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Although numerous regulatory connections between pre-mRNA splicing and chromatin have been demonstrated , the precise mechanisms by which chromatin factors influence spliceosome assembly and/or catalysis remain unclear . To probe the genetic network of pre-mRNA splicing in the fission yeast Schizosaccharomyces pombe , we constructed an epistatic mini-array profile ( E-MAP ) and discovered many new connections between chromatin and splicing . Notably , the nucleosome remodeler SWI/SNF had strong genetic interactions with components of the U2 snRNP SF3 complex . Overexpression of SF3 components in ΔSWI/SNF cells led to inefficient splicing of many fission yeast introns , predominantly those with non-consensus splice sites . Deletion of SWI/SNF decreased recruitment of the splicing ATPase Prp2 , suggesting that SWI/SNF promotes co-transcriptional spliceosome assembly prior to first step catalysis . Importantly , defects in SWI/SNF as well as SF3 overexpression each altered nucleosome occupancy along intron-containing genes , illustrating that the chromatin landscape both affects—and is affected by—co-transcriptional splicing .
Recent work has uncovered extensive crosstalk amongst chromatin , transcription and RNA processing machineries . Changes to chromatin typically involve nucleosomes—histone octomers wrapped by approximately 147 nucleotides of DNA . We now know that nucleosomes are enriched in exons relative to introns [1 , 2] and that intronic and exonic histones are marked differentially [3] suggesting that nucleosomes may be involved in defining intron/exon junctions and that certain histone marks might influence splicing decisions . Importantly , nucleosomal contacts with DNA are constantly modulated by ATP-dependent chromatin remodeling complexes ( e . g . SWI/SNF , Ino80 , and RSC ) , that function to deposit , remove , and/or slide nucleosomes [4] . Although primarily studied in the context of regulation of transcription , nucleosome remodeling is also likely to influence splicing in numerous ways: altering RNA polymerase II elongation rates , promoting RNAPII pauses , and/or recruiting the spliceosome to chromatin via protein-protein interactions ( Reviewed in [5] ) . Most of what we know about co-transcriptional splicing regulation comes from studies of alternative splicing in mammals , in which histone modifications ( e . g . H3K36me3 [6] ) and chromatin remodelers ( e . g . SWI/SNF [7 , 8] ) have been shown to modulate exon skipping ( reviewed in [9 , 10] ) . However , most of this work has focused on a small set of alternatively spliced reporter genes and has not revealed mechanistic insights into how specific steps of spliceosome activation and/or catalysis can be influenced by changes to chromatin . Additionally , while there is good evidence that splicing can direct histone H3K36 tri-methylation [11 , 12] and H3K4 tri-methylation [13] , we still know very little about how splicing may more broadly influence chromatin states . Despite the relatively simple intron/exon architecture of the Saccharomyces cerevisiae genome , there is mounting evidence that chromatin and transcription also play an important role in promoting splicing in budding yeast . Specifically , ubiquitination of histone H2B has been linked to spliceosome assembly and function [14 , 15] and histone acetylation has been shown to promote pre-catalytic spliceosome assembly [16 , 17] . RNA polymerase speed has also been correlated with splicing efficiency in S . cerevisiae [18 , 19] . Taken together , these results suggest that many of the fundamental mechanisms linking chromatin and splicing are conserved throughout evolution . Here , we present work showing extensive connections between pre-mRNA splicing and chromatin in the fission yeast , Schizosaccharomyces pombe . This yeast is a uniquely attractive model organism—it is as genetically tractable as its distant cousin , S . cerevisiae , but much more metazoan-like in its dependence on splicing; over half of fission yeast genes contain an intron and of those , half contain more than one intron [20] . Though examples of alternative splicing are rare to date [21 , 22] it is notable that many fission yeast introns have non-consensus splice sites [23] , suggesting that so-called constitutive introns vary in efficacy of removal . Using high-throughput genetic interaction mapping , we uncovered multiple connections between the chromatin and splicing machineries . Importantly , we found that deletion of SWI/SNF nucleosome remodeling complex components left cells particularly sensitive to overexpression of the SF3 subcomplex of the U2 snRNP . We present evidence that SWI/SNF contributes to splicing catalysis by promoting recruitment of the Prp2 ATPase , which acts to destabilize SF3 immediately prior to first step catalysis . Our data provide a functional role for SWI/SNF in the splicing of introns with weak splice sites and promote the idea that nucleosomes , while serving as barriers to RNAPII elongation , may actually promote co-transcriptional splicing .
In order to define the genetic interaction space of all pre-mRNA splicing factors in fission yeast , we created a set of mutants in which each gene involved in splicing was individually removed or perturbed . We first manually compiled a list of 81 genes whose products have either been experimentally implicated in fission yeast splicing or whose orthologs have been functionally characterized as splicing factors in other species ( S2 Table ) . As was originally described in Roguev et al . , ( 2008 ) , for genes annotated as non-essential , we systematically replaced the open reading frame with a NAT resistance cassette to create deletion strains . For essential genes ( which encode the majority of splicing factors in S . pombe ) , we created DAmP alleles [24] by replacing the 3’UTR with a NAT resistance cassette . DAmP alleles have been successfully used in the past to perturb mRNA levels of essential genes in order to shed light on their function in high-throughput genetic and drug screens [25] . Using the Pombe Epistasis Mapper ( PEM ) system our group developed [26] , we screened 74 S . pombe splicing factors ( several splicing mutants were too sick to be propagated through the E-MAP screen; see S2 Table ) against a fission yeast mutant library containing more than 2 , 000 non-essential deletions ( library as described in [27] ) . This collection represented virtually every major known biological process in the cell , creating a Splicing E-MAP with approximately 120 , 000 pairwise measurements . Positive genetic interactions between two mutants ( > +2 . 0 ) represent epistasis or suppression , while negative genetic interactions ( < -2 . 5 ) represent synthetic sickness , or in some cases , synthetic lethality . We present this data in S1 Table . Previous work ( summarized in [28] ) has shown that genes belonging to the same complex or pathway tend to have similar genetic interaction profiles . To gain an unbiased overview of the splicing factors in our E-MAP we clustered them according to the similarity of their genetic interaction profiles , i . e . based on how individual splicing factor mutants genetically interact with all factors represented in the deletion library . Several clusters ( correlation coefficient > 0 . 3 , see methods ) emerged containing splicing factors from multiple steps of the splicing cycle ( Fig . 1A and S2 Table ) . Consistent with our understanding of early spliceosome assembly , our E-MAP revealed a high degree of correlation between a pair of splicing factors , prp11 ( PRP5; where appropriate , S . cerevisiae orthologs will be listed parenthetically ) and the branchpoint binding protein bpb1 ( MSL5/BBP1 ) , which are both known to function at the step of intron recognition ( Fig . 1A , Cluster 9 ) . Interestingly , cwf23 ( CWC23 ) is also part of Cluster 9 , perhaps indicating a role for this DNAJ domain-containing protein at an early step of spliceosome assembly . Generally , splicing genetic interaction profile clusters did not breakdown into U snRNPs , the small nuclear ribonuclear protein complexes that assemble stepwise to form a functional spliceosome . Interpretation of the E-MAP genetic interaction profile correlation scores for splicing factors is complicated by the fact that most of them are essential and thus are represented in the E-MAP as DAmP alleles . Integration of the DAmP cassette is not expected to affect expression of all genes equally . Indeed , as was the case in the S . cerevisiae RNA processing E-MAP [29] , many of the S . pombe DAmP strains showed little growth defect and had weak genetic interaction profiles , making clustering uninformative in some cases and resulting in clusters comprised mostly of mutants with strong genetic interaction profiles . Despite these caveats , our observation that certain “early” splicing factors and “late” splicing factors had similar genetic interaction profiles reinforces the dynamic nature and functional complexity of the spliceosome . We next asked what cellular pathways were enriched for positive or negative genetic interactions with splicing as a whole . Positive genetic interactions can identify genes whose products act in the same biological pathway , or in some cases , form a protein complex . Negative genetic interactions are often observed between two genes whose products act in parallel pathways that lead to a common biological outcome [28] . Strikingly , of all the pathways represented in the E-MAP [27] , splicing was specifically enriched for negative genetic interactions with factors related to “transcription , ” “regulation of transcription , ” “chromatin modifications” and “chromosome organization” ( Fig . 1B , methods ) . Looking next at negative genetic interactions between splicing and known protein complexes , we saw additional evidence for significant crosstalk between splicing and chromatin: S . pombe splicing mutants had strong negative genetic interactions with chromatin remodeling factors , such as the histone exchange complex SWR1 , and histone modifying factors , including the histone demethylase Lid2 and the histone deacetylase complex Rpd3L ( Fig . 1B ) . Interestingly , there appears to be some specificity regarding which splicing factors have genetic interactions with particular chromatin complexes . For example , splicing factors in Cluster 8 exhibited strong negative genetic interactions with Rpd3L factors , supporting a role for histone deacetylation in promoting splicing , while Cluster 10 factors did not ( Fig . 1C ) . Although the composition of U snRNPs was not immediately evident from the clustering , we still wanted to know whether there was any specificity in how different U snRNPs interacted genetically with chromatin complexes . We found that several chromatin complexes were enriched for negative genetic interactions with multiple U snRNPs ( e . g . Set1/COMPASS , Lid2 , SWI/SNF , and SWR1 ) ( Fig . 1D ) . However , a smaller subset of chromatin complexes showed enrichment for negative genetic interactions with a limited set of U snRNPs—for example , tri-snRNP ( U4/U5/U6 ) showed strong negative interactions with two ubiquitin ligase complexes ( CLRC and Cul4B-RING ) and the U1 snRNP was strongly negative with the SWR1 histone exchange complex . Notably , the nucleosome remodelers SWI/SNF and Ino80 were particularly enriched for negative interactions with the U2 snRNP ( Fig . 1D ) , as well as with downstream tri-snRNP components . SWI/SNF and Ino80 are ATP-dependent chromatin remodeling complexes whose activities include ordering and reorganizing nucleosomes on the DNA template in order to regulate RNA polymerase II elongation . A subunit of SWI/SNF has been previously shown to influence alternative splicing in mammals [7] , although its mechanism for doing so remains unclear . We therefore set out to elucidate the precise interplay between SWI/SNF and splicing in fission yeast . We identified two splicing factors with strong negative genetic interactions with components of SWI/SNF: sap145 and sap62 . Sap145 ( CUS1 ) is a component of SF3b , a subcomplex of U2 that associates with the pre-mRNA branchpoint region [30] . Sap62 ( PRP11 ) is a component of the SF3a complex , a group of three conserved proteins that binds to SF3b and also makes contacts with the intron branchpoint . Because both sap145 and sap62 are essential genes in S . pombe , they were represented in the E-MAP as DAmP alleles . In order to confirm the E-MAP results , as well as to assess temperature sensitivities , we crossed these DAmP alleles with two non-essential SWI/SNF deletions , Δsnf5 and Δsol1 . Snf5 is a core component of SWI/SNF while Sol1 ( SWI1 ) is an ARID ( AT-rich interaction domain ) -containing protein that makes specific contacts with DNA [31] . As predicted by the negative genetic interaction score in the EMAP , combining the SF3 DAmP alleles with Δsnf5 or Δsol1 caused synthetic sickness at all temperatures , most severely at 37°C and 16°C ( Fig . 1E ) . Results from the E-MAP also showed negative genetic interactions between SWI/SNF and other components of SF3 , namely sap114 and prp10 ( PRP21 and HSH155 , respectively ) ( S1 Fig ) , further suggesting there might be specific interplay between the SWI/SNF nucleosome remodeling complex and the U2 snRNP . We thus set out to understand the molecular phenotype behind the synthetic sickness in our SWI/SNF-SF3 double mutants . In order to ascertain whether defective splicing was responsible for the growth defect in the SWI/SNF-SF3 double mutant strains , we performed genome-wide splicing microarray analysis . These arrays contain oligonucleotide probes specific for the exon , intron , and exon-exon junction for nearly every canonically spliced intron in fission yeast—allowing us to compare the levels of total , pre-mRNA and mature mRNA , respectively , for any given mutant strain against an isogenic wild-type ( Fig . 2A ) . The experiment was performed as a competitive two-color hybridization and results are reported as the log2 fold change ( logFC ) in mutant RNA compared to wild-type RNA ( Lipp et al . , in preparation ) , with yellow in the Fig . 2B heatmap signifying accumulation of pre-mRNA relative to mature mRNA . We observe little accumulation of pre-mRNA in any of the single mutants ( sap145DAmP , sap62DAmP , Δsnf5 , Δsol1 , Fig . 2B ) , which we interpret to mean there is little to no change in splicing efficiency in these strains . However , upon combining mutations in splicing factors and SWI/SNF , we see a broad yet intron-specific accumulation of pre-mRNA ( Fig . 2B ) . Average hierarchical linkage clustering reveals a high degree of correlation between sets of double mutants: SWI/SNF-SF3a double mutants have a correlation of 0 . 85 and the SWI/SNF-SF3b double mutants have a correlation of 0 . 76 ( S2 Fig ) . Together , the SWI/SNF-SF3 strains correlate to a relatively high degree ( 0 . 65 ) , indicating that similar introns are affected in all SWI/SNF-SF3 double mutant backgrounds . To quantify the exacerbated splicing defect in the double mutants , we imposed a significance cut-off of a logFC of 0 . 5 and calculated the number of introns that were spliced poorly ( in yellow ) or spliced better ( in blue ) for each of the single and double mutant strains ( S2 Fig ) . Again , we observed few changes in the number of introns retained in the single mutants ( <300 in each case ) , but many more retained introns in the SWI/SNF-SF3 double mutants ( >1000 in most cases ) , and of those , the double mutants had more introns retained to higher degrees ( logFC > 1 . 5 , in darker yellow ) . While these results are consistent with a splicing defect , we cannot rule out that combination of SWI/SNF deletion with the SF3 DAmP alleles induces changes in rates of pre-mRNA decay by nuclear and/or cytoplasmic surveillance pathways . We validated our microarray results by reverse transcriptase quantitative PCR ( RT-qPCR ) , choosing two representative multi-intron containing genes , spo14 and nda3 , using primers that amplify the intron-exon junction ( intron ) and the exon ( total ) ( Fig . 2C ) . Again , we only saw intron accumulation in the double mutant strains , relative to wild type , providing further evidence that the splicing defect is only seen when both SF3 and SWI/SNF are mutated . Interestingly , when we looked at a multi-intron containing gene like nda3 , we saw that intron 4 accumulated to much higher levels in the double mutants than did other introns in the same gene , indicating that the splicing defect is intron-specific , even when affected introns are very close to other introns in the gene . The specificity of intron retention suggested to us that there might be cis-splicing signals that make an intron more or less sensitive to the SWI/SNF-SF3 mutations . To interrogate this , we computationally assessed the contribution of different intron features to the splicing defect . While the cis-splicing signals of S . pombe introns are less constrained then those of S . cerevisiae , there are consensus sequences maintained by the majority of S . pombe introns . Analysis of the microarray data revealed that deviation from consensus in those sequences ( the 5’ splice site ( 5’SS ) , the branchpoint ( BP ) , and the 3’ splice site ( 3’SS ) ) increased the likelihood that an intron would be retained in the Δsol1sap145DAmP double mutant strain ( Fig . 2D ) . Interestingly , we observed that splicing in the double mutant is most sensitive to changes to the 5’SS , which may reflect cooperativity between SWI/SNF and the U6 snRNA’s ability to engage the 5’SS , perhaps shedding light on the negative genetic interactions between SWI/SNF and the tri-snRNP from the E-MAP ( Fig . 1D ) . Looking back to the introns that accumulated in our RT-qPCR experiments , we saw that intron 3 of spo14 has a rare 5’SS ( GTATGC , used by less than 200 introns in S . pombe ) and intron 4 of nda3 contains a disfavored AAG 3’SS ( YAG is preferred , where Y = pyrimidine ) . No intron features significantly increased or decreased probability of retention in any of the single mutant strains , which is consistent with the overall lack of splicing phenotype in those strains . Several of the mutants had a population of introns whose splicing was improved ( in blue on S2 Fig ) , but there were insufficient numbers for statistical analysis of enriched cis-splicing signals . Taken together , this analysis suggests that non-consensus introns have a unique requirement for interplay between the SWI/SNF nucleosome remodeler and SF3 , and surprisingly , suggests these two machineries may play a concerted role in splicing . In order to look more closely at the mechanistic causes of the splicing defect in the SWI/SNF-SF3 double mutant strains , we needed to understand the effect of the DAmP cassette on SF3 transcript levels . Generally , the DAmP cassette is thought to compromise mRNA processing and stability by removing a gene’s 3’UTR , thus creating hypomorphic alleles . To interrogate the expression level of the sap145 and sap62 DAmP alleles , we performed RT-qPCR on total RNA isolated from each mutant strain . Unexpectedly , we found significantly higher levels of mRNA for both sap145 and sap62 , indicating that these are in fact , overexpression alleles ( Fig . 3A ) . While this result contradicts the paradigm of 3’UTR disruption creating hypomorphic alleles ( DAmP = decreased abundance by mRNA perturbation ) , we propose that DAmP allele overexpression may be a general phenomenon for genes encoding RNA binding proteins , many of which are known to negatively regulate their own expression through binding to their 3’UTRs [32] . Removal of snf5 and sol1 alone did not affect sap145 or sap62 mRNA levels , although the overexpression appeared slightly exacerbated in the double mutant background . Importantly , overexpression of the sap145 ortholog CUS1 in S . cerevisiae has previously been purported to promote a tighter association between SF3b , SF3a , and Prp5 [33] . As measuring protein levels would require tagging the proteins , most simply at either the N- or C-terminus , and would likely unintentionally re-create the DAmP alleles , we set out to ectopically overexpress sap145 and sap62 and ask whether this recapitulated the synthetic sick phenotype seen with the DAmPs when combined with Δsnf5 . Sap145 and sap62 , along with sap49 and sap114 , other components of SF3b and SF3a , respectively , were cloned into the pREP4x expression vector , which is under the control of the thiamine-repressible nmt1 promoter [34] Overexpression of any of the SF3 factors in WT cells did not confer any growth defect while overexpression in the Δsnf5 strain background resulted in synthetic sickness ( Fig . 3B ) . We took this as confirmation that the DAmP cassette caused overexpression of sap145 and sap62 and that overexpression of these splicing factors causes synthetic sickness and intron retention when combined with SWI/SNF mutants . S3B Fig shows levels of SF3 factor overexpression in the pREP4x strains , which are comparable to levels induced by the DAmP cassette . As controls , we also overexpressed a non-SF3 U2 factor , uap2 ( CUS2 ) , and a U1 snRNP factor , usp104 ( PRP40 ) , in WT and Δsnf5 cells . We did not observe any specific genetic interaction between Δsnf5 and overexpression of uap2 or usp104 , indicating that merely overexpressing any early splicing factor is not sufficient to induce a growth defect in Δsnf5 cells ( Fig . 3C ) . We also overexpressed sap145 in other chromatin mutants ( Δswr1 ( SWR1 ) , Δpht1 ( HTZ1 ) and Δset1 ( SET1 ) ) and observed no synthetic sickness ( S3 Fig ) , consistent with a specific interaction between deletion of SWI/SNF and SF3 overexpression . Because we only see intron accumulation when the two mutations are combined , we reasoned that SWI/SNF might contribute to an SF3-dependent step of splicing , possibly by promoting spliceosome recruitment and/or pre-catalytic spliceosome rearrangements . Because we observed no synthetic sickness between Δsnf5 and mutants of the Prp5 RNA-dependent ATPase ( Prp11 in S . pombe ) ( S3 Fig ) [35] , we hypothesized that SWI/SNF may act at a step of splicing that follows spliceosome assembly . The microarray results and synthetic sick phenotype suggested that SWI/SNF and SF3 contribute to splicing in a concerted fashion , perhaps both influencing a particular step in the splicing cycle . Having confirmed that our SF3 DAmP alleles were being overexpressed , we wanted to look at specific steps of splicing that might be affected by an overabundance of SF3 components . Importantly , SF3 needs to be destabilized by the ATPase Prp2 prior to the first step of splicing in order for the branchpoint adenosine on the pre-mRNA intron to be available for nucleophilic attack ( Fig . 4A ) [36–38] . We therefore hypothesized that having an excess of SF3 factors could disfavor first step catalysis and would exacerbate a splicing defect occurring at the Prp2 step . To follow spliceosome rearrangements in the context of chromatin , we performed splicing factor chromatin immunoprecipitation ( ChIP ) ( Fig . 4B ) , which has been used successfully in the past as a read-out for the relative timing and efficiency of splicing factor recruitment [39 , 40] . We chose several intron-containing genes to ChIP based on the following criteria: 1 . ) The gene had one or more introns whose splicing was defective in a SWI/SNF-SF3 double mutant by microarray , RT-qPCR or both , 2 . ) The gene was highly expressed , 3 . ) The gene had introns long enough ( >200nt ) to provide enough spatial resolution for us to detect co-transcriptional loading and rearrangements of the individual spliceosome factors . To test the hypothesis that the Prp2 step is affected in the double mutant , we HA-tagged cdc28 ( PRP2 ) and performed ChIP qPCR on at set of representative genes . Strikingly , we found levels of Cdc28 to be significantly lower in the Δsnf5/sap145DAmP strain compared to wild type at three different genes with distinct intron architectures: rps3 , dbp2 and SPBC660 . 16 ( Fig . 4C and S4 Fig ) , indicating that defective splicing in the double mutant may be caused by a lack of Cdc28 ( PRP2 ) recruitment or stabilization . If reduced Cdc28 ( PRP2 ) levels are due to decreased recruitment , we would expect a defect in Cdc28-dependent remodeling of the spliceosome . One way such a defect would manifest itself is by retention of the proteins released or destabilized by Cdc28 ( PRP2 ) , including the Nineteen Complex ( NTC ) -related protein Cwf24 ( CWC24 ) or the RES ( REtention and Splicing ) factor Cwf26 ( BUD13 ) [37] . We chose to tag and ChIP Cwf24 ( CWC24 ) . As expected , in the Δsnf5sap145DAmP cells where we saw less Cdc28 ( PRP2 ) , we observed much higher levels of Cwc24 in all three genes we looked at , especially at primer sets at or downstream of affected introns ( Fig . 4C and S4 Fig ) . We interpret this to mean that in wild-type cells , Cwf24 is displaced by the Prp2 ATPase and is thus not in the vicinity of chromatin to be crosslinked . In the absence of optimal Cdc28 ( PRP2 ) recruitment , the likelihood of Cwf24 ( CWC24 ) remaining part of the pre-catalytic spliceosome increases , and this is borne out by higher Cwf24 ChIP signal in the Δsnf5sap145DAmP double mutants . We chose not to look at SF3 by ChIP directly for two reasons: first , because sap145 overexpression may change the local concentration of binding partners and complicate ChIP interpretation and second , because SF3 is known to be destabilized but not completely released from the spliceosome [37] and such nuanced rearrangements are likely not detectable by ChIP . Notably , we do see a modest increase in Cdc28 ( PRP2 ) ChIP in the sap145DAmP single mutant cells and a decrease in Cdc28 ChIP in the Δsnf5 single mutant cells , similar to the levels in Δsnf5sap145DAmP ( S4C Fig ) , providing some mechanistic insight into how each of these mutations individually affects pre-spliceosome activation . We believe that the overabundance of Sap145 results in a greater requirement for Prp2 remodeling , leading to a higher local concentration of the enzyme , but in the absence of Snf5 , Prp2 is not recruited as well . When the two mutations are combined , it creates a situation where the cell needs more Prp2 but has less , which leads to poor splicing outcome . To provide further evidence that the splicing defect in the Δsnf5sap145DAmP double mutants occurs at the step of spliceosome activation , we performed ChIP on a HA-tagged U1 snRNP factor , Usp105 ( PRP39 ) as a control . Compared to wild type , we observed little change in U1 snRNP ChIP along the single intron-containing rps3 gene ( Fig . 4D and S4A Fig ) . The overall lack of U1 ChIP defect is consistent with our genetic data ( showing no synthetic sickness between U1 overexpression and Δsnf5 ) and supports a model in which SWI/SNF contributes downstream of early spliceosome assembly . Our ChIP results support a model whereby SWI/SNF contributes to splicing at the Prp2 step of splicing activation . To provide additional lines of evidence to these ends , we again turned to genetics . If our hypothesis that SWI/SNF helps recruit Cdc28 ( PRP2 ) is correct , we would expect that abrogation of Cdc28 ( PRP2 ) function via an alternative method would phenocopy the synthetic sickness seen between SWI/SNF and sap145 overexpression . We therefore mutated the conserved Gly553 to an Aspartate in S . pombe cdc28 ( PRP2 ) , based on the well-characterized prp2-1 temperature-sensitive allele in S . cerevisiae , which has been shown to stall splicing after assembly but before first-step catalysis [41 , 42] . S . pombe cdc28-1 ( G553D ) was synthetic sick when combined with Δsnf5 , especially at the non-permissive temperature ( 37°C ) ( Fig . 5A ) , consistent with an increased requirement for SWI/SNF activity at the Prp2 ATPase step . In S . cerevisiae , Prp2 is known to act with the cofactor Spp2 , an essential G-patch protein that binds to Prp2 and promotes the first step of splicing [43] . Looking back at the S . pombe splicing E-MAP , we saw that the cwf28DAmP ( SPP2 in S . cerevisiae ) allele had a strong positive genetic interaction with SWI/SNF ( +2 . 0 and +2 . 6 with Δsnf5 and Δsol1 , respectively; Fig . 1C ) , suggesting epistasis or suppression . To confirm that cwf28 and SWI/SNF interacted genetically , we re-created the double mutant by mating , and observed that the Δsol1/cwf28DAmP double mutant grew better at 16°C than did the Δsol1 strain alone , confirming the E-MAP positive genetic interaction ( Fig . 5B ) . In examining the cellular levels of cwf28 mRNA in the DAmP background , we saw that the DAmP cassette again led to overexpression of cwf28 ( S5 Fig ) . Interestingly , SPP2 was originally identified as a high-copy suppressor of prp2-1 [44] . Because SWI/SNF appears to recruit Prp2 and overexpression of Spp2 can overcome the cold-sensitivity of Δsol1 , we think Spp2 is also acting as a high-copy suppressor of ΔSWI/SNF . To further implicate SWI/SNF in promoting Prp2-dependent spliceosome remodeling , we overexpressed several non-SF3 proteins that are released by Prp2 , predicting that their overexpression—like that of SF3—would result in synthetic sickness with Δsnf5 . Indeed , using the pREP4x vector , ectopic overexpression of cwf26 ( BUD13 ) and cwf24 ( CWC24 ) caused a severe growth defect in Δsnf5 but not wild-type cells ( Fig . 5C ) , consistent with a role for SWI/SNF in promoting Prp2-recruitment and the subsequent release of Cwf26 ( BUD13 ) and Cwf24 ( CWC24 ) from the spliceosome . In summary , we have provided several lines of evidence that SWI/SNF contributes to the Prp2 step of splicing: Prp2 recruitment is defective in Δsnf5sap145DAmP cells; SWI/SNF has opposite genetic interactions with cdc28-1 ( PRP2-1 ) and overexpression of cwf28 ( SPP2 ) ; and overexpression of cwf26 and cwf24 is synthetic sick with Δsnf5 , presumably due to exacerbating the block in splicing imposed by defective Cdc28 ( PRP2 ) ATPase recruitment . SWI/SNF is generally thought to deposit and remodel nucleosomes on a DNA template in order to regulate RNAPII transcription . To test whether nucleosome positioning and/or occupancy could be altered in ΔSWI/SNF cells , we performed a nucleosome-scanning assay , as described in Infante et al . , 2012 [45] . Following micrococcal nuclease ( MNase ) treatment , mononucleosome-protected DNA fragments ( ~150bp ) were excised from an agarose gel and tiling qPCR was performed along several fission yeast genomic loci ( Fig . 6A ) . At all expressed genes examined , we saw a striking decrease in signal from protected DNA in Δsnf5 and Δsnf5sap145DAmP cells , implicating Snf5 in depositing or maintaining the position of nucleosomes along coding regions ( Fig . 6B , replicate experiment in S6B , C , D Fig ) . Both the Δsnf5 single mutant and the Δsnf5sap145DAmP double mutant displayed lower nucleosome occupancy at intron-containing genes , although certain nucleosomes were differentially protected; generally , nucleosomes downstream of introns exhibited the biggest changes in nucleosome occupancy relative to wild type ( Fig . 6B , bold circles ) . We propose that a certain level/distribution of nucleosomes is required for optimal recruitment of Prp2 and that SWI/SNF contributes to creation and/or maintenance of this nucleosome environment at intron-containing genes . In order to show that the nucleosome occupancy phenotypes we report in Δsnf5 and Δsnf5sap145DAmP cells were not simply due to differences in mononucleosome recovery or MNase accessibility we amplified two regions that we did not expect to be under the control of SWI/SNF: the silenced Mat3-Mm ORF in the mating type locus and an untranscribed gene-poor region . At these two silent regions , we observed no significant change in overall abundance of nucleosomes in the different mutant strains ( Fig . 6C and S6B Fig ) . In our assay , nucleosomes appeared to be less strongly positioned overall ( leading to lower signal relative to undigested genomic DNA ) at Mat3_Mm and the untranscribed region . This is consistent with published genome-wide data , which show weak nucleosome positioning in these regions during mitotic growth [46] . To correlate the lower nucleosome signal we report at rps3 and dbp2 in the Δsnf5 single mutant and the Δsnf5/sap145DAmP double mutant with RNAPII transcription , we examined the ChIP profile of RNA polymerase II , using an antibody against the unmodified RNAPII CTD , antibody ( 8GW16 ) . RNAPII levels were consistently lower in Δsnf5/sap145DAmP strains compared to wild type at almost all points throughout the gene bodies we looked at , even though signal at the promoter was comparable between the two strains ( Fig . 6D and S6E Fig ) . Because this phenotype was strongest in the double mutant , we propose that RNAPII is subject to a level of regulation that relies on both SWI/SNF and splicing being intact . ΔSnf5 single mutants generally displayed wild-type RNAPII density in ChIP using the 8GW16 antibody ( Fig . 6D ) , although interestingly , additional ChIP experiments using an antibody directed against Ser5 phosphorylated CTD residues ( 4H8 , Abcam ) showed reproducibly lower levels of Ser5P CTD in Δsnf5 and Δsnf5sap145DAmP strains , consistent with results from Batsche et al . , which correlate Ser5P with the SWI/SNF subunit Brm at alternative exons [7] . While it is difficult to garner mechanistic understanding from these results alone , lower RNAPII levels are consistent with lower nucleosome occupancy in the Δsnf5 and Δsnf5/sap145DAmP strains: if nucleosomes act as a barrier to transcription [47 , 48] , then lower nucleosome density should allow for enhanced RNAPII elongation and thus decrease the probability of crosslinking the polymerase molecule to any one point in a gene , though we can not rule out the possibility of changes to RNAPII initiation or processivity . Surprisingly , we also observed a dramatic increase in nucleosome occupancy in the sap145DAmP single mutant cells at intron-containing genes ( Fig . 6E ) . Importantly , when we looked at an intronless gene , act1 , we did not observe higher nucleosome occupancy in the sap145DAmP single mutant , suggesting that sap145 overexpression is only inducing chromatin changes in the context of splicing at intron-containing genes . These results support a model wherein splicing itself may influence chromatin dynamics . We propose that overexpression of sap145 inhibits spliceosome activation and that higher levels of nucleosomes serve to compensate for this defect by slowing RNAPII to promote co-transcriptional splicing . Notably , this increase in nucleosome occupancy presumably requires the integrity of the SWI/SNF complex , since , as we describe above , nucleosome occupancy decreased relative to WT when we combined sap145 overexpression with deletion of snf5 in our double mutant . Data from humans has shown that in addition to higher nucleosome abundance in exons vs . introns , nucleosome occupancy further peaks at exons that are flanked by long introns or weak splice sites [49] , consistent with our data and with the idea that SWI/SNF-dependent nucleosome positioning is important for pre-mRNA splicing . Taken together , these results point to a role for SWI/SNF in maintaining nucleosome occupancy at expressed genes in S . pombe and hint at an additional role for splicing factors , or the act of splicing itself , in further promoting nucleosome occupancy .
One reason we set out to create a Splicing E-MAP in fission yeast was because of its metazoan-like intron/exon architecture . Over half of intron-containing genes in fission yeast contain more than one intron and the cis-splicing signals in S . pombe introns are highly variable , in contrast to S . cerevisiae but mirroring the situation in metazoans . For example , the branch-site consensus sequence in S . cerevisiae is almost always UACUAAC , whereas the S . pombe branch-site consensus CURAY ( where R is a purine and Y is a pyrimidine ) , is more similar to that found in mammals ( reviewed in [50] ) . Metazoan splicing decisions are aided by an abundance of SR and hnRNP proteins that regulate exon inclusion or skipping by binding to specific sequences in pre-mRNAs [51] . While the fission yeast genome encodes a small number of SR-like proteins , their roles in intron recognition remain unclear [52 , 53] . The abundance of degenerate cis-splicing signals combined with the dearth of canonical regulatory proteins in S . pombe prompts questions of how introns with degenerate splicing signals are recognized in fission yeast and whether there are other pathways to promote co-transcriptional intron recognition and spliceosome assembly . Our results indicate that multiple cellular pathways contribute to efficient splicing in fission yeast , most notably chromatin and RNAPII transcription . If we take a closer look at the particular chromatin complexes enriched for negative genetic interactions with splicing , we can infer some mechanistic insights . Both SWI/SNF and Ino80 are known to deposit and position nucleosomes in an ATP-dependent manner . Although to date there are no reports implicating Ino80 in splicing , there is solid evidence linking the SWI/SNF catalytic subunit Brm to variant exon inclusion in several human genes . Overexpression of the Brm subunit in human breast cancer cells led to increased exon inclusion in a handful of genes examined and siRNA knockdown of Brm abolished RNAPII accumulation on the same variant exons [7] . The authors of this report proposed SWI/SNF contributes to splicing by decreasing RNAPII elongation rate , thus facilitating co-transcriptional spliceosome recruitment and allowing for recognition of introns with suboptimal spice sites . The idea that a “slow” RNA polymerase promotes spliceosome assembly and recognition of weak splice sites—often referred to as the “kinetic model” of co-transcriptional splicing—is supported by numerous lines of evidence , and seems to be conserved from budding yeast [18] to humans ( reviewed in [10] ) . Intriguingly , most of the chromatin complexes that were synthetic sick with splicing factors in the E-MAP have been implicated in negative regulation of RNAPII transcription . For example , Set1/Compass , which deposits H3K4 trimethylation has recently been shown , in coordination with H3K4 dimethylation , to repress transcription at coding genes [54] . Similarly , Rpd3L is involved in downregulating transcription at gene promoters by deacetylating histones [55 , 56] . The cycle of histone acetylation and deacetylation has already been shown to be important for spliceosome recruitment and rearrangements in S . cerevisiae , wherein deletion of histone deacetylases led to retention of U2 factors and a decrease in recruitment of later splicing factors [16 , 17] . Taken together , our SWI/SNF data and the Splicing E-MAP as a whole support the general premise of the kinetic model ( i . e . repressive chromatin complexes should promote splicing ) and provide strong evidence that constitutive splicing decisions also rely on transcriptional inputs . We propose a model whereby SWI/SNF contributes to the deposition and/or maintenance of nucleosomes in the coding regions of expressed genes in fission yeast ( Fig . 7 ) . These nucleosomes may influence splicing in several ways: by influencing polymerase speed and/or promoting pausing , by recruiting splicing factors directly or via adapter proteins , or by directing changes to the CTD modification status . Our data correlate lower nucleosome occupancy with a decrease in RNAPII ChIP and we hypothesize that this reflects changes to RNAPII elongation along coding regions . We propose that these SWI/SNF-dependent changes to nucleosomes and consequently to RNAPII , lead to decreased Prp2 recruitment and defective co-transcriptional spliceosome activation . Our report that deletion of snf5 leads to a decrease in nucleosome occupancy across spliced genes is consistent with recent findings from Tolstorukov et al . , who saw a decrease in nucleosome occupancy at promoters in snf5-deficient mammalian cells [57] . Both these results are in some ways at odds with the general view that SWI/SNF removes nucleosomes from DNA to allow RNAPII to elongate , thus activating gene expression . However , the function and composition of SWI/SNF is remarkably complex and there is good evidence for SWI/SNF also having a role in transcriptional silencing , likely through nucleosome positioning [58] . Our results may necessitate a reworking of how we think about activation of gene expression: to date , SWI/SNF was thought to activate gene expression by removing nucleosomes , but perhaps in some cases it does the opposite—by promoting deposition of nucleosomes that downregulate RNAPII elongation and encourage co-transcriptional splicing , leading to greater levels of steady-state processed mRNAs . Our observations that sap145 overexpression led to higher level of nucleosomes at intron-containing genes was at first perplexing in light of the fact that we did not see a splicing defect in this strain . However , upon further contemplation , we contend that this nucleosome phenotype is actually compensation for the defect conferred by SF3 overexpression . Overexpression of SF3 alone is deleterious to the cell , in that it disrupts dynamics between SF3b , SF3a and Prp5 interactions [30] and likely interferes with Prp2-dependent spliceosome activation ( this work ) . We argue that this defect is sensed and , through an unknown feedback mechanism , leads to an increase in nucleosome deposition or stabilization of existing nucleosomes in order to inhibit RNAPII elongation and re-establish the timing and balance of co-transcriptional splicing . We believe that this “feedback” is dependent on SWI/SNF and that nucleosome occupancy cannot be restored in the absence of SWI/SNF ( accounting for the low nucleosome signal observed when you combine the sap145 overexpression with deletion of snf5 ( i . e . the double mutant phenocopies the Δsnf5 single mutant ) . These data fit nicely with reports from the Beggs and Neugebauer labs , both of which report RNAPII accumulation downstream of introns in S . cerevisiae [59 , 60] . The Beggs lab went on to show , quite elegantly , that mutating the branch point sequence in a reporter gene intron led to an accumulation of RNAPII around the 3’SS [60] . They refer to this accumulation as an RNAPII “pause” and propose that splicing itself acts as a checkpoint to stall RNAPII and promote co-transcriptional splicing . We propose that the presence of a nucleosome at or near the 3’SS could contribute to the RNAPII pause reported by the Beggs lab and also believe that splicing itself provides this checkpoint . One outstanding question is how SWI/SNF and nucleosome occupancy directly impact RNA polymerase II elongation through open reading frames . While our ChIP data are consistent with a faster moving RNAPII decreasing ChIP signal , it is difficult to provide direct evidence of these dynamics in vivo . One line of experimentation that could address this question is combining SWI/SNF-SF3 double mutants with RNAPII “fast” and “slow” mutants that have been made and characterized in S . cerevisiae [18 , 61] . We would predict that if a slow polymerase mutant could phenocopy a paused polymerase at spliced genes , it would also alleviate the splicing defects seen in the double mutant . However , these mutants have yet to be made and validated in fission yeast . The multiple connections between the splicing and chromatin machineries , and the specific regulatory connection we show here , support the exciting hypothesis that each of the ATP-dependent steps of the splicing cycle could be regulated by a separate input—which may come from different chromatin remodeling or histone modifying complexes . Implications for such interplay are of great consequence , as mutations in both splicing and chromatin factors—SWI/SNF in particular—are known to promote tumorigenesis in metazoans [62] . Understanding connections between these pathways will be essential not only to elucidate pre-mRNA splicing mechanism , but also to create a full picture of tumor suppression .
A list of strains used in and made for this study are available in S2 Table . Analysis and generation of the E-MAP data was performed as previously described ( Ryan et al . , 2012 , Roguev et al . , 2008[63] ) . To identify splicing clusters , we performed a thresholded version of UPGMA hierarchical clustering using Pearson’s correlation as the similarity metric . Initially each cluster contains single gene . Clusters were merged as long as the average Pearson correlation between their members was >0 . 3 . Clusters generated using this approach are presented in Fig . 1 and S1 Table . To evaluate links between splicing factors and processes or complexes ( Fig . 1B , D ) we used the permutation method of Bandyopadhyay et al [64] . A p-value was calculated by comparing the mean observed interaction score between two groups to that expected by drawing 106 equal-sized random samples of interactions from the E-MAP . All p-values were corrected using the Bonferonni method . Cultures were grown overnight in YES5 to saturation and were diluted to 0 . 005 OD600 to allow for overnight growth to mid log-phase . Strains were harvested by centrifugation between 0 . 5–0 . 8 OD600 . Pellets were either snap-frozen in liquid nitrogen or immediately processed for RNA isolation . Total cellular RNA was isolated using hot acid phenol followed by isopropanol precipitation as described in ( Bergkessel et al . , 2011 ) . Capped error bars represent Standard Error of the Mean ( SEM ) of 2–4 biological replicates . Uncapped error bars represent 3 technical replicates of a single biological sample . S . pombe splicing-specific microarrays were designed by , and were a generous gift from , Jeffrey Pleiss . The arrays were designed to measure the relative ( mutant vs . wild type ) amount of every intron and constitutive junction in fission yeast . A single exon probe is used as a measure of total expression for each intron-containing gene . Microarray experiments were performed as outlined in Inada and Pleiss , 2010 . Briefly , RNA was isolated as described above . cDNA synthesis was performed according to the manufacturer’s protocol ( Invitrogen , SuperScript III ) but with slight modifications: 5 mg/ml dN9 random primers were added to 20 ug RNA per synthesis reaction . Reactions were left at 42°C for at least 4 hours . RNA was removed from samples by NaOH treatment and cDNA was labeled with Cy3 ( wild-type ) and Cy5 ( mutant ) dyes . Labeled cDNAs were hybridzed to Agilent arrays . Agilent protocols were followed for hybridization and washing conditions . Splicing changes were expressed as ( intron*exon ) /junction , to take into account changes to overall gene expression for a given transcript . The R package limma was used to analyze the arrays and the data was normalized by loess normalization . To identify changes to cis-splicing signals that increased the likelihood of an intron being retained in our mutants , intron features were compiled and nucleotides were classified into common ( >50% of introns in the fission yeast genome contain that nucleotide at that position ) or rare ( all other bases ) . Log odds and their standard errors were calculated based on a logistic regression model for each nucleotide position . S . pombe strains were diluted to 0 . 005 OD600 in 50–100 mL of YES5 media and allowed to grow overnight . Cultures were crosslinked in 1% formaldehyde for 15 minutes when they had reached mid-long phase OD600 0 . 3–0 . 8 . Formaldehyde was quenched with 125 mM glycine for 5 min . Crosslinked cells were spun down at 300 rpm and washed twice with cold dH2O . Pellets were flash frozen in liquid nitrogen and stored at -80°C or were immediately processed for ChIP . ChIP was performed as described in Kress et al . , 2008 , with certain changes for S . pombe: protease inhibitors were increased ( 1 mM PMSF , 20 μg/mL leupeptin ) , bead beating was increased to 1 . 5’ x 10 cycles and sonication was increased to 6 cycles on high ( 10 min , 30 sec ON , 1 min OFF ) . Total and IPed DNA was analyzed by qPCR as described above . Capped error bars represent Standard Error of the Mean ( SEM ) of 2–4 biological replicates . Uncapped error bars represent 3 technical replicates of a single biological sample . Mononucleosomes were prepared as described in Infante et al . , 2012 , except for the following changes . MNase treatment was performed for 5 minutes using 150U MNase , which resulted in >80% mononucleosome-sized fragments as assayed by agarose gel electrophoresis . Crosslinking was reversed in 1% SDS at 65°C overnight in the presence of Proteinase K ( 30μl of 30mg/ml ) . RNase treatment ( 10μl of 10mg/ml ) followed . Total and mononucleosome DNA was analyzed by qPCR as described above using overlapping tiling primer sets . | It has recently become apparent that most introns are removed from pre-mRNA while the transcript is still engaged with RNA polymerase II ( RNAPII ) . To gain insight into possible roles for chromatin in co-transcriptional splicing , we generated a genome-wide genetic interaction map in fission yeast and uncovered numerous connections between splicing and chromatin . The SWI/SNF remodeling complex is typically thought to activate gene expression by relieving barriers to polymerase elongation imposed by nucleosomes . Here we show that this remodeler is important for an early step in splicing in which Prp2 , an RNA-dependent ATPase , is recruited to the assembling spliceosome to promote catalytic activation . Interestingly , introns with sub-optimal splice sites are particularly dependent on SWI/SNF , suggesting the impact of nucleosome dynamics on the kinetics of spliceosome assembly and catalysis . By monitoring nucleosome occupancy , we show significant alterations in nucleosome density in particular splicing and chromatin mutants , which generally paralleled the levels of RNAPII . Taken together , our findings challenge the notion that nucleosomes simply act as barriers to elongation; rather , we suggest that polymerase pausing at nucleosomes can activate gene expression by allowing more time for co-transcriptional splicing . | [
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] | [] | 2015 | Genetic Interaction Mapping Reveals a Role for the SWI/SNF Nucleosome Remodeler in Spliceosome Activation in Fission Yeast |
Genomic loci with regulatory potential can be annotated with various properties . For example , genomic sites bound by a given transcription factor ( TF ) can be divided according to whether they are proximal or distal to known promoters . Sites can be further labeled according to the cell types and conditions in which they are active . Given such a collection of labeled sites , it is natural to ask what sequence features are associated with each annotation label . However , discovering such label-specific sequence features is often confounded by overlaps between the labels; e . g . if regulatory sites specific to a given cell type are also more likely to be promoter-proximal , it is difficult to assess whether motifs identified in that set of sites are associated with the cell type or associated with promoters . In order to meet this challenge , we developed SeqUnwinder , a principled approach to deconvolving interpretable discriminative sequence features associated with overlapping annotation labels . We demonstrate the novel analysis abilities of SeqUnwinder using three examples . Firstly , SeqUnwinder is able to unravel sequence features associated with the dynamic binding behavior of TFs during motor neuron programming from features associated with chromatin state in the initial embryonic stem cells . Secondly , we characterize distinct sequence properties of multi-condition and cell-specific TF binding sites after controlling for uneven associations with promoter proximity . Finally , we demonstrate the scalability of SeqUnwinder to discover cell-specific sequence features from over one hundred thousand genomic loci that display DNase I hypersensitivity in one or more ENCODE cell lines .
Many regulatory genomics analyses focus on finding DNA sequence features that are characteristic of a biological property . Given a set of sequences that are bound by a particular transcription factor ( TF ) , for example , we typically aim to discover short , degenerate DNA patterns that may represent the DNA binding preferences of the TF itself , the binding preferences of coincident TFs , or general properties of the regions that make them favorable for binding . The de novo DNA motif-finding problem is typically cast in the context of two mutually exclusive sequence sets . Most popular motif-finding methods use unsupervised machine-learning approaches to discover motifs in ‘foreground’ input sequences that are over-represented with respect to a set of ‘background’ sequences ( e . g . “bound” vs . “unbound” , respectively ) [1 , 2] . Several other methods explicitly solve a two-class classification problem , where the goal is to find sequence features that discriminate between two mutually exclusive class labels [3–6] . Current characterizations of regulatory sites move beyond binary labels such as “bound” and “unbound” . For example , in a given cell type , each regulatory element could be labeled as bound or unbound by each of several TFs and enriched or depleted for several chromatin states [7–9] . As we add more regulatory class labels , it becomes difficult to define mutually exclusive sets of sequences that are representative of each label . Relatedly , our analyses may become confounded by uneven degrees of overlap between the class labels , leading to incorrect associations between sequence features and regulatory activities . Therefore , a simple recasting of discriminative motif-finding as a multi-class classification problem ( where classes are required to be mutually exclusive ) is not always appropriate . As an example , consider the hypothetical scenario presented in Fig 1A . In this example , a given TF’s binding sites have been profiled in types A , B , and C . Thus , each TF binding event can be labeled as specific to a cell type or common to all or a subset . Let’s assume that after further labeling the sites as being proximal or distal to promoters ( Pr and Di , respectively ) , we find that the TF’s binding sites in cell A are more likely to be promoter proximal than sites in other cell types . Promoter regions have sequence features that are distinct from distal regions ( e . g . the presence of core promoter elements and distinct GC-content patterns ) . Therefore , if we search for sequence features that are discriminative of cell A’s sites without accounting for the uneven overlaps with other labels , it is likely that some discovered features will actually be generic properties of proximal regions . Such results could in turn affect our conclusions regarding the biological mechanisms of TF binding in cell A . To resolve DNA features associated with each cell type’s label from those associated with confounding labels ( e . g . promoter proximity ) , we need motif-finders that are able to analyze multiple labels in parallel . Almost all existing discriminative motif-finders assume that the class labels are mutually exclusive , and therefore cannot appropriately handle scenarios such as that outlined in Fig 1A . For example , the multi-class discriminative sequence feature frameworks proposed by Tavazoie and colleagues [3 , 10 , 11] are limited to analysis of mutually exclusive classes . A few existing methods do allow a limited analysis of datasets where annotation labels partially overlap , but these approaches were designed for two-class classification problems where the multi-task framework enables modeling of the “common” task in addition to the two classes . For example , Arvey , et al . [4] used a multi-task SVM classifier to learn sequence features associated with cell type-specific TF binding across two cell types , along with features shared by TF binding sites in both cell types . The group lasso based logistic regression classifier SeqGL [5] also implements a similar multi-task framework to identify features that are discriminative between two classes and features that are common to both . No existing discriminative feature discovery method is applicable to multi-label classification scenarios where a set of genomic sequences contains several annotation labels with arbitrary rates of overlap between them . In this work , we present SeqUnwinder , a hierarchical classification framework for characterizing interpretable sequence features associated with overlapping sets of genomic annotation labels . We demonstrate the unique analysis abilities of SeqUnwinder using both synthetic sequence datasets and collections of real TF ChIP-seq and DNase-seq experiments . In each demonstration , SeqUnwinder cleanly associates interpretable sequence features with various cell- or condition-specific annotation labels , while simultaneously removing the effects of confounding signals . SeqUnwinder scales effectively to large collections of genomic loci that have been annotated with several overlapping labels , and is thus designed to deal with the complexity of modern data sets .
The intuition behind SeqUnwinder is that sequence features associated with a particular annotation label should be similarly enriched across all subclasses spanned by the label ( regardless of how the subclasses have been defined ) . SeqUnwinder’s analysis begins by defining genomic site subclasses based on the combinations of labels annotated at these sites ( Fig 1B ) . The site subclasses are treated as distinct classes for a multi-class logistic regression model that uses k-mer frequencies as predictors . At the same time , k-mer models are also learned for each label by incorporating them in an L1 regularization term ( see Methods ) . In other words , while the k-mer weight parameters for each subclass are learned directly from the data , the weight parameters for the labels are learned exclusively through the regularization constraint . The regularization encourages each label’s model to take the form of the features that are consistently enriched across the subclasses spanned by that label ( Fig 1B ) . The trained classifier encapsulates weighted k-mer models specific to each label and each subclass ( i . e . combination of labels ) . The label- or subclass-specific k-mer model is scanned across the original genomic sites to identify focused regions ( which we term “hills” ) that contain discriminative sequence signals ( Fig 1C ) . Finally , to aid interpretability , SeqUnwinder identifies over-represented motifs in the hills and scores them using label- and subclass-specific k-mer models ( Fig 1D ) . SeqUnwinder is easy to use , taking as input a list of DNA sequences or genomic coordinates that are each annotated with a set of user-defined labels . The labels can come from any source , enabling a high degree of analysis flexibility . SeqUnwinder implements a multi-threaded version of the ADMM [12] framework to train the model and typically runs in less than a few hours for most datasets . Output includes both k-mer models and position-specific scoring matrices and weights associating these motifs with each subclass and label . To demonstrate the properties of SeqUnwinder , we simulated 9 , 000 regulatory regions and annotated each of them with labels from two overlapping sets: A , B , C and X , Y ( Fig 2A ) . We assigned a different motif to each label . At 70% of the sequences associated with each label , we inserted appropriate motif instances by sampling from the distributions defined by the position-specific scoring matrices of label assigned motifs ( Fig 2A ) . We used this collection of sequences and label assignments to compare SeqUnwinder with a simple multi-class classification approach ( MCC ) . In MCC training , each label was treated as a distinct class and therefore each regulatory sequence is included multiple times in accordance with its annotated labels . SeqUnwinder and the MCC model correctly identify motifs similar to all inserted motifs ( Fig 2B ) . However , the MCC approach makes several incorrect motif-label associations , potentially due to high overlap between labels . In contrast , the label-specific scores of the identified motifs in the SeqUnwinder model are not confounded by overlap between annotation labels . For example , even though labels X and A highly overlap , SeqUnwinder correctly assigns each motif to its respective label . Next , we assessed the performance of SeqUnwinder at different levels of label overlaps . We simulated 100 datasets with 6000 simulated sequences , varying the degree of overlap between two sets of labels ( {A , B} and {X , Y} ) from 50% to 99% ( Fig 2C ) . We then compared SeqUnwinder with MCC and DREME [1] , a popular discriminative motif discovery tool . Since DREME takes only two classes as input: a foreground set and a background set , we ran four different DREME runs for each of the four labels . We calculated the true positive ( discovered motif correctly assigned to a label ) and false positive ( discovered motif incorrectly assigned to a label ) rates based on the true label assignments . We used these measures to calculate the F1 score ( harmonic mean of precision and recall ) at different overlapping levels ( Fig 2D ) . Fig 2D demonstrates the range of label overlap rates in which SeqUnwinder outperforms the alternative approaches . When the labels are uncorrelated ( i . e . low or random overlap ) , the sequence features associated with each label do not confound one another and thus all methods perform similarly well in characterizing label-specific motifs . On the other hand , when the labels are highly correlated ( i . e . high overlap ) , it becomes impossible for any method to correctly assign sequence features to the correct labels . SeqUnwinder performs better than the other approaches in the intermediate range of label overlaps , and accurately characterizes label-specific sequence features even when the simulated labels overlap at 90% of sites . More specifically , SeqUnwinder consistently has a false positive rate ( incorrectly assigning motifs to labels ) of zero at the cost of a modest decrease in true positive rates ( recovering all motifs assigned to a label ) ( Fig 2E and Fig 2F ) . To demonstrate its unique abilities in a real analysis problem , we use SeqUnwinder to study TF binding during induced motor neuron ( iMN ) programming . Ectopic expression of Ngn2 , Isl1 , and Lhx3 in mouse embryonic stem ( ES ) cells efficiently converts the resident ES cells into functional spinal motor neurons [13 , 14] . We recently characterized the dynamics of motor neuron programming by studying TF binding , chromatin dynamics , and gene expression over the course of the 48hr programming process [14] . We found that two of the ectopically expressed TFs , Isl1 & Lhx3 , bind together at the vast majority of their targets during the programming process . Using MultiGPS [15] , we also found that this cooperative pair of TFs shifted their binding targets during programming , and we used three mutually exclusive labels–early , shared , and late–to annotate Isl1/Lhx3 binding sites according to their observed dynamic occupancy patterns . Early sites were bound by Isl1/Lhx3 only during earlier stages of programming , shared sites were constantly bound over the entire programming process , and late sites were only bound during the final stage of programming . In our previous work , we demonstrated that the early Isl1/Lhx3 sites were more accessible in the initial pluripotent cells , and we suggested that some early sites are the result of opportunistic Isl1/Lhx3 binding to ES enhancer regions [14] . However , this raises a question that was not addressed in our earlier work: if we discover sequence features at early sites , how can we tell if those features are specifically associated with Isl1/Lhx3 as opposed to reflecting on coincident properties of ES enhancers ? In order to assess the potential confounding effects of ES regulatory sites , we trained a random forest classifier to further categorize all Isl1/Lhx3 bound sites using two additional labels: “ES-active and “ES-inactive” ( see Methods ) . Annotating Isl1/Lhx3 sites using both sets of labels ( Isl1/Lhx3 binding dynamics and ES activity ) results in six different subclasses . As can be seen from Fig 3A , early sites have a higher propensity to also be active prior to ectopic TF expression in the starting ES cells . Conversely , the late sites are more likely to be inactive in ES cells . We next trained SeqUnwinder on the multi-label Isl1/Lhx3 dataset , and compared the results with those of DREME and the simple MCC approach described in the previous section ( Fig 3B , S1 Fig , S2 Fig , S1 Table ) . All methods discover similar sets of motifs . For example , both the SeqUnwinder and MCC approaches find motifs corresponding to the binding preferences of Oct4 , Zfp281 , Onecut-family TFs , and homeodomain TF motifs corresponding to the cognate Isl1/Lhx3 binding preference ( Fig 3B ) . However , the different approaches produce different associations between motifs and labels . For example , the MCC approach associates the Oct4 motif with both the “early” and “ES-active” labels , and it associates the Onecut motif with both “late” and “ES-inactive” labels ( S1 Fig ) . DREME similarly makes ambiguous associations ( S2 Fig ) . SeqUnwinder , in contrast , makes much cleaner associations; the Oct4 motif is only associated with the “early” label , and the Onecut motif is only associated with the “late” label , suggesting that these motifs are not merely coincidental features due to the ES activity status of the binding sites . The SeqUnwinder motif-label associations suggest that Isl1/Lhx3 bind cooperatively with Oct4 and Onecut TFs at subsets of early and late binding sites , respectively . As described in our earlier work , we characterized Onecut2 binding to be highly enriched at late Isl1/Lhx3 sites during iMN programming [14] . We also found that late sites are not bound by Isl1/Lhx3 ( and iMN programming does not proceed ) in cellular conditions under which Onecut TFs are not expressed [14] , supporting a model in which late Isl1/Lhx3 binding is dependent on Onecut TFs . Analysis of motif log-odds scores and Onecut2 ChIP enrichment further support SeqUnwinder’s prediction that the Onecut motif is not merely a general feature of ES-inactive sites ( Fig 3C ) . Conversely , Oct4 is predicted by SeqUnwinder to be a specific feature of “early” binding sites , and not merely an artifact associated with “ES-active” sites . Using ChIP-seq , we profiled the binding of Oct4 immediately before NIL induction . As shown in Fig 3D , Oct4 motif log-odds scores and ChIP-seq tags show a preferential enrichment at early Isl1/Lhx3 sites , in line with SeqUnwinder’s prediction . Interestingly , the motif features that are most highly associated with shared binding sites all correspond to homeodomain TF motifs of the type bound by Isl1/Lhx3 ( Fig 3B and S1 Fig ) . One possible explanation is that there are stronger or more frequent cognate motif instances at sites bound by a given TF across multiple timepoints , or indeed across multiple unrelated cell types . We further assess this hypothesis in the following section . Our analysis of Isl1/Lhx3 binding during iMN programming thus serves as an example analysis scenario in which SeqUnwinder identifies motif features associated with multiple overlapping labels , leading to testable hypotheses about co-factors that serve mechanistic roles at subsets of binding sites . The sequence properties of tissue-specific TF binding sites have been extensively studied [4 , 5 , 16] . As might be expected , sites that are bound by a given TF in only one cell type are often enriched for motifs of other TFs expressed in that cell type . Therefore , a given TF’s cell-specific binding activity is likely determined by context-specific interactions with other expressed regulators . Most TFs also display cell-invariant binding activities; each TF typically has a cohort of sites that appear bound in all or most cellular conditions in which that TF is active . Despite the potential regulatory significance of such multi-condition binding sites , little is known about the sequence properties that enable a TF to bind them regardless of cellular conditions . Studies of individual TFs suggest that binding affinity to cognate motif instances may play a role in distinguishing multi-condition binding sites from tissue-specific sites [15 , 17] . In order to characterize sequence discriminants of multi-condition TF binding sites across a wider range of TFs , we curated multi-condition ChIP-seq experiments from the ENCODE project . We restricted our analysis to the 17 sequence-specific TFs profiled in all 3 primary ENCODE cell-lines ( K562 , GM12878 , and H1-hESC; see Methods ) [18] . For each TF , we used MultiGPS analysis to curate sets of tissue-specific sites in each cell type , and a further set of sites that are “shared” across all three cell types ( see Methods ) . For most examined TFs , the majority of shared binding sites were located in promoter proximal regions ( S3 Fig ) . As outlined in the Introduction , promoter proximal sites are known to have distinct sequence biases , which could confound the discovery of sequence features associated with shared sites . We therefore further labeled each TF’s binding sites as being located proximal or distal to annotated TSSs . Introducing the proximal and distal labels should marginalize the proximal bias at shared sites , as the sequence features learned by SeqUnwinder at shared sites must be consistently enriched at both proximal and distal sites . Each examined TF’s binding sites is thus categorized into eight subclasses , each of which is composed of combinations of six distinct labels . We applied SeqUnwinder to each labeled sequence collection in order to characterize label-specific sequence features ( see S2 Table for cross-validation classification performance values ) . We illustrate the process with SeqUnwinder’s results for YY1 . We started with a total of ~35 , 000 YY1 binding events called by MultiGPS across the three cell types , categorized into the eight aforementioned subclasses ( Fig 4A ) . SeqUnwinder identifies several de novo motifs in this collection ( Fig 4B ) . Interestingly , SeqUnwinder predicts that a motif matching the cognate YY1 motif is strongly associated with the “shared” label . The cell-type specific , proximal and distal labels had low or negative scores for this cognate motif . Note here that a non-positive label-specific score for a motif does not necessarily imply complete absence of instances of that motif . A significant depletion of motif instances at sites annotated by a label compared to other labels can very likely result in non-positive scores . Cell-type specific sites had higher scores for co-factor motifs . For example , H1-hESC specific sites were enriched in instances of a TEAD-like motif , while K562-specific sites and GM12878-specific sites were enriched for a GATA-like motif and an IRF-like motif , respectively . In fact , GATA2 ChIP-seq reads in K562 , IRF4 ChIP-seq reads in GM12878 , and TEAD4 ChIP-seq reads in H1hESC showed striking enrichment at corresponding cell-specific YY1 binding sites ( Fig 4A ) . Analogous results were observed for many of the examined factors . For 13 out of the 17 examined factors , SeqUnwinder predicts that the cognate motif is highly associated with the “shared” label ( Fig 5A ) . Despite significant overlaps between shared sites and promoter proximal sites ( S3 Fig ) , the cognate motifs were not found to be predictive of any TF’s “proximal” label ( Fig 5A ) . Furthermore , the cognate motif was not specifically predictive of cell-type-specific labels for the examined TFs , with the exception of H1-hESC-specific sites for CEBPB , NRSF and SRF . An orthogonal analysis of log-odds motif scoring distributions across each TF’s labels is consistent with the SeqUnwinder results ( S4 Fig ) . When we ran DREME on the same datasets for comparison , the association of cognate motif to shared sites was less clear . For 9 of the tested factors , DREME results associated the cognate motif with more than one label ( S5 Fig ) . We also examined the motifs that SeqUnwinder predicts to be associated with cell-type-specific binding labels . Interestingly , we found IRF and RUNX motifs enriched at GM12878-specific binding sites for 11 and 7 of the 17 examined TFs , respectively . Similarly , the GATA motif was predictive of K562-specific binding for 14 of the 17 examined TFs . A TEAD-like motif was predictive of H1-hESC specific sites for 11 of the 17 TFs ( Fig 5B ) . The observation that cell-type-specific sites are depleted for cognate motif instances but are enriched for motif instances of other lineage-specific regulators is consistent with the “TF collective” model proposed by Junion and colleagues [19] . Under this model , the cooperative binding of large numbers of TFs is driven by the presence of motifs for a subset of lineage-specific factors that drive recruitment of the rest ( i . e . the motifs for some TFs need not always be present ) . To further support the “TF collective” interpretation of SeqUnwinder’s results , we tested the degree to which TSS-distal cell-type-specific sites are bound by numerous other TFs . We first defined a binding site’s “collective degree” as the number of distinct TFs with nearby ChIP-seq peaks . To calculate collective degree , we used a total of 158 , 102 , and 202 ChIP-seq datasets in GM12878 , H1-hESC , and K562 cell-types , respectively . From Fig 5C , it is clear that distal K562- and GM12878-specific sites lacking a cognate motif instance have higher collective degrees . Similar findings were previously identified at the “high occupancy of transcription-related factors ( HOT ) ” regions [20] . Finally , we aim to demonstrate the utility of SeqUnwinder in identifying sequence features at large numbers of genomic loci annotated with several labels . We first annotated a large collection of DNase I hypersensitive ( DHS ) sites with six cell-line labels depending on the enrichment of DNase-seq reads ( Fig 6A ) . If we had used analysis methods that rely on mutually exclusive categories , we would need to restrict analysis to ~97 , 000 sites labeled as either shared or exclusive to one of the six cell types [21] . Indeed , these strict category definitions may introduce sequence composition biases into each category . However , by taking advantage of SeqUnwinder’s unique framework to pool information from all subclasses , we can analyze ~140 , 000 DHS sites that we annotate into 22 subclasses as shared ( i . e . enriched in 5 or more cell types ) or specific to one or two cell types ( Fig 6A , S3 Table ) . SeqUnwinder identifies several motifs in this large collection of DHS sites , including those previously associated with specific cell-types [22–24] ( Fig 6B ) . For example , components of the CTCF motif were highly predictive of shared DHS sites . This result is consistent with previous findings suggesting relatively invariant CTCF binding across cellular contexts [25 , 26] . RUNX , IRF and NF-κB motifs were enriched at GM12878-specific DHS sites . These motifs were also discovered by others at GM12878-specific DHS sites [5 , 23] . Motifs corresponding to GATA TFs , key regulators of erythroid development [27–29] , were enriched at K562-specific DHS sites . SNAI and TEAD motifs were enriched at H1-hESC sites , consistent with previous observations [5] . JUND and FOS motifs were enriched at HeLa-S3-specific DHS sites . Motifs for HNF4A and FOX TFs , known master regulator of hepatocytes [30–33] , were enriched at HepG2-specific DHS sites . Finally , motifs belonging to the ETS class of TFs were enriched at HUVEC-specific DHS sites ( Fig 6B ) . ETS factors have been shown to directly convert human fibroblasts to endothelial cells [34] . Interestingly , some of the motifs associated with cell-type specific DHS sites were also found in our analyses of cell-type specific TF binding sites above ( Fig 5B ) . For example , IRF , GATA , and TEAD motifs associated with GM12878 , K562 , and H1-hESC specific DHSs were also predictive of corresponding cell-type specific binding for many of the analyzed TFs . These results demonstrate that SeqUnwinder scales effectively in characterizing sequence features at thousands of regulatory regions annotated by several different overlapping labels .
Classification models have shown great potential in identifying sequence features at defined genomic sites . For example , Lee et al . [3] trained an SVM classifier to discriminate putative enhancers from random sequences using an unbiased set of k-mers as predictors . The choice of kernel function is key to the performance of an SVM classifier . Several variants of the basic string kernel ( e . g . mismatch kernel [35] , di-mismatch kernel [4] , wild-card kernel [5 , 35] , and gkm-kernel [36] ) have been proposed and have been shown to substantially improve the classifier performance . Several complementary methods using DNA shape features in a classification framework have also provided insight on the role of subtle shape features that distinguish bound from unbound sites [37–39] . More recently , deep learning models have also been harnessed to predict TF binding sites from unbound sites [6] . In this work , we focus not on the form of the training features , but rather on the tangential problem of identifying sequence features that discriminate several annotations applied to a set of genomic locations . Most existing methods have been developed and optimized to identify sequence features that discriminate between mutually exclusive classes ( e . g . bound and unbound sites ) . However , when considering different sets of genomic annotation labels , overlaps between them are very likely and can confound results . To systematically address this , we developed SeqUnwinder . Using three analysis scenarios based on real ChIP-seq and DNase-seq datasets , we have demonstrated that SeqUnwinder provides a unique ability to deconvolve discriminative sequence features at overlapping sets of labeled sites . Our applications are chosen to demonstrate that SeqUnwinder has the ability to predict the identities of TFs responsible for particular regulatory site properties , while accounting for potential sources of bias . For example , in our previous characterization of Isl1/Lhx3 binding dynamics during motor neuron programming , we discovered motifs that were enriched at early and late binding site subsets [14] . However , our analyses were potentially confounded by a correlation between TF binding dynamics and the chromatin properties of the sites in the pre-existing ES cells . Therefore , the motifs that we previously assigned to early or late TF binding behaviors could have been merely associated with ES-active and ES-inactive sites , respectively . By implicitly accounting for the effects of overlapping annotation labels , SeqUnwinder can deconvolve sequence features associated with motor neuron programming dynamics and ES chromatin status . Our analyses support an association between Oct4 binding and early Isl1/Lhx3 binding sites , along with our previously confirmed association between Onecut TFs and late Isl1/Lhx3 binding sites [14] . Our analyses of ENCODE ChIP-seq and DNase-seq datasets demonstrate the flexibility and scalability of SeqUnwinder . In analyzing TF binding across multiple cell types , we used SeqUnwinder to account for promoter proximity as a potential confounding feature . Our results add to the growing evidence that multi-condition TF binding sites tend to be distinguished by better quality instances of the primary cognate motif [15 , 17] . For example , Gertz et al . , showed that ER ( estrogen receptor ) binding sites bound in both ECC1 and T4D7 , two human cancer cell lines , had higher affinity instances of EREs ( estrogen response elements ) compared to cell-specific binding sites . Indeed , even the “shared” binding sites for Isl1/Lhx3 in our first demonstration are characterized by stronger instances of the Isl1/Lhx3 cognate binding motifs ( Fig 3B ) . These results suggest that many TFs have a set of binding sites that are bound across a broad range of cellular contexts , and which are characterized by better quality cognate motif instances . Furthermore , our results support a model in which cell-type-specific sites lacking cognate motif instances are bound in a “TF collective” fashion [19] . Interestingly , SeqUnwinder discovers consistent motif features to be predictive of cell-specific binding sites across several examined TF ChIP-seq collections . For example , SeqUnwinder discovers GATA , IRF and TEAD motifs at K562- , GM12878- and H1hESC-specific TF binding sites , respectively . These same motifs are also discovered by SeqUnwinder to be predictive of appropriate cell-specific DNase I hypersensitivity in a large collection of DHS sites across 6 different cell types . SeqUnwinder’s characterization of cell-specific motif features in collections of DNase-seq datasets may therefore serve as a source of predictive features for efforts that aim to predict cell-specific TF binding from accessibility experimental data alone [39–41] . There remain several areas of possible future improvement within SeqUnwinder’s hierarchical multi-label classification approach to discriminative motif-finding . SeqUnwinder does not currently model any relationships or correlations between class labels . For example , we might expect similar cell types to have similar cell-specific motif features within their regulatory regions . Incorporating graphs defined by label similarities [42 , 43] may thus be productive in the context of analyses across cell lineages or developmental time-series . SeqUnwinder may also be easily extended to incorporate different kinds of sequence kernels and DNA shape features [35 , 36 , 44] . In summary , SeqUnwinder provides a flexible framework for analyzing sequence features in collections of related regulatory genomic experiments , and uniquely enables the principled discovery of sequence motifs associated with multiple overlapping annotation labels .
The core of SeqUnwinder is a multi-class logistic regression classifier trained on subclasses of genomic sites . The training features for the classifier are based on k-mer frequencies in a fixed window around input loci . The value or range of k is user-definable in the SeqUnwinder software , but all analyses in this work use models based on all 4-mers and 5-mers . When counting frequencies , we map each k-mer to the same entry as its reverse complement . To account for differences in the ranges of k-mer frequencies , we standardize the feature vectors such that each k-mer has a zero mean and unit variance across the entire training dataset . The parameters of SeqUnwinder are k-mer weights for each subclass ( combination of annotation labels ) . In addition , SeqUnwinder also models the label-specific k-mer weights by incorporating them in the L1 regularization term . Briefly , label-specific k-mer weights are encouraged to be similar to k-mer weights in all subclasses the label spans by regularizing on the differences of k-mer weights . Note that our approach is conceptually similar to hierarchical classification approaches such as that described by [45] , although we use L1 regularization as opposed to L2 . The overall objective function of SeqUnwinder is: - −∑i=1M∑n∈Tbiyinlog[exp ( wnxi ) ∑n∈Texp ( wnxi ) ]+λ∑n∈T∑p∈Π ( n ) ‖wn−wp‖1 ( 1 ) In the above equation; M is the total number of genomic loci in all subclasses , T is the set of all subclasses , bi is the weight given to the genomic site i , wn is the k-mer weight vector for subclass n , xi is a vector of k-mer counts for the genomic site i , yin is a binary indicator variable denoting the subclass of genomic site i , λ is the regularization co-efficient , ∏ ( n ) is the set of all labels spanning the subclass n , and wp is the k-mer weight vector for label p . Values for bi are chosen to account for class imbalances . Hence , the value of bi for a genomic site i belonging to class n is defined as |nmax|/|n| , where |n| denotes the number of genomic sites in subclass n and |nmax| denotes the number of genomic sites in the subclass with maximum sites . The wn and wp update steps separate out and are iteratively updated until convergence . The wp update step has a simple closed form solution given by the equation: wpk=median ( cpk ) ;wherecpk={wjk|j∈C ( p ) } Where wpk is the kth term of the label-p weight vector . cpk is a set of the kth terms of the weight vectors of all the subclasses the label p spans . The wn update step is: - wn=argminwn[−∑i=1M∑n∈Tbiyinlog ( exp ( wnxi ) ∑n∈Texp ( wnxi ) ) +λ∑n∈T∑p∈Π ( n ) ‖wn−wp‖1] The above equation is solved using the scaled alternating direction method of multipliers ( ADMM ) framework [12] . Briefly , the ADMM framework splits the above problem into 2 smaller sub-problems , which are much easier to solve . ADMM introduces an additional variable znp initialized as follows znp=wn−wp; wn and znp are iteratively estimated until convergence of the ADMM algorithm . Sub-problem 1: wnt+1=argminwn[−∑i=1M∑n∈Tbiyinlog ( exp ( wnxi ) ∑n∈Texp ( wnxi ) ) +ρ2∑n∈T∑p∈Π ( n ) ‖wn−znpt−wp+unpt‖22] Where unp is the scaled dual variable . The above sub-problem is solved using the LBFGS ( limited-memory Broyden Fletcher Goldfarb Shanno ) algorithm [46] . Sub-problem 2: znpt+1=argminznp[λ‖znp‖1+ρ2‖wnt+1−znp−wp+unpt‖22] The solution to the above equation is given by the shrinkage function defined as follows: - znpt+1=δ2λρ ( wnt+1+znpt−wp+unpt ) δk ( a ) ={a−k , ifa>k0 , if‖a‖≤ka+k , ifa<−k The update step for the scaled dual variable unp is: - unpt+1=unpt+wnt+1−znpt+1−wp wnt , znpt , and unpt are iteratively estimated until convergence . The stopping criteria for the ADMM algorithm is: ‖ρ ( znp−znpold ) ‖2<ϵabs*K+ϵrel*‖ρ*unp‖2 and ‖wn−znp−wp‖2<ϵabs*K+ϵrel*max ( ‖wn‖2 , ‖znp‖2 , ‖wp‖2 ) Where ϵabs and ϵrel are the absolute and relative tolerance , respectively . Of note , to speed up the implementation of SeqUnwinder , a distributed version of ADMM was implemented . Intuitively , the wnt+1 update step is distributed across multiple threads by splitting the M training examples into smaller subsets . The znpt+1 and the unpt+1 update steps act as pooling steps where the estimates of different threads are averaged . To further speed up convergence , a relaxed version of ADMM was implemented as described in [12] . In the relaxed version , wnt+1 is replaced by αwnt+1+ ( 1−α ) znpt for the znpt+1 and unpt+1 update steps , where α is the over-relaxation parameter and is set to 1 . 9 as suggested in [12] . While SeqUnwinder models label-specific sequence features using high-dimensional k-mer weight vectors , it is often desirable to visualize these sequence features in terms of a collection of interpretable position-specific scoring matrices . To do so , we use a combination of k-mer model scanning and local motif-finding in an approach similar to that used by SeqGL for producing interpretable motifs [5] . Specifically , we first scan the k-mer models learned during the training process across fixed-sized sequence windows around the input genomic loci to identify local high-scoring regions called “hills” . Label-specific hills are focused regions ranging from 10 to 15bp and are composed of high scoring k-mers . We use a threshold of 0 . 1 to define hills . Next , we cluster the hills using K-means clustering with Euclidean distance metric and k-mer counts as features . To speed-up implementation , we restrict the unbiased k-mer features to only those k-mers that are present in at least 5% of the hills . We use silhouette index [47] to choose the appropriate value for K . Briefly , we test a range of K values from 2 to 6 . For each value of K , we calculate the silhouette index on 30 bootstrap samples . The value of K with highest median silhouette index is chosen as the best value for K . Finally , any clusters with membership size ( i . e . numbers of clustered hills ) less than 10% of the largest cluster’s membership size are merged with their next closest cluster . MEME [48] is used to identify motifs in different clusters resulting in label-specific discriminative motifs . Each k-mer model further scores MEME-identified motifs as follows: Scorewp ( motifx ) =∑j∈motifxwpj where j ∈ motifx is the set of all k-mers that belong to motif “motifx” . Note that the heatmaps in each figure which display these label-specific discriminative scores have been generated with a shared color scheme; i . e . , the maximum shade of yellow is defined to correspond to a model-specific score of +0 . 4 , while the maximum shade of blue is set to a score of -0 . 4 . In each figure , individual motifs sometimes have scores outside of these bounds , but we chose to use a shared color scheme for consistency of interpretation across figures . In our experience , the above “hill-finding” method provides a convenient way to convert high-dimensional k-mer models into interpretable position-specific scoring matrices , and is less error-prone than alternative k-mer clustering or assembly approaches . One advantage of the “hill-finding” approach is that it implicitly takes into account positional relationships between high-scoring k-mers on the genome; short stretches that contain multiple high-scoring k-mers will form larger “hills” . Focused motif searches in the hills thus can find motifs that are longer than the longest k-mers in the underlying SeqUnwinder model . To test SeqUnwinder in simulated settings , we generated various synthetic datasets . The sizes of simulated datasets ( 6 , 000–9 , 000 sequences ) were chosen to roughly reflect the number of peaks in a typical ChIP-seq dataset . First , we generated 150bp sequences by sampling from a 2nd-order Markov model of the human genome . Our use of a 2nd-order Markov model is motivated by a desire to capture typical di- and tri-nucleotide compositional biases of vertebrate genomes ( e . g . CG dinucleotide depletion and poly-A tracts ) . The exact choice of order of the background Markov model ( i . e . 2nd-order versus a higher order ) is arbitrary , but should not be expected to affect the relative performances of the methods in correctly associating embedded motifs with correct labels . Next , we randomly assigned labels to the simulated sequences at different frequencies . The overlap between the labels at the sequences was varied from 0 . 5 to 0 . 99 . Arbitrarily chosen TF binding motifs were assigned to labels . Each motif instance was sampled from the probability density function defined by the PWM of the motif . Sampled motif instances were inserted at labeled sites at a frequency of 0 . 7 . TF ChIP-seq datasets: We analyzed 17 TF ChIP-seq ENCODE datasets in three primary cell-lines ( GM12878 , K562 , and H1-hESC ) . The binding profiles for the factors were profiled using MultiGPS [15] . All called binding events for TFs were required to have significant enrichment over corresponding input samples ( q-value <0 . 01 ) as assessed using MultiGPS’ internal binomial test . For a site to be labeled as “shared” , the binding site was required to be called in all the 3 cell-lines . Further , binding sites showing significantly differential binding in any of the possible 3 pair-wise comparisons were removed from the shared set . Binding sites labeled as cell-type specific sites were required to have significantly higher ChIP enrichment compared to other cell-lines . All TF binding sites within 5Kbp of a known TSS ( defined using UCSC hg19 gene annotations ) were labeled as “promoter proximal” , while all sites that were more than 5Kbp from known TSSs were labeled as “distal” . DNase-seq datasets: We analyzed the DHS sites at 6 different tier 1 and 2 ENCODE cell-lines ( GM12878 , K562 , H1-hESC , HeLa-S3 , HepG2 , HUVEC ) . The DHS sites were called using in-house scripts . Briefly , contiguous 50bp genomic bins with significantly higher read enrichment compared to an input experiment were identified ( binomial test , p-value < 0 . 01 ) . Further , contiguous blocks within 200bp were stitched together to call enriched domains . A 150bp window around the maximum point of read density at enriched domains was considered as the DHS . All de novo motifs identified using SeqUnwinder were annotated using the cis-bp database . Briefly , de novo motifs were matched against the cis-bp database using STAMP [49] . The best matching hit with a p-value of less than 10e-6 was used to name the de novo identified motifs . SeqUnwinder is freely available under the MIT open source license from: https://github . com/seqcode/sequnwinder . Complete output files produced by the SeqUnwinder runs described in this work , along with scripts and data for reproducing all analysis figures , are available from: https://github . com/ikaka89/sequnwinderPaper . | Transcription factor proteins control gene expression by recognizing and interacting with short DNA sequence patterns in regulatory regions on the genome . Current genomics experiments allow us to find regulatory regions associated with a particular biochemical activity over the entire genome; for example , all regions where a particular transcription factor interacts with the genome in a given cell type . Given a collection of regulatory regions , we often aim to discover short DNA sequence patterns that are more common in the collection than in other regions . Performing such “DNA motif-finding” analysis can give us hints about the patterns that determine gene regulation in the analyzed cell type . Here we describe a new method for DNA motif-finding called SeqUnwinder . Our approach analyzes collections of regulatory regions where each has been labeled according to various biological properties . For example , the labels could correspond to various cell types in which the regulatory region is active . SeqUnwinder then performs machine-learning analysis to unravel DNA sequence features that are characteristic of each label ( e . g . features that distinguish regulatory regions in each cell type from other cell types ) . SeqUnwinder is the first method to enable analysis of regulatory region collections that contain several overlapping labels . | [
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] | 2017 | Deconvolving sequence features that discriminate between overlapping regulatory annotations |
Characterizing the force of infection ( FOI ) is an essential part of planning cost effective control strategies for zoonotic diseases . Echinococcus multilocularis is the causative agent of alveolar echinococcosis in humans , a serious disease with a high fatality rate and an increasing global spread . Red foxes are high prevalence hosts of E . multilocularis . Through a mathematical modelling approach , using field data collected from in and around the city of Zurich , Switzerland , we find compelling evidence that the FOI is periodic with highly variable amplitude , and , while this amplitude is similar across habitat types , the mean FOI differs markedly between urban and periurban habitats suggesting a considerable risk differential . The FOI , during an annual cycle , ranges from ( 0 . 1 , 0 . 8 ) insults ( 95% CI ) in urban habitat in the summer to ( 9 . 4 , 9 . 7 ) ( 95% CI ) in periurban ( rural ) habitat in winter . Such large temporal and spatial variations in FOI suggest that control strategies are optimal when tailored to local FOI dynamics .
The force of infection ( FOI ) is a crucial epidemiological parameter and characterizing its dynamics is an essential part of planning cost effective control strategies for infectious diseases [1] . Mechanistically , disease intervention strategies are typically targeted at decreasing the per capita infection rate . If successful , this will then cause a decrease in observed prevalence . As such , quantification of the FOI provides a key measure of efficacy when assessing or comparing interventions [2] . The FOI can be extremely difficult to estimate directly , i . e . observationally , in wildlife populations . Even in human populations this is not without considerable challenges , and requires accurate longitudinal monitoring of the target population in order to capture all new infections which arise [3] . An alternative approach is to estimate the FOI indirectly , through access to prevalence data , in conjunction with either an explicit mathematical model describing the disease transmission processes , or else some assumed disease risk function [4] , [5] . Foxes are typical definitive hosts for the parasite Echinococcus multilocularis , with different rodent species being the primary intermediate host in which the alveolar hydatid cysts grow . In humans , which are aberrant hosts , this parasite causes the important emerging zoonosis alveolar echinococcosis ( AE ) . This is a serious disease with a high fatality rate in the absence of appropriate treatment [6] . In Europe there have been increasing numbers of AE cases reported in the Baltics [7] , Poland [8] , Austria [9] and in Switzerland [10]: the latter associated with an increase in fox populations . The disease is also emergent in central Asia with a huge increase in the numbers of human cases in Kyrgyzstan recorded in recent years [11] . This disease also has a considerable impact on human health in Western China , particularly on the Tibetan plateau [12] . Alveolar echinococcosis is also an emerging public health concern in North America due , at least in part , to the increasing urbanization of wild canids [13] . Red foxes ( Vulpes vulpes ) are high prevalence hosts of E . multilocularis [14] , where zoonotic transmission may occur through environmental contamination [15] or through contaminated food [16] . In addition , dogs are susceptible definitive hosts [17] and may be very important for transmission to humans where prevalences in dogs are high , such as in China [18] or central Asia [19] . In Europe , dogs are low pravalence hosts [20] , but nevertheless may pose a high risk of introducing the parasite in non endemic countries such as the UK if appropriate treatment is not given when dogs enter the country [21] . In terms of potential control measures for reducing the risk of AE , a number of different studies have investigated anthelmintic baiting in foxes [22] . The impact of such approaches on reducing prevalence appears to strongly depend on the specific design used , in relation to how the baits are delivered and choices of location , and frequency . In Switzerland , year round monthly anthelmintic baiting is an effective control measure in foxes [22] . The E . multilocularis transmission cycle is , however , dynamically highly complex with many known temporal-spatial heterogeneities ( for example [23] ) . Adopting , therefore , a baiting strategy in close concordance with FOI dynamics could optimize existing intervention strategies . In planning such intervention studies knowledge of the dynamics and magnitude of the FOI can be invaluable , as this potentially allows the frequency of baiting to be tailored to the changing levels of exposure throughout time and across space . This may enable considerable cost saving , as opposed to , for example , monthly all year round baiting across all habitat types . In Switzerland it has been shown that there are considerable differences in the spatial and seasonal distribution of the prevalence of E . multilocularis in definitive hosts [14] , [15] and intermediate hosts [23] . These studies indicated that 129 of 857 Arvicola terrestris were infected of which 12 harboured protocolices . Ten of these animals had between 61 and 452 , 000 protoscolices . Seasonal patterns of infection in intermediate hosts were seen with highest prevalences seen in over-wintered animals . Thus seasonal anthelmintic treatment of foxes , with a focus on the autumn and winter months , is likely to be a more efficient strategy in reducing the parasite biomass [23] . Likewise although fox densities are highest in urban settings , they consume fewer rodents and have a greater reliance on anthropomorphic food supplies compared to rural foxes [24] , which is likely to significantly affect transmission dynamics on a spatial scale . Consequently , the intensity of intervention strategies could also be tailored to exploit these spatial differences . Such differences in prevalences clearly indicate that relative differences in the FOI exist between rural and urban areas , and between winter and summer seasons . We develop a statistically robust quantitative characterization of the FOI for E . multilocularis in foxes to address three specific research questions: i ) firstly , is the FOI constant or dynamic ( with age of the host ) , and what is its value accounting for complexities such as statistical uncertainty; ii ) secondly , how much does FOI vary quantitatively with habitat type , in particular between more or less urbanized regions; iii ) and thirdly how much does the FOI of infection vary quantitatively on a temporal basis between winter and summer seasons .
The data to which we fit our transmission models is an extension of that previously described in [14] and [24] , and includes only samples taken prior to the anthelmintic baiting intervention described in [27] . Samples were collected from in or around the city of Zurich in Switzerland . Three key variables were utilized: i ) presence ( absence ) of E . multilocularis infection based on necropsy ( details given in [14] , [24] ) ; ii ) the age of each fox , and following previous studies , and as described in [14] , cubs were assumed to be born on 1st April and age determination of foxes sampled after 1st July was done via examination of teeth ( details given in [14] ) . Along with the date of death ( which is known as these animals were culled by hunters ) and the weight at death , each animal's approximate age in years and days was estimated . The final variable utilized was habitat type , where this comprised three zones reflecting differing degrees of urbanization: urban; border; and periurban . The characteristics of these are described in detail in [27] . The urban zone comprises of mostly residential dwellings with relatively few green spaces , the periurban zone is rural comprising of forests , fields , pastures , and meadows . The border zone separates urban from rural , and was defined as extending 250 meters from the edge of the urban area and into 250 meters of the periurban surroundings . The border zone includes largely residential areas , public spaces , allotments and pastures . The data used in the study is in the Supporting Information Data S1 . Out of the foxes aged three years or less in the study data , 160 were sampled in the periurban zone , 167 in the border zone and 131 in the urban zone . The overall observed prevalence across all 458 animals was 42 . 1% , within the periurban , border and urban zones this was 65 . 6% , 38 . 9% and 17 . 6% respectively . The median age across these 458 animals was 0 . 80 years . In the periurban , border and urban zones the median respective ages were 0 . 87 , 0 . 77 and 0 . 59 years . The most general form of hypothesized transmission model we consider for E . multilocularis is given in Figure 1 . The structure of this model is based on initial work by [25] which has provided a basis for many subsequent disease modelling studies involving in E . granulosus and E . multilocularis , ( e . g . [5] , [28] ) . Figure 1 depicts an intuitively reasonable representation of the possible disease states and flows between them based on current known biology of E . multilocularis in foxes . The model dynamics here are over age of the host ( foxes ) , as is typical when modelling E . multilocularis or E . granulosus . We assume a fully susceptible population at birth , i . e . no vertical transmission and therefore . This dynamic system can be described in a series of ordinary differential equations ( ODEs ) . State variables are , , and , where represents the proportion of hosts which are not infected and not immune at age , is the proportion of hosts which are infected and not immune at age . Variables and are defined similarly but for cohorts –not infected and immune} and –infected and immune} respectively . The following system of ordinary differential equations defines the dynamics over age of this system:with initial conditions: , , and . Parameter denotes infection pressure ( force of infection - FOI ) , measured in insults ( exposures ) per year; is the probability of immunity on exposure; is the duration of host immunity; is the parasite death rate . Note that to simplify the notation we have suppressed any explicit dependency of the parameters on age , e . g . where FOI is dependent upon age , but such dependencies are considered during the model selection process making this potentially an inhomogeneous ODE system . The observed data comprise of randomly sampled binary observations each denoting whether a fox was infected ( not infected ) . This gives a sampling model comprising of Bernoulli trials where the likelihood function for observations is , where is the age of the th fox in the data , is an indicator variable where if the th fox is infected and otherwise , and is the prevalence in foxes of age . The ODE transmission model provides which will generally be some unknown function of the epidemiological parameters of interest , where ( Figure 1 ) : is the probability of immunity on exposure; the force of infection ( measured in insults per unit time ) ; the rate of loss of immunity; and the parasite death rate . It is not necessary to know function explicitly , all that is required is that for any given values of , along with appropriate initial conditions for state variables , , , , an estimate for for any suitable value of can be computed . This is readily possible using standard numerical techniques for solving ODEs ( e . g . [29] ) . The likelihood function ( parameter priors as we are using Bayesian inference ) can therefore be evaluated , and thus the key unknown epidemiological parameters of interest such as can be estimated from the study data —conditional on the chosen form of ODE model . Gaussian distributed prior distributions for parameters and were used , where these were each implemented within a log link function . For the probability parameter , a logit link function was used , again with a Gaussian prior distribution . Highly diffuse priors were used for all parameters except , where these each had a mean of zero and standard deviation of . In effect , this introduces no prior biological knowledge into the estimation of these parameters . For , a Gaussian prior ( again on a log link ) was used and chosen via expert opinion based on data presented in [17] . The latter study comprised of longitudinal observation of five foxes experimentally infected with E . multilocularis . The parasite burden in 80% ( three of five ) animals was very low at 90 days , suggesting an 80th percentile for the death rate of approximately per year , in addition we consider that parasites in 50% of infected animals may survive to around 120 days ( death rate per year ) , with 2 . 5% possibly surviving beyond 150 days ( death rate per year ) . A Gaussian distribution on a log link with a mean of 1 . 2 and standard deviation of , gives quantiles for ( on real scale ) of approximately 2 . 24 ( 2 . 5% ) , 3 . 32 ( 50 . 0% ) and 3 . 93 ( 80% ) per year , which we choose as an informative prior for . In addition we also examine a wider , but still highly informative prior , with a mean of 1 . 3 and standard deviation of 0 . 3 which has corresponding quantiles of 2 . 04 ( 2 . 5% ) , 3 . 67 ( 50 . 0% ) and 4 . 72 ( 80% ) per year . Sensitivity to prior assumptions is a crucial aspect of Bayesian inference , so we also present modelling results which use the same highly diffuse ( uninformative ) prior for as for and . Bayesian model selection — used to identify an optimal ODE transmission model — was performed using the marginal likelihood goodness of fit metric . This is equivalent to comparing Bayes factors between two models when each has an equal a priori probability of being the preferred model . The marginal likelihood is generally more difficult to compute than other commonly used metrics , such as the Bayesian Information Criterion ( BIC ) or Deviance Information Criterion ( DIC ) , but is the standard and preferred theoretical choice in Bayesian inference [26] , [30] . This metric allows Bayesian model selection to be interpreted as simply an extension of maximum likelihood model selection , where evidence ( i . e . statistical support ) for any given model is that obtained by multiplying the best fit likelihood by the “Occam factor” , so-named as this metric has been shown to be conceptually consistent with Occam's Razor ( as explained in [30] ) . The marginal likelihood was computed using Laplace approximations , a standard numerical technique in statistical inference [31] , [32] . These were also used to estimate posterior distributions for the epidemiological parameters . All numerics were implemented in R [33] using a number of well tested internal functions borrowed from the R abn library [34] . See Supporting Information Text S1 for technical details . An approximate guide for the size of differences in marginal likelihoods which may be considered notable is given in Table 2 . 1 page 27 in [26] . Using the terminology from [26] , a difference of is suggested as weak support for the model with higher marginal likelihood , is support , is strong evidence and greater than 10 very strong evidence .
Exploratory analyses of the observed prevalence data is illustrated in Figure 2 . Choosing a smoothing parameter of f = 0 . 072 in ( lowess ( ) in R ) gives smoothed data which appear relatively consistent with the observed data in Figure 2 ( a ) , and provides a more refined visualization of the data rather than in 30-day blocks . Figure 2 ( a ) and 2 ( b ) suggest that it may be appropriate to consider the inclusion of periodicity into one or more of the epidemiological parameters in our transmission model . This suggests that for our model to adequately capture the gross dynamic features of disease transmission we should consider both age independent FOI , , and also FOI parametrized as a function of age , , with as some polynomial or periodic function . It is clear from Figure 2 ( c ) that there appears very little identifiable dynamic structure after 36 months , which is perhaps unsurprising given this only comprises some 14% on observations , and thus very sparse sampling at these older ages . This is consistent with life expectancy estimates for foxes which suggest that only a small proportion of foxes survive beyond 2–3 years years in the wild [35] . As foxes aged less than three years present the vast majority of zoonotic risk , combined with foxes of older ages being sampled very sparsely in the data , subsequent analyses focus on foxes less than three years of age . For completeness some modelling results are also presented considering all ages . Figure 2 ( d ) shows the smoother applied to data of all ages . A range of transmission models of increasing complexity were fitted to the observed data ( Table 1 ) with separate results shown for the two informative priors for . See Supporting Information Text S2 for results using an uninformative prior for , and Supporting Information Text S3 for the equivalent of Table 1 but for the models fitted to data from foxes of all ages . Estimates of the posterior modes for all the parameters in models presented in Table 1 can be found in Supporting Information Text S4 . We commenced with a model comprising no immunity ( Model 1-C ) , i . e . only state variables and , and constant FOI . This was followed by similar models but where the FOI was parametrized as a linear ( 1-L ) , quadratic ( 1-Q ) and periodic ( 1-P ) function of age , with the latter using a sinusoidal forcing term as is commonly used for diseases with periodic transmission rates ( e . g . measles [36] ) . The particular form of sinusoidal function used was . A log link function ensures that all estimates of are positive , and also avoids the potentially complex task of having to specifying a proper ( i . e . integrates to unity ) joint parameter prior for , and which would otherwise be required to ensure that the posterior distribution for was positive . This parametric form of has a period of one year , with ( on a log scale ) denoting the lifetime average ( or baseline ) FOI , the amplitude beyond the lifetime average . The term is to allow , if necessary , a time shift compared with the standard sinusoidal function . A logit link function is used here as we are only interested in time shifts in the interval [0 , 1] . Parameters and each have diffuse Gaussian priors with means of zero and standard deviations of . From Table 1 is it clear that periodic infection pressure is strongly supported over the other forms . Retaining periodic infection pressure , we next consider models with a more complex cohort structure comprising of all four state variables , allowing for the presence of lifelong immunity ( Model 2 ) , and transient immunity ( Model 3 and the “full” model in Figure 1 ) . It is again apparent from Table 1 that the observed data are less supportive of these two more complex models , and hence there is little evidence in the data for the presence of immunity . Based purely on the goodness of fit results in Table 1 our preferred model is Model 1-P . The next more complex best fitting model was Model 2 . These two models cross a rather large biological divide — no immunity verses lifelong immunity . To provide additional empirical justification for choosing Model 1-P over Model 2 we briefly examine the magnitude of the parameters in the latter model using the posterior modes ( which are estimated as part of the marginal likelihood computation ) . In Model 2 , using the prior for with mean of 1 . 2 , we have a logit for of −5 . 3 giving an approximate probability of becoming immune per exposure of 0 . 005 . Posterior mode estimates for the FOI in this model , , gives an ( approximate ) average lifetime number of exposures , , of per year . Based on the observed prevalence data , then suppose that 86% of animals have a lifetime of at most three years and the remaining 14% live for a full nine years . Then , in a population of 100 animals these parameters give a total of 768 exposures for all animals over their entire lifetime . For this then gives , on average , at most only four animals becoming immune during the entire lifetime of the population . This is a very fine scale population change , and it is therefore of little surprise that , statistically , the empirical data are not supportive of the presence of immunity . Having arrived at a preferred transmission model we now use this to provide the first of our main results: quantification of the FOI , i . e . . Of most interest here are the baseline and amplitude parameters and , specifically we wish to estimate the joint marginal posterior distribution for these two parameters and then examine the range of values for the FOI which arise when are within their joint 95% posterior confidence interval ( to account for sampling uncertainty ) . It would be possible to consider a joint density comprising of all three parameters in ; . It is , however , difficult to visualize such a density ( with four dimensions - three parameters plus the density estimate ) , and as epidemiological interest is focused on we therefore marginalize out and giving a joint posterior density for . Note that this distribution , therefore , also incorporates the statistical uncertainty in and ( i . e . the latter are not simply fixed at constant values ) . Before computing the joint marginal density for we first summarize , , and through their marginal posterior 95% confidence intervals ( Supporting Information Text S5 provides full marginal posterior densities ) . Using the informative prior for with mean = 1 . 2 and sd = 0 . 2 gives ( on the real scale ) , , and , with approximate medians of , , ; and . The corresponding estimates when using the informative prior for with mean = 1 . 3 and sd = 0 . 3 are , , and , with approximate medians of , , ; and . Using the diffuse prior for gives , , and , with approximate medians of , , ; and . A contour plot of the joint marginal posterior density for , Figure 3 panel a , clearly shows strong dependency between and — when one is lower the other is higher and vice-versa . This demonstrates why it is more intuitively reasonably to consider these parameters jointly . To visualize the statistical uncertainly in our estimate of FOI over age we choose two points and , which lie on the contour defining the 95% region for this two-dimensional density . We then solve the ODE model for these sets of parameter estimates ( the other two parameters are set to their modal values ) . These two “extreme” sets of parameters provide an approximate 95% confidence interval for the mean force of infection over age ( Figure 3 panel b ) , and similarly the mean prevalence ( Figure 3 panel c ) . We estimate the ( mean ) minimum FOI during an annual population cycle as 0 . 27 to 1 . 27 insults ( with 95% confidence ) , and rising to a maximum of between 6 . 87 and 7 . 05 insults ( with 95% confidence ) . The summary statistics suggest that there may be a difference between the prevalence of E . multilocularis in populations of foxes within the different habitat types . To provide a measure of statistical rigour to these observations we fit Model 1-P to these data , where now heterogeneity is introduced into to allow the force of infection to vary across each of the different zones . If the inclusion of such heterogeneity improves the model goodness of fit then that provides formal statistical evidence of a different in FOI between habitats . We consider two versions of Model 1-P , Model 1-P0 and Model 1-P01 . The first allows the baseline force of infection , , to vary with zone and assumes the amplitude is homogeneous across all zones . The second model allows both and to vary within each habitat zone . For simplicity , the period shift and parasite death rate are assumed homogeneous over all three zones . Model 1-P0 has a goodness of fit of −285 . 4 , with Model 1-P01 having −292 . 6 . This is strong evidence that: i ) there is a difference in baseline force of infection between different habitat zones; ii ) there is no evidence of any difference in periodic amplitude between the different habitats . We use , therefore , Model 1-P0 to quantify differences in FOI across habitat . Following a similar approach as for our analyses of Model 1-P , we derive approximate confidence intervals for the force of infection using the joint marginal posterior densities for and , where this time we have three , two dimensional distributions , , , for urban , border and periurban . First we summarize and through their marginal posterior 95% confidence intervals ( Supporting Information Text S6 provides full marginals posterior densities ) . Using the informative prior for with mean = 1 . 2 and sd = 0 . 2 gives ( on the real scale ) , , , , and , with approximate medians of , , , , and . It is clear that the marginal densities in the urban and periurban habitats do not overlap at the 5% significance level . Supporting Information Text S7 provides a comparison of the modal estimates of prevalence over age in each of the three habitat types . Finally we consider the statistical uncertainty in our FOI estimates over age within each habitat type . Figure 4 panel a is similar to Figure 3 panel a and shows the joint marginal posterior densities for , , . As for the one-dimensional marginal estimates of in each habitat , it is very clear that the FOI baseline is statistically different between the urban and periurban zones i . e . the 95% contours do not overlap . The FOI in the border zone is indistinguishable from that in either the periurban or rural zones . We repeat the same approach to estimate approximate 95% confidence intervals for the FOI within each habitat as for the homogeneous habitat model ( Model 1-P ) , this is shown in Figure 4 panel b . These uncertainty limits are clearly rather more approximate here than for those in Model 1-P — as can be seen by the fact that the urban and periurban trajectories overlap slightly , while they are clearly very distinct at the 95% contours in Figure 4 panel a . The limits for the border habitat also cross each other . This behavior is not entirely unexpected in that we are collapsing a six dimensional posterior probability distribution ( comprising of all the parameters in Model 1-P0 ) into effectively only two dimensions . This gives joint statistical estimates which are far more manageable , but as we see here , does makes the resulting confidence limit estimates rather approximate . We estimate with approximate 95% confidence that the ( mean ) minimum FOI during an annual cycle in the urban habitat is 0 . 1 to 0 . 8 insults , rising to a maximum of between 1 . 6 and 2 . 0 insults . For the periurban habitat we have minimum and maximum force of infections of 0 . 7 to 3 . 9 insults and 9 . 35 to 9 . 7 insults respectively . Despite these minor statistical discrepancies in relation to the differing comparisons of confidence limits , the overall result is very clear: there is a large difference in FOI during annual cycles in the urban and periurban habitats .
The FOI is a key parameter in models estimating the effectiveness and cost effectiveness of infectious disease prevention [37] . Using a simple —and empirically justified — mathematical model we have estimated the force of E . multilocularis infection in a fox population in Switzerland , and shown how much it quantitatively varies with season and geography , i . e . through time and across space . There have been a number of trials aimed at reducing the prevalence of infection in foxes by distributing baits containing the anthelmintic praziquantel . Several studies , in Switzerland and in Germany , with baiting intervals of 12 times per year , resulted in a substantive decline in the numbers of foxes infected ( reviewed in [22] , [38] , [39] ) . These studies typically resulted in a decrease in prevalence from 35% and 67% to between 1% and 6% . Provided most foxes are treated , this would be expected as the baiting interval is similar to the prepatent period of E . multilocularis in foxes and hence it should prevent transmission . Other baiting campaigns have used lower frequencies and have had variable results . For example in Germany a baiting frequency of 5 times per year resulted in a decrease in the prevalence in foxes of 32% ( 95% CIs 16–52 ) to 4% ( 95% CIs 2–7 ) . Other studies with less frequent baiting intervals have not shown such a clear reduction . Our estimates and modelling methodology for computing the pre-intervention baseline FOI provides a rigorous framework which can be used to optimize baiting intervals , in order to trade off the need to reduce infection in foxes , and thus the potential for zoonotic transmission , and the cost of implementing such intervention programmes . Based on Swiss data we estimate that there is a high infection pressure in the winter months for non urban foxes of close to 10 infections per year ( i . e . greater than 1 per month ) , baiting at monthly intervals would therefore be required . This conclusion is in accordance with the results of an epidemiological study on the intermediate hosts which showed most rodents become infected during the winter [23] . However , in the summer when the FOI is lowered to between 0 . 7 to 3 . 9 insults per year , then decreasing the baiting frequency to once every three months would be more appropriate . In addition , baiting frequency , at least in theory , could be further reduced in urban habitats where the FOI is between 0 . 1–0 . 8 and 1 . 6–2 . 0 insults per year . However in practice , this would be a challenge in Zürich as the spatial separation of such zones is as little as 500 meters . A decreased cost of baiting foxes increases the cost benefit as a similar reduction in the numbers of human AE cases would be expected to be achieved as earlier suggested [15] based on epidemiological data [23] , [24] . Theoretical models [40] , [41] , have also suggested seasonal transmission of E . multilocularis in Japan . However , our model is also challenged with field data , where as the conclusions of previous models are based on simulations . In addition , our model does not depend upon parameters from the intermediate host and therefore should be applicable for FOI calcualtions in any area where suitable prevalence data from foxes is available . Our estimates of FOI are dependent on the estimate of the life expectancy of the infection in the definitive host . Experimental infections of foxes indicate that parasites can survive in foxes beyond 90 days [17] , although most parasites are lost earlier . This model is based on the presence or absence of parasites , with even a single parasite being found in a fox defining the fox as infected . Therefore an estimated life expectancy of 120 days was used in the model as being a reasonable period extrapolating from the data of [17] . By which half of foxes might be estimated to be free of parasites . If the life expectancy is less then the FOI will be higher than reported here . The corollary is also true . A longer life expectancy would result in a lower FOI . It is possible that low worm burdens in foxes could persist for some considerable time as all foxes in the experimental study by Kapel and others [17] remained infected at 90 days , albeit with low burdens . However , if this were the case , decreasing baiting frequency in the summer months and in urban areas , as suggested would still be effective in lowering the parasite biomass , as the numbers of infections per year would be lower than calculated here . However , as infection is highly overdispersed only a few infected foxes will be responsible for most of the transmission . Using a non zero threshold worm burden for foxes that are relevant to transmission could give important information with regard to the FOI in heavily infected foxes . An alternative approach , in a future study , using abundance data may help clarify this issue . An obvious related key question is quantifying the transmission probability from environmental contamination , e . g . via the distribution of fox faeces , to human infection . To finish , a brief comment on the basic reproduction ratio ( ) , arguably the most important epidemiological parameter in any disease system , although it is not without its critics [42] . Robust estimation of is often difficult , especially with parasites with complex life cycles . Roberts [43] described how could be estimated if prevalence data from foxes and small mammal intermediate hosts were available together , along with a number of assumptions regarding various transmission parameters . However , when it is difficult to estimate , estimates of FOI become highly relevant [37] . We have shown that with a relatively simple transmission model empirically justified from study data , an estimate of the FOI can be made , and how this can be practically applied for optimizing the interval of baiting to lower the prevalence of E . multilocularis in foxes . | Human alveolar echinococcosis ( AE ) is caused by the fox tapeworm E . multilocularis and has a high fatality rate if untreated . The frequency of the tapeworm in foxes can be reduced through the regular distribution of anthelmintic baits and thus decrease the risk of zoonotic transmission . Here , we estimate the force of infection to foxes using a mathematical model and data from necropsied foxes . The results suggest that the frequency of anthelmintic baiting of foxes can be optimised to local variations in transmission that depend upon season and type of fox habitat . | [
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] | 2014 | Dynamics of the Force of Infection: Insights from Echinococcus multilocularis Infection in Foxes |
The gene encoding the forkhead box transcription factor , FOXP2 , is essential for developing the full articulatory power of human language . Mutations of FOXP2 cause developmental verbal dyspraxia ( DVD ) , a speech and language disorder that compromises the fluent production of words and the correct use and comprehension of grammar . FOXP2 patients have structural and functional abnormalities in the striatum of the basal ganglia , which also express high levels of FOXP2 . Since human speech and learned vocalizations in songbirds bear behavioral and neural parallels , songbirds provide a genuine model for investigating the basic principles of speech and its pathologies . In zebra finch Area X , a basal ganglia structure necessary for song learning , FoxP2 expression increases during the time when song learning occurs . Here , we used lentivirus-mediated RNA interference ( RNAi ) to reduce FoxP2 levels in Area X during song development . Knockdown of FoxP2 resulted in an incomplete and inaccurate imitation of tutor song . Inaccurate vocal imitation was already evident early during song ontogeny and persisted into adulthood . The acoustic structure and the duration of adult song syllables were abnormally variable , similar to word production in children with DVD . Our findings provide the first example of a functional gene analysis in songbirds and suggest that normal auditory-guided vocal motor learning requires FoxP2 .
Genetic aberrations of FOXP2 cause developmental verbal dyspraxia ( DVD ) , which is characterized by impaired production of sequenced mouth movements and both expressive and receptive language deficits [1–4] . Brain imaging studies in adult FOXP2 patients implicate the basal ganglia as key affected regions [5–7] , and FOXP2 is prominently expressed in the developing human striatum [8] . These findings raise the question whether the speech and language abnormalities observed in individuals with DVD result from erroneous brain development or impaired function of differentiated neural circuits in the postnatal brain , or a combination of both . Human speech and learned vocalizations in oscine birds bear behavioral and neural parallels [9] . Thus songbirds are a suitable model for studying the neural mechanisms of imitative vocal learning , including speech and its pathologies . The FoxP2 expression patterns in songbird and human brains are very similar , with strong expression in the basal ganglia , thalamus , and cerebellum [8 , 10 , 11] . Moreover , FoxP2 expression in the basal ganglia song nucleus , Area X , which is important for normal song development [12 , 13] , transiently increases at the time when young zebra finches learn to sing . In adult canaries , FoxP2 expression in Area X is elevated during the late summer months , coincident with the incorporation of most new syllables to their seasonally changing song [10] . FoxP2 is down-regulated in Area X when adult zebra finches sing slightly variable , undirected song , but not when they sing more stereotyped female-directed song [14] . Together , these correlative findings raise the question whether FoxP2 and vocal plasticity are causally related . Using lentivirus-mediated RNA interference ( RNAi ) during song development , we now show that zebra finches with reduced FoxP2 expression levels in Area X imitated tutor songs incompletely and inaccurately . This effect was already evident during vocal practice in young birds . Moreover , the acoustic structure and the duration of song syllables in adults were abnormally variable , similar to word production in children with DVD [15] . These findings are consistent with a role of FoxP2 during auditory-guided vocal motor learning in songbird basal ganglia .
Vocal learning in zebra finches proceeds through characteristic stages . In the sensory phase that commences around 25 d after hatching ( post-hatch day [PHD] ) , young males memorize the song of an adult male tutor . Concomitantly , they start vocalizing the so called “subsong , ” consisting of quietly uttered , poorly articulated , and nonstereotypically sequenced syllables [16] . Following intensive vocal practice and improvement toward matching the tutor song during the period of “plastic song , ” they eventually imitate the song of their tutor with remarkable fidelity around PHD90 . The structural and temporal characteristics of adult “crystallized” song remain essentially stable throughout adult life . To study the function of FoxP2 during song learning of zebra finches , we reduced the levels of FoxP2 expression bilaterally in Area X in vivo , using lentivirus-mediated RNAi . In this approach , short interfering hairpin RNA ( shRNA ) containing sense and antisense sequences of the target gene connected by a hairpin loop are expressed from a viral vector . The virus stably integrates into the host genome , enabling expression throughout the life of the animal [17] . We designed two different shRNAs ( shFoxP2-f and shFoxP2-h ) targeting different sequences in the FoxP2 gene . Both hairpins strongly reduced the levels of overexpressed FoxP2 protein in vitro ( Figure 1F ) , but did not change the levels of overexpressed protein levels of FoxP1 , the closest homolog of FoxP2 . For further control experiments , we generated a shRNA designed not to target any zebra finch gene ( shControl ) . As expected , this nontargeting shRNA did not affect expression of either FoxP2 or FoxP1 in vitro ( Figure 1F ) . Since shFoxP2-f and shFoxP2-h targeted FoxP2 with similar efficiency , both of them were interchangeably used for subsequent in vivo experiments ( shFoxP2-f/-h ) . On PHD23 , at the onset of sensory-motor learning [16] we injected either FoxP2 knockdown or control viruses ( shControl and shGFP; see below ) stereotaxically into Area X to achieve spatial control of knockdown ( Figure 1A ) . Starting on PHD30 , each young bird , here called pupil , was kept in a sound isolation chamber , together with an adult male zebra finch as tutor . At PHD65 , PHD80 , and between PHD90 and PHD93 , we recorded the pupils' vocalization for subsequent song analysis ( for timeline of experiments , see Figure S1 ) . After the last song recording , brains were histologically analyzed for correct targeting of virus to Area X . All lentiviral constructs expressed the green fluorescent protein ( GFP ) reporter gene , allowing the detection of infected brain areas by fluorescence microscopy ( Figure 1B ) . On average , 20 . 3% ± 9 . 9% ( mean ± standard deviation [STDV]; n = 24 hemispheres from 12 animals ) of the total volume of Area X was infected . Importantly , there was no difference in the volume of Area X targeted with FoxP2 knockdown or control viruses ( two-tailed Mann-Whitney U test , p > 0 . 5; shFoxP2-f/-h , n = 6 , shControl , n = 7 ) . Quantification of Area X volume targeted by virus injection in an equally treated group of birds , but sacrificed at PHD50 , confirmed the results obtained for PHD90 ( mean volume 20 . 4% ± STDV 4 . 0%; two-tailed Mann-Whitney U test , p > 0 . 6; shControl n = 3 hemispheres from 3 animals; shFoxP2-f/-h , n = 3 hemispheres from 3 animals ) . To quantify the neuronal extent of lentivirus expression in Area X , we used immunohistochemical staining with the neuronal marker Hu [18] ( Figure S2 ) . Of all virus-infected cells , 78 . 5% ± 3 . 5% were neurons ( mean ± standard error of the mean [SEM]; no significant difference between shFoxP2 and shControl , two-tailed Mann-Whitney U test , p > 0 . 7; shControl injections n = 3 hemispheres from 3 animals , shFoxP2 injections n = 4 hemispheres from 4 animals; ) . This result is consistent with Wada et al . [19] , who used the same viral constructs in the zebra finch brain in vivo . Among the infected cells were FoxP2-positive spiny neurons , which are assumed to be the most common cell type in Area X [20] ( Figure 1C–1E ) . To quantify FoxP2 knockdown in vivo , we determined FoxP2 protein levels in Area X on PHD50 , the time of peak FoxP2 expression [10] in birds injected on PHD23 with shFoxP2-f/-h in one hemisphere and shControl into the contralateral hemisphere . The signal of the immunofluorescent staining with a FoxP2 antibody was significantly lower in knockdown Area X than in control Area X ( Figure 1G and 1H ) . We also assessed FoxP2 mRNA levels after knockdown in Area X . Birds were injected on PHD23 with shFoxP2-f/-h in one hemisphere and shControl in the contralateral hemisphere . On PHD50 , we punched out Area X of injected birds and measured FoxP2 mRNA levels by real-time PCR . FoxP2 levels were normalized to two independent RNAs coding for the housekeeping genes Hmbs and Pfkp . FoxP2 mRNA was reduced on average by approximately 70% in the shFoxP2-infected region of Area X compared to the shControl-infected region of Area X ( Figure 1I ) . Of note , RNAi-mediated knockdown approximates FOXP2 levels in DVD patients , since haploinsufficiency , a 50% reduction of functional FOXP2 protein , is apparently the common feature of all reported human FOXP2 mutations [4 , 21] . To demonstrate that RNAi-mediated gene knockdown can persist in vivo throughout the entire song-learning phase , we used a virus expressing shRNA against the viral reporter GFP ( shGFP ) in conjunction with the virus expressing a shRNA lacking a target gene ( shControl ) . We injected young zebra finches on PHD23 with equal amounts of equally infectious shGFP and shControl virus in the left and right hemisphere , respectively . More than 3 mo later , on PHD130 , the GFP signal in the shGFP-injected hemisphere was still 70 . 5% ± 5 . 8% less intense than in the shControl-injected hemisphere ( mean ± SEM; n = 2; Figure 1J ) . To rule out potential side effects of FoxP2 knockdown on cellular survival in Area X , we investigated apoptosis in Area X 6 d after surgery with terminal deoxyribonucleotide transferase-mediated dUTP nick end labeling ( TUNEL ) . The TUNEL method detects genomic DNA double-strand breaks characteristic of apoptotic cells . Of 1 , 149 GFP-positive cells counted in six hemispheres from three animals , only five were TUNEL-positive ( Figure S3 ) . ShControl-injected and uninjected animals had similar low levels of apoptotic cells ( unpublished data ) . Thus , FoxP2 is not a gene essential for short-term survival of postmitotic neurons . Since the TUNEL method does not capture any long-term changes in neuronal viability that might follow after reduction of FoxP2 , we used the neuronal marker Hu to determine neuronal densities in Area X 30 d after injecting either shFoxP2-f/-h or shControl virus ( Figure S4 ) . Neuronal densities in the infected region in Area X did not differ in knockdown and shControl-injected birds ( two-tailed Mann-Whitney U test , p > 0 . 39; shControl , n = 4 hemispheres; shFoxP2-f/-h , n = 3 hemispheres ) . Density of neurons were also comparable inside and outside of the virus-infected region of Area X for all viruses ( two-tailed Mann-Whitney U test , p > 0 . 6 for both shFoxP2-f/-h and shControl ) . In sum , these data demonstrate that virus-mediated RNAi can induce specific , long-lasting knockdown of gene expression in zebra finch Area X without causing cell death . Adult zebra finch song consists of different sound elements , here called syllables , that are separated by silent intervals . Syllables are rendered in a stereotyped sequential order , constituting a motif . During a song bout , a variable number of motifs are sung in short succession . To obtain a first descriptive account of the song of knockdown and control pupils , we measured mean acoustic features for all syllables recorded from all pupils using the software Sound Analysis Pro ( SAP ) [22] . The features extracted were mean pitch , mean frequency , mean frequency modulation ( FM; change of frequency in time ) , mean entropy , and mean pitch goodness ( PG; periodicity of sound ) , as well as mean duration . The comparison of the distribution of these features across the repertoire of knockdown and control pupils did not reveal any significant differences , indicating that knockdown pupils , control pupils , and tutors sang syllables with similar acoustic features ( Figure S5 ) . Next , we analyzed the behavioral consequences of bilateral FoxP2 knockdown in Area X for the outcome of song learning at PHD90 . When a juvenile male finch is tutored individually by one adult male , the pupil learns to produce a song that strongly resembles that of his tutor [23] . We therefore determined learning success by the degree of acoustic similarity between pupil and tutor songs . Analysis of song recorded at PHD90 revealed that pupils with experimentally reduced FoxP2 levels in Area X imitated tutor songs with less fidelity than control animals did ( see also Audio S1–S6 ) . The comparison of sonograms from shControl-injected ( Figure 2A ) and shFoxP2-injected pupils ( Figure 2B and 2C ) with their respective tutors shows the characteristic effects caused by reduction of FoxP2 . Typical features of FoxP2 knockdown pupils included syllable omissions ( Figure 2B , syllables C , D , F , and G; Figure 2C , syllable B ) , imprecise copying of syllable duration ( Figure 2B , syllable E longer; Figure 2C , syllable D shortened ) , and inaccurate imitation of spectral characteristics ( Figure 2B , syllable E; Figure 2C , syllable D ) . In addition , in four out of seven knockdown pupils , the motif contained repetitions of individual syllables or syllable pairs ( e . g . , see Figure 2B and 2C ) . In contrast , none of the control or tutor motifs contained repeated syllables . Pupils did not reverse the sequential order of syllables in the tutor motifs , except for one control ( unpublished data ) and one FoxP2 knockdown pupil ( Figure 3A ) . Acoustic similarity between pupil and tutor song was measured with SAP by pairwise comparison of user-defined pupil and tutor motifs . SAP provides a similarity score that indicates how much of the tutor sound material was imitated by the pupil , regardless of syllable order . The distinction between imitated and non-imitated sounds in SAP is based on p-value estimates derived from the comparison of 250 , 000 sound interval pairs , obtained from 25 random pairs of zebra finch songs ( see Materials and Methods and [22] for further details ) . The similarity score was significantly lower in FoxP2 knockdown than in control animals ( Figure 2D ) . In addition , we also manually counted the number of user-defined syllables copied from the tutors , confirming that knockdown animals imitated fewer syllables ( Figure S6 ) . Even though knockdown animals copied tutor syllables , their imitation appeared to be less precise than in control animals . Figure 3A illustrates the inaccurate syllable imitation ( syllables A and B ) in a knockdown pupil that learned from the same tutor as the shControl-injected pupil shown . To quantify how well the syllables of a motif were imitated on average , we obtained motif accuracy scores in SAP from pairwise motif comparisons between pupil and tutor . The motif accuracy score measures the extent to which the pupil's sounds are closer to the tutor than expected by chance . The average accuracy per motif was significantly lower in knockdown pupils than in shControl-injected pupils ( Figure 3B ) . Of note , both shFoxP2 hairpins ( shFoxP2-f and shFoxP2-h ) affected motif similarity and motif accuracy scores to a similar degree ( Figure S7 ) , which is consistent with their comparable efficiency in reducing FoxP2 mRNA in vitro ( Figure 1F ) . Neither the similarity score nor the accuracy score correlated with the volume of Area X targeted in the pupil . Possibly , there were too few values to observe such a correlation or the absolute volume targeted by shFoxP2 virus has only a small influence on the outcome of learning . To investigate whether inaccurate imitation affected all or only some syllables , we compared corresponding syllable pairs between tutors and pupils using a syllable identity score . The syllable identity score reflects both the degree of similarity ( i . e . , quantity of imitation ) and the degree of accuracy ( i . e . , quality of imitation ) in a single measure . The frequency distribution of identity scores of all syllables from FoxP2 knockdown pupils was shifted towards lower scores compared to control pupils . This suggests that imprecise imitation was not skewed towards particular syllables or syllable types ( Figure 3C ) , pointing to a generalized lack of copying precision . Consistent with the reduced accuracy of motif imitation ( Figure 3B ) , we also found that syllable identity scores were significantly lower in knockdown pupils compared to control pupils ( syllable identity score averaged for each animal , two-tailed Mann-Whitney U test , p < 0 . 02; n = 7 for both shFoxP2 and shControl ) . To rule out that the lower imitation success of knockdown animals was related to specific song characteristics of the tutors or their lacking aptitude for tutoring , we used some males to tutor both knockdown and control pupils . Direct comparison of the motif similarity and accuracy scores from control and knockdown pupils tutored by the same male revealed significantly lower scores for knockdown compared to control pupils ( average similarity score 82 . 6 ± 3 . 6 for shControl and 61 . 9 ± 5 . 6 for shFoxP2; average accuracy score 73 . 8 ± 0 . 7 for shControl and 71 . 7 ± 0 . 4 for shFoxP2; ± SEM; n = 5 , two-tailed Mann-Whitney U test , p < 0 . 03 for similarity and p < 0 . 03 for accuracy; see also Figure 3A ) . Because the shControl hairpin , in contrast to shFoxP2-f/-h , has no target gene , it might not stably activate the RNA-induced silencing complex ( RISC ) essential for knockdown of gene expression during RNAi . Because recent work suggests an involvement of the RISC in the formation of long-term memory in the fruitfly [24] we addressed a possible influence of RISC activation during song learning . For this , we compared song imitation in shGFP virus–injected pupils , in which virally expressed GFP is lastingly knocked down ( Figure 1J ) , and shControl-injected pupils . Similarity and accuracy scores did not differ significantly between shGFP-injected and shControl-injected animals , ruling out that RISC activation contributed to the effects of shFoxP2 on song imitation ( Figures 2D and 3B ) . Finally , we investigated the precision of syllable imitation on the level of individual acoustic features by comparing the mean values of acoustic features of pupil syllables to those of their respective tutor . The divergence of imitated syllables from the tutor tended to be larger in all acoustic measures in the FoxP2 knockdown pupils than in the controls . For average syllable duration and mean entropy measures , the difference was significant ( Figure 3D ) . Area X is part of a basal ganglia–forebrain circuit , the anterior forebrain pathway ( AFP ) , which bears similarities with mammalian cortical–basal ganglia loops [25] . The pallial target of the AFP , nucleus lateral magnocellular nucleus of the nidopallium ( lMAN ) , may act as a neural source for vocal variability in juvenile zebra finches [13 , 26] . Similarly , in adult zebra finches , neural variability in AFP outflow is associated with the variability of song [27] , and experimental manipulations inducing adult song variability require an intact AFP [28 , 29] . To explore AFP function in FoxP2 knockdown and control zebra finches , we investigated the variability of their song syllables . The comparison of sonograms from different renditions of the same syllable revealed that knockdown pupils sang their syllables in a more variable fashion than control pupils ( Figure 4A and 4B ) . Both the spectral ( syllables I and III ) and the temporal domain ( syllables II and IV ) were affected . Of note , the first three syllable examples shown in Figure 4A and 4B ( syllables I , II , and III and I′ , II′ , and III′ ) , stem from different animals , but were learned from the same tutor . To quantify the acoustic variability of syllables , we used the syllable identity score mentioned above . Pairwise comparison between different renditions of the same syllable revealed that shFoxP2-injected pupils sang syllables slightly , but significantly , more variably than control pupils or tutors ( Figure 4C ) . As expected , shControl-injected pupils , shGFP-injected pupils , and tutors performed their syllables with equal stability ( Figure 4C ) . Next , we quantified the variability of syllable duration between different renditions of the same syllable . The coefficient of variation of syllable duration was significantly higher in knockdown than in control pupils and tutors , suggesting imprecise motor coordination on short temporal scales ( Figure 4D ) . Notably , the timing of syllables in control pupils ( shControl and shGFP ) was as stable as in tutors ( Figure 4D ) . The variability of syllable duration in tutor and control birds varied in the same range as reported previously [30] , emphasizing how tightly adult zebra finches normally control syllable duration . Finally , we analyzed the sequential order of syllables over the course of many motifs . To this end , we first annotated sequences of 300 user-defined syllables with the positions in their respective motifs . We then measured the stereotypy of a motif by calculating for each syllable the entropy of its transition distribution . Based on this entropy measure , we generated a sequence consistency score ( 1 − entropy ) , which reflects song stereotypy . An entropy score of 0 indicates random syllable order , whereas a score of 1 reflects a fixed syllable order . The mean sequence consistency was similar in shControl and shFoxP2-f/-h animals ( Figure S8 ) . Because stereotypy of motif delivery is a hallmark of “crystallized” adult song , it seems plausible that both knockdown animals and controls had reached the end of the sensory-motor learning period [31] . To investigate this question in more detail , we next analyzed the song of knockdown and control pupils recorded at earlier stages of song development . To explore the developmental trajectory of song learning in knockdown and control pupils , we analyzed songs recorded during plastic song at PHD65 and towards the end of the learning phase at PHD80 . Since syllables are not yet rendered in a stereotyped motif structure at PHD65 , we quantified song imitation success and vocal variability on the level of the syllables only . To avoid the necessity of identifying individual syllables based on their morphology , we made use of an automated procedure provided by SAP to compare all song material from a given day to the tutor's typical motif . The vocalizations of pupils were first segmented into syllables . All segments were subsequently compared to the typical motif of the tutor in a pairwise fashion ( between 1 , 000–3 , 000 comparisons per pupil per day ) . The output variable of these measurements is an accuracy score , which describes the extent to which the pupil's sounds match those of the tutor ( see Materials and Methods and [22] for further details ) . We found that knockdown pupils imitated their tutors less accurately than control pupils already at PHD65 ( Figure 5A ) . The frequency distribution of accuracy values also suggests that imprecise syllable imitation was not skewed towards particular syllables or syllable types ( Figure S9 ) . This result is in line with the observation made earlier for the syllables at PHD90 ( Figure 3C ) . In contrast to control pupils , knockdown pupils did not improve in accuracy after PHD80 , suggesting they had reached the end of the learning phase ( Figure 5A ) . For each pupil , we also calculated the change of accuracy from one age to the next ( accuracy [agen − agen−1] ) . The change of accuracy from PHD65 to PHD80 was indistinguishable between knockdown and control pupils ( two-tailed Mann-Whitney U test , p > 0 . 9; n = 5 for shFoxP2-f/-h and n = 7 for shControl ) , suggesting that up to this age , syllable imitation followed largely similar dynamics . However , from PHD80 to PHD90 , accuracy of syllable imitation continued to improve only in control , but not in knockdown pupils ( two-tailed Mann-Whitney U test , p < 0 . 04; n = 6 for shFoxP2-f/-h and n = 6 for shControl ) . In order to investigate variability of syllable production during song development , we compared the variance of accuracy values between knockdown and control pupils . Whereas the variance was similar between the two experimental groups at PHD65 and at PHD80 , it was significantly higher in knockdown pupils compared to controls at PHD90 ( Figure 5B ) . This difference resulted from an increase of variance with age in shFoxP2-injected birds ( Figure 5B ) . Of note , the similarity batch analysis , which does not require assumptions about the identity of individual motifs or syllables , confirmed the results on both lower imitation success and higher vocal variability obtained in our prior analysis of the songs from PHD90 ( Figures 2D and 3B ) .
Our goal was to investigate the requirement of FoxP2 for normal song development in the zebra finch , a model for studying the basic principles of vocal learning . To this end , we analyzed the behavioral consequence of an experimental reduction of FoxP2 during song development . Using lentivirus-mediated RNAi for the first time in the songbird brain , we reduced FoxP2 mRNA and protein levels in Area X with either of two different knockdown constructs . We found that this prevented complete and accurate imitation of the tutors' song , an effect already evident during plastic song . Reduced FoxP2 levels also led to more variable performance of syllables in adults . In contrast , we observed no such abnormalities in birds with Area X injections of virus knocking down an exogenously expressed gene ( GFP ) or expressing a nontargeting control construct . In addition , we verified in vitro that knockdown of FoxP2 did not affect protein levels of FoxP1 , the closest homolog of FoxP2 . FoxP2 knockdown also did not cause apoptotic cell death in Area X , and it did not alter the density of neurons in this nucleus . Consistent with this , FoxP2 knockdown pupils showed different song abnormalities than birds with electrolytic lesions in Area X . Juvenile Area X lesions result in low sequence consistency , and the repertoire of birds with juvenile Area X lesions contains unusually long syllables [13] , which were not observed in FoxP2 knockdown finches ( Figure S5 ) . Together , these data rule out that unspecific effects of RNAi induction , viral infection , or damage to Area X influenced our results . We further eliminated the possibility that specific song features of the tutor birds contributed to the behavioral differences . The outcome of song learning was affected by virus infection in approximately 20% of the volume of Area X . This result is consistent with a previous study on virally injected rats , in which blocking neural plasticity in 10%–20% of lateral amygdala neurons was sufficient to impair memory formation [32] . Taken together , these data strongly suggest that insufficient levels of FoxP2 in Area X spiny neurons lead to incomplete and inaccurate vocal imitation , implicating FoxP2 in postnatal brain function . The incomplete and inaccurate vocal imitation of tutor song in FoxP2 knockdown pupils raises the question whether knockdown pupils were unable to generate particular sounds . Given that syllables with similar spectral features could be learned or omitted by the same pupil ( e . g . , in Figure 2B , tutor syllables E and G are similar; pupil imitated E , but not G ) , this does not seem likely . Also , omitted syllables did not differ in their spectral feature composition from those that were learned by knockdown animals ( unpublished data ) . Consistent with this , the distributions of mean syllable feature values and mean duration across the syllable repertoire were indistinguishable between knockdown and control pupils ( Figure S5 ) . However , it is still possible that FoxP2 knockdown affected the motor control of singing . The fact that FoxP2 knockdown pupils produced syllables more variably than controls at PHD90 would be consistent with this . Importantly though , this increased variability of syllable rendition in FoxP2 knockdown pupils was not yet evident at PHD65 , when tutor imitation was already less proficient ( Figure 5B ) . Thus , the increased syllable variability is apparently not causally related to the observed tutor imitation deficit . Unfortunately , song analysis alone cannot ultimately distinguish between impairments in motor production and motor learning . Any motor production deficit likely affects the auditory feedback signal , which in turn is bound to reduce the quality of tutor imitation . Knockdown of FoxP2 in adult zebra finches might help to clarify the contribution of FoxP2 to motor control . Although knockdown animals were apparently not unable to produce particular syllable types , given the involvement of the basal ganglia in the acquisition and performance of motor sequences [33] , knockdown pupils might have been impaired in producing particular sequences of syllables , i . e . , in moving from one syllable to the next . We found that knockdown pupils could in principle imitate adjacent tutor syllables in the same order ( e . g . , Figure 2B , syllables A and B , and H and I; Figure 2C , syllables C and D ) . There was also no preferred position ( i . e . , beginning or end of song ) for imitated and non-imitated syllables . Moreover , potential sequencing problems might occur at different syllable transitions within the motif or intermittently in different renditions of the motif . Both scenarios would result in low sequence stereotypy , which we did not find ( Figure S8 ) . The limited imitation success of FoxP2 knockdown pupils could also result from an imprecise neural representation of the tutor model . There is evidence for an involvement of Area X in sensory learning at PHD35 [34] , but the up-regulation of FoxP2 in Area X at PHD50 and PHD75 rather speaks for an involvement of FoxP2 in sensory-motor learning [10] . Under the assumption of a model of reinforcement-based motor learning mediated through the basal ganglia , the animal initially generates variable motor output . Progressively , particular motor actions are reinforced [33] . In view of this model , FoxP2 knockdown pupils might have either experienced a limitation in generating enough sound variability or difficulties with reinforcing the “right” motor patterns , a possibility that includes both difficulties in detecting similarity to the target or adjusting song appropriately . Since knockdown pupils sing as variable as control pupils early during song development and even more variable as adults ( Figures 4 and 5B ) , we favor the hypothesis that knockdown pupils were impaired in adjusting their motor output according to the memorized tutor model in the course of song learning . This hypothesis is supported by the phenotypic overlap of song deficits observed in FoxP2 knockdown pupils and birds that were prevented from matching vocal output with memorized tutor song . For instance , perturbed auditory feedback provokes syllable repetitions [35] , and deafening in juveniles brings about syllables with large acoustic variability [36] . Although we cannot ultimately rule out the possibility that the impairment observed after FoxP2 knockdown in juvenile birds was primary motor in nature , an interpretation involving a deficit with auditory-guided motor learning seems more consistent with the knockdown song phenotype . What is the mechanism by which FoxP2 contributes to song development ? In Area X , spiny neurons receive pallial glutamatergic input from Area X–projecting neurons in HVC [37] . These neurons process auditory information and are active during singing [38 , 39] . FoxP2 expressing spiny neurons also receive nigral dopaminergic input [10 , 40] . As has been suggested for motor learning in mammals [41] , midbrain dopaminergic activity could act as reinforcement signal during song learning . Therefore , the integration of pallial and dopaminergic signals provides a candidate mechanism for tuning the motor output to the tutor model during learning . The increase of FoxP2 expression in Area X of zebra finches during times of vocal plasticity could be functionally related to this process . FoxP2 might mediate adaptive structural and functional changes of the spiny neurons while the song is learned . During the seasonal phase of vocal plasticity in canaries , increased FoxP2 expression in the fall months might similarly be involved in seasonal song modifications . Since FoxP2 is a transcription factor , it could act by positively or negatively regulating plasticity-related genes . If FoxP2 functions as a plasticity-promoting factor , knockdown pupils should have been less plastic during learning , resulting in impoverished imitation and abnormally invariant song . Syllable omissions of FoxP2 knockdowns are consistent with this notion , but more variable syllable production is clearly not . Alternatively , if FoxP2 restricts neuronal plasticity , knockdown pupils should sing more variable song . In fact , this is the case , but syllable omissions are not easily explained then . The identification of the downstream target genes of FoxP2 and the electrophysiological characterization of spiny neurons with reduced FoxP2 levels will shed light on the mechanisms by which FoxP2 affects the outcome of vocal learning . The vocal behavior of FoxP2 knockdown zebra finches offers a new interpretation of the speech abnormality in individuals with genetic aberrations of FOXP2 [5] , possibly extending to apraxia of speech in general [42] . The human core deficit affects the production of rapid , sequential mouth movements , which are required for speech articulation [43] , and is thought to be caused by erroneous brain development . Perhaps the speech impairment results from a problem with motor learning rather than motor performance during speech learning , a hypothesis that is in line with recent theories on basal ganglia dysfunction in various developmental disorders [44] . Our results extend the similarities between learned birdsong and human speech to the molecular level , emphasizing the suitability of songbirds for investigating the basic principals of speech and its pathologies . It will be interesting to test , whether “dyspraxic song” is also perceived as different by other finches and interferes with communication , as DVD does in humans . Given female songbirds' preference for well-learned , experimentally unaltered song [45 , 46] , we would expect this to be the case . Finally , the fact that a reduction of FoxP2 affects the outcome of both song learning and speech development provides further evidence for the hypothesis [4 , 21] that during evolution , ancestral genes and neural systems were adapted in the human brain and gave rise to the uniquely human capacity of language .
For FoxP2 nomenclature , we followed the convention proposed by the Nomenclature Committee for the forkhead family of genes ( FOXP2 in Homo sapiens , Foxp2 in Mus musculus , and FoxP2 in all other species , including zebra finches ) [47] . Proteins are in roman type , genes and RNA in italics . Initially , we designed eight different constructs for the expression of short hairpin RNA ( shRNA ) targeting the zebra finch FoxP2 mRNA . All FoxP2 target sequences were located within the minimum common sequence of all isoforms ( ORF of isoform IV ) , thus targeting all FoxP2 isoforms described in [10] . In order to minimize potential cross-reactivity of the hairpins , we chose target sequences that contained at least six dissimilar bases with FoxP1 , the closest homolog of FoxP2 . and were not located within the highly conserved forkhead box domain of FoxP2 . This shRNA design is stringent in comparison to a recently published guideline [48] that recommends including at least three mismatches to untargeted sequences . The structure of the linear DNA encoding shRNA hairpins was sense-loop-antisense . The sequence of the loop was GTGAAGCCACAGATG . Each hairpin construct was tested for knockdown efficiency in HEK293 T cells in vitro by simultaneous overexpression with zebra finch FoxP2 , tagged with the V5 epitope . Subsequent western blot analysis using a V5 antibody revealed two hairpins ( shFoxP2-f , target sequence AACAGGAAGCCCAACGTTAGT , and shFoxP2-h , target sequence AACGCGAACGTCTTCAAGCAA ) that strongly reduced FoxP2 expression levels . To demonstrate the sequence specificity of the hairpins to the FoxP2 gene , we also simultaneously overexpressed them with FoxP1 , cloned from adult zebra finch brain cDNA and tagged with the V5 epitope . The DNA fragments encoding the hairpins shFoxP2-f and shFoxP2-h were subcloned into a modified version of the lentiviral expression vector pFUGW [17] containing the U6 promoter to drive their expression . To use as controls , we subcloned fragments encoding a hairpin targeting GFP ( shGFP , target sequence GCAAGCTGACCCTGAAGTTCA ) and a nontargeting hairpin ( shControl , sequence AATTCTCCGAACGTGTCACGT ) into the modified pFUGW . All viral constructs expressed GFP under control of the human ubiquitin C promoter . Recombinant lentivirus was generated as described in [17] . Titers were adjusted to 1–2 × 106/μl . The general procedure for studying the behavioral consequences of locally reduced FoxP2 levels in Area X was as follows ( Figure S1 ) . Young zebra finches from our colony at the Max-Planck-Institute for Molecular Genetics were sexed as described [49] at approximately PHD10 . By PHD20 , fathers and older male siblings were removed from family cages to prevent experimental zebra finches from instructive auditory experience prior to the onset of tutoring . At PHD23 , animals were anaesthetized with xylazine/ketamine and stereotaxically injected with recombinant lentivirus . The stereotaxic coordinates for Area X injections were anterior/posterior 3 . 6 and 4 . 0 , medial/lateral 1 . 4 and 1 . 6 , and dorsal/ventral 3 . 8 and 4 . 0 . Per injection site , approximately 200 nl of lentiviral solution were injected over a period of 2 min with a hydraulic micromanipulator ( Narishige ) . On PHD30 , each pupil received an adult male song tutor , and both birds were kept together for 2 mo in a sound-isolated box with automated song-recording equipment . By PHD93–95 , trained pupils were perfused with 4% paraformaldehyde in 0 . 1 M PB and their brains dissected for histological analysis ( see Figure S1 for timeline of experiments ) . We determined that the virus infected FoxP2 immunopositive neurons using immunostaining as described [10] . Moreover , we used immunohistological staining with antibody Hu ( 1:200; Chemicon ) to stain neurons and quantify the percentage of them infected by virus . Immunofluorescent sections were analyzed with a 40× oil objective , using a Zeiss confocal microscope ( LSM510 ) with the LSM-510 software package . On average , we counted 417 virus-infected cells in five to six sections per hemisphere ( seven hemispheres from five animals ) and determined how many of those were also Hu+ . We quantified the neuronal density by counting the number of Hu+ cells in scanning windows of 230 . 3 μm × 230 . 3 μm ( two scanning windows per section ) inside and outside the injection site in Area X ( presented as a number of cells/mm2 ) . To identify apoptotic cells , we used a fluorescein TUNEL assay ( Roche ) in 50-μm sagittal sections from PHD29 male zebra finch brains , injected with shFoxP2 virus on PHD23 . To increase signal intensity , we stained the sections by fluorescent immunohistochemistry with an anti-FITC antibody , followed by incubation with an Alexa568-conjugated secondary antibody . TUNEL-positive cells were counted using a fluorescence microscope . In general , the total number of TUNEL-positive cells was very low ( approximately eight cells per 50-μm brain section ) . There was no difference between knockdown and control animals in the total number of TUNEL-positive cells . In order to quantify the volume of Area X targeted by virus injection , we measured the area of Area X in all brain sections ( thickness , 50 μm ) containing it , and quantified the region visibly expressing GFP within Area X under 5× magnification on a fluorescence microscope . We then summed the values from all sections for both areas separately and calculated the ratio of GFP-positive area to total Area X , which is equivalent to the ratio of GFP-positive volume to total Area X volume . The values from left and right hemispheres were averaged per animal . In one knockdown animal , GFP expression in Area X was detected only in the right hemisphere . Since this pupil had a motif imitation score of 50 . 8% , which is below the range of controls ( 68 . 1 ± 2 . 7% mean ± SEM ) , but better than knockdown pupils ( 39 . 6 ± 5 . 0 mean ± SEM ) , it could be that knockdown of FoxP2 in Area X of only the right hemisphere suffices to impair song learning consistent with right hemispheric dominance in zebra finches [50] . In six animals injected with either shFoxP2-f/-h or shControl virus , no GFP was detectable after histological analysis . We quantified imitation success in three of the six animals without GFP , and found it to be similar to zebra finches with shControl injection ( similarity score = 90 . 7; accuracy score = 77 . 7; two-tailed Mann-Whitney U test , p > 0 . 8 for both similarity and accuracy ) . Young male zebra finches received an injection of shFoxP2-f/-h virus in one hemisphere and an injection with control virus ( shControl ) in the contralateral hemisphere on PHD23 as described above . For the quantification of protein levels after FoxP2 knockdown , we performed an immunohistological staining with the FoxP2 antibody on 50-μm sections 30 d after virus injection . Immunohistological staining was performed as described [10] , but using an antibody dilution of 1:5 , 000 . All sections were processed at the same time with the same batch of antibody solution . Images of stained brain sections were taken with a digital camera using the Simple PCI software ( Compix ) at 40× magnification . For each section , we acquired multiple Z-stacked images of the virus-infected area ( 230 . 3 μm × 230 . 3 μm ) , and reconstructed a maximal projection . All images from the same bird were taken with the same microscope and software settings . Finally , we quantified fluorescence intensity levels in the images . The intensity of the green fluorescence from the viral GFP was not significantly different between shFoxP2-f/-h–injected and shControl-injected hemispheres ( two-tailed Mann-Whitney U test , p > 0 . 3 ) . For the quantification of FoxP2 knockdown mRNA levels , young male zebra finches were injected with shFoxP2-f/-h virus in one hemisphere and control virus ( shControl ) in the contralateral hemisphere on PHD23 , as described above . This permitted analysis of FoxP2 knockdown in the same bird while avoiding confounding differences in gene expression levels between birds . On PHD50 , we sacrificed the birds and excised the GFP-expressing brain area with a 1-mm–diameter glass capillary ( Brand ) under a fluorescence dissecting microscope . RNA was extracted with TRIZOL ( Invitrogen ) ; yield was determined by UV spectroscopy at 260/280 nm with a Nanodrop device . FoxP2 expression was quantified by real-time PCR using SybrGreen ( Applied Biosystems ) . We determined relative FoxP2 expression levels through normalization to the expression levels of two internal control genes , which were identified in a BLAST homology search for the mouse housekeeping genes Hmbs and Pfkp in the database from the Songbird Neurogenomics Initiative ( http://titan . biotec . uiuc . edu/songbird/ ) and the Songbird Brain Transcriptome Database ( http://songbirdtranscriptome . net/ ) . The expression of Hmbs and Pfkp in the left and right hemisphere in both injected and untreated animals was equivalent ( numbers indicate fold change between left and right hemispheres; untreated: Hmbs = 1 . 4 ± 0 . 5 and Pfkp = 1 . 3 ± 0 . 6; injected: Hmbs = 1 . 0 ± 0 . 4 and Pfkp = 1 . 1 ± 0 . 4 , n=5 birds ) . Relative expression levels were determined with the comparative cycle time ( Ct ) method . All primers used in this study amplified the cDNA with similar efficiency ( E = 1 ± 5% ) in a validation experiment . Normalized Ct values from the same animal were calibrated to the shControl-injected hemisphere . FoxP2 expression levels are thus presented as the ratio of expression in shControl- to shFoxP2 -injected hemispheres . Vocalizations were recorded between 9 am and 4 pm on PHDs 65 , 80 , and between 90 to 93 in absence of the tutor . Quantitative song analysis was performed using the SAP software , version 1 . 04 [22 , 51] . We analyzed song at the level of the syllables , the motif , and syntax . We define “syllable” as a continuous sound element , surrounded by silent intervals . The “typical song motif” was defined as the succession of syllables that includes all syllable types ( except introductory notes ) , and occurs in a repeated manner during a song bout . Syntax refers to the sequence of syllables in many successive motifs . Motif analysis . We quantified how well pupils had copied the motif of their tutor using a similarity score and an accuracy score obtained in SAP from ten asymmetric pairwise comparisons of the pupil's typical motif with the tutor motif . In asymmetric comparisons , the most similar sound elements of two motifs are compared , independent of their position within a motif . The smallest unit of comparison are 9 . 26-ms–long sound intervals ( FFT windows ) . Each interval is characterized by measures for five acoustic features: pitch , FM , amplitude modulation ( AM ) , Wiener entropy , and PG . SAP calculates the Euclidean distance between all interval pairs from two songs , over the course of the motif , and determines a p-value for each interval pair . This p-value is based on p-value estimates derived from the cumulative distribution of Euclidean distances across 250 , 000 sound-interval pairs , obtained from 25 random pairs of zebra finch songs . Neighboring intervals that pass the p-threshold value ( p = 0 . 1 in this study ) form larger similarity segments ( 70 ms ) . The amount of sound from the tutor's motif that was included into the similarity segments represents the similarity score; it thus reflects how much of the tutor's song material was found in the pupil's motif . To measure how accurately pupils copied the sound elements of the tutor motif , we used the accuracy score from SAP . The accuracy score is computed locally , across short ( 9 ms ) FFT windows and indicates how well the sound matched to the sound in the tutor song . SAP calculates an average accuracy value of the motif by averaging all accuracy values across the similarity segments . Syllable analysis—manual counting of imitated syllables . For manual counting of imitated syllable types , two individuals who were blind to treatment counted all syllables that matched a tutor syllable by visual inspection of sonograms . Their interobserver reliability was 80% . Syllable analysis—syllable acoustic features . We extracted the mean pitch , mean FM , mean entropy , and mean PG , as well as mean duration from 25 renditions of each syllable . To compare the similarity of individual spectral features between pupil and tutor syllables , we subtracted each mean feature value of each tutor syllable from the mean feature value of the corresponding pupil syllable . Next , we normalized the absolute differences between the values of tutor and pupil syllables to the values of the tutor syllable to obtain the difference of a pupil syllable in a given feature from the tutor syllable in percent . To describe the variability of syllable duration between different renditions , we calculated the coefficient of variation of duration values among 25 renditions of each syllable . Syllable analysis—syllable identity score . We quantified the acoustic similarity between different syllables using symmetric comparisons to obtain syllable identity scores . In contrast to asymmetric comparison , no similarity segments are identified during symmetric comparisons . Instead , the FFT windows are compared sequentially from beginning to the end of the two sounds . Thus , similarity reflects how many sound intervals were above p-value , and accuracy indicates the average ( 1 − p-value ) . To comprehensively capture the acoustic similarity between syllables in a single measure we used the product of similarity and accuracy to obtain the syllable identity score . As for the motif analysis the p-threshold value was set to p = 0 . 1 . To quantify how accurately pupils learned individual syllables , we performed ten symmetric comparisons of each pupil syllable with its corresponding tutor syllable . To assess how variable the same pupil performed a particular syllable in multiple renditions of his motif , we compared 20 renditions of each syllable , two at a time . Because minute temporal shifting of FFT windows is allowed in symmetric comparisons ( 10 ms in this study ) , the more variable duration of syllables in FoxP2 knockdown animals did not bias the identity score . The syllable identity score rather reflects spectral differences between syllables . Syntax analysis . For each pupil , we manually annotated sequences of 300 user-defined syllables with the positions in their respective motifs . That is , each syllable of a motif was given a unique integer . Based on these data , we computed the Markov chain for each pupil , i . e . , all transition probabilities between syllables . To measure the stereotypy of a motif , we calculated for each syllable the entropy of its transition distribution [52] . Because motif duration differed between birds , these entropy values were rescaled by the maximal possible entropy for each given motif duration . The entropy score for a pupil was then represented by the average of these fractions of maximal entropy over all syllables . Based on this entropy measure , we generated a sequence consistency score ( 1 − entropy measure ) , which reflects song stereotypy . An entropy score of 0 indicates random syllable order , whereas a score of 1 reflects a fixed syllable order . Analysis of song development . To determine tutor similarity and vocal variability during plastic song and towards the end of the learning phase , we analyzed songs recorded on PHD65 , PHD80 , and PHD90–93 ( PHD ± 1 d; in one control pupil , recordings were only available from PHD75 instead of PHD80 ) . First , all sound files from one day were segmented into sounds in the feature batch mode of SAP . Here , the pupils' vocalization is separated from nonvocalization background using two thresholds ( Wiener entropy and amplitude ) . The thresholds were adjusted for each pupil individually to obtain an optimal segmentation . We validated the segmentation for each pupil by visual inspection of the segments and confirmed that segments correspond to syllables . Next , all segments from a given day ( between 1 , 000 and 3 , 000 segments ) were automatically compared to the tutor motif . That is , in each comparison , SAP identifies the best possible match to the tutor motif for each segment . Of all segments analyzed from PHD65 , PHD80 , and PHD90 , 11 . 0% ± 0 . 9% were less similar to the tutor model than two random zebra finch sounds are to each other , and thus did not receive any accuracy value in SAP . These sounds were found to represent cage noise , mostly . There were no differences between the amount of sounds excluded between knockdown and control pupils for any of the ages ( two-tailed Mann-Whitney U test , p > 0 . 9 for PHD65; p > 0 . 8 for PHD80; p > 0 . 7 for PHD90 ) .
The GenBank ( http://www . ncbi . nlm . nih . gov/Genbank ) accession numbers for the genes and gene products discussed in this paper are FoxP1 ( AY549152 ) , FoxP2 isoform I ( AY549148 ) , FoxP2 isoform IV ( AY549151 ) , Hmbs ( NM_013551 ) , and Pfkp ( NM_019703 ) . The Online Mendelian Inheritance in Man ( OMIM; http://www . ncbi . nlm . nih . gov/sites/entrez ? db=OMIM ) accession number for FOXP2 is 605317 . | Do special “human” genes provide the biological substrate for uniquely human traits , such as language ? Genetic aberrations of the human FoxP2 gene impair speech production and comprehension , yet the relative contributions of FoxP2 to brain development and function are unknown . Songbirds are a useful model to address this because , like human youngsters , they learn to vocalize by imitating the sounds of their elders . Previously , we found that when young zebra finches learn to sing or when adult canaries change their song seasonally , FoxP2 is up-regulated in Area X , a brain region important for song plasticity . Here , we reduced FoxP2 levels in Area X before zebra finches started to learn their song , using virus-mediated RNA interference for the first time in songbird brains . Birds with experimentally lowered levels of FoxP2 imitated their tutor's song imprecisely and sang more variably than controls . FoxP2 thus appears to be critical for proper song development . These results suggest that humans and birds may employ similar molecular substrates for vocal learning , which can now be further analyzed in an experimental animal system . | [
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] | 2007 | Incomplete and Inaccurate Vocal Imitation after Knockdown of FoxP2 in Songbird Basal Ganglia Nucleus Area X |
During the development of neural circuitry , neurons of different kinds establish specific synaptic connections by selecting appropriate targets from large numbers of alternatives . The range of alternative targets is reduced by well organised patterns of growth , termination , and branching that deliver the terminals of appropriate pre- and postsynaptic partners to restricted volumes of the developing nervous system . We use the axons of embryonic Drosophila sensory neurons as a model system in which to study the way in which growing neurons are guided to terminate in specific volumes of the developing nervous system . The mediolateral positions of sensory arbors are controlled by the response of Robo receptors to a Slit gradient . Here we make a genetic analysis of factors regulating position in the dorso-ventral axis . We find that dorso-ventral layers of neuropile contain different levels and combinations of Semaphorins . We demonstrate the existence of a central to dorsal and central to ventral gradient of Sema 2a , perpendicular to the Slit gradient . We show that a combination of Plexin A ( Plex A ) and Plexin B ( Plex B ) receptors specifies the ventral projection of sensory neurons by responding to high concentrations of Semaphorin 1a ( Sema 1a ) and Semaphorin 2a ( Sema 2a ) . Together our findings support the idea that axons are delivered to particular regions of the neuropile by their responses to systems of positional cues in each dimension .
During the development of neural circuitry , neurons of different kinds must establish specific synaptic connections by selecting appropriate targets from large numbers of different alternatives . The range of these alternative targets is reduced by well organised patterns of growth , termination , and branching that deliver the terminals of appropriate pre- and postsynaptic partners to restricted regions of the developing nervous system . The mechanisms that control the coordinate projection of pre- and postsynaptic neurites to a common region are incompletely understood . Although there has been substantial progress in identifying molecular mechanisms of axon growth and guidance , far less is known about the way in which appropriate target areas are identified , leading to termination and branching [1]–[4] . The extent to which these processes depend on target specific signals as opposed to pervasive guidance cues , to which many different neurons can respond , is far from clear . We have used the axons of embryonic Drosophila sensory neurons as a model system in which to study the way in which growing neurons are guided to terminate in a specific region of the developing nervous system . These neurons have their cell bodies in the periphery of the embryo , either close to or embedded in the body wall . Their axons grow into a central ganglion where they terminate in a neuropile that consists of a dense meshwork of interweaving axons and dendrites . Anatomically the neuropile shows few overt signs of organisation apart from clear regularities such as the commissures that cross the midline and a set of longitudinal axon bundles at stereotyped positions that provide a series of landmarks with respect to which other structures can be mapped [5] . Functionally however the neuropile is an obviously well-organised structure , with , for example , motor neuron dendrites and the endings of sensory neurons terminating and branching in distinct and characteristic domains . Thus it is clear that there must be cues operating in the neuropile that deliver terminals to these specific destinations within the forming network . In the case of the sensory neurons , it is clear that specific types of neurons serving particular modalities terminate in well ordered and characteristically different parts of the neuropile . These termination zones together with the overall structure of the neuropile are shown in diagrammatic form in Figure 1 . Because the sensory neurons provide us with an accessible set of cells whose terminals grow to different parts of the forming neuropile , we can readily use these neurons to investigate the guidance mechanisms that operate to determine these distinctive patterns of growth and termination . We previously showed that Slit secreted at the midline and acting through its Robo receptors constitutes a repellent gradient to which sensory neurons respond by terminating and branching at specific positions in the medio-lateral axis of the neuropile [6] . Expression of a particular Robo receptor by a sensory axon is necessary and sufficient to determine the distance from the midline at which that axon will terminate . Thus , in the medio-lateral axis at least , the position at which an axon terminates within the forming neuropile is determined not by some putative signals from its postsynaptic target , but by the presynaptic neuron's response to a pervasive cue secreted from the midline . However , the neuropile is a 3-D structure and there must therefore be additional cues that determine the dorso-ventral and antero-posterior termination domains for each axonal and dendritic arbor . Our previous study provided evidence for at least one further signal that operates to determine positions in the dorso-ventral axis . Sensory terminals that are shifted experimentally along the medio-lateral axis of the neuropile maintain their characteristic dorso-ventral location in their new position , suggesting that the factor that determines this position may be a “dorso-ventral” patterning cue that is present at different positions in the medio-lateral axis . This additional finding led us to propose a general model for the cues that delineate domains within a neuropile in which presynaptic axons and their postsynaptic partners terminate and form connections [6] . In this model , termination sites depend on the response of axons to a system of positional cues that dictate the behaviour and final location of many , perhaps all terminals within a developing network of pre- and postsynaptic neurons . Specific locations are given not by the target , but by the set of receptors for these positional cues that each neuron expresses . Here , we test and augment this model by using a genetic screen to identify cues and their receptors that guide terminating axons in the dorso-ventral axis of the neuropile . We find that dorso-ventral layers of neuropile contain different levels and combinations of semaphorins . We demonstrate the existence of a central to dorsal and central to ventral gradient of Sema 2a , perpendicular to the Slit gradient . We show that a combination of Plexin A ( Plex A ) and Plexin B ( Plex B ) receptors specifies the ventral projection of sensory neurons by responding to high concentrations of Semaphorin 1a ( Sema 1a ) and Semaphorin 2a ( Sema 2a ) . These signals together with the Slit/Robo system acting in the medio-lateral axis limit the arborisations of sensory axons to specific termination domains within the neuropile . Since these are the domains within which specific functional sets of connections will be formed , the terminating sensory axons , by responding to pervasive positional cues , are able to lay out part of the characteristic functional architecture of the forming network .
Previous studies have shown that the axons of sensory neurons project to distinct medio-lateral , dorso-ventral , and antero-posterior domains in the neuropile in correlation with their modality and dendritic morphology [7]–[9] . We have extended these studies using Fasciclin II ( Fas II ) positive tracts as reference points ( Figure 1A ) [6] , [10] . We divide the neuropile into three medio-lateral domains and four dorso-ventral layers ( Figure 1C ) . With the exception of the chordotonal ( ch ) neurons , sensory axons terminate in the medial domain of the neuropile . Ch axons terminate and branch in the intermediate domain . There is very little sensory input to the dorsal-most layer ( layer 1 ) where motor neurons establish their dendritic arbors . The proprioceptive dorsal bipolar dendritic ( dbd ) and class I md ( multidendritic ) neurons terminate in the upper central layer ( layer 2 ) [6] , [9] , [11] . The ch neurons terminate in the lower central layer ( layer 3 ) [6] , whereas nociceptive class IV md neurons terminate in the ventral-most layer ( layer 4 ) . Class IV md neurons can be identified with ppkEGFP , which labels one intersegmental nerve ( ISN ) and two segmental nerve ( SN ) neurons in each hemisegment [9] , [12] . The position of termination in the neuropile does not correlate with the nerve route by which the sensory neurons reach the neuropile ( see Figure S1 ) . Sensory axons whose cell bodies are located ventrally in the body wall travel in the SN , whereas axons whose cell bodies are located dorsally or laterally in the body wall travel in the ISN [13] . Sensory axons running in the SN and ISN terminate in layers 2 , 3 , or 4 , in correlation with their modality and dendritic morphology [9] , [11] . Since each of the three modality-specific sensory termination domains contains some neurons that have travelled through the SN , and others that have travelled through the ISN , differences in axon routing to the neuropile cannot account for differences in termination within the neuropile . To investigate mechanisms that confine sensory projections of different modalities to different dorso-ventral layers of the neuropile , we carried out a gain-of-function screen for trans-membrane proteins , which , when expressed selectively in sensory neurons , shift sensory terminals with respect to Fas II tracts . We used PO163GAL4 , UAS-n-synaptobrevin-GFP flies to target gene expression selectively to sensory neurons and simultaneously to visualise their terminals ( Figure 2A ) [14] . As a test of our method , we confirmed that expressing the Robo 3 receptor for Slit in sensory neurons shifts their terminals away from the medial domain of neuropile ( Figure 2B ) [6] . We screened 418 lines with UAS inserts in front of trans-membrane protein coding genes ( see Materials and Methods and Table S1 for detailed results of the screen ) by systematically expressing them in sensory neurons and analysing the pattern of sensory terminals in abdominal segments ( A1–A7 ) at 21-h after egg laying ( AEL ) . We identified 11 genes ( 2 . 6% ) that change the pattern of sensory terminals , without altering the number of neurons or preventing sensory axons from reaching the central nervous system ( CNS ) ( Table S1 ) . Of the 11 genes expressed , two produced obvious shifts along the dorso-ventral axis . Both belong to the same family: plex B and A . If plex B is expressed in all sensory neurons , sensory terminals are excluded from layer 2 ( Figure 2C ) . If plex A is expressed , terminals are excluded from the intermediate regions of layer 3 and from layer 1 ( Figure 2D ) . We also co-expressed Robo3 and Plex B in sensory neurons and found that this produces a “combination” of Robo 3 and Plex B expression phenotypes . In these embryos sensory terminals are now mostly confined to the lateral-most portion of layers 3 and 4 ( Figure 2E ) . The Plexins are receptors for the Semaphorins ( Semas ) , a diverse family of secreted and membrane-associated proteins [15]–[19] . In Drosophila there are two Plexins ( A and B ) and five Semas: 1a , 1b , and 5c ( transmembrane ) and 2a and 2b ( secreted ) . Plex B binds Sema 2a and mediates the Sema 2a-dependent repulsion of motor and sensory axons in the periphery and the fasciculation of longitudinal tracts in the ventral nerve cord ( VNC ) [20] , [21] . Plex A binds strongly to Sema 1a and Sema 1b and mediates the Sema-dependent repulsion of embryonic motor axons in the periphery and the repulsion of adult olfactory receptor axons by Sema 1a in the antennal lobes [22]–[24] . The Plexin overexpression phenotypes suggested that their Sema ligands might act as cues to position the terminals of neurons along the dorso-ventral axis of the forming neuropile . We therefore used antibody labelling to analyse the expression of Semas 2a and 1a in the CNS at different stages of embryogenesis: prior to sensory axon ingrowth ( 11-h AEL ) , at stages when sensory axons form their terminal arbors ( 13-h AEL ) , and several hours after sensory axons have completed their terminal arbors ( 21-h AEL ) . Sema 2a expression first becomes detectable at 11 h as the outgrowth of sensory axons begins , persists strongly until 16 h , but has disappeared by 21 h , when the embryo is mature and ready to hatch . At 13 h , when sensory axons are forming their terminal arbors , the highest levels of Sema 2a are in layer 2 in the centre of the neuropile ( Figure 2F and 2H ) . Strikingly , the protein forms gradients of expression in the neuropile that extend dorsally and ventrally from layer 2 ( Figure 2L ) , at right angles to the mediolateral gradient of Slit ( Figure 2F , 2G , 2J , and 2K ) . There is no detectable expression in layer 4 . Our experiments show that the effect of overexpressing Plex B in sensory neurons is to shift their terminals away from regions with high Sema 2a levels . This effect is still detectable at 21 h when there is no Sema 2a expression and we conclude that misplaced terminals do not compensate by delayed growth into central neuropile ( Figure 2C ) . Sema 1a expression is present at 10-h AEL , before sensory axons have entered the neuropile and persists throughout embryogenesis ( unpublished data ) . By 13 h the highest levels of Sema 1a are in the lateral and intermediate portions of layers 1 and 3 , at lower levels in layer 2 , and not detectable in layer 4 ( Figure 2F and 2I ) . In addition to differences in the levels of Sema 1a expression in different dorso-ventral layers of the neuropile , we also find an apparent decrease in concentration from intermediate ( high ) to medial ( low ) in layers 1 and 3 . At 21 h Sema 1a is still strong in intermediate portions of layers 1 and 3 . The effect of overexpressing Plex A is to exclude sensory terminals from these high levels of Sema 1a expression ( Figure 2D ) . We also analyzed the distributions of Sema 1a and Sema 2a in the antero-posterior axis , at the time of sensory axon ingrowth into the CNS , and found they appear uniform ( Figure S2A and S2B ) . To confirm that Sema 2a and Sema 1a act as the ligands for Plex B and Plex A in our experiments , we tested the sema 2a and sema 1a dependence of the Plex B and Plex A overexpression phenotypes in sensory neurons . We analysed patterns of sensory terminals in sema 2a03021 loss of function embryos [25] and in embryos in which plex B was overexpressed in sensory neurons in a sema 2a03021 background . In sema 2a03021 embryos we find ectopic sensory terminals in layer 2 ( Figure 3A ) . Overexpression of Plex B in sensory neurons in a sema 2a03021 background fails to exclude sensory terminals from central and dorsal neuropile ( compare Figures 2C and 3B ) . The pattern of sensory terminals in these embryos is similar to their pattern in sema 2a03021 mutants ( compare Figure 3A and 3B ) . We conclude that Sema 2a is the functional ligand for Plex B in this system . We recombined the UAS-plex A-HA [24] transgene with the sema 1aP1 [26] mutation to express Plex A in a sema 1a mutant background . We analysed patterns of sensory terminals in sema 1aP1 mutant embryos and in embryos in which Plex A was overexpressed in sensory neurons in a sema 1aP1 background . In sema 1aP1 embryos , we found ectopic sensory terminals in layers 1 and 3 ( Figure 3C ) . Overexpression of Plex A in sensory neurons in a sema 1aP1 background failed to exclude them from layers 1 and 3 ( compare Figures 2D and 3D ) . The pattern of sensory terminals in these embryos is strikingly similar to their pattern in sema 1aP1 mutants ( compare Figure 3C and 3D ) . We conclude that Sema 1a is the functional ligand for Plex A in this system . We were able to identify potential cellular sources of the transmembrane Semaphorin Sema 1a by looking for neuronal populations that project to layers 1 and 3 ( Figure S3 ) . One such population are the motorneurons , most of which project dendrites to layer 1 ( Figures 1 and S3A ) . Using the OK371-GAL4 we targeted the expression of the cell death gene reaper and of the CD8GFP reporter ( OK371-GAL4 , UASCD8GFP;UAS-reaper ) to the motor neurons [27] . This resulted in the death of most motor neurons by the early first instar larval stage ( as judged both by the onset of larval paralysis and by the loss of GFP signal ) ( Figure S3C ) . Immunofluorescence visualisation of Sema 1a shows a significant reduction in Sema 1a levels in layer 1 in animals that lack motor neurons , compared to animals with intact motor neurons ( Figure S3B , S3D , and S3E ) . We conclude that the motorneuron dendrites are likely to be a source of Sema 1a in the dorsal neuropile . Another cell population that projects to layer 1 , as well as to layer 3 , are the GABAergic interneurons ( Figure S3F ) . We used GADGAL4 [28] , [29] to visualise and kill both the motor neurons and the GABAergic interneurons and found that this resulted in nearly complete loss of Sema 1a staining from both layers 1 and 3 ( Figure S3G , n = 10 embryos ) . We conclude that the GABAergic interneurons are likely to be a significant source of Sema 1a in layer 1 and also in layer 3 . We have so far been unable to identify cellular populations that project exclusively to layer 2 , but since the expression is continuous across the midline ( see Figure S2A ) , at least some midline cells could be involved . One possibility is that the recently described extensions of midline glial cells , the gliopodia [30] , might provide a vehicle by which high levels of Sema 2a are deployed across the developing neuropile . Interestingly these extensions of the glial cells have a limited life span , becoming much reduced late in embryogenesis and we find that Sema 2a expression also declines in these late stages . The VNC of embryos that lack midline glial cells ( for example in single minded mutants; [31] ) are too fragile and disorganized to allow analysis of levels of Sema 2a along the dorso-ventral axis . Instead we restored Sema 2a expression in the midline glial cells using the single mindedGAL4 line [31] , in an otherwise sema 2a mutant background ( sema 2a , UAS-sema 2a;single-mindedGAL4 ) ( see Figure S4A–S4C for details of these experiments ) . We were able to restore Sema 2a expression in the neuropile ( Figure S4B ) , in layers 1 , 2 , and 3 ( Figure S4C ) , but in a pattern that appeared broader than the endogenous stripe in layer 2 . Thus a source of the Sema 2a gradients could potentially be a subset of the midline cells , although we cannot exclude the possibility that some other cells are the endogenous source of this cue in the CNS . To investigate the role of the Sema/Plexin system in determining the position at which axons terminate within the layered structure of the neuropile we decided to focus our experiments on a single class of sensory cells with well defined terminal branches , the nociceptive class IV md neurons . Class IV md neurons can be identified with ppkEGFP [9] , [12] , which labels one ISN and two SN neurons in each hemisegment . The axons of these cells terminate medially in the ventral-most part of the neuropile , layer 4 ( Figure 1C ) , where they branch asymmetrically in the antero-posterior axis ( Figure S7A ) . By examining the location of these ppkEGFP-expressing axons with respect to Sema expression we confirmed that at 13-h AEL these axons terminate in a region of low Sema 2a ( Figure 4A ) and just below regions of high Sema 1a expression levels ( Figure 4B ) . At 21-h AEL the class IV terminals remain in a region with low Sema 1a expression ( Figure 4C ) . We now asked whether sema 1a and sema 2a are required to confine class IV projections to layer 4 . In embryos mutant for sema 1aP1 [26] , sema 2a03021 [25] , and in sema 1aP1 , sema 2a03021 double mutants , the class IV axons have aberrant patterns of termination and/or growth in the dorso-ventral axis ( compare Figure 5A with 5B , 5C and 5D; see also Figure S6 for details of the effects of these mutations on the dorso-ventral position of Fas II tracts ) . We make a distinction between growth and termination phenotypes of class IV axons ( For details of this distinction and examples of different kinds of growth and termination phenotypes see Figure S5 ) . We found a significant increase in the percentage of hemisegments with aberrant terminals in sema 1aP1 , sema 2a03021 , and sema 1aP1 , sema 2a03021 double mutants with respect to sema 1aP1/+ controls ( Figure 5A–5H ) . Moreover , the percentage of hemisegments with aberrant termination in sema 1aP1 , sema 2a03021 double mutants , was significantly higher than in either sema 1aP1 or sema 2a0302 single mutants ( Figure 5E ) . We found that in sema 1aP1 mutants aberrant class IV axons tend to terminate in layer 1 more often than in layer 2 ( Figure 5F ) . Conversely , in 2a03021 mutants , we found that aberrant class IV axons tend to terminate in layer 2 more often than in layer 1 ( Figure 5G ) . In sema 1aP1 , sema 2a03021 double mutants ( Figure 5H ) class IV axons terminate with roughly equal probability in layers 1 , 2 , or 3 . Sema 1a appears to play a more important role in preventing termination in layer 1 , followed by layer 3 , and a minor role in preventing termination in layer 2 . Sema 2a appears to play a more important role in preventing termination in layer 2 , and a minor role in preventing termination in layers 1 and 3 . Our results suggest that Sema 1a and Sema 2a are instructive for termination of class IV axons along the dorso-ventral axis . We also assessed the potential roles of Sema 1a and Sema 2a in controlling the termination of class IV axons in the antero-posterior axis by analysing their projections in a top-down view of the neuropile in wild type and in sema 1a , sema 2a double mutants ( Figure S7 ) . We chose the sema 1a , sema 2a double mutant for this analysis , because it exhibited the strongest phenotypes in the dorso-ventral axis . Wild-type class IV axons grow asymmetrically , within their normal ventral and medial termination domain , forming thicker terminal in the anterior than in the posterior portion of the segment ( Figure S7A ) . We did not observe a significant loss of this asymmetry in the sema 1a , sema 2a double mutant compared to wild type ( Figure S7B and S7C ) . In top down view , class IV terminals do appear disorganized compared to wild type , but we assume this disorganization is a consequence of the major defects in growth and termination in the dorsoventral axis . Thus Sema 1a and Sema 2a do not appear to play a major role in confining class IV terminals to the anterior portion of the segment . Consistent with this idea is also our finding that the distributions of Sema 1a and Sema 2a appear uniform in the antero-posterior axis , at the time of sensory axon ingrowth into the CNS ( Figure S2A and S2B ) . In some cases membrane-bound Sema 1a acts as a receptor [18] , [32] . Thus , rather than a requirement to act as a cue , the class IV mutant phenotypes could reflect a cell-autonomous requirement for Sema 1a in the sensory neurons themselves . To resolve this , we performed two kinds of rescue experiments . First , we restored sema 1a expression to sensory neurons in sema 1aP1mutant embryos using PO163GAL4 . Antibody labelling confirms that Sema 1a is successfully targeted to embryonic sensory terminals using this driver ( compare Figure 6A , 6C , and 6E ) and shows that in the mutant a large fraction of Sema 1a-expressing sensory neurons aberrantly project to the dorsal part of the neuropile ( Figure 6E ) . We then analysed specifically the projections of class IV neurons in embryos where sema 1a expression had been restored to sensory neurons in the sema 1aP1 mutant background ( compare Figure 6B , 6D , and 6F ) . There was no rescue of the dorsal termination phenotype of class IV axons in these embryos . Quantification revealed no significant reduction in dorsal termination of class IV axons , compared to sema 1aP1 mutants ( Figure 6I ) . Thus , sema 1a is not required in class IV neurons themselves to exclude their terminals from dorsal neuropile . In a second set of experiments , we selectively restored Sema 1a to dorsal neuropile in an otherwise sema 1aP1 mutant background , by using HB9GAL4 to drive its expression in a subset of motor neurons [33] . We used the HB9GAL4 line for this rescue experiment , because it is expressed before sensory neurons grow into the neuropile , unlike GADGAL4 or OK371GAL4 , which are expressed later . We confirmed that Sema 1a is selectively present in dorsal neuropile in these experiments ( compare Figure 6A , 6C , and 6G ) , and we observed a significant reduction in the dorsal termination of class IV axons compared to sema 1aP1 mutants ( Figure 6H and 6I ) . Thus in an otherwise mutant background , the mutant phenotype of class IV axons can be partially rescued by expressing sema1a dorsally in the dendrites of motor neurons . We also asked whether restoration of Sema 2a expression in the midline glial cells using the single mindedGAL4 line in an otherwise sema 2a mutant background ( sema 2a , UAS-sema 2a;single-mindedGAL4 , ppkeGFP ) rescues the sema 2a mutant phenotype of class IV axons ( see Figure S4 for details of these experiments ) . We observed a significant reduction in the aberrant termination of class IV axons in layer 2 compared to sema 2a mutant embryos ( Figure S4D and S4E ) . The experiments we describe suggest that both Sema 1a and Sema 2a are required as cues to confine class IV sensory axons to ventral neuropile . We also find that expressing either plex A or plex B in sensory neurons is sufficient to shift their terminals away from regions with high levels of Sema 1a and 2a . Thus , a combination of Plexins could be required in ventrally projecting sensory neurons to exclude them from dorsal and central neuropile . By in situ hybridization we confirmed previous reports [20] that ch , dbd , and class I–IV md neurons express plex B at the time that sensory axons grow into and terminate in the VNC ( Figure S8D ) . By double labelling with anti-Plex A and anti-horseradish peroxidase we confirmed that Plex A is expressed in sensory neuron cell bodies at 13-h AEL ( Figure S8A ) , and by double labelling with anti-Plex A and anti-GFP showed that Plex A is strongly expressed in the ppk-expressing class IV neurons ( Figure S8B ) . Unfortunately , none of these experiments allows us to draw quantitative conclusions about levels of expression in different cells . High background levels also prevented a reliable analysis of Plex A expression along the dorso-ventral axis of the CNS . However antibody labelling against Plex A does reveal expression in the neuropile at 13-h AEL ( Figure S8C ) . To show whether both Plexins are required to exclude the ventrally projecting sensory neurons from central and/or dorsal neuropile , we analysed the projection pattern of class IV axons in plex A and B mutants . In plex ADf ( 4 ) C3 mutants , class IV axons project aberrantly to central and/or dorsal neuropile ( Figure 7A and 7B ) . Quantification reveals significantly more terminals in dorsal and central neuropile , compared to wild type ( Figure 7G ) . In plex BKG00878 mutants class IV axons also project to dorsal or central neuropile ( Figures 7D and 7E ) . Quantification reveals significantly more terminals in dorsal and central neuropile compared to wild type ( Figure 7G ) . We also quantified the proportion of terminals in each of the different layers of the neuropile ( Figure 7H and 7I ) . We found that in plex B mutants Class IV axons terminate with roughly equal probability in layers 1 , 2 , or 3 . This suggests that Plex B may normally have a role in preventing termination in layers with high levels of Sema 2a or Sema 1a and may therefore be a functional receptor for both ligands . We also observed that embryos transheterozygous for plex B and either sema 1a ( sema 1a/+; plex B/+ ) , sema 2a ( sema 2a/+; plex B/+ ) , or plex A ( plex B/plex A ) all exhibit class IV termination phenotypes , indicating a genetic interaction between these mutations ( unpublished data ) . To further test whether Plex B functions to prevent termination in regions with high Sema 1a levels we analysed the patterns of sensory terminals that overexpress Plex B in a sema 1a mutant ( Figure S9 ) . In these embryos we observed a striking expansion of sensory terminals into the intermediate region of layer 1 that normally contains highest Sema 1a levels within this layer ( compare Figure S9A and S9B ) . In a wild-type background Plex B-overexpressing sensory terminals remain confined in the most medial portion of layer 1 , even though they become excluded from layer 2 ( Figures 2C , S9A ) . A similar expansion is also observed in plex ADf ( 4 ) C3 mutants , indicating that Plex A function is required to prevent termination in regions with highest levels of Sema 1a ( Figure S9C ) . Interestingly , we found that in the absence of plex A , Plex B overexpression in sensory neurons is sufficient to prevent their expansion into regions with highest Sema 1a levels ( in PO163GAL4 , UAS-plex B; plex ADf ( 4 ) C3 embryos ) ( Figure S9D ) . To exclude ( a ) the possibility that plex A and B are required in the central targets of sensory neurons , in which case the mutant phenotypes might be a result of aberrations in normal target directed growth and ( b ) the possibility that Plex A and B are acting as guidance cues we restored their expression selectively to sensory neurons using the P0163 GAL4 driver . Restoration of Plex B expression selectively in sensory neurons in a plex BKG00878 mutant background , rescues the phenotype of class IV md neurons ( Figure 7E ) . Quantification reveals significantly fewer aberrant class IV terminals in plex B-rescue embryos as compared to plex B mutants ( Figure 7G ) . The Fas II tracts continued to exhibit mutant phenotypes in these experiments , as would be expected if the rest of the neuropile , other than the sensory neurons , remained mutant ( Figure S10 ) . Likewise , restoration of Plex A expression in sensory neurons in a plex ADf ( 4 ) C3 mutant background , rescued the phenotype of class IV md neurons ( Figures 7C ) . Quantification reveals significantly fewer aberrant class IV terminals in plex A-rescue embryos compared to plex A mutants ( Figure 7G ) . We conclude that both plex A and plex B are required in sensory neurons for the appropriate targeting of class IV axons to the ventral neuropile .
Our initial approach of using a misexpression screen targeted to all sensory axons was sufficient to reveal the existence of the Semas as putative cues in the dorso-ventral axis by showing that there were generalised redistributions of sensory endings in this axis when the cells concerned were forced to express either of the two Sema receptors , Plex A or Plex B . These shifts were readily detectable when the nervous system was viewed in a plane at right angles to the neuraxis . Viewing the nervous system in this plane also reveals the largely complementary patterns of expression for the two Semas . The membrane bound Sema 1a is distributed in an alternating pattern across the neuropile with high levels in both layers 1 and 3 . The secreted Sema , Sema 2a , on the other hand , is expressed at high levels in a central strip that extends across the midline and in gradients that decline ventrally and dorsally orthogonal to the Slit gradient ( Figure 2 ) . A gradient of Sema 2a has also been described in the embryonic limb of the grasshopper [38] . There it contributes to the polarized growth of pioneer sensory axons away from the region of highest Sema 2a expression at the tip . In the developing Drosophila embryo selective overexpression of the putative receptors for Sema 1a and Sema 2a in sensory neurons acts in a predictable fashion to exclude sensory axons and terminals from those regions where the ligands are highly expressed: overexpression of Plex A excludes projections from high levels of Sema 1a expression in layers 1 and 3 . Overexpression of Plex B shifts sensory terminals further away from the central layer of the neuropile . These findings suggest that Sema 2a and Sema 1a provide guidance cues to the growth cones of sensory neurons that express Plex A and Plex B . It is consistent with this idea that in the absence of Sema 1a , Plex A overexpression in sensory neurons does not exclude their terminals from regions that normally contain high Sema 1a levels . Similarly , in the absence of Sema 2a , Plex B overexpression in sensory neurons does not exclude their terminals from the central layer of the neuropile . The manipulations of the pattern of sensory terminals in the dorso-ventral axis found with Plexin overexpression are analogous to the manipulations in the medio-lateral axis that are found with Robo3 misexpression . In both dimensions the position at which sensory neurons form their terminals is determined by their expression of receptors for positional cues . The most ventrally located sensory terminals , the ppk-expressing md neurons are derived from axons that actually enter the nervous system dorsally and grow downwards , skirting alternative neuropile regions before turning inwards to reach their characteristic medial , ventral domain of termination . A consequence of Sema signalling is that these ventrally targeted axons are excluded from more dorsal regions of the neuropile and channelled instead through a limited lateral region where the expression of both proteins is low , so that their inward migration towards the midline is blocked until they reach the most ventral region . In the absence of either of the Semas or their Plexin receptors , ppk-expressing axons aberrantly enter and terminate in more dorsal regions of the neuropile . This suggests that the growth cones of these cells are attracted towards to midline ( we assume by Netrins ) [39] as soon as they enter the CNS , but that entry and termination in the more dorsal region of the neuropile is prevented by high levels of Sema 1a in layer 1 . In vertebrates genetic studies show that proprioceptive axons are excluded from the superficial dorsal horn by Sema 6D/6C signalling mediated by Plex A1 . Loss of Plex A1 allows proprioceptive collaterals to invade the superficial dorsal horn although most succeed in projecting through it to their normal more ventral target zones [19] . In an analogous ( though not topologically equivalent ) fashion , ventrally projecting afferents in Drosophila require Sema signalling through Plex A for their proper exclusion from the most dorsal neuropile . Loss of plex A appears to affect class IV terminals less strongly than loss of sema 1a . One explanation could be that Plex B might also function as a receptor for Sema 1a in this system . Our observation that in plex B mutants class IV axons aberrantly terminate in layers 1 , 2 , or 3 supports this possibility . We also find that Plex B overexpression in sensory neurons in plex A mutant embryos , prevents aberrant expansion of sensory terminals into intermediate portion of layer 1 , which contains very high levels of Sema 1a ( Figure S9 ) . Such an expansion occurs in both plex A and sema 1a mutant embryos . High levels of Plex B signalling thus appear to be able to substitute for the absence of Plex A signalling and prevent expansion into regions with high Sema 1a levels . These findings could be explained if Plex B were to function as a lower affinity receptor for Sema 1a , as well as a high affinity receptor for Sema 2a . Sema 1a and Sema 2a are unlikely to be the only cues that operate in the dorso-ventral axis . The incomplete penetrance of the termination phenotype in the sema 1a , sema 2a double mutant suggests that additional factors may operate to control the ventral targeting of class IV axons . There may be long range ventral attractants or local substrate bound attractive cues for these axons in the neuropile . It is also likely that dorsally and centrally located sensory and interneuron terminals , as well as dendrites of motor neurons may require additional signals to exclude them from ventral neuropile . Such signals could be the other Semas . Alternatively , by analogy with the optic tectum , where Wnt signalling drives dorsal projections and Ephrins dictate ventral projections , it is possible that some other signalling system may operate with Semas to confine dorsally projecting neurons to dorsal neuropile [3] , [40] , [41] . In the fly antennal lobe , during the formation of the olfactory map , Sema 1a expression on the surfaces of antennal olfactory receptor neuron ( ORN ) axons excludes Plex A expressing maxillary palp ORN axons from inappropriate glomeruli [22] , [23] . Our findings suggest that much of the Sema 1a expression in the neuropile of the VNC is on the surfaces of motor neuron dendrites and on the projections of the GABAergic interneurons . Thus , there appear to be two kinds of positional cues in the neuropile . Slit and Sema 2a are examples of secreted and possibly glia-mediated positional cues . Sema 1a on the other hand is presented on membranes of particular neuronal classes ( GABAergic interneurons and motorneurons ) and is a repellent for the axons of at least one other type of neuron ( class IV md neurons ) . Thus , the presentation of repellent molecules on the surfaces of subsets of neurons can act to exclude specific classes of axons from particular regions of the neuropile . Theoretical models for gradient-guided axonal growth and targeting during the formation of 2-D neural maps , such as the retinotopic projections , require at least one gradient in each of the two—not necessarily Cartesian—dimensions [42] . These ideas have been borne out by experimental findings . Gradients of attractants and repellents in one dimension have been implicated in providing positional information for terminating sensory axons during the formation of both continuous and discrete neural maps [18] , [43] , [44] . Furthermore , a recent study has shown that two orthogonal systems of graded cues operate to specify position of termination along each axis of a somatotopic map in the optic tectum . Our findings address the larger issue of how termination of distinct neuron classes is regulated within a complex meshwork of differentiating axons and dendrites . They suggest that similar mechanisms that are used for the establishment of neural maps , only involving generalized positional cues in each dimension , control targeting of many different classes of neurons to specific termination domains within a complex neuropile . Although the evidence we provide here suggests that positional cues can specify particular domains for the termination of sensory neurons , we do not suppose that the control of termination and branching by a pervasive system of positional cues would necessarily be sufficient to allow connections to form selectively and specifically between appropriate pre- and postsynaptic partners . What such a system does provide is a framework of signals that could regulate simultaneously the growth of axons and dendrites of many different neurons and induce their termination and branching in appropriate parts of the developing network . Within these restricted regions it is likely that further , localised mechanisms , including competitive interactions , patterns of activity , and target derived cues might all be required to control synaptogenesis and determine the emergence of precise patterns of connectivity within a termination domain . If the pattern of sensory axon termination within the neuropile is controlled by a system of positional cues , most likely , in three dimensions , it may well be that the location of their postsynaptic dendrites is determined in a similar fashion . If this were the case , the matched expression of receptors for the same system of signals by pre- and postsynaptic neurites would guide them to a common volume as a prelude to the formation of synaptic connections between them . Recent studies that show that developing motor neuron dendrites respond to some of the same cues as terminating sensory axons provide indirect evidence for common systems of positional cues leading to the coordinate targeting of presynaptic axons and postsynaptic dendrites [45] , [46] . A direct test of this hypothesis , however , must await the identification of the postsynaptic interneurons with which developing sensory neurons form connections . It will then be possible to make a direct investigation of the molecular mechanisms that control the termination and branching of pre- and postsynaptic endings and thereby lay out a ground plan for connectivity within the developing neuropile .
For mutant analyses sema 2a03021 [25] , sema 1aP1 [26] , plex ADf ( 4 ) C3 [47] , and plex BKG00878 [21] , [48] were crossed into the ppkEGFP [9] stock . Stocks were made using GFP balancers [49] . Homozygous mutant embryos were identified by lack of GFP . For misexpression we used the following stocks: UAS-robo3 [50] , [51] inserts on second and third chromosome , UAS-plexB [21] and UAS-plexA-HA [47] , UAS-robo2 [51] , UAS-ephrin [52] , [53] , UAS-eph [52] , UAS-unc5 [54] , UAS-frazzled [55] , UAS-drl-DN [56] , UAS-comm [57] , UAS-robo , 410 EP-lines from the Rorth collection [58] , [59] . For the misexpression screen the UAS-lines were crossed into the PO163GAL4 , UAS-n-syb-GFP stock [14] , [60] . For rescue experiments the following embryos were analysed: UAS-sema 1a , sema 1aP1; PO163GAL4 , ppkEGFP [26] , UAS-sema 1a , sema 1aP1; HB9GAL4 , ppkEGFP; UAS-plexA-HA/+; PO163GAL4 , ppkEGFP/+; plex ADf ( 4 ) C3 , and UAS-plex B/PO163GAL4 , ppkEGFP; plex BKG00878 . We also used OK371GAL4 ( gift of M . Landgraf ) , GADGAL4 [29] , single mindedGAL4 [31] , UAS-reaper [27] and wnt5D7 stocks [56] . Embryos were staged and VNCs dissected out embryos as previously described [6] , [61] , [62] . For overexpression experiments embryos were grown at 29°C . VNCs were mounted with brain lobes down and VNC up to allow rapid , high-resolution , confocal imaging of transverse planes , perpendicular to the neuraxis . We used the following primary antibodies: anti-Sema 2a ( MAb 19C2 , developed by C . Goodman ) , anti-Slit ( MAb C555 . 6D , developed by S . Artavanis-Tsakonas ) , anti-Fas II ( MAb 1D4 , developed by C . Goodman ) , and anti-Repo ( MAb 8D12 , developed by C . Goodman ) supplied by the Developmental Studies Hybridoma bank ( 1∶10 dilution ) ; anti-Sema 1a ( 1∶1 , 000 dilution , kindly provided by A . Kolodkin [26]; anti-Plex A ( 1∶500 dilution , kindly provided by L . Luo ) [23] , and Cy5-conjugated goat anti-horseradish peroxidase ( 1∶100 dilution; Jackson ImmunoResearch ) . Secondary antibodies were used at 1∶500 dilution: Alexa488-conjugated donkey anti-goat , Alexa488-conjugated goat anti-rabbit , Alexa633-conjugated goat anti-mouse , Alexa633-conjugated rabbit anti-mouse ( Molecular Probes ) . Standard immunocytochemical procedures were followed [63] , and immunofluorescence was visualised with Leica SP1 and Zeiss LSM confocal microscopes . Images are maximum projections of confocal z series processed with Adobe Photoshop software . For quantification of Sema 2a gradients at 13-h AEL nine VNCs stained for Sema 2a were randomly chosen and A7 imaged using a Leica SP1 . A confocal section was randomly chosen from each stack , the dorso-ventral axis manually drawn , and the neuropile was divided into nine equal dorso-ventral stripes , perpendicular to the midline and the average fluorescence intensity in each stripe was calculated . Values from different nerve cords were normalized such that the average intensity from each nerve cord was 1 . For quantification of the Slit gradient at 13 h , 12 VNCs stained for Slit were randomly chosen , and A7 was imaged using a Leica SP1 . A confocal section was randomly chosen from each stack , a line on either side of the midline was manually drawn , and the neuropile on either side of the midline was divided into four mediolateral stripes . The average fluorescence intensity in each stripe was calculated and normalised as above . For a statistical analysis of defects in the pattern of sensory terminals ( visualized with PO163GAL4 , UAS-n-syb-GFP ) along the medio-lateral axis we quantified the normalised surface area occupied by sensory terminals ( sensory area , SA ) in the medial domain of the neuropile ( SAM/T = SA[medial]/SA[medial+intermediate+lateral] ) in randomly chosen transverse confocal sections from 30 different hemisegments for each genotype . Within a single embryo , we selected every tenth section ( all confocal sections were 1-µm thick so that the analyzed sections were 10 µm apart from each other ) . A Student's t-test was used to compare the mean SAM/T for the different genotypes . For a statistical analysis of expansion or exclusion of sensory terminals ( visualized with PO163GAL4 , UAS-n-syb-GFP ) into different dorso-ventral layers we compared SA in layer 2 ( SA2/h = SA ( layer 2 ) /[hemisegment surface area] ) or SA in layers 1 , 3 , and 4 ( SA1+3+4/h = SA[layer 1+3+4]/[hemisegment surface area] ) in randomly chosen transverse confocal sections from more than 30 different hemisegments for each genotype . Within a single embryo , we selected every tenth section ( all confocal sections were 1-µm thick so that the analyzed sections were 10 µm apart from each other ) . A Student's t-test was used to compare the mean SA2/h or SA1+3+4/h for the different genotypes . For a statistical analysis of termination defects class IV md axons in the dorso-ventral axis , we quantified the percentage of hemisegments with aberrant terminals ( Hat ) in layers 1 , 2 , and 3 per embryo ( per 14 hemisegments ) : Hat ( 1 , 2 , or 3 ) = ( n hemisegments with terminals in 1 , 2 , or 3/14 ) ×100 and the total percentage of hemisegments with aberrant terminals per embryo [Hat ( 1+2+3 ) = Hat ( 1 ) +Hat ( 2 ) +Hat ( 3 ) ] . A Student's t-test was used to compare the mean Hat for the different genotypes . In some cases we also quantified the average ( per embryo ) relative proportion of hemisegments with aberrant terminal in each layer: Hat ( 1 ) /Hat ( 1+2+3 ) , Hat ( 2 ) /Hat ( 1+2+3 ) , and Hat ( 3 ) /Hat ( 1+2+3 ) . We counted as “aberrant” only those hemisegments with terminals in layers 1 , 2 , or 3 ( Figure S5A and S5B ) . We did not count as “aberrant” those axons that exhibit the aberrant growth , with normal termination phenotype ( Figure S5C ) . | Axons and dendrites of synaptic partners must be targeted to a common region of the developing neural network so that appropriate connections can be formed . The mechanisms underlying this targeting are incompletely understood . We showed previously that a positional cue ( Slit ) acting in the medio-lateral axis of the Drosophila nerve cord controls the position of sensory terminals independently of their synaptic partners . This work revealed that there might be additional cues operating in a similar fashion in the dorso-ventral axis of the nerve cord . Here we report the discovery of a dorso-ventral system of positional cues , in the form of a gradient of secreted Semaphorin 2a acting at right angles to the Slit gradient , and membrane bound Semaphorin 1a differentially distributed across the neuropile . The two Semaphorins dictate the termination positions of sensory axons in the dorso-ventral axis . Together with a third signal acting in the antero-posterior axis , Semaphorins and Slit deliver axons to appropriate volumes of the neural network . These studies support a model in which axons branch and terminate , independently of synaptic partners , in response to pervasive systems of volumetric positional cues . | [
"Abstract",
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] | [
"developmental",
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] | 2009 | Positional Cues in the Drosophila Nerve Cord: Semaphorins Pattern the Dorso-Ventral Axis |
Botrytis cinerea is the causal agent of gray mold diseases in a range of dicotyledonous plant species . The fungus can reproduce asexually by forming macroconidia for dispersal and sclerotia for survival; the latter also participate in sexual reproduction by bearing the apothecia after fertilization by microconidia . Light induces the differentiation of conidia and apothecia , while sclerotia are exclusively formed in the absence of light . The relevance of light for virulence of the fungus is not obvious , but infections are observed under natural illumination as well as in constant darkness . By a random mutagenesis approach , we identified a novel virulence-related gene encoding a GATA transcription factor ( BcLTF1 for light-responsive TF1 ) with characterized homologues in Aspergillus nidulans ( NsdD ) and Neurospora crassa ( SUB-1 ) . By deletion and over-expression of bcltf1 , we confirmed the predicted role of the transcription factor in virulence , and discovered furthermore its functions in regulation of light-dependent differentiation , the equilibrium between production and scavenging of reactive oxygen species ( ROS ) , and secondary metabolism . Microarray analyses revealed 293 light-responsive genes , and that the expression levels of the majority of these genes ( 66% ) are modulated by BcLTF1 . In addition , the deletion of bcltf1 affects the expression of 1 , 539 genes irrespective of the light conditions , including the overexpression of known and so far uncharacterized secondary metabolism-related genes . Increased expression of genes encoding alternative respiration enzymes , such as the alternative oxidase ( AOX ) , suggest a mitochondrial dysfunction in the absence of bcltf1 . The hypersensitivity of Δbctlf1 mutants to exogenously applied oxidative stress - even in the absence of light - and the restoration of virulence and growth rates in continuous light by antioxidants , indicate that BcLTF1 is required to cope with oxidative stress that is caused either by exposure to light or arising during host infection .
Botrytis cinerea Persoon: Fries ( teleomorph Botryotinia fuckeliana ( de Bary ) Whetzel ) is a necrotrophic pathogen with a broad host range causing gray mold disease in several economically important plants including grape vine and strawberry [1]–[2] . Sources of infection by B . cinerea are the conidia that are ubiquitously distributed in the air . After landing on the plant surface , the conidia germinate and form short germ tubes which directly penetrate . Infection may also occur from already established mycelia , in this particular case , multicellular infection structures ( infection cushions ) are formed . Usually , after penetration the epidermal and underlying cells die and B . cinerea establishes a primary ( restricted ) infection . Then , the fungus starts a massive outgrowth ( spreading ) that results finally in the maceration of the plant tissue ( soft rot ) and formation of conidia for new infections . During the interaction , the fungus produces phytotoxic metabolites e . g . botrydial ( BOT ) and botcinic acid ( BOA ) , necrosis-inducing proteins , reactive oxygen species ( ROS ) , cell wall-degrading enzymes , peptidases and phytohormones [1] , [3]–[5] . Gene replacement approaches have identified only a few virulence factors to date , probably because of their redundancy [6] , [2] . Hence , the importance of the toxic secondary metabolites BOT and BOA for infection becomes apparent only when strains are lacking both toxins [7]–[9] . The asexual macroconidia formed by branched conidiophores are undisputedly the main source of primary inoculum under given environmental conditions , such as high humidity and moderate temperatures . The dark pigmented sclerotia , however , serve as survival structures e . g . for over-wintering . They may germinate vegetatively to yield new mycelia and conidia , or they can act as the female parent in sexual reproduction when microconidia ( male parent ) carrying the opposite mating type are present . Apothecia , containing the linear asci with the ascospores , develop on the fertilized sclerotia [10] . It is difficult to estimate how frequently sexual reproduction happens in nature; nevertheless , it is assumed that it significantly contributes to genetic variation of B . cinerea [11] . If isolates produce sclerotia and microconidia they can function as female and male partners in reciprocal crosses [12]; however , not all B . cinerea isolates form sclerotia . As early as 1929 , a comparative morphological study reported on three groups of B . cinerea isolates predominantly forming sclerotia , sterile mycelia or conidia [13] . When isolates are able to produce both conidia and sclerotia such as the sequenced strain B05 . 10 [14] , [2] , it happens in a light-dependent fashion: full-spectrum/white light induces the formation of conidia while its absence results in formation of sclerotia , and even limited exposures to light are sufficient to suppress sclerotial development [15] . B . cinerea was found to respond to different wavelengths of light: UV light results in conidiation , red light promotes sclerotial development and blue light leads to the accumulation of undifferentiated aerial mycelia [15]–[16] . Based on these observations it was hypothesized in 1975 that two receptors sensing UV/blue light and red/far-red light , respectively , are involved in regulation of asexual reproduction in B . cinerea [17] . In accordance with this model , the inspection of the genome sequence has revealed the existence of two UV light-sensing cryptochromes , two blue light sensors , two opsin-like proteins that may sense green light as well as three red/far-red light-sensing phytochromes . Notably , the number of phytochromes has been expanded in B . cinerea in comparison to Aspergillus nidulans and Neurospora crassa with one and two representatives , respectively , suggesting a special role of red light in its life cycle [18] , [2] . The best studied fungus in terms of light signaling is N . crassa that mainly responds to blue light in order to regulate mycelial carotenoid biosynthesis , conidiation , the circadian clock , and formation of protoperithecia [19] . Most of these responses are mediated via the WHITE COLLAR transcription factors WC-1 and WC-2 that form a complex ( WCC ) in response to blue light leading to the activation of gene expression [20] . In accordance with the fact that blue light is the most active wavelength in N . crassa , deletions of the cryptochromes , opsins and phytochromes did not result in obvious phenotypes [21]–[24] . In contrast , and similarly to B . cinerea , A . nidulans does respond to UV , blue and red light and forms asexual spores in the light , but preferentially undergoes sexual reproduction ( cleistothecia ) in the dark [25]–[26] . It was demonstrated that the single cryptochrome CryA represses the formation of cleistothecia in the light [27] , and that blue light sensed via the WCC complex ( LreA/LreB ) and red light sensed via the single phytochrome FphA act in concert to trigger the formation of conidia in the light [28] . However , unlike A . nidulans , B . cinerea possesses homologues of VVD-1 and FRQ-1 that are involved in photoadaptation and the circadian clock in N . crassa [18] , [20] , [29] . In B . cinerea , the photoreceptors , with the exception of the opsin BOP1 whose deletion affected neither affect light-dependent differentiation nor virulence [30] , have not been functionally studied so far . However , “blind” B . cinerea mutants will be helpful to study the role of light in the fungus-host interaction without interfering with the host's metabolism . Recently , the reason for the “blind: always conidia” phenotype of two natural isolates ( T4 , 1750 ) has been identified: different single nucleotide polymorphisms ( SNPs ) in bcvel1 encoding a VELVET family protein result in truncated proteins and consequently in the deregulation of the light-dependent differentiation and in severely reduced virulence suggesting an interrelationship between light perception and virulence in B . cinerea [31]–[32] . This study describes the identification of a novel virulence-associated gene by a random mutagenesis approach using Agrobacterium tumefaciens-mediated transformation ( ATMT ) . The identified gene bcltf1 encodes a GATA-type transcription factor homologous to A . nidulans and A . fumigatus NsdD , N . crassa SUB-1 , and Sordaria macrospora PRO44 for whom the involvement in differentiation programs were demonstrated [33]–[38] . To our knowledge , the functions of this GATA transcription factor are described for the first time in a plant pathogen and reveal some overlapping features with the characterized homologues with regard to regulation of reproduction . Additionally , we report on specific functions of this transcription factor with regard to maintenance of ROS homoeostasis ( production vs . scavenging of ROS ) , secondary metabolism and virulence .
To identify genes in B . cinerea that are associated with the infection process , 2 , 367 hygromycin-resistant mutants obtained through A . tumefaciens-mediated transformation ( ATMT ) were screened for virulence on detached tomato leaves . The assay revealed 560 mutants that were either avirulent or less virulent than the parental wild-type strain B05 . 10 [39] . So far , phenotypes have been confirmed for 193 mutants on primary leaves of living bean plants ( Phaseolus vulgaris ) , including the mutants PA31 , PA2411 , and PA3417 ( Fig . 1A ) . These mutants are impaired in their ability to penetrate ( PA3417 ) or to colonize the host tissue ( PA31 , PA2411 ) , and are furthermore affected in light-dependent differentiation ( data not shown ) . TAIL- ( thermal asymmetric interlaced ) PCR analyses revealed that the T-DNA integrations in the genomes of the mutants have occurred in the same genomic region ( Fig . 1B ) . Identical integration events occurred for the RB flanks of the T-DNAs in all mutants i . e . 1 . 608 kb upstream of the annotated open reading frame ( ORF ) B0510_3555 ( Broad Institute; for details see Materials and Methods ) , while different situations were found for the LB-flanking regions . In PA3417 , the T-DNA insertion caused the deletion of four base pairs , in the other two mutants TAIL-PCR failed because no amplicons were obtained ( PA31 ) , or the remaining vector sequence was amplified ( PA2411 ) . BlastP analyses using the sequence of B0510_3555 as query revealed the GATA-type transcription factors NsdD and SUB-1 from A . nidulans ( e-value 6e-46 ) and N . crassa ( e-value 9e-40 ) , respectively , as putative orthologues . The alignment of protein sequences of B0510_3555 , NsdD and SUB-1 showed low levels of sequence similarity ( 48% and 35% , respectively ) , except for the highly conserved GATA zinc finger domains in the C-terminal regions ( Fig . 1C ) . Due to its expression profile and functions in B . cinerea - as described in the following - the protein is referred to as BcLTF1 ( B . cinerea light-responsive transcription factor 1 ) . To confirm the linkage between the identified T-DNA integrations upstream of bcltf1 and the observed phenotypes of PA31 , PA2411 , and PA3417 , bcltf1 was deleted and over-expressed in the B05 . 10 genomic background . Two different replacement mutants exhibiting identical phenotypes were generated by deleting either the ORF only ( Δbcltf1-A6 ) or the ORF including 1 . 7-kb of the 5′-noncoding region ( Δbcltf1-B1 ) . Expression of bcltf1 under control of a constitutive promoter ( PoliC ) in the wild-type background resulted in two over-expressing mutants ( OE::bcltf1-T6 and -T7 ) . Complementation of deletion mutants was done by re-introduction of bcltf1 into the native gene locus ( bcltf1COM ) ( for details , see Materials and Methods , Table 1 , Fig . S1 , Table S1 ) . Given that BcLTF1 is implicated in regulation of light-dependent differentiation as suggested by the ATMT phenotypes , we studied its expression pattern during conditions inducing either conidiation ( incubation in continuous light ( LL ) or in 12 h light/12 h dark rhythm ( LD ) ) or sclerotia formation ( incubation in continuous darkness ( DD ) ) in the wild-type strain B05 . 10 . Northern blot analyses revealed that bcltf1 was highly expressed during incubation in the light while only very weak expression occurred during sclerotial development in the dark . For comparison , expression of bcssp1 ( homologue of S . sclerotiorum “sclerotia-specific protein 1” [40] ) is restricted to developing sclerotia while expression of bcpks13 ( polyketide synthase ( PKS ) putatively involved in melanin biosynthesis [41] ) is restricted to conidiation in LL ( Fig . 2A ) . To further analyze the effect of light on the expression of bcltf1 , we exposed wild-type cultures that were grown for 2 d in DD for different periods to white light ( 5 to 300 min ) . High expression levels of bcltf1 became visible from 15 min light pulse ( LP ) while only low expression occurred in cultures that were kept in DD . In contrast to other light-induced genes such as bop1 ( opsin-like protein [30] ) and bcccg1 ( homologue of N . crassa “clock-controlled gene-1” [42] ) whose expression levels decreased again after prolonged exposure to light ( from exposure times of 120 min and longer ) , expression levels of bcltf1 remained stable ( Fig . 2B ) . Light induction of bcltf1 , bop1 , and bcccg1 was also observed during infection of P . vulgaris leaves ( in stage of lesion spreading at 3 dpi ) , even though expression levels of bcltf1 were very low in comparison to those found in cultures grown on solid PDAB medium made from mashed bean leaves ( Fig . 2C ) . Finally , dark-grown submerged cultures of B05 . 10 were exposed to ROS under dark conditions . Exposure to light , but neither the addition of hydrogen peroxide ( H2O2 ) , nor the presence of singlet oxygen ( 1O2 ) , superoxide anions ( O2−• ) and hydroxyl radicals ( OH• ) affected the expression of bcltf1 . Expression levels of bcccg1 increased in response to all treatments in contrast to bcgpx1 ( encoding a glutathione peroxidase ) that is specifically induced by H2O2 ( Fig . 2D ) . Taken together , among the conditions tested bcltf1 is exclusively highly expressed in response to light , and represents therefore an eligible candidate for controlling differentiation processes ( conidiation vs . sclerotial development ) and virulence in a light-dependent manner . To localize BcLTF1 in living hyphae , the coding sequence of bcltf1 was fused to the codon-optimized gene encoding the green fluorescent protein ( GFP ) [43] and expressed under control of a constitutive promoter in the bcltf1 deletion mutant ( Δbcltf1+PoliC::bcltf1-gfp ) ( for details , see Materials and methods , Table 1 , Fig . S1E ) . Merged images of GFP fluorescence and nucleic acid-specific Hoechst staining patterns showed that BcLTF1-GFP localizes to the nuclei in germinated conidia irrespective of the applied light conditions ( Fig . 3A ) . Nuclear localization of BcLTF1-GFP was also observed in infectious hyphae during infection of onion epidermal cells ( Fig . 3B ) . Similar results were obtained for BcLTF1-GFP that was expressed from its native promoter ( Δbcltf1+Pbcltf1::bcltf1-gfp ) . GFP fluorescence , although weaker when compared to that of the constitutively expressed fusion protein , was detected in the nuclei independently of the illumination treatment ( data not shown ) . Expression of BcLTF1-GFP under control of PoliC or its native promoter in the Δbcltf1 genomic background indicated the functionality of the fusion protein , as the native expression rescued all phenotypes of the deletion mutant while PoliC-driven expression resulted in phenotypes that are presumably due to the inappropriate expression of BcLFT1 especially in dark conditions ( Fig . S2 , Fig . S1F ) . Accordingly , mutants expressing PoliC::bcltf1-gfp in the wild-type background were generated as BcLTF1-overexpressing strains hereafter referred to OE::bcltf1 ( T6 , T7 ) ( Fig . S1F , Table 1 ) . The deletion as well as the over-expression of bcltf1 cause severe growth phenotypes that are related to the light treatment . Deletion mutants ( Δbcltf1-A6 , -B1 ) failed to grow on minimal medium ( CD ) in any light condition ( Fig . 4A ) , and radial growth rates on synthetic complete medium ( CM ) correlated with the exposure times to light: while growth rates nearly equivalent to the wild type were observed in DD ( 92% of WT ) , incubation in LD and LL conditions significantly decreased growth rates to 43% and 27% of DD , respectively ( Fig . 4B ) . The inhibitory effect of white light could be assigned to the blue light fraction , as growth rates were similarly decreased under incubation in continuous white and blue light , while growth rates in green , yellow and red light were comparable to those of Δbcltf1 cultures grown in DD ( Fig . S3 ) . Although Δbcltf1 mutants were severely impaired in growth in LD , they colonized the whole Petri dish after two weeks of incubation accompanied by reduced aerial hyphae formation and precocious and enhanced conidiation ( 155% of conidia produced by WT; data not shown ) . In contrast , the overexpressing mutants produced more aerial mycelia and reduced numbers of conidia giving the colonies a fluffy appearance . Light of different wavelengths ( in LD rhythm ) failed to alter the phenotypes of Δbcltf1 and OE::bcltf1 mutants . In contrast , the wild type formed more aerial hyphae and fewer conidia and sclerotia during incubation in blue and yellow/red light , respectively ( data not shown ) . In general , phenotypes of the dark-grown cultures were more pronounced than those incubated in LD as both Δbcltf1 and OE::bcltf1 mutants failed to produce any sclerotia . Instead , the deletion mutants produced conidia and the overexpression mutants accumulated cotton ball-like mycelial aggregates in the dark ( Fig . 4C ) . Evidently , light and BcLTF1 have an impact on colony morphology and the mode of reproduction , however , other differentiation programs in B . cinerea are not affected by light and mutations of bclft1 . Conidial germination is induced by nutrients or hydrophobic surfaces in a similar fashion in wild type , OE::bcltf1 and Δbcltf1 mutants in LL and DD , and germling fusions via conidial anastomosis tubes ( CATs ) were observed for all strains ( Fig . S4A; data not shown ) . Nevertheless , the forced expression of bcltf1 that is marked by bright nuclear BcLTF1-GFP signals , results in malformed germ tubes ( Fig . S4B ) . The lack of the GFP signal accompanied by a wild-type-like morphology in a percentage of germ tubes suggests that the overexpression construct might become lost during conidiogenesis , so the outcome of studies involving OE::bcltf1 conidia needs to be carefully considered . Though all T-DNA insertions upstream of bcltf1 impair virulence , differences exist between mutant PA3417 , that is not able to penetrate resulting in the accumulation of mycelia on the top of the leaf , and mutants PA31 and PA2411 that are impaired in their ability to colonize the host tissue ( Fig . 1A ) . However , the penetration defect of PA3417 is restricted to the conidia as infections from mycelia have been observed ( data not shown ) . To examine how BcLTF1 is implicated in the infection process , the defined mutants ( Δbcltf1 , OE::bcltf1 ) were tested for their capabilities to penetrate and to proliferate inside a living host . To monitor the penetration events , onion epidermal strips were inoculated with conidial suspensions or agar plugs with non-sporulating mycelia ( Fig . 5A ) . At 24 hpi , all strains had formed branched infection structures ( infection cushions ) originated from the already established mycelia on the agar plugs . Presumably , OE::bcltf1 mutants produced them more frequently than the wild type due to the significantly increased proliferation of aerial hyphae . In contrast , infection cushions were less often observed for Δbcltf1 mutants known to form fewer aerial hyphae ( data not shown ) . Conidia derived from wild-type and Δbcltf1 strains formed short germ tubes that immediately penetrated the host cells indicating that the transcription factor is dispensable for entering the host . However , the majority of conidia with elevated bcltf1 expression levels produced elongated and branched germ tubes that failed to penetrate . Primary leaves of living bean plants ( P . vulgaris ) were inoculated with conidial suspensions or mycelia-containing agar plugs derived from wild-type and mutant strains , and incubated in LD and DD to study the impact of light on infection . As can be seen from the time course experiments , Δbcltf1 mutants were significantly impaired in the infection process irrespective of the inoculation method and incubation conditions ( in DD or LD ) because in all cases distinct primary lesions emerged one day later than for the wild type and the OE::bcltf1 mutants ( Fig . 5B , Fig . S5A , B ) . The reduced proliferation of Δbcltf1 hyphae inside the host became visible by 1 dpi in onion cells , and was likewise visualized in living bean tissues by trypan blue staining at 2 and 3 dpi ( Fig . S5C ) . However , infection by the deletion mutant progressed until the whole leaf was colonized ( 9 dpi ) and conidia were formed . Although germ tubes of OE::bcltf1 were severely impaired in their capacity to penetrate onion epidermal cells , infections of P . vulgaris derived from conidia and mycelia were comparable to those caused by the wild type with regard to lesion expansion . In contrast , colonization of the host tissue by OE::bcltf1 was accompanied by increased accumulation of aerial hyphae and reduced conidiation ( Fig . 5B , Fig . S5A ) . In summary , light or its absence did not affect the infection process by wild type and bcltf1 mutant strains , and elevated and reduced expression levels of bcltf1 resulted in reduced penetration rates via germ tubes ( OE::bcltf1 ) and retarded infection ( Δbcltf1 ) , respectively . B . cinerea induces an oxidative burst and hypersensitive cell death in the host , and it was shown that the degree of virulence on Arabidopsis correlates with the levels of superoxide ( O2− ) and hydrogen peroxide ( H2O2 ) [44] . To monitor the accumulation of H2O2 in wild type- and Δbcltf1-infected plant tissues , inoculated bean leaves were sampled at 1 to 4 dpi and treated with 3 , 3′-diaminobenzidine ( DAB ) ; a compound that is oxidized by H2O2 in the presence of peroxidases yielding a dark-brown product . Microscopic observations revealed H2O2 accumulation in anticlinal walls of epidermal cells that were in close contact with wild-type germlings at 1 dpi . In contrast , no H2O2 accumulation was observed beneath the Δbcltf1 infection droplets at this time point even though conidia had germinated and entered the plant tissue ( data not shown ) . However , after a further two days , significantly increased H2O2 accumulation in spreading lesions of the bcltf1 deletion mutant was observed ( Fig . 5C ) . Therefore , in this particular case , increased ROS accumulation does not positively correlate with virulence . To explore the possibility that the elevated ROS levels may negatively affect the ability of the Δbcltf1 mutant to colonize the host tissue , we added ascorbic acid as an antioxidant to the conidial suspensions of wild type and bcltf1 mutants prior to inoculation . Whereas no differences were observed for infections by wild type and OE::bcltf1 strains , the addition of the antioxidant restored virulence of Δbcltf1 mutants as deduced from lesion sizes at 3 dpi ( Fig . 5D ) . Consequently , the abnormal ROS accumulation may account for the virulence defect of the Δbcltf1 mutants . For reasons thus far un-established , BcLTF1 is required to tolerate light during saprophytic growth on solid media , but not during plant colonization . To unravel the mechanism of light sensitivity , we modified the standard growth medium with different supplements and monitored colony growth and morphology during incubation in LL , LD and DD . Osmotic stabilization of the medium by adding 0 . 7 M sorbitol elevated growth rates of Δbcltf1 mutants in LL and LD conditions almost to those found in DD ( 66% and 94% of DD ) ( Fig . 6A ) suggesting that the mutants have difficulty to cope with hypo-osmotic conditions , especially during illumination . On the other hand , the addition of 0 . 7 M NaCl appeared to cause ionic stress and prevented growth of Δbcltf1 mutants also during incubation in DD ( 25% of DD ) . These findings may imply that cell wall integrity and/or the osmotic stress response is impaired when bcltf1 is absent . Considering the finding of the altered ROS accumulation in Δbcltf1-infected plant tissues and the compensating effect of ascorbic acid in virulence , we pursued two strategies to study whether ROS play a role in the light-dependent growth defect of the deletion mutants . Firstly , the effect of oxidative stress caused by addition of 300 µM menadione ( artificial source of superoxide radicals ( O−2• ) ) and 7 . 5 µM H2O2 ( a source of highly reactive hydroxyl radicals ( OH• ) upon reaction with metal ions ) , respectively , was monitored revealing that the deletion mutants were not able to cope with these conditions irrespective of the light conditions ( Fig . 6A , B ) . Secondly , the use of antioxidants was intended to protect fungal strains against ROS . Both the addition of 800 µM dithiothreitol ( DTT ) and 28 mM ascorbic acid restored radial growth rates of Δbcltf1 mutants in LL and LD , but not the proliferation of aerial hyphae ( Fig . 6C ) . To study whether the growth defect of the Δbcltf1 mutants on minimal medium ( CD ) can be rescued by reducing oxidative stress , we supplemented CD medium containing either nitrate or ammonium a single nitrogen source with ascorbic acid . In fact , the supplementation increased the growth rates of the mutants to certain extents in LD and DD , but failed to restore wild-type growth rates ( Fig . S6 ) . The fact that Δbcltf1 mutants are hypersensitive to exogenously applied oxidative stress even in the absence of light , and that on the contrary , the supplementation with antioxidants facilitated growth during incubation in LL indicate that the deletion mutants are unable to cope with oxidative stress that arises during the exposure to light . To detect ROS production by the different strains , colonies of wild type and bclft1 mutants grown on CM and DTT-supplemented CM were stained with DAB solution . The intensive brown coloration of mutant colonies , even in presence of DTT that creates a reducing environment , indicated an increased accumulation of H2O2 in the range of the Δbcltf1 colonies in comparison to the wild type ( Fig . 6D ) . In a second approach , the H2O2 production by equal amounts of biomasses of wild type , bcltf1 deletion and overexpressing mutants was studied , confirming the increased H2O2 production by the deletion mutants and revealing furthermore the decreased production by mutants overexpressing BcLTF1 ( Fig . 6D ) . Formation of O−2• was monitored by treating wild type and mutant mycelia with nitro blue tetrazolium ( NBT ) . Blue precipitates were found in hyphal tip segments of all strains ( data not shown ) demonstrating that mutations of bcltf1 do not alter the net accumulation of O2− . The fact that Δbcltf1 mutants accumulate higher amounts of H2O2 under all light conditions , either caused by its inappropriate production and/or detoxification , may explain the general hypersensitivity of the mutants to ( additional ) exogenously applied oxidative stresses such as the exposure to menadione , H2O2 or light . To see whether the cellular redox status ( glutathione pool ) is altered in Δbcltf1 mutants , we expressed the redox-sensitive green fluorescent protein ( roGFP2 ) in the mutant background and compared the ratio of fluorescence intensities ( 395 nm = oxidized state/488 nm = reduced state ) with those of the wild type expressing roGFP2 [45] . Similar initial redox statuses were found in Δbcltf1 and wild-type hyphae , and roGFP2 was oxidized and subsequently reduced in a similar fashion in both strains after addition of 10 mM H2O2 ( Fig . 6E ) . Thus , the altered ROS homoeostasis of the mutants that is characterized by the overproduction of H2O2 and the hypersensitivity to oxidative stress and light is not accompanied by an altered cytosolic redox status . To gain broader insight into the functions of BcLTF1 especially in those that are related to illumination , a genome-wide approach was initiated to compare gene expression profiles in the wild type and the Δbcltf1 mutant in response to a light stimulus . For these experiments , strains were cultivated in DD , and then exposed to white light for 60 min ( WT:B05 . 10+LP; Δbcltf1+LP ) or kept in darkness for additional 60 min ( WT:B05 . 10-D; Δbcltf1-D ) ( for details , see Material and Methods ) . RNA from four biological replicates was extracted , labeled and hybridized to NimbleGen microarrays containing oligonucleotides representing all predicted B . cinerea genes as well as non-matching expressed sequence tags ( ESTs ) [2] . Statistical tests were performed to detect differentially expressed genes: light responses in wild type ( +LP/D in WT ) and mutant ( +LP/D in Δbcltf1 ) , and Δbcltf1 effects in darkness ( Δbcltf1/WT in D ) and light ( Δbcltf1/WT in LP ) . In total , 2 , 156 differentially expressed spotted genes corresponding to 2 , 074 distinct genes were identified and could be assigned to 11 expression profiles ( Fig . 7 , Table S2 ) . 293 genes showed a light-responsive pattern in the wild-type background: the expression of 244 genes was induced by light ( referred to as light-induced genes; bclig1-244 ) , and the expression of 49 genes was repressed by light ( referred to as light-repressed genes; bclrg1-49 ) . The expression levels of the majority of light-responsive genes were altered in the absence of BcLTF1 , i . e . light induction/repression did not happen ( genes in profiles 5 and 6 ) or just disappeared as genes are already de-regulated in Δbcltf1 in the absence of light ( genes in profiles 7 and 8 ) . Only 34% of light-responsive genes ( in profiles 3 and 4 ) exhibited similar light responses in both genomic backgrounds , demonstrating that the absence of bcltf1 , which itself is responsive to light , severely affects the expression pattern of other light-responsive genes . Interestingly , some genes were found whose expression responded to light exclusively in the absence of BcLTF1; they were either induced ( 12 genes in profile 9 ) or repressed by light ( 44 genes in profile 10 ) . Nevertheless , the loss of the BcLTF1 had a more pronounced effect on genome-wide gene expression than the light stimulus . In total , the expression levels of 1 , 972 spotted genes corresponding to 1 , 905 distinct genes were changed in the Δbcltf1 mutant and most of them ( 1 , 539 genes; 81% ) were not related to a light response: 748 genes were overexpressed ( profile 1 ) and 791 genes were underexpressed ( profile 2 ) irrespective of the illumination condition ( Fig . 7 , Table S2 ) . Functional enrichment analyses using the GSEA ( Gene Set Enrichment Analyses [46]–[47] ) method and defined sets of genes were performed to reveal biological processes that are affected by light and/or in the absence of BcLTF1 . Hence , the group of light-induced genes in wild type is enriched for genes implicated in the oxidative stress response ( OSR ) or light perception , while amino acid-transporter-encoding genes are overrepresented in the group of light-repressed genes . Even though a majority of bcltf1-dependent genes display similar expression profiles in darkness and light , the group of overexpressed genes in the Δbcltf1 mutant in darkness is significantly enriched in genes involved in light perception and respiration , and the group of overexpressed genes in the light is enriched in secondary metabolism-related genes . In both light conditions , the group of underexpressed genes in Δbcltf1 is enriched in transporter-encoding genes ( Table 2 ) . Currently , it is not known how light and its absence , trigger differentiation in B . cinerea . Because young undifferentiated mycelia of the wild type , that are able to form conidia or sclerotia dependent on the illumination conditions , were subjected to the transcriptomic analysis , two sets of “developmental genes” among the light-responsive genes can be expected , those implicated in induction of conidiation or in suppression of sclerotial development . Light responses are based on the perception of light as a signal and the induction of subsequent signaling events . Though it is not clear why photoreceptors should be regulated on the level of gene expression , their light-responsive expression is a known phenomenon in N . crassa [37] , [48] . Accordingly , expression of most of the predicted photoreceptor-encoding genes of B . cinerea was induced by light ( Fig . 8A , Table S3 ) . Bcvvd1 encoding a putative blue light receptor was the strongest up-regulated gene with a 37-fold increase . Further light-induced genes are those encoding cryptochromes ( bccry1 , bccry2 ) , opsins ( bop1 , bop2 ) and one of three phytochromes ( bcphy2 ) . Whereas similar intensities were found for bcvvd1 in the wild type and the Δbcltf1 mutant , amplitudes of light induction of the other photoreceptor-encoding genes were much lower in the mutant than in the wild-type background as the genes in Δbcltf1 were already higher expressed in the dark ( bccry1 , bccry2 , bop2 , bcphy2 ) or less induced by light treatment ( bop1 , bop2 ) than in the wild type . Bcwcl1 encoding a putative blue light-sensing transcription factor was not found among the light-induced genes in this microarray experiment . However , further analysis showed that bcwcl1 is expressed higher during incubation in LL than in DD ( data not shown ) , indicating that light also affects the expression of this gene . Among the light-induced genes bclov3 ( PAS/LOV protein 3 ) might be related to light perception as the deduced protein sequence shares similarities with N-terminal parts of plant phototropins . These proteins are autophosphorylating protein kinases harboring N-terminal blue light-sensory ( LOV ) and C-terminal kinase domains [49] . Regulators that function downstream of signaling cascades are transcription factors ( TFs ) whose activities might be transcriptionally and/or post-transcriptionally modified . The genome of B . cinerea contains 406 genes encoding TFs of different families [2] , [50] , 45 TF-encoding genes were differentially expressed in at least one of the four conditions , i . e . displaying a light response in wild type or Δbcltf1 mutant , or a Δbcltf1 effect in darkness or in response to light ( Fig . 8B , Table S3 ) . Hierarchical clustering of the selected TFs across the 16 hybridizations highlights that the differential expression is mainly due to the bcltf1 mutation ( 43 TF-encoding genes ) and to a smaller extent to the light condition ( seven TF-encoding genes ) . In addition to bcltf1 , the expression of five further TF-encoding genes was induced by light and the genes were accordingly named bclft2-bclft6 . BcLTF2 and BcLTF3 are C2H2-TFs homologous to the light-responsive TFs SAH-1 and CSP-1 of N . crassa [37] , [48] . BcLTF4 to BcLTF6 belong to the family of Zn2Cys6-TFs , and only a homologue of BcLTF6 is present in N . crassa . While the expression of bcltf3 , bcltf4 and bcltf5 was similarly induced by light in both genomic backgrounds , the light-dependent expression pattern of the other two TF-encoding genes are affected in different ways in the absence of BcLTF1 . Both bcltf2 and bcltf6 were overexpressed in the Δbcltf1 mutant in darkness , but only the expression level of bcltf6 was further increased in response to the light stimulus . Therefore , BcLTF1 is involved in repressing the transcription of bcltf2 and bcltf6 in the dark . Moreover , it can be assumed that BcLTF1 represses the expression of 16 other TF-encoding genes in a light-independent manner , including bcboa13 that belongs to the botcinic acid ( BOA ) gene cluster ( see next section ) . The expression of one TF-encoding gene ( BofuT4_P161860 ) was repressed by light in the wild type and the effect was found to be absent in the Δbcltf1 background , as the gene was no longer expressed ( even in darkness ) . The gene encodes a member of the Zn2Cys6-TF family and is physically linked with genes of unknown functions displaying a similar expression pattern ( Bclft1g1499-1505; Table S2 ) . Fungal secondary metabolite ( SM ) -biosynthetic genes are typically located adjacent to each other and exhibit similar patterns of expression . These clusters usually include a gene encoding the key enzyme responsible for synthesis of the raw product , genes encoding enzymes for modifications and the transport of the compound , and genes whose products regulate cluster gene expression [51] . The genome of B . cinerea comprises 43 genes encoding SM key enzymes including 21 polyketide synthases ( PKSs ) , nine non-ribosomal peptide synthetases ( NRPSs ) , six sesquiterpene cyclases ( STCs ) , three diterpene cyclases ( DTCs ) and one dimethylallyl tryptophan synthase ( DMATS ) [2] . In our transcriptomic approach , 38 of the 43 genes were significantly expressed in at least one condition ( see Materials and Methods ) . The expression patterns of 17 of these genes ( 45% ) are significantly altered in the absence of BcLTF1 , including 9 PKS- , 3 NRPS- , 4 STC- and the single DMATS-encoding gene ( Fig . S7A ) . Remarkably , the majority ( 15 ) of these BcLTF1-dependent genes are not responsive to light . This group includes bcbot2 ( bcstc1 ) that is part of the BOT-biosynthetic cluster ( bcbot1-5 ) as well as bcboa6 and bcboa9 ( PKSs ) that belong to the two gene clusters ( bcboa1-6 , bcboa7-17 ) required for BOA biosynthesis . The other cluster genes were overexpressed as the key enzymes ( Fig . S7B ) , suggesting that the mutants may produce more toxins under the tested conditions . Additionally , the two PKS-encoding genes bcpks12 and bcpks13 were under- and overexpressed in the Δbcltf1 mutant , respectively , and are considered to be associated with dihydroxynaphthalene ( DHN ) -melanin biosynthesis [41] . Beside the PKS yielding the polyketidic precursor , probably three other enzymes encoded by bcbrn1 , bcbrn2 and bcsd1 are involved in melanin biosynthesis ( Fig . S8 ) . Increased expression levels of bcpks13 , bcbrn1 , bcbrn2 , and bcscd1 in Δbcltf1 mutants were associated with the dark pigmentation of the culture medium , while expression of bcpks12 was exclusively detected in developing sclerotia of the wild type . Therefore , bcpks13 and bcpks12 are differentially expressed in Δbcltf1 mutants as well as during the life cycle of the wild type . The expression patterns of only three key enzyme-encoding genes ( bcnrps2 , bcphs1 , bcstc5 ) were significantly affected by light . Notably , one of them corresponds to a cluster of light-induced genes ( bcphs1 , bcphd1 , bccao1 ) that encode homologues of enzymes involved in the biosynthesis of retinal , the chromophore for opsin , in Fusarium fujikuroi ( phytoene synthase , phytoene dehydrogenase , and carotenoid oxygenase ) . As in F . fujikuroi , the cluster contains a forth co-regulated gene encoding an opsin ( bop2 ) [52]–[54] ( Fig . S9 ) . Though genes were still induced by light in the absence of BcLTF1 , the absolute expression levels were much lower than those found in the wild type in response to light , suggesting decreased retinal production and an altered function of BOP2 in the absence of BcLTF1 . Given the finding that mutations of bcltf1 affect the accumulation of H2O2 , we inspected the transcriptomic data for genes involved in ROS metabolism to gain insight whether the net over-accumulation is due to an inappropriate detoxification and/or generation of ROS . Oxidative stress response ( OSR ) systems include superoxide dismutases ( SODs ) that convert O−2• into the less toxic H2O2 , and catalases ( CATs ) , peroxidases ( PRDs ) , and peroxiredoxins ( PRXs ) that detoxify H2O2 and thereby prevent the formation of OH• via the Fenton reaction . Important non-enzymatic mechanisms include the oxidation of compounds such as glutathione , ascorbate and carotenoids . Reduced glutathione ( GSH ) provided by glutathione reductases ( GLRs ) is used by glutathione peroxidase ( GPX ) , glutaredoxins ( GRXs ) as well as by glutathione S-transferases ( GSTs ) that conjugate GSH with various substrates such as peroxides [55] . In B . cinerea , several enzyme activities are represented by multiple genes , thus , four SODs , eight CATs , 16 PRDs , nine PRXs , one TRX , five GRXs and 26 GSTs have been identified ( Table S4 ) . The set of light-induced genes in the wild type is significantly enriched for OSR-related genes ( Table 2 ) , among them are two CATs ( bccat3 , bccat4 ) , two PRDs ( bcprd1 , bcprd2 ) and three GSTs ( bcgst3 , bcgst8 , bcgst15 ) ( Table S4 ) . These genes are not induced by light in the absence of bcltf1 suggesting that their products may contribute to coping with light-related oxidative stress . Nevertheless , twenty genes among them SOD- , CAT- , PRD- , PRX- and GST-encoding genes exhibited increased or decreased expression levels in the mutant background , reflecting a general deregulation of OSR-related genes . Several enzymes of B . cinerea may generate ROS either as secondary messenger ( e . g . formation of O−2• by the NADPH oxidase ( NOX ) ) or as side products of metabolic processes ( e . g . oxidation of carbohydrates , fatty acids and amino acids yielding H2O2 ) . Forty-eight putative oxidase-encoding genes were identified in the genome , and expression levels of twenty-five genes were affected by either the light stimulus and/or the deletion of bcltf1 . Four oxidase-encoding genes were induced by light in both genomic backgrounds , and a further 11 oxidase-encoding genes exhibited increased expression levels in the mutant background . Seven out of the 11 identified DAO ( D-amino acid oxidase ) -encoding genes are less expressed in the bclft1 mutant than in the wild type , irrespective of the illumination condition , a fact that might be related with the weak expression of amino acid transporter-encoding genes ( Table 2 ) . Additionally , two out of the four identified putative NADH:flavin oxidoreductase/NADH oxidase-encoding genes showed increased expression levels in the absence of BcLTF1 while the genes encoding the catalytic subunits of the NOX complex ( bcnoxA and bcnoxB ) were slightly underexpressed ( Table S4 ) . Beside the mentioned ROS-generating enzymes , the mitochondrial electron transport chain ( ETC ) massively contributes to intracellular ROS levels . Complexes I and III are forming O2− that is in turn dismutated to H2O2 to prevent the formation of OH• [56] . Though expression levels of nuclear-encoded subunits of the different protein complexes of the respiratory chain were not affected in the Δbctlf1 mutant ( data not shown ) , strongly increased expression levels were observed for the gene encoding the alternative oxidase ( BcAOX1 ) ( Table S4 , Fig . S10 ) . This enzyme accepts electrons from the ubiquinone pool and reduces oxygen directly , hence bypassing the electron flux through complexes III and IV ( cytochrome c oxidase , COX ) , and related ROS generation [57] . In addition to complex I ( NADH:ubiquinone oxidoreductase ) , B . cinerea possesses five putative NADH dehydrogenases that may deliver electrons to the ubiquinone pool thereby bypassing complex I . In fact , bcnde2 and bcndi1 encoding external and internal NADH dehydrogenases , respectively , were overexpressed in the deletion mutants . Additionally , genes encoding cytochrome c ( bccyc1 ) , a cytochrome c peroxidase ( bcccp2 ) , an uncoupling protein ( bcucp1 ) and several other mitochondrial carrier proteins were more highly expressed in the absence of BcLTF1 , further supporting the deregulation of the ETC in the mutant background . CCP2 may accept electrons from complex III/cytochrome c for reduction of H2O2 , hence avoiding the electron flux through COX [58] , and uncoupling proteins are considered to decrease ROS production by facilitating the transport of ions across the membrane [56] , [59] . Taken together , the observed transcriptional changes in the Δbcltf1 mutant suggest that alternative enzymes compete with the ETC complexes for electrons to decrease the generation of mitochondrial ROS .
Random mutagenesis via ATMT is successfully used in plants and filamentous fungi to generate new phenotypes and to identify the corresponding genes . In a recent study , we reported on an ATMT approach in B . cinerea yielding 2 , 367 mutants with 560 exhibiting reduced virulence in a first screening . For two out of four randomly chosen candidates tested , the genes could be linked to the ATMT phenotype by deletion approaches . As reported for other fungal ATMT libraries , T-DNA integrations were mostly random , single copy , and occurred preferentially in noncoding ( regulatory ) regions [39] . The latter facts hold true for the ATMT mutants we are describing in this study , however , the fact that three independent mutants out of 77 virulence-attenuated mutants analyzed so far were found to contain T-DNA insertions at the same gene locus , indicates that “hot spots” for T-DNA insertions exist in the genome of B . cinerea . While the deletion of bcltf1 mainly affects the advanced stages of infection , the overexpression of bcltf1 impairs specifically the penetration process by germ tubes which exhibit an abnormal branching pattern . In contrast , penetration via infection cushions from already established mycelia is not impaired indicating that these modes of penetration are independently regulated processes . This is in agreement with previous reports on mutants that are impaired in mycelia- but not in conidia-derived infection [60] . The retarded colonization of the host tissue by Δbcltf1 mutants is accompanied and likely caused by increased accumulation of ROS as the addition of antioxidants to the inoculation droplets restored virulence to wild-type levels . Since the deletion mutants accumulate more H2O2 than the wild type under axenic conditions , the increased H2O2 levels in Δbcltf1-infected plant tissues probably are the consequence of inappropriate ROS production by the fungus rather than by the plant . However , the increased accumulation has a negative impact on invasion of the host tissue by the mutant , whereas the application of antioxidants does not interfere with virulence of the wild type , raising the question to which extent ROS produced by the host ( in terms of an oxidative burst ) or the fungus are contributing to the outcome of the interaction . Homologues of BcLTF1 have been characterized so far only in very few fungal species , revealing their requirement for sexual reproduction in Sordariomycetes ( N . crassa , S . macrospora ) , and Eurotiomycetes ( A . nidulans and A . fumigatus ) [33] , [35] , [36] , [38] . Due to the lack of sclerotia , Δbcltf1 as well as OE::bcltf1 mutants are female sterile , and for crossings of Δbcltf1 microconidia ( MAT1-1 ) with sclerotia of the reference strain SAS405 ( MAT1-2 ) no apothecia were obtained ( data not shown ) indicating that the TF is required for sexual reproduction in B . cinerea as well . However , significant differences also exist that may reflect the requirement to adapt to different ecological niches . NsdD in A . flavus clearly regulates the mode of asexual reproduction , either via formation of conidia or sclerotia in a light-independent manner [61] . Regardless of the fact that the mentioned fungi are distantly related and exhibit very different modes of reproduction including different asexual and sexual structures , reproduction is controlled by different environmental cues . Therefore , protoperithecia formation in N . crassa requires nutrient limitation and light [19] , and limited air exchange favors sexual reproduction in A . nidulans while nutrient limitation , light and other stresses shift the ratio from sexual to asexual reproduction [25]–[26] . On the contrary , light alone controls asexual reproduction in B . cinerea by inducing conidiation and suppressing sclerotial development preventing thereby the simultaneous formation of both structures . In contrast , the formation of microconidia is induced by nutrient limitation [62] , and also the formation of apothecia in the laboratory takes place in the absence of nutrients and requires a photoperiod [10] . Hence , light has an outstanding function in B . cinerea because it determines the mode of reproduction . In accordance with the findings that B . cinerea undergoes photomorphogenesis and that its germ tubes , conidiophores and apothecia stipes exhibit phototropic responses [63] , a number of light-responsive genes ( 293 ) could be identified by genome-wide expression analysis . Similar studies have revealed 314 , 533 , and 250 light-responsive genes in N . crassa , A . nidulans , and A . fumigatus , respectively [37] , [64]–[65] . Fifteen min of white light was sufficient to induce the transcription of bcltf1 and two other tested light-responsive genes ( bop1 , bcccg1 ) , and maximal induction was achieved at exposure times of 45–60 min . BcLTF1 is an important modulator of the transcriptional responses to light as it influences the expression patterns of the majority of light-responsive genes ( 66% ) . Similar observations have been made for SUB-1 in N . crassa which acts in a hierarchical light-sensing cascade , being – as an early light-responsive gene – involved in induction of the majority of late light-responsive genes [37] . Noteworthy , transcript levels of both bcltf1 and sub-1 increase in response to light but no light responsiveness of A . nidulans nsdD has been reported [64] , indicating that NsdD is unlikely to be involved in light signaling in the same fashion as its homologues in B . cinerea and N . crassa . White light is composed of different wavelengths; therefore different photoreceptors are assumed to be involved in perception and subsequent signaling . The inducible effect of the blue light fraction on expression of bcltf1 is likely mediated via a WHITE COLLAR-like transcriptional complex ( WCC ) formed by the GATA transcription factors BcWCL1 and BcWCL2 that were reported to interact in the nuclei [66] . In fact , induction of bcltf1 , bop1 and bcccg1 by white light requires BcWCL1 because in its absence the expression levels are much lower than in the wild type [P . Canessa , J . Schumacher , M . Hevia , P . Tudzynski , L . Larrondo , unpublished] . Nevertheless , an induction is still visible suggesting the involvement of other photoreceptors/wavelengths in transcriptional activation of bcltf1 and other light-induced genes . Δbcltf1 mutants exhibit similarly decreased radial growth rates in blue and white light suggesting that the blue light fraction accounts for the toxicity of white light , possibly by provoking greater oxidative stress than other wavelengths . Remarkably , the light-protecting function seems to be unique to BcLTF1 because N . crassa Δsub-1 and S . macrospora Δpro44 mutants exhibit comparable vegetative growth in light and darkness [L . Larrondo , pers . commun . , 38] . The observation that expression levels of light-induced genes ( bop1 , bcccg1 ) decreased after prolonged exposure to light ( from 120 min on ) indicates the existence of photoadaptation processes in B . cinerea . In N . crassa , photoadaptation involves a further blue light-sensing protein ( VIVID ) , whose expression is induced by light in a WCC-dependent manner , and that then negatively acts on the WCC to prevent further activation of light-responsive genes [67]–[68] . The homologue of B . cinerea ( bcvvd1 ) is highly induced by light in both wild-type and Δbcltf1 strains , however , whether BcVVD1 impacts light-induced gene expression and is required for light tolerance like its counterpart in N . crassa needs to be studied . VIVID , the WCC and the FREQUENCY ( FRQ ) protein are furthermore implicated in running the circadian clock in N . crassa . Like vvd , expression of frq is turned on by the WCC in response to light , and subsequently FRQ blocks its own transcription by physically interacting with the WCC [20] . B . cinerea possesses a homologue of frq whose expression is induced by light in wild type and the bcltf1 deletion mutants to a similar extent , suggesting that the circadian clock is set by light in a BcLTF1-independent manner , as it was described for SUB-1 in N . crassa [37] . The fact that expression of bcltf1 in contrast to its homologue in N . crassa is not subjected to photoadaptation underlines the importance of the transcription factor for mediating the tolerance towards light in B . cinerea . Based on the finding that the deletion of BcLTF1 results in hyperconidiation , it can be concluded that BcLTF1 acts as a general repressor of conidiation . This result was unexpected because the light-responsiveness of bcltf1 may suggest a stimulatory role of the TF on light-dependent differentiation processes . Additionally , a direct role for BcLTF1 in the induction of sclerotia development appears unlikely as its overexpression results in the accumulation of undifferentiated aerial mycelia instead of sclerotia . This fluffy phenotype resembles those of wild type colonies that have been illuminated with blue light [15]–[16] supporting the role of BcLTF1 in mediating blue light responses . Hence , it is concluded that conidiation is initiated by the differentiation of aerial hyphae ( triggered by blue light , involving WCC and BcLTF1 ) followed by their differentiation to conidiophores ( possibly triggered by the other light wavelengths/photoreceptors ) . In addition to BcLTF1 that represses conidiation in light and darkness , five other light-responsive TFs were identified that may regulate differentiation in a light-dependent fashion . Homologues of bcltf2 and bcltf3 in N . crassa ( sah-1/short aerial hyphae and csp-1/conidial separation ) are inducible by light and control asexual development [36]–[37] . Increased expression levels of bcltf2 in the Δbcltf1 background in darkness correlate with the hyper-conidiation phenotype of the mutant , suggesting that BcLTF2 indeed may act as a positive regulator of conidiation . Light-responsive TFs may be also implicated in suppressing sclerotial development in the light , for instance by induction of transcriptional repressor ( s ) of sclerotia-related gene expression . Further studies on the light-responsive TFs are envisaged to reveal whether and how they are involved in differentiation of reproductive structures . Likewise these TFs may be involved in regulation of DHN-melanin biosynthesis , a pigment that is incorporated in the reproductive structures giving conidiophores , conidia and sclerotia of B . cinerea their characteristic gray to black color [69]–[70] . Accordingly , Δbcltf1 and Δbcvel1 mutants [31]–[32] exhibiting a hyper-conidiation phenotype produce melanin in excess leading to a dark coloration of the culture broths . In contrast to other DHN-melanin-producing fungi , B . cinerea possesses two highly similar PKS-encoding genes ( bcpks12 and bcpks13 ) [41] , while single copies for the other melanogenic genes exist . The relevance of having two copies of the PKS appears obscure when both enzymes are functional . However , considering the differential expression pattern of bcpks12 and bcpks13 in the Δbcltf1 background and during the life cycle of the wild type and the fact that B . cinerea produces , in contrast to other fungi , the same pigment under very different conditions , it is conceivable that BcPKS12 and BcPKS13 are responsible for sclerotia- and conidiophore/conidia-specific melanin biosynthesis , respectively . The observation that non-sporulating B . cinerea mutants produce an orange pigment during intensive illumination suggests furthermore that the fungus is able to produce carotenoids , a group of pigments that is produced by plants and fungi to protect cells from free radicals , ROS and singlet oxygen [71] . Consequently , increased carotenoid production may result in extended life spans as demonstrated in Podospora anserina [72] . Genes putatively involved in retinal biosynthesis in B . cinerea are inducible by light as their homologues in N . crassa and F . fujikuroi , but unlike the situation in N . crassa , genes in the two other fungi are organized together with the opsin-encoding gene in a gene cluster [37] , [54] . Nevertheless , as described in N . crassa , light-induction of carotenogenesis in B . cinerea relies on the WCC [P . Canessa , J . Schumacher , M . Hevia , P . Tudzynski , L . Larrondo , unpublished] . Given the protective function of the carotenoids , their decreased production by Δbcltf1 mutants , as concluded from lower gene expression levels , may contribute to the hypersensitivity of the mutant to light and oxidative stress . Several observations suggest that BcLTF1 is essential for maintaining the ROS homoeostasis . Expression levels of many ROS-related genes are altered in the absence of BcLTF1 , though it is still ambiguous which of the changes ( decreased scavenging , increased generation in the peroxisomes and/or mitochondria ) cause the significantly increased net accumulation of H2O2 and therefore rendering Δbcltf1 mutants more sensitive to oxidative stress arising from exogenous ROS , light , and during infection . Some but not all phenotypes of Δbcltf1 mutants can be restored by antioxidants illustrating that the unbalanced ROS homoeostasis accounts for decreased virulence and mediates light toxicity , but it does not cause the observed developmental defects . Although clear evidences suggest an altered cellular redox status in the deletion mutants , the use of roGFP2 as sensor failed to confirm that , because the initial cytosolic glutathione pool is reduced and recovers after a short-term oxidation event caused by H2O2 in a manner almost as in the wild-type . However , given that the equilibrium of the glutathione pool is limited between different cellular compartments , as it was reported for plant cellular compartments [73] , the mutant may experience oxidative stress in another compartment other than the cytosol . The overexpression of nuclear genes encoding enzymes of the alternative respiration pathway in Δbcltf1 mutants indicates the reprogramming of the mitochondria probably as a consequence of their dysfunction ( retrograde signaling ) [74] . The key component of the alternative pathway is the AOX that functions as the terminal oxidase ( instead of the copper-containing COX in cytochrome ETC ) transferring electrons to oxygen . Studies in different fungi reported an increased aox expression level in response to the inhibition of later steps of cytochrome ETC [75]–[78] , to oxidative stress [79]–[82] , and to copper deficiency causing COX assembly defects [83]–[84] . AOX activity has been reported to be dispensable for virulence in M . oryzae , B . cinerea and A . fumigatus , however , in the latter it contributes to oxidative stress resistance [85]–[86] , [77; C . Levis , pers . commun . ] . In P . anserina , AOX activity in complex III-deficient mutants leads to an extended lifespan associated with a decreased ROS production [87] , [76] . On the contrary , the long-lived phenotype of mutants with a nonfunctional COX ( deletion of the copper chaperone PaCOX17 ) is accompanied by increased ROS production [88] . Accordingly , studies in plant cells demonstrated the oxidation of the mitochondrial glutathione pool in response to the inhibition of COX ( without affecting the cytosolic redox status ) , but not in response to inhibition of complex III [89] . From the present data it is difficult to define the reason for increased alternative respiration in Δbcltf1 mutants , i . e . whether increased overall ROS production or an impairment of the cytochrome ETC causes the expression of the corresponding genes . However , the observation that the homologues of the copper chaperone COX17 and the high-affinity copper transporter CTR3 are significantly overexpressed in the Δbcltf1 mutant may indicate an altered copper homoeostasis in the mutants which in turn may affect the activity of COX and other enzymes requiring copper as cofactor . Regardless of how increased alternative respiration is achieved , the altered mode of respiration is expected to reduce mitochondrial ROS production and to cause further global changes in the metabolism of the fungus , because the electron flux through the alternative enzymes is not coupled with the translocation of protons across the membrane and therefore does not allow for proton gradient-driven ATP synthesis . Retrograde signaling in yeast cells was shown to affect glycolysis , the tricarboxylic acid cycle , peroxisomal fatty acid oxidation as well as the glyoxylate cycle [90] . We suggest therefore that the significantly altered secondary metabolism in Δbcltf1 mutants is due to an altered primary metabolism rather than to a direct involvement of BcLTF1 in regulation of secondary metabolism-related genes . Taken together , it can be assumed that light affects B . cinerea in two ways ( Fig . 9 ) ; directly via generation of singlet oxygen that damage macromolecules , and indirectly via its sensing by fungal photoreceptors altering gene expression . Products of light-responsive genes are implicated in ROS homoeostasis ( via generation or scavenging ) , photoadaptation , the circadian clock , in promoting conidiation and apothecia formation and in preventing sclerotial development . Many , but not all , responses are altered in the absence of BcLTF1; and a hallmark of the deletion mutants is the unbalanced ROS homoeostasis ( net ROS overproduction ) accompanied by a retrograde response ( alternative respiration ) accounting for reduced virulence , light toxicity and potentially for altered secondary metabolism . Phenotypic and expression data suggest that BcLTF1 exerts repressing as well as activating functions in constant darkness and in response to light , though its transcription level is very low in darkness .
Strain B05 . 10 of B . cinerea Pers . Fr . [B . fuckeliana ( de Bary ) Whetzel] is an isolate from Vitis ( Table 1 ) and is used as the recipient strain for genetic modifications . Genome sequences of B05 . 10 and T4 were recently published [2] , [91] . Strains were cultivated on plates containing solid synthetic complete medium ( CM ) [92] , modified Czapek-Dox ( CD ) as minimal medium ( 2% sucrose , 0 . 1% KH2PO4 , 0 . 3% NaNO3 , 0 . 05% KCl , 0 . 05% MgSO4×7 H2O , 0 . 002% FeSO4×7 H2O , pH 5 . 0 ) , or solid potato dextrose medium ( Sigma-Aldrich , Germany ) supplemented with 10% homogenized bean leaves . Acidification of the culture medium ( solid CM pH 8 ) was monitored using the pH indicator bromothymol blue ( 0 . 1% ) ( Sigma-Aldrich , Germany ) . Cultures were incubated at 20°C under white light ( 12 h light/12 h darkness ( LD ) or continuous light ( LL ) ) for conidiation , and in continuous darkness for induction of sclerotial formation . White light ( 9000 lx at culture level ) was generated by using a set of Sylvania Standard F18W/29-530 “warm white” and F36W/33-640 “cool white” fluorescent bulbs . Wavelengths of transmitted light were controlled using Petri dish chambers covered with Roscolux polyester filters ( #381 Baldassari Blue , #389 Chroma Green , #312 Canary , #27 Med Red; http://www . rosco . com/filters/roscolux . cfm ) ; transmission spectra of the filters are shown in Fig . S3 . Crossings using SAS405 ( MAT1-2 ) as reference strain were performed as described previously [31] . For DNA and RNA isolation , mycelia were grown on solid CM medium with cellophane overlays . Fungal genomic DNA was prepared according to Cenis [93] . For Southern blot analysis , fungal genomic DNA was digested with restriction enzymes ( Fermentas , Germany ) , separated on 1% ( w/v ) agarose gels and transferred to Amersham Hybond-N+ filters ( GE Healthcare Limited , UK ) . Blot hybridizations with random-primed α-32P-dCTP-labelled probes were performed as described previously [94] . PCR reactions were performed using the high-fidelity DNA polymerase Phusion ( Finnzymes , Finland ) for cloning purposes and the BioTherm Taq DNA Polymerase ( GeneCraft , Germany ) for diagnostic applications . Replacement fragments and expression vectors were assembled in Saccharomyces cerevisiae by exploiting its homologous recombination machinery [36] , [66] . Sequencing of DNA fragments was performed with the Big Dye Terminator v3 . 1 sequencing kit ( Applied Biosystems , USA ) in an ABI Prism capillary sequencer ( model 3730; Applied Biosystems ) . For sequence analysis , the program package DNAStar ( Madison , USA ) was used . Protocols for protoplast formation and transformation of B . cinerea were described by Schumacher [66] . Regenerated protoplasts were overlaid with SH agar containing 70 µg/ml hygromycin B ( Invitrogen , The Netherlands ) and 140 µg/ml nourseothricin ( Werner-Bioagents , Germany ) , respectively . Resistant colonies were transferred to agar plates containing CM medium supplemented with the respective selection agent in a concentration of 70 µg/ml . Single conidial isolates were obtained by spreading conidial suspensions on selective media and transferring single colonies to new plates . Total RNA was isolated making use of the TRIzol reagent ( Invitrogen , The Netherlands ) . Samples ( 25 µg ) of total RNA were transferred to Hybond-N+ membranes after electrophoresis on a 1% ( w/v ) agarose gel containing formaldehyde , according to the method of Sambrook et al . [95] . Blot hybridizations were carried out by use of random-primed α-32P-dCTP-labelled probes [94] . Analyzed genes are ( Fig . 2 ) : Bcssp1 ( BC1G_03185 ) encoding the homologue of S . sclerotiorum ssp1 [40]; bcpks12 ( AY495617 ) and bcpks13 ( AY495618 ) encoding polyketide synthases putatively involved in melanin biosynthesis [41]; bop1 ( BC1G_02456 ) encoding an opsin-like protein , expression is induced upon H2O2 [30] and light treatment ( this study ) ; bcccg1 ( BC1G_11685 ) representing the homologue of N . crassa ccg-1 [42] ) , expression is induced upon H2O2 and light treatment ( this study ) ; bcactA ( AJ000335 ) encoding actin is used in this study to detect fungal RNA in mixed ( in planta ) samples; bcgpx1 ( BC1G_02031 ) encoding the glutathione peroxidase , expression is induced upon H2O2 treatment [96] . Strains PA31 , PA2411 and PA3417 belong to an insertional mutant library of B . cinerea B05 . 10 that was generated by A . tumefaciens-mediated transformation ( ATMT ) . Southern blot analyses demonstrating the existence of single T-DNA copies in the three mutants ( data not shown ) were performed according to Giesbert et al . [39] . The T-DNA-flanking regions were identified by TAIL- ( thermal asymmetric interlaced ) PCR as described previously [39] . BlastN analyses using the amplified T-DNA flanking regions revealed discontinuous sequences at the respective locus in both genome sequences of T4 ( 1-kb gap upstream of BofuT4_P09344; 480 aa ) and B05 . 10 ( 2-kb gap upstream of BC1G_10441; 133 aa ) ( B . cinerea genome project , URGI; http://urgi . versailles . inra . fr/Species/Botrytis/ ) [2] . Re-sequencing of both strains yielded continuous sequences ( B . cinerea Database , Broad Institute; http://www . broadinstitute . org/annotation/genome/botrytis_cinerea/ [91] , hence , the T-DNA insertion occurred 1 . 608 kb upstream of the ORF hereafter called bcltf1 ( Fig . 1B ) . Bcltf1 of B05 . 10 encodes a protein of 481 aa ( BcLTF1B05 . 10: B0510_3555 ) , that of T4 a protein of 480 aa ( BcLTF1T4: BofuT4_P09344 , BcT4_9306 ) . Putative transcriptional start ( TSS ) and PolyA signals ( Fig . 1B ) were predicted using the FGENESH tool ( http://linux1 . softberry . com/ ) . The nuclear localization signal ( NLS ) and the GATA zinc finger motif were predicted by WoLF PSORT ( http://wolfpsort . org/ ) and Pfam Search ( http://pfam . sanger . ac . uk ) , respectively ( Fig . 1C ) . No nuclear export signals ( NES ) were identified by using NetNES 1 . 1 ( http://www . cbs . dtu . dk/services/NetNES/ ) . Putative phosphorylation sites for protein kinase A ( PKA: S78 , S95 , S184 , S185 ) and protein kinase C ( PKC: T91 , S105 , S124 , S194 , T453 , T457 , S467 , S468 ) in BcLTF1B05 . 10 were identified by running NetPhosK ( http://www . cbs . dtu . dk/services/NetPhosK/ ) . For generation of fragments mediating the replacement of bcltf1 ( Δbcltf1-A ) and bcltf1+1 . 7 kb of the 5′-noncoding region ( Δbcltf1-B ) , respectively , different 5′ flanks ( A , B ) were assembled with the same 3′ flank and a hygromycin resistance cassette by yeast recombinational cloning [36] ( Table S1 , Fig . S1A ) . Replacement fragments were transformed into B . cinerea B05 . 10 . Homologous integration events in hygromycin-resistant transformants were detected by diagnostic PCR using the primers pCSN44-trpC-T and pCSN44-trpC-P , binding in the hygromycin resistance cassette and the primers bcltf1-A-hi5F/B-hi5F and bcltf1-hi3R , binding upstream and downstream of the bcltf1-flanking regions , respectively ( Table S1 , Fig . S1B ) . Single spore isolates were screened for the absence of bcltf1 alleles using primers bcltf1-WT-F and bcltf1-WT-R matching the substituted coding region . For Southern blot analysis , genomic DNA of the mutants and the recipient strain B05 . 10 was digested with PstI , and the blot was hybridized with the 3′ flank of the replacement fragment . The hygromycin resistance cassette contains an additional PstI restriction site resulting in smaller hybridizing fragments in the replacement mutants ( 2 . 3 kb ) than in the wild type ( 7 . 2 kb ) ( Fig . S1C ) . Strain Δbcltf1-A1 was discarded because a second ectopic integration event occurred . Taken together , two independent mutants were generated lacking either only bcltf1 ( Δbcltf1-A6 ) or bcltf1 and its 5′-noncoding region ( Δbcltf1-B1 ) . The mutant Δbcltf1-A6 was complemented by targeted integration of bcltf1 or bcltf1-gfp at the native gene locus thereby replacing the hygromycin resistance cassette . For vector construction , all fragments indicated in Fig . S1A were amplified by PCR using oligonucleotides listed in Table S1 , and assembled in one step in S . cerevisiae yielding plasmids pbcltf1-COM and pbcltf1-gfpin loco . Replacement constructs were transformed into Δbcltf1-A6 , and diagnostic PCR confirmed the targeted integration in two transformants per construct ( bcltf1COM:T2 , T3; and Δbcltf1+Pbcltf1::bcltf1-gfp: T2 , T5 ) ( Fig . S1D ) . For over-expressing the BcLTF1-GFP fusion protein , the ORF was introduced in vector pNAN-OGG comprising gene flanks for targeted integration at bcniiA ( locus of nitrite reductase ) , a nourseothricin resistance cassette and the expression cassette with gfp under control of the constitutive oliC promoter [66] ( Fig . S1E ) . The construct was transformed into B05 . 10 yielding transformants WT+PoliC::bcltf1-gfp ( T6 , T7 ) , and into Δbcltf1-B1 yielding transformants Δbcltf1+PoliC::bcltf1-gfp ( T3 , T4 ) . Targeted integration at bcniiA was verified by diagnostic PCR as described previously [66] ( data not shown ) . Northern blot analyses revealed the constitutive expression of bcltf1-gfp in the mutant background and the overexpression of bcltf1- ( gfp ) in the wild-type background , respectively ( Fig . S1F ) . Conidial germination was monitored according to Doehlemann et al . [97] . For testing nutrient- and hydrophobicity-induced germination , cleaned conidia were incubated in Gamborg B5+10 mM glucose on glass surfaces and in double distilled water on polypropylene foil , respectively , in LL or DD conditions . Germination rates were determined in triplicates after 4 h and 8 h of incubation for glucose-induced and after 24 h for polypropylene-induced germination . Germling fusions via conidial anastomosis tubes were monitored according to Roca et al . [98] . Briefly , conidia were plated on solid Vogel's medium and incubated for 17 h at 21°C in the dark . Samples were analyzed by using light microcopy . For penetration assays , onion epidermal layers were prepared as described [31] . Conidial suspensions or non-sporulating mycelia from 3-d-old cultures were used for inoculation . The staining with lactophenol aniline blue ( Sigma-Aldrich , Germany ) allowed the identification of the penetrations sites after 24 h of incubation . For infection assays on French bean ( Phaseolus vulgaris cv . 90598 ) , primary leaves of living plants were inoculated with 7 . 5-µl droplets of conidial suspensions ( 2×105 conidia/ml Gamborg B5+2% glucose ) or non-sporulating mycelia [31] . Infected plants were incubated in plastic boxes at 22°C under natural illumination conditions ( LD ) and continuous darkness ( DD ) , respectively . Trypan blue and DAB ( 3 , 3′-diaminobenzidine ) staining of lesions were performed as described previously [31] . Bright field images were taken using the Zeiss SteREO Discovery V . 20 stereomicroscope or the AxioScope . A1 microscope equipped with an AxioCamMRc camera and the AxiovisionRel 4 . 8 software package ( Zeiss , Germany ) . To detect accumulating H2O2 , strains were grown on solid CM covered with cellophane for 3 d in DD . Fresh mycelia ( 25 mg ) were weighed , placed in wells of a 24-well plate and flooded with 1 ml DAB solution ( 0 . 5 mg/ml DAB in 100 mM of citric acid buffer , pH 3 . 7 ) . Samples were incubated for 2 h in darkness at room temperature . The colorless DAB is oxidized by H2O2 in the presence of fungal peroxidases and horseradish peroxidase ( positive control ) resulting in a brown coloration of the staining solution . To detect accumulating O2 . − , germinated conidia were incubated for 2 h in NBT staining solution ( 0 . 5 mg/ml nitrotetrazolium blue chloride in 50 mM sodium phosphate buffer , pH 7 . 5 ) . Samples were analyzed by using light microcopy . The colorless substrate NBT is converted to a blue precipitate in the presence of O2− . Vector pNAN-GRX1-roGFP2 carrying the reduction-oxidation sensitive green fluorescent protein ( roGFP ) and a nourseothricin resistance cassette [45] , was transformed into the mutant Δbcltf1-A6 ( Table 1 ) . The homologous integration of the construct at bcniiA in two independent transformants ( T2 , T3 ) was verified by diagnostic PCR as described previously [66] . Reactions of roGFP2 in wild type B05 . 10 ( WT+grx-rogfp2 ) and bcltf1 deletion mutants ( Δbcltf1+grx-rogfp2 ) were monitored using a fluorometer as described by Heller et al . [45] . Briefly , germinated conidia ( conidia cultivated for 12 h in liquid CM ) were washed twice and transferred to 96-well plates . Fluorescence was measured at the bottom with 3×3 reads per well and an excitation wavelength of 395±5 nm for the oxidized state and 488±5 nm for the reduced state of roGFP2 using a Tecan Safire fluorometer . Relative fluorescence units ( RFU ) were recorded to calculate the Em395/Em488 ratio . For microscopy , conidia were suspended in Gamborg B5 solution supplemented with 2% glucose and 0 . 02% ammonium phosphate , and incubated overnight under humid conditions on microscope slides or on onion epidermal strips . Nuclei were stained using the fluorescent dye Hoechst 33342 [66] , and fungal cell walls using calcofluor white ( CFW ) as described previously [60] . Fluorescence and light microscopy was performed with a Zeiss AxioImager . M1 microscope equipped with the ApoTome . 2 technology for optical sectioning with structured illumination . Differential interference microscopy ( DIC ) was used for bright field images . Specimens stained by Hoechst 33342 and CFW were examined using the filter set 49 DAPI shift free ( excitation G 365 , beam splitter FT 395 , emission BP 445/50 ) , GFP fluorescence using filter set 38 ( excitation BP 470/40 , beam splitter FT 495 , emission BP 525/50 ) . Images ( optical sections and Z-stacks ) were captured with an AxioCam MRm camera and analyzed using the Axiovision Rel 4 . 8 software package ( Zeiss , Germany ) . Wild type B05 . 10 and the Δbcltf1 mutant were grown for 3 d on solid CM in LD before non-sporulating mycelia were transferred to solid CM covered with cellophane overlays . Cultures were incubated for 52 h in DD , and then half of the cultures were transferred to white light ( +LP ) . After 60 min all cultures ( WT-D; WT+LP; Δbcltf1-D; Δbcltf1+LP ) were harvested . Material derived from four independent experiments was used for RNA isolation ( Trizol procedure ) . Total RNA was treated with the DNA-free kit to remove any trace of DNA ( Ambion - Applied Biosystems , France ) . Synthesis of double-stranded cDNA , Cy3-labeling and hybridization on microarrays were done by PartnerChip ( http://www . partnerchip . fr/ ) using the procedures established by NimbleGen ( Roche ) and the reagents from Invitrogen ( Life technologies , France ) . To study the complete transcriptome of B . cinerea , NimbleGen 4-plex arrays with 62 , 478 60-mer specific probes covering all the 20 , 885 predicted gene models and non-mapping ESTs of B . cinerea [2] were used . The arrays also include 9 , 559 random probes as negative controls . Data processing , quality controls and differential expression analysis were performed using ANAIS methods [99] . Probe hybridization signals were subjected to RMA-background correction , quantile normalization , and gene summarization [100]–[101] . Genes with a normalized intensity over the threshold ( 1 . 5×95th percentile of random probes ) in at least one hybridization were considered as expressed and kept for further analyses . Differentially expressed genes ( light response in wild type and mutant , Δbcltf1 effect in darkness and light ) were then identified using a one-way ANOVA test . To deal with multiple testings , the ANOVA p-values were then submitted to a False Discovery Rate correction . Genes with a corrected p-value <0 . 05 , and more than a 2-fold change in transcript level were considered as significantly differentially expressed . BlastN analyses for the 20 , 885 spotted genes [2; URGI database] were performed to identify the matching gene models derived from the re-sequencing approaches by Staats & van Kan [91; Broad Institute] . By this , the number of differentially expressed genes was reduced because incorrect gene models derived from discontinuous sequences were revised and furthermore previously non-matching ESTs could be mapped . The four gene lists were then pooled and cluster analyses were performed to highlight the different expression profiles encountered . For this purpose , the log2-normalized intensities scaled by gene across the 16 hybridizations were clustered using Genesis tools [102] . Gene enrichment analyses were further performed with GSEA toolkit [46]–[47] to highlight significantly enriched functions compared to the complete list of functionally annotated B . cinerea genes [2] . Details on the experiments , raw and normalized values are available at NCBI GEO ( http://www . ncbi . nlm . nih . gov/geo/ ) ( Accession: GPL17773 ) . Furthermore , lists of differentially expressed genes are available at http://urgi . versailles . inra . fr/Data/Transcriptome . | Both fungal pathogens and their host plants respond to light , which represents an important environmental cue . Unlike plants using light for energy generation , filamentous fungi use light , or its absence , as a general signal for orientation ( night/day , underground/on the surface ) . Therefore , dependent on the ecological niche of the fungus , light may control the development of reproductive structures ( photomorphogenesis ) , the dispersal of propagules ( phototropism of reproductive structures ) and the circadian rhythm . As in other organisms , fungi have to protect themselves against the detrimental effects of light , i . e . the damage to macromolecules by emerging singlet oxygen . Adaptive responses are the accumulation of pigments , especially in the reproductive and survival structures such as spores , sclerotia and fruiting bodies . Light is sensed by fungal photoreceptors leading to quick responses on the transcriptional level , and is furthermore considered to result in the accumulation of reactive oxygen species ( ROS ) . In this study , we provide evidence that an unbalanced ROS homoeostasis ( generation outweighs detoxification ) caused by the deletion of the light-responsive transcription factor BcLTF1 impairs the ability of the necrotrophic pathogen Botrytis cinerea to grow in the presence of additional oxidative stress arising during illumination or during infection of the host . | [
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] | 2014 | The Transcription Factor BcLTF1 Regulates Virulence and Light Responses in the Necrotrophic Plant Pathogen Botrytis cinerea |
While many models of biological object recognition share a common set of “broad-stroke” properties , the performance of any one model depends strongly on the choice of parameters in a particular instantiation of that model—e . g . , the number of units per layer , the size of pooling kernels , exponents in normalization operations , etc . Since the number of such parameters ( explicit or implicit ) is typically large and the computational cost of evaluating one particular parameter set is high , the space of possible model instantiations goes largely unexplored . Thus , when a model fails to approach the abilities of biological visual systems , we are left uncertain whether this failure is because we are missing a fundamental idea or because the correct “parts” have not been tuned correctly , assembled at sufficient scale , or provided with enough training . Here , we present a high-throughput approach to the exploration of such parameter sets , leveraging recent advances in stream processing hardware ( high-end NVIDIA graphic cards and the PlayStation 3's IBM Cell Processor ) . In analogy to high-throughput screening approaches in molecular biology and genetics , we explored thousands of potential network architectures and parameter instantiations , screening those that show promising object recognition performance for further analysis . We show that this approach can yield significant , reproducible gains in performance across an array of basic object recognition tasks , consistently outperforming a variety of state-of-the-art purpose-built vision systems from the literature . As the scale of available computational power continues to expand , we argue that this approach has the potential to greatly accelerate progress in both artificial vision and our understanding of the computational underpinning of biological vision .
The study of biological vision and the creation of artificial vision systems are naturally intertwined—exploration of the neuronal substrates of visual processing provides clues and inspiration for artificial systems , and artificial systems , in turn , serve as important generators of new ideas and working hypotheses . The results of this synergy have been powerful: in addition to providing important theoretical frameworks for empirical investigations ( e . g . [1]–[6] ) , biologically-inspired models are routinely among the highest-performing artificial vision systems in practical tests of object and face recognition [7]–[12] . However , while neuroscience has provided inspiration for some of the “broad-stroke” properties of the visual system , much is still unknown . Even for those qualitative properties that most biologically-inspired models share , experimental data currently provide little constraint on their key parameters . As a result , even the most faithfully biomimetic vision models necessarily represent just one of many possible realizations of a collection of computational ideas . Truly evaluating the set of biologically-inspired computational ideas is difficult , since the performance of a model depends strongly on its particular instantiation–the size of the pooling kernels , the number of units per layer , exponents in normalization operations , etc . Because the number of such parameters ( explicit or implicit ) is typically large , and the computational cost of evaluating one particular model is high , it is difficult to adequately explore the space of possible model instantiations . At the same time , there is no guarantee that even the “correct” set of principles will work when instantiated on a small scale ( in terms of dimensionality , amount of training , etc . ) . Thus , when a model fails to approach the abilities of biological visual systems , we cannot tell if this is because the ideas are wrong , or they are simply not put together correctly or on a large enough scale . As a result of these factors , the availability of computational resources plays a critical role in shaping what kinds of computational investigations are possible . Traditionally , this bound has grown according to Moore's Law [13] , however , recently , advances in highly-parallel graphics processing hardware ( such as high-end NVIDIA graphics cards , and the PlayStation 3's IBM Cell processor ) have disrupted this status quo for some classes of computational problems . In particular , this new class of modern graphics processing hardware has enabled over hundred-fold speed-ups in some of the key computations that most biologically-inspired visual models share in common . As is already occurring in other scientific fields [14] , [15] , the large quantitative performance improvements offered by this new class of hardware hold the potential to effect qualitative changes in how science is done . In the present work , we take advantage of these recent advances in graphics processing hardware [16] , [17] to more expansively explore the range of biologically-inspired models–including models of larger , more realistic scale . In analogy to high-throughput screening approaches in molecular biology and genetics , we generated and trained thousands of potential network architectures and parameter instantiations , and we “screened” the visual representations produced by these models using tasks that engage the core problem of object recognition–tolerance to image variation [10]–[12] , [18] , [19] . From these candidate models , the most promising were selected for further analysis . We show that this large-scale screening approach can yield significant , reproducible gains in performance in a variety of basic object recognitions tasks and that it holds the promise of offering insight into which computational ideas are most important for achieving this performance . Critically , such insights can then be fed back into the design of candidate models ( constraining the search space and suggesting additional model features ) , further guiding evolutionary progress . As the scale of available computational power continues to expand , high-throughput exploration of ideas in computational vision holds great potential both for accelerating progress in artificial vision , and for generating new , experimentally-testable hypotheses for the study of biological vision .
In order to generate a large number of candidate model instantiations , it is necessary to parameterize the family of all possible models that will be considered . A schematic of the overall architecture of this model family , and some of its parameters , is shown in Figure 2 . The parameterization of this family of models was designed to be as inclusive as possible–that is , the set of model operations and parameters was chosen so that the family of possible models would encompass ( as special cases ) many of the biologically-inspired models already described in the extant literature ( e . g . [1]–[4] , [7] , [9] ) . For instance , the full model includes an optional “trace” term , which allows learning behavior akin to that described in previous work ( e . g . [4] , [20]–[22] ) . While some of the variation within this family of possible models might best be described as variation in parameter tuning within a fixed model architecture , many parameters produce significant architectural changes in the model ( e . g . number of filters in each layer ) . The primary purpose of this report is to present an overarching approach to high-throughput screening . While precise choices of parameters and parameter ranges are clearly important , one could change which parameters were explored , and over what ranges , without disrupting the integrity of the overarching approach . An exhaustive description of specific model parameters used here is included in the Supplemental Text S1 , and is briefly described next . Model parameters were organized into four basic groups . The first group of parameters controlled structural properties of the system , such as the number of filters in each layer and their sizes . The second group of parameters controlled the properties of nonlinearities within each layer , such as divisive normalization coeffients and activation functions . The third group of parameters controlled how the models learned filter weights in response to video inputs during an Unsupervised Learning Phase ( this class includes parameters such as learning rate , trace factors , etc . ; see Phase 2: Unsupervised Learning below ) . A final set of parameters controlled details of how the resulting representation vectors are classified during screening and validation ( e . g . parameters of dimensionality reduction , classification parameters , etc . ) . For the purposes of the work presented here , this class of classification-related parameters was held constant for all analyses below . Briefly , the output values of the final model layer corresponding to each test example image were “unrolled” into a vector , their dimensionality was reduced using Principal Component Analysis ( PCA ) keeping as many dimensions as there were data points in the training set , and labeled examples were used to train a linear Support Vector Machine ( SVM ) . Each model consisted of three layers , with each layer consisting of a “stack” of between 16 and 256 linear filters that were applied at each position to a region of the layer below . At each stage , the output of each unit was normalized by the activity of its neighbors within a parametrically-defined radius . Unit outputs were also subject to parameterized threshold and saturation functions , and the output of a given layer could be spatially resampled before being given to the next layer as input . Filter kernels within each stack within each layer were initialized to random starting values , and learned their weights during the Unsupervised Learning Phase ( see below , see Supplemental Text S1 ) . Briefly , during this phase , under parametric control , a “winning” filter or filters were selected for each input patch , and the kernel of these filters was adapted to more closely resemble that patch , achieving a form of online non-parametric density estimation . Building upon recent findings from visual neuroscience [18] , [23] , [24] , unsupervised learning could also be biased by temporal factors , such that filters that “won” in previous frames were biased to win again ( see Supplemental Text S1 for details ) . It should be noted that while the parameter set describing the model family is large , it is not without constraints . While our model family includes a wide variety of feed-forward architectures with local intrinsic processing ( normalization ) , we have not yet included long-range feedback mechanisms ( e . g . layer to layer ) . While such mechanisms may very well turn out to be critically important for achieving the performance of natural visual systems , the intent of the current work is to present a framework to approach the problem . Other parameters and mechanisms could be added to this framework , without loss of generality . Indeed , the addition of new mechanisms and refinement of existing ones is a major area for future research ( see Discussion ) . While details of the implementation of our model class are not essential to the theoretical implications of our approach , attention must nonetheless be paid to speed in order to ensure the practical tractability , since the models used here are large ( i . e . they have many units ) , and because the space of possible models is enormous . Fortunately , the computations underlying our particular family of candidate models are intrinsically parallel at a number of levels . In addition to coarse-grain parallelism at the level of individual model instantiations ( e . g . multiple models can be evaluated at the same time ) and video frames ( e . g . feedforward processing can be done in parallel on multiple frames at once ) , there is a high degree of fine-grained parallelism in the processing of each individual frame . For instance , when a filter kernel is applied to an image , the same filter is applied to many regions of the image , and many filters are applied to each region of the image , and these operations are largely independent . The large number of arithmetic operations per region of image also results in high arithmetic intensity ( numbers of arithmetic operations per memory fetch ) , which is desirable for high-performance computing , since memory accesses are typically several orders of magnitude less efficient than arithmetic operations ( when arithmetic intensity is high , caching of fetched results leads to better utilization of a processor's compute resources ) . These considerations are especially important for making use of modern graphics hardware ( such as the Cell processor and GPUs ) where many processors are available . Highly-optimized implementations of core operations ( e . g . linear filtering , local normalization ) were created for both the IBM Cell Processor ( PlayStation 3 ) , and for NVIDIA graphics processing units ( GPUs ) using the Tesla Architecture and the CUDA programming model [25] . These implementations achieve highly significant speed-ups relative to conventional CPU-based implementations ( see Figure 1 and Supplemental Figure S1 ) . High-level “outer loop” coordination of these highly optimized operations was accomplished using the Python programming language ( e . g . using PyCUDA [26] ) , allowing for a favorable balance between ease of programming and raw speed ( see Supplemental Text S2 ) . In principle , all of the analyses presented here could have been performed using traditional computational hardware; however , the cost ( in terms of time and/or money ) of doing so with current CPU hardware is prohibitive . Figure 1 shows the relative speedup and performance/cost of each implementation ( IBM Cell on Sony's PlayStation 3 and several NVIDIA GPUs ) relative to traditional MATLAB and multi-threaded C code for the linear filtering operation ( more details such as the raw floating point performance can be found in the Supplemental Figure S1 ) . This operation is not only a key component of the candidate model family ( see below ) but it's also the most computationally demanding , reaching up to 94% of the total processing time ( for the PlayStation 3 implementation ) , depending on model parameters ( average fraction is 28% ) . The use of commodity graphics hardware affords orders-of-magnitude increases in performance . In particular , it should be noted that the data presented in this work took approximately one week to generate using our PlayStation 3-based implementation ( 222x speedup with one system ) on a cluster of 23 machines . We estimate that producing the same results at the same cost using a conventional MATLAB implementation would have taken more than two years ( see Figure S1 ) . Our approach is to sample a large number of model instantiations , using a well-chosen “screening” task to find promising architectures and parameter ranges within the model family . Our approach to this search was divided into four phases ( see Figure 3 ) : Candidate Model Generation , Unsupervised Learning , Screening , and Validation/Analysis of high-performing models .
As a first exploration of our high-throughput approach , we generated 7 , 500 model instantiations , in three groups of 2 , 500 , with each group corresponding to a different class of unsupervised learning videos ( “petri dishes”; see Methods ) . During the Screening Phase , we used the “Cars vs . Planes” object discrimination task [11] to assess the performance of each model , and the most promising five models from each set of 2 , 500 models was submitted to further analysis . The raw computation required to generate , train and screen these 7 , 500 models was completed in approximately one week , using 23 PlayStation 3 systems [41] . Results for models trained with the “Law and Order” petri dish during the Unsupervised Learning Phase are shown in Figure 6A . As expected , the population of randomly-generated models exhibited a broad distribution of performance on the screening task , ranging from chance performance ( 50% ) to better than 80% correct . Figure 6B shows the performance of the best five models drawn from the pool of 2 , 500 models in the “Law and Order” petri dish . These models consistently outperformed the V1-like model baseline ( Figure 7 ) , and this performance was roughly maintained even when the model was retrained with a different video set ( e . g . a different clip from Law and Order ) , or with a different random initialization of the filter kernel weights ( Figure 6C ) . Since these top models were selected for their high performance on the screening task , it is perhaps not surprising that they all show a high level of performance on that task . To determine whether the performance of these models generalized to other test sets , a series of Validation tests were performed . Specifically , we tested the best five models from each Unsupervised Learning petri dish on four test sets: two rendered object sets , one rendered face set , and a modified subset of the MultiPIE face recognition image set ( see Validation Phase in Methods ) . Performance across each of these validation sets is shown in Figure 7 ( black bars ) . While the exact ordering of model performance varied somewhat from validation set to validation set , the models selected during the Screening Phase performed well across the range of validation tasks . The top five models found by our high-throughput screening procedure generally outperformed state-of-the-art models from the literature ( see Methods ) across all sets , with the best model found by the high-throughput search uniformly yielding the highest performance across all validation sets . Even greater performance was achieved by a simple summing of the SVM kernels from the top five models ( red bar , Figure 7 ) . Of note , the nearest contender from the set of state-of-the-art models is another biologically-inspired model [7] , [8] . Interestingly , a large performance advantage between our high-throughput-derived models and state-of-the-art models was observed for the MultiPIE hybrid set , even though this is arguably the most different from the task used for screening , since it is composed from natural images ( photographs ) , rather than synthetic ( rendered ) ones . It should be noted that several of the state-of-the-art models , including the sparse C2 features ( “SLF” in Figure 7 ) , which was consistently the nearest competitor to our models , used filters that were individually tailored to each validation test–i . e . the representation used for “Boats vs . Planes” was optimized for that set , and was different from the representation used for the MultiPIE Hybrid set . This is in contrast to our models , which learned their filters from a completely unrelated video data set ( Law and Order ) and were screened using an unrelated task ( “Cars vs . Planes” , see Methods ) . While even better performance could no doubt be obtained by screening with a subset taken from each individual validation test , the generalizability of performance across a range of different tasks argues that our approach may be uncovering features and representations that are broadly useful . Such generality is in keeping with the models' biological inspiration , since biological visual representations must be flexible enough to represent a massive diversity of objects in order to be useful . Results for the 2 , 500 models in each of the other two “petri dishes” ( i . e . models trained with alternate video sets during unsupervised learning ) were appreciably similar , and are shown in Supplemental Figures S7 and S8 , using the same display conventions set forth in Figures 6 and 7 .
While our approach has yielded a first crop of promising biologically-inspired visual representations , it is another , larger task to understand how these models work , and why they are better than other alternatives . While such insights are beyond the scope of the present paper , our framework provides a number of promising avenues for further understanding . One obvious direction is to directly analyze the parameter values of the best models in order to understand which parameters are critical for performance . Figure 8 shows distributions of parameter values for four arbitrarily chosen parameters . While in no way conclusive , there are hints that some particular parameter values may be more important for performance than others ( for quantitative analysis of the relationship between model parameters and performance , see Supplemental Text S3 , Figures S9 and S10 ) . The speed with which large collections of models can be evaluated opens up the possibility of running large-scale experiments where given parameters are held fixed , or varied systematically . Insights derived from such experiments can then be fed back into the next round of high-throughput search , either by adjusting the parameter search space or by fundamentally adjusting the algorithm itself . Such iterative refinement is an active area of research in our group . The search procedure presented here has already uncovered promising visual representations , however , it represents just the simplest first step one might take in conducting a large-scale search . For the sake of minimizing conceptual complexity , and maximizing the diversity of models analyzed , we chose to use random , brute-force search strategy . However , a rich set of search algorithms exist for potentially increasingly the efficiency with which this search is done ( e . g . genetic algorithms [42] , simulated annealing [43] , and particle swarm techniques [44] ) . Interestingly , our brute-force search found strong models with relatively high probability , suggesting that , while these models would be hard to find by “manual” trial-and-error , they are not especially rare in the context of our high-throughput search . While better search algorithms will no doubt find better instances from the model class used here , an important future direction is to refine the parameter-ranges searched and to refine the algorithms themselves . While the model class described here is large , the class of all models that would count as “biologically-inspired” is even larger . A critical component of future work will be to adjust existing mechanisms to achieve better performance , and to add new mechanisms ( including more complex features such as long-range feedback projections ) . Importantly , the high-throughput search framework presented here provides a coherent means to find and compare models and algorithms , without being unduly led astray by weak sampling of the potential parameter space . Another area of future work is the application of high-throughput screening to new problem domains . While we have here searched for visual representations that are good for object recognition , our approach could also be applied to a variety of other related problems , such as object tracking , texture recognition , gesture recognition , feature-based stereo-matching , etc . Indeed , to the extent that natural visual representations are flexibly able to solve all of these tasks , we might likewise hope to mine artificial representations that are useful in a wide range of tasks . Finally , as the scale of available computational resources steadily increases , our approach naturally scales as well , allowing more numerous , larger , and more complex models to be examined . This will give us both the ability to generate more powerful machine vision systems , and to generate models that better match the scale of natural systems , providing more direct footing for comparison and hypothesis generation . Such scaling holds great potential to accelerate both artificial vision research , as well as our understanding of the computational underpinnings of biological vision . | One of the primary obstacles to understanding the computational underpinnings of biological vision is its sheer scale—the visual system is a massively parallel computer , comprised of billions of elements . While this scale has historically been beyond the reach of even the fastest super-computing systems , recent advances in commodity graphics processors ( such as those found in the PlayStation 3 and high-end NVIDIA graphics cards ) have made unprecedented computational resources broadly available . Here , we describe a high-throughput approach that harnesses the power of modern graphics hardware to search a vast space of large-scale , biologically inspired candidate models of the visual system . The best of these models , drawn from thousands of candidates , outperformed a variety of state-of-the-art vision systems across a range of object and face recognition tasks . We argue that these experiments point a new way forward , both in the creation of machine vision systems and in providing insights into the computational underpinnings of biological vision . | [
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] | 2009 | A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation |
Development of prefrontal and other higher-order association cortices is associated with widespread changes in the cortical transcriptome , particularly during the transitions from prenatal to postnatal development , and from early infancy to later stages of childhood and early adulthood . However , the timing and longitudinal trajectories of neuronal gene expression programs during these periods remain unclear in part because of confounding effects of concomitantly occurring shifts in neuron-to-glia ratios . Here , we used cell type–specific chromatin sorting techniques for genome-wide profiling of a histone mark associated with transcriptional regulation—H3 with trimethylated lysine 4 ( H3K4me3 ) —in neuronal chromatin from 31 subjects from the late gestational period to 80 years of age . H3K4me3 landscapes of prefrontal neurons were developmentally regulated at 1 , 157 loci , including 768 loci that were proximal to transcription start sites . Multiple algorithms consistently revealed that the overwhelming majority and perhaps all of developmentally regulated H3K4me3 peaks were on a unidirectional trajectory defined by either rapid gain or loss of histone methylation during the late prenatal period and the first year after birth , followed by similar changes but with progressively slower kinetics during early and later childhood and only minimal changes later in life . Developmentally downregulated H3K4me3 peaks in prefrontal neurons were enriched for Paired box ( Pax ) and multiple Signal Transducer and Activator of Transcription ( STAT ) motifs , which are known to promote glial differentiation . In contrast , H3K4me3 peaks subject to a progressive increase in maturing prefrontal neurons were enriched for activating protein-1 ( AP-1 ) recognition elements that are commonly associated with activity-dependent regulation of neuronal gene expression . We uncovered a developmental program governing the remodeling of neuronal histone methylation landscapes in the prefrontal cortex from the late prenatal period to early adolescence , which is linked to cis-regulatory sequences around transcription start sites .
Prolonged maturation of the human cerebral cortex , which extends into the third decade of life , is critical for proper development of executive functions , such as higher order problem solving and complex cognition [1] , [2] . Little is known about changes within the nuclei of post-mitotic neurons during this prolonged maturation period , including possible changes in epigenetic regulation of DNA and histone proteins . This lack of knowledge is remarkable given that neurogenesis and subsequent permanent exit from the cell cycle by newly generated cortical neurons takes place within the first 3–4 months of prenatal development . It is likely that dynamic changes in epigenetic regulation of neuronal gene expression extend far beyond the first trimester of pregnancy . For example , various periods of growth and differentiation in early and late childhood have been found to be followed by highly dynamic waves of pruning and remodeling of synapses and neuronal connectivity in the prefrontal cortex ( PFC ) and related areas [3] . Human postmortem brain studies have revealed robust transcriptional changes during the transition from the prenatal period to childhood and into adulthood , with broad implications for inhibitory and excitatory neurotransmission , myelination , metabolism , and various other cellular functions [4] , [5] . Thus , it is reasonable to hypothesize that the developmental milestones of cortical neurons are linked to ‘pre-programmed’ changes in neuronal gene expression . These mechanisms are likely to be associated with the process of neural differentiation because genes controlling the process of cell division show a general decline in expression during prenatal development and infancy , while genes associated with synaptic functions and neurotransmission show an increased expression during the same period [5] . Likewise , adolescence and early adulthood ( 15–25 years ) are accompanied by a transient increase in transcripts for energy metabolism as well as protein and lipid synthesis , in conjunction with a further decline in genes implicated in neurodevelopment and plasticity [6] . Each of these developmentally defined transcriptional changes simultaneously involves hundreds to thousands of genes distributed throughout the entire genome , raising the question of whether there is a coordinated unfolding of neuronal gene expression as the PFC matures . Quantifying developmentally regulated changes in neuronal transcriptomes and epigenomes , however , is confounded by the rapidly occurring and dramatic shifts in cell composition during maturation of the human cerebral cortex . For example , there is a post-natal increase of several fold in the number of astrocytes generated by cell division from local precursors [7] . Although the prenatal cortical plate is overwhelmingly comprised of post-mitotic neurons , neuron-to-glia ratios in the mature primate cerebral cortex are in the range of 0 . 6 ( human ) to 1 . 7 ( macaque ) [8] . Developmental changes in the cortical transcriptome may largely reflect this underlying change in cell composition and , when explored in tissue homogenate , could mask cell type-specific regulation . To circumvent the potential confound of shifting cell compositions , we have developed the technique of sorting and separating neuronal and non-neuronal nuclei of human post-mortem PFC specimens for subsequent preparation of mono-nucleosomes ( nucleosomes are the fundamental units of chromatin , composed of a histone core—the H2A/H2B/H3/H4 octamer—and 146 bp DNA wrapped around it ) for genome-wide histone methylation mapping [9] , [10] . For this work , we focused on histone H3 trimethylated at lysine 4 ( H3K4me3 ) , a histone modification sharply regulated around transcription start sites ( TSS ) and other regulatory sequences [11] , [12] . The H3K4me3 mark broadly correlates with RNA polymerase II occupancy at sites of active gene expression [13] , and is largely non-overlapping with promoter-associated DNA cytosine methylation and other repressive marks [14] , which provides an additional component of transcriptional regulation [15] , [16] . In an earlier study , we presented high-resolution maps of H3K4me3 in neuronal and non-neuronal nuclei collected from the PFC of 11 individuals ranging from 0 . 5 to 69 years [9] . We identified thousands of genes that showed highly enriched H3K4me3 levels in neuronal but not non-neuronal PFC chromatin . We also identified hundreds of genes ( many function in developmental processes ) with decreased H3K4me3 during the first year after birth . Here , building upon our earlier results , we present H3K4me3 maps of 36 human PFC specimens collected from the late prenatal period to 81 years of age , with an emphasis on comparing the H3K4me3 landscape in prenatal PFC with that of older ages . We present evidence that 1157 loci within the neuronal epigenome of the PFC are subject to ongoing remodeling of the local H3K4me3 landscape . This remodeling is defined by a unidirectional trajectory of progressive gain or loss of H3K4me3 , with steeper changes in the late prenatal period until the first year after birth , and slower changes thereafter extending into the adolescence and adult period . The genes near these age-dependent H3K4me3 peaks showed similar temporal expression patterns , i . e . , genes near regions with increasing H3K4me3 also show increasing expression as a function of age and vice versa . Furthermore , in neuronal chromatin , genes near developmentally up-regulated H3K4me3 peaks were enriched in various functional categories related to mature neurons . In contrast , non-neuronal chromatin was defined by a progressive H3K4me3 increase at transcription start sites ( TSS ) associated with myelination and other glial-related functions . Our results draw a connection between cis-regulators of chromatin structure and function as well as the molecular mechanisms governing the maturation of the human prefrontal cortex , starting with prenatal development and continuing into adolescence and beyond .
We analyzed 36 datasets of H3K4me3 chromatin immunoprecipitation followed by sequencing ( ChIP-seq ) of sorted nuclei from the human prefrontal cortex . Thirty-one of these datasets were for neuronal NeuN+ nuclei and as control , the other five datasets were for non-neuronal ( NeuN– ) nuclei . The neuronal ( NeuN+ ) samples covered an age range from 34 gestational weeks ( gw ) to 81 years ( yr ) , including three prenatal samples ( C1–C3 ) and three infant samples ( <1 yr old; C4–C6 ) . The NeuN– samples spanned an age range of 40 gw to 69 yr , including one prenatal sample and one infant sample . Among these datasets , 16 neuronal samples ( including the 3 prenatal ) and two NeuN– samples were newly generated and sequenced for this study , and the remaining 18 datasets were taken from our previous publications [9] , [10] . We also used one input dataset based on the NeuN+ cells of C28 [10] that had been previously generated , but for which no antibody was added though processed in the same way as the above ChIP-seq samples ( Table 1 ) . Samples were sequenced using Illumina GAII and a total of 390 million ( M ) 36-bpreads were obtained , where 318 M reads mapped to unique locations in the human genome ( Table 1 ) . The data are available at http://zlab . umassmed . edu/zlab/publications/ShulhaPLOSGen2013 . html . On average , 50% uniquely mapped reads in ChIP-seq samples were in promoters ( within 2 kb of the TSS ) of RefSeq genes ( http://www . ncbi . nlm . nih . gov/RefSeq/; 35 , 519 transcripts in total ) , and 4% reads of the input sample were in promoters , consistent with the prior knowledge that H3K4me3 is strongly enriched around TSS [17] . We used the MACS algorithm [18] to detect the genomic regions significantly enriched in H3K4me3 ( called peaks ) in each ChIP-seq sample compared with the input sample . We then constructed a pool of 47 , 281 neuronal peaks detected in at least one neuronal sample , and another pool of 42 , 693 peaks detected in at least one NeuN– sample . We performed further analysis on these two pools of peaks . For each peak , we counted the total number of reads in each sample , normalized by the total number of reads in that sample that mapped to within 2 kb of all RefSeq TSSs . To investigate promoter H3K4me3 occupancy profiles in neurons , we constructed a vector for each neuronal sample with 21 , 084 elements ( the total number of unique RefSeq TSSs excluding chromosome Y ) , each element being the normalized number of reads in a RefSeq promoter . Then we computed the Pearson correlation coefficient for all pairs of samples and presented the results as a heatmap ( Figure 1A ) . All 31 samples are highly similar , with correlations above 0 . 9 . The most distinct samples are the three prenatal samples C1–C3 , followed by the three infants C4–C6 , as shown by the darker shades of the first six columns in Figure 1A . Thus , from the prenatal period to less than 1 year of age to older childhood and adulthood , there is an age-dependent progression of the genome-wide H3K4me3 profile in annotated promoter regions . These findings are consistent with observations we previously reported [9] , [10] using much smaller cohorts and a narrower age range . To determine how the overall population of H3K4me3 peaks ( regardless of their association with any annotated TSS ) differ across the neuronal samples , we performed principal components analysis on all H3K4me3 peaks as previously described [10] . Principal components analysis is a mathematical method that reduces the dimensionality of data , in our case , a 31 by 31 matrix for which each element is the number of H3K4me3 peaks found in one sample contrasted with another sample . The analysis identifies directions , called principal components , which maximize the variation in the data . Samples are then plotted along the principal components to reveal clusters . Figure 1B shows the 31 neuronal samples plotted against the first two principal components , which in combination account for 95% of the variation in our data . Strikingly , the first principal component separates the prenatal samples ( in green ) away from all other samples , and the second principal component separates the prenatal samples and the infant samples ( in blue ) from the remaining samples ( in red ) . The third and fourth principal components do not further separate the samples older than 1 yr into subgroups ( data not shown ) . In order to investigate whether the clear separation is due to the larger number of samples in the >1 yr group , we also performed PCA with 12 samples , 3 samples in each age range: gestational , 0–1 years , 3–14 years , and 15–25 years . The results looked very similar , namely the first two principal components could clearly distinguish the gestational and infant groups from the remaining samples ( Figure S1 ) . Thus , neurons in the prefrontal cortex undergo substantial changes in their H3K4me3 landscapes during the transition from the last 2 months of gestation to postnatal life , to a somewhat lesser degree during the first year after birth , and comparatively minor changes for the remainder of the lifespan . With the exception of the three prenatal samples who were all females , all other age groups comprised males and females . However , neither sample-to-sample Pearson correlation analysis of RefSeq promoters nor PCA analysis ( Figure 1A–1C ) showed any evidence for a role of gender in the age-dependent remodeling of neuronal and non-neuronal H3K4me3 landscapes in the PFC . Notably , a recent study in a cohort of 269 postmortem specimens collected across the lifespan reported developmentally regulated changes in gene expression and a consistent molecular architecture of the PFC across the human race [5] . Our histone methylation studies support this conclusion because each of our age groups of prenatal , infant , older children and adults included subjects from at least two races ( information on race was available for 14 brains: 8 Caucasian and 6 African American ) . To identify H3K4me3 peaks subject to age-dependent regulation , we systematically compared the three prenatal samples with the 25 samples older than 1 year of age and found 742 ( 415 ) peaks with at least two-fold higher or lower levels in the prenatal samples ( p-value<0 . 05; see Methods for details ) . We called these ‘down’ and ‘up’ peaks , reflecting the change of H3K4me3 upon aging ( Table S1 ) . We computed the average H3K4me3 levels for the up and down peaks in each sample . We observed a progressive increase ( for the up peaks; Figure 2A ) and decrease ( for the down peaks; Figure 2B ) as a function of age that continued at least for the first 10–20 years of life . Note that each of the three infant samples ( <1 yr; blue dots in Figure 2A , 2B ) was positioned in between the prenatal samples and the older child/adult samples although these three samples were not used to define the up and down peaks . Figure S2 uses boxplots to illustrate the distributions of H3K4me3 levels for the peaks within the ‘up’ and ‘down’ groups , respectively . It is clear that the variation among genes within each group is smaller than the difference between the two groups . From these results , we conclude that the PFC neuronal population undergoes a steady and continuous developmental remodeling of H3K4me3 peaks , starting during prenatal life and extending into the first childhood years . These up and down peaks were within 2 kb of the TSSs of 247 and 508 RefSeq genes respectively , and using the DAVID tool , we asked whether these two groups of genes were enriched in any gene ontology ( GO ) categories [19] . The 508 genes near the down peaks were enriched in 117 GO processes with a false discovery rate ( FDR ) of less than 5% ( Table S2 ) . Some of the most significant GO categories found in the down peaks included ‘anatomical structure’ , ‘organ’ , ‘systems’ , ‘nervous system’ development ( FDR ranges from 1 . 1e-9 to 1 . 6e-6 ) , and many other processes of critical importance of early embryonic development ( Figure 3A , Table S2 ) . A representative example is the transcription factor SOX11 ( SRY-related HMG-box ) which among other functions , regulates neuronal differentiation during early development [20] ( Figure 3B ) . The 247 genes near the up-peaks were enriched in 28 GO categories with FDR<5% ( Table S2 ) , and the most significant categories included neuron projection ( FDR = 2 . 2e-5 ) , synapse ( FDR = 6 . 1e-4 ) , and axon ( FDR = 2 . 5e-3 , Figure 3A ) . Thus , H3K4me3 levels near neuronal genes related to the function of mature neurons , including synaptic transmission and connectivity are up-regulated during the transition from pre- to post-natal life . A representative example is synaptopodin ( SYNPO ) , encoding an actin-associated protein enriched in dendritic spines and postsynaptic densities [21] , [22] ( Figure 3C ) . The genes near the down peaks and genes near the up peaks were similarly enriched in 11 GO categories , mostly involving cell-cell communication and signal transduction function ( colored blue in Table S2 ) ; however , distinct sets of genes led to the enrichment of each shared category . Some of these genes contain multiple promoters that overlapped with multiple age-dependent H3K4me3 peaks . The first group contained eleven genes whose alternative promoters overlapped H3K4me3 peaks with opposing developmental changes ( one up peak and one down peak ) . Some of these genes are essential for normal brain development , including SHANK2 , encoding a synaptic scaffolding protein that when mutated is responsible for mono- or oligogenic causes of autism and other neurodevelopmental disease [23] , [24]; LFC/ARHGEF2 , encoding a Rho-specific guanine nucleotide exchange factor important for neurogenesis and dendrite spine morphology [25] , [26]; and PLEKHG5 , a Pleckstrin homology domain-containing gene that is responsible for an autosomal recessive form of motor neuron disease [27] ( Table S3 ) . There were also five genes each with two promoters subject to a developmental increase in H3K4me3 , including the calcium sensor CABP1 , which regulates voltage-gated Ca2+ channels and synaptic short term plasticity [28] , and SEPT9 , a member of the cytoskeleton-related septin family , which is responsible for hereditary neuropathic syndromes such as amyotrophic neuralgia [29] ( Table S3 ) . Furthermore , the third set of nine genes harbored multiple promoters that were subject to a coordinated developmental downregulation of H3K4me3 , including the trans-membrane protein NOTCH3 , which is essential for preventing premature death of young differentiating neurons [30] and responsible for some forms of vascular disease in the mature brain [31] , the Wingless-Int ( WNT ) protein , WNT7B , which shows distinct regional expression patterns during human brain development , including progenitor cells and sub-layers of the developing cortical plate [32] , and DPYSL3 , which encodes for a dihydropyrimidinase-like protein important for neuronal differentiation and regulated by NMDA glutamate receptors [33] ( Table S3 ) . Earlier studies reported that the process of differentiation from pluripotent stem cells was associated with dynamic changes in the shape of the H3K4me3 profile , due to spreading into neighboring nucleosomes in differentiated tissue , thereby resulting in broader peaks [34] , [35] . To find out whether maturation of PFC is associated with similar changes in neuronal H3K4me3 landscapes , we determined for each subject numbers and proportion of peaks across six different length categories ( from 500 bp to 5 kb ) . However , no age-dependent trends emerged , because peaks <1 kb in length comprised the large majority of the total pool of peaks , while longer peaks ( >4 kb ) contributed little ( 3% or less ) to each sample ( Table S4 ) . This result , however , is unsurprising , because the marker used in our study for nuclei sorting ( NeuN ) specifically labels postmitotic neurons and excludes stem cells . Furthermore , in good agreement with the above studies [34] , [35] reporting that genes specifically expressed in differentiated tissues show wider histone methylation peaks , the broadest peaks ( >4 kb ) in neurons in the present study also showed a highly significant enrichment of Gene Ontology categories defining neurons . These categories include nervous system development , neurogenesis , neuron differentiation , axonogenesis , neuron projection , synapse , and postsynaptic density among others ( Table S5 ) . The up and down H3K4me3 peaks described above show roughly monotonic changes of H3K4me4 levels as a function of age ( largely driven by rapidly changing H3K4me3 levels during the transitions from the prenatal period to infancy and from infancy to early childhood ) . To test whether our dataset harbors genomic loci with a different , or non-monotonic age profile , we performed k-means clustering , an unsupervised learning technique to separate all H3K4me3 peaks into five clusters ( k = 5 ) so that peaks within each cluster have similar age profiles . In Figure S3 , peaks were shown to be increased in Cluster 1 and decreased in Clusters 2 and 3 with age ( with Cluster 2 showing a greater decrease than Cluster 3 , but both clusters are defined by the largest shifts occurring within the first few years of life ) . Clusters 4 and 5 showed age-invariant H3K4me3 profiles . We also performed clustering using larger k values but the results were qualitatively the same , with additional clusters showing similar patterns . Therefore , consistent with the hypothesis-driven method described above for identifying the up and down peaks , unbiased k-means clustering again resulted in the two main patterns of age-dependent H3K4me3 changes , with some loci going monotonically up and other loci going monotonically down upon aging . Thus we concluded that the overwhelming majority of the developmentally regulated H3K4me3 changes in neuronal chromatin of the prefrontal cortex are unidirectional and monotonic , with the changes during the successive transitions from the prenatal period to early infancy and from infancy to later childhood ages being much more pronounced than any shifts that may occur later in life . We also performed GO enrichment analysis on the genes near Cluster 1 and 2 peaks , and the results are shown in Table S6 . Because the peaks in Clusters 1 and 2 match the up and down peaks defined in the previous section , the results of GO enrichment are highly consistent . Specifically , the genes near Cluster 1 peaks are enriched in developmental processes , with ‘anatomical structure’ , ‘system’ , ‘organ’ development , ‘neurogenesis’ ( and many other GO categories in Table S2 ) again among the most significantly enriched , and the genes near Cluster 2 peaks are enriched for many neuron-related categories , including ‘axon’ , ‘neuron projection’ and others . H3K4me3 level is a good indicator of gene expression , and we wanted to further test whether the genes near the age-dependent H3K4me3 peaks show similar age-dependent expression patterns . Colantuoni et al . performed microarray experiments to assay the genome-wide transcription levels in the prefrontal cortex of 269 subjects spanning the majority of the human lifespan , including 38 prenatal samples ( 14–20 gws ) and 18 infants ( <1 yr ) [5] . Using their data , we plotted , in Figure 4 , the average expression levels of the 202 and 419 genes that are near our up and down H3K4me3 peaks which also assayed by Colantuoni et al . as a function of age . The expression signal as defined by Colantuoni et al . is the log2 density ratio of a particular sample over the reference sample produced by pooling all test samples [5] . It is clear that the genes that are near the up peaks are more highly expressed in older samples ( Figure 4A ) and the genes that are near the down peaks are expressed at lower levels in older samples ( Figure 4B ) . Similarly , we plotted the average expression patterns for the genes near each of the H3K4me3 ChIP-seq peaks in the five clusters determined by k-means clustering ( Figure S4 ) . Indeed , the average expression profile for genes in each cluster follows the same trend as the average H3K4me3 profile: genes near Cluster 1 peaks increased expression upon aging , genes near Cluster 2 and 3 peaks decreased expression upon aging with Cluster 2 showing a greater decrease than Cluster 3 , and genes near Cluster 4 and 5 peaks show invariant expression across the age span . Note that the prenatal gene expression data in Colantuoni et al . were for the 14–20 gw period [5] , earlier than the stage of our H3K4me3 data ( 34–40 gw ) . They reported four groups of genes that showed significant changes in expression across age: genes that increased expression in both prenatal and infant stages ( up-up genes ) , genes that decreased expression in both stages ( down-down genes ) , genes that increased expression during the prenatal stage and then decreased expression during the infant stage ( up-down genes ) , and genes that decreased expression during the prenatal stage and then increased expression during the infant stage ( down-up genes ) . We plotted the H3K4me3 profiles for these four groups of genes ( Figure S5 ) . Indeed , both the up-up and down-up genes show a monotonic increasing H3K4me3 pattern ( the left two panels of Figure S5 ) , and both the down-down and up-down genes show a monotonic decreasing H3K4me3 pattern ( the right two panels of Figure S5 ) However , the changes in these patterns are not as pronounced as our up and down H3K4me3 peaks ( Figure 2 and Figure S3 ) . Detailed examination of the expression patterns in Figure 2 of Colantuoni et al . indicates that the reversal of the expression patterns for the down-up and up-down genes occurs at around 19 gw . Because our H3K4me3 data are for later time points ( 34–40 gw ) , our two H3K4me3 patterns are consistent with the four expression patterns by Colantuoni et al . In the human cerebral cortex , the genome-wide distribution of H3K4me3 is largely anti-correlated with methyl-cytosine densities [14] and many genes in the genome show a robust DNA methylation increase in CpG dense sequences , including those residing in proximal promoters [36] , [37] . Using the datasets from [37] , prenatal to postnatal changes in methyl-CpG densities in cortical tissue homogenates were available for 394 ( 744 ) CpGs that were in the promoters of genes subject to developmental H3K4me3 up- ( down- ) regulation in our study on PFC neurons ( Table S7 and Table S8 ) . Much higher percentages of the CpGs ( 59 . 9% and 60 . 1% respectively ) were in the promoters of the genes near the up or down H3K4me3 peaks than the remaining 26 , 446 CpGs ( 42 . 0% ) which show significantly different ( FDR<0 . 05 ) methylation levels between prenatal samples and samples older than 1 yr . Moreover , 15 . 7% of CpGs associated with an increase in H3K4me3 during development showed a significant decrease in methyl-CpG levels ( FDR<0 . 05 ) , while only 6 . 2% genes associated with declining H3K4me3 levels also showed a significant decrease in methyl-CpG levels ( Table S9 ) . Thus , there is a significant anti-correlation between the age-dependent change of H3K4me3 levels and the age-dependent change in DNA methylation levels ( p-value = 7 . 0e-8; hypergeometric test ) . These results are robust regardless of the cutoff for calling a DNA methylation as significantly different between prenatal samples and samples older than 1 yr ( the lower half of Table S9 shows the results for the cutoff of FDR<1e-10 ) . Examples of genes with significant and opposing changes in H3K4me3 and DNA methylation include ARC , NR4A1 and other transcription factors with critical roles in synaptic plasticity , learning and memory [38] , [39] , ANK3 , a psychiatric susceptibility genes encoding a synaptic scaffolding proteins [40] , and ligand-gated ion channels including the GABAA receptor subunit GABRD , which has been linked to mood and seizure disorders [41] , [42] . We also tested whether the sequence motifs of any transcription factors ( TF ) are enriched in the up or down peaks using the Clover algorithm [43] with all TF binding motifs in the TRANSFAC database [44] . We used two types of background sequences , one was generated by shuffling the sequences of the up or down peaks while preserving dinucleotide frequencies , and the other was the H3K4me3 peaks that did not vary their intensities across age ( peaks in Clusters 4 and 5 ) . Table S10 lists the motifs that are significantly enriched in the up or down peaks according to both types of backgrounds ( FDR<0 . 05 ) . Among them , the motif ofAP-1 is significantly enriched among the up peaks . AP-1 ( heterodimer of c-Jun and c-Fos ) is a classical early response transcription factor and master regulator of the axonal response in neurons . It also functions as a negative regulator of myelination in Schwann cells ( SCs ) and is strongly reactivated in SCs upon axonal injury [45] . The Pax motif is highly enriched in the down peaks . Among the Pax family of transcription factors , Pax2 and Pax8 specify GABAergic cell fate [46] , Pax6 is the master regulator of the visual system [47] , and Pax8 is important for hindbrain neurons [48] . The motif of Rp58 is also enriched in the down peaks , and Rp58 is recently shown to be essential for the patterning of the cerebellum and for the development glutamatergic and GABAergic neurons [49] . We also noticed significant enrichment for Signal Transducer and Activator of Transcription ( STAT ) motifs in the down H3K4me3 peaks from PFC neurons , which agrees well with the finding that STAT-dependent signaling pathways primarily promote astrocytic and other non-neuronal differentiation in the developing cortical plate [50]–[52] . To further explore which , if any , of the above mentioned developmental H3K4me3 changes are specific to PFC neurons , we explored H3K4me3 landscapes in non-neuronal ( NeuN– ) nuclei obtained from one prenatal , one infant and three adult specimens ( Table 1 ) . Similar to the findings in the 31 neuronal samples described in Figure 1A , computation of Pearson correlation coefficient for all five pairs of NeuN– samples revealed higher correlations between samples of similar age ( Figure 1C ) . The smaller number of NeuN– samples prevented us from using the same approach for identifying up and down H3K4me3 peaks as for NeuN+ samples , i . e . , directly comparing prenatal samples with samples older than 1 yr , due to the lack of statistical power . Instead , we used the k-means clustering algorithm to identify 2224 TSS-proximal peaks ( within 2 Kb of a TSS ) subject to decline upon aging , and 785 TSS-proximal peaks that increased upon aging . The genes whose TSSs are proximal to the NeuN– H3K4me3-down peaks partially overlapped with the genes that are proximal to the NeuN+ H3K4me3-down peaks ( 1889;241;392 for the NeuN– unique , shared , and NeuN+ unique genes; chi-square test p-value<2 . 2e-16 ) . Yet , these two sets of H3K4me3-down genes fall into similar GO categories ( compare Tables S6 and S11 ) . We determined the GO categories that each set of genes was enriched in and observed a strong correlation between the enrichment scores of the two sets of GO categories ( Pearson correlation coefficient R = 0 . 70; p-value<1e-6 ) . A GO category was included in the calculation if the FDR was less than 0 . 85 for either gene set and the enrichment score was defined as the –log10 ( FDR ) . Both gene sets were highly enriched in five GO categories related to development ( multicellular organismal development , systems development , developmental process , anatomical structure development , and nervous system development ) . These results indicate that similar functional pathways are pruned epigenetically between neuronal and non-neuronal cells even though the genes that belong to these pathways differ between the two cell types . Similar analysis for H3K4me3-up genes revealed a different picture . The genes whose TSSs are proximal to the NeuN– H3K4me3-up peaks overlapped partially with the genes that are proximal to the NeuN+ H3K4me3-up peaks ( 675;83;615 for the NeuN– unique , shared , and NeuN+ unique genes; chi-square test p-value<2 . 2e-16 ) . However , these two sets of genes were enriched in mostly non-overlapping GO categories ( compare Tables S6 and S11 ) . The Pearson correlation coefficient between the enrichment scores of the two sets of GO categories was –0 . 11 ( p-value = 0 . 24 ) . The most enriched GO categories for NeuN– H3K4me3-up genes included compact myelin ( FDR = 0 . 0029 ) and myelin sheath ( FDR = 9 . 0e-5 ) , consistent with one important function of glial cells , which make up the vast majority of NeuN– cells , and these GO categories were not enriched in any of the other three groups of genes ( NeuN+ H3K4me3-up , NeuN+ H3K4me3-down , or NeuN– H3K4me3-down ) . On the other hand , the NeuN+ H3K4me3-up genes were more enriched in axon , neuron projection , and signal transduction ( FDR = 0 . 0013–0 . 0052 ) than the NeuN– H3K4me3-up genes . We conclude that while both neuronal and nonneuronal cells undergo a major remarking of TSS-associated histone methylation during PFC development and maturation , different areas of the genome are affected , depending on cell type .
The present study provides detailed analyses into the developmental regulation of a transcriptional mark , H3K4me3 , in neuronal and non-neuronal chromatin during the extended course of PFC maturation . There were 1157 loci , including 768 TSS-proximal and many other regulatory sequences that showed evidence for the developmental remodeling of H3K4me3 . Strikingly , these peaks showed similar kinetics as defined by an unidirectional course with the largest decline or increase occurring within the first 1–2 years of postnatal life , followed by a gradual slowing of age-related changes that apparently continue at least until early adolescence or even beyond . We show that these developmentally regulated H3K4me3 peaks at transcription start sites are associated with age-related changes in the expression of the corresponding RNA . We further showed that a subset of regulatory motifs , including Pax and AP-1 transcription factor recognition sites , are overrepresented among the developmental regulated peaks showing a decrease ( Pax , Stat ) , or increase ( AP-1 ) respectively , during the course of PFC maturation . Finally , the developmental remodeling of H3K4me3 landscapes in PFC is cell type specific . Collectively , our results draw a connection between cis-regulators of chromatin structure and function and the molecular mechanisms governing maturation of the human prefrontal cortex . The findings presented here , when taken in conjunction with studies exploring developmental changes in DNA methylation [36] , [37] and gene expression across the lifespan of the human PFC [5] , paint a picture in which immature PFC , during the weeks and months preceding and following birth , undergoes a larger scale reprogramming of transcriptomes and promoter-associated epigenetic regulators , including promoter-bound DNA and histone methylation . This reprogramming involves hundreds of TSS that define cellular functions that are either characteristics of differentiated neurons and glia ( e . g . synaptic transmission , myelination ) or functions related to earlier stages of development ( e . g . neurogenesis , nervous system development ) that decline as the PFC matures . By charting a developmental map for the neuronal and non-neuronal constituents of the PFC separately , the present study and our earlier studies [9] , [10] largely extends the previous work on tissue homogenate that allows for limited data interpretation due to age-related shifts in neuron-to-glia ratios and other confounding factors . The present study further emphasizes the prenatal stage and shows that developmental remodeling of TSS-associated histone methylation in PFC neurons rapidly changes in prenatal and infant stages but continues at a slower pace deep into the second decade of life . Our study faces several important limitations . Given that no technique with single cell resolution exists to map histone methylation levels at specific loci , our assays by design only inform about H3K4me3 profiles averaged across millions of cell type-specific nuclei that are required for the ChIP-seq assays . Thus , the cell population-based developmental kinetics of H3K4me3 with the simple and unidirectional exponential curves , as presented here , leave open whether individual PFC neurons would show a more dynamic interplay between H3K4 methylation and demethylation . Furthermore , our prenatal specimens were limited to the mid- and late stages of the third trimester , and it remains possible that brains of an earlier prenatal age could show a more complex or multi-layered regulation of the H3K4me3 marks , compared to what is reported here . For example , there are four groups of genes reported by Colantuoni et al . , who compared RNA expression at earlier stages of gestation ( 14–20th week of pregnancy ) with those of infant and older brains [5] . Nonetheless , these four groups of genes could be recognized by their age-dependent H3K4me3 profiles in our study ( Figure S5 ) , reaffirming the view that these gene expression networks are co-regulated both on the level of the transcriptome and the epigenome . The present study suggests that the developmental remodeling of TSS-associated histone methylation in PFC neurons in the weeks and months before and after birth continues at a slower pace deep into the second decade of life . Presently , nothing is known about the molecular ‘clocks’ or ‘pacemakers’ that orchestrate such steadfast remodeling of TSS-associated H3K4me3 during the first 10–20 years of life and , to the best of our knowledge , these phenomena await further investigation in laboratory animals . Indeed , a recent H3K4me3 ChIP-seq study in whole tissue of four macaque PFC specimens found evidence for histone methylation changes during the course of maturation and aging [53] . Insights into these mechanisms bear great promise for a better understanding of normal development and the pathophysiology of schizophrenia and autism and other neurodevelopmental disorders associated with DNA and histone methylation changes in the PFC [10] , [54]–[58] . To this end , it is interesting that AP-1 transcription factor motifs are enriched in H3K4me3 peaks that are upregulated during the course of PF maturation . Of note , antipsychotic drug treatment administered over the course of 2–3 weeks resulted in lasting increases of AP-1 protein and transcript in rat rostromedial cortex ( considered the functional homologue to primate PFC ) [59] . Because AP-1 in PFC and other brain regions is highly regulated by neuronal activity [60] , up-regulation of AP-1 expression and AP-1 mediated transcriptional activity , either during normal development or in the context of psychopharmacological treatments , could serve as one of the molecular drivers for the regulation of H3K4 trimethylation at AP-1 bound promoters and other active TSS in the mature PFC .
All postmortem tissue work was done in compliance with the Institutional Review Board regulations of the University of Massachusetts Medical School and the Mount Sinai School of Medicine . Freshly frozen ( never fixed ) tissues from the rostral prefrontal cortex of subjects ranging in age from the 34th week of gestation to 81 years , was provided by four independent brain banks ( Table 1 ) . Tissue aliquots ( 200–500 mg/subject ) were extracted in hypotonic lysis buffer , purified by ultracentrifugation and resuspended in 1x PBS , immunotagged with anti-neuronal nucleus ( anti-NeuN , Millipore ) antibody and sorted into NeuN+ and NeuN– fractions using a FACSVantage SE flow cytometer , as described [61] , [62] . Mononucleosomal preparations from at least 1×106 sorted nuclei were prepared for subsequent chromatin immunoprecipitation with anti-H3K4me3 antibody ( Upstate/Millipore ) , and ChIP-seq libraries prepared from the immunoprecipitated DNA by blunt-ending , A-tailing and ligation to adaptors ( Genomic Adaptor Oligo Mix , Illumina ) and PCR amplification and sequencing on an Illumina Genome Analyzer II platform , as described [9] , [63] . All of our sequencing libraries contained single-end 36-bp reads and we mapped them to the human genome with Bowtie ( version 0 . 11 . 3 ) [64] . We allowed up to one mismatch and mapped all sequences to the gender appropriate genome HG19 . Reads that mapped to multiple locations were discarded . Unique mappers constitute 66–90% of all reads . Detailed statistics is presented in Table 1 . As previously reported , H3K4me3 levels at promoters did not show correlations with postmortem interval and tissue pH [9] , [65] . Critical ChIP-seq parameters , including the proportion of uniquely mappable sequence tags , and the percentage of uniquely mappable sequences at gene promoters were very similar between samples from the four brain banks , without significant differences ( Table 1 ) ( %uniquely mappable , % uniquely mappable at promoters: Harvard Brain Tissue Resource Center ( HBTRC ) : 82 . 3 ± 4 . 9 , 45 . 3 ± 19 . 3; Maryland Psychiatric Research Center ( MPRC ) 84 . 4 ± 2 . 9; 51 . 9 ± 11 . 4; University of California at Irvine/Davis ( UCI/UCD ) 78 . 7 ± 8 . 7; 60 . 3 ± 8 . 7; University of Maryland Brain and Tissue Bank for Developmental Disorders ( UM-BTB ) , 80 . 7 ± 4 . 5; 58 . 2 ± 10 . 5 ) . To construct the heatmap in Figure 1 , we calculated Pearson correlation coefficients for each pair of samples . We took the genomic coordinates of all TSSs ( except chrY ) from RefSeq and expanded them in both directions by 2 kb . If some regions overlapped with each other , we merged them together . For each of these 21 , 084 non-overlapping regions , we computed the total number of mapped H3K4me3 ChIP-seq reads and normalized by size of the region . The resulting read densities were used to compute Pearson correlation coefficients . The MACS software ( version 1 . 3 . 5 ) [18] was used to identify statistically enriched H3K4me3 regions ( called H3K4me3 peaks or peaks in short ) . We contrasted each sample against the input sample using bw = 230; tsize = 36 and default values for the remaining parameters in MACS . Principal Component Analysis was performed on a matrix that contains peaks unique to each sample . Each sample was compared against every other sample using the MACS software with parameters mentioned above . The H3K4me3 peaks thus obtained were further filtered using the following criteria: ( 1 ) MACS p-value must be less than 1e-20 , ( 2 ) read density ratio between the two samples must be greater than 4 , and ( 3 ) normalized read density must be greater than 0 . 005 . To search for age-dependent H3K4me3 peaks , all peaks from NeuN+ samples were combined and overlapping peaks merged , resulting in 47 , 281 peaks . The 742 down peaks ( ≥1 k bp ) were defined as: ( 1 ) the average read density in prenatal samples must be greater than or equal to 0 . 01 , ( 2 ) the ratio of average read density of prenatal samples and the 25 samples older than 1 year must be greater than or equal to 2 , and ( 3 ) the t-test p-value for comparing the three prenatal samples with the 25 samples older than 1 year must be less than or equal to 0 . 05 . The reciprocal criteria were used for defining the 415 up peaks . We performed k-means clustering on the age profiles of all H3K4me3 peaks . We limited our calculations to regions that were ≥1 k bp and had an average H3K4me3 read density ≥0 . 01 in prenatal samples or in the 25 samples older than 1 year . The small number of peaks in chrY was excluded from this analysis . This resulted in 14 , 708 regions that were further normalized by the strongest signal ( across all samples ) for each region . We then used the “k-means” procedure from the R software with 5 clusters . We averaged the H3K4me3 levels across the regions in each cluster and plotted the resulting average H3K4me3 profile for each cluster in Figure S3 . We used the same approach to perform k-means clustering for NeuN– samples . We used the DAVID web-server for detecting enriched Gene Ontology categories . For each set of H3K4me3 peaks , genes whose TSSs were within 2 k bp of an H3K4me3 peak are included in the analysis . We used false discovery rate ( FDR ) to quantify statistical significance , because it accounts for multiple testing correction . To compare our H3K4me3 data with DNA methylation [37] we downloaded the data from the author's website ( http://braincloud . jhmi . edu/downloads . htm ) . The dataset contains methylation level for CGs in a set of gene promoters . CGs in every gene that matched our proximal H3K4me3 peaks were analyzed for age-dependent changes ( Tables S7 and S8 ) . We performed a t-test for every gene , comparing all prenatal samples with all samples older than 1 year , and computed false discovery rate ( FDR ) after multiple testing correction . Cases with significant DNA methylation changes ( using two cutoffs , FDR<0 . 05 or FDR<1e-10 ) were used to establish relationship with H3K4me3 ( Table S9 ) . | Prolonged maturation of the human cerebral cortex , which extends into the third decade of life , is critical for proper development of executive functions such as higher-order problem-solving and complex cognition . Little is known about changes of post-mitotic neurons during this prolonged maturation period , including changes in epigenetic regulation , and more broadly , in genome organization and function . Such knowledge is critical for a deeper understanding of human development , cognitive abilities , and psychiatric diseases . Here , we identify 1 , 157 genomic loci in neuronal cells from the prefrontal cortex that show developmental changes in a chromatin mark , histone H3 trimethylated at lysine 4 ( H3K4me3 ) , which has been associated with regulation of gene expression . Interestingly , the overwhelming majority of these developmentally regulated H3K4me3 peaks were defined by rapid gain or loss of histone methylation during the late prenatal period and the first year after birth , followed by slower changes during early and later childhood and minimal changes thereafter . The genomic sequences showing these dynamic changes in H3K4me3 were enriched with distinct transcription factor motifs . Our findings suggest that there is highly regulated , pre-programmed remodeling of neuronal histone methylation landscapes in the human brain that begins before birth and continues into adolescence . | [
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] | 2013 | Coordinated Cell Type–Specific Epigenetic Remodeling in Prefrontal Cortex Begins before Birth and Continues into Early Adulthood |
Many bacteria mediate important life-style decisions by varying levels of the second messenger c-di-GMP . Behavioral transitions result from the coordination of complex cellular processes such as motility , surface adherence or the production of virulence factors and toxins . While the regulatory mechanisms responsible for these processes have been elucidated in some cases , the global pleiotropic effects of c-di-GMP are poorly understood , primarily because c-di-GMP networks are inherently complex in most bacteria . Moreover , the quantitative relationships between cellular c-di-GMP levels and c-di-GMP dependent phenotypes are largely unknown . Here , we dissect the c-di-GMP network of Caulobacter crescentus to establish a global and quantitative view of c-di-GMP dependent processes in this organism . A genetic approach that gradually reduced the number of diguanylate cyclases identified novel c-di-GMP dependent cellular processes and unraveled c-di-GMP as an essential component of C . crescentus cell polarity and its bimodal life cycle . By varying cellular c-di-GMP concentrations , we determined dose response curves for individual c-di-GMP-dependent processes . Relating these values to c-di-GMP levels modeled for single cells progressing through the cell cycle sets a quantitative frame for the successive activation of c-di-GMP dependent processes during the C . crescentus life cycle . By reconstructing a simplified c-di-GMP network in a strain devoid of c-di-GMP we defined the minimal requirements for the oscillation of c-di-GMP levels during the C . crescentus cell cycle . Finally , we show that although all c-di-GMP dependent cellular processes were qualitatively restored by artificially adjusting c-di-GMP levels with a heterologous diguanylate cyclase , much higher levels of the second messenger are required under these conditions as compared to the contribution of homologous c-di-GMP metabolizing enzymes . These experiments suggest that a common c-di-GMP pool cannot fully explain spatiotemporal regulation by c-di-GMP in C . crescentus and that individual enzymes preferentially regulate specific phenotypes during the cell cycle .
Cyclic di-GMP is a ubiquitous second messenger that serves as key regulator of bacterial life-style decisions . While low intracellular concentrations of c-di-GMP promote a planktonic , single-cell life-style , where cells are generally motile and express virulence determinants , high levels of c-di-GMP lead to biofilm formation and persistence [1] , [2] . Intracellular c-di-GMP levels are controlled by the antagonistic diguanylate cyclases ( DGCs ) and phosphodiesterases ( PDEs ) that either synthesize c-di-GMP from GTP or degrade it . These catalytic activities reside in GGDEF ( DGC ) and EAL or HD-GYP ( PDE ) domains , respectively . Typically , multiple proteins that contain GGDEF , EAL , and/or HD-GYP domains are encoded in the genome of a single bacterial species . In the most extreme cases , over 100 proteins are potentially involved in c-di-GMP metabolism , emphasizing the importance of c-di-GMP for bacterial signaling and adaptation [3] . This is also reflected by an ever-increasing number of established c-di-GMP receptors that regulate a wide range of cellular processes on the transcriptional , translational , or post-translational level [2] , [4] . This includes the synthesis of virulence factors and toxins , the production of adhesins and biofilm matrix components , the regulation of different forms of cell motility , as well as cell cycle progression [2] , [4] . Receptor affinities were reported from the low nM to the mid µM range ( e . g . see [2] , [5]–[11] ) . The physiological significance of such large differences in affinity is unclear . In Caulobacter crescentus , the c-di-GMP mediated motile-sessile switch is integrated into a bimodal reproductive cycle , providing a simple and accessible cellular tool to study the c-di-GMP dynamics in time and space . C . crescentus divides asymmetrically to produce two daughters with distinct behavior and replication potential , a motile swarmer cell and a sessile stalked cell . The swarmer cell , equipped with a single polar flagellum and polar pili , remains in a motile but replication inert state for an extended period termed the G1-phase . The replication block is suspended concurrent with the transition of the swarmer cell into a stalked cell , during which the flagellar motor and the pili are lost and replaced by a stalk and an exopolysaccharide adhesin , the holdfast . At the same time , the density of the cells changes from a state of low to high buoyancy . Concurrently with these morphological changes , stalked cells proceed into S-phase to double their chromosomes and – after re-synthesizing a flagellum at the pole opposite the stalk - undergo an asymmetric cell division ( G2-phase ) . Thus , Caulobacter cells continuously oscillate between different developmental and reproductive stages , offering an exemplary model system to dissect the molecular and cellular basis for the motile-sessile switch in bacteria and its coordination with cellular reproduction . This transition bears behavioral changes that are highly relevant for growth and persistence of many environmental and pathogenic bacteria . For example surface colonization and biofilm formation are key features of chronic infections of numerous human pathogens [12] . Just how exactly this behavioral change is staged and adjusted to the environment is not fully understood . Several studies implicated that c-di-GMP is an important regulatory component of the C . crescentus developmental and cell cycle program [11] , [13]–[17] . Different processes of pole development during the swarmer-to-stalked transition require c-di-GMP , including flagellar ejection , stalk elongation and holdfast biogenesis [13]–[17] . In addition , c-di-GMP interacts with the machinery that regulates the G1-S transition [11] . The C . crescentus genome encodes a total of 14 GGDEF/EAL domain proteins that are classified in three groups , GGDEF domain only , EAL domain only , and GGDEF-EAL composite proteins ( Figure S1 ) . The best-studied member of this group of proteins is PleD ( CC2462 ) , a DGC that is required for efficient pole remodeling during the motile-sessile transition . PleD is inactive in swarmer cells and is activated by phosphorylation during the swarmer-to-stalked cell differentiation [14] , [18] . Intriguingly , activation of PleD is coupled to its subcellular sequestration to the differentiating pole , suggesting that PleD activates some nearby downstream effectors involved in pole remodeling [14] , [19] . DgcB ( CC1850 ) is an additional DGC involved in C . crescentus holdfast biogenesis and surface attachment during the swarmer-to-stalked cell transition . In contrast to PleD , DgcB is not controlled by cell cycle-dependent phosphorylation , but instead is antagonized in the swarmer cell by the PDE PdeA ( CC3396 ) . PdeA itself is only present in swarmer cells , where it counteracts DgcB and helps to keep the c-di-GMP levels low thereby licensing cell motility [15] . Specific proteolysis of PdeA during the cell cycle ‘releases’ DgcB activity to contribute to the sessile life style of the stalked cell [15] . DgcB also sequesters to the cell pole , again emphasizing a possible spatial coupling of controlled c-di-GMP production and the activation of downstream target processes [15] . Finally , DgcA ( CC3285 ) was shown to possess DGC activity in vitro and in vivo but its physiological role is unknown [6] . At least two members of the GGDEF/EAL protein family , PopA ( CC1842 ) and TipF ( CC0710 ) , are enzymatically inactive and have adopted novel roles [11] , [16] . PopA is c-di-GMP specific effector proteins that binds c-di-GMP through its GGDEF domain and , in response , sequesters to the incipient stalked cell pole where it helps to recruit the replication initiation inhibitor CtrA to deliver it to the polar protease ClpXP [11] . The specific removal of CtrA then licenses cells for replication initiation ( G1-S ) . The EAL domain protein TipF localizes to the pole opposite the stalk , where it contributes to the proper placement of the motor organelle in the polarized predivisional cell [16] . The proposed role of c-di-GMP in C . crescentus cell fate determination is consistent with the observed bimodal distribution of c-di-GMP during the cell cycle [18] , [20] . Measurements of c-di-GMP indicated that motile swarmer cells and sessile stalked cells exhibit low and high levels of the signaling compound , respectively . Accordingly , the characteristic upshift of c-di-GMP during the SW-to-ST transition and the drop of c-di-GMP during birth of a new swarmer progeny are critical determinants of the differential developmental and replicative programs . However , this model raises several questions that need to be addressed . First , does c-di-GMP control additional cellular processes ? Second , what are the molecular and cellular details of their execution in time and space ? Third , which DGCs/PDEs are involved in the formation of c-di-GMP gradients during the C . crescentus cell cycle ? Fourth , what is the minimal set of enzymes required to maintain c-di-GMP fluctuations , which in turn mediate oscillatory cell fate determination ? To address the above questions this study takes advantage of the moderate complexity of the c-di-GMP signaling network in C . crescentus . To generate a strain that is free of c-di-GMP , we have deleted all components that are potentially involved in the synthesis and degradation of the second messenger . We show that a c-di-GMP free mutant ( cdG0 ) shows remarkable developmental and reproductive defects and looses morphological hallmarks of cell polarity . We then use this strain to re-construct the c-di-GMP signaling network , to functionally characterize the role of individual DGCs and PDEs and to generate a c-di-GMP dose response curve for individual c-di-GMP dependent processes using a heterologous dgc expression system . Our results indicate that different c-di-GMP dependent processes have distinct activation thresholds in vivo and provide strong evidence for a spatially structured mode of signaling .
Next , we carried out a careful in-depth analysis of the cdG0 strain to define the role of c-di-GMP in C . crescentus development more precisely . Transmission electron microscopy revealed that the c-di-GMP free strain lacked flagella , offering an explanation for its non-motile phenotype ( Figure 3A ) . In agreement with this , specific flagellar proteins are not synthesized in this background . Levels of proteins encoded by class III and class IV genes of the flagellar hierarchy [24] were either not present ( FlgH ) or strongly reduced ( flagellins ) . In contrast , products of class II flagellar genes are present at normal concentrations ( Figure 3C ) . Likewise , transcription of class II genes is largely unaltered in the cdG0 strain , while transcription of class III and class IV genes is severely reduced ( Table 1 ) . Rapid and irreversible C . crescentus surface attachment depends on polar type IV pili and the presence of a polar adhesive holdfast exopolysaccharide [25] , [26] . The latter can be visualized with fluorescent wheat germ agglutinin [27] . While C . crescentus wild type showed bright fluorescent staining at the stalked cell pole , no staining was observed for the cdG0 strain ( Figures 3B ) . To detect the presence of pili , we employed the pilus specific phage φCbK [28] . Serial dilutions of φCbK form plaques on lawns of wild-type C . crescentus . In contrast , the sensitivity of the cdG0 strain is reduced to levels of a mutant lacking the major pilin subunit ( Figure 3D ) . When C . crescentus cells were briefly exposed to the phage , fixed , and investigated with transmission electron microscopy , phage particles could readily be found at the pole of wild-type cells , where they decorated the polar pili . In the mutant lacking c-di-GMP , no phage particles were detected anywhere on the surface ( Figure 3E ) . Together , these results indicate that the assembly of both adhesive organelles , pili and holdfast , requires c-di-GMP . While analyzing the cdG0 strain we noticed that the different cell types could no longer be separated based on their different densities . Density gradient centrifugation allows separating low-density stalked and predivisional cells from high-density swarmer cells in the wild type . In contrast , all cells of the cdG0 mutant accumulated at the high-density position ( Figure 3F ) , indicating that cell type specific density differences are dependent on the second messenger . Marks et al . [23] showed that differential cell density depends on a mobile genetic element ( MGE ) that is located on the chromosome and is linked to mucoidy and phage φCR30 susceptibility . A mutant lacking this mobile genetic element behaved like the cdG0 strain with respect to cell density ( Figure 3F ) , phage sensitivity ( Figure 3G ) , and mucoidy on sucrose containing agar plates ( data not shown ) . Together this indicated that c-di-GMP regulates differential cell density in C . crescentus and that this process is linked to genes located on a mobile genetic element . Microscopic images of the cdG0 strain also revealed characteristic morphology changes ( Figures 3A , B , 4A ) . Cells lacked stalks and were often elongated with division septa frequently forming close to one end of the cell ( Figure 3A ) . This suggested that c-di-GMP is important for morphological processes that are associated with proper re-direction of cell wall growth during the cell cycle . We have recently exposed replication initiation as another c-di-GMP dependent cell cycle process . This process involves the GGDEF protein PopA ( Figure S1 ) , which , upon binding to c-di-GMP dynamically localizes to the old cell pole to deliver the replication initiation inhibitor CtrA to the polar protease ClpXP [11] . PopA also localizes to the new cell pole in a c-di-GMP independent manner [11] . As shown in Figure 3H , PopA localization to the stalked cell pole is unaffected in a mutant lacking the first four DGCs , but then gradually decreases with deletions of additional DGC genes . This emphasizes the importance of c-di-GMP for C . crescentus cell cycle progression and reiterates the redundant nature of DGCs for most of the c-di-GMP dependent processes . Altogether , these data strongly imply that c-di-GMP is a critical regulatory determinant of C . crescentus cell polarity and cell fate determination , and that all processes involved in C . crescentus pole morphogenesis are regulated by the second messenger . As indicated above , c-di-GMP is required for multiple developmental processes that need to be timed appropriately during the cell cycle . This raised the questions if these processes are mediated by cell cycle dependent changes of the c-di-GMP concentration , and how they respond to altered cellular levels of c-di-GMP . To address these questions , strains were constructed that allow the controlled expression of a heterologous DGC , YdeH from E . coli . For this , ydeH was expressed from the IPTG inducible lac promoter in single copy on the chromosome , on the low copy number plasmid pRK2 [29] , or the medium copy number plasmid pBBR [30] ( Figure S4 ) . The combination of an inducible promoter and different copy numbers allowed fine-tuning of ydeH expression ( Figure S5A ) at constant levels during the C . crescentus cell cycle ( Figure S5B ) . YdeH production was homogenous as expression differences at the single cell level were quite low and no sign for subcellular compartmentalization was detected ( Figure S5C ) . Determination of the total c-di-GMP concentration [31] then allowed estimating the average intracellular c-di-GMP concentration at different levels of ydeH induction . For this , we determined the average cell volume ( Figure S6A ) from precise measurements of cell length ( Figure S6B ) and width ( Figure S6C ) , as well as the relation between optical density and colony forming units ( CFU ) ( Figure S6D ) . Using different ydeH expression constructs in the cdG0 strain and different inducer concentrations it was possible to vary the cellular c-di-GMP content from zero to approximately 60-fold of the average wild-type concentration , which was estimated to be about 130 nM ( Figure S2 ) . To determine in vivo activation thresholds for specific c-di-GMP-dependent cellular processes , we next asked at which internal c-di-GMP levels individual processes are restored in the cdG0 strain . This includes cell morphology , φCbK and φCR30 phage sensitivity , cell density , motility and surface attachment . Interestingly , while most processes were restored to wild-type behavior at intermediate c-di-GMP levels , they showed distinct behavior at very low and very high c-di-GMP concentrations . This is illustrated for cell morphology in Figure 4A , S7A and Table 2 . In the absence of c-di-GMP , cells are elongated , lack stalks and their characteristic curvature , and have misplaced division septa . At increasing second messenger concentrations , cells shorten and increase curvature until they are morphologically indistinguishable from the wild type . Upon further increase of the c-di-GMP concentration cells become even more curved and stalks and cell bodies continuously elongate . These morphological changes , and all other investigated phenotypes , are not influenced by IPTG , the inducer of ydeH expression ( Figure S8A–F ) . Also , despite of this strong effect on cell morphology , cell growth was not affected by changing c-di-GMP levels ( Figure S9 ) . A similar distribution was observed for the biogenesis of polar pili . While cells without c-di-GMP were completely resistant against the pili-specific phage φCbK , the lowest possible induction of YdeH restored phage sensitivity to wild-type levels ( Figure 4B , S7B and Table 2 ) . Changes in phage sensitivity occur at c-di-GMP concentrations where cell morphology is clearly still different from that of the wild type , arguing that the two processes differ with respect to c-di-GMP regulation . At high c-di-GMP levels phage sensitivity drops again with plaques becoming more turbid . Under these conditions , 10–100 times higher phage titers were required to form a visible plaque in the bacterial lawn . When challenging the cdG0 strain with phage φCR30 that uses the S-layer protein of C . crescentus as receptor , cells are hypersensitive to the phage . This is illustrated by clear and larger plaques and a 10-fold lower titer required for plaque formation as compared to the wild-type situation ( Figure 4C ) . Similar to φCbK infections , small amounts of c-di-GMP restored normal phage sensitivity , while high c-di-GMP levels led to complete φCR30 resistance ( Figure 4C , S7C and Table 2 ) . Together , this suggested that c-di-GMP is critical for pili biogenesis but not for S-layer formation and that at high c-di-GMP levels another envelope structure conceals the surface exposed phage receptors . A possible candidate for such a structure is a capsule exopolysaccharide that could also be responsible for the cell type specific density difference of C . crescentus [23] , [32] . Cell density behavior exactly parallels the φCR30 sensitivity pattern . In the absence of c-di-GMP , all cells accumulated in the high-density fraction , while the wild-type density distribution and synchronizability was restored at intermediate c-di-GMP levels , with high c-di-GMP levels forcing all cells into a low-density state ( Figure 4D , S7D and Table 2 ) . Expression of ydeH restored the density switch in a cell type specific manner as judged by the isolation of a pure population of swarmer cells , which when released into fresh media proceeded through the cell cycle synchronously , as indicated by the characteristic patterns of protein fluctuations ( Figure 4E ) . It is worth emphasizing that cell cycle-dependent CtrA degradation , a process known to be regulated by c-di-GMP , is also fully functional in these cells ( Figure 4E ) [11] , [15] . Together these findings indicate that c-di-GMP is required for the temporal and spatial regulation of developmental transitions and cell fate determination in C . crescentus , without affecting the overall growth rate under the conditions tested . Because surface attachment and motility behavior can easily be quantified , we used these two c-di-GMP dependent processes to determine dose response curves . As shown in Figure 5 , both curves follow an inverted U-shape , albeit with different peak positions . Without c-di-GMP cells fail to assemble a flagellum and hence are non-motile ( Figure 5A ) . Flagellar biogenesis and motility was restored at relatively low intracellular c-di-GMP concentrations similar to the overall c-di-GMP levels in wild type . When c-di-GMP levels were increased further , motility again dropped until cells were completely non-motile on motility agar plates ( Figure 5A ) and also in liquid culture ( data not shown ) . However , flagellar proteins are synthesized normally and flagellar biogenesis was not impaired ( Figure S8G , H ) , arguing that motors are likely to be paralyzed under these conditions . Similarly , the attachment defect of the cdG0 strain was restored with increasing ydeH expression strength . While motility was restored at relatively low levels of the second messenger , reconstituting surface attachment to wild-type levels required significantly higher c-di-GMP concentrations ( Figure 5B ) . At low c-di-GMP concentrations surface attachment correlated well with the amount of holdfast produced under these conditions ( Figure 5C ) indicating that c-di-GMP dependent adhesin formation is a main factor driving surface colonization . As c-di-GMP levels reached their highest values , surface attachment dropped significantly , despite of increased holdfast production ( Figure 5B , C ) . Several points about the data in Figure 5 are worth highlighting . First , flagellar biogenesis , motor interference and holdfast production are induced by c-di-GMP at distinct cellular concentrations , arguing for distinct in vivo activation thresholds of these processes . Second , despite of covering a wide range of c-di-GMP levels , the cdG0::ydeH strain failed to reach the same motility levels as observed for the wild type or for defined C . crescentus DGC mutants ( e . g . ΔdgcB ) . Finally , when motility and attachment were scored in defined DGC and PDE mutants [15] large phenotypic changes were observed within a relatively narrow concentration window of the second messenger ( Figure 5A , B ) . The observation that in the cdG0::ydeH strain the respective regulatory transitions occur in a much wider c-di-GMP concentration range argues that homologous components involved in cell cycle dependent c-di-GMP metabolism must be subject to specific regulatory fine-tuning that cannot be mimicked by the constitutive expression of a heterologous DGC . At very high c-di-GMP concentrations , we observed interference with all processes that we investigated . The observation that reduced surface attachment , phage sensitivity and differential cell density were all triggered at concentrations above 1 µM c-di-GMP raised the question if these changes result from the same underlying c-di-GMP-dependent process . φCR30 sensitivity and differential cell density were recently linked to a mobile genetic element ( MGE ) that contains candidate genes involved in the biosynthesis of capsule exopolysaccharides [23] , [32] . If genes located on the mobile genetic element mediate the observed changes at high c-di-GMP levels , one would expect these phenotypes to become c-di-GMP insensitive in a mutant lacking the MGE . To test this , ydeH was overexpressed in C . crescentus wild type or in a strain carrying a deletion of the MGE region . Cells lacking the MGE failed to switch to the low-density state and were hypersensitive to phage φCR30 ( Figures 6A , B , S10A , B ) [23] . In the wild type , high c-di-GMP concentrations led to migration of all cells to the low-density band in a density-gradient centrifugation . In contrast , the same high c-di-GMP levels did not affect the phenotype of cells lacking the MGE . Similarly , high c-di-GMP concentrations increased the resistance against phage φCR30 , while a strain lacking the MGE remained hypersensitive . In contrast , the presence of the mobile genetic element has no influence on c-di-GMP mediated φCbK sensitivity , motility and attachment ( Figure 6C–E , S10C–E ) . Together , this indicated that c-di-GMP affects components encoded by the MGE to modify cell density and φCR30 sensitivity . Furthermore , this pathway seems to be distinct from the regulatory mechanisms that govern a reduction of φCbK sensitivity , motility and attachment at high c-di-GMP levels . If c-di-GMP is homogenously distributed throughout the cytoplasm , changes in global c-di-GMP content should directly mediate changes in bacterial behavior . But how does the intracellular c-di-GMP concentration during the cell cycle compare to the measured dose-response curves ? We determined the c-di-GMP concentration throughout the cell cycle using synchronized populations of cells ( Figure S11A ) . Because our synchronization technique harvests all high-density swarmer cells irrespective of their exact age after division and because the cell cycle length of individual cells varies , experimentally determined c-di-GMP concentrations represent population averages rather than exact values corresponding to a distinct cell cycle stage . Knowing the population composition at each cell cycle time point ( Figure S11B ) would allow inferring the exact single cell concentration of c-di-GMP at any given time of the cell cycle . To obtain the population composition , we developed a mathematical model that describes the growth of a C . crescentus population and the relative age of its constituents ( Figure S11C , D ) . This model was parameterized with measurements of cell cycle length variation and relative swarmer and stalked cell cycle lengths combined with growth curves during the respective experiment ( see Materials and Methods ) . Numerical simulations yielded the population composition ( Figure S11B ) , from which we calculated the internal c-di-GMP content ( Figure 7 ) . We find that the c-di-GMP concentration peaks during the swarmer-to-stalked cell transition , falls slowly to a lower level in the stalked cell and is lowest in swarmer cells just after division . Qualitatively , this pattern is in line with the observed phenotypes in the cdG0::ydeH strain , where holdfast formation occurs at very high levels , while processes taking place in the predivisional cell ( e . g . pili and flagellum assembly ) require intermediate levels , and motility being promoted by low c-di-GMP levels . However , c-di-GMP concentrations related to specific phenotypes are much lower in synchronized wild type cells as compared to the dose response curves in the cdG0::ydeH strain ( Figure 5 ) . For example even peak concentrations measured during the swarmer-to-stalked transition are too low to explain the behavior of populations of the cdG0::ydeH strain . This demonstrates that although a heterologous DGC can qualitatively restore all c-di-GMP dependent processes in a mutant strain lacking all homologous enzyme systems , significantly higher cellular c-di-GMP concentrations are needed in such a context . This again argues for a specific regulatory arrangement of c-di-GMP signaling components that permits the proper fine-tuning of processes driving differentiation and growth in C . crescentus . To determine the minimal set of components required for c-di-GMP cell cycle fluctuations we made use of the observed c-di-GMP dependent density switch at the swarmer-to-stalked cell transition . While in the cdG0 strain all cells accumulate at the high density band , intermediate level expression of ydeH restored differential cell density , synchronizability and normal cell cycle progression in this background ( Figure 4D , E ) . This strongly argues that c-di-GMP oscillation is at least partially restored under these conditions . Since YdeH is constitutively expressed and is unlikely subject to cell cycle regulation , the production of c-di-GMP in this strain should be constant . Normal cell cycle oscillation of this strain could thus be explained by varying sensitivities of downstream effectors during the cell cycle or by cell cycle-dependent breakdown of c-di-GMP by PDEs . To distinguish between these possibilities , we deleted all genes encoding potential PDEs ( cc1086 , cc0091 , CC3148 and pdeA ) in the cdG0 strain , thereby generating a strain lacking all enzymes involved in c-di-GMP metabolism ( rcdG0 ) . This strain was phenotypically indistinguishable from the cdG0 strain ( data not shown ) . In particular , all cells accumulated in the high-density fraction during density gradient centrifugation . However , when introducing a single copy of ydeH into the chromosome of this strain , we observed that low levels of YdeH expression already lead to a complete shift of cells to the low-density fraction ( Figure 8A ) . This excluded the possibility of varying effector sensitivities mediating cell type specific density and indicated that one or several PDEs are responsible for cell-cycle dependent c-di-GMP fluctuations in the cdG0::ydeH strain . When cc1086 , cc0091 or pdeA were re-introduced into their original chromosomal loci of the rcdG0::ydeH strain , only pdeA was able to restore differential cell density ( Figure 8A ) . Light microscopy analysis confirmed that the high-density band of this strain contains a pure population of swarmer cells ( data not shown ) . Moreover , when these cells were followed over time , the characteristic fluctuations of several indicator proteins confirmed their synchronous progression through the cell cycle ( Figure 8C ) . We have recently shown that PdeA is a swarmer cell-specific PDE [15] . This argues that a constant source of c-di-GMP ( originating from YdeH ) and a swarmer cell specific PDE is sufficient to establish c-di-GMP oscillations leading to proper cell type-specific cell density . We next asked if a DGC , which is subject to cell cycle regulation , was able to create the same fluctuations and restore cell cycle timing . For this , we re-introduced the pleD gene into the rcdG0 strain . PleD is a cell-cycle controlled DGC that is inactive in swarmer cells [14] , [19] . Similar to the constitutive YdeH , PleD derived c-di-GMP led to an accumulation of low-density cells in this strain background ( Figure 8B ) . Limiting the production of c-di-GMP to specific times during the cell cycle alone is therefore not sufficient to establish the cell type specific program . However , when we also introduced a heterologous PDE , PA5295 from Pseudomonas aeruginosa [33] , the cell type specific programs were restored ( Figure 8C ) . By itself , or in combination with the constitutive DGC YdeH , PA5295 is unable to restore the bimodal program of C . crescentus ( Figure 8B ) . Together , these results indicated that the correct cell-type specific control of either a DGC or a PDE is sufficient to maintain the bimodal developmental program of C . crescentus .
Numerous studies have demonstrated that c-di-GMP negatively interferes with bacterial motility at different levels ( e . g . [10] , [15] , [35]–[41] ) . This is also the case in C . crescentus where c-di-GMP obstructs motility in at least two different ways , by interfering with motor function in the swarmer cell [13] , [15] , [42] and by triggering flagellar ejection during the swarmer-to-stalked cell transition [13] , [42] . One of the surprising findings of this study is that c-di-GMP also plays a critical role in flagellar assembly . This is in accordance with c-di-GMP being important for flagellar assembly in Salmonella [34] and argues that a specific c-di-GMP window defines both the lower and upper limits of motility in bacteria . It remains to be shown if such a bipartite role of c-di-GMP in flagellar-based motility regulation is a general phenomenon in bacteria . In C . crescentus , c-di-GMP mediated control may coordinate the assembly and function of the flagellar motor in time and space during the cell cycle . Dividing Caulobacter cells need to continuously re-orient their flagellar polarity . While the flagellum is removed from the incipient stalked pole during cell differentiation , it is reassembled in the predivisional cell at the pole opposite the stalk . At this stage of the cell cycle c-di-GMP levels are likely to be high enough to initiate the assembly of the structure and might also be high enough to obstruct its rotation until c-di-GMP levels drop after cell division releases a functional swarmer cell . This might assist the assembly process or facilitate a tight coordination between cell division and swimming behavior of the swarmer progeny . The latter is supported by the observation that mutants with lower levels of c-di-GMP show premature swimming behavior as predivisional cells [13] , [14] . Since most of the c-di-GMP dependent processes in C . crescentus show inverted U-shaped dose-response curves , counteracting c-di-GMP dependent mechanisms - one activating and the other inhibitory - might represent a more general phenomenon . Our study demonstrates that c-di-GMP not only orchestrates C . crescentus pole development , but also strongly contributes to cell morphology . While cells lacking c-di-GMP are straight and elongated , their characteristic length and crescentoid curvature is restored at intermediated levels of c-di-GMP and strongly increased at very high c-di-GMP concentrations . C . crescentus cell curvature depends on the intermediate filament Crescentin [43] , while division septum placement depends on FtsZ and its organizer MipZ [44] . Although c-di-GMP has not been linked to elements of the cytoskeleton so far , it remains to be shown at which stage c-di-GMP interferes with these processes . Likewise , c-di-GMP is required for stalk elongation , a process that resembles cell elongation and that originated as an adaptation to surface growth in oligotrophic environments [13] , [45] , [46] . In the absence of c-di-GMP , stalks are not detectable in complex media . Under phosphate-limited conditions , stalk growth is partially rescued ( data not shown ) , arguing that c-di-GMP regulation represents only one of several regulatory inputs into this process . Finally , we find evidence that c-di-GMP influences the cell type specific expression of a capsule-like exopolysaccharide . The cdG0 strain is hypersensitive to phage φCR30 , which docks to the surface exposed S-layer [23] , [47] . This indicated that under these conditions a protective layer on the outside of the cell is missing leading to increased exposure of phage receptors . Likewise , differential cell density , a feature that is used to synchronize C . crescentus populations by density gradient centrifugation , is abolished in the cdG0 strain and restored upon ydeH expression with a dose-response curve indistinguishable from the corresponding dose –response curves of φCR30 sensitivity . While in the absence of c-di-GMP all cells show swarmer cell-like high density and φCR30 hypersensitivity , at high c-di-GMP concentrations all cells show a stalked cell-like low density and φCR30 resistance . In addition , cells without c-di-GMP form rough colonies on sugar-containing media in comparison to the mucoid wild type ( data not shown ) . These phenotypes , but none of the other c-di-GMP dependent processes , hinge on a mobile genetic element ( Figure 6A , B ) that harbors several predicted glycosyl-transferases and other genes involved in carbohydrate metabolism and polymerization [23] . Together this argued that the two phenotypes are linked , and suggested that both processes are contingent on a surface exposed , cell type specific capsule-like exopolysaccharide . While such a structure has been described in C . crescentus [32] , its cellular and biochemical properties remain to be characterized . Our studies predict that c-di-GMP regulation limits the expression of such a capsular structure to the sessile cell types , while it keeps the motile swarmer cell free of this extra surface layer , thereby lending this cell type its high density and phage sensitivity . Efficient surface attachment of C . crescentus requires the concerted action of a rotating flagellum , type IV pili , and an adhesive holdfast [25] , [26] , [48] . We show here that the formation of all of these organelles depends on c-di-GMP . However , while flagellum and pili biogenesis are restored in the cdG0::ydeH strain at very low c-di-GMP levels , holdfast production and attachment only kick in at moderately high c-di-GMP concentrations . In principle , differential regulation of these processes can be explained by concentration differences of c-di-GMP in either time or space [1] , [20] . A temporally oscillating global pool of c-di-GMP could elicit a graded response through the serial activation of processes with different activation thresholds for c-di-GMP . For example , the order of assembly of flagellum , pili and holdfast during the cell cycle could directly follow from temporal fluctuations of c-di-GMP levels , which are very low in the swarmer cell , peak at the swarmer-to-stalked cell transition and later drop to an intermediate level in the predivisional cell . In this model , c-di-GMP signaling specificity could be achieved through differences in receptor affinities as recently indicated with engineered receptor affinity mutants in Salmonella enterica serovar Typhimurium [5] . While these c-di-GMP binders could govern single specific phenotypes , we cannot exclude that one pathway regulates several traits . In the C . crescentus swarmer cell c-di-GMP levels are below 100 nM ( Figure 7 and [20] ) . Consistently , we find that swarmer cell specific c-di-GMP regulated processes like motor function , pili expression and high cell buoyancy operate at low c-di-GMP levels . In contrast , stalked cell specific processes like holdfast and stalk biogenesis are not induced at such low concentrations , but coincide with a peak of c-di-GMP of about 275 nM during the motile-sessile transition . When considering the c-di-GMP dose response curves determined with a strain expressing a single heterologous DGC , these concentrations would not be sufficient to induce the motile-sessile switch . In a mixed culture of the cdG0::ydeH strain , the average c-di-GMP concentration might be a poor predictor of behavioral changes because the temporal c-di-GMP profile and thus the effective concentrations triggering specific phenotypes are unknown . Alternatively , it is possible that we underestimate the c-di-GMP concentration in synchronized cells . C-di-GMP measurements were not carried out in single cells but in populations of synchronized cells . Cells synchronized via density gradient centrifugation retain a certain degree of heterogeneity because of varying cell cycle length and different internal age of the harvested swarmer cells . Although our mathematical model adjusts for this heterogeneity , it makes assumptions regarding cell cycle length ( normal distribution ) and internal age at harvesting ( uniform distribution ) that might simplify reality . Also , because of limited temporal resolution ( 20-minute intervals ) we might underestimate the effective maximum of the c-di-GMP concentration peak . Single cell based c-di-GMP measurements indicated that the second messenger reaches levels above 500 nM in the stalked cell [18] , [20] . Interestingly , these FRET-based experiments failed to observe the c-di-GMP peak during the swarmer-to-stalked cell transition . This discrepancy is either due to the fact that FRET fails to accurately measure moderate c-di-GMP changes or because LC/MS based measurements reported previously [18] and in this study underestimate c-di-GMP levels specifically in the stalked and predivisional cell . The observation that different levels of c-di-GMP are required to initiate distinct processes in the stalked and predivisional cell ( e . g . holdfast synthesis vs . flagellum assembly ) argues that the c-di-GMP metabolism in the sessile cell types is more complex than anticipated by FRET measurements . The observed reduction of the second messenger concentration during the stalked cell phase also indicated that one or several phosphodiesterases are active during this stage of the cell cycle . The observation that a cc0091 deletion has no effect on motility but strongly interferes with surface attachment is in agreement with a role for this PDE in the sessile stalked cell . C-di-GMP thresholds required to restore specific processes in the cdG0 strain largely correlate with the concentrations measured in different cell types in vivo . Although this is consistent with a global pool model where c-di-GMP mediates differential cell behavior primarily through different effector affinities , other observations indicated that c-di-GMP control goes beyond mere temporal variation . The global pool model predicts that c-di-GMP dependent processes that coincide during the cell cycle have similar activation thresholds and shared upstream components . This is not the case for developmental and cell cycle processes that coincide during the swarmer-to-stalked cell transition . Mutants lacking the DGCs PleD and DgcB fail to assemble a holdfast , while the coincident activation of the PopA pathway leading to replication initiation is not affected under these conditions [14] , [15] . This strongly argues that these processes , although running in parallel , must have different activation thresholds for c-di-GMP . This , in turn implies that they are fueled by specific enzyme combinations . In such a scenario , distinct pathways might be individually regulated within spatially separated c-di-GMP environments , thereby providing more complex possibilities for regulatory fine-tuning . Compartmentalized pools could e . g . originate from a distinct arrangement of DGCs and/or PDEs , as observed for PleD , DgcB and PdeA in C . crescentus [14] , [15] or in other bacteria [49] , [50] . Alternatively , it could result from macromolecular complexes of DGCs and/or PDEs with their downstream effectors or from a similar arrangement of bifunctional trigger enzymes [51] . Although the finding that a heterologous source for c-di-GMP can complement all defects of the cdG0 strain could be interpreted in favor of a global pool model , phenotypic behavior and overall levels of c-di-GMP do not match the behavior of individual dgc and pde mutants . For example , although both motility and surface attachment are strongly affected in a dgcB mutant , the overall c-di-GMP levels show only a minor reduction as compared to the wild type . Similarly , compared to the overall changes in c-di-GMP content , mutants lacking PleD or PdeA show disproportionally strong behavioral changes . Finally , YdeH mediated rescue of c-di-GMP dependent processes in a cdG0 often remained below wild-type level and occurred at c-di-GMP concentrations higher than those observed in wild type . This suggested that specific c-di-GMP effectors might be more accessible for homologous DGCs and/or PDEs in the original signaling context , while in the cdG0::ydeH strain higher concentrations are required to ‘invade’ these signaling units and to compensate for the missing functions . Taken together , our findings highlight the central importance of c-di-GMP in bacterial development and life-style decisions . They further indicate that both temporal gradients of a global c-di-GMP pool and insulated c-di-GMP micro-pools facilitate the complex coordination of development and cell cycle progression in C . crescentus .
The bacterial strains and plasmids used in this study are listed in Table S1 . E . coli strains were grown in Luria Broth ( LB ) medium at 37°C , supplemented with the appropriate antibiotic ( solid/liquid media; in µg/ml: kanamycin 50/30 , gentamycin 20/15 , oxytetracycline 12 . 5/12 . 5 ) . C . crescentus strains were grown in peptone yeast extract ( PYE ) or M2 minimal medium supplemented with 0 . 1% glucose ( M2G ) at 30°C . These media were also supplemented with the appropriate antibiotic ( solid/liquid media; in µg/ml: kanamycin 20/5 , gentamycin 5/0 . 5 , oxytetracycline 5/2 . 5 , nalidixic acid 20/n . a . ) and the inducers vanillate ( Van; 1 mM ) and isopropyl 1-thio-b-D-galactopyranoside ( IPTG; 31–1666 µM ) where applicable . To solidify the medium , either 1 . 5% ( regular growth plates ) or 0 . 3% ( motility plates ) agar was added . The optical density of cultures were either determined individually using a photo-spectrometer at 660 nm ( Genesys6 , Thermo Spectronic , WI , USA ) or in 96-well format using clear bottom plates ( BD Falcon , NJ , USA ) and a plate reader at 660 nm ( Molecular Devices , CA , USA ) . The E . coli strain DH5α was used for cloning and plasmid propagation , while S17-1 was used for plasmid transfer in C . crescentus by conjugation as described by Ely et al . [52] . Plasmid construction is described in the supporting information ( Text S1 ) and primers used for plasmid construction are listed in Table S2 . Deletion mutants were generated by allelic exchange as described before [53] . In brief , the suicide plasmid pNPTS138 was used as plasmid backbone harboring two regions of homology that flank the gene of interest . After mobilization of the plasmid into C . crescentus , Kanamycin resistant first recombinants were selected , followed by a sucrose counter selection step . Sucrose resistant second recombinants were tested by PCR and confirmed by sequencing . To exclude the possibility that spontaneous mutations might have emerged during the construction of the cdG0 strain to suppress essential functions of c-di-GMP , the NA1000 cdG0 strain was re-sequenced . Comparison of ancestor and cdG0 mutant identified a total of five SNPs . Back-crossing experiments confirmed that none of the genetic changes had an influenced on the behavior of the cdG0 strain ( data not shown ) . For microscopy , cells were harvested at mid-exponential phase ( OD660∼0 . 3 ) . Except for holdfast stains , all strains used for microscopy were generated in NA1000 background . For transmission electron microscopy ( TEM ) , the cells were washed twice with water and absorbed to a glow-discharged , carbon-coated colloid film on a copper grid . The grids were then washed several times with deionized water and negative stained with 0 . 75% ( w/v ) uranyl formate . In case cells were infected with φCbK , the phage was added after the first washing steps 15 min before cells were fixed with an aqueous formaldehyde solution ( 1% ) . Cells were examined with a Hitachi 7000 , 100 kV instrument . For light microscopy , cells were placed on agarose pads ( 1% in water , Sigma , USA ) . Fluorescence , phase contrast ( PH ) , and differential interference contrast ( DIC ) pictures were taken with a DeltaVision Core microscope ( Applied Precision , USA ) equipped with an UPlanSApo 1003/1 . 40 Oil objective ( Olympus , Japan ) and a coolSNAP HQ-2 CCD camera ( Photometrics , USA ) . Images were processed with softWoRx version 5 . 0 . 0 ( Applied Precision , USA ) and Photoshop CS6 ( Adobe , USA ) software . Cellular dimensions , single cell fluorescence , and number of foci were analyzed using MicrobeTracker version 0 . 931 [54] . To visualize the holdfast , cells ( CB15 or NA1000 hfsA+ background ) in mid-exponential growth phase were stained with Oregon Green-conjugated wheat-germ agglutinin ( 0 . 2 mg/ml , Invitrogen , USA ) , washed twice with water and visualized by fluorescence microscopy . To quantify the holdfast production , images of stained cells were segmented by setting a threshold that removes background signal . Individual stained holdfasts were identified in these bitmap images by using the “analyze particle” tool from imageJ [55] with selection for >0 . 7 circularity and >2 pixel size . The intensities of holdfast stains were then quantified in the identified regions as measure for holdfast production and analyzed in R [56] . For small-scale density gradient centrifugations , cells were grown in PYE medium until they reached mid-exponential growth phase . A 20× staining solution ( 0 . 1% Coomassie brilliant blue R in 40% methanol; 10% acetic acid; 50% water ) was added to optimize the visibility of high and low density bands and incubated for 10 min at room temperature . Cells were washed twice with cold synchrony phosphate buffer ( 12 . 25 mM Na2HPO4; 7 . 75 mM KH2PO4 ) and resuspended in cold 33% Ludox ( in synchrony phosphate buffer ) . After 45 min centrifugation at 9000×g at 4°C , pictures were taken ( Nikon Coolpix 990 ) with back light illumination to document the band distribution . Big scale density gradient centrifugations for isolating swarmer cells were performed in M2G medium as described before [53] . If necessary , inducible promoters were spiked three hours prior to the density gradient centrifugation . After release of isolated swarmer cells in fresh medium , samples for light microscopy and immunoblot analysis were taken every 20 min for a total of 160 min . All density gradient centrifugation experiments were performed with NA1000 derived strains . To determine the β-galactosidase activities of promoter lacZ fusions , strains carrying the reported constructs were grown to mid-exponential growth phase in M2G-medium containing tetracycline . The cells were permeabilized with chloroform and SDS and assayed in triplicate as described by Miller et al . [57] . C . crescentus cells were harvested by centrifugation at ∼20 , 000×g at 4°C . Pellets were resuspended in SDS loading buffer ( 50 mM Tris-HCl ( pH 6 . 8 ) ; 2% sodium dodecyl sulfate ( SDS ) ; 10 mM dithiothreitol ( DTT ) ; 10% glycerol ) and normalized for the optical density of the culture . Total protein lysates were separated by 12 . 5% SDS-polyacrylamide gel electrophoresis ( PAGE ) and transferred to PVDF-membranes ( Immobilon-P , Millipore , MA , USA ) . Proteins were detected using specific polyclonal antibodies ( anti-CtrA 1∶5 , 000; anti-McpA 1∶10 , 000; anti-CcrM 1∶10 , 000; anti-PdeA 1∶1 , 000 ) and polyclonal anti-rabbit secondary antibodies conjugated to horseradish peroxidase ( 1∶10000; Dako , Denmark ) . Flag-tagged YdeH was detected by using M2-antibodies ( 1∶10000; Invitrogen , USA ) and anti-mouse secondary antibodies conjugated to horseradish peroxidase ( 1∶10000; Dako , Denmark ) . After incubation with ECL chemiluminescent substrate ( Perkin Elmer , USA ) , Super RX X-ray films ( Fuji , Japan ) were used to detect luminescence . Band intensities were quantified using the integrated density tool from imageJ after scanning the exposed X-ray films . Motility of cells was determined on semi-solid PYE plates and surface attachment was quantified in 96-well polystyrene plates in PYE as described before [14] , [25] . Attachment assays were performed with cells derived from CB15 or NA1000 hfsA+ and grown for 24 h before the biofilm was quantified . Motility assays in Figure 1 , 2 , 5 and S7 show mutants created in CB15 background , Figure 6 and S10 were performed with strains created in NA1000 or NA1000 hfsA+ background . To determine the sensitivity of C . crescentus cells to φCbK and φCR30 , cells were grown to mid-exponential phase , embedded in molten PYE containing 0 . 45% agar at 37°C and spread on PYE plates . After the agar had solidified , a 1∶10 serial dilution of the appropriate phage was spotted on the bacterial lawn . The plates were incubated for 48 h at 30°C and pictures were taken ( Nikon Coolpix 990 ) using an indirect illumination box [58] . For the transduction of wild-type alleles of cc0091 , cc1086 , and pdeA , φCR30 lysates were prepared on wild-type NA1000 cells carrying a kanamycin marker in the vicinity of the gene of interest ( CMS0 , CMS12 , and CMS37 , respectively ) [59] . These lysates were used to infect the recipient strains . After selection for kanamycin , the presence of the correct allele was determined by PCR . Lysate preparation and general transduction were performed as described before [59] . C-di-GMP was extracted from NA1000 and NA1000 derived strains and quantified by liquid chromatography-tandem mass spectrometry as described previously [31] . The average intracellular concentration was determined by normalizing the c-di-GMP measurements to the total bacterial volume as determined by the median cell volume and the CFU per OD660 . Genomic DNA was extracted from NA1000 cdG0 and the parental strain ( NA1000jenal ) using standard guanidium thiocyanate extraction and isopropanol/ethanol precipitation . The DNA was sequenced at Fasteris ( Switzerland ) on the Genome Analyzer GAIIx platform generating 2×38 bp paired end reads . These data were mapped on the NA1000 reference genome ( GenBank accession CP001340; [23] ) using VAAL [60] . Sanger sequencing was used to confirm polymorphisms . Synchronized populations of C . crescentus cells retain a certain degree of heterogeneity . To infer stage-specific c-di-GMP contents we employed a mathematical model that extracts these data from population measurements . The first step was to determine the exact composition of the cell population at any given time point . We considered two sources of heterogeneity in synchronized populations of C . crescentus: i ) variations in cell cycle length and ii ) variations in age of newborn swarmer cells harvested by density gradient centrifugation . We followed the individual ( “internal” ) age of each cell in a virtual bacterial population , where cells with a characteristic cell cycle length tC divide asymmetrically into two daughter cells with the internal age 0 ( swarmer cell ) and tS ( stalked cell ) ( Figure S11C , D ) . The cell cycle length was determined from OD660 measurements in the cultures in which c-di-GMP was quantified ( tC = 137 min ) . We assume that the length of G1 ( swarmer cell ) and S+G2 ( stalked and predivisional cell ) are ¼ and ¾ of the full cell cycle length , such that tS = 0 . 25 tC . Additionally , we make the assumption that stalked cells have the same c-di-GMP content independent of their origin , e . g . differentiated swarmer cells or newborn stalked cell originating from cell division . The cell cycle length of individual bacteria in the population was assumed to follow a normal distribution with a standard deviation as determined from previously published data ( +/−35% ) [61] . To calculate c-di-GMP concentrations in individual cells from the population measurements , we grouped the bacterial population in seven 20-min intervals i and solved the resulting system of equations for the measured c-di-GMP content:with m describing the timepoint of the measurement ( 0–180 min ) , [c-d-GMP]P , m the average c-di-GMP content in a C . crescentus population at this timepoint ( Figure S11A ) , fi , m the fraction of cells at the timepoint m that have an internal age in the interval i and [c-d-GMP]C , i as the average c-di-GMP concentration in cells in the age interval i . All calculations were done in R . | Bacterial processes like virulence , motility or biofilm formation are governed by the second messenger c-di-GMP . In most bacteria , c-di-GMP is produced and degraded by a complex network comprising dozens of enzymes . This has hindered a comprehensive analysis of the cellular role of c-di-GMP . Here we mutate the entire c-di-GMP network in Caulobacter crescentus , a model organism with inherent cell polarity and bimodal life-cycle . We find that a c-di-GMP free strain ( cdG0 ) shows severe developmental defects , a loss of cell polarity and defective cell division . By determining c-di-GMP dose-response curves for individual processes and relating these to c-di-GMP levels , which were modeled for single cells progressing through the cell cycle , we define a quantitative frame for the c-di-GMP dependent program during the C . crescentus life cycle . We then show that the defects of cdG0 can be largely rescued by restoring c-di-GMP levels with a single heterologous enzyme producing c-di-GMP . However , much higher levels of the second messenger are required under these conditions as compared to the contribution of homologous enzymes . Our data argue for specific regulatory fine-tuning of the enzymes mediating c-di-GMP oscillation during the cell cycle and provide evidence for both global as well as insulated c-di-GMP pools . | [
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] | [] | 2013 | Bi-modal Distribution of the Second Messenger c-di-GMP Controls Cell Fate and Asymmetry during the Caulobacter Cell Cycle |
Classical decision theory postulates that choices proceed from subjective values assigned to the probable outcomes of alternative actions . Some authors have argued that opposite causality should also be envisaged , with choices influencing subsequent values expressed in desirability ratings . The idea is that agents may increase their ratings of items that they have chosen in the first place , which has been typically explained by the need to reduce cognitive dissonance . However , evidence in favor of this reverse causality has been the topic of intense debates that have not reached consensus so far . Here , we take a novel approach using Bayesian techniques to compare models in which choices arise from stable ( but noisy ) underlying values ( one-way causality ) versus models in which values are in turn influenced by choices ( two-way causality ) . Moreover , we examined whether in addition to choices , other components of previous actions , such as the effort invested and the eventual action outcome ( success or failure ) , could also impact subsequent values . Finally , we assessed whether the putative changes in values were only expressed in explicit ratings , or whether they would also affect other value-related behaviors such as subsequent choices . Behavioral data were obtained from healthy participants in a rating-choice-rating-choice-rating paradigm , where the choice task involves deciding whether or not to exert a given physical effort to obtain a particular food item . Bayesian selection favored two-way causality models , where changes in value due to previous actions affected subsequent ratings , choices and action outcomes . Altogether , these findings may help explain how values and actions drift when several decisions are made successively , hence highlighting some shortcomings of classical decision theory .
Classical decision-making theory states that when facing a choice , agents consider the cost attached to potential actions and the value of their expected outcomes , and select the option that gives the maximal net benefit [1–5] . In this classical view , values are subjective estimates of anticipated outcomes that drive action selection . However , an opposite perspective has been suggested where the reverse inference is made [6]: agents may infer values from the observation of their own behavior . The general logic is: “I have engaged this action in order to get that outcome , therefore this is how much I like that outcome” . This reversed logic has been adopted in the well-known cognitive dissonance theory [7 , 8] . According to this theory , people adjust their preferences in order to justify their actions , i . e . to reduce the dissonance between choices and likeability judgments . A paradigmatic task used to demonstrate cognitive dissonance is the free choice paradigm , in which participants rate the likeability of several items , then choose between items of similar ratings , and then rate these items again [9] . An impressive number of studies showed that relative to the first rating , the second rating is increased for chosen items and decreased for unchosen items . This effect has been termed choice-induced spread of preference [10–13] , which implies reverse causality from actions to values . However , this line of research has been recently challenged [14 , 15] . Indeed , if both ratings and choices proceed from probabilistic distributions over internal value estimates , then the famous spread of preference ( between chosen and unchosen items ) can be observed without any causal determination from choice to rating . This statistical artifact is analogous to a “regression to the mean” ( see Izuma & Murayama [16] for a review but also Alós-Ferrer & Shi [17] for an opposite view ) . The idea is that because value judgments are noisy , two different items A and B may be given a similar first rating by accident , even in presence of a preference ( say A>B ) , i . e . a difference between means of probabilistic distributions . However , this difference should be expressed on average , so it is likely that during following choice , A will be selected over B , and that during next judgment , A will receive a higher rating than B . Therefore , choice can appear as predicting a spread of preference , even if values are stable ( i . e . , if probabilistic distributions are not affected by choice ) . Chen & Risen suggested a clever way to assess this statistical artifact , with a control condition in which both ratings are performed before choices ( RRC condition ) . This control condition has been implemented in several studies , beginning with Chen & Risen themselves , who confirmed that apparent spread of preference is observed even in the RRC condition [14 , 15] . To assess whether choice-induced spread of preference can occur on top of the statistical artifact , the critical test is then to compare the control RRC condition to the classical free choice paradigm ( RCR condition ) . Initial results were mixed [14 , 15] but then several studies have validated the presence of an effect beyond the artifact , although not all experiments were conclusive [18 , 19] , [20 , 21] . Another way to get around the artifact is to assign participants choices that they did not make , and therefore cannot incorporate information about underlying values . Even these fake choices were found to spread preferences [22–25] . Also , the spread of preference was more robust when choices were remembered at the time of the second rating [26 , 27] , providing evidence for a psychological mechanism ( post-hoc justification of choice ) above and beyond statistical regression to the mean . The first aim of the present study was to address this question through a different approach: by comparing computational models in which values are stable versus models in which values can evolve as a function of choice . Using this computational approach , we assessed which model would best account for behavioral data acquired in a new version of the free choice paradigm , following the standard rating / choice / rating sequence . The second aim of our study was to generalize the notion that actions determine values , beyond choice . In decision theory , expected values not only drive which action is selected but also with how much vigor it is performed [28–30] . Here again , a reverse inference could be postulated: agents may revise their values depending on the degree of effort they notice to have exerted . This may explain some everyday life phenomena that have been described as ‘fruit of labor’ or ‘effort justification’ or ‘contrast effect’: for example a beautiful landscape would be even more enjoyable after an exhausting walk [31] . In the laboratory , it has been shown that abstract shapes associated with high effort ( to get a reward ) are subsequently preferred to shapes associated with low effort ( for the same reward ) , suggesting a positive impact of effort on value [32–34] . This logic may extend to the success or failure of the action engaged ( whether the outcome was obtained or not ) , which can be taken as a proxy for the effort invested . Agents may devalue outcomes of actions that were chosen and initiated but not completed , whatever the reason . This post-failure devaluation may account for the case of the fox in the famous Aesop’s fable , who revised his judgment about the desirability of grapes that he failed to reach , leading to the saying “any fool can despise what he cannot get” . This story is often considered as a typical example of cognitive dissonance and received different psychological interpretations: pretending that grapes were sour could for instance attenuate frustration or temper the reputation of being a loser [13 , 35 , 36] . Surprisingly however , previous studies have not intended to disentangle the impact of choice , success vs . failure and actual effort expenditure on subsequent valuation . To do so , we implemented choice as a decision about whether to perform an effortful action for a particular reward , which is why the choice task is thereafter denoted effort task . The required effort was varied such that participants sometimes failed to complete the action . This design therefore enabled assessing the effects of three action-related variables ( not only choice but also effort and success ) on outcome value . The third aim of our study was to determine whether choice only impacts declarative judgments or all value-based decisions . We previously showed that rating , effort and choice tasks elicited the same value function [37 , 38] . However , many would argue that to faithfully reflect preferences , behavioral responses must bear consequences . Otherwise , if there is no cost in doing so , subjects may fake their preference , notably for social reputation concerns . It is correct that in many paradigms ( including ours ) , declarative judgments have no further consequence , while choice and effort have an impact on the outcome: they determine the reward that participants get in the end . In our case , the outcome was a food reward , which mattered to participants because they were actually hungry . In behavioral economics , choice and effort would thus be considered as "incentivized" and therefore more tightly linked to underlying values than declarative judgments . Indeed , evidence supporting cognitive dissonance theory in the free choice paradigm comes precisely from explicit ratings , leaving open the interpretation that participants just pretend having preferences that are more aligned to their choices than they truly are . However , this does not imply that underlying internal values remain stable , as it might be difficult to maintain separate declarative judgments and internal values for a vast collection of items in the long run . Indeed , changes in ( explicit ) preferences have been shown to persist for one week [19] and up to three years [20] after choices were made . The question remains whether such long-lasting spread of preference corresponds to changes in the internal values driving other behavioral outputs than explicit ratings , such as choice and effort allocation . If the famous fox was presented in a follow-up story with the same grapes , would he try again ? And would he try even harder ? To answer these questions , we examined whether choices would affect not only subsequent ratings but also other value-based behaviors , such as subsequent choice or effort production . To do so , we extended the experimental task sequence to rating / effort / rating / effort / rating . To recapitulate , we designed a new free-choice paradigm that alternates rating tasks , in which participants make judgments about the desirability of food items , and effort tasks , in which they decide whether or not to produce handgrip force to obtain these items ( Fig 1 ) . Different force levels were assigned to different food items , orthogonally to desirability ratings . Some targets were beyond the maximal force that participants could reach , generating failure events analogous to the sour grape story . This design therefore dissociated three action-related dependent variables: choice ( decision to engage effort exertion ) , effort ( how much physical force was actually produced ) and success ( whether or not the outcome was eventually obtained ) . Note that the force produced , which was an observed behavior , was largely influenced by target force level , which was imposed by the experimenter . The three action-related variables were orthogonal , because success was only considered in accepted trials , and force in successful trials . We then examined the effects of these three action-related factors on all subsequent value-related behaviors: not only rating but also choice , effort , and success . In any case , there was no feedback in the sense that subjects never experienced outcomes , so change in values could only arise from action-related factors . The influence of past actions on subsequent behaviors was then assessed using computational modeling .
We assumed that in all versions of the behavioral tasks , action-related variables ( choice , success , and force produced ) would result from a cost/benefit trade-off , the cost being here associated with physical effort , while the benefit varied with the value of items presented as potential outcomes [28] . We took target force level as a proxy for effort cost and likeability rating as a proxy for value . Therefore , we expected ratings and targets to predict all behavioral variables ( choice , success , force ) in the effort task . To test these predictions , for each participant , choice C , success S and force F were regressed across trials ( i . e . , across items ) against target force level L and the desirability rating R given in the preceding rating session . The regression was run on the two effort tasks pooled together , but separately for each participant . Logistic regression models were used to account for choice and success ( balanced accuracies of 0 . 78±0 . 01 and 0 . 82±0 . 01 respectively ) , while a linear regression model was used for force ( R2 = 0 . 92±0 . 006 ) . Individual regression estimates ( betas ) were then entered into group-level random-effect analyses . For success , only accepted trials were included ( i . e . , with force > 20% Fmax for Exp 1–2 , and yes response in Exp 3 ) . For force , only successful trials ( i . e . , where required force was exerted ) were included . As expected ( Fig 2 ) , ratings had a significant positive effect on subsequent choice , success , and exerted force ( all p<0 . 01 ) , denoting a higher motivation to win the food item . Conversely , target force level has significant negative effect on subsequent choice and success ( both p<0 . 001 ) , as well as a positive effect on exerted force ( p<0 . 001 ) . Note that the latter effect was trivially imposed by instructions: a successful trial means that exerted force reached target level . The negative effect on choice and success could be interpreted as a deterrent influence of anticipated effort cost ( at the decision level ) and experienced effort cost ( at the execution level ) . As in usual free-choice paradigms , we expected post-choice ratings ( relative to pre-choice ratings ) to be higher for chosen items than for unchosen items . In other words , the choice variable should have a positive influence on the change from pre-choice to post-choice rating ( the so-called spread of preference , hereafter denoted Δ-rating ) . We also tested the influence of the additional action-related factors that we integrated in our paradigm . According to the so-called ‘sour-grape’ effect , post-choice ratings should be higher for earned items ( successful trials ) than for lost items ( failed trials ) , predicting again a positive influence of success on Δ-rating . Finally , according to the so-called ‘fruit of labor’ effect , the amount of effort expenditure should also have a positive influence on Δ-rating . To test these three predictions , we computed Δ-rating for each participant ( ratings were z-scored within subjects before computing the difference ) , each item , and each repetition ( i . e . , R2—R1 and R3—R2 ) . This Δ-rating was then linearly regressed against choice C , success S , and force F as predictors , pooling together the two repetitions . In order to orthogonalize regressors , all trials were z-scored within subjects for choice , while only chosen trials were z-scored for success ( unchosen was coded 0 ) and only successful trials were z-scored for force ( unsuccessful was coded 0 ) . We also included a constant intercept term T that captured the effect of time . Individual regression estimates were then entered into group-level random-effect analyses ( Fig 3B ) . The effects of choice and success were significantly positive ( βC = 0 . 09±0 . 01; βS = 0 . 04±0 . 01 , both p<0 . 001 ) . This means that participants rated items higher after deciding to obtain these items , and beyond this effect , after successfully achieving the required force level . Note that even if we changed the definition of choice in Exp1/2 versus Exp3 , and the pairing between ratings and targets in Exp1 versus Exp2/3 ( see Fig 1 for details ) , the effect of choice on ratings was unchanged: it was significant in all experiments ( Exp 1: βC = 0 . 07 , p = 0 . 008 , Exp 2: βC = 0 . 08 , p<0 . 001 , Exp 3: βC = 0 . 11 , p<0 . 001 ) and not significantly different between experiments ( p = 0 . 389 ) . Finally , there was no significant effect of exerted force ( βF = 0 . 009±0 . 01 , p = 0 . 461 ) and the intercept was not different from 0 ( T = 0 . 003±0 . 03 , p = 0 . 892 ) . Thus , we did not validate the hypothesis of a ‘fruit of labor’ effect , making items more valuable when they are obtained with more effort . Also , there was no evidence for a drift in rating , independent of task factors ( i . e . , when comparing R3-R2 to R2-R1 , p = 0 . 484 ) . As all tasks were self-paced , one may wonder to what extent duration of exposure could be a confound , as difficult trials could take longer and thus prolong the exposition to related food items . In order to address this concern , we re-ran the same analysis but including cumulative duration of exposure ( computed for each item at each rating ) as an additional regressor . Critically , this regressor was not significant ( p = 0 . 533 ) and the pattern of results was not modified . Finally , we assessed whether the change in rating ( i . e . , Δ-rating ) was driven by chosen items being rated higher , or unchosen items being rated lower , or both . We took advantage of the 30 items that were rated but randomly excluded from the effort task in Exp 2-3 . Critically , Δ-rating for excluded items ( +0 . 03 ) was in-between that of chosen items ( +0 . 10 ) and unchosen items ( -0 . 07 ) , with a significant difference in both cases ( both p< 0 . 001 ) . Relative to excluded items , there was no difference in the magnitude of Δ-rating induced by acceptance and rejection ( p = 0 . 194 ) . Similarly , there was no difference in Δ-rating between excluded items and selected items ( i . e . , chosen and unchosen items taken together; p = 0 . 183 ) . Thus , model-free analyses provided evidence for an influence of choice and success on likeability ratings . However , these effects remain susceptible to the same statistical artifact ( regression to the mean ) as in the classical free choice paradigm . In addition , the complex interplay between experimental variables and behavioral measures was not properly assessed in the above model-free analyses that tested the different effects separately . To better assess whether the effect of past actions reflected an actual change in the underlying values , we therefore turned to a computational approach . As there was no evidence for a change in behavior across experiments , we pooled all participants in the following model-based analyses . The general strategy was to formalize the null hypothesis , in which ratings are solely noisy observations of hidden values , and choice effects on ratings only statistical artifacts , and compare this null model to models that integrate the influence of past behaviors . The null model can be written as follows: Ri=Vi+ε ( 1 ) where Ri and Vi are respectively the rating and value of item i , and ε is a Gaussian noise with variance σR2 . Thus , although the null hypothesis implies that values are strictly immutable , this null model predicts fluctuations in ratings due to the noise term . Fitting Eq ( 1 ) to actual ratings ( i . e . , estimating Vi and ε ) , therefore allows computing a likelihood that all changes in ratings are due to mere chance . In the following , we detail a set of alternative models that could account for the influence of past actions on subsequent behaviors ( see graphical illustration in Fig 4 ) . In brief , on top of our null hypothesis ( H0 ) , we formalized three alternative scenarios with growing effects of action-related factors involved in effort tasks: in H1 , only the next declarative judgment is affected ( i . e . , E1 could influence R2 but not R3 nor E2 ) ; in H2 , all subsequent declarative judgments are affected but not the other actions ( i . e . , E1 could modulate R2 and R3 but not E2 ) ; in H3 , all subsequent behaviors are modulated by previous actions ( i . e . E1 could modulate R2 , E2 , and R3 ) . The latter model corresponds to a change in underlying values that would affect all subsequent behaviors . For all three scenarios , each of the three action-related behaviors ( choice , success , and force ) could have or not a significant influence on the designated subsequent behaviors . All alternative models were assessed using Bayesian model comparison against the null model ( Eq 1 ) . This procedure allows us to derive statistical evidence for possible effects of past behaviors while controlling for the ‘regression to the mean’ confound . More precisely , any influence of past behaviors is only considered if it can explain subsequent ratings above and beyond the expected random fluctuations . In this section , we use Bayesian model selection to assess whether action-related factors affect subsequent ratings ( model H1 ) , as in the classical free-choice paradigm . We start by presenting the equations used to capture the direct bias that past actions may exert on next ratings . As in model-free analyses , the effort task was decomposed into three behavioral variables: choice Cik , success Sik , and force Fik where k denotes task session . They were orthogonalized to form three independent regressors , as follows: C={+1iftrialisaccepted ( F>20%FmaxinExp1−2;yeschoiceinExp3 ) −1otherwise ( F<20%FmaxinExp1−2;nochoiceinExp . 3 ) S={+1ifitemissuccessfullyearned ( F>L ) −1iftrialisfailed ( 20%Fmax<F<L ) 0otherwise ( declinedtrial ) F={zFz‑scoreofFiftrialissuccessful ( F>L ) 0otherwise ( failedordeclinedtrial , F<L ) Then , total bias bik induced by effort task k on the rating k+1 of item i can be formalized as the weighted sum of choice , success and force effects: bik=bCCik+bSSik+bFFik+bT ( 2 ) The free parameters bC , bs , and bF represent respectively the weight of choice , success , and exerted force . The parameter bT captures the effect of time ( i . e . , repetition of ratings ) and allows the model to capture non-specific trends like boredom ( negative effects ) or hunger ( positive effect ) . Finally , the hypothesis that these action-related variables could affect subsequent ratings can be written as: Rik+1=Vi+bik+ε ( 3 ) When all parameters bC , bS , bF , and bT are ( strictly ) set to 0 , Eq 3 is equivalent to Eq 1 , which formalizes the null hypothesis H0: ratings are not affected by action-related variables and item values are solely determined by the constant free parameters Vi . Conversely , if bias parameters b are allowed to differ from zero , then ratings will consistently change according to behavioral variables derived from the effort task , on top of spurious fluctuations driven by the noise term . Switching on or off the four bias parameters resulted in 16 different models . In order to test the possibility that actions have an impact on subsequent ratings ( hypothesis H1 ) , we grouped with H0 the model containing only an effect of task repetition ( all b set to 0 except bT ) and considered as belonging to H1 all other models that included at least one effect of choice , success , or force ( at least one b ≠ 0 other than bT ) . Note that Eq 2 is equivalent to the linear model that was used in the model-free analysis to assess the influence of action-related variables on subsequent ratings . However , because the Bayesian inference relies on the comparison to a prior assumption that all effects might be due to chance , any evidence for an influence of past actions in this model-based analysis is immune to the statistical artifact raised by Chen & Risen [14 , 15] . A family-wise model comparison [39 , 40] provided evidence that hypothesis H1 was far more plausible than H0 ( Ef = 0 . 99 , xp = 1 ) . Furthermore , grouping models by action-related variables ( choice , success , force ) showed that all types of bias but force were significantly present in the population ( see Table 1 ) . This result does not imply that all biases had a consistent direction across subjects , since model inversion was performed at the individual level . To assess the consistency of effects across subjects , we estimated the amplitude of each bias at the individual level by computing the Bayesian Model Average ( BMA ) [41] of the posterior b parameters and entered them into group-level random-effect analyses ( see Table 1 ) . In accordance with model-free analyses , both choice and success effects were strongly positive ( both p < 0 . 001 ) . The effects of exerted force and task repetition were not sufficiently consistent across subjects to pass significance threshold . This model-based analysis demonstrated that ratings were actually modulated by two action-related variables of the preceding effort task: choice and success . Indeed , higher ratings were assigned to items for which the trial was accepted ( meaning that the subject decided to try and reach the target ) and for which the item was earned ( meaning that the subject succeeded in reaching the target ) . However , these effects may be short-lived and only affect ratings provided just after the effort task . In the next section , we examined whether the choice and success effects could extend to behaviors beyond the next immediate rating . We first assessed whether actions would affect not only the subsequent rating session ( from E1 to R2 or E2 to R3 ) , as in the classical free-choice paradigm , but also the distant rating session ( from E1 to R3 ) . For this purpose , the model was extended to allow action-related biases to accumulate across sessions of the effort task: Rik+1=Vi+∑k′=1kbik′+ε ( 4 ) This equation makes similar predictions to Eq 3 regarding the first two ratings ( R1 and R2 ) . However , while Eq 3 assumes that the last rating ( R3 ) is only affected by actions made in E2 , Eq 4 now suggests that R3 will also be affected by actions made in E1 . As seen before , switching on or off the respective bias parameters corresponding to the four factors ( choice , success , force and time ) generates 16 models . Among these 16 models , two belong to the null hypothesis H0 ( no effect at all or only an effect of time ) , and 14 represent the hypothesis H2 that actions induce a durable bias that can be expressed in distant ratings . A family-wise comparison between H0 ( 2 models ) , H1 ( 14 models ) , and H2 ( 14 models ) sets showed that H2 provided the best explanation of the data ( Ef = 0 . 78 , xp = 1 ) , suggesting that actions had a lasting effect on distant ratings . Furthermore , as in previous analyses , grouping models by action-related variables showed that choice and success bias , but not force bias , were significantly present in the population ( all xp = 1 ) . Although this analysis suggests that actions have a lasting effect , it does not imply that this effect would affect other value-driven behaviors than ratings . In other words , the effect could only change declarative judgments , with no influence on subsequent actions and related markers ( choice , success , and force ) . The question here is to assess whether past actions can influence not just rating but a common hidden value that would drive behavior in both the rating and effort tasks . For this purpose , we needed a quantitative model of how value drives choice , success and force . Inverting this model could then provide evidence that observed choice , success and force data were generated by constant values versus values evolving under the influence of past actions . Following our framework , we modelled all action-related behaviors ( choice , success and force ) as resulting from a cost/benefit trade-off . More precisely , we modeled probabilities of choice and success using sigmoid functions , and force using an affine function , of a weighted balance between the cost ( required force level L ) and the benefit ( proposed item value V ) involved in every trial . Formally , dropping the indexing of items ( i ) and repetitions ( k ) for the sake of readability , we have: choicefunction:p ( C=1 ) =sig ( ρCV+ηCL−C0 ) ( 5 ) successfunction:p ( S=1 ) =sig ( ρSV+ηSL+S0 ) ( 6 ) forcefunction:F=ρFV+ηFL+F0+ω ( 7 ) where ω ~ N ( 0 , σF ) is some Gaussian noise , ρX , ηX and the offsets X0 ( with X standing for C , S , or F ) are free parameters that were estimated through Bayesian inversion . Focusing for example on choice , Eq 5 predicts that the probability of accepting the trial will be higher than average ( sig ( C0 ) ) if ρCV + ηCL > 0 , i . e . if the benefit ( item value ) is higher than the required cost ( force target ) . As a sanity check , we verified with t-tests that ρ parameters were significantly positive , irrespective of the considered hypothesis ( see S1 Table for details ) . This confirms that values were susceptible to conveying the effects of past actions on subsequent behaviors , since they enhanced the probability of choice and success , and the amount of exerted force . Critically , in this new set of alternative models , the biases induced by past actions ( according to Eq 2 ) do not impact the ratings directly ( as in Eqs 3 or 4 ) , but the underlying values . This is formalized in the following update rule: Vk+1=V1+∑k′=1kbk′ ( 8 ) Where Vk is the value of an item ( index i has been dropped for readability ) during task k , and V1 is a free parameter that captures the initial value of this item . The dynamical values ( Vk ) were then used to predict not only ratings , using Eq 1 , but also choice , success and force , using Eqs 5–7 , with V being replaced by Vk . In fact , the predicted ratings are the same as in the previous set of models , because combining Eqs 1 and 8 is mathematically equivalent to Eq 3 . The key difference is therefore the prediction of behavior in the effort task ( choice , success and force ) , which is allowed to vary according to past actions . Again , switching on or off the different bias parameters in Eq 8 yields 16 models: two belonging to the null hypothesis ( no bias or time bias only ) , and 14 to the new hypothesis labeled H3 , which implements a two-way causality between values and actions . We could not compare H3 directly to H1-H2 models as they were formulated in previous sections , because Bayesian model selection can only be performed between models fitted to the same data . Yet previous model comparisons exclusively considered rating data , while the additional prediction brought by H3 is about choice , success and force data . Thus , we rephrased H1 and H2 by including Eqs 5–7 , where values were constant since the biases affected not values but ratings ( through Eq 3 for H1 and Eq 4 for H2 ) , as schematized in Fig 4 . Importantly , these extended H1 and H2 models provide a null hypothesis for the behavior in the effort task: they postulate that past actions have an influence on ratings but not on choice , success and force . Thus , comparing H3 to extended H1-H2 models enables testing for the presence of a bias in the effort task above and beyond chance , properly controlling for potential regression to the mean artifacts . A family comparison including all models ( H0 to H3 ) showed that indeed H3 best explained the data ( Ef = 0 . 49 , xp = 0 . 98 ) . Post-hoc analyses of the fitted bias parameters in H3 confirmed a positive effect of past choice and success on underlying value , corroborating our previous results , and a non-significant effect of past force . Furthermore , extending models for H1 and H2 did not alter previous conclusions , as significant effects reported in previous sections were still present ( see Table 2 for details ) . We also checked that critical effects captured in our best model ( H3 ) were significant in each experiment analyzed separately , ensuring replicability of our findings across participant groups and task versions ( see S2 Table ) . Finally , we verified that our Bayesian model comparison approach was indeed immune to the ‘regression to the mean’ artifact . For this purpose , we ran Monte-Carlo simulations to a ) generate mock behavioral data under the null hypothesis H0 and then b ) estimate the effect of past actions in these random data under H1 , H2 or H3 ( see S1 Text , S3 Table and S4 Table ) . This resulted in an approximate distribution of the bias parameters ( weights of choice , success , force and time ) under the null hypothesis ( see Fig 5 ) . We then compared these parameter estimates recovered from simulated data to parameters estimated on the data acquired in real subjects . To do so , we compared the 63 real parameters to 63 simulated parameters ( randomly selected from the distribution ) using Welch’s ( unequal variance ) two-samples t-tests . This procedure was repeated 1000 times to obtain stable p-values ( by averaging over repetitions ) . These p-values ( Fig 5 ) show that bias parameters estimated in real subjects largely deviated from chance ( parameters simulated under H0 ) for both choice and success , but not force . This was true whether we used only rating data ( H1-H2 ) or both data from both the rating and effort tasks ( H1-H2-H3 ) . It confirms that the effects of past actions reported in previous analyses are unlikely to have arisen from a statistical artifact . Moreover , this bootstrap approach also provides for all action-related factors an estimate of the effect size that should be expected from chance ( in the absence of a true influence on values ) , corresponding to the statistical confound pointed by Chen & Risen ( see Supplementary Material for details ) . Critically , these simulations show that our Bayesian approach did not inflate the amplitude ( estimates are always close to zero ) nor the significance ( false positive rate is kept under nominal threshold ) of bias parameters , as it would be the case with classical analyses ( see S3 and S4 Tables ) . More than a mere sanity check , it therefore demonstrates that the statistical method developed in this paper is immune to the artifact identified by Chen & Risen .
In this paper , we examined the possibility of reverse causality from actions to the underlying values that drive these actions . This idea has a long history and has been notably defended in the context of cognitive dissonance theory [7 , 8] . We see three major advances in our findings: 1 ) a novel Bayesian approach discarding the statistical artifacts that had undermined fifty years of research on cognitive dissonance [14 , 16] , 2 ) evidence that beyond choice , other action-related variables , such as success , can modulate underlying values , and 3 ) a demonstration that actions impact not only declarative value judgments but also other value-driven behaviors , such as subsequent decision , effort production and eventual success . These findings are globally consistent with the notion of reverse causality , meaning that the brain would update values based on the behavior which it has itself triggered under environmental constraints [6] . In the following , we successively discuss these findings and their general implications . The cornerstone of cognitive dissonance theory is the free choice paradigm , which implements a choice-rating-choice series of tasks . The classical result that has been repeatedly observed is that choice seems to affect the change from first to second ratings assigned to the two items . The critique raised by Chen & Risen [14] has casted doubt on this result , which could arise from a regression to the mean artifact . If ratings are considered as noisy projections of hidden values , then two items could get similar first ratings by chance , while second ratings would be closer to the means and reveal a difference that would also be expressed in choice . One experimental solution suggested by Chen & Risen is to add a control condition that implements a rating-rating-choice sequence . This condition enables measuring the statistical artifact in isolation , i . e . without any possible influence of choice on rating , since the choice task is performed after the second rating task . Using this condition as a reference , several authors confirmed a spread of preference beyond any statistical artifact [13 , 18 , 20 , 27] , but see [15 , 16 , 18] for null or mixed results . To directly address this issue , and rather than introducing a new experimental control condition , we utilized a novel computational strategy that simply compare models with and without an effect of choice on rating . This involves using Bayesian model inversion and comparison techniques to derive the plausibility of an effect beyond what can just be explained by noise in rating and choice behaviors [42–44] . This procedure also provides an estimate of the true effect , and its coherence across subjects , above and beyond ( i . e . , controlling for ) statistical artifacts . In line with classical statistical analyses , we indeed found a choice-induced spread of preference that cannot be reduced to a statistical artifact . This may be an important contribution to the debate but also an important methodological point , since such modeling approach could be applied to any dataset compromised by the same statistical concern . Although we kept the term choice to underline the link with the existing literature , our choice task was quite different from the typical free choice paradigm . Instead of expressing a preference between two items , participants decided whether or not to exert a given force for a particular food item . The spread of preference was therefore measured between items for which participants made an effortful attempt and the others . We observed no significant impact of the way choice was expressed in the different versions of the paradigm ( pressing on accept / decline buttons versus just trying or not to reach the target ) . This null result suggests that choice does not have to be explicit in order to induce spread of preference . Beyond choice , our task was designed in order to incorporate two candidate effects of actions on values: the so-called ‘sour grape’ and ‘fruit of labor’ effects . As the grapes in Aesop’s fable , some food items were paired with unreachable force levels , thereby introducing cases of failure . We indeed found that , as the famous fox , our subjects rated items that they failed to obtain as less desirable than initially judged . The usual interpretation for the fox despising what he could not get is that he is trying to avoid regret or humiliation , and maintain the illusion that he is in control . However , following the idea of reverse causality , it could also represent a true change of preference . Indeed success may be a rough proxy for the amount of resources deployed , which should scale with the desirability of the goal . Thus , the brain would logically conclude from observing failure that the goal value is lower than initially estimated . This inference might represent an adaptive mechanism: decreasing the value of unreachable goals could save time and energy . The implication is that updating value would be easier or more efficient than monitoring action feasibility . Yet exploring the environments in which this speculative statement is correct would require comprehensive simulations , using an evolutionary game theoretical approach . Finally , we note that , even if we focus in this discussion on the effect of failure to push the analogy with the sour grapes fable , our model-based analysis does not distinguish between failure and success effect ( or accept and decline effects for choice ) . Thus , all we can conclude is that relative to the initial difference , the difference in desirability between obtained and missed items tends to increase . Regarding effort expenditure , the reverse causality hypothesis would predict a positive effect of force produced on value , since deploying more resources would signal a more desirable goal . This sort of inference would explain what has been coined ‘fruit of labor’ or ‘effort justification’ effect–the fact that a reward is much appreciated when it comes as an output of strenuous effort [31] . By extension , it could also explain the so-called ‘sunk-cost’ effect [45 , 46]–the fact that the propensity to invest resources for attaining a given goal augments with the amount of resources invested in the past with the same goal . Indeed , if the goal value increases with the expended effort , subsequent cost-benefit arbitrage may shift to producing even more effort . However , we found no consistent evidence for produced force to affect subjective value of items , neither with family-wise model nor with classical test on ( BMA ) posterior estimates . Nonetheless , we believe that this null finding should not be taken as a strong argument against effort affecting value , as it could result from a particular feature of our design . Indeed , as the force produced was mainly driven by imposed target level , it was not so informative about how valuable an item is . It remains possible that an effect may be observed in situations where the outcome would not be binary but proportional to invested effort ( as when payoff scales with the amount of work ) . More generally , cognitive dissonance theory invokes self-consistency mechanisms to account for the influence of choice on rating . The idea is that agents align their ratings onto their choices because they want to maintain coherence ( reduce dissonance ) , either for themselves or for other people ( experimenters ) . Although this mechanism has long been considered subconscious , it was recently suggested that it might require an explicit memory of the choices made [27 , 47] . Here , we did not control for episodic memory effects , so we cannot discard the possibility that our findings represent an explicit adjustment aiming at reducing dissonance between behaviors . Yet this assumption of explicit adjustments may be tempered by the observation that past actions modulated not only subsequent ratings but also next choice , success and force outputs . Notably , adjusting force just for the sake of showing coherence would entail a deterrent cost . Moreover , keeping in mind all past behaviors , so as to ensure coherence with current behavior , would rapidly become intractable . Updating one value that drives all behaviors appears as a more reasonable strategy , and a more parsimonious explanation . This is even more critical for cognitive dissonance effects that were shown to last for days to years [19 , 20] . Value updating could also be an explanation for choice repetition observed in real life , e . g . for supermarket customers aligning their current purchases to their previous brand selection [48] . In the first version of the paradigm , a direct causality from past behavior to next behavior may have strengthened the effects . This was facilitated by the fixed pairing between food items and force levels . In other words , memory of failure could lead to no-try , not because reward value was decreased but because one would expect to fail again . However , such a mechanism would not explain the observed modulation on subsequent rating . Moreover , we observe a similar spread of preference in subsequent versions of the paradigm , which proposed different pairings between food items and force targets in the two effort tasks . So we conclude that reverse causality from actions to values , with values then driving subsequent behaviors , is a more parsimonious account for our set of observations . This mechanism may be seen as a shortcut implemented by the brain to avoid repeating bad choices and experiencing failure . However , there are a number of limitations in our study . A first limitation is that we did not consider the possibility that overt ratings may be processed similarly to other actions , eventually resulting in some sort of rating-induced value changes . This was omitted for the sake of simplicity but in principle , people could get information about their values from their own statements . A second limitation is that in our design , choice was always expressed actively . Indeed , to accept an offer , participants had to lightly squeeze the grip ( in Exp 1 ) or to press a button ( in Exp 2 and 3 ) . In other words , we did not include a "no-go" condition in which acceptance would be indicated by inaction . Different results might have been obtained in this condition , since it has been shown that positive and negative items are revaluated differently depending on whether choice is active or inactive [21] , possibly due to the implication of dopaminergic signaling in linking action to reward [49] . A third limitation is that we did not specify the computational mechanism used to update values . In our update equation , action variables have linearly cumulative effects , again for the sake of simplicity . This was sufficient to afford statistical evidence for the effect of interest but may be deemed unrealistic , because such a linear accumulation would eventually yield diverging values when exposed to repetitive choice and success ( something like ‘the more I choose it the more I like it’ , then ‘the more I like it the more I choose it’ , and so on ) . Exploring the dynamics of this iterative logic would be way beyond the scope of this paper , since we only have two repetitions in our design . One natural solution would be Bayesian updating: value should be updated according to ( i ) its precision: the higher the confidence associated to the value of an item , the less susceptible to change it should be , ( 2 ) the prediction error: the influence of a choice should be lower if it was strongly predicted by value . Note that choice and success are binary ( 0 or 1 ) , so they necessarily differ from their prior probability . Consistent with the latter principle , it has been shown with the free choice paradigm that easy choices ( i . e . , choices that have prior probability close to 1 , because of a high difference between option values ) have a lesser influence on rating change than hard choices [16] . The former principle would ensure convergence , since precision would increase after choice , stabilizing values even if choice ( and success or failure ) were repeated over and over . To make sense , such a model would need to assume some encapsulation of the brain systems that generate action and those that update value . If the latter system were fully informed about the computations performed by the former system , there would be no discrepancy between observed and predicted behavior , and therefore no possibility of learning . With two separate systems , one could learn from the other: the value-updating system would integrate a rough initial prediction from a superficial inspection of the choice situation , and then learn from the discrepancy between that prediction and the eventual behavioral response generated by the action-selection system after a more careful consideration . In summary , we have provided a computational method to test the effects of actions on underlying values while properly controlling for statistical artifacts . Using this method , we have extended the classical effect of choice on likeability judgment , showing that not only choice but also experience of success versus failure may modulate the hidden values that in turn impact not only preference but also subsequent effort expenditure and eventual outcomes . Altogether , this work generalizes the notion of a two-way dynamic interaction between actions and values , which may invite a serious reconsideration of decision theory .
The research has been approved by the Ethics Committee for Biomedical Research ( ‘Comité de Protection des Personnes’ ) of the Pitié-Salpêtrière Hospital . All participants gave written informed consent prior to participating in the study . Three groups of participants ( n: 18 , 24 , 24; gender: 13/5 , 17/7 , 15/9 female/male; age±SEM: 23± 0 . 7 , 24±0 . 9 , 24±0 . 8 correspondingly ) were recruited from a volunteer database in Paris to participate in experiments 1 to 3 . They were asked to refrain from eating at least 3 hours before arriving in the lab , but were allowed to drink water ( sugary drinks were not allowed ) . On average their last meal was 5h43±51min , 6h18mn±42min and 5h48±39min before the start of the study . They received 30 euros for their participation and up to two food products , corresponding to the two trials that were drawn at random at the end of the two effort tasks . Before the experiment , participants were shown a large subset of the potential food prizes stored in the testing room . They were then informed that they could only get the food items that they successfully obtained during the task , and that there would be a lottery to select the two trials that determined their food reward . Three participants were excluded because they had too few failures ( they almost always succeeded after accepting a trial ) , meaning that the effects of choice and success were not separable . The analysis was therefore conducted on a total of 63 participants . We used 150 standardized images of sweet and savory snack products available in French supermarkets ( 106 taken from the French INSEAD food database , and 44 were created in the lab ) . Food products were photographed frontally over a black background , with some of the contents displayed in front of the packaging . Size of the photographs was 400x300 pixels . All experimental stimuli were presented via MATLAB ( www . mathworks . com ) and Psychtoolbox ( http://psychtoolbox . org [50] ) . The experiment was run on a Windows-based PC . A handmade pneumatic handgrip device was used in Exp 1 ( as in Ref 34 ) , whereas Vernier Hand Dynamometers ( https://www . vernier . com ) were used in Exp 2–3 . Three versions of the behavioral paradigm were designed for the three experiments . All analyses were run with Matlab ( www . mathworks . com ) . Computational modeling , estimation , and simulation , as well as follow-up Bayesian analyses [40] were implemented using the VBA toolbox [42] ( http://mbb-team . github . io/VBA-toolbox/ ) . Regression coefficients are given as mean + sem . Goodness of fit is the balanced accuracy for logistic regression ( choice , success ) and R2 for linear regression ( force ) , also given as mean + sem . All t-tests are two-tailed . Details on computational models are provided in the results section . | The standard way to explain decisions is the so-called valuation/selection model , which includes 1 ) a value function that calculates desirability for every possible outcome of alternative actions and 2 ) a choice function that integrates outcome values and generates selection probability for every action . In this classical view , choices are therefore determined ( in a probabilistic sense ) by hidden values . However , some authors have argued that causality could also be reversed , meaning that values may in turn be influenced by choices . Yet existing demonstrations of reverse causality have been criticized because pseudo-effects may arise from statistical artifacts . Here , we take a novel computational approach that directly compares models with and without the existence of reverse causality , on the basis of behavioral data obtained from volunteers in a new task . The winning model is a generalization of the reverse causality hypothesis , showing that people tend to like more the items that they previously chose to pursue , and even more if they did obtain these items . These effects were manifest not only in desirability ratings but also in subsequent actions , showing that value changes were more profound than just verbal statements . Altogether , our results invite reconsideration of decision theory , showing that actions are not neutral to the values driving them , hence suggesting that the history of actions should be taken into account . | [
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] | 2019 | Sour grapes and sweet victories: How actions shape preferences |
Poly-β ( 1 , 6 ) -N-acetyl-D-glucosamine ( PNAG ) is a major biofilm component of many pathogenic bacteria . The production , modification , and export of PNAG in Escherichia coli and Bordetella species require the protein products encoded by the pgaABCD operon . PgaB is a two-domain periplasmic protein that contains an N-terminal deacetylase domain and a C-terminal PNAG binding domain that is critical for export . However , the exact function of the PgaB C-terminal domain remains unclear . Herein , we show that the C-terminal domains of Bordetella bronchiseptica PgaB ( PgaBBb ) and E . coli PgaB ( PgaBEc ) function as glycoside hydrolases . These enzymes hydrolyze purified deacetylated PNAG ( dPNAG ) from Staphylococcus aureus , disrupt PNAG-dependent biofilms formed by Bordetella pertussis , Staphylococcus carnosus , Staphylococcus epidermidis , and E . coli , and potentiate bacterial killing by gentamicin . Furthermore , we found that PgaBBb was only able to hydrolyze PNAG produced in situ by the E . coli PgaCD synthase complex when an active deacetylase domain was present . Mass spectrometry analysis of the PgaB-hydrolyzed dPNAG substrate showed a GlcN-GlcNAc-GlcNAc motif at the new reducing end of detected fragments . Our 1 . 76 Å structure of the C-terminal domain of PgaBBb reveals a central cavity within an elongated surface groove that appears ideally suited to recognize the GlcN-GlcNAc-GlcNAc motif . The structure , in conjunction with molecular modeling and site directed mutagenesis led to the identification of the dPNAG binding subsites and D474 as the probable catalytic acid . This work expands the role of PgaB within the PNAG biosynthesis machinery , defines a new glycoside hydrolase family GH153 , and identifies PgaB as a possible therapeutic agent for treating PNAG-dependent biofilm infections .
A major determinant of Bordetella pathogenicity is their ability to form biofilms on biotic and abiotic surfaces [1–9] . Bordetella pertussis and parapertussis are the causative agents of whooping cough in humans , whereas Bordetella bronchiseptica has a broad host range , colonizing and causing respiratory diseases in a wide variety of animals including kennel cough in dogs [10 , 11] . Biofilm formation by B . bronchiseptica requires production of poly-β ( 1 , 6 ) -N-acetyl-D-glucosamine ( PNAG ) . PNAG produced by B . bronchiseptica was originally called Bordetella polysaccharide , or Bps , and was shown to be important for innate immune resistance and colonization of the mouse respiratory tract [6 , 7 , 12 , 13] . PNAG production , modification , and export in Bordetella species are dependent on the bpsABCD operon [12 , 14] . This operon has recently been re-annotated in databases as pgaABCD . PNAG is an important virulence factor for infection by other bacteria such as Escherichia coli [15] , Staphylococcus aureus [16] , Staphylococcus epidermidis [17] , and Klebsiella pneumoniae [18] , and is also produced by numerous fungal and eukaryotic organisms including Plasmodia spp . which are the causative agent of malaria [19] . PNAG production in Gram-negative bacteria is initiated by the PgaC and PgaD synthase complex [20] . PgaC is an inner-membrane protein that contains a glycosyltransferase domain and interacts with the inner-membrane protein PgaD in a bis- ( 3’ , 5’ ) -cyclic-dimeric-guanosine monophosphate ( c-di-GMP ) dependent manner [21] . Together PgaCD synthesize and transport PNAG across the cytoplasmic membrane [21] . PgaA is a two-domain protein that contains an N-terminal periplasmic tetratricopeptide repeat ( TPR ) interaction domain and a C-terminal outer-membrane porin that facilitates PNAG export across the outer membrane [22] . The final protein is PgaB , a two-domain periplasmic protein with an N-terminal family four carbohydrate esterase ( CE4 ) domain that displays metal-dependent deacetylation activity on PNAG oligomers [14 , 23] . In B . bronchiseptica the formation of deacetylated PNAG ( dPNAG ) does not appear to be required for polymer transport through the outer-membrane , but is required for the formation of a robust three-dimensional biofilm [14] . The C-terminal domain of PgaB is annotated as a member of the glycoside hydrolase ( GH ) 13-like family . For the E . coli PgaB orthologue we previously hypothesized this domain may play a role in binding and translocating PNAG through the periplasmic space [24] . To date , there is no experimental evidence demonstrating hydrolytic activity for this putative GH domain . However , carbohydrate-cleaving enzymes have been identified and characterized in other exopolysaccharide ( EPS ) biosynthetic systems revealing important roles for their hydrolase activity in polymer production and/or biofilm formation . E . coli BcsZ is a periplasmic cellulase that has been shown to hydrolyze agar embedded carboxymethylcellulose and is required for efficient cellulose biosynthesis and export [25 , 26] . Listeria monocytogenes PssZ is a glycoside hydrolase important for production of the N-acetylmannosamine-galactose rich EPS that is required for cell aggregation [27] . In alginate biosynthesis the periplasmic lyase AlgL has been implicated in polymer biosynthesis [28] and is proposed to be a part the alginate trans-envelope complex . Glycoside hydrolases have also been shown to play a role in fungal EPS biosynthesis , as galactosaminogalactan produced by Aspergillus fumigatus requires Sph3 [29] . Although the exact function of these GHs in polymer biosynthesis remains unclear , there exists a strong body of data that shows EPS biosynthetic systems frequently contain an active and specific carbohydrate-cleaving enzyme . Herein , we demonstrate that the C-terminal domain of B . bronchiseptica PgaB ( formerly called BpsB , but referred to as PgaBBb henceforth ) and E . coli PgaB ( PgaBEc ) are dPNAG hydrolases that can cleave purified or in situ produced dPNAG , disrupt pre-formed PNAG-dependent biofilms , and potentiate antibiotic killing by gentamicin . Furthermore , our structure-function analysis of PgaB suggests that deacetylated PNAG is the substrate for hydrolysis , identifies a GlcN-GlcNAc-GlcNAc motif required for cleavage of the polymer , and defines a new GH family .
Our previous studies using PNAG oligomers ( up to a hexamer ) or artificial para-nitrophenyl glycoside substrates and PgaBEc failed to demonstrate glycoside hydrolase activity [23 , 24] . As we obtained similar results for PgaBBb [14] , we hypothesized that PgaB may require longer , high molecular weight PNAG for GH activity to occur . Since a reducing sugar assay with purified exopolysaccharide had been successfully used previously to demonstrate GH activity for PslG and Sph3 [29 , 30] , we purified dPNAG ( ~5% deacetylated ) from S . aureus and tested whether incubating this substrate with PgaBEc or PgaBBb resulted in an increase of soluble reducing sugars . We tested full-length constructs of PgaB that contain both the deacetylase ( DA ) and GH domain ( Ec-DAGH and Bb-DAGH , which share 38% sequence identity ) , the isolated GH domains ( Ec-GH and Bb-GH , which share 44% sequence identity ) , as well as the PNAG glycoside hydrolase dispersin B ( DspB ) ( Fig 1 ) . Incubation of purified dPNAG ( Fig 1B ) with 2 μM Ec-DAGH , Ec-GH , Bb-DAGH , Bb-GH , and DspB for 24 h resulted in an increase in concentration of soluble reducing sugars of 305 ± 19 , 232 ± 7 , 981 ± 22 , 886 ± 13 , and 3125 ± 124 μM respectively ( Fig 1C ) . Given that the initial dPNAG substrate solution had 103 ± 10 μM of reducing sugars , this equates to a 3 . 0 , 2 . 3 , 9 . 5 , 8 . 6 and 30 . 6 -fold increase in the number of reducing sugars for Ec-DAGH , Ec-GH , Bb-DAGH , Bb-GH , and DspB , respectively , over the course of the assay . As PgaBBb exhibited a higher level of GH activity than PgaBEc , we monitored the reaction of PgaBBb over 2 h . The 2 h time-course showed an increase in reducing sugars that approached saturation with similar amounts of reducing sugar produced to that of the 24 h samples ( Fig 1C ) . DspB has been shown to disperse PNAG-dependent biofilms in ex vivo assays [12 , 31–33] . Therefore , to further probe the activity of PgaB , we examined whether PgaBBb could disrupt pre-formed PNAG biofilms produced by E . coli and B . pertussis ( Fig 1D ) . We utilized three different bacterial strains that have been engineered to over-produce PNAG from either the pgaABCDEc or pgaABCDBb operons [6 , 34] . Incubating Bb-DAGH and Bb-GH with preformed E . coli or B . pertussis biofilms completely disrupted the biofilms within 2 h . To examine whether biofilm disruption was dependent on PgaBBb glycoside hydrolase activity and not the presence of the protein , we compared the sequence of PgaBBb to members of the GH13 family and identified D474 as the putative nucleophile required for hydrolysis . Thus , we constructed a D474N variant and examined its ability to disrupt preformed biofilms . The D474N mutant was unable to disrupt the PNAG biofilms produced by E . coli yet displayed a small , but significant ( p <0 . 01 ) , ability to disrupt the PNAG biofilm produced by B . pertussis . These data suggest that the observed reduction in biofilm biomass is a direct result of the catalytic activity of PgaBBb . B . pertussis is a slow growing bacterium that requires at least 72 h to produce a relatively small amount of biomass ( OD540 levels of around 1 ) . Therefore , to determine the effective concentration to disrupt 50% of the biofilm ( EC50 ) for PgaBBb we utilized the faster growing E . coli K-12 and S . carnosus TM300 PNAG-overproducing strains , and a clinical S . epidermidis strain [35] . Treatment of the preformed biofilms for 2 h with Bb-DAGH and Bb-GH disrupted the biofilms with EC50 values in the nM range . The EC50 values for the Bb-DAGH and Bb-GH were 6 . 7 ± 0 . 9 and 6 . 4 ± 0 . 6 nM , respectively , for S . epidermidis; 11 . 4 ± 0 . 3 and 11 . 7 ± 0 . 6 nM , respectively , for S . carnosus; and 80 . 6 ± 14 . 9 and 108 . 1 ± 8 . 3 nM , respectively for E . coli ( Fig 1E ) . Similar to the end-point assay , the D474N mutant showed little to no ability to disrupt the biofilm biomass . Collectively , these data suggest that the C-terminal domain of PgaBEc and PgaBBb exhibit dPNAG hydrolase activity and their catalytic function can disrupt biofilms produced from various biological sources . Bacteria form biofilms as a mechanism to evade the host immune response and to limit killing by antibiotics . To evaluate the potential of PgaB as a therapeutic agent , we tested the effect of serum on PgaB activity , examined whether PgaB could potentiate antibiotic killing , and compared these activities to DspB . Preformed biofilms of S . epidermidis and E . coli grown in the presence of TSB media were treated with Bb-GH or DspB in the presence and absence of fetal bovine serum and EC50 values determined . Serum had a minimal effect on the disruption activity of Bb-GH with an observed EC50 = 11 . 4 ± 1 . 3 nM ( without serum ) and 10 . 0 ± 1 . 3 nM ( with serum ) for S . epidermidis biofilms; and EC50 values of 38 . 8 ± 8 . 0 nM ( without serum ) and 18 . 3 ± 9 . 2 nM ( with serum ) for E . coli biofilms ( Fig 2A ) . DspB displayed 60–100 times higher activity compared to Bb-GH with EC50 values of 171 ± 27 pM ( without serum ) and 99 . 7 ± 11 . 2 pM ( with serum ) , and 526 ± 47 pM ( without serum ) and 202 ± 37 pM ( with serum ) for the S . epidermidis and E . coli biofilms , respectively ( Fig 2A ) . Next , preformed S . epidermidis and E . coli biofilms were incubated with gentamicin alone or in combination with either Bb-GH or DspB , and bacteria were enumerated after 20 and 4 h treatments , respectively ( Fig 2B ) . Gentamicin was chosen for these experiments as vancomycin has been shown previously to have minimal bactericidal effect on non-replicating cells [36] , and it displays bactericidal activity against Gram-positive and -negative bacteria . Biofilm-embedded S . epidermidis showed a modest but significant 1-log reduction in cfu/ml in the presence of 500 μg/ml gentamicin and Bb-GH , versus antibiotic alone ( Fig 2B ) . The enzyme was more effective at potentiating bacteria killing for biofilm-embedded E . coli , as there was a greater than 2-log reduction in cfu/ml in the presence of 50 μg/ml gentamicin and Bb-GH relative to antibiotic treatment alone ( Fig 2C ) . Biofilm treatment with the hydrolase alone had no effect on the bacteria . In comparison , DspB displayed a 2-log reduction versus gentamicin alone for both S . epidermidis and E . coli . Together these results further support the potential of PgaB as an antimicrobial agent albeit it has a higher EC50 value than DspB for biofilm dispersal . As the isolated and biofilm forms of PNAG are both partially deacetylated , we next set out to determine whether deacetylation of PNAG was required for PgaB glycoside hydrolase activity . To test this hypothesis we developed an assay similar to those utilized previously [21] , that allowed us to synthesize fully acetylated PNAG in situ using PgaCDEc-containing membranes and the UDP-GlcNAc activated sugar precursor as a substrate ( Fig 3A ) . After incubating the reaction mixture for 24 h and centrifugation of the product we noticed that it formed an insoluble pellet that was mechanically resistant to pipetting or crushing . Since control reactions without membranes or substrate did not yield a pellet , we hypothesized that the pellet was primarily constructed of PNAG . As high molecular weight PNAG is insoluble , we reasoned that if we could hydrolyze the polymer into short fragments it could then be analyzed using mass spectrometry ( MS ) . We therefore incubated the pellets with either Bb-DAGH or Bb-GH and found that the insoluble pellet was only disrupted in the presence of the full-length Bb-DAGH protein ( Fig 3A , Table 1 ) . We verified the in situ production of PNAG oligomers by analyzing the purified products of the hydrolysis reaction by MALDI-MS . This analysis revealed masses consistent with PNAG oligomers between 4–19 residues in length that were predominantly mono- or di-deacetylated ( Fig 3B ) . PNAG oligomer masses could only be detected in Bb-DAGH-hydrolyzed samples , suggesting that deacetylation of the polymer by the N-terminal domain of PgaB is required for Bb-GH activity on the fully acetylated in situ produced PNAG . To confirm that PNAG hydrolysis requires the deacetylase activity of PgaB and is not the consequence of increased polymer binding affinity due to the presence of the N-terminal domain , we repeated the hydrolysis reactions with the inactive deacetylase H49A and D114A Bb-DAGH variants [14] . These mutants were unable to disintegrate the PNAG pellet and did not produce any MS-detectable products ( lower panels in Fig 3B , Table 1 ) . Together these experiments strongly suggest that deacetylation of the polymer is required for the hydrolysis of in situ produced PNAG . To determine whether there was a preferred cleavage motif we further characterized the hydrolyzed dPNAG oligomers using MALDI-TOF MS-MS . Fragmentation of the most intense ion at a m/z ratio of 2031 . 71 , representing the reduced mono-deacetylated 10-mer , revealed a ( GlcNAc ) 7-GlcN- ( GlcNAc ) 2 sequence ( Fig 4A ) . MS-MS fragmentation analysis of all the mono-deacetylated PNAG oligomers observable above the m/z ratio of 1000 revealed that all oligomers had a fragmentation pattern consistent with a GlcN-GlcNAc-GlcNAc motif at their reducing end ( Fig 4B and S1 Fig ) . The di-deacetylated PNAG oligomers were also analyzed by MS-MS fragmentation ( S2A Fig ) . While structural heterogeneity in these samples precluded unambiguous assignment of the GlcN residues , GlcN was not observed at the reducing end of the oligomers . The spectra are consistent with the two GlcN units positioned between sites -2 and -5 ( S2B Fig ) . While the samples might not contain all possible combinations of sugars at the reducing end , the GlcNAc-GlcNAc-GlcNAc combination can be excluded . The exclusive occurrence of the GlcN-GlcNAc-GlcNAc motif in mono-deacetylated samples suggests that this motif is recognized during dPNAG cleavage . The presence of the same motif in the di-deacetylated oligomers is also likely , although a GlcNAc-GlcN-GlcNAc motif cannot be fully ruled out due to ambiguity in data interpretation . Based on amino acid sequence , Bb-GH and Ec-GH ( 44% sequence identity ) have been grouped into the putative GH13-like family [37] . The GH13 family in the CAZy database [38] is a large sequence and structurally diverse family that hydrolyzes substrates containing α-glucoside linkages . Despite their functional diversity , the GH13 family shares a core ( β/α ) 8 TIM-barrel fold , and enzymatically active members contain a conserved triad of residues that include a nucleophile ( aspartate ) , an acid/base ( glutamate ) , and a residue that stabilizes the substrate transition state ( aspartate ) . Our previous structural studies revealed that Ec-GH had a similar overall structure to GH18 , GH20 , and GH13 family members , but lacked the respective catalytic consensus motifs found in these GH families [14 , 24] . This also holds true for Bb-GH , prompting us to initiate structural studies on Bb-GH to gain a better understanding of the structure and function of this GH domain . Bb-GH crystallized in the monoclinic space-group P21 . Diffraction data were collected to 1 . 76 Å resolution and the structure was determined by molecular replacement . Structural refinement produced a final model with good geometry and R factors Rwork and Rfree of 15 . 1% and 16 . 7% , respectively ( Table 2 ) . Examination of the crystal packing in the asymmetric unit revealed two Bb-GH molecules packed in a head to head fashion . We do not believe this assembly is biologically relevant as size exclusion chromatography shows that Bb-GH elutes as a monomer in solution ( S3A Fig ) . Bb-GH adopts a ( β/α ) 8 barrel fold observed in GH13 family members , and also common to a wide range of other glycoside hydrolases families ( Fig 5A ) . Along the top face of the ( β/α ) 8 barrel is an extended electronegative groove that is approximately 43 Å long and 11 Å wide . The groove is formed by residues located at the C-terminal end of the core barrel β-strands and the eight β-α-connecting loops ( Fig 5A ) . There is also a defined pocket at the center of the groove ( Fig 5B ) . This central pocket and most of the electrostatic groove contain residues that are highly conserved among orthologues ( Fig 5C ) . The central pocket is the deepest part of the groove; it has a narrow section ( slice 1 in Fig 5D ) and a wide section ( slice 2 in Fig 5D ) that contains the center of the β-barrel . Surveying the PDB for structurally related proteins using the DALI webserver [41] returned results similar to those previously reported for Ec-GH [23 , 24] . The CAZy database groups glycoside hydrolases based on their primary sequence [38] , however members of a given GH family act on similar ( if not the same ) substrate ( s ) . The top hits had good Z-scores of 19–24 and were predominately from families GH42 , GH35 , GH14 , GH18 , and GH20 . The GH20 family contains the only currently characterized PNAG hydrolase , DspB [31–33 , 42 , 43] . Superposition of Bb-GH and DspB revealed that D474 of Bb-GH aligns with the catalytic aspartate residue of DspB , D183 . However , Bb-GH lacks a structural equivalent to the second catalytic residue of DspB , E184 ( Fig 6A and 6B ) . A similar scenario is observed when Bb-GH is superposed with the GH18 chitinase AMCase ( Fig 6C ) . Bb-GH also displays significant structural differences when compared to the canonical representative of the GH13 family , α-amylase . Bb-GH does not contain the D206 , E230 , and D297 catalytic triad residues found in α-amylase ( Fig 6D ) . Most notable is the absence of a residue that contributes to the stabilization of the transition state . In Bb-GH the closest aspartate and glutamate residues D618 , E613 , E585 , and D326 are over 8 Å displaced and thus unlikely to replace D297 . Considering that neither PgaBBb nor PgaBEc have sequence or structural similarity to the active site to DspB or other closely related GH family members , we propose that the C-terminal domain of PgaB defines a new GH family , GH153 . Given our hypothesis that substrate recognition and the PgaBBb active site differ from previously characterized glycoside hydrolases , we utilized mutagenesis to determine which residues are important for dPNAG hydrolase activity . Initially , we constructed a set of Bb-GH mutants focusing on conserved charged residues in the active site groove within 10 Å of the central cavity . The function of each mutant was evaluated using the reducing sugar and biofilm disruption assays . The reducing sugar assay revealed that mutation of D326 , D328 , H473 , D474 , and E585 to alanine reduced the activity of the enzyme by more than 90% ( Fig 7A ) . We were unable to assess the activity of the D364A variant as this protein was unstable and aggregated during purification . To help differentiate between residues involved in substrate binding and catalysis , we also mutated the charged residues to either asparagine or glutamine as appropriate . An increase in activity relative to the alanine variant was observed for D326N , D328N and E585Q , with E585Q recovering wild-type levels of activity . No increase in activity was observed for the D474N mutant relative to its alanine counter-part . The D364N mutant exhibited ~25% of wild-type activity comparable to the activity observed for the D326N and D328N mutants . The central cavity of the electronegative groove also contains three tyrosine residues and a methionine . To probe the role of these residues , tyrosine to phenylalanine and methionine to glutamate variants were constructed . Mutation of Y549 and Y648 to phenylalanine had little effect on the activity , however , the Y329F variant showed a 2-fold reduction in activity relative to wild-type . Although we had hypothesized that replacing M584 with glutamate may aid in the binding or catalysis of dPNAG , the M584E variant was completely inactive ( Fig 7A ) . We further analyzed the Bb-GH variants using our biofilm disruption assay , to determine whether differences in the activity would be observed when E . coli dPNAG was used as the substrate . We observed that results from the biofilm disruption assay correlate well with the reducing sugar assay . In general , variants with less than 30% residual activity in our reducing sugar assay were unable to disrupt biofilms ( Fig 7B and 7C ) . Taken together , the mutagenesis data suggests that residues D326 , D328 , D364 , and E585 play a role in substrate binding as the asparagine or glutamine mutants displayed detectable activity , while D474 is the most probable residue to be involved in catalysis as it is the only residue whose activity is ablated in both the asparagine and alanine variants . Our hydrolase activity analysis revealed that Bb-GH is 4 times more active than Ec-GH ( Fig 1C ) , however , a pairwise sequence alignment does not show any major difference in the residues tested by our mutagenesis analysis that could explain this result ( Figs 7D and 8A ) . Structural alignment of Bb-GH ( PDB 6AU1 ) and Ec-GH ( PDB 4P7L ) shows strong conservation with a root mean square deviation of 1 . 1 Å over 337 equivalent Cα atoms ( Fig 8B ) . The most significant structural differences are seen in loop 3 and 7 , which are involved in forming the sidewalls of the central pocket ( Fig 8B and 8C ) . Loop 7 in E . coli PgaB extends much further over the groove than in B . bronchiseptica , almost completely occluding the binding groove ( Fig 8C ) . Loop 3 is the longest loop in the orthologous PgaB structures and folds back into the active site pocket ( Fig 8C ) . Examination of the residues in loop 3 reveal significant amino-acid variation between Bb-GH and Ec-GH that may partially explain the difference in activity between the two enzymes . Although it is hard to predict the conformational flexibility of these loops , these findings suggest that a more restricted active site groove in PgaBEc might lead to reduce hydrolase activity due to lower binding affinity or reduced accessibility for dPNAG . Structures of Ec-GH bound to GlcNAc and GlcN monomers and a GlcNAc tetramer ( GlcNAc ) 4 have been determined previously [24] . Since the groove region is highly conserved across PgaB enzymes , we modeled these ligands into the Bb-GH structure to gain insight into the catalytic mechanism and how the GlcN-GlcNAc-GlcNAc motif is recognized by the enzyme ( Fig 9A ) . The ( GlcNAc ) 4 binding site is formed mainly by the loop region between β5 and α5 ( Fig 5A ) . The oligosaccharide is oriented with its non-reducing end closest to the central pocket ( Fig 9A ) . The O3 and O4 hydroxyls of the GlcNAc unit closest to the central pocket form hydrogen bonds with carboxylate side chain of E585 ( Fig 9A ) . This interaction is compatible with our mutagenesis data that suggests E585 plays a role in polymer binding but not catalysis . Given the location of the catalytic reside D474 and the distance to E585 , we hypothesize that the ( GlcNAc ) 4 oligomer occupies positions +2 to +5 relative to the site of cleavage ( -1/+1 ) ( Fig 9B ) . Reminiscent of position +2 , the oligomeric GlcNAc unit at position +4 forms hydrogen bonds via O3 and O4 hydroxyls to the carboxylate side chain of D480 , suggesting that this residue plays a role in polymer binding similar to E585 ( Fig 9B ) . Modeling of the GlcN and GlcNAc monomers identifies the -3 and -1 sugar binding sites . GlcN and GlcNAc monomers bind in the central pocket interacting with the proposed catalytic residue D474 , and three aspartate residues , D326 , D328 and D364 , respectively ( Fig 9A , S4 Fig ) . An additional GlcNAc monomer also binds to the +4 site ( Fig 9A ) . Examination of this GlcNAc reveals that its orientation is not the same as observed in the ( GlcNAc ) 4 oligomer , and not compatible with polymer binding . This is perhaps not unexpected as monomeric sugar units when binding to the protein would be able to adopt the most favourable conformation possible without strict constraints imposed when part of a longer polymer . Inspection of the individual sugar moieties in the -3 and -1 positions also suggests that while the sugar identifies a binding site , the orientations are unlikely compatible with polymer binding . For example , the C6 atom of GlcN that participates in the 1 , 6-linkage in the PNAG polymer is pointing towards the β-barrel cavity and not along the groove . In addition , the C1 atom , which would define the reducing end of a polymer , would result in a polymer that is oriented in the opposite direction relative to ( GlcNAc ) 4 . In the case of GlcNAc , the position of atom C1 would result in a polymer that has the same orientation as the ( GlcNAc ) 4 oligomer , but the acetyl-group is oriented along the groove and would clash with the GlcN in the -3 site ( S4 Fig ) . As demonstrated by the GlcNAc unit at the +4 site , we assume that monomeric sugar compounds indicate approximate binding sites of polymeric sugar units , but not necessarily the correct orientation . Combining the individual ligand binding sites suggest that a dPNAG oligomer containing 9 sugar units binds along the elongated and conserved binding groove of PgaB ( Fig 9B ) .
Treating chronic biofilm-associated infections is a significant medical problem . A better understanding of mechanisms involved in biofilm formation as well as new methods to disrupt or inhibit biofilms that render the bacteria more susceptible to antimicrobial agents and host defense mechanisms are in urgent need . In this report , we show that PgaB can hydrolyze dPNAG and demonstrate its potential use as a biofilm disruption agent . Our previous characterization of PgaB led to the hypothesis that the C-terminal domain was catalytically inactive [23 , 24] . However , leveraging our experience from investigating other exopolysaccharide systems , we show herein that the C-terminal domain of PgaB is a glycoside hydrolase that can cleave dPNAG . The presence of an active GH or carbohydrate-cleaving enzyme within bacterial EPS biosynthetic operons or fungal EPS gene clusters is an emerging trend [20 , 44] . The biological role of EPS-cleaving enzymes within the biosynthesis machinery is not fully understood and appears to be system specific . In some cases , such as for Acetobacter xylinum BcsZ [26] , A . fumigatus Sph3 [29] , and P . aeruginosa AlgL [28] , the enzyme is required for or imparts efficient exopolysaccharide biosynthesis and/or export . While in other cases the enzyme is dispensable for biofilm formation , like the P . aeruginosa PSL-polysaccharide hydrolase PslG [30] . However , overexpressing PslG results in impaired biofilm formation and less surface associated Psl , suggesting intracellular levels are critical during the Psl biosynthetic process [30] . Various functions for these carbohydrate-cleaving enzymes have been proposed , such as controlling polymer length , generating a secretion-competent form of the polymer , as well as degradation of excess polymer in the periplasm [25 , 45 , 46] . Previous studies showed complementation of a ΔpgaB strain with constructs lacking part of , or all of the C-terminal domain resulted in cell-retained material and abolished biofilm formation [46] . This suggests that the glycoside hydrolase activity of PgaB may be required for biofilm formation in E . coli . However , this interpretation is complicated by the observation of proteolytic degradation of the truncated PgaB constructs [46] and the fact that deacetylase activity of the N-terminal domain of PgaBEc requires the C-terminal GH domain [24] . As the PgaBEc N-terminal domain is not active in isolation , the observed phenotype could have arisen from the loss of deacetylase activity . This conclusion is supported by alanine mutagenesis studies on the C-terminal domain of the Y . pestis PgaB homologue , PgaBYp ( also known in the literature as HmsF ) as mutating conserved residues , equivalent to those required for dPNAG hydrolysis herein ( D364 and D474 ) , showed a normal biofilm phenotype [47] . However , phenotypic defects from single alanine variants can easily be masked by the increased gene copy number from complementing in trans . Interestingly , in Salmonella enterica the cellulase BcsZ , part of the cellulose biosynthesis system , has been reported to efficiently enable host colonization by functioning as a negative regulator of cellulose biosynthesis [48] . Negative regulation of biofilm related cellulose production has been linked earlier to increased virulence in Salmonella species [49] , indicating a role in balancing short- and long-term fitness . Further studies will be necessary to uncover the exact role for PgaB-dependent hydrolysis of dPNAG during biosynthesis , export , and biofilm maturation . In addition to functioning as a GH , exopolysaccharide-cleaving enzymes that reside in the periplasm have also been suggested to form part of larger protein complexes within the biosynthetic machinery . This provides a scaffold for the polymer to cross the periplasmic space bridging the inner and outer membrane components [20 , 44 , 50] . In E . coli , PgaB forms a complex with the periplasmic TPR domain of the outer membrane porin , PgaA [22] . This interaction appears to be critical as deletion of the TPR domain abolishes biofilm formation [22] . A similar interaction between PgaAYp and PgaBYp ( also known as HmsH and HmsF ) , the Y . pestis homologues , has also been observed using in vivo cross-linking studies [51] . Similarly , a recent study from our lab on the PEL polysaccharide system showed that PelA , an enzyme with PEL deacetylase and glycoside hydrolase activities , interacts with the TPR domain of the outer membrane porin PelB [52] . PelB has the same domain architecture as PgaA . Furthermore , when the activities of PelA were assessed in vitro , we found that the interaction of the TPR domain of PelB with full length PelA resulted in increased deacetylase and decreased glycoside hydrolase activity compared to PelA alone [52] . Considering these findings , it is tempting to speculate that the TPR domain of PgaA could also modulate the enzymatic activities of PgaB and regulate the temporal modification of PNAG during biosynthesis . Our previous molecular dynamic simulations [24] suggested a continuous inter-domain PNAG binding surface , and that the C-terminal GH domain would preferentially bind dPNAG over PNAG . Therefore , we postulated that the PNAG polymer is first deacetylated and then wraps around PgaB to the GH domain prior to export through PgaA . Unfortunately we were not able to determine the exact level of deacetylation in small-scale biofilms due to the low solubility of PNAG , which makes it difficult to unambiguously prove that deacetylation is required for hydrolysis in these assays . Instead , we used the PgaCD synthase complex to produce fully acetylated PNAG in vitro , and subsequently tested the effect of PgaB deacetylation on glycoside hydrolase activity . Our mass spectrometry data strongly suggests that the deacetylation of PNAG is required for PgaB glycoside hydrolase activity . This conclusion is supported by our hydrolase assays using in situ produced PNAG ( i . e . fully acetylated ) , which was only cleaved by full-length Bb-DAGH and not the isolated Bb-GH domain ( Fig 3 ) . However , until longer soluble PNAG oligomers are available , including those with single site-specific deacetylation of the polymer , we cannot completely rule out the possibility that PgaB could display glycoside hydrolase activity on fully acetylated PNAG . Our data also suggest a model where Bb-GH is an endo-acting GH , and is supported by MS/MS data that detected dPNAG oligomer cleavage products of 4 to 19 sugar units in length from the hydrolase assays . Furthermore , Bb-GH and Ec-GH lack hydrolytic activity on pNP-glycoside substrates and short PNAG oligomers and contain a pronounced and well-conserved substrate-binding groove suited to binding long stretches of polymer [24] . This is in contrast to the only other characterized PNAG hydrolase , DspB , which displays both exo and endo-acting hydrolysis of PNAG and has a shallow substrate-binding groove [53 , 54] . However , it does correlate with the bacterial cellulose and Psl biosynthesis systems , as BcsZ and PslG are both endo-acting glycoside hydrolases with long and deep substrate-binding clefts [25 , 26] . MS/MS analysis of the hydrolyzed dPNAG oligomers revealed unambiguously a GlcN-GlcNAc-GlcNAc motif at the reducing end of mono-deacetylated cleavage products , suggesting that the polymer orients itself in the binding groove with GlcN and GlcNAc sugar units positioned at sites -3 and -1 during catalysis , respectively ( Fig 9B ) . The position of the positively charged GlcN could be stabilized at site -3 by the negatively charged aspartate residues D326/D328/D364 . This is supported by our mutagenesis study that show residues in the -3 site are important for activity and suggests they may be involved in pre-orientating the polymer for cleavage at site -1 by the proposed catalytic residue D474 ( Figs 7 and 9B ) . Furthermore , the -3 site is located at the narrowest part of the groove ( Fig 5D ) and steric hindrance could therefore play a role in substrate specificity as the N-acetyl group may clash with residue side chains that line the wall . The observed hydrolysis of dPNAG by the GH domain of PgaB ( Fig 1C ) is consistent with the observation that the secreted form of PNAG found in bacterial biofilms is partially deacetylated [34 , 55] . The extent of deacetylation depends on the bacterial species and growth conditions , but has been reported to be between 3–25% [34 , 46 , 55] . Furthermore , we exploited the dPNAG hydrolase activity of Bb-GH to disrupt biofilms formed by PNAG-overproducing strains of B . pertussis , S . carnosus , and E . coli as well as a clinical isolate of S . epidermidis with EC50 values in the nanomolar range ( Fig 1D and 1E ) . This is underscored by the ability of PgaB to disrupt biofilms in the presence of serum and potentiate bacterial killing by gentamicin ( Fig 2 ) . The ability to disrupt biofilms formed by a variety of different Gram-negative and Gram-positive bacteria suggests that , like the previously characterized glycoside hydrolases DspB [31–33] , PelA [56 , 57] , PslG [30 , 56] , Sph3 [29 , 57] , NghA [58] , and PssZ [27] , PgaB may have therapeutic potential for treatment of a broad range of infections caused by PNAG-producing bacteria such as E . coli [34] , Staphylococcus spp . [55 , 59 , 60] , Bordetella spp . [6 , 7] , and others [19 , 33 , 61–66] . In direct comparison PgaB displays a higher EC50 value than DspB ( Fig 2A ) , which might be related to their individual biological roles or their substrate specificity . While we have no evidence to propose that PgaB plays a biological role in biofilm dispersal , a surface self-dispersal mechanism is employed by Aggregatibacter actinomycetemcomitans and potentially by Yersinia pseudotuberculosis that express DspB and NghA , respectively [58] . Bb-GH illustrates the challenge of categorizing glycoside hydrolases based solely on primary sequence without structural or functional characterization , as our data strongly suggest that Bb-GH comprises a new GH family , GH153 . Furthermore , the structural and functional characterization of PgaB presented herein suggests that the mechanism of periplasmic processing and outer-membrane export of dPNAG polymer involves deacetylation and limited hydrolysis in concurrent order , and establishes Bb-GH as a potential therapeutic agent for selective treatment of PNAG-dependent biofilm infections .
Bacterial strains , plasmids , and oligonucleotide primers used in this study are described in Table A in S1 Text . PgaBBb constructs were derived from B . bronchiseptica RB50 ( accession number WP_010926292 ) , and the C47S variant of PgaBBb was used for the Bb-DAGH and Bb-DA constructs to eliminate non-specific cysteine cross-linking during purification . The C-terminal domain from PgaBBb encoding residues 318–670 was cloned into the pET28a expression vector using PCR with pET28-Bb-DAGH as template and primers 318 Fwd and 670 Rev that contain an NdeI and HindIII site , respectively . The resulting plasmid pET28-Bb-GH , encodes a thrombin-cleavable N-terminal hexa-histidine tag fused to Bb-GH . The Bb-GH variants were all cloned using the QuikChange Lightning site-direct mutagenesis kit using plasmid pET28-Bb-GH as template and the respective variant primers listed in Table A in S1 Text . All proteins were expressed and purified as described previously for Bb-DAGH [14] and Ec-DAGH [67] . The purified PgaBBb and PgaBEc constructs were >95% pure as judged by SDS-PAGE and stable for approximately 2 weeks at 4°C or 6 months at -20°C . A codon-optimized gene encoding DspB ( accession number AAP31025 ) was purchased from BioBasic and sub-cloned into the pET24a expression vector using the NdeI/XhoI restriction sites . The resulting plasmid pET24a-DspB encodes a non-cleavable C-terminal hexa-histidine tag fused to DspB , which was used to express and purify DspB as described previously [31] . To probe for GH activity , the following artificial glycoside hydrolase substrates were tested: pNP-α-galactose , pNP-β-galactose , pNP-α-GalNAc , pNP-β-GalNAc , pNP-α-glucose , pNP-β-glucose , pNP-β-mannose , and pNP-β-GlcNAc . The pNP glycoside substrates were dissolved in DMSO at a concentration of 50 mM . The enzymatic reaction ( 100 μl ) contained 2 . 5 mM pNP glycoside substrate and 40 μM Bb-GH in 100 mM HEPES buffer pH 7 . 0 . Reactions were initiated by the addition of substrate and allowed to proceed for 120 min . The reaction was monitored for the appearance of 4-nitrophenyl in real time at 405 nm . The extraction , purification , and level of deacetylation of dPNAG from S . aureus strain MN8m were performed as described previously [62 , 68] . Lyophilized dPNAG was dissolved in ice-cold 5 N HCl by pipetting and vortexing . The solution was then placed on ice and titrated to neutrality ( pH 7–8 ) by slowly adding ice cold 5 N NaOH . The final solution of dPNAG was slightly turbid and ranged between 3–6 mg/ml . Determination of carbohydrate reducing-ends was performed as described previously [69] . Briefly , in a 50 μl reaction 2 μM of a PgaB construct or DspB was incubated with 2 mg/ml of dPNAG in 100 mM HEPES pH 7 . 0 for 2 or 24 h . The sample was split into two 20 μl aliquots and treated with 0 . 5 M NaOH , then the dithiotreitol/3-methyl-2-benzothiazolinone hydrozone solution ( 1:3 mg/ml ratio ) , and heated at 80°C for 15 min . A solution containing 0 . 5% ammonium iron ( III ) sulfate , 0 . 5% sulfamic acid , and 0 . 25 N HCl was added , mixed , and cooled to room temperature . A volume of 100 μL was then transferred to a 96-well clear bottom plate and the absorbance was measured at 620 nm using a SpectraMax M2 plate reader ( Molecular Devices , Sunnyvale , CA ) . Protein and dPNAG in 100 mM HEPES pH 7 . 0 were used as background controls . GlcN solutions were used to determine a standard curve to determine the concentration of reducing sugars . E . coli DH5α biofilm assay: E . coli DH5α containing plasmid pMM11 was grown overnight in LB broth supplemented with 40 μg/ml chloramphenicol with shaking at 200 rpm , diluted to an OD600 of 0 . 05 into fresh media , and 100 μl was added to individual wells of 96 well cell culture plates ( Costar 3596 ) and incubated statically for 24 h at 25°C . Non-adherent cells and media were removed by washing the plate three times with deionized water . The wells were then incubated with 100 μl of 100 mM HEPES pH 7 . 0 containing 1 . 3 μM of PgaBBb at room temperature for 2 h with gentle shaking . The wells were then washed three times with deionized water , stained with 150 μl of 0 . 1% ( w/v ) crystal violet for 10 min , and washed again three times with deionized water . The remaining dye was solubilized with 100 μl of 95% ( v/v ) ethanol for 10 min with rotation , after which time the absorbance was measured at 540 nm . B . pertussis 536 biofilm assay: B . pertussis strain 536 Δbps carrying plasmid pMM11 was grown overnight in Stainer Scholte ( SS ) media supplemented with 10 μg/ml chloramphenicol , diluted to an OD600 of 0 . 1 into fresh media , and 250 μl of normalized culture was added to individual chambers of coverglass slides ( 8 chamber Lab Tek II coverglass system ) and incubated for 72 h at 37°C . Non-adherent cells and media were removed by washing the chamber three times with deionized water . Chambers were then incubated with 250 μl of 100 mM HEPES pH 7 . 0 containing 1 . 3 μM of PgaBBb at room temperature for 2 h . The reaction was then quenched by washing the coverslides three times with deionized water , and the remaining biomass was quantified using crystal violet as described above for E . coli DH5α biofilm . S . epidermidis biofilm assay: Cultures of S . epidermidis clinical isolate SE801 were grown overnight at 37°C with shaking at 200 rpm in LB broth without antibiotics . The next day cultures were sub-cultured 1:100 into tryptic soy broth ( TSB ) , mixed thoroughly and 100 μl was added to individual wells of a sterile Cellbind surface plate ( Corning ) and the plates were incubated statically for 24 h at 26°C to allow for biofilm formation . To eliminate edge effects , ~200 μl of sterile water was placed in all outside wells and the plate was sealed with parafilm . After incubation non-adherent cells and media were removed by washing the plate with deionized water three times . The wells were filled with 95 μl of 100 mM HEPES buffer pH 7 . 0 followed by 5 μl of varying concentrations of protein . Reactions were incubated for 2 h at 25°C on a rotating nutator at which time , the reaction was quenched by washing the plates with deionized water three times . The wells were then stained with crystal violet , solubilized , and quantified as outlined above for DH5α biofilms but using 595 nm as the wavelength . S . carnosus TM300 biofilm assay: Cultures of S . carnosus TM300 containing pTXicaADBC were grown overnight at 37°C with shaking at 200 rpm in LB broth supplement with 5 μg/ml tetracycline . The next day cultures were sub-cultured 1:100 into tryptic soy broth ( TSB ) supplemented with 0 . 5% ( w/v ) xylose and 5 μg/ml tetracycline , mixed thoroughly and 100 μl was added to individual wells of a sterile Cellbind surface plate ( Corning ) and the plates were incubated statically for 24 h at 26°C to allow for biofilm formation . To eliminate edge effects , ~200 μl of sterile water was placed in all outside wells and the plate was sealed with parafilm . After incubation non-adherent cells and media were removed by washing the plate with deionized water three times . The wells were filled with 95 μl of 100 mM HEPES buffer pH 7 . 0 followed by 5 μl of varying concentrations of protein . Reactions were incubated for 2 h at 25°C on a rotating nutator at which time , the reaction was quenched by washing the plates with deionized water three times . The wells were then stained with dye , solubilized , and quantified as outlined above for DH5α biofilms but using 595 nm as the wavelength . E . coli K-12 biofilm assay: Cultures of E . coli K-12 TRXWMGΔABCD complimented with pPGA372 were grown overnight at 37°C with shaking at 200 rpm in LB broth supplemented with 100 μg/ml kanamycin and 200 μg/ml ampicillin . Overnight cultures were sub-cultured 1:100 into LB broth supplemented with antibiotics , mixed thoroughly , and 100 μl was added to individual wells of a sterile 96-well polystyrene round bottom microtiter plate ( BD Falcon ) . The cultures were incubated statically for 24 h at 26°C to allow for biofilm formation . Biofilm formation quantification and dose response curves were determined as described above for the DH5α ( 540 nm ) and S . epidermidis ( 595 nm ) biofilms , respectively . Biofilm disruption assays including serum were conducted in 96-well Cellbind surface plates ( Corning ) as described above for S . epidermidis SE801 and E . coli K-12 strains but with 10% fetal bovine serum ( FBS ) added in the disruption solution containing TSB and varying concentrations of Bb-GH or DspB . Biofilms were grown for over 24 h in 96-well Cellbind surface plates ( Corning ) as described previously for the S . epidermidis SE801 and E . coli K-12 biofilm disruption assays but using TSB as growth media for both strains . TSB was removed and biofilms were washed twice with PBS . Reactions were performed in TSB supplemented with 10 μM Bb-GH or DspB and 500 or 50 μg/ml gentamicin for S . epidermidis SE801 and E . coli K-12 biofilms , respectively . Cultures that were not treated or were only treated with hydrolase or antibiotic served as controls . Reactions were incubated for 20 h ( S . epidermidis SE801 ) and 4 h ( E . coli K-12 ) at 37 °C . Afterwards , for reactions in the presence of hydrolase the reaction media was serially diluted in PBS . For reactions in the absence of hydrolase the supernatant was removed and kept on ice while biofilms exposed to 10 μM Bb-GH or DspB to disrupt the biofilm-embedded bacteria . After 10 min the suspended cells and supernatant were combined and serially diluted in PBS . Dilutions were plated on LB agar plates at 37 °C and enumerated after 24 h . E . coli K-12 pgaC and pgaD were cloned into the pETDuet-1 co-expression vector using PCR with pPGA372 as template , restriction digest , and ligation cloning ( BamHI and HindIII , and NdeI and XhoI sites , respectively ) . This produced the expression plasmid pETDuet-PgaCD that encodes for the co-expression of PgaC with a non-cleavable N-terminal His-6 tag and PgaD . The wspR-R242A variant was subcloned from pET28-WspR R242A [70] into the pET24 vector between the NdeI and XhoI sites . This produced the expression plasmid pET24-WspR R242A that encodes WspR R242A without a purification tag . BL21 CodonPlus cells transformed with pETDuet-PgaCD and pET24-WspR R242A were grown overnight at 37°C with shaking in LB with 100 μg/ml ampicillin and 50 μg/ml kanamycin , and then sub-cultured into 1 L LB and grown to an OD600 = 0 . 4–0 . 5 . The culture was moved to 18°C for 20–30 min until the OD600 = 0 . 6–0 . 7 and was induced with 1 mM IPTG and incubated overnight . Cells were harvested by centrifugation at 5 , 000 x g for 15 min , re-suspended in 40 ml of PgaCD lysis buffer ( 50 mM HEPES pH 7 . 5 , 300 mM NaCl , 1 mM TCEP , 5% ( v/v ) glycerol , and a protease tablet ) , and lysed by homogenization through an Emulsiflex-c3 ( 4 passes , 10-15K psi ) . Cellular debris was removed by centrifugation at 20 , 000 x g for 30 min , and the supernatant was further centrifuged at 200 , 000 x g for 60 min to pellet the membranes . Membranes were isolated and homogenized in a hand press twice in PgaCD lysis buffer with 20% ( v/v ) glycerol . Aliquots ( 50–100 μL ) were placed into 1 . 5 mL tubes and utilized immediately in assays or stored at -20°C until required . Production of c-di-GMP for the hydrolase assays was synthesized and purified as described previously [70] . PgaCD membrane aliquots were incubated with 10 mM UDP-GlcNAc , 5 mM MgCl2 , and 1 mM c-di-GMP to a final volume of 200 μl . Tubes were allowed to sit for 24 h at 30°C and after 5 min of centrifugation at 7 , 400 x g on a table-top centrifuge a pellet was formed . Pipetting lifted the pellet from the Eppendorf tube bottom and it was washed three times with water . The pellet was then incubated to 200 μl of 100 mM HEPES buffer pH 7 . 0 and 2 μM PgaB protein for 12 h . Reactions were quenched by purification as described below . Samples were purified by modifying a protocol used for the purification of alginate oligosaccharides [71] . Reactions were quenched by applying the entire reaction to a pre-equilibrated Grace Alltech Extract-Clean Carbograph column . The column was washed with 6 ml of dH20 , 25% ( v/v ) acetonitrile , and 100% ( v/v ) acetonitrile . All fractions were then dried using a speed vacuum and reconstituted in 0 . 2% ( v/v ) trifluoroacetic acid ( TFA ) for MALDI-TOF MS . MS/MS analysis was ran after reduction of the sample . Samples were incubated overnight in 1 mg/ml NaBH4 in 1 M NH4OH prior to being neutralized with acetic acid and purified on Hypersep Hypercarb SPE column ( ThermoFisher ) and eluted with 50% ( v/v ) acetonitrile . Samples were spotted in a 1:1 ratio with 5 mg/mL of 2 , 5-dihydroxybenzoic acid matrix in acetonitrile-0 . 2% TFA ( 70:30 , v:v ) . Analysis was performed on the UltrafleXtreme MALDI-TOF/TOF in positive reflector mode recording 5 , 000 laser shots per sample in MS , and 10 , 000 for MS/MS . PNAG oligomers were only found in the 25% ( v/v ) acetonitrile fractions . Purified Bb-GH was concentrated to 10 mg/ml and screened for crystallization conditions at 20°C using hanging-drop vapour diffusion in 48-well VDX plates ( Hampton Research ) with the MCSG 1–4 sparse matrix suites ( Microlytic ) . An initial crystallization hit was obtained from MCSG-1 condition #77 . Optimized crystals approximately 500 × 150 × 50 μM in dimension were grown using 8 . 6 mg/mL Bb-GH with a 3 μl drop with equal amounts of protein and precipitant equilibrated against 250 μl precipitant solution ( BIS-TRIS pH 6 . 9 , 0 . 2 M lithium sulfate , and 1 . 7 M ammonium sulfate ) after one week of incubation . The crystals were cryoprotected for 5–10 s in reservoir solution supplemented with 25% ( v/v ) ethylene glycol prior to vitrification in liquid nitrogen . Diffraction data were collected at a wavelength of 1 . 075 Å on beam line X29A at the National Synchrotron Light Source ( NSLS ) ( Table 2 ) . The data were indexed , integrated , and scaled using HKL2000 [72] . The structure was determined by molecular replacement with PHENIX AutoMR [40] using Ec-GH ( PDB 4P7L ) [24] as a search model . The resulting electron density map enabled PHENIX AutoBuild [40] to build ~95% of the protein . The remaining residues were built manually in COOT [73] and alternated with refinement using PHENIX . REFINE [40] . Translation/Libration/Screw ( TLS ) groups were used during refinement and determined automatically using the TLSMD web server [74 , 75] . Structure figures were generated using PYMOL Molecular Graphics System ( DeLano Scientific , http://www . pymol . org/ ) , quantitative electrostatics were calculated using PDB2PQR [76 , 77] and APBS [78] , and conservation levels were generated with ConSurf [79] . Programs used for crystallographic data processing and analysis were accessed through SBGrid [80] . | From plaque on teeth to infections in the lungs of cystic fibrosis patients , biofilms are a serious health concern and difficult to eradicate . One of the key building blocks involved in biofilm formation are polymeric sugar compounds that are secreted by the bacteria . Our work focuses on the biopolymer poly-β ( 1 , 6 ) -N-acetyl-D-glucosamine ( PNAG ) , which is produced by numerous pathogenic organisms . Deacetylation of PNAG by the N-terminal domain of PgaB is a critical step in polymer maturation and is required for the formation of robust biofilms . Herein , we show that the C-terminal domain of PgaB is a glycoside hydrolase active on partially deacetylated PNAG , and that the enzyme disrupts PNAG-dependent biofilms and potentiates killing by antibiotics . Only deacetylated PNAG could be cleaved , suggesting that PgaB deacetylates and hydrolyses the polymer in sequential order . Analyzing the chemical structure of the cleaved dPNAG fragments revealed a distinct motif of sugar units . Structural and functional studies identify key amino acids positioned in an elongated polymer-binding groove that potentially recognize the sugar motif during cleavage . Our study provides further insight into the mechanism of periplasmic PNAG modification , and suggests PgaB could be utilized as a therapeutic agent to eliminate biofilms . | [
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] | 2018 | PgaB orthologues contain a glycoside hydrolase domain that cleaves deacetylated poly-β(1,6)-N-acetylglucosamine and can disrupt bacterial biofilms |
Hypermutation of the immunoglobulin ( Ig ) genes requires Activation Induced cytidine Deaminase ( AID ) and transcription , but it remains unclear why other transcribed genes of B cells do not mutate . We describe a reporter transgene crippled by hypermutation when inserted into or near the Ig light chain ( IgL ) locus of the DT40 B cell line yet stably expressed when inserted into other chromosomal positions . Step-wise deletions of the IgL locus revealed that a sequence extending for 9 . 8 kilobases downstream of the IgL transcription start site confers the hypermutation activity . This sequence , named DIVAC for diversification activator , efficiently activates hypermutation when inserted at non-Ig loci . The results significantly extend previously reported findings on AID-mediated gene diversification . They show by both deletion and insertion analyses that cis-acting sequences predispose neighboring transcription units to hypermutation .
Vertebrate B cells are able to diversify their rearranged immunoglobulin ( Ig ) genes by hypermutation , gene conversion and class switch recombination . All three phenomena require expression of Activation Induced cytidine Deaminase ( AID , NC_006088 ) [1]–[3] which most likely initiates Ig gene diversification by deaminating cytidines within the mutating and recombining sequences [4] , [5] . A further requisite for hypermutation and switch recombination is the transcription of the Ig genes and the switch regions respectively [6] , [7] . Sequence analysis of transcribed non-Ig genes from AID expressing B cells revealed either no or only a low number of mutations compared to Ig genes [8] . A recent study of a large number of expressed genes in B cells found a significantly higher number of mutations in wild-type mice than in AID knock-out mice [9] . However , the mutation rates for the non-Ig genes in AID expressing B cells were still orders of magnitude lower than for the Ig genes . To explain this difference between Ig and non-Ig genes it has been postulated that cis-acting sequences in the Ig loci activate hypermutation possibly by recruiting AID . However , intense efforts did not succeed to unambiguously define these sequences for the murine and human Ig loci [10] . Whereas studies using chimeric reporter genes in transgenic mice indicated that certain Ig enhancers and their surrounding sequences conferred hypermutation activity [11]–[14] , deletion of Igκ enhancers in knock-out mice did not prevent hypermutation of the Igκ gene ( CAA36032 ) [15] , [16] . At least one murine B cell line [17] and AID expressing fibroblasts [18] mutated transcribed transgenes in the absence of nearby Ig locus sequences , further confounding the issue of whether cis-acting regulatory sequences are needed for hypermutation . The chicken B cell line DT40 diversifies its rearranged Ig light chain ( IgL ) gene by gene conversion in the presence of nearby pseudo V ( ψV ) genes [2] and by hypermutation , if the ψV genes are deleted [19] . Both activities strictly depend on the expression of AID . Consistent with the idea that the absence of homologous gene conversion donors leads to hypermutation , a Green Fluorescent Protein ( GFP , AAB08058 ) transgene is rapidly diversified by mutations when inserted into the rearranged IgL locus [20] . The hypermutation activity of DT40 appeared however to be limited to the IgL locus , because no mutations were found in the highly transcribed Elongation Factor 1 alpha gene ( NP_989488 ) [19] . This was confirmed by a recent study showing that neither the VpreB3 ( NC_006102 ) nor the Carbonic Anhydrase ( XP_415218 ) gene , immediate upstream and downstream neighbors of the IgL locus respectively , showed sequence heterogeneity in DT40 [21] . DT40 has been proposed as a model to study the mechanism of Ig hypermutation [19] . Compared to mice and humans , the chicken IgL locus including the ψV genes is compact spanning only 30 kb . Furthermore , targeted integration of transfected DNA constructs in DT40 allows the introduction of deletions and insertions at defined chromosomal positions . Encouraged by these advantages we decided to search for the elusive cis-acting hypermutation control sequence in the IgL locus of DT40 .
We have previously demonstrated that a GFP transgene in DT40 rapidly accumulates mutations , when integrated at the position of the promoter of the rearranged IgL locus [20] . The hypermutation activity depended on AID expression and could be visualized by the appearance of cells displaying decreased green fluorescence due to detrimental GFP mutations . To exploit this phenomenon , we designed a new expression cassette named GFP2 which consisted of the strong RSV promoter followed by the GFP coding region , an internal ribosome entry site ( IRES ) , the blasticidin resistance gene ( P19997 ) and the SV40 polyadenylation signal . GFP2 was incorporated into the targeting construct pIgLGFP2 ( Figure 1A ) in the opposite transcriptional orientation of the IgL gene to minimize interference between transcriptional and post-transcriptional regulation of the GFP2 transgene and the IgL gene . Transfection of pIgLGFP2 into the conditionally AID expressing clone AIDR2 yielded a number of transfectants named IgLGFP2 in which targeted integration had substituted the IgL promoter by the GFP2 transgene . Fluorescence activated cell sorting ( FACS ) analysis of subclones from two independent primary transfectants revealed median values of 12 . 8% and 14 . 5% decreased green fluorescence two weeks after subcloning ( Figure 1C and 1D ) . The result confirms our previous study indicating that the GFP2 transgene is mutated at high rate within the rearranged IgL locus and that cell populations with decreased green fluorescence can be used to quantify this hypermutation activity . Targeted integration was used to insert the GFP2 reporter at various distances from the IgL locus into chromosome 15 [22] ( Figure 1B and Figure S1A ) . FACS analysis of primary transfectants ( Figure 1C and Table S1 ) and their subclones ( Figure 1D ) revealed that the medians of decreased green fluorescence fell to about 3% at the +26 kb and the −15 kb positions , to 0 . 5% at the +52 kb position and to about 0 . 05% at the −135 kb position . The medians of decreased green fluorescence were only around 0 . 001% at the +52 , +26 and −135 kb positions in the absence of AID ( Figure 1C and 1D and Table S1 ) , indicating that the decreased green fluorescence was dependent on AID expression . Although we did not determine for the GFP2 insertions outside the IgL locus , whether the rearranged or the unrearranged allele was targeted , the results were representative for a large number of independent primary transfectants ( Table S1 ) . Thus , hypermutation of the GFP2 reporter was detectable at insertions up to 52 kb away from the IgL locus , but mutations declined with increasing distance and were barely detectable at the −135 kb position . Since surrounding sequences were unlikely to influence the post-transcriptional processing and translation of GFP2 transcripts , GFP2 transcription should be reflected by the green fluorescence of the cells independent of the transgene insertion site . Even in the case of mutating transgenes , GFP2 transcription levels could be deduced from the average green fluorescence of the major cell populations which most likely expressed the un-mutated GFP sequence . As seen by FACS analysis , the average green fluorescence of the major cell populations varied slightly among the primary transfectants ( Figure 1C ) most likely reflecting chromosomal position effects . However , the transfectants +52IgLGFP2 and IgLGFP2 differed more than 20 fold in their median fluorescence decreases despite similar green fluorescence of their major cell populations . This strongly suggested that the hypermutation differences among the transfectants reflected the distance of the GFP2 insertion sites to the IgL locus and not variation in GFP2 transcription . The results could be explained by the presence of a cis-acting sequence which activated hypermutation in a distance dependent manner . We have named this putative regulatory sequence Diversification Activator ( DIVAC ) and attempted to map it by combining insertions of the GFP2 reporter with deletions of the IgL locus . To address the role of the ψV part of the IgL locus , a GFP2 construct ( Figure 2A , upper part ) was transfected into the clone ψV−AIDR1 [20] in which the entire 20 kb containing the ψV genes had been deleted . The transfectants ψV−IgLGFP2 expressed the GFP2 reporter at the position of the IgL promoter in the absence of the ψV locus . FACS analysis of ψV−IgLGFP2 primary transfectants showed sizable populations of cells showing decreased green fluorescence ( Figure 2B ) indicating that the GFP gene is diversified by hypermutation . To rule out that the decrease of green fluorescence is caused by gene silencing , the populations of high ( GFP-high ) and low ( GFP-low ) green fluorescence were sorted from one of the ψV−IgLGFP2 primary transfectants ( Figure 2C , left ) , and GFP mRNA levels was analyzed by semi-quantitative reverse transcription polymerase chain reaction ( RT-PCR ) , ( Figure 2C , right ) . RT-PCR of the EF1α mRNA served as a control . Although GFP-low cells showed on average more than 100-fold lower green fluorescence than GFP-high cells , the levels of GFP mRNA were comparable between sorted GFP-low , GFP-high and non-sorted cells , confirming that the decrease of green fluorescence is not due to silencing of GFP gene expression . FACS analysis of ψV−IgLGFP2 subclones revealed medians of 5 . 2% and 7 . 5% decreased green fluorescence ( Figure 2D ) , only one fold lower than the medians of the ψV positive IgLGFP2 subclones . As the difference between IgLGFP2 and ψV−IgLGFP2 subclones could be due to fluctuation effects or different AID expression levels in the AIDR2 and ψV−AIDR1 precursors , the ψV locus seems to exert little , if any stimulation on the hypermutation activity of the GFP2 reporter . ψV−IgLGFP2 still contained a 9 . 8 kb fragment of the rearranged IgL locus extending from the IgL transcription start site until the 3′ end of the carbonic anhydrase gene and referred to in the following as fragment ‘W’ . To test the relevance of this fragment , a GFP2 construct was transfected into the clone ψV−IgL− in which the entire rearranged IgL locus had been replaced by the puromycin resistance gene ( P42670 ) . The resulting transfectants ψV−IgL− , GFP2 had the puromycin resistance gene replaced by the GFP2 reporter at the position of the deleted IgL locus ( Figure 2A , middle part , and Figure 2B ) . Subclones of ψV−IgL− , GFP2 showed medians of only 0 . 01% and 0 . 02% decreased green fluorescence ( Figure 2D ) , more than 100 fold lower than the medians of ψV−IgLGFP2 subclones . This indicated that the ‘W’ fragment , absent in ψV−IgL− , GFP2 but present in ψV−IgLGFP2 , was required for hypermutation of the GFP2 transgene . ψV−IgL− cells were then transfected by a construct including the GFP2 transgene and the ‘W’ fragment ( Figure 2A , lower part ) . Subclones of the transfectants ψV−IgLW , GFP2 showed median green fluorescence decreases similar to the medians of ψV−IgLGFP2 subclones ( Figure 2D ) . Thus , the ‘W’ fragment efficiently activates hypermutation after reinsertion into the IgL locus as expected for a true DIVAC sequence . Controls confirmed that the appearance of cells with decreased green fluorescence reflected hypermutation in the GFP2 gene . As expected , the decrease of green fluorescence in ψV−IgLGFP2 cultures depended on AID , because subclones of the AID negative transfectant ψV−IgLGFP2AID−/− showed only very low medians of 0 . 001% decreased green fluorescence ( Figure 2D ) . Furthermore , 723 bp of the GFP open reading frame amplified from ψV−IgLGFP2 cells six weeks after subcloning showed an average of 0 . 9 nucleotide substitutions per sequence ( Figure 2E ) . As the doubling time of the DT40 cell line is about 10 hours , the mutation rate of the GFP gene of ψV−IgLGFP2 was calculated to be 1 . 3×10−5 mutation/bp/generation , which was similar to the mutation rate of the human hypermutating RAMOS cell line ( 2 . 2×10−5 mutation/bp/generation ) [23] , [24] . The most prevalent mutations were C to G and G to C transversions as previously observed for hypermutation of the IgL VJ segments from ψV deleted DT40 clones [19] . In contrast , only a very low number of nucleotide substitutions , most likely reflecting polymerase chain reaction ( PCR ) artifacts , were found in the GFP gene of ψV−IgL− , GFP2 cells ( Figure 2E ) . A new series of targeting constructs was transfected into ψV−IgL− to characterize the ‘W’ fragment by step-wise deletions ( Figure 3A ) . FACS analysis of subclones from the different transfectants showed a variable but progressive loss of hypermutation activity when the ‘W’ fragment was shortened from either end ( Figure 3C ) . The 4 kb ‘S’ fragment in the middle of the ‘W’ fragment , which included the previously identified IgL enhancer [25] , still produced median green fluorescence decreases of 2 . 7% and 1 . 7% . In contrast , the upstream ‘B’ and the downstream ‘P’ fragments on their own produced median green fluorescence decreases of 0 . 13% and 0 . 05% respectively , which are low in absolute terms , but clearly above the medians of ψV−IgL− , GFP2 . If either one of these fragments was combined with the ‘S’ fragment in the ‘F’ and ‘K’ fragments respectively , the median decreases of green fluorescence were elevated about 3 times . This suggested that the DIVAC of the chicken IgL locus consisted of a central core region and partially redundant flanking regions which contributed to the overall activity . Clearly , more detailed analysis is needed to define the location , the nature and the configuration of the active motifs within the IgL DIVAC . The average green fluorescence in the main population of ψV−IgLW , GFP2 was increased compared to ψV−IgL− , GFP2 ( Figure 2B ) perhaps due to the additional stimulation of the RSV promoter in GFP2 by the IgL enhancer of the ‘W’ fragment . However , the relatively small decrease of GFP2 transcription seen in ψV−IgL− , GFP2 was unlikely to be responsible for the more than 300 fold reduction of hypermutation . Analysis of the ‘W’ fragment deletions also strongly argued against the possibility that differences in hypermutation were caused by alterations of GFP2 transcription since primary transfectants of all fragments shown in Figure 3B showed similar GFP transcription levels . Similar levels of steady-state GFP2 transcripts were confirmed by RT-PCR ( Figure S2 ) . To confirm that the GFP2 reporter on its own is stably expressed at non-Ig loci , six loci on five different chromosomes [22] were targeted by transfection of GFP2 constructs into ψV−AIDR1 ( Figure 4A and Figure S1B ) . Neither the primary transfectants ( Figure 4B and Table S1B ) nor their subclones ( Figure 4C ) showed high percentages of decreased green fluorescence . Depending on the experiment and the insertion site , the medians of the subclones ranged from 0 . 02% to 0 . 22% indicating that the mutation rates of the GFP2 reporter at the chosen loci were 50 to 500 fold lower than at the IgL locus . However , these medians were about 2–10 fold higher than the medians of various subclones from AID negative transfectants ( Figures 2D and 5C ) confirming a slight increase in the background mutation rates in AID expressing B cells [9] . GFP2 was then inserted together with the ‘W’ fragment into the respective AID , BACH2 ( NC_006090 ) and RDM1 ( BAC02561 ) loci of ψV−AIDR1 ( Figure 5A and 5B ) . Subclones of the transfectants ψV−AIDW , GFP2 , ψV−BACH2W , GFP2 and ψV−RDM1W , GFP2 showed high median decreases of green fluorescence between 4 . 0% and 9 . 4% ( Figure 5C ) similar to the medians for ψV−IgLGFP2 and ψV−IgLW , GFP2 subclones . GFP2 hypermutation was AID dependent , since subclones of the AID negative transfectants ψV−AIDW , GFP2/− , ψV−BACH2W , GFP2/−AID−/− and ψV−RDM1W , GFP2/−AID−/− showed very low medians of decreased green fluorescence in the range of 0 . 001% to 0 . 03% . These results demonstrated that the ‘W’ fragment was able to activate AID mediated hypermutation at loci which otherwise did not support hypermutation .
We have identified a cis-acting sequence that is needed for hypermutation at the chicken IgL locus and able to activate hypermutation at other loci upon insertion . The 9 . 8 kb sequence , named DIVAC for diversification activator , extends from the IgL transcription start site towards the next downstream gene . DIVAC seems to be composed of multiple interacting regions . Whereas a 4 kb core sequence which includes the known IgL enhancer activates hypermutation more than 100 fold above background level , the flanking regions possess less activity on their own , but stimulate hypermutation when combined with the core . Surprisingly , DIVAC can act on either side of the hypermutation reporter and over long distances . Given the conservation of AID mediated Ig gene diversification during vertebrate evolution , the identification of the chicken IgL DIVAC should also be of relevance for mammals . Searches for cis-acting hypermutation regulatory sequences in transgenic mice showed that regions surrounding the Ig enhancers conferred hypermutation activity [11]–[14] . Intriguingly , the location and functional characteristics of these regions appear to be similar to the core of the chicken IgL DIVAC . In hindsight , the difficulty to unambiguously prove the existence of hypermutation activator sequences may relate to the large size of the murine Ig loci and the fact that DIVACs seem to be composed of multiple interacting regions . As each of the murine Ig loci possesses at least two enhancers at different positions , murine DIVACs may be composed of multiple discontinuous sequences . Bursal B cells and DT40 in the presence of nearby ψV genes diversify their rearranged IgL loci by gene conversion , suggesting that one of the physiological roles of the IgL DIVAC is activation of gene conversion . This is supported by a recent publication showing that a deletion of the rearranged IgL locus downstream of the C region stopped IgL gene diversification in ψV positive DT40 [26] . It seems also likely that DIVACs play a role for switch recombination which is accompanied by hypermutation of the recombining switch regions [27] . Possibly a dedicated DIVAC near the switch regions activates switch recombination . As the chicken IgL DIVAC can activate hypermutation in both directions over large distances , it is also conceivable that a single DIVAC in the heavy chain loci regulates both hypermutation and switch recombination . The mechanism of how a cis-regulatory sequence can activate hypermutation in neighboring transcription units remains speculative . Intriguingly , the chicken IgL DIVAC not only includes the IgL enhancer , but also seems to act like an enhancer by activating hypermutation over long distances in upstream or downstream target genes . A plausible hypothesis may be that DIVAC promotes the formation of protein complexes which first bind AID and then hand it over to the neighboring transcription initiation complex . Candidates for proteins involved in building such an AID docking station would be DNA binding factors which recognize sequence motifs within DIVAC . The described experimental system offers unique advantages to test this hypothesis .
The GFP2 construct was made by combining the RSV promoter-GFP open reading frame of pHypermut2 [20] with a PCR amplicon including an IRES [19] , the blasticidin resistance gene and the SV40 polyadenylation signal [28] . PCR was performed using the primers described in the Table S2 . GFP2 was flanked by unique BamHI restriction sites for easy cloning into the targeting vectors . All targeting constructs except the ones belonging to the series of ‘W’ fragment deletions and reconstitutions were made by cloning the arms sequences into pBluescriptKS+ ( Stratagene , CA ) and then inserting GFP2 either into unique BamHI or BglII sites as shown in Figure 5A and Figure S1 . Targeting of AID [28] and RDM1 [29] have been previously described . PCR amplifications of all target arms were performed using the Expand long template PCR System ( Roche , Switzerland ) , DT40 genomic DNA as template and primers as described in the Table S2 . Since the ‘W’ fragment was difficult to amplify as a single sequence , it was sequentially cloned by combining upstream and downstream PCR amplicons with a 2 . 2 kb AvrII/SpeI restriction fragment excised from the rearranged IgL targeting construct ‘Construct R’ [30] . The sequence of the AvrII/SpeI restriction fragment is A/T rich and localized between the J segment and the C region . The assembled ‘W’ fragment of 9784 nucleotides was sequenced and deposited into Genbank under the accession number FJ482234 . It starts at position −7 relative to the first base of the IgL start codon and corresponds to the chicken genome coordinates chr15:8165070–8176699 but lacks the VJ intervening sequence . Constructs belonging to ‘W’ fragment deletion series were made by cloning GFP2 between the target arms and then inserting the ‘W’ fragment or parts thereof into unique NheI/SpeI sites . A BamHI fragment containing GFP2 and the ‘W’ fragment was incorporated into the AID , BACH2 and RDM1 targeting vectors to test the activity of the ‘W’ fragment in non-Ig loci . Cells were cultured in chicken medium ( RPMI-1640 or DMEM/F-12 with 10% fetal bovine serum , 1% chicken serum , 2 mM L-glutamine , 0 . 1 µM β-mercaptoethanol and penicillin/streptomycin ) at 41°C with 5% CO2 . Transfections were performed by electroporation , using 40 µg of linearized plasmid DNA with a Gene Pulser Xcell ( BIO-RAD ) at 25 µF and 700 V . Stable transfectants were selected by culturing in 15 µg/ml of blasticidin . Transfectants having integrated the transgenic constructs by targeted integration were identified by PCR using an inside primer from the SV40 polyadenylation signal sequence of GFP2 together with a primer derived from the sequence outside the target arm ( Table S2 ) . In case of insertions into the IgL locus , targeted integration into the rearranged allele was verified by amplifying the VJ intervening sequence of the unrearranged locus . The AID reconstituted clone AIDR2 was generated from the AID deleted clone AID−/− [2] by transfection of a construct which targeted an AID cDNA expression cassette into one of the deleted AID loci . The AID negative transfectants were produced by transfecting AID−/− and ψV−AID−/− [19] , respectively . The phenotype of each mutation was determined by FACS analysis of at least two independent targeted transfectants and twenty-four subclones of each . The primary transfectants were analyzed by FACS about three weeks after transfection and the subclones two weeks after subcloning . As the green fluorescence levels in the main populations varied slightly among the transfectants , the gates to separate the main population of green fluorescent cells from cells showing decreased or lost green fluorescence were adapted accordingly . At least 5000 events falling into the live cell gate were collected for each primary transfectant or subclone . Subclones in which more than 50% of the live cell events fell into the gates for decreased or lost green fluorescence were excluded from the analysis as they might represent the expansion of a precursor cell already expressing a mutated GFP2 transgene at the time of subcloning . To minimize PCR-introduced mutations , Pfu Ultra hotstart polymerase ( Stratagene ) was used for the amplifications of the GFP open reading frames prior to sequencing . Sequencing and sequence analysis were performed as previously described [19] . RT-PCR was performed as previously described [2] . Primer pairs used for amplification of the GFP and elongation factor 1α transcripts are shown in Table S2 . | It remains an open question how AID-mediated gene diversification is targeted to the immunoglobulin loci . Here we define a cis-acting sequence , named DIVAC for diversification activator , which is required for hypermutation of the Ig light chain gene and sufficient to activate hypermutation at various non-Ig loci in the DT40 B cell line . DIVAC is composed of multiple interacting sequences and able to work over considerable distances both upstream and downstream of its target gene . This work provides the first conclusive evidence that AID-mediated gene diversification is targeted to the Ig loci by cis-acting sequences . The conservation of AID-mediated Ig gene diversification during vertebrate evolution suggests that DIVACs also play a role in gene conversion , hypermutation , and switch recombination in mammalian B cells . The findings should be of general interest not only for molecular immunology and the pathogenesis of B cell lymphomas but also the whole field of biology as a unique example of how locus-specific gene diversification is controlled . The described experimental system offers unique advantages to further clarify the molecular mechanism of DIVAC . | [
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] | 2009 | A cis-Acting Diversification Activator Both Necessary and Sufficient for AID-Mediated Hypermutation |
In June 2014 , Suriname faced the first Chikungunya outbreak . Since international reports mostly focus on hospitalized patients , the least affected group , a study was conducted to describe clinical characteristics of mainly outpatients including children . In addition , the cumulative incidence of this first epidemic was investigated . During August and September 2014 , clinically suspected Chikungunya cases were included in a prospective follow-up study . Blood specimens were collected and tested for viral RNA presence . Detailed clinical information was gathered through multiple telephone surveys until day 180 . In addition , a three stage household-based cluster with a cross-sectional design was conducted in October , December 2014 and March 2015 to assess the cumulative incidence . Sixty-eight percent of symptomatic patients tested positive for Chikungunya virus ( CHIKV ) . Arthralgia and pain in the fingers were distinctive for viremic CHIKV infected patients . Viremic CHIKV infected children ( ≤12 years ) characteristically displayed headache and vomiting , while arthralgia was less common at onset . The disease was cleared within seven days by 20% of the patients , while 22% of the viremic CHIKV infected patients , mostly women and elderly reported persistent arthralgia at day 180 . The extrapolated cumulative CHIKV incidence in Paramaribo was 249 cases per 1000 persons , based on CHIKV self-reported cases in 53 . 1% of the households and 90 . 4% IgG detected in a subset of self-reported CHIKV+ persons . CHIKV peaked in the dry season and a drastic decrease in CHIKV patients coincided with a governmental campaign to reduce mosquito breeding sites . This study revealed that persistent arthralgia was a concern , but occurred less frequently in an outpatient setting . The data support a less severe pathological outcome for Caribbean CHIKV infections . This study augments incidence data available for first outbreaks in the region and showed that actions undertaken at the national level to mount responses may have positively impacted containment of this CHIKV outbreak .
Chikungunya fever is caused by a classical arbovirus ( genus Alphavirus , family Togaviridae ) , which is transmitted to humans primarily through Aedes aegypti and Aedes albopictus mosquitoes [1] . Acute onset of fever and polyarthralgia , mainly affecting the extremities ( wrists , ankles , phalanges ) , are the primary reported clinical characteristics [2 , 3] . Joint pain is often severe [4] and arthralgia may persist for weeks to years [5 , 6] . Other reported symptoms include rash , headache and back pain [1 , 7] . Despite the low hospitalization rate of Chikungunya patients ( 0 . 3% during the outbreak in La Reunion in 2005–2006 [8] ) , at present more is known about the clinical presentation and outcome ( i . e . recovery or persistent pain ) of hospitalized patients during outbreaks . Chikungunya is endemic in tropical Africa , South-East Asia and on the Indian subcontinent [1 , 3] . In recent years , outbreaks have been appearing outside the endemic zone probably due to increased global air travel and seaborne trade [9–12] and Chikungunya has recently emerged as a major public health concern in the Caribbean Region [13] . In December 2013 , the first Chikungunya virus ( CHIKV ) infections were reported among non-travelers on the Caribbean island of Saint Martin [14] . Since then , the virus has spread rapidly into the Caribbean region and neighboring countries [15] . The Chikungunya viruses studied from this region belonged to the Asian genotype [16] . Recently , it was demonstrated that this strain causes a less severe pathological outcome compared to the East Central South African ( ECSA ) genotype [17] . In June 2014 , the first locally acquired case of Chikungunya was reported in Suriname [18] . In this period , no other outbreaks of vector-borne diseases ( e . g . yellow fever , malaria , leptospirosis ) were recorded by the Bureau of Public Health , BOG ( BOG Annual Report ) . A study was initiated with two objectives , firstly to assess the manifestation and course of Chikungunya infection following acute illness in a naïve population , and secondly to determine the cumulative incidence of the Chikungunya outbreak in Suriname . To assess the extent and severity of the symptoms during this first outbreak , patients with suspected CHIKV diagnosis were enrolled in the period between August and September 2014 in three different outpatient settings . CHIKV presence was determined with Real-Time reverse transcription-polymerase chain reaction ( RT-rPCR ) . Detailed clinical information from Chikungunya infected patients was collected and the symptom development was recorded through multiple telephone interviews until 6 months after onset . To determine the cumulative incidence of this first Chikungunya outbreak in Suriname , a community surveillance consisting of three successive household-based cluster investigations was conducted in the capital in October 2014 , December 2014 and March 2015 , gathering information on 1169 households with 4842 participants . The validity of self-reported CHIKV infections was crosschecked with serological analysis . To our knowledge , the study is unique in describing in detail the clinical evolution of autochthonous CHIKV outpatients in a Caribbean country . Furthermore , clinical follow-up was conducted for six months for almost one hundred viremic CHIKV infected patients including 10 children ( 2–12 years ) . We therefore also add to the records internationally available for clinical manifestation of Chikungunya in children . The prospective follow-up study was complemented with cluster-based cross-sectional data , adding valuable data to the international Chikungunya outbreak information .
Suriname is a tropical country , located along the North Coast of South-America , bordering Brazil to the south , Guyana to the west and French Guiana to the east . Approximately 80% of Surname is covered by tropical rainforest . Suriname has a highly multiethnic population of nearly 550 , 000 , most of whom live in the coastal area in and around the capital Paramaribo [19] . This study took place in Paramaribo and in Commewijne , a rural district adjacent to Paramaribo . All participants provided oral informed consent . Acquired informed consent was registered prior to inclusion either by the attending physician on the short questionnaire in the clinical setting or by the trained pollster in the community surveillance . Participants , whose blood specimens were shipped abroad for testing , provided written informed consent . The national ethics committee within the Ministry of Health approved both studies ( VG018-14 and VG008-15 ) . All reported signs and symptoms are presented descriptively , namely the presence/absence of arthritis , periarticular edema , arthralgia , fever , myalgia , skin rash , itching , headache , back pain , eye pain , vomiting , nausea , fatigue , asthenia and joint pain intensity and location . The Chi-square test was used to compare characteristics and symptoms of the participants . To evaluate differences in age distribution between the viremic CHIKV infected and the CHIKV- group , the Mann-Whitney U test was used . The Statistical Packages for Social Sciences ( SPSS 21 . 0 ) were used for analysis excluding observations with missing data . Statistical significance was set at p = 0 . 05 .
Between October 2014 and March 2015 , a total of 4842 participants ( 1637 , 1583 and 1622 in the three surveys respectively ) from 1169 households ( 385 , 392 , and 392 , respectively ) in all 12 regions in Paramaribo were included . The random distribution of households and the cross-sectional study design allowed inference and valid analysis . The data presented encompass the whole study period . The gender distribution of this survey followed the general Surinamese population distribution ( m/f ratio: 1 . 1 vs 1 . 0 ) . The limited number of participants in each of the twelve regions in Paramaribo did not allow for comparisons per region .
After the emergence of CHIKV infections in the Caribbean region in 2013–2014 [15] , autochthonous infections were continuously reported in Suriname since June 2014 , as illustrated in this study . The prospective follow-up study included 180 symptomatic patients with 68% testing positive for viral RNA ( viremic CHIKV infected patients ) , of which 70 . 8% had severe joint pain . At disease onset 73 . 5% viremic CHIKV infected patients had fever and 84 . 4% had arthralgia which was still reported at D180 for 22 . 2% . The combination of fever and severe arthralgia/arthritis in the clinically suspected sample population ( 68% RT-rPCR positive ) , demonstrated that with these cardinal symptoms , general practitioners in Suriname were able to correctly diagnose the majority of the patients during this CHIKV outbreak . The fact that only RT-rPCR was used to define CHIKV infected patients may have induced a classification bias , especially in the context of the Asian lineage strain , circulating in the Caribbean Region [17] . However , we still observed clear differences between viremic CHIKV infected and CHIKV- patients . The manifestation of arthralgia in the fingers was characteristic for viremic CHIKV infected patients , while CHIKV- patients had more frequent pain in the eyeball compared to viremic CHIKV infected patients , and less frequent arthralgia . Moreover , rash in CHIKV- patients was mostly observed at D5 and D6 , in contrast to the viremic CHIKV infected patients with rash mostly at D3 . These symptoms may point towards Dengue virus infection , a common infection in South-America . Furthermore , the CHIKV outbreak substantiates the vector presence and Dengue is clinically difficult to differentiate from Chikungunya [23] . In the clinical differentiation , the symptoms arthralgia and onset of rash may therefore be good markers to differentiate Chikungunya from other exanthematous diseases . Besides , additional laboratory tools as leukocyte and thrombocyte count could be utilized , since patients with Chikungunya often have lymphocytopenia which is seldom seen in Dengue patients [24] , whereas thrombocytopenia as seen in patients with Dengue hemorrhagic fever is not common in Chikungunya . The gathered detailed information about daily clinical symptoms of acute disease and chronic illness of viremic CHIKV infected outpatients in Suriname revealed that the most common reported symptoms were abrupt onset of fever , arthralgia , asthenia and myalgia . These findings , in particular the very high frequency of fever and the incapacitating peripheral pain in multiple smaller joints ( i . e . ankles , hands , feet , knees ) matched the main reported features of CHIKV outpatients and hospitalized patients in other regions [3 , 7 , 25 , 26] . However , the frequency of pain by location ( ankles 54 . 3% , hands 48 . 9% , feet 47 . 8% , knees 40 . 2% ) during acute CHIKV infection was lower than those reported by the TELECHIK cohort study [27] ( ankles 74 . 9% , hands 75 . 7% , feet 73 . 1% , knees 67 . 6% ) and the French soldiers cohort ( ankles 68% , fingers and palms 76% , feet 68% , knees 58% ) [28] . This finding is consistent with the presumed circulation of the Asian lineage in Suriname , which is less virulent than the ECSA strain circulating in the latter population-based studies in La Reunion [17] . The overall assigned score of pain intensity in other studies was generally high ( i . e . NRS score ≥7 ) [25] as was corroborated by our findings ( score 8 ) . The intense joint pain caused walking difficulties in almost all our viremic CHIKV infected patients ( 92 . 1% ) , which is even higher than during the outbreak in La Reunion ( 2005–2006 ) , where 46 . 4% to 75 . 0% [21 , 25] of the viremic and/or serologically-confirmed patients reported discomfort in performing daily activities such as walking . The presence of periarticular edema which was described earlier [29] , was also observed at the day of consultation in 25 . 5% of individuals positive for CHIKV-RNA in our cohort . This corresponds with a hospital-based study ( periarticular edema in 25 . 6% RT-rPCR CHIKV+ patients ) [30] and another study from La Reunion where 30% of patients presented with soft-tissue swelling [1 , 7] . The presence of acute arthritis in 35 . 8% of our viremic CHIKV infected cohort is lower in comparison to the French soldiers cohort study from La Reunion with 44 . 8% reporting polyarthritis [28] , as could be expected from the Asian lineage . Moreover , a study from India ( Maharashtra State ) even observed arthritis in 68 . 8% [31] . Symptoms improved gradually during the acute phase , except for skin rash and itching , consistent with previous reports [4 , 32] . The presence of skin rash , in 37 . 9% of our patients , falls within the broad range that is reported worldwide ( 10% to 81% ) [33 , 34] . The occurrence of skin rash towards the end of the febrile phase [35] is substantiated by the peak presence on day 3 in our study . The general observations on maculopapular rash [35] , mostly reported on limbs and trunk , rarely affecting the face and occasionally spreading over the entire body was corroborated by our findings . At day 7 , fever receded for most patients , but asthenia and arthralgia were still reported by more than 40% of the patients , in coherence with the study of Thiberville et al . [4] . This study also reported a considerable percentage of patients with headache and myalgia at day 7 , in contrast to our findings . Twenty percent of our cohort was symptom-free within 7 days after disease onset , which seems slightly more favorable than in La Reunion with a duration of symptoms <15 days for 23% of the cases [25] . The manifestation and the course of CHIKV symptoms are variable and depend on several factors such as virus strain , age , gender , immune status [32 , 36 , 37] and possibly also on the genetic predisposition [38] . The latter is supported by the finding that Maroons were underrepresented in our study . This corresponds with a recent study where hospitalizations due to Dengue , which is transmitted by the same vector as Chikungunya , occurred least in the Maroons [39] . However , the underrepresentation of Maroons in our study could also be due to either the low population density of this ethnic group in at least one of our study sites ( Commewijne ) or other factors as differences in health care access . Earlier studies report that women are more prone to CHIKV infection [31 , 33 , 40 , 41] , consistent with our results , probably because of greater home-based activities and different clothing behaviors enabling an increased accessibility for mosquitoes . This is further supported by a report in the Midwest region of Brazil , where women were significantly more frequent victims of Dengue [42] . On the other hand , a cross-sectional study in Mayotte ( Indian Ocean ) , found that CHIKV seroprevalence was higher in men than women [43] and during an outbreak in Italy no difference in sex incidence was noticed [12] . The inconsistency of sex as factor in exposure to viral infection across countries/communities may be related to different lifestyles and behaviors . The occurrence of Chikungunya viral infections in Suriname in all age groups substantiates the general finding . Moreover , the observation that children were the least affected group during first epidemic periods in La Reunion ( 18% of the cases <10 years ) and India ( Maharashtra State: 5% of the cases <15 years ) [31 , 41] was also corroborated with our findings ( 10 . 7% ≤12 years ) . In line with recent studies [44] , arthralgia ( 50% at onset ) had a milder course in the children younger than 13 years and back pain was less common ( 14 . 3% at D3 ) . Most studies report a low frequency of headache in children ( 15% to 35 . 3% ) [44 , 45] , whereas a higher frequency of headache for children was reported by our cohort ( 85 . 7% at onset ) . The frequency of vomiting ( 28 . 6% at D0 ) falls within the reported range from studies in India ( 12 . 2% to 47% ) [33 , 45] . However , these studies did not report our observation of a significant higher frequency of vomiting for children compared to adults at onset . This discrepancy could be due to different study settings . The presence of maculopapular rash in 44 . 4% of the children at D4 was in line with earlier reports ( 33–60% ) [44] . However , the earlier reported more prominent presence of skin rash in children versus adults during a CHIKV outbreak in southern Thailand [24] , could not be substantiated in this study . The standard triad of fever , joint pains and rashes for the diagnosis of Chikungunya in children may not be as effective in Suriname . The low number of children included in our study however could obscure this presumption . Persistent arthralgia was described in La Reunion and Italy in more than half of the Chikungunya patients in outpatients and hospitalized patients [21 , 25 , 46] , while in our setting , arthralgia was observed in 22% of the patients six months after disease onset . In contrast to our study , patients in the aforementioned studies reported underlying osteoarthritis or pre-existing rheumatic diseases , which are independent markers for persistent pain [21 , 46] . A recent retrospective study from Columbia also reported a higher percentage of persistent polyarthralgia ( 44 . 3% ) [47] . Our results are more in line with another study in Reunion Island including only outpatients , where the residual arthralgia was significantly lower ( 23% ) after 300 days [4] . Our results are also consistent with a study done in Indonesia , where CHIKV infections by the Asian genotype caused mild and short lasting clinical symptoms [48] . An even lower percentage of only 13 . 3% of residual arthralgia , 2 to 3 months after illness , was observed in a study in Singapore , with patients without underlying medical conditions [32] . Other variables associated with persistent joint pain are age ≥ 45 years and gender ( women are more likely to have persistent arthralgia ) [4 , 25 , 26 , 49] , as was corroborated by our results . The frequency of patients with continuous pain ( 25 . 0% ) was lower than reported by the French soldiers cohort ( 41 . 3% ) [28] . Moreover , in this cohort 93 . 7% of the symptomatic patients still complained about chronic pains 6 months after infection ( our study: 22% ) . This difference could be caused by the less pathological outcome of the Asian lineage presumably present in Suriname compared to the Indian Ocean strain [17] . The reported relapse of arthralgia in some Chikungunya patients in the months after acute infection [21 , 23] , was also observed in our study ( 8 . 9% ) . However , relapse was less common in Suriname than in the hospitalised-based study from Borgherini et al . ( 21% ) [21] . Moreover , in the population-based French soldier cohort , the more virulent ECSA strain caused a relapse in 58 . 7% of the patients [28] . Our investigation of the clinical manifestations had some limitations . Firstly , we only performed qualitative PCR analysis and did not determine the average viral load at inclusion of the study . We could therefore not relate viral load to symptomatology and severity of symptoms . Secondly , clinical symptoms were only described for Chikungunya infections in patients consulting a physician in the initial stage of disease . The described symptoms are therefore expected to be more severe than the general symptoms , since less severe or asymptomatic cases were not included . Thirdly , symptomatic differences between viremic CHIKV infected and CHIKV- patients could be obscured , since symptoms of CHIKV- patients were characterized only in the acute phase . Furthermore , no serology was performed to differentiate between real negative CHIKV cases and potential CHIKV+ cases in a late stage with no detectable viremia . These reasons may have skewed the estimation of some symptoms in this cohort . However , because of the robust size of the viremic CHIKV infected sample ( 2 fold higher than the CHIKV- group ) , we feel that our conclusions about the viremic CHIKV infected outcome and symptoms are well supported . The prospective follow-up study was complemented with cluster-based cross-sectional data and the cumulative incidence in Paramaribo from July 2014 to March 2015 was 249 cases per 1000 , which by inference is similar to the cumulative incidence of La Reunion in 2005/2006 ( 350 cases per 1000 ) over a 12-month period [41] . Caution is warranted in the country comparison for epidemic impact , since several factors as virus strain , influence of the weather , the density of the population and the effectiveness of vector control measures are involved . The high value of serologically confirmed CHIKV infections ( 90 . 4% IgG ) in self-reported cases clearly demonstrated the acquired skill of the population to recognize and diagnose CHIKV infection . Internationally , not much data is available about the health care seeking behavior during Chikungunya outbreaks . In Mayotte , 52% of participants with confirmed CHIKV sought medical advice [50] , while 79 . 5% of the CHIKV suspected persons in our study population sought medical care . The good access to health care in Paramaribo may account for this high percentage . In the community surveillance , more women self-reported to be Chikungunya positive , underlining the observation among the laboratory confirmed CHIKV cases that women are more prone to CHIKV infection . Moreover , also the health care seeking behavior was higher for women than men , corroborating the results of a general study on the utilization of health care services in the USA [51] and underlining the generally poor recovery observed in women . The use of insect repellent ( 47 . 4% ) in Paramaribo is lower than in outbreaks in La Reunion ( 67 . 9% ) [52] and India ( Chennai ) ( 88 . 4% ) [53] , despite the fact that in contrast to Reunion Island , Suriname was warned for an upcoming Chikungunya outbreak . However , the latter two studies reported individual use of insect repellent while in our study household data were gathered . Moreover , country comparisons may not be very useful without data of the mosquito index and data on actual repellent use . In Suriname , the highest CHIKV incidence was noted in the long dry season , which may have been associated with increased storage of water filled objects in dry periods , positively influencing mosquito breeding and thus CHIKV transmission . This premise is further supported by the observation that the sharp increase in CHIKV cases occurred five weeks after the dry season had set in , similar to the largest Dengue epidemic in Suriname in 2005 ( personal communication ) and in coherence with findings in Thailand , where Chikungunya peaked in several provinces 6 weeks after the start of the dry period [54] . The sharp decline of CHIKV after week 40 , coincided with the start of the governmental campaign to eliminate mosquito breeding sites , which may have positively impacted the containment of this outbreak . These results thus support country actions to increase effectiveness of vector control and to raise population awareness . In conclusion , this is the first report about the emergence of CHIKV in Suriname . The clinical course of most symptoms in our naïve cohort was similar to those of other countries that faced a Chikungunya outbreak . The earlier finding that women and elderly persons are more at risk for persistent arthralgia was substantiated , although our study highlighted that persistent arthralgia , mostly intermittent , is a less frequent concern among viremic CHIKV infected patients in an outpatient setting . Furthermore , our data support the presumption of the circulation of the Asian lineage of CHIKV in Suriname . Our findings also provided more insight into the manifestation and course of this arboviral disease in children . Still , data from larger children cohorts in CHIKV affected areas are required to further enhance patient management . We also observed some clinical differences between viremic CHIKV infected and CHIKV- patients , which could be useful to differentiate Chikungunya from other diseases ( such as exanthematous diseases as Dengue ) in resource-limited countries lacking testing facilities . The finding that 20% of the patients cleared the disease within one week , while 22% still displayed persistent arthralgia after six months offers valuable indicators for countries facing their first outbreak . Furthermore , our study adds to the international data on cumulative Chikungunya incidence during first outbreaks , which is particularly important for the Caribbean region . | Chikungunya virus is transmitted to humans by mosquito bites and causes fever and joint pain . Chikungunya was first detected in Africa , but recently became a worldwide concern with outbreaks in many ( sub ) -tropical countries . We report the characteristics of the first outbreak in Suriname ( 2014–2015 ) . Mainly non-hospitalized patients were followed-up to study the clinical manifestations and course of the disease , after presentation in the respective clinics with the standard Chikungunya symptoms ( fever and arthralgia ) . Twenty percent of follow-up patients could clear the disease within one week and 22% ( mostly women and elderly ) still had complaints about arthralgia up to 6 months after infection . This is consistent with the assumption that Caribbean Chikungunya viral infection has a less severe pathological outcome . Furthermore , more insight was gained into the symptomatology of children ( ≤12 years ) . In addition , house-to-house surveys in Paramaribo were carried out to identify suspected cases to assess the incidence . Almost 25% of the survey participants experienced symptoms consistent with Chikungunya during the nine months spanning the investigation . The launch of a governmental campaign to eliminate mosquito breeding sites coincided with a sharp decline of Chikungunya cases , suggesting that such measures may be important in the containment of future CHIKV outbreaks . | [
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] | 2016 | First Chikungunya Outbreak in Suriname; Clinical and Epidemiological Features |
BALB/c mice are highly susceptible while C57BL/6 are relatively resistant to experimental Trypanosoma congolense infection . Although regulatory T cells ( Tregs ) have been shown to regulate the pathogenesis of experimental T . congolense infection , their exact role remains controversial . We wished to determine whether Tregs contribute to distinct phenotypic outcomes in BALB/c and C57BL/6 mice and if so how they operate with respect to control of parasitemia and production of disease-exacerbating proinflammatory cytokines . BALB/c and C57BL/6 mice were infected intraperitoneally ( i . p ) with 103 T . congolense clone TC13 and both the kinetics of Tregs expansion and intracellular cytokine profiles in the spleens and livers were monitored directly ex vivo by flow cytometry . In some experiments , mice were injected with anti-CD25 mAb prior or post T . congolense infection or adoptively ( by intravenous route ) given highly enriched naïve CD25+ T lymphocytes prior to T . congolense infection and the inflammatory cytokine/chemokine levels and survival were monitored . In contrast to a transient and non significant increase in the percentages and absolute numbers of CD4+CD25+Foxp3+ T cells ( Tregs ) in C57BL/6 mouse spleens and livers , a significant increase in the percentage and absolute numbers of Tregs was observed in spleens of infected BALB/c mice . Ablation or increasing the number of CD25+ cells in the relatively resistant C57BL/6 mice by anti-CD25 mAb treatment or by adoptive transfer of CD25+ T cells , respectively , ameliorates or exacerbates parasitemia and production of proinflammatory cytokines . Collectively , our results show that regulatory T cells contribute to susceptibility in experimental murine trypanosomiasis in both the highly susceptible BALB/c and relatively resistant C57BL/6 mice .
Tse-Tse-transmitted trypanosomiasis is a complex disease in both humans and animals caused by several species of the protozoan parasite Trypanosoma [1] . Trypanosoma brucei gambiense and Trypanosoma brucei rhodensiense cause disease in humans ( sleeping sickness ) while trypanosomiasis in animals ( Nagana ) is caused by Trypanosoma congolense , Trypanosoma brucei brucei and Trypanosoma vivax . It is estimated that human African trypanosomiasis ( HAT ) accounts for 1 . 6 million disability adjusted life years in sub-saharan Africa per year , thereby putting a huge burden on poor rural farmers [1] . Out of the three species of animal trypanosomiasis , T . congolense is the most important disease for livestock [2] . Some cattle breeds indigenous to West Africa ( such as the Ndama ) are relatively resistant to trypanosomiasis , whereas the European breeds , particularly of the Zebu background are highly susceptible . The immunologic mechanisms that regulate this difference in susceptibility are not clearly understood . Similarly , different strains of inbred mice show varying degree of susceptibility to experimental T . congolense infection . For example , BALB/c mice are highly susceptible while the C57BL/6 mice are relatively resistant to infection with T . congolense as measured by the ability to control parasitemia and survival period [3] , [4] . Resistance is typically linked with the early production of interferon gamma ( IFN-γ ) , nitric oxide and parasite-specific IgG2a antibodies , which are essential for parasite clearance [5] , [6] , [7] . However , the over production of IFN-γ as well as other proinflammatory cytokines , ( particularly IL-1β , IL-6 , IL-12 and TNF ) contributes to disease and death in the highly susceptible BALB/c mice [8] . Indeed , IL-10 , which has powerful anti-inflammatory properties , is critical for survival of mice infected with T . congolense . Blockade of IL-10 signaling by treatment with anti-IL-10R mAb leads to the elevation of serum levels of proinflammatory cytokines and acute death in otherwise relatively resistant C57BL/6 mice [8] and LTβR−/− mice on C57BL/6 background [9] . Gershon et . al [10] reported the presence of thymic CD4+ lymphocyte populations capable of suppressing antigen-specific immune responses and these cells were later characterized as naturally occurring regulatory T cells ( Tregs ) [11] . Tregs constitutively express CD25 ( interleukin IL-2 receptor α chain ) and the transcription factor; forkhead box protein 3 ( Foxp3 ) [12] and are mainly involved in the regulation and control of autoreactive T cells . In addition , Tregs have also been shown to influence the pathogenesis of several infectious diseases including bacteria [13] fungi [14] and parasites [15] , [16] . However , the role of Tregs in the pathogenesis of African Trypanosomiasis is controversial . While a report indicates that Tregs play a crucial role in enhanced resistance [17] another report showed that they contribute to susceptibility to the infection [18] . This discrepancy could be related to differences in mouse strains ( BALB/c versus C57BL/6 ) used in these experiments by the two different groups . Furthermore , these studies relied solely on antibody depletion of CD25+ cells and did not directly test the role of Tregs by adoptive transfer . Here , we asked whether Tregs contribute to the distinct disease outcomes observed in T . congolense-infected BALB/c and C57BL/6 mice . Our findings indicate that Tregs play pathogenic roles in African trypanosomiasis in both the relatively resistant and highly susceptible mice . The extent of this effect is highly variable , being more pronounced in the relatively resistant mice and only observable in the highly susceptible mice following an intradermal infection .
All experimental protocols were approved by the Animal Care and Use Committee of University of Manitoba and all animals were housed and used according to the guidelines stipulated by the Canadian Council for Animal Care . Six to eight ( 6–8 ) weeks old female C57BL/6 , BALB/c and outbred CD-1 mice were purchased from the University of Manitoba Central Animal Care Services ( CACS ) breeding facility . All mice were maintained in specific-pathogen free environment at the University of Manitoba CACS . Cryopreserved Trypanosoma congolense , variant antigenic type ( VAT ) TC13 were passaged in CD-1 mice as previously described [19] . Three days post-passage , mice were sacrificed and bloodstream forms of the parasites were isolated by DEAE-cellulose anion exchange chromatography [20] . For infection , C57BL/6 and BALB/c mice were injected intraperitoneally with 103 T . congolense VAT TC13 in 100 µl Tris-saline-glucose supplemented with 10% heat-inactivated fetal bovine serum ( FBS , Invitrogen , Burlington , ON ) . In some experiments , BALB/c mice were infected in the footpad ( intradermal ) with 104 TC13 . At indicated times; a drop of blood from the tail vein was collected onto a glass slide ( Fisher Scientific Ottawa , ON ) and parasitemia was determined by counting the number of parasites in three or more fields at 40× objective as previously described [21] . At indicated times , mice were anesthetized by intraperitoneal injection of xylazine ( 10 mg/kg ) and ketamine ( 150 mg/kg ) and blood was collected by cardiac puncture using a 1 ml syringe and 25G needle . Blood samples were kept at 4 degree for 4 hr , spun at 2400 rpm for 10 min and , serum was collected and stored at −20°C until used . Serum levels of trypanosome-specific IgM and IgG2a antibodies in infected mice were determined by ELISA as previously described [22] . At various times after infection , mice were sacrificed and single cell suspensions of the spleens were lysed of contaminating red blood cells ( RBC ) , washed in PBS , counted and resuspended at 4 million/ml in complete medium ( DMEM supplemented with 10% heat-inactivated FBS , 2 mM L-glutamine , 100 U/ml penicillin , and 100 µg/ml streptomycin ) . The cells were plated at 1 ml/well in 24-well tissue culture plates ( Falcon , VWR Edmonton , Canada ) , incubated at 37°C for 72 hr and the culture supernatant fluids were collected and stored at −20°C until assayed for cytokines by ELISA . In some experiments , splenocytes were directly stained ex vivo for surface expression of CD3 , CD4 and CD25 intracellular expression of Foxp3 using Treg staining kit ( BD Bioscience , Mississauga , ON , Canada ) according manufacturers suggested protocols . Intrahepatic lymphocytes were isolated from liver tissues as previously described [23] with minor modifications . Briefly , infected or uninfected mice were anesthetized with isoflourane and blood was collected by cardiac puncture . The chest cavity was opened and the livers were perfused by injecting 10 ml ice-cold PBS into the right ventricle . After 5 min , the liver was minced in collagenase solution ( 1 mg/ml ) , digested at 37°C for 1 hour and passed through a 70 µm cell strainer ( VWR , ON , Canada ) . The slurry was washed with 30 ml Hanks balanced salt solution ( HBSS ) ( Invitrogen , ON , Canada ) at 1200 rpm for 5 min and the contaminating RBCs were lysed , washed once with HBSS and the cells were resuspended in 4 ml 40% percoll ( Sigma ) . Liver lymphocytes were separated by layering the cells on top of 70% percoll ( Sigma ) and centrifuging at 750 g at 22°C for 20 min without brakes . The interface containing lymphocytes was carefully collected , washed twice with PBS , re-suspended in complete DMEM medium and stained directly ex vivo for cytokines and Tregs as for spleen cells above . The concentrations of cytokines ( IL-6 , IL-10 , TNF , and IFN-γ ) in serum or culture supernatant fluids were assayed by sandwich ELISA using antibody pairs ( Ebioscience San Diego , CA ) according to the manufacturer suggested instructions . The sensitivities of our ELISA ranges from 7 . 5–15 pg/ml . In some experiments , mice were injected intraperitoneally with 100 µg of anti-CD25 mAb ( clone PC61 ) 24 hrs prior to infection with T . congolense . Previous studies from our laboratory have shown that this dose of antibody causes complete and sustained ( up to 1 week ) depletion of CD25+ and FoxP3+ cells in T . congolense-infected mice [9] . CD4+CD25− and CD4+CD25+ T cells were isolated from spleen of uninfected C57BL/6 and BALB/c mice by using Stem Cell Treg isolation kit ( Stem Cell , Vancouver , BC Canada ) according to manufacturers insrtuctions . The purity of CD4+CD25+ cells was >95% as assessed by flow cytometry . Intracellular staining indicated that isolated CD4+CD25− and CD4+CD25+ cells were ≤5% and ≥ 90% FoxP3+ , respectively . Groups of mice received four million CD4+CD25− or CD4+CD25+ T cells in PBS by intravenous injection and were infected 24 hrs later with T . congolense . Two-tailed student T test was used to compare mean and standard error of mean ( SEM ) between two groups . In some other experiments , non-parametric one-way analysis of variance ( ANOVA ) was used to compare mean and standard deviation ( SD ) of more than two groups . Tukeys test was used where there was significant difference in ANOVA . Differences were considered significant when p<0 . 05 .
Following infection , parasitemia was first detected on day 5 post-infection and was not significantly different in both strains of mice up to day 6 post-infection ( Figure 1A ) . Thereafter , infected BALB/c mice had significantly ( p<0 . 05 ) higher parasitemia than infected C57BL/6 mice ( Figure 1A ) , consistent with previous report [22] . To determine whether there were differences in expansion of Tregs following T . congolense infection , we sacrificed infected mice at days 0 ( no infection ) , 2 , 4 , 6 and 8 post-infection and determined the total number of cells , the percentages and absolute numbers of CD4+CD25+FoxP3+ cells in the spleens directly ex vivo by flow cytometry . There was no significant difference in the total number of cells in the spleens of infected BALB/c and C57BL/6 mice ( Figure 1B ) . Splenocytes from uninfected BALB/c mice had slightly ( but not significantly ) higher basal numbers of CD4+CD25+ T cells , which increased steadily after infection compared to infected C57BL/6 mice ( p<0 . 01 Figure 1C & Figure S1A ) . In addition , uninfected BALB/c mice spleens contain slightly higher basal levels of CD4+CD25+FoxP3+ cells , which transiently increased at days 2 and 4 and dropped to the base line by day 8 post-infection ( Figure 1D & Figure S1B ) . In contrast , the percentage of CD4+CD25+FoxP3+ cells in spleens of infected C57BL/6 mice remained either relatively unchanged or slightly decreased ( Figure 1D ) . Furthermore , the absolute numbers of CD4+CD25+ and CD4+CD25+FoxP3+ cells in spleens of infected BALB/c mice were higher than those from infected C57BL/6 mice ( Figure 1E & F ) . Collectively , these results show a differential expression and/or expansion of Tregs in the spleens of the highly susceptible and relatively resistant mice after infection with T . congolense . Next , we assessed the immune response to determine how this correlates with the differences in numbers of regulatory T cells in infected C57BL/6 and BALB/c mice . Spleen cells from infected mice were directly stained ex vivo for intracellular IFN-γ and IL-10 . As shown in Figure 2A & B , there was no significant difference in the percentage of CD4+IFN-γ+ and CD4+IL-10+ cells in the spleens of infected BALB/c and C57BL/6 mice although there was a trend towards higher IFN-γ+ cells in spleens from infected BALB/c mice ( Figure S2 ) . Furthermore , there was no difference in the percentages of Tregs ( CD4+CD25+Foxp3+ ) that produce IL-10 ( Figure 2C ) . Interestingly , the absolute numbers of CD4+IFN-γ+ and CD4+IL-10+ cells were significantly higher in infected BALB/c than in the C57BL/6 mice ( Figure 2D & E ) . In addition , except for day 8 post-infection , there was no significant difference in the absolute numbers of Tregs ( CD4+CD25+Foxp3+ ) that produce IL-10 ( Figure 2F ) in the two mouse strains . Because liver Tregs have been proposed to contribute to resistance in experimental African Trypanosomiasis [17] , we compared the expansion of Tregs and cytokine production by immune cells in the liver of T . congolense-infected C57BL/6 and BALB/c mice . As shown in Fig . 3A , although the total number of lymphocytes in the liver increased as the infection progressed , the numbers remain comparable between the two strains of mice . In addition , the absolute numbers of CD25+FoxP3+ T cells was not different between the two groups until day 8 post-infection when the numbers in infected BALB/c mice were higher ( although not significant ) than in the C57BL/6 mice ( Figure 3B & C ) . Furthermore , there was also no significant difference in the absolute numbers of CD4+IFN-γ+ , CD4+IL-10+ and FoxP3+IL-10+ T cells ( Figure 3D–F ) in the livers of infected mice of both strains of mice . Overall , the data presented here show that unlike the spleen , the expansion of Tregs in the liver was comparable between BALB/c and C57BL/6 mice during infection with T . congolense . To determine whether Tregs play a differential role in the pathogenesis of experimental T . congolense infection in BALB/c and C57BL/6 mice , we injected BALB/c and C57BL/6 mice with anti-CD25 monoclonal antibody one day prior to infection with T . congolense , followed by weekly injection in infected C57BL/6 mice for the next 2 weeks . Although anti-CD25 mAb could also potentially deplete all CD25+ cells , it preferentially caused sustained depletion of CD4+Foxp3+ cells ( Tregs ) throughout the treatment period [9] . Depletion of Tregs resulted in longer prepatent period ( not statistically significant ) and significantly ( p<−0 . 05–0 . 001 ) lower first and second peaks of parasitemia ( Figure 4A ) but did not affect the survival period of infected C57BL/6 mice ( data not shown ) . In addition , serum levels of IL-10 and IFN-γ; cytokines that have been shown to regulate resistance to T . congolense infection [9] , [21] , [22] , [24] , as well the percentages of cells producing these cytokines in the spleens ( Figure S3A & B ) were significantly ( p<0 . 05 ) higher in Tregs-depleted mice compared to those treated with isotype control ( Figure 4B & C ) , suggesting that anti-CD25 mAb treatment did not negatively impact on effector Th1 ( IFN-γ-producing ) cells . Paradoxically , depletion of Tregs led to a significant reduction in serum levels of IL-6 ( Figure 4D ) and TNF ( Figure 4E ) in infected mice . Interestingly , depletion of Tregs also caused a 2-day delay in onset of parasitemia but did not significantly affect the peak parasitemia ( Figure 4F ) , survival period ( data not shown ) and serum levels of IFN-γ ( Figure 4G ) in infected BALB/c mice . However , while the serum IL-10 level was significantly ( p<0 . 01 ) increased in Treg-depleted mice , serum levels of IL-6 and TNF ( Figure 4 I & J ) were significantly decreased when compared to control Ig . Because the previous report that showed anti-CD25 mAb treatment enhanced resistance in BALB/c mice utilized intradermal infection route [18] , we wondered whether the marginal effect we observed was related to the use intraperitoneal route , which causes rapid and uncontrolled infection in BALB/c mice [25] . Therefore we repeated the antibody treatment experiment but infected mice with TC13 in the footpad . As shown in Figure 5A & B , 100% of Treg-depleted mice infected with T . congolense in the footpad controlled their first wave of parasitemia and survived up to 21 days when the experiment was terminated . We conclude that as in C57BL/6 mice , naturally occurring regulatory T cells contribute to susceptibility in BALB/c mice and this effect is more apparent following intradermal infection . To more clearly determine the role of Tregs in the pathogenesis of experimental African trypanosomiasis , we adoptively transferred highly enriched ( >97% , Fig . 6A ) naïve syngeneic CD4+CD25+ ( Tregs ) and CD4+CD25− ( non Tregs ) cells ( 4 million cells/mouse ) into naïve C57BL/6 mice and after 24 hr infected them with T . congolense . As shown in Figure 6B , recipients of CD4+CD25+ T cells had higher peak parasitemia and took longer time to control their first wave of parasitemia compared to recipients of CD4+CD25− cells or PBS . In addition , while there was no significant difference in serum IFN-γ levels among the groups ( Figure 6C ) , recipients of CD4+CD25+ T cells had significantly higher levels of proinflammatory cytokines ( including TNF , IL-6 and MCP-1 ) than those that received CD4+CD25− or PBS on day 13 post-infection ( Figure 6D–F ) . Interestingly , this difference in serum levels of proinflammatory cytokines was transient , such that when mice were sacrificed at day 21 post-infection ( a time when the difference in parasitemia was no longer significant ) , this difference in serum levels of TNF , MCP-1 and IL-6 between recipient and PBS control groups was no longer apparent ( data not shown ) . Interestingly , adoptive transfer of CD4+CD25+ T cells also caused a significant reduction in serum levels of trypnosome-specific IgM and IgG2a in infected mice ( Figure S4A & B ) . Collectively , these results suggest that naturally occurring Tregs suppress efficient early parasite control in the relatively resistant C57BL/6 mice infected with T . congolense .
In this study , we investigated the role of regulatory T cells in pathogenesis of experimental T . congolense infection in mice . Our findings indicate that naturally occurring regulatory T cells contribute to enhanced disease in both the relatively resistant ( C57BL/6 ) and highly susceptible ( BALB/c ) strains of mice . Following T . congolense infection of BALB/c mice , the percentage and absolute numbers of splenic Tregs steadily increased as the infection progressed . This was in sharp contrast to the relatively unchanged numbers of these cells in the infected C57BL/6 mice . Interestingly , there was no difference in the expansion of Tregs in the liver of infected BALB/c and C57BL/6 mice , suggesting that differences in hepatic Tregs do not contribute to the differences in the outcome of T . congolense infection in these mice . Depletion studies showed that Tregs negatively affect efficient parasite control while adoptive transfer studies confirmed the role of these cells in control of parasitemia and production of disease exacerbating proinflammatory cytokines . Unlike cutaneous leishmaniasis , where the role of regulatory T cells have been well characterized both in primary [26] and secondary [16] immunity , the role of these cells in pathogenesis of experimental T . congolense infection is still unclear . While some reports suggest that Tregs enhance susceptibility [18] , others indicate that they are protective [27] . Although , both studies used T . congolense clone TC13 ( as in our study ) for infection , they used different strains of mice and different dose of anti-CD25 mAb to deplete CD25+ T cells . We have studied the role of Tregs in T . congolense infection in both the susceptible and relatively resistance strains of mice under identical experimental conditions . Collectively , our depletion and adoptive transfer studies show that Tregs contribute in part to impaired parasite control ( BALB/c and C57BL/6 ) and early death ( BALB/c ) in experimental T . congolense infection . Thus , our results support the findings of Wei et al [27] and show that Tregs play a pathogenic role in experimental African trypanosomiasis independent of mouse genetic background . Because some studies have shown that anti-CD25 mAb is not very specific at depleting only Tregs , [28] , we also used adoptive transfer experiments to further define the role of Tregs in experimental T . congolense infection cells . We showed that adoptive transfer of highly enriched naïve CD4+CD25+ T cells ( Tregs ) led to increased peak parasitemia and production of disease exacerbating inflammatory cytokines , including IFN-γ , IL-6 , TNF and MCP-1 early during infection in C57BL/6 mice ( Figure 4 A–E ) . Interestingly , adoptive transfer of Tregs did not alter parasitemia or serum levels of proinflammatory cytokines in the BALB/c mice , most likely because these mice are already highly sensitive and succumb rapidly to intraperitoneal infection with T . congolense . The finding that depletion and/or adoptive transfer of Tregs decreases and increases the production of proinflammatory cytokines , respectively , is paradoxical because Tregs are normally known to dampen inflammatory responses [29] . However , recent reports have shown that Tregs could also enhance CD4+ T cell-mediated inflammatory lung fibrosis [30] and IL-6 production by mast cells [31] . Thus , it is conceivable that as in these reports , Tregs could enhance inflammation in experimental T . congolense infection although the exact mechanism ( s ) remains to be elucidated . How does Tregs contribute to enhanced susceptibility to experimental African trypanosome infection ? Previous reports suggest that production of IL-10 by Tregs might be critically important in dampening excessive inflammatory response leading to enhanced survival in T . congolense infected mice [32] . However , we found no significant difference in the percentage and absolute numbers of IL-10-producing Tregs ( CD4+FoxP3+IL-10+ ) cells in the spleens and livers of infected BALB/c and C57BL/6 mice . Thus , our results do not support the proposal that Treg-derived IL-10 contributes to enhanced-resistance to experimental African trypanosomiasis . We speculate that these cells might act by suppressing the production of IFN-γ and IL-10 by helper T cells or other as yet uncharacterized cells that contribute to efficient parasite clearance and enhanced resistance in infected mice . In line with this , we found that depletion of CD25+ T cells by anti-CD25 mAb led to a significant increase in serum levels of IFN-γ and IL-10 , and this was associated with increased prepatent period and lower parasitemia in infected mice ( Figure 3A–C ) . Another possible mechanism by which Tregs may enhance susceptibility to experimental T . congolense infection is by suppressing antibody response . Tregs have been shown to directly or indirectly ( via their effect on CD4+ helper T cells ) suppress B cell responses leading to impaired antibody production [33] , [34] . Since antibodies are critically important for controlling parasitemia and resistance to African trypanosomes , a suppressive effect on their production will result in enhanced susceptibility to the disease . In line with this , we found that adoptive transfer of Tregs was associated with a significant reduction in serum levels of anti-T . congolense IgM and IgG2a antibodies in infected mice ( Figure S4A & B ) . Generalized immunosuppression is a prominent feature of both natural ( cattle ) and experimental ( mice ) African trypanosomiasis [35] , [36] , [37] . However , the exact mechanism through which African trypanosomes induce immunosuppression is still not well defined . While earlier studies implicated macrophages [36] others suggest that T cells may be involved in this process [38] , [39] . In an exhaustive review , Tabel et al [32] suggested that Tregs maybe involved in immunosuppression via the production of IL-10 , which down-regulates and/or inhibits IFN-γ and nitric oxide production [21] . In our study , we found no difference in percentages and absolute numbers of IL-10-producing CD4+FoxP3+ cells in infected highly susceptible and relatively resistant mice . On the contrary , depletion of Tregs ( by anti-CD25 mAb treatment ) was associated with increased IFN-γ and IL-10 serum levels and increased frequency of IFN-γ and IL-10-producing CD4+ T cells in infected mice . IFN-γ and IL-10 play critical roles in resistance to experimental T . congolense infection in mice [9] , [40] , [41] . Thus , our studies suggest that IL-10-derived Tregs may not be involved in susceptibility to T . congolense infection . They suggest that the effects of Tregs maybe directly related to their suppression of IFN-γ and antibody responses , leading to failure to control parasitemia . Several studies have shown that co-production of IL-10 by IFN-γ producing CD4+ Th1 cells is critically important for regulation of excessive Th1 and inflammatory responses in many infections [42] . In experimental T . gondii infection , Jankovic et al [43] demonstrated that IL-10 producing CD4+T-bet+ Th1 cells ( and not Foxp3+ T cells ) are the major regulators of inflammation in infected mice . Similarly , Anderson et al [44] showed in a model of chronic cutaneous leishmaniasis caused by L . major that in addition to CD25+Foxp3+ Treg cells , IFN-γ-producing CD4+CD25−Foxp3− ( Th1 cells ) that co-produce IL-10 are more important than Tregs at regulating inflammation and disease chronicity . In this study , we carefully analyzed our data to determine whether there were CD4+IFN-γ+IL-10+ ( double producers ) T cells but failed to observe any significant number of such cells in the spleens or livers of infected mice ( Figure S2 ) . Therefore , it is unlikely that these so called “Th1 regulatory effector cells” play a significant role in immunoregulation in experimental T . congolense infection in mice . In conclusion , we have shown that there are differences in the kinetics and relative expansion of Tregs following T . congolense infection in both the highly susceptible and relatively resistant mice . Our results show that Tregs contribute to enhanced susceptibility in experimental murine trypanosomiasis in both the highly susceptible and relatively resistant mice . | BALB/c mice are highly susceptible while C57BL/6 is relatively resistant to experimental Trypanosoma congolense infection . Acute death observed in infected BALB/c mice is usually associated with the excessive production of pro-inflammatory cytokines . Regulatory T cells ( Tregs ) have been shown to play a significant role in the pathogenesis of many diseases including those caused by parasites . However , the role of Tregs in the pathogenesis of T . congolense infection remains unclear . We were interested in addressing the following questions: Do Tregs contribute to the distinct phenotypic outcomes observed in T . congolense-infected BALB/c and C57BL/6 mice ? If so , where and how do they operate with respect to parasitemia and cytokine response ? By selectively altering the numbers of these cells either by targeted depletion with monoclonal antibody or adoptive transfer of highly enriched naïve CD25+ cells prior to infection , we show that Tregs impairs efficient parasite control and impacts on production of disease-exacerbating proinflammatory cytokines . Collectively , our findings suggest that Tregs contribute to enhanced susceptibility to experimental T . congolense infection in mice . | [
"Abstract",
"Introduction",
"Materials",
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"Results",
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] | [
"medicine"
] | 2012 | Regulatory T Cells Enhance Susceptibility to Experimental Trypanosoma Congolense Infection Independent of Mouse Genetic Background |
A genome-wide screen for large structural variants showed that a copy number variant ( CNV ) in the region encoding killer cell immunoglobulin-like receptors ( KIR ) associates with HIV-1 control as measured by plasma viral load at set point in individuals of European ancestry . This CNV encompasses the KIR3DL1-KIR3DS1 locus , encoding receptors that interact with specific HLA-Bw4 molecules to regulate the activation of lymphocyte subsets including natural killer ( NK ) cells . We quantified the number of copies of KIR3DS1 and KIR3DL1 in a large HIV-1 positive cohort , and showed that an increase in KIR3DS1 count associates with a lower viral set point if its putative ligand is present ( p = 0 . 00028 ) , as does an increase in KIR3DL1 count in the presence of KIR3DS1 and appropriate ligands for both receptors ( p = 0 . 0015 ) . We further provide functional data that demonstrate that NK cells from individuals with multiple copies of KIR3DL1 , in the presence of KIR3DS1 and the appropriate ligands , inhibit HIV-1 replication more robustly , and associated with a significant expansion in the frequency of KIR3DS1+ , but not KIR3DL1+ , NK cells in their peripheral blood . Our results suggest that the relative amounts of these activating and inhibitory KIR play a role in regulating the peripheral expansion of highly antiviral KIR3DS1+ NK cells , which may determine differences in HIV-1 control following infection .
The KIR receptors are expressed mainly on the surface of lymphocyte subsets including natural killer ( NK ) cells and a small subset of T cells , and they have a unique role in fine-tuning the balance between self-tolerance and cytotoxicity . KIRs bind to major histocompatibility complex ( MHC ) class I ligands on the surface of target cells . The degree of inhibition and/or activation mediated by interactions between co-inherited KIR and MHC class I gene products determines the activation threshold for NK cells ( Figure 1 ) [1] . The KIR region , on chromosome 19q13 . 4 , is highly polymorphic in humans [2] and its extensive polymorphism has been repeatedly associated with the natural history of HIV-1 infection [3] . The KIR3DL1 and KIR3DS1 genes segregate as allelic variants at the same locus and both are thought to encode receptors for molecules that fall within the Bw4 subfamily of HLA-B alleles ( HLA-Bw4 ) . There is also evidence for a single chromosome to have both KIR3DS1 and KIR3DL1 [4] , [5] . Both allele groups at this locus have been shown to be involved in HIV-1 pathogenesis . The activating allele KIR3DS1 , in combination with HLA-Bw4 molecules that have an isoleucine at position 80 ( Bw4-80I ) , has been associated with lower viral load , slower decline in CD4+ T cells , and delayed progression to AIDS , as well as with protection against opportunistic infections [6] , [7] . In addition , KIR3DS1 has recently been shown to correlate with strong inhibition of HIV-1 replication [8] . However , some reports have shown no protective effect associated with the KIR3DS1+HLA-Bw4-80I genotype [9] , [10] or show no evidence for a synergistic effect from the KIR3DS1+HLA-Bw4-80I genotype on viral load or on CD4+ T cell counts [11] . Recent reports have also shown that NK cells expressing KIR also directly place pressure of the virus , driving HIV viral evolution [12] . Similarly , various distinct allelic combinations of the inhibitory KIR3DL1 receptor and HLA-Bw4 ligands have been associated with lower HIV-1 viral load and slower progression to AIDS [13] . Two proposed functional explanations may account for the latter result . The first relates to the education process of NK cells during development , in which inhibitory receptors must recognize autologous MHC class I ligands for the NK cell to be functional upon maturation [14]–[16] , suggesting that ligand engagement by more highly expressed inhibitory KIR3DL1 allotypes during NK cell development ultimately may result in stronger NK cell responses in the event of viral infection when the ligand is missing or altered [14] , [17] . The second underlying explanation may relate to the fact that KIR3DL1 is involved in monitoring the circulation for normal MHC class I expression; however upon HIV infection , HIV Nef protein rapidly downregulates MHC class I expression . Thus , it is equally plausible that higher expression of KIR3DL1 may allow NK cells to recognize reduced MHC class I expression on infected cells more readily . KIR receptors are expressed on NK cells in a variegated manner , where only a fraction of all NK cells express a particular KIR gene product . Certain KIR receptors are consistently expressed on a large fraction of NK cells , while others are expressed on a smaller fraction of NK cells [18] . In individuals with one copy of KIR3DS1 , roughly 20%–50% of NK cells express the KIR3DS1 receptor [18] , [19] , and in individuals with two copies of KIR3DS1 , 60% or greater of NK cells express the KIR3DS1 receptor [18] , [19] . To add to the complexity , some KIR3DL1 allotypes have different surface expression levels [20] , which have been shown to have varying impacts on HIV-1 outcomes [13] , and correlate with genealogical groups of KIR3DL1 alleles [21] . The primary outcome studied here is HIV-1 viral load at set point , which has been shown to be a genetically tractable HIV outcome [22] . We used a genome-wide screen to identify a copy number variable region that associated with HIV-1 control , as measured by plasma viral load at set point , and that encompassed the KIR3DL1-KIR3DS1 locus . Further dissection of the region and of the interactions between KIR3DL1 , KIR3DS1 , and their HLA ligands demonstrated that the number of gene copies of the inhibitory KIR3DL1 receptor and activating KIR3DS1 receptor plays an important role in modulating HIV-1 control , but that this effect is only detectable after epistatic interactions between HLA molecules and KIR receptors are taken into account . Furthermore , functional and transcriptional studies on cells derived from individuals with these particular KIR CNV/HLA combinations demonstrated a dramatic expansion of KIR3DS1+ NK cells , which are able to robustly inhibit HIV replication in vitro . Thus , these data support the genetic association results , suggesting novel mechanisms of regulation of the antiviral activity of NK cells .
We investigated the role of large CNVs on HIV-1 control in a cohort of 2 , 102 patients of European ancestry from the Euro-CHAVI Consortium and the Multicenter AIDS Cohort Study ( MACS ) . Genome-wide single nucleotide polymorphism ( SNP ) genotyping was performed using Illumina's HumanHap550 , Human1M , or Human1MDuo BeadChips , and we used the PennCNV software [23] to identify large CNVs . For each SNP in a CNV region , we assigned copy number status ( zero , one , two , three , or four copies ) to each sample and carried out a linear regression analysis on HIV-1 viral load at set point . We examined duplications and deletions separately using copy number status in additive genetic models and included as covariates age , sex , and 12 EIGENSTRAT ancestry axes to control for population stratification [24] . We tested 5 , 384 deletions and 3 , 553 duplications with a minimum frequency of 0 . 004 , and none of them associated significantly with viral set point after correction for multiple testing using straight Bonferroni correction ( p threshold = 9 . 2×10−6 for deletions and 1 . 4×10−5 for duplications ) . However , we note that this is a conservative correction , because several CNVs can reflect the same association signal due to the difficulty of distinguishing between nearby CNVs when inferring them from the genotyping data . We manually inspected all CNVs that showed an association with set point at p<0 . 05 ( unadjusted ) ( Table S1 ) . One associated CNV was located in the KIR region , where both duplications and deletions associated to variable degrees with HIV-1 control . The duplications and deletions each occurred in around 3%–5% of the study population . Many , although not all , of these identified duplications and deletions covered the KIR3DL1-KIR3DS1 locus , which has been the subject of intensive study related to control of HIV-1 [7] , [13] . Focusing on SNPs included in this copy number variable region ( rs631717 , rs649216 , rs581623 ) , HIV-1 viral load at set point was lower for individuals with more copies and higher for individuals with fewer copies ( p = 0 . 010 for duplications and p = 0 . 001 for deletions , as compared to samples that did not show copy number variability and have two total copies of KIR3DL1 and/or KIR3DS1 ) . If we assign an overall copy number to each sample based on the PennCNV call for these three SNPs ( zero , one , two , three , or four copies ) , the CNV in the KIR region shows an even stronger association with viral load at set point ( p = 3×10−5 ) . To assess the individual impact of KIR3DS1 and KIR3DL1 on HIV-1 control and to further investigate the copy number variability observed in the KIR region , we developed a real-time PCR assay to quantify the number of copies of each gene ( Figure S1 ) . Individuals without a CNV in the region have a total of two copies of these genes ( one KIR3DL1 or one KIR3DS1 on each chromosome ) , whereas in individuals with a deletion or a duplication their sum corresponds to the copy number state ( e . g . , four alleles are measured in individuals with a homozygous duplication , or zero alleles with a homozygous deletion ) ( Table S2 ) . As can be seen in Figure S1 , our assay is able to count nearly all known alleles of KIR3DS1/KIR3DL1 that appear in populations of European descent . Overall , we found high repeatability of the assay ( Figure S2 ) and a good correspondence with copy number assignments called by PennCNV [23] using SNPs in the KIR3DL1-KIR3DS1 region ( Text S1 , Table S3 ) . In the KIR3DL1-KIR3DS1 region , about 3 . 6% of our samples had a deletion according to the real-time PCR data , and about 5% had a duplication . The frequencies of the various genotypes that show evidence of duplication are listed in Table S4 . It is clear that a single chromosome can have two copies of KIR3DS1 or KIR3DL1 , since respectively 6% and 14 . 5% of the samples with duplications had three total copies of either KIR3DS1 or KIR3DL1 , presumably two on one chromosome and one on the opposite chromosome . The diversity of genotypes present at this locus and our inability to discern which genes occur on the same chromosome make it challenging to determine if this locus is in Hardy-Weinberg equilibrium . However , previous work has shown that KIR3DL1/KIR3DS1 are indeed in Hardy-Weinberg equilibrium , in spite of the existence of the relatively rare haplotypes containing multiple copies of this gene [25] . We first checked to see if the raw number of KIR3DS1 and KIR3DL1 copies per individual ( without accounting for presence of the cognate HLA-B ligand ) associated with HIV-1 set point: neither raw count associated with viral control ( n = 1 , 736 , p = 0 . 230 for raw KIR3DS1 count and p = 0 . 508 for raw KIR3DL1 count , Table 1 ) . The functionality of a KIR receptor hinges on the presence of its cognate ligand . The activity of KIR3DS1 and KIR3DL1 therefore depends on the expression of appropriate HLA-Bw4 molecules on the surface of target cells . To take this epistatic feature into account , we created an “effective” gene count , in which each copy of KIR3DL1 or KIR3DS1 was counted only when its specific Bw4 ligand was present . HLA-Bw4-80I and HLA-Bw4-80T have both been demonstrated to be ligands for KIR3DL1 [26] , [27] . Although the direct interaction between KIR3DS1 and HLA-Bw4-80I is less definitive because a physical interaction between HLA-Bw4-80I and KIR3DS1 has not been demonstrated [28]–[30] , epidemiological and functional evidence suggests that under some conditions , such as HIV-1 infection , HLA-Bw4-80I serves directly or indirectly as a ligand for KIR3DS1 ( Table 2 ) [3] . HLA-Bw6 is not a ligand for either KIR3DS1 or KIR3DL1 , and there has not been evidence to show that any other KIR receptors interact with HLA-Bw4 molecules . Some HLA-A alleles also carry the HLA-Bw4-80I motif ( 16 . 1% of all HLA-A in dbMHC Project Anthropology , http://www . ncbi . nlm . nih . gov/gv/mhc/ihwg . cgi ? ID=9&cmd=PRJOV ) , but for simplicity we restricted our analyses to HLA-B alleles with Bw4-80I , since it is not clear that all HLA-A-Bw4-80I molecules serve as ligands for KIR3DL1 . All subsequent analyses used this “effective” gene count . In order to determine these “effective” KIR3DL1 and KIR3DS1 counts , we required that samples had ( 1 ) successful real-time quantitation for both KIR3DL1 and KIR3DS1 , ( 2 ) HLA-B data , and ( 3 ) available KIR3DL1 allelic subtyping , if at least one KIR3DL1 was present . A total of 706 samples fit all the criteria . Allelic subtyping was not included for KIR3DS1 since it shows little variation [21] . The KIR3DL1 subtyping data were used to separate the alleles that are expressed on the cell surface ( “KIR3DL1-surface” ) , at either high or low levels , from the special case of KIR3DL1*004 , which is not expressed at the cell surface [13] , [31] . Each of the effective counts was tested separately . We found that the effective KIR3DS1 and effective KIR3DL1-surface gene counts associated with HIV-1 set point ( p = 4 . 2×10−6 and 0 . 020 , respectively ) ( Table 3 ) , with an increase in effective gene count leading to lower viral loads . When KIR3DS1 and KIR3DL1-surface effective gene counts were considered in the same regression model , they remained separately significant ( p = 0 . 00028 and 0 . 0085 , respectively ) ( Table 3 , Figure 2A–B ) . Regardless of KIR3DL1 status , an increase in the effective count of KIR3DS1 associated with improved viral control ( Table 4 , Figure 3A ) . In contrast , an increase in the effective count of KIR3DL1 did not show any association in the absence of KIR3DS1 , but did impact HIV-1 set point in the presence of one or more effective copies of KIR3DS1 ( p = 0 . 0015 , Table 4 , Figure 3B ) . In the subset of study participants who had two HLA-B alleles from the Bw6 subfamily ( Bw6/Bw6 homozygotes ) , the raw counts for KIR3DS1 and KIR3DL1-surface showed no association with HIV-1 viral load at set point , further supporting the critical nature of particular KIR-HLA combined genotypes ( Table S5 ) . The interpretation of KIR-HLA epistatic influences on HIV-1 control is complicated by the fact that particular HLA class I alleles ( -A , -B , and -C ) have previously been independently implicated in modulating HIV-1 control , with HLA-B alleles placing the greatest immune pressure on HIV-1 replication [32] . Specifically , HLA-B*57 and HLA-B*27 are associated with slower HIV-1 disease progression , whereas HLA-B*35Px associates with a stronger susceptibility to developing AIDS rapidly [33]–[35] . The differential impact of these HLA alleles has been attributed to differences in epitope presentation in conserved versus variable regions within the viral genome . The protective HLA-B*57 and HLA-B*27 generate strong antiviral CD8+ T cell responses that target highly conserved proteins , where escape mutations place a great impact on viral fitness . In contrast , HLA-B*35Px restricted CD8+ T cells tend to target highly variable regions that likely place little pressure on the virus . However , in addition to CD8+ T cells , NK cells are also able to interact differentially with these three alleles via KIR3DL1 and KIR3DS1 . HLA-B*57 molecules are a subset of the HLA-Bw4-80I group ( ligand for KIR3DL1 and possibly KIR3DS1 ) , HLA-B*27 are mainly HLA-Bw4-80T ( ligand for KIR3DL1 ) , and HLA-B*35Px are primarily HLA-Bw6 ( except HLA-B*5301 , which is HLA-Bw4-80I ) ( Table S6 ) . Thus , although HLA-B*27 and HLA-B*57 may themselves interact with KIR , these favorable and unfavorable HLA alleles ( in terms of HIV-1 control ) are present in different proportions within each of the groups of alleles that are and are not ligands for KIR3DL1 and KIR3DS1 , and could therefore drive an association with viral control independently of KIR genotypes . For this reason , to assess the specific effects of KIR , these HLA-B alleles must be accounted for in the analysis . To do this , we added all three HLA-B allotypes as covariates to the model considered earlier . We found that an increase in the effective count of KIR3DS1 still associated with a decrease in viral load ( p = 0 . 0075 ) , but the effective count of KIR3DL1-surface did not ( p = 0 . 220 ) ( Table 3 ) . We note that correction for these controlling alleles could reduce power and create instabilities in the signal due to colinearity . Nevertheless , we retain this test as one established approach [4] for evaluating whether it appears to be a credible alternative explanation that the apparent signal is due simply to the contribution of these controlling alleles . We also note that some fraction of the protection conferred by HLA-B*57 and HLA-B*27 is due to their interaction with KIR receptors [13] . Previous work examining the role of protective KIR/HLA genotypes on NK cell functionality showed that NK cells from healthy HIV-uninfected individuals who expressed KIR3DS1 and that also expressed HLA-Bw4-80I were associated with a robust capacity to inhibit HIV-1 replication in vitro , compared to individuals who expressed KIR3DS1 in the absence of its putative ligand [8] , potentially conferring an enhanced capacity of these individuals to respond to the virus soon after infection . We were therefore interested in determining whether individuals with the observed duplication showed a differential capacity to inhibit viral replication in vitro , and whether this effect was due to KIR3DS1 , KIR3DL1 , or both . To determine whether NK cells generated in individuals with increased effective counts of KIR3DS1 and KIR3DL1 showed any variation in NK cell function , we performed an NK cell viral inhibition assay using fresh blood collected from HIV-negative individuals with different KIR/HLA genotype combinations , including several individuals with one effective copy of KIR3DS1 and two effective copies of KIR3DL1 . We found that NK cells from HIV-negative individuals with one effective copy of KIR3DS1 and one effective copy of KIR3DL1 inhibited HIV-1 replication more potently than NK cells from individuals who did not possess at least one effective copy of both KIR3DL1 and KIR3DS1 ( Figure 4 , mean inhibition = 42% , p<0 . 005 ) . Interestingly , individuals who had one effective copy of KIR3DS1 and two effective copies of KIR3DL1 exhibited even more robust NK-cell-mediated inhibition of HIV replication in vitro than did individuals who had one copy of effective KIR3DS1 and just one copy of effective KIR3DL1 ( Figure 4 , mean inhibition = 88% ) . Individuals who did not have HLA-Bw4-80I or who did not have both KIR3DL1 and KIR3DS1 showed markedly less inhibition ( mean inhibition<15% ) . These data support the association results described in the first part of the article , with the only discrepancy being that individuals with two effective copies of KIR3DS1 do show a decrease in viral load at set point , but do not show an increase in viral inhibition . Overall , these results demonstrate that , prior to infection , NK cells generated in the presence of more effective copies of KIR3DS1 and KIR3DL1 have enhanced HIV-1 antiviral activity . Mounting evidence suggests that KIR/HLA compound genotypes heavily influence the frequency of NK cells expressing a given KIR receptor [36] , [37] , and it has previously been shown that KIR3DS1+ NK cells expand in acute HIV infection in the presence of their putative ligand , HLA-Bw4-80I [38] . This preferential early expansion of highly antiviral KIR3DS1+ NK cells could potentially provide enhanced viral control . Given that increased effective counts of KIR3DL1 in the presence of KIR3DS1 demonstrated an enhanced capacity to inhibit HIV replication ( Figure 4 ) , we speculated that the increasing doses of inhibitory KIRs may be associated with unique patterns of KIR3DL1 and KIR3DS1 expression levels in NK cells . This could potentially account for their superior antiviral activity . Using freshly isolated purified NK cells from healthy controls with distinct KIR/HLA genotypes , we found that increasing effective counts of KIR3DL1 , in the presence of an effective KIR3DS1 , were associated with an elevated number of KIR3DS1 transcripts in purified NK cell populations ( Figure 5A , p<0 . 05 ) . This suggests that the increasing effective KIR3DL1 counts potentiate the expression of KIR3DS1 in the circulating NK cell pool but have little impact on their own expression . More interestingly , increasing levels of KIR3DS1 RNA transcripts were strongly associated with the level of HIV-1 inhibition in all KIR3DS1-carrying individuals expressing the putative ligand ( r2 = 0 . 81 , p<0 . 001 , Figure 5B ) , whereas the relative expression of KIR3DL1 was not associated with NK-cell-mediated inhibition of HIV infection ( Figure 5C ) . Similarly , in addition to the impact of increasing KIR3DL1 effective counts on KIR3DS1 transcript expression , individuals with the protective genotype of an effective copy of KIR3DS1 and two effective copies of KIR3DL1 showed a trend towards an expansion of KIR3DS1+ NK cells in the peripheral circulation , as compared to individuals with a single effective copy of KIR3DL1 in the presence of an effective copy of KIR3DS1 ( Figure 5D , E ) . The shift in the whole NK cell population ( Figure 5D ) could reflect an increase in the quantity of KIR3DS1 expressed on the surface of NK cells in the presence of two copies of KIR3DL1 , concomitant with an expansion of the frequency of KIR3DS1+ NK cells [39] . Individuals with the protective genotype of an effective copy of KIR3DS1 and two effective copies of KIR3DL1 also show an increase in the percent of KIR3DS1+ NK cells , but not an increase in the percent of KIR3DL1+ NK cells , when compared to individuals with just one effective copy of KIR3DS1 and one effective copy of KIR3DL1 ( Figure 5E ) . These data suggest that the increasing KIR3DL1 effective gene count , in the presence of an effective KIR3DS1 , is associated with more robust HIV antiviral activity due to a genotype-driven natural expansion of KIR3DS1+ NK cells in the peripheral blood prior to infection . This population of KIR3DS1+ NK cells may expand even further upon HIV infection , potentially providing these individuals with an antiviral advantage .
We conducted a genome-wide CNV screen that allowed us to locate a variable region involved in HIV-1 control . The observed association signal was due to KIR3DL1 and KIR3DS1 copy number variation encompassed within the region , and to the interaction of these receptors with their cognate HLA-B ligands . The proportion of variance explained by the CNV and the proportion of variance explained by the effective KIR3DL1 and KIR3DS1 counts are both approximately equal to 0 . 7% ( in models that also include age , gender , 12 significant EIGENSTRAT axes , HLA-B*57 , HLA-B*27 , and HLA-B*35Px ) . This compares favorably with the proportion of variance explained by CCR5 ( 1 . 7% ) and CCR2 ( 1% ) and is substantially lower than that explained by HLA-B*57 ( 5 . 8% ) [40] . The effective count model that we developed uses one term to describe the interaction between KIR molecules and their known or suggested HLA-B ligands , under the assumption that a receptor is not functional unless its HLA-B ligand is present . The model is based on the well-established interaction between KIR3DL1 and HLA-Bw4 , and the possible interaction between KIR3DS1 and HLA-Bw4-80I . Our results actually further support the proposed epistatic interaction between KIR3DS1 and HLA-Bw4-80I , as can be seen in the striking difference in viral inhibition exhibited by KIR3DS1+ cells from KIR3DL1+ individuals who do and do not have HLA-Bw4-80I . HLA class I alleles are key players in the adaptive immune response , having marked differences in their abilities to restrict HIV through presentation of diverse HIV epitopes to cytotoxic T lymphocytes ( CTL ) , which will in turn kill the infected cells . But HLA molecules also interact with KIR to modulate NK cells , thereby acting within the innate arm of the immune response . In fact , the three alleles described above also are subsets of the HLA-Bw4-80I ( for HLA-B*57 ) , the HLA-Bw4-80T ( for HLA-B*27 ) , and the HLA-Bw6 groups ( for HLA-B*35Px ) . Their impact on HIV control through T-cell-mediated immunity is therefore also measured in the global assessment of the effect of the KIR3DS1 and KIR3DL1 effective counts . We confirmed that our results , showing an association between a decrease in viral load at set point and an increase in the effective KIR3DS1 and KIR3DL1 counts , were not merely due to this confounding factor by including the relevant HLA alleles as covariates in combined models . As expected , the effective KIR3DS1 and KIR3DL1 association signals were weaker after adjustment for the HLA alleles , but the effective KIR3DS1 count remained significantly associated with viral load , and there is evidence for a KIR3DL1 effect when KIR3DS1 is present . Indeed , part of the protective effect of HLA-B*57/HLA-B*27 and the susceptibility effect of HLA-B*35Px are likely attributable to their interaction ( or lack thereof ) with KIR3DL1 and/or KIR3DS1 [13] . Thus , these interactions between KIR and HLA provide an additional contribution to HIV-1 control . Increasing copies of effective KIR3DS1 had a clear impact on viral load at set point regardless of the presence of an effective KIR3DL1 and even after accounting for HLA-B controlling alleles . However , NK cells derived from individuals with multiple copies of effective KIR3DS1 in the absence of an effective KIR3DL1 did not appear to mediate robust antiviral activity in vitro . This is potentially due to the fact that this assay may not detect the antiviral activity of these cells , as these cells may recognize or respond to target cells in a distinct manner than NK cells from individuals who co-express KIR3DL1 . Or this discrepancy could be related to the manner in which KIR3DS1 and KIR3DL1 recognize , compete , and interact with the same ligands on infected cells . Increasing effective copies of KIR3DL1 in the absence of an effective KIR3DS1 had no impact on viral control after correcting for known protective HLA-B alleles . Individuals without an effective KIR3DS1 also did not show an increase in the capacity to inhibit HIV-1 replication in vitro . However , individuals who had additional copies of effective KIR3DL1 in addition to at least one effective copy of KIR3DS1 exhibited remarkable control of HIV-1 set point viral loads . Moreover , this finding was supported by our functional data , where individuals with effective KIR3DS1 and KIR3DL1 also showed an elevated capacity to inhibit HIV replication in vitro . Interestingly , individuals with two effective copies of KIR3DL1 and an effective copy of KIR3DS1 showed even more inhibition than individuals with just one copy of each gene . They also showed a significant elevation in KIR3DS1 transcript levels and in KIR3DS1+ NK cells expressing this activating KIR receptor as compared to individuals with just one effective copy of KIR3DS1 and one effective copy of KIR3DL1 ( Figures 4 , 5A and 5E ) . Surprisingly , having additional effective copies of KIR3DL1 also increased the proportion of KIR3DS1+ NK cells . To our knowledge , this is the first study to show evidence for a beneficial interaction between KIR3DS1 and KIR3DL1 , and these data imply that an elevated KIR3DL1 effective count may specifically provide more robust licensing of KIR3DS1+ NK cells that are then able to expand and mediate strong antiviral control . Alternatively , HIV proteins such as Nef [41] and Vpu [42]–[44] specifically interfere with the capacity of NK cells to recognize infected cells via the downregulation of various NK cell receptor ligands . Thus , it is equally possible that increased expression of KIR3DL1 may provide a more sensitive measure of reduced MHC class I expression , potentiating the triggering of other activating NK cell receptors . Previous data suggest that individuals with increasing copies of KIR3DS1 exhibit an expansion of the frequency of KIR3DS1+ NK cells in their peripheral circulation [39] . However , such patterns have not been observed for inhibitory KIR such as KIR3DL1 , perhaps due to the fact that there are many unique KIR3DL1 alleles , which can be grouped into high , low , and unexpressed variants . Thus , increasing the dosage of KIR3DL1 may alter NK cell functionality even though it does not necessarily increase the frequency of KIR3DL+NK cells . Perhaps having additional copies of KIR3DL1 could contribute to enhanced licensing , similar to the manner in which additional copies of HLA-Bw4 enhance bulk NK cell activity [45] . However , additional work is required to tease out the mechanism underlying the potential interaction between KIR receptors . The analyses that we have conducted required several types of data ( genome-wide genotyping , real-time quantitation for KIR3DL1 and KIR3DS1 , HLA-B allelic determination , KIR3DL1 subtyping ) , which limited our final sample size . However , the results of our association studies appear clear and biologically reasonable , and are strongly supported by functional data , providing a plausible mechanism by which this CNV may impact HIV-1 control . KIR receptors are expressed on NK cells in a stochastic manner and are involved in modulating NK cell functions . The CNV that we have observed in the KIR region can influence the proportion of NK cells expressing KIR3DS1 , and possibly the overall expression level of KIR3DS1 on the surface of NK cells . It also appears to affect the ligand specificity , licensing , or the ability of the NK cells to recognize virally infected cells , as evidenced by the differences in inhibition of HIV replication that are seen in individuals with different genotypes . Interestingly , KIR3DS1+ NK cells expand aggressively following acute infection in the presence of HLA-Bw4-80I , potentially allowing these anti-viral cytolytic effector cells to expand in sufficient numbers to gain effective control of the incoming virus [38] . However , based on the data presented here , individuals with increased numbers of effective copies of KIR3DL1 in the presence of KIR3DS1 may possess an enlarged pool of KIR3DS1+ NK cells prior to infection , which can potentially contribute to enhanced anti-viral control immediately upon transmission , without any proliferative delay to control HIV-1 replication , if their ligand is present . The fact that an increased KIR3DS1 effective count appears to impact relatively early measures of HIV-1 disease control , such as viral load at set point , reinforces the notion that elevated levels of KIR3DS1+ NK cells in acute infection may provide the needed effector cells to contain early viral replication until the HIV-specific CD8+ T cells are able to respond . These observations add a new element to what is known about how genetic variation in the KIR locus modulates the immune response to HIV-1 . It has already been shown that particular KIR variants interact with their ligands to influence control of HIV-1 , with a strong interaction reported between some KIR3DL1 and HLA-B*5701 [13] . The novelty of our findings is that the counts of individual genes in the KIR locus directly influence early aspects of HIV-1 control , with individuals who have an effective copy of KIR3DS1 , in combination with an effective copy of KIR3DL1 , achieving the highest degree of viral suppression . This effect was first apparent from our association data and is strongly supported by functional experiments . In order to assess the possible implications of these findings for vaccine development , it is now a priority to elucidate the functional basis of how NK cells expressing sufficient quantities of both KIR3DL1 and KIR3DS1 suppress HIV-1 , and in particular whether such suppression involves elements of adaptive immunity [46] , or allow for the potential of specific recognition of infected cells by KIR3DS1+ NK cells that may drive viral evolution [12] .
All samples used in this analysis were de-identified . Samples were received from collaborators at outside institutions and were approved under an IRB exemption by the Duke University Health System Institutional Review Board . The Massachusetts General Hospital institutional review board approved functional analyses , and each individual gave written informed consent for participation in the study . Participants were recruited from the Euro-CHAVI Consortium and from the Multicenter AIDS Cohort Study ( MACS ) . All samples were consenting according to the IRB guidelines at their respective sites . The Euro-CHAVI cohort represents a consortium of eight European and one Australian Cohorts/Studies that agreed to participate in the Host Genetic Core initiative of the Center for HIV-AIDS Vaccine Immunology ( CHAVI ) . CHAVI is a consortium of universities and academic medical centers established by the National Institute of Allergy and Infectious Diseases , part of the Global HIV Vaccine Enterprise . The Multicenter AIDS Cohort Study ( MACS ) is an ongoing prospective study of the natural and treated histories of HIV-1 infection in homosexual and bisexual men conducted by sites located in Baltimore , Chicago , Pittsburgh , and Los Angeles . A total of 6 , 973 men have been enrolled . From April 1984 through March 1985 , 4 , 954 men were enrolled; an additional 668 men were enrolled from April 1987 through September 1991 . A third enrollment of 1 , 351 men took place between October 2001 and August 2003 . The 3 , 427 participants were HIV-seronegative at study entry and were tested for seroconversion semiannually by ELISA , with confirmation of positive tests by Western blotting . A total of 978 healthy participants from the Boston area were typed for the KIR CNV . A total of 76 participants were recruited for this study . A seroconverting patient is defined as reaching set point after acute HIV infection , when at least two consecutive HIV-1 viral load values , taken at least a month apart , are within a 0 . 5 log range . All plasma viral load measurements within the set point range are then averaged to determine the phenotype [22] . A potential limitation is that our definition of set point may exclude some rapid progressors who never maintain a stable stage of infection . To call CNVs , we used PennCNV [23] , a software that applies a hidden Markov-model-based approach for kilobase-resolution detection of CNVs from Illumina SNP genotyping data . PennCNV uses Log R ratio ( LRR ) and B allele frequency ( BAF ) measures automatically computed from the signal intensity files by BeadStudio , and we used the standard hg18 PennCNV hidden Markov model and population frequency of B allele ( pfb ) files . For data from the 1MDuo BeadChip , for which there is no standard pfb file , we used our own data to design a pfb file that would include the SNPs specific to this genotyping platform . For better handling of low-quality genotype data , we implemented GC-model signal pre-processing using standard files from PennCNV . We ran QC analysis on the samples and removed those with an LRR standard deviation greater than 0 . 28 , a BAF median outside the range of 0 . 45 to 0 . 55 , a BAF drift greater than 0 . 002 , or a waviness factor not between −0 . 04 and 0 . 04 ( n = 204 ) . Samples were also removed if they were near threshold for more than one of the QC measurements , exhibited karyotype abnormalities , or were gross outliers for number of CNVs called ( n = 59 , total removals = 256 as some failed more than 1 QC parameter ) . CNV calls were restricted to autosomes . The CNV calls were prepared for regression analysis by creating separate duplication and deletion files , each containing a list of the SNPs that were deleted or duplicated , and indicating the number of copies the participant possessed ( zero , one , two for deletion analysis; two , three , four for duplication analysis ) . The calls for each SNP were then run as genotypes in a regression using an additive genetic model , testing for association with HIV-1 set point . All samples that were determined to have a deletion or duplication of the KIR3DS1-KIR3DL1 locus were visually inspected in BeadStudio to confirm the CNV . KIR3DS1 and KIR3DL1 copy number was measured using a quantitative real-time PCR assay . Primer sequences were: KIR3DS1 forward primer , 5′- CTCGTTGGACAGATCCATGA -3′; KIR3DS1 reverse primer , 5′- GTCCCTGCAAGGGCAC -3′; KIR3DL1 forward primer , 5′- GCCTCGTTGGACAGATCCAT-3′; KIR3DL1 reverse primer 5′- TAGGTCCCTGCAAGGGCAA-3′; KIR3DL1-KIR3DS1 probe , 5′-VIC- GGGTCTCCAAGGCCAATTTCTCCAT-MGB-3′; Beta-globin forward primer , 5′- GGCAACCCTAAGGTGAAGGC -3′; Beta-globin reverse primer , 5′-GGTGAGCCAGGCCATCACTA-3′; Beta-globin probe , 5′-6FAM- CATGGCAAGAAAGTGCTCGGTGCCT-MGB-3′ . Primers were purchased from Integrated DNA Technologies ( Coralville , IA , USA ) , and probes were purchased from Applied Biosystems ( Foster City , CA , USA ) . Since the sequences for KIR3DL1 and KIR3DS1 are so similar , we use the same probe for both KIR3DL1 and KIR3DS1 . Both reverse primers were validated in reference [47] as being specific for KIR3DS1 and KIR3DL1 , respectively . We designed new forward primers that create shorter products which are better suited to real-time PCR analysis . Concentration of DNA samples was determined by absorbance at 260 nm , and samples were diluted to achieve a concentration range of 1–20 ng/µL , of which 1 µL was used per reaction in a total volume of 10 µL per reaction . Serially diluted DNA from the CEPH lines GM11840 and GM12752 was used as a standard , with concentrations ranging from ∼100 ng/µL to 8 pg/µL . Both lines have one KIR3DL1 and one KIR3DS1 , which was determined by running them against a standard that did not show copy number variability in the KIR region and that an external assay determined had both KIR3DL1 and KIR3DS1 . Thermal cycling was performed on the Applied Biosystems 7900 Sequence Detection System and data were captured using Sequence Detection System software v1 . 0 . Cycling conditions were: 50°C×2 min , 95°C×10 min , followed by 40 cycles of two-step PCR with 15 s at 95°C and a 1 min extension at 60°C . The threshold ΔRn was set manually after visual inspection of the real-time PCR results . The cycle at which the threshold was achieved ( Ct ) for each CEPH standard reaction was plotted against the base 10 log of the input DNA amount , and the line of best fit through this standard curve was used to estimate the relative input amount for each gene ( KIR3DL1/KIR3DS1 or Beta-globin ) in the unknown samples , which were run in duplicate . The lower limit of detection of the assay was approximately 16 pg of input DNA . Samples with estimated DNA input amounts of less than 16 pg , or for which the coefficient of variation ( CV ) in duplicate samples exceeded 0 . 25 , were excluded from analysis . Samples with a CV greater than 0 . 1 were manually curated and were excluded if the results looked irregular for any reason ( such as duplicates were not conclusive or a low DNA concentration ) . Thirty-nine samples ( 1 . 6% ) were excluded due to low DNA concentration . The copy number of the unknown samples was estimated by the ratio of the KIR3DL1 or KIR3DS1 amount to the Beta-globin amount . Individual samples were then assigned a copy number by rounding the KIR/Beta-globin value to the nearest integer . Of the samples that had a high enough DNA concentration to use in the real-time assay , we were able to make 98 . 8% of the KIR3DL1 calls and 98 . 4% of the KIR3DS1 calls . The KIR3DL1 alleles were characterized according to the protocol in ref . [47] . A total of 204 of our samples overlapped with the samples in the VL cohort in ref . [13] . We used the genealogy in ref . [21] to categorize all of the KIR3DL1 alleles found in our sampled population . KIR3DL1-high: *001 , *00101 , *002 , *015 , *01501 , *01502 , *008 , *009 , *020 , *022 , *023 , *029 , *033 , *035 , *052; KIR3DL1-low: *005 , *006 , *007 , *019 , *028 , *053 , *054 , 3DL1-Lv2 , N9; KIR3DL1*004: *004 , *00401 , *00402 . KIR3DL1-surface includes all KIR3DL1-high and all KIR3DL1-low alleles . Four samples were dropped due to an inconclusive allele type or because they had a rare KIR3DL1 allele for which the quantity or presence of any cell surface expression was not known . We were unable to count the number of KIR3DL1-high , KIR3DL1-low , and KIR3DL1*004 alleles for six samples where the KIR3DL1 real-time assay counted three copies of KIR3DL1 , since the allele genotyping assay is not quantitative and thus we could not discern if one of the alleles was duplicated . These samples were not included in the analysis . HLA-B genotyping was performed by amplification of genomic DNA with primers that flank exons 2 and 3 . PCR products are cleaned using Ampure ( Beckman Coulter ) . The cleaned products are sequenced using appropriate nested primers . The sequenced products are cleaned using CleanSEQ ( Beckman Coulter ) and then run on the ABI PRIZM 3730 . Sequence analysis is carried out using Assign ( Conexio Genomics ) . A total of 2 , 724 individuals with age and gender data were identified as whites by a principal component analysis of the genome-wide genotyping data ( EIGENSTRAT method ) and were eligible for the study . A total of 126 of these were dropped since they did not have a set point value . A total of 236 of these were dropped due to being outliers in the EIGENSTRAT analyses . This left 2 , 362 samples , of which 2 , 102 had Illumina genotype data and a sufficient PennCNV quality score to be included in the PennCNV analysis . Of the 2 , 362 patients who had a set point value and were not EIGENSTRAT outliers , 1 , 751 had KIR3DL1 and KIR3DS1 real-time results . A total of 771 of these had KIR3DL1 genotype data or had a KIR3DL1 count of zero . Of these 771 patients , 48 could not be included because they were missing HLA-B data . Six samples were not included because the sum of KIR3DL1+KIR3DS1 real-time counts did not equal the PennCNV call . Six samples were not included where the KIR3DL1 real-time assay counted three copies of KIR3DL1 ( see previous section ) . Four samples were dropped where the KIR3DL1 genotyping assay had two unique alleles and the real-time assay only counted one KIR3DL1 . One sample was dropped for inconclusive HLA-B results . Of the samples that were not EIGENSTRAT outliers , 738 samples had complete KIR3DS1 and KIR3DL1 effective calls , meaning that each had KIR3DL1 and KIR3DS1 real-time counts , HLA-B data , and KIR3DL1 allele typing when the KIR3DL1 assay showed the presence of at least one KIR3DL1 allele . Of those with complete effective calls , 706 had stable HIV-1 viral load set point . The individuals who were included in the NK cell inhibition assays were HIV-negative healthy controls from the Boston area . A total of 76 participants were recruited for these assays , including eight individuals expressing two copies of KIR3DL1 and one copy of KIR3DS1 . The numbers of included individuals with each genotype are listed in the legend for Figure 4 . NK inhibition assays were performed as previously described [38] . Activated CD4+ T cells were generated from each donor for 4 d in culture with a bispecific antibody to CD3/CD8 . The cells were then infected with a JRCSF ( R5 ) at a multiplicity of infection of 0 . 01 for 4 h at 37°C . Cells were then washed twice , and 105 CD4+ T cells were plated in quadruplicate in the presence of 50 U/ml IL-2 . NK cells were enriched from whole blood by negative selection ( RosetteSep , Stem Cell technologies ) on the same day as CD4+ T cells were infected . NK cells were then added at a 10∶1 NK∶CD4 ratio . Supernatant was collected every 3–4 d for quantification of p24 Gag production by ELISA ( p24 ELISA; Perkin Elmer ) . The percent inhibition was calculated as the difference between the level of p24 produced in wells containing medium alone and those also containing NK cells divided by the total level viral replication in medium alone wells . NK cell populations were isolated from peripheral blood mononuclear cells ( PBMCs ) by high-speed cell sorting using a fluorescence-activated cell sorter ( BD FACSAria ) . For these cell-sorting experiments , PBMCs were purified from whole blood , which were then labeled with anti-CD3-phycoerythrin-Cy5 . 5 ( anti-CD3-PE-Cy5 . 5 ) , anti-CD56-PE-Cy7 , anti-CD16-allophycocyanin-Cy7 ( anti-CD16-APC-Cy7 ) , anti-CD14-PE-Cy5 , anti-CD19-PE-Cy5 , DX9-FITC ( KIR3DL1 , BD Biosciences ) , and z27-PE ( Beckman Coulter ) antibodies . Gates were set to only include CD3− CD14− CD19− CD56+/− CD16+/− NK cells , and all CD3+ CD14+ CD19+ cells were excluded . The average purity of sorted NK cell populations was 97 . 7% ( range , 95 . 8% to 99 . 1% ) . Sorted NK cells were collected directly in RNA stabilizing buffer ( RLT; Qiagen ) and stored at −80°C . RNA was prepared using the RNeasy kit ( Qiagen ) and then used to prepare cDNA using the Superscript III kit ( Invitrogen ) . All sorted events were recorded on the FACSAria and the frequency of CD3− CD14− CD19− CD56+/− CD16+/DX9+ ( KIR3DL1+ NK cells ) and CD3− CD14− CD19− CD56+/− CD16+/Z27+ DX9− ( KIR3DS1+ NK cells ) were analyzed . The level of transcription of all KIRs was measured by quantitative PCR with SYBR green ( Stratagene ) as described previously [37] . To ensure specificity , dissociation curves were analyzed upon each run . The relative expression of KIR mRNA was normalized to the expression of glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) in sorted NK cell RNA preparations . The levels of KIR transcripts were then expressed as 250-cycle number above threshold . The statistical analyses were performed using Stata/IC 10 . 0 for Windows . The association with set point was tested by using a linear regression of the effective gene count after correcting for age , gender , and ancestry by using the 12 significant EIGENSTRAT axes . Statistical significance refers to two-sided p values of<0 . 05 . The NCBI ( http://www . ncbi . nlm . nih . gov/sites/entrez ) accession numbers for the sequences discussed in this article are: HLA-B ( GeneID 3106; NC_000006 . 11 ) , KIR3DL1 ( GeneID 3811; NC_000019 . 9 ) , KIR3DS1 ( GeneID 3813 ) . | There is marked intrinsic variation in the extent to which individuals are able to control HIV-1 . We have identified a genetic copy number variable region ( CNV ) in humans that plays a significant role in the control of HIV-1 . This CNV is located in the genomic region that encodes the killer cell immunoglobulin-like receptors ( KIRs ) and specifically affects the KIR3DS1 and KIR3DL1 genes , encoding two KIRs that interact with human leukocyte antigen B ( HLA-B ) ligands . KIRs are expressed on the surface of natural killer ( NK ) cells , which serve as important players in the innate immune response , and are involved in the recognition of infected and malignant cells through a loss or alteration in “self” ligands . We use both genetic association and functional evidence to show a strong interaction between KIR3DL1 and KIR3DS1 , indicating that increasing gene counts for KIR3DL1 confer increasing levels of protection against HIV-1 , but only in the presence of at least one copy of KIR3DS1 . This effect was associated with a dramatic increase in the abundance of KIR3DS1+ NK cells in the peripheral blood , and strongly associated with a more robust capacity of peripheral NK cells to suppress HIV-1 replication in vitro . This work provides one of the few examples of an association between a relatively common CNV and a human complex trait . | [
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] | 2011 | Copy Number Variation of KIR Genes Influences HIV-1 Control |
The transition from vegetative growth to multicellular development represents an evolutionary hallmark linked to an oxidative stress signal and controlled protein degradation . We identified the Sem1 proteasome subunit , which connects stress response and cellular differentiation . The sem1 gene encodes the fungal counterpart of the human Sem1 proteasome lid subunit and is essential for fungal cell differentiation and development . A sem1 deletion strain of the filamentous fungus Aspergillus nidulans is able to grow vegetatively and expresses an elevated degree of 20S proteasomes with multiplied ATP-independent catalytic activity compared to wildtype . Oxidative stress induces increased transcription of the genes sem1 and rpn11 for the proteasomal deubiquitinating enzyme . Sem1 is required for stabilization of the Rpn11 deubiquitinating enzyme , incorporation of the ubiquitin receptor Rpn10 into the 19S regulatory particle and efficient 26S proteasome assembly . Sem1 maintains high cellular NADH levels , controls mitochondria integrity during stress and developmental transition .
The 26S proteasome represents the major cytoplasmic and nuclear ubiquitin-dependent protein degradation machinery and is composed of a barrel-like 2 . 5 MDa 20S proteolytic core particle ( CP ) capped with one or two 19S regulatory particles ( RP ) . Proteins destined for degradation are unfolded , de-ubiquitinated and translocated by the RP to the CP where proteolytic activity takes place . Each regulatory particle consists of a lid and a base . The lid is composed of a nine-subunit protein composed of six PCI ( Proteasome/COP9/Initiation factor ) domain proteins ( Rpn3 , 5 , 7 , 9 , 12 ) , two MPN ( Mpr1p and Pad1p N-terminal ) domain proteins ( Rpn 8 , 11 ) and the Sem1 ( Suppressor of Exocytosis Mutation 1 ) / Dss1 ( deletion of split hand/split foot 1 ) protein . Sem1 was originally identified in Saccharomyces cerevisiae in genetic suppression studies of the exocyst [1] . It is a multifunctional and intrinsically disordered protein , associating with several functionally diverse protein complexes where it is also supporting assembly without being a subunit of the final active complex itself [2] . This includes the human BRCA2 complex ( breast cancer complex ) required for homologous recombination , the TREX2 complex ( transcription export complex 2 ) needed for mRNA elongation and nuclear export , or the yeast Csn12-Thp3 complex involved in RNA splicing [2] . Deletion of the Sem1 encoding gene in yeasts resulted in viable cells with phenotypes including temperature sensitivity , enhanced filamentation and cell cycle delay [3] . Dss1 represents the human homolog and was able to rescue the growth defect of both S . cerevisiae and S . pombe mutant strains , suggesting a conserved molecular function from yeast to humans [4 , 5] . The in vivo function of Sem1 in multicellular metazoans is difficult to address , as deletion of the corresponding gene in C . elegans revealed essential functions in oogenesis resulting in embryonic lethality and larval arrest [6] . The fungus Aspergillus nidulans can be used as model system as it not only grows by forming polar reiterated cellular units but can also differentiate and produce fruiting bodies including specialized cell types , which are surrounded by a tissue of auxiliary cells for nursing [7 , 8] . The function of the fungal Sem1 counterpart was analysed in A . nidulans . The encoding gene was named here for convenience sem1 and corresponds to semA according to A . nidulans nomenclature . Fungal Sem1 protein is associated with the 19S RP and links an appropriate oxidative stress response to cellular differentiation and coordinated fungal development . The majority of the proteasomes in the Δsem1 mutant strain were 20S core particles , which provides an increased ATP-independent protease activity . The small proportion of cellular Δsem1 deficient 19S and 26S proteasomes lacked any interaction with the chaperon Ecm29 , which facilitates the association of the 19S RP with the 20S CP . The Δsem1 19S regulatory particles were also deprived from detectable incorporation of the ubiquitin receptor Rpn10 , which facilitates the association of the lid with the base . The cellular redox state in A . nidulans is linked to Sem1-dependent transition from vegetative growth to differentiation . Sem1 links increased 26S proteasome stability to mitochondria integrity and is a prerequisite for an appropriate oxidative stress response required for multicellular development .
Sem1 proteins are conserved throughout eukaryotes from fungi to humans ( S1 Fig ) . The impact of Sem1 on developmental programmes of a multicellular fungus was analysed . Filamentous fungi perform the transition from vegetative growth as hyphae to multicellular development by forming fruiting bodies consisting of tissues with distinct specialized cells . A . nidulans exhibits an asexual and a sexual life cycle propagated by spores . Light promotes asexual development and reduces sexual development , whereas darkness and oxygen limitation promote the sexual life cycle [9] . Deletion of the gene encoding fungal Sem1 resulted in a viable Δsem1 deletion strain , which exhibited a reduced growth rate with a smaller colony size compared to wildtype ( Fig 1A and 1B ) . The mutant strain accumulated a reddish pigment similar to a fungal strain defective in the COP9 signalosome , required for specific ubiquitination of substrates ( Fig 1A and 1B ) [10 , 11] . Conidia are the asexual spores of A . nidulans and are formed at conidiophores . Conidia formation , which is normally favoured in light , is delayed in the absence of Sem1 ( Fig 1C ) . Conidia formation was examined during 6 days of asexual growth . In the absence of Sem1 , significant reduction of conidia was observed compared to wildtype and complementation strains . The wildtype and complementation strain produced ≈ 150x106 spores/ml after only 2 days , whereas the mutant strain was able to produce less than 1% after 2 days ( 5000 spores/ml ) . The number of spores in the mutant strain increased over time , reaching a maximum after 5 days with approximately 64% of the spores produced by wildtype or complementation strains ( Fig 1C ) . To determine whether the delay in conidiophore formation can explain the reduced conidia in the absence of Sem1 , the morphology of conidiophores was examined after 20h , 26h and 48h ( Fig 1D ) . No conidiation was observed in the absence of Sem1 after 20h . After 26h , most of the conidiophores of wildtype and complementation strains showed several lines of conidia , whereas the deletion strain only produced metulae and phialides . After 48h , all strains were able to produce conidiophores ( Fig 1D ) . A gene encoding endogenously tagged Sem1-GFP complements these developmental phenotypes resulting in a functional gene that can be used to investigate in vivo Sem1 localization and interaction partners . Growth of the deletion strain was examined in the dark , which promotes the formation of sexual fruiting bodies named cleistothecia , containing meiotic ascospores ( Fig 1E and 1G ) . Cleistothecia maturation includes the formation of hyphal nests and primordia of fruiting bodies , a development that requires one week for wildtype or the sem1-gfp complemented strain . In contrast , the Δsem1 strain showed after one week mostly white air mycelium and only nests with primordia ( Fig 1F ) . Cleistothecia from wildtype and complementation strains contained meiotic ascospores , which were able to germinate , suggesting the ascospores are viable ( Fig 1F , squeezed panel ) . Cultivation of Δsem1 strain for 10 days still resulted in only white air mycelium and nests with primordia ( Fig 1F , 10 days panel ) . Even after 10 days , cleistothecia from Δsem1 strain contained only an empty cleistothecia-envelop without any ascospores , indicating a specific blockage in sexual fruiting body formation ( Fig 1F , squeezed panel ) . The average size of these empty cleistothecia-envelops was 5850±823 μm2 . In contrast , cleistothecia smaller than 5000 μm2 from wildtype or complementation strains ( Fig 1G , 15% ) were pigmented and contained ascospores ( Fig 1F , squeezed panel ) . These data demonstrate that the Δsem1 mutant strain can grow vegetatively but is delayed in asexual development and blocked in sexual development with a misregulated secondary metabolism , as indicated by the accumulation of an orange dye . The finding that sem1-gfp can complement all these developmental phenotypes makes A . nidulans an attractive system to compare cellular 26S proteasome composition and assembly with or without Sem1 in a multicellular organism . Cellular proteasome fractions from Δsem1 , wildtype and sem1-gfp complementation strain were isolated and the ratios of intact 26S proteasomes versus 20S CP were compared . Negative staining electron microscopy revealed three forms of proteasome complexes in the cellular fractions , including a 20S CP , composed of a single capped ( 20S+19S ) and a double-capped ( 19S+20S+19S ) proteasomes , respectively ( Fig 2A ) . Cellular proteasomes with functional Sem1 comprise approximately half of the proteasomes as composed particles , ranging from 45% for Sem1 to 59% for Sem1-GFP . This includes 18% and 41% double-capped proteasomes for wildtype and Sem1-GFP complemented strain , respectively . Single capped proteasomes vary between 18% for the complemented and 27% for the wildtype strain in the analysed fungal extracts . The ratio between assembled 26S and 20S proteasomes was significantly shifted within Δsem1 mutant strain , which comprised only 6% composite proteasomes including only 2% double-capped proteasomes . Accordingly , the Δsem1 mutant strain produced primarily 20S proteasome complexes ( 94% ) , whereas the 20S proteasome complexes of wildtype ( 55% ) and Sem1-GFP complementation ( 41% ) strains represent approximately half of the total cell extract derived proteasome fraction . This increase in the percentage of 20S proteasomes from 50% to more than 90% of the total cellular proteasomes corroborates that Sem1 is required for an efficient in vivo assembly of 26S proteasomes and that Sem1-GFP can fulfil this function . The function of Sem1 might either be to accelerate the assembly or to stabilize functional 26S proteasomes or a combination of both . 20S CP might represent , even in the presence of Sem1 , a substantial part of the cellular proteasome complexes during vegetative growth of the fungus . Activities of purified proteasomes were measured by monitoring the hydrolysis of a fluorogenic peptide in the presence of ATP and KCl . Δsem1 strain proteasomes showed 2 . 3 time higher rates of peptidase activities compared to the wildtype strain in the presence of ATP and KCl ( Fig 2B , blue curves ) . Proteasome specificity was further demonstrated by the addition of the proteasome inhibitor MG132 , which inhibited these peptidase activities almost completely to 3% or less ( Fig 2B , green curves ) . The overall proteasome activities in the presence of ATP were compared to the basal ATP-independent peptidase activities derived primarily from 20S CP in the absence of ATP and potassium ions ( Fig 2B , red curves ) . These ATP-independent peptidase activities were lower than the overall activities of proteasomes in all strains , as the absence of ATP and potassium ions retard spontaneous activation of the 20S core particle [12] . In the presence of Sem1 , the ATP-independent overall peptidase activity was 26% compared to ATP-dependent activity . This value increased to 75% in the Δsem1 proteasome fraction . The ratio of 26S/20S peptidase activity in the wildtype and complementation strain was 3 . 8 times and 2 . 7 times higher , respectively , indicating that 26S proteasomes are more active than the 20S proteasomes . This ratio of peptidase activity was only 1 . 3 in Δsem1 mutant strain , indicating similar peptidase activities in Δsem1 regardless to the presence or absence of ATP and 19S RP . These data further support the finding of the electron microscopy analysis that Sem1 is required for the efficient assembly of functional 26S proteasomes . The high ATP-independent relative to ATP-dependent peptidase activity of Δsem1 cells in comparison to wildtype could considerably contribute to the observed mutant phenotypes including the failure to establish developmental programmes . Significant changes in the ubiquitination pattern were detected in the Δsem1 mutant strain with only 6% of conjugated substrates in Δsem1 compared to wildtype cells ( Fig 3A ) . This loss of ubiquitin conjugated proteins in the Δsem1 mutant strain suggests a direct or indirect Sem1 effect on cellular ubiquitination process or on the control of components of the ubiquitin conjugation pathway . Sem1-dependent transcription of six genes providing cellular ubiquitin was examined to analyse whether Sem1 affects cellular ubiquitin homeostasis . The ubi1 gene encodes a protein where ubiquitin is fused to the small ribosome subunit; thereby synthesis of fusion protein will yield ribosomal protein and ubiquitin . The ubi4 gene product contains four head to tail repeats of ubiquitin and supplies monoubiquitin to the cell . RT-PCR revealed that the transcription levels of both de novo synthesis genes of ubiquitin were similar in Δsem1 mutant strain compared to wildtype ( Fig 3B ) . The genes doa4 , ubp6 and ubp14 encode deubiquitinating enzymes ( DUBs ) , which recycle polyubiquitin chains . The rfu1 gene encodes an inhibitor of Doa4 and balances the amount of monoubiquitin and polyubiquitin chains [13] . Transcript levels of these four genes involved in recycling of polyubiquitin were comparable in strains with or without functional Sem1 ( Fig 3B ) . These data suggest that cellular ubiquitin synthesis and recycling functions were independent of Sem1 . A possible function of Sem1 on the ubiquitination pathway was analysed . The last step of the ubiquitination cascade is the attachment of ubiquitin to target substrates by neddylated E3 ubiquitin cullin RING ligases ( CRLs ) . CRLs are under the control of the COP9 signalosome and its deneddylase Csn5/CsnE . COP9 is required for sexual fungal development and physically interacts with Den1/DenA , a second conserved deneddylase , which promotes asexual development [14–16] . The Δsem1 strain showed reduced amounts of neddylated ( 65% ) and unneddylated Cul1/CulA ( 40% ) compared to wildtype ( Fig 3C ) . The majority of the CulA proteins were in their unneddylated and inactive form . RT-PCRs revealed that the expression levels of csnE , culC or culD genes were decreased in the Δsem1 mutant strain compared to wildtype , whereas denA transcripts were unchanged ( S2A Fig ) . This could be due to Sem1’s chaperon functions for the assembly of various protein complexes involved in transcription , RNA splicing or nuclear export [2] . Reduced levels of neddylated cullins and subsequently a limited ubiquitin conjugation of substrates in the absence of Sem1 suggest an important function of Sem1 in the ubiquitination pathway of target proteins due to its impact on transcription and on proteasome assembly and function . Accelerated proteolysis due to increased numbers of proteasomes can cause decreased levels of ubiquitin conjugates [17 , 18] . Transcript levels of genes encoding proteasomal subunits or ubiquitin receptors were compared between strains with or without Sem1 to examine whether increased transcription contributes to decreased ubiquitination observed in the Δsem1 strain ( Figs 3B and S2B ) . The rpn3 transcription of wildtype cells was significantly higher than in the Δsem1 mutant strain , where transcription was reduced . The rpn3 gene encodes a protein which is tethered by Sem1 to the proteasome lid during biogenesis and interacts in the mature lid with Rpn7 [19] . The reduced transcription of rpn3 transcripts suggest limited incorporation of Rpn3 protein into the 26S proteasome . The rpn10 gene encodes one of the intrinsic ubiquitin receptors of the proteasome and is located at the interface of the regulatory particle between the base and the lid . Expression of rpn10 as well as of other rpn transcripts for the lid subunits were similar in strains with or without Sem1 , except for rpn11 mRNA ( Figs 3B and S2B ) . The transcription level of rpn11 encoding the intrinsic ubiquitin isopeptidase of the 26S proteasome was doubled in the mutant strain compared to wildtype . Controlled rpn11 expression using an inducible PTetOn-rpn11 fusion gene was applied to examine whether increased amounts of rpn11 transcripts result in higher deubiquitinase activity and reduce the overall population of ubiquitinated proteins . The PTetOn-rpn11 strain was only able to grow in the presence of a threshold concentration of at least 5μg/ml doxycycline , indicating that Rpn11 is essential for growth ( S3A Fig ) . Eight-fold increase in the transcription of rpn11 did not affect the expression of the control genes sem1 or csn5 , but led to an overall decrease in ub-conjugated proteins compared to wildtype ( S3B and S3C Fig ) . This supports that significantly increased Rpn11 isopeptidase activity can contribute to the reduction in ubiquitin conjugates as observed in the Δsem1 mutant strain ( Fig 3A ) . Cellular Rpn11 protein levels for the deubiquitinating enzyme were compared to ubiquitin receptor Rpn10 levels in cells with or without Sem1 . The genes encoding Rpn11 or Rpn10 were replaced by functional Rpn-GFP fusions . Rpn10-GFP derived from Δsem1 or wildtype strain resulted in stable Rpn10 protein levels . In contrast , Rpn11-GFP was instable and resulted in only 35% of full-length protein in the Δsem1 strain compared to wildtype ( Fig 3D ) . These data imply a possible effect of Sem1 on the transcription of specific proteasomal genes . Increased rpn11 transcripts result in less full-length Rpn11 protein in a Δsem1 mutant strain lacking the conserved zinc–binding site in the MPN+ domain ( S3D Fig ) , suggesting that Sem1 supports cellular Rpn11 stability . Reduced amounts of an intact 26S proteasomes observed by electron microscopy correlate with the reduced Rpn11 protein levels in Δsem1 . Incorporation of the deubiquitination enzyme into the 26S proteasomes presumably provides a Sem1-mediated Rpn11 stabilization in fungal wildtype cells . The cellular localization of functional GFP fusions of Sem1 and the four RP subunits ubiquitin receptor Rpn10 , deubiquitinating protein Rpn11 , its inhibitor Rpn5 , and Rpn3 that is tethered by Sem1 were compared ( Fig 4A ) . Identical microscopy settings and the same number of spores were used for cultivation to obtain relative concentrations of 19S RP subunits in the hyphae , reflected by GFP intensities . Significant nuclear staining was observed in the sem1-gfp strain including a minor cytoplasmic and a major nuclear Sem1 subpopulation ( Fig 4B ) . The weakest monitored GFP signal in the cytoplasm and the nucleus was observed for Rpn3 , Rpn10 and Rpn11 , indicating similar cytoplasmic and nuclear abundance . Rpn5-GFP and Sem1-GFP had similar intensities , which were significantly higher than the Rpn3 , Rpn10 or Rpn11 levels . Rpn5 inhibits the Rpn11 deubiquitinase and the increased intensities of Rpn5 might reflect its importance to reduce false DUB activity . Increased cellular Sem1 levels might be required , because it is not only part of the RP of the proteasome but also functions as chaperone in the assembly of several other complexes for cellular processes including transcription . Affinity purifications of endogenously GFP-tagged Sem1 , Rpn3 , Rpn5 , Rpn11 and Rpn10 combined with subsequent protein identification by mass spectrometry resulted in 34 putative interaction partners for Sem1 and 29 for Rpn GFP-fused subunits ( Fig 5 ) . Sem1-GFP recruited two proteins of the transcription export complex 2 ( TREX2 ) and two proteins homologous to subunits of the yeast transcription regulator complex , Csn12-Thp3 , in agreement with previous approaches [2] . These associations were not observed with the other lid subunits and were Sem1-specific . An additional Sem1-specific association was found with the hypothetical protein HypoP2 ( encoded by the AN4931 gene ) , which is conserved among 21 Aspergillus species but not in the unicellular yeasts S . cerevisiae or S . pombe . 29 proteins were identified associating both with Sem1 and the other GFP- tagged 19S RPs . The 29 interaction partners were grouped into five clusters: ( 1 ) protein degradation by the proteasome , ( 2 ) proteins involved in mitochondria-related activities , ( 3 ) proteins associated with ribosomes , ( 4 ) tubulin of the cytoskeleton and ( 5 ) conserved hypothetical protein HypoP1 ( encoded by AN2234 ) with orthologs only in Aspergillus-related species and no conserved domain ( Fig 5A lower panel ) . These associations point to a link between Sem1 as part of the regulatory particle and protein homeostasis , transport and mitochondria-related activities . GFP pull-downs corroborated that A . nidulans Sem1 associates as part of the lid , with the complete 19S RP consisting of all 19S RP subunits ( Fig 5A ) . Four identified in vivo interactions , which were also identified with the other analysed 19S RP subunits , support an important contribution of Sem1 to proteasome assembly , enabling the lid to associate with the base and the 19S RP to associate with the 20S CP . The Sem1-Rpn10 interaction might stabilize the connection between the proteasome lid and the base [20 , 21] . The Sem1 interactions with base and lid associated chaperons , namely Nas6 ( PSMD10 in human ) and Hsp90 , corroborates a Sem1 assembly function [22–25] . Sem1 also interacted with Ecm29 , which stabilizes the 26S proteasome by tethering the 20S CP to the 19S RP [23] . Neither the ubiquitin receptor Rpn10 nor the tethering protein Ecm29 could be identified with any of the rpn-gfp strains when Sem1 was absent ( Fig 5B ) . Consistently , in the absence of Sem1 , Rpn10-GFP failed to pull any of the base and lid related proteasome subunits ( Fig 5B ) . This supports an in vivo function of Sem1 through the ubiquitin receptor Rpn10 and Ecm29 for the interaction of base , lid and CP , which is essential for the assembly of intact 26S proteasomes . The domain architecture of the lid of the proteasome is conserved in eukaryotic cells ( Fig 6A ) . The Δsem1 mutant strain possesses a lid where the Rpn10 ubiquitin receptor is missing and the Rpn11 deubiquitinase protein levels are reduced , although rpn11 transcript levels are increased . This suggests that Sem1 supports the assembly of stable functional capped 26S proteasomes by a molecular mechanism , which includes the physical interaction between Sem1 and Rpn10 to assemble lid to base and that Sem1 protects Rpn11 protein integrity , which is required for the specific ATP/ubiquitin-dependent 26S proteasome activity . Bimolecular fluorescence complementation studies ( BiFC ) were performed to determine whether Sem1 interaction with Rpn10 and Rpn11 can be monitored in fungal cells in vivo . Fungal strains expressing functional Sem1 fused through its C-terminus to the C-terminal half of YFP and C-terminal Rpn10 and Rpn11 fusions to the N-terminal half of YFP were examined ( strains sem1-yfpc+rpn10-yfpn and sem1-yfpc+rpn11-yfpn , respectively ) . The fluorescence observed in strains containing fused Sem1-Rpn10 and fused Sem1-Rpn11 was significantly higher compared to the respective control strains ( Fig 6B right panel ) . These cellular interactions of Sem1 could be due to an escorting of these proteins for lid assembly . A homology model of the A . nidulans 19RP based on the cryoEM structure of human proteasome was generated ( EMDB-4002 , PDBs: 5L4K and 5L46 [26] ) to examine the possibility of interactions between Sem1-Rpn10 and/or Sem1-Rpn11 upon assembly of the lid ( Fig 6C ) . The modeled C-terminal fragment of Sem1 is bound in a structurally conserved cleft between the lid subunits Rpn3 and Rpn7 . This structural conservation results in a very similar binding mode of Sem1 observed for yeast and human proteasomes ( Fig 6A ) . In that binding mode , the extension of Sem1 towards the N-terminus reaches to the other side of the lid ( opposite ) due to an opening in the center of the lid . The missing ( not modeled ) N-terminal tail , comprising approximately 30 amino acids , could be responsible for direct interactions of Sem1 with both Rpn10 and Rpn11 forming the opposite surface of the lid ( Fig 6C ) . These data corroborate direct interactions between Sem1-Rpn10 as well as Sem1-Rpn11 in the fungal cell . Sem1 might escort Rpn10 and Rpn11 proteins to the lid and support the assembly and positioning of both proteins into a stable capped 26S proteasome through the assistance of its flexible N-terminal tail . In contrast to wildtype , the 19S regulatory particle lacking Sem1 associated to proteins related to NADH or ATP production ( Figs 5 and 7A ) . The dihydrolipoamide acetyltransferase Pdh1 ( AN6708 ) is part of the pyruvate dehydrogenase complex for the oxidative decarboxylation of pyruvate to acetyl-CoA . Cytoplasmic Pcy1 ( AN4462 ) converts pyruvate to oxaloacetate . Both enzyme products are used by the mitochondrial TCA cycle . The Sem1-interacting protein Rpn3 associated only in the absence of Sem1 with the ADP/ATP carrier Pet9 ( AN4064 ) of the mitochondrial inner membrane , which exchanges cytosolic ADP for mitochondrial synthesized ATP . Rpn3 , Rpn5 or Rpn10 interacted in the absence of Sem1 with the mitochondrial porin Por1 ( AN4402 ) . This outer membrane protein is required for maintenance of mitochondrial osmotic stability and membrane permeability . The 19S RP without Sem1 associates with the β-subunit of the mitochondrial processing protease Mpp ( AN0747 ) , which cleaves the N-terminal targeting sequence from mitochondrial-imported proteins . Subunit II of complex III ( AN8373 ) and NADH-ubiquinone oxidoreductase , complex I ( AN4288 ) are two components of the mitochondrial inner membrane electron transport chain which interact with RPs without Sem1 . These findings suggest a specific physical interaction of RP subunits in the absence of Sem1 with mitochondria , which is not found when Sem1 is present . The morphology of the mitochondria in Δsem1 and wildtype strains were compared to examine the impact of the association of lid subunits with the TCA cycle and respiratory chain related proteins , which were exclusively found in the absence of Sem1 ( Fig 7A ) . The mitochondria of Δsem1 cells showed dots of disrupted filaments and differed significantly from the wildtype ( Fig 7B ) . This phenotype suggests a defect in the dynamic equilibrium between mitochondria fusion and fission processes , that could be caused by the physical interactions of Δsem1 RPs with the mitochondrial machinery , which is suppressed in wildtype where Sem1 is present . The total cellular NADH production was determined in Δsem1 and compared to wildtype ( Fig 7C ) . Strains expressing Sem1 or Sem1-GFP showed similar high concentrations of NADH produced per gram mycelium after 20h of vegetative growth ( 63 . 0±15 . 2 and 53 . 3±2 . 4 ΔOD460nm/g mycelium , respectively ) . Deleting sem1 resulted in only 26% of NADH compared to wildtype ( 13 . 84±3 . 7 ΔOD460nm/g mycelium ) . The fragmented mitochondria observed in the mutant strain might be defective and less active compared to mitochondria in the wildtype strain . A Δsem1 mutant strain showed fragmented mitochondria , produced less NADH and accumulated orange/red pigments ( Fig 7B–7D ) . Mutant strains with a deficient COP9 signalosome or CAND-proteins controlling cellular cullin E3 ubiquitin ligase activities also accumulated red dyes and were linked to a misregulated secondary metabolism and an inappropriate oxidative stress response [27 , 28] . Consistently , Δsem1 mutant strain was not able to grow on hydrogen peroxide and could hardly grow in the presence of menadione , whereas strains with functional Sem1 germinated and produced normal looking colonies ( Fig 7D ) . The oxidative stress response was monitored at the transcriptional level of three superoxide dismutase encoding genes sodA , sodB and sodM and the catalase encoding gene catA ( Figs 7E and S2B ) . Deletion of sem1 resulted in significant 2 . 5-fold up-regulation of transcripts for catalase A and 1 . 6-fold increase for superoxide dismutase B compared to wildtype ( Figs 7E and S2B ) . These results underline a critical Sem1 function in the oxidative stress response . The mutant strain presumably tries to minimize the damaging effects of ROS caused by the damaged mitochondria , thereby inducing an antioxidative defence system . Wildtype mitochondria were not damaged when exposed to moderate oxidative stress . Transcription levels of mitochondrial genes fzo1 for fusion or fis1 and dnm1 for fission were similar in fungal wildtype cells with an intact Sem1 in absence or presence of oxidative stress . The encountered oxidative stress was reflected in a response of increased expression of catalase encoding catA , sodB for a superoxide dismustase or the regulatory gene for oxidative stress nap1 corresponding to yeast yap1 ( Figs 7F and S6 ) . It was analysed , whether Sem1 expression levels varied in response to oxidative stress . The inflicted oxidative stress resulted in a significantly increased transcription of sem1 and rpn11 genes ( Fig 7F ) . This increasing level of sem1 and rpn11 transcripts represents a yet undescribed physiological cellular oxidative stress response , which might protect the cell from increased 20S proteasome levels . Increased transcription to produce more Sem1 and Rpn11 proteins might counteract the damaging interaction of aberrant 19S regulatory particles with the mitochondria , which is detrimental for vegetative cells and for fungal differentiation , requiring an oxidative stress signal as developmental trigger [29 , 30] .
The conserved Sem1 protein supports the assembly of multiple cellular complexes and represents the ninth bona fide subunit of the 19S regulatory particle of the 26S proteasome . A novel cellular function was detected , which connects the proteasome function and the cellular redox state at the molecular level . Oxidative imbalances in the multicellular ascomycete Aspergillus nidulans resulted not only in increased transcription of genes for detoxification enzymes such as catalases , but also in increased transcription of sem1 and rpn11 encoding the proteasomal deubiquitinating enzyme . Sufficient amounts of Sem1 and Rpn11 proteins are necessary during oxidative stress to provide higher amounts of correctly assembled 26S proteasomes . A lack of Sem1 resulted in increased oxidation-driven 20S proteasomes and instable capped proteasomes lacking the Rpn10 ubiquitin receptor , a functional Rpn11 deubiquitinating enzyme and the chaperone Ecm29 that connects the CP to the RP . Decreased amounts of Sem1 compromise multicellular fungal development , which requires internal reactive oxygen signals as triggers ( Fig 8 ) . Sem1 is required for morphological integrity and functionality of the mitochondria , evident by the structural defects caused by the absence of Sem1 . A physiological link between dysfunctional mitochondria due to mistransferred proteins and a proteostatic response had been described [31] . Lack of Sem1 results in a five-fold decrease in cellular NADH production compared to a wildtype strain . Consistently , Sem1 is required to allow fungal vegetative growth in the presence of oxidative-stress inducing compounds such as H2O2 or menadione . An appropriate oxidative stress response therefore includes in a wildtype fungus not only increased transcription of genes for detoxificating enzymes such as catA or sodB genes , but also increased transcripts for subunits of the 19S RP of the proteasome such as sem1 or rpn11 . This corroborates that increased protein levels for these subunits are part of the cellular answer to stress . Mutant strains without Sem1 protein are hypersensitive towards stress , although they constitutively induce transcription of genes for detoxification enzymes . The Sem1-dependent stress response is linked to coordinated fungal secondary metabolism . This link is reminiscent to genetic studies with mutant strains defective in COP9 signalosome or CAND proteins , which control the activity of cullin E3 ubiquitin ligases . Impaired function of COP9 signalosome , CAND or Sem1 results in a redox imbalance and in accumulation of red orcinol derived secondary metabolites visible in the fungal colony as red dye [27 , 28] . Multicellular development specifically requires protection against oxidative stress , as internal cellular stress signals are required for the progression of differentiation . This includes transient increase in reactive oxygen species ( ROS ) for developmental programmes in animals [32] or fungi [29 , 30] . In fungi , increased ROS production interferes with hyphal fusion as one of the initial steps from vegetative hyphal growth to multicellular development [33] . In humans , increased ROS production is associated with mitochondria disorders , aging or neurodegenerative diseases , where unfolding of oxidized proteins promotes accumulation of protein aggregates [34 , 35] . Sem1 of the unicellular yeast had been proposed to stabilize the interactions between the lid and the base [36] . In multicellular A . nidulans , Sem1 is not only required for correct 26S assembly , but represents a lid subunit which is mandatory for 26S proteasome composition , stability and specificity [36–38] . A . nidulans can form a lid without Sem1 , as it had been described for S . cerevisiae , where a comparison between wildtype and Δsem1 lids by single particle cryo-EM analyses revealed significant structural differences with rearrangements of Rpn3 and Rpn7 in the Δsem1 lid [5 , 39] . Sem1-dependent functions on assembly and stabilization of A . nidulans 26S proteasome were visualized by negative staining electron microscopy , where Δsem1-deficient proteasomes from mutant strains consist mostly of 20S proteasomes with only low abundance of 26S proteasome complexes . Sem1 fulfils its stabilization function by the recruitment of Rpn10 , which is mandatory to stabilize the interaction between lid and base [19 , 21] . In addition to the incorporation of Rpn10 , Sem1 is required for the recruitment of Ecm29 as facilitator , which associates the 19S regulatory particle to the 20S core particle . Direct interactions between Sem1 and the base were not reported , but Rpn10 makes extensive connections with different lid and base subunits , namely the Rpn11/Rpn8 heterodimer , Rpn9 and Rpn12 or the base subunits Rpn1 and the Rpt4/Rpt5 heterodimer [40–43] . A . nidulans Rpn10 is a stable protein , which is unable to associate with any lid or base subunit without Sem1 . The molecular function of Sem1 could be to escort Rpn10 to the lid and to stabilize it during the assembly . The homology model of the 19S regulatory particle of A . nidulans positions the C- terminal fragment of Sem1 between Rpn3 and Rpn7 and the last modeled N-terminal residue of Sem1 in the cleft formed by Rpn3 . This cleft is located in a close proximity to an opening in the centre of the lid , which could be a structural feature allowing the N-terminal tail of Sem1 to pass through and interact with Rpn10 and Rpn11 . Thereby , it is conceivable that this cleft accommodates the N-terminal tail of Sem1 and stabilizes the interaction of Rpn10 and Rpn11 within the lid . This is in agreement with recent cross-linking experiments between the N-terminal part of Sem1 and C-terminal part of Rpn11 in the fully assembled lid but not in LP2 ( lid particle 2 ) , the lid intermediated consisting all eight lid subunits except of Rpn12 [44] . A cross-linking between Sem1 and Rpn10 was not yet described . Modelling the corresponding regions of Sem1 from human or S . cerevisiae into the A . nidulans model leads to similar results . The in vivo BiFC study show direct physical interaction between Sem1 and Rpn10 and supports an escorting and assembly function of Sem1 for Rpn10 prior and during 26S proteasome assembly . Conclusively , the data suggests that the presence of Sem1 is a prerequisite to Rpn10 and is essential for accurate and efficient assembly of a stable capped 26S proteasome . Oxidation drives 26S disassociation presumably by posttranslational modifications of α5 , α6 , α7 rings of the 20S CP [45 , 46] . It was demonstrated that S-glutathionylation through redox-regulation promotes gate opening of the 20S CP , which is otherwise closed unless 19S RP is bound to it [47] . Cellular proteolysis of the oxidation-driven 20S proteasomes derived from the Δsem1 mutant strain impaired in redox regulation resulted in higher degradation rates compared to the wildytype 20S proteasomes . As ATP/ubiquitin-independent degradation requires 20S proteasome complexes but no ATP and no polyubiquitinated proteins [48] , this presumably allows the Δsem1 strain to maintain efficient degradation . In the mutant strain the 20S proteasomes are kinetically favoured regardless of the absence of 19S RP or ATP . The Δsem1 mutant strain produces less NADH , which potentially reduces oxidative respiration and ATP production . Mutant analysis revealed that the presence of the sem1 gene correlated with increased ubiquitin-conjugates and reduced ATP/ubiquitin-independent degradation in comparison to the mutant strain lacking sem1 . A ubiquitin receptor function has been described for the human counterpart Dss1 with a ubiquitin binding site overlapping the Rpn3-Rpn7 binding sites [49] . This suggests that Sem1 can associate with the proteasome leaving the two acidic patches available for ubiquitinated substrates and/or can even dynamically associate with the proteasome to escort ubiquitinated substrates to the proximity of proteasomes [2] . The observed decreased levels of ubiquitinated substrates in the A . nidulans mutant strain lacking sem1 could be related to CRLs and the attachment of ubiquitin to substrates . In yeast , the deletion of Sem1 resulted in an accumulation of ubiquitinated substrates presumably due to non-functional proteasomes [3] . Decreased cellular levels of ubiquitin-conjugates were also observed in mammalian epithelial cells exposed to H2O2 , as a consequence of the oxidation of cysteine residues in the active sites of E1-E3 ubiquitin-conjugating enzymes [50–52] . In A . nidulans , oxidative stress not only reduces the amount of the E3 Cul1 scaffold subunit , but also influenced the transcription of culC for Cul3 , culD for Cul4 and csnE for the COP9 signalosome subunit Csn5 . This suggests that ubiquitinated proteins are regulated in A . nidulans in response to oxidative stress where ATP/ubiquitin-independent degradation takes place . Sem1 represents a novel molecular link between proteasome assembly and specificity , mitochondrial integrity and cellular development . The viable Δsem1 mutant strain from A . nidulans is a valuable tool to investigate ATP/ubiquitin-independent proteolysis to elucidate the cross talk between cullins , COP9 signalosome and proteasome in response to oxidative stress . Understanding Sem1 function during mitochondrial stress will provide new insights for our understanding of mitochondrial-associated pathogenesis . Increased Sem1 activity might delay mitochondria dysfunction and can be used for further therapies . Elucidating the mechanisms by which Sem1 affects and regulates oxidative stress is beneficial in the efforts to understand and treat age-related human diseases and explore potential therapies .
The study did not involve human participants , specimens or tissue samples , or vertebrate animals , embryos or tissues . A . nidulans strains used in this study are listed in Table A ( S1 File-supporting information ) . The A . nidulans sem1 gene corresponds to semA in the fungal nomenclature . Spore concentration was determined by Z2 Coulter particle count and size analyser ( Beckmann counter ) . Vegetative growth was performed in flasks with indentations containing supplemented liquid media and 5x105 spores/ml at 37°C for 20 hours . For asexual and sexual development 10 μl of 10000 spores/ml were spotted on supplemented MM and incubated at 37°C . For the time point experiment , asexual spores were counted with Thoma chamber . Incubating the plates in constant white light triggered asexual sporulation , whereas sexual fruiting body formation was induced by oxygen limitation ( the plates were sealed ) and darkness . Asexual spores were counted using Thoma chamber . For sexual growth , 100 μl of 1x106 spores/ml were spared on supplemented MM and incubated at 37°C and incubated in the dark with limited oxygen for 7 and 10 days . Samples were bound to a glow discharged carbon foil covered grid . After staining with 1% uranyl acetate , the samples were evaluated with a CM 120 transmission electron microscope ( FEI , Eindhoven , and The Netherlands ) . Images were taken with a TemCam F416 CMOS camera ( TVIPS , Gauting , Germany ) . The total NADH production was measured as described earlier [53] . 8x 5ml mycelia of each strain were used for the measurements . Activity was measured at 460nm and was normalized to DCW ( g mycelium ) after drying for 3 days at 60°C . Proteasomes were purified from 8 g grained mycelium using rapid 26S proteasome purification kit from UBPBio . Concentrations were determined by Nanodrop and activity assays were performed according to manufacturer recommendations . The activity was measured in an assay buffer containing 12 . 5 mM Tris HCl pH = 7 . 5+10 mM KCl+1 . 25 mM MgCl2+0 . 125 mM ATP+0 . 25 mM DTT+0 . 0125 mg/ml BSA . The release of AMC from 100 μM fluorgenic peptide , Suc-LLVY-AMC , was measured over 30–60 min and background fluorescence was subtracted from all measurements . t-test was used to determine significance of the results ( http://www . quantitativeskills . com/sisa/statistics/oneway . htm ) . The intensity of western blot bands was determined with ImageJ v1 . 48 analysis software . Mean intensities from biological replicates ( n ) were relative to glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) serving as loading control and were normalized to wildtype ( % ) . Expression levels assayed by RT-PCR are shown as relative expression compared to wilidtype and represents mean value and standard error of the indicated independent experiments ( n ) . | The cellular ubiquitin-proteasome pathway is essential to control cell cycle , gene expression or the response to oxidative stress . Sem1 is conserved in eukaryotes from single cell yeasts to humans as intrinsically disordered and multifunctional protein . Sem1 supports the assembly of several multiprotein complexes but becomes eventually exclusively a subunit of the lid of the 26S proteasome , a cellular machine with a molecular mass of about two megadalton . Defects in the function of the proteasome , which degrades a large fraction of intracellular proteins , result in cancer or neurodegenerative diseases . We showed that Sem1 from a multicellular fungus is required for accurate 26S proteasome assembly and specific activity as prerequisites for mitochondria integrity , oxidative stress response and cell differentiation . Our findings of the complex and dynamic interplay between multiple cellular processes mediated by a small conserved intrinsically unordered protein sheds light and supports current efforts to understand and explore in more details potential therapies to eventually treat age-related human diseases . | [
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] | 2018 | Sem1 links proteasome stability and specificity to multicellular development |
Many cancers are understood to be the product of multiple somatic mutations or other rate-limiting events . Multistage clonal expansion ( MSCE ) models are a class of continuous-time Markov chain models that capture the multi-hit initiation–promotion–malignant-conversion hypothesis of carcinogenesis . These models have been used broadly to investigate the epidemiology of many cancers , assess the impact of carcinogen exposures on cancer risk , and evaluate the potential impact of cancer prevention and control strategies on cancer rates . Structural identifiability ( the analysis of the maximum parametric information available for a model given perfectly measured data ) of certain MSCE models has been previously investigated . However , structural identifiability is a theoretical property and does not address the limitations of real data . In this study , we use pancreatic cancer as a case study to examine the practical identifiability of the two- , three- , and four-stage clonal expansion models given age-specific cancer incidence data using a numerical profile-likelihood approach . We demonstrate that , in the case of the three- and four-stage models , several parameters that are theoretically structurally identifiable , are , in practice , unidentifiable . This result means that key parameters such as the intermediate cell mutation rates are not individually identifiable from the data and that estimation of those parameters , even if structurally identifiable , will not be stable . We also show that products of these practically unidentifiable parameters are practically identifiable , and , based on this , we propose new reparameterizations of the model hazards that resolve the parameter estimation problems . Our results highlight the importance of identifiability to the interpretation of model parameter estimates .
Parameter estimation is an important aspect of computational modeling in the life sciences because parameter estimates can shed light on underlying biological mechanisms and processes and provide a way to link dynamic models to real-world data . However , the dynamics of many living systems have evolved to be robust to changes in underlying parameters , which necessitates an understanding of which parameters or combinations of parameters can even be estimated from data , known as identifiability . Here , we leverage computational identifiability tools to determine what cancer incidence data can tell us about the biology of carcinogenesis . Cancers arise from the accumulation of genetic ( and epigenetic ) abnormalities and mutations . Although a single change is thought to be sufficient for certain cancers ( certain leukemias , lymphomas , and sarcomas in particular ) , many cancers are thought to require two or more hits [1] . For example , retinoblastoma is a two-hit cancer—indeed , a two-hit model of retinoblastoma predicted the existence of the tumor suppressing gene pRb before it was discovered [2]—and colorectal cancer can be described by three or more hits to the APC , RAS , and P53 genes [1] . Similarly to the development of precancerous polyps for colorectal cancer , many esophageal cancers begin with a transition to a condition called Barrett’s esophagous [3] before accumulating additional abnormalities . These genetic ( or epigenetic ) hits are often described as starting different phases of carcinogenesis: initiation , the first destabilizing mutation ( s ) ; promotion , the unchecked growth of a tumor; and malignant conversion , the spread into other tissues . This classification is useful because different exposures may act on different stages of carcinogenesis . Multistage clonal expansion ( MSCE ) models are a class of continuous-time Markov chain models that capture this initiation–promotion–malignant-conversion hypothesis of carcinogenesis . Originally posed as a two-stage model [4 , 5] using birth–death–mutation branching-process theory , this class of models has been expanded to three or more stages , multiple pathways , and other variations . These models have been successfully used to analyze epidemiological population-level cancer incidence data [6–11] , to assess the impact of time-varying exposures on cancer risk using individual-level data [12–16] , and to project the impact of prevention and control strategies on population cancer rates [10 , 17–19] . Although models that use multiple clonal expansion steps have been considered , models with multiple initiation stages but only a single , final clonal expansion stage are more common in the literature and appear to capture the incidence patterns of many cancers ( e . g . [6–8 , 20] . We are concerned here with parameter estimation for MSCE models because it can lead to better understanding of the rates of biological processes like tumor growth or adverse mutations . Indeed , knowing the approximate speed at which an abnormality arises may help to classify the underlying abnormal event ( e . g . single nucleotide mutation , chromosomal translocation , or epigenetic change ) . Identifiability is the study of the parametric information available in a data set when viewed through the lens of a model , and identifiability analysis is an important precursor to accurate parameter estimation . A model is said to be identifiable if all model parameters may be uniquely determined from observed data [21–23] . There are two kinds of identifiability analyses: structural—which analyzes the model in the context of perfectly measured and noise-free data in order to uncover the inherent limitations of the model structure in the context of parameter estimation—and practical—which considers obstacles to parameter estimation that arise from noise , sampling frequency , bias , and other issues in real-world data sets [24] . Identifiability analysis can identify parameter combinations that embody the parametric information available in the data and lead to useful reparameterizations of the model [23] . That MSCE models are not fully identifiable is well established [6 , 25–27] . In particular , finding the closed-form solution of a model’s hazard function—the model output corresponding to age-specific incidence data—gives an upper bound on the number of identifiable parameter combinations available for that model from the age-specific incidence data and constrains the forms of those combinations . We previously computed the exact structural identifiability for the class of MSCE models with constant parameters and one clonal-expansion step [28] . However , this is not the last word on the identifiability of MSCE models . In particular , it is known that there is a practical identifiability problem with the clonal expansion models with three or more stages: the information contained in the asymptote of the corresponding hazard function is not available in the usual age-specific cancer incidence data because the asymptote is not reached within human lifespans [8] . In this analysis , we examine this practical identifiability problem with a profile-likelihood approach . We consider pancreatic cancer , which has linear age-specific incidence at older ages [8] and can be fit by an MSCE model with two or more stages . We demonstrate that the two- , three- , and four-stage models have only three practically identifiable parameter combinations and that , for the three- and four-stage models , several parameters that are theoretically structurally identifiable individually , are , practically , identifiable only in their product . This practical unidentifiability means the incidence data contains information about the overall rate of progression from normal to cancer-initiated cells but not the expected information on the rates of the individual steps leading to initiation .
The mathematics of multistage clonal expansion models have been detailed elsewhere [4 , 5 , 8 , 25 , 29–36] , so we only give a basic description here . The n-stage clonal expansion model ( Fig 1 ) is a continuous-time Markov chain with the following states: X ( t ) , the number of normal cells at age t; Y1 ( t ) , … , Yn−2 , the number of cells in subsequent preintiation states; Yn−1 ( t ) , the number of initiated cells; and Z ( t ) , the number of malignant cells . Let ν be the initial mutation rate , μ1 , … , μn−3 the following preinitiation mutation rates , μn−2 the initiation mutation rate , μn−1 the malignant transformation rate , α the clonal expansion rate , and β the cell death rate . If the parameters and X ( t ) are constant , then we may denote p n , q n ≔ 1 2 - α - β - μ n - 1 ∓ α - β - μ n - 1 2 + 4 α μ n - 1 , ( 1 ) and write hazard functions [6 , 8] of the two- , three- , and four-stage models ( a derivation is provided in S1 Text ) : h 2 ( t ) = ν X α p 2 q 2 ( e - q 2 t - e - p 2 t ) q 2 e - p 2 t - p 2 e - q 2 t , ( 2 ) h 3 ( t ) = ν X 1 - q 3 - p 3 q 3 e - p 3 t - p 3 e - q 3 t μ 1 / α , ( 3 ) h 4 ( t ) = ν X 1 - exp ∫ 0 t μ 1 q 4 - p 4 q 4 e - p 4 ( t - u ) - p 4 e - q 4 ( t - u ) μ 2 / α - 1 d u . ( 4 ) From the hazard functions , we can see that the two- , three- , and four-stage models have at most three ( νX/α , p2 , q2 ) , four ( νX , μ1/α , p3 , q3 ) , and five ( νX , μ1 , μ2/α , p4 , q4 ) structurally identifiable parameter combinations . In this case , these parameter combinations are structurally identifiable [28] . Multistage clonal expansion model hazards share similar characteristics , including an exponential region , a linear region , and an asymptote ( Fig 2 ) . The transition from the linear phase to the asymptote occurs on different time scales for the different models , and , for biologically reasonable ranges of the parameters , only h2 can achieve this asymptote within human lifespans . The other hazards achieve their asymptotes on the order of 1 , 000 to 100 , 000 years , depending on the parameters . For example , the asymptote of the three-stage model occurs on the order of ( μ1 ( 1 − β/α ) ) −1 [8] , and mutation rate estimates are typically on the order of 10−7–10−5 [4–8] ( note that 0 < ( 1 − β/α ) < 1 , so that this term can only exacerbate the time span ) . Thus , because real data cannot access the information contained in the asymptote and other late appearing features , one may expect inherent practical identification issues for MSCE models with more stages . We consider cancers reported to the Surveillance , Epidemiology , and End Results ( SEER ) cancer registries , using SEER 9 data 1973–2012 ( data available in S1 and S2 Data ) . We use the International Classification of Diseases ( ICD-10 ) codes to identify incidence of pancreatic cancer ( C25 ) . More thorough treatments of identifiability of dynamical systems are presented elsewhere [23 , 24 , 37 , 38] , and we previously described a framework to apply dynamical systems identifiability techniques to stochastic time-to-event models , including multistage clonal expansion models [28] . Nevertheless , we provide the basic identifiability framework and definitions here for reference . Consider a vector of states x ( t ) ( unobserved ) , vector of parameters to be estimated ρ , and observed ( known ) input u ( t ) and output v ( t ) in the dynamical systems model , x ˙ ( t ) = f ( x ( t ) , u ( t ) , ρ ) , v ( t ) = g ( x ( t ) , ρ ) . ( 5 ) Definition 1 Parameter ρi in the model given in Eq ( 5 ) is ( globally ) structurally identifiable if , for almost all values ρ i * and initial conditions , the observation of an output trajectory ( v ( t ) = v* ( t ) ) uniquely identifies ρi ( ρ i = ρ i * ) , i . e . if only one value of ρi could have resulted in the observed output . Definition 2 The model given in Eq ( 5 ) is ( globally ) structurally identifiable if each ρi is structurally identifiable . The definition of structural identifiability concerns perfectly measured input and output . However , because real data may not capture all of the parameteric information available in a theoretic trajectory , parameters that are structurally identifiable in a model for a kind of theoretical data may be practically unidentifiable given a corresponding real-world dataset . Practical non-identifiability can arise from poor data quality ( uncertainty , infrequent sampling , etc ) , but it can also be inherent to the type of data measured . For example , the saturation constant of a Michaelis-Menten equation may not be identifiable from low-dose data [39] , and the amplitude of a circadian rhythm will not be identifiable if a value is measured once a day at the same time [40] . Thus , even if there are a large number of data points ( e . g . as is often the case for cancer registry data ) , practical identifiability may still be an issue . It is this kind of inherent limitation of the data that we explore for the multistage clonal expansion models . Practical identifiability is difficult to define in a rigorous way without choosing a threshold ( e . g . width of a confidence interval ) and thus has a “I know it when I see it” quality . Nevertheless , descriptions of practical identifiability are possible and typically consider the confidence bounds for the estimated parameters , found by Fisher Information Matrix ( FIM ) [22 , 23 , 41 , 42] or likelihood-based methods [24] . In this analysis , we use likelihood-based confidence intervals , which are defined as follows . Let L ( ρ ) be the likelihood for the model given the data set as a function of the parameters ρ , and let ρ ^ the maximum-likelihood estimator . Definition 3 Let L * ( ρ i ) denote the maximum likelihood when the ith parameter is fixed to value ρi , and call it the profile likelihood of ρi . Then , the likelihood-based confidence interval for ρi at level of significance α is the set of values of ρi for which the relative negative log-likelihood at ρi is less than a threshold determined by α , that is , { ρ i : log ( L ( ρ ^ ) ) - log ( L * ( ρ i ) ) < Δ α } , ( 6 ) where 2 Δ α = χ 2 ( α , df ) ( 7 ) is the chi-squared distribution with a number of degrees of freedom ( df ) equal to the number of parameters ( for simultaneous confidence intervals ) or equal to 1 ( for pointwise confidence intervals ) . [24 , 43] . We would like to say that parameter ρi in the model given in Eq ( 5 ) is practically identifiable if the likelihood-based confidence interval for ρi has finite length . However , this definition is neither well-defined ( the confidence interval may be finite for one level of significance but infinite at another ) nor practically verifiable . Ultimately , parameters with confidence intervals that are sufficiently large—typically orders of magnitude—as to cause uncertainty and parameter estimation problems at the desired parameter scale and level of significance can be said to be practically unidentifiable . We use profile likelihood [24] and subset profiling [42] methods to investigate the practical identifiability of the two- , three- , and four-stage models . We assume that cancer incidence is Poisson distributed ( details in S1 Text ) . Profile likelihoods were computed by fixing the value of one parameter at each of a series of values within an interval and numerically optimizing the negative log-likelihood as a function of the remaining parameters . Numerical optimization was done in R ( v . 3 . 0 . 1 ) using the Bhat package [44] .
We profile the relative negative log-likelihood of the maximum-likelihood two-stage hazard as a function of each of the parameter combinations p3 , q3 , and νX/α ( Fig 4 ) . All three parameters combinations are practically identifiable because of the trough-shape of the negative log-likelihood , giving finite confidence intervals . The parameter estimates are given in Table 1 . We profile the relative negative log-likelihood of the maximum-likelihood three-stage hazard as a function of each of the parameter combinations p3 , q3 , νX , and μ1/α ( Fig 5 ) . Parameter combinations p3 and q3 are practically identifiable as above , but parameter combinations νX and μ1/α are not practically identifiable because their likelihoods flatten out , resulting in infinite confidence intervals . To identify the form of the practically-identifiable parameter combination of νX and μ1/α , we plot the fitted value of μ1/α as we vary the value of νX ( Fig 6 ) . Because the relationship is linear on the log–log scale , νX and μ1/α exist in a practically identifiable product . From the biological perspective , this means that we can only know the net rate of transition from normal to initiated cells but not the rates of the individual intermediate steps . Our analysis thus demonstrates that there are three parameter combinations that are practically identifiable for the three-stage model from age-specific cancer-incidence data . Since there are three pieces of information in the data and four degrees of freedom in the full model ( Eq 3 ) , one might assume that one additional constraint on the model is sufficient to reduce the number of parameters estimated to three and simultaneously resolve the non-identifiability problem . However , the most reasonable simplifying assumption , namely that the first two mutation rates are the same ( ν = μ1 ) , such as for biallelic gene inactivation [1] , does not do this; the three-stage model with ν = μ1 still has four structurally identifiable parameter combinations , namely p3 , q3 , νX , and ν/α , but only three pieces of practically identifiable information , so another constraint would be needed for a fully identifiable model . In this case , the constraint would need to designate the relative values of νX and μ1/α , which assuming ν = μ1 does not do . The ν = μ1 assumption does , however , suggest a new reparameterization of Eq 3 . Denote r 3 ≔ ( ν X ) ( μ 1 / α ) , ( 8 ) and fix X and α at reasonable values , i . e . at values where the likelihood profiles are flat ( see Fig 5 ) . Then , assuming ν = μ1 , we parameterize ν X = r 3 α X and μ 1 / α = r 3 / α X , and write h 3 ( t ) = r 3 α X 1 - q 3 - p 3 q 3 e - p 3 t - p 3 e - q 3 t r 3 / α X . ( 9 ) As long as X and α are chosen so that νX and μ1/α are within a the range of values for which the likelihood is flat , their exact values do not affect the model fit and can be fixed . Caution is advisable here , however: although the exact values of these parameters do not affect the fit in this context , it is important to not take these values into other contexts where the exact values may be relevant , e . g . prediction in context of time-varying exposures . Nevertheless , this parameterization has several advantages . In particular , multiplicative effects on r3 , such as relative period or cohort effects , can be thought of as affecting both ν and μ1 equally: under the assumption μ ≔ ν = μ1 , r3 simplifies to r 3 = μ X / α , and , more generally , we can write , for some scalar ξ , ξ r 3 = ( ξ ν ) ( ξ μ 1 ) ( X / α ) . We see that the profile relative NLL of r 3 = ν μ 1 X / α has a finite confidence interval ( Fig 7 ) , as p3 and q3 did in Fig 5 . The best-fit parameters for the three-stage model—parameterized as in Eq ( 9 ) and fit to the age-specific pancreatic cancer incidence data—are given in Table 1 . We similarly profile the relative negative log-likelihood of the maximum-likelihood four-stage hazard as a function of each of the parameter combinations p4 , q4 , νX , μ1 , and μ2/α ( Fig 8 ) . As before , parameters combinations p4 and q4 have finite confidence intervals and are practically identifiable , while combinations νX , μ1 and μ2/α have infinite confidence intervals and are not practically identifiable . To determine the combination structure , we use subset profiling [42] . However , rather than using FIM to determine the profiled parameter subsets , we note that the analysis of the three stage model leads us to suspect that the three parameter combinations νX , μ1 , and μ2/α are in a practical product . We use this structure to propose our nearly-full rank subsets . To verify this proposal , we plot the fitted value of one parameter combination while another is fixed and the third is varied ( Fig 9 ) . The three selected plots presented are sufficient to verify that the three parameter combinations indeed exist in a practically identifiable product . As for the three-stage case , that νX , μ1 , and μ2/α can only be identified up to their product means that we can only know the net rate of transition from normal to initiated cells but not the rates of the individual intermediate steps . We can define a quantity analogous to r3 in the three stage case . Here , r 4 = ν X μ 1 μ 2 / α 1 / 3 ( 10 ) and , for some reasonable fixed values of X and α , h 4 ( t ) = r 4 X 2 α 1 / 3 1 - exp ∫ 0 t r 4 α / X 1 / 3 q 4 - p 4 q 4 e - p 4 ( t - u ) - p 4 e - q 4 ( t - u ) r 4 / α 2 X 1 / 3 - 1 d u . ( 11 ) We see that the profile relative NLL of r4 = ( νXμ1μ2/α ) 1/3 has the expected trough shape ( Fig 10 ) , as seen in Fig 8 for p4 and q4 . The best-fit parameters for the four-stage model—parameterized as in Eq ( 11 ) and fit to the age-specific pancreatic cancer incidence data—are given in Table 1 .
Practical unidentifiability is a significant barrier to parameter estimation . Indeed , because it—unlike structural identifiability—can be so dependent on the quality of the data , it can be a moving target . From this perspective , ironically , it is perhaps fortunate that the practical identifiability issue described herein is inherent to any age-specific cancer incidence data that is linear at older ages . This way , such problems can be anticipated and handled systematically , e . g . by reparameterizing the model appropriately . In theory , we could gain additional information if people were to live long enough to see the incidence plateau , but , as previously discussed , the expected timing of the plateau in the three- and four-stage clonal expansion models is well beyond conceivable human life spans . While the observation of a plateau might suggest that either the underlying mechanism is the two-stage model or the presence of heterogeneities or temporal effects , the absence of a plateau leaves room for various interpretations . Indeed , the two- , three- , and four-stage models were all able to reasonably fit the pancreatic cancer incidence data ( Fig 3 ) . For each of the two- , three- , and four-stage models , only three parameter combinations were practically identifiable . In each case , these combinations are most easily interpreted in the following forms: α - β - μ n - 1 ( 12 ) the net cell proliferation rate of initiated cells , α μ n - 1 ( 13 ) the scaled malignant conversion rate , and ν ∏ i = 1 n - 2 μ i ( X / α ) ( 14 ) the product of all preinitation rates scaled by the number of normal cells and the cell growth rate . Note that the first two combinations are together equivalent to pn and qn . Because the last combination is a product of individually structurally identifiable combinations , we know that information about mutation rates at the intermediate steps is only available in later features of the MSCE hazards , i . e . the asymptote and the transition from the linear phase to the asymptote . Because there are only three practically identifiable combinations , successful parameter estimation can only be achieved if the models are reparameterized in terms of these combinations . For example , with the three-stage model parameterized as in Eq ( 3 ) , parameter estimates for νX and μ1/α are not stable . Here , we proposed one possible solution with the reparameterizations in Eqs ( 9 ) and ( 11 ) and show that it does indeed resolve the practical unidentifiabilities , though an infinite number of reparameterizations will give equivalent fits as long as the parameter combinations are preserved . Each reparameterization represents a different assumption about the relative sizes of its constituent parameter combinations . Our reparameterizations are inspired by the assumption that the preinitation mutation rates are equal ( ν = μ1 = … ) but do not actually codify this assumption in the models . Nevertheless , it is consistent with a scenario in which multiple copies of a tumor suppressor gene must be “knocked out” [1] . Traditional approaches to parameter estimation that use asymptotic confidence intervals do not always reveal practical identifiability issues . Because asymptotic confidence intervals are based on the local curvature of the likelihood around the parameter estimate , they may give finite confidence bounds when the likelihood is curved on one side of the estimate but flat on the other . Numerical optimization algorithms may provide results that give the appearance of practical identifiability but have in fact simply pushed the estimate to the point where the likelihood begins to curve . Hence , the fact that our group and others have previously reported values of μ2/α with finite confidence intervals in four-stage models [6 , 8] is not inconsistent with our results . Some of these previous works have interpreted the larger-than-expected values for μ2 ( fixing α ) as being too fast to represent a genetic mutation , suggesting that the four-stage model may represent two , slow genetic mutations followed by a fast epigenetic change , a transient event , or other transformation . Our results suggest that a large range of values μ2/α would have resulted in equivalent fits , and we note that the values presented in these previous works are of the same order of magnitude where we see curvature in our likelihood function . In particular , a previous fit of pancreatic cancer incidence in SEER ( 1973–2004 ) using the four-stage model [8] estimated νXμ1μ2/α to be 1 . 88E-5—the same value that we find here with the new parametrization ( for pancreatic cancer in SEER 1973–2012; Table 1 ) —but also separately estimated μ2/α to be 4 . 0E-1 , which falls exactly where the profile likelihood begins to curve up ( Fig 8 ) . Hence , such parameter estimates may be an artifact of the algorithm numerically optimizing the likelihood , and one should then be careful when giving a biological interpretation to those results . This analysis also speaks to the question of model selection and model reduction . Although the four-stage model gives the best statistical fit to the data in Fig 3 , its hazard nearly entirely overlaps with the that of the other models . Hence , we must question whether or not the larger model is actually capturing some nuance in the data . Given the practical identifiability issues we have presented , does the two-stage already capture all of the information ? Possibly so . Are the results of both models equivalent ? Unfortunately not: although each model is estimating the same biological parameters ( i . e . the product of initiation rates , the final promotion rate , and the malignant conversion rate ) , a perusal of Table 1 reveals that the parameter estimates are not particularly consistent across the three models ( although are generally within an order of magnitude ) . Moreover , the different dynamics of each model will become important as we move away from simply analyzing incidence and consider prediction or individual time-varying exposures . Nevertheless , in this situation , one might be inclined to take an ensemble approach and to consider uncertainty quantification not only within a model but across the models , perhaps weighting in some way by statistical fit . Additional empirical science , by better elucidating carcinogenesis mechanisms common to cancer at given site , could aid modelers in model selection . The guidance we have presented in this study is important as three- and four-stage clonal expansion models are commonly used to model certain cancers at the population level , and successful parameter estimation is dependent on the model being identifiable with respect to the available data . Ultimately , our analysis demonstrates the need for future studies to verify the practical identifiability of model parameters whenever feasible , which should strengthen the validity of the analyses and aid in the interpretation of estimated parameter values and modeling results . | Parameter estimation from data is an important part of mathematical modeling , and structural identifiability is the study of what parametric information exists , for a given model , in ideal data . Unfortunately , for a variety of reasons , there is often less information available in our real data sets . The study of these problems is called practical identifiability . In this study , we consider a family of models of cancer biology that are commonly used to explain cancer incidence in terms of underlying biological parameters . Using profile likelihoods , a widely applicable numerical tool , we demonstrate that even though the more complex models we consider have theoretically more identifiable parameters , the data contains only three pieces of practically identifiable information for each model: the product of the initiating mutation rates , the net cell proliferation rate , and the scaled malignant conversion rate . This result can be interpreted biologically: we can determine only the product of cell mutation rates not the intermediate rates themselves . Our result limits the interpretability of previous work , but we propose a novel parameterization to resolve the identifiability issue . Ultimately , our analysis demonstrates the importance of verifying the practical identifiability of parameters before assigning too much weight to the interpretation of their estimated values . | [
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] | 2017 | Parameter estimation for multistage clonal expansion models from cancer incidence data: A practical identifiability analysis |
The protozoan parasite Leishmania possesses a single flagellum , which is remodelled during the parasite’s life cycle from a long motile flagellum in promastigote forms in the sand fly to a short immotile flagellum in amastigotes residing in mammalian phagocytes . This study examined the protein composition and in vivo function of the promastigote flagellum . Protein mass spectrometry and label free protein enrichment testing of isolated flagella and deflagellated cell bodies defined a flagellar proteome for L . mexicana promastigote forms ( available via ProteomeXchange with identifier PXD011057 ) . This information was used to generate a CRISPR-Cas9 knockout library of 100 mutants to screen for flagellar defects . This first large-scale knockout screen in a Leishmania sp . identified 56 mutants with altered swimming speed ( 52 reduced and 4 increased ) and defined distinct mutant categories ( faster swimmers , slower swimmers , slow uncoordinated swimmers and paralysed cells , including aflagellate promastigotes and cells with curled flagella and disruptions of the paraflagellar rod ) . Each mutant was tagged with a unique 17-nt barcode , providing a simple barcode sequencing ( bar-seq ) method for measuring the relative fitness of L . mexicana mutants in vivo . In mixed infections of the permissive sand fly vector Lutzomyia longipalpis , paralysed promastigotes and uncoordinated swimmers were severely diminished in the fly after defecation of the bloodmeal . Subsequent examination of flies infected with a single paralysed mutant lacking the central pair protein PF16 or an uncoordinated swimmer lacking the axonemal protein MBO2 showed that these promastigotes did not reach anterior regions of the fly alimentary tract . These data show that L . mexicana need directional motility for successful colonisation of sand flies .
Eukaryotic flagella / cilia are complex multifunctional organelles conserved from protists to humans [1] . Protists use flagella for swimming , feeding , cell-to-cell communication , adherence to substrates and morphogenesis [2] . Single-celled organisms , most prominently among them the green algae Chlamydomonas reinhardtii , have served as important model organisms to study molecular mechanisms of ciliogenesis and ciliary function [3] , spurred on by the recognition that ciliary defects cause human genetic disorders collectively termed “ciliopathies” [4] . The eukaryotic flagellum is a complex , highly structured organelle and dissection of the molecular mechanisms underpinning its diverse functions requires detailed knowledge of its component parts . Proteomic studies of isolated flagella or axonemes from diverse species typically identified at least 300 distinct proteins [5–9] and phylogenetic profiling identified a set of 274 evolutionarily conserved ciliary genes [10] . All of these datasets comprise many “hypothetical” proteins still awaiting functional characterisation in addition to well-characterised core components of the microtubule axoneme , associated motor proteins and regulatory complexes . Insights into conserved ciliary biology have helped elucidation of flagellar function in eukaryotic microbes , with a particular focus on human pathogens [11 , 12] . Among these , flagella have been most extensively studied in the causative agent of African trypanosomiasis , Trypanosoma brucei [13] , which uses flagellar motility for locomotion and immune evasion [14] and exhibits close spatio-temporal coordination between flagellum assembly and cell morphogenesis during division [15] . The T . brucei bloodstream form is particularly sensitive to the loss of flagellar function [6 , 16] , highlighting a potential Achilles’ heel that might be exploitable for new anti-parasitic treatments . The Leishmania flagellum is also a multi-functional organelle , which undergoes striking structural changes during the parasite’s life cycle [17–19] . Amastigote forms proliferating in mammalian macrophages possess a short sensory-type 9+0 microtubule axoneme , which is remodelled to a canonical long motile 9+2 axoneme during differentiation to promastigote forms , which live in blood-feeding phlebotomine sand flies ( Diptera: Psychodidae ) . In the fly , nectomonad promastigote forms attach via their flagella to the microvilli of the posterior midgut [20] to protect the parasites from being cleared during defecation of remnants of the blood meal . In the oesophageal valve , broad haptomonad forms attach to the cuticular lining via their flagellar tips , forming hemidesmosomes [20] . These life cycle descriptions ( S1 Fig ) [21 , 22] imply that periods of attachment must be followed by migration to more anterior regions of the alimentary tract and the propulsive function of the Leishmania flagellum is presumed to drive this forward migration but this has not been directly tested . To enable a detailed genetic dissection of flagellar functions and mechanisms in Leishmania , we defined here a flagellar proteome for motile L . mexicana promastigotes . We used new CRISPR-Cas9 genome editing methods [23] to generate a Leishmania knockout library of 100 mutants , over half of which showed altered swimming speed . We also developed a barcode sequencing ( bar-seq ) protocol to test the fitness of mutants in the permissive sand fly vector Lutzomyia longipalpis . This study identified new genes required for flagellar motility and shows that whilst culture-form promastigotes tolerated loss of the flagellum , paralysed mutants and uncoordinated swimmers failed to colonise sand flies indicating that flagellum movement is required for completion of the parasite’s life cycle . Furthermore this flagellum movement must be able to give effective translocation and if cells cannot undergo directional motility then they cannot be transmitted through the fly .
To enable a systematic genetic dissection of flagellar functions we sought to isolate L . mexicana promastigote flagella comprising the axoneme , extra-axonemal structures and the surrounding membrane for subsequent analysis by protein mass spectrometry ( MS ) . Mechanical shearing in the presence of 75 mM Ca2+ successfully separated cells into flagella ( F ) and deflagellated cell bodies ( CB ) ( Fig 1A and 1B ) . Subsequent centrifugation on sucrose gradients allowed isolation of F and CB fractions with little cross-contamination: the CB fraction contained only 2 . 03% ( ±0 . 69% ) isolated flagella and the F fractions contained 0 . 56% ( ±0 . 15% ) deflagellated cell bodies ( S2A Fig ) . Isolated flagella still retained their membrane: First , examination of F fractions by transmission electron microscopy ( TEM ) confirmed that most axonemes were bounded by a membrane ( S2B–S2E Fig ) and second , tracking an abundant promastigote flagellar membrane protein , the small myristoylated protein 1 ( SMP-1 , [24] ) tagged with enhanced green fluorescent protein ( eGFP ) showed that it remained associated with isolated flagella ( Fig 1; 75% of flagella retained SMP-1::eGFP signal , N = 906 ) . Analysis of the SMP-1::eGFP signal also facilitated flagellar length measurements in whole cells , F and CB fractions , which showed that flagella were separated from the cell body near the exit point from the flagellar pocket . The average break point was 2 . 7 μm distal to the base of the flagellum . The length of the isolated flagella was similar to those on intact cells , indicating that isolated flagella remained in one piece , with little fragmentation ( S3 Fig ) . Two independently prepared sets of F and CB fractions were separated into detergent soluble ( S ) and insoluble fractions ( I ) , yielding four fractions , FS , FI , CBS and CBI ( Fig 1C ) . All four fractions for both replicates were analysed by liquid chromatography tandem mass spectrometry ( MS ) , which detected a total of 2711 distinct proteins ( Fig 1D ) . Enrichment of detected proteins between biological replicates correlated well ( Pearson’s r > 0 . 72 , Spearman’s rs > 0 . 83 , S4 Fig ) . To discover proteins enriched in each of the four fractions , we used a label-free normalized spectral index quantitation method ( SINQ , [25]; S1 , S2 and S3 Tables ) to generate a SINQ enrichment plot ( Fig 2A ) . The promastigote flagellar proteome , defined as proteins enriched in F vs . CB fractions consisted of 701 unique proteins detected in at least one MS run; 352 of these were enriched in F vs . CB fractions in both MS runs . To validate the data , we mapped well-characterised flagellar proteins onto the enrichment data plot ( Fig 2A ) . Axonemal , paraflagellar rod ( PFR ) , flagellar tip and flagellar membrane proteins mapped to the FI and FS quadrants . Basal body , FAZ and tripartite attachment complex ( TAC ) proteins mapped to the CBI and CBS quadrants because F fractions contained exclusively the cell-external portion of the flagellum . Intraflagellar transport ( IFT ) proteins clustered around the midpoint of the plot , indicating their abundance was similar in the F and CB fractions , which is consistent with their known dynamic association with the flagellar basal body and axoneme . We also found substantial overlaps between L . mexicana proteins in the FI quadrant and proteins detected in previously published flagellar proteomes of L . donovani and T . brucei ( S5A–S5D Fig ) . However , L . mexicana proteins in the FS quadrant showed only a moderate overlap with reported soluble T . brucei flagellar proteins ( S5C Fig ) . We designed a website ( www . leishgedit . net/leishgedit_db ) for interactive browsing of proteins in the enrichment plots shown in Figs 2 and S5 . Prediction of lipid modification sites identified 15 proteins in the Fs fraction with an MGXXXS/T N-terminal myristoylation site indicating possible association with the flagellar membrane . Proteins with predicted trans-membrane domains ( TMD; annotation from TritrypDB . org ) were predominantly detected in the detergent soluble fractions ( S5F Fig ) . Overall , TMD proteins were however underrepresented in the proteome compared to their frequency predicted from the genome ( Chi-squared test , p < 0 . 0001 ) , as were proteins smaller than 10 kDa ( S6 Fig ) . Underrepresentation of small and hydrophobic proteins could be due to technical limitations of the sample preparation and MS protocol [26] , for example through loss of proteins at the gel fractionation stage due to their size or their propensity to aggregate , or use of a suboptimal protease for proteolytic cleavage . Although ribosomal proteins were detected in individual F fractions , the enrichment plot clustered them around the midpoint , with many enriched in the cell body fractions ( S5E Fig ) . Our simple strategy of testing for enrichment thus successfully filtered out likely contaminating proteins from the promastigote flagellar proteome , as recently observed for enrichment of other cytoskeleton structures in T . brucei [27 , 28] . Interestingly , a comparison of these proteomics data with L . mexicana RNA-seq data from promastigotes and amastigotes [29] showed that proteins enriched in the flagellar fractions were significantly more likely to have higher RNA abundance in promastigotes vs . amastigotes , compared to proteins detected in the cell body fraction ( Fig 2B; Chi-squared test , p < 0 . 0001 ) . This is consistent with the disassembly of the motile axoneme during differentiation from promastigotes to amastigotes [17] . Whilst on a global scale transcript levels correlate poorly with protein abundance in Leishmania spp . [30] these data indicate that modulation of mRNA levels is a key regulatory step in Leishmania flagellar biogenesis and differentiation from a 9+2 to a 9+0 flagellum . Many of the proteins detected in the F fractions had orthologs in previously defined flagellar and ciliary proteomes yet lacked any functional characterisation . Arguably , endowing cells with motility is the primary function of the promastigote flagellum and we took advantage of our high-throughput CRISPR-Cas9 toolkit [23] to identify proteins required for motility and subsequently study the phenotypes of the mutant Leishmania . In our knockout ( KO ) library ( S4 Table ) we included 19 highly conserved axonemal proteins known to be involved in the regulation of flagellar beating , three intraflagellar transport ( IFT ) proteins , 60 flagellar proteins with transcript enrichment in promastigotes [29] and eight additional soluble and four insoluble flagellar proteins . Twenty of the selected proteins were detected in the promastigote flagellar proteome but have to our knowledge not been linked to flagella before . We also made deletion mutants for two genes implicated in membrane protein trafficking , BBS2 and Kharon1 . Finally , deletion mutants for four glycoconjugate synthesis genes encoding phosphomannose isomerase ( PMI ) , phosphomannomutase ( PMM ) and GDP-mannose pyrophosphorylase ( GDP-MP ) were produced as control cell lines for sand fly infection experiments . Flagellar localisation of a subset of proteins was independently examined by generating cell lines expressing proteins tagged with a fluorescent protein at the N- and/or C-terminus ( S7 Fig ) . For 35 proteins , both N- or C-terminally tagged fusion proteins were examined and 28 showed consistent localisations . For CFAP44 and CMF10 , the C-terminal tag gave a clear flagellar localisation whereas the N-terminal tag resulted in flagellar and cell body signal . For six proteins ( LmxM . 17 . 0800 , LmxM . 29 . 3360 , LmxM . 08_29 . 1000 , LmxM . 27 . 0670 , PKAC1 and CD047 ) a clear flagellar localisation was observed with N-terminal tags but the C-terminally tagged proteins were exclusively seen in the cell body . Addition of a fusion protein can in some cases result in protein mis-localisation , and further analysis of these discrepancies may reveal sequence features controlling flagellar targeting in these proteins . Orthofinder [31] was used to generate genome-wide orthologous protein sequence families using genome sequences of 33 ciliated and 15 non-ciliated species from across eukaryotic life , including L . mexicana and T . brucei ( S5 Table ) . Twenty-two proteins were only found in kinetoplastids ( L . mexicana and T . brucei ) , 30 were conserved specifically in ciliated organisms and 23 widely conserved across eukaryotes whilst the remainder showed no clear pattern . In the following , we refer to genes of unknown function by their GeneID from TriTrypDB . org [32] and where we identified named orthologs we used the corresponding gene names . The target genes were then deleted as described previously [23] . To facilitate high-throughput generation of knockout ( KO ) cell lines , PCR reactions and transfections were performed in 96-well plates . Analysis of drug-resistant transfectants by PCR confirmed loss of the target ORF and integration of the drug-resistance gene in 94 of 98 cell lines ( S8 Fig ) . This 96% success rate highlights the power of our gene deletion strategy . The reason for the presence of the target ORF in the remaining four cell lines was not further investigated , but was confirmed by diagnostic PCR of two independently isolated samples of genomic DNA from the relevant mutants . The flagellar mutants generated in this study , the previously generated paralysed cell line ΔPF16 [23] , the parental line L . mex Cas9 T7 , and wild type promastigotes were subjected to motility assays using dark field microscopy to track the swimming behaviour of cells and measure swimming speed and directionality as previously described [33] . Parental cells immobilised though formaldehyde fixation were also measured . Wild type L . mexicana promastigotes use a tip-to-base flagellar beat for propulsive motility , interrupted by episodes of base-to-tip ciliary beats [34] and their swimming trajectories follow curving paths , with occasional changes in direction . The majority of cells achieve a large displacement from their starting position over time; this is directional motility of the cell , albeit in a random direction in a homogenous culture environment [33] . More than half of all mutant lines showed a significant deviation from the normal average swimming speed measured for the parental cell line and wild type controls ( Fig 3A and 3B ) : 52 ( 53 . 6% ) mutants showed a significant reduction in speed and 4 ( 4 . 1% ) swam faster ( Student’s t-test , p<0 . 005; Fig 3A , S4 Table ) . We used the ratio of velocity to speed per cell as a measure of swimming path directionality per cell , this is equivalent to the ratio of displacement achieved to the distance travelled to reach that point . Plotting mean swimming speed against mean directionality shows broad groups of mutants ( Fig 3B ) : Those which are paralysed , slower swimmers , slow uncoordinated swimmers , faster swimmers and a single mutant that had faster and more directional swimming ( ΔLmxM . 36 . 3620 ) . The mechanistic contribution to swimming behaviour remains to be clarified for many proteins in this set . Loss of flagellar waveform modulators would cause altered motility patterns , and this is exemplified by two mutants in this set: the ΔdDC2 mutant , which lacks the outer dynein arm docking complex protein dDC2 and can perform a ciliary beat but no flagellar beat [35] clusters with the uncoordinated group . By contrast , ΔLC4-like , which lacks a distal regulator of outer dynein arms and spends more time doing a flagellar beat at a higher beat frequency [35] , was among the faster swimmers . The most severe loss of motility was observed in three cell lines that had no visible external flagellum ( Fig 4 ) ; all of these were deletions of conserved intraflagellar transport ( IFT ) proteins ( ΔIFT122B , ΔIFT139 and ΔIFT88 ) . Ablation of the central pair ( CP ) protein hydin also resulted in almost complete paralysis , comparable to the deletion of the CP protein PF16 [23] . In a subset of paralysed or slow-swimming uncoordinated mutants ( Fig 3C ) we noted that the flagella tended to be in a curled rather than straight conformation . Δhydin mutants had the highest proportion of curled-up flagella ( 62 . 6% , Fig 4 and S9 Fig ) while fewer than 1% of flagella were curled-up in the parental cell line and many other slow swimming mutants ( S9 Fig ) . A high proportion ( >10% ) of curled-up flagella was also found in four paralysed KO lines ( inner dynein arm intermediate chain protein mutant ΔIC140 , 57%; ΔPF16 , 14%; tether and tether head complex protein mutants ΔCFAP44 , 15% and ΔCFAP43 , 19% ) and three uncoordinated KO lines ( ΔMBO2 , 26%; nexin-dynein regulatory complex protein mutant ΔDRC4 , 13%; ΔLmxM . 33 . 0560 , 12% ) . The curls were observed in aldehyde fixed cells as well as in live cells in culture , indicating they were not an artefact of microscopy sample preparation . This novel phenotype might be caused by disrupted dynein regulation and warrants further investigation . We generated 13 add-back cell lines to rescue mutant phenotypes by transfecting episomes containing the deleted ORF . Four complemented mutants fully recovered parental swimming speed ( complemented ΔIFT88 , ΔLmxM . 14 . 1220 , ΔLmxM . 18 . 1090 and ΔLmxM . 08_29 . 2440; Fig 3 ) and complemented ΔCFAP44 and ΔMBO2 lines showed fewer curled flagella ( S9 Fig ) . Complementation of the other 7 slow swimming mutants resulted in a significant increase in swimming speed close to parental levels ( Fig 3 ) and reduction of curling compared to the KO lines ( S9 Fig ) . Null mutants for the major PFR protein PFR2 , lacking the paracrystalline PFR lattice structure , are known to have impaired motility [36] . To compare motility of a ΔPFR2 mutant with other mutants generated in this study , we used CRISPR-Cas9 to delete both allelic copies of the PFR2 array ( PFR2A , PFR2B and PFR2C ) and confirmed loss of PFR2 expression by western blot ( S10 Fig ) . This ΔPFR2 line had slower and less directional swimming compared to the parental cells , clustering with other slow swimming mutants defined in Fig 3B . To test whether gene deletion in other slow swimming mutants had a major disruptive effect on the PFR , which might explain their motility defect , we expressed PFR2::mNG in KO lines and looked for changes to PFR length or loss of PFR integrity ( defined as gaps in the PFR2::mNG signal ) ( Figs 4 and 5; S8 Table ) . Three mutants had shorter flagella compared to the parental cell line , but the PFR remained proportional to the overall flagellar length and was uninterrupted ( ΔARL-3A , ΔCFAP44 , and ΔFLAM2 ) . Six mutants had PFR-specific defects ( Fig 5B ) : a shorter flagellum with a disproportionately shorter PFR ( ΔLmxM . 27 . 0860; ΔTTC29; ΔLmxM . 14 . 1220 ) , a normal-length flagellum with a shorter PFR ( ΔFM458 ) or a shorter PFR with gaps ( ΔLmxM . 21 . 1110 , 25 . 3% of all flagella; ΔMBO2 only 4 . 1% of all flagella ) . Interestingly , these comparatively subtle alterations to PFR length and integrity reduced swimming speed to similar levels as PFR2 deletion ( Fig 3C ) . Thus , our screen readily identified promastigote mutants with impaired motility and even the most severe phenotype , ablation of flagellar assembly caused by loss of IFT components , was compatible with promastigote survival in vitro , in line with earlier reports [37] , [38] , [39] . Whilst flagellar motility is generally believed to be required for development in sand flies , enabling Leishmania migration from the midgut to the mouthparts [40–42] , this has not been directly tested . To interrogate the phenotypes of larger cohorts of Leishmania mutants in parallel , we developed a multiplexed bar-seq strategy inspired by pioneering phenotyping screens in yeast [43] and the malaria parasite Plasmodium berghei [44 , 45] . We pooled mutant L . mexicana lines that were each tagged with a unique 17 bp barcode . This enabled us to measure the relative abundance of each line at different time points after sand fly infection ( S11 Fig ) . Seventeen were flagellar mutants described above and five were parental control cell lines tagged with unique barcodes in their small subunit ( SSU ) ribosomal RNA locus ( S11 Fig ) . The flagellar mutants were chosen to represent different phenotypes which may impact in different ways on their persistence and migration in the fly: aflagellate parasites , parasites with a short flagellum , paralysed parasites with a flagellum of normal length , slow swimming parasites with more ( “uncoordinated” ) or less severe defects in directionality and parasites lacking proteins implicated in flagellar protein trafficking . We also generated a barcoded ΔLPG1 KO mutant , which is only defective in LPG synthesis [23 , 46] and three barcoded mutants defective in the pathway leading to mannose activation for synthesis of LPG and other glycoconjugates: KOs of phosphomannose isomerase [47] ( ΔPMI ) , phosphomannomutase [48] ( ΔPMM ) and GDP-mannose pyrophosphorylase [49] ( ΔGDP-MP ) . These mutants were included as control lines expected to be outcompeted by the parental cell lines based on prior demonstration that loss of the LPG coat is detrimental to parasite development in the fly [50 , 51] . The barcoded cell lines were pooled in equal proportions and first we determined their relative growth rates in culture . Over the 96h observation period , five cell lines became depleted: ΔIFT88 , ΔLPG1 , ΔPMI , ΔPMM and ΔGDP-MP ( S12 Fig ) . These showed also the longest doubling times when measured in individual cultures ( S12 Fig ) . To generate pools to infect L . longipalpis , the cell lines were divided into four sub-pools according to their in vitro growth rates and grown for 48 hours until they reached late log phase and then these were pooled in equal proportions just before the infection . The relative abundance of each line was determined by sequencing DNA isolated from the mixed promastigote pool and from flies at two , six and nine days after infection . The results show progressively diminishing proportions for the control mutants defective in LPG synthesis ( ΔLPG1 ) or a broader range of glycoconjugates including LPG ( ΔPMI , ΔPMM and ΔGDP-MP ) ( Fig 6 , S9 Table ) indicating that parasites lacking these molecules were at a competitive disadvantage in these infections . This effect was apparent as early as two days after infection , consistent with a protective role for PG-containing glycoconjugates in the digesting bloodmeal [51] and a role for LPG in L . mexicana attachment to L . longipalpis [50] . Paralysed and uncoordinated mutants also became noticeably scarcer as the infection progressed ( Fig 6 , S9 Table , S13 Fig ) . The aflagellate ΔIFT88 mutant showed the most severe phenotype and a significant decrease over time was also measured for ΔPF16 , ΔCFAP43 , ΔCFAP44 , ΔIC140 , ΔdDC2 and ΔRSP4/6 . By contrast , mutants with a mild swimming defect ( slower swimmers ΔLmxM . 21 . 1110 , ΔFM458 and ΔLmxM . 18 . 1090 and faster ΔLC4-like ) ( Fig 3D , S13 Fig ) remained as abundant as the normal swimmers throughout the infection ( Fig 6 , S9 Table ) . The exceptions were the slower swimmers ΔKharon1 ( Fig 3D , S13 Fig ) , which is also defective in the transport of a flagellar glucose transporter [52] , and ΔARL-3A , which has a short flagellum ( Fig 5 ) . Both of these were rarer in the fly compared to the starting pool . To gain anatomical resolution and determine whether an immotile mutant fails to migrate to anterior portions of the fly gut , we infected separate batches of L . longipalpis with motile parasite lines and complemented KO lines as controls , with the motile ΔBBS2 mutant , which lacks a component of the BBSome complex [53] which is expected to play a role in flagellar membrane trafficking , and with the paralysed ΔPF16 mutant ( Fig 7 ) . The ΔPF16 mutants are among the least motile cells that retain a long flagellum ( Fig 5 ) , while having only moderate levels of flagellar curling ( S9 Fig ) . The axonemal defect resulting in paralysis is a well-characterised disruption of the central pair in kinetoplastids ( Fig 3B and [23 , 54 , 55] ) and is similar to the defect of the pf16 Chlamydomonas reinhardtii mutant [56] indicating it is a well-conserved core axoneme component . Two days post blood-meal ( PBM ) , the L . mexicana wild type and L . mex Cas9 T7 [23] control cell lines and the ΔBBS2 mutant developed well , with infection rates above 70%; the ΔPF16 mutant produced the lowest infection rate ( below 50% ) . The introduction of an add-back copy of PF16 into the ΔPF16 line restored infection levels ( Fig 7A ) . In all lines , promastigotes were localized in the abdominal midgut , within the bloodmeal enclosed in the peritrophic matrix ( Fig 7B ) . After defecation ( day 6 PBM ) , all control lines and the ΔBBS2 mutant replicated well and developed late-stage infections with colonisation of the whole mesenteron including the stomodeal valve ( Fig 7B ) which is a prerequisite for successful transmission . Their infection rates ranged from 56% to 83% . By contrast , ΔPF16 Leishmania failed to develop; the infection rate was less than 2% ( a single positive fly out of 62 dissected ( Fig 7A ) , with parasites restricted to the abdominal midgut ( Fig 7B ) ) , indicating that ΔPF16 parasites were lost during defecation and were unable to develop late stage infections in L . longipalpis . Since the pooled data ( Fig 6 ) showed that uncoordinated swimmers were also progressively lost during an infection , we tested whether the uncoordinated swimmer ΔMBO2 would also fail to reach the stomodeal valve . Dissection of flies at 2 and 6 days after infection with the ΔMBO2 mutant line or a complemented ΔMBO2 line showed that ΔMBO2 Leishmania failed to thrive . At day 6 the infection rate was 7 . 5% ( 4 positive flies out of 53 dissected ( Fig 7A ) , with parasites restricted to the abdominal midgut ( Fig 7B ) ) , similar to the ΔPF16 mutants . This defect was rescued by restored expression of MBO2 ( Fig 7 ) . Our data provide strong evidence that flagellum-driven directional motility is an essential requirement for successful Leishmania development in sand flies and , by implication , parasite transmission .
This study demonstrates the power of high-throughput CRISPR-Cas9 knockout screens to discover mutant phenotypes in Leishmania . We first defined a flagellar proteome by pursuing a flagellar isolation protocol yielding a defined section of intact flagella and comparing both the flagella and the deflagellated cell body fractions to define a relative enrichment score for each protein . The SINQ method [25 , 27 , 28] eliminated from our analysis abundant cell body proteins that were likely cross-contaminants in the flagellar fractions . The flagellar proteins ( Fig 2 ) defined by this method showed similarities in numbers and types of proteins to other analyses of eukaryotic flagella and cilia ( S5 Fig , S4 Table , [5] ) . We then used these high-confidence flagellar proteome data in conjunction with transcriptomics data and prior knowledge of conserved axonemal proteins to demonstrate a role in motility for >50 genes from a set of one hundred . We also show the importance of directional flagellar motility in the colonisation of sand flies . The data from the pooled mutant population show a progressive loss of paralysed or uncoordinated swimmers over nine days from infection . Because these data report total abundance of each genotype in the whole fly without discriminating between regions of the gut , we probed this question further in infections with the ΔPF16 mutant , which is essentially paralysed and incapable of sustained directional motility due to a defined defect in the central pair complex of the axoneme [23] . The results show that ΔPF16 Leishmania were rapidly lost from most of the dissected flies , consistent with the depletion of this mutant from the mixed pool , and additionally shows that none of the few remaining parasites reached anterior parts of the alimentary tract . A similarly severe defect in colonisation was observed in the ΔMBO2 mutant . MBO2 is an evolutionarily conserved axonemal protein [57] and derives its name from Chlamydomonas mutants that move backwards only because the algal flagella remain locked in a flagellar beat and cannot readily switch to a ciliary beat [58] . Whilst the precise function of MBO2 remains unknown , it is likely that the uncoordinated swimming behaviour of Leishmania ΔMBO2 mutants ( Fig 3B , S13 Fig ) is also the result of defective waveform control . Taken together , these findings show that parasite motility is required for completion of the Leishmania life cycle , in line with the essential role of motility in other vector-transmitted protists . For example , Rotureau et al . , [59] showed that loss of forward motility , caused by ablation of outer dynein arms though KO of DNAI1 , rendered T . brucei unable to reach the tsetse fly foregut . It seems likely that loss of motility also contributed to the inability of L . amazonensis to progress beyond the abdominal midgut of L . longipalpis when the parasites overexpressed GTP-locked ADP-ribosylation factor-like protein 3A ( Arl-3A ) and as a result grew only short flagella [60] . The interesting question remains to what extent flagellar motility and attachment via the flagellum are linked . Observations of attached Leishmania in dissected sand flies show adhesion specifically via the flagellum but the precise molecular interactions between flagellum and the microvillar gut lining remain to be clarified . The dominant cell surface glycoconjugate LPG which covers the entire parasite surface including the flagellum is known to be important in Leishmania attachment to sand fly guts [61] and our results support the view that LPG plays an important role in L . mexicana infection of L . longipalpis [50] . The proportion of ΔLPG1 mutants had decreased by two days after infection and reduced further as infection progressed . The observed loss of fitness of the ΔPMM , ΔGDP-MP and ΔPMI mutants is likely the cumulative effect of the loss of LPG and a broader range of mannose-containing glycoconjugates which were shown to protect Leishmania in the digesting bloodmeal [51] . The consistency of the pooled mutant data with the reported phenotypes of individual glycoconjugate-deficient mutants demonstrates the power of this new rapid method for mutant phenotyping in Leishmania . However , whilst a role for LPG in L . mexicana attachment to the fly is well established , the possible contribution of flagellum-specific surface molecules [62] has not yet been conclusively resolved . Zauli et al . , [38] reported isolation of L . braziliensis from a patient’s skin lesion which differentiated to promastigotes with an “atypical” morphology . These cells had a short flagellum barely protruding from the flagellar pocket , with an amorphous tip suggestive of a defect in flagellum elongation . In experimental infections of L . longipalpis , these parasites persisted in the fly following defecation of the blood meal , suggesting that they remained sufficiently anchored without a long flagellum . It would be interesting to follow up the subsequent development of this mutant in the fly . Interestingly , here only 1 . 6% of flies infected with the paralysed ΔPF16 mutant and 7 . 5% infected with ΔMBO2 were still positive 6 days post infection , compared to 65% of dissected flies infected with the parental cell line . It is possible that loss of directional motility impedes traversal of the peritrophic matrix and it would be informative to look for differences between mutants in the subsequent colonisation of the microvillar lining . Several lines of evidence suggest a role for the trypanosomatid flagellum in environmental sensing [42 , 63–65] . Evidence for specific signal transduction pathways aiding promastigote navigation through the sand fly is however limited . Cyclic nucleotide signal transduction pathways may have important roles in coupling environmental sensing with regulation of flagellar beat patterns [66 , 67] and have been shown to be involved in the migration of T . brucei in the tsetse fly [68] . In our flagellar proteome we identified several adenylate cyclases ( ACs ) , cAMP-specific phosphodiesterases ( PDEs ) and PKA subunits and mapped their localisations to distinct flagellar subdomains by protein tagging ( S7 Fig ) . The motility assays showed that deletion of PKA subunits ( ΔLmxM . 34 . 4010 ( partial KO only ) and ΔFM458 ) reduced swimming speed , whereas deletion of two different PDEs ( ΔLmxM . 18 . 1090 and ΔLmxM . 08_29 . 2440 ) increased it , pointing to an activating role for cAMP in Leishmania motility . Knockout of receptor-type adenylate cyclase a-like protein LmxM . 36 . 3180 had no effect on swimming speed in our motility assay but given the possible redundancy with other flagellar ACs , this preliminary finding should be followed up by examination of other AC mutants individually and in combinations . In our pooled KO screen in sand flies , KOs of PDE LmxM . 18 . 1090 and PKA RSU ( FM458 ) remained as abundant as the controls , indicating that the mild motility phenotypes measured in vitro did not significantly impair colonisation of flies . Perturbation of the flagellar membrane might be expected to interfere with sensory functions mediated through the flagellum . Ablation of membrane proteins LmxM . 17 . 0870 and LmxM . 23 . 1020 ( S7 Fig ) did not significantly enhance or reduce the relative abundance of the respective mutants in sand flies over the nine-day observation period . BBS2 is an integral part of the core BBSome complex which is highly conserved across ciliated eukaryotes [69] and functions as a cargo adaptor for ciliary membrane protein trafficking in Chlamydomonas flagella and metazoan cilia [70] . Our pooled mutant data and infections with the BBS2 deletion mutant alone found that loss of this gene had no discernible detrimental effect on survival in sand flies and the parasites’ ability to reach the anterior gut . By contrast , KO of Kharon1 , a protein shown to be required for trafficking of the glucose transporter LmGT1 , and perhaps other proteins , to the promastigote flagellum [52] led to slightly reduced fitness in the flies from the earliest time point . The ΔArl-3A mutants were also less abundant compared to the controls . This is reminiscent of the previously published abortive phenotype of L . amazonensis overexpressing the constitutively GTP bound LdARL-3A-Q70L [60] . This mutant formed only a short flagellum , similar to the ΔArl-3A mutant generated in the present study ( Fig 5 ) . Failed attachment as a result of the shortened flagellum was thought to be a likely cause for the rapid clearance of LdARL-3A-Q70L-expressing parasites but it was noted that an inability to migrate at later stages of development would also lead to the disappearance of the mutants [60] . In our study the phenotype of the ΔArl-3A mutants was however mild compared to the aflagellate ( ΔIFT88 ) or paralysed mutants . Arl-3A acts as guanine nucleotide exchange factor in the transport of lipidated proteins to the flagellar membrane [71] and protein mis-targeting could contribute to the phenotype in addition to flagellar shortening . Further insights into the contribution of flagellar membrane proteins to attachment or directional swimming behaviour may be uncovered by further biochemical studies into flagellar membrane composition and subjecting different mutants ( with or without overt motility phenotypes in culture ) to chemotaxis assays and fly infections . Flagella isolated by the method used in this study provide suitable starting material for further targeted experiments to identify integral membrane proteins . This could be achieved by using for example carbonate fractionation , as used for the enrichment of membrane proteins in olfactory cilia [72] , or combining surface labelling with subsequent affinity purification prior to mass spectrometry [73] In contrast to the absolute requirement of motility for movement through the sand fly vector , flagellar motility is dispensable for promastigote proliferation in culture . Promastigotes are viable and able to divide even if they fail to assemble a flagellum at all , as demonstrated originally by the deletion of cytoplasmic dynein-2 heavy chain gene LmxDHC2 . 2 [37] and IFT140 [39] and the phenotypes of knockouts of anterograde and retrograde IFT components in the present study . The ensuing prediction that most gene deletions affecting flagellar function are expected to yield viable promastigotes in the laboratory is borne out by our high success rate of obtaining 96% of attempted knockouts . Thus , in Leishmania , flagellar mutant phenotypes can be observed in replication-competent cells over many cell cycles and our mutant library enables detailed systematic studies of KO phenotypes to probe protein functions in flagellum assembly , motility and signal transduction . A fruitful area for further studies will be dissection of PFR function and assembly mechanisms . This extra-axonemal structure is required for motility as demonstrated through deletion of the major structural PFR components , PFR1 and PFR2 in Leishmania [36 , 74] and ablation of PFR2 by RNAi in T . brucei procyclic forms [75] but its precise role remains unclear . The PFR comprises more than 40 proteins , some with structural roles , others with roles in adenine nucleotide homeostasis , cAMP signalling , calcium signalling and many uncharacterised components [76 , 77] and it may anchor metabolic and regulatory proteins as well as influencing the mechanical properties of the flagellum . Our results showed that fragmentation of the PFR caused by loss of LmxM . 21 . 1110 reduced swimming speed to levels similar to the structurally more severe PFR2 KO . Whether LmxM . 21 . 1110 is required for correct PFR assembly or stabilisation is currently unknown . Motility mutants analysed in our screen also included deletions of genes with human orthologs linked to ciliopathies ( such as hydin ) or male infertility ( CFAP43 and CFAP44 ) [78] . Leishmania offers a genetically tractable system to gain further mechanistic insight into their functions . The hydin mutant has been extensively characterised in other species: in mammals , mutations in the hydin gene cause early-onset hydrocephalus [79] and subsequent studies on C . reinhardtii , T . brucei and mice showed that hydin localises to the C2 projection of the central pair complex [80] , and that loss of hydin function causes mispositioning and loss of the CP [81] and motility defects [80–82] . The motility phenotype in the L . mexicana Δhydin mutant was consistent with these existing data and we made the new observation that the mutant flagella show extensive curling ( Fig 4 , S9 Fig ) . Interestingly , hydin knockdown in C . reinhardtii caused flagella to arrest at the switch point between effective and recovery stroke , leaving cells with one flagellum pointing up and the other down , prompting speculation that this may indicate a role for hydin in signal transmission to dynein arms [80] . Consistent with this hypothesis , cilia of hy3/hy3 mouse mutants frequently stalled at the transition point between the effective and recovery stroke [82] . Curling may represent the failure of flagellum bending to reverse during progression of the normal flagellum waveform down the flagellum , leaving the flagellum locked at one extreme of bending , analogous to the ciliary beat hydin phenotype . In L . mexicana , the Δhydin mutant presented the most severe manifestation of the curling phenotype , which was also observed in a lower proportion of other mutants ( S9 Fig ) . This phenotype may be a consequence of mis-regulated dyneins and the set of mutants exhibiting curling will facilitate further experiments to establish the mechanistic basis for flagellar curling . Genetic , biochemical and structural studies have provided elegant and detailed models for the mechanisms of flagellar motility [83 , 84] . Phylogenetic profiling and comparative proteomics studies have yielded insights into the evolutionary history , core conserved structures and lineage-specific adaptations of eukaryotic flagella . Our CRISPR-Cas9 KO method enables rapid targeted gene deletion and characterisation of loss-of-function phenotypes for large cohorts of Leishmania genes in vitro and in vivo and hence new opportunities to interrogate the functions of hitherto poorly characterised flagellar proteins in motility regulation , environmental sensing and axoneme remodelling from 9+2 to 9+0 . The bar-seq strategy for phenotyping of mutants can also be used to probe parasite-host interactions in mammals .
Promastigote-form L . mexicana ( WHO strain MNYC/BZ/62/M379 ) were grown at 28°C in M199 medium ( Life Technologies ) supplemented with 2 . 2 g/L NaHCO3 , 0 . 005% haemin , 40 mM 4- ( 2-Hydroxyethyl ) piperazine-1-ethanesulfonic acid ( HEPES ) pH 7 . 4 and 10% FCS . The modified cell line L . mexicana SMP1:TYGFPTY [17] was cultured in supplemented M199 with the addition of 40 μg/ml G-418 Disulfate . L . mex Cas9 T7 [23] was cultured in supplemented M199 with the addition of 50 μg/ml Nourseothricin Sulphate and 32 μg/ml Hygromycin B . To avoid proteolytic degradation , all procedures were performed on ice . 2·109 L . mexicana SMP1:TYGFPTY cells were collected at 800g for 15 min at 4°C , washed once in 20 ml phosphate buffered saline ( PBS ) and resuspended in 5 ml 10 mM PIPES [10 mM NaCl , 10 mM piperazine-N , N′-bis ( 2-ethanesulfonic acid , 1 mM CaCl2 , 1 mM MgCl2 , 0 . 32 M sucrose , adjusted to pH 7 . 2] . 0 . 375 ml of 1 M Ca2+ solution ( final conc . 0 . 075 M ) and a protease inhibitor cocktail [final concentration , 5 μM E-64 , 50 μM Leupeptin hydrochloride , 7 . 5 μM Pepstatin A and 500 μM Phenylmethylsulfonyl fluoride ( PMSF ) ] were added to the cell suspension . Cells were deflagellated by passing them 100 times through a 200 μl gel loading pipette tip ( Starlab ) attached to a 10 ml syringe . Flagella and cell bodies were separated through density gradient centrifugation , using a modified version of the protocol in [85] . The sample was loaded on top of the first sucrose-bed containing three layers of 10 mM PIPES with 33% ( upper ) , 53% ( middle ) and 63% ( bottom ) w/v sucrose [10 mM NaCl , 10 mM piperazine-N , N′-bis ( 2-ethanesulfonic acid , 1 mM CaCl2 , 1 mM MgCl2 , adjusted to pH 7 . 2 with either 0 . 96M , 1 . 55M or 1 . 84M sucrose] and centrifuged at 800g for 15 min at 4°C . The pellet in the 63% sucrose layer was diluted with 10 ml 10 mM PIPES and centrifuged at 800g for 15 min at 4°C . The supernatant was discarded and the pellet resuspended in 40 μl 10 mM PIPES . This was the cell body fraction . The top layer of the first sucrose-bed , containing flagella , was collected and sucrose sedimentation was repeated with a second sucrose-bed containing only one layer of 10 mM PIPES with 33% w/v sucrose . The resulting top layer of the second sucrose bed was transferred to an ultra-centrifugation tube ( Beckmann tubes ) and collected by ultra-centrifugation at 100 , 000g for 1 h at 4°C ( Beckman Coulter ) . The supernatant was discarded and the pellet resuspended in 40 μl 10 mM PIPES . This was the flagellar fraction . All other sucrose layers contained a mixture of flagella and cell bodies and were discarded . 1 μl of flagellar and cell body fractions was used for counting and imaging and 36 μl of each fraction were used for proteomic analysis . Cell body and flagellar fractions were supplemented with 4 μl protease inhibitor cocktail ( see above ) and 10 μl octylglycoside ( 1% ( w/v ) final conc . ) , incubated for 20 min on ice and centrifuged at 18 , 500g for 1 h at 4°C to separate soluble ( supernatant ) from insoluble ( pellet ) proteins . 50 μl ice cold reducing 2x Laemmli buffer was added to the resulting supernatant . Pellets were dissolved in 100 μl 1x Laemmli buffer . To avoid aggregation of hydrophobic proteins , fractions were not boiled prior to SDS-PAGE [86] . 20 μl of flagella fractions and 10 μl of cell body fractions ( ~5–20 μg protein ) were pre-fractionated on a 10% polyacrylamide gel , stained overnight with SYPRO Ruby Protein Gel Stain ( Molecular Probes ) and destained for 30 min in 10% ( v/v ) Methanol / 7% ( v/v ) acetic acid . Sample preparation in the following was carried out as described in [87] . Briefly , gel pieces were destained with 50% acetonitrile , reduced with 10mM TCEP ( Tris ( 2-carboxyethyl ) phosphine hydrochloride ) for 30 minutes at RT , followed by alkylation with 55 mM Iodoacetamide for 60 minutes in the dark at RT . Samples were deglycosylated with PNGase F over two days at RT and digested overnight at 37°C with 100 ng trypsin . Samples were acidified to pH 3 . 0 using 0 . 1% trifluoroacetic acid and desalted by reversed phase liquid chromatography . Samples were analysed on an Ultimate 3000 RSLCnano HPLC ( Dionex , Camberley , UK ) system run in direct injection mode coupled to a QExactive Orbitrap mass spectrometer ( Thermo Electron , Hemel Hempstead , UK ) . MS-data were converted from . RAW to . MGF file using ProteoWizard ( S6 Table ) and uploaded to the Central Proteomics Facilities Pipeline ( CPFP [88] ) . Protein lists were generated by using CPFP meta-searches ( S6 Table ) against the predicted L . mexicana proteome ( gene models based on [29] , followed by label-free SINQ quantification ( S1 and S6 Tables ) . For SINQ enrichment plots detected GeneIDs were filtered ( p ≥ 0 . 95 , ≥ 2 peptides ) and plotted using normalized spectral indices . For missing indices pseudo spectral indices of 10−10 were inserted . The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [89] partner repository with the dataset identifier PXD011057 . Tagging was achieved by insertion of a drug-selectable marker cassette and fluorescent protein gene into the endogenous gene to produce an in-frame gene fusion . The fusion PCR method described in Dean et al . , [90] was used for tagging with eYFP , using pJ1170 ( pLENT-YB ) as the template plasmid for PCR and selection with 5 μg/ml Blasticidin-S deaminase . The CRISPR-Cas9 method described in Beneke et al . , [23] was used for tagging with mNeonGreen . The online primer design tool www . LeishGEdit . net was used to design primers for amplification of the 5’ or 3’ sgRNA template and primers for amplification of donor DNA from pPLOTv1 blast-mNeonGreen-blast or pPLOTv1 puro-mNeonGreen-puro . Transfectants were selected with either 5 μg/ml Blasticidin-S deaminase or 20 μg/ml Puromycin Dihydrochloride . Gene deletions were essentially done as described in Beneke et al . , [23] . The online primer design tool www . LeishGEdit . net was used to design primers for amplification of the 5’ and 3’ sgRNA templates and for amplification of donor DNA from pTBlast and either pTPuro or pTNeo . Primers for deletion of PFR2A-C were designed with the EuPaGDT primer design tool [91] using the PFR2 array sequence from L . mex Cas9 T7 . For amplification of the sgRNA template DNA , primers: For amplification of a pTNeo donor DNA fragment with 80 bp homology arms , primers: For transfections on 96-well plates the protocol was modified as follows ( similar to descriptions in [92] ) : 52 x 107 late log phase L . mex Cas9 T7 cells ( 1 x 107 cells per reaction ) were collected by centrifugation at 800g for 15 min . A transfection buffer was prepared by mixing 2 ml 1 . 5 mM CaCl2 , 6 . 5 ml modified 3x Tb-BSF buffer ( 22 . 3 mM Na2HPO4 , 7 . 67 mM NaH2PO4 , 45 mM KCl , 450 mM sucrose , 75 mM HEPES pH 7 . 4 ) and 1 . 5 ml ddH2O . The cells were re-suspended in 3 ml of this transfection buffer and centrifuged again as above . The heat-sterilised sgRNA and donor DNA PCR products were placed into 48 wells of a 96-well disposable electroporation plate ( 4 mm gap , 250 μl , BTX ) such that a given well combined all of the donor DNAs and the sgRNA templates for a given target gene . After centrifugation , cells were re-suspended in 5 . 2 ml transfection buffer and 100 μl of the cell suspension dispensed into each of the 48 wells containing the PCR products . Plates were sealed with foil and placed into the HT-200 plate handler of a BTX ECM 830 Electroporation System . Transfection used the following settings: 1500 V , 24 pulses , 2 counted pulses , 500 ms interval , unipolar , 100 μs . After transfection cells were recovered in 1 ml supplemented M199 in 24-well plates and incubated for 8–16 h at 28°C / 5% CO2 before addition of the relevant selection drugs by adding 1 ml of supplemented M199 with double the concentration of the desired drugs . Mutants were selected with 5 μg/ml Blasticidin-S deaminase in combination with either 20 μg/ml Puromycin Dihydrochloride or 40 μg/ml G-418 Disulfate and further incubated . Drug resistant populations typically emerged after one week . Drug-selected populations were passaged at least twice ( one with at least a 1:100 dilution ) before extraction of genomic DNA using the protocol described in [93] . A diagnostic PCR was done to test for the presence of the target gene ORF in putative KO lines and the parental cell line ( S8 Fig ) . Primer3 [94] was used to design , for the entire L . mexicana genome ( gene models based on [29] ) , primers to amplify a short 100–300 bp unique fragment of the target gene ORF ( S7 Table ) . In a second reaction , primers 518F: 5’-CACCCTCATTGAAAGAGCAAC-3’ and 518R: 5’-CACTATCGCTTTGATCCCAGGA-3’ were used to amplify the blasticidin resistance gene from the same genomic DNA samples . For ΔPFR2 additional primers were used to confirm deletions ( S10 Fig; Leishmania cells expressing fluorescent fusion proteins were imaged live . Samples were prepared as described in [17] . Cells were immediately imaged with a Zeiss Axioimager . Z2 microscope with a 63× numerical aperture ( NA ) 1 . 40 oil immersion objective and a Hamamatsu ORCA-Flash4 . 0 camera or a 63× NA 1 . 4 objective lens on a DM5500 B microscope ( Leica Microsystems ) with a Neo sCMOS camera ( Andor Technology ) at the ambient temperature of 25–28°C . For transmission electron microscopy , deflagellated cell bodies and isolated flagella were prepared with a modified version of the chemical fixation protocol described in Höög et al . , [95] . Pellets of cell fractions were fixed with 2 . 5% glutaraldehyde in 10 mM PIPES ( buffer as described above ) overnight at 4°C . Pellets were washed four times for 15 min in 10 mM PIPES and overlaid with 10 mM PIPES , containing 1% osmium tetraoxide and incubated at 4°C for 1 h in darkness , then washed five times with ddH2O for 5 min each time and stained with 500 μl of 0 . 5% uranyl acetate in darkness at 4°C overnight . Samples were dehydrated , embedded in resin , sectioned and on-section stained as described previously [95] . Electron micrographs were captured on a Tecnai 12 TEM ( FEI ) with an Ultrascan 1000 CCD camera ( Gatan ) . Micrographs were processed using Fiji [96] . To enable comparisons between the parental and tagged cell lines , the same display settings for the green fluorescence channel were used . PFR length ( defined by reporter signal ) and flagellar length ( distance between stained kinetoplast DNA and flagellar tip ) was measured with the ROI manager in Fiji [96] . Mutant and parental cell lines were tracked using the previously described method in [33] with three modifications . Firstly , the scripts were modified for batch analysis of multiple files . Secondly , prior to calculation of swimming statistics any ‘drift’ due to bulk fluid flow in the sample was subtracted . As swimming direction of each cell in the population is in a random direction any drift is visible as a mean population movement between successive frames . We treated drift as an apparent translation and scaling of cell positions between successive video frames , which was then subtracted . Finally , the primary statistics we considered were mean speed ( using the path at 200 ms resolution ) and directionality ( mean velocity as a fraction of mean speed ) . Swimming behaviour was measured in triplicates at approximately 6·106 cells/ml and analysed from 5 μl of cell culture on a glass slide in a 250-μm deep chamber covered with a 1 . 5 mm cover slip using darkfield illumination with a 10× NA 0 . 3 objective and a Hamamatsu ORCA-Flash4 . 0 camera on a Zeiss Axioimager . Z2 microscope at the ambient temperature of 25–28°C . The sample was stored inverted prior to use , then turned upright immediately prior to imaging to ensure consistent motion of immotile cells through sedimentation between samples . A sample of the parental cell line killed with a final concentration of 1% formaldehyde was used as a reference for motion of completely paralysed cells through sedimentation and Brownian motion alone . Sand flies were either infected with pooled mutant populations of L . mexicana or individually with L . mexicana MNYC/BZ/62/M379 wild type ( WT ) , parental line L . mex Cas9 T7 , knockout cell line ΔPF16 , its add-back ( PF16AB ) [23] , knockout cell line ΔBBS2 , its add-back ( BBS2AB ) , knockout cell line ΔMBO2 and its add-back ( MBO2AB ) . For pooling , parasites were pooled into four sub-pools with different starting densities , depending on the mutant growth rates , so that the pools would reach late log phase at the same time . Sub-pools were seeded at 5·106 cells/ml , 3·105 cells/ml , 1·105 cells/ml or 8·104 cells/ml , respectively , and grown for 48 hours . The sub-pools were mixed in equal proportions just before the infection . All parasites were cultivated at 23°C in M199 medium supplemented with 20% foetal calf serum ( Gibco ) , 1% BME vitamins ( Sigma ) , 2% sterile urine and 250 μg/ml amikin ( Amikin , Bristol-Myers Squibb ) . Before experimental infections , logarithmic growing parasites were washed three times in PBS and resuspended in defibrinated heat-inactivated rabbit blood at a concentration of 106 promastigotes/ml . Lutzomyia longipalpis ( from Jacobina , Brazil ) were maintained at 26°C and high humidity on 50% sucrose solution and a 12h light/12h dark photoperiod . Sand fly females , 3–5 days old , were fed through a chick skin membrane as described previously [97] . Fully-engorged females were separated and maintained at 26° C with free access to 50% sucrose solution . They were dissected on days 2 or 6 post blood-meal ( PBM ) and the guts were checked for localisation and intensity of infection by light microscopy . Parasite load was graded as described previously by [98] into four categories: zero , weak ( <100 parasites/gut ) , moderate ( 100–1000 parasites/gut ) and heavy ( >1000 parasites/gut ) . Mutant Leishmania lines were grown separately or in sub-pools as described above to late log phase and mixed in equal proportions ( 1·107 cells per cell line ) . This pool was divided equally into three aliquots . DNA was extracted using the Roche High Pure Nucleic Acid Kit or Qiagen DNeasy Blood & Tissue Kit according to the manufacturers instructions and eluted in 20 μl elution buffer . Each aliquot was then either kept in promastigote culture over 96 hours , where DNA was extracted every 24 hours from approximately 1·107 cells as above , or used to infect three separate batches of 150 sand flies . The three batches were kept separate and DNA was extracted from 50 whole sand flies two , six and nine days post blood meal , using the same kit as follows: Sand flies were placed in two 1 . 5 ml microcentrifuge tubes ( 25 flies per tube ) , overlaid with 100 μl tissue lysis buffer and frozen at -20°C . The dead flies were defrosted and after addition of 100 μl tissue lysis buffer and 40 μl proteinase K , flies were disrupted with a disposable plastic pestle ( Bel-Art ) and DNA purified according to the kit manufacturer’s instructions . DNA was eluted with 50 μl elution buffer and eluates from the same timepoint were combined , yielding 100 μl in total . For each sample , 600 ng isolated DNA was incubated with exonuclease VII ( NEB ) overnight at 37°C , purified using SPRI magnetic beads and amplified using PAGE purified custom designed p5 and p7 primers ( Life Technologies ) , containing indexes for multiplexing and adapters for Illumina sequencing . Amplicons were again bead purified and multiplexed by pooling them in equal proportions . The final sequencing pool was once again bead purified and quantified by qPCR using NEB Library Quant Kit . Library size was determined using Agilent High Sensitivity DNA Kit on a 2100 Bioanalyzer instrument . The pool was diluted to 4 nM and spiked with 30% single indexed Leishmania genomic DNA , prepared using Illumina TruSeq Nano DNA Library kit according to the manufacturers instructions . The final library was spiked with 1% PhiX DNA and the Illumina sequencer was loaded with 8 pM to allow low cluster density ( ~800 K/mm2 ) . Sequencing was performed using MiSeq v3 150 cycles kit following the manufactures instructions with paired-end sequencing ( 2x75 cycles , 6 and 8 cycles index read ) . MiSeq raw files were de-multiplexed using bcl2fastq ( Illumina ) . De-multiplexed samples were subjected to barcode counting using a bash script . Each gene in the Leishmania genome was assigned a unique barcode—the number of times each of these barcodes appeared in the sequencing output was recorded ( S9 and S10 Tables ) . The criteria for barcode counting was a 100% match to the 17 nt total length . Counts for each barcode were normalized for each sample by calculating their abundance relative to all 25 barcodes . One of the mutants selected for the pooled screen was excluded from the analysis because sequencing of the cell line showed eight nucleotide mismatches in the p5 sequence ( likely introduced during oligonucleotide synthesis ) which precluded amplification of the barcode region . To calculate “fitness” normalized barcode counts in the pooled population before feeding were divided by normalized counts at the relevant time point post blood meal . Orthofinder 1 . 1 . 2 was used to generate orthogroups for predicted protein coding genes from 48 eukaryotic genomes ( 32 ciliated species and 16 non-ciliated species ) : The 45 previously used by Hodges et al . [69] ( with Leishmania major replaced with Leishmania mexicana ) and supplemented with Aspergillus nidulans , Plasmodium berghei and Schistosoma mansoni . | Leishmania are protozoan parasites , transmitted between mammals by the bite of phlebotomine sand flies . Promastigote forms in the sand fly have a long flagellum , which is motile and used for anchoring the parasites to prevent clearance with the digested blood meal remnants . To dissect flagellar functions and their importance in life cycle progression , we generated here a comprehensive list of >300 flagellar proteins and produced a CRISPR-Cas9 gene knockout library of 100 mutant Leishmania . We studied their behaviour in vitro before examining their fate in the sand fly Lutzomyia longipalpis . Measuring mutant swimming speeds showed that about half behaved differently compared to the wild type: a few swam faster , many slower and some were completely paralysed . We also found a group of uncoordinated swimmers . To test whether flagellar motility is required for parasite migration from the fly midgut to the foregut from where they reach the next host , we infected sand flies with a mixed mutant population . Each mutant carried a unique tag and tracking these tags up to nine days after infection showed that paralysed and uncoordinated Leishmania were rapidly lost from flies . These data indicate that directional swimming is important for successful colonisation of sand flies . | [
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] | 2019 | Genetic dissection of a Leishmania flagellar proteome demonstrates requirement for directional motility in sand fly infections |
Caloric restriction extends lifespan , an effect once thought to involve attenuation of reactive oxygen species ( ROS ) generated by aerobic metabolism . However , recent evidence suggests that caloric restriction may in fact raise ROS levels , which in turn provides protection from acute doses of oxidant through a process called adaptation . To shed light on the molecular mechanisms of adaptation , we designed a series of genome-wide deletion fitness and mRNA expression screens to identify genes involved in adaptation to hydrogen peroxide . Combined with known transcriptional interactions , the integrated data implicate Yap1 and Skn7 as central transcription factors of both the adaptive and acute oxidative responses . They also identify the transcription factors Mga2 and Rox1 as active exclusively in the adaptive response and show that Mga2 is essential for adaptation . These findings are striking because Mga2 and Rox1 have been thought to control the response to hypoxic , not oxidative , conditions . Expression profiling of mga2Δ and rox1Δ knockouts shows that these factors most strongly regulate targets in ergosterol , fatty-acid , and zinc metabolic pathways . Direct quantitation of ergosterol reveals that its basal concentration indeed depends on Mga2 , but that Mga2 is not required for the decrease in ergosterol observed during adaptation .
Oxidative stress is caused by a number of reactive oxygen species ( ROS ) generated as a result of aerobic metabolism or chemical exposure . These compounds damage a variety of cellular products , including DNA , proteins , and lipid membranes , and are associated with a number of human pathologies . For example , in cardiovascular disease , oxidation of low-density lipoprotein causes an inflammatory response [1] . The sensitivity of neurons to oxidative stress implicates ROS in neurodegenerative diseases , such as Parkinson's and Alzheimer's [2]–[4] . A continuing source of controversy is the role of oxidative stress in aging . Caloric restriction has been shown to extend lifespan in a number of species [5] . Initially , it was hypothesized that the effect on lifespan occurs primarily because caloric restriction reduces the level of aerobic respiration , a major source of ROS [6] . Newer evidence is challenging this hypothesis , since caloric restriction paradoxically increases respiration [7] . Increased respiration , in turn , can generate mild levels of ROS which protect against high doses of oxidant [8] . This process is known as adaptation or hormesis [9] and is widely conserved among eukaryotes [8] , [10]–[12] . One hypothesis is that adaptation to oxidative stress is the basis for the lifespan-extending effect of caloric restriction [13] , [14] . Thus , further efforts to understand the process of adaptation may have broad implications on models of aging and disease . In one model of adaptation , the cell increases the activity of the enzymes and pathways required to rid the cells of ROS , leaving it better equipped to process acute dosages of oxidant when they arise . Under this model , genes involved in the adaptive response are expected to be a subset of those that become active in the acute response [15] . Many such candidates have been identified , including a variety of biosynthetic enzymes which produce small molecular compounds or proteins with reduction potential , such as glutathione ( GSH ) , thioredoxin , NADPH , and trehalose [16]–[20] . Different enzymes facilitate this process for different ROS , including catalases and peroxidases ( which deal with peroxide radicals ) [21] , [22] and superoxide dismutases ( which deal with superoxide radicals ) [23] , [24] . Additional proteins serve to repair the damage caused by oxidative stress . Heat shock proteins act as chaperones within the cell , allowing damaged proteins to fold properly or preparing them for disposal [25] . DNA repair genes are also vital , as oxidative stress can damage both nucleotides and the phosphodiester DNA backbone [26] . Several studies have implicated classical oxidative stress proteins and pathways in adaptation , including the transcription factor Yap1 [27] and glutathione synthesis [28]–[30] . In contrast to this model , a second body of evidence suggests that adaptation may be governed by novel pathways not directly involved in the response to acute oxidation . In a study of adaptation to the oxidant linoleic acid , Alic et al . found that adaptation can occur without induction of oxidative or general stress response genes following pretreatment [31] . Instead , various metabolic processes were activated and protein synthesis was inhibited . Moreover , machinery with a central role in the acute response , such as the mitochondria [9] , [32] or the Msn2/4 environmental stress response factors , are not required for adaptation [27] , [33] . Nonetheless , expression studies of acute oxidative damage have helped to identify a set of genes involved in the common environmental stress response ( ESR ) and implicated the Msn2/4 transcription factors in control of this gene set [34]–[36] . In fitness studies of yeast deletion strains , Thorpe et al . identified a set of genes required for the response to hydrogen peroxide , mainly dealing with the proper functioning of the mitochondria [37] . However , to-date these genome-scale approaches have focused on the acute , rather than the adaptive , response . One study to date that has screened for adaptive genes focused on a set of 268 genes selected based on previous literature [38] . Here , we use the rich functional genomics toolbox of yeast to identify pathways involved in adaptation to hydrogen peroxide . To accomplish this goal , we use barcode arrays to screen the Saccharomyces cerevisiae gene deletion collection [39] for genes required in the acute and adaptive responses , and we couple these data with genome-wide mRNA expression profiles to build a system-wide model of adaptation .
As shown in Figure 1A , we elicited adaptation using a protocol consisting of a mild pretreatment of hydrogen peroxide ( 0 . 1 mM H2O2 for 45 min ) followed by a later high dose ( 0 . 4 mM H2O2 for 1 hr ) . For purposes of comparison , we also conducted an acute protocol which exposed cells to the high dose only ( 0 . 4 mM H2O2 for 1 hr ) . Consistent with previous findings [9] , we observed that yeast cells undergoing the adaptation protocol exhibited a smaller reduction in viability compared to cells exposed to the acute treatment protocol ( Figure 1B and Figure S1 ) . Given these protocols , we designed a series of yeast genome-wide phenotyping experiments using the publicly available pool of 4 , 831 viable single-gene deletion strains [40] . Each strain in the pool incorporates a pair of unique oligonucleotide barcode tags , which allow the relative prevalence of all strains to be tracked in growth experiments by hybridization of pooled genomic DNA to a barcode microarray . In a first experiment , two identical pools of deletion mutants were treated with the adaptation or acute protocol , respectively , and directly compared on a barcode array ( with multiple biological replicates; see Methods ) . In a second experiment , a pool subjected to the acute treatment was compared against an untreated pool . These experiments were used to identify genes required for adaptation or for the acute response , as shown in Figure 1C . Fitness in the acute response was defined as the difference in viability between the acute and untreated conditions ( determined from the log ratio of intensities measured in the direct comparison of the acute and untreated pools , see Methods ) . Adaptive fitness was defined as the difference in viability between the acute and adapted conditions , normalized by the magnitude of the acute effect ( Figure 1C ) . A total of 156 versus 108 genes were found to be required for the adaptive versus the acute responses , with an overlap of 88 genes ( Figure 2A ) . A complete list of acute and adaptation-sensitive genes is provided in the Dataset S1 . Surprisingly , neither the adaptive nor the acute screen was enriched for oxidative stress response genes ( GO Biological Process 0006979 ) which encode enzymes involved in processes such as ROS detoxification and homeostasis . This may be due to the ability of this response to compensate for the loss of single gene activities , confirming earlier observations regarding the acute response by Thorpe et al . ( Table S1 ) [37] . Instead , both the adaptive and acute gene sets were heavily enriched for functions in the mitochondrial ribosome and aerobic respiration ( Figure 2B ) . The identification of these functions is puzzling in light of an earlier finding that yeast with defective mitochondria ( rho− mutants ) adapt to oxidative stress [9] , [32] . In these studies , a milder high dose was required to demonstrate adaptation; therefore , the observed deficiency in adaptation of mitochondrial mutants in our screen may be due to increased sensitivity to the high dose . Both sensitivity screens also highlighted several transcription factors ( Figure 2A ) , which are particularly interesting due to their potential roles in regulation of adaptation . These factors include YAP1 and SKN7 which , in contrast to the above enrichment results , do have known involvement in the response to oxidative stress [41] , [42] . YAP1 and SKN7 were previously identified as adaptive-sensitive in the restricted screen conducted by Ng et al . [38] . The transcription factor MGA2 was required for the adaptive but not the acute response . MGA2 has been implicated in fatty-acid biosynthesis and the response to hypoxia [43] . To confirm the requirement of these transcription factors for oxidative adaptation , we performed additional adaptation experiments specifically in yap1Δ , skn7Δ , mga2Δ , and wild type strains . For each , we quantified the severity of each protocol ( acute , adapted , untreated ) as the time required to recover to a specific OD600 threshold following treatment ( Figure 1B ) [32] . Adaptive fitness was calculated as the reduction in viability of the adapted culture , relative to that of the acute-treated culture ( see Methods ) . Figure 3 displays the computed fitness values for each strain over a range of OD600 thresholds . All of these strains were indeed confirmed to have fitness values less than wild type . Next , we performed mRNA expression profiling on each of the three treatment protocols ( pretreated , adapted , acute , see Figure 1A ) in comparison to untreated conditions . These profiles were analyzed to identify two types of adaptive response genes: early versus late . Early adaptive genes were defined as those that were differentially expressed after the 45 min . pretreatment relative to untreated conditions ( 169 genes at p<1 . 0×10−5 , see Methods ) . Late adaptive genes were defined as those that were differentially expressed after the 1 hr . high dose following pretreatment ( 391 genes ) . In comparison , a much larger set of 1 , 893 genes was differentially expressed in response to the high dose in the absence of pretreatment . The overlap of the acute expression response with either the early or late adapted responses was significant ( p = 2 . 1×10−2 versus p = 6 . 8×10−36 by hypergeometric test , respectively ) ; nonetheless the overlap with the early response was much less than with the late adapted response ( 38% versus 60% , see Figure 2C ) . In addition , 26 genes that would be expected to be increasing in expression based on the acute expression data were decreasing in expression during adaptation , such as genes involved in the response to oxidative stress ( GO Biological Process 0006979 ) ( Figure 2B ) . Other sets of genes were expressed uniquely during early and late adaptation , including ergosterol metabolism , fatty acid synthesis , and zinc homeostasis ( GO Biological Processes 0008204 , 0006631 , 0055069 , respectively ) ( Figure 2B ) . Unlike the fitness profiling , oxidative stress genes were strongly implicated in the acute expression response ( as also found by others; Tables S2 and S3 ) . To map the transcriptional program underlying adaptation , we computed the activity of each yeast transcription factor based on the significance of differential expression among its set of known targets ( Figure 4 ) . Lists of targets for each factor were drawn from YeastRACT , a database of literature-curated regulatory interactions [44] ( Methods ) . Application of this method to the acute treatment protocol identified Msn2/4 , Yap1 , and Skn7 as key factors , all of which had been previously associated with the acute response to oxidative stress . All of these factors were also moderately active during pretreatment and became more so after transitioning to the high dose ( Figure 4 ) . Other factors exhibiting this behavior include Adr1 , Hsf1 , and Pdr1/3 . On the other hand , targets of Mga2 and Rox1 exhibited highly significant activity during pretreatment , but not during the acute response ( Figure 4 ) . As Rox1 is a transcriptional repressor , the up-regulation of its targets suggests a decrease in Rox1 activity [45] . While mga2Δ was also identified as an adaptive-deficient strain in the high-throughput screen ( Figure 2 ) , rox1Δ was not . Both of these findings were confirmed with targeted investigations of individual deletion strains ( Figure 3 ) . Like Mga2 , Rox1 had previously been associated with the hypoxic , not oxidative , stress response [46] . Thus , our analysis appears to classify transcription factors into two categories: early response factors activated by mild doses of oxidant during pretreatment only ( Rox1 , Mga2 ) , and late damage response factors whose level of activation responds in proportion to treatment dose ( Msn2/4 , Yap1 , Skn7 ) . The involvement of Mga2 in early adaptation is supported by its requirement for adaptive growth in the deletion profiling experiments ( Figures 2 and 3 ) and the striking behavior of its targets in the expression profiling experiments ( Figure 4 ) . To further confirm the activity of Mga2 , pretreatment with hydrogen peroxide was repeated in an mga2Δ background and gene expression was profiled versus untreated cells using quadruplicate whole-genome microarrays . In this experiment , the number of up-regulated Mga2 targets was significantly decreased ( Figure 5A , p = 1 . 2×10−2 by Fisher's Exact Test ) , supporting the activation of Mga2 by mild pretreatment with hydrogen peroxide . Moreover , the MGA2 gene is itself up-regulated following pretreatment and the transition to the high dose ( p = 1 . 4×10−3 and 5 . 3×10−5 , respectively ) . Rox1 ( Repressor of Hypoxic Genes ) is a repressor under transcriptional control of Hap1 [45] . The decrease in expression of the ROX1 gene following both the pretreatment and adapted treatment protocols ( p = 3 . 6×10−11 and 1 . 4×10−7 , respectively ) suggests that this repressor is deactivated in the process of adaptation . To confirm this observation we profiled a rox1Δ strain and found that the number of Rox1 targets with increased expression following pretreatment falls significantly ( p = 0 . 046 by Fisher's Exact Test ) ( Figure 5B ) . However , as we cannot demonstrate a fitness requirement for Rox1 , it is unclear whether the expression changes due to de-repression by Rox1 are functionally relevant . A similar expression analysis suggests that Yap1 is an active regulator during both the pretreatment and high-dose phases of adaptation . To confirm the activity of Yap1 during pretreatment , we profiled the expression response of a yap1Δ strain versus wild type cells under the pretreatment protocol . This experiment revealed widespread changes in patterns of expression ( Figure 5C ) . The expression responses of Yap1 , Rox1 , and Msn2/4 targets following mild pretreatment in the yap1Δ strain most closely resembled their expression responses in the wild type following acute treatment ( Figure 5B–D ) . Thus , it is clear that Yap1 is required for many of the expression changes associated with adaptation . Interestingly , Mga2 , Rox1 , and Yap1 targets were not enriched for genes that were required for adaptation in the competitive fitness screen ( Figure 5A , B , C; Dataset S2 gives a list of all required targets ) . In the case of Mga2 , not a single target gene was required for adaptation . This suggests significant functional redundancy in the genes targeted by these factors , or that their requirement for adaptation is mediated by targets that are essential for viability and therefore are not included in the deletion strain collection used in the screen for competitive fitness . The mechanisms by which Mga2 and Rox1 might be activated by mild pretreatment with oxidants are unknown , but several lines of evidence suggest they are shared with the hypoxic response . Rox1 is expressed in a heme-dependent manner [47] . While falling heme levels typically signal hypoxic conditions [48] , hydrogen peroxide may also reduce heme levels via degradation [49] . Dirmeier et al . found that ROS levels transiently increase following exposure to anoxic conditions , suggesting that this could signal the expression of hypoxic genes [50] . They did not believe the activation of hypoxic genes could be replicated with exogenously supplied ROS , based on the H2O2 expression profiling data of Causton et al . [36] . We contradict this earlier hypothesis with the observation of increased expression of hypoxic genes as a result of treatment with H2O2 . The apparent discrepancy may be a result of the higher dose of H2O2 used by Causton et al . [36] . In response to mild pretreatment with hydrogen peroxide , Mga2 and Rox1 activate targets involved in ergosterol metabolism , zinc homeostasis , and fatty acid metabolism . Ergosterol is a cholesterol-like component of the plasma membrane with diverse effects on its function [51] . Branco et al . observed that adaptation is associated with an increase in membrane rigidity , an effect that is abrogated in the ergosterol-deficient erg3Δ and erg6Δ strains [52] . Thus , a potential mechanism for Mga2's requirement during adaptation is that it promotes an increase in ergosterol which inhibits diffusion of H2O2 across the plasma membrane . Zinc homeostasis genes may play a similar role , as these genes also influence ergosterol metabolism [53] . Conversely , Tafforeau et al . observed a decrease of both squalene synthase ( Erg9 ) activity and ergosterol content during adaptation in S . pombe , highlighting the complex relationship between ergosterol and membrane permeability [54] . To elucidate the role of ergosterol biosynthesis in adaptation , we profiled ergosterol concentration in both untreated and adaptive conditions in wild type , mga2Δ , and rox1Δ strains ( see Methods ) . Relative to wild type , the basal concentration of ergosterol was significantly lower in the mga2Δ strain and slightly higher in the rox1Δ strain ( Figure 6 ) . This finding agrees with the regulatory roles of Mga2 and Rox1 as an activator and repressor of ergosterol biosynthesis genes , respectively . It also provides some evidence that ergosterol may be a precondition for adaptation to occur , since mga2Δ is the only strain tested that had low ergosterol concentration and is also the only one with an adaptation defect ( Figure 3 ) . On the other hand , in all strains ergosterol content decreased significantly from untreated to mild pretreated conditions ( p = 1 . 4×10−2 , 4 . 1×10−3 , and 3 . 1×10−2 for wild type , mga2Δ , rox1Δ strains , respectively using a paired t-test ) . This decrease supports the earlier work of Tafforeau et al . [54] but is surprising given it occurs uniformly in all strains , and given that the expression of ergosterol biosynthetic genes increases from untreated to pretreated conditions . One explanation is that expression of ergosterol biosynthetic genes rises in order to compensate for lowered ergosterol levels . Therefore , we conclude that high ergosterol concentration requires Mga2 , supporting a possible role for the influence of Mga2 on ergosterol levels as a precondition of adaptation . However , the change in ergosterol in response to pretreatment does not depend on Mga2 or Rox1 , suggesting the involvement of other regulators of ergosterol or of other mechanisms of adaptation that are ergosterol independent . Two of the most highly expressed genes following pretreatment with hydrogen peroxide were OLE1 ( oleic acid requiring ) and FAS1 ( fatty acid synthetase ) , essential genes required for synthesis of fatty acids . Both genes are direct transcriptional targets of Mga2 ( YeastRACT database ) , suggesting fatty acid pathways as an alternative to ergosterol for the key mechanism of action of Mga2 during adaptation . Although fatty acid pathways could influence the stability and permeability of the plasma membrane [55] , these enzymes could also affect the mitochondrial membrane [56] , and mutations in OLE1 have been linked to mitochondrial morphology and inheritance [57] . Because OLE1 and FAS1 are essential genes , their specific requirement for adaptation was difficult to assay . However , we found that the high expression of OLE1 was maintained in a rox1Δ background but was greatly reduced in an mga2Δ strain ( Dataset S3; p = 7 . 2×10−3 ) . Previous work by Matias et al . reported decreased expression of FAS1 mRNA 30 minutes after treatment with 0 . 15 mM H2O2 [55] . By 1 hour , no significant differential expression was detected . In comparison , we observed increased expression of FAS1 one hour after treatment with 0 . 10 mM H2O2 and demonstrated that adaptation occurs under these conditions . Thus , FAS1 has been observed to be both up- and down-regulated during adaptation to H2O2 , albeit at slightly different doses and times . In order to determine the influence of H2O2 dose and treatment time on FAS1 expression , we performed RT-PCR profiling of FAS1 following treatment with both 0 . 10 mM and 0 . 15 mM H2O2 . As detailed in Figure S2 , we observed an increase in FAS1 levels following treatment with 0 . 10 and 0 . 15 mM H2O2 , although the measurement at 0 . 15 mM was not significant . This is consistent with both our microarray results and the work of Matias et al . Further testing of FAS1 mRNA levels at 30 minutes following 0 . 15 mM H2O2 revealed no significant differential expression ( p = 5 . 6×10−1 ) ( Figure S3 ) . Therefore , we have been unable to confirm the previous report of down-regulation of fatty-acid biosynthetic genes during the process of adaptation . Increased expression was also confirmed by RT-PCR for OLE1 ( Figure S2 ) . While the precise adaptation program mediated by Mga2 remains to be elucidated , fatty acid synthesis warrants further study as a possible mechanism . Figure 7 shows a summary of our findings integrated with previous literature . The expression response during adaptation may be segregated into “early” and “late” phases . “Early” genes respond to pretreatment only and not to the later high dose . Mga2 and Rox1 are likely regulators of the genes involved in the early expression response , with functions in ergosterol biosynthesis , zinc homeostasis , and fatty acid synthesis . Mga2 , but not Rox1 , is required for maximal adaptive fitness . Conversely , the expression response of “late” genes increases strongly following the high dose of the adaptation protocol . The transcription factors Yap1 and Skn7 have been previously shown to regulate many genes associated with the “late” response , such as those involved in redox homeostasis . In addition , both of these transcription factors are required for adaptation . One goal for future work is to investigate whether the mechanisms of adaptation identified here also function in higher organisms or in lifespan extension . Of the 156 genes identified in this study as required for the adaptive response , 97 have some homology to higher eukaryotes [58] . In humans , fibroblasts and smooth muscle cells exhibit extended replicative lifespan in response to hypoxic external conditions . This effect requires the generation of ROS inside the cell and the presence of hypoxia inducible factor ( HIF ) . Like Mga2 and Rox1 in S . cerevisiae , HIF is a transcription factor that mediates the response to hypoxic conditions , although it is not orthologous to either protein [59] , [60] . Further work will be required to see if HIF can be activated not only by hypoxia but also by caloric restriction . In conclusion , we have completed the first genome-wide scan for genes required for the adaptive response to oxidative stress . By integrating these data with results from expression profiling , we have identified pathways with novel involvement in the response to oxidative stress , including the hypoxic response factor Mga2 . The activation of Mga2 under adaptive conditions provides additional information about the sensing mechanism of the hypoxic response , given that we have demonstrated this response can be initiated by exogenous oxidative stress . Future studies can interrogate the manner in which the homologs of these genes are necessary for adaptation in higher organisms and explore their role in aging and disease .
The high dose of 0 . 4 mM H2O2 was selected to be comparable to other previous expression studies of acute hydrogen peroxide exposure ( 0 . 4 mM , 0 . 24 mM , 0 . 32 mM , for Causton , Shapira , Gasch , respectively ) [34]–[36] . This dose resulted in a reduction of growth rate by approximately two thirds as measured by OD600 . The pretreatment dose was selected as the largest dose that did not result in impaired growth or viability . This criteria and the length of pretreatment ( 45 minutes ) were selected in accordance with previous studies of adaptation to oxidative stress [9] , [61] , [62] . We profiled the response to three hydrogen peroxide treatment protocols ( pretreatment , adapted , and acute ) over a series of microarray experiments . Each series consisted of four biological replicates . For each replicate in the acute treatment protocol , a single colony of BY4741 ( ATCC , Manassas , Virginia , USA ) was used to inoculate 10 mL of YPD media . Following overnight growth at 30°C , this culture was resuspended in 100 mL of YPD media at an OD600 of 0 . 1 and placed in an orbital shaker at 30°C . At OD600 = 0 . 6 cells were split into two 50 mL portions . In the acute treatment protocol growth continued for 45 minutes , at which point a high dose of hydrogen peroxide ( final concentration in media: 0 . 4 mM H2O2 ) was administered to one member of the pair ( with the other receiving a sham treatment of 100 mM phosphate buffer ) . Treatment continued for 1 hour at which point cells were harvested by centrifugation at 3000 rpm for 5 min . Pellets were immediately frozen in liquid nitrogen and stored at −80°C . The pretreatment protocol was identical except for the final concentration of hydrogen peroxide ( 0 . 1 mM ) . For the adapted treatment , a pretreatment dose of hydrogen peroxide ( 0 . 1 mM ) and corresponding sham treatment were administered directly after splitting the culture , but otherwise the treatment was identical to the acute protocol . All single deletions were obtained from the complete yeast deletion collection in the BY4741 background ( ATCC , #2013888 ) and verified by PCR ( http://www . sequence . stanford . edu/group/yeast_deletion_project/single_tube_protocol . html ) . RNA from each sample was isolated via phenol extraction followed by mRNA purification [Poly ( A ) Purist , Ambion , Catalog # 1916] . Purified mRNA from the control experiments was labeled with dUTP incorporating either Cy3 or Cy5 dye ( CyScribe First-Strand cDNA labeling kit , Amersham Biosciences ) . Cy3 and Cy5 labelings were alternated between replicates to create a balanced design . Complementary labelings ( Cy3 versus Cy5 ) were hybridized to Agilent expression arrays ( Catalog # G4140B ) . Arrays were scanned using a GenePix 4000A or PerkinElmer Scanarray Lite microarray scanner and quantified with the GenePix 6 . 0 software package . Data from each array were subjected to background and quantile normalization [63] . Intensity values are available at the GEO database ( www . ncbi . nlm . nih . gov/geo/ ) under the accession number GSE12602 . The VERA software package was used with dye bias correction [64] to assign a significance value λ of differential expression to each gene . In a negative control experiment ( quadruplicate untreated vs . untreated arrays ) , the distribution of significance values λ over all genes was fit parametrically as 1 . 7 * χ21 , where χ21 is the chi square distribution with one degree of freedom . This null distribution was used for assignment of p-values . RNA from each sample was isolated by TRIzol extraction ( Invitrogen , Catalog # 15596-026 ) [65] . The purified RNA samples were then used as template for first-strand cDNA synthesis ( SuperScript III First-Strand Synthesis for qRT-PCR , Invitrogen , Catalog # 11752-050 ) . For each sample , an RT-PCR reaction was performed with both a gene-specific pair of primers as well as primers targeted to ACT1 . Sequences for primer pairs are available in Table S4 . Each reaction was monitored in triplicate on a 96-well real-time PCR detection system ( BIO-RAD MyIQ ) . For each reaction , this system reports a Ct value representing the number of PCR cycles required to exceed a particular fluorescence threshold . The average Ct value was calculated across technical replicates for both gene-specific and ACT1 primer pairs . The mRNA level ( reported as the log2 ratio relative to the concentration of ACT1 mRNA ) was determined by subtracting the average gene-specific Ct value from the average ACT1 Ct value . A pool of the 4 , 831 viable haploid deletion strains was created from individual collections kept in glycerol stock and divided into 1 mL aliquots stored at −80°C . Two separate types of treatment protocols ( acute and adapted ) were studied consisting of four and six replicate arrays , respectively . For each replicate , a single aliquot of pooled deletion strains was diluted in 15 mL YPD media and grown in a rotating wheel at 30°C to OD600 = 0 . 6 . The sample was then split into two 6 . 5 mL portions . In the adapted treatment protocol , one member of the paired samples was immediately treated with a mild dose of oxidant ( final concentration in media: 0 . 1 mM ) and the other received a sham treatment . After 45 minutes of continued growth at 30°C , a high dose was administered ( final concentration in media: 0 . 4 mM ) to both samples . After 1 hour of treatment , the cells were harvested by centrifugation at 3000 rpm for 5 min and resuspended in 50 mL of YPD media . After 5 hours of growth , the cells were once again harvested by centrifugation and the pellets were immediately frozen in liquid nitrogen and stored at 80°C . The acute treatment protocol was identical , except that no sample was treated with a mild pretreatment dose and only one member of the sample pair was treated with the high dose . Genomic DNA was extracted from cell pellets using a glass bead preparation [66] . Subsequent DNA labeling , hybridization , and microarray design followed the protocol of Yuan et al . [67] . Briefly , asymmetric PCR was used to amplify unique tag sequences in the genomic DNA of the deletion strains . In each PCR reaction , 1 µg of gDNA was used for labeling . Arrays were scanned and quantified in the same manner as the arrays prepared for the expression profiling experiments . Array intensity values are available in the GEO database ( www . ncbi . nlm . nih . gov/geo/ ) under the accession number GSE12733 . The hoptag package ( implemented in R ) was used to analyze the intensity data from the scanned arrays . Briefly , median and loess correction were performed on the intensity distributions [67] , after which each deletion strain was assigned an UPTAG ratio and a DNTAG ratio for each array . The logs of these ratios were averaged to derive one measurement per gene per array . Across multiple arrays measuring the same treatment protocol comparison ( acute vs . untreated or acute vs . adapted ) , the distribution of log ratio values was quantile normalized [63] . To determine an acute fitness value , we assumed that the signal intensity for a given gene deletion strain is:where Ii , treatment and fi , treatment are the observed signal intensity and viability of gene deletion strain i subject to the designated treatment protocol , [Ci] and Ri are the initial concentration and growth rate , respectively , of the deletion strain i , and t is time . Ntreatment is a constant factor applied to all intensities from the same treatment representing the shared effect of normalization procedures . For each gene deletion strain i , the log ratio of the acute and untreated signal intensities is therefore:Thus , the log ratio is proportional to the acute fitness metric as defined in Figure 1 . Since each intensity distribution was normalized to share the same median , the distribution of log ratios was centered on zero . In order to indentify genes that deviate significantly from this expected value , we performed a one sample t-test testing the difference of the mean against zero . This test was regularized to share the estimate of variance among all genes . Similarly , the log ratio obtained from the direct comparison of the acute and adapted samples was centered on zero and proportional to the log ratio of the viabilities , . Furthermore , due to median normalization of the intensity distributions , the scales of both log ratio distributions were approximately equal . Thus , for most genes without a defect in adaptive fitness , the log ratio was strongly correlated to the log ratio , . A gene with a large difference between the values and indicated a deviation from the average adaptive fitness measure . A two-sample regularized t-test comparing the log ratios determined from each direct comparison was used to identify such cases . For both adaptive and acute fitness measures , the threshold for significant p-value was set at 5 . 0×10−3 . To verify that the identified sensitive genes are meaningful , the sensitivities of specific gene deletions were verified in small-scale experiments . In these , a colony of a specific deletion strain of interest was incubated in YPD overnight . Following dilution to OD600 0 . 1 in 30 mL YPD media , the culture was grown to OD600 0 . 6 and split into three aliquots . Each aliquot was treated according to one treatment protocol ( untreated , adapted , or acute ) . Following ten-fold dilution in YPD , growth was monitored in a 96 well optical density plate reader in 12-fold replicate . Examples of recovery following treatment for individual biological replicates are available for wild type , yap1Δ , and mga2Δ in Figures S1 , S4 , and S5 , respectively . For each treatment protocol , the average time required to recover to a particular OD600 threshold was determined ( Figure 1B ) . In Figure 3 , the specific value of this OD600 threshold is varied between 0 . 3 and 0 . 95 to illustrate that the substance of the results is not dependent on the selection of any particular value for the threshold . We calculate adaptive fitness as the difference in viability ( f ) between the adaptive and acute treatments relative to the difference between untreated and acute . For each treatment protocol , the formula for exponential growth relates the recovery time ( ttreatment ) to the fractional reduction in viability associated with that treatment ( ftreatment ) , whre Cthreshold is the threshold concentration , Cinitial is the concentration before treatment , and rstrain is the growth rate of the strain . The following derivation illustrates how we can use this information to express the adaptive fitness measure in terms of recovery time , An unpaired t-test was used to determine the significance of the difference from results obtained when applying the same procedure to wild type ( BY4741 ) colonies . The determination of ergosterol was adapted from Arthington-Skaggs et al . [68] . Following overnight incubation , a culture was grown in YPD to OD600 0 . 6 and split into two aliquots of 50 mL . One of the aliquots was treated with 0 . 1 mM H2O2 for 1 hour , after which the OD600 of each aliquot was measured . Each aliquot was pelleted and washed once with water . The cleaned pellet was incubated for 1 hour at 85°C with 3 mL 25% alcoholic KOH . After cooling for 15 minutes , 1 mL water and 3 mL n-heptane were added and the mixture was vortexed for 3 minutes . The n-heptane layer was extracted and the presence of ergosterol was detected via absorbance at OD281 . The ergosterol concentration for each aliquot of the paired trial was reported as the ratio of OD600/OD281 . We investigated the significance of enrichment for functional classes among both differentially expressed and sensitive genes . Functional classes were defined in one of two ways: ( 1 ) classes of genes with common annotation in the Gene Ontology ( GO ) hierarchy [69] ) or ( 2 ) classes of genes targeted by the same transcription factor as recorded in the YeastRACT online database [44] . In this database , the list of targets for each factor is compiled from literature sources where each regulatory interaction is backed with experimental evidence . To prevent the identification of redundant or overly general gene ontology categories , we limited the GO analysis to those categories that contained between 5 and 100 genes . Similarly , the YeastRACT database contained several transcription factors with an excessive number of annotated targets ( Yap1 alone was annotated with over 1 , 500 ) . To reduce the incidence of false positives , those studies which contributed over 100 targets for a given factor were discarded ( on a per factor basis ) . While this may eliminate some true interactions , the goal is to generate a smaller set of high-confidence interactions which may be used to accurately assess the activity of given transcription factor . The final set of targets for each transcription factor is available as Dataset S4 . A hypergeometric test was used to assess the enrichment of each gene set in the lists of differentially expressed or sensitive genes . Since the true number of differentially expressed or sensitive genes was unknown and poorly defined , we varied the cutoff for significance between 100 and 500 genes . The minimal p-value for each gene set was returned , and the activity/sensitivity of each gene set was reported as the negative log of this minimal p-value . Since the corresponding p-value was no longer strictly accurate as a consequence of multiple hypothesis testing , significance was assessed by repeated randomization trials in which the order of genes was shuffled . Every gene set was tested and the maximum significance value was retained in each trial . Only those gene sets which exceeded the 95th quantile in this set were determined to be significant . | Reactive oxygen species ( ROS ) damage a variety of structures within the cell , resulting in disease and aging . In a seemingly paradoxical effect termed adaptation , it is possible to prevent damage caused by ROS by pre-treating the cell with a small amount of oxidant . We studied this process in order to identify the mechanisms that provide this protection . Our study identified a number of genes and processes with previously unappreciated roles in adaptation . The mechanisms we identified are remarkable because they are distinct from those previously known to protect the cell from ROS . Although this study is conducted in yeast , the wide conservation of adaptation among many organisms suggests that the results from this study may be widely applicable . | [
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] | 2009 | Genome-Wide Fitness and Expression Profiling Implicate Mga2 in Adaptation to Hydrogen Peroxide |
Sole reliance on one drug , Praziquantel , for treatment and control of schistosomiasis raises concerns about development of widespread resistance , prompting renewed interest in the discovery of new anthelmintics . To discover new leads we designed an automated label-free , high content-based , high throughput screen ( HTS ) to assess drug-induced effects on in vitro cultured larvae ( schistosomula ) using bright-field imaging . Automatic image analysis and Bayesian prediction models define morphological damage , hit/non-hit prediction and larval phenotype characterization . Motility was also assessed from time-lapse images . In screening a 10 , 041 compound library the HTS correctly detected 99 . 8% of the hits scored visually . A proportion of these larval hits were also active in an adult worm ex-vivo screen and are the subject of ongoing studies . The method allows , for the first time , screening of large compound collections against schistosomes and the methods are adaptable to other whole organism and cell-based screening by morphology and motility phenotyping .
Infection with parasitic worms ( helminths ) causes a huge burden of human disease [1] and economic loss to the livestock industry [2] . Currently the major control strategy for the human diseases is by large scale drug administration to schools or by mass drug administration [3] . However , the drugs available are limited in number and efficacy and their increasing use worldwide raises concerns about the development of drug resistance [4] , [5] . Schistosomiasis affects an estimated 600 million people [6] but only one drug , praziquantel ( PZQ ) , is commercially available for its treatment and control . PZQ is poorly effective against the immature worms [7] and its increasingly widespread use [8] fuels concerns about drug resistance developing [9] . There have been sporadic reports of treatment failures with PZQ [10] , [11] , [12] and strains isolated from such cases show lower susceptibility to PZQ [13] . However , since the development of PZQ [14] there has been limited interest in discovery of new schistosomicides apart from the recent identification of oxadiazole-2-oxides as lead compounds [15] and of anti-schistosome activities for some anti-protozoal drugs [16] , [17] . The approved anthelmintics invariably were discovered by in vivo screening in animal models . However the low throughput and high costs of these models limits the discovery of new anti-helminth agents . Therefore new high-throughput , in vitro phenotypic screening methods are necessary to advance the discovery of new anthelmintics including anti-schistosome compounds . In the recent past small , focussed , compound collections have been screened against adult schistosomes recovered from rodents [18] , [19] . To facilitate screening of larger compound collections microplate-based visual assays were developed using in vitro-derived larval stages , schistosomula , which can be generated in very large numbers [20] , [21] . With a view to standardization and automation methods other than manual visual assessment have recently been applied to evaluate drug-induced damage to schistosomula [21] , [22] , [23] , [24] . However bright-field microscopy is simpler to set up , reveals drug-specific morphological effects , and is 100% effective in detecting compounds active in the adult ex-vivo assays [20] , [21] . To overcome the need for visual assessment we have developed a label-free , high content screen ( HCS ) using automatic bright-field image analysis to establish and validate a high throughput screen ( HTS ) for primary drug screening against schistosomes . Compound efficacy is assessed by a combination of larval motility and larval morphology quantified by Bayesian analysis . The methods make it feasible for the first time to screen very large compound collections against schistosomes and are applicable to other larval helminths .
Experimentation was carried out under the United Kingdom Animal's Scientific Procedures Act 1986 with approval from the London School of Hygiene and Tropical Medicine Ethics committee . CD1 mice supplied by Charles River , UK were maintained at St Mary's Hospital , Imperial College London . Schistosoma mansoni was maintained by routine passage and schistosomula were prepared and cultured in M169 [25] as previously described [21] . Adult worm ex-vivo drug testing was as previously described [19] . The reference anti-schistosome compounds praziquantel ( PZQ ) and dihydroartemisinin ( DHA ) were obtained from Sigma-Aldrich ( UK ) , methylclonazepam ( MCZ ) and Ro15-5458 ( Ro15 ) were a gift from Dr H . Stohler ( Hoffman-La Roche , Basle , Switzerland ) , oxamniquine ( OX ) was from Pfizer Ltd ( Sandwich , UK ) and oltipraz ( OPZ ) from WHO Special Programme for Research and Training in Tropical Diseases ( WHO-TDR; Geneva , Switzerland ) . Compounds were dissolved in DMSO ( Sigma-Aldrich , UK ) . A 10 , 041 compound library comprising lead-like compounds was provided by the Division of Biological Chemistry and Drug Discovery , University of Dundee . The last two columns of each test plate were reserved for controls . The test compound solvent , DMSO , was used as the negative control and added to 16 wells . Our initial testing of the image analysis models revealed that OPZ induced the lowest phenotype and motility scores reflecting the visual assessment that OLT caused the most severe effects of all of the anti-schistosome compounds tested . Therefore , OLT was chosen as the positive reference standard and applied to 8 wells . PZQ , the current therapy for schistosomiasis , induced intermediate phenotype and motility scores and so 4 wells of PZQ were included on each plate as an arbitrary check on plate performance . Black 384-well clear-bottomed plates were selected for imaging ( PerkinElmer , UK Cat no 6007460 ) . Into each well 0 . 5 µl of test compound or DMSO was dry stamped using the Biomek FXp ( Beckman Coulter , High Wycombe , UK ) . When necessary a prior intermediate dilution step in DMSO was carried out in V-bottomed dilution plates ( Greiner bio-one , UK , cat no 781280 ) . Schistosomula ( 120/well ) were added to each well in 80 µl of M169 media using a Matrix WellMate ( Thermo Scientific , Basingstoke , UK ) . Plates were then incubated in a Cytomat C2 automatic incubator ( Thermo Scientific , UK ) at 37°C , 5% CO2 for 3 days . A Scara Robot ( KiNEDx Robot KX-300-470 , Peak Robotics , Colorado , USA ) controlled by Overlord 3 ( Process Analysis and Automation , Hemel Hempstead , UK ) was used for all plate movements . After 3 days culture schistosomula were redistributed and disaggregated by using the Biomek FXp programmed to aspirate and dispense 40 µl of the well contents in each of the 4 corners of each well ( ×3 ) . Bright-field images were collected using an ImageXpressMicro HCS microscope ( IXM; Molecular Devices , Wokingham , UK ) fitted with a PhotometricsCoolSnapHQ camera ( Roper Scientific , Germany ) . Focussing of the plate and well bottom was achieved by the IXM high-speed laser auto-focus , with a 25 µm offset to focus on the larvae . For motility analysis 5×6 sec interval time-lapse images were collected using a 4× S Fluor 0 . 2NA Nikon objective . For detailed morphology a 10× Ph1 Plan Fluor DL 0 . 3NA Nikon objective was used to collect 4 adjacent images , which were tiled together to maximise larval numbers for phenotype analysis . After imaging , the plates were visualized by two independent assessors using an inverted microscope ( LeitzDiavertWetzlar , Germany ) . Differences in phenotype and motility scores were measured by one-way ANOVA with a Dunn's post-test to measure significant differences between DMSO control wells and individual drug treatments . Z factors for both the phenotype and motility scores were measured on a per plate basis in Pipeline Pilot 8 . 5 ( Accelrys Inc . , San Diego , USA ) with an acceptable score being >0 . 5 [26] .
Following preliminary assessment of appropriate screening concentration/hit rate , the 10 , 041 compound library was screened at 10 µM . All of the plates were also visually scored by two independent assessors [21] . Using the HCS hit thresholds defined above , all the visual hits ( 379 ) apart from four were determined to be hits by the automatic analysis ( Figs . 5A & B ) . Three of the failures were ascribed scores which fell just outside the hit threshold and one failed to segment due to the parasites remaining aggregated . The hit region also contained 109 wells which were visual non-hits ( i . e . false positives by HCS ) . Of these , 86 were wells containing compound crystals . All of these were readily rejected as hits on manual review of the corresponding images in the automated HCS plate reports by marking the “Non-Hit checkbox” ( Fig . S3E ) . Overall , during the manual plate reading , 780 wells were found to have crystals , of which 130 were deemed to be hits . Importantly , all of these fell in the HCS hit region . A novel phenotypic effect ( internal vacuolation ) was also identified during visual assessment/plate reporting for a number of compounds , a proportion of which were scored as hits by the HCS . Visually , larval viability was not considered sufficiently reduced to designate these as hits and none of the compounds were active in the adult assay . Z factor scores were reviewed to assess plate performance during screening ( Fig . 5C ) all of which were within an acceptable range . Hits from the screen were also analysed and grouped according to larval phenotype by the Bayesian categorization model to determine which anti-schistosome compound they most resembled . From the 378 hits , 175 were ascribed to the OPZ treatment class , 60 to PZQ , 13 to MCZ , 83 to Ro15 , 34 to OX and 13 to DHA . The assay was further validated by re-testing a selection ( 796 ) of hits and non-hits from the 10 , 041 compound library along with compounds from the WHO-TDR set . There was a high level of concordance between the initial and repeat testing ( 92 . 3% for morphology , Fig . 6A; 95 . 2% for motility , Fig . 6B ) . In vitro testing against ex-vivo adult worms is a crucial secondary screen since the adult worm is the key target of drug action . Preliminary testing of a few of the larval hits in the secondary adult worm assay [19] at 10 µM yielded a very low hit rate and so the hits were all tested at 20 µM which gave 45 adult hits . Plotting the larval phenotype and motility scores for these hits ( Fig . 7 ) showed that the majority corresponded to severe larval phenotypes but a few were scattered throughout the hit threshold region . The number of hits ascribed to different treatment classes were OPZ 28 , PZQ 6 , DHA 4 , Ro15 4 , OX 2 and MCZ 1 . Subsequent IC50 testing of the adult hits identified 7 compounds which had IC50s of <10 µM and which are the subject of on-going studies . These compounds were attributed the drug treatment classes OLT 5 , PZQ 1 , OX 1 .
Whole organism screens have an advantage over more target-based approaches as hit compounds can be directly translated into new therapeutics and it has been suggested that the lack of these screens has impacted on the discovery of new compounds [28] . We have established the first HTS for whole organism screening of helminths based on bright-field HCS analysis of morphological and motility changes in schistosome larvae . The published examples of primary high content drug screens are predominately cell-based [29] , [30] . A few involving whole organism screens for Caenorhabditis elegans [31] , zebrafish embryos [32] , Leishmania major [33] and Plasmodium falciparum [34] have been reported . These exploit use of fluorescent probes or proteins since fluorescence provides more contrast , sharpness and discrimination compared with transmitted-light imaging . However , use of fluorescent probes often involves more manipulations and use of potentially toxic fluorophores [35] . Furthermore , fluorescent transgenic lines are not available for certain organisms of interest including parasitic helminths . Transmitted-light imaging and analysis has been developed for whole well segmentation of C . elegans [36] and to demonstrate drug-induced motility changes using optical flow in adult Brugia malayi [37] . Our approach focussed on the development of bright-field imaging of morphology and motility of larval schistosomes directly comparable to the manual visualization used previously [20] , [21] . This requires segmentation and analysis of whole organisms as has been described for C . elegans [31] , [38] , [39] . Our segmentation method differs from previously reported approaches applied to schistosomula , which used a single threshold and aimed to segment touching larvae [40] , [41] . The protocol we describe uses an adaptive threshold ( relative to regional background intensity ) and avoids the need to segment touching organisms due to successful larval resuspension prior to imaging . This resulted in capture of sufficient individual larvae ( ≥10/well ) at 4× and 10× for analysis in 97 . 8% wells from the 10 , 041 compound library . Analysis of cells or whole organisms by HCS has the potential to generate image profiles to define characteristic drug phenotypes [42] which may ultimately be interpretable in relation to compound activity , modes of action and molecular targets , currently undefined for any of the known schistosomicides [43] . Different schistosomicides induce a range of distinct morphological effects in both adults [19] and larvae [20] which led us to develop the treatment class model , grouping test compounds causing similar effects to the anti-schistosome drugs . In screening the 10 , 041 compound library , larval treatment classes ascribed to adult hits with IC50<10 µM were OLT 5 , PZQ 1 , OX 1 . Using an alternative approach to our Bayesian treatment class model , agglomerative hierarchical clustering or DBSCAN has been recently developed [41] to ascribe larval phenotypes according to defined morphological classes . The utility of such classifications models according to phenotype may become clear as more hits are detected and structure/activity relationships are understood . The Bayesian models developed here can also be readily modified by uploading larval images of any novel phenotypic effects identified during image review for any compounds of particular interest e . g . those which show adult worm activity . Similarly the models could be modified by addition of images of novel phenotypes deemed on manual review not to warrant hit status e . g . those causing internal vacuolation identified during our screening . The HCS was validated using several compound collections . At 10 µM , a commonly used concentration for primary screening , the models reliably and reproducibly distinguished all of our reference schistosomicides from the controls with the exception of DHA and Ro15 . This is not considered a failure of the primary assay since both of these compounds are inactive in the visual larval and adult worm in vitro assays at 10 µM . Based on the results with the anti-schistosome drugs , hit thresholds of −0 . 15 for phenotype and −0 . 35 for motility were established and proved robust in detecting 40 previously tested adult hit compounds [19] and 99 . 8% of visually assessed hits from the 10 , 041 compound library . The HCS produced a false positive rate of 1 . 1% , mostly ( 78% ) due to the precipitation of test compounds in wells which in fact contained healthy parasites and which were readily redefined as non-hits during routine reviewing of the automated plate reports . The HCS offers significant advantages over other recent approaches to objective quantitation of schistosomula damage . Peak et al . ( 2010 ) [23] successfully assessed severe drug effects based on uptake of fluorescent markers but the assay involves multiple wash steps and was unable to detect more subtle effects e . g . caused by PZQ . An assay based on use of the redox indicator , Alamar Blue , was similarly less sensitive than visual assessment for more subtle effects and was influenced by variation in parasite numbers per well [21] . Isothermal microcalorimetry [44] , assessment of motility via electrical impedance measurement [45] and optical flow [37] are also able to quantitate drug effects in schistosomes , but are not currently readily adaptable to high throughput applications . Much of the HTS described is automated and simple to operate . Once test plates have been set up and left for 3 days in the Cytomat incubator , the image capture and analysis systems would run automatically after opening “Overlord 3” , the automation control software and pressing “run” . Thereafter , plates emerge from the Cytomat and are robotically moved around for barcode reading , parasite resuspension , imaging and then return to the cytomat . Subsequent analysis involves opening Pipeline Pilot 8 . 5 and running the analysis protocol . Once complete the plate reports are accessible within an intranet web-port . To become sufficiently familiar with the current customized system would require only a couple of days of training and completing a few test runs . Basic modification of the analysis protocols e . g . to run fewer test compounds in a plate , would require some familiarity with Pipeline Pilot 8 . 5 as well as MetaXpress software which controls the IXM microscope . More significant alterations of the protocols would require in-depth knowledge of all the different software involved . Currently the platform takes 2 hrs to image a 384 well plate and a further 2 hrs to analyse the phenotype and motility images . The system can be programmed to start imaging at any time of the day and could run close to continuously . So if test plates were set up on each of 4 days per week , the throughput , limited by plate reading , would be ∼48 plates or 16 , 896 compounds/week which would require around 2×106 cercariae/week . In fact it is cercarial production which is limiting our current throughput capability to around 10 plates twice per week ( ∼7 , 000 compounds/week , 350 , 000/year ) . In conclusion , the HCS described is suitable for primary screening of large compound collections for activity against schistosomes . Further studies are ongoing to adapt this system to screen against several species of nematode larvae of medical and veterinary importance , which may allow parallel testing of libraries against various helminths . | Schistosomiasis is a severe helminth infection affecting an estimated 600 million people . The one drug widely available , praziquantel ( PZQ ) , is not ideal . PZQ kills the adult worms but not the developing juveniles so the treated patient may not be cured long-term . In addition , use of repeated mass treatment campaigns with PZQ to control morbidity raises concerns about the development of drug resistance . Our work is aimed at providing starting points for drug discovery programs for schistosomiasis by screening large compound libraries against whole organisms . Praziquantel and several other known anti-schistosomal drugs are also active in vitro against the adult worms and the larval stages , schistosomula . The latter are ideal for novel drug screening as they can be produced in large numbers in vitro , are small and so are amenable to screening in microwell plates . Drug activity can be assessed visually but this is subjective and laborious . We have built an automated system for assessing drug action involving the collection of images of the larvae and the development of computer algorithms to analyze their morphology and motility , defining them as "hits" or "nonhits . " The method is reliable , consistent and efficient , making it feasible , for the first time , to screen large compound collections . | [
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] | 2012 | Whole Organism High-Content Screening by Label-Free, Image-Based Bayesian Classification for Parasitic Diseases |
Menopause , the permanent cessation of ovulation , occurs in humans well before the end of the expected lifespan , leading to an extensive post-reproductive period which remains a puzzle for evolutionary biologists . All human populations display this particularity; thus , it is difficult to empirically evaluate the conditions for its emergence . In this study , we used artificial neural networks to model the emergence and evolution of allocation decisions related to reproduction in simulated populations . When allocation decisions were allowed to freely evolve , both menopause and extensive post-reproductive life-span emerged under some ecological conditions . This result allowed us to test various hypotheses about the required conditions for the emergence of menopause and extensive post-reproductive life-span . Our findings did not support the Maternal Hypothesis ( menopause has evolved to avoid the risk of dying in childbirth , which is higher in older women ) . In contrast , results supported a shared prediction from the Grandmother Hypothesis and the Embodied Capital Model . Indeed , we found that extensive post-reproductive lifespan allows resource reallocation to increase fertility of the children and survival of the grandchildren . Furthermore , neural capital development and the skill intensiveness of the foraging niche , rather than strength , played a major role in shaping the age profile of somatic and cognitive senescence in our simulated populations . This result supports the Embodied Capital Model rather than the Grand-Mother Hypothesis . Finally , in simulated populations where menopause had already evolved , we found that reduced post-reproductive lifespan lead to reduced children’s fertility and grandchildren’s survival . The results are discussed in the context of the evolutionary emergence of menopause and extensive post-reproductive life-span .
Menopause , the permanent cessation of ovulation , occurs in women well before the end of their expected lifespan; reproductive senescence occurs substantially earlier than somatic senescence , leading to a particularly long post-reproductive life [1] . This is a rather uniform pattern across traditional and modern human societies . For example , if a man or a woman reaches age 45 , he or she can expect to live at least an additional two decades [2–5] . However , and remarkably consistently across populations , reproductive senescence in women is largely completed by age 45 [6] . Extensive post-reproductive life-span ( PRLS ) in humans is thus not a consequence of modern improvements to nutrition , hygiene or medicine . Rather , reproductive cessation occurring approximately twenty years before the end of the expected lifespan appears as a constant feature of human biology [5 , 7] . Among other species , only pilot and killer whales also exhibit extensive female PRLS . For instance , female killer whales can live into the 90s although they usually stop reproducing around age 40 [8–10] . However , patterns of reproductive and somatic senescence in killer whales differ from those of humans in some other ways , especially for males . Indeed , males rarely live beyond 50 years . Moreover , they do not undergo reproductive cessation [11] . In contrast , observations in traditional human populations have suggested that men may often undergo reproductive cessation once their wives reach menopause [12] . Understanding the conditions involved in the evolution of menopause and extensive PRLS is a long-standing challenge for biologists . First , an early end to reproduction seems contrary to maximizing Darwinian fitness . Second , the selective advantage associated with long life after the end of reproduction is not trivial . Various hypotheses have been proposed ( for a review see [13] ) , including the Maternal hypothesis ( MH ) , the Grand-mother Hypothesis ( GMH ) , and the Embodied capital model ( ECM ) . The MH is the idea that menopause has evolved in humans to avoid the risk of dying at childbirth , which is higher in older women , and to ensure the survival of the last offspring [14 , 15] . This hypothesis might thus explain why ageing women stop reproduction . However , as it relies on costs but not on benefits , the MH seems unlikely to explain alone the particularly long duration of PRLS observed in women . Indeed , whereas age-related costs of reproduction may explain early end of reproduction , it cannot explain why additional life after reproduction may be advantageous . Furthermore , death in childbirth may not be common enough to constitute a sufficient cost [16] . According to GMH [7] and ECM [12] , both menopause and long life after reproduction may have evolved as two parts of the same allocation strategy consisting of ceasing to allocate resources to direct reproduction ( i . e . producing new children ) to favor indirect reproduction ( i . e parental or grandparental care ) . Indeed , menopause and extensive PRLS may allow additional resource allocation to grandoffspring care and , therefore , increased fertility of the children and survival of the grandchildren . There are two main differences between the GMH and the ECM . The first resides in the specific causal hypotheses involved [17] . Indeed , according to the GMH , strength ( e . g . proxied by body size ) is the primary determinant of resource production [18–21] . Children productivity is low because foraging requires strength . As human growth is particularly slow , benefit of grand-mothering for grand-children survival and fertility is high , generating selection for older women to increase longevity [21] . According to the ECM , neural capital development and the skill intensiveness of the human foraging niche play the major role in shaping the age profile of resource production and transfers . In traditional societies , a peak of resource production is reached approximately twenty years after the peak of strength ( mid-twenties ) [12] . This is because earlier-life investments in neural capital lead to later-life energetic returns from such investments , with the consequences that individuals still acquire more resources than they need for survival until age 70 [12] . These extra resources could be used either for direct reproduction or for indirect reproduction . However , if the cost of reproduction increases with age ( for instance , due to physiological constraints ) , it may be more advantageous to use these resources for increasing condition and fertility of the children and grandchildren , rather than increasing the number of children . The second difference between GMH and ECM resides in the fact the ECM is a two-sex model , whereas males may not be considered in the GMH . Indeed , as the traditional hunter-gatherer pattern of production , reproduction , and parental investment depends fundamentally on a cooperative division of labor between men and women , the ECM predicts that both aging women and men may stop producing new children to allocate resources to existing children and grand-children . To test the MH [22 , 23] and the GMH [24 , 25] , empirical studies have compared the fitness of children and grandchildren of women who experienced different durations of post-reproductive life-span . However , it is unclear if these studies help to understand the emergence or maintenance of menopause and extensive PRLS [26] . Indeed , the conditions favoring their maintenance are not the same as the conditions favoring their emergence . This is because female reproductive strategies in a population alter the social environment and determine the benefits of a trait . This change affects competition for reproductive resources and the average relatedness between interacting individuals [26] . Thus , the evolution of menopause and PRLS should not be studied outside of its ecological context or without considering the feedback between the evolution of this trait and the resulting ecology . To empirically study the evolutionary emergence of extensive PRLS , the fitness of rare mutant females who experience menopause should be compared to the fitness of resident females who do not . However , this is a possibility neither in humans , as menopause and extensive PRLS is already present in all populations [26] , nor in our closest relative species , as reproductive senescence in midlife seems to be absent in non-human primates [27] . Regarding the ECM , the prediction that both aging women and men may stop producing new children to allocate resources to existing children and grand-children has been already supported by observations in traditional human populations [12] . However , the relation between neural capital development and skill intensiveness of the foraging niche on the one hand , and the duration of PRLS on the other hand , have not been demonstrated yet . Here , we tested the MH , GMH and ECM for both the emergence and the persistence of menopause and extensive PRLS using a modeling approach based on life-history theory . Life history theory is the idea that living organisms must divide the total energetic potential available to them over their lifetime to perform different tasks , mainly survival , growth , direct reproduction , and parental care [28 , 29] . As this energetic potential is limited , trade-offs occur among these tasks , resulting in different life-history strategies . The first trade-off occurs between immediate and future reproduction ( via investment in growth and survival ) . The second trade-off occurs between the quantity and quality of offspring ( i . e . , having more offspring versus a larger investment in each of them ) . Modeling the evolution of allocation strategies should allow investigating the conditions for a switch from allocation to direct reproduction to allocation to indirect reproduction , i . e . for the emergence and persistence of both menopause and extensive PRLS . However , it requires a comprehensive model that considers both all of the allocation decisions that an individual has to make during his or her life , and how these decisions are shaped by complex interactions between genes , environment , and the internal state of the individual at the time when he has to make the decision . We used Artificial Neural Networks ( ANNs [30] ) to simulate the evolution of resource allocation strategies , including all types of complex , even unforeseen , trade-offs in populations subject to diverse ecological conditions . Allocation decisions were allowed to freely evolve , and menopause and extensive PRLS emerged under some ecological conditions . We then tested for the following predictions: ( 1 ) Under the MH , menopause ( and thus extensive PRLS ) should not be observed without including age-dependent risk of dying at childbirth in the model; ( 2 ) Under both the GMH and the ECM , menopause and extensive PRLS should not emerge , whatever the ecological conditions , if resource transfers to grand-offspring are not allowed; ( 3 ) Under the ECM only , cognitive resources , because of delayed benefits of investment in neural development , should be a required condition for the emergence of menopause and extensive PRLS . Note that the ECM , as mentioned before , also predicts that both aging women and men may stop producing new children to allocate resources to existing children and grand-children , a prediction which has been supported by observations in traditional human populations [12] . Due to methodological issues ( see limitation section ) , we did not test this prediction here . We rather focused on the relation between cognitive resources and extensive PRLS , which has never been tested before . Finally , we also tested whether MH , GMH and ECM may explain the persistence of extensive PRLS in simulated populations where this trait has already evolved . In these populations , GMH predicts that mother death at the age of menopause or delayed menopause of the mother should lead to decreased fertility of the children and/or decreased grandchildren survival . MH predicts that , under the same conditions , survival of the children should be decreased .
With the exception of the flow rate of available resources , all of the ecological parameters included in the model ( α , the skill intensiveness of the foraging niche; β , the rate of skills acquisition; γ2 , the difficulty of acquiring resources in the environment; δ , the depletion rate of somatic and cognitive capital; and σ2 , the dangerousness of the environment ) somehow influenced the duration of PRLS ( Fig 1 ) . Lower values for α were associated with shorter PRLS regardless of the values of the other parameters . However , high values of α were not always sufficient to generate a duration of PRLS higher than 5 time units , suggesting the presence of interactions with other parameters . The highest durations of PRLS observed ( >5 time units ) were associated with parameters of intermediate ( for β ) or high values ( for δ and γ2 ) , suggesting multiple complex interactions among them . The maximization procedure confirmed that at least one combination of ecological parameters ( α = 0 . 91; β = 0 . 47; γ2 = 157; δ = 0 . 87; σ2 = 27 ) lead to menopause and extensive PRLS with our model . Among all tested combinations of parameters ( see Material and method section ) , this set , referred to as EP* , led to evolution of the longest duration of PRLS in the simulated population . With EP* , 1 , 121 individuals were born and died during the final 2 , 000 time units of the simulation process . Their average duration of PRLS was 21 . 92 ( +/- 3 . 04 ) time units . A total of 78 . 1% of these individuals had exactly the same allocation strategy ( S1 Fig ) , as defined by the combination of synaptic weights of the artificial neural network ( see the Material and Methods section ) . Their average duration of PRLS was 18 . 84 ( +/- 0 . 60 ) . The typical life history of an individual with this allocation strategy achieving reproduction was the following ( Fig 2 ) : The first resource allocation was to growth , survival and maintenance until the quantity of somatic capital reached the value of 0 . 6 ( at t = 18 ) . Then , the first reproductive event occurred and somatic senescence started , thus suggesting that resources were allocated to reproduction at the expense of investment in the quality of somatic capital . Investment in maternal care for a given child was maximal following birth and then decreased over time . A second reproductive event occurred at t = 28 , again at the expense of the quality of somatic capital . At t = 32 , a grandchild was born . Then , the individual started to allocate resources for maternal care for both the first child , who has given birth , and the second child , who is not autonomous yet . The resulting increase in maternal care occurred at the expense of the quality of somatic capital but also at the expense of investment in direct reproduction . Indeed , no individual along the simulation process gave birth to an additional child after the birth of a grandchild . Despite the decrease of the quality of somatic capital , the quality of both cognitive capital and survival probability remained stable until t = 42 . Then , they started to decrease until death , which occurred at t = 47 . Whatever the ecological parameters used , menopause and extensive PRLS did not emerge in the simulated populations when grandoffspring care was not allowed . In that case , the mean duration of PRLS obtained after applying the maximization procedure was 1 . 6 time units , a 92 . 6% reduction compared to the mean duration of PRLS ( 21 . 9 time units ) obtained when grand-mothering was a possible option . Similarly , menopause and extensive PRLS did not emerge when cognitive resources were not differentiated from somatic resources in the model ( i . e . both resources are interchangeable and had the same properties , including no delayed benefits ) . In that case , the mean duration of PRLS obtained after applying the maximization procedure was 2 . 1 time units , a 90 . 6% reduction compared to the mean duration of PRLS obtained with the full model . Finally , allowing resource transfers between siblings lead to no substantial changes in the results ( duration of the PRLS of 22 . 04 with the full model , 1 . 71 without grand-mothering , and 2 . 03 without delayed benefit of investment in cognition ) . In populations where menopause and extensive PRLS had already evolved , condition 1 ( death at the age of menopause ) had no detectable effect on the survival of the first-generation children ( G1 ) , although these children had reduced fertility . In the subsequent generations , the survival and fertility of the manipulated individuals with condition 1 were lower than the control ( Fig 3 ) and they decreased in frequency ( Fig 4 ) . Manipulated individuals with condition 2 ( delayed menopause , i . e . one additional reproductive event at the age of menopause ) were significantly more frequent in the population than control individuals at G1 , which was expected given the nature of the condition . Then , they decreased in frequency and were significantly less frequent than the control individuals from G5 to G10 ( Fig 4 ) . The probability to survive until reproduction was significantly lower for the manipulated individuals with condition 2 than for the control individuals , from G1 to G10 . At G1 , this difference was explained by a low probability of survival ( 0 . 23 ) for the last child , who was born at the mother’s expected age of menopause . Conversely , the other children had a probability of surviving until reproduction of 0 . 48 , which is equal to those of the control individuals . The fertility of the manipulated individuals with condition 2 was significantly higher than that of the control individuals at G0 , as expected given the nature of the condition . Then , however , fertility of the manipulated individuals was not significantly different from that of the control individuals from G1 to G10 ( Fig 5 ) . Finally , the lifespan of the manipulated individuals with condition 2 was significantly shorter than that of control individuals ( on average 42 units of time rather than 47 , p-value: 0 . 003 ) .
Studying the correlations between the ecological parameters used for the simulations and the resulting duration of PRLS revealed that high values of α ( i . e . skill intensiveness of the ecological niche ) were necessary to generate duration of PRLS higher than 5 time units . This result supports the ECM [12] . However , high values of α were not sufficient to generate a duration of PRLS higher than 5 time units , suggesting the presence of complex interactions with other parameters . Therefore , studying the correlations between ecological parameters and the resulting duration of PRLS was insufficient to clearly understand the conditions favoring the emergence of menopause and extensive PRLS . We thus used the maximization procedure to investigate the evolution of life history traits in the simulated populations and to identify required conditions for emergence of menopause and extensive PRLS . When allocation decisions were allowed to freely evolve in a simulated population , menopause and extensive PRLS emerged under at least one set of ecological parameters ( Fig 2 ) . The patterns of somatic and reproductive senescence obtained were strikingly similar in some ways to those observed in traditional human populations [12] . In particular , we observed a cognitive senescence beginning about twenty units of time after somatic senescence , and stable productivity until cognitive senescence begins . In contrast , some other characteristics of the evolved strategy were less realistic when compared to observations in traditional human populations ( e . g . number of offspring per individual , inter-birth intervals , see Fig 2 ) . However , note that we did not aim here to simulate precisely all the aspects of a human life-cycle . Indeed , there are substantial differences in the timing of life-history between human populations around the world , and all this variability cannot be captured here . Moreover , there is no indication that the trait values observed now in hunter-gatherers ( mainly living in marginal habitats ) , reflect the values in the ancestral hunter-gatherers , at a time when menopause evolved . For these reasons , we have designed the maximization procedure to optimize the ecological parameters in order to obtain the longest PRLS under various simulated conditions . This approach allowed us to identify some factors which are required for the emergence of extensive PRLS , whatever the ecological parameters used , the species considered , and the other characteristics of the allocation strategy . An advantage from this approach is that our findings apply to any species with menopause and extensive PRLS , not only humans . Another is that we assumed a minimal number of physiological or environmental constraints . In particular , menopause and extensive PRLS evolved without imposing a starting condition with the presence of a somatic senescence . Rather , somatic senescence , reproductive senescence ( i . e . menopause ) and extensive PRLS evolved as an allocation strategy . Similarly , no prior assumption was made on an increase of the cost of direct reproduction with age . To explain reproductive senescence , MH assumes that the cost of direct reproduction increases with age due to the higher risk of dying at childbirth [14 , 15] . ECM also assumes increasing costs of reproduction due to physiological constraints ( e . g . decreasing oocyte quality ) , although Kaplan et al . [12] recognized that additional costs to late-life reproduction beyond physiological costs ( e . g . reduced future productivity from maternal depletion ) may exist . Here , the cost of direct reproduction is only defined by the amount of resources allocated for direct reproduction and for parental care , which are allowed to freely evolve . However , when individuals were forced to reproduce at the age of menopause , their own lifespan was significantly reduced , and the child had a higher probability of dying before achieving reproduction , compared to previous children . Late reproduction is thus costly for survival and weakly advantageous for gene transmission , as assumed by MH and ECM . However , this is a result of an evolved allocation strategy rather than the consequence of pre-existing physiological constraints . Similarly , mortality was only a probabilistic consequence of a reduced quantity of resources invested in survival . Extrinsic mortality was not included in the model , as it can be considered that evolved organisms exert some control over many possible causes of mortality ( e . g . , by altering patterns of travel to avoid predators , by investing in immune functions , etc . ; see [31] ) . Most types of mortality could thus be seen as the result of an allocation strategy . Investigating how patterns of reproductive senescence were shaped by the evolution of allocation decisions under different simulated conditions allowed us to test three hypotheses ( MH , GMH and ECM ) for the emergence and the persistence of menopause and extensive PRLS . By supporting key assumptions from the GMH and ECM ( but not the MH ) , our results support the idea that both grand-mothering ( GMH ) and cognitive resources ( ECM ) are required for the emergence of menopause and extensive PRLS . We also support the importance of GMH , but not MH , in explaining the persistence of extensive PRLS in populations where this trait has already evolved . Indeed , in a population where extensive PRLS had already evolved , when maternal mortality was enforced at the age of menopause ( i . e . , on average 4 . 3 time units after the second childbirth ) , the children’s fertility was affected , but not their survival until reproduction ( Fig 3 ) . This non-reduced survival of motherless children did not result from allocare [37] , as children without their mother could not receive resources from other individuals . Rather , it was the result of an evolved strategy consisting in prioritizing survival rather than fertility when facing a lack of resources . In contrast , grand-mothering is likely pivotal to maintain extensive post-reproductive life-span once it has evolved . Indeed , when the grand-mothering effect was suppressed at the age of menopause ( the grandmother was forced to die ) or reduced ( the grandmother was forced to have an additional child so that parental resources were reduced for any given child ) , this was associated with a reduced fitness and the corresponding strategy decreased in frequency ( Figs 3 and 5 ) . This is consistent with several empirical studies [23–25 , 28–42] . The main limitations in this study were due to the use of a one-sex model . Up to now , no validated and reliable method has been published to use neural networks in the context of a two-sex diploid model . We hope that further methodological developments will allow overcoming this limitation in the near future . It would make possible to complement this study by testing another key prediction of the ECM , i . e . both aging women and men may stop producing new children to reallocate resources to existing children and grand-children . Note however that this prediction has been already supported by observations in traditional populations [12] . In contrast , the relation between cognitive resources and duration of the PRLS had not been previously tested . Using a two-sex model would have also allowed testing the reproductive conflict hypothesis [23 , 39 , 43] . The idea is that , when old and young women are co-breeding in the same family unit , as in patrilocal societies , menopause could be the result of a limitation in resources due to competition . Relatedly , some authors suggested that , in this context of intra-familial competition , younger females should benefit from a decisive advantage as compared to older females [25 , 43 , 44] due to asymmetric relatedness . Indeed , the daughter-in-law is not related to the children of her mother-in-law , but the mother-in-law is related to the children of her daughter-in-law . Testing of these hypotheses require using a two-sex model , as they are explicitly based on relatedness within a family . Therefore , it cannot be excluded here that these processes , in addition with grand-mothering and cognitive resources , may have also played a role in the emergence or persistence of menopause and extensive PRLS in humans . More generally , future developments may be envisaged to make the model more realistic . For instance , this may include taking into account migration and patterns of patri or matri-locality ( i . e . the individuals can invest for their kin only if they are co-resident ) , modelling resource transfers between non-kin or distant kin , considering separately different kind of resources ( e . g . time and energy ) , or allowing different degradation rates for somatic and cognitive capital . Indeed , in the absence of any published evidence that the respective degradation rates of somatic and cognitive capital are different , for the sake of simplicity , we assumed that these two rates are equal . Note that we speak here of physiological degradation rates , which are different from observed rates of decrease in performance . Indeed , there is published evidence that age-related decline in physical strength follows a very different trajectory than age-related decline in various cognitive abilities [35 , 45] . However , age-related decline in performance depends both on the physiological degradation rate and on investment in maintenance . Finally , we assumed here a maximum of five children living simultaneously . This assumption was also imposed by computational limits . However , this is unlikely to have affected the results , as no individuals gave birth to more than three children in our simulated populations using the EP* parameters . To conclude , we hope to stimulate further interest to use artificial neural networks ( or any other adequate tool ) to study the evolution of allocation decisions to address these questions , as well as many other issues in evolutionary biology . Indeed , allocation decisions are central to various long-standing questions in this field ( e . g . , the evolution of senescence , cognition , social interactions , … ) , and modelling their evolution may result in significant improvement .
The model ( Fig 6 ) was coded in C++ . The neural networks were fully connected multi-layer perceptrons with a single hidden layer of 5 neurons . The inputs to the networks were information on the internal state and social environment perceived by the individual . The outputs were the proportions of resources allocated to each function . Preliminary exploration showed that increasing the amount of available resources at each time unit , all else being equal , lead only to a proportional increase in population size , without changing the average duration of post-reproductive period . This parameter was established at 20 , 000 , resulting in population sizes of at least 500 individuals . A random value was attributed to each of the five other parameters ( α , β and δ were drawn from a uniform distribution between 0 and 1 and γ2 and σ2 from a uniform distribution between 0 and 200 ) , and a simulated population with an initial size of 1 , 000 individuals was allowed to evolve during 10 , 000 time units with these parameters . PRLS was measured as the average time interval between the last reproduction and death , calculated over the individuals who were born and died during the final 2 , 000 units of time . This process was repeated 100 times to detect the influence of each parameter on the variation of PRLS . The maximizing function “rbga” ( package “genalg” [47] ) , implemented in R v3 . 2 [48] , was used to test whether at least one combination of ecological parameters lead to extensive PRLS with our model . To this end , we identified the combination of ecological parameters values able to promote the evolution of the longest PRLS , and we measured the average PRLS duration in a population which has evolved under these conditions . For each set of parameters ( α , β , δ , γ2 and σ2 ) , a population with an initial size of 1 , 000 individuals was simulated and was allowed to evolve during 10 , 000 units of time ( or 20 , 000 units of time , without changing the results ) . PRLS was the variable to be maximized in the space of parameter values . For each combination of ecological parameters , a combination of synaptic weights evolved ( i . e . , became the most frequent in the population ) . This procedure thus allowed identifying both the combination of ecological parameter values which led to the longest PRLS ( referred to as EP* ) , and the associated synaptic weights ( referred to as the best weights ) . To observe and describe the allocation strategy corresponding to the best weights , a population with an initial size of 1 , 000 individuals was simulated using the EP* , with all individuals having the best synaptic weights , without possible mutations . With these conditions , the demographic characteristics were allowed to freely evolve during 10 , 000 units of time ( or 20 , 000 units of time , without changing the results ) . Although individuals with the same synaptic weights necessarily have a similar strategy , decisions could vary based on the local perceived conditions . This step allowed a reduction in the inter-individual variation of realized life histories . To test the GMH , the same procedure was performed with the outputs corresponding to allocation in maternal care for a child who had already reproduced set to 0; grand-parenting was thus no longer a possible allocation option . Indeed , as mentioned before , grand-parenting was modeled in this study by allowing the individuals to adapt their parental investment for a given child depending on its own number of children , rather than allowing direct resource transfers to grandoffspring . If the GMH is determinant to explain the emergence of extensive PRLS , we thus expected that it cannot emerge under this condition , whatever the combination of ecological parameters . To test the ECM , the procedure was performed after removing delayed benefits of investing in cognition from the model ( i . e . the integral term and the β parameter were removed from Eq 3 ) . Indeed , without delayed benefits of investment , cognitive resources were not differentiated from somatic resources in the model ( i . e . both resources are interchangeable and had the same properties ) . Strong delayed benefits of investment are a specificity of cognitive capital [12] . Indeed , investing in neural development at time t promotes accumulation of skills and experience all along the life . Returns from cognitive capital can thus continue to increase ( not only to be maintained ) even after stopping investment in it . This is not the case for somatic capital . Indeed , although investment in somatic capital at time t can provide benefits later ( for resource production , protection , … ) , these benefits will not increase without further investment . Therefore , this procedure allowed testing for the relation between cognitive resources and the duration of PRLS . Indeed , without delayed benefits of investment , cognitive resources were not differentiated from somatic resources in the model ( i . e . both resources are interchangeable and had the same properties ) . In addition , as resource transfers are sometimes also provided by older siblings in humans , we also tested whether allowing transfers between siblings change the results . To identify the costs of suppressed or reduced PRLS in a population where extensive PRLS has already evolved , we simulated 200 populations with initial size of 1 , 000 individuals , where all individuals had the same allocation strategy ( best synaptic weights ) . We allowed each population to evolve using the EP* during 20 , 000 units of time , without possible mutations . 100 populations were attributed to condition 1: death at age of menopause , and the 100 other populations were attributed to condition 2: one additional reproductive event at age of menopause . At t = 10 , 000 , we applied the condition to half of the individuals in each population . The condition was heritable and was also applied to their offspring at each generation . No condition was applied to the other individuals and their offspring ( control ) . For each population , the proportion of individuals who received the condition among the successive generations , up to the 10th , was tested for a significant departure from the expected frequency ( 0 . 5 ) using two-sided binomial tests ( R-based function binom . test ) . The average fertility and the proportion of individuals who survived until reproduction were compared between the control and condition across the first ten generations using two-sided student tests . The data fitted the requirements for these tests . | In all human populations , regardless of environmental and socioeconomic conditions , menopause occurs in women well before the end of their expected lifespan . Conversely , extensive post-reproductive life-span is rare in other species; except in some cetaceans . Evolutionary theory predicts that menopause and extensive post-reproductive lifespan should emerge and persist in populations only if it is advantageous for gene transmission . Identifying this advantage is a long-standing issue . We provide a better understanding by demonstrating that humans’ cognitive abilities , in association with grand-mothering , are required for the emergence of this pattern . Indeed , cognitive abilities allow accumulation of skills and experience over the lifespan , thus providing an advantage for resource acquisition . These surplus resources can then be used to increase the number of offspring or be transmitted to existing offspring and grandoffspring . Stopping reproduction during aging allows allocating more resources to assist offspring and grandoffspring , thus increasing children’s fertility and grandchildren’s survival . | [
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] | 2017 | Grandmothering and cognitive resources are required for the emergence of menopause and extensive post-reproductive lifespan |
Retroviral capsid recognition by Trim5 blocks productive infection . Rhesus macaques harbor three functionally distinct Trim5 alleles: Trim5αQ , Trim5αTFP and Trim5CypA . Despite the high degree of amino acid identity between Trim5αQ and Trim5αTFP alleles , the Q/TFP polymorphism results in the differential restriction of some primate lentiviruses , suggesting these alleles differ in how they engage these capsids . Simian immunodeficiency virus of rhesus macaques ( SIVmac ) evolved to resist all three alleles . Thus , SIVmac provides a unique opportunity to study a virus in the context of the Trim5 repertoire that drove its evolution in vivo . We exploited the evolved rhesus Trim5α resistance of this capsid to identify gain-of-sensitivity mutations that distinguish targets between the Trim5αQ and Trim5αTFP alleles . While both alleles recognize the capsid surface , Trim5αQ and Trim5αTFP alleles differed in their ability to restrict a panel of capsid chimeras and single amino acid substitutions . When mapped onto the structure of the SIVmac239 capsid N-terminal domain , single amino acid substitutions affecting both alleles mapped to the β-hairpin . Given that none of the substitutions affected Trim5αQ alone , and the fact that the β-hairpin is conserved among retroviral capsids , we propose that the β-hairpin is a molecular pattern widely exploited by Trim5α proteins . Mutations specifically affecting rhesus Trim5αTFP ( without affecting Trim5αQ ) surround a site of conservation unique to primate lentiviruses , overlapping the CPSF6 binding site . We believe targeting this site is an evolutionary innovation driven specifically by the emergence of primate lentiviruses in Africa during the last 12 million years . This modularity in targeting may be a general feature of Trim5 evolution , permitting different regions of the PRYSPRY domain to evolve independent interactions with capsid .
The anti-retroviral activity of Trim5α was discovered in a screen to identify rhesus macaque cDNAs conferring resistance to HIV-1 replication [1] . Antiretroviral activity has since been demonstrated for a large number of primate Trim5 orthologs , including prosimians , as well as homologs from cow and rabbit [2] , [3] , [4] , [5] . While no single ortholog of Trim5 universally restricts all retroviruses , the collective breadth of restriction , coupled with the observation that some orthologs can restrict viruses from two or more genera , suggests that Trim5 recognizes a conserved , pathogen-associated molecular pattern common to members of the Retroviridae [2] , [6] , [7] . Trim5α is composed of four domains: the RING , the B-Box and the Coiled-coil domains , which make up the tripartite RBCC of TRIM proteins , and a C-terminal PRYSPRY domain [8] , [9] . The PRYSPRY domain is thought to recognize the viral capsid [1] , [10] , [11] . In the case of lentiviruses , the cone-shaped capsid is composed of 12 pentamers and approximately 200 hexamers , each in turn comprised of identical copies of monomeric capsid ( CA ) protein [12] , [13] . An HIV-1 CA monomer has two α-helical domains connected by a flexible linker [14] . The N-terminal domain makes up the outer surface of the capsid and mediates interactions with cellular cofactors [15] , [16] , [17] , [18] , [19] , [20] , [21] . Comparisons between reported CA structures from viruses representing five Orthoretrovirinae genera show that the overall architecture of the N-terminal domain is conserved , despite little conservation of protein sequence . All reported retroviral N-terminal domain structures contain a conserved five α-helix core , from which a conserved surface feature , the β-hairpin , protrudes into the cytoplasm . Structural variation can be found among additional features on the CA surface . These differences include the presence and arrangement of 1–2 additional α-helices and/or the presence of an extended loop connecting helices 4 and 5 ( 4–5 loop ) [22] , [23] , [24] , [25] , [26] , [27] . Reports suggest that multiple sites within retroviral CAs modulate Trim5α sensitivity [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] , [47] , [48] , [49] , [50] , [51] , [52] . The majority of these sites map to the N-terminal domain and are enriched within the CA surface features . Perplexingly , engineered CA mutations , naturally occurring variants , and escape mutations can have similar phenotypes even when separated by distances in excess of 25 Å . Understanding how these sites relate to one another is critically important for defining how Trim5α recognizes retroviral capsids , and how viruses evolve to evade Trim5α restriction . We previously reported that the Trim5 locus of rhesus macaques ( Macaca mulatta ) is highly polymorphic , and that the different allelic lineages of rhesus Trim5 ( rhTrim5 ) have been maintained by long-term balancing selection [53] , [54] . Based on functional assays and gene association studies , rhTrim5 alleles can be grouped into 3 classes , rhTrim5αTFP , rhTrim5αQ and rhTrim5CypA [31] , [55] , [56] , [57] , [58] , [59] , [60] . When tested against a panel of primate lentiviruses , the 3 alleles give differing patterns of restriction [31] , [53] , [57] – an indication that rhTrim5 has at least 3 distinct ( or incompletely overlapping ) targets on the lentiviral CA protein . SIVmac emerged in captive macaque colonies in the 1970s , most likely the result of an unintentional interspecies transmission of SIV from sooty mangabeys ( SIVsm ) [61] , [62] , [63] , [64] . We previously reported that SIVsm isolates are resistant to rhTrim5αQ , but sensitive to rhTrim5αTFP and rhTrim5CypA alleles [31] . Because rhTrim5αTFP , rhTrim5αQ and rhTrim5CypA likely have deferring targets within CA and because all are present at moderate-to-high frequency , emergence of SIVmac in rhesus macaque colonies required adaptations permitting simultaneous resistance to all three . Thus , comparisons between SIVmac and other restricted isolates provide a unique opportunity to understand the basis of recognition by Trim5α proteins and to identify specific features of CA that determine sensitivity and resistance to rhTrim5α-mediated restriction . The structural basis for CA recognition by rhTrim5Cyp is clear: the cyclophilin A domain ( CypA ) specifically binds the 4–5 loop [65] , [66] . In contrast , rhTrim5αTFP and rhTrim5αQ interact with capsids via a C-terminal PRYSPRY domain , but the basis for capsid recognition by Trim5 PRYSPRY domains remains poorly understood . There are several factors that complicate studies of the interaction . For example , Trim5α destabilizes capsid complexes [10] , [11] , [67] , [68] , [69] , the nature of the interaction is believed to be high avidity and low affinity [69] , [70] , [71] , [72] , the interaction site may extend beyond a single CA monomer or hexamer [11] , [67] , [70] , [72] , [73] , retroviral capsids and presumably the Trim5α lattice surrounding them have variable morphology and composition [70] , [74] , and there is considerable diversity among Trim5α orthologs and retroviral CA sequences . To investigate how Trim5α recognizes retroviral CAs , we combined genetic , phylogenic and structural investigations with an alternative mutational strategy to separate and map the determinants for the differential restriction of HIV-1 and SIVmac by rhTrim5α alleles . The resolution of our mapping , together with the structural determination of the SIVmac239 CA N-terminal domain and consideration of primate lentivirus diversity , allowed us to identify two conserved CA surface elements that appear to be targets of rhTrim5α recognition . The first , the β-hairpin , is a structural feature that is present in all reported retroviral CA structures . Mutations in the β-hairpin affected targeting by both rhTrim5αQ and rhTrim5αTFP alleles . The second element , a patch of highly conserved amino acids among primate lentivirus CAs , maybe a unique target of the more recently evolved rhTrim5αTFP allele . Strikingly , this patch is a surface-exposed extension of the recently indentified CPSF6 binding site [18] . Therefore , similar to the exploitation of the interaction between cyclophilin A and Nup358 by Trim5CypA , it appears that rhTrim5αTFP has evolved to target the binding site of a required cellular cofactor . Taken together , the observations made from investigating the differential breadth and specificities of rhTrim5α alleles have revealed a complex evolutionary relationship between retroviruses and Trim5α orthologues .
Differential restriction by rhTrim5αQ and rhTrim5αTFP has been mapped to a length polymorphism in the PRYSPRY domain ( TFP339-341Q ) [57] . Despite the fact that the protein sequences are >98% identical , the rhTrim5αQ and rhTrim5αTFP alleles yield different patterns of restriction when tested in parallel against divergent retroviruses [31] , [53] , [56] , [57] . We tested both alleles against multiple primate lentiviruses and found that even among these related viral strains , the rhTrim5αQ and rhTrim5αTFP alleles give different patterns of restriction ( Figure 1 ) . Specifically , rhTrim5αQ restricted a human viral isolate , HIV-1nl4 . 3 , but failed to restrict any of the lentiviruses isolated from Cercopithecine primates ( SIVmac239 from rhesus macaques , SIVsmE041 and SIVsmE543-3 from sooty mangabeys , and SIVagmTAN-1 from African green monkeys ) or HIV-2ROD ( which originated by cross-species transmission of SIVsm [75] ) . In contrast , rhTrim5αTFP restricted HIV-1nl4 . 3 , SIVsmE041 , SIVsmE543-3 , SIVagmTAN-1 and to a lesser extent , HIV-2ROD . Only the rhesus macaque isolate , SIVmac239 , was resistant to both alleles . Thus , while both alleles are functional , the differing patterns of restriction are consistent with the hypothesis that rhTrim5αQ and rhTrim5αTFP proteins differ in the way they recognize primate lentivirus CAs . HIV-1 and SIVmac239 had opposite restriction profiles when tested for restriction on rhTrim5α expressing cells . HIV-1nl4 . 3 was restricted by both rhTrim5αTFP and rhTrim5αQ alleles , whereas SIVmac239 was resistant to both alleles . At least three lines of evidence support the existence of multiple sites of rhTrim5α recognition within the HIV-1 CA . First , HIV-1 is restricted by both rhTrim5αTFP and rhTrim5αQ alleles while other tested primate lentiviruses are resistant to the rhTrim5αQ allele . Second , attempts to evolve an HIV-1 with resistance to rhTrim5α have not yielded fully resistant viruses [42] , while other viruses have successfully evolved resistance to rhTrim5α-mediated restriction with genuine escape mutations both in vitro and in vivo [30] , [31] . Third , mutagenesis approaches in which elements of the SIVmac239 CA were inserted into the HIV-1 CA resulted in rhTrim5α restricted viruses [28] , [29] , [37] , [38] . With 79 amino acid differences between the two viruses ( Figure 2A ) , we hypothesized that isolating each determinant would allow us to resolve the specific amino acids involved in rhTrim5α recognition at each target site . We therefore chose to take an alternative approach , based on identifying gain of sensitivity mutations of the inherently rhTrim5α-resistant SIVmac239 CA . We inserted individual features of the HIV-1nl4 . 3 CA into the SIVmac239 CA and measured the impact on restriction . The ability of Trim5α orthologs to restrict highly divergent retroviruses with little to no sequence identity suggests Trim5α may target conserved , structural elements of CA . All reported retroviral N-terminal domain structures have a conserved five α-helix core . To determine whether differences within the five α-helix core impact rhTrim5α recognition , we generated SIV-HIVinterior , by replacing most of the five α-helix core of SIVmac239 with that of HIV-1nl4 . 3 . This virus retained the SIVmac239 residues at the first and last amino acid of each α-helix ( Figure S1 ) . We then tested this virus for restriction by rhTrim5αTFP and rhTrim5αQ alleles . This mutant was 2 . 3-fold more sensitive to rhTrim5αTFP than the SIVmac239 parent ( Figures 3A–3C and Figure S2 ) . This differed markedly from SIV-HIVsurface , in which three surface elements , the β-hairpin , 4–5 loop and helix 6 , were derived from HIV-1nl4 . 3 . This virus was restricted by all rhTrim5α alleles tested , at levels similar to HIV-1nl4 . 3 ( Figures 3A–3D ) . Because SIV-HIVsurface was phenotypically similar to HIV-1nl4 . 3 , we asked whether a reciprocal chimera was sufficient to render HIV-1nl4 . 3 restriction resistant . Therefore , we replaced the HIV-1nl4 . 3 CA surface features with the three SIVmac239 surface features ( the β-hairpin , 4–5 loop and helix 6 ) to create HIV-SIVsurface ( Figure S1 ) . This HIV-1 variant differed from HIV-1nl4 . 3 by 28 amino acids and was highly resistant to restriction by rhTrim5αTFP and rhTrim5αQ alleles ( Figure 3E ) . Within the linker that connects the β-hairpin to helix 1 , HIV-1nl4 . 3 and SIVmac239 differ at three positions ( amino acids 13–15 ) ( Figure 2 and Figure S1 ) . Using a second HIV-1-SIV chimera , HIV-SIVsurface25 , we determined that these three differences do not influence restriction ( Figure 3F ) . To our knowledge , HIV-SIVsurface and HIV-SIVsurface25 represent the first description of an HIV-1 strain resistant to all allelic classes of rhTrim5 . Titration of these viruses and abrogation assays confirm that resistance was not due to saturation of rhTrim5α in the target cell lines ( Figures S2 and S3 ) . To examine the individual contributions of each of the three surface features to restriction , we produced a series of SIVmac239 CAs each grafted with a single HIV-1nl4 . 3 surface feature . To take into account the fact that the β-hairpin is one amino acid shorter in SIVmac239 , we generated two SIV variants: SIV-HIVbhp , with a full length HIV-1nl4 . 3 β-hairpin , and SIV-HIVbhpQ7Δ , with a single amino acid deletion in the HIV-1nl4 . 3 β-hairpin . We also generated SIVmac239 variants with the HIV-1nl4 . 3 4–5 loop or helix 6 ( SIV-HIV4–5L and SIV-HIVh6 , respectively ) . Rhesus Trim5αTFP alleles restricted all four of these viruses ( SIV-HIVbhp , SIV-HIVbhpQ7Δ , SIV-HIV4–5L , and SIV-HIVh6 ) . With the exception of SIV-HIVh6 , the chimeras had little effect on restriction by rhTrim5αQ ( Figure 3G–J ) . Together , these mutants suggest that the HIV-1 restriction-sensitive and SIVmac239 restriction-resistant phenotypes involve contributions from all three capsid surface features . Based on results obtained from the HIV-SIVsurface25 chimera , we generated a series of SIVmac239 CA mutations in which the amino acid at each of the 25 positions of interest was substituted with the amino acid found at the homologous position in HIV-1nl4 . 3 ( Figures 2A , 3F , S1 , S2 and Table 1 ) . Two of the 25 mutations in the SIVmac239 CA , R117H and N123P , resulted in loss of infectivity . Although a His is found at position 117 in HIV-1nl4 . 3 , an Asp is more common among HIV-1 isolates . We found that an SIVmac239 in which R117 was substituted with Asp instead of His retained infectivity ( Figure S2 ) . The 24 infectious SIVmac239 variants with single amino acid substitutions in CA were tested for sensitivity to restriction by rhTrim5αTFP and rhTrim5αQ . Restriction was quantified by determining the level of infectivity relative to SIVmac239 ( Table 1 ) . Only two single amino acid substitutions ( Q3V and G6L ) , both in the β-hairpin , resulted in gain-of-sensitivity to both rhTrim5αTFP and rhTrim5αQ . There were 12 additional mutations that caused gain-of-sensitivity to rhTrim5αTFP , but not to rhTrim5αQ . These mutations were spread among all three CA surface features . Together these results indicate that the targets of the two alleles partially overlap , and that the overlap involves elements within the β-hairpin . The observation that a large number of residues outside of the β-hairpin exclusively affect rhTrim5αTFP without altering rhTrim5αQ sensitivity raises the possibility that rhTrim5αTFP either has a larger footprint on the CA surface than rhTrim5αQ , or that it has the capacity to target more than one determinant in CA . Most notably , there were no mutations that affected only the rhTrim5αQ allele ( that is , none of the mutations tested caused gain-of-sensitivity to rhTrim5αQ but not to rhTrim5αTFP ) . This trend was mirrored among the 14 other viruses tested , including both naturally occurring viruses and chimeric viruses generated for this study ( Figures 1 and 3 ) . To provide a relevant structural context for evaluating the mutagenesis results , we determined the structure of the SIVmac239 CA N-terminal domain ( Figures 4A , S4 , S5 and Table S1 ) . The SIVmac239 CA N-terminal domain was very similar to reported structures of HIV-1 ( PDB: 2X2D ) ( RMSD at Cα positions: 2 . 29 Å ) and HIV-2 ( PDB: 2WLV ) ( RMSD at Cα positions: 1 . 42 Å ) ( calculations used SuperPose [76] ) . In particular , the five α-helices of the SIVmac239 N-terminal domain core did not deviate from those of HIV-1 or HIV-2 , consistent with the observation that the SIV-HIVinterior chimera remained largely resistant to restriction ( Figure 3C ) . Since the amino acids governing rhTrim5α restriction mapped to the CA surface , we were particularly interested in structural differences between SIVmac239 and HIV-1 in the β-hairpin , 4–5 loop and helix 6 . We compared the SIVmac239 CA N-terminal domain structure to all of the previously reported wild type HIV-1 and HIV-2 CA N-terminal domain structures in which the surface features were properly folded ( Figure 4B and Figure S5 ) . This dataset includes structures of CA monomers , CA monomers from cyclophilin A bound HIV-1 CAs , HIV-1 hexamers and HIV-1 pentamers . From this analysis , we found a clear distinction between the HIV-1 structures and those of the more closely related HIV-2 and SIVmac239 . Specifically , the 4–5 loops and β-hairpins formed two clusters; one composed of HIV-1 structures , and the other composed of SIVmac239 and HIV-2 structures . Measurements between the HIV-1 Cα of Gly94 or Gln95 and the corresponding Gly91 and Gln92 of SIVmac239 indicate that these two groups are separated by 3 . 3–11 Å in the structural alignment . Similarly , measurements between the Cα of HIV-1 Gly8 and the homologous SIVmac239/HIV-2 Gly7 show the two groups are separated by 4–8 . 5 Å in the structural alignment ( Figure 4B ) . These CA structural differences may help to explain the observed changes in restriction between the reciprocal SIV-HIVsurface and HIV-SIVsurface chimeras ( Figures 3D and 3E ) . To determine the spatial arrangement of the single amino acid substitutions associated with rhTrim5α restriction , we mapped the restriction data for rhTrim5αQ and rhTrim5αTFP onto the structure of the SIVmac239 N-terminal domain ( Figures 4C and 4D respectively ) as well as the structure of the HIV-1 CA hexamer ( Figure S6 ) . The two individual point mutations associated with rhTrim5αQ restriction were confined to the β-hairpin and were within 10 Å of each other . This differed from rhTrim5αTFP , which in addition to being affected by the same two sites in the β-hairpin , also recognized amino acid substitutions outside the β-hairpin , spanning approximately 30 Å of the CA surface . In contrast to rhTrim5αQ , we found that rhTrim5αTFP restricts at least three phylogenetically distinct primate lentiviruses: HIV-1 , SIVagmTan , and SIVsm ( Figure 1 ) . While single amino acid substitutions affecting rhTrim5αQ were confined to the β-hairpin , substitutions that increased sensitivity to rhTrim5αTFP were spread across the N-terminal domain surface ( Figures 4C and 4D ) . Based on these two observations , we hypothesized that rhTrim5αTFP may have evolved to target a conserved element ( s ) unique to the primate lentivirus CA N-terminal domain . To identify uncharacterized sites of primate lentivirus conservation , we generated an alignment of CA N-terminal domains using one representative virus from eleven different primate lentivirus lineages ( Figure S7 ) . We then scored the number of unique amino acids found at each position , and mapped the results onto the SIVmac239 structure ( Figure 5A ) . Despite significant sequence diversity among primate lentiviruses , we found a cluster of conserved residues on the CA surface . This site overlapped with the structurally conserved C-terminus of the 4–5 loop , and helices 5 and 6 . In SIVmac239 , this patch is composed of residues Lue93 , Arg94 , Pro96 , Gly98 , Asp100 , Ile101 , Ala102 , Gly103 , Thr105 , Ser106 , Ser107 , Glu110 , Gln112 and Trp114 ( Figures 5 , S4 , S5 , and S7 ) . This patch of conservation extends into a larger site of conservation formed by α-helices 3 , 4 and 5 . This site of conservation has recently been identified as the binding site for nuclear import factor CPSF6 [18] . Mutations that specifically increased sensitivity of SIVmac239 to rhTrim5αTFP include S100R , V111L , D112Q and Q116G , which ring the boundaries of this patch , and Q86V , P87H , A89G , Δ91I , Q93P and L96M which are in the 4–5 loop just above the patch ( Table 1 , Figure 5 and Figure S5 ) . In the immediate vicinity of the surface exposed conserved patch there were three observed trends for amino acid substitutions that influenced rhTrim5αTFP restriction: 1 ) mutations in the variable regions of the 4–5 loop , 2 ) amino acid differences at the periphery of the surface patch , and 3 ) amino acid differences extending into the surface patch . There were five amino acid substitutions within the highly variable regions of the 4–5 loop that had an impact on rhTrim5αTFP restriction . The SIVmac239 4–5 loop , like that of HIV-2 , is positioned further over the conserved surface patch than that of most HIV-1 loops . ( Figures 4B and 5B ) . It has been documented that amino acid substitutions can alter the conformation or the dynamics of the 4–5 loop [77] , [78] . It is therefore possible that Q86V , P87H , A89G , Δ91I and Q93P may alter the conformation or dynamics of the 4–5 loop in such a way as to enhance rhTrim5α recognition of the conserved surface patch . Structurally , the surface patch was conserved across SIVmac239 , HIV-1 and HIV-2 . The C-terminus or the 4–5 loop , helix 5 and helix 6 were in very close agreement with the structures of HIV-1 and HIV-2 , indicative of strong selection to preserve the overall architecture and amino acid composition of this site . Rather than changes to the structure or sequence of the patch , a majority of substitutions that altered rhTrim5αTFP sensitivity were found at its periphery . For example , we found that altering Ser97 in SIVmac239 to the corresponding HIV-1 Arg had the largest effect of any single substitution tested . An Arg at this position is found in an overwhelming majority of reported SIVsm sequences , and importantly , the Arg to Ser mutation was found to be a critical adaptive change acquired by SIVsm to evade rhTrim5αTFP-mediated restriction in vivo [31] . In HIV-1 and HIV-2 an Arg at this position contributes to a hydrogen bond bridging the base of the 4–5 loop . In SIVmac239 the corresponding Ser97 does not participate in a similar contact , but rather , it appears to engage in additional contacts within helix 5 which are not observed in HIV-1 or HIV-2 . SIVmac239 Gln109 and HIV-1 Asp112 are oriented similarly , however the presence of an acidic group would alter the chemical environment at the periphery of the patch ( Figure 5B ) . There was no obvious difference to explain why the V111L mutant in helix-6 was six-fold more sensitive to restriction than parental SIVmac239 . Perhaps slight differences between the side-chains of these residues can impact rhTrim5αTFP restriction . Two substitutions that were associated with increased rhTrim5αTFP sensitivity extend into the conserved surface patch itself . We found that substituting the Leu at position 93 ( which sits over the surface patch ) for the less-bulky Met residue resulted in a 7-fold gain in sensitivity to rhTrim5αTFP ( Figure 5B and Table 1 ) . Notably , Leu93/Met96 cover Trp114 and Arg94 , both of which are absolutely conserved among primate lentiviruses . Finally , SIVmac239 residue Gln113 reaches deeper into the patch than the corresponding Gly116 in HIV-1nl4 . 3 ( Figure 5B ) . Together , mutagenesis and structural data suggests that rhTrim5αTFP targets a surface-exposed patch of CA that is conserved in both structure and sequence across primate lentiviruses . Furthermore , differences between SIVmac239 and HIV-1 at the periphery of this patch account for their differential sensitivity to rhTrim5αTFP . At the same time , Trim5αTFP and Trim5αQ are both affected by changes in the β-hairpin , suggesting that restriction by both alleles involves recognition of this conserved feature of retroviral CAs . To reconstruct the evolutionary origins of the Q/TFP polymorphism , we analyzed multiple primate Trim5α sequences . We found that Gln341 in rhTrim5α is present at the homologous location in Trim5α of hominoids ( Homo sapiens and Pan troglodytes ) , colobines ( C . guereza and P . nemaeus ) and macaques ( M . mulatta and M . fasicularis ) ( Figure 6 ) . In contrast , the insertion is found only in Papionins , including sooty mangabeys ( Cercocebus atys ) , baboons ( P . anubis ) , geladas ( T . gelada ) , mandrills ( M . sphinx ) , Barbary macaques ( M . sylvanus ) , rhesus macaques ( M . mulatta ) and crab-eating macaques ( M . fasicularis ) . Therefore , the insertion most likely originated in a common ancestor of the Papionini . Strikingly , a 60-nucleotide insertion/duplication at an identical position is found in Trim5 of cercopithecins ( E . patas and other Cercopithecus species . ) . We therefore cannot rule out an earlier origin of the insertion in a common ancestor of the Cercopithecini and Papionini . Together , these observations give a range of insertion times between 9 . 8 and 11 . 6 million years ago ( MYA ) ( Figure 6 ) [79] . Thus , Gln341 is the ancestral state at this position , and TFP is the evolutionarily derived state – consistent with our hypothesis that rhTrim5αTFP alleles may be the result of selection to recognize the CA of primate lentiviruses . We also noted considerable variation in the first codon of the inserted element itself , finding ( in addition to TFP ) orthologs encoding SFP , MFP and LFP among extant species ( Figure 6 ) . To ask whether this variation is consistent with continued positive selection since the time of insertion , we calculated dN/dS for each codon in the PRYSPRY domain using an alignment representing sixteen species of old world primate , including 4 species for which multiple haplotypes are available ( M . mulatta , M . sylvanus , P . anubis and C . atys ) . We identified five codons in the PRYSPRY ( 332 , 334 , 337 , 339 and 341 ) with high posterior probabilities of positive selection , including two in the 6 b . p . insertion itself ( 339 and 341 ) , a pattern consistent with sequences evolving under continuous or repeated cycles of positive selection .
Rhesus macaques have three functionally distinct Trim5 alleles , rhTrim5αTFP , rhTrim5αQ , and rhTrim5CypA [53] , [54] , [55] , [58] , [59] , [60] . Of these , the structural basis for recognition of CA by rhTrim5Cyp is best understood , and is attributed to interactions between the CypA domain and the 4–5 loop [65] , [66] . In contrast , CA recognition by C-terminal PRYSPRY domains , such as those found in rhTrim5αTFP and rhTrim5αQ , is not well understood . Using genetic , mutagenic , and structural approaches we found evidence that restriction by rhTrim5α proteins involves at least two structurally conserved elements of the primate lentivirus CA N-terminal domain . There are four possible phenotypes for viruses that encounter rhTrim5αTFP and rhTrim5αQ alleles: resistance to both , sensitivity to both , and sensitivity to one or the other but not both . We observed only three of the four possibilities: resistance to both ( SIVmac239 ) , sensitivity to both ( HIV-1nl4 . 3 ) , and sensitivity to rhTrim5αTFP but resistance to rhTrim5αQ ( SIVagmTAN , SIVsmE04 , SIVsmE543 and HIV-2Rod ) ( Figure 1 ) . We did not observe the converse , resistance to rhTrim5αTFP combined with sensitivity to rhTrim5αQ . Moreover , none of the 34 chimeric viruses assayed displayed a rhTrim5αTFP-res/rhTrim5αQ-sens phenotype , and there are no reports of other retroviruses displaying a rhTrim5αTFP-res/rhTrim5αQ-sens phenotype . In fact , the only mutations in SIVmac239 that resulted in sensitivity to rhTrim5αQ also resulted in sensitivity to rhTrim5αTFP ( Figures 3 , S2 and Table 1 ) . The substitutions that increased sensitivity to both alleles map to the β-hairpin of CA . Structurally , the β-hairpin is the most conserved retroviral surface feature and is present in structures from five different genera [22] , [23] , [24] , [25] , [26] , [27] . Thus , it appears that the β-hairpin is a retrovirus-associated molecular pattern by which Trim5α evolved to “recognize” retroviruses . In support of these hypotheses , we note that experimental evolution of a rhTrim5αTFP-resistant N-MLV in cell-culture selected for a single change in the β-hairpin of the MLV capsid [30] . When we superimposed the MLV and lentiviral CA structures , the identified resistance mutation in MLV overlaps with Y9 , a residue we identified in the SIVmac239 β-hairpin that modulates recognition by rhTrim5αTFP ( Figure S8 ) . In addition to substitutions in the β-hairpin that increased sensitivity to both rhTrim5αQ and rhTrim5αTFP , there were twelve additional mutations specifically associated with rhTrim5αTFP restriction ( Table 1 ) . We interpret this to mean that the rhTrim5αTFP allele has retained the CA-recognition capacity of rhTrim5αQ , but has evolved to interact with an additional target or targets in the lentiviral CA . These mutations map to surface features that distinguish primate lentivirus CAs from other retroviral CAs . Specifically , these substitutions ring a spatially clustered group of amino acids that are conserved across primate lentiviruses , altering this site at its periphery . Interestingly , these mutations also overlap the binding sites of lentivirus-specific cellular cofactors , including CypA , NUP358 and CPSF6; notably , when these factors are fused to a Trim5 RBCC , the resulting fusion proteins function as restriction factors [18] , [65] , [66] , [80] , [81] . Primate lentiviruses have extended 4–5 loops that productively interact with at least two cellular cyclophilins , CypA and the CypA domain of a nuclear import factor , NUP358 [16] , [17] , [82] . In nature , these interactions have been independently exploited at least four times during primate evolution in the form of Trim5-CypA fusion proteins , two of which have been maintained in modern day lineages of owl monkeys and macaques [54] , [55] , [58] , [59] , [60] , [83] , [84] , [85] . SIVmac239 residue Ala86 corresponds to Gly89 in the HIV-1 CypA binding motif , while SIVmac239 Gln88 and Gln89 are previously identified sites of an adaptive change permitting SIVmac to resist rhTrim5CypA restriction [16] , [31] . We demonstrate that both of these sites influence rhTrim5αTFP restriction ( Table 1 ) . Resistance mutations to both rhTrim5CypA and rhTrim5αTFP may explain why SIVmac239 does not utilize Nup358 , which is required by other primate lentivirusess for efficient nuclear import and optimal target site integration [17] . The conserved surface patch is an extension of the CPSF6 binding site , which is conserved among primate lentiviruses [18] . Our data suggest that this site is targeted by the rhTrim5αTFP PRYSPRY domain ( Figure 5 ) . We therefore propose that the targeting of this site is analogous to exploitation of the CypA binding site in the 4–5loop by rhTrim5CypA , since rhTrim5αTFP also exploits a critical , conserved CA interface that is necessary for its interaction with a host co-factor that facilitates lentiviral replication . Recent structural determination of the rhesusTrim5α PRYSPRY domain shows the four discrete variable regions are arranged on the surface of a β-sandwich core [71] , [72] . Ohkura et al . reported that the variable regions may make independent contributions to CA recognition [86] . Thus , differences in targeting by the rhTrim5αQ and rhTrim5αTFP proteins may reflect contributions from different regions of the PRYSPRY domain . For example , the TFP insertion in variable region 1 ( V1 ) may directly confer specificity for the conserved face of lentiviral CAs , whereas the interactions of both rhTrim5αTFP and rhTrim5αQ with the β-hairpin may involve contributions from one or more of the other variable loops . The original insertion in V1 that gave rise to rhTrim5TFP in modern macaques arose after the Cercopithecinae-Colobinae split , but prior to divergence of the Macaca and Papio lineages , providing an estimate for the time of insertion between 9 . 8 to 11 . 6 million years ago [79] . In contrast , the Trim5CypA allele has only been found in Asian macaques , but not in Barbary macaques or any other old world primates [54] , [55] , [58] , [59] , [60] , [87] , and may therefore have arisen less than 5–6 million years ago , after the lineage leading to Asian macaques ( Macaca sp . ) diverged from the African lineages [79] . These dates , and the observation that rhTrim5αTFP and rhTrim5CypA target lentiviral-specific features of CA , constitute indirect but compelling evidence that viruses related to modern primate lentiviruses were infecting ancestral primates as far back as 12 million years ago , driving selection of Trim5 variants with enhanced capacity to restrict lentiviral replication . Recently , similar conclusions were independently obtained from a study of APOBEC3G variation in Old World monkeys [88] . Endogenous lentiviral sequences found in the genomes of European brown rabbits [89] , Malagasey lemurs [90] and weasels [91] , [92] support the conclusion that lentiviruses were extant at this time , and structural studies indicate that the CA proteins of at least two of these ( RELIK and pSIVgml ) were very similar to modern lentiviruses [93] . The natural history of African primate lentiviruses , and the species that harbor them , suggests lentiviruses were a driving force for the selection and maintenance of TFP-like Trim5α alleles during the last 12 million years . Based on these observations , we propose an evolutionary model in which different regions of the PRYSPRY can evolve independently to recognize different features of retroviral CAs ( Figure 7 ) . β-hairpin recognition was conserved between the ancestral Trim5αQ allele and the evolutionary derived rhTrim5αTFP allele . Therefore , it is likely that the region encompassing the Q/TFP polymorphism in variable loop 1 ( V1 ) does not contribute to β-hairpin recognition . Instead , this region may be free to make additional contacts with the CA . Due to its dynamic and unstructured nature , V1 may readily tolerate mutations and insertions ( such as the 6-nucleotide insertion ) affording the molecule enhanced evolutionary plasticity [71] , [72] . The SIV-HIVh6 mutant was restricted by rhTrim5αQ , implying that the rhTrim5αQ PRYSPRY could recognize one edge of the conserved surface patch ( Figure 7A ) . The modern day presence of Trim5α orthologs with the 6-nucleotide insertion indicate that the insertion event conferred a selective advantage ( likely against primate lentiviruses ) . The simplest explanation is that the insertion allowed V1 to make additional contacts or possibly even extend beyond helix 6 and further into the conserved surface patch . We have shown that the first and last positions of the rhesus TFP polymorphism have been under positive selection , indicative of continued refinement of its ability to recognize the conserved surface patch over evolutionary time . This model is likely a snapshot of a larger evolutionary scenario in which an ancestral PRYSPRY domain may first have acquired the ability to recognize a highly conserved retroviral CA element ( such as the β-hairpin ) . On top of this intrinsic recognition ability , modularity of Trim5α proteins allowed them to explore additional targets on the CA surface in response to pressures from specific viruses or viral families , perhaps by taking advantage of inherent plasticity within the variable loops ( Figure 7B ) . Such a process , played out over the course of tens of millions of years of evolution , could help to explain both the collective breadth and species-specificity of modern primate Trim5α proteins .
Crandell-Rees Feline Kidney ( CRFK ) cells and Human Embryonic Kidney 293T/17 ( HEK293T/17 ) cells were obtained from American Type Culture Collection ( Manassas , VA ) and grown in DMEM/10% FBS . CRFK cell lines stably expressing N-terminally HA-tagged Trim5 orthologs were previously described [31] . Stable cell lines were maintained in DMEM/10% FBS supplemented with 0 . 5 mg/ml G418 . All cultured cells were maintained at 37°C with 5% CO2 . The SIVmac239-based retroviral vector pV1EGFP ( gift from Hung Fan , University of California , Irvine , CA ) was previously modified to contain a functional gag-pol ORF [31] . All single cycle chimeric viruses are in either the pV1EGFP-SIV or HIV-1nl4 . 3 pNL43DenvGFP background as indicated . To facilitate the rapid production of chimeric viruses , a capsid and gag shuttle vector system was engineered through DNA synthesis by GENEART ( Regensburg , Germany ) . Silent nucleotide changes within the capsid allowed for chimerization between capsids from either virus ( Figure S9 ) . All chimeric capsids with the exception of single amino acid point mutants were produced through gene synthesis by GENEART ( Regensburg , Germany ) and were then cloned into the proper viruses using our shuttle vector system . Single amino acid substitutions on the SIVmac239 surface were made using site directed mutagenesis . The S100R mutant was described in a previous publication [31] . A CFP expressing HIV-1 derived lentiviral vector was created for abrogation assays . A CFP gene was introduced into using AgeI an XhoI sites into pNL-EGFP/CMV-WPREDU3 [6] , a vector based on pNL-EGFP/CMV ( which features the WPRE element for increased mRNA stability and a deleted U3 region for added safety ) . All single-cycle viruses were produced in HEK293T/17 cells by cotransfection of the appropriate viral plasmid and pVSV-G ( Clontech Laboratories , Mountain View , CA ) , using the GenJet transfection system ( SignaGen; Ijamsville , MD ) . Culture supernatants containing the single-cycle , GFP/EGFP expressing , VSV-G-pseudotyped virions were titered on untransfected CRFK cells; supernatant volumes resulting in approximately 25% GFP/EGFP+ CRFK cells were used for infectivity assays on the cell lines expressing the indicated Trim5α . Information regarding viral infectivity appears in Figure S2 . The CFP expressing HIV-1 lentiviral vector was made from 293T transfection of a 3∶2∶1 plasmid ratio of pNL-ECFP/CMV-WPREDU3 [6] , pCD/NL-BH*DDD [94] and pVSV-G ( Clontech Laboratories , Mountain View , CA ) ( pNL-ECFP/CMV-WPREDU3 and pCD/NL-BH*DDD were kindly provided by Dr . Jakob Reiser , Louisiana State University Health Sciences Center ) . Stably expressing Trim5 CRFK cells were seeded at a concentration of 5×104 cells per well in 12-well-plates and infected with the appropriate amount of VSV-G pseudotyped , single-cycle , GFP/EGFP expressing viruses . All infections were done in triplicate . After 2 days , expression of GFP/EGFP was analyzed by fluorescence-activated cell sorting ( FACS ) performed on a FACSCaliburTM flow cytometer ( BD , Franklin Lakes , NJ ) , and data were analyzed using FlowJo software ( Tree Star , Inc . , Ashland , OR ) . Viral titers were determined using the appropriate p24 ( HIV-1 ) or p27 ( SIVmac ) antigen capture kit from Advanced Bioscience Labs ( Rockville , MD ) . Information regarding viral titers appears in Figure S2 . A codon optimized N-terminal fragment of the SIVmac239 capsid corresponding to residues 1–144 was synthesized with a C-terminal factor Xa cleavage site and 6x-His Tag by GENEART ( Regensburg , Germany ) . Using engineered XbaI and XhoI sites the N-terminal fragment was cloned into pET303 ( Invitrogen ) and expressed from BL21 ( DE3 ) E . coli cells . The SIVmac239 capsid was purified by Ni-NTA agarose ( Qiagen ) followed by gel filtration chromatography on a Superdex 200 column ( GE Healthcare ) . The C-terminal 6x-His tag was removed by treatment with factor Xa ( New England Biolabs ) , re-purified by orthogonal Ni-NTA agarose chromatography and gel filtration chromatography . Purified SIVmac239 capsid protein was crystallized by the hanging drop method over a reservoir solution containing 10% ( w/v ) PEG 2000 MME , 10 mM nickel chloride and 100 mM TRIS , pH 8 . 5 at 24 C . Crystals were harvested from 0 . 2 ul drops and cryoprotected by addition of 10–15% PEG 400 or glycerol to the reservoir solution , then flash cooled in liquid nitrogen . Protein concentration ranged from 10–15 mg/ml . We recorded diffraction data at beamline 24-ID-E at the Advanced Photon Source . Data sets from individual crystals were processed with HKL2000 [95] . Molecular replacement ( MR ) was carried out with PHASER [96] using the HIV-2 capsid as an initial search model . One molecule of SIVmac239 completes the asymmetric unit . Refinement was carried out using PHENIX [97] , [98] and all model modifications were done in COOT [99] . Initial rigid body refinement followed by simulated annealing and positional refinement was done . The 4–5 loop ( residues 83–97 ) was initially removed from the model and rebuilt into modest density . There was no clear density for residue proline 88 and it was omitted from the structure . The model was further refined by additional cycles of positional and B-factor refinement , followed by TLS . The quality of the data was assessed using MolProbity [100] . Data collection and refinement statistics can be found in Table S1 . Coordinates and diffraction data have been submitted to the PDB , accession number: PDB:4HTW . Trim5α sequences were identified by BLAST search of the non-redundant nucleotide database , aligned in Geneious Pro v . 5 . 5 . 4 using the Translation Align option . The alignment was adjusted manually , converted back to nucleotide and the best-fit tree identified with MrBayes . dN/dS analysis was performed with CODEML in v4 . 4 of PAML ( Table S2 ) [101] . | TRIM5α is an intrinsic immunity protein that blocks retrovirus infection through a specific interaction with the viral capsid . Uniquely among primates , rhesus macaques harbor three functionally distinct kinds of Trim5 alleles: rhTrim5αTFP , rhTrim5αQ and rhTrim5CypA . SIVmac239 , a simian immunodeficiency virus that causes AIDS in rhesus macaques , is resistant to all three , whereas its relative , the human AIDS virus HIV-1 , is inhibited by rhTrim5αTFP and rhTrim5αQ alleles . We exploited this difference between these two retroviruses to figure out how Trim5α proteins recognize viral capsids . By combining mutagenesis , structural biology and evolutionary data we determined that both rhTrim5αTFP and rhTrim5αQ recognize a conserved structure common to all retroviral capsids . However , we also found evidence suggesting that rhTrim5αTFP evolved to recognize an additional target that is specifically conserved among primate immunodeficiency viruses . Molecular evolutionary analysis indicates that this expanded function appeared in a common ancestor of modern African monkeys sometime between 9–12 million years ago , and that it thereafter continued to be modified by strong evolutionary pressure . Our results provide insight into the evolutionary flexibility of Trim5α-capsid interactions , and support the notion that viruses related to modern HIV and SIV have been present in Africa for millions of years . | [
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] | 2013 | Gain-of-Sensitivity Mutations in a Trim5-Resistant Primary Isolate of Pathogenic SIV Identify Two Independent Conserved Determinants of Trim5α Specificity |
An animal’s ability to survive depends on its sensory systems being able to adapt to a wide range of environmental conditions , by maximizing the information extracted and reducing the noise transmitted . The visual system does this by adapting to luminance and contrast . While luminance adaptation can begin at the retinal photoreceptors , contrast adaptation has been shown to start at later stages in the retina . Photoreceptors adapt to changes in luminance over multiple time scales ranging from tens of milliseconds to minutes , with the adaptive changes arising from processes within the phototransduction cascade . Here we show a new form of adaptation in cones that is independent of the phototransduction process . Rather , it is mediated by voltage-gated ion channels in the cone membrane and acts by changing the frequency response of cones such that their responses speed up as the membrane potential modulation depth increases and slow down as the membrane potential modulation depth decreases . This mechanism is effectively activated by high-contrast stimuli dominated by low frequencies such as natural stimuli . However , the more generally used Gaussian white noise stimuli were not effective since they did not modulate the cone membrane potential to the same extent . This new adaptive process had a time constant of less than a second . A critical component of the underlying mechanism is the hyperpolarization-activated current , Ih , as pharmacologically blocking it prevented the long- and mid- wavelength sensitive cone photoreceptors ( L- and M-cones ) from adapting . Consistent with this , short- wavelength sensitive cone photoreceptors ( S-cones ) did not show the adaptive response , and we found they also lacked a prominent Ih . The adaptive filtering mechanism identified here improves the information flow by removing higher-frequency noise during lower signal-to-noise ratio conditions , as occurs when contrast levels are low . Although this new adaptive mechanism can be driven by contrast , it is not a contrast adaptation mechanism in its strictest sense , as will be argued in the Discussion .
A natural environment is an ever-changing sensory landscape . Sensory systems adapt to these changes , increasing an animal’s ability to extract important information and survive under a wide range of conditions . In the natural world , the mean luminance and variations around the mean luminance—i . e . , contrast—are poorly correlated [1] . This relative independence of contrast and luminance is reflected in the functional organization of the visual system , as retinal neurons adapt independently to these two basic features of natural scenes [1] . In vertebrates , most cone luminance adaptation takes place in the phototransduction cascade [2] . By adapting to the luminance level , photoreceptors primarily encode contrast . Although contrast levels can vary widely between different natural scenes and even between locations within a natural scene , they have strong regularities . In natural scenes , the power spectrum declines as the frequency increases in a 1/fβ fashion ( 0 . 7 < β < 3 ) [3 , 4] , reflecting the preponderance of larger and slower-moving objects over smaller and faster ones . Consequently , as noise in the responses of cones declines in power with frequency at a slower rate ( ~0 . 3 < β < 0 . 4 ) [5–8] , the signal-to-noise ratio ( SNR ) of a cone’s response will decrease with increasing frequency . At some point , the signal becomes indistinguishable from the noise , at which point mostly noise is transmitted to the rest of the visual system . This “threshold frequency” will depend on the contrast level . In low-contrast conditions , it will occur at a lower frequency than in high-contrast conditions . Cone noise largely originates from the outer segment and has intrinsic and extrinsic sources . One primary intrinsic source , the gating transition in cyclic guanosine monophosphate ( cGMP ) -gated channels , generates noise ranging from low frequencies to those well beyond the flicker fusion frequency . On the other hand , the photoreceptor inner segment conductances contribute little cone response noise [8] . In principle , the inner segment membrane acts as a band-pass filter , thereby reducing the amount of higher-frequency outer-segment noise transmitted by cones . However , to do this optimally , the filter should adapt such that its cutoff frequency remains close to the threshold frequency in all stimulus conditions . If this is not the case , information will be lost or additional noise will enter the system . Adaptive filtering by photoreceptors would maximize the amount of sensory information extracted from a natural scene and reduce the amount of noise transmitted under the various contrast conditions encountered . Thus , the entire visual system would benefit if photoreceptors could act as activity-dependent adaptive filters . In this paper , we show that long- and mid-wavelength sensitive cones ( L- and M-cones ) do indeed act as activity-dependent adaptive filters . Cone frequency responses change such that L- and M-cones become slower under stimuli that induce minor membrane potential deflections as happens in low-contrast conditions . When the induced voltage deflections are larger such as in high-contrast conditions , L- and M-cones responses speed up . This form of adaptation has a time constant of less than a second , and the cone hyperpolarization-activated current ( Ih ) is a critical component of the underlying mechanism . Consistent with this result is the finding that short-wavelength sensitive cones ( S-cones ) have a significantly smaller Ih compared to L- and M-cones and contrast did not affect their frequency responses .
To study the behavior of cones under naturalistic stimulus conditions , we recorded the voltage light responses of goldfish cones to a natural time series of chromatic intensities ( NTSCI ) . Cones were stimulated with a 20-μm spot of light , which was modulated by a 40-s-long NTSCI segment obtained from van Hateren's natural-stimulus collection [9] . The NTSCI segment was preceded and followed by a 4 . 5-s period of white light with the same mean luminance . We first calculated the “cone-specific” stimuli the NTSCI presented to the L-or M-cones in photons/μm2/s absorbed by the specific cone types ( see Fig 1 ) by using the measured spectral output of the stimulus-generating light-emitting diodes ( LEDs ) and the spectral sensitivity of goldfish cones [10] . Fig 1A shows the frequency distributions of both the L-and the M-cone-specific stimuli , which are approximately equal . The mean luminance was approximately the same ( 0 . 04 log unit difference ) , whereas the contrast experienced by the M-cones was about 15% lower than that for the L-cones . The NTSCI was repeated multiple times for as long as the light response of the cones remained stable ( 4 . 8 ± 0 . 25 times; n = 8 ) . Fig 1Bi shows a 1 . 5-s section of the two cone-specific NTSCI stimuli , and Fig 1Bii the corresponding responses for an L- ( red traces ) and an M- ( green traces ) cone . Although the L-and M-cone responses were rather similar , scrutinizing the traces revealed significant differences in their response kinetics . For example , in the section shown in Fig 1Bii , the M-cone response clearly lags behind the L-cone response and did not follow the rapidly changing aspects of the stimulus with the same fidelity . To investigate these differences further , we calculated the stimulus-response transfer functions for the L- and M- cones for the 40-s NTSCI period . When expressed as impulse-response functions ( Fig 1C ) , the M-cone ( n = 4 ) function peaked 14 . 3 ± 4 . 6 ms later ( p = 0 . 021 ) , and its full width at half maximum ( FWHM ) was 38 . 2 ± 8 . 06 ms ( p = 0 . 0032 ) wider than for L-cones ( n = 4 ) . These differences indicate that the average M-cone response to the NTSCI was slower than that of the L-cones . These characteristics are also apparent when the transfer functions are expressed as frequency response curves ( Fig 1Di; non-normalized data in S1A Fig ) . The larger lower-frequency content of the M-cone response is demonstrated by the higher gain levels at all frequencies lower than 2 Hz , compared to that of the L-cones ( 0 . 002 < p < 0 . 047 in all cases ) . Similarly , the faster response kinetics of the L-cones are demonstrated by the slower rate of decline of gain for higher frequencies ( Fig 1Dii , −1 . 4 ± 0 . 18 dB/Hz ) compared to the M-cones ( −1 . 9 ± 0 . 06 dB/Hz; p = 0 . 032 ) . The faster response kinetics of the L-cones are also indicated by their broader bandwidths , which is conventionally defined as the frequency at which the gain had reduced by 3dB ( f3dB ) . The L-cone f3dB ( 6 . 3 ± 1 . 02 Hz ) was nearly twice that of M-cones ( 3 . 2 ± 0 . 37 Hz , p = 0 . 032 ) . To obtain a better intuition about the size of the effect , we estimated the integration time by dividing the integral of the initial hyperpolarizing lobe of the impulse-response function by its maximum amplitude [11] and found that the integration time of the M-cones was 75% longer than for the L-cones ( 91 . 6 ± 7 . 03 ms versus 51 . 6 ± 5 . 46 ms; p = 0 . 0041 ) . We asked whether this difference between L- and M-cones kinetics is an intrinsic or a stimulus-induced phenomenon . To address this issue , we calculated the impulse-response functions for M- and L-cones for 1-s periods at random time points throughout the NTSCI and used the time to peak ( T2P ) as an estimate of the cone response kinetics . T2P was constantly changing for both cone types throughout the NTSCI ( Fig 1E ) . Most of the time , the L-cones were faster than the M-cones , but on some occasions , the M-cones were faster than the L-cones ( e . g . , arrowheads , Fig 1E ) . This suggests that the response kinetics of the cones were reacting to features of the NTSCI and did not represent an intrinsic difference between the L- and M-cones . Next , we assessed whether the change in kinetics of cones depended on the levels of luminance or contrast . To do this , we calculated the joint and conditional probabilities of the T2P with either the “effective” luminance or “effective” contrast level ( see Materials and Methods and S6 Fig for “effective” level calculations ) . Fig 2A shows the relationship between the T2P and luminance for an L-cone , using 1-s periods at random time points . The random time points and T2P data are the same as shown in Fig 1E . T2P was largely statistically independent of luminance in both the joint ( Fig 2Ai ) and conditional probability ( Fig 2Aii ) distributions . On the other hand , T2P did covary with contrast as is demonstrated by the joint and conditional probabilities ( Fig 2B ) . When contrast levels were higher , the T2P occurred earlier than when contrast was lower . This pattern was consistent for all four L- and all four M- cones we recorded from ( see S1B and S1C Fig for the M-cone shown in Fig 1E ) . These results suggest that under naturalistic stimulus conditions M- and L-cone responses have faster kinetics during higher-contrast periods of the stimulus than when contrast levels are lower . The results shown in Fig 1 and Fig 2 demonstrate a novel and unexpected form of adaptation of cones: their response kinetics were faster during stimuli epochs with higher contrast levels than when contrast levels were lower . This result is unexpected , as previously , studies using white noise stimuli have indicated that cone response kinetics are unaffected by the level of contrast [12 , 13] . So far , we analyzed cone responses to the NTSCI . The advantages of such a stimulus are that it resembles the natural condition , where large fluctuations in light intensity can occur [3] . On the other hand , such a stimulus is rather erratic and not well suited for systems analysis . In the following sections , we study cone adaptation under more controlled conditions using artificial stimuli that are better suited for linear systems analysis . Stimuli were generated by summing a range of sinusoids ( sum of sinusoids , SoS ) with different frequencies , equal amplitude , and randomized phase ( see Materials and Methods ) , qualitatively similar to those previously used by Victor and colleagues [14] . Unlike classic stimuli such as white noise , these constructed stimuli retain a key feature typifying natural stimuli . By spacing the sine wave frequencies at approximately equal intervals on a log10 scale , much of the power of the stimuli came from the lower frequencies , similar to natural stimuli ( S2 Fig ) . Such stimuli are well suited for standard fast Fourier transform techniques , as the transfer functions derived from the cone responses to the SoS stimuli predicted 95 ± 0 . 7% ( n = 36 ) of the cone’s light dependent structure ( see Materials and Methods ) ( S3A and S3B Fig and S1 Table ) [12] . Fig 3A shows a SoS stimulus ( upper trace ) , and the resulting cone response is depicted in the lower trace . The frequency response curves for L- and M-cone voltage light responses to the high- and low-contrast versions of this stimulus are shown in Fig 3B ( see S3C and S3D Fig for non-normalized impulse-response functions ) . Note that both stimuli had the same mean luminance . As for the NTSCI stimuli , f3dB of L- and M-cones were the highest in the high-contrast condition and the lowest in the low-contrast condition ( S2 Table ) . The slopes of the frequency response curves for L- and M-cones at higher frequencies were also shallower during high contrast than during low contrast ( S2 Table ) . In addition , f3dB and frequency response curve slopes for L-cones and M-cones did not differ from each other in either contrast condition ( S2 Table ) . These results confirm our original observations with the NTSCI; L- and M-cone frequency response characteristics are not intrinsically different but are dependent on stimulus characteristics—in this case , the level of temporal contrast present . Interestingly , the frequency response of S-cones remained the same in the two contrast conditions ( Fig 3B ) , and their f3dB was lower and the slope of their frequency response curve steeper than for L- and M-cones in any contrast condition ( S2 Table ) . Next , we asked whether the change in frequency response characteristics of L- and M-cones was reversible within the same stimulus application . Cones were presented with an SoS stimulus that switched from high to low temporal contrast and back again ( Fig 3Ci ) , and f3dB of their frequency responses were determined ( Fig 3Cii and 3Ciii ) . Since the behavior of L- and M-cones did not differ significantly , we pooled their data here and in all subsequent experiments using switching stimuli . On average , when going from high contrast to low contrast , f3dB dropped by approximately 23% ( Δf3dB = −1 . 2 ± 0 . 20 Hz , n = 11 , p = 0 . 00012 ) , and when the stimulus went from low to high contrast , it increased by approximately 22% ( Δf3dB = 1 . 1 ± 0 . 19 Hz , n = 11 , p = 0 . 00015 ) ( Fig 3Ciii ) . These changes in f3dB did not differ significantly from each other ( p = 0 . 33 ) , showing that adaptation in cones is reversible within the time course of this response . To study the time course of adaptation , cones were stimulated with the SoS contrast-switching stimulus , and f3dB values were determined in 250-ms steps using frequency responses for 4-s periods with 93 . 75% overlap . The upper panel of Fig 3D shows the measured mean ( ±SEM ) f3dB of 11 L- or M-cones as a function of time during the switch from high contrast to low contrast and back again ( blue trace ) . The curve was smoothed with an eight-point Savitzky-Golay filter ( black trace ) . However , this result does not reflect the true time course of the change in f3dB . The 4-s time windows needed to accurately determine frequency responses when using this stimulus obscure the true time course . For instance , if f3dB were to change at the same time as the contrast level switched , it would still appear as if the f3dB change developed over the following 4 s as the analysis time window slides from one condition to the other . To estimate the true time course of adaptation , we simulated the adaptational process ( S4 Fig ) . Cone filtering characteristics were systematically varied at different time points and convolved with the stimulus until we simulated a cone response that replicated the measured change in f3dB . Our best approximation of the cone mean f3dB is shown in the middle panel of Fig 3D ( red trace; smoothed with an eight-point Savitzky-Golay filter ) . The lower panel of Fig 3D shows f3dB values used to produce this simulated response . When contrast levels switch from high to low , after a short delay , f3bB steadily declines over about 1 s and then rebounds slightly over the next several seconds . When contrast switches back to high from low , after a short delay , the f3dB rapidly increases over the next second , overshooting its final value , which it returns to over the next several hundred milliseconds . This analysis indicates that L- and M-cones start adapting relatively quickly after a change in contrast occurs and that the full adaptational process takes about a second to complete . Previous studies using Gaussian white noise ( WN ) stimuli have not found adaptation in cones when contrast levels were varied [12 , 13] . We tested whether this was also true in our hands using WN stimuli that switched between high and low contrast ( Fig 4A ) . To directly compare the cone responses , we presented three cones with both the SoS ( Fig 3Ci ) and WN ( Fig 4A ) switching contrast stimuli . Both stimuli were the same in terms of their maximum frequency ( 31 . 75 Hz ) , total stimulus power , and mean “stimulus” luminance and “stimulus” contrast level ( see Materials and Methods and S6 Fig for “stimulus” contrast-level calculations ) . For these cones , under the WN condition , f3dB remained unchanged when going from high to low ( Δf3dB = 0 . 004 ± 0 . 0799 Hz , p = 0 . 97 ) contrast or from low to high ( Δf3dB = 0 . 034 ± 0 . 029 Hz , p = 0 . 36 ) contrast ( Fig 4B , open symbols ) . However , for the SoS stimulus , f3dB decreased when contrast went from high to low ( Δf3dB = 0 . 72 ± 0 . 149 Hz , p = 0 . 040 ) and increased when contrast went from low to high ( Δf3dB = 0 . 65 ± 0 . 143 Hz , p = 0 . 046 ) ( Fig 4B , closed symbols ) . What stimulus difference might account for the absence of adaptation when WN is used ? Since cones have a relatively long integration time , fast intensity variations will be averaged out . Hence , faster elements are increasingly “perceived” as a sustained light intensity with less variance until eventually , above the flicker fusion frequency , they simply appear as a sustained light stimulation . Consequently , stimuli like WN consisting largely of higher-frequency light intensity variations will have less “effective” contrast with which to drive cone responses than stimuli with a larger lower-frequency content ( also see S2A–S2D Fig ) . To test this , we compared cone responses to stimuli in which we varied the power-frequency distribution ( Fig 4C ) . Two stimuli had power-frequency distributions that declined in a 1/fβ fashion ( β1 and β1* ) and thus resembled natural scenes , and one was a WN version , as each frequency had equal power ( β0 ) ( Fig 4D ) . All stimuli were 4 s long , with a minimum and maximum frequency of 0 . 5 and 40 Hz , respectively . Both the β0 ( Fig 4C , black trace ) and β1 ( Fig 4C , blue trace ) stimuli had the same total power . For β1* ( Fig 4C , red trace ) , we increased the power at each frequency of the β1 stimulus by 4 dB to maximize the light intensity variation delivered; hence , β1* is a higher-contrast version of β1 . The cone responses differed markedly under these different conditions . Fig 4C shows that the β1 stimulus induced a larger cone response than the β0 stimulus did . Across the seven cones tested under these conditions , the range of membrane potentials during β1 was more than twice that during β0 ( 2 . 5 ± 0 . 35 mV versus 6 . 1 ± 0 . 91 mV , p = 0 . 00035 , Fig 4C and 4E ) , indicating that β1 was more effective at driving the cone response . The filtering characteristic and temporal response properties of cones also differed under these two stimulus components . Under the β1 condition , the cone f3dB increased , and the temporal integration time decreased , compared to β0 ( Fig 4E ) . These differences were even greater for five cones that received both the β0 and β1* stimuli . This suggests that the “effective” contrast “perceived” by the cones is lower in the β0 condition than in the β1 and β1* conditions . Indeed , when these stimuli are weighted by a function mimicking the temporal process of the phototransduction cascade ( see Materials and Methods and S6 Fig ) , the resulting mean “effective” contrast levels for the β1 and β1* stimuli were approximately 2 and 3 . 5 times higher than for the β0 stimulus . These results indicate that ( 1 ) stimuli with a frequency distribution resembling natural stimuli drive cones more effectively and ( 2 ) adaptation was not found when using WN stimuli because the “effective” contrast in these stimuli was too low to modulate the cone membrane potential sufficiently to drive the adaptational process ( also see S2E Fig ) . What mechanism underlies the adaptation we find ? First , we determined whether it is an intrinsic process of L- and M-cones by blocking either photoreceptor synaptic transmission with 2 mM CoCl2 or cone input to horizontal cells with 50 μM DNQX , a glutamate receptor antagonist . In both cases , adaptation was unaffected ( Fig 5Ai and 5Aii ) ( for both DNQX and Co2+: 0 . 0001 < p < 0 . 045 for f3dB changes going from high contrast to low or from low contrast to high in control and drug conditions ) . These results show that the adaptation process is cone intrinsic . If changes in the phototransduction cascade were underlying this form of cone adaptation , then it should also be present in the photocurrent . To test this , we voltage clamped cones and determined their frequency response characteristics for two contrast conditions using the SoS stimuli shown in Fig 3A . Unlike Fig 3B , the frequency response curves of both the L- and M-cones fully overlapped in the two contrast conditions , showing that adaptation was absent in this condition ( S5 Fig , S1 Table , S2 Table ) . The difference between the voltage-clamp and current-clamp experiments is exemplified in Fig 5B , where impulse-response functions of cones for both high- and low-contrast conditions under both recording configurations are compared ( see S3C and S3D Fig for un-normalized impulse-response functions ) . As expected , the voltage impulse-response function measured in current clamp of the L- and M-cones varied with contrast ( Fig 5B , left and middle panel ) . Under low contrast , the T2Ps were longer , and the FWHMs broader ( S3 Table ) . This did not happen in S-cones ( Fig 5B , right panel , S3 Table ) . In comparison , the current impulse-response functions measured under voltage-clamp conditions for all cone types were the same under the different contrast conditions ( Fig 5B , S3 Table ) . This result suggests that the adaptation depends on voltage-activated processes in the membrane of L- and M-cones and that such a component is largely missing in S-cones . Which voltage-activated membrane process is critical for the L- and M-cone adaptation ? The two most likely currents are ( 1 ) the delayed rectifying potassium current ( IK ) [15] and ( 2 ) the hyperpolarization-activated inward rectifying current ( Ih ) [16–18] . Using the SoS contrast-switching stimuli ( Fig 3Ci ) , f3bd was determined for both high and low contrast in conditions when either Ih or IK were pharmacologically blocked . In the following current-clamp experiments , current was injected in the cells to correct for drug-induced sustained changes to the light-adapted resting membrane potential . Twenty mM tetraethylammonium ( TEA ) , a blocker for IK , reduced f3dB in L- and M-cones by 54 ± 4 . 8% ( p = 0 . 015 , n = 3 ) but did not affect the adaptive changes ( Fig 5Ci , f3dB changes from high contrast to low or from low contrast to high; control , p < 0 . 007; TEA , p < 0 . 04 ) . On the other hand , 5 mM CsCl , a blocker of both the potassium current and Ih , reduced f3dB in L- and M-cones by 28 ± 3 . 1% ( p = 0 . 0010 , n = 3 ) and prevented adaptation ( Fig 5Cii , f3dB changes from high contrast to low or from low contrast to high; control , p < 0 . 009; CsCl , p > 0 . 46 ) . These results suggest that both IK and Ih are speeding up the cone responses but only Ih , and not Ik , is involved in the cone adaptation we find . To confirm the contribution of Ih in the adaptation process , we tested whether adaptation occurred when Ih was blocked by 50 μM ZD7288 , a specific Ih antagonist [18] . In this condition , neither f3dB nor the slopes of the frequency response curves in the two contrast conditions were significantly different ( Fig 5D , S2 Table ) . Combined , these experiments indicate that the mechanism underlying the M- and L-cone adaptation is strongly dependent on Ih . This makes the adaptive process we identified a “hyperpolarization-activated adaptation , ” which in turn emphasizes an essential property of the process: it is asymmetrical . The absence of adaptation in S-cones ( Fig 3B , S2 Table ) suggests that they may have no or a smaller Ih compared to L- and M-cones . We tested this next . Whole cell current-voltage ( I-V ) relations were determined when the light responses of the cones were saturated with a small spot of light and cones were clamped at −40 mV and stepped to potentials ranging from −80 to −50 mV for 2 s . L- , M- , and S-cones developed a slow increase in an inward current ( Fig 5Ei ) , a characteristic feature of Ih activation . The amplitude of Ih was determined by taking the difference between the peak of the initial current and the minimum current occurring within the next 1 s . This value was plotted as function of potential ( Fig 5Eii ) . Ih did not differ significantly between L- and M-cones ( 0 . 66 < p < 0 . 97 for all potentials ) but was significantly smaller in S-cones ( for all potentials: versus L-cones , 0 . 0019 < p < 0 . 012; versus M-cones , 0 . 00005 < p < 0 . 0014 ) . Inhibiting Ih with ZD7288 reduced Ih in L- and M-cones to the level found in S-cones ( Fig 5Eii , 0 . 43 < p < 0 . 76 for all potentials ) . These results indicate that S-cones do not adapt like L- and M-cones because they lack Ih , corroborating its importance as a critical membrane component required for the form of adaptation we find . The comparison of voltage-clamp and current-clamp data also demonstrates another important role of Ih . The current impulse-response functions of all cone types in low-contrast conditions were faster and narrower compared to the voltage impulse-response functions , and the current impulse-response functions of the different cone-types did not differ from each other ( Fig 6A , S3 Table ) . This indicates two points: ( 1 ) The phototransduction cascade of all cone types have the same kinetic properties , and ( 2 ) membrane properties of the cones , most likely the membrane capacitance , slow down the responses under current clamp . Fig 6B shows that Ih speeds up the kinetics of the cone responses . When Ih is blocked by ZD7822 , the voltage impulse-response functions of L- and M-cones became as slow as those of the S-cones ( S3 Table ) . This slowing down of the response is prominent in L- and M-cones and absent in S-cones since the latter have no Ih ( Fig 6A , S3 Table ) . This difference between the S-cone kinetics and the L- and M-cone kinetics was also evident when cones were stimulated with light flashes of 200 ms , which were 50% brighter or 50% dimmer than the mean luminance of the SoS stimuli . The responses under voltage clamp overlapped for the L- , M- , and S-cones ( Fig 6Ci ) , but under current clamp , S-cone responses were substantially slower ( Fig 6Cii ) . Combined , these results indicate that ( 1 ) the kinetics of the phototransduction cascade in L- , M- , and S-cones are equal; ( 2 ) the membrane properties of cones , most likely the membrane capacitance , slow down the kinetics of their voltage light responses considerably; and ( 3 ) in L- and M-cones , but not in S-cones , Ih speeds up the cone voltage light responses such that they approach the cone photocurrent responses . Furthermore , the presence of Ih allows the cone kinetics to be adaptive .
How might Ih change the frequency characteristics of cones in different temporal contrast conditions ? Ih is known to affect the kinetic properties of neurons [19 , 20] . Experiments in which Ih was blocked , either pharmacologically or by knocking out the hyperpolarization-activated cyclic nucleotide-gated ( HCN ) channels mediating Ih , showed that Ih makes rod light responses more transient , changes the filter characteristics of rods from low pass to band pass , and increases the cutoff frequency [18 , 21 , 22] . Similarly , increased activation of Ih moves the peak of the cones' transfer function to higher frequencies , in this way extending the operational frequency range of cones [18] . Ih can affect a neuron in various ways . Activation of Ih by hyperpolarization decreases the input resistance , depolarizes the resting membrane potential , and speeds up the kinetics of neurons [19 , 20 , 23 , 24] . Activation of Ih also leads to a reduction of low frequencies because of the channel’s slow kinetics . On the other hand , the increase in membrane conductance due to the activation of Ih leads to a decrease of the membrane time constant because the membrane capacitance can be discharged faster and thus speeds up responses [18] . These effects would lead to a reduction in gain mostly at low frequencies . However , we also find that a moderate increase in gain at high frequencies can occur ( S3C Fig ) , indicating that Ih may also modulate a mechanism for an overall gain increase . Although we have shown that the activation of Ih is essential for the form of adaptation we find for L- and M-cones , we cannot discount the possibility that other factors contribute to this overall gain increase . These additional factors might include second messenger modulation of the Ih activation potential , local changes in ion concentration near the cone membrane [25] , and indirectly activated ion channels [26] . In addition , Ih becomes faster when the membrane potential is hyperpolarized [27] . Ih seems to be essential for the fast response kinetics of cones . When Ih is not active , the impulse-response functions of cones under voltage clamp are faster than under current clamp ( S3 Table ) , indicating that the membrane capacitance of the cones slows down the voltage light responses considerably . Ih counteracts this such that the L- and M-cone responses under current clamp in high contrast are about equally fast as under voltage clamp ( Fig 6A , S3 Table ) . This does not happen in S-cones since they lack a prominent Ih ( Fig 5E ) . In addition , IK also helps to speed up the cone light response . The adaptation we have found in L- and M-cones may therefore reflect the activation of Ih . This may be a consequence of larger cone membrane potential fluctuations around the resting membrane potential in high-contrast conditions . As Ih becomes larger and activates faster at more hyperpolarizing potentials [19 , 27] , it will be larger in the high-contrast condition and smaller in the low-contrast condition . When Ih is more activated , it will speed up the light response . In low-contrast conditions , Ih will be smaller , so the voltage response will remain slow , dominated by the passive low-pass filter properties of the membrane . Once activated by hyperpolarization , Ih can remain active for hundreds of milliseconds , even if the membrane potential has since depolarized [28] . During high-contrast conditions , Ih remains activated since sufficiently hyperpolarizing events occur while Ih is still active from previous events and cone response kinetics therefore remain faster . However , the situation is different for a luminance step in which the cone experiences a sustained increase in light intensity . Here , Ih activation is transient as the additional adaptation processes of the phototransduction cascade come into play . Over time courses of hundreds of milliseconds to seconds , the phototransduction cascade becomes increasingly adapted . This depolarizes the cone membrane potential back towards its preluminance step potential [29] to a far greater extent than any depolarization resulting from Ih activation . Consequently , Ih activation will reduce , and any increase in cone kinetics that occurred at the beginning of the luminance step will disappear . Hence , the adaptive responses to dynamic and static changes in light intensity are different: high-contrast-like conditions will keep Ih activated , whereas luminance-step-like conditions will not . Is hyperpolarization-activated adaptation similar to the dynamic light adaptation proposed by Clark et al . [30] ? Their phenomenological model consists of two kernels that extend over similar time scales , but one is broader and delayed relative to the other . The two kernels combine to produce an “effective” kernel in which the time scale and dynamics are dependent on the recent stimulus history . Using this model , they were able to reproduce many features of cone responses , including previously unreported gain changes in cone responses to a WN stimulus . Although we were able to fit their model to our WN data , despite our best efforts , we were unable to adequately fit their model to our SoS data . The dynamic light adaptation model overestimated the cone response to decrements in light intensity and underestimated the time course for these changes under the SoS conditions . One potential reason that the model of Clark et al . [30] may not adequately simulate our results is that it uses one nonlinearity to describe both the asymmetry between cone responses to light increments and decrements ( Fig 6C ) and a change in response kinetics under differing light intensity . However , for our results we find that this asymmetry and the changes to the kinetics of cone response have different origins . For example , under voltage clamp while we find that the asymmetry between cone responses to light increments and decrements is present ( Fig 6C ) , the response kinetics are unaffected by stimulus contrast ( Fig 5B , S3 Fig , S5 Fig ) . Hence , these two processes have a different origin and therefore cannot be adequately described by one nonlinearity . This illustrates the limits of simplistic linear/nonlinear modelling . How does the time constant of hyperpolarization-activated adaptation compare with other known adaptational processes in the retina ? Light adaptation occurs throughout the animal kingdom and is active at many levels in the visual system , occurring over multiple scales in time and space . For example , the retinal cone pathway adapts to small changes in luminance at the bipolar to ganglion cell synapse , whereas larger changes induce adaptation within the cones themselves that occur over time scales ranging from tens of milliseconds to minutes [2 , 30–32] . Similarly , some mechanisms for temporal contrast adaptation are relatively fast , in the hundreds-of-milliseconds range or less [14 , 33] , whereas others are relatively slow , on the order of several seconds [13 , 34] . The spatial extent ranges from the size of ganglion cell receptive field subunits to the whole retina [33] . The hyperpolarization-activated adaptation we find for L- and M-cones appears to be one of the retina’s faster mechanisms . It seems to begin within 100 ms of an abrupt change in the modulation depth of the membrane potential , as happened when our SoS stimuli switched from one contrast condition to another , and once started , the adaptational process continues over about a second . Hyperpolarization-activated adaptation does not seem to be symmetrical; the time course is different when the membrane modulation depth decreases ( i . e . , going from high to low contrast ) versus when it increases ( i . e . , going from low to high contrast ) . Interestingly , optimal adaptation to nonstationary variance has been suggested to have asymmetric dynamics , as an abrupt increase is more readily detectable than an abrupt decrease [35 , 36] . So far , we have shown that L- and M-cones possess a novel form of adaptation: hyperpolarization-activated adaptation . However , is this novel form of adaptation a type of luminance or contrast adaptation ? In Fig 2 ( and S2 Fig ) , we show that cones change their kinetics in response to variations in contrast and not to luminance . Therefore , one might be tempted to call hyperpolarization-activated adaptation a form of contrast adaptation . However , we will not do so for the following reason . Contrast can be positive or negative . Positive contrast corresponds with cone hyperpolarization , while negative contrast corresponds to depolarization . A true contrast adaptation mechanism should be activated by both positive and negative contrast . Since the mechanism we have identified is hyperpolarization-activated adaptation , it can only be activated by positive contrast and is therefore not a true contrast adaptation mechanism . However , it should be noted that previous investigations into mechanisms of contrast adaptation at various retinal locations have not always used such a strict definition . Often the mechanism under study is only invoked by changes in the “preferred” contrast , such as positive contrast for ON-bipolar cells and negative contrast for OFF-bipolar cells [37–40] . Does that mean that the mechanism we have identified is a form of luminance adaptation ? Light stimulation will hyperpolarize cones , which activates Ih and induces adaptation . Thus , one might be tempted to call hyperpolarization-activated adaptation a form of luminance adaptation . However , as we describe above , Ih is transiently activated by sustained changes in light intensity . Hence , the hyperpolarization-activated mechanism we describe cannot remain adapted to a luminance level over longer time periods . It is a transient luminance adaptation mechanism . Indeed , this situation is reflected in the results found for the NTSCI . Hyperpolarization-activated adaptation did not correlate with the mean luminance over 1 s periods , whereas it did correlate with contrast . Therefore , we cannot call this adaptive mechanism a form of true luminance adaptation . Fast luminance adaptation has been described and can occur in cones on time scales from tens to hundreds of milliseconds [30–32] . However , extensive modelling [41] and direct cone measurements [32 , 42] indicate that fast luminance adaptation arises from the phototransduction cascade , and hence , they are distinct from the form of adaptation we find . Is the hyperpolarization-activated adaptation we describe even a form of light adaption ? Light adaptation as the name implies depends on light , but hyperpolarization-activated adaptation does not . It is fully determined by the modulation of the cone membrane potential . Therefore , in the most exacting sense of the name , the form of cone adaptation we find is not truly light adaptation , but it can be induced by light stimuli . In essence , the mechanism we have identified is rather difficult to classify within the boundaries of the existing and well-known terminology . It is neither luminance , nor contrast , nor even light adaptation within their strictest definitions . This raises the question of how best to name these adaptational phenomena . Do we name them according to the stimulus feature driving them best or by stricter criteria ? Here , we have chosen the latter and name the form of adaptation we find “hyperpolarization-activated adaptation . ” Here , we have shown that the main aspect of the stimulus that activates hyperpolarization-activated adaptation in a natural condition is the ability of a stimulus to engage and modulate the cone membrane potential . In other words , the more one stimulus condition can hyperpolarize the cone membrane potential away from the mean membrane potential and modulate Ih compared to another stimulus condition , the bigger the relative changes in kinetic properties of the cone will be . Hence , under natural conditions , cone responses will be faster during prolonged periods when the dispersal of light intensities are broader , especially if skewed towards higher values , than when the dispersal is narrower . This dispersal could be estimated by the variation of intensities occurring around the prolonged period’s mean value ( contrast ) as we have done . However , it could just as easily be described by the distributions of mean intensities calculated over shorter time windows ( luminance ) within each period . As low-frequency-dominated stimuli like natural scenes have long autocorrelation times , distinguishing between these two measures of dispersal becomes increasingly difficult as the time window used to perform the calculations becomes shorter . Consequently , while we cannot say that the adaptive response we find is exclusively a form of contrast or fast and transient luminance adaptation , we can say that when measured as we have done , cone responses speed up in high-contrast conditions and slow down in low-contrast conditions and thus behave like a contrast adaptation mechanism . How does hyperpolarization-activated adaptation relate to known contrast adaptation mechanisms ? Adaptation to temporal contrast is thought to only occur in higher-order neurons such as bipolar cells , amacrine cells , and ganglion cells [13 , 40 , 43 , 44] . However , as we show here , this is not entirely the case , as contrast changes can also induce an adaptive response in L- and M-cones . What could be the reason for this different result ? Previous studies based their conclusions on experiments using WN stimuli , which were confirmed in the present paper ( Fig 4 ) . However , such stimuli differ substantially from the naturalistic and artificial stimuli that induced hyperpolarization-activated adaptation here . Natural stimuli typically have long-term serial correlations , and as their power spectra decreases with 1/fβ ( 0 . 7 < β < 3 ) , they predominately contain lower frequencies [3 , 4] , whereas WN stimuli contain no serial correlations , and as the power of each frequency is equal , WN signals mostly consist of higher frequencies . As cones have relatively long integration times , the high-frequency component of the WN stimulus will be perceived by the cone as a sustained light stimulus , thereby reducing the “effective” contrast ( S2 Fig , S6 Fig ) . Consequently , stimuli like WN that consist primarily of higher frequencies will not modulate the membrane potential of cones as effectively as stimuli like natural stimuli that largely consist of lower temporal frequencies , even though they may be equal in terms of both the total photon number and variance ( Fig 4 , S2E Fig ) . If the membrane potential of the cone is not sufficiently modulated , then Ih will not activate enough to cause measurable hyperpolarization-activated adaptation . In the studies of Rieke [12] and Baccus and Meister [13] , the high-contrast noise stimuli generated membrane potential changes in cones that were similar to or smaller than those found by us in SoS low-contrast conditions ( S2E Fig ) . Hence , one should determine how effective a stimulus is at modulating the membrane potential of cones , as this will determine their adaptational state . Here , we estimated this using the “effective” contrast metric . Previous studies have also noted that “effective” contrast is a better measure of a stimulus’s ability to engage other neurons in the visual system [14 , 45–47] and lateral geniculate nucleus ( LGN ) neurons [48] than “stimulus” contrast is . That WN stimuli cannot induce hyperpolarization-activated adaptation in cones whereas it can induce adaptive changes in later visual system neurons indicates that independent contrast adaptation mechanisms exist at different stages of the visual system . These contrast adaptation processes have been studied mostly with WN stimuli . Since cones respond better to naturalistic and “natural-like” stimuli , as we have shown in this paper , it is likely that these higher-contrast adaptation mechanisms might become more pronounced if stimuli that at least preserve features of the natural world are used . Presumably , the higher effective contrasts delivered by WN stimuli , when band limited to lower frequencies ( e . g . , 10 Hz ) so that the waveforms of light intensities match the operational range of photoreceptors , would also engage these adaptation mechanisms more fully . Thus , in the broader perspective , it would be very interesting to re-examine temporal contrast adaptation in neurons throughout the visual pathway using stimuli that can deliver high levels of effective contrast , like natural stimuli . As sensory systems have evolved to process natural stimuli , it is highly likely that the whole visual pathway is optimized to process stimuli with spectral power distributions and serial correlations similar to those occurring in natural scenes . Indeed , sensory neurons display different response kinetics and filter characteristics as well as increased encoding efficiency when natural stimuli are used . Responses to classic stimuli are often poorly predictive for responses to natural stimuli , and as we show here , some response properties are only apparent when naturalistic stimuli are used [49–55] . Finally , with regards to the gain changes that are typically associated with contrast adaptation , what we find for cones is unique . Later visual neurons typically adapt to an increase in contrast by reducing their gain and integration time , processes that can occur independent of each other [13 , 40 , 43 , 44 , 56] . However , the gain at higher frequencies of the cone response increased , not decreased , with increased contrast levels . This increased higher-frequency gain is entirely consistent with a reduced integration time . How might hyperpolarization-activated adaptation improve the performance of cones ? Van Hateren [57] proposed that sensory neurons will act as adaptive filters that maximize the level of information in different SNR conditions . In high-SNR conditions , they will integrate the incoming signal over a shorter period of time and thereby transmit more information . However , in low-SNR conditions , they will increase their integration time , effectively sacrificing the higher-frequency components of the stimulus to reduce the higher-frequency noise contained in their responses . In this way , the sensory neurons restrict and adjust their response range to frequencies at which the signal can be reliably transmitted . In this paper , we have shown that in a natural scene L- and M-cones adapt independently to contrast via a process called hyperpolarization-activated adaptation . The consequence is that within the same scene , cones of different types and of the same type at different retinal locations experience distinct visual environments and adjust their output accordingly . For instance , an M-cone looking at foliage experiences a low-contrast environment , while an L-cone looking at a red flower in the foliage experiences a high-contrast environment . In such a scene , L- and M-cones adapt their filtering properties such that they may sample their “cone-specific” scene more optimally . The finding that S-cones are slower than L- and M-cones and do not adapt to temporal contrast might reflect the ecological properties of the short-wavelength environment . In natural scenes , the short-wavelength environment has a narrower distribution of contrast levels than the mid-wavelength environment [58] , making it less important for S-cones to adapt to the temporal contrast than for L- and M-cones . The results presented in this paper show that L- and M-cones use hyperpolarization-activated adaptation to extract the most reliable information from their “cone-specific” scene .
All animal experiments were carried out under the protocol NIN10 . 31 issued by the ethical committee of the Royal Netherlands Academy of Arts and Sciences acting in accordance with the European Communities Council Directive of 24 November 1986 ( 86/609/EEC ) . Goldfish , Carassius auratus , were killed , and the eyes were enucleated , with the retina isolated under infrared illumination with the aid of IR viewers , placed photoreceptor side up in a recording chamber ( volume: ~300 μl , model RC-26G , Warner Instruments ) mounted on a Nikon Eclipse 600FN microscope , secured under a tissue harp , and continuously superfused ( 1 . 5 ml . min−1 ) with oxygenated Ringer’s solution at room temperature ( 20°C ) . The preparation was viewed on an LCD monitor by means of a 60× water-immersion objective ( N . A . 1 . 0 ) , a CCD camera , and infrared ( λ > 800 nm; Kodak wratten filter 87c , United States ) differential interference contrast optics . Cone photoreceptor outer segments were visually inspected for damage , and whole cell recordings from undamaged cones with resting membrane potentials between −35 and −45 mV ( mean: −39 . 6 ± 0 . 73 mV ) were made under voltage or current clamp ( series resistance 25–40 MΩ ) . The current light response was measured by voltage clamping cones at their resting membrane potential , whereas the voltage light response was measured by current clamping cones . When current clamped , holding currents were only applied when CoCl2 , CsCl , or TEA-Cl were in the perfusate in order to restore the light-adapted membrane potential back to its original value . The data were not corrected for junction potentials . Ringer solution consisted of the following ( in mM ) : 102 . 0 NaCl , 2 . 6 KCl , 1 . 0 MgCl2 , 1 . 0 CaCl2 , 28 . 0 NaHCO3 , 5 . 0 glucose continuously gassed with 2 . 5% CO2 and 97 . 5% O2 to yield a pH of 7 . 8 ( osmolarity 245–255 mOsm ) . When using TEA , the NaCl concentration was equimolar reduced to maintain the chloride equilibrium potential . Pipette solution was made fresh every 2–3 days and contained ( in mM ) 96 K-gluconate , 10 KCl , 1 MgCl2 , 0 . 1 CaCl2 , 5 EGTA , 5 HEPES , 5 ATP-K2 , 1 GTP-Na3 , 0 . 1 cGMP-Na , 20 phosphocreatine-Na2 , and 50 units ml−1 creatine phosphokinase , adjusted with NaOH to pH 7 . 27–7 . 3 ( osmolarity 265–275 mOsm ) . All chemicals were supplied by Sigma-Aldrich ( Zwijndrecht , the Netherlands ) , except for ZD7288 ( Tocris Biosciences , Bristol , United Kingdom ) . Patch pipettes ( resistance 8–12 MΩ , PG-150T-10; Harvard Apparatus , Holliston , Massachusetts ) were pulled with a Brown Flaming Puller ( Model P-87; Sutter Instruments Company ) . Pipettes were placed in a PCS-5000 micromanipulator ( Burleigh Instruments , Union City , California ) , connected to an Axopatch 200A patch clamp amplifier ( Molecular Devices , Sunnyvale , California , four-pole low-pass Bessel filter setting: 1 kHz ) . Data were digitized and stored with a PC using a CED 1401plus AD/DA converter at 2 kHz sampling frequency using Signal software ( v . 3 . 07; Cambridge Electronic Design [CED] , Cambridge , UK ) to acquire data , generate voltage command outputs , and drive light stimuli . The light stimulator consisted of a homemade LED stimulator based on a three-wavelength high-intensity LED ( Atlas , Lamina Ceramics , Westhampton , New Jersey , US ) . The peak wavelengths of the LEDs were 624 , 525 , and 465 nm , respectively , with bandwidths smaller than 25 nm . An optical feedback loop ensured linearity . The output of the LEDs was coupled to the microscope via fiber optic light guides . Stimuli were projected onto the retina via a 20-μm light spot focused on the cone outer segment though a 60× water-immersion objective at a presentation rate of 166 . 67 Hz for the NTSCI and 200 Hz for all other stimuli . White light consisted of equal quantal output of the three LEDs . Using Welch's averaged periodogram method [62] , frequency responses Fsr ( f ) ( Eq 1 ) were calculated as the quotient of the cross power spectral density of S ( f ) and R ( f ) and the power spectral density of S ( f ) : Fsr ( f ) =⟨S* ( f ) R ( f ) S ( f ) S ( f ) ⟩ , ( 1 ) where * is the complex conjugate and < > denotes averaging over multiple stimulus presentation repeats . Where frequency response curves are used , only the magnitude data are shown . Frequency responses were calculated using the following parameters: For the frequency response analysis , the stimulus and cone response were first detrended . f3dB was calculated by a least squares linear fit between the last frequency that had a gain drop of less than 3 dB and the very next frequency to estimate the frequency at which the level of gain reached −3dB ( f3dB ) . Filter slopes were determined by least squares linear fits from the peak frequency response ( i . e . , gain = 0 dB ) to 20 Hz for the NTSCI or to 27 . 5 Hz for the SoS stimulus . Within these ranges , the frequency response was essentially linear for every cone analyzed , reflected by their high coefficient of determinations ( NTSCI; r2 = 0 . 99 ± 0 . 003 , 8 cones; SoS; r2 = 0 . 99 ± 0 . 002 , 18 cones ) . The ability of the frequency response to describe a cone’s response to the SoS stimuli was determined by the approach given in Rieke [12] . Here , the cone’s response to the stimulus was estimated by multiplying the light input and the interpolated frequency response in the frequency domain and converting the product back to the time domain by inverse Fourier transform . The mean correlation of this predicted response with each individual measured response was then compared to the mean correlation of the cone’s mean response with each individual measured response . For example , using the values of a typical cone , when the average correlation between the predicted and individual responses was 0 . 94 and between the mean and individual response was 0 . 96 , then the transfer function was said to predict 98% of the light-dependent structure of the cone’s measured response ( S3A and S3B Fig ) . Using the noninterpolated frequency response reduced this value by 0 . 01% . The interpolated frequency response was therefore considered to be a reliable “full frequency” model of a cone’s response . Impulse-response functions were generated by inverse Fourier transformation of the frequency response . Where SoS stimuli were used , the interpolated frequency response was used . To prevent the overall shape of average impulse-response functions being distorted by variations in response latency , the T2P of each individual cone impulse-response function was first time shifted to zero , the average and SEM calculated , and then the average impulse-response function time shifted back such that its T2P matched the group’s average T2P . A description of the impulse-response functions associated with f3dB values was developed using 24 impulse-response functions of L- and M-cones stimulated with the SoS contrast-switching stimulus . This description was used to generate simulated cone responses for which f3dB values were known at all times . The f3dB values were systematically varied at the different time points with 5-ms precision until a simulated cone response replicating both the magnitude and time course of the f3dB change for cones was found . Full details are given in S4 Fig . Impulse response function T2P values were binned into 1-ms intervals . Contrast and luminosity values were calculated as described above using the “effective stimuli . ” Contrast levels were binned into 0 . 0307 unit intervals ranging from 0 . 14 to 0 . 97 . Luminosity levels were binned into 959 unit intervals ranging from 3 , 436 to 29 , 339 . The probabilistic relationships were generated via Bayes’ rule . For joint probabilities: p ( ri , si ) =p ( ri∩si ) ( 2 ) For conditional probabilities: p ( ri|si ) =p ( ri∩si ) p ( si ) orp ( si|ri ) =p ( si∩ri ) p ( ri ) , ( 3 ) where p ( ri ) is the probability that the T2P value occurs within bin ri , and p ( si ) is the probability that a contrast ( or luminosity ) level is within bin si . All data are presented as mean ± SEM unless otherwise stated . Differences between groups were tested using two-tailed paired or independent t-tests as appropriate . Where the differences between means are given , the SEM was calculated as the Satterhwaite approximation of the standard error: SE=s12n1+s22n2 , ( 4 ) where s and n are the standard deviation and sample size . | An animal’s ability to survive depends on its ability to adapt to a wide range of light conditions , by maximizing the information flow through the retina . Here , we show a new form of adaptation in cone photoreceptors that helps them optimize the information they transmit by adjusting their response kinetics to better match the visual conditions . The adaptive mechanism we describe is independent of the cone phototransduction process and is instead mediated by membrane processes in which the hyperpolarization-activated current , Ih , plays a critical role . Consistent with the critical role of this current , we also found that cones sensitive to short wavelengths lacked a prominent Ih current and did not show this new form of adaptation . As voltage-dependent processes underlie the adaptational mechanism , it is only apparent when the stimuli are able to sufficiently modulate the membrane potential of cones . This happens with natural stimuli , which are able to deliver high levels of “effective” contrast . However , even though this new adaptive mechanism can be driven by contrast , we argue in the Discussion that in its strictest sense it is not a contrast adaptation mechanism per se . | [
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] | 2017 | A novel mechanism of cone photoreceptor adaptation |
A recent paper ( Nehrt et al . , PLoS Comput . Biol . 7:e1002073 , 2011 ) has proposed a metric for the “functional similarity” between two genes that uses only the Gene Ontology ( GO ) annotations directly derived from published experimental results . Applying this metric , the authors concluded that paralogous genes within the mouse genome or the human genome are more functionally similar on average than orthologous genes between these genomes , an unexpected result with broad implications if true . We suggest , based on both theoretical and empirical considerations , that this proposed metric should not be interpreted as a functional similarity , and therefore cannot be used to support any conclusions about the “ortholog conjecture” ( or , more properly , the “ortholog functional conservation hypothesis” ) . First , we reexamine the case studies presented by Nehrt et al . as examples of orthologs with divergent functions , and come to a very different conclusion: they actually exemplify how GO annotations for orthologous genes provide complementary information about conserved biological functions . We then show that there is a global ascertainment bias in the experiment-based GO annotations for human and mouse genes: particular types of experiments tend to be performed in different model organisms . We conclude that the reported statistical differences in annotations between pairs of orthologous genes do not reflect differences in biological function , but rather complementarity in experimental approaches . Our results underscore two general considerations for researchers proposing novel types of analysis based on the GO: 1 ) that GO annotations are often incomplete , potentially in a biased manner , and subject to an “open world assumption” ( absence of an annotation does not imply absence of a function ) , and 2 ) that conclusions drawn from a novel , large-scale GO analysis should whenever possible be supported by careful , in-depth examination of examples , to help ensure the conclusions have a justifiable biological basis .
The Gene Ontology ( GO ) Consortium has , over the last 10 years , revolutionized the use of structured , controlled vocabularies in biology , and provides GO annotations of gene products that describe biological function from the molecular to organism level [1] , [2] . During this time , the biocuration community , and in particular the curators associated with the major model organism databases ( MODs ) , have contributed tens of thousands of GO annotations—associations between a specific gene or gene product and a term in the GO—based on experimental results reported in the biomedical literature . As this corpus of experimental annotations has grown , it has become increasingly powerful to mine the annotations within the context of the ontology structure not only to generate biological hypotheses but also to examine precepts of comparative biology . In a recent publication Nehrt et al . [3] used these experimentally-derived GO annotations to test the hypothesis that orthologous genes ( separated by a speciation event ) have more closely related functions than paralogous genes ( separated by a gene duplication event ) . Here we discuss the applicability of GO annotations for their analysis , issues that impact the interpretation of the results they report , and some overall guidelines that should govern use of functional annotations in computational analysis . The Nehrt et al . paper highlights some potential pitfalls of using GO annotations without considered evaluation of the sources and semantics of these annotations [4] . In brief , the “ortholog conjecture” derives from a simple observation of genomic evolution: during evolution , genomes have often expanded via intra-genome copying of genomic regions ( a process called “gene duplication” ) , and there are many documented cases in which one or more of the duplicates either adopted a new or modified function ( “neofunctionalization” ) or lost a function ( “subfunctionalization” ) , resulting in duplicated genes with functions that differ to some degree [5] . These duplicates are referred to as “paralogs , ” whether they are from the same genome ( e . g . human hemoglobin vs . human myoglobin ) or different genomes ( e . g . human hemoglobin vs . mouse myoglobin ) [6] . “Orthologs , ” on the other hand , occur only in different genomes because they are separated by a speciation event ( e . g . human myoglobin vs . mouse myoglobin ) [6] . Because of the apparent importance of gene duplication in generating genes with novel or modified functions , it is generally assumed that orthologs tend , on average , to share a greater functional similarity than paralogs , the so-called “ortholog conjecture . ” This hypothesis has been questioned [7] . Nehrt et al . claim to perform the first large-scale test of this hypothesis . The primary evidence the authors use to draw their conclusions is a score based on the normalized intersection of the experimentally-supported Gene Ontology annotations for different pairs of genes . The authors interpret the score as representative of functional similarity . We contend that the score is more accurately described as annotation congruence . These two interpretations are very different: functional similarity refers to similarity in the actual biological function of two gene products , while annotation congruence refers to agreement in the representation of the functions that have been experimentally demonstrated so far for two gene products . If our experimental knowledge of biological function were complete , and adequately represented by GO annotations , these would be equivalent . Unfortunately this is not yet the case in general . It is very important to note that GO annotations are subject to an “open world assumption” , i . e . absence of a GO annotation does not mean that a function is absent from a particular gene product . Even the limited knowledge that we do have about biological function is not yet completely represented by GO annotations , due to limitations of time and resources . Perhaps most importantly for this discussion , different model organisms are used to study different aspects of biology using different assays , and so the annotation of orthologs in different species will reflect these systematic differences in experimental systems and outcomes . In fact , complementarity with other established systems is a key factor in the development of different model organism experimental systems . As a result of these and other considerations , we suggest that the authors , rather than testing the “ortholog conjecture , ” instead tested an “unbiased annotation conjecture . ” Similar suggestions have been made in post-publication review forums ( http://f1000 . com/12462957 ? key=5g7rjmt7xzv2y32 ) and blogs ( http://phylogenomics . blogspot . com/2011/09/special-guest-post-discussion . html ) , but not yet in the peer-reviewed literature . As Nehrt et al . describe , it would indeed be contrary to expectations if paralogous genes in humans or mice were functionally more similar than orthologous genes between these species . This would not only challenge the so-called “ortholog conjecture”: it would challenge the longstanding research programs in model systems and comparative biology , and even the tenets of current evolutionary theory with its emphasis on inheritance and divergence from a common ancestor . Surprisingly , then , the rejection of the “ortholog conjecture” by Nehrt et al . is based almost entirely on statistical analysis of existing GO annotations , with no in-depth analysis of specific examples . In particular , the section entitled “Case studies” provides no citation of experimental evidence for the authors' claims , thus complicating overall evaluations . Here , we examine these specific cases , and find no evidence for the conclusion that within-species paralogs are more functionally similar than orthologs . Instead , we suggest that the statistical bias observed by Nehrt et al . is better explained by a bias in annotations arising at least in part because research programs in human and mouse experimental systems tend to discover aspects of orthologous gene function that are complementary rather than conflicting .
Mitogen activated protein kinase kinase kinase kinases ( MAP4K ) are protein kinases that participate in the MAP kinase signal transduction cascade [8] . The authors state that an “example of a violation of the ortholog conjecture is… MAP4K2…While the human hMAP4K2 shares 94% sequence identity with its ortholog in mouse , their functional similarity is only 5% ( 45 annotated terms in human , 13 in mouse ) . In contrast , its functional similarity with its own outparalogs was 69% on average , including 82% similarity with hMAP4K3 , a within-species outparalog . ” The GO biological process annotations for human MAP4K2 , mouse Map4k2 and human MAP4K3 are shown in Table 1 . Both human MAP4K2 and human MAP4K3 are annotated with intracellular protein kinase cascade ( GO:0007243 ) and protein phosphorylation ( GO:0006468 ) , while mouse Map4k2 is only annotated with vesicle targeting ( GO:0006903 ) . So the finding that the annotation congruence for MAP4K2 and MAP4K3 in humans ( paralogs ) is greater than for human MAP4K2 and mouse Map4k2 ( orthologs ) is correct . However , decreased annotation congruence can be explained more easily in terms of annotation incompleteness ( arising from incompleteness in actual experimental results ) and complementarity rather than functional differences between orthologs . MAP4Ks are upstream of MAP3Ks in the mitogen-activated protein kinase ( MAPK ) cascade , and both MAP4K2 and MAP4K3 are known in humans to participate specifically in the JNK ( c-Jun N-terminal kinase ) cascade , one of four different known MAPK pathway variants [8] . Thus , from a functional standpoint , it is generally accepted that human MAP4K2 and MAP4K3 , and mouse Map4K2 can all participate in an intracellular protein kinase cascade . However , from a GO annotation standpoint , only human MAP4K2 and MAP4K3 have been experimentally characterized as participating in an intracellular protein kinase cascade . Mouse Map4K2 , the ortholog of human MAP4K2 , has apparently not been characterized at the molecular level , though there are several reported effects of mouse mutants lacking Map4k2 , including an effect on vesicle targeting . This lack of experimental characterization cannot , however , be taken as evidence that Map4k2 differs from its human ortholog in that it does not participate in MAPK signaling . On the contrary , a molecular link between the JNK cascade and vesicle targeting ( through the conserved JNK-interacting protein JIP-1 ) has been established in Drosophila [9] , suggesting a mechanism by which mouse Map4k2 ( and likely its human ortholog ) may affect vesicle targeting through MAPK signaling . In summary , the different annotations for mouse and human orthologs of MAP4K2 do not constitute evidence that the orthologous genes have different functions; a more likely explanation is that they are instead providing complementary information about a conserved biological system , representing the current , incomplete , state of experimentation results . Nuclear receptors are transcription factors , influencing transcription of specific target genes , that are activated by binding a specific ligand . The authors find that , in this family , “a paralog was more functionally similar than the ortholog for the majority of the targets , and the specific paralog with the highest functional similarity was most often an outparalog in the same species . ” The biological functions of nuclear receptors are known to be highly dependent upon their biological ligands , and the evolution of ligand specificity has been studied for some members of this family [10] , [11] . The authors provide no specific comparisons in this family , we therefore selected an example to illustrate that quantitative differences in annotation congruence score as defined in this paper may not be functionally meaningful . The thyroid hormone receptor alpha ( THRA in human , Thra in mouse ) gene product binds thyroid hormone , a tyrosine-based hormone , and has effects on tissue growth , differentiation and metabolism [12] . The estrogen receptor alpha ( Esr1 in mouse ) gene product binds the steroid hormone estrogen ( the primary female hormone in mammals ) , and has physiological effects ranging from reproduction to cognition [13] . The thyroid receptor and estrogen receptor bind chemically different ligands , and activate very different sets of target genes . There is no known biological evidence that the mouse thyroid receptor is more similar in its actual biological function to its paralog Esr1 , than to its human ortholog THRA . Indeed , such a convergence in function between paralogs would be a revolutionary finding . Yet the molecular function annotation congruence for mouse Thra is greater with mouse Esr1 than with human THRA ( Table 2 ) . Is there any evidence that mouse Thra is actually more similar in function to its paralog Esr1 than to its human ortholog , even in the GO annotations ? There is not: the GO annotations are correct , if incomplete . The observed greater annotation similarity for Thra-Esr1 is driven largely by the greater specificity in the annotations of human THRA as compared to either mouse gene . Both mouse genes are annotated with 1 ) protein binding , and 2 ) ligand-activated sequence-specific DNA binding RNA polymerase II transcription factor activity , while THRA is annotated with 1 ) TBP-class protein binding and 2 ) thyroid hormone receptor activity . TBP-class protein binding is a subclass of protein binding , while thyroid hormone receptor activity is a subclass of ligand-activated sequence-specific DNA binding RNA polymerase II transcription factor activity . It is important to consider the semantics of a non-specific GO annotation: an annotation of mouse Thra as possessing ligand-activated sequence-specific DNA binding RNA polymerase II transcription factor activity means that the gene product functions as a nuclear receptor for some ( unspecified ) ligand , which of course does not preclude that the ligand is thyroid hormone . Thus differences in annotation specificity , a form of annotation incompleteness , cannot generally be interpreted as differences in actual biological function . Differences in annotation specificity , even for similar experiments , may arise for non-biological reasons such as variability in annotation processes between different curation groups ( note that most GO annotations for human genes are made by GOA [14] while for mouse genes most are made by MGI [15] ) , differences in the experimental systems employed in different research laboratories , and the differences in availability of terms in the ontology at the time of annotation . Nevertheless , assuming that the annotation similarity scores are calculated correctly , the statistical differences reported by Nehrt et al . between orthologs and paralogs are significant . However if , as suggested above , the differences are not biological in origin , is there an alternative interpretation ? The authors observed that the greatest differences in annotation similarity scores occur between two groups: 1 ) inparalogs/within-species outparalogs , versus 2 ) orthologs/between-species outparalogs . In short , within-species comparisons yielded greater annotation similarity scores on average than between-species comparisons . The authors claim that “the sparsity of annotation… is unlikely to affect comparisons between classes of homologs , ” but this claim is essential for their interpretations and requires supporting evidence . As shown in the examples above , annotation incompleteness can result in annotation differences even in the absence of functional differences . We reasoned that the bias uncovered by Nehrt et al . , in which within-species comparisons showed greater annotation similarity than between-species comparisons , would arise if GO annotations for mouse genes in general—not just for paralogous genes—are more similar to each other than to human GO annotations , and vice versa . To test this alternative explanation , we compared the set of all human experimental annotations to the set of all mouse experimental annotations in the GO database . Table 3 lists several examples of molecular functions and biological processes that are very unequally represented in the annotations for one species relative to the other . For molecular function , human annotations are enriched in protein binding and some enzymatic functions , while mouse annotations are enriched in transcription factors and ion channels . In agreement with Nehrt et al . 's results ( but contrary to their interpretation ) , biological process annotations are even more biased , with mouse being enriched for organism-level processes including development and cell differentiation , and human for cellular biochemical-level processes such as protein modification and molecular catabolism . These differences in overrepresented functional classes are very unlikely to reflect actual functional differences between human and mouse orthologs; rather they reflect biases both in the kinds of experiments that are performed in that organism , and in the curation process ( e . g . which published papers are prioritized for annotation by a given curation group ) . Some of the most significant biases can be explained by the fact that mouse is used in genetics experiments to probe organism level processes that cannot be approached experimentally in humans , while many of the experiments in human systems are performed on isolated cells and proteins .
We have shown that the interpretation of Nehrt et al . 's metric of GO annotation congruence as functional similarity is problematic , and therefore it cannot be used to draw valid conclusions about the ortholog functional conservation hypothesis . From a theoretical standpoint , the semantics of GO annotations must be interpreted using an “open world assumption” in which absence of an annotation does not mean absence of a function ( a true negative ) . Thus , lack of annotation congruence may simply be due to false negatives: incompleteness either in the state of our experiment-derived knowledge of a particular gene's function , or in representing that knowledge as GO annotations . From an empirical standpoint , we demonstrate that the bias noted by Nehrt et al . between different classes of homologous gene in human and mouse , is likely to be reflecting a global bias over all human and mouse genes . This global bias is consistent with the common use of mouse as a genetic system for probing system-level processes via observed phenotypes , and of the use of human cell lines for probing cellular-level processes . It may also reflect a tendency for researchers not to “repeat” a particular experiment that has already been carried out in a closely related organism . We note that Nehrt et al . did attempt to address potential sources of bias in GO annotations , though they apparently missed a major contributor as discussed above . The authors' observation that there are “preferences toward the same annotation when multiple homologs were functionally annotated in the same article: functional similarity went up 0 . 1–0 . 3 across orthologs and paralogs for both Biological Process and Molecular Function” supports the “biased annotation conjecture” interpretation we propose here . We would also expect annotation congruence to increase accordingly if homolog annotations were derived from research groups and co-authors addressing the same biological questions , or for annotations made during the same time period , when they would be constrained by the availability of similar GO terms . Nevertheless , whenever a novel type of GO-based statistical analysis is presented , a manual review of key examples or case studies should be considered as an important component of validating its biological implications . GO-based analysis can be an excellent way to generate biological hypotheses , but in order to draw defensible conclusions , it is important to verify actual biological examples , particularly if analyses may be affected by global differences between the sets of annotations being compared . Between-species comparisons based on different annotation sources ( i . e . organisms ) , are particularly sensitive to subtle differences in annotation and experimental testing bias . Users of GO should ensure that they test for , and adjust for , potential biases prior to interpretation . Our re-analysis of the case studies presented by Nehrt et al . confirmed a greater annotation congruence between paralogs as compared to orthologs , but showed that this difference is due to incomplete and complementary annotations , and not to functional divergence among orthologs or convergence among paralogs . This in-depth analysis suggested possible types of bias that we explored with further interrogation of biological knowledge and statistical analysis . If the annotation congruence is not appropriate , are there alternative ways in which GO annotations might be used to test the ortholog functional conservation hypothesis ? One way that functional differences between orthologs and paralogs could be addressed using GO would be to consider homologs for which similar experiments had been performed , and where negative results were captured as negative GO annotations ( using the “NOT” qualifier ) to indicate the absence of functionality . We note that GO curators have already made numerous negative annotations—though these are still very incomplete—often where a particular function was suspected/expected for a gene ( one possible reason being that it was found for an ortholog ) but shown not to be present . Two examples of orthologs with divergent functions are SUV3 ( Saccharomyces cerevisiae ) /rpm2 ( Schizosaccharomyces pombe ) and MGT1 ( S . cerevisiae ) /atl1 ( S . pombe ) . In these cases , the gene product in S . pombe has been demonstrated to lack a function found in the S . cerevisiae ortholog , and this has been captured with negative annotations for the S . pombe genes [16] , [17] . To date , negative GO annotations are relatively rare and probably insufficient to refute or support the ortholog functional conservation hypothesis in general , though a detailed and careful analysis might be interesting . Indeed , several functional differences between orthologous genes in humans and mice have been documented [18] , but it is unclear how prevalent such cases will prove to be as more experimental data accumulate . We applaud the use of the Gene Ontology resources in new and creative ways . At the same time , we strongly encourage careful consideration of the interpretations of such uses . Do they reflect actual biological insights , or are they in fact due to inherent biases in annotation and or the experimental data or systems available ? This phenomenon is certainly not limited to GO analyses . The process of knowledge representation of any kind will always introduce issues that must be properly considered in meta-analyses . We strongly and actively encourage researchers to contact us when proposing a novel type of GO-based analysis , to ensure appropriate interpretation and use of the GO .
Term overrepresentation analysis ( Table 3 ) was performed on the sets of human and mouse annotations from the 2011-09-10 release of the GO database , using the cumulative hypergeometric probability distribution in Microsoft Excel . Only annotations with the following evidence codes were considered: EXP , IPI , IDA , IMP , IGI , IEP ( http://www . geneontology . org/GO . evidence . shtml ) . For the MAP4K2 and nuclear receptor examples ( Tables 1 and 2 ) , GO annotations ( same evidence codes as above ) were retrieved using AMIGO ( http://www . geneontology . org ) on 2011-11-29 . | Understanding gene function—how individual genes contribute to the biology of an organism at the molecular , cellular and organism levels—is one of the primary aims of biomedical research . It has been a longstanding tenet of model organism research that experimental knowledge obtained in one organism is often applicable to other organisms , particularly if the organisms share the relevant genes because they inherited them from their common ancestor . Nevertheless this tenet is , like any hypothesis , not beyond question . A recent paper has termed this hypothesis a “conjecture , ” and performed a statistical analysis , the results of which were interpreted as evidence against the hypothesis . This statistical analysis relied on a computational representation of gene function , the Gene Ontology ( GO ) . As representatives of the international consortium that produces the GO , we show how the apparent evidence against the “ortholog conjecture” can be better explained as an artifact of how molecular biology knowledge is accumulated . In short , a complementarity between knowledge obtained in mouse and human experimental systems was incorrectly interpreted as a disagreement . We discuss the proper interpretation of GO annotations and potential sources of bias , with an eye toward enhancing the informed use of the GO by the scientific community . | [
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] | 2012 | On the Use of Gene Ontology Annotations to Assess Functional Similarity among Orthologs and Paralogs: A Short Report |
During oogenesis in metazoans , the meiotic divisions must be coordinated with development of the oocyte to ensure successful fertilization and subsequent embryogenesis . The ways in which the mitotic machinery is specialized for meiosis are not fully understood . cortex , which encodes a putative female meiosis-specific anaphase-promoting complex/cyclosome ( APC/C ) activator , is required for proper meiosis in Drosophila . We demonstrate that CORT physically associates with core subunits of the APC/C in ovaries . APC/CCORT targets Cyclin A for degradation prior to the metaphase I arrest , while Cyclins B and B3 are not targeted until after egg activation . We investigate the regulation of CORT and find that CORT protein is specifically expressed during the meiotic divisions in the oocyte . Polyadenylation of cort mRNA is correlated with appearance of CORT protein at oocyte maturation , while deadenylation of cort mRNA occurs in the early embryo . CORT protein is targeted for degradation by the APC/C following egg activation , and this degradation is dependent on an intact D-box in the C terminus of CORT . Our studies reveal the mechanism for developmental regulation of an APC/C activator and suggest it is one strategy for control of the female meiotic cell cycle in a multicellular organism .
Developmental regulation of meiosis is crucial for generating viable eggs and sperm and , thus , a successful fertilization event . Meiosis is a modified cell cycle in which segregation of homologous chromosomes is followed by segregation of sister chromatids without an intervening S phase . These unique divisions are controlled by general mitotic cell-cycle regulators as well as specialized meiotic proteins [1] . During oogenesis in multicellular organisms , meiosis presents a particular regulatory challenge . The meiotic divisions must be coordinated tightly with growth and development of the oocyte to allow for oocyte differentiation and to ensure that the completion of meiosis is coordinated with fertilization . To achieve this coordination , oocytes arrest at prophase I and again at metaphase I or metaphase II and are released from these arrests through processes called oocyte maturation and activation , respectively [2 , 3] . Furthermore , additional specialized cell-cycle regulation is required for the transition between meiosis and restart of the cell cycle in embryogenesis . In Drosophila , meiosis is completed without cytokinesis in the same common cytoplasm in which the rapid mitotic divisions of embryogenesis begin . Upon fertilization , the oocyte must quickly inactivate meiotic regulators to prevent interference with embryonic mitotic cycles . The ways in which general mitotic proteins act together with meiosis-specific proteins to meet the multiple regulatory challenges of meiosis in metazoans are not well understood . The anaphase-promoting complex/cyclosome ( APC/C ) plays a critical role in mitosis , but much remains to be understood about its function in meiosis . The APC/C is a large E3 ubiquitin ligase composed of at least 12 core subunits , which targets specific substrate proteins for degradation by the 26S proteasome [4] . In mitosis , the APC/C is crucial for proper cell division through targeting of key substrates . Securin , an inhibitor of separase , must be degraded to allow for cleavage of cohesin and subsequent segregation of sister chromatids , and mitotic cyclins must be degraded to allow for the metaphase to anaphase transition and events associated with mitotic exit [5–8] . In addition , the APC/C targets many other proteins for degradation including proteins involved in spindle function and regulators of DNA replication [9 , 10] . Substrate specificity is conferred to the APC/C by activator proteins Cdc20/Fizzy and Cdh1/Fizzy-related , which recognize substrate proteins containing D-box or KEN box motifs [11–15] . Regulation of these specificity factors is one crucial way by which APC/C activity is modulated . Cdc20 is transcribed and translated during S phase and G2 , phosphorylated in mitosis , and degraded in an APC/CCdh1-dependent manner in G1 [13 , 16–19] . Phosphorylation of several APC/C subunits in mitosis facilitates the ability of Cdc20 to bind to and activate the APC/C [18 , 20–23] . Levels of Cdh1 are constant in mitosis and lowered in late G1 and S , but inhibitory phosphorylation of Cdh1 prevents its association with APC/C during S , G2 , and M phases [16 , 18 , 24] . Thus , differential regulation of Cdc20 and Cdh1 directs their transient association with the APC/C at different times during the cell cycle to target specific subsets of proteins for degradation . In meiosis , the role of the APC/C and its regulation is less clear . An appealing hypothesis is that the meiotic divisions are driven in part by the degradation of specific meiotic APC/C substrates , and thus , the APC/C must require unique regulation during these divisions . In yeast , it is known that disjunction of homologous chromosomes in meiosis I and sister chromatids in meiosis II requires APC-mediated destruction of Pds1/securin to release separase for cleavage of cohesin [25–27] . In multicellular organisms , however , a requirement for the APC/C in meiosis has been more difficult to demonstrate . Mutations in or RNA interference against APC/C subunits in Caenorhabditis elegans result in a metaphase I arrest [28 , 29] . In Drosophila , mutations in fzy cause both meiosis I and meiosis II arrests [30] . Several studies in mouse oocytes have shown that APC/C-mediated protein degradation is required for homolog disjunction and polar body extrusion [31–34] . However , inhibiting APC/C subunits by depletion or antibody injections in Xenopus laevis does not prevent the metaphase I to anaphase I transition but does cause arrest in metaphase II [35 , 36] . The reasons behind this apparent inconsistency in Xenopus remain unknown . One way in which the APC/C may be regulated uniquely in meiosis is through its association with meiosis-specific activators . Cdc20/FZY family members that are expressed exclusively in meiosis have been identified in yeast . In Saccharomyces cerevisiae , Ama1 activates the APC/C after meiosis I and is required for spore wall assembly [37–39] . Similarly , in S . pombe , fzr1/mfr1 is required for proper spore formation after the completion of the meiotic divisions [40 , 41] . The identification and study of meiosis-specific APC/C activators are starting points from which to understand the unique regulation of the APC/C during meiosis as well as to identify meiosis-specific substrates of the APC/C . Drosophila provides the best candidate for a meiosis-specific APC/C activator in metazoans . cortex ( cort ) encodes a distant member of the Cdc20/FZY family and is expressed exclusively in oogenesis and early embryogenesis [42] . cort is required for proper female meiosis . Eggs laid by cort mutant mothers display aberrant chromosome segregation in meiosis I and arrest terminally in metaphase II [43 , 44] . In addition , cort mutant eggs contain elevated levels of mitotic cyclins , and misexpression of cort causes a decrease in levels of mitotic cyclins [30] . cort presents a unique opportunity for understanding the function and the developmental regulation of the APC/C during meiosis in a multicellular organism . Drosophila is an ideal system for studying female meiosis because different meiotic stages can be distinguished easily by cytology and isolated by microdissection . In this study , we demonstrate that CORT interacts biochemically with the APC/C during female meiosis and reveal a mechanism for developmental regulation of cort through both post-transcriptional and post-translational processes .
A recent study suggested that the cortex gene encodes a functional activator of the APC/C [30] . This assertion was based on the ability of cort to affect cyclin protein levels . Levels of mitotic cyclins are elevated in cort mutants , and misexpression of cort causes decreased levels of cyclins in wing imaginal discs . While these data are strong evidence of cort's function as an activator of the APC/C , demonstration of a physical association between CORT and the APC/C during oogenesis is lacking . We looked for an association between CORT and the APC/C by co-immunoprecipitation . We made transgenic lines with a MYC-tagged form of cort under control of the UAS response element and drove MYC-CORT expression in the female germline using nos-gal4 . MYC-CORT is functional , because expression of this transgene rescued the meiotic arrest in progeny of cortRH65 mutant females ( Table S1 ) . We immunoprecipitated MYC-CORT from ovary extracts using a MYC antibody . Cdc27 and Cdc16 , tetratricopeptide repeat core subunits of the APC/C , co-immunoprecipitate with MYC-CORT but not with a control mouse immunoglobulin G ( IgG ) ( Figure 1A ) ( unpublished data ) . Morula ( MR ) , the APC2 homolog in Drosophila , does not co-immunoprecipitate with MYC-CORT ( Figure 1A ) . Data from S . cerevisiae suggest that the APC/C exists in two distinct subcomplexes bridged together by Apc1 . One subcomplex contains Apc2 and Apc11 , while the other contains the tetratricopeptide proteins Cdc27 , Cdc16 , and Cdc23 [45] . Failure of MR to co-immunoprecipitate with MYC-CORT may be explained by a tighter and more direct association of CORT with the tetratricopeptide protein-containing subcomplex but not with the Apc2-containing subcomplex . Furthermore , buffer conditions may be causing CORT to dissociate from MR , as extensive high-salt washes of human APC cause dissociation of Apc2 and Apc11 from the rest of the complex [46] . On the basis of previous studies of APC/C , we still think it is likely that CORT acts together with MR and Cdc27 in one complex . As an additional control , we immunoprecipitated an unrelated MYC-tagged protein , PLU-MYC , from ovary extracts to confirm that the associations of Cdc27 and Cdc16 with CORT are specific . Neither Cdc27 nor Cdc16 co-immunoprecipitate with PLU-MYC , indicating that they are associating with CORT and not with the MYC tag ( Figure 1A ) ( unpublished data ) . In the reciprocal experiment , we immunoprecipitated Cdc27 from ovary extracts ( Figure 1B ) . MYC-CORT co-immunoprecipitates with Cdc27 but not with a control rabbit IgG , suggesting that there is a strong physical interaction between CORT and Cdc27 . In addition to demonstrating the physical association between CORT and the APC/C , we identified motifs in the CORT protein sequence that have been shown to contribute to binding of APC/C activator proteins to the APC/C ( Figure 1C ) . CORT contains an internal motif called the C-box that is important for binding to the APC/C and is conserved in Cdc20 and Cdh1 proteins throughout many species [47 , 48] . In addition , CORT contains the C-terminal IR ( isoleucine-arginine ) tail motif that is present in all APC/C activators as well as Doc1 and has been shown to mediate a direct interaction between Cdh1 and Cdc27 [46 , 49] . Together , the co-immunoprecipitation of CORT with core subunits of the APC/C in oogenesis and the presence of conserved motifs in the CORT sequence confirm CORT's identity as a meiosis-specific APC/C activator . An additional way to determine if cort functions as an APC/C activator is to ask whether it can provide the function of another APC/C activator when over- or misexpressed . To investigate whether cort acts similarly to fzy , we asked if cort can functionally substitute for fzy in the early embryo . fzy6/fzy7 females lay eggs that arrest in metaphase after a few rounds of mitotic divisions [50] . If cort can provide fzy function , we would expect these embryos to progress further in embryogenesis . We overexpressed myc-cort in the germline of fzy6/fzy7 females , collected embryos from these females , visualized the DNA and the spindles by immunofluorescence , and counted the number of mitotic spindles . Embryos overexpressing cort did not contain more mitotic spindles compared to fzy alone , thus we did not observe any rescue of the fzy phenotype . In contrast , overexpression of cort slightly worsened the fzy phenotype ( presented below ) . This result suggests either that cort does not function as an APC/C activator , or , more likely , that CORT confers significantly different substrate specificity to the APC/C than FZY and , therefore , cannot provide its function . In mitosis , cyclins are degraded sequentially by the APC/C , with Cyclin A being degraded prior to Cyclin B and Cyclin B3 [51 , 52] . For a more detailed analysis of CORT function in meiosis , we examined levels of putative APC/CCORT substrates at different time points during the meiotic divisions . We can isolate different meiotic stages by dissecting egg chambers from ovaries: immature ovaries with egg chamber stages 1–12 contain oocytes arrested in prophase I , and mature stage 14 oocytes are arrested in metaphase I . We performed western blots on extracts from immature egg chamber stages and stage 14 oocytes to assay levels of Cyclin A at these meiotic time points in cort mutants . We found that Cyclin A levels are slightly reduced in cort mutant immature ovaries ( stages 1–11 ) , although the significance of this effect is not clear as we have not observed any defects in these stages of cort mutant ovaries . Cyclin A levels are elevated in mutant stage 14 oocytes compared to a heterozygous control ( Figure 2A ) . The heterozygous control blots indicate that Cyclin A is normally degraded at some point between release of the prophase I arrest and establishment of the metaphase I arrest . In cort mutants , the failure to degrade Cyclin A by the metaphase I arrest indicates that APC/CCORT is required for Cyclin A degradation at this time . In contrast , levels of Cyclin B , Cyclin B3 , and PIMPLES ( PIM ) , the securin homolog in Drosophila , are not elevated in cort mutant stage 14 oocytes compared to heterozygous controls , suggesting that these proteins are not subject to degradation by APC/CCORT at this developmental stage ( Figure 2B ) . Upon egg activation in Drosophila , the metaphase I arrest is released , and meiosis is rapidly completed as the egg is ovulated and laid [53] . Meiosis is completed regardless of whether the oocyte is fertilized . Thus , unfertilized eggs represent a time point just after the completion of meiosis . We examined cyclin levels in eggs laid by cort mutant females , which do not complete meiosis ( Figure 2C ) . As a control we used heterozygous unfertilized eggs . We found that all three mitotic cyclins , as well as PIM , are elevated in cort mutant eggs , which suggests that APC/CCORT targets all of these substrates for degradation after release of the metaphase I arrest . Complementary results for cyclin levels have been previously observed [30] . This degradation may take place at the metaphase I to anaphase I transition , the metaphase II to anaphase II transition , or during both transitions . Cyclin B , at least , is likely degraded at both transitions as expression of a nondegradable form of Cyclin B in the female germline results in both meiosis I and meiosis II arrests [30] . The sequential CORT-dependent degradation of cyclins we observe in Drosophila female meiosis is parallel to observations made in mitosis that degradation of Cyclin A begins just after nuclear envelope breakdown in prometaphase , while degradation of Cyclin B is initiated at the beginning of metaphase [52 , 54–56] . Nuclear envelope breakdown occurs in Drosophila female meiosis in stage 13 , and just after this stage is when we see an increase of Cyclin A but not Cyclins B or B3 in cort mutants compared to heterozygous controls . We see CORT-dependent degradation of all three cyclins in eggs , consistent with degradation of Cyclin B and Cyclin B3 not occurring until the metaphase I to anaphase I transition . To our knowledge , this is the first observation of sequential cyclin degradation during meiosis in a metazoan . Given the difference in timing of CORT-dependent degradation of cyclins , we examined the protein expression pattern of CORT during meiosis to see if differential protein regulation of CORT correlates with differential cyclin degradation . Eggs laid by cort mutant mothers arrest in metaphase II and never complete meiosis [44] . This strong arrest phenotype indicates a critical role for cort specifically in meiosis . However , cort mRNA is present throughout oogenesis and early embryogenesis , suggesting a much broader developmental role [42] . We determined the timing of CORT protein expression to define better the scope of its activity . To investigate the developmental control of CORT protein expression , we made a polyclonal antibody against a glutathione S-transferase ( GST ) -tagged N-terminal fragment of CORT . Anti-CORT serum recognizes a band of approximately 47 kDa in wild-type oocyte extracts ( Figure 3A ) . To test for antibody specificity , we probed oocyte extracts from cortRH65 mutants that contain a cort allele with a premature stop codon [42] . The serum does not recognize a band of the same size in these mutants . We also did not detect an N-terminal fragment in this mutant extract . In addition , we probed extracts from grauzone ( grau ) mutant oocytes . grau encodes a transcription factor that activates expression of cort [57] . The CORT band of 47 kDa is reduced in grau mutants , confirming the specificity of our antibody . CORT expression is specific to oogenesis , as we detected a CORT band in whole female fly extracts but not in female fly extracts from which the ovaries were removed ( Figure 3A ) . We also did not detect CORT in whole male fly extracts . We performed developmental western analysis on different stages of oogenesis to determine specifically when CORT protein is expressed ( Figure 3B ) . CORT is undetectable in early stage 1–8 egg chambers , and very low levels are detectable in stages 9–10B egg chambers . CORT levels increase dramatically in stage 12–13 egg chambers and remain high in mature stage 14 oocytes . The appearance of CORT protein occurs at the same time that Cyclin A degradation is triggered ( Figure 2A ) , indicating that APC/CCORT targets Cyclin A as soon as CORT protein is expressed , while simultaneously being prevented from targeting Cyclins B and B3 and PIM until after release of the metaphase I arrest . The timing of appearance of CORT protein correlates with timing of the unmasking of maternal mRNA by cytoplasmic polyadenylation . Many organisms utilize cytoplasmic polyadenylation as a strategy to turn on the translation of specific transcripts at specific developmental time points [58] . Elongation of the poly ( A ) tail of these transcripts is thought to allow for a stable closed-loop conformation of the translational machinery and thus to activate translation . This process occurs during oocyte maturation when oocytes are released from prophase I arrest to reenter the meiotic cell cycle [2] . In Drosophila , oocyte maturation takes place in stage 13 of oogenesis [59] . Given the correlation of the appearance of CORT protein with the timing of oocyte maturation , we investigated the lengths of cort poly ( A ) tails at different developmental time points . We conducted ligase-mediated poly ( A ) tail ( PAT ) assays on immature egg chambers and mature stage 14 oocytes to determine if the poly ( A ) tail length of cort changes upon oocyte maturation [60] . We observed an elongation of the poly ( A ) tail in stage 14 oocytes compared with stage 1–11 egg chambers ( Figure 3C ) . Poly ( A ) tails are approximately 20 As in immature stages and elongate to approximately 70 As in mature oocytes . As a positive control , we measured the poly ( A ) tail length of cyclin B mRNA in these stages , because cyclin B mRNA is known to be polyadenylated upon oocyte maturation ( Figure 3C ) [61] . We observed a similar increase in cyclin B mRNA poly ( A ) tail lengths as has been previously shown . The appearance of CORT protein in stage 13 of oogenesis when oocyte maturation occurs together with the elongation of cort's poly ( A ) tail in mature oocytes suggests that cort translation is developmentally regulated by cytoplasmic polyadenylation . If APC/CCORT activity is necessary for meiosis but dispensable for mitosis , cort may be inactivated in the early embryo to prevent its association with a mitotic APC/C complex . Early Drosophila embryos are transcriptionally quiescent , so post-transcriptional control is essential for regulating the activity of maternal gene products . In many organisms , egg activation triggers destabilization of a subset of maternal transcripts [58] . As deadenylation is often the rate-limiting step in mRNA decay , we investigated the polyadenylation status of cort mRNA after the completion of meiosis . We performed PAT assays to measure the poly ( A ) tail length of cort mRNA in mature stage 14 oocytes and 0–1-h embryos . We found that cort mRNA is deadenylated in early embryos compared to mature oocytes ( Figure 4A ) . The tail decreases from a length of approximately 70 As to 20 As . We used cyclin B mRNA as a positive control that , in contrast , is further polyadenylated upon egg activation [61] . CCR4 is the main catalytic subunit of the Ccr4-Pop2-Not deadenylase complex in S . cerevisiae [62] . A CCR4 homolog exists in Drosophila and has deadenylase activity [63] . We measured the poly ( A ) tail length of cort mRNA in embryos from ccr4 mutant mothers and found that cort's poly ( A ) tail length is elongated in the mutant ( Figure 4B ) . Thus , deadenylation of cort in the early embryo is dependent on the conserved CCR4-NOT deadenylase complex . We performed western analysis on CORT after the completion of meiosis to determine when CORT protein is expressed in the early embryo ( Figure 5A ) . Surprisingly , we found that CORT protein levels drop dramatically by the time meiosis is completed in unfertilized eggs . We detect CORT at very low levels for up to 1 . 5 h of embryogenesis before it disappears . Given the rapid timing of CORT degradation by the end of the meiotic divisions , we hypothesized that CORT itself may be a target of the APC/C . To test whether the APC/C plays a role in CORT degradation , we looked at CORT levels in mr/APC2 mutants ( Figure 5B ) . We found that CORT levels are unaffected in mr mutant ovaries , but CORT levels increase strongly in eggs from mr mutant females . As a positive control , we probed for Cyclin B in these samples and found that it is also elevated in mr mutant eggs ( unpublished data ) . These results parallel the timing of changes in CORT protein levels in a wild-type background; CORT levels normally drop by the time that meiosis is completed , and , similarly , the dependence of CORT degradation on mr is only apparent in unfertilized eggs , in which meiosis has been completed . These results strongly suggest that CORT is targeted for degradation by the APC/C at some point after the release of the metaphase I arrest and by the time that the meiotic divisions are completed . The specific timing of CORT degradation suggests that it is critical for development of the embryo that CORT protein levels be greatly reduced by the time meiosis is completed . The APC/C targets proteins for degradation through recognition of specific motifs in its substrates . The two most common motifs are D-boxes ( R-X-X-L-X-X-X-X-N ) and KEN boxes ( K-E-N-X-X-X-E/D/N ) , although additional motifs have been identified [4 , 14 , 64] . We identified a putative D-box in the C terminus of CORT ( residues 424–432 ) but no KEN box ( Figure 5C ) . To determine whether the putative D-box in CORT is functional , we constructed a D-box mutant form and asked whether protein stability is affected in an embryo injection experiment . We mutated all of the conserved residues in CORT's D-box to alanine ( Figure 5B ) and tagged it with MYC to distinguish the protein from endogenous CORT . We know that a MYC-tagged form of CORT is regulated in a similar way to endogenous CORT , because transgenic MYC-tagged CORT is degraded with similar developmental timing in embryos to endogenous CORT in vivo ( Figure S1 ) . MYC-tagged D-box mutant cort and MYC-tagged wild-type cort were transcribed in vitro . The RNA was microinjected into 0–30-min post-deposition wild-type embryos . After incubating the embryos to allow for translation of the RNA and post-translational modifications of the proteins , extracts were made and analyzed by western blot ( Figure 5D ) . We found that D-box mutant CORT is stabilized compared to wild-type CORT . Thus , the D-box motif in CORT is required for its degradation in early embryos . Given our previous observation that cort mRNA is deadenylated in early embryos , we wondered if this regulatory mechanism also contributes to the drop in CORT protein levels after the completion of meiosis . To determine whether Ccr4-mediated deadenylation of cort mRNA is required for low levels of CORT in early embryos , we looked at CORT protein levels in ccr4 mutants by western blot ( Figure 5E ) . We found that CORT protein levels are unchanged in both ccr4/Df stage 14 oocytes and 0–1-h embryos when compared to heterozygous sibling controls . This result suggests that although ccr4 is required for cort deadenylation , it is not required for a subsequent decrease in protein levels . Thus , APC/C-mediated degradation of CORT is the primary mechanism by which CORT protein levels are lowered in early embryos . It is likely that deadenylation serves as a backup mechanism to block future synthesis of CORT after fertilization . Given the dependence of CORT degradation on mr and an intact D-box , we wanted to determine which APC/C activator is responsible for CORT's destruction . FZR protein is undetectable in 0–2-h embryos and , thus , is not a good candidate [65] . FZY , however , is present in 0–2-h embryos and is the most likely activator of APC/C-mediated degradation of CORT [65] . To test this hypothesis , we first looked at CORT levels in eggs laid by fzy mutant females . We were unable to detect an increase of CORT protein in these embryos , but these alleles are hypomorphic and may not show an effect on CORT ( unpublished data ) . Next , we looked for genetic interactions between cort and fzy . If CORT is a substrate of APC/CFZY in the early embryo , we would expect them to antagonize each other in a genetic pathway . Reducing the level of cort expression in fzy mutants should suppress the fzy phenotype , and increasing the amount of cort expression in fzy mutants should enhance the fzy phenotype . We carried out these genetic tests using fzy female-sterile mutants in which embryos arrest in metaphase after a few mitotic divisions [50] . Reducing the gene copy number of cort by one in a fzy mutant background causes a modest suppression of the fzy phenotype . Over 75% of embryos laid by these double mutant females arrest with three or more mitotic spindles , whereas only 33% of embryos from fzy single mutants develop this far ( Figure 6A ) . Conversely , overexpressing cort in the germline of fzy mutant females slightly enhances the fzy phenotype . In this case , fzy embryos containing excess cort arrest with fewer mitotic spindles compared to fzy alone ( Figure 6B ) . The results of these genetic interaction tests are consistent with CORT being a substrate of APC/CFZY and suggest that the arrest phenotype of fzy embryos is due in part to the presence of excess CORT protein .
In this study , our demonstration of a physical interaction between CORT and the APC/C strengthens and confirms previous suggestions that cort encodes a functional meiosis-specific APC/C activator . A strong metaphase II arrest phenotype in cort mutant eggs and distant homology to the Cdc20/FZY protein family initially suggested that CORT might function as an APC/C activator [42 , 44] . More recently , cort was shown to negatively regulate levels of mitotic cyclin proteins , which is consistent with a role for CORT in activating the APC/C [30] . However , biochemical evidence linking CORT to the APC/C in vivo is crucial for this argument . We have shown that CORT physically associates with core subunits of the APC/C in ovaries , strongly supporting CORT's role as an APC/C activator . Coordination of the meiotic divisions with oogenesis and the transition from meiosis to restart of the mitotic cell cycle in embryogenesis present unique regulatory challenges for the organism . Our studies of cortex in Drosophila suggest that developmental control of levels of a meiosis-specific APC/C activator is one way in which meiosis is developmentally regulated , which has not been previously observed in a multicellular organism . This strategy exploits ongoing regulatory mechanisms occurring during meiosis and embryogenesis: cytoplasmic polyadenylation during oocyte maturation , deadenylation after egg activation , and APC/C-dependent degradation in the early embryo . Cytoplasmic polyadenylation upon oocyte maturation has been shown to translationally activate maternal transcripts of genes that are required for meiotic entry , transition between meiosis I and meiosis II , and metaphase II arrest in vertebrates [58] . We have shown that cort mRNA is polyadenylated at oocyte maturation , which adds an APC/C subunit to this group of transcripts that are translationally unmasked for entry into the meiotic divisions . What is the signal for polyadenylation of cort ? Masked transcripts contain a cis-acting cytoplasmic polyadenylation element ( CPE ) to which CPE binding protein ( CPEB ) is bound . Phosphorylation of CPEB upon oocyte maturation triggers elongation of the poly ( A ) tail and activation of translation [66] . We have not yet identified a CPE in the 3′ UTR of cort , although CPE sequences are quite variable . In addition , we have tested but have not observed a dependence of cort polyadenylation on orb , the CPEB homolog in Drosophila ( unpublished data ) . Because the orb alleles we used are hypomorphic , we cannot fully rule out the possibility that polyadenylation of cort is orb/CPEB-dependent . Egg activation triggers maternal transcript destabilization in several organisms , some of which occurs through ccr4-dependent deadenylation , and this is likely to be important for localization of maternal transcripts in the embryo and proper zygotic development [58] . We showed in this study that cort mRNA is deadenylated in the early embryo in a ccr4-dependent manner , but this deadenylation is not required for lowering CORT protein levels . However , we may not be able to detect a difference in protein levels because of the rapid APC/C-dependent degradation of CORT protein that occurs after release of the metaphase I arrest . Deadenylation could serve as a backup mechanism to ensure that CORT protein levels remain low in the early embryo by destabilizing cort mRNA . The APC/C drives degradation of Cyclin B and other substrates during the rapid syncytial mitotic divisions of early embryogenesis in Drosophila [50 , 65 , 67 , 68] . We found that CORT is targeted for APC/C-dependent degradation by the completion of meiosis in the early embryo . The targeting of an APC/C activator for degradation by another form of APC/C is not unprecedented , as APC/CCdh1 targets Cdc20 for degradation in G1 [16 , 17 , 19] . Our data support the conclusion that CORT is targeted by APC/CFZY . First , FZY is thought to be the only activator present in early embryos [65] . Second , we show here that cort and fzy interact genetically in a way that is consistent with cort being a negative downstream target of fzy in embryos . Third , in our embryo injection experiments , we showed that exogenous MYC-CORT is degraded in a D-box–dependent manner in injected embryos . Because the only APC/C activator in early embryos is FZY , degradation of MYC-CORT is likely to occur through APC/CFZY in this assay . It is also possible the APC/CCORT regulates itself in a negative feedback loop by targeting CORT for degradation when levels of CORT reach a certain threshold at the end of meiosis . To address this possibility , we looked at the degradation of CORT in a homozygous cortQW55 background in which there is no functional CORT protein . CORTQW55 mutant protein is not degraded at the transition from mature stage 14 oocytes to unfertilized eggs , unlike in a heterozygous control background ( unpublished data ) . These results suggest that CORT could be targeted by itself , but it remains a possibility that the lesion in the cortQW55 allele prevents an interaction between CORTQW55 mutant protein and the APC/C machinery . The lesion does not disrupt the D-box , but it could affect proper folding and structure of the protein . In summary , we conclude that CORT is targeted for degradation by the APC/C . It is most likely that FZY is the participating APC/C activator , but CORT may also contribute to targeting itself for degradation . Recent work has shown that both cort and fzy are required for the meiotic divisions in Drosophila female meiosis . Mutant analysis suggests that cort and fzy act redundantly to control the metaphase I to anaphase I transition , whereas they seem to act with different temporal and spatial specificity in targeting Cyclin B for destruction along the meiosis II spindles [30] . We showed in this study that cort cannot functionally substitute for fzy in the early embryo , suggesting that they target nonredundant sets of substrates . However , in this experiment , we cannot rule out the possibility that MYC-CORT was not present in sufficient levels in early embryos for rescue because of low expression levels or protein instability ( Figure S1 ) . Although MYC-CORT is expressed at high levels in stage 14 oocytes , it appears to be subject to degradation after the completion of meiosis , like the endogenous CORT protein . Furthermore , homozygous cort mutants alone exhibit a strong metaphase II arrest , indicating that the wild-type levels of fzy in this background are not able to act in place of cort to control passage through metaphase II [44] . Finally , we have observed that FZY is expressed at a uniform level during oogenesis and embryogenesis ( Figure S2 ) ( unpublished data ) , which is in contrast to our results in this study showing that CORT expression is specifically upregulated during the meiotic divisions . On the basis of all of these observations , we think it is likely that in addition to the mitotic cyclins , APC/CCORT targets a unique set of substrates in meiosis that are not recognized by APC/CFZY . The identification of these meiotic substrates will be crucial for understanding how the meiotic divisions are controlled in the oocyte . The study of meiotic control of the APC/C is especially intriguing in Drosophila , because in addition to cort , a female meiosis-specific activator , the genome contains fizzy-related 2 ( fzr2 ) , another member of the Cdc20/FZY family . fzr2 is expressed exclusively in testes and may act as a male meiosis-specific activator [69] . Further study of both cort and fzr2 will be important for understanding differential developmental regulation of the APC/C during meiosis in females versus males . In mitosis , cyclins are targeted sequentially for destruction by the APC/C . Degradation of Cyclin A begins just after nuclear envelope breakdown in prometaphase , while degradation of Cyclin B does not occur until the metaphase to anaphase transition [52 , 54–56] . Sequential degradation of Cyclin A , Cyclin B , and , finally , Cyclin B3 in Drosophila triggers a series of distinct events leading to exit from mitosis [51 , 70] . We have found that a similar situation exists in Drosophila female meiosis , in which degradation of Cyclin A by APC/CCORT initiates upon nuclear envelope breakdown , but degradation of Cyclin B and Cyclin B3 does not occur until after the metaphase I to anaphase I transition . The difference in timing of Cyclin A and Cyclin B degradation in mitosis is due to regulation of the APC/C by the spindle assembly checkpoint . The spindle assembly checkpoint inhibits APC/CCdc20 from initiating anaphase until all chromosomes are bioriented on the spindle , in part through direct binding of Cdc20 to Mad2 and BubR1 [71] . Spindle assembly checkpoint proteins specifically inhibit APC/C-dependent ubiquitination of Cyclin B but not of Cyclin A [52 , 55 , 56] . APC/CCORT may be regulated in a similar manner during meiosis I . Indeed , the spindle assembly checkpoint is likely to function during meiosis I in Drosophila , as the conserved spindle checkpoint kinase Mps1 is required for delaying entry into anaphase I to allow for proper segregation of achiasmate homologs and maintenance of chiasmate homolog connections in Drosophila oocytes [72 , 73] . Furthermore , a functional Mad2-dependent checkpoint exists during meiosis I in mouse oocytes , and spindle checkpoint components have been shown to regulate the APC/C during meiosis I in C . elegans [33 , 74 , 75] . To determine whether APC/CCORT is regulated by the spindle checkpoint , we asked if BubR1 or Mad2 physically associate with CORT in stage 14-enriched ovaries . We were unable to detect an association with BubR1 or Mad2 ( unpublished data ) . Although this negative result does not rule out the possibility of regulation of APC/CCORT by the spindle checkpoint , it suggests that APC/CCORT may be subject to other types of regulation that inhibit it from targeting Cyclin B and Cyclin B3 for degradation until after the metaphase I arrest . In conclusion , through the investigation of cortex , a meiosis-specific APC/C activator , we have found one way in which the meiotic cell cycle may be developmentally controlled during oogenesis . cort is developmentally regulated by existing post-transcriptional and post-translational mechanisms , resulting in expression of CORT protein being restricted to the meiotic divisions . Further study of APC/CCORT will continue to elucidate the ways in which developmental control of the APC/C contributes to proper female meiosis in a metazoan .
Crosses were performed , and flies were maintained between 22 °C and 25 °C using standard techniques [76] . The wild-type stocks used were Oregon R and yw . The cortRH65 and cortQW55 alleles have been described [42 , 44 , 77] . To obtain ccr4 mutant flies , ccr4KG877 , a ccr4 allele generated by the Berkeley Drosophila Genome Project ( http://www . fruitfly . org ) , was placed in trans to Df ( 3R ) crb-F89-4 , a large deficiency that deletes the ccr4 locus [63] . Female-sterile alleles of morula , mr1 and mr2 , were originally isolated from natural populations and have been described [78–80] . Female-sterile alleles of fizzy , fzy6 and fzy7 , have been described [50] . UASp myc-cort was made by PCR amplification of cort cDNA ( LD43270 ) and subcloning into pUASp with a 6xMYC tag at the N terminus . The LD43270 clone is missing coding sequence for nine amino acids on the 5′ end that we added during PCR amplification . Expression of 6xmyc-cort was driven in the female germline with the nanos-Gal4-VP16 driver [81] . The plu-myc transgenic line has been described [82] . To generate unfertilized eggs , we crossed virgin females to sterile males , which do not produce sperm but are able to stimulate females to lay eggs . The sterile males are from strain T ( Y;2 ) #11cn bwD mr2/b cn mr1 bs2/SM6A , a gift from B . Reed . To prepare ovary extracts for immunoprecipitations , whole ovaries were dissected in Grace's insect medium ( Gibco ) from 32 females fattened 3 d on wet yeast at 25 °C . Ovaries were homogenized in 3× volume homogenization buffer ( 25 mM HEPES [pH 7 . 5] , 0 . 4 M NaCl , 0 . 1 mM EDTA , 0 . 1 mM EGTA , 1 mM PMSF , 10% glycerol , complete mini EDTA-free protease inhibitors , 1 tablet/10 ml [Roche] ) , snap frozen in liquid nitrogen , and stored at −80 °C . A total of 30 μl Protein A Sepharose beads ( Amersham ) were precoupled to antibodies for 1 h at 4 °C . For precoupling , antibodies were as follows: 2 μl mouse IgG ( Sigma I5381 ) ; 12 μl mouse monoclonal anti-myc , 9E10 ( Covance ) ; 2 μl rabbit IgG ( Sigma I5006 ) ; or 10 μl affinity-purified rabbit anti-Cdc27 [68] . After removing an aliquot for input , 70 μl ovary extract was added to antibody-bound beads and incubated for 2–4 h at 4 °C . Beads were washed once in ice-cold IP buffer ( 25 mM HEPES [pH 7 . 5] , 100 mM NaCl , 1 mM EGTA , 0 . 1% Triton X-100 , 10% glycerol , complete mini EDTA-free protease inhibitors , 1 tablet/10 ml [Roche] ) , once in IP buffer plus 0 . 5 M NaCl , and 4 times in IP buffer . Inputs , immunocomplexes , and supernatants were resolved by SDS-PAGE and analyzed by immunoblot as described below . A fusion between GST and 152 amino acids from the N terminus of CORT was used to produce antibodies in guinea pigs . The construct encoding GST-CORT_N was made by PCR amplification of cort cDNA ( clone number LD43270 ) as described above , followed by subcloning into pGEX-4T-1 expression vector ( Pharmacia ) . GST-CORT_N was expressed in TOP10 E . coli cells by IPTG induction . The majority of GST-CORT_N was insoluble so it was gel purified from the insoluble material after cell lysis . Crude lysate was clarified , and the insoluble pellet resuspended in 5× Sample Buffer ( 60 mM 1 M Tris-HCl [pH 6 . 8] , 25% glycerol , 2% SDS , 14 . 4 mM 2-mercaptoethanol , 0 . 1% bromophenol blue ) . Sample was resolved by SDS-PAGE on a preparative 10% 150:1 ( 30% acrylamide/2% bis-acrylamide ) gel . Vertical strips from either side of the gel were stained with GelCode Blue ( Pierce ) and used as a guide to cut out the unstained band of GST-CORT_N . Gel slice was pulverized with cold 1× SDS Electrophoresis Buffer ( 25 mM Tris base , 192 mM glycine , 0 . 1% SDS ) through a 10 ml syringe and gently rocked for 30 min at 4 °C to elute protein . Gel slice mixture was filtered through 125 μm nylon mesh ( Tetko ) , and the eluate concentrated in Amicon Centricon YM-10 ( Millipore ) . Concentrated protein was injected into guinea pigs for antibody production ( Covance ) . The anti-CORT antibody recognizes a band of approximately 47 kDa that is the CORT protein . Protein extracts were made by homogenizing staged egg chambers , whole ovaries , unfertilized eggs , or embryos in 3:1 Urea Sample Buffer ( 8 M urea , 2% SDS , 100 mM Tris [pH 7 . 5] , 5% Ficoll ) /embryo ( vol/vol ) . Unfertilized eggs were collected for 0–2 h . Whole fly extracts were made by homogenizing flies directly in 5× Sample Buffer . Protein extracts were resolved by SDS-PAGE and transferred to Immobilon-P membranes ( Millipore ) . We used 10 . 5%–14% acrylamide gels for immunoprecipitations ( Figure 1 ) and substrate blots ( Figure 2 ) . We used 10% acrylamide gels for all CORT blots and RNA injection assays ( Figures 3 and 5 ) . Equal amounts of protein were loaded per lane and confirmed by anti-α-Tubulin blotting . Blots were probed with the following antibodies: mouse monoclonal anti-MYC , 9E10 ( 1:1000 , Covance ) ; affinity-purified rabbit anti-Cdc27 ( 1:500 [68] ) ; affinity-purified rabbit anti-MR ( 1:200 [83] ) ; guinea pig anti-CORT serum ( 1:2000 ) ; rat monoclonal anti-α-Tubulin , YL1/2 and YOL1/34 ( 1:200 , Harlan Sera-lab ) ; mouse monoclonal anti-Cyclin A , A19 ( 1:50 , gift of P . O'Farrell ) ; mouse monoclonal anti-Cyclin B , F2F4 ( 1:200 [84] ) ; rabbit anti-Cyclin B3 serum ( 1:4000 [51] ) ; rabbit anti-PIM serum ( 1:10 , 000 [85] ) ; and affinity-purified rabbit anti-FZY ( 1:1000 [65] ) . Alkaline phosphatase- or horseradish peroxidase-conjugated secondary antibodies were used to detect bound primary antibodies . Protein was detected using ECL Plus ( Amersham ) . Ovary or embryo mRNA was isolated using the PolyATtract System 1000 ( Promega ) . LM-PAT assays were performed using 100 ng mRNA as described [60] . cDNA was made using the Reverse Transcription System ( Promega ) . PCRs were performed with message-specific primers , and a fraction of the PCR product was tested on a gel to permit approximately equal loading of the PCR product for the experiment . PCR products were separated on a 2% MetaPhore agarose gel and stained with ethidium bromide . Point mutations were introduced into cort cDNA using the Phusion Site-Directed Mutagenesis Kit ( Finnzymes ) . Wild-type or mutated D-box cort cDNA was subcloned into pCS2 containing a 6xMYC tag at the N terminus . Capped mRNAs were synthesized from these vectors using the SP6 mMessage mMachine Kit ( Ambion ) . mRNA was purified using the MEGAclear Kit ( Ambion ) . yw embryos that were 0–30 min postdeposition were dechorionated and prepared for injection . Samples were prepared containing 250 ng/μl wild-type or mutant cort RNA in injection buffer ( 5 mM KCl , 0 . 1 M K2HPO4 [pH 7 . 8] ) . A no RNA control contained injection buffer alone . Each sample was injected into 150 embryos . After 40 min at room temperature , the embryos were harvested in heptane , washed 2 times in embryo wash ( 0 . 4% NaCl , 0 . 03% Triton X-100 ) , and homogenized in 20 μl USB . Extracts were resolved by SDS-PAGE and analyzed by immunoblotting as described above . The experiment was repeated 5 independent times to confirm results . Eggs were collected for 0–3 h for Figure 6A and for 2–4 h for Figure 6B , dechorionated in 50% bleach , devitellinized in methanol and heptane , and fixed in methanol for 3 h at room temperature or overnight at 4 °C . Eggs were stained for DNA with Propidium Iodide and for Tubulin with rat monoclonal anti-α-Tubulin , YL 1/2 , and YOL 1/34 ( 1:20 , Harlan Sera-lab ) . Antibodies were detected using fluorescent secondary antibodies ( Jackson Immunoresearch ) . Imaging was performed using a Zeiss Axioskop .
The FlyBase ( http://flybase . bio . indiana . edu/search/ ) accession numbers for genes and gene products discussed in this paper are bubR1 ( FBgn0025458 ) , ccr4 ( FBgn0011725 ) , cdc16 ( FBgn0025781 ) , cdc27 ( FBgn0012058 ) , cort ( FBgn0000351 ) , cycA ( FBgn0000404 ) , cycB ( FBgn0000405 ) , cycB3 ( FBgn0015625 ) , fzr ( FBgn0003200 ) , fzr2 ( FBgn0034937 ) , fzy ( FBgn0001086 ) , grau ( FBgn0001133 ) , mad2 ( FBgn0035640 ) , mr ( FBgn0002791 ) , orb ( FBgn0004882 ) , and pim ( FBgn0003087 ) . | Meiosis is a modified cell cycle that generates four gametes , each containing half the genetic content of the parent cell , through a reductional division followed by an equational division without an intervening DNA synthesis phase . During oogenesis of multicellular organisms , proper coordination of the meiotic divisions with the development of the oocyte is crucial for successful fertilization and the initiation of zygotic development . Very little is known about how general cell-cycle regulators as well as meiosis-specific regulators contribute to this coordination . In this study we describe the role and developmental regulation of cortex , a meiosis-specific activator of the anaphase-promoting complex/cyclosome ( APC/C ) . CORT protein physically associates with the APC/C and triggers the sequential degradation of mitotic cyclins in meiosis . We find that cortex is subject to both post-transcriptional and post-translational regulatory mechanisms , which result in expression of CORT protein being restricted to the meiotic divisions . This developmental regulation may be important for proper meiosis as well as the transition from the completion of meiosis to mitotic divisions in the early embryo . | [
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] | 2007 | Developmental Role and Regulation of cortex, a Meiosis-Specific Anaphase-Promoting Complex/Cyclosome Activator |
The RRM-type RNA-binding protein Mei2 is a master regulator of meiosis in fission yeast , in which it stabilizes meiosis-specific mRNAs by blocking their destruction . Artificial activation of Mei2 can provoke the entire meiotic process , and it is suspected that Mei2 may do more than the stabilization of meiosis-specific mRNAs . In our current study using a new screening system , we show that Mei2 genetically interacts with subunits of CTDK-I , which phosphorylates serine-2 residues on the C-terminal domain of RNA polymerase II ( Pol II CTD ) . Phosphorylation of CTD Ser-2 is essential to enable the robust transcription of ste11 , which encodes an HMG-type transcription factor that regulates the expression of mei2 and other genes necessary for sexual development . CTD Ser-2 phosphorylation increases under nitrogen starvation , and the stress-responsive MAP kinase pathway , mediated by Wis1 MAPKK and Sty1 MAPK , is critical for this stress response . Sty1 phosphorylates Lsk1 , the catalytic subunit of CTDK-I . Furthermore , a feedback loop stemming from activated Mei2 to Win1 and Wis4 MAPKKKs operates in this pathway and eventually enhances CTD Ser-2 phosphorylation and ste11 transcription . Hence , in addition to starting meiosis , Mei2 functions to reinforce the commitment to it , once cells have entered this process . This study also demonstrates clearly that the stress-responsive MAP kinase pathway can modulates gene expression through phosphorylation of Pol II CTD .
The cell cycle programs for mitosis and meiosis appear to be strictly segregated from each other , although they are likely to have molecular mechanisms in common . Analyses in lower eukaryotes have shown that factors required exclusively for meiosis , generated through the transcriptional activation of meiosis-specific genes , are largely responsible for the segregation of these two processes [1] , [2] . In addition , we have reported previously in fission yeast that meiosis-specific mRNAs transcribed at the wrong time during the mitotic cell cycle are removed selectively by nuclear exosomes , thereby preventing the inappropriate expression of the meiotic program in mitotic cells [3] , [4] . The master meiotic regulator in fission yeast , Mei2 , an RNA-binding protein with three RRM domains [5]–[7] , suppresses the function of this selective removal system by sequestering a key component Mmi1 , which is an RNA-binding protein of the YTH family [3] . Mei2 thus ensures full expression of meiosis-specific genes and facilitates execution of the meiotic program ( reviewed in [8] ) . However , it is unlikely that the function of Mei2 in meiosis is confined to the tethering of Mmi1 as the artificial inactivation of Mmi1 does not induce the full meiotic program , whereas the experimental induction of the activated form of Mei2 does so [3] , [6] . The mechanisms and pathways by which Mei2 promotes the entire meiotic program is therefore a subject of considerable interest . To identify possible upstream or downstream effectors of Mei2 , we devised a new screening system and found that a subunit of CTDK-I , which is a CDK-like kinase complex that phosphorylates the C-terminal repeat domain of the largest subunit of RNA polymerase II ( Pol II CTD ) [9] , [10] , could genetically interact with Mei2 . More specifically , the phosphorylation of Pol II CTD by CTDK-I was found to affect the expression of ste11 , which encodes a key transcription factor that regulates the mei2 gene . Pol II CTD serves as a binding scaffold for a variety of nuclear factors , and its phosphorylation status has been implicated in regulation of an ever-increasing number of functions necessary to execute complex transcriptional processes [9] , [10] . Our aforementioned findings indicate that the phosphorylation of Ser-2 residues on Pol II CTD in fission yeast is unique in that it is required mainly for the meiotic program , via the activation of ste11 transcription , but is not absolutely necessary for the mitotic program . Essentially the same conclusions have been reached independently by others , through global gene expression analysis [11] . Here we further show that the stress-responsive MAP kinase cascade is crucial for the phosphorylation of Ser-2 residues under nutrient starvation , which is a condition suitable for meiosis . We also show that artificially activated Mei2 has the potential to promote the phosphorylation of Ser-2 residues on Pol II CTD via the stress-responsive MAP kinase cascade , irrespective of the nutrient conditions . Taken together , the results of our present study demonstrate a new regulatory paradigm for meiosis by Mei2 in fission yeast , i . e . , that this master meiotic regulator ensures the commitment to meiosis by strengthening the transcription of ste11 via a feedback loop comprising the stress-responsive MAP kinase cascade and the phosphorylation of Pol II CTD by CTDK-I .
The haploid fission yeast strain JV312 harbors the mei2-L-SATA allele driven by the authentic mei2 promoter . This allele contains a combination of two mutations , mei2-L and mei2-SATA . The former mutation confers temperature-sensitivity to the Mei2 protein ( our unpublished results ) , whereas the latter activates this gene constitutively , overriding the inhibitory phosphorylation by Pat1 kinase [6] . JV312 cells arrest during vegetative growth and induce ectopic meiosis at 25°C because the Mei2-L-SATA protein is functional at this temperature . However , these cells continue vegetative growth at 32°C because Mei2-L-SATA is then inert and does not interfere with cell growth pathways . To identify novel upstream regulators or downstream effectors of Mei2 , we screened for suppressor mutants that could grow at 25°C by insertional mutagenesis of JV312 ( see Materials and Methods ) . Several suppressor mutants were thereby isolated , one of which was found to contain an insertion in SPBC4B3 . 08 , which is annotated in the fission yeast database ( http://old . genedb . org/genedb/pombe/ ) to encode a homologue of the γ subunit of RNA polymerase II C-terminal domain kinase I ( CTDK-I ) . CTDK-I belongs to the CDK family , but in addition to the catalytic subunit α and the cyclin-like regulatory subunit β conserved among these family members , it contains a third γ subunit [12] , [13] . In fission yeast , the lsk1 and lsc1 genes encode the α and β subunits of the CDK proteins , respectively [14] , [15] . Hereafter , we designate SPBC4B3 . 08 as lsg1 . Because the level of homology between fission yeast Lsg1 and Saccharomyces cerevisiae CTDK-I γ ( CTK3 ) was found not to be high ( a 24% amino acid identity; Figure S1 ) , we examined whether Lsg1 was indeed a functional homolog of CTDK-I γ . We constructed the lsg1-deletion strain by replacing the entire lsg1 ORF with a drug-resistant cassette , and compared its phenotype with that of lsk1Δ and lsc1Δ . The lsg1Δ strain exhibited no significant defects in mitotic growth , like the lsk1Δ and lsc1Δ strains previously analyzed [14] , [15] ( Figure 1A ) . The doubling time in liquid YE medium at 30°C was 2 . 1 h for the wild-type , 2 . 2 h for lsg1Δ , 2 . 3 h for lsk1Δ , and 2 . 2 h for lsc1Δ , respectively . However , lsg1Δ cells showed hypersensitivity to Latrunculin A , an inhibitor of actin polymerization , which was a phenotype reported previously for lsk1Δ and lsc1Δ [14] , [15] ( Figure 1A ) . In addition , both lsk1Δ and lsc1Δ could suppress the growth defect of mei2-L-SATA at 25°C as efficiently as lsg1Δ ( Figure 1B ) . These observations confirmed that lsg1 indeed encodes the CTDK-I γ subunit , and indicated that loss of CTDK-I activity is responsible for the suppression of mei2-L-SATA . Although deletion of the gene encoding each CTDK-I subunit led to no obvious defect under normal growth conditions , these deletion mutants all showed impairments in conjugation and sporulation under starved conditions . Under these conditions , haploid lsg1Δ , lsk1Δ or lsc1Δ cells conjugated at a lower frequency than wild-type cells , and diploid lsg1Δ , lsk1Δ or lsc1Δ cells underwent azygotic meiosis and sporulation at a lower frequency than wild-type cells ( Figure 1C ) . We further found that the progression of the meiotic cell cycle was significantly retarded in the CTDK-I subunit mutants . Fluorescence-activated cell sorting ( FACS ) analysis indicated that diploid lsg1Δ , lsk1Δ or lsc1Δ cells began to arrest in G1 phase as late as eight hours after the shift to nitrogen starvation and showed minimal premeiotic DNA synthesis even after 24 hours . In contrast , wild-type cells began to arrest in G1 phase after two hours and completed premeiotic DNA synthesis at between 2 and 6 hours ( Figure 1D ) . Our observations that the CTDK-I deletion mutants were defective in sexual development and could suppress growth deficiency , evoked by the mei2-L-SATA allele , led us to speculate that the expression of ste11 , which encodes an HMG-family transcription factor , might be impaired in these mutants . Our reasoning was that 1 ) Ste11 regulates the transcription of many genes essential for sexual development , including mei2 [16]; 2 ) the deletion of ste11 has been shown to suppress ectopic meiosis induced by the pat1 mutation and restore vegetative growth , by blocking the expression of mei2 [17] , [18]; and 3 ) we had noticed that ste11Δ cells show G1 arrest retardation under conditions of nitrogen starvation , even more extensively than lsg1Δ , lsk1Δ or lsc1Δ cells , while mei2Δ cells are not so much affected ( Figure S2A ) . We thus analyzed the transcription of ste11 in lsg1Δ cells and found that it was significantly suppressed ( Figure S2B ) . Because requirement of CTDK-I for the expression of ste11 has been independently discovered and already reported by Hermand and his colleagues [11] , we briefly summarize our data that confirm their conclusions in Figures S2 and S3 . We tested whether the forced expression of ste11 could recover sexual development in the CTDK-I deletion mutants . The overexpression of ste11 from the nmt1 promoter , which is roughly four to five times as strong as the physiological expression , effectively recovered conjugation and subsequent meiosis in lsg1Δ , lsk1Δ and lsc1Δ homothallic haploid cells ( Figure S2C ) , indicating that the loss of ste11 expression is a major cause of the mating and sporulation deficiency in the CTDK-I mutants . We then determined the range of genes whose expression is regulated by CTDK-I , by comparing the gene expression profiles between lsg1Δ and wild-type cells starved of nitrogen for 2 . 5 hours . Genome-wide microarray analysis indicated that the expression of 64 genes was downregulated more than two-fold in the lsg1Δ mutant , whereas 22 genes showed upregulation by more than two-fold in the mutant ( Figure S3A ) . Notably , 33 out of the 64 downregulated genes identified , including ste11 itself , have been shown previously to be controlled by Ste11 [19] . These genes are listed in Table S1 . In contrast , the expression of atf1 , pcr1 , rst2 , and other genes that also encode an upstream regulator of ste11 transcription [20]–[25] , was not significantly affected by the deletion of lsg1 ( Figure S3B ) , suggesting that CTDK-I may exert its effects on ste11 transcription directly . Previous work has shown that Lsk1 is involved in the phosphorylation of Ser-2 residues within the heptad repeats of the carboxy terminal domain ( CTD ) of RNA polymerase II [15] . To determine whether the Pol II CTD phosphorylation status might be changed by the induction of sexual development , we analyzed phosphorylation of Ser-2 and Ser-5 residues within the CTD before and after the shift to nitrogen-depleted medium . Extracts were prepared from wild-type and lsg1Δ homothallic haploid cells , either growing or shifted to nitrogen-free minimal medium , and the phosphorylation of CTD was examined using monoclonal antibodies that recognize either phospho-Ser-2 , phospho-Ser-5 , or unphosphorylated CTD . As shown in Figure 2A , the phosphorylation of Ser-2 residues on the CTD repeats was increased by nitrogen starvation in wild-type cells , but not in lsg1Δ cells . The level of phospho-Ser-5 was unaffected by nitrogen starvation in both strains . These results suggest that nitrogen starvation induces the phosphorylation of CTD Ser-2 residues by CTDK-I . We next evaluated the possibility that the insufficient phosphorylation of CTD Ser-2 residues in the CTDK-I mutants underlies their sexual development deficiency . For this purpose we examined the phenotypes caused by two rpb1 alleles ( reported by J . Karagiannis and kindly provided to us ) , namely rpb1-12×CTD and rpb1-12×S2ACTD . The former allele produces Rpb1 carrying a CTD that consists of 12 copies of the authentic heptad repeat ( YSPTSPS ) , whereas the latter produces Rpb1 with 12 copies of a mutant heptad repeat in which Ser-2 is substituted by alanine ( YAPTSPS ) [15] . Wild-type Rpb1 carries 29 repeats of the heptad [26] , but the previous work has shown that 12 repeats are sufficient for cell viability [15] . Cells carrying the rpb1-12×S2ACTD allele were impaired severely in terms of conjugation and sporulation ( Figure 2B ) , and the transcription of ste11 was greatly reduced in them ( Figure 2C ) . Furthermore , the sterility of the rpb1-12×S2ACTD strain was rescued , although not completely , by the overexpression of ste11 ( Figure 2D ) . These results strongly suggest that CTDK-I facilitates the transcription of ste11 by phosphorylating Ser-2 residues on Pol II CTD . In general , the rpb1-12×S2ACTD strain showed severer phenotypes than the CTDK-I mutants with regard to sexual development , probably because CTD Ser2 could also be phosphorylated supplementarily by Cdk9 [11] . We wished to determine the mechanism by which nitrogen starvation caused the increased phosphorylation of CTD Ser-2 by CTDK-I . The concentration of CTDK-I subunits per cell was not found to be significantly altered upon nitrogen starvation ( Figure S4A ) . We also measured the levels of Fcp1 , a phosphatase that has been shown to preferentially remove phosphate groups from synthetic CTD peptides phosphorylated on Ser-2 [27] , [28] . However , the levels of this protein were also not changed significantly upon nitrogen starvation ( Figure S4B ) . It has been reported in S . cerevisiae that CTD Ser-2 phosphorylation increases both upon heat shock and during the diauxic shift [29] . The phosphorylation of CTD Ser-2 is also known to be elevated by an exposure to hydroxyurea or UV irradiation [30] . We speculated therefore that nitrogen starvation may be recognized as a stress , which could then affect the phosphorylation status of the CTD in fission yeast . We hence examined the possible involvement of Sty1 ( also called Spc1/Phh1 ) , a MAP kinase known to be crucial to the response to stress [31]–[33] , in CTD phosphorylation . As shown in Figure 3A , the phosphorylation of CTD Ser-2 in response to nitrogen starvation was dramatically reduced in sty1Δ cells compared with wild-type cells . Deletion of the atf1 gene , which encodes a target of Sty1 MAPK , also significantly affected Ser-2 phosphorylation , whereas the ste11 and mei2 genes appeared to be dispensable for this phosphorylation event in response to nitrogen starvation ( Figure 3A ) . Deletion of pcr1 , which encodes a bZIP protein that forms a heterodimer with Atf1 [21] , [23] , did not affect Ser-2 phosphorylation significantly ( Figure S5 ) , and produced a much less severe phenotype compared with mutants lacking atf1 , as observed previously for other features [23] , [34] . The deletion of rst2 , which encodes a transcription factor necessary to activate ste11 in response to glucose starvation and cAMP reduction [24] , [25] , also had no affect on Ser-2 phosphorylation ( Figure S5 ) . We then examined the effects of a forced activation of the Sty1 MAPK pathway , by expressing a constitutively active form of Wis1 MAPKK in the yeast cells . Phosphorylation of Ser-2 was induced by expression of the active MAPKK from a plasmid , even in the presence of ample nitrogen ( Figure 3B ) . However , this ectopic phosphorylation was not observed in lsk1Δ cells ( Figure 3B ) , indicating that the observed phosphorylation was mediated by CTDK-I . These results suggest that the activation of Sty1 MAP kinase in response to nitrogen starvation is pivotal to the promotion of CTD Ser-2 phosphorylation by CTDK-I . To examine if the stress-responsive MAPK Sty1 directly phosphorylates CTDK-I , we prepared an in vitro phosphorylation system as detailed in Materials and Methods . Each subunit of CTDK-I , namely Lsk1 , Lsc1 or Lsg1 , was fused with GST , and the fusion proteins were affinity-purified . Pk-tagged Sty1 MAPK ( Sty1-Pk ) and its kinase-dead form ( Sty1-KD-Pk ) were prepared respectively from S . pombe strains NJ761 and NJ767 , provided kindly by N . Jones , as described previously [34] . The kinase preparation and each GST-fusion protein were mixed and incubated in the kinase reaction buffer supplemented with [γ-32P]-ATP . As shown in Figure 3C , GST-Lsk1 appeared to be phosphorylated by Sty1 , although the full-length protein apparently underwent extensive proteolysis and a possible degradation product was the most heavily labeled . GST-Lsc1 and GST-Lsg1 , as well as the control GST , did not appear to be a good substrate of Sty1 in this analysis ( Figure 3C ) . To confirm that Sty1 could phosphorylate Lsk1 , we divided Lsk1 into two parts , the N- and C-terminal halves , and fused each of them to GST ( Figure 3D ) . These fusion proteins were relatively stable , and when mixed with active Sty1 , the N-terminal half was significantly phosphorylated ( Figure 3D ) . Moreover , our preliminary analysis has shown that at least serine 109 on Lsk1 , which constitutes a MAPK substrate consensus sequence PGSP , is a preferred phosphorylation site for Sty1 ( data not shown ) . Analysis of Lsg1 dissected into two parts confirmed that it was not likely to be a substrate of Sty1 ( data not shown ) . These results indicate that Sty1 MAPK is likely to phosphorylate Lsk1 directly and thereby activate CTDK-I , which in turn phosphorylates CTD Ser-2 residues . We made a surprising observation when we analyzed the status of CTD Ser-2 phosphorylation in cells undergoing ectopic meiosis induced by artificial expression of the activated form of Mei2 , i . e . , Mei2-SATA . As we reported previously [6] , these cells underwent meiosis in the presence of ample nitrogen , a condition that does not stimulate the stress-responsive Sty1 MAP kinase cascade . However , the phosphorylation of CTD Ser-2 was observed in these meiotic cells ( Figure 4A ) . Given this finding , we speculated as to whether the phosphorylation of CTD Ser-2 during Mei2-SATA-induced meiosis was dependent on CTDK-I and/or Sty1 . We further tested relevant mutant strains and found that the Mei2-SATA-induced Ser-2 phosphorylation was abolished in lsk1Δ and reduced dramatically in sty1Δ , indicating its stringent dependency on both of these factors ( Figure 4B ) . Sty1 has been positioned upstream of mei2 expression in the stress-responsive signal transduction pathway and in cooperation with a chromatin-remodeling factor Atf1 , activates the transcription of ste11 [20]–[22] . The produced Ste11 in turn binds to the upstream region of mei2 and activates the transcription of this gene [16] . We thus hypothesized that activated Mei2 can affect its upstream factors through a feedback regulation . To identify the component of the stress-responsive signaling pathway that is feedback-regulated by Mei2 , we examined mutants that are defective in components of the pathway that function upstream of Sty1 . Sty1 MAPK is activated by Wis1 MAPKK [31] , [32] , [35] , [36] , which in turn is activated by either Wis4/Wak1 MAPKKK or Win1 MAPKK [37]–[39] . A response regulator protein , Mcs4 , associates with Wis4/Wak1 , and probably also with Win1 , to regulate the MAPKKK activity [38] , [40] . We investigated the phosphorylation of Ser-2 during Mei2-SATA-induced meiosis in mcs4Δ , wis4Δ , win1Δ , and wis4Δ win1Δ mutant strains , together with control wild-type , lsk1Δ , sty1Δ , and ste11Δ strains . As summarized in Figure 4B , the phosphorylation of Ser-2 was observed in mcs4Δ and ste11Δ cells , indicating that Mcs4 and Ste11 are not directly involved in the feedback activation of Ser-2 phosphorylation . Ser-2 phosphorylation was observed also in the wis4Δ and win1Δ mutants but was found to be greatly reduced in the wis4Δ win1Δ double mutant . These results indicated that the feedback signals from activated Mei2 might ultimately merge with the stress-responsive MAPK cascade at the Wis4/Wak1 and Win1 MAPKKKs , although there could be a third target because Ser-2 phosphorylation was not completely abolished in wis4Δ win1Δ ( Figure 4B ) . We observed that the level of Sty1 MAPK phosphorylation increased during Mei2-SATA-induced meiosis ( Figure 4C ) , which reinforces the presence of a signaling pathway from Mei2 to the MAPK cascade . To evaluate physiological significance of the feedback , we examined whether activation of Mei2 would result in enhancement of ste11 expression during meiosis . Firstly , we induced ectopic meiosis by shifting the mei2-L-SATA strain from 32°C to 25°C in the presence of rich nutrition . As shown in Figure 5A , expression of ste11 was evident in this strain but not in the wild-type , and this expression was dependent on lsk1 . Secondly , we induced ectopic meiosis by shifting the temperature-sensitive pat1-114 mutant from 25°C to 34°C . Again , expression of ste11 was induced significantly in pat1-114 cells under rich nutrition , in an lsk1-dependent manner ( Figure 5B ) . Deletion of mei2 blocked ste11 expression in these cells . The temperature-shift did not induce ste11 expression in wild-type ( Figure 5B ) or mei2Δ cells ( not shown ) . These results indicate clearly that activation of Mei2 can stimulate expression of ste11 through phosphorylation of PolII CTD . We finally evaluated the contribution of the feedback regulation to the expression of ste11 during meiosis under physiological conditions . To do so , we used the mei2-FA allele , which produces inactive Mei2 protein [5] , [6] . We compared expression of ste11 and mei2 in wild-type and mei2-FA cells subjected to nitrogen starvation . As shown in Figure 5C , the level of ste11 mRNA , normalized by ribosomal RNA , and that of mei2 mRNA also , were higher in wild-type cells than in mei2-FA cells , and the difference became greater in later stages . This suggests that activated Mei2 protein in wild-type cells indeed enhances ste11 expression via feedback . Taken together , we propose that fission yeast possess a regulatory circuit , as depicted in Figure 5D , which is likely to be crucial in ensuring an irreversible commitment to meiosis and a strict differentiation of the mitotic and meiotic cell cycle programs .
In our present study , we have demonstrated that a genetic interaction exists between the subunits of CTDK-I , a protein kinase complex that phosphorylates RNA polymerase II CTD , and the master meiotic regulator in fission yeast , Mei2 . Furthermore , our analyses indicate that a loss of CTDK-I function impairs the transcription of the ste11 gene , which encodes a transcription activator essential for the expression of mei2 and other genes crucial for sexual development . However , this loss of function does not significantly affect the gene expression required for vegetative growth . In an independent study , Hermand and colleagues have performed genome-wide mapping of three kinds of CTD kinases and also of serine 2- and 5-phosphorylated Pol II in fission yeast to investigate the link between CTD phosphorylation and specific cellular events [11] . Consequently they have found that the CTDK-I catalytic subunit Lsk1 and Ser-2-phosphorylated Pol II associate with a rather limited number of transcription units and play only minor roles during vegetative growth , but become essential during sexual development . These authors have further reported that nitrogen starvation enhances recruitment of Lsk1 to the ste11 gene , and remarked that the phosphorylation of CTD Ser-2 plays a highly specialized role in gene regulation in fission yeast , unlike in other organisms , and is virtually confined to the regulation of a single key gene controlling sexual differentiation . Our study fully supports this notion . While a subsequent study [26] suggests that the deleterious effects of loss of Ser-2 phosphorylation on ste11 transcrition can be compensated partially by loss of Ser-7 phosphorylation , the nature of such extreme specification and its evolution is an intriguing enigma . Our present data have further shown that the stress-responsive MAP kinase pathway is crucial for the activation of CTDK-I under conditions of nitrogen starvation . The requirement for Sty1 MAPK and its target Atf1 for the expression of ste11 has been known for some time [20]–[22] , but the details of the molecular mechanisms involved have remained unknown . It now appears that CTD Ser-2 phosphorylation is a key step in the activation of ste11 expression by the Sty1 MAPK cascade . It has been shown that when phosphorylated and activated by Wis1 MAPKK , the Sty1 protein migrates to the nucleus and resides on the promoter regions of stress-responsive genes [31] , [34] , [41] . This is also the case for the Sty1 ortholog in S . cerevisiae Hog1 [42] , [43] . As shown above , Sty1 can directly phosphorylate Lsk1 in vitro . While the phosphorylation of Lsk1 in vivo remains to be confirmed , it appears to be conceivable that Sty1 may also be recruited to the ste11 promoter and phosphorylate CTDK-I staying there , which in turn phosphorylates CTD and licenses RNA polymerase II to transcribe the gene . In this regard , it is noteworthy that hsp9 , which encodes a small heat-shock protein [44] and is one of the genes responsible for the “core environmental stress response” or CESR in fission yeast [45] , was detected among our possible target genes upregulated by CTD Ser-2 phosphorylation ( Table S1 ) . Interestingly , Reiter et al . have shown previously that Sty1 MAPK is recruited to the promoter of hsp9 and other CSRE genes upon osmotic stress in an Atf1-dependent manner , but does not necessarily phosphorylate Atf1 as a substrate [34] . This suggests that ste11 and hsp9 may be similarly regulated by the Sty1 – CTDK-I – CTD phosphorylation system . However , conventional Chip analyses have not provided convincing evidence for the association of Sty1 with the ste11 promoter , and we are conducting further experiments to scrutinize this possible scheme . The results of our present analyses demonstrate unambiguously that a feedback-regulatory system operates in fission yeast during the meiotic cell cycle . In this feedback loop , the active form of Mei2 can eventually stimulate the stress-responsive MAPKKKs and enhance the transcription of ste11 through the Sty1 – CTDK-I – CTD phosphorylation system . From our findings we can outline a framework of the molecular mechanisms that differentiate the mitotic and meiotic programs in fission yeast as in Figure 5D . However , it remains currently unknown how the RNA-binding protein Mei2 can fulfill such a never-anticipated task and how many steps may mediate between Mei2 and the MAPKKKs , raising another challenging scientific query as represented by the broken line in Figure 5D .
The S . pombe strains used in this study are listed in Table S2 . The general genetic procedures used in the S . pombe experiments were as described previously [46] . Complete medium YE , minimal medium SD , minimal medium MM and its nitrogen-free derivative MM-N [47] , synthetic sporulation medium SSA [48] were used to culture the cells . Transformation of S . pombe was performed using the lithium acetate method [49] . The ura4+ cassette used for insertion mutagenesis was amplified by PCR using the primers N18AGCTTAGCTACAAATCCCACTGGCT and N18TGTGATATTGACGAACTTTTTGAC ( N18: 18b random DNA sequence ) . The PCR products were then introduced into JV312 ( mei2-L-SATA ura4-D18 ) cells , and transformants were plated onto SD lacking uracil and incubated at 25°C . Colonies were selected , and the site of ura4+ integration was determined via the sequencing of inverse PCR products [50] . Samples were prepared for flow cytometry essentially as described previously [51] and then analyzed using a FACScan ( Becton-Dickinson , San Jose , CA ) . JY450 ( wild-type ) and JT659 ( lsg1Δ ) cells were grown to mid-log phase in MM medium and shifted to MM-N medium . The cells were collected 2 . 5 h after the shift , and total RNA was extracted as described previously [52] . Data acquisition and normalization were performed by Roche Applied Science , Japan . The microarray data was deposited to the GEO database under the accession number of GSE32516 . Northern blot analysis was performed as described [53] . DNA fragments used to probe for transcripts of ste11 , rpb1 and mei2 were labeled with [α-32P] dCTP using random primers . Cell extracts were prepared and separated essentially as described earlier [54] . Briefly , cells grown to the mid-log phase were shifted to nitrogen-free medium , and sampled at various intervals . Total lysates were extracted and resolved by SDS-PAGE . Immunoblotting was performed using primary antibodies specific to unphosphorylated CTD ( 8WG16 , Covance , Princeton , NJ , used at 1∶2000 ) , Ser-5 phosphorylated CTD ( H14 , Covance , used at 1∶2000 ) , Ser-2 phosphorylated CTD ( H5 , Covance , used at 1∶1000 ) , Mei2 ( Our lab preparation , used at 1∶1000 ) , the phosphorylated form of Sty1 MAPK ( P-p38 MAPK , Cell Signaling Technology , Danvers , MA , used at 1∶500 ) , or GFP ( clones 7 . 1 and 13 . 1 , Roche Applied Science , Indianapolis , IN , used at 1∶1000 ) . As secondary antibodies , donkey anti-rabbit IgG conjugated with horseradish peroxidase ( GE Healthcare , Waukesha , WI ) was used for the Mei2 and P-p38 MAPK antibodies at a dilution of 1∶2000 . Sheep anti-mouse IgG conjugated with horseradish peroxidase ( GE Healthcare ) was used to detect all other primary antibodies at a dilution of 1∶2000 . Immunoblotting with a monoclonal antibodies specific for α-tubulin , either TAT-1 ( a gift from Dr . Keith Gull , University of Birmingham ) [55] , or Clone B-5-1-2 ( Sigma Aldrich , St . Louis , MO ) , was performed as a loading control . Cells expressing chromosomally tagged Sty1-3Pk ( NJ761 ) , or Sty1KD-3Pk ( NJ767 ) were subjected to nitrogen starvation for 1 h . Extracts were prepared , protein immunoprecipitated , and the immuno-complexes tested for kinase activity as described [34] . Affinity purified GST-fusion proteins were used as substrates . | Hundreds of genes are newly expressed during meiosis , a process to form gametes , and the control of meiosis-specific gene expression is not simple . The master regulator of meiosis in fission yeast , Mei2 , blocks an RNA destruction system that selectively degrades meiosis-specific mRNAs , highlighting the importance of post-transcriptional control in meiotic gene expression . Here we present another example of unforeseen regulation for meiosis . Ste11 is a key transcription factor responsible for the early meiotic gene expression in fission yeast . The ste11 gene is transcribed robustly only when serine-2 residues on the C-terminal domain ( CTD Ser-2 ) of RNA polymerase II are phosphorylated . We show that the stress-responsive MAP kinase cascade transmits the environmental signal to stimulate CTD Ser-2 phosphorylation . Sty1 MAP kinase appears to phosphorylate and activate the catalytic subunit of CTDK-I , which in turn phosphorylates CTD Ser-2 . We demonstrate further that Mei2 , expression of which depends on Ste11 , can activate the MAP kinase cascade , forming a feedback loop . Thus , we clarify here three important issues in cellular development: the physiological role of CTD Ser-2 phosphorylation , the molecular function of the stress-responsive MAP kinase pathway , and the presence of positive feedback that reinforces the commitment to meiosis . | [
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] | 2011 | The Fission Yeast Stress-Responsive MAPK Pathway Promotes Meiosis via the Phosphorylation of Pol II CTD in Response to Environmental and Feedback Cues |
The interactions that occur during HIV Pr55Gag oligomerization and genomic RNA packaging are essential elements that facilitate HIV assembly . However , mechanistic details of these interactions are not clearly defined . Here , we overcome previous limitations in producing large quantities of full-length recombinant Pr55Gag that is required for isothermal titration calorimetry ( ITC ) studies , and we have revealed the thermodynamic properties of HIV assembly for the first time . Thermodynamic analysis showed that the binding between RNA and HIV Pr55Gag is an energetically favourable reaction ( ΔG<0 ) that is further enhanced by the oligomerization of Pr55Gag . The change in enthalpy ( ΔH ) widens sequentially from: ( 1 ) Pr55Gag-Psi RNA binding during HIV genome selection; to ( 2 ) Pr55Gag-Guanosine Uridine ( GU ) -containing RNA binding in cytoplasm/plasma membrane; and then to ( 3 ) Pr55Gag-Adenosine ( A ) -containing RNA binding in immature HIV . These data imply the stepwise increments of heat being released during HIV biogenesis may help to facilitate the process of viral assembly . By mimicking the interactions between A-containing RNA and oligomeric Pr55Gag in immature HIV , it was noted that a p6 domain truncated Pr50Gag Δp6 is less efficient than full-length Pr55Gag in this thermodynamic process . These data suggest a potential unknown role of p6 in Pr55Gag-Pr55Gag oligomerization and/or Pr55Gag-RNA interaction during HIV assembly . Our data provide direct evidence on how nucleic acid sequences and the oligomeric state of Pr55Gag regulate HIV assembly .
The assembly of the HIV particle is orchestrated by the HIV-1 Gag precursor protein ( Pr55Gag ) . As the major structural protein forming the virus , it is essential that Pr55Gag packs tightly with other Pr55Gag molecules to shape the virus shell while also encapsulating the genomic RNA required for its replication . Fittingly , Pr55Gag is comprised of multi-functional domains that facilitate key interactions involved in the early stages of the assembly process . The nucleocapsid ( NC ) domain is responsible for genomic RNA packaging and the capsid ( CA ) domain mediates the key interactions to promote Pr55Gag-Pr55Gag oligomerization ( for review see [1–3] ) . While both Pr55Gag-nucleic acid and Pr55Gag-Pr55Gag interactions have been identified as major contributors to the assembly process [4] , the mechanistic details of how they regulate HIV assembly are not well defined , specifically in the context of full length Pr55Gag . It is generally accepted that nucleic acid binds to Pr55Gag and acts as a scaffold for Pr55Gag oligomerization [5–7] , although short oligonucleotides ( 10 nucleotides or less ) are not sufficient to bridge multiple Pr55Gag molecules to support assembly [8 , 9] . The p6 C-terminus truncated recombinant Gag protein ( Pr50GagΔp6 ) , with the addition of nucleic acid and lipid-mimicking inositol phosphate , has previously been shown to assemble in vitro , forming virus-like particles ( VLPs ) in the absence of other viral proteins [7 , 8] . Cellular imaging and immunoprecipitation studies with virion producing cells have suggested that nucleic acid initially binds to cytosolic Pr55Gag , therefore promoting the formation of low order oligomers of Pr55Gag in the cytosol; higher order Pr55Gag oligomers only occur upon Pr55Gag binding to the plasma membrane [10–13] . However , cytoplasmic HIV-1 RNA has also been shown to traffic to the membrane by passive diffusion independent of Pr55Gag , suggesting that the viral RNA genome may bind to Pr55Gag molecules on the plasma membrane where a high local concentration of Pr55Gag has already been achieved for oligomerization [14] . At the plasma membrane , the assembly of Pr55Gag has also led to the reorganization of the lipid membrane , such as the nano-clustering of phosphatidyl inositol ( 4 , 5 ) biphosphate lipid [15] . Clearly , the relationships between nucleic acid binding and Pr55Gag oligomerization that lead to virus assembly are still unclear . Moreover , the energy requirements and thermodynamic properties that drive these two basic components to facilitate the formation of the viral particle are currently not known . In this regard , a thermodynamic analysis of the assembly process will provide critical information ( such as , the affinity and the stoichiometry between ligands and substrates , plus the energetics involved in these reactions ) to define the mechanisms of the process . Specifically , enthalpic and entropic components of the binding reaction derived from the analysis will enable us to gain insight into the mechanisms of the interactions that take place . Apart from the packaging signal ( Psi ) of the genomic RNA that is involved in the encapsidation of viral genome , the contributions from the rest of the viral RNA sequence to the assembly process is not well defined . Early studies have indicated that the mature nucleocapsid domain on its own displays preferential binding to selected nucleic acid sequence motifs in addition to the Psi packaging sequences [9 , 16 , 17] . Studies comparing Psi and non-Psi RNA binding to C-terminal truncated Pr55Gag protein reported differences in the electrostatic and non-electrostatic interactions of Pr55Gag proteins with different RNAs [18] , and we have shown how Pr55Gag selected between unspliced and spliced HIV RNA in vitro for viral assembly [19 , 20] . Recent cross-linking immunoprecipitation experiments have shown that different RNA motifs interact with Pr55Gag at different stages of virus biogenesis [21] . However , the functional significance and the mechanisms of these specific Pr55Gag-RNA interactions in regulating the assembly of Pr55Gag have yet to be shown . A key bottleneck in acquiring fundamental information about the mechanistic details of Pr55Gag assembly has been the limited availability of full-length recombinant Gag protein for analysis , as large quantities of full-length recombinant Pr55Gag are difficult to produce [8 , 22] . Here , we have produced large quantities of recombinant Pr55Gag and use in vitro systems to study the early steps of HIV assembly . Using the entire form of recombinant Pr55Gag in isothermal titration calorimetry ( ITC ) studies , we present the first thermodynamic analysis of how HIV Pr55Gag and RNA regulate assembly . More specifically , we directly demonstrate that: ( 1 ) the specific interaction between Pr55Gag and Adenosine ( A ) -containing RNA motifs; and ( 2 ) the oligomerization of Pr55Gag; are energetically favourable reactions that facilitate virion particle formation . Our findings benchmark the thermodynamic regulations of retroviral assembly , and provide a platform to interrogate , and ultimately to reveal , structural and biophysical properties between HIV Pr55Gag and host cell factors during viral replication .
The binding of RNA to Pr55Gag and the oligomerization of Pr55Gag molecules are two of the basic components of viral assembly . However , a thermodynamic analysis unpacking how these two components drive the assembly process has never been conducted in the context of full length Pr55Gag . We produced sufficient quantities of recombinant full-length Pr55Gag ( S1 Fig ) and validated its capacity to oligomerize and form VLPs in the presence of RNA in vitro , similar to previous observations with the truncated Pr50GagΔp6 [8 , 23–26] ( S2 Fig ) . Using ITC , we first characterized the binding between Pr55Gag protein ( domain arrangements of Pr55Gag proteins used schematized in Fig 1A ) and the packaging signal stem loop 3 RNA ( Psi SL3 RNA ) , a region of the viral RNA involved in genomic packaging and known to bind to NC with high affinity [17 , 27] . Representative ITC curves were shown in Fig 1B , while data in Fig 1C–1F reflect the means of all three experiments . The representative ITC curves in Fig 1B are the raw values prior to adjustments ( such as fluctuations of detected temperature of the reaction; fluctuation of cabinet temperature of the ITC chamber for each point of the measurement; and the normalization of baseline temperature with the corresponding buffer controls ) . The ITC curves also acted as quality assurance with these ITC experiments , demonstrating the reliability of the data . We first benchmarked the thermodynamics properties of HIV assembly using Pr55Gag molecules that are capable of oligomerizing ( Pr55Gag and Pr50GagΔp6 ) and Psi SL3 RNA that is the primary determinant for genomic RNA selection in the cytoplasm . Both Pr55Gag and Pr55GagΔp6 displayed a favourable binding enthalpy [ΔH ~ -17 kcal mol-1] as measured by the maximum heat energy release during ITC titrations of Psi SL3 RNA into the Pr55Gag protein ( Fig 1B and 1C ) . The reaction is also thermodynamically favourable with ΔG<0 . The detected energy release in these reactions is likely to be the sum of both Pr55Gag-RNA interaction and Pr55Gag-Pr55Gag oligomerization . To estimate the fraction of energy released that is contributed by Pr55Gag-RNA vs Pr55Gag-Pr55Gag oligomerization , we have used a mutant , Pr55Gag WM316-7AA ( or Pr55Gag WM ) [28] that is incapable of supporting the dimerization of Pr55Gag via the CA-hexamer dimerization domain . Therefore , mutant Pr55Gag WM is known to partially suppress the assembly of HIV via the dimerization defect of the CA hexamers [4] . As measured by the maximum heat energy release during ITC titrations of Psi SL3 RNA into the Gag protein , interaction of Psi SL3 RNA with Pr55Gag WM displayed a favourable binding enthalpy [ΔH ~ -10 kcal mol-1] ( Fig 1B and 1C ) , but it was only 60% of the energy release of the wild type Pr55Gag-RNA interaction ( Fig 1B and 1C ) . These data implied that the oligomerization of Pr55Gag during Pr55Gag-RNA interaction contributed to at least 40% of energy detected in the ITC experiment , and the Pr55Gag- Pr55Gag interactions increased its favourable binding enthalpy with Psi SL3 RNA . In support of this , interaction of Psi SL3 RNA with both processed NC proteins ( p15NC-SP2-p6 and p7NC that lack the CA-CA interaction domains for oligomerization ) also displayed less favourable binding enthalpies [ΔH ~ -5-6 kcal mol-1] compared to the full-length Gag proteins Pr55Gag . The interaction of Pr55Gag and Pr50Δp6Gag with Psi SL3 RNA however had a more unfavourable entropic contribution compared to that of Pr55Gag WM ( Fig 1D ) . As entropy is a measure of the disorder of the reaction [29] , the unfavourable entropy likely results from loss of conformational freedom due to Pr55Gag-Pr55Gag oligomerization . Nevertheless , the larger accompanying favourable enthalpic contributions observed with Psi SL3 binding to Pr55Gag and Pr50Δp6Gag compensates for the unfavourable entropic component to produce an overall favourable Gibbs free energy ( ΔG<0 ) of binding ( Fig 1E ) , highlighting that the Pr55Gag-RNA ( SL3 ) interaction is an energetically favourable process overall . On the other hand , reactions with processed NC proteins ( p15NC-SP2-p6 and p7NC ) were characterized by favourable entropic contributions , likely driven by displacement of ordered water molecules [30] as the result of NC—Psi RNA interaction ( Fig 1D ) . The favourable entropic component of Psi SL3 binding to p15NC-SP2-p6 and p7NC , combined with the accompanying favourable enthalpic component contributed to an overall favourable Gibbs free energy ( ΔG<0 ) of binding . Interestingly , calculated binding affinities for Pr55Gag [Kd = 0 . 081 ± 0 . 025 μM] , Pr50Δp6Gag [Kd = 0 . 098 ± 0 . 003 μM] , Pr55Gag WM316-7AA [Kd = 0 . 088 ± 0 . 035 μM] , p15NC-SP2-p6 [Kd = 0 . 057 ± 0 . 011 μM] and p7NC [Kd = 0 . 048 ± 0 . 023μM] did not differ significantly ( Fig 1F ) . These data imply that the binding between Pr55Gag and SL3 RNA for genomic RNA selection during viral assembly is independent of the oligomerization state of Pr55Gag . Our ITC results demonstrate that the binding of Psi SL3 RNA to Pr55Gag is energetically favourable ( ΔG<0 ) . Bindings of Psi SL3 to p15NC-SP2—p6 and p7NC are results of both favourable entropic and enthalpic contributions , suggesting the involvement of both polar and hydrophobic interactions [30] . By definition , these interactions must occur between the Psi SL3 RNA and the nucleocapsid protein . Reactions of Psi SL3 with full length Pr55Gag that has an increased capacity to oligomerize were driven by a larger favourable enthalpy , which helps in overcoming the unfavourable entropy brought about by conformational restrictions due to oligomerization . During the assembly of HIV , there are only 2 sets of SL3 sequences in the packaged HIV dimeric RNA genome . As a result , most of the 1500–2500 Pr55Gag molecules in the assembled virion must bind to other segments of the RNA genome . HIV Pr55Gag has been previously reported to transiently interact with different RNA sequence motifs during the viral assembly process [21] . It has been suggested that cytosolic Pr55Gag consisting of low-order Pr55Gag oligomers interact with Guanosine Uridine ( GU ) -containing RNA sequence motifs , while high-order Pr55Gag oligomers in the immature virion are more likely to associate with Adenosine ( A ) -containing RNA sequence motifs ( schematized in Fig 2A ) . However , the role these non-packaging segments of the RNA genome play in viral assembly still remains unanswered . These GU-containing RNA sequences and A-containing RNA sequences are also interspersed throughout the HIV genome and in many cellular RNA sequences ( S3 Fig ) . To investigate whether the thermodynamic relationship with Pr55Gag can provide insight into the function of these sequences , we conducted ITC analyses of Gag interaction with the top 4 RNA sequence motifs that were previously identified to interact with HIV Gag either in the cytosol ( GU-containing RNA motifs: 5’GAUGG3’ and 5’UGUGG3’ ) or within the immature virion ( A-containing RNA motifs: 5’GAGAA3’ and 5’AAGGA3’ ) [21] . Binding of the 20 mer of the GU-containing RNA motif 4x 5’-GAUGG-3’ RNA to the oligomerization-impaired Pr55Gag WM ( mimicking the low-order oligomeric form of cytosolic Gag ) resulted in an enthalpy release [ΔH ~-20 kcal mol-1] ( Figs 2B and 3A ) , which is comparable to that released in the binding to processed NC proteins ( p15NC-SP2-p6 and p7NC ) ( Figs 2B and 3A ) . In contrast , higher enthalpy was released in the binding of the same 20mer GU-containing RNA motif 4x 5’-GAUGG-3’ RNA with the oligomeric forms of Gag ( Pr55Gag and Pr50GagΔp6 ) with means of ΔH ~-45 kcal mol-1 and -35 kcal mol-1 , respectively ( Fig 3A ) ( or raw values of ΔH ~-35 kcal mol-1 and -30 kcal mol-1 , respectively , in the un-adjusted representative ITC curves , Fig 2B ) , suggesting that oligomerization contributes at least in part to the favourable enthalpy release of Pr55Gag binding with GU-containing RNA . These data are consistent with those observed with Psi SL3 RNA based Pr55Gag-RNA ITC studies ( Fig 1 ) . In support of this , more enthlapy release was also observed in the interaction between the 20 mer of A-containing RNA motif 4x 5’-GAGAA-3’ RNA and the oligomeric forms of Gag ( Pr55Gag and Pr50GagΔp6 ) , with means of ΔH ~-80 kcal mol-1 ( Fig 3A ) ( or raw values of ΔH ~-70 kcal mol-1 and -60 kcal mol-1 , respectively , in the un-adjusted representative ITC curves , Fig 2B ) , over the interaction between the 20 mer of A-containing RNA motif 4x 5’-GAGAA-3’ RNA and the oligomeric-deficient forms of Gag ( Pr55Gag WM ) [ΔH ~ -60 kcal mol-1] ( Fig 3A ) ( or raw value of -40 kcal mol-1 in the un-adjusted representative ITC curve , Fig 2B ) . Agreeably , ITC analyses repeated with the second set of GU-containing and A-containing RNA sequence motifs ( 4x 5’-UGUGG-3’ and 4x 5’-AAGGA-3’ , respectively ) produced a similar trend in binding enthalpies measured across the Gag constructs ( Figs 2C and 3C ) . Our results showed that the favourable enthalpy and Gibbs Free energy release from oligomerization can potentially be a general feature of RNA-Pr55Gag protein interaction during viral assembly . Conversely , across the Gag constructs tested independent of their size and oligomeric capacity , thermodynamic analysis also indicated that the interaction of the A-containing RNA motifs with Gag were characterized by about 2-times more favourable enthalpy compared to that of the GU-containing RNA motifs ( Fig 3A and 3C ) . This result suggests that the type of RNA sequence interacting with the Gag protein also contributes to the favourable release of enthalpy . Additionally , the type of RNA sequence may also be a determinant of how tightly packed Gag can bind . An analysis of the stoichiometry of binding showed a trend that 1 A-containing RNA molecule would bind to 5 Gag molecules ( RNA:Gag N~0 . 2 ) in comparison to 1 GU-containing RNA molecule would bind to 3 Gag molecules ( RNA:Gag N~0 . 33 ) ( Fig 4 ) . However , it is important to acknowledge that the binding stoichiometry data are related to a single type of GU-containing RNA motif and a single type of A-containing RNA motif , and these motifs are artificially presented in 4 consecutive repeats within a synthetic RNA . The potential importance of different RNA-Gag binding stoichiometry must be validated using multiple different natural A-containing RNA motifs and natural GU-containing RNA motifs within the context of HIV RNA genome . Furthermore , a larger unfavourable entropy was detected when A-containing RNA motifs were used in ITC experiments compared to GU-containing RNA motifs ( Fig 3B and 3D ) , suggesting that these designated A-containing RNA motifs might in part better support tight packing of Pr55Gag-RNA and/or Pr55Gag-Pr55Gag interaction during the biological process , leading to greater loss of conformational freedom . Nevertheless , the larger favourable binding enthalpy associated with binding of A-containing RNA motifs to Pr55Gag overcomes the unfavourable entropy to drive the reaction forward . These differential thermodynamic relationships between Pr55Gag and A-containing RNA motifs in immature virus vs Pr55Gag and GU-containing RNA motifs in cytoplasm have provided insight on how viral particle formations are regulated . The Gibb’s free energy ( ΔG ) consistently remained at ~-10 kcal mol-1 ( Fig 3E and 3F ) , indicating the process is an energetically favourable spontaneous event . We have also performed additional control experiments showing that less than 5% of materials can be pelleted from solution after our ITC experiments using Gag and RNA ( S4A Fig ) . Furthermore , similar amount of pelletable materials ( <5% ) were collected in parallel ITC experiments when nucleic acid free buffer was injected into the ITC chamber containing recombinant Gag ( S4B Fig ) . These data implied that no virus-like-particles were generated in these ITC reactions under our experimental conditions when ‘low’ concentrations of Pr55Gag and RNA were used ( S4 Fig ) . In addition to the more favourable binding enthalpy of A-containing RNA to various Gag constructs , calculated binding affinities ( kd ) from ITC A-containing and GU-containing RNA binding curves revealed that oligomeric capable Pr55Gag displayed ~3-times stronger binding affinity for the immature virus A-containing RNA motif over the cytosolic GU-containing RNA motif ( Fig 5A and 5B ) . This occurrence was not observed when oligomerization-impaired forms of Gag ( Pr55Gag WM ) were used in parallel experiments ( Fig 5A and 5B ) . Unexpectedly , Pr50Δp6Gag , which is capable of high-order Gag oligomerization , also did not display binding preference toward A-containing RNA motif ( Fig 5A and 5B ) . It is possible that the p6 domain in Pr55Gag plays a part in facilitating the binding of A-containing RNA to oligomeric forms of Pr55Gag , implying a previously unknown role of p6 late domain in the packing of Pr55Gag-Pr55Gag and/or Pr55Gag-RNA interaction at the late stage of HIV assembly . To independently assess whether cytosolic low-order Gag oligomer binding with A-containing RNA is a less energetically favourable process than the interaction between high-order Gag oligomer and A-containing RNA , three additional Gag oligomerization defective mutants were engineered for analysis . These Gag oligomierization-impaired mutants are: ( 1 ) Pr55Gag ( CA Helix 6 mutations , TTSTLQ 239–44 AASALA ) , Pr55Gag [CA Helix 6] ( Fig 6A left panel ) [28 , 31]; ( 2 ) Pr55Gag ( CA Helix 10 mutation , D329A ) , Pr55Gag [CA Helix 10] ( Fig 6A middle panel ) [28 , 31]; and ( 3 ) multiple sites Gag oligomerization mutant ( designated Pr55Gag [CA All 4] ) ( Fig 6A right panel ) that includes 4 sets of mutations at the: Pr55Gag ( CA dimerization interface WM 316–7 AA ) , Pr55Gag ( CA helix 6 TTSTLQ 239–44 AASALA ) , Pr55Gag ( CA helix 10 D329A ) , and Pr55Gag CA major homology region ( MHR K290A ) [28] . These respective mutations ( ie helix 6 , helix 10 and MHR mutations ) were chosen based on their reported inhibitory effects on viral assembly [28 , 31] , likely through their disruption of important intra-hexameric contacts within the immature CA hexamer [32] ( Fig 6A ) . ITC analyses of these three Pr55Gag oligomerization impaired mutants with the 20mer of cytosol GU-containing RNA ( 4x 5’-GAUGG-3’ ) showed minimal to no detectable binding ( Fig 6A ) . In contrast , the binding of these same mutants with the 20mer of immature HIV A-containing RNA ( 4x 5’-GAGAA-3’ ) have led to energy release in the range of [ΔH ~ -18-24 kcal mol-1] ( Fig 6A ) . These data are consistent with that of the previously described oligomerization impaired mutant , Pr55Gag WM ( Fig 2 ) , showing that oligomerization of Pr55Gag can help to increase the level of energy being released during Pr55Gag-RNA interaction in HIV assembly . Given that the C-terminus His-Tag is present in all of the recombinant Gag used in this study thus far , it is unlikely that the His-Tag would selectively interfere with the binding for one of these three groups of RNA ( Psi RNA , GU-containing RNA , and A-containing RNA ) with Gag . However , to directly rule out any potential selective bias that might be introduced by the C-terminus His-Tag , a Tobacco Etch Virus ( TEV ) protease cleavage site was engineered in between the C-terminus end of Gag sequences and the His Tag for Pr55Gag , Pr50GagΔp6 , and Pr55Gag WM . The His-Tag was removed via TEV protease digestion , and the His-Tag free recombinant Gag proteins ( Pr55Gag-TEV , Pr50GagΔp6-TEV , and Pr55Gag WM -TEV ) were subsequently column purified for analysis . ITC analyses of Pr55Gag-TEV [Fig 6B left panel] , Pr50GagΔp6-TEV [Fig 6B middle panel] , and Pr55Gag WM-TEV [Fig 6B right panel] with either GU-containing RNA ( 4x 5’-GAUGG-3’ ) or A-containing RNA ( 4x 5’-GAGAA-3’ ) resulted in energy release ranging from ΔH ~ -8 kcal mol-1 to ~ -50kcal mol-1 ( Fig 6B ) . More specifically , the ΔH are: ( 1 ) ~-10 kcal mol-1 for Pr55Gag-TEV-GU-containing RNA binding [Fig 6B left panel , dark blue]; ( 2 ) ~-10 kcal mol-1 for Pr50Gag Δp6-TEV-GU-containing RNA binding [Fig 6B middle panel , dark orange]; ( 3 ) ~-10 kcal mol-1 for Pr55Gag WM-TEV-GU-containing RNA binding [Fig 6B right panel , green]; ( 4 ) ~-45 kcal mol-1 for Pr55Gag-TEV-A-containing RNA binding [Fig 6B left panel , light blue]; ( 5 ) ~-25 kcal mol-1 for Pr50Gag Δp6-TEV-A-containing RNA binding [Fig 6B middle panel , light orange]; ( 3 ) ~-25 kcal mol-1 for Pr55Gag WM-TEV-A-containing RNA binding [Fig 6B right panel , dark yellow] . Analogous to the ITC results using the His-Tag containing Gag ( Pr55Gag , Pr50GagΔp6 , and Pr55Gag WM ) ( Fig 2 ) , Gag binding with A-containing RNA ( 4x 5’-GAGAA-3’ ) were consistently shown to be a more energetically favourable reaction than parallel analyses that used GU-containing RNA ( 4x 5’-GAUGG-3’ ) as substrates ( Fig 6B ) . Furthermore , the binding between Pr55Gag-TEV and A-containing RNA was a more thermodynamically favourable reaction than that between Pr50GagΔp6-TEV and A-containing RNA , highlighting the potential role of p6 in this process . Although the C-terminus His-Tag has no selective bias on the overall data interpretation , it is important to acknowledge that reduced levels of enthalpy ( ΔH ) were also detected with the Gag-RNA binding when the C-terminus His-Tag was removed from the recombinant Gag ( Fig 2 vs Fig 6B ) , implying that the C-terminus His-Tag has consistently increased the amounts of enthalpy release during Gag-RNA interaction . It is conceivable that the synthetic 20mer RNA with the 4 consecutive A-containing RNA repeats ( 4x 5’-GAGAA-3’ ) does not truly represent the binding between HIV Gag and RNA sequences during viral assembly . To directly examine the relationship between authentic HIV RNA sequences and HIV Pr55Gag protein during viral assembly , a 37mer RNA fragment representing the coding region of HIV reverse transcriptase ( RNA positions 2671–2707 ) was used for ITC analyses . Using CLIP-sequencing , this RNA sequence ( HIVNL4 . 3RNA2671-2707 ) , consisting 3 independent A-containing RNA motifs , has been previously identified to be important for Pr55Gag binding in immature HIV [21] . The same 37mer RNA fragment with mutations of the AGAAA RNA motifs ( HIVNL4 . 3RNA2671-2707 with AGAAA mutation ) was used as a control . ITC analyses of Pr55Gag-TEV , Pr50GagΔp6-TEV , and Pr55Gag WM-TEV with the 37 mer RNA fragment HIVNL4 . 3RNA2671-2707 resulted in energy release ranging from ΔH of ~ -30 kcal mol-1 to ~ -45kcal mol-1 ( Fig 6C ) , and the level of energy release with Pr55Gag-TEV was 50% and 28% more than when Pr50GagΔp6-TEV and Pr55Gag WM-TEV were used , respectively ( Fig 6C ) . In contrast , mutations of these identified A-containing RNA motifs within these authentic HIV RNA sequences eliminated most to all of its bindings with HIV Gag proteins ( Fig 6C ) . The distinct level of energy release between Pr55Gag-TEV and Pr50GagΔp6-TEV based ITC experiments further support a potential role of p6 in the Gag-RNA and/or Gag-Gag interaction at the late stage of HIV assembly . Overall , our ITC analyses provide direct evidence that the interaction between HIV Gag and RNA for Pr55Gag oligomerization is an energetically ( ΔG<0 ) favourable reaction , and it is associated with a favourable enthalpy ( ΔH ) and unfavourable entropy ( ΔS ) . There is a general binding preference of HIV Gag toward A-containing RNA during viral assembly . Unexpectedly , our data have revealed that the p6 domain within Pr55Gag also has a role in this Pr55Gag-A-containing RNA binding preference during virion biogenesis , which has raised a new question on a novel contribution of the p6 domain in the process of Gag-RNA interaction during viral assembly .
Our thermodynamic analyses showed that Pr55Gag-RNA binding was energetically favourable in terms of free energy exchange ( ΔG<0 ) and that both Pr55Gag-Pr55Gag interactions and the type of RNA sequence can contribute to the favourable change in enthalpy . Many studies have focused on the interaction of nucleic acids with the mature forms of the NC domain to probe the chaperone activity [16 , 33] and nucleic acid binding properties of NC during reverse transcription [34 , 35] . Our ITC studies represent the first thermodynamic characterization of the binding interaction between nucleic acids and full length Pr55Gag , allowing us to study the effects of Pr55Gag-Pr55Gag interaction and Pr55Gag-RNA interaction on energy release . In the context of Pr55Gag assembly , these ITC analyses reflect the net energy exchange when nucleic acids are injected into the Pr55Gag-Pr55Gag and Pr55Gag-RNA complexes . It is important to emphasize that some of the observed energy exchange detected via ITC would be the consequence of Pr55Gag-Pr55Gag oligomerization . Our analyses between Pr55Gag and SL3 RNA suggested that at least 40% of energy release in our ITC experiment is derived from Pr55Gag oligomerization . Our ITC study with RNA and full length Pr55Gag ( that is capable of forming higher-order Pr55Gag oligomers ) suggest that the additional release of energy from Pr55Gag-Pr55Gag interactions could drive the binding of Pr55Gag with nucleic acid and the oligomerization of Pr55Gag . This interpretation is consistent with recent in vitro membrane-bound Pr55Gag assembly data showing that the genomic RNA selection by Pr55Gag and the self-assembly of Pr55Gag are interdependent [36] . Likewise , our observation that the Pr55Gag binding with immature HIV A-containing RNA motifs has a greater ΔH than Pr55Gag binding with the GU-containing cytosolic Pr55Gag-interacting RNA motifs during HIV biogenesis . These data suggest the additional heat release ( that is associated with a greater entropic penalty ) could help drive the Pr55Gag-complex to interact preferentially with A-containing RNA sequences during assembly . Furthermore , our thermodynamic analyses indicated that the oligomeric capable Pr55Gag has 3-times greater affinity for immature virus A-containing RNA motifs compared to cytosolic GU-containing RNA sequences , but that oligomerization-impaired form of Gag ( Pr55Gag WM ) does not . Similar to Pr55Gag WM , other oligomerization-impaired forms of Gag ( Pr55Gag [CA Helix6] , Pr55Gag [CA Helix 10] and Pr55Gag [CA All 4] ) are consistent to have a less favorable [ΔH] in comparison to the wild type Pr55Gag and A-containing RNA ITC analyses . The nucleic acid sequence dependence of the binding enthalpy is likely related to the process of HIV assembly and virion genesis . Early work by Campbell and Rein [37] has shown that recombinant Pr50GagΔp6 has distinct behavior with different nucleic acids during in vitro assembly . Recent work by Kutluay et al have reported that HIV Pr55Gag would have a different preference towards distinct RNA sequence during virion genesis [21] , although the precise mechanistic details of this relationship between HIV Pr55Gag and specific RNA sequences during viral assembly will require further investigation . These data allow us to propose a model for how viral assembly steps are regulated . The unfavorable entropic reaction with A-containing RNA motif and an increased binding stoichiometry between Gag and A-containing RNA sequences are also in agreement with a much tighter packing of Pr55Gag molecules in the immature virus stage over low-order oligomeric Pr55Gag in the cytoplasm . It is known that the HIV-RNA genome has a bias towards A-containing codons [38 , 39] . While many of these A-rich sequences might be a consequence of G to A hyper mutation from APOBEC3G pressure [40 , 41] , HIV has found ways to utilise them to its own gain . Indeed , these A-rich viral sequences are already known to be important for RNA trafficking from nucleus to cytoplasm via the RRE-Rev relationship [42 , 43] , and also for supporting the synthesis of viral cDNA during reverse transcription [44] . Now , we have evidence to suggest the A-rich RNA codon ( in the form of identified A-containing RNA motifs ) [21] may also have a role in regulating the viral assembly process thermodynamically . It is important to note that HIV Pr55Gag consistently displayed more favourable enthalpy [ΔH] when it binds to A-containing RNA motifs over GU-containing RNA motifs , and it is remains to be determined why HIV Pr55Gag has a binding preference with GU-containing RNA motifs in the cytoplasm and the plasma membrane [21] . One potential explanation could be that the context of how these GU-containing RNA motifs are being presented in HIV genomes ( or cellular mRNA ) is also an important determinant for Pr55Gag-RNA binding during HIV assembly , and further investigations are needed to dissect this process . Moreover , it would be important to highlight that the preference of Pr55Gag toward A-containing RNA is in part associated with the p6 domain . This unexpected observation suggests a previous unknown role of p6 in Pr55Gag-RNA interaction , and how p6 ( and potentially in conjunction with ESCRT protein ) might achieve this mechanistically would require further evaluation . Taken together , our findings show how the virus might derive the energy required to drive the Pr55Gag-Pr55Gag assembly and the mechanism by which HIV-1 assembles . We propose a model ( Fig 5C ) wherein many Pr55Gag molecules bind to GU-containing RNA in the cytosol forming low-order oligomers . The Pr55Gag-RNA complex then traffics to the plasma membrane and acts as a nucleation site for Pr55Gag assembly; with energy released from Pr55Gag-Pr55Gag and Pr55Gag-RNA interactions driving the formation of higher order Pr55Gag oligomerization complexes . The high-order Pr55Gag complex in turn interacts preferentially with A-containing viral RNA sequences with a lower Kd , and the process is further enhanced by the additional release of heat through interaction with the A-containing viral RNA sequences to assist in the packaging of the genome , thus driving the completion of the particle formation .
Recombinant Gag proteins ( Pr55Gag , Pr50Δp6Gag , Pr55Gag WM 316–7 AA , Pr55Gag [CA Helix 6 TTSTLQ 239–44 AASALA] , Pr55Gag [CA Helix 10 D329A] , Pr55Gag [CA All 4] ) and processed NC proteins ( p15NC-SP2-p6 , p7NC ) were expressed with C-term His-tag and purified as previously described [45] . Large scale production and purification of Pr55Gag is described herein . Defined medium ( DM1 ) used for seed cultures contained per litre: KH2PO4 , 13 . 3 g; ( NH4 ) 2HPO4 , 4 . 0 g; citric acid , 1 . 7 g; glucose , 10 g; MgSO4 . 7H2O , 0 . 62 g; kanamycin , 50 mg; thiamine hydrochloride , 4 . 4 mg; and trace salts solution , 5 mL . Defined medium ( DM2 ) used in the bioreactors contained per litre: KH2PO4 , 10 . 6 g; ( NH4 ) 2HPO4 , 4 . 0 g; citric acid , 1 . 7 g; glucose , 25 g; MgSO4 . 7H2O , 1 . 23 g; kanamycin , 50 mg; thiamine hydrochloride , 4 . 4 mg; and trace salts solution , 5 mL . The trace salts solution contained per litre: CuSO4 . 5H2O , 2 . 0 g; NaCI , 0 . 08 g; MnSO4 . H2O , 3 . 0 g; Na2MoO4 . 2H2O , 0 . 2 g; boric acid , 0 . 02 g; CoCl2 . 6H2O , 0 . 5 g; ZnCl2 , 7 . 0 g; FeSO4 . 7H2O , 22 . 0 g; CaSO4 . 2H2O , 0 . 5 g and H2SO4 , 1 mL . As required , glucose , magnesium , trace salts , thiamine and kanamycin were aseptically added as concentrated stock solutions to media after sterilisation . Primary seed cultures were prepared from single colonies taken from a fresh transformation plate , and grown in 10 mL of DM1 ( in a 30 mL bottle ) . The cultures were incubated at 37°C shaking at 200 rpm for 23 h . A volume ( 0 . 5 mL ) of the primary seed culture was used to inoculate 500mL of DM1 ( in a 2 L Erlenmeyer flask ) . These secondary seed cultures were incubated at 37°C shaking at 200 rpm for 16 h . Recombinant HIV Gag proteins were produced in 2 L stirred tank bioreactors connected to a Biostat B ( Sartorius Stedim , Germany ) control system . The initial volume of medium in the bioreactor was 1 . 6 L and glucose as used as the carbon source . A volume of the secondary seed culture was added to the bioreactor to attain an initial optical density ( measured at 600 nm ) of 0 . 25 . Foaming was controlled via the automatic addition of 10% ( v/v ) polypropylene glycol 2025; 3 mL of the antifoam solution ( Sigma , Antifoam 204 ) was added prior to inoculation . The pH set-point was 7 . 0 and controlled by automatic addition of either 10% ( v/v ) H3PO4 or 10% ( v/v ) NH3 solution . The dissolved oxygen set-point was 30% of saturation and a two-step cascade control was used to maintain the dissolved oxygen above the specified set-point . The agitator speed ranged from 500 rpm to 1200 rpm and airflow ( supplemented with 5% pure O2 ) ranged from 0 . 3 L min-1 to 1 . 5 L min-1 . The ratio of air to oxygen was manually changed as required . To assist with correct folding of the Gag proteins , the medium was supplemented with 50 μM ZnSO4 added 1 . 8 h after inoculation . A high cell density fed-batch process was used , with the feed solution comprised of 400 mL of 660 g L-1 glucose solution to which 40 mL of 1 M MgSO4 . 7H2O was added . The feed flow rate was 21 mL hr-1 and commenced once the initial glucose supply was exhausted ( typically 8 to 9 hr after inoculation ) . Two hours after the fed-batch process was initiated the bioreactor temperature set-point was reduced to 18°C , with culture temperature dropping to 19°C within 30 min . After cooling , protein expression was induced via the addition of 1 mM isopropyl-β-D-thiogalactopyranoside ( IPTG ) and 0 . 02% ( w/v ) arabinose and the feed flow rate reduced to 4 mL hr-1 . Cells were harvested by centrifugation ( 12000 g , 4°C , 10 min ) 22 to 23 h after inoculation and cell pellets stored at -80°C . Cell pellets ( ~200 g ) were thawed and re-suspended in 1 . 0 L of ice cold lysis buffer ( 1 M NaCl , 50 mM TRIS-HCl pH 8 . 0 , 5 mM MgCl2 , 10 mM imidazole , 1 ( v/v ) % Tween-20 , 10% ( v/v ) glycerol , 5 mM DTT ) containing 50 , 000 units of DNase I , 5 mM benzamidine-HCl and 1 mM phenyl methyl sulfonyl fluoride ( PMSF ) . The cell suspension was homogenized ( EmulsiFlex-C5 homogenizer , Avestin ) pre-chilled to 4°C , three times at 700 bar pressure . The lysate was clarified by centrifugation 12 , 000 g , 4°C , 30 min ) , and the supernatant filtered using a 0 . 45 μm Stericup filter ( Millipore ) . The supernatant was loaded at 5 mL min-1 onto a 30 mL HisTRAP fast flow IMAC column ( consisting of 6 x 5 mL cartridges connected in series; GE Healthcare ) using a MINIPULS peristaltic pump ( Gilson ) . The column had previously been equilibrated with 5 column volumes ( CV ) binding buffer ( 1 . 0 M NaCl , 50 mM TRIS-HCl pH 8 . 0 , 5 mM MgCl2 , 10 mM imidazole , 1% ( v/v ) Tween-20 , 10% ( v/v ) glycerol , 5 mM DTT ) . The column was washed with 10 CV wash buffer ( 1 . 0 M NaCl , 50 mM TRIS-HCl pH 8 . 0 , 5 mM MgCl2 , 25 mM imidazole , 1% ( v/v ) Tween-20 , 10% ( v/v ) glycerol , 5 mM DTT ) and bound proteins eluted with 5 CV elution buffer ( 1 . 0 M NaCl , 50 mM TRIS-HCl pH 8 . 0 , 5 mM MgCl2 , 250 mM imidazole , 1% ( v/v ) Tween-20 , 10% ( v/v ) glycerol , 5mM DTT ) . Pr55Gag eluting from the IMAC column was concentrated to ~ 3 . 0 mg mL-1 using a centrifugal concentrator ( Amicon Ultra-15 , 10 , 000 molecular weight cut-off membrane; Millipore ) . The concentrated protein ( 5x 10 mL ) was fractionated by size exclusion chromatography ( SEC ) using a Superdex 200 26/60 column ( GE Healthcare ) previously equilibrated in SEC buffer ( 50 mM TRIS-HCl , 1 . 0 M NaCl , 5 mM DTT , pH 8 . 0 ) using an ÄKTApurifier chromatography workstation ( GE Healthcare ) . Peak fractions ( UV 280nm ) containing Pr55Gag were collected , pooled and concentrated to 1–2 mg mL-1 as described above , and snap frozen in liquid nitrogen before storage at -80°C . Purified protein containing the TEV sequence is digested with 1:25 ( w/w ) TEV protease ( produced in-house ) at 4°C for 14 hrs . Efficiency of the cleavage is assessed by sodium dodecyl sulfate—poly acrylamide gel electrophoresis ( SDS-PAGE ) . A 1ml NI-NTA column pre-equilibrated with SEC buffer and the TEV digested protein is applied to the column and the flowthrough is collected and passed over the column two more times . The cleaved His6-Tag and uncut fusion protein is eluted from the column using 5 CV elution buffer ( 1 . 0 M NaCl , 50 mM TRIS-HCl pH 8 . 0 , 5 mM MgCl2 , 250 mM imidazole , 1% ( v/v ) Tween-20 , 10% ( v/v ) glycerol , 5mM DTT ) . Protein was buffer exchanged into 1x Na/K 10mM phosphate buffer ( pH 7 . 4 ) with different NaCl concentrations and concentrated to 1 mg mL-1 . Yeast tRNA ( Sigma Aldrich ) ( 10% ( w/w ) ratio of nucleic acid to protein ) was added to the protein solution and incubated for 30 min at room temperature , followed by addition of paraformaldehyde ( PFA ) ( final concentration of 0 . 2% w/v ) . After incubating the solution for further 30 min at room temperature , the crosslinking was stopped by addition of 50 μL of 3M TRIS pH 8 . 0 . Samples were centrifuged 10 min at 10 , 000 g and supernatants were analyzed by size exclusion chromatography using a Superdex 200 10/30 column ( GE Healthcare ) , previously equilibrated in phosphate-buffered saline ( PBS ) with respective NaCl concentrations . Peak fractions were concentrated to 2 mg mL-1 as previously described . Fractions were electrophorised on a NuPAGE Novex 3–8% TRIS-Acetate protein gel under denaturing conditions before being transferred onto nitrocellulose membranes for Western analysis . Pr55Gag and Pr55GagΔp6 were concentrated to 2 . 0 mg mL-1 in 50 mM TRIS pH 8 . 0 containing 1 . 0 M NaCl and 10 mM dithiothreitol and mixed with TG30 at a nucleic acid to protein ration of 4% ( w/w ) prior to dialysis against 50 mM TRIS pH 8 . 0 containing 150 mM NaCl and 10 mM dithiothreitol overnight at 4°C . Particles were imaged using both negative stain transmission electron microscopy and cryo-electron microscopy . For negative stain imaging , the stock solution was diluted 100-fold to give a single layer of well-separated particles in most fields of view . Carbon-coated grids were glow discharged in nitrogen prior to use to facilitate sample spreading . Aliquots of approximately 4 μL were pipetted onto each grid and allowed to settle for 30 s . Excess sample was drawn off with filter paper , and the remaining sample stained with a drop of 2% aqueous phosphotungstic acid . Again , excess liquid was drawn off with filter paper . Grids were air dried until required . Samples were examined using a Tecnai 12 Transmission Electron Microscope ( FEI , Eindhoven ) at an operating voltage of 120kV . Images were recorded using a Megaview III CCD camera and AnalySIS camera control software ( Olympus . ) For cryo-electron microscopy , virus particles were prepared , processed and imaged as previously described [46] . For the nucleic acid mediated in vitro assembly , protein ( 2 . 5 mg mL-1 ) and a DNA 30-mer oligonucleotide with alternating TG motifs ( TG30; Macrogen ) at a 10% ( w/w ) ratio of nucleic acid to protein were mixed in 50 mM TRIS , 500 mM NaCl , pH8 . 0 prior to adding inositol hexaphosphate ( IP6 ) ( 10 μM ) and slowly decreasing the NaCl concentration by dialysis into 50 mM TRIS , 150 mM NaCl , pH8 . 0 buffer to initiate the assembly process . The buoyant density of the particles produced and their in vitro assembly efficiency was assessed by layering the assembly reaction mixture onto a 32 . 5 to 55% ( w/w ) linear sucrose gradient in TBS ( 150 mM NaCl , 50 mM Tris , pH 7 . 6 ) . Assembled HIV-1 Gag samples were layered onto the gradient and centrifuged for 16 h at 110 , 000 g ( SW41 rotor: Optima L-90k ultracentrifuge; Beckman ) . 750 μL fractions were collected from the top in separate 1 . 5 mL tubes after completion of the run and prepared for trichloroacetic acid ( TCA ) precipitation . 350 μL of 50% ( w/v ) TCA was added to each fraction and incubated at 4°C for 30 min . Tubes were centrifuged at 14 , 000 g for 10 min at 4°C and the pellet was resuspended in 200 μL of prechilled acetone and incubated for 5 min at 4°C . The centrifugation step was repeated and excess supernatant aspirated and tubes air dried . The dried pellet was resuspended in 20 μL of 4x NuPAGE reduced LDS Sample Buffer ( Life Technologies ) and heated for 5 min at 95°C . Samples were briefly centrifuged and 10 μL of each sample was loaded onto a NuPAGE 4–12% Bis-TRIS Midi gel and electrophoresed using NuPAGE MES SDS Running Buffer ( Life Technologies ) at 150 volts . PAGE gels were transferred to a Nitrocellulose membrane ( PerkinElmer ) using XCell II Blot Module ( 30 volts for 60 mins ) , and the membrane blocked overnight in blotto ( 5% ( w/v ) skim milk powder in TBS + 0 . 05% ( v/v ) Tween-20 ) at 4°C . Membrane was washed 3x with wash buffer ( TBS + 0 . 05% ( v/v ) Tween-20 ) for 5 min each and probed with a mouse anti-CA monoclonal antibody ( Hybridoma Clone 183-H12-5C; NIH AIDS Reagent program ) for 1 h at room temperature . Membrane was washed 3x with wash buffer , incubated with a secondary goat anti-mouse IRDye 800CW conjugate ( LI-COR; diluted1:30 , 000 in blotto ) for 1 hr at room temperature . Membrane was washed again 3x and analysed using an ODYSSEY CLx system ( LI-COR ) . Isothermal titration calorimetry ( ITC ) experiments were performed at 30°C using a Microcal Auto-ITC200 MicroCalorimeter ( Malvern ) . For each ITC experiment , the cell contained soluble Gag protein ( 6–10 μM ) in TBS with 1mM tris ( 2-carboxyethyl ) phosphine ( TCEP ) and the syringe contained 15–50 μM of the DNA 30-mer oligonucleotide with alternating TG motifs ( TG30 ) or RNA 20-mer oligonucleotides with 4 consecutive repeating GU-containing ( 4x 5’-GAUGG-3’; 4x 5’-UGUGG-3’ ) or A-containing ( 4x 5’-GAGAA-3’; 4x 5’-AAGGA-3’ ) sequence motifs . The SL3 DNA and the 20mer SL3 RNA ( 5’GGACUAGCGGAGGCUAGUCC3’ ) as well as the HIVNL4 . 3RNA2671-2707 ( 5’-CTTAGAAATAGGGCAGCATAGAACAAAAATAGAGGAA-3’ ) and the HIVNL4 . 3RNA2671-2707 ( with AGAAA mutation ) ( 5’-CTATCTTTTAGGGCAGCAATCTTCAAAAATAGTCCTT-3’ ) are based on NL4 . 3 HIV proviral RNA . DNA and RNA oligonucleotides were purchased from Macrogen and Integrated DNA Technologies , respectively , and dissolved in TBS with 1mM TCEP . The volume of the first injection for each ITC run was set to 0 . 4 μL over 0 . 8 s to minimize the experimental impact caused by dilution effects at the injection syringe tip; this initial injection was excluded from data analysis . The first injection was followed by 25 injections of 1 . 5 μL over 3 s or 38 injections of 1 μL over 2 s each with the interval between each injection set to 300 s . The reference power was set to 5 μcals-1 . The syringe stirring speed was set to 750 rpm . A baseline was drawn by linear extrapolation using the data points collected from control experiments and subtracted from the whole data set to correct for the heat of dilution . The total heat signal from each injection was determined as the area under the individual peaks and plotted against the [nucleic acid]/[Gag] molar ratio . The corrected data were analyzed to determine number of binding sites ( n ) , and molar change in enthalpy of binding ( ΔH ) in terms of a single site model derived as follows: The quantity r is defined as the moles of nucleic acid [D] bound per mole of protein [P] with an association constant ( Ka ) : r=Ka[D]1+Ka[D] ( Eq 1 ) Solving Eq 1 for Ka leads to: Ka=r ( 1+r ) [D] ( Eq 2 ) Since the total concentration of nucleic acid [D]T in the cell is known , it can be represented by Eq 3 wherein [P]T equals the total protein concentration in the cell and n the number of binding sites: [D]T=[D]+nr[P]T ( Eq 3 ) Since Eq 3 shows nr[P]T = [PD] , the fraction of sites occupied by the nucleic acid , combining Eqs 2 and 3 leads to: r2−r[[D]Tn[P]T+1nKa[P]T+1]+[D]Tn[P]T=0 ( Eq 4 ) Solving the quadratic for the fractional occupancy ( r ) gives: r=12[ ( [D]Tn[P]T+1nKa[P]T+1 ) − ( [D]Tn[P]T+1nKa[P]T+1 ) 2−4[D]Tn[P]T] ( Eq 5 ) The total heat content ( Q ) of the solution in the volume of the sample cell ( Vo; determined relative to zero for the apo-species ) at fractional saturation r is given by Eq 6 , where ΔH represents the molar heat of nucleic acid binding: Q=nr[P]TΔHV0 ( Eq 6 ) Substituting Eq 5 into Eq 6 gives: Q=nr[P]TΔHV02[ ( [D]Tn[P]T+1nKa[P]T+1 ) − ( [D]Tn[P]T+1nKa[P]T+1 ) 2−4[D]Tn[P]T] ( Eq 7 ) The total heat content , Q can be calculated as function of n , Ka , ΔH because [P]T , [D]T and Vo are known experimental parameters . The parameter Q defined in Eq 7 only applies to the known starting volume of protein solution in the sample cell ( Vo ) . In order to correct for the displaced volume ( Vi ) , the change in heat content Q ( i ) at the end of the ith injection is defined by Eq 8 to obtain the best fit for n , Ka , and ΔH by standard Marquardt methods until no further significant improvement in fit occurs with continued iteration . The Gibbs free energy ( ΔG0 ) was calculated from the fundamental equation of thermodynamics Eq 9: ΔG°=ΔH−TΔS=−RTlnKa ( Eq 9 ) All data fitting operations were performed with Origin V7 . 0 software ( OriginLab , Northampton , MA ) . Following ITC analysis , the solutions containing HIV protein and RNA complex was spun at 100 , 000 g for 1 hr ( TLA 100 . 2 rotor; optima max ultracentrifuge; Beckman ) . The supernatant was removed and the pellet was resuspended in 50μl of TBS . Protein estimation was done using UV-Vis ( A280 ) on both the supernatant and the pelletable materials ( NanoDrop1000; Thermo Scientific ) via Bradford Protein Assay [47] . | Formation of any virus particle will require energy , yet the precise biophysical properties that drive the formation of HIV particles remain undefined . Isothermal titration calorimetry ( ITC ) is a biophysical technique that is the gold standard to reveal parameters governing biochemical and biophysical reaction . However , ITC requires large amount of proteins for analysis . As large quantities of full-length recombinant HIV Pr55Gag proteins have not been available in the past 30 years due to technical limitation , a comprehensive thermodynamic analysis of full-length HIV Pr55Gag has not been possible . Here , we have generated sufficient amount of full-length recombinant HIV Pr55Gag protein for isothermal titration calorimetry analysis . Our analyses have shown that the major interactions amongst HIV proteins and RNA sequences during viral assembly are energetically favourable reactions . In other words , HIV Pr55Gag proteins and viral RNA have evolved to overcome the energy barrier for virus formation by utilising energy obtained from protein-RNA interactions in order to facilitate the viral assembly process . Furthermore , HIV also use the oligomeric states of HIV Pr55Gag proteins and the RNA sequences as means to regulate the viral assembly process . | [
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] | 2017 | The thermodynamics of Pr55Gag-RNA interaction regulate the assembly of HIV |
In eukaryotic organisms , histones are dynamically exchanged independently of DNA replication . Recent reports show that different coding regions differ in their amount of replication-independent histone H3 exchange . The current paradigm is that this histone exchange variability among coding regions is a consequence of transcription rate . Here we put forward the idea that this variability might be also modulated in a gene-specific manner independently of transcription rate . To that end , we study transcription rate–independent replication-independent coding region histone H3 exchange . We term such events relative exchange . Our genome-wide analysis shows conclusively that in yeast , relative exchange is a novel consistent feature of coding regions . Outside of replication , each coding region has a characteristic pattern of histone H3 exchange that is either higher or lower than what was expected by its RNAPII transcription rate alone . Histone H3 exchange in coding regions might be a way to add or remove certain histone modifications that are important for transcription elongation . Therefore , our results that gene-specific coding region histone H3 exchange is decoupled from transcription rate might hint at a new epigenetic mechanism of transcription regulation .
The nucleosome is the basic repeating unit of the chromatin and comprises 147 bp of DNA wrapped around an octamer of histone proteins ( two copies of H2A , H2B , H3 and H4 ) . Nucleosome disassembly and reassembly is tightly coupled with replication , transcription , DNA repair and heterochromatin silencing ( e . g . , [1] , [2] ) . Under normal circumstances , histones are associated with specific histone chaperones that assist their assembly and disassembly [3] . During disassembly and reassembly of nucleosomes , the original histones might be exchanged ( replaced ) by newly synthesized histones [4]–[8] . Outside of replication , histone H3 exchange occurs predominantly at promoters , whereas H3 exchange in coding regions is significantly lower [7] , [8] . Coding region replication-independent H3 exchange varies from gene to gene . Recent studies have shown that this variation among coding region is linked to differences in transcription rate [6]–[9] . For example , genome-wide studies demonstrate a strong association between transcription rate and replication-independent histone H3 exchange in yeast coding regions 7 , 8 . This association is expected: During transcription elongation , nucleosomes are disassembled in front of the elongating RNA polymerase II ( RNAPII ) complex to enable its passage , and reassemble almost immediately behind it ( reviewed by [10]–[12] ) . Behind RNAPII , the original H3/H4 histones might be either exchanged ( replaced ) or retained ( not replaced; [13] ) . Therefore , the amount of coding region H3 exchange is expected to reflect the number of transcripts produced by RNAPII . Although it is widely accepted that coding region replication-independent H3 exchange differences are a consequence of transcription rate , it is still unknown whether this variability is also controlled independently of transcription rate . In other words , it is not clear whether different coding regions can have substantially different replication-independent H3 exchange even if they have the same transcription rate . This leads us to investigate genome-wide coding region H3 exchange independently of both replication and transcription rate . We address two key questions: First , is there evidence that transcription rate–independent and replication-independent H3 exchange in coding regions is a consistent feature of genes ? Second , is there evidence for an active regulation of this feature ? In this study , we analyzed published data sets of replication-independent histone H3 exchange in yeast [7] , [8] . The measured amount of replication-independent histone H3 exchange is simply called total exchange , whereas the calculated transcription rate–independent total exchange is referred to as relative exchange . Positive ( negative ) relative exchange implies that the total exchange is higher ( lower ) than what is expected based on transcription rate alone . Importantly , although relative exchange is independent of transcription rate , it is still likely to be influenced by the transcription process . We found that relative exchange varies from gene to gene and is a reproducible feature of genes . Elevated or reduced relative exchange occurs along the entire coding region and not only in a specific part of it . Moreover , relative exchange is a gene-specific property rather than a regional effect . Finally , we revealed that H3K79 trimethylation is depleted in coding regions with hyper relative exchange and enriched in coding regions with hypo relative exchange . Taken together , our data provides evidence that coding regions have a characteristic relative exchange , a new feature of genes . Genes might have either hyper or hypo relative exchange , irrespective of their total exchange or transcription rate . Histone exchange in coding regions might be a way to add or remove certain histone modifications that are important for transcription elongation . Therefore , decoupling replication-independent histone exchange from transcription rate is a process with potential for epigenetic gene regulation . Asf1 is a histone H3/H4 chaperone that has been implicated in histone H3/H4 exchange during elongation [4] , [14] . Recently it was shown that outside of replication , Asf1-mediated H3 exchange globally correlates with the amount of total exchange and with transcription rate [8] . In addition to Asf1's role in histone exchange , Asf1 is also known as a global regulator of gene expression [15] . Interestingly , many genes are down-regulated by Asf1 , whereas other genes are up-regulated by its influence . This dual function of Asf1 as specific negative and positive regulator of gene expression is well documented [2] , [14] , [15] , but is still largely unexplained . Here we show a global association between Asf1-mediated gene expression and relative exchange ( but no direct association with total exchange ) . Genes with hyper relative exchange tend to be down regulated by Asf1 , whereas genes with hypo relative exchange are up-regulated by Asf1 . Therefore , the relative exchange property provides insights into the longstanding question as to the selective positive and negative transcriptional influence of Asf1 .
The present work is focused on the understanding of replication-independent histone H3 exchange in coding regions . For our study , we used published genome-wide measurements of histone H3 exchange and RNAPII densities [7] , [8] . The data was taken from G1-arrested cells , hence eliminating the contribution of histone exchange during replication . In the following , transcription rate is defined as RNAPII density averaged over the coding region . Total exchange is the measured replication-independent histone H3 exchange averaged over the coding region . Recent reports show that outside of replication , there is a clear correlation between coding region replication-independent histone H3 exchange and RNAPII density ( Figure 1A , and Figure S1 in Text S1; [6]–[8] ) . Beyond this global relationship , there is a wide distribution around the diagonal . Hence , even at the same transcription rate , different genes differ in their amount of replication-independent histone H3 exchange . To determine whether this variation has a biological basis , we extracted and interpreted this information in a systematic manner as follows: Relative exchange is the distance of total exchange to a running average of the total exchange along the transcription rate axis ( Figure 1B ) . In case that we analyze relative exchange of each single tiling-array probe ( denoted probe's relative exchange ) , we used measurements of total exchange and RNAPII density in a single probe without averaging over the entire coding region ( see Methods ) . The calculated relative exchange values eliminate the contribution of transcription rate from the total exchange in coding regions . Relative exchange is substantially different and is not monotonic with the amount of total exchange ( see an illustrative example in Figure 1 ) . Total exchange is replication-independent , whereas relative exchange is replication-independent and transcription rate–independent . The observed relative exchange variation among genes might be a consequence of biological or experimental noise . To exclude the latter , we grouped genes into pairs with minimal difference in transcription rate . Such a gene pair is termed similar-transcription genes . The relative exchange difference between two similar-transcription genes was calculated by subtracting the relative exchange of the gene whose transcription rate is lower from the paired ( higher transcription rate ) gene . For comparison , we have computed the difference between relative exchange replicates ( calculated based on replicates from [8] ) . Figure 2A demonstrates that the distribution of relative exchange differences between similar-transcription genes is broader than the distribution of differences between replicates , indicating that experimental noise can only partially account for relative exchange variation [F-test P<10−200 ( F-test for significance of difference between variances ) ] . This observation is particularly significant because of the bias toward both negative and positive differences between similar-transcription genes . Whereas the bias toward positive values can be attributed to the global relationships with transcription rate , this effect cannot explain the bias toward negative values . Next , we have investigated the reproducibility of relative exchange in different laboratories . Total exchange ( together with transcription rate ) was measured in two different laboratories [7] , [8] . Therefore , we asked whether relative exchange calculated based on measurement from Rufiange et al . correlates with relative exchange based on Dion et al . Our rationale is that if the relative values are only noise , there will be a poor correlation between relative exchange values taken from two different laboratories . On the other hand , if relative exchange is informative , measurements from the two laboratories should show good correlation . Remarkably , we found that relative exchange measured by Rufiange et al . exhibit good correlation with relative exchange measured by Dion et al . ( Spearman correlation = 0 . 84 , P-value<10−200 , Figure 2B ) . The correlation among relative exchange from the two laboratories is almost as good as the correlation among the measured total exchange replicates ( Spearman correlation = 0 . 85 ) . We validated that this reproducibility is not a byproduct of ( i ) a bias in total exchange or RNAPII density measurements , ( ii ) using average RNAPII density as an approximation of transcription rate , ( iii ) GC-content and sequence properties , ( iv ) averaging genes of different lengths ( see Text S1 for details ) . Taken together , Figure 2A and 2B suggest that total exchange is informative even after eliminating the contribution of transcription rate ( for a quantitative evaluation , see Text S2 ) . To examine whether relative exchange is a general property of genes , we wished to analyze the reproducibility of relative exchange along the entire coding region . To that end , we divided the genes into five subsets according to their relative exchange based on Dion et al . For each subset , we plotted a profile of probe's relative exchange throughout the coding region based on Rufiange et al . ( Figure 2C ) . Note that the probe's relative exchange values were calculated based on measurements in single probes and were not averaged over the coding region ( see above and Methods ) . We observe that the reproducibility of probe's relative exchange is found in each small segment of the coding region . We next considered the consistency of relative exchange along the coding region . To that end , we have split each coding region into six segments of equal length . The relative exchange of a segment ( denoted segmental relative exchange ) was calculated using only probes located within this segment ( see Methods ) . Figure 2D shows that relative exchange differences between segments of the same coding region ( denoted within genes ) tend to be smaller than relative exchange differences between segments from neighboring genes ( denoted between genes ) . In agreement , relative exchange variation between genes is significantly larger than the variation within genes [P<10−200 ( F-test for significance of difference between variances ) ] , indicating the consistency of relative exchange along the coding region . Notably , the distribution of relative exchange differences between neighboring genes is similar to the distribution of relative exchange differences between random genes ( Figure 2D ) . The same comparison on total exchange ( rather than relative exchange ) gives similar results ( Figure S2 in Text S2 ) . Taken together , it appears that each gene has a characteristic relative exchange along the coding region , and relative exchange is a gene-specific property rather than a regional effect . Some coding regions have hyper relative exchange ( or hypo relative exchange ) , based on their total exchange that is relatively higher ( or lower ) from what can be expected from their transcription rate alone . A genome-wide mapping shows that hyper relative exchange coding regions are scattered throughout the genome ( Figure 3 ) . To systematically characterize relative exchange , we compared it with published genome-wide profiles of histone H3 modifications ( data sources are [8] , [16]–[19]; see Text S3 for details ) . The analysis was limited to coding regions . Figure 4A demonstrates the correlation between each modification and relative exchange vis-à-vis the correlation with total exchange . For each modification , we calculated its average enrichment in each coding region , and compared it with the ( average ) relative or total exchange of the coding regions . To avoid complications arising from averaging on coding regions with different lengths , we used Spearman correlation calculated independently of transcript length . Further , the total exchange was factored out from the correlation with relative exchange and vise versa ( see Methods ) . In agreement with previous observations [8] , total exchange is mainly associated with H3K56 acetylation ( H3K56Ac; see the table Figure S3 in Text S3 ) . Unexpectedly , we found that relative exchange is tightly related to H3K79 trimethylation ( H3K79me3 [17] ) . H3K79me3 anti-correlates with relative exchange ( Spearman correlation = −0 . 42 , P-value = 10−88 , Figure 4A and 4B ) but not with total exchange ( Spearman correlation = 0 . 01 , P-value = 10−1 ) , indicating that H3K79me3 is specifically associated with relative exchange . In agreement , Figure 4C clearly demonstrates that H3K79me3 is linked to relative exchange rather than to total exchange ( compare with the bottom panel of Figure 1B ) . The association holds throughout the entire coding region ( Figure 4D ) . We obtained the same results when the association is computed independently of GC content ( data not shown ) . We conclude that the pattern of H3K79me3 is related to relative exchange . Previous studies show that in coding regions , there is a correlation between H3K79me3 and transcription rate ( e . g . , [17] ) . We asked whether this correlation holds even when eliminating the effect of histone exchange . Interestingly , the general correlation between H3K79me3 and transcription rate ( Spearman correlation = 0 . 19 ) becomes much higher when eliminating the effect of histone exchange ( total exchange-independent Spearman correlation = 0 . 46 ) . This demonstrates that any genome-wide analysis of H3K79me3 must take into consideration the effect of histone H3 exchange . Histone H3K36 can be a target of acetylation , mono- , di- and trimethylation ( Ac , me , me2 , me3 , respectively ) . Recent report shows that H3K36Ac pattern is inversely related to H3K36me2 and H3K36me3 patterns in coding regions , suggesting that H3K36 is an ‘acetyl/methyl switch’ [19] . Here we found that relative exchange is significantly associated with H3K36me3 ( Spearman correlation = −0 . 28 , P-value = 10−35 , Figure S4 in Text S4 ) , but the association with H3K36Ac is significantly lower ( Spearman correlation P-value = 10−3 ) . Therefore , our analysis indicates that although both H3K36me3 and H3K36Ac are inversely related , they are associated differentially with relative exchange . The histone chaperone Asf1 is important for disassembly and reassembly of H3/H4 histones during DNA replication , repair , and heterochromatin silencing . Asf1 is the only yeast histone chaperone that was implicated in histone H3/H4 exchange during elongation [4] , [14] . Outside of replication , the contribution of Asf1 to histone H3 exchange strongly correlates with both total exchange and transcription rate [8] . The fact that Asf1 has a role in histone exchange prompted us to examine its relations with relative exchange . To that end , we used log change total exchange in wild type vs . asf1Δ , denoted Asf1-mediated exchange ( data taken from [8] ) . The higher Asf1-mediated exchange , the higher the contribution of Asf1 to total exchange . Using this data , we have confirmed the correlation between Asf1-mediated exchange and total exchange ( Figure 5A; Spearman correlation = 0 . 54 , P-value<10−154 , [8] ) . We next sought to investigate the relations between Asf1-mediated exchange and transcription rate . In agreement with previous reports , we found that Asf1-mediated exchange indeed correlates with transcription rate ( Spearman correlation = 0 . 25 , P-value<10−53 ) . However , given total exchange , the conditional correlation is insignificant ( total exchange-independent Spearman P-value>0 . 1 ) . This can be clearly seen in Figure 5A: in each small range of total exchange ( a row in the 2D heat map ) , the level of Asf1-mediated exchange ( color-coded ) is similar along the entire row . Therefore , Asf1-mediated exchange is associated with transcription rate only indirectly , through its association with total exchange . Next , we analyzed the influence of Asf1 on gene expression ( i . e . , transcription rate ) . The presence of Asf1 at promoters is important for disassembly of H3/H4 upon activation and for reassembly of H3/H4 upon loss of activation ( reviewed in [2] , [3] , [20] ) . In coding regions , Asf1 travels with elongating RNAPII and influences RNAPII density [14] . Asf1 is a global transcription factor that influences transcription of hundreds of genes distributed over the entire yeast genome [15] . Asf1 has both positive and negative effect on gene expression . For example , Asf1 up-regulates transcription of SRL3 and HYR1 ( confirmed by RT-PCR , [15] ) . On the other hand , Asf1 down-regulates PYK1 , PMA1 , and RPS9B ( confirmed by RNAPII occupancy in promoter and coding region , [14] ) . This dual activity of Asf1 as transcription activator and transcription repressor is still largely unexplained . To analyze the transcriptional influence of Asf1 , we used genome-wide gene expression change in asf1Δ mutant vs . wild type , referred to as Asf1-mediated gene expression ( data taken from [15] , see Text S3 for details ) . We observe that Asf1-mediated gene expression is related to relative exchange ( Spearman correlation = 0 . 175 , P-values<10−17 ) but is not associated with total exchange ( Spearman P-values>0 . 1; Figure 5B and 5C ) . The association with relative exchange appears in at least seven out of ten transcription rate bins ( heat map columns in Figure 5B , Spearman correlation>0 . 15 in seven independent columns ) . Moreover , this significant association is independent of histone exchange in the corresponding promoters ( see details in Text S4 ) . We obtained the same results when the association is computed independently of GC content or transcript length ( data not shown ) . Consistent with our observation , SRL3 and HYR1 are indeed hypo relative exchange genes , whereas PYK1 , PMA1 , and RPS9B are hyper relative exchange genes ( data not shown ) . Next , we considered the possibility that the association between Asf1-mediated gene expression and relative exchange is not a consistent feature of the entire coding region . Therefore , we divided the genes into three subsets according to their Asf1-mediated gene expression . For each subset , we have plotted a profile of probe's relative exchange throughout the coding region ( Figure 5D ) . We observe that in each segment of the coding region , the Asf1-mediated gene expression is associated with relative exchange . Therefore , Asf1-mediated gene expression corresponds to relative exchange throughout the entire coding region . While the reason for this result is yet unclear , it appears that hyper relative exchange genes are down-regulated by Asf1 , whereas hypo relative exchange genes are up-regulated by Asf1 .
In this study we investigated transcription rate–independent replication-independent histone H3 exchange in coding regions , called relative exchange ( based on data from [7] , [8] , see Figure 1 ) . By calculating relative exchange values , we eliminated the contribution of RNAPII transcription rate from replication-independent histone H3 exchange . Many studies investigate transcription-independent exchange , where the histone exchange is measured in the absence of transcription processes ( e . g . , [5] ) . Unlike those studies , our calculated relative exchange is not independent of transcription , but only independent of transcription rate . Therefore , relative exchange may still represent histone exchange during transcription , as long as the histone exchange is not determined solely by transcription rate . Our analysis provides evidence that total exchange does not reflect only transcription rate . First , we show that relative exchange variability , which is independent of transcription rate , cannot be explained solely by experimental noise ( Figure 2A and 2B; see corroborations in Text S2 ) . Next , several analyses suggest that relative exchange is a feature of an entire coding region rather than a regional effect: ( i ) Relative exchange characterizes the entire coding region and not only a specific part of it ( Figure 2C ) , ( ii ) Neighboring genes in the genome do not have a similar relative exchange ( Figure 2D ) , ( iii ) relative exchange variation between neighboring genes is larger than the relative exchange variation within coding regions ( Figure 2D ) , ( iv ) hyper relative exchange genes are scattered throughout the genome ( Figure 3 ) , and ( v ) functional enrichment tests show that hypo relative exchange genes are up-regulated by Asf1 and enriched with H3K79me3 ( Figures 4 and 5 , respectively ) . Taken together , this collection of evidence indicates that total exchange variability at the same transcription rate is a biological property of genes . Among the numerous modified histone H3 residues , methylated H3K79 is the only one in the globular core domain , rather than in the exposed N-terminal tail . H3K79me3 occurs predominantly in the coding regions of genes and is associated with transcription activity [17] . Dot1 directly methylates H3K79 and is the main source of H3K79 methylation [21] . On the other hand , none of the identified demethylation enzyme families can remove H3K79 methylation , suggesting that H3K79 methylation might be enzymatically irreversible ( e . g . , [22] ) . This study demonstrates that relative exchange is mainly associated with H3K79me3 ( Figure 4 ) . Many possible mechanisms might explain this association . For example , H3K79me3 might be a signal for the required level of relative exchange in coding regions . Another attractive hypothesis is that histone exchange is a functional alternative to active enzymatic removal of H3K79me3 . For instance , the enrichment of H3K79me3 might reflect the balance between transcription-coupled H3K79 methylation and exchange-coupled removal in each round of RNAPII transcription . If this hypothesis is correct , the slight influence of H3 exchange on the overall enrichment of H3K79me3 could be easily detected due to the simple methylation/demethylation system of H3K79 ( i . e . , only one methylase and probably no demethylase ) . Nucleosomes are dynamically exchanged during many DNA metabolism processes , including replication , transcription initiation and elongation , DNA repair , heterochromatin silencing and basal histone exchange . Therefore , it is hard to determine the process that generates relative exchange . We assume that relative exchange is not related to replication , since the data was measured in G1-arrested cells . Several lines of evidence show that relative exchange is not established during repair or heterochromatin silencing: First , relative exchange is reproducible in different datasets ( Figure 2B ) , and thus it is not likely to reflect a temporary cellular repair status . Second , hyper relative exchange genes are scattered in the entire genome and not localized to heterochromatin regions ( Figure 2D and Figure 3 ) . Finally , we validate that the association between Asf1 and relative exchange is independent from molecular features that are related to repair or heterochromatin silencing ( Figure S5 in Text S5 ) . The hypothesis that relative exchange variation is generated during transcription elongation is highly attractive . Asf1 travels with RNAPII along the coding region and is the only known histone chaperone that mediates histone exchange during transcription elongation [3] , [4] , [14] . Since Asf1 activity is related to relative exchange ( Figure 5 ) , we hypothesize that Asf1 has a gene-specific level of activity during elongation , thereby increasing or decreasing the proportion of histone H3 exchange per RNAPII passage . This generates the observed total exchange variability across genes that have similar transcription rate . Recent reports provide evidence for specific targeting of Asf1 to promoters as part of transcription initiation [23] , but to the best of our knowledge , it is still not clear whether Asf1 has a gene-specific targeting also in coding regions during elongation . Several studies show that Asf1 has a selective positive and negative effect on gene expression , but this dual function is still largely unexplained [2] , [14] , [15] . In this study , we show that Asf1 has a positive transcriptional influence on hypo relative exchange genes , but negative transcriptional influence on hyper relative exchange genes ( Figure 5B–5D ) . This provides an important insight as to the selective positive and negative transcriptional influence of Asf1 . The connection might be direct , e . g . , Asf1 activity might be related to RNAPII poising or a slow elongation . Alternatively , Asf1 can influence transcription rate indirectly by promoting relative exchange that removes or adds important chromatin modifications that are important for transcription . The latter alternative is supported by recent reports in several eukaryotes , demonstrating that histone exchange in promoters regulates gene expression by incorporation/removal of histone variant H3 . 3 ( reviewed in [24] , [25] ) . In yeast , there is no such histone H3 variant and thus detailed experiments will be necessary to reveal the precise role of gene-specific relative exchange in epigenetic transcription regulation .
We retrieved yeast ORFs and intergenic regions from the Saccharomyces Genome Database ( http://www . yeastgenome . org , July 2007 ) . To avoid biases related to genes that are not transcribed by RNAPII and global effects on histone H3 exchange , we removed non-coding genes , 25-kbp regions near the telomeres and centromeres , and 1-kbp regions near rRNA , tRNA , ARS and mitochondrial DNA ( see Text S5 ) . In total , we applied our analysis to 3760 coding regions . Replication-independent histone H3 exchange data and RNAPII density were taken from [7] , [8] ( see Text S5 ) . In many cases there is dependency between two molecular features x and y . To analyze x independently of confounding influences due to y , we define y-independent x-values as the distance of each x value from a running average of x values along the y-axis . This procedure is commonly used in noise analysis ( e . g . , [26] ) and can be applied recursively to analyze x independently of y1 , … , yn values . For example , relative exchange was defined as a transcription rate–independent total exchange . Therefore , relative exchange is the vertical distance of a given total exchange point from the running average line in the total exchange–transcription rate plot ( Figure 1A ) . Segmental relative exchange was calculated as follows: we split the coding regions into six equal segments . The relative exchange of each segment was calculated using only measurements from probes located within this segment . Figure 2D presents segmental relative exchange differences between 1-st segments of different genes , and between the 1-st vs . 4-th and 1-st vs . 6-th segments of the same gene . In Figures 2C , 4D and 5D , relative exchange values are reported per single tiling array probes from Rufiange et al . [8] . For each probe , we used total exchange and RNAPII density that were measured only on this probe ( referred to as probes' total exchange and probe's RNAPII density , respectively ) . To calculate relative exchange , we needed the running average curve of total exchange along the RNAPII density . To avoid complications arising from different total exchange along the coding region ( slightly higher near 5′ and 3′ end and lower in the middle , see [8] ) , we did not calculate a common running average curve for all probes . Instead , the coding regions were split into six segments of equal length , and all probes were assigned to one of six groups according to their coding region segment . All probes nearby a coding region were split into four segments ( 50 bp each ) upstream or downstream the coding region . For each segment , we calculated a running average curve using only probes within it . The relative exchange of a probe , denoted probe's relative exchange , is the distance of its probe's total exchange to its segment's running average curve ( on the coordinate of the probe's RNAPII density ) . In order to present the relation between relative exchange and other molecular features , we used a heat map , which illustrates the functional relationship between transcription rate ( x-axis ) , total exchange ( y-axis ) and an additional molecular feature ( color coded; Figures 1A , 4C and 5AB ) . The heat map has the shape of a scatter plot , but additionally visualizes the values of the data points with respect to the third feature . In the heat map , each cell represents a 2D bin including all genes with total exchange and transcription rate in a defined range . Empty and near-empty 2D bins ( below five genes ) are colored white . Bins with more then five genes are color-coded according to the level of a molecular feature averaged over the genes contained within the bin . The information in each cell is therefore independent of the information in neighbor cells . In case of functional relation between relative exchange and a molecular feature , hyper exchange bins would have different color than hypo exchange bins ( as illustrated in Figure 1B bottom panel ) . In this study , all correlations and their P-values are based on the non-parametric Spearman correlation test [27] . The correlation of x and z independently of y , referred also as y-independent Spearman correlation , is the correlation of two variables x and z when eliminating the contribution of a third or more other variables y . This was calculated as the Spearman correlation between y-independent x and y-independent z . Therefore , we applied a non-parametric equivalent to the statistical calculation of partial correlation [27] . All reported Spearman correlations between relative ( total ) exchange and an additional molecular feature , were calculated independently of total ( relative ) exchange and transcript length . This way , the correlation with any exchange measure is calculated only after factoring out the contribution of the other exchange measure and after eliminating potential effects of transcript length . For each coding region , we used the molecular feature value averaged over the coding region . Transcript lengths were computed based on sequencing of cDNA library ( [28] , see Text S5 for details ) . | During nucleosome disassembly and reassembly , evicted histones are exchanged with newly synthesized histones . Histone exchange occurs in several DNA metabolism processes , including replication , transcription , and repair . Recent reports from several labs show that replication-independent histone H3 exchange in yeast coding regions is tightly correlated with transcription rate . We have computationally shown that histone exchange variability among genes is not only a consequence of transcription rate . Instead , each coding region has a characteristic amount of replication-independent histone exchange , even when excluding the confounding effect of transcription rate . We show that this transcription rate–independent exchange , referred to as relative exchange , is a reproducible and consistent feature of the entire coding region and cannot be explained by regional effects . Next , we characterize the relations between relative exchange and a variety of histone H3 modifications , as well as the histone chaperone Asf1 . Taken together , our analysis shows that gene-specific replication-independent histone H3 exchange in coding regions is mediated independently of transcription rate , thus constituting a new mechanism in epigenetic transcription regulation . | [
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] | 2009 | Evidence for Gene-Specific Rather Than Transcription Rate–Dependent Histone H3 Exchange in Yeast Coding Regions |
Epidemiological studies have reported a higher incidence of rare disorders involving imprinted genes among children conceived using assisted reproductive technology ( ART ) , suggesting that ART procedures may be disruptive to imprinted gene methylation patterns . We examined intra- and inter-individual variation in DNA methylation at the differentially methylated regions ( DMRs ) of the IGF2/H19 and IGF2R loci in a population of children conceived in vitro or in vivo . We found substantial variation in allele-specific methylation at both loci in both groups . Aberrant methylation of the maternal IGF2/H19 DMR was more common in the in vitro group , and the overall variance was also significantly greater in the in vitro group . We estimated the number of trophoblast stem cells in each group based on approximation of the variance of the binomial distribution of IGF2/H19 methylation ratios , as well as the distribution of X chromosome inactivation scores in placenta . Both of these independent measures indicated that placentas of the in vitro group were derived from fewer stem cells than the in vivo conceived group . Both IGF2 and H19 mRNAs were significantly lower in placenta from the in vitro group . Although average birth weight was lower in the in vitro group , we found no correlation between birth weight and IGF2 or IGF2R transcript levels or the ratio of IGF2/IGF2R transcript levels . Our results show that in vitro conception is associated with aberrant methylation patterns at the IGF2/H19 locus . However , very little of the inter- or intra-individual variation in H19 or IGF2 mRNA levels can be explained by differences in maternal DMR DNA methylation , in contrast to the expectations of current transcriptional imprinting models . Extraembryonic tissues of embryos cultured in vitro appear to be derived from fewer trophoblast stem cells . It is possible that this developmental difference has an effect on placental and fetal growth .
Several epidemiological studies have reported a higher incidence of rare disorders involving imprinted genes ( Angelman syndrome [1]–[3] and Beckwith-Wiedemann syndrome [4]–[8] ) among children conceived using assisted reproductive technologies ( ART ) . Studies on imprinted gene expression and parental allele-specific DNA methylation in animal models have also suggested that epigenetic marks may be altered by treatments and procedures commonly employed in ART ( ovarian stimulation , egg retrieval , in vitro fertilization , intracytoplasmic sperm injection , preimplantation embryo culture , embryo transfer ) [9]–[13] . CpG sites in differentially methylated regions ( DMRs ) of imprinted genes are methylated on the allele contributed by one parent and unmethylated on the allele contributed by the other . This pattern of differential allelic methylation is established during male and female gametogenesis [14] , [15] and the differences are maintained after fertilization such that cells from most somatic tissues are expected to exhibit the same parental allele-specific methylation pattern [16] , [17] . However , if ART treatments and procedures in the human are disruptive to imprinted gene methylation patterns , as they are in the mouse [18] , one might predict that alterations could occur in some cells of the early embryo but not in others , resulting in individuals who were mosaic to varying degrees for loss or relaxation of proper imprinted allelic methylation . In addition , different degrees of relaxation of allele-specific methylation could be observed between different tissues of the same individual , depending on when the disruption occurred during development . One of the loci shown to be susceptible to alteration of epigenetic modifications by in vitro culture and ovarian stimulation in the mouse is Igf2/H19 [11] , [13] , [19]–[23] . Because IGF2 is an important placental growth factor and one of the phenotypes most strongly associated with human ART procedures is low birth weight , we reasoned that the human IGF2/H19 locus might also be susceptible during ART treatments and procedures . We compared parental allele-specific methylation between children conceived in vitro or in vivo at the DMR that functions as an imprint control region ( ICR ) at IGF2/H19 [24] , [25] and also at an IGF2 receptor ( IGF2R ) DMR [26] , [27] . We examined a sample of cord blood , a section of umbilical cord and five sections of placenta in each child for abnormal methylation of maternal alleles at the IGF2/H19 ICR/DMR and for abnormal methylation of paternal alleles at the IGF2R DMR [28] . Under our null hypothesis , little variation in parental allele-specific methylation was expected within an individual , or between individuals , because the methylation status of each CpG site in the DMR is set in the gametes [29] and faithful replication of this status during development is expected to result in the same allelic methylation ratio in each individual and in each tissue . We also measured steady-state IGF2 , H19 and IGF2R transcript levels to determine whether mRNA levels were correlated with abnormal allelic methylation ratio or birth weight . To our knowledge , this is the first study to examine intra-individual variation in epigenetic markings in children conceived using assisted reproduction .
We investigated intra- and inter-individual variation in allele-specific methylation at the IGF2/H19 DMR . We measured the relative level of CpG methylation on maternal and paternal alleles at this locus in cord blood , cord and five sections of placenta taken from a population of children conceived either in vitro or in vivo . The imprinted IGF2/H19 DMR is located between the IGF2 and H19 genes and is normally methylated on only the paternal allele [24] , [30] . We used a single nucleotide polymorphism ( a C/T SNP at a CfoI site ) to identify informative ( heterozygous ) individuals and a methylation-sensitive restriction endonuclease ( MluI ) to determine the methylation status of a specific CpG site within the DMR , as described previously [28] . Methylation at the MluI site , and an adjacent MaeII site , have been shown previously to be characteristic of the methylation at surrounding CpGs by bisulfite sequencing ( Fig . 7 in Sandovici et al . , 2003 ) [28] . We identified 45 in vitro and 56 in vivo individuals who were informative and for whom DNA was available from cord blood , cord and five sections of placenta . Because previous studies have indicated that loss or relaxation of imprinting is a quantitative trait [31] , [32] , we measured the ratio between DNA methylation levels on maternal and paternal ( M/P ) alleles as an indicator of imprinting status . A ratio of zero corresponds to exclusive methylation of the MluI site on the paternal allele , while a ratio of one signifies methylation of this site on an equal number of maternal and paternal alleles ( n . b . : Although we have not determined the parental origin of each allele in the present study because DNA was not available from parents , we have shown previously that the less methylated allele was maternal in all 163 individuals for whom we were able to determine parental origin by pedigree analysis [28] . We therefore assume that the less methylated allele is maternal in the population examined here , also . ) In the case of controls , no uncleaved C alleles were detected in any C/C homozygous individuals , indicating that CfoI cleaved the “hot-stop” PCR products with >99% efficiency [33] . Figure 1 shows the distribution of M/P methylation ratios observed from 56 informative in vivo ( Figure 1A ) and 45 informative in vitro ( Figure 1B ) individuals . The M/P ratios were measured for cord blood , cord and five sections of placenta from each informative individual . The data are represented as a series of symbols on a vertical line , ranked from individuals showing the greatest range of variation on the left side of the graph to individuals showing the least range of variation on the right side of the graph . The distribution of individual M/P methylation ratios in cord blood ( red circles in Figure 1 ) shows that the great majority of informative individuals have <15% methylation on the maternal allele in both groups . Approximately 8% of the population examined here has ≥15% of methylation of CpG sites on the maternal allele in cord blood . These findings are similar to those reported previously by Sandovici et al . ( 2003 ) for M/P methylation ratios measured in peripheral blood samples from the CEPH families [28] . The distribution of individual M/P methylation ratios in cord showed a similar pattern to that observed in cord blood ( Figure 1 ) , while the five sections of placenta taken from each individual showed a broad range of intra-individual variation in M/P methylation ratios . When individual tissues are compared , both M/P ratio mean and variance are greater in the in vitro group in each tissue , although two of these comparisons ( cord blood means , in which the fewest samples are compared between the two groups , and placenta variance , in which the greatest range of variation is observed ) do not reach statistical significance ( Table 1 ) . However , because the a priori expectation for the mean M/P methylation ratio of the IGF2/H19 DMR is near zero , independent of the embryonic origin of the tissue ( because the methylation status of maternal and paternal DMR alleles is assumed to be determined in the gametes and to escape the genome wide demethylation/remethylation that occurs in preimplantation embryos ) [16] , it is not inappropriate to combine all samples in each group to determine whether the two groups differ in mean M/P ratio and whether the groups have equal variance . When samples from all tissues are combined , both allele-specific methylation ratio mean and variance are significantly greater in the in vitro group ( P = 0 . 0001 and P = 0 . 0006 , respectively , Table 1 ) . The fact that the intra-individual variation in M/P ratios is also greater in the in vitro group may be seen , simply , by comparing the fraction of individuals in each group in which all samples have M/P ratios below any arbitrarily chosen value . For example , only 31% of in vitro individuals ( 14/45 ) maintain M/P ratios of <0 . 1 in cord blood , cord and all five sections of placenta while 46% of in vivo individuals ( 26/56 ) are below this threshold . One mechanism by which greater variance in an epigenetic character may occur is through a sampling effect that depends on the number of stem cell progenitors that give rise to a particular tissue; the fewer the number of stem cells , the greater the variance . Because much of the difference in variance observed between the two groups occurs as a result of intra-individual differences in umbilical cord and placenta samples , we estimated the number of trophoblast stem cells that give rise to the placenta in each group by comparing the distribution of X-inactivation scores in females from each comparison group [34] , [35] and by comparing the distribution of M/P IGF2/H19 methylation ratios from Figure 1 . We note that the assay used in each case amounts to a simple yes/no binomial trial of the form “is the CpG site being examined methylated ( in which case it gives a signal ) or not ( in which case it does not ) ” , each of which is expected to yield a “success” ( the DNA molecule in question is methylated ) with probability “p” ( p = 0 . 5 in the case of which X chromosome is inactivated and p = 0 . 1 in the case of methylation of the maternal IGF2/H19 DMR , see below ) or a failure with probability “q” ( which is equal to 1-p ) . The number of trophoblast stem cells may be estimated from the distribution of X-inactivation scores by comparing the actual distribution of X-inactivation scores with the distribution estimated from the variance of the binomial distribution ( pq/N ) , setting the probability that either allele is methylated ( p or q ) to 0 . 5 and generating the distribution for different values of N ( number of stem cells ) [34] , [35] . We determined the X-inactivation score distribution for each group ( using DNA samples from five sections of placenta from 50 in vitro and 54 in vivo females ) by comparing allele-specific methylation at HpaII sites adjacent to the CAG trinucleotide repeat in the highly polymorphic Androgen Receptor locus ( AR ) [36] . The closest fit to the distribution of X-inactivation scores in placenta in children conceived in vitro corresponds to nine trophoblast stem cells and the closest fit in children conceived in vivo corresponds to 11 trophoblast stem cells ( Table 2 ) . We additionally estimated the number of trophoblast stem cells in each group by comparing the distribution of M/P IGF2/H19 DMR methylation ratios ( Figure 1 ) from the two comparison groups , using 0 . 1 and 0 . 9 as values for p and q ( these values were selected based on the observation that ∼10% of individuals have significant methylation on the maternal DMR while ∼90% have very few cells carrying maternal DMR methylation [28] . These values are also in close agreement with the probability that any maternal DMR DNA molecule is methylated ( placenta in vivo mean = 0 . 0801 , in vitro mean = 0 . 1017 , Table 1 ) . Using these parameters , the closest fit to the distribution of M/P ratios in placenta in children conceived in vitro corresponds to eight trophoblast stem cells and the closest fit in children conceived in vivo corresponds to 10 trophoblast stem cells ( Table 2 ) . Overall , these very similar independent estimates of between-group epigenetic variation ( n . b . : not only are the two loci examined on different chromosomes but many of the individuals in the X-inactivation groups , composed of only females , and the IGF2/H19 groups were different ) are consistent with the prediction that overall greater variance in M/P IGF2/H19 DMR methylation ratios in the in vitro group is associated with fewer trophoblast stem cells . Because the methylation-sensitive restriction endonuclease assay used to generate the data shown in Figure 1 provides a ratio of maternal alleles at which MluI sites are methylated to paternal alleles at which MluI sites are methylated rather than an absolute fraction of all alleles , we also assayed DNA methylation at the IGF2/H19 DMR by bisulfite pyrosequencing . The assay we used quantifies the methylation status of five CpGs in the IGF2/H19 DMR that are adjacent to the CpG queried in the MluI assay . If an M/P ratio greater than zero ( Figure 1 ) represents methylation of maternal alleles in addition to methylation of DMRs on all paternal alleles , then the CpG sites in these samples/individuals should be methylated on greater than 50% of molecules assayed by bisulfite pyrosequencing ( i . e . all paternal alleles plus some fraction of maternal alleles ) . The placenta samples with M/P ratios greater than zero show greater than 50% methylation at all five CpGs in almost all cases ( Figure 2 ) by bisulfite pyrosequencing , indicating that both the methylation-sensitive restriction endonuclease assay and the bisulfite pyrosequencing assay are measuring gain of methylation on maternal alleles , as has also been reported in the mouse [19] , [22] , [23] . The current model for imprinted transcriptional control of IGF2/H19 correlates methylation at the ICR/DMR with an inability to bind CTCF , transcriptional silencing of H19 and transcriptional activation of IGF2 [37] , [38] . Because our analysis of DMR methylation indicates that all paternal and some maternal DMRs are methylated , we examined mRNA expression in individuals with varying levels of bi-allelic DMR methylation , for the presence of bi-allelic transcripts of IGF2 and H19 . We assayed individuals who were informative for an ApaI polymorphism ( in exon 9 ) for the presence of transcripts from both IGF2 alleles [32] . We also assayed individuals who were informative for an RsaI polymorphism ( in exon 5 ) for the presence of transcripts from both H19 alleles [39] , which would be expected to occur if paternal DMRs became demethylated . Although we detected minor amounts of presumed maternal IGF2 mRNA in several individuals , there was no correlation between maternal DMR methylation and amount of transcript from the maternal allele ( Table 3 ) . We also observed only small amounts of presumed paternal allele expression of H19 mRNA , which suggested that there was no loss of methylation on paternal alleles in these samples ( Table 3 ) , also consistent with the overall greater than 50% methylation observed by pyrosequencing ( Figure 2 ) . We investigated intra-individual variation in allele-specific methylation at an IGF2R DMR we have also examined previously [28] . The rationale for examining allele-specific methylation at this non-transcriptionally imprinted locus is that this locus may be a more sensitive reporter of any disruption in CpG site methylation by environmental factors because such changes are not predicted to affect transcription and are less likely to be selected against . The relative level of CpG methylation on paternal and maternal alleles at this locus was measured in cord blood , cord and five sections of placenta taken from populations of children conceived either in vitro or in vivo . The less methylated allele is assumed to be paternal because we found no individuals in which the paternal allele was more methylated than the maternal allele in 112 informative individuals for whom allelic inheritance could be confirmed by pedigree analysis [28] . The parental origin-specifically methylated IGF2R DMR is located in the second intron of IGF2R and is normally methylated on the maternal allele . Although the human IGF2R gene is transcribed from both alleles [40] differential methylation of maternal and paternal alleles is maintained in the human , as it is in the mouse [26] and a small fraction of the human population may have transcriptional imprinting of IGF2R [27] . We used a single nucleotide polymorphism within an MspI site to identify maternal and paternal alleles of informative ( heterozygous ) individuals and a methylation-sensitive restriction endonuclease ( NotI ) to determine the methylation status of a specific CpG site within the DMR [28] . We identified 28 in vitro and 27 in vivo individuals who were informative and assayed allele-specific methylation as described previously [28] . We calculated the ratio between the DNA methylation levels on paternal and maternal ( P/M ) alleles as an indicator of methylation imprint status . A ratio of zero corresponds to exclusive methylation of the NotI site on the maternal allele , while a ratio of one signifies methylation of this site on an equal number of paternal and maternal alleles . In the case of controls , no uncleaved C alleles were detected in any C/C homozygous individuals , indicating that MspI cleaved the PCR products with >99% efficiency . Although preferential methylation of the presumed maternal allele was observed in almost all individuals ( Figure 3 ) , the distribution of paternal/maternal ( P/M ) methylation ratios at the IGF2R DMR in cord blood and cord showed that most individuals have an easily measurable level of methylation at the CpG within the NotI cleavage site on the presumed paternal allele ( P/M>0 . 1 , Figure 3 ) , as has also been observed previously in peripheral blood from the CEPH families [28] . In cord blood , only 34% of the total population has low levels of methylation on the paternal allele ( P/M allele ratios of <0 . 1 ) while a very small fraction of individuals ( 2% ) have P/M allelic methylation ratios greater than 0 . 5 . The distribution of individual P/M methylation ratios in cord ( Figure 3 ) also showed a similar pattern to what was observed in cord blood . However , results from five placenta sections taken from the same individuals showed nearly complete loss of the methylation imprint ( i . e . P/M ratios close to 1 ) at this locus in samples from multiple individuals in both in vitro and in vivo groups ( Figure 3 ) . We found no difference in mean P/M ratios in cord blood , cord or placenta between the in vitro and in vivo groups , either comparing individual tissue types or combining all samples ( Table 4 ) . Cord blood allele-specific methylation ratio variance was greater in the in vitro group ( P = 0 . 0016 ) but we did not attempt to calculate a cord blood stem cell number comparison because of the small number of samples on which to model the distribution . There was no significant difference in the population variance in the in vitro group in cord or placenta , although the presence of a substantial fraction of samples in both groups for which nearly complete loss of the methylation imprint ( P/M>0 . 9 ) was observed is likely to affect our ability to distinguish such a difference . We measured steady-state IGF2 , H19 and IGF2R mRNA levels in cord blood and placenta from children conceived in vitro or in vivo ( Table 5 , Figure 4 ) . In addition to the children who were informative for allele specific DMR methylation ( Figure 1 and Figure 3 ) , we also measured mRNA levels in the children who were not informative . Mean cord blood IGF2R mRNA levels were significantly lower in the in vitro group ( fold change = 0 . 61 , P = 0 . 0039 ) . Mean placental IGF2 and H19 mRNA levels were also significantly lower in the in vitro group ( fold change = 0 . 52 , P<0 . 0001 , and fold change = 0 . 72 , P = 0 . 0193 , respectively ) .
We have examined intra- and inter-individual variation in DNA methylation at the differentially methylated regions ( DMRs ) of the IGF2/H19 and IGF2R loci in cord blood , cord and five sections of placenta from a population of children conceived in vitro or in vivo . Although a significant fraction of individuals in both groups do appear to maintain the IGF2/H19 methylation imprint “correctly” , with M/P ratios of <0 . 1 ( 93 . 6% of peripheral blood samples are below this M/P ratio , Sandovici et al . , 2003 ) [28] , we found substantial intra-individual and inter-individual variation in allele-specific methylation in both groups in all three tissues: 8/46 individuals in the in vivo group and 7/40 individuals in the in vitro group have cord blood M/P ratios above this level ( n . b . of the 56 informative in vivo and 45 informative in vitro individuals shown in Figure 1 , cord blood samples were unavailable for 10 of the in vivo and five of the in vitro children ) . The acquisition of CpG methylation on only a fraction of maternal IGF2/H19 DMRs in so many individuals suggests that there is extensive population level variation in the time at which methylation imprints become set during development , especially in extraembryonic lineages . This assertion receives further support from the analysis of intra-individual variation in P/M methylation ratios at an IGF2R DMR . Very few individuals ( one in the in vivo group , two in the in vitro group ) maintained this imprint “correctly” in all samples ( even if the threshold for “correctly” is reduced to P/M ratios <0 . 2 ) and the discrepancy between maintaining the imprint in embryonic and extraembryonic tissue is even more pronounced ( Figure 3 ) . The observation of greater epigenetic variation in extraembryonic than in embryonic tissues is consistent with observations at a number of imprinted loci , including IGF2/H19 , in the mouse [11] . We believe this is the first time this observation has been made in the human . Parental allele-specific mean methylation ratios at the IGF2/H19 DMR were greater in the in vitro group , indicating acquisition of methylation on maternal alleles . This finding is consistent with observations made at Igf2/H19 in the mouse [23] . The total variance in M/P ratio at IGF2/H19 was significantly greater in cord blood and cord from the in vitro group , with a suggestive P-value ( 0 . 0620 ) for placenta . These findings indicate an association between ART and the magnitude of the variance in parental allele-specific methylation patterns . The mechanism by which greater variance might be created in the in vitro group is unclear but could be related to the number of trophoblast stem cells that give rise to the placenta in each group . If fewer trophoblast stem cells give rise to the placenta in the in vitro group , one expects greater intra-individual variation in somatically heritable epigenetic marks if the population of stem cells contains cells with more than one epigenetic state ( i . e . IGF2/H19 DMRs that are methylated only on the paternal allele in some cells and methylated on both maternal and paternal alleles in others ) . As an independent test of this prediction , we measured X chromosome inactivation ratios in five sections of placenta in females from the in vitro ( 50 individuals ) and in vivo groups ( 54 individuals ) in order to estimate the number of trophoblast stem cells that give rise to the placenta in each group . The closest fit to the in vitro X-inactivation distribution corresponds to nine trophoblast stem cells while the closest fit to the in vivo distribution corresponds to 11 trophoblast stem cells . We additionally estimated the number of trophoblast stem cells in each group by comparing the distribution of M/P IGF2/H19 DMR methylation ratios from the two comparison groups . The closest fit to the distribution of M/P ratios in placenta in children conceived in vitro corresponds to eight trophoblast stem cells and the closest fit in children conceived in vivo corresponds to 10 trophoblast stem cells . Overall , these estimates of between group intra-individual epigenetic variation are consistent with the prediction that overall greater variance in M/P IGF2/H19 DMR methylation ratios in the in vitro group is associated with fewer trophoblast stem cells . Although our statistical estimates of the number of trophoblast stem cells is imprecise , it gives one some confidence that similar absolute numbers are obtained ( Table 2 ) from estimates of epigenetic variance at two different loci , examining different individuals with each assay . Whether the calculated difference in number of trophoblast stem cells reflects designation of trophoblast stem cells at an earlier stage of development ( when fewer cells are present in the embryos ) or whether embryos from the in vitro group have fewer cells than in vivo embryos at comparable stages cannot be determined from these data , however , previous reports suggest that in vitro mouse embryos may contain fewer cells than in vivo embryos at the same developmental time [41] . Our approach of using epigenetic variance to calculate the number of trophoblast stem cells is , for obvious reasons , the only opportunity for estimating this number in the human because direct comparisons of cell numbers in in vitro and in vivo embryos is not possible . We note that while we have uncovered locus-specific differences in the level of epigenetic variation in children conceived in vitro , we cannot distinguish whether the differences are due to some aspect of the assisted reproduction process or is related to the underlying infertility . In fact , the characteristic of epigenetic variance , itself , may be under genetic control and may also be influenced by the environment [42] . Greater variance in trait value , even without changes in trait mean , is predicted to have a substantial positive effect on fitness in a changing environment [42] . In this regard , we note that in vitro conception is associated with at least two changes of environment ( hormonal stimulation , retrieval of ova from the maternal environment to fertilization and culture in vitro , followed by return to the maternal environment ) . A larger-scale epigenetic screen is required in order to determine whether there is a tendency for in vitro conception to be associated with overall increased variance of epigenetic marks . Several observations are noteworthy about the steady-state mRNA levels measured for IGF2 in placenta . First , IGF2 mRNA levels in placentas from in vitro conceived children , as a group , were approximately half of what was observed in children conceived in vivo . This observation is consistent with experiments demonstrating reduced Igf2 mRNA levels in placentas from mouse embryos subject to in vitro manipulations [20] . Second , reduction in IGF2 mRNA levels in the human placentas does not occur in conjunction with loss of methylation at the paternal DMR , as expected if transcript levels are controlled by genomic imprinting alone . Furthermore , we did not observe that increased levels of methylation at the maternal DMR induced a coordinate level of transcription from the maternal IGF2 allele . In fact , given that IGF2 transcript levels vary by more than an order of magnitude between individuals ( Figure 4 ) and almost by that much between samples within a single placenta ( Figure S1 ) , the mechanism by which natural selection might act in a population , on a process whose postulated design is to reduce transcription by half ( from two alleles to one ) is unclear . Along these same lines , we observed no correlation between birth weight and IGF2 transcript levels in either placenta or cord blood , whether or not birth weights were corrected for gestational age ( Figure S2 ) . This last observation was not completely unexpected , as several laboratories have failed to observe a correlation between IGF2 mRNA levels and birth weight [43]–[45] . In this regard , it is likely that epigenetic marking of genes according to parental origin plays an important role in other processes associated with reproduction and the formation of gametes , such as chromosome pairing and recombination [46]–[49] . The selective force for the maintenance of imprinting in these processes is both direct ( successful recombination is required for successful gametogenesis ) and related to reproductive success . We also observed that mean steady-state levels of IGF2R mRNA were lower in cord blood from the in vitro group . This locus does not appear to be transcriptionally imprinted in most humans [40] , [50] , although the preferential methylation of only one parent's allele ( the maternal ) is conserved [26] , [27] . Although not transcriptionally imprinted , we did note an inverse correlation between methylation of the paternal allele and overall transcript level , indicating that “aberrant” methylation of the “incorrect” , paternal allele does have a small effect ( accounting for ∼10% of the variance , Figure S3 ) . Lower IGF2R mRNA level in the in vitro group is , on the face of it , in contrast to expectations . If IGF2R is a receptor that acts as a “sink” for IGF2 [51] , [52] , one might expect children conceived in vitro to have higher levels of IGF2R because they have a higher probability of low birth weight [53] . In any case , we did not find any correlation between birth-weight , IGF2 levels , IGF2R levels or IGF2/IGF2R ratios ( Figure S2 ) . Overall , our results indicate that epigenetic modifications at IGF2/H19 and IGF2R are subject to frequent changes during early development , especially in extraembryonic tissues . Although not all of the epigenetic changes appear to be manifested as significant differences in DNA methylation , conception in vitro is associated with gene expression differences for all three genes in some tissues . Whether the gene expression differences between in vitro and in vivo groups are also a manifestation of what appears to be a smaller number of trophoblast stem cells in children from the in vitro group is a subject for future investigation .
The cases/in vitro group are newborns conceived by assisted reproductive technology at a single infertility treatment center so that the clinical and laboratory procedures are uniform . The parents of the control/in vivo group had no prior history of infertility and the index pregnancy was achieved without medical assistance , such as the use of infertility medications or treatments . All the in vitro patients were stimulated with commercially available gonadotrophin preparations . The embryo culture media and the incubation parameters were all the same . The cases and controls were matched with regards to maternal age , race and gestational age ( Table S1 ) . Written , informed consent was obtained in advance from the mother of each newborn ( University of Pennsylvania I . R . B . approved protocol no . 804530 ) . A summary of the assays used , number of individuals studied and the tissues investigated is provided in Table S2 . Cord blood , cord and placenta samples were collected from each in vitro and in vivo newborn . All cord blood samples were collected within 20 min of delivery . Tissue samples were stored at 4°C after delivery , and samples were collected within five hours of delivery [54] . The umbilical cord was wiped with normal saline and the cord vein was punctured with a 21G needle . Whole cord blood ( 6–10 ml ) was collected in lavender topped vacutainer tubes at room temperature . The sample was shaken thoroughly to prevent clotting as the tube contains EDTA , ethylenediaminetetraacetic acid . An aliquot ( 3–4 ml ) of cord blood was transferred to a 15 ml Falcon tube containing RNALater RNA Stabilization Reagent ( Ambion , USA ) , following the manufacturers guidelines , to stabilize the RNA . The remaining cord blood in the lavender topped vacutainer tubes was saved for blood DNA extraction . All cord blood DNA and RNA samples were initially stored at 4°C , and nucleic acid extractions were performed within 2–4 days of collection . Placental tissue ( 1 . 5–2 . 5 cm3 ) was excised from the fetal surface of the placenta and rinsed extensively with sterile saline solution to minimize maternal blood contamination . Each placenta was sampled from four quadrants and from directly behind the cord insertion site ( this sample was used for the RT-PCR and pyrosequencing assays , as well as for the allele-specific methylation assays ) . A segment of umbilical cord ( 2 cm ) was cut and treated in a similar fashion . Placental and cord tissue for RNA extraction were chopped into small pieces ( 0 . 5 cm3 ) and immersed in RNALater RNA Stabilization Reagent ( Ambion , USA ) , following the manufacturers guidelines , as soon as possible after collection . All tissue DNA and RNA samples were initially stored at 4°C , and tissue digestion and nucleic acid extractions were performed within 2–4 days of collection . Approximately 4–5 mg of tissue was used for the DNA and RNA extraction procedures , and the remaining tissue was stored at −80°C . Cord blood DNA was isolated using the ArchivePure DNA Blood Kit ( Fisher Scientific Company , USA ) following the manufacturers guidelines . Tissue genomic DNA was extracted using standard phenol-chloroform extraction methods . The isolated DNA was dissolved in 10 mM TrisCl , pH 8 . 0 , quantified using a spectrophotometer and stored at −80°C until further use . Cord blood RNA was isolated using the PerfectPure RNA Blood Kit ( Fisher Scientific Company , USA ) following the manufacturers guidelines . Total cellular RNA was extracted from each tissue sample using TRIzol Reagent ( Invitrogen Corporation , USA ) , according to the manufacturers instructions . The isolated RNA was dissolved in Milli-Q water , quantified using a spectrophotometer and stored at −80°C until further use . There are many DMRs on chromosome 11 , but the most consistent observations indicating a role in the control of transcription of the IGF2 and H19 genes involve a CpG island located in a 5 kb region centromeric to the H19 gene , known as the IGF2/H19 DMR [25] . CpG sites within this DMR on the paternal allele are normally methylated , while those on the maternal allele are normally unmethylated [30] , [55]–[58] . This region also contains seven different binding sites for the CTCF protein [38] and the methylation status of the sixth binding site was found to be most consistently associated with the transcriptional status of both IGF2 and H19 [24] . The upstream H19 sequence used in this study is available from GenBank ( accession number AF125183 ) . Allele-specific methylation was investigated by screening the DNA samples for a C/T polymorphism recognized by CfoI at the IGF2/H19 DMR ( near the sixth binding site for CTCF ) [24] . After identifying maternal and paternal alleles of heterozygous individuals , a methylation-sensitive restriction endonuclease ( MluI ) was used to determine the methylation status of specific CpG sites within the DMR . If all paternal alleles are methylated and all maternal alleles are unmethylated at these sites in a sample of genomic DNA , then all maternal alleles should be cleaved by MluI while all paternal alleles will remain uncleaved . Amplification of the region by PCR using primers that flank the MluI site should amplify only paternal alleles ( identified by post-PCR cleavage with CfoI ) . Amplification of maternal alleles indicates resistance to cleavage by MluI . This may occur as a result of methylation of the CpG site within the MluI recognition sequence ( the principle upon which the assay is based ) , mutation of the MluI site or technical artifact . The latter two possibilities may be distinguished from the first by DNA sequencing , assay reproducibility and use of additional methylation-sensitive restriction endonucleases . The IGF2R DMR is located in the second intron of IGF2R and is normally methylated on the maternal allele . The sequence of IGF2R is available from GenBank ( accession number AF069333 ) . Allele-specific methylation at the IGF2R DMR was investigated by screening the DNA for a C/T polymorphism recognized by MspI . After identifying maternal and paternal alleles of heterozygous individuals , a methylation-sensitive restriction endonuclease ( NotI ) was used to determine the methylation status of specific CpG sites within the DMR . Genomic DNA ( 100 ng ) from informative individuals was digested overnight at 37°C with an excess of a methyl-sensitive restriction endonuclease: MluI and NotI for the IGF2/H19 and IGF2R DMRs , respectively . Control individuals who were homozygous for C alleles and homozygous for T alleles were also analyzed in each experiment . After digestion , the enzymes were denatured and the digested DNA was amplified in a hot-stop PCR assay using the following primers: IGF2-F 5′-GAGATGGGAGGAGATACTAGG-3′ and IGF2-R 5′-GTCAGTTCAGTAAAAGGCTGG-3′ for the IGF2/H19 DMR , and IGF2R-F 5′-GGCCGAGGCCTGGCATGTTGG -3′ and IGF2R-R 5′-TGGGGAAGCGCGAGAGGCCTAGG-3′ for the IGF2R DMR . After 30 cycles at 94°C for 30 s , 50°C for 30 s ( IGF2/H19 ) or 63°C for 30 s ( IGF2R ) , and 72°C for 1 min , we added 3 µCi α-32P dCTP for one additional cycle and a final elongation step ( 72°C for 7 min ) . PCR products were then digested overnight at 37°C with the enzyme used for identifying the parental origin of the alleles ( CfoI for IGF2/H19 and MspI for IGF2R ) . The samples were separated on denaturing 5% polyacrylamide gels and the intensity of the bands ( alleles ) were quantified using a PhosphoImage Reader FLA 5000 ( FUJIFILM Medical Systems USA , Inc . ) . We used a custom pyrosequencing assay for the IGF2/H19 DMR ( NCBI36:11 , 2019856-2019740 ) which included five CpGs . Genomic DNA ( 500 ng per sample ) was bisulfite treated using EZ Gold DNA Methylation Kit ( Zymo Research , USA ) following the manufacturers protocol . Bisulfite treated DNA was used for generating PCR amplified templates for pyrosequencing . The PCR primer sequences were: forward 5′- GGGGTTATTTGGGAATAGG-3′ and biotin labeled reverse , 5′- CCAAACCATAACACTAAAACCCTC-3′ . The PCR reaction ( 30 µl ) was following: 25 ng of bisulfite DNA , 0 . 75 U HotStar Taq Polymerase ( Qiagen , USA ) , 1X PCR buffer , 3 mM MgCl2 , 200 µM of each dNTPs , 6 pmol forward primer and 6 pmol reverse primer . Recommended PCR cycling conditions were: 95°C for 15 min; 45 cycles ( 95°C for 30 s; 60°C for 30 s; 72°C for 30 s ) ; 72°C for 5 min . The biotinylated PCR product ( 10 µl ) was used for each sequencing assay with the following sequencing primer: 5′- GAATAGGATATTTATAGGAG-3′ . Pyrosequencing was done using the PSQ96HS system according to standard procedures using Pyro Gold Reagent kits ( Biotage , Sweden ) . Methylation was quantified using Pyro Q-CpG Software ( Biotage , Sweden ) , which calculates the ratio of converted C's ( T's ) to unconverted C's at each CpG and expresses this as a percentage methylation . First-strand cDNA was obtained using SuperScript III Reverse Transcriptase ( RT ) ( Invitrogen Corporation , USA ) . To produce cDNA from total RNA , a mixture containing 0 . 5–1 µg extracted total RNA , 0 . 5 µg oligo ( dT ) 18 primer and 1 µl dNTP mix ( 10 mM each ) in a final 13 µl reaction volume was heated to 65°C for 5 min , cooled down on ice for 1 min , and then added to a 7 µl reaction mixture containing 4 µl SuperScript III RT buffer ( 10 ) , 1 µl DTT ( 0 . 1 M ) , 1 µl RNaseOUT Recombinant RNase inhibitor ( 40 U/µl; Invitrogen Corporation , USA ) and 1 µl SuperScript III M-MLV reverse transcriptase ( 200 U/µl ) . The samples were mixed and incubated at 50°C for 60 min . Reactions were terminated at 70°C for 15 min and the RT products were stored at −20°C until further use . Quantitative real-time RT-PCR assays were carried out using a 7700 Sequence Detector ( Applied Biosystems , USA ) . GAPDH , which has previously been used as a housekeeping gene in placenta by several investigators [59]–[61] , was used as the housekeeping gene . All the placental tissue samples were from the third trimester . There was a positive correlation between GAPDH expression and the expression of another commonly used housekeeping gene HPRT , when studied in the same samples ( Figure S4 ) . Steady-state mRNA levels of IGF2 , H19 , IGF2R and housekeeping gene GAPDH were measured using gene-specific primers and QuantiFast SYBR Green PCR Master Mix ( Qiagen , USA ) . The primer sequences were following: IGF2 Forward 5′-TCTGACCTCCGTGCCTA-3′ , IGF2 Reverse 5′-TTGGGATTGCAAGCGTTA-3′ , H19 Forward 5′-AGAAGCGGGTCTGTTTCTTTA-3′ , H19 Reverse 5′-TGGGTAGCACCATTTCTTTCA-3′ , IGF2R Forward 5′-ACCTCAGCCGTGTGTCCTCT-3′ , IGF2R Reverse 5′-CTCCTCTCCTTCTTGTAGAGCAA-3′ , GAPDH Forward 5′-GAGTCAACGGATTTGGTCGT-3′ , and GAPDH Reverse 5′-TTGATTTTGGAGGGATCTCG-3′ . PCR reactions were performed by mixing 1 µl of cDNA ( 50 ng/µl placenta , 25 ng/µl cord blood ) with 24 µl of reaction mixture ( 12 . 5 µl QuantiFast SYBR Green PCR Master Mix ( 2X ) , 2 . 5 µl forward primer ( 10 µM ) , 2 . 5 µl reverse primer ( 10 µM ) , and 6 . 5 µl nuclease free dH2O ) and amplified under the following conditions: 95°C for 5 min , followed by 40 cycles of 95°C for 10 s and 60°C for 30 s . A melting curve analysis of the PCR products was performed to verify their specificity and identity . PCR products were also run on 2% agarose gels to confirm the size of the amplified products . Relative gene expression levels were obtained using the ΔΔCT method [62] . To avoid genomic DNA contamination during imprinting analysis , PCR was done across an intron-exon boundary and the cDNA products were gel-purified . The primers used for assaying IGF2 imprinting were: primer 1 , 5′−ATCGTTGAGGAGTGCTGTTTC−3′; primer 2 , 5′−CGGGGATGCATAAAGTATGAG−3′; primer 3 , 5′−CTTGGACTTTGAGTCAAATTGG−3′; and primer 4 , 5′−GGTCGTGCCAATTACATTTCA−3′ [32] . Heterozygosity of an ApaI polymorphism in exon 9 of IGF2 was ascertained by doing PCR with genomic DNA using primers 3 and 4 across the ApaI site , and the PCR product was digested with ApaI . Imprinting status was ascertained by doing RT-PCR , using primers 1 and 2 in exons 8 and 9 , respectively . The cDNA PCR product , which is shorter than any possible contaminating genomic DNA product because of intron splicing , was electrophoresed and purified from a 2% agarose gel using the QIAquick Gel Extraction Kit ( Qiagen , USA ) following the manufacturers protocol . Hot-stop PCR was then done using primers 3 and 4 , with α-32P dCTP added before the last cycle . The PCR product was digested with ApaI and then separated on denaturing 5% polyacrylamide gels and the intensity of the bands ( alleles ) were quantified using a PhosphoImage Reader FLA 5000 ( FUJIFILM Medical Systems USA , Inc . ) . The primers used for assaying H19 imprinting were: H19-1 , 5′-GGAGTTGTGGAGACGGCCTTGAGT-3′; H19-2 , 5′-CCAGTCACCCGGCCCAGATGGAG-3′; and H19-3 , 5′-CTTTACAACCACTGCACTACCTGAC-3′ . Heterozygosity of an RsaI polymorphism in exon 5 of H19 was ascertained by doing PCR with genomic DNA using primers H19-1 and H19-2 across the RsaI site , and the PCR product was digested with RsaI . Imprinting status was ascertained by doing RT-PCR , using primers H19-3 and H19-2 in exons 4 and 5 , respectively . The cDNA PCR product , which is shorter than any possible contaminating genomic DNA product because of intron splicing , was electrophoresed and purified from a 2% agarose gel using the QIAquick Gel Extraction Kit ( Qiagen , USA ) following the manufacturers protocol . Hot-stop PCR was then done using primers H19-1 and H19-2 , with α-32P dCTP added before the last cycle . The PCR product was digested with RsaI and then separated on denaturing 5% polyacrylamide gels and the intensity of the bands ( alleles ) were quantified using a PhosphoImage Reader FLA 5000 ( FUJIFILM Medical Systems USA , Inc . ) . X-chromosome inactivation ratios were assayed using previously published modifications of a methylation-sensitive PCR assay [33] , [36] , [63]–[65] . We measured the methylation status of a CpG site that is correlated with the expression of alleles at the X-linked , highly polymorphic androgen receptor ( AR ) locus . Genomic DNA from cord blood , cord and five sections of placenta was available for 50 in vitro and 54 in vivo females who were heterozygous for AR alleles that differed by more than one CAG repeat . Genomic DNA from females who were heterozygous ( informative ) at the highly polymorphic ( CAG ) n repeat of the X-linked AR gene was amplified after previous overnight digestion with HhaI methyl-sensitive restriction endonuclease , with primers ( AR1: 5′-AGAGGCCGCGAGCGCAGCAC-3′ and AR2: 5′-ACTCCAGGGCCGACTGCGGC-3′ ) , which flank the repeat and two HhaI sites . We added a radiolabeled nucleotide for the last cycle of ‘hot-stop’ PCR , rather than a single end-labeled primer , to increase the signal . After 27 cycles at 94°C for 1 min , 68°C for 1 min , and 72°C for 1 min , 3 µCi α-32P dCTP was added for one additional cycle . PCR products were separated on denaturing 5% polyacrylamide gels and the intensity of the alleles was quantified by using the PhosphoImage Reader FLA 5000 ( FUJIFILM Medical Systems USA , Inc . ) [65] . As a way of quantifying the degree of skewing , i . e . , the degree to which the somatic cells of an individual female deviated from a 1∶1 ratio , the intensity of the upper allele divided by the sum of the intensities of both alleles was computed for each individual . The statistical significance of the methylation datasets representing the in vivo and in vitro group was examined using the Wilcoxon Rank Sums Test . Data from the real time RT-PCR experiments were analyzed using Student's T-test . The number of cells was estimated using the method described by Amos-Landgraf et al . ( 2006 ) and Mclaren A ( 1972 ) [34] , [35] . P-values ≤0 . 05 were considered significant . | We have screened a population of children conceived in vitro for epigenetic alterations at two loci that carry parent-of-origin specific methylation marks . We made the observation that epigenetic variability was greater in extraembryonic tissues than embryonic tissues in both groups , as has also been demonstrated in the mouse . The greater level of intra-individual variation in extraembryonic tissues of the in vitro group appears to result from these embryos having fewer trophoblast stem cells . We also made the unexpected observation that variability in parental origin-dependent epigenetic marking was poorly correlated with gene expression . In fact , there is such a high level of inter-individual variation in IGF2 transcript level that the presumed half-fold reduction in IGF2 mRNA accounted for by proper transcriptional imprinting versus complete loss of imprinting would account for less than 5% of the total population variance . Given this level of variability in the expression of an imprinted gene , the presumed operation of “parental conflict” as the selective force acting to maintain imprinted gene expression at the IGF2/H19 locus in the human should be revisited . | [
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] | 2010 | Inter- and Intra-Individual Variation in Allele-Specific DNA Methylation and Gene Expression in Children Conceived using Assisted Reproductive Technology |
It is well established that persistent viral infection may impair cellular function of specialized cells without overt damage . This concept , when applied to neurotropic viruses , may help to understand certain neurologic and neuropsychiatric diseases . Borna disease virus ( BDV ) is an excellent example of a persistent virus that targets the brain , impairs neural functions without cell lysis , and ultimately results in neurobehavioral disturbances . Recently , we have shown that BDV infects human neural progenitor cells ( hNPCs ) and impairs neurogenesis , revealing a new mechanism by which BDV may interfere with brain function . Here , we sought to identify the viral proteins and molecular pathways that are involved . Using lentiviral vectors for expression of the bdv-p and bdv-x viral genes , we demonstrate that the phosphoprotein P , but not the X protein , diminishes human neurogenesis and , more particularly , GABAergic neurogenesis . We further reveal a decrease in pro-neuronal factors known to be involved in neuronal differentiation ( ApoE , Noggin , TH and Scg10/Stathmin2 ) , demonstrating that cellular dysfunction is associated with impairment of specific components of the molecular program that controls neurogenesis . Our findings thus provide the first evidence that a viral protein impairs GABAergic human neurogenesis , a process that is dysregulated in several neuropsychiatric disorders . They improve our understanding of the mechanisms by which a persistent virus may interfere with brain development and function in the adult .
Upon entrance in the brain , viruses most often induce inflammation , fever , and brain injury , all signs symptomatic of acute encephalitis . A strong immunological response is typically triggered and generally limits viral dissemination and resolves infection . In some cases , however , viruses may not be recognized by the immune system , thus allowing their life-long persistence in the central nervous system ( CNS ) . Continuous viral replication may then interfere with cellular functions , and while not causing overt tissue damage may nevertheless lead to disease . This was first recognized in the early 80s when the lymphocytic choriomeningitis virus ( LCMV ) was shown to disrupt homeostatic and cognitive functions without obvious tissue injury in its natural murine host [1–4] . Rabies is another well-known virus that disrupts brain function in the absence of cell lysis and causes dramatic alteration in behavior in both animals and humans [5] . Such studies conducted in animal models , together with epidemiological analyses of human neuropsychiatric conditions , have suggested that persistent viral infection may play a role in human mental disorders of unclear etiology [6–8] . Understanding the mechanisms by which persistent viruses impair brain function and how this may be related to neurological and neuropsychiatric diseases has thus become a major challenge in neuro-virology . Borna disease virus ( BDV ) is a highly neurotropic virus that persists in the CNS of infected individuals for their entire lifespan . It is an enveloped virus with a non-segmented , negative-sense , single-stranded RNA genome that belongs to the Bornaviridae family within the Mononegavirales order [9 , 10] . Its small genome of 8 . 9 kb encodes 6 proteins , the nucleoprotein ( N ) , phosphoprotein ( P ) , X protein ( X ) , matrix protein ( M ) , glycoprotein ( G ) and polymerase ( L ) . N , P , X and L form the polymerase complex , the smallest unit necessary for genome replication . Natural BDV infection has been identified in a wide range of vertebrates , including horses , sheep , cattle , dogs , cats , shrews , ostriches and non-human primates [11–16] . Infected hosts develop a wide spectrum of neurological disorders , ranging from immune-mediated disease to behavioral alteration without inflammation . The latter includes deficits in learning and social behavior that are reminiscent of symptoms observed in human psychiatric diseases [17 , 18] . In humans , evidence supporting the presence of BDV in the brain of a schizophrenic patient has been reported [19] and some epidemiologic studies have supported BDV infection [20] . A possible association between BDV infection and psychiatric diseases has , however , been debated for years [21] , with the most recent study showing no evidence of association [22] . This controversy may not be fully resolved until measures to ensure reliability , such as those described by Hornig et al . , ( 2012 ) [22] , are adopted by all investigators performing BDV diagnosis . Moreover , since detection of BDV is currently hampered by reason of its sequestration in the brain and its weak immunogenicity , the development of new diagnostic tools would improve association studies in the future . Nevertheless , BDV infection provides an excellent model to study the relationship between persistent viruses and the development of chronic neurological symptoms , with possible consequences for public health . BDV interference with cellular signaling has been evidenced in neuronal and glial cells . In neurons , BDV has been shown to block neurotrophin-induced signaling , leading to diminished neuritic outgrowth [23] and synaptogenesis [24] . It is also known to block synaptic vesicle recycling in response to stimuli-induced synaptic potentiation [25 , 26] and to limit through its phosphoprotein the mobility of the GABA receptor , two mechanisms by which it may impair synaptic transmission [27] . As regards glial cells , the selective expression of the viral phosphoprotein P in astrocytes led to neurobehavioral disturbances in transgenic mice [28] , suggesting that BDV infection of astrocytes may also contribute to behavioral disorders . Beyond its role in highly specialized cells , we have recently demonstrated that BDV infects human neural progenitor cells ( hNPCs ) in culture and impairs their capacity to produce neurons , thus , identifying a new mechanism by which BDV may interfere with brain function [29] . Neurogenesis is a process that occurs not just during development but also throughout life , although it is restricted to discrete brain areas in adults . Given that abnormality in the generation of neurons , in pre- and post-natal life , is viewed as characteristic of human mood disorders , such as depression , dementia and psychosis [30–32] , our work has opened a new field in BDV research . The goal of this study was to unravel the molecular mechanisms by which BDV impairs neurogenesis in hNPCs . Using transgenic hNPCs expressing the viral genes encoding the P or X protein , we have identified P as a protein responsible for alteration of human neurogenesis . We then provide evidence that P damages GABAergic neurogenesis and , finally , we show that cellular dysfunction is associated with impairment of specific components of the intrinsic molecular program responsible for neurogenesis . To our knowledge , this is the first demonstration that a viral protein interferes with human GABAergic neurogenesis , a process that is dysregulated in several neuropsychiatric disorders . This is also the first study that addresses and elucidates some of the molecular mechanisms responsible for virally induced alteration of human neurogenesis .
To identify the viral proteins responsible for BDV-induced alteration of neurogenesis , we chose to study the phosphoprotein and the X protein ( henceforth referred to P and X ) , as they have been previously described to interact with many cellular pathways in neural cells . We thus established transgenic populations of hNPCs expressing either bdv-p or bdv-x gene , or as a control , the gfp gene . At 10 weeks of expansion , adherent hNPCs were transduced with highly purified lentiviral vectors encoding the different genes of interest and amplified for a further 2 to 4 week period before epidermal growth factor ( EGF ) and basic fibroblast growth factor ( bFGF ) withdrawal and analysis of the effect of the transgene on neural differentiation ( Fig 1A ) . The level of expression of gfp , bdv-p or bdv-x genes in transduced hNPCs was first verified . In undifferentiated cells ( day 0 ) more than 90% of hNPCs were GFP- ( 91 . 28 +/- 2 . 9% ) or P-positive ( 96 +/- 2% ) and approximately 80% were X-positive ( 79 +/- 4 . 3% ) , as determined by enumeration of cells labeled with antibodies directed against the P or X viral proteins ( Fig 1B and 1C ) . A similar percentage of transgene-expressing hNPCs was observed after 14 days of differentiation ( 87 . 6 +/- 1 . 1% , 96 . 2 +/- 1 . 5% , and 79 . 9 +/- 3 . 3% of cells expressing the gfp , bdv-p or bdv-x gene , respectively ) ( Fig 1B and 1C ) . Thus , high efficiencies of transduction were obtained with lentiviral vectors and differentiation did not affect gfp , bdv-p and bdv-x gene expression . In keeping with the presence of a nuclear localization signal [33] , P was strictly nuclear , whereas X , which contains both a nuclear localization signal and a short helix responsible for mitochondrial targeting [34 , 35] , was observed in both nuclear and cytoplasmic structures , in undifferentiated and differentiated hNPCs ( Fig 1B ) . At the undifferentiated stage , transgenic hNPCs expressing either the gfp , bdv-p or bdv-x gene were morphologically indistinguishable from their non-transduced ( NT ) matched controls ( S1 Fig ) . Transgene expression had no impact on cell survival , as observed by light microscopy examination ( S1 Fig ) , and the presence of the viral proteins , P or X , did not modify the expression of Nestin and Sox2 , two markers of the undifferentiated stage ( Fig 2A and 2B ) . We next examined whether the viral genes influenced the proliferative capacity of hNPCs . Transgene-expressing hNPCs and their NT matched controls were cultured in the presence of growth factors—EGF and bFGF—and proliferation was determined by evaluation of both BrdU incorporation and mitochondrial dehydrogenase activity . No significant difference was observed between bdv-p- and bdv-x-expressing hNPCs and their matched NT controls ( Fig 2C , 2D and 2E ) . Since , however , an effect on proliferation may have been masked by the presence of growth factors , we measured proliferation after growth factor withdrawal by enumerating hNPCs at day 0 and day 4 of differentiation using DAPI staining . At day 4 , NT hNPCs were 5 to 7 . 5 fold more numerous than at day 0 , showing that cells continued to proliferate in the first days of differentiation . No difference , however , was observed in bdv-p- and bdv-x-expressing hNPCs compared with their matched NT controls ( Fig 2F and 2G ) , demonstrating that transgenic hNPCs were not impaired in their capacity to proliferate . Thus , the viral P and X proteins did not alter hNPCs at the undifferentiated stage . When growth factors are withdrawn from the medium , hNPCs differentiate into a mixed culture composed mainly of neurons and astrocytes . Very few oligodendrocytes are produced in our conditions of culture . To evaluate the effect of viral bdv-p and bdv-x gene on neural differentiation , transgene-expressing hNPCs and their NT matched controls that had undergone differentiation for 14 days were fixed and immunostained with antibodies directed against the neuronal marker βIII-Tubulin and the astroglial marker glial fibrillary acidic protein ( GFAP ) . In keeping with our previous findings , differentiated NT hNPCs exhibited a pattern typical of mixed culture , being composed of 55% to 65% of neurons and 20% to 30% of astrocytes ( Fig 3 , non-transduced ) . No difference was observed in the percentage of either neurons or astrocytes generated in gfp-expressing hNPCs as compared with their matched NT controls ( Fig 3A , 3Aa–3Ad and 3B ) . Thus , lentiviral transduction per se did not affect differentiation of hNPCs . We then evaluated the impact of bdv-p and bdv-x genes . In bdv-p-expressing hNPCs , we observed a decrease of up to 50% of neurons upon enumeration of immunostained cells ( Fig 3A , 3Aa , 3Ae and 3C , left ) . A similar result was obtained when cells were immunostained with an antibody directed against a second neuronal marker , the microtubule-associated protein 2 ( MAP2 ) , ( S2A and S2B Fig ) , thus confirming the observed diminution in the percentage of neurons . By contrast , the percentage of astrocytes was not modified ( Fig 3A , 3Ab , 3Af and 3C , right ) . In bdv-x-expressing hNPCs , neither neurons nor astrocytes were altered , as their pattern and percentage were similar to those observed in their matched NT controls ( Fig 3Aa , 3Ag , 3Ab , 3Ah and 3D ) . Thus , our results demonstrate that expression of the viral bdv-p but not bdv-x gene alters neuronal differentiation but spares the astrocytic lineage . The decrease in the number of neurons in bdv-p-expressing hNPCs may have been due to increased death of cells committed to neuronal fate , or alternatively , to blockade in neuronal differentiation . To address the first possibility , we sought evidence of a cytopathic effect or apoptosis . Observation by phase-contrast microscopy at 4 , 7 , 10 and 14 days of differentiation did not reveal any obvious cytopathic effect in either bdv-p- or bdv-x-expressing hNPCs , as compared with their matched NT controls ( Fig 4A , day 14 ) . Apoptosis was sought in cells differentiated for 14 days by immunostaining using an antibody directed against cleaved caspase 3 , a well-known apoptotic marker , and by TUNEL assay ( Fig 4B and 4C ) . Observation and enumeration of cleaved-caspase-3- ( Fig 4B ) and TUNEL- ( Fig 4C ) positive cells revealed very few apoptotic cells in hNPCs , whether NT or expressing bdv-x or bdv-p . This showed that P did not compromise the survival of differentiating cells . Thus , P-induced reduction in the number of neurons appears to be due to the decreased capacity of hNPCs to differentiate into neurons rather than to impairment of their survival . To define the stage at which neuronal differentiation was impaired with greater precision , we performed a time-course study in which the number of Sox2- and HuC/D-positive cells was monitored throughout differentiation . Sox2 is a universal marker of neural progenitors that is known to be down-regulated during differentiation when progenitors become post-mitotic [36] . We reasoned that if a pool of bdv-p-expressing hNPCs were blocked at the progenitor stage , Sox2-positive cells would be more numerous than in control cells . We thus labeled bdv-p-expressing hNPCs and their NT matched controls with an antibody directed against Sox2 from 0 to 28 days of differentiation . As expected , at the progenitor stage ( day 0 ) , 100% of NT hNPCs were Sox-2 positive and their number continuously decreased during differentiation ( Fig 5A and 5B ) . They represented approximately 60% of the population at day 14 and 40% at day 28 . It was somewhat surprising to observe that as many as 60% of hNPCs still expressed Sox2 after 14 days of differentiation , since at that time approximately 90% of the cells had already differentiated into either βIII-Tubulin- or GFAP-positive cells . This indicated that loss of Sox2 expression is gradual during the differentiation process . Most notably , no difference was observed in the percentage of Sox2-positive cells between bdv-p- and NT- cells at any time point studied , indicating that P does not prevent the cells from exiting the progenitor stage . Next , to address whether P blocks cell entry into the neuronal pathway , bdv-p-expressing hNPCs and their NT matched controls were labeled with an antibody directed against HuC/D , a nuclear neuronal marker that is expressed as soon as the neuroblasts exit the proliferation cell cycle [37] . In NT cells , approximately 60% of cells were HuC/D-positive at day 7 and their number rose up to day 10 , at which time it remained constant up until day 28 ( Fig 5C ) . This showed that by day 10 , commitment to the neuronal lineage has been completed . The estimate of the neuronal population based on HuC/D immunostaining at 14 days of differentiation in NT cells was somewhat higher ( approximately 80% ) than that previously determined on the basis of βIII-Tubulin immunostaining ( approximately 60% , Fig 3 ) . This is possibly due to variability between experiments and/or to differences in the manner in which cells were enumerated ( automatically for HuC/D and manually for βIII-Tubulin ) . To address this issue , HuC/D- and βIII-Tubulin-positive cells were both manually enumerated in one single experiment , at day 14 of differentiation . Similar values were obtained ( approximately 70% ) , indicating that , at that time , all NT cells that had entered a neuronal pathway had acquired the βIII-Tubulin marker . Most notably , at every time point studied , no significant difference in the percentage of HuC/D-positive cells was observed between bdv-p-expressing cells and their NT controls ( Fig 5C ) , indicating that P does not impair neuronal commitment . Thus , altogether , our results suggest that P impairs the acquisition of a mature neuronal phenotype . We then sought to define the neuronal subpopulations that were altered in bdv-p-expressing cells . Depending on the age of the fetus , the area of the brain from which cells were collected and the composition of the culture medium , GABAergic and glutamatergic neurons may be produced . To determine which subtypes were present in our cultures , we used the antibody directed against HuC/D for recognition of all neuronal subtypes and antibodies specifically directed against either GABAergic or glutamatergic neurons . We also used an antibody specific for dopaminergic neurons . At day 28 of differentiation , approximately 80% of the neurons were GABAergic , 0 . 3% were dopaminergic and none of them were glutamatergic ( S3 Fig ) . It is unlikely that the lack of glutamatergic neurons in our cultures is attributable to defective antibodies because two of the three antibodies we tested are routinely used for staining glutamatergic neurons generated from human embryonic stem ( hES ) cells [38] . Thus , our cultures were predominantly composed of GABAergic neurons . In a time-course study , we verified whether acquisition of the GABAergic phenotype was impaired in bdv-p-expressing cells and at which time ( Fig 5D and 5E ) . At the earliest time point examined ( day 4 ) , the GABAergic phenotype had not yet been acquired in either NT or bdv-p-expressing cells . In NT cells , it was first observed at day 7 , at which time GABAergic neurons constituted approximately 30% of the total neuronal population . Their percentage continuously rose to reach approximately 80% at day 28 ( Fig 5D and 5E ) . In bdv-p-expressing cells , while the number of GABAergic neurons also continuously rose from day 7 to day 28 , their proportion was reduced throughout differentiation , as compared with that of NT cells ( Fig 5E ) . Thus , bdv-p expression in differentiating hNPCs diminishes GABAergic neurogenesis . On the contrary , from day 7 to day 28 of differentiation , the percentage of GABAergic neurons was similar in bdv-x-expressing cells and their matched NT controls ( S4A and S4B Fig ) , confirming that BDV-X does not impair human neurogenesis and further demonstrating the specificity of bdv-p-induced alteration . The serine ( S ) residues at position 26 and 28 ( S26 and S28 ) are known to be phosphorylated by PKC and to form the major site of phosphorylation in the phosphoprotein P [39] . Recently , two atypical PKC , zeta and iota , have been shown to facilitate neuronal differentiation [40] . This prompted us to investigate whether S26 and S28 might be involved in bdv-p-induced alteration of GABAergic neurogenesis through alteration of PKC signaling . To address this question , we used a modified P protein in which the S residues were replaced by alanine ( A ) residues ( Fig 6A ) . In the modified protein , called Paass , P phosphorylation by PKC was shown to be completely abolished [34] . Using lentiviral vectors as previously described , we established new transgenic hNPCs expressing either the bdv-p or bdv-paass gene . A similar percentage of cells was transduced in the two transgenic lines , as 96 +/- 2% and 87 +/- 5% of cells expressing the bdv-p or bdv-paass gene , respectively , were P-positive ( Fig 6B ) . Both cellular localization and level of expression of P were also comparable in the two cell lines , as shown by immunofluorescence ( Fig 6B ) and Western blotting ( Fig 6C ) . The bdv-p- and bdv-paass-expressing hNPCs , together with their matched NT controls , were induced to differentiate for 14 days and the percentage of GABAergic neurons generated was determined . The phosphorylation status of S26/28 residues clearly did not modify P-induced inhibition of GABAergic neurogenesis , as a similar percentage of GABAergic neurons was generated in bdv-p- and bdv-paass-expressing hNPCs . In both cases , the percentage was significantly lower than in the NT matched controls ( Fig 6D ) . These results establish that the phosphorylation of the serine residues S26/28 is not essential for P-induced impairment of GABAergic neurogenesis . Neuronal differentiation is tightly regulated by an intrinsic genetic program . To determine whether this program was impaired in bdv-p-expressing hNPCs , we analyzed the differential expression of 84 human genes involved in neural differentiation , using a PCR array approach . Under the assumption that transcriptional alterations should precede cellular alterations , transcripts from bdv-p-expressing hNPCs were pooled from biological triplicates and compared with their matched NT controls at an early time point of differentiation ( day 4 ) . All genes studied are shown in Fig 7 and genes significantly modulated , after application of an arbitrary cut-off of 3-fold , are listed in Table 1 . In confirmation of our finding that bdv-p expression had no effect at the undifferentiated stage , the genes involved in proliferation and maintenance of hNPCs were not significantly regulated in bdv-p-expressing cells . In contrast , 10 genes known to be involved in either neuronal specification ( ApoE , Tnr , noggin ) or neuronal maturation and survival ( TH , Pou4f1 , Adora2A , Bdnf , AchE ) or glial differentiation ( Bmp4 , Olig2 ) were down-regulated . Among these , tyrosine hydroxylase ( TH ) and apolipoprotein E ( ApoE ) were the most dramatically regulated , with a 42- and 10-fold decrease , respectively . TH is a well-known marker of dopaminergic neurons , although it is also expressed to a lesser extent in neural progenitor cells , in which its function remains uncertain . In order to validate our PCR array data , primers were specifically designed to re-analyze its expression by RT-qPCR on individual samples . Down-regulation of TH mRNA in bdv-p-expressing hNPCs differentiated for 4 days was confirmed , although with a more modest 5-fold decrease ( Fig 8A , left ) . In contrast , there was no modification in the TH mRNA level in bdv-x-expressing hNPCs ( Fig 8A , right ) , demonstrating that down-regulation was specifically induced by the P protein . Decrease in TH mRNA occurred as early as day 0 , in undifferentiated bdv-p-expressing hNPCs ( Fig 8B ) , but this difference was no longer observed at 14 days of differentiation ( Fig 8C ) . At day 4 , P-induced down-regulation was confirmed at the protein level , as shown by Western blotting ( Fig 8D ) . In contrast and as expected , X did not alter the level of TH protein ( Fig 8D , right ) . TH , BDNF and SCG10/Stathmin2 are three pro-neuronal factors that are known to be regulated by REST/MeCP2 signaling , a major pathway in neurogenesis [41 , 42] . As we observed a down-regulation in TH and Bdnf in the PCR array , we wondered whether Scg10/Stathmin2 , which had previously been shown to be altered in BDV-infected rat hippocampal neurons [43] , was also down-regulated in bdv-p-expressing hNPCs . This was indeed the case , as a 1 . 8-fold decrease was observed ( Fig 8E , left ) . Again , no alteration was shown in bdv-x-expressing hNPCs ( Fig 8E , right ) . Like TH , Scg10/Stathmin2 down-regulation was observed before the initiation of differentiation , at day 0 ( Fig 8F ) , but in contrast to TH it was durable and still evident at day 14 ( Fig 8G ) . This was confirmed at the protein level ( Fig 8H ) . To determine whether down-regulation of genes regulated by REST and MeCP2 might result from a modification of their expression , we evaluated REST and MeCP2 levels in bdv-p-expressing hNPCs . RT-qPCR and Western blotting analyses , however , did not reveal any alteration at mRNA or protein levels at any time point studied ( S5A–S5D Fig for REST and S5E–S5H Fig for MeCP2 ) . ApoE was the second most down-regulated gene ( 10-fold decrease ) in bdv-p-expressing hNPCs . In a study by Li et al . [44] , it was shown that invalidation of the ApoE gene was linked to decrease in noggin and to alteration in neurogenesis . As Noggin was also shown to be decreased in the PCR array ( by a 3-fold factor , Fig 7 and Table 1 ) , we sought to confirm the down-regulation of these two genes by RT-qPCR . Down-regulation of ApoE was clearly confirmed at day 4 of differentiation , as a 4 . 4-fold decrease was observed ( Fig 9A , left ) . In contrast , as already shown for TH and Scg10/Stathmin2 , there was no alteration in bdv-x-expressing hNPCs ( Fig 9A , right ) . Also , similar to observations made for Scg10/Stathmin2 , down-regulation of the ApoE gene occurred before the initiation of differentiation , at day 0 ( Fig 9B ) , and was still observable at day 14 ( Fig 9C ) . It was also further confirmed at the protein level ( Fig 9D ) , with again , no alteration due to the X protein . Similar results were obtained for noggin as a 2 . 9-fold decrease occurred at day 4 and was observable at day 0 and day 14 of differentiation ( Fig 9E , 9F and 9G ) . Taken together , our results demonstrate that P impairs the developmental program involved in hNPC differentiation and strongly suggest certain genes to be responsible for bdv-p-induced inhibition of neurogenesis .
BDV alters the behavior of infected hosts by mechanisms that remain largely unknown . Recently , we have uncovered a new mechanism by which it may do so , as we demonstrated its capacity to impair human neurogenesis [29] . Here , we sought to identify the viral proteins and molecular pathways that are responsible for BDV impairment of neurogenesis . We demonstrate that the phosphoprotein P , but not the X protein , reduces human neurogenesis and affects GABAergic neurons . In addition , we reveal an alteration in expression of pro-neuronal factors controlling neurogenesis that precedes and accompanies cellular dysfunction . Using specific markers , we have defined the stage at which neuronal differentiation is impaired by P . We show that bdv-p-expressing hNPCs exit the cell cycle and enter the neuronal pathway , suggesting that P expression impairs the acquisition of a mature neuronal phenotype , including expression of βIII-Tubulin , MAP2 and GABA markers . This reduction in neurogenesis was not accompanied by cellular death , in contrast with our previous findings , which showed extensive cell death in BDV-infected differentiating hNPCs [29] . This difference may be related to the necessity for viral replication or for a combination of viral proteins to induce death . One may speculate , for example , that interactions between P and X would lead to the localization of P in not only the nucleus , as observed in our study , but also in the cytoplasm [33] . This would most likely induce interference with signaling pathways other than those described here , possibly resulting in cell death . Thus , although the mechanisms underlying the cytocidal effect of BDV during neuronal differentiation remain to be fully elucidated , we clearly show that a single protein , the P , is sufficient to reduce neurogenesis . Gamma-aminobutyric acid ( GABA ) is the chief inhibitory neurotransmitter that regulates neuronal excitability in the mammalian brain . It is important to note that BDV has been reported to alter this system through different mechanisms . In vivo , BDV infection of newborn rats induces a strong reduction in the volume of cortical areas , which has been associated with depletion of GABAergic neurons [45] . In vitro , studies have shown that P binds to the GABA receptor-associated protein ( GABARAP ) , a molecule that links the GABA receptor ( GABAR ) to microtubules , which may lead to alteration in GABAergic synaptic transmission through limitation of GABAR mobility [27] . Finally , our finding that P impairs GABAergic neurogenesis has revealed a new mechanism by which BDV may alter GABAergic neurotransmission in the brain . This is particularly intriguing because abnormalities in the generation of GABAergic neurons are viewed as characteristic of several neuropsychiatric disorders [30 , 46] . Whether P induces a definitive loss in GABAergic neurons or simply a delay in neuronal maturation could not be determined in our study , as by 28 days of differentiation , the latest time point studied , the neurons had not all acquired the GABAergic phenotype . In both cases , however , brain damage would be expected to occur , as brain development is dependent not only on the acquisition of the proper number of neurons , but also on their acquisition at the proper time . Whether this neuronal subpopulation is affected in the context of BDV infection has not yet been specifically addressed , but appears highly probable , as we have shown that most of the neurons derived from hNPCs are predestined towards a GABAergic fate . The fate of NPCs is tightly controlled by an intrinsic genetic program that is still incompletely understood , especially in human NPCs . Here , we show that 11 genes known to be involved in neural differentiation are down-regulated in bdv-p-expressing hNPCs . Among these , TH , Bdnf and Scg10/Stathmin2 are known to be pro-neuronal factors . Their regulation by BDV has been previously observed in rat hippocampal and cortical neurons [43] or neuronal-like PC12 cells [23] . Here , we further show that P is responsible for their regulation in hNPCs . Notably , these three pro-neuronal factors are under the control of the REST/MeCP2 signaling , a pathway known to be critically important for neurogenesis [41 , 47–50] . This observation , along with the fact that BDV had been previously shown to regulate MeCP2 expression in rat cortical neurons [43] , prompted us to analyze this pathway . P , however , regulated neither Rest nor Mecp2 expression in hNPCs . The difference between our study and that of Suberbielle et al . [43] may be explained either by the implication of viral proteins other than P in Mecp2 regulation or by differences that can be attributed to cell type or species . Nevertheless , it is striking that expression of several pro-neuronal factors that are directly under the control of REST/MeCP2 is altered . It is tempting to suggest that P may induce other modifications in MeCP2 and/or REST that have an impact on their expression . Post-translational modifications in MeCP2 [47 , 51 , 52] , modification in REST activity through alteration of either the nuclear complex that is necessary for its repressive activity or cellular localization , or differential regulation of its isoforms [53] are all possibilities that represent exciting challenges for the future . Several studies have concurred that ApoE is involved in adult neurogenesis in mice . In particular , its expression was evidenced in vivo in NPCs found in two neurogenic areas , the sub-granular zone ( SGZ ) of the dendate gyrus ( DG ) and the sub-ventricular zone ( SVZ ) [54] and was shown to be up-regulated in vitro in murine NPCs induced to differentiate with retinoic acid [55] . In vitro analyses using NPCs isolated from adult ApoE-/- mice further demonstrated that invalidation of the ApoE gene induced a reduction in the number of neurons that were generated during adult neurogenesis [44] . This was accompanied by an 80% decrease in Noggin expression , suggesting that Noggin , a factor that is known to facilitate neurogenesis [56] , plays a role in this mechanism . These observations , together with our findings showing a decrease in both ApoE and Noggin expression in bdv-p-expressing hNPCs , strongly suggest that the ApoE-Noggin pathway is a mediator of P-induced impairment of human neurogenesis . The underlying mechanisms , however , appear to differ between the study by Li et al . [44] and our own . Whereas the decrease in neurogenesis was due to altered neuronal specification and was accompanied by an increase in astrogliogenesis in the Li et al . study [44] , neuronal specification and astrocyte number were not altered in ours . This may reveal differences between adult and fetal neurogenesis or between rodent and human neurogenesis . Whether the ApoE-Noggin pathway plays a role in the generation of different neuronal subtypes may be questioned . This is suggested by both studies as one addresses glutamatergic neurogenesis while the other focuses on the generation of GABAergic neurons . However , differences in timing and species in the two studies preclude direct comparison and future studies are required to address this issue in human neurogenesis . Another candidate that may potentially play a role in P-induced inhibition of GABAergic neurogenesis is the tenascin R ( tnr ) gene . This extracellular matrix glycoprotein has very recently been shown to regulate the genesis of GABAergic neurons [57] . Our observation , by PCR array , that its expression is decreased in bdv-p-expressing hNPCs , suggests its involvement in P-induced reduction of neurogenesis . Xu et al . [57] , however , showed that the deletion of the tnr gene was associated with an increase in GABAergic neurons in mice , which is in apparent contradiction to our observation . Further studies thus will be required to evaluate the functional importance of each of these molecules in P-induced reduction in human neurogenesis . How P is linked to downstream signaling is not yet understood . Several studies have shown that it interferes with cellular signaling by acting as a decoy substrate for cellular kinases . This was shown to have dramatic consequences for the immune response [58] and neuronal transmission [25] . In the latter case , it was established that the damage to synaptic transmission in BDV-infected rat hippocampal neurons was due to P-mediated interference with PKC signaling [59] . Here , we used a modified P protein in which the PKC phosphorylation site had been abrogated and showed that it was not necessary for P-induced impairment of GABAergic neurogenesis . We therefore suggest that the viral P protein interferes with signaling in the brain in another manner , independent of PKC signaling . Identification of the missing factor that interacts with P and links it to downstream signaling would cast light on the mechanisms by which the viral phosphoprotein P interferes with brain function . The development of hNPC cultures has allowed the direct impact of viruses on human neurogenesis to be addressed , a process that is fundamental for both brain development and normal brain function in the adult . Several neurotropic viruses , including BDV , are now known to affect human neurogenesis [29 , 60] . In this study , we provide the first evidence that a viral protein , the phosphoprotein P , impairs GABAergic neurogenesis , a pathological process that is characteristic of several psychiatric disorders . This result strengthens the view that persistent viral infection of the CNS may play a role in human mental disorders , as has been suggested by others [8] . Although future studies will have to be conducted to describe in full the mechanisms by which this occurs , we provide the first molecular clues as to how a viral protein impairs the genetic program that leads to neuronal differentiation . Our study thus improves our understanding of the mechanisms by which BDV interferes with brain function and identifies an original molecular tool , the phosphoprotein P , that can be used to characterize the process by which human GABAergic neurons are generated . Our findings may contribute to a better understanding of psychiatric disorders and to development of improved therapies in the future .
Human fetuses were obtained after legal abortion with written informed consent of the patient . The procedure for the procurement and use of human fetal CNS tissue was approved and monitored by the “Comité Consultatif de Protection des Personnes dans la Recherche Biomédicale” of Henri Mondor Hospital , France . hNPCs were prepared and cultured as previously described in [29] . The genes encoding the viral P and X proteins or green fluorescent protein ( GFP ) were amplified by PCR and cloned into the pTrip lentiviral vector backbone downstream of the constitutive cytomegalovirus ( CMV ) enhancer/chicken ß-actin ( CAG ) promoter ( a kind gift of Dr . P . Charneau , Pasteur Institute , Paris , France ) , using BamHI and XhoI restriction sites . To produce the lentiviral vectors , 10 T150 flasks plated with 1 . 2 x107 HEK-293T cells each were cotransfected with the two packaging plasmids , psPAX2 and pMD2 . G ( Addgene , France ) , and the pTrip plasmid expressing one of the genes of interest ( 14 . 6 , 7 . 9 and 22 . 5 μg of each endotoxin-free prepared plasmids were used , respectively ) mixed with 100 μl of GeneJuice ( Merck , France ) . Culture medium was removed the next day and replaced by warm OptiMEM medium ( Gibco , France ) . Supernatants were collected 48 h and 72 h post-transfection , cleared by low-speed centrifugation and filtered using a 0 . 45 μm filter . Lentiviral particles were then purified by ultracentrifugation through a 20% sucrose cushion ( 25 , 000 rpm , 2 h , 4°C; SW32Ti rotor , Beckman Coulter ) . Pellets were resuspended in ice-cold PBS under gentle agitation overnight at 4°C , aliquoted and stored at -80°C . Titers of the lentiviral vectors were determined by counting foci 72 h after transduction of HEK-293T cells with serial dilutions of vectors . In all our experiments , titers varied from 8x108 to 3x109 transduction units ( TU ) ml-1 and lentivectors were used at a multiplicity of transduction ( transduction units of vector for each cell ) of 1 to 10 . Three to five passages after transduction , transduced hNPCs and their matched NT controls were plated in 24-well plates or 6 cm-dishes at a density of 53000 cell/cm2 and induced to differentiate upon withdrawal of EGF and bFGF ( Abcys , Eurobio , France ) , as previously described [29] . Of note , as the cell density at plating determines the percentage of each cellular types generated upon differentiation [61] , special care was taken to ensure homogeneous cell numbers . In particular , the number of plated transduced and NT hNPCs was systematically verified by counting cells at day 0 of differentiation ( DAPI staining , 1 μg/ml , Life technologies , France ) . Experiments in which the number of cells varied for different conditions were discarded . Two tests were used to quantify hNPC proliferation in the presence of growth factors , EGF and bFGF . The Wst1 Cell Proliferation Assay ( Roche , France ) was used as described previously [29] , except that transduced hNPCs and their matching NT controls were plated on 48-well plates pre-coated with matrigel ( Corning , France ) , at a density of 5000 cells/well . For the Cell Proliferation ELISA , BrdU kit ( Roche , France ) , transduced hNPCs and their matched NT controls were plated at a density of 15000 cells/well in 96-well plates ( Falcon , Corning , France ) and maintained in proliferation for 4 days before addition of BrdU for 5 h at 37°C . Experiments were then processed according to the manufacturer’s instructions . To analyze hNPC proliferation in the absence of growth factors ( early phase of differentiation ) , transduced hNPCs and their matched NT controls were plated in 48-well plates at a density of 40000 cells/well and stained with DAPI at day 0 and day 4 of differentiation . Images were acquired from 4 fields per well and an average of 130 cells per well were counted . Primary antibodies were as follows: anti-βIII-Tubulin ( mouse monoclonal , Sigma , France ) , anti-βIII-Tubulin ( rabbit polyclonal , Abcam , France ) , anti-actin ( mouse monoclonal , Sigma , France ) , anti-ApoE ( goat polyclonal , Millipore , France ) , anti-Cleaved caspase-3 ( rabbit polyclonal , Cell Signaling , France ) , anti-GABA ( rabbit polyclonal , Sigma , France ) , anti-GFAP ( rabbit polyclonal , DAKO , France ) , anti-vGLUT1 ( rabbit polyclonal , Abcam , France ) anti-glutamate transporter neuronal ( EAAC1 ) ( goat polyclonal , Millipore , France ) , anti-vGLUT1 and anti-vGLUT2 ( rabbit polyclonal , Synaptic Systems , Germany ) , anti-HuC/D ( mouse monoclonal , Molecular Probes , Life Technologies , France ) , anti-MAP2 ( mouse monoclonal , Sigma , France ) , anti-MeCP2 ( rabbit , a generous gift from Dr E . Joly , Toulouse , France ) , anti-REST ( rabbit polyclonal , Millipore , France ) , anti-SCG10 ( rabbit polyclonal , a generous gift from Dr A . Sobel , Paris , France ) , anti-TH ( rabbit polyclonal , Abcam , France ) , anti-SOX2 ( rabbit polyclonal , Millipore , France ) and anti-BDV-P and anti-BDV-X ( rabbit polyclonal antibody ) . Transgene-expressing hNPCs and their matched NT controls were plated at a density of 53 , 000 cell/cm2 on 24-well plates optimized for automated image acquisition ( Ibidi , France ) . Undifferentiated and differentiated hNPCs were fixed 20 min in 4% paraformaldehyde ( Electron Microscopy Sciences , France ) and standard immunofluorescence was performed as described previously , for βIII-Tubulin , MAP-2 , GFAP , cleaved-caspase 3 and BDV-P antibodies [29] . For all other antibodies , cells were blocked for 1h in PBS-3%BSA and primary antibodies were incubated in PBS-0 . 1%Triton-X-100-1%BSA overnight at +4°C . Secondary antibodies were anti-mouse IgG coupled with Alexa Fluor-546 , or anti-rabbit IgG coupled with Alexa Fluor 488 or 647 ( Molecular Probes , Invitrogen , France ) . Nuclei were stained with DAPI . Images of cells immunostained with βIII-Tubulin , GFAP or MAP2 antibodies were acquired with the AxioObserver Z1 inverted microscope ( Zeiss ) using either Axiovision or ZEN software ( Zeiss ) . In every experiment , six wells per condition were analyzed and an average of 1 , 000 cells per well were manually enumerated . Images of cells immunostained with HuC/D , GABA and Sox2 antibodies were acquired using the Cellomics ArrayScan automated microscope ( Thermofisher Scientific , France ) and cell enumeration was automatically performed . In every experiment , six wells per condition were analyzed and an average of 3 , 000 cells per well were enumerated . Total RNA was extracted using the RNeasy mini kit ( Qiagen , France ) according to the manufacturer’s instructions . Five hundred nanograms of RNA were reverse-transcribed with the RT² First Strand Kit ( SA Biosciences , Qiagen , France ) and cDNA was subjected to a PCR array specific for human neurogenesis ( RT2 Profiler PCR array—Human Neurogenesis PAHS-404ZA , SA Biosciences , Qiagen , France ) . The manufacturer's instructions were strictly followed for reverse transcription and PCR array . Genes were analyzed from RNA pooled from biological triplicates for each condition . Data were normalized using 5 house-keeping genes and analyzed using the -2ΔΔCt method for relative quantification . Total RNA was extracted using Nucleospin RNA/protein ( Macherey Nagel , France ) according to the manufacturer’s instructions . Reverse transcription and quantitative PCR were performed as described previously [29] . Primers used are listed in S1 Table . PCR efficiencies ranged between 93% and 100% , depending on primer pairs . For relative quantification , the -2ΔΔCt method was used . The reference gene was Gapdh and each gene was analyzed from two independent experiments performed in triplicate . Proteins and RNA were extracted from the same biological samples using the Nucleospin RNA/protein kit ( Macherey Nagel , France ) . Proteins were quantified using the Protein Quantification Assay kit ( Macherey Nagel , France ) , according to the manufacturer’s instructions . Western blot analyses were performed as described previously [29] except that blots were blocked for 1h at RT in PBS-0 . 1%Tween-20-5% dry milk . The GenBank ( www . ncbi . nlm . nih . gov/Genbank ) accession numbers for genes mentioned in the article are ApoE ( NM_000041 ) , Gapdh ( NM_002046 ) , MeCP2 ( NM_004992 ) , nestin ( NM_006617 ) , Nog/Noggin ( NM_005450 ) , Rest ( NM_005612 ) , Scg10 ( NM_007029 ) , Sox2 ( NM_003106 ) , Th ( NM_000360 ) . The accession numbers for proteins cited in the text are ApoE ( AAH03557 ) , BDV Phosphoprotein ( BDV-P , P0C798 ) , BDV X protein ( Q912Z9 ) , GFAP ( AAB22581 ) , HuC ( NP_001411 ) , HuD ( NP_001138246 ) , MAP2 ( AAB 48098 ) , MeCP2 ( P51608 ) , REST ( NP_005603 ) , SCG10 ( AAB36428 ) , Sox2 ( NP_003181 ) , and TH ( P07101 ) . Data represent the means ± standard deviation ( SD ) . Statistical analyses were performed using the Mann-Whitney test . *** , p < 0 . 001 or p < 0 . 005 depending on experiments; ns , non-significant ( p > 0 . 05 ) . | When a virus enters the brain , it most often induces inflammation , fever , and brain injury , all signs that are indicative of acute encephalitis . Under certain conditions , however , some neurotropic viruses may cause disease in a subtler manner . The Borna disease virus ( BDV ) is an excellent example of this second class of viruses , as it impairs neural function without cell lysis and induces neurobehavioral disturbances . Recently , we have shown that BDV infects human neural progenitor cells ( hNPCs ) and impairs neurogenesis , revealing a new mechanism by which BDV may interfere with brain function . In the present study , we identify that a singled-out BDV protein called P causes similar impairment of human neurogenesis , and further show that it leads to diminution in the genesis of a particular neuronal subtype , the GABAergic neurons . We have also found that the expression of several genes involved in the generation and the maturation of neurons is dysregulated by this viral protein , which strongly suggests their implication in P-induced impairment of GABAergic neurogenesis . This study is the first to demonstrate that a viral protein interferes with human GABAergic neurogenesis , a process that is frequently impaired in neuropsychiatric disorders . It may thus contribute to elucidating the molecular bases of psychiatric disorders . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Borna Disease Virus Phosphoprotein Impairs the Developmental Program Controlling Neurogenesis and Reduces Human GABAergic Neurogenesis |
Understanding the mechanisms of protein–protein interaction is a fundamental problem with many practical applications . The fact that different proteins can bind similar partners suggests that convergently evolved binding interfaces are reused in different complexes . A set of protein complexes composed of non-homologous domains interacting with homologous partners at equivalent binding sites was collected in 2006 , offering an opportunity to investigate this point . We considered 433 pairs of protein–protein complexes from the ABAC database ( AB and AC binary protein complexes sharing a homologous partner A ) and analyzed the extent of physico-chemical similarity at the atomic and residue level at the protein–protein interface . Homologous partners of the complexes were superimposed using Multiprot , and similar atoms at the interface were quantified using a five class grouping scheme and a distance cut-off . We found that the number of interfacial atoms with similar properties is systematically lower in the non-homologous proteins than in the homologous ones . We assessed the significance of the similarity by bootstrapping the atomic properties at the interfaces . We found that the similarity of binding sites is very significant between homologous proteins , as expected , but generally insignificant between the non-homologous proteins that bind to homologous partners . Furthermore , evolutionarily conserved residues are not colocalized within the binding sites of non-homologous proteins . We could only identify a limited number of cases of structural mimicry at the interface , suggesting that this property is less generic than previously thought . Our results support the hypothesis that different proteins can interact with similar partners using alternate strategies , but do not support convergent evolution .
Protein-protein interaction is the basis of numerous biological functions , such as immune response , supra-molecular assembly , enzymatic reactions , and many more . Understanding the way proteins interact is thus a fundamental challenge . The collection of all protein-protein interactions , the interactome , is also of great importance for drug discovery [1] . Given their variety and often transient nature , the number of protein-protein complexes for which crystallographic structures are available is very limited compared to the number of individual protein structures in the Protein Data Bank [2] . But even with this limited amount of data , the observation of available complexes has helped to decipher some rules for protein-protein interactions . Among the properties playing a role in this process , hydrophobicity was suggested as a major factor by Chothia and Janin in their pioneering work [3] . Other characteristics that are important for interaction , or that can be used to describe binding sites , include size , shape complementarity , residue propensity and packing density [4]–[6] . Sequence conservation is also widely acknowledged as an important feature of protein-protein recognition [7] , [8] . Additional studies have further refined the picture . For example , binding sites are organized as a core of buried residues , surrounded by a rim of accessible residues , with distinct amino-acid composition and evolutionary conservation patterns [9] , [10] . Nicola and Vakser found that the binding site is , on average , closer to the center of mass of the protein compared to other surface residues [11] . Different types of complexes ( e . g . homo- or hetero-dimers , transient or permanent ) display different properties [8] , [12] , [13] . A notable element to understand the mechanism of protein-protein interaction is the existence of hot spots , residues that make major contributions to the binding energy , see for example [14] for a review . In their landmark paper , Bogan and Thorn showed that hot spots are localized at the center of interfaces , and surrounded by a ring of energetically unimportant residues , that protect them from the solvent [15] . This is called the O-ring theory , and has been recently refined by Li and Liu [16] . Several groups have addressed the question of the evolutionary conservation of protein-protein binding sites and binding modes . At first found to be insignificant [17] , the conservation of interface residues has since been shown to be more pronounced in biological interfaces than in crystallographic ones or over the rest of the protein surface [18] , [19] . This change of viewpoint probably comes from the increase of available data , as well as the variety of computational approaches developed to quantify conservation , and also the fact that some proteins have multiple interfaces [20] . The link between evolutionary conservation and hot spots is unclear: overall difference in conservation between hot spot and non hot spot residues is marginal [21] , [22]; conservation used in combination with other features has been found to improve hot spot prediction in [21] but not in [22] . From a more macroscopic point of view , complexes that share more than 35% identity commonly share similar structures and interaction modes [23] . The localization of a binding site on a protein is preserved within SCOP families , but not necessarily at the super-family level [24] , [25] . Another important notion we want to introduce here is the existence of promiscuous proteins . Promiscuity , also called multi-functionality or moonlighting , denotes the ability of one protein to perform distinct functions , see reviews [26] , [27] . A recent review reveals that promiscuity is not as rare as previously thought [28] . Examples notably include transcription regulatory proteins that can act as transcription coactivators or enzymes [29] . More generally , a promiscuous protein can interact with different partners . These multi-partner proteins have been the subject of dedicated studies . For example , Keskin et al have shown that multi-partner protein interfaces have original properties: they are smaller and less packed than other interfaces [30] . A recent survey of proteins with multi-binding protein interfaces involving 97 pairs of complexes from 49 protein families revealed that multi-binding interfaces are not more conserved than other interface sites [31] . The energetic determinants of multi-partner proteins have also been addressed: interactions involving specific binding sites display higher affinities than those of promiscuous binding sites [32] . In an earlier work , Humphris and Kortemme employed a computational design procedure to optimize the binding site of 20 multi-specific proteins , so that they maintained interactions with all their known partners ( multi-constraint protocol ) or with each partner separately ( single-constraint protocol ) [33] . For half of the tested cases , they obtained different results using the single and the multi-constraint protocol , suggesting that promiscuous binding sites are optimized for multi-specificity in such a way that each partner prefers its own set of residues on the binding site . A recent analysis using state-of-the-art computational methods applied on calmodulin , whose structure is available in complex with 16 different targets , confirmed this hypothesis [34] . These analyzes focused on the common , promiscuous binding sites , but not on the binding sites of the multiple partners . The fact that a promiscuous protein can bind to different partners using the same binding site is puzzling , but also of outstanding interest to further understand the mechanisms of protein-protein interactions . Does this observation imply that radically different proteins possess similar binding sites in order to recognize a single promiscuous protein ? At first sight , it might seem hopeless to look for similar binding sites on non-homologous proteins that differ in structure , function and ancestry . However , the literature is rich in examples of approaches employing - or searching for - such local similarities between unrelated proteins . This is the case for at least three distinct targets: catalytic sites , ligand binding sites and protein-protein binding sites . In the case of catalytic sites , the well-known example of the catalytic triad pattern , found in diverse serine proteases , has motivated a number of developments [35]–[40] . Concerning ligand binding sites , their generic nature among unrelated proteins has lead to the development of many comparison approaches [41]–[50] . Lastly , for protein-protein interactions , the similarity between proteins with very different folds has been investigated in several studies . An important corpus of work on this problem comes from Nussinov and colleagues . Using geometric hashing , they created clusters of similar interfaces based on the C geometry [51] and found clusters with similar interfaces despite different overall structures , as well as clusters where only one side of the interface was conserved [52] , [53] . Shulman-Peleg et al . subsequently developed the I2I-SiteEngine software , dedicated to structural alignment of protein-protein interfaces , based on the similarity of their physico-chemical properties and shapes [54] , [55] . These observations have been applied to the prediction of protein-protein interactions , with the development of the PRISM database [56] , [57] , and to structural alignment of protein-protein interfaces , with the MAPPIS web server [49] . Other groups have also investigated this question . Zhu et al . proposed the Galinter method , based on the representation of interfaces by vectors representing van der Waals interactions and hydrogen bonds between protein chains , allowing binding site comparison using graph algorithms [58] . Very recently , Konc et al . have proposed ProBis , a graph-based method for binding site prediction [59] . Convergent evolution thus seems to exist also for protein-protein interactions [60] , [61] . In this paper , we analyze a set of protein-protein complexes involving homologous proteins in interaction with different partners . These examples come from an analysis of PDB complexes in terms of SCOP domains , and are stored in the ABAC database [61] . Truly speaking , these complexes do not illustrate promiscuity , since they involve homologous ( same SCOP family ) rather than identical proteins . We therefore term this promiscuous binding at the family level . Our goal is to understand how unrelated proteins can bind to similar targets . In particular , we looked for similar atoms or groups of atoms at the interface of different proteins that bind similar partners and assessed the significance of the similarity between interfaces using a bootstrap procedure . We also considered evolutionarily conserved residues , as they probably play a dominant role in the binding . Our results support the hypothesis that different partners often interact with a single partner using alternate strategies , and do not point to convergent evolution .
We first compute the number of similar elements - atoms , pseudo-atoms or residues - in each partner of the protein complexes after structural superimposition of the common partners A and A′ . Domains A and A′ are from homologous domains from the same SCOP family . Consequently , we expect a good level of similarity between them . However , since such similarity results from divergence from a common ancestor and fold conservation , it does not necessarily imply that the similar elements are key determinants for the protein-protein interaction . Domains B and C are from different SCOP superfamilies . They thus have very different structures , but a common ability to bind to the same , or , at least , a similar partner . Similar elements between B and C could thus be a sign of evolutionary convergence to a given binding motif , or indicate which functional groups are essential for the binding . Figure 3 presents the number of superimposed and similar elements at the interface in the 433 pairs of complexes , and the ratio of similarity , with different interface representations ( separate Figures for each category are given in Figures 4 to 8 in Text S1 ) . For each ABAC pair , the number of superimposed and similar elements is computed separately for each domain , and we compare the statistics on the homologous sides ( A and A′ ) versus the non-homologous sides ( B and C ) of each complex . Each ABAC pair is thus represented by two points: one for complex AB and one for complex A′C . We previously checked that the sizes of the binding sites on A/A′ and B/C sides are roughly similar ( see Figure 3 and Table 3 in Text S1 ) , which is true , except for complexes of the M category , due to their protruding/interwound geometry as illustrated in Figure 2 . As expected , there is a positive correlation between the number of superimposed elements - defining the size of the overlap - on the A/A′ domains versus B/C domains , see Figures 3A , D and G , resulting from geometrical considerations . The number of superimposed elements is almost always lower on the B/C side than on the A/A′ side , for every interface representation . This is due to the fact that the structural superimposition is guided by domains A and A′ , which favours better overlap on the A/A′ side , as illustrated in Figure 4 . This bias introduced by the superimposition results in a mean ratio of overlap sizes equal to 1 . 3–1 . 8 , depending on the interface representation: for 100 elements superimposed on the B/C side , there is an average of 130 to 180 elements on the A/A′ side ( statistics for each pair category are presented in Table 4 in Text S1 ) . Because of this effect alone , the number of similar elements on B/C sides is expected to be lower than on the A/A′ sides . It can be seen , in Figures 3B , E and H , that the number of similar elements on the B/C side is effectively lower . The mean numbers of similar elements for the five categories are given in Table 2 . The mean ratio is around 2 for all-atom and coarse-grain representations and 3 for residues: there is , on average only one similar residue on the B/C side for 3 residues on the A/A′ side . Interestingly , the correlation between the similarity ratios , i . e . , number of similar elements normalized by the number of superimposed elements ( see Figures 3C , F and I ) is lower . For example , the Pearson correlation coefficient between the numbers of similar atoms ( see Figure 3B ) is equal to 0 . 8 , versus 0 . 4 between the corresponding similarity ratios ( see Figure 3C ) . In other words , a greater similarity between A/A′ interfaces does not automatically correspond to a greater similarity between B/C interfaces . It thus seems that the low level of similarity in B/C domains is not only the result of the superimposition bias , but reflects a real sparsity of common binding determinants in different proteins that bind to similar partners . Indeed , some ABAC pairs with very similar common domains can exhibit very low similarity on the B/C sides . As an example , when complex 1m4u_BA ( human bone morphogenetic protein-7 complexed with noggin ) is compared with complex 1nys_DC ( human activin A complexed with rat activin receptor ) 11 out of 16 superimposed residues are similar for the A/A′ domain , and only 2 residues out of 9 for the B/C domain . Similar binding sites can thus bind two proteins that present a very restricted set of similar residues . To go further with this analysis , we computed similarity P-values as explained in the Materials and Methods section . Similarity P-values , computed using a bootstrap procedure , are presented as histograms in Figure 5 for the ABAC pairs of category O . A P-value equal to x% means that in x% of the randomly sampled interfaces , the number of similar elements is greater or equal to the number of similar elements in the real interface . Consequently , a high P-values indicates that the similarity has a high probability to occur by chance . Inversely , a very low P-value means that the similarity is significantly higher than expected with a random model . A value of 5% is classically used as the significance cut-off . It is clear from Figures 5A and 5B that the distribution of similarity P-values is very different between A/A′ and B/C sides , with a bias toward low P-values on the A/A′ sides , and high P-values on the B/C sides . For A/A′ interfaces , we intuitively expect low P-values , indicating a significant similarity , since A and A′ domains belong to the same SCOP family and share a common ancestor . This is the case , see Figure 5A . What is less expected , is that the P-values for the B/C sides are rather high , indicating that the similarity between binding sites of the B and C domains is , most of the time , insignificant , see Figure 5B . We note that the all-atom model ( see Figure 5A ) can however result in high P-values for A/A′ domains . This can be due to the background model used for bootstrapping , in which the atom type labels are randomly re-distributed among atom positions . In an all-atom representation , atoms of the same type appear as clusters , simply because they are part of the same amino acid . Such a random model is thus not optimal , because it neglects this aspect . Furthermore , with a distance cut-off equal to 3 Å to detect similar superimposed points , several atoms can be matched by the same point after superimposition . The result is an artificially high number of random similar points , and consequently , high P-values . Another source of error , with a probable significant impact , is the inherent sensitivity of the all-atom model to side chain flexibility . Since the same side chain , upon binding to multiple partners , might undergo different conformational changes , the all-atom model might under-estimate the real level of similarity . For these reasons we considered coarse-grain and C representations only in the following analysis . As shown in Figures 5C and D , the coarse-grain representation overcomes the high P-value artifact on the A/A′ side . On the B/C histogram , however , a number of complexes still display high P-values , meaning that the similarity level is not significant compared to random . This holds true using a C representation , see Figure 5E and F . We obtained similar results for other categories of ABAC pairs ( Figures 4 to 7 in Text S1 ) , although with more noisy results ( less significant P-values on the A/A′ side ) for the E , I and S categories , as expected due to the difficulty of the structural alignments for these categories . We next considered the restricted set of evolutionarily conserved residues detected using the ConSurf database ( as explained in the Materials and Methods section ) and analyzed the interface similarity in this light . More precisely , we repeated the same analysis as for the C representation , but instead of considering five classes of residues , we labelled the residues by their conservation status , i . e . , conserved or non-conserved . Then , we considered only the conserved residues at the interface , to see if they are co-localized with conserved residues after domain superimposition . As before , we computed separately the number of conserved residues superimposed on the A/A′ interfaces and the B/C interfaces , and the corresponding P-values . The P-value histograms follow the same trend as for binding site similarity: low P-values on the A/A′ side , but not on the B/C side , see Figures 8 and 9 in Text S1 . Note that a considerable number of protein domains have no superimposed conserved residues in their binding sites , limiting the P-value analysis to a more restricted data set . As shown in Figure 6 , interfaces are only partially overlapping after structural superimposition of A/A′ domains . We thus cannot exclude that some residues located outside the overlap play dominant roles in the binding . The correlation between the fraction of similar atoms and the fraction of atoms that are overlapping is weak but positive ( see Figure 21 in Text S1 ) . The fact that binding sites with a small fraction of similar atoms tend to have a small fraction of binding site overlap ( meaning that a significant proportion of the binding site is excluded from the comparison ) suggests that key binding determinants could indeed be missed .
In the same way that there is a limited number of protein folds , it is tempting to speculate that there is a limited number of protein-protein binding interfaces [62] . Since protein structures are made of recurrent local conformations , i . e . , -helices and -strands , protein-protein interfaces might be made by the assembly of recurrent binding modules . The present study was motivated by the search for such modules . Indeed , the fact that unrelated , dissimilar proteins are able to bind similar , homologous proteins suggests that common binding strategies might be re-used by different proteins . It is logical to look for generic binding modules in the promiscuous binding sites thus formed . We were not however able to confirm this hypothesis . Starting from a discrete physico-chemical model , in which interfaces are described by points - be they atoms , pseudo-atoms or residues - belonging to five different classes , we found that , in most of the cases , the similarity between different proteins that bind to homologous partners is not greater than random ( but the similarity between the homologous partners is significant , suggesting that the random model is appropriate ) . It thus seems that protein interfaces with no detectable similarity can nevertheless bind similar partners . We should temper this result by noting that the energetic contribution of interfacial residues is uneven; some hot spot residues make major contribution , while other residues are unimportant . Unfortunately , energetic information - requiring extensive mutation analysis - is not available for our full data set , we thus approached this particularity in an indirect way . Although evolutionary conservation is a poor discriminant of hot spots [21] , [22] , it has been shown to improve the prediction when used in combination with other features [21] . Conserved residues do not translate into hot spots but might contain some information . We thus considered conserved residues at protein-protein interfaces , and assessed their co-localization in our complex pairs . This time , the criteria was not to know if superimposed residues are from the same physico-chemical class , but to know if they are both conserved during evolution , independently of their class . The rationale was to restrict the analysis to the subsets of conserved residues . The co-localization of conserved residues in different proteins that bind homologous partners was found to be largely insignificant . Further studies using in silico hot spot prediction methods could bring additional information . Altogether , our results suggest the following picture for promiscuous protein-protein binding: similar , homologous proteins present binding sites with great similarity , via which they interact with diverse , dissimilar proteins . The binding interfaces of these dissimilar proteins exhibit different atomic/residue patterns , and their conserved residues are not co-localized . It thus suggests that different proteins use their own set of atoms/residues to perform the recognition , as illustrated in Figure 7A . There is also the possibility that atom groups interacting specifically with a single partner could play a dominant role , i . e . , different partners use residues or group of residues that are outside the overlap between the two binding sites , see Figure 7B . The mechanism illustrated in Figure 7A is in agreement with the elegant work of Humphris and Kortemme , who have shown that multi-specific binding can be achieved by different mechanisms [33] . Using computational design to “optimize” the interfaces of promiscuous proteins , they observed two distinct patterns: ( i ) for half of the tested case , all partners shared key interactions; ( ii ) for the other half , each binding partner preferred its own set of wild-type residues in the common binding site . Some experimental studies of promiscuous proteins support this second pattern . For example , TRAF3 ( Tumor Necrosis Factor Receptor-associated Factor ) is able to bind two targets , CD40 and Lymphotoxin- receptor , at the same interface , although they present motifs with distinct sequence and structure motifs for the binding [63] . Another example of promiscuous protein is protein kinase A , which is able to bind to different proteins using the same binding site . Entropy calculations suggest that the binding site of protein kinase A provides alternative contact points for the partner side chains [64] . In a recent study of BirA , a protein able to form a homodimer as well as heterodimer using the same binding site , hot spot residues were identified specifically for the homodimerization , but not for the heterodimerization [65] . This suggest that each complex forms using its own preferred and distinct interactions . This has also been observed for protein/ligand complexes . For example , different non-peptidic haptens have been shown to bind to the same site of an antibody , by forming different hydrogen bonds , dependent upon their particular chemistry and the availability of complementary antibody residues [66] . A last point to discuss is the existence of structural mimicry at interface . Protein mimicry is an intuitive concept , that has been successfully used in rational design [67] . Examples of protein interface mimicry - present in our data set - include several chymotrypsin inhibitors with various global folds ( 49 ABAC pairs ) , the viral protein M3 that mimics the binding site of chemokines for homodimerization ( 1 ABAC pair ) , and different subtilase inhibitors ( 3 ABAC pairs ) . Surprisingly , the similarity P-value analysis of these 53 pairs revealed that the physico-chemical similarity of the mimicking binding sites is not significant . However , their structural similarity is obvious , see Figure 2 . This might indicate that the shape - not taken into account by our atomic or residue-based representations - is an important determinant for interface mimicry . Indeed , local surface comparison has been successfully used to retrieve chymotrypsin inhibitors [68] . The present study focused on promiscuous binding at the family level . The goal was to find the key determinants that allow unrelated proteins to bind to homologous partners . Our main conclusions are summarized below . We were not able to find evidence of convergent evolution . Our results support the hypothesis that promiscuous binding is rather achieved by alternative binding strategies for different partners .
PDB files of protein-protein complexes were retrieved from the PQS database [70] . Starting from a non-redundant list of ABAC pairs with only one instance per SCOP family combination , we selected pairs that fulfilled two criteria: ( i ) the two partners are from different chains , i . e . , we do not consider intra-chain interactions , ( ii ) SCOP domains spanning several protein chains involved in the binding site are excluded from the analysis for computational simplicity . We also removed complexes with missing atomic coordinates at the binding site , and pairs with very low overlap between the binding sites resulting in no superimposed atoms on the B/C side . Details concerning the minimum overlap size in the data set are given in Table 5 in Text S1 . The final data set comprises 433 ABAC pairs . These 433 pairs were further classified into 5 categories , based on a visual assessment of the quality of the superimposition between A and A′ domains , particularly at the interfaces: For the category M , the geometry of the main chain of B and C domains in the binding site was taken into account . Globally , O and M categories correspond to smaller rmsd between A and A′ domains , and smaller irmsd ( rmsd between interfacial residues ) compared to category S; categories E and I are intermediate; and categories overlap in terms of rmsd values , see Figure 2 in Text S1 . Note that rmsd and irmsd are average values of structural deviation , hence they only reflect global tendencies; furthermore , they depend on the extent of the structural alignments . Also , irmsd computation does not take into account insertion of residues , because they are unaligned . Structural mimicry of B and C domains cannot be detected using rmsd , since domains B and C are unrelated and hence not superimposable . The classification thus ultimately results from a careful visual examination that takes into account all these parameters . Our data set is non-redundant in the sense that every SCOP family combination is unique . However , the ABAC pairs are not independent , since the same SCOP family can be shared by several pairs . For example , the SCOP family 49504 ( Plastocyanin/azurin-like ) is shared by the A/A′ domains of two ABAC pairs: Overall , 68 SCOP families are present in only one ABAC pair if we consider their A/A′ domains , and the most abundant family - family 52592 , G proteins - is represented in 130 pairs . This probably indicates both the capacity of some particular families for promiscuous binding at the family level , but may also reflect the bias of structures deposited in the PDB toward proteins with biomedical interest . The number of distinct SCOP families , for A/A′ domains and B/C domains are reported in Table 1 , for each category of ABAC pairs . It can be seen that the number of different SCOP families in A/A′ domains is 105 for the full data set . This apparent redundancy is not a limitation in our context , since we consider the similarity between pairs of complexes . In particular , considering ABAC pairs with unique SCOP domain combinations is enough to explore how different B/C domains interact with similar A/A′ domains . Interfacial atoms were detected by applying a cut-off of 5 Å between heavy atoms from interacting chains , as in the SCOPPI database [71] , [72] . Residues were considered to be part of the binding site if they had at least one interfacial atom . Atoms were classified into five groups adapted from those proposed by Mintseris and Weng [73] ( see Figure 1 in Text S1 ) . These groups were determined by an optimization procedure , so as to maximize the mutual information of the pairwise matrix of atomic contacts at protein-protein interfaces . Although they have been determined by statistical optimization , they are in excellent agreement with biochemical criteria and roughly make the distinction between positively charged/negatively charged/polar/non-polar and hydrophobic groups of atoms . As in [61] , homologous partners of the ABAC pairs , i . e . , domains A and A′ , were superimposed using Multiprot [74] . After structural superimposition , interfacial atoms from A ( resp . B ) were considered as superimposed if there was an interfacial atom from A′ ( resp . C ) less than Å away , and similar if both atoms were from the same group . Cutoff was set to 3 Å , as in [61] . Note that this cut-off is used to compute the number of similar atoms between two binding sites after superimposition , and should not be confused with the cut-off equal to 5 Å that is used to detect atoms that are part of the interface . Binding site similarity was also quantified on a per-residue basis , by representing each residue by its C . In addition , we considered an intermediate coarse-grain model introduced by Zacharias [75] , in which residues - except GLY - are modeled by two or three pseudo-atoms: the C , and one side-chain pseudo-atom ( residues ALA , SER , THR , VAL , LEU , ILE , ASN , ASP and CYS ) or two side-chain pseudo-atoms ( residues PHE , MET , PRO , TRP , HIS , TYR , GLN , GLU , LYS , ARG ) . Residues and pseudo-atoms were clustered into five groups , deduced from the atom groups ( see Tables 1 and 2 in Text S1 ) . In order to take into account the fact that residues are described by a reduced number of points using these simplified representations , the cut-off to detect similar points after complex superimposition was empirically set to 4 Å for the C and the coarse-grain representations . The significance of the similarity between binding sites was assessed by bootstrapping . The principle is to generate random binding sites by randomly re-assigning the atom types in the overlapping interfaces . The advantage of this re-sampling is that the sizes of the compared objects are preserved . The procedure was repeated 500 times in order to obtain the distribution of the number of similar atoms ( or pseudo-atoms or residues ) between two binding sites that can be expected with a random model . The extent of the observed similarity could then be assessed by computing the corresponding P-value , , where and denote respectively the number of similar atoms obtained between random binding sites , and observed between real binding sites . For each ABAC pair , we thus computed four P-values: one for each of A , A′ , B and C binding sites . Evolutionarily conserved residues were detected using the ConSurf database [76] . This database contains pre-calculated conservation scores , obtained after multiple alignment of homologous sequences using an empirical Bayesian algorithm [77] . For each residue of a protein , a normalized conservation score is assigned . Residues with normalized scores lower than -1 were considered as evolutionarily conserved . In some cases , when the number of homologous sequences is too low , the conservation scores were not available . In such cases , all residues were considered as unconserved . During the comparison of binding sites , 131 comparisons out of 433 involved a binding site with no conserved residues when considering A/A′ domains , and 178 out of 433 when considering B/C domains . The analysis of evolutionarily conserved residues is thus inherently based on a smaller data set . | Interaction between proteins is a fundamental process , generic to most biological pathways . The increasing number of protein–protein complexes with atomic data should help us to understand the major factors that guide protein interactions . In particular , a number of examples are available of similar proteins that interact with proteins that are very different in terms of structure and function . An intuitive hypothesis to explain the ability of these different proteins to recognize the same partner is that they display the same local region for interaction , in other words , they imitate the same binding site . Here , we quantify the similarity between these putatively mimicking binding sites . We show that it is not statistically significant . We confirm this observation on the small sets of evolutionarily conserved residues . Our results suggest that different proteins that bind the same protein do not imitate binding sites , but probably target specific locations or residues at the binding site . | [
"Abstract",
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] | 2010 | Beauty Is in the Eye of the Beholder: Proteins Can Recognize Binding Sites of Homologous Proteins in More than One Way |
Object conceptual processing has been localized to distributed cortical regions that represent specific attributes . A challenging question is how object semantic space is formed . We tested a novel framework of representing semantic space in the pattern of white matter ( WM ) connections by extending the representational similarity analysis ( RSA ) to structural lesion pattern and behavioral data in 80 brain-damaged patients . For each WM connection , a neural representational dissimilarity matrix ( RDM ) was computed by first building machine-learning models with the voxel-wise WM lesion patterns as features to predict naming performance of a particular item and then computing the correlation between the predicted naming score and the actual naming score of another item in the testing patients . This correlation was used to build the neural RDM based on the assumption that if the connection pattern contains certain aspects of information shared by the naming processes of these two items , models trained with one item should also predict naming accuracy of the other . Correlating the neural RDM with various cognitive RDMs revealed that neural patterns in several WM connections that connect left occipital/middle temporal regions and anterior temporal regions associated with the object semantic space . Such associations were not attributable to modality-specific attributes ( shape , manipulation , color , and motion ) , to peripheral picture-naming processes ( picture visual similarity , phonological similarity ) , to broad semantic categories , or to the properties of the cortical regions that they connected , which tended to represent multiple modality-specific attributes . That is , the semantic space could be represented through WM connection patterns across cortical regions representing modality-specific attributes .
One of the most challenging questions in cognitive neuroscience is how abstract knowledge emerges from more basic dimensions of information , such as visual shapes and patterns of motor action . How do we proceed from the visual shape of a pair of scissors to the knowledge that they can be used to cut things and that they are semantically related to an axe , which looks different and is manipulated differently from scissors ? Research on the neural basis of semantic memory—the storage of general knowledge about the world—has revealed widely distributed brain regions supporting modality-specific attributes of objects , such as shape , color , and motion ( e . g . , [1 , 2]; see review in [3] ) . Nonetheless , such attribute-specific knowledge and its simple pairings are not adequate to explain the actual semantic space of objects that have quite different sensory/motor attributes but that may nonetheless be considered to be semantically similar ( e . g . , [4–7] ) . To achieve such a semantic space , various steps of binding and abstraction are assumed to occur at specific gray matter ( GM ) regions [6 , 8–11] . Although past research on semantic representation has focused on the roles of cortical regions , specific white matter ( WM ) tracts have been found to be necessary for semantic processing , including the left inferior fronto-occipital fasciculus ( IFOF ) , the left uncinate fasciculus ( UF ) , and the left anterior thalamic radiation . Damage to these tracts is associated with semantic deficits in patients [12–17] . WM is classically assumed to relay information [18–20] . In accord with this general notion , these WM tracts that are necessary for semantic processing are assumed to relay distributed information to particular GM regions ( e . g . , the anterior temporal lobe or angular gyrus ) for binding , where concepts are represented and the “deep structures” of semantic space are formed [6 , 7 , 21] . The nature of the potential information carried by WM has never been discussed or examined . Herein , we present results for a new notion that the WM connections , being natural binding structures , provide an alternative basis to achieve semantic representation . Distributed GM regions that represent different attribute dimensions ( e . g . , shape , color , manner of interaction ) of the same object are connected by WM . The WM linking pattern itself would then contain multiple dimensions of information in these GM regions and , importantly , additional information about the manner of mapping among various attributes . The incorporation of these elements has been argued to be necessary for the “higher-order” semantic similarity relationships , which are not explained by attribute-specific spaces , to emerge ( e . g . , [7 , 22] ) . To investigate the information coded in WM connections , we extended representational similarity analysis ( RSA ) [23] , a highly productive method that tests the nature of representation in functional magnetic resonance imaging ( fMRI ) studies of cortical regions [24–26] , to lesion data and WM connections . RSA examines the relationship between the representational dissimilarity matrix ( RDM ) derived from neural patterns and RDMs based on various types of stimulus information as a measure of information representation . The conventional neural RDM is measured by the dissimilarity of brain activity patterns induced by stimulus conditions . Here , we compute the neural RDMs with a machine-learning model using the voxel-wise lesion patterns as features to predict behavioral performance in patients with brain damage ( see Fig 1 ) . The performance in picture naming of 100 object items and the structural MRI data of 80 patients were collected . For each WM connection , a training model was built for each item ( e . g . , scissors ) using the support vector machine ( SVM ) classifier with patients’ voxel-wise lesion patterns as predictive features and the naming performances of that item as labels ( 0 , incorrect; 1 , correct ) . The correlation between the predicted score using the classifier from that item and the actual scores of another item ( e . g . , axe ) was taken as the neural similarity basis of these two items , based on the assumption that if this connection pattern contains certain aspects of information shared by the naming process of these two items , models trained with one item ( useful features relevant for such information ) should also predict naming accuracy of the other item . Once the neural RDMs are obtained from various WM connections or GM regions using this method , they can be correlated with behavioral RDMs of various object property dimensions , including the semantic RDM and four modality-specific attribute RDMs ( shape , manipulation , color , and motion ) . Neural RDMs that are correlated with the semantic RDM even after controlling for the attribute RDMs are considered to contain “higher-order” semantic information .
Behavioral RDMs for the semantic , shape , manipulation , color , and motion features of 100 objects ( 20 animals , 20 fruits and vegetables , 20 tools , 20 non-tool small objects , and 20 large non-manipulable objects ) were generated using a multi-arrangement method [29] . In this task , 20 college students were instructed to arrange the items by a particular dimension of interest on a computer screen , and the distance among items was derived , resulting in an RDM ( see Fig 2A ) . The semantic RDM was visually clustered into three domains: animals , fruits and vegetables , and man-made objects ( tools , small non-tool objects , large non-manipulable objects; see Fig 2B & 2C ) . Visualization of the semantic RDM using multidimensional scaling ( Fig 2C ) further revealed that within each category , words with closer semantics tended to share similar function ( e . g . , scissors and knife ) , share certain distinct features , or belong to finer subordinate categories ( e . g . , peanut and potato ) . The semantic RDM and the four modality-specific attribute RDMs were intercorrelated to various degrees ( Fig 2D; semantic with shape: r = 0 . 35; with manipulation: r = 0 . 47; with color: r = 0 . 23; with motion: r = 0 . 27; p < 10−9 ) . Neural RDMs were generated for each of the 688 WM connections ( S1A Fig ) that were identified through deterministic tractography across 90 automated anatomical labeling ( AAL ) regions based on the diffusion tensor imaging ( DTI ) data of 48 healthy controls [27] . To generate the neural RDM for each WM connection , we performed lesion-naming model decoding using voxel-wise lesion patterns and item-level naming responses . For 80 patients with brain damage , lesion patterns in each WM connection ( with each voxel in the WM connections labeled as “lesion” or “intact” ) for each patient were obtained by overlapping the manually traced lesion mask ( converted to the MNI ) space ) with the WM mask ( see Fig 1 ) . A total of 680 out of 688 WM connections with adequate lesion coverage ( see Materials and methods; see also S1E Fig for the lesion distribution map ) were included in the following analyses . The patients’ naming performances for each of the 100 pictures were collected ( performance distribution in S1B Fig ) . WM neural RDMs were generated using item-based lesion-naming prediction models . For 197 connections , the lesion-naming models had successful within-item prediction averaged across all items ( Bonferroni p < 0 . 05; diagonal in Fig 1D ) . That is , they yielded successful naming prediction models and were the connections that we considered in the following analyses . Of these connections , 185 were located in the left hemisphere and 12 in the right hemisphere ( S1C Fig ) . For each of these WM connections , we computed the correspondence between the predicted scores using SVM classifiers built using the training patients’ lesion patterns and the naming scores of one item and the actual naming score of another item in the testing samples across testing iterations . This between-item correlation was taken as the similarity value for this item pair in the neural RDM , based on the assumption that if this connection pattern contains certain aspects of information shared by the naming process of these two items being captured by the SVM model , models trained with one item should also predict naming accuracy of the other item . Worth clarifying is that this procedure does not depend fully on the correlation between the actual naming accuracies across item pairs but also to what degree the potentially shared underlying properties for their naming process are supported by each WM connection ( as captured by the SVM models ) . For example , for connections supporting phonological processing , the SVM models may pick up phonological properties and result in higher correlation between phonologically related pairs; those supporting semantic processing may pick up semantic properties and result in correlation between semantically related pairs . The resulting 100 × 100 ( -item ) lesion-naming prediction similarity matrix was transformed to be the neural RDM of this connection ( 1-prediction similarity , Fig 1D ) . Using RSA , the correlations between the WM neural RDMs and the semantic RDM were assessed . Significantly positive correlations were obtained in 60 WM connections ( r = 0 . 03–0 . 11 , false discovery rate [FDR] q < 0 . 05; see S1D Fig ) . These WM connections connected widely distributed regions across the left hemisphere , and approximately half ( 31/60 ) of the connections had at least one of the connected nodes located in the temporal lobe . The most densely connected regions ( degree z-score > 1 ) were the middle temporal gyrus ( MTG ) , superior temporal gyrus ( STG ) , orbital part of middle frontal gyrus , inferior parietal lobule ( IPL ) , and precentral gyrus . What about semantic effects that could not be explained by modality-specific attributes , peripheral factors , or broad semantic categorical effects ? We controlled for the effects of all four modality-specific attributes , two peripheral variables ( the early visual and phonological ) and semantic category matrix ( labeling within-category pairs 1 and between-category pair 0 ) using partial correlation . The semantic effect was consistently significant in eight WM connections ( r = 0 . 03–0 . 07 , FDR q < 0 . 05; Fig 3A–3C ) . Table 1 presents the detailed statistical results before and after , including these variables as covariates . These eight connections were considered to represent ( relatively ) higher-order semantic space . Five of them were located in the left ventral visual pathway and connected occipital regions ( middle occipital gyrus , calcarine sulcus , and lingual gyrus ) and temporal regions ( STG , MTG , superior anterior temporal lobe [ATL] , and middle ATL ) . The three remaining WM connections were located in the right hemisphere , connecting the postcentral gyrus with the thalamus , lingual gyrus , and parahippocampal gyrus . These reconstructed connections are shown in Fig 3B and S2 Fig . To examine the degree to which the semantic effects we observed on these WM connections reflect effects of broad semantic category , we also checked the RSA effect of the category matrix ( correlating the neural RDM and the category RDM ) and found that none of these connections had significant effects of the semantic category ( p > 0 . 05 , Table 1 ) . To consolidate the main results above , we further performed validation analyses to test the following concerns: ( 1 ) The WM mask we adopted was constructed using DTI data acquired on a scanner with a low magnetic field ( 1 . 5 T ) and 32 directions . Was the WM connection construction accurate and unaffected by crossing-fiber problems ? ( 2 ) To maximize power , we included patients with multiple etiologies ( 84% stroke and 16% traumatic brain injury [TBI] ) and lesion distributions ( 37 . 5% lesion in the left hemisphere only , 43 . 8% lesion in bilateral hemispheres , and 18 . 8% in the right hemisphere only ) . Were the results systematically affected by disease type or hemispheric differences ? What types of representations are linked by the WM connections that represent the semantic space ? Do the WM connections simply relay semantic information that has already been encoded in the GM nodes , or do they contain information that cannot be accounted for by representation in the GM nodes ? We tested the representational contents of the seven GM nodes that were connected by the five higher-order semantic WM connections whose effects remained robust in the validation tests ( see Table 1 ) . Four GM regions had successful within-item naming prediction and were considered in the RSA analysis: superior ATL , middle ATL , MTG , and STG . The neural RDM for each GM node was constructed using the same method as with the neural RDMs of the WM connections . We found that the higher-order semantic representation in the five semantic WM connections cannot be simply explained by GM information ( Fig 3D; S1 Table ) : when correlating the GM neural RDMs with the semantic RDM ( controlling for peripheral and categorical matrices ) , only the superior ATL reached significance ( r = 0 . 04 , FDR q < 0 . 05 ) . However , this effect could be explained by modality-specific attribute representations . After controlling for the four modality-specific attribute matrices , none of the four GM nodes significantly correlated with the semantic RDM at either the conventional threshold ( FDR q < 0 . 05 ) or a less stringent threshold ( uncorrected p < 0 . 05 , see S1 Table ) . Additionally , when testing the higher-order semantic representation in the five WM connections by further adding the neural RDMs of the two GM nodes being connected as additional confounding variables , the results remained unchanged ( see Table 1 ) . We further constrained our WM connection mask with a WM mask constructed by T1 segmentation ( conducted using SPM8 in MNI T1 template , default parameters ) to offer a clear WM boundary , i . e . , containing only WM voxels . We then recomputed the higher-order semantic RSA in these WM connections using only the voxels within the WM mask and found that the effects in all five WM connections remained significant ( FDR q < 0 . 05 , r = 0 . 03–0 . 07 , SD = 0 . 01 ) . If not semantic , do these GM nodes code modality-specific attributes ? We correlated the neural RDM of each GM node with each of the four modality-specific attribute RDMs ( shape , manipulation , color , and motion; Fig 3D & S1 Table; the three control matrices—low-level visual , phonological , category—were controlled for ) . The superior ATL , MTG , and STG were significantly correlated with the shape and manipulation RDMs ( shape: r = 0 . 04–0 . 08 , manipulation: r = 0 . 12–0 . 16 , FDR q < 0 . 05 ) . The middle ATL was significantly correlated with the shape and color RDMs ( shape: r = 0 . 04 , color: r = 0 . 06 , FDR q < 0 . 05 ) . Finally , we conducted a whole-brain analysis across all 90 AAL GM nodes . In addition to superior ATL , the neural RDMs of the left IPL , precentral gyrus , and postcentral gyrus were significantly correlated with the semantic RDM ( r = 0 . 04–0 . 05 , FDR q < 0 . 05 ) , but none of these or any other GM regions retained significance after controlling for the four modality-specific attribute matrices ( FDR q < 0 . 05 ) .
To test the potential WM basis of semantic representation , we developed a structural-property-pattern-based RSA approach by applying machine learning to lesion and behavioral data in patients to derive item-based neural RDMs for WM connections . We found that a set of WM connections connecting occipital/middle temporal regions and anterior temporal regions represented a semantic space that was not explained by broad semantic categories or the effects of modality-specific attributes and , hence , was addressed as higher-order semantic representation . Such semantic effects were not fully explained by the properties of the GM nodes that were connected . Although the neural RDM of a connecting node—the superior ATL—correlated with the semantic RDM , such effect diminished after controlling for modality-specific attributes . Instead , these GM nodes tended to represent modality-specific attributes , including shape and manipulation in the superior ATL , MTG , and STG and shape and color in the middle ATL . First , it should be noted that we inferred semantic effects to be higher-order when they were not explained by linear combinations of the classical modality-specific attributes for objects . The potential effects of some untested modalities or certain nonlinear combinations across various modalities could not be fully excluded . Also , subjectively judged semantic distance might be a rather composite measure that is driven by multiple semantic dimensions , which may have different neural bases ( e . g . , [30] ) . Under the current ( conventional ) operation , these WM connections that represent higher-order semantics tend to lie in several major pathways that have been associated with semantic processing using univariate lesion-behavior correlation or intraoperative stimulation [12 , 16 , 21 , 27 , 31] . These connections partly belong to IFOF , and the inferior longitudinal fasciculus ( ILF ) ( the overlapped voxels with the Johns Hopkins University WM template: IFOF [32%] , ILF [71%] , and minimally on the minor forceps [6%] and superior longitudinal fasciculus [8%] ) . Lesion or atrophy in IFOF is associated with semantic deficit severity in patients with stroke and in patients with semantic dementia [12 , 27 , 32] . A similar result was also found with ILF in semantic dementia [16 , 33] . Additionally , direct intraoperative stimulation of IFOF induces semantic errors [34 , 35] . Our current findings based on multivariate RSA demonstrate that the organization of specific connections among these large WM tract bundles represent the fine-grained semantic space . Items closer in semantic space are represented by more similar WM patterns in these specific connections . Note that it is well known that patients’ specific naming errors may vary from session to session [36] . The WM lesion pattern observed here is likely associated with some aspects of semantic space rather than with specific items . The damage of such specific aspects of semantic space would result in noisy/impaired representation for a range of items sharing that space , resulting in potentially different outputs at different time points . Such semantic space was nonetheless much finer than broad semantic categories , however , as the RSA results were robust after controlling for the categorical matrix . It is also well known that patients may make different types of errors , such as phonological and semantic paraphasias , which may be originated from different cognitive stages . Our approach here pulled all types of naming errors together , and the RSA results of correlating the neural RDM with different RDMs ( semantic versus phonological/visual ) presumably reflect the neural basis of different error types , which should be directly examined in future research . What is the relationship between the WM representations and the nature of the GM regions that they connect ? First , we indeed observed that one of the seven linked GM regions was related to semantic space—superior ATL . The finding that lesion-pattern-behavior ( neural ) RDM in the superior ATL correlated with semantic space before regressing out the effects of modality-specific attributes converges nicely with the accumulated evidence about the cortical representation of semantics from fMRI and neuropsychological studies . ATL is the region with the strongest atrophy in patients with semantic dementia , which is marked by semantic deficits [6 , 7 , 31 , 37 , 38] and is sensitive to multiple modalities of object attributes [39 , 40] . Unlike the WM connections related to higher-order semantic space , the semantic effect in the superior ATL could be explained by the effects of modality-specific attributes . Worth noting is that ventral ATL was not scrutinized because it was not a node in the AAL parcellation we used but was included in the fusiform and inferior temporal nodes . What should be highlighted , however , is that the positive effects of higher-order semantic representation in the WM connections are significant and are not simply inheriting the properties of the connected GM nodes . Several higher-order semantic WM connections observed here connected ATL with other regions , inviting further questions about whether it is the integrity of ATL or of the ATL-related WM connections that make stronger contributions to the semantic deficits in semantic dementia patients . While our results certainly do not argue against the possibility that there are specific GM regions supporting semantic representation , we found that the GM nodes being connected by the WM connections obtained here tended to represent multiple modality-specific object properties . Of the four GM regions we could test , the MTG , STG , and superior ATL represented shape and manipulation properties , and the middle ATL represented shape and color properties . These results converge nicely with the fMRI literature studying the sensitivity of these regions for object attributes . For instance , the effects of various attributes were recently tested using parametric modulation analyses [2] , which found that the posterior MTG was sensitive to both shape and manipulation knowledge . Coutanche and Thompson-Schill [39] found that the ATL codes the integration of color and shape , and Peelen and Caramazza [40] found that the ATL codes both manipulation and location . The STG was sensitive to motion properties in Fernandino et al . [2] but not in our study , perhaps due to different parcellation scales regarding the finer structure within this region . Note that many studies about the attribute-specific property representations have revealed results in sensory and motor cortices ( e . g . , shape in the lateral occipital/temporal cortex: [26 , 41]; color in the ventromedial occipital cortex such as lingual gyrus: [42–44] ) . However , these regions could not be tested in our data given their chance-level lesion-naming prediction performance , which could either be due to low lesion distribution in these regions ( see S1E Fig for lesion distributions ) or because the specific dimensions they represent are unnecessary for object picture-naming behavior . It may also be the case that higher-order semantic space is formed by binding multiple , rather than single , pairs of attributes . Consistent with this speculation , it has been shown that computation simulation models with a convergent architecture , in which intermediate units code multiple types of dimension pairings , were better at capturing the “deep” structure of conceptual space and promoting generalizations across semantically related items that were not apparently similar along single dimensions [22] . What is the mechanism of coding higher-order semantic information in WM that connects multiple modality-specific attributes ? One potential mechanism could be through synchronized firing of specific sensory and motor patterns for objects . Consider when people use a pair of scissors: the neurons that represent the attributes across various modalities—e . g . , shape , haptics , ways of grasping and manipulating it , seeing the consequence of using it ( things being cut ) —fire together . Such functional co-activation across a wide range of attributes occurs often when we see or use scissors , which enhances the structural connection between neurons within and across dimensions of the same object . WM provides a basis for such synchronization between distant cortical regions [45] . These synchronizations also lead to the building and tuning of WM connections , because neuronal activity traveling through axons can affect the properties of myelin sheaths in the active circuit; for example , electrical activity in the axon induces myelination [46 , 47] . This interactive process results in the WM basis of a multidimensional representation of “scissors , ” which is closer in the higher-order semantic space to concepts such as “axe” or “paper . ” The formation and modulation of the WM microstructure underlying these representations can be affected by our experiences , which is the basis of acquiring new concepts and of the coloring of existing concepts . Ample evidence describes how WM is affected by experience . Early-life experiential deprivation in animals and humans leads to decreased myelin sheath thickness and WM volume [48 , 49] , whereas these parameters increase when the organism is placed in a rich experiential environment [50] . Reading training [51] and music practice [52 , 53] during childhood lead to increased fractional anisotropy in WM . The acquisition of motor skills changes the WM microstructure [54 , 55] . The exact relationship between WM microstructure and the functional coupling between cortical regions for various representational dimensions warrants further studies . A final methodological note is that the approach we developed here—building neural RDMs using machine learning with structural lesion data and condition-specific performances—could be easily adapted to other cognitive issues and all kinds of brain structural integrity measurements , including DTI indices ( e . g . , fractional anisotropy , mean diffusivity ) or voxel-based morphometry measures for both patient and healthy populations . For the current study , we chose to focus on manually traced lesion on the T1 image ( with reference to T2 ) because it captures the structural damage in our specific patient group ( mostly stroke ) in a most straightforward fashion . RSA , an approach that connects major branches of systems neuroscience—brain-activity measurement , behavioral measurement , and computational modeling [23]—could now be extended to an additional branch , i . e . , brain structural measurement . In conclusion , using a structural-property-pattern-based RSA approach , we found that the WM structures mainly connecting occipital/middle temporal regions and anterior temporal regions represent fine-grained higher-order semantic information . Such semantic relatedness effects were not attributable to modality-specific attributes ( shape , manipulation , color , and motion ) or to the representation contents of the cortical regions that they connected and were above and beyond the broad categorical distinctions . By connecting multiple modality-specific attributes , higher-order semantic space can be formed through patterns of these connections .
Eighty patients with brain damage participated in the present study . The patient group ( 60 males , 20 females ) was recruited from the China Rehabilitation Research Center with at least 1 month post-onset ( mean = 6 . 09; SD = 11 . 69; range: 1–86 months ) and premorbidly right-handed . The majority suffered from stroke ( n = 67 ) and others suffered from TBI ( n = 13 ) . The patients’ mean age was 45 years ( SD = 13; range: 19–76 years ) and mean years of formal education was 13 ( SD = 3; range: 2–19 ) . Twenty additional college students ( 10 males; mean age = 22 . 9 , SD = 2 . 45 , range = 19–27 ) participated in the multi-arrangement experiment for the behavioral RDMs . This study was approved by the Institutional Review Board of the State Key Laboratory of Cognitive Neuroscience and Learning , Beijing Normal University ( IORG0004944 ) , adhering to the Declaration of Helsinki for research involving human subjects . All participants gave informed written consent . Each subject was scanned using a 1 . 5T GE SIGNA EXCITE scanner with an 8-channel split head coil at the China Rehabilitation Research Center . We collected two types of images: ( 1 ) high-resolution 3D T1-weighted MPRAGE images in the sagittal plane with a matrix size = 512 × 512 , voxel size = 0 . 49 × 0 . 49 × 0 . 70 mm3 , repetition time ( TR ) = 12 . 26 ms , echo time ( TE ) = 4 . 2 ms , inversion time = 400 ms , field of view ( FOV ) = 250 × 250 mm2 , flip angle = 15° , and slice number = 248; and ( 2 ) FLAIR T2-weighted images in the axial plane with a matrix size = 512 × 512 , voxel size = 0 . 49 × 0 . 49 × 5 mm3 , TR = 8 , 002 ms , TE = 127 . 57 ms , inversion time = 2 s , FOV = 250 × 250 mm2 , flip angle = 90° , and slice number = 28 . To improve the image quality , the T1 image was scanned twice . The two scans were then co-registered and averaged for the following analyses . All imaging data can be found at the Open Science Framework database ( URL: https://osf . io/h7upk/ ? view_only=52b8f86cffa14ed4844e4a1b9cd429cb ) . We used structural-property-pattern ( lesion ) -based RSA to investigate semantic and modality-specific attribute representation in WM connections and GM regions . Similar to the conventional RSA , which is a highly fruitful method to research the neural representation in cortical regions using functional imaging data , the structural-property-pattern ( lesion ) -based RSA computes the relationship between the neural RDMs and behavioral or theoretical RDMs . The main difference is that the neural RDMs in this study were constructed by machine-learning models based on performances on neuropsychological tests and patients’ brain structural lesion patterns . The main rationale for this neural similarity measure is that if a WM connection pattern contains certain aspects of information shared by the naming process of two items ( e . g . , some semantic features ) , models trained with one item should also be able to predict naming accuracy of the other item to some degree . We first extracted the lesion features , balanced item labels by bootstrapping , input the lesion features and balanced labels into SVM training and testing to obtain the neural RDM , and used permutation to estimate the significance level of the neural RDM . The full pipeline is shown in Fig 1 and the details for each of these steps are described below in turn . The scripts of the full pipeline can be found at https://osf . io/h7upk/ ? view_only=52b8f86cffa14ed4844e4a1b9cd429cb . The neural RDMs were correlated with behavioral RDMs using Spearman correlation . Specifically , for each WM connection , its neural RDM ( a 100 [-item] × 100 [-item] matrix ) and the semantic RDM ( a 100 [-item] × 100 [-item] matrix ) were both converted to a 1 × 4 , 950 vector . Correlation was computed on these two vectors ( 4 , 950 pairs of values ) . The r values were used to determine the extent of specific information encoded in the WM connections/GM regions . The FDR ( q < 0 . 05 ) was used for multiple comparison correction . To investigate the higher-order semantic effects beyond modality-specific attributes , partial correlation analyses were performed between the semantic RDM and neural RDMs , with the modality-specific attribute RDMs ( and the peripheral and categorical matrices ) as nuisance variables . As explained in the “Behavioral RDM Construction” session , we adopted two ways of treating missing values in the modality-specific attributes ( e . g . , animal items were not rated on “manipulation” property ) —setting it to be 1 ( most dissimilar with other items on this modality ) or to “NaN” ( missing value ) . The RSA mapping procedure was implemented using a custom MATLAB function . | One of the most challenging questions in cognitive neuroscience is how semantic knowledge , for example , that “scissors” and “knives” are related in meaning , can emerge from primary sensory dimensions such as visual forms . It is often assumed that in the human brain , semantics are stored in regions of the brain cortex , where distinct types of modality-specific information are transferred to and bind together . We tested an alternative hypothesis—“representation by connection”—in which higher-order semantic information could be coded by means of connection patterns between cortical regions . Combining data from behavior and brain imaging of 80 patients with brain lesions , we applied machine learning to construct the mapping models between the lesion patterns on axonal tracts ( white matter ) and item-specific object-naming performances . We found that specific white matter lesions produced deficits in object naming associated with the object’s semantic space , but not relevant to its primary dimension . The naming performances of semantically related objects were better predicted from white matter lesion-pattern models . That is , the higher-order semantic space could be coded in patterns of brain connections by linking cortical areas that do not necessarily contain such information . | [
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] | 2018 | Semantic representation in the white matter pathway |
Dendritic cells ( DCs ) are essential antigen-presenting cells for the induction of immunity against pathogens . However , HIV-1 spread is strongly enhanced in clusters of DCs and CD4+ T cells . Uninfected DCs capture HIV-1 and mediate viral transfer to bystander CD4+ T cells through a process termed trans-infection . Initial studies identified the C-type lectin DC-SIGN as the HIV-1 binding factor on DCs , which interacts with the viral envelope glycoproteins . Upon DC maturation , however , DC-SIGN is down-regulated , while HIV-1 capture and trans-infection is strongly enhanced via a glycoprotein-independent capture pathway that recognizes sialyllactose-containing membrane gangliosides . Here we show that the sialic acid-binding Ig-like lectin 1 ( Siglec-1 , CD169 ) , which is highly expressed on mature DCs , specifically binds HIV-1 and vesicles carrying sialyllactose . Furthermore , Siglec-1 is essential for trans-infection by mature DCs . These findings identify Siglec-1 as a key factor for HIV-1 spread via infectious DC/T-cell synapses , highlighting a novel mechanism that mediates HIV-1 dissemination in activated tissues .
HIV-1 can infect CD4+ cells of the lymphoid and myeloid lineage with a strong preference for CD4+ T cells . Myeloid DCs exhibit innate resistance against HIV-1 infection . HIV-2 , on the other hand , efficiently infects myeloid DCs due to its accessory viral protein Vpx , not present in HIV-1 , which is able to counteract the myeloid restriction factor SAMHD1 [1] , [2] . Despite lacking these additional target cells , HIV-1 exhibits a higher pathogenicity than HIV-2 and has dominated the global AIDS pandemic . Indeed , myeloid DCs can contribute to the spread of HIV-1 through trans-infection of CD4+ T cells [3] , [4] . This process requires HIV-1 binding to the DC surface , viral capture and release of trapped viruses at the infectious synapse , a cell-to-cell contact zone between uninfected DCs and interacting CD4+ T cells , which facilitates infection by locally concentrating virus and viral receptors [5] . Classical myeloid DCs patrol submucosal surfaces , where they capture and internalize microbial pathogens through various cell surface receptors . Pioneering studies suggested that HIV-1 is trapped by immature DCs ( iDCs ) in mucosal tissues through binding of its envelope glycoproteins to the C-type lectin DC-SIGN , with subsequent transfer of infectious particles to secondary lymphoid tissues , where trans-infection occurs [4] , [6] . Later reports indicated , however , that HIV-1 captured by iDCs is rapidly degraded [7]–[9] , arguing against this original “Trojan horse” hypothesis . Conversely , maturation of DCs with lipopolysaccharide ( LPS ) , a microbial product significantly augmented in the plasma of HIV-1-infected individuals [10] , markedly enhanced the capacity of DCs to capture HIV-1 and mediate trans-infection [5] , [7] , [9] . These results suggested that HIV-1 capture by LPS-matured mDCs ( LPS mDCs ) plays an essential role in HIV-1 pathogenesis , facilitating viral spread in the densely populated lymphoid tissue , where many uninfected T cells contact virus-presenting mDCs . Other receptors besides DC-SIGN have been identified as binding factors for HIV-1 but do not explain why LPS mDC capture of HIV-1 is independent of viral glycoproteins [9] . Instead , HIV-1 capture is markedly sensitive to reductions in viral sphingolipid content [11] and relies on HIV incorporation of membrane gangliosides [12] , [13] . Furthermore , we recently showed that sialyllactose in gangliosides serves as the viral attachment factor for LPS mDCs [13] . Since HIV-1 and cellular secreted vesicles , termed exosomes , use the same pathway for mDC capture [11] , HIV-1 may have hijacked a pre-existing cellular route for vesicle capture to facilitate efficient transfer to multiple target cells .
To identify the molecule on DCs that mediates HIV-1 and exosome capture , we performed transcriptome analysis on differentially matured DCs with a highly divergent capacity to capture and transmit HIV-1 . We used efficiently trans-infecting LPS mDCs and compared them to DCs matured in the presence of the clinical grade cocktail ITIP ( ITIP mDCs ) , which exhibit strongly reduced HIV-1 capture and trans-infection capacity ( Figure 1A ) [14] . We focused our analysis on the Siglec family ( including CD83 ) because these type I transmembrane proteins have an amino-terminal V-set domain that had been shown to interact with sialylated ligands [15] . Most members of the family were equally expressed in LPS mDCs and ITIP mDCs , and this was also observed for the maturation marker CD86 ( Figure 1B ) . DC-SIGN , SIGLEC7 , and SIGLEC14 were slightly up-regulated in LPS mDCs , but this difference was not statistically significant for DC-SIGN and marginally significant for SIGLEC14 and SIGLEC7 , respectively ( p = 0 . 03 and p = 0 . 04 ) . In contrast , SIGLEC1 expression was strongly up-regulated in LPS mDCs compared to ITIP mDCs with genome-wide significance ( p = 3 . 5×10−4; Figure 1B ) . Furthermore , SIGLEC1 ranked 20th of all differentially regulated genes in comparative transcriptome analysis . The differential expression of Siglec-1 in LPS and ITIP mDCs was confirmed by quantitative real-time PCR ( qRT-PCR; Figure 1C ) and Fluorescence Activated Cell Sorting ( FACS; Figure 1D ) . Comparison with iDCs also revealed a significantly higher expression level and surface density of Siglec-1 in LPS mDCs ( Figure 1C , D ) . To test whether Siglec-1 is the surface molecule on LPS mDCs responsible for the capture of vesicles and viruses that carry sialyllactose-containing gangliosides in the outer leaflet of their membrane , we used a previously established FACS assay [11] , [13] . This assay makes use of HIV-1 virus-like particles lacking the viral envelope glycoproteins and carrying a fusion of the viral structural protein Gag with eGFP ( VLPHIV-Gag-eGFP ) . These fluorescent VLPs follow the same trafficking route as wild-type HIV-1 in LPS mDCs [11] . VLP capture of LPS mDCs was evaluated in the presence of antibodies ( Abs ) against different Siglecs or mannan , a C-type lectin inhibitor blocking the HIV-1 interaction with DC-SIGN . Besides Siglec-1 , we included Abs against CD83 , highly expressed in ITIP and LPS mDCs ( Figure 1B ) ; Siglec-7 , moderately up-regulated in LPS mDCs ( Figure 1B ) ; and Siglec-5/14 too , due to their high homology to the V-set domain of Siglec-1 . VLP capture was almost completely abolished when LPS mDCs were pre-treated with the α-Siglec-1 monoclonal Ab ( mAb ) 7D2 ( Figure 2A; p<0 . 0001 ) . However , pretreatment with Abs against other Siglec family members or blockade of DC-SIGN with mannan had no effect ( Figure 2A ) . We have previously shown that Texas Red ( tRed ) labeled Large Unilamellar Vesicles ( LUV ) mimicking the size and lipid composition of HIV-1 and containing the ganglioside GM1 ( LUVHIV-tRed ) follow the same trafficking route as VLPHIV-Gag-eGFP in LPS mDCs . Binding and capture in both cases depends on the recognition of sialyllactose exposed in gangliosides of the vesicle membrane [13] . Accordingly , capture of GM1-containing LUVHIV-tRed by LPS mDCs was efficiently and specifically inhibited by the α-Siglec-1 mAb 7D2 ( Figure 2B; p<0 . 0001 ) . The residual capture by 7D2-treated LPS mDCs was similar to that exhibited by untreated LPS mDCs capturing LUVHIV-tRed containing GM1 without the sialic acid group ( Asialo GM1 ) , confirming that sialic acid in the vesicle membrane is crucial for Siglec-1 recognition ( Figure 2B; p<0 . 0001 ) . We extended this analysis to cellular exosomes , which also carry sialyllactose-containing gangliosides in their membrane [16] and can be internalized by LPS mDCs [11] . Fluorescent exosomes were efficiently captured by LPS mDCs , and this capture was almost abolished by mAb 7D2 treatment ( Figure 2C; p<0 . 0001 ) . Titration of the α-Siglec-1 mAb 7D2 revealed a dose-dependent inhibition of VLP capture ( Figure 2D ) . Specificity of the mAb 7D2–mediated inhibition was confirmed by pre-incubation of this mAb with different Siglec proteins . Pre-incubation with purified Siglec-1 completely restored VLP capture , while pre-incubation with purified Siglec-7 , -5/14 , or CD83 had no effect ( Figure 2E ) . Although the epitope recognized by 7D2 mAb might not constitute the actual viral binding site , since 7D2 Fab fragments did not lead to a block in VLP capture , titration with 7–239 , a different α-Siglec-1 mAb , confirmed a dose-dependent inhibition of VLP capture ( Figure S1 ) . Hence , α-Siglec-1 mAb 7D2 , raised against the four N-terminal domains of Siglec-1 [17] , or α-Siglec-1 mAb 7–239 , raised against the full 17-domain protein [18] , specifically blocked sialyllactose-dependent LPS mDC capture of VLPs , LUVs , and exosomes , identifying Siglec-1 as the relevant recognition receptor . If Siglec-1 serves as a recognition receptor on DCs , its surface expression should correlate with their respective VLP capture ability . Capture was low in iDCs and stable over time ( Figure 2F , left graph ) , while VLP capture was strongly enhanced following LPS treatment ( Figure 2F , right graph ) . This increased VLP capture ability directly correlated with a strong up-regulation of Siglec-1 surface expression on LPS mDCs ( Figure 2F , right graph ) . We also performed quantitative FACS analysis to determine the absolute number of Siglec-1 Ab Binding Sites ( ABS ) on ITIP mDCs , iDCs , and LPS mDCs ( Figure 2G ) . The VLP capture capacity of these distinct DC subtypes was strongly correlated with the mean number of Siglec-1 ABS expressed per cell ( ρ = 0 . 9695; Figure 2G ) . Furthermore , Siglec-1 expression also correlated with the relative VLP capturing capacity of LPS mDCs derived from the same donor ( Figure S2 ) . These experiments show a direct correlation between Siglec-1 expression on the DC surface and their respective VLP capture capacity . To extend these observations to authentic virus , we performed similar experiments with infectious HIV-1 . Again , LPS mDCs captured significantly more virus than iDCs or ITIP mDCs ( Figure 3A ) . The α-Siglec-1 mAb 7D2 inhibited HIV-1 capture of LPS mDCs by 80% ( Figure 3A; p = 0 . 0019 ) , while pre-treatment with mannan had no effect . Noteworthy , α-Siglec-1 mAbs also blocked binding of HIV-1 to LPS mDCs at 4°C ( Figure S3 ) . Similarly , pre-treatment of iDCs with the mAb 7D2 reduced HIV-1 capture by 60% ( Figure 3A; p = 0 . 0005 ) , indicating that even at lower surface expression levels of Siglec-1 on iDCs ( Figure 1D ) , this receptor still constitutes an important capture moiety . Consistently , capture inhibition by mAb 7D2 was much weaker on ITIP mDCs ( Figure 3A; p = 0 . 001 ) , which exhibited the lowest Siglec-1 surface expression ( Figure 1D ) . The effect of the mAb 7D2 on HIV-1 capture was dependent on blocking cell surface Siglec-1 , as addition of the inhibitor after virus exposure had no effect ( Figure 3B ) . Importantly , primary blood myeloid DCs exposed to LPS also up-regulated Siglec-1 expression levels ( Figure S4 ) and showed increased HIV-1 capture capacity that could be blocked by pretreatment with α-Siglec-1 mAb 7D2 ( Figure 3C; p = 0 . 0022 ) . These results strongly suggest that Siglec-1 is the molecule responsible for HIV-1 capture by DCs , especially upon triggering of Siglec-1 expression by LPS . Next , we investigated whether Siglec-1 traffics together with sialylated ligands , such as ganglioside-containing liposomes , VLPs , or HIV-1 , reaching the same sac-like compartment where these particles are stored [11] , [13] . LPS mDCs were pulsed with these different fluorescent particles and subsequently stained with the α-Siglec-1 Alexa 488 mAb 7–239 ( Figure 3D ) . Confocal microscopy revealed extensive co-localization of Siglec-1 with GM1-containing LUVHIV-tRed , VLPHIV-Gag-Cherry , and HIV-1Cherry in the same compartment ( Figure 3D , Movies S1 , S2 , S3 , and Figure S5 ) . We then assessed whether binding of α-Siglec-1 mAb 7D2 to LPS mDCs would be sufficient to internalize Siglec-1 into a similar compartment . Following incubation for 4 h at 37°C , most of the bound α-Siglec-1 mAb 7D2 was indeed found within a sac-like compartment ( Figure 3E ) . Hence , binding of mAb 7D2 , probably causing Siglec-1 cross-linking at the cell surface , is sufficient to induce Siglec-1 internalization . To assess the relevance of Siglec-1 for HIV-1 trans-infection , we pulsed distinct DCs with equal amounts of infectious virus in the presence or absence of blocking reagents and cocultured them with a CD4+ reporter cell line ( Figure 4A ) . Controls performed with the protease inhibitor saquinavir , which abolishes production of infectious virus , demonstrated that this assay measured only trans-infection of reporter cells by DC-captured virus without a contribution from potentially de novo infected DCs ( Figure 4A–B , last bars ) . Pretreatment of LPS mDCs with the α-Siglec-1 mAb 7D2 inhibited HIV-1 trans-infection by 85% ( Figure 4A; p = 0 . 0052 ) , while blocking DC-SIGN through mannan had no effect . Analogously , pretreatment of iDCs with 7D2 reduced HIV-1 trans-infection by 55% ( Figure 4A; p = 0 . 0091 ) . In contrast , ITIP mDC-mediated trans-infection was not affected by 7D2 but was blocked by mannan ( Figure 4A; p = 0 . 0014 ) . Addition of any of the inhibitors tested after DC viral pulse had no significant effect on trans-infection ( Figure 4B ) , except for the mAb 7D2 in LPS mDCs ( p = 0 . 0069 ) . This latter inhibitory effect could not be explained by differences in viral capture ( Figure 3B ) but is most likely attributed to the cell-to-cell adhesion function of Siglec-1 [19] , where establishment of infectious synapses may be partially impaired when Siglec-1 is blocked in LPS mDCs . Indeed , when we analyzed infectious synapse formation between HIV-1Cherry pulsed LPS mDCs cocultured with CD4+ T cells , Siglec-1 polarized towards the site of the cell-to-cell contact zone where viruses were also concentrated ( Figure 4C ) . The importance of Siglec-1 for HIV-1 trans-infection was also confirmed for blood myeloid DCs . LPS stimulation strongly enhanced their potential for trans-infection ( Figure 4D; p<0 . 0001 ) , and this increase could be abolished by pre-incubation with mAb 7D2 ( Figure 4D; p<0 . 0001 ) . To verify the essential role of Siglec-1 during HIV-1 capture and trans-infection , we applied two complementary experimental strategies: RNA interference to reduce Siglec-1 expression levels in LPS mDCs and transfection of Siglec-1 into cells devoid of this receptor . In the first approach , we transduced DCs with lentiviral particles coding for different shRNAs by co-infection with vpx-expressing lentiviruses to counteract the restriction factor SAMHD1 and facilitate DC productive infection . Transduction of two different SIGLEC1-specific shRNAs , but not of a nontarget shRNA control , led to a drastic decrease in Siglec-1 surface expression and a concurrent loss of VLPHIV-Gag-eGFP capture ( Figure 5A , B ) . Furthermore , transduction of a SIGLEC1-specific shRNA , but not of a control shRNA , decreased LPS mDC capacity for HIV-1 trans-infection to a reporter CD4+ cell line ( Figure 5C ) . We next assessed whether endogenous Siglec-1 expression in cells lacking this molecule on their surface could rescue their capacity for HIV-1 capture and trans-ifection . This was first attempted for the monocytic cell line THP-1 , but could not be pursued since transfection with any of the plasmids tested up-regulated Siglec-1 expression , probably through TLR signaling ( Figure S6A , top panels ) . Thus , we chose Raji B cell line instead , which lack endogenous expression of Siglec-1 and could be efficiently transfected without unspecific up-regulation of Siglec-1 ( Figure S6A , bottom panels , and S6B ) . Transfection of a Siglec-1 expression vector significantly enhanced VLPHIV-Gag-eGFP capture in the Siglec-1-positive cell population , and this effect was abolished by pretreatment with the α-Siglec-1 mAb 7D2 ( p = 0 . 0005; Figure 5D , E ) . No increased capture was seen in the Siglec-1-negative population of Siglec-1 transfected cells or following transfection of Siglec-5 or Siglec-7 expression plasmids ( Figure 5D , E ) . Pre-incubation with sialyllactose also blocked VLP capture in Siglec-1 transfected Raji cells ( Figure S7 ) . Accordingly , transfection of a Siglec-1 expression vector into Raji cells significantly increased their capacity for HIV-1 trans-infection to a reporter CD4+ cell line ( Figure 5F ) , and this effect was again abolished by pre-incubation of transfected cells with the mAb 7D2 ( p<0 . 0001; Figure 5F ) . Equivalent results were obtained when Siglec-1 transfected HEK-293T cells were analyzed ( Figure S8 ) . We finally verified that as opposed to DC-SIGN , Siglec-1 viral capture does not rely on the recognition of envelope glycoproteins ( Figure S9 ) . Transfection of a Siglec-1 expression vector in Raji cells allowed for efficient capture of HIV-1 with or without envelope glycoproteins , whereas Raji DC-SIGN cells only captured viruses bearing glycoproteins ( Figure S9 ) . The complementary approaches of SIGLEC1 knockdown and de novo expression on heterologous cells strongly support our conclusion that Siglec-1 is a central molecule mediating HIV-1 capture and trans-infection .
Three lines of evidence identify Siglec-1 as a novel DC receptor for HIV-1 capture and trans-infection: ( i ) Siglec-1 expression correlates with viral capture and trans-infection capacity of DCs , ( ii ) mAbs against Siglec-1 specifically inhibit HIV-1 capture in a dose-dependent manner , and ( iii ) SIGLEC1 knockdown reduces viral capture and trans-infection , while heterologous de novo expression of Siglec-1 enhances HIV-1 capture and trans-infection . An important role for Siglec-1 in HIV-1 infection is in line with previous studies reporting increased expression of Siglec-1 on CD14+ monocytes and macrophages in HIV-1 infection [20]–[22] . However , these studies analyzed Siglec-1 interactions with sialylated viral envelope proteins , while our results clearly show that HIV-1 capture depends on sialyllactose on viral membrane gangliosides interacting with Siglec-1 , but does not require viral glycoproteins . DC-SIGN was initially proposed as the HIV-1 attachment factor concentrating virus particles on the surface of DCs [4] , but later studies showed a variable contribution of DC-SIGN to HIV-1 capture and trans-infection [23] . Our results indicate that both DC-SIGN and Siglec-1 contribute to trans-infection by iDCs , while HIV-1 capture by highly trans-infecting LPS mDCs is independent of DC-SIGN and requires Siglec-1 . Hence , although Siglec-1 viral binding via sialyllactose recognition does not discriminate between infectious or noninfectious HIV-1 particles , the greater the expression of Siglec-1 , the greater the amount of virions captured and transmitted by DCs , diminishing the relative contribution of DC-SIGN gp120-mediated viral capture to trans-infection . Given that lectins such as DC-SIGN and Siglec-1 generally achieve high-avidity binding by clustering of both receptor and ligand [15] , recognition of thousands of sialyllactose containing gangliosides in the viral membrane by Siglec-1 should be clearly superior to the interaction of DC-SIGN with only 14±7 envelope trimers per virion [24] . Siglec-1 is the only Siglec family member tested that mediated HIV-1 capture , although all Siglecs interact with sialic acid through their respective V-set domains . This could be caused by different specificities , but is most likely due to Siglec-1 containing the largest number of Ig-like C2-type domains of all Siglecs; these domains act as spacers separating the ligand-binding site from the cell surface . Therefore , Siglec-1 extends beyond the glycocalix of the cell , and is thus available for interaction with external ligands , while other family members mainly bind ligands in cis [15] . Although Siglec-1 expression is restricted to myeloid cells , particularly to tissue macrophages found in secondary lymphoid tissues [17] , [25] , its expression can be rapidly induced and up-regulated once myeloid cells are activated [26] . Indeed , DCs exhibit a characteristic mature phenotype in HIV-1 viremic patients [27] , and up-regulation of Siglec-1 on mDCs is therefore likely to play an important role in HIV-1 dissemination in lymphoid tissues , thus contributing to HIV-1 disease progression . DC maturation is probably not directly induced by HIV-1 [28] , but is more likely a consequence of factors released upon HIV-1 infection . Circulating LPS has been shown to be significantly augmented in HIV-1 patients due to the increased translocation of microbial products from the gastrointestinal lumen once infection is established [10] . Thus , LPS may facilitate HIV-1 progression by local and systemic stimulation of DCs , which ( i ) leads to Siglec-1 up-regulation and enhanced viral spread and ( ii ) creates the pro-inflammatory milieu associated with HIV-1 infection and immune activation . This work together with several other recent reports indicates that HIV-1 uses a highly sophisticated strategy to evade DC immune surveillance and facilitate disease progression . Viral capture through Siglec-1 on the mDC surface is beneficial for viral spread through trans-infection , but could also be detrimental for the virus if leading to successful antigen presentation . However , captured HIV-1 do not appear to reach the endolysosomal compartment of LPS mDCs [29] , where antigen processing occurs . Furthermore , interaction of HIV-1 with DC-SIGN can cause down-regulation of MHC class II molecules and interferon genes , impairing antiviral immune responses while triggering infectious synapse formation [30] . If productive fusion of the viral and cellular membrane occurs , HIV-1 replication is blocked by the myeloid-specific restriction factor SAMHD1 [1] , [2] , thus preventing viral antigen production . On the other hand , if DC resistance to infection is bypassed , the interaction of newly synthesized HIV-1 proteins with a cell-intrinsic sensor elicits antiviral immune responses , not typically engaged owing to the absence of DC infection [31] . Siglec-1 captures HIV-1 through its interaction with sialyllactose-containing gangliosides exposed on viral membranes , and therefore functions as a general recognition receptor for vesicles carrying sialyllactose in their membrane . These include exosomes [16] and probably other sialyllactose-containing viruses . Gangliosides have been observed in the membrane of , for example , vesicular stomatitis virus ( VSV ) , semliki forest virus , or murine leukemia virus [32] , [33] , but are likely to be present in the membrane of many other enveloped viruses as well . Siglec-1-dependent viral capture may be important for direct infection of DCs in these cases , but may also enhance immune recognition , thus benefiting the host . Accordingly , Siglec-1 has been shown to efficiently capture VSV in vivo and facilitate antiviral responses and prevent viral neuroinvasion [34] , [35] . The observation that Siglec-1 also captures cellular microvesicles suggests that this pathway normally leads to antigen presentation through exosomes [36] and has been hijacked by HIV-1 for infectious virus storage and spread . The discovery of the role of Siglec-1 in capturing sialylated viruses expands our understanding of HIV-1 transmission mechanisms and warrants novel therapeutic approaches aimed to prevent viral dissemination .
The institutional review board on biomedical research from Hospital Germans Trias i Pujol approved this study . Peripheral blood mononuclear cells ( PBMCs ) were obtained from HIV-1-seronegative donors , and monocyte populations or myeloid DCs were isolated and cultured as described in [9] . Monocyte-derived mature DCs were differentiated for 48 h with 100 ng/ml of LPS ( Sigma-Aldrich ) or ITIP ( 300 IU/ml IL-1β , 1 , 000 IU/ml IL-6 , 1 , 000 IU/ml TNF-α , all from CellGenix , and 1 µg/ml PGE2 from Sigma-Aldrich ) . LPS myeloid DCs were differentiated for 24 h with 100 ng/ml of LPS . Autologous and heterologous CD4+ T cells were enriched from PBMCs using the RossetteSep α-CD8+ cocktail ( Stem cell ) and maintained in RPM1 with 10% fetal bovine serum ( FBS , Invitrogen ) supplemented with 10 IU/ml of IL-2 ( Roche ) . DCs ( 3×106 ) were centrifuged and resuspended in RNAlater solution ( Ambion ) . After lysate homogenization using QIAshredder spin columns ( Qiagen ) , total RNA isolation was performed with the RNeasy Mini Kit ( Qiagen ) , including a 15-min DNAse I treatment step . Affymetrix GeneChip Human Gene 1 . 0 ST arrays were processed with R using affy and limma Bioconductor packages [37] , [38] . After robust multichip average and quantile normalisation , differential expression was computed using moderated paired t test . Adjusted p values were computed with the Benjamini & Hochberg method [39] , and a 0 . 05 cutoff was applied to select significant genes . In total , 1 µg of RNA obtained as in the previous section was reverse transcribed using the TaqMan reverse transcription reagents ( including multiscribe reverse transcriptase and random hexamers; Applied Biosystems ) . Predesigned TaqMan gene expression assays and the comparative Ct ( ΔΔCt ) method [40] were used to determine relative SIGLEC1 gene expression . SIGLEC1 mRNA quantification ( FAM dye-labeled probe ) was normalized using the endogenous control gene Beta Glucuronidase ( VIC/TAMRA dye labeled probe ) in multiplex qPCR experiments run on the Applied Biosystems 7500/7500 Fast Real-Time PCR System and analyzed with the 7500 Software v2 . 0 . 4 . A cDNA sample from PBMCs was used as a reference for all relative quantification results . DCs were blocked with 1 mg/ml of human IgG ( Baxter , Hyland Immuno ) and stained with α-Siglec-1-PE 7–239 mAb ( AbD Serotec ) following the manufacturer's instructions at 4°C for 20 min . Samples were analyzed with FACSCalibur ( Becton-Dickinson ) using CellQuest and FlowJo software to evaluate collected data . HEK-293T and TZM-bl ( obtained through the U . S . National Institutes of Health [NIH] AIDS Research and Reference Reagent Program , from JC Kappes , X Wu , and Tranzyme Inc . ) were maintained in D-MEM ( Invitrogen ) . Raji B cell line ( kindly provided by Y . van Kooyk ) was cultured in RPMI ( Invitrogen ) . Raji DC-SIGN B cell line ( kindly provided by Y . van Kooyk ) was maintained in RPMI with 1 mg/ml of G418 ( Invitrogen ) . All media contained 10% FBS , 100 IU/ml of penicillin , and 100 µg/ml of streptomycin ( all from Invitrogen ) . VLPHIV-Gag-eGFP and VLPHIV-Gag-Cherry were obtained as previously described [11] . HIVNL4-3 was obtained following transfection of the molecular clone pNL4-3 ( NIH AIDS Research and Reference Reagent Program from M . Martin ) . HIVNL4-3-Cherry was obtained following cotransfection of pCHIV and pCHIV mCherry in a 1∶1 ratio [41] . HIVNL4-3 lacking the envelope glycoprotein was obtained as described elsewhere [9] . The p24Gag content of the viral stocks and VLP was determined by ELISA ( Perkin-Elmer ) or by a quantitative Western blot [13] . HIVNL4-3 used in infectious assays was titrated employing the TZM-bl reporter cell line as described in [42] . Large unilamellar vesicles ( LUVs ) were prepared as in [13] , and exosomes were isolated from Jurkat cells as described in [11] . LPS mDCs ( 2×105 ) were pre-incubated at 16°C for 30 min with 10 µg/ml of α-Siglec-1 mAb 7D2 ( HSn 7D2 , Abcam ) , IgG1 isotype control mAb ( 107 . 3 , BD Bioscience ) , α-Siglec-7 cell-adhesion neutralizing pAb ( R&D Systems ) , α-Siglec-5/14 cell-adhesion neutralizing mAb ( 194128; R&D Systems , which recognizes both Siglec-5 and Siglec-14 , sharing 99% of amino acid homology in the three extracellular distal domains ) or α-CD83 mAb ( HB15e; R&D Systems ) or with 500 µg/ml of mannan from Saccharomyces cerevisiae ( Sigma-Aldrich ) . Capture experiments were performed maintaining compound concentration and pulsing mDCs in parallel applying either 200 µM of the respective LUVHIV-tRed formulations or 150 ng of VLPHIV-Gag-eGFP Gag per 2×105 cells for 30 min at 37°C . ExosomeDiI capture was performed pulsing 1×105 pretreated LPS mDCs with 150–250 µg of exosomes for 4 h at 37°C . After extensive washing , positive DCs were acquired by FACS . To test for potential cross-reactivity of α-Siglec-1 mAb 7D2 , 2 . 2 µM of the mAb were pre-incubated or not with more than 100-fold molar excess of recombinant human protein Siglec-1 , and more than 200-fold molar excess of Siglec-7 , Siglec-5/14 , or CD83 ( all from R&D Systems ) 30 min at RT prior addition to the LPS mDCs . After incubation with mixes , LPS mDCs were pulsed with VLPs as indicated earlier . Fab fragments were generated from α-Siglec-1 7D2 and Isotype mAbs using the Fab Micro Preparation kit ( Pierce ) according to the manufacturer's instructions . Quality of Fab preparations was assessed with SDS-PAGE and Coomassie staining . Titration of a different α-Siglec-1 mAb was performed with functional grade clone 7–239 ( AbD Serotec ) . DCs were also assessed for VLP capture for 1 h as described above but starting 5 d after isolation ( when LPS was added to LPS mDCs ) and continuing 6 , 24 , and 48 h after LPS addition . In parallel , DCs were labeled with α-Siglec-1-PE 7–239 mAb and α-HLA-DR-PerCP ( clone L243 , BD Biosciences ) . The mean number of Siglec-1 Ab binding sites per cell was obtained with a Quantibrite kit ( Becton Dickinson ) at day 7 as previously described for DC-SIGN [9] . HIVNL4-3 capture was assessed pre-incubating 2 . 5–3×105 distinct monocyte-derived DCs or blood myeloid DCs at 16°C for 30 min with 10 µg/ml of the α-Siglec-1 mAb 7D2 , the isotype control , or 500 µg/ml of mannan . Subsequently , DCs were pulsed with HIVNL4-3 at an MOI of 0 . 1 ( 50–80 ng of p24Gag estimated by ELISA ) for 5 h at 37°C . In parallel , untreated DCs equally pulsed with HIVNL4-3 were exposed to inhibitors right after viral capture . After extensive washing , cells were lysed with 0 . 5% Triton X-100 to measure p24Gag antigen content by ELISA . HIVNL4-3 binding was performed pre-incubating LPS mDCs with the indicated mAbs , but maintaining cells at 4°C during viral pulse . Cells were lysed to detect p24Gag or stained with Siglec-1-Alexa 488 7–239 mAb ( Ab Serotec ) to confirm arrested endocytosis of Siglec-1 at 4°C as compared to cells exposed to the virus at 37°C by FACS . To assess whether Siglec-1 traffics to the same compartment as sialyllactose-containing vesicles , we adapted our previously described method [13] . Briefly , LPS mDCs were incubated with GM1-LUVHIV-tRed , VLPHIV-Gag-Cherry , HIVNL4-3-Cherry , or HIVNL4-3 for 4 h as described above . When indicated , α-HLA-DR-Alexa 647 ( Clone L243 , Biolegend ) was used to reveal LPS mDC membranes . Cells were then fixed , permeabilized , and labeled with Siglec-1-Alexa 488 7–239 mAb . HIV-1 was revealed with α-p24Gag-PE ( Clone RD1 , Coulter ) . To identify the cytoplasm of pulsed LPS mDCs , some permeabilized cells were also labeled with CellMask Deep Red ( Molecular Probes ) . To detect trafficking of Siglec-1 , LPS mDCs were pre-incubated with 10 µg/ml of the mAb 7D2 30 min at 16°C , revealed with a secondary Alexa 488 goat α-mouse IgG mAb ( Molecular Probes ) , washed , and incubated 4 h at 37°C . To determine whether Siglec-1 redistributes to the infectious synapse , LPS mDCs previously pulsed with HIVNL4-3-Cherry for 4 h , extensively washed , and co-cultured with autologous or heterologous CD4+ T cells for an additional 2 h were stained with α-CD4-Alexa 647 ( Clone OKT4 , Biolegend ) , fixed , permeabilized , and labeled with α-Siglec-1-Alexa 488 7–239 . Confocal acquisition and analysis was performed as in [13] . DCs were treated and pulsed with HIVNL4-3 as described above . After extensive washing , cells were co-cultured with the TZM-bl CD4+ target cell line to measure trans-infection . Pulsed monocyte-derived DCs or myeloid DCs were co-cultured in quadruplicate or duplicate at a ratio of 1∶1 or 5∶1 , respectively . Cells were assayed for luciferase activity 48 h later ( BrightGlo Luciferase System; Promega ) in a Fluoroskan Ascent FL luminometer ( Thermo Labsystems ) . Background values consisting of non-HIV-1-pulsed co-cultures or reporter CD4+ cells alone were subtracted for each sample . To detect possible productive infection of pulsed cells or re-infection events , some DCs were cocultured in the presence of 0 . 5 µM of the protease inhibitor Saquinavir . VSV-G-Pseudotyped SIV3 lentivector ( kindly provided by A . Cimarelli ) was produced as in [43] . Isolated monocytes ( 5×105 ) were infected with SIV3 particles and transduced with two different SIGLEC1-specific or one nontarget shRNA control MISSION Lentiviral Transduction Particles ( Sigma-Aldrich ) at an MOI = 50 . Transduced monocytes were differentiated into LPS mDCs and assessed for VLP capture and HIV-1 trans-infection as described above . Adequate phenotypic maturation of DCs was evaluated as in [9] . Lentiviral transduction particles carrying the GFP reporter gene cloned in the same pLKO . 1-puro vector backbone ( MISSION TurboGFP Control Transduction Particles ) were used to evaluate transduction efficiency by FACS ( estimated 75%–98% at day 7 , when cells were employed ) . Raji cells ( 2×106 ) were transfected with vector backbone pCMV6-Entry ( Origene ) comprising the coding region of Siglec-1 , Siglec-5 , or Siglec-7 using Amaxa nucleofector as recommended by the manufacturer . At 36 h posttransfection , cells were assessed for VLP capture and HIV-1 trans-infection ( at a ratio 2∶1 ) as described above . When indicated , cells were pre-incubated with decreasing concentrations of 3′-Sialyllactose ( Carbosynth ) or Lactose ( Sigma-Aldrich ) 30 min prior to VLP pulse . In experiments with envelope-deficient viruses , 5×105 cells were pulsed with 100 ng of p24Gag estimated by ELISA for 4 h at 37°C and assessed for capture and trans-infection ( at a ratio 2∶1 ) as aforementioned . HEK-293T cells were transfected using Fugene HD ( Promega ) and assessed 24 h posttransfection as described for Raji cells . Trans-infection of HEK-293T was tested in a different luminometer ( Luminoskan Ascent , Thermo Labsystems ) , and collected data were normalized to 100% . Transfection efficiency in both cell types was assessed staining cells with α-Siglec-1-PE 7–239 mAb , α-Siglec-7-PE 5–386 mAb ( AbD Serotec ) , and α-Siglec-5/14-PE 1A5 mAb ( Biolegend ) and evaluated by FACS . Stable Raji DC-SIGN cells were labeled with α-DC-SIGN-PE DCN46 mAb ( BD Pharmigen ) . Statistics were performed using paired t test ( considered significant at p≤0 . 01 ) or Spearman correlation with GraphPad Prism v . 5 software . | Mature dendritic cells ( mDCs ) capture and store infectious HIV-1 and subsequently infect neighboring CD4+ T cells in lymphoid organs . This process , known as trans-infection , is a key contributor to HIV pathogenesis , but the precise mechanism and the identity of the receptor on the mDC surface that recognizes viral particles remain controversial . Although the interaction of HIV-1 envelope glycoproteins with the C-type lectin DC-SIGN has been suggested to mediate HIV-1 capture and trans-infection , later studies revealed an envelope glycoprotein-independent virus capture mechanism in mDCs . Here , we identify Siglec-1 as the surface receptor on mDCs that boosts their uptake of HIV-1 and their capacity to trans-infect CD4+ cells , leading in turn to HIV-1 disease progression . Siglec-1 captures the virus by interacting with sialyllactose-containing gangliosides exposed on viral membranes . This indicates that Siglec-1 functions as a general binding molecule for any vesicle carrying sialyllactose in its membrane , including exosomes and other viruses . We suggest that this natural pathway through mDC , which would normally lead to antigen processing and presentation , has been subverted by HIV-1 for its own storage and transmission . | [
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] | 2012 | Siglec-1 Is a Novel Dendritic Cell Receptor That Mediates HIV-1 Trans-Infection Through Recognition of Viral Membrane Gangliosides |
The recruitment of RNA-Pol-II to the transcription start site ( TSS ) is an important step in gene regulation in all organisms . Core promoter elements ( CPE ) are conserved sequence motifs that guide Pol-II to the TSS by interacting with specific transcription factors ( TFs ) . However , only a minority of animal promoters contains CPEs . It is still unknown how Pol-II selects the TSS in their absence . Here we present a comparative analysis of promoters’ sequence composition and chromatin architecture in five eukaryotic model organisms , which shows the presence of common and unique DNA-encoded features used to organize chromatin . Analysis of Pol-II initiation patterns uncovers that , in the absence of certain CPEs , there is a strong correlation between the spread of initiation and the intensity of the 10 bp periodic signal in the nearest downstream nucleosome . Moreover , promoters’ primary and secondary initiation sites show a characteristic 10 bp periodicity in the absence of CPEs . We also show that DNA natural variants in the region immediately downstream the TSS are able to affect both the nucleosome-DNA affinity and Pol-II initiation pattern . These findings support the notion that , in addition to CPEs mediated selection , sequence–induced nucleosome positioning could be a common and conserved mechanism of TSS selection in animals .
An essential step in gene regulation is the recruitment of RNA-Pol-II ( Pol-II ) to the transcription start sites ( TSS ) at gene promoters [1–3] . This is often facilitated by the presence of conserved sequence motifs known as core promoter elements ( CPEs ) , which are found at a fixed or nearly fixed distance from the TSS [4 , 5] . Among them , the TATA-box , located 25–30 base-pairs ( bp ) upstream of the TSS , and the Initiator ( Inr ) , located at the TSS , are the best known and most widely conserved CPEs among species [6 , 7] . The TATA-box is bound by general transcription factors ( TFs ) that guide and anchor Pol-II to the TSS [8] . As a consequence , promoters with a TATA-box are generally characterized by a focused , almost to the single base , start site [9 , 10] . In spite of the CPE’s demonstrated capability to select a TSS with high precision , only a minority of promoters have a CPE ( in human 10% a TATA-box , 30% an Inr motif ) [11] . A central question in gene expression is how Pol-II selects the TSS in their absence [12 , 13] . It has been shown that nucleosomes in promoter regions can regulate gene expression via TF binding site occlusion [14] but their role in TSS selection by Pol-II remains unclear . Promoters have a remarkably conserved chromatin architecture consisting of a nucleosome free region that spans 100–150 bp upstream the TSS followed by a well-positioned nucleosome ( +1 nucleosome ) [15 , 16] . This general conformation can be altered by diverse factors . Contrary to intuition , so called broad promoters with dispersed initiation sites have the most focused and regular nucleosome architecture whereas narrow promoters ( also referred as peak promoters ) have less organized nucleosomes [17] and an atypical chromatin architecture [18] . In zebra fish , the chromatin architecture of the same promoter has been shown to change from one developmental stage to another [19] but there again , the conformation with the more structured nucleosome architecture shows a broader initiation site pattern . In mammals , promoters have traditionally been classified according to the presence or absence of CpG islands ( CGI ) , 500–1000 bp long regions enriched in C+G [20–22] . CGI-promoters are often TATA-box depleted [23] , have broad TSS [9] , exhibit characteristic histone marks [24] and have a precisely positioned +1 nucleosome which is present even when the promoter is not transcribed [25] . In essence , CGI-promoters resemble the broad promoters described in other species and thus may not be considered a separate class . An open question in gene regulation is whether the chromatin at promoters is organized by sequence-intrinsic features or indirectly by the transcription machinery occupying the nucleosome-free region and thereby forcing the nucleosome to bind to the nearest free space downstream the TSS . On a genome level , two types of sequence features have been reported to participate in nucleosome positioning: dinucleotide periodicity and base composition [26] . A theoretical model suggests that the same dinucleotide repeated at 10 bp intervals leads to intrinsic curvature that favors the wrapping of the DNA around the histone octamer [26 , 27] . This model theorizes that the periodic dinucleotide always occurs with the same orientation relative to the histone-octamer surface , for instance having the major groove facing outwards , and implies a rotational positioning of the nucleosome . Some authors have identified WW ( W for A or T ) and SS ( S for C or G ) dinucleotides in counter-phase as major contributors of rotational positioning [28 , 29] , others emphasized the importance of RR ( R for A or G ) and YY ( Y for C or T ) motifs [27] . DNA base composition can also affect nucleosome positioning . Highly AT-rich sequences , in particular poly ( dA:dT ) tracts , strongly disfavor nucleosome formation [30 , 31] , whereas G+C rich sequence tend to have high nucleosome occupancy [32 , 33] . Unlike dinucleotide periodicity , sequence composition can position nucleosomes in a narrow DNA region without specific preference for rotational setting , a condition termed translational positioning . As said before , the role of sequence-intrinsic features in chromatin organization around promoters remains a matter of debate [34] . Zhang and colleagues concluded that its positioning is the result of Pol-II binding to the DNA [35] . Recent studies done in yeast have shown that chromatin remodelers play an important role in organizing chromatin both at a genome [36] and promoter level [37] and that they act synergistically with DNA sequences [38] . Others have reported the presence of nucleosome-favoring and disfavoring sequences in yeast promoters [27 , 39–41] , with a high correlation between in-vitro and in-vivo nucleosome organization in these regions [42 , 43] . Recently , a 10 bp periodic signal has been observed in cumulative WW frequency plots of promoters sequences aligned with respect to the major TSS as defined by CAGE [44] . A similar WW periodicity can be seen in WW heat map plots published in [19] . The phasing of WW periodicity with the TSS is the first indication that the rotational setting of the DNA in the +1 nucleosome is guiding the TSS selection process . In this paper , we investigate the molecular mechanisms of TSS selection by jointly analyzing experimentally determined chromatin architectures , DNA-encoded nucleosome signals , Pol-II initiation site patterns and natural genetic variation in promoters stratified by the presence or absence of specific CPEs and/or the breadth of the initiation patterns . The analysis on five model organisms ( Homo sapiens , Mus musculus , Danio rerio , Drosophila melanogaster and Caenorhabditis elegans ) confirms that different species have an overall similar chromatin organization with nevertheless some noteworthy species-specific differences . All five organisms have sequence-intrinsic nucleosome-positioning signals that are predictive of in-vivo nucleosome organization , but only in promoters that lack TATA-boxes . Additionally , we show that broad promoters associated with strong sequence-encoded nucleosome +1 have 10 bp periodic initiation patterns . By analyzing the effects of genetic variants on promoter initiation site patterns and dinucleotide periodicity , we provide genetic evidence that rotational nucleosome positioning is mechanistically involved in TSS selection .
To verify that DNA sequences around animal promoters had rotational nucleosome-positioning properties and that the 10–11 bp was the prevailing frequency , 1 kb regions on each side of H . sapiens , M . musculus , D . rerio , D . melanogaster and C . elegans TSSs were scanned for the presence of periodic signals of any length for each individual WW , SS , YY , or RR dinucleotide ( S1 Fig ) . Confirming our expectations , all organisms showed a peak in signal intensity for periods of 10–11 bp ( S2 Fig ) that are typical of nucleosomal DNA with a minimum in correspondence of the nucleosome free region and a maximum in the N+1 region ( S3 Fig ) . To further study the rotational properties of single promoters sequences and their effect on chromatin conformation , the strength of 10 . 3 bp periodic signals for each dinucleotide was evaluated in each promoter and compared to their in-vivo nucleosome maps . As expected , the WW dinucleotide ( or SS for D . melanogaster ) showed the highest correlation with in-vivo nucleosome signals ( Fig 1A and S4 Fig ) . In H . sapiens , about one third of promoters ( top promoters of Fig 1A ) had low WW periodicity upstream the TSS and a peak in periodicity immediately downstream . This was reflected in the chromatin organisation with a clear nucleosome free region ( NFR ) and a focused N+1 . As expected , this group of promoters was also depleted of TATA-box and enriched in CpG islands . Approximately 25% of promoters showed an opposite signal , with a peak upstream the TSS and a valley downstream ( promoters at the bottom of Fig 1A ) . They were characterized with a less pronounced NFR , a broader N+1 , an enrichment in TATA-box and depletion in CpG islands , in agreement with earlier studies . CpG-enriched promoters were previously reported to have an open chromatin conformation and to be enriched in active histone marks . On the other end , CpG-depleted promoters were reported to have a close chromatin conformation and low levels of histone modifications [45 , 46] . Fig 1A shows that a large fraction of human promoters had a WW signal that , although depleted in the NFR , did not show a clear enrichment in the N+1 region . These promoters might have had other dinucleotide signals that peaked in this region allowing for a correct nucleosome positioning . To test this hypothesis , we identified promoters with periodic signal intensity ( for each dinucleotide ) in the proximal promoter region that could favour the average in-vivo nucleosome distribution . To do so , we compared the average 10 bp periodic signal in the NFR with that of the N+1 region and identified promoters with a higher signal downstream of the TSS ( named hereafter as concordant signal ) . The organisms had heterogeneous number of promoters with concordant signals ( Fig 1B ) . H . sapiens and M . musculus promoters were characterised for having the YY and RR dinucleotides as the most common and , at the same time , the WW signal was less frequent . This could have been the consequence of the presence of CpG islands that , with their high GC content , could affect the dinucleotide frequencies and the possibility to generate a periodic signal . WW signal was more frequent in all other organisms but only in D . rerio it was the most frequent . In fact , D . melanogaster showed that more then 40% of promoters had a concordant SS signal , whereas C . elegans promoters were enriched in YY signal but strongly depleted of SS signal . Nonetheless , in all organisms 80% of promoters had at least 1 concordant signal ( Fig 1C ) and 20% 3 or more . The presence of multiple concordant signals in the proximal promoter region was clearly reflected in chromatin organisation ( Fig 1D ) with more focused nucleosomes even outside the proximal-promoter area used in this analysis . Our analyses showed that more then one dinucleotide periodic signal was often present in the N+1 region of a promoter ( Fig 1 ) . However , it was not clear how the dinucleotides were positioned compared to each other within the same sequence . The mutually exclusive WW and SS are expected to be found in counter-phases [28] as YY and RR [27] . Trifonov [47] concluded that the general DNA consensus sequence for genomic nucleosomes could be summarized with the following 2 motifs , SSRRNWWNYY or SSYYNWWNRR ( note the relative position of the YY and RR in the two motifs ) , but little is known about the relative position of the 4 dinucleotides in the N+1 region . We addressed this using aggregate plots as in [44] where patterns of WW frequency were revealed in the N+1 region of H . sapiens promoters that were remarkably similar to the dinucleotide periodicities seen in MNase-seq data [28] . Using this observation , we evaluated and compared the periodic frequencies of DNA consensus sequences of the N+1 and genomic nucleosomes . To do so , promoters of the 5 organisms under study were aligned to the TSS and , using aggregated plots , the strength of a 10 bp periodic signal was evaluated in the N+1 region of all possible motifs of length 10 bp generated permuting the 4 dinucleotides and two N bases ( 240 motifs ) . A similar analysis was performed on genomic nucleosomes defined by high-resolution MNase data and aligned to the inferred center position . In H . sapiens ( Fig 2A ) there was a very high correlation between the 10 bp frequency strengths measured in DNA sequences coming from genomic nucleosomes and signal from the DNA sequences of the N+1 region with a clear separation between motifs with high signal and all the rest . Confirming the expectations from [47] , motifs with strong periodicity were all characterized for having the WW dinucleotide in counter phase to the SS as well as the YY and RR and to share the same dinucleotide order: the SS dinucleotide was always followed by YY , then by WW and RR . The average intensities of this motif class around H . sapiens promoters showed a pattern that closely resembled in-vivo nucleosome maps ( S5 Fig ) with signal depletion in correspondence of the NFR and a peak at the N+1 . Moreover , the class of motifs belonging to the first motif in Trifonov model ( SS-RR-WW-YY ) [47] , showed very week signal in both regions . These findings indicated that in H . sapiens , the DNA wrapped around the histones in the N+1 region had almost identical dinucleotide periodicity patterns of the DNA found in genomic nucleosomes . M . musculus , D . melanogaster and D . rerio showed a preference for motifs belonging to the same class as H . sapiens ( Fig 2B and S6 Fig ) with a strong correlation between signals coming from genomic and promoter nucleosomes ( S5 Fig ) . C . elegans was the only organism analyzed that shows a clear difference between the DNA code used on genomic nucleosome and the one used at promoters . On a genome level C . elegans showed no difference with the other organisms ( Fig 2B left panel ) with a clear preference for the motifs class SS-YY-WW-RR . C . elegans promoters , instead , showed strong signals also for the class SS-RR-WW-YY ( Fig 2B and 2C , S5 and S7 Figs ) . Analysis of the average distribution of the two motif classes around C . elegans promoters showed signal for both ( S8 Fig ) , suggesting the presence of two promoter groups characterized by the presence of one motif and not the other ( S8 Fig ) . To identify them , promoters were grouped on the bases of the signal intensity for one consensus as twice as strong compared to the other . 1344 promoters had strong signal from the SS-RR-WW-YY class and 806 from the SS-YY-WW-RR . These two promoter groups did not have very different chromatin architectures with the SS-YY-WW-RR class showing only a slightly more focused N+1 and more pronounced NFR ( S8 Fig ) but not a difference in H3K4me3 distribution ( S8 Fig ) . The finding that promoters with a broad initiation pattern have phased dinucleotide periodicities in the N+1 region compared to focused promoters [44] that , on the other end , are enriched in TATA-box motifs [9 , 17] suggests that TATA-box and chromatin conformation could have different effects on transcription initiation [12 , 13] . The TATA-box can direct Pol-II to the TSS with high precision [1] whereas in its absence , chromatin organization could guide the Pol-II complex but less precisely . To analyze the quantitative effect of rotational properties of DNA on Pol-II positioning , the correlation between the strength of the dinucleotide signals in the N+1 region and the spread of Pol-II initiation were studied in grater detail . To do so , promoters were first grouped according to their TATA-box state ( with and without the motif ) and , for the TATA-less promoters , according to their average dispersion of Pol-II initiation around the TSS ( from very focused to very broad promoters ) evaluated using CAGE data and summarized with a Dispersion Index ( DI , it could be considered as the standard deviation around the most likely initiation site ) . Then , for each group , the average strength of the four dinucleotide signals in the N+1 region was evaluated . In all organisms tested there was a strong inverse correlation between promoters DI and the average dinucleotide strength ( for example for H . sapiens: R2 = 0 . 76 and p-value = 0 . 0002 ) ( Fig 3A and S9 Fig ) . Focused promoters without a TATA-box were characterized for the presence of a strong periodic signal , whereas broad promoters showed a weak periodicity . TATA-box promoters were outliers: they showed low DI values and weak periodic signals . In D . melanogaster another large group of promoters ( 5628 promoters , 1/3 of the total ) was characterized for having focused initiation and weak periodicity . All these promoters had a DPE [48] and an Inr element , both of which are found at conserved distance from the TSS . Moreover in all organisms , only promoters without TATA-box ( or Inr-DPE ) had the signal in phase with the TSS suggesting that there was a fixed distance between the TSS and the N+1 ( S10 Fig ) . To test if the periodic signal in the N+1 affects also the level of activity of Pol-II , the average expression of promoters was correlated with the average dinucleotide strength in the N+1 region . In this case , no correlation between the two was found ( R2 = 0 . 18 , p-value = 0 . 21 ) ( S11 Fig ) . To further elucidate the relationship between periodic DNA signals and Pol-II , we studied the primary and secondary transcription initiation patterns in promoters . In fact , rotational nucleosome positioning due to a 10 bp periodic signal does not require the occurrence of the nucleosome center at exactly the same base: it tolerates shifting by multiples of 10 bp [26 , 27] . To validate our model that the rotational setting of the +1 nucleosome influences TSS selection by Pol-II , CAGE tags were used to analyze the distribution of transcription starts at promoters . In order to detect these secondary Pol-II initiation sites , a “micro-peak” method was applied to the data that consisted in extracting positions that corresponded to a local maximum in CAGE tag coverage within a window of 5 bp . This method emphasized the stronger initiation sites compared to a simple cut-off value and also reduced the background noise given by spurious signals ( S12 Fig ) . Subsequently , the average distributions of secondary TSS around promoters grouped by their TATA and DI statuses were evaluated . In H . sapiens , each promoter subclass showed a similar level of primary TSS activity with comparable frequencies of micro-peaks at the TSS ( Fig 3B ) . Away from the primary TSS , two opposite Pol-II behaviors were detected . The first had a strong 10 bp periodic pattern in secondary initiation sites distribution around promoters and corresponded to TATA-less promoters regardless of their DI values with both focused and broad promoters showing strong secondary initiation patterns . The second had no clear periodic signal near the central peak and corresponded to TATA-box promoters . This subclass had also poor affinity values ( Fig 3A ) with the absence of a phase signal downstream the TSS ( S10 Fig ) . The other organisms showed similar patterns of Pol-II initiation ( S13 Fig ) with TATA-box containing promoters the only group that did not show any periodicity in secondary initiation . In D . melanogaster , Inr-DPE promoters had a micro-peak distribution similar to TATA-box containing promoters . The 10-bp periodic distribution of secondary initiation sites could be due to local curving of the DNA at the major initiation site or one-sided protection by components of the pre-initiation complex . To rule out this possibility and to establish a direct link between TSS phasing and the +1 nucleosome signal , we selected promoters with the strongest pattern in secondary initiation sites and studied their DNA properties in the N+1 region . Results showed that promoters with a strong periodic TSS initiation pattern ( Fig 3C ) also showed high phasing with the +1 nucleosome periodic signal ( Fig 3D ) , further suggesting the presence of a direct relation between the two . The strong correlation observed between DNA-encoded nucleosome positioning signals near the TSS and transcription initiation patterns ( Fig 3 ) was an indication that the DNA sequence of promoters had a crucial role in guiding Pol-II to the initiation site via a possible N+1 interaction . To gain further evidence that there was a causative link between DNA sequence and Pol-II initiation and to identify the region that had the greatest influence , we studied the effect of natural variation on promoters’ DI . To do so , we used CAGE data from the ENCODE tier 1 cell line GM12878 ( a lymphoblastoid cell line ) for which the genome had been sequenced by the 1000Genome consortium [49] . Using data from this cell line , it was possible to study the effect of natural variation , such as SNPs and Indels ( deletion or insertion of few bases ) , on Pol-II initiation expressed as variation in DI . To address this we compiled promoters’ variants for which the GM12878 was homozygous for the minor allele . In total there were 15548 SNPs mapping near promoters ( 2kb window around TSS ) and 1849 indels . The two distributions were similar ( S14 Fig ) , both showed low frequencies near the TSS , but were not exactly the same . SNPs minimum was centered slightly upstream the TSS whereas indels minimum downstream , in a region that coincided with the N+1 . GM12878 CAGE tags were then used to evaluate DI values for all promoters . As a reference , we used CAGE data from blood-derived cells from a different origin that should not contain the same mutations [44] and assigned them to a reference genome containing always the major allele ( most likely genome ) . To identify the promoter region that had the greatest impact on TSS dispersion , we first selected promoters that had natural variants in the GM12878 cell line and grouped them according to the distance of the variants from the TSS ( in windows of 150 bp and 10 bp shift ) . Then the average variation in DI between the two cell lines was evaluated for each group of promoters and plotted as a function of the distance of the window from the TSS ( Fig 4A ) . It was possible to evaluate the impact on initiation patterns made by natural variants at any given distance from the TSS . Both SNPs and indels had a measurable effect on TSS dispersion if located in the proximal promoter region . Overall , SNPs had a weaker effect on TSS dispersion , with a maximum for SNPs mapping 120 bp downstream the TSS , in the central region of the N+1 ( Fig 4A ) . Conversely , Indels had a much stronger impact in a region that extended from the TSS until the end of the N+1 and peaked within the first half of the N+1 . Interestingly , SNPs and indels mapping in the NFR did not coincide with a strong variation in DI . We then investigated the relationship between alterations of the nucleosomes-DNA affinity ( measured as variation in dinucleotide 10 bp frequency ) produced by natural variants and their effects on Pol-II initiation . To assess this , we scanned the promoter region with a sliding window of 150 bp ( 10 bp shift ) and investigated the linear relationship between the variation in 10 bp frequency for the WW dinucleotide ( produced by GM12878 natural variants that mapped in that region ) and the variation in the observed DI for the corresponding promoters . The N+1 region was the only one showing a negative correlation between the variation measured in the nucleosome-DNA affinity and the variation in promoters’ DI , with a minimum centered at base +110 ( p-value = 0 . 022 , Pearson’s r = -0 . 184 ) ( Fig 4B ) . On a single promoter level , natural variants that mapped in this region with disruptive effect on the nucleosome binding corresponded to promoters with increased DI compared to WT ( Fig 4C ) . On the other end , natural variants that increased the nucleosome affinity had an effect on lowering the DI .
Two pathways for TSS selection by POL-II have been described in the literature . According to the conventional model the TSS position is defined by the presence of CPE [5] . However , the majority of eukaryotic promoters lack CPEs , including a TATA-box and an Inr [11] . Jiang and Pugh proposed that TSS selection in yeast might be linked to the position of the N+1 in the absence of CPEs [12] . Here , through a comparative analysis of DNA-encoded nucleosome signals in animal promoters and Pol-II initiation patterns , we report that the DNA signals underlying both mechanisms are conserved across species and , through the study of DNA natural variants , we show that the level of affinity between N+1 and DNA affects TSS selection in the absence of CPEs . The function of sequence-intrinsic features in chromatin organization around promoters is still a matter of discussion [34] . Although studies done in yeast have shown an important role of chromatin remodeler in organizing chromatin at a genome [36] and promoter level [37] , a growing body of evidence favors the functional role of sequence-intrinsic features at promoters [27 , 39–41] . Moreover , in a recent study Drillon et colleagues have shown that around 1/3 of nucleosomes in the human genome are positioned based on DNA sequence determinants [50] . Here , through comparative analysis of promoters DNA sequence composition , we show that in 5 model organisms ( H . sapiens , M . musculus , D . rerio , D . melanogaster and C . elegans ) the position of nucleosomes at the majority of promoters is at least partly determined by DNA encoded signals , with some remarkably species-specific differences . Promoters of all organisms show a 10 bp periodic signal for the four dinucleotides tested ( WW , SS , YY and RR ) . H . sapiens is the only organism showing also a strong signal for YY and RR dinucleotides for a periodicity of 8 bp , that is probably the consequence of the presence of specific CT rich microsatellite sequences in human promoters [51] ( S1 Fig ) . As expected , the dinucleotide that shows the highest correlation with in-vivo nucleosome maps is WW ( Fig 1A ) . Regardless of this , multiple periodic signals reinforce each other in organizing chromatin around promoters ( Fig 1D ) , suggesting an additive effect of the affinity of the four dinucleotides to histones . When we study the spatial relationships between the four dinucleotides within a promoter sequence we find the same consensus as in genomic nucleosomes ( SS-YY-WW-RR ) for all organisms tested with the exception of C . elegans . Interestingly , on a genome level the DNA that is wrapped around C . elegans nucleosomes has the same consensus sequence as all other organisms but at promoter level we find that there are two distinct group of promoters characterized for having the SS-YY-WW-RR or SS-RR-WW-YY consensus . This finding is intriguing since the difference in the two sequences is not purely semantic , but has been predicted to alter the affinities to histones [47] . Although SS-RR-WW-YY has been predicted to have the higher affinity to nucleosomes allowing for perfect bendability of the DNA around the histone octamer [52] , our analysis show that C . elegans promoters with this sequence in the N+1 region do not have any difference in chromatin conformation compared to promoters with the other consensus . The reason for this unexpected observation is unknown and need further investigation . The identification of promoters by the transcription machinery is a process that is guided by the general transcription factor TFIID [53] , a multi-subunit protein that is not only able to interact with the TATA-box or the DPE element [5] but also with chromatin [54–56] via the TAF3 subunit , suggesting the presence of a motif-independent TFIID recruitment at promoters that rely on the N+1 [57] . In agreement with this hypothesis , TATA-box mutation studies have shown a direct effect on Pol-II initiation both in term of TSS position and level of promoter activity [19 , 58] . On the other end , no study , to our knowledge , has investigated the effect that nucleosome-DNA affinity in the N+1 region has on TSS selection . Correlation analysis shows that in all organisms promoters without CPEs have the predicted level of nucleosome-DNA affinity anti-correlated with TSS initiation patterns ( Fig 3A and S9 Fig ) . Broad promoters generally have lower DNA-encoded nucleosome affinity . Conversely , narrow promoters , often presented as a homogeneous class in the literature , vary greatly in this respect , with only the CPE-less subset ( TATA-less and Inr-DPE-less in D . melanogaster ) showing strong affinity in the N+1 region . Moreover , the 10 bp periodicity seen in Pol-II initiation in all promoters , focused and broad , that lack CPEs ( Fig 3B and S13 Fig ) is another indication of a direct interaction between Pol-II and the N+1 in the absence of other DNA signals . In fact , a model of Pol-II initiation that relies on the interaction with the N+1 , which in turn is rotationally positioned and able to tolerate shifting by multiples of 10 bp [26 , 27] , would allow Pol-II to start transcription at 10 bp intervals . Furthermore , the study of DNA natural variants in H . sapiens have shown that the region with grater influence on TSS selection is the N+1 ( Fig 4A ) and that there is a negative correlation between variation in nucleosome affinity and Pol-II initiation ( Fig 4B and 4C ) . That is , the presence of a variant in the N+1 region that decreases the nucleosome-DNA affinity results in an increase in TSS dispersion and vice-versa . These results strongly support the model of a motif-independent TFIID recruitment mediated by N+1—TAF2 interaction [57] . We can speculate that , in the absence of the TATA-box or Inr-DPE , the relative stability of the histones-DNA complex in the N+1 region could be transferred to the PIC via interaction with TFIID leading to a more or less focused transcription initiation by Pol-II . An alternative mechanism of PIC recruitment at promoters in the absence of CPE has been proposed by recent work by Afek and Lukatsky done in yeast in which they used a non-consensus based free-energy function to predict PIC affinity instead of nucleosome affinity [59] . Interestingly , they found that the free-energy distribution around promoters ( Fig 1 and Fig 2 in [59] ) is very similar to our nucleotide periodicity profile we see in human ( S5A Fig ) with a minimum located in the nucleosome-free region upstream of the TSS followed by spikes in free-energy in correspondence of the nucleosome occupied regions . On the other end , in all organisms studied , CPEs containing promoters are outliers compared to non-CPE promoters: they are focused but have weak nucleosome affinity and do not show any TSS periodicity . In this class of promoters the initiation site appears to be specified solely by the presence of the CPE [8 , 10] .
The promoter sets and the corresponding dominant TSS positions were taken from EPDnew [11]: version 2 for H . sapiens and D . melanogaster , version 1 for all other species . Pol-II initiation patterns were based on CAGE or GRO-Cap data from the following sources: H . sapiens: ENCODE data , GEO ID GSE34448 [60] , FANTOM5 [44]; M . musculus: FANTOM5 [44]; D . rerio SRA ID SRA055273 [61]; D . melanogaster SRA ID SRP001602; C . elegans GRO-cap data GSE43087 [62] . Nucleosome maps are from paired-end MNase-seq data or alternatively from single-end MNase–seq data . H . sapiens: paired-end MNase-seq data for the lymoblastoid cell line GM18507 , SRA ID SRP012024 , GEO ID GSM907783 [28] , M . musculus: single-end MNase data from HAFTL cell line , GEO-ID GSM1293995 [63]; D . rerio: single-end MNase-seq data from embryos in dome stage , GEO ID GSM1081554 [64]; D . melanogaster: paired-end MNase-seq data , GEO ID GSM1293957 [65]; C . elegans: paired-end MNase data from adults , SRA ID SRP000191 [66] . Promoter lists were stratified based on the presence or absence of core promoter elements using the TATA-box and Inr position weight matrices ( PWMs ) from [6] . Promoter sequences were scanned with these PWMs using the cut-off values suggested in the original paper . Promoters were classified as TATA+ if a TATA-box was present at position -29±3 relative to the TSS , while as Inr+ if this motif occurred exactly at the TSS . The D . melanogaster Inr-DPE matrix is posted at http://epd . vital-it . ch/promoter_elements/init-dpe . php , including the recommended cut-off values . CGI coordinates for human and mouse were downloaded from the UCSC genome browser [67] . Promoters with a CGI that spans the TSS ( starting before and ending after the TSS ) were attributed to the CGI+ class . Promoter sequences from position -1074 to position 1075 relative to the TSS were extracted from the corresponding genome assembly ( H . sapiens: hg19; M . musculus: mm9; D . rerio: danRer7; D . melanogaster: dm3; C . elegans: ce6 ) and scanned for the presence of four dinucleotide types ( identified by IUPAC codes ) : WW ( W = A or T ) , SS ( S = C or G ) , RR ( R = A or G ) and YY ( Y = C or T ) . The resulting binary sequences were individually scanned in a sliding window of 150 bp , shifted by 10 bp at a time . A Fourier transform was applied to each window in order to extract the power spectrum . From the resulting spectrum , the value corresponding to a frequency of 0 . 097 ( corresponding to a period of 10 . 3 bp ) was extracted . This value was directly used as a periodicity score . For paired-end samples , nucleosome positions were restricted to paired-reads that formed fragments of exactly 147 bp as previously reported in [28] . In a similar way , to reproduce analogous results on single-end samples , reads were selected if they had another read mapped on the opposite strand 147 bp downstream . For both single- and paired-end samples , multiple fragments that mapped to the same location were considered only once . For both paired- and single-end samples , the midpoints of the fragments were used as the inferred nucleosome position . Consensus motifs were generated by permuting the 4 dinucleotide ( WW , SS , YY , RR ) and two Ns . Sequences starting with an N were discarded resulting in a total of 240 sequences . These consensus motifs were then mapped to promoters and MNase-seq enriched regions . For the analysis of nucleosome +1 , the region from position -99 to 300 relative to the TSS of the corresponding genome assembly was used for mapping each consensus motif allowing a maximum of 3 mis-matches . Then , the average occurrence frequency for each motif was evaluated from base +50 to +200 relative to the TSS and a Fourier transform was applied in order to identify the intensity of the frequency of 0 . 097 ( corresponding to a period of 10 . 3 bp ) . This value was then stored as the motifs’ score for the nucleosome +1 and the procedure was repeated for all consensus motifs . For the genomic nucleosomes a similar analysis was performed . In order to speed-up the analysis , 80 . 000 inferred positions were randomly selected from each sample . Subsequently , each consensus motif was mapped around the inferred nucleosome position and the average occurrence frequency was calculated from position -75 to +75 relative to it . A Fourier transform was then applied as before and the value for a period of 10 . 3 bp was used as the motif score in genomic nucleosomes . CAGE data from different samples belonging to the same species were first merged into one file . TSS profiles were then extracted for promoter regions extending from -103 to +104 relative to the dominant TSS using the ChIP-Extract tool from the ChIP-Seq web server [68] . The resulting integer arrays were then converted into binary “micro-peak” arrays . Briefly , a micro-peak corresponds to a 5bp window with a minimal number of 100 tags . The position of the micro-peak is then assigned to the position with the highest number of tags within the corresponding window . Each micro-peak was then given a maximum value of 1 tag . The cumulative frequency of micro-peaks was then determined at single-base resolution within a 200bp region around the TSS . To identify promoters with a strong 10 bp periodicity in micro-peaks signals , promoters were ranked according to the covariance between their micro-peaks distribution and a cosine function of period 10 bp . Promoters with weak micro-peak signal ( with low covariance values ) were selected for having a cumulative covariance equal to 0 . Nucleosome distributions for promoter subsets were computed from nucleosome mapping data using the ChIP-Cor program from the ChIP-Seq web server [68] . MNase- or ChIP-seq tags were centered by 70 bp to account for the estimated fragment size of about 140 bp ( centering parameter of the ChIP-Seq server ) . Multiple tags mapping to the same genomic location were removed from the analysis ( parameter “Count cut-off” set to 1 ) and tag frequencies were calculated in a 10 bp sliding window . The spread of CAGE tags in a window of 100 bp around the TSS was expressed as a Dispersion Index ( DI ) using the following formula: DIK=∑i=1N ( xi−x¯ ) 2N Where N is the total number of tag starts in the window around promoter k , and xi is the mapped position of the 5’ end of tag i . For each species , DI values were calculated for each promoter using CAGE data from individual samples . A DI was calculated only if more then 5 tags mapped in the selected region . The sample–specific DI were then averaged to obtain a final unique and robust DI value for each promoter . VCF files of Indels ( version 2010_07 ) and SNPs ( version 2010_03 ) for the GM12878 cell line were downloaded from the 1000Genomes ftp web server . All homozygous variants were extracted from these files and used to generate a GM12878 genome . On the other end the frequencies of these variants were evaluated using the allele frequency calculated by the final version of the 1000Genome project ( phase 3 , 20130502 ) . For each variant , the most frequent allele was stored and used to generate the Most Likely genome that was then used as reference . The final list of SNPs and Indels for GM12878 cell line was restricted to the variants that differ compared to the ML genome . | Gene transcription is a complex process that starts with the recruitment and positioning of Pol-II enzyme at the transcription start site ( TSS ) . Specific promoter sequences , known as core promoter elements ( CPEs ) facilitate this process . Surprisingly , only a fraction of promoters contain them . It is still unknown how Pol-II choses the start site in their absence . A recently proposed alternative mechanism implicates positioned nucleosomes in the TSS selection . Here , we provide new evidence of the existence of such mechanism with a comparative analysis of promoter’s features across the animal kingdom . We analysed the promoter’s DNA sequence composition in 5 organisms and found conserved and unique consensus sequences used to organize chromatin in the region of the first nucleosome downstream the TSS ( N+1 ) . Moreover , we found that all organisms show a strong correlation between the spread of Pol-II initiation and the strength of the DNA-encoded signal in the N+1 region . A detailed analysis of Pol-II initiation sites reveals also the presence of a 10 bp periodicity that is correlated with the intensity of the DNA signal in the N+1 region . Importantly , we report that genetic variants that alter the DNA-nucleosome affinity in that region alter Pol-II initiation spread as well . | [
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] | 2016 | Influence of Rotational Nucleosome Positioning on Transcription Start Site Selection in Animal Promoters |
Nearly all mechanochemical models of the cross-bridge treat myosin as a simple linear spring arranged parallel to the contractile filaments . These single-spring models cannot account for the radial force that muscle generates ( orthogonal to the long axis of the myofilaments ) or the effects of changes in filament lattice spacing . We describe a more complex myosin cross-bridge model that uses multiple springs to replicate myosin's force-generating power stroke and account for the effects of lattice spacing and radial force . The four springs which comprise this model ( the 4sXB ) correspond to the mechanically relevant portions of myosin's structure . As occurs in vivo , the 4sXB's state-transition kinetics and force-production dynamics vary with lattice spacing . Additionally , we describe a simpler two-spring cross-bridge ( 2sXB ) model which produces results similar to those of the 4sXB model . Unlike the 4sXB model , the 2sXB model requires no iterative techniques , making it more computationally efficient . The rate at which both multi-spring cross-bridges bind and generate force decreases as lattice spacing grows . The axial force generated by each cross-bridge as it undergoes a power stroke increases as lattice spacing grows . The radial force that a cross-bridge produces as it undergoes a power stroke varies from expansive to compressive as lattice spacing increases . Importantly , these results mirror those for intact , contracting muscle force production .
Radial forces are the same order of magnitude as axial forces in contracting muscles [1]–[3] . These forces , along with axial force acting in the direction of muscle contraction , depend on myofilament lattice spacing [4] , [5] . At the same time , structural information about myosin cross-bridges suggests that they generate force by applying torque to a lever arm [6]–[8] . This lever arm generates the strain accompanying the power stroke via a change in the rest angle at which the lever is attached to S1 region [8] , [9] . This change in angle occurs at the converter region , a flexible area in myosin S1 which acts as a torsional spring . These phenomena may be related: the radial forces a cross-bridge creates are results of the lever arm geometry ( as suggested by Schoenberg [10] ) . Existing theoretical and computational models of cross-bridge force generation at the level of the half-sarcomere assume that force is generated by a simple extensional linear spring oriented parallel to the long axis of the myofilaments ( Figure 1A ) . This assumption has persisted from the earliest fundamental models of muscle contraction to more elaborate and spatially explicit models [11]–[15] . These single-spring models yielded insight into the processes that regulate production of force in the direction of contraction , parallel to the long axis of the myofilaments . However , these prior models of muscle contraction have paid less attention to radial forces and the effects of changes in filament lattice spacing . As a result , geometries of the single spring cross-bridge models have changed little while kinetic schemes governing transitions between conformational states have increased in complexity [11] , [12] , [16] , [17] . To analyze the radial forces that occur during muscle contraction , a different cross-bridge geometry is needed: a geometry that produces both forces aligned with and forces orthogonal to the long axis of the myofilaments . A lever arm of several springs can: ( 1 ) simulate the deformations a cross-bridge undergoes as it generates force through the power stroke , ( 2 ) provide a geometry which is practical for use in cross-bridge models , and ( 3 ) account for both axial and radial forces [9] . Here we detail two models of cross-bridges that use multiple springs to replicate the lever arm mechanism and capture its biologically relevant effects ( Figure 1B–C ) . Both models are affected by changes in lattice spacing as well as axial offset from binding sites along the thin filament , and both account for the radial component of force produced during the power stroke . The first model ( referred to as the 4sXB model ) simulates the cross-bridge as a system of four linearly elastic springs arranged in a geometry based upon the structure of the S1 and S2 regions of myosin II ( Figure 1C ) . Our second model ( referred to as the 2sXB model ) consists of two linearly elastic springs and provides greater computational efficiency than the 4sXB model while replicating many of the more complex model's behaviors ( Figure 1B ) . A prior two spring cross-bridge model was proposed by Schoenberg ( 1980 ) , with the S2 arm represented as an extensional spring and the S2-S1 junction as a torsional spring [10] , [18] . Both the 4sXB model and the 2sXB model use a three-state model of cross-bridge cycling kinetics , consisting of an unbound state , a low-force pre-power stroke state , and a force-producing post-power stroke state . The kinetics of transition from one state to another in our models are similar to those used previously but are generalized for use in two dimensions; our kinetics calculate transition probabilities using the free energy landscape of the cross-bridges instead of the offset of the cross-bridge head ( Figure 1D and Figure S1 ) [12] , [14] , [16] , [19] . We compare the 4sXB and 2sXB models to a single spring model of the cross-bridge ( referred to as the 1sXB model ) , similar to those used previously . We quantify both the axial and the radial forces of our two cross-bridge models . Additionally , we show how changes in lattice spacing and axial offset affect kinetics and forces in our multiple-spring models .
At rest lattice spacing , the free energies and kinetics of the of the single- and multi-spring cross-bridge models are largely similar , as seen in Figure 2 ( where the 1sXB values used are calculated as in Figure 10 of Tanner et al . ( 2007 ) [14] ) . These properties share a common base that is intentionally conserved , where possible , between the multiple-spring and single-spring cross-bridges [16] . The free energies of the multi-spring cross-bridges are a result of both extensional springs that are at an angle to the thick filament and torsional springs sensitive to the angle they make with the thick filament . As the multi-spring cross-bridges move in the axial direction , their angles to the thick filament backbone change . This angle dependence skews the free energies of the multi-spring cross-bridges from the symmetric hyperbola of the 1sXB ( Figure 2A ) . The two-dimensional diffusion-based binding probability function that governs the multi-spring cross-bridges ( as described in the binding rate calculation section ) causes the likely binding areas to occupy a greater range of axial positions than those of the single-spring cross-bridge ( Figure 2B ) [20] , [21] . Multi-spring cross-bridges are thus less likely than the 1sXB model to bind near their rest position , but are more likely to bind than the 1sXB at greater offsets from their rest position . This flattening and spreading of the binding probability function is a result of the extra degrees of freedom of motion in the two-dimensional models . The power stroke rate constants of the multi-spring cross-bridges are the same as those of the single-spring cross-bridge , with energy-dependent terms using the sum of the free energy of every spring comprising a cross-bridge ( Figure 2C ) . The detachment rate constant of the 1sXB explicitly relies on cross-bridge head position as well as energy . This position dependence was removed in adapting the 1sXB model's detachment rate constant for the multi-spring cross-bridges . The detachment rate constant thus loses the intentional asymmetry that the position term provided and retains only the asymmetry created by the spring geometries of the 2sXB and 4sXB models ( Figure 2D ) . The rate of detachment and the other cross-bridge kinetic rate constants remain close to those of the 1sXB , even though the kinetics of the multi-spring cross-bridges are based not on axial position but on the free energy of the cross-bridge in multiple dimensions . The axial offset of a cross-bridge property is the axial distance from the point where the cross-bridge attaches to the thick filament to the point where the cross-bridge property reaches an extreme value or inflection point . These axial offsets are depicted in Figure 3 and Figure S2 where , for example , the axial offset of the 2sXB attachment rate constant at 34 nm is approximately 12 nm . As lattice spacing increases , the axial offsets of most multi-spring cross-bridge kinetic rates and free energies grows smaller . This relationship is shown in Figures 3A and B and Figure S2 A and B , where the axial offset of the 4sXB or 2sXB model's lowest energy point is more than 3 nm greater at a lattice spacing of 32 nm than at a lattice spacing of 38 nm . The positions where cross-bridges are most likely to bind shift to smaller axial offsets at larger lattice spacings , decreasing how extended a cross-bridge is likely to be upon binding ( Figures 3C–D and Figure S2C–D ) . Similarly , as lattice spacing increases , decreases in the axial offset of the power stroke rate constant inflection point cause the size of the power stroke to change with lattice spacing ( Figures 3E–F and Figure S2E–F ) . The 4sXB model's rate of detachment is the only cross-bridge property whose axial offset is predominately invariant with changes in lattice spacing ( Figure 3G and Figure S2G ) . This exception is explained by the largely radially aligned post-power stroke orientation of , the 4sXB model's final spring . Combined , these effects reduce the axial force a cross-bridge generates at larger lattice spacings with implications for the sarcomere length dependence of force production and relaxation . These multi-spring cross-bridge models are the first to be capable of reproducing these lattice spacing dependent effects on force production and kinetics . The number of cross-bridges in a force generating state depends on lattice spacing . At any axial location , as lattice spacing diverges from its 34 nm rest value , the rate of attachment decreases while the rate of detachment increases ( Figure 3C–D and 3G–H ) . These kinetic rate constants change with lattice spacing because they depend on the difference in free energy between the unbound state and the pre- or post-power stroke state , a difference which increases with lattice spacing . This increase in energy makes a cross-bridge increasingly likely to transition to the unbound state and remain there ( Figure 3C–D and 3G–H ) . An example of the decrease in the likelihood of a cross-bridge remaining bound can be seen in the 4sXB model , where the slowest rate of detachment is 20/sec at a lattice spacing of 34 nm but rises to 260/sec at 38 nm ( Figure 3G ) . As a result of these changes , individual cross-bridges spend less time in a bound state and are less likely to generate force as lattice spacing diverges from its rest value . The axial and radial forces at a given axial offset correlate with lattice spacing ( Figures 4 and 5 ) . When lattice spacing is compressed , more expansive radial forces and smaller axial forces are produced . When lattice spacing is expanded , more compressive radial forces and larger axial forces are produced . An example of increased forces with increased lattice spacing is seen in the 4sXB model which , at a 10 nm axial offset , produces half the radial and half the axial force at 35 nm as it does at 38 nm ( Figure 5A–B ) . Similarly with the 2sXB model at a 12 nm axial offset , a lattice spacing of 35 nm produces two thirds of the axial and radial forces as does a lattice spacing of 38 nm ( Figure 5C–D ) . At large lattice spacings , this greater force per cross-bridge competes with the decreased probability a cross-bridge will bind and generate force , an interaction that requires a model of the half-sarcomere using multi-spring cross-bridges to fully evaluate [22] . The force landscapes of Figure 5 also show that no lattice spacing is free of radial force at all axial offsets . The radial force produced by a cross-bridge , even at rest lattice spacing , increases in magnitude as the cross-bridge tip moves away from its unstrained axial offset . The step size of both multi-spring models varies with lattice spacing ( Figure 6 ) . We define step size at a given lattice spacing as the axial distance between the pre- and post-power stroke positions of the myosin head . Put another way , step size at one lattice spacing is the distance from the axial offset with the lowest free energy in the pre-power stroke state , to the axial offset with the least amount of energy in the post-power stroke state . Both models have a peak step size at a relatively uncompressed lattice spacing , with decreasing step size as lattice spacing diverges from that value . The 4sXB model has a maximum step size of 5 . 0nm near 34nm lattice spacing and the 2sXB model has a maximum step size of 6 . 1nm near 36nm lattice spacing . The radial and axial components of force , produced by a 4sXB model or 2sXB model moved from its rest position to an axial offset , are of the same order of magnitude ( Figures 2E–F and 4A–D ) . The values of the axial and radial forces produced by the multiple-spring cross-bridge models at rest lattice spacing are compared to those produced by the single-spring cross-bridge model in Figure 2E–F . The relative values of the radial and axial forces are visualized as the angles of the force vectors in Figure 4A–D . Axial locations and lattice spacings with balanced axial and radial forces produce force vectors which are neither vertical nor horizontal , but in some intermediate orientation . Most axial and radial offsets are populated by such vectors , particularly regions a cross-bridge would be most likely to occupy ( unlikely regions are not shown in the vector plots ) . The few regions dominated by one force , notably some small offset positions in the 2sXB model ( Figure 4D ) , are dominated by radial forces . This presence of large radial forces suggests that , in all but the least strained locations at the smallest axial offsets , radial forces will be present in magnitudes comparable to those of axial forces .
The lattice spacing of the filaments around an attached multi-spring cross-bridge determine the energy landscape of the cross-bridge and thus the force it can generate . The forces and strains a cross-bridge produces at most axial offsets grow more positive as lattice spacing increases ( Figure 4E–H ) . While this increased cross-bridge strain translates into greater axial and radial force per post-power stroke cross-bridge , the probability that these cross-bridges will bind decreases as lattice spacing increases ( Figure 3C–D ) . The decrease in attachment rate constants at extreme lattice spacings , while power stroke rate constants remain unchanged ( Figure 3E–F ) , suggests lattice spacing influences muscle fiber force generation by altering the rate of cross-bridge attachment rather than the power stroke rate [22] . Spatially explicit effects in the compliant sarcomere , such as cross-bridge induced realignment of binding sites , may act to balance the decreased binding and increased detachment at larger lattice spacings . The energies , kinetics , and forces generated by the 2sXB model are subject to the same governing trends as those of the 4sXB model , and can be made similar by deliberate parameter choice ( Table 1 and Figures 2 , 3 , 4 , and 5 ) . That the 2sXB model can replicate the results of the 4sXB model indicates two things: first , the 2sXB can be used in place of the 4sXB in larger simulations , enabling work that would otherwise require prohibitive resources , and second , a feature shared between our two models is responsible for the interesting properties of our simulations , the use of a lever arm which undergoes an angle change to generate force . While the energies , binding rate constants , and power stroke rate constants of the multi-spring cross-bridges are almost identical , there are some smaller differences between the two models . The rate constant of detachment is rotated by approximately 20 between the two systems due to differences in the way the post-power stroke position is achieved ( Figure 3 ) . The 4sXB model and the 2sXB model generate somewhat different forces; the axial force produced by each model increases with lattice spacing , but that produced by the 4sXB does so more steeply ( Figure 5A , C ) . In a reversal of this pattern , the 2sXB model's radial force is more dependent on lattice spacing ( Figure 5B , D ) . In each of these cases , the forces generated by both multi-spring cross-bridges are subject to the same trend . The close agreement between the forces and other properties of the two cross-bridge representations supports the position that the key feature of our multi-spring models is the use of a lever arm to generate force , rather than a factor unique to the 4sXB model , such as the simulation of interaction between the lever arm and the S2 domain . Substituting the 2sXB model for the 4sXB model reduces the runtime of a simulation by two orders of magnitude and puts multi-spring cross-bridge simulations of the half-sarcomere within reach . The geometries of the multi-spring models require a change in step size accompany a change in lattice spacing . This is because , while the length of the lever arm changes as lattice spacing varies , the pre- and post-power stroke angles do not . Step size varies more in the 4sXB model as the 4sXB model's spring configuration causes the pre- and post-power stroke free energies to differ more than in the 2sXB model . As the detachment rate constant is a product of the post-power stroke free energy , the greater rotation in the 4sXB's post-power stroke free energy , relative to that of the 2sXB model , can be seen in Figure 3 G–H . Experimental measurements of step size vary , and it has been postulated that this is due to more than experimental error , but to our knowledge these results are the first prediction of a step size that varies with lattice spacing [24] . Experimental confirmation of these predictions is not possible with current literature: existing in vivo measurements of step size are from isolated myosin preparations which are unable to simulate a change in muscle lattice spacing [25] , [26] . While our single cross-bridge models lack the predictive power of a multi-filament model , the dependence of step size on lattice spacing offers insight into unloaded shortening velocity . Maximum unloaded shortening velocity is commonly interpreted as a function of both myosin's step size and drag from attached post-power stroke cross-bridges [27] . A decrease in unloaded shortening velocity is observed when lattice spacing is compressed via dextran [28] , [29] . This slower unloaded shortening is supported by the multi-spring models: their step size exhibits a similar decrease as lattice spacing shrinks ( Figure 6 ) . However , a moderate increase in the rate of detachment at highly compressed lattice spacings , seen in Figure 3 G–H , may balance smaller steps sizes . This increased detachment rate is due to the greater post-power stroke strain that is present with greater radial displacement of the cross-bridge . Changes in modeled detachment rates and step size are both likely to be needed , along with changes in filament overlap , to explain the complicated dependence of unloaded shortening velocity on sarcomere length [30] . The 4sXB and the 2sXB produce radial forces of the same order of magnitude as the axial forces generated by a cross-bridge . These forces range between 10% and 50% of the axial force at the least strained axial and radial offsets where a cross-bridge is most likely to enter the post-power stroke state ( Figure 4 ) . Muscle fibers display these radial forces by resisting width changes as osmotic pressure is applied [1] . Direct measurement of lattice spacing by X-ray diffraction has confirmed fiber width estimates of radial force [31] . Checchi et al . ( 1990 ) [2] observed large radial forces by examining lattice spacing during redevelopment of tension following length changes . A spatially explicit model , even one using multiple thick and thin filaments arranged in a lattice , is insensitive to lattice spacing if it uses a version of the 1sXB model . Embedding multi-spring cross-bridges in a multi-filament model allows the simulation of radial force regulation in a lattice of thick and thin filaments . The inclusion of radial forces in a multi-filament model permits examination of previously unavailable kinds of cooperativity , ones where radial force can be transmitted through the backbone lattice to affect the kinetics of other cross-bridges . Radial force is a potential regulator of lattice spacing and of sensitivity as lattice spacing and sarcomere length vary [3] . A multi-filament model using the 4sXB or 2sXB can simulate the interaction of radial force generated by a cross-bridge with radial forces provided by other mechanisms , e . g . titin or electrostatic repulsion [3] , [22] , [32] . Thus multi-spring cross-bridges make it possible to evaluate the influence of these radial forces , posited to be regulators of lattice spacing , and processes which may depend on lattice spacing or myosin head to thin filament distance , such as the Frank-Starling mechanism; something not possible with a 1sXB model [33] . In future studies , these models will permit the investigation of radial forces and lattice spacing in multi-filament models , and will allow us to examine disease states that alter myosin compliance . The inclusion of radial forces and lattice spacing in half-sarcomere models will illuminate regulatory mechanisms of shortening velocity and length-dependent axial force generation . Other efforts may use existing studies of how disease-related mutations alter myosin compliance to produce disease state mimicking cross-bridge models [34] . Multi-filament simulations using these altered cross-bridge models have the potential to explain how symptoms of disease states such as hypertrophic cardiomyopathy arise from myosin-level changes .
To describe the kinetics we use a simplified three-state model of the cross-bridge cycle originally described by Pate and Cooke ( 1989 ) [16] and modified by Tanner et al . ( 2007 ) [14] . This relatively simple scheme directly links the cross-bridge's kinetics and mechanics; the three kinetic states are directly comparable to the myosin configurations described in Houdusse ( 2000 ) [36] . The kinetic rates are independent of the number of springs used in a model cross-bridge , allowing the 4sXB and the 2sXB models to use the same system . The three states represented in the kinetic scheme are ( 1 ) an unbound state: Myosin- ( 2 ) a loosely-bound state:Actin-Myosin- and ( 3 ) a force-generating post-power stroke state: Actin-Myosin-ADP ( Figure 1D ) . These kinetics replicate those of a generic cross-bridge , and are aimed at reproducing properties shared between cardiac , skeletal , and insect myosin types . The kinetics of both the 4sXB and the 2sXB models are strain dependent and are essentially transforms of the free energy landscapes experienced by the cross-bridges in their different states . These free energies are a function of the distortion necessary to move the point representing the simulated myosin head's tip to the proposed binding site . Examples of these free energy landscapes are shown in Figure 3A and B , with cuts through them at the rest lattice spacing visible in Figure 2A . As the free energies of the cross-bridges are functions of their spring rest values and stiffnesses , changing the geometry and stiffness of the springs used by the model also changes the kinetics of the model . The binding probabilities of both the 4sXB and the 2sXB models are determined by Monte-Carlo simulations of their diffusion as a result of being perturbed by Boltzmann-derived energy distributions [21] . After a new head location is found , a binding probability is calculated which decreases exponentially with distance from the potential binding site . This probability is tested against a random number from a uniform distribution to determine if binding occurs in our chosen time step of 1 ms . | The molecular motor myosin drives the contraction of muscle , but doesn't just produce force in the axis of shortening . Models of muscle contraction have primarily treated myosin as a simple spring oriented parallel to its direction of movement . This assumption does not allow prediction of the relationship between the forces produced and the spacing between contractile filaments or of radial forces , perpendicular to the axis of shortening , all of which are observed during muscle contraction . We develop an alternative model , still computationally efficient enough to be used in simulations of the sarcomere , that incorporates both extensional and torsional ( angle dependent , like those found in a watch ) springs . Our model captures much of the spacing-dependent kinetics and forces that are missing from single-spring models of the cross-bridge . | [
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] | 2010 | Axial and Radial Forces of Cross-Bridges Depend on Lattice Spacing |
Although leptospirosis is traditionally considered a disease of rural , agricultural and flooded environments , Leptospira spp . are found in a range of habitats and infect numerous host species , with rodents among the most significant reservoirs and vectors . To explore the local ecology of Leptospira spp . in a city experiencing rapid urbanization , we assessed Leptospira prevalence in rodents from three locations in Malaysian Borneo with differing levels of anthropogenic influence: 1 ) high but stable influence ( urban ) ; 2 ) moderate yet increasing ( developing ) ; and 3 ) low ( rural ) . A total of 116 urban , 122 developing and 78 rural rodents were sampled , with the majority of individuals assigned to either the Rattus rattus lineage R3 ( n = 165 ) or Sundamys muelleri ( n = 100 ) . Leptospira spp . DNA was detected in 31 . 6% of all rodents , with more urban rodents positive ( 44 . 8% ) , than developing ( 32 . 0% ) or rural rodents ( 28 . 1% ) , and these differences were statistically significant . The majority of positive samples were identified by sequence comparison to belong to known human pathogens L . interrogans ( n = 57 ) and L . borgpetersenii ( n = 38 ) . Statistical analyses revealed that both Leptospira species occurred more commonly at sites with higher anthropogenic influence , particularly those with a combination of commercial and residential activity , while L . interrogans infection was also associated with low forest cover , and L . borgpetersenii was more likely to be identified at sites without natural bodies of water . This study suggests that some features associated with urbanization may promote the circulation of Leptospira spp . , resulting in a potential public health risk in cities that may be substantially underestimated .
Leptospirosis is the most widespread zoonotic disease globally , with over a million cases of severe disease and around 60 , 000 deaths reported annually [1] . Occurring in a wide variety of environmental settings , and with the greatest impact on public health in tropical and subtropical regions , it is a significantly under-diagnosed disease due to its broad clinical picture and symptoms that are common to several other diseases [2] . Leptospirosis is caused by spirochaetes of the genus Leptospira , of which 22 species and >300 serovars are currently recognized . Ten species have been definitively associated with severe human disease , whilst a further five have been linked to milder disease [3] . In addition , 12 novel species have recently been identified from tropical soils , although none have yet been associated with disease [4] . Human infection with Leptospira spp . occurs via several routes , including through direct contact with urine or tissues from infected animals , or indirectly through contamination of ( usually humid ) environments with infected urine . The two species responsible for the majority of human infections , L . interrogans and L . borgpetersenii , differ in their transmission routes; L . interrogans remains viable for extended periods in aquatic or humid environments , whilst L . borgpetersenii , which has lost several genes related to environmental sensing , now relies primarily on direct transmission between hosts [5] . These differences impact the ability of each species to persist in the environment and have led to differences in distribution and zoonotic potential [6] . As such , whilst exposure to wetlands has traditionally been considered a significant risk factor for this disease , Leptospira spp . have been detected in a number of environments , including cities [7–10] . Although relatively little is known about the ecology and epidemiology of Leptospira spp . in urban environments , zoonotic transmission has been repeatedly documented and often associated with poor sanitation and slum conditions [11–14] . By 2050 , 66% of the global human population is predicted to reside in urban environments and as such , the majority of human-wildlife interactions are likely to occur in these areas [15] . Critically , features of the urban environment can impact disease dynamics in wildlife hosts and increase the frequency of human exposure to zoonotic pathogens . Indeed , Leptospira spp . , infection prevalence has been found to be higher in wildlife occupying urban habitats than natural environments , and this trend appears to be particularly significant for rodents [16] . Several species of rodent , including Rattus norvegicus , R . rattus and R . exulans , appear to benefit from urbanization and thrive in city environments , resulting in regular human exposure to these species and their excreta [17 , 18] . Despite the obvious risks posed by urban rodent infestation , the distribution , prevalence , diversity and dynamics of Leptospira spp . in urban populations remains largely unknown , impacting the ability of local authorities to develop effective prevention and control strategies . In Southeast Asia , the number of reported cases and outbreaks of leptospirosis has increased dramatically in recent years , due in part to improvements in diagnosis and surveillance , but also as a result of the rapid environmental changes occurring in this region [19–21] . At least six zoonotic species have been detected in Southeast Asian rodents to date: L . borgpetersenii , L . interrogans , L . kirschneri , L . weilli , L . noguchii and L . wolfii [6 , 22] . In Malaysia , the annual number of reported cases increased more than 14-fold between 2004 and 2012 , which led to the classification of leptospirosis as a mandatory notifiable disease at the end of 2010 [23] . Although many recent Malaysian outbreaks have been associated with outdoor recreational activities , human infections have also been documented in urban environments [24] . Some studies have begun to assess the prevalence of Leptospira spp . in urban reservoir species in Southeast Asia [22 , 25] ) , but none have yet compared how distribution and transmission varies with the degree of anthropogenic influence across an urban landscape . In this study , we screened native and invasive rodents found in urban , developing and rural locations around the city of Kuching , Sarawak for Leptospira spp . , to begin to explore how urbanization effects the presence and prevalence of Leptospira in Malaysian Borneo .
This study was approved by the CSIRO Australian Animal Health Laboratory’s Animal Ethics Committee ( #1750 ) and the Sarawak Forests Department ( Permit: NCCD . 907 . 4 . 4 ( JLD . 12 ) -131 ) . For this study , sites were considered to be a circle with a 110 m radius centered at the point where GPS coordinates were taken during rodent trapping . All site-specific environmental variables were measured or estimated over the complete circle . The 110 m radius was chosen to correspond with the approximate home range of R . rattus that has been estimated under similar environmental conditions [26] . As home range data is not available for the other rodent species studied , we used the R . rattus estimate to delineate sites throughout the study . To classify the degree of urbanization and the intensity of anthropogenic influence at each site , the following estimates of land use were considered: 1 ) Mean forest cover was estimated using QGIS v 2 . 14 . 0 and previously published forest cover and loss datasets at the Landsat pixel scale . Mean estimates were ranked and grouped into tertiles , which were categorized as minimal , moderate or maximal forest cover ( https://earthenginepartners . appspot . com/science-2013-global-forest ) [21] . 2 ) Dominant land-cover type ( gray , green or gray/green interface ) was determined by assessing the proportion of vegetated ( forest , scrub , etc . ) or impervious ( buildings , roads , etc . ) space within and around each site using QGIS ( as above ) and ground-truthing . Gray sites were considered to be completely within and primarily surrounded by human infrastructure , green sites were those dominated by unmanaged vegetation , gray interface sites were within human infrastructure but adjacent to substantial vegetation , and green interface sites were within managed/unkempt vegetation and adjacent to human infrastructure . Other site-specific environmental features recorded included the presence or absence of a natural water body at a site , and the local environment in which individual rodents were caught , referred to as ‘trap location’ . Trap locations were recorded as: 1 ) inside domestic dwellings , 2 ) household gardens and yards , 3 ) forests , 4 ) sewers , and 5 ) scrub ( areas of vegetation dominated by unkempt bushes and grasses ) . Where buildings were present at a site , the relative condition ( i . e . , poor , fair , good , excellent ) and type of building ( s ) ( i . e . , residential , mixed commercial/residential , institutional ) were also recorded . Rodents were collected from multiple sites between September 2015 and April 2016 at each of the three locations described above . At each site , multiple wire mesh traps ( ~30cm x 14cm ) were baited with meat and banana , placed at intervals >1m for between one and seven nights , and checked every morning . Trapping effort varied substantially between sites in an effort to collect equal numbers of animals/species/location . Rodents were euthanized by over-anesthetization in isoflurane , followed by bilateral thoracotomy . Sex , reproductive status , weight ( as a proxy for age ) and tentative species assignment ( by morphological assessment ) were recorded , and tissues were collected and frozen directly on dry ice . The species identity of each animal was confirmed by sequencing the product of a PCR assay using primers BatL5310 and R6036R , which amplify 726bp of the cytochrome oxidase I gene [27] . Approximately 30mg of rodent kidney was homogenized in 600ml of Buffer RLT Plus ( Qiagen ) containing 1% β-mercaptoethanol using the TissueLyser II ( Qiagen ) , and a 5mm stainless steel bead . Homogenized tissue was clarified by centrifugation and the resultant supernatant transferred to a new tube and used for DNA extraction with the AllPrep DNA/RNA mini Kit ( Qiagen ) , as per the manufacturer’s instructions . DNA quantity and quality were assessed using a NanoDrop ( Thermo Scientific ) , diluted to <400ng/ul , and subjected to six previously described PCR assays targeting the rpoB , flaB and 16S rRNA genes [28–34] . Multiple PCR assays were chosen to maximize the probability of detecting any and all Leptospira spp . DNA present , including both pathogenic and non-pathogenic species . Samples were considered positive if they produced a visible band on an electrophoresis gel that could be confirmed as Leptospira spp . by Sanger sequencing ( conventional PCRs ) , or if they demonstrated a Ct value of 35 or lower by Leptospira-specific TaqMan PCR . The resultant sequences ( S1 Appendix ) were trimmed for quality and length and subjected to BLAST ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ) analysis to assess sequence similarity and determine putative species [3] . Sequences were considered to belong to a species if they shared ≥99% nucleotide similarity with publicly available sequences from verified species . Statistical analyses were conducted for all Leptospira spp . , as well as for L . interrogans and L . borgpetersenii separately , due to documented differences in transmission routes [5] . We further considered all rodent hosts collectively to avoid conflating Leptospira ecology with rodent ecology , as no evidence exists at present to suggest that these rodent species differ in competence [6] . Chi squared tests were used to assess differences in Leptospira prevalence in rodents between all three locations ( i . e . , urban , developing , and rural ) , as well as between each pair of locations . To interrogate the relationships between site-specific environmental variables , the GoodmanKruskal package ( version 0 . 01 ) implemented in R was used to run Goodman and Kruskal’s tau ( τ ) statistic ( https://CRAN . R-project . org/package=GoodmanKruskal ) [35] . This test measures the strength of associations between categorical data , with values ranging from −1 ( perfectly negative association ) to +1 ( perfectly positive association ) . A multivariate analysis of mixed data was also performed using the package PCAmixdata ( version 3 . 1 ) implemented in R [36] . To examine links between the probability of Leptospira infection of rodents and site-specific environmental variables , a generalized linear ( mixed ) model ( GLMM or GLM ) with logit function was created using the lme4 package implemented in R ( version 1 . 1–15 ) [37] . Analyses were performed using two initial models with explanatory variables chosen according to the strength of association identified in the Goodman and Kruskal’s tau statistics , and the results of the multivariate analysis . The first model ( a GLMM , henceforth referred to as the global model ) , included the following explanatory variables: site location , trap location , forest cover , dominant land-cover type and waterbody , with no interactions added among independent variables and rodent species as a random effect . The second model ( a GLM , referred to as the built environment model ) was intended to investigate aspects of the built environment that may be relevant to Leptospira prevalence , and included site location , trap location , building type and building condition , with no interactions among them . Only the infection status of rodents trapped in sites with buildings present were included in this model . Support for competing models was evaluated using the Akaike information criterion adjusted for small sample sizes ( AICc ) in the package AICcmodavg ( version 2 . 1–1 ) , and Akaike weights wr [38] . Selection of the best models was made using the R package glmulti ( version 1 . 0 . 7 ) [39] . The three top best models selected for the global and built environment modes are given in S2 Appendix and S3 Appendix , respectively .
A total of 316 animals were caught across all locations . Of these , nine species from four genera were identified by COI sequence analysis , with most individuals classified as S . muelleri ( n = 100 individuals ) or as R . rattus R3 ( n = 165 ) , one of the lineages within the R . rattus super-group ( Table 1 ) [40 , 41] . A total of 31 . 6% of all animals were positive for Leptospira spp . , and Leptospira spp . prevalence varied significantly by site location , with rodents from urban and developing locations more likely to be infected than rural rodents ( Fig 2; S2 Appendix ) . Sequence analysis revealed the presence of two distinct Leptospira species: L . borgpetersenii and L . interrogans . L . borgpetersenii was identified in 38 rodents ( six S . muelleri and 32 Rattus spp . ) , whilst L . interrogans was identified from 57 rodents , comprising Maxomys ochraceiventer ( N = 1 ) , M . whiteheadii ( N = 1 ) , S . muelleri ( N = 15 ) , and Rattus spp . ( N = 40 ) . Sequence information could not be obtained from five samples , which were positive only by a qPCR assay that yielded amplicons too small to sequence [34] . Goodman and Kruskal’s τ and our multivariate analysis showed moderate positive associations between pairs of environmental variables , but as all values were <0 . 60 , they were not considered to be fully redundant in this case ( S3 Appendix ) ( Fig 3 ) . As a result , no variables were excluded from the subsequent analyses . Of the variables considered in the global GLMM , trap location , forest cover , dominant land-cover type and water body were each important in explaining the infection of rodents with Leptospira ( S4 Appendix ) . Significant associations were identified in the first top model between infection with any Leptospira species and sites characterized by minimal forest cover ( P = 0 . 001 ) , and the absence of natural water bodies ( P = 0 . 01 ) , which may be reflective of the association between minimal forest cover and L . interrogans , and the absence of a natural water body with L . borgpetersenii ( Table 2 ) . The built environment GLM further investigated the effects of building type , building condition , trap location and site location on rodent infection with Leptospira ( S5 Appendix ) . The first top model demonstrated that individual rodents trapped in or near buildings with mixed commercial and residential uses were more likely to be infected by both Leptospira species ( P = 0 . 001 , Table 3 ) . Moreover , the presence of institutional buildings within a site ( e . g . , churches , schools , etc . ) also appeared to increase the risk of rodent infection by L . interrogans ( P = 0 . 038 , Table 3 ) .
The results of this study reveal that the prevalence of Leptospira spp . in rodents is influenced by a number of environmental factors , and that these may vary depending on the species of Leptospira considered . Overall , Leptospira prevalence increased with increasing anthropogenic influence across the landscape , with a significantly higher proportion of infected rodents observed at the urban location . In particular , L . borgpetersenii was most commonly found at sites without a natural body of water , whilst L . interrogans infection was most prevalent among rodents inhabiting sites with low forest cover . For sites within the built environment , the type of buildings present was also found to have an impact on the prevalence of both Leptospira species . The overall prevalence of pathogenic Leptospira spp . in rodents in this study was slightly higher ( 32% ) than those observed in other studies from the Southeast Asian region ( 6–27% ) , and was also higher than previously identified from rodents trapped in urban areas of Sarawak ( 5 . 6% , N = 107 rodents ) [6 , 22 , 42–46] . While the discrepancy between our results and those of Pui et al . [22] are not straightforward to explain , they may be due to differences in sampling sites , organs tested and laboratory methodology . Pui et al . , cultured all samples prior to detection with a single PCR assay , unlike the direct-detection methodology with multiple primer sets applied in the current study . Although the approach of Pui et al . is common , growing Leptospira in vitro is well-known to be challenging and can be biased by species and serovar , which may result in a lower reported prevalence . Rural habitats have repeatedly been associated with an increased risk of leptospirosis due to associations with some types of agriculture ( e . g . rice farming ) and outdoor recreational activities [47 , 48] . As a result , the majority of research on the ecology and distribution of Leptospira spp . in rodents has been performed in rural environments , and the ecological drivers and risk factors for zoonotic infection in these habitats are relatively well documented . In contrast , Leptospira ecology in urban environments has received considerably less attention , despite the abundance of rodents and other potential hosts in urban environments , and clear evidence of human infection [49–52] . In addition , and as observed in this study , most surveys of urban rats have found a high prevalence of pathogenic Leptospira spp . [42 , 53–56] . The high population densities that rodents can reach in urban areas and the resulting frequency of human-rodent contact suggests that a real risk of human infection is present , even when infection prevalence in rodents is low [57 , 58] . From the results of this study we are unable to determine if the Leptospira spp . carried by rodents in and around Kuching are associated with human infection , or what the relevant risk factors for zoonotic transmission may be . However , previous work has assessed serovar diversity in both soil and rodents in urban Sarawak , and predominantly identified L . interrogans serovar Icterohaemorrhagiae [22] . This serovar is commonly associated with rodents and has been linked to human disease in Sarawak and other regions of Malaysia [59–62] . In addition , serovar Sarawak ( Lepto 175 ) has also been detected in both humans and rodents in Sarawak but has not yet been confirmed as an agent of human disease [25 , 62 , 63] . Taken together this suggests that rodents , including the species sampled in this study , are likely a source of human infection in Sarawak and the Southeast Asian region . In recent years , several leptospirosis outbreaks in Malaysia have been linked to outdoor activities ( e . g . hiking , water-sports ) in natural environments [23 , 42] . Reflective of this risk , both species of Leptospira were detected in vegetated areas across the landscape in this study . Infected rodents were detected in disturbed forests , recreational parks and vacant lots , all of which are utilized to varying degrees by people , and which provide additional interfaces for human exposure to Leptospira . Across our study sites , city parks are used extensively for sporting and social activities , vacant lots for edible plant foraging and small-scale fruit and vegetable cultivation ( personal observation ) , and disturbed forests for farming , hunting and foraging , as well as recreational activities [64 , 65] . In contrast , we identified an unexpected association between the presence of L . interrogans and reduced forest cover at a site , which may indicate that transmission is favored in more cleared ( and disturbed ) habitats [6 , 66] . However , this trend may be related to the ecology of the dominant rodent species assessed in this study . Across Southeast Asia , both members of the R . rattus super-group and S . muelleri are often found at higher abundance in disturbed and urban habitats compared to more pristine , forested habitats [67 , 68] . It is therefore possible that rodent population density , which was not measured here but did appear low in forested areas , is a factor that inhibits Leptospira spp . transmission . Alternatively , the lower infection prevalence observed at sites characterized by high forest cover may simply be a result of the relatively small number of rodents trapped at these sites . Rodent abundance may also be related to the association we identified between the presence of buildings with mixed commercial and residential uses , and an increased prevalence of both Leptospira investigated here . This type of building , which often has a shop , restaurant or market on the ground floor and higher-density accommodation above , is the primary building type found in the center of Kuching and at focal points of human activity in its suburbs . The disposal of waste from these premises is often informal and directed towards the sewerage system , providing an ample source of food for rodents . As sewers also provide access to water and shelter from most predators , they are regularly favored by urban rodents such as R . norvegicus , which can become extremely abundant in urban environments [55] . The high rodent population densities that can occur in such settings may promote the circulation of Leptospira and increase the risk of zoonotic transmission in urban environments . Indeed , living close to open sewers has been identified as a risk factor for human Leptospira infection in Salvador , Brazil , and occupational risks have been identified for town cleaners and sewage workers in other cities [24 , 69–71] . The drivers behind the association between L . interrogans positive rodents and sites with institutional buildings is less clear , although this may be an artifact of our analysis as there were only four sites in this category . However , it is worth noting that as none of these sites were rural , this association may also be reflective of benefits related to higher levels of urbanization . It is surprising that the presence of L . borgpetersenii at a site was significantly more likely if natural bodies of water were absent , and may reflect the evolution of this species towards direct transmission between hosts [5] . This is reflected in the findings of another Southeast Asian study , which found L . borgpetersenii to be abundant in both dry and humid habitats , with the highest prevalence in non-floodable lands such as orchards , plantations and shrubby wasteland [6] . However , it may be worth noting that although sewers were accounted for by the variable ‘trap location’ in our analyses , they were not part of the variable ‘water body’ due to their artificial nature and the inconsistent presence of water in this environment . As approximately half of all Leptospira-positive rodents ( 51/100 ) were trapped in sewers , their exclusion from this category may have influenced these results . In addition , ‘trap location’ was not selected in the best top model for L . borgpetersenii ( Table 2 ) , but it was selected in the second and third top models , indicating that it may have some influence on L . borgpetersenii infection prevalence ( S4 Appendix ) . This study focused on identifying environmental factors that influence the prevalence of Leptospira spp . in urbanizing environments; however , the environment can both directly and indirectly influence the circulation of Leptospira ( i . e . by shaping host ecology ) . While we are unable to distinguish between these two modes of action in this study , our choice to explore the ecology of Leptospira across all rodents collectively is supported by several factors: 1 ) Leptospira are host generalists; 2 ) rodent species are not known to differ in competence; 3 ) a similar ecological study of Leptospira in Thailand found no impact of rodent species [6]; 4 ) individuals from R . rattus R3 and S . muelleri ( comprising 84% of all captures in this study ) were both found in urban , developing and rural locations , including at some of the same sites . However , infection prevalence did vary between these two species , with 42 . 5% of R . rattus R3 and 23 . 0% of S . muelleri individuals infected , suggesting that either the former is more likely to become infected , or that this species prefers to inhabit environments that promote the circulation of Leptospira . For example , R . rattus R3 was commonly caught in sewers in our study ( 73/165 captures ) , and large numbers of animals were often observed at these sites , suggestive of high population density . These conditions may promote the circulation of Leptospira , particularly for species such as L . borgpetersenii , which rely on direct transmission between hosts [5] . The number of reported cases of leptospirosis in Malaysia has increased considerably in recent years [23] . Although many cases are still documented in rural areas , zoonotic transmission is also clearly a feature of urban living , with some occupations ( i . e . garbage collectors , town cleaners ) associated with a higher risk of infection [24 , 69] . The high prevalence of Leptospira observed here and the importance of rodents as sources for human disease , suggests that the ecology and dynamics of rodent-associated transmission in urban Kuching warrants further study and may be required to prevent ongoing human disease . With the increasing loss of natural habitats and continuing urbanization occurring across the globe , the majority of zoonotic transmission events are anticipated to occur in urban settings . It is therefore essential to develop a thorough understanding of the drivers of pathogen transmission and zoonotic infection that occur in the ecologically and demographically complex urban environment . | Leptospirosis is a significant zoonotic disease that is found in a range of environments worldwide , most notably tropical regions prone to flooding . The bacterial agents of this disease , Leptospira spp . , are most often associated with rodents , including species frequently found in urban areas . In cities , rodent populations are often larger and denser than those found in natural environments , which can lead to higher rates of contact with people and impact human disease risk . To investigate the impacts of urbanization on Leptospira spp . , we sampled rodents at locations with differing levels of human influence , from highly urbanized to rural , surrounding a city in Malaysian Borneo . We found that 31 . 6% of all rodents were positive for Leptospira spp . DNA , and that two primary species were present , L . interrogans and L . borgpetersenii , both of which are known human pathogens . Statistical analyses revealed that infected animals were more common in areas with higher levels of human influence , and were more likely to occur at sites with limited forest cover , and mixed commercial and residential activity . Our study adds to a growing body of evidence suggesting that there is a significant yet underappreciated risk of leptospirosis for people living in urban environments . | [
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] | 2019 | Association of rodent-borne Leptospira spp. with urban environments in Malaysian Borneo |
Cells infected with dengue virus release a high proportion of immature prM-containing virions . In accordance , substantial levels of prM antibodies are found in sera of infected humans . Furthermore , it has been recently described that the rates of prM antibody responses are significantly higher in patients with secondary infection compared to those with primary infection . This suggests that immature dengue virus may play a role in disease pathogenesis . Interestingly , however , numerous functional studies have revealed that immature particles lack the ability to infect cells . In this report , we show that fully immature dengue particles become highly infectious upon interaction with prM antibodies . We demonstrate that prM antibodies facilitate efficient binding and cell entry of immature particles into Fc-receptor-expressing cells . In addition , enzymatic activity of furin is critical to render the internalized immature virus infectious . Together , these data suggest that during a secondary infection or primary infection of infants born to dengue-immune mothers , immature particles have the potential to be highly infectious and hence may contribute to the development of severe disease .
Dengue virus ( DENV ) represents a major emerging arthropod-borne pathogen . There are four distinct serotypes of DENV which , according to WHO estimates , infect about 50-100 million individuals annually , mostly in the ( sub ) tropical regions of the world . While most DENV infections are asymptomatic or result in self-limited dengue fever ( DF ) , an increasing number of patients present more severe , potentially fatal clinical manifestations , such as dengue hemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) . It is well established that a major risk factor for the development of DHF/DSS is secondary infection with a heterotypic virus serotype [1]–[3] . Also primary infection of infants born to dengue-immune mothers may lead to severe disease [1] , [4] , [5] . These observations have led to the hypothesis of antibody-dependent enhancement ( ADE ) of infection [3] , [6] , [7] . Increased disease severity appears to correlate with high circulating virus titers [3] , [8]–[11] , suggesting that antibodies directly influence the infectious properties of the virus . The molecular mechanisms by which antibodies enhance DENV infection however remain elusive . DENV , as well as other major human pathogens like West Nile virus ( WNV ) , yellow fever virus , and tick-borne encephalitis ( TBEV ) belong to the Flavivirus genus within the family Flaviviridae . Flaviviruses enter cells via clathrin-mediated endocytosis and fuse from within acidic endosomes , through which the viral genome gains access to the target cell cytoplasm[12] . Following RNA replication and protein translation , immature virions , which contain heterodimers of the transmembrane proteins E and a precursor form of M ( prM ) , are assembled within the ER . Subsequently , the particles mature by passing through the Golgi and trans-Golgi network ( TGN ) [13] . In the acidic environment of the TGN , the virion undergoes a conformational change and the cellular endoprotease furin cleaves prM into M and a peptide ( “pr” ) that remains associated with the virion [14] . Upon release , the pr peptide dissociates from the virion , resulting in the formation of mature progeny virions . Cells infected with DENV secrete high levels ( ∼30% ) of prM-containing immature particles [15] , [16] suggesting that cleavage of prM to M is not efficient . These DENV particles are released from infected cells as fully immature prM-containing particles and partially immature particles containing both prM and M proteins in the viral membrane [17] . Extensive functional analyses have revealed that fully immature flaviviruses lack the ability to infect cells , as the presence of uncleaved prM in the virion blocks the E glycoprotein from undergoing the pH-induced conformational changes that are required for membrane fusion [16] , [18]–[22] . Although immature particles are therefore generally considered as irrelevant by-products of infected cells , the rates of prM antibody responses are significantly higher in patients with secondary infection compared to those with primary infection [23] . Furthermore , previous reports show that prM antibodies can enhance DENV infection . Enhancement of infection was observed for wild-type virus [24] , [25] , presumably due to the presence of uncleaved prM in these preparations , and with DENV particles containing high levels of prM generated from cells treated with chloroquine [26] . It is thus quite puzzling if indeed the presence of prM obstructs DENV infectivity , how immature particles contribute to disease pathogenesis and what role do anti-prM antibodies play in the enhancement of infection ? The present study addresses these questions .
First , we investigated if prM antibodies are able to render fully immature DENV infectious . To this end , immature DENV-2 strain 16681 particles were produced in furin-deficient LoVo cells . We have used this procedure before and showed that LoVo-derived particles have an average content of 94%±9% prM [16] . Furthermore , we demonstrated that the specific infectivity of LoVo-derived fully immature DENV is at least 10 , 000-fold reduced compared to that of wild-type virus on cells highly permissive to infection [16] . The infectious properties of fully immature DENV virions were determined in Fc-receptor-expressing K562 cells in the absence or presence of increasing concentrations of the 70–21 antibody . This is an IgG2a antibody that has been isolated from DENV-infected mice and is mapped to amino acids 53-67 of prM [25] . Antibodies recognizing this epitope are abundantly present in sera of DHF/DSS patients [16] , [27] . K562 cells were infected with DENV at a multiplicity of 100 genome-containing particles per cell ( MOG 100 ) . The number of genome-containing particles ( GCP ) was determined by quantitative PCR analysis of reverse-transcribed viral RNA [15] . At 24–48 hours post-infection ( hpi ) , cells were fixed and prepared for flow-cytometric analysis to determine the number of infected cells , measured on the basis of dengue E protein expression . We observed that 43 hpi is optimal for read-out as it represents a single round of infection together with a high mean fluorescence intensity per infected cell ( Fig . S1 ) . In agreement with our previous study [15] , [16] , we observed that fully immature DENV particles are essentially non-infectious as the number of E-positive cells did not exceed the limit of detection ( Fig . 1A ) . Remarkably however , substantial numbers of E-positive cells were observed upon infection of cells with fully immature particles opsonized with the anti-prM antibody ( Fig . 1A ) . Subsequent titration of the cell supernatants at 43 hpi revealed that opsonization of immature DENV with anti-prM antibody dramatically enhanced ( up to 30 , 000-fold ) virus particle production ( Fig . 1B ) . The results show that prM antibodies render essentially non-infectious immature DENV nearly as infectious as wild-type virus ( Fig . 1B ) . Enhancement of immature DENV infectivity was seen in a broad antibody concentration range , even at conditions of high antibody excess . To ensure that the observed high level of enhancement is not restricted to a single antibody we performed additional experiments with the murine IgG2a prM antibody 2H2 [28] . The results show that 2H2 stimulated the infectious properties of fully immature particles up to 1 , 000 fold ( Fig . S2A ) . Although , this antibody has previously been shown to enhance DENV infectivity [26] , the power of enhancement observed here is striking and demonstrates that prM antibodies render essentially non-infectious fully immature DENV highly infectious . Subsequently , we investigated the enhancing properties of both prM antibodies in Fc-receptor-bearing human monocytic U937 cells and observed that the antibodies again significantly stimulate the infectivity of immature particles ( Fig . 1C , S2B ) . Thereafter , we studied the infectious properties of immature DENV particles in primary human PBMCs , cells which are known to be involved in dengue pathogenesis . The results show that , also under these conditions , prM antibodies render fully immature particles infectious ( Fig . 1D , S2C ) . To better understand the mechanism by which prM antibodies trigger infectivity of immature DENV , we analyzed the distinct steps in the cell entry pathway of the virus . First , the binding of immature virions to K562 cells was determined by quantitative-PCR . In order to determine the number of bound GCP per cell , the amount of virus added per cell was increased 10-fold compared to the concentration used in the infectivity experiments . The results show that antibody-opsonized immature DENV binds approximately 30-fold more efficiently to cells than immature particles in the absence of antibody ( Fig . 2A ) . Indeed , immature particles opsonized with anti-prM bound almost as efficiently to cells as wild-type DENV in the absence of antibody . Moreover , immature DENV particles failed to interact efficiently with baby hamster kidney cells ( BHK-15 ) , cells which are highly permissive for dengue infection ( data not shown ) suggesting that the observed lack of infectivity is partially related to the poor binding efficiency of immature particles to cells . It is likely that binding of virus-antibody complexes is mediated by direct interaction of the antibody with the Fc-receptor expressed on the cell surface . Indeed , treatment of cells with an anti-CD32 antibody to block FcγII-receptor interaction , or opsonization of particles with mAb70-21 F ( ab' ) 2 fragments severely reduced virus particle production upon infection of K562 cells with opsonized immature virions , whereas it had no effect on infection with wild-type virus ( Fig . 2B ) . Although this antibody has been previously described to enhance the infectious properties of wild-type DENV in cells with or without Fc-receptors [25] , [27] , clearly in the case of immature particles interaction with the Fc-receptor is important for infectivity . Taken together , these data indicate that prM antibodies facilitate efficient interaction and cell entry of virus-immune complexes via the FcγII-receptor . Efficient FcγIIR-mediated cell entry does not however clarify what is the trigger for immature virions to become infectious , since the presence of prM has been shown to obstruct membrane fusion activity of the virus [14] , [16] , [18] . One could speculate that anti-prM antibody bound to immature virions induces a conformational change that would enable the E protein to trigger membrane fusion irrespective of the presence of prM . Another scenario might be that prM-containing virions mature upon cell entry since furin , although predominantly present in the TGN , also shuttles between early endosomes and the cell surface . To verify the potential involvement of furin during virus cell entry , we investigated the infectious properties of antibody-opsonized immature DENV in cells treated with furin inhibitor , decanoyl-L-arginyl-L-valyl-L-lysyl-L-arginyl-chloromethylketone ( decRRVKR-CMK ) . In aqueous solution , decRRVKR-CMK has a half-life of 4–8 h [29] and therefore it is not expected to interfere with the maturation process of newly assembled virions within the infected cell . The results show that inhibition of furin activity completely abrogated virus particle production in cells infected with antibody-opsonized immature virions , whereas infection of cells with wild-type virus remained unaffected under these conditions ( Fig . 3A ) . To further substantiate the role of furin in triggering viral infectivity , we generated a furin cleavage-deficient virus ( pDENprMΔ90 ) by deletion of the lysine on the position 90 ( 87-R-R-E-K-R-91 ) within the furin recognition sequence . Subsequently , DENVprMΔ90 virus and wild-type DENV-2 16681 ( generated from pD2/IC-30P ) virus were produced by transfection of RNA transcripts derived from the cDNA plasmids into BHK-15 cells . Virus production was measured by determining the number of physical particles based on GCP and the number of infectious units as measured by plaque assay . The presence of physical particles was further evaluated in three-layer ELISA experiments , by coating plates with a similar number of genome-containing DENVprMΔ90 particles and LoVo-derived immature particles . Similar OD values were measured for DENVprMΔ90 and LoVo-derived viruses ( data not shown ) , which confirms the presence of physical particles and suggests that the number of genome-containing particles is accurately determined . Subsequent titration studies revealed that the specific infectivity of DENVprMΔ90 mutant virus is reduced by a factor of 12 . 000 compared to that of wild-type virus ( generated from pD2/IC-30P ) and is comparable to LoVo-derived immature virus . Next , K562 cells were infected with DENVprMΔ90 mutant virus opsonized with increasing concentrations of prM antibody 70–21 . Figure 3B shows that disruption of the furin-recognition motif within the prM protein of the virus abrogates the enhancing activity of the anti-prM-antibody , demonstrating that enzymatic cleavage of prM to M by furin is critical to render immature DENV infectious . To address the question as to whether prM to M cleavage can occur upon interaction of immature DENV particles with antibodies , we incubated 35S-methionine-labeled immature particles in the absence and presence of antibodies with exogenous furin for 16 h at pH 6 . 0 [16] . Protein visualization was done by SDS-PAGE analysis . In agreement with previous studies , we observed that exogenous furin treatment induces efficient cleavage of prM to M ( Fig . 3C ) . Importantly , we found that the presence of prM antibodies does not affect DENV maturation , as virtually complete cleavage of prM to M was observed ( Fig . 3C ) . It has been postulated that antibody-mediated entry of DENV leads to a higher production of virus particles per infected cell , a phenomenon often referred to as intrinsic ADE [30] . In this part of the study , we investigated whether prM-mediated entry of immature DENV supports intrinsic ADE . Since immature particles are essentially non-infectious in the absence of antibodies , we compared the production of prM-opsonized immature DENV particles with wild-type virus in K562 cells . For accurate comparison , we first searched for a condition that gives a similar percentage of infected cells . Infection of K562 cells with prM-opsonized immature DENV at a MOG of 100 leads to 0 . 53%+/−0 . 14 infected cells ( Fig . 1 , 4A ) . Comparable numbers of infected cells were detected for wild-type DENV at MOG 10 ( Fig . 4A ) . Under these experimental conditions , no differences were observed in E protein expression and production of virus particles ( Fig . 4B–C ) , which indicates that the presence of prM antibodies , while evidently stimulating the infectious properties of immature virions , has no enhancing effect on the number of progeny virions produced per cell . Given the high number of prM-containing particles in wild-type DENV preparations it is possible that prM antibodies also enhance the infectious properties of wild-type DENV . Indeed , in agreement with previous studies , opsonization of wild-type virus with prM antibodies results in a significant increase of viral infectivity ( Fig . 5A-C , Fig . S3 ) [24] , [25] . The level of enhancement is dependent on the cell type used and comparable to what has been described before for E antibodies [31] , [32] . Enhancement of wild-type DENV infection was observed at higher antibody concentrations compared to that of immature particles . Although we do not completely understand these differences , we think that this may be related to the presence of structurally distinct immature virus particles ( individual variations in prM/M content ) in wild-type preparations [17] . Importantly , no enhancement of infection was observed in cells treated with furin inhibitor ( Fig . 5D ) , demonstrating that furin activity in the target cells plays a vital role in triggering the infectious properties of antibody-opsonized immature particles in wild-type DENV preparations . Collectively , these results illustrate that prM antibodies enhance the infectious properties of prM-containing particles in wild-type DENV preparations and therefore may be important in disease pathogenesis . As a first step towards elucidation of the implications of our findings in disease pathogenesis we evaluated the enhancing properties of 7 convalescent serum samples from patients infected with DENV-2 . The infectious properties of immature particles opsonized with various dilutions of polyclonal sera were determined in U937 cells , since this cell line expresses both Fc receptors CD32 and CD64 on the cell surface . At 43 hr post-infection , the medium was harvested and the production of virus particles was measured by plaque assay . No plaques were found in the absence of sera and in the presence of DENV-naïve serum ( Fig . 6 ) . Convalescent sera from two distinct DENV-2 infected patients significantly enhanced the infectious properties of immature DENV particles at a 10 , 000 dilution ( Fig . 6 ) . Sera from two other patients enhanced the infectivity of immature particles to a minor extent as only a low number of plaques ( average of 1 . 5 plaques ) was observed . The three remaining patient sera did not show any effect on viral infectivity of immature particles as no viral plaques were observed . As expected , nearly all of the analyzed patient sera enhanced the infectious properties of wild-type virus particles ( Fig . S4 ) .
Multiple studies have shown that immature particles are non-infectious , the presence of prM obstructing the low-pH-induced conformational changes in the viral E glycoprotein required for membrane fusion of the virus [14] , [16] , [18] , [21] , [22] , [33] . On the other hand , prM antibodies have been shown to enhance DENV infection [24] , [25] . In this report , we show that the lack of infectivity of fully immature particles in the absence of antibodies is primarily related to inefficient binding of immature virions to the cell surface . If binding is facilitated through anti-prM antibodies , immature DENV particles become highly infectious presumably due to efficient intracellular processing of prM to M by the endoprotease furin . Maturation upon entry has been previously reported for other enveloped viruses . Zhang and co-workers [34] showed that the infectivity of immature particles of Semliki Forest virus , an alphavirus , can be triggered by furin during viral endocytosis . It is likely that DENV maturation also occurs within acidic endosomes , since previous in vitro experiments have revealed that cleavage of immature particles by furin is dependent on the exposure of the virus to low pH [16] . We propose that the acidic conditions of the endosome , similar to those in the acidic TGN during processing of newly assembled virions , triggers an initial conformational change in the virion such that furin is able to cleave prM to M and the “pr” peptide . Interestingly , a recent report has shown that upon cleavage of prM a large fraction of pr peptide remains associated with the virion and that back-neutralization to pH 8 . 0 is required to release the pr peptide from the virion [14] . The authors interpreted this as a mechanism preventing newly assembled cleaved virions from undergoing membrane fusion in the acidic TGN . However , this notion is difficult to reconcile with our present observations , since virions that have matured within acidic endosomes of target cells do not return to neutral-pH conditions before initiating infection . One may speculate that the pr peptide stabilizes the E protein to such an extent that it survives the mildly acidic lumen of the TGN ( ∼pH 6 . 0 ) , but is released at the more acidic pH of endosomes ( ∼pH 5 . 0 ) such that the E proteins have the capacity to rearrange to their fusion-active conformation . Another possibility is that upon cleavage of prM the pr peptide associates with the prM antibody instead of the E protein , thereby enabling the E proteins to adopt the fusion-active conformation . The observed infectious potential of immature DENV virions in the presence of anti-prM antibodies may have important implications for our understanding of the processes involved in dengue pathogenesis . We speculate that in the early stages of a primary infection , before the appearance of virus-specific antibodies , immature virions would fail to penetrate host cells and therefore are of minor significance in disease development . On the other hand , during a secondary infection or primary infection of infants born to dengue-immune mothers , immature particles may become highly infectious due to the presence of anti-prM antibodies and hence may contribute to an increased dengue-infected cell mass and a high circulating virus titer , one of the preludes for the development of severe disease symptoms [3] , [8]–[11] . Importantly , anti-prM antibodies may activate the infectious properties of a large population of virus particles , since we recently observed that a typical DENV-2 preparation of the prototype strain 16681 contains as much as 30% prM [16] . Taken together , our results suggest that immature DENV particles act as a veiled pathogen and can , like mature DENV contribute to the disease pathogenesis . Variable levels of enhancement were seen with DENV-immune sera . As expected , virtually all of analyzed DENV-immune sera stimulated the infectivity of wild-type DENV . Interestingly , sera from 2 out of 7 patients significantly enhanced the infectious properties of immature particles . This suggests that individual patients develop different responses to prM . On the basis of these results , we believe that it is important to further investigate the antibody responses in DENV-infected patients and to unravel if patients with prM antibodies are more susceptible to develop severe disease . In this respect , it is interesting to note that the rates of prM antibody responses are significantly higher in patients experiencing a secondary infection compared to a primary infection [23] . Clearly , future clinical studies are required to obtain further evidence for the role of immature particles and prM antibodies in disease development .
Aedes albopictus C6/36 cells were maintained in minimal essential medium ( Life Technologies ) supplemented with 10% fetal bovine serum ( FBS ) , 25 mM HEPES , 7 . 5% sodium bicarbonate , penicillin ( 100 U/ml ) , streptomycin ( 100 µg/ml ) , 200 mM glutamine and 100 µM nonessential amino acids at 28°C , 5% CO2 . Baby Hamster Kidney-21 clone 15 cells ( BHK-15 ) cells were cultured in DMEM ( Life Technologies ) containing 10% FBS , penicillin ( 100 U/ml ) , streptomycin ( 100 µg/ml ) , 10 mM HEPES , and 200 mM glutamine . Human adenocarcinoma LoVo cells were cultured in Ham's medium ( Life Technologies ) supplemented with 20% FBS at 37°C , 5% CO2 . Human erythroleukemic K562 cells were maintained in DMEM supplemented with 10% FBS , penicillin ( 100 U/ml ) , and streptomycin ( 100 µg/ml ) at 37°C , 5% CO2 . Human leukemic monocyte lymphoma U937 cells were maintained in Iscove's modified Dulbecco's medium ( GIBCO ) supplemented with 10% FBS , 4 mM L-glutamine , penicillin ( 100 U/ml ) , and streptomycin ( 100 µg/ml ) and adjusted to contain 1 . 5 g/l sodium bicarbonate , 10 mM HEPES and 1 . 0 mM sodium pyruvate ( GIBCO ) . Cells were incubated at 37°C at 5% CO2 . Human peripheral blood mononuclear cells ( PBMCs ) were maintained in RPMI 1640 medium supplemented with 10% FBS , penicillin ( 100 U/ml ) , and streptomycin ( 100 µg/ml ) . PBMCs were isolated from heparinized blood samples collected from healthy persons using standard density centrifugation procedures with Lymphoprep™ ( AXIS-SHIELD ) . The PBMCs were used immediately after isolation or cryopreserved at −150°C . On the day of infection , the percentage of CD14+ , CD19- population within isolated PBMCs was determined ( 5%–10% depending on the blood donor ) using cell surface markers CD-14 -FITC and CD19-R-PE purchased from commercial source ( IQ Products ) . DENV-2 strain 16681 , kindly provided by dr . Claire Huang ( Center for Disease Control and Prevention , USA ) , was propagated on C6/36 cells as described before [16] . Briefly , monolayer of C6/36 cells was infected at multiplicity of infection ( MOI of 0 . 1 ) . At 96 hpi , the medium was harvested , cleared from cellular debris by low-speed centrifugation , aliquoted , and stored at −80°C . Immature DENV particles were produced on LoVo cells as described previously [16] . Briefly , LoVo cells were infected at MOI 10 . Virus inoculum was removed after 1 . 5 h and fresh medium was added after washing the cells twice with PBS . At 72 hpi , the medium containing the virus particles was harvested , cleared from cellular debris by low-speed centrifugation , aliquoted , and stored at −80°C . [35S] methionine-labeled immature virus was prepared , as described previously [16] . Briefly , cells were infected at a MOI of 10 . At 2 hpi , 400 µCi of [35S]methionine ( Amersham Biosciences ) was added to 20 ml of medium and incubation was continued overnight . At 23 hpi , the medium was supplemented with an additional 200 µCi of radioactive label . At 72 hpi , the supernatant containing the viral particles was cleared from cell debris by low-speed centrifugation and the virions were pelletted and further purified on a discontinuous ( 20 and 55% w/v ) Optiprep™ gradient ( Axis-Shield ) by ultracentrifugation . Virus was harvested from the gradient interface , aliquoted and stored at −80°C . Virus preparations were analyzed with respect to the infectious titer and the number of genome-containing particles , as described previously [15] , [16] . The furin-cleavage mutant ( pDENprMΔ90 ) was generated by deletion of the lysine codon within the furin-recognition site at position 90 of prM . The mutation was introduced in the DENV-2 16681 infectious cDNA clone ( pD2/IC-30P ) [35] . Briefly , two PCR fragments were generated using the following primers: forward primer A ( 5′-CTC AAC GAC AGG AGC ACG ATC AT- 3′ ) and reverse primer A ( 5′- GAG TGC CAC TGA TCT TTC TCT TC-3′ ) and forward primer B ( 5′- GAA GAG AAA Δ GAT CAG TGG CAC TCG TT-3′ ) and reverse B ( 5′-GTG TCA TTT CCG ACT GCA TGC TCT-3′ ) . The PCR fragments were ligated , cut with SacI and Sph1 , and ligated into pD2/IC-30P . The introduced deletion was confirmed by DNA sequence analysis using an automated capillary sequencing system ( ABI ) . RNA transcripts were generated from pDENprMΔ90 and pD2/IC-30P using T7 RNA polymerase and transfected into BHK-15 cells cells by electroporation ( Bio-Rad Gene Pulser apparatus; two pulses at 1 . 5 kV , 25 µF , and 200 Ω ) . At 12 hours post transfection ( hpt ) cells were washed extensively to remove remaining RNA copies . Virus preparations were harvested at 72 hpt , cleared from cellular debris by low-speed centrifugation , aliquoted , and stored at −80°C . Virus preparations were analyzed with respect to the infectious titer and the number of genome-containing particles , as described previously . The antigenic reactivity of DENVprMΔ90 was compared to LoVo-derived virus by standard three-layer ELISA . Briefly , microtiter ELISA plates ( Greiner bio-one ) were coated with 4×106 GCP of different virus preparations per well in 100 µl coating buffer , overnight . After blocking with 2% milk in coating buffer for 45 min , 100 µl of two-fold serial dilutions of anti-DENV mAbs were applied to the wells and incubated for 1 . 5 h , in triplicate . Subsequently , 100 µl of horseradish peroxidase-conjugated goat anti-mouse IgG-isotype antibody ( Southern Biotech ) was applied for 1 h . All incubations were performed at 37°C . Staining was performed using o-phenylene-diamine ( OPD ) ( Eastman Kodak Company ) and absorbance was read at 492 nm ( A492 ) with an ELISA reader ( Bio-tek Instruments , Inc . ) . Convalescent sera from DENV-2 immune , hospitalized patients were kindly provided by dr . G . Comach ( Biomed-UC , Lardidev , Maracay , Venezuela ) and dr . T . Kochel ( U . S . Naval Medical Research Center Detachment , Lima , Peru ) . All sera samples analyzed were obtained between 20–28 days following DENV-infection . Virus or virus-antibody complexes were added to 2×105 K562 cells , at a multiplicity of 100 genome-containing particles ( MOG ) per cell . After 1 . 5 h incubation at 37°C , the inoculum was removed and fresh medium was added to the cells . At 24–48 hpi , the medium was harvested and virus production was analyzed by plaque assay on BHK-15 cells , as described previously [36] . To measure the number of infected cells , cells were fixed at 24–48 hpi , stained with 3H5-conjugated Alexa647 , and analyzed using a FACS Calibur cytometer . For virus-antibody complex formation , virus particles were incubated for 1 h at 37°C with various dilutions of monoclonal prM antibody 70-21 and 2H2 in cell culture medium containing 2% FBS prior to the addition to cells . To investigate the involvement of the Fc receptor , mAb 70–21 F ( ab' ) 2 fragments produced by use of the immobilized pepsin ( Pierce ) were used . Alternatively , K562 cells were pretreated with 25 µg/ml of anti-FcγRII antibody ( MCA1075PE , Serotec ) for 1 h at 37°C , after which access antibody was removed by extensive washing . In furin blockage experiments , cells were treated with 25 µM of furin-specific inhibitor , decanoyl-L-arginyl-L-valyl-L-lysyl-L-arginyl-chloromethylketone ( decRRVKR-CMK ) ( Calbiochem ) prior and during virus infection . In control sample for the decRRVKR-CMK activity , additional 25 µM of the inhibitor was added to ensure blockage of the progeny virus maturation . To determine the number of bound genome-containing particles per cell , virus or virus-antibody complexes were incubated with 2×105 K526 cells at MOG 1000 for 1 h at 4°C . Subsequently , cells were washed three times with ice-cold PBS containing MgCl2 and CaCl2 ( Life Technologies ) to remove unbound virus-antibody complexes . Then , viral RNA was extracted from the cells by use of the QIAamp Viral RNA mini Kit ( QIAGEN ) . Thereafter , cDNA was synthesized from the viral RNA with reverse transcription-PCR ( RT-PCR ) , copies of which were quantified using quantitative PCR [15] . [35S]methionine-labeled immature particles or viral immune complexes were incubated with furin [New England BioLabs] for 16 h at pH 6 . 0 , as described previously [16] . Following furin treatment samples were subjected to sodium dodecyl sulphate-polycrylamide gel electrophoresis ( SDS-PAGE ) analysis to visualize the protein composition . | Dengue virus represents a major emerging arboviral pathogen circulating in the ( sub ) tropical regions of the world , putting 2 . 5 billion people at risk of infection . Each of the four circulating serotypes can cause disease ranging from febrile illness to devastating manifestations including dengue hemorrhagic fever and dengue shock syndrome . Severe illness is observed in individuals experiencing a re-infection with a heterologous dengue virus serotype and in infants born to dengue-immune mothers , presumably due to antibody-dependent enhancement of infection . Interestingly , it has been recently reported that patients experiencing a secondary infection have elevated levels of antibodies directed against the prM protein of immature dengue virus particles . Although it is known that cells infected with dengue virus release substantial amounts of prM-containing virions , numerous functional studies have demonstrated that immature particles lack the ability to infect cells . Herein , we show that essentially non-infectious fully immature dengue virions become virtually as infectious as wild type virus particles in the presence of prM antibodies . Anti-prM antibodies facilitate efficient binding and entry of immature dengue virus into cells carrying Fc-receptors . Furthermore , furin activity in target cells is critical for triggering infectivity of immature virus . These data indicate that immature dengue virus has the potential to be highly infectious and hence may contribute to disease pathogenesis . | [
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] | 2010 | Immature Dengue Virus: A Veiled Pathogen? |
It has unambiguously been shown that genetic , environmental , demographic , and technical factors may have substantial effects on gene expression levels . In addition to the measured variable ( s ) of interest , there will tend to be sources of signal due to factors that are unknown , unmeasured , or too complicated to capture through simple models . We show that failing to incorporate these sources of heterogeneity into an analysis can have widespread and detrimental effects on the study . Not only can this reduce power or induce unwanted dependence across genes , but it can also introduce sources of spurious signal to many genes . This phenomenon is true even for well-designed , randomized studies . We introduce “surrogate variable analysis” ( SVA ) to overcome the problems caused by heterogeneity in expression studies . SVA can be applied in conjunction with standard analysis techniques to accurately capture the relationship between expression and any modeled variables of interest . We apply SVA to disease class , time course , and genetics of gene expression studies . We show that SVA increases the biological accuracy and reproducibility of analyses in genome-wide expression studies .
Large-scale gene expression studies allow one to characterize transcriptional variation with respect to measured variables of interest , such as differing environments , treatments , time points , phenotypes , or clinical outcomes . However , a number of unmeasured or unmodeled factors may also influence the expression of any particular gene . Besides inducing widespread dependence in measurements across genes [1 , 2] , these influential factors create additional sources of differential expression , which , unlike gene-specific fluctuations , represent common sources of variation in gene expression that can be observed among multiple genes . We call “primary measured variables” ( or primary variables ) those variables that are explicitly modeled in the analysis of an expression study . These variables may or may not be associated with any given gene's expression variation . We classify all the remaining sources of expression variation into three basic types . “Unmodeled factors” are sources of variation explained by measured variables , but are not explicitly included in the statistical model ( e . g . , because their relationship to expression is intractable or the relevant measured variables were excluded because of sample size restrictions ) . “Unmeasured factors” are sources of expression variation that are not measured in the course of the study , so we also call these unmodeled factors . Finally , “gene-specific noise” refers to random fluctuations in gene expression independently realized from gene to gene . As a simple example meant only for illustrative purposes , consider a human expression study where disease state on a particular tissue type is the primary variable . Suppose that in addition to changes in expression being associated with disease state , the age of the individuals also has a substantial influence on expression . Thus , some genes exhibit differential expression with respect to disease state , some with respect to age , and some with respect to both . If age is not included in the model when identifying differential expression with respect to disease state , we show that this may ( a ) induce extra variability in the expression levels due to the effect of age , decreasing our power to detect associations with disease state , ( b ) introduce spurious signal due to the fact that the effect of age on expression may be confounded with disease state , or ( c ) induce long-range dependence in the apparent “noise” of the expression data , complicating any assessment of statistical significance for differential expression . In practice , even if age were known , it may be one of dozens of available measured factors , making it statistically intractable to determine which to include in the model . Furthermore , even measured factors such as age may act on distinct sets of genes in different ways , or may interact with an unobserved factor , making the effect of age on expression difficult to model . “Expression heterogeneity” ( EH ) is used here to describe patterns of variation due to any unmodeled factor . Major sources of expression variation are due to technical [3 , 4] , environmental [5 , 6] , demographic [7 , 8] , or genetic [9–11] factors . It is well known that sources of variation due to experimental design or large-scale systematic sources of signal may be present in expression data [3 , 4 , 12 , 13] , sometimes even after normalization has been applied [14] . Genetic factors can also have a large-scale impact on gene expression levels . Specific genetic loci have been shown to influence the expression of hundreds or thousands of genes in several organisms [10 , 11 , 15] . Expression heterogeneity is particularly pronounced in human expression data , especially in the study of complex systems , such as cancer or responses to stress [16–18] . Recently , Lamb et al . proposed the “Connectivity Map” for identifying functional connections between cancer subtypes , genetic background , and drug action [19] . Lamb et al . noted EH ( e . g . , due to cell type and batch effects ) presented a major hurdle for extracting relevant biological signal from the Connectivity Map . In each of these studies , expression variation with respect to one or at most a handful of variables is explored . However , it is likely that in each study multiple sources of EH will act on distinct , but possibly overlapping , sets of genes . Normalization techniques are commonly used to detect and adjust for systematic expression variation due to well-characterized laboratory and technical sources [12 , 13 , 20] . However , to date there has been no approach for identifying and accounting for all sources of systematic expression variation , including variation due to unmeasured or unmodeled factors of both biological and technical sources . We show here that biological sources of variation not modeled in the analysis can be just as problematic as technical sources of variation . Here , we introduce “surrogate variable analysis” ( SVA ) to identify , estimate , and utilize the components of EH . Figure 1 shows the effects of failing to account for unmodeled factors in a differential expression analysis , and the potential benefits of the SVA approach . EH causes drastic increases in the variability of the ranking of genes for differential expression ( Figure 1A ) , distorts the null distribution potentially causing highly conservative or anticonservative significance estimates ( Figure 1B ) , and reduces the power to distinguish true associations between a measured variable of interest and gene expression ( Figure 1C ) . However , employing SVA in these studies produces operating characteristics nearly equivalent to what one would obtain with no EH at all . We apply SVA to three distinct expression studies [7 , 21 , 22] , where each study contains clear patterns of EH ( Figure S1 ) . These studies represent major classes of gene expression studies performed in practice: genetic dissection of expression variation , differential expression analysis between disease classes , and differential expression over time . We show that SVA is able to accurately identify and estimate the impact of unmodeled factors in each type of study , using only the expression data itself . We further show that SVA improves accuracy and consistency in detecting differential expression . SVA orders the significant gene lists to more accurately and reproducibly reflect the ordering of the genes with respect to their true differential expression signal . SVA is particularly useful in producing reproducible results in microarray studies , because adjusting for surrogate variables reduces differential expression due to sources other than the primary variables . These results indicate that EH is prevalent across a range of studies and that SVA can be used to capture and account for these patterns to improve the characterization of biological signal in expression analyses .
We have developed an approach called surrogate variable analysis that appropriately borrows information across genes to estimate the large-scale effects of all unmodeled factors directly from the expression data . Figure 2A shows a simulated example of EH . The primary variable distinguishes the first ten arrays from the last ten ( Figure 2B ) ; however , the unmodeled factor may have a variety of effects on expression ( Figure 2C ) . The SVA approach flexibly captures signatures of EH , including highly irregular patterns not following any simple model , by estimating the signatures of EH in the expression data themselves rather than attempting to estimate specific unmodeled factors such as age or gender . After the surrogate variables are constructed , they are then incorporated into any subsequent analysis as covariates in the usual way . The SVA algorithm , described in mathematical detail in Materials and Methods , can conceptually be broken down into four basic steps: ( Step 1 ) Remove the signal due to the primary variable ( s ) of interest to obtain a residual expression matrix . Apply a decomposition to the residual expression matrix to identify signatures of EH in terms of an orthogonal basis of singular vectors that completely reproduces these signatures . Use a statistical test to determine the singular vectors that represent significantly more variation than would be expected by chance . ( Step 2 ) Identify the subset of genes driving each orthogonal signature of EH through a significance analysis of associations between the genes and the EH signatures on the residual expression matrix . ( Step 3 ) For each subset of genes , build a surrogate variable based on the full EH signature of that subset in the original expression data . ( Step 4 ) Include all significant surrogate variables as covariates in subsequent regression analyses , allowing for gene-specific coefficients for each surrogate variable . The four-step procedure is necessary both to ensure that the surrogate variables indeed estimate EH and not the signal from the primary variable ( Step 1 ) , to ensure an accurate estimate of each surrogate variable by identifying the specific subset of genes driving each EH signature ( Step 2 ) , to allow for correlation between the primary variable and the surrogate variables by building the surrogate variables on the original expression data ( Step 3 ) , and to take into account the fact that a surrogate variable may have a different effect on each gene ( Step 4 ) . The third and fourth steps are particularly important for maintaining unbiased significance with SVA , as demonstrated below . The overall goal of SVA is to provide a more accurate and reproducible parsing of signal and noise in the analysis of an expression study when EH is present . One way in which signal is commonly quantified is through a significance analysis [23] . The most basic definition of a significance analysis being performed “correctly” is if the null distribution is calculated properly [24] . A straightforward means for determining whether this is true is to assess whether the p-values corresponding to true null hypotheses are Uniformly distributed between zero and one . Indeed , p-values are specifically defined so that those corresponding to true null hypotheses have a Uniform ( 0 , 1 ) distribution if and only if the null distribution has been correctly calculated [25] . Throughout this paper , we examine the distribution of p-values from null genes to determine whether various procedures are able to recover the correct null distribution in the presence of EH . To assess statistically any deviations from the Uniform distribution for the null p-values , we apply a nested Kolmogorov-Smirnov test that is robust to chance fluctuations that may be present in a single simulated dataset ( see Text S1 ) . We performed a simulation study to investigate the properties of SVA with respect to large-scale significance testing . Specifically , we show that the SVA algorithm ( a ) accurately estimates signatures of expression heterogeneity , ( b ) corrects the null distribution of p-values from multiple hypothesis tests , ( c ) improves estimation of the false discovery rate ( FDR ) [23 , 26] , and ( d ) is robust to confounding between the primary variable and surrogate variables . The primary variable for our simulation was a binary variable indicating two disease classes . We simulated 1 , 000 expression studies , drawn from the same hypothetical population . For each study , we simulated expression for 1 , 000 genes on 20 arrays divided between the two disease states . The first 300 genes were simulated to be differentially expressed between disease states and genes 200–500 were affected by an independent unobserved factor to simulate a randomized study ( Materials and Methods ) . Several recent studies have carried out the genetic dissection of expression variation at the genome-wide level [10 , 11 , 15] . Brem et al . [10 , 21] measured expression genome wide in 112 segregants of a cross between two isogenic strains of yeast . They also obtained genotypes for each segregant at markers covering 99% of the genome ( Materials and Methods ) . It was shown that many gene expression traits are cis-linking , i . e . , the quantitative trait locus ( QTL ) linkage peak coincided with the physical location of the open reading frame for the expression trait [36] . At the same time , it was also shown that a number of gene expression traits are trans-linking , with linkage peaks at loci distant from the physical location of their open reading frames . In particular , several “pivotal” loci each appear to influence the expression of hundreds or even thousands of gene expression traits . Similar highly influential loci have been observed in other organisms [11 , 15] . These pivotal loci act as a major source of EH , regardless of whether genotypes have been measured in an expression study . As proof of concept , the Brem et al . [10 , 21] dataset was used to show that well-defined EH exists in actual studies and that SVA can properly capture and incorporate this EH structure into the statistical analysis of measured variables of interest . First , we analyzed the full dataset to identify the expression traits under the influence of these pivotal trans-acting loci , as well as the patterns of EH induced by these loci . Then we applied SVA to only the expression data , ignoring the genotype data to identify relevant surrogate variables capturing EH . Linkage analysis was performed again including the surrogate variables as covariates , showing that the effects from the pivotal loci are now negligible . In other words , SVA was able to capture and remove the effects of these few pivotal loci without the need for genotypes . A number of expression traits have significant trans-linking eQTL mapping to pivotal loci on Chromosomes II , III , VIII , XII , XIV , and XV ( Figure 3A ) . In the SVA-adjusted analysis , the majority of the trans-linkages to the pivotal loci have been eliminated ( Figure 3B ) . The pervasive trans-linkage signal mapping to the pivotal loci can be viewed as global expression heterogeneity . The reduction in trans-linkage to these loci in the SVA-adjusted significance analysis indicates that SVA effectively captures genetic EH . Pivotal trans-linkage signals indicate large-scale effects of a few loci . However , subtle and potentially more interesting cis-linkage may be lost in the presence of substantial genetic heterogeneity . To assess the impact of SVA on power to detect cis-linkage , we calculated linkage p-values only for markers located within three centimorgans of the open reading frame of each trait . On chromosomes without a pivotal locus ( Chromosomes I , IV , V , VI , VII , IX , X , XI , and XIII ) the SVA-adjusted analysis finds substantially more cis-linkage signal . At an FDR cutoff of 0 . 05 , the adjusted analysis finds 1 , 894 significant cis-linkages , compared with 1 , 604 for the unadjusted analysis . This increase is consistent across a range of FDR cutoffs ( Table 1 ) and illustrates the potential increase in power obtained from applying SVA . We applied the SVA approach to two human studies [7 , 22] , representing the two common human study designs: disease state and timecourse .
Expression heterogeneity due to technical , genetic , environmental , or demographic variables is common in gene expression studies . Here we have introduced a new method , SVA , for identifying , estimating , and incorporating sources of EH in an expression analysis . SVA uses the expression data itself to identify groups of genes affected by each unobserved factor and estimates the factor based on the expression of those genes . Simulations show that SVA accurately detects expression heterogeneity and improves significance analyses . We performed SVA on experiments involving recombinant inbred lines , individuals of varying disease state , and expression measured over time to illustrate the broad range of studies on which SVA can be applied . One advantage of the SVA approach is the ability to disentangle correlated and overlapping differential expression signals . This approach may be particularly useful in clinical studies , where a large number of clinical variables may have a complicated joint impact on expression . We have implemented SVA in an open source package available for downloading at http://www . genomine . org/sva/ .
Three publicly available datasets were employed to represent a broad range of gene expression studies performed in practice . The first dataset consists of gene expression measurements for 6 , 216 genes in 112 segregants of a cross between two isogenic strains of yeast , as well as genotypes across 3 , 312 markers [10 , 21] . The second dataset consists of gene expression for 3 , 226 genes in seven BRCA1 and eight BRCA2 mutation–positive tumor samples [22]; several genes with apparent outliers were removed as described [23] for a total of 3 , 170 genes . The third dataset consists of gene expression measurements in kidney samples from normal kidney tissue obtained at nephrectomy from 133 patients [7]; the 34 , 061 genes analyzed in [8] were also analyzed here . Seventy-four of the tissue samples were obtained from the cortex and 59 from the medulla . Details of the protocol for each study appear in the corresponding references . All expression data were analyzed on the log scale . The SVA algorithm identified 14 significant surrogate variables from the expression data . We performed both an unadjusted and an SVA-adjusted linkage analysis for each expression trait . In the unadjusted analysis , linkage p-values were calculated from an F-test comparing an additive genetic model to the null model of no genetic association [42] . SVA-adjusted p-values were calculated from an F-test comparing the full model of genetic association and the null model of no association , both models including all significant surrogate variables as additive terms . For each study , we simulated expression for 1 , 000 genes on 20 arrays divided between the two disease states . For simplicity , the expression measurements for each gene were drawn from a normal distribution with mean zero and variance one . We simulated expression heterogeneity with a dichotomous unmodeled factor independent of the disease state . The mean differences between disease states and states of the unmodeled factor were drawn from two independent normal distributions . For the real data example , we calculated the residuals from the regression of BRCA tumor type on expression for the Hedenfalk data [22] . Then , for each simulated study , we independently permuted each row of the expression data to create a matrix of residuals . To this matrix , we added signal , as in the case of the purely simulated data . The simulation studies were based on data generated using the R programming language [43] . All differential expression analyses were performed by a t-test based on standard linear regression . The genes were ranked for relative significance by the absolute values of their t-statistics . Differential expression was calculated using a t-test based on standard linear regression for the disease class data . The method of Storey et al . [8] was applied for the time-course data . q-Values were estimated using previously described methodology [23] . Let Xmxn = ( x1 , . . , xm ) T be the normalized m × n expression matrix with n arrays for m genes , where xi = ( xi1 , . . , xin ) T is the vector of normalized expression for gene i . Let y = ( y1 , . . , yn ) T be a vector of length n representing the primary variable of interest . Without loss of generality model xij = μi + fi ( yj ) + eij , where μi is the baseline level of expression , fi ( yj ) = E ( xij | yj ) − μi gives the relationship between measured variable of interest and gene i , and eij is random noise with mean zero . As a simple example , for a dichotomous outcomes yj ∈ {−1 , 1} we would employ the linear model xij = μi + βi yj +eij and estimate μi and βi by least squares . We could then perform a standard test of whether βi = 0 or not for each gene . This hypothesis test is exactly equivalent to performing a test of differential expression between the two classes . Suppose in a microarray study there are L biologically meaningful unmodeled factors , such as age , environmental exposure , genotype , etc . Let gℓ = ( gℓ1 , . . . , gℓn ) be an arbitrarily complicated function of the ℓth factor across all n arrays , for ℓ=1 , 2 , . . . , L . Therefore , we can now model the expression for gene i on array j as xij = μi + fi ( yj ) + , where γℓi is a gene-specific coefficient for the ℓth unmodeled factor . If unmodeled factor ℓ does not influence the expression of gene i , then γℓi = 0 . The fact that we employ an additive model is actually quite general: it has been shown that even complicated nonlinear functions of factors can be represented in an additive fashion for a reasonable choice of a nonlinear basis [44]; we simply define the gℓ to be as nonlinear as necessary and make L as large as necessary to best fit the additive effect . Since there are n arrays , each gene's expression can be modeled by at most n linearly independent factors , and hence any dependence structure between genes can be represented using L ≤ n vectors in this additive fashion . Due to this formulation , the inter-gene dependent eij have now been replaced with , where is the true gene-specific noise , now sufficiently independent across genes . In other words , we have broken the error eij into two terms , one that represents dependent variation across genes due to unmodeled factors , , and one that represents gene-specific independent fluctuations in expression . It is not possible in general to directly estimate the unmodeled gℓ , and SVA does not attempt to do so . The key observation is to note that for L vectors in n space , it is possible to identify an orthogonal set of vectors hk , k = 1 , . . . , K ( K≤L ) that spans the same linear space as the gℓ In other words , for any set of vectors gℓ and coefficients γℓi , it is possible to identify mutually orthogonal vectors hk and coefficients λki such that and Therefore , we do not need to estimate the specific variables gℓ . We only need to estimate the linear combination , so we can choose a set of vectors that spans the same space but is statistically tractable . Here we choose the set of K orthogonal vectors ( denoted by the hk ) to be those that are the right non-zero singular vectors provided by the singular value decomposition of the m × n matrix with ( i , j ) entry . This justifies the use of the singular value decomposition to identify orthogonal signatures of expression heterogeneity for surrogate variable estimates . We call these h1 , h2 , . . . , hK the “surrogate variables . ” An intuitive question that arises from an inspection of this formulation is about the model assumptions of the gℓj . Whereas the term fi ( yj ) is a model of the measured variable , yj , it is not generally possible to analogously formulate gℓj as a function of a well-defined , measured variable . Since we estimate the outcomes directly from the expression data ( as ) , it is not necessary to determine a model of the gℓj in terms of a biologically meaningful variable . Thus , we can bypass the need to know what the most relevant model of a measured variable is for gℓj for the purposes of estimating the EH . The goal of the SVA algorithm is therefore to identify and estimate the surrogate variables , hk , = ( hk1 , . . . , hkn ) T , based on certain consistent patterns of expression variation . Methods for empirically identifying [37] and estimating [40] expression trends or clusters have previously been developed . However , care must be taken when estimating expression trends for use in subsequent analyses of measured variables of interest . Specifically , surrogate variables must represent signal due to sources other than the primary variable and allow for potential overlap with the primary variable . The SVA algorithm is designed to estimate surrogate variables that meet both requirements . We assume that n < m and , for simplicity , that there is only a single primary variable; the extension to multiple primary variables simply requires one to include all of them in the model fit occurring in each Step 1 below . The algorithm is decomposed into two parts: detection of unmodeled factors and construction of surrogate variables . The basic form of the first algorithm has been proposed previously [27] . The second algorithm has been proposed and justified in this manuscript Algorithm to detect unmodeled factors . 1 . Form estimates and by fitting the model xij = μi + fi ( yj ) + eij , and calculate the residuals rij = xij − ( yj ) to remove the effect of the primary variable on expression . Form the m × n residual matrix R , where the ( i , j ) element of R is rij . 2 . Calculate the singular value decomposition of the residual expression matrix R = UDVT . 3 . Let dℓ be the ℓth eigenvalue , which is the ℓth diagonal element of D , for ℓ=1 , . . . , n . If df is the degrees of freedom of the model fit μ̂i + f̂i ( yj ) , then by construction the last df eigenvalues are exactly zero and we remove them from consideration . For eigengene k=1 , . . . , n-df set the observed statistic to be which is the variance explained by the kth eigengene . 4 . Form a matrix R* by permuting each row of R independently to remove any structure in the matrix . Denote the ( i , j ) entry of R* by . 5 . Fit the model and calculate the residuals to form the m × n model-subtracted null matrix R0 . 6 . Calculate the singular value decomposition of the centered and permuted expression matrix R0 = U0D0 . 7 . For eigengene k form a null statistic as above , where d0ℓ is the ℓth diagonal element of D0 . 8 . Repeat steps 4−7 a total of B times to obtain null statistics for b = 1 , . . . , B and k = 1 , . . . , n-df . 9 . Compute the p-value for eigengene k as: Since eigengene k should be significant whenever eigengene k′ is ( where k′>k ) , we conservatively force monotonicity among the p-values . Thus , set pk = max ( pk−1 , pk ) for k = 2 , . . . , n-df . 10 . For a user-chosen significance level 0≤α≤1 , call eigengene k a significant signature of residual EH if pk ≤ α . Algorithm to construct surrogate variables . 1 . Form estimates and by fitting the model xij . = μi + fi ( yj ) + eij , and calculate the residuals rij = xij − ( yj ) to remove the effect of the primary variable on expression . Form the m × n residual matrix R , where the ( i , j ) element of R is rij . 2 . Calculate the singular value decomposition of the residual expression matrix R = UDVT . Let ek = ( ek1 , . . . , ekn ) T be the kth column of V ( for k=1 , . . . , n ) . These ek are the residual eigengenes and represent orthogonal residual EH signals independent of the signal due to the primary variable . 3 . Set to the number of significant eigengenes found by the above algorithm . Note that “significant” means that the eigengene represents a greater proportion of variation than expected by chance . For each significant eigengene ek k=1 , . . . , . 4 . Regress ek on the xi ( i = 1 , . . . , m ) and calculate a p-value testing for an association between the residual eigengene and each gene's expression . This p-value measures the strength of association between the residual eigengene ek and the expression for gene i . 5 . Let π0 be the proportion of genes with expression not truly associated with ek; form an estimate , as described previously [23] and estimate the number of genes associated with the residual eigengene by . Let be the indices of the genes with smallest p-values from this test . 6 . Form the × n reduced expression matrix . Since is an estimate of the number of genes associated with residual eigengene k , the reduced expression matrix represents the expression of those genes estimated to contain the EH signature represented by some hk as described above . As was done for R , calculate the eigengenes of Xr , and denote these by for j=1 , . . . , n . 7 . Let j* = argmax1≤j≤n cor and set . In other words , set the estimate of the surrogate variable to be the eigengene of the reduced matrix most correlated with the corresponding residual eigengene . Since the reduced matrix is enriched for genes associated with this residual eigengene , this is a principled choice for the estimated surrogate variable that allows for correlation with the primary variable . 8 . In any subsequent analysis , employ the model xij = μi + fi ( yj ) + , which serves as an estimate of the ideal model xij = μi + fi ( yj ) + . The singular value decomposition is employed in these SVA algorithms . It may be possible to utilize other decomposition methods , but since the singular value decomposition provides uncorrelated variables that decompose the data in an additive linear fashion with the goal of minimizing the sum of squares , we found this to be the most appropriate decomposition . If the primary variables are modeled for data that are not continuous , then it may make sense to decompose the variation with respect to whatever model-fitting criteria will be employed SVA has been made freely available as an R package at http://www . genomine . org/sva/ . | In scientific and medical studies , great care must be taken when collecting data to understand the relationship between two variables , such as a drug and its effect on a disease . In any given study there will be many other variables at play , such as the effects of age and sex on the disease . We show that in studies where the expression levels of thousands of genes are measured at once , these issues become surprisingly critical . Due to the complexity of our genomes , environment , and demographic features , there are many sources of variation when analyzing gene expression levels . In any given study , it is impossible to measure every single variable that may be influencing how our genes are expressed . Despite this , we show that by considering all expression levels simultaneously , one can actually recover the effects of these important missed variables and essentially produce an analysis as if all relevant variables were included . As opposed to traditional studies , the massive amount of data available in this setting is what makes the method , called surrogate variable analysis , possible . We hypothesize that surrogate variable analysis will be useful in many large-scale gene expression studies . | [
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As targets of adaptive immunity , influenza viruses are characterized by the fluidity with which they respond to the selective pressure applied by neutralizing antibodies . This mutability of structural determinants of protective immunity is the obstacle in developing universal influenza vaccines . Towards the development of such vaccines and other immune therapies , our studies are designed to identify regions of influenza viruses that are conserved and that mediate virus neutralization . We have specifically focused on viruses of the H3N2 subtype , which have persisted as a principal source of influenza-related morbidity and mortality in humans since the pandemic of 1968 . Three monoclonal antibodies have been identified that are broadly-neutralizing against H3 influenza viruses spanning 40 years . The antibodies react with the hemagglutinin glycoprotein and appear to bind in regions that are refractory to the structural variation required for viral escape from neutralization . The antibodies demonstrate therapeutic efficacy in mice against H3N2 virus infection and have potential for use in the treatment of human influenza disease . By mapping the binding region of one antibody , 12D1 , we have identified a continuous region of the hemagglutinin that may act as an immunogen to elicit broadly protective immunity to H3 viruses . The anti-H3 monoclonal antibodies were identified after immunization of mice with the hemagglutinin of four different viruses ( A/Hong Kong/1/1968 , A/Alabama/1/1981 , A/Beijing/47/1992 , A/Wyoming/3/2003 ) . This immunization schedule was designed to boost B cells specific for conserved regions of the hemagglutinin from distinct antigenic clusters . Importantly , our antibodies are of naturally occurring specificity rather than selected from cloned libraries , demonstrating that broad-spectrum humoral immunity to influenza viruses can be elicited in vivo .
Under non-pandemic conditions , the global mortality attributed to influenza virus infection is considerable , with 200 , 000–500 , 000 associated deaths occurring each year [1] . In the setting of the 1918 influenza pandemic , the global mortality reached 50 million people in one year , equivalent to twice the number of people killed by HIV/AIDS since its emergence almost thirty years ago [2] . Notably , in 1918 and in the current swine-origin influenza virus pandemic , the populations normally considered the fittest are observed to be among the most vulnerable [3] , [4] . Four kinds of influenza viruses are circulating in the human population at this time: influenza A viruses of the hemagglutinin H3 and H1 subtypes ( H1 viruses are further divided into those of human and swine origin ) and influenza B viruses . Influenza A viruses are responsible for the bulk of seasonal disease , with H3 viruses dominating eight of the past twelve influenza seasons in the United States [5] . In 1968 , an H3 virus caused one of the three major influenza pandemics of the twentieth century and H3 viruses have persisted since that time as a significant agent of human disease . In addition to humans , H3 influenza viruses commonly infect birds , swine , and horses . It is not known whether H3 viruses will persist as human pathogens or how they may evolve to become more or less virulent in humans . Immunity to influenza viruses is currently achieved by vaccination with strains representing those predicted to circulate in the coming flu season . In a healthy person , the virus acts as a robust immunogen , eliciting neutralizing serum antibody that protects against influenza disease . Both the humoral and cell-mediated arms of the adaptive system are involved in resolution of active influenza infection , with neutralizing antibody titers correlating with protection in vivo [6] . The hemagglutinin glycoprotein is the primary target of antibodies that confer protective immunity to influenza viruses . Antibodies to other influenza proteins likely act in: Fc-receptor mediated uptake of virus particles , antibody-dependent cell cytotoxicity , delay of replication kinetics and , in aggregate , they may contribute to virus neutralization . On a monoclonal level , however , only antibodies specific for the viral hemagglutinin have been shown to block/neutralize infection [7] . Neutralizing monoclonal antibodies ( mAbs ) act by preventing either of the two functions of the hemagglutinin molecule: virus attachment or virus fusion with the host cell [8] . Antibodies that prevent attachment bind antigenic sites surrounding the receptor binding pocket in the membrane distal HA1 subunit of the hemagglutinin and restrict the association with host cell receptors ( sialic acids ) [9] . These antibodies drive the outgrowth of antigenic variants , resulting in a continuum of changes in the hemagglutinin structure known as ‘antigenic drift’ . Relatively few examples of fusion-inhibiting mAbs are available , but they are most commonly described to interact with the membrane proximal HA2 portion of the hemagglutinin in the region of the fusion peptide [10] , [11] , [12] . The sixteen subtypes of the influenza hemagglutinin are divided broadly into two phylogenetic groups that correlate with two basic structures taken by the stalk of the molecule [13] . In 1993 , mAb C179 , an antibody with broad neutralizing activity against viruses in Group 1 ( of H1 and H2 subtypes ) was described [14] . More recently , several other monoclonal antibodies that neutralize a broad array of Group 1 viruses ( including representative H1 and H5 viruses ) were identified [11] , [12] , [15] , [16] . These antibodies have consistently been shown to interact with the stalk of the hemagglutinin and neutralize virus by preventing fusion with the host cell . This report constitutes the first description of broadly neutralizing antibodies against viruses in Group 2 .
In order to enhance the production of cross-reactive antibody specificities , we immunized mice by sequential administration with DNA coding for the hemagglutinin from H3 viruses arising approximately 10 years apart: A/Hong Kong/1/1968 , A/Alabama/1/1981 , A/Beijing/47/1992 . Finally , three days prior to fusion , mice were boosted with the H3 virus A/Wyoming/3/2003 . By performing the fusion rapidly after virus boost we ensured that only hemagglutinin-specific B cells were present in the spleen at time of fusion . The hemagglutinins chosen were from viruses that arose over several decades , thus representing multiple H3 antigenic clusters [17] . Post-fusion , hybridoma supernatants were screened for the ability to bind A/Hong Kong/1/1968 by western blot or by ELISA and successive rounds of subcloning were performed on positive supernatants until monoclonal hybridoma populations were isolated . The immunization schedule we utilized successfully elicited the production of antibodies with broad reactivity against H3 viruses . Approximately 120 clones were isolated that reacted with A/Hong Kong/1/1968; of those , eight mAbs were cross-reactive against all of the H3 hemagglutinins tested . Interestingly , the particular immunization protocol also preferentially elicited the production of antibodies specific for the HA2 subunit of the hemagglutinin . Of the 8 mAbs identified , 5 mAbs react with HA2 and 1 mAb reacts with HA1 by western blot . The remaining 2 mAbs bind conformational epitopes present in the HA trimer as detected by western blot of purified H3 virus proteins separated under non-reducing gel conditions . All mAbs were reactive in a purified H3 virus ELISA . Three of the mAbs , 7A7 , 12D1 , 39A4 , had the highest activity by ELISA and were selected for thorough characterization ( Table 1 , Figure 1 ) . Antibodies 7A7 , 12D1 and 39A4 react by ELISA with purified A/Alabama/1/1981 and purified A/Hong Kong/1/1968 viruses ( Figure 2 ) . MAb XY102 is specific for the hemagglutinin of A/Hong Kong/1/1968 virus [18] . MAbs 7A7 , 12D1 and 39A4 show broad reactivity by immunofluorescence against cells infected with all H3 viruses spanning 40 drift years . MAbs 7A7 and 39A4 also react by immunofluorescence with other influenza A viruses chosen at random , including representative H1 , H2 and equine H3 viruses ( Table 2 ) . The anti-H3 mAbs were first evaluated for their ability to neutralize H3 influenza viruses by microneutralization assay . Viruses used in this assay contain a gene segment coding for firefly luciferase in place of the viral hemagglutinin; a hemagglutinin is present on the viral envelope due to propagation of virus in cells stably expressing a particular H3 hemagglutinin protein ( see methods ) . Luciferase viruses were generated that express the hemagglutinin of A/HK/1968 or A/Panama/99 viruses . Neutralization of viruses by anti-H3 mAbs was determined based on luciferase activity after single-cycle replication; mAbs 7A7 , 12D1 and 39A4 were determined to neutralize the hemagglutinin of both A/HK/1968 and A/Pan/99 ( Figure 3 ) . Next , we evaluated neutralization activity by plaque reduction assay . The anti-H3 mAbs were able to prevent infection ( not simply reduce plaque size ) of Madin Darby canine kidney cells by H3 viruses arising over 40 drift years: A/HK/1968 , A/BJ/1992 , A/Pan/99 , A/Bris/07 , A/NY/08 ( Figure 4 ) . We tested 7A7 , 12D1 and 39A4 against representative H4 and H7 viruses ( Group 2 ) as well as an H1 virus ( Group 1 ) and found that they did not neutralize the non-H3 subtype viruses ( Figure 4 ) . The three mAbs were tested in vivo for use as passive transfer therapies in disease caused by H3 virus infection . Mice were given 30mg/kg mAb intraperitoneally either 1 hour before , 24 hours post or 48 hours post challenge with 10 mouse LD50 reassortant H3 virus ( the A/HK/68 reassortant virus contains the six non-hemagglutinin , non-neuraminidase segments from the mouse-adapted A/PR/8 virus ) . Mice were weighed daily and were sacrificed if they reached 75% of their starting weight . Treatment of mice with mAb 12D1 either prophylactically or therapeutically was 100% protective . mAb 39A4 was evaluated for efficacy by prophylactic treatment and was similarly 100% protective in vivo . Mice treated prophylactically with mAb 7A7 were only 40% protected against the A/HK/68 reassortant virus ( Figure 5 ) . Next , the effect of prophylactic treatment with mAb 12D1 or 39A4 on lung damage caused by H3 viral pneumonia was assessed by histologic evaluation of tissue taken 4 days post infection with the A/HK/68 reassortant virus . Without treatment , lungs showed degenerative changes with focal hemorrhaging , dense neutrophilic infiltrates and diffuse alveolar damage with edema . Treatment with either anti-H3 mAb significantly diminished pathologic changes ( Figure 6 ) . Having demonstrated protective activity in vivo against the A/HK/68 reassortant virus we sought to evaluate cross-protection mediated by mAbs 12D1 and 39A4 against a second H3 virus , A/Georgia/1981 . MAbs 12D1 and 39A4 were administered as described above to BALB/c mice one hour prior to infection . Mice were then infected intranasally with 2700 pfu A/Georgia/1981 and lung titers were evaluated two days post infection . The anti-H3 mAbs were found to reduce lung titers by 97 . 75% ( 12D1 ) or 99 . 03% ( 39A4 ) ( Figure 7 ) . In order to determine the mechanism of virus neutralization by our anti-H3 mAbs , we first looked at the ability of the mAbs to inhibit virus hemagglutination of chicken red blood cells . We found that none of the three mAbs had hemagglutination inhibition activity , suggesting that the mAbs did not act by obstructing the binding of virus to the host-cell . Next , we tested the effect of the anti-H3 mabs on virus fusion . MAbs 7A7 , 12D1 and 39A4 were determined to inhibit the low-pH fusion of A/HK/1968 virus with chicken red blood cells by at least 80% at 10ug/ml ( Figure 8 ) . Finally , we aimed to identify the region of the H3 hemagglutinin that might elicit antibodies with fine specificities mirroring those of 12D1 or 39A4 . Sixteen passages of A/HK/1968 virus in the presence of the anti-H3 mAbs 12D1 or 39A4 did not yield escape variants that might have assisted in identification of the binding epitopes . Also , the hemagglutinin of six plaques present after incubation of A/HK/1968 virus with 50ug/ml mAb 12D1 or 39A4 in a plaque assay was sequenced and we were surprised to find no changes from the wild-type hemagglutinin . Because mAb 12D1 mediates protection against influenza disease in vivo and reacts with a continuous epitope of the viral hemagglutinin ( no trimeric structure required ) , as evidenced by reactivity with the denatured hemagglutinin monomer by western blot ( Figure 1 ) , we focused on identification of the 12D1 binding epitope . Hemagglutinin truncation mutants consisting of hemagglutinin segments of varying length fused to GFP were generated . GFP expression was utilized to assess expression of the constructs in transfected 293T cells . By analysis of the truncation mutants , it was determined that the 12D1 paratope makes dominant interactions with the HA2 subunit in the region of amino acids 30–106 . Diminished 12D1 binding without diminished GFP expression in the 76–184 and 91–184 truncations along with loss of binding with the 106–184 truncation suggested that 12D1 binding is dependent on contacts with amino acids in the HA2 76–106 region ( Figure 9 ) . These 30 amino acids fall within the membrane distal half of the long alpha-helix of HA2 ( Figure 10 ) . The 12D1 paratope may have additional contacts with amino acids outside of this region ( in HA1 or HA2 ) that are not required for binding by western blot .
For this study , we developed an immunization schedule that elicited broadly-neutralizing antibodies against H3 influenza viruses in vivo . The finding that such antibody specificities can be elicited by vaccination of mice suggests that with the proper immunogen ( s ) and vaccination protocol , such a response might also be elicited in humans . Several recent studies describe antibodies isolated from human phage display libraries that have cross-neutralizing activity against Group 1 influenza viruses [11] , [12] , [15] , [16] . Mabs isolated from human display libraries have proved extremely useful in the characterization of structural epitopes that mediate heterosubtypic neutralization . Caveats to this methodology exist , however , since the diversity of combinatorial display libraries is typically orders of magnitude greater than the diversity of the true human variable region repertoire [19] . Additionally , phage display libraries are generated by random combination of immunoglobulin VH and VL genes and are therefore not restricted , as the in vivo repertoire is , by mechanisms regulating the production of auto-reactive specificities . Until now , broadly neutralizing antibodies reactive with H3 viruses have not been described . Interestingly , mAbs 7A7 and 39A4 react by immunofluorescence with the hemagglutinin of multiple subtypes , though neutralizing activity appears to be limited to H3 viruses . Binding by these mAbs to other subtypes may be of relatively low avidity such that they no longer mediate neutralization , or , they may simply bind an epitope of non-H3 hemagglutinins that does not mediate neutralization . The identification of anti-H3 mAbs 12D1 and 39A4 complements recent works describing antibodies F10 and CR6261 that neutralize an array of Group 1 viruses [11] , [12] , [16] . One might envision a passive transfer therapy consisting of multiple broadly neutralizing mAbs for general use against pandemic and seasonal influenza virus strains . With the increasing resistance of influenza virus isolates to available anti-viral drugs , such an antibody cocktail could be of great value in severe disease . Mouse monoclonal antibodies such as the anti-H3 mabs described herein are commonly used in the development of therapeutic antibodies for use in humans . Once characterized , rodent antibodies are readily humanized by methods typically involving grafting of non-human complimentary determining regions into appropriate human frameworks followed by cloning of variable region segments into complete human immunoglobulin constructs [20] . The fact that escape mutants were not selected after multiple passages of virus in the presence of anti-H3 mabs 12D1 and 39A4 is intriguing . Sui et al . reported that they were similarly unable to isolate escape mutants using their fusion-inhibiting mAb F10 [11] . MAb F10 makes multiple interactions with the hydrophobic pocket of the hemagglutinin including with the fusion peptide itself and prevents the low-pH triggered conformation change required for fusion . Considering the rigid structural and electrostatic requirements involved in membrane fusion , the hemagglutinin might not readily accommodate mutations at the F10 binding epitope . Anti-H3 mAb 39A4 binds a conformational epitope of the hemagglutinin trimer; the region of binding may bridge two monomers , therefore interacting with two different portions/faces of each monomer . A mutation at one region of contact ( that does not affect trimer formation ) may not be sufficient to ablate 39A4 binding . Anti-H3 mAb 12D1 likely binds within the long alpha-helix of HA2 . This region may not accommodate changes that would affect 12D1 binding due to required secondary helix structure and specific van der Waals interactions that stabilize the hemagglutinin trimer [21] . Generally , mutations in the stalk of the hemagglutinin are more likely to affect the architecture of the entire molecule than are mutations in the classical antigenic sites [9] . The development of HA2-based vaccine constructs is of significant interest given recent reports of anti-HA2 mAbs with broad neutralizing activity against influenza viruses . Original studies of immunogens consisting of virus particles lacking the HA1 subunit demonstrated that design of an effective construct , however , will likely not be straightforward [22] . This is in large part due to the difficulty involved in maintaining the native configuration of the hemagglutinin stalk , which has complex tertiary structure and incorporates a portion of HA1 in addition to the HA2 subunit . Recent reports of mAbs with broad neutralizing activity against influenza viruses that are not active by western blot and that make contacts with amino acids in both HA1 and HA2 underscore the importance of maintaining non-contiguous epitopes in HA2 vaccine contructs [12] , [16] , [20] . In contrast to these mAbs , anti-H3 mAb 12D1 does not rely on a structural/non-contiguous epitope of the hemagglutinin stalk for binding . The observation that 12D1 makes dominant contacts within a continuous segment of the HA2 subunit suggests the design of an immunogen , perhaps consisting of that HA2 segment coupled to a carrier protein , that would direct an immune response to the region . The identified region , HA2 76–106 , is 100% conserved between the H3 viruses used in this study and all other H3 viruses that we have examined . In contrast , the H1 viruses A/New Caledonia/20/99 and A/PR/8/34 share only 56 . 7% identity with the equivalent region in the H3 hemagglutinin . A vaccine construct incorporating this region , therefore , would likely not provide protection against H1 influenza viruses . This study and other structural studies [11] , [12] , [14] of the influenza hemagglutinin provide groundwork for the design of novel vaccine constructs aimed at providing broad-spectrum immunity to influenza viruses .
6 week old female BALB/c mice from Jackson Laboratory were used for all experiments . All animal procedures performed in this study are in accordance with Institutional Animal Care and Use Committee ( IACUC ) guidelines , and have been approved by the IACUC of Mount Sinai School of Medicine . Madin Darby canine kidney cells from ATCC were used for all cell based assays . Cells were maintained in minimum essential medium supplemented with 10% fetal bovine serum , and 100 units/ml of penicillin-100 µg/ml of streptomycin . All viruses were propagated in eggs . Viruses used in various studies: A/Hong Kong/1/1968 ( HK/68 ) ( H3 ) , A/Alabama/1/1981 ( AL/81 ) ( H3 ) , A/Georgia/1981 ( H3 ) , A/Beijing/47/1992 ( BJ/92 ) ( H3 ) , A/Wyoming/3/2003 ( H3 ) , A/Wisconsin/67/2005 ( WI/05 ) ( H3 ) , A/Brisbane/10/2007 ( BR/07 ) ( H3 ) , A/New York/2008 ( NY08 ) ( H3 ) , A/Texas/36/1991 ( TX/91 ) ( H1 ) , A/New Caledonia/20/99 ( N . Cal/99 ) ( H1 ) , A/Duck/England/1962 ( Dk/62 ) ( H4 ) , A/Turkey/England/1963 ( Tky/63 ) ( H7 ) , A/Equine/Kentucky/2002 ( e/KY/02 ) ( H3 ) , A/Ann Arbor/6/1960 ( AA/60 ) ( H2 ) , A/Fort Monmouth/1/1947 ( FM/47 ) ( H1 ) . Purified virus was prepared by high speed centrifugation ( 43 , 000 rpm , 1 hour ) of allantoic fluid through a 20% sucrose cushion . Hybridoma supernatants were used for screening of mAbs for reactivity by enzyme-linked immunosorbent assay ( ELISA ) and by western blot . For other assays , purified monoclonal antibody or ascites preparations treated with receptor-destroying enzyme [23] were used . RDE –treated ascites was used for measurement of binding by ELISA , microneutralization , plaque reducion and fusion assays . Antibodies were purified by methods previously described [24] . Because of differences in isotypes , Protein A-agarose ( Roche ) was used for purification of mAbs 7A7 and 39A4 while protein G-agarose ( Roche ) was used for purification of mAb 12D1 . 6-week old BALB/c mice were immunized with DNA constructs coding for the open-reading frame of influenza virus hemagglutinin in the pCAGGS plasmid [25] . Individual immunizations were given intramuscularly , 3-weeks apart and consisted of 100ug DNA in 100ul PBS . Hemagglutinins utilized in the immunization schedule were cloned from the following parental viruses - primary immunization: A/Hong Kong/1/1968 , secondary immunization: A/Alabama/1/1981 , tertiary immunization: A/Beijing/47/1992 HA . Three days prior to fusion , mice were boosted with 50ug purified A/Wyoming/3/2003 virus . B cell hybridomas were produced by methods previously described [26] , [27] . Hybridoma supernatants were screened by blot and by ELISA for reactivity with A/Hong Kong/1/1968 virus . For the ELISA , direct binding to wells coated with 5ug/ml purified virus , 50ul per well was assessed . For the blot assay , 10ug purified virus was adsorbed onto nitrocellulose strips and individual strips were incubated with hybridoma supernatants . For the ELISA and blot assays , binding of antibody to virus was detected using goat anti-mouse γ-chain horse radish peroxidase secondary antibody ( SouthernBiotech , Birmingham , Al ) . All wells that had activity in either assay against A/Hong Kong/1/1968 virus were subcloned repeatedly to ensure the monoclonality of the hybridoma populations . Blots were produced by methods previously described [28] . Samples were boiled for 5 minutes at 100°C in loading buffer containing SDS and 0 . 6M DTT . SDS migration buffer was used for electrophoresis . For non-reducing gel conditions samples were prepared in loading buffer with SDS but without reducing agent and were not boiled . MDCK cells were infected with virus at a multiplicity of infection of 1 and incubated for 6 hours at 37°C . Infected and uninfected cells were incubated with 1ug/ml mAb for 1 hour at room temperature . Goat anti-mouse fluorescein conjugate ( SouthernBiotech ) was used for detection of mAb binding . Two stable cell lines were generated that expressed the HA of A/Hong Kong/1/1968 virus or A/Panama/2007/1999 virus . Pseudotyped viruses expressing the HA of either cell line were generated by infection of cells with a virus that carries seven segments from A/WSN/33 virus ( all except the HA segment ) and one segment encoding Renilla luciferase . Pseudotyped viruses expressing the HA of A/Hong Kong/1/1968 virus or A/Panama/2007/1999 virus were used as the neutralization target . Viruses were incubated with mAb at room temperature for 30 minutes , rocking . Purified polyclonal mouse IgG ( Invitrogen ) was used for the negative control . The mixture containing virus and mAb was then transferred to wells of a 96-well plate seeded to confluency with MDCK cells and incubated for 12 hours at 37°C . Individual determinants were performed in triplicate . After incubation , luciferase activity in cell-lysates was measured as a read-out of virus infection ( Renilla luciferase assay system , Promega ) . Antibody and virus ( ∼50 pfu/well ) were co-incubated at room temperature for 30 minutes , rocking . 6 well plates seeded with MDCK cells were washed once with PBS and 200ul of virus and mAb was added to each well then incubated for 20 minutes , 37°C . Virus with mAb was aspirated from cells and an agar overlay containing antibody was added to each well . Plates were incubated for 3 days , 37°C and plaques were counted by crystal violet staining . Purified mouse IgG ( Invitrogen ) was used for the negative control . Before infection , mice were anesthetized by intraperitoneal administration of a ketamine ( 75 mg/kg of body weight ) /xylazine ( 15 mg/kg of body weight ) mixture . 6 week old BALB/c mice were given 30mg/kg mAb intraperitoneally either one hour before , 24 hours after or 48 hours after challenge with 10 LD50 A/Hong Kong/1/1968 , A/PR/8/34 reassortant virus or 2700 pfu A/Georgia/1981 virus ( lung titer experiment ) . Purified mouse IgG ( Invitrogen ) was used for the negative control . Virus was suspended in PBS and administered intranasally in 50ul ( 25ul per nostril ) . Mice were weighed daily and sacrificed if they fell to 75% of starting weight . For the lung titer experiment , mouse lungs were harvested 4 days post infection with A/Georgia/1981 and virus titers in lung homogenates were determined by plaque assay . For histologic evaluation of lung damage , lungs were harvested 4 days post infection with A/Hong Kong/1/1968 - A/PR/8/34 reassortant virus . Tissues were imbedded in paraffin and sections were stained with hematoxylin and eosin . MAbs were tested in a standard hemagglutination inhibition assay [29] using chicken red blood cells and A/Hong Kong/1/1968 virus . For the red blood cell fusion assay , virus was incubated with chicken red blood cells ( 2% final red cell concentration ) on ice for 10 minutes . Dilutions of antibody were added and samples were incubated on ice for 30 minutes . Sodium citrate buffer , pH 4 . 6 was then added to bring the final pH to 5 . 0 and samples were incubated for 30 minutes at room temperature . Samples were centrifuged for 3 minutes at 3000rpm to pellet red blood cells and supernatants were then transferred to an ELISA plate for determination of NADPH content by optical density measurement ( 340nm ) . NADPH was present in the supernatant as a function of fusion-induced red blood cell lysis . DNA constructs were generated in the pCAGGS plasmid that coded for truncations of the A/HK/1/68 virus hemaggluinin fused to green fluorescent protein . All constructs were sequenced and confirmed . 293T cells were then transfected using Lipofectamine 2000 ( Invitrogen , Inc . ) with the various pCAGGS encoding the HA-GFP fusion gene . Cell lysates were resolved in a 4–20% Tris-HCl SDS-PAGE gel ( Bio-Rad Laboratories ) and proteins were blotted onto a Protran nitrocellulose membrane ( Whatman ) . GFP and truncated HA fragments were detected using rabbit anti-GFP ( Santa Cruz Biotechnology , Inc . ) and anti-H3 mAb 12D1 respectively . Secondary antibodies were anti-rabbit IgG HRP ( Dako ) and anti-mouse Ig HRP ( GE Healthcare ) . | Influenza viruses remain a formidable public health threat . Because of a dramatic increase in drug resistant strains of influenza viruses and due to the semi-regular emergence of pandemic virus strains , the development of novel antibody-based therapies and influenza vaccine constructs is of great interest . Recently , monoclonal antibodies with broad neutralizing activity against an array of Group 1 influenza viruses ( including H5 and H1 subtypes ) were identified; studies using these antibodies have expanded our understanding of structural aspects of the viral hemagglutinin , the molecule mediating protective immunity to influenza viruses . We have identified the first broadly neutralizing antibodies against viruses in Group 2—specifically , they are active against H3 influenza viruses spanning 40 years . The antibodies react with the hemagglutinin and appear to bind in regions that are refractory to the structural variation required for viral escape from neutralization . The antibodies demonstrate therapeutic efficacy in mice against H3N2 virus infection and have potential for use in the treatment of human influenza disease . By mapping the binding region of one antibody , 12D1 , we have identified a continuous region of the hemagglutinin that may act as an immunogen to elicit an immune response conferring broad protection against H3 viruses . | [
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] | 2010 | Broadly Protective Monoclonal Antibodies against H3 Influenza Viruses following Sequential Immunization with Different Hemagglutinins |
Chikungunya virus ( CHIKV ) is a mosquito-transmitted alphavirus that can cause fever and chronic arthritis in humans . CHIKV that is generated in mosquito or mammalian cells differs in glycosylation patterns of viral proteins , which may affect its replication and virulence . Herein , we compare replication , pathogenicity , and receptor binding of CHIKV generated in Vero cells ( mammal ) or C6/36 cells ( mosquito ) through a single passage . We demonstrate that mosquito cell-derived CHIKV ( CHIKVmos ) has slower replication than mammalian cell-derived CHIKV ( CHIKVvero ) , when tested in both human and murine cell lines . Consistent with this , CHIKVmos infection in both cell lines produce less cytopathic effects and reduced antiviral responses . In addition , infection in mice show that CHIKVmos produces a lower level of viremia and less severe footpad swelling when compared with CHIKVvero . Interestingly , CHIKVmos has impaired ability to bind to glycosaminoglycan ( GAG ) receptors on mammalian cells . However , sequencing analysis shows that this impairment is not due to a mutation in the CHIKV E2 gene , which encodes for the viral receptor binding protein . Moreover , CHIKVmos progenies can regain GAG receptor binding capability and can replicate similarly to CHIKVvero after a single passage in mammalian cells . Furthermore , CHIKVvero and CHIKVmos no longer differ in replication when N-glycosylation of viral proteins was inhibited by growing these viruses in the presence of tunicamycin . Collectively , these results suggest that N-glycosylation of viral proteins within mosquito cells can result in loss of GAG receptor binding capability of CHIKV and reduction of its infectivity in mammalian cells .
Chikungunya virus ( CHIKV ) is a mosquito-transmitted , single-stranded RNA virus belonging to the genus Alphavirus of the family Togaviridae . In humans , CHIKV infection can cause fever , headache , maculopapular rashes , myalgia , acute joint swelling , persistent arthritis , and even life-threatening neurological or cardiovascular complications [1–5] . CHIKV was first identified in Africa in 1952 and has been endemic in the tropical Indian Ocean countries for decades [6] . In recent years , this virus has caused more widespread and noticeable outbreaks . From 2004 to 2011 , approximately six million cases of CHIKV infection were reported from nearly forty countries in Africa , Asia , and Europe [6–9] . In recent years , CHIKV mosquito transmission vectors Aedes aegypti and Ae . albopictus have spread from tropical to temperate climates , making CHIKV an emerging pathogen within these climate zones [10 , 11] . In line with this , CHIKV cases have been recently reported from more than twenty-five countries in the Caribbean islands , thereby posing a potential threat to North America [12] . Unfortunately , CHIKV pathogenesis is not well understood , and there is no vaccine or specific antiviral treatment currently available for CHIKV infection [13–15] . CHIKV circulates between mammalian and mosquito hosts and this cyclical transmission may provide a suitable environment for increased viral fitness and the emergence of more pathogenic strains [16 , 17] . Interestingly , re-emergence of CHIKV during the 2005–2006 epidemic on Reunion Island was associated with a single point mutation in its genome , which increased CHIKV fitness within its mosquito vector Ae . albopictus [18] . Additionally , CHIKV and other alphaviruses differ in their ability to infect mammalian and mosquito cells . For example , alphaviruses can cause cytopathic effects in mammalian cells and can also shut-down the mammalian macromolecular machinery involved in cellular protein synthesis at both the transcription and translational levels [19–21] . In contrast , alphavirus infection of mosquito cells causes little to no cytopathic effects and does not affect the cellular transcription and translational processes [21–24] . Mammalian and mosquito cells have distinct cellular enzymatic systems for protein glycosylation; therefore , different post-translational processing of viral surface proteins are possible in these host cells [25] , which can influence replication [26–28] , pathogenesis [28 , 29] , transmission [30] , and evolution [17] of mosquito-transmitted viruses . In line with this , mammalian- and mosquito-generated arboviruses can bind to different receptors expressed on the surface of host cells . For instance , differential glycosylation of viral receptor-binding proteins in mammalian- and mosquito-generated Sindbis virus [31] , West Nile virus ( WNV ) [32] , and dengue virus [33] , can affect binding of these virus to host cell receptors . Similarly , mammalian cell-generated Ross River virus ( RRV ) , Venezuelan equine encephalitis virus ( VEEV ) , and WNV can induce more potent interferon responses compared to their mosquito cell-generated counterparts [34 , 35] . However , it remains unclear whether CHIKV generation in mosquito and mammalian cells can affect its infectivity and virulence . Glycosaminoglycans ( GAGs ) are highly sulfated polysaccharides that are ubiquitously expressed on the cell surface and the extracellular matrix of mammalian cells [36 , 37] . Many viruses including CHIKV can utilize GAGs as receptors to infect host cells [38] . However , research on the role of GAG receptor binding in CHIKV and other alphaviruses has been inconclusive . The GAG receptor binding of CHIKV and other alphaviruses can be acquired through acquisition of basic amino acids in viral receptor-binding proteins via mutations during their continuous passage in cell culture [36 , 38] . Although such dependence on GAG receptor binding increases viral infectivity in vitro , it can potentially decreases viral fitness in vivo [39] . In contrast , GAG binding properties have also been described in non-cell culture adapted alphaviruses , including a clinical strain of CHIKV [40] and a wild-type strain of eastern equine encephalitis virus ( EEEV ) [41] , suggesting that some other mechanisms that are independent of cell culture adaptation may also control GAG binding and virulence of CHIKV and other alphaviruses . Biochemically , GAG receptors possess a negative charge that enables virus-GAG receptor interaction [40–42] . In addition , mosquito and mammalian cells have different N-glycosylation mechanisms that can generate different configurations of viral glycoproteins [43] and modulate charge dependent interaction of viruses to host cell receptors . Thus , virus generation in these different host cells can potentially influence receptor binding and infectivity of CHIKV . However , the role of mosquito- and mammalian cells on GAG binding capability of CHIKV and other alphaviruses is unclear . Herein , we report that CHIKV generated in mammalian cells replicates more efficiently during its subsequent infection of both human and murine cells in vitro and is more virulent in a mouse model of CHIKV arthritis , when compared to its mosquito cell-generated counterpart . We further demonstrate that the reduced replication of mosquito cell-generated CHIKV is associated with its failure to bind to cell surface GAG receptors on mammalian cells due to differential glycosylation of viral proteins in mosquito cells .
This study was carried out in strict accordance with the recommendations described in the Guide for the Care and Use of Laboratory Animals of the National Research Council of The National Academies . The Institutional Animal Care and Use Committee at the University of Southern Mississippi ( Animal Welfare Assurance # A3851-01 ) reviewed and approved all the animal care and use procedures under the protocol #12041201 . All in vitro experiments and animal studies involving CHIKV were performed by certified personnel in biosafety level 3 ( BSL3 ) laboratories , following standard biosafety protocols approved by the University of Southern Mississippi Institutional Biosafety Committee . Low-passaged , Vero cell-generated CHIKV Ross strain ( provided by Dr . John F . Anderson , Connecticut Agricultural Experiment Station ) and LR OPY1 2006 strain ( provided by Dr . Robert B . Tesh , University of Texas Medical Branch ) were used as parental viral stocks in this study . Majority of experiments were performed using the Ross strain and to test strain specificity , some experiments were repeated using the LR OPY1 2006 strain . The viral stocks used in this study were prepared by a single passage of parental viruses in C6/36 ( ATCC , CRL-1660 ) or Vero cells ( ATCC , CCL-81 ) and designated as CHIKVmos and CHIKVvero , respectively . All viral stocks were titered in Vero cells by a plaque-forming assay . C6/36 cells were cultured at 28°C with 5% CO2 in Eagle’s minimum essential media ( EMEM , ATCC ) supplemented with 10% fetal bovine serum ( FBS ) . Vero cells were cultured at 37°C with 5% CO2 in Dulbecco’s modified Eagle’s medium ( DMEM , Life Technologies ) supplemented with 10% FBS . L929 ( ATCC , CCL-1 ) , NIH3T3 ( ATCC , CRL-1658 ) , Raw 264 . 7 ( ATCC , TIB-71 ) , human foreskin fibroblasts ( HFF , ATCC , CRL-2522 ) , human THP-1 cells ( ATCC , TIB-202 ) and human dermal fibroblasts were cultured at 37°C with 5% CO2 in DMEM supplemented with 10% FBS . To generate murine bone marrow-derived dendritic cells ( mBMDC ) , healthy C57BL/6J mice ( 7 weeks old ) were euthanized and bone marrow cells were recovered from both femurs . After red blood cells were lysed , the bone marrow cells were cultured in R10 medium supplemented with 10% J558L cell supernatant ( as a source of granulocyte-macrophage colony-stimulating factor ) at a final density of 1 × 106 cells/ml at 37°C with 5% CO2 . The medium was changed every 3 days and mBMDCs were ready for infection at day 10 . Dermatan sulfate ( from porcine intestinal mucosa ) , chondroitin sulfate A ( from bovine trachea ) , heparin ( from porcine intestinal mucosa ) , sepharose CL-4B , heparin-sepharose , yeast mannan , tunicamycin , and neutral red were all purchased from Sigma . Cells were plated 24 h before infection in 6- , 12- or 24-well plates to 60–80% confluence . CHIKVmos or CHIKVvero ( MOI = 1 ) were added to the cells and incubated at 37°C for 1 h to allow for viral adsorption and penetration . The inoculation medium was then replaced with fresh medium to remove unadsorbed viruses . Cells were washed once with fresh medium and further incubated at 37°C with 5% CO2 and collected at selected time points for analysis of viral genome replication and host’s gene expression . Cells were infected with CHIKV ( MOI = 1 or 5 ) for 48 h and fixed with 4% paraformaldehyde ( PFA , Electron Microscopy Science ) . Phase contrast images were acquired using Zeiss LSM510 META confocal imaging system ( Carl Zeiss Microscopy , NY ) . Cell viability was quantified by toluidine blue ( TB ) staining , according to the previously published method [44] . Viable cells were assayed by measuring the absorbance of TB at 630 nm using a microplate reader ( BIO-TEC ) . Percentage of viable cells was calculated after normalization to uninfected controls . CHIKV infected cells were subjected to total RNA extraction using TRI-reagent ( Molecular Research Center , Inc . ) . For RNA isolation from mouse blood samples , RNeasy mini kit ( Qiagen ) was used . The first-strand complementary DNA ( cDNA ) was synthesized using the iSCRIPT cDNA synthesis kit ( Bio-Rad ) . RT-qPCR assays were performed in a CFX96 Real-Time system ( Bio-Rad ) using SYBR Green supermix ( Bio-Rad ) . Viral RNA copy numbers were expressed as the ratio of CHIKV envelope-1 ( CHIKV E1 ) to cellular β-actin . For cytokine RT-qPCR assay , data were presented as relative fold change ( RFC ) in expression by the ΔΔCT method after normalized to cellular β-actin . Primer sequences for β-actin of mice [45] and human [46] were previously described . Primers for CHIKV E1 gene ( Forward: TCC GGG AAG CTG AGA TAG AA; Reverse: ACG CCG GGT AGT TGA CTA TG ) , and Ae . albopictus ribosomal protein 7 gene ( Forward: CTC TGA CCG CTG TGT ACG AT; Reverse: CAA TGG TGG TCT GCT GGT TC ) were designed using NCBI online primer designing tool . Primer sequences for host immune genes ( Ifn-α , Ifn-β , Tlr3 , Rig-I , Mda-5 , and Il-1β ) were described in a previous report [44] . All primers were synthesized by Integrated DNA Technologies . Plaque assays were performed according to our previous report with some modifications [47] . Briefly , Vero , L929 or NIH3T3 cells were plated at 5 × 105 cells/well in 6-well plates one day before infection . Virus-containing samples were added to cell monolayers to allow viral adsorption/penetration at 37°C with 5% CO2 for 1 h . After removing unadsorbed viruses , cells were overlaid with 1% SeaPlaque agarose ( Lonza ) containing medium and further incubated at 37°C with 5% CO2 for an additional 48 h . Plaques were counted after staining with 0 . 3% neutral red . CHIKV particles in viral stocks were also quantified by RT-qPCR , as described previously [38] . Briefly , 200 μl of viral stocks were treated with 50 units ( U ) of RNase A ( Affymetrix ) for 1 h at 37°C . TRI-reagent was added to inactivate RNase and lyse viral particles , and viral RNA was isolated after adding 5 μg of tRNA as carrier . First strand cDNA synthesis and CHIKV E1 gene quantification by RT-qPCR were performed as described above . Five week old , sex-matched C57BL/6J mice ( The Jackson Laboratory ) were subcutaneously inoculated on the ventral side of the right hind footpad toward the ankle with 105 plaque forming units ( PFUs ) of CHIKVvero or CHIKVmos ( Ross strain or LR 2006 OPY 1 ) in 50 μl phosphate buffer saline ( PBS ) , or with 50 μl PBS for mock controls , according to previous publications [48–50] . Blood samples were collected in 0 . 5M EDTA by retro-orbital bleeding and viral RNA in these samples were quantified by RT-qPCR . The height ( thickness ) and breath ( width ) of the perimetatarsal area of inoculated feet were measured daily from day 0 to day 10 post infection ( d . p . i . ) by using a digital caliper ( Electron Microscopy Science ) , and the relative increase in swelling was calculated as previously described [50] . Briefly , footpad swelling was expressed as the relative increase in swelling compared to pre-infection ( x d . p . i . – 0 d . p . i . ) /0 d . p . i . ) . Mice were euthanized and inoculated footpad tissues were collected at 6 d . p . i . and fixed overnight in 4% PFA , followed by decalcification in 10% EDTA for over 10 days . Tissues were then dehydrated , paraffin embedded , and sectioned ( 10 μm ) with a microtome ( American Optical Spencer 820 ) , followed by staining with hematoxylin and eosin ( H&E ) . The images were acquired using a bright-field microscope ( Olympus BH2 ) . CHIKVs ( MOI = 1 or 5 ) were added to cell monolayer for attachment at 4°C for 1 h followed by washing with fresh medium to remove unattached viruses . The attached viruses were quantified either by RT-qPCR or by a plaque assay in the same cells , as described above . In some experiments , media containing the unattached viruses were also collected and the unattached viruses were quantified by a plaque assay in Vero cells to confirm equal numbers of virions were added to each sample . In addition , CHIKV attachment was analyzed by flow cytometry . CHIKVvero or CHIKVmos ( MOI = 2 . 5 ) were added to NIH3T3 cells in triplicates in PBS supplemented with 2% FBS ( staining buffer ) and were incubated at 4°C for 45 min . Unbound viruses were removed by washing twice with the staining buffer and the cells were fixed with 2% PFA ( Electron Microscopy Science ) for 15 min at room temperature ( RT ) . After washing , the cells were probed with mouse monoclonal anti-CHIKV antibody ( Abcam ) and Cy5 conjugated goat anti-mouse IgG ( KPL ) secondary antibody , both for 1 h at RT . The cells were then washed twice and re-suspended in the staining buffer and analysed in a BD LSRFortessa ( BD Biosciences ) using FACSDiva version 6 . 0 software ( BD Biosciences ) . To assess CHIKVvero and CHIKVmos entry into the host cells , we blocked the endosome acidification process using a lysomotrophic agent alone or in combination with a low pH medium ( pH 5 . 5 ) , the latter mediated viral envelope and cytoplasmic membrane fusion , as previously described [51 , 52] . Briefly , infection was carried out in the medium containing 20 mM NH4Cl to block endosomal acidification . For the direct membrane fusion assay , viruses were allowed to attach onto cells and then immediately treated with low pH medium for 2 minutes . The internalized viruses were quantified by RT-qPCR and plaque assays . To analyze virus binding to GAG receptors , we performed a GAG neutralization assay , in which viruses were pre-incubated with soluble GAGs to inhibit their attachment to cell surface GAG receptors . Briefly , viruses ( 2 . 5 x 106 PFU/ml ) were pre-incubated with heparin , chondroitin sulfate A or dermatan sulfate ( concentration indicated in figures ) in DMEM containing 2% FBS at 37°C for 1 h . The virus-GAGs mixtures were then added to cells ( MOI = 1 ) at 4°C for 1 h to allow attachment . The unattached virus-GAGs mixtures were removed and cells were washed once with fresh culture medium . The viruses attached to cells were quantified by RT-qPCR and plaque assays . In some experiments , the effect of heparin-pretreatment on viral replication was measured at 24 hours post-infection ( h . p . i . ) by RT-qPCR . To measure virus binding to lectin receptors such as DC-SIGN and L-SIGN , we performed a blocking assay in the presence of yeast mannan that disrupts interaction of viruses to cell surface lectin receptors . Briefly , cells were pretreated with different concentrations of yeast mannan for 30 minutes at room temperature . Viruses were then added to the cells ( MOI = 1 ) and incubated at 4°C for 1 h . The viruses attached on cells were quantified by RT-qPCR . For flow cytometric analysis of GAG neutralization , CHIKVvero or CHIKVmos ( 2 . 5 × 106 PFU/ml ) were pre-incubated with different concentrations of GAGs in DMEM containing 2% FBS at 37°C for 1 h . Virus-GAGs mixtures were added to NIH3T3 cells in PBS supplemented with 2% FBS ( MOI = 2 . 5 ) and incubated at 4°C for 45 min . Cells were washed twice at 4°C to remove unbound virus and immediately fixed with 2% PFA for 15 min . Cells were then probed with anti-CHIKV antibody and analyzed by flow cytometry , as described above . Heparin-conjugated sepharose beads or unconjugated control beads were purchased from Sigma . The beads ( 60 μl ) were washed twice in 200 μl DMEM , and mixed with 105 PFUs of CHIKV in a total of 60 μl DMEM containing 2% FBS , and incubated at 4°C for 30 min . The beads were then washed three times in DMEM containing 2% FBS and the washed solution was collected for subsequent plaque assays to quantify the unbound viruses . Viruses bound to beads were lysed in 50 μl of Laemmli sample buffer ( Bio- Rad ) , and viral proteins were separated by 10% SDS-polyacrylamide gel electrophoresis and transferred to a nitrocellulose membrane ( Bio-Rad ) . After blocked with 5% bovine serum albumin ( BSA ) for 1 h at RT , the membranes were probed with mouse monoclonal anti-CHIKV primary antibody ( Abcam ) at 4°C for overnight on a rocker . The membranes were then washed five times ( 5 min each ) with Tris-buffered saline with Tween 20 ( TBS-T ) buffer and reacted with horseradish peroxidase conjugated goat anti-mouse IgG secondary antibody ( Jackson Immunoresearch ) for 1 h at RT . The membranes were then washed and developed using SuperSignal West Pico Chemiluminiscence Substrate ( Thermo Scientific ) and images were acquired using a ChemiDoc MP system ( Bio-Rad ) . Parental CHIKV viruses ( original stocks received from suppliers ) , single-passaged CHIKV in Vero cells ( CHIKVvero ) or mosquito cells ( CHIKVmos ) , and single-passaged CHIKVvero and CHIKVmos in NIH3T3 cells were subjected to RNA isolation using RNeasy Mini Kit ( Qiagen ) . cDNA was prepared using the iScript cDNA synthesis kit ( Bio-Rad ) . Complete CHIKV E2 gene was amplified using a Q5 high fidelity polymerase ( New England Biolab ) . The PCR primers were used according to a previous report [38] . The PCR fragments were purified by PureLink quick PCR Purification Kit ( Life Technologies ) and sequenced by Functional Biolab . Stocks of CHIKV ( Ross strain ) were prepared in Vero and C6/36 cells , UV-inactivated , and viruses were concentrated by pelleting with 20% sucrose at 28 , 000 rpm for 2 h in an ultracentrifuge ( Beckman Coulter ) . Deglycosylation of viral proteins were carried out using peptide-N-glycosidase F ( PNGase F , Sigma ) treatment following the manufacturer’s instruction . Viral proteins were separated in a 10% Mini-PROTEAN Precast Gels ( Bio-Rad ) and imaged in a ChemiDoc MP system ( Bio-Rad ) after coomassie brilliant blue staining . Tunicamycin ( TM ) was purchased from Sigma and dissolved ( 10 mg/ml ) in cell culture grade dimethyl sulfoxide ( DMSO , ATCC ) . Vero cells and C6/36 cells were plated for 24 h and infected with a 0 . 1 MOI of parental CHIKV ( Ross strain ) . Viruses were allowed to adsorb and penetrate for 1 h at 37°C . After unadsorbed viruses were removed , the cells were further cultured with medium containing 0 . 1 μg/ml of TM or the same final concentration of DMSO ( 0 . 001% ) as vehicle controls . Cell culture media were collected at 24 h for virus quantification by plaque assays and RT-qPCR assays . Virus stocks generated in Vero and C6/36 cells in the presence of TM or DMSO were used to infect NIH3T3 cells ( MOI = 0 . 1 ) and viral genome replication was analyzed at 24 h by RT-qPCR . Data were analyzed using GraphPad Prism ( version 6 . 0 , GraphPad software ) and p < 0 . 05 was considered statistically significant . Data were compared using the two-tailed student's t-test or analysis of variance ( ANOVA ) .
Previous reports have suggested that passage of virus through mosquito and mammalian cells can modulate arboviral infectivity [29 , 31 , 43] . To investigate the difference between mosquito and mammalian cells generated CHIKV , we prepared CHIKV stocks ( Ross strain ) by infecting African green monkey ( mammal ) kidney cell line ( Vero cells ) or an Ae . albopictus ( mosquito ) cell line ( C6/36 cells ) . Thus generated CHIKV stocks in Vero or C6/36 cells were titered by plaque assay in Vero cells and designed as CHIKVvero and CHIKVmos , respectively . CHIKV replicates more efficiently in fibroblastic cells compared to hematopoietic cells [53 , 54] , therefore we infected mouse embryonic fibroblasts ( NIH3T3 cells ) with CHIKVmos or CHIKVvero at a multiplicity of infection ( MOI ) of 1 . At 24 h . p . i . , the cells were collected for total RNA extraction and the first-strand complementary DNA ( cDNA ) synthesis . CHIKV envelope-1 ( E1 ) gene RNA copy numbers were quantified by reverse transcription quantitative polymerase chain reaction ( RT-qPCR ) and cellular β-actin was used as an internal control . The RT-qPCR results showed that the level of CHIKVmos replication was significantly lower ( approximately 25-folds ) than CHIKVvero at 24 h . p . i . ( Fig 1A , p < 0 . 01 ) . In addition , we also confirmed that CHIKVmos had lower replication in mouse subcutaneous fibroblasts ( L929 cells , Fig 1B , p < 0 . 05 ) , human foreskin fibroblastic cells ( HFF cells , Fig 1C , p < 0 . 05 ) and human dermal fibroblasts ( HDF cells , Fig 1D , p < 0 . 01 ) at 24 h . p . i . by RT-qPCR assay . To further test whether CHIKVmos had lower replication over CHIKVvero in cells other than fibroblasts , we compared their replication in a mouse macrophage cell line ( Raw 264 . 7 cells ) , primary mouse bone marrow derived dendritic cells ( mBMDC ) , and a human monocyte cell line ( THP-1 ) . Although CHIKV replication levels were relatively lower in these immune cells when compared to fibroblasts , similar reduction of CHIKVmos replication over CHIKVvero was also observed in Raw 264 . 7 cells ( Fig 1E , p < 0 . 005 ) , mBMDC ( Fig 1F , p < 0 . 05 ) , and THP-1 cells ( Fig 1G , p < 0 . 0005 ) . In contrast to murine and human cells , both CHIKVvero and CHIKVmos replicated similarly when their gene copy numbers were compared in mosquito ( C6/36 ) cells ( Fig 1H ) . To further study the kinetics of CHIKVvero and CHIKVmos replication over a longer infection period , we infected NIH3T3 cells with CHIKVvero or CHIKVmos ( MOI = 1 ) and cells were collected at various time points to analyze CHIKV E1 gene replication by RT-qPCR . In NIH3T3 cells , levels of CHIKVmos replication was about 30-fold lower than CHIKVvero at 12 and 24 h . p . i ( Fig 1I , p < 0 . 0001 ) , but both viruses replicated at comparable levels at the later time points ( 36 , 48 and 60 h . p . i ) . Both CHIKVvero and CHIKVmos also replicated at comparable levels at 48 h when assayed in L929 cells ( Fig 1J ) . These observations suggest that CHIKVvero replicates more efficiently than CHIKVmos in murine and human cells during the early time points . However , over the course of infection in mammalian cells , CHIKVmos may gain its infectivity and replicates similarly to CHIKVvero . Besides using PFU/ml as a standard titer for infection assays , we also determined viral titers by measuring viral genome copies in our viral stocks using RT-qPCR . We infected NIH3T3 cells with equal amounts of genome copies of CHIKVvero and CHIKVmos , and compared their replication levels by RT-qPCR . Similarly , we observed a significantly lower replication of CHIKVmos compared to CHIKVvero ( S1A Fig ) , which suggests that the lower replication of CHIKVmos over CHIKVvero was not due to the difference in unencapsidated viral genome present in our viral stocks . To test whether our results were specific to the CHIKV Ross strain , we also compared the replication levels of Vero cell-generated and C6/36 cell-generated CHIKV-LR OPY1 strain . Similarly , we observed a lower replication of C6/36 cell-generated CHIKV-LR OPY1 when NIH3T3 cells were infected ( S1B Fig ) . Collectively , these results demonstrate that mosquito cell-generated CHIKV has reduced levels of replication in both murine and human cells during early stage of infection , when compared to Vero cell-generated CHIKV . CHIKV infection can cause cytopathic effects and lysis of mammalian cells [21] . Since CHIKVmos has a much slower replication than CHIKVvero in both mouse and human cells at the early time points post infection , we expected that CHIKVmos might also cause less cytopathic effects in these cells . To test this , we infected NIH3T3 , L929 , HFF , and C6/36 cells with CHIKVmos and CHIKVvero ( Ross strain , MOI = 1 or 5 ) for 48 h , a time point when cytopathic effects were clearly visible under a microscope . The microscopy results showed that CHIKVvero caused more morphological distress and cell death than CHIKVmos in both human and mouse fibroblasts , but no cytopathic effect was observed in C6/36 cells ( Fig 2A ) . This observation was further confirmed by a cell viability assay using toluidine blue staining , which showed that CHIKVmos only caused moderate cytopathic effects compared to CHIKVvero in NIH3T3 ( Fig 2B , p < 0 . 005 ) , L929 ( Fig 2C , p < 0 . 005 ) and HFF cells ( Fig 2D , p < 0 . 005 ) . In contrast to murine and human cells , both CHIKVmos and CHIKVvero did not cause any apparent cytopathic effects in C6/36 cells ( Fig 2E ) . To rule out the possibility that the differences in cytopathic effects were not due to Vero and mosquito cell-specific proteins that could be released in culture supernatant and might be present in our virus stocks , we examined the cytopathic effects of UV-inactivated CHIKVvero and CHIKVmos stocks in L929 cells . We did not observe any cytopathic effects until 72 h post-treatment ( S1C Fig ) , suggesting that the observed cytopathic effects were specific to CHIKVvero and CHIKVmos infection . Some mosquito cell-derived viruses including RRV , VEEV and WNV have been reported to exhibit enhanced infection in primary myeloid dendritic cells due to their inhibition of type I interferon production when compared to corresponding mammalian cell-derived viral preparations [34 , 35 , 55] . While our results of CHIKVvero and CHIKVmos were opposite to those of RRV , VEEV and WNV in terms of replication [34 , 35 , 55] , we asked whether the difference in CHIKVmos and CHIKVvero replication was due to differential induction of cellular antiviral or inflammatory responses by these viruses . To test this , we measured the expression profiles of selected pattern recognition receptors ( PRRs ) and inflammatory cytokines in CHIKVmos or CHIKVvero ( Ross strain ) infected Raw 264 . 7 , L929 , NIH3T3 , and mBMDC ( MOI = 1 ) by RT-qPCR assay . In consistent with its lower replication , CHIKVmos induced significantly lower levels of antiviral cytokines ( Ifn-α and Ifn-β ) , proinflammatory cytokine ( Il-1β ) , and PRRs ( Tlr3 , Rig-I , and Mda-5 ) in all of the tested cell types at 24 h . p . i . ( Fig 3 , p < 0 . 05 ) . Difference in expression of these genes in CHIKVvero and CHIKVmos infected cells correspond with replication levels of these viruses in respective cells , suggesting that higher replication of CHIKVvero over CHIKVmos may not be due to an inhibition of antiviral or inflammatory cytokine expression by these cells . Thus , the slower replication of CHIKVmos in the early stage of infection might be due to a mechanism that is independent of the host cell antiviral responses . Differences in in vitro replication of mammalian and mosquito-generated viruses may not always produce the similar clinical symptoms in a mouse model , as previously reported with WNV infection [56] . Therefore , we asked whether CHIKVmos and CHIKVvero also differed in their virulence in vivo . To test this , we infected five-week-old , sex-matched C57BL/6J mice subcutaneously via footpad inoculations with 1 × 105 PFUs of CHIKVmos or CHIKVvero ( Ross strain ) or PBS as a vehicle control ( mock ) , according to the previous reports [48–50] . Blood samples were collected on day 1 , 2 , 4 and 6 post-infection ( d . p . i . ) for viremia measurement by RT-qPCR , and footpad swelling was measured daily from 0 to 10 d . p . i . . CHIKVmos produced lower viremia ( presented as CHIKV E1 / β-actin ) in mice over the course of infection when compared to CHIKVvero , which reached statistical significance at 2 d . p . i . ( Fig 4A , p < 0 . 05 ) . These results suggest that CHIKVmos displays reduced infectivity in mice . Consistent with the viremia results , CHIKVmos induced milder footpad swelling than CHIKVvero throughout the experiment ( Fig 4B and 4D ) . Similar results were also obtained when footpad swelling was compared in mice infected with mosquito cell- and Vero cell-generated CHIKV LR OPY1 strain ( Fig 4C and 4D ) . To further test whether CHIKVmos causes less pathology in mice compared to CHIKVvero , we collected inflamed footpad tissue at 6 d . p . i . and performed a histological analysis . We found that CHIKVmos induced less leukocyte infiltration and limited subcutaneous necrosis in the inflamed foot when compared to CHIKVvero ( Fig 4E ) . Since type I interferons have been shown to play important roles in CHIKV pathogenesis [53 , 57] , we also measured expression of Ifn-α and Ifn-β in the blood of CHIKVvero and CHIKVmos infected mice at 1 , 2 and 4 d . p . i . by RT-qPCR . . No significant difference in expression of these genes was observed between mice infected with CHIKVvero and CHIKVmos ( S1D and S1E Fig ) , suggesting that lower level of swelling in mice infected with CHIKVmos may not be due to difference in IFN expression , but due to lower infectivity of this virus . All these in vivo data suggest that CHIKVmos displays lower virulence than CHIKVvero in a mouse model of footpad swelling . To further dissect the mechanism by which CHIKVmos has reduced replication and virulence , we next compared the plaque-forming phenotypes of CHIKVvero and CHIKVmos in various cells by plaque assays and counted plaques at 48 h post infection . Consistent with RT-qPCR results ( Fig 1 ) , the plaque assays showed that CHIKVmos had an approximately 25-fold reduction in PFUs over CHIKVvero in both NIH3T3 and L929 cells ( Fig 5A and 5B , p < 0 . 01 ) when equal amounts of virus ( ~70 PFUs ) were used for plaque development . In contrast to the numbers of PFUs , both CHIKVvero and CHIKVmos formed plaques at 48 h and no difference in plaque size was observed in all the tested cells ( Fig 5A ) . These results suggest that the lower replication of CHIKVmos in murine and human cells may be due to its reduced ability to attach or enter into these cells . To test whether CHIKVmos attaches to cell receptors at a lower affinity compared to CHIKVvero , we measured the attachment of CHIKVmos and CHIKVvero ( Ross strain , MOI = 5 ) on L929 , NIH3T3 and HFF cells . Viruses were allowed to bind to the target cells for 1 h at 4°C , a condition at which most of the viruses attach to cell surfaces but do not enter into cells [58] . Unattached viruses were removed by washing with fresh medium and the viruses attached to cells were quantified by measurement of CHIKV E1 RNA copies by RT-qPCR . The results showed that CHIKVmos had significantly reduced attachment to L929 , NIH3T3 and HFF cells ( Fig 5C ) when compared to CHIKVvero . Similar results were also obtained when mosquito- and Vero cell-generated CHIKV-LR OPY1 viruses were assayed for their attachment to these cells by RT-qPCR ( Fig 5D ) . In contrast to murine and human cells , no difference in attachment between CHIKVvero and CHIKVmos was observed in C6/36 cells ( S1F Fig ) . To further confirm lower attachment of CHIKVmos , we incubated NIH3T3 cells with CHIKVvero or CHIKVmos ( Ross strain , MOI = 2 . 5 ) at 4°C for 45 min , immediately fixed the cell surface bound viruses with 4% PFA , probed these cells with anti-CHIKV monoclonal antibody , and analyzed them by flow cytometry . The results showed that CHIKVvero attached to 95% of these target cells while CHIKVmos attached to only 18% ( Fig 5E , p < 0 . 005 ) , which confirmed our hypothesis that CHIKVmos has a reduced attachment to host cells , when compared to CHIKVvero . To further test whether the reduced attachment of CHIKVmos correspond to its lower infectivity , the viruses attached to these cells were also visualized by plaque development . For this , we incubated 100 PFUs of CHIKVmos and CHIKVvero ( Ross strain ) with L929 cells at 4°C for 1 h and washed away unattached viruses . The attached viruses were allowed to develop plaques for 48 h in a 37°C incubator . To ensure that equal amounts of viruses were used during this experiment , unattached viral particles were also quantified by a plaque assay in Vero cells . As expected , the sum of attached and unattached virus matched between CHIKVvero and CHIKVmos . The attachment results are expressed as percentage using the sum of attached viruses and unattached viruses as denominator ( Fig 5F , p < 0 . 005 ) . These results showed that only 2% of CHIKVmos , compared to 45% of CHIKVvero , developed plaques in L929 cells , suggesting a 22 . 5-fold reduction in plaque development , when viruses were allowed to attach at 4°C . These results were in agreement with the RT-qPCR and plaque assay results showing that CHIKVmos had approximately 40-fold reduced replication ( Fig 1C , p < 0 . 005 ) and approximately 23-fold reduction in plaque numbers ( Fig 5A , L929 cells , p < 0 . 01 ) in L929 cells when compared to CHIKVvero . Collectively , these attachment assay results measured by RT-qPCR , flow cytometry and plaque assays , all suggest that lower replication of CHIKVmos is due to its reduced attachment to the host cells . Alphaviruses , including CHIKV , primarily use receptor-mediated endocytosis to enter into host cells , in which viruses enter the endosome through clathrin-independent endocytosis followed by a low pH dependent viral uncoating process in the endosome to gain entry into the cytoplasm [59 , 60] . Besides the receptor-mediated endocytosis pathway , direct viral membrane fusion with the cytoplasmic membrane has also been described as a possible mechanism that mediates the entry of some alphavirus ( e . g . Semliki Forest virus ) [61] . To test the possibility that whether CHIKVvero or CHIKVmos may use different pathways to enter into host cells , we performed plaque assays in the presence or absence of a lysomotrophic agent ( NH4Cl ) in growth media that blocks endosomal acidification and inhibits viral entry through endosomes . To test direct viral membrane fusion with cytoplasmic membrane , we induced a low pH mediated viral fusion with the plasma membrane and further cultured cells with or without NH4Cl . The plaque assay results showed that both CHIKVmos and CHIKVvero fail to develop plaques in L929 cells ( Fig 5G ) and Vero cells ( S1G Fig ) when endosomal acidification was blocked , suggesting that both types of viruses use receptor-mediated endocytosis , but not direct viral-plasma membrane fusion , to enter into cells . To further confirm that the receptor-mediated entry of CHIKV occurs through the endosome , we infected L929 cells ( MOI = 1 ) with CHIKVvero and CHIKVmos in the presence or absence of NH4Cl and analyzed the expression of CHIKV E1 at 24 h by RT-qPCR . These results further demonstrated that both CHIKVvero and CHIKVmos enter cells via the endosome pathway ( Fig 5H , p < 0 . 0001 ) . CHIKV E2 is the major viral protein that mediates binding of CHIKV to host cell surface receptors [62] . Although a number of cell surface receptors have been described for CHIKV and other alphaviruses [59 , 60] , the cell surface GAG receptors have been suggested to play a role in alphavirus infectivity [40–42 , 63 , 64] . We asked whether lower attachment of CHIKVmos was due to its failure to bind to the GAG receptors on host cells . To test this , we performed a GAG neutralization assay , in which we pre-incubated CHIKVmos or CHIKVvero ( Ross strain ) with a range of soluble GAGs ( dermatan sulfate and heparin from porcine intestinal mucosa and chondroitin sulfate A from bovine trachea ) at 37°C for 1 h . The virus-GAG mixtures were added to NIH3T3 cells and cells were further incubated at 4°C for 1 h for viral attachment . After removal of unattached viruses , the cells were washed once with fresh medium and the attached viruses were quantified by RT-qPCR . The results showed that pre-treatment with all the tested GAGs reduced CHIKVvero attachment to NIH3T3 cells in a concentration-dependent manner , but did not affect the attachment of CHIKVmos ( Fig 6A ) . These results suggest that only CHIKVvero , but not CHIKVmos , may utilize GAG receptors to enter into cells . It has been previously shown that mosquito cell-generated alphaviruses preferentially bind to lectin receptors to enter into cells [31] . Thus , it is possible that CHIKVmos uses lectin receptor to enter into cells . To test this , we performed mannan-blocking assay , in which pre-incubation of cells with yeast mannan blocks the binding of viruses to lectin receptors , such as DC-SIGN , L-SIGN and mannose receptors [31 , 59] . However , preincubation of both NIH3T3 and HFF cells with yeast mannan ( up to 200 μg/ml ) did not affect attachment of CHIKVmos or CHIKVvero in these cells ( S1H Fig ) . To further confirm GAG receptor binding of CHIKVvero , we also assayed GAG neutralization by flow cytometry . Similarly , we observed a concentration-dependent reduction of CHIKVvero attachment to NIH3T3 cells after GAGs pre-treatment , while attachment of CHIKVmos was not affected ( Fig 6B and 6C ) . In addition , pretreatment with GAGs also reduced the plaque development of CHIKVvero but not CHIKVmos ( S1I Fig ) . Similar results were also obtained when CHIKVmos and CHIKVvero ( LR OPY1 strain ) were tested for heparin neutralization in NIH3T3 cells by RT-qPCR ( Fig 6D , p < 0 . 005 ) . These results suggest that CHIKVvero , but not CHIKVmos , binds to the cell surface GAG receptors . To further confirm differential effects of pre-incubation with soluble GAGs in CHIKVvero and CHIKVmos attachment to host cells , we performed a direct GAG binding assay using heparin-conjugated sepharose beads . Equal PFUs of CHIKVvero or CHIKVmos or their parental viruses ( Ross strain , all generated in Vero cells ) were incubated with heparin-conjugated sepharose beads or unconjugated control beads at 4°C for 30 min . The beads were washed three times in DMEM containing 2% FBS , and the unbound viruses in the wash solution were quantified by a plaque assay . Approximately 95% of CHIKVvero , CHIKVmos and their parental viruses were recovered in the wash solution after incubation with the control beads ( Fig 6E , bottom ) , suggesting that none of the tested viruses bound onto the control beads . While approximately 95% of CHIKVmos were recovered in the wash solution after incubation with the heparin sepharose beads , only about 25% of CHIKVvero , and the parental viruses were recovered ( Fig 6E , bottom ) , suggesting that only Vero cell-generated CHIKV ( CHIKVvero and their parental viruses ) bound to heparin . These results were also confirmed by western blot by measuring viruses bound to the heparin sepharose and control beads ( Fig 6E , top ) . Collectively , the results of GAG neutralization by RT-qPCR , flow cytometry , and direct heparin-conjugated sepharose bead binding assays demonstrate that only CHIKV generated in Vero cells , but not in mosquito cells , can attach to cell surface GAG receptors . To test whether the difference in GAG receptor binding contributes to the different levels of replication of CHIKVmos and CHIKVvero , we infected NIH3T3 and HFF cells with heparin pre-treated ( 1000 U/ml ) or untreated CHIKVmos and CHIKVvero ( Ross strain ) at 37°C and measured viral replication at 24 h by RT-qPCR . Significant reduction in replication of CHIKVvero ( Fig 6G , p < 0 . 05 ) , but not CHIKVmos ( Fig 6F , p > 0 . 05 ) , was measured in both NIH3T3 cells and HFF cells ( Fig 6G ) after heparin pre-treatment . Of note , CHIKVvero pre-incubated with heparin had comparable replication to CHIKVmos in both mouse ( Fig 6F ) and human cells ( Fig 6G ) , suggesting that the GAG receptor binding of CHIKVvero contributes to its higher replication , when compared to CHIKVmos . CHIKV E2 is the receptor binding protein of CHIKV . It has been suggested that charge-dependent interaction between the conserved basic amino acid residues in the alphavirus E2 domains and the negatively charged GAG receptors on mammalian host cell surface facilitate viral attachment [16 , 60] . In line with this , GAG receptor binding has been previously reported in cell-culture adapted alphaviruses , including CHIKV , whereby continuous cell-culture passages of virus result in acquisition of basic amino acid ( s ) in the viral E2 glycoprotein via mutations [38 , 64] . To test the possibility of potential mutations that could be acquired during CHIKVvero and CHIKVmos generation through a single passage , we sequenced the receptor binding protein-encoding gene E2 of CHIKVvero , CHIKVmos , and their parental viral stocks ( both Ross and LR-OPY1 strains ) . The sequence alignment showed that the E2 gene sequences were identical among CHIKVvero , CHIKVmos , and their parental stock within each strain ( S2 Fig ) , which suggested no mutations were acquired in the E2 gene of our CHIKVvero and CHIKVmos stocks after a single cell passage . In addition , the heparin-sepharose bead assays showed that the parental viruses , also generated in Vero cells , bound to soluble GAGs ( Fig 6E ) , which indicated that Vero cell-generated CHIKV lost its GAG binding capability after a single passage in mosquito cells . To further confirm this , we generated CHIKVmos-NIH and CHIKVvero-NIH by growing CHIKVmos and CHIKVvero ( Ross strain ) in NIH3T3 cells for 48 h ( single passage ) and compared their replication levels in NIH3T3 and HFF cells at 24 h . p . i . by RT-qPCR . The results showed that both CHIKVmos-NIH and CHIKVvero-NIH produced a comparable amount of viral RNA and replicated similarly to CHIKVvero in NIH3T3 cells ( Fig 7A ) and HFF cells ( Fig 7B ) . We similarly passaged CHIKVmos and CHIKVvero once for 48 h in L929 to generate CHIKVmos-L929 and CHIKVvero-L929 , or in Vero cells to generate CHIKVmos-VERO and CHIKVvero-VERO . No differences in replication were observed when CHIKVmos and CHIKVvero passaged once in L929 cells ( Fig 7C ) or in Vero cells ( Fig 7D ) were used to infect NIH3T3 cells for 24 h . These data demonstrate that CHIKVvero and CHIKVmos no longer differ in replication after their single passage in mammalian cells , suggesting that mosquito cell-mediated reduction of CHIKVmos replication can be regained after a single passage of CHIKVmos in mammalian cells . To further test whether CHIKVvero and CHIKVmos after a single passage in mammalian cells have a similar level of attachment and GAG receptor binding , we performed attachment assays and GAG receptor neutralization assays of CHIKVmos-NIH and CHIKVvero-NIH , and measured the CHIKV E1 gene by RT-qPCR , as described above . The RT-qPCR data showed that both CHIKVmos-NIH and CHIKVvero-NIH had similar levels of attachment ( Fig 7E ) . In addition , the RT-qPCR results confirmed that pre-treatment with heparin , dermatan sulfate and chondroitin sulfate A significantly inhibited attachment of both CHIKVmos-NIH and CHIKVvero-NIH onto NIH3T3 cells at similar levels , confirming that CHIKVmos had regained GAG receptor binding after a single passage through the mammalian cells ( Fig 7F ) . To ensure this was not due to viral mutation , we also sequenced and compared the E2 gene in CHIKVmos-NIH and CHIKVvero-NIH , which showed that no mutation occurred in CHIKV E2 gene ( S2 Fig ) . Collectively , these results indicate that a single passage within mosquito cells can reduce CHIKV infectivity by eliminating its GAG receptor binding capability . Mosquito and mammalian cells use different cellular enzymes for N-glycosylation of proteins , and generate different carbohydrate residues in viral glycoproteins that influence receptor binding and virulence of viruses [31 , 43 , 65] . CHIKV E2 , the receptor binding protein , is comprised of 423 amino acids with two putative N-linked glycosylation sites at positions 263 and 345 [66] . Therefore , we hypothesized that differential glycosylation of CHIKV receptor binding protein in mammalian and mosquito cells might lead to differential GAG receptor binding and therefore influence replication of CHIKVvero and CHIKVmos . To confirm that N-glycosylation occurs in CHIKV proteins , we treated CHIKVvero and CHIKVmos with PNGase F , an enzyme that removes N-linked glycan from glycoproteins [67] , and analyzed the viral proteins by SDS-PAGE . We observed shifts in protein bands in the gel after PNGase treatment ( Fig 8A ) , suggesting that both E1 and E2 proteins are glycosylated in CHIKVvero and CHIKVmos . However , the composition of glycan at these glycosylation sites differs because the post-translational modifications within insect cells generate high-mannose oligosaccharides at all glycosylation sites of viral proteins , while such modifications in vertebrate cells generate both complex and high-mannose carbohydrates chains at similar glycosylation sites [43 , 65 , 68] . To test whether removal of N-glycosylation from CHIKVvero and CHIKVmos affect replication of these viruses , we infected C6/36 and Vero cells for 20 h with the parental stocks of CHIKV ( Ross strain ) in the presence of tunicamycin ( TM , 0 . 1μg/ml ) , an antibiotic that specifically inhibits N-glycosylation of proteins [26] , or with DMSO as a vehicle control . We analyzed the effect of TM on virus production in mosquito cells by plaque assays and RT-qPCR . Treatment with TM reduced infectious viral particle production in Vero cells ( Fig 8B , p < 0 . 001 ) , but not in C6/36 cells ( Fig 8C ) , when measured by plaque assay . Similar to the plaque assay results , genome quantification in culture media by RT-qPCR also showed that TM treatment similarly reduced CHIKV production from Vero cells ( Fig 8D , p < 0 . 001 ) , but not in C6/36 cells ( Fig 8E ) . In addition , CHIKV generated in C6/36 and Vero cells in the presence of DMSO or TM has comparable genome to PFU ratio , suggesting that our observations are not due to presence of defective viral particles between groups . These results suggest that N-glycosylation of CHIKV protein can influence its replication in mammalian cells but not in mosquito cells . We further compared the replication of non-glycosylated CHIKVvero and CHIKVmos that were generated after TM treatment . For this , we infected NIH3T3 cells and measured viral RNA at 24 h by RT-qPCR . As expected , CHIKVmos has approximately 30-fold lower replication than CHIKVvero when these viruses are generated in respective cells treated with DMSO ( designated as CHIKVvero-DMSO and CHIKVmos-DMSO , respectively ) as vehicle controls ( Fig 8F , p < 0 . 001 ) . Interestingly , CHIKVvero and CHIKVmos generated in the presence of TM ( designated as CHIKVvero-TM and CHIKVmos-TM , respectively ) no longer differed in their replication in NIH3T3 cells at 24 h ( Fig 8G ) . To further test whether CHIKVvero-TM and CHIKVmos-TM differ in GAG receptor binding , we performed GAG receptor neutralization assays of these viruses by RT-qPCR as described above . The RT-qPCR data showed that GAGs pre-treatment did not inhibit binding of both CHIKVmos-TM and CHIKVvero-TM to NIH3T3 cells ( Fig 7H ) . These results collectively indicate that mosquito cell-specific N-glycosylation of viral protein does not favor binding of CHIKV to cell surface GAG receptor and reduces its infectivity in murine and human cells .
Since viruses utilize host cell enzymatic systems to replicate their genome and synthesize functional proteins , viral infection may interrupt normal cellular functions and cause death of the host cells . However , mosquito-transmitted viruses replicate efficiently in mosquito cells , but do not cause apparent cellular damage to these cells [22 , 24] . Therefore , viruses may have adapted a mechanism to minimize their harmful effects within mosquito cells while replicating in mosquito vectors . This may be a viral fitness strategy , whereby a virus can efficiently replicate and generate a high titer in mosquito vectors , which is necessary for its efficient transmission to mammalian hosts . Since viral replication uses mammalian and mosquito cellular enzymatic systems to modify viral structural components [43 , 69] , it could be possible that this fitness advantage developed in mosquito cells may reduce viral infectivity in mammalian cells [70] . To test whether mosquito or mammalian cell passages can influence CHIKV infectivity , we compared the replication levels of CHIKVmos and CHIKVvero generated respectively in C6/36 ( mosquito , CHIKVmos ) or Vero ( mammal , CHIKVvero ) cells through a single passage . We observed that CHIKVmos had a significantly lower replication than CHIKVvero in both human and murine cells , and it also induced moderate cytopathic effects and lower antiviral immune responses . In contrast , both CHIKVmos and CHIKVvero replicated similarly in C6/36 cells and did not caused apparent cytopathic effects to these cells ( for up to 72 h . p . i . ) . In a mouse model of CHIKV-induced footpad swelling , CHIKVmos caused lower viremia , footpad swelling , and milder histological profiles within the inoculated foot when compared to CHIKVvero . These results suggest that generation through mosquito cells reduces CHIKV replication in both human and murine cells and cause a less severe disease in mice . Previous studies have demonstrated that arboviruses ( e . g . Sindbis virus , WNV and dengue virus ) generated in mammalian or mosquito cells can bind to different cell surface receptors [31–33] . In this study , CHIKVmos replicated at lower levels at the early stages of infection ( 6 to 24 h ) and also developed fewer plaques in both human and murine fibroblasts , compared to CHIKVvero . However , both CHIKVvero and CHIKVmos produced plaques at the same time point ( approximately 48 h . p . i . ) and no difference in plaque size was observed , suggesting only a portion of inoculated CHIKVmos may initiate productive infection in these cells . Moreover , the cytokine expression profiles suggest that the higher replication of CHIKVvero over CHIKVmos may not be due to suppression of antiviral cytokines , but rather an intrinsic property of the viral particles generated in two different cell lines . Therefore , we hypothesized that CHIKVmos might have a lower binding capability to murine and human cell surface receptors compared to CHIKVvero , which may account for lower replication of CHIKVmos at an early phase of infection . This hypothesis was proven by the attachment assay and viral entry assay results , which showed that CHIKVmos had reduced receptor binding to murine and human cells . Although the cellular receptors for CHIKV and other alphaviruses remain elusive , mammalian cell surface glycosaminoglycan ( GAG ) receptors are the most extensively studied receptors for alphaviruses [64 , 71 , 72] . Interestingly , it has been previously demonstrated that a single passage of RRV ( T48 strain ) in C6/36 cells resulted in loss of GAG receptor-binding capability , while RRV derived from mammalian cells bound to GAG receptors [73] . Consistent with this report , our GAG receptor neutralization and heparin-sepharose bead binding assays showed that CHIKVmos does not bind to GAG receptors . These results provided evidence for reduced receptor binding and infectivity of CHIKVmos compared to CHIKVvero . In addition , we also demonstrated that GAG receptor binding of CHIKVmos could be regained after a single passage in mammalian cells , indicating that mosquito cells can reduce infectivity of CHIKV by eliminating its ability to bind to GAG receptors on murine and human cells . It is likely that CHIKVmos may use some other receptors to enter into cells , and acquire GAG receptor-binding capability after replication in mammalian cells , which could eventually enhance its infectivity . Viruses that are generated in different host cells can acquire host cell-specific modifications on their structural components . For instance , during the viral budding process , enveloped viruses acquire a portion of the host cell membrane as the viral envelope membrane , resulting in variable carbohydrate and lipid compositions of these viruses , depending on the cell types in which they are generated [43 , 69 , 74] . In addition , mosquito and mammalian post-translational modifications , particularly the N-glycosylation , uses different cellular enzymes to modify the viral glycoproteins [31 , 65] . Our PNGase F treatment assay results confirm that CHIKV E2 , the receptor binding protein of CHIKV , has N-linked glycosylation sites . However , the composition of the carbohydrate residues at the glycosylation sites of E2 may vary depending on the host cell types used for CHIKV generation . Insect cells , including mosquitoes , are deficient in enzymes for carbohydrate synthesis , including N-acetylglucosaminyl- , galactosyl- , and sialyltransferases , resulting in high-mannose oligosaccharides at all glycosylation sites . In contrast , vertebrate cells can generate both complex and high-mannose carbohydrates chains at these glycosylation sites [43 , 65 , 68] . Glycosylations of Sindbis virus ( SINV ) E2 at positions 196 and 318 have been documented to play critical roles in receptor binding and infectivity in both cell culture and in a mouse model [28] . Thus , we hypothesized that differential glycosylation patterns in mammalian and mosquito cells may play a role in differential infectivity of CHIKV generated in these cell lines . TM is a potent inhibitor of N-glycosylation and it has been previously shown that viral stocks prepared in the presence of TM lose glycan at their glycolysation sites [26] . We demonstrated that CHIKVvero and CHIKVmos no longer differed in their replication when these viruses were prepared in the presence of TM , suggesting that mosquito and mammalian cell-specific glycosylation can affect the replication of CHIKVvero and CHIKVmos in murine and human cells . Although we could not completely exclude the possibility that mosquito cell-specific processing of other structural and nonstructural viral proteins might also influence the infectivity of CHIKVmos , our data provided several lines of evidence to support that mosquito cell-specific glycosylation of E2 does not favor GAG receptor binding and reduces CHIKV infectivity . Previous studies suggested that mammalian cell-generated RRV , VEEV , and WNV induce potent IFN responses , but these viruses generated in mosquito cells inhibit IFN production and replicate more efficiently in mammalian cells in vitro [34 , 35] . It has been suggested that mosquito cell-generated WNV has higher infectivity in vitro [32] , however , it produced significantly lower viral load in mice compared to mammalian cell-generated WNV during an early phase of infection , indicating greater infectivity of mammalian cell-generated WNV in vivo [56] . We found that mammalian ( Vero ) cell-generated CHIKV possessed greater infectivity in both in vitro and in vivo conditions . It is possible that the lack of GAG receptor binding of mosquito-generated CHIKV may favor its replication in mosquito cells , however this can potentially reduce its infectivity in mammalian cells . This hypothesis is also supported by a recent report , which showed that RRV had increased fitness in mosquito cells but produced less severe disease in a mouse model [70] . The role of GAG receptors in viral pathogenesis has been extensively studied in human immunodeficiency virus [75] , herpes simplex virus [76] , Echovirus [77] , and human papillomavirus [78] . However , the role of GAG receptor binding in alphavirus pathogenicity remains controversial . Several reports document that cell-culture adapted alphaviruses ( about 10–20 generations ) can acquire GAG receptor binding capability in vitro via mutations in the E2 gene that leads to acquisition of basic amino acids [38 , 64 , 71 , 72] . Such GAG receptor dependence , often described as a cell-culture adapted property , has been suggested to reduce viral pathogenicity in vivo , presumably because of rapid clearance of these viruses from circulation , as shown in VEEV [39] and SFV [79] . In contrast , GAG receptor binding of cell-culture adapted SINV has been shown to enhance its infectivity and neurovirulence in mice [42] . It has been believed that wild-type ( non cell-culture adapted ) alphaviruses generally do not use heparan sulfate ( HS ) receptors , a prototype of GAG receptors , for host cell attachment but depend on other cell surface receptors including DC-SIGN , L-SIGN , and C-type lectin molecules [31 , 59] . However , it has been demonstrated that wild-type isolates of eastern equine encephalomyelitis virus ( EEEV ) and VEEV that bound to GAG receptors were neurovirulent in mice [41 , 80] . Unlike other cell culture adapted alphaviruses , the GAG receptor binding capability of wild-type EEEV does not occur through acquisition of additional basic amino acids in E2 [41] . Similarly , GAG receptor binding property has been also described in a non cell-culture adapted clinical CHIKV strain , which also occurred independent of additional basic amino acids acquisition in E2 [40] . However , a non cell-culture adapted CHIKV LR strain does not bind to GAG receptors [57] , suggesting that GAG receptor binding may not be a property of all wild-type CHIKV strains . Herein , we report that mosquito cell-generated CHIKV does not bind on GAG receptors when compared to Vero cell generated counterpart , but it can acquire GAG receptors binding capability after a single passage in mammalian cells , such as NIH3T3 and L929 cells . CHIKV E2 sequencing results further confirmed that the GAG binding ability of mammalian cell-generated CHIKV was not due to mutation . It is noteworthy that we did not detect any previously published E2 amino acids in our CHIKV strains that were described to facilitate GAGs binding of the tissue-culture adapted CHIKV strain [38] , suggesting that GAG receptor binding in our mammalian cell generated CHIKV does not reflect cell-culture adaptation and occurs without acquisition of basic amino acids . Thus , our results support the notion that the GAG receptor binding capability in CHIKV and other alphavirus can also be acquired through mechanisms independent of basic amino acid acquisition in E2 and such GAG binding capability can also enhance CHIKV infectivity both in vitro and in vivo . Attenuation of viruses through a continuous cell-culture passage has been used as a strategy to develop live-attenuated arboviral vaccines [81–84] . Live-attenuated yellow fever virus vaccine ( 17D strain ) is one of the most successful vaccines generated through this approach . Unfortunately , uses of this approach in an attempt to generate similar vaccines to other viruses were not successful . Although the detailed molecular mechanisms for attenuation of 17D vaccine strain remain elusive , it has been proposed that viral dependence on GAG receptor binding , particularly HS receptors , which can be acquired during continuous cell-culture passage , might play a role [82 , 84 , 85] . Recently , GAG-receptor binding CHIKV strains generated through continuous cell-culture passage has been evaluated as potential vaccine candidates [38 , 86 , 87] . Although GAG receptor dependence has been believed to attenuate alphaviruses in vivo , some of these CHIKV vaccine strains have been shown to be pathogenic [38] . For example , CHIKV vaccine strain ( 181/25 ) has been reported to cause transient arthralgia in clinical trials [88] . Although the selection of GAG receptor dependence has been proposed as a vaccine developmental strategy , accumulating evidence [38 , 40 , 41 , 89] suggests that other mechanisms are also possible in attenuation of cell-culture adapted arboviruses . Therefore , strategies to generate and/or evaluate CHIKV and other arboviral vaccine candidates should not be solely based on selection of GAG receptor dependence . In addition , viruses may have different affinity of GAG receptor binding or they may bind to different types of GAG receptors that may affect GAG receptor-dependent CHIKV infectivity in vitro and in vivo , which requires further investigation . The mechanisms by which mosquito-transmitted viruses cause minimal or no damage to mosquito vectors , yet can cause cell death and diseases in mammalian hosts , are not well understood . The cellular machinery of mosquitos , which prevents CHIKV to bind to GAG receptors , may favor viral replication in mosquito cells without causing significant cytopathic effects . Our observation of reduced infectivity of mosquito-generated CHIKV puts forward an intriguing question of how CHIKV manages to infect hosts during natural infection , when viruses are inoculated into human skin by a mosquito bite . It has been previously reported that the mosquito saliva facilitates pathogenicity of some arboviruses , including CHIKV , La Crosse , dengue , and WNV by inducing CD4+ T helper-2 ( Th2 ) cell dominant anti-inflammatory responses [90–95] . It is likely that CHIKV may take advantage of mosquito salivary proteins to initiate early infection in humans . A recent report showed that an Ae . aegypti saliva serine protease enhanced dengue virus infectivity by increasing the attachment of viruses to HS proteoglycans in mammalian hosts [96] , suggesting that the reduction in viral GAG receptor binding by mosquito cells can be compensated by mosquito saliva during natural infections . After inoculation into the human skin through a mosquito bite , the mosquito cell-derived CHIKV may use alternative receptors to initiate a low level of infection in human cells . During the course of infection , CHIKV replicates using human cellular machinery and acquires GAG receptor binding , resulting in its enhanced infectivity . In conclusion , this study demonstrates that the infectivity of CHIKV is reduced when generated in mosquito cells . We show that mosquito cell-generated CHIKV does not bind to GAG receptors and has reduced attachment to mammalian cells . Furthermore , we provide several lines of evidence to support that N-glycosylation within mosquito cells may lower infectivity of CHIKV by removing its ability to bind GAG receptors on murine and human cells . This new understanding of how mosquito and mammalian host cells alter CHIKV receptor binding and infectivity may help with the development of effective therapeutics or vaccines against CHIKV and other mosquito-transmitted viruses . | Chikungunya virus ( CHIKV ) is a chronic arthritis-causing pathogen in humans , for which no licensed vaccine or specific antiviral drug is currently available . Due to the global spread of its mosquito vectors , CHIKV is now becoming a public health threat worldwide . CHIKV can replicate in both mammalian and mosquito cells , however it does not cause apparent damage to mosquito cells , yet it rapidly kills mammalian cells within a day after infection . In addition , mosquito and mammalian cells have different mechanism of protein glycosylation , which can result in different glycan structures of viral glycoproteins . In this study , we report that mosquito cell-generated CHIKV has lower infectivity in cell culture and causes less severe disease in mice , when compared to mammalian cell-generated CHIKV . We demonstrate that only mammalian cell-generated CHIKV , but not mosquito-cell generated CHIKV , binds to mammalian cell surface glycosaminoglycan receptors . Interestingly , mosquito-cell generated CHIKV can re-acquire glycosaminoglycan receptor binding capability after a single passage in mammalian cells and replicate at similar levels with mammalian cell-generated CHIKV , suggesting that passage of CHIKV in mosquito cells can reduce its infectivity . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Loss of Glycosaminoglycan Receptor Binding after Mosquito Cell Passage Reduces Chikungunya Virus Infectivity |
In response to impending anoxic conditions , denitrifying bacteria sustain respiratory metabolism by producing enzymes for reducing nitrogen oxyanions/-oxides ( NOx ) to N2 ( denitrification ) . Since denitrifying bacteria are non-fermentative , the initial production of denitrification proteome depends on energy from aerobic respiration . Thus , if a cell fails to synthesise a minimum of denitrification proteome before O2 is completely exhausted , it will be unable to produce it later due to energy-limitation . Such entrapment in anoxia is recently claimed to be a major phenomenon in batch cultures of the model organism Paracoccus denitrificans on the basis of measured e−-flow rates to O2 and NOx . Here we constructed a dynamic model and explicitly simulated actual kinetics of recruitment of the cells to denitrification to directly and more accurately estimate the recruited fraction ( ) . Transcription of nirS is pivotal for denitrification , for it triggers a cascade of events leading to the synthesis of a full-fledged denitrification proteome . The model is based on the hypothesis that nirS has a low probability ( , h−1 ) of initial transcription , but once initiated , the transcription is greatly enhanced through positive feedback by NO , resulting in the recruitment of the transcribing cell to denitrification . We assume that the recruitment is initiated as [O2] falls below a critical threshold and terminates ( assuming energy-limitation ) as [O2] exhausts . With = 0 . 005 h−1 , the model robustly simulates observed denitrification kinetics for a range of culture conditions . The resulting ( fraction of the cells recruited to denitrification ) falls within 0 . 038–0 . 161 . In contrast , if the recruitment of the entire population is assumed , the simulated denitrification kinetics deviate grossly from those observed . The phenomenon can be understood as a ‘bet-hedging strategy’: switching to denitrification is a gain if anoxic spell lasts long but is a waste of energy if anoxia turns out to be a ‘false alarm’ .
Denitrification is a key process in the global nitrogen cycle and is also a major source of atmospheric N2O [2] . A plethora of biogeochemical models have been developed for understanding the ecosystem controls of denitrification and N2O emissions [3] . A common feature of these models is that the denitrifying community of the system ( primarily soils and sediments ) in question is treated as one homogenous unit with certain characteristic responses to O2 and concentrations . This simplification is fully legitimate from a pragmatic point of view , but in reality any denitrifying community is composed of a mixture of organisms with widely different denitrification regulatory phenotypes [4] . Modelling has been used to a limited extent to analyse kinetic data for various phenotypes ( See [5] and references therein ) and for understanding the accumulation of intermediates [6] . To our knowledge , however , no attempts have been made to model the regulation during transition from aerobic to anaerobic respiration in individual strains , despite considerable progress in the understanding of their regulatory networks . It would be well worth the effort , since the regulatory phenomena at the cellular level provide clues as to how denitrification and NO and N2O emissions therefrom are regulated in intact soils [7] . Explicit modelling of the entire denitrification regulatory network , however , would take us beyond available experimental evidence , with numerous parameters for which there are no empirical values . Considering this limitation , here we have constructed a simplified model to investigate if a stochastic transcriptional initiation of key denitrification genes ( nirS ) could possibly explain peculiar kinetics of e−-flow as Paracoccus denitrificans switch from aerobic to anaerobic respiration [4] , [8] . Although denitrification is widespread among bacteria , the α-proteobacterium Pa . denitrificans is the ‘paradigm’ model organism in denitrification research . Recent studies [4] , [8] , [9] have indicated a previously unknown phenomenon in this species that , in response to O2 depletion , only a marginal fraction ( ) of its entire population appears to successfully switch to denitrification . In these studies , however , is inferred from rates of consumption and production of gases ( O2 , NOx , and N2 ) , and a clear hypothesis as to the underlying cause of the low is also lacking . To fill these gaps , we formulated a refined hypothesis addressing the underlying regulatory mechanism of the cell differentiation in response to O2 depletion . On its basis , we constructed a dynamic model and explicitly simulated the actual kinetics of recruitment of the cells from aerobic respiration to denitrification . The model adequately matches batch cultivation data for a range of experimental conditions [4] , [8] and provides a direct and refined estimation of . The exercise is important for understanding the physiology of denitrification in general and of Pa . denitrificans in particular and carries important implications for correctly interpreting various denitrification experiments . Generally , the transcription of genes encoding denitrification enzymes is inactivated in the presence of O2 . A population undertaking denitrification typically responds to full aeration by completely shutting down denitrification and immediately initiating aerobic respiration [10] . Thus , O2 controls denitrification at transcriptional as well as metabolic level , and both have a plausible fitness value . The transcriptional control minimises the energy cost of producing denitrification enzymes , and the metabolic control maximises ATP ( per mole electrons transferred ) because the mole ATP per mole electrons transferred to the terminal e−-acceptor is ∼50% higher for aerobic respiration than for denitrification [10] . Denitrification enzymes produced in response to an anoxic spell are likely to linger within the cells under subsequent oxic conditions ( although , this has not been studied in detail ) , ready to be used if O2 should become limiting later on . However , these enzymes will be diluted by aerobic growth , since the transcription of their genes is effectively inactivated by O2 . Hence , a population growing through many generations under fully oxic conditions will probably be dominated by the cells without intact denitrification proteome . When confronted with O2 depletion , such a population will have to start from scratch , i . e . , transcribe the relevant genes , translate mRNA into peptide chains ( protein synthesis by ribosomes ) and secure that these chains are correctly folded by the chaperones , transport the enzymes to their correct locations in the cell , and insert necessary co-factors ( e . g . , Cu , Fe , or Mo ) . In E . coli grown under optimal conditions , the whole process from the transcriptional activation to a functional enzyme takes ≤20 minutes [11] and costs significant amount of energy ( ATP ) . Synthesis of denitrification enzymes is rewarding if anoxia lasts long and NOx remains available , but it is a waste of energy if anoxia is brief . Since the organisms cannot sense how long an impending anoxic spell will last , a ‘bet-hedging strategy’ [12] where one fraction of a population synthesises denitrification enzymes while the other does not may increase overall fitness . Most , if not all , denitrifying bacteria are non-fermentative and completely rely on respiration to generate energy [13] , [14] . This implies that their metabolic machinery will run out of energy whenever deprived of terminal e−-acceptors . When [O2] falls below some critical threshold , the cells will ‘sense’ this and start synthesising denitrification proteome , utilising energy from aerobic respiration [10] . However , if O2 is suddenly exhausted or removed , the lack of a terminal e−-acceptor will create energy limitation , restraining the cells from enzyme synthesis , hence , entrapping them in anoxia . This was clearly demonstrated by Højberg et al . [15] , who used silicone immobilised cells to transfer them from a completely oxic to a completely anoxic environment . Such a rapid transition is unlikely to occur in nature; however , the experiment illustrates one of the apparent perils in the regulation of denitrification: the cells that respond too late to O2 depletion will be entrapped in anoxia , unable to utilise alternative electron acceptors for energy conservation and growth . Højberg et al . 's [15] observations have largely been ignored in the research on the regulation of denitrification , and it is implicitly assumed that , in response to O2 depletion , all cells in cultures of denitrifying bacteria will switch to denitrification . Contrary to this , however , Bergaust et al . [4] , [8] , [16] followed by Nadeem et al . [9] proposed that in batch cultures of Pa . denitrificans , only a small fraction of all cells is able to switch to denitrification . During transition from oxic to anoxic conditions , they observed a severe depression in the total e−-flow rate ( i . e . , to O2+NOx , see Fig . 1 ) , which was estimated on the basis of measured gas kinetics . Had all of the cells switched to denitrification as O2 exhausted , the total e−-flow rate would have carried on increasing , without such a depression . The depression was followed by an exponential increase in the e−-flow rate , which was tentatively ascribed to anaerobic growth of a small ( fraction recruited to denitrification ) . It was postulated that this fraction escaped entrapment in anoxia by synthesising initial denitrification proteins within the time-window when O2 was still present , whereas the majority of the cells ( ) failed to do so , thus remained unable to utilise NOx . To represent the batch cultivation conducted by Bergaust et al . [4] , [8] , the model explicitly simulates growth of two sub-populations , one with denitrification enzymes ( ) and the other without ( ) ; both equally consume O2 , but cannot reduce NOx to N2 . Once oxygen concentration in the liquid falls below a critical level [22] , the cells within are assumed to initiate nirS transcription ( and thereby ensure recruitment to ) with a rate described by a probabilistic function: ( cells h−1 ) , where is assumed to be an dependent probability ( h−1 ) for any cell within to initiate nirS transcription ( leading to a full denitrification capacity ) . When falls below , triggers and holds a constant value as long as is above a critical minimum . For , is zero ( assuming the inactivation of NNR by O2 ) ; is also zero for ( assuming the lack of energy for protein synthesis ) . The recruitment of to is simulated as an instantaneous event; thus , the model does not take into account the time-lag between the initiation of nirS transcription and the time when the transcribing cell has become a fully functional denitrifier . This simplification is based on the evidence that this lag is rather short . Experiments with E . coli [11] under optimal conditions suggest lags of ∼20 minutes between the onset of transcription and the emergence of a functional enzyme . In Pa . denitrificans [8] , [22] , the lag observed between the emergence of denitrification gene transcripts and the subsequent gas products suggests that the time required for synthesising the enzymes is within the same range . In a series of experiments with denitrifying bacteria ( Pseudomonas denitrificans , Pseudomonas fluorescens , Alcaligenes eutrophus and Paracoccus pantotrophus ) [24]–[26] , oxic cultures were sparged with N2 to remove O2 and were monitored by measuring optical density ( OD550 ) . All the strains except Ps . fluorescens went through a conspicuous ‘diauxic lag: a period of little or no growth’ [26]; the OD remained practically constant during the lag period , lasting 4–30 hours , which was eventually followed by anaerobic growth . To understand the diauxic lag , Liu et al . [24] used the common assumption that all cells would eventually switch to denitrification . They constructed a simulation model based on the assumption that all the cells contained a minimum of denitrification proteome ( even after many generations under oxic conditions ) . This minimum would allow them to produce more denitrification enzymes when deprived of O2 , albeit very slowly due to energy limitation . The time taken to effectively produce adequate amounts of denitrification enzymes ( = the diauxic lag ) was taken to be a function of the initial amounts of these enzymes per cell . Although their model may possibly explain short time-lags , it appears unrealistic for lag phases as long as 10–30 hours [25] because to produce such long lags , conceivably , the initial enzyme concentration would be less than one enzyme molecule per cell , which is mathematically possible but biologically meaningless . The model presented in this paper provides an alternative explanation for the apparent diauxic lags: a sudden shift from fully oxic to near anoxic conditions ( by sparging with N2 ) would leave the medium with only traces of O2 , which would be quickly depleted due to aerobic respiration . As a consequence , the available time for initiating the synthesis of denitrification proteome would be marginal , allowing only a tiny fraction ( ) of the cells to switch to denitrification . This marginal fraction would grow exponentially from the very onset of anoxic conditions , but it would remain practically undetectable as measured ( OD ) for a long time , creating the apparent 4–30 h lag . The length of the lag depends on the fraction of the cells switching to denitrification . To demonstrate this alternative explanation , we adjusted our model to the reported conditions and simulated the experiment of Liu et al [24] . The model produced qualitatively similar ‘diauxic lags’ in the simulated cell density ( OD ) , although the time length of the lag could be anything ( depending on assumptions regarding the residual O2 after sparging , which was not measured ) .
Bergaust et al . [4] , [8] studied aerobic and anaerobic respiration rates in Paracoccus denitrificans ( DSM413 ) . The cells were incubated ( at 20°C ) as stirred batches in 120 mL gastight vials , containing 50 mL Sistrom's medium [27] ( Fig . 3 ) . The medium was supplemented with various concentrations of KNO3 or KNO2 . Prior to inoculation , air in the headspace was replaced with He to remove O2 and N2 ( He-washing ) , followed by the injection of no , 1 , or 7 headspace-vol . % O2 . Finally , each vial was inoculated with ∼3×108 aerobically grown cells . The model effectively represents the physical phenomena mentioned above , so as to ensure that the simulation results match the measured data for the right reasons . Net effect of sampling ( dilution and leakage ) is included in the simulation of O2 kinetics at the reported sampling times . Transport of O2 between the headspace and the liquid is modelled using an empirically determined transport coefficient and the solubility of O2 in water at 20°C . To simulate the metabolic activity ( O2 consumption and N2 production ) and growth , the model divides the cells into two sub-populations: one without and the other with denitrification enzymes ( and pools , respectively , see Fig . 3 ) . Both equally consume O2 if present , but cannot reduce to N2 . Those cells that , in response to O2 depletion , are able to initiate nirS transcription ( see Fig . 2 ) are recruited to the pool , where = 0 prior to the recruitment . The recruitment rate ( ) is modelled according to a probabilistic function described below ( Eqs . 7–8 ) . The model ignores sampling effect on N2 ( leakage and loss ) , thus calculating the cumulative N2 production as if no sampling took place . That is because the experimentally determined N2 accumulation ( which is to be compared with the model predictions ) was already corrected for the net sampling effect . The model is developed in Vensim DSS 6 . 2 Double Precision ( Ventana Systems , Inc . http://vensim . com/ ) using techniques from the field of system dynamics [29] . The model is divided into three sectors: I . O2 kinetics , II . Population dynamics of and , and III . Denitrification kinetics ( Fig . 4 ) . Structural-basis for the O2 kinetics is mapped in Fig . 4A: the squares represent the state variables , the circles the rate of change in the state variables , the shaded ovals the auxiliary variables , the arrows mutual dependencies between the variables , and the edges represent flows into or out of the state variables . Briefly , Fig . 4A ( left to right ) shows that O2 in the vial's headspace ( ) is transported ( ) to the liquid-phase ( ) , where it is consumed ( ) by both the and populations ( lacking and carrying denitrification enzymes , respectively ) in proportion to an identical cell-specific velocity of O2 consumption ( ) . represents net marginal changes in due to sampling . Below we present equations and a detailed explanation of the structural components shown for this sector . Fig . 4B represents the structure governing the population dynamics of and . Briefly , the figure shows that both the populations are able to grow by aerobic respiration ( and , respectively ) . Initially , = 0 and is populated through recruitment ( ) of the cells from the pool , where the recruitment is a product of and an [O2] dependent specific-probability ( h−1 ) of the recruitment ( , see Eqs . 7–8 ) . The growth rate of is primarily based on denitrification ( ) , but the cells that are recruited before O2 is completely exhausted also grow by consuming the remaining traces of O2 . Below we present equations and a detailed explanation of the structural components shown for this sector . The structure controlling the denitrification kinetics is mapped in Fig . 4C . Briefly , the figure shows that the cells with denitrification proteome ( ) control the consumption rate of ( ) , recovered as , in proportion to a cell-specific velocity of consumption ( ) . The denitrification intermediates NO and N2O are not explicitly modelled , as they accumulated to miniscule concentrations only [4] , [8] . Most of the parameter values used in the model are well established in the literature ( See Table 2 ) . However , somewhat uncertain parameters include , , , and the assumed parameter :
To test the assumption of a single homogeneous population , we forced our model to achieve 100% recruitment to denitrification by setting = 1 h−1 . In consequence , the simulated N2 accumulation ( molN vial−1 ) showed gross overestimation as compared to the measured for all the treatments ( as illustrated for some randomly selected ones in Fig . 6 ) . To find a more adequate value , was calibrated to produce the best possible match between the simulated and measured N2 through optimisation . ( The optimisation was carried out in Vensim DSS 6 . 2 Double Precision , http://vensim . com/ ) . Table 4 presents the optimal for each treatment; no consistent effect of initial [O2] and [] was found on the optimal results . The average for all the treatments = 0 . 0052 , which appears to give reasonable fit between the simulated and measured N2 ( See Figs . 7 , 8 , and 9 ) . This indicates that the simulations with = 0 . 0052 should provide a reasonable approximation of ( the fraction recruited to denitrification ) during the actual experiment . To investigate whether the recruitment of a small fraction of the cells to denitrification could explain the ‘diauxic lag’ observed by Liu et al . [24] , we used our model to simulate the conditions they reported for their experiment . In short , Liu et al . [24] incubated Ps . denitrificans ( ATCC 13867 ) in oxic batch cultures , which were sparged with N2 as the cultures had reached different cell densities ( OD550 = 0 . 05–0 . 17 ) . The sparging resulted in apparent diauxic lags , i . e . , periods with little or no detectable growth . The length of such lags increased with the cell density present at the time of sparging . Two sensitivity analyses were run to investigate the system's response to initial O2 in the headspace , : one corresponding to a range of initial [O2] in the liquid-phase below ( see Eqs . 7–8 ) and the other for a range much higher than . All other model parameters and initial values remained as listed in Tables 2 and 3 , respectively . The exercise helps illustrate the relative importance of aerobic growth versus the recruitment ( ) in determining the time taken to deplete the pool . The prevailing wisdom in denitrification research is that , under impending anoxic conditions , all cells in a batch culture of denitrifying bacteria will switch to denitrification . However , recent experiments with batch cultures of Pa . denitrificans have provided evidence that , in response to O2 depletion , only a small fraction ( ) of the entire population is able to switch to denitrification [4] , [8] , [9] . The evidence is based on indirect analyses of e−-flow rates to O2 and NOx during the transition of the cells from aerobic to anaerobic respiration . To provide a direct and refined estimation of , we constructed a dynamic model and directly simulated kinetics of recruitment of the cells to denitrification . We first formulated a hypothesis as to the underlying regulatory mechanism of cell differentiation under approaching anoxia . Briefly , it is that the low is due to a low probability of initiating transcription of the nirS genes , but once initiated , the transcription is greatly enhanced through autocatalytic positive feedback by NO , resulting in the recruitment of the transcribing cell to denitrification . Then , as we implemented this hypothesis in the model , the simulation results showed that the specific-probability ( ) of 0 . 0052 ( h−1 ) for a cell to switch to denitrification is sufficient to robustly simulate the measured denitrification gas kinetics . The model estimated the resultant between 3 . 8–16 . 1% only ( average = 8 . 2% ) . The phenomenon may be considered as a ‘bet-hedging’ regulation ‘strategy’ [12]: the fraction switching to denitrification benefits if the anoxic spell is long and NOx remains available , whereas the non-switching fraction benefits , by saving energy required for the protein synthesis , if the anoxic spell is short . The strategy has important implications for the interpretation of numerous experiments on Pa . denitrificans and other denitrifying organisms , as this study has illustrated by presenting a more plausible explanation of the apparent diauxic lags [24] on the basis of the low . | In response to oxygen-limiting conditions , denitrifying bacteria produce a set of enzymes to convert / to N2 via NO and N2O . The process ( denitrification ) helps generate energy for survival and growth during anoxia . Denitrification is imperative for the nitrogen cycle and has far-reaching consequences including contribution to global warming and destruction of stratospheric ozone . Recent experiments provide circumstantial evidence for a previously unknown phenomenon in the model denitrifying bacterium Paracoccus denitrificans: as O2 depletes , only a marginal fraction of its population appears to switch to denitrification . We hypothesise that the low success rate is due to a ) low probability for the cells to initiate the transcription of genes ( nirS ) encoding a key denitrification enzyme ( NirS ) , and b ) a limited time-window in which NirS must be produced . Based on this hypothesis , we constructed a dynamic model of denitrification in Pa . denitrificans . The simulation results show that , within the limited time available , a probability of 0 . 005 h−1 for each cell to initiate nirS transcription ( resulting in the recruitment of 3 . 8–16 . 1% cells to denitrification ) is sufficient to adequately simulate experimental data . The result challenges conventional outlook on the regulation of denitrification in general and that of Pa . denitrificans in particular . | [
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] | 2014 | Low Probability of Initiating nirS Transcription Explains Observed Gas Kinetics and Growth of Bacteria Switching from Aerobic Respiration to Denitrification |
Invasive aspergillosis ( IA ) is a common and life-threatening infection in immunocompromised individuals . A number of environmental and epidemiologic risk factors for developing IA have been identified . However , genetic factors that affect risk for developing IA have not been clearly identified . We report that host genetic differences influence outcome following establishment of pulmonary aspergillosis in an exogenously immune suppressed mouse model . Computational haplotype-based genetic analysis indicated that genetic variation within the biologically plausible positional candidate gene plasminogen ( Plg; Gene ID 18855 ) correlated with murine outcome . There was a single nonsynonymous coding change ( Gly110Ser ) where the minor allele was found in all of the susceptible strains , but not in the resistant strains . A nonsynonymous single nucleotide polymorphism ( Asp472Asn ) was also identified in the human homolog ( PLG; Gene ID 5340 ) . An association study within a cohort of 236 allogeneic hematopoietic stem cell transplant ( HSCT ) recipients revealed that alleles at this SNP significantly affected the risk of developing IA after HSCT . Furthermore , we demonstrated that plasminogen directly binds to Aspergillus fumigatus . We propose that genetic variation within the plasminogen pathway influences the pathogenesis of this invasive fungal infection .
Invasive infection with Aspergillus fumigatus ( AF ) is a common and life-threatening infection among severely immunocompromised individuals . Despite aggressive surveillance and prophylaxis , the incidence of invasive aspergillosis ( IA ) in hematopoietic stem cell transplant ( HSCT ) recipients remains approximately 10% , and the three month mortality following infection approaches 30% [1] , [2] . AF infection leads to a potentially hemorrhagic bronchopneumonia , with angio-invasion and tissue destruction . Several factors have been shown to affect risk for developing IA in allogeneic HSCT recipients . These include immunosuppresion required for HSCT , graft-vs-host-disease , and corticosteroid use[2] . However , despite having similar risk profiles , only a subset of at-risk individuals will develop IA . Since most of the identified risk factors for IA affect the immune system of the recipient , we hypothesize that genetic variation within key innate or adaptive immune response genes could influence susceptibility to or outcome of this invasive fungal infection . The susceptibility of different inbred murine strains for developing IA after immunosuppression was analyzed to determine if genetic factors affect susceptibility to aspergillosis . Haplotype-based computational genetic analysis of this survival data identified PLG as a candidate susceptibility gene . A SNP that caused a significant amino acid substitution in human PLG was identified , and human PLG alleles were found to affect risk for developing IA in HSCT recipients .
Ten inbred mouse strains were transiently immunosuppressed with cyclophosphamide and cortisone acetate prior to exposure to AF conidia . The strain-specific survival patterns were then evaluated over a 14 day period . This immunosuppressive regimen disrupts macrophage and neutrophil-mediated defense against inhaled conidia . In control experiments , no significant mortality was observed in the nine different immunosuppressed strains of mice that were not exposed to AF . However , the immunosuppressive regimen caused significant mortality in the DBA/2 strain . Therefore , subsequent analyses were performed with and without the data from this strain . Since the immunosuppressive regimen was initiated 3 days prior , all strains were rendered neutropenic at time of exposure to AF . The neutropenia extended through day 7 with neutrophil recovery by day 10 ( Table S1 ) . Quantitative PCR ( qPCR ) measurements performed on lung tissue harvested 24 hours after AF exposure indicated that the inbred strains were exposed to equivalent amounts of AF and had an equivalent pulmonary burden of A . fumigatus ( Table S1 ) . Thus , the inbred strains received the same immunosuppressive regimen , experienced identical periods of neutropenia , and received the same pulmonary fungal inoculum . Despite this , the inbred strains exhibited significant and reproducible differences in survival after exposure to AF ( Figure 1A ) . The “susceptible” inbred strains ( A/J and C3H/HeJ ) exhibited 100% mortality by day 6 . The DBA/2J strain also experienced 100% mortality by day 6; however the survival curves for immune suppressed unexposed DBA/2J mice were not significantly different from the mice that were exposed to AF ( data not shown ) . The “intermediate” strains ( MRL/MPJ and NZW/LacJ ) also experienced 100% mortality; however the majority of deaths in these strains occurred between days 5 and 10 . Five strains exhibited a “resistant” pattern of survival: ( AKR/J , C57/Bl6J , 129/SvJ , Balb/CJ , and Balb/CByJ ) ; they had between 30–60% survival at the end of the 14 day observation period ( Figure 1B ) . The survival patterns were reproducible on repeated experimental trials . Histological evaluation of the lungs of moribund mice revealed Aspergillus hyphae and foci of pneumonitis ( Figure 1C , D ) . Overt histologic differences between moribund mice from each strain were not noted , however histologic sections from surviving mice noted resolving pneumonia ( data not shown ) . The amount of AF in the lung 48 hours after infection was also measured by qPCR . At 48 hours , the fungal burden in the sensitive strains ( median conidial equivalents 4 . 7 log cells/gram lung tissue; range 3 . 8–5 . 0 ) was significantly greater ( p = 0 . 02 ) than in the resistant strains ( median conidial equivalents 3 . 7 log cells/gram lung tissue; range 2 . 7–5 . 3 ) ( Table S1 ) . Despite having the same level of immunosuppression and initial exposure to AF , the inbred strains exhibited significant differences in fungal burden and survival after AF exposure . These findings suggest that genetic factors affect the control of fungal burden , and survival in immune suppressed mice after AF exposure . Notably , C3H/HeJ mice are known to have defective TLR-4 signalling [3] . To evaluate the role of TLR-4 hypofunction in outcome following inhalation of A . fumigatus , both non-immune compromised and exogenously immune compromised C57Bl6tlr4−/− mice were evaluated . These mice exhibited a “resistant” phenotype upon exposure to A . fumigatus in this model ( data not shown ) . Thus , the survival pattern exhibited by C3H/HeJ mice in our model was not felt to be solely due to hypofunction of TLR4 . To identify genetic factors affecting survival after AF exposure , the inbred strain survival data were analyzed using haplotype-based computational genetic analysis [4] , [5] , [6] . This analysis identifies genomic regions where the pattern of genetic variation among inbred murine strains correlates with a pattern of phenotypic responses . The area under the survival curve ( AUC ) after AF exposure was analyzed; and 2 haplotype blocks were identified where the pattern of genetic variation had a strong correlation with survival ( Figure 2 , Figure S1A ) . The top two predicted loci contained the genes UDP-glucose ceramide glucosyltransferase-like 1 ( Ugcgl1 ) and Plasminogen ( PLG ) . While Ugcgl1 , a glycoprotein glucosyltransferase , may have some role in CD4/CD8 thymocyte function [7] , recent reports linking the fibrinolytic system with host response to infectious pathogens made PLG an attractive candidate for influencing susceptibility to IA [8]–[18] . Because the immunosuppressive regimen induced mortality in the DBA/2 strain , the computational analysis was also repeated using survival data that did not include this strain . When this reduced dataset was analyzed , PLG was among the genomic regions whose pattern of genetic variation correlated survival ( Figure S1B , Figure S2 ) . PLG was sequenced across 20 inbred strains , and 423 SNPs were identified . Among the 10 inbred Mus musculus strains characterized here , the pattern of genetic variation in PLG was organized into 2 haplotype blocks . The 129 , AKR , Balb/c , Balb/cBy , C57 , and NZW strains had one haplotype , while the AJ , MRL , DBA and C3H strains had the other haplotype . There was a single non-synonymous SNP that altered an amino acid ( G110S ) . This non-conservative amino acid substitution is within the first kringle domain , which regulates the initial binding of plasminogen to fibrin as well as plasmin-induced cell-detachment [19] , [20] . The glycine at this position is conserved across all five kringle domains of plasminogen in mice and humans , suggesting that this amino acid change may have significant functional importance . Notably , the susceptible strains had the minor allele ( Ser ) at this position , which was not present in the resistant strains . Since an allelic difference in murine PLG was associated with susceptibility in a mouse model , it was possible that polymorphisms in human plasminogen ( PLG ) could also affect susceptibility to IA . If true , the best chance for detecting a genetic effect would be through analysis of a cohort of immunosuppressed patients that is at increased risk of developing invasive aspergillosis . The incidence of invasive aspergillosis in allogeneic HSCT recipients , which undergo intensive immunosuppression is approximately 10% [1] , [2] . Therefore , HSCT recipients , followed for at least one year after transplant , provide an ideal population for identifying the genetic factors associated with IA susceptibility ( Table 1 ) . To perform the human genetic study , we first had to characterize the pattern of genetic variation in the human plasminogen gene . To do this , all exons and the promoter region of PLG were sequenced in 20 HSCT donor-recipient pairs ( 40 DNA samples from 20 stem cell donors and 20 stem cell recipients ) . Although 4 SNPs causing an amino acid change were identified , only one of these SNPs had a minor allele frequency above 1% . Therefore , our analysis focused upon SNP rs4252125 ( Asp472Asn ) , which had a minor allele frequency ( Asn472 ) of 25% . This non-conservative ( neutral to acidic amino acid ) substitution occurs in a loop region that connects the 4th and 5th kringle domains of plasminogen . This loop region can form highly variable structures based on the surrounding environment . Similar to the murine polymorphism , this amino acid change could have a significant functional impact through altering the alignment of kringle domains or ligand binding . The genotype at Asp472Asn for the remainder of the HSCT cohort was then determined . Genotype was interpreted in a blinded manner , such that the two independent persons analyzing genotype data were unaware of each subject's status as a case or control . Analysis of these data indicated that HSCT recipients that were homozygous or heterozygous for Asn472 ( AA or AG genotype ) were at a significantly increased risk of developing IA after transplant . The Asn472 allele was present with greater frequency in HSCT recipients who developed IA ( 47/83; 56% ) as compared to those who did not ( 62/147; 42% ) . Importantly , the genotype-specific risk for developing IA was not constant over time after transplant . Risk for development of IA following HSCT is bimodal , with peak risk periods occurring during the pre-engraftment phase ( <day 40 ) and a second peak during the post-engraftment phase , during days 40–100 [2] . To allow for possible violations of the proportional hazards assumption , the genotypic hazard ratios were evaluated during 3 distinct time periods post HSCT: days 0–40 , days 40–100 and days 100–365 . There was a constant and substantial elevation in the genotype-specific hazard ratio for IA among recipients who carried at least one “Asn” allele ( AA or AG genotype ) between 40–365 days following HSCT ( Figure 3 , Table 2 ) . Perhaps due to a low number of cases of IA occurring during days 0–40 or to the confounding effects of known strong risk factors pre-engraftment ( e . g . neutropenia ) , an association between PLG genotype and IA was not observed during the first 40 days after transplant . For follow-up after day 40 , the recipient plasminogen genotype remained a significant risk factor for IA susceptibility ( p<0 . 0005 ) when a multivariate analysis was used to simultaneously account for effects of other factors that were significant in univariate analysis ( HLA matching status , source of stem cells , and underlying malignancy ) . In addition , there was an apparent gene-dosage effect: homozygous AsnAsn individuals were at a 5 . 6-fold increased risk of developing IA , while heterozygous individuals had a 3 . 0 fold increased risk , relative to AspAsp individuals . A majority ( 96% ) of subjects received fluconazole prophylaxis ( no anti-Aspergillus activity ) , with the remainder receiving itraconazole , which does possess anti-Aspergillus activity ( 4% ) . Type of prophylaxis did not differ significantly between cases and controls . Inclusion of prophylaxis in the risk model revealed that the type of prophylaxis was not predictive of acquiring invasive aspergillosis , nor did the results related to genotype change with adjustment for type of prophylaxis . Notably , donor polymorphism status was not associated with development of aspergillosis . This served as an important negative control; plasminogen is synthesized hepatically , and thus , the donor plasminogen genotype should not associate with disease acquisition . During the 40–365 days following transplant , 59 HSCT recipients developed IA; and 135 HSCT recipients who survived for more than 40 days following HSCT did not develop IA . The analysis of survival following diagnosis of IA showed somewhat reduced risk of death among IA patients with the same genotypes , with the hazard ratio for AsnAsn = 0 . 32 95% CI = ( 0 . 11 , 0 . 89 ) and the hazard ratio for AspAsn = 0 . 79 , 95% CI = ( 0 . 43 , 1 . 44 ) ( Table 2 ) . Thus , an allelic difference causing an amino acid change in plasminogen is associated with increased risk for developing IA in a high-risk human cohort , as well as in an experimental murine model , and may also be associated with survival in HSCT patients who develop IA ( p = 0 . 07 ) . We next sought to identify possible mechanisms by which plasminogen may influence susceptibility to invasive aspergillosis . The lysine binding sites in the 1st and 4th kringle domains of plasminogen mediate plasminogen binding to fibrinogen and other targets [20] . Analysis of the theoretical crystal structure of the murine kringle domain 1 revealed that substitution of serine for glycine at this position increased the negativity of the electrostatic potential around the principal lysine binding site . Such a change could increase the affinity of lysine binding to the 1st kringle domain ( Figure S3 ) . Since plasminogen is more easily activated after binding , this amino acid substitution could increase the rate of plasminogen activation [12] , [21] , [22] . Analysis of the crystal structure of human plasminogen ( Figure 4 ) kringle 4 ( K4 ) and kringle 5 ( K5 ) revealed that a substitution of asparagine for aspartic acid at position 472 , located in the loop connecting K4 and K5 , could potentially influence K4–K5 interactions or binding at the K4 lysine binding site ( LBS ) . Although an interaction between plasminogen and AF has not been previously described , plasminogen does bind to other fungal pathogens [14] , [23] . Therefore , immunofluorescence microscopy and flow cytometric analysis were used to determine if plasminogen bound to AF . Immunofluorescence microscopy demonstrated that FITC-labeled murine ( Figure 5C , D ) bound to both swollen AF conidia and hyphae in a dose dependent manner . Neither murine nor human plasminogen bound resting conidia ( data not shown ) . The dose-dependent binding of plasminogen to AF was confirmed and further quantified by flow cytometry ( Figure 5A ) . Furthermore , specificity of the interaction was tested using pre-incubation of swollen conidia with unlabeled plasminogen . This pre-incubation inhibited the binding of the labeled plasminogen ( Figure 5A ) . Human plasminogen bound to swollen conidia and hyphae ( Figure 5B ) , and demonstrated similar specificity as murine plasminogen , with inhibition of binding by pre-incubation with unlabeled human plasminogen ( Figure 5B ) . FITC-labeled bovine serum albumin ( BSA ) bound to AF conidia to a lesser degree than labeled plasminogen , and pre-incubation of AF conidia with 0 . 1% BSA did not inhibit binding of labeled plasminogen ( data not shown ) , highlighting the specificity of the plasminogen-AF interaction . These observations indicate that both infectious forms of AF can directly bind plasminogen , which supports the possibility that the plasminogen system plays an important role in the pathogenesis of IA . This also suggests a plausible mechanism by which the polymorphisms could alter susceptibility to invasive aspergillosis . A polymorphism that enhances plasminogen binding to the pathogen will increase plasminogen activation on the pathogen surface , which could facilitate pathogen entry and pathogen-induced tissue damage [11] .
Our genetic analyses in mice and humans indicate that plasminogen alleles affect risk for developing invasive aspergillosis in the immunocompromised state . This association is also supported by functional in vitro studies demonstrating that plasminogen binds to AF . To our knowledge , this is the first attempt to identify genetic polymorphisms affecting risk of IA using a multi-species genetic mapping approach , with a murine model system targeting a polymorphic susceptibility gene which is then assessed in a well-defined human cohort . This approach , unlike a pure candidate gene approach , allowed evaluation of a gene that would be unlikely to be selected a priori for evaluation . While clinical applications of this finding will require further prospective evaluation , the finding can serve as a proof-of-principle paradigm for this staged approach to identifying host genetic polymorphisms that can affect outcome in immune compromised patients . Identification of a genetic polymorphism that influences infectious outcome after HSCT has important implications for pre and post transplant care , and may also have implications for the management of other immune-compromised patients . For example , genetic testing could identify high risk individuals who may benefit from use of broad-spectrum antifungal agents or enhanced monitoring for infection . While plasminogen polymorphisms are associated with development of invasive aspergillosis in HSCT recipients , it remains to be determined whether this allelic variant affects the risk of developing invasive aspergillosis after chronic corticosteroid use , semi-invasive aspergillosis or allergic bronchopulmonary aspergillosis . Additionally , these findings implicate the fibrinolytic pathway as an important mediator of fungal infection . The fibrinolytic pathway is increasingly recognized in infectious disease pathogenesis , and this is the first report to link plasminogen with a filamentous fungus . Several mechanisms may explain how genetic changes affecting the function of plasminogen and the fibrinolytic system could influence host susceptibility to IA . IA is characterized by hemorrhage and tissue destruction , which are mediated by the plasminogen system . As previously demonstrated for streptococcal infection [18] , AF-induced plasminogen activation could trigger plasminogen-mediated destruction of extracellular matrix components , which in turn enhances tissue invasiveness of the pathogen . Genetic differences that alter the coagulative function of plasminogen may also influence disease pathogenesis by promoting pulmonary hemorrhage and infarction . Immobilization of plasminogen on the pathogen surface allows for easier activation of this zymogen[10] . This brings the activated enzyme into close contact with key substrates in the basement membrane , which increases the virulence of bacteria [9] , [16] , [24] and other fungi [13] , [14] , [23] . Since AF adheres to components in the extracellular matrix and basement membrane [25] , [26] , its ability to bind plasminogen could be an important virulence mechanism . Since plasminogen plays an important role is inflammation and host defense , this provides another potential mechanism for the plasminogen system to affect the pathogenesis of IA . Plasminogen is a direct chemotaxin and is an activator of monocytes , and has recently been shown to prevent monocyte apoptosis [27]–[30] . A bidirectional relationship between coagulation and inflammation has long been recognized [15] , [22] . Since monocytes/macrophages are critical for host defense , genetic variation in plasminogen could cause subtle differences in immune function that may also affect outcome after exposure to AF in severely immune compromised hosts . Although death rates following infection tended to be reduced among carriers of the same genotypes that conferred increased risk , this finding may well be due to chance , as it was not statistically significant ( p = 0 . 07 ) . However , puzzling the finding , if this proves statistically significant in another population , it is of profound importance since it dissociates risk from acquisition of disease from survival . Clinical outcome following development of IA hinges not only on immune recovery to limit fungal related pulmonary hemorrhage and tissue infarction , but also on control of an over-exuberant inflammatory response as the immune system recovers . Thus , some of those who develop IA despite carrying the protective AspAsp genotype may respond differently to infection than those carrying the AsnAsn genotype . An additional possibility is that carriers of the AsnAsn genotype who develop IA tend to have a more evident phenotype and consequently are either more readily diagnosed or are diagnosed at an earlier stage of disease . Thus carriers would have elevated risk of diagnosis but would present with a more clinically manageable form of IA . Human association studies in smaller independent HSCT cohorts have implicated Toll-like receptor 1 and 6 [31] and IL-10 receptor [32] polymorphisms as risk factors for development of invasive aspergillosis , While the published human association studies target viable candidate genes based on the prior literature , they are hampered by small size ( n = 22 [31] and n = 9 , including 4 cases of “possible” IA [32] ) . Additionally , conflicting data exist on the role of other common innate immune deficiencies ( i . e . TLR2 and TLR4 hypofunction ) in the pathogenesis of IA [33]–[38] . Our work with C57Bl6tlr4−/− mice did not find TLR4 to be essential for host defense against IA ( data not shown ) , thus unlikely to be the sole reason for enhanced susceptibility of C3H/HeJ mice who are known to have TLR4 hypofunction . Similarly , where deficiency in complement 5a ( C5a ) may influence outcome in invasive aspergillosis [39] , the varying phenotypes between A/J and AKR/J ( both C5a deficient ) mice indicate that C5a deficiency alone is unlikely to account for the poor survival of A/J mice . A polymorphism in the gene for plasminogen may confer increased risk of invasive aspergillosis following bone marrow transplantation . The identification of murine and human genetic polymorphisms associated with susceptibility to invasive fungal infection in immunosuppressed hosts enables subsequent epidemiologic and clinical studies that can produce improved methods for management of transplant patients .
Inbred 6–8 week-old female mice ( Jackson Laboratories; Bar Harbour , ME ) were housed and fed under aseptic conditions and their sterile water was supplemented with tetracycline ( 1 mg/ml ) changed once daily . Ten inbred strains were utilized ( BalbC/ByJ , Balb/CJ , AKR/J , 129/SvJ , C57Bl/6J , MRL/MPJ , NZW/LACJ , A/J , DBA/2J and C3H/HeJ ) . Mice weighed between 18–24 grams , with the exception of MRL/MPJ mice ( 28–32 grams ) . The mice were immunosuppressed with an intraperitoneal injection of cyclophosphamide ( Sigma Biochemicals , St . Louis , MO ) ( 150 mg/kg ) on day −3 , and a subcutaneous injection of cortisone acetate ( Sigma Biochemicals , St . Louis , MO ) ( 250 mg/kg ) on day −1 of infection . The immunosuppressive regimen also included additional doses of cyclophosphamide ( 150 mg/kg ) on days +1 , and +4 of infection . All animal work was approved by the Institutional Animal Use and Care Committee at Duke University Medical Center and followed the standard guidelines for ethical treatment of animals . AF strain 293 was used in all experiments ( provided by Dr . William J . Steinbach , Duke University Medical Center ) . AF conidia were grown on Sabouraud dextrose agar ( Difco; Becton Dickinson; Sparks , MD ) for 7 days and harvested in 0 . 01% Tween 80 in sterile water on the day prior to inoculation . Conidia were washed and resuspended in sterile water and counted on a Neubauer hematocytometer to create a conidial suspension of 3×108 conidia/ml . 10 mice per strain were immunosuppressed . A total of 40 ml of the 3×108 conidia/ml suspension was aerosolized in four separate nebulizers ( Aerotech II , CIS-US , Inc . , Beford , MA ) in a Hinners-style exposure chamber [40] for 25 minutes as previously described [41] . Select strains ( C3H/HeJ , A/J , C57Bl/6J and Balb/CJ ) were evaluated a total of 3 times each to ensure reproducibility of findings , thus the total number of mice evaluated was 160 . Haplotype-based computational genetic analysis of the phenotypic data was performed as previously described [4] , [6] , [42] , [43] , [44] . In brief , allelic data from multiple inbred strains were analyzed and a haplotype block map of the mouse genome was constructed . SNPs were organized into haplotype blocks . Only a limited number of haplotypes-typically 2 , 3 or 4-are present within a haplotype block . This analysis identifies haplotype blocks in which the haplotypic strain grouping within a block correlates with the distribution of phenotypic data among the inbred strains analyzed . To do this , a p-value that assesses the likelihood that genetic variation within each block could underlie the observed distribution of phenotypes among the inbred strains is calculated as described using ANOVA [6] , [43] , [44] . The phenotypic data was evaluated using the average value for each strain , obtained by assessing 10–30 mice per strain . The haplotype blocks are then ranked based upon the calculated p-value . The genomic regions within haplotype blocks that strongly correlated with the phenotypic data are then analyzed . When the computational analysis was performed , there were 1745 haplotype blocks that were generated from analysis of 160 , 000 SNPs with alleles characterized across 18 inbred strains covering 2 , 171 genes . For this analysis , the candidate haplotype blocks that were empirically selected had p-value<0 . 005 . This was the best p-value achieved by blocks in which the 10 strains are grouped into two haplotypes such that the phenotypes of strains in one haplotype are separated from those of strains in another haplotype . Genomic sequencing of PLG in inbred mouse strains was performed as previously described [4]; and covered the 2 kb 5′ upstream , 1 kb 3′ downstream , intron-exon junctions and all exonic regions . AF burden was quantified in murine lung at 24 and 48 hours following inhalation of 3 . 0×108 AF 293 conidia according to the methods of Bowman , et al [45] . Briefly , organ samples were mechanically disrupted using a roller-bottle method [46] and then vigorous agitation in a FastPrep 120 ( Qbiogene; Carlsbad , CA ) homogenizer . DNA was extracted from the homogenate using the DNeasy 96 Tissue Kit ( Qiagen; Valencia , CA ) according to the manufacturer's instructions . Oligonucleotide amplification primers and a dual-labeled fluorogenic oligonucleotide hybridization probe complementary to sequence from the 18S rRNA gene utilized by Bowman , et al [45]; were designed using Primer Express version 1 . 5 ( Applied Biosystems; Foster City , CA ) . The sequences of these oligonucleotides are- Modifications to the published protocol include the normalization of DNA content by a spike addition of 2×106 copies of the non-murine , non-fungal plasmid Eimeria tenella PKG cDNA ( Accession Number AF411961 ) , with normalization of results to amount of E . tenella DNA extracted ( Bowman , et al; unpublished data ) . After homogenization and DNA isolation , samples are analyzed by TaqMan with the following primers and probe specific for the parasite gene sequence: TaqMan quantification of the PKG target sequence allows for an estimate of the recovery of DNA in the experiment from the crude homogenate through the TaqMan reaction . This assessment of DNA recovery was made for each experimental sample . The percent recovery of the PKG target sequence was used to estimate the recovery of tissue DNA from the sample . Accordingly , each TaqMan data point for the AF18S rRNA gene target was normalized based on the recovery of DNA predicted by the PKG standard . Data were analyzed using GraphPad Prism version 4 . 0 for Windows , ( GraphPad Software , San Diego , CA , www . graphpad . com ) . Purified mouse plasminogen prepared from outbred mice ( Haematologic Technologies ) was dialyzed against PBS and labeled with AlexaFluor 488 per the manufacturers instructions ( Invitrogen ) . The moles dye per mole protein binding ratio of labeled plasminogen was 5 . Free dye was removed by dialyzing labeled protein for 24 hours against PBS at 4°C . Purified human glu-plasminogen ( Haematologic Technologies ) was labeled in the same manner , with a moles dye per mole protein binding ratio of 6 . Swollen A . fumigatus conidia were prepared by incubating freshly harvested A . fumigatus 293 conidia in RPMI for 6 hours at 37°C . Swollen conidia were washed with PBS prior to incubation with AlexaFluor 488 labeled murine or human plasminogen . Resting and swollen A . fumigatus conidia ( 1×107 conidia/ml ) were treated with several concentrations ( 0 . 2 to 10 µg/ml ) of AlexaFluor 488 labeled mouse or human plasminogen in PBS and incubated at 37°C for 30 minutes with shaking . As controls , swollen conidia were incubated with increasing concentrations ( 0 . 2 to 4 µg/mL ) of FITC-bovine serum albumin ( BSA ) ( Invitrogen ) . Plasminogen binding was quantified by flow cytometric analysis ( Duke Human Vaccine Institute Flow Cytometry Core Facility and Flow Cytometric Core Facility , National Institute of Environmental Health Sciences ) . For competitive inhibition binding assays A . fumigatus conidia ( 1×107 ) were first treated with unlabelled mouse or human plasminogen ( Haematologic Technologies ) then AlexaFluor 488 labeled mouse or human plasminogen . Controls included swollen conidia pre-incubated in PBS-0 . 1% BSA overnight prior to incubation with AlexaFluor 488 labeled human plasminogen as described above . Conidia were incubated at 37°C for 30 minutes with shaking for each treatment . Plasminogen binding was quantified by flow cytometric analysis ( Duke Human Vaccine Institute Flow Cytometry Core Facility and Flow Cytometric Core Facility , National Institute of Environmental Health Sciences ) . For microscopy , conidia were treated with Calcofluor white ( Sigma Biochemicals ) to visualize the cell wall . Binding was visualized using a Zeiss Axioskop 2 Plus ( Carl Zeiss MicroImaging ) fluorescent microscope with an AxioCam MRM digital camera . 10 ml of RPMI-1640 medium in a T-25 vented cap tissue culture flask was inoculated with AF conidia ( 1×107 ) and incubated overnight at 37°C with 5% CO2 . Hyphae were collected by gentle vortexing of the tissue culture flask and 10 µl aliquots of hyphae were spotted onto glass slides and allowed to dry . Hyphae were then treated with AlexaFluor 488 labeled mouse or human plasminogen for 30 minutes at 37°C at varying concentrations and with Calcofluor white to visualize the cell wall . For competitive inhibition binding assays A . fumigatus hyphae were first treated with unlabelled mouse or human plasminogen ( Haematologic Technologies ) for 30 minutes at 37°C then Alexa Fluor 488 labeled mouse or human plasminogen ( 0 . 2 µg/ml ) for 30 minutes at 37°C . Binding was visualized using a Zeiss Axioskop 2 Plus fluorescent microscope with an AxioCam MRM digital camera . Genomic DNA was prepared from patients who received allogeneic hematopoietic stem cell transplant ( HSCT ) after myeloablative therapy at the Fred Hutchinson Cancer Research Center ( Seattle , WA ) , and their donors . An immortalized lymphocyte cell line was created from each subject using peripheral blood mononuclear cells obtained prior to transplantation[47] . Genomic DNA was isolated using a Qia-Amp DNA blood Kit ( Qiagen ) . The cohort represented a sample of 83 patients who developed proven or probable invasive aspergillosis , according to standardized criteria[48] , and 147 patients who did not . Cases of “possible” aspergillosis were not included in the study . The plasminogen ( PLG ) gene extending from 2 kb 5′ upstream to 1 kb 3′ downstream of human PLG transcript was sequenced in 20 IA affected donor/recipient pairs using Applied Biosystems ( AB ) Version 3 . 1 Dye Terminators and an ABI 3730 Genetic Analyzer ( Applied Biosystems ) . Polymorphisms were identified , and corresponding genotypes determined , by analysis of the resulting DNA sequence data using PHRED and PHRAP . Four coding change SNPs were identified and exons containing these SNPs ( exons 2 , 3 , 11 , and 12 , Ensembl Human V . 36 ) were further sequenced for all members of the cohort to determine their genotypes using PolyPhred . To identify potentially important sequence changes in hPLG , all exons and the 500 bp promoter region of the hPLG gene were sequenced in 20 HSCT donor-recipient pairs; nineteen SNPs were identified . Of the 19 SNPs , 11 SNPs were located in exons and 4 of them induced a change in an amino acid . Exons containing these nonsynonymous coding change SNPs ( exons 2 , 3 , 11 , and 12 ) were sequenced in the remaining sample of 210 Caucasian donor/recipient pairs ( 63 with proven/probable IA , 147 without IA ) ( Demographics; Table 1 ) . SNP Asp472Asn ( rs4252125 ) at position 827 in exon 11 was the only one of the nonsynonymous coding change SNPs considered to be of further interest as it had a minor allele frequency of approximately 25% , while the other 3 SNPs had minor allele frequencies of <1% . Statistical Methods: A Cox proportional hazards approach was used to model the hazard of IA in relation to the recipient genotype , over follow-up time after receipt of stem cells . Death and relapse were treated as censoring mechanisms [49] . Although the required proportional hazards assumption was not significantly violated , in the final modeling the follow-up for analysis was begun on day 40 following transplantation , though this left-truncation did not materially affect the results . Because patients developing IA were sampled with probability 0 . 25 , while those who had remained free of IA were sampled with probability 0 . 09 , the differential sampling had to be accounted for statistically . This was accomplished by designating a random 9/25 of the IA cases ( 31 of 83 ) , together with all noncases , as members of a synthetic random subcohort , and applying software for case-cohort analysis [50] . In this way , the variances for parameter estimation were increased appropriately to account for the dependency induced by using the same controls in successive risk sets in modeling the risk of IA . This weighting was also accounted for in calculating Kaplan-Meier cumulative incidence curves . For multi-category variables , the improvement in fit provided by their inclusion in a model was tested by means of chi-squared statistics based on scores . To assess the proportionality assumption required by the Cox proportional hazards model , the relative risks associated with the PLG genotype were permitted to vary across two defined periods of time: days 40–100 and days more than 100 following receipt of stem cells , as these time intervals represent known differential risk periods for IA [2] . Additionally , we included models that allowed genotype ( and certain other factors , such as HLA matching status ) to have different effects during different epochs of follow-up . Division of time into epochs was based on knowledge of how clinical approaches to management are altered in the months following HSCT . Analyses were performed both for the entire available follow-up time , and with follow-up truncated at one year . Results are shown for the analysis beginning at 40 days and truncated at one year , but the inference is not materially different with inclusion of longer follow-up . Covariates considered for adjustment as possible confounders or effect modifiers included age , cell type , malignancy category , antifungal prophylaxis , gender , genotype of the donor , and HLA status of graft [matched related ( 6/6 match from relative ) ; matched unrelated ( 6/6 match from non-relative ) or mismatched] . Adjustment for development of graft-versus-host disease was considered , but judged not to be defensible as a legitimate potential confounder of recipient genotype , based on any plausible causal directed acyclic graph [51] . Possible effects of genotype on survival following IA were explored through a Cox proportional hazards analysis , with follow-up beginning the day after diagnosis of IA . The contribution of each variable to the final model was tested by means of score tests and all p values given are two-sided . Hardy-Weinberg equilibrium for the recipient patients was tested based on the members of the sub-cohort , while that for the donors were tested based on all the donors . Work was approved by the Institutional Review Boards of the Fred Hutchinson Cancer Research Center , Duke University Medical Center and the National Institute of Environmental Health Sciences . A model for murine kringle-1 ( K1 ) was created using the X-ray crystal structure of the kringle-1 domain of human plasminogen ( pdb entry: 1CEA ) . All appropriate mutations required for this homology modeling were carried out using Sybyl 7 . 1 ( Tripos , Inc . ) . The structure was then energy minimized in vacuum using the Amber force field ( FF03 ) [52] . Gly6 was then mutated to Ser and the structure was re-minimized using the above force field . Minimization was performed using the Sander module of the molecular dynamics package Amber 9 . 0 [53] . All hydrogens were added to the crystal structure of human plasminogen K1 ( pdb entry: 1CEA ) and the sidechains were energy minimized ( in vacuum ) using the Amber force field ( FF03 ) . The electrostatic potential surfaces of the minimized structures were constructed using the program Grasp [54] . For modeling the change in human plasminogen , the combined K4K5 domains along with the connecting region was modeled using the crystal structures of the individual kringle domains ( K4 from PDB entry 2PK4 and K5 from PDB entry 5HPG ) and the X-ray crystal structure of K1-K2-K3 domains of human angiostatin . The K1-K2 relative domain arrangement in angiostatin was used as the initial template for K4 superimposed on K1 and K5 superimposed on K2 and the loop building using homology modeling package Sybyl 7 . 1 ( Tripos , Inc . ) yielded the initial structure of K4K5 domains . A subsequent minimization followed by a nanosecond molecular dynamics simulation using Generalized Born solvation model ( Amber 9 . 0 ) was used to obtain the final representative structure for K4K5 of plasminogen . Asp472 in the native K4K5 structure was mutated to Asn and the resultant structure was subjected to a minimization followed by a nanosecond generalized Born salvation dynamics trajectory calculation to obtained the representative structure of the K4K5 ( Asp472Asn ) mutant structure . | Invasive aspergillosis ( IA ) is the most common invasive mould infection among highly immune compromised hosts . While exogenous immune suppression is the greatest risk factor for infection acquisition , polymorphic variation in key immune effector genes likely contributes to disease susceptibility as well . We hypothesized that susceptibility to invasive aspergillosis was , in part , due to host genetic variation . By screening strains of inbred mice , we demonstrated differential susceptibility to invasive aspergillosis . Using the Roche Mouse single nucleotide polymorphism ( SNP ) Database , we localized an area on murine chromosome 17 as a putative “quantitative trait locus” governing disease susceptibility . Within this interval , the gene encoding plasminogen ( Plg ) was found to have a coding change SNP in murine strains that were highly susceptible to disease . Many micro-organisms are recognized to hijack the plasminogen to be more prevalent in human hematopoietic stem cell transplant recipients who developed IA as compared to those who did not . Thus , we demonstrated a genetic basis for increased susceptibility to IA in both a murine model and human cohort , and implicated the fibrinolytic system in the pathogenesis of this disease . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"infectious",
"diseases/fungal",
"infections",
"genetics",
"and",
"genomics/complex",
"traits",
"oncology/hematological",
"malignancies"
] | 2008 | Plasminogen Alleles Influence Susceptibility to Invasive Aspergillosis |
Concerns over the possibility of resistance developing to praziquantel ( PZQ ) , has stimulated efforts to develop new drugs for schistosomiasis . In addition to the development of improved whole organism screens , the success of RNA interference ( RNAi ) in schistosomes offers great promise for the identification of potential drug targets to initiate drug discovery . In this study we set out to contribute to RNAi based validation of putative drug targets . Initially a list of 24 target candidates was compiled based on the identification of putative essential genes in schistosomes orthologous of C . elegans essential genes . Knockdown of Calmodulin ( Smp_026560 . 2 ) ( Sm-Calm ) , that topped this list , produced a phenotype characterised by waves of contraction in adult worms but no phenotype in schistosomula . Knockdown of the atypical Protein Kinase C ( Smp_096310 ) ( Sm-aPKC ) resulted in loss of viability in both schistosomula and adults and led us to focus our attention on other kinase genes that were identified in the above list and through whole organism screening of known kinase inhibitor sets followed by chemogenomic evaluation . RNAi knockdown of these kinase genes failed to affect adult worm viability but , like Sm-aPKC , knockdown of Polo-like kinase 1 , Sm-PLK1 ( Smp_009600 ) and p38-MAPK , Sm-MAPK p38 ( Smp_133020 ) resulted in an increased mortality of schistosomula after 2-3 weeks , an effect more marked in the presence of human red blood cells ( hRBC ) . For Sm-PLK-1 the same effects were seen with the specific inhibitor , BI2536 , which also affected viable egg production in adult worms . For Sm-PLK-1 and Sm-aPKC the in vitro effects were reflected in lower recoveries in vivo . We conclude that the use of RNAi combined with culture with hRBC is a reliable method for evaluating genes important for larval development . However , in view of the slow manifestation of the effects of Sm-aPKC knockdown in adults and the lack of effects of Sm-PLK-1 and Sm-MAPK p38 on adult viability , these kinases may not represent suitable drug targets .
Schistosomiasis is a parasitic disease caused by a trematode of the genus Schistosoma , affecting around 200 million people in the poorest areas of the world and despite progress in development of a vaccine against schistosomiasis , none is yet available [1–3] . Therefore , currently , both treatment and most disease control initiatives [4] rely on chemotherapy using a single drug , praziquantel ( PZQ ) , which is active against adult worms of all the medically important Schistosoma species [5] . Moreover , it has proved to be generally safe and effective using a single oral dose [6 , 7] . However , there have been a number of reports of poor in vivo efficacy of PZQ [8–11] and strains isolated from such cases have shown lower susceptibility to PZQ experimentally [12] although as yet there is no convincing evidence of development and selection of heritable resistance even following repeated rounds of treatment [9 , 13–15] . However , its increasingly extensive use , especially in mass drug administration programs , raises concerns about drug resistance emerging and this has led to renewed interest in research into drug discovery including: development and application of whole organism screens for compound testing [16]; rational drug discovery [17]; and identification of putative molecular targets by analysis of the annotated schistosome sequences [18] . Following the availability of transcriptome data and the complete genome sequence of Schistosoma mansoni [19–23] a number of groups have set out to identify likely drug targets from amongst the >11 , 000 genes predicted for the S . mansoni genome and to prioritise them for validation by molecular/biochemical techniques that can offer a quicker and more selective tool to identify a subset of possible essential genes . As recently reviewed [24] this in silico analysis has highlighted various “druggable” targets [25] in schistosomes [19 , 26–28] and the application of comparative genomics has identified orthologues in schistosomes of druggable genes shown to be essential in other organisms . Such approaches have led to the compilation of a number of partially overlapping lists of putative targets [19 , 20 , 29] . The demonstration that effective gene knockdown can be achieved in a variety of life cycle stages of schistosomes using RNA interference ( RNAi ) [24 , 30–36] has led to this method being applied to validate putative drug targets based on changes to phenotype and/or viability in culture or in vivo [32 , 36–44] . In parallel with our ongoing whole organism high throughput screen ( HTS ) approach [45] we have also undertaken studies aimed at target identification and validation using RNAi . At the outset we were interested to compare the use of adult and larval schistosomes with a view to possible use of our automated image-based HTS [45] for drug-induced damage to larval schistosomes to develop RNAi HTS . Studies were initiate by selecting putative essential genes in S . mansoni which were orthologues of genes previously shown , through RNAi methodology , to be essential in C . elegans [46] . Moreiver the RNAi-induced phenotype of Sm-Protein Kinase C together with the highlighted potential of kinases as key targets for drug design in schistosomes [28 , 47 , 48] led us to focus on kinases in our bioinformatics list . We also identified some other potential targets known to interact with known small molecule , drug-like compounds ( i . e . “druggable” targets ) . Early detection was based on compounds found to be active in whole organism screening of a focused kinase inhibitor library ( from GlaxoSmithKline ) and a diverse collection of lead-like compounds from the Division of Biological Chemistry and Drug Discovery , University of Dundee . Thus , a number of additional potential kinases were selected and targeted for silencing in adult and larval stages to establish their essentiality . Overall , phenotypic effects were seen following the dsRNA-mediated knockdown of 4 out of 16 genes ( Smp_026560 . 2 , Smp_096310 , Smp_009600 and Smp_133020 in SchistoDB ) coding for: Calmodulin , atypical Protein kinase C , Polo-like kinase 1 and p38-MAPK family member , respectively .
Orthologues were detected using OrthoMCL [49] version 1 . 4 . The program was installed locally and modified to use the NCBI BLAST+ program [50] . The default OrthoMCL parameters , for e-value and MCL inflation index were used , as it had been shown previously [51] that tightening these parameters only increased specificity at a cost on sensitivity . OrthoMCL produced clusters of genes within the two given genomes representing co-orthologous groups . Where the cluster contained a single gene from each genome , these genes were considered true orthologs . Where a cluster contained one gene from a species and multiple genes from the other species , the single gene was considered the ortholog , and the multiple genes considered paralogs . Where a cluster contained multiple genes from both genomes , all the genes were considered paralogs to each other . In order to obtain some ideas about the potential targets that the DDU set may be targeted a multi categorical Laplacian-modified naïve Bayesian model , as described by Nidhi et al . [52] with ChEMBL version 6 as training set [53] , using Pipeline Pilot v8 . 0 ( Biovia ) was built . Compounds from ChEMBL were considered active if they displayed an activity ( Ki , EC50 , Kd , Kb ) below 10 μM and targets were kept if they had at least 10 active compounds , the dataset contains 254 , 253 compounds and 1 , 028 targets . The descriptors used in the model were molecular weight , ALogP , numbers of hydrogen bond donors , acceptors and rotatable bonds , fractional Topological Polar Surface Area ( TPSA/total surface area ) [54] and Extended-connectivity fingerprints of depth 6 ( ECFP_6 ) [55] . For every compound the top prediction from the model was calculated and assigned as potential target of interest for that particular compound . To visualize the results for the 2 sets of compounds , similarity networks were drawn where nodes are compounds and edges linked two similar compounds ( one network per set ) . The similarity cut-off used is a Tanimoto score [56] higher than 0 . 7 using ECFP_4 as descriptors [55] . The networks were displayed in Cytoscape [57] . Individual sub networks were then analyzed to identify those which were enriched in in-vivo active molecules and to identify known or predicted targets of interest ( S1 and S2 Figs ) . Experimentation was carried out using the NC3Rs and ARRIVE guidelines . It was approved following local ethical review by the LSHTM Animal Welfare and Ethical Review Board and was performed in strict accordance with the U . K Home Office Animals ( Scientific Procedures ) Act 1986 ( approved H . O . Project License 600456 ) . Female CD1 mice ( aged 6–8 weeks ) supplied by Charles River , UK were maintained at St Mary’s Hospital , Imperial College London in SPF conditions with access to food and water ad libitum . Experiments were performed using the Puerto Rican strain of S . mansoni maintained in Biomphalaria glabrata and CD1 mice . Schistosomula were mechanically prepared as previously described [45] using medium 169 ( M169 ) [58] with 5% FCS . For some experiments the M169 was supplemented with 0 . 25% packed A+ human red blood cells ( hRBC ) ( National blood transfusion service , Collindale , UK ) . Mice were infected subcutaneously under mild isoflurane ( Merial Animal Health Ltd ( UK ) anaesthesia with 400 cercariae in 100μl water . Adult worms were recovered from infected mice using sterile techniques by portal perfusion 6–8 weeks post-infection using warm perfusion medium ( Dulbecco’s Modified Eagle’s Medium [DMEM] , 2mM L-glutamine , 100u/ml penicillin , 100μg/ml streptomycin , 20mM Hepes , 10units/ml heparin [Sigma , UK] [16] . We used DMEM since this was the medium used in our previous studies [16] and has also been used by others for long term culture [59 , 60] . As in these earlier studies we found good viability using cDMEM but would also expect M169 which we used for the schistosomula to work equally well [24 , 61] . In some experiments the mice were infected with male only worms . These were produced by infection of snails with single miracidia and the cercariae produced screened by PCR using the female-specific W probe [62] and only male cercariae were used for mice infections . Adult parasites were washed free of red blood cells using the perfusion medium and finally placed in culture in complete medium ( cDMEM: DMEM , 2mM L-glutamine , 100u/ml penicillin , 100μg/ml streptomycin , 10% foetal calf serum ( FCS ) at 37°C , in an atmosphere of 5% CO2 [59] . For all the targets above , specific T7 promoter-tagged primers were designed in order to amplify a PCR product of ~500 base pair ( bp ) for each gene ( S1 Table ) . We also produced an exogenous ( irrelevant [IRR] ) non-schistosome dsRNA fragment from the yeast expression vector pPIC9K as previously described [42] . The sequences of the primers were tested against the S . mansoni genome using the BLAST program at National Center for Biotechnology Information ( NCBI ) to avoid the risk of possible off-targeting . PCR products were used to synthesize in vitro the long dsRNAs using the T7 Megascript RNAi Kit ( Ambion ) following the instructions reported by the manufacturer . Briefly , all synthesis reactions were carried out for 4 hours at 37°C incubation followed by DNAse and RNAse treatments . At the end dsRNA products were run in a 1% agarose gel to check their integrity and concentrations assessed at OD260 using the ND 1000-Spectrophotometer . In addition , for each target short interfering RNAs ( siRNAs ) were synthesized commercially from both Applied Biosystem ( AB ) and Integrated DNA Technology ( IDT ) which use a different algorithm to design them . From both companies two siRNAs were designed based on different regions of the sequence in order to avoid any possible annealing problems due to mRNA secondary structure . A siRNA irrelevant control was obtained from IDT based on pPIC9K sequence , as mentioned above . To assess the level of gene knock down RNA was extracted from parasites using the Trizol method ( Invitrogen ) following the instructions reported by the manufacturer . Parasites were homogenized on ice using a sterile electronic pestle and the RNA precipitated overnight in ethanol , sodium acetate ( 3M , pH 5 . 2 ) and glycogen ( 1 μg/ml ) and subsequently treated with DNase ( Invitrogen ) to remove any possible genomic DNA residues . cDNA was synthesized using 500 ng of total extracted RNA from each sample in the Super Script III kit ( Invitrogen ) and an oligo ( dT ) 20 primer . Quantitative real-time PCR was performed in triplicate using custom TaqMan Gene Expression Assays including a specific set of primers and a probe labeled with 6-carboxyfluorescin ( FAM ) , obtained from Applied Biosystem . All TaqMan probes have been designed using a gene sequence outside the region in which the long dsRNA was synthesized . PCR reactions were carried out in triplicate on a 7500 ABI PRISM Sequence Detection System Instrument using an equivalent of 10 ng of parasite RNA according to the manufacturer’s instructions . For relative quantification , the ΔΔCt method was employed , using alpha tubulin as the endogenous standard for each sample [64] . Results obtained from parasites treated with irrelevant dsRNA were used as calibrators [65] . For graphical representation , the ΔΔCt values were normalized to controls and expressed as percent difference . Newly transformed schistosomula were soaked in presence of specific long dsRNA or control as described above . After the treatment parasites were maintained in culture overnight in 1 ml of complete M169 without blood . The following morning parasites were washed three times with M169 without serum , counted and resuspended in an appropriate volume to contain 1000 schistosomula/100 μl of medium . This was injected into CD1 mice ( 5 age and weight matched mice per group ) maintained under mild isoflurane ( Merial Animal Health Ltd ( UK ) anaesthesia . In order to increase the chance of administration of comparable numbers of larvae to each mouse , two separate intramuscular injections each of 50 μl suspension were administered into different thigh sites of each mouse using a 30 g needle ( BD Micro-FineTM+ ) . Samples of the treated parasites were kept in culture in order to assess gene knock down 7 days and 28 days after the dsRNA treatment . Mature worms were recovered from mice 4 weeks later by mesenteric vein perfusion [66] and checked for number and morphological changes . Parasites were then used to assess the gene suppression levels by qRT-PCR as described above . 500 newly transformed schistosomula were cultured in wells of 48 well plates ( Nunc , UK ) in 1 ml of M169 medium supplemented with 100 U/ml Penicillin , 100 μg/ml Streptomycin and 5% foetal calf serum ( Sigma , UK ) in the presence of 100 nM of BI2536 inhibitor ( Axon MedChem , Netherlands ) dissolved in DMSO ( final DMSO concentration was 0 . 1% ) . Control schistosomula were cultured in medium with 0 . 1% DMSO . For adult worms , cultures were set up in 24 well plates ( Nunc , UK ) and 5 worm pairs recovered from mice 6 weeks post infection added to each well in 2 ml cDMEM . As above , 100nM of Bl12536 was added to test wells and DMSO to controls . Cultures were set up with or without 2 . 5μl/ml packed washed human hRBC . Parasites were maintained in 5% CO2 at 37°C and the medium ± inhibitor ± hRBC renewed twice a week . Differences between groups were assesses for statistical significance using unpaired Student’s t-test using GraphPad Prism 4 . 0 Software . Results are considered significant if p value < 0 . 05 .
Genes predicted to be both essential and druggable ( i . e . interact with small molecule ligands ) in S . mansoni , were selected for study using RNAi . Due to the lack to whole genome essentiality studies in S . mansoni essentiality was inferred from C . elegans data , as the genome sequence [67] and a systematic functional analysis of the C . elegans genome , using RNAi , are available [46] . The latter study assigned each gene to a functional class based on the phenotype , and the functional classes used to define “essential” genes here were: ( i ) Embryonic lethality ( Emb ) , defined as >10% dead embryos; ( ii ) Sterile ( Ste ) , required a brood size of <10 ( wild-type worms under similar conditions typically have >100 progeny ) ; ( iii ) Sterile progeny ( Stp ) , progeny brood size of <10 . Gene essentiality in S . mansoni was inferred from the C . elegans data [46] using a logical model: the predicted gene has an ortholog or out-paralogs , but no in-paralogs and any one of the predictor co-orthologs has been experimentally verified as essential . The assumptions of the model were that if a gene was retained after a speciation event , despite a large evolutionary time and adaption to new conditions , then its function was potentially essential . However , if the gene had subsequently been duplicated there was a potential redundancy of function and the gene was not individually essential . Of the 11 , 809 genes in the S . mansoni genome , 323 were predicted to be essential using this method . The 323 putative essential S . mansoni genes were searched against the protein targets in ChEMBL [53] ( http://www . ebi . ac . uk/chembl/ ) using BLAST+ , ( E-value cut-off of 1×10-03 and target coverage of >50% ) . Only ChEMBL targets that had at least one potent compound ( <10nM ) were considered . This filter reduced the S . mansoni set to just 24 genes ( S2 Table ) . Interestingly these genes cover a variety of functional categories including kinases , phosphatase and proteasome subunits . One of those selected genes , glycogen synthase kinase 3 , GSK-3 , ( Smp_008260 . 1 ) was previously proposed as a potential drug target in S . mansoni [68] . The 2 libraries of compounds were tested and the DDU set similarity network with active and inactive compound is shown in S1 Fig , while the activity of the PKIS dataset in-vivo are shown in S3 Table and the network similarity network in S2 Fig . Some sub networks were identified as being enriched in active compounds and the associated known or predicted targets for those compounds were investigated . Some of the corresponding genes in the S . mansoni genome were selected by searching for the homologous gene directly in the schisto DB database ( http://schistodb . net/schisto/ ) ( i . e . EGFR ) . Other genes have been identified via information relative to the genes function and/or mechanism of action available in literature ( i . e . Insulin receptor and PLK1 ) . For all the other genes , the protein sequences of the human genes were used to query the schistosome genome via a BLAST search and the genes having the highest sequence similarity were selected . In this way a total of 10 different genes were selected for RNAi ( some examples are indicated in S2 Fig ) . Despite effective mRNA knockdown no differences between dsRNA-treated and controls were observed for any of the genes tested in adult worms ( S4 Table ) . However the silencing of 2 of the genes ( Smp_133020 and Smp_009600 ) led to phenotypic changes and loss of viability when performed in larvae as described below . In the first part of this work we present the results obtained from the silencing of several genes selected from S2 Table . In order to establish and confirm the optimal conditions to perform the RNAi in adult and larval parasites we started with the gene at the top of the predicted essential list , a putative calmodulin gene ( Smp_026560 . 2 ) . Then , in the second part of this study we focused on kinases selected from phenotypic screening of kinase inhibitor libraries . Initial experiments involved electroporation of adult male worms in the presence of dsRNA since we and others had previously found this to be successful [34 , 69] . We tested the ability of specific long Sm-Calm dsRNA ( dsRNA ) versus synthetic small interfering RNA ( siRNA ) ( designed and supplied by Applied Biosystems [AB] and Integrated DNA Technology [IDT] ) to efficiently suppress Sm-Calm expression . A week after the RNAi treatment relative gene expression was determined using qRT-PCR . We found that all dsRNA constructs exerted strong transcript level suppression , ranging from 70 to 95% compared to controls . Comparable knockdown was achieved with both ds and siRNA from two different companies used separately or in combination ( S3 Fig ) . This and subsequent comparisons of ds and siRNAs with seven other genes showed both to be effective but in general the dsRNA gave somewhat higher levels of inhibition and , therefore , in later experiments we only used long dsRNA ( normally ~500bp ) . Fig 1A shows data representative of 3 repeat experiments . Relative expression of Sm-Calm a week after the electroporation with dsRNA was reduced by 90% . Between 1–2 weeks after treatment the worms started to manifest the characteristic waves of contraction/dilation ( Fig 1C ) . In contrast control worms treated with irrelevant dsRNA ( IRR ) had a smooth and regular shape ( Fig 1B ) . This phenotype did not seem to affect worm viability initially but by 5 weeks , when the waving phenotype had reduced , the treated worms appeared darkened , had damage to the tegument and showed lower motility compared with controls . Two months after the silencing Sm-Calm was still 50% lower compared to controls . Electroporation of a mixture of male and female worms , some in copula , resulted in mRNA suppression of 89% in male worms but only 26% in females . The contraction phenotype was not seen in the females but did occur in the males even when in copula although this did not seem to affect pairing . The same phenotype was seen with the siRNA suppression of Sm-Calm suppression . Whilst evaluating the effects of Sm-Calm RNAi in schistosomula we carried out numerous experiments to confirm the optimal protocols based on various methods reported in the literature: use of dsRNA delivery ( by soaking or electroporation ) , age of parasites ( newly transformed or one week old cultured schistosomula ) , the effect of different culture media on efficiency of knockdown and parasite survival ) and culture with and without human red blood cells ( hRBC ) . These findings , replicated with several other genes , are reported in brief since similar conclusions have been reached by others [24 , 34 , 39 , 40] i . e . markedly better survival in M169 than in RPMI , better survival following delivery of dsRNA by soaking than electroporation , comparable mRNA knockdown in newly transformed and 7 days old schistosomula whether by electroporation or soaking . Based on these results the soaking method with newly transformed schistosomula using M169 was used for all further experiments . Although marked Sm-Calm suppression ( up to >95% ) was consistently observed , no phenotypic changes were observed in terms of larval survival or development when silenced parasites were cultured in M169 with or without hRBC . High levels of mRNA knockdown were also achieved with schistosomula as seen in Fig 3A . Initial experiments showed modest effects on schistosomula viability and so further experiments tested cultures with or without hRBC , since this triggers larval development and transcriptional changes in gene expression [70] . In the presence of hRBC the phenotypic effects were much more marked ( Fig 3B ) . The control and Sm-aPKC treated schistosomula looked similar up to one week of culture with many ingesting hRBC and showing haematin pigment in their developing guts . After 2 weeks there was increased death of the Sm-aPKC treated schistosomula ( Fig 3D ) and a marked drop in the proportion of those with developing guts so that virtually all remaining by 4 weeks showed the elongate shape of the lung stage schistosomula ( Fig 3C ) . This indicated high mortality once development had been initiated by onset of blood feeding in vitro . The data shown is representative of three independent experiments . Due to the effects induced by silencing Sm-aPKC and the fact that kinases are considered to be an important class of potential drug targets , owing to the essential roles many of them play [28 , 47] we focused our RNAi experiments on some other kinases in the list of putative essential genes i . e . caseine kinase ( Smp_180400 ) , serine/threonine protein kinase ( Smp_080730 ) , putative serine/threonine-protein kinase vrk ( Smp_141380 ) and glycogen synthase kinase 3 ( Smp_008260 . 1 ) . As can be seen from S5 Table high levels of mRNA reductions were observed in adults for each of the genes although levels for the larvae were more variable . However , knockdown of none of these genes caused any phenotypic changes in either adult or larval stages . Sm-PLK1 suppression experiments were performed three times using adult male and female worms cultured with or without RBC . Despite high levels of silencing ( >90% mRNA reduction ) no phenotypic changes were observed during culture for 5 weeks . RNAi treatment of schistosomula induced >95% suppression of mRNA for Sm-PLK1 as assessed one week later . As shown in Fig 4 this silencing led to severe morphological abnormalities and reduced survival between two and three weeks of culture . As with Sm-PKCa , this effect was markedly more pronounced with schistosomula cultured with hRBC , with only 15% of those in hRBC surviving at 4 weeks ( Fig 4B ) compared with 60% cultured without hRBC ( Fig 4A ) . The above evidence of involvement of Sm-PLK1 in early worm development was corroborated by using the specific inhibitor , BI2536 , as previously used with adult worms [71] . Triplicate cultures of 500 schistosomula with or without hRBC were set up as above in 48-well plates with or without 100nM Bl2536 . During the first 2 weeks the inhibitor caused no obvious effect but by 3 weeks a proportion of larvae in the cultures with inhibitor showed morphological damage and death . This was markedly more pronounced in the cultures with hRBC ( Fig 5 ) such that by 4 weeks very few remained alive and those that did were larvae which retained the lung form and had not started to feed ( e . g . Fig 5E ) . BI2536 at 100nM was also used in adult worm cultures . During culture for 7 weeks no differences were seen in the morphology , behaviour or viability of the worms with or without inhibitor . However , in contrast to the controls the eggs produced in cultures with the inhibitor failed to embryonate . To investigate if this could be due to effects of the inhibitor on egg development , the unembryonated eggs produced by ex-vivo adults maintained for 3 days in control medium were recovered from the cultures and incubated with or without inhibitor in quadriplicate . After 10 days 41±9 . 3% of eggs in control cultures contained viable miracidia or had hatched whereas none of the eggs in the inhibitor had developed to contain miracidia ( Fig 6 ) . This clearly indicates that PLK-1 is necessary for embryonation of viable eggs . Marked suppression ( >85% ) of p38-MAPK mRNA was observed a week after the long dsRNA treatment in both adult worms and schistosomula . Again no phenotype was seen in the adult worms but the RNAi silencing consistently reduced viability of schistosomula starting after 2 weeks and , as previously observed for Sm-aPKC and Sm-PLK1 , such effects on parasite viability were more evident when p38-MAPK silenced schistosomula where cultured in the presence of hRBC ( Fig 7 ) . Groups of 5 mice were injected intramuscularly with 1 , 000 control , Sm-aPKC or Sm-PLK1 treated schistosomula/mouse and perfused 4 weeks later . Aliquots of the same batches of schistosomula were cultured in vitro . Data representative of two independent experiments is shown in Fig 8 . Mice injected with both Sm-aPKC and Sm-PLK1 silenced schistosomula yielded significantly fewer parasites compared with untreated controls or mice injected with IRR dsRNA . However , none of the surviving suppressed parasites showed any differences in size or shape compared to the controls . The recovered worms also showed no persisting gene suppression in contrast to the Sm-aPKC or Sm-PLK1 treated larvae maintained in culture for 4 weeks which showed significant knockdown ( ~70% and 80% respectively ) comparable to that assessed one week after treatment .
In this study we set out to contribute to RNAi based validation of putative drug targets for schistosomiasis . S . mansoni possesses a quite large ( 270 Mb ) and complex genome containing between 15 , 000 and 25 , 000 genes [22 , 72] . It is thus crucial to find new in silico strategies to select genes which have a potential to be target candidates for new drugs or vaccines . In looking for essential genes in pathogens a variety of characteristics can be considered . Crowther et al . , [20] used the targets database ( TDR ) ( http://tdrtargets . org/ ) as a tool to illustrate how to prioritize proteins which have a potential to be considered drug targets in neglected-disease causing pathogens . Such target selection has been applied to several parasitic diseases such as Chagas disease , parasitic worm infections and malaria . The strategy is based on a multi-criteria search through the use of the open-access resource in TDR in which it is possible to manually select proteins that fit specific desired traits ( druggability , assayability , essentiality ) . In S . mansoni proteins are not yet scored for druggability in the TDR database , so their druggability was assessed by comparing the proteins’ amino acid sequences to those of known targets in the protChEMBL database . In addition to this , proteins’ functional importance have been compared to gene knockout data of their orthologs in C . elegans and D . melanogaster . This method yielded a top list of 170 Schistosoma targets . Caffrey et al . [68] , focused their search on a comparative chemogenomics approach based on the proteome of two model organisms , the nematode Caenorhabditis elegans and the fruit fly , Drosophila melanogaster in which gene knockout studies had identified proteins which appear to be essential or producing a deleterious phenotype in both organisms . The search aimed to highlight proteins in these organisms having clear sequence similarities to orthologues in schistosomes . This type of manual screening led to a selection of 35 S . mansoni druggable targets 8 of which were also present in the earlier list [20] . Our strategy was similar to the above and aimed at identifying potential essential and druggable targets for S . mansoni by inference from the ChEMBL database of drug targets and orthology essential data from C . elegans , as a one of the few model systems where whole genome functional genomics studies using RNAi have been performed . This analysis produced a list of 24 target candidate genes to be analysed by RNAi of which only the glycogen synthase kinase 3-related ( gsk3 ) gene ( Smp_008260 ) was also present in the earlier lists [68] . In the case of Sm-Calm , the first gene on our list of 24 genes , robust suppression was induced in adult worms and resulted in regular waves of contraction and dilation in male worms but not females . Interestingly , siRNA knockdown of a 24kDa calcium-regulated heat-stable protein of S . japonicum ( CRHSP-24 ) induces what appears to be a similar contraction wave phenotype in juvenile S . japonicum worms [73] . Calmodulin as a transporter of Ca2+ plays a key role in calcium signalling , affecting numerous functions including muscle contraction and metabolism . Selective inhibitors of calmodulin in schistosomes block egg hatching [74] and both inhibitors [75] and siRNA knockdown [76] inhibit miracidial transformation to sporocysts . Exposure of worms to PZQ results in disruption of Ca2+ homeostasis mediating its action by inducing Ca2+ influx [5] and it has been suggested that molecules in the calcium signalling pathway may be potential targets for drug discovery . After 5 weeks the contraction phenotype waned but by this time the worms were slow , darkened and had tegumental damage . Despite the important role that calmodulin plays during different stages of S . mansoni development the very slow induction of damage to adults and the fact that the gene exhibits more than 90% identity with mammalian calmodulin [76] reduce its suitability as a target for drug development . Silencing of the atypical protein kinase C ( Sm-aPKC ) similarly had no immediate effect on adult worms but after 2 weeks in culture the worms became darkened , non-adherent and developed blebs . By the third week they were severely damaged . Sm-aPKC silencing in schistosomula also induced decreased viability and development after 2 weeks of in vitro culture especially when cultured in the presence of hRBC . Reduced viability was also seen when Sm-aPKC silenced schistosomula were injected in vivo supporting a role for this gene in schistosomula development . Interestingly we found that parasites recovered from mice in vivo showed no more silencing resulting in mRNA levels comparable to controls . Such phenomenon is not surprising as it has been previously observed in other studies [32 , 42 , 63] . As suggested by Krautz-Peterson et al . , one hypothesis is that different schistosomula can receive variable amounts of dsRNA or , alternatively , this disparity could be due to a more active metabolism of parasites in vivo that can determine a shorter half life of the dsRNA [77] . Members of the family of atypical PKCs are considered to be key components of downstream signalling pathways activated by the EGF receptors which can activate diverse signal transduction cascades [78] . Earlier studies on another S . mansoni protein kinase C ( SmPKC1 ) , with high homology to mammalian PKCβ , was found to be differentially expressed during the schistosome life cycle , with the highest mRNA and protein levels being found in miracidia and sporocysts , respectively . The protein was also immunolocalized to the acetabular glands of newly transformed schistosomula suggesting a possible role in larval transformation [48] . A further study demonstrated activated schistosome PKCβ expression during postembryonic development of the miracidium to mother sporocyst suggesting its role in regulating this transformation [79] . The effect that Sm-aPKC silencing had on both adult and larvae schistosomes in our studies indicates an important role in the parasite although the slow onset of effects does not support it as a likely drug target . Given the importance of kinases as potential drug targets and having shown some phenotypic effects following knockdown of the atypical protein kinase C ( Sm-aPKC ) we focused our attention on additional kinases . Andrade et al . , [28] identified and classified 252 S . mansoni eukaryotic protein kinases ( ePKs ) , describing some of them as good drug candidates as they may perform an essential function in the parasite . Although they often had high sequence similarity to their human homologues , new and effective drugs can overcome such similarity by binding protein kinases close but not in the ATP site occluding ATP access to the kinase to retard enzyme activity [28] . Initially we carried out RNAi suppression on four other kinases in our list of putative essential genes alongside three other non-kinase genes but failed to demonstrate any phenotype with these knockdowns . So attention was switched to selecting genes through computational prediction of putative targets based on either searching the ChEMBL database for compounds found to be active against adult S . mansoni in previous whole organism screens [16 , 45 , 80] or from the focused PKIS kinase inhibitor collection and the kinase inhibitors represented in the DDU compound collection we had tested previously [45] . A similar study was recently conducted in zebrafish by Laggner et al . that sought to identify putative targets of 681 neuroactive molecules from a 14 , 000-compound phenotypic screen in zebrafish embryos [81] . Putatively homologous genes for the identified kinases were mapped in the S . mansoni genome and thereafter 10 kinases highlighted to be validated by RNAi phenotypic screening . From this screening we obtained detectable phenotype in schistosomula but not in adults following the knockdown of 2 of the 10 genes tested , Sm-PLK1 and Sm-MAPK p38 . As for Sm-aPKC the loss in schistosomula viability with Sm-PLK-1 and Sm-MAPK p38 started after 2–3 weeks and were more marked in cultures with hRBC in which the larvae were developing rapidly indicating a key role for these kinases during development . In the case of PLK-1 the RNAi effects were exactly comparable when the PLK-1 specific inhibitor BI2536 was used . The failure to demonstrate activity in the adults with the RNAi knockdowns despite the adult stage having been used initially to screen the kinase inhibitors may be explained by the relatively modest effects seen against adults in the initial in vitro testing i . e . the highest percentage inhibition of adult motility achieved with the five GSK kinase inhibitors identified to target PLK-1 was 65% and the next highest only 25% . P38 MAPK was implicated by two inhibitors inducing 82% and 76% inhibition of motility . So none of these inhibitors actually killed the adults which combined with the only partial , albeit high , RNAi knockdown may have explained why no effect on phenotype was seen with the RNAi in adults . By comparison the phenotypic effects seen in schistosomula may reflect greater sensitivity of this developing stage to knockdown of these two genes . It also cannot be ruled out that the compounds inhibit additional targets ( possibly protein kinases ) within the schistosomes , complicating the analysis . PLKs constitute a family of serine threonine kinases which have a highly conserved function in all eukaryotes . Along with other mitotic kinases , they play a crucial role in coordinating cell cycle progression and are potent regulators of M phase [82] . In vertebrates , there are four well-known PLKs ( PLK 1–4 ) whilst schistosomes only possess two , Sm-PLK1 and Sm-Sak , both well characterized [71 , 83 , 84] . Sm-PLK1 has been shown to be abundantly expressed in germinal and vitelline cells and more abundant in sporocysts than in other parasite stages , consistent with its potential role in mitosis and/or meiosis in schistosomes . Our data suggest that Sm-PLK1 might play an important role also at the schistosomula stage which is characterized by rapid growth and body remodeling particularly when hRBC are added , processes which would require active mitotic machinery . Such an important role in schistosomula can also be observed in vivo where a reduced viability was also seen in silenced parasites injected in mice thus supporting the involvement for this gene in schistosomula development . In addition , previous studies have shown that adult worms treated with BI2536 , a specific inhibitor of Sm-PLK1 , which showed altered morphology of reproductive organs with significant effects on oogenesis and spermatogenesis [83] . In our studies neither RNAi knockdown or BI2536 affected adult worm viability but BI2536 inhibited embryonation of eggs produced in vitro as would be expected from inhibition of mitosis . Similar effects in terms of morphology and kinetics were seen with silencing of the Sm-MAPK p38 family member ( Fig 7 ) . It has been previously shown how p38 MAPK inhibition with pyridinylimidazole can have crucial effects on survival and replication of some pathogens such as P . falciparum and T . gondii [85 , 86] . The active form of this gene in S . mansoni has been associated with the cilia of miracidia , playing a role in the ciliary motion [87] and also during the early stages of post-embryonic development [88] . Treatment performed with a specific inhibitor ( SB203580 ) is able to retard the development of the miracidium to post-miracidium stage and also from post-miracidium to mother sporocyst stage , without affecting larval viability [88] . We demonstrated here that Sm-MAPK p38 might also play an important role in schistosomula development and that its suppression affects growth and survival of parasites in vitro . Despite the coincidence of the nature and timing of the effects seen in vitro with these different RNAi gene knockdowns in schistosomula we believe the effects to be specific for the following reasons: ( i ) IRR dsRNA prepared in exactly same way as that for the specific genes was used in all experiments and cause no significant phenotypes; ( ii ) the effect was reproducible for the genes in question whereas no effects were seen for a number of other genes in which the RNAi was prepared in exactly the same way; ( iii ) in a recent study of RNAi knock-down of 3-hydroxy-3-methyl-glutaryl-CoA reductase ( HMGR ) , the target for statins , there was , similarly little effect on schistosomula viability until 2–3 weeks of culture [36]; ( iv ) in the case of the Sm-PLK-1 knockdown the phenotype and time of its onset were exactly replicated with the PLK-1 inhibitor , BI2356; ( v ) in the cultures with hRBC the loss of viability is seen once larval development characterized by gut formation is initiated whereas schistosomula which retain a “lung-form” morphology remain unaffected . Although we were able to demonstrate phenotypic effects on both larval and adult schistosomes , the earliest effects seen were at 2 weeks ( calmodulin in adults ) whilst most of the effects on larvae were not seen until the second/third week . Furthermore , with some of the gene knockdowns effects were seen with larvae but not adults and vice versa . In addition the deleterious effects on schistosomula were markedly enhanced and visualized when hRBC were added and the larvae induced to feed . Indeed it was commonly seen that larvae which did not feed and retained the lung form phenotype seemed unaffected ( e . g . Fig 5E ) . It is not surprising that the induction of development following initiation of red blood cells is associated with expression of genes vital for survival . Microarray studies on schistosomula cultured for 5 days have characterized the transcriptional changes in genes encoding surface and secreted proteins during the first 5 days in in vitro culture and show upregulation of a number of genes in the presence of hRBC [70] . We conclude that RNAi of schistosomula combined with culture in the presence of hRBC is a valuable approach to understanding key roles played by individual genes . However , as mentioned at the outset one of our intentions was to assess whether the automated image analysis of larval motility and morphology which we have developed for drug screening [45] could be developed for HTS RNAi . Effects which take weeks to manifest , vary markedly between individual worms , and rely on addition of hRBC are not conducive to this sort of analysis not least because once hRBC are added to schisostomula , the individual worms develop at different rates some developing guts quickly and others remaining like lung forms . So there is even more variation in shape , size , motility and appearance than with newly transformed parasites making development of segmentation and image analysis of morphological changes more challenging . Nevertheless , inhibition of motility would be easier to develop although the presence of hRBC would interfere with segmentation of the parasites for image analysis . This proof of concept study illustrates the potential of combining bioinformatics target prioritization with RNAi viability studies as a method to identify putative drug targets in S . mansoni . | Schistosomiasis is a neglected tropical disease affecting 200 million people in the poorest areas of the world . To date no vaccine exists against this parasitosis , and the only possible cure relies on the use of the one available drug , praziquantel . Praziquantel is a safe and effective drug active against adult worms in a single dose administration . However the increasing threat of emerging resistance makes the search for novel drugs a compelling priority . S . mansoni possesses a quite large and complex genome so new in silico strategies to select genes which might represent potential novel drug target candidates are an important approach to drug development . Here , we report application of RNA interference , which knocks down gene function , to validate a number of putative drug targets for schistosomiasis . Some of these genes were selected because they had been shown to be essential for life in another worm species , C . elegans , and others because they were potential targets of compounds shown to be active against schistosomes in vitro . Although none of the identified genes looked promising as drug targets , our results showed important roles for some of the genes in schistosome development . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Application of RNAi to Genomic Drug Target Validation in Schistosomes |
Disruption of protein homeostasis in chloroplasts impairs the correct functioning of essential metabolic pathways , including the methylerythritol 4-phosphate ( MEP ) pathway for the production of plastidial isoprenoids involved in photosynthesis and growth . We previously found that misfolded and aggregated forms of the first enzyme of the MEP pathway are degraded by the Clp protease with the involvement of Hsp70 and Hsp100/ClpC1 chaperones in Arabidopsis thaliana . By contrast , the combined unfolding and disaggregating actions of Hsp70 and Hsp100/ClpB3 chaperones allow solubilization and hence reactivation of the enzyme . The repair pathway is promoted when the levels of ClpB3 proteins increase upon reduction of Clp protease activity in mutants or wild-type plants treated with the chloroplast protein synthesis inhibitor lincomycin ( LIN ) . Here we show that LIN treatment rapidly increases the levels of aggregated proteins in the chloroplast , unleashing a specific retrograde signaling pathway that up-regulates expression of ClpB3 and other nuclear genes encoding plastidial chaperones . As a consequence , folding capacity is increased to restore protein homeostasis . This sort of chloroplast unfolded protein response ( cpUPR ) mechanism appears to be mediated by the heat shock transcription factor HsfA2 . Expression of HsfA2 and cpUPR-related target genes is independent of GUN1 , a central integrator of retrograde signaling pathways . However , double mutants defective in both GUN1 and plastome gene expression ( or Clp protease activity ) are seedling lethal , confirming that the GUN1 protein is essential for protein homeostasis in chloroplasts .
Endosymbiotic organelles such as mitochondria and chloroplasts play fundamental roles in eukaryotic organisms . They both contain their own genome but most of their proteins are encoded by the nuclear genome . As a consequence , mechanisms to adjust nuclear gene expression to particular organelle needs are required to ensure an appropriate supply of functional proteins [1–4] . Nuclear-encoded proteins are translocated into organelles in unfolded form , and then their transit peptide is cleaved before they are properly folded , assembled , or/and targeted to their particular suborganellar destination . Inside the organelles , the lifespan and activity of proteins depend on protein quality control ( PQC ) systems formed by chaperones and proteases that promote correct protein folding , prevent the formation of insoluble aggregates , and remove irreversibly damaged proteins . When misfolded proteins accumulate and aggregate in mitochondria , an adaptive transcriptional response known as unfolded protein response ( UPR ) is activated to communicate with the nucleus and induce the expression of nuclear genes encoding mitochondria-targeted chaperones and proteases [5–7] . The existence of a chloroplast UPR ( cpUPR ) has only recently been proposed based on work with the unicellular green alga Chlamydomonas reinhardtii [8 , 9] . In particular , gradual depletion of the catalytic capacity of the stromal Clp protease in algal cells was found to trigger the accumulation , both at the RNA and protein level , of small heat shock proteins , chaperones , and proteases [8] . Arabidopsis thaliana mutants with constitutively decreased Clp proteolytic activity also show highly increased levels of stromal chaperones from different families , including Cpn60 , Hsp70 , Hsp90 , and Hsp100/ClpB [10–17] . Interestingly , the Clp protease is a key component of the UPR mechanism in mitochondria [6 , 18] . While these observations suggest that a UPR conceptually similar to that observed in mitochondria might operate in chloroplasts , the physiological signal ( s ) triggering this putative cpUPR and the specific consequences for chloroplast function remain unexplored . Recently , we characterized the role of chloroplast PQC systems to control the levels and activity of Arabidopsis deoxyxylulose 5-phosphate synthase ( DXS ) , the enzyme that catalyzes the first and main rate-determining step of the methylerythritol 4-phosphate ( MEP ) pathway [16 , 19 , 20] . The MEP pathway is localized in the plastid stroma and synthesizes the metabolic precursors for isoprenoids such as carotenoids and the prenyl chains of chlorophylls , tocopherols , or plastoquinone ( Fig 1A ) . DXS is prone to misfold and aggregate , resulting in insolubility and loss of enzymatic activity [16 , 19 , 21] . Misfolded and aggregated forms of DXS are primarily degraded by the Clp protease complex through a pathway involving the DnaJ-like protein J20 , an adaptor that delivers the inactive enzyme to stromal Hsp70 chaperones . Then , interaction with the Hsp100/ClpC1 chaperone allows unfolding of the DXS protein for degradation by the catalytic core of the complex . Alternatively , direct interaction of Hsp70 with Hsp100/ClpB3 eventually results in the refolding and hence reactivation of DXS ( Fig 1A ) . ClpB3 is the only ClpB-type Hsp100 chaperone targeted to Arabidopsis plastids , where it is presumed to disaggregate protein clumps and promote protein solubilization either alone or in synergy with Hsp70 chaperones [22 , 23] . Unlike ClpB3 , ClpC1 and the other two plastidial ClpC-type Hsp100 chaperones found in Arabidopsis ( ClpC2 and ClpD ) contain a ClpP-loop motif for interaction with proteolytic subunits of the Clp complex [16 , 22 , 24] . Notably , mutants defective in ClpC1 show an increase in ClpB3 protein levels that prevents the formation of DXS aggregates , eventually resulting in higher levels of enzymatically active DXS protein [16] . An enhanced accumulation of DXS protein levels ( but not transcripts ) was also observed upon genetic or pharmacological inhibition of plastome gene expression ( PGE ) . Screening collections of Arabidopsis T-DNA insertion lines for plants able to survive in the presence of an otherwise lethal concentration of the MEP pathway inhibitor fosmidomycin ( FSM ) led to the isolation of resistant to inhibition with FSM ( rif ) mutants such as rif10 , impaired in plastid RNA processing [25] , and rif1 , defective in plastidial ribosome assembly [26 , 27] . Besides DXS , both rif10 and rif1 mutants show enhanced accumulation of other MEP pathway enzymes , including deoxyxylulose 5-phosphate reductoisomerase ( DXR ) , the specific target of FSM ( Fig 1A ) . Accumulation of DXS and DXR enzymes resulting in enhanced FSM resistance was also observed when PGE was partially inhibited in wild-type ( WT ) seedlings grown in the presence of sublethal concentrations of chloramphenicol ( CAP ) , an inhibitor of protein synthesis in plastids . By contrast , treatment with norflurazon ( NFZ ) , an inhibitor of carotenoid biosynthesis ( Fig 1A ) that causes a similar visual phenotype without directly affecting PGE [28] , did not result in FSM resistance [25] . As the catalytic ClpP1 subunit of the Clp protease is encoded by the plastome and its levels are altered in rif mutants and CAP-treated plants [26] , it was proposed that interference with PGE disrupts stoichiometry and consequently reduces proteolytic activity of the Clp complex , eventually resulting in the accumulation of protein clients such as MEP pathway enzymes . The increase in ClpB3 protein levels observed when Clp activity is decreased would then contribute to maintain these enzymes in a correctly folded ( i . e . active ) form [16] . While increase of DXS and DXR protein levels in PGE-defective or Clp-impaired plants does not involve transcriptional changes [16 , 25 , 26] , the mechanism up-regulating ClpB3 levels is currently unknown . In this work , we aimed to test the hypothesis that this mechanism might involve a cpUPR , i . e . that insufficient Clp protease activity resulting from PGE defects might elicit a retrograde signaling pathway eventually increasing the levels of plastidial chaperones ( including ClpB3 ) by inducing the expression of the corresponding nuclear genes . Besides confirming this hypothesis and providing mechanistic insights , our results show that the plastidial protein GUN1 ( a major integrator of signaling pathways from the chloroplast to the nucleus ) is not required for the cpUPR-associated transcriptional changes but it is essential for chloroplast to respond to challenges disrupting protein homeostasis .
In mitochondria , UPR can be unleashed through interference with the expression of the organelle genome , e . g . by inhibiting protein translation [6] . To verify the possible existence of a cpUPR acting in response to alterations in PGE , we initially tested different concentrations of lincomycin ( LIN ) for their capacity to up-regulate the accumulation of plastidial chaperones . LIN specifically inhibits chloroplast translation even though it also has secondary effects on mitochondrial gene expression at high concentrations , i . e . those causing seedling bleaching [29 , 30] . WT ( Columbia ) Arabidopsis plants were germinated and grown for 10 days under long-day ( LD ) conditions in the presence of LIN at concentrations causing from no visible symptoms ( 5 μM ) to complete bleaching ( 100 μM ) ( Fig 1B ) . Then , we analyzed the abundance of plastidial chaperones ( Hsp70 , ClpB3 , and ClpC ) by immunoblot analysis ( Fig 2 ) . While chaperones recognized by antibodies against plastidial Hsp70 and ClpC proteins hardly changed in LIN-exposed plants , the levels of ClpB3 did increase even at low concentrations of LIN ( Fig 2 ) . As concentration of LIN increased and plants became paler , levels of the ClpB3 unfoldase were progressively higher ( Fig 2 ) . We next used PGE-defective mutants to test whether ClpB3 accumulated to higher levels in their chloroplasts ( Fig 3 ) . In particular , we used mutants defective in the exoribonuclease RIF10 , implicated in the processing of all major classes of plastid RNAs [25] , or the chloroplast 50S ribosomal protein L24/SVR8 [31] . WT plants and T-DNA insertion alleles rif10-2 and svr8-2 were grown for 10 days under LD and then analyzed for chlorophyll and protein content . While both mutants showed similarly reduced chlorophyll levels ( S1 Fig ) , rif10-2 seedlings displayed green cotyledons and pale true leaves whereas svr8-2 seedlings showed reduced pigmentation in both cotyledons and leaves ( Fig 3A ) . WT plants growing on medium supplemented with 15 μM LIN , a concentration that reduced overall chlorophyll levels to those found in the mutants ( S1 Fig ) , showed a general pale phenotype similar to the svr8-2 mutant ( Fig 3A ) . Similar to LIN-treated plants , levels of ClpB3 , but not those of Hsp70 and ClpC chaperones , were increased in both rif10-2 and svr8-2 mutants compared to untreated WT controls ( Fig 3A ) . Together , our data confirm that interference with PGE triggers the accumulation of particular chloroplast-targeted chaperones involved in releasing protein folding stress , such as ClpB3 . ClpB3 helps to refold misfolded and aggregated forms of DXS to recover their solubility and enzymatic activity , hence preventing their Clp-mediated degradation [16] ( Fig 1A ) . As a consequence , it was expected that up-regulation of ClpB3 upon interference with PGE would correlate with higher levels of soluble ( i . e . enzymatically active ) DXS . Indeed , DXS protein levels were increased in PGE-defective rif10-2 and svr8-2 mutants and LIN-treated WT plants ( Figs 2 and 3A ) whereas levels of DXS-encoding transcripts remained unchanged ( S1 Fig ) . Transgenic 35S:DXS-GFP lines constitutively expressing a GFP-tagged DXS protein [19] also showed enhanced levels of both endogenous DXS and recombinant DXS-GFP proteins when treated with 15 μM LIN ( Fig 3B ) , consistent with the conclusion that DXS protein accumulation in plants with PGE defects does not rely on transcriptional changes . Furthermore , the vast majority of the DXS protein accumulated in plants with a genetically or pharmacologically impaired PGE remained in the soluble ( stromal ) fraction ( Fig 3C ) . Similar to DXS , the next enzyme of the MEP pathway , DXR , was also found to be mostly soluble in untreated WT plants and to strongly accumulate in the soluble fraction upon interference with PGE ( Fig 3C ) . These results suggest that both DXS and DXR enzymes might accumulate in an enzymatically active form when PGE is disrupted . To confirm this conclusion , we first quantified the resistance of rif10-2 and svr8-2 mutants to FSM . When present in the growth medium , this MEP pathway inhibitor causes concentration-dependent developmental arrest and chlorophyll loss ( Fig 1B ) . Both phenotypes are alleviated when DXS or DXR activity are artificially increased in transgenic plants [21 , 26 , 32] . FSM resistance had also been shown for the rif10-2 mutant [25] . Quantification of both seedling establishment and chlorophyll levels in plants grown with 30 μM FSM confirmed that resistance to the inhibitor was increased in rif10-2 but also svr8-2 compared to the WT ( Fig 4 ) , consistent with the presence of higher levels of active DXS and DXR enzymes in the mutants ( Fig 3 ) . Improved seedling establishment and higher chlorophyll levels were also observed in WT plants when the growth medium containing 30 μM FSM was additionally supplemented with 15 μM LIN to disrupt PGE ( Fig 5 ) . These results are consistent with previous observations that FSM resistance of several plant species ( including Arabidopsis ) can be improved by supplementing the growth medium with sublethal concentrations of the PGE inhibitor CAP but not with the carotenoid biosynthesis inhibitor NFZ , indicating a PGE-specific effect [25] . The FSM resistance phenotype linked to enhanced accumulation of active ( i . e . soluble ) DXS and DXR enzymes is virtually identical to that previously observed in mutants impaired in Clp protease activity such as clpr1-2 , defective in the nuclear-encoded ClpR1 subunit of the catalytic core of the protease [16 , 21 , 26] ( Fig 4 ) . However , LIN treatment did not affect FSM resistance in clpr1-2 seedlings ( Fig 5A ) , suggesting that both LIN and Clp protease act in the same pathway eventually upregulating the levels of DXS and DXR enzymes . Together , the data support the conclusion that interference with PGE can cause defects in Clp protease activity ( likely via the alteration in the levels of plastome-encoded ClpP1 subunit ) that eventually lead to FSM resistance by triggering the accumulation of soluble and enzymatically functional MEP pathway enzymes ( DXS and DXR ) . At least in the case of DXS , this regulatory mechanism involves an enhanced activity of the ClpB3 chaperone to favor the refolding of misfolded and aggregated forms of the enzyme that might otherwise accumulate when their degradation is impaired [16] . Higher ClpB3 levels are expected to also alleviate folding stress of many other protein substrates , therefore impacting other metabolic pathways . In fact , mutants defective in PGE or Clp protease activity ( including clpr1-2 ) were identified in a screening for mutants able to green in the presence of the carotenoid pathway inhibitor NFZ [33] . We confirmed that NFZ resistance was also gained by partial disruption of PGE in rif10-2 and svr8-2 mutants ( Fig 4 ) as well as in LIN-treated plants ( Fig 5B ) . The described results suggested that alteration of chloroplast protein homeostasis following defects in PGE promoted the accumulation of chaperones such as ClpB3 , presumably to deal with protein aggregation and folding stress . To confirm whether interference with PGE actually resulted in increased protein aggregation , we analyzed the accumulation of aggregated proteins after treating isolated chloroplasts with 1 mM LIN for several hours to completely inhibit PGE . The rationale of using isolated chloroplasts instead of whole plants was that protein folding stress might be easier to detect in chloroplasts unable to communicate with the nucleus and to increase their folding capacity by importing newly synthesized ClpB3 or other plastid-targeted chaperones . After treatment , chloroplasts were lysed using Triton X-100 , a mild non-ionic detergent that solubilizes membrane proteins without denaturing them . Lysates of LIN-treated and untreated ( control ) chloroplasts were then ultracentrifuged to separate soluble and membrane proteins ( supernatant ) from insoluble aggregates ( pellet ) . As predicted , blockage of PGE after addition of LIN to the chloroplast preparation led to increased accumulation of aggregated proteins only hours after treatment ( Fig 6A and 6B ) . In the case of DXS , the amount of protein in insoluble fractions was higher in LIN-grown and PGE-defective seedlings compared to untreated WT controls ( Fig 3C ) . Enhanced association of DXS proteins with insoluble fractions was also observed in mutants impaired in Clp protease [16 , 21] . Consistent with the conclusion that this likely results from enhanced protein aggregation , the characteristic DXS-GFP aggregates observed as plastidial fluorescent spots in 35S:DXS-GFP lines [19] increased when PGE was impaired in LIN-grown or svr8-2 35S:DXS-GFP seedlings ( Fig 6C ) . Immunoblot analyses of DXS accumulation in insoluble fractions from leaves infiltrated for 3h with either 400 μM LIN or 400 nM NFZ ( as a PGE-unrelated control ) confirmed that DXS aggregates were formed in planta soon after inhibiting PGE ( Fig 6D ) . If the response to PGE defects is part of a true cpUPR mechanism , it would be expected that the protein aggregation stress caused by LIN treatment would rapidly trigger changes in the expression of nuclear genes encoding the corresponding plastidial chaperones . To test this possibility , we analyzed the expression of the Arabidopsis genes encoding ClpB3 and Hsp70 chaperones , as they can act synergistically to prevent the wasteful accumulation of insoluble aggregates [22 , 23 , 34] . We also analyzed the expression of the nuclear gene encoding Hsp21 ( also known as HSP25 . 3-P ) , a plastid-localized small heat shock protein proposed to provide fast protection against stress-induced protein aggregation in cooperation with Hsp100 and Hsp70 chaperones [35–37] . As shown in S2 Fig , genes encoding Hsp21 , ClpB3 , and the two plastidial Hsp70 isoforms found in Arabidopsis , Hsp70 . 1 and Hsp70 . 2 [38 , 39] , are rapidly but transiently induced after a heat shock , when protein folding stress occurs in all cell compartments . After a brief lag period ( 30 min ) in which gene expression does not change , transcripts encoding ClpB3 and Hsp70 . 2 peak at 1h , earlier than those for Hsp70 . 1 ( 3h ) . They all return to normal levels relatively soon ( 6h ) after heat treatment ( S2A Fig ) . By contrast , Hsp21 transcript levels already increase 2-fold at 30 min , reach about 3 , 000-fold higher levels at 3h , and still remain higher than before the treatment ( 40-fold ) after 12h ( S2B Fig ) . Genes encoding Clp protease subunits of the catalytic core ( S2C Fig ) or the chaperone domain of the complex , including ClpC1 ( S2D Fig ) , remained unchanged . DXS gene expression was also unaffected by the heat treatment ( S2D Fig ) . To test whether plastidial protein folding stress caused by LIN also had the capacity to elicit changes in the expression of specific nuclear genes ( e . g . those encoding plastid-targeted chaperones that alleviate this stress ) , we designed an experiment to trigger the response at a certain timepoint and then follow the accumulation of selected transcripts and proteins . We grew WT plants on a mesh placed on top of solid growth medium for 7 days under LD conditions . Then , we transferred the mesh with the seedlings to fresh medium supplemented with 400 μM LIN to ensure a rapid inhibition of PGE . To distinguish between PGE-specific and unspecific effects , we did a similar experiment using 400 nM NFZ instead of LIN . Samples were collected at different time points after transfer to extract RNA and protein for quantitative real-time PCR and immunoblot analysis , respectively ( Fig 7 ) . A transient accumulation of transcripts encoding ClpB3 and Hsp70 . 2 was detected after LIN treatment , but not in NFZ-exposed seedlings ( Fig 7A ) . Transcript levels peaked at 2h and then returned to initial levels by 6h . Hsp21 transcripts were also upregulated by LIN but not by NFZ , confirming that the observed effect is specifically caused by direct interference with PGE . In the case of Hsp21 , however , the induction was much more dramatic ( about 50-fold at the 2h peak , compared to 3-fold in the case of ClpB3 and Hsp70 . 2 ) and they remained higher ( 3-fold ) after 9h ( Fig 7A ) , somehow paralleling that observed in the response to heat shock ( S2 Fig ) . By contrast , transcripts for Hsp70 . 1 only showed minor changes after exposure to LIN ( Fig 7A ) . Most interestingly , seedlings germinated and grown in the presence of glycine betaine , a plastid-synthesized chemical chaperone that protects proteins against stress [40 , 41] , showed a decreased up-regulation of ClpB3 , Hsp70 . 2 and Hsp21 expression after exposure to LIN ( Fig 8 ) , suggesting that alleviation of protein aggregation desensitizes the retrograde pathway involved in this response . At the protein level , both ClpB3 and Hsp70 chaperones tended to progressively accumulate in LIN-treated samples ( Fig 7B ) . However , statistical analysis only detected significant ( p<0 . 05 ) differences between untreated ( 0h ) and LIN-treated samples in the case of ClpB3 . Higher levels of DXS protein were also found in LIN-treated plants ( Fig 7B ) . In agreement with the conclusion that the increase in DXS protein levels is a consequence of interfering with PGE and eventually down-regulating Clp protease activity , DXS-encoding transcripts were found to oscillate during the timeframe of the experiment ( as expected based on the reported circadian control of DXS gene expression; [42] ) but not to differentially respond to LIN ( Fig 7A ) . Similarly , the level of transcripts for the Clp proteolytic core protein ClpR1 and the ClpC1 chaperone remained virtually unchanged in response to LIN ( S3 Fig ) . This result , together with the lack of response of these and other genes encoding Clp protease subunits to heat shock ( S2 Fig ) , suggests that episodes of protein misfolding and aggregation in chloroplasts do not elicit changes in this proteolytic complex . Based on the described results , we conclude that LIN-mediated interference with PGE results in protein aggregation and elicits a retrograde transcriptional response aimed to deal with protein folding stress , the characteristic features of a cpUPR . This response might eventually allow the proteins that fail to be degraded by a saturated Clp protease to remain correctly folded ( e . g . soluble in the case of DXS ) and hence active . Several retrograde signals and pathways have been reported in the literature to regulate nuclear gene expression when chloroplast normal functions are compromised [2–4] . The plastidial pentatricopeptide repeat protein GUN1 integrates multiple retrograde signals ( including those related to PGE ) and has been recently proposed to participate in a putative cpUPR signaling pathway [43] . However , GUN1-defective plants of the knock-out gun1-101 allele [44] showed a virtually WT profile of chaperone gene expression after LIN treatment ( Fig 7A ) . These results suggest that GUN1 is not required to produce or/and transduce the PGE-related plastidial signal that eventually regulates nuclear gene expression in LIN-treated seedlings . Strikingly , the gun1-101 mutant was unable to respond to LIN treatment in terms of improving FSM resistance ( Fig 5A ) despite having a WT phenotype of LIN-induced accumulation of ClpB3 and DXS proteins ( Fig 7B ) . GUN1 was recently found to interact with proteins rather than nucleic acids [45] . GUN1 interactors include proteins related to PGE ( such as components of the plastome transcription , RNA editing , and translation machinery ) and PQC ( including the Hsp70 and ClpC chaperones present in the chloroplast ) . It is therefore conceivable that GUN1 acts as a coordinator of PGE , PQC , and cpUPR at the protein-protein interaction level to ensure that protein homeostasis is properly maintained in chloroplasts exposed to environmental challenges [43 , 45] . Consistent with this possibility , the gun1-101 mutant shows increased sensitivity to a partial blockage of PGE with LIN , the MEP pathway with FSM , or the carotenoid pathway with NFZ , compared to WT seedlings ( Fig 9A ) . A genetic confirmation of this central role of GUN1 came from the analysis of double mutants with gun1-101 and mutants defective in PGE and PQC ( clpr1-2 , rif10-2 and svr8-2 ) . In all the cases , double mutants germinated but were unable to develop beyond the cotyledon stage ( Fig 9B ) . Based on the expression profile of chaperone-encoding genes , the response to PGE disruption with LIN is similar ( but weaker ) to that observed after a heat shock , perhaps because both involve protein aggregation but the latter affects all cell compartments . The response to heat stress is orchestrated at the transcriptional level by heat shock transcription factors such as HsfA2 . Because Arabidopsis HsfA2 has been shown to participate in heat-responsive retrograde pathways [46] and to directly induce genes encoding plastidial chaperones such ClpB3 and Hsp21 [47 , 48] , we investigated the participation of this transcription factor in the cpUPR mechanism . The level of HsfA2 transcripts dramatically increased after exposing WT seedlings to LIN ( but not in response to NFZ ) and , consistent with a role for HsfA2 in up-regulating ClpB3 and Hsp21 expression , HsfA2 induction preceded that of the chaperone-encoding genes ( Fig 7A ) . Again , this is a similar but weaker response compared to heat shock ( S2 Fig ) . As expected based on the behavior of target genes ClpB3 and Hsp21 , the induction of HsfA2 gene expression in response to LIN was repressed by glycine betaine ( Fig 8 ) and did not require the activity of GUN1 ( Fig 7A ) .
The observed cpUPR was triggered by using the PGE inhibitor LIN . The first link between altered PGE , reduced Clp protease activity , and enhanced accumulation of MEP pathway enzymes ( including DXS ) was provided by the isolation of FSM-resistant ( rif ) mutants such as rif1 [26 , 27] and rif10 [25] . Here we report that a rif phenotype is also observed in other PGE mutants such as svr8-2 ( Figs 3 and 4 ) and can be induced in WT plants by partially inhibiting PGE with sublethal concentrations of LIN ( Figs 3 and 5 ) , similar to that previously described using CAP [25] . The observation that FSM resistance of clpr1-2 seedlings does not change in the presence of concentrations of LIN that do improve FSM resistance in the WT ( Fig 5A ) further supports the conclusion that PGE defects and the Clp protease act in the same pathway eventually resulting in the accumulation of active MEP pathway enzymes . When the capacity of the Clp protease to remove non-functional proteins is compromised ( e . g . by treatment with LIN or in mutants ) , the excess accumulation of aggregated proteins appears to trigger a cpUPR mechanism to rescue protein homeostasis ( Fig 10 ) . We show that chloroplast proteostasis is carried out by a dynamic balance between degradation and repair of structurally compromised proteins . In the case of DXS , degradation is specifically carried out by the Clp protease with the involvement of Hsp70 and ClpC1 unfolding chaperones , whereas the combined unfolding and disaggregating actions of Hsp70 and ClpB3 chaperones allow solubilization and hence reactivation of the enzyme [16 , 19] ( Fig 1A ) . The characteristic M domain of ClpB3 allows direct interaction with Hsp70 [16] . Unlike that observed in bacterial homologues ( such as E . coli ClpA ) , this domain is not completely absent in Arabidopsis ClpC1 [16] , raising the possibility that the unfolding of substrates for degradation by the plastidial Clp protease complex might also be under the direct control of Hsp70 . In agreement , Hsp70 and ClpC1 chaperones have been co-immunoprecipitated , suggesting a direct ( or close ) interaction [49 , 50] . It was proposed that the relative abundance of ClpC1 and ClpB3 might determine the eventual fate of DXS and other Hsp70-delivered client proteins [16] . Under normal growth conditions , the levels of ClpB3 transcripts and proteins are much lower than those of ClpC1 and hence it would be expected that damaged or inactivated ( misfolded ) forms of DXS were preferentially degraded ( Fig 1A ) . Here we show that when the degradation capacity of this system is compromised , a cpUPR diverts the Hsp70-mediated unfolding of DXS towards the recovery pathway by upregulating ClpB3 ( but not ClpC1 ) . The existence of these opposite pathways has major biological implications . Degrading misfolded and aggregated chloroplast proteins such as DXS not only consumes energy for Hsp70 and ClpC1-mediated unfolding and subsequent Clp protease degradation but it also involves a high cost to resynthetize new proteins , import them into the chloroplast , and properly fold them to substitute the ones that were removed . In contrast , disaggregation and refolding of DXS proteins that lose their active conformation in the chloroplast by the alternative Hsp70-ClpB3 pathway ensures that the protein function will be restored in a much faster and energy-saving way . While this adaptive mechanism might be particularly useful under stress conditions , its fundamental relevance for normal plant development is illustrated by the seedling lethal phenotype of double mutants impaired in both Clp protease ( i . e . degradation ) and ClpB3 ( i . e . reactivation ) activities [13] . The Clp protease not only regulates the accumulation of MEP pathway enzymes but it impacts many other processes in chloroplasts [51] . In particular , reduced Clp proteolytic activity in mutants causes accumulation of many plastidial proteins specifically involved in PGE , including components of the RNA processing and editing as well as protein translation machinery [51] . Therefore , it is tempting to speculate that the two-way reciprocal regulation of PGE and Clp protease might be a compensatory mechanism . Thus , defects in PGE would result in decreased Clp protease activity , which in turn would lead to increased levels of PGE-related proteins to regain overall protein homeostasis in individual plastids ( i . e . without the need to regulate nuclear gene expression ) . In agreement with this model , concentrations of LIN that hardly produced visual symptoms were able to dramatically increase the levels of DXS ( Fig 2 ) , suggesting a relatively large decrease in Clp protease activity ( i . e . in the capacity to degrade DXS ) in response to moderate alterations of PGE . Higher concentrations of LIN led to a concomitant increase in the levels of plastidial chaperones such as ClpB3 ( Fig 2 ) , likely because failure to achieve protein homeostasis elicits a cpUPR mechanism to release protein folding stress with the help of nuclear-encoded chaperones . Similar to the Arabidopsis cpUPR described here , reduction of Clp protease activity by controlled depletion of ClpP1 in Chlamydomonas reinhardtii [8] caused up-regulation of both transcript and protein levels for small heat shock proteins ( including several Hsp21 homologues ) and chaperones such as Hsp70B and ClpB3 , the only plastidial homologues of the Hsp70 [52] and ClpB-type Hsp100 [22] families in the alga . We detected transiently increased accumulation of transcripts encoding Arabidopsis Hsp21 , ClpB3 and Hsp70 . 2 ( but not those encoding Hsp70 . 1 ) when WT plants were exposed to LIN ( Fig 7A ) . However , our immunoblot analysis was not able to detect Hsp21 proteins and only detected a clear increase in protein levels in the case of ClpB3 ( Fig 7B ) . While Hsp70 chaperones also appeared to accumulate at higher levels after LIN treatment ( Fig 7B ) , statistical analysis did not allow to conclude that such differences were significant , mostly due to large differences between replicates . The commercial anti-Hsp70 serum that we use , raised against both Arabidopsis Hsp70 . 1 and Hsp70 . 2 isoforms ( http://www . agrisera . com/en/artiklar/hsp70-heat-shock-protein-70-chloroplastic- . html ) , is presumed to be specific for plastidial Hsp70 proteins . This antibody , however , failed to detect higher Hsp70 levels in Clp protease mutants such as those defective in ClpR1 or ClpC1 [16] , whereas proteomic approaches have consistently detected increased levels of these chaperones in clpr1 [12] , clpc1 [17] , and other Clp-defective Arabidopsis mutants [51] . We therefore conclude that our immunoblot analysis might not be sensitive enough to detect actual changes in plastidial Hsp70 levels . In any case , it is remarkable that the putative cpUPR-mediated elevation of Hsp70 chaperone supply to the chloroplasts of LIN-treated WT plants might rely mostly on the up-regulation of the Hsp70 . 2 gene ( Fig 7A ) . Genes encoding Hsp70 . 1 and Hsp70 . 2 are expressed at similar levels in photosynthetic tissues under normal growth conditions [38] . But in response to heat stress , the Hsp70 . 2 gene is activated faster than Hsp70 . 1 [39] ( S2 Fig ) . Arabidopsis mutants defective in Hsp70 . 2 do not show a visual phenotype , but those impaired in Hsp70 . 1 show variegation and delayed growth despite they accumulate Hsp70 . 2 proteins at levels higher than those of the two Hsp70 chaperones combined in the WT [38] . It is therefore possible that Hsp70 . 1 preferentially plays housekeeping functions while Hsp70 . 2 might be more specialized in responding to plastidial protein folding stress . Unlike LIN , NFZ treatment did not trigger the accumulation of chaperone transcripts ( Fig 7A ) . While both inhibitors cause similar bleaching symptoms ( Fig 1 ) , only LIN has a direct impact on PGE [28–30] . Blockage of carotenoid biosynthesis with NFZ can eventually alter PGE as it leads to decreased photoprotection and photooxidation , but LIN directly inhibits the translation of plastome-encoded proteins and has a much stronger impact on RNA transcription and processing than NFZ [30 , 53] . In any case , the secondary effects of NFZ on PGE are not expected to be relevant in green plants at short times like those used to analyze the existence of a cpUPR after transferring WT plants to inhibitor-supplemented media ( Fig 7A ) . The absence of a cpUPR in NFZ-treated plants was also deduced from the lack of a rif phenotype of enhanced FSM resistance when NFZ was added to the growth medium of WT plants [25] . These results further support the contribution of PGE-triggered changes in Clp protease activity to the cpUPR ( Fig 10 ) . NFZ has been widely used to identify retrograde signals and pathways communicating the chloroplasts with the nucleus [1–4] . For example , genomes uncoupled ( gun ) mutants , including gun1 [54] , were identified based on their ability to de-repress the expression of nuclear genes encoding photosynthetic proteins in NFZ-supplemented medium . These studies were typically conducted using very high ( μM ) concentrations of the inhibitor that caused massive photooxidative damage and complete bleaching . By contrast , screening for happy on norflurazon ( hon ) mutants able to green in the presence of lower ( nM ) concentrations of NFZ showed that mutants with altered PGE ( hon23 ) or Clp protease activity ( such as hon5 , defective in the ClpR4 subunit of the complex ) gained resistance to NFZ [33] , consistent with our results using rif10-2 , svr8-2 , and clpr1-2 mutants ( Fig 4 ) . It was concluded that perturbance of chloroplast protein homeostasis in hon mutants caused a relatively mild stress that led to an activated protection against further stress such as that imposed by NFZ treatment [33] . This proposed stress acclimatization response might be related with the cpUPR unveiled here . Mutants defective in PGE and Clp protease activity were also repeatedly identified in screenings for Arabidopsis mutants with a phenotype of suppressor of variegation ( svr ) of the yellow variegated 2 ( var2 ) mutant , defective in one of the subunits of the chloroplast FtsH protease complex [55 , 56] . In particular , svr1 [57] , svr3 [58] , svr4 [59] , svr7 [60] , svr8 [31] , svr9 [61] , and svr10/rif1 [62] are defective in PGE processes such as RNA editing or protein translation , whereas those impaired in Clp protease activity include svr2/clpr1 and clpc1 [57] . Furthermore , PGE inhibitors such as CAP [57] and LIN ( S4 Fig ) can also suppress var2 variegation . The existence of a cpUPR in Arabidopsis as supported here could explain why interference with PGE and Clp protease activity generates rif , hon and svr phenotypes , as they can be considered as ultimate consequences of triggering a PQC-based stress protection mechanism in chloroplasts . Thus , higher levels of plastidial chaperones such as ClpB3 in rif10-2 , svr8-2 , or cpr1-2 [16] ( Fig 3 ) would contribute to remove protein aggregates and to maintain chloroplast proteins in a correctly folded form , hence preventing their degradation and eventually resulting in enhanced resistance to herbicides such as FSM or NFZ ( Fig 4 ) . Increased chaperone levels might also mitigate the deleterious effects produced by accumulation of VAR2 substrates as misfolded polypeptides and protein aggregates , hence causing a reversion of the variegation phenotype of the var2 mutant . Besides confirming the existence of a cpUPR in plants , our work has unveiled some of the molecular components of the signal transduction pathway ( Fig 10 ) . In particular , the kinetics of transcript accumulation following sudden inhibition of PGE with LIN ( Fig 7A ) suggests that the chloroplast signal first regulates the expression of the nuclear gene encoding the transcription factor HsfA2 ( peaking 1h after LIN treatment ) . Then , HsfA2 induces the expression of target genes , including those encoding Hsp21 and ClpB3 ( with a peak at 2h ) . It is possible that Hsp70 . 2 might also be a gene regulated by HsfA2 ( either directly or indirectly ) , as it also peaks at 2h ( Fig 7A ) . Strikingly , the gene expression response to LIN is very similar ( but weaker ) to that observed after a heat shock ( S2 Fig ) . As heat stress causes protein aggregation in all cell compartments , including the chloroplast , it is possible that the enhanced response to heat shock in terms of HsfA2 and chaperone gene expression was the consequence of converging signaling pathways triggered by protein aggregation in different cell locations ( Fig 10 ) . This model predicts that heat stress should also increase the refolding capacity of chloroplasts , as it induces the expression of HsfA2 and downstream genes encoding plastid-targeted chaperones . In agreement , exposure of WT seedlings to heat dramatically improved their resistance to FSM ( S5 Fig ) . The observation that Chlamydomonas lines with reduced levels of the HsfA2 homologue HSF1 failed to induce the expression of genes encoding plastid-targeted chaperones after a heat shock [63] further suggests that these transcription factors are conserved targets of the cpUPR-associated retrograde signal . Several retrograde signaling pathways have been proposed to mediate the communication of chloroplast stress ( including heat stress ) to the nucleus [1–4] . GUN5 , a protein involved in tetrapyrrole-mediated signaling , was recently shown to mediate the retrograde control of Hsp21 gene expression under heat stress [64] . Interestingly , heat-triggered induction of HsfA2 expression is also regulated by retrograde signals [46] . Because a GUN5-dependent tetrapyrrole metabolite has been shown to inhibit the activity of cytosolic Hsp90 chaperones [65] and Hsp90 binds Chlamydomonas HSF1 to presumably prevent its activity [9] , it is tempting to speculate that a tetrapyrrole produced by stressed chloroplasts might decrease Hsp90 activity , hence releasing HsfA2 to regulate cpUPR-related gene expression . However , gun5 mutants were found to normally induce ClpB3 expression in response to LIN ( S6 Fig ) . Besides the tetrapyrrole-dependent pathway , GUN1 integrates signals derived from perturbations in two other major retrograde pathways: PGE and redox homeostasis [1–4] . The observation that gun1-101 plants showed a WT profile of LIN-mediated gene expression and protein accumulation in response to LIN treatment ( Fig 7 ) , however , suggests that this central integrator of retrograde pathways is not required to trigger cpUPR-related transcriptional responses . In agreement with this conclusion , mutants defective in ABI4 , a transcription factor involved in GUN1-dependent chloroplast retrograde signaling [54] , also showed a normal induction of ClpB3 expression in response to LIN ( S6 Fig ) . However , the inability of gun1-101 plants to unfold the subsequent acclimatory response ( Fig 5A ) and to efficiently cope with stress caused by inhibition of chloroplast function [33] ( Fig 9A ) as well as the seedling lethal phenotype of double mutants defective in GUN1 and PGE or PQC ( Fig 9B ) strongly suggest that the GUN1 protein is a pivotal component of the overall cpUPR response at the protein level . This is consistent with the observation that GUN1 interacts with many proteins involved in PGE and PQC processes [45] and with the conclusion that GUN1 is a coordinator of chloroplast PGE , protein import , and protein homeostasis [43] . It has been proposed that GUN1 might act as a platform to bring different protein together to promote or/and prevent particular interactions [43] . Among the GUN1-independent retrograde signals , isoprenoid-related metabolites such as the MEP pathway intermediate methylerythritol cyclodiphosphate ( MEcPP ) and carotenoid-derived products such as β-cyclocitral were found to participate in stress responses [66 , 67] . In particular , MEcPP ( Fig 1A ) mediates the rapid and transient induction of general stress response genes and triggers the endoplasmic reticulum ( ER ) UPR in advance of the accumulation of misfolded proteins in this cell compartment [68 , 69] . If any of these isoprenoid metabolites were involved in the cpUPR , it would be expected that their differentially altered levels in WT plants treated with FSM or NFZ ( Fig 1A ) resulted in opposite responses to LIN . However , plants growing with FSM or NFZ showed a similar response to LIN ( Fig 5B ) . Arabidopsis mutants accumulating high levels of MEcPP also showed a WT response to LIN treatment in terms of gene expression ( S6 Fig ) and WT levels of plastidial chaperones ( ClpB3 and Hsp70 ) and MEP pathway enzymes ( DXS and DXR ) at both transcript and protein levels ( S7 Fig ) . Transcripts encoding HsfA2 and Hsp21 were also unaltered MEcPP-overaccumulating mutants ( S7 Fig ) . Together , an involvement of MEcPP on the transduction pathway activating cpUPR is unlikely . While much work is still ahead to unveil the detailed molecular pathway connecting disturbed proteostasis in the chloroplast with increased expression of HsfA2 , work in Caenorhabditis elegans and mammals suggest several possibilities based on mitochondrial and ER UPR mechanisms [6 , 7 , 70] . In C . elegans , the small peptides that result from degradation of protein clients by the mitochondrial Clp protease are exported to prevent mitochondrial import and promote nuclear translocation of ATFS-1 , a bZIP transcription factor that orchestrates expression of mitochondrial UPR-related genes [18 , 71 , 72] . Plant ER UPR also relies on the differential targeting of bZIP transcription factors , namely bZIP60 and bZIP28 . They are normally anchored to the ER under non-stressed conditions . However , accumulation of misfolded proteins in the ER lumen triggers BiP chaperone-dependent pathways eventually producing nuclear-targeted versions of these transcription factors by differential splicing ( bZIP60 ) or proteolytic cleavage ( bZIP28 ) [70] . Some plastidial retrograde signaling pathways also rely on transcription factors that can relocate from the chloroplast to the nucleus . Among them , the homeodomain transcription factor PTM is normally anchored to the chloroplast envelope but upon a GUN1-mediated stimulus translocates to the nucleus , where it enhances ABI4 expression [73] . The observation that neither GUN1 nor ABI4 are required for normal cpUPR-associated gene expression ( Fig 7A and S6 Fig ) suggests that this response might also be independent of PTM . WHIRLY1 ( WHY1 ) is another example of transcription factor with alternate plastid-nucleus localization [74] . It has been proposed that changes in the redox state of the chloroplast cause destabilization of WHY1 oligomers and release of monomeric proteins , which are then translocated to the nucleus to regulate transcription [75] . Interestingly , WHY1 is involved in maintaining plastome stability [76] and accumulates in ClpC1-defective mutants [15] . It is conceivable that protein folding stress resulting from reduced plastome function or/and Clp protease activity ( e . g . by LIN treatment ) might also destabilize WHY1 oligomers , eventually promoting the nuclear targeting of this transcription factor . A high-throughput quantitative proteomic analysis of chloroplasts and nuclei after LIN treatment should contribute to confirm this hypothesis and identify other dual-localized transcription factors potentially involved in the cpUPR-associated retrograde signaling pathway in Arabidopsis . In combination with RNAseq , it should also provide a comprehensive picture of how this cpUPR impacts chloroplast functions to efficiently overcome stress .
Arabidopsis thaliana WT , transgenic 35S:DXS-GFP [19] , and mutant rif10-2 ( SALK_037353 ) , svr8-2 ( SALK_010822 ) , clrp1-2 ( SALK_088407 ) , gun1-101 ( SAIL_33_D01 ) , var2-8 [77] , abi4-1 [78] , gun5-1 [79] , and csb3/clb4-3 [80] lines were in the Columbia ecotype . For generation of double mutants , single homozygous mutants were crossed and the F2 progeny was screened for the characteristic pale phenotype associated to the clpr1-2 , rif10-2 or svr8-2 mutations in homozygosis . Then , pale individuals were PCR-genotyped to identify the T-DNA insertion of the gun1-101 allele as described [44] . Individuals confirmed to be homozygous for clpr1-2 , rif10-2 or svr8-2 and heterozygous for gun1-101 were allowed to self-cross . Double mutants , segregated as tiny albino plants in the F3 generation , were confirmed by PCR analysis of the T-DNA insertions in both genes . The 35S:DXS-GFP transgene was introgressed into the svr8-2 mutant background by cross-fertilization . Seeds were surface-sterilized and germinated on solid 0 . 5 X Murashige and Skoog ( MS ) medium without sucrose or vitamins , and plates were incubated in a growth chamber at 22°C under LD conditions as described [19] . For long-term inhibitor treatments , the medium was supplemented with the indicated concentrations of FSM ( Life Technologies ) , NFZ ( Zorial ) or/and LIN ( Sigma ) . Quantification of resistance was based on seedling establishment ( in 14-day-old plants ) and chlorophyll levels ( in 7 to 10-day-old plants ) measured as described [81] . For short-term inhibitor treatments , seeds were germinated on a sterile mesh disc ( SefarNitex 03-100/44 ) placed on top of solid MS medium without inhibitors or supplemented with 10 mM glycine betaine ( Sigma ) . Plates were incubated under LD for 7 days and then the disc with the seedlings was transferred to fresh medium supplemented with 400 μM LIN or 400 nM NFZ . Samples were collected at different time points for qPCR and immunoblot analysis . Adult WT plants grown on soil for 4 weeks at 22°C under LD conditions were used for inhibitor infiltrations and chloroplast isolation . Rosette leaves were infiltrated with either 400 μM LIN or 400 nM NFZ , and samples were collected 3h later for immunoblot analyses of insoluble fractions . Intact chloroplasts were isolated as described [26] . Equal volumes of the chloroplast suspension were then treated with 1mM LIN or a mock solution of the same volume without the inhibitor and incubated in the light for up to 4h . Total protein extraction , separation of soluble and insoluble fractions from whole seedlings extracts , immunoblot analyses , and quantification of protein abundance were performed as described [16 , 19] . The following antibodies ( and dilutions ) were used: DXS ( 1:500 ) , DXR ( 1:7 , 000 ) , Hsp70 ( 1:7 , 000 ) , ClpC ( 1:1 , 500 ) , and ClpB3 ( 1: 2 , 000 ) . The last three antibodies were supplied by Agrisera . For the detection and quantification of protein aggregates , LIN-treated and control chloroplasts were lysed with TKMES buffer [19] supplemented with 0 . 3% Triton X-100 in a total volume of 3 . 7 ml . After collecting a 0 . 2 ml aliquot as the “total” fraction , lysates were subjected to centrifugation at 125 , 000 x g for 30 min . Then , the supernatant ( “non-aggregated” fraction ) was collected and the pellet ( “aggregated” fraction ) was directly resuspended in 0 . 2 ml of SDS-PAGE loading buffer . Equal volumes of each fraction were run on TGX Stain-Free gels ( BioRad ) to estimate protein content from fluorescence intensity using the Image lab ( BioRad ) software . Total RNA was extracted from whole seedlings using the Maxwell 16 LEV Plant RNA Kit ( Promega ) . RNA was quantified using a NanoDrop ( Thermo Scientific ) and its integrity was analyzed by agarose gel electrophoresis . cDNA was synthetized using the cDNA Synthesis Kit ( Roche ) . Real-time quantitative PCR was performed in a total reaction volume of 20 μL using LightCycler 480 SYBR Green I Master ( Roche ) and gene-specific primers ( S1 Table ) on a LightCycler 480 Real-Time PCR System ( Roche ) . The normalized expression of target genes was calculated using UBC as the endogenous reference gene ( S1 Table ) . Subcellular localization of DXS-GFP and chlorophyll fluorescence was determined with an Olympus FV 1000 confocal laser-scanning microscope using an argon laser for excitation ( at 488 nm ) and 500–510 nm filter for detection of GFP fluorescence and 610–700 nm filter for detection of chlorophyll fluorescence . All images were acquired using the same confocal parameters . | Chloroplasts are central metabolic factories for plant cells . Yet , they are constantly challenged by stress episodes that alter protein homeostasis and disrupt normal chloroplast functions . To deal with this problem , protein quality control pathways involving particular chaperones and proteases promote correct protein folding and remove irreversibly damaged proteins . In the case of DXS , the main regulatory enzyme of the isoprenoid pathway , misfolded and aggregated forms of the enzyme are refolded back to its active form by stromal chaperones of the Hsp70 and Hsp100/ClpB families , hence preventing their degradation by the Clp protease complex . In this paper we report that saturated or defective Clp protease activity triggers a chloroplast unfolded protein response that results in the up-regulation of nuclear genes encoding chloroplast chaperones . Higher levels of these chaperones ( particularly the disaggregase ClpB3 ) eventually restore the activity of DXS and other chloroplast proteins that accumulate in a non-functional form when Clp protease activity and chloroplast functions are compromised . | [
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"organisms"
] | 2017 | Interference with plastome gene expression and Clp protease activity in Arabidopsis triggers a chloroplast unfolded protein response to restore protein homeostasis |
Metabolic homeostasis and circadian rhythms are closely intertwined biological processes . Nuclear receptors , as sensors of hormonal and nutrient status , are actively implicated in maintaining this physiological relationship . Although the orphan nuclear receptor estrogen-related receptor α ( ERRα , NR3B1 ) plays a central role in the control of energy metabolism and its expression is known to be cyclic in the liver , its role in temporal control of metabolic networks is unknown . Here we report that ERRα directly regulates all major components of the molecular clock . ERRα-null mice also display deregulated locomotor activity rhythms and circadian period lengths under free-running conditions , as well as altered circulating diurnal bile acid and lipid profiles . In addition , the ERRα-null mice exhibit time-dependent hypoglycemia and hypoinsulinemia , suggesting a role for ERRα in modulating insulin sensitivity and glucose handling during the 24-hour light/dark cycle . We also provide evidence that the newly identified ERRα corepressor PROX1 is implicated in rhythmic control of metabolic outputs . To help uncover the molecular basis of these phenotypes , we performed genome-wide location analyses of binding events by ERRα , PROX1 , and BMAL1 , an integral component of the molecular clock . These studies revealed the existence of transcriptional regulatory loops among ERRα , PROX1 , and BMAL1 , as well as extensive overlaps in their target genes , implicating these three factors in the control of clock and metabolic gene networks in the liver . Genomic convergence of ERRα , PROX1 , and BMAL1 transcriptional activity thus identified a novel node in the molecular circuitry controlling the daily timing of metabolic processes .
In most living organisms , metabolic and behavioral processes are orchestrated in a timely fashion approximating a 24 hr daily cycle . In mammals , light/dark ( LD ) cycles regulate the diurnal activity of the master pacemaker within the suprachiasmatic nuclei ( SCN ) and in turn synchronize autonomous molecular clocks in peripheral tissues [1]–[3] . A small network of core clock genes coordinate the initiation and regulation of the circadian expression of genes and are interconnected by positive and negative transcriptional and translational feedback loops [4] . The primary loop is comprised of the positive transcriptional regulators BMAL1 and CLOCK and the transcriptional repressors PERIOD ( PER ) and CRYPTOCHROME ( CRY ) [5]–[7] . Upon heterodimerization , BMAL1 and CLOCK work together to activate the cyclic expression of core clock genes and mediators of the molecular clock called clock-controlled genes ( CCGs ) . PER and CRY proteins function to repress BMAL1/CLOCK transcriptional activity to ensure the continous daily rhythmic expression of genes . Integrity of the mammalian clock is vital as dysfunction in the timed oscillation of genes has been associated with various diseases including obesity and cancer [8] , [9] . The peripheral clock in metabolic tissues such as liver is reset by physiological cues such as food availability [10] , [11] and CCG networks are responsible for the circadian timing of metabolic processes including glucose homeostasis , fatty acid oxidation and cholesterol synthesis and degradation [9] , [12]–[14] . Although integral components of the molecular clock can directly regulate some metabolic genes , the output from the circadian oscillator is believed to be in large part mediated through the action of transcription factors whose patterns of expression are rhythmic in metabolic tissues [15] . In this regard , members of the nuclear receptor superfamily are well suited for this function . Nuclear receptors can translate nutrient and hormone signals into specific expression signatures of metabolic genes and several members of the family are expressed in a rhythmic fashion in metabolic tissues [16] , [17] . Indeed , evidence of functional cross-talk between various nuclear receptors and core clock genes is rapidly accumulating . First , nuclear receptors such as REV-ERBα and REV-ERBβ ( NR1D1 and NR1D2 ) and RORα , β and γ ( NR1F1 , NR1F2 and NR1F3 ) are directly linked to BMAL1 and CLOCK via interconnecting positive and negative transcriptional feedback loops [18]–[20] . Second , nuclear receptors implicated in metabolic control such as PPARα ( NR1C1 ) and TRα ( NR1A1 ) have been shown to act as indirect mediators of BMAL1 and CLOCK to carry out specific metabolic outputs in a circadian manner [21] , [22] . Third , PER2 has recently been shown to propagate clock information to metabolic genes by directly interacting with and acting as a coregulator of nuclear receptor-mediated transcription [23] . The orphan nuclear receptor estrogen related receptor α ( ERRα , NR3B1 ) plays a critical role in the control of cellular energy metabolism [24] , [25] . In addition , ERRα transcripts display a diurnal rhythm in several tissues including liver , kidney , uterus and bone [17] , [26] , [27] . The transcriptional activity of ERRα is highly dependent on interactions with coregulatory proteins , most notably members of the family of PGC-1 coactivator proteins [28]–[32] . Interestingly , PGC-1α and PGC-1β were shown to be rhythmically expressed in liver and skeletal muscle and PGC-1α was shown to enhance the expression of molecular clock genes [33] . The rhythmic expression of clock and metabolic genes were altered in PGC-1α-null mice consequently resulting in abnormal circadian physiological rhythms including locomotor activity , body temperature and metabolic rate . Conversely , we have recently shown that the metabolic function of the ERRα/PGC-1α complex can be antagonized in liver cells by the transcriptional regulator Prospero-related homeobox 1 ( PROX1 ) [34] . PROX1 directly interacts with both ERRα and PGC-1α , represses the transcriptional activity of the ERRα/PGC-1α complex on metabolic gene promoters and opposes the effects of ERRα on the respiratory capacity of liver cells in culture . In support of these observations , functional genomic analyses using a ChIP-on-chip approach in mouse liver revealed that ERRα and PROX1 share approximately 50% of their target genes . These common targets include a broad range of metabolic genes involved in carbohydrate and fatty acid metabolism , tricarboxylic acid cycle ( TCA ) cycle , electron transport and oxidative phosphorylation ( OXPHOS ) [34] . PROX1 has also been recently identified as a genetic locus implicated in fasting glucose homeostasis in human subjects [35] . Whether ERRα and PROX1 participate in the rhythmic control of metabolism and/or have functional interaction with integral components of the molecular clock is currently unknown . In this report , we first show that proper maintenance of diurnal glucose , insulin , bile acid , cholesterol , non-esterified fatty acid ( NEFA ) and triglyceride levels as well as locomotor rhythms in mice is dependent on the presence of ERRα . Analyses of ChIP-on-chip and gene expression datasets as well as functional studies further demonstrate that ERRα PROX1 and BMAL1 are involved in both transcriptional regulatory loops and rhythmic control of components of all major metabolic pathways in the liver . In particular , we show that BMAL1 directly targets a significantly large number of genes linked to diverse biological processes , including cellular metabolism . Our results thus demonstrate that BMAL1 plays a more comprehensive role in the circadian output pathways than previously anticipated and uncover a novel node in the intricate transcriptional network necessary to sustain proper metabolic and circadian rhythms .
We have recently observed that mice lacking ERRα have impaired diurnal blood pressure levels that are associated with changes in the expression of Na+ and K+ transporters in the kidneys [27] . These results indicate that ERRα could be involved in orchestrating other physiological and behavioral processes in a timely fashion . Given the importance of ERRα in the regulation of genes involved in glycolysis and gluconeogenesis [34] , we first sought to compare blood glucose levels in wild-type ( WT ) and ERRα-null mice fed ad libitum across the day . As expected [8] , WT mice display noticeable diurnal variation of glucose levels in circulating blood ( Figure 1A ) . However , mice lacking ERRα had significantly lower glucose levels at Zeitgeber times ( ZT ) 12 and ZT 0/24 , coinciding with the start and end of the dark cycle , respectively ( Figure 1A ) . The observed hypoglycemia may be due to altered glucose uptake by muscle and fat and/or glucose output from the liver . Food availability appears to be a major determinant in the observed differences in circulating glucose levels in ERRα-null mice as the time-dependent hypoglycemia was lost under fasting conditions ( Figure 1B ) . Subsequently , we performed glucose tolerance tests at ZT 12 and ZT 0/24 to investigate whether differences in insulin secretion and response could account for the deregulated glucose homeostasis observed in fed ERRα-null mice . At ZT 12 following a 6 hr fast , basal glucose measurements were significantly lower in ERRα-null mice but no difference in glucose levels were seen post-glucose injection ( Figure 1C ) . In Figure 1D , the data is illustrated as the total area under the curve ( AUCglucose ) calculated using the trapezoidal rule . In contrast , glucose tolerance tests at ZT 0/24 revealed that ERRα-null mice have improved glucose handling demonstrated by significantly decreased basal blood glucose levels after a 6 hr fast and at each subsequent time-point post-glucose administration ( Figure 1E and 1F , p = 0 . 0046 ) . Our data suggest that the hypoglycemia observed in ERRα-null mice under ad libitum feeding may in part be due to enhanced insulin secretion and/or response . We thus measured circulating serum insulin levels and determined however that fed ERRα-null mice have significantly reduced insulin levels at ZT 16 ( Figure 1G ) . The delay in insulin secretion during the dark cycle observed in ERRα-null mice reflects the trend towards latency in glucose uptake seen under fed ad libitum conditions ( Figure 1A and 1G ) . Glucose tolerance tests at ZT 16 were performed to determine whether the lower circulating insulin levels in ERRα-null mice at this time would result in impaired glucose tolerance . As shown in Figure 1H , loss of ERRα expression in mice had no impact on glucose tolerance at ZT 16 . Next , we investigated whether ERRα expression is important in maintaining the diurnal levels of other circulating metabolites . Bile acid , total cholesterol , NEFA and triglycerides were measured in both fed and fasted mice . ERRα-null mice were found to have significantly greater serum bile acids at ZT 0/24 and ZT 4 under fed and fasted conditions , respectively ( Figure 2A and 2B ) . On the other hand , ERRα-null mice have less circulating cholesterol , NEFA and triglycerides determined at least at one time point during a 24 hr cycle regardless of food availability ( Figure 2C–2H ) . Differences in these circulating metabolite levels in both fed and fasted ERRα-null mice are more apparent when measurements are compiled throughout a complete 12 hr light or dark cycle ( Figure 2C–2H ) . We next monitored locomotor activity in WT and ERRα-null mice . Mice in running wheel cages were first entrained in LD conditions prior to wheel-running activity recordings over a 5 day period . Under these light-entrained conditions , ERRα-null mice were found to display significantly lower activity levels , ran significantly less in the hours preceding lights off ( ZT 10–12 ) , and presented an earlier activity offset ( Table 1 , Figure 3A ) . Subsequently , the mice were put in dark/dark ( DD ) conditions for 20 days and the recordings from day 3 to day 20 were used to define circadian locomotor measurements in free-running conditions . Representative actograms of WT and ERRα-null mice are shown in Figure 3B . The ERRα-null mice were found to exhibit a free-running period significantly shorter than that of WT mice ( 23 . 42 hrs vs . 23 . 65 hrs , p = 0 . 008 ) ( Table 1 , Figure 3C ) . In addition , mice lacking ERRα displayed lower activity levels over a 24 hr period and a lower proportion of their activity in the subjective day under DD conditions ( Table 1 ) . Overall , these results implicate ERRα as a potential regulator of the circadian clock . We have recently shown that ERRα regulates a large number of target genes involved in a broad range of molecular functions in the liver as determined by genome-wide ChIP-on-chip analyses [34] . This dataset has not only reinforced the importance of ERRα in the control of cellular metabolism but now provides evidence for its regulation of the molecular clock . Figure 4A displays ERRα ChIP-on-chip binding profiles on the core clock genes Arntl ( known as Bmal1 ) , Clock , Cry1 , Per2 , Nr1d1 ( known as Rev-erbα ) , Nr1d2 ( known as Rev-erbβ ) , Csnk1d and Bhlhe40 ( known as Dec1 ) . Putative ERR binding elements ( ERREs ) within the major binding peaks are denoted by an asterisk ( Figure 4A ) . ChIP-qPCR validation of these binding events are shown in Figure 4B . The gene encoding ERRα , Esrra , is expressed rhythmically in the liver of ad libitum fed mice ( Figure 4C ) . The lowest level of Esrra expression was consistently observed at ZT 4 but we identified 2 peak levels at ZT 12 and ZT 20 whereas other groups identified Esrra expression peaks at ZT 12–16 and ZT 16 [17] , [26] . The slight variability in the peak expression times of Esrra between laboratories may be due to differences in the housing conditions of the mice and/or exact timing and delay in tissue isolation between mice at the different time points . Overall , Esrra displays a rhythmic expression pattern with trough and peak expression levels at ZT 4 and ZT 12–20 , respectively . Moreover , Esrra expression is under the control of the circadian clock as the hepatic rhythmic expression of Esrra under constant darkness in Clock mutant mice is lost [26] . We next sought to investigate whether ERRα is required to maintain the diurnal rhythm of clock gene expression in mouse liver . As shown in Figure 4D , mice lacking ERRα have altered diurnal rhythms of the clock genes Bma1l , Clock , Cry1 and Rev-erbα . In all cases , a difference in expression amplitude rather than a phase-shift was observed in ERRα-null livers compared to wild-type . The livers of ERRα-null mice express a significantly higher level of Bmal1 between ZT 16–24 , less Clock and Rev-erbα levels at ZT 8 and more Cry1 levels at ZT 20 . Diurnal protein levels of ERRa , BMAL1 and CLOCK were determined by Western blot analysis as shown in Figure 4E . In wild-type mice , nuclear ERRα protein levels were found to increase during the dark cycle of the day and interestingly found to be expressed anti-phase to that of BMAL1 . Esrra mRNA expression precedes that of Bmal1 which helps explain the observed anti-phase diurnal protein profiles of these two factors . As expected from the mRNA profiling data , BMAL1 protein levels are significantly increased across the day in mouse liver lacking ERRα ( Figure 4E ) . This result demonstrates that ERRα activity results in strong repression of BMAL1 . In contrast , CLOCK protein levels were found to be relatively constant throughout the day in both WT and ERRα-null livers ( Figure 4E ) . Our data provide evidence for ERRα as a direct regulator of clock gene expression in mouse liver under ad libitum feeding . Unexpectedly , Esrra , Bmal1 , Clock , Cry1 and Rev-erbα expression oscillate less robustly under fasting conditions and loss of ERRα expression alters diurnal clock gene expression to a much lesser extent compared to that seen in fed liver ( Figure 4F ) . We next explored the importance of ERRα expression in regulating the diurnal expression of genes involved in metabolism under fed conditions as many metabolic genes are known to be under the direct transcriptional control of the receptor [34] . First , we examined the hepatic diurnal expression of transcriptional regulators known to play a role in metabolic control . As shown in Figure 5A and Figure S1A , we found altered transcript profiles of the transcription factors Dbp , Esrrg ( encoding ERRγ , NR3B3 ) , Ppargc1a ( encoding PGC-1α ) and Nr0b2 ( encoding SHP ) in ERRα-null mice . No significant change in expression patterns of Ppargc1b ( encoding PGC-1β ) , Prox1 and Nr1h4 ( encoding FXR ) were found ( Figure 5A and Figure S1A ) . In addition , the levels of the mature form of the liver enriched ERRα microRNA target miR-122a , involved in the regulation of cholesterol and lipid metabolism [36] , were found to oscillate in the liver and to display a disrupted cyclic expression in ERRα-null mice ( Figure S1A ) . The observation that the expression of the mature form of miR-122a and not solely the primary transcript oscillates in liver is distinct from that of a recent report [37] , but the reason for this difference is unknown . Furthermore , mature microRNA miR-378* , recently shown to act as a negative regulator of the TCA cycle and oxidative metabolism by down-regulating ERRγ and GABPA expression [38] was also found to have an altered diurnal expression in ERRα-null mice ( Figure S1A ) . Subsequently , we examined the rhythms of genes involved in glycolysis/gluconeogenesis , insulin and AMPK signaling , lipid metabolism , the TCA cycle and OXPHOS . In ERRα-null liver , altered mRNA oscillations of the genes Pck1 , Pdha1 , Pdk4 , G6pc and Gck involved in glycolysis/gluconeogenesis was observed ( Figure 5B and Figure S1B ) . In contrast , no significant difference in expression profiles of Slc2a2 ( encoding GLUT2 ) , Pklr , Pcx and Pdk1 were detected . Of note , expression of the gluconeogenic genes Pck1 and G6pc are significantly up-regulated in ERRα-null liver at ZT 4 during the fasting phase of the LD cycle where ERRα expression is normally at its lowest ( Figure 5B ) . At this time point , the transcript encoding PGC-1α , a known activator of the gluconeogenic transcriptional program , is also drastically increased in the absence of ERRα ( Figure 5A ) . Our data thus validate the previous report demonstrating that ERRα acts as a repressor of gluconeogenesis in contrast to the overall positive action of PGC-1α in this process [39] . Next , we analyzed genes associated with insulin and AMPK signaling . Briefly , ERRα-null livers express altered diurnal rhythms of the genes Gys2 , Pik3r1 , Gys1 , Lipe , Stk11 ( encoding LKB1 ) , Acacb ( encoding ACC2 ) and Mlycd with no significant change in the mRNA profiles of Insr , Pik3c2g and Prkag2 ( Figure 5C ) . In Figure 5D , we demonstrate that the diurnal expression patterns of genes involved in fatty acid , cholesterol and bile acid metabolism are dependent on ERRα . Specifically , Acadm , encoding the enzyme MCAD important in fatty acid β-oxidation [40] and Hmgcr , encoding the rate-limiting enzyme HMG-CoA reductase in cholesterol biosynthesis [41] are up-regulated in ERRα-null mice at specific times during the day . Despite the increased expression of Hmgcr found in the liver , ERRα-null mice have reduced circulating levels of cholesterol during the light phase of the day as shown earlier . Moreover , hepatic genes involved in bile acid biosynthesis and transport Cyp7a1 , Cyp8b1 and Abcb11 ( encoding BSEP ) respectively , are generally down-regulated in the absence of ERRα ( Figure 5D ) . Consequently , decreased circulating bile acids would be anticipated in ERRα-null mice but , as described above , we found increased levels at ZT 0/24 . Reduced Cyp7a1 and Cyp8b1 gene expression in ERRα-null liver may be attributable to possible negative feedback inhibition exerted by the higher bile acid levels but the exact mechanism is unknown . The expression profiles of genes associated with mitochondrial energy production were also explored . Overall , we found a significant reduction in the expression profiles across a 24 hr cycle of many TCA cycle and OXPHOS genes including Cs , Aco2 , Sdhd , Cycs , and Ndufb5 in ERRα-null liver ( Figure S1C ) . Taken together , our data clearly defines ERRα as an important player in the diurnal regulation of clock gene expression and of many metabolic genes involved in clock-controlled physiological outputs . Notably , oscillations of several metabolic genes that are not known to be direct targets of ERRα were altered in the liver including Esrrg , Ppargc1a and Cyp7a1 . Our data suggest that ERRα participation in the maintenance of cyclic gene expression is mediated by direct and indirect transcriptional control of genes via regulation of the molecular clock and other transcriptional regulators . We next sought to determine the extent of the functional relationship between BMAL1 and ERRα in the direct control of metabolic gene networks . To this end , we first performed a mouse liver BMAL1 ChIP-on-chip experiment using tiled arrays covering extended promoter regions ( −5 . 5 to +2 . 5 kb from transcriptional start sites ) of ∼17 , 000 genes , the same platform previously used to identify ERRα occupancy in the genome of mouse livers taken at ZT 4 in LD conditions [34] . We identified 2 , 555 high-confidence BMAL1 binding sites mapping to the promoter regions of 2 , 522 genes ( Table S1 ) . First , we classified the target genes associated with a known function into general cellular functional categories as shown in Figure 6A . BMAL1 was found significantly enriched at promoters of genes involved in a broad range of metabolic processes , including amino acid , lipid , carbohydrate and TCA cycle/OXPHOS . In addition , ∼15% of the BMAL1 target genes are involved in transcriptional regulation , including genes encoding numerous transcription factors , nuclear receptors involved in metabolic control ( see below ) , splicing factors and polymerases . De novo motif discovery using MDscan [42] analysis of enriched binding segments revealed the expected consensus E-box motif , CACGTG ( Figure 6A ) . BMAL1 enrichment at the extended promoter regions of core clock genes including Cry2 , Dbp , Dec1 , Dec2 , Per1 , Per2 , Per3 and Rev-erbα further validates the approach using extended promoter arrays ( Figure 6B and Figure S2 ) . Furthermore , our analysis revealed previously unidentified binding sites for BMAL1/CLOCK within the promoters of Bmal1 and Clock themselves as well as in the Csnk1d promoter ( Figure 6B and Figure S2 ) . Cry1 , a known BMAL1/CLOCK target gene , missed the ChIP-on-chip p-value cutoff but did validate in standard BMAL1 and CLOCK ChIP-qPCR experiments along with other tested target genes ( Figure 6C ) . Our data not only shows that BMAL1/CLOCK can directly bind to the core clock genes but that they can also directly bind to their own promoters , thus identifying a previously unrecognized autoregulatory loop . We next compared the overlap in target genes between ERRα and BMAL1 in mouse liver . As shown in Figure 6D , comparison of the datasets revealed that a total of 891 target genes are shared by both factors ( 37 . 5% of all ERRα targets ) . A significant number of common targets , which include Pck1 , Hmgcr , Nr0b2 and miR-122a , were found to have altered cyclic expression patterns in ERRα-null mice ( Figure 5 and Figure S1 ) , amplifying the importance of ERRα as a transcriptional regulator of clock-controlled genes . We next identified significantly enriched biological pathways associated with target genes that are specific to ERRα , BMAL1 or both factors . A subset of the analysis is shown in Figure 6E . ERRα-specific targets were highly enriched in metabolic and energy producing processes including inositol metabolism , lipid metabolism , the TCA cycle , ubiquinone biosynthesis and OXPHOS . Targets shared by ERRα and BMAL1 were also associated with metabolic processes such as AMPK and insulin receptor signaling in addition to glycolysis/gluconeogenesis . ChIP qPCR validation of ERRα and BMAL1 enrichment at the promoters of several genes involved in these processes including Stk11 , Prkag2 , Insr , Pik3c2g , Gys2 and G6pc is shown in Figure S3 . Moreover , there was a strong enrichment of common ERRα and BMAL1 target genes involved in immune response as well as in circadian rhythm signaling ( Figure 6E ) . Targets specific to BMAL1 were enriched in a wide variety of processes associated with protein assembly , nucleic acid metabolism as well as renin-angiotensin , hormone and cancer signaling ( Figure 6E ) . In order to execute clock-controlled physiological and behavioral processes in a timely and efficient manner , BMAL1/CLOCK regulate a number of factors involved in transcription to mediate clock outputs . As we previously noted , BMAL1 was found to be enriched at over 300 target genes associated with transcriptional regulation including genes encoding the nuclear receptors ERRα , COUP-TFII ( NR2F2 ) , GCNF ( NR6A1 ) , HNF4α ( NR2A1 ) , LRH-1 ( NR5A2 ) , PPARa PPARγ ( NR1C3 ) , PXR ( NR1I2 ) , SHP ( NR0B2 ) , REV-ERBα , RORγ , RXRβ ( NR2B2 ) , SF-1 ( NR5A1 ) and TRα , the transcription factors ATF2-7 , DBP , HIF1α , IRF8 , GABPA , and STAT3 , and the transcriptional coregulators PGC-1α , PGC-1β , NCoR1 and PROX1 . In addition , the microRNAs miR-17 , -22 , -101a , -122a , -200c and let-7c-2 were also identified as BMAL1 targets . BMAL1/CLOCK ChIP validation of a subset of these genes is shown in Figure 6F . In particular , our data show that ERRα is indeed a direct downstream target of BMAL1/CLOCK . Figure 6G displays the binding profile of BMAL1 on the mouse Esrra promoter with close examination of the DNA sequence under the peak . We identified a BMAL1/CLOCK consensus E-box binding motif ( CACGTG ) located adjacent to the well characterized , duplicated ERR binding sites ( TNAAGGTCA ) [29] that is conserved in mouse and human ( Figure 6G ) . Luciferase reporter assays in COS-1 cells show that BMAL1 alone induces the activity of the Esrra promoter which was further enhanced in the presence of PGC-1α ( Figure 6H ) . Prox1 is a direct BMAL1/CLOCK target gene ( Figure 6F ) and consequently , we next sought to examine if Prox1 expression , like that of other components of the ERRPGC-1 transcriptional pathway , is rhythmic in mouse liver . Wild-type and Clock mutant mice kept in constant darkness were used to measure the circadian expression of Prox1 . As expected , Rev-erbα was found to have an expression peak at circadian time ( CT ) 6 and the mRNA oscillation was abrogated in Clock mutant mice ( Figure 7A , top panel ) . Interestingly , we observed a cyclic expression in Prox1 transcript levels with two expression peaks at CT 10 and CT 18 that was abolished in Clock-mutant mice under DD conditions ( Figure 7A , bottom panel ) . Unlike in constant darkness , Prox1 oscillates with one expression peak during the night in light-entrained conditions as shown earlier ( Figure 5A ) suggesting that Prox1 expression is influenced by light . Taken together , our data indicate that Prox1 expression is rhythmic and under direct control of the molecular clock . We next sought to determine if PROX1 was also involved in a regulatory loop with BMAL1/CLOCK . Indeed , ChIP-on-chip experiments revealed PROX1 recruitment to the clock target genes Bmal1 , Clock , Cry1 , Cry2 , Csnk1d , Dec1 , Per1 , Rev-erbα and Rev-erbβ as shown in Figure 7B . ChIP-qPCR validation of these binding events are shown in Figure 7C . We next wanted to investigate PROX1 regulation of the molecular clock . Unlike ERRα-null mice , PROX1-deficient mice are embryonic lethal and die at embryonic day E14 . 5-E15 [43] . Consequently , we used the HepG2 liver cell line to analyze the expression of direct clock target genes in the presence or absence of siRNA pools targeting PROX1 . Ablation of PROX1 in HepG2 cells resulted in an increase in Bmal1 , Clock , Cry2 and Dec1 expression as well as a decrease in Csnk1d , Rev-erbα and Rev-erbβ levels ( Figure 7D ) . Subsequently , serum shock was used to synchronize clock gene oscillation in HepG2 cells to determine the effects of loss of PROX1 on the rhythmic expression of these genes . qRT-PCR analysis of clock gene expression over a 24 hr period was studied . Overall , ablation of PROX1 in synchronized HepG2 cells resulted in significantly altered oscillations and abundances of clock transcripts ( Figure S4A ) . Our data show that PROX1 is a regulator of the molecular clock by acting as either an activator or repressor of clock gene expression . Moreover , we observed altered expression rhythms of Pck1 , Slc2a2 and Aldoc involved in glucose homeostasis in the absence of PROX1 in synchronized HepG2 cells ( Figure S4B ) . As a whole , PROX1 can function both as an upstream clock regulator and direct downstream mediator of clock function . A schematic representation of the integration of ERRα and PROX1 as well as BMAL1 in transcriptional control of mammalian clock components is shown in Figure 7E . Analysis of the gene networks commonly regulated by ERRα , PROX1 and/or BMAL1 shows that 905 ( ∼35 . 8% of all BMAL1 targets ) target genes are common to PROX1 and BMAL1 and that 891 ( ∼35 . 3% of all BMAL1 targets ) target genes are shared by ERRα and BMAL1 ( Figure 8A and Table S2 ) . Overall , 512 targets are shared by all 3 factors ( ∼20% of all 3 datasets ) , indicating a significant level of coordination in the control of specific gene networks . Comparative analysis of these datasets with mouse liver circadian expression data compiled from five different experiments performed with mice under basal conditions maintained in constant darkness [12] , [44]–[47] indicates that a large subset of ERRα , BMAL1 and PROX1 target genes displays rhythmic expression in the liver ( Figure S5 and Table S3 ) . A gene found to be rhythmic in at least one dataset was considered in our analysis . Interestingly , we also observed that genes commonly targeted by either BMAL1 and ERRα , BMAL1 and PROX1 or by all three factors are enriched for metabolic genes ( e . g . fatty acid and carbohydrate metabolism ) as compared to genes targeted by BMAL1 alone ( Figure 8B ) . A schematic representation of the metabolic and nutrient sensing pathways targeted by BMAL1 alone or in combination with ERRα and PROX1 is shown in Figure 8C .
As a direct target of BMAL1/CLOCK and a major regulator of energy metabolism , ERRα may also play a central role in clock-controlled output pathways linked to metabolic homeostasis . Indeed , the importance of ERRα as an integrator of the molecular clock and metabolism is evident in vivo as the absence of ERRα in mice results in altered hepatic diurnal expression rhythms of genes associated with diverse metabolic processes , the majority of which are known to display circadian rhythms ( e . g . Cyp7a1 , Hmgcr , Pck1 , Pdk4 , PGC-1α and Stk11 ) . In this context , the finding of the potential regulation of Per2 by ERRα is also particularly interesting . PER2 has recently been shown to interact with several nuclear receptors and serve as a coregulator of nuclear receptor-mediated transcription and it has thus been proposed that PER2 could confer more precise oscillator information to metabolic genes [23] . Given that ERRα has been shown to occupy the regulatory region of genes encoding a plethora of nuclear receptors that includes the PER2 partners HNF4α , PPARα and REV-ERBα [34] , ERRα could serve as an amplifier of the PER2-dependent clock output . Diurnal serum chemistry profiling revealed an important role of ERRα in maintaining metabolic homeostasis . Altered diurnal glucose , insulin , bile acid , cholesterol , NEFA and triglyceride levels were observed in ERRα-null mice . Of particular interest , fed ERRα-null mice exhibit time-dependent hypoglycemia and hypoinsulinemia with no apparent impairment in insulin secretion as determined by glucose tolerance tests . We thus hypothesize that the observed hypoglycemia in ERRα-null mice is a result of enhanced glucose uptake due to increased insulin sensitivity . In complete agreement with our findings in ERRα-null mice , a report published during preparation of this manuscript showed that administration of a novel highly selective ERRα inverse agonist ( compound 29 ) in diet-induced murine models of obesity and an overt diabetic rat model resulted in improved insulin sensitivity and glucose tolerance accompanied by reduced circulating glucose , free fatty acid and triglyceride levels [48] . Nuclear receptors have been previously implicated as components of the output pathways of the molecular clock but their involvement in these processes has usually been inferred through the regulation of one to a few specific genes [22] . Previous in situ hybridization experiments revealed that 19 of 49 nuclear receptors are expressed in the mouse SCN [49] . However , lack of apparent expression of any ERR isoform in the SCN suggests that the ERRs are not critically involved in SCN physiology and clock entrainment . This is supported by the modest change in free-running period of locomotor activity rhythms ( Figure 3 ) . Instead , ERRs may act as regulators of clock function in peripheral tissues . Indeed , this study demonstrates that ERRα plays a key role in hepatic regulation of the molecular clock . We show that ERRα , as a regulator of the core clock mechanism , targets the promoter region of many core clock genes , including Bmal1 , Clock , Cry1 , Per2 , Rev-erbα and Rev-erbβ and that the presence of ERRα is necessary to maintain the diurnal rhythm of many of these genes in the liver . Of particular interest , ERRα was found to display very potent repressor activity on BMAL1 ( Figure 4D and 4E ) . Unlike the nuclear receptor REV-ERBα that couples the positive and negative limbs of the molecular oscillator , the role of ERRα in the clock is more likely to contribute to the robustness of circadian oscillation in response to specific physiological cues . In this context , physiological rhythms dependent on ERRα transcriptional activity is expected to be modulated via changes in the levels of PGC-1α in response to nutritional signals [33] , and/or the activity of the SIRT1 histone deacetylase complex which is known to act as an intracellular metabolic sensor and a post-translational modifier of both PGC-1α and ERRα [50] , [51] . This is particularly relevant in regard to the superimposed metabolic and circadian clock feedback loops involving interplay between the rhythmic NAD+ biosynthesis , SIRT1 , and CLOCK/BMAL1 [52] , [53] . BMAL1 is an important regulator of metabolism . In particular , liver-specific disruption of Bmal1 in mice results in hypoglycemia , higher glucose clearance and loss of rhythmic expression of clock-regulated metabolic genes , highlighting the importance of the peripheral oscillators in modulating circadian physiology [14] , [52] . Our BMAL1 ChIP-on-chip study in mouse liver indicates that BMAL1 plays a more comprehensive role in dictating metabolic clock outputs . We find that BMAL1 binding sites are particularly enriched in genes involved in amino acid ( e . g . Got1 , mTor ) , lipid ( e . g . Acadm , Lipe ) and carbohydrate ( e . g . G6pc , Insr ) metabolism as well as in the TCA cycle/OXPHOS ( e . g . Cs , Atp5g ) ( Figure 8C and Table S1 ) . Of particular interest is the finding that BMAL1 binds to two distinct sites within the promoter of Slc2a2 , which encodes glucose transporter type 2 ( GLUT2 ) . This finding thus provides a molecular mechanism for the loss of rhythmic expression of Slc2a2 in liver-specific BMAL1-deficient mice which has been proposed to account for the observed circadian hypoglycemia in these mutant mice [14] . More globally however , our study demonstrates that direct BMAL1 target genes are not exclusively associated with metabolism but with a large set of diverse biological functions . This finding is in agreement with previous expression studies showing that temporal hepatic gene regulation is extensive and impinges on a wide variety of processes [12] , [45] , [46] . Our work suggests that the extensive identification of BMAL1 target genes supports a more direct participation of core clock proteins in driving clock output pathways . Our study provides evidence for a direct participation of PROX1 in transcriptional regulation of the molecular clock . In particular , clock gene synchronization in HepG2 cells revealed that ablation of PROX1 results in altered oscillation of core clock genes . In this context , we also show that PROX1 is required to maintain the rhythmic expression of genes involved in glucose homeostasis . Taken together with the recent identification of PROX1 as an important regulator of the ERRα/PGC-1α axis involved in the regulation of broad transcriptional programs implicated in the control of energy homeostasis in the liver [34] and the observation that the PROX1 locus is associated with fasting glucose levels and increased risk for type II diabetes [35] , this study implies that PROX1 possesses all the necessary attributes to be an important factor linking metabolism and circadian rhythms . We have shown that BMAL1 targets the promoter region of a substantial number of metabolic genes . However , a comparative analysis of BMAL1 , ERRα and PROX1 targets indicates that the presence of BMAL1 alone is insufficient to regulate the expression of metabolic gene networks ( Figure 8B and 8C and Table S2 ) . In contrast , these results suggest that the regulation of metabolic genes involves coordinated action by the three factors . Whether BMAL1 can directly interact with ERRα and/or PROX1 is currently unknown . We suggest that ERRα may function by contributing to the robustness of the rhythmic expression of BMAL1 target genes . Similarly , the overlap between BMAL1 and PROX1 target genes , especially those not shared by ERRα , probably denotes the presence and action of other partners of PROX1 such as the metabolic regulators HNF-4α and LRH-1 [54] , [55] . The physiological significance of the functional interaction between these factors is further supported by the observation that a considerable subset of ERRα/BMAL1/PROX1 target genes displays circadian expression in the liver ( Figure S5 and Table S3 ) . Finally , as ERRα expression is regulated by metabolic cues , we can anticipate that the rhythmic expression of ERRα and many of its target genes will be driven by both feeding and the clock . We have identified the orphan nuclear receptor ERRα as a novel transcriptional regulator of both the molecular clock and its output pathways that shares extensive transcriptional cross-talk with the core clock protein BMAL1 . As such and given the known property of ERRα to translate signals propagated by physiological sensors such as PGC-1α and SIRT1 into metabolic gene expression networks , ERRα may serve as the key bidirectional regulator connecting the peripheral liver clock and cellular energy metabolism . Similarly , we show that the ERRα corepressor PROX1 can act both upstream and downstream of the endogenous clock . Furthermore , our study showed that the direct participation of BMAL1 in the clock output pathways is highly extensive , suggesting that other core clock proteins might play a similar role . Therefore , additional investigations of the functional relationship between ERRα , PROX1 and core clock genes in diverse tissues are bound to reveal other key molecular mechanisms and physiological phenotypes linked to the daily timing of biological processes .
Animal use followed the guidelines of the Canadian Council on Animal Care . The animal use protocol was approved by the local Facility Animal Care Committee ( FACC ) at McGill University . Male wild-type and ERRα-null mice [56] 2–3 months old in a C57BL/6J genetic background were housed and fed standard chow in the animal facility at McGill University Life Sciences Complex . Mice were entrained to a 12 hr light/12 hr dark LD cycle for 2 weeks prior to the start of the experiment . Animals were sacrificed by cervical dislocation at 4 hr intervals over a 24 hr period from ZT 4 to ZT 24 ( n = 4 per ZT ) . ZT 0 is the time of lights on , ZT 12 is the time of lights off . Livers were isolated , frozen in liquid nitrogen and grinded using a mortar and pestle and kept frozen until further processing . Male wild-type and Clock mutant mice [57] 2–3 months old in a 50% C57BL/6J and 50% BALB/c genetic background were housed and fed standard chow in the animal facility at the Douglas Mental Health University Institute . For circadian experiments , livers from 4 WT and Clock mutant mice kept in DD conditions were collected every 4 hrs from circadian time ( CT ) 2 to CT 22 and immediately frozen in liquid nitrogen until further processing . ERRα and PROX1 ChIP assays were performed as previously described [34] , [58] on adult male mouse livers at ZT 4 . For BMAL1 and CLOCK ChIP assays , chromatin corresponding to 0 . 4 g of initial liver mass taken from a pool of 44 livers at ZT 4 was used and pre-cleared chromatin was immunoprecipitated with 6 µg of an anti-BMAL1 antibody ( Santa Cruz , sc-48790x ) , 6 µg of an anti-CLOCK antibody ( Santa Cruz , sc-25361x ) or not ( no antibody control ) with subsequent addition of 50 µl of a 50% slurry of salmon sperm DNA/protein A beads for 3 hrs at 4°C . To assess the enrichment of ERRα , PROX1 , BMAL1 or CLOCK at specific promoters , quantitative PCR ( qPCR ) was performed as described previously [58] . Enrichment of DNA fragments was normalized against two amplified regions using the control primers , located approximately 4 kb upstream of the ERRα and 49 kb upstream of the PROX1 transcriptional start site . Specific mouse primers designed and used for ChIP-qPCR analysis are shown in Tables S4 and S5 . Duplicate mouse liver BMAL1 ChIP-on-chip experiments using tiled extended promoter arrays covering −5 . 5 to +2 . 5 kb from transcriptional start sites of ∼17 , 000 genes ( mm8 ) from Agilent were performed as previously described [34] , [58] with the following modifications . Chromatin corresponding to 3 . 1 g of initial liver mass taken from a pool of 44 livers was used and pre-cleared chromatin was immunoprecipitated with 45 µg of an anti-BMAL1 antibody ( sc-48790x ) or not ( no antibody control ) with subsequent addition of 400 µl of a 50% slurry of salmon sperm DNA/protein A beads for 3 hrs at 4°C . The ChIP-on-chip target genes were classified by biological function based on GO annotation ( http://fatigo . org/ ) and NCBI gene descriptions . The mouse liver BMAL1 ChIP-on-chip bed file can be found in Dataset S1 . Analysis of the ChIP-on-chip target genes for significant biological pathways and networks were done using Ingenuity Pathways Analysis software v7 . 6 ( Ingenuity Systems , www . ingenuity . com ) . Canonical pathways analysis identified significant pathways from the Ingenuity's Pathways Analysis library of canonical pathways . Fisher's exact test was used to calculate a p-value determining the probability that the association between the genes in a dataset and the canonical pathway is explained by chance alone . Networks of genes were algorithmically generated based on their connectivity by overlaying the target genes onto a global molecular network developed from information contained in the Ingenuity Pathways Knowledge Base . pCMX , pCMX-hERRα and mESRRA-luciferase were described previously [29] . The expression vector pcDNA3/HA-hPGC-1α was provided by A . Kralli ( La Jolla , CA ) . The expression vectors pSG5-mCLOCK and pCS2 ( 5× Myc ) -mBMAL1 were described previously [59] . Cos-1 or HepG2 cells were transfected using FUGENE in 12-well plates with 300 ng luciferase reporter , 100 ng CMX expression vector ( empty vector or hERRα ) , 100 ng CMV β-galactosidase , 200 ng pSG5 expression vector ( empty vector or mCLOCK ) , 200 ng pCS2 ( 5× myc ) expression vector ( empty vector or mBMAL1 ) , with 300 ng of HA-PGC1α or pcDNA3 . Cells were harvested and assayed for luciferase activity 24 hrs post-transfection . Experiments were performed in triplicate and each experiment was replicated multiple times . HepG2 cells were cultured in DMEM ( Invitrogen ) supplemented with 10% FBS and pen/strep and maintained at 70% confluency . HepG2 were transfected with either On-Target Smartpool control ( siCtrl ) from Dharmacon or a specific siRNA pool against PROX1 ( siProx1 ) using HiPerfect reagent ( according to the manufacturer's instructions ) . HepG2 cells treated with either control siRNA ( siCtrl ) or siProx1 were grown and maintained in high glucose ( 25 mM ) Dulbecco's modified Eagle's medium ( DMEM ) supplemented with 10% fetal bovine serum ( FBS ) . 48 hrs later , the cells were then starved in DMEM containing 0 . 5% fetal bovine serum for 24 hrs . Subsequently , 50% horse serum was added ( T = 0 ) for 2 hrs , and then the medium was changed back to starvation medium . RNA was isolated from cells harvested every 4 hrs during a 24 hr period for qRT-PCR analysis . For quantitative reverse-transcription PCR , cDNA was prepared from total RNA isolated from mouse livers and the HepG2 siRNA knock-down samples . cDNA was obtained from 2 µg of total RNA by reverse transcription with Oligo ( dT ) primer , dNTPs , 5× 1st strand buffer , DTT , RNase inhibitor , and Superscript II RNase H Reverse Transcriptase . cDNA was amplified using specific primers ( Tables S6 and S7 ) along with the SYBR PCR Master Mix ( Qiagen ) and a LightCycler instrument ( Roche ) . Relative fold expression levels of the analyzed genes in mouse livers were normalized to Arbp levels and expressed as mean values +/− SEM at the indicated time points relative to the mean value of the WT livers at ZT 4 set at 1 . For all genes tested , primer efficiencies were taken into account based on a serial dilution of cDNA . Relative fold expression levels of the analyzed genes in serum shocked HepG2 cells were normalized to HPRT1 levels and expressed as mean values +/− s . d . at the indicated time points relative to the mean value of siCtrl samples at T = 0 set at 1 . For all genes tested , primer efficiencies were taken into account based on a serial dilution of cDNA . For microRNA quantification , RNA was isolated from mouse livers using the Qiagen miRNeasy kit . microRNA levels were detected and normalized to snoRNA412 levels using Taqman miRNA RT-PCR following the manufacturer's instructions ( Applied Biosystems , snoRNA412 #1243 , miR-122a #2245 , miR-378* #567 ) . Real-Time PCR reactions were carried out in a Corbett Research Rotor-Gene instrument . Nuclear extracts were prepared from livers of WT and ERRα-null mice collected during a 12∶12 h LD schedule . Briefly , the livers were homogenized in cell lysis buffer ( HEPES 5 mM , KCl 85 mM , NP40 0 . 5% ) containing protease and phosphatase inhibitor cocktails ( Roche ) and the nuclei collected were prepared in lysis buffer ( sodium phosphate 20 mM , NaCl 150 mM , NP40 1% , EDTA 5 mM , PMSF 1 mM ) containing protease and phosphatase inhibitor cocktails ( Roche ) . Equal amounts of protein were pooled from 4 WT and 4 ERRα-null liver nuclear extracts at the indicated time points and a total of 20 µg protein extract at each time point were used for immunoblot analysis . Immunoblot detection was done using a custom made anti-ERRα ( 1∶10 , 000 ) antibody [29] , anti-BMAL1 ( Santa Cruz , sc-8550X , 1∶1 , 000 ) and anti-CLOCK ( Santa Cruz , sc-6927X , 1∶1 , 000 ) antibodies . Whole cells lysates from the HepG2 knock-down samples were prepared in lysis buffer ( sodium phosphate 20 mM , NaCl 150 mM , NP40 1% , EDTA 5 mM , PMSF 1 mM ) containing protease and phosphatase inhibitor cocktails ( Roche ) . 50 ug of total protein lysate was used for immunoblot analysis . Immunoblot detection was done using anti-PROX1 ( Proteintech Group , 51043-1-AP , 1∶400 ) antibody and anti-RPLP ( Proteintech Group , 11290-2-AP , 1∶2 , 000 ) antibody was used as a loading control . Wheel-running activity data were collected from male adult age-matched WT ( n = 9 ) and ERRα-null ( n = 10 ) mice . Animals were put in running wheel cages ( Actimetrics , Wilmette , IL , USA ) set in a light-proof ventilated cabinet . Activity under a 12 hr light ( ∼200 lux ) , 12 hr dark cycle ( LD ) was recorded over 5 days following entrainment of the animals to this schedule . Animals were then kept in DD conditions for 20 days and the recordings of day 3 to day 20 in DD were used for defining circadian parameters in free running conditions . All data were analyzed using the Clocklab program ( Actimetrics , Wilmette , IL , USA ) . Chi2 periodogram analysis was used for measurement of the free-running period . Serum metabolic measurements of insulin , bile acid , total cholesterol , NEFA and triglycerides were conducted on male WT and ERRα knock-out mice ( n = 4 ) under basal ( fed ad libitum ) and fasted conditions . For fasting experiments , mice were housed in cages with woodchip bedding instead of corncob bedding and food was removed from cages 24 hrs prior to the start of blood collection every 4 hrs during a 24 hr period . Blood was isolated by cardiac puncture from mice anesthetized with isofluorene gas . Blood was allowed to clot for 45 min in serum separation tubes ( Sarstedt , 41 . 1378 . 005 ) with subsequent centrifugation at 5000 rpm for 30 min . Serum was isolated and stored at −80°C until further processing . Total cholesterol , triglyceride , bile acid and NEFA levels were detected by enzymatic colorimetric rate assays performed at IDEXX Laboratories ( Markham , Ontario ) . Specifically , cholesterol CHOD-PAP ( Roche ) , triglyceride GPO-PAP ( Roche ) , total bile acids assay ( Diazyme ) and VetSpec NEFA ( Catachem Inc . ) kits were used and samples were run on a Roche Hitachi H917 chemistry analyzer . Radioimmunoassay detection of insulin with rat insulin RIA kits ( Millipore ) was performed at the Animal Health Diagnostic Center at Cornell University . Blood glucose levels were measured using a OneTouch Ultra2 glucose meter ( LifeScan ) on male WT and ERRα knock-out mice . For glucose tolerance tests , mice were housed in cages with woodchip bedding and fasted for 6 hrs prior to intraperitoneal injection of a 20% glucose solution in 0 . 9% NaCl at 2 mg/g of body weight . Glucose levels were measured prior to glucose administration and at the indicated time points post-injection . | The molecular basis for coordinated control of circadian rhythms and metabolism is not well understood . Although integral components of the molecular clock such as the transcription factor BMAL1 can directly regulate some metabolic genes , the output from the circadian oscillator is believed to be in large part mediated through the action of transcription factors whose patterns of expression are rhythmic in metabolic tissues . The estrogen-related receptor α ( ERRα , NR3B1 ) and its corepressor PROX1 , two major metabolic regulators , could be well suited for this function . Indeed , we show that proper maintenance of daily glucose , insulin , bile acid , lipid , and locomotor rhythms in mice are dependent on the presence of ERRα . Ablation of PROX1 in synchronized HepG2 cells revealed the importance of PROX1 in regulating the rhythmic expression of clock and metabolic genes . Using genome-wide analysis of promoter occupancy and gene expression analyses , we identify ERRα and PROX1 as novel transcriptional regulators of the molecular clock and show that the direct participation of BMAL1 in the clock output pathway related to metabolic control is highly extensive . ERRα and BMAL1 thus serve as key bidirectional regulators connecting the peripheral clock and cellular energy metabolism . | [
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] | 2011 | Genomic Convergence among ERRα, PROX1, and BMAL1 in the Control of Metabolic Clock Outputs |
Bacterial contact-dependent growth inhibition ( CDI ) is mediated by the CdiA/CdiB family of two-partner secretion proteins . Each CdiA protein exhibits a distinct growth inhibition activity , which resides in the polymorphic C-terminal region ( CdiA-CT ) . CDI+ cells also express unique CdiI immunity proteins that specifically block the activity of cognate CdiA-CT , thereby protecting the cell from autoinhibition . Here we show that many CDI systems contain multiple cdiA gene fragments that encode CdiA-CT sequences . These “orphan” cdiA-CT genes are almost always associated with downstream cdiI genes to form cdiA-CT/cdiI modules . Comparative genome analyses suggest that cdiA-CT/cdiI modules are mobile and exchanged between the CDI systems of different bacteria . In many instances , orphan cdiA-CT/cdiI modules are fused to full-length cdiA genes in other bacterial species . Examination of cdiA-CT/cdiI modules from Escherichia coli EC93 , E . coli EC869 , and Dickeya dadantii 3937 confirmed that these genes encode functional toxin/immunity pairs . Moreover , the orphan module from EC93 was functional in cell-mediated CDI when fused to the N-terminal portion of the EC93 CdiA protein . Bioinformatic analyses revealed that the genetic organization of CDI systems shares features with rhs ( rearrangement hotspot ) loci . Rhs proteins also contain polymorphic C-terminal regions ( Rhs-CTs ) , some of which share significant sequence identity with CdiA-CTs . All rhs genes are followed by small ORFs representing possible rhsI immunity genes , and several Rhs systems encode orphan rhs-CT/rhsI modules . Analysis of rhs-CT/rhsI modules from D . dadantii 3937 demonstrated that Rhs-CTs have growth inhibitory activity , which is specifically blocked by cognate RhsI immunity proteins . Together , these results suggest that Rhs plays a role in intercellular competition and that orphan gene modules expand the diversity of toxic activities deployed by both CDI and Rhs systems .
Many bacteria lead social lives in communities where they cooperate and compete with members of their own species , as well as those of other species [1] . One mechanism of bacterial communication is quorum sensing , in which small signaling molecules are released to coordinate group behavior when a critical cell density has been attained [2] . Other modes of communication based on direct cell-to-cell contact have recently been identified in bacteria . Contact-dependent signaling helps to coordinate cell aggregation and fruiting body formation in Myxococcus xanthus [3] , and is also exploited to inhibit the growth of neighboring cells . Contact-dependent growth inhibition ( CDI ) was first described in the Escherichia coli isolate EC93 [4] , and has subsequently been demonstrated in Dickeya dadantii 3937 [5] . CDI is mediated by the CdiB-CdiA two-partner secretion system . CdiB is a predicted outer membrane β-barrel protein that is required for secretion and presentation of the CdiA exoprotein on the cell surface [6] , [7] . Like other two-partner secretion exoproteins , CdiA contains an N-terminal transport domain followed by a hemagglutinin repeat region that is predicted to adopt an extended filamentous β-helical structure [7]–[9] . The CDI growth inhibitory activity resides within the C-terminus of CdiA ( CdiA-CT ) . The cdi locus also encodes a small CdiI immunity protein immediately downstream of cdiA . CdiI protects EC93 cells from CdiA-mediated growth autoinhibition [5] . CDI systems are widespread amongst α- , β- , and γ-proteobacteria [5] . CdiA exoproteins are related throughout most of their length , which varies from 1 , 400 to 2 , 000 amino acid residues in Neisseria and Moraxella species to over 5 , 600 residues for some Dickeya and Pseudomonas strains [5] . However , the CdiA-CT regions are highly variable , with CdiA sequences diverging abruptly after a VENN peptide motif found within the conserved DUF638 domain ( Pfam PF04829 ) . Similarly , CdiI sequences are also highly variable , suggesting that these immunity proteins specifically bind to cognate CdiA-CTs and neutralize their toxic activities . In support of this model , we recently showed that CdiA-CTs from Dickeya dadantii 3937 and uropathogenic E . coli ( UPEC ) 536 possess distinct toxic nuclease activities , and that the corresponding CdiI proteins bind to their cognate CdiA-CT and block nuclease activity both in vitro and in vivo [5] . Thus , CdiA-CT/CdiI pairs constitute a polymorphic family of toxin/immunity modules that allow CDI systems to deploy a wide variety of growth inhibition activities . CdiA proteins share a number of characteristics with the Rhs protein family . The rhs genes were first identified in E . coli by C . W . Hill and colleagues , and were named rearrangement hotspots based on their role in chromosome duplications [10] , [11] . Rhs proteins are widely distributed throughout the eubacteria , but their function is poorly understood . Like CdiA , Rhs proteins are large , ranging from ∼1 , 500 residues in Gram-negative bacteria to over 2 , 000 residues in some Gram-positive species . Rhs proteins also possess a central repeat region , though the characteristic YD peptide repeats of Rhs proteins are unrelated to the hemagglutinin repeats in CdiA . Moreover , Rhs proteins have variable C-terminal domains that are sharply demarcated by a conserved peptide motif ( PxxxxDPxGL in the Enterobacteriaceae ) [12] . Remarkably , we find that some CdiA and Rhs proteins share related C-terminal sequences , suggesting the protein families may be functionally analogous . Rhs proteins from a number of species appear to be exported to the cell surface [13]–[15] , consistent with a role in cell-to-cell communication . Here we show that many CDI systems have an unusual genetic organization similar to that described for some Rhs loci [12] . Downstream of the cdiBAI genes , CDI systems often contain fragmentary gene pairs that resemble cdiA-CT/cdiI toxin/immunity modules . The predicted cdiA-CT fragments generally lack translation initiation signals but encode the VENN peptide motif that demarcates the CdiA-CT region in full-length CdiA proteins . These “orphan” CdiA-CT proteins possess growth inhibitory activities , which are specifically neutralized by the corresponding orphan CdiI immunity proteins . Moreover , we show that the orphan cdiA-CT/cdiI region is actively transcribed in E . coli EC93 . Although the orphan CdiA-CT does not appear to be synthesized , functional orphan CdiI immunity protein is produced in EC93 . We also show that the Rhs systems of D . dadantii 3937 encode toxin/immunity pairs . Rhs-CTs from D . dadantii 3937 inhibit cell growth when expressed in E . coli , and this toxic activity is specifically neutralized by the cognate RhsI protein encoded immediately downstream . These results suggest that Rhs constitutes another class of cell-surface proteins involved in intercellular competition , and that orphan CT/immunity modules may represent a reservoir of toxin/immunity diversity for both CDI and Rhs systems .
Examination of the cdi locus in E . coli EC93 revealed two short open reading frames ( ORFs ) immediately downstream of the cdiI immunity gene ( Figure 1 ) . The first ORF lacks a translation initiation codon but encodes the VENN motif that typically demarcates variable CdiA-CT regions ( Figure 1 ) , suggesting the first ORF encodes a detached CdiA-CT remnant and the second ORF is its associated cdiI gene . A TBLASTN search of bacterial genomes revealed that the encoded proteins are related to the CdiA-CT/CdiI toxin/immunity pair from E . coli UPEC 536 ( Figure S1 ) . Thus , the cdiBAI gene cluster in E . coli EC93 is followed immediately by an “orphan” cdiA-CT/cdiI module related to the cdi locus of a different E . coli strain . To differentiate these modules from main cdiBAI clusters , we indicate orphan genes with a subscripted “o” and a number that indicates the position of the module in the cdi locus . Additionally , throughout the text we will indicate bacterial strains as superscripts . According to this nomenclature , the genes in the EC93 orphan module are designated cdiA-CTo1EC93 and cdiIo1EC93 . Examination of CDI regions in other bacteria shows that orphan cdiA-CT/cdiI pairs are quite common . Although some CDI systems are comprised solely of the cdiBAI gene cluster , many loci are closely followed by one or more cdiA gene fragments that usually encode the VENN peptide motif ( Figure 2 ) . These orphan cdiA-CT genes are typically followed by small ORFs representing potential cdiI immunity genes . For example , the region II CDI system in Yersinia pseudotuberculosis PB1/+ contains four additional cdiA-CT gene fragments encoding the VENN motif , each associated with a putative cdiI gene ( Figure 2 and Figure S2A ) . Three of these gene fragments ( cdiA-CTo2PB1 ( II ) , cdiA-CTo3PB1 ( II ) and cdiA-CTo4PB1 ( II ) ) share significant regions of homology with the upstream cdiAPB1 ( II ) gene . The extent of these homologous regions varies between orphans , but the homology to full-length cdiAPB1 ( II ) is limited to regions upstream of the VENN encoding sequences for cdiA-CTo2PB1 ( II ) and cdiA-CTo3PB1 ( II ) ( Figure S2 and Figure S3 ) . In contrast , the orphan cdiA-CTo1PB1 ( II ) gene shares no significant identity with the full-length cdiAPB1 ( II ) gene beyond the sequence encoding the VENN peptide ( Figure S2C ) . Downstream of the VENN encoding region , the orphan cdiA-CToPB1 ( II ) genes are unrelated to one another , but have homology to cdiA genes and cdiA-CT fragments from other bacteria . The predicted orphan CdiA-CTo1PB1 ( II ) ( UniProt B2K3A6 ) is related to CdiA-CTs from E . coli A0 34/86 ( Q1RPM1; 87% identity over 107 residues ) and Enterobacter cloacae subsp . cloacae ATCC 13047 ( D5CBA0; 50% identity over 183 residues ) , as well as to orphan CdiA-CTs from Dickeya zeae Ech1591 ( C6CGV6; 88% identity over 111 residues ) and Citrobacter rodentium ICC168 ( D2TJP2; 86% identity over 107 residues ) . Orphan CdiA-CTo2PB1 ( II ) ( B2K3A4 ) is related to the CdiA-CT from Serratia proteamaculans 568 ( A8GK56; 63% identity over 131 residues ) . Orphan CdiA-CTo3PB1 ( II ) ( B2K3A2 ) is related to a CdiA-CT from Erwinia amylovora CFBP1430 ( D4HWF3; 63% identity over 127 residues ) and to orphan CdiA-CTs from E . coli EC869 ( B3BM80; 75% identity over 297 residues ) and Neisseria meningitidis MC58 ( Q9K0T4; 57% identity over 136 residues ) . Finally , orphan CdiA-CTo4PB1 ( II ) ( B2K3A0 ) is related to the CdiA-CT encoded by the adjacent cdiAPB1 ( II ) gene ( 39% identity over 179 residues ) , and also to CdiA-CTs from Klebsiella pneumoniae 342 ( B5Y0C2; 61% identity over 263 residues ) and Dickeya dadantii Ech586 ( D2BZ75; 59% identity over 263 residues ) . Although some CDI systems , such as those in Y . pseudotuberculosis PB1/+ and Neisseria meningitidis FAM18 , have well-ordered arrays of orphan cdiA-CT gene fragments downstream of cdiBAI , other species have more complex genomic arrangements . Klebsiella variicola At-22 and Erwinia pyrifoliae DSM 12163 both contain cdiA-CT fragments that do not encode VENN and lack associated cdiI genes ( Figure 2 ) . In several instances , the orphan cdiA-CT genes are disrupted by IS elements and transposon genes . For example , the orphan cdiA-CTo9EC869 from E . coli EC869 is interrupted by an SSuT antimicrobial resistance element [16] . Orphan cdiA-CT fragments can retain varying amounts of cdiA sequence upstream of the VENN-encoding region , but in some cases these homologous sequences are absent ( Figure 2 ) . For example , the orphan cdiA-CTo7EC869 gene from E . coli EC869 is unrelated to the adjacent cdiA-CToEC869 fragments , but is almost identical ( 99% identity from the VENN region onward ) to the orphan cdiA-CTo1254 from E . coli strain 254 ( Figure S4 ) . Moreover , the associated cdiI immunity genes are also nearly identical . A comparison of the E . coli EC869 and E . coli strain 254 genomes shows that the homology between these orphan cdiA-CT/cdiI modules begins at the VENN encoding sequence and extends precisely to the VENN sequence of the next cdiA-CT orphan ( Figure S4 ) . All sequenced Y . pestis strains share two large blocks of conserved DNA that contain cdi loci . For the region I CDI system ( positioned between the mannitol transporter and dioxygenase β-subunit genes ) , the predicted CdiA ( I ) protein is identical in all fully sequenced Y . pestis strains with the exception of the Microtus 91001 strain . CdiA ( I ) sequences N-terminal to the VENN motif are essentially identical in Microtus 91001 and other Y . pestis strains , though there is a six amino acid residue deletion within the hemagglutinin repeat region of Microtus 91001 . However , following the VENN motif , the CdiA-CT91001 ( I ) of Microtus 91001 diverges from that of other Y . pestis strains ( Figure S5 ) . Pairwise comparison of the Y . pestis CO92 and Y . pestis Microtus 91001 genomes revealed a 3 , 557 base-pair deletion in the Microtus 91001 region I cdi locus that has fused an orphan cdiA-CT/cdiI module to the upstream cdiA91001 ( I ) gene ( Figure 3A ) , producing a CdiA/CdiI toxin/immunity pair that is different from other Y . pestis strains . Thus , the CdiA91001 ( I ) protein from Microtus 91001 contains an orphan CdiA-CT effector domain from the CO92 strain . Another possible example of CdiA-CT interchange is found in the region II CDI system ( located between tellurium resistance genes and a predicted autotransporter ) shared by Y . pseudotuberculosis PB1/+ and IP31758 strains . The main cdiA-CTIP31758 ( II ) and cdiIIP31758 ( II ) sequences of Y . pseudotuberculosis IP31758 are essentially identical to the cdiA-CTo4PB1 ( II ) /cdiIo4PB1 ( II ) orphan module from strain PB1/+ ( 99% identity over 3 , 646 nucleotides ) ( Figure 3B ) . Additionally , comparison of these loci revealed other complex rearrangements . The orphan cdiA-CTo1PB1 ( II ) /cdiIo1PB1 ( II ) module of strain PB1/+ is nearly identical to cdiA-CTo2IP31758 ( II ) /cdiIo2IP31758 ( II ) from IP31758 ( 98% identity over 2 , 656 nucleotides ) , and the orphan cdiA-CTo2PB1 ( II ) /cdiIo2PB1 ( II ) from PB1/+ is 98% identical ( over 1 , 073 nucleotides ) to a cdiA gene fragment ( which lacks the VENN encoding sequence ) and its associated cdiI gene in strain IP31758 ( Figure 3B ) . There are at least two possible explanations for these observations . The two Y . pseudotuberculosis strains could have independently acquired the same modules and incorporated them at different sites within the cdi locus . Alternatively , the cdi locus of a common ancestor may have rearranged during strain diversification . Although the mechanisms underlying these complex exchanges are unknown , these observations suggest that orphan cdiA-CT fragments are a potential source of CdiA/CdiI toxin/immunity diversity . If orphan cdiA-CT/cdiI modules are merely unused , nonfunctional remnants of full-length cdiA/cdiI genes , then there should be no selective pressure to maintain CdiA-CT toxin activity and CdiI immunity function . To determine whether orphan cdiA-CT/cdiI modules encode functional proteins , we characterized the orphan CdiA-CT/CdiI proteins from E . coli EC93 . As described above , CdiA-CTo1EC93 and CdiIo1EC93 are very similar to the UPEC 536 CdiA-CTUPEC536/CdiIUPEC536 pair that we have previously characterized [5] . We first tested whether CdiA-CTo1EC93 and CdiIo1EC93 bind to one another as predicted for a toxin/immunity pair . We introduced a translation initiation signal upstream of the cdiA-CTo1EC93 fragment and co-expressed orphan CdiA-CTo1EC93 with CdiIo1EC93 immunity protein carrying a hexa-histidine ( His6 ) epitope tag at its C-terminus . Ni2+-affinity purification of His6-tagged CdiIo1EC93 under non-denaturing conditions resulted in co-purification of CdiA-CTo1EC93 ( data not shown ) , demonstrating that the two proteins bind each other . Given the similarity between CdiA-CTo1EC93 and CdiA-CTUPEC536 , we next tested whether the CdiIUPEC536 and CdiIo1EC93 immunity proteins are able to bind near-cognate CdiA-CTs . His6-tagged CdiI proteins were first separated from their cognate CdiA-CT proteins by Ni2+-affinity chromatography under denaturing conditions . The individual proteins were then refolded and tested for binding to their cognate partners . Refolded CdiA-CTo1EC93 and CdiA-CTUPEC536 were able re-bind to their cognate CdiI proteins ( Figure 4A ) . However , the His6-tagged CdiI proteins were unable to bind stably to the near-cognate CdiA-CTs ( Figure 4A ) . These results show that the EC93 orphan CdiA-CT/CdiI proteins physically interact with one another , but appear to have diverged enough from the UPEC 536 system that high-affinity binding between the two systems no longer occurs . We previously demonstrated that CdiA-CTUPEC536 is a nuclease that cleaves tRNA [5]; therefore we asked whether CdiA-CTo1EC93 also possesses this biochemical activity . Purified CdiA-CTo1EC93 cleaved a number of different tRNA species in a manner that was indistinguishable from CdiA-CTUPEC536 tRNase activity ( Figure 4B and data not shown ) . This tRNase activity was inhibited by the addition of equimolar His6-tagged CdiIo1EC93 ( Figure 4B ) . Intriguingly , His6-tagged CdiIUPEC536 was also able to neutralize the tRNase activity of CdiA-CTo1EC93 , but CdiIo1EC93 had no effect on CdiA-CTUPEC536 activity ( Figure 4B ) . Presumably , CdiIUPEC536 interacts with CdiA-CTo1EC93 , but this binding is not of sufficient affinity to allow co-purification by Ni2+-affinity chromatography . Together , these results show that the EC93 orphan CdiA-CT/CdiI proteins retain the biochemical features of a functional toxin/immunity module . We next asked whether the EC93 orphan CdiA-CT retains growth inhibitory activity . In general , cdiA-CT gene fragments are very toxic and can only be maintained on plasmids if the cognate cdiI immunity gene is also present . However , CdiI immunity proteins efficiently block CdiA-CT activity , making it difficult to assess CdiA-CT toxicity . To circumvent this problem , we used the controllable proteolysis system of McGinness and Sauer [17] to activate CdiA-CTo1EC93 through degradation of the CdiIo1EC93 immunity protein . This strategy uses the SspB adaptor protein to deliver ssrA ( DAS ) peptide-tagged proteins to the ClpXP protease . We tagged the C-terminus of CdiIo1EC93 with the ssrA ( DAS ) peptide and co-expressed it with CdiA-CTo1EC93 in E . coli ΔsspB cells . Degradation of tagged CdiIo1EC93 was then initiated by induction of SspB synthesis from a plasmid-borne arabinose-inducible promoter , resulting in growth arrest after approximately 2 hours ( Figure 5A ) . In contrast , growth continued unabated upon induction of SspB ( Δ47 ) ( Figure 5A ) , which lacks the C-terminal motif required for binding to ClpXP . Analysis of total cellular RNA revealed cleavage of tRNAs in the cells expressing wild-type SspB , but not in those expressing SspB ( Δ47 ) ( Figure 5B ) . These results demonstrate that CdiA-CTo1EC93 activity is unmasked upon CdiIo1EC93 degradation . Additionally , the temporal correlation between growth arrest and tRNA cleavage strongly suggests that the tRNase activity of CdiA-CTo1EC93 is responsible for growth inhibition . To test whether other orphan cdiA-CT/cdiI modules have growth inhibition activity , we examined orphan gene pairs from Dickeya dadantii 3937 and E . coli EC869 . D . dadantii 3937 contains one orphan cdiA-CT/cdiI module in the region I cdi locus . We cloned this module and added a C-terminal His6 epitope tag onto the predicted CdiIo13937 protein . Overproduced CdiA-CTo13937 protein co-purified with CdiIo13937-His6 during Ni2+-affinity chromatography , indicating a binding interaction between these proteins ( Figure S6A ) . Moreover , CdiA-CTo13937 inhibited the growth of E . coli cells upon degradation of ssrA ( DAS ) -tagged CdiIo13937 ( Figure S6B ) . Examination of the orphan cdiA-CTo11/cdiIo11 module from E . coli EC869 gave similar results , except growth inhibition was delayed compared to the D . dadantii 3937 orphan system ( Figure S6 ) . These data demonstrate that other orphan cdiA-CT/cdiI modules also encode functional toxin/immunity pairs . The EC93 orphan cdiA-CTo1EC93 lacks translation initiation signals , suggesting the encoded protein is not synthesized under normal conditions . By analogy with other two-partner secretion proteins , full-length CdiA proteins are secreted through the inner membrane via the general secretory pathway and assembled onto the cell surface through interactions with CdiB [4] , [18] . The EC93 orphan cdiA-CTo1EC93 gene also lacks a signal sequence and thus would not be delivered to the cell surface if it were expressed . Therefore to test whether CdiA-CTo1EC93 and CdiIo1EC93 are functional in the context of cell-mediated CDI , we replaced the EC93 cdiA-CTEC93/cdiIEC93 region with the corresponding sequences from the EC93 orphan module . The resulting construct produces a chimeric molecule in which the CdiA-CTo1EC93 is fused to CdiAEC93 at the VENN motif ( Figure 6A ) . E . coli expressing the CdiAEC93-CTo1EC93 chimera inhibited the growth of target cells expressing the CdiIEC93 immunity protein ∼105-fold compared to control CDI− inhibitor cells , but target cells expressing the orphan CdiIo1EC93 were completely protected from growth inhibition ( Figure 6B ) . In contrast , CdiIo1EC93 was unable to protect target cells from growth inhibition mediated by CdiAEC93 ( Figure 6B ) . Thus , the EC93 orphan CdiA-CTo1 is functional in contact-dependent growth inhibition when it is part of a full-length CdiA protein . To determine whether orphan cdiA-CT/cdiI modules are expressed , we isolated total RNA from E . coli EC93 and performed quantitative RT-PCR using primer pairs to amplify from potential cdiA-CTEC93 , cdiIEC93 , cdiA-CTo1EC93 and cdiIo1EC93 transcripts ( Figure 7 ) . This analysis revealed that transcripts encoding orphan cdiA-CTo1EC93 and cdiIo1EC93 are expressed in wild-type EC93 cells , and that orphan message levels are very similar to those encoding the main cdiA-CTEC93 and cdiIEC93 ( Figure 7 ) . Additionally , the orphan transcript was expressed at approximately the same level in an EC93 strain deleted for the main cdiA-CTEC93/cdiIEC93 region ( Figure 7 ) , indicating that the promoter driving orphan region transcription is upstream of the main cdiA-CTEC93 . These results suggest that the orphan region is co-transcribed with the upstream cdiA and cdiI genes . Indeed , a large RT-PCR product was obtained with the forward cdiAEC93 and reverse cdiA-CTo1EC93 primers ( data not shown ) , confirming that all of these ORFs are present on a single transcript . We next sought to detect the CdiA-CTo1EC93 protein by immunoblot using polyclonal antibodies raised against the homologous CdiA-CTUPEC536 from UPEC 536 . Although this antiserum detects CdiA-CTo1EC93 produced from a heterologous expression system in E . coli K-12 , we were unable to detect CdiA-CTo1EC93 in whole-cell lysates of EC93 ( data not shown ) . In contrast to the orphan cdiA-CTo1EC93 fragment , the EC93 cdiIo1EC93 gene has an initiation codon and a properly spaced Shine-Dalgarno sequence , indicating that the CdiIo1EC93 protein is likely to be synthesized in EC93 cells . To determine if orphan CdiIo1EC93 is indeed expressed , we tested whether EC93 cells are resistant to CDI mediated by the chimeric CdiAEC93-CTo1EC93 protein . Because EC93 cells are CDI+ , the chimeric inhibitor strain must itself be immune to EC93-mediated CDI . Therefore , we introduced a cosmid encoding the CdiAEC93-CTo1EC93 chimera into EC93 and used the resulting strain as the inhibitor for these experiments . Wild-type EC93 was not inhibited by EC93 expressing the CdiAEC93-CTo1EC93 chimera , but the EC93 ΔcdiA-CTo1EC93/ΔcdiIo1EC93 strain was inhibited approximately 103-fold ( Figure 8 ) . This growth inhibition was completely abrogated when orphan CdiIo1EC93 immunity protein was expressed from a plasmid in the EC93 ΔcdiA-CTo1EC93/ΔcdiIo1EC93 cells ( Figure 8 ) . Taken together , these results demonstrate that functional CdiIo1EC93 immunity protein is produced from the orphan locus in EC93 . In the course of our bioinformatic analyses , we found that many CdiA-CT sequences share significant identity with the C-terminal regions of Rhs proteins ( Table S1 ) . For example , the CdiA-CT91001 ( I ) of Y . pestis Microtus strain 91001 ( Q74T84 ) is similar to the C-terminal region of an Rhs/YD-repeat protein from Waddlia chondrophila WSU 86–1044 ( D6YTT8 ) ( Table S1 and Figure S7 ) . The W . chondrophila rhs gene is followed by a small ORF that encodes a protein with similarity to CdiI91001 ( I ) from Y . pestis Microtus 91001 ( 27% identity over 48 residues ) , suggesting this locus encodes a toxin/immunity protein pair . Intriguingly , Rhs-CT sequences are variable and demarcated by a well-conserved peptide motif ( PxxxxDPxGL ) analogous to the VENN motif in CdiA proteins [12] , [19] . The parallels between CDI and Rhs systems extend to their genetic organization , with many rhs loci containing numerous “silent cassettes” that resemble CDI orphan modules [12] . Rhs systems also appear to undergo complex rearrangements that diversify Rhs-CT sequences . For example , in one Rhs region shared by Y . pseudotuberculosis strains IP31758 and IP32953 , the rhs-CT and putative rhsI sequences are completely unrelated to one another , but the surrounding genomic regions are clearly homologous between the strains ( Figure 9 ) . This homology includes rhs coding sequence upstream of the region encoding DPxGL and nearly identical rhs-CT/rhsI orphan modules downstream of the rhs genes ( Figure 9 and Figure S8 ) . In addition , orphan rhs-CT/rhsI modules from a given species are often found fused to full-length rhs genes in other bacteria . For example , one of the rhs loci in D . dadantii 3937 contains two orphan rhs-CT/rhsI modules . Both of these predicted orphan Rhs-CT3937 proteins are related to the C-terminal regions of full-length Rhs proteins: Rhs-CTo13937 ( E0SGM0 ) is related to the CT region of a putative Rhs repeat protein ( C3K5K6; 49% identity in 147 residues following the DPxGL ) from Pseudomonas fluorescens SBW25 , and Rhs-CTo23937 ( E0SGM2 ) is related to the CT of a predicted YD-repeat protein ( C6CNW6; 99% identity in 116 residues ) from Dickeya zeae 1591 . In general , the functions of Rhs proteins are unknown , but data from Hill and colleagues suggest that that the E . coli rhsA locus may encode a toxin/immunity pair [20] . In conjunction with the similarities to CDI systems , these observations suggest that Rhs proteins may be involved in intercellular competition . To test whether other Rhs systems encode toxin/immunity pairs , we examined rhs genes from D . dadantii 3937 , which contains three full-length rhs genes that we have termed rhsA ( Dda3937_01758 ) , rhsB ( Dda3937_02773 ) and rhsC ( Dda3937_04312 ) . Each of these rhs genes is closely followed by a small ORF that encodes a possible immunity protein . Additionally , the rhsC locus contains the two orphan rhs-CT gene fragments described above . We first tested RhsB-CT3937 for growth inhibitory function , because this domain contains an HNH endonuclease motif found in other cytotoxic proteins [21] . We expressed RhsB-CT3937 together with an ssrA ( DAS ) -tagged version of the putative RhsIB3937 immunity protein in E . coli cells , and found that growth was arrested upon degradation of RhsIB3937- ( DAS ) ( Figure 10 and data not shown ) . The same results were obtained with the two orphan modules from D . dadantii 3937 ( Figure 10 and data not shown ) , indicating that Rhs-CTo13937 and Rhs-CTo23937 also have growth inhibition activity . These results also demonstrate that the putative rhsI genes do in fact encode proteins with immunity function . Finally , we examined the specificity of RhsI-mediated immunity . Although each Rhs-CT/RhsI- ( DAS ) expression construct can be maintained stably in E . coli ΔsspB strains , these plasmids cannot be transformed into sspB+ cells due to the Rhs-CT toxicity that results from RhsI- ( DAS ) degradation ( Figure 10 and data not shown ) . Therefore , we asked whether untagged RhsI protein could rescue cells from Rhs-CT toxicity and allow stable transformation of Rhs-CT/RhsI- ( DAS ) plasmids into sspB+ cells . Each of the three Rhs-CT/RhsI- ( DAS ) plasmids were introduced into sspB+ cells expressing individual RhsI proteins , and the transformed cell suspensions plated onto selective media containing L-arabinose to induce Rhs-CT/RhsI- ( DAS ) synthesis . Transformants carrying the Rhs-CT/RhsI- ( DAS ) expression plasmid were only obtained if the cells also expressed the cognate RhsI immunity protein ( Figure 10 ) . Therefore , each RhsI protein only confers immunity to its cognate Rhs-CT , demonstrating that the Rhs systems of D . dadantii 3937 encode polymorphic toxin/immunity pairs .
The results presented here show that many CDI systems have a complex genetic organization in which the cdiBAI genes are followed by an array of orphan cdiA-CT/cdiI modules . These arrays can be extensive , with eleven orphan gene pairs in the cdi locus of E . coli EC869 . The widespread occurrence of orphan cdiA-CT/cdiI modules in diverse bacterial species argues that these gene pairs confer a selective advantage . Although the majority of orphan cdiA-CT genes lack translation initiation signals , orphan cdiI genes appear to be fully capable of expression . Therefore , bacteria could maintain orphan modules to build a repertoire of immunity genes to protect themselves from neighboring bacteria that express diverse CdiA proteins . Broad range immunity would clearly confer an advantage and our data demonstrate that EC93 does in fact express its orphan CdiI immunity protein . However , if bacteria are collecting immunity genes , then it is unclear why the toxin-encoding cdiA-CT fragments are retained in the process . One model to explain these findings is that orphan modules allow the bacterium to change its toxin/immunity profile by recombination between the highly conserved VENN-encoding sequences . This may have occurred in the cdi locus of Y . pestis Microtus 91001 , where a large deletion has loaded an orphan cdiA-CT/cdiI module onto the main cdiA gene . However , such a strategy for generating CdiA-CT diversity could have serious shortcomings . First , although reloading CdiA with a new toxic C-terminal domain would produce a novel weapon , the recombination would also delete cdiI and render the cell susceptible to the original CdiA protein expressed by neighboring bacteria that have not recombined their cdi locus . Second , simple recombination between cdiA and distal orphan cdiA-CT genes would delete all intervening cdiA-CT/cdiI modules . An alternative model is that recombination between cdiA ( or rhs ) and the orphan regions occurs following tandem duplication of the loci . This mechanism would allow cdiA-CT/cdiI modules to be rearranged without the loss of immunity or genetic diversity , because a copy of the original cdi/rhs system would be present [22] , [23] . The results presented here have also revealed a connection between CDI and Rhs systems . The rhs genes were first described in E . coli , and were originally thought to be rearrangement hot spots because of a recombination event between nearly identical sequences within the rhsA and rhsB genes [10] , [11] . These proteins are widely distributed throughout the eubacteria and related proteins containing YD peptide repeats are also found in metazoans including all vertebrates . Despite their prevalence , the function of these proteins is almost completely unknown . Our data show that at least some Rhs systems encode toxin/immunity protein pairs , and a recent bioinformatic study has proposed that Rhs proteins contain toxic nuclease domains [24] . These results are consistent with work from Hill and colleagues showing that the C-terminal region of E . coli RhsA blocks the recovery of stationary phase cells [20] . Moreover , this growth inhibition was neutralized by expression of the ORF ( yibA ) encoded immediately downstream of rhsA [20] . More recently an Rhs-related protein from Pseudomonas savastanoi pv . savastanoi was found to be associated with bacteriocin activity [25] . Because of the parallels between CDI and Rhs , we suspect that Rhs proteins are also exported to block the growth of neighboring cells and thereby impart a competitive advantage to the inhibitor cell . However , many Rhs proteins from Gram-negative bacteria lack recognizable signal sequences , so the export pathway is unclear in several instances . Many rhs genes are linked to valine-glycine repeat ( Vgr ) -encoding genes that are associated with Type VI secretion systems [26] . VgrG proteins associated with Type VI secretion systems have structural similarity to bacteriophage cell-puncturing proteins [27] , [28] , and are therefore ideal for penetrating bacterial envelopes . Indeed , recent work has demonstrated that Type VI systems are used to deliver protein toxins into target bacteria [29] . Whether the close genetic linkage between rhs and vgr genes extends to a functional relationship remains to be determined . Although the Rhs systems examined here appear to be growth inhibitory , there are indications that Rhs proteins have other signaling modalities . Youdarian and Hartzell found that an Rhs-related YD repeat protein ( Q1CXS7 ) from Myxococcus xanthus plays an important role in social motility [15] , which occurs when individual bacteria make contact with one another to coordinate cell movement . The M . xanthus YD repeat protein has a signal sequence and is presumably exported to the surface . The gene encoding Q1CXS7 ( MXAN_6679 ) is closely followed by a small ORF that is suggestive of an rhsI gene . However , it is not clear that immunity would be required in this signaling pathway . Perhaps this small ORF encodes a peptide that is involved in receiving the signal from the C-terminal region of the YD repeat protein . Recent work on the entomopathogen Pantoea stewartii suggests another function for a class of Rhs-related proteins termed Ucp ( for you cannot pass ) [30] . The P . stewartii genome contains seven ucp homologs that encode large proteins ( 1 , 200–1 , 300 residues ) with similar N-terminal regions and variable C-terminal sequences . The Ucp1 protein mediates bacterial aggregation and is a virulence factor required for pathogenicity against the aphid host . It was postulated that Ucp1 and other Ucp proteins function primarily as adhesins and that C-terminal variability is driven by the need to evade host immune responses [30] . Our results suggest an alternative possibility . The C-terminal peptide of Ucp1 could be delivered into insect cells and exert a toxic effect in a manner similar to that proposed for CDI [5] . In this model , the other six Ucp proteins could be targeted against other bacterial competitors or different eukaryotic hosts . Finally , another intriguing example of Rhs signaling has been proposed for teneurins . The teneurin protein family is comprised of four paralogs that are present in all vertebrates [31] . These proteins are type II integral membrane proteins ( single transmembrane span with the C-terminus presented extracellularly ) that play a role in axon guidance and neural patterning during development [31] , [32] . Like Rhs proteins , the teneurins are large ( 2 , 500–2 , 800 residues ) and the C-terminal half is comprised of several YD peptide repeats . Remarkably , all teneurins contain a C-terminal associated peptide ( TCAP ) that is similar to neuroendocrine signaling peptides [33] , [34] . The TCAP region is adjacent to a phylogenetically conserved furin protease cleavage site ( RxRR ) [31] , suggesting that the TCAPs are released and enter target cells to exert neuromodulatory effects . Because some CdiA-CTs and Rhs-CTs have toxic nuclease activity , presumably they must also be cleaved for delivery into the cytoplasm of target cells . The parallel between CDI/Rhs-mediated growth inhibition and the proposed teneurin signaling pathway is striking , suggesting that intercellular communication through the delivery of cleaved C-terminal peptides is ubiquitous and possibly ancient . Perhaps these systems have arisen from a common YD-repeat protein ancestor .
Pairwise sequence comparisons were performed using GCG Gap or pairwise BLAST . Related CdiA-CT , Rhs-CT , CdiI , RhsI and orphan sequences were found using TBLASTN against assembled bacterial genomes , because these regions are often not annotated and thus not represented in the non-redundant protein database . Pairwise comparisons of genomic regions were performed using WebACT ( www . webact . org ) with BLASTN comparisons . The UniProt , GenBank and gene locus accession numbers for each full-length CdiA and Rhs protein discussed in this work are presented in Table S2 . The orphan cdiA-CTo1EC93/cdiIo1EC93 module from E . coli EC93 was amplified using oligonucleotides , EC93orph-Nco ( 5′ - TTG CCA TGG AGA ATA ACT CGC TGA GC ) and EC93orph-Spe ( 5′ - ATC ACT AGT GGC ATT AGA TAG CTT ATC TAT TTT TGC ) ( restriction endonuclease sites are underlined ) , followed by digesting with NcoI and SpeI , and ligation to plasmid pET21S [5] . The resulting construct overproduces CdiA-CTo1EC93 and C-terminally His6-tagged CdiIo1EC93 . The EC93 orphan immunity gene was amplified using primers #1658 ( 5′ - CAA CAA GCA TGC CCC GAC TTT GAG ACC AGA ATA TC ) and #1664 ( 5′ - ATC AGG AGC ATG GTA TAT GAC AAC ATT TAG ATC ) . The resulting PCR product was digested with SphI and ligated to EcoRV/SphI digested plasmid pBR322 under control of the tet promoter . Orphan cdiA-CT/cdiI modules from D . dadantii 3937 and E . coli EC869 were amplified using 3937orph-Nco ( 5′ - AAG CCA TGG TGG AGA ATA ACT ATC TGA GCA G ) and 3937orph-Spe ( 5′ - TCT ACT AGT AGG CTG GTA ATC TTC ATA TTC C ) ; and EC869orph11-Nco ( 5′ - ATT CCA TGG GCA CAA ACC AGT CTC TGA CCT TCG ) and EC869orph11-Spe ( 5′ - TCT ACT AGT ACC TTT GCA GCG ACT CAA GGC CAG ) , respectively . The rhs-CT3937/rhsI3937 modules from D . dadantii 3937 were amplified using the following primer pairs: rhsB-Nco ( 5′ - CAG CCA TGG AAA GTA ATT ACG GTT ATG TCC ) and rhsB-Spe ( 5′ -AAA CTA GTA ATT TTT CTT GAT TTA TAT TTT ACA AGC ) ; rhs-orph1-Nco ( 5′ - TCC CAT GGG GTT GGT GGG ATG TCC GC ) and rhs-orph1-Spe ( 5′ - AAA ACT AGT GCC ATC AAG GTA TAC AGA AGG ) ; and rhs-orph2-Nco ( 5′ - ACC CCA TGG GGC TGG CAG GGG GGC TG ) and rhs-orph2-Spe ( 5′ - TTT ACT AGT AAC AGC TTT GTA ATA ATC GTG ) . All PCR products were digested with NcoI and SpeI and ligated to pET21S . The D . dadantii rhsI3937 genes were amplified from pET21S constructs using primer pET-Pst ( 5′ - CGG CTG CAG CAG CCA ACT CAG TGG ) in conjunction with primers: rhsIB-Nco ( 5′ - TAA CCA TGG ATA TTG AAA ATG C ) , rhsIo1-Nco ( 5′ - TCA CCA TGG ATT CTA GTG ATA AG ) , and rhsIo2-Nco ( 5′ - AAT CCA TGG ATG CTG AAC AAT TTG ) . The resulting PCR products were digested with NcoI and PstI and ligated to plasmid pCH450 [35] . A cassette encoding the ssrA ( DAS ) peptide tag ( AANDENYSENYADAS ) was generated from oligonucleotides , DAS-top ( 5′ - CTA GTG CTG CGA ACG ATG AAA ATT ACT CCG AAA ATT ATG CGG ATG CGT CTT AAT G ) and DAS-bot ( 5′ - GAT CCA TTA AGA CGC ATC CGC ATA ATT TTC GGA GTA ATT TTC ATC GTT CGC AGC A ) , and ligated to SpeI/BamHI digested plasmid pKAN [36] . As part of an unrelated study , a fragment of the E . coli hisS gene was cloned into pKAN ( DAS ) using SacI and SpeI sites . The resulting hisS- ( DAS ) SacI/BamHI fragment was subcloned into plasmid pTrc99A ( Amersham Pharmacia ) to generate plasmid pTrc ( DAS ) . All cdiA-CT/cdiI and rhs-CT/rhsI modules were subcloned into pTrc ( DAS ) using NcoI and SpeI restriction sites . The sspB and sspB ( Δ47 ) genes were amplified using SspB-Nde ( 5′ - GAG TTA ATC CAT ATG GAT TTG TCA CAG C ) in combination with SspBΔ47-Bam ( 5′ - TGC GGA TCC TTA ATT CAT GAT GCT GGT ATG TTC ATC GTA GGC ) and SspB-Bam ( 5′ - ATA TGA TTG CCA GGA TCC CGC TAT TTT ATT AAG TC ) , respectively . Both PCR products were digested with NdeI and BamHI and ligated to plasmid pCH410 , allowing L-arabinose control of SspB and SspB ( Δ47 ) expression [37] . The orphan cdiA-CTo1EC93/cdiIo1EC93 module from E . coli EC93 was fused to the full-length EC93 cdiAEC93 gene in multiple steps . A fragment of cdiAEC93 ( upstream of the VENN encoding sequence ) was amplified using primers , #1527 ( 5′ - GAA CAT CCT GGC ATG AGC G ) and #1758 ( 5′ - CAA GCT CAG CGA GTT ATT CTC AAC CGA GTT CCT ACC TGC CTG ) . The EC93 orphan module was amplified using primers , #1759 ( 5′ - CAG GCA GGT AGG AAC TCG GTT GAG AAT AAC TCG CTG AGC TTG ) and #1663 ( 5′ - GGT CTG GTG TCT AAC CTT TGG G ) . The two products were combined by overlapping-end PCR using primers #1527 and #1663 , and the resulting product digested with SphI and AvrII and ligated to plasmid pDAL660Δ1-39 [4] . All CdiA-CT/CdiI-His6 complexes were overproduced and purified by Ni2+-affinity chromatography as described [5] . Complexes were eluted from Ni2+-nitrilotriacetic acid resin with native elution buffer [20 mM sodium phosphate ( pH 7 . 0 ) –10 mM β-mercaptoethanol – 250 mM imidazole] , followed by dialysis in storage buffer [20 mM sodium phosphate ( pH 7 . 0 ) – 150 mM NaCl – 10 mM β-mercaptoethanol] . CdiA-CT and CdiI-His6 proteins were separated from one another by Ni2+-affinity chromatography with denaturing buffer [20 mM sodium phosphate ( pH 7 . 0 ) – 10 mM β-mercaptoethanol – 6 M guanidine-HCl] . Denatured proteins were refolded by dialysis into storage buffer . All purified proteins were quantified by absorbance at 260 nm using the following molar extinction coefficients: CdiA-CTUPEC536 , 12 , 950 M−1 cm−1; CdiIUPEC536 , 8 , 480 M−1 cm−1; CdiA-CTo1EC93 , 11 , 460 M−1 cm−1; and CdiIo1EC93 , 11 , 460 M−1 cm−1 . The specificity of CdiA-CT/CdiI-His6 binding interactions was determined by Ni2+-affinity co-purification as described [5] . The tRNase activity of isolated and refolded CdiA-CTUPEC536 and CdiA-CTo1EC93 proteins was determined as described [5] , [37] . E . coli strain CH4180 ( ×90 ΔsspB ) was co-transformed with pTrc ( DAS ) orphan module constructs and either the SspB or SspB ( Δ47 ) arabinose-inducible expression plasmids . The resulting strains were grown at 37°C with aeration in LB media supplemented with 150 µg/mL ampicillin and 10 µg/mL tetracycline ( to maintain plasmids ) to mid-log phase and re-diluted into fresh media to an optical density at 600 nm ( OD600 ) of 0 . 05 . After 40 min , SspB or SspB ( Δ47 ) expression was induced by the addition of 0 . 4% L-arabinose . Cell growth was tracked by measuring the OD600 every 30 min after induction . Cells expressing CdiA-CTo1EC93/CdiIo1EC93-DAS were harvested into an equal volume of ice-cold methanol and RNA extracted as described [38] . Northern blot hybridizations were conducted as described [37] , using radiolabeled oligonucleotides tRNAHis probe ( 5′ - CAC GAC AAC TGG AAT CAC ) and tRNA1BAla probe ( 5′ - TCC TGC GTG AGC AG ) as probes . E . coli sspB+ cells expressing RhsIB3937 , RhsIo13937 and RhsIo23937 were transformed with plasmids encoding rhs-CT/rhsI ( DAS ) modules under control of the PBAD promoter [39] . Competent cells were incubated with 0 . 5 µg of purified supercoiled plasmid for 20 min on ice , then heat-shocked at 42°C for 45 s . The treated cells were recovered in 1 . 0 mL of LB media for 2 hr without selection , then 20 µL of the cell suspension was plated onto LB-agar supplemented with ampicillin ( 150 µg/mL ) , tetracycline ( 10 µg/mL ) and 0 . 4% L-arabinose . E . coli ΔsspB cells were also transformed in the same manner with arabinose-inducible rhs-CT/rhsI ( DAS ) constructs to confirm that growth inhibition was dependent upon RhsI-DAS degradation . The main cdiA-CTEC93 region and cdiIEC93 gene were deleted from rifampicin-resistant E . coli strain EC93 ( DL3852 ) using allelic exchange as described [4] , [40] . Sequence upstream of cdiA-CTEC93 was amplified using oligonucleotides #1683 ( 5′ - CAA CAA GAG CTC GAA CAT CCT GGC ATG AGC G ) and #1684 ( 5′ - CAG CGA GTT ATT CTC AAC AAC AAC TA CGA GTT CCT ACC TGC CTG ) ( SacI restriction endonuclease site is underlined ) . Sequence downstream of cdiIEC93 ( including the cdiIEC93 stop codon ) was amplified using oligonucleotides #1685 ( 5′ - CAG GCA GGT AGG AAC TCG tag TTG TTG TTG AGA ATA ACT CGC TG ) and #1686 ( 5′ - CAA CAA TCT AGA CCC GAC TTT GAG ACC AGA ATA TC ) ( XbaI restriction endonuclease site is underlined ) . The two PCR products were combined by overlapping-end PCR using primers #1683 and #1686 , and the resulting product digested with SacI and XbaI and ligated to suicide vector pRE112 [40] . The orphan cdiA-CToEC93/cdiIoEC93 module was deleted from rifampicin-resistant EC93 in a similar manner . Sequence upstream of cdiA-CTo1EC93 was amplified using oligonucleotides #1714 ( 5′ - CAA CAA GAG CTC GTG AAG GTG GGC TTA CTC AG ) and #1715 ( 5′ - CGA CTT TGA GAC CAG AAT ATC TAT TTA CTC AAC AAC AAC TAT TTT CTG TCT AAG ) ( SacI restriction endonuclease site is underlined ) . Sequence downstream of cdiIo1EC93 ( including the cdiIo1EC93 stop codon ) was amplified using oligonucleotides #1716 ( 5′- CTT AGA CAG AAA ATA GTT GTT GTT GAG TAA ATA GAT ATT CTG GTC TCA AAG TCG ) and #1717 ( 5′ - CAA CAA TCT AGA CCC GTA AGT ATG CTT ATC CCA TG ) ( XbaI restriction endonuclease site is underlined ) . The two PCR products were combined by overlapping-end PCR using primers #1714 and #1717 , and the resulting product digested with SacI and XbaI and ligated to suicide vector pRE112 [40] . E . coli strain EPI100 carrying plasmids pWEB-TNC ( CDI− ) , pDAL660Δ1-39 ( CdiAEC93 ) , or pDAL879 ( CdiAEC93-CTo1EC93 chimera ) were grown overnight at 37°C in LB media supplemented 100 µg/mL of ampicillin . Overnight cultures were diluted into fresh medium to OD600 of 0 . 05 , and incubated at 37°C with aeration until the culture reached mid-log phase ( OD600≈0 . 3 ) . The log-phase inhibitor cultures were then mixed with target E . coli cells – CAG18439 pBR322 ( no CdiI ) , pDAL741 ( CdiIEC93 ) , or pDAL867 ( CdiIo1EC93 ) – at an inhibitor to target cell ratio of 10∶1 . The co-cultures were incubated for 2 hr at 37°C with aeration . Viable target cell counts were determined by serially dilution of the co-cultures into M9 salt solution followed by plating onto LB agar supplemented with 10 µg/mL of tetracycline . To assay CdiIo1 expression in EC93 , growth competitions were conducted with streptomycin-resistant EC93 ( DL6104 ) carrying pDAL879 ( CdiAEC93-CTo1EC93 chimera ) as the inhibitor strain . Target strains were rifampicin-resistant EC93 carrying plasmid pBR322 ( no CdiI ) , and rifampicin-resistant EC93 ΔcdiA-CTo1 ΔcdiIo1 cells carrying pBR322 ( no CdiI ) or pDAL867 ( CdiIo1EC93 ) . Growth competitions were conducted as described above except that mid-log phase cells were mixed and co-cultured at an inhibitor to target ratio of 1∶1 . Viable target cell counts were determined by serial dilution of the co-cultures into M9 salt solution followed by plating onto LB agar supplemented with 150 µg/mL of rifampicin . Total RNA was isolated from wild-type EC93 and EC93 strains deleted for cdiA-CTEC93/cdiIEC93 and cdiA-CTo1EC93/cdiIo1EC93 , followed by treatment with RNase-free DNase I ( Roche ) to remove contaminating chromosomal DNA . RNA ( 0 . 5 µg ) was reverse transcribed using the iScript cDNA synthesis kit ( Bio-Rad ) . A control without reverse transcriptase was also prepared to assess chromosomal DNA contamination . Quantitative PCR was carried out on a Bio-Rad MyiQ single-color real-time PCR detection system using SYBR green supermix . The following primers sets were used for amplification: #1700 ( 5′ - GGT GAA GGT GGG CTT ACT CA ) and #1701 ( 5′ - TGA TGT GAC AGA GCC AAA GC ) for cdiA-CTEC93; #1698 ( 5′ - TGC TAT GTA CTG TAC TTG GTC ) and #1699 ( 5′ - TAA AGC CTA TGG GAT TCC T ) for cdiIEC93; #1647 ( 5′ - ACT GAC CGC TGA TGA ACT GG ) and #1648 ( 5′ - AGT AGC CGC TTG AAC CTG CAC ) for cdiA-CTo1EC93; #1649 ( 5′ - TGA ACC CAA CAG TCG CTC TTC ) and #1650 ( 5′ - GTC TTC CCC AGC CAG AGG AT ) for cdiIo1EC93; #1568 ( 5′ - TCA CCC CAG TCA TGA ATC AC ) and #1569 ( 5′ - TGC AAC TCG ACT CCA TGA AG ) for 16S rRNA . Thermal cycling conditions were: 95°C for 5 min for polymerase activation and collection of experimental well factors and 40 cycles at 95°C for 10 s; 56°C for 30 s and 72°C for 30s followed by a melting curve ( 55°C to 95°C ) to analyze the end product . Data were analyzed using the iQ5 optical system software ( Bio-Rad ) and exported to Microsoft Excel and Prism 5 . 0 for further analysis . For each target gene , a standard curve was generated to assess PCR efficiency ( E ) allowing the expression level ( e ) to be determined , where ( e ) = ( Etarget ) −Ct target/ ( Eref ) −Ct ref [41] . Gene expression was normalized to a 16S rRNA RT-PCR product amplified from the corresponding sample , and the reported values represent the mean ± SEM from three independent RNA extractions . | Recent work from our laboratories has shown that many bacteria express contact-dependent growth inhibition ( CDI ) systems in which stick-like proteins on the cell surface deliver toxic tips into target cells . Over 60 distinct toxic tips have been identified in bacteria , and our data indicate that each CDI+ cell expresses a specific immunity protein that binds to its cognate toxin and inactivates it to prevent cell suicide . Here we identify genes for toxic tips that are not attached to the stick protein . Each of these “orphan” tips has toxic activity , which is blocked by its associated immunity protein . Remarkably , the orphan tips of some bacterial species are often found on the stick proteins in other species , suggesting that cells load and deliver different tips . We also report on a system called Rhs , which encodes another predicted stick-like protein that also carries variable tips . We found that the tips of Rhs proteins are toxic and that Rhs systems encode immunity proteins that specifically block toxin activity . CDI and Rhs toxin tip diversity may represent a microbial arms race , driven by the competition for environmental resources . | [
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] | 2011 | Identification of Functional Toxin/Immunity Genes Linked to Contact-Dependent Growth Inhibition (CDI) and Rearrangement Hotspot (Rhs) Systems |
Mutations in the human MECP2 gene cause Rett syndrome ( RTT ) , a severe neurodevelopmental disorder that predominantly affects girls . Despite decades of work , the molecular function of MeCP2 is not fully understood . Here we report a systematic identification of MeCP2-interacting proteins in the mouse brain . In addition to transcription regulators , we found that MeCP2 physically interacts with several modulators of RNA splicing , including LEDGF and DHX9 . These interactions are disrupted by RTT causing mutations , suggesting that they may play a role in RTT pathogenesis . Consistent with the idea , deep RNA sequencing revealed misregulation of hundreds of splicing events in the cortex of Mecp2 knockout mice . To reveal the functional consequence of altered RNA splicing due to the loss of MeCP2 , we focused on the regulation of the splicing of the flip/flop exon of Gria2 and other AMPAR genes . We found a significant splicing shift in the flip/flop exon toward the flop inclusion , leading to a faster decay in the AMPAR gated current and altered synaptic transmission . In summary , our study identified direct physical interaction between MeCP2 and splicing factors , a novel MeCP2 target gene , and established functional connection between a specific RNA splicing change and synaptic phenotypes in RTT mice . These results not only help our understanding of the molecular function of MeCP2 , but also reveal potential drug targets for future therapies .
Rett syndrome ( RTT ) is a progressive neurodevelopmental disorder that predominantly affects females[1 , 2] . Classic RTT patients develop normally in the first 6–18 months , and then undergo a rapid regression of higher brain functions that eventually leads to the loss of speech and purposeful hand movement , microcephaly , dementia , ataxia and seizure[3] . Mutations in the human X-linked methyl-CpG-binding protein 2 ( MECP2 ) gene are responsible for over 90% of classic RTT cases[4] . MeCP2 is abundantly expressed in the mammalian central nervous system ( CNS ) and binds to methylated CpG site throughout the genome[5] . Despite decades of work , the underlying molecular mechanism of how mutations of MECP2 lead to RTT is not fully understood . In order to reveal the RTT disease mechanism , it is necessary to study the molecular function of MeCP2 . Previous research on the molecular function of MeCP2 has focused on the localization of MeCP2 in the nucleus and the proteins that physically interact with MeCP2 . At the microscopic level , MeCP2 appears to be colocalized with heterochromatin and thus is hypothesized to induce large-scale chromatin reorganization during terminal differentiation[6] . At the genomic level , MeCP2 can bind to unmethylated DNA[7] , methylated cytosine[5] , and hydroxymethylated cytosine[8] , and may preferentially modulate the expression of long genes[9] . In parallel to research on MeCP2 localization , many proteins have been identified to physically interact with MeCP2 . Based on the known functions of identified MeCP2-interacting proteins , previous studies have suggested a role for MeCP2 in maintaining DNA methylation[10] , regulating transcription[11–16] , chromatin structure[17–22] , and RNA processing[23–25] . Future effort to combine the insights from the two approaches described above may allow more detailed understanding of the regulation of each of these specific protein-protein interactions across the entire genome , as well as the relevance of each interaction to RTT disease pathogenesis . Misregulation of RNA alternative splicing has been implicated in a number of neurological disorders , which can be classified into two categories: cis-acting splicing disorder and trans-acting disorder[26] . Cis-acting disorder is caused by mutations that affect splicing of the mutant gene itself and therefore the function of that gene . An example of this type is frontotemporal dementia with Parkinsonism linked to chromosome 17 ( FTDP-17 ) , in which mutations in the MAPT ( Tau ) gene alter the function of Tau by increasing the exon 10 containing isoform[27] . In contrast , trans-acting disorders are caused by the loss of function of genes with regulatory roles in RNA splicing . For instance , the loss of survival motor neuron protein 1 ( SMN1 ) function affects biogenesis of small nuclear RNA ( snRNA ) and lead to widespread splicing changes in spinal muscular atrophy ( SMA ) [28] . Relevant to RTT , the Zoghbi lab has identified RNA-dependent interaction between MeCP2 and Y box-binding protein 1 ( YB1 ) in cultured cells , and further reported many altered RNA splicing events in a mouse model of RTT[23] . However , it is not clear if the splicing alterations are indeed dependent on the MeCP2/YB1 interaction and no link has been discovered between any gene-specific splicing change and specific neuronal phenotypes in RTT . Therefore the mechanistic and functional links between MeCP2 , splicing regulation , and RTT phenotypes remain elusive . To facilitate systematic identification of MeCP2-interacting proteins in the brain , we created a knockin mouse line ( Mecp2-Flag ) that expresses Flag-tagged MeCP2 from the endogenous Mecp2 locus[29] . This unique tool gives us two main advantages . First , it ensures that the MeCP2-Flag protein is expressed at the physiological level , so that non-specific protein-protein interactions caused by the overexpression of MeCP2-Flag is minimized . Second , it allows us to use a highly efficient anti-Flag antibody in the co-immunoprecipitation . The choice of antibody is not a trivial issue , because in the past , different anti-MeCP2 antibodies in co-immunoprecipitation experiments have generated conflicting results in the identification of MeCP2-interacting proteins[30 , 31] . Mass spectrometry analysis of proteins co-immunoprecipitated by the anti-Flag antibody from the nuclear extract prepared from the Mecp2-Flag mouse brains showed that MeCP2 interacted with multiple splicing factors . Some of these physical interactions were disrupted by RTT-causing mutations in MeCP2 . Furthermore , ChIP-seq analysis of revealed MeCP2 occupancy at exon/intron injections , which provides additional support of the role for MeCP2 in modulating alternative splicing . Consistent with previous findings , hundreds of splicing events were found to be misregulated in the cortex of Mecp2 knockout ( KO ) mice . More importantly , a specific splicing change in the Mecp2 KO cortex-a shift in the balance between the flip and flop exon in the AMPA receptor ( AMPAR ) genes was causally linked to synaptic phenotypes of faster desensitization kinetics of AMPAR-gated current and altered synaptic transmission . Together , our findings substantiate the role of MeCP2 in regulating alternative splicing of RNA by revealing direct physical interaction between MeCP2 and multiple splicing factors , association of MeCP2 at exon/intron junction , and providing the first functional link between a specific splicing alteration and synaptic phenotypes in RTT mice .
To facilitate identification of MeCP2-interacting proteins in the mouse brain , we generated the Mecp2-Flag knockin mouse line that expresses Flag-tagged wild-type MeCP2 from the endogenous locus . We purified nuclei from the brains of male Mecp2-Flag mice , prepared nuclear extract and performed co-immunoprecipitation ( co-IP ) using the anti-Flag antibody . Eluted protein sample was then subjected to protein identification by mass spectrometry . Forty-eight proteins were identified using highly stringent statistical filters ( S1 Table ) . Identified proteins included previously known MeCP2-interacting transcriptional regulators and chromatin remodeling proteins , such as HDAC1 and components of the SWI/SNF complex . Consistently , gene ontology ( GO ) analysis showed that proteins identified by co-IP/MS were enriched with GO terms of chromatin organization , chromatin modification and regulation of transcription . Interestingly , these proteins were also enriched for RNA splicing ( Fig 1a ) . MeCP2 has been implicated in regulating splicing , but its role in pre-mRNA splicing has not been studied extensively . Therefore we decided to focus our study on the interaction between MeCP2 and several splicing factors . To validate the physical interaction between MeCP2 and splicing factors , we performed anti-Flag co-IP in nuclear extract from the Mecp2-Flag knockin mouse brain and probed it with antibodies against TDP-43 , LEDGF , DHX9 , FUS , hnRNP H , and hnRNP F , respectively . Western blot results showed that TDP-43 , LEDGF , DHX9 and FUS were co-immunoprecipitated with MeCP2 , whereas hnRNP H and hnRNP F were not ( Fig 1b ) . Next , we performed reverse co-IP and detected MeCP2 in immunoprecipitate of anti-TDP-43 , anti-LEDGF , anti-DHX9 , anti-FUS , and anti-hnRNPH+F ( Fig 1c ) , further confirming that MeCP2 physically interacts with these proteins in the mouse brain . In addition , the interaction between MeCP2 and splicing factors were not sensitive to Benzonase treatment , which digest and remove all nucleic acids , suggesting that these interactions were most likely direct interactions independent of either DNA or RNA ( S1 Fig ) . The interaction between MeCP2 and LEDGF has been previously reported in cancer cells[32] , but not in the brain . MeCP2 interacts with the N-terminal PWWP-CR1 domain of LEDGF , but which domain of MeCP2 that LEDGF binds to is not defined . DHX9 has recently been revealed as an interacting partner of MeCP2[33] , but the interaction domain is not known either . To examine which domain of MeCP2 is required for interaction with LEDGF and DHX9 , we expressed HA-tagged MeCP2 with different deletions ( Fig 2a ) and Myc-tagged full-length LEDGF/p52 , LEDGF/p75 and DHX9 , respectively , in HEK293 cells . Co-IP with anti-HA antibody followed by Western blot with anti-Myc antibody showed that deletion of amino acids 163–380 of the MeCP2 protein significantly reduced the interaction between MeCP2 and LEDGF/p52 , LEDGF/p75 or DHX9 ( Fig 2b and 2c; S2a Fig ) . Reverse co-IP with anti-Myc antibody followed by Western blot with anti-HA antibody also demonstrated that amino acids 163–380 of the MeCP2 protein was required for interaction between MeCP2 and LEDGF/p52 ( S2b Fig ) . Collectively , these results strongly suggested that the transcription repression domain ( TRD ) of MeCP2 is essential for the interaction of MeCP2 with RNA binding proteins . Several RTT disease causing mutations locate in the region of amino acids 163–380 ( R168X , R255X , R270X and R294X ) and may disrupt the TRD domain , therefore we asked whether these mutations affect the interaction between MeCP2 and LEDGF or DHX9 . To test this , we co-transfected MeCP2 constructs encoding MeCP2 WT , MeCP2R168X , MeCP2R255X , MeCP2R270X , and MeCP2R294X , respectively , with LEDGF/p52 or DHX9 in HEK293 cells . Co-IP assay showed that interaction between LEDGF/p52 and MeCP2R168X , MeCP2R255X , and MeCP2R270X was significantly impaired ( Fig 2d and 2e ) . Interestingly , the interaction between MeCP2R294X ( retaining a large fraction of TRD ) and LEDGF/p52 was not significantly different from that between wild type MeCP2 and LEDGF/p52 , suggesting that amino acids 270–294 of MeCP2 are required for its binding to LEDGF/p52 ( Fig 2d and 2e ) . Similarly , we found that MeCP2R168X and MeCP2R255X interacted poorly with DHX9 , while MeCP2R270X and MeCP2R294X had intact binding capability , indicating that amino acids 255–270 of MeCP2 are required for its binding to DHX9 ( Fig 2f and 2g ) . The newly identified interactions between MeCP2 and multiple splicing factors prompted us to determine whether there are widespread RNA splicing changes upon loss of MeCP2 . We conducted high-throughput sequencing of RNA ( RNA-Seq ) from the cortex of wild type and Mecp2 knockout ( KO ) mice . As a measure of the quality of the RNA-Seq data , we first examined whether our data reflect transcriptional changes consistent with previous findings . We examined transcriptional changes in our RNA-Seq data by applying a negative binomial model in edgeR[34] . Recently , a meta-analysis of transcriptional changes across multiple brain regions in Mecp2 KO or overexpression ( OE ) mouse identified 466 MeCP2-repressed genes based on high degree of consistency ( log2FC > 0 in KO or log2FC < 0 in OE in at least 7 out of 8 datasets; FC: fold change ) [9] . Of these genes , 315 genes ( ~68% ) were also found to be up-regulated ( log2FC[KO/WT] > 0 ) in our analysis result , suggesting significant overlap between transcriptional changes identified in our study and previous studies ( S2 Table ) . In addition , we selected seven previously known misregulated genes in Mecp2 KO[9 , 35] as well as six novel differentially expressed gene identified by our study for further validation . qRT-PCR results show that all of them show similar changes as observed in our RNA-Seq data ( Pearson’s r = 0 . 95 ) ( S3 Fig ) . Taken together , these data indicate that our RNA-Seq data are robust in identifying transcriptional changes . Next , we applied the Mixture of Isoforms ( MISO ) [36] algorithm to the RNA-Seq data and identified 263 alternative splicing ( AS ) events that were significantly changed in the cortex of Mecp2 KO mice using a stringent filter ( S3 Table; see Methods for detail ) . Loss of MeCP2 affects various types of AS events , including skipped exon ( SE ) , mutually exclusive exons ( MXE ) , retained intron ( RI ) , alternative 5’ ss exon ( A5E ) , and alternative 3’ ss exon ( A3E ) ( Fig 3a ) . Subsequent analysis indicated that although more RI or MXE events had slightly reduced percent spliced in ( PSI ) value , SE , A5E and A3E events , which in total represented the majority of events , had similar number of events with increased or decreased PSI ( Fig 3b ) . These data suggest the loss of MeCP2 affects alternative splicing in both directions , which is similar to the knockdown or overexpression of a typical splicing factor[37 , 38] . Additionally , functional enrichment analysis using DAVID showed that genes with splicing changes were enriched with splice variant , alternative splicing , phosphoprotein , cell junction , compositionally biased region ( Ser-rich ) and plasma membrane part ( S4a Fig ) . Interestingly , gene expression analysis on the 232 genes associated with splicing changes revealed that the majority of them have similar total expression level between WT and Mecp2 KO ( only 15 genes show larger than 1 . 25-fold change and only one shows larger than 1 . 5-fold change ) ( Fig 3c ) , suggesting that MeCP2-mediated transcriptional regulation and splicing regulation are independent of each other . To validate the splicing changes , we performed qRT-PCR with isoform specific primers to evaluate 20 SE events . We observed consistent changes in 13 genes as identified by MISO ( 65% ) , including 6 events with decreased PSI and 7 events with increased PSI in Mecp2 KO ( S5 Fig ) . The overall validation rate from our study of using biological replicates of tissue is comparable to the success rate using cell lines in two recent studies ( 74% and 71% , respectively ) [39 , 40] , when more than 20 events were selected for validation . To generalize our observation that loss of MeCP2 leads to global splicing changes , we analyzed RNA-Seq data generated from Mecp2 KO hypothalamus and visual cortex in two recent studies[9 , 35] , respectively . Using a cutoff of |ΔPSI| ≥ 5% and Bayes factor≥1 , 482 and 719 SE events were identified by MISO to be changed in Mecp2 KO hypothalamus and visual cortex , respectively . 150 of the 482 SE events identified in the Mecp2 KO hypothalamus and 171 of the 719 SE events identified in the Mecp2 KO visual cortex were also found in our study ( S4–S6 Tables ) . We focused our meta-analysis on SE events because this is the best-annotated category of alternative splicing events in the mouse genome . In summary , the large number of alternative splicing changes in independent RNA-seq data sets and the significant overlap between data sets generated from different brain regions of different lines of Mecp2 KO mice at different ages are consistent with the notion that loss of MeCP2 results in global splicing alterations . To further study whether MeCP2 may be directly involved in modulating splicing , we examined MeCP2 occupancy across the genome . ChIP-Seq analysis was performed using the anti-Flag antibody on chromatin prepared from the cortex of the Mecp2-Flag knockin mice . 20 , 652 high confidence MeCP2 ChIP-seq peaks were identified ( S7 Table , see Methods for detailed description on ChIP-seq analysis and quality control statistics . ) . Based on statistical ranking and robustness of primer design , 5 of the identified peaks were selected for independent validation ( highlighted in S7 Table ) . ChIP-qPCR on a separate cohort of Mecp2-Flag mice detected significant occupancy of MeCP2 at the genomic locations corresponding to these 5 peaks relative to Gapdh promoter ( S7 Table ) . To gain an overall picture of MeCP2 distribution across the genome relative to genes , we examined the MeCP2 ChIP-seq signal in the 2kb region immediately upstream of all transcriptional start sites ( TSS ) , the region from TSS to the transcription end sites ( TES ) , and the 2kb region immediately downstream of TES across the genome . This analysis revealed that MeCP2 occupancy was depleted at promoters ( ~0 read count per million mapped reads [ChIP minus Input] from TSS to -2 , 000bp . In contrast , MeCP2 binding is enriched in the gene body ( 0 . 05–0 . 33 read count per million mapped reads [ChIP minus Input] from TSS to TES , S4b Fig ) . To assess the correlation between MeCP2 ChIP-seq signal and DNA methylation , we calculated the average percentage of mCG and mCH across all of our MeCP2 ChIP-seq peaks using previously published whole genome base-resolution methylation data in mouse cortex [42] , and found that the percentage of mCG in MeCP2 ChIP-seq peaks is slightly higher than genome average ( ~83% vs 78% ) , and the percentage of mCH in MeCP2 ChIP-seq peaks is significantly higher than genome average ( 2 . 78% vs 1 . 30% ) , suggesting MeCP2 ChIP signal is correlated with mCH and mCG across the genome . These results are consistent with several previous studies that demonstrated that MeCP2 occupancy tracks DNA methylation across the genome[35 , 43 , 44] . Moreover , the average GC content in MeCP2 ChIP-seq peaks is ~53 . 8% , significantly higher the genome average of 42% [45] . The correlation between MeCP2 occupancy and GC content is consistent with findings reported in earlier this year [44] . Interestingly , gene ontology analysis found that genes with MeCP2 ChIP-seq peak ( s ) were enriched with GO terms of alternative splicing ( Fig 3d ) . Indeed , alignment of MeCP2 ChIP-seq reads with the 5’ and 3’ ends of exons revealed a significant enrichment of MeCP2 ChIP-seq peaks around the exon/intron boundary and over exons ( Fig 3e ) . Consistently , significant enrichment of hmC and mCG signals were also found at intron/exon boundary and on exons , while a modest enrichment of mCH signal was observed at the 3’ end of exons ( S4c–S4e Fig ) . Taken together , the physical interaction between MeCP2 and splicing factors , the widespread changes in RNA splicing , and the enriched MeCP2 occupancy around exon/intron boundary are consistent with each other and strongly suggest that MeCP2 could play an important role in regulating alternative splicing . Gria2 is a major component of the AMPA receptor ( AMPAR ) , which mediates the vast majority of fast synaptic transmission in the CNS . Two electrophysiologically distinct isoforms for Gria2 are generated by a mutually exclusive splicing event of the Gria2 pre-mRNA . Depending of the usage of either the flip or the flop exon , Gria2 pre-mRNA can be spliced into either the flip or the flop isoform . Our RNA-Seq data revealed that ~ 51% of all Gria2 transcripts contained the flip exon in wild type mice . In contrast , only ~ 28% Gria2 transcripts included the flip exon in the Mecp2 KO mice ( Fig 4a ) . qRT-PCR analysis in a separate cohort of animals confirmed a shift of flip/flop ratio toward a flop dominant state in Mecp2 KO mice , while the total expression level of the Gria2 gene remained unchanged ( Fig 4b and 4c ) . The reduction of flip/flop ratio in Mecp2 KO mice are not likely due to delayed development of the brain because the flip isoform is more abundant during early brain development and the flop isoform gradually increases to a comparable level of the flip isoform toward adulthood[46] . Since alternative splicing of flip/flop exons is a common feature in all AMPAR genes , we asked whether similar changes also occurred in the Gria1 , Gria3 , and Gria4 genes . Quantification result showed that flip/flop ratio of Gria1 , Gria3 , and Gria4 genes was significantly reduced in the cortex of Mecp2 KO mice ( Fig 4d ) , implicating a biased usage of flop exon in the mature transcripts of all AMPAR genes . Importantly , the total mRNA level of Gria1 was unchanged and only subtle trend of decreasing Gria3 and Gria4 mRNA level was observed in Mecp2 KO mice ( Fig 4e ) . Interestingly , analysis of RNA-Seq data from visual cortex and hypothalamus of Mecp2 KO mice also showed that percentage of flip isoform is significantly decreased ( S8 Fig ) . Note that these two studies used the Bird allele ( Mecp2tm1 . 1Bird ) and our data was generated from the Jaenisch allele ( Mecp2tm1 . 1jae ) . The consistent flip/flop splicing changes across different brain regions from different knockout mouse lines suggested that reduction of flip/flop ratio is a common defect due solely to loss of MeCP2 . More importantly , we also found that the percentage of flip isoform in the hypothalamus of Mecp2 OE mice was significantly increased , which is opposite to the changes in Mecp2 KO ( S8 Fig ) . Together , these results strongly suggest that MeCP2 directly modulates the regulation of Gria2 flip/flop splicing . Finally , we tested whether splicing alteration of AMPAR genes also occurs in the cortex of heterozygous female Mecp2-/+ mice . Although not as drastic as that observed in Mecp2 KO male mice , Mecp2-/+ mice also displayed a significant reduction of flip/flop ratio in Gria1 , Gria2 , Gria3 , and Gria4 ( Fig 4f ) . Similar to Mecp2 KO male mice , Mecp2-/+ mice had unchanged total mRNA level in all four AMPAR genes ( Fig 4g ) . These data suggest similar change in flip/flop usage may exist in female RTT patients . To determine how loss of MeCP2 affects the splicing of flip/flop exon , we focused on the Gria2 gene to explore the potential involvement of several recent models of splicing regulation . Modulation of PolII elongation rate has been proposed as one model of how epigenetic mechanisms influence splicing . Slow PolII elongation rate allows longer time for spliceosome to assemble and hence increase the chance of the alternative exon being included in the mature transcript[47] . A recent study suggested that MeCP2 is enriched in particular alternative exons and facilitates exon inclusion by pausing PolII in cultured cells[48] . We set out to test whether MeCP2 regulates Gria2 flip/flop splicing through similar mechanism in the brain . Chromatin immunoprecipitation ( ChIP ) followed by qRT-PCR showed a significant enrichment of MeCP2 on the flip and flop exons of Gria2 gene ( Fig 5a ) . However , no significant difference in PolII occupancy on the flip and flop exons between the wild type and Mecp2 KO mice was found by PolII ChIP ( Fig 5b ) , suggesting the involvement of a PolII-independent mechanism underlying the flip/flop splicing change in Mecp2 KO brain . Another interesting epigenetic model for alternative splicing regulation is that histone modification can be bound by adaptor proteins which in turn recruit specific splicing factor to alternative exons[47] . It has been previously shown that trimethylated histone H3 lysine 36 ( H3K36me3 ) is enriched on exons and can be bound by LEDGF , which recruits splicing factors such as SRSF1 to regulate splicing[49] . Although significant LEDGF occupancy was detected on the flip and flop exons ( Fig 5c ) , no significant difference in the occupancy of H3K36me3 on the flop and flip exons was detected between the wild type and Mecp2 KO brain ( Fig 5d ) . To determine whether LEDGF is functionally involved in the regulation of Gria2 flip/flop splicing , we tested the effect of knockdowning LEDGF on flip/flop ratio in a neuroblastoma cell line , Neuro-2A , using a Gria2 minigene . The Gria2 minigene spans the genomic region from exon 13 to exon 15 of Gria2 ( S9a Fig , either the flip or flop exon can be included as exon 14 ) . As a control , we co-transfected Mecp2 shRNA , Gria2 minigene along with a MeCP2 overexpression plasmid into Neuro-2a cells and found that flip/flop ratio is significantly reduced upon Mecp2 knockdown ( Fig 5e and 5f ) , indicating that this artificial assay is capable of discovering factors that potentially affect flip/flop splicing . Next , we transfected a Ledgf shRNA in the cells and tested its effect on flip/flop splicing . Similar to Mecp2 knockdown , Ledgf knockdown also leads to a reduction of flip/flop ratio ( Fig 5g and 5h ) , suggesting that LEDGF is functionally involved in the regulation of Gria2 flip/flop splicing . Flip/flop exon encodes a 38 amino acids sequence in the ligand binding domain of AMPARs that controls desensitization rate . Compared to flip-containing receptors , flop-containing receptors desensitize with faster kinetics[50 , 51] . To determine the functional consequence of altered flip/flop splicing in the cortex of Mecp2 KO mice , we performed outside-out patch clamp recording of glutamate-evoked current on layer 2/3 pyramidal neurons in acute brain slices . We found that the decay time constant τ was significantly reduced in Mecp2 KO mice ( Fig 6a and 6b ) . In addition to evoked response , regular whole cell patch clamp recording of spontaneous synaptic events also detected a faster decay in miniature excitatory postsynaptic current ( mEPSC ) in layer 2/3 pyramidal neurons from the Mecp2 KO mice . Finally , bath application of cyclothiazide ( CTZ ) , a positive allosteric modulator of AMPARs that inhibits desensitization of AMPARs[52] , slowed down the decay kinetics in Mecp2 KO slice to a comparable level of wild type cells ( Fig 6c and 6d ) . Together , these results uncover a previously unappreciated defect of faster desensitization kinetics of AMPAR-gated current in the Mecp2 KO mice , which correlates with altered flip/flop splicing and can be modulated by pharmacological reagents . To causally link the change in flip/flop splicing and the altered AMPAR desensitization kinetics , we used engineered splicing factors ( ESF ) [53 , 54] to specifically manipulate flip/flop splicing in the brain of Mecp2 KO mice . ESF is composed of a sequence-specific RNA-binding domain derived from human Pumilio1 ( PUF domain ) and a functional domain that suppresses ( Gly domain ) or enhances ( SR domain ) inclusion of a specific exon . We evaluated the effect of four ESFs ( ESF-flop-Gly [flop suppressor] , ESF-flop-SR [flop enhancer] , ESF-flip-Gly [flip suppressor] and ESF-flip-SR [flip enhancer] ) on flip/flop splicing using a Gria2 minigene ( S9a Fig ) . We found that ESF-flop-Gly significantly increased the flip/flop ratio ( Fig 6e ) , an effect opposite to the change we observed in the cortex of Mecp2 KO mice . Moreover , ESF-flop-Gly didn’t change the level of total Gria2 minigene ( Fig 6f ) . To further test the effect of ESF-flop-Gly on flip/flop splicing of the endogenous Gria2 transcript in neurons , we infected primary cortical neurons with adeno-associated virus ( AAV ) encoding either mCherry alone or ESF-flop-Gly and mCherry . As expected , AAV-ESF-flop-Gly-mCherry significantly altered the flip/flop splicing balance to favor the use of the flip exon ( S9b and S9c Fig ) , suggesting that ESF-flop-Gly could be used in vivo to reverse the flip/flop splicing defect in Mecp2 KO mice . To that end , we injected lentivirus expressing ESF-flop-Gly into the cortex of Mecp2 KO mice , and measured the decay time constant τ of glutamate-evoked AMPAR-gated current in the outside-out patch clamp mode in acute brain slices 2 weeks post injection . Compared to neurons infected with control virus ( KO+Ctrl ) , ESF-flop-Gly expressing neurons ( KO+ESF ) had a significant larger decay time constant τ , which was indistinguishable from that of WT cells ( Fig 6g ) . These results strongly suggest that altered flip/flop splicing is required for a specific synaptic phenotype in the Mecp2 KO mice . To further examine the effect of altered flip/flop splicing on synaptic transmission , we applied repetitive stimulation on the neurons in an interval of 100 ms and recorded the AMPAR-gated current . Upon repetitive stimulation , a fraction of AMPA receptors desensitizes and the short interval between stimulation does not allow full recovery . As a result , fewer AMPA receptors can respond to the subsequent stimulation and therefore current diminished . Comparing to WT neurons , KO neurons displayed even more drastic decrease in current amplitude over the course of five stimulations ( Fig 6h and 6i ) . This difference could be partially due to a higher percentage of flop isoform that are more easily desensitized in the KO neurons . Consistent with this hypothesis , overexpressing ESF-flop-Gly in KO neurons partially rescued this phenotype ( Fig 6h and 6i ) . These data suggest the altered flip/flop splicing ratio has important impact on synaptic transmission in the Mecp2 KO cortex , which can be reversed by ESF designed to specifically target flip/flop exons . To determine the functional outcome of Ledgf knockdown-induced change in Gria2 flip/flop splicing , we injected lentivirus encoding shLedgf and control shRNA into the cortex of wild type mice . We found that Ledgf knockdown resulted in a significantly reduced decay time constant τ of glutamate-evoked AMPAR-gated current ( Fig 6j ) , an effect similar to that caused by the loss of MeCP2 ( Fig 6a and 6b ) . In addition , Ledgf knockdown led to significantly weaker response upon repetitive stimulations ( Fig 6k and 6l ) , another phenotype caused by the loss of MeCP2 ( Fig 6h and 6i ) . These data correlate well with the Gria2 minigene assay in Fig 5 and further support that both LEDGF and MeCP2 are required for the normal splicing of Gria2 flip/flop exons .
MeCP2 has been previously implicated in regulating alternative splicing of RNA in two studies . In 2005 , Young et al reported RNA-dependent interaction between MeCP2 and YB1 in a neuroblastoma cell line forced to overexpress MeCP2 and some changes in alternative splicing in the Mecp2308/y brain[23] . In 2013 , Maunakea et al reported intragenic DNA methylation-dependent MeCP2 binding to alternatively spliced exons in cancer cell lines[48] . Our work substantially extends these previous studies in several ways , and to our knowledge , this is the first report of the functional consequence for MeCP2-mediated splicing . First , the physical interaction between MeCP2 and its interacting partners identified in our study are independent of any nucleic acid , suggesting that MeCP2 does not need to bind to RNA in order to regulate splicing . Additionally , these physical interactions have more physiological relevance , because they were identified in the mouse brain where MeCP2 is expressed from its endogenous locus . Furthermore , we identified multiple splicing factors as novel MeCP2-interacting partners in the brain . Since these factors are not part of the core splicing machinery but rather affect splicing as accessory splicing factors[55] , the biochemical mechanism underlying their involvement in splicing regulation is not well known . Their interaction with MeCP2 , a known chromatin protein , provides novel clues for studying how these factors regulate splicing . In addition , because we used a different RTT mouse model ( Mecp2 KO mice in our study vs . Mecp2308/y mice in Young et al[23] ) and a more sensitive method to profile alternative splicing ( RNA-seq vs . microarray ) , the altered splicing events identified in our study were different from those previously identified by Young et al[23] . Nonetheless , combining results from three independent unbiased approaches ( Co-IP mass spectrometry , RNA-seq and ChIP-seq ) , our study provides strong evidences for a significant involvement of MeCP2 in regulating RNA splicing . Second , we discovered significant MeCP2 occupancy around exon/intron boundary and exons in the mouse brain , and characterized gene exon specific interaction between MeCP2 and two splicing regulators , providing a potential mechanism for MeCP2-dependent splicing regulation . Recent evidence suggests intragenic DNA methylation recruits MeCP2 and regulates pre-mRNA splicing through altering DNA polymerase II elongation rate[48] . However , our data suggests that it is not responsible for the altered flip/flop splicing in the cortex of Mecp2 KO mice . Instead , our results suggest a new model that co-occupancy of MeCP2 and LEDGF on the chromatin is required for the normal flip/flop splicing in the Gria2 gene . Finally , and most importantly , we established a functional link between specific splicing changes caused by the loss of MeCP2 function to synaptic changes in RTT mice . The fact that a ESF specifically rescues the flip/flop splicing defect can reverse the corresponding synaptic changes in RTT brain strongly suggest that the specific change in synaptic property ( AMPAR kinetics ) is caused by altered flip/flop splicing . Given the central role of AMPARs in synaptic transmission , it is likely the altered AMPAR kinetics will lead to altered synaptic functions other than the repetitive stimulation paradigm employed in our study . Future study is needed to mechanistically link the altered AMPAR kinetics with specific neuronal defects in RTT symptoms , and to evaluate the effect of reversing flip/flop splicing on RTT disease progression . In addition to the flip/flop choice in AMPARs , alterative splicing of several other genes ( e . g . Nrxn1 , Dscam , lin7a ) that play important roles in synaptic functions were changed in the RTT mouse cortex , indicating that additional synaptic changes may be caused by splicing deficits . Thus , altered RNA splicing appears to be a novel molecular mechanism underlying synaptic dysfunction in RTT . Splicing misregulation has been increasingly recognized as a significant contributor to a number of neurological diseases , such as SMA[56] , FTDP-17[27] , ALS[57] and myotonic dystrophy[58] . The mechanistic study of how the genes mutated in neurological diseases can directly affect alternative splicing , as well as the functional consequences of splicing alteration in such diseases , will have important implications in human health . Our study adds to the growing list of studies on the novel links between specific events of altered splicing and neurological diseases .
All animal procedures were performed according to protocols approved by the Institutional Animal Care and Use Committee at the University of Wisconsin-Madison . All mice in this study were euthanized by CO2 asphyxiation , according to the guidelines of the RARC at the University of Wisconsin-Madison and the recommendations of the Panel on Euthanasia of the American Veterinary Association . The Mecp2-Flag mice have a Flag sequence inserted intermediately before the stop codon of the Mecp2 locus[34] . The Mecp2 KO mice used in this study are the Jaenisch strain ( Mecp2tm1 . 1jae ) [59] . Mice were housed in a facility with 12-hr light/12-hr dark cycle . pRK5-HA-MeCP2-WT , Δ1–77 , Δ1–162 , Δ78–162 and Δ381–492 were a gift from Dr . Zilong Qiu[25] . DNA encoding Δ163–380 , MeCP2R168X , MeCP2R255X , MeCP2R270X and MeCP2R290X were PCR amplified and inserted into pRK5-HA by replacing the sequence between SalI site and NotI site of pRK5-HA-MeCP2 using Gibson cloning ( NEB ) . To construct Myc-tagged protein expression plasmid , cDNA of LEDGF/p52 , LEDGF/p75 , and DHX9 were amplified from a mouse cortex cDNA library using a Myc sequence-containing primer and inserted into pRK5 backbone . LEDGF is also known as Psip1 . Engineered splicing factor ( ESF ) were designed to target the Flip ( GCCAAGGA ) and the Flop ( GCAGCGGG ) exons[38] . Gria2 minigene was constructed by amplifying Gria2 Exon13 to Exon15 from mouse genomic DNA and inserted into pEGFP-C1 backbone . The Ledgf shRNA ( shLedgf ) target sequence ( 5’-GCA GCT ACT GAA GTC AAG ATT C-3’ ) was adapted from a previous study[60] and cloned into pLL3 . 7 backbone . The Mecp2 shRNA ( shMecp2 ) construct was used in our previous study[61] . A scrambled sequence ( 5’-GGA ATC TCA TTC GAT GCA TAC-3’ ) was used as negative control ( shCtrl ) . Nuclei were extracted from the whole brain of WT and Mecp2-Flag mice as previously described[62] . Purified nuclei were resuspended in lysis buffer containing 20mM Tris , 150mM NaCl , 1 . 5mM MgCl2 , 1mM EDTA , 10% Glycerol , 0 . 2% NP-40 and 1X proteinase inhibitors cocktail ( Roche ) and sonicated using a Misonix 3000 . After centrifuging at 20 , 000g for 20min at 4°C , supernatant was incubated with 50ul of Anti-Flag M2 Magnetic Beads ( Sigma ) overnight at 4°C . In the following day , beads were washed with lysis buffer for 6 times . Bound protein was eluted by competition with 100 mg/ml of Flag Peptide ( Sigma F3290 ) . Eluted proteins from 5 IPs per genotype were pooled together and precipitated by adding 8 volume of pre-chilled acetone . Pellet was resuspended in 100mM Ammonium Bicarbonate solution . After DTT and IOAA treatment , protein was digested into peptides using Trypsin Gold ( Promega ) and Proteinase Max ( Promega ) overnight at 37°C . Peptides were separated by a nano HPLC and analyzed by a Thermo LTQ mass spectrometer . MS/MS spectra data was analyzed using Bioworks software ( Thermo ) . Only proteins identified in Flag IP eluate from Mecp2-Flag mice but not WT mice were considered to be potential MeCP2-interacting proteins . Co-IP was performed as described above except using Dynabeads ( Life Technologies ) . For co-IP with Benzonase treatment , lysate was treated with 250 Unit of Benzonase per mouse brain for 1hr at 4°C before incubating with beads . Proteins were eluted by adding 1X LDS sample buffer ( Life Technologies ) and heated at 95°C for 10min . Proteins were resolved in a 10% SDS-PAGE gel and transferred into a nitrocellulose membrane . Membrane was blocked with 5% non-fat milk in PBS for 1 hour followed by incubating with primary antibody overnight at 4°C . Membrane was washed 3 times with PBST and incubated with DyLight Fluor Secondary Antibodies ( Pierce ) for one hour at room temperature . Membrane was imaged on a LI-COR Odyssey Imager . Western blot quantification was done using ImageJ . Primary antibodies used in this study were: anti-DHX9 ( Abcam ab26271 , 1:2000 ) , anti-FLAG ( Sigma M2 , 1:1000 ) , anti-FUS ( Bethyl A300-293A , 1:10000 ) , anti-HA ( Covance MMS-101P , 1:5000 ) , anti-hnRNP F+H ( Abcam ab10689 , 1:3000 ) , anti-LEDGF ( Bethyl A300-847A , 1:1500 ) , anti-MeCP2 ( Abcam ab50005 , 1:2000 ) , anti-Myc ( Cell signaling 71D10 , 1:1000 ) , and anti-TDP-43 ( ProteinTech 10782-2-AP , 1:1000 ) . HA-MeCP2 construct was co-transfected with Myc-LEDGF or Myc-DHX9 ( 1:1 ratio ) into HEK293 cells using GenJet transfection reagent ( Signagen ) . 24 hours after transfection , cells were washed with PBS twice and directly lysed with Pierce IP Lysis Buffer ( Thermo Scientific ) for 10min on ice . Lysate was centrifuged at 16 , 000g for 10min at 4°C and pellet was discarded . Six hours before lysate preparation , 30ul Dynabeads protein G was incubated with 3ug of anti-HA ( Covance ) or anti-Myc ( Millipore ) at 4°C to form the antibody-proteinG-bead complex . After washing off excess antibody , beads were incubated with lysate overnight at 4°C . Beads were washed with lysis buffer 6 times and then eluted by adding 1X LDS sample buffer ( Life Technologies ) and heated at 95°C for 10min . Total RNA was extracted from cortices of 6-weeks-old WT and Mecp2 KO mice using Qiagen RNeasy Mini Plus kit . Genomic DNA was removed by a gDNA Eliminator column . 150ng total RNA was used to prepare sequencing library according to manufacturer’s instructions ( Nugen Encore Complete ) . Each Library was subject to one lane of 100bp single end sequencing using Illumina Hi-Seq 2000 . Reads were mapped to the mouse genome ( mm9 ) using Tophat ( 2 . 0 . 8 ) . Reads count for each gene was calculated using htseq-count function in the HTSeq package . Differential gene expression analysis was done using edgeR in R . Splicing analysis was performed using the Mixture of Isoforms pipeline ( MISO 0 . 4 . 7 ) . Considering the high similarity of the two replicates for each genotype ( Correlation = 0 . 98 for each ) , reads from two replicates were combined for each genotype and processed with MISO . A stringent filter ( total reads for the event ≥ 1000 , reads supporting inclusion or exclusion isoform ≥ 50 , total reads supporting inclusion and exclusion isoform ≥ 100 , |ΔPSI| ≥ 0 . 20 and Bayes-factor ≥ 20 ) was used to generate a list of differential splicing events . Read density plot was generated using sashimi plot built in MISO . RNA-Seq data from Chen et al[35] and Gabel et al[9] were processed as above for splicing analysis . A less stringent filter ( total reads for the event ≥ 20 , |ΔPSI| ≥ 0 . 05 and Bayes-factor ≥ 1 ) was applied to allow for generating more events for further overlap analysis . Gene Ontology ( GO ) analysis was done using DAVID[63] . Briefly , official gene symbols were submitted to DAVID . We used our own RNA-seq data and applied a cutoff of RPKM ≥ 0 . 5 to generate a list of genes expressed in the mouse cortex ( 13846 genes ) . This set of genes expressed in the mouse cortex was used as background for all GO analysis in this manuscript . Terms with Benjamini adjusted P-value < = 0 . 05 was considered as significant . Total RNA was extracted from cortices of 6-8-week-old wild type ( WT ) and Mecp2 KO male mice or 15-18-month-old WT and Mecp2 KO female mice using Qiagen RNeasy Mini Plus kit with on-column DNase treatment . RNA extraction from HEK293 or N2A cells was performed using TRIzol ( Life Technology ) . RNA was reverse transcribed into cDNA using qScript cDNA SuperMix ( Quanta Biosciences ) . qPCR was performed on an ABI Step-One plus machine using SYBR Green qPCR Master Mix ( Biotool ) . Gapdh was used as endogenous control and 2−ΔCt method was used to calculate fold change . See S8 Table for primer sequence . Chromatin immunoprecipitation ( ChIP ) was performed as previously reported[29] . Briefly , cortex tissue was dissected from 6-8-week-old mice , minced and crosslinked in 1% formaldehyde ( wt/vol ) and sonicated using a Misonix 3000 . Antibody was first bound to Dynabeads and then incubated with sheared chromatin overnight at 4°C . After 4 washes with RIPA buffer and 1 wash with TE buffer , bound chromatin was eluted and reverse crosslinked at 65°C overnight . Eluted DNA was treated with RNase A ( Thermo Scientific ) and proteinase K ( Promega ) , purified by phenol-chloroform extraction and dissolved in water . Antibodies used were: anti-Flag ( Sigma M2 ) , anti-LEDGF ( Bethyl A300-847A ) , anti-H3K36me3 ( Abcam ab9050 ) , and anti-PolII ( Abcam ab5408 ) . Primer sequence for ChIP-qPCR is provided in S8 Table . ChIP-Seq data were generated from two biological replicates ( referred to as WT1 and WT2 ) . Raw data was aligned to the mouse genome version mm9 with Bowtie ( 0 . 12 . 7 ) . After excluding non-mapping reads , we had 72 , 221 , 924 reads for WT1 ChIP and 31 , 333 , 769 for its input and 84 , 871 , 157 reads for WT2 ChIP and 22 , 412 , 408 for its input . We firstly evaluated the quality of these data with respect to ENCODE’s ChIP-seq quality control metrics[64] . The Normalized Strand Cross Correlation ( NSC ) for WT1 ChIP and WT2 ChIP is 1 . 3 and 1 . 4 , respectively . Another quality control measure is PCR Bottleneck Coefficient ( PBC ) , which gives an estimate of the complexity of the ChIP-seq library[65] . PBC<0 . 5 indicates PCR bottlenecks are present in sequenced libraries . The PBC ranged within [0 . 63 0 . 83] across WT1 ChIP sample and [0 . 85 , 0 . 94] for the WT1 input sample . Similarly , the PBC ranged within [0 . 63 0 . 83] across WT2 ChIP sample and was 0 . 93 for the WT2 input sample . These numbers suggest our libraries were of good quality . We carried out peak calling using MOSAiCS package in R[66] using default parameters except for fdrRelaxed = 0 . 1 for WT1 and WT2 and fdrRelaxed = 0 . 2 for pooled replicates . Bin and fragment sizes were set to 200 bps for all the runs . We followed a conservative strategy and obtained peaks for individual replicates at false discovery rate of 0 . 1 and for pooled sample run at 0 . 2 . Then , we identified the peaks in the intersection of the three peak lists and filtered them with mosaics parameters: logMinP > = -log10 ( 0 . 05 ) & peakSize > = 150 & aveLog2Ratio > = log2 ( 1 . 5 ) . This resulted in a total of 20 , 652 peaks with median size of 1731 bps . We performed location analysis using mm9 Refseq genes and the nomenclature in Blahnik et al[67] . The previously published independent datasets were used[42] . DNA methylation data in the frontal cortex of adult mouse ( 10-wk-old ) were downloaded under accession number GSM 1173784 . Each context of the cytosine methylation and the two following bases from the same strand was considered independently: CG , CHG or CHH ( where H = A , C or T ) . To determine the frequency of each context , the frequency of the cytosine methylation of each context in MeCP2 ChIP-seq peaks was estimated as the average of ratio / ( x ) = nm ( x ) /ntot ( x ) , where nm ( x ) is the number of reads supporting a methylated cytosine at position x and ntot ( x ) is the total number of reads at that position . ESF construct was co-transfected into 293 cells with Gria2 minigene ( 8:2 ratio ) using GenJet transfection reagent ( Signagen ) . To test the effect of Mecp2 knockdown on Gria2 splicing , shMecp2 , Mecp2 overexpression construct and Gria2 minigene ( 4 . 5:4 . 5:1 ratio ) was co-transfected into N2A cells with using GenJet . To test the effect of Ledgf knockdown on Gria2 splicing , shLedgf construct and Gria2 minigene ( 9:1 ratio ) was co-transfected into N2A cells using GenJet . Cells were lysed in TRIzol 48 hours after transfection for qRT-PCR analysis . Male mice at 4–6 weeks postnatal were used . Coronal brain slices ( 400 μm ) were prepared in ice-cold modified artificial cerebrospinal fluid ( aCSF ) ( in mM: 124 NaCl , 2 . 5 KCl , 1 CaCl2 , 2 MgSO4 , 1 . 25 NaH2PO4 , 26 NaHCO3 , and 15 glucose ) bubbled with 95%O2/5%CO2 . Then the slices were incubated in normal aCSF ( in mM: 124 NaCl , 2 . 5 KCl , 2 . 5 CaCl2 , 1 . 2 MgSO4 , 1 . 25 NaH2PO4 , 25 NaHCO3 , and 15 glucose ) at room temperature for at least 1 hour and then transferred to a submerged recording chamber perfused with 95%O2/5%CO2 saturated aCSF for electrophysiological recordings . Whole-cell recording of mEPSCs and outside-out patch recording of glutamate-evoked currents was performed from the Layer 2/3 pyramidal neurons at room temperature . TTX ( 1 μM ) , D-APV ( 20 μM ) , bicuculline ( 50 μM ) were added into the perfused aCSF to block voltage gated Na+ channels , NMDA receptors and GABA receptors respectively . The patch pipette ( 3–4 MΩ ) solution contained ( in mM ) : 140 Cs-Gluconate , 7 . 5 CsCl , 10 HEPES , 0 . 5 EGTA-Cs , 4 Mg-ATP , and 0 . 3 Li-GTP , pH 7 . 4 . Raw data were amplified with a Multiclamp 700B amplifier and acquired with pClamp10 . 2 software ( Molecular Devices ) . Neuronal currents were recorded under voltage clamp at the holding potential of -70 mV . An ALA fast perfusion system was used to perform application of glutamate ( 10 mM ) . In some experiments , CTZ ( 50 μM ) was added . The detection of mEPSCs and exponential fitting were performed using Clampfit 10 . 2 . The decay of glutamate evoked currents was fitted with double-exponential functions , and the fast- and slow- time constant were obtained . Signals were filtered at 2 Hz and sampled at 10 kHz by Digidata 1440A ( Molecular Devices ) . mEPSCs were analyzed using the Template Search tool of the Clampfit10 . 2 . To create the template , several well-shaped mEPSCs traces were picked and averaged to the template window . The mEPSCs events were accepted manually . Amplitude and the weighted time constant of decay phase of both mEPSCs and glutamate evoked currents were acquired . To investigate whether enhanced depression of AMPAR responses to burst-type stimulations is expressed at synapses , we recorded excitatory postsynaptic potentials evoked through a bipolar stimulating electrode ( FHC Inc . ) placed in the white matter ( eEPSCs , five pulses at 10 Hz ) . AMPAR-mediated eEPSCs were recorded in the presence of D-APV ( 20 μM ) and bicuculline ( 50 μM ) at a holding potential of -70 mV . The data was analyzed with Clampfit 10 . Lentivirus preparation was performed as described[68] except that we use minimum amount of media ( leftover in the tubes ) to resuspend the virus . Stereotaxic injection was done as previously described[61] . Custom AAV encoding ESF was generated by Vigene . DIV7 primary cortical neurons were infected with AAV at a MOI of 105 . AAV was removed 48 hours after infection and cells were collected 7 days after infection for qRT-PCR analysis . No statistical procedure was used to predetermine sample size . Student’s t-test was used to compare means between two groups . Multiple t-test comparisons were corrected using Benjamini-Hochberg procedure . One-way ANOVA followed by Tukey’s multiple comparison tests was used to test difference in experiments with multiple groups . Two-way ANOVA with repeated measure followed by Bonferroni's multiple comparisons test was used for analysis in the repetitive stimulation experiments . Statistical calculation was performed using Microsoft Excel and Graphpad Prism . | Rett syndrome ( RTT ) is a debilitating neurodevelopmental disorder with no cure or effective treatment . To fully understand the disease mechanism and develop therapies , it is necessary to study all aspects of the molecular function of methyl-CpG binding protein 2 ( MeCP2 ) , mutations in which have been identified as the genetic cause of RTT . Over the years , MeCP2 has been shown to maintain DNA methylation , regulate transcription and chromatin structure , control microRNA processing , and modulate RNA splicing . Among these known functions , the role of MeCP2 in modulating RNA splicing is less well understood . We took several unbiased approaches to investigate the how MeCP2 may regulate splicing , what splicing changes are caused by the loss of MeCP2 , and what functional consequences are caused by altered splicing . We discovered that MeCP2 interacts with splicing factors to regulated the splicing of glutamate receptor genes , which mediate the vast majority of excitatory synaptic transmission in the brain; and linked the altered splicing of glutamate receptor genes to specific synaptic changes in a RTT mouse model . Our findings not only advance the understanding of RTT disease mechanism , but also reveal a potential drug target for future development of therapies . | [
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] | 2016 | Misregulation of Alternative Splicing in a Mouse Model of Rett Syndrome |
The INhibitor of Growth ( ING ) proteins act as type II tumor suppressors and epigenetic regulators , being stoichiometric members of histone acetyltransferase and histone deacetylase complexes . Expression of the alternatively spliced ING1a tumor suppressor increases >10-fold during replicative senescence . ING1a overexpression inhibits growth; induces a large flattened cell morphology and the expression of senescence-associated β-galactosidase; increases Rb , p16 , and cyclin D1 levels; and results in the accumulation of senescence-associated heterochromatic foci . Here we identify ING1a-regulated genes and find that ING1a induces the expression of a disproportionate number of genes whose products encode proteins involved in endocytosis . Intersectin 2 ( ITSN2 ) is most affected by ING1a , being rapidly induced >25-fold . Overexpression of ITSN2 independently induces expression of the p16 and p57KIP2 cyclin-dependent kinase inhibitors , which act to block Rb inactivation , acting as downstream effectors of ING1a . ITSN2 is also induced in normally senescing cells , consistent with elevated levels of ING1a inducing ITSN2 as part of a normal senescence program . Inhibition of endocytosis or altering the stoichiometry of endosome components such as Rab family members similarly induces senescence . Knockdown of ITSN2 also blocks the ability of ING1a to induce a senescent phenotype , confirming that ITSN2 is a major transducer of ING1a-induced senescence signaling . These data identify a pathway by which ING1a induces senescence and indicate that altered endocytosis activates the Rb pathway , subsequently effecting a senescent phenotype .
Cellular senescence was first described as a consequence of the limited replicative capacity of human diploid fibroblasts by Hayflick in the early 1960s [1] . It was later characterized as an intrinsic tumor-suppressive mechanism that acts to limit the proliferative capacity of precancerous cells . Replicative senescence is triggered by telomere erosion [2] , the loss of TTAGGG nucleotide repeats that occurs as a consequence of the end replication problem of linear chromosomes , where DNA polymerase is unable to synthesize the extreme termini of lagging DNA strands [2] , [3] . Senescence , resulting in permanent cell cycle arrest , can also be induced independent of telomere loss as a consequence of various forms of stress , including oncogenic [4] and oxidative stress [5] , [6] , and has been referred to as stress-induced premature senescence , or SIPS [7] . Markers for senescence include senescence-associated β-galactosidase activity ( SA-β-gal ) [8]; formation of senescence-associated heterochromatic foci ( SAHF ) [9]; accumulation of lipofuscins [10]; changes in nuclear morphology [11]; increased p16INK4a [12] , cyclin D1 [13] , and cyclin D2 [14] levels; loss of gene inducibility [15]; and hyperactivation of the pRb [16] and p53 [17] tumor suppressors . In addition , alternative splicing of mRNAs from diverse genes [18] including those encoding proteins that affect chromatin structure such as p53 [19] , p16 [20] , Pot-1 [21] , lamin A [22] , and ING1a [23] has been reported to increase during replicative senescence , and the telomere-initiated stress signal has been implicated in promoting the production of alternative splice products [22] . The INhibitor of Growth ( ING ) family consists of five genes ( ING1–5 ) encoding multiple splice products [24] , [25] . All ING proteins contain plant homeodomains ( PHDs ) through which they bind the histone H3 epigenetic mark H3K4Me3 [26]–[28] , thus serving as epigenetic readers . They are also stoichometric members of histone acetyltransferase ( HAT ) and histone deacetylase ( HDAC ) complexes [29] , directing their activities to adjacent histone amino acid residues to alter chromatin structure [30] and affect transcription [31] . The ING proteins also contain a sequence unique in the human proteome called the lamin interacting domain through which they physically interact with lamin A [32] , suggesting that altered localization and levels of the INGs may contribute to the Hutchinson Gilford Progeria Syndrome ( HGPS ) form of premature aging . HGPS cells show altered chromatin conformation and nuclear membrane structure that is caused by alternative splicing of the lamin A gene and subsequent production of a truncated form of lamin A called progerin [33] . The INGs function as type II tumor suppressors , being frequently down-regulated or mislocalized in different tumor types [34]–[37] , and murine knockout models of ING1 show development of B cell lymphoma independent of p53 status [38] , although ING1 protein can increase p53 levels through effects upon p53 polyubiquitination [39] . The ING1 gene encodes four variants , with p33ING1b and p47ING1a being the best characterized and predominant isoforms [23] , [37] , [40] . Overexpression of the major isoform , ING1b , initially induces features of stress-induced senescence such as SA-β-gal activity , increased expression of p16 and growth arrest [41]–[43] , and culminates in cells acquiring pyknotic nuclei and undergoing apoptosis [44] . In contrast , overexpression of ING1a blocks cell growth in a state that resembles replicative senescence by a number of criteria including high SA-β-gal activity , presence of SAHF , increased cell size , altered nuclear morphology , increased expression of p16 and Rb , and growth arrest [23] . Furthermore , as cells undergo replicative senescence , the ratio of ING1a:ING1b increases by ∼30-fold [23] , and knocking down ING1 [45] or ING2 [46] in senescing fibroblasts significantly increases their replicative life span in culture , suggesting roles for the INGs in transducing telomere-initiated senescence signaling . Despite these observations linking ING1a to the induction of senescence , its role in replicative senescence and the mechanism by which it induces SIPS have yet to be determined . Here we ask what genes are regulated by altered ING1a levels in order to better understand how ING1a functions in senescence . We find that ING1a affects growth factor receptor internalization by transcriptional up-regulation of a group of genes whose products affect endocytosis , subsequently activating the retinoblastoma tumor suppressor pathway . Furthermore , inhibition of endocytosis in young fibroblasts by several methods results in phenotypes resembling senescence , supporting the idea that alterations in signal transduction , at least partly as a consequence of ING1 alternative splicing , contribute to establishing the senescence phenotype .
To investigate how ING1a induced SIPS when overexpressed and to elucidate its role in replicative senescence , we identified genes that are differentially regulated by ING1a using microarray-based analysis in human diploid fibroblasts . Hs68 cells were infected with replication-deficient adenoviral vectors encoding ING1a and GFP under separate promoters ( Ad-ING1a ) or control virus encoding GFP ( Ad-GFP ) alone , and grown for 48 h . The analysis identified 242 up-regulated and 172 down-regulated genes that showed significantly different expression levels upon ING1a overexpression ( Tables S1 and S2 ) . Figure 1A shows the functional categories of the up-regulated genes as estimated by various pathway analyses . A list of genes that were reproducibly altered by mean fold changes greater than ±2 . 5-fold is shown in Table 1 . Among the genes that exhibited significant differences in expression , >40% were known to function in endocytosis , vesicular trafficking , or related signaling ( marked with asterisks in Table 1 ) . A subset of these genes was analyzed by qPCR to confirm the array results , and all the genes tested validated the microarray experiment ( Figure 1B ) . The gene showing the largest fold change in response to ING1a expression , was intersectin 2 ( ITSN2 ) , a key component of endocytosis . ITSN2 is a 180 kDa multidomain adaptor protein , containing two Eps homology ( EH ) domains , a coiled coil ( CC ) domain , and five Src homology 3 ( SH3 ) domains . Alternative splicing generates a longer isoform that has an additional Dbl homology ( DH ) domain , a pleckstrin homology ( PH ) domain , and a C2 domain [47]–[49] . ITSN2 facilitates the assembly of endocytic proteins for the formation of clathrin pits during clathrin-mediated endocytosis of growth factor receptors . It interacts with epsin , a clathrin pit component , and with AP2 , a clathrin adaptor complex , through its EH domains [50] , [51] , and binds to dynamin and synaptojanin , two proteins needed for the pinching off of clathrin vesicles from the membrane surface , through its SH3 domains [49] , [52] . ITSN2 forms heterodimers with EPS15 , an essential component of the endocytic pathway [53] , through its CC domain . Interestingly , we found that EPS15 expression was also altered by ING1a in our microarray ( Table 1 ) and RT-PCR analyses ( Figure 1 ) . It has previously been reported that overexpression of ITSN2 inhibits transferrin ( TR ) and epidermal growth factor receptor ( EGFR ) internalization and blocks clathrin-mediated endocytosis [54]–[56] . Intersectin proteins may do this by virtue of their five SH3 domains , since overexpression of the SH3 domain of ITSN affected its interaction with dynamin and also inhibited endocytosis by causing the formation of constricted clathrin-coated pits [57] . To study the effect of ITSN2 expression in fibroblasts , we ectopically expressed ITSN2 in Hs68 cells and checked for EGF receptor internalization . We found that cells overexpressing ITSN2 had reduced EGFR uptake after 10 min of EGF stimulation ( Figure S1 ) . The second most highly ING1a-regulated gene was JAK2 ( Table 1 ) , the Janus kinase that regulates the internalization and turnover of several receptors including the growth hormone receptor [58] and the interleukin-5 receptor [59] . The fact that ITSN2 , JAK2 , and EPS15 , as well as other proteins that affect endocytosis , were selectively regulated by ING1a suggested that ING1a might affect endocytosis , a process that regulates cell signaling and growth in response to extracellular stimuli . In order to test the hypothesis that ING1a was inducing features of cellular senescence through its effects on endocytosis , we studied the effect of ING1a expression on endocytosis of the EGF receptor , since it is the best characterized receptor in terms of internalization and trafficking [60] . EGFR uptake and retention were analysed in ING1a-expressing Hs68 cells at various time points after EGF stimulation . As shown in Figure 2A , immunofluorescence analysis showed that control cells had more EGFR puncta ( endosomes ) after 15 min of EGF stimulation compared to ING1a-expressing cells . Furthermore we found that EGFR staining was retained in ING1a-expressing cells at later time points ( 3 h of EGF stimulation ) , while they were absent in the control cells . These observations suggested that ING1a expression delayed both the internalization of EGF receptor as well as its degradation . Similar pulse chase experiments were also carried out to study the colocalization of EGFR with Rab5 ( an early endosome marker ) and Rab7 ( a late endosome marker ) in control and ING1a-expressing fibroblasts , and in all the cases we found that ING1a-expressing cells showed delayed trafficking of EGF receptor ( unpublished data ) . To further confirm the difference in EGFR internalization , surface biotinylation assays were carried out in A431 cells , which express high levels of endogenous EGFR . Consistent with the immunofluorescence results , ING1a-expressing A431 cells retained EGFR on the cell surface for a longer time compared to GFP-expressing cells ( Figure 2B ) . We also checked the tyrosine phosphorylation status of EGF receptor to see if there was a difference in the activation of the receptor , prior to internalization , in A431 cells . We found delayed tyrosine phosphorylation on EGFR immunoprecipitated from ING1a-expressing A431 cells . Control cells had tyrosine-phosphorylated EGFR starting within 2 min of EGF stimulation , while in ING1a-expressing cells , a significant amount of phosphorylation was visible only after 15 min ( Figure 2C ) . These results confirmed that EGFR internalization is significantly delayed when ING1a was overexpressed . To study the degradation of EGF receptor , ING1a-expressing cells were treated with cycloheximide , harvested at different time points after EGF stimulation , and were analyzed by western blotting for EGFR levels . ING1a-expressing cells retained significant levels of EGFR even 90 min after EGF stimulation , while in the control cells , most EGFR was degraded by 60 min ( Figure 2D ) . This result corroborated the observation of immunofluorescence ( Figure 2A ) and confirmed that EGFR degradation was delayed when ING1a was overexpressed . While these results indicate that ING1a inhibited endocytosis and processing of the EGF receptor , these assays all relied on ING1a overexpression and were thus done under supraphysiological levels of ING1a . To verify whether this effect on endocytosis was also mediated by endogenous levels of ING1a , we compared the kinetics of EGF-dependent EGFR degradation in wild-type and in ING1 knockout mouse embryo fibroblasts . As shown in Figure 2E , EGFR levels were lower , and its degradation in ING1−/− cells was more rapid than in the control MEF WT cells . Furthermore , the expression levels of Ese2 , the mouse homologue of ITSN2 , were significantly reduced in the ING1−/− cells compared to WT MEFs ( Figure 2F ) . These observations confirmed that ING1 is a regulator of ITSN2 expression and has a negative effect on endocytosis . Although the mouse ING1 splice variants are not well characterized , the presence of a murine ING1a isoform , with homology to human ING1a , is predicted based on sequence analysis . We tested for the presence of this ING1a-specific motif by PCR using cDNA obtained from mRNA of MEF WT and ING1 knockout cells . MEF WT cells expressed this region , while ING1 KO cells did not show any expression , confirming that mouse ING1 KO cells lacked this isoform with sequence homology specific for human ING1a ( Figure S2 ) . Since the expression of ING1a is induced during replicative senescence [23] and we had found that ING1a induced ITSN2 expression , we next examined ITSN2 levels in senescent cells . As shown in Figure 3A , endogenous ITSN2 levels were , indeed , significantly higher in senescent cells compared to low passage young fibroblasts . As we have previously reported , p16 and ING1a levels were up-regulated in senescent cells [23] . However , other genes that were induced by ING1a , such as EPS15 , HSP70 , and JAK2 , did not show significant changes in senescent cells . To check if the higher endogenous levels of ING1a and ITSN2 in senescent cells might correlate with delayed endocytosis during senescence , we compared EGFR degradation at different times after EGF stimulation in young and old Hs68 cells . As shown in Figure 3B , EGFR persisted considerably longer in senescent cells after stimulation with EGF compared to young cells . These data support our hypothesis that , like cells in which ING1a is ectopically expressed , an increase in endogenous ING1a may also contribute significantly to the process of replicative senescence via inhibiting the endocytic pathway . To further ask whether levels of ING1a , similar to those seen during normal cell senescence , affected ITSN2 , we ectopically expressed ING1a to levels comparable to its physiological levels in senescent cells using plasmid transfections . We found that a 2-fold increase in ING1a did not significantly induce ITSN2 , while a 5-fold increase in ING1a induced ITSN2 to levels similar to those seen in senescent cells ( Figure S3 ) . We next tested if ING1a levels directly regulated ITSN2 induction in senescent cells . We measured the expression of ITSN2 in senescent cells after knocking down ING1a using siRNA . We found that knockdown of ING1a in senescent cells led to significant down-regulation of ITSN2 mRNA levels , further suggesting a role for ING1a in regulating ITSN2 expression ( Figure 3C ) . To test if ING1a functions in other forms of stress-induced premature senescence ( SIPS ) , we induced senescence using tert-butyl hydroperoxide ( chronic oxidative stress ) and doxorubicin ( DNA damaging agent ) . While both agents induced SA-β-gal staining , we observed that ING1a levels increased in cells undergoing oxidative stress-induced senescence but not DNA-damage-induced senescence ( Figure 3D ) . ITSN2 levels were also induced by oxidative stress , but not by doxorubicin , consistent with the induction of ITSN2 by ING1a in senescing cells . We further asked whether these forms of senescence also displayed aspects of defective endocytosis . We found that EGF receptor endocytosis was significantly delayed in cells induced to senesce using tert-butyl hydroperoxide . In contrast , the DNA-damaging agent doxorubicin had little effect upon endocytosis of the EGFR ( Figure S4 ) . As noted previously , ING1a induced the expression of both p16 and Rb when ectopically expressed in young fibroblasts [23] . ING1a also induced senescence-associated β-galactosidase staining and cell cycle arrest at the G0/G1 phase of the cell cycle after about 48 h of ectopic expression [23] . If ITSN2 that is induced by ING1a contributes to the cellular senescence phenotype , we hypothesized that its expression should precede that of the senescence markers associated with ING1a expression . We tested this hypothesis by doing a time course experiment to check the expression levels of p16 , Rb , and ITSN2 , after ING1a overexpression in young fibroblasts . We found that ING1a levels begin to increase significantly between 12 and 24 h post-infection with Ad-ING1a . ITSN2 levels increased 24 h after infection with Ad-ING1a and reached maximum levels 36 h post-infection . In contrast , mRNA levels of p16 and Rb did not increase until 36 h post-infection ( Figure 4A ) . The other differentially regulated microarray target gene , EPS15 , also increased , but only 36 h post-infection . Thus , ING1a induced ITSN2 levels well ahead of Rb and p16 , suggesting an upstream , causative role for ITSN2 in mediating the ING1a-initiated senescence signal . To ask if the transcriptional induction of ITSN2 and EPS15 by ING1 was a direct or indirect effect , we checked whether ING1a binds to the promoters of these genes by chromatin immunoprecipitation using an ING1-specific monoclonal antibody [61] . Although no binding to the EPS15 promoter was seen , we detected binding to a region 200 bp upstream of the ITSN2 gene start site . As shown in Figure 4B , the ING1 antibody but not the control IgG recovered the ITSN2 promoter . These observations support the idea that ING1a drives the expression of ITSN2 by directly binding its promoter , leading to its induction before the appearance of the known senescence markers . The specificity of the antibody used for this assay was confirmed using western blotting ( Figure S5 ) . To confirm the role of ITSN2 in the induction of senescence , we overexpressed ITSN2 in young primary fibroblasts and checked for senescence markers . Ectopic expression of ITSN2 by itself was able to induce SA-heterochromatic foci ( SAHF ) and SA-beta galactosidase staining in young fibroblasts ( Figure 4C ) . In contrast , ITSN2-expressing cells did not exhibit the enlarged or flattened nuclear and cellular morphology typical of senescent cells and ING1a-expressing cells , suggesting that ITSN2 transduced many , but not all of the ING1a senescence signal and that ITSN2 induction is necessary , but not sufficient for ING1a-induced SIPS . To investigate the role of signaling changes associated with altered endocytosis in cells expressing ING1a , we examined the phosphorylation of signaling proteins after EGF stimulation . As noted in Figure 5A , there was a significant delay or attenuation of the phosphorylation of Src ( S416 ) , Erk ( T202/Y204 ) , p38MAPK ( T180/Y182 ) , and Akt ( S473 ) in ING1a-expressing cells compared to control cells . We next examined if changes in growth factor signaling pathways affected the retinoblastoma protein ( Rb ) . Modulation of Rb function by phosphorylation is one of the key mechanisms of senescence induction in cells and mitogenic stimuli alters the phosphorylation status of Rb . Analysis of the Rb phosphorylation sites that inhibit its role as an inhibitor of E2F transcription factor [62]–[64] showed that ING1a expression blocked S807/811 and S795 phosphorylation and strongly inhibited S780 phosphorylation ( Figure 5A ) . These results suggested that , in ING1a-expressing cells , Rb remained tightly bound to E2F , blocking its ability to promote cell proliferation . We next examined the phosphorylation status of Rb in ING1a-expressing cells during growth in complete medium containing serum . Low passage primary fibroblasts synchronised by serum starvation were released in the presence of Ad-GFP or Ad-ING1a for the indicated time points , and the phosphorylation status of Rb was checked using western blotting with site-specific antibodies . Unlike control cells infected with adenovirus-expressing GFP , which phosphorylated Rb on S780 and S795 , ING1a-expressing fibroblasts failed to phosphorylate RB at these residues ( Figure 5B ) . However , under these growth conditions , there was no significant difference in S801/811 phosphorylation . ING1a-expressing cells also expressed significantly higher levels of Rb , consistent with the transcriptional induction of Rb by ING1a [23] . Since hypophosphorylated Rb is the active form that binds and inhibits E2F , we next asked whether the Rb in ING1a-expressing cells physically associated with E2F . Western blot analysis of immunoprecipitated E2F1 in these samples confirmed that E2F bound Rb , avidly in the presence of ING1a compared to the GFP-expressing cells ( Figure 5C ) . Serum-starved quiescent cells were used as a positive control for this experiment . We also noted that higher amounts of Rb protein were immunoprecipitated in ING1a-expressing cells , further confirming the induction of Rb in these cells . As predicted , Rb immunoprecipitated from ING1a-expressing cells was hypophosphorylated at S795 compared to Rb from cells infected with control virus . These observations confirmed that the increased level of Rb in ING1a-expressing cells was maintained in an active , hypophosphorylated state that bound tightly to E2F . Hypophosphorylated Rb binds E2F to block transcription . Since ING1a-expressing cells showed hypophosphorylated Rb , we measured mRNA levels of a representative number of E2F target genes known to function in different processes including cell cycle progression , DNA synthesis and replication , DNA repair , and checkpoints [65] . As shown in Figure 5D , nearly all the E2F targets investigated were expressed at significantly reduced levels in ING1a-expressing cells . However , one E2F target gene that is a negative regulator of cell cycle progression , p57KIP2 , was induced in ING1a-expressing cells . Given that p57KIP2 is a potent inhibitor of the cyclinE–CDK2 and cyclin D–CDK4 complexes [66] that phosphorylate and inactivate Rb in response to mitogens , these data suggested that the greater amounts of Rb protein expressed in response to ING1a were also maintained in an active state by both p16 and p57KIP2 to inhibit cell proliferation and contribute to the induction of senescence . To check if the induction of p57KIP2 and p16 in ING1a-expressing cells was mediated through ITSN2 , we overexpressed ITSN2 to levels comparable to those seen in untransfected senescent cells and found that ITSN2 was able to independently induce both p16 and p57KIP2 , but not Rb ( Figure 5E ) , suggesting that the CDK inhibitors were induced as a consequence of ITSN2 , while induction of Rb by ING1a occurred through an ITSN2-independent pathway . We next tested if ITSN2 was a downstream mediator of the ING1a-induced defect in endocytosis and the subsequently generated senescence signal . When ITSN2 was knocked down in ING1a-expressing cells , we found that EGFR internalization kinetics were similar to control GFP-expressing cells ( Figure 2B ) and were significantly restored when compared to the ING1a-expressing A431 cells ( Figure 6A ) . We next checked if loss of ITSN2 in ING1a-expressing cells affected the senescence phenotype . We found that senescence-associated β-gal staining was reduced significantly in these cells ( Figure 6B ) , and they showed increased proliferation when assayed using the BrdU incorporation assay ( Figure 6C ) . High levels of p16 were no longer induced by ING1a ( Figure 6D ) , suggesting that ITSN2 was indeed a direct mediator of ING1a-induced senescence signal . Since we previously noted higher levels of both ING1a and ITSN2 in senescent versus low passage fibroblasts ( Figure 3A ) , we next tested if ITSN2 knockdown in senescent cells could ameliorate any aspects of the senescent phenotype . Knockdown of endogenous ITSN2 in senescent fibroblasts resulted in ∼50% reduction of p16 levels , while ING1a and Rb levels remained unchanged ( Figure 6E ) . The fact that p16 was not completely repressed implies that ITSN2 is a potent effector , but not the sole inducer of replicative senescence . We next wanted to check if knocking down ITSN2 in ING1a-expressing cells could restore the levels of E2F targets . As shown in Figure 6F , ITSN2 knockdown restored most of the E2F targets tested , suggesting that it is the up-regulation of ITSN2 by ING1a that attenuates growth factor signaling and Rb phosphorylation , leading to E2F inactivation . To further test whether inhibition of endocytosis by ING1a was initiating the Rb-mediated senescence signal , we asked whether inhibition of endocytosis by other methods would also lead to a senescent phenotype . Dynasore , a soluble pharmacological inhibitor of endocytosis , interferes with the GTPase activity of dynamin and thus blocks the internalization step of endocytosis [67] . Treating fibroblasts with concentrations of dynasore known to block endocytosis , resulted in cells assuming a large senescent phenotype and intense staining for SA-β-gal ( Figure 7A ) . While Dynasore showed a clear ability to induce senescence , other nonspecific effects of this pharmacological agent cannot be ruled out . To test if inhibiting endocytosis by more specific genetic methods also induced senescence , we disrupted the stoichiometry of endocytic components , blocking endocytosis at different stages . Wild-type and dominant negative forms of Dynamin1 , Rab5 , and Rab7 that affect the internalization , early , and late endosomal stages of endocytosis respectively , were transfected into young fibroblasts . These cells were examined for the senescence markers: SAHF ( Figure 7B and D ) , HP1γ ( Figure 7B ) , SA-β-gal ( as measured by direct X-gal staining in Figure 7B or by 4-MU fluorescence in Figure 7C ) , and cyclin D1 protein levels ( Figure 7D ) . With the exception of the dominant negative form of Rab7 ( T22N ) , which would be expected to interfere with the final stages of endocytosis , all wild-type and mutant endosomal proteins induced senescence phenotypes to varying degrees . Robust induction of SA-β-gal and cyclin D expression was seen in response to all constructs except Rab7 ( T22N ) , at levels very similar to those induced by ING1a . Why expression of the wild-type but not the mutant form of Rab7 induces senescence is unknown , but it is possible that inhibition of Rab7 might have other effects on cells , since Rab7 has diverse functions , including autophagosomal formation and lysosomal biogenesisis .
The ING proteins are encoded by the multiple splicing products of five ING genes , several of which have been implicated in the regulation of cell senescence . Overexpression of many of the ING proteins blocks cell replication , induces apoptosis , or induces indices of SIPS , depending upon the cell type and experimental model employed [24] , and at least one ING protein also affects the differentiation/aging of epidermal stem cells [81] . Consistent with these observations , knocking down ING1 [45] or ING2 [46] extends cell replicative lifespan , implying that both gene products contribute to transducing the senescence signal initiated by the attrition of telomeres . This is consistent with reports that ING1 accumulates in chromatin as cells senesce [42] , [82] . Increased expression of ING1a and ITSN2 during replicative senescence , in a premature cell aging model ( HGPS ) , and in response to other forms of stress suggests that premature aging syndromes such as HGPS , SIPS , and replicative senescence may have many components in common , despite being initiated by different agents . Our data also reveal that dysregulation of cytoplasmic signal transduction pathways by various means activates the Rb tumor suppressor axis through inducing Rb expression and blocking Rb inactivation , contributing to induction of the senescent phenotype .
Hs68 and WI38 fibroblast cell strains were obtained from the American Type Culture Collection ( ATCC ) and were maintained in DMEM ( Lonza ) supplemented with 10% fetal bovine serum ( Gibco; Invitrogen ) at 37°C under 5% CO2 . Low passage young cells used were between 14 and 35 mean population doublings ( MPDs ) for Hs68 cells and between 20 and 30 MPDs for WI38 cells . Senescent fibroblasts were between 80 and 85 MPDs for Hs68 cells and 55 and 60 MPDs for WI38 cells . The A431 cells were maintained in high glucose DMEM supplemented with 10% FBS . Wild-type and ING1−/− mouse embryonic fibroblasts were gifts from Dr . Stephen N . Jones ( University of Massachusetts ) and were maintained in high glucose DMEM supplemeted with 10% FBS . Plasmid and siRNA transfections in Hs68 and WI38 cells were done using lipofectamine LTX ( Invitrogen ) according to the manufacturer's protocol . ING1 and ITSN2 siRNA smartpools were obtained from Dharmacon , and a scrambled siRNA was used as a control . Hs68 cells , infected with either Ad-GFP or Ad-GFP-ING1a ( GFP and ING1a under separate promoters ) , were harvested 48 h after infection and RNA was isolated . The microarray hybridization was done as described previously [31] . Briefly , the quality of RNA isolates from cells were checked using a Bioanalyzer ( Agilent ) , and cDNA was made using indirect labelling of cDNA with the dyes Cy3 and Cy5 using a FairPlay microarray labelling kit ( Stratagene ) according to the manufacturer's protocol . The labelled cDNAs were then purified and combined with yeast tDNA ( Stratagene ) and hybridized to human oligonucleotide chips ( Southern Alberta Microarray facility , University of Calgary ) at 37°C for 18 h . The slides were then washed and scanned using a fluorescence laser microarray scanning device ( Virtex ) . The data from two independent replicates and two dye reversal experiments were quantitated and normalized using Array-Pro and GeneTraffic software . Functional annotation of the genes reproducibly affected in response to ING1a , was done using Ingenuity Pathway Analysis ( IPA ) , PANTHER , the Database for Annotation , Visualization and Integrated Discovery ( DAVID ) , and GFINDer bioinformatic tools . Genes falling under the same functional annotation , as predicted by at least two of these bioinformatic tools , were categorized as shown in Figure 1A Total RNA from cells were isolated using TRIzol ( Invitrogen ) according to the manufacturer's suggestions and were reverse transcribed using an Omniscript Reverse Transcription kit ( Qiagen ) . Gene-specific primer sequences are available from the authors upon request . Real-time PCR was carried out in triplicate using Maxima SYBR Green qPCR Mastermix ( Fermentas ) on an Applied Biosystems 7900HT Fast Real-time PCR system using a standard protocol . β-Actin or GAPDH were used as endogenous normalization controls . Relative fold changes were determined using the comparative threshold ( CT ) method . Total cell lysates for western blotting experiments were prepared by lysing cells in Laemmli sample buffer and boiling at 95°C for 10 min . Proteins were resolved by SDS-PAGE and then transferred to nitrocellulose membranes . We used 5% bovine serum albumin ( BSA ) in PBST as a blocking solution for 1 h at room temperature , and membranes were then incubated with primary antibodies for 2 h in blocking solution , washed 3 times for 10 min , and then incubated with horse-radish peroxidase ( HRP ) –conjugated secondary antibodies in blocking solution for 45 min at room temperature . After washing , proteins were visualized using ECL . α-EGFR , -Cyclin D1 , and -GFP were from Santacruz Biotechnology; all α-phospho-antibodies were obtained from Cell Signalling; α-actin antibody was from Cell Signalling , and α-ING1 was a mouse monoclonal from the SACRI antibody facility , University of Calgary [61] . For EGFR degradation assays , cells were serum starved overnight and stimulated with 100 ng/ml of human recombinant EGF ( Invitrogen ) for indicated time points together with 10 µg/ml of cycloheximide ( Sigma ) . For immunoprecipiatation of Rb-bound E2F complex , the cells were serum starved for 24 h and then released in the presence of Ad-GFP and Ad-ING1a containing complete medium . Twenty-four hours later , the cells were lysed in lysis buffer [50 mM Tris , pH 8 . 0 , 150 mM NaCl , 1% NP40 , 10 mM EDTA , 5% glycerol , 1 mM phenylmethylsulfonylflouride ( PMSF ) , 10 µg/ml aprotinin , and 10 µg/ml leupeptin ) and immunoprecipitated with anti-Rb ( BD Pharmingen ) . We used 30-h serum-starved cells as the quiescent cell control . The immunoprecipiated E2F was detected by using α-E2F antibody ( Cell Signalling ) . For immunofluorescence experiments , cells were grown on coverslips and were fixed with 4% paraformaldehyde in phosphate buffered saline ( PBS ) for 15 min at room temperature , permeablilized using 0 . 1% Triton X-100 in PBS for 5 min , and then blocked in 5% BSA in PBS for 1 h at room temperature . Cells were then incubated with primary antibodies in blocking solution for 1 h and then incubated with Alexa-488 , -568 , or -633 goat α-mouse or α-rabbit secondary antibodies in blocking solution for 1 h at room temperature . Cells were then washed with PBS , stained with Hoechst stain , and imaged using an LSM 510 or Axiovert 200 microscope . Immunoflourescence in Figure 2A was performed after serum starvation and EGF stimulation as described above for western blotting . Serum-starved A431 cells , expressing either Ad-GFP or Ad-GFP-ING1a , were stimulated with 100 ng/ml of EGF in serum-free DMEM for the indicated time points at 37°C . Cells were then washed with ice-cold PBS thrice and incubated with 0 . 5 mg/ml Biotin-X-NHS ( Calbiochem ) , dissolved in borate buffer ( 10 mM boric acid , 150 mM NaCl , pH 8 . 0 ) for 1 h at 4°C . Biotinylation was terminated by washing twice with ice-cold 15 mM glycine in PBS and twice with ice-cold PBS . Cells were then lysed [150 mM NaCl , 50 mM Tris-HCl , pH 8 . 0 , 1% Triton X-100 , 1 mM orthovanadate , 1 mM phenylmethylsulfonyl fluoride ( PMSF ) , 10 mg/ml aprotinin , and 10 mg/ml leupeptin] and immunoprecipitated with α-EGFR antibody ( sc-03 ) . Cell surface biotinylated EGFR was detected using horse-radish-peroxidise–conjugated streptavidin ( Calbiochem ) . For studying the tyrosine phosphorylation status of EGFR , A431 cells were infected with Ad-GFP/ING1a for 24 h , serum starved for 14 h , and stimulated with 100 ng/ml EGF in serum-free DMEM for the indicated time points at 37°C . After washing with ice-cold PBS , the cells were lysed in the buffer described above and immunoprecipiated with α-EGFR . The samples were resolved by 8% SDS-PAGE and blotted with phospho-tyrosine antibody ( Millipore ) . The SA-β-gal assay was carried out as described previously [8] . Briefly , cells were fixed using 3% paraformaldehyde in PBS ( pH 6 . 0 ) and stained for 14–16 h at 37°C . The staining solution contained 1 mg/ml 5-bromo-4-chloro-indolyl-β-D-galactopyranoside ( X-gal ) , 5 mM potassium ferrocyanide , 5 mM potassium ferricyanide , 150 mM NaCl , and 2 mM MgCl2 in PBS ( pH 6 . 0 ) . For quantification of SA-β-gal activity , we followed the protocol of Gary & Kindell [83] using methyl-umbulliferryl-β-D-galactopyranoside ( MUG ) . Cells were grown in 60 mm plates and transfected with the indicated constructs using Lipofectamine LTX ( Invitrogen ) according to the manufacturer's protocol . After 24 h , cells were washed thrice with PBS to remove serum and other growth media components and were then lysed in a buffer containing 5 mM CHAPS , 40 mM citric acid , 40 mM sodium phosphate , 0 . 5 mM benzamidine , and 0 . 25 mM PMSF at pH 6 . 0 . The clarified supernatant was treated with an equal volume of 2× reaction buffer at pH 6 . 0 ( 40 mM citric acid , 40 mM sodium phosphate , 300 mM NaCl , 10 mM β-mercaptoethanol , 4 mM MgCl2 , and 1 . 7 mM MUG ) . Samples were incubated at 37°C for 2 h , and the reaction was quenched using the stop solution containing 400 mM sodium carbonate . Fluorescence was measured using a 96-well plate reader , with excitation at 360 nm and emission at 465 nm . SA-β-gal activity was normalized to the total protein concentration measured by a standard Lowry assay . Hs68 cells grown on glass coverslips were treated with 30 mM 5-bromo-2′-deoxyuridine for 6 h , fixed for 20 min with acid ethanol ( 90% ethanol , 5% acetic acid ) , and then washed with PBS . Cells were then denatured in 2 M HCl for 20 min at room temperature , washed with PBS+3% BSA , and stained with 1∶500 α-BrdU antibody ( Invitrogen ) for 1 h at room temperature . Following a brief wash , cells were then incubated with Alexa-568 goat α-mouse secondary antibodies in PBS/BSA for 1 h at room temperature . Cells were then washed with PBS , imaged , and counted using an Axiovert 200 microscope . ING1a binding to the promoter of ITSN2 was tested using ChIP analysis as described previously [84] . Briefly , about 3×108 cells infected with either Ad-GFP or Ad-GFP-ING1a adenoviruses were cross-linked using 1% formaldehyde ( Sigma ) for 15 min at 37°C . Cells were harvested after quenching with 0 . 125 M glycine and lysed in ChIP lysis buffer ( 150 mM NaCl , 50 mM Tris , pH 8 . 0 , 1% Triton X-100 , 0 . 1% deoxycholate , 1 mM EDTA , 1 mM PMSF , 1 µg/ml aprotinin , 1 µg/ml pepstatin , and 1 µg/ml leupeptin ) . Extracts were sonicated eight times for 10 s each , and lysates were clarified by centrifugation at 13 , 000 rpm for 15 min at 4°C . We used 100 µL of this sample as input . The clarified supernatants were immunoprecipiated with either α-ING1 or with mouse IgG antisera ( negative control ) at 4°C for 3 h , followed by protein G Sepharose ( GE Healthcare ) for 1 h at 4°C . The immunoprecipitates were sequentially washed with 1 mL of ChIP lysis buffer twice , ChIP lysis buffer with 500 mM NaCl twice , and LiCl/detergent solution ( 10 mM Tris-HCl , pH 8 . 0 , 250 mM LiCl , 0 . 5% NP-40 , 0 . 5% sodium deoxycholate , 1 mM EDTA ) twice , and finally with TE buffer ( 10 mM Tris and 1 mM EDTA , pH 8 . 0 ) . The beads were eluted using 1% SDS and 0 . 1 M sodium bicarbonate solution . The eluent and the input samples were reverse-cross-linked using NaCl for 6 h at 65°C . The DNA from the samples was isolated by phenol-chloroform , followed by ethanol precipitation . Promoter binding was tested using polymerase chain reaction using primers spanning the upstream regions of ITSN2 and EPS15 start sites ( primer sequences available upon request ) , and the primer sequences for PCNA and Cyclin A promoters were obtained from our previous report [23] . Young ( low passage ) fibroblasts were exposed to 70 µM t-Butyl hydroperoxide ( tbhp ) as oxidative stress . Tbhp , freshly diluted in DMEM containing 10% FBS , was added to cells in 1 h doses , once a day for 8 d . After the 1 h exposure to tbhp , the cells were washed twice with PBS and allowed to grow in DMEM with 10% FBS . Cells induced to senescence by doxororubicin were exposed to 100 ng/ml of the drug for 6 d . The control cells were grown in DMEM with 10% FBS without the stressing agents . Senescence induction in the stressed and control cells was checked periodically using the SA-β-gal assay using X-gal . All data are expressed as mean ± standard deviation . The statistical analyses were done using t tests for two samples and one-way analysis of variance for differences among groups , using GraphPad Prism software . A probability of p<0 . 05 was considered to be statistically significant . | Alternative splicing of several genes including the p16 and p53 tumor suppressors has been reported to increase during replicative senescence of normal diploid cells , but the biological functions of most alternative transcripts are unknown . We have found that a splicing product of the ING1 epigenetic regulator , ING1a , also increases during senescence; moreover , forced expression of ING1a at these levels in otherwise growth-competent cells can induce senescence . In this study we have determined that a major mechanism by which ING1a induces senescence is through inhibiting endocytosis; this subsequently activates the retinoblastoma ( Rb ) tumor suppressor pathway by increasing Rb levels and preventing its inactivation through multiple mechanisms . Our study also establishes a link between endocytosis and oxidative stress and suggests that multiple mechanisms that induce cellular senescence may do so by inhibiting normal endocytic processes , thereby affecting normal signal transduction pathways including those mitogenic pathways required for cell growth . | [
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] | 2013 | The ING1a Tumor Suppressor Regulates Endocytosis to Induce Cellular Senescence Via the Rb-E2F Pathway |
Major depressive disorder ( MDD ) associated with chronic neglected tropical diseases ( NTDs ) has been identified as a significant and overlooked contributor to overall disease burden . Cutaneous leishmaniasis ( CL ) is one of the most prevalent and stigmatising NTDs , with an incidence of around 1 million new cases of active CL infection annually . However , the characteristic residual scarring ( inactive CL ) following almost all cases of active CL has only recently been recognised as part of the CL disease spectrum due to its lasting psychosocial impact . We performed a multi-language systematic review of the psychosocial impact of active and inactive CL . We estimated inactive CL ( iCL ) prevalence for the first time using reported WHO active CL ( aCL ) incidence data that were adjusted for life expectancy and underreporting . We then quantified the disability ( YLD ) burden of co-morbid MDD in CL using MDD disability weights at three severity levels . Overall , we identified 29 studies of CL psychological impact from 5 WHO regions , representing 11 of the 50 highest burden countries for CL . We conservatively calculated the disability burden of co-morbid MDD in CL to be 1 . 9 million YLDs , which equalled the overall ( DALY ) disease burden ( assuming no excess mortality in depressed CL patients ) . Thus , upon inclusion of co-morbid MDD alone in both active and inactive CL , the DALY burden was seven times higher than the latest 2016 Global Burden of Disease study estimates , which notably omitted both psychological impact and inactive CL . Failure to include co-morbid MDD and the lasting sequelae of chronic NTDs , as exemplified by CL , leads to large underestimates of overall disease burden .
Cutaneous leishmaniasis ( CL ) is the most prevalent form of leishmaniasis and 1 of 22 highly prevalent neglected tropical diseases ( NTD ) [1] . Current disease classifications differentiate aspects of the active ( nodular , ulcerative or plaque ) CL lesion in terms of its transmission route ( “zoonotic” vs “anthroponotic” ) , geographical location ( “New World” vs “Old World” ) , and extent of its dermatological manifestations ( “diffuse” vs “localised” ) [2] . However , none capture the characteristic stigmatisation and psychological sequelae of life-long residual CL scarring that accompanies active infection in almost all cases . As such , we recently expanded the spectrum of CL disease by introducing new terminology—active ( aCL ) and inactive ( iCL ) scarring cutaneous leishmaniasis—to describe the dermatological changes of CL in relation to its disease activity [3] . Such a classification is also inclusive of long-term sequelae such as mucocutaneous leishmaniasis ( MCL ) , which develops in a minority of CL cases ( ~4% ) [4] mainly in the Americas and East African regions and which may represent a reactive form of CL [5] . The stigmatisation resulting from visible active and inactive CL lesions can be traced back centuries and was probably a major driver in establishing the ancient practice of leishmanisation [6] . Nevertheless , this defining psychosocial aspect of cutaneous leishmaniasis has been almost completely overlooked by successive disease burden studies [7–10] . Furthermore , the prevalence of inactive CL has not previously been estimated and as such is not presently incorporated into burden estimates . This unfortunately underlines a habitual lack of consideration for the chronic sequelae of NTDs . Regrettably , as CL is not a life-limiting infection , policy-makers often neglect CL as a priority disease [11–13] despite its importance to endemic communities and its links to poverty [14] . This oversight is particularly problematic given the increasing CL incidence in highly endemic conflict zones of Afghanistan , Iraq , the Syrian Arab Republic , and Yemen , creating a major public health problem [15 , 16] . Major Depressive Disorder ( MDD ) is the most prevalent form of mental disorder , affecting 4 . 4% of world’s population [17] . The diagnosis of MDD is symptom-based and follows the Disease Statistical Manual ( DSM ) . MDD is one of two depressive disorders that account for the fifth largest cause of disability ( years of life lived with disability; YLD ) in the latest 2016 Global Burden of Disease ( GBD ) Study [18] . There is also a growing recognition by the global mental health community of the importance of adopting a more inclusive approach to mental health and disease , from wellness to subclinical distress to clinical “disorder” , known as the staged model of depression [19] . The psychological impact of NTDs is an area that has only recently been emphasised in the NTD community [20] . For example , mental ill health was not included in recent calculations of disability-adjusted life years ( DALYs ) by NTD programmes , suggesting that the psychological impact of these conditions is not a primary outcome of such programmes [21] . It is therefore unsurprising that previous global burden of depression studies appear to exclude NTDs from their prevalence and burden estimates [17 , 22 , 23] This omission is highly significant for two reasons: Many NTDs are uniquely stigmatizing [20] , and collectively , WHO estimates that NTDs affect over 1 billion ( or 1 in 6 ) people worldwide [1] . In summary , CL is often ignored at the policy level due to its lack of mortality , and is therefore a prime example of a stigmatising , prevalent NTD whose associated mental illness is disregarded . The aims of the present study are two-fold: 1 ) To conduct a systematic review of the psychological impact of cutaneous leishmaniasis; 2 ) To quantify the burden of co-morbid major depressive disorder in this highly prevalent and stigmatising condition for the first time .
Our study reflects the current approach to disease burden estimates , which are based upon MDD as classified by the DSM [22] . We have also adopted the staged model of depression to use additional evidence from psychological and quality of life studies . These latter studies were used to calculate stages of subclinical distress associated with CL and to quantify its overall psychosocial impact . There are four steps to calculating the burden of co-morbid depression ( in DALYs ) due to CL . Firstly , we conducted a systematic review of the psychosocial impact of all forms of CL ( including MCL ) . To quantify the overall impact of iCL as part of the burden of CL , we also had to generate estimates of iCL prevalence for the first time . Following these first two steps , we then estimated the prevalence of MDD co-morbidity and its severity in aCL and iCL patients . We did not calculate the burden of co-morbid MCL as the associated mortality rate is not known and therefore prevalence estimates could not be reliably calculated . Finally , we multiplied the prevalence of aCL and iCL with co-morbid MDD by the disability weight ( DW ) for MDD at three severity levels ( mild , moderate , and severe ) following the methodology of Ton et al ( 2015 ) [24] ( see Fig 1 ) . The search strategy queried four Ovid databases–Medline [25] , EMBASE [26] , Global Health [27] , and PSYCInfo [28]–as well as LILACS [29] , using English , French , Spanish , and Portuguese search terms on 4th December 2017 . Additional searches through Google Scholar [30] were performed in Arabic and English , along with back referencing of relevant articles and a grey literature search . The search strategy accounted for common terms and abbreviations for cutaneous leishmaniasis ( e . g . “CL” and “cutaneous leishmaniasis” ) , and combined these with key words for major depressive disorder and its symptoms , as well as general psychological impact ( e . g . “psych*” , “major depressive disorder” , “distress” ) . We included all relevant psychological studies in CL patients and those with reliable knowledge of their experiences ( i . e . their caregivers and their care providers ) ( Fig 2 ) . As such , community studies were excluded from our final analysis except to further contextualise our findings . Please see S1 Appendix for further details of the search strategy and individual terms queried . Please see S2 Appendix for our inclusion and exclusion criteria , and S3 Appendix for the reasons for excluding studies from final analysis .
Twenty-nine studies were included in the final analysis of the psychosocial impact of CL ( see S4 Appendix ) . The large majority ( 25/29 ) of studies were based in middle-income countries ( 18/29 UMIC , 7/29 LMIC ) [32] . Similarly , most studies took place in the highest burden world regions ( 12/29 in the Eastern Mediterranean Region ( EMR ) and 11/29 in the Americas Region ( AMR ) ) , and included 11 of the 50 highest burden countries for CL in the world [9] . Studies that quantified an MDD diagnosis or symptoms using both validated ( e . g . SCID-1; BDI ) and unvalidated tools ( e . g . self-reported depression symptoms ) were used to determine rates of co-morbid MDD in both aCL and iCL ( See Table 1 ) . Additional quality of life , stigma , socioeconomic , and qualitative studies were used to generate an estimate of subclinical “distress” as per the staged model of depression ( see Tables 2 and 3 ) . A diagnosis of MDD was consistently reached within the mean or one standard deviation of the mean in CL patients [33 , 34 , 36 , 38] , equating to MDD rates of 30–50% . Meanwhile , quantification of symptoms of MDD mostly relied upon self-reporting . As such , symptoms of low mood and depression in CL patients ranged from 12 . 5–90 . 9% [34 , 40–45] aCL patients had significantly higher rates of MDD compared to controls in both children and adults [36] aCL was also found on multivariate analysis to be an independent risk factor for mental disorder in the primary care setting [33] . It is therefore unlikely that these results are a product of significant selection bias . Equally , whilst rates of MDD were not measured for children with iCL , significantly higher rates of MDD were found in adults compared to controls . iCL patients were also at significantly higher suicide risk than controls [34] . In the only study to measure co-morbid MDD in both aCL and iCL , CL scarring was associated with non-statistically significantly higher MDD scores [38] . These findings are important , as considerably more patients are in the inactive ( scarring ) phase of CL than in the active phase . Although the data suggest that rates of MDD in iCL are at least equal to those found in aCL patients , the majority of studies ( 16/29 ) focused exclusively on aCL . More broadly , quality of life was found to be significantly decreased in CL patients compared with controls . Stigma was a characteristic feature of CL in most quantitative and qualitative studies , whilst psychological distress was found to be between 50–90% [46 , 55] . Similarly , issues of disfigurement and reduced capacity to work affected the majority of sufferers ( see Table 2 ) . Interestingly , the psychological burden extended to CL caregivers , who were also found to have significantly elevated depression rates [36] and diminished quality of life [36 , 49] compared to controls . Overall , CL is associated with a high degree of psychological morbidity irrespective of country , age , and disease activity . We present two other important patient- and disease-specific variables considered during our analysis: patient sex and lesion location . These were chosen due to multiple reports linking them with increased psychosocial impact . Indeed , despite findings of qualitative studies that facial lesions are the most psychologically damaging [42 , 45 , 63 , 67] , none of the four quantitative studies [34 , 46 , 52 , 54] providing subgroup analysis demonstrated a statistically significant association with facial lesions and worsening psychological outcomes . Moreover , facial iCL scars were actually associated with lower rates of depression and suicidality than those located on other parts of the body [34] . Instead , it may be more appropriate to differentiate the visibility of lesions in future studies . A significant number of studies focused solely on women ( 5/29 ) on the basis that women are generally at greater risk of depression [17] . It is therefore important to consider possible sex differences in MDD rates given that men have more reported cases of CL than women in most endemic countries [4] Interestingly , women-only studies were found to have comparable MDD rates to mixed sex studies , although differences in self-reported symptoms of MDD were noted in some countries [43 , 44] . The reasons for these findings could perhaps be explained by community [68] , socio-economic [62] , and qualitative studies [67] . For example , whilst women are commonly more concerned by bodily appearance and marital prospects , a roughly equal impact is placed upon men through incapacity to work and perform leadership responsibilities [52] due to the disease . Based on the available evidence , we conservatively estimate that 70% of individuals with both active and inactive CL will experience some degree of psychological morbidity . This ranges from subclinical “distress” ( 50% ) to clinical “disorder” ( 20% ) , in accordance with the staged model of depression [19] As such , 30% of CL patients fall into the “wellness” category of the model , in view of regional differences in psychosocial impact [55 , 65] and the small number of countries and endemic communities in which CL is less stigmatizing [59] and perceived as less severe [69] ( see Table 4 ) . The 2016 GBD Study provides CL prevalence estimates that account solely for aCL and that also include MCL within them unseparated . As such , the prevalence of inactive ( scarring ) CL has not been previously estimated , and is not incorporated formally into the GBD burden estimates for CL . The methodology for calculating the prevalence of inactive CL has been previously described [3] . In short , our calculations are derived from the latest reported aCL incidence data from WHO spanning 2006–2015 [70] that have been adjusted for underreporting [10 , 71] and the presence of MCL within them [72–74] ( see Table 5 ) . We assume zero CL-associated mortality and a life expectancy of 30 years with scarring; this is a conservative longevity estimate considering the life expectancy of at-risk populations in high burden countries [74] . For further information on this methodology , please see S5 Appendix . GBD Studies differentiate the severity of episodes of MDD at three levels—mild , moderate , and severe–each with its own disability weight [22] . Therefore , it is necessary to calculate the severity of co-morbid MDD in CL patients to calculate the disability burden ( YLD ) component of the DALY . In the studies we identified , the mean depression scores of CL patients equated to mild MDD , with moderate MDD scores being reached within one standard deviation in most studies . Furthermore , in a study of depression in inactive CL using Beck’s Depression Inventory , ~70% of cases with depression scored in “mild” severity [34] . Due to the relatively small sample sizes and difficulties in comparing MDD severity from different measurement tools , we used data from the 2010 GBD study on depressive disorders to help inform our estimates ( see Table 6 ) . In that study , the patient MDD cohort was classified accordingly: 72 . 7% with Mild severity; 16 . 5% with Moderate severity; and 10 . 8% with Severe MDD [22] . Applying the estimate for MDD severity to our prevalence estimates for cutaneous leishmaniasis , the following YLDs were calculated: 200 , 000 for active CL , and 1 . 7 million for inactive CL ( combined total 1 . 9 million YLDs for CL ) ( see Table 7 and Table 8 ) . We assumed no mortality burden associated with MDD co-morbid to cutaneous leishmaniasis , and as such our YLD figures equalled the overall DALY figures ( see S6 Appendix for in-depth calculations ) . These figures only represent the impact of co-morbid MDD in this condition and do not account for the impact of other mental disorders such as anxiety disorders or the subclinical state of distress as per the staged model of depression [19] .
In the latest 2016 iteration of the GBD study , the psychological impact of CL scarring has been incorporated into the disease burden estimates for the first time via a modification of disability weights ( DW ) ( IHME personal communication ) . As such , the disability burden of CL has increased from 41 , 500 [77] to 273 , 000 [18] YLDs . Despite this modification , relying upon DWs to capture the unique psychosocial aspects of NTDs has unfortunately led to some of the most stigmatising ( namely CL and leprosy ) diseases yielding some of the lowest disability ( YLD ) estimates of all the NTDs in past iterations [18 , 77–79] . CL is currently viewed as a “level two disfigurement” , meaning that its DW reflects “a visible physical deformity that causes others to stare and comment . As a result , the person is worried and has trouble sleeping and concentrating” . This corresponds to a DW of 0 . 067 in GBD 2016 , where 0 indicates perfect health and 1 indicates death [75] Thus , we can be confident that our findings represent an unrecognized mental disease burden of CL . Instead , we strongly recommend that inactive ( scarring ) CL be included with active CL infection in future CL prevalence estimates , and that MCL and aCL estimates be presented separately for further information . We have shown that with inactive CL , such a large increase in prevalence ( 10-fold higher ) and burden of co-morbid MDD ( 8-fold increase ) is not sufficiently accounted for by simply altering the DWs for active CL given the evidence of mental illness in patients with residual scarring . As we have only included the “disorder” stage of depressive burden in our YLD estimates , our estimate of CL-related distress ( 50% ) using the staged model approach to depression is not accounted for . Here adjustments to DWs for both aCL and iCL would be justified , as a large proportion of affected individuals with both forms of CL experience some degree of quantifiable distress or socially adverse consequences . Finally , it is important to highlight that the 2016 GBD Study estimates of aCL incidence [18] are almost half those of previously accepted incidence estimates published in 2012 [71] . This is despite the marked increase in CL incidence due to ongoing conflict and displacement in the Middle East [15] . Similarly , our aCL burden estimates are based upon the 2016 GBD Study estimates of aCL prevalence to allow for comparisons to be made . However , it is unclear why these prevalence estimates are almost seven times lower than the annual incidence of aCL [18] when the majority of cases of aCL self-heal within 6–12 months [2] . For these reasons , we did not include GBD estimates in our calculations of iCL prevalence . Although our study is the first to generate prevalence estimates of inactive ( scarring ) CL , we were cautious of the life span of patients with iCL lesions , which is currently unknown . Whilst the majority of CL infections occur in older children and young adults [4] we took a conservative approach to our iCL prevalence estimates by assuming just 30 years lived with residual scars . Nevertheless , given that the majority of aCL cases occur in the young and working adult populations , this figure could be significantly higher . We also conservatively assume no mortality burden with CL , yet suicidal risk and ideation has been noted in both aCL and iCL patients [34 , 63] . Secondly , we acknowledge our failure to include prevalence and isolated burden estimates for co-morbid MDD in mucocutaneous leishmaniasis ( MCL ) . As discussed , MCL prevalence ( and YLD burden ) has not been separated from that of aCL in GBD Studies . A further complicating factor is the mortality rate of MCL , which has not been established and consequently prevented us from generating reliable MCL prevalence estimates from WHO incidence data . Nevertheless , the experience of shame in CL patients [45 , 59] was surprisingly higher than that found in a study of mixed MCL and CL patients [61] . However , in a study of MCL patients alone [80] , notably those with severe disease , rates of social exclusion and reduced quality of life were comparable to those found in CL patients [45 , 52 , 54 , 62] . It is possible that the prevalence of co-morbid MDD in MCL patients is similar to that of aCL patients ( ~20% of cases ) , meaning that our aCL burden estimates may be relatively unaffected by the presence of MCL cases within them . This is the first study to estimate the burden of a co-morbid mental disorder in aCL and iCL . One major limitation of our estimates is the evidence underpinning them . We recognize that our 29 studies represent only a relatively small proportion of the global CL caseload . Nevertheless , our systematic literature review has identified the most evidence of psychological impact in CL patients to date , and doubled the evidence of previous recent attempts [81] . Moreover , these studies represent a range of geographically diverse populations across several levels of economic development . In our analysis , studies quantifying MDD using robust and internationally recognised criteria ( i . e . DSM ) were given the most weight in generating our final estimates of MDD co-morbid to CL . We were also selective and chose to only utilize studies of CL patients and their care providers . In order to minimize the effects of bias we accounted for patient- and disease-specific variables such as sex , age , lesion location , and country of study . As results for co-morbid MDD were comparable when these variables changed , we were confident that none of these variables could have significantly biased our overall estimates . Finally , whilst depressive disorders represent the most prevalent form of mental disorder worldwide , CL patients are affected by a range of other mental disorders , which have not been included in our estimates . Indeed , CL patients may be at even greater risk of multiple mental disorders [33] . These include generalised anxiety disorder , which may predominate in the active CL phase [36 , 38] post-traumatic stress disorder [33] and mixed anxiety and depressive disorder [41] , the latter of which is not independently considered within the GBD framework at present . Social stigma , disfigurement , and patient suffering are some of the most identifiable features of NTDs , as emphasized by the case of cutaneous leishmaniasis . However , the suffering of those with active infection as well as those who remain disfigured by NTDs post-infection is not adequately factored into NTD programmes or burden estimates . We reason that there is value in striving for both goals by placing the individual at the centre of such programmes to achieve the holistic care of individuals affected by NTDs . After all , focusing on the disease alone ignores the characteristic disability associated with NTDs such as cutaneous leishmaniasis , leprosy , and filariasis , and risks leaving affected individuals behind . | Cutaneous leishmaniasis is a highly prevalent vector-borne disease affecting large parts of Latin America and the Middle East , as well as parts of Northern Africa . There are several types of Cutaneous leishmaniasis , almost all of which have an active phase characterized by a disfiguring lesion ( typically on exposed parts of the body ) , which then becomes a permanent scar ( the inactive phase ) . We recently published an article highlighting the impact of the inactive scarring phase of CL on affected individuals , which is associated with high levels of stigma . Nevertheless , this aspect of the disease is not considered in its own right when calculating the overall disease burden by the Global Burden of Disease ( GBD ) Studies . In this article we estimate the prevalence of depression ( major depressive disorder ) in cutaneous leishmaniasis , in both the active and inactive forms . We then show the contribution of inactive CL to the overall disease burden estimates when included , which is due to the large psychological impact it has on those affected by it . We also highlight the importance of further similar efforts for other NTDs which have a chronic course , and which are also not sufficiently included in disease burden calculations at present . | [
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] | 2019 | Cutaneous leishmaniasis and co-morbid major depressive disorder: A systematic review with burden estimates |
Sequence logos are frequently used to illustrate substrate preferences and specificity of proteases . Here , we employed the compiled substrates of the MEROPS database to introduce a novel metric for comparison of protease substrate preferences . The constructed similarity matrix of 62 proteases can be used to intuitively visualize similarities in protease substrate readout via principal component analysis and construction of protease specificity trees . Since our new metric is solely based on substrate data , we can engraft the protease tree including proteolytic enzymes of different evolutionary origin . Thereby , our analyses confirm pronounced overlaps in substrate recognition not only between proteases closely related on sequence basis but also between proteolytic enzymes of different evolutionary origin and catalytic type . To illustrate the applicability of our approach we analyze the distribution of targets of small molecules from the ChEMBL database in our substrate-based protease specificity trees . We observe a striking clustering of annotated targets in tree branches even though these grouped targets do not necessarily share similarity on protein sequence level . This highlights the value and applicability of knowledge acquired from peptide substrates in drug design of small molecules , e . g . , for the prediction of off-target effects or drug repurposing . Consequently , our similarity metric allows to map the degradome and its associated drug target network via comparison of known substrate peptides . The substrate-driven view of protein-protein interfaces is not limited to the field of proteases but can be applied to any target class where a sufficient amount of known substrate data is available .
The degradome , the complete set of proteolytic enzymes [1] ( herein excluding their binding partners , although this term has also been used for proteases and their substrates and inhibitors together ) , comprises more than 500 proteases in humans , where every single one is linked to a particular cleavage pattern [2] . Although they all share the same catalytic principle , which is the hydrolytic cleavage of a peptide bond [3] substrate spectra range from the specific degradation of single peptides to promiscuous non-specific degradation of multiple substrates [4] . Therefore , proteases can execute a wide range of biological functions , from specific signaling tasks to unspecific digestion of nutrition proteins [5] . Proteases initiate , modulate and terminate a wide range of fundamental cellular functions [6] , making them attractive targets for drug design [7] . Substrate specificity of proteases is determined via molecular interactions at the protein-protein interface of the substrate with the proteolytic enzyme . Specificity subpockets necessary for recognition of substrates as well as substrate positions are numbered according to the convention of Schechter and Berger [8]: Peptide amino acids P are indexed with position 1 around the scissile bond , with P1′ being oriented towards the C-terminal . Indices are incrementally increased for subpockets farther away from the bond about to be cleaved . Protease subpockets binding the substrates are numbered Sn-Sn′ , ensuring consistent indices for substrate and enzyme pockets interacting directly . The peptide substrate is typically locked in a canonical beta conformation [9] spanning several subpockets flanking the catalytic center explaining specificity for the substrate sequence [10] , [11] . Known proteases cover several types of catalytic machineries including aspartic , cysteine , metallo , serine and threonine proteases according to the MEROPS database [12] . Still , some of these protease groups include non-homologous members allowing further subdivision into clans and families . Serine proteases may be subdivided into homologous clans such as the chymotrypsin fold , the subtilisin fold , or the carboxypeptidase Y fold . This inherent complexity of proteolytic systems [13] , [14] is tackled by a broad range of research activities to profile protease specificity [15] . Established methods for substrate profiling include chromatography-based methods [16] , [17] , [18] , phage display [19] , usage of substrate libraries [20] , [21] and fluorogenic substrates [22] as well as N-terminal labeling techniques [23] , [24] . Still , inherent similarities in protease substrate readout have by now only been examined qualitatively ( e . g . [25] ) . Apart from a solid classification of known proteases , MEROPS contains a collection of known substrates [26] even exceeding 10000 known substrates in case of trypsin 1 . This substrate sequence data is frequently depicted as sequence logos [27] or heat maps [16] to highlight individual substrate preferences of proteases . Recently , substrate information from MEROPS has been successfully employed in the prediction of protease cleavage sites using machine learning techniques [28] or the calculation of cleavage entropy , a quantitative measure of substrate promiscuity [4] . In our current study , the peptide substrate data set from the MEROPS database forms the basis of an approach to map the complex world of proteases into intuitively accessible diagrams by highlighting similarities in substrate readout between individual proteases . An extraction of known protease inhibitors from the ChEMBL database [29] shows how knowledge from peptide substrates can be directly transferred into predictions on small molecules . Overlaps in cleaved peptides correlate with binding of similar small molecules , thus indicating overlaps in the chemical space covered . This observation renders our approach promising for the prediction of off-target effects or general chemogenomic approaches in drug discovery .
Data on known substrates were downloaded from the MEROPS database [12] ( database accession 8 . 5 . 2013 ) containing the largest collection of substrate sequences when compared to other online resources as CutDB [30] or Proteolysis MAP [31] . We retained cleavage information from all experimental sources to ensure maximum statistics . All proteases with at least 100 annotated substrates were selected for further analysis , forming an initial set of 65 proteases . Three aminopeptidases were discarded , as half of their binding site remains unoccupied , yielding a final set of 62 proteases ( see Supporting Table S1 for a detailed list ) . Sequence logos depicting respective substrate preferences were generated with WebLogo [32] . For each protease , a sequence matrix covering eight positions S4 to S4′ based on the frequency of each of the 20 natural amino acids was generated . This definition restricts the coverage of specificity directly at the active site , skipping differences in in allosteric sites and exosite interactions . Residue frequencies at P4 to P4′ were normalized to their natural abundance [33] to ensure a proper reflection of protease substrate preferences . For each subpocket we extracted a vector of length 20 containing the respective amino acid frequencies at that position from the sequence matrix , thereby containing information about over- as well as underrepresented amino acids as visualized via iceLogo [34] . In order to facilitate a comparison of the whole binding frame or regions within , respective vectors for subpockets were combined and normalized to yield a substrate vector v of length one and dimension 160 for the eight binding pockets . Apparently , comparison of smaller binding site regions results in lower dimensional vector spaces . Similarities between vectors were calculated as scalar products ( dot products ) . The scalar projection of one normalized vector on another yields an overlap of 1 for identical vectors and an overlap of 0 for orthogonal vectors . Thus , such a metric is perfectly suitable to quantify similarities s of amino acid distributions encoded in the vectors v ( see Formula 1 and Figure 1 for a summary ) . Formula 1: Calculation of protease similarities s based on substrate vectors v1 , v2 containing amino acid frequencies p at each subpocket of the binding site A complete pairwise comparison of all 62 cleavage site sequence logos stored as vectors yield a symmetric matrix of dimension 62 with values of 1 for the comparison of identical substrate vectors in the main diagonal . A distance matrix was created by subtraction of all elements of the similarity matrix from 1 . Hence , a pairwise distance of 0 represents identical substrate recognition , whereas 1 depicts maximal distance in protease space . The resulting distance matrix stores differences in substrate recognition of all 62 proteases in the test set . The distance matrix of 62 protease substrate recognition patterns was diagonalized using SciPy [35] . Principal components of the matrix were extracted as eigenvectors in protease space . Corresponding eigenvalues normalized to the sum of all eigenvectors depict the individual contribution of the eigenvector to the total variance in the data set . Principal components were sorted according to their contribution and depicted as loadings plots . Subpocket-wise cleavage entropies and total cleavage entropies were calculated as described earlier [4] . Apart from directly analyzing the protease distance matrix via principal component analysis , we visualized similarities in protease substrate recognition as dendrograms . We used fkitsch from the EMBOSS server [36] employing a Fitch Margoliash method [37] for tree construction . 100 random starts were performed to ensure robustness of constructed similarity trees . Interactive Tree of Life ( iTOL ) was used to visualize the constructed substrate-driven protease specificity trees [38] . Although we think that the statistical term “selectivity” would better fit our presented analysis , we stick to the long-established phrase “protease specificity” . We used the ChEMBL database version 16 [29] as resource for small molecule bioactivity data . ChEMBL lists 1 . 5 million compounds with more than 11 million associated bioactivities . We extracted all 426 protease targets , associated selectivity groups as well as annotated ligands . A list of matched MEROPS and ChEMBL identifiers is provided in Supporting Table S1 . We discarded covalent inhibitors from our analysis and mapped the remaining bioactivities to our protease specificity trees . We did not employ a stringent activity cutoff but rather preserved all annotated target affinities as positives to provide a comprehensive picture of protease-ligand recognition . Only empty fields or zero percent inhibition annotations were discarded .
Data mining in the MEROPS database showed the increasing importance and promise of knowledge-based approaches in recent years , as for example described by Ekins et al [39] . Within 18 months , the set of proteases with more than 100 cleavage sites annotated in MEROPS increased from 47 [4] to currently 65 . After discarding three aminopeptidases , the set of 62 proteases spans the four major catalytic types of proteases: serine , metallo , cysteine , and aspartic proteases ( see Supporting Table S1 for details ) . No member of glutamic and threonine proteases qualified for inclusion into our study due to insufficient substrate data for all their members . Our presented approach yields quantitative distance values between known protease substrate preferences . A value of 0 represents identical substrate readout , whereas 1 shows an orthogonal cleavage pattern in all subpockets investigated . Calculated distance values within the set of proteases span a wide range . Distances in substrate readout range from 0 . 003 to 0 . 79 when calculated over the whole range of eight subpockets flanking the cleavage site ( S4-S4′ ) . This finding highlights the diversity of substrate recognition among known proteases . A group with nearly identical substrate recognition are the proprotein convertases of MEROPS subfamily S8B , which uniformly cleave after two basic residues [40] , reflected in a distance lower than 0 . 1 between all members except kexin . In contrast to all other members , kexin does not recognize arginine residues in the S4 pocket , hence leading to higher distance values up to 0 . 29 . A further group with highly similar substrate recognition are the apoptotic signalling caspases 3 and 7 [41] with a distance lower than 0 . 05 . Although both share DEVD as ideal substrate in the non-prime region [42] , they were found to have functionally different effects [43] . Further groups that recognize highly similar substrates comprise thrombin and plasmin cleaving after basic residues [22] , [44] , as well as unspecific matrix metallo proteases showing a high degree of overlap between substrates [45] , [46] . Except cathepsins K , L , B , S , H and V , these groups of similar substrate recognition coincide with annotated protease selectivity groups within ChEMBL . The cell signaling peptidases neurolysin and thimet oligopeptidase were found to form a group with similar substrate readout ( distance = 0 . 033 ) which is very distinct to all other proteases within the set ( all distances >0 . 45 ) . Both peptidases hydrolyze a narrow spectrum of intracellular oligopeptides [47] , [48] whilst sequence readout is spanning over the whole binding site region from S4 to S4′ [4] . We expect parts of this similarity to stem from the origin of MEROPS substrates: A large part of annotated substrates for both proteases is derived from a comparison of these two proteases using fluorogenic substrates derived from neurotensin [49] . The largest distance within the protease set is found between KPC2type peptidase of Caenorhabditis elegans , a subtilisin-like proprotein convertase , that specifically cleaves a group of neuropeptides [50] , and the unspecific matrix metallo protease 13 [51] . Intuitively , distances between unspecific proteases are smaller , e . g . , the distance between substrate recognition of both highly promiscuous thermolysin and chymotrypsin is found to be lower than 0 . 22 and hence highly similar to the distance of trypsin and chymotrypsin ( distance = 0 . 21 ) . See Figure 1b for an example set of proteases and their respective distances calculated from MEROPS substrates . Compiling all pairwise protease substrate similarities yields a symmetric matrix representing distances in substrate readout of the 62 investigated proteases . Principal component analysis of this matrix reveals that the first principal component , depicting a linear combination of protease substrate recognition patterns , is sufficient to cover 50 percent of variance within the data set . Second and third axis contribute 8 . 9 and 5 . 9 percent respectively , while the seventh principal component shows the last contribution exceeding 2 percent . These first seven principal components cover more than 77 percent of total variance in the data set and thus represent the main features in protease substrate recognition . Both first and second principal component ( PC1 , PC2 ) strongly correlate with substrate promiscuity measured as total cleavage entropy [4] . Pearson's linear correlation coefficient r for these two axes is 0 . 87 and 0 . 79 respectively , indicating a pronounced positive linear correlation . While PC1 shows a strong correlation over the whole binding site region S4-S4′ , PC2 mainly contains information on substrate specificity in the S4-S1 region . PC2 outnumbers PC1 especially in terms of S1 readout ( r = 0 . 72 for PC2 versus r = 0 . 42 for PC1 , see Supporting Figure S1 for more details ) . Hence , the scatter plot of PC1 versus PC2 shows a separation of specific and unspecific proteases as well as via PC2 a separation of serine proteases specifically recognizing positively charged amino acids in the P1 position ( see Figure 2a , 2b ) . The third principal component ( PC3 ) does not correlate with overall substrate promiscuity but rather with a single substrate position P3′ ( correlation to subpocket-wise cleavage entropy for P3′: r = 0 . 73 ) . Several matrix metallo proteases are known to show amino acid preferences at this position besides the S1′ pocket , being the main carrier of substrate specificity in matrix metallo proteases [52] . For example matrix metallo protease 13 is known to preferably cleave peptides having a small residue as glycine or alanine at position P3′ [53] . As a consequence , PC3 separates metallo proteases . Still , completely unspecific matrix metallo proteases , as for example thermolysin , are not separated from other proteases via PC3 ( see Figure 2c , 2d and Supporting Figure S1 for more details ) . Further principal components rather represent single amino acid preferences at specific positions than general substrate promiscuity . The sixth principal component ( PC6 ) separates aspartic proteases from other catalytic types , as several of them show a preference for apolar residues in P1 position . Therefore , a scatter plot of PC3 versus PC6 nicely clusters the different catalytic types present in the test set of 62 proteases . Necepsin 1 of Caenorhabditis elegans is the only aspartic protease not well separated from other catalytic types . For this particular protease involved in neurodegeneration [54] no stringent substrate criteria are known [55] . The distance matrix of proteases investigated via principal component analysis was also employed to construct a similarity tree based on protease substrate recognition over the whole binding site S4-S4′ . Tree construction was found to yield a consistent result at a minimum of 100 random starts with a standard error of seventeen percent on distance reproduction for the tree over the whole binding site . In contrast to evolutionary trees based on protein sequences or domains ( e . g . [56] , [57] , [58] , [4] ) , similarity trees based on substrate readout allow to compare enzymes of different evolutionary origin because no assumption on homology has to be made . Hence , individual evolutionary trees of proteases are merged to yield a complete picture of diversity in substrate readout of proteases ( see Figure 3 ) . Even though no protease sequence information was used in tree construction , information on evolutionary subgroups of proteases is recovered from substrate-driven protease specificity trees . Homologues thimet oligopeptidase and neurolysin are grouped in a separate branch distinct in degradome space from all other members , as both of them cleave oligopeptides with substrate readout over the whole binding site region [48] , [4] . A second branch is formed by the subfamily S8B around the subtilases kexin and furin . Non-homologous kallikrein-related peptidase 4 is added to the branch , though being overall more unspecific . Still , it shares the main features of substrate readout: peptides containing positively charged residues at P1 [59] as well as arginine-containing substrates at P4 are preferred ( see also Supporting Table S1 ) . Chymotrypsin-like serine protease ( MEROPS family S1 ) are scattered over a wide range in our similarity tree . This reflects the broadness of specificities and substrate promiscuities within this family containing digestive enzymes as well as signaling proteases . A similar result was recently obtained by a structure-based analysis of protease binding sites [60] . A cavity-based clustering scattered all members present in our set into separate clusters . In analogy to our study , Glinca and Klebe found pure protein sequence data to be less informative for an analysis of substrate recognition . In general , proteases are grouped with respect to substrate promiscuity as measured by total cleavage entropy [4] . The main branch of the protease specificity tree first splits off caspases and granzyme B sharing a preference for aspartate residues at P1 , although evolutionary not related and not even sharing the catalytic type . Caspases form a separate fold of cysteine proteases C14 [61] , whereas granzymes are members of the chymotrypsin fold of serine proteases S1 [62] . After splitting off several singletons with unique substrate readout , residual proteases form a branch of unspecific matrix metallo proteases M10 as well as the digestive enzymes within the pepsin family A1 . Overall , the large branch comprising most proteases spans from individual specific proteases to completely unspecific enzymes . Apart from a comparison over the whole binding site region , similar analyses were performed for regions of interest within . An analog protease specificity tree was constructed only based on substrate data of the non-prime region S4-S1 ( see Figure 4 ) . Similar grouping of proteases was obtained as compared to the protease specificity tree over the whole binding site region . This highlights the importance of interactions within the non-prime region for specific protease substrate recognition . By narrowing the region of interest , catalytic types of proteases as well as evolutionary families are clustering more and more , still preserving the overall trend to group specific as well as unspecific proteases . When narrowing down the substrate positions analysed to amino acids at P1 , the readout at this particular subpocket can be investigated in detail ( see Figure 5 ) . The degradome again splits into three main branches in the protease specificity tree . First , proteases recognizing aspartate residues at P1 such as caspases and granzyme B are split off . Second , proteases cleaving after positively charged residues , as for example trypsin [22] , are separated . This branch shows an internal branching pattern according to the preference of arginine over lysine or vice versa . The third branch splits off several proteases showing unique substrate preferences: elastase preferring hydrophobic residues [63] , glutamyl peptidase I specifically cleaving after glutamate residues [64] as well as neurolysin and thimet oligopeptidase mainly cleaving after proline residues ( see also Supporting Table S1 ) . The branch containing the latter two proteases is not as clearly separated from other proteases when compared to the protease specificity tree based on the substrate recognition over the whole binding site . The residual tree contains unspecific proteases of all catalytic types sorted by increasing subpocket-wise cleavage entropy within P1 and hence unspecific substrate cleavage . Finally , we mapped targets of known protease inhibitors from the ChEMBL database to the protease specificity trees . We chose benzamidine ( ChEMBL20936 ) as a well-studied protease inhibitor that occupies only a single protease subpocket S1 in bound state ( e . g . [65] ) . We mapped known targets to the protease specificity tree based on S4-S1 amino acid frequencies ( see Figure 6 ) . Despite the wide usage of benzamidine as protease inhibitor in biochemistry ( e . g . [66] ) , ChEMBL only lists bioactivity data for three protease targets in our test set . All three proteases plasmin , trypsin 1 , and thrombin are serine proteases of the chymotrypsin fold known to prefer positively charged amino acids at P1 position and hence nicely group in one branch of the protease specificity tree . Several ligands in ChEMBL are annotated to bind to even more than three different proteases . We chose BI 201335 ( ChEMBL1241348 ) as example for a promiscuous non-covalent protease ligand inhibiting a wide range of proteases even distributed over different catalytic types ( see Figure 7 ) . BI 201335 is a known inhibitor of the Hepatitis C Virus NS3-NS4A protease [67] . Mapping all 21 annotated targets in our protease set to the protease specificity tree over the whole binding site region , we observe a clustering of all known protease targets in particular branches of the degradome . Similarity between these targets is not observed on sequence or structure basis , as targets span all four major catalytic types of proteases . Similar to BI 201335 , the linear depsipeptide grassystatin A ( ChEMBL567893 ) binds several targets annotated in ChEMBL . Kwan et al performed a screening campaign against 59 proteases in an effort to rationalize selectivity of grassystatins A-C [68] , thus providing broad bioactivity data for these three compounds . Known targets tend to cluster to groups within our protease specificity tree ( see Figure 8 ) . Grassystatin A binds to several matrix metallo proteases forming one branch , similarly several caspases as well as cathepsins D and E forming two groups in the tree are known targets . As for BI 201335 known targets of promiscuous grassystatin A span all catalytic types of proteases . A mapping of known targets of 2-[ ( 4-methoxybenzyl ) sulfanyl]-6-methylpyrimidin-4-ol ( ChEMBL500351 ) reveals promiscuous binding to several metallo proteases ( see Figure 9 ) . The main data source for this compound is a screening study by Nakai et al aiming at characterization of the selectivity of small molecule MMP13 inhibitors [69] . The screening set included various members of the matrix metallo proteases as well as neprilysin . All these targets cluster in one region of our substrate-based degradome map , whereas the metallo protease thermolysin , which is an unknown target for this compound , is omitted .
We present a novel approach to intuitively map the degradome based on substrate readout rather than protease sequence . The underlying methodology to construct a similarity matrix is solely based on subpocket amino acid frequencies , the same information visualized in common sequence logos of protease substrates . Therefore , the presented method is suitable for comparison of any kind of position-specific scoring matrix , e . g . , a multiple sequence alignment or sequence motif . We encourage the community to use our method for comparison of sequence logos also in research apart from the protease universe . Navigating the protease space using the approach described here has three major advantages over a protein sequence-driven view: First , protease similarity is inherently captured in the interaction with a substrate . Accordingly , binding site similarities are directly probed by using substrate data . Natural amino acids contain a variety of chemical features and provide multiple anchor points for interactions . By mapping known targets of small molecules to our protease specificity trees , we can directly translate knowledge from peptide and protein substrates to drug design of small molecules . Secondly , our approach is not limited to the analysis of similarities and differences between homologous proteases as sequence-based analyses . Taking the common feature , the cleaved substrates as basis , we are able to compare proteases of different evolutionary origin . Therefore , we can engraft individual evolutionary trees of proteases to a complete map of the degradome . Finally , with increasing knowledge and availability of large scale data in protease databases , our data-driven mapping of the degradome can be more and more refined to result in a highly detailed view of protease substrate recognition . An annotation of true negatives , whether in the field of small molecule binding data or protease substrate data , would be especially helpful to refine current models . Likewise , quantitative data of binding affinity and kinetics could provide new insights into protease substrate recognition . The presented principal component analysis on the similarity matrix of 62 proteases highlights the most important features of protease substrate recognition . The main variance in the data set results from substrate promiscuity quantified as total cleavage entropy and covered in PC1 and PC2 . PC2 especially correlates with specificity of S1-P1 interactions , the major specific interaction point for most proteases , directly adjacent to the scissile bond . Further principal components read special subpocket interactions and hence group proteases by catalytic types , rendering a complex picture of protease-substrate recognition characteristics . This close interplay of protease catalytic types , evolutionary relations and diversification of specificity and function has been discussed over years ( e . g . [70] ) , and are recovered by the statistical analysis presented in this study . Analysis of protease-ligand annotations within ChEMBL shows a striking promiscuity of small molecules within the degradome . Broad binding profiles even overlap between catalytic types of proteases , adding an additional layer of complexity to the understanding or protease specificity . Current protease assay panels are usually limited to proteases with the same catalytic mechanism ( e . g . [71] ) . Therefore , promiscuous binding to proteases of other catalytic type but similar substrate preferences would not be detected within these assays . We strongly encourage to setup broader protease assay panels to further trace ligand promiscuity within the protease field . Availability of suitable data sets would be of high interest for academic research . We observe different exchange probabilities for chemically closely related residues within the protease set covered in our study . Cleavage profiles show large overlaps in substrate recognition between proteases preferring positively charged residues , arginine or lysine , such as several members of the chymotrypsin fold . Still , we do not observe similar overlaps amongst substrate spectra of proteases recognizing negatively charged residues . Caspases and granzyme B are highly specific for aspartate residues , whereas glutamyl peptidase I predominantly binds glutamate residues at the S1 pocket . No overlaps between aspartate and glutamate preferring binding pockets are present in our set . As our analysis of protease similarities based on cleaved substrates directly uncovers similarities in substrate recognition , we propose to apply our methodology for the prediction of off-target effects and understanding of polypharmacology within the protease field . We expect similarities in sequence specificity and thus substrate recognition to correlate with ligand recognition . Hence , proximity of proteases in specificity trees and principal component analyses should indicate possible off-target effects . Figure 6 shows how benzamidine binds to a branch of the protease specificity tree , whilst other members are omitted . We assume that benzamidine would also bind to the other proteases recognizing highly similar peptide substrates , e . g . , granzyme A . This similarity in peptide binding is captured in our substrate-driven trees but not directly visible from sequence or structure due to different evolutionary origins . Still , more and more ligands binding to multiple similar but non-homologous binding sites are described in the literature [72] . Intuitively , drug repurposing efforts within the field of proteases can directly be based on our study via capturing substrate similarity . Current strategies to predict or probe off-target effects include analysis of similarities in ligand structure , target structure as well as combinations thereof [73] , [74] , [75] . Especially , three-dimensional information has been described to be crucial in this field [76] . Computational techniques applied to capture target structure similarity include molecular docking [77] as well as pharmacophore-based approaches [78] . Similar binding sites are expected to result in polypharmacology as a consequence of binding of similar ligands [79] , [80] . Apart from prediction of polypharmacology , ligand-based network analyses have recently been found useful in the identification of unknown mechanisms of action of known drugs [81] . Our presented study introduces a novel ligand-based methodology to the field , the comparison of enzymes based on their peptide substrates . Keeping in mind that substrate promiscuity is a general prerequisite for drug design [82] , analyses of substrate promiscuity and specificity are of high importance for the protease field . Larger analyses of binding site similarities of protease mainly cover structure-based comparisons [52] , [60] , [83] , but neglect existing information on substrates . Following the general trend towards drug polypharmacology [84] , [85] and the high potential of multi target drugs [86] , we think that our study is an important step to fill that particular gap . By mapping the degradome from the perspective of substrates , similarities of protein binding sites can be captured directly . We have shown the straightforward applicability of information from peptide substrates in the chemical space of drug molecules and expect that similar studies are feasible for all kinds of protein-protein interfaces where sufficient substrate data is available . | We present a novel approach to intuitively map the degradome , the set of proteolytic enzymes , based on their substrates rather than on the protease sequences . Information stored in cleavage site sequence logos is extracted and transferred into a metric for similarity in protease substrate recognition . By capturing similarity in substrate readout , we inherently focus on the biomolecular recognition process between protease and substrate . Furthermore , we are able to include proteases of different evolutionary origin into our analysis , because no assumption on homology has to made . In a second step , we show how knowledge from peptide substrates can directly be transferred into small molecule recognition . By mining protease inhibition data in the ChEMBL database we show , how our substrate-driven protease specificity trees group known targets of protease inhibitors . Thus , our substrate-based maps of the degradome can be utilized in the prediction of off-target effects or drug repurposing . As our approach is not limited to the protease universe , our similarity metric can be expanded to any kind of protein-protein interface given sufficient substrate data . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2013 | Substrate-Driven Mapping of the Degradome by Comparison of Sequence Logos |
Plant development is remarkably plastic but how precisely can the plant customize its form to specific environments ? When the plant adjusts its development to different environments , related traits can change in a coordinated fashion , such that two traits co-vary across many genotypes . Alternatively , traits can vary independently , such that a change in one trait has little predictive value for the change in a second trait . To characterize such “tunability” in developmental plasticity , we carried out a detailed phenotypic characterization of complex root traits among 96 accessions of the model Arabidopsis thaliana in two nitrogen environments . The results revealed a surprising level of independence in the control of traits to environment – a highly tunable form of plasticity . We mapped genetic architecture of plasticity using genome-wide association studies and further used gene expression analysis to narrow down gene candidates in mapped regions . Mutants in genes implicated by association and expression analysis showed precise defects in the predicted traits in the predicted environment , corroborating the independent control of plasticity traits . The overall results suggest that there is a pool of genetic variability in plants that controls traits in specific environments , with opportunity to tune crop plants to a given environment .
Nitrogen is a limiting nutrient in plant growth that is typically taken up from the soil by the root system [1] . However , because the soil environment often varies over space and time , a single genotype needs to adjust its root architecture in response to different soil conditions , an example of developmental plasticity . One imperative in agriculture is to develop crops that can grow efficiently while reducing expensive and environmentally detrimental nitrogen supplements; current high yield crops are typically optimized for a single environment of high nitrogen . To breed crops for different or naturally fluctuating nitrogen environments , mechanisms that mediate traits conditioned on the environment may be important targets of crop improvement . In plants , root architecture is a complex phenotype that arises from adult meristematic activity in primary and lateral roots and lateral root initiation [2] , [3] . These traits collectively determine the root's three-dimensional body plan , where specific shapes can provide advantages in certain environments [4] . For example , deeper primary roots are often associated with plants with a greater tolerance to drought [5] , [6] . The dynamic and patchy nature of the soil environment also appears to make the post-embryonic adjustment of the body plan a valuable attribute . For example , a strong association was found between local proliferation of lateral roots and nitrogen uptake in competition assays in grasses [7] , [8] . Collectively , these studies show that different attributes of root architecture and the ability of individuals to adjust that architecture can confer advantages in the heterogeneous soil environment . Here , we systematically characterize the way in which root traits can vary in different environments across accessions in one species , Arabidopsis thaliana . At one extreme , a set of traits may be correlated ( or anti-correlated ) such that trait 1 and 2 may both consistently increase , decrease or show opposite trends in a new environment when examined in many different accessions [7] . At the other extreme , traits may be independent with respect to each other , such that a change in trait 1 has no predictive value in a change in trait 2 when examining many genotypes [4] . We expect that specific genes mediate the response to extrinsic signals to affect intrinsic development programs [2] . For example , genes that mediate the activation of transient stem cell niches in the pericycle will influence lateral root density [4] . In previous work , plasticity mechanisms active in the pericycle were found to coordinate the control of root initiation and outgrowth by nitrogen . It was found that an increase in expression of the transcription factor Auxin Response Factor 8 in response to high nitrogen treatment decreased lateral root growth and increased lateral root initiation [9] . This was an example of trait coupling that could result in an anti-correlation between traits across many genotypes . Another study showed that C∶N ratio appeared to specifically control lateral root initiation without strongly influencing other root traits – a potential example of trait independence [10] . A few other cases of regulatory genes that control root architecture in response to nitrogen have been identified [11] , [12] . However , the overall level of customization of phenotype to environmental variation and the genetic architecture underlying plasticity are not well understood . To characterize the tunability of root traits in response to different environments , we merge concepts from two different fields . The field of phenotypic integration has documented the level of correlation vs . independence in traits , typically across different genetic variants within a species or a set of closely related species [13] . The field of phenotypic plasticity has documented the ability of single genotypes to show variable phenotypes in different environments [13]–[15] . Here , we examine the correlation vs . independence of the difference in root traits in two environments to ask how finely the plant can manipulate its developmental plasticity . In addition , we also seek to determine the genetic mechanisms that mediate plasticity , as there is growing interest in the genes underlying phenotypic plasticity [13] , [16] , [17] . By carrying out comparative phenotypic analysis of key features of root architecture , we have been able to both analyze the correlation of individual root traits as well as assess the extent of plasticity within and between root traits that form root architecture . The use of genomic expression profiling in combination with high-density genetic marker association analysis enabled identification of genes implicated in controlling independent root parameters . By combining the phenotypic and genomic analyses we were able to functionally validate a number of new root regulators that mediate the response to nitrogen levels in discrete environments .
We characterized the level of correlation vs . independence in root plasticity by asking how traits vary in two distinct nitrogen environments among 96 well-characterized natural accessions or ecotypes of the model species Arabidopsis thaliana [18] ( see Materials and Methods ) . We define developmental plasticity as the ability of a single genotype to exhibit different phenotypes in different environments . If root trait differences in the two environments are correlated , accessions should exhibit similar suites of changes in root architecture in distinct nitrogen environments . Alternatively , if a high level of trait independence exists , genotypes that are similar in one nitrogen environment could alter a subset of root traits in a new nitrogen environment . To address this hypothesis , we first clustered accessions based on seven root traits capturing root size and architecture ( see Methods ) in each of the two environments: high and low nitrogen ( Figure 1 ) . There was a dramatic rearrangement of the tree topology in the two environments , as shown by the dispersal of clusters formed in low nitrogen mapped onto the high nitrogen phenotype tree ( Figure 1 ) . To observe trait behaviors , we also clustered accessions based on their trait differences in the two nitrogen growth conditions , and mapped average trait differences in each accession onto the tree as bar graphs ( Figure 2A , B ) or a heatmap ( Figure S4 ) . In one example , NFA-8 and Sq-8 have similar architectures on low nitrogen , but exhibit much different phenotypes in the high nitrogen environment , where Sq-8 outgrows lateral roots much more dramatically ( Figure 2C ) . In another clade , Kas-2 is a super-responder , dramatically increasing almost all root traits measured in high nitrogen to the extreme levels observed ( Figure 2C ) . On the other hand , roots of Bil-7 are almost completely unresponsive to nitrogen ( Figures 1 , 2A ) . Overall , the cluster analysis indicates that sharing a phenotype in one nitrogen environment does not predict similarity in root architecture in a second nitrogen environment , arguing that different trait responses are independent of one another . We used a Principal Components Analysis ( PCA ) to investigate the degree of correlation vs . independence in the root traits . In the first PC , which accounted for 64% of variation , almost all traits showed the same magnitude and direction in their contribution ( Figure 2D , blue lines ) . This trend suggested that the greatest variation among the difference of traits on high compared to low nitrogen were correlated changes in traits , meaning overall size differences ( Figure 2D ) . However , the different traits made highly varied contributions to the second and third PCs , as shown by the vectors ( blue lines ) representing the magnitudes and sign of trait coefficients in each component ( Figure 2E ) . The second and third components represented about 17% and 10% of the variance , respectively , indicating a substantial amount of variation in these two components . Interestingly , the star-shaped configuration of the coefficient vectors indicates that traits are highly orthogonal in the space of the second and third principal components . In other words , traits show a high degree of independence and lack of correlation . The same trends were found in a PCA analysis of trait data from high or low nitrogen conditions or the combined high plus low nitrogen dataset ( Figure S5 ) . The substantial variation in PCs 2 and 3 shows that there is a significant component of variation in which traits vary freely among accessions in the transition from one environment to another . Similarly , a mixed model ANOVA of the trait data showed that almost all traits have accession-by-treatment interactions ( see Methods ) . For example , in the ANOVA model , Kas-2 has a high interaction coefficient in LRtot , in which it changes phenotype dramatically in the two nitrogen conditions ( Figure 2C ) . In a different type of trait interaction , Kas-2 also has a high interaction coefficient in LB/PR ( root length between hypocotyl and most distal lateral root ) , but it is one of the few accessions to show almost no phenotypic difference between nitrogen environments ( Table S5 ) . Overall , the analysis shows that individual accessions adjust to differing nitrogen environments with variable increases in overall size , which demonstrates trait correlation as in PC1 . However , there is a prominent secondary source of variability in which traits vary independently among the accessions and between environments , as demonstrated in PCs 2 and 3 . The result shows that much of the variation observed when growing the 96 accessions in two environments is comprised of overall size effects , but , importantly , another large component of variation reflects a high level of fine tuning of each accession to a particular nitrogen environment . To identify mechanisms involved in plasticity , we employed a genome-wide association study ( GWAS ) [19] . We associated known SNPs with root traits from plants grown on low or high nitrogen environments , or the difference in a trait value between the two environments ( Table S6 ) . In addition , we calculated the total proportion of trait heritability that the SNPs explain ( Tables S6 , S7 ) . We used 96 accessions , as previous work suggested this number is sufficient to identify associations with relatively strong effect [19] . In total , we found 53 highly significant SNP hits that could be grouped , based on proximity , into 17 SNP groups ( a SNP window included all genes within 10 kb on either side of the SNP and such intervals were joined into “groups” if their windows overlapped ) . In total , the 17 SNP groups encompassed 106 genes ( Table S8 ) . Surprisingly , out of 17 SNP groups , only a third of the groups associated with the same trait in the two nitrogen environments . This could mean that we either lacked power to detect SNPs in one environment , or , that there is genetic variation that specifically influences phenotype in one environment . We sought to test the hypothesis that specific genes mediate distinct traits in one nitrogen environment by testing whether mutants in any of the genes found within intervals showed a phenotype in the associated trait in the predicted environment . We focused on lateral root average length because 7 SNP groups encompassing 53 genes showed high significance and because a number of insertional mutants were available for genes in these windows ( Table S8 ) . We sought to narrow candidates within genomic intervals that were implicated by SNPs by focusing on potential plasticity regulators that showed variable gene expression among accessions or between conditions or both . Thus , we profiled root gene expression of seven accessions that represent diverse root architectures ( Col-0 , Kas-2 , Var2-1 , Tamm-27 , NFA-8 , Sq-8 , Ts-5; Figures 1 , 2A–C ) using ATH1 microarrays in response to a 2-hour treatment of nitrate vs . control to identify early growth regulators that respond to new conditions ( Methods; Table S9 ) . ANOVA followed by a model simplification assignment ( FDR<0 . 1 , see Methods and Tables S10 , S11 ) identified 5 , 043 genes that varied among accessions but with no response to nitrogen and 279 genes with a range of effects due to nitrogen ( Figure 3 ) . Of these 279 genes , 29 genes responded to nitrogen in all accessions with no accession-specific variation in the degree of response or direction of nitrogen-regulation ( “nitrogen-only effect” ) . 123 genes responded to nitrogen across all accessions , with the same direction of response in all accessions but with a variation in the degree of response ( “nitrogen , accession effect’ ) . The remaining 127 genes had a nitrogen*accession interaction effect whereby the direction and/or degree of nitrogen regulation varied over the seven accessions . To validate the expression analysis for nitrogen responses , we analyzed the 152 genes that responded across all accessions ( 29 genes with nitrogen effect only; 123 genes with nitrogen and accession effect; Table S11 ) . These 152 core response genes include two key nitrate response genes ( AtNRT2 . 1 ( nitrate transporter 2 . 1 , At1g08090 ) and NIR1 ( nitrite reductase , At2g15620 ) ) and there is an overrepresentation of the GO term ‘response to nitrogen’ ( 8 genes , P = 1 . 06E-02 ) . In addition , there is an overrepresentation of a number of metabolic functional terms including GO term ‘cellular metabolic process’ ( 78 genes , P = 3 . 88E-04 ) and GO term ‘small molecule biosynthetic process’ ( 23 genes , P = 6 . 69E-03 ) , supporting common nitrogen regulation of cellular and metabolic pathways . We also defined a more stringent list of regulated genes following the hypothesis that genes controlling the nitrogen response of root traits across accessions should have varied nitrogen-response levels across accessions . To generate such a “stringent set” of candidate genes , we took genes that showed a significant nitrogen*accession effect in ANOVA ( 127 genes ) and those in expression clusters that correlated with average lateral root length in either low or high nitrogen or the difference between the two levels of nitrogen ( 321 genes ) . We then conducted a reverse genetic screen in Col-0 to ask whether GWAS refined by expression analysis could identify genes that mediate specific traits in specific nitrogen environments . As a proxy for examining the phenotypic effects of natural alleles , we evaluated T-DNA mutants in 13 genes that fit two criteria for predicting a specific phenotype: the genes were found within genomic intervals associated with lateral root average length and their transcripts demonstrated a significant ANOVA effect ( accession-only , nitrogen or nitrogen*accession effect ) among the seven profiled accessions ( 13/53; Table S8 ) . Out of the 13 loci , three genes passed our criteria for demonstrating root phenotypes with ( 1 ) consistent , quantifiable phenotype in specific root traits for two separate T-DNA mutant alleles and , ( 2 ) absent or reduced gene expression in the mutant gene ( Figure 4; Methods; Table S12; Figure S10 ) . In addition we carried out crosses of the pairs of allelic mutants and confirmed trans non-complementation , supporting that the mutant alleles were responsible for the phenotypes ( Table S12 ) . In support of a model in which genes control traits in specific environments , mutant phenotypes from two loci precisely matched predictions for mediating specific traits in specific environments . One GWAS hit included a block of genes containing JR1 ( JASMONATE RESPONSIVE 1 ) , which was associated with lateral root length in low nitrogen and the difference between low and high nitrogen ( Figure 4A ) . In addition , JR1 met stringent expression criteria , belonging to a cluster that correlated to the difference in lateral root length between nitrogen environments ( Figure S9 ) . The two mutant alleles tested showed a specific defect in lateral root average length in low nitrogen but not high nitrogen , as we predicted from analysis of GWAS and expression data ( Figure 4B–C ) . In one plausible functional role for JR1 in controlling the length of lateral roots specifically when there are low levels of nitrogen in the environment , the jasmonate pathway has been shown to have a role in lateral root development [20] . A second gene , PhzC , which was significantly regulated across accessions , was also consistent with GWAS predictions , having shorter lateral roots on low nitrogen but not high nitrogen ( Figure 4D–F ) . For a third gene identified from GWAS analysis , UBQ14 ( polyubiquitin ) , an association with lateral root length in low nitrogen but not high nitrogen was tested ( Figure 4G ) . However , phenotypic analysis showed phenotypes in both nitrogen conditions . In addition , phenotypes were observed in total lateral root length ( LRtot ) and total root length ( PR+LRtot ) , as might be expected with severe defects in lateral root length ( Figure 4H–I ) . Thus , this mutant implicates UBQ14 in trait specificity but not environmental specificity . Nonetheless , for two out of three cases for which we identified a root trait phenotype ( JR1 and PhzC ) the combination of GWAS with expression profiling identified genes that affected specific traits in specific environments , showing that , with this combination of techniques , we can map genotype to both trait and environment . Within the narrower set of only three candidate genes that met the dual criteria of belonging to the gene expression “stringent set” and a GWAS “group , ” two genes showed mutant phenotypes in precisely the predicted trait and environment . We cannot rule out that expression criteria alone could have identified candidates with mutant phenotypes . However , in a preliminary screen on the same data , we used expression criteria to examine mutants in 13 genes , with some showing pleiotropic phenotypes ( data not shown ) but none demonstrating specific defects in one or even two traits . Thus , we believe the combination of genome-wide association and gene expression greatly assists in identifying genes involved in specific traits in specific environments with high precision . Overall , mutations in two out of the three loci that we identified by GWAS affected root systems in low nitrogen environments , where the lateral root system was relatively small , but showed normal root length in high nitrogen environments , where the root system was more extensive . This suggests that the mutant phenotypes are not simply due to general defects in lateral root growth , but rather the gene's specific role in one environment . We point out that we do not know the causal polymorphisms for the phenotypic variation in root traits among natural variants . Even if causal polymorphisms map to the same loci as the mutations we identified , the genetic polymorphisms responsible for the trait variation likely control trait values in a different manner than loss-of-function mutations . However , the mutant analysis provides some corroboration that these loci contribute to controlling plasticity in the traits that we identified . Furthermore , the mutant analysis suggests that different mechanisms may predominate in the control of specific traits in specific environments , perhaps because fewer redundant mechanisms are expressed in one condition . Genotype x environment effects have traditionally been seen as a detriment to crop breeding programs , although there is growing interest in accounting for such effects [17] . Our result suggests that mechanisms that alter traits in specific environments may be quite common . Such genes could be exploited to customize crop phenotypes to a specific environment , such as low nitrogen , without , for example , changing an optimal phenotype in high nitrogen environments .
All seeds were obtained from ABRC ( set of 96 ‘Nordborg’ lines , CS22660 ) [21] or NASC ( SALK or SAIL T-DNA lines: SAIL_167_A06 , SAIL_658_G04 , SAIL_448_B08 , SALK_026383 ( BE ) , SALK_108492C , SALK_000461C , SALK_026685C , SALK_011676 ( A ) , SALK_030620 ( AI ) , SALK_020347C , SALK_028332 ( BO ) , SALK_112558 , SALK_022578C , SALK_060146 , SALK_025883C , SALK_068266 ( BA ) , SALK_104906C , SALK_045666 , SALK_057714 ( CF ) , SALK_123616 ( BV ) , SALK_047601 , SALK_047837 ( AS ) , SALK_059126C , SALK_064966 ( AP ) , SALK_075567C , SALK_107827C , SALK_086488C , SALK_121520C , SALK_086554C , SALK_111688C ) [22]; Table S8 lists location for each T-DNA line . Our overall goal in preliminary growth experiments was to find conditions that maximized trait differences between high and low nitrogen environments . To carry out this exploratory phase , Arabidopsis seedlings were grown on a combination of different levels of carbon ( 0 , 3 , 10 , 15 , 30 , 60 mM sucrose ) and nitrogen ( 0 , 0 . 03 , 0 . 05 , 0 . 1 , 0 . 5 , 1 , 5 , 10 , 20 mM KNO3 ) . For each seedling , primary root length was measured and the number of lateral roots counted; lateral root density was calculated from these two parameters ( Table S1 , Figure S1 ) . Our previous work [9] showed that high levels of nitrogen induce lateral root primordium development and repress lateral root emergence , resulting in a higher pre-emergent∶emergent lateral root ratio than on low nitrate conditions . As a ratio this is also the case here , although total lateral root numbers on high nitrate are larger ( due to the nutrient effect and longer primary roots; overall size effect ) . An increasing concentration of nitrate was found to result in increased primary root length , particularly with concentrations of 0 . 5 mM KNO3 or more ( Figure S1A ) . This inductive effect tended to level off at 5 mM KNO3 , with primary root length remaining fairly constant at 10 and 20 mM KNO3 . At lower levels of nitrate the primary root was longer with no or low sucrose in the media , but as the nitrate concentration increased this effect was reversed ( primary root length was longer on higher sucrose concentrations . This is likely due to a C∶N balance effect [23] . It was also on higher sucrose concentrations that the nitrate inductive effect was more pronounced . A similar C/N effect was found on regulation of lateral root number , and again at more than 0 . 5 mM KNO3 the N effect was most pronounced , leveling off at 5 mM ( Figure S1B ) . At the highest sucrose concentrations ( 30 and 60 mM sucrose ) a significant increase in lateral root numbers was observed . Lateral root density was found to be relatively constant over all C∶N conditions , suggesting that in general , increases in lateral root number were proportional to primary root length ( Figure S1C ) . However , there was a higher lateral root density for combinations of the highest C∶N levels ( 30 , 60 mM sucrose∶5 , 10 , 20 mM KNO3 ) , suggesting that at these concentrations there is a developmental effect that leads to larger numbers of lateral roots developing . Thus , in order to understand the genetic basis of this developmental effect we decided to use 30 mM sucrose , 5 mM KNO3 as our ‘high N’ condition; on this combination there also appeared to be strong and near-maximal induction of primary root length and lateral root development ( as indicated by the leveling off described above ) . As a comparative low N condition we decided to use 0 . 03 mM KNO3 ( also at 30 mM sucrose ) since root growth and development was significantly different from seedlings grown on 5 mM , and the plants would be N-depleted/starved but still viable and growing ( compared to 0 mM KNO3 , complete N starvation ) . To confirm that the root architecture difference that we observed were due to the effect of different nitrate levels rather than potassium levels we grew Col-0 seedlings on either 5 mM KNO3 , 5 mM CaNO3 , or 2 . 5 mM KNO3 , 2 . 5 mM CaNO3 and found no major differences between overall root architecture ( Table S2 , Figure S2 ) . Finally , we have some evidence that the root architecture observed in our chosen conditions correlates with that in field conditions , for example Var2-1 is found in sandy regions and exhibits a highly elongated primary root with very few lateral roots as seen in our experiments ( see Figure 1 ) . For phenotypic analysis , seeds from each of 96 Arabidopsis thaliana accessions [18] or T-DNA lines were grown on vertical agar plates containing custom nitrogen and sucrose-free 1× Murashige and Skoog basal medium ( GibcoBRL , Gaithersburg , USA ) supplemented with 30 mM sucrose and either low ( 0 . 03 mM ) or high ( 5 mM ) KNO3 with 0 . 8% agar ( pH 5 . 7 ) . To confirm the effect of nitrate , KNO3 was replaced with CaNO3 for Col-0 . For microarray studies 6 , 000 seeds ( per replicate , in triplicate ) of each accession ( Col-0 , Kas-2 , Var2-1 , Tamm-27 , NFA-8 , Sq-8 , Ts-5 ) were sterilized and sown on liquid 1× Murashige and Skoog basal medium containing no nitrogen or sucrose supplemented with 3 mM sucrose and 0 . 5 mM ammonium succinate for hydroponic growth as previous [9] . Plants were grown for 12 days in 16 hr light ( 50 mmol photons m−2 s−1 light intensity ) /8 hr dark cycles at 22°C in growth chambers . For determining growth conditions and for phenotyping of the 96 accessions , 10 seedlings were measured for one replicate of each condition/accession in New York in a Percival growth cabinet ( Percival Scientific Inc . , Perry , IA , ) . For T-DNA allele phenotyping , an average of 10 seedlings were measured for each of three independent replicates of each condition/allele: New York , T-DNA phenotyping Rep 1 in a Percival Scientific Inc; Warwick , T-DNA phenotyping Reps 2 , 3 in a Sanyo MLR-351 , Panasonic Biomedical , Loughborough ) . To confirm presence of T-DNA insertions and loss-of-expression of candidate genes , roots were harvested for genotyping of isolated DNA and qPCR of isolated RNA ( see Table S12 ) . For treatments , KNO3 was added to the media to a final concentration of 5 mM for two hours [9] . Control plants were mock-treated by adding the same concentration of KCl . At the end of the two hour treatment , roots were harvested and flash-frozen in N2 ( l ) for subsequent RNA extraction . To confirm trans non-complementation among alleles for each gene , we crossed the pairs of alleles to each other via reciprocal crossing . As a crossing control , individual alleles were also crossed to Col0 . Root phenotypes in the F1s were compared to selfed Col0 plants grown in parallel . In each KNO3 environment , parameters relating to root architecture were measured using ImageJ: primary root length ( i , PR ) , number of lateral roots ( ii , LR# ) , lengths of all LRs and LR distribution ( number of LRs per cm of PR ) . From this the following were calculated: lateral root density ( iii , LRdensity ) , the proportion of the PR that is the root branching zone ( the zone of the parent root that extends from the most rootward emerged LR to the shoot base , LB , terminology following Dubrovsky and Forde ( 2012 ) [24] ) ( iv , LB/PR ) , total LR length ( v , LRtot ) , total LR plus PR length ( vi , PR+LRtot ) , average LR length ( vii , LRlengthave ) ; Table S3 , Figure S3 . Traits designated with roman numerals were used for GWAS . Shoot area was estimated to calculate shoot area to primary root length . Data was scaled from 0 to 1 using the scaling factor ( n - low val ) / ( high val – low val ) ; Table S4 . Clustering of phenotyping values was carried out using hierarchical clustering with an average linkage and Pearson correlation using the clustergram function in MATLAB ( The MathWorks , Natick , MA , USA ) . NA values were considered to have a value of 0 . Silhouette widths were plotted in MATLAB using the silhouette function for each hierarchical tree and used to determine where to cut the trees and define clusters . A Perl script was written that produces a line drawing illustrating average seedling PR length , and lengths and distribution of LRs in each cm of the PR . This script can be accessed via URL: http://coruzzilab . bio . nyu . edu/cgi-bin/manpreetkatari/drawplant/drawplant . cgi . A positive hit in the reverse genetic screen was determined by satisfying the following criteria: ( 1 ) two separate mutant alleles showed the same phenotype , ( 2 ) mutant alleles showed a reduction or complete loss of expression using qPCR , 3 ) both mutant alleles showed a consistent , quantifiable phenotype in three independent screens including separate trials in New York and Warwick growth facilities . To calculate heritabilities of the within-environment variables , we used the “lmer” function of the lme4 package [25] in R v . 2 . 15 . 1 [26] and fit a restricted maximum likelihood ( REML ) -based analysis of variance ( ANOVA ) model of the form: Phenotype = Accession+Error , where Accession was treated as a random effect [27] . Heritabilities were calculated as σG/σP , where σG is the genetic variance component ( the genetic variance component attributable to variation among accessions ) and σP is the total phenotypic variance . To calculate the heritabilities of the response variables , we used the same function in R to fit a REML-based ANOVA model of the form: Phenotype = Accession+Nitrogen Level+Accession-by-Nitrogen Level+Error , where Nitrogen Level ( high or low ) was treated as a fixed effect , and Accession and Accession-by-Nitrogen Level were treated as random effects . Heritabilities were calculated as σGxE/σP , where σGxE is the variance component of the Accession-by-Nitrogen Level interaction effect [28] . GWAS was carried out using the EMMA package in R as described in Atwell et al ( 2010 ) [19] . The kinship matrix was constructed using the full set of ∼214k SNPs and SNPs with a minor allele frequency of 0 . 1 were mapped ( ∼178k/214k SNPs ) ; see Figure S7 . We opted to avoid what we believe is the overly stringent criteria of the Bonferroni correction and adjusted for multiple testing following Moran ( 2003 ) and Storey and Tibshirani ( 2003 ) [29] , [30] . Thus , we calculated Q-values , in which the distribution of P-values is used to correct for the false positive rate [30] . Q-values were calculated separately for each trait using the “qvalue” package [31] in R , a well-established method for finding significant fold changes in the microarray literature ( e . g . [32] ) . We note that , because Q-values are based on the distribution of the raw P-values and because Q-values are calculated separately for each trait , the raw P-value corresponding to our target threshold of Q = 0 . 05 ( the significance threshold for SNP-trait associations ) varies for different traits ( see Figure S7 ) . Compared to Atwell et al ( 2010 ) [19] we used a more stringent location criteria for selection of genes: for each significant SNP association , a window of 20 kb ( rather than 40 kb ) centered on the SNP ( using the SNP-mapped TAIR8 genome version ) was used to select genes predicted to be responsible for the association . To understand the relationship between minor allele frequency ( MAF ) , additive genetic effect size , and the power to detect an additive genetic effect , we performed a power simulation sensu Yu et al . ( 2006 ) [33] . Specifically: ( i ) the empirical phenotypic values for each accession were treated as random deviates; ( ii ) based on the empirical phenotypic variation , we calculated a genetic effect equal to 0 . 1 , 0 . 2 , 0 . 5 , 0 . 7 , 0 . 9 , or 1 times the standard deviation of the phenotypic mean; ( iii ) x accessions out of the total n accessions were randomly assigned to one simulated genotype , and the rest of the individuals were assigned to the other simulated genotype , so that x/n equaled the minor allele frequency of interest; ( iv ) the genetic effect corresponding to an accession's simulated genotype was added to the empirical phenotypic value for that accession; ( v ) structured association mapping was performed using the real ( non-simulated ) kinship matrix; ( vi ) steps 1 to 4 were repeated 1000 times , and the power to detect the additive genetic effect was the proportion of times that the P value from the mapping analyses ( see step v ) was below the 0 . 05 significance threshold . We performed this power simulation for each trait and for MAFs ranging from 0 . 1–0 . 5; see Figure S8 . The power simulations show similar results to Yu et al . ( 2006 ) [33] , namely that the power to detect a genetic effect is low at small MAFs and at small genetic effect sizes; the power to detect a genetic effect increases dramatically with an increase in the genetic effect size , such that a genetic effect half as large as the random background variation will usually be statistically significant even at a low MAFs . RNA from the whole root samples for microarray analysis was extracted with TRIzol ( Invitrogen , Carlsbad , CA ) . Standard Affymetrix protocols were then used for amplifying , labeling and hybridizing 1 µg of RNA samples to the ATH1 GeneChip ( Affymetrix , Santa Clara , USA ) . For qPCR tests , RNA was extracted with the RNAeasy kit ( Qiagen ) then first DNAase-treated using a Precision DNase kit and double stranded cDNA was synthesized using the nanoscript RT kit ( both from Primer Design Ltd , Southampton , UK ) according to manufacturer's instructions . qPCR was carried out using the Precision-SY MasterMix kit using primers designed by Primer Design Ltd according to manufacturer's instructions on a Roche 480 LightCycler . The mRNA levels were normalized relative to the UBQ10 housekeeping gene using the geNorm REF gene kit ( Primer Design Ltd ) and quantified using standard curves generated for each primer pair . Expression of At3g16470 , At4g02860 and At4g02890 transcripts were used to confirm loss-of expression in the SALK lines vs . Col-0 ( primers designed by Primer Design Ltd ) . SALK lines were PCR-genotyped with primer designed using T-DNA Primer Design ( http://signal . salk . edu/tdnaprimers . 2 . html ) ; for all primer sequences see Table S12 . Affymetrix GCOS software was used to verify that the arrays had similar hybridization efficiencies and background intensities for all accessions . We carried out an analysis to address the use of the Affymetrix Col-0 chip for other Arabidopsis accessions . Given the rate of SNPs between accessions and Col-0 , we first observed that mismatches to any of the 11 probes ( 25mers ) for any given gene were likely to be rare . In addition , we compared only N-deplete with N-replete Affymetrix signal values within each accession directly and only focused on genes that showed a difference between the two . Therefore any genes that cannot be detected because of sequence-associated probe hybridization problems do not confound our analysis . However , to ensure that we account for over/under-estimations of N-regulation significance that might result from stronger hybridization of a sequence in one accession compared to another ( due to sequence difference ) , we developed an algorithm to rank the signal values of each element in each probe set across the experiments ( 7 accessions , 2 conditions ( N-treatment and KCl control ) , 3 replicates ) . This was based on the expectation that , while overall signal from the probe sets of a given gene may change , the relative hybridization to each probe set for a given should not . The method identifies elements within probe sets whose expression is indicative of that element not hybridizing to accession-derived sequences due to the presence of SNP ( s ) using Col-0 as a reference . Significant deviation from this order could indicate sequence divergence altering the binding strength of a sequence to a probe element . We derived a null distribution of signal strength orders for Col-0 and then used this to identify significant probe element outliers in hybridizations from the other ( see Table S9 for lists of all element outliers ) . These probe elements were discarded . Microarray data was subsequently normalized with MAS5 using all but these element values and implemented in the Affymetrix GCOS software ( Table S10 ) . On average , 10% of all probe sets were analyzed with the complete set of 11 elements and a further 70% analyzed with 9 or 10 probe elements ( see Table S9 for details for each replicate set ) . The reproducibility of replicates was analyzed using the correlation coefficient and r2 value of replicate pairs in R; r2 values were typically in the range of 0 . 92 to 0 . 98 , with the lowest being 0 . 91 . Probe-gene mapping was made using the latest annotation file ( TAIR10 annotation ) ( ftp://ftp . arabidopsis . org/home/tair/Microarrays/Affymetrix/affy_ ATH1_array_elements-2010-12-20 . txt ) . The following classes of probes were flagged ( Table S10 ) : probes matching non-nuclear Arabidopsis thaliana genes or that had no gene match ( flag #1 ) , probes that had an ambiguous match to nuclear genes , i . e . matched more than one gene ( flag #2 ) , probes where several probes match a single gene ( flag #3 ) , probes whose average expression level was found to be below the detection cutoff ( flag #4 ) . To identify flag #4 probes we analyzed genes known to be expressed or absent in the root to calculate an expression signal of 100 as a cutoff for detection . All genes were fit to the following ANOVA model: Y = μ+αaccession+αtreatment+αaccession* treatment+ε , where Y is the normalized signal of a gene , μ is the mean of the reference accession and treatment ( intercept ) , the α coefficients correspond to the effects of accession , treatment ( nitrogen ) and the interaction between accession and treatment , and ε represents unexplained variance . Potential location effects were handled by growing plants together in a highly controlled environment and randomizing the placement of accessions and treatments in different shelves and locations of the growth chamber . The replicate trials were conducted in rapid succession in identical conditions , where we have not found significant time-effect variation . Thus , the ANOVA was modeled without block effects , where potential confounding effects were handled by randomization . Genes with a model P value less than a cutoff determined by setting the FDR [34] to 0 . 1 were analyzed further using model simplification to test these genes for significant N*Accession interaction effects , response to N , and variation across Accession . We did this by removing terms from the model one by one and then comparing the models to see if there was a significant difference in explanatory power between the simplified model and the more complex model using an FDR of 0 . 1 . Gene expression values were averaged for each treatment , log2 converted , row normalized and clustered using hierarchical clustering with an average linkage and Pearson correlation using the clustergram function in MATLAB . Silhouette widths were plotted in MATLAB using the silhouette function for each hierarchical tree and used to determine where to cut the trees and define clusters . Clustering was carried out separately for genes that were determined by ANOVA to have a N , Accession effect , a N*Accession effect , or a N only effect , then the cluster patterns visualised together in MATLAB using the clustergram function to create Figure 3A . Two-tailed t-tests assuming equal variance were used to compare trait values for wild-type and mutant seedling roots , and trait values for Col-0 grown on different levels of sucrose and nitrate . For analysis of overrepresentation of GO terms we used the BioMaps function in VirtualPlant with default settings [35] . Root phenotypes for the seven transcriptionally profiled accessions ( Figure S6 ) were analyzed using a mixed interaction model ANOVA using MATLAB ( anovan function ) with the following model: ROOT_TRAITn = ENVn+GENn+GENn * ENVn+en where Root Traitn is one of n = 7 root traits measured ( PR , LRtot , PR+LRtot , LRlengthave , LR# , LRdensity , and LB/PR ) , ENV is environment , GEN is genotype , and e is error . Environment was modeled as a fixed effect and genotype was modeled as a random effect . P values were taken for each trait separately for the main and interaction effects . Coefficients generated from the ANOVA were used to determine the specific traits that contributed most to significant interaction effects ( Table S5 ) . As in the design for expression analysis , placement of plants was randomized in chambers , and this experiment was conducted at one time point . Principal Components Analysis was performed in MATLAB using the princomp function with default parameters . Rows were accessions and traits were columns , where dimensionality reduction was performed on traits . The biplot function was used to map accessions in specific treatments ( average trait values ) in the new trait space and observe the contribution of original traits to each new component . We performed separate analyses on the combined HighN and LowN treatments for each accession , each condition alone , and δ highN-lowN of each accession to changes in nitrogen . We plotted two components on each biplot ( 1 vs 2; 2 vs 3 ) to analyze the first three principal components . See Figure S5 . We created a network of expression modules to traits by first clustering responses ( expression in low nitrogen – expression in high nitrogen ) using Pearson correlation and hierarchical clustering ( average linkage , tree cut at R = 0 . 7 ) . To determine significant clusters , we randomized the data and used the same clustering routine . This routine showed that clusters greater than 50 genes were observed less than 10% of the time by chance . Using that cutoff to define major clusters , we took the mean response of these major clusters in all 7 accessions . We then concatenated mean scaled trait values for δ highN-lowN and generated a correlation matrix , where R>0 . 7 or <−0 . 7 resulted in a significant edge . This resulted in a correlation matrix between gene expression clusters and traits that was used to generate the network depicted in Figure S9 using the biograph function in MATLAB . | Plants can dramatically alter their development in order to cope with new environmental conditions . Such plasticity is especially evident in the root system since it adopts a particular architecture under one condition , but can change architecture by altering the extent of lateral root branching in a different condition . To explore the extent of root plasticity to the critical nutrient nitrogen we analyzed a natural population of the model plant Arabidopsis in both nitrogen-limiting and nitrogen-rich environments . This revealed that root architecture plasticity appears to be the combined effect of many individual root responses to the environment that are independently modulated . Each aspect , such as lateral root length , number , or density seems to be turned on or off separately , giving the whole system flexibility . We then identified specific genes that control these individual component responses by exploring the genetic variation across the natural population in combination with analyzing which genes respond to nitrogen . Together the results help us gain insights into how the environment shapes plant development . This knowledge can be used to better understand how the growth of our existing crop species might change as the climate varies , and identify new crop varieties that will be robust to such variation . | [
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] | [] | 2013 | Plasticity Regulators Modulate Specific Root Traits in Discrete Nitrogen Environments |
We characterise the evolutionary dynamics of influenza infection described by viral sequence data collected from two challenge studies conducted in human hosts . Viral sequence data were collected at regular intervals from infected hosts . Changes in the sequence data observed across time show that the within-host evolution of the virus was driven by the reversion of variants acquired during previous passaging of the virus . Treatment of some patients with oseltamivir on the first day of infection did not lead to the emergence of drug resistance variants in patients . Using an evolutionary model , we inferred the effective rate of reassortment between viral segments , measuring the extent to which randomly chosen viruses within the host exchange genetic material . We find strong evidence that the rate of effective reassortment is low , such that genetic associations between polymorphic loci in different segments are preserved during the course of an infection in a manner not compatible with epistasis . Combining our evidence with that of previous studies we suggest that spatial heterogeneity in the viral population may reduce the extent to which reassortment is observed . Our results do not contradict previous findings of high rates of viral reassortment in vitro and in small animal studies , but indicate that in human hosts the effective rate of reassortment may be substantially more limited .
Genome sequencing has provided multiple insights into the evolution of the influenza virus . At the global level , sequences collected from circulating strains of the virus have enabled the identification of codons in the virus evolving under positive selection [1 , 2] , and demonstrated the importance of interactions between selected alleles [3–5] . Collected sequence data have been used to understand the global migration dynamics of different seasonal strains [6–8] and to make predictions for the future evolution of the virus [9–11] . Considering infection at the level of a single host , sequencing technology has been used to explore the evolution of the influenza virus over the course of a single infection and during the process of viral transmission [12 , 13] . Studies of this form have highlighted both the potential for rapid changes in sequence composition within a series of transmission events , as well as diversity in the antigenic properties of a viral population within a single host [14–16] . Experiments conducted in ferrets have considered the within-host evolution and transmission of novel viral strains [17–20] . While some phenotypic properties of the influenza virus can be understood in terms of genetic changes occurring within a single gene [21] , interactions between genes have increasingly been identified as of importance for viral evolution . For example , evidence for epistasis in influenza has been found between variants both within single influenza genes [22–24] and across different gene segments [25–27] , including the occurrence of permissive mutations which facilitate protein evolution and immune escape . A key multi-gene effect in influenza dynamics is the process of reassortment , whereby distinct influenza viruses within a single cell produce viruses with novel combinations of genome segments , potentially leading to the emergence of new , beneficial strains [28]; evidence for frequent reassortment has been identified in global viral populations [29] . While precise rates of reassortment are hard to quantify , both in vitro and in vivo experiments have suggested that reassortment between compatible strains of influenza occurs readily [30 , 31] . Although the extent of reassortment in experimentally induced infections can depend upon the number of viruses received by a host [32] , viral transmission gives a sufficient dose for within-host reassortment to be observed in small animal infection [33] , suggesting that reassortment plays an important role in within-host viral dynamics . Quantitative modelling of within-host influenza infection has a long history [34] , but it is only recently that sequencing studies have provided the necessary information to fit evolutionary models to data . Standard techniques for detecting selection , such as dN/dS , are not appropriate for such populations , in which the time-scale for evolution is extremely short [35 , 36] . Homologous recombination is rare [37–39] , or potentially non-existent , such that selection acting upon one variant may , via linkage disequilibrium , cause changes in allele frequency across a single segment . Under the influence of selection , a population evolves according to a ‘fitness landscape’ , which describes the expected within-host growth rate of each virus as a function of viral genotype [40] . Given time-resolved sequence data , approaches which account for the haplotype structure of the viral population have been applied in order to infer the core components of this landscape as it influences viral evolution [41 , 42] . However , even in these inferences , the potential role of reassortment has not been considered . Given anything other than a rapid rate of reassortment , linkage disequilibrium between alleles in different segments may have an effect on viral dynamics . Studies of within-host influenza growth and transmission have been conducted in a variety of animals , including birds , dogs , ferrets , pigs , horses and small mammals [13 , 43–45] . Such studies are of great inherent importance , and have long been acknowledged as contributing to our understanding of human infections [46] . However , from the specific perspective of human health , while ferrets in particular provide a valuable model for understanding influenza infection [47 , 48] , studies conducted in human hosts remain the ultimate reference point . We here describe results from one of the first studies to examine influenza evolution in humans from the perspective of time-resolved viral sequence data , which describe in detail the process of viral evolution as it occurs . Using longitudinal viral sequence data collected from subjects in two related influenza challenge studies ( described in previous publications [15 , 49–54] ) we evaluate how fitness effects shape within-host viral evolution . Using a maximum likelihood approach , we infer the location and magnitude of the selective forces underlying observed changes in the genotypic structure of the viral population . Capturing reassortment in real time on the scale of an infected individual has been noted as a difficult task [55] . Here , using an inference method which incorporates the effect of linkage disequilibrium between alleles on distinct viral segments , we estimate the effective rate of reassortment within the viral population in human subjects on the basis of changes in the genetic composition of the viral population observed over time . On the basis of the collected data , we infer that selection acting upon variants in the HA , NP , and PA viral genes is responsible for driving within-host evolution in the subjects from which data were collected . Further , we find strong evidence to suggest that the effective rate of reassortment between variants found on different gene segments is limited , such that linkage disequilibrium between alleles on different segments is maintained throughout the course of an infection . The inferences derived via our model are consistent with previous findings that the extent of within-host recombination is linked to the dose of virus received by a patient . Our results are reconcilable with reports of high reassortment rates in in vitro and small animal studies given a scenario in which the absolute rate of reassortment is high , but the within-host influenza population is spatially distributed , forming a metapopulation within a patient . We conclude that interactions between variants on different viral segments may substantially affect within-host viral evolution .
Our approach to estimating reassortment rates is based upon some elementary principles of population genetics . Given the relatively short length of an influenza infection , and the large number of viruses created during that infection , changes in the viral population are driven by the influence of selection . Selection changes allele frequencies over time , favouring individuals with variants that grant beneficial traits to the virus . However , the manner in which allele frequencies changes under selection depends upon whether variants at different nucleotides are free to change in a manner independent from one another , or whether they are linked together , perhaps by physical linkage , through epistatic effects , or via a lack of reassortment . Examples of how different reassortment rates affect the evolution of a system are shown in Fig 1 . Here we exploit this idea to make the converse step , using observed changes in the genetic composition of multiple viral populations to infer the rate of reassortment within human subjects . Our model exploits the fact that , within a single segment of influenza , short sequence reads may potentially describe alleles at more than one locus . Such reads grant direct information into the associations between alleles in a single segment . These multi-locus data can be exploited to gain a clearer view of the evolution of the population; [42] . Under our approach , we first identify sites in the influenza genome at which polymorphism exists , and at which the minor allele frequency changes significantly over time . The set of observed polymorphisms is used to construct a set of potential virus-wide haplotypes , from which variant alleles are observed . We then construct a model under which the frequencies of viral haplotypes change in frequency over time under mutation and selection , and under the effect of reassortment between viral segments . A likelihood optimisation method is used to fit the model to the data under a wide range of possible rates of reassortment , allowing an inference to be made of the effective rate of reassortment between segments . Full details of our model are given in the Methods section . The data used for this study were next-generation sequencing samples generated from two human challenge studies with influenza A/Wisconsin/67/2005 ( H3N2 ) virus [56] . Briefly , individuals enrolled in these studies were inoculated intranasally with an influenza H3N2 viral inoculum and monitored throughout maximal symptom development , a total of 7 days . The two studies differed in the treatment given to individuals . In the first study , individuals received standard treatment , with oseltamivir being administered on the evening of the 5th day post-challenge . In the second study , individuals received either standard treatment or early treatment , defined as the administration of oseltamivir on the evening of the 1st day post-challenge . As such , subjects were divided into two cohorts , a ‘standard treatment’ cohort , and an ‘early treatment’ cohort ( Table 1 ) . The viral inoculum used for both studies consisted of a genetically heterogeneous population of virus generated from passaged viral reference strain A/Wisconsin/67/2005 ( H3N2 ) . The virus was passaged 3X in chicken cells , 4X in eggs and 2X in vero cells . Subjects were challenged with doses of the viral inoculum ranging from 3 . 08–6 . 41 log10 ( TCID50/ml ) [57] . Previous analysis of this experiment showed there was no association between the inoculum dose and whether the challenge subject became infected [58] . For those subjects who were infected , there was no association between the inoculum dose and the degree of disease symptoms or the magnitude/duration of viral shedding [58] . Nasal wash samples were collected daily and tested for the presence of virus using culture or quantitative PCR . Those samples containing virus ( a total of 60 samples out of 266 samples obtained ) and the viral inoculum , were sequenced at the JCVI , as described in [59 , 60] . The next generation sequencing pipeline at the JCVI uses both the Illumina HiSeq 2000 and Ion Torrent platforms to compensate for platform specific errors [19] . The sequences from both platforms were used in this analysis . Sequences were obtained for 42 of the nasal wash samples from 17 subjects . As this analysis infers selection based upon on the trajectory of variant frequencies over the course of infection , we excluded samples from 4 individuals ( Flu008 , Flu010 , Flu015 and Flu5017 ) who were only sequenced at one time-point . The remaining dataset consisted of 38 samples from 13 individuals ( six of whom received early treatment and seven of whom received standard treatment ) , with between two and five samples per subject . Data showing the distribution of read lengths and the depth of sequencing are shown in S1 and S2 Figs . Sequence data are available from the SRA with the project accession number SRP091397 . Considering data from all time-points and all individuals , polymorphisms were identified at a total of 110 loci ( nucleotide sites ) across the influenza genome . Allele frequencies at these loci measured across time , which we refer to as trajectories , are shown in S3–S10 Figs . The inoculum sample was used as a proxy measurement of the population establishing infection within each host . Trajectories were assessed using a single-locus model of allele frequency change ( c . f . [61] ) , looking for statistically significant deviation from a neutral model , under which the frequency of an allele remained constant over time . Applying this model a total of 16 loci , distributed across six of the eight influenza genome segments , showed potential evidence of non-neutral allele frequency change ( Fig 2; equivalent data by segment are shown in S11 Fig ) . The absence of evidence for significant allele frequency change in either the NA or NS segments implies the absence of strong positive selection in each case . In NA the emergence of drug resistance mutations , notably the amino acid substitution H274T arising from the nucleotide variant C820T , has previously been reported following prolonged oseltamivir treatment in an immunocompromised host [62] . However , no such event was observed here , in either the standard or the early-treatment populations . A single-segment , multi-locus model ( SGML ) [42] was applied to alleles at the potentially selected loci , accounting for the correlations between alleles that arise due to the lack of homologous recombination in influenza segments . Under this method , short-read data from each segment are used to identify potential haplotypes spanning the segment [63] . Considering potential haplotypes , we note that in a case where two alleles are observed at each of n different loci , a total of 2n potential haplotypes may exist . However , where reads span multiple loci , and these multi-locus reads indicate limited diversity across sites , the number of potential haplotypes required by the model may be reduced . Potential segment-wide haplotypes were used under a model of mutation and selection to construct the most parsimonious model of selection acting within each segment , inferring the initial frequency of each haplotype and noting that selection at one locus may explain a change in allele frequency at another . Under the assumption of a consistent model of selection acting across all individuals , we identified potential evidence for selection at 9 loci , found in the HA , NP , PA , and PB2 segments ( Table 2 ) . A negative epistatic interaction between two variants in the HA segment was also inferred; negative epistasis implies that each variant incurs a fitness penalty in the presence of the other , in addition to its inherent effect upon viral fitness . This reduction in the number of potentially selected sites confirms the importance of linkage disequilibrium in influenza dynamics; changes in allele frequencies that under the single locus model appeared to result from the direct effect of selection were often better explained by the indirect effect of selection acting at another locus . For example , four different loci in the PA segment were identified as having significant changes in allele frequency using the single locus model , but when the segment was considered as a whole , selection at locus 1680 was sufficient to explain the changes observed across all four loci; adding selection at a second locus did not significantly improve the ability of the multi-locus model to fit the data .
We have applied a novel multi-segment model of within-host evolution to data from infections in multiple individuals during two influenza challenge studies . Our approach for inferring selection is conservative with regard to the quantity of information contained within next-generation sequencing data , and in preferring simpler explanations of variant frequency change . Using our approach , we infer the key evolutionary effects influencing the adaptation of the within-host population in the human subjects under study . Building upon previous approaches for inferring selection from influenza populations [41 , 42 , 61] , our method infers selection not only at the level of events occurring at a single locus , or within a single segment , but across the entire influenza genome . As inferences were conducted across greater numbers of loci , in the SGML and MGML models , a decrease was observed in the number of loci at which selection was inferred . While the single locus test identifies potential non-neutrality at 16 loci over 6 gene segments , under the MGML model , selection was inferred to apply at only four of these positions within three gene segments . Two reasons likely underlie this reduction in the apparent complexity of the selective model . Firstly , due to linkage disequilibrium , selection acting at one locus can cause changes in the frequency of alleles at multiple other loci; inter-segment associations between alleles may cause changes in the frequencies of alleles not directly under selection . Accounting for linkage disequilibrium between alleles reduces false positive inferences of selection caused in this manner . Secondly , as more data are brought into the model , the model penalty for adding another parameter for selection increases . Where a polymorphism is observed in only a single individual , evidence of genuine selection may not be sufficient to be detected by our model , leading to false negative calls of selection . As such , our inferences describe the core set of fitness effects shaping the evolution of the virus , rather than a comprehensive fitness landscape . Across all subjects , we infer the key driver of evolution to be the reversion of mutations in HA acquired during passaging before inoculation into individual patients . However , selection was further inferred for variants in the NP and PA segments . The time of administration of oseltamivir to patients did not have a statistically significant effect upon the inferred fitness landscape . Although the emergence of oseltamivir resistance has been documented as having arisen during a single infection [68] , in this case no evidence of drug resistance was observed; with regard to the genetic composition of the virus , drug therapy had no apparent impact . While early oseltamivir treatment led to reductions in the severity of symptoms and the length of infection [69] , it did not have a distinct evolutionary effect on the viral genome . Among the variants identified as being under selection , those occuring in HA could be attributed to the reversion of mutations acquired during passaging . The variants C327T in NP and T1680C in PA are both synonymous in nature , and identifying a biological rationale for selection acting upon them requires a little more speculation . Both variants were called as polymorphisms in all subjects , though the explanation for how these variants could affect viral fitness is less clear . One possible explanation would be that the variants interfere with a packaging signal within the vRNA . Packaging signals are a necessary component in the selective packaging model for the influenza virus , which asserts that there is some specific element within the primary sequence of the viral RNA ensuring that each of the eight gene segments is incorporated to new virions [70] . While the packaging signals so far identified have tended to be within the UTRs rather than the coding regions of the segments , a few exceptions to this rule exist [71 , 72] . However , none of the packaging sequences identified within the coding regions for NP or PA coincide with the loci described here . Another possible explanation for selection occurring on the synonymous mutations is that these variants interfere with the secondary structure of the viral RNA [73] , structure being potentially related to viral fitness due to the need for the RNA to have a specific thermal stability [74] . For example , the synonymous variant C327T in NP identified by this analysis as under selection is located within a region of highly conserved secondary structure [75] . This region , spanning nucleotides 1031–1250 ( numbers indicating the ( + ) RNA ) , has been characterized as having high stability . As such , this variant ( at position 1193 of the ( + ) RNA ) , may be neutral or beneficial within the in ovo environment , but confer a fitness disadvantage in the human challenge subjects . While secondary structural elements have been identified within the PA segment [76] , the regions described have not included the locus 1680 , noted here . In addition to inferring selection for specific variants , we also inferred the presence of epistatic effects acting between alleles in different segments of influenza . Following the discovery that reassortment viruses generated from distinct strains do not reassort in a random manner [77] , a considerable body of literature has been developed studying the propensity for gene segments from distinct viruses to be separated , or found together , following a reassortment event . More formal statistical approaches for identifying associations between segments have been developed [78] . Biases in reassortment have been found in human influenza A and B viruses [79 , 80] , in cell culture [78 , 81] , and in swine influenza viruses [82] . The focus of this study is somewhat different to these , being concerned with the rate at which effective reassortment takes place rather than what is produced given a reassortment event . Nevertheless , epistasis is an important factor in our study due to the potential for linkage disequilibrium to be maintained between alleles on separate segments via epistatic effects . By accounting for epistasis in our model , we separate the maintenance of linkage disequilibrium caused by epistasis from that arising from a lack of reassortment such that our result cannot be fully explained by non-random reassortment between segments . Our model infers the existence of epistatic interactions between the HA , NP , and PA segments . However , our study is not the ideal vehicle for which to consider such factors; the study of more diverse strains in an environment in which there was rapid effective reassortment would be desirable for making such inferences . The restricted extent of effective reassortment between segments inferred during the course of infection is perhaps the most surprising result of our study , with a model of zero reassortment granting the best fit to the data . This result was supported by extremely strong model evidence , with a model of low reassortment providing a far better explanation of the data than a model with rapid reassortment . Our model is limited in so far as it requires the polymorphisms in a segment to be observed at significant allele frequencies; as such , our estimates of reassortment were made across a subset of the segments in the influenza genome . Our result should also be understood in proper context; a lower implied within-host reassortment rate does not contradict observations of reassortment events in global influenza populations . Our study contains a result matching earlier findings in suggesting that the rate of reassortment within a host is linked to the dose received by a single patient . However , our study contrasts with previous work in the extent of reassortment that was observed; while we identify low reassortment rates , an earlier study noted 86% of viruses reassorting during the course of a single infection in a case where a guinea pig was infected with 106 PFU of virus [31] . We suggest that these results may be reconciled under the assumption of a metapopulation model of infection ( c . f . [83 , 84] ) in which interactions between viruses are limited by spatial diversity within the host . Such a model is well-supported by existing knowledge of influenza virus; viruses in cell culture form distinct plaques [85] , while genetically distinct influenza populations have been observed at different locations within a single ferret [19] , implying that viruses are not uniformly distributed throughout a host . Under such a model , reassortment , as a local process occurring in single cells , will tend to occur between viruses that are genetically more self-similar than viruses drawn randomly from the population; the effective rate of reassortment , as we define it , will be lower than the absolute rate at which segments are swapped between viruses . Assuming a constant absolute rate of reassortment throughout the host , the rate of effective reassortment will be a function of the local genetic diversity of the population , which itself will depend upon the number of distinct viruses founding an infection in any given region of the host respiratory system . As the total number of viruses founding infection in a host increases , more , and therefore statistically more diverse viruses , found each local infection , such that the observed extent of reassortment also increases . This understanding suggests that in a larger host , where the respiratory system occupies a larger physical space , more viruses would be required to create the same local genetic diversity , and hence the same observed rate of reassortment; given the same infectious dose , the observed rate of reassortment will be lower . Our study does not oppose the vital role played by animal experiments in understanding influenza infection . However with specific regard to reassortment , experiments conducted in vitro or in small animal experiments may over-estimate the effective reassortment rate that might be expected in a human infection . Multi-segment effects may have a substantial and previously under-appreciated role in the evolutionary dynamics of within-host influenza .
The SAMFIRE package [63] was used to call time-resolved multi-locus sequence variants from the data . High quality short reads were identified using the criteria of a median PHRED quality score of at least 30 , trimming sequences where necessary to obtain this . Single nucleotide polymorphisms ( SNPs ) were then identified from reported nucleotides with PHRED score at least 30 , for which at least 10 reads , and at least 2% of the reads in a sample , reported the variant . To account for possible PCR bias , variant alleles were only considered if they were identified in at least two samples from the dataset . SNP trajectories , describing the frequency of each SNP over time , were calculated . In order to quantify the influence of selection , a conservative estimate was made of the extent of noise in the reported sequence data . Under the assumption that changes in allele frequency over time resulted purely from noise , the extent of noise was characterized within the framework of a Dirichlet multinomial model . Where in a given individual i , n i a ( t ) is the number of copies of a variant allele observed at the polymorphic locus a at time t , and N a i ( t ) is the total number of alleles observed in this individual at this locus at time t , we calculated the mean allele frequency across all reads q ^ a i = ∑ t n a i ( t ) ∑ t N a i ( t ) ( 1 ) for each allele . Assuming that observed changes in allele frequency occur only through noise , we then optimised the parameter C to maximise the likelihood L = ∑ a , i , t L D ( C , { n a i ( t ) } , { q ^ a i } ) ( 2 ) where L D is the Dirichlet multinomial distribution with parameters α i = C q a i . This approach produces a conservative estimate of the extent of noise in the system , characterised by C . Given an estimate of the extent of noise in the sequenced data , a likelihood model was used to identify potentially non-neutral alleles across the influenza genome using a single-locus model of selection similar to those applied in earlier studies of viral evolution [61] . Here , for data from allele a within individual i , we generate a set of model allele frequencies q a i ( t ) , calculating the model likelihood as L m = ∑ t L D ( C , { n a i ( t ) } , { q a i ( t ) } ) ( 3 ) and comparing models using the Bayesian Information Criterion ( BIC ) for model selection [86]; under this comparison , a difference of 10 in BIC values indicates strong evidence for the more complex model [67] . In fitting a model to data , our model assumes a “generation time” , representing a round of intracellular reproduction , as occurring in 12 hours [87] . Fitted models describe the deterministic evolution of an allele frequency under either a neutral model , or a model of selection for the allele in question . In order to account for potential hitchhiking effects , we also examined a time-dependent model of selection , in which a different magnitude of selection was used to model selection between each pair of samples within an individual . Neutral model: q a ( t ) = q ∀ t ( 4 ) Constant selection: q a ( t ) = q a ( 0 ) exp ( σ t ) 1 - q a ( 0 ) + q a ( 0 ) exp ( σ t ) ( 5 ) Time-dependent selection: q a ( t ) = q a ( t - 1 ) exp ( σ t ) 1 - q a ( t - 1 ) + q a ( t - 1 ) exp ( σ t ) ( 6 ) At the single-locus level , time-dependent selection was considered so as to identify significant non-monotonic changes in allele frequency , as might occur via linkage disequilibrium between alleles; in further models where linkage disequilibrium between alleles was explicit to the model , only models of constant selection were considered . As in the models described below , fitness differences between viruses are understood to arise from differences in the respective within-host growth rates of genetically distinct viral strains . Where more than one variant occurs within a single segment , selection at a single locus may result in changes in the genetic composition of the virus across multiple loci [88]; under such conditions , single-locus measures of selection can produce misleading results [89] . A multi-locus model was used to account for such effects . Data within each segment were compiled to call multi-locus variants across potentially non-neutral loci . Multi-locus variants were then used to construct a set of potential viral haplotypes for each segment . Our approach to haplotype reconstruction , implemented within the SAMFIRE package , is two-fold by design . Given only single-locus reads , the number of potential haplotypes in a segment is an exponential function of the number of loci at which variation was observed . However , given multi-locus reads , limitations in the diversity of the population may be directly observed . Firstly , therefore , we infer a set of potential segment-wide haplotypes sufficient ( though perhaps not necessary ) to explain the observed multi-locus data , with no concern for the frequencies at which these haplotypes may or may not exist in the population . Secondly , we fit an evolutionary model describing mutation and selection to the variant data , under the constraint that the population is composed of the identified segment-wide haplotypes in some proportion at each recorded point in time , the frequencies of these haplotypes changing according to the evolutionary models . Mutation was modelled as occurring at constant rate between haplotypes: M : q a ( t + 1 ) = q a ( t ) + μ ∑ b ( q b ( t ) - q a ( t ) ) ( 7 ) where qa ( t ) is the frequency of the haplotype a in the popluation at time t , and the sum is conducted over haplotypes differing from a by a single nucleotide . The mutation rate was assumed to be μ = 10−5 per cite per replication cycle [90] . Selection was modelled to occur deterministically , changing the frequencies of different viral haplotypes according to their fitness . S : q a ( t + 1 ) = q a ( t ) exp ( σ a ) ∑ b q b ( t ) exp ( σ b ) ( 8 ) where the sum is conducted over all haploytpes b , and the fitness of haplotype a is determined by exp ( σa ) , where σ a = ∑ i s i + ∑ i , j χ i , j + ∑ i , j , k χ i , j , k + ⋯ ( 9 ) is a sum of single- and multi-locus ( i . e . epistatic ) terms , over variant nucleotides i , j , k , … , contained within the haplotype a . Hierarchical models of fitness were considered , gradually increasing the number of component terms in the fitness model , and using BIC to select the model giving the best explanation of the observed changes in viral genetic composition [41 , 42] . While reassortment between influenza segments is potentially a rapid process [31] , associations have been observed to exist between alleles on different segments [91] . Further to previous modelling approaches , an inference was made of the effect of reassortment upon the viral dynamics , via the use of a multi-segment , multi-locus model . Sites in each segment at which potential non-neutrality was inferred in the multi-locus model were combined , forming potential viral haplotypes spanning all segments at which selection had been inferred . As above , selection and mutation were evaluated across haplotypes , the fitness of any given viral genotype being calculated at the multi-segment haplotype level . However , in addition , reassortment was evaluated between segments , being modelled to occur between haplotypes with probability r: R : q a ( t + 1 ) = ( 1 - r ) q a ( t ) + r ∏ g q a , g ( t ) ( 10 ) where qa now denotes the frequency of viruses with a specific multi-segment haplotype , and qa , g ( t ) is the total frequency at time t of viruses having the same alleles as a across all loci in the segment g . The precise mechanism via which influenza segments are packaged into virions is a subject of ongoing research [92] . Following our assumptions of a large , well-mixed influenza population , we here assume a multi-parental model of reassortment , whereby reassortment occurs freely between a proportion of viruses in each generation . Recalculation of statistics under a strict bi-parental model of reassortment reproduced our key inference of a low effective reassortment rate; see S1 Text and S19 Fig . Given a Poisson model , we note that an equivalent reassortment rate ρ can be calculated as: ρ = - log ( 1 - r ) ( 11 ) In the above model , reassortment occurs between random genotypes in the population , as would occur if the population was well-mixed . In the same way that the effective population size is used in population genetics to seek to quantify the extent of genetic drift in terms of an idealised population [93] , the statistic we measure in this way describes an effective reassortment rate in the viral population . We note that this effective rate may be significantly lower than the absolute rate of reassortment between viruses , as in a case where reassortment primarily occurs between viruses with similar genotypes . In order to reduce the computational cost of the calculation , the multi-segment model was evaluated only across sites within each individual at which polymorphism was identified; as such , selection for a genetic variant was only considered to affect the evolution within a host if the variant under selection was observed in that host . Polymorphic loci in an individual form a subset of the total set of loci; individual-specific multi-segment haplotypes were constructed as a projection of the complete set of multi-segment haplotypes onto these loci , evaluating the effect of a consistent model of selection on the dynamics of each system . Calculations involving reassortment rate were conducted across a range of values of r . Subject-specific estimates of reassortment were derived from the component of the likelihood function corresponding to the data from that subject . A further measure quantifying the extent of evidence for limited reassortment in an individual subject was calculated as the normalised difference between the maximum likelihood reassortment model at any value of r , and under a model of rapid reassortment . max { L r } r - L ( r = 1 ) N ( 12 ) where N was the total number of datapoints collected from that individual . The details of this human challenge study have been previously described in [15 , 49–54] . The procedures performed under this study were in accordance with the Declaration of Helsinki . The protocols followed for this study were approved by the institutional review boards ( IRBs ) of Duke University Medical Center ( Durham , NC ) , the Space and Naval Warfare Systems Center San Diego ( SSD-SD ) of the US Department of Defense ( Washington , DC ) , the East London and City Research Ethics Committee 1 ( London , UK ) , and the Independent Western Institutional Review Board ( Olympia , WA ) . All participants enrolled in this challenge study provided written consent , as in accordance with standard IRB protocol . Code for the MGML model is available from http://websvc . gen . cam . ac . uk/~cjri2/MGML . The SAMFIRE code contains the single-locus model and is available at https://github . com/cjri/samfire . The SGML code is available as part of a previous publication [42] . | The influenza virus is an important cause of disease in the human population . During the course of an infection the virus can evolve rapidly . An important mechanism of viral evolution is reassortment , whereby different segments of the influenza genome are shuffled with other segments , producing new viral combinations . Here we study natural selection and reassortment during the course of infections occurring in human hosts . Examining viral genome sequence data from these infections , we note that genetic variants that were acquired during the growth of viruses in culture are selected against in the human host . In addition , we find evidence that the effective rate of reassortment is low . We suggest that the spatial separation between viruses in different parts of the host airway may limit the extent to which genetically distinct segments reassort with one another . Within the global population of influenza viruses , reassortment remains an important factor . However , reassortment is not so rapid as to exclude the possibility of interactions between genome segments affecting the course of influenza evolution during a single infection . | [
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"organisms"
] | 2017 | The effective rate of influenza reassortment is limited during human infection |
Rapidly increasing temperatures in the mountain region of Nepal and recent reports of dengue fever and lymphatic filariasis cases from mountainous areas of central Nepal prompted us to study the spatio-temporal distribution of the vectors of these two diseases along an altitudinal transect in central Nepal . We conducted a longitudinal study in four distinct physiographical regions of central Nepal from September 2011 to February 2012 . We used BG-Sentinel and CDC light traps to capture adult mosquitoes . We found the geographical distribution of the dengue virus vectors Aedes aegypti and Aedes albopictus along our study transect to extend up to 1 , 310 m altitude in the Middle Mountain region ( Kathmandu ) . The distribution of the lymphatic filariasis vector Culex quinquefasciatus extended up to at least 2 , 100 m in the High Mountain region ( Dhunche ) . Statistical analysis showed a significant effect of the physiographical region and month of collection on the abundance of A . aegypti and C . quinquefasciatus only . BG-Sentinel traps captured significantly higher numbers of A . aegypti than CDC light traps . The meteorological factors temperature , rainfall and relative humidity had significant effects on the mean number of A . aegypti per BG-Sentinel trap . Temperature and relative humidity were significant predictors of the number of C . quinquefasciatus per CDC light trap . Dengue fever and lymphatic filariasis cases had previously been reported from all vector positive areas except Dhunche which was free of known lymphatic filariasis cases . We conclude that dengue virus vectors have already established stable populations up to the Middle Mountains of Nepal , supporting previous studies , and report for the first time the distribution of lymphatic filariasis vectors up to the High Mountain region of this country . The findings of our study should contribute to a better planning and scaling-up of mosquito-borne disease control programmes in the mountainous areas of Nepal .
Dengue fever ( DF ) is a mosquito-borne viral disease which has become a major international public health concern in recent years . Dengue virus ( DENV ) , the causative agent of this disease , belongs to the genus Flavivirus , family Flaviviridae , and is transmitted by Aedes mosquitoes , especially by the yellow fever mosquito ( Aedes [Stegomyia] aegypti ) and the Asian tiger mosquito ( Aedes [Stegomyia] albopictus ) which are respectively considered to be its primary and secondary vectors in Southeast Asia [1] , [2] . In the last five decades , the incidence of DF has increased 30-fold , and geographical expansions to new countries and , in the present decade , from urban to rural settings have occurred [3] . For example , in the World Health Organization ( WHO ) South-east Asia Region ( SEARO ) , the area with autochthonous DENV transmission has extended to the sub-Himalayan foothills of Bhutan and Nepal since 2004 and 2006 , respectively [3] , [4] . In the past , DF had often been considered a public health problem of lesser concern because of its low mortality rate and an infrequent occurrence of epidemics [5] . However , rapid economic development and urban growth in developing countries with a lack of careful planning of housing , water resources , sewage and waste management , along with the globalization of trade and travel , have since contributed to rendering DF the most important mosquito-borne viral disease of humans [5] . Despite progress with the development and clinical trials of vaccines against DENV infection , no such vaccine is available on the market yet [6] , and there is no specific antiviral treatment either . Thus , controlling the population of dengue vector mosquitoes , especially A . aegypti and A . albopictus , and limiting their dispersal to new regions remains crucial for the prevention and control of DENV transmission [4] . The first case of DF in Nepal was a Japanese volunteer in 2004 [7] , and the presence of the disease in the country was officially confirmed after an outbreak in 2006 [8] . In this first DF outbreak , 32 confirmed cases were reported , followed by 27 cases in 2007 , 10 in 2008 , 30 in 2009 , and 917 cases including five deaths in 2010 when a major epidemic occurred in the Chitwan and Rupandehi districts of central and western Nepal , respectively [9] . More importantly , laboratory tests confirmed the presence of all four DENV serotypes in Nepal which portends the emergence of more severe DENV infections like dengue haemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) [8] . During the 2010 epidemic , a DF case with no recent travel history to known DF affected areas was for the first time found in Kathmandu [10] . This case along with reports of A . aegypti collections in Kathmandu [11] suggest that local transmission of DENV has already occurred in Kathmandu , the capital city of Nepal which is located at altitudes around 1 , 300 m above sea level ( asl ) . Furthermore , DF cases admitted to Sukraraj Tropical Diseases Hospital in 2010 , one of the referral hospitals of Kathmandu , came from 24 of the 75 districts of Nepal , spanning the geographical regions of the Terai , Siwalik and Middle Mountains and indicating a rapid spread of DENV in the country [10] . Entomological investigations from the 1950s on had demonstrated the presence of A . albopictus in the Terai plains , Siwalik hills and Middle Mountain region [12] , [13] , but A . aegypti had not been found at the time . Entomological surveys carried out in 2006 and following years showed that A . aegypti was now present locally [8] , [11] . However , apart from a few recent surveys of mosquito larvae in Kathmandu valley [11] , [14] , no detailed entomological studies on DENV vectors in Nepal have been conducted . Moreover , it remained to be determined how frequently the immature stages that had been found actually emerge as adult mosquitoes in the mountains compared to the Terai lowlands of Nepal . Lymphatic filariasis ( LF ) , one of the oldest known and most devastating neglected tropical diseases , is caused by three species of parasitic worms ( Wuchereria bancrofti , Brugia malayi and Brugia timori ) . Globally about 90% of all LF infections are caused by W . bancrofti [15] . These parasites are transmitted between humans by different mosquito species depending on the geographical setting , e . g . , species of the genus Culex mainly in urban and semi-urban areas of Asia , Anopheles species mainly in rural areas of Africa and Aedes species mainly in disease endemic islands of the Pacific , and by various species of the genus Mansonia [15] . In 2011 , approximately 120 million people in 73 countries were infected with LF , and more than 1 . 3 billion were at risk [16] . The WHO SEARO shared the highest burden of LF among the six WHO regions , accommodating 65% of the global population at risk and 50% of the infected cases ( 60 million ) [16] . In Nepal , LF is a major public health problem in terms of morbidity , primarily due to lymphedema , which causes the swelling of arms , legs , breasts and genitalia , and hydrocele , the swelling of the scrotum in male patients , hindering the socio-economic development in disease endemic areas and leading to social exclusion and stigmatization [17] . The disease is poverty-related , and marginalised groups and the poorer sector of the communities are predominantly affected [17] , [18] . An LF mapping of Nepal using immunochromatographic card tests ( ICT ) was completed in 2005 . It revealed that the disease is endemic in 60 out of the 75 administrative districts ranging from the lowlands ( around 90 m ) to more than 1 , 765 m above sea level in the mountains [17] . Wuchereria bancrofti , the only recorded LF parasite in Nepal , has been detected in all of these 60 endemic districts and is reported to be transmitted by Culex quinquefasciatus mosquitoes in Nepal [17] . The Epidemiology and Diseases Control Division ( EDCD ) under the Department of Health Services ( DoHS ) of the Ministry of Health and Population of the Government of Nepal has formulated a National Plan of Action ( 2003–2020 ) with an aim to eliminate LF in Nepal by 2020 [17] . Accordingly , the national LF programme has started the interruption of transmission by yearly mass drug administration ( MDA ) using a two drug regimen ( diethylcarbamizine [6 mg/kg] plus albendazole [400 mg] in a single dose ) in Parsa district in 2003 . Since then the programme has expanded gradually to other endemic districts . In five districts , the MDA was completed and stopped after five rounds while the programme is going on in another 46 endemic districts with plans to start MDA in the remaining nine endemic districts in 2014 and achieve <1% microfilaria prevalence by 2018 [17] . However , a low acceptance of MDA due to severe adverse effects in some people , low coverage in urban areas and the movement of people between endemic and non-endemic areas pose challenges for LF elimination in Nepal [17] . Although MDA continues to be the mainstay for interrupting LF transmission , vector control is increasingly recognized as an important supplementary strategy for achieving LF elimination goals [19] , [20] . An earlier mapping of LF in 37 districts of Nepal in 2001 had shown a high prevalence in Middle Mountain districts including Kathmandu [21] . The locality with the highest altitude sampled in that study was located at 1 , 400 m . Similarly , a sentinel surveillance study conducted in 2007 revealed that the highest microfilaria infection rates occurred in High Mountain district [22] . Therefore , there is a need of further study to better define the geographical limits of the endemic zone of LF in high mountain areas . However , to the best of our knowledge , no such work has yet been reported from Nepal . Mosquito vector monitoring and control play a complementary role in LF elimination at two stages: by adding to the reduction of microfilaria density and prevalence due to an active reduction of transmission during MDA , and by preventing recurrence or new infections in the surveillance phase after transmission has been interrupted [20] . Furthermore , monitoring the mosquito vectors and their infection status helps to identify new endemic areas and to make informed decisions about the need for an extension and scaling-up of MDA and vector control programmes in Nepal . There is increasing evidence that global change , including climate change , affects the geographical distribution of vector-borne diseases . For example , model projections show a potential increase in the latitudinal and altitudinal range of DF as well as an increase in the potential duration of the transmission season and epidemic potential in temperate regions [23] , [24] . The analysis of maximum temperature data from 49 stations in Nepal for the period 1971–1994 reveals warming trends after 1971 , ranging from 0 . 06°C year−1 in most of the Middle Mountain and High Mountain regions , while the lowland regions of the Siwalik hills and Terai plains show warming trends of less than 0 . 03°C year−1 or even cooling trends ( −0 . 03°C year−1 ) [25] . An extended analysis of temperature data after 1994 also shows a continuing warming trend without decrease [26] . A recent analysis carried out with data of 13 mountain stations of Nepal ( 1980–2009 ) shows that the maximum temperature and annual temperature are likely to increase while variability is too high to estimate a trend of the minimum temperature [27] . However , a general warming trend of minimum temperatures is observed in Nepal when data of the 36 years between 1971–2006 are analysed , showing a higher rate in mountain regions and lower rates in lowland Terai regions [28] . These trends of increasing temperature at higher altitudes of Nepal and the frequent reports of DF and LF cases from the mountain region of this country prompted us to conduct a study on the occurrence and abundance of DENV and LF vectors along an altitudinal transect of Nepal . This article documents the spatial and temporal distribution of the important vectors A . aegypti , A . albopictus and C . quinquefasciatus in central Nepal .
Nepal is a landlocked country that is administratively divided into five development regions ( equivalent to provinces ) , 14 zones and 75 districts . The central development region ( referred to later as central Nepal ) consists of 19 districts and covers an area of 27 , 410 km2 . As per the latest census of 2011 , the total population of this region is 9 , 656 , 985; this constitutes the highest population density of the country with 352 people per km2 ( the national population density is 180 per km2 ) [29] . Most urban areas ( 20 of total 58 ) are located in central Nepal . Central Nepal comprises all types of physiographical regions: the lowlands of the Terai and Siwalik , Middle Mountain , High Mountain and High Himalayan regions [30] . For the entomological survey , the urban sites Birgunj ( 26°59′59″N , 84°52′00″E , 80 m asl ) , Hetauda ( 27°25′02″N , 85°01′59″E , 465 m asl ) and Kathmandu ( 27°41′59″N , 85°20′01″E , 1 , 310 m asl ) were selected from the Terai , Siwalik and Middle Mountain regions , respectively . In addition , the densely populated rural sites Ranipauwa ( 27°49′54″N , 85°14′21″E , 1 , 825 m asl ) and Dhunche ( 28°06′45″N , 85°17′45 . 50″E , 2 , 100 m asl ) were selected from the High Mountain region . The altitudes of the study sites range from less than 85 m asl at the southern border with India to more than 2 , 100 m asl near the border with China in the north , representing a vertical cross-section of each physiographical region of the country to the exception of the High Himalayan region which we did not attempt to sample for the present study . The sites are connected to each other by highways and hence population mobility along the transect is high . The geographical locations of the study sites are presented in Figure 1; their socio-demographic characteristics are provided in Dhimal et al . [31] . Birganj is a sub-metropolitan city , headquarters of Parsa district and border town in southern Nepal . According to the 2011 census of Nepal , Birganj has a population of 139 , 068 . It is located 91 km ( airline distance ) south of the capital Kathmandu and 3 km north of the border with the Indian state of Bihar . Being the main entry point to Nepal from the Indian cities Patna and Kolkata , it is also known as the gateway to Nepal . As a large part of the country's imported goods enters Nepal through Birganj , the town has a significant economic importance . The climate of Birganj is considered to be sub-tropical . Hetauda is a municipal area and headquarters of Makwanpur district within the Siwalik region . It has 85 , 653 inhabitants and is one of the important industrial areas of Nepal . The airline distance between Hetauda and Kathmandu is 43 km . Hetauda is situated in a unique geographical structure called ‘doon’ , which means valley-like geography , and is surrounded by the Middle Mountains in the north and the low rolling Siwalik range with sub-tropical climate in the south . Kathmandu is the capital of Nepal and , with close to one million inhabitants , its largest metropolitan city . The city lies in a bowl-shaped valley in central Nepal that is surrounded by four mountains: Shivapuri , Phulchowki , Nagarjun and Chandragiri . Among the five major climatic zones of Nepal , Kathmandu valley belongs to the warm temperate zone . Ranipauwa is a settlement with 7 , 320 inhabitants in the Kakani Village Development Committee of Nuwakot district in central Nepal . The airline distance between Ranipauwa and Kathmandu is 20 km . It has a cool temperate climate , is a popular picnic and trekking destination , and has a good road connection . Dhunche , the headquarters of Rasuwa district , is located in the High Mountain region , has a cool temperate climate and is home to about 2 , 744 people . The airline distance of Dhunche from Kathmandu is 45 km . In the north , Rasuwa district is bordered by the Langtang mountain range and national park . This area has a good road connection from Kathmandu to China border . The longitudinal entomological survey covered five administrative districts of central Nepal representing four physiographical regions ( the Terai and Siwalik lowlands , Middle and High Mountain regions ) . In each region , adult mosquitoes were collected for half a year from September 2011 to February 2012 ( end of monsoon , post-monsoon , and winter seasons ) using BG-Sentinel traps ( Biogents , Regensburg , Germany ) and Centers for Disease Control ( CDC ) light traps ( BioQuip Products , USA ) . Mosquito eggs were collected using ovitraps . Adult mosquitoes were captured in each site using twelve BG-Sentinel mosquito traps ( two traps per site per month ) . As one BG-Sentinel trap in Hetauda failed in February 2012 , a total of 59 BG-Sentinel trap collections were performed . Each trap was operated for 24 hours using a 12 V battery at each time and site with BG-Lure attractant ( Biogents , Regensburg , Germany ) according to the manufacturer's protocol . In addition , we used dark activated CDC light traps fitted with double ring fine mesh collection bags ( BioQuip Products , USA ) and operated for 12 hours for the outdoor collection of mosquitoes . Two BG-Sentinel traps and one CDC light trap were randomly allocated to three fixed places in each of the study sites and operated monthly in the same places . We assumed the maximum adult longevity of A . aegypti , A . albopictus and C . quinquefasciatus to be three weeks [32] and that adult mosquitoes captured on consecutive sampling dates should have emerged between capture dates . Thus , this sampling strategy was expected to avoid generation overlap and temporal auto-correlation . The ovitraps used in this survey consisted of 300 ml cups made of black plastic . The upper three quarters of the inside of the cup were lined with one layer of an inexpensive , locally purchased , indigo-coloured low thread count cotton fabric which was held in place by a single paper clip [33] . The fabric was labelled with the house number and location of the ovitrap ( indoors or outdoors ) using a permanent marker . In addition , the same plastic cups were used to make ‘traditional’ ovitraps with a single , plain wooden tongue depressor paddle instead of fabric as the oviposition substrate . The wooden paddle was labelled on its back with the house number and location of the ovitrap using a permanent marker . Both types of ovitrap were filled to three quarters with distilled water and deposited outside of 30 houses in low-lying areas of the study sites that were protected from sun and rainfall by a roof or other cover . We then recorded geographical coordinates of each sampling point using portable global positioning system ( GPS ) devices ( Garmin eTrex H ) . Permission was obtained from the inhabitants of the houses before ovitraps were placed . Ovitraps were monitored every month from September 2011 to February 2012 on the dates when BG-Sentinel and CDC light trap collecting was performed . Mosquito eggs were counted under a stereo microscope , identified to genus level , and the number of eggs per ovitrap and day was calculated . As the proportion of other Aedes spp . among our adult mosquito collections was very low in the study sites , it was assumed that most of the collected eggs that looked like A . aegypti and A . albopictus eggs had indeed been deposited by these species . However , no efforts were made to verify this identification or distinguish between the eggs of A . aegypti and A . albopictus , e . g . , by molecular analysis or hatching and raising them to adulthood . Daily records of the average minimum and maximum temperature , morning and evening relative humidity and rainfall from August 2011 to February 2012 for each study site were obtained from the Department of Hydrology and Meteorology , Ministry of Science , Technology and Environment , Government of Nepal . The meteorological stations were within 1–5 km from mosquito collection spots . Using these data , we computed the derived variables adjusted rainfall ( ADJRAIN ) and adjusted temperature ( ADJTEMP ) [32] . ADJTEMP is the mean of the daily average temperatures during the three weeks prior to the collection date , and ADJRAIN is the accumulated rainfall during the third and second week before each mosquito collection date . Furthermore , we also calculated the adjusted relative humidity ( ADJRH ) using the same procedure as for the ADJTEMP . In the absence of an active case detection surveillance system of DENV infection in Nepal , we collected reported DF cases from the passive surveillance system and published literature . Dengue fever cases were reported from all three study areas ( Terai , Siwalik and Middle Mountain regions ) of central Nepal except the High Mountain region . Lymphatic filariasis cases were recorded only during campaign programmes for MDA which take place in selected districts each year and during the mapping phase of the LF elimination program , and were reported from all study sites except Dhunche , Rasuwa district . Ethical approval for conducting this study was granted by the Ethical Review Board ( ERB ) of the Nepal Health Research Council . Stakeholders at the central level as well as local authorities were briefed about the objectives and procedures of the study before the beginning of the surveys . Oral informed consent to install traps and collect mosquitoes was obtained from all owners of households and their premises . Taking oral consent only was approved by the ERB . The identity of patients was not disclosed in the process of data transfer or analysis . The data were entered into Microsoft Excel spreadsheets and further cleaned and manually edited if necessary . The data were then transferred to and analysed using R computing software [34] . The proportion of female mosquitoes of each species in each trap was calculated using an exact binomial test with female and male as outcomes . The GPS coordinates of trap placement locations and of those traps that were found positive were projected onto maps with ArcGis software ( ArcGis10 , ESRI ) . In most cases , the data for comparing the mean abundance of each species among study groups were not normally distributed and primarily right skewed . Therefore , generalized linear models ( GLM ) were fitted assuming negative binomial distribution and a log link function for each species using the “MASS” package in R [35] . The negative binomial distribution model is reported to be a robust analysis especially with respect to count data sets [36]–[38] . The full model was fitted including all possible interactions of selected variables . The selected predictor variables were trap type , month of collection and physiographic region . We fitted separate models for each species , mean abundance , and eggs per ovitrap as response variables and the trap type , month of collection and physiographic region as predictor variables . We also calculated Spearman's rank correlation between the abundance of each mosquito species in each trap and meteorological variables . The resulting correlation coefficients showed that meteorological variables were better correlated with the number of A . aegypti in BG-Sentinel traps and the number of C . quinquefasciatus in CDC light traps but not significantly correlated with A . albopictus numbers in either trap . Therefore we further fitted generalized linear models for predicting the number of mosquitoes in each trap using the derived meteorological variables . We used Akaike's information criterion ( AIC ) to select the final model using an automated step function . When comparing two models , the smaller AIC was accepted as indicating the better fit [39] . We assessed multicollinearity of predictors for each model using variance inflation factors ( VIFs ) . The VIFs for all predictor variables were less than 2 . 0 . We compared the fit of models with ( full ) and without ( reduced ) the term of interest using an F-ratio test statistics [40] . Pearson's as well as deviance residuals were calculated for model checking and standardized residuals were plotted to find evidence whether the model assumptions were met or not . If the final model included a variable with more than two levels , Tukey's multiple comparisons were applied using the “multcomp” package in R [41] . Differences between groups were considered to be significantly different at a family error rate of p<0 . 05 . The parameter coefficients and their 95% confidence intervals ( CI ) were transformed into original scale using exponential functions for easy interpretation of results . Effects display graphs were generated using “effects” package in R for GLM [42] and GraphPad Prism for few figures .
From September 2011 to February 2012 , a total of 1 , 164 mosquitoes from the genera Aedes , Anopheles , Armigeres and Culex were captured in 59 BG-Sentinel trapping sessions ( eight species ) and 747 mosquitoes in 30 CDC light trap sessions ( seven species ) ( Table 1 ) . The GPS points of the collection sites and number of mosquitoes per month per site by trap type is provided in Table S1 . From the viewpoint of LF and DENV transmission , C . quinquefasciatus , A . albopictus and A . aegypti are the most important vector species in this geographic setting . Culex quinquefasciatus was the predominant species in both trap types . Apart from Culex vishnui , all of the seven species captured in CDC light traps were also commonly caught in BG-Sentinel traps . Overall , more than 68% of all mosquitoes caught with BG-Sentinel traps ( except Armigeres spp . ) were female ( Table 1 ) . In the case of Anopheles culicifacies , C . vishnui and Culex fuscocephala , only females were captured with BG-Sentinel traps during the study period . Similarly , significantly higher female catches ( >70% ) were recorded in CDC light traps except for C . fuscocephala ( Table 1 ) . Only one male of Aedes sp . and only three females of Anopheles culicifacies were recorded in CDC light traps . Aedes aegypti , A . albopictus and C . quinquefasciatus were the predominant species in both trap types with significantly higher proportions of females of all three species in BG-Sentinel traps and only C . quinquefasciatus females in CDC light traps ( Table 1 ) . Therefore , we included only females of these species in subsequent analyses . The mean abundance of females of these three species by trap type is shown in Figure 2 . Throughout the study period , A . aegypti and C . quinquefasciatus were continuously recorded in central Nepal whereas A . albopictus was not trapped in December . Trap method , month and region were not significant variables for predicting the mean number of A . albopictus . The effects of trap method ( F = 20 . 91; df = 1; p<0 . 001 ) , region ( F = 10 . 89; df = 2; p<0 . 001 ) and month ( F = 12 . 82; df = 5; p<0 . 001 ) were significant variables for predicting the mean number of female A . aegypti . Compared to CDC light traps , BG-Sentinel traps collected 19 times more female A . aegypti ( 95% CI = 3 . 7–101 . 3; p<0 . 001 ) ( Table S2 ) . Adult A . aegypti and A . albopictus were collected from the lowland Terai , Siwalik and Middle Mountain regions up to 1 , 310 m asl . The highest abundance of female A . aegypti was recorded in lowland Birganj followed by the Middle Mountains in Kathmandu ( Figure 3A ) . The abundance of female A . aegypti significantly decreased in the Siwalik ( β = 0 . 07; 95%CI = 0 . 01–0 . 31; p = 0 . 001 ) and Middle Mountain regions ( β = 0 . 23; 95%CI = 0 . 06–0 . 82; p = 0 . 024 ) compared to the lowlands of the Terai . Similarly , compared to September , the abundance of female A . aegypti increased significantly reaching its peak in November ( β = 36 . 98; 95%CI = 5 . 56–245 . 99; p<0 . 001 ) and then declined abruptly ( Figure 3B ) . The regression model for predicting female A . aegypti mean abundance using categorical explanatory variables is provided in Table S2 . For predicting the mean number of female C . quinquefasciatus , only region ( F = 21 . 34; df = 3 , p<0 . 001 ) and month ( F = 12 . 82; df = 5 , p<0 . 001 ) were significant variables . Compared to the Terai lowlands , the abundance of female C . quinquefasciatus significantly decreased in the Siwalik ( β = 0 . 27; 95%CI = 0 . 06–0 . 84; p = 0 . 026 ) , Middle Mountain ( β = 0 . 23; 95%CI = 0 . 06–0 . 82; p<0 . 001 ) and High Mountain regions ( β = 0 . 004; 95%CI = 0 . 001–0 . 015; p<0 . 001 ) ( Figure 3C ) . The mean abundance of female C . quinquefasciatus increased reaching its peak in October and then gradually declined ( Figure 3D ) . The regression model for predicting female C . quinquefasciatus mean abundance is given in Table S3 . No significant interactions were identified between predictor variables in either regression model . In addition , there was a significant effect of only region ( F = 3 . 31; df = 3; p<0 . 01 ) on the mean number of Aedes eggs per ovitrap . The highest mean number of female A . albopictus was recorded in Birganj ( 2 . 58±1 . 74 individuals [±SE] ) followed by Kathmandu ( 1 . 5±0 . 94 individuals ) . The chi-square test indicated a significant association between abundance of female A . albopictus and region ( χ2 = 13 . 45; df = 2; p = 0 . 001 ) , month ( χ2 = 91 . 40; df = 5; p<0 . 001 ) and trap method ( χ2 = 34 . 32; p<0 . 001 ) . In the High Mountain locations Ranipauwa and Dhunche , however , A . aegypti and A . albopictus were not recorded . Among the Aedes mosquitos , A . aegypti was the most abundant species captured in all of the regions except the High Mountain locations where both A . aegypti and A . albopictus were not found during our survey . Interestingly , the frequency of co-occurrence of A . aegypti and A . albopictus in traps was very low indicating that one species may dominate over the other locally ( Figure 4 ) . The generalized linear model with ADJRAIN ( mm ) , ADJTEMP ( °C ) and ADJRH ( % ) as covariates for the mean abundance of female A . aegypti per BG-Sentinel trap indicated significant effects of these three variables ( Figure 5 ) . Each degree rise in ADJTEMP increased female A . aegypti abundance ( β = 1 . 63; 95%CI = 1 . 34–1 . 98; p<0 . 001 ) ; increased ADJRAIN reduced abundance ( β = 0 . 94; 95%CI = 0 . 92–0 . 97; p<0 . 001 ) and increased ADJRH also reduced abundance ( β = 0 . 59; 95%CI = 0 . 44–0 . 77; p<0 . 001 ) . Likewise , an increase of the ADJRAIN had a negative effect ( β = 0 . 98; 95%CI = 0 . 96–1 . 00; p = 0 . 050 ) , ADJTEMP had a significantly positive effect ( β = 1 . 36; 95%CI = 1 . 16–1 . 60; p<0 . 001 ) , and ADJRH had a significantly negative effect ( β = 0 . 68; 95%CI = 0 . 54–0 . 85; p<0 . 001 ) on the number of female C . quinquefasciatus per CDC light trap . Moreover , all these three covariates had significant effects on the number of C . quinquefasciatus female per BG-Sentinel trap ( data not shown ) . The ADJRH had significantly negative effects on the mean number of Aedes eggs per ovitrap ( β = 0 . 83; 95%CI = 0 . 71–0 . 97; p<0 . 001 ) . We did not find any significant effect of rainfall and temperature on the number of Aedes eggs per ovitrap ( p>0 . 05 ) . The number of Aedes eggs in ovitraps was positively correlated with the number of female A . aegypti per BG-Sentinel trap ( rs = 0 . 64; p<0 . 001 ) . The number of Aedes eggs per ovitrap was likewise positively ( but not significantly ) correlated with the number of A . albopictus per BG-Sentinel trap ( rs = 0 . 20; p>0 . 05 ) .
This is the first longitudinal entomological survey of adult Aedes and Culex mosquito vectors of DENV and LF using BG-Sentinel together with CDC light traps in Nepal . As the use of CO2 for monitoring mosquitoes is difficult in Nepal because of logistic and economic reasons , one aim of our field study was to test the regular monitoring of DF and LF vectors with BG-Sentinel and CDC light traps without the use of CO2 as an attractant . We captured a substantial number of various mosquito species in both traps , and the species captured were common in both traps with only one additional species ( C . vishnui ) in BG-Sentinel traps . Culex quinquefasciatus was the predominant species in both types of traps ( Table 1 ) , but the mean number of female C . quinquefasciatus per trap was nearly two times higher in CDC light traps than in BG-Sentinel traps ( Figure 2 ) . These findings suggest that CDC light traps without CO2 and BG-Sentinel traps with BG-Lure attractant can both be used in Nepal for monitoring adult C . quinquefasciatus . On the other hand , BG-Sentinel traps with BG-Lure attractant were much more efficient capturing A . aegypti and A . albopictus than CDC light traps . The trapping method obviously had a significant effect on the mean number of female A . aegypti caught , and the abundance of female A . aegypti was 19 times higher in BG-Sentinel than in CDC light traps . This indicates that , although both traps were able to capture A . aegypti , the BG-Sentinel trap was the more efficient trap for collecting A . aegypti in our study areas . The black and white visual target and contrast characterizing the BG-Sentinel trap are factors that contribute to the successful collection of this diurnal mosquito species . Also , the BG-Sentinel trap does not need CO2 for collecting DENV vectors and its BG-Lure attractant remains active for several months . Thus , this trap can be used for the long-term monitoring of DENV vectors and certain other mosquitoes of medical importance that it reliably attracts [44] . As previously demonstrated in other studies [44]–[47] , the BG-Sentinel trap equipped with BG-Lure attractant was the superior surveillance tool for A . aegypti populations in our study compared to CDC light traps . In agreement with findings from Puerto Rico [32] and Brazil [46] , a higher number of C . quinquefasciatus than A . aegypti were trapped in BG-Sentinel traps in our study . Although the difference was statistically not significant due to the overall relatively low number of A . albopictus that were collected along our transect , the BG-Sentinel traps also collected more A . albopictus compared to the CDC light traps in the present study which is consistent with previous reports [48]–[50] . The strongly significant positive correlation between the number of female A . aegypti caught in the BG-Sentinel trap and the number of Aedes eggs collected in the ovitraps , non-significant positive correlation between the number of female A . albopictus in BG-Sentinel traps and the number of Aedes eggs in ovitraps , and low number of other Aedes spp . collected in our study sites , suggest that the majority of Aedes eggs in the ovitraps might have been from A . aegypti . This positive correlation between the number of eggs and number of female A . aegypti in BG-Sentinel traps is similar to findings from Puerto Rico [32] . In addition , many Culex eggs which might have been mostly from C . quinquefasciatus were recovered from these ovitraps . This indicates that such simple ovitraps or similar devices may be useful tools for simultaneously monitoring populations of gravid female mosquitoes as indicators of potential DENV [32] and LF transmission in Nepal . Our study clearly demonstrated differences in the spatial distribution of these three important vector mosquito species and a strong effect of the physiographical region on the mean abundance of female A . aegypti and female C . quinquefasciatus . Similar findings on the effect of geographical region on the abundance of A . aegypti and A . albopictus have been reported from Vietnam [51] where A . aegypti predominated in urban-rural areas in the southern lowlands and A . albopictus in the northern region with more mountainous areas . In our survey , A . aegypti and A . albopictus were not collected in the High Mountain region which included some rural areas , and A . aegypti was predominant throughout the urban agglomerations of the Middle Mountain , Siwalik and Terai regions in Nepal ( Figure 3 ) . Aedes albopictus and C . quinquefasciatus had been recorded as early as the 1950s in Nepal [13] and were later recorded from the Terai lowlands , Siwalik hills ( Makwanpur ) and Middle Mountain regions ( Kathmandu ) [12] . In contrast , A . aegypti was recently introduced to Nepal where it was first recorded in a few cities in lowland areas near the southern border with India in 2006 [8] . In the Middle Mountain region of Nepal it was first found in Kathmandu in 2009 [11] . A study conducted in the Gharwal Himalayan region of neighbouring India reported the distribution of adult A . albopictus from 300–1 , 300 m , A . aegypti up to 800 m and C . quinquefasciatus up to 2 , 000 m asl [52] , roughly similar to our distribution records . The high spatial heterogeneity of the three species in the present study is consistent with reports that DENV is highly focal in nature [53] and DF outbreaks closely linked to the abundance of A . aegypti [54] . In our study , a BG-Sentinel trap installed in the Terai captured 84 male and 151 female A . aegypti during 24 hours in November . The fact that no adult A . aegypti and A . albopictus were collected in the High Mountain localities in our study suggests that both species are either not yet established at this altitude in this region , or could not be trapped due to their low population density during the study period . Other Aedes species were recorded up to the highest studied location at 2 , 100 m asl . The frequency of co-occurrence of both species in the Siwalik and Middle Mountain regions in the present study was consistent with findings from Vietnam [51] . Because of the low frequency of occurrence and low abundance of A . albopictus , no meaningful statistical analysis of this species could be performed . One possible explanation for the rarity of A . albopictus in this study might be our sampling in highly urbanized sites since A . albopictus has been reported to be mainly found in sub-urban and rural areas [55]–[57] . On the other hand , this species might be displaced by the newly introduced A . aegypti as has been reported in many studies in other South Asian countries [58]–[61] . In contrast , in settings with more temperate climate like the USA [62] where cold winters are a limiting factor for these mosquitoes , the recent establishment and spread of A . albopictus has largely displaced previously established A . aegypti populations , and A . albopictus has become the most abundant mosquito in man-made containers in much of the south-eastern USA [62]–[64] . The effect of the month of collection was significant for the mean abundance of females of A . aegypti and the mean abundance of female C . quinquefasciatus . The mean abundance of A . aegypti and A . albopictus was higher in the end of monsoon and post-monsoon rainy seasons ( September–November ) than in the winter season ( December–February ) with a peak in November in each region except the High Mountains where these species were not recorded . The peak mean abundance of these two Aedes species in the month of November may , on the one hand , be attributed to conditions of only slight rainfall , a moderate mean temperature ( 20°C ) and an optimal temperature range ( 10–30°C ) . In addition , by November large populations of these species could have built up since the preceding monsoon rainy season ( June–August ) using the numerous available water-filled containers . The populations of adult female C . quinquefasciatus peaked in October in the High Mountain region and in November in the Terai and Siwalik lowlands and Middle Mountains . In agreement with our findings , effects of the month or season of collection on the abundance of Aedes and Culex species elsewhere have been reported in many previous studies [51] , [65]–[68] . Regional environmental conditions may strongly determine the local abundance and distribution of mosquito species . The meteorological factors adjusted temperature , rainfall and relative humidity had significant effects on the mean number of female C . quinquefasciatus collected per BG-Sentinel trap . However , rainfall was not significant predictor of the mean number of female C . quinquefasciatus per CDC light trap which might be because of small sample size ( n = 30 ) compared to BG-sentinel trap ( n = 59 ) . Effects of temperature and rainfall on the population dynamics of C . quinquefasciatus observed along an altitude gradient in Hawaii [69] are consistent with our findings . Culex quinquefasciatus is a common domestic species whose preferred habitats range from clean freshwater to brackish , turbid and polluted water , and which is commonly found in ground pools , ditches , drains , sewerage , latrines , septic tanks and artificial containers such as discarded tires in Nepal [12] . Being highly anthropophilic , biting predominantly at night but also at daytime in dark rooms , and feeding both indoors and outdoors , it is an efficient vector for maintaining low levels of microfilaria within a population [19] , [70] . Therefore , unplanned urbanization , poor sanitation and drainage systems and the expansion of transportation systems in rural areas and highlands including pronounced rise of temperature in mountain regions may have led to a present and future expansion of the distribution of LF vectors . Falling temperatures coupled to increasing elevation , on the other hand , have negative effects on mosquito survival and parasite development rates [71] , [72] . Model projections show a wide distribution of LF in Africa , and that climate change and population growth will expand both the range and risk of LF infection in an endemic region of Africa [73] . Another study suggests that population growth rather than climate change is the dominant factor for predicting the prevalence and spread of LF on the African continent [74] . However , predicting the impact of climate change on LF is difficult owing to the chronic condition of this disease . We found significant effects of adjusted temperature , relative humidity and rainfall on the mean number of female A . aegypti per BG-Sentinel traps which is consistent with previous findings [32] . These reports indicate that temperature , relative humidity and rainfall play a significant role for the abundance of these mosquito species but not necessarily as a direct driver . Important effects of temperature on these species and the diseases they can transmit are those that shorten the extrinsic incubation period of pathogens , lead to increases in biting frequency and extensions of the average life span of mosquitoes [75]–[77] . Hence , increasing temperature can make temperate regions of Nepal vulnerable to DF epidemic . Interestingly , we collected A . aegypti and A . albopictus in the BG-Sentinel traps in the Terai lowlands even when minimum temperatures had dropped to around 8°C suggesting a considerable adaptive capacity of local A . aegypti and A . albopictus populations to low temperatures . No Aedes species could be captured in the Middle Mountain collection sites after November when mean temperatures had dropped to around 10°C and minimum temperatures to around 2°C during a few days . However , a substantial number of Aedes eggs was recorded in ovitraps that had been set up indoors in these sites , indicating an ongoing reproductive activity of A . aegypti during the cold winter months . Thus , low temperatures outdoors , where the BG-Sentinel traps had been positioned , may have been compensated by an indoor environment with temperatures that were still sufficient for oviposition in another example of the ability of vector insects to exploit human activities to their advantage [78] . Rainfall can be an important abiotic factor for mosquitoes in areas where their breeding sites are produced by rainfall . However , the effect of rainfall is more complex . In some cases , increased rainfall may increase the vector population size by creating new or better larval habitats while excessive rain would eliminate habitats through flooding , thus decreasing the vector population [79] , [80] . Drought events , while having a negative impact on natural breeding sites , can increase mosquito abundance by increasing man-made breeding sites created by household water storage [81] . In Nepal , the observed decline in the abundance of mosquitoes during the monsoon season which lasts from June to September ( low abundance in September ) might not only be attributed to the flushing of drains and the flooding of other outdoor breeding foci , but also to increased mortalities due to physical impact by heavy rain . During the post-monsoon season lasting from October to November with very low or no rainfall , we observed higher numbers of A . aegypti , A . albopictus and C . quinquefasciatus compared to September with high rainfall . In our study areas , many containers such as discarded tires , cemented tanks and metal drums with water provided suitable breeding sites in the post-monsoon season . Entomological indices are used to predict dengue risk transmission in many studies . To prevent DENV transmission , Barrera et al . [32] suggested that the number of female A . aegypti per BG-Sentinel trap and the number of eggs per ovitrap should be maintained well below two and ten , respectively . Accordingly , Mogi et al . [82] did not report DHF cases in Chiang Mai , Thailand , when the number of eggs in ovitraps was less than two . In our study , we found more than two female A . aegypti per BG-Sentinel trap and more than ten eggs per ovitrap in the Terai and Middle Mountain regions indicating a risk of DENV transmission in both regions . Interestingly , DF cases have been reported from the Terai and Middle Mountain regions , but also from the Siwalik regions [83] , [84] where the number of female A . aegypti per BG-Sentinel trap and the number of eggs per ovitrap were well below two and ten , respectively . Similarly , LF cases and high prevalence of microfilaria were reported from all study sites [17] , [21] where C . quinquefasciatus was recorded in the present study except Dhunche located at more than 2 , 000 m asl . This may be due to a lower vector density or lack of disease diagnosis as this district is regarded as LF free . Moreover , LF is a chronic condition and recently infected asymptomatic cases may not have reported . Unfortunately , because of the short study period and lack of sufficiently disaggregated data , the temporal dynamics of DF and the association with its vectors could not be established in the present study . The reported DF cases in Nepal show a clear seasonal pattern [8] , [83] , [84] that can be related to temperature and rainfall with almost all cases in the monsoon and post-monsoon seasons . This pattern is consistent with reports from other countries in South-East Asia [4] , [85] . Possible explanations for this seasonal transmission include vertical transmission of DENV in mosquitoes and , given the high number of asymptomatic DENV infections in the communities , silent transmission in people by a reduced number of vectors between the seasonal peaks [2] . A recent study suggests that a DENV-1 strain which is phylogenetically close to Indian viruses was responsible for the 2010 epidemic in Nepal and that this epidemic started in the southern lowland areas bordering India and then expanded to the mountain areas [84] . As DENV is a relatively recently introduced virus in Nepal and rapidly expanding its geographical range in the country , the implementation of both vertical ( top-down ) government-led and horizontal ( bottom-up ) community-led programmes is urgently required to limit its further spread and/or epidemic impact in the future . Further studies that integrate serological , entomological , parasitological , socio-economic , climatic and environmental data along different altitudinal transects of Nepal appear urgently needed in the context of the rapid climate change that especially the higher altitudes of this country are experiencing . Moreover , there should be routine surveillance of DENV vectors and DF cases to prevent outbreaks . Similarly , xenomonitoring could be used for LF surveillance as the national programmes scales down MDA in known endemic areas , and to investigate the expansion of LF in Himalayan districts that were previously considered non-endemic . The findings of our study may contribute to a better planning and scaling-up of mosquito-borne disease control programmes in the mountainous areas of Nepal that had previously been considered risk free . There is a higher preponderance and establishment of DENV vectors and DF cases up to the Middle Mountain region ( 1 , 310 m asl ) supporting previous studies , while LF cases are reported up to 1 , 800 m asl and LF vectors up to 2 , 100 m asl in the High Mountain region . Climate change can increase the altitudinal ceiling of vector distribution and potentially put regions of Nepal that are presently sub-tropical and temperate at risk of DF and LF in the future provided that the domestic environments commonly exploited by the vectors are available in the climatically newly suitable areas . The knowledge of the vector dynamics and infection status of mosquitoes using xenomonitoring should be used for vector-borne disease surveillance in Nepal . In view of the high population density of mosquitoes in central Nepal , an integrated vector management programme based on control operations should be developed and implemented to particularly control the three mosquito species A . aegypti , A . albopictus and C . quinquefasciatus . We believe that xenomonitoring and control measures from the lowlands up to the High Mountain region are essential for the prevention of DF and LF in Nepal and therefore recommend that insecticide treatments or larval habitat perturbations should be conducted based on the surveillance of larvae in the areas at risk defined herein . The rapid range expansion of DENV vectors in a relatively short time up to the Middle Mountain region together with the presence of all four DENV serotypes and a large vulnerable human population in Nepal suggest that the risk of DF and its severe forms like DHF and DSS is much higher in this country than previously thought . Moreover , the abundance of LF vectors above 2 , 000 m in areas previously considered to be LF free challenges the government efforts to eliminate LF in Nepal by 2020 . Therefore , urgent and responsible actions must be taken in coordination with related stakeholders to control DF and LF vectors in Nepal and their expansion into new areas . In addition to crucial community education and participation , cross-sectoral integration ensuring due consideration of the public health implications of transport management , urban development and planning , water supply , and waste and sewerage management are essential in this context . | Dengue fever , a viral disease transmitted by the bites of infected Aedes aegypti and Aedes albopictus mosquitoes , has been rapidly spreading in Nepal since it was first reported in this country in 2004 . Similarly , lymphatic filariasis , a parasitic disease transmitted by Culex quinquefasciatus mosquitoes in Nepal , is a public health problem in terms of morbidity and impact on the social and economic status of poor people living in rural and slum areas . Evidence for more pronounced temperature rises in higher altitudes of Nepal and an increasing frequency of dengue fever and lymphatic filariasis cases reported from mountain areas , in the absence of recent data on the mosquito vectors of these diseases , prompted us to investigate their distribution and abundance in this country . In our study , we document the distribution of A . aegypti and A . albopictus from the lowlands up to 1 , 310 m altitude in Kathmandu , and the distribution of C . quinquefasciatus up to Dhunche ( 2 , 100 m altitude ) , the highest locality included in this study . The wide distribution of these important disease vectors in the mountains , previously considered non-endemic for dengue fever and lymphatic filariasis , calls for an extension and scaling-up of vector-borne disease surveillance and control programmes in Nepal . | [
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] | 2014 | Spatio-Temporal Distribution of Dengue and Lymphatic Filariasis Vectors along an Altitudinal Transect in Central Nepal |
The genome-sequencing gold rush has facilitated the use of comparative genomics to uncover patterns of genome evolution , although their causal mechanisms remain elusive . One such trend , ubiquitous to prokarya and eukarya , is the association of insertion/deletion mutations ( indels ) with increases in the nucleotide substitution rate extending over hundreds of base pairs . The prevailing hypothesis is that indels are themselves mutagenic agents . Here , we employ population genomics data from Escherichia coli , Saccharomyces paradoxus , and Drosophila to provide evidence suggesting that it is not the indels per se but the sequence in which indels occur that causes the accumulation of nucleotide substitutions . We found that about two-thirds of indels are closely associated with repeat sequences and that repeat sequence abundance could be used to identify regions of elevated sequence diversity , independently of indels . Moreover , the mutational signature of indel-proximal nucleotide substitutions matches that of error-prone DNA polymerases . We propose that repeat sequences promote an increased probability of replication fork arrest , causing the persistent recruitment of error-prone DNA polymerases to specific sequence regions over evolutionary time scales . Experimental measures of the mutation rates of engineered DNA sequences and analyses of experimentally obtained collections of spontaneous mutations provide molecular evidence supporting our hypothesis . This study uncovers a new role for repeat sequences in genome evolution and provides an explanation of how fine-scale sequence contextual effects influence mutation rates and thereby evolution .
A major challenge of evolutionary genetics is to determine the mechanisms underlying cryptic patterns of mutation rate variation and how they influence evolutionary outcomes [1] . One of the most striking of these trends is the association between indel mutations and nucleotide substitutions [2]–[7] . Inter-species genome comparisons have revealed this trend to be universal to all prokaryotic and eukaryotic genomes examined thus far [4]–[6] . The prevailing explanation for this association is that indels , as “universal mutators” [4] , cause the accumulation of nucleotide substitutions in the hundreds of base pairs of sequence surrounding the indel [4] , [6] . Although such studies have been unable to unequivocally determine if the clusters are due to a single multimutational event ( multiple mutation hypothesis ) , the indel per se ( the mutagenic indel hypothesis ) , or the region of sequence in which the indel is found ( the regional differences hypothesis ) , the mutagenic indel hypothesis has been adopted by workers in the field [8]–[12] . The mechanism of indel mutagenicity proposed by Tian and co-workers is that indels , when heterozygous , cause paired chromosomes to form heteroduplex DNA during meiosis [4] . This is posited to cause error-prone DNA repair systems to target indel-containing regions , leading to an increased likelihood of nucleotide substitution in the sequence surrounding the indel . Over time , this increase in mutation rate is predicted to leave as its signature the clustering of nucleotide substitutions in the DNA surrounding indels , while corresponding non-indel-containing orthologous sequences should have a lower number of substitutions , in accordance with the background substitution rate . In addition , because the proposed mutagenic effect of the indel is postulated to be dependent on its heterozygosity , the accumulation of substitutions should cease as soon as the indel becomes homozygous in the population . These predictions contrast with the regional differences hypothesis; regional effects are predicted to cause both indel and non-indel haplotypes to accumulate substitutions whether the indel is heterozygous or not . The multiple mutations hypothesis differs from both the regional and indel hypotheses in that clusters of mutations are due to a one-off mutation event . Determining whether mutations have accumulated over time or are due to a single mutation event is difficult without the ability to examine indel divergence on a temporal scale . Here we use a population genomics approach to tease apart the dynamics of indel divergence using the genomes of Escherichia coli , Saccharomyces paradoxus ( S . paradoxus ) , Drosophila , and humans . We show that it is not the indel but rather the sequence region in which the indel occurs that is associated with the accumulation of nucleotide substitutions over evolutionary time scales . We propose a mechanism whereby a DNA sequence that is prone to cause replication fork stalling causes the recurrent recruitment of error-prone DNA polymerases to certain DNA sites , resulting in an increased likelihood of nucleotide substitutions in the surrounding DNA sequence .
The detection of indel-associated mutation in bacterial species poses a dilemma for the mutagenic-indel hypothesis . Prokaryotes are haploid; following the indel-causing event , the cell has only a brief heterogenote period during which , according to the mutagenic-when-heterozygous hypothesis , the indel is mutagenic . After a few cell divisions , the daughter cell will produce only indel-containing copies of the genome and will not have a non-indel version to recognize that the indel is present ( Figure 2 ) . The mutagenic-when-heterozygous theory then predicts ( at least in prokaryotes ) that nucleotide diversity does not accumulate over time . To test this prediction , we generated pre-defined , non-overlapping sets of old and new indels in E . coli . Old indels are those determined ( using an appropriate outgroup ) to have occurred before the divergence of the two strains under comparison; new indels are those that have occurred after their divergence ( Materials and Methods , Figure S2 ) . As shown in Figures 3A and S3 , D values are significantly higher for old indels ( black lines ) than those for new indels ( grey lines ) . This result demonstrates that , contrary to the mutagenic-when-heterozygous and multiple mutation hypotheses , mutations are accumulating at a higher rate in regions surrounding indels over time . Background D ( Db ) is the average difference in the DNA sequences of two aligned orthologous regions . An increase in the number of differences between the nucleotide sequences of two aligned orthologous regions above this average indicates an increase in the rate of the accumulation of substitutions . The mutagenic indel hypothesis states that the indel per se is the cause of an increase in mutation rate and the accumulated nucleotide diversity in the surrounding sequence . A consequence of this is that , of two aligned fragments of DNA , the indel-containing fragment should have a highly elevated D close to the indel and its corresponding non-indel-containing orthologous fragment should have a D equivalent to the background . These predictions can be tested by choosing an orthologous sequence from a third E . coli genome as an outgroup to infer the ancestral state of the aligned sequence , thus allowing us to pinpoint in which of the two aligned genome fragments the indel event has occurred . This is dependent on the assumption of parsimony—if indels are a convergent character , the indel haplotype could be mistakenly assigned . D can be calculated for the sequence windows surrounding an indel-containing region ( the indel haplotype ) and the corresponding orthologous region without the indel ( the non-indel haplotype ) with which it is paired . In order to minimize the bias caused by differences in the selective constraints upon aligned sequences , we employed stringent filters to ensure that the sequences compared are strictly orthologous ( see Materials and Methods ) . Figure 3D shows that the values of D for both the indel- and non-indel-containing haplotypes , Di and Dni , are elevated in window 1 as compared to the background nucleotide diversity Db . Although the values of Di in window 1 are often higher than Dni ( an average 14% difference in D ) , this was not significant ( two-sample Kolmogorov-Smirnov test , p>0 . 05 , Table S2 ) for any of the strains compared . By contrast , when Di and Dni are compared to Db , in five out of six comparisons Di is significantly greater than Db ( an average 57% difference in D ) , while Dni is significantly greater than Db in four cases ( two-sample Kolmogorov-Smirnov test , p <0 . 05 , Table S2; average 40% difference in D ) . Thus , for nearly as many instances as the indel haplotype , the non-indel haplotype has a D significantly higher than the background nucleotide divergence , confirming that the regional effect plays a role in the accumulation of nucleotide substitutions . These results raise the possibility that the accumulation of mutations surrounding indels ( Figure 3C ) is mainly due to regional effects and not attributable to indels per se . However , this conflicts with the inferences of previous studies [2] , [4] , [6] , that concluded that indels , not regions , are mutagenic . In order to find the cause of this disagreement , we took a closer look at the results of those studies as well as our own data . We noticed that the strains that are less diverged tended to have the largest difference between the indel and non-indel haplotypes ( Table S2 , Figure S4 ) . Indels detected in the comparisons of two highly similar strains must have happened since their relatively recent divergence . The fact that the more diverged strains differed less between the indel and non-indel haplotypes suggests that the indel-associated effect diminishes over time . When we studied the results of [4] and [6] , we found the same trend . For example , using data from [6] , when bacterial divergence was plotted against difference between Di and Dni , it showed that the difference between Di and Dni decreases with increasing divergence ( Figure S4 ) . A further example is provided by Tian et al . 's [4] analysis of heterozygote alleles at one-third and two-thirds frequencies in yeast . The mutagenic-when-heterozygous mechanism predicts that indels occurring at a higher frequency in a population have been accumulating mutations for longer periods and should thus have a higher D value and a greater difference between Di and Dni . Conversely , the indels at two-thirds frequency have a smaller Di/Dni ( 1 . 40 ) than the indels at one-third frequency ( 2 . 23 ) . The fact that indels that have been segregating for longer time have a smaller difference between the indel and non-indel haplotypes indicates that spending more time as a heterozygote actually diminishes the indel-associated effect , contrary to the prediction of the mutagenic-when-heterozygous hypothesis . The separation of D into Di and Dni allows us to calculate the proportion of D on the indel haplotype that can be attributed to the indel effect and to the regional effect , respectively ( see Materials and Methods ) . Under the assumption that indel-causing events are uniformly distributed since the time of divergence , it follows that the level of divergence between two strains is correlated with the average age of the indels found during comparison . If an indel constantly influences the accumulation of nucleotide substitutions in the surrounding sequence while polymorphic , we expect to see an increase in the difference between Di and Dni over time . Conversely , if indels have a one-time-only effect on nucleotide diversity , we expect to find a decline in this difference over time . We compared Di and Dni for alignments identifying new and old indels ( Materials and Methods , Table S3 ) . Figure 4A shows that the difference between Di and Dni decreases with increasing divergence ( Pearson's correlation coefficient , r = −0 . 769 , p = 0 . 0093 ) . This negative correlation is striking when compared to the positive correlation between time since divergence and nucleotide diversity when the indel and region effects are not separated ( Figure 3C , Pearson's correlation coefficient r = +0 . 711 , p = 0 . 00092 ) . This result suggests that it is the region , but not the indel , that is constantly influencing the accumulation of substitutions over evolutionary time scales . To test whether the aforementioned phenomenon is specific to prokaryotes , we carried out analogous indel analyses using the budding yeast Saccharomyces paradoxus . This organism is suitable for analysis because genome sequences are now available for a variety of its strains [14] and because S . paradoxus , like many multicellular eukaryotes , spends most of its life as a diploid [15] . The results of the analyses with S . paradoxus ( Figures 3B , 3E and 4B , Table S3 ) were in agreement with those obtained using E . coli sequences . The S . paradoxus strains used here ( Table S1B ) cover a wider range of divergence than the E . coli strains [16]; this allowed us to view the diminishing proportion of the indel-dependent component of D on a longer time scale ( Figure 4B , Pearson's correlation coefficient r = −0 . 963 , p = 0 . 008 ) . We then extended our analysis to Drosophila species ( Figure 4C ) ( see Materials and Methods ) . Although few species diverged recently enough to be suitable for analysis , the results corroborate our prior findings that the proportion of D attributable to the indel decreases over time ( Pearson's correlation coefficient r = −0 . 980 , p = 0 . 128 ) . It should be noted that the ratio of ( Di − Db ) / ( Dni − Db ) was calculated for several yeast and fly alignments with greater divergence than shown in Figure 4; in all cases , this ratio was approximately one ( Table S3 ) . All these results suggest that a difference between the indel and non-indel haplotype exists following the indel-causing event but that this difference decreases over time until stabilising with both haplotypes having the same amount of nucleotide diversity . Because our study is able to track indel divergence within a species , this analysis provides unequivocal evidence that nucleotide diversity associated with indels decreases over time . Mutations arise from inaccurate processing of DNA damage or errors incurred during DNA replication . E . coli possesses five DNA polymerases of which two , Pol IV and Pol V , are error-prone . These polymerases are recruited to stalled replication forks [17] , [18] and double-strand breaks [19] to restart DNA replication . Errors made by DNA Pol IV are biased towards frameshifts [20] , and though genomes exhibit a bias towards transitions [16] , DNA Pol V most often causes transversion mutations [21]–[23] . We analysed the ratio of transition to transversion changes for all aligned E . coli genomes and found that transversions are enriched close to indel and non-indel haplotypes ( two-sample Kolmogorov-Smirnov test , p <0 . 0001 ) ( Figure 5 ) ; this is also true for S . paradoxus and other eukaryotes [4] . The accumulation of mutations at a specific site at a higher rate is uncharacteristic of mutations caused by a mutagenic chemical or another random event and is most likely due to the persistent recruitment of error-prone polymerases to that site over evolutionary time . Impediments imposed by polynucleotide repeats or other repeat sequences are suggested to be common causes of DNA replication fork arrest [24] . We performed a computational analysis on the 20 bp immediately flanking our collection of E . coli , S . paradoxus , and Drosophila indels to determine the distribution of repeats around indels . We defined an indel as contiguous with a repeat if it occurred inside or immediately next to a repeat , and as repeat-proximal if some part of a repeat was positioned within 5 bp on either side of the indel . For E . coli , 43% of indels were contiguous with a homopolymer , while 20% were proximal . The corresponding numbers were 45% and 25% for yeast and 31% and 34% for flies , respectively ( Figure 6A ) . The association between repeat sequences and indels is well understood: repeat sequences are prone to sustain strand slippage mutations [25] , [26] , which tend to cause indels [19] , [27] . We propose a mechanism distinct from strand slippage for the regional increase in nucleotide substitutions , whereby repeat sequences and other polymerase-stalling motifs persistently cause the recruitment of error-prone DNA polymerases . Each time DNA replication is restarted by an error-prone polymerase , DNA surrounding the region will be synthesized with a higher rate of error [17] , [18] , [28] , leading to an increased likelihood of nucleotide substitution . The stalled fork also suffers a high rate of double-strand breaks , another route to error-prone repair [19] , [27] , [29] . The 3R hypothesis predicts that regions of a genome with increased sequence diversity should be able to be identified by repeat sequence abundance . We tested this prediction by using the recently sequenced genomes of three E . coli strains that we had previously not analysed . We searched for repeat-rich regions by first generating pairwise alignments as for our indel analysis , dividing these into non-overlapping 100-bp windows , and then binning each window according to its number of 4-nucleotide homopolymer repeats ( see Materials and Methods ) . We found that , even when indel-containing windows were excluded , windows with a higher number of repeat sequences had more nucleotide substitutions than those without ( 83% increase for SE11/REL606 and 71% increase for SE15/REL606 in windows with six repeats ) . As for indel-based analyses , the more diverged two-strain comparison had a higher value of D , supporting that repeats cause the accumulation of substitutions over time ( Figure 6B ) . We also found that the number of transversions relative to transitions was increased in repeat-rich regions ( 88% increase in windows with six repeats ) ( Wilcoxon Sum Rank , p<0 . 05 , Figure 6C , Table S5 ) . The “bump” in nucleotide substitutions associated with the indel ( the difference between Di and Dni ) that we and others [4] , [6] often observe requires an explanation . The declining ratio of Di/Dni shows that this bump is smoothened over evolutionary time ( Figure 4 ) . One explanation for this is that indel mutagenicity is transient because the indel-containing allele is only mutagenic as a heterozygote and its mutagenic effect will vanish when it becomes homozygous . The period for which bacteria exist as heterogenotes for an indel is orders of magnitude less than that for diploid eukaryotes . However , a consistent decrease in Di/Dni is found across taxonomic kingdoms , an observation at odds with the proposal that heterzygosity/heterogenosity causes the indel “bump . ” An alternative explanation is that the indel-associated bump in D may be due to the indel-causing event resulting in multiple nucleotide changes . This possibility is not implausible considering the spectrum of mutations in baker's yeast . Lang and Murray [30] found that in 63% of instances where two mutations occurred at the same time one was an indel and the other a nucleotide substitution; yet indels constituted only 6 . 67% of all mutations observed in that study . Whichever explanation is correct , it is evident that the indel effect is transient and that it is the surrounding sequence that is associated with the accruement of substitutions over evolutionary time scales . All the inferences made about indels , nucleotide substitutions , and repeat sequences have so far been drawn only from the comparisons of genomes . In order to test predictions made by the 3R and mutagenic indel hypotheses , we utilized the comprehensive collection of spontaneous ura3 mutants gathered by Lang and Murray [30] . This collection comprises 207 ura3 mutant alleles , each of which resulted from a single mutational event in a haploid ( and non-indel-containing ) gene . The mutagenic indel hypothesis predicts that the clustering of mutations is caused by indels; thus , this set of independently occurring mutants should not cluster . Conversely , the 3R hypothesis states that repeat sequences cause an increase in the likelihood of the surrounding sequence sustaining both indels and nucleotide substitutions; thus , according to this hypothesis , indels and substitution mutations collected from independent mutants should cluster around repeats . Using a model based on a hyper-geometric distribution ( Materials and Methods ) , we first found that indels and substitutions cluster together ( p = 0 . 019 ) , even though most substitutions occurred without a co-occurring indel ( 97% ) . Next , we tested for the association of indel/nucleotide substitution mutations with any of the 264 four-nucleotide combinations of A , T , C , and G ( e . g . , ATCG , ATCA , ATCT , etc . ) . It is expected by chance that 2 or 3 four-nucleotide combinations should be found to be significant; however , significant associations were found only with the repeat sequences TGTG ( p = 0 . 00027 ) , AAAA ( p = 0 . 0093 ) , and GTGT ( p = 0 . 0098 ) . These results confirm that indels , substitutions , and repeat sequences are associated independently of any initiating mutator indel . We directly tested whether insertions of repeat sequences could increase the mutation rate of nearby regions in yeast . We engineered a copy of the URA3 gene to contain either a poly ( A ) repeat , a poly ( G ) repeat , a poly ( TG ) repeat , or a random 12-mer sequence in the promoter , verified that these constructs did not abolish URA3 function , and then performed fluctuation tests using the maximum likelihood method to determine the mutation rate to URA3 inactivation . We observed that ( G ) 11 and ( G ) 12 conferred a significant increase in the phenotypic mutation rate compared to the wild type ( paired t test , p<0 . 001 , Figure 7 ) . Insertion of a shorter poly ( G ) sequence also conferred an increased rate , but the changes were less significant . On the other hand , the insertion of a random 12-mer sequence , poly ( A ) , and poly ( TG ) showed no effect on the mutation rate . The fact that poly ( G ) causes an increase in the mutation rate is interesting considering that tetranucleotides composed of G or C bases are absent in the URA3 gene and are 5–10-fold less common across E . coli , S . cerevisiae , and Drosophila genomes than A or T tetranucleotides ( unpublished data ) . In order to determine if clusters of indels and substitutions influenced coding sequences in humans , we used alignments of recent segmental duplications ( <5% diverged ) [31] to detect indels in the human genome , restricting our analysis to those sequences that had been confirmed as expressed ( see Materials and Methods ) . We found that indels and nucleotide substitutions occurring in human transcribed sequences follow the same patterns observed in other species , confirming that indel/region/repeat-associated mutation impacts genes expressed in humans ( Figure 8 ) . Here we have provided evidence suggesting that regional effects have a strong influence on the accumulation of nucleotide substitutions over evolutionary time scales . Although an indel effect is also observed , we have shown the proportion of D attributable to an indel effect diminishes over time . In addition , it is not possible to formally exclude whether this effect is due to a mutagenic indel effect or a single multiple mutation causing event . Although we found that many indels are associated with repeat sequences , many are not . This finding may be explained by the existence of other non-repeat polymerase stalling sequence motifs; another possible explanation is that repeat sequences were destroyed by mutation , while the indel remained . So what is the impact of the indel/region effect on phenotypic evolution ? Most indels in E . coli are within 100 bp of the nearest gene ( Figure S5 ) . In S . cerevisiae , 25% of promoters contain repeat sequences [32] and 600 seven-nucleotide homopolymer runs have been identified in essential genes [33] , putting cis-regulatory regions and coding sequences well within the range of the effect of indel/repeat-associated mutation .
The genomes and accession numbers used for E . coli/Shigella and S . paradoxus analyses are shown in Table S1 . Genome sequences for alignments between Drosophila species were downloaded from the UCSC database ( http://www . biostat . wisc . edu/~cdewey/fly_CAF1/ ) , while those for melanogastor/melanogastor alignments were downloaded from http://www . dpgp . org . The alignments of recent human segmental duplications were provided by [31] . For pairwise comparisons , genome sequences were aligned using BLAST with default parameters and divided into orthologous regions of at least 3 kb in length and >80% nucleotide sequence identity . Any region that could be aligned to multiple locations was not considered for analysis , ensuring that only orthologous sequences were used . A program was written in Perl script to find indel mutations within orthologous regions; those regions not containing indels were discarded . For three and four genome alignments , orthologous regions that were not common to all strains were discarded and those regions remaining were realigned using ClustalW . In order to determine in which of two aligned fragments an indel has occurred , an appropriate outgroup was selected using the phylogenetic tree [34] and confirmed by our own approximations of relatedness ( Table S4 ) . In addition to establishing in which of the fragments the indel had occurred , the number of nucleotide substitutions occurring in the indel containing haplotype ( Di ) and non-indel containing haplotype ( Dni ) was determined by comparison with an outgroup sequence . For instance , when three genomes were aligned to determine indel and non-indel haplotypes , the number of mutations on the non-indel haplotype was counted by comparison of the non-indel fragment with the outgroup , and the number of substitutions on the indel haplotype was calculated by comparing the indel haplotype and the outgroup . Statistical comparisons between indel- and non-indel-containing haplotypes were carried out using the non-parametric Kolmolgorov-Smirnov paired test . See the statistical analysis plan below for more details . An indel was designated as contiguous with a repeat for cases where the indel occurred inside the repeat ( A-AAA , AA-AA , or AAA-A ) , or immediately next to it ( −AAAA or AAAA− ) where − denotes the position of the indel . It was defined as near a repeat if any part of a repeat was within five nucleotides on either side of the indel ( AAAANNN− , AAAAN− , etc . ) . For the search for regions of high D on the basis of repeat sequence density , we used three E . coli strains not previously used in this study ( E . coli SE11 , E . coli SE15 , and E . coli B Str . REL606 ) . We searched for repeat-rich regions by first generating pairwise alignments ( as described for the indel analysis above ) , followed by generating non-overlapping 100-bp windows and binning of windows according to the number of homopolymer repeats of at least 4 nt in length . Repeat sequences interrupted by a substitution mutation so that the homopolymer was less than four continuous nucleotides in length were not included . We then calculated total D for each window as well as the D for these classes of mutation: substitution , indel , transition , and transversion . To test for statistically significant differences between different classes or 100-bp windows , we used the Wilcoxon Sum Rank test . In order to extract indel-flanking sequences for analysis , the positions of indels were recorded in each orthologous region . Next , the sequences ( 1 kb ) both up- and downstream were extracted and examined for additional indels . If one of the flanking sequences was found to contain additional indels , that flanking region was discarded . The sequence surrounding the indel was named and ordered into windows ( Figure S1 ) . For every analysis in this study , the nucleotide divergence ( D ) was calculated for each window using the Jukes-Cantor method [35] . Pairs of recently diverged strains were chosen based on a phylogenetic tree ( Figure 1B ) . Each of these designations as highly related was supported by our own estimations of divergence provided by pairwise alignments ( Table S4 ) . Two pairs of recently diverged strains were aligned by performing a new alignment of all four orthologous fragments in ClustalW , giving a total of four aligned genomes . New indels were those that occurred within pairs of recently diverged strains; for indels to be detectable , they must have occurred since the recent divergence of these two strains ( see Figure S2 ) . D for new indels was calculated using the alignment of two similar strains , of which one had been found to contain the indel . Old indels were those sites which concurred within recently diverged pairs but were different between the two pairs ( see Figure S2 ) . Such indels must have happened before the divergence of the highly similar strains yet after the divergence of the two sets of strains . For calculating D , one from each of the sets of similar genomes was selected , so that two highly diverged genomes were compared and from this comparison D is calculated for old indels . If there are double mutations ( sites where the two similar genomes are different from each other and the other diverged pair ) , these are scored as one substitution because the difference between the two similar strains must have happened since the divergence of the two diverged sets of strains and have already been scored in the new-indel analysis . The background divergence ( Db ) used for the regression shown in Figure 3C was calculated as the average D from windows 3 to 10 for each E . coli pairwise alignment ( window 1 comprises the 50 bp closest to the indel; windows 3 to 10 were assessed as consistently outside the range of influence of the indel ) ( see Figure 1A ) . The indel-associated divergence was calculated by subtracting the values obtained for Db from the value of D at window 1 . For pairwise comparisons between indel and non-indel haplotypes , previous studies have used paired t tests , however we found that our data was not normally distributed ( Shapiro-Wilk test for normality , p<0 . 05 ) . We used the two-sample Kolmogorov-Smirnov test to test for the appropriateness of the non-parametric Wilcoxon Sum Rank test for our samples . If the samples were found to be different by the Kolmogorov-Smirnov test , the Kolmogorov-Smirnov test was named and p value given ( as was the case for the indel/non-indel analysis ) . If the two-sample Kolmogorov-Smirnov test found the samples under comparison to be of the same shaped distribution , we carried out and presented the Wilcoxon Sum Rank test and p values ( this was the case for the repeat/window analysis ) . A comparison of the amount of nucleotide substitutions attributable to the indel and regional effects for indels of different ages would provide for a test of the hypothesis that indel-associated mutations accumulate over time . In principle , this could be achieved by using the sets of old and new indels used for the analysis presented in Figure 3A and 3B; however , the generation of the set of old indels required a four-genome alignment; a fifth genome needs to be added to determine the indel and non-indel haplotypes . Because of our strict criteria for defining orthologous regions , the partitioning of the old and new indel sets into indel and non-indel haplotypes leaves prohibitively few orthologous regions for analysis . An alternative is to consider pairwise sets of alignments . The background nucleotide diversity for each pairwise comparison ( Figure 1 ) provides a measure of relatedness; the greater the average value of background D , the more diverged the two strains . In order to gauge the range and degree of difference across these pairwise comparisons , the sets of background diversity values ( provided by the D values for windows 3 to 10 , which were chosen because they are outside the range of indel/region-associated influence ) were compared . We found that most strains had distinct levels of sequence divergence from each other ( Tukey's HSD , p<0 . 05 , Table S4 ) , with an approximately 20-fold difference in D values between the most and least diverged strains ( see Table S4 for details ) . In order to cover a range of pairwise comparisons of increasing divergence , we chose four strains and systematically compared them to strains from clades of increasing divergence . The least divergent outgroup was always chosen . Each value of D can be partitioned into composite fractions ( Figure 3D and 3E ) . Di is attributable to the effect of the indel and the region together , whereas Dni is attributed to the region alone . ( Di − Db ) / ( Dni − Db ) provides a measure of the total proportion of Di that is influenced by the indel . If ( Di − Db ) / ( Dni − Db ) = 1 , none of the increase in nucleotide diversity can be attributed to the indel . As the value increasingly exceeds one , more of the nucleotide substitutions surrounding indels can be attributed to the indel effect . The indels detected in pairwise comparisons of more diverged strains cannot be strictly called “old” indels; these pairwise alignments will also include indels that have occurred relatively recently . However , increasingly divergent strains will be composed of a greater proportion of relatively old indels . This method of comparing indels between less diverged and more diverged strains will therefore underestimate the negative association between indels and the accumulation of nucleotide substitutions . In order to explore indel divergence in a metazoan genus , we aligned sequenced genomes of the genus Drosophila . However , all pairwise comparisons ( except the alignment of D . sechelia and D . simulans ) were diverged so much that the difference between Di and Dni was undetectable ( ( Di − Db ) / ( Dni − Db ) = 1 ) . To possibly obtain alignments of less diverged genomes , we used alignments of 37 genomes available from the D . melanogastor 50 genome project ( http://www . biostat . wisc . edu/~cdewey/fly_CAF1/ ) . However , the alignment of any two of these genomes could not give enough indels suitable for analysis; most indels detected within D . melanogastor tended to cluster , leading to the rejection from our analysis of many indel-containing regions . To overcome this , suitable indels found from the alignment of all 35 strains from the Raleigh collection [36] to two of the Malawi strains ( MW63 and MW27 ) [37] were used; indels found in more than one alignment were discarded , and from this set the 100 most and 100 least diverged indel-containing alignments were taken ( background divergence was taken as Db and calculated based on the average D of windows 3 to 10 ) . Each nucleotide site of URA3 was classified as being mutable or not , based on the 5 bp of sequence on each side of that nucleotide , creating a stringent null model for the expected distribution of nucleotide substitutions and indel mutations . The probability of obtaining the observed distribution under the null model was calculated using the hypergeometric distribution: where for the test for association between indels and substitutions , m is the total number of windows which are defined as mutable , k is the number of times an indel is in a region defined as mutable , N is the number of sliding windows , n is the total number of indel mutations , and for the test for association between repeat sequences and indels and substitutions , k is the number of times a tetranucleotide sequence x is contiguous with a nucleotide site defined as mutable and n is the total number of times a tetranucleotide sequence x appears in URA3 . A single ( TG ) 6 , ( G ) 12 , or ( A ) 12 tract ( or a random 12-mer ( AAGTGTCAAATA ) as a control ) was inserted between positions −4 and −5 of URA3 . Because these sequences are inherently unstable , multiple lengths of a homonucleotide tract were recovered during the cloning process , all of which left URA3 functional—providing evidence that alteration in the length of this sequence could not confer the Ura- , 5-FOA-resistant phenotype . Fluctuation tests were carried out in order to determine the mutation rate of altered URA3 genes . These were carried out by first setting up overnight cultures of each strain to be assayed in CSM-Ura media to ensure maintenance of the functional URA3 gene . The following day each strain's culture was diluted so that low numbers of cells ( ∼1 , 000 ) were inoculated into at least 10 independent 100 µl YPD cultures per strain in 96 well plates . Cultures were incubated at 30°C for 2 d without shaking and then spot plated onto dry 5-FOA plates . Aliquots ( 5 µl ) of each culture were pooled , diluted , and subsequently plated onto three YPD plates to determine the total cell count . Each experiment was repeated three times . Mutation rates were calculated using the equation µ = m/Nt , where m is the mutant frequency and Nt is the total number of cells in the culture . m was determined by counting the number of 5-FOA resistant colonies for each of the 3 sets of 10 independent cultures; then calculations were carried out using FALCOR software [38] ( http://www . keshavsingh . org/protocols/FALCOR . html#interface ) , which employs a maximum likelihood method developed by Sarkar , Ma , and Sandri [39] . The resultant value for m ( mean mutant frequency ) is divided by the total number of cells in the culture Nt . Nt provides a measure of the total cell divisions that have occurred in the culture; therefore , our final unit is number of Ura− mutants per cell division . Error bars are 95% confidence intervals as calculated by FALCOR using a formula devised by [40] . t tests were used to compare all strains to the wild-type strain , using formula 5 on the FALCOR website . In order to identify indels occurring within the human lineage that may have influenced phenotypic evolution , we used a collection of recent segmental duplications ( <5% diverged ) [31] and identified them as expressed by comparing with the human mRNA sequence collection ( refseq , NCBI ) . We used the Chimpanzee genome as an outgroup to identify indel and non-indel haplotypes ( http://hgdownload . cse . ucsc . edu/downloads . html#chimp ) . All human segmental duplications were present as a single copy in the chimpanzee genome . The non-indel haplotype corresponds to the human copy that is the same as the chimp single copy at the indel site , while the indel-containing copy is the one that differs from the chimp version at the indel site . We searched for an association between indel sites and various sequence elements that could have been associated with an increased nucleotide substitution rate . We generated a list of indels found in the E . coli K12 MG1655 genome , the best studied of all E . coli strains for which such sequence elements are well characterized . For each indel , the sequence region flanking 1 kb of the indel was designated as an indel-containing portion of the genome . The frequency with which sequence elements of interest were found in indel-containing portions of the genome compared to the rest of the genome was scored . The sequence elements that were searched were transposable elements and insertion sequences , tRNA genes , recombination sites ( as indicated by the chi site ) , DNA sites prone to breakage ( sites identified by the program Twist Flex ) , and repeat sequences . | An intriguing observation made during the comparison of genomes is that insertion and deletion mutations ( indels ) cluster together with nucleotide substitutions . Two ( not mutually exclusive ) hypotheses have been proposed to explain this phenomenon . The first postulates that an indel mutation causes an increase in the likelihood of the surrounding sequence incurring nucleotide substitutions , while the second claims that the region of DNA in which such a cluster is located is more likely to sustain both indels and substitutions . Here , we present evidence suggesting that the region of DNA , and not the indel , is associated with the accumulation of clusters of mutations over evolutionary time scales . We find that repeat sequences are closely associated with a large proportion of indels and that the abundance of repeat sequences is linked with regions of increased nucleotide diversity . By analysing molecular data and measuring the mutation rates of genes engineered to contain repeats , we find that the mutation rate can be manipulated by the insertion of long repeat sequences . On the basis of these results , we propose a model in which repeat sequences are prone to cause stalling of the high-fidelity DNA polymerase , leading to the recruitment of error-prone repair polymerases which then replicate the surrounding sequence with a higher-than-average error rate . | [
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] | 2011 | Clusters of Nucleotide Substitutions and Insertion/Deletion Mutations Are Associated with Repeat Sequences |
HIV and SIV infection dynamics are commonly investigated by measuring plasma viral loads . However , this total viral load value represents the sum of many individual infection events , which are difficult to independently track using conventional sequencing approaches . To overcome this challenge , we generated a genetically tagged virus stock ( SIVmac239M ) with a 34-base genetic barcode inserted between the vpx and vpr accessory genes of the infectious molecular clone SIVmac239 . Next-generation sequencing of the virus stock identified at least 9 , 336 individual barcodes , or clonotypes , with an average genetic distance of 7 bases between any two barcodes . In vitro infection of rhesus CD4+ T cells and in vivo infection of rhesus macaques revealed levels of viral replication of SIVmac239M comparable to parental SIVmac239 . After intravenous inoculation of 2 . 2x105 infectious units of SIVmac239M , an average of 1 , 247 barcodes were identified during acute infection in 26 infected rhesus macaques . Of the barcodes identified in the stock , at least 85 . 6% actively replicated in at least one animal , and on average each barcode was found in 5 monkeys . Four infected animals were treated with combination antiretroviral therapy ( cART ) for 82 days starting on day 6 post-infection ( study 1 ) . Plasma viremia was reduced from >106 to <15 vRNA copies/mL by the time treatment was interrupted . Virus rapidly rebounded following treatment interruption and between 87 and 136 distinct clonotypes were detected in plasma at peak rebound viremia . This study confirmed that SIVmac239M viremia could be successfully curtailed with cART , and that upon cART discontinuation , rebounding viral variants could be identified and quantified . An additional 6 animals infected with SIVmac239M were treated with cART beginning on day 4 post-infection for 305 , 374 , or 482 days ( study 2 ) . Upon treatment interruption , between 4 and 8 distinct viral clonotypes were detected in each animal at peak rebound viremia . The relative proportions of the rebounding viral clonotypes , spanning a range of 5 logs , were largely preserved over time for each animal . The viral growth rate during recrudescence and the relative abundance of each rebounding clonotype were used to estimate the average frequency of reactivation per animal . Using these parameters , reactivation frequencies were calculated and ranged from 0 . 33–0 . 70 events per day , likely representing reactivation from long-lived latently infected cells . The use of SIVmac239M therefore provides a powerful tool to investigate SIV latency and the frequency of viral reactivation after treatment interruption .
A major obstacle to developing a cure for HIV is the establishment in early infection of long-lived viral reservoirs , defined as sources of virus that can persist over extended periods despite seemingly effective suppressive combination antiretroviral therapy ( cART ) , that can cause recrudescent viremia if cART is interrupted . While multiple anatomic sites and cell compartments likely act as viral reservoirs , it has been argued that latently infected resting CD4+ T cells represent the most significant long-lived viral reservoir for HIV-1 [1–7] . During latency , these reservoirs are unrecognized by host immune responses and cells containing integrated latent proviruses are unaffected by current cART , which acts only by blocking new rounds of infection . For patients to safely stop treatment , the immune system must be able to control rebound infection ( sustained cART free remission or functional cure ) , or all reactivatable replication-competent virus must be completely eradicated . Numerous studies are in progress to test therapies designed to decrease viral reservoir size and prolong ART-free remission . A critical element for evaluating the effectiveness of these therapies is an accurate measurement of reservoir size before and after treatment . These assessments have typically involved ex vivo estimates and have been based on total cell-associated viral DNA ( CA-vDNA ) measurements [8–10] , stimulation of PBMCs or enriched CD4+ T cells to measure the frequency of cells producing viral RNA ( vRNA induction assay or TILDA ) [11–14] or the frequency of cells harboring replication competent virus ( quantitative viral outgrowth assays , QVOA ) [1 , 3 , 13 , 15 , 16] . However , each method for estimating reservoir size has shortcomings . Ho et al . demonstrated that the QVOA tends to underestimate the amount of replication competent virus present in any sample , as not all latent proviruses will reactivate after a single stimulation event [16] . Additionally , QVOA requires large source specimens , is time and labor intensive , and has limited precision and dynamic range . On the other hand , PCR detection of viral DNA tends to greatly overestimate the reservoir size , as much of the viral DNA detected in these assays does not encode full length replication competent virus due to large deletions or APOBEC mediated mutations . While the vast majority of intact , APOBEC mutation-free genomes are replication competent and could contribute to rebound viremia [16] identifying and quantifying these genomes requires near-full genome sequencing which is time consuming and expensive , necessarily precluding its use in large cohorts of patients . While accurate assessment of the size of the viral reservoir is central to the evaluation of HIV cure strategies , none of the ex vivo assays directly assess the size of the viral reservoir that can lead to recrudescent viremia after cART interruption . Most studies evaluating the effects of novel therapies on viral reservoir size are dependent on these ex vivo assays , however due to sample size and assay sensitivity issues , “undetectable” viral measurements do not necessarily indicate an absence of reactivatable virus , so as experimentation progresses , ultimately these treatments still require testing in HIV+ patients with the eventual discontinuation of cART to test for functional cure . In these instances , time to rebound after treatment interruption is considered the most direct measure of cure intervention treatment efficacy [17] . This approach might be effective for revealing large differences between treatment groups , which cause significant differences in time to detectable rebound viremia , however effects of treatments that result in small but potentially meaningful changes in reservoir size may be too subtle to be detected with this approach [17] . This will be particularly true for individuals with large reservoirs where even large differences between treatment groups will be difficult to detect using only time to rebound , and in groups with highly divergent interpatient reservoir size which will affect time to detectable rebound viremia . Therefore , alternative approaches for evaluating the functional reservoir size ( i . e . the cells that can contribute to systemic viremia once therapy is removed ) and the effects of new therapeutic interventions on the reservoir size are needed . AIDS virus infected non-human primates ( NHPs ) represent useful models to study viral reservoir establishment and to evaluate changes in reservoir size with novel interventions . Until recently , consistent and complete viral suppression was difficult to achieve in SIV-infected rhesus macaques with cART regimens developed for HIV-1 infection in humans . However , there are now several classes of drugs , including nucleos ( t ) ide reverse-transcriptase inhibitors , protease inhibitors , and integrase inhibitors that have been evaluated and shown to be effective for suppression of SIV and SHIVs in infected macaques . Recently , cART regimens have been developed that can effectively , durably and sustainably reduce plasma vRNA to clinically relevant levels ( below 15–50 copies per mL ) [18–22] . These regimens result in similar viral suppression dynamics to those observed in humans . Additionally , drugs are typically administered daily without any “drug holidays” or accidental missed doses . Frequent blood sampling and standardized assays provide assurances of successful suppression . Finally , NHPs may be removed from cART without the ethical implications involved in removing HIV-1 infected humans from treatment . To more fully realize the potential of NHP models for evaluation of candidate cure approaches , we developed a novel , barcoded virus system that allows for a deep genetic assessment of the number of rebounding viruses , in conjunction with time to rebound viremia measurements . This novel barcoded virus is fully and stably replication competent in vitro and in vivo and can be used to establish infection with a large number of otherwise sequence identical viral clonotypes bearing unique barcode sequences . Following cART treatment and interruption , the number and relative proportion of each rebounding clonotype can be measured with next generation sequencing of the barcodes , using high template input that allows for the discrimination of individual rebounding clonotypes . By combining viral growth rates ( the rate at which the virus grows once achieving detectable systemic infection ) and the relative proportion of each rebounding clonotype , the frequency of rebound of each clonotype can be estimated in each animal . This approach is likely more sensitive than measuring time to detectable viremia alone because it is less affected by natural variation among individual animals , and consequently requires smaller group sizes to distinguish statistically significant differences in reservoir size . This approach allows for detection of both small or large changes in the viral reservoir population , a distinction which may be critical for evaluating interventions resulting in real , but only modest changes in reservoir size . Our use of this system in initial in vivo studies demonstrates that the time of initiation and duration of cART administration in NHPs can alter the size of the reservoir , allowing for tightly controlled experimental design and execution , an idea also introduced by Whitney et al ( 19 ) . These data will help inform HIV cure research by providing a basic understanding of the biology of latency establishment , maintenance , and reactivation and will facilitate evaluation of potential therapies intended to reduce reservoir size .
Twenty-six purpose-bred Indian-origin male rhesus macaques ( Macaca mulatta ) weighing on average 7kg ( range 5-9kg ) were housed at the National Institutes of Health ( NIH ) and cared for in accordance with the Association for the Assessment and Accreditation of Laboratory Animal Care ( AAALAC ) standards in an AAALAC-accredited facility and all procedures were performed according to protocols approved by the Institutional Animal Care and Use Committee of the National Cancer Institute ( Assurance #A4149-01 ) . Animals were maintained in Animal Biosafety Level 2 housing with a 12:12-hour light:dark cycle , relative humidity 30% to 70% , temperature of 23 to 26°C and all animals were observed twice daily by the veterinary staff . Filtered drinking water was available ad libitum , and a standard commercially formulated nonhuman primate diet was provided thrice daily and supplemented 3–5 times weekly with fresh fruit and/or forage material as part of the environmental enrichment program . Environmental enrichment: Each cage contained a perch , two portable enrichment toys , one hanging toy , and a rotation of additional items ( including stainless steel rattles , mirrors , and challenger balls ) . Additionally , the animals were able to listen to radios during the light phase of their day and were provided with the opportunity to watch full-length movies at least three times weekly . At the start of the study , all animals were free of cercopithecine herpesvirus 1 , simian immunodeficiency virus ( SIV ) , simian type-D retrovirus , and simian T-lymphotropic virus type 1 . All animals were treated with enrofloxacin ( 10 mg/kg once daily for 10 days ) , paromomycin ( 25 mg/kg twice daily for 10 days ) , and fenbendazole ( 50 mg/kg once daily for 5 days ) followed by weekly fecal culture and parasite exams for 3 weeks to ensure they were free of common enteric pathogens . At least a 4-week post-treatment period allowed time for stabilization of the microbiome prior to use in this study . Primers were designed to introduce an MluI restriction site between the vpx and vpr accessory genes . These primers contain regions complementary to either the vpx or vpr genes with the MluI restriction site appended to the 3’ end of each primer . Amplicons were generated from SIVmac239 template using a generic primer upstream of SbfI and downstream of the EcoRI restriction site . The vpx-containing fragment was digested with MluI and SbfI , and the vpr-containing fragment was digested with MluI and EcoRI . SIVmac239 plasmid digested with SbfI and EcoRI was ligated with the two digested amplicons overnight at 16°C using T4 DNA ligase ( NEB ) . 5μL of the ligation reaction was transformed into Stbl2 cells ( Invitrogen ) and plated on agar plates containing 100μg/mL ampicillin . Resulting colonies were checked for correct assembly and insertion of the MluI site . This clone was termed SIVmac239-Mlu . The barcode insert was synthesized as single stranded forward and reverse barcoded templates ( IDT ) that were comprised of 10 random bases , flanked on either end by a stretch of bases complementary to the same region on the opposite primer to function as a molecular “clamp” with MluI sticky ends on both ends of the dimer ( Fig 1A ) . To generate primer dimers , the forward and reverse barcode primers were mixed in equal proportion and heated to 95°C . The temperature was slowly lowered at a rate of 1 . 5°C/min to allow primer pairs to anneal . SIVmac239-Mlu was digested with MluI and the DNA was purified with a Qiaex II kit . The digested SIVmac239-Mlu and primer dimers were mixed and ligated at 16°C overnight . The primer sequence was designed such that upon ligation of the primer into the MluI site of the SIVmac239-Mlu , the MluI site would be destroyed . Thus , the ligation product was digested again with MluI to linearize any genome not containing a primer dimer insert , and the digestion product cleaned and purified with a Qiaex II kit . The eluted product was transformed into Stbl2 cells , and transformants were grown up in LB amp overnight . The plasmid library was extracted from the bacterial preparations using the Qiagen MaxiPrep kit . Virus was prepared in HEK-293T cells transfected with the prepared SIVmac239M plasmid library using Mirus Trans-IT 293 transfection reagent as described by the manufacturer . Culture medium was changed at 24hr post-transfection , and culture supernatants were collected at 48hr . Supernatants were passed through a 0 . 45μm filter and stored at −80°C in 0 . 5 or 1 mL aliquots . Viral infectivity was determined using TZM-bl reporter cells ( reference no . 8129; NIH AIDS Research and Reference Reagent Program ) , which contain a Tat-inducible luciferase and β-galactosidase gene expression cassette . Infectivity was determined by assessing the number of β-galactosidase expressing cells present after infection with serial dilutions of viral stocks . After dilution correction , wells containing blue cell counts falling within a linear range were averaged and used to determine the titer of infectious units ( IU ) per mL in the viral stock as previously described [23] . RNA was isolated from plasma or viral stock using QIAamp Viral RNA mini kit per manufacturer’s instructions . RNA was eluted from the column with 65μL elution buffer . cDNA was synthesized from the extracted DNA using Superscript III reverse transcriptase ( Invitrogen ) and a reverse primer ( Vpr . cDNA3: 5’-CAG GTT GGC CGA TTC TGG AGT GGA TGC-3’ at position 6406–6380 ) . The reaction mixture was prepared as previously described with initial incubation at 50°C for one hour then increased to 55°C for an additional hour . Temperature was increased to 70°C , and the reaction incubated for 15 minutes . Each reaction was then treated with RNaseH and incubated at 37°C for 20 minutes . qRT-PCR was used to quantify the cDNA synthesized in the previous step using the primers VpxF1 5’-CTA GGG GAA GGA CAT GGG GCA GG-3’ at 6082–6101 and VprR1 5’-CCA GAA CCT CCA CTA CCC ATT CATC-3’ at 6220–6199 . PCR was used to amplify the cDNA and add MiSeq adaptors directly onto the amplicon . Reactions were prepared using High Fidelity Platinum Taq per the manufacturer’s instructions , using primer VpxF1 and VprR1 combined with either the F5 or F7 Illumina adaptor sequence containing a unique 8 nucleotide index sequences . Template input values ranged from 5x103 copies to 1x106 copies . Reaction conditions used are as follows: 94°C , 2min; 40x [94°C , 15sec; 60°C , 1:30min; 68°C , 30sec]; 68°C , 5min . Following PCR , 10μL from each reaction was pooled and purified using the QIAquick PCR purification kit . The resulting eluted DNA was quantified using the QuBit . The combined DNA sample was diluted to 3 . 0nM and 5μL of this diluted sample was placed in a new tube and denatured with 5μL 0 . 2N NaOH . This sample was vortexed and centrifuged at 280xg for 1 minute . The sample incubated at room temperature for 5 minutes , and 990μL of chilled HT1 buffer added . This sample was then diluted to 12 . 5pM . The control PhiX library was treated similarly . 2μL of the PhiX library was combined with 3μL Tris-HCl pH 8 . 5 , 0 . 1% tween-20 . 5μL of 0 . 2N HCl was added to the library , and the sample vortexed and centrifuged at 280xg for 1 minute . The sample was incubated at room temperature for 5 minutes , and 990μL of chilled HT1 buffer added . Multiplexed samples and PhiX library were then loaded on the MiSeq reagent tray , and the run initiated . For low-template samples , we used single genome amplification ( SGA ) followed by direct Sanger sequencing to assess the frequency and number of unique barcodes . cDNA synthesis and PCR was performed as described above but using a limiting dilution of cDNA prior to PCR amplification . This method provides representative proportionality and excludes PCR-induces errors [24] . Samples that were multiplexed were separated into individual samples using Geneious software and the unique 8-nucleotide index . After barcode splitting , individual barcoded clonotypes were identified by sequencing the first 50 bases of vpr . The 34 bases immediately upstream of this alignment were extracted and presumed to encode the inserted barcode region . Sequences obtained from infected animals were identified by comparison to all barcodes identified in the stock ( 14 , 357 ) . Only identical matches to a defined barcode were counted as an authentic input sequence . Given the short duration of infection prior to initiation of cART and the limited size of the insert , the vast majority of sequences were identical to a known barcode and were thus identified . Some identified barcodes contained 1 or more deletions in the insert region . This deletion resulted in 1 or more bases of vpx necessarily included in the extracted “barcode” . Although these unique but shorter barcodes represent only 0 . 7% of all inserts observed , they were included in the comprehensive tally of all barcodes because they remain a unique and genetically identifiable insert . Since all samples were quantified by real-time PCR , the theoretical limit of detection was estimated for each sample as the minimum number of sequences that would result from a single copy of an input template . Sequences below this threshold were discarded . Viral replication curves were prepared by culturing CD8+ T-cell-depleted Indian-origin rhesus macaque peripheral blood mononuclear cells ( PBMCs ) ( CD8+ depletion performed using Miltenyi Biotec CD8+ microbeads ) in RPMI supplemented with 10% fetal bovine serum ( FBS ) , 2mM l-glutamine , and 100U/mL penicillin and 100μg/mL streptomycin ( RPMI-complete ) , stimulated for 3 days with 5μg/mL phytohemagglutinin ( PHA ) and IL-2 ( 100U/mL ) . Stimulated PBMC cultures were infected with SIVmac239 or SIVmac239M at an MOI of 0 . 01 or 0 . 001 ( as determined by TZM-bl ) . 24hr post-inoculation , cell cultures were washed with phosphate buffered saline ( PBS ) twice and once with RPMI-complete to remove excess virus . Viral replication was monitored over 14 days by detection of supernatant SIV p27 antigen in an enzyme-linked immunosorbent assay ( ABL ) according to the manufacturer’s provided protocol . In total , 26 animals were intravenously infected with 2 . 2x105 IU ( 1mL ) of transfection produced SIVmac239M . All 26 animals were used to enumerate the number of detectable barcodes measured during primary infection . Of these 26 animals , two animals were followed for over 3 months to assess early viral replication kinetics ( peak and set point viral load ) of SIVmac239M . Four of the other infected animals began antiretroviral treatment beginning at day 6 post infection and continued for 82 days . Each animal received a combination antiretroviral therapy ( cART ) regimen comprising a co-formulated preparation containing the reverse transcriptase inhibitors tenofovir ( TFV: ( R ) -9- ( 2-phosphonylmethoxypropyl ) adenine ( PMPA ) , 20 mg/kg ) and emtricitabine ( FTC; 50 mg/kg ) administered by once-daily subcutaneous injection , plus raltegravir ( RAL; 150-200mg ) given orally twice daily . At the time of interruption from cART , three animals were infused at the day of cART interruption with autologous CD8+ T cells transduced with an anti-SIV Gag T-cell receptor ( animals MK9 and KTM ) or with an irrelevant receptor ( animals KMB and KZ2 ) plus daily subcutaneous injections of IL-2 at 10 , 000 IU/kg for 10 days . The total number of infused cells ranged from 4 . 6 to 6 . 4x109 cells with <1% of the cells CD4+ . In these animals , infused cells did not traffic to lymphoid or GI tissues and persistence of the cells was poor . Animal KTM died due to procedural complications at the time of cART interruption and was therefore excluded from subsequent analyses . In a separate cohort of 6 animals ( study 2 ) , therapy was initiated on day 4 post-infection with the same therapeutic regimen ( TFV , FTC , RAL ) described above with the addition of the protease inhibitor indinavir ( IDV; 120mg BID ) and ritonavir ( RTV; 100mg BID ) for the first 9 months . In study 2 , cART treatment was continued for 305 , 374 , or 482 days , with two animals discontinuing therapy at each time point . The remaining 14 animals were used to enumerate the number of replicating clonotypes during primary infection . Whole blood was collected from sedated animals . Plasma for viral RNA quantification and PBMCs for proviral DNA assays were prepared from blood collected in EDTA Vacutainer tubes ( BD ) . Following separation from whole blood by centrifugation , plasma aliquots were stored at 80°C . PBMCs were isolated from whole blood by Ficoll-Paque Plus ( GE Healthcare ) gradient centrifugation . Plasma viral load determinations for SIV RNA were performed over the duration of the study using quantitative real-time PCR as described previously [25] . The limit of detection of this assay is 15 vRNA copies/mL . Quantitative assessment of cell-associated viral DNA and RNA in PBMC pellets was determined by the hybrid real-time/digital RT-PCR and PCR assays essentially as described in Hansen et al . [26] but specifically modified to accommodate cell pellets . 100μL of TriReagent ( Molecular Research Center , Inc ) was added to cell pellets in standard 1 . 7mL microcentrifuge tubes and the tubes sonicated in a Branson cup horn sonicator ( Emerson Electric , St . Louis ) for 15 seconds at 60% amplitude to disrupt the pellet . Additional TriReagent was added to a final volume of 1mL and the remainder of the protocol was carried out as described previously [26] . Limit of detection is evaluated on a sample by sample basis , dependent on the number of diploid genome equivalents of extracted DNA assayed . SIVmac239M viral stock was randomly distributed into 168 aliquots with 5 , 000 viral cDNA templates per aliquot . After next-generation sequencing of the barcode region , a bimodal frequency distribution of the number of copies of a given sequence in a single aliquot was observed . Many sequences were present at very low copy number , likely representing erroneous sequences generated during the PCR amplification and/or sequencing process . By contrast , sequences present at high copy numbers ( representing authentic ‘input’ barcode sequences ) were also observed in each aliquot . A mixture model approach was used to model the frequency of both the erroneous and input sequences . If X is a random variable corresponding to the number of copies of an individual sequence , then the distribution of X in an aliquot f ( x ) can be modeled as a mixture distribution of X for the erroneous sequences fE ( x ) and the authentic barcode input sequences fI ( x ) . This can be written as: f ( x ) =pfE ( x ) + ( 1−p ) fI ( x ) ( 1 ) where p is the proportion of erroneous sequences in an aliquot , and ( 1−p ) is the proportion of input sequences in an aliquot . Based on observed sequences , we fitted a model where the number of copies of the input sequences follows a lognormal distribution , while the erroneous sequences follow a power law distribution . The above distribution function is fitted to the number of copies of each unique sequence in an aliquot , using the function mle from MATLAB ( R2014b ) . An optimal cutoff number of copies of a sequence for each aliquot was determined as the value where the theoretical distribution in the mixture model reaches a minimum . The sequences above the cutoff were designated putative input sequences and the sequences below the cutoff putative erroneous sequences . Moreover , the percentage of input sequences classified as erroneous and the number of erroneous sequences classified as input was estimated . The method above identifies the number of putative input sequences in each aliquot , however we also estimate that 2–5% of these are actually erroneous sequences that are classified as input barcodes . Since generation of PCR/sequencing error is likely a random event in a given aliquot , we might expect that most erroneous sequences will be confined to one or a few of the 168 aliquots . However , since we expect ≈10 , 000 total input sequences in the stock , and observe around 2000 sequences per aliquot , then we should see most input sequences in many aliquots . Based on this observation , the probability of observing a sequence in n aliquots is given by the mixture of binomial distributions: p ( n ) =fBin ( N , p1 ) + ( 1−f ) Bin ( N , p2 ) ( 2 ) in which p1 is the probability of observing the erroneous sequences , p2 is the probability of observing an input sequence , and f is the proportion of erroneous sequences . The above distribution function is fitted to the histogram of the number of aliquots each sequence is observed in ( using the function mle in MATLAB v . R2014b ) . Using the fitted distribution function , we could find a cutoff value that can be used to determine the total number of input sequences across all aliquots . We can also estimate the false positive and false negative rates around this cutoff . Additionally , we also tested for a binomial model with non-constant proportion in the input sequences . However , allowing for a distribution in the proportion of input sequences did not yield a better fit ( p = 0 . 78 , likelihood ratio test ) , hence we found no evidence for a distribution in clone size of the input sequences . In order to estimate frequency of reactivations , we assumed exponential viral growth at the earliest stage of infection . The time between ith and ( i + 1 ) th reactivations , Δi = ti+1 − ti , can be estimated from ratios Ri=ViVi+1=eg ( ti+1−ti ) , i = 1 , … , n − 1 of rebounders as shown by the following formula: Δi=lnRig . ( 4 ) In order to find the growth rate g of each rebounder ( assumed to be the same ) , we assume that reactivation occurs in average every Δ days . Thus the total viral load ( i . e . : the sum of all variants ) at time t after treatment can be expressed by formula: V ( t ) =V0eg ( t−t0 ) +V0eg ( t−t0−Δ ) +V0eg ( t−t0−2Δ ) …+V0eg ( t−t0− ( n−1 ) Δ ) , ( n−1 ) =⌊ ( t−t0 ) /Δ⌋ . ( 5 ) Taking into account that ( e−gΔ ) m , m = 0 , … , n − 1 , is a geometric progression , we can reduce the function ( 5 ) so it will take the form: V ( t ) =V0eg ( t−t0 ) 1−e−gΔ ( ⌊t−t0Δ⌋+1 ) 1−e−gΔ , ( 6 ) where ⌊x⌋ is the largest integer not greater than x . The function ( 6 ) has discontinuity that may create some obstacle in finding the global minimum during fitting . Thus , for the purpose of fitting we removed the discontinuities in ( 6 ) by substituting ⌊ ( t − t0 ) /Δ⌋ with ( t − t0 ) /Δ and rewrite the expression ( 6 ) for the log of viral load: lnV ( t ) =lnV0+ln ( eg ( t−t0 ) −e−gΔ ) −ln ( 1−e−gΔ ) ( 7 ) In order to use average time between reactivations that can be obtained from the ratios of founder virus data , as it was described above , we substitute Δ in ( 7 ) by the estimate of the mean , Δ¯=L¯g , where L¯=1 ( n−1 ) ∑i=1n−1lnRi . Thus , we obtain the formula: lnV ( t ) =lnV0+ln ( eg ( t−t0 ) −e−L¯ ) −ln ( 1−e−L¯ ) , ( 8 ) where n is the number of founder viruses in the dataset . Model was fitted ( using Prism 6 . 07 , GraphPad Software Inc . San Diego , Ca , USA ) to exponential phase of growth of virus in monkeys having V0 as a shared parameter .
We reasoned that a molecularly barcoded SIV clone would have great utility for studies of HIV/SIV latency , viral reservoir establishment and maintenance , and viral rebound upon therapeutic interruption . To generate this barcoded virus , the MluI restriction recognition sequence ( ACGCGT ) was introduced into the SIVmac239 infectious molecular clone ( IMC ) between the stop codon of vpx and the start codon of vpr . A genetic cassette consisting of 10 random bases with 7 complementary bases flanking each end was ligated into the SIVmac239 clone using the introduced MluI restriction site ( Fig 1A ) . Importantly , the genetic insert is bidirectional , effectively doubling the discriminating power of the barcode . Following ligation , a large bacterial plasmid library was generated and was then used for large-scale virus production via transfection of HEK-293T cells . All produced virus was collected , pooled , and aliquoted , such that single aliquots contain a representative sampling of all genetic variants generated . Thus , the generated virus stock contained variants of SIVmac239 that differed only within a 34-nucleotide insertion harboring a 10 base-stretch of random nucleotides in an otherwise genetically clonal genome . These 34 bases comprise the viral barcode and the virus stock was designated SIVmac239M . The goal of generating SIVmac239M was to produce a phenotypically homogeneous viral population with extensive diversity contained entirely within a small region of the genome suitable for deep sequencing and with a known distribution of the genetically distinct viral barcodes ( or viral clonotypes ) . Therefore , it was necessary to determine the genetic diversity and abundance of each clonotype in the virus stock . When sequencing such a large potential number of genetic variants , it can be difficult to discern between sequences arising from PCR or sequencing error and those representing true input viral clonotypes . To distinguish these sequences , viral RNA was extracted , synthesized into cDNA , and distributed into 168 aliquots each containing 5 , 000 viral templates . Following PCR and Illumina-based sequencing of each aliquot , the number of unique sequences was compared to the total sequence count . Using a limited template input with massive oversampling of sequencing ( at least 100-fold over-sequencing per template ) , we found a clear bimodal distribution of both PCR-induced errors ( power-law distributed , with a high proportion of single sequences ) , and authentic clones ( log-normally distributed , with sequences present at high frequency ) ( Fig 1B ) . We then identified the threshold number of copies separating the erroneous from the authentic barcode sequences in each aliquot . Using this approach , we detected a total of 14 , 357 unique clonotypes across the 168 aliquots . These clonotypes were then rank ordered by the number of replicate aliquots in which each was found . Of the 5 , 021 sequences found in only one aliquot , we estimated that only approximately 100 of these were likely to be authentic input barcodes based on the distribution of the 168 aliquots ( Eq 2 ) . Therefore , the vast majority of input barcodes were contained within the top 9 , 336 sequences . Phylogenetic analysis of these 9 , 336 identified clonotypes was performed to determine the genetic relatedness of each barcoded clone ( S1 Fig ) . Of the 9 , 336 identified barcodes , 5 , 519 were inserted in one direction , and 3 , 817 were inserted in the inverse direction . To quantify genetic relatedness between barcodes , we performed pairwise comparisons of each barcode ( S2 Fig ) . We found two distinct populations ( representing the two barcode orientations ) , with an average nucleotide difference of 7 bases . Genetic analysis also revealed 105 barcodes with one or more base pair deletions generating slightly smaller barcodes . These short inserts are likely due to errors in the molecular generation of the barcoded clone and although these barcodes are truncated , they retain their usefulness because they can still be genetically distinguished from the rest of the variant pool . Overall , these data support the conclusion that we have generated a genetically diverse , synthetic viral population approaching 10 , 000 individual viral clonotypes . Prior to use in nonhuman primates , viral infectivity and replication of SIVmac239M was assessed using TZM-bl reporter cells and primary rhesus lymphocytes , respectively . This stock contained 2 . 2x105 IU/mL , which was equivalent to the infectious titer of a stock of parental SIVmac239 produced using the same approach . To assess the replication capacity of SIVmac239M , CD8+ T-cell-depleted PBMCs were inoculated with equivalent infectious units of SIVmac239M or the parental SIVmac239 and samples were collected every 2–3 days ( Fig 2A ) . SIVmac239M displayed peak virus replication levels on day 7 , corresponding to a detected 1 . 8ng of reverse transcriptase ( RT ) /mL of culture supernatant . The viral growth curves were comparable between SIVmac239M and parental SIVmac239 , which also peaked on day 7 with 2 . 0ng of RT/mL . These results demonstrate that the insertion of the barcode into the viral genome did not have a measurable deleterious effect on either infectivity or replicative capacity in vitro . To confirm that SIVmac239M did not have any replication defects , we assessed its replication capacity in vivo in rhesus macaques . Two rhesus macaques ( ZK37 and ZK56 ) were infected with 2 . 2x105 IU of SIVmac239M via intravenous injection . Plasma viral loads were monitored regularly using qRT-PCR . SIVmac239M displayed viral replication kinetics comparable to wild type SIVmac239 [27] resulting in peak viremia in both SIVmac239M infected animals at day 13 with viral RNA copies at 5 . 0x107–1 . 0x108 copies/mL ( Fig 2B ) . Viral RNA set-point was reached by day 49 with titers at ~106 copies/mL , which was similar to parental SIVmac239 [27] . These data indicate that SIVmac239M is fully functional in vivo with viral kinetics indistinguishable from wild-type SIVmac239 and suggest that the insertion of the barcode did not result in impaired infectivity or replicative function . Furthermore , sequence analysis through 3 months post infection revealed no loss of detected barcodes or accumulated changes that precluded barcode tracking and variant enumeration . Sequence analysis of plasma during chronic viremia revealed all viral genomes contained a barcoded insert . It was also observed that while mutations did occur , they were uncommon in the 34 bases of the barcode insert . On occasions when mutations did affect the barcoded region , the parental barcode was identifiable phylogenetically . The underlying design premise in using SIVmac239M for studies of viral reservoirs was to establish a disseminated systemic infection with a large number of sequence-discriminable viral variants that are isogenic outside of the barcode and biologically equivalent . As each different variant represents the progeny of a distinct chain of infection events , barcode sequence analysis in SIVmac239M infected animals undergoing viral recrudescence after cART discontinuation should allow for unprecedented facility and depth of analysis while limiting confounding diversification and differences in viral replication capacity or other biological properties that can accumulate over time in the infected host prior to initiation of cART . To achieve reservoir seeding with numerous barcode variants , we employed a relatively high dose intravenous infection , while allowing limited time for viral replication before initiation of cART to control the size of the reservoir . The design premise of this study was to mimic the diversity of viral variants capable of seeding persistent viral reservoirs in chronic HIV infection without the attendant biological variability . It was therefore necessary to determine whether SIVmac239M could establish a genetically diverse infection in rhesus macaques . To assess the number of detectable barcodes in plasma during primary infection , plasma from day 4 to day 14 post-infection was obtained from 26 animals infected with SIVmac239M ( including ZK37 and ZK56 ) . Sequence analysis revealed an average of 1 , 247 clonotypes per animal ( 244–4800 ) . The number of barcodes identified for individual animals varied based on duration of infection prior to treatment . Of the 9 , 336 confirmed barcodes in the stock , 7 , 991 ( 85 . 6% ) were identified in at least one animal . Furthermore , each barcode was identified in a mean of 5 . 2 out of 26 animals , with an interquartile range of 1–8 animals . Because the large majority of barcodes from the stock could also be detected in animals , we conclude that at least 85 . 6% of the barcodes in our total stock are both infectious and functional . Because variation in the proportion of each clonotype within the stock was observed , the correlation between the frequency of the clonotype in the stock and frequency of detection of the clonotype during acute infection in animals was determined . Comparing the number of viral stock replicates in which a particular clonotype was identified ( out of 168 total replicates ) against the number of animals in which each clonotype was found yielded a linear correlation with an R2 value of 0 . 77 ( p<10−5 Pearson/Spearman , Fig 3A ) . Thus , clonotypes that were identified frequently in the 168 replicates in the stock were also found in more animals after in vivo challenge . Importantly however , clonotype abundance in each animal ( i . e . , the relative number of copies of each clonotype ) was only weakly correlated to their abundance in the viral stock ( Spearman p-values ranging from 0 . 07–0 . 18 ) , suggesting that although clonotypes found more frequently in the stock were more likely to infect an animal , they did not necessarily become the dominant clonotypes within the animal . These results highlight the biologically consistent nature of the different clonotypes and a lack of negative impact on viral fitness due to a barcode insertion . Further analysis of the distribution of barcodes in animals revealed an inverse relationship between the number of animals in which a barcode was found ( i . e . animals sharing a common clonotype ) , and the number of common barcodes found in that number of animals ( Fig 3B ) . That is , individual barcodes were found most frequently in only 1 animal , and least frequently in all 26 animals . The mean relative frequency of each clonotype was calculated by averaging the proportional abundance at which it was found in each individual animal . This value was then correlated with the number of animals in which that clonotype was identified . The number of animals in which each clonotype was found appears to be largely independent of its mean relative frequency in these animals , and each clonotype was found at nearly equal proportions in each animal with no single clonotype dominating the population . The fact that a barcode was found in many animals was therefore not due to greater fitness , as barcodes found in more animals did not represent larger proportion of the total sequences than barcodes observed in only a few animals . ( Fig 3C ) . Thus , while the proportion of each clonotype in the stock correlates with the likelihood of establishing systemic infection , there is no indication that clonotypes differ in their replicative capacity . Our major goal in generating a barcoded virus was to facilitate the ability to discriminate between individual rebound events contributing to viremia following treatment interruption . A pilot study using short-duration cART treatment was initiated to test the feasibility of using the barcoded virus model system to discriminate distinct viral lineages following cART interruption ( study 1 ) . Here , 4 rhesus macaques ( MK9 , KMB , KZ2 , and KTM ) were each infected intravenously with 2 . 2x105 IU of SIVmac239M followed by daily cART ( TFV/FTC/RAL ) administration from day 6 to day 88 post-infection , at which time cART was discontinued ( Fig 4 ) . Cell associated-viral RNA ( CA-RNA ) levels in PBMC peaked on day 6 post-infection at an average of 7 . 6x105 copies/106 cells ( S3A Fig ) . CA-RNA levels dropped dramatically on cART to an average of 6 . 3 copies/million cells at time of interruption . CA-viral DNA ( CA-DNA ) also peaked on day 6 at an average of 2 . 0x104 copies/106 cells ( S3B Fig ) . These DNA levels declined over the course of cART treatment , but more gradually than CA-RNA , reaching an average of 5 . 5x102 copies/ 106 cells at the time of interruption . The plasma SIV RNA viral load was below the assay quantification limit ( 15 copies/mL ) from day 68 to treatment interruption for 3 of 4 animals ( MK9 , KMB , and KTM ) . Viral rebound was detectable in plasma 1–2 days following cART interruption , and plasma viral loads at rebound peak were between 1 . 2x105–9 . 2x106 copies/mL . Animal KZ2 never achieved full viral suppression , and had a detectable viral load ( 40 SIV RNA copies/mL ) on the day therapy was interrupted , which rapidly increased thereafter , highlighting the lack of full suppression in this cohort . KTM died due to procedural complications at the time of cART interruption and was therefore excluded from subsequent analyses . These data reveal typical early replication dynamics , with a greater than 5-log reduction in plasma vRNA and CA-RNA during cART treatment and a less than 2-fold decrease in the CA-DNA levels during therapy . These animals displayed rapid rebound kinetics following cART interruption . Sequence analysis just prior to initiation of therapy identified 1 , 872 distinct clonotypes in animal MK9 , 1 , 815 in animal KMB , and 3 , 739 in animal KZ2 . To assess the number of detectable rebounding clonotypes , next generation sequence analysis was performed 7 days post-interruption . In contrast to the pre-treatment viral diversity , following cART interruption , recrudescent rebound viremia contained only 118 distinct clonotypes for MK9 , 136 for KMB and 87 for KZ2 ( S4 Fig ) . Therefore , despite the limited duration of therapy , the number of detectable clonotypes in plasma was greatly reduced from the pre-therapy time point . For animal KZ2 , which had detectable virus at the time of interruption , of the 87 detectable rebounding variants , one clonotype was found at 2 logs higher proportion than the next detectable clonotype . The relative proportion of all other clonotypes were within half a log of its nearest neighbor . Overall , these results indicate that individual clonotypes can be detected in plasma viremia immediately following cART interruption and , with sufficient template input , the relative proportion of each clonotype may be accurately assessed across 5 logs . To confirm the reproducibility of the MiSeq sequencing to consistently provide proportional representation of virus populations and to determine if additional barcodes could be identified with a larger template input , barcode sequencing of rebound plasma viremia was repeated for MK9 but with a starting template input 10-fold higher than first assayed ( total 1x106 vRNA template copies assessed ) . Upon comparison of the relative abundances of sequenced clonotypes , nearly identical proportions of detected clonotypes were observed ( S5 Fig ) . Furthermore , 32 additional barcodes were identified below the previous lower-limit of detection of 0 . 001% . These results confirm the reproducibility of our sequencing approach and that template input quantity determines the lower limit of detection . This short-term cART treatment study demonstrated that SIVmac239M could be used to enumerate transmitted/founder variants before cART and the rebound/founder variants following cART interruption . However , 82 days of therapy was insufficient for full suppression and was likely not long enough for the decay of all short-lived viral reservoirs , therefore , a longer-term cART suppression study was conducted ( study 2 ) . Six rhesus macaques infected intravenously with SIVmac239M were given cART ( TFV/FTC/RAL/IDV/RTV ) starting on day 4 post-infection . Viral load measurements revealed rapid acute phase kinetics with viral load measurements ranging from 3 . 3x104–9 . 1x105 copies/mL at day 4 post-inoculation . Plasma viremia decreased over the next 9 weeks , and by day 67 , all animals were suppressed to below 15 copies/mL ( Fig 5A ) . Plasma viral loads were maintained below 15 copies/mL apart from one viral blip ( 30 copies/mL ) in animal H105 at day 269 post-infection , which happened to coincide temporally with a diagnosis of dermatitis and associated topical antibiotic treatment . At day 305 post-infection , therapy was discontinued in 2 animals ( DEJX and DFGV ) . Viremia was first detected at day 9 post-cART for DEJX and day 16 for DFGV . Peak post-cART viral loads of 2 . 3x105copies/mL for DEJX and 2 . 7x106 copies/mL for DFGV were measured at day 16 and day 23 , respectively . At day 374 post-infection , therapy was discontinued in an additional 2 animals ( H090 and DEJW ) . Viremia was detected at days 7 and 9 post-cART with peak viral loads of 3 . 9x105 copies/mL on day 15 , and 3 . 5x105 copies/mL on day 29 for H090 and DEJW , respectively . The final two animals ( H105 and DEPI ) discontinued treatment on day 482 . Viremia was first detected on days 7 and 11 with peak rebound viral loads of 1 . 5x106 copies/mL on day 18 for H105 , and 4 . 9x105 copies/mL on day 18 for DEPI . In this study , the time to detectable viremia ranged from 7 to 16 days post-cART , which was significantly longer than animals in study 1 ( p = 0 . 004 , Log-rank Mantel-Cox test ) . Notably , the time to rebound is much faster in these studies than has been reported for human studies [28] . However , there was no significant difference in peak rebound viremia between study 1 ( mean 3 . 4x106 copies/mL ) and study 2 ( mean 9 . 4x105 copies/mL ) ( p = 0 . 26 ) . CA-RNA and DNA were isolated from PBMCs collected regularly over the course of the study and quantified using real-time PCR/RT-PCR . For animals in study 2 , CA-RNA levels peaked at 3 . 0x103–2 . 5x105 copies/106 cells on day 4 ( immediately prior to the initiation of therapy ) ( Fig 5B ) . CA-RNA fell below 10 copies/106 cells by day 53 in all animals and remained at or below the limit of quantification until interruption from cART . CA-DNA levels peaked at 1 . 3x102–1 . 4x103 copies/106 cells immediately prior to the initiation of suppressive therapy which diminished more slowly than CA-RNA levels , but reaching 10 copies/106 cells by day 283 ( Fig 5C ) . Animal H105 showed a spike in CA-DNA on day 313 , but was again near the limit of quantification by day 430 and remained suppressed for the duration of therapy . This CA-DNA spike was preceded by a blip in plasma viral load at day 269 . No corresponding increase in CA-RNA was observed . The peak levels of CA-DNA and CA-RNA at cART initiation and the levels at interruption were markedly higher in animals in study 1 where treatment began on day 6 and lasted for only 82 days compared to those in study 2 in which treatment began on day 4 and lasted for more than 300 days ( p<0 . 001 , t-test ) . These differences in viral DNA and RNA levels highlight the importance of the dynamics of acute infection and timing of treatment initiation on the establishment of the viral reservoir ( 19 , 37 ) and provide a means to control the reservoir size based on time to cART initiation . Sequence analysis of the clonotypes detected in plasma prior to cART treatment in study 2 revealed patterns similar to those seen prior to therapy in study 1 . The average number of detectable barcodes at day 4 ( peak pre-therapy ) was 1 , 274 . Post-rebound sequencing was performed on samples obtained from ramp-up to early set-point viremia to identify the number of rebounding clonotypes and assess their relative abundance over time ( Fig 6 ) . Across all six animals , we detected a total of 34 unique rebounding clonotypes ranging from 4–8 variants per animal ( overall mean of 5 . 7 ) , with a maximum of 7 clonotypes detected at any given time , with no clear difference in animals that discontinued therapy on day 305 ( mean of 5 . 5 clonotypes ) , day 374 ( mean of 5 . 0 clonotypes ) , or day 482 ( mean of 6 . 5 clonotypes ) . For animals DEJX , DFGV , H090 , DEPI , and H105 , the proportions of variants remained substantially consistent over time in the initial weeks after cART discontinuation , with only the minor clonotypes showing any notable variation . Interestingly , for animal DEJW , the dominant clonotype during rebound ( clone 4886 ) was replaced at day 35 post interruption by the second most dominant clonotype ( clone 997 ) . Overall , these data are consistent with previous work showing a stable viral population over time with limited changes in variant proportion once those proportions are established [27] . Next , each rebounding clonotype was compared to its relative proportion in the same animal prior to cART initiation and to the SIVmac239M stock ( S6 Fig ) . Of the 34 total detected rebound clonotypes , 27 were identified in the pre-therapy sample from the same animal . We considered whether some clonotypes may be more fit than others , despite their inherent clonality . While this was not directly measurable , it was noted that if some clonotypes were more fit , they would presumably emerge as rebounders in most or all animals used in this study . In fact , in study 2 , no rebounding clonotype was observed in more than one animal , and even in study 1 , in which we counted an average of 114 rebounding clonotypes per animal , less than 25% were identified in more than one animal , and only 1 was found in all three . Additionally , in both study 1 and 2 , the clonotypes most abundantly represented prior to therapy were more likely to be detected following cART interruption , presumably because they were more likely to seed a larger number of cells capable of harboring stable residual virus ( S7 Fig ) . We find significant correlation between the pre-therapy and post-rebound clonotypes in study 1 ( p<0 . 001 ) , but no significance in study 2 , likely due to the limited number of rebounding genomes in this study . These observations are key as they demonstrate that it is possible to enumerate and identify clonotypes both before and after ART treatment . The observed clonotypes following ART interruption represent progeny from the activation of a long-lived viral reservoir . A major goal in generating a barcoded virus was to establish a model in which both the number of sources of recrudescent virus and the dynamics of each rebounding variant could be directly estimated from the number and relative proportion of clonotypes detected in plasma . We propose that each reactivation event leading to systemic viremia can be identified using the relative proportion of each rebounding variant and the overall slope of the rebounding viral load curve . Assuming equivalent replication of each clonotype , the differences in the relative proportion of each detectable barcode can be used to infer the rate of reactivation from latency for each animal . The dynamics of viral recrudescence leading to measurable plasma viremia and the relative abundance of each clonotype sequence detected in plasma during acute recrudescence ( time point highlighted by asterisk in Fig 6 ) were used to calculate the estimated frequency of reactivation from latency ( or reactivation rate ) ( Eq 8 ) . In animals in study 2 treated beginning at day 4 post infection for 305 , 374 , or 482 days , the estimated reactivation rates per day averaged 0 . 64 , 0 . 59 , and 0 . 41 respectively with an overall average of 0 . 54 –that is roughly one reactivation event every 2 days . In study 1 , while the calculated reactivation rate was 16–31 events per day ( mean 22 . 7 , p = 0 . 02 , Mann-Whitney Test ) , these rates likely reflect the residual presence of virus that was actively replicating or being actively produced during the relatively brief duration of cART prior to discontinuation , and thus may not represent reactivation from long-lived , latent cells . This interpretation is supported by the detectable viral load values for animal KZ2 prior to interruption from cART . Therefore , we cannot distinguish between plasma virus representing reactivating latently infected cells and residual viremia that , upon drug levels reaching a minimum threshold concentration , was immediately available to initiate a spreading infection . Using the proportion of the total viral load represented by each clonotype , and the exponential growth rate in plasma viremia , we extrapolated the slope of the viral load curve of each animal from study 2 to below the limit of detection for each detected clonotype to the theoretical concentration of a single virion in the total blood volume , calculated using a standard clinical volume of plasma in a rhesus macaque ( 54mL plasma/kg body weight ) which equates to 2 . 6x10-3 viral RNA copies/mL in a typical 7kg animal ( Fig 7 ) . The reactivation rate was plotted across the x-axis , starting at time 0 post interruption from cART , indicating the estimated average time interval between each reactivation event within each animal . Although reactivation of a latent provirus in an individual cell is stochastic in nature , we found most reactivations occurred within the predicted window of time ( i . e . , within the time-frame predicted by the calculated reactivation rate ) . In 3 animals , we hypothesized that a theoretical reactivation event occurred within the first window of reactivation ( DEJX , H090 , and H105 ) , and posited that reactivation occurred shortly after therapy was discontinued with limited time for drug washout . For animal DEJW , an activation event likely did not occur within the first predicted reactivation window , but afterwards all three detectable clonotypes fit within the inferred reactivation windows . For animal DEPI , a reactivation event did not occur in the first 2 reactivation windows , but did for subsequent windows . The time to rebound in animal DFGV was the most delayed and we estimate that the first 6 reactivation windows were missed prior to a robust reactivation of many viral lineages . The theoretical initial reactivation events occurred in animals DEJX , DFGV , DEJW , H090 , DEPI , and H105 on days 1 . 8 , 7 . 3 , 3 . 2 , 0 . 5 , 4 . 3 , and 0 . 3 days after treatment interruption .
Accurately assessing the size and nature of the residual viral reservoir that can give rise to recrudescent viremia is essential for studies focused on prolonging cART-free remission . In addition to various ex vivo assays , all of which have significant limitations , many current assessments of reservoir-targeting therapeutic strategies include in vivo testing in HIV+ individuals , followed by cART interruption and monitoring of time to detectable viremia . However , treatment interruption likely causes reseeding of the viral reservoir during viral recrudescence off therapy , regardless of how short a time interval the interruption lasts , and the potential consequences of this must be carefully considered . Using treatment interruption and time-to-detection of virus to measure changes in the frequency of reactivation and then extrapolating this frequency to estimate changes in reservoir size after reservoir-targeting therapeutic strategies has low statistical power to detect the effects of therapy [17] . Moreover , if reactivation rates are high , or highly variable , then this will further reduce the probability of successfully assessing new interventions [29] . In the absence of any identified strategies that robustly diminish the size of the residual virus pool on cART by multiple logs , it will be important to develop approaches that allow for the identification of those therapies that have a more modest effect on the size of the viral reservoir but that may be improved upon or useful in combination with other agents . Nonhuman primates offer useful models for the assessment of intervention strategies as reservoirs can be established to recapitulate those established in HIV-1 infected patients , and interruption can be studied without risk to patients . For studies of reservoir targeting strategies , it is advantageous for the size of the viral reservoir to be controlled and normalized between experimental groups . This kind of experimental stringency is possible in NHP models in a way that is impossible in humans . Here we demonstrate a barcoded virus model to assess reservoir size which , when taken together with time to rebound measurements , provides a more sensitive and robust assessment of therapeutic changes to the latent reservoir . Viral RNA collected from human plasma following rebound has been sequenced to estimate the number of latent cells reactivating [17] , as the diverse viral quasi-species in chronic HIV infection allows for enumeration and characterization of viral reservoirs and recrudescence . While this previous work has shown that multiple viral lineages can contribute to rebounding viremia ( 28–31 ) , they are limited in the depth of sequencing , the small region of viral sequence analyzed , and limited overall viral diversity in HIV infection to distinguish individual reactivation events . One of the possible problems with attempting to use this method with human samples is that sequencing of env or pol requires SGA to evaluate the genetic diversity of each sample [30–32] , analysis that is labor intensive and can feasibly only yield a maximum of approximately 100 sequences . Analysis of 100 sequences from a patient with viral loads greater than 104 copies/mL will only allow for the detection of lineages representing the largest proportions of that population . An alternative approach would be to preselect individuals with greater overall viral heterogeneity and use a diverse region of the genome to identify the relative proportion of variants [17 , 33] . While the relative proportions of each lineage can still be estimated with these approaches , utilizing current technology , they lack sufficient depth and dynamic range to accurately assess reactivation rates in most HIV-1 infected individuals . By contrast , our clonal barcoded virus is genetically homogeneous , with only the short stretch of 34 bases harboring the entire genetic disparity between clonotypes . Because only this short portion of the genome requires analysis to distinguish between lineages , we are able to use next generation sequencing , a method that reads tens of thousands of individual sequences , allowing for detection of rare clonotypes and consistent evaluation of the relative proportions of rebounding lineages . When evaluated in this manner , the proportion of clonotypes is reflective of the timing of the individual reactivation events that led to each viral lineage . This measured rate may therefore be used to assess the successful intervention of novel therapeutics that can reduce the overall viral reservoir size . This type of evaluation is entirely dependent on the assumption that each individual clonotype replicates at an identical rate , emphasizing the utility of our clonal virus model , and further highlights the complication of utilizing this approach in HIV-1 infected patients . Admittedly , despite the clonal nature of SIVmac239M , one factor potentially complicating analyses based on both time to detection and reactivation rates is the anatomic location of each rebounding lineage . Reactivation at sites with a limited number of target cells available for rapid viral expansion might alter the inferred reactivation rate . However , this error is minimized when using reactivation rates as a measure of reservoir size because the rates are based on an average of multiple reactivation events and variation in growth rate of a few rebounding clonotypes will not alter the calculated rate . By contrast , if using only time to detection as an estimate of reservoir size , the variation in target cell availability would significantly impact the time to viral detection , because this assessment is based only on a single measurement ( i . e . , the time needed for the first reactivating latent reservoir to produce detectable virus ) . In order for a barcoded virus to allow the evaluation of the latent reservoir , each viral variant must be functionally equivalent . With only a 34-nucleotide cassette inserted between the vpx and vpr genes , there is no apparent effect on in vitro or in vivo infectivity or replication capacity of the virus . Of 9 , 336 distinct , apparently biologically equivalent clonotypes present in the stock , 105 clonotypes were found to bear truncated barcode inserts , representing 1 . 1% of the total stock population . Hypothesizing that these smaller barcodes might confer some fitness advantage compared to clonotypes with the full 34 base insertion and that resultant viral populations might show bias for these clonotypes , we examined all pre- and post-therapy populations to determine the relative contribution of these clonotypes to the total viral load . We found that these barcodes do not overwhelm the pre-therapy population nor do they prevent full length clonotypes from also establishing infection and reservoirs . Even the presence of wild-type SIVmac239 and the SIVmac239 with only the MluI restriction site but no barcode did not cause a disproportionate bias in the population . This emphasizes the fact that the insertion of the genetic cassette has no discernable negative effects on successful replication of the virus . Additionally , stochastic mutations elsewhere in the genome could potentially allow for selection , however in this study , animals were placed on suppressive therapy on days 4 and 6 , which limited the time for a fitness-conferring mutation to arise , and therefore upon therapy release , no genome was advantageously poised to dominate the population . One of the most common methods for evaluating the relative size of the latent reservoir in HIV+ individuals is through the quantification of CA-DNA in PBMCs . In our study , early treatment appears to have constrained the level of CA-DNA in the animals , with all of the animals receiving long-term therapy in study 2 having fewer than 10 copies of CA-DNA per million cells by day 53 post-infection , and even the animals receiving short-term therapy in study 1 reaching minimum levels of only 387–650 CA-DNA copies per million cells . These low levels unfortunately prevented the collection of a meaningful number of CA-DNA and RNA sequences to compare with rebounding clonotypes in the plasma . More extensive sampling and analysis in future studies may allow assessment of potential correlations between various estimates of reservoir size based on ex vivo assays and based on the method presented here . Early treatment also likely prevented elevated levels of immune activation/inflammation that persist in cART suppressed individuals and animals initiating cART during chronic infection [34 , 35] . One of the major advantages to using NHP models for reservoir research is the ability to manipulate reservoir size and the resultant number of rebounding variants following treatment interruption by controlling standardized inocula , timing of cART initiation , and duration of treatment . In our study , the animals that received cART beginning on day 6 post-infection for 82 days ( study 1 ) had approximately 10 times higher frequency of rebounders than animals started on cART beginning on day 4 for 305–482 days ( study 2 ) . There may be two explanations for this . First , these animals were treated on day 6 , two days later than the day 4 treated animals in study 2 , therefore viral seeding , which expands logarithmically during the acute ( pre-peak viremia ) phase of infection , reached higher levels prior to suppression . This hypothesis is supported by the fact that the CA-DNA levels just prior to cART initiation and just prior to cART discontinuation are approximately 2 logs higher in the animals in study 1 ( averaging approximately 103 copies/106 cells ) , compared to the animals in study 2 ( averaging approximately 10 copies/106 cells ) . Because the seeded latent reservoir is larger , this could explain why both the rate of reactivation and the number of rebounding clonotypes is significantly higher ( p = 0 . 01 , Mann-Whitney Test ) . An additional factor potentially contributing to the increased number of detectable rebounders is that the duration of suppressive therapy was much shorter in the animals in study 1 , and it is likely that cells actively producing replication competent virus have not had sufficient time to decay down to minimal quantities . This would dramatically increase the apparent size of the reservoir , and result in a much greater rate/number of reactivating cells . Additionally , full viral suppression was likely not achieved in these animals , as evidenced by the detectable viral load of KZ2 on the day of therapy interruption . This suggests the presence of residual production of virus in the animal which , upon degradation/metabolization of cART , could potentially initiate recrudescent infection , given susceptible target cells . As such , there is no way to distinguish between those clonotypes that arose from spread of variants present as residual viremia and those that resulted from activation of latently infected cells . These confounding factors likely contribute to rebound viremia in the animals in study 1 , whereas by the time of cART interruption in the animals in study 2 , these shorter-lived reservoirs likely had decayed or been eliminated , and residual viremia cleared from the system . Furthermore , animals DEJX , H090 , and H105 were also detectable quickly within the first few days , suggesting drug washout is rapid in these animals . Interestingly DFGV had a similar rate of reactivation to the other animals in study 2 , but had a considerable delay in time to first detectable viremia likely due to variability in drug washout kinetics . This animal to animal variation might be important for studies that utilize only time to detection as the measure of viral reservoir size . To estimate the decrease in reactivation rate in animals in study 2 , a decay curve was fitted to our data ( y = y0ekt , where y is the reactivation rate , and t is the duration of treatment ) . We find that time required for the reactivation frequency to decrease by 50% is 299 days . Ultimately , it is most likely that both peak viral load and duration of therapy account for the differences in the number of rebound variants we observe in the two studies . This then illustrates a major advantage of an animal infection model paired with our barcoded virus , namely , that we may control the timing of cART initiation and treatment duration to modulate reservoir size to fit the desired parameters of the study . Most animal studies conducted to measure reservoir size allow infection to reach chronic phase prior to initiation of therapy , and then utilize time to detection of viral load to identify changes in reservoir size . Time to detection is a valid method for monitoring changes in reservoir size , however it requires large sample group sizes to detect the effects of reservoir reduction [17 , 29] . Our model adds power and depth to the traditional time to detection measurement . By using a barcoded virus and next generation sequencing , we greatly increase the sensitivity of detecting changes in the reservoir size by increasing the amount of information that can be derived from each animal . The measured number of rebounding clonotypes and the corresponding reactivation rates are reflective of the functional reservoir size , and thus may be used as a direct measurement of the latent reservoir that effectively contributes to rebound viremia . When this study was initiated , it was theorized that early treatment itself could limit the viral reservoir and prevent rebounding viremia [36] . One sobering observation made here was that even after very early treatment ( day 4 post-infection ) and over a year of suppressive therapy , viremia rapidly recrudesced after treatment interruption . Despite the early treatment start date , a time frame which is virtually impossible to achieve for newly infected humans , we still detected viral rebound within eleven days of treatment interruption . Although delayed when treated early , previous studies in both humans [28 , 37] and NHPs [19] demonstrate similar findings: that once viremia is detectable , despite early treatment , viral reservoir is irreversibly established and causes recrudescent viremia . These studies and our findings highlight the urgency of developing novel therapeutics to target the reservoir directly . Furthermore , it remains to be determined if post-rebound control of viremia can be augmented by some intervention strategy , thereby providing a functional cure if elimination of viral reservoirs cannot be obtained . There are likely numerous applications for a barcoded virus . This would be an ideal system for sensitive detection of minor changes in reservoir size induced by latency reversing agents or other adjunctive therapies . Additionally , this barcode could be introduced into other lentiviruses used in nonhuman primate research , including SHIV clones and minimally chimeric HIV . It may also be useful if introduced into HIV-1 clones for in vitro testing and in humanized mice . This approach is also not limited to lentiviruses , and might be useful for other viruses that would tolerate a small genetic barcode . Furthermore , this approach might extend to other replicating biological systems that could benefit from genetic tracking , including bacteria and fungi . A major advantage of the model is that because the genetic insert is small , it reduces the probability of exerting any inhibitory effect on growth or infectivity and is it unlikely to be extruded from the genome . | Elucidation of HIV dynamics is essential for a thorough understanding of viral transmission , therapeutic interventions , pathogenesis , and immune evasion . The complex dynamics of reservoir establishment and viral recrudescence upon therapy removal present the primary obstacles to developing a functional cure . We sought to develop a virus model system for use in nonhuman primates that allows for the genetic discrimination of nearly 10 , 000 otherwise isogenic clones . This “synthetic swarm” adds a genetic component to viral dynamics where individual viral lineages can be tracked and monitored during infection . Here we utilized this model to identify the dynamics of viral reservoir establishment and rebound . We found that after 300 or more days of therapy , between 4 and 8 distinct viral lineages could be detected upon therapeutic intervention . Using the relative proportion of each distinct genetic barcoded virus and the overall viral load curve , we could estimate the time and rate of reactivation from latency . On average , we found 1 reactivation event every 2 days with reactivation of the first rebounding variant within days of therapeutic interruption . This virus model will be useful for testing various approaches to reduce the latent viral reservoir and to molecularly track viral dynamics in all stages of infection . | [
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] | 2017 | Genetically-barcoded SIV facilitates enumeration of rebound variants and estimation of reactivation rates in nonhuman primates following interruption of suppressive antiretroviral therapy |
Pathogen clearance and host resilience/tolerance to infection are both important factors in surviving an infection . Cells of the myeloid lineage play important roles in both of these processes . Neutrophils , monocytes , macrophages , and dendritic cells all have important roles in initiation of the immune response and clearance of bacterial pathogens . If these cells are not properly regulated they can result in excessive inflammation and immunopathology leading to decreased host resilience . Programmed cell death ( PCD ) is one possible mechanism that myeloid cells may use to prevent excessive inflammation . Myeloid cell subsets play roles in tissue repair , immune response resolution , and maintenance of homeostasis , so excessive PCD may also influence host resilience in this way . In addition , myeloid cell death is one mechanism used to control pathogen replication and dissemination . Many of these functions for PCD have been well defined in vitro , but the role in vivo is less well understood . We created a mouse that constitutively expresses the pro-survival B-cell lymphoma ( bcl ) -2 protein in myeloid cells ( CD68 ( bcl2tg ) , thus decreasing PCD specifically in myeloid cells . Using this mouse model we explored the impact that decreased cell death of these cells has on infection with two different bacterial pathogens , Legionella pneumophila and Streptococcus pyogenes . Both of these pathogens target multiple cell death pathways in myeloid cells , and the expression of bcl2 resulted in decreased PCD after infection . We examined both pathogen clearance and host resilience and found that myeloid cell death was crucial for host resilience . Surprisingly , the decreased myeloid PCD had minimal impact on pathogen clearance . These data indicate that the most important role of PCD during infection with these bacteria is to minimize inflammation and increase host resilience , not to aid in the clearance or prevent the spread of the pathogen .
Pathogen clearance and host resilience/tolerance are both important in surviving a given infection [1 , 2] [3] [4] [5] . A main purpose of the immune response is to identify and clear invading pathogens . However , highly resilient hosts can survive infection with a given pathogen , independent of the ability of the immune response to clear it . One aspect of host resilience is prevention and repair of extensive tissue damage . Both the immune response and pathogens themselves can cause damage to the infected host [2] [3] [4] [5] . So while the immune system must act to clear a pathogen , it must also be carefully controlled in order to prevent excessive damage . This study seeks to understand the role that myeloid cells , cells of the innate immune response , play in both pathogen clearance and host resilience . Myeloid cells , including monocytes , macrophages , dendritic cells ( DCs ) , and neutrophils , are an essential part of the innate immune system . During the early stages of the immune response they are essential in both direct phagocytosis and destruction of pathogens , and activation of other immune cells by secretion of cytokines and chemokines [6–9] [10] [8] . They also have important roles in immunoregulation and tissue repair that are crucial in surviving an infection [11–13] [14] [15] [16] . If myeloid cells are not carefully controlled they can cause excessive inflammation that can lead to immunopathology and decreased host resilience [17–20] [10] [11] [21] . The importance of myeloid cells in the innate immune response against infection often makes them a target of pathogens . Microorganisms will manipulate the cells in order to survive , proliferate , and spread to other cells [7 , 22] [23] . One mechanism of both controlling pathogen replication and host inflammation is programmed cell death ( PCD ) . There are many different PCD pathways of which apoptosis and autophagic cell death are largely non-inflammatory [6 , 16 , 24 , 25] and pyroptosis and necrosis are considered inflammatory [15 , 22 , 26] [27] . Apoptotic cell death is controlled by a caspase cascade , of which caspase-3 and caspase-7 are central players [28] . Pro- and anti-apoptotic proteins regulate apoptotic cell death . One key protein that regulates many types of PCD , but in particular apoptosis is B-cell lymphoma ( bcl ) -2 . Infected myeloid cells will undergo cell death in order to control pathogen dissemination and replication [27] [29] [30] [31] [32] . Once the pathogen is cleared myeloid cells will undergo apoptosis to prevent excessive inflammation and immunopathology [24 , 25] [33] [34] [35] [26] . We have developed a mouse model with decreased myeloid cell death in order to understand the impact it has on pathogen clearance and host resilience to infection . This mouse expresses the anti-apoptotic protein human bcl-2 under the control of the CD68 promoter ( CD68 ( bcl2 ) tg ) . This limits ectopic bcl-2 expression to cells of the myeloid lineage including monocytes , macrophages , neutrophils , and DCs . Bcl-2 primarily prevents apoptotic and autophagic cell death [25] [36] [37 , 38] , thus making this an ideal model for studying the role of non-inflammatory myeloid PCD in pathogen clearance and host resilience . This study uses two bacterial pathogens , L . pneumophila and S . pyogenes , that infect distinct sites , to examine how decreasing myeloid cell death impacts pathogen clearance and host resilience . While many studies have demonstrated mechanisms of myeloid cell death in in vitro infection models of S . pyogenes and L . pneumophila [39–44] [33] , it remains unclear what role myeloid cell death plays during in vivo infection . L . pneumophila infection remains confined to the lung under most circumstances where it causes a severe pneumonia [45] [46] . This bacteria is found in contaminated water supplies , such as air-conditioning systems , and infects alveolar macrophages [45 , 47 , 48] [46] . It can cause complications in people with immunosuppression or other health problems , making it an important hospital-acquired infection [49] [50] . In mice , pulmonary infection can be mimicked using an intranasal infection model of L . pneumophila . S . pyogenes is a versatile pathogen that infects many areas of the body including the upper respiratory tract and soft tissue [51] . Invasive soft tissue infections can result in the systemic spread of bacteria causing a severe toxic shock syndrome ( TSS ) [35] [50] [29] [52] . To mimic this type of infection , we used a cutaneous infection model that rapidly causes a systemic infection . Using these two models we examined the roles that myeloid cell death play during both pulmonary and systemic infections . L . pneumophila primarily infects lung macrophages , and actively delays apoptosis of these cells in order to replicate [53] [54] [55] [56] [31] . Infection with L . pneumophila induces an early pyroptotic cell death under the control of caspase-1 [57 , 58] [59] [60] [43] [61] [40] [62] [42] . There is also a caspase-11-dependent cell death that has shown in vitro to be independent of flagellin [40 , 57] . The later apoptotic cell death is at least partly also under the control of caspase-3 , and as such can be inhibited by bcl-2 [63] [64] . Human macrophages do not express the Naip5 inflammasome that is triggered by L . pneumophila flagellin , so to better mimic the human infection we use a strain of L . pneumophila lacking flagellin A ( ΔflaA ) . Deletion or inhibition of the pro-survival factor BCL-XL in macrophages results in decreased L . pneumophila replication [65] , indicating that delaying PCD is a strategy that L . pneumophila may have for surviving in cells . When macrophages eventually undergo apoptosis this may enable the pathogen to spread to other cells . Unlike macrophages , DCs do not support the growth of L . pneumophila as they undergo rapid cell death in response to infection . When apoptotic cell death is blocked in DCs by overexpression of bcl-2 L . pneumophila will proliferate in DCs [27] . It was hypothesized that since DCs migrate throughout the body this DC cell death may be a mechanism to prevent spread of the bacteria . Similar to L . pneumophila , S . pyogenes is thought to cause PCD by pyroptosis and apoptosis [29] [66] . The role that this PCD plays during infection is not well understood . The severe inflammatory response caused by S . pyogenes infection may be tempered by PCD in myeloid cells such as macrophages and neutrophils [67] [35] [68] [69] . S . pyogenes causes lysis of myeloid cells in a streptolysin O-dependent manner , that is thought to increase pathogen spread [68] [29] [52] . The PCD induced by S . pyogenes could be an immune evasion technique , and strains that cause less PCD have reduced virulence [29] . Therefore myeloid PCD may impact both pathogen clearance and host resilience to S . pyogenes infection . This study explores in vivo the role that myeloid PCD plays during infection with two distinct pathogens . While the role of PCD in response to infection is well documented in vitro , less is known about what sort of balance is struck between controlling pathogen clearance and maintaining host resilience during in vivo infections . Both of the bacterial pathogens used in this study interact with myeloid cell death pathways , and this study focuses on the role that cell death controlled by bcl-2 plays during infection . Our data demonstrates that CD68 ( bcl-2 ) tg mice infected with either pathogen have decreased host resilience that occurs largely independent of any changes in pathogen clearance . This indicates that the rate of myeloid cell death is calibrated to preserve host resilience , and manipulations of this rate are detrimental to the host .
Bone marrow derived macrophages ( BMDM ) were infected with L . pneumophila . Human macrophages are more permissive to infection by L . pneumophila than macrophages derived from most common mouse strains [70] . Triggering of the NAIP5 inflammasome by flagellin , results in rapid pathogen clearance in mouse macrophages . In order to better recapitulate the infection that is seen in human macrophages for our mouse model we used a ΔflaA strain of L . pneumophila . To compensate for the lack of motility the bacteria are spun briefly onto cells . Twenty-four hours after infection with L . pneumophila there was an increased number of apoptotic cells as indicated by flow cytometry staining using a stain for activated caspase-3/7 . ( Fig 1A ) . Similarly there was an increase in caspase-3/7 activation in BMDMs infected with S . pyogenes ( Fig 1B ) . This apoptosis was dependent on the dose of bacteria given . L . pneumophila induced the most cell death at a multiplicity of infection ( MOI ) of 20 ( Fig 1C ) . S . pyogenes induced cell death at an MOI as low as . 001 ( Fig 1D ) . Macrophages were derived from mice expressing human bcl-2 under the control of the CD68 promoter . Most of these macrophages constitutively express bcl-2 ( Fig 2A ) . When BMDMs are exposed to the DNA damaging agent etoposide there is an increase in apoptotic cells as demonstrated by staining with annexin V and propidium iodide and flow cytometry analysis . Macrophages constitutively expressing bcl-2 had significantly decreased apoptotic cell death after exposure to etoposide ( Fig 2B ) . As further evidence that ectopic expression of bcl-2 prevents etoposide-induced apoptosis cells were stained with the cell event reagent that is activated by activated caspase-3/7 , and the DNA dye sytox that indicates permeable cells . The substantial caspase-3/7 activation induced by etoposide was abrogated in BMDMs derived from CD68 ( bcl2 ) tg mice ( Fig 2C ) . The ectopic expression of bcl2 was effective in decreasing apoptosis at both high and low doses of etoposide ( Fig 2D ) . Constitutive expression of bcl-2 also decreased the cell death in BMDMs infected with L . pneumophila ( Fig 3A ) , and S . pyogenes ( Fig 3B ) when examined with a fixable live/dead stain 24 hours after infection . The cell death prevented by bcl-2 during infection was largely apoptotic , as indicated by caspase-3/7 activation and cell permeability . Macrophages derived from CD68 ( bcl2 ) tg mice , infected for 24 hours with L . pneumophila , had decreased caspase-3/7 activation compared to macrophages derived from their littermate controls . This is shown by flow cytometry staining ( Fig 3C ) , and quantified ( Fig 3D ) . Likewise flow cytometry performed on macrophages infected with S . pyogenes indicated that both apoptotic and late apoptotic/necrotic stages were inhibited by ectopic expression of bcl-2 ( Fig 3E and 3F ) . Bone marrow , spleen and lymph nodes were examined for expression of transgenic bcl-2 . The transgene was expressed primarily in CD11b+ cells in these organs ( Fig 4A ) . While the transgene is expressed in all myeloid cell subsets it is expressed at the highest level in F4/80+CD11b+ macrophages and lower in Ly6G+ neutrophils ( Fig 4B ) . It is expressed at an intermediate level in CD11c+ MHC class II+ DCs , and not expressed in cells of the lymphocyte lineage ( Fig 4B ) . In 12 week-old mice there is a slight increase in Ly6G+Ly6Clow neutrophils and Ly6G-Ly6Chigh inflammatory monocytes , while in mice aged 6–8 weeks the myeloid compartments are comparatively normal ( Fig 4C ) . These changes are most noticeable in the spleens of older mice ( Fig 4C ) . The spleens of 12 week old mice were slightly larger than their littermate controls ( Fig 4D ) , therefore the total number of inflammatory monocytes and neutrophils was also higher . The spleen cellularity was comparable between CD68 ( bcl2 ) tg mice and littermate controls at 8 weeks . There was no indication of cancer development as the mice aged . The mice were healthy and lived a normal lifespan . However , given this slight accumulation of inflammatory cells as mice aged we used mice that were between 6–8 weeks of age for the infection experiments . This enabled us to focus primarily on the impact that decreased myeloid cell death had on infection , and not on homeostatic effects of constitutive bcl-2 expression . Since the expression of bcl2 is expressed at varying levels in the different myeloid cell types , we examined the ability of the transgene to rescue DCs , neutrophils , and resident peritoneal macrophages from etoposide-induced cell death . Bone marrow-derived DCs ( BMDCs ) had a large increase in caspase-3/7 activation after treatment with etoposide , but this was greatly decreased with the presence of bcl-2 ( Fig 5A ) . Neutrophils had an increase in late apoptotic or necrotic cells after treatment with etoposide based on staining for caspase-3/7 and sytox ( Fig 5B ) . This PCD was also prevented by the ectopic expression of bcl-2 ( Fig 5B ) . To show the effect of bcl-2 on a different subset of macrophages we used resident peritoneal macrophages . Etoposide-induced apoptosis and necrosis was decreased in peritoneal macrophages isolated from CD68 ( bcl2 ) tg mice , compared to littermate controls ( Fig 5C and 5D ) . In order to determine the impact that decreased myeloid cell death had on pathogen clearance and host resilience responses during pulmonary infection , mice were infected with the bacterial pathogen L . pneumophila . Mice infected intranasally with 1X106 L . pneumophila start to steadily lose weight 2 days after infection ( Fig 6A ) . After infection CD68 ( bcl2 ) tg mice lose more weight and have a longer recovery time when compared to littermate controls ( Fig 6A ) . We next examined the specific cause of this decreased health status in infected CD68 ( bcl2 ) tg mice . One possibility was that the increased survival of myeloid cells , in particular DCs aided in the systemic spread of the bacteria . However , the infection was confined to the lung and there were no detectable bacteria in the spleen , liver or kidneys of infected mice ( S1A Fig ) . The decreased health of the CD68 ( bcl2 ) tg mice was therefore due to activity and responses in the lung . CD68 ( bcl2 ) tg mice infected with L . pneumophila had significantly increased damage in their lungs compared to littermate controls as soon as 48 hours after infection and this damage was even greater 96 hours after infection in transgenic mice ( Fig 6B and 6C ) . The histological damage score ( HDS ) measured a number of parameters including area of damage and immune cell infiltration into the alveolar space . In order to determine if this increased lung damage was due to an increased bacterial burden in the lung of CD68 ( bcl2 ) tg mice we determined the colony forming units ( CFUs ) in the lungs from infected mice 1 hours , 48 hours , and 96 hours after infection . There was no statistically significant difference between genotypes in bacterial uptake in the lung or early proliferation as indicated by the bacterial counts at 1 hour after infection and 48 hours after infection . Interestingly , the lung damage preceded the small increase in bacteria counts in transgenic mice observed 96 hours after infection ( Fig 6D ) . Ten days after infection both transgenic mice and littermate controls had cleared the infection ( S1B Fig ) . In order to determine the cause of the increased inflammation and lung damage , the pulmonary immune response to L . pneumophila was analyzed in CD68 ( bcl2 ) tg mice and littermate controls . Both transgenic mice and littermate controls had the same amount of immune cell infiltrate in the bronchoalveolar lavage fluid ( BALF ) 48 hours after infection ( Fig 7A ) . The number of infiltrating cells remained about the same in littermates 96 hours after infection , but continued to increase in the lungs of transgenic animals ( Fig 7A ) . The increased infiltrating immune cells in transgenic mice were mostly neutrophils as determined by cytospin analysis ( Fig 7B ) . Neutrophils migrated into the lungs of infected animals by 48 hours after infection , and their number was increased in transgenic animals compared to littermate controls 96 hours after infection . Surprisingly , other cell types had no significant increase in the lungs of infected CD68 ( bcl2 ) tg mice compared to infected littermate controls ( Fig 7B ) . Macrophages increased in the lungs of both CD68 ( bcl2 ) tg mice and littermate controls at the same rate ( Fig 7B ) . In addition to looking at the immune cell infiltrate , expression levels of several cytokines and chemokines were measured in the lungs of infected animals ( Fig 8A and 8B ) . The peak of expression of these inflammatory genes in the lungs of both transgenic mice and littermate controls was 48 hours after infection , but the expression of several cytokines and chemokines were elevated in transgenic animals compared to littermate controls ( Fig 8A and 8B ) . The large increase of IL-6 indicates an increase in inflammation in the lungs of these animals [71] . IL-1 receptor antagonist ( Il-1rn ) expression is also increased 48 hours after infection in transgenic animals . This gene is known to be important in resolution of lung inflammation and the expression is increased during times of acute inflammation [72] Also CXCL1 and CCL7 are known chemoattractants for neutrophils [73] and the increased expression of these chemokines at 48 hours may lead to the increase recruitment of the neutrophils by 96 hours . While there is a mild increase in bacterial load in lungs of infected CD68 ( bcl2 ) tg mice 96 hours after infection , the increased inflammation precedes this increase in bacterial burden . Therefore it seems that decreased myeloid cell death impacts host resilience , but does not affect the spread of the bacterial pathogen , and only slightly delays the clearance . Given the impact that the CD68 ( bcl2 ) tg has on in vivo infection with L . pneumophila , we explored how L . pneumophila-induced PCD is affected by expression of bcl2 in relevant myeloid cell types . DCs from CD68 ( bcl2 ) tg mice had decreased PCD after infected with L . pneumophila , compared to littermate controls , as demonstrated with caspase-3/7 activation and cell permeability assays ( Fig 9A and 9B ) . Alveolar macrophages isolated from CD68 ( bcl2 ) tg mice also had decreased L . pneumophila-induced early and late apoptosis compared to littermate controls ( Fig 9C and 9D ) . While there is limited detection of PCD in neutrophils infected with L . pneumophila , the small amount of apoptosis observed is rescued by ectopic expression of bcl-2 ( Fig 9E and 9F ) . During infection with L . pneumophila there is an increase in cytokines detected in the lung . To investigate if this is caused by an increase of cells producing cytokines , or if the transgenic myeloid cells make more cytokines , infected DCs and macrophages were stained intracellularly for IL-6 and TNFα . There was not a significant increase in the percentage of DCs making either cytokine when CD68 ( bcl2 ) tg mice were compared to littermate controls infected with L . pneumophila ( Fig 10A and 10B ( top ) ) . Also the mean fluorescent intensity was the same in DCs from transgenic animals or littermate controls ( Fig 10B ( bottom ) ) , indicating that on a per cell basis the cytokine production is the same . Similar results were observed for macrophages . Cytokine production was equivalent between macrophages from transgenic mice and littermate controls , on a per cell and population basis ( Fig 10C and 10D ) . We interpret this to mean that the increase in cytokines observed in the lungs of CD68 ( bcl2 ) tg mice is due to the increased number of inflammatory cells . In order to determine how decreased myeloid cell death would impact the response to a systemic pathogen we infected CD68 ( bcl2 ) tg mice and littermate controls with S . pyogenes . In order to mimic a common course of severe infection with S . pyogenes , a cutaneous infection that causes systemic disease , the bacteria were injected subcutaneously . The bacteria spread from the skin into the blood stream within a few hours and colonize various organs . Mice constitutively expressing bcl-2 in myeloid cells had decreased survival after infection with S . pyogenes ( Fig 11A ) . However , there was no statistically significant change in the bacterial load in these mice ( Fig 11B ) . The initial site of infection , the skin , had similar bacterial loads between transgenic mice and littermate controls 2 days after infection . In addition , the systemic spread was also similar as the spleen , and the liver had similar levels between the two genotypes ( Fig 11C ) . The decreased survival despite the unchanged pathogen clearance rates between the genotypes indicated a decrease in host resilience , and we examined a number of factors that could contribute to this . We examined the site of infection to determine if there were changes in inflammatory immune cell infiltration . There were increased infiltrating cells in transgenic mice into the area of bacterial infection ( Fig 12A and 12B ) . To get a clearer understanding of the types of immune cells infiltrating into the site of infection S . pyogenes was injected intraperitoneally . There was a significant increase in infiltrating cells into the peritoneal cavity of CD68 ( bcl2 ) tg mice 24 hours after infection with S . pyogenes ( Fig 12C ) . However , the types of cells that responded to the infection did not change between the littermate and transgenic mice ( Fig 12D ) . The responding cells were primarily neutrophils as identified by Ly6G and Ly6C expression in both genotypes of mice ( Fig 12D ) , however the transgenic mice had more cells ( Fig 12C ) . Given the increase in different myeloid cell types during infection with S . pyogenes in CD68 ( bcl2 ) tg mice , the effect of ectopic expression of bcl2 on S . pyogenes-induced cell death was examined . Neutrophils ( Fig 13A ) , DCs ( Fig 13B ) , and resident peritoneal macrophages ( Fig 13C ) from CD68 ( bcl2 ) tg mice all had significantly decreased S . pyogenes-induced caspase-3/7 activation . During in vivo infection apoptotic cells are rapidly cleared [74] , also the processes involved in cell isolation often cause cell death , therefore detection of apoptosis from ex vivo samples can be challenging . However , rapid assaying of whole blood cells from mice infected with S . pyogenes allows for the detection of cells undergoing PCD . S . pyogenes induces more than half of neutrophils in the blood to undergo PCD , but in CD68 ( bcl2 ) tg mice significantly less cells were undergoing PCD ( Fig 13D ) . The same is true for blood monocytes ( Fig 13E ) , and DCs ( Fig 13F ) . Since death caused by systemic infection with S . pyogenes is due to a toxic shock syndrome induced by systemic inflammation we examined the systemic responses . There were many systemic changes during infection . Most notably there were significant changes in the inflammatory cytokines TNFα , IL-1α , and IFN-γ ( Fig 14A ) . Transgenic mice sustained significant liver damage after infection as evidenced by increased levels of alanine aminotransferase ( ALT ) in the serum ( Fig 14B ) . The decreased myeloid cell death in this systemic infection resulted in increased systemic inflammation that exacerbated the disease progression and led to decreased host resilience . It seems likely that the increased systemic cytokine production in transgenic mice was due to the increased cellularity of infected mice . However , it is also possible that myeloid cells from CD68 ( bcl2 ) tg mice produce more cytokines . To investigate these non-mutually exclusive possibilities DCs and macrophages were stained intracellularly for IL-6 and TNFα . There was not a significant increase in the percentage of DCs making either cytokine when CD68 ( bcl2 ) tg mice were compared to littermate controls infected with S . pyogenes ( Fig 15A and 15B ( top ) ) . Also the MFI was the same in DCs from transgenic animals or littermate controls ( Fig 15B ( bottom ) ) , indicating that on a per cell basis the cytokine production is the same . Similar results were observed for macrophages . Cytokine production was equivalent between macrophages from transgenic mice and littermate controls , on a per cell and population basis ( Fig 15C and 15D ) .
Pathogen clearance and host resilience are both important in surviving a given infection . While many studies have examined different mechanisms of pathogen clearance , recent studies have highlighted the importance that host resilience plays in survival of a given infection [2] [3] [4] [5] . The fact that cells of the myeloid lineage play important roles in both of these important processes [6–9] [10] [8 , 11–13] [14] [15] [16] , we sought to determine how manipulation of myeloid cell death influenced the response to two bacterial pathogens . To do this we developed a mouse model that has decreased myeloid PCD . Ectopic expression of bcl-2 decreased PCD in response to numerous stimuli in myeloid cells of these mice ( Figs 2 , 3 , 5 , 9 and 13 ) [27] . We used two pathogens that interact with myeloid cells , but infect different areas of the host . The first pathogen , L . pneumophila , infects lung macrophages and remains confined to the lung [45 , 47 , 48] [46 , 75] , while the second pathogen S . pyogenes spreads systemically [67] [35] [68] [69] . When myeloid cell death is prevented during systemic infection with S . pyogenes there is a significant decrease in host resilience ( Figs 11 , 12 and 14 ) . Infected transgenic mice have decreased survival compared to littermate controls , and increased systemic inflammation . There is not a significant increase in bacterial load in the transgenic mice . The effects of decreased cell death during a pulmonary infection with L . pneumophila on host resilience are milder , but there is also an increase in inflammation in the lung . This increased inflammation precedes the small increase in bacterial load that is seen at the later stages of infection . Unlike infection with S . pyogenes both littermate and transgenic mice survive infection with L . pneumophila , indicating that the myeloid cell death has a greater impact on the systemic infection with S . pyogenes than on the pulmonary infection with L . pneumophila . In both infection models the primary cell type that is increased are neutrophils , which are known to cause tissue damage [34] . Also in both infection models the increase in inflammatory cytokine levels appears to be linked to the increased number of immune cells , and not by increased cytokine production on a per cell basis . The lung is a delicate and essential organ thus the response to lung infections is particularly challenging , in that pathogen clearance must be balanced with host resilience mechanisms . As L . pneumophila is confined to the lung , we used this as a model of lung infection . Decreased cell death of lung myeloid cells leads to increased inflammation and immune cells infiltrate into the lung in response to this pathogen . Interestingly , this increased inflammation precedes any increase in bacterial load in the lung . There is a small , but statistically significant increase in the bacterial load in the lungs of transgenic mice compared to littermate controls , however surprisingly the decreased myeloid cell death did not lead to systemic spread of the bacteria . Transgenic mice had a decrease in health status compared to littermate controls as indicated by rapid and sustained weight loss . However , both genotypes of mice were able to survive and eventually clear the infection . The mild effect of decreased myeloid cell death observed upon infection with L . pneumophila may be for several reasons . Many different types of cell death are caused by infection with L . pneumophila , including pyroptotic cell death . Transgenic expression of bcl-2 has limited influence on pyroptotic cell death . However , the apoptotic cell death observed in later stages of infection of macrophages and also seen in DCs is profoundly affected ( Fig 3 ) [27] . It could also be that the lung is able to cope using alternative resilience mechanisms , such as tissue repair pathways , with a threshold level of inflammation . It is likely , given the fragility and importance of the lung that there are multiple pathways that serve to protect this organ from damage . The increase in neutrophils observed in the transgenic mice could be a cause for the decreased health status , but pro-resilience pathways are able to maintain tolerance to the infection and survival is not impacted . In order to determine what role myeloid cell death plays in pathogen clearance and host resilience during an infection that can spread systemically we infected mice with S . pyogenes . In our model this subcutaneous infection rapidly spreads systemically . This enabled us to examine what role myeloid cell death plays at the site of infection , in spread of the infection , and in the systemic response to infection . In contrast to what was observed after lung infection with L . pneumophila , where both genotypes were able to survive the infection , transgenic mice infected with S . pyogenes had significantly decreased survival compared to littermate controls . Interestingly , the decrease in myeloid cell death in the transgenic mice did not significantly influence pathogen spread and clearance . However , it did cause a significant increase in inflammation both at the site of the infection , and systemically . There was also an increase in liver damage in transgenic mice . These data suggest host resilience to systemic S . pyogenes infection is compromised by decreased myeloid cell death due to an excessive inflammatory response . During infection with S . pyogenes the bacteria spread systemically and the increase in inflammation at multiple sites may overwhelm other host resilience mechanisms . This indicates that PCD of myeloid cells is an essential disease resilience mechanism during systemic infections with S . pyogenes . This study provides new insight into the roles that myeloid cell death plays in response to bacterial infections . While the decreased myeloid cell death caused minor inflammation in the lung , most likely other pathways were able to compensate for the increase in inflammation and prevent a large decrease in host resilience . However , during a systemic infection decreased PCD and consequently increased inflammation overwhelms the host and leads to decreased resilience as measured by survival . Interestingly , in both infections the main cell type that is increased are neutrophils . This increase may be caused either directly by the expression of bcl-2 in these cells or by the increase in other myeloid cell types that recruits neutrophils to the sites of infection . Neutrophils are known to cause damage in many different disease models [76–79] , and they are the probable cause of the decreased host resilience with these infection models . As neutrophils are also essential for pathogen clearance depletion of them could result in decreased host-mediated damage , but the increase in bacteria will most likely cause an increase in pathogen-mediated damage . These data demonstrate how tightly regulated PCD in myeloid cells is , and how important it is for host resilience . Disruption of this regulation changes an infection that is potentially survivable to one that has a high rate of lethality . These findings can be applied to models of sepsis and other infections where host resilience processes are important factors in survival [67 , 80] .
A transgenic construct was made using the CD68 promoter and regulatory sequences as described by Gough et al . [81] and human bcl-2 cDNA was cloned into the XbaI restriction digest sites . The transgenic animals were made using standard methods . Four initial founders were selected based on screening by Southern Blot . The line used in this study Tg535 ( bcl2 ) rm had the highest expression based on intracellular staining for the bcl-2 protein . The mice were initially on a 129/J background , but were backcrossed greater than 20 times to C57BL/6J mice . Animals were bred and maintained in a specific pathogen free facility ( SPF ) All procedural protocols involving mice were approved by the appropriate Institutional Animal Care and Use Committee at the site where the work was performed . In Vienna all of the experiments have been approved by the Vienna University of Veterinary Medicine institutional ethics committee and performed according to protocols approved by the Austrian law called BMWF 68 . 205/0032-WF/II/3b/2014 . General condition and behavior of the animals during the experiments was controlled by FELASA B degree holding personnel . The animal protocol number approved by University of Veterinary Medicine institutional ethics committee on 2/28/11 is 535233 . Brown University adheres to the “U . S . Government Principles for the Utilization and Care of Vertebrate Animals Used in Testing , Research , and Training” , “PHS Policy on Humane Care and Use of Laboratory Animals” , “USDA: Animal Welfare Act & Regulations” , and “the Guide for the Care and Use of Laboratory Animals” . The University is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International ( AAALAC ) . Brown University’s PHS Assurance Number: A3284-01 and it expires July 1 , 2018 The USDA Registration Number is 15-R-0003 . Brown University IACUC approved on October 8 , 2013 , and the animal protocol number is 1308000011 . The L . pneumophila strain used in this study was JR32ΔflaA provided by Craig Roy ( Yale University School of Medicine , New Haven USA ) . The S . pyogenes strain used in this study was serotype M1 strains ISS3348 [82] . For L . pneumophila infections mice were infected intranasally with 1X106 L . pneumophila in 40 μl sterile saline . Bacteria were grown from a heavy patch overnight in autolyzed yeast extract broth . For S . pyogenes infections mice were infected with 1X107 bacteria in 50 μl subcutaneously in the left flank . A single colony of S . pyogenes was grown overnight in Todd Hewitt Broth ( THB ) . This overnight culture was diluted into 100 ml of THB and grown for approximately 6 hours until in the log phase . Bacterial amounts were estimated using OD600 readings , and confirmed by colony forming unit ( CFU ) quantification . To ensure proper infectivity of cells when using the non-motile strain of L . pneumophila , bacteria were inoculated using a spinfection protocol . Bacteria were added to the cells in antibiotic-free media , and plates were spin for 5 minutes at 1200 rpm ( 290 RCF ) . To determine the inoculation load and the bacterial loads in various organs at indicated time points after infection CFU assays were done . At several time points after L . pneumophila infection lungs , spleens , and livers from infected mice were homogenized in 2 ml of sterile water using an electronic homogenizer ( Polytron 2100 ) . One-hundred μl of serially diluted homogenate was plated on charcoal yeast extract ( CYE ) plates . Colonies were quantified after 2–3 days of incubation at 37 degrees C . To determine bacterial counts in skin and organs from mice infected with S . pyogenes , organs were homogenized in PBS , as described for L . pneumophila infection . Serially diluted homogenate was plated on blood agar plates and incubated overnight at 37 degrees C . Bacterial colonies were quantified the next day . To collect bronchoalveolar lavage fluid ( BALF ) , the trachea was exposed , and a flexible tube placed on a 23-gauge cannula was inserted into the trachea . The lung was rinsed with 1ml PBS using an attached syringe . The viability of the isolated cells was determined by Trypan blue exclusion , and the cells were counted in a hemacytometer . For isolation of cells from lungs , they were perfused with 20 ml of PBS . The lung tissue was diced into small pieces and incubated for 45min at 37 degrees C in 4ml of media containing collagenase and DNAse . Afterwards , digested lung tissue was made into a single cell suspension by passage through a cell strainer . After centrifugation the cells were re-suspended in 4ml 40% Percoll/Roswell Park Memorial Institute ( RPMI ) and carefully layered over 4ml of 80% Percoll/PBS . The formed gradient was centrifuged at RT for 20min at 652rcf ( Eppendorf 5810R centrifuge ) with minimal acceleration and deceleration . Cells assembled in the interphase were collected , and washed with 10ml RPMI media containing 5% fetal calf serum . All steps of staining were performed on ice unless mentioned otherwise . All staining was done in V-bottom 96-well plates . Isolated cells were pelleted by centrifugation and washed twice with PBS containing 1% BSA and 0 . 001% w/v sodium azide ( FB ) . Cells were re-suspended in FB containing rat anti-mouse CD16/CD32 antibodies ( 1:100 ) and incubated for 10min to block the Fc receptors , followed by two washes Cells were re-suspended in FB containing the desired antibodies for the surface staining in an appropriate dilution determined by titration . After 20min incubation , cells were washed twice with FB . Antibodies used included CD11c ( M1/70 ) Biotin , Ly6C ( AL-21 ) V450 ( BD Biosciences ) , Ly6G ( 1A8 ) FITC , CD11c ( N418 ) PE , F4/80 ( BM8 ) APC , and 570 Fc receptor block ( 93 ) ( Biolegend ) . In addition , the fixable viability dye eFluor 780 ( eBioscience ) ( room temperature staining ) and Streptavidin Brilliant Violet were used . Apoptotic cells were detected using either Annexin V ( eBioscience ) and PI ( Sigma ) , or the CellEvent reagent and sytox ( Thermofisher ) . To identify specific cell types these stains were combined with the cell surface markers described above . Stained cells were acquired on a FACSAria III cell sorter , equipped with a 488nm ( blue ) laser , a 633nm ( red ) laser , and a 407nm ( violet ) laser . Flow-cytometry was also performed on the Attune NxT . Finally , collected data were analyzed by FlowJo Software ( Tree Star , Inc ) . For histological analysis , perfused lungs or excised skin were placed in 1% paraformaldehyde ( PFA ) overnight at 4 degrees C . The samples were transferred into 70% ethanol and the samples were processed in the Excelsior tissue processor . The samples were embedded in paraffin blocks and 5 μMsections were made with a microtome ( Leica ) . Standard staining protocols were used for Hematoxylin and Eosin staining of rehydrated sections . All histological samples were assessed twice in a blinded manner . To determine the histological damage score ( HDS ) , the following criteria were considered: the frequency ( none = 0; sporadic = 0 . 5; few = 1; many = 2 , excessive = 3 ) of neutrophils , macrophages and lymphocytes and their location ( perivascular , peribrochial , parenchymal , subpleural , and alveolar lumen ) , as well as the activation of the pleura pulmonalis ( none = 0; little = 0 . 5; medium = 1; strong = 2 , excessive = 3 ) . The points were added together and a maximum of 48 points per sample could be achieved . To assign the samples into 10 HDS groups ( 0–1 = 1; 1 . 1–2 = 2; 2 . 1–3 = 3; 3 . 1–4 = 4; 4 . 1–5 = 5; 5 . 1–6 = 6; 6 . 1–7 = 7; 7 . 1–8 = 8; 8 . 1–9 = 9; 9 . 1–10 = 10 ) , the obtained sum was divided by 4 . 8 . A low HDS value indicates minor tissue damage , whereas high HDS values mark increased damage of the lung . In addition , also the frequency of neutrophils , macrophages , and lymphocytes as well as the level of pleura activation was analyzed separately . Skin sections were also scored in a blinded manner . To determine the cytokine and chemokine protein levels , the Mouse Th1/Th2/Th17/Th22 13plex FlowCytomix Multiplex assay was performed according to the manufacturer’s instructions . RNA from tissues was purified using Reliaprep RNA Miniprep System ( Promega ) . Quantitative PCR was performed on a Roche LC96 using standard methods . Alanine transaminase in the serum was measured with the use of a colorimetric kit ( Cayman Chemical ) according to manufacturer’s instructions . | Multicellular organisms are constantly interacting with microbes . Pathogens are microbes that can cause harm to the host if not properly controlled , therefore pathogen clearance is an essential part of survival of all multi-cellular organisms . Equally important factors in survival are host resilience mechanisms , or host processes that increase survival independent of pathogen burden . Not only can pathogens themselves cause damage to the host , the immune response that eradicates pathogens can cause harm in the form of immunopathology . Controlling and repairing damage are important factors in host resilience , and depending on the site of infection the specific mechanisms vary . This study examines the multiple roles that cells of the innate immune response play in both pathogen clearance and host resilience in response to both systemic and pulmonary pathogens . | [
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] | 2016 | The Influence of Programmed Cell Death in Myeloid Cells on Host Resilience to Infection with Legionella pneumophila or Streptococcus pyogenes |
RNA viruses are the only known RNA-protein ( RNP ) entities capable of autonomous replication ( albeit within a permissive host environment ) . A 33 . 5 kilobase ( kb ) nidovirus has been considered close to the upper size limit for such entities; conversely , the minimal cellular DNA genome is in the 100–300 kb range . This large difference presents a daunting gap for the transition from primordial RNP to contemporary DNA-RNP-based life . Whether or not RNA viruses represent transitional steps towards DNA-based life , studies of larger RNA viruses advance our understanding of the size constraints on RNP entities and the role of genome size in virus adaptation . For example , emergence of the largest previously known RNA genomes ( 20–34 kb in positive-stranded nidoviruses , including coronaviruses ) is associated with the acquisition of a proofreading exoribonuclease ( ExoN ) encoded in the open reading frame 1b ( ORF1b ) in a monophyletic subset of nidoviruses . However , apparent constraints on the size of ORF1b , which encodes this and other key replicative enzymes , have been hypothesized to limit further expansion of these viral RNA genomes . Here , we characterize a novel nidovirus ( planarian secretory cell nidovirus; PSCNV ) whose disproportionately large ORF1b-like region including unannotated domains , and overall 41 . 1-kb genome , substantially extend the presumed limits on RNA genome size . This genome encodes a predicted 13 , 556-aa polyprotein in an unconventional single ORF , yet retains canonical nidoviral genome organization and expression , as well as key replicative domains . These domains may include functionally relevant substitutions rarely or never before observed in highly conserved sites of RdRp , NiRAN , ExoN and 3CLpro . Our evolutionary analysis suggests that PSCNV diverged early from multi-ORF nidoviruses , and acquired additional genes , including those typical of large DNA viruses or hosts , e . g . Ankyrin and Fibronectin type II , which might modulate virus-host interactions . PSCNV's greatly expanded genome , proteomic complexity , and unique features–impressive in themselves–attest to the likelihood of still-larger RNA genomes awaiting discovery .
Radiation of primitive life as it took hold on earth was likely accompanied by genome expansion , which was associated with increased complexity and a proposed progression from RNA-based through RNA-protein to DNA-based life [1] . The feasibility of an autonomous ancient RNA genome , and the mechanisms underlying such fateful transitions , are challenging to reconstruct . It is especially unclear whether RNA entities ever evolved genomes close to the 100–300 kilobase ( kb ) range [2 , 3] of the “minimal” reconstructed cellular DNA genome [4] . This range overlaps with the upper size limit of nuclear pre-mRNAs [5] , which is likely the upper size limit for functional RNAs due to the relative chemical lability of RNA compared to DNA . However , pre-mRNAs are incapable of self-replication , the defining property of primordial genomic RNAs . RNA viruses may uniquely illuminate the evolutionary constraints on RNA genome size [6–9] , whether or not they descended directly from primitive RNA-based entities [10–13] . The same constraints may also inform research on the biology and pathogenesis of RNA virus infections , because they shape the diversity of viral proteomes and RNA elements . The causes and consequences of changes in genome size can be understood in the context of a relationship that locks replication fidelity , genome size , and complexity within a unidirectional triangle [14] . RNA viruses appear to be trapped in the low state of this relationship ( Eigen trap ) [15] , which is characterized by low fidelity ( high mutation rate ) , small genome size ( 10 kb average ) , and low complexity ( few protein/RNA elements ) . Specifically , low-fidelity replication without proofreading constrains genome expansion [16] , since accumulation of mutations [17] would lead to the meltdown of larger genomes during replication ( error catastrophe hypothesis ) [18 , 19] . This constraining relationship is supported by evidence from nidoviruses ( order Nidovirales ) : enveloped viruses with positive-stranded RNA genomes in the range of 12 . 7 to 33 . 5 kb–the largest known RNA genomes [20–23] ( Fig 1A and 1B , S1 Table ) . The Nidovirales is composed of two vertebrate families , Arteriviridae and Coronaviridae ( subfamilies Coronavirinae and Torovirinae ) , and two invertebrate families , Mesoniviridae and Roniviridae [24 , 25] , and includes important pathogens of humans ( Severe acute respiratory syndrome coronavirus , SARS-CoV; Middle East respiratory syndrome coronavirus , MERS-CoV ) and livestock ( different arteriviruses , coronaviruses and roniviruses ) [26–30] . All known nidoviruses with genomes larger than 20 kb also encode a proofreading exoribonuclease ( ExoN ) [14 , 31–34] ( Fig 1B ) , which , once acquired by an ancestral nidovirus , may have relieved the constraints on all three elements of the triangular relationship simultaneously , providing a solution to the Eigen trap [14] . In the last 20 years of virus discovery , however , despite the application of unbiased metagenomics to RNA virus discovery [35 , 36] , the largest-known RNA viral genome has only increased ~10% in size–a mere fraction of the nearly ten-fold increase observed for DNA viruses [37–39] ( Fig 1A ) . Thus , other constraints have apparently limited genome size , even in RNA viruses equipped with proofreading capability . Further characterization of nidovirus molecular biology , variation , and evolution may provide insight into these other factors . Nidovirus genomes are typically organized into many open reading frames ( ORFs ) , which occupy >90% of genome and can be divided into three regions: overlapping ORF1a and ORF1b , and multiple ORFs at the 3’-end ( 3’ORFs ) [14] ( Fig 2 ) . The products of these regions predominantly control genome expression/replication , and virus assembly/dissemination , respectively . ORF1a and ORF1b are expressed by translation of the genomic RNA that involves a -1 programmed ribosomal frameshifting ( PRF ) at the ORF1a/ORF1b overlap [40 , 41] . The two polyproteins produced without or with frameshifting , pp1a ( ORF1a-encoded ) and pp1ab ( ORF1a/ORF1b-encoded ) , vary in size from 1 , 727 to 8 , 108 aa . They are processed to a dozen or more proteins by the virus’ main protease ( 3CLpro , encoded in ORF1a; Fig 2 ) with possible involvement of other protease ( s ) [42] . These and other proteins form a membrane-bound replication-transcription complex ( RTC ) [43 , 44] that invariably includes two key ORF1b-encoded subunits: the Nidovirus RdRp-Associated Nucleotidyltransferase ( NiRAN ) fused to an RNA-dependent RNA polymerase ( RdRp ) [45 , 46] , and a zinc-binding domain ( ZBD ) fused to a superfamily 1 helicase ( HEL1 ) , respectively [47–50] . The RTC catalyzes the synthesis of genomic and 3’-coterminal subgenomic RNAs , the latter via discontinuous transcription that is regulated by leader and body transcription-regulating sequences ( lTRS and bTRS ) [51–53] . Subgenomic RNAs are translated to express virion and , in ExoN-positive viruses , accessory proteins encoded in the 3’ORFs [23 , 54–59] . Most nidovirus proteins are multifunctional , but some released from the N-terminus of pp1a/pp1ab and/or encoded in the 3’ORFs are specialized in the modulation of virus-host interactions [26 , 60–65] . Intriguingly , despite the large variation in genome size among extant nidoviruses , the size of ORF1b varies extremely little within either the ExoN-negative ( 12 . 7–15 . 7 kb genome range ) or ExoN-positive ( 19 . 9–33 . 5 kb genome range ) nidoviruses [66] . There is no overlap between these two groups of viruses in the size range of ORF1b: the smallest ORF1b of an ExoN-positive nidovirus is almost double the length of the largest ExoN-negative ORF1b . In contrast , the ORF1a and 3’ORFs regions exhibit considerable size variation , and their sizes overlap between the ExoN-positive and ExoN-negative clades . A current theoretical model of nidoviral genome dynamics , the three-wave model , proposes that a genome expansion cycle is initiated by a bottleneck increase of ORF1b ( the first wave ) in a common ancestor of ExoN-positive nidoviruses , which then permits parallel expansion of ORF1a and , often , 3’ORFs in subsequent overlapping waves in separate lineages [66] . Extant nidovirus genomes of different sizes have reached particular points on this trajectory of genome size , apparently due to the lineage-specific interplay of poorly understood genetic and host-specific factors . A single cycle of this process can account for genome expansion from the lower end of genome sizes ( 12 . 7 kb ) to the upper end ( 31 . 7 kb ) ; expansion of genomes far beyond that size range has been hypothesized to require a second cycle , beginning with a new wave of ORF1b expansion [66] . In the absence of newly discovered RNA viruses with significantly larger genomes since the time of that analysis , and due to the unknown nature of the ORF1b size constraint ( s ) , however , the feasibility of a second cycle has remained uncertain , and the notion that ~34 kb is close to the actual limit of RNA virus genome size [35] has seemed plausible . To examine whether this limit applies beyond the currently recognized ~3000 RNA virus species ( isolated from only a few hundred host species ) , further sampling of virus diversity is required , particularly from host species in which viruses have thus far remained virtually unknown . To this end , we analyzed de novo transcriptomes from both major reproductive biotypes ( strains ) of the planarian Schmidtea mediterranea [67]: a hermaphroditic sexual strain , and an asexual strain whose members reproduce via transverse fission [68] . We report the discovery and characterization of the first known planarian RNA virus , dubbed the planarian secretory cell nidovirus . PSCNV has the largest RNA genome by a considerable margin–a feat made more remarkable by the fact that its genome is organized as a single ORF . Concomitantly , it has adapted the nidoviral regulatory toolkit in novel ways , and acquired many features that revise the known limits of viral genomic and proteomic variation–some of these features being unique among nidoviruses , others among RNA viruses , and still others among all known viruses . Our results imply that viruses with the nidoviral genetic plan have the potential to expand RNA genomes further along the trajectory envisioned by the multi-cycle , three-wave model .
To identify potential nidovirus-like sequences in the planarian transcriptome , we queried two in-house de novo-assembled Schmidtea mediterranea transcriptomes [67] for sequences that significantly resembled a reference coronavirus genome . Two nearly identical ( 99 . 97% ) nested transcripts , txv3 . 2-contig_1447 ( originating from the sexual strain ) and txv3 . 1-contig_12746 ( from the asexual strain ) , showed a statistically significant similarity to known nidoviruses as reciprocal BLAST top hits . We hypothesized that these transcripts are genomic fragments of a new nidovirus species . We further identified several overlapping EST clones with >99% nucleotide identity to the transcriptome contigs , and assembled these into a putative partial genome ( S1 Fig ) . Finally , with additional transcriptome search iterations and Sanger sequencing of the transcript 5’-end , we assembled a 41 , 103-nt transcript ( excluding the polyA tail ) . Based on several criteria ( see below ) , we assigned this RNA sequence to the genome of a virus we dubbed Planarian Secretory Cell Nidovirus ( PSCNV ) ( S1 Fig ) . This sequence was the reference genome used for further analyses ( see Materials and Methods for more detail ) . The complete PSCNV genome encodes a single 40 , 671-nt ORF that is flanked by a 128-nt 5’-UTR and a 304-nt 3’-UTR ( Figs 1B and 2 ) . In addition , we found the main ORF overlapping multiple small ORFs in other reading frames , whose lengths exceeded 150 nt: 8 ORFs in the same strand as the large ORF ( plus-strand ) , lengths ranging from 156 to 267 nt , 5 of which mapped to the 3’-terminal quarter of the genome; and 24 ORFs in the reverse complement strand ( minus-strand ) , distributed throughout the genome , with lengths ranging from 153 to 681 nt . To further verify the presence of the viral genome in vivo , we amplified large overlapping genomic subregions by RT-PCR ( S2 Table , S1 Fig ) [69] . These sequences could not be amplified from S . mediterranea genomic DNA , nor could they be found in the reference planarian genome [70]; thus , they appear to derive from an exogenous source . A survey of 16 S . mediterranea RNA-seq datasets from nine laboratories worldwide uncovered PSCNV reads in five datasets from three American locations . Of the positive datasets , three originated from the sexual strain , and two from the asexual strain . Overall , viral sequences were much more abundant in transcriptomes obtained from sexual strains ( S3 Table ) . The PSCNV sequences detected in these studies vary little from one another . The three most complete sequences ( tentatively reconstructed from PRJNA319973 , PRJNA79031 , and PRJNA421285 ) are characterized by >99 . 9% identity across a nearly 13 kb span of the genome , where at least 2 reads ( and at least 10 reads for >95% of positions ) from each dataset mapped to each position of the reference genome . Indeed , sequences from PRJNA319973 and PRJNA79031 –the two datasets from the Newmark laboratory–exhibit only a single mutation relative to the reference genome , and the sequence from PRJNA421285 –from the Sanchez Alvarado laboratory–differs at only 9 positions ( S4 Table ) . This low variation is notable , as two of the datasets analyzed ( PRJNA79031 and PRJNA421285 ) are derived from sexual S . mediterranea , and the other one ( PRJNA319973 ) from an asexual S . mediterranea lab strain . The source populations of these two ( freshwater ) strains are separated from each other by about 500 km of the Mediterranean Sea: the asexual laboratory strain was established from a population in Barcelona [71] , and the sexual strain originates from a Sardinian population . A recent study of the evolutionary history of S . mediterranea suggests that these populations diverged from each other at least 4 million years ago [72] . Given the long-separate history of these two planarian strains prior to becoming research subjects and the relatively high mutation rate in characterized nidoviruses , the detection of nearly identical viral transcripts in both strains is strong evidence that the virus is transmissible . The absence of viral sequences from asexual strains in most labs , and their presence in all labs that have reported RNA-seq data from the sexual strain , strongly suggest that the virus first infected ( or was endemic to ) the sexual strain , and has subsequently spread to asexual laboratory stocks . We examined PSCNV infection in planarian tissues by whole-mount in situ hybridization ( ISH ) . PSCNV RNA was detected abundantly in cells of the secretory system in both sexuals and asexuals ( Fig 3A ) . Fluorescent ISH revealed viral RNA in gland cell projections that form secretory canals ( Fig 3B ) . Notably , viral RNA was detected largely in ventral cells ( Fig 3C ) whose localization corresponds to mucus-secreting cells that produce the slime planarians use for gliding locomotion , and to immobilize prey [73] . We then analyzed planarians by electron microscopy ( EM ) for the presence of viral structures . In one specimen , membrane-bound compartments containing 90–150 nm spherical-to-oblong particles resembling nidoviral nucleocapsids [74 , 75] were found in the cytoplasm of mucus-secreting cells . These sub-epidermal gland cells are notable for their abundant rough endoplasmic reticulum and long projections into the ventral epithelium , through which they secrete mucus ( S2 Fig ) . These cells provide an ideal environment for nidoviral replication , which co-opts host membranes to produce viral replication complexes [76 , 77] . Putative viral particles were found both in deep regions of these cells , and in their trans-epidermal projections ( Fig 4A–4C ) . The latter location suggests a route for viral transmission . Notably , particles in sub-epidermal layers have a “hazy” appearance and are embedded in a relatively electron-dense matrix ( Fig 4D ) . In contrast , particles closer to the apical surface of the epidermis appear as relatively discrete structures , standing out against electron-lucent surrounding material ( Fig 4E ) . The size , ultrastructure , and host-cell locations are all consistent with these structures being nidoviral nucleocapsids [74 , 75] . In 280 images from the positive specimen , all other ultrastructural features were normal . Importantly , typical mucus vesicles were evident in this specimen , often immediately adjacent to vesicles containing putative virions ( Fig 4C , see also S2 Fig ) . As such , we determined that these structures do not represent artefacts caused by atypical fixation of this specimen . The genome and proteome of PSCNV are by far the largest yet reported for an RNA virus . Its RNA genome is ~25% larger than that of the next-largest known RNA virus ( BPNV , [21] ) , which is separated by a comparable margin from the first nidovirus genome sequenced 30 years ago ( IBV , [78] ) ( Fig 1A ) . The size of the predicted PSCNV polyprotein ( 13 , 556 amino acids , aa ) is 58–67% larger than the largest known RNA virus proteins produced from a single ORF ( 8 , 572 aa; Gamboa mosquito virus , [79] ) or multiple ORFs through frameshifting ( 8 , 108 aa; BPNV , [21] ) ( Fig 5 ) . Functional annotation of the PSCNV polyprotein by comparative genomics [14 , 31 , 80 , 81] presented a distinct bioinformatics challenge , due to its weak similarity to other proteins and its extremely large size , which exceeds the average size of protein domains by approximately 75-fold . We delineated at least twenty domains in the PSCNV polyprotein , including twelve domains conserved in nidoviruses or other entities , using a multistage computational procedure that combined different analyses within a probabilistic framework ( Fig 2; S3–S16 Fig; S5 Table; see Materials and Methods ) . We initially identified six regions highly enriched in hydrophobic residues characteristic of transmembrane domains , named TM1 to TM6 accordingly ( Fig 2 ) . The number and relative location of the TM domains resemble those found in the proteomes of nidoviruses , which commonly have five or more TM domains in non-structural and structural proteins [82–85] . We then identified fourteen regions enriched in individual amino acid residues ( S4 Fig ) , with the strongest signal observed for Thr-rich region ( residues 10429–10559 , 44 . 3% Thr residues , up to 13 . 4 SD above the mean ) . Notably , the Thr-rich region overlaps with a Ser-rich region ( 10461–10501 aa , 19 . 5% Ser residues , up to 5 . 5 SD above the mean ) . Subsequently , two tandem repeats were identified toward the N-terminus of the polyprotein ( residues 1616–1682 and 1686–1751 , Probability 96 . 6% , S5 Fig ) , which showed no significant similarity to other proteins in the databases using HHsearch . We used the domains described above to split the polyprotein into nine regions , which were analyzed by an iterative HHsearch-based procedure ( outlined in S3 Fig and S1 Materials and Methods ) . Our approach identified eight domains that , together with TM2 and TM3 , form a canonical synteny of replicative domains in the central part of the polyprotein ( genome ) , which is characteristic of known invertebrate nidoviruses ( Fig 2 ) : 3CLpro , NiRAN , RdRp , ZBD , HEL1 , ExoN , and S-adenosylmethionine ( SAM ) -dependent N7- and 2’-O-methyltransferases ( N-MT and O-MT , respectively ) . Five of these domains ( 3CLpro , NiRAN , RdRp , HEL1 , and O-MT ) were identified by hits exceeding the 95% Probability threshold , while three others were based on weaker hits: 35 . 0% for ZBD , 39 . 1% for ExoN , and 80 . 8% for N-MT . Despite the lower Probability values obtained for the latter three domains , synteny and conservation of essential functional residues strongly suggest that they encode true homologs of canonical nidoviral proteins . Overall , the analysis demonstrates the existence of the three definitive nidoviral genomic subregions in the PSCNV single-ORF genome: ORF1a- , ORF1b- , and 3’ORFs-like . Within these regions , TM2 , 3CLpro , and TM3 map to the ORF1a-like region , while NiRAN , RdRp , ZBD , HEL1 , ExoN , N-MT , and O-MT map to the ORF1b-like region . In addition to the canonical replicative domains present in the canonical order and location , we found four domains that are novel for nidoviruses: one upstream and three downstream of the array of the conserved replicative domains ( S5 Table ) . These include a homolog of ribonuclease T2 ( RNase T2 , Probability 80 . 0% ) upstream of the TM2 , two fibronectin type II domains ( FN2a and FN2b , 91 . 3% and 78 . 5% , respectively ) , and an ankyrin repeats domain ( ANK , 98 . 9% ) downstream of the O-MT . For the three domains identified with the under-threshold hits , additional support came from conservation of functionally important residues ( see below ) . We subsequently generated multiple sequence alignments ( MSAs ) of these domains for a representative set of established nidovirus species , followed by phylogenetic reconstruction to characterize PSCNV by revealing common and unique features of its conserved domains . The next three sections summarize the salient features of the replicative , novel , and structural domains of the polyprotein . The 3’ORFs region of nidoviruses encodes components of the enveloped virion [23 , 54] , which define receptor specificity [55–57] and typically include the nucleocapsid protein ( N ) , characterized by biased amino acid composition and structurally disordered region ( s ) [104 , 105] , spike glycoprotein ( s ) ( S protein in corona- and toroviruses ) and transmembrane matrix protein ( M in corona- and toroviruses ) enriched with TM regions [58 , 59 , 106] . As expected from the weak sequence conservation of this region in other nidoviruses [14 , 107] and its weak similarity with other viruses [108] , we were unable to find statistically significant similarity between the PSCNV polyprotein and structural proteins of the known nidoviruses . Nevertheless , important nidoviral themes are evident . First we noted that the genome distribution of the TM-encoding regions in PSCNV conformed to that observed in other nidoviruses , with TM1 and TM2 located upstream of 3CLpro , TM3 C-terminal to 3CLpro , and TM4–TM6 downstream , in the 3’ORFs-like region ( Fig 2 ) . In nidoviruses , the TM domains encoded in the 3’-genome region are known to be part of the S and M proteins or their equivalents , and occasionally additional accessory proteins [14 , 58 , 59 , 106 , 109] . The extracellular portion of the S protein is supported by multiple disulfide bridges between conserved Cys residues [56] . In PSCNV , a Cys-rich region was observed downstream of TM5 ( S4 Fig ) . In an approximately 650 aa region surrounding the TM6 domain ( 4 . 7% of the polyprotein length ) , we identified six areas enriched in Pro , Leu , Gly , Gln , Asn , or Arg , in close proximity to each other ( S4 Fig ) . This region accounted for 43% of all residue-enriched areas in the polyprotein; such an exceptionally high concentration of sequences enriched with specific amino acids is indicative of unusual properties . Accordingly , this area was predicted to include the longest stretch of disordered regions . In nidoviruses , disordered hydrophilic-rich areas are characteristic of N proteins . In PSCNV , the polyprotein region downstream of O-MT is ~4000 aa , more than twice as large as the largest known structural protein of nidoviruses [106] . We reasoned that this part of its polyprotein might be processed by cellular signal peptidase ( SPase ) and/or furin to produce several proteins , as documented for maturation of the structural proteins of many RNA viruses , including nidoviruses [110–114] . Indeed , our analysis of potential cleavage sites of these proteases revealed highly uneven distributions ( S4 Fig ) , with sites predicted only in the N- and C-terminal parts of the polyprotein: 1400–3100 aa ( one SPase and four furin sites ) and 10200–13200 aa ( three SPase and five furin sites ) . All of these are outside of the region that must be processed by 3CLpro . With the exception of the most C-terminal furin site , all predicted sites are in close vicinity to provisional borders of the domains described above , as would be expected if these domains function as distinct proteins . Specifically , if the predicted SPase and furin sites are cleaved , TM1 , TM4 , TM5 , and TM6 would end up in separate proteins , with one protein including the TM4 and ANK domains . With predicted cleavage sites flanking it from both sides , TM5 may be released as a separate protein , most similar to M proteins in size and hydrophobicity . We also note that two putative proteins may combine a FN2 module with a disordered region: FN2a with a Thr/Ser-rich region and FN2b with the Pro/Leu/Gly/Gln/Asn/Arg-rich region , respectively . Based on the reasoning outlined above , the latter combination may constitute a region of the N protein . Overall , our analysis of the predicted PSCNV proteins suggests that its genome is functionally organized in much the same manner as in the multi-ORF nidoviruses: with the non-structural and structural proteins encoded in the 5’- and 3’- regions , respectively . Next we sought to determine when PSCNV’s lineage emerged , relative to other nidoviruses . The proteome analysis described above indicates that PSCNV shares the main features characteristic of invertebrate nidoviruses , although it also exhibits distinctive properties indicative of a distant relationship with previously characterized nidoviruses . To resolve very deep branching , we used an outgroup in our analysis , and selected astroviruses for this purpose [23] . Astroviruses [115] and nidoviruses share multi-ORF genome organization , a central role for 3CLpro in polyprotein processing , and similarities in the RdRp domain . Conversely , astroviruses do not encode a HEL1 , NiRAN or ZBD , and their 3CLpro is highly divergent . Given the divergent 3CLpro of PSCNV , RdRp remained as the only domain most suitable for phylogeny reconstruction; this domain has been used in many studies on macroevolution of nidoviruses [21 , 23 , 35 , 116] . We performed phylogenetic analysis of the RdRp core region by Bayesian inference ( BEAST software , LG+I+G4 model , relaxed clock with uncorrelated log-normal rate distribution ) . Nidoviruses including PSCNV formed a monophyletic group in >90% of the trees in the analyzed Bayesian sample , with PSCNV being one of the basal branches in the cluster of invertebrate nidoviruses in 88 . 7% of the trees , basal to either mesoni- and roniviruses ( 54 . 7% of the trees ) , or roniviruses ( 20 . 6% ) , or mesoniviruses ( 13 . 4% ) ( Fig 7 and S17 Fig ) . In addition , we built a nidovirus phylogeny without an outgroup ( BEAST software , LG+I+G4 model , relaxed clock with uncorrelated log-normal rate distribution ) , based on a concatenated alignment of five domains conserved in all nidoviruses ( 3CLpro , NiRAN , RdRp , ZDB , HEL1 ) . Again , PSCNV belonged to the cluster of invertebrate nidoviruses in the majority of trees and was basal to either mesoni- and roniviruses ( 11 . 8% of the trees ) , or roniviruses ( 83 . 0% ) , or mesoniviruses ( 3 . 6% ) . Is the unique single-ORF genomic organization of PSCNV an ancestral characteristic of nidoviruses , or has it evolved from an ancestral multi-ORF organization ? To choose between these alternative scenarios , we need to reconstruct a genomic ORF organization of the most recent common ancestor ( MRCA ) of nidoviruses . Such reconstruction by orthology , which was used for RdRp-based phylogeny , is not feasible with the current dataset , as none of the open reading frames or their overlaps ( with the exception of the ORF1a/ORF1b junction ) are conserved in all known multi-ORF nidoviruses . To address this challenge , we noted that nidoviruses with multi-ORF organization , unlike PSCNV , recurrently use initiation and termination codons to delimit ORF-specific proteins in the 3’ORFs region , indicative of pervasive selection forces that operate in all recognized nidovirus species . Therefore , we reasoned that multi- and single-ORF organizations in nidoviruses could be treated as two alternative discrete states of a single trait ( ORF organization ) , regardless of the complexity of their actual evolutionary relations in the 3’ORFs region and assuming the rate of transition between any two multi-ORF organizations to be extremely high compared to that between single- and multi-ORF organizations . This reasoning allows us to reformulate the question in the framework of ancestral state reconstruction analysis: if each extant nidovirus is characterized by one of the two states of a trait ( ORF organization ) , which state of the trait existed in their MRCA ? To conduct this analysis , we applied the BayesTraits [117] program to the RdRp-based Bayesian sample of phylogenetic trees including the outgroup , which accounts for uncertainty in the phylogeny inference of nidoviruses . The results strongly favored multi-ORF organization of the ancestral nidovirus ( Log Bayes Factor ( BF ) 6 . 06 and 6 . 16 , when multi-ORF genome organization , or no information about genome organization , were specified as states of the trait for astroviruses , respectively ) ( S17 Fig ) . Similarly , strong support ( Log BF 4 . 79 ) for multi-ORF ancestral organization was obtained when the analysis was conducted based on a phylogeny without an outgroup , reconstructed using five nidovirus-wide conserved domains . Each of the three main regions of the PSCNV genome is larger than its counterparts in all other nidoviruses ( Fig 8A , S1 and S6 Tables ) . However , the size differences between PSCNV and the next largest nidovirus in each of these regions are smaller than those observed for complete genomes ( Fig 8A: 5 . 7% , 20 . 6% and 15 . 6% for ORF1a , ORF1b and 3’ORFs , respectively , vs 22 . 9% for the genome ) . This paradoxical observation is due to profound differences in regional size variation among nidoviruses [66] such that different nidoviruses are the next largest to PSCNV for each of the three main regions ( S1 Table ) . To account for these and other differences in sizes of the three regions while assessing the regional size increases of PSCNV , we employed two measures in addition to the percentage size increase between PSCNV and the next largest nidovirus ( see Materials and Methods , formulas D2 and D3 versus formula D1 ) . First , for each genome region , we normalized the size difference between PSCNV and the next largest virus against the difference between the latter and the median-sized virus for that region ( formula D2 ) . Second , we checked how much the deviation calculated with formula D2 differs from that expected under a hypothesis that size changes are uniform across the three genome regions , and therefore proportional to genome-wide changes ( formula D3 ) . These measures show that , relative to the size variation among known ExoN-positive nidoviruses , the size increase in the ORF1b region was extraordinarily large ( D2 = 1270 . 5% and D3 = 968 . 1% ) , while the corresponding increases in the two other regions were modest and smaller than could be expected ( 18 . 9% and 14 . 4% for ORF1a , and 44 . 3% and 33 . 7% for 3’ORFs ) ( Fig 8B , S6 Table ) . Virus reproduction requires different viral protein stoichiometries at distinct replicative cycle stages , a challenge for a single-ORF genome theoretically producing equimolar quantities of encoded polypeptides . To this end , all previously described nidoviruses employ -1 PRF to translate ORF1a+ORF1b , in addition to ORF1a alone , to produce two polyproteins from a genomic template: pp1ab and pp1a , respectively [40 , 41] . The net result of this mechanism is relatively high expression of the ORF1a- compared to ORF1b-encoded proteins , since PRF occurs at the ORF1a/1b junction in 15–60% of ORF1a translation events . In contrast , proteins encoded in the 3’ORFs region are produced by translation of subgenomic ( sg ) mRNAs , synthesized on specific minus-strand templates [51–53] , which are in turn produced by discontinuous RNA synthesis on genomic templates . Discontinuous minus-strand template synthesis relies on lTRS and bTRS , which are nearly identical , short repeats at sites where RNA synthesis pauses ( upstream of 3’ORFs ) and resumes ( in the 5’-UTR ) , respectively . Templates of some sg mRNAs may be terminated at bTRS . Both transcription and translation of sg mRNAs provide a means to produce relatively large quantities of structural proteins , compared to non-structural ( replicative ) proteins , late in the replicative cycle , and to regulate production of accessory proteins . We analysed the PSCNV genome for evidence of such mechanisms . Finally , we used the PSCNV polyprotein as a query sequence to survey several flatworm species’ transcriptomes in the PlanMine database [119] for the presence of other nidoviruses related to PSCNV . We identified six contig sequences with highly significant similarity to PSCNV indicative of at least two nidoviruses ( S18 Fig ) . These contigs originate from transcriptomes of S . mediterranea ( uc_Smed_v2 and ox_Smed_v2 assemblies , two and one contigs , respectively; the latter contig was excluded from consideration due to being almost identical to one of the former contigs ) and another planarian species , Planaria torva ( dd_Ptor_v3 assembly , three contigs ) . Translations of the two uc_Smed_v2 contigs of 814 nt and 1839 nt gave hits of >99% aa identity to the very C-terminus of PSCNV polyprotein , indicative of a variant of PSCNV circulating in the same host species ( see section above ) . In contrast , the dd_Ptor_v3 transcriptome included two short contigs ( 283 nt and 289 nt ) with hits to the PSCNV RdRp domain ( 38 and 48% aa identity ) as well as an 8811-nt contig , whose translation in the +1 frame gave 3 discontinuous hits , one to the O-MT domain of the ORF1b-like region ( 37% aa identity ) and two to the 3’ORFs-like region and its FN2b domain ( 25% and 37% aa identity ) . These domains are separated by different distances in PSCNV and the 8811-nt contig . It is notable that all three hits from the P . torva contig correspond to its translation in the same frame , uninterrupted by stop-codons , suggesting that ORF1b-like and 3’ORFs-like regions of this putative and divergent virus could also be expressed from a single ORF .
The PSCNV polyprotein includes distant homologs of all ten domains common to invertebrate nidoviruses , as well as the vertebrate Coronavirinae subfamily [14 , 45] . These were identified with high statistical confidence , using an iterative bioinformatics procedure with profile searches at its core . These domains include the definitive nidovirus markers NiRAN and ZBD , and all ten are syntenic between PSCNV and other nidoviruses . Most are located in the ORF1b-like ( replicase ) region , which also includes four subregions left unannotated ( Fig 2 ) . Of these unannotated subregions , one flanked by ZBD and HEL1 may correspond to the regulatory domain 1B , which is uniformly present but poorly conserved in helicases of nidoviruses [48 , 49] , while the other three may represent domains uniquely acquired by a PSCNV ancestor . Like all characterized invertebrate nidoviruses , but unlike most vertebrate nidoviruses [14 , 129] , PSCNV does not encode a homolog of an uridylate-specific endonuclease ( NendoU ) [31] . Accordingly , our rooted RdRp-based phylogenetic analysis assigned PSCNV to a monophyletic clade of invertebrate nidoviruses . Another topologically similar tree was inferred using five nidovirus-wide conserved domains with a dataset that did not include an outgroup . The observed tree topology is also broadly compatible with other observations of this study ( see below ) , and with RdRp-based trees of known nidoviruses produced in other studies [14 , 21 , 35] . Given that PSCNV infects planarian hosts , consistent placement of this virus in the invertebrate nidovirus clade by different analyses makes biological sense . On the other hand , the precise position of PSCNV in the invertebrate nidovirus clade remains poorly resolved for several reasons , including the highly skewed host representation in the analyzed small sample of 57 nidoviruses , and the large divergence of invertebrate nidoviruses from each other . The dominant tree topology placed PSCNV in a very long and deeply rooted branch , which has been recognized as a suborder in the pending taxonomic proposal [130] . This is further supported by the presence of the GDD tripeptide in the RdRp C motif ( S9 Fig ) , most common in ssRNA+ viruses other than nidoviruses , which typically ( except for the arterivirus Wobbly possum disease virus , WPDV , [81] ) have an SDD signature instead [131] . The pronounced divergence of PSCNV is also evident in other conserved protein domains , 3CLpro , NiRAN and ExoN , each of which carries substitutions not observed in other invertebrate or all nidoviruses . Two prominent replacements in PSCNV 3CLpro are functionally meaningful ( S7 Fig ) . The replacement of the otherwise invariant His by Val in the putative substrate pocket is indicative of a modified P1 substrate specificity for this enzyme , which exhibits a strong preference for Glu or Gln residues in P1 position in most other ssRNA+ viruses , including vertebrate nidoviruses [42 , 88–91] . Accordingly , we were unable to identify typical 3CLpro cleavage sites at the expected inter-domain borders in the portion of the PSCNV polyprotein that must be processed by 3CLpro . Furthermore , the nucleophilic catalytic residue of PSCNV’s 3CLpro is Ser , while its counterpart in other characterized invertebrate nidoviruses is Cys . Similar variation of this residue has been described among vertebrate arteri- and toroviruses versus coronaviruses [42 , 88–91] , with distinct variants being associated with deeply separated virus lineages at the rank of ( sub ) family . Diversification of the nucleophile residue was also observed in other ssRNA+ viruses that employ 3C ( L ) proteases [132 , 133] . This recurrent Ser-Cys toggling of the catalytic nucleophile in other well-established viral families argues against independent origins of 3CLpros in PSCNV and other nidoviruses , despite their weak sequence similarity . Besides its exceptionally large genome size , the single-ORF organization of the PSCNV genome is unprecedented for nidoviruses . This single-ORF organization was unexpected , given that multi-ORF organization is conserved across the vast diversity of nidoviruses separated by large evolutionary distances , and infecting vertebrate or invertebrate hosts . In contrast , other large monophyletic groups of ssRNA+ viruses with comparable host ranges ( e . g . , the order Picornavirales or Flavi-like viruses ) , include many viruses with either single- or multi-ORF organizations , which intertwine phylogenetically [79 , 132 , 133] . The use of 3CLpro as the main protease responsible for the release of key RTC subunits from polyproteins would be anticipated to remain essential in the single-ORF PSCNV . In contrast , two other conserved mechanisms of genome expression , ORF1a/1b -1 PRF and discontinuous transcription , might not be expected to operate in this virus , since they are associated with the use of multiple ORFs in nidoviruses . We reasoned otherwise , however , on the grounds that these mechanisms allow differential expression of three functionally different regions of the nidovirus genome , which are also conserved in PSCNV . We located a potential -1 PRF signal in the PSCNV genome . This signal is located at the canonical position observed in other nidoviruses , and could potentially attenuate in-frame translation downstream of the ORF1a-like region in a manner different from a mechanism used by other characterized nidoviruses , but with similar end-products ( Fig 9 ) . Such a postulated mechanism is used by encephalomyocarditis virus to attenuate the expression of replicase components in favor of capsid proteins from its main long ORF [134] . Likewise , we obtained several lines of evidence for upregulated transcription of the 3’ORFs-like region as a subgenomic RNA ( Fig 10 ) . The products of this region may also be derived from the polyprotein , but are likely required in greater abundance toward the end of the viral replication cycle , and separate expression from sg mRNA would more efficiently address this need . Importantly , no evidence , either bioinformatic or experimental , was obtained for other sg mRNAs , although we cannot exclude their existence . PSCNV’s putative TRSs are exceptionally long for nidoviruses ( 59 and 57 nt versus typically a dozen nt ) , perhaps because smaller repeats might emerge in its extraordinarily long genome by chance , interfering with transcription accuracy . Other unknown factors may also contribute to this large TRS repeat size . The putative leader TRS ( lTRS ) and body TRS ( bTRS ) , along with their predicted RNA secondary structures , suggest a model for transcriptional regulation of the PSCNV genome . We postulate that during anti-genomic RNA synthesis , the virus RTC unwinds two bTRS hairpins ( Fig 10C , top ) . As a result , the region immediately upstream of the bTRS ( yellow in the figure ) becomes available for base-pairing with the 5’-terminus of the lTRS ( Fig 10C , middle ) . This interaction will bring the two distant regions of the genome in close proximity , facilitating translocation of the nascent minus-strand from body to leader TRS ( Fig 10C , bottom ) . The latter step is considered routine in the current model of sg RNA synthesis in well-characterized arteriviruses and coronaviruses [51 , 135] . However , its mechanistic details are poorly understood and may operate differently among nidovirus families . Although we cannot exclude the possibility that smaller ORFs are expressed by PSCNV , it seems unlikely that they would contribute substantially to the virus proteome , in line with the apparent inverse relationship between genome size and gene overlap [136] . Rather , such ORFs could be used for regulatory purposes , as in the case of the very small ORF at the border of ORF1a- and ORF1b-like regions , through the PRF mechanism proposed above . The combined genomic and proteomic characteristics of PSCNV defy the central role of multiple ORFs in the life cycle and evolution of nidoviruses , despite their universal presence in all other nidoviruses [26 , 60] . Contrary to conventional wisdom , single-ORF genome expression can involve the synthesis of subgenomic mRNAs . Rather than multi-ORF genome organization , functional constraints linked to the synteny of key replicative enzymes may be the hallmark characteristic of nidoviruses [137] . Most of the domains that we annotated in the PSCNV giant polyprotein are homologs of canonical nidovirus domains . However , we also mapped several unique domains . Below , we discuss possible functions of five small domains , all of which plausibly modulate different aspects of virus-host interaction . PSCNV encodes a ribonuclease T2 homolog upstream of the putative 3CLpro in the ORF1a-like region ( Fig 2 ) . Ribonucleases of the T2 family ( RNase T2 ) are ubiquitous cellular enzymes that non-specifically cleave ssRNA in acidic environments [138] . DNA polydnaviruses and RNA pestiviruses are the only two other virus groups that are known to encode related enzymes [139 , 140] . In pestiviruses , the RNase T2 homolog is a domain of secreted glycoprotein Erns found in virions , but dispensable for virus entry [141] . The Erns structure is supported by four disulfide bridges that are formed by eight conserved Cys residues [139] . None of these residues were found in the PSCNV RNase T2 homolog , consistent with its location in the polyprotein region that produces cytoplasmic proteins in other nidoviruses . In polydnaviruses and pestiviruses , the RNase T2 homolog modulates cell toxicity and immunity [139 , 140] , and a similar role could be considered for the PSCNV RNase T2 homolog . The origin of this domain in PSCNV remains uncertain due to the lack of close homologs in either its host , S . mediterranea , or other cellular and viral species . Two other unique domains of PSCNV are fibronectin type II ( FN2 ) homologs , protein modules of approximately 40 aa with two conserved disulfide bonds , which are ubiquitous in extracellular proteins of both vertebrates and invertebrates [142 , 143] . Because of the low similarity of FN2a and FN2b to each other and other homologs , it is not clear whether they emerged by duplication or were acquired independently . No other known virus encodes an FN2 homolog ( although the putative nidovirus identified in P . torva may include an ortholog of FN2b , S18 Fig ) , suggesting that PSCNV’s FN2 domains function in a unique aspect of its replication cycle . FN2 domains are known to possess collagen-binding activity , and are found in a variety of proteins that bind to and remodel the extracellular matrix [144 , 145] . Thus , it is conceivable that these domains might play a role in the shedding or transmission of PSCNV virions . This hypothesis is compatible with the accumulation of PSCNV RNA and particles , presumably virions , in the planarian mucus-secreting cells . Besides FN2 domains , this process might also involve the Thr/Ser-rich region adjacent to FN2a in polyprotein , since Thr-rich and Thr/Ser-rich regions have been implicated in mediating adherence of fungal and bacterial extracellular ( glyco ) proteins to various substrates [146 , 147] . The identification of the ankyrin repeats domain ( ANK ) in PSCNV is unprecedented and intriguing . In proteins of other origins , the ANK domain is a tandem array of ankyrin repeat motifs ( ~33 residues each ) of variable number and divergence that fold together to form a protein-binding interface [148] . Ankyrin-containing proteins are involved in a wide range of functions in all three domains of cellular life . In viruses described to date , they have been identified exclusively in large DNA viruses with genome sizes ranging from ~100 kb to 2474 kb , the latter of Pandoravirus salinus , the largest viral genome described so far [38 , 148–150] . Acquisition of this domain , likely from a planarian host , might have provided a PSCNV ancestor with a mechanism to evade host innate immunity . Notably , according to SmedGB [102] annotation , host proteins SMU15016868 and SMU15005918 , whose C-terminal domains are the closest homologs of PSCNV ANK ( Fig 6 ) , contain a Rel homology domain ( RHD ) at their N-termini . This N-RHD-ANK-C domain architecture is typical of the NF-ĸB protein , a precursor of a cellular transcription factor that triggers inflammatory immune responses upon virus infection or other cell stimulation [151] . NF-ĸB is activated for translocation to the nucleus by degradation of its inhibitor , C-terminal ANK domain of NF-ĸB protein or its closely related paralog , IĸB protein [148 , 152 , 153] . Several large DNA viruses have been shown to encode IĸB-mimicking proteins that prevent NF-ĸB from entering the nucleus in response to the infection , and thus downregulate the host immune response [154 , 155] . PSCNV ANK may represent the first example of an IĸB-mimicking protein in RNA viruses , although RNA viruses including nidoviruses can target NF-ĸB protein using other mechanisms [156] . This striking parallel between PSCNV and large DNA viruses blurs the distinction between these viruses regarding how they adapt to hosts [157] . It further highlights the exceptional coding capacity of PSCNV genome among RNA viruses . The single-ORF organization of PSCNV’s exceptionally large genome is intriguing , but we cannot determine whether this association between genome size and organization is causal or coincidental from observation of a single species . In this respect , determining whether the putative nidovirus we identified in P . torva also employs a single-ORF organization could be illuminating . An evolutionary switch between multi- and single-ORF organizations , regardless of its direction , must be a multi-step process , since it affects many translation regulatory signals . In our study , we used a simple model of this process with two character states within a Bayesian phylogenetic framework , to obtain support for the single-ORF organization of PSCNV emerging from the multi-ORF organization . This approach is apparently not sensitive to the choice of domains used for phylogeny reconstruction , or inclusion of an outgroup . However , given the deep position of the PSCNV lineage in the nidovirus tree , the ambiguous rooting of PSCNV relative to other invertebrate nidovirus families , and PSCNV being the only single-ORF nidovirus known , further analysis of this transition using improved sampling of nidoviruses and their sister clades [35 , 36] , and more sophisticated models is warranted . In the few experimentally characterized coronaviruses with genomes of 27–31 kb , the mutation rate is low by RNA virus standards , due to ExoN proofreading activity [34 , 158 , 159] . This observation is in line with the inverse relationship between genome size and mutation rate in viruses and prokaryotes [160 , 161] . Accordingly , we may expect mutation rates to differ among ExoN-containing nidoviruses with different genome sizes , with PSCNV having a particularly low mutation rate . While characterization of mutation rates of PSCNV and other nidoviruses must await future studies , we already note a distinctive similarity between cellular proofreading exonucleases and ExoN of PSCNV , which separates it from its orthologs in other ExoN-positive nidoviruses . Specifically , there is a correlation between the presence of the Zn-finger motif in the exonuclease active site [33 , 92] and the genome size of the biological entity encoding the exonuclease: non-PSCNV nidoviruses with genome sizes in the range of 20–34 kb include a Zn-finger embedding catalytic His , while PSCNV and DNA-based entities with genome sizes >41 kb do not ( S12 Fig ) [162] . Based on these observations , it is plausible that this Zn-finger might limit ExoN's capacity to improve replication fidelity while providing other benefits , and its loss in the PSCNV lineage could have been a factor promoting genome expansion . Besides the lack of the Zn-finger in ExoN , the reported size increase of the ORF1b-like region in PSCNV relative to other nidoviruses ( about 10-fold greater than expected under an assumption of uniform expansion in all genome subregions ) is particularly notable in the context of the theoretical framework presented in the introduction . Briefly , expansion of RNA genomes requires escape from the so-called Eigen trap ( or Eigen paradox ) : such genomes are confined to a low-size state , in which low replication fidelity prevents the evolution of larger genomes , which in turn prevents the evolution of greater complexity , which could introduce tools to increase replication fidelity [15] . The three-wave model of genome expansion in nidoviruses notes that the ORF1b region , which encodes the core replicative machinery , appears to play a central role in such constraints . It proposes that a wave of expansion in the ORF1b region of a common ancestor precedes and permits subsequent lineage-specific waves in the ORF1a and 3’ORFs subregions . The wave of expansion in ORF1b involved the acquisition of the ExoN proofreading exonuclease , which permitted further expansion of other subregions due to a reduced mutation rate . Until now , however , the genomes of large nidoviruses ( the 20-to-34 kb size range ) appeared to have reached a plateau at the low-30 kb range , associated with very little variability in the size of ORF1b among members of this group ( 6 . 9-to-8 . 2 kb ) . The three-wave model predicts that further genome expansion far beyond 34 kb would require a second cycle of waves , beginning again with ORF1b [66] . The disproportionate increase in PSCNV’s ORF1b-like region is consistent with this prediction . The acquisition of additional , still-uncharacterized domains in this region of the PSCNV genome , as well as the distinctive features of its ExoN domain , may help to explain this “second escape” from the Eigen trap . Further characterization of the PSCNV ExoN and novel ORF1b domains are required , to assess their contribution to replication fidelity and other characteristics that may be critical for faithful replication and expression of exceptionally large RNA genomes . Our discovery of PSCNV , and analysis of its genome , show that nidoviruses can overcome the ORF1b-size barrier and adopt divergent ORF organizations . If the multi-cycle three-wave model of genome expansion in RNA viruses holds , one would expect that a large expansion of ORF1b , as evident in PSCNV , would permit yet greater expansion of the ORF1a and 3’ORFs regions in other viruses of the PSCNV lineage . Thus , nidoviruses of yet-to-be-sampled hosts might prove to have evolved even larger RNA genomes than that reported here , further decreasing the gap between virus RNA and host DNA genome sizes .
The genome sequence of human coronavirus OC43 ( GenBank KY014282 . 1 ) was used to query two in-house de novo-assembled Schmidtea mediterranea transcriptomes ( transcripts assembled from multiple asexual and sexual planarian stocks , designated with txv3 . 1 and txv3 . 2 prefixes , respectively ) [67] using tblastx ( BLAST+ v2 . 2 . 29 [163] ) . With E-value cut-off 10 , 25 S . mediterranea transcripts were identified and used in reciprocal BLAST searches against the NCBI NR database . Two nested transcripts , txv3 . 2-contig_1447 ( assembled from sexual planarians , GenBank BK010449 ) and txv3 . 1-contig_12746 ( assembled from asexual planarians , GenBank BK010448 ) , showed statistically significant similarity to other nidoviruses , which exceeded its similarity to other entries . Sequences of these two transcripts overlap by 23 , 529 nt with only 7 nt mismatches ( 0 . 03% ) . The larger transcript , txv3 . 1-contig_12746 , was used to search in planarian EST clones [69 , 164] , which found the following overlapping clones showing >99% nucleotide identity: PL06016B2F06 , PL06005B2C04 . PL06007A2B12 , PL06008B2B03 PL08002B1C07 , and PL08001B2B04 ( GenBank DN313906 . 1 , DN309834 . 1 , DN310382 . 1 , DN310925 . 1 , HO005314 . 1 , and HO005110 . 1 , respectively ) . Transcripts txv3 . 1-contig_12746 and txv3 . 2-contig_1447 , and the six EST clones were assembled into an incomplete putative genome . Conflicts between overlapping sequences were always resolved in favor of the txv3 . 1-contig_12746 sequence . Fifteen 3’-terminal nt of the reverse complement of txv3 . 1-contig_12746 ( “TATTATGTGATACAC” ) and two 3’-terminal nt of HO005314 . 1 and HO005110 . 1 ( “TG” ) were discarded due to their likely technical origin . The assembled sequence contains a stop codon followed by a short untranslated region and a polyadenylated ( polyA ) tail . The planarian transcriptomes were surveyed again for transcripts with >50 nt overlap at the 5’-end of the incomplete genome by consecutive rounds of nucleotide BLAST . This identified txv3 . 1-contig_349344 ( from asexual planarians; 11 , 647 nt; 100-nt overlap with txv3 . 1-contig_12746 with no mismatches; GenBank BK010447 ) upstream of the original transcripts , and no further extension was achieved with more BLAST iterations . The 5’-end of the genome was then extended using 5’-RACE followed by Sanger sequencing ( primers in S2 Table ) . Reads from planarian RNA-seq datasets ( used to assemble the two transcriptomes described above , and those available from EBI ENA [165] ) were mapped to the PSCNV genome sequence by either CLC Genomics Workbench 7 , or Bowtie2 version 2 . 1 . 0 [166] . Read counts and coverage were estimated using SAMtools 0 . 1 . 19 [167] , and genome sequence variants were called by BCFtools 1 . 4 [168] . Freshly prepared RNA from mature sexual planarians was used for cDNA synthesis ( iScript , Bio-Rad ) or 5’-RACE ( RLM-RACE , Ambion ) according to manufacturer instructions . Large overlapping amplicons across the PSCNV genome ( primers in S2 Table ) were amplified by standard Phusion® High-Fidelity DNA polymerase reactions , with 65°C primer annealing temperature and 10 min extension steps . Colorimetric and fluorescent in situ hybridizations were done following published methods [169] . Digoxigenin ( DIG ) -labelled PSCNV probes were generated by antisense transcription of the planarian EST clone PL06016B2F06 ( GenBank DN313906 . 1 ) [69] . Following color development , all samples were cleared in 80% ( v/v ) glycerol and imaged on a Leica M205A microscope ( colorimetric ) or a Carl Zeiss LSM710 confocal microscope ( fluorescent ) . Sexual and asexual planarians originating from the Newmark laboratory were fixed and processed for epoxy ( Epon-Araldite ) embedding as previously described [170] . For light-microscopic histology , 0 . 5 μm sections were stained with 1% ( w/v ) toluidine blue O in 1% ( w/v ) borax for 30 s at 100°C , and imaged on a Zeiss Axio Observer . For transmission electron microscopy , 50–70 nm sections were collected on copper grids , stained with lead citrate [171] and imaged with an AMT 1600 M CCD camera on a Hitachi H-7000 STEM at 75 kV . Putative virions were seen by TEM in sections from a single worm , which led us to re-examine a collection of 1697 electron micrographs , drawn from 16 additional worms ( 12 sexuals , four asexuals ) from cultures known to harbor PSCNV . All images that included some portion of a mucus cell were chosen for further examination ( n = 165 ) ; the total number of cells represented cannot be determined without three-dimensional reconstruction from serial sections , which is not practical for such large and irregularly shaped cells . No additional examples of putative viral structures were found among the specimens included in these samples . For various analyses we used the following databases: PlanMine [119] , Smed Unigene [102] , scop70_1 . 75 , pdb70_06Sep14 and pfamA_28 . 0 supplemented with profiles of conserved nidovirus domains [172–174] , Uniprot [175] , genome sequences representing the current 57 nidovirus species that were delineated by DEmARC [176] and recognized by ICTV on year 2016 [177] , NCBI Viral Genomes Resource [178] , GenBank [179] and RefSeq [180] . To predict RNA secondary structure and PRF sites we used Mfold web server [181] and Knot-InFrame [182] , respectively . Blastn ( BLAST+ v2 . 2 . 29 ) [163] was used to identify RNA repeats . Virus protein sequences were analyzed to predict disordered regions ( DisEMBL 1 . 5 [183] ) , transmembrane regions ( TMHMM v . 2 . 0 ) , secondary structure ( Jpred4 [184] ) , signal peptides ( SignalP 4 . 1 [185] ) , N-glycosylation sites ( NetNGlyc 1 . 0 ) and furin cleavage sites ( ProP 1 . 0 [186] ) . Multiple sequence alignments of RNA virus proteins were generated by the Viralis platform [187] . Protein homology profile-based analyses were assisted with HMMER 3 . 1 [188] , and HH-suite 2 . 0 . 16 [189] . To identify sites enriched with amino acid residue , distribution of each residue along polyprotein sequence was assessed using permutation test executed with a custom R script . To establish homology for ZBD , ExoN , and N-MT , for which top HHsearch hits were under the 95% Probability threshold , we considered several criteria about the source hits: 1 ) being among the top three for the respective query of a database; 2 ) being similar to several homologous profiles in two or three databases; 3 ) residing in the polyprotein position conserved in nidoviruses for the respective domain ( S3 Fig , S5 Table ) ; and 4 ) including most residues that are critical for function of the respective domain . For ZBD , we also observed a statistically significant enrichment in cysteine ( Cys ) residues ( S4 Fig ) , in line with the coordination of three Zn2+ ions by characterized ZBDs , which involves predominantly Cys and His residues [48 , 49] . Size differences between genome regions of PSCNV and nidoviruses ( S1 Table ) were estimated using three measures , D1 , D2 , and D3 , that accounted for: 1 ) the region size , D1 ( region ) = ( p-M ) /M*100%; 2 ) the region size variation , D2 ( region ) = ( p-M ) / ( M-m ) *100%; and 3 ) the region size variation and genome size increase , D3 ( region ) = D2 ( region ) /D2 ( genome ) *100% , where m and M are median and maximum sizes of the region in ExoN-containing nidoviruses , respectively , and p is the region’s size in PSCNV . Phylogeny was reconstructed by a Bayesian approach using a set of tools including BEAST 1 . 8 . 2 package [190] and ProtTest 3 . 4 [191] as described in [81] . BayesTraits V2 [117] was used to perform ancestral state reconstruction . Preference for a state at a node was considered statistically significant only if Log BF exceeded 2 [192] . Protein alignments were visualized with the help of ESPript 2 . 1 [193] . To visualize Bayesian samples of trees , DensiTree . v2 . 2 . 1 was used [194] . R was used for visualization [195] . | RNA viruses are the only known RNA-protein ( RNP ) entities capable of autonomous replication . The upper genome size for such entities was assumed to be <35 kb; conversely , the minimal cellular DNA genome is in the 100–300 kilobase ( kb ) range . This large difference presents a daunting gap for the proposed evolution of contemporary DNA-RNP-based life from primordial RNP entities . Here , we describe a nidovirus from planarians , named planarian secretory cell nidovirus ( PSCNV ) , whose 41 . 1 kb genome is 23% larger than any riboviral genome yet discovered . This increase is nearly equivalent in size to the entire poliovirus genome , and it equips PSCNV with an unprecedented extra coding capacity to adapt . PSCNV has broken apparent constraints on the size of the genomic subregion that encodes core replication machinery in other nidoviruses , including coronaviruses , and has acquired genes not previously observed in RNA viruses . This virus challenges and advances our understanding of the limits to RNA genome size . | [
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] | 2018 | A planarian nidovirus expands the limits of RNA genome size |
Phytopathogenic ascomycete fungi possess huge effector repertoires that are dominated by hundreds of sequence-unrelated small secreted proteins . The molecular function of these effectors and the evolutionary mechanisms that generate this tremendous number of singleton genes are largely unknown . To get a deeper understanding of fungal effectors , we determined by NMR spectroscopy the 3-dimensional structures of the Magnaporthe oryzae effectors AVR1-CO39 and AVR-Pia . Despite a lack of sequence similarity , both proteins have very similar 6 β-sandwich structures that are stabilized in both cases by a disulfide bridge between 2 conserved cysteins located in similar positions of the proteins . Structural similarity searches revealed that AvrPiz-t , another effector from M . oryzae , and ToxB , an effector of the wheat tan spot pathogen Pyrenophora tritici-repentis have the same structures suggesting the existence of a family of sequence-unrelated but structurally conserved fungal effectors that we named MAX-effectors ( Magnaporthe Avrs and ToxB like ) . Structure-informed pattern searches strengthened this hypothesis by identifying MAX-effector candidates in a broad range of ascomycete phytopathogens . Strong expansion of the MAX-effector family was detected in M . oryzae and M . grisea where they seem to be particularly important since they account for 5–10% of the effector repertoire and 50% of the cloned avirulence effectors . Expression analysis indicated that the majority of M . oryzae MAX-effectors are expressed specifically during early infection suggesting important functions during biotrophic host colonization . We hypothesize that the scenario observed for MAX-effectors can serve as a paradigm for ascomycete effector diversity and that the enormous number of sequence-unrelated ascomycete effectors may in fact belong to a restricted set of structurally conserved effector families .
Pathogenic microorganisms have to cope with the immune system of their host and therefore deploy measures to hide their presence , disturb host immunity or inactivate defense responses . In all these strategies , proteins secreted by the pathogen during infection and acting on host proteins and cellular processes play a key role [1–3] . These proteinaceous virulence factors named effectors act either extra-cellularly or inside host cells and can possess , depending on the microorganism , very different molecular features . In fungal pathogens , the main class of effectors are small secreted proteins of less than 200 amino acids expressed specifically during infection and often rich in cysteins [4–6] . Genome sequencing and expression analysis identified hundreds of such effector candidates in individual plant pathogenic fungal species . Few of them , mainly those acting extra-cellularly , are widely distributed among phytopathogenic fungi and contain known motifs or domains , such as NLPs ( necrosis and ethylene-inducing peptide 1 ( Nep1 ) -like proteins ) , LysM domain-containing proteins or protease inhibitors [5 , 6] . The vast majority of the fungal effectors do not share sequence similarities with other proteins and do not contain conserved motifs . This is very different from the situation in other phytopathogens and in particular oomyctes , an important class of plant pathogens that have similar lifestyles and infection strategies and whose virulence relies also on large effector repertoires . In oomycete pathogens , large families of cytoplasmic effectors with hundreds of members in individual species are defined by the presence of the RXLR or the LFLAK host cell translocation motifs [7–9] . The effector domains of these RXLR and Crinkler ( CRN ) effectors that mediate virulence functions are highly diversified but contain , in the majority of cases , conserved motifs or domains that are shared between effectors from the same or other species allowing their classification in distinct families . On the contrary , most fungal effectors are species-specific while few are lineage specific and occur in closely related species . In most phytopathogenic fungi , no large effector gene families were identified [5 , 6] . The majority of their effectors are singletons and a small proportion belongs to small paralogous groups of rarely more than 3 members . Effector repertoires dominated by gene families of large size counting more than 5 members were only detected in particular cases such as powdery mildew and rust fungi lineages [10–13] . Due to their high diversity and the lack of similarity with other proteins , the mode of action and the role in infection of fungal effectors have to be elucidated case by case and remain still largely unknown [5 , 6] . In addition , this tremendous diversity raises the question of the evolutionary trajectories of fungal effectors that do not show traces of common origins . Rice blast disease caused by the ascomycete fungus M . oryzae is present in all rice growing areas and causes important harvest losses . Since rice is the main source of calories for half of the human population and since disease control strategies are frequently overcome by the pathogen due to its high genetic plasticity , blast is considered one of the most dangerous plant diseases threatening global food security and hampering attempts to increase rice yield in many parts of the world [14–16] . Due to its economic importance , the status of the host plant rice as a model plant and the ease of cultivation and genetic manipulation of M . oryzae , blast disease has become a model for the molecular and genetic investigation of fungal plant diseases [14] . In particular , molecular mechanisms of fungal disease development were studied intensively in M . oryzae uncovering important features of fungal virulence [17 , 18] . Key steps in infection by M . oryzae are ( i ) penetration into epidermal cells by the breakage of the leaf cuticle and epidermal cell walls by an appressorium , a specialized unicellular structure , ( ii ) biotrophic growth inside the first invaded host cells , followed by ( iii ) necrotrophic growth associated with active killing of host tissue and the development of disease symptoms and finally , ( iv ) clonal reproduction and sporulation . Effectors and in particular cytoplasmic effectors are key elements in M . oryzae virulence and particularly important during the biotrophic phase of infection [6 , 19 , 20] . However , the function of individual effectors in the infection process has only been established for the LysM effector SLP1 that sequesters chitin fragments and thereby interferes with their recognition by the rice chitin receptor CEBiP , and AvrPiz-t that interferes with host immunity by inhibiting the E3 ubiquitin ligase APIP6 [21 , 22] . Mutant analysis aiming to demonstrate that individual effectors are important for virulence have often been unsuccessful , probably due to functional redundancy among effectors [23 , 24] . Approximately 700 of the 1300–1500 secreted proteins encoded in the M . oryzae genome are considered effector candidates according to their size of less than 200 amino acids and their lack of homology to proteins of known function [25 , 26] . Hundreds of them were found to be expressed during appressoria formation or infection [23 , 26–28] . Some effectors are recognized in certain plant accessions by immune receptors localized either at the plasma membrane or in the cytosol leading to the induction of strong defense responses and resistance to pathogen isolates possessing this effector [29] . The recognized effector is , in these cases , named an avirulence ( Avr ) protein . In M . oryzae , 8 different effectors acting as Avr proteins named PWL2 , AVR-Pia , AVR1-CO39 , AVR-Pii , AVR-Pik , AvrPiz-t , AVR-Pita and Avr-Pi9 have been cloned molecularly [26 , 30–35] . They are all translocated into host cells and do not show similarities to proteins of known function with the exception of AVR-Pita that shows homology to neutral zinc proteases [6] . For 7 of them , the matching rice immune receptors that are in all cases cytoplasmic nucleotide-binding and leucine-rich repeat domain proteins ( NLRs ) have been identified [36–41] . In the present study , the 3-dimensional structures of the M . oryzae effectors AVR-Pia and AVR1-CO39 were investigated to deepen our understanding of fungal effector function and diversity . NMR analysis revealed that the structures of both proteins consist of two anti-parallel β-sheets , each having three strands , and linked by one disulfide bond Structural similarity searches revealed that the M . oryzae effector AvrPiz-t and the effector ToxB from the wheat pathogen Pyrenophora tritici-repens have similar 6 β-sandwich structures with the same topology [42 , 43] . Comparisons of the structures of the four effectors that we named MAX-effectors revealed that they share a common architecture but no sequence consensus . Structure-informed and pattern-based searches identified large numbers of weakly homologous MAX-effector candidates in M . oryzae and M . grisea , and limited numbers or no homologs in other phytopathogenic ascomycete fungi . Expression profiling indicated that the majority of the M . oryzae MAX-effector candidates are expressed during early infection . MAX-effectors therefore seem to have undergone a lineage-specific expansion in the Pyricularia genus that may be driven by duplications and rapid adaptation to new functions involving important changes of surface properties but conservation of protein architecture . This evolutionary process has the potential to generate large families of structurally related proteins without sequence similarity and may serve as a paradigm for effector evolution and diversification in phytopathogenic ascomycete fungi .
AVR1-CO39 and AVR-Pia proteins , deleted for their endogenous secretion signal , were expressed in E . coli with an N-terminally fused signal peptide for secretion in the bacterial periplasm that is cleaved upon secretion , an N-terminal His6-tag for purification and a TEV1 cleavage site . Recombinant proteins were soluble and were purified to homogeneity from periplasmic protein extracts by Ni-agarose affinity and gel exclusion chromatography ( S1 Fig ) . Both recombinant Avr proteins eluted as monomers from gel exclusion chromatography . Recombinant , 15N and 13C-labelled AVR1-CO39 and AVR-Pia proteins produced in 15N and 13C-labelled minimal medium were used for structure determination by two- and three-dimensional NMR experiments . Three-dimensional ( 3D ) HNCO , HNCA , HN ( CO ) CACB , HN ( CA ) CO , HNCACB , 2D 13C-detected CON , CACO and 2D-COSY-DQF ( D2O ) and TOCSY ( D2O ) experiments were used for the backbone and aliphatic side chain resonance assignments . 3D 15N-edited NOESY-HSQC and 2D-NOESY ( D2O ) spectra were collected to confirm the chemical shift assignments and generate distance restraints for structure calculations . ( Fig 1 and S1 Table ) . The assigned 1H , 15N-HSQC spectra were well dispersed . Residues from the N-terminal tags are still resolved . All amino acids of AVR-Pia and almost all of AVR1-CO39 have {1H-15N} NOE values above 0 . 8 indicating highly defined structures with low flexibility ( S2 Fig ) . Only N-terminal tags , below residue number 22–23 , and C-terminal sequences of AVR1-C039 ( amino acids 80–89 ) show increased flexibility . The strong dαN ( i , i+1 ) NOEs and weak dNN ( i , i+1 ) NOEs are indicative of a β-structure and consistent with the six β-strands observed in AVR-Pia and AVR1-CO39 ( S3 Fig ) . NHs in slow exchange were consistent with hydrogen bonding networks and were used to derive constraints for the structure calculations . The ratios of R2 to R1 relaxation rates of AVR-Pia and AVR1-CO39 were consistent with a monomeric molecular size ( AVR-Pia τc = 6 . 2 ± 0 . 3 ns and AVR1-CO39 τc = 5 . 7 ± 0 . 4 ns ) and thus confirm that both Avrs form monomers in solution ( S1 Table ) [44] . The solution structures of AVR-Pia and AVR1-CO39 were determined based on 1541 and 1286 NOE-derived distance restraints , 90 and 72 dihedral angle restraints and 20 and 15 hydrogen bond restraints , respectively ( Fig 2 and Table 1 and S4 Fig ) . A disulfide bridge between Cys25-Cys66 for AVR-Pia and between Cys26-Cys61 for AVR1-CO39 was added based on cysteine 13Cβ chemical shifts and DTNB quantification of free thiols . The Pro65 in AVR1-CO39 has been determined to be in a cis-conformation according to the 13Cβ chemical shift at 34 . 4 ppm and strong sequential Hα-Hα NOE . The best conformers with the lowest energies , which exhibited no obvious NOE violations and no dihedral violations > 2° were selected for final analysis . Surprisingly , both AVR-Pia and AVR1-CO39 proved to possess the same secondary structure elements arranged with the same topology in similar three-dimensional structures ( Fig 2 ) . Both proteins are composed of 6 β-strands that form two antiparallel β-sheets packed face-to-face and connected by loops ( Fig 2 ) . The first sheet is formed by the three β-strands β1 , β2 and β6 while the second sheet contains β3 , β4 and β5 . In both cases , the two β-sheets pack together by an internal core of hydrophobic residues and one disulfide bridge and the structures belong to the β-sandwich classification . In both Avrs , the β-strands overlay and are similarly oriented ( vide infra ) but loops differ in length and structure . The surface properties of AVR-Pia and AVR1-CO39 are different with the exception of a hydrophobic patch located in both proteins on the side of the β-sandwich that is formed by the first β-sheet ( β1-β2-β6 ) ( Fig 2C and 2D ) . In AVR-Pia , this solvent exposed hydrophobic surface is constituted by the residues F24 , V26 and Y28 in β1 , V37 , L38 and Y41 in β2 , and Y85 in β6 , and has an area of 372 Å2 . In AVR1-CO39 , the solvent exposed hydrophobic surface of the first β-sheet is formed by the residues I27 and Y31 in β1 , V36 and I39 in β2 and V73 in β6 , as well as W23 from the N-terminus and Y82 from the C-terminus , and has a surface area of 280 Å2 . To identify structural homologs of AVR-Pia and AVR1-CO39 , structural similarity searches were performed using the Dali server and the Protein Data Bank [45] . Both queries , with AVR1-CO39 and AVR-Pia , identified the secreted effector protein ToxB from the wheat tan spot pathogen Pyrenophora tritici-repentis as well as its natural allele Toxb as the closest structural homologs with the highest Z-scores ( S2 Table and Fig 3 ) [43] . Like , AVR-Pia and AVR1-CO39 , ToxB is secreted during infection and is an important determinant of virulence for the tan spot fungus [46] . In addition , the search with AVR1-CO39 identified AvrPiz-t , another avirulence effector of M . oryzae that is sequence-unrelated to AVR-Pia and AVR1-CO39 but structurally similar [42] . A pairwise similarity matrix using root-mean-square deviation ( rmsd , measured in Å ) and DALI Z-scores [45] was established revealing that all proteins are structurally related and that ToxB is closer to all other three structures than the others among them ( S2 Table ) . ToxB and AvrPiz-t are like AVR-Pia and AVR1-CO39 , composed of two three-stranded antiparallel β-sheets , β1-β2-β6 and β3-β4-β5 , forming a six β-sandwich ( Fig 3A–3D ) . Structure-based sequence alignments provided by DALI revealed , at a first glance , no obvious conservation , but also no clear consensus except buried hydrophobic residues alternating with exposed polar amino acids in the β-strands ( Fig 3E ) . The β-strands β1 and β2 are very similar in length and position in all four proteins , while β3 , β4 and β6 display more variation . β5 is the shortest and the most irregular strand . As expected for β-strands , buried and exposed residues alternate , with the exception of β1 where residues have a tendency to be more buried . This is due to the packing of β1 in between the β2 and β6 strands . The loops connecting the β-strands have variable length , and are the sites where most of the residue insertions occur . The disulfide bond between C2 and C43 ( ToxB numbering ) is well conserved but shifted “in phase” by two residues in AVR-Pia ( Fig 3E ) . The unexpected finding , that all three M . oryzae effectors that have been characterized for their structure so far and one effector from an only very distantly related fungal group are structurally related raised the possibility that these four effectors are members of a widely distributed and abundant fungal effector family characterized by a common β-sandwich structure and high sequence divergence . Simple Blast searches are not suited to identify such distantly related proteins and when performed with the protein sequence of effectors from ascomycete fungi , generally identify no or only very few conserved homologs in the same species . Therefore , more sensitive Psi-Blast searches that use position-specific scoring matrices were performed with AVR-Pia , AVR1-CO39 , AvrPiz-t and ToxB . The searches were performed on a protein sequence database combining the protein sequences of the M . oryzae reference isolate 70–15 , of 5 other rice-infecting M . oryzae isolates ( TH16 , TH12 , PH14 , FR13 and Guy11 ) , three M . oryzae isolates with other host specificities ( BR32 , US71 and CD156 specific for wheat , Setaria italica and Eleusine coracana ) and one isolate of the sister species M . grisea ( BR29 ) . These additional M . oryzae and M . grisea protein sequences were obtained by whole genome re-sequencing and de novo annotation of proteins and are accessible at http://genome . jouy . inra . fr/gemo [47] ) . After 4 Psi-Blast iterations and filtering of the results for sequences having an alignment length of at least 40 residues , an overall protein size of less than 180 amino acids and the presence of a predicted signal peptide , 3 , 8 and 4 homologs of AVR-Pia , 16 , 25 and 16 homologs of AVR1-CO39 and 5 , 9 and 6 homologs of ToxB were detected in respectively 70–15 , TH16 and BR29 ( S3 Table , orthologous sequences present in 70–15 and TH16 were only counted for 70–15 ) . For the other M . oryzae isolates similar numbers of homologs as in TH16 were found . The elevated number of homologs present in these isolates but not in 70–15 are due to the fact that the pipeline used for protein annotation in the re-sequenced genomes identified many additional small secreted proteins that are not annotated in 70–15 although the corresponding coding sequences are present in its genome [47] ) . The similarities were weak ( frequently less than 25% identity ) but they were consistent with the structural alignment ( Fig 3 ) and included the two cysteine residues . For AvrPiz-t , no homologs that were not already identified by standard Blast were identified in the Psi-Blast search . When 25 additional fungal genomes , including the closely related fungi M . poae and Gaeumannomyces graminis were added to the database for the Psi-Blast searches , only very limited numbers of homologs ( 0 , 1 or 2 ) with frequently low e-value scores were identified in other fungi . This suggested that effectors with similaritiy to Magnaporthe Avrs and ToxB named in the following MAX-effectors that potentially also have 6 β-sandwich structure are present with low frequency in other fungal pathogens but were strongly amplified and diversified in M . oryzae and M . grisea that both belong to the genus Pyricularia in the Pyriculariae family [48] . To exclude that the Psi-Blast search missed MAX-effectors in the additional fungal genomes due to biases in the search matrix or too low sensitivity and to deepen the search for this class of effectors in M . oryzae and M . grisea genomes , a hidden Markov model ( HMM ) -based profile search was performed . This type of profile search is among the most powerful procedure for detecting with high accuracy remote homologies between proteins . As a first step , a high stringency Blast search with the three M . oryzae effectors and a Psi-Blast search with ToxB was performed and the resulting set of closely related sequences was aligned in a multiple sequence alignment constrained by the structural alignment of AVR-Pia , AVR1-CO39 , AvrPiz-t and ToxB ( S5A Fig ) . For the M . oryzae effectors , the Blast search identified orthologs of the effectors with few polymorphisms in different M . oryzae isolates . In addition , for each M . oryzae effector , one paralog was identified in M . oryzae and one or two paralogs were identified in the M . grisea isolate BR29 ( S5B Fig ) . For the M . oryzae paralogs , generally several different alleles were identified . For ToxB , in addition to highly homologous sequences from P . tritici-repentis and P . bromi , 1 homolog was identified in M . oryzae , Bipolaris oryzae and Colletotrichum higginsianum , 2 in C . fioriniae , 3 in C . orbiculare and 4 in C . gloeosporioides . ( S5B Fig ) . As a second step , an HMM profile was built , starting from the structure-guided multiple sequence alignment from step1 ( S5A Fig ) and by iteratively searching for homologs in a database containing the small secreted proteins ( <170 amino acids ) of 25 pathogenic and non-pathogenic ascomycete fungi and of the 9 re-sequenced M . oryzae and M . grisea isolates from which completely redundant sequences had been removed . At each iteration , the recovered sequences were filtered for alignment of the two cysteins with a spacing of 34 to 49 amino acids and used to generate a new profile used in the next iteration . The interval of 34 to 49 amino acids was fixed , based on the frequencies of cystein spacings in HMM searches run without this constraint . This search recovered 161 new , more distantly related sequences of which 154 were from M . oryzae or M . grisea , 5 from 3 different Colletotrichum species , 1 from Lepthosphaeria maculans and 1 from Mycosphaerella graminicola ( recently renamed Zymoseptoria tritici ) ( S6A Fig ) . This suggests that MAX-effectors have been massively and specifically expanded in M . oryzae and M . grisea . However , it also indicates their presence in other fungal species , i . e . in Colletrichum spp . where they seem to occur at elevated frequencies . The alignment and clustering of the set of 200 sequences combining the 39 sequences used for the initial profile and the 161 new sequences revealed clusters of orthologous sequences originating from the different M . oryzae isolates with weak sequence polymorphism between orthologs ( S6A and S6B Fig ) . Frequently , orthologs of M . oryzae can be identified in M . grisea but never in other fungi . Sequences from different orthologous clusters have high sequence diversity . Only in 3 cases , statistically significant clusters , supported by bootstrap values bigger than 50% can be identified that contain 2 distantly related MAX-effectors or MAX-effector clusters of M . oryzae . A sequence logo derived from the multiple alignment shows the invariant cysteine residues ( position 2 and 43 in mature ToxB ) that constitute the alignment framework , as well as additional positions that are specifically enriched ( Fig 4A ) . There is a propensity for hydrophobic residues in positions 4 and 6 , corresponding to hydrophobic positions in strand β1 , in position 27 , corresponding to a hydrophobic residue in β3 and in positions 35 , 37 and 39 corresponding to β4 . Positions 10 , 23 , 40 and 49 are in loop regions between the pairs of strands β1-β2 , β2-β3 , β4-β5 and β5-β6 respectively , and are enriched in glycine , polar or charged residues . The resulting HMM profile was used to search with a relaxed cut-off two different databases: ( i ) the UniRef90 database that contains non-redundant sequences from a wide range of different organisms and that was used to determine in which type of organisms proteins with the MAX-effector motif occur and to evaluate by this the specificity of the motif and ( ii ) the previously described fungal genomes and M . oryzae and M . grisea database to get a precise view of the occurrence of MAX-effectors in a broad range of ascomycete fungi . The search of the UniRef90 database recovered 70 sequences . All but 3 were from phytopathogenic ascomycete fungi ( S7A Fig ) . The exceptions were from a bacteria , Pseudomonas sp . StFLB209 , living in association with plants , from tomato ( Solanum lycopersicum ) and from a nematode-parasitic fungus ( Arthrobotrys oligospora ) and had low e-values . Among the fungal sequences , 49 were from M . oryzae and included AVR1-CO39 and AVR-Pia . The remaining 18 corresponded to previously identified effectors from Colletotrichum species ( 5 C . orbiculare , 2 C . higgensianum , 3 C . gloeosporioides , 2 C . fioriniae ) that belong as M . oryzae to the class of Sordariomycetes and Z . tritici , L . maculans and B . oryzae as well as ToxB from P . tritici-repentis and P . bromi that are all from the class of Dothideomycete fungi . Clustering of the sequences revealed high sequence diversity and , apart from the Tox-B cluster , no or extremely limited relatedness could be identified ( S7A and S7B Fig ) . Interestingly , with slightly different settings , this search also recovered the well characterized AVR-Pik effector from M . oryzae [26] . AVR-Pik clearly fits the MAX-effector pattern but was discarded in the other searches since its secretion signal is not recognized by the SignalP4 . 1 program used for filtering of the results . The search of the previously described Magnaporthe and other fungal genomes database not filtered for redundancy recovered only limited numbers of MAX-effectors in non-Magnaporthe fungal genomes that had , with the exception of one effector from Fusarium fujicuroi , already been retrieved in the other searches ( Fig 4B and S8A Fig ) . In M . oryzae , between 67 and 38 MAX-effectors per isolate were identified while in M . grisea , 37 MAX-effectors were identified ( Fig 4B ) . 46 of the 55 MAX-effectors identified by Psi-Blast in M . oryzae 70–15 and TH16 and in M . grisea BR29 ( S3 Table ) were also found by this HMM search . Alignment and clustering shows that the M . oryzae MAX-effectors are generally present in the majority of M . oryzae isolates and are grouped in clusters of orthologs ( S8A and S8B Fig ) . Many of these orthologous clusters also contain an ortholog from the M . grisea isolate BR29 that shows however higher sequence divergence . Only six statistically significant clusters ( bootstrap > 50% ) that contain more distantly related M . oryzae effectors from different orthologous groups are identified . Otherwise , the sequence diversity between proteins from different M . oryzae ortholog clusters is so strong that classical tree building methods do not detect statistically significant sequence relatedness . The non-Magnaporthe MAX-effectors do not cluster significantly with Magnaporthe MAX-effectors and 8 of the 10 Colletotrichum effectors are comprised in three different Colletotrichum-specific clusters . Taken together , the different HMM searches reveal that the MAX-effector motif is specific for effectors from phytopathogenic ascomycete fungi . MAX-effectors are identified with low frequencies in phytopthogenic ascomycete fungi from the class of Dothideomycetes and seem to have expanded moderately in different Colletotrichum species ( i . e . Colletotrichum orbiculare ) . Only in M . oryzae and M . grisea , MAX-effectors expanded and diversified massively to become a dominating family of virulence effectors in these pathogens . To test if the M . oryzae MAX-effectors identified by the HMM profile search could be involved in plant infection , the expression of 50 different candidate MAX-effector-coding genes was analyzed by qRT-PCR in infected rice leafs and in in vitro grown mycelium ( S4 Table ) . 30 genes showed early infection-specific expression with a majority of profiles ( 25 ) that strongly resemble the biotrophy effector marker gene BAS3 ( Fig 5 and S9A and S9D Fig ) [23] . The expression pattern of all these genes and of 3 genes coding for MAX-effectors identified only by Psi-Blast searches was clearly different from the markers of very early or late infection ( Orf3 and MGG01147 , respectively ) . For 18 genes , no significant expression was detected and only 2 genes were expressed constitutively with significant expression in the mycelium ( Fig 5 and S9C and S9D Fig ) . Therefore , the majority of the MAX-effector candidates seems specifically expressed during biotrophic infection and can therefore be considered as potential virulence effectors .
The only similarity of the surfaces of AVR1-CO39 and AVR-Pia is an extended hydrophobic area on the surface formed by β1 , β2 and β6 . Such extended and exposed hydrophobic areas are uncommon since protein surfaces are generally in contact with solvent water molecules and they are frequently involved in protein-protein interactions . Previous studies on the recognition of AVR-Pia by the rice NLR immune receptor RGA5 support that the hydrophobic surface of AVR-Pia could indeed be involved in protein binding [37] . AVR-Pia binds physically to a C-terminal domain of RGA5 homologous to heavy metal-associated ( HMA ) domain proteins related to the copper chaperone ATX1 from Saccharomyces cerevisiae ( RATX1 domain ) . This binding is required to derepress a second NLR RGA4 that activates resistance signaling [49] . A natural allele of AVR-Pia ( AVR-Pi-H3 ) where the surface exposed phenylalanine 24 and threonine 46 situated respectively in and at the border of the hydrophobic patch are replaced by serine and asparagine loses binding to RGA5RATX1 and does not trigger resistance [37] . Structural information will now guide further functional studies to elucidate if other amino acids situated in or at the border of the hydrophobic patch are also involved in RGA5RATX1-binding and to validate by this the role of the hydrophobic patch as a protein-protein interaction surface . Common features of the M . oryzae MAX-effectors are that they act intracellular in host cells [21 , 24 , 32] and are recognized by NLR immune receptors in resistant rice genotypes: AVR1-CO39 and AVR-Pia by the same NLR pair RGA4/RGA5 and AvrPiz-t by the NLR immune receptor Piz-t [37 , 39 , 41] . While the molecular bases of the recognition of AVR1-CO39 and AVR-Pia by RGA4/RGA5 are beginning to be elucidated , details of AvrPiz-t recognition are not known . Also , whether the three M . oryzae MAX-effectors target similar host processes and host proteins is not known . AvrPiz-t was described to target the host ubiquitin proteasome system by binding and inactivating the RING E3 ubiquitin ligase APIP6 [21] but virulence targets of AVR-Pia and AVR1-CO39 have not been described . However , it has been hypothesized that both proteins target RATX1 proteins homologous to the RGA5RATX1 domain that was suggested to act as a mimic for AVR-Pia and AVR1-CO39 targets [50] . Therefore , we assume that AvrPiz-t on the one hand and AVR-Pia and AVR1-CO39 on the other have different molecular activities and target different host proteins . This would be in accordance with the high divergence of their shapes and their surface properties . That AVR-Pia and AVR1-CO39 interact with the same immune receptor by binding to the same sensor domain and potentially interact with the same host targets is striking because apart from the extended hydrophobic patch on the β1β2β6 surface they share no apparent similarities with respect to their shapes and surfaces . It will therefore be important to elucidate in the future which amino acids of AVR-Pia and AVR1-CO39 bind to RGA5RATX1 and which surfaces of RGA5RATX1 are involved in binding to each of the two effectors to better understand specificity in effector recognition . In addition , identification of AVR1-CO39 and AVR-Pia targets as well as ToxB targets for which molecular details of activity are also lacking will be important to understand how MAX-effectors promote virulence and to understand the link between MAX-effector structure and function . Structure-informed pattern searches identified huge numbers of MAX-effector candidates that possess as the structurally characterized MAX-effectors very high sequence diversity and probably also possess a 6 β-sandwich structure stabilized by buried hydrophobic residues from β-strands and a disulfide bond between conserved cysteins connecting β1 and β5 . Systematic prediction of the secondary structure of the MAX effector candidates using SSPRO 5 software identified with high frequency two β-strands , β1 located after the first cysteine and β4 located before the second cysteine ( S10 Fig ) . The other regions of the sequences had more variable secondary structure predictions which is also reflected by a less defined pattern in these regions ( Fig 4A ) . High sequence diversity among MAX-effector candidates could as in the case of the structurally characterized MAX-effector be the consequence of interchangeability of buried hydrophobic core residues , variation in the lengths of some β-strands ( i . e . β5 ) , exchange of surface exposed residues and deletion or insertion of residues in exposed loops . MAX-effectors were specifically detected in phytopathogenic ascomycetes from the classes of Sordariomycetes and Dothideomycetes . One MAX-effector per species was detected in phytopathogenic fungi of the class of Dothideomycetes ( L . maculans , P . tritici-repentis , Z . tritici and B . oryzae ) and higher numbers ( 2–6 ) occur in fungi from the genus Colletotrichum . Only in M . oryzae and M . grisea that are both from the genus Pyricularia huge numbers of MAX-effector candidates were detected and expression profiling confirmed that most of them are likely bona fide effectors expressed specifically during biotrophic early infection . With 40–60 effectors which represents 5–10% of the candidate effectors of individual M . oryzae or M . grisea isolates , MAX-effectors can be considered a dominant class of effectors in these fungi [24 , 47] . This is further supported by the finding that 5 of the 51 biotrophy-associated proteins identified by transcriptome analysis are MAX effectors ( MG02546 , MG08414 , MG08482 , MG09425 and MG09675 ) [23] . Also , the M . oryzae effector AVR-Pik fits the MAX-effector pattern further highlighting the outstanding importance of this effector family that comprises 4 out of 8 cloned Avr effectors in the blast fungus [6] . It is striking that the only other group of fungi with elevated numbers of MAX effectors are Colletotrichum species . Colletotrichum fungi are phylogenetically only distantly related to M . oryzae and M grisea but employ a similar hemibiotrophic infection strategy characterized by appressorium-mediated penetration into the host and growth inside invaded plant cells during biotrophic infection . It will be interesting to determine in the future whether MAX effectors play similar roles in these early infection processes in both groups of fungi . In Gaeumannomyces graminis and M . poae that belong to the closely related Magnaporthaceae family no MAX-effectors were detected [48] . The expansion of MAX-effectors therefore occurred probably in a common ancestor of M . oryzae and M . grisea since clear orthologous relations can be established between many MAX-effectors from M . oryzae and M . grisea but after the split of the Magnaporthaceae . Expansion and diversification of the MAX-effectors is clearly continuing since frequently orthologs in M . oryzae or M . grisea cannot be identified and duplication , loss and diversification of MAX-effectors in host specific lineages of M . oryzae is observed ( S8B Fig ) . Genome sequencing of additional species from Pyricularia and other genera in the Periculariae will allow to further strengthen the hypothesis of lineage-specific expansion of MAX-effectors . Lineage specific expansion of effector families has been observed in other fungi such as mildew and rust fungi whose effector repertoires are dominated by effector families that contain frequently numerous members and are for their majority restricted to individual species or precise clades [10 , 51] . However , in these cases , sequence divergence is not as strong as in MAX-effectors since sequence-based comparisons allow the establishment to these effector families . On the contrary , the effector repertoires of ascomycete phytopathogens outside the mildew lineage contain hundreds of sequence-unrelated effectors and the evolutionary origin of these huge amounts of species or clade specific genes is an open question . Duplication and diversification eventually driven by localization of the genes in transposon rich regions , genome reshuffling or transfer of accessory chromosomes were convincingly proposed as potential mechanisms to create effector diversity but the apparent lack of relatedness of ascomycete effectors remains unexplained [52–55] . Establishment of a huge effector family in M . oryzae and M . grisea that is also present at much lower frequency in other ascomycete pathogens sheds new light on the origin and relatedness of ascomycete effectors . Theoretically , convergent evolution as well as diversifying evolution can explain the situation observed for the MAX-effectors characterized by a broad and patchy distribution , high diversification and limited sequence homology as well as a shared sequence pattern and probably the same structure . Convergent evolution would apply if these proteins with similar functions and a similar fold appeared repeatedly in phytopathogenic ascomycetes and eventually evolved independently in different clades . Under diversifying evolution , a protein or protein family present in a common ancestor has been strongly diversified in different lineages of ascomycete fungi and frequently lost during evolution in certain lineages and species . The scenario of convergent evolution of MAX-effectors cannot be excluded but is clearly less parsimonious . It raises the question why MAX-effectors do not occur in organisms with similar lifestyles outside the Sordariomycete and Dothideomycete pathogens such as phytopathogenic basidiomycetes or oomycetes . In addition , there are no well-documented examples of convergent evolution towards similar folds or sequence patterns for pathogenic effectors or secreted fungal proteins involved in adaption to the environment while comparative genomics studies in fungi and oomycetes are beginning to identify such widely distributed gene families that are shaped by strong diversifying selection and that can only be properly reconstructed when pattern-based searches and structure information are taken into consideration . The best documented example is certainly the WY-domain family among the RXLR effectors that is specific to the Peronosporales clade in oomycetes and evolves by diversifying evolution [8 , 9 , 56–58] . Careful sequence analysis involving pattern searches identified the W , Y and L sequence motifs in the effector domains of a majority of the Phytophtora RXLR effectors that are frequently completely sequence unrelated [9] . Functional analysis confirmed the importance of these motifs for effector function [59] and structure analysis of the effector domain of different RXLR effectors with limited sequence homology revealed that conserved sequence motifs reflected a conserved , highly similar 3-dimensional structure named the WY-domain fold [56 , 60–62] . PexRD2 and AVR3a11 show e . g . only 14% amino acid identity in a structure-based alignment but overlay of their structures has an RMSD score of 0 . 73 Å . As in the case of the β-sandwich fold of the MAX-effectors , the WY-domain fold tolerates insertion or deletion of amino acids in the loops , exchange of surface exposed amino acids and is stabilized by hydrophobic core residues that can be exchanged as long as hydrophobicity is maintained [56] . This flexible structure allows to generate effectors with highly variable shapes and surface properties and studied WY-domain effectors showed very diverse molecular activities , target different host proteins and are recognized by different NLR immune receptors [7 , 56] . An example of rapidly evolving proteins from fungi that are structurally but not sequence-conserved are hydrophobins that are low molecular mass secreted proteins important for the impermeabilization of fungal cell walls , adhesion to hydrophobic surfaces and pathogenicity [63] . Hydrophobins were shown to evolve rapidly according to a birth-and-death mechanism [64] , are widely distributed in a broad range of basidio- and ascomycete fungi and are characterized by sequence patterns but no sequence homology [63 , 65] . Structure analysis demonstrated that distantly related hydrophobins are structurally related supporting a common evolutionary origin [66] . Another example of a fungal gene family that is rapidly evolving according to a birth-and-death model are the Hce2 proteins ( homologs of Cladosporium fulvum ECP2 ) that are present in a wide range of basidio and ascomycete fungi and seem to act as effectors in pathogenic fungi and potentially in stress responses in non-pathogenic fungi [67] . Much like MAX-effectors they show patchy distribution , lineage-specific expansions and high sequence diversification . Based on our discovery of the MAX-effector family and the widely accepted concept that fungal effectors evolve according to a birth-and-death model we propose the hypothesis that the majority of the immense number of different ascomycete effectors could in fact belong to a restricted set of structurally defined families whose members are phylogenetically related . These families of structurally conserved effectors are expected to be , as the MAX-effectors widely distributed with frequent losses on the one hand and lineage specific expansions on the other leading to effector families that are particularly important in certain fungal clades but not in others . The evolution of individual effectors is so rapid and their adaptation to new functions so profound that sequence homology and resulting phylogenetic signals are rapidly lost although the basic protein architecture may frequently be conserved because it represents a good solution to many general constraints effectors have to face such as stability in the fungus-host interface or translocation into host cytosol . Sequence homology can therefore only be detected in orthologs from closely related species but in paralogs from the same species or homologs from more distantly related species no similarity is detected on the sequence level . Only structure-informed and pattern-based searches reveal the hidden relatedness of ascomycete effectors . This hypothesis is also supported by the recent identification of an effector super family in the powdery mildew fungus Blumeria graminis fsp hordei by structural modelling [51] . 72 effectors from different families established by sequence homology or with no homology to other proteins had 3D structure models with similarity to ribonucleases suggesting a common origin and a conserved structure in this superfamily of sequence diverse effectors . Knowledge on the structures of fungal effector proteins is extremely limited and outside of the MAX-effectors the structures of only three cytoplasmic fungal effectors have been determined . AvrL567 from the rust fungus Melampsora lini and ToxA from P . tritici-repentis have distantly related β-sandwich structures whose topologies are completely different from the MAX-effectors and AvrM has a helical structure [68–70] . Therefore , the elucidation of the 3-dimensional structures of additional fungal effectors is a priority for a better understanding of their diversity and will teach us to what extent structurally conserved but sequence-diversified effector families dominate the huge and extremely diverse effector repertoires of phytopathogenic fungi .
The sequence for the mature protein ( residues 20–85 for AVR-Pia , and residues 23–89 for AVR1-CO39 ) was inserted into the pET-SP vector by ligation of PCR using NdeI-BamHI sites . PCR products were generated using the forward and reverse oligos tatcatatggctGCGCCAGCTAGATTTTGCGTCTAT and tatggatccCTAGTAAGGCTCGGCAGCAAG or tatcatatGCTTGGAAAGATTGCATCATCCA and tatggatccGATCAACAAGACTCATCGTCGTCA for respectively AVR-Pia or AVR1-CO39 . The pET-SP vector was constructed from pET-15b ( Merck-Millipore , Darmstadt Germany ) by inserting a periplasmic secretion sequence , a hexahistidine tag and a TEV cleavage site at the N-terminus of the protein adding an extra 31 amino acid sequence at the N-terminus of the recombinant proteins ( sequence MKKTAIAIAVALAGFATVAQA_APQDNTSMGSSHHHHHHSSGRENLYFQGHMA ) . The plasmids pET-SP-AVR-Pia and pET-SP-AVR1-CO39 were used to transform E . coli BL21 ( DE3 ) . Transformed cells were grown in an autoinducing minimal media C-750501 [71] at 37°C for 24h . To generate isotopically-labeled samples for NMR spectroscopy , we used 15NH4Cl , 13C3-glycerol and 13C6-glucose as the primary nitrogen and carbon sources . Cells were harvested by centrifugation and the pellet was resuspended in lysis buffer ( 200 mM TrisHCl pH8 , 200mM Sucrose , 0 . 05mM EDTA , 50μM lysozyme ) . After 30 minutes incubation , cell debris were removed by centrifugation at 12 000 g for 15 min at 4°C . The resulting crude protein extracts were loaded on an AKTA basic system into a HisTrap 5ml HP columm ( GE Healthcare ) , equilibrated in buffer A ( 50 mM TrisHCl , pH 8 . 0 , 300 mM NaCl , 1 mM DTT , 0 . 1 mM Benzamidine ) . The His-tagged protein was eluted from the affinity column with buffer B ( buffer A supplemented with 500 mM imidazole ) . Fractions containing the protein were identified by SDS-PAGE and pooled . The protein was further purified by gel filtration using a Superdex S75 26/60 ( GE Healthcare ) column in buffer A and pure fractions were pooled . The elution profiles indicated that AVR-Pia and AVR1-CO39 eluted as single monomeric species ( Fig 1 ) . Ellman’s reagent , 5 , 5’-dithio-bis- ( 2-nitrobenzoic acid ) , DTNB , was used for quantitating free sulfhydryl groups [72] . Briefly , aliquots of standard ( cysteine , Sigma , 12 . 5 μM to 75 μM ) or sample ( 50 μM ) were reacted with 0 . 1 mM DTNB reagent in 100 mM sodium phosphate pH 8 . 0 , 1mM EDTA buffer . Free sulfhydryl groups were also measured in denaturating conditions using the same buffer supplemented with 6M Guanidinium Chloride . Absorbance was read at 412 nm on a NanoDrop 2000 , and the concentration of free thiols was determined from the standard curves . The NMR samples were prepared with 1mM of purified protein at 10% D2O and 0 . 5 mM DSS as a reference . For AVR-Pia the purification buffer was exchanged with phosphate buffer ( 20 mM potassium-sodium phosphate , pH 5 . 4 and 150 mM NaCl ) , by filtrating with Centricon . The purified AVR1-CO39 proteins were dialyzed in 20 mM sodium phosphate , pH 6 . 8 , 150 mM NaCl and 1 mM DTT . For the D2O experiments , a non-labeled sample was lyophilized and dissolved in D2O . Spectra were acquired on 500 and 700 MHz Avance Bruker spectrometers equipped with triple-resonance ( 1H , 15N , 13C ) z-gradient cryo-probe at 305 K . Experiments were recorded using the TOPSPIN pulse sequence library ( v . 2 . 1 ) ( S2 Fig ) . 2D-NOESY experiments with excitation sculpting water suppression were acquired at 305K , with mixing times from 100 to 150 msec . All spectra are referenced to the internal reference DSS ( 4 , 4-dimethyl-4-silapentane-1-sulfonic acid ) for the 1H dimension and indirectly referenced for the 15N and 13C dimensions [73] . NMR data was processed using Topspin ( v . 3 . 2 ) and were analyzed using strip-plots with Cindy in house software and CCPN [74] [analysis v 2 . 3] . Side chain assignments were carried out using 2D-NOESY , 2D-TOCSY and COSY-DQF experiments with D2O samples , combined with 15N-NOESY-HSQC and 15N-TOCSY-HSQC 3D spectra . For AVR-Pia , the two N-terminal residues Ala-Pro and the His-tag , Ser-His6-Ser were not assigned . For AVR1-CO39 , the tag-residues Asp ( -7 ) -Asn ( -8 ) and the stretch Ser2-His6-Ser2 were not assigned . The 15N and 13C assignments were derived from the 3D spectra at 500 MHz . Relaxation data were acquired at 305K on a Bruker Avance 500 MHz spectrometer using R1 , R2 and 15N{1H} heteronuclear NOE pulse sequences ( TOPSPIN library , v 2 . 1 ) . NMR samples of 500 μL at 0 . 85 mM and 0 . 3 mM were used for AVR-Pia and AVR1-CO39 , respectively . R1 experiments were performed with nine relaxation delays ( 18 , 54 , 102 , 198 , 294 , 390 , 582 , 774 and 966 ms ) . R2 experiments were carried out employing a Carr–Purcell–Meiboom–Gill ( CPMG ) pulse train [75 , 76] with eight relaxation delays ( 16 , 32 , 48 , 64 , 96 , 128 , 192 and 256 ms ) . A recycle delay of 2 . 5 s was employed in R1 and R2 , experiments , and 15N decoupling during acquisition was performed using a GARP-4 sequence . In heteronuclear 15N{1H}NOEs , proton saturation was achieved during the relaxation time by application of high-power 120° pulse spaced at 20 ms intervals for 3 s prior to the first pulse on 15N [77] . A relaxation delay equal to 6 s between each scan was used . Relaxation parameters , R1 , R2 and NOEs were determined from the analysis module of CCPN [74] . The programs CYANA [78] and CNS [79] were used for automatic NOE assignments and structure calculations . The NH , Hα , 15N , 13Cα and 13Cβ chemical shifts were converted into Φ/Ψ dihedral angle constraints using TALOS+ ( v . 1 . 2 ) [80] . The CANDID procedure of CYANA ( v 2 . 1 ) was used to assign the 3D-peaks list from the 15N-NOESY-HSQC spectra . NOE assignments were inspected and used in a new CANDID assignment run including peaks from the 2D-NOESY spectra ( with 100 and 150 msec mixing times for AVR-Pia and 100 and 200 msec for AVR1-CO39 ) . A disulfide bridge Cys25-Cys66 for AVR-Pia and between Cys26-Cys61 for AVR1-CO39 was added based on cysteine Cβ chemical shifts and DTNB quantification of free thiols . NOE constraints were inspected and classified from very strong , strong , medium weak and very weak , corresponding to 2 . 4 , 2 . 8 , 3 . 6 , 4 . 4 and 4 . 8 Å upper bound constraints , respectively . Final structure calculations were performed with CYANA ( v . 2 . 1 ) using 1541 and 1286 distance restraints , for AVR-Pia and AVR1-CO39 , with 90 and 72 Φ/Ψ dihedral angle constraints , respectively . The 30 conformers with lowest target function starting from 200 initial structures , were refined by CNS ( v . 1 . 2 ) using the refinement in water of RECOORD [81] . The final 20 conformers were selected with the lowest NOE and dihedral angle violations . These are the structures discussed herein and deposited ( PDBs , 2MYV and 2MYW ) . The final 20 structures contained no NOE violations greater than 0 . 3 Å and no dihedral angle constraint violations greater than 2° . Structures were validated using PROCHECK [82] . Two sequence databases were used , the UniRef90 release 2015_03 [83] and a database build from the genomes of the ascomycete fungi Magnaporthe oryzae ( reference isolate 70–15 ) , Colletotrichum graminicola , Colletotrichum higginsianum , Fusarium graminearum , Fusarium oxysporum , Gaeumannomyces graminis , Magnaporthe poae , Neurospora crassa , Pyrenophora tritici-repentis , Verticillium dahliae , Aspergillus fumigatus , Aspergillus nidulans , Blumeria graminis , Botrytis cinerea , Colletotrichum gloeosporioides , Colletotrichum orbiculare , Dothistroma septosporum , Fusarium fujikuroi , Fusarium pseudograminearum , Fusarium verticillioides , Leptosphaeria maculans , Phaeosphaeria nodorum , Pyrenophora teres , Trichoderma virens , Tuber melanosporum and Zymoseptoria tritici ( all from the Ensembl Fungi database http://fungi . ensembl . org ) as well as the genomes of eight M . oryzae isolates specific for Eleusine coracana ( CD156 ) , Triticum aestivum ( BR32 ) , Setaria italica ( US71 ) and Oryza sativa ( TH16 , GY11 , FR13 , TH12 , PH14 ) and one M . grisea isolate ( BR29 ) pathogenic to Digitaria ssp ( genome sequences at http://genome . jouy . inra . fr/gemo ) [47] . Sequences without signal peptide ( according to SIGNALP 4 . 1 [84] ) bigger than 170 amino acids or with less than 2 cysteine residues were removed . For the initial HMM search , identical sequences were reduced to only one occurrence in the databases . The start of the search was a structural alignment with TM-align [85] and the structures of AVR-Pia , AVR1-CO39 , AvrPiz-t and ToxB complemented with sequence homologues found by single queries using BLAST ( v 2 . 2 . 27+ ) with a stringent cut-off E-value = 1e-6 . For the ToxB query , two iterations of NCBI PSI-BLAST were used on the NR database with a cut-off E-value = 1e-4 ( S5A Fig ) . This initial alignment was used as input to look for homologs in the filtered and non-redundant fungi database using HMMERsearch program from the HMMER package v 3 . 0 [86] with a 1e-6 E-value cut-off . For each run , only sequences where the two cysteine residues were aligned were kept , and the output alignment was used as input query for a new HMMERsearch run . This run was repeated until reaching convergence . New iterations were then done with increased E-value cut-off at 1e-5 and 1e-4 . From the last alignment , a histogram indicated that the two aligned cysteine residues were separated by at least 34 and at most 49 amino acids . The full homolog search was re-started , as described above , but this time using also the aligned cysteine separations as an additional constraint for filtering homologs after each HMMERsearch run . The HMMERsearch runs were repeated until convergence for raised threshold E-values 1e-6 , 1e-5 , 1e-4 and finally 1e-2 . The homolog ensembles obtained for the three E-values cut-off , 1e-6 , 1e-4 and 1e-2 were aligned with Muscle v3 . 8 [87] ( S6A Fig for E-value 1e-4 ) . The derived logo was built from the HMMER search with E-value of 1e-4 using Weblogo3 [88] . The multiple sequence alignment ( MSA ) derived from the HMMER search with E-value 1e-4 was used as input to look for homologs in the redundant fungi database and the UniRef90 database , using HMMERsearch with an E-value threshold of 1e-1 . Diversity trees were built from alignments generated with Muscle v3 . 8 using the Neighbor-Joining method with the MEGA6 package [89] . For analysis of gene expression in vitro grown mycelium , M . oryzae isolate Guy11 was grown in liquid medium ( glucose 10g/L , KNO3 3g/L , KH2PO4 2g/L and yeast extract 2g/L ) at 120 rpm on a rotary shaker at 25°C for five days . Mycelium was harvested over a piece of cheese-cloth ( Merck-Millipore , Darmstadt Germany ) . For production of spores for infection assays , M . oryzae isolate Guy11 was grown on rice flour agar for spore production [90] . A suspension of fungal conidiospores was prepared at a density of 2x105 spores/ml and spotted on detached leaves of the japonica rice variety Saraceltik grown for 3 weeks as described [91] . Infected leaf samples were harvested 16 , 24 , 48 and 72 hours post inoculation ( hpi ) . RNA extraction and reverse transcription was performed as described [92] . Quantitative PCR were performed with a LightCycler 480 instrument ( Roche , Basel , Switzerland ) using LC 480 SYBR Green I Master Mix ( Roche ) and the primers listed in the S4 Table . Amplification was performed as follows: 95°C for 10 min; 40 cycles of 95°C for 15 s , 60°C for 20s and 72°C for 30 s; then 95°C for 5 min and 40°C for 30 s . Data were analyzed using the delta-delta Ct method and applying the formula 2-∆CT , where ∆CT is the difference in threshold cycle ( CT ) between the gene of interest and the housekeeping gene Actin ( MGG_03982 ) used as a constitutively expressed reference gene . For each condition , three biological replicates were analyzed . | Fungal plant pathogens are of outstanding economic and ecological importance and cause destructive diseases on many cultivated and wild plants . Effector proteins that are secreted during infection to manipulate the host and to promote disease are a key element in fungal virulence . Phytopathogenic fungi possess huge effector repertoires that are dominated by hundreds of sequence-unrelated small secreted proteins . The molecular functions of this most important class of fungal effectors and the evolutionary mechanisms that generate this tremendous numbers of apparently unrelated proteins are largely unknown . By investigating the 3-dimensional structures of effectors from the rice blast fungus M . oryzae , we discovered an effector family comprising structurally conserved but sequence-unrelated effectors from M . oryzae and the phylogenetically distant wheat pathogen Pyrenophora tritici-repentis that we named MAX-effectors ( M . oryzae Avrs and ToxB ) . Structure-informed searches of whole genome sequence databases suggest that MAX-effectors are present at low frequencies and with a patchy phylogenetic distribution in many ascomycete phytopathogens . They underwent strong lineage-specific expansion in fungi of the Pyriculariae family that contains M . oryzae where they seem particularly important during biotrophic plant colonization and account for 50% of the cloned Avr effectors and 5–10% of the effector repertoire . Based on our results on the MAX-effectors and the widely accepted concept that fungal effectors evolve according to a birth-and-death model we propose the hypothesis that the majority of the immense numbers of different ascomycete effectors could in fact belong to a limited set of structurally defined families whose members are phylogenetically related . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | Structure Analysis Uncovers a Highly Diverse but Structurally Conserved Effector Family in Phytopathogenic Fungi |
Trachoma is the leading infectious cause of blindness in the world and is associated with precarious living conditions in developing countries . The aim of the present study was to evaluate the prevalence of trachoma in three municipalities of the Marajó Archipelago , located in the state of Pará , Brazil . In 2008 , 2 , 054 schoolchildren from the public primary school system of the urban area of the region and their communicants were clinically examined; in 2016 , 1 , 502 schoolchildren were examined . The positive cases seen during the clinical evaluation were confirmed by direct immunofluorescence ( DIF ) laboratory tests . The presence of antibodies against the genus Chlamydia was evaluated by indirect immunofluorescence ( IIF ) , and the serotypes were determined by microimmunofluorescence ( MIF ) . In 2008 , the prevalence of trachoma among schoolchildren was 3 . 4% ( 69 cases ) and it was more frequent in children between six and nine years of age and in females; among the communicants , a prevalence of 16 . 5% was observed . In 2016 , three cases of trachoma were diagnosed ( prevalence of 0 . 2% ) , found only in the municipality of Soure . The results of the present study showed that in 2008 , trachoma had a low prevalence ( 3 . 4% ) among schoolchildren in the urban area of Marajó Archipelago; eight years after the first evaluation and the introduction of control and prevention measures ( SAFE strategy ) , there was a drastic reduction in the number of cases ( 0 . 2% ) , demonstrating the need for constant monitoring and effective measures for the elimination of trachoma .
Trachoma , an eye disease caused by Chlamydia trachomatis ( serotypes A , B , Ba , and C ) , continues to be the leading infectious cause of blindness in the world [1 , 2] . The clinical presentation of trachoma is characterized by follicular and papillary hyperplasia in the conjunctiva , forming grayish-yellow follicles; the development of scars on the eyelid conjunctiva; trichiasis ( inverted lashes touching the eyeball ) ; and corneal opacity ( which includes formation of pannus , epithelial vascularization , and infiltration ) , responsible for the blindness stage of trachoma [3] . The disease occurs mainly in places with precarious and crowded living conditions , poor sanitation , and low educational and cultural levels , which favors its direct ( eye to eye or via contaminated hands ) or indirect ( including clothing and insect vectors ) transmission . It is considered a household infection with higher incidence in childhood , which facilitates reinfections [2 , 4 , 5] . Currently , trachoma is a public health problem in 42 countries and is responsible for the blindness or visual impairment of approximately 1 . 9 million people . Almost 182 million people live in areas endemic for trachoma and are at risk of developing blindness [1] . In Brazil , serological evidence of the infection indicates that serotype A of C . trachomatis was introduced in the Amazon region during the eighteenth century from a community of immigrants from North Africa who settled in the region [6] . A study evaluating the presence of trachoma among Brazilian schoolchildren revealed a prevalence of 5% , and the states of Ceará , Acre , and Pará presented the highest prevalence rates ( 8 . 7% , 7 . 9% , and 6 . 6% , respectively ) [7] . The state of Pará , an extensive geographic area located in the Amazon region , contains municipalities with prevalence rates of trachoma varying from zero to 29 . 4% [8] . However , some municipalities were not evaluated in the last surveillance surveys conducted in the state , including the 15 municipalities that are part of the Marajó Archipelago [8] , although this region is one of the poorest in the state , exhibiting one of the lowest human development indices ( HDI ) in the country [9] . The World Health Organization ( WHO ) leads an international alliance for the elimination of trachoma by the year 2020 ( GET 2020 ) , which proposed recommendations and guidelines for collective and specific treatments , through the SAFE strategy ( Surgery for entropion/trichiasis , Antibiotics for infectious trachoma , Facial cleanliness to reduce transmission , and Environmental improvements ) , which includes surgery , antibiotic therapy , constant facial hygiene and environmental improvements , and health education for changes in habits [10] . Brazil is part of this alliance and adheres to the elimination targets , working in conjunction with the state agencies coordinating trachoma surveillance and control programs [11] . The objective of the present study was to evaluate the prevalence of trachoma in the Marajó Archipelago in 2008 and 2016 and the impact of the introduction of educational and preventive measures on the disease during an eight-year interval .
The study was conducted in three municipalities of the Marajó Archipelago , located in the state of Pará , in the Eastern Amazon estuary , considered the world's largest fluvial-maritime archipelago [12] . The areas involved in the present study were selected based in a previous study [13] which detected a large dissemination of trachoma among the children population . Seroepidemiological information showed a high prevalence of antibodies to C . trachomatis [14 , 15] in several areas of the Amazon region of Brazil which is commonly found among deprived population groups with a low HDI . The Marajo Archipelago is almost uniform in its poverty index and lack of health access and , indeed , the lowest HDI in Brazil is found in this geographical area . Fig 1 shows the geographic locations of the three municipalities included in the study , Soure , Salvaterra , and Cachoeira do Arari . A draw was performed to randomly select 16 schools visited from the municipalities of Soure ( n = 7 ) , Salvaterra ( n = 6 ) and Cachoeira do Arari ( n = 3 ) . They were located in the urban areas because of the absence of regular travelling facilities within the Marajo . There were approximately 13 , 210 registered children in school during the period of examination , and the study included 2 , 054 children from 6 to 14 years , attending the first four school years . There were no revisits for missing children as they were present during the period of sample collection . The visits were preceeded by an extensive discussion with the community to ensure their presence during the investigation . A certified nurse trained by the Brazilian Ministry of Health program against trachoma conducted the examinations and the grading system for classification was that of Thylefors et al ( 1987 ) [16] . In 2008 , the study evaluated 2 , 054 children of both sexes , attending public primary schools located in the urban area , of the municipalities of Soure ( 922 children ) , Salvaterra ( 572 ) , and Cachoeira do Arari ( 560 ) . The initial evaluation consisted of a clinical examination of both eyes using a 2 . 5x magnifying glass under sunlight and light from a flashlight , a common protocol used in field work , and children with severe disease were referred to a medical service in ophthalmology at the Universidade Federal do Para . The grading system for classification was that of Thylefors et al . , 1987 [16] . The clinical cases were compared with photograph grading cards of the WHO Trachoma Program for the prevention of blindness and deafness and the cases were confirmed by a second examiner , following the instructions of the Brazilian Ministry of Health . The children who presented clinical diagnosis of trachoma were addressed for confirmation of the laboratory diagnosis . The individuals responsible for the diseased children answered an epidemiological questionnaire . All communicants ( individuals who had household contact with the children ) who presented clinical signs of trachoma were also evaluated through clinical and laboratory examinations . In March 2016 , a new evaluation was conducted in the same schools of the three municipalities evaluated in 2008 . A total of 1 , 502 children were evaluated , of whom 754 resided in Soure , 533 in Salvaterra , and 215 in Cachoeira do Arari . The epidemiological information was collected , and the clinical and laboratory tests were performed in the same way during the two investigative periods . Clinical cases of trachoma were defined as the presence of five or more follicles in the upper tarsal conjunctiva ( trachomatous inflammation follicular–TF ) , marked inflammatory thickening of the conjunctiva with obstruction of more than half of the deep tarsal vessels ( trachomatous inflammatory intense–TI ) , scars on the upper tarsal conjunctiva ( trachomatous scarring–TS ) , inverted lashes touching the eyeball ( trachomatous trichiasis–TT ) , and corneal opacity with reduced visual acuity ( trachomatous corneal opacification–TCO ) [17 , 18] . Children were classified as presenting a clean face when it was not possible to detect secretions either in the eyes or in the nose . To confirm trachoma in children who presented clinical signs , blood samples and tarsal conjunctival scrapings were collected . A total of 5 mL of blood was collected by intravenous puncture into a tube with anticoagulant to obtain serum , which was used to detect antibodies to the genus Chlamydia and to the serotypes of C . trachomatis . The conjunctival scrapings were deposited on slides and were used for the detection of elementary body antigens of C . trachomatis . The samples were stored at -20°C and were transported to the Virology Laboratory , Biological Sciences Institute , Federal University of Pará and to the STD/Trachoma Laboratory , Bacteriology Section , Evandro Chagas Institute/Health Surveillance Department/Ministry of Health , where the laboratory tests were performed . In the evaluation conducted in 2008 , educational actions were developed to eliminate trachoma in the region , which included guideline instructions for the use of health staff and parents of the students for the adequate treatment of patients and the importance of constant facial cleaning of patients and communicants . Lectures were also held for the community dealing with preventive measures against trachoma , such as the adoption of hygienic practices in the elimination of infectious diseases , personal and environmental hygiene , avoiding the attraction of disease-transmitting animals , drinking boiled water , and adequate storage of food . All identified cases of active trachoma were treated with 20 mg/kg ( up to 1 g ) of oral azithromycin in a single dose , as recommended by the Brazilian Ministry of Health [17] . The information was collected and a data bank was prepared . Prevalence rates were calculated as usual using the number of persons with the characteristic involved over the total number of persons . Percentages were calculated using the rate between the number of persons with the characteristic and the total examined . When applicable , the binomial test was used for the comparison of two proportions . All study participants were informed about the objectives of the study , and the parents who agreed authorized the participation of the children by signing an informed consent form . The project was submitted and approved by the ethics committee on research with human beings of the Evandro Chagas Institute ( protocols n . 0008/08 and n . 1 . 400 . 011 ) .
In 2008 , 1 , 030 ( 50 . 14% ) of the 2 , 054 children investigated were males with an age range from six to 16 years . The prevalence of clinically diagnosed trachoma was 3 . 4% ( 69/2 , 054 ) , all of them presenting the follicular form of the disease; most of the children had clean faces . The age of the children with trachoma ( Table 1 ) ranged from 6 to 14 years , most were females , and the highest number of cases was seen in the municipality of Soure . The majority of the children lived in homes with four rooms , where they lived with six to nine persons , with a family income that varied between one and three times the minimum wage . Environmental variables which are commonly associated with the presence of trachoma were also present as part of the daily routine of the diseased children . There were plenty of domestic animals , garbage collection was not regular and all had contact with flies and mosquitoes . It was quite a homogeneous situation commonly involving also the non-diseased persons . DIF tests confirmed the 69 clinical cases and all presented antibodies to the genus Chlamydia . The MIF test showed that most of the children in the three municipalities investigated were infected or have experienced previous infections by the four serotypes responsible for causing trachoma ( Table 2 ) . Among the 243 communicants of the schoolchildren investigated in 2008 , 40 ( 16 . 5% ) had clinical and laboratory diagnosis of trachoma . The ages of the communicants ranged from six months to 94 years , and the majority were female , residing in the municipality of Salvaterra . The trachomatous FT form was the most evidenced in children ( 95% , 38/40 ) , while the ST form was most evidenced in adults ( 5% , 2/40 ) . The socio-demographic characteristics related to the communicants are listed in Table 3 . In 2016 , 1 , 502 school children were examined in the three municipalities ( Cachoeira do Arari , Salvaterra and Soure ) , and three cases of trachoma ( 0 . 2% ) were found in Soure , which presented the FT form; none of the 13 communicants presented clinical suspicion of disease . The ages of the children with trachoma varied between seven and eight years , and all of them were female and resided in Soure . Five to six individuals lived in the households of these children , and the family income ranged from less than one to three times the minimum wage . The visits occurred in the months of June ( in 2008 ) and March ( 2016 ) when rain precipitation , humidity and river conditions were similar .
WHO estimates that the prevalence of trachoma , including trichiasis and blindness , has decreased from 317 million in 2010 to 200 million in 2016 as a result of the implementation of the SAFE strategy [22] . The present study evaluated the influence of control and prevention measures on the prevalence rates of the disease in 2008 and 2016 in schoolchildren from three municipalities of the Marajó Archipelago , a geographical area with rural economic activity and a low development index [9] . The prevalence of trachoma in the initial visit in 2008 ( 3 . 4% ) was similar to that reported among schoolchildren in other regions of Brazil ( 5 . 0% ) [7] , but it was below the prevalence found in the municipality of Cachoeira do Arari in a collection performed in 2007 ( 24 . 1% ) in children aged from 0–15 years [13] . In other studies performed in the Amazon region , high prevalence rates among Indians of the upper Rio Negro region ( 28 . 5% ) [23] and among Xingu Indians ( 28% ) were identified [24] , differing from the rates found in this study , which were similar to the prevalence rates recorded in other states , such as Maranhão ( 4 . 1% ) , Tocantins ( 5 . 6% ) , Bahia ( 3 . 5% ) , Goiás ( 5 . 2% ) , and São Paulo ( 4 . 1% ) [7] . The difference in the prevalence rates of trachoma among these studies is possibly related to the location where the studies were conducted , since those in rural communities reported higher prevalence rates , whereas those studies performed in the urban parts of the cities or nearby found lower prevalence rates . Trachoma is one of the leading causes of blindness in many developing countries , especially in poor rural areas [25] . All of the diseased children presented the follicular form of trachoma , which is most commonly found among children and adolescents [7 , 22 , 26] . This clinical form is characterized by a mild and self-limited but highly contagious disease that may be responsible for the maintenance of infection in a community and the cause of reinfections , leading to the development of more severe forms of trachoma [17] . Children are the main reservoir of infection [27] , and as they grow older there is a significant risk factor for the development of trichiasis , corneal opacity , and visual loss [28] . Thus , the correct diagnosis of the disease and treatment of children are important for the elimination of the most severe forms and the possible eradication of the disease . The main age group affected with trachoma includes children aged one to five years [4] . Although the present study did not evaluate children in this age group , a high frequency of infection was observed among children aged six to nine years , which corroborates the suggestion of decreased frequency of trachoma with increasing age [29 , 30 , 31] . The frequency of trachoma was twice as high in females , differing from the frequencies found in other regions of Brazil , where the prevalence was higher among males or did not differ in its prevalence according to sex [7 , 29 , 31] . It is possible that the greater frequency of trachoma cases among females could be related to the more affective behavior among them , as direct person-person contact is an important form of infection transmission [2] . The majority of schoolchildren with trachoma presented clean faces , but they reported living with large numbers of people and did not enjoy a standard of living that kept them away from crowding , hygienic standards and other conditions which would lead them to a healthier life . These conditions favour the occurrence of C . trachomatis infection in the region . Trachoma is associated with precarious living conditions , poverty , lack of hygiene , and poor housing and sanitation conditions [4 , 29] . The transmission of trachoma may be maintained via the sharing of domestic and peridomestic environment among infected individuals and of these with animals such as buffaloes , horses , dogs and birds , among other animals [2 , 32] such as those of the Marajó fauna , which can lead to an unhealthy environment , attracting winged insects often implicated as vectors of bacterial conjunctivitis , including eye gnats and flies ( Liohippelates and Hippelates ) [33] . The mechanical transmission of C . trachomatis occurs when these mosquitoes land on the face of the diseased children with trachoma and transfer the bacteria to others , closing the transmission cycle [5] . It is important to emphasize this form of transmission because of the large number of vectors and the finding that all infected children had constant contact with flies and other mosquitoes , as was previously shown regarding the involvement of synanthropic flies in the transmission of C . trachomatis and trachoma dissemination [13] . Most of the children investigated were positive for the four C . trachomatis serotypes associated with trachoma . All of these serotypes are usually present in developing countries where trachoma is endemic [34] . They are frequently associated with the precarious life conditions of the population , demonstrating the endemic character of the disease in Marajó Island , requiring a continuous monitoring program for trachoma prevention and treatment . Secondary infections of the communicants showed a high prevalence of the disease with different presentations in children and adults , confirming the findings of the different forms of trachoma according to age . The clinical form , FT , decreases inversely to advancing age , in contrast to the prevalence of ST [3 , 29] . The presence of adults with scars among the communicants indicates that the individual was exposed to trachoma at a very young age and that the children with the FT form are possibly responsible for the maintenance of transmission [35] . The return to the same communities in 2016 , eight years after the first investigation , showed a significant reduction in the prevalence of trachoma ( 0 . 2% ) . Three positive cases were diagnosed in one municipality ( Soure ) , in one single school; the three children belonged to three different families in the municipality . The presence of these cases may be related to visits by the families to rural areas , which are usually difficult to reach and that have shown a high prevalence of trachoma [13] . The decrease in the prevalence of trachoma is associated with disease control and prevention measures enacted by the team involved in the study together with the communities . According to the recommendations of the WHO and the Trachoma Program of the Brazilian Ministry of Health , the SAFE strategy ( Surgery , Antibiotics , Face , Environment ) for the elimination of trachoma was tentatively applied [10] . Cases which were more severe were referred to a University hospital for surgery . Outpatients and their communicants were treated with antibiotics with the suggested regimens of the Trachoma Program . Health and educational authorities , nurses and community leaders of the municipalities were joined in a net with school teachers , parents and children who received educational talks with the distribution of folders and banners regarding the disease , and ways to prevent it , including the maintenance of clean faces to avoid bacterial transmission . Finally , environmental improvements were strongly stressed to the municipality net regarding individual hygiene , the appropriate use of latrines , the elimination of vectors , the water supply and public garbage disposal . The intervention had to be done by the municipality and the power of convincing the community certainly helped to decrease the number of trachoma cases eight years later . Although it was not possible to intervene directly with the authorities regarding public services , there was a change in their behavior regarding the prophylaxis of trachoma . The strategy may not have been applied in full , but it was certainly an enormous help to change the epidemiological distribution of trachoma in the area of the study . It is also important to articulate measures ranging from combating poverty-related diseases to adopting public policies aimed at improving basic sanitation and environmental conditions [11 , 17] . In conclusion , the present study showed that trachoma presented a low prevalence ( 3 . 4% ) among schoolchildren in the urban area of the Marajó Archipelago in 2008 , and eight years later following the introduction of control and prevention measures , there was a drastic reduction in the number of cases ( 0 . 2% ) , emphasizing the need for constant monitoring and the implementation of effective measures for the elimination of trachoma . | Trachoma is one of the main neglected infectious diseases and carry a considerable burden to human health as a consequence of the clinical severity of the disease which may evolve to blindness . The lack of hygiene , education and other indicators of low social and economic markers occurring in developing and underdeveloped countries favour the spread of Chlamydia trachomatis , the bacterium causing trachoma . Although there is an easy , cheap and available treatment , reinfections are common and transmission is a consequence of bad hygienic habits and the various serotypes of the bacterium . The Marajó territory , in the North of the Amazon region of Brazil , is a large area with an ill educated , poor population , with almost no access to health resources and with almost no chance of transportation to major urban centers . Trachoma was detected a long time ago in the island and now , for the first time , a clear effort was produced during an eight year period in order to improve health hygienic habits among children and their relatives . The number of new cases following an initial diagnosis , was significantly reduced by the application of the WHO SAFE ( Surgery , Antibiotics , Facial hygiene and Education for better habits ) strategy . | [
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] | 2018 | Prevalence of trachoma in school children in the Marajó Archipelago, Brazilian Amazon, and the impact of the introduction of educational and preventive measures on the disease over eight years |
The nematode intestine is a tissue of interest for developing new methods of therapy and control of parasitic nematodes . However , biological details of intestinal cell functions remain obscure , as do the proteins and molecular functions located on the apical intestinal membrane ( AIM ) , and within the intestinal lumen ( IL ) of nematodes . Accordingly , methods were developed to gain a comprehensive identification of peptidases that function in the intestinal tract of adult female Ascaris suum . Peptidase activity was detected in multiple fractions of the A . suum intestine under pH conditions ranging from 5 . 0 to 8 . 0 . Peptidase class inhibitors were used to characterize these activities . The fractions included whole lysates , membrane enriched fractions , and physiological- and 4 molar urea-perfusates of the intestinal lumen . Concanavalin A ( ConA ) was confirmed to bind to the AIM , and intestinal proteins affinity isolated on ConA-beads were compared to proteins from membrane and perfusate fractions by mass spectrometry . Twenty-nine predicted peptidases were identified including aspartic , cysteine , and serine peptidases , and an unexpectedly high number ( 16 ) of metallopeptidases . Many of these proteins co-localized to multiple fractions , providing independent support for localization to specific intestinal compartments , including the IL and AIM . This unique perfusion model produced the most comprehensive view of likely digestive peptidases that function in these intestinal compartments of A . suum , or any nematode . This model offers a means to directly determine functions of these proteins in the A . suum intestine and , more generally , deduce the wide array functions that exist in these cellular compartments of the nematode intestine .
Parasitic nematodes cause major diseases of humans , directly affecting more than two billion people on a global scale [1] . Diseases they cause in food animal species also pose significant constraints to agricultural production and thus , indirectly impact human health in regions of the world where nutritional resources are limited . Acquisition of resistance to contemporary anthelmintics by many species of parasitic nematodes is on the rise [2] , [3] . Hence , the need for research to identify new targets for therapies to treat and control infections by these pathogens has never been greater . The nematode intestine is one tissue of importance in this context . The intestine of parasitic nematodes is formed by a single cell layer . The apical intestinal membrane ( AIM ) forms an intestinal lumen ( IL ) , and both the AIM and IL are accessible from the outside host environment . In combination , these two intestinal cell compartments form a major parasite interface with the host that performs a wide range of biochemical and cellular functions essential for survival of these pathogens . Consequently , knowledge of functions sited at this interface , and cellular processes needed to maintain it , could lead to approaches that disrupt intestinal cell functions critical for parasite survival . Research on the AIM of the parasitic nematode Haemonchus contortus , a gastrointestinal nematode of small ruminants , has demonstrated the importance of AIM glycoproteins as targets for vaccines [4]–[6] , advances on which have been extended to hookworms [7] , and in stimulating host mucosal immune responses during an infection [8] . These advances have underscored the unique value of the nematode intestine as a target for vaccines . Importance of the H . contortus intestine as a drug target was demonstrated relative to a benzimidazole anthelmintic [9] , which when used to inhibit AIM biogenesis caused catastrophic damage to the H . contortus intestine . The nematode AIM is also a primary target for crystal protein toxins produced by Bacillus spp . , which have efficacy against multiple parasitic nematodes [10] . Properties unique to the nematode intestine appear to account for anthelmintic effects of both the benzimidazole and crystal protein treatments . Consequently , the nematode intestine presents multiple biological characteristics that can be targeted by new therapies for treatment and control of parasitic nematodes . Nevertheless , details of intestinal cell functions remain obscure , in part due to research challenges imposed by the small size of many parasitic nematodes , including H . contortus . In this context , the size of adult Ascaris suum , the large round worm of swine ( 20–35 cm compared to ca . 2 . 5 cm for adult H . controtus ) , offers research capabilities absent with many other nematode species . The close relationship to A . lumbricoides , the most globally prevalent parasitic helminth of humans [11] , makes A . suum research particularly relevant to this human pathogen and global health . Previous comparisons of transcripts expressed in the intestines of H . contortus , Caenorhabditis elegans and A . suum identified likely , orthologous intestinal proteins in common among these species , which represent a phylogenetic distance spanning an estimated 350 million years [12] . These similarities were detected despite the utilization of markedly different nutrient sources by these species , e . g . host blood , H . contortus; bacteria , C . elegans; and host intestinal content , A . suum . This comparative approach , coupled with larger genome projects [13] , has begun to clarify orthologous protein groups with potential for broad biological application to many species of nematodes , whether parasitic or not . In research reported here , we coupled advantages conferred by the large size of A . suum , and past progress made with H . contortus , to develop an improved model for investigating functions located on the AIM and in the IL of A . suum . Previous research clarified properties of some glycoproteins and peptidases located on the H . contortus AIM surface [4] , [6] , [14] , [15] , and intestinal transcript analysis identified candidate , orthologous intestinal proteins from A . suum [12] , which we hypothesize perform related functions in these two species . In addition to peptidases , many other proteins are expected to reside at the nematode AIM , or in the IL , fulfilling roles in digestion and nutrient transport , as examples . The approach reported here facilitated identification of an extensive set of apparent A . suum peptidases that are sited at the AIM and IL , in addition to many other putative AIM and IL proteins . The results have greatly expanded concepts on apparent functions that reside at the A . suum AIM and in the IL . The approach also established A . suum as a unique model to directly investigate i ) functions that are located in these nematode intestinal cell compartments and ii ) methods to inhibit those functions , in context of improved or novel therapies and methods of parasite control .
Adult female Ascaris suum were obtained from swine infected as weanling pigs ( mixed breed , Swine Center , Washington State University ) 60 to 70 days post-infection . Infections were initiated with A . suum eggs collected from uteri of three or more patent female worms . Eggs were cultured in distilled water at room temperature for 30 days to allow eggs to embryonate , and then treated with 0 . 25% sodium hypochlorite for up to seven minutes to decoat the eggs , followed by three rounds of washes in 50 ml distilled water , and then stored at 5C in the refrigerator until use . Parasite intestinal samples were dissected from two or more freshly isolated worms maintained in ice cold phosphate buffered saline ( PBS , pH 7 . 4 ) . Intestinal samples were stored −80C until used . Intestinal tissue samples used for centrifugal fractionation and Concanavalin A ( ConA ) isolation and analysis were not necessarily from the same worm preparations . 10% formalin fixed adult female worms embedded in paraffin ( Histology Laboratory , Washington State University ) were sectioned , attached to glass slides and deparaffinized and steam treated . To assess specificity of ConA binding , sections were treated with sodium periodate ( 5 mM in 50 mM sodium acetate buffer , pH 4 . 5 ) , followed by sodium borohydride ( 50 mM in PBS , pH 7 . 4 ) to disrupt carbohydrates containing vicinyl hydroxyl groups . Slides were ten treated with 0 . 3% hydrogen peroxide in methanol for 30 min at 25°C to eliminate endogenous peroxidases and then incubated with ConA-horse radish peroxidase ( HRPO ) . Binding was localized by development with Metal Enhanced DAB Substrate ( Thermo Scientific , Rockford , IL . ) . Sections were then counterstained with Mayer's haematoxylin ( Thermo Scientific , Fremont , CA ) . Protein samples were separated by SDS-PAGE under non-reducing conditions , using 17% to 7% polyacrylamide gradient gels using previously described methods [15] . Proteins were either stained with Coomassie Brilliant Blue R-250 or transferred to nitrocellulose filters . Filters were then incubated with ConA-HRPO to localize glycoproteins by chemiluminescence ( Pierce ECL Western Blotting Substrate , Thermo Scientific , Rockford IL ) recorded on x-ray film ( Kodak O-MAT ) . To assess specificity of ConA binding , replicate nitrocellulose filters were treated with sodium periodate as described for tissue sections , and control filters were treated the same , but excluding sodium periodate . For analysis of soluble protein , samples were adjusted to 1% TX-100 and used in assays at 0 . 5 to 4 µgs ( depending on assay ) in each well . Buffers used were 100 mM citrate phosphate ( pH 5 . 0–7 . 0 ) and 100 mM phosphate ( pH 8 . 0 ) . Samples were incubated with Bodipy FL casein ( E6638 , Life Technologies , Grand Island , NY ) for two hours in a C-1000 Touch thermal cycler with a CFX96 Optical Reaction Module ( Bio-Rad , Hercules , CA ) at 37°C , in a total volume of 50 µl . Assays were conducted in triplicate using 96 well PCR plates ( Bio-Rad , Hercules , CA ) . Fluorescence signal was measured ( excitation 490 nm; emission 530 nm ) . Net fluorescence signal was determined by subtraction of starting values from end values . Mean fluorescence was calculated for no protein controls after incubation with Bodipy FL casein at a given pH for two hrs . This background value was subtracted from each fluorescence value obtained for samples with protein . Activity was expressed as mean relative fluorescence units per µg of protein . For analysis of ConA binding proteins , intestinal supernatant S1 solubilized in 1% TX-100 was incubated with beads ( 1 mg per 100 µl packed beads ) for 2 hours with inversion , then washed with Binding Buffer containing divalent cations ( 50 mM TRIS , [pH 7 . 4] , 500 mM NaCl , 1 mM each MgCl2 , MnCl2 , and CaCl2 ) . ConA-agarose beads with bound proteins ( 10 µl ) were transferred to wells of 96 well flat-bottomed plates ( Corning Costar , Corning , NY ) in 50 µl total volume for assays as described for peptidase assays . Beads with no proteins were used for no protein controls . The 96 well plates were rotated during incubation ( 50 rpm ) to ensure mixing . Reaction supernatants from which beads were eliminated were transferred to a 96-well PCR plate ( Bio-Rad , Hercules , CA ) for fluorimetric measurements , as described for peptidase assays . Alternatively , SDS ( 1 . 0% ) solubilized intestinal lysates were used for ConA bead isolation and analysis by mass spectrometry and testing in peptidase assays , but produced no detectable activity . Mean fluorescence units generated from treatments in peptidase assays were compared first by analysis of variance ( ANOVA ) , followed by Tukey's multiple comparison of means . ANOVA was conducted among treatment groups for individual pHs . For inhibition experiments , ANOVA and multiple means comparisons were conducted to identify means of inhibitor treated groups that differed from the untreated control group at a given pH . For significance with ConA-bead isolated proteins , mean fluorescence at each pH tested was assessed by 95% confidence intervals to determine if the intervals were above zero . ConA bead-isolated proteins were separated on SDS-PAGE gels in two lanes , one of which was Coomassie Blue stained and the second was transferred to nitrocellulose and probed with Con A-HRPO . Bands detected in blots were used to align with stained bands in the gel , which were excised and then prepared for in situ trypsin digestion and analysis by LC-MS/MS as described [8] . Proteins in PBS and 4MU perfusates , PF , and P2 pellets were precipitated with the “2-D Clean-up kit” ( GE Healthcare Life Sciences ) , and then resolubilized in 8M urea , 100 mM Tris , pH 8 . 5 ( 20 µl ) for 30 min at 37°C . Samples were then reduced with 1 mM tris ( 2-carboxyethyl ) phosphine TCEP ( 2 µl of 10 mM TCEP stock ) at room temperature for 30 minutes , followed by alkylation with 20 mM Iodoacetamide for 30 minutes in the dark . Then , samples were quenched with 10 mM DTT for 15 minutes and diluted 1∶4 dilution in Tris ( pH 8 . 5 ) . Proteins were digested sequentially with endoprotease Lys-C ( cleaving lysine at the C-terminus ) and trypsin as previously described [16] and then processed for liquid chromatograpy-tandem mass spectrometry ( Supplemental Materials ) . LC-MS data files ( MS2 centroided ) were used for database searching with MASCOT ( Matrix Science , version 2 . 3 . 0 . 0 ) using previously described software settings [17] , against the deduced A . suum proteome [18] and the Sus scrofa proteome ( Uniprot , downloaded Sept . 2012 ) to identify potential host contamination . Thresholds for detection were set in accordance to the suggestions by the Scaffold documentation , and as described in other recent proteomics studies [19]–[21] . Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony . The research involving use of swine was reviewed and approved by the Washington State University Institutional Animal Care and Use Committee , protocol #04097-004 . , approved on 12/19/2013 . Guidelines are provided by the Federal Animal Welfare Act , USA .
Peptidases from four different major classes ( Aspartic , cysteine , metallo and serine ) have been directly or indirectly localized to the AIM of H . contortus [14] , [15] , [22] . Predicted homologs or orthologs of these H . contortus AIM peptidases are members of intestinal protein families ( IntFam-241 , [12] ) found to be conserved among this parasite , A . suum and C . elegans . Nucleotide sequences for these H . contortus AIM proteins encode signal peptides , but not always hydrophobic sequences that are expected for integral membrane proteins , suggesting a peripheral association with the AIM . EST cluster sequences that encode the A . suum IntFam-241 homologues of H . contortus AIM peptidases were next mapped by BLASTN to A . suum gene models from the published A . suum genome sequences [18] . The A . suum gene and corresponding protein designations for these IntFam-241 peptidases are listed in Table 1 . Accordingly , we hypothesized that the deduced A . suum proteins listed are AIM or IL peptidases . In previous research , intestinal homogenates and lysates from adult female H . contortus proved valuable for dissecting biological properties of AIM proteins , of which a large family of cathepsin B-like ( CBL ) cysteine peptidases comprise a prominent component [4] , [13] , [15] , [22] , [23] . This general approach was used to assess peptidase activity in the adult female A . suum intestine . Activity was detected in A . suum intestinal lysates using a Bodipy casein substrate predominately at pH 5 . 0 and 6 . 0 , with lower activity at pH 7 . 0 and 8 . 0 ( Fig . 1A ) . The potential for A . suum peptidases to exist as membrane associated proteins was determined by enrichment for peptidase activity in pellet fractions P1 ( large debris ) and P2 , ( 5 , 000 to 50000×g ) ( Fig . 1A ) . Although not exclusive to this fraction , the results show that intestinal peptidase activity was enriched in both P1 and P2 pellets relative to lysate of whole intestine or supernatant fractions in tested , which is likely to reflect membrane associated proteins . General inhibitors effective against distinct peptidase classes were used to identify peptidases that might contribute to the activities detected in whole lysates ( Fig . 1B ) and the membrane enriched fraction P2 ( Fig . 1 C ) . Inhibitors of aspartic , metallo and serine peptidases each caused inhibition of activity in intestinal lysates , and often in a pH dependent manner ( Fig . 1B ) . For instance , the aspartic and metallo peptidase inhibitors , pepstatin and 1 , 10 phenanthroline , respectively , each inhibited activity at the more acidic range . In contrast , the serine peptidase inhibitor , phenylmethylsulphonylfluoride ( PMSF ) inhibited activity at neutral and basic pH conditions , while some inhibition by 1 , 10 phenanthroline was also observed under these pH conditions . No significant inhibition ( p>0 . 05 ) was observed with an inhibitor of cathepsin B-like cysteine peptidases ( E-64 ) , and similar results were obtained with the general cysteine peptidase inhibitor , iodoacetamide ( S1 Fig . ) . Results with the P2 pellet were similar to the lysate , except that PMSF showed greater inhibition under acidic conditions by comparison to the whole lysate . The samples that were assayed are very complex . Although distinct effects were observed relative to pH and inhibitors , the results should be viewed as providing gross indications of peptidase activity in the intestinal homogenate and other fractions of interest , described below . The inhibitor results provided a sense of diversity relative to peptidase class and pH dependency for the peptidase activities detected , which will be evaluated in more detail by mass spectrometry in subsequent sections . Other intestinal fractions that will be described below were assayed for peptidase activity when incubated with Bodipy casein . However , use of denaturants with several samples described below introduced constraints on interpretations of those results . Nevertheless , inhibitors were routinely tested at pH 5 . 0 to assess peptidase involvement in producing signal in the assays . Several known proteins , including peptidases , located on the H . contortus AIM are glycosylated [4] , [14] . Hence , we investigated the use of a lectin , concanavalin A ( ConA ) , to identify glycosylated proteins on the A . suum AIM . Glycans located on the A . suum AIM were previously detected by ConA [24] , and this observation was confirmed here ( Fig . 2A , B ) . Pretreatment of tissue with periodate prevented binding of ConA to the AIM surface , indicating specificity of ConA binding to the A . suum glycans . ConA binding was most obvious on and at the base of the A . suum AIM . There was also evidence of ConA binding to material that appeared loosely attached to the AIM and extended into the lumen , which may reflect insoluble cellular material on the AIM surface that is released from the AIM into the lumen . ConA was used to probe blots of SDS-PAGE gels in which proteins of intestinal lysates were separated ( Fig . 2C ) . A large number of protein bands were identified that ranged in Mr from 14 to over 225 kDa . Binding of ConA to these bands was inhibited by pretreatment of the blot with periodate , which supported the dependence of binding on the presence of periodate-sensitive glycans . Next , ConA-agarose beads were used to isolate ConA binding proteins from Triton X-100 lysates of the intestine . Proteins that remained on the beads following washes hydrolyzed Bodipy casein at pHs that ranged from 5 . 0 to 8 . 0 ( Fig . 2 D ) . In this case , inhibitors of aspartic , cysteine , metallo and serine peptidases significantly reduced peptidase activity associated with beads . ( Fig . 2 E ) . Although the extended time required for ConA binding may have differentially affected the various peptidase activities , the results support that peptidases were isolated on ConA beads . On the contrary , methods used to isolate ConA binding proteins for analysis by mass spectrometry included SDS treatment , and no peptidase activity was detected with beads from these preparations . Collectively , the results showed that i ) ConA bound both to the AIM and IL content of A . suum , ii ) multiple ConA binding proteins exist in the A . suum intestine , and iii ) intestinal peptidase activity was isolated on ConA-agarose beads . The foregoing experiments provided general information on characteristics of A . suum intestinal proteins that may include AIM or IL glycoproteins and peptidases . Cannulation and perfusion of the intestinal lumen provided a more direct approach uniquely supported by the large size of A . suum , as compared to other nematodes such as H . contortus and C . elegans , Fig . 3 A-C indicates the steps utilized in cannulation to perfuse the A . suum intestine . Injection of dye into the intestinal lumen of worms with an intact posterior end demonstrated confinement of dye to the lumen ( Fig . 3D ) . IL content was first collected by perfusion with phosphate buffered saline ( PBS perfusate ) . This perfusion was followed in the same worms by perfusion with PBS containing 4 M urea ( 4MU perfusate ) . The urea chaotrope was perfused because ConA binding was most evident on the AIM , by comparison to the IL , suggesting that at least some predicted AIM glycoproteins ( peptidases ) are peripheral AIM proteins that may be solubilized by 4MU and collected for analysis in this perfusate . Blots of isolated proteins in both perfusates were probed with ConA ( Fig . 4A ) , which showed somewhat similar banding patterns overall between the PBS and 4MU perfusates , but with different relative abundances of ConA binding proteins in each . Periodate pre-treatment of the nitrocellulose filter ( blot ) eliminated detectable ConA binding to protein bands from both perfusates . In peptidase assays , mean activity of the PBS perfusate was generally higher than the 4MU perfusate and the pseudocoelomic fluid ( PF ) , in which negligible peptidase activity was detected , except for the low level at pH 6 . 0 . The activity in the PBS perfusate at pH 5 . 0 was largely inhibited by pepstatin ( Fig . 3B , C ) . Despite treatment with denaturant , peptidase activity was detected in the 4MU perfusate under all pH conditions tested . Mean fluorescence was significantly higher than PF at both pH 5 . 0 and 8 . 0 . In contrast to the PBS perfusate , significant inhibition was observed with PMSF and 1 , 10 phenanthroline at pH 5 . 0 ( Fig . 3F ) . Although these results suggest enrichment for different classes of peptidases , between the PBS and 4MU perfusates , inhibitory effects of urea on peptidases in the 4MU fraction also seems likely , rendering this point inconclusive . These results showed that 1 ) PBS and 4MU intestinal perfusates contained ConA binding proteins , 2 ) ConA binding proteins were differentially obtained from the intestine according to specific perfusion conditions , and 3 ) both perfusates contained peptidase activity that was distinct from potential contamination by PF . Proteins from each of the fractions described above ( ConA agarose beads , PBS and 4MU perfusates , and P2 pellet ) were analyzed by LC-MS/MS mass spectrometry . A complete listing of results is provided in S1 Table . The smallest subset of proteins identified was generated with proteins eluted from ConA agarose beads ( full set in S2 Table , peptidases in Table 2 ) . These bead-isolated proteins were separated by PAGE , and ConA binding bands located on nitrocellulose filters were then excised from corresponding gels for analysis ( as described in the methods ) . Twenty seven proteins were identified , most individual proteins of which were confined to one or two unique PAGE gel slices ( S2 Table ) . Eighteen of the 27 proteins fell into the two major functional categories of peptidases ( 10 ) and O-glycosyl hydrolases ( eight ) , of which the glycosyl hydrolases may be related to saccharidase activity previously detected in A . suum intestinal brush border preparations [25] , [26] . Only three proteins ( GS_16354 , GS_12574 , GS_21785 ) from the entire ConA set were not detected in either of the perfusate fractions , and only one ( GS_21785 ) was absent from both the perfusate and P2 pellet fractions ( Table 2 ) . For proteins detected in both the ConA binding proteins and the perfusates , representation was often the highest in the 4MU versus PBS perfusate , which might be expected for peripheral membrane proteins . Therefore , the ConA isolated proteins identified appear to account , at least in part , for ConA binding proteins located in situ on the AIM , and for peptidase activity associated with ConA agarose beads and perfusates . A more complex group of peptidases was detected in perfusate samples ( Table 2 ) . These samples were also compared to LC-MS/MS results obtained for the PF , a most likely contaminant of the perfusate samples . Predicted peptidases identified in the PBS and 4MU perfusates by LC-MS/MS showed both similarities and differences . Perfusate proteins annotated as peptidases corresponded to aspartic , cysteine , metallo , serine carboxy and threonine peptidases , or unassigned peptidases , according to MEROPS [27] . These proteins were represented in one or both perfusates , although representation of mass spectra was better in the 4MU perfusate for many proteins shared with the PBS perfusate . In total , 29 peptidases were detected in ConA and perfusate fractions ( Table 2 ) . All but four of these were also detected in the P2 pellet . The largest group of peptidases identified in the Con A and perfusate fractions were categorized as metallopeptidases ( M01 , nine; M13 , four; M17 , one; M20 , two ) , followed by aspartic peptidases ( A01 , five ) , serine carboxy peptidases ( S10 , three; S28 , one ) , cathepsin B-like cysteine peptidases ( C01 , one ) , threonine peptidase ( T01A , one ) and unassigned amino peptidase N ( - , two ) . Peptidase classes represented in the perfusates and ConA binding fraction were largely consistent with our previously published predictions ( Table 1 ) , although the results significantly expanded the subclasses , increased the number and provided direct evidence for A . suum AIM and IL peptidases identified relative to these predictions ( Table 3 ) . The relatively high representation of some peptidase sequences in the 4MU fraction and occurrence in the ConA fraction is consistent with localization to the AIM as peripheral membrane proteins . Although information from the P2 fraction was not specific regarding compartmentalization , this fraction contained 21 additional putative peptidases , including seven S10 and S28 serine carboxypeptidases , which may also function on the AIM or in the IL . In contrast to H . contortus in which cathepsin B-like cysteine peptidases make up a prominent fraction of AIM-IL peptidases [15] , [23] , cysteine protease activity was variably indicated in inhibitor assays and a single peptide was detected in the PBS perfusate for an A . suum cathepsin B-like ( CBL ) cysteine peptidase . Thus , A . suum CBLs appear to represent a relatively minor constituent of A . suum AIM and IL digestive peptidases . Also , based on acidic preference of serine carboxypeptidases , no AIM or IL peptidases were identified that would obviously account for the apparent serine peptidase activity detected at pH 8 . 0 . Although host trypsin and chymotrypsin apparently localize to the A . suum intestine [28] , use of porcine trypsin for the mass spectrometry analysis obviated clarification based on detection of this protein . Nevertheless , peptides of porcine chymotrypsin were also detected in perfusates , raising the possibility of host serine peptidase activity in perfusates analyzed at higher pHs . Several of the peptidase protein sequences were predicted to have signal peptides for secretion , or are predicted non-classical secretory proteins , each of which is consistent with compartmentalization in the IL or on the AIM . However , neither of these two characteristics was predicted for three of the ConA and perfusate peptidases based on existing A . suum protein models ( Table 2 ) . A recent publication on the A . suum secretome [29] identified 10 of the ConA and perfusate peptidases identified here as excretory-secretory products of adult female A . suum ( Table 2 ) , six of which lack apparent signal peptides , and five of those were classified as non-classical secretory proteins . While our results clarify the likely origin of these “secretory” products , their localization in excretory-secretory products is also consistent with secretion from the AIM . It is possible that inaccurate gene models account for lack of detectable signal peptides on some proteins and for some discrepancies between bioinformatics annotation and experimental results . We also detected intestinal transcripts for each of the predicted peptidase genes under evaluation [30] and many of these showed relative abundances that are equal to or greater in intestinal cells compared to other tissues investigated ( Table 2 ) . We next assessed representation of all A . suum peptidases that can be accounted for by intestinal peptidases detected in the compartments under investigation . All peptidases annotated in the entire proteome were evaluated relative to transcript expression and over-expression of transcripts in the intestine compared to other adult tissues , based on previous measures [30] , and then , those peptidases detected by LC-MS/MS in fractions analyzed here . The total number of peptidases for which intestinal transcripts have been detected represent from 78 to 100% of all peptidases annotated in the A . suum proteome ( Table 4 ) . However , the range of percentages decreased substantially ( 5 to 26% of all peptidases ) when considering peptidase for which transcripts were relatively overexpressed in intestinal tissues . In addition , representation of peptidases identified by LC-MS/MS was much higher for the “overexpressed” group . For instance , the number of peptidases detected in both perfusates account for a high percentage of metallo ( 68% ) , aspartic ( 100% ) and unassigned ( 100% ) peptidases classified in the “overexpressed” group . Whereas serine and cysteine peptidases classified as “overexpressed” were less well represented in the perfusates ( 6 and 13% , respectfully ) , representation of serine peptidases improved when data for the P2 pellet fraction was included ( 62% ) . The results show that peptidases in perfusates accounted for a high percentage of intestinal peptidases identified in the group with transcripts comparatively overexpressed in the A . suum intestine . While but a relatively small fraction of all prospective intestinal peptidases , this connection between AIM-IL peptidases and the “overexpressed” group conveys relative importance of these proteins for the intestine and the parasite . Although peptidases were used as markers for the IL and AIM compartments , fractions containing these proteins were expected to include IL and AIM proteins that have other functions , which is evident in S1 Table . More detailed analysis will be reported on these other proteins a separate publication .
Parasitic nematodes present numerous challenges for conducting research on individual worm tissues , and even more so on individual cells . Such is the case for biological questions pertaining to the intestine and intestinal cells of these pathogens . Here we describe progress towards comprehensive identification of proteins that function in the IL and/or on the AIM of adult female A . suum , which is one of a relatively few parasitic nematodes with sufficient size to support the progress reported by using relatively straightforward perfusion methods . Knowledge of H . contortus AIM peptidases gained from past research guided efforts to identify A . suum homologs ( likely orthologs or paralogs ) that were detected in ConA binding fractions and intestinal perfusates . Collective observations reported here indicated that most of the predicted aspartic , metallo and serine carboxy peptidases identified in Table 1 , are indeed A . suum glycoproteins that function in the IL and/or on the AIM . Moreover , testing of this hypothesis led to identification of a greatly expanded set of apparent peptidases that function in these compartments , while also identifying many proteins with other functions that are likely sited in these compartments . The foregoing conclusions are supported by multiple considerations , different subsets of which apply to different peptidases; i ) detection of peptidase activity in the PBS and 4MU perfusates of the A . suum IL , ii ) detection by LC-MS/MS of proteins in the perfusates that are predicted to have relevant peptidase properties , iii ) localization of these peptidases , or subsets of peptidases in fractions enriched for membranes ( P2 pellet ) and glycosylated proteins ( ConA binding proteins ) , iv ) evidence of classical or non-classical secretion for proteins encoded by corresponding A . suum genes of many , but not all of the these proteins [30] . Many peptidases were detected in multiple fractions and those preferentially detected in the 4MU perfusate are also candidate peripheral AIM proteins . Their detection in the lumen could reflect an origin as a peripheral membrane protein on the AIM with subsequent release into the lumen as part of a normal process of protein turnover . A peripheral association is supported by other characteristics including detection in the ConA and/or P2 fractions , presence of a signal peptide and lack of a transmembrane domain . While none of the individual characteristics is sufficient alone , in combination these characteristics increase the probability for a given protein to have a peripheral membrane association . Nevertheless , apparent lack of a signal peptide must be tempered in these considerations pending improvement of A . suum gene models . We expect that signal peptides will be recognized for additional proteins identified here with future refinement of these gene models . AIM and IL peptidases have proven valuable as targets in control strategies against parasitic nematodes of humans , animals and plants [26] , [31] . Approaches have included i ) vaccination , and ii ) delivery of peptide inhibitors and double stranded RNA to disrupt intestinal peptidase functions in multiple parasitic nematodes [31]-[33] . Results presented here provide a broad and deep assessment of peptidases that A . suum likely relies upon for digestion of ingested proteins . The number and diversity of peptidases identified was not necessarily expected for a parasite that consumes intestinal content already digested to a large extent by the host . The results indicated that the intestine of A . suum is rich in peptidases actively engaged in digestion of proteins obtained from the host intestinal content . Comparisons made at the whole genome and intestinal transcriptome levels provided quantitative context for the subset of A . suum peptidases that are likely involved in protein digestion within the adult female intestinal lumen . This subset reflects a relative small fraction of the entire set of prospective peptidases derived from A . suum genome and intestinal transcriptome analyses [18] , [30] . Many of the peptidases identified in perfusates are encoded by transcripts determined to be relatively overexpressed in intestinal cells as compared to other tissues [30] . This combination of data adds confidence that these proteins contribute significant roles in the A . suum intestine and indicates that the up-regulation of expression of these peptidases is an important aspect of development in intestinal cells . The foregoing knowledge will contribute significantly to research directed at disruption of protein digestion in A . suum by chemical or immunological means . For instance , in contrast to some other plant and animal parasitic nematodes [23] , [32] , the single cathepsin B-like cysteine peptidase detected had low representation in intestinal samples analyzed and therefore may be a low value target for disrupting protein digestion in A . suum . Alternatively , aspartic peptidases represent a major group of A . suum endopeptidases implicated in protein digestion , although the moderately large number identified will be an important consideration relative to inhibitory methods that rely on antibodies or RNA interference . Several of the aspartic peptidases were annotated with similarity to necepsins , which are vaccine candidates for hookworms [7] . Likewise , a group of 16 apparent IL-AIM metallopeptidases were identified in intestinal perfusates , and far more than is known from any other nematode species . Predicted amino ( M01 ) and endo ( M13 ) peptidases were prominent components of this class , and mass spectra for two apparent M13 peptidases were overrepresented in perfusates ( GS_08219 and GS_19140 ) , perhaps reflecting the relative biological importance of these peptidases . Serine carboxypeptidases present a less complex picture , although mass spectra specific to the P2 fraction identified additional serine carboxypeptidases , which may foretell a more complex picture . While the value of these proteins as vaccine targets is unclear , transcripts encoding serine carboxypeptidases are highly abundant in H . contortus intestine [22] , [23] and these peptidases have been implicated as vaccine candidates against this parasite [34] . Accordingly , the ability to perfuse the A . suum intestine offers an approach for testing methods of inhibition in vitro using perfusates isolated from the worm and in vivo by perfusing inhibitors into the intestine contained within otherwise intact worms . The similarities in profiles of IL-AIM peptidases among diverse species offer potentially broad application of results , while differences represent adaptations that may be explained in several ways: 1 ) influence of trophic niche ( food from host intestinal content versus blood ) between species like A . suum and H . contortus , respectively; 2 ) unique evolutionary solutions for nutrient acquisition among phylogenetic lineages; or 3 ) combinations of these factors . For instance , our results confirm previous findings [23] that A . suum apparently expresses a single intestinal CBL , which has a relative minor role in protein digestion , whereas H . contortus expresses numerous intestinal CBLs that appear to have a prominent role in nutrient digestion . Given the high number of IL-AIM metallopeptidases , A . suum may have comparatively high representation of this class of peptidase among nematodes , although more comparative information is needed to address this point . While differences may clarify biological distinctions among individual pathogens , it will also be of interest to learn at what phylogenetic level differences of this kind have been acquired . Because adaptations related to nutrient acquisition likely represent major determinants in parasite evolution , additional information here may clarify factors critical for the evolution of parasite lineages and/or species . In addition to AIM and IL peptidases , our approach has provided a major step towards comprehensive identification of IL proteins free in the lumen because most ( all ) of these proteins are expected to be collected in the PBS perfusate . Use of 4 M urea was expected to extract many peripheral AIM proteins , but not integral membrane proteins , which are likely to have gone largely unidentified in this analysis . Nevertheless , the ability to perfuse the intestine should facilitate use of other methods to further elucidate A . suum AIM proteins . The ability to perfuse the intestine has also expanded experimental capabilities to directly investigate in vivo intestinal functions that serve the IL , AIM and worm in general . Capability of this kind is lacking for most other nematodes , including C . elegans , and represents an important adjunct to experimental dissection of intestinal cell functions of relevance to all nematodes . Beyond peptidases , proteins predicted to perform numerous other functions were identified in the PBS and 4MU perfusates . While analysis of these proteins is presented in more detail elsewhere , some may have roles that are complementary with the peptidases discussed here . An example is the VHA subunits detected ( A , B , C , D , E and G ) in the 4MU perfusate which may function to create acidic conditions in the A . suum intestinal lumen and aid in digestion of ingested nutrients . VHAs translocate H+ across membranes and have been implicated as AIM determinants of acidic pH in the intestinal lumen of C . elegans [35] . The peptidase activity detected at more alkaline pH notwithstanding , an acidic pH supported peptidase activities from intestinal fractions inclusive of apparent aspartic , serine and metallo peptidases , which is consistent with A . suum proteins identified in intestinal perfusates . Similarly , A . suum intestinal saccharidases associated with AIM brush border preparations ( microvilli ) had acidic pH preference [25] , [26] . This association with the brush border was suggested to reflect compartmentalization of carbohydrate digestion at the AIM surface that facilitates localized coupling of nutrient transport across the AIM [26] . Our results extend this possibility to include coupling of protein digestion and peptide transport that is facilitated by close association on the AIM . Although acidic conditions may prevail throughout the A . suum intestinal lumen , detection of intestinal peptidase activity at neutral and basic pHs prevents exclusion of possible regional variation of pH along the length of the lumen . In any case , this research provides new capabilities to develop and investigate questions on mechanisms of nutrient acquisition by A . suum , and by extension , methods to inhibit those processes . Research leading to the progress reported here stemmed from comparative analysis of intestinal cDNAs from A . suum , H . contortus and C . elegans . The large size of A . suum uniquely facilitated identification of many proteins that appear to be sited in the intestinal lumen and on the AIM . How the current findings apply to other parasitic nematodes is now of interest to determine . The perfusion method described here cannot be effectively applied to address this question in many other species of nematodes important to the health of humans and agricultural species . Nevertheless , transcriptomic/genomic methods provide a means to link experimental results obtained with A . suum to other nematodes of medical importance . Accordingly , computational research is ongoing to elucidate orthologous intestinal functions of basic importance among nematodes ( [12] and unpublished research ) , which will complement well the progress that can be made using the experimental intestinal model that is provided by A . suum . | Past research has demonstrated that the nematode intestine has value for developing new methods of therapy and control of parasitic nematodes , as related to both vaccines and other anthelmintics . Yet , information related to basic intestinal cell biology is very limited . Research progress reported here moves towards the comprehensive identification of proteins ( peptidases and others ) , and hence functions , that are sited on the apical intestinal membrane and within the intestinal lumen of adult female Ascaris suum . These advances provide an unprecedented research model to determine critical functions sited at these locations and to develop approaches to inhibit those functions . Comparative analysis among diverse parasitic species raises expectations that the results from A . suum can be applied to many parasitic nematodes for which similar research is technically impossible to perform . | [
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] | 2015 | Peptidases Compartmentalized to the Ascaris suum Intestinal Lumen and Apical Intestinal Membrane |
Genes required for infection of mice by Salmonella Typhimurium can be identified by the interrogation of random transposon mutant libraries for mutants that cannot survive in vivo . Inactivation of such genes produces attenuated S . Typhimurium strains that have potential for use as live attenuated vaccines . A quantitative screen , Transposon Mediated Differential Hybridisation ( TMDH ) , has been developed that identifies those members of a large library of transposon mutants that are attenuated . TMDH employs custom transposons with outward-facing T7 and SP6 promoters . Fluorescently-labelled transcripts from the promoters are hybridised to whole-genome tiling microarrays , to allow the position of the transposon insertions to be determined . Comparison of microarray data from the mutant library grown in vitro ( input ) with equivalent data produced after passage of the library through mice ( output ) enables an attenuation score to be determined for each transposon mutant . These scores are significantly correlated with bacterial counts obtained during infection of mice using mutants with individual defined deletions of the same genes . Defined deletion mutants of several novel targets identified in the TMDH screen are effective live vaccines .
Salmonella enterica serovar Typhimurium ( S . Typhimurium ) infection of mice is well established as a model of systemic typhoid fever in humans [1] . The system is particularly useful for identifying the genetic determinants of Salmonella virulence , usually by comparing the in vivo growth of mutants with their wild type parents . If a mutant is unable to survive in the mouse model it is inferred that the disrupted gene is important for infection . Several such mutants have been used to induce an immune response that is protective against subsequent infection with wild type S . Typhimurium , and have subsequently been translated for use as live attenuated vaccines in humans and food-producing animals [2] . Most often , the mouse model has been used to investigate Salmonella infection on a gene-by-gene basis . However , recent developments in molecular biology allow pools of many mutants to be screened in parallel . The first example of this was signature-tagged mutagenesis ( STM ) , which employs transposons containing unique sequence tags that can be amplified by PCR and identified by Southern hybridisation . These transposons are used to generate mixed pools of mutants , which are used to infect an animal . Bacterial DNA recovered from the animal can be interrogated for the presence of the tags , and compared to similar data obtained from the inoculum to identify attenuated mutants that do not survive in vivo . This method was developed in a study of S . Typhimurium in the mouse model of typhoid fever [3] , using input pools of 96 tagged mutants per animal . This led to the discovery of Salmonella pathogenicity island 2 ( SPI-2 ) , which encodes a type III secretion system ( T3SS ) that is critical for systemic infection and intracellular pathogenesis [4] . The island was so named to distinguish it from the well-characterised SPI-1 , which also encodes a T3SS that is important in intestinal adhesion and invasion [5] . Morgan et al . [6] used STM to identify S . Typhimurium genes required for colonisation of calves and chicks , using the same STM mutant library to infect the different hosts , hence enabling comparison of the gene sets required for infection of different livestock species . This study showed that SPI-1 and SPI-2 mutants that were defective in colonization of calves were able to colonize chicks and led to further characterisation of a SPI-4 locus , required for infection of calves , but which appeared to be less important in the intestinal colonization of pigs in a further STM screen with this library [7] . There are various limitations associated with STM , including the size of mutant pools given the number of unique tag sequences that it is practical to use , and the fact that STM may not lead to the identification of mutations that cause only a small reduction in fitness to survive within a host [8] . An alternative approach employs DNA microarray technology to screen a library of transposon mutants . The parallel nature of microarrays allows the simultaneous screening of much larger pools of mutants than is possible using STM . The use of a custom transposon containing an outward facing promoter allows the in vitro production of labelled transcripts that are homologous to the regions flanking the sites of transposon insertion . These can be used to hybridise to the microarray , bypassing the need for a PCR amplification step that can lead to non-reproducible results [9] . Microarray-based screening approaches have recently been applied to the study of S . Typhimurium infection of mice . Using such methods , Chan et al . [10] identified S . Typhimurium mutants that cannot survive in RAW 264 . 7 macrophage-like cells . Within the population of negatively selected mutants there was a significant overrepresentation of genes located in SPI-1 ( 36/395 ) and SPI-2 ( 51/395 ) indicating their importance in this infection model . Screening the same transposon library through BALB/cJ mice similarly revealed significant overrepresentation in the negatively selected population of SPI-2 ( 27/187 ) and genes involved in lipopolysaccharide ( LPS ) biosynthesis ( 9/187 ) , with an overrepresentation of SPI-1 genes ( 5/187 ) that fell marginally short of significance at the 5% level . In a follow-up study , Lawley et al . [11] used similar methods to study genes required by S . Typhimurium to survive for up to 28 days in the spleens and livers of Nramp1r mice , a model of the carrier state in human typhoid fever . In this model genes from SPI-1 to SPI-6 were important for systemic infection . The involvement of SPI-1 was a novel finding since that island had previously been thought to be involved only in the gastrointestinal phase of infection . It is possible that SPI-1 genes may be required for S . Typhimurium to re-establish intracellular growth during persistent infection . Other genes important for long-term systemic infection included LPS genes , fimbrial genes , and horizontally acquired genes on the virulence plasmid and within prophage elements . Here we describe transposon mediated differential hybridisation ( TMDH ) [12] , and its application for the simultaneous , genome-wide identification of genes required for growth of S . Typhimurium in an acute infection of BALB/c mice over 2 days . Our method improves significantly on earlier studies through the use of high-density tiling microarrays and a novel bioinformatics algorithm to determine the positions of transposon insertions with a high degree of accuracy . This has allowed the unambiguous identification of transposon mutants within 2824 S . Typhimurium genes . Many of these are attenuated in the mouse typhoid model , and may encode potential targets for the development of novel antimicrobial therapeutics . We also show that defined deletion mutants of some of the targets identified in the TMDH screen can act as novel live-attenuated vaccines that protect against subsequent challenge with wild-type S . Typhimurium . The technology we present here is generic and can be applied to any pathogen for which there is a genome sequence , a method of generating large numbers of insertion mutants ( in this case , transposition ) and a model of infection in which to identify mutants that are attenuated for virulence .
The TMDH procedure is outlined in Figure 1 . A library of around 10 , 000 transposon mutants was generated using custom Tn5 and Mu transposons , containing outward-facing T7 and SP6 promoters . Genomic DNA was isolated from the library , and in vitro transcription was induced from the T7 and SP6 promoters in the presence of fluorescently-labelled dNTPs . DNaseI was used to remove the genomic DNA , leaving labelled RNA run-offs , which were hybridised to high-density tiling microarrays . Analysis of the microarray data allows the genomic position of the transposon insertions to be determined . This process was performed for the original library ( input ) , and for mutant pools recovered from the livers of duplicate intravenously ( i . v . ) infected mice ( output ) . Mutants that are present in the input pool but which are absent or less prevalent in the output pools are inferred to be attenuated in vivo . TMDH transposons were based on Tn5 and Mu transposasome constructs from Epicentre ( EZ:Tn5 R6Kγori/KAN-2 Transposon and HyperMu R6Kγori/KAN-1 Transposon ) . The constructs were adapted for use in TMDH by the addition of outward-facing T7 and SP6 promoters , which allow the generation of both left- and right-arm RNA products corresponding to the regions flanking the site of transposon integration in genomic DNA [13] , and incorporation of homing endonuclease recognition sites for I-Sce-I and PI-Psp-I . These are rare-cutting enzymes and incorporation of their recognition sites into the transposons permits the introduction of the sites into a bacterial genome following transposition . Following digestion of transposed genomic DNA with the rare-cutting enzyme , a ligation-capture method can be carried out to rescue transposon ends and obtain flanking sequence data ( see below ) . The Tn5 and Mu TMDH transposons were cloned into pBAD-TOPO ( Invitrogen Life Technologies ) and transformed into TOP10 E . coli chemically competent cells according to the Invitrogen protocol , with selection on LB agar ( Sigma-Aldrich ) plates supplemented with 30 µg/ml kanamycin ( Sigma-Aldrich ) . Colonies were picked and grown in L-Broth ( Sigma-Aldrich ) and plasmids were isolated using a Qiagen QIAprep spin miniprep kit . The Tn5 and Mu constructs were sequenced and have been deposited into the EMBL database ( accession numbers AX828670 and FN394965 ) . The plasmids were transformed into electrocompetent S . Typhimurium SL1344 with selection on 30 µg/ml kanamycin . Single colonies were picked and grown up further in order to purify the plasmids using a Qiagen QIAfilter Mega Plasmid Prep , which produced 2 mg of plasmid in 2 ml of TE ( pH 8 . 5 ) . Tn5 and Mu TMDH libraries of around 5 , 000 mutants each were generated in S . Typhimurium SL1344 . 100 µg of Tn5 or Mu pBAD-TOPO TMDH constructs were digested overnight at 37°C with XmnI and NcoI ( New England Biolabs ) in NEB buffer 2 and BSA in a final volume of 200 µl . The entire digest was then run out on a 0 . 8% agarose gel , the insert bands were cut out and the DNA was extracted from the gel using the Qiagen QIAquick Gel Extraction kit and eluted in 50 µl TE ( pH 8 . 5 ) . The purified Tn5 and Mu transposon DNA was used in the Epicentre in vivo transposition protocol using EZ-Tn5 Transposase or HyperMu™ MuA Transposase as appropriate . Transposon DNA was added to transposase and glycerol in a 1∶2∶1 ratio and incubated at room temperature for 1 h . The transposasome complex was then electroporated into electrocompetent SL1344 cells using a 2 mm cuvette , at 200 Ω , 25 mF and 12 kV/cm . Recovery was in 1 ml SOC medium ( Gibco ) at 37°C for 1 h then the bacteria were plated out on Tryptic Soy Agar ( TSA , Oxoid ) , supplemented with 50 µg/ml kanamycin , and grown overnight at 37°C . Colonies were picked and grown overnight at 37°C in 0 . 5 ml L-Broth supplemented with 25 µg/ml kanamycin , in 2 ml 96-well blocks ( Fisher Scientific ) . Overnight cultures of 5 , 184 Tn5 and 5 , 184 Mu TMDH transposon mutants were stored at −80°C in 20% glycerol in L-Broth in 96-well microtitre plates . 96 of the Tn5 mutants were streaked on LB agar plates supplemented with 100 µg/ml ampicillin to check for sensitivity . All were sensitive , showing that there had been no carry-through of undigested pBAD-TOPO construct into the gel-purified transposon DNA . Tn5 mutants 1 to 50 were screened on microscope slides for agglutination with anti-O4 antiserum ( Remel Europe Ltd . ) . All mutants agglutinated with the antiserum indicating the bacteria expressed an intact O-antigen following electroporation . To prepare inocula for in vivo TMDH in mice , 5 , 184 Tn5 and 5 , 184 Mu TMDH transposon mutants were grown up individually in 1 ml L-Broth in 2 ml 96-well blocks , at 37°C overnight . Cultures were combined into 20 pools of 480 mutants and 2 pools of 384 mutants for inoculation into mice . 3 ml of each pooled culture was removed for measurement of OD600 in order to estimate bacterial numbers . The remainder was used for preparation of input-pool genomic DNA ( see below ) . Cells from 3 ml cultures were recovered by centrifugation ( 4300×g for 10 min ) , resuspended in 3 ml phosphate buffered saline , pH 7 . 5 ( PBS ) and diluted to give an appropriate number of bacteria for the inoculum . An aliquot of the inoculum was plated on LB agar in triplicate in order to obtain an accurate viable count . A dose of 106 colony-forming units ( CFU ) was chosen empirically as sufficient to prevent random dropout of mutants , and was inoculated in 0 . 2 ml PBS into the tail veins of duplicate six- to eight-week-old BALB/c mice ( Harlan ) . All animal procedures were carried out in accordance with the Animals ( Scientific Procedures ) Act ( 1986 ) . Spleens and livers were recovered 2 days post-infection and homogenised in 10 ml distilled water , then 100 µl aliquots were plated for viable counts on LB agar . The remainder of the homogenate was plated on three 50 ml LB agar plates and incubated at 37°C overnight . For each pool of mutants , genomic DNA from the input pool and the two liver output pools was prepared . For the input pools , bacteria were collected from 200 ml of pooled overnight culture by centrifugation in a benchtop centrifuge at 4300×g at 15°C for 10 min . For the output pools , bacteria from confluent plates were harvested by adding 10 ml of L-Broth to each plate , then bacterial suspensions were pooled and vortexed , and cells from a 20 ml aliquot were recovered by centrifugation at 4300×g for 10 min . In each case , the pellet was resuspended in 20 ml of Tris ( 10 mM , pH 8 ) EDTA ( 10 mM ) ( TE ) , then 400 µl of 10 mg/ml lysozyme ( Sigma-Aldrich ) in water was added and incubated at 42°C for 30 min . 200 µl of Qiagen Proteinase K , 40 µl of Qiagen RNaseA , and 2 ml of 10% ( w/v ) N-lauryl sarcosine ( Sigma-Aldrich ) were added and incubated for 1 hr ( input pools ) or 2 hr ( output pools ) , or until completely lysed ( clear ) at 50–55°C . Lysates were extracted by adding 1 volume of buffered phenol ( Sigma-Aldrich ) , shaking , and centrifuging at 7000×g for 10 min . The aqueous layer was removed and extracted with 1 volume of phenol∶chloroform∶isoamyl alcohol ( IAA ) ( 25∶24∶1 ) , then once more with chloroform∶IAA ( 24∶1 ) . DNA was precipitated with 2 volumes of ethanol , recovered , washed in 70% ( v/v ) ethanol , then transferred to a 1 . 5 ml tube containing 0 . 5 ml of TE and left at 4°C to dissolve . The DNA concentration and A260/A280 ratio were measured on a NanoDrop 1000 spectrophotometer ( Thermo Scientific ) . Genomic DNA was also prepared similarly from a 50 ml wild-type S . Typhimurium SL1344 culture grown overnight at 37°C in L-Broth for use as an untransposed control ( see below ) . 10 µg of genomic DNA from each TMDH input and output pool , and from the untransposed control DNA , was digested using RsaI ( Promega ) overnight at 37°C . Digested DNA was cleaned using a Qiagen QIAquick PCR Purification Kit and eluted in 30 µl RNase-free water ( Qiagen ) . The DNA concentration and A260/A280 ratio were measured on a NanoDrop 1000 spectrophotometer . Equal amounts of digested DNA from 2 or 3 pools of mutants were combined to give 8 sets of 960–1440 mutants each for microarray analysis ( see Table 1 ) . In vitro transcription ( IVT ) was induced from the transposon T7 and SP6 promoters using 500 ng of genomic DNA in a 20 µl MEGAscript T7 Kit or MEGAscript SP6 Kit reaction ( Ambion Inc . ) , with half the UTP replaced with 5- ( 3-aminoallyl ) -UTP ( aa-UTP; Ambion Inc . ) . RNA run-offs were treated with TURBO DNase ( Ambion ) and purified on Qiagen RNeasy MinElute columns . Purified RNA from input or output pools was post-labelled with Cy5 and RNA from wild-type ( untransposed ) control DNA with Cy3 , using CyDye Post-Labelling Reactive Dye Packs ( GE Healthcare ) , and the reactions were stopped with 4 M hydroxylamine ( Sigma-Aldrich ) . The resultant labelled RNA was purified again on Qiagen RNeasy MinElute columns , and used for hybridization to DNA microarrays . A set of 60-mer oligonucleotide probes was designed based on the S . Typhimurium LT2 genome sequence ( accession numbers AE006468 and AE006471 ) . The probes were spaced approximately every 100 bases on both strands across the whole genome , with the exception of repetitive regions for which unique probes could not be designed . As a different S . Typhimurium strain , SL1344 , was used for the TMDH experiment it was necessary to identify the positions of the SL1344 genome that corresponded to each probe . The SL1344 genome sequence and preliminary annotation was obtained from the Wellcome Trust Sanger Institute ( http://www . sanger . ac . uk/Projects/Salmonella ) . Each microarray probe sequence was used as the query in a blastn search of the SL1344 genome sequence , and was considered to match uniquely if the top hit showed >80% identity over >50% of the length of the probe , and the second hit did not fulfil both those criteria . Uniquely matching probes were used in the TMDH analysis , with their hybridization position on the SL1344 genome determined from the BLAST result . Any probes that contained an RsaI restriction site within the central 30 bases were omitted from the analysis , since the probe signal could reflect transcript from either side of the restriction site . Custom microarrays with 2×105K features per slide were obtained from Agilent Technologies . Each Cy5-labelled RNA run-off from an input or output pool was combined with an aliquot of Cy3-labelled control RNA generated from the same promoter , and fragmented at 60°C for 30 minutes using Agilent Fragmentation Reagent . Fragmented RNA was then hybridised to microarrays in Agilent Gene Expression HI-RPM hybridisation buffer at 65°C for 17 hours in Agilent hybridisation chambers and backings in an Agilent hybridisation oven . Following hybridisation , the arrays were washed with Agilent wash buffers 1 and 2 according to the manufacturer's instructions , followed by one wash in acetonitrile ( Sigma-Aldrich ) and drying in Agilent Stabilisation and Drying Solution . The arrays were scanned using an Agilent G2565BA scanner using an extended dynamic range of 10% and 100% PMT , and the images analyzed using the Agilent Feature Extractor software version 9 . 3 . 5 . 1 . The raw microarray data were uploaded to ArrayExpress ( accession number E-MEXP-2076 ) . A separate microarray experiment was performed for each set of 960–1440 mutants . Each experiment consisted of six microarrays , one each for the T7 and SP6 RNA run-offs from the input pool and from the two biological replicate output pools . The IVT product from each mutant pool was labelled with Cy5 and hybridised to the microarray together with the Cy3 labelled IVT product from an untransposed control strain . The untransposed control has two purposes: it allows normalisation of the T7 and SP6 signals without any problems associated with dye bias , and acts as a control to allow identification of the sites of transposon insertion in the input pool . Microarray data were imported into R [14] using the Bioconductor package Limma [15] . The raw signals were normalised to account for sequence-dependent variation using the Naef and Magnasco method [16] , [17] , implemented in R by Royce et al . [18] . Briefly , this procedure uses a linear model to predict the contribution to the probe signal intensity of each possible nucleotide at each position in the probe sequence . A predicted log signal intensity is computed for each probe based on the model , and subtracted from the observed log signal intensity . Since the transcripts from each transposon promoter are likely to influence the signal from multiple probes , the individual probe signals cannot be treated as statistically independent . To circumvent this problem the data from all the probes within a single RsaI restriction fragment were summarised to give a single data point . This procedure is illustrated in Figure 2 . A single transposon insertion within a restriction fragment is expected to produce transcripts that will hybridise to probes 5′ from the transposon on both strands ( “on” probes ) . All other probes in the restriction fragment should produce only a background signal ( “off probes” ) . The most likely position of a transposon is therefore determined as the position where the sum of the signals from the “on” probes minus the sum of the “off” probe signals is maximal , and the summary score is calculated based on the “on” probe signals . For large restriction fragments it is not appropriate to use an average of all these signals as a summary score , since signal intensities tend to decrease for probes that are distant from the transposon . A sliding window approach was used , with the geometric mean of the signals calculated for each window of three consecutive probes . The highest value was used as the summary score for that restriction fragment . The situation is complicated somewhat if more than one transposon is present within a single restriction fragment within the same array set . It is possible to distinguish between two inserts within the same fragment if they are in opposite orientations , since the transcripts that correspond to the same strand will be derived from different promoters and hence will be detected on different arrays and not interfere with each other . For this reason each restriction fragment is assigned two summary scores , one for each possible transposon orientation . If multiple transposons are present in the same orientation then the signals will interfere , and it may be impossible to determine the location of each insert . However , since no more than 1440 mutants were investigated on the same array , this is unlikely to represent a significant problem for the analysis of our data . To determine the position of the transposon insertions present in the input pool of each set of mutants , summary scores were calculated for the Cy5 signals from the Input T7 and SP6 arrays , and separately for the Cy3 ( control ) signals from the same arrays . The summary scores are analogous to data derived from a traditional expression microarray , and can be analysed in a similar manner . To do this , the input and control summary scores were converted to the red and green signals of a Limma RGList object [15] . The signals were normalised using a loess curve , and displayed on a plot of log2 signal intensity ratio ( M ) against log2 average signal intensity ( A; see Figure 3 ) . An M cut-off value of 2 was used to identify summary scores likely to correspond to transposon insertions . To compare the input and output pools , a similar procedure was followed . In this case the untransposed control signals were used as a standard reference to normalise between arrays , and the ratio of sample:reference signal for each probe was used to calculate the summary scores . This was done separately for the input and two output pools , and the resulting scores analysed in the same manner as single-colour Affymetrix data in Limma [15] . The scores were normalised using a loess curve , as implemented in the Bioconductor package affy [19] , and log2 fold change ( logFC ) and P-values were calculated using the linear models implemented in Limma , applying the method of Benjamini and Hochberg [20] to account for multiple testing . These logFC values were normalised between array sets by dividing by the median absolute deviation . The normalised logFC scores and P-values from each set were combined and used to assess the in vivo survival of each of the mutants previously identified within the input pool ( see Table S1 ) . As part of the TMDH development process , 50 Tn5 mutant colonies were selected from Set6 for DNA sequencing-based verification of the position of the transposon insertion using a ligation capture procedure . The mutants were grown up individually in 2 ml TSB ( Oxoid ) in the presence of 50 µg/ml kanamycin overnight at 37°C and 200 rpm . 1 . 5 ml of each was used to prepare chromosomal DNA using the Qiagen DNeasy tissue kit . 5 µl of each of the fifty chromosomal preps was digested with EcoRV ( NEB ) in a final volume of 20 µl , overnight at 37°C . The EcoRV was heat inactivated at 80°C for 20 minutes . The 20 µl digest was then re-ligated in 100 µl final volume using Gibco T4 DNA ligase for 48 hours at 4°C . Each re-ligation was then individually cleaned up using a Qiagen gel extraction spin column and eluted in 50 µl water . 4 µl of cleaned up , re-ligated , EcoRV-digested chromosomal DNA from each mutant was electroporated into 40 µl of electrocompetent pir+ cells using a 1 mm cuvette , 100 Ω , 25 µF and 20 kV/cm , outgrowth was in 1 ml SOC medium ( Gibco ) at 37°C for 1 h , each of the 50 transformations was then recovered by centrifugation and the pellet resuspended in 50 µl L-Broth and plated out on LB agar supplemented with 30 µg/ml kanamycin , and incubated overnight at 37°C . From each plate where colonies grew , one colony was picked and grown up in 5 ml L-Broth plus 30 µg/ml kanamycin at 37°C overnight . 46 Qiagen minipreps were carried out and 10 µl plasmid DNA was sequenced according to the Beckman CEQ protocol using a transposon-specific primer . Salmonella sequence data was obtained for 37 of the mutants . Additionally , 13 transposon mutants ( see Table S2 ) were selected from Set1 and Set3 based on the results of the microarray analysis . For each mutant , the region flanking the transposon was amplified by PCR , using a transposon-specific primer and a gene-specific primer . The gene-specific primer was designed to anneal ∼200 bp from the approximate position of the transposon as predicted during the automated microarray analysis . PCR was performed using the Expand High Fidelity PCR System ( Roche ) on a Biometra T3000 thermocycler . Reactions contained 0 . 5 mM dNTPs ( Bioline ) , 1 µM each primer ( Sigma-Genosys ) , 100 ng template DNA , 1× Expand PCR buffer and 0 . 75 µl of Expand polymerase mix in a total volume of 50 µl . Bands matching the expected products were excised from a 0 . 8% agarose ( Invitrogen ) gel , extracted with a QIAquick Gel Extraction kit ( Qiagen ) , purified further with a QIAquick PCR Purification kit ( Qiagen ) , and then sequenced directly using the transposon-specific primer by the Department of Biochemistry DNA Sequencing Facility at the University of Cambridge . The sequence data were examined using FinchTV v 1 . 4 . 0 ( Geospiza ) , then used as queries in blastn searches against a database containing the SL1344 genome , together with the sequences of the Mu and Tn5 transposons . For each mutant , the transposon could be identified as Mu or Tn5 based on which of those sequences gave a higher scoring BLAST hit . The position of the highest-scoring alignment with the SL1344 genome allowed the insertion location and orientation of the transposon to be determined . Defined deletion mutants of S . Typhimurium were constructed by a modification of the ET-cloning procedure [21] . Genes to be deleted were replaced with a kanamycin resistance cassette from pUC4Kan ( Amersham ) . PCR was used to amplify the antibiotic resistance cassette with 5′ and 3′ 60 bp homology arms complementary to the flanking regions of the gene to be deleted . PCR products were electroporated into S . Typhimurium LB5010 [22] containing the plasmid pBADλred expressing the phage lambda genes exo , bet and gam under an inducible arabinose promoter , having induced these cells with 0 . 2% ( w/v , final concentration ) arabinose ( Sigma-Aldrich ) prior to making them electrocompetent . Candidate mutant colonies were selected on LB agar plates supplemented with 25 µg/ml kanamycin . Verification of allelic replacement was carried out by a test PCR using primers designed to regions 150 bp upstream and downstream of the gene of interest . The PCR products were then TA-cloned into pCR®2 . 1-TOPO ( Invitrogen-Life Technologies ) following the manufacturer's instructions , and the resultant plasmids were sequenced to confirm the DNA sequence at the junction of the antibiotic resistance cassette and the disrupted gene . The mutations generated in S . Typhimurium LB5010 were transduced using bacteriophage P22 [23] into strain SL1344 for in vivo virulence studies [24] . Transductants were selected on LB agar supplemented with 25 µg/ml kanamycin and were screened for agglutination with anti-O4 serotype-specific antiserum ( Remel Europe Ltd . ) in addition to verifying the presence of the mutation by PCR and sequencing , as described above . Six- to eight-week-old BALB/c mice ( Harlan ) were used for infection studies using the individual defined deletion mutants . Bacteria were inoculated into L-Broth , supplemented with 25 µg/ml kanamycin if appropriate , and left to stand overnight at 37°C . Bacteria were harvested and re-suspended in PBS and adjusted as appropriate ( usually 5×103 CFU ml−1 for i . v . infections ) and the viable count of the inoculum was confirmed by plating serial dilutions on LB agar . Mice were inoculated with 0 . 2 ml of the appropriate dilution of the bacterial suspension via the tail vein . Three or four mice per group were killed by cervical dislocation at each time-point post-infection . The spleens and livers of infected mice were removed and placed in 10 ml sterile distilled water and homogenised using a Stomacher® 80 Lab System ( Seward ) . Viable mutants were quantified by plating various dilutions of homogenised organs in LB agar . For protection experiments , six- to eight-week old BALB/c mice ( Harlan ) were immunised i . v . with 1 . 0×105 CFU per mouse of the defined deletion mutant , grown as above , and 4 months later challenged i . v . with 104 CFU per mouse of the virulent parent strain , SL1344 . One day prior to the challenge two mice per group were culled and spleens and livers were plated on LB agar to assess the pre-challenge bacterial counts in the organs . When no bacteria were present in these pre-challenge counts , the challenge experiment was undertaken . Non-immunised , age-matched mice were challenged with the same dose of virulent bacteria at the same time as the immunised mice . Three to eight mice per group were killed at each time-point post-challenge and organs plated for viable counts of bacteria as above .
A list of the transposon mutants identified from the microarray data , together with normalised log2-fold change ( attenuation ) scores and P-values is shown in Table S1 . In the table and below , all SL1344 genes are referred to using the name of their LT2 homologue . The list is also available as an online database at http://www-tmdh . vet . cam . ac . uk . This database includes a facility to inspect manually the normalised microarray data and its relationship to the genome sequences of S . Typhimurium SL1344 and LT2 ( see Figure 4 ) . This provides a powerful tool to confirm the position of transposons determined by the automated algorithm . A graphical representation of the distribution of attenuation scores is shown in Figure 5 . The majority of transposon insertions do not affect virulence in this mouse model and show an attenuation score not significantly different from 0 . Of the mutants that significantly differ between the input and output pools , the majority are attenuated and under-represented in the output , with an attenuation score of <0 . We infer that these mutants have a transposon that disrupts a gene important for infection . A few mutants are over-represented in the output pool relative to the input , suggesting that they may have a mutation that leads to an increase in competitiveness during the infection process . The TMDH analysis procedure allows the positions of transposons to be determined to within a region of around 200 bp . From a total of 10368 mutants , the position of the transposon insertion could be identified using TMDH for 8533 . Of these , 6108 could be unambiguously mapped to 2824 different S . Typhimurium genes . To support the accuracy of the algorithm used to predict transposon insertion sites from the microarray data , sequence data from the regions flanking 50 transposons were obtained ( see Methods ) . 13 of these were selected for sequencing as there was a clear indication of the location of the transposon from the microarray data . This set was chosen to include several mutants that appeared to be attenuated in vivo . The other 37 mutants chosen for sequencing were selected randomly from set 6 . BLAST searches against the SL1344 genome using the obtained sequence data allowed the position and orientation of the transposon to be accurately determined . The results of this analysis are shown in Table S2 . From the 50 transposons investigated , the positions of 46 were within the range predicted by the TMDH analysis . The remaining 4 were situated in positions that did not allow their detection using TMDH , due to the distribution of RsaI restrictions sites and microarray probes ( see Discussion ) . Defined deletion mutants were constructed in SL1344 for 47 different genes . These genes were selected to include a representative range of attenuation scores . The in vivo growth of each of these was assessed by i . v . infection of BALB/c mice , and compared to infection with the parental SL1344 as a control ( see Table S3 ) . Figure 6 shows a plot of the mean bacterial viable counts in the mouse organs on day 3 post-infection , as a function of the mean attenuation score for all the transposon mutants that were unambiguously identified as being within that gene . This plot shows a significant correlation ( R2 = 0 . 54 , P = 1 . 7×10−9 ) . SL1344 trxA and SL1344 atpA were chosen from the TMDH screen as live-attenuated vaccine candidates . BALB/c mice were immunised i . v . with 1 . 0×105 CFU per mouse of SL1344 trxA or SL1344 atpA . SL3261 , an aroA mutant of SL1344 which is a well-characterised live-attenuated vaccine strain [24] , was used as a control . After 4 months , these mice and age-matched , un-immunised controls were challenged with an i . v . dose of 1×104 CFU SL1344 per mouse . Figure 7 shows bacterial loads in spleens and livers following this intravenous challenge with SL1344 . The bacterial counts are represented as the geometric means and standard errors of one representative experiment from two with similar results . Immunisation with SL1344 trxA and SL1344 atpA resulted in protection of mice against i . v . challenge with the virulent parent strain , as shown by the fact that the viable counts in the organs of immunised animals were lower by several fold than the viable counts in the control un-immunised animals . Infections in un-immunised mice were only allowed to proceed to day 4 before mice were too ill to survive and were culled . In contrast , immunised mice were still well 14 days after challenge with SL1344 showing that atpA and trxA mutants when delivered intravenously can be used as live-attenuated vaccine strains to protect against a subsequent intravenous challenge . We also investigated the potential of a tolA mutant to protect against virulent challenge . tolA was selected from the TMDH screen , and encodes part of the Tol-Pal complex in the inner membrane of Gram negative bacteria . SL1344 tolA was attenuated in mice via the oral and i . v . routes and i . v . immunisation with SL1344 tolA provided significant protection against subsequent challenge with SL1344 , delivered i . v or orally [25] .
We have developed TMDH , a microarray-based screen that exploits customised transposons with outward-facing promoters [13] to allow identification of the disrupted genes in pools of transposon mutants [12] . High-density tiling microarrays and a novel bioinformatic algorithm are used to locate the site of transposon insertion with high resolution . This technology is generic with many potential applications . One possibility is the use of large transposon pools to saturate potential sites of insertion , and hence infer genes essential for survival and replication as those which do not contain transposons [26] . An alternative application is comparative in vivo TMDH , which exploits the TMDH method to compare a pool of mutants harvested from an infection model with the same pool grown in vitro , to identify mutants that are attenuated in vivo . Again this is a generic technology and could be applied to the study of any bacterial pathogen for which a genome sequence , a suitable transposon and a reproducible model of infection are available . In the work presented here we have used TMDH to investigate genes important for S . Typhimurium SL1344 infection of BALB/c mice . There are several previous examples of the use of DNA microarrays to investigate the distribution of transposon insertions in bacteria . The majority of these employ large PCR-product microarrays , with a small number of probes for each gene [9] , [10] , [27]–[31] . The use of high density tiling microarrays offers a significant improvement over these methods , allowing sub-genic resolution of individual transposon insertion sites . An additional advantage of tiling arrays derives from having probe coverage of the entire genome . This removes the reliance on annotated genome features , reduces the possibility that transposons will be in regions not covered by the microarray , and allows intergenic regions that may be of interest to be determined . One previous study has reported the use of genome tiling microarrays to identify the position of transposons within a bacterial genome [32] . However , the analysis procedure used in that study was a straightforward examination of the microarray signals using arbitrary signal-strength cut-offs and manual inspection . This sort of analysis would not be suitable for interrogation of large pools of mutants such as those screened in the current work . Our analysis method is more sophisticated , allowing an unbiased quantitative measure of the relative fitness of each transposon mutant to be determined . In total , 10368 transposon mutants were obtained and investigated using TMDH . From these , 8533 ( 82 . 3% ) putative insertion sites were identified during the automated microarray analysis . The remainder includes transposons that could not be located as they were inserted in regions of the SL1344 genome not covered by the microarray ( including repetitive regions and plasmids or strain-specific islands not present in LT2 ) . Other undetected transposons include those that inserted within small RsaI restriction fragments , and some that could not be unambiguously located due to the presence of two or more transposons in the same orientation within the same restriction fragment . It would be possible to increase the recovery rate by repeating the TMDH procedure using additional restriction enzymes and alternative microarray designs . The locations of the transposon insertion sites were independently verified by obtaining DNA sequence data from the regions flanking the insert for 37 randomly selected mutants . BLAST searches of the SL1344 genome using the sequence data allowed the location and orientation of each of the transposons to be unambiguously determined . 33 ( 89 . 1% ) of these were in positions within the range identified by the automated TMDH analysis . The analysis of the remaining 4 insertion events highlights some of the limitations inherent in the method: two were incorrectly identified due to the presence of additional transposons in the same orientation within the same restriction fragment , one was not identified as the insertion was within a small restriction fragment , and one gave a low signal that did not exceed the threshold value used to filter false positives from the dataset . Nevertheless , the correct location of a high percentage of the transposons suggests that the technology is suitable for use as a screening method . Additional support was obtained by PCR amplification of the regions flanking 13 transposons , using a transposon-specific primer and one designed to be adjacent to the insertion position predicted by the automated analysis . Again , sequence data were obtained using a transposon-specific primer and the position of the transposon relative to the SL1344 genome was determined using BLAST . For this set , all 13 transposons were located within the range predicted by the automated analysis . The TMDH analysis procedure distils the microarray data into a form equivalent to a standard expression microarray , and allows the direct comparison of the input and output signals to give a fold-change . This is expressed as a log2 value and referred to as an “attenuation score” . Negative values indicate that the mutant is attenuated , while positive values suggest that the mutant has a competitive advantage in vivo . Use of replicate mice additionally allows the estimation of a P-value . The data for all 8533 mutants are available in Table S1 , ordered by attenuation score . Importantly , the top ( most attenuated ) end of the list includes many genes that have well established roles in virulence . These include numerous genes that encode structural components of the SPI-2 T3SS or associated regulators , chaperones and secreted effector proteins . The requirement of this system for S . Typhimurium infection and persistence is well established [4] , and virtually all the transposon mutants within this region are highly attenuated ( see Figure 8 ) . Interestingly , transposon insertions within the other SPI regions present in S . Typhimurium are not identified as strongly attenuating in our screen ( see Figure 9 ) – this is probably a consequence of the chosen mode of infection ( i . v . ) and the use of the mouse as the model species . Beside SPI-2 , other well established virulence-related genes that are identified in our dataset include: the aromatic amino acid biosynthesis ( or pre-chorismate ) pathway genes aroA , aroC and aroD , mutants of which are prototype live attenuated Salmonella vaccines [24] , [33] , [34]; the purine biosynthesis genes purA , purD , purF , purG , purH , guaA and guaB [3] , [35] , [36]; LPS core biosynthesis genes [37]; O-antigen biosynthesis genes [38]; and the virulence plasmid spv genes [39] . Presumably because of the requirement for the spv operon , plasmid partitioning is required for virulence , and both parA and parB mutants are attenuated . The latter is surprising since parB was described as being dispensable for partitioning in pSLT [40] . As might be expected , bacteria with mutations in many genes previously associated with resistance to acid , high temperature and oxidative stress are unable to survive in vivo . Some of these are known to be involved in virulence , including slyA [41] , [42] , htrA ( degP ) and degS [21] , [43] , [44] . Other genes that have been associated with stress responses but not previously demonstrated to be attenuated in vivo include the tRNA base modification gene miaA [45] . The fatty acid biosynthesis gene fabF ( atrB ) is associated with the acid tolerance response in S . Typhimurium LT2 , but previous investigation of a mutant in the SL1344 background did not find any significant effect on acid tolerance or virulence [46] . In contrast , our results suggest that transposon insertions within this gene are attenuating . This may indicate that the fabF mutation leads to a mild effect on virulence that is only evident in competitive assays . DNA recombination and repair is also important , with our data confirming the importance of dam [47] , recA [48] and recG [49] for virulence . Mutations in the genes recD , and to a lesser extent recF , recJ and recQ also appear to be attenuating from our data , however previous studies have not found recD , recF and recJ mutants to be attenuated during individual infection assays [50] , [51] . Other systems that are important for infection and persistence in our model include carbon metabolism , with attenuating mutants found in a number of genes involved in glycolysis ( pgm , ptsG , crr and tpiA ) , the TCA cycle ( sucABC and sdhABC ) , mannose utilisation ( mtlA , mtlD , rfbM and manA ) and oxidative phosphorylation ( the nuo locus , atpABFI , cyoAB and cydA [52] ) . Zinc transport ( znuABC ) [53] and phosphate transport ( pstABCS ) are also important for virulence , as is pyrimidine metabolism ( pyrBCDE , and carAB ) . Mutants of two genes involved in the thioredoxin system , trxA and trxB , are identified as attenuating by TMDH . Of these trxA is known to be important for infection of mice [54] . trxB mutants are not attenuated in individual infections [54] but do show reduced intracellular proliferation , which may account for their attenuation in the TMDH competitive infection screen . Genes identified in our screen that had not previously been associated with Salmonella virulence include components of the Tol-Pal system that contributes to membrane stability ( tolA and tolB ) [25]; yqiC , which encodes a putative cytoplasmic protein with no functionally characterised homologues; the putative regulator encoded by STM4030; ychK , which encodes a patatin-like lipolytic enzyme; and ybjT , which encodes a putative nucleoside-diphosphate-sugar epimerase . Interestingly , the patatin-like ExoU is a type III-secreted cytotoxin and virulence factor of Pseudomonas aeruginosa [55] , and the E . coli O157:H7 homologue of ybjT is induced during human infection [56] . Although the TMDH screen is primarily intended to identify attenuating mutants , it also may identify mutants that perform better in vivo than in vitro , with attenuation scores significantly greater than 0 . The genes disrupted in these “hypercompetitive” mutants may impair the infection process in wild-type strains . There are fewer examples of such mutants in our dataset than attenuating mutants ( see Figure 5 ) – intuitively it is easier to see how the infection process may be disrupted than enhanced – and many of the genes at this end of the list have poor P-values . Nevertheless a few plausible candidate hypercompetitive mutants are identified . These include several genes involved in flagellar biosynthesis ( flgCEFKI , flhB and fliFHIJKRT ) , mutants of which are known to display enhanced virulence [57]; the global transcriptional regulator gene fnr , mutants of which show enhanced entry into and proliferation within HEp-2 epithelial cells [58]; dsdA , which encodes a positive regulator of D-serine deaminase and which when mutated enhances the ability of uropathogenic E . coli to infect the bladder and kidneys of mice [59]; and araH , the loss of which is associated with virulence in Burkholderia mallei [60] . There are also several putative hypercompetitive mutants associated with Type II secretion ( hofQ , hofC , hopD and ppdA ) . 47 of the genes identified in the TMDH screen were subjected to further investigation by generating defined deletion mutants and performing single mutant infection assays . The set of 47 genes was chosen to reflect a range of attenuation scores , and to include a number of potential live vaccine candidates . It should be noted that some mutants may be attenuated in parallel infection screens such as TMDH due to their inability to compete with the other mutants in the pool , but not show any evidence of attenuation during a single mutant infection . The reverse is also possible , since a mutant may be able to overcome its deficiency in the presence of other genotypes , for example through the uptake of a compound secreted by the other bacteria . Also , within the TMDH screen , different transposon mutants within the same gene are not necessarily comparable . The transposon may be inserted in a different position within the gene , or a mutant may perform differently in the context of a different pool of mutants . Despite these caveats , Figure 6 demonstrates a significant correlation between the average attenuation score and the average log10 colony counts from day 3 of the single mutant infection experiments . This indicates that TMDH attenuation scores accurately reflect the levels of attenuation seen when defined deletion mutants are generated and investigated individually . TMDH therefore represents an effective screen for genes that are important for infection . Some of the attenuating mutants selected by TMDH were investigated further for their ability to elicit an immune response that would be protective against subsequent challenge with a wild type strain . atpA and trxA defined deletion mutants protected against wild-type challenge ( Figure 7 ) and further details of similar experiments for another promising candidate , tolA , have recently been published [25] . This demonstrates the power of TMDH as a screen for identifying mutants that act as novel live-attenuated vaccine strains . | Salmonella Typhimurium infection of mice is an established model of systemic typhoid fever in humans . Mutations that inactivate genes that are important for virulence produce attenuated S . Typhimurium bacteria that have potential for use as live vaccines . To investigate the infection process we have produced a large pool of random insertion mutants , and developed a novel microarray-based technology , Transposon Mediated Differential Hybridisation ( TMDH ) , that allows us to determine the gene disrupted by each insertion . Comparison of data obtained from the mutant pool grown in laboratory culture ( input ) with equivalent data produced after passage of the pool through mice ( output ) enables genes that are important for the infection process to be determined , since they are absent or less prevalent in the output pool . We have constructed defined deletion mutants of several of the candidate genes identified in the TMDH screen , and have shown that they are attenuated for virulence and effective live vaccines . | [
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] | 2009 | Comprehensive Identification of Salmonella enterica Serovar Typhimurium Genes Required for Infection of BALB/c Mice |
Staphylococcus aureus is a prominent bacterial pathogen that is known to agglutinate in the presence of human plasma to form stable clumps . There is increasing evidence that agglutination aids S . aureus pathogenesis , but the mechanisms of this process remain to be fully elucidated . To better define this process , we developed both tube based and flow cytometry methods to monitor clumping in the presence of extracellular matrix proteins . We discovered that the ArlRS two-component system regulates the agglutination mechanism during exposure to human plasma or fibrinogen . Using divergent S . aureus strains , we demonstrated that arlRS mutants are unable to agglutinate , and this phenotype can be complemented . We found that the ebh gene , encoding the Giant Staphylococcal Surface Protein ( GSSP ) , was up-regulated in an arlRS mutant . By introducing an ebh complete deletion into an arlRS mutant , agglutination was restored . To assess whether GSSP is the primary effector , a constitutive promoter was inserted upstream of the ebh gene on the chromosome in a wildtype strain , which prevented clump formation and demonstrated that GSSP has a negative impact on the agglutination mechanism . Due to the parallels of agglutination with infective endocarditis development , we assessed the phenotype of an arlRS mutant in a rabbit combined model of sepsis and endocarditis . In this model the arlRS mutant displayed a large defect in vegetation formation and pathogenesis , and this phenotype was partially restored by removing GSSP . Altogether , we have discovered that the ArlRS system controls a novel mechanism through which S . aureus regulates agglutination and pathogenesis .
Staphylococcus aureus is a Gram-positive opportunistic pathogen that exists as part of the normal human microflora in approximately one third of the human population [1] . This pathogen is responsible for causing a wide range of acute and chronic infections resulting in significant morbidity and mortality in both the hospital and community settings . Further , the spread of methicillin-resistant S . aureus into the community setting ( CA-MRSA ) and the severe disease associated with these infections has led to its emergence as a major public health problem [2] , [3] . The success of S . aureus as a pathogen arises in part from its genetic malleability , enabling the rapid acquisition of antimicrobial resistance mechanisms and advantageous virulence factors , and the diverse signal transduction mechanisms that can respond to both environmental and host cues [4] . S . aureus is the single most frequent cause of healthcare-associated infections , many of which are persistent and remain recalcitrant to treatment [2] . These types of infections are typically chronic , such as endocarditis , osteomyelitis , and implant-associated infections , and have been linked to the organism's ability to form biofilms [5] , [6] . Biofilms can be defined as a community of cells encased in a protective matrix containing eDNA , proteins , and polysaccharides that are attached to a surface . This survival mechanism provides an increased capacity for bacteria to persist in hostile environments created by exposure to antibiotics and host immune defenses [5] , [7] , [8] . Recently , studies evaluating chronic infections with Pseudomonas aeruginosa suggested this pathogen predominates in cellular aggregates , as opposed to existing as surface-attached biofilms [9]–[11] . Upon further investigation , these aggregates were shown to perform a similar function as biofilms by maintaining resistance properties to antibiotics and host defenses . Additionally , P . aeruginosa aggregates consisted of a variety of matrix components commonly associated with biofilm formation , including eDNA and polysaccharides [10] . However , investigation of aggregate composition of another pathogen , S . aureus , revealed important differences between biofilm and aggregates . S . aureus aggregates are predominately composed of polysaccharide and display increased metabolic activity as opposed to typical bacterial biofilms and P . aeruginosa aggregates [10] , [12] . However , it is difficult to predict how the identified properties of aggregates relate to pathogenesis since S . aureus exploits host proteins for agglutination in vivo [13] , [14] . S . aureus agglutination is a process through which cells bind matrix proteins and form stable clumps that aid evasion of host defenses and establishment of infection . While some initial studies have been performed , the mechanisms through which S . aureus controls agglutination remain largely undefined . This study investigates the mechanisms required for S . aureus agglutination in vitro and defines the two-component system , ArlRS , as a novel regulator of clumping and pathogenesis . Fournier and Hooper initially identified and characterized ArlRS as a regulator of autolysis [15] . However , recent studies by Memmi et al . revealed the regulatory role of ArlRS in autolysis is limited to methicillin-sensitive S . aureus ( MSSA ) strains , with autolysis in MRSA controlled by a distinct , yet undefined mechanism [15]–[17] . We demonstrate the ArlRS system controls S . aureus agglutination by negatively regulating the expression of the Giant Staphylococcal Surface Protein ( GSSP ) [18] , also called the extracellular matrix binding protein homologue ( Ebh ) [19] . We also demonstrate the ArlRS system is essential for pathogenesis in a rabbit model of sepsis and infective endocarditis .
To understand the agglutination process more clearly and determine how closely these interactions resemble biofilm communities , we developed an approach to quantify S . aureus agglutination in vitro using human plasma . Briefly , early growth phase S . aureus cells were prepared and resuspended in PBS , and the cell suspensions were incubated with an increasing concentration of human plasma ( HP ) to determine a dose response statically at room temperature ( Fig . 1A ) . Aliquots of supernate were removed over time for turbidity readings to assess the amount of remaining cells ( Fig . 1B ) . We define agglutination as the production of large clumps that clear the supernate up to 70% of the initial cells present ( Fig . 1C ) . After repeated trials , we selected 2 . 5% HP ( 1∶1 mixture of HP and heparin/dextran ) as the standard amount used in experiments and 2 hr incubation as the standard incubation time ( unless otherwise noted ) . As a control , cell suspensions incubated without HP took over 16 hr for cells to sediment and reach 70% agglutination ( data not shown ) , demonstrating the importance of the plasma proteins to the phenotype . Several divergent S . aureus strains were tested for their ability to agglutinate , including USA300 ( LAC-WT ) , USA400 ( MW2 ) , and Newman . The addition of 2 . 5% HP induced clump formation within 2 hr for all strains tested , which could be visualized as a white mass at the bottoms of the tubes ( Fig . 1C ) . LAC-WT , MW2 , and Newman displayed 78% , 82% , and 70% , agglutination , respectively 2 hr post-addition of 2 . 5% HP ( Fig . 1D ) . A time course of MW2 was performed ( Fig . 1E ) , revealing that this strain agglutinated at a faster rate than LAC-WT ( Fig . 1B ) . The effect of anticoagulant on S . aureus agglutination ( heparin/dextran sulfate ) was also tested , and the cells sedimented at the same rate as those without HP ( data not shown ) . To assess the impact of other anticoagulants , LAC-WT agglutination was tested with 2 . 5% citrated HP over time and revealed similar results ( Fig . 1F ) , further demonstrating the anticoagulant does not affect S . aureus clumping . Thus , we have developed a straightforward and simple assay to quantify S . aureus agglutination with HP that shows a robust phenotype conserved across strains . Previous studies on agglutination indicate that the human matrix protein , fibrinogen ( Fg ) , is a major component of HP required to induce clumping in S . aureus [20] . To test the potential role of Fg in agglutination , the protein was added at a final concentration of 18 . 5 µg/mL ( roughly the concentration calculated to be present in 2 . 5% HP ) . At this concentration , Fg induced agglutination similar to HP in all S . aureus wildtype strains tested with LAC-WT , MW2 , and Newman displaying 73% , 82% , and 81% agglutination after 2 . 5 hr , respectively ( Fig . 2A ) . This agglutination was specific to Fg; as a dose-response of fibronectin added up to 25 times the amount found in 2 . 5% HP was unable to agglutinate wildtype S . aureus strains comparable to HP ( data not shown ) . Similarly , the addition of a dose-response of human serum was insufficient to agglutinate strains to levels equivalent to HP ( data not shown ) . To visualize the agglutinated clumps , Fg conjugated to the fluorophore Oregon Green ( Fg-OG ) was used in the agglutination assay , and the clumps were examined using fluorescence microscopy . LAC-WT was used as a representative strain in the experiment . Similar to the tube-based assay , LAC-WT formed large aggregates as seen with light microscopy . In fluorescence mode it was evident that Fg-OG was incorporated and embedded in the clumps ( Fig . 2B ) . Our observations indicate that Fg is the major component of human plasma promoting agglutination and this ECM protein becomes an integral component of the clump . Several genetic factors encoded by S . aureus required for agglutination have been described [21] including clumping factor A ( ClfA ) [13] , [22] , a fibrinogen-binding protein and a member of the microbial surfaces components recognizing adhesive matrix molecules ( MSCRAMMs ) , and the two coagulating proteins , coagulase A ( Coa ) and von Willebrand factor binding protein ( vWbp ) [14] . These factors , along with several other MSCRAMMs , were shown to be involved in abscess formation during systemic infection and contribute to persistent disease [13] , [14] , [23] . To compare our agglutination assay to these published reports , we examined the ability of a strain with a mutation in sortase A ( ΔsrtA ) , a transpeptidase responsible for proper MSCRAMM localization , to agglutinate [24] . The LAC ΔsrtA mutant displayed a defect in the ability to agglutinate compared to LAC-WT ( Fig . 3A ) . Newman ΔsrtA was also assessed for the ability to agglutinate . Newman ΔsrtA was indeed deficient for agglutination at early time points , although not to the same degree LAC ΔsrtA ( data not shown ) . Since a number of MSCRAMMS have defined roles in adherence and biofilm formation , we also assessed their role in S . aureus agglutination . To characterize the contribution of individual MSCRAMMs , we examined whether strains with mutations in the coding sequences of clumping factor A ( ClfA ) , clumping factor B ( ClfB ) [25] , fibronectin binding proteins ( FnbpA and B ) [26] , or protein A ( Spa ) [27] could agglutinate in our assay [13] . A LAC clfA mutant showed a significant defect at early time points , displaying only 53% agglutination compared to LAC-WT which exhibited 80% agglutination at the same time point ( Fig . 3B ) . Interestingly , the ΔclfB , ΔfnbpAB , and Δspa mutants , in strain LAC-WT , displayed no phenotype in the agglutination assay ( Fig . 3B & 3C ) . Due to the important role of the polysaccharide intercellular adhesin ( PIA ) in biofilm and aggregate formation [12] , we also tested strain LAC-WT with a deletion of the ica genes , which encode the PIA biosynthesis components , and again no defect was observed in agglutination of the LAC Δica mutant ( Fig . 3C ) . To assess the generality of the observations , the contribution of MSCRAMMs to agglutination in strain Newman was also determined and revealed strikingly similar results ( data not shown ) . Taken together , these studies demonstrate that in vitro agglutination of S . aureus requires MSCRAMMs , specifically clfA , for proper interactions with matrix proteins , and many known factors important for biofilm formation are not essential for the agglutination phenotype . To better understand the mechanisms that control agglutination , we assessed the impact of mutations in global regulators on the ability of S . aureus to agglutinate in the presence of HP or Fg . We initially selected regulators with reported roles in biofilm formation , including SarA [28] , sigma factor B ( SigB ) [29] , agr quorum sensing [30] , SaeRS [31] , and ArlRS [15] , [32] . The addition of HP revealed that strains with mutations in agr , sigB , or saeRS displayed no phenotype in the assay , while a sarA mutant had a minor defect in clumping ( Fig . 4A ) . Addition of purified Fg revealed the same result ( data not shown ) . Surprisingly , we observed that a ΔarlRS mutant had a dramatic phenotype in agglutination compared to LAC-WT ( Fig . 4A ) . The agglutination inhibition occurred throughout a LAC ΔarlRS time course ( Fig . 1B ) . It was also conserved across doses of HP ( from 0 . 5%–40%; Fig . 1A ) and in the presence of other anti-coagulants , such as citrated HP ( Fig . 1F ) . Similar results were obtained with MW2-WT and MW2 ΔarlRS strains ( Fig . 1E and data not shown ) . This phenotype could be complemented with a plasmid encoding the arlRS genes under regulatory control of the native promoter ( Fig . 5A ) , demonstrating that the absence of the arlRS genes was responsible for the observed phenotypes . The arlRS mutation was reconstructed in the MW2 and Newman backgrounds and assessed for agglutination with either HP ( Fig . 1C and 1D ) or purified human Fg ( Fig . 2A ) . In all strains tested , agglutination was markedly inhibited when the arlRS genes were disrupted . To better appreciate the agglutination phenotype , Fg-OG was utilized to visualize the clumps formed by LAC-WT and its isogenic arlRS mutant . As noted above , LAC-WT bound Fg-OG and formed large , tight clumps with embedded Fg , while the LAC ΔarlRS clumps were small , spatially separated , with only trace levels of Fg-OG detectable ( Fig . 2B ) . These observations , using multiple assessments of agglutination , demonstrate that strains missing the ArlRS two-component system prevent agglutination by inhibiting proper interactions with Fg . With the identification of ArlRS as a regulator of agglutination , we examined a previously published microarray to determine how the two-component system contributes to agglutination [33] . Surprisingly , mRNA transcript abundance of factors known to be involved in agglutination , such as ClfA , sortase and the coagulases , were not identified as being significantly altered in an arlRS mutant . To confirm these observations in the LAC-WT strain background , we employed quantitative real time PCR ( qPCR ) to determine transcript levels of these factors in LAC-WT and LAC ΔarlRS . There were no significant differences in transcript levels of sortase and the coagulases in the arlRS mutant compared to LAC-WT ( Fig . 4B ) . However , clfA transcript expression was reduced two-fold in LAC ΔarlRS compared to LAC-WT . The transcript level of staphylokinase ( sak ) , which is known to inhibit clumping through plasminogen activation [34] , [35] , was also compared between LAC-WT and LAC ΔarlRS and revealed no significant changes in expression ( Fig . 4B ) . These regulatory observations suggested ArlRS was controlling agglutination by a unique , undefined mechanism . The reported microarray revealed striking transcriptional changes in a set of genes , ebhA and ebhB , that had the potential to be involved in agglutination [33] . Characterization of these genes in Mu50 and N315 suggested ebhA and ebhB were originally a single open reading frame and a frameshift mutation resulted in their permanent separation [19] . Analysis of these genes in other S . aureus strains , including MW2 and LAC-WT , revealed these strains contained one intact gene , termed ebh . The ebh gene is the largest open reading frame on the S . aureus chromosome at 33 kb , effectively comprising 1% of the genome [19] . Ebh , also called Giant Staphylococcal Surface Protein ( GSSP ) , is predicted to be membrane anchored and protruding from the cell surface in a fiber-like manner [36] , [37] . The protein contains numerous sugar binding and albumin binding repeat domains that are thought to be important for binding extracellular matrix proteins [19] . To test whether ebh is upregulated in the LAC ΔarlRS mutant , we employed qPCR to assess transcript levels . During early logarithmic growth , ebh expression was increased almost 50 fold in LAC ΔarlRS compared to LAC-WT ( Fig . 4B ) . To confirm with a different assay , the ebh promoter was fused to sGFP to create a transcriptional reporter , and the levels of GFP in LAC ΔarlRS increased markedly throughout growth compared to LAC-WT ( Fig . 4C ) . To address the production of GSSP from the ebh gene , the H2 domain of GSSP was purified and antibodies were generated . The H2 domain is a 392-residue peptide composed of three sugar-binding motifs in one of the repeat regions [19] . Dot blots to assess GSSP production were performed in a manner similar as done recently with S . epidermidis [18] , and mutations in Protein A ( Spa ) were engineered into the strains to eliminate background response on the blots . As shown in Figure 4D , GSSP levels are low in LAC spa mutant and they rise dramatically in a LAC ΔarlRS spa double mutant . As a control , a LAC ΔarlRS Δebh spa mutant was constructed and the GSSP response was eliminated , demonstrating the specificity of the H2 antibody . These findings revealed that a functional ArlRS is required to repress the transcription of ebh , preventing the expression of GSSP on the surface of the cell . To assess the potential role GSSP may play in inhibiting agglutination , LAC Δebh single mutant and LAC ΔarlRS Δebh double mutant strains were constructed in the LAC-WT background . Using the agglutination assay described above , the LAC Δebh mutant displayed no phenotype in the presence of either HP or Fg ( Fig . 5A and C respectively ) . Importantly , the LAC ΔarlRS Δebh double mutant displayed an agglutination level similar to LAC-WT in either HP or Fg ( Fig . 5A and C ) , effectively eliminating the phenotype observed with LAC ΔarlRS . This observation suggests that the overexpression of GSSP in an arlRS mutant is responsible for the inability of LAC ΔarlRS to agglutinate , and this phenotype is abrogated upon mutational disruption of GSSP expression in an arlRS mutant background . To visualize agglutination by each strain , light microscopy was performed . The images revealed that the LAC Δebh and LAC ΔarlRS Δebh mutants both formed tight clumps similar to LAC-WT ( Fig . 5B ) , while the LAC ΔarlRS mutant cells associated in loose collections , consistent with our previous observations . Together these data indicate the overexpression of the ebh gene in the LAC ΔarlRS mutant is the major factor responsible for the inhibition of agglutination . Another large surface protein , SasC , is up-regulated more than 10-fold in an arlR mutant [33] , and we investigated the potential that SasC plays a similar function as GSSP in agglutination . For this experiment , we constructed a LAC ΔarlRS ΔsasC double mutant using a sasC::φΣ insertion from the Nebraska Transposon Mutant Library [38] . However , LAC ΔarlRS ΔsasC strain showed the same agglutination phenotype as the LAC ΔarlRS mutant ( data not shown ) , suggesting that , unlike GSSP , SasC is not involved in blocking agglutination under these conditions . The sasC mutation was also introduced into the LAC ΔarlRS Δebh strain , and again this additional mutation did not alter agglutination phenotypes ( data not shown ) . To define the roles of ArlRS and GSSP in the agglutination phenotype , flow cytometry was employed to assess agglutination . Background fluorescence controls ( LAC-WT and LAC-WT incubated with dextran sulfate conjugated to Oregon Green ) were set as a baseline to less than 0 . 1% ( Fig . 6A and B ) . A shift in population into the upper right quadrant ( Q1 ) was positive for Fg-OG incorporation and agglutination . In Figure 6 , representative examples of the flow observations are shown ( Fig . 6C–E ) , along with data from all of the strains normalized to LAC-WT set to 100% ( Fig . 6F ) . Incubating LAC-WT with Fg-OG revealed a shift of 12 . 1% of the population from the left quadrants into Q1 , indicating LAC-WT bound Fg-OG in a productive manner , resulting in clump formation ( Fig . 6C ) . Similar to our previous observations , LAC ΔarlRS did not agglutinate and displayed less than 2% shift into Q1 ( Fig . 6D ) . However , the shift does indicate the ΔarlRS mutant can bind Fg to some degree . The removal of GSSP in the LAC ΔarlRS Δebh double mutant resulted in an 8 . 5% shift in population into Q1 , translating to 70% agglutination relative to LAC-WT ( Fig . 6E and F ) . Again , the introduction of the Δebh mutation restored much of the LAC ΔarlRS defect . The LAC ΔarlRS complemented strain , and the LAC Δebh mutant , behaved similar to LAC-WT , agglutinating 98% and 78% relative to LAC-WT , respectively ( Fig . 6F ) . As controls , single mutants in clfA and srtA , were also assessed in the flow cytometry assay . LAC ΔclfA and LAC ΔsrtA agglutinated to 0% and 5% of LAC-WT respectively ( Fig . 6F ) , demonstrating the importance of ClfA to agglutination . These strains appear to exhibit a more severe phenotype in the flow cytometry assay compared to the gravity agglutination assay . However , the flow cytometry assay is only assessing Fg binding and incorporation , and the results shown are gated ( Fig . 6F ) , while experiments using HP contained many other host ECM proteins that potentially additively contribute to agglutination . These flow data corroborate our other agglutination observations and confirm the role of GSSP in an arlRS mutant as a major inhibitor of agglutination . To further assess whether the LAC-WT and LAC ΔarlRS clumps are morphologically distinct , scanning electron microscopy ( SEM ) was employed . Images revealed LAC-WT formed large , tight clumps in the presence of Fg ( Fig . 7A ) with visible cross-links binding the cells together at a magnification of 45K ( Fig . 7B ) . Clumps formed by LAC ΔarlRS lacked the structured cross-links displayed by LAC-WT at high magnification , and instead loose collections with long string-like fibers appeared to connect the cells ( Fig . 7C and D ) , resulting in abnormal clump formation . Agglutination of LAC Δebh , LAC ΔarlRS complemented , and LAC ΔarlRS Δebh displayed similar characteristics to LAC-WT , revealing tight interactions between cells ( data not shown and Fig . 7E and F , respectively ) . The flow data suggests that the LAC ΔarlRS can bind some level of Fg , however , the clumps are characteristically distinct from LAC-WT in SEM , suggesting the arlRS mutants interact with Fg inefficiently . Importantly , the deletion of ebh in an arlRS background rescues agglutination , providing further evidence that the proper regulation of GSSP is essential . To confirm that GSSP is the major factor responsible for the inhibition of agglutination in an arlRS mutant , we replaced the wild-type ebh promoter with the fabI promoter in LAC-WT . PfabI was inserted into the chromosome of LAC-WT displacing the ebh promoter , resulting in a strain that constitutively drives a low level of ebh expression ( Fig . 8A ) . LAC PfabI-ebh was assessed in the agglutination assay , where it displayed a defect in clumping similar to LAC ΔarlRS ( Fig . 8B ) . Further , this construct was tested with the flow assay , and similar to LAC ΔarlRS , the LAC PfabI-ebh strain displayed a population shift of less than 1% into Q1 , indicating little Fg-OG incorporation and clumping ( Fig . 8C ) . Using the flow assay , both LAC ΔarlRS and LAC PfabI-ebh agglutinated less than 10% relative to LAC-WT ( Fig . 8D ) . These findings confirm that increased expression of ebh is responsible for the inhibition of agglutination in an arlRS mutant . With the newly identified role of ArlRS in agglutination , we evaluated ΔarlRS mutants using in vitro and in vivo assessments of pathogenesis . Preliminary reports suggest that ArlRS regulates alpha toxin ( Hla ) [17] , an important virulence factor in animal models [39] , [40] , but this gene was not identified as significantly regulated in the published microarray [33] . To determine whether ArlRS plays a role in the transcriptional regulation of alpha toxin , hla transcript levels were measured by qPCR in both LAC-WT and LAC ΔarlRS . Transcript levels of hla displayed no significant differences between the strains ( data not shown ) . Since Hla is also regulated at the post-transcriptional level [41] , rabbit red blood cell lysis titers were performed , and again no significant differences were observed between LAC-WT and LAC ΔarlRS ( data not shown ) . These observations indicate that the ArlRS system does not regulate Hla in CA-MRSA USA300 strains or potentially other clinical isolates . The ArlRS system was identified as being important for pathogenesis in murine models of systemic infection in a random mutagenesis screen to discover virulence determinants [42] . However , these experiments were performed using laboratory strains and the phenotypes have not been reassessed . In this study , we took advantage of rabbit models , which more accurately replicates the disease state seen in humans than mouse models [43] . Importantly , agglutination by other pathogens has been shown to be required for virulence in this model [44] . With the newly identified role of ArlRS in S . aureus agglutination , and the established link between agglutination and pathogenesis [13] , [14] , [44] , we tested the contribution of ArlRS to pathogenesis using a combined model of sepsis and infective endocarditis in rabbits . The high level of Hla production in USA300 strains is lethal to rabbits , making endocarditis vegetation formation difficult to monitor [45] . To circumvent these difficulties we used the relevant USA400 MW2 isolate for these experiments . A dose of ∼106–107 CFU MW2-WT or MW2 ΔarlRS was injected into rabbits via the marginal ear vein . Rabbits were examined four times a day over a four-day period , and were euthanized upon signs of illness or at the completion of the experiment . Hearts were removed and assessed for the presence of vegetations . Vegetations at the valve sites were removed , weighed , homogenized , and plated for bacterial counts . Four of the six rabbits infected with MW2-WT died before day four: one rabbit on day two; and three rabbits on day three ( Fig . 9A ) . All rabbits infected with MW2-WT developed vegetations with an average weight of 12 mg and 2 . 6×107 CFU/vegetations ( Fig . 9B and C ) . In contrast , all rabbits infected with MW2 ΔarlRS survived until day four ( Fig . 9A ) and had either no vegetations or very small vegetations , which was statistically significant ( p<0 . 05 ) . The average vegetation weight derived from these infections was less than 0 . 5 mg ( Fig . 9B ) and bacteria were recovered from only one vegetation , which contained less than 1 . 0×104 CFU ( Fig . 9C ) . All other vegetations recovered from rabbits infected with MW2 ΔarlRS were sterile . To investigate the infective endocarditis defect in the MW2 ΔarlRS mutant , a bacterial attachment assay was performed with the MW2-WT and MW2 ΔarlRS strains . The sepsis and endocarditis model was performed in a similar manner , except that rabbits were sacrificed 2 hrs post-infection . The hearts were harvested and washed to remove any unbound bacteria , and the bacterial load in the damaged areas was an average of 386 CFU for MW2-WT and 47 CFU for MW2ΔarlRS . Although the MW2-WT trended higher , hese differences did not reach statistical significance , suggesting that the ability of MW2-WT or MW2 ΔarlRS to adhere to heart valves is similar . These data indicate the reduced virulence of the MW2 ΔarlRS mutant in infective endocarditis is not due to a defect in initial attachment to the damaged heart valve . To determine the impact of GSSP on agglutination in vivo , we tested the MW2 Δebh single and MW2 ΔarlRS Δebh double mutants for the ability to cause infective endocarditis . One of the rabbits challenged with MW2 Δebh succumbed at day two of the experiment , while all rabbits that were infected with MW2 ΔarlRS Δebh survived the entire experiment ( Fig . 9A ) . The average vegetation weight from rabbits infected with MW2 Δebh was 17 . 5 mg and ∼3 . 1×108 CFU recovered , which are numbers that even exceed MW2-WT ( Fig . 9B and C ) . In comparison , the MW2 ΔarlRS Δebh double mutant developed vegetations with an average weight of 7 . 7 mg and 1 . 0×106 CFU recovered ( Fig . 9B and C ) . These are larger than the MW2 ΔarlRS vegetations , but the results did not reach statistical significance . Taken together , the ArlRS system is essential for pathogenesis in a rabbit model of sepsis and infective endocarditis , and this phenotype is in part explained by the overexpression of GSSP .
Staphylococcus aureus has the ability to form different types of community structures , such as biofilms , that enable this pathogen to persist during infection . It has been well-established that S . aureus produces numerous self-adhering factors necessary for establishing these communities , and recent studies have highlighted the importance of matrix protein coated surfaces in biofilm development [46]–[48] . In these in vitro models of biofilm formation , it is thought that biofilm initiation occurs though the initial attachment of the cells to the underlying matrix coating , followed by direct cell-cell accumulation . These methods for examining biofilm formation are being recognized as more clinically relevant since they more closely mimic in vivo conditions where S . aureus attaches to conditioned surfaces [5] , [8] . In the absence of a traditional substratum , S . aureus has the ability to complex with itself to form aggregates [12] , or it can clump in the presence of host proteins [20] , [49] through a process called agglutination [13] . The mechanisms of agglutination are complex and only just becoming fully appreciated . When S . aureus binds host ECM proteins , the bacterial cells are able to form large clumps , presumably providing protection against various host and antimicrobial exposures . Importantly , in many in vivo situations , such as in the bloodstream or peritoneal cavity [50] , the local concentration and accessibility of matrix proteins may render agglutination advantageous since direct adherence to an endothelial or epithelial layer may be difficult . Agglutination may also increase S . aureus survival in vivo by creating microenvironments that retain a functional agr quorum-sensing system , thereby facilitating virulence factor expression [49] . Recently , agglutination was linked to the development of abscess communities [13] , [21] , [51] , suggesting that the general ability of S . aureus to form tightly packed groups of cells aids survival in various host situations . While there are substantial reports to indicate these communities are important during infection [13] , [50] , [52] , the mechanisms behind the agglutination process remain incompletely defined . In this study , we characterized a new role for the ArlRS two-component system in S . aureus agglutination and pathogenesis . Our findings herein demonstrate that ArlRS regulates agglutination by modulating the levels of GSSP ( see model in Fig . 10 ) . The ebh gene encodes the surface bound protein GSSP , by far the largest protein encoded by the S . aureus genome at 1 . 1 MDa . GSSP is thought to bind matrix components and protect the cell against osmotic pressure changes [19] , [53] . Despite these initial reports , remarkably little is known about the physiological and pathogenic role of this giant surface structure . Only one of the repeat domains has been tested for function , called the H2 domain , which was found to bind fibronectin [19] . The ebh gene is transcribed at low levels during growth of wildtype strains , but our findings demonstrate that expression and protein levels rise dramatically in the absence of ArlRS ( Fig . 4 ) . It is not known whether the response regulator directly binds the ebh promoter element ( Fig . 10A ) , as the ArlR binding site has not yet been identified . Therefore the ArlRS repressive effect on GSSP could be direct or indirect through an intermediary . Our findings demonstrate that GSSP inhibits Fg-mediated agglutination , but the mechanism ( s ) of inhibition is not known ( Fig . 10B ) . One possibility is that GSSP functions like an Fg sink , absorbing excess Fg and preventing proper cell-cell interactions mediated by this matrix protein . However , the published report that GSSP binds fibronectin and not Fg , at least in one repeat domain [19] , does not favor this possibility . Another potential inhibitory mechanism is steric hindrance due to the tremendous size and protruding rod-like structure of GSSP [37] . By literally pushing away neighboring cells , clump formation could be prevented , but as of yet , there is no experimental evidence to support this model . Further investigation is necessary to understand the specifics of the mechanism through which GSSP prevents agglutination . With the growing reported links between agglutination and S . aureus pathogenesis [13] , [14] , we assessed the in vivo contribution of the ArlRS system for virulence using a rabbit model of infective endocarditis and sepsis . The endocarditis development program requires S . aureus cell-cell interactions and incorporation of matrix proteins to form the vegetation [54] , which has parallels to the agglutination mechanism . Our data indicate that wildtype strain MW2 , an excellent cause of infective endocarditis [45] , resulted in significantly larger cardiac vegetations and higher bacterial load in the vegetations than a strain lacking the ArlRS system . Additionally , more animals infected with MW2-WT succumbed prematurely compared to those infected with the arlRS mutant . The mutant defect is likely in the vegetation development pathway , as the adherence of WT and arlRS mutant strains to heart valves was similar . An assessment of MW2 Δebh and MW2 ΔarlRS Δebh mutants indicates that GSSP is a contributor to these rabbit model phenotypes . The MW2 Δebh was similar , if not superior , in virulence characteristics compared to MW2-WT , while the MW2 ΔarlRS Δebh displayed increased pathogenesis compared to the MW2 ΔarlRS mutant . These findings demonstrate that restoring the ability of an arlRS mutant to agglutinate also restores pathogenicity , which is consistent with our in vitro observations . However , the inability of the MW2 ΔarlRS Δebh strain to completely recover virulence indicates that the GSSP overproduction is not the only ArlRS-regulated factor involved in pathogenesis . Future studies will be needed to further elucidate the ArlRS function in vivo during infection . Our findings are consistent with the hypothesis that bacterial pathogens causing infective endocarditis depend on the ability to agglutinate . In a recent study by Schlievert et al . , it was shown that self-aggregation of Enterococcus faecalis through its surface aggregation substance ( AS ) , or enhanced aggregation through the combined effects of AS and IgG antibodies against AS , led to increasingly serious diseases compared to organisms lacking AS [44] . In their studies , organisms with AS were better able to cause infective endocarditis than those lacking AS , and the presence of AS IgG antibodies increased the severity of illness . These studies highlight the importance of agglutination in disease causation , and collectively , the data presented herein are consistent with these prior findings , suggesting S . aureus agglutination plays a key role in its ability to cause infective endocarditis and sepsis . The important contribution of ArlRS to the formation of endocarditis vegetations raises questions about the regulation of GSSP in pathogenesis and expression among S . aureus strains . The development and resolution of agglutination-based infections could benefit from a control switch like GSSP that modulates agglutination behavior . What environmental or host cues are sensed by the ArlRS system , and how these signals are transmitted to regulate GSSP levels , are all unknown at this time . However , it is tempting to speculate that GSSP levels might be low in the formation of a staphylococcal abscess community and high during the resolution event to break up the community [51] . Similarly , the formation of an endocarditis vegetation could benefit from low GSSP levels to promote S . aureus - matrix protein interactions and clump formation . We observed that ebh mutants accumulated even larger vegetations than wild-type ( Fig . 9B ) , supporting the hypothesis that the absence of GSSP is beneficial for infective endocarditis . Interestingly , the most frequent causes of infective endocarditis are clonal complex 30 ( CC30 ) strains , causing up to 20% of infections [55] . Bioinformatics analysis indicates the ebh gene is truncated in a number of these genomes , including MN8 ( data not shown ) . MN8 is an excellent cause of infective endocarditis [45] , and the absence of full-length GSSP further suggests this protein is not important for causing infective endocarditis and is perhaps even inhibitory . However , whether there is a statistically significant trend in ebh gene defects across clinical CC30 endocarditis isolates remains to be determined . Looking at other S . aureus strain lineages , there is considerable sequence variation in GSSP [56] , which could be another contributing factor to agglutination . In this report , we identified a new function for the S . aureus ArlRS system as a regulator of agglutination and pathogenesis . The unexpected discovery that GSSP is the primary ArlRS output controlling agglutination suggests that S . aureus has the ability to control clump formation using a surface structure . One goal of our future studies is to decipher the mechanism ( s ) through which GSSP carries out the agglutination inhibitory activity . The increasing link between in vivo agglutination and S . aureus pathogenesis emphasize the importance of understanding the regulatory pathways and components involved in clump formation .
The animal studies were reviewed and protocol approved by the University of Iowa Institutional Animal Care and Use Committee . The approved protocol was assigned number 1106138 . Accordingly , animals were administered pain relieving medications throughout experimentation . Additionally , animals that could not simultaneously maintain upright positions and exhibit normal escape behavior were prematurely euthanized; these criteria are 100% predictive of death in the model used . The University of Iowa is AAALAC accredited , and the centralized facilities meet and adhere to the standards in the “Guide and Care of Laboratory Animals . ” The wildtype and mutant S . aureus strains used in this study are listed in Table 1 . Trypticase soy broth ( TSB ) was used to maintain S . aureus cultures or prepare overnight cultures for experiments . Escherichia coli strains were maintained in Luria-Bertani ( LB ) broth or on LB agar plates . For the agglutination experiments , all S . aureus strains were subcultured and grown in brain heart infusion ( BHI ) broth . To maintain S . aureus mutants and their complementing clones , antibiotics obtained from Sigma-Aldrich were added to the media at the following concentrations: chloramphenicol ( Cam ) 10 µg/mL , kanamycin 50 µg/mL , erythromycin 10 µg/mL , and spectinomycin 100 µg/mL . To maintain pJMB insertion plasmids , Cam was increased to 30 µg/mL in the media . E . coli strains with plasmids were maintained on media supplemented with ampicillin at 150 µg/mL . Other chemical reagents were purchased from Sigma-Aldrich or Fisher Scientific unless otherwise noted . E . coli DH5α was used as a cloning host for plasmid constructions . Restriction enzymes , DNA ligase , and Phusion DNA polymerase were purchased from New England Biolabs . The plasmid mini-prep and gel extraction kits were purchased from Qiagen . Lysostaphin , used for S . aureus DNA extractions , was purchased from Ambion products . Plasmids were electroporated into RN4220 as described previously [57] . Bacteriophage transductions between S . aureus strains were performed with phage 80α or 11 as described previously [58] . All oligonucleotides were ordered from IDT Technologies ( Coralville , IA ) and are listed in Supplementary Table S1 . DNA sequencing was performed at the University of Iowa DNA Core Facility or Genewiz , South Plainfield , NJ . To assess the ability of S . aureus to agglutinate , we modified an aggregation assay originally described by Walter et . al . [59] to include the addition of human plasma and other extra cellular matrix proteins . S . aureus strains were grown overnight , subcultured in BHI to a starting optical density ( OD ) at 600 nm wavelength of 0 . 05 , and grown to a final OD600 of 1 . 5 . Cells were washed two times with one volume of 1X phosphate buffered saline ( PBS ) and resuspended in one volume of 1X PBS . Cells were vortexed two times for 20 sec each to ensure all clumps were dispersed . Human plasma ( HP ) was prepared from donors at the University of Iowa Inflammation Program with all necessary approvals or purchased from Sigma-Aldrich . HP prepared from donors at the University of Iowa was diluted 1∶1 with heparin/dextran sulfate to prevent clotting , and for the purposes of this study , this level of HP was considered a final concentration of 100% . Thus , a 2 . 5% HP listed throughout is actually 1 . 25% HP mixed with 1 . 25% heparin/dextran ( vol/vol ) . Citrated HP purchased from Sigma-Aldrich was resuspended according to manufacturer's instructions and was considered to be a final concentration of 100% . A dose response of HP was used to determine the minimum amount of HP required to induce agglutination , which was 2 . 5% vol/vol ratio of HP in PBS . For all subsequent experiments HP or Fg was added to initiate agglutination at a final concentration of 2 . 5% or 18 . 5 µg/mL ( the predicted concentration in 2 . 5% HP ) , respectively . As agglutination proceeds large clumps form and quickly fall to the bottom of the tube , leaving the supernate clear of bacterial cells . To quantify agglutination , 100 µL of supernate was removed and measured at an OD595 in a 96 well plate using a Tecan Infinite M200 microtiter plate reader . Measurements were performed every 30 min after addition of ECM proteins for total of 6 hr . Clumping ability was correlated with sedimentation speed . To calculate percent agglutination the following formula was used: Microscopy was employed to confirm clumps formed by LAC ΔarlRS were distinct from LAC-WT . Briefly , the agglutination assay was performed as described above with either human Fg or Fg-OG . At 2 . 5 hr post addition of the ECM protein , 25 µL aliquots were removed and spun onto a microscope slide at 400 rpm for 5 min using a Shandon Cytospin 3 ( Thermo Scientific Shandon ) . For light microscopy slides were fixed and stained using the hema-3 stain kit according to manufacturer's instructions ( Fisher Scientific , Ca #23-123-869 ) . A Zeiss Axioplan 2 microscope and AxioCam MRm camera ( Carl Zeiss Inc . ) with Axio Vision 4 . 1 software was used to capture images of the clumps . For fluorescence microscopy coverslips were added and clumps were assessed by a Zeiss Axioplan 2 microscope and a AxioCam MRn camera ( Carl Zeiss Inc . ) with Axio vision 4 . 1 software was used to capture images of the clumps . Approximately 500 base pairs upstream and downstream of the arlRS gene pair ( SAUSA300_1307-1308 ) were amplified using PCR with S . aureus strain AH1263 as chromosomal DNA template and the following primer pairs; 1308up5EcoRI and 1308up3fuse; 1308dwn5fuse and 1308dwn3salI . Amplicons were gel purified and joined by PCR using the 1308up5EcoRI and 1308dwn3salI primer pair . The PCR product was gel purified , digested with EcoRI and SalI , and ligated into similarly digested pJB38 [60] . The ligation was transformed into E . coli DH5α , selecting on ampicillin and colonies were screened for the correct insert ( final plasmid pJMB202 ) . Plasmid pJMB202 was isolated and transformed into RN4220 selecting on TSA containing Cam at 30°C . Plasmid pJMB202 was transduced into AH1263 , and single colonies were inoculated into 5 mL of TSB containing Cam . Cultures were grown at 42°C overnight to select for single recombinants . Single colonies were inoculated into 5 mL of TSB medium , grown overnight , and cultures were diluted 1∶25 , 000 before plating 100 µL on TSA-anhydrotetracycline ( 150 ng/mL ) to screen for loss of pJMB202 . Colonies were screened for the double recombination event using PCR with primers 1308verify5 and 1308verify3 , and also screened for loss of plasmid by Cam sensitivity . The Δebh::Kan mutant was created using the same protocol as outlined above with the following exceptions: 1 ) the upstream and downstream portions of ebh ( SAUSA300_1327 ) were amplified using the following primer pairs: 1327up5EcoRI and 1327up3fuse; 1327dwn5fuse and 1327Dwn3SalI; 2 ) the 1327up5EcoRI and 1327Dwn3SalI primers were used for joining the two PCR products; and 3 ) colonies were screened for both Cam sensitivity and Kan resistance after the double recombination event . The 1327 deletion was verified by primer walking . Fourteen sets of primers were designed to amplify 2 Kb regions across the entire ebh gene in LAC-WT . The forward primer ebh1for and the reverse primer ebh14rev were used to amplify across the entire gene ( 33 Kb ) for wildtype and 1 . 8 Kb for the ebh deletion . For construction of the Δebh::Tet , the same approach was used , except that G+tet_nheI and G+tet_mluI oligos were used to amplify the tetM marker . PCR was used to amplify the arlRS gene pair ( SAUSA300_1307-1308 ) and its native promoter using AH1263 chromosomal DNA as a template and the arlcomp5BamHI and arlcomp3SalI primer pair . The PCR product was gel purified and digested overnight with BamHI and SalI . The digested fragment was gel purified , ligated into similarly digested pCM28 , and transformed into E . coli DH5α cells , resulting in plasmid pJMB219 . The constructed plasmid was transformed into RN4220 , selected on TSA supplemented with Cam , and subsequently transduced into LACΔarlRS . A transcriptional promoter fusion with GFP was created using primers JNW47EbhGFPFor and JNW48EbhGFPrev to amplify the region 500 base pairs upstream of the ebh predicted start site . The PCR product was gel purified and digested overnight with SphI and BamHI . The gel fragment was cloned into similarly digested pCM11 [61] and transformed into E . coli DH5α cells . This construct was transduced into either LAC-WT or LAC ΔarlRS . To assess fluorescence , overnight cultures were diluted to a starting OD600 of 0 . 05 in BHI and incubated at 37°C with shaking . Time points were taken periodically by transferring 100 µL to a 96 well plate and measuring the OD600 and the fluorescence intensity with excitation at 495 nm and emission at 515 nm using a Tecan Infinite M200 plate reader . For qPCR , cultures were grown in BHI to an OD600 of 1 . 5 . RNA was isolated using the RNeasy Mini Kit ( Qiagen ) , with the exception that S . aureus cells were lysed with 100 µL of lysostaphin for 30 min at 37°C prior to the cell lysis step . Primers were designed for coa , sak , vWbp , srtA , clfA and ebh based on published S . aureus sequences using IDT online software ( see Supplemental Table S1 for oligonucleotide sequences ) . Five sets of primers were designed for ebh across the entire gene . qPCR was performed by amplifying 10 ng of RNA with Express Super Script Mix ( Invitrogen ) under the following conditions: 5 minutes at 50°C , 2 minutes at 95°C , 40 cycles of 15 seconds at 95°C and 1 minute at 53°C , followed by a dissociation curve . All samples were run in triplicate and DNA gyrase ( gyrB ) was used as a reference gene . Primer sets one , four and five displayed consistent transcript levels across several experiments and primer set one ( SA-ebh-8091 and SA-ebh-7908 ) , located 1/3 of the way into the gene , was chosen as a representative set for all experiments . PCR was performed using oligonucleotides H2 for and H2 rev with Phusion polymerase ( New England Biolabs ) on AH1263 genomic DNA as template . The PCR fragment was Topo TA ( Life Technologies ) cloned , transformed into E . coli and transformants were screened by PCR . Plasmid DNA from a positive transformant was digested by NdeI and XhoI and ligated into pET28c ( Novagen ) digested by the same enzymes . The resulting plasmid is called pET28-H2 , and the plasmid was transformed into E . coli expression ER2566 and saved as AH2930 . To purify H2 peptide , cultures of AH2930 were grown at 37°C in LB broth supplemented with Kan to an OD600 of 0 . 5 . Isopropyl β-D-thiogalactoside ( Research Products International ) was added to a final concentration of 0 . 1 mM , the culture shifted to 30°C and grown for 4 hr . After centrifugation , the pellets were resuspended in equilibration buffer ( 50 mM sodium phosphate , 0 . 3 M sodium chloride , 10 mM imidazole , pH 8 ) with Sigmafast ( Sigma ) protease inhibitor . Cells were lysed by two passages through a Microfluidics LV1 . After centrifugation , the cleared lysate was loaded onto an equilibrated His-Select HF Nickel affinity ( Sigma ) column . The nonbinding proteins were removed by washing and the 6xHis H2 protein was eluted with the same buffer containing 250 mM imidazole . Fractions containing the H2 protein were pooled , dialyzed versus phosphate buffered saline and concentrated . To address aggregation problems with the protein , it was dialyzed into 8 M urea and purified over an S200 column in 8 M urea . Fractions containing the H2 protein were pooled , dialyzed versus phosphate buffered saline and concentrated in an Amicon Ultra ( Millipore ) centrifugal filter . This protein was used to generate rabbit polyclonal antisera against H2 . GSSP expression was monitored by dot blotting based on the method described [18] . Strains AH3007 , AH3008 and AH3019 were grown in 25 ml of BHI to an OD600 of ∼6 and the cell densities were normalized before harvesting . Cells were washed with PBS before resuspending in 10 ml PBS and sonicating for 3 min with a Branson 450 Sonifier ( power level 3 , 50% duty ) to shear off surface proteins . Intact cells were removed by centrifugation and the supernatants were serially diluted ( 2-fold dilutions ) in PBS . Aliquots ( 5 µL ) were spotted on a nitrocellulose membrane and allowed to dry before blocking for 1 h at room temperature with TBS containing 0 . 05% Tween 20 ( TBST ) and 5% milk . The membrane was incubated with GSSP H2 antiserum ( diluted 1∶1000 in TBST+5% milk ) for 1 h , washed three times in TBST , and incubated with HRP-conjugated goat anti-rabbit antibodies ( diluted 1∶20 , 000 in TBST+5% milk ) . The membrane was washed three times in TBST before incubation with SuperSignal West Pico chemiluminescent substrate for 5 min and exposure to X-ray film . To assess clumps by flow cytometry , the agglutination assay was performed as described above with the following modifications: fibrinogen conjugated to Oregon Green ( Invitrogen , Ca# F7496 ) was substituted for human Fg , which induced agglutination similarly to fibrinogen as measured by OD . After 2 . 5 hrs of agglutination , clumps were analyzed on a C6 Accuri flow cytometer and C-flow Plus software using a threshold of 20 , 000 for data collection and channels FL-1 and FSC-H to measure fluorescence intensity and clump size , respectively . LAC-WT suspended in PBS ( negative control ) was used to exclude unbound , individual bacteria and cellular debris . Dextran sulfate conjugated to Oregon green ( Invitrogen; no clumping control ) was used to determine background fluorescence and lack of clumping as dextran sulfate and dextran sulfate conjugated to Oregon green failed to induce agglutination . LAC-WT incubated with Fg-OG was considered 100% agglutination , and the percent agglutination of all strains was set relative to LAC-WT . The agglutination assay was performed as described above with the following strains: LAC-WT , LAC Δarl , LAC Δebh , LAC ΔarlRS complemented strain , and LAC ΔarlRS Δebh . At 2 . 5 hr post addition of Fg ( 18 . 5 µg/mL ) , 25 µL aliquots of supernate were removed and spun onto a 12 mm round coverslips ( Fisherbrand ) at 400 rpm for 5 min using a Shandon Cytospin 3 ( Thermo Scientific Shandon ) . Coverslips were transferred to a 24 well plate ( Costar ) and fixed in 500 µL of 2 . 5% gluteraldehyde for at least 1 hr . To remove the fix , coverslips were washed three times for 5 min each with a buffer rinse , 0 . 1 M sodium cacodylate . Samples were then treated with 1% osmium tetroxide for 20 min and washed again with the buffer rinse . The buffer was removed , and samples were washed once in double distilled water and subsequently with increasing concentrations of ethanol for 6 min each to dehydrate the samples . Coverslips were incubated in 95% ethanol for 10 min and 100% ethanol twice for 5 min each . Samples were cross-linked with hexamethyldisilizane ( HMDS ) twice for 10 min each , HMDS was removed , and samples were allowed to air dry in a tissue culture hood overnight . Coverslips were mounted on stubs and coated with gold particles using an Emitech K550 sputter coater . Images were captured with a Hitachi S-4800 scanning electron microscope with Super ExB filter technology . Approximately 500 base-pairs upstream of the start codon from the fabI gene was amplified using S . aureus strain AH1263 chromosomal DNA as a template and the cmR_fabIF and fabI_ebhAR primer pair ( see Supplemental Table S1 for sequences ) . The first 300 base pairs of the ebh gene was amplified using S . aureus chromosomal DNA as a template and the fabI_ebhAF and Ebh pUC3 primer pair . The CamR gene was amplified using pJB38 as a template and the yeast_CmF and cmR_fabIR primer pair . A yeast cloning cassette containing the 2μ origin of replication and Saccharomyces cerevisiae URA3 gene with the native promoter ( Boyd JM and Belden WJ unpublished ) was amplified using the pUCYeast5 and yeast_CmR reverse primers . The pUC19 plasmid was linearized using AatII and SapI restriction enzymes . All DNA fragments were gel purified and approximately 1 µg of each DNA construct was combined and plasmid circularized with gap repair cloning using Saccharomyces cerevisiae creating plasmid JMB449 [62] . The pJMB449 was transformed into in S . aureus RN4220 via electroporation , and strains with integrated plasmids were selected for on TSA supplemented with Cam ( 30 µg/mL ) . The integrated plasmid was transduced into AH1263 and verified using the 1327 internal verify and cmR_fabIF primer pair . Plasmid integration into AH1263 was verified by DNA sequencing using the 1327 internal verify primer . Rabbit blood was purchased from Hemostat Labs . Red blood cell lysis titers to assess alpha-toxin levels were performed as previously described [63] . New Zealand white rabbits ( approximately 2–3 kg ) , either sex , were purchased from Bakkom Rabbitry , Red Wing , MN and used according to University of Iowa IACUC approved protocol 1106138 . Rabbits were anesthetized with ketamine ( 25 mg/kg ) and xylazine ( 25 mg/kg ) ( Phoenix Pharmaceuticals , Burlingame , CA ) . Their necks were shaved , and 5 cm incisions were made to expose the left carotid arteries . Hard plastic catheters were inserted into the carotid arteries until the catheters just abutted against the aortic valves . The catheters were then tied in place and allowed to cause damage to the aortic valves for 2 h . Subsequently , the catheters were removed and carotid arteries tied off , and the animals were closed . Animals were injected intravenously through the marginal ear veins with S . aureus strains in 2 ml PBS ( approximately 106–107 CFU/ml ) . The rabbits were monitored for health status for up to 4 days; during this time , animals that simultaneously failed to exhibit escape behavior and failed to be able to right themselves , 100% predictive of lethal infection , were prematurely euthanized with 1 ml/kg of Beuthanasia D ( Shering-Plough , Westlake , TX ) . After 4 days ( or at the time of premature euthanasia ) , the animals were euthanized , hearts removed , and vegetation formation determined . Vegetations , cauliflower-like clumps of bacteria and host cells , were removed , weighed , and homogenized for CFU determination . Statistical differences in vegetation weights and CFUs were determined by Student's t test analysis of normally-distributed , non-paired data . Infective endocarditis was induced in New Zealand white rabbits as described above . Briefly , catheters were inserted into the left carotid artery and allowed to induce damage on the aorta for 2 hrs . Catheters were removed after 2 hrs , the arteries were tied off , and the incision site was closed . Rabbits were infected via marginal ear vein with 2 . 5×107 CFU . Infection was allowed to proceed for 2 hrs . Hearts were then harvested and washed twice with 30 mLs of sterile PBS to remove bacteria that remained unattached to the heart valve . The leaflets of the aorta were examined and damaged areas were excised . Tissue was homogenized in 1 mL TH media for CFU determination . Statistical differences in CFUs were determined Student's t-test analysis of normally-distributed , non-paired data . | Staphylococcus aureus is a bacterial pathogen that is responsible for causing significant disease in humans . The development of antibiotic resistant strains has made these infections more difficult to treat , and an improved understanding of how this pathogen causes infections will facilitate the development of new tools for treatment . It has long been recognized that S . aureus can bind human matrix proteins to form stable clumps in a process called agglutination , but the importance of agglutination during infection is only just becoming understood . In this work , we developed several techniques to investigate the S . aureus agglutination mechanism . We discovered that the ArlRS two-component regulatory system controls agglutination by regulating the expression of the ebh gene , which encodes the Giant Staphylococcal Surface Protein ( GSSP ) . When ArlRS is non-functional , S . aureus agglutination is prevented through the action of GSSP . These phenotypes were confirmed in a rabbit model of sepsis and infective endocarditis , demonstrating that ArlRS is an important regulator of virulence . Taken together , the identification of ArlRS as a regulator of S . aureus agglutination and pathogenesis may lead to innovative directions for therapeutic development . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | The Staphylococcus aureus ArlRS Two-Component System Is a Novel Regulator of Agglutination and Pathogenesis |
Kaposi’s Sarcoma associated Herpesvirus ( KSHV ) , an oncogenic , human gamma-herpesvirus , is the etiological agent of Kaposi’s Sarcoma the most common tumor of AIDS patients world-wide . KSHV is predominantly latent in the main KS tumor cell , the spindle cell , a cell of endothelial origin . KSHV modulates numerous host cell-signaling pathways to activate endothelial cells including major metabolic pathways involved in lipid metabolism . To identify the underlying cellular mechanisms of KSHV alteration of host signaling and endothelial cell activation , we identified changes in the host proteome , phosphoproteome and transcriptome landscape following KSHV infection of endothelial cells . A Steiner forest algorithm was used to integrate the global data sets and , together with transcriptome based predicted transcription factor activity , cellular networks altered by latent KSHV were predicted . Several interesting pathways were identified , including peroxisome biogenesis . To validate the predictions , we showed that KSHV latent infection increases the number of peroxisomes per cell . Additionally , proteins involved in peroxisomal lipid metabolism of very long chain fatty acids , including ABCD3 and ACOX1 , are required for the survival of latently infected cells . In summary , novel cellular pathways altered during herpesvirus latency that could not be predicted by a single systems biology platform , were identified by integrated proteomics and transcriptomics data analysis and when correlated with our metabolomics data revealed that peroxisome lipid metabolism is essential for KSHV latent infection of endothelial cells .
Viruses have evolved functions to reprogram the proteomic landscape of their host and modulate cellular signaling pathways to adjust the regulation of cellular machinery . These cellular alterations support the survival of infected cells to allow replication and spread of the virus . Many viruses rewire host cell signaling pathways to activate the host cell and to enable lytic replication , and in the case of the herpesviruses , to support long-term latent infection [1 , 2] . During latency , herpesviruses are known to modulate host cell signaling pathways that lead to inhibition of apoptosis , subversion of the host immune response , and alteration in host carbon and lipid metabolism among many other pathways . Importantly , alteration of these pathways by some oncogenic gamma-herpesviruses may influence tumor formation given the optimal cellular milieu [3 , 4] . Kaposi’s Sarcoma Associated Herpesvirus ( KSHV ) , a human gamma-herpesvirus , is the etiological agent of Kaposi Sarcoma and two B-cell lymphoproliferative diseases , Primary Effusion Lymphoma ( PEL ) and Multicentric Castleman Disease ( MCD ) [5–7] . KS is the most common AIDS-associated malignancy worldwide and among the most common tumors overall in Sub-Saharan Africa [8] . KSHV is found in the main KS tumor cells , the spindle cells , which are cells of endothelial origin [9 , 10] . In the KS spindle cells , KSHV is predominantly in the latent state ( >90% ) where only a handful of the more than 90 annotated viral genes are expressed as well as a number of viral microRNAs [11 , 12] . A limited number of spindle cells ( < 5% ) express markers of lytic replication as well [13] . While there are limited animal models for the disease , there are well-established mammalian cell culture systems that recapitulate the latent and lytic infection rates seen in KS tumors [14–17] . We and others have successfully used these cell culture models to demonstrate that KSHV promotes angiogenesis , modulates carbon utilization and alters lipid profiles in KSHV latently infected endothelial cells [18–21] . Our previous work showed that latent KSHV infection leads to profound changes in central carbon metabolism and fatty acid ( FA ) synthesis and that both are required for the survival of latently infected cells indicating the importance of altered metabolism and lipid homeostasis to latent infection [19 , 22] . Many of these cellular changes induced by KSHV are similar to phenotypes that commonly occur in cancer cells [3] . Several of the signaling pathways modulated by KSHV infection have been studied through traditional approaches of identifying individual host proteins or pathways predicted to play a role in the phenotype investigated . Here we are applying a more comprehensive approach where the global response of cell host in response to KSHV infection during latency at the protein and transcript levels are evaluated . Systems biology approaches can be utilized to identify important cellular networks on a cell-wide scale . In particular , advancement of recent mass spectrometry-based techniques using affinity-based phosphopeptide enrichment coupled with chemical labeling and high-resolution chromatography have been adapted to query changes in protein phosphorylation [23–25] . In addition , the assembly of large-scale , high-quality , protein-protein interaction databases provide an extensive and detailed context for interpreting proteome changes [26] . To evaluate gene expression profiles , next generation sequencing technology provides comprehensive analysis of the presence and quantity of the transcriptome . The use of transcriptomics data to predict transcription factor ( TF ) activity as a function of changes in mRNA provides an effective tool to link proteomics to transcriptomics data [27] . The integration of these two different data types has been successfully demonstrated in several biological systems , including glioblastoma [28] , breast cancer [29] , epithelial-mesenchymal transition [30] , yeast salt stress response [31] and influenza virus infection [32] . These studies have provided insights and a comprehensive view of cellular networks from stimuli to gene expression/suppression . We performed a systems-level data integration approach to identify global changes in cellular networks that are important for KSHV latent infection . To dissect cellular changes and examine the signal transduction from upstream signaling to downstream targets induced by KSHV infection , we first conducted a mass spectrometry-based proteomics and phosphoproteomics analysis , including both tyrosine and serine/threonine phosphoproteomics . We also evaluated gene expression profiles following KSHV infection using high throughput sequencing to generate global cellular transcriptomics data . Virally induced changes in both the proteome and the transcriptome were integrated using an inference algorithm to predict TF activation . A comprehensive protein-protein interaction network was used to identify predicted cellular pathways subverted by KSHV . This integrated systems biology approach identified multiple pathways altered by KSHV infection including peroxisome metabolism . Peroxisomes have been identified as a nexus of lipid metabolism and signaling [33 , 34] . While we have previously shown that FA synthesis is required for the survival of endothelial cells latently infected with KSHV , how these downstream FAs are utilized and why they are necessary have not been determined [19] . Peroxisomes have been studied in the context of infection with RNA viruses including influenza [35–40] . Interestingly , infection with influenza virus led to an increase in peroxisomes while infection with flaviviruses led to a significant decrease in peroxisomes metabolism . Our results show that KSHV latent infection of endothelial cells leads to increased numbers of peroxisomes . One major function of peroxisomes is to metabolize very long chain fatty acids ( VLCFAs ) . Peroxisomal defects have been associated with several clinical disorders , including . Zellweger syndrome , a disease characterized by abnormal peroxisome lipid metabolism presenting with deficiency of ACOX1 function , D-bifunctional protein ( D-BP ) and X-linked adrenoleukodystrophy ( X-ALD ) [41] . Lipidomics analysis in fibroblasts cells from these patients present with abnormal lipid profiles specifically high levels of VLCFs and low levels of DHA indicating abnormal function of VLCFs breakdown and DHA synthesis [42] . ABCD3 is a peroxisomal lipid transporter of VLCFAs involved in transporting 24:6n3 , the precursor of DHA [34] . After 24:6n3 is transported into the peroxisome; it gets further metabolized by ACOX1 , a peroxisomal enzyme . ACOX1 synthesizes DHA by partial β-oxidation of 24:6n3 [43 , 44] . In the current studies , transient knockdown of ACOX1 and ABCD3 led to cell death in the KSHV latently infected endothelial cells but not the mock-infected control . Overall , these findings validate our integrated global approach and strongly suggest that KSHV modulates peroxisomal lipid metabolism for the increased maintenance of latently infected cells .
To quantify global signaling events modulated by KSHV , we used quantitative phosphoproteomics and proteomics to compare mock and KSHV infected endothelial cells ( Fig 1A ) . Tert-immortalized microvascular endothelial cells ( TIME ) [16] were mock or KSHV infected and harvested at 48 hours- post-infection ( hpi ) , when latency has been established . Three biological replicates were performed with separate infections performed on different days ( Fig 1A ) . Latent infection , in greater than 90% of the cells was confirmed by immunofluorescence ( IFA ) assays . This approach identifies the presence of a latent protein ( ORF73 ) and the absence of ORF59 , a protein marker of lytic infection . ORF59 stained positive in less than 2% of the infected cells in all experiments . To quantify differential peptide expression levels between mock and KSHV infected cells , each sample was chemically labeled with isobaric tags for relative and absolute quantification ( iTRAQ ) [45] ( Fig 1A ) . Peptide quantification was normalized prior to labeling using quantitative fluorimetric peptide assay to ensure that similar amounts of peptides were labeled across all samples . Labeled peptides from each biological sample were pooled ( Fig 1A ) . Peptides were , then separated , sequenced and analyzed using one or two-dimensional ( 1D or 2-D ) HPLC tandem high-resolution mass spectrometry ( LC- MS/MS ) for phosphotyrosine enrichment and total phosphroteome and proteome , respectively ( Fig 1A ) . LC-MS/MS analyses were conducted in three stages . First , low-abundance phosphotyrosine ( pT ) containing peptides were identified and quantified after enrichment by immunoprecipitation ( IP ) ( Fig 1A ) . The IP flow-through was then used to enrich and quantify peptides containing phosphorylated serine ( pS ) , threonine ( pT ) and the remaining tyrosine residues using immobilized metal affinity chromatography ( IMAC ) . Finally , the IMAC flow-through was used to quantify total protein levels ( Fig 1A ) . Upon data acquisition and analysis , we confirmed there was not a statistically significant difference between the mean relative abundance of peptides across the samples , indicating that the sample labeling was equally effective in each case ( S1A–S1D Fig ) . A total of 2304 unique proteins from the proteome and 1038 unique phospho-proteins from the phosphoproteome runs were analyzed that includes phospho-tyrosine/threonine and serine . Activation of a phosphorylated residue within the same protein can vary; therefore , we analyzed individual peptides . From the phosphoproteome , we analyzed 1644 unique phospho-peptides , including 175 unique phosphotyrosine peptides that comprised 75 unique phosphotyrosine proteins ( Fig 1B ) . The protein and peptide population distribution for the proteome and phosphoproteome , respectively , were plotted based on the sum of relative peptide intensities from the iTRAQ reporter ions from mock and KSHV infected samples versus log10 of the ratios/fold change of KSHV over mock ( Fig 1D and 1E and S2 Table ) . Of the 1644 unique phospho-peptides identified , 192 were differentially phosphorylated , of which half were upregulated and half were down regulated ( paired t-test p < . 05 ) . Phosphorylated signal transducer and activator of transcription 3 ( STAT3 ) is the top hit of the tyrosine-phosphorylated residue from the phosphoproteome analysis ( Fig 1D ) . Our lab has previously shown that KSHV induces persistent activation of phospho-STAT3 during latency validating the phosphoproteomic results [46] . From the upregulated hits including both phosphoproteome and proteome , there are several proteins involved in metabolism , immunity , insulin resistance , endocytosis , NFk-B signaling and others , providing potentially interesting targets for future study . From the proteome analysis , we measured 2304 unique proteins among which 289 were altered by KSHV latent infection; 164 were upregulated and 125 downregulated . This corresponds to viral induced changes in 13% of the proteins detected and 12% of the phosphorylated residues , including unique phosphotyrosine ( Fig 1B ) . Kyoto Encyclopedia of Genes and Genomes ( KEGG ) pathway analysis of the phosphoproteins and proteins measured and altered during KSHV infection identified several pathways consisting of more than 4 proteins annotated [47] ( Fig 1C ) . These pathways include metabolic processes involved in carbon and lipid metabolism as well as hypoxia inducible factor ( HIF ) signaling , both of which had been previously associated with KSHV latent infection , providing internal positive controls for our proteomic data [18 , 22 , 48] . Gene Ontology analysis also provided similar results ( S2B Fig ) . To identify changes at the transcriptional level , high throughput cDNA sequencing from mRNA was performed to identify genes expression differences between mock and KSHV infected endothelial cells at 48 hpi . Three separate mock and KSHV infections of TIME cells performed on different days were analyzed by high throughput sequencing of cDNA . Expression of 12 , 375 cellular genes in all replicates were identified . Of the genes measured , 985 cellular genes were significantly upregulated following KSHV infection of endothelial cells and 1 , 134 were significantly downregulated at a 1% FDR using a method based on the negative binomial distribution ( Fig 2A and S3A Fig ) . The transcriptomic data was used to predict the activities of transcription factors based on binding motifs in the promoter regions of transcripts that are activated or repressed following latent KSHV infection . The enrichment of a motif in the promoters of genes whose expression is significantly altered implicates the motif’s associated TF as a possible regulator ( Fig 2B ) . The software FIMO identified putative binding sites by motif presence [49] , and two-sided Wilcoxon rank-sum tests [50] quantified and assigned p-values to the enrichment of those binding sites in promoters of genes significantly changed in expression after KSHV infection ( Fig 2B ) . FIMO was used to scan for the locations of 426 TF binding motifs , from a curated database of position-weight matrices compiled and derived from multiple experimental types , in 1000bp regions upstream of annotated transcription start sites [27] . The enrichment scores of the 261 motifs whose corresponding TF’s mRNAs were reliably detected in the RNA-seq data and the mRNA’s fold-change in expression after KSHV infection ( Fig 2C ) . A positive enrichment z-score indicates that the motif’s putative target genes increase in expression on average , and a negative enrichment score indicates that the motif’s putative target genes decrease in expression on average . Wilcoxon rank-sum tests assigned statistical significance to the motif enrichments and found that five motifs were significantly enriched at a 5% FDR ( p-value < 0 . 001 ) and twenty-four more were enriched at a less stringent cutoff of p-value < 0 . 05 ( Fig 2C ) . The motif of four of these TFs , interferon regulatory factors 1 , 2 , 7 ( IRF1 , IRF2 , IRF7 ) and STAT2 , are enriched only in upregulated promoters . However , because the motifs for these factors are similar ( S3C Fig ) , it is not clear which of the TFs or TF complexes are actually relevant from just the motifs . The mRNA of IRF1 exhibits a 3 . 3-fold increase in gene expression ( p-value < 0 . 001 ) as measured by our transcriptomics data , which may imply that IRF1 is a more relevant player . A motif associated with the transcriptional repressor zinc finger protein 148 ( ZNF148 ) , was significantly enriched in downregulated promoters ( S3C Fig ) , suggesting that ZNF148’s repressor activity increased post-infection . The repressor E2F5’s motif was also significantly enriched in downregulated promoters ( p-value = 0 . 0038 ) . It has been shown that E2F5 is inhibited by retinoblastoma protein 1 , which is directly inhibited by the KSHV protein , LANA [51] . These data support that motif enrichment scores can successfully denote prediction of transcription factor activity for use in the analyses below . To build a comprehensive network model that describes the host response to viral infection , we used the Prize-Collecting Steiner forest algorithm to integrate the proteomic and TF motif analyses [52] . This algorithm parsimoniously identifies the protein-protein interaction most likely to be relevant for connecting the relevant factors identified in the two types of analyses . In addition , it identifies Steiner nodes , which are proteins that were not implicated in the proteomic or TF analyses but form crucial connections between other important proteins identified as altered by KSHV in the global data sets generated . The proteomic data was integrated with the TF enrichment scores rather than the differentially expressed genes from the RNAseq data because TF transcript levels do not necessarily reflect regulatory activity [53] . When combining gene expression data with other types of protein scores for pathway reconstruction , it is therefore preferable to use inferred TF activities [28 , 29 , 32 , 54] . The complete predicted Steiner forest network is large , connecting hundreds of proteins that respond to KSHV infection and the TFs inferred to regulate the transcriptional changes ( S4 Fig ) . Randomization analysis shows that the selected proteins and interactions are specific to KSHV infection and do not reflect biases in the protein-protein interaction network or Steiner forest algorithm ( S7A and S7B Fig ) . To focus on specific biological functions , we assessed the overlap between the proteins in our Steiner forest network and KEGG pathways . We required a minimum overlap of 5 proteins and used the Benjamini and Hochberg multiple hypothesis test correction ( FDR < 10% ) . The significantly enriched pathways included pathways involved in phagocytosis , immune response and several metabolic processes among others ( Fig 3A and S1 Table for complete list ) . Our lab has shown that metabolism is altered during latent KSHV infection , including carbon and lipid metabolism , which supports our integrated network analysis [19 , 22 , 48] . There are several interesting pathways that are predicted to be altered during KSHV latent infection ( S1 Table ) . From this analysis , we decided to follow up on proteins that clustered together , particularly those involving peroxisome metabolic lipid signaling . We have previously identified that lipid metabolism is required during latency [19] , but how these metabolites are further utilized during KSHV latent infection still unknown . Therefore , we chose to further analyze activation of peroxisomes by KSHV . Peroxisome related proteins identified in the subnetwork including SCP2 , PRDX5 , ACSL3 , MLYCD , AGPS , EHHADH , PEX19 were upregulated following KSHV infection and two Steiner nodes PEX12 and PEX5 were predicted by the algorithm to be activated by KSHV ( Fig 3C ) . The proteins in this cluster are involved in lipid metabolism ( SCP2 , ACSL3 , MLYCD , AGPS , EHHADH ) and peroxisome organelle biogenesis and transport ( PEX19 , PEX12 and PEX5 ) . Therefore , this sub- network predicts that KSHV induces peroxisome pathways involved in lipid metabolism and biogenesis . In addition , IRF3 is an interferon inducible gene activator that was predicted in the TF analysis to have increased transcriptional activity . It is not annotated as a peroxisome pathway protein , but the Steiner forest algorithm includes IRF3 in our peroxisome subnetwork due to its predicted relationship with PEX19 and previous evidence of a direct IRF3-PEX19 interaction [55] cataloged in the iRefIndex database . This observation suggests that peroxisomes might also play a role in immune signaling during latency , which might be an important regulatory control point of KSHV infection . In addition , we incorporated a protein-protein interaction database from a study that mapped global interactions between KSHV genes and host proteins using viral gene pulldowns [56] . Since our study is mainly focused on latency , we included only the KSHV latent viral proteins and host proteins hits with our predictive Steiner forest network . From the database , we found that two viral genes have been shown to interact with proteins associated with peroxisome biogenesis . The latent KSHV proteins that are predicted to be associated with peroxisome biogenesis are shown as purple diamonds in Fig 3C . The KSHV protein-protein interaction database used total protein pull downs and therefore does not demonstrate direct interactions , rather it shows associations with the identified protein . All the proteins from the KSHV major latent locus were included in the Steiner forest analysis shown in figure S5 Fig . The utilization of KSHV protein-protein interaction dataset with the other protein-protein interaction databases advances our predictions of pathways that could be important to KSHV pathogenesis . To validate the prediction that peroxisome pathways involved in lipid metabolism and likely peroxisome biogenesis are increased during latent infection , we examined the protein levels of ABCD3 , an ATP Binding Cassette Subfamily D Member 3 , a lipid transporter specific to peroxisomes and a common marker to study peroxisomes , using flow cytometry at 48 and 96 hpi ( Fig 4A–4H ) . Staining with an antibody to ABCD3 showed a significant increase in fluorescent staining in the KSHV infected cells compared to mock infected cells in three different infections at 48 and 96 hpi in TIME cells and 96 hpi in primary human dermal microvascular endothelial cells ( hDMVECs ) and lymphatic endothelial cells ( LECs ) ( Fig 4E–4H ) , indicating that during latent infection KSHV significantly upregulates ABCD3 protein expression . In addition , we evaluated MLYCD and PEX19 and a non-clustered peroxisome protein PEX3 levels , in TIMECs , hDMVECs and LECs . PEX3 is a PEX19 docking factor required for PEX19 to deliver proteins into the peroxisome matrix [57] . Staining with MLYCD and PEX3 antibody showed a significant increase of the protein levels in KSHV infected TIME cells , primary hDMVECs and primary LECs compared to mock infected at 96 hpi ( S6 Fig ) while PEX19 was significantly upregulated in TIME cells ( S6 Fig ) . We next evaluated the peroxisome organelle number using confocal imaging analysis of mock and KSHV infected TIME cells stained with antibody to ABCD3 . Peroxisome size ranges approximately between . 4–1 uM . We evaluated 3D particles using z-stacks imaging and then quantified the particle number as a proxy of peroxisome organelle per cell with a minimum threshold of . 5 uM . There is an approximately 50% increase in the number of peroxisomes per cell in the KSHV infected endothelial cells as compared to mock infected cells ( Fig 4I and 4J ) . Representative pictures of the mock and KSHV infected cells stained with an antibody to ABCD3 are shown in Fig 4I . Combined , these observations support the prediction from the Steiner forest analysis that KSHV promotes peroxisome biogenesis . To determine that the increase of peroxisome numbers per cell was not a cellular response to infection but rather induced by virally encoded genes , cells infected with UV irradiated KSHV were stained with ABCD3 antibody and measured by flow cytometry . UV irradiated virus can bind and enter cells but does not express viral genes . Flow cytometry analysis showed no increase in the expression of ABCD3 following infection with UV irradiated virus ( Fig 5A and 5B ) . Therefore , the increase of ABCD3 in latently infected cells requires KSHV gene expression and it is not a cellular response to virus entry into the cell . The KSHV latent locus is comprised of LANA , vCyclin , vFlip , Kaposins and 12 microRNA loci . To assess the role of the KSHV latent locus in increasing the number of peroxisomes , we evaluated whether the KSHV latency associated region ( KLAR ) is sufficient to induce the increase of ABCD3 protein expression levels . The KLAR locus ( a kind gift from Dr . Rolf Renne ) was cloned into a helper-dependent gutted adenovirus vector that does not express any adenovirus genes . Cells were infected with a control gutted adenovirus ( Ad ) only expressing GFP ( AdGFP ) and the gutted adenovirus expressing KLAR ( AdKLAR ) and stained with ABCD3 antibody . Infection rates for AdGFP and AdKLAR were 59% and 97% respectively as determined by expression of GFP or LANA . To adjust for the differences in the infection rates , we gated on the GFP positive cells from the AdGFP infected cells and then compared to the AdKLAR cells . Cells infected with the AdKLAR expressing gutted adenovirus exhibited increased ABCD3 protein expression compared to mock infected cells and AdGFP ( Fig 5C and 5D ) . Therefore , the latency genes are sufficient to induce ABCD3 protein expression levels . Peroxisomes are involved in lipid signaling and metabolism . Our previous metabolomics screen indicated that several lipid metabolites are altered by KSHV during latent infection of endothelial cells , including two metabolites generated in the peroxisome , dihydroxyacetone phosphate ( DHA-P ) and docosahexaenoate ( DHA; 22:6n3 ) [19] and metabolites upstream of DHA are also upregulated as indicated in red numbers ( Fig 6A ) . DHA is synthesized from 24:6n3 by Acyl-CoA Oxidase 1 ( ACOX1 ) an enzyme mainly expressed in the peroxisome and it is involved in the first step of peroxisomal β-oxidation [43] ( Fig 6A ) . To determine if ACOX1 is necessary during KSHV latent infection , small interfering RNA ( siRNA ) was used to knockdown its gene expression ( Fig 6B ) . Loss of ACOX1 did not alter the cellular proliferation of uninfected cells or the KSHV infection rates but resulted in a significant increase in cell death of the KSHV infected cells compared to controls at 96 hpi ( Fig 6C–6E ) . As ACOX1 is the main enzyme involved in metabolizing DHA , these observations suggest that DHA might be required during infection . The precursor of DHA , 24:6n3 is transported into the peroxisome by the lipid transporter ABCD3 [34 , 58] . Therefore , we evaluated if ABCD3 is required during latency by transiently silencing its gene expression . Similarly , to ACOX1 , loss of ABCD3 did not alter cellular proliferation of uninfected cells or KSHV infection rates but resulted in a significant increase in cell death of the KSHV infected cells compared to controls at 96 hpi ( Fig 6C–6E ) . Therefore , both ACOX1 and the ABCD3 transporter are required for the survival of endothelial cells latently infected with KSHV . In parallel , we treated cells with a pan-caspase inhibitor , QVD , to test whether apoptosis was the main cell death mechanism . KSHV-siABCD3 and KSHV-siACOX1 cells treated with QVD showed a 3-fold decrease in cell death , indicating that apoptosis was the main cell death mechanism ( Fig 6D ) . Therefore , this data strongly indicates that peroxisomal proteins involved in lipid metabolism are required for the survival of endothelial cells latently infected with KSHV .
We integrated transcriptomics , proteomics and metabolomics analyses , to provide a comprehensive view of cell signaling in an oncogenic virus infection in human endothelial cells , the cell type likely to be most relevant to KS tumor cells ( Fig 7 ) . From quantitative measurements of the phosphoproteome and proteome analysis of endothelial cells latently infected with KSHV , we found that latent infection alters the levels of at least 289 proteins , approximately 13% of the proteome quantified , as well as 192 altered phosphorylation sites , approximately 12% of the phosphosites quantified in this study . Previous studies using mass spectrometry based proteomics and KSHV , was done with targeted proteomics to identify protein-protein interactions specific to single viral proteins , LANA and K5 for example , using immunoprecipitation and 2D-gel mass spectrometry [59–69] . Our dataset is the first that we are aware of , that analyzes the global response to latent KSHV infection with both phosphoprotein and proteomic studies in endothelial cells . The list of phosphosites altered by KSHV infection may provide deeper insights into cell signaling activation following KSHV infection of endothelial cells and should serve as a useful dataset for future studies . From transcriptomics analysis , we found that KSHV infection leads to alterations in approximately 17% of the host cellular transcriptome . This dataset was generated using next generation sequencing providing more comprehensive gene expression profiles in endothelial cells latently infected with KSHV than previously published . Transcriptomic analysis of KSHV infection has been done in KS tumors and in PEL cells , but only older microarray technology for endothelial cells has been previously done [70–78] . The activity of several TFs was predicted to be activated or repressed by latent infection as identified from transcription factor motifs found in the promoters of host genes that were up or down regulated following KSHV infection of endothelial cells . These TFs serve as a link to map protein-protein interactions , connecting upstream signaling to downstream gene-expression targets . The goal of this integrated systems biology approach was to identify novel pathways that could not be predicted by one platform alone . Various functional networks , including phagosomes , endocytosis and multiple metabolic pathways including peroxisome biogenesis were identified by the Steiner forest analysis providing a rich data set for future studies . We chose to further analyze peroxisome biogenesis as peroxisomes are involved in several pathways likely to be important for KSHV pathogenesis including redox control and the breakdown of very long chain fatty acids . A sub-network cluster of peroxisomal proteins predicted to be activated by the Steiner forest analysis is shown in Fig 3C . The presence of this sub- network implies increased peroxisome activity in KSHV latently infected endothelial cells . The integrated analysis is further substantiated by a significant increase in the number of peroxisomes per cell during KSHV latency , induced specifically by KSHV latent gene expression as opposed to a cellular response to a viral infection . Upregulation of peroxisomes was further validated by identifying the upregulation of several peroxisomal proteins in TIME cells , primary dermal microvascular endothelial cells as well as in primary lymphatic dermal microvascular endothelial cells , the cell type that most closely resembles KS spindle cells [78] . We previously found that KSHV latent infection dramatically alters the lipid profile of endothelial cells [19] . In the KSHV infected cells , there was a significant increase in most of the LCFAs measured . We also found that FAs synthesis was necessary for the survival of endothelial cells latently infected with KSHV [19] . In our metabolomic screen we also noted that DHA and its precursors , as well as DHA-P , were increased following KSHV infection during latency [19] ( Fig 6A ) . DHA is an important metabolite involved in anti-inflammatory responses and cellular development and is mainly produced in the peroxisome by partial β-oxidation [43 , 44 , 79] . Knockdown of ACOX1 , the enzyme that produces DHA , results in a significant increase in the death rate of latently infected endothelial cells but not their mock infected counterparts . Furthermore , the peroxisome-specific lipid transporter ABCD3 , which transports VLCFAs including a precursor of DHA , is also essential for KSHV-infected endothelial cell survival . Therefore , lipid metabolism in the peroxisome is essential for the survival of endothelial cells latently infected with KSHV . Peroxisomes appear to play a role in the response to lytic viral infection for several viruses . This organelle serves as a signaling platform for antiviral response against unrelated non-enveloped and lipid-enveloped RNA and DNA viruses including Reovirus , Sendai virus , Dengue virus and Influenza virus in infected mouse embryonic fibroblast cells [36–39] . Interestingly , one study established that Influenza virus modulates and requires peroxisomal ether lipid metabolism for efficient virion replication in A549 epithelial cells [37] . These observations underscore complex and sometimes paradoxical cellular changes that involve peroxisomes during viral infection; while Influenza requires peroxisome metabolism for virion production , peroxisomes also play an important role in the immune response triggered by infection . The interplay between peroxisome responses and viral infection may depend on the cellular environment and virus type . Our study elucidates a novel mechanism by which a latent herpesvirus infection manipulates peroxisomal lipid signaling required for survival in a long-term infection . KSHV is known to activate COX-2/PGE2/EP vector during de novo infection mediating an underlying pro-inflammatory state conducive to long-term latency [21] . COX-2 converts Arachidonic Acid ( 20:4n6 , or AA ) to PGE2 , which then regulates autocrine and paracrine signaling . Our previously published metabolomics screen demonstrates that KSHV latent infection upregulates precursors of the COX-2/PGE2/EP signaling , such as AA indicating that signaling upstream of the COX-2/PGE2/EP pathway is active during latency . Furthermore , upregulation of DHA-P and DHA as shown by our metabolomics screen indicates , that the peroxisomes are enzymatically active and producing these metabolites [33] . Therefore , the peroxisome represents a crossroads of lipid signaling and bridges the gap between upstream essential fatty acids ( AA , EPA and DPA ) and how they are metabolized downstream ( DHAP , DHA ) during latent KSHV infection ( Fig 6A ) . AA is a pro-inflammatory metabolite and DHA has been associated with anti- inflammatory responses [80]; however , both are upregulated during latency . The KS tumor environment is characterized by a chronic inflammatory state [11] . Therefore , we hypothesize that KSHV commandeers control of cellular metabolic pathways to fine-tune a higher level of chronic inflammation by altering homeostatic mechanisms and maintaining a shifted equilibrium in this new inflammatory state required for the maintenance of latency . Further work is required to elucidate whether the primary role of peroxisomes is to regulate lipid signaling and inflammation , if peroxisomal ether lipid metabolism is required or if peroxisomes are also involved in regulating H2O2 , which often occurs in parallel . It has been shown that in primary endothelial cells during exogenous stress , inflammatory cytokine expression is downregulated by using DHA as anti-inflammatory treatment [81] . It would be interesting to determine if altering DHA synthesis influences inflammatory signaling proteins in endothelial cells latently infected with KSHV . Currently , pharmacological approaches that target herpesvirus infection focus on lytic replication and there are no treatments specific for latent infections . Since KS tumors primarily exhibit latent infection , this study elucidates critical control point mechanisms in the latent phase offering an understanding of KSHV viral pathogenesis and provides potential novel and combinatorial molecular therapeutic targets through large scale identification of pathways activated by KSHV latent infection of endothelial cells .
QVD-OPH ( SMBiochemicals ) was dissolved in DMSO and used at a final concentration of 20 μM . YOYO-1 and SytoGreen were purchased from Thermofisher scientific . Tert-immortalized microvascular endothelial ( TIME ) cells were obtained from the McMahon lab and previously described in Venetsanakos , et . al . [82] , human dermal microvascular endothelial cells ( hDMVECs ) and lymphatic endothelial cells ( LECs ) ( LONZA Walkersville , MD ) were maintained as monolayer cultures in EBM-2 media ( LONZA Walkersville , MD ) supplemented with a bullet kit containing 5% FBS , vascular endothelial growth factor , basic fibroblast growth factor , insulin-like growth factor 3 , epidermal growth , and hydrocortisone . KSHV for phosphoproteomic , proteomic and transcriptomic experiments was purified from BCBL-1 cells , a primary effusion lymphoma cell line that maintains wild type KSHV , as described previously [22] . For most of the subsequent studies , KSHV was isolated from iSLK cells containing a recombinant KSHV made from KSHV-Bac16 containing the GFP gene as described previously [83] . For all experiments KSHV was titered and used to infect TIME cells as previously described [46] . Infections were performed in serum-free EBM-2 media and subsequently overlaid with complete EBM-2 media . Infection rates were assessed for each experiment by immunofluorescence and only experiments where greater than 90% of the cells expressed LANA , a latent marker , and less than 2% of the cells expressed ORF59 , a lytic marker , were used as previously described [46] . To express the KSHV latent genes in the absence of other viral gene expression , The 12 . 6 kbp KSHV latency associated region ( KLAR ) containing the native LANA promoter , LANA , vCyc , vFLIP , all 12 miRNA loci and the kaposins through the native polyadenylation signal downstream of the kaposins , was obtained from the Renne lab . The helper dependent Adenovirus contains the adenovirus packaging signal but no adenovirus genes and was purchased from MicroBix . To create AdKLAR and AdGFP , the KSHV KLAR region or GFP was cloned into a shuttle vector ( pBShuttle ) flanked by adenoviral sequences . The KLAR/adenovirus expression cassette was then excised from this plasmid and electroporated into BJ5183 cells ( Stratagene ) along with pC4Hsu helper adenovirus vector ( Microbix Biosystems ) to allow for homologous recombination . The resulting plasmid ( AdKLAR or AdGFP ) was transfected into 293Cre cells , which stably express a Cre recombinase enzyme , selectable with puromycin . Cells were passaged in the presence of helper adenovirus ( HD14; Microbix ) , which contains the adenovirus coding regions and allows to produce AdKLAR adenovirus . The Helper Adenovirus contains a modified packaging sequence flanked by loxP sites; therefore , the helper adenovirus is not packaged due to an excision of the packaging sequences . After expansion of the adenovirus , cells were collected , pelleted , and freeze-thawed three times using liquid nitrogen and 37 C water bath . Cell debris was spun out at 2000rpm and the cell-free supernatant was collected . The cleared lysate was layered onto a continuous 15% to 40% CsCl gradient and centrifuged for 2–3 hours at 35 , 000g using a SW41Ti rotor ( Beckman Coulter , Inc . , Fullerton , CA ) . The mature virus band was collected and purified in a second CsCl density gradient . The virus band was collected , dialyzed against three changes of A195 buffer . Infections were performed in serum-free EBM-2 medium supplemented with 1μg/mL poly-L-lysine for 1 hour , after which the medium was replaced with complete EGM-2 media . Infection rates were assessed for each experiment by immunofluorescence for LANA , a latent marker , and GFP for AdGFP expression . Mock- , KSHV-infected cells were washed with PBS and removed by trypsinization , fixed , with 4% paraformaldehyde for 30 min on ice and processed for flow cytometry . Cells were permeabilized and blocked with . 1% triton and 1% NGS . The ABCD3 transporter was detected with the PMP70 ( ABCD3 ) antibody from thermoscientific product# PA 1–650 , MLYCD Proteintech Group ( 15265-1-AP ) , PEX3 Novus a Biotechne brand ( NBP1-86210 ) and PEX19 Abcam ( ab 137072 ) . After staining with the primary antibody for 1 hour , the cells were reacted with a secondary Alexa Fluor 594 and Alexa Fluor 488 both anti-rabbit ( ThermoFischer P#A11072 ) antibodies . Samples were analyzed by FLOWJO , flow cytometry analysis software . TIME cells were seeded on a 4-well glass chamber and were processed for confocal microscopy by fixing in paraformaldehyde ( 4% in 1XPBS ) at 37°C . Samples were permeabilized with Triton X-100 ( 0 . 5% in 1XPBS ) . Incubations with primary antibodies diluted ( 1:1 , 000 ) in blocking buffer ( 3% bovine serum albumin [BSA] and 1XPBS ) were carried out at room temperature ( RT ) for 30 minutes . Samples were then incubated with secondary antibodies ( Alexa Fluor 488 anti-rabbit ) in blocking buffer for 25 min at RT . Prior to mounting; samples were incubated with DAPI for 5 min at RT coverslips were mounted on microscope slides . Confocal images were acquired using Zeiss LSM 510 Meta confocal microscope Olympus . 2-5um Z-stacks were acquired using a Zeiss 510 META confocal microscope equipped with a 63X / 1 . 4 NA Oil DIC objective . The exported images were then processed using Imaris 7 . 2 . 3 software ( Bitplane ) for peroxisome quantification and ImageJ was use for figure images . Cytoplasmic peroxisomes were quantified based on voxels graphics . The data were then analyzed using student’s t-tests . siRNAs specifically targeting ACOX1 ( pre-validated ) were purchased from Santa Cruz Biotechnology ( Cat . Sc-94104 ) . A negative-control siRNA ( siSCRB ) and ABCD3 ( pre-validated ) were designed and synthesized by Ambion . TIME cells were transfected with siRNA using the Amaxa Nucleofector Kit by Lonza per the manufacturer’s protocol . At 24 hour post transfection , cells were Mock- or KSHV-infected . At 96 hpi cell death was measured using Trypan blue assay and cell were quantified using TC20 cell counter from BioRad . In parallel , cell death fluorescent images were acquired using the IncuCyte from Essen Bioscience using YOYO-1 or SytoGreen ( both probes from Thermofisher scientific ) . Approximately 5 million cells were lysed in 2 mL of 8M Urea . Protein concentration was determined by the BCA assay ( Pierce ) . Samples were reduced with 5 mM dithiothreitol at 56 C for 1 hour , and then alkylated with 15 mM iodoacetamide for 1 hour at RT in the dark . Samples were diluted 4-fold with 100 mM Ammonium Acetate , pH 8 . 9 , and digested with Sequencing Grade Modified Trypsin ( Promega ) at a ratio of 1:100 ( trypsin to total protein ) , overnight at RT . Following digestion , peptides were desalted and concentrated using Sep-Pak Plus C18 cartridges ( Waters , cat . no . WAT020515 ) per the manufacturer’s recommendations . Samples were then dried by vacuum centrifugation , lyophilized , and stored at -80 C until further processing . Phosphorylated samples were labeled with 8-plex iTRAQ reagents ( AB Sciex ) . Lyophilized peptides derived from approximately 1 million cells were resuspended in 30 uL of dissolution buffer ( 0 . 5 M N ( Et ) 3HCO3 pH 8 . 5–9 ) . iTRAQ labels were resuspended in 70 uL of isopropanol and added to the peptide mixture . Samples were incubated at RT for 2 hours , combined , and dried overnight by vacuum centrifugation . The following day , samples were desalted and concentrated using Sep-Pak Vac 1cc ( 50mg ) cartridges ( Waters , cat . no . WAT054955 ) according to the manufacturer’s recommendations . Samples were then dried by vacuum centrifugation , lyophilized , and stored at -80 C until further processing . Approximately 100 uL of packed Ni beads ( Ni-NTA Superflow beads , Qiagen ) were washed three times in water and stripped with 100 mM EDTA pH ~8 . 9 for 30 min . Beads were then washed three times with water and once with 80% ACN in 0 . 1% trifluoroacetic acid . Lyophilized iTRAQ samples were resuspended in 1 . 5 mL of ACN in 0 . 2% TFA and incubated with prepared beads for 1 hour at RT . Beads were then washed three times with 80% ACN in 0 . 1% TFA , and phosphopeptides were eluted from beads with 2 incubations in 75 uL of 1 . 4% Ammonia . Samples were then vacuum centrifuged down to ~20 uL . 2 uL of 200 mM ammonium formate pH 10 was added and samples were directly analyzed by mass spectrometry . Peptide samples were loaded onto a first-dimension trap column ( Waters Xbridge , C18 , 10 uM particle size , 100 Å pore size , 4 cm packing length 150 uM column inner diameter ) . Online peptide separation coupled to MS/MS was performed with a 2D-nanoLC system ( nanoAcquity UPLC system , Waters ) and a Velos-Pro/Orbitrap-Elite hybrid mass spectrometer ( ThermoFisher Scientific ) . Six discrete elutions were performed at 1 . 5 uL/min with 5mM ammonium formate pH 10 using increasing concentrations of ACN ( 1% , 3% , 6% , 15% , 25% and 44% ) and diluted with 6 uL/min 0 . 1% formic acid ( FA ) prior to loading onto a second dimension trap column ( Dr . Maisch ReproSil-Pur , C18 , 5 uM particle size , 120 Å pore size , 4 cm packing length 150 uM column inner diameter ) connected to an analytical column ( Orochem Reliasil , C18 , 3 uM particle size , 90 Å pore size , 20–25 cm packing length 50 uM column inner diameter ) with an incorporated electrospray emitter . Peptide separation was achieved using a gradient from 3 to 80% ( V/V ) of ACN in 0 . 1% FA over 115 minutes at a flow rate of 200 nL/min . The mass spectrometer was operated in data-dependent mode using a Top 10 method . Full MS scans ( m/z 300–2000 ) were acquired in the Orbitrap analyzer ( resolution = 120 , 000 ) , followed by high energy collision induced dissociation ( HCD ) MS/MS ( fm/z 100–2000 , resolution = 15 , 000 ) at a normalized collision energy of 35% . MS data files were searched using the COMET [24 , 84] algorithm and the output was imported into the Trans-Proteomic Pipeline [85] with the following parameters: variable oxidation of methionine , variable phosphorylation of Serine , Threonine , or Tyrosine , up to 4 variable modifications per peptide , fixed oxidation of Cysteine , and fixed iTRAQ labeling of Lysines and the N-terminus , maximum charge of 7 . Peptide false discovery rate ( FDR ) was set to 5% for phosphorylation analysis . Peptide quantification based on the iTRAQ labels was determined using the LIBRA software embedded in the Trans-Proteomic Pipeline . Phosphopeptides were normalized to an internal control peptide ( VNQIGpTLSESIK ) from the enolase digest containing phosphorylated peptides . For each biological replicate , 2 technical replicates were run . A total of 12 fractions for the phosphoproteome , 12 fractions for the proteome and 2 fractions for phosphotyrosine- enriched runs were analyzed . 3 , 579 peptide spectra profiles were analyzed for the proteome , 4982 for the phosphoproteome including the phosphotyrosine residues . From the phosphotyrosine enrichment 1053 total spectra were analyzed . There was approximately 70% overlap of proteins identified from both technical runs and approximately 50% overlap in the phosphoproteome ( S2A Fig ) . Each peptide included in the analysis was identified in a minimum of 2 spectra , and each protein included in the analysis was identified by a minimum of two unique peptides . To deconvolve complex overlapping spectra profiles , we used the Hardklör algorithm [86 , 87] in conjunction with the MassSpecUtil tool to merge spectra for iTRAQ analysis quantification . This was done to separate any possible overlapping isotopic envelope and providing better peptide identification . Comet search algorithm was used to identify the peptide spectra with a 5% FDR . To assess significantly changing protein phosphorylation and abundance , peptide-spectrum matches that did not have an intensity of at least 10 in all channels were removed . All channels were median normalized , and the intensities were summed over all peptides of the same protein for each condition . The KSHV-specific effects were assessed by computing the log2 ( K/M ) fold change for the means of the KSHV- ( K ) and mock ( M ) -infected biological replicates . A paired t- test was used to calculate the significance of the changes . Each technical replicate was analyzed independently . TIME cells were Mock- or KSHV-infected with virus isolated from BCBL-1 cells , as described above , and incubated for 48 hours . Total mRNA was isolated from TIME cells using the NucleoSpin RNA kit ( Machery-Nagle , Bethlehem , PA ) . mRNA was further concentrated and purified using the RNA Clean and Concentrator kit ( Zymo Research , Irvine , CA ) . Purified mRNA samples were processed at the Benaroya Research Institute Genomics core facility and sequenced using an Illumina HiSeq 2500 . Image analysis and base calling were performed using RTA v1 . 17 software ( Illumina ) . Reads were aligned to the Ensembl's GRCh37 release 70 reference genome using TopHat v2 . 08b and Bowtie 1 . 0 . 0 [88 , 89] . Counts for each gene were generated using htseq-count v0 . 5 . 3p9 . The data have been deposited in NCBI's Gene Expression Omnibus [90] and are accessible through GEO Series accession number GSE84237 . DNA sequences 1000bp upstream of annotated transcription start sites were downloaded from the Genome Reference Consortium at http://hgdownload . cse . ucsc . edu/goldenpath/hg19/bigZips/upstream1000 . fa . gz on May 29 , 2015 . Position-frequency matrices of 426 motifs were taken from a database of consensus motifs compiled from a variety of experimental techniques downloaded from http://meme-suite . org/meme-software/Databases/motifs/motif_databases . 12 . 11 . tgz . For each motif , FIMO software was used to find the top 1000 instances by p-value of each motif in the sequences . A motif was considered to flank a gene if an instance of the motif exists within 1000bp upstream of the gene’s transcription start site . For each TF with at least 50 reads in all three mock-infected replicates or all three KSHV- infected replicates , a two-tailed Wilcoxon rank-sum test compared the change in expression post- infection of the genes flanked by of its motif locations to the change in expression of genes that are not . For the test , the genes were ranked by the significance and direction of their change in expression as analyzed by DESeq , where the highest-ranking genes were associated with low DESeq p-values and increased expression post-infection , the lowest-ranking genes were associated with low DESeq p-values and decreased expression , and the intermediate genes had high p-values . The Wilcoxon rank-sum tests compared the sum of the ranks of binding-site- flanked genes to a null normal model . We conducted network analysis with the Prize-Collecting Steiner Forest ( PCSF ) algorithm from the Omics Integrator package , which uses msgsteiner for optimization [91] . PCSF identifies a sparse sub-network that connects the proteins highlighted by mass spectrometry with the TFs identified by the motif-based analysis . It assigns positive scores ( prizes ) to the proteins and TFs that reflect how relevant they are to KSHV infection and edge costs to protein-protein interactions that represent how trustworthy they are , with reliable edges receiving lower costs . The sub-network maximizes the cumulative prizes of the included proteins while minimizing the edges costs . It can include Steiner nodes , which are proteins that were not assigned prizes but form critical links between other proteins . We created an endothelial-specific weighted interaction network by integrating our RNA-seq data with the iRefIndex PPI network [26] . The PCSF algorithm requires protein prizes that quantify how relevant they are to the biological process of interest and PPI edge costs that describe how reliable they are . To calculate protein prizes , we scaled the p-values from the proteomic and phosphoproteomic technical replicates into the range [0 , 1] by computing -log10 ( p-value ) , subtracting the minimum value across all proteins , and dividing by the maximum value across all proteins . Each proteomic and phosphoproteomic replicate was scaled independently . For the TF prizes , we transformed the Wilcoxon rank-sum q-values as -log10 ( q-value ) but did not rescale them . Visualizing the histograms of the proteomic and TF scores revealed that the proteomic scores would dominate the TF scores if the TF scores were rescaled . For each protein , we then selected the maximum score from the two proteomic technical replicates , two phosphoproteomic technical replicates , and TF motif scores , which produced prizes for 3080 unique proteins . We used the iRefIndex ( version 13 . 0 ) PPI network [26] . The iRefIndex database aggregates PPI from multiple primary interaction databases such as BioGRID [92] , DIP [93] , HPRD [94] , and IntAct [95] . All edges represent direct , experimentally-detected physical interactions between two proteins as opposed to predicted PPI or other types of functional relationships . We calculated edge costs between 0 and 1 based on the interaction metadata such as the interaction type , experimental assay , and number of supporting publications as in Ceol et al . [96] . PPIs identified using reliable , low-throughput experiments ( for example , co- crystallization ) are assigned much lower costs than interactions detected in large-scale screens . Interactions reported in multiple publications similarly receive lower costs . Low cost edges are more likely to be selected by PCSF so the PCSF subnetwork preferentially includes trustworthy protein-protein relationships . We created an endothelial-specific network by removing all unexpressed genes from the iRefIndex PPI network , which initially contains interactions from many types of human cells and tissues . Originally , the network contained 175854 interactions among 15404 proteins . After filtering genes that were not expressed at 50 counts or greater in all six of the RNA-seq replicates , the endothelial-specific network contained 121059 interactions among 9489 genes . To select PCSF parameters , we performed a grid search testing all combinations of β from 0 . 25 to 5 . 0 with a step size of 0 . 25 , μ from 0 to 1 . 0 with a step size of 0 . 005 , and ω from 0 . 5 to 3 . 0 with a step size of 0 . 5 . Under some parameter combinations , the hub node ubiquitin C ( UBC ) was directly connected to a large portion of genes in the network so we discarded all networks containing UBC . The parameters β = 4 . 75 , μ = 0 . 02 , and ω = 2 . 5 produced the largest network without UBC , and we used these parameters for further analysis . We ran PCSF 1000 times with these parameters and additionally set r = 0 . 01 to add random noise to the edge costs . The multiple executions of PCSF with random noise were used to identify parallel paths between proteins in the different runs . Our final network was the union of the 532 of these 1000 PCSF networks that did not contain UBC . The union network contained 1253 interactions among 734 proteins , of which 44 were Steiner nodes . For visualization , we calculated edge frequency as the fraction of the 532 PCSF networks that contain a particular edge . The edge thickness in the network figures reflects edge frequency in the collection of PCSF networks , not the original interaction confidence score in the PPI network . To assess whether the PCSF subnetworks are specific to KSHV infection , we ran PCSF 1000 times with the same parameters and randomized protein prizes . Each random run reassigned the observed KSHV protein prizes to random proteins in the PPI network . We removed the random PCSF outputs that contained the hub node UBC and computed node and edge frequencies with the 131 remaining forests . We supplemented the PCSF union network with KSHV-human protein-protein interactions obtained from VirHostNet 2 . 0 [56 , 97] ( downloaded January 7 , 2016 ) . We considered only latency-related KSHV genes as defined by Davis et al . : K1 ( K1_HHV8P ) , K2 ( VIL6_HHV8P ) , K12A ( K12_HHV8P ) , K12B , K12C , ORF71 ( VFLIP_HHV8P ) , ORF72 ( VCYCL_HHV8P ) , and ORF73 ( ORF73_HHV8P ) . We queried VirHostNet and the Davis et al . interactions for all host-virus PPI between these latency-related KSHV genes and any human protein in our PCSF network . We then added all the relevant KSHV-human interactions to our network figures and did not use the PCSF algorithm to filter these edges . We used WebGestalt [98] to identify KEGG pathways [47] that are enriched for proteins in our PCSF network . Because the predicted network can only contain proteins from the initial iRefIndex interaction network , we used all proteins in the protein-protein interaction network as the reference set . We required a minimum overlap of 5 proteins and used the Benjamini and Hochberg multiple hypothesis test correction ( FDR < 10% ) . To visualize the network regions that are relevant to the enriched KEGG pathways we used Cytoscape [99] to select all proteins in the enriched pathway and their directly connected neighbors in the PCSF network . We then included all KSHV- human PPI that involve the KEGG pathway members and their PCSF neighbors . | Kaposi’s Sarcoma herpesvirus ( KSHV ) is the etiologic agent of Kaposi’s Sarcoma , the most common tumor of AIDS patients . KSHV modulates host cell signaling and metabolism to maintain a life-long latent infection . To unravel the underlying cellular mechanisms modulated by KSHV , we used multiple global systems biology platforms to identify and integrate changes in both cellular protein expression and transcription following KSHV infection of endothelial cells , the relevant cell type for KS tumors . The analysis identified several interesting pathways including peroxisome biogenesis . Peroxisomes are small cytoplasmic organelles involved in redox reactions and lipid metabolism . KSHV latent infection increases the number of peroxisomes per cell and proteins involved in peroxisomal lipid metabolism are required for the survival of latently infected cells . In summary , through integration of multiple global systems biology analyses we were able to identify novel pathways that could not be predicted by one platform alone and found that lipid metabolism in a small cytoplasmic organelle is necessary for the survival of latent infection with a herpesvirus . | [
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] | 2017 | Integrated systems biology analysis of KSHV latent infection reveals viral induction and reliance on peroxisome mediated lipid metabolism |
To work towards reaching the WHO goal of eliminating soil-transmitted helminth ( STH ) infections as a public health problem , the total number of children receiving anthelmintic drugs has strongly increased over the past few years . However , as drug pressure levels rise , the development of anthelmintic drug resistance ( AR ) is more and more likely to appear . Currently , any global surveillance system to monitor drug efficacy and the emergence of possible AR is lacking . Consequently , it remains unclear to what extent the efficacy of drugs may have dropped and whether AR is already present . The overall aim of this study is to recommend the best diagnostic methods to monitor drug efficacy and molecular markers to assess the emergence of AR in STH control programs . A series of drug efficacy trials will be performed in four STH endemic countries with varying drug pressure ( Ethiopia and Brazil: low drug pressure , Lao PDR: moderate drug pressure and Tanzania: high drug pressure ) . These trials are designed to assess the efficacy of a single oral dose of 400 mg albendazole ( ALB ) against STH infections in school-aged children ( SAC ) by microscopic ( duplicate Kato-Katz thick smear , Mini-FLOTAC and FECPAKG2 ) and molecular stool-based diagnostic methods ( quantitative PCR ( qPCR ) ) . Data will be collected on the cost of the materials used , as well as the time required to prepare and examine stool samples for the different diagnostic methods . Following qPCR , DNA samples will also be submitted for pyrosequencing to assess the presence and prevalence of single nucleotide polymorphisms ( SNPs ) in the β-tubulin gene . These SNPs are known to be linked to AR in animal STHs . The results obtained by these trials will provide robust evidence regarding the cost-efficiency and diagnostic performance of the different stool-based diagnostic methods for the assessment of drug efficacy in control programs . The assessment of associations between the frequency of SNPs in the β-tubulin gene and the history of drug pressure and drug efficacy will allow the validation of these SNPs as a marker for AR in human STHs . The trial was retrospectively registered the 7th of March 2018 on Clinicaltrials . gov ( ID: NCT03465488 ) .
Currently , the reduction in number of eggs excreted in stool after drug administration ( egg reduction rate , ERR ) is the recommended method for monitoring the efficacy of anthelmintic drugs against STHs [27] . In contrast to other available assays , it allows for the assessment of the efficacy of any drug against all STHs [28–31] . Today , a variety of egg counting methods have been recommended by WHO for the assessment of drug efficacy [27] , of which Kato-Katz thick smear is the most commonly applied . Egg counting still requires a minimum amount of laboratory equipment and experienced laboratory technicians to ensure quality of the data obtained [22 , 24 , 32] . Furthermore , it is essential that samples are analysed quickly after collection to ensure the visualisation of hookworm eggs and that results are adequately analysed , applying the most appropriate summary statistics [15 , 27] and are then reported to the responsible authorities . There is often limited availability of laboratory capacity and a poor reporting of the results obtained . This prompts the need for an improved diagnostic method that enables ( i ) egg counts to be performed without the use of a microscope , ( ii ) automated egg counting , and ( iii ) submission of results from remote locations via the internet for quality control of egg counts , analysis and reporting . Such a method would eliminate the need for a microscope or highly skilled technicians/clinicians or administrators while providing and centralizing drug efficacy results . A method that largely meets this target product profile is the FECPAKG2 platform . Recently , it has been launched as a complete remote-location diagnostic method for sheep/cattle farmers and their veterinarians to assess the intensity of helminth infections and efficacy of drugs [33] . The platform accumulates helminth eggs into one microscopic view after which digital images are taken [34] . Images are stored by the associated software and can be uploaded to a remote server when an Internet connection is available . Later , a web-based laboratory technician can count the eggs visible in the images , after which the results are returned to the user by e-mail . The FECPAKG2 platform thus potentially eliminates the need for skilled technicians on-site . The online software allows easy access for quality control of egg counting and the production of standardized analysis and reports . More importantly , it opens the door to automated egg counting by egg recognition software [35] . Recently , Ayana et al . ( in press ) [36] modified and optimized a FECPAKG2 protocol for the detection and quantification of human STH eggs in stool . However , it remains unclear whether it provides equivalent ERR results to those obtained by diagnostic methods recommended by WHO , and whether it would result in more efficient use of financial and technical resources under field conditions . From a research perspective , there is a growing interest to apply quantitative polymerase chain reaction ( qPCR ) for the assessment of drug efficacy . In contrast to the egg counting methods , qPCR can differentiate the different hookworm species , and thus allow evaluation of BZ efficacy against the different hookworm species [37 , 38] . This is of particular interest given that there could be differential susceptibility towards BZ drugs among hookworm species , and that there is increasing evidence of zoonotic transmission of Ancylostoma ceylanicum between dogs and humans [39–42] . Although studies have linked amount of DNA with egg output [37 , 43] , to date , qPCR has not yet been applied for the assessment of drug efficacy . Early detection of BZ resistance is essential to anticipate the efficacy of drugs in STH control programs , as it is more difficult to mitigate the spread of AR when the initial frequency is already high [44] . However , both in vivo ( e . g . ERR ) and in vitro assays ( e . g . egg hatch assay ) lack the sensitivity to detect the emergence of AR at an early stage . For example , in animals ERR was only able to detect AR when the proportion of resistant STHs exceeded 25% [45] , a proportion that may impede mitigating the spread of AR . A more sensitive alternative is a molecular test based on resistance-associated mutations in targeted genes . Due to the unfortunate and costly experiences of AR in veterinary medicine , BZ resistance has been researched in detail in animal STHs , providing strong evidence of molecular markers associated with BZ resistance . An overview of the markers for BZ resistance in animal STH is provided by Von Samson-Himmelstjerna et al . , [46] . Generally , BZ resistance in animal STHs is caused by single nucleotide polymorphisms ( SNPs ) in the gene encoding β-tubulin at codons 167 ( TTC to TAC ) , 198 ( GAA to GCA ) or 200 ( TTC to TAC ) , but the relative association of these SNPs with BZ resistance varies considerably across animal STH species [47–49] . Various methodologies to detect and quantify these SNPs in animal STH have been developed through an international Consortium for Anthelmintic Resistance SNPs ( CARS ) , [50] of which some have now also been developed and applied for human STHs [29 , 30 , 51–53] . The few studies assessing SNPs in human STHs suggest that ( i ) polymorphisms are predominantly found in codon 200 , and ( ii ) that resistance-associated mutations increased after drug administration ( seen for T . trichiura ) , ( iii ) but that there was no clear association with reduced drug efficacy . However , the results should be interpreted with caution . First , they were based on a small number of participants ( ranging from 3 to 31 participants ) across a limited number of geographical areas ( Kenya , Haiti , Panama and Tanzania ) . Second , STH data were collected applying a variety of study methodologies . For example , the samples originated from a wide range spectrum of the population ( pre-SAC , SAC and adults ) and were examined applying different egg counting methods ( Kato-Katz thick smear , McMaster egg counting method and FLOTAC ) , which makes comparison across studies difficult . Most technologies to assess SNPs linked to BZ resistance are only available in well-equipped laboratories . Recently , Rashwan et al . , [54] published a loop-mediated isothermal amplification ( LAMP ) assay for the assessment of β-tubulin polymorphisms in human STHs . Due to its simplicity , ruggedness and low cost , LAMP could provide major advantages in detecting AR on-site . However , the method needs to be further validated on field samples . Another strategy to further reduce the operational costs is to apply a pooled examination strategy . Examination of pooled stool has already proved to allow for reduction in technical and financial resources for other egg counting methods such as Kato-Katz thick smear [55] . The overall aim of this study is to recommend the best diagnostic methods to monitor drug efficacy and status of AR in STH programs . These will then be applied in work package 2 ( the establishment of a surveillance system to monitor the global patterns of drug efficacy and emergence of AR in STH programs ) . The specific objectives are to: Assess equivalence of ERRs measured by Kato-Katz thick smear , Mini-FLOTAC and FECPAKG2 Assess the diagnostic performance of Kato-Katz thick smear , Mini-FLOTAC , FECPAKG2 and qPCR Assess the costs linked with assessing drug efficacy by Kato-Katz thick smear , Mini-FLOTAC and FECPAKG2 Provide proof-of-principle for qPCR to assess intensity of infection and drug efficacy Assess associations between frequency of SNPs linked to BZ resistance measured by pyrosequencing and history of drug pressure and drug efficacy Compare pyrosequencing and LAMP for the assessment of SNPs linked to BZ resistance Assess pooling samples as a cost-saving strategy to determine infection intensity ( qPCR ) and frequency of SNPs linked to BZ resistance ( pyrosequencing and LAMP )
The Starworms protocol has been reviewed and approved by the Institutional Review Board ( IRB ) of the faculty of medicine of Ghent University , Belgium ( Ref . No B670201627755 ) . The trial protocol was subsequently also reviewed and approved by the IRBs associated with each trial site ( Ethical Review Board of Jimma University , Jimma , Ethiopia: RPGC/547/2016; National Ethics Committee for Health Research ( NECHR ) , Vientiane , Lao PDR: 018/NECHR; Zanzibar Health Research Council , United Republic of Tanzania: ZAMREC/0002/February/2015 and the Institutional Review Board from Centro de Pesquisas René Rachou , Belo Horizonte , Brazil: 2 . 037 . 205 ) . Parent ( s ) /guardians of participants will sign an informed consent document indicating that they understand the purpose of and procedures required for the study and that they are willing to have their child participate in the study . If the child is ≥5 years , he/she has to orally assent in order to participate in the study . Participants of ≥12 years of age are only included if they sign an informed consent document indicating that they understand the purpose of the study and procedures required for the study and are willing to participate in the study . A series of drug efficacy trials will be performed in four STH endemic countries ( Brazil , Ethiopia , Lao PDR and Tanzania ) . These trials will be designed to assess the efficacy of a single oral dose of 400 mg ALB against STH infections in SAC by a variety of stool-based diagnostic methods . At the start of each trial , schools will be visited by the local principal investigator ( PI ) and a team of field officers , who will explain the planned trial and sampling method to the parents and teachers and the children . At baseline , SAC will be asked to provide a fresh stool sample . All children that meet all inclusion criteria and none of the exclusion criteria ( see Table 1 ) will be enrolled in the study . They will be treated with a single oral dose of 400 mg ALB under supervision . The ALB to be used in the different studies is manufactured by GlaxoSmithKline and donated to WHO and originates from the same production batch ( Batch Nr: 335726 ) . All collected stool samples will be processed to determine the fecal egg counts ( FECs; expressed in eggs per gram of stool ( EPG ) ) for each STH using Kato-Katz thick smear ( single and duplicate ) , Mini-FLOTAC and FECPAKG2 . During baseline evaluation , only stool samples found to contain at least 13 eggs on duplicate Kato-Katz or at least 15 eggs on Mini-FLOTAC for at least one of the three STHs will be preserved for further molecular analysis . Fourteen to 21 days after drug administration , a second stool sample will be collected from all the children that were excreting eggs of any STH at baseline based on Kato-Katz thick smear and Mini-FLOTAC . Stool samples will be examined by Kato-Katz thick smear ( single and duplicate ) , Mini-FLOTAC and FECPAKG2 . All follow-up samples will be preserved for further molecular analysis . To gain insights into the operational costs for assessing drug efficacy , the time to process stool with Kato-Katz thick smear , Mini-FLOTAC and FECPAKG2 , and the time needed to enter data and to draft reports will be assessed . The different steps of the trials are schematized in Fig 1 . The study will focus on SAC ( age 5–14 ) since they are the major target of PC programs targeting STH , and they usually represent the group with highest worm burdens for A . lumbricoides and T . trichiura [56] . This study will be conducted in Ethiopia , Tanzania , Lao PDR and Brazil . The selection of these sites is based on their experience in assessing drug efficacy [13 , 15] , evaluating the performance of diagnostic methods [57–59] and the availability of well-equipped diagnostic facilities and skilled personnel , and PC history ( Table 2 ) . A sample size was calculated to test the alternative hypothesis that FECPAKG2 , Mini-FLOTAC and single Kato-Katz thick smear provide equivalent drug efficacy results measured by ERRs compared to a duplicate Kato-Katz thick smear . Given the differences in drug efficacy of ALB across the STH species [15 , 21] ( Ascaris: ~99% , hookworms: ~96% , Trichuris: ~65% ) , a level of equivalence that is acceptable for Trichuris may not be acceptable for Ascaris . Therefore , a species-specific level of equivalence was used . For Ascaris the level of equivalence was set at 2 . 5-point percentage , for hookworms and Trichuris the level of equivalence was set at 5 . 0 and 10 point percentage , respectively . Both type I and II errors were set at 0 . 05 . To calculate the corresponding sample size for each of the STH species , we performed a simulation study . This simulation study considered ( i ) variation in ERR and baseline FECs across and within STH species , ( ii ) variation in FECs introduced by the egg counting process , ( iii ) the paired ERR results across egg count methods , and ( iv ) a post-hoc correction for a pair-wise comparison . Based on the simulation , at least 110 , 100 and 12 complete cases are required for Trichuris , hookworm and Ascaris , respectively . A detailed description of the sample size calculation is available in the supplementary information ( S1 Info ) . We will provide participants with a container for the collection of stool samples . The samples will be collected and stored in cooler boxes with ice-packs , which will then be transported to the laboratory at the respective study site for further analysis . Upon arrival in the laboratory , samples will be thoroughly homogenized using a wooden spatula to obtain a more homogeneous distribution of STH eggs in the stool [60] . A detailed standard operating procedure ( SOP ) on sample homogenisation is available in the supplementary information ( S2 Info ) . To make objective estimates of the costs associated with the different diagnostic methods , we will collect data on the cost of the materials used as well as the time required to prepare and examine stool samples for the different stool-based diagnostic methods . Detailed pricing information for the list of materials needed to perform Kato-Katz thick smear and Mini-FLOTAC will be obtained from the local PI . The costs for the materials needed for the FECPAKG2 method will be obtained from the manufacturer ( Techion Group Ltd . ) and is expected to be the same for all four study sites . A list of materials included in the cost calculation for the Kato-Katz thick smear and Mini-FLOTAC method is provided in S11 Info . The costs associated with molecular analysis of the samples will include the cost for preservation and shipment of the stool samples as well as the laboratory materials used to perform DNA extractions and qPCR . Timers will be provided to each laboratory technician to measure the time needed to perform the different steps of each of the diagnostic methods . For all egg counting methods , the time taken to complete each step will be measured . In addition , the time needed for data entry and drafting a report will be recorded . The protocols of the egg counting methods include more details on the different timing steps ( S3–S5 , S8 and S10 Info ) . All data collected during the studies will be recorded in specifically designed record forms ( S12 Info ) . The original results written down on the paper documents will be scanned and stored digitally . This data will later be entered into customized Excel-files by two separate individuals ( S13–S15 Info ) . After completion of the data entry , the files of both data entry clerks will be compared for discrepancies ( S16 Info ) . Potential mismatches will be highlighted and the true values verified using the scanned original record form . The PIs from each site are responsible for acquiring ethical approval by their local Institutional Review Board ( IRB ) and conducting the trial procedures as described in the original study protocol and study SOPs . The studies in the different countries will be coordinated by the project PI and the Starworms Coordinating Team at Ghent University . One week prior to the start of each study , a member of the Starworms Coordinating Team will visit the study site . Local team members will be informed on the study design , familiarised with the different study documents and receive both theoretical and practical training on how to perform different egg counting methods according to the Starworms SOPs . The Starworms staff member will remain on-site for at least 7 days after the start of the trial to ensure SOP adherence and to solve any issues that might arise . After data collection and quality control , all data will be combined in a final , protected dataset for statistical analysis . After finishing work package 1 , this dataset will be made available on the Starworms website ( www . starworms . org ) . All statistical analysis will be performed in R ( R Development Core Team , 2016 ) . We will evaluate both the prevalence and intensity of each different STH species in each of the four different study sites . The percentage of individuals included in the different classes of infection intensity will be reported in order to assess the efficacy of the PC intervention conducted until then in eliminating STH infection of moderate and heavy intensity . We will report the efficacy of a single oral dose of 400 mg ALB separately for A . lumbricoides , T . trichiura and hookworms , and for each FEC method by means of ERR , using the formula below: ERR=100%× ( arithmeticmean ( FECatbaseline ) −arithmeticmean ( FECatfollow-up ) ) / ( arithmeticmean ( FECatbaseline ) ) A bootstrap analysis will be used to determine the corresponding 95% confidence intervals ( 95% CI ) for each egg counting method and for the pair-wise differences in ERR across diagnostic methods . A permutation test will be used to assess the equivalence in ERR between a duplicate Kato-Katz thick smear , and a single Kato-Katz thick smear , Mini-FLOTAC and FECPAKG2 . Tukey’s method will be applied for multiple comparison between methods . In addition , the agreement between a duplicate Kato-Katz thick smear and the other egg counting methods in the assignment of drug efficacy into ‘satisfactory’ , ‘doubtful’ and ‘reduced’ will be evaluated by Fleiss’ kappa statistic ( κFleiss ) . This classification of the drug efficacy will be based on the criteria recently proposed by the WHO ( Table 4 ) [27] . The value of this statistic indicates a slight ( κFleiss <0 . 2 ) , fair ( 0 . 2 ≤ κFleiss <0 . 4 ) , moderate ( 0 . 4 ≤ κFleiss <0 . 6 ) , substantial ( 0 . 6 ≤ κFleiss <0 . 8 ) and an almost perfect agreement ( κFleiss ≥ 0 . 8 ) . For each diagnostic method , the sensitivity will be calculated using the combined results of the four methods ( duplicate Kato-Katz thick smear , Mini-FLOTAC , FECPAKG2 and qPCR ) as the diagnostic ‘gold’ standard . This means that a sample is considered positive if it tested positive on at least one of the four methods . The species specificity of all methods will be set at 100% , as indicated by the morphology of the eggs or by the species-specific primer sets used in qPCR . The corresponding 95% CI for sensitivity will be calculated separately for each diagnostic method . Differences in sensitivity between methods will be assessed by a permutation test ( 10 , 000 iterations ) . Tukey’s method will be applied for pair-wise comparisons between the methods . The impact of infection intensity on the sensitivity within each method will be explored by a logistic regression model , which will be fitted for each of the methods with their test result ( positive/negative ) as the outcome and the natural log transformed mean FEC across the three methods as covariate . The predictive power of the final models will be evaluated by the proportion of the observed outcome that was correctly predicted by the model . To this end , an individual probability >0 . 5 will be set as a positive test result , and negative if different . Finally , the sensitivity for each of the pre-defined values of FEC will be calculated based on these models . For egg counting methods , the agreement in egg counts will be evaluated by permutation tests ( 10 , 000 iterations ) based on ( i ) Pearson’s correlation coefficient and ( ii ) the differences in FECs . Tukey’s method will be applied for pair-wise comparisons within each STH species . For qPCR , the agreement in GE numbers and FECs obtained by duplicate Kato-Katz thick smear will be evaluated by permutation tests ( 10 , 000 iterations ) based on Pearson’s correlation coefficient . Samples containing helminth eggs will be classified into low , moderate , and heavy infection intensity according to FECs obtained by duplicate Kato-Katz and following the thresholds proposed by WHO; for A . lumbricoides these are 1–4 , 999 EPG , 5 , 000–49 , 999 EPG , and >49 , 999 EPG; for T . trichiura these are 1–999 EPG , 1000–9 , 999 EPG , and >9 , 999 EPG; and for hookworm these are 1–1 , 999 EPG , 2 , 000–3 , 999 EPG and >3 , 999 EPG ( WHO , 1998 ) . For each of the other diagnostic methods , a receiver operating characteristic analysis will be performed to determine the corresponding FECs / GE numbers that allow classification of the infections into low , moderate and heavy using duplicate Kato-Katz thick smear as a reference . Finally , the proportion samples correctly classified as having a low , moderately and heavy infection intensity will be assessed separately for single Kato-Katz thick smear , Mini-FLOTAC , FECPAKG2 and qPCR . The mean time to process and examine one stool sample will be calculated for each separate egg counting method . We will also calculate the mean time needed to enter the demographic data and test results of a single subject , and the time to analyze and report the data . To reach an estimation of the total cost associated per sample , we will combine these data with the estimation of the costs of the raw materials and labour time needed to analyze a sample by each of the different methods . To calculate the costs for analysing samples a fixed rate will be used . Finally , a one-way sensitivity analysis will be performed to assess the impact of each parameter on the total cost for each diagnostic method separately . The efficacy of a single oral dose of 400 mg ALB will be reported separately for A . lumbricoides , T . trichiura and hookworms by means of genome equivalent reduction rate ( GERR ) , using the formula below: GERR = 100% x ( arithmetic mean ( GE at baseline ) –arithmetic mean ( GE at follow-up ) ) / ( arithmetic mean ( GE at baseline ) ) . The corresponding 95% CI for GERR will be determined by bootstrap analysis ( 10 , 000 iterations ) . A permutation test will be used to assess the equivalence in drug efficacy between a duplicate Kato-Katz thick smear by means of ERR and qPCR by means of GERR for each of the STH species separately . The same level of equivalence will be applied as described in section 4 . 8 . 1 . In addition , the agreement between a duplicate Kato-Katz thick smear and qPCR in the assignment of drug efficacy into ‘satisfactory’ , ‘doubtful’ and ‘reduced’ will be evaluated by κFleiss statistic . To assess the association between mutant SNPs linked to BZ resistance and history of drug pressure , a permutation test will be applied to evaluate differences in frequency of mutant SNPs linked to BZ in the baseline samples across the study sites . Tukey’s method will be applied for pair-wise comparisons between the study sites . To assess the association between mutant SNPs linked to BZ resistance and drug efficacy , a permutation test will be applied to evaluate any difference in frequency of mutant SNPs linked to BZ across the four levels of individual response to ALB ( Table 3 ) . Tukey’s method will be applied for pair-wise comparisons between the four levels of individual response . The sensitivity of the LAMP method to detect mutant SNPs linked to BZ resistance will be evaluated using the pyrosequencing method as the gold standard . The impact of SNP frequency on the sensitivity of LAMP will be explored by a logistic regression model with the LAMP result as outcome ( negative/positive ) and the frequency of SNPs linked to BZ resistance as covariate . The predictive power of the final models will be evaluated by the proportion of the observed outcome that was correctly predicted by the model . To this end , an individual probability >0 . 5 will be set as a positive test result , and negative in all other cases . The sensitivity for each of the pre-defined values of FEC will be calculated based on these models . Finally , the κ-statistic will be determined to assess the agreement in the LAMP test result across the different study sites , Canada ( McGill University ) , Ethiopia ( Jimma University ) and Tanzania ( Public Health Laboratory—Ivo de Carneri ) ) . The value of this statistic indicates a slight ( κ <0 . 2 ) , fair ( 0 . 2 ≤ κ <0 . 4 ) , moderate ( 0 . 4 ≤ κ <0 . 6 ) , substantial ( 0 . 6 ≤ κ <0 . 8 ) and an almost perfect agreement ( κ ≥ 0 . 8 ) . For qPCR , we will apply the Pearson’s correlation coefficient to assess the agreement between the mean frequency of GE number based on the examination of individual samples and the GE based on the examination of pooled samples will be evaluated . In addition , a permutation test ( 10 , 000 iterations ) will be applied to test for differences in mean GE number between the examination of individual and pooled samples . For pyrosequencing , the agreement between the mean frequency of SNPs based on the examination of individual samples and the frequency of SNPs based on the examination of pooled samples will be evaluated by the Pearson’s correlation coefficient . In addition , a permutation test will be applied to test for differences in mean frequencies between examination of individual and pooled samples . For LAMP , the κ-statistic will be determined to assess the agreement in the LAMP test results obtained by an individual and a pooled examination strategy . The impact of SNP frequency on the sensitivity of LAMP will be explored by a logistic regression model with the LAMP result as outcome ( negative/positive ) and the frequency of SNPs linked to BZ resistance as covariate . The predictive power of the final models will be evaluated by the proportion of the observed outcome that was correctly predicted by the model . To this end , an individual probability >0 . 5 will be set as a positive test result , and negative in all other cases . The sensitivity for each of the pre-defined values of SNP frequency will be calculated based on these models .
The potential development of AR is a real threat for PC programs targeting STHs . It is facilitated by the ever-increasing quantity of anthelmintic drugs reaching at risk populations , the administration of drugs with the same mode of action at a suboptimal dose and the lack of alternative treatment options should AR eventually emerge and spread . It is therefore of paramount importance that the efficacy of the administered BZ drugs is regularly monitored . However , in order to establish a robust surveillance system to monitor drug efficacy there is a need for diagnostic methods that effectively mitigate important obstacles of performing , analysing and reporting drug efficacy surveys in resource poor settings , and a validated molecular marker to detect emergence of AR at an early stage . Based on the results from this study within the Starworms project , we aim to recommend diagnostic methods to monitor drug efficacy and molecular markers to assess the status of AR in STH control programs . We will be the first to report a comprehensive comparison of two novel egg counting methods ( Mini-FLOTAC and FECPAKG2 ) and DNA-based methods ( qPCR ) with the current WHO diagnostic standard ( Kato-Katz thick smear ) . We will not limit our comparison to the diagnostic performance but expand our analysis to compare them for the intended use-case ( assessment of drug efficacy by means of egg reduction rate ) and consider the associated costs and time required to perform each of the different methods . The inclusion of FECPAKG2 may create a game-changing shift in how drug efficacy can be monitored at a global scale . The online connectivity of FECPAKG2 allows easy access for quality control of egg counts and the production of standardized analysis and reports . It also opens the door to automated egg counting by egg recognition software , which when successful , could further increase throughput , reduce personnel costs and variation in egg counts between technicians or laboratories . We will provide proof-of-principle for qPCR to assess infection intensity and drug efficacy by means of GE numbers . Inclusion of qPCR will also allow for more accurate estimates of the sensitivity of the different diagnostic tests . This is of particular importance since there is no gold standard diagnostic method for STHs [67] . By standardizing the drug efficacy trials ( e . g . , egg count method , follow-up period , anthelmintic drug and statistical analysis ) and strategically selecting the study sites ( varying drug pressure history , and hence possibly differences in drug efficacy ) , we hope to provide complementary insights into SNPs in the β-tubulin gene as a marker for BZ resistance in human STHs . If these SNPs play a role in AR , we expect an increase in frequency in mutant SNPs in STHs as a function of increased drug pressure history and decreased individual response to treatment . However , this molecular part of the study may face some challenges , which may impede a straightforward interpretation of the results . First , if the expected trends in SNPs are not detected , it is not possible to completely exclude them as a marker for BZ resistance . Furthermore , it could also indicate that resistance to BZ has not yet emerged , which would also jeopardize any downstream objectives ( e . g . comparison of pyrosequencing and LAMP for the assessment of SNPs linked to BZ resistance and assessing pooling samples as a cost-saving strategy to determine the frequency of SNPs linked to BZ resistance ) . It is also important to note that any observed trends would only suggest an association rather than causal relationship . Second , it remains unclear whether we will be able to select the number of cases per level of individual response for each of the different STH species . Finally , the classification of the individual response is based on the outcome of imperfect tests ( sub-optimal sensitivity ) , further complicating the interpretation . To conclude , the combined results of this study are expected to answer a number of outstanding questions with regards to how to best monitor drug efficacy and measure AR in populations that are being subjected to PC for STH infections . The results could be translated into novel recommendations for the scientific community on what is the best diagnostic method to assess drug efficacy in ongoing control programs and will create the basis to set up a robust surveillance system . | Soil-transmitted helminths ( STHs ) affect 1 . 4 billion people worldwide and cause significant morbidity when the intensity of infection is high . Currently , these infections are controlled in school-aged children by preventive chemotherapy with the benzimidazole drugs albendazole ( ALB ) or mebendazole ( MEB ) . However , for the success of these control programs , it is essential to keep track of the efficacy of these drugs and to screen parasite populations for a possible rise of anthelmintic resistance ( AR ) . In this light , a series of trials will be performed to assess the efficacy of ALB treatment against STH in four endemic countries with varying drug pressure . Both microscopic and molecular stool-based diagnostic methods will be used to evaluate drug efficacy . DNA samples will also be analysed for the presence and prevalence of mutations in a gene that was previously linked to AR in animal STHs . The results of these trials will provide evidence on the efficacy of ALB , help select the optimal diagnostic method to assess drug efficacy and provide information regarding the usefulness of genetic markers for AR detection in human STHs . | [
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] | 2018 | Comprehensive evaluation of stool-based diagnostic methods and benzimidazole resistance markers to assess drug efficacy and detect the emergence of anthelmintic resistance: A Starworms study protocol |
Multiple genome-wide association studies ( GWAS ) have been performed in HIV-1 infected individuals , identifying common genetic influences on viral control and disease course . Similarly , common genetic correlates of acquisition of HIV-1 after exposure have been interrogated using GWAS , although in generally small samples . Under the auspices of the International Collaboration for the Genomics of HIV , we have combined the genome-wide single nucleotide polymorphism ( SNP ) data collected by 25 cohorts , studies , or institutions on HIV-1 infected individuals and compared them to carefully matched population-level data sets ( a list of all collaborators appears in Note S1 in Text S1 ) . After imputation using the 1 , 000 Genomes Project reference panel , we tested approximately 8 million common DNA variants ( SNPs and indels ) for association with HIV-1 acquisition in 6 , 334 infected patients and 7 , 247 population samples of European ancestry . Initial association testing identified the SNP rs4418214 , the C allele of which is known to tag the HLA-B*57:01 and B*27:05 alleles , as genome-wide significant ( p = 3 . 6×10−11 ) . However , restricting analysis to individuals with a known date of seroconversion suggested that this association was due to the frailty bias in studies of lethal diseases . Further analyses including testing recessive genetic models , testing for bulk effects of non-genome-wide significant variants , stratifying by sexual or parenteral transmission risk and testing previously reported associations showed no evidence for genetic influence on HIV-1 acquisition ( with the exception of CCR5Δ32 homozygosity ) . Thus , these data suggest that genetic influences on HIV acquisition are either rare or have smaller effects than can be detected by this sample size .
Variation in infection susceptibility and severity is a hallmark of infectious disease biology . This natural variation can be attributed to a variety of host , pathogen and environmental factors , including host genetics . Several genome-wide association studies ( GWAS ) of HIV-1 outcomes have been performed primarily to assess the impact of human genetic variation on plasma viral load and/or disease progression [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] . These studies have confirmed the key role of major histocompatibility complex ( MHC ) polymorphisms in HIV-1 control , with a minor impact of variants in the CCR5 gene region . A smaller number of GWAS have also investigated host genetic influences on HIV-1 acquisition using samples of individuals with known or presumed exposure to an HIV-1 infected source [12] , [13] , [14] , [15] , [16] . With the exception of CCR5Δ32 homozygosity ( known to explain a proportion of HIV-1 resistance in Europeans [17] ) , no reproducible associations with increased or reduced HIV-1 acquisition have been observed . Additionally , several variants reported to influence HIV-1 acquisition by candidate gene studies have either failed to be replicated or lacked sufficient investigation as to be considered confirmed . We here describe a large study of human genetic determinants of HIV-1 acquisition , performed under the auspices of the International Collaboration for the Genomics of HIV , a collaborative research effort bringing together the HIV-1 host genetics community . By collecting for the first time all available genome-wide single nucleotide polymorphism ( SNP ) data on HIV-1 infected individuals and comparing them with population-level control data sets we sought to uncover common genetic markers that influence HIV-1 acquisition .
Genome-wide genotype data were collected from 25 cohort studies and clinical centers ( listed at the end of the paper and in Note S1 in Text S1 ) . We obtained a data set of 11 , 860 HIV-1 infected individuals genotyped at multiple centers using several platforms ( Table S1 in Text S1 ) . The present analysis focused on the subset of these individuals that are of European ancestry as assessed by principal components ( PCs ) analysis ( see methods ) . For two of the genotyping centers , matched HIV-1 uninfected controls were available . For the remaining samples , large population-level control data sets were accessed from the Illumina Genotype Control Database ( www . illumina . com ) and the Myocardial Infarction Genetics ( MIGen ) Consortium ( genotyped using the Affymetrix 6 . 0 platform ) [18] . Sample-level quality control and case-control matching ( Figure S1 in Text S1 ) resulted in six non-overlapping data sets including 6 , 334 HIV-1 infected cases and 7 , 247 controls ( Table S1 in Text S1 ) . After imputation , each variant was individually tested for association with HIV-1 status by logistic regression including PCs to correct for residual population structure , under additive and recessive genetic models . Association results were then combined across data sets . Restricting to variants observed in all six data sets with >1% frequency and a minimum imputation quality of 0 . 8 in at least 2 groups , approximately 8×106 common variants ( SNPs and indels ) were tested . The overall distribution of p-values was highly consistent with the null hypothesis ( λ1000 = 1 . 01 ) suggesting that the matching strategy was successful in minimizing inflation ( Figure 1a ) . We observed 11 SNPs with combined evidence for association passing the genome-wide significance threshold ( p<5×10−8 , Figure 1b ) under an additive genetic model . All genome-wide significant SNPs were located in the MHC region , centered on the class I HLA genes HLA-B/HLA-C ( Figure 2a and Table S2 in Text S1 ) . The top SNP , rs4418214 ( p = 3 . 6×10−11 , odds ratio ( OR ) for the C allele = 1 . 52 ) has previously been associated with control of HIV-1 viral load [8] , with the C allele tagging the classical HLA-B alleles 57:01 and 27:05 , both known to associate with lower viral load and longer survival after infection . Analysis assuming a recessive genetic model did not identify any genome-wide significant associations ( data not shown ) . Since variation in the HLA region is well known to impact rate of HIV-1 disease progression and not acquisition , we sought to better understand the observed associations at this locus . Due to their shorter survival time , patients with rapid disease progression are underrepresented in seroprevalent cohorts , while individuals with prolonged disease-free survival times are more likely to be included , leading to an enrichment of factors that protect against disease progression in such populations . Additionally , some of the cohorts accessed for this analysis specifically recruited long-term non-progressors ( LTNPs , Groups 2 , 3 and 4 ) . Inspection of the effect estimates at the top SNP ( rs4418214 ) per data set showed that the majority of the association signal was driven by groups specifically enriched for LTNPs ( Figure 2b ) suggesting a possible frailty bias in the overall results . To assess the potential contribution of frailty bias , we ran association testing as previously but restricting the case population to 2 , 173 individuals with a known date of seroconversion that were not enrolled in LTNP cohorts . Association testing in this sample showed no variants passing the genome-wide significance threshold . Additionally , rs4418214 dramatically dropped in strength of association to p = 0 . 02 , with all other previously genome-wide significant SNPs suffering a similar loss in association strength ( Figure 2c and Table S2 in Text S1 ) . In order to address whether this loss of association signal could be due to the reduced size of the case population rather than frailty bias , we performed a sensitivity analysis where we tested for association at rs4418214 restricting the HIV+ cases to 2 , 173 individuals randomly selected from the full case sample . We repeated this procedure 1 , 000 times and compared the p-value from the random case selection to that obtained when restricting to seroconverters . Of these 1 , 000 tests , only one resulted in a loss of association signal that was similar to what was observed when restricting to seroconverters ( Figure S2 in Text S1 ) . This suggests that the signal observed in the full acquisition analysis is most likely due to frailty bias . Previous studies in large cohorts have shown that multiple genetic variants with small effect sizes that contribute to complex traits , but fall below the genome-wide significance threshold , can be detected by examining the consistency of their combined effects across studies [19] . We sought to test for evidence of such polygenic inheritance in our study population . To do this ( and to avoid overfitting ) , we split our sample into a discovery set ( Groups 1 , 2 , 4 , 5 and 6 ) and a test set ( Group 3 ) and performed genome-wide association testing and meta-analysis on the discovery set . Based on these results , we generated sets of high-quality SNPs ( minor allele frequency >0 . 1 , imputation accuracy >0 . 9 ) in relative linkage equilibrium ( r2<0 . 1 , informed by p-value in the discovery set , see methods ) falling below various p-value thresholds ( PT ) . Scores were then generated for all individuals in Group 3 by summing the weighted genotype dosage ( using the log odds ratio from the discovery set as weights ) of all SNPs below a given PT . Phenotype was then regressed on this score using logistic regression including covariates . We assessed both the significance of the score and the phenotypic variance explained ( using Nagelkerke's pseudo-R2 [20] ) . We did not observe a significant association between the calculated score and phenotype in the discovery set at any PT ( Figure 3 ) . This further suggests that effects of common variants on HIV-1 acquisition detectable by this study design are negligible . Since different modes of HIV-1 transmission may be influenced by different host factors , we further investigated if genetic variants may contribute to enhanced HIV-1 acquisition within transmission risk sub-groups . We stratified the study population by reported risk groups that were either primarily sexual ( homosexual and heterosexual , n = 3 , 311 ) or parenteral ( injection drug use and transfusion , n = 1 , 046 ) . Association results in these sub-groups were consistent with those observed in the full set with no genome-wide significant signals detected ( data not shown ) . With the exception of CCR5Δ32 ( addressed in the next section ) , many variants reported to influence HIV-1 acquisition have remained unconfirmed . We sought to assess the evidence for association of 22 variants previously reported to influence HIV-1 acquisition in this sample . All 22 of these variants could be measured in this sample either through direct genotyping or imputation . Of these , only one variant ( rs1800872 ) showed nominal significance ( p<0 . 05 , Table 1 ) although it did not survive correction for the number of variants tested ( p>2 . 5×10−3 ) . Thus , none of the previously reported associations can be considered confirmed in this large sample . Parameters required for determining power for variant detection , specifically the trait prevalence and the level of enrichment of enhanced HIV-1 acquisition , are difficult to estimate given this study design . Thus , we sought to determine the extent to which we could detect known genetic influences on HIV-1 acquisition in this sample by assessing the depletion of CCR5Δ32 homozygosity in the HIV-1 infected sample . Although this variant is not captured by commercial arrays ( and is not included in the 1 , 000 Genomes Project reference panel ) , genotypes of the deletion were available for a majority of the HIV-1 infected individuals ( n = 4 , 854 ) . As expected , we observed very few Δ32/Δ32 homozygous individuals in this sample ( n = 4 ) and a large deviation from Hardy-Weinberg equilibrium ( Table S3 in Text S1 ) . To assess the association strength of this variant , we used a subset of our sample with available CCR5Δ32 genotypes to build a reference panel , which was then used for imputation of CCR5Δ32 in both cases and controls ( see methods ) . Overall the imputation accuracy was acceptable ( average information score = 0 . 82 ) and we observed good correspondence between typed and imputed dosage ( Figure S3 in Text S1 ) . Using a recessive genetic model , we observed a genome-wide significant association between CCR5Δ32 homozygosity and HIV-1 acquisition ( p = 5×10−9 , OR = 0 . 2 ) . No impact on HIV-1 acquisition was observed under any other genetic model . To address whether the association signal at CCR5Δ32 was subject to the same frailty bias as the MHC SNPs , we next tested for association between CCR5Δ32 and HIV acquisition restricting only to the 2 , 173 HIV+ individuals with known dates of seroconversion . Using these individuals , CCR5Δ32 remains strongly associated ( p = 1×10−6 for the recessive model ) , suggesting that the observed association statistic in the full set is not simply due to frailty bias . This demonstrates that , despite an inability to precisely estimate power , other variants of similar or somewhat weaker effect could also have been detected in this sample .
By assembling a large collaborative network of cohorts and institutions involved in HIV-1 host genetic studies we sought to test for common genetic polymorphisms that influence HIV-1 acquisition . Through this network , we were able to combine genome-wide SNP data on over 6 , 300 HIV-1 infected patients of European ancestry . In order to maximize power , we further accessed large population-level genotype data sets to use as controls . Where necessary , case/control samples were iteratively matched to limit inflation in the test statistic due to platform or cohort effects . Genome-wide imputation using the 1 , 000 Genomes Project CEU sample as a reference panel resulted in a set of approximately 8×106 high-quality variants that were tested for association with HIV-1 acquisition . We observed 11 variants that passed the genome-wide significance threshold , all located in the MHC region . Imputation and association testing of the CCR5Δ32 polymorphism demonstrated that this sample size and study design are appropriate to detect strong associations that impact HIV-1 acquisition . The fact that the top association in the full analysis ( rs4418214 ) is a tag SNP for HLA-B*57:01 and 27:05 highlights the frailty bias inherent to studies of diseases with high mortality rates . HLA-B alleles have been associated with reduced HIV-1 transmission in heterosexual couples [21] , likely due to the effects of HLA-B on HIV-1 viral load , which decreases infectiousness . To further explore the possibility that HLA-B alleles are also associated with HIV-1 acquisition , we ran an analysis restricting the case population to the 2 , 173 individuals with a known date of seroconversion , assuming that cohorts of patients recruited soon after HIV-1 acquisition are less likely to suffer from frailty bias . This analysis resulted in an almost complete loss of signal at rs4418214 that is unlikely to be due to the reduction in size of the case population . Thus , the most parsimonious explanation for the association result in the HLA class I region is that it reflects an enrichment of alleles that protect against disease progression ( hence survival ) rather than increasing acquisition . Under ideal circumstances , this sample size provides approximately 80% power to detect a common variant ( MAF = 0 . 1 ) with genotypic relative risk of 1 . 3 at genome-wide significance . However , we recognize that the present study design allows for a proportion of the sample to be misclassified ( i . e . individuals at average or low susceptibility to HIV-1 infection included as cases ) which can reduce power [22] . Nevertheless , even under assumptions including a large proportion of controls in the case group , this sample size is suitable to discover large effect variants ( GRR>3 , Figure S4 in Text S1 ) . This is further evidenced by our ability to detect the known effect of CCR5Δ32 homozygosity on HIV-1 acquisition in this sample , even given imperfect imputation . Additionally , the lack of enrichment of the control population for individuals with proven or suspected resistance against HIV-1 infection may also influence power [23] . However , in line with our results , GWAS looking at HIV-1 acquisition in mother-to-child transmission pairs [12] , discordant couples [13] , areas of heightened prevalence [14] and in hemophiliacs exposed to potentially contaminated blood products [16] ( although much smaller than the present study ) have been similarly unable to discover novel associations . This large study population is useful for attempting to replicate previous associations , particularly with genetic variants thought to reduce HIV-1 acquisition , as they would be depleted in infected individuals . None of the 22 previously reported variants tested in this sample were associated with HIV-1 acquisition after correcting for multiple tests . This lack of replication is consistent with other , smaller GWAS of this phenotype [14] . These data suggest that many or all of these variants do not appreciably impact HIV-1 acquisition . Thus , evidence is mounting that common polymorphisms affecting acquisition are either very difficult to detect ( perhaps due to weak effects ) or absent , with the exception of CCR5Δ32 homozygosity . The early observation that CCR5Δ32 influences both acquisition ( when homozygous ) and disease progression ( when heterozygous ) suggested shared biology between these phenotypes . However , this proved not to be a generalizable observation since variation at other loci , such as HLA class I and KIR , associate with disease progression but are not generally believed to modulate acquisition . Mechanisms mediating acquisition i . e . permissiveness to HIV upon parenteral or mucosal exposure , likely involve cellular targets and innate immune factors that play none or a limited role in disease progression . On the other hand , mediators of host tolerance ( as defined by [24] ) and of acquired immunity are only expected to exert their effects after infection has been established . Although this study focuses on the host genetics of HIV-1 acquisition , it is possible that the extensive variation in HIV-1 genotype also plays a role in determining susceptibility . This notion is supported by the observation that amino acid changes , generally in response to host HLA pressure , result in decreased viral fitness ( reviewed in [25] ) . However , defining viral variants that limit or enhance infection would require large-scale epidemiological investigations in HIV-1 endemic areas . Despite the large sample size and comprehensive genotype information obtained through imputation , this study is still limited to analysis of common variation with detectable effects present in European samples . Thus , we cannot rule out whether multiple common variants of small effect , population-specific variants or rare variants exist that influence HIV-1 acquisition . Of particular note , in light of the well-known effect of CCR5Δ32 on HIV acquisition , is the inability to comprehensively test structural variation using array-based genotyping platforms . Although SNPs contained on commercial arrays have been shown to tag a large proportion of common structural variants [26] it is still possible that unobserved or poorly tagged structural variants contribute to HIV acquisition . Detection of these types of effects will require large-scale sequencing efforts , preferably in samples with known levels of exposure to HIV-1 .
Ethical approval for this study was obtained from institutional review boards at each of the Cohorts , Studies and Centers listed at the end of the manuscript . All subjects provided written informed consent . The International Collaboration for the Genomics of HIV was established as a platform to combine all available genome-wide SNP data sets obtained on HIV-1 infected individuals worldwide . Patient material was collected at multiple clinical centers across North America , Europe , Australia and Africa ( a list of contributing cohort studies and centers is given at the end of the paper ) . Genotypes for uninfected control individuals were obtained directly from three of the participating centers ( GRIV , ACS , CHAVI ) and from the Illumina genotype control database ( www . illumina . com/icontroldb ) and the Myocardial Infarction Genetics Consortium ( MIGen ) ( NIH NCBI dbGaP Study Accession: phs000294 . v1 . p1 ) [18] . Each data set was subject to quality control procedures performed prior to centralization of all data for the combined analysis . However , to ensure consistency , all data were subject to further quality control once submitted . Per data set , samples with high missingness ( <95% of sites successfully genotyped ) and high heterozygosity ( inbreeding coefficient >0 . 1 ) were removed . Ancestry was determined using EIGENSTRAT to project sample data onto the HapMap III reference panel . For the present analysis , only individuals clustering with the CEU/TSI subset were retained . To remove samples genotyped by multiple centers ( and those with high relatedness ) we performed identity-by-state analysis taking the intersection of SNPs across all genotyping platforms , using PLINK version 1 . 07 [27] . In the case of duplicates , the sample contributing the larger number of genotyped SNPs was retained . We further filtered out individuals with relatedness higher than 0 . 125 , adopting a strategy to maximize sample retention . After sample removal , SNPs with high missingness ( >2% ) , low minor allele frequency ( <1% ) or that were out of Hardy-Weinberg equilibrium ( p<1×10−6 ) were removed . To limit bias introduced due to the majority of the control samples being genotyped separately from cases we used a 2-stage case/control matching strategy . In the first round , cases and controls were matched by platform and geographic origin . This resulted in four clusters; The Netherlands ( Illumina , Group 1 ) , France ( Illumina , Group 2 ) , North America and non-Dutch/non-French European ( Illumina , Groups 3 and 4 ) , North America and non-Dutch/non-French European ( Affymetrix , Groups 5 and 6 ) . To test the success of this method at controlling inflation , we ran association testing on all genotyped SNPs including the top PCs as covariates per cluster and assessed lambda ( Figure S1 in Text S1 ) . For samples ascertained from France and The Netherlands , this was sufficient to control inflation in the test statistic ( λ∼1 , Figures S1a–d in Text S1 ) . For the remaining two clusters , we plotted each sample based on their coordinates across the top two PCs and split each cluster into two sub-clusters based on these coordinates . Sub-clusters then underwent either 1∶3 or 1∶1 case/control matching using Euclidean distance across the top 10 PCs ( with the top PC given twice the weight of the others ) . Samples were removed if no suitable match could be identified . This strategy proved sufficient to control inflation in these remaining clusters ( Figures S1e–l in Text S1 ) . After sample matching and per group quality control , unobserved SNP genotypes were imputed using the 1 , 000 Genomes Project Phase I release integrated SNPs and indels ( March 2012 ) . Two teams from this Collaboration performed the analysis independently using different tools . The first team used BEAGLE [28] , the second team used the pipeline IMPUTE2 , SNPTEST and META [29] , [30] with a pre phasing step using ShaPEIT [31] . Per group , phenotype was regressed on genotype dosage including population covariates calculated by EIGENSTRAT to control for residual structure under both additive and recessive genetic models . Association results were then combined using inverse-variance weighted meta-analysis including a covariate to correct for group-specific effects . The results obtained by each team were compared for cross-validation and found to be highly consistent ( Figure S5 in Text S1 ) . SNPs were considered associated if the combined p-value was below the accepted level of genome-wide significance ( p<5×10−8 ) . We performed analysis to test for evidence of polygenic effects using five of the six groups as a discovery set and Group 3 ( the largest single group ) as the test set . To build a SNP set we first filtered out all SNPs with low minor allele frequency ( MAF<0 . 1 ) and low imputation quality ( R2<0 . 9 ) and removed the MHC region . We then performed LD pruning informed by the p-value calculated in the discovery set such that the SNP with the lowest p-value was selected and all other SNPs in LD ( r2>0 . 1 ) were removed . The SNP with the lowest remaining p-value was then selected and again all other SNPs in LD were removed . This procedure was repeated until no remaining SNPs fell below the selected PT . In the test set , per individual scores were generated by summing the dosage of all SNPs in a set weighted by the effect size ( beta ) calculated in the discovery set . We then regressed phenotype on this score using logistic regression including top PCs . SNP set pruning was performed using PLINK version 1 . 07 [27] , logistic regression , calculation of variance explained and results visualization was performed using R version 2 . 12 ( www . r-project . org ) and the Design package [32] . A list of SNPs previously reported to associate with HIV-1 acquisition was taken from Petrovski et al [14] and updated to include recently reported associations . All SNPs were either directly genotyped or imputed , and tested in the same logistic regression/meta-analysis framework as all other variants . CCR5Δ32 genotypes were obtained by individual cohorts using either Sequenom genotyping , PCR or direct sequencing as described in the original publications . Since genotype of this deletion was not available in the control populations we used a subset of the HIV+ sample with both genome-wide genotypes and CCR5Δ32 types as a reference panel for imputation . For this , we used the subset typed on the Illumina 1M platform ( n = 1 , 100 ) to maximize SNP coverage . Additionally , we included 383 non-overlapping individuals with known CCR5Δ32 genotype from a recent GWAS in hemophiliacs [16] . Phasing of the reference panel and imputation was performed using ShaPEIT [31] and IMPUTE2 [29] , [30] . We imputed CCR5Δ32 genotype in both cases and controls using a leave-one-out strategy such that , if an individual was present in both the reference and test sample , their genotype information was removed from the reference panel and imputation was carried out using the remaining samples as reference . Association was tested under a recessive model and assuming an additive or heterozygous advantage model . Power for variant detection was estimated over a wide range of possible proportions of controls being misclassified as cases ( Figure S4 in Text S1 ) . Calculations were made under an additive genetic model assuming a risk variant of 10% frequency for a study of 6 , 300 cases and 7 , 200 controls at genome-wide significance ( p<5×10−8 ) . Calculations were performed using PAWE-3D [22] , [33] . | Comparing the frequency differences between common DNA variants in disease-affected cases and in unaffected controls has been successful in uncovering the genetic component of multiple diseases . This approach is most effective when large samples of cases and controls are available . Here we combine information from multiple studies of HIV infected patients , including more than 6 , 300 HIV+ individuals , with data from 7 , 200 general population samples of European ancestry to test nearly 8 million common DNA variants for an impact on HIV acquisition . With this large sample we did not observe any single common genetic variant that significantly associated with HIV acquisition . We further tested 22 variants previously identified by smaller studies as influencing HIV acquisition . With the exception of a deletion polymorphism in the CCR5 gene ( CCR5Δ32 ) we found no convincing evidence to support these previous associations . Taken together these data suggest that genetic influences on HIV acquisition are either rare or have smaller effects than can be detected by this sample size . | [
"Abstract",
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"Results",
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"Methods"
] | [
"medicine",
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] | 2013 | Association Study of Common Genetic Variants and HIV-1 Acquisition in 6,300 Infected Cases and 7,200 Controls |
When organisms need to perform multiple tasks they face a fundamental tradeoff: no phenotype can be optimal at all tasks . This situation was recently analyzed using Pareto optimality , showing that tradeoffs between tasks lead to phenotypes distributed on low dimensional polygons in trait space . The vertices of these polygons are archetypes—phenotypes optimal at a single task . This theory was applied to examples from animal morphology and gene expression . Here we ask whether Pareto optimality theory can apply to life history traits , which include longevity , fecundity and mass . To comprehensively explore the geometry of life history trait space , we analyze a dataset of life history traits of 2105 endothermic species . We find that , to a first approximation , life history traits fall on a triangle in log-mass log-longevity space . The vertices of the triangle suggest three archetypal strategies , exemplified by bats , shrews and whales , with specialists near the vertices and generalists in the middle of the triangle . To a second approximation , the data lies in a tetrahedron , whose extra vertex above the mass-longevity triangle suggests a fourth strategy related to carnivory . Each animal species can thus be placed in a coordinate system according to its distance from the archetypes , which may be useful for genome-scale comparative studies of mammalian aging and other biological aspects . We further demonstrate that Pareto optimality can explain a range of previous studies which found animal and plant phenotypes which lie in triangles in trait space . This study demonstrates the applicability of multi-objective optimization principles to understand life history traits and to infer archetypal strategies that suggest why some mammalian species live much longer than others of similar mass .
Mammals can have very different lifespans , and it is of great interest to understand why longevity differs between species . Recent studies use comparative approaches to understand mechanisms for longevity in diverse mammalian species , especially species which are long lived [1–3] . In order to expand such studies to a wider range of species , a good understanding of how longevity varies with other traits , such as mass , is needed . It is well known that larger animals live longer , and some authors suggest an allometric relation in which L ∼ M1/4 [4–7] . Outliers to this relation have long been noted such as flying animals which are long lived given their mass [8 , 9] , as well as arboreal species [10] . In contrast to well-known power-laws such as the ¾-power law relating mass and metabolic rate ( Fig 1A ) , longevity and mass do not fall along a clean line in log space ( Fig 1B ) , suggesting that a simple allometric law may not apply . In general , the allometric law explains only about 35% of the lifespan variation [11] . More recent work used multi-dimensional linear [12] regression to connect multiple traits such as brain size with longevity [13–17] . This diversity of lifespan is of interest , since it may point to ways of understanding the regulation of longevity [2 , 3 , 18–21] . Life history traits such as longevity and reproductive parameters are closely linked with the ecological niche and environmental interactions of each species [22–26] . Short and long lifetimes have been associated with different global life-history strategies , namely rapid growth and reproduction versus long-term survival with slow reproduction . Work in the 1970s suggested a theory of r and K strategies based on the logistic equation[27–30] , where r-strategists have rapid reproduction and short lifespan , and K-specialists have long lifetime and slow reproduction . More recent work has challenged r-K theory and the use of the logistic equation; Recent work empirically supports the existence of a continuum of life-history traits known as the fast–slow continuum[12 , 31 , 32 , 33] . A different approach , within Stearn’s life-history theory [34] , focuses on physiological tradeoffs defined as negative correlations between traits which are hypothesized to result from competition over physiological resources . Here , we approach the relation of mass , longevity and other traits by analyzing a large database of endotherm ( mammal and bird ) life-history traits . We use concepts from a recent theoretical advance by Shoval et al [35] in understanding suites of variations when organisms need to perform multiple tasks[36] , based on Pareto optimality . A Pareto optimal system cannot perform better in all task , but rather in order to improve in a task it has to reduce its performance in at least one other task . The Pareto front is the collection of all systems for which no other system exists that is better at all tasks at once . Shoval et al . showed that when two tasks are important for fitness , species should fall on a line segment in the space of traits . The ends of this line are called archetypes: phenotypes optimal at one of the two tasks . If three tasks are important , data should fall on a plane and within that plane on a triangle . The vertices of the triangle are the three archetypes . Four tasks lead to a tetrahedron and so on . The position of each species in this polygon or polyhedron relates to the relative importance of the tasks in its niche ( Fig 1C–1F ) : specialist in one task should be near the corresponding archetypes , and generalists should be in the middle of the polygon . To apply this theory , one does not need to know the tasks in advance . If the data is found to fall in a polygon with clear vertices , the tasks can be inferred: clues about the tasks are gained by noting the special features of the animals closest to each archetype- these features provide hints about the task optimized by that archetype [37] . The tradeoffs in the Pareto theory can result in either positive or negative correlations between traits ( Fig 1G ) , and thus differ fundamentally from the tradeoffs of Stearn’s life history theory . Pareto theory was previously used to identify tasks in animal morphology [35 , 38 , 39] , behavior [40] and gene expression [35 , 37 , 41 , 42] . It was not previously applied to life-history traits . We find that the mass-longevity dataset for mammals and birds is well-described by a triangle in trait space . The triangle is filled nearly uniformly . We demonstrate how the theory can be used to infer three tasks from the mass-longevity triangle . A putative fourth task is identified by analyzing additional life history traits . This provides a new coordinate system for comparative studies of animal longevity , and more generally an approach for understanding the geometry of life history trait variation .
To comprehensively study the geometry of life history space of endothermic animals ( mammals and birds ) , we analyzed a large dataset of life history traits taken from AnAge build 13 [43] . We filled some of the gaps in the database by literature search , adding 20 longevity values for species with missing data , and 168 values for other life history traits ( Table 1 summarizes the taxa , and S1 Table lists the entire data ) . We also added brain size from a different source [17] resulting in 324 additional entries . We analyzed species with both mass and longevity known , resulting in a dataset with 2105 endotherms , each with 13 relevant life-history features ( See Methods and S1 Table ) . We begin by focusing on mass and longevity ( Fig 2A ) . We find that the data is much better explained by a triangle ( the minimal area triangle enclosing the data up to a defined number of outliers is shown in Fig 2A , see Methods , and S1 Text ) than by a line such as an allometric power law ( Fig 1B ) , as well as higher order polygons such as a four-vertex polygon ( quadrangle ) . Statistical model selection tests ( Akaike information criterion , cross validation tests ) indicate that a triangle is a better fit than a line , even when taking into account that fitting a triangle requires more parameters than a line ( see Methods ) . Fits to higher order polygons ( quadrangle , pentagon etc . ) do not provide enough improvement in fit quality to justify their extra parameters . We also tested the statistical significance that a triangle describes the data by comparison to randomized data , according to a test developed in [35] , ( p = 0 . 005 , see Methods ) . We conclude that a triangle is a good description of the geometry of the data in the log mass-log longevity plane . We also asked whether the way longevity is defined- the longest lived animal on record- may affect the geometry . We aimed to allow for a shorter longevity value for each species , perhaps reflecting natural conditions . We thus generated a dataset in which longevity of each species was multiplied by a random number between 0 . 8 and 1 . 0 , thus shortening its longevity entry . We find that a triangle still significantly fits the data in over 95% of 1000 such randomly perturbed datasets ( See S1 Text ) . Two of the edges of the mass-longevity triangle are approximately parallel to the axes . The third edge has a slope of 0 . 274±0 . 007 . This value agrees with the ¼ allometric relation between mass and longevity ( Fig 2A ) , L ∼ M1/4 . Linear regression applied to the full dataset also gives a slope close to ¼ , when ‘outliers’ such as bat species are removed , explaining previous findings of an allometric ¼-law . The three vertices of the triangle correspond–in Pareto theory- to archetypes . The error in the vertex position is about 3% as estimated by bootstrapping ( Fig 2A ) . The first archetype is close to high mass–high longevity species such as whales , elephants , and the hippopotamus . We name this the W-archetype , for whales . The second archetype is next to species of low mass and low longevity such as shrews , mice and hummingbirds . We name it the S-archetype , for shrews . The third archetype is near low mass—high longevity species . These include bats and the bat-like myotis , the naked mole rat , and birds like canaries . We name this the B-archetype for bats ( for values of mass and longevity see Table 2 ) . Flying birds lie in a region closest to the B ( bat ) archetype ( Fig 2A ) . Marsupials ( in green ) are spread over much of the triangle ( Fig 2C ) . Non-flying birds lie farthest from this archetype ( Fig 2D ) . Primates are relatively close to the B archetype ( Fig 2D ) , as are arboreal squirrel species ( S1 Fig ) . A phylogenetic analysis of this dataset shows that above the taxonomic level of family , the position on the triangle cannot be wholly explained by phylogenetic history ( S2 Text and S2 Fig ) . We next asked which tasks might be at play in each archetype . We approach this by looking for commonalities in the species near the archetypes . Quantitatively , we ask which life history features ( other than mass or longevity ) are maximal or minimal at the archetype . If Pareto theory is applicable , certain traits should be maximal or minimal in animals closest to the archetype; these traits should decline ( or rise ) with distance from the archetype . The S-archetype lies at low mass and short lifespan . The closest animals are shrews and rodents ( orders Rodentia and Soricomorpha ) . We tested 11 different life history traits of the animals from the AnAge database [43] as a function of their distance from the S-archetype . We normalized traits with units of time ( female maturity , weaning , inter-litter interval ) by the organism lifespan , and normalized traits with units of mass ( birth weight , weaning weight ) by the adult weight , to obtain dimensionless traits . Some information was lacking in the database , because some species had missing trait values ( Ranging from 82% for Body temperature to 28% for Litter/Clutch size ) . To search for enriched ( depleted ) features next to the archetypes , we followed the algorithm of Ref [37] . Briefly , we divided species into bins according to their distance from a given archetype , such that each bin had the same number of species . Then , we looked for features that have significantly maximal ( minimal ) values in the bin next to an archetype . According to Pareto theory , traits associated with the task of the archetype should have their maximal or minimal value at the bin closest to the archetype . We find ( see Methods , S3 Text , including multiple hypothesis tests ) that several life history traits are highly enriched near the S-archetype ( Fig 3 and Table 3 ) . Specifically , two traits associated with high reproduction rate—litter/clutch size and litter/clutch per year—are both maximal at this archetype . In addition , normalized brain size ( for mammals ) is highest at this archetype , as well as the relative gestation/incubation period relative to lifespan ( see Fig 3 ) . The enrichment for these traits is rather sharply peaked at the bin closest to the archetype , and falls off rapidly with distance from the archetype . This is the behavior expected from Pareto theory . These results support the conclusion that a task including rapid reproduction , many offspring , and rapid growth is maximized at the S-archetype . This inference makes sense: these animals are generally thought to face high predation . High predation has been suggested as an evolutionary pressure for resource allocation to early life , which correlates with short lifespan [1 , 8 , 44] in mammals ( but see exception [45] in guppies ) . The tasks most important in this situation may be rapid reproduction , many offspring , and rapid growth [27] . The S-archetype is a point deduced from the overall shape of the dataset- an extrapolated vertex of a triangle that encompasses the dataset; the region in the immediate proximity of the S-archetype is empty . This is because the endotherms in the dataset do not have longevity shorter than 2 years . There may be a physiological or ecological limit that prevents endotherms from going below a minimal longevity . For example , the heart-rate of mammals smaller than a shrew is expected to approach the physiological limitations of the circulatory system [46] . The closest animals to the W-archetype are whales , elephants , hippopotamus and orcas , which have large mass and high longevity . Enrichment analysis shows that at this archetype , animals have relatively long gestation/incubation periods . They have smallest and least frequent clutches/litters [47] ( Fig 3 ) . They have a relatively small brain compared with their mass . Again , the enriched traits are maximal at the bins closest to the archetypes , and decay with distance from the archetype . The task inferred for this archetype is appropriate for these large animals according to previous literature . High mass may allow them to escape predation , so that selection pressure to live longer becomes more meaningful [48–50] . Furthermore , they invest resources in a single offspring , which probably has good odds to reach adulthood . The third archetype , the B-archetype , has a low mass ( close to the mass of the S-archetype ) but high longevity values ( almost half of the longevity of the W-archetype ) . The animals closest to this archetype are bats , the naked mole rat , and many songbirds such as the Eurasian goldfinch , Broad-tailed hummingbird and Purple sugar bird . These species are thought to have lower extrinsic mortality rates by finding niches that reduce the number of possible predators . Both bats and birds fly , whereas the naked mole rat lives underground . Somewhat farther from the archetype are arboreal species ( primates , Southern flying squirrel , Pygmy marmoset , Sugar glider ) , which also have relatively low predation due to their niche ( Fig 2 ) [10 , 51] . Among birds , there appears to be a correlation between flying ability and the distance from this archetype , where the birds farthest from this archetype are flightless , such as the penguin , ostrich and hen ( Fig 2D ) . Related species occur in layers at different distance from this archetype , suggesting different levels of protective environments . The bats are closest to the archetype , then the primates . In a similar distance from the archetype are the family of Sciuridae ( squirrels ) , and farther away a strip with a high concentration of “armored” species such as hedgehogs , pangolin , armadillos and porcupines ( S1 Fig ) . Enrichment analysis indicates that species closest to the B-archetype have shorter pregnancies ( or incubations ) relative to lifespan . They wean the fastest relative to their longevity . This means that a larger part of their life is spent in puberty and adulthood . Compared to their mass , these species have large brain and they are born large and wean large . ( Fig 3 ) These traits are maximally enriched at the archetype and decay rapidly with distance from it . We went beyond two-dimensional trait-space of mass and longevity , by considering additional life history traits . One challenge is missing data- many of the 13 traits in the database are sparsely filled . We therefore chose the four traits with the most data , in the sense that there is the largest number of species in the database with all four traits available . These traits are mass ( adult weight ) , longevity , female maturity and birth weight ( see Methods ) . There are 550 mammals with data in all four traits . We removed 142 marsupials from the dataset , because birth weight in marsupials has a different physiological context and clusters far from other mammals . We analyzed the species in this four dimensional trait space . We performed principal component analysis ( PCA ) to determine the effective dimensionality of the data . We find that the first 3 PCs capture ~99 . 5% of the variance ( p < 10−4 compared to shuffled data ) . This suggests that data falls effectively in three dimensions . The three PCs are loaded heavily with the following traits: PC1 is related to mass ( adult weight and birth weight ) , PC2 to time ( longevity and female maturity ) , and PC3 to the ratio between the adult weight and birth weight . We went on to test whether the data can be reasonably described by a polyhedron in this three dimensional space ( Fig 4A ) . We find that a tetrahedron captures the data well , and with high significance compared to randomized data ( p < 2⋅10−4 ) [37] ( see Methods ) . Higher-order polyhedra do not improve the fit to justify their extra parameters ( Methods ) . Fig 4C–4E shows this tetrahedron from three points of view in the space of the first 3 principal components . The tetrahedron has four vertices , which we consider as four archetypes . Three of the archetypes match those we discussed so far . The fourth archetype lies at an intermediate point of mass-longevity ( M~3 . 5kg , L~18 years ) ( Fig 4B ) , and at low birth weight . The animals nearest this archetype are the panda and bears , while somewhat farther are other carnivorous mammals ( order Carnivora including the feline family—lions and tigers , and the canine family wolves ) . Enrichment analysis did not supply significant enrichment of other life-history traits at this archetype , perhaps due to the small number of data points . The potential task or strategy related to this archetype is thus mainly performed by large predators . We conclude by discussing previous work in the context of Pareto theory . In the field of life history , several triangle-shaped suites of variation were found in the past ( summarized in S2 Table ) . Grime [52] considered a trait space of plants , including life history traits such as leaf longevity and litter , and morphological traits such as root morphology and leaf from . This study identified three syndromes of traits that go together . Each syndrome is specialized for a different strategy: stress tolerant ( S ) , ruderal ( R ) , and competitive ( C ) , which apply to extreme niches: High stress and low disturbance , low stress and high disturbance , and low stress and low disturbance . The C , S and R survival strategies can be considered as tasks . The CSR model is widely used when describing plants life history strategies , and was implemented also to reef corals [53] and fungi [54] . The original CSR model by Grime was depicted in performance space , by triangle whose vertices represent each strategy . More recent work [53] analyzed trait space using principal coordinate analysis ( PcoA ) , to find that high dimensional trait data falls approximately on two dimensions . Projecting on this 2D plane , data falls in a triangle , with generalists in the middle and specialist at each strategy towards the vertices . This series of studies fits well with Pareto theory , where the vertices of the triangles in trait space define three archetypes , whose traits correspond to Grimes syndromes . In another pioneering study [55] a triangle was found in the life history trait space of fresh and marine fish . Here the triangle was found in trait-space defined by the measured juvenile survival , fecundity , and onset and duration of reproductive life . The three strategies ( tasks ) matching three niches in this case are: ( i ) periodic strategy in environments with predictable patterns of change , ( ii ) opportunistic strategy in marginal habitats and large predation , and ( iii ) equilibrium strategy in temporally stable and resource limited environments . Evidence for a triangle due to different resource limitations was found in phytoplankton , where competitive ability for nitrate , phosphate as well as cell volume exert tradeoffs [56] . In many of these studies it is noted that the strategies described are extremes , while intermediate strategies lie in the middle of the triangle . These studies thus seem to be consistent with predictions of the Pareto theory of Shoval et al .
The present study indicates that variation in longevity and other life history traits can be understood using the multi-objective optimality approach of Shoval et al [35] . We studied life history traits in a large dataset of endothermic animals . We find that mass and longevity fall in a triangle , and not a line , suggesting three major life history strategies . Analysis of additional traits suggests a tetrahedral geometry with a fourth putative strategy related to large carnivores . Using the multi-objective optimality approach offers a new theoretical framework and a testable model for future research . It also offers a new way to consider tradeoffs that complements Stearns theory [34] . This study demonstrates how Pareto theory can be used to systematically infer tasks from the dataset geometry . The triangular geometry of longevity-mass suggests three tasks or strategies . Animals closest to the vertices are predicted to specialize in particular strategies , and to have extreme values of certain life history traits ( other than mass and longevity ) . This prediction is fulfilled . These enriched features provide clues to the possible tasks at play . The three putative strategies of whales , shrews and bats are summarized in Table 3 . They relates to classical notions of fast and slow strategies ( fast reproducing shrew versus long-lived whale ) , with a third strategy for animals that are thought to have low predation niches ( bats , arboreal animals , flying birds etc . ) . Animals in the middle of the triangle are generalists; their distance from the different vertices suggests their relative use of the different strategies . The three-strategy picture that emerges for endothermic animals echoes the three-strategy picture for plants ( competitive , stress tolerant and ruderal ) [52] and fish [55] . A fourth strategy , related to large carnivores , remains to be better defined when more data becomes available , and has the potential to further enhance our understanding of life histories interplays and tradeoffs . This study also makes a distinction between different definitions of the concept of trade-offs ( Fig 1G ) . In life-history theory [34] , tradeoffs are defined by negative correlations between measured features such as survival and fecundity . In terms of Pareto theory , features such as survival and fecundity are understood as performances in tasks . Stearns theory thus treats performance space as opposed to trait space . Correlations in trait space result from the fact that no phenotype can be optimal at multiple tasks . This leads to negative or positive correlations in trait space ( traits such as mass , longevity , litter size ) , and also to the possibility of triangles and other polygons/polyhedra . It is of major interest to identify genetic and molecular changes that cause differences in longevity . To date , most studies use model species such as C . elegans [57] and mice [58–60] . Using the present triangle and future molecular data on a very large number of mammalian species [3 , 61 , 62] , one can hope to gain sufficient statistical power to address longevity in a new way . In particular , using many species may help discard molecular changes that are irrelevant , and to highlight the changes that directly relate to mass-longevity . For this purpose , one can correlate molecular changes with the distance of each species from the three archetypes . In summary , biological life-history traits , traditionally analyzed using allometric lines and multidimensional linear regression , can be shown to have more complex configurations such as triangles , tetrahedrons and maybe even higher dimensional simplexes . Pareto theory provides a theoretical framework to understand such polygons . The vertices of these shapes correspond to specialists at key tasks that combine to generate fitness in different niches , and the edges are the tradeoffs between such two tasks . Mass and longevity of mammals and birds fall on a triangle and not an allometric line , suggesting three main tasks or strategies . These strategies were analyzed using a systematic way to infer life-history tasks- by noting which features are enriched near the archetypes . A novel fourth task/strategy related to carnivores is suggested at the next level of resolution of the Pareto approach . Future work can employ Pareto archetype analysis to other life-history traits with the hope of better understanding biological diversity in form and function .
Data was downloaded from AnAge database of animal longevity , build 13 [43] . We considered 13 traits: female maturity ( days ) , male maturity ( days ) , gestation/incubation ( days ) , weaning ( days ) , litter/clutch size , litters/clutches per year , inter-litter/interbirth interval , birth weight ( g ) , weaning weight ( g ) , adult weight ( g ) , maximum longevity ( yrs ) , metabolic rate ( W ) , and temperature ( K ) . Maximum longevity is manually curated data on the oldest age on record [63] . Values of other traits were averaged across measurement methods , geographical location and literature source [63] , with field data preferred over captive data . When there was a substantial difference between age at sexual maturity and age of first reproduction , the former was used . Sexual maturity is defined as the time from conception to physiological sexual maturity [9] . Further information about AnAge database can be found in refs [9 , 43 , 63] and on the website: http://genomics . senescence . info/help . html#anage . Four traits in the AnAge database were not used: growth rate because it is a fitting parameter of a model where for birds and mammals different models were used , body mass because it strongly and tightly correlates with the adult weight entry; we also did not use mortality rate doubling time ( MRDT ) for a given species using the Gompertz equation and the infant mortality rate ( IMR ) . Because of missing data , we added to this dataset information from several sources on longevity and other life history traits [64–67] . We consider only species with data in both adult weight and longevity ( S1 Table lists the data ) , which amounts to 2105 endotherms ( rows ) , each with its taxonomic tree and 13 relevant life-history features ( columns ) . See Methods for more details . We also added the brain size from [17] . We took only the species from this database that existed on AnAge this resulted in 324 values for brain size . We tested several models for the log mass-log longevity data . This includes a linear fit , l = am + b , a triangle enclosing he data and more generally an n-vertex polygon . We first used the PCHA algorithm to find the best n points on the convex hull of the data that accounts for most of the variance in the data as described in [41] . A line is defined by two archetypes , a triangle by three etc . For each number of archetypes n , we compute the explained variance given by the mean relative distance of the N data points to the polygon EV ( n ) =1N∑i=1N ( 1−‖pi−si‖/‖pi‖ ) . Here pi is the ith data point and si is the closest point to pi in the polygon [41 , 68]; points inside the polygon have ‖pi − si‖ = 0 . The normalization term ‖pi‖ , refers to the distance to the center of mass of all the data points . We seek a number of archetypes for which adding an additional archetype does not increase EV by much . We find that explained variance for n = 2 , 3 , 4 , 5 is EV = 0 . 9711 , 0 . 9989 , 0 . 9993 , 0 . 9999 . Thus a triangle is better than a line , and four-vertex or five-vertex polygons make only a tiny improvement over the triangle ( 2% vs . 0 . 04% ) . We approached model selection also in a second way , by using a criterion that takes into account the number of free parameters in the model , namely the Akaike information criterion ( AIC ) [69] . The first step in calculating AIC is to calculate how likely each model is . Likelihood is calculated by assuming that the data was drawn from a distribution ( the model ) . According to standard practice , we used a Gaussian error model: the convolution of the line/triangle/polygon with a Gaussian to account for the possibility of noise . The standard deviation of the Gaussian distribution was chosen to be 30% of the value at the data-point . Similar results were obtained with a 10% or 100% std . In all of these cases the triangle was overwhelmingly more likely than a line ( AIC/2 ~–log likelihood ~ 7500 , 3600 for line and triangle in the 30% case , 56000 , 5600 for the 10% case , and 4300 , 3900 in the 100% case ) . A triangle was more likely than a 4-vertex model for the 30% and 100% cases . To estimate the statistical significance of the description of the data by a triangle or a tetrahedron ( polygon ) , we calculated the t-ratio . The t-ratio is the ratio of the polygon’s volume to the volume of the convex hull of the data [35] . It is a measure for the extent that the data fills the polygon . A t-ratio of one occurs when the data convex hull is exactly the desired polygon . We then generate randomized datasets by sampling from the cumulative distribution created by each coordinate independently from its ensemble of measured values . This eliminates correlations between traits while conserving the distribution of values of each parameter . We calculate the t-ratios for each randomized dataset in comparison to its own minimal volume enclosing polygon , and set the p-value to be the proportion of randomized sets with a smaller or equal t-ratio than the original data . See S1 Text for more details . In order to determine the position of the three archetypes on the mass-longevity plane we used the algorithm described by Shoval et al . [35] . This algorithm finds the minimal triangle enclosing all the data point , without allowing any outliers . To be robust to outliers , we applied a peeling procedure–removing the convex hull of the data . We repeated this procedure , and after each peeling we looked for the archetypes of the new dataset . The change of position of the W archetype was large after one peeling step ( ~17% of the large edge of the triangle ) , but remained relatively unchanged after more peelings ( ~3% ) . Thus , the final position of the archetypes presented here is given after the data was peeled once . All the data points inside the polygon obey the rule , X = θA . Here , X is the matrix of the data ( namely , mass and longevity ) , A is the matrix of the archetypes , where each column is a different archetype and θ is the weight vector representing the compromises between the tasks ( archetypes ) . For each data point ∑θi = 1 . For every point inside the triangle ∀θi ≥ 0 . Values of theta are given in S1 Table . We computed the uncertainty of the archetype positions by bootstrapping , resampling the 2105 data point with replacements and computing the new triangle archetypes . We obtained archetype position distribution by repeating this procedure 1000 times . The standard deviations of the archetype positions are depicted as ellipses in Fig 2 . The intersection between the data in the phylogeny of [70] and the present dataset includes 966 mammals . We represented each data points by its first two weights ( the third is determined by the demand that ∑θi = 1 ) . On this resulting triangle we measured distance of two species , d , by the Euclidean distance between the points . The distance on the phylogenetic tree was measured as the shortest path distance , namely , for each two species , we looked for their nearest common ancestor , and summed the length ( years ) of the branches leading to each species ( see S2 Text and S2 Fig ) . For each archetype , we divide the data into equally populated bins according to their distance from the archetype . Bin size was determined using a bootstrapping test ( see S3 Text ) . Then we check whether the median of each trait in each bin is maximal ( or minimal ) with respect to the rest of the bins . We then test whether the distribution of points in the first bin is significantly different than the rest of the data ( the Mann-Whitney test [71] ) . Then we used the Benjamini-Hochberg procedure for multiple hypothesis testing [72] . All the enriched features in Table 3 are significant after the multiple hypothesis testing . For further explanations on the way we tested for feature robustness and the choosing of bin-size see S3 Text . We scanned all 715 possible combinations of four traits of the 13 in the database , and chose the four traits with the largest number of species that have data on all four traits . We found the best fit tetrahedron using SISAL algorithm [73] , evaluated the errors in archetypes by bootstrapping ( 12% errors ) , and the significance by the t-ratio test ( p < 2⋅10−4 ) . | Understanding why some mammals live longer than others is of crucial interest . Here we study how longevity relates to other life-history traits , using data on about 2000 species of mammals and birds . In contrast to the tradition in which traits fall on a line in logarithmic coordinates , called allometric lines . We find that mass and longevity of mammals and birds fills out a triangle in logarithmic axes , not a line . Thus , mammals of low and intermediate mass can have a wide range of life spans . We interpret this geometry using Pareto optimality theory: The triangle suggests three different life strategies or tasks . Animals near the vertices of the triangle are specialists at one of the strategies , animals near the center are generalists . Analyzing the data at higher resolution suggests an additional fourth strategy related to carnivores . The mass-longevity triangle offers a new coordinate system for comparative studies of animal longevity . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | The Mass-Longevity Triangle: Pareto Optimality and the Geometry of Life-History Trait Space |
Dynamic exchange of a subset of nucleosomes in vivo plays important roles in epigenetic inheritance of chromatin states , chromatin insulator function , chromosome folding , and the maintenance of the pluripotent state of embryonic stem cells . Here , we extend a pulse-chase strategy for carrying out genome-wide measurements of histone dynamics to several histone variants in murine embryonic stem cells and somatic tissues , recapitulating expected characteristics of the well characterized H3 . 3 histone variant . We extended this system to the less-studied MacroH2A2 variant , commonly described as a “repressive” histone variant whose accumulation in chromatin is thought to fix the epigenetic state of differentiated cells . Unexpectedly , we found that while large intergenic blocks of MacroH2A2 were stably associated with the genome , promoter-associated peaks of MacroH2A2 exhibited relatively rapid exchange dynamics in ES cells , particularly at highly-transcribed genes . Upon differentiation to embryonic fibroblasts , MacroH2A2 was gained primarily in additional long , stably associated blocks across gene-poor regions , while overall turnover at promoters was greatly dampened . Our results reveal unanticipated dynamic behavior of the MacroH2A2 variant in pluripotent cells , and provide a resource for future studies of tissue-specific histone dynamics in vivo .
All genomic transactions in eukaryotes occur in the context of chromatin . While histones are generally among the most stably-associated DNA-binding proteins known [1] , a subset of histones exhibit dynamic replication-independent exchange with the soluble pool of nucleoplasmic histones [2]–[4] . Dynamic histone exchange is intimately linked to a variety of key aspects of chromatin biology . In all eukaryotes studied , histone H3 exchange is most rapid at promoters [5]–[12] , and is generally slowest over heterochromatic regions . In addition , H3 exchange is rapid at boundary elements that block the spread of heterochromatin [5] , [7] , raising the possibility that rapid histone exchange could function mechanistically to erase laterally spreading chromatin states . These correlations , in which histone exchange is slow over epigenetically-heritable heterochromatin domains but is rapid at boundary elements , raise the question of how histone dynamics contribute to epigenetic inheritance . Interestingly , H3/H4 tetramers carrying the H3 . 3 variant “split” during replication to a greater extent than do H3 . 1-containing tetramers [13] , consistent with the hypothesis that dynamic regions of chromatin could potentially self-perpetuate through replication [2] . In addition , the rapid histone turnover observed at promoter regions of actively transcribed genes suggests that histone turnover may have an important role in gene regulation , as higher histone turnover rates could provide greater access of regulatory proteins to specific DNA elements . Yet much remains to be learned about the mechanistic basis for , and the biological consequences of , dynamic chromatin states . Embryonic stem ( ES ) cells are a key model for mammalian pluripotency and cell state inheritance . ES cells are characterized by unusual chromatin packaging [14] , and a wide variety of chromatin regulators have been implicated in control of pluripotency and differentiation [15]–[19] . One curious feature of ES cell chromatin is its “hyperdynamic” state—photobleaching experiments show that many histone variants exchange more rapidly in ES cells than in differentiated cell types [20] . This hyperdynamic state has been proposed to maintain the ES cell genome accessible as a relatively permissive ground state that becomes “locked down” during the process of lineage commitment and subsequent differentiation . Understanding histone exchange dynamics in ES cells , and during differentiation , is therefore of great interest for understanding the roles for chromatin in cell state inheritance . The histone variant MacroH2A plays a key role in cell state stabilization in mammals . Mammals encode three MacroH2A variants , MacroH2A1 . 1 and MacroH2A1 . 2 , which are alternatively spliced isoforms of a single gene , and the distinct gene product MacroH2A2 . All three MacroH2A variants are distinguished by the presence of the unusual “Macro” domain fused to their relatively well-conserved H2A cores . It has been suggested that MacroH2A plays a role in fixing the epigenetic state of differentiated cells ( reviewed in [21] ) . Support for this notion comes from observations that MacroH2A deposition increases with cellular age and senescence [22] , [23] , and that epigenetic reprogramming via somatic cell nuclear transfer is accompanied by an active removal of MacroH2A1 from the donor chromatin upon transfer into the ooplasm [24] . More recent studies have indicated that MacroH2A depletion from somatic cells increases their propensity for undergoing epigenetic reprogramming [25]–[28] —in several of these studies , depletion of either MacroH2A1 or MacroH2A2 enhances reprogramming , with depletion of both having an additive effect . These studies suggest that removal of MacroH2A from the somatic genome may be prerequisite for acquisition of pluripotency during epigenetic reprogramming . MacroH2A may further contribute to fixing the epigenetic state of differentiated female cells due to its accumulation on the inactive X chromosome ( Xi ) [29] . However , association of MacroH2A1 with the Xi appears to occur after the random inactivation of the X chromosome ( XCI ) [30] , and in conditional Xist deletions gene silencing is maintained despite the loss of MacroH2A1 on the Xi [31] . Nonetheless , while MacroH2A1 appears to be dispensable for XCI , removal of this variant from the Xi could still potentially represent a barrier to epigenetic reprogramming of a differentiated , post-XCI somatic cell to the pre-XCI ground state of pluripotency . Despite the general characterization of MacroH2A as being a “repressive” histone variant , there are numerous examples where Macro incorporation is associated with increased gene expression , particularly during early lineage specification after embryoid body formation from ES cells [32] , and more recently in embryonic fibroblasts where MacroH2A1 is present at high levels in the active Thy1 gene , but nearly completely absent when this gene is silent in pluripotent ES cells [27] . Determining the dynamics of MacroH2A turnover in both pluripotent ES cells and somatic cells is therefore of paramount interest for gaining an in-depth understanding of the epigenetic processes underlying cellular reprogramming . Three methods are currently used to study histone dynamics [33] . First , the original discovery that the H3 . 3 variant marks sites of replication-independent histone exchange [3] , [4] has enabled many labs to infer histone dynamics simply from steady-state H3 . 3 localization patterns [6] , [7] , [9]–[11] . Second , genetically encoded “pulse-chase” systems have been utilized in which an epitope-tagged histone molecule is induced , and mapping of the epitope tag at various times after induction provides a detailed kinetic view of histone exchange dynamics [5] , [8] , . Finally , a metabolic labeling strategy termed “CATCH-IT” enables kinetic analysis of overall chromatin dynamics [37] . Here , we extend the approach of inducible expression of epitope-tagged histone variants to study chromatin dynamics in murine embryonic stem cells . We generated ES lines carrying doxycycline ( “Dox” ) -inducible HA-tagged versions of several histone variants , including H3 . 3 and MacroH2A2 . These cells allowed us to monitor the rate of incorporation of HA-tagged variants by ChIP-Seq at varying times following Dox induction . For the well-studied H3 . 3 variant , we validate our method by recapitulating known aspects of H3 . 3 localization and dynamics . We also characterized the dynamics of the understudied MacroH2A2 variant in detail in ES cells and in their embryonic fibroblast ( MEF ) derivatives . MacroH2A2 exhibited broad , likely replication-coupled , incorporation throughout large stretches of the ES cell genome , along with unexpectedly rapid turnover behavior at highly-expressed promoters . In contrast , MacroH2A2 in more differentiated MEFs was additionally associated with a subset of gene-poor genomic loci , and its exchange at promoters slowed considerably . These results reveal surprising aspects of MacroH2A2 localization and dynamics and suggest that the view of MacroH2A2 as simply an indicator and/or mediator of repressed chromatin states is not accurate . Moreover , these studies establish a model system for investigation of histone variant dynamics in tissue culture systems as well as in complex organ systems in vivo .
In order to assay genome-wide histone variant dynamics in embryonic stem cells and cell types derived from them , we generated ES cells based on the murine KH2 ES cell line [38] , which harbors a modified reverse tetracycline transactivator ( M2rtTA ) targeted to the ROSA26 locus and an FRT recombination site targeted into safe-haven chromatin downstream of the Type I Collagen ( Col1A1 ) locus . Introduction of a donor plasmid carrying another FRT recombination site along with HA-tagged cDNA sequences encoding the histone variants of interest under transcriptional control of the tetracycline operator ( TetO ) , along with an additional plasmid encoding the FLP recombinase , allows for site-specific integration of the tetracycline-inducible HA-tagged histone cassette into the genome ( Figure 1A ) . Subsequent addition of the tetracycline analog doxycycline ( “Dox” ) to these ES cell clones or mice derived from them results in induction of the tagged histone variant ( Figure 1A , bottom panel ) . Cell lines were generated and validated for several different histone variants , including MacroH2A2 ( hereafter called Macro in some contexts ) , and H3 . 3 ( Figure S1A ) . For all cell lines analyzed , no expression of tagged histones was detected in the absence of Dox by Western blotting or immunofluorescence staining with an HA antibody . Robust activation of the tagged proteins was detectable within 2–3 hours of Dox addition ( Figure 1B , Figure S1B ) . Several controls show that ectopic expression of tagged histone variants did not significantly perturb ES cell pluripotency . First , even after 12 hours of induction , ectopically expressed histones were far less abundant than the endogenous proteins levels ( Figure S1C ) . Second , after 72 hours of overexpression , ES colonies maintained their pluripotent state as assessed by cell morphology , alkaline phosphatase ( AP ) staining , and expression of pluripotency markers such as Oct4 , Sox2 and Nanog ( Figure S2 ) . The only exception was H3 . 3 , where 72 ( but not 24 ) hours of ectopic expression resulted in a slightly reduced proliferation rate , but did not compromise pluripotency based on Oct4 or AP staining ( not shown ) . Finally , as shown below , mapping of total MacroH2A2 ( for which a high quality commercial antibody exists ) both before and after HA-Macro induction yielded nearly identical results , demonstrating that ectopic expression did not drive nonphysiological incorporation of this histone variant into ectopic sites throughout genome . To validate our system , we first sought to determine whether a pulse-chase experiment is consistent with steady state mapping of H3 . 3 localization [4] , [6] , [7] , [9] , and what additional information it provides . ES cells carrying doxycycline-inducible HA-H3 . 3 were treated with Dox , and harvested after 0 , 3 , or 6 hours . HA-H3 . 3-containing chromatin was mapped genome-wide by chromatin immunoprecipitation followed by Illumina deep sequencing ( ChIP-Seq ) . Sequencing reads were mapped back to the genome . Importantly , HA mapping at t = 0 ( no doxycycline ) did not show enrichment over specific loci but rather genome-wide nonspecific background , demonstrating the specificity of the anti-HA antibody ( see below ) . Because H3 . 3 replacement is strongly associated with the 5′ ends of genes [6] , [7] , we aligned all annotated genes by their transcription start sites ( TSSs ) , and averaged all mapped reads at each position relative to the TSS ( Figure S3 ) . Consistent with studies in flies and murine ES cells [6] , [7] , [9] , we find that H3 . 3 is localized to two peaks surrounding the TSS , and that H3 . 3 levels correlate with the mRNA abundance of the associated gene . We also confirmed that the rapid H3 . 3 dynamics observed at Polycomb-bound regulatory elements in flies [7] are also present in the mouse embryonic stem cell genome at regions occupied by polycomb proteins Rnf2 and Suz12 ( Figure S3E ) . Our results therefore recapitulate major known aspects of histone H3 . 3 dynamics . As H3 . 3 replacement has been extensively studied , we therefore turned to the understudied MacroH2A . 2 variant . We next extended our studies to a histone variant with unknown dynamic properties , MacroH2A2 . Because MacroH2A2 localization in ES cells has not been characterized , we first carried out genome wide mapping of MacroH2A2 in murine ES cells using a commercially available antibody ( Figure 2 , Tables S1 , S2 ) . MacroH2A2 was broadly localized to large ( megabase-scale ) blocks across the mouse genome , where it colocalized with regions of high gene density ( Figure 2A , Figure S4 , Table S3 ) —the correlation between average MacroH2A2 enrichment and gene density was 0 . 46 for 100 kb windows , and rose to 0 . 59 when considering 1 MB windows of the genome ( Figure 2B ) . In addition to broad localization over gene-rich regions , we noted that MacroH2A2 exhibited a tight ( ∼500 bp ) peak on average over promoters ( Figure 2C ) . Counterintuitively , genes lacking MacroH2A2 were generally poorly expressed ( Figure 2D and Figure S4B , see Cluster 3 ) , stemming largely from the absence of MacroH2A2 at repressed gene families such as those encoding olfactory receptors or zinc finger transcription factors . Interestingly , among genes associated with promoter MacroH2A2 , tighter localization was correlated with higher expression levels ( Figure S4B , Clusters 1 and 2 ) . Consistent with the surprising correlation between MacroH2A2 localization and active promoters , we found a moderately positive correlation between our MacroH2A2 dataset and H2A . Z localization [39] in ES cells ( Figure S5 ) . Confidence in these surprising observations comes from four lines of evidence . First , localization datasets obtained before and after HA-MacroH2A2 induction were highly-correlated ( Figure 2C ) . Second , anti-HA ChIP-Seq in uninduced HA-Macro cells yielded a nearly flat genome-wide background ( Figure 2A and Figure S4A , top panel , Figure S6 , left panel ) . Third , MacroH2A2 localization obtained using the MacroH2A2 antibody was very highly correlated with the localization pattern observed using anti-HA ChIP-Seq from cells expressing HA-MacroH2A2 ( Figure 2A and Figure S6 ) , but not HA-H3 . 3 ( Figure S3 ) . Finally , MacroH2A2 localization patterns were strongly correlated , but not identical , between ES cells and MEFs ( see below ) . Thus , we find MacroH2A2 localizes to large blocks of gene-rich chromatin in ES cells , and within these blocks exhibits strong promoter localization at expressed genes . We next carried out genome wide mapping of HA-MacroH2A2 at 3 time points ( 3 , 6 , and 12 hours ) after Dox induction . Reads were mapped back to the genome and genes were aligned by TSS as above . HA mapping in the no Dox control revealed a primarily flat genomic background ( Figures 2A , 3A ) , with trace levels of promoter localization likely resulting from low levels of leaky expression of HA-MacroH2A2 ( Figure S6 ) . Data from 3 , 6 , and 12 hours after HA-Macro induction was strongly correlated with endogenous MacroH2A2 localization ( Figure 3A , Figure S6 ) . The strong correlation between all 3 time points and the steady-state localization is to be expected from the fact that ES cells are rapidly cycling , so even at 3 hours of induction a substantial subpopulation of cells will have gone through S phase and carried out any replication-dependent MacroH2A2 incorporation . In yeast , cell cycle arrest can be used to explicitly assay replication-independent histone replacement dynamics [5] , [8] . However , this is impractical in ES cells , and our data come from asynchronously cycling cells . Nonetheless , such data can be used to study histone turnover . Two considerations , one conceptual and the other empirical , will aid in understanding how pulse-chase data obtained from cycling cells can be used to infer turnover dynamics ( see Figure S7 ) . Conceptually , we expect that loci exhibiting replication-coupled histone deposition ( or slow replication-independent deposition ) will gradually accumulate epitope-tagged histone variants over a time course of induction ( Figure S7A ) . In contrast , because replication-independent replacement will initially occur in a greater fraction of cells than the subset of cells that are actively transiting S phase , such loci will exhibit more rapid accumulation of tagged histone . Given that genome-wide measurement methods typically normalize for sequencing depth ( with the underlying assumption/hypothesis being equivalent total amounts of material between samples ) , the end result of this is that loci exhibiting rapid turnover will exhibit high levels of epitope tag enrichment early in a time course , but later in the time course this normalized relative enrichment will decrease as the bulk of cells transit S phase and replication-coupled deposition results in a greater total amount of epitope tag incorporated into the genome . In other words , relative enrichment of the rapidly exchanging population is high at early time points before population-wide assembly of HA-histone into the slower subpopulations , whereas at later time points normalization relative to the extensive HA-histone in cold domains results in a diminishing peak at “hot” loci ( Figure S7B ) . Importantly , the assessment of relatively hot and cold loci is robust to normalization methods ( Figure S7B , Methods ) . This predicted behavior is exactly what we have previously observed [5] empirically in yeast—here , replication-independent H3 turnover was directly measured in G1-arrested yeast . A parallel experiment was carried out using asynchronous cells , and those loci shown to exhibit rapid replication-independent turnover exhibited precisely the above-predicted behavior—rapid enrichment of tagged H3 , followed by diminishing tag enrichment as the bulk of the genome was assembled into tagged H3 via replication-coupled assembly . Consistent with the above considerations , in addition to the genome-wide HA incorporation observed at all 3 time points , we also observe extensive locus-specific variation in HA-Macro dynamics ( Figure 3A , red and green-bordered boxes identify regions of rapid and slow HA incorporation , respectively ) . Regions exhibiting high levels of HA at 3 hours relative to 12 hours were inferred to be “hot” ( Figure S7 , [5] ) , and typically occurred in highly delimited peaks associated with promoters ( see below ) , whereas cold regions generally covered broad chromosomal stretches , often in intergenic regions ( Figures 3B–D , S8 , Table S2 ) . These trends can also be seen in detail when focusing on promoter proximal Macro dynamics ( Figure 4 ) . On average , the TSS-proximal peak of MacroH2A2 diminished from 3 hours to 6 hours to 12 hours , consistent with rapid replication-independent replacement . This observation was reproduced in a second HA-Macro induction time course ( Figure S9 ) . In contrast , genes associated with broad domains of MacroH2A2 across their promoters ( Figures 2B and 4A , Cluster 2 ) exhibited consistent HA-MacroH2A2 mapping patterns at all three time points , as would be expected if these broad domains were relatively stable and incorporated Macro either via slow replacement or only during replication . These results are consistent with at least two populations of MacroH2A2-containing chromatin that can be distinguished by their dynamic behavior . We infer that the TSS-proximal MacroH2A2 that is enriched at early time points before diminishing in enrichment represents a rapidly exchanging population of Macro that is present at moderate steady state occupancy , while larger Macro domains undergo either slow turnover or replication-coupled assembly . These larger domains tend to be gene poor , often occurring over gene deserts ( Figures 3C–D ) but occasionally encompassing individual genes as well ( Figure 4A , cluster 2 ) . To gain further insight into the population of dynamic promoter-proximal Macro , we sorted genes with tight promoter Macro peaks ( Cluster 1 ) according to their relative inferred Macro dynamics ( Figure 5A ) —note that relative dynamic behavior is completely insensitive to whether data are normalized assuming equivalent levels of Macro , or taking increasing total Macro incorporation over time into account . Genes with rapidly exchanging MacroH2A2 were enriched for GO processes consistent with housekeeping functions such as “translation” or “metabolism” ( not shown ) that are generally highly expressed , suggesting a potential link to expression level . Indeed , we found a strong correlation ( r = 0 . 46 ) between MacroH2A2 dynamics and mRNA abundance ( Figure 5B ) , as poorly expressed genes were associated with more stable MacroH2A2 than were highly expressed genes ( see Figures 5C–D for examples ) . This link between promoter Macro dynamics and mRNA abundance supports our hypothesis that a pattern of diminishing HA enrichment over our time course is diagnostic of rapid MacroH2A2 replacement . These results are also consistent with the rapid histone H3 dynamics at promoters observed in a variety of organisms . What is the function of rapid MacroH2A2 replacement at highly-expressed promoters ? Knockdown of MacroH2A2 resulted in extremely modest changes in global mRNA abundance ( Table S4 ) , likely reflecting compensatory gene regulation by MacroH2A1 , which is present in ES cells at ∼10-fold higher abundance than is MacroH2A2 . Nonetheless , mRNA abundance exhibited greater changes at genes associated with slow MacroH2A2 exchange dynamics than at genes with rapid MacroH2A2 replacement ( Figure S10 ) . Given that several histone variants are “hyperdynamic” in ES cells [20] and that the Macro content in somatic cells is considered an epigenetic barrier for epigenetic reprogramming to pluripotency [24] , [26] , we sought to characterize the changes in MacroH2A2 dynamics between ES cells and mouse embryonic fibroblasts ( MEFs ) . We generated transgenic mouse embryos by injecting TRE-HA-MacroH2A2 ES cells into blastocysts , derived MEFs from E12 . 5 chimeric embryos , then purified a homogenous population of TRE-HA-MacroH2A2 MEFs after selection against host blastocyst-derived cells . Importantly , HA-Macro protein induction dynamics were similar in ES cells and MEFs ( Figure S11 ) , enabling comparisons of Macro dynamics using this system . We first mapped MacroH2A2 in MEFs using an anti-MacroH2A2 antibody . As observed for ES cells , Macro localization patterns were strongly correlated before and after Dox induction ( Figure S12 ) , and were strongly correlated with HA mapping data from Dox-induced cells ( r = 0 . 98 , see below ) , providing strong evidence for antibody specificity . Overall , MacroH2A2 patterns were similar ( r = 0 . 67 using 100 kb bins ) between ES cells and MEFs ( Figure 6A , Figure S13 , Tables S1–S2 ) , supporting prior reports showing good correlations for MacroH2A1 localization between different cell types [40] . There was a general increase in MacroH2A2-enriched regions in MEFs relative to ES cells ( Figure S13A ) , consistent with the fact that MacroH2A2 levels are higher in MEFs than in ES cells . Overall , while MacroH2A2 was generally maintained at gene-rich regions in MEFs as well as ES cells ( Figure 6A , S13 ) , we identified a large number of additional regions that gained Macro in MEFs relative to ES cells . Interestingly , MEF-specific Macro domains typically occurred in gene-poor chromosomal regions ( Figure S13 ) . In terms of gene categories associated with the sparse genes found in these gene-poor regions , MEFs gained MacroH2A2 at a broad set of genes involved in alternative differentiation programs including neural , leukocyte , muscle , and spermatogenesis programs ( Table S5 ) . This broadly supports the idea that the more plastic pluripotent chromatin state becomes progressively restricted during differentiation , with unused genes in each variety of differentiated cell type becoming “locked down” via MacroH2A2 incorporation . In addition to broad gains of MacroH2A2 over gene-poor regions , we observed widespread changes in Macro enrichment over promoters between MEFs and ES cells . The average peak of MacroH2A2 over promoters exhibited an apparent decrease in MEFs ( Figure 6B ) , although given that genome-wide there is more MacroH2A2 signal distant from promoters in MEFs relative to ES cells , this loss is overestimated as a result of dataset normalization . Accounting for this possibility , we nonetheless noted extensive redistribution of promoter-localized MacroH2A2 between ES cells and MEFs ( Figure 6C ) . Curiously , MacroH2A2 changes between ES cells and MEFs correlated poorly ( r = 0 . 02 ) with gene expression changes between these cell types , although we did note that exceptionally unpregulated genes characteristic of fibroblasts such as collagen and extracellular matrix factors ( Col1a1 , Col5a1 , Lox , Tgfb2 , Fib1 , etc . ) generally lost MacroH2A2 at their promoters in MEFs ( Tables S1–S2 ) . Instead of correlating with gene expression changes , we found that loss of Macro in MEFs tended to occur at promoters exhibiting dynamic Macro turnover in ES cells ( Figures 6C–D ) . In contrast , stably Macro-associated promoters in ES cells preferentially retained Macro in MEFs . Together , these results suggest that dynamic assembly and disassembly of MacroH2A2 at highly expressed promoters is a specific feature of ES cells that is lost upon differentiation . In other words , while ribosomal protein genes ( Rpl8 , Rpl32 , etc . ) are highly expressed in both ES cells and MEFs , in ES cells their promoters are associated with rapidly-exchanging Macro , whereas these promoters are depleted of Macro in MEFs . To explicitly characterize Macro dynamics in MEFs , we carried out HA-Macro mapping at 3 , 6 , and 12 hours after Dox induction . As with ES cells , HA localization at all 3 time points was highly correlated ( r = 0 . 98 for all three time points using 100 kb windows ) with mapping data obtained using the anti-Macro antibody . In contrast to ES cells , however , inspection of genome browser tracks yielded many fewer instances of 3 hour HA peaks that diminished at 6 and 12 hours . More systematically , we found that the average TSS-proximal HA peak was nearly identical at all three time points ( compare Figures 7A and B ) . Not only was the average promoter HA peak nearly identical at all three time points , but there was less variation from t = 3 to t = 12 in our MEF data than in our ES data ( Figure 7C ) . Sorting genes by inferred turnover behavior in MEFs revealed a subtle correlation between promoter turnover kinetics and mRNA abundance in MEFs ( Figure 7D ) , but this relationship was far less robust ( r = 0 . 17 versus r = 0 . 46 ) than that observed in ES cells ( Figure S14 ) . Taken together , these data show that rapid MacroH2A2 turnover is a specific feature of ES cells , and that upon differentiation to MEFs Macro is lost from dynamic promoters but retained in larger blocks of stably-associated Macro .
We primarily focus here on the relatively unstudied MacroH2A2 variant . Overall , we observe extensive differences in the localization and dynamics of this variant between pluripotent ES cells and committed mouse embryonic fibroblasts . In ES cells , we observed widespread localization of MacroH2A2 across gene rich domains , along with a strong TSS-proximal peak of MacroH2A2 . From our time course mapping studies , we infer that MacroH2A2 is rapidly replaced at promoters , and that this replacement is positively correlated with a gene's expression level . It is worth noting that H3 . 3 replacement is also rapid at promoters and correlates with mRNA abundance , indicating that in ES cells promoters exhibit rapid turnover of multiple histone variants . Upon differentiation to embryonic fibroblasts , MacroH2A2 is broadly gained over gene poor domains , resulting in increased MacroH2A2 levels over genes associated with alternative differentiation programs such as neural or immune cell differentiation . Intriguingly , MacroH2A2 becomes far less dynamic in MEFs , and moreover MacroH2A2 is generally lost from those promoters where it is most dynamic in ES cells . Among other things , this observation argues that dynamic MacroH2A2 replacement inferred at highly-expressed genes in ES cells does not simply reflect nonspecific association of ectopically expressed histones with “open” promoters , as the highly expressed genes in MEFs exhibit far more subtle Macro dynamics than do the same genes in ES cells . Removal of the X chromosome from all key analyses ( Figure S15 ) does not alter any of the conclusions regarding the change in Macro behavior between ES cells and MEFs , which is unsurprising as both cell types used in this study are male and thus data from the X chromosome reflects only the active X . Together , these results are broadly consistent with the idea that pluripotent cells are characterized by “hyperdynamic” chromatin [20] . Interestingly , in contrast to the global hyperdynamic state observed by photobleaching for other histone variants , here we observe local , rather than global , dynamic MacroH2A2 behavior at a small fraction of loci—promoters of highly expressed genes . It will be interesting to identify factors contributing to ES-specific promoter MacroH2A2 dynamics in future studies . Our findings that the dynamics of Macro turnover decrease as pluripotent ES cells become developmentally committed , and that stable MacroH2A2 becomes incorporated in gene poor regions and at genes associated with alternative cell fates in MEFs , have implications for the interpretation of several recent studies suggesting that the MacroH2A content of somatic cells acts as a barrier to epigenetic reprogramming of the genome to a pluripotent state . It is widely appreciated that Macro content increases during cellular differentiation and ageing , and studies employing somatic cell nuclear transfer ( SCNT , or cloning ) , revealed that somatic MacroH2A1 is actively removed from the genome prior to the acquisition of pluripotency [24] . These observations , coupled with the accumulation of MacroH2A on the Xi during the process of X chromosome inactivation in female cells , suggest that removal of MacroH2A from the somatic genome may facilitate , or even be a prerequisite for , reprogramming to pluripotency . Indeed , a recent study found that depletion of MacroH2A1 and 2 from somatic cells prior to initiation of epigenetic reprogramming via the ectopic expression of Oct4 , Sox2 , Klf4 , and c-Myc ( the Yamanaka factors—[41] ) greatly improved reprogramming efficiency [27] . This study implicated repression of pluripotency-associated genes ( Oct4 , Sox2 ) with high MacroH2A1 content in somatic cells as the epigenetic barrier , such that removal of MacroH2A from pluripotency-associated promoters might allow for the reprogramming factors to more readily activate these genes . While this may be a contributing factor , in general MacroH2A content does not strongly predict gene repression . For example , in MEFs MacroH2A1 is highly enriched at the active Thy1 gene , but in ES and iPS cells , where Thy1 is silent , MacroH2A1 is nearly completely absent [27] . Indeed , in ES cells we find that MacroH2A2 is associated with active promoters ( Figure 2 ) , further arguing against a simple model for a universally repressive function of MacroH2A . Instead , we speculate that stable association of MacroH2A ( Figure S10 ) , rather than average MacroH2A occupancy per se , is more likely to play a role in gene repression . Consistent with this idea , we observe Macro enrichment over Sox2 in both ES cells and in MEFs , but in ES cells this gene is marked by rapid Macro replacement whereas Macro association is much more stable in MEFs ( not shown ) . Our findings that ( 1 ) dynamic incorporation of MacroH2A2 in gene-rich regions is correlated with highly active promoters , and that ( 2 ) stable MacroH2A2 incorporation in gene-poor regions ( harboring genes associated with alternative cell fates ) in MEFs is correlated with gene silencing , suggests that Macro removal during reprogramming may be most critical at these stable loci for re-establishing the “permissive” chromatin state characteristic of pluripotent cells . To date , the majority of studies on histone dynamics have been carried out in cell culture systems . However , it will be of great interest to begin understanding the tissue-specific differences in chromatin dynamics in vivo , both under control conditions and in response to environmental perturbations . Thus , we generated a inducible histone variant mouse strain after blastocyst injection of the TRE-HA-H3 . 3 ES cell line and successful germline transmission of the R26-M2rtTA and TRE-HA-H3 . 3 alleles ( Figure S16 ) . Administration of 2 mg/mL doxycycline in the drinking water of TRE-HA-H3 . 3 mice resulted in HA-H3 . 3 induction in liver nuclear extracts ( Figure S16C ) . These animals will therefore provide a unique and exciting resource for characterization of histone dynamics in different tissues and cell types , and provide a proof of principle for the application of our approach in vivo .
All procedures involving mice were reviewed and approved by the Institutional Animal Care and Use Committee of the University of Pennsylvania ( Animal Welfare Assurance Reference Number #A3079-01 , approved protocol #803415 granted to Dr . Lengner ) and were in accordance with the guidelines set forth in the Guide for the Care and Use of Laboratory Animals of the National Research Council of the National Institutes of Health . cDNA of various histone variants ( ( H2a-MMM1013-98478233; H2Az-MMM1013-9498090; MacroH2a2-MMM1013-9201250; H3 . 3-MMM1013-98478016 , H1o-MMM1013-65296 , Open Biosystems & human H3 . 1-Kind gift of Eric Campeau ) ) were initially subcloned in-frame with the HA-tag , then were cloned into the unique EcoRI restriction site of the pBS31 vector containing a PGK promoter followed by an ATG start codon and an FRT recombination site , followed by a splice acceptor-double polyA cassette , the tetracycline operator with a minimal CMV promoter , the unique EcoRI site , and an SV40 polyadenylation signal . The pBS31 vector containing the histone/histone variant cDNA was then electroporated along with a Flpe recombinase-expressing vector into KH2 embryonic stem cells harboring the modified reverse tetracycline transactivator ( M2rtTA ) targeted to and under transcriptional control of the ROSA26 locus , as well as an FRT-flanked PGK-neomycinR cassette followed by a promoterless , ATG-less hygromycinR cassette targeted downstream of the Collagen1a1 locus [38] . Selection for hygromycin resistance upon flip-in yielded numerous colonies which were verified for proper site-specific recombination at the Coll1a1 locus by digestion of genomic DNA and Southern blotting with a 3′ internal probe , yielding a 6 . 2 kb wildtype band , a 6 . 7 kb band for the FRT-containing knock-in allele , and a 4 . 1 kb band for the successfully flipped-in inducible allele . Together , the components of this system enable tetracycline induction of the epitope-tagged histone variant of choice in embryonic stem cells from a genomically-integrated construct . Activation of the TetOn HA-tagged histone expression was carried out by addition of 2 µg/mL doxycycline hyclate ( Sigma D9891 ) to the culture media . Cells were collected at different induction time points and induction of HA tagged histone variants in ES cells was assayed via Western blot . ES cell cultures were fixed in 4% paraformaldehyde for 5 minutes prior to staining for pluripotency markers alkaline phosphatase and Oct4 . Alkaline phosphatase was detected by enzymatic reaction using a Vector Red substrate kit ( Vector Labs ) . Immunofluorescence staining for Oct4 was carried out by first permeabilizing and blocking in 5% FBS , 0 . 1% Triton-X 100 for 15 minutes , then incubating with an anti-Oct4 primary antibody at 1∶100 for 1 hr at room temperature ( Rabbit polyclonal H-134 , Santa Cruz Biotech ) . After 3 washes with PBS , cells were incubated with an anti-rabbit secondary antibody labeled with Cy3 , washed , stained with DAPI for total DNA , and imaged . HA-MacroH2A2 or HA-H3 . 3-inducible ES cells were injected into BDF2 blastocysts and transplanted into pseudopregnant recipient females . For HA-MacroH2A2 MEF isolation , pregnant females were euthanized at E12 . 5 , embryos were dissected followed by removal of internal organs . Embryos were then minced in the presence of 0 . 25% Trypsin-EDTA and incubated at 37°C for 20 minutes . MEF medium was then added and cell suspension was titrated followed by plating cells onto two 15 cm culture dishes per embryo . Cells were cultured for 12 hours at 37°C , 3% CO2 , and 5% O2 after which puromycin was added to the MEF culture media to select against host blastocyst-derived cells ( by virtue of a constitutively active puromycin resistance cassette targeted to the ROSA26 locus along with the M2rtTA ) . After 48 hours of puromycin selection , homogenous populations of HA-MacroH2A2 MEFs were trypsinized and frozen at passage 1 . ChIP-Seq experiments on MEFs were carried out after thawing and one additional passage ( i . e . , p2 MEFs ) . For generation of HA-H3 . 3 mice , blastocyst injection was performed as above , but embryos were carried to term . High contribution chimeras ( >95% by coat color ) were backcrossed to Bl/6 mice to establish an inducible HA-H3 . 3 mouse colony . ES cells were grown in standard ES media containing Lif ( ES Gro , Millipore ) on mitotically inactivated feeder MEFs until approximately 80% confluence . ES cells were then pre-plated on gelatin and incubated for 45 min to deplete feeder MEFs by virtue of their faster adherence than ES cells ( roughly 3 hours ) . ES cells were then split onto three gelatinized plates each of which was induced at different time points by the addition of final 2 µg/mL doxycycline hyclate ( Sigma ) . A similar procedure was used for induction of MEFs at passage 2 . All time points were crosslinked with formaldehyde to a final concentration of 1% for 10 minutes , and were quenched with 125 mM glycine . Crosslinked cells were resuspended in 270 µl SDS-Lysis Buffer ( 1% SDS , 10 mM EDTA and 50 mM Tris-Cl , pH 8 . 1 ) including protease inhibitor complex ( Sigma ) and PMSF ( Sigma ) , and chromatin was sonicated in Bioruptor ( UCD-200 ) to an average size of 150–400 base pairs . 70 µg of chromatin of each time point was immunoprecipitated either with HA antibody ( Abcam ) or MacroH2A2 antibody ( Abcam ) . Eluted ChIP materials were PCI ( Phenol-Chloroform-Isoamylalcohol ) extracted , RNAse ( Qiagen ) and CIP ( NEB ) treated . ChIP material was then gel-purified and DNA fragments were blunt-ended and phosphorylated with the End-it-Repair kit ( EPICENTRE ) . Illumina genome sequencing adaptors were ligated using the Fast-Link ligation kit ( EPICENTRE ) after the addition of adenosine nucleotide , using exo- Klenow . And samples were PCR amplified with Illumina genomic DNA sequencing primers . PCR products ( 250 to 450 bp in size ) were gel purified and sent for Illumina GA2 “Solexa” sequencing at the UMass Worcester deep sequencing core facility . Data will be available at Gene Expression Omnibus , Accession #GSE57665 . Raw FastQ reads were first collapsed by their sequences while the occurrences were kept . We then mapped reads to the mm9 genome using bowtie allowing at most one mismatch in the alignment . Only one mapping was randomly picked by the -M 1 parameter setting for dealing with multimappers . Each aligned coordinate was extended toward its 3′ end to reach 150 bp length ( although extension was clipped if it exceeded the length of the chromosome ) . We calculated the relative distance to the nearest TSS for all named genes , and for each TSS tallied the sum of read occurrences from 4 kb upstream to 4 kb downstream . The occurrences were normalized to p . p . m . and binned in 20 bp intervals . For TSS-centered averages ( as in Figure 3C , for example ) data were additionally normalized relative to the average of the first 2 kb ( from −4 kb to −2 kb ) . Importantly , for turnover analyses , relatively hot and cold regions are insensitive to the normalization method used—if we normalized all datasets to the hottest regions of the genome , rather than observing decreasing HA enrichment at promoters over time , we would observe very slow incorporation across the rest of the genome with increasing enrichment over time . However , in the absence of a true benchmark with known absolute occupancy ( eg a set of promoters with 100% occupancy of MacroH2A2 at t = 3 hours ) , we choose to utilize standard genome-wide normalization and interpret our dataset with these considerations in mind . The mouse genome was segmented into nonoverlapping 100 kb tiles ( eg chromosome 1 1–100 , 000 , chromosome 1 100 , 001–200 , 000 , etc . ) . For each tile , total normalized Macro or HA levels were calculated , and number of annotated TSSs was counted ( using only TSSs for named genes ) . Tiles with the top 1% of signal in the anti-HA dataset from uninduced cells were discarded , as these typically covered regions adjacent to extensive repeats that show artifactual “enrichment” in all public datasets examined , including pre-ChIP input sequencing . For computing correlations between datasets , unmappable tiles with zero mapped reads were also removed . For all named genes , data were aggregated into 20 bp bins from −4 kb to +4 kb surrounding the annotated TSS . These data were used for clustering and visualization throughout . In addition , we calculated a summary statistic based on total enrichment values for the 1 . 2 kb stretch from −600 to +600 bp—this value was used for analyses such as Figures 5A , 6D , or 7C ( and related Supporting Figures ) . For comparisons between ES cells and MEFs , we used all genes with an average promoter MacroH2A2 enrichment of at least 0 . 1 , in one of the two datasets , for the 1 . 2 kB surrounding the TSS . | The ability of cells to remember the correct cell fate is at least partly dependent on how the genome is packaged . Embryonic stem ( ES ) cells , which have the ability to become any cell type in the body , are a particularly well-studied system for understanding how the packaging of the genome – chromatin – controls cell state . One of the more curious aspects of ES cell chromatin is its “hyperdynamic” nature , as the histone proteins that comprise chromatin have been reported to exchange rapidly on and off the DNA in these cells . Here , we report a pulse chase system for studying histone dynamics in mouse ES cells , and report on the dynamics of two histone variants , H3 . 3 and MacroH2A2 . Notably , MacroH2A2 is highly dynamic in ES cells , with rapid exchange occurring over gene promoters , alongside much more stably-bound domains that cover large blocks of the genome . Upon differentiation to fibroblasts MacroH2A2 becomes much more stably-bound to the genome , consistent with the idea that this histone variant plays a role in “locking down” repressed regions the genome . These results provide further evidence for a key role of histone dynamics in control of cell state inheritance . | [
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] | 2014 | A System for Genome-Wide Histone Variant Dynamics In ES Cells Reveals Dynamic MacroH2A2 Replacement at Promoters |
The cyclin-dependent kinase inhibitor p27KIP1 is a tumor suppressor gene in mice , and loss of p27 protein is a negative prognostic indicator in human cancers . Unlike other tumor suppressors , the p27 gene is rarely mutated in tumors . Therefore misregulation of p27 , rather than loss of the gene , is responsible for tumor-associated decreases in p27 protein levels . We performed a functional genomic screen in p27+/− mice to identify genes that regulate p27 during lymphomagenesis . This study demonstrated that decreased p27 expression in tumors resulted from altered transcription of the p27 gene , and the retroviral tagging strategy enabled us to pinpoint relevant transcription factors . inhibitor of DNA binding 3 ( Id3 ) was isolated and validated as a transcriptional repressor of p27 . We further demonstrated that p27 was a downstream target of Id3 in src-family kinase Lck-driven thymic lymphomagenesis and that p27 was an essential regulator of Lck-dependent thymic maturation during normal T-cell development . Thus , we have identified and characterized transcriptional repression of p27 by Id3 as a new mechanism decreasing p27 protein in tumors .
p27KIP1 binds to and thereby prevents cyclin-CDK complexes from phosphorylating their protein substrates [1] . The biological consequence of this molecular interaction in cultured cells is cell cycle arrest , primarily in the G1 phase . Analysis of p27−/− mice provides further evidence that p27 is an important regulator of cell proliferation in vivo . p27 null mice exhibit gigantism and contain proportionally larger , hypercellular organs compared to wild-type siblings [2] . The p27-null thymus was disproportionately enlarged compared to the whole animal , though developing thymocyte subsets were maintained in normal proportions . Increased T-lineage proliferation was also seen in the spleen [3] . Further analysis demonstrated that p27 controls cytokine stimulated T-cell proliferation [4 , 5] . In addition to the cell proliferation phenotype , p27−/− mice develop spontaneous pituitary adenomas . Analyses of tumor susceptibility demonstrated that the p27 gene is a dose-dependent tumor suppressor gene [6] . That is , a 50% reduction in p27 protein levels was sufficient to predispose p27+/− mice to tumors in multiple organs , especially following the administration of exogenous carcinogens , or when genetically combined with various oncogenes or deletions of tumor suppressors . Thus , loss of p27 accelerated the rate of tumor development in p27−/−; Rb+/− , p27;pten , and p27−/−;ApcMin+/− mice [7–9] . In different tumor models , p27 deficiency increased both the number of tumors and their rate of progression to more aggressive cellular phenotypes [10] . In humans , decreased expression of p27 protein is a negative prognostic indicator in breast , colon , prostate , lung , esophageal , and gastric cancer [11] . Additionally , loss of p27 expression is one of the most clinically significant negative prognostic markers in human breast cancers [11] , and its prognostic value improves when combined with other markers [12] . Although reduced p27 protein levels are observed in tumors , both alleles of the p27 gene are rarely mutated [11] . Therefore , the quantity of p27 protein appears to mediate tumor susceptibility , and misregulation of p27 expression , rather than loss of its gene , is responsible for decreases in p27 protein levels . For this reason , if the pathways that cause p27 misregulation could be inhibited , then the tumor suppressor function of p27 could potentially be restored in cancer cells . Although the regulation of p27 in normal cells occurs at the transcriptional , translational , and post-translational levels [13–18] , most attention in cancer cells has focused on p27 misregulation via ubiquitin-dependent protein degradation , specifically by the SCF-Skp2 E3 ubiquitin-protein ligase [19] . Recently , we analyzed tumorigenesis in knock-in mice expressing a mutant p27 protein ( p27T187A ) that cannot be ubiquitinated by the SCF-Skp2 pathway . The p27T187A protein was down-regulated in lung tumors induced by an activated K-ras allele to the same extent as wild-type p27 protein , and moreover the p27T187A mice had the same rate of tumor-dependent death as p27 wild-type mice [20] . Additionally , we observed a substantial decrease in p27 mRNA in these lung tumors . These data imply that mechanisms other than SCF-Skp2-mediated protein degradation play significant roles in misregulating p27 during tumorigenesis . To elucidate the molecular mechanisms that contribute to p27 misregulation during lymphomagenesis , we used a functional genomics assay to identify genes that repress p27 . We discovered inhibitor of DNA binding 3 ( Id3 ) as a candidate negative regulator of p27 in our screen . Id3 is an HLH protein that lacks a DNA binding domain and antagonizes transcriptional activators by stably forming heterodimers unable to bind DNA sequences within target gene promoters and thus down regulates gene expression [21] . We demonstrated that Id3 decreased p27 transcription during lymphomagenesis and further established that Id3 was part of the pathway by which the src-family protein tyrosine kinase ( PTK ) p56lck repressed p27 expression to stimulate proliferation of T-cell progenitors in the thymus .
We used retroviral insertional mutagenesis to screen the mouse genome for genes that modulate p27 protein levels during tumorigenesis . Moloney murine leukemia virus ( M-MuLv ) integrates throughout the genome and primarily up-regulates nearby genes [22] , which induces tumors in vivo and tags the relevant oncogenes by their physical proximity to the retrovirus . Wild-type , p27−/− , and p27+/− heterozygous mice were infected with M-MuLv , which is tropic for the thymus and therefore predominantly induced T-cell lymphomas . Previously , it was demonstrated that after exposure to M-MuLv the p27+/− heterozygous mice developed lymphomas at an intermediate rate compared to the wild-type and knockout mice [23] . Therefore , given that the p27+/− heterozygotes contain 50% as much p27 protein as wild-type mice [6] , this genotype may represent a sensitized background for revealing M-MuLv-induced changes in gene expression that caused further reductions of p27 protein . Our strategy was to identify the subset of tumors arising in p27+/− heterozygotes that contained very low or undetectable quantities of p27 potentially resulting from insertional activation of genes that down-regulated p27 . Western analysis of 44 lymphomas induced by M-MuLv in p27+/− mice showed that all of them expressed lower amounts of p27 protein than did normal p27+/− thymus . Indeed , 10/44 tumors had almost no detectable p27 protein ( Figure 1A ) . The uniformly lower amounts of p27 protein in lymphomas compared to normal thymus may be a normal consequence of the increased proliferation characteristic of many tumors . However , we hypothesized that the ten lymphomas with undetectable amounts of p27 protein had suffered an M-MuLv-mediated event that caused a further , pathological reduction in p27 expression . From these ten tumors , retroviral junction fragments were cloned by inverse PCR and verified by DNA sequencing as legitimate M-MuLv-genomic DNA junction fragments ( see Materials and Methods ) . From these we identified 55 unique insertional junction fragments ( Table 1 ) , which was in good agreement with the 53 retroviral insertions we had estimated on the basis of Southern data from the tumors ( unpublished data ) . DNA sequences of the 55 cloned junction fragments were analyzed using the National Center for Biotechnology Information and the Celera mouse databases . Insertion sites from at least two independent tumors that colocalized to within a 125-kb genomic interval were designated as common insertion sites ( CIS ) . Our analysis revealed seven CIS ( Table 2 ) . The gene tagged most frequently in the p27+/− lymphomas was myc ( seven out of ten ) . Also , the C14 CIS [23] was found in four of the ten tumors analyzed; myb , RORC , cux-1 , CIS4 , and LDLR/Mc7 were each detected in two independent tumors . In a large-scale retroviral insertional mutagenesis to identify cancer genes , 62% of the CIS were found in only two tumors [24] . Similarly , our data indicate 71% of the CIS in p27+/− mice were detected in two tumors . In addition to the above CIS , two CIS defined by insertions in or near different members of a gene family were detected . Eya1 and Eya3 , and Wnt10b and Wnt16 were each identified in this way . We categorized these as CIS because , although they were only found near each gene a single time , they involved functionally related members of a gene family . Single insertions in genes within signaling cascades have been detected in other screens , including the Wnt signaling pathway [24] . Finally , single insertions in known CISs were identified for Jdp2 , pim-1 , Gfi , rasgrp1 , and Notch1 ( Table 2 ) . The key question in our approach was how to sort among the many retrovirally tagged genes for the candidates that were most likely to participate in the regulation of p27 . We postulated that three types of genes would be tagged in our screen: genes that modulated p27 levels; genes that were oncogenic by mechanisms other than down-regulation of p27; and genes that represented random , unselected sites of retroviral integration . We tentatively assigned a tagged gene to the first , p27-regulatory category if it: ( 1 ) was tagged in multiple independent tumors with low levels of p27 protein expression and was therefore unlikely to represent a random , unselected site of integration , and ( 2 ) it was not tagged in any tumor arising in p27−/− mice . Activation of a gene that down-regulated p27 should provide no selective advantage to cells already lacking the p27 protein . We compared the p27+/− CIS in Table 2 to the CIS identified from 277 junction fragments that were isolated in a similar manner from p27−/− lymphomas [23] . Only CIS4 fulfills our criteria for a regulator of p27 because it was defined by insertions from two independent lymphomas ( Figure 1B ) , and insertions at CIS4 were not found in lymphomas from p27−/−mice ( p < 0 . 03 ) . In addition to CIS4 , the CIS representing the Eyes Absent ( Eya ) family and the CIS representing the Wnt family were not isolated from p27−/− mice . Therefore , while the Eya and Wnt genes may also represent candidate negative regulators of p27 , we focused here on CIS4 . CIS4 was previously isolated in five retroviral insertional mutagenesis screens in p27 wild-type mice and designated Evi62 ( Retrovirus Tagged Cancer Gene Database ) . Evi62 insertions were mapped near the Id3 and E2F2 genes , but the target gene of the retroviral insertions in Evi62 had not been established . In addition to the Id3 and E2F2 genes , the Ddefl1 , Tcea3 , Zfp46 , and Hnrpr genes mapped to the region ( Figure 1B ) . Since Id3 and E2F2 regulate the cell cycle , we focused on these two as candidate genes . We used western analysis to determine if Id3 or E2F2 was up-regulated by the retroviral insertions in our tumors . Analysis of Id3 protein levels in the lymphomas detected high Id3 expression in the tumor with the retroviral insertion immediately adjacent to the Id3 gene , and moderate Id3 expression in the other , more distantly tagged tumor ( Figure 1C ) . In total , four of the ten lymphomas screened for CIS had high to moderate Id3 expression ( unpublished data ) . One of the tumors with elevated Id3 protein had a retroviral insertion adjacent to the Notch gene , which is a known activator of Id3 [25] . The mechanism up-regulating Id3 in the fourth lymphoma was not investigated further . In the other set of 34 lymphomas , with higher expression of p27 , 30% had low expression Id3 , and no Id3 expression was detectable in the others ( unpublished data ) . No increase in E2F2 protein was observed in any of our tumors with the M-MuLV insertions ( unpublished data ) . Additionally , no effect on p27 transcription by E2F2 was detected by DNA transcription array experiments [26–28] . Therefore , our data suggest Id3 was the likely target of the retroviral insertion at Evi62 . In addition to Id3 , other genes in the region may also be activated by the retroviral insertions and could potentially regulate p27 in coordination with Id3; however , we analyzed Id3 alone as a negative regulator of p27 . In the experiments below , we tested whether there was a causal relationship between Id3 up-regulation and p27 down-regulation . Since Id3 regulates transcription , we measured the p27 transcript levels in the lymphomas . Quantitative real time ( RT ) PCR data revealed that p27 mRNA was reduced in all of the p27+/− lymphomas relative to normal p27+/− thymus , with a significantly greater reduction in p27 mRNA abundance in the set of lymphomas expressing very low or absent p27 protein ( Figure 2A ) . Therefore , control of mRNA expression appeared to be a general mechanism for decreasing p27 protein levels in these lymphomas . To determine whether Id3 regulates p27 transcript levels , we knocked down Id3 expression in NIH3T3 cells using Id3 siRNAs . Three concentrations of pooled Id3 siRNAs were transfected into cells , and all siRNA concentrations increased p27 protein levels compared to cells transfected with a nonspecific control siRNA ( Figure 2B ) . Quantitative RT-PCR on RNA isolated from these cells confirmed that Id3 mRNA decreased and that p27 mRNA increased , dependent on the presence of the Id3 siRNA ( Figure 2C ) . Thus , Id3 repressed p27 transcript levels in NIH 3T3 cells . The HLH protein Id3 represses transcription by interacting with transcription factors to form heterodimers unable to bind DNA . Previous experiments demonstrated that coexpression of the bHLH proteins E12 and NeuroD2 led to increased p27 protein in cells [29] . However , these experiments did not address whether this increased p27 expression was a direct or indirect effect of the bHLH proteins , nor whether it occurred at the level of p27 transcription . The 2 . 0-kb mouse p27 promoter contains six potential E-box sequences ( CANNTG ) . We tested the ability of bHLH heterodimers to stimulate transcription from a full-length p27 promoter-luciferase fusion gene and a minimal promoter containing two of the six E-box sequences ( Figure 3A ) . While neither E12 nor NeuroD2 alone affected p27 promoter activity ( unpublished data ) as expected [30] , cotransfection of increasing amounts of E12/NeuroD2 caused a dose-dependent increase in both the full length and minimal p27 promoter activity ( Figure 3B ) . Expression of the p27 promoter-luciferase fusion gene constructs was also stimulated by E12/E47 heterodimers , which are bHLH proteins expressed in lymphocytes ( unpublished data ) . This activation was dependent on the presence of the two E boxes ( CANNTG ) and one imperfect E box ( GACCTG ) in the p27 minimal promoter , as the p27 minimal promoter with these sites mutated to CGNNAT and GGCCAT respectively could only be induced to 26% of the wild-type promoter ( Figure 3C ) . This residual induction was likely due to fortuitous E-box sequences in the expression vector itself [31] . Furthermore , titration of Id3 abrogated the stimulatory effect of E12/ND2 on the p27 minimal promoter ( Figure 3D ) . The effect of Id3 was specifically to antagonize bHLH protein-stimulated transcription , because in the absence of cotransfected bHLH proteins , Id3 had almost no detectable effect on basal , low-level p27 promoter expression ( unpublished data ) . The results of these experiments suggest that bHLH proteins directly activate the p27 promoter and that Id3 represses p27 transcription by inhibiting bHLH protein function . To further examine Id3 regulation of p27 in cancer cells , we studied lymphoma cell lines in which the src-family protein tyrosine kinase p56lck could be conditionally regulated . p56lck activity increased Id3 transcript levels in cultured lymphocytes [32] , and cell lines derived from lymphomas arising in mice overexpressing p56lck [33] provided a system to assay Id3-mediated repression of p27 . Cells were treated with a pharmacologic inhibitor highly specific for src-family protein tyrosine kinases , PP1 [34] . Analysis of Id3 mRNA abundance by quantitative RT-PCR confirmed that this transcript rapidly declined 5-fold within 1 h following inhibition of p56lck activity . After Id3 expression declined and Id3 protein disappears because of its short half-life [35] , p27 mRNA increased an average of 2-fold by 2 h ( Figure 4A ) . A cell line derived from thymic lymphomas arising in SV40 large T-protein transformed mice was used as a control for PP1 specificity for p56lck [36] . As expected if p56lck is the kinase required for modulating Id3 and p27 transcript abundance , Id3 and p27 mRNA levels remained essentially unchanged after addition of PP1 to these cells ( Figure 4B ) , consistent with the transformed phenotype of these cells being independent of Lck activity . Thus , p56lck activity was required for maintaining Id3 mRNA expression and repressing p27 transcript levels . We further showed that enforced Id3 expression rescued p27 repression in lymphoma cells in cells treated with the pharmacologic inhibitor of p56lck . Lck-transformed cells infected with an Id3-expressing retrovirus or control retrovirus were treated with PP1 and assayed for Id3 and p27 transcript abundance . Cells transduced with the Id3 expression vector continued to express the Id3 mRNA at close to physiological levels ( >50% ) after addition of PP1 , and the p27 mRNA remained repressed ( Figure 4C ) . The results from the control retrovirus infected cells were indistinguishable from the uninfected cells; the Id3 transcript was decreased 5-fold , and the p27 transcript was increased almost 3-fold after addition of PP1 ( Figure 4A and 4D ) . Since Id3 acts as transcription repressor , the p27 transcription rate was examined using nuclear run-on assays . A rapid 3-fold induction of p27 transcription was observed following p56lck inhibition and decreased Id3 expression , and the converse transcriptional silencing of the p27 gene soon after inhibitor removal and reactivation of p56lck ( Figure 4E ) . These results demonstrate that Id3 was sufficient to repress accumulation of the p27 mRNA via a reduced rate of p27 transcription . The increased p27 transcription that follows p56lck inhibition delayed progression through the cell cycle . Initially , Lck inhibition resulted in depletion of early S-phase cells , followed over time by an accumulation of cells with a 2N ( G1 ) content of DNA ( Figure 4F ) . Removal of PP1 resulted in a near-synchronous progression of the culture into S phase ( Figure 4F ) . Western-blot analysis of lysates from PP1-treated cells showed a rapid accumulation of p27 , its association with cyclin E-containing complexes , and a loss of in vitro histone kinase activity in immunoprecipitates containing cyclin E ( unpublished data ) . Conversely , following washout of PP1 , p27 mRNA levels rapidly fell to baseline though the protein remained elevated for an additional 12 h falling coincident with induction of cyclin E-associated kinase activity and S-phase entry ( unpublished data ) . Therefore , the increased transcription of p27 due to loss of Lck and Id3 activity resulted in a reversible cell cycle arrest in lymphoma cells . To investigate the repression of p27 by Id3 and p56lck in vivo , we tested whether a genetic interaction could be detected during normal thymic development in mice . Thymic maturation is punctuated by differential induction and silencing of CD4 and CD8 expression . The most immature lymphoid cells are CD4−CD8− double-negative ( DN ) cells [37] . Within the DN compartment , expression of CD25 and CD44 further defines four subsets ( DN1 through DN4 ) of increasing maturity . The DN4 subset is the immediate antecedent to the double-positive ( DP ) population [38] . Mice deficient in p56lck have impaired DN3 to DN4 maturation and consequent constriction of the DP compartment , resulting in hypocellular thymi proportionately enriched in DN cells [39] . Similarly , overexpression of p27 also inhibits the DN3 to DN4 transition in a dose-dependent fashion [40] . Therefore , as observed in lymphoma cells Lck activity may repress p27 gene expression during thymocyte maturation . As a test of whether p27 is downstream of Lck during thymocyte maturation , p27−/− mice were crossed to lck−/− mice , and the phenotypes of the progeny were analyzed for rescue of the proliferative and developmental arrest caused by the loss of Lck activity . The lck−/−p27+/− and lck−/−p27−/− animals displayed a proportionate and absolute expansion of the DP compartment ( Figure 5A ) , consistent with an increase in either the number of cells maturing or proliferating , or both . Also , total thymus cellularity progressively increased in inverse relationship to p27 gene dosage , although it did not reach wild-type numbers ( Figure 5B ) . This reflected a numeric expansion of both the DN and DP compartments , with proportionately greater increase in the latter . Additionally , the DN compartment in the lck−/−p27−/− mice appeared enriched in the DN4 subset , indicative of a reduced threshold for DN3 to DN4 progression ( unpublished data ) . Thus loss of p27 partially rescued the defects in lck−/− mice . If Lck repressed p27 through Id3 , we would expect increased levels of Id3 at the time p27 transcript is reduced . We compared p27 and Id3 transcript levels in small DN3 cells and DN4 cells from wild type mice . As cells transitioned to the DN4 stage p27 mRNA decreased 3-fold compared to the DN3 stage , and Id3 mRNA increased 1 . 3-fold ( Figure 5C ) . Therefore , as observed in the lymphoma cells , there was a correlation between decreasing p27 transcript levels and increasing Id3 transcript levels , suggesting that Id3 repressed p27 gene expression at the DN3 to DN4 transition . These results extend our molecular observations connecting Lck and p27 through the action of Id3 in lymphoma cells to normal thymocyte development .
We describe a functional genomics assay employing M-MuLv insertional mutagenesis screen to detect negative regulators of a tumor suppressor gene . We analyzed lymphomas from p27+/− mice and discovered Id3 as a repressor of p27 in cancer cells . Since the Id3 CIS was detected in p27+/− tumors with low p27 protein , we propose Id3 contributed to lymphomagenesis by causing the observed decrease in p27 protein . Consistent with Id3 functioning as a transcriptional repressor , p27 mRNA amounts were greatly reduced in the tumors with Id3 tagged by the retrovirus . Although decreases in p27 transcription in tumors or cancer cell lines have not been widely reported , the predisposition of p27+/− mice to tumors demonstrates a 50% reduction in p27 mRNA levels is physiologically relevant . Moreover , we previously observed significantly reduced p27 transcript in a murine lung tumor model and in a subset of human breast cancers [20] . Therefore , decreased p27 mRNA abundance may occur in multiple types of cancer , and our isolation of Id3 as a negative regulator of p27 implies a novel mode of regulation for p27 in lymphomas . In agreement with transcriptional regulation of p27 decreasing p27 protein in tumors , all of the p27+/− lymphomas with low p27 protein analyzed for CIS had reduced levels of p27 mRNA . In addition to Id3 , we identified single specific insertions in p27+/− mice near Wnt10b and 16 as well as Eya1 and 3 . Cofactors of Eya proteins and Wnt1 repress p27 transcription [41 , 42] . In total , among the ten lymphomas in which we were able to document very low p27 protein and mRNA expression , seven had either up-regulated Id3 protein or retroviral insertions near the Wnt or Eya genes . Two of the remaining lymphomas had insertions in the Myb locus . The p27 promoter contains a Myb binding site [43] , and Myb collaborates with Hes1 , a known negative regulator of p27 [44] . However , the Myb CIS was also found in lymphomas arising in p27−/− mice . Thus , if Myb is a negative regulator of p27 it is also likely to have p27-independent effects on tumorigenesis . Therefore , transcription factors or cofactors were identified in nine out of the ten tumors; however , Id3 represented the sole CIS not found in p27−/− lymphomas . Id3 and p27 have opposing effects on proliferation and differentiation . Overexpression of Id1–3 genes increased cell proliferation , and antisense Id1–3 delayed reentry of arrested cells into the cell cycle [45] , whereas increased p27 protein arrests cells in G1 . Also , overexpression of Id1 and Id2 in the thymus promotes the development of lymphomas [46 , 47] . In Id1−/−Id3−/− knockout mice , neuroblasts prematurely withdraw from the cell cycle and p27 protein levels are elevated [48] consistent with our luciferase assay data that Id3 regulates p27 transcription via interference with neural bHLH proteins . Additionally , in a wound healing model , Id3 repressed ELK1 activation of p27 gene expression [49] . Furthermore , inverse mRNA expression patterns of p27 and Id3 have been observed in cells [50] . Increased Id3 and decreased p27 protein levels were observed when an oncogene was transfected into cells [51]; however , a function for Id3 regulating p27 in tumorigenesis was not investigated . Although regulation of p27 transcription has been reported by Id3 in wound healing [49] and other transcription factors in cell culture [18] , our results offered the first evidence that Id3 directly regulated p27 transcription in cancer cells , and moreover indicated that , in vivo , it could cause the misregulation of p27 during tumorigenesis . The variety of mechanisms controlling transcriptional misregulation of p27 in human cancers remains to be fully investigated . Id3 is overexpressed in many cancer types [52] , and loss of p27 is a significant negative prognostic indicator for many types of human cancers [11] . Furthermore , analysis of human leukemias by DNA microarrays found increased Id3 gene expression and decreased p27 gene expression in the samples ( Ross_Leukemia and Schmidt_Leukemia , Oncomine database [53] ) . Therefore , Id3 misregulation of p27 transcription may be an important mechanism in human cancers . Our data demonstrating the control of p27 transcription as a central mode of regulation downstream of Lck suggested p27 may be regulated similarly in normal developmental processes dependent on p56lck signaling , including β-selection [54] . The role of bHLH/Id proteins in lymphoid development is well established [30] . However , the genes regulated by bHLH/Id proteins during β-selection remain unknown . Previously , a cell cycle defect was observed in E2A−/− lymphocytes when the bHLH protein E47 was added to these cells , a phenotype that could indicate increased p27 expression [30] . Other observations also suggest p27 may be regulated by the bHLH/Id proteins during β-selection . Specifically , that DN4 cells had reduced p27 expression compared to small , quiescent DN3 cells [55] , and impaired DN3 to DN4 maturation was seen with increasing dosage of transgenic p27 expression [40] . Our data confirmed that p27 transcript was decreased in DN4 cells relative to DN3 cells and correlated the p27 mRNA decrease with increased Id3 mRNA at the DN3 to DN4 transition . Therefore , we concluded p27 may be a critical target of regulation by Lck through Id3 during thymic β-selection . We further observed that in a gene-dosage fashion , p27+/− and p27−/− partly relieved the lck−/− phenotype , augmenting the efficiency of DN to DP maturation and consequent thymus cellularity . That the DN compartment expanded in absolute cell numbers was an unexpected observation and may reflect more developmental niches available throughout the thymus , as the thymic epithelium increases in response to both maturation and expansion of the lymphoid compartment [56 , 57] as well as a consequence of the p27 mutation [58] . In more preliminary studies , we examined the effect of p27 mutation on the absolute block in development in a lck/fyn double mutant ( M . Tasch and R . Perlmutter , unpublished observations ) . The residual thymic maturation in the lck mutant is due to functional redundancy between Lck and a related Src-family kinase , Fyn , and loss of both p56lck and p59fyn results in severe thymic hypocellularity and developmental arrest at the DN3 stage . Here , p27−/− relieved the DN3 arrest and allowed accumulation of a DN4 compartment . In parallel , very low level of expression for CD4 and CD8 was observed , consistent with thymocytes making the initial step in the DN to DP transition . However , this maturation was abortive as bona fide DP cells failed to accumulate . These results are consistent with p27 being a critical target of Lck signaling during thymic maturation , but also indicate that Lck has targets in addition to p27 that are essential for full thymic maturation . In conclusion , our novel retroviral insertional mutagenesis screen discovered a repressor of the tumor suppressor p27 , and our subsequent analysis identified transcriptional misregulation of the p27 gene by Id3 as a new mechanism controlling p27 levels in lymphomas . This study highlights the fact that developmental pathways that regulate gene expression in normal cells are often coopted during tumor cell evolution . Thus , Id3 is an essential downstream target of Lck during normal thymic maturation and is activated to drive p27 down-regulation in Lck-driven thymic lymphomas . Finally , having demonstrated the importance of misregulating p27 at the level of gene transcription during lymphomagenesis , it now becomes crucial to understand which transcriptional pathways cause misregulation of p27 in human cancers , and how frequently this occurs .
M-MuLv infection and genotyping of the p27+/− mice was previously described in [23] . Animals were euthanized when they developed signs of morbidity , and the lymphomas were snap frozen in liquid nitrogen at necropsy . For thymus analysis , p27−/− mice on the 129 background were mated to lck−/− mice maintained on the C57BL/6 background , and the compound heterozygous F1 mice were then backcrossed with lck−/− animals . The lck−/−p27+/− mice were mated with either lck−/−p27+/− or lck−/−p27−/− mice and the thymi from lck−/−p27+/+ , lck−/−p27+/− , and lck−/−p27−/− progeny analyzed . Mice carrying targeted disruptions of the p27 and lck genes , and the procedures and reagents used in genotyping , have been described previously [3 , 39] . Small pieces of normal thymus from wild-type mice and lymphomas from p27+/− mice were homogenized in lysis buffer ( 1× PBS , 1% NP-40 , 0 . 1% SDS , 0 . 5% sodium deoxycholate , 1 mM dithiothreitol , 10 mM sodium flouride , 1 mM sodium orthovanadate , 10 μg/ml leupeptin , 10 μg/ml aprotinin , 10 μg/ml pepstatin-A , and 1 mM phenylmethylsulfonylfluoride ) . NIH 3T3 cells were lysed in the same buffer as above . Samples were sonicated , and protein concentrations were determined using the Bio-Rad protein assay . A total of 40 μg of protein was separated on 12% SDS-PAGE gels , and the proteins were transferred onto polyvinylidene diflouride membranes ( Perkin Elmer ) . The membranes were blocked in 1× PBS , 0 . 1% Tween-20 , and 5% milk . After blocking , the membranes were incubated with primary antibodies diluted 1:1 , 000 in 1× PBS , 5% milk , and 0 . 1% Tween-20 overnight at 4 °C . Id3 ( C-20 ) and p27 ( C-19 ) rabbit polyclonal antibodies from Santa Cruz Biotechnology and the α-tubulin ( clone DM1A ) mouse monoclonal antibody from Sigma were used . ECL ( Amersham ) was used for immunodetection . Genomic DNA was isolated from 44 p27+/− lymphomas and inverse PCR performed as previously described [23] . After two rounds of I-PCR the products were gel purified and cloned using the Topo cloning system ( Invitrogen ) following manufacturers protocol . Plasmids were sequenced using M13–20 or M13 reverse primers and sequencing reactions were carried out at the FHCRC automated sequencing shared resource . The DNA sequence data were blasted against the NCBI , Celera , and Ensembl mouse databases . The statistical significance of the CIS4 retroviral insertions in the p27+/− versus the absence in the p27−/− lymphomas was determined using Fisher's exact test at http://www . exactoid . com/fisher/index . php . HEK 293T and Phoenix ecotropic cells were grown in Dulbecco's modified Eagle medium ( DMEM ) containing 10% bovine growth serum ( Hyclone ) , 1mM sodium pyruvate , 2 mM L-glutamine , and 2 μg/ml penicillin-streptomycin ( Life Technologies ) . NIH 3T3 cells were grown in same medium as above with the exception of 10% fetal bovine serum ( Hyclone ) . Thymic lymphoma cell lines LGY-6871 , LGY-10442–2 , and SV40–180 were maintained in RPMI 1640 supplemented with 10% fetal bovine serum , 2 mM L-glutamine , 0 . 1 mM nonessential amino acids , 50 U/ml penicillin G , and 50 μg/ml streptomycin , 2 . 0 mM HEPES buffer ( pH 7 . 4 ) , and 100 μM β-mercaptoethanol . All cell lines were maintained at 37 °C in 5% CO2 . For retroviral infections , the mouse Id3 cDNA ( provided by B . Christy ) was cloned into the pQCXIP vector ( BD-Clontech ) . Ecotropic Phoenix cells were transfected with 15 μg pQCXIP-Id3 or pQCXIP using Fugene 6 ( Roche ) . After 24 h the medium was changed to cRPMI , and the cells were moved to 34 °C . The LGY-6871 cells were plated in a 10-cm dish and allowed to recover 4 h before infection . The viral supernatant was collected 48 and 72 h after transfection and passed through a 0 . 22-μm filter onto the LGY-6871 cells and 1 μg polybrene added . The infections were done at 34 °C . Twenty-four hours after the last infection the cells were centrifuged at 1 , 000 rpm for 5 min , and the pellets resuspended in cRPMI medium and moved to 37 °C . Selection with 0 . 5 μg/ml puromycin ( Calbiochem ) began 48 h after infection for 4 d . Western analysis confirmed the expression of Id3 . RNA was extracted from pieces of frozen lymphomas , normal thymi , normal thymocytes , NIH3T3 cells , or thymic lymphoma cells LGY-6871 and SV40–180 following the Trizol protocol ( Invitrogen ) . cDNAs were generated by reverse transcribing 1 μg of total RNA using oligo dT and the Taqman reverse transcription kit ( Applied Biosystems ) . The cDNAs were diluted 1:10 , and 5 μl added to each reaction containing Taqman master mix at 1× concentration and the p27 Mm00438167_g1 , Hprt1 Mm00446968_m1 , Id3 Mm00492575_m1 , or Id3 Mm01188138_g1 Assay on Demand primers and probe ( Applied Biosystems ) . Each 50-μl reaction was done in triplicate . The Taqman analysis for the PP1-inhibited cells was performed on two to three separate experiments . Taqman RT-PCR reactions were performed using an ABI PRISM 7900HT sequence detector and analyzed by the SDS2 . 2 software . Probe sequences were as follows: Mouse p27: AGGAAGCGACCTGCTGCAGAAGATT Mouse HPRT: AGGTTGCAAGCTTGCTGGTGAAAAG Mouse Id3: GGCACCTCCCGAACGCAGGTGCTGG Mouse Id3: CCGATCCAGACAGCTGAGCTCACTC NIH 3T3 cells were transfected with 20 nM , 35 nM , or 50 nM pooled Id3 siRNA duplexes or siControl nontargeting siRNA duplexes ( Dharmacon ) using siLentfect lipid ( Bio-Rad ) according to the manufacturer's protocol . Cells were harvested for RNA isolation or protein lysates 24 h after transfection . The mouse p27 promoter constructs were generated by cloning a 2 . 2-kb BamHI-BspEI fragment or a 550-bp SacI-BspEI fragment into the pGL-2 luciferase vector ( Promega ) . The mouse E12 and mouse NeuroD2 plasmids were provided by S . Tapscott and J . Olson , respectively . The internal control plasmids were SV40-β-galactosidase or CMV-β-galactosidase , and the CMV-luciferase plasmid was used as a positive control . HEK293T cells were plated on 60-mm dishes , transfected using calcium chloride method or Fugene 6 ( Roche ) , and harvested 48-h after transfection for assays . Each transfection experiment was repeated three to four times to ensure reproducibility . The luciferase assays using Steady-Glo luciferase substrate ( Promega ) and β-Galactosidase enzyme assay system ( Promega ) were used according to manufacturers protocol . An EG&G plate reader was used for all luciferase experiments , and a Labsystems Multiskan Plus plate reader was used for the β-Galactosidase assays . Each luciferase and β-Galactosidase reaction was done in triplicate and the data averaged . E boxes 1 and 2 and the imperfect E box were mutated to CGNNAT and GGNNAT using the QuikChange Multi Site-Directed Mutagenesis kit ( Stratagene ) following the manufacturer's protocol . Primer sequences were as follows: Ebox1: GCCCTCCAGTACGCTATATCACTGAAGCCTCGAG Ebox2: CCTGGCTCTGCTCCGTTATACTGTCTGTGTGCAGTCG I E box: GCCTCTCTTCCCCAGGCCATCGCGCTACTGCG The src-family–specific tyrosine kinase inhibitor PP1 ( Biomol ) was used in in vitro cultures at 5 mM , diluted from a 5-mM stock in DMSO directly into LGY-6871 , LGY-6871+Id3 , LGY-6871+pQCXIP , or SV40–180 cultures . Aliquots were removed from cultures at indicated time points , washed in cold PBS , and cell pellets either ( 1 ) snap frozen and maintained at −80 °C for later protein extraction , ( 2 ) immediately resuspended in Trizol for subsequent RNA extraction , ( 3 ) used immediately for DNA staining , or ( 4 ) prepped for nuclei isolation . LGY experimental and SV40 control cells were washed in cold PBS and resuspended in a solution of 4 . 0 mM sodium citrate ( Sigma ) , 30 U/ml RNase , 0 . 1% Triton X-100 ( Sigma ) , and 50 mg/ml propidium iodide ( Sigma ) . Cells were incubated at 37 °C in the dark for 10 min , after which 1/10 volume of 1 . 38 M sodium chloride was added . Cells were then analyzed using a FACScan flow cytometers ( Beckton-Dickenson ) and Cell Quest ( Beckton-Dickenson ) analysis software . Intact nuclei from LGY experimental and SV40 control cells were isolated in hypotonic Tris buffer containing 0 . 25% nonident P-40 ( Sigma ) , and nascent mRNA transcripts were radiolabeled by incubation in buffer containing 60 mM each ATP , CTP , GTP , and 20 ml a32P-UTP ( 4 , 000 Ci/mmol ) [59] . Nuclei were treated with RNase-free DNase and proteinase K , and RNA was isolated by phenol:chloroform extraction and ethanol precipitation . Target DNA was linearized and denatured prior to immobilization on polyvinylidene fluoride ( PVDF ) membranes according to the manufacturer's instructions ( Schleicher and Scheull ) . Target sequences included pBluescript ( SK ) ( Stratagene ) , murine p27 cDNA , and murine elongation factor1a ( EF1a ) cDNA . Hybridization was carried out for 18 h at 65 °C , and subsequently membranes were washed stringently and exposed to autoradiography media and quantitated by phosphorimage analysis . Thymus tissue from lck−/−p27+/+ , lck−/−p27+/− , and lck−/−p27−/− mice was harvested after euthanasia , and single-cell lymphocyte suspensions prepared by mechanical disruption with scalpel followed by maceration with frosted glass slides . Cell preparations were washed with cold PBS and red blood cells depleted with hypotonic ammonium chloride per published protocols . Antibodies specific for murine CD4 , CD8 , CD44 , and CD25 and conjugated to fluorescein isothiocyanate ( FITC ) , R-phycoerytherin ( PE ) or biotin , and streptavidin conjugated to PE-Cy5 were obtained from BD/Pharmingen and used at empirically derived concentrations on samples of 1 × 106 cells . Staining buffer consisted of PBS with 5% FCS , 5 mM HEPES buffer ( pH 7 . 4 ) , and 1 mM sodium azide . After staining and extensive washing , cells were fixed in 2% paraformaldehyde and analyzed with a FACScan flow cytometers and Cell Quest software ( both Beckton-Dickenson ) . To isolate DN3 and DN4 cells for mRNA , cells were sorted using a FACS ARIA flow cytometer and FACSDiva software ( both products from Beckton Dickinson ) on the basis of CD44 and CD25 expression and cell size , as determined by forward light scatter characteristics . Sorted cells were then disrupted and extracted for total RNA as detailed above .
Accession numbers for genes mentioned in this paper from the National Center for Biotechnology Information ( NCBI ) ( http://www . ncbi . nlm . nih . gov ) are Id3 ( NM_008321 ) , E2F2 ( NM_177733 ) , Ddefl1 ( NM_001008232 ) , Tcea3 ( NM_011542 ) , Zfp46 ( NM_009557 ) , and Hnrpr ( NM_028871 ) . | Many human cancers express abnormally low amounts of the p27 protein , and this is associated with aggressive tumor behavior and a poor clinical outcome . Surprisingly , the p27 gene is rarely mutated in these tumors and retains the potential to produce normal amounts of p27 protein . Therefore , understanding the pathways that cause the decrease of p27 protein in cancer cells may lead to the development of new therapies that restore p27 gene expression to normal levels . We undertook a survey of the mouse genome to identify genes that modulate p27 protein levels in lymphomas . Our analysis discovered inhibitor of DNA binding 3 ( Id3 ) as a negative regulator of p27 gene expression . Additionally , we demonstrated that the p27 gene is controlled by Id3 during normal embryological development of the thymus . Our results underscore the fact that cancer cells frequently exploit normal developmental pathways as they evolve into increasingly aggressive transformed states . | [
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Loiasis , a filarial infection caused by Loa loa usually thought to cause relatively minor morbidity , can cause serious and often fatal reactions in patients carrying very high levels of circulating Loa loa microfilariae ( mf ) following administration of microfilaricidal drugs . An experimental model of this condition would greatly aid the definition of the optimal management of this important clinical presentation . Fifteen baboons ( Papio anubis ) were infected with 600 infective larvae ( L3 ) isolated from Chrysops vector flies . Animals were observed for any clinical changes; blood samples were collected every 1–2 months for 22 months , and analysed for parasitological , hematological and biochemical profiles using standard techniques . All animals became patent but remained clinically normal throughout the study . The parasitological pre-patent period was between 4–8 months , with a majority ( 60% ) of animals becoming patent by 5 months post infection ( MPI ) ; all animals were patent by 8 MPI . Microfilarial loads increased steadily in all animals and reached a peak at 18 MPI . By 10 MPI >70% of animals had mf >8 , 000 mf/mL , and at 18 MPI >70% of animals had mf >30 , 000mf/mL with 50% of these animals with mf >50 , 000mf/mL . Absolute eosinophil , creatinine , Ca2+ and K+ levels were generally above normal values ( NV ) . Positive associations were seen between microfilariaemia and eosinophilia , Hb , Ca2+ , and gamma-GT values , whilst significant negative associations were seen between microfilariaemia and potassium , glucose and mononuclear leukocyte levels . Infection of splenectomised baboons with L . loa can induce levels of circulating microfilariae , and corresponding haematological profiles , which parallel those seen in those humans in danger of the severe post-microfilariacide clinical responses . Utilization of this experimental model could contribute to the improved management of the loiasis related adverse responses in humans .
Loa loa is a parasitic filarial nematode of humans , and a member the super family Filariodea which includes infections that are targeted for elimination , such as lymphatic filariasis ( Wuchereria bancrofti , Brugia sp ) , and onchocerciasis ( Onchocerca volvulus ) . L . loa is found in the tropics [1] restricted to the rainforest and forest fringes of West and Central Africa [2] and causing a relatively well tolerated medical condition known as loiasis . The geographic distribution of this infection is limited by the presence of the two biting tabaniid vectors , Chrysops silicea and C . dimidiata , which generally prefer rainforest-like environment; however , recently the disease has been described in the Guinean savannah [3] . It is estimated that some 14 . 4 million people live in high risk areas where the prevalence of loiasis ( i . e . a history of “eye worm” ) is greater than 40% , with 15 . 2 million in intermediate risk areas where the estimated eye worm prevalence is between 20 and 40% [4] . This disease has been recognized as one of public health importance , not so much because of its own clinical manifestations , but because of its negative impact on the control of onchocerciasis and lymphatic filariasis in areas of co-endemicity . There have been increasing reports in the past decade of serious adverse events ( SAE ) following the administration of ivermectin for these two major filariae , onchocerciasis and lymphatic filariasis , in L . loa endemic areas . These SAE are characterized by a severely disabling and potentially fatal encephalopathy . Evidence exists that these SAE appear to correlate with high loads of L . loa microfilariaemia ( >30 , 000 mf/mL ) [5–8] . Despite advances in defining the epidemiological aspects of these L . loa associated SAE , their pathogenesis and treatment still remains obscure . It is not known if the genesis of the encephalopathy is associated with the increased presence of L . loa in the brain tissue , although a vasculopathy associated with the presence of microfilariae has been proposed as a possible aetiology [9 , 10] . To be able to properly manage these cases , it is necessary to better understand the mechanism of pathogenesis of the post-ivermectin events in these heavily infected individuals . Very limited progress has been made in research on the pathogenesis of encephalopathy , due in most part to the lack of material from human cases , as well as a lack of any useful animal models to investigate the etiology and test new potential clinical management procedures . Although the natural hosts of L . loa are humans , the entire life cycle of L . loa can be maintained experimentally . L . loa readily infects mandrills ( Mandrillus leucophaeus ) [11] , baboons ( Papio anubis ) [12] , and patas monkeys ( Erythrocebus patas ) [12] . Non-human primates ( NHPs ) are therefore considered the best models for the much needed investigation in human loiasis , especially as suitable in vitro models for loiasis have not yet been developed , and indeed such artificial models are not easily extrapolated to humans [13–15] . Although the mandrill is an excellent experimental host , there are ethical concerns with using this now protected animal for research , and it is no longer used in biomedicine . The use of Patas monkeys also is limited as the parasite does not behave in the same way like it does in the more human-like drill [12] . The baboon ( P . anubis ) however , has potential as an experimental model to study the mechanisms behind the SAEs that develop in L . loa infected people as in this animal the parasite here behaves essentially in the same way as it does in the drill [12] , and therefore is comparable with the situation in humans . Secondly , the use of baboons in biomedical research is accepted by the International Union for Conservation of Nature-IUCN [16] . In the simian host , the spleen usually becomes enlarged and granulomatous in filarial infections as it is the site of destruction of a large proportion of circulating Loa microfilariae [17 , 18] . If the spleen is removed very high levels of circulating microfilariae develop in the blood ( >50 , 000 mf/mL ) , levels that are similar to those found in patients developing the post-treatment Loa-associated encephalopathy . Thus the baboon , with its remarkable similarities to humans in many anatomical and physiological parameters [19 , 20 , 21] , appears to likely be a suitable model for studying this important clinical phenomenon .
Ethical and administrative clearances for the use of baboons in this study were obtained from the Ministry of Scientific Research and Innovation of Cameroon ( Research permit #028/MINRESI/B00/C00/C10/C12 ) . The animal procedures were conducted in accordance with the guidelines with animal care and use committee at the National Institutes of Health ( USA ) and University of Georgia , Athens , USA . Ethical clearance for the involvement of human subjects in the production of infective larvae was obtained from the Institutional Review Board of the Medical Research Station of Kumba , Cameroon . All volunteers were handled according to the Helsinki declaration on the use of humans in biomedical research . The use of non-human primates for research was approved by the Committee on the Ethical Use of Animals in Research ( CEUAR ) within the Research Foundation for Tropical Diseases and Environment ( REFOTDE ) , Cameroon . All relevant aspects of the International Primatological Society ( IPS ) 2007 guidelines on the acquisition , care and breeding of non-human primates for research were followed . Baboons of both sexes were trapped in different parts of Cameroon according to IPS standard accepted procedures These animals were transported to the animal facilities in the Tropical Medicine Research Station , Kumba , South West Region and quarantined for a period of two months during which they were pre-screened for a panel of natural infections ( loiasis , other blood-borne parasites , and intestinal worms ) . Each animal was observed daily by the veterinary staff to ensure that they were healthy , and any animal found to be ill was immediately given appropriate treatment , both in the quarantine period and during the main study period . The animals were housed individually in large custom built cages that allowed the animals to move about freely and be allowed to display their normal repertoire of locomotor behavior ( walking , climbing , running , jumping and swinging ) by providing them with vertical climbing surfaces and perches . Horizontal surfaces were also provided to allow them to rest comfortably and perform their social interactions such as sprawling during grooming . The housing facility was well aerated and equipped with a system that provided water ad libitum for each animal . Each baboon’s behavior was regularly monitored to identify any indications of poor welfare . Baboons received a diet of food that mimicked their natural diet ( leaves , grass , roots , bark , flowers , fruit , lichens , tubers , seeds , mushrooms , corms , and rhizomes ) . They were also fed a supplement of a nutritionally complete commercial-available diet . The health and well-being of the baboons were regularly assessed during the study by an animal welfare officer who advised on matters such as disease prophylaxis , zoonoses , anesthesia , and methods of humane euthanasia and provision of health certificates . All measures were taken to minimize suffering during capture , captivity and experimentation . Health screening of workers in contact with the baboons was performed regularly to prevent animal losses from diseases transmitted from humans to baboons as well as zoonotic transmission of disease from baboons to workers . A total of 15 animals ( 6 males , 9 females ) were used in this study with each animal being given a project animal number ( BAB-1 to BAB -15 ) . Splenectomy was carried out by a licensed veterinarian following previously published procedures [17 , 18] . The animals were anaesthetized using an intra-peritoneal injection of 4 mg of betamethasone ( Septon , Europe ) and 5 mg ketamine ( Imalgene , Merial , France ) ; 2mg/kg morphine sulphate ( Hamelin Pharmaceuticals Ltd , UK ) as also added to the administration as an analgesic . Spleens were removed in approximately 25 minutes under aseptic conditions , the skin wound sutured and disinfected with an antibiotic spray ( 2 . 0g Chlortetracycline , 0 . 5g Gentian violet , 100 mL excipient ) , and the incision site bandaged . Dressing were changed daily and the animals given a daily 1 mL injection containing 1 . 2 million units of penicillin and 5 mg of streptomycin with 1 mL of anticoagulation factor ( Vitamin K ) for a week post-surgery . The surgical wound was dressed daily using an antimicrobial and insect repellent ( Veto Spray—Vétoquinol ) to protect against flies . The sutures were removed 7 to 8 days after splenectomy . The general welfare of the animals was monitored is on a daily basis for a period of approximately 2 months before infecting the animals with L . loa . The normal ranges for various blood parameters in baboons used for comparison were those provided by the Association of Primate Veterinarians Primate Formulary ( 1999 ) as listed for baboons housed individually in large custom cages . The data were entered into Epi Info version 3 . 5 . 3 ( C . D . C . Atlanta , GA , USA ) and analysed using the Software Package SPSS version 20 . Descriptive statistical analyses were performed to compute the mean , median and standard deviations of Loa microfilarial counts , different haematological and biochemical parameters in the general study group , and in both males and females . Graph PadPrism software was used to draw the scatter plots comparing microfilariaemia and the different haematological and biochemical parameters to test for any association . The Kruskal and Wallis test was used to test for significant differences in levels of microfilariaemia , haematological and biochemical parameters before inoculation and at different time points of observation in the general study population . The Mann-Whitney test was used to test for significant differences in the different haematological and biochemical parameters between males and females . The Jonchkeere-Terpstra ( J-T ) test was used to test for any trend of linearity in the different parameters at different months . All tests were performed to a 5% significance level .
All fifteen animals appeared well fed and remained healthy throughout the study . The surgical incision sites post-splenectomy all healed without any evidence of infection , and no animal showed fever nor any intestinal disturbances during the study . The pre-study screening did not detect the presence of malaria or any intestinal parasites , in the test animals . The pre-patent period of this infection in baboons ranged from 4–8 months , with a median of 5 months . 1 of 15 ( 6 . 7% ) , 9 of 15 ( 60% ) baboons , 4 of 15 ( 26 . 7% ) , 1 of 15 ( 6 . 7% ) had pre-patent periods respectively of 4 , 5 , 6 , and 8 MPI ( Figs 1 and 2 ) . The pre-patent period for males ranged from 4–6 months ( median 5 months ) with 3 out of 6 males ( 50% ) becoming patent at 5 months post infection ( Fig 1 ) . The pre-patent period of females ranged from 5–8 months ( median 5 months ) with 6 out of 9 females ( 66 . 67% ) becoming patent at month 5 post infection ( Fig 1 ) . There was no significant difference between the median pre-patent period of males and that of females ( p = 0 . 504 ) . The month at which each baboon started having microfilariae in blood ( pre-patency period ) and the month at which each baboon developed its highest microfilariaemia is shown in Fig 1 . Animals were followed up for 22 months by which time all animals had become patent ( Fig 2 ) . By month 4 post inoculation ( MPI ) about 7% of infected baboons had microfilariae present in their circulation , and by month 5 MPI >70% of infected animals had developed microfilariaemia . By month 8 MPI all animals were parasitologically positive ( Fig 2B ) . Generally , the mf increased steadily in all animals from the onset of patency to reach a median of 48 , 790 mf/mL by month 18 . The mf loads at different time points during the course of infection was highly variable ( Fig 2A ) . Male baboons generally developed higher microfilariaemia than females ( Fig 1 ) , although this difference was not statistically significant ( p = 0 . 06 ) . By 6 MPI about 7% of animals had developed mf loads >8 , 000 mf/mL; at 8 MPI 50% of them had developed mf loads >8 , 000 mf/mL , and at 10 MPI >70% of animals had developed mf loads >8 , 000 mf/mL ( Figs 1 and 2A ) . With regards especially high blood microfilarial loads , about 20% of animals had developed mf loads >30 , 000 mf/mL by 10 MPI . At 14 MPI , 50% of infected animals had developed mf loads that were >30 , 000 mf/mL and at 18 MPI >70% of infected animals had developed mf loads >30 , 000 mf/mL ( Fig 2B ) . By 10 MPI about 7% of infected animals had developed extremely high blood microfilarial loads of >50 , 000 mf/mL and by 18 MPI almost 50% of them had developed these very high microfilarial loads ( Fig 2B ) . RBC counts ranged from 2 . 65–4 . 3 x106 cells/mm3 ( median 3 . 48 x106 cells/mm3; NV = 3 . 76–5 . 61 x106 cells/mm3 ) . Again males differed significantly from females in RBC values ( p<0 . 001 ) . Most RBC values recorded in all animals were below the NV at the different time points ( Fig 3A ) . Hemoglobin ( Hb ) values ranged from 10–16 . 2g/dl ( median: 13 . 90g/dl ) essentially close to normal values ( NV ) of 9 . 5–14 . 5g/dl ( Fig 3B ) , with a slight upward trend during the infection ( see S1 File ) . There was significant difference between males and females: Hb values in males ranged from 10–16 . 2g/dl ( median: 14 . 4g/dl ) whilst for females the values were 11–15 . 2g/dl ( median 13 . 6g/dl ) ; these were significantly different ( p<0 . 01 ) . The total white cell counts were generally all within the NV ( Fig 4A ) . Absolute neutrophil counts also stayed within NV ranging from 1 , 000–19 , 500 cells/mm3 ( Median: 2 , 250 cells/mm3 ) ( Fig 4B ) . Absolute mononuclear ( lymphocyte + monocyte ) cell counts ranged from 2 , 400–34 , 760 cells/mm3 and were within the NV ( see S1 File ) although was a marked variation between different time points ( Fig 4C ) . with a median of 3 , 796 cells/mm3 ( NV: 810–19 , 728 cells/mm3 ) . The values for males were 1 , 800–39 , 000 cells/mm3 ( median: 3 , 788 cells/mm3 ) while in females these counts ranged from 1 , 848–6 , 030 cells/mm3 ( median: 3 , 810 cells/mm3 ) . These mononuclear cell counts did not vary significantly between males and females ( p = 0 . 904 ) . Absolute counts at different time points however did vary significantly ( p<0 . 001 ) and showed a significant linear trend ( p<0 . 001 ) . Mononuclear counts recorded were within the NV ( Fig 4C ) . Absolute eosinophil counts ranged from 0–6 , 500 cells/mm3 ( Median: 978 cells/mm3; NV: 0–822 cells/mm3 ) . Absolute eosinophil values in males ranged from 0–6 , 500 cells/mm3 ( Median: 1 , 013 cells/mm3 ) while in females absolute eosinophil levels values ranged from 0–2 , 640 cells/mm3 with a median of 972 cells/mm3; these were not significantly different ( p = 0 . 473 ) . Eosinophil counts at different time points , however did vary significantly ( p<0 . 001 ) and showed a general increase over the 18 months studied . All animals recorded absolute eosinophil values out of the normal range at different time points ( Fig 5A ) The absolute eosinophil results for each animal are given in Fig 5B . Basophils were not identified in these study samples . There was no significant difference in SGPT values between males and females ( p = 0 . 342 ) , nor did the SGPT values at different time points vary significantly ( p = 0 . 086 ) or show a significant linear trend ( p = 0 . 110 ) with the majority of SGPT values being within the NV ( Fig 6A ) . The SGOT values at different time points varied significantly ( p<0 . 05 ) although there was no significant linear trend ( p = 0 . 356 ) . The majority of SGOT values were within the NV , even though all baboons except BAB 04 showed SGOT values both below or above the NV at different time points ( Fig 6B ) . The γ-GT values at different time points varied significantly ( p<0 . 001 ) , and there was a significant linear relationship between microfilariaemia and the duration of infection ( p<0 . 05 ) . However , most of the γ-GT values were within the NV ( Fig 6C ) . The creatinine values at different time points varied significantly ( p<0 . 001 ) , although the values did not show any significant linear relationship with the duration of infection ( p = 0 . 068 ) . Most of the creatinine values were above the NV at the different time points ( Fig 7A ) . Glucose values varied significantly ( p<0 . 001 ) at different time points , and there was significant linear relationship between blood glucose level and the duration of infection ( p<0 . 001 ) . Majority of the glucose values were within the normal range , although all baboons showed glucose values out of the normal range at different time points ( Fig 7B ) . Calcium values varied significantly at different time points ( p<0 . 001 ) and showed a significant linear relationship with the duration of infection ( p<0 . 001 ) . Majority of the calcium values in all animals at different time points were below the NV ( Fig 8A ) . The potassium values at different time points varied significantly ( p<0 . 001 ) , and showed a significant negative linear relationship with the duration of infection ( p<0 . 001 ) . The majority of the potassium values in all animals were out of the NV ( Fig 8B ) . Hb values showed a slow increase from 12 . 4 g/dL before inoculation to 14 . 1 g/dL at 3 MPI after which the values dropped to 13 . 1 g/dL at 6 MPI and increased steadily over time to 14 . 1 g/dL at 16 MPI where a slight dropped to 13 . 3 g/dL was noticed as mf culminated at 18 MPI . Overall , there was a positive significant association ( r = 0 . 180 , p>0 . 05 ) between mf and Hb values ( Fig 9B ) . The eosinophil count increased sharply from 0 cells/mm3 before inoculation to 1 , 500 cells/mm3 at 18 MPI when the mf reached its highest level . Overall , there was a strong positive significant association ( r = 0 . 730 , p<0 . 001 ) between eosinophil and mf ( Fig 10C ) . The mononuclear count decreased steadily from 4 , 800 cells/mm3 at 1 MPI to 3 , 800 cells/mm3 at 16 MPI from where its values dropped slightly to 3 , 200 cells/mm3 at 18 MPI when the mf peaked . Overall , there was a negative significant association ( r = -0 . 368 , p<0 . 001 ) between mf and mononuclear counts ( Fig 10D ) . There was no significant association between mf and RBC , WBC and neutrophil ( Figs 9A , 10A and 10B ) . The enzymes SGPT and SGOT did not show any significant association with mf ( Fig 11A and 11B ) . Overall , there was a positive significant association ( r = 0 . 281 , p<0 . 001 ) between γ-GT and mf ( Fig 11C ) . γ-GT values increased sharply from 0 IU/L before inoculation to 50 IU/L at 8 MPI , then decreased slightly over time to 29 at 14 MPI after which time its values decreased gradually over time to 42 IU/L at 18 MPI when mf peaked and then dropped again . There was a negative , non-significant , association between mf and creatinine ( Fig 12A ) . Glucose values decreased gradually from 1 . 5 g/L at 1 MPI to 0 . 6 g/L at 6 MPI from where the values increased slightly to 1 . 1 g/L at 10 MPI after which its values decreased to 0 . 6 g/L at 18 MPI . Overall , there was a negative significant association ( r = -0 . 171 , p<0 . 05 ) between glucose and mf , ( Fig 12B ) . Calcium values increased steadily over time from 9 mg/L before inoculation to 25 mg/L at 10 MPI at which point it increased sharply to 74 mg/L at 12 MPI after which its values decreased steeply over time to 15 mg/L at 20 MPI . Overall , there was a positive significant association ( r = 0 . 410 , p<0 . 001 ) between calcium and mf ( Fig 13A ) . Potassium values plummeted from 170 mmol/L before inoculation to 5 mmol/L at 18 MPI when the mf peaked . Overall , there was a negative significant association ( r = -0 . 423 , p<0 . 001 ) , between mf and potassium ( Fig 13B ) .
This study is the first showing parasitological , haematological and biochemical characterization of hyper-microfilaraemic loiasis in the splenectomized baboon ( P . anubis ) . Parasite pre-patency was between 4–8 months , with the majority ( 60% ) of animals becoming patent 5 months post inoculation; all animals developed patency; Microfilariaemia rose steadily in all animals and culminated at a peak level by month 18 post infection with males showed higher microfilariaemia than females . By month 10 post inoculation >70% of infected animals developed microfilariaemia >8 , 000mf/mL; by month 18 post inoculation >70% of infected animals had developed microfilariaemia >30 , 000mf/mL , and 50% of them developed >50 , 000mf/mL a level where in humans that predisposed for severe adverse reactions post treatment . Significant positive associations were seen between microfilariaemia and eosinophil , haemoglobin , calcium and gamma-GT , whilst there was a negative significant correlation between microfilariaemia and mononuclear leucocytes , glucose and potassium . This model has the potential of helping to understand the mechanism ( s ) involved in the development of Loa-encephalopathy post-ivermectin treatment in heavily Loa microfilariaemic humans , and could help in designing improved management of such cases . | Loiasis is a filarial infection of humans that , in addition to causing severe direct clinical effects , is of concern to the global community’s efforts to eliminate the important filarial diseases , onchocerciasis and lymphatic filariasis , through causing interruption to mass drug distribution activities . Hyper-microfilariaemia has been seen to be the characteristic parameter in patients suffering from post ivermectin encephalopathy , a condition which sometimes leads to death . Understanding and developing appropriate approaches to the treatment and prevention of these severe adverse reactions has been difficult due to the lack of suitable models . As primates can be infected with human L . loa , and can develop hyper-microfilariaemia , it is likely that they therefore can serve as suitable models for the investigation of this syndrome in humans . This current study shows that following splenectomy the circulating microfilarial loads are similar to those seen in humans , and that the clinical pathology profile following infection also appears to be similar . The consistent ability to induce microfilariae levels of above 30 , 000 mf/ml in more than 70% of the tested animals suggests that this is indeed a practical model for investigating the adverse events occurring in hyper-loiasis . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Parasitological, Hematological and Biochemical Characteristics of a Model of Hyper-microfilariaemic Loiasis (Loa loa) in the Baboon (Papio anubis) |
Snakebite envenoming ( SBE ) is a major problem in rural areas of West Africa ( WA ) . Compared to other Neglected Tropical Diseases ( NTD ) , the public health burden of SBE has not been well characterized . We estimated the impact of snakebite mortality and morbidity using the Disability Adjusted Life Years ( DALYs ) metrics for 16 countries in WA . We used the reported annual number of SB deaths and mean age at time of SB and converted these into years of life lost ( YLL ) . Similarly , the years of life lived with disability ( YLD ) were estimated by multiplying the number of amputations by the respective disability weight of 0 . 13 . In WA , the annual cases of SB mortality and amputations ranged from 24 ( 95% Confidence Interval: 19–29 ) and 28 ( 17–48 ) respectively in Guinea-Bissau with the highest estimates of 1927 ( 1529–2333 ) and 2368 ( 1506–4043 ) respectively in Nigeria . We calculated that the annual DALYs associated with a SB death ranged from 1550 DALYs ( 95%CI: 1227–1873 DALYs ) in Guinea Bissau to 124 , 484 DALYs ( 95%CI: 98 , 773–150 , 712 DALYs ) in Nigeria . The annual DALYs associated with amputation for the two countries were 149 DALYs ( 95%CI: 91–256 DALYs ) and 12 , 621 DALYs ( 95%CI: 8027–21 , 549 DALYs ) respectively . The total burden of SBE was estimated at 319 , 874 DALYs ( 95% CI: 248 , 357–402 , 654 DALYs ) in the 16 countries in WA . These estimates are similar , and in some instances even higher , than for other NTDs encountered in WA ( e . g . , Buruli ulcer , Echinococcosis , Intestinal Nematode Infections , Leishmaniasis , Onchocerchiasis , Trachoma and Trypanosomiasis ) as reported in the Global Burden of Diseases 2010 ( GBD ) . The public health burden of SBE in WA is very substantial and similar to other more widely recognized NTDs . Efforts and funding commensurate with its burden should be made available for the control of snakebite in the sub-region .
Snakebite envenoming ( SBE ) is a major public health problem among communities of the savanna region of West Africa , notably in Benin , Burkina-Faso , Cameroon , Chad , Ghana , Nigeria , Senegal and Togo [1 , 2 , 3 , 4] . The precise incidence of snakebite is difficult to determine and is often grossly underestimated . An early estimate in northeastern Nigeria reported a bite incidence of 500 per 100 , 000 population per year with a 12–20% natural mortality , with carpet vipers ( Echis ocellatus ) accounting for at least 66% [5] . However , this study probably exaggerated the overall incidence by extrapolating from data in selected areas notorious for their incidence of snakebites . Up to 10% of hospital beds may be occupied by SBE patients in certain areas of the country . A recent global reappraisal estimated 10 , 001 to 100 , 000 snakebite envenomings with an incidence of 8 . 9–93 . 3/100 , 000 persons per year with an estimated 1 , 001 to 10 , 000 deaths and a mortality rate of 0 . 5–5 . 9/100 , 000 persons per year occurring in the West African sub-region [6] . A more recent study estimated over 314 , 000 bites , 7300 deaths and nearly 6000 amputations occurring annually in sub-Saharan Africa ( SSA ) [4] . As a condition affecting poor vulnerable rural dwellers , it is not only a major health problem but also a major impediment to economic prosperity from loss of income following initial incapacitation , hospitalization , long-term disabilities and premature deaths [7] . It is preventable and treatable with antivenom which has been shown to be cost effective [8] . In this analysis , we estimated the impact of snakebite mortality and morbidity using the Disability Adjusted Life Years ( DALYs ) metrics for 16 countries in Western Africa ( WA ) . This will allow for comparison to other diseases as well as guide prioritization of resource allocation .
From the most recent reliable literature available , projected annual burden of SBE in Sub-Saharan Africa was derived using a meta-analytic approach which has been described in detail elsewhere [4] . In summary , SBE data was obtained using a meta-analytic approach based on indexed , non-indexed or grey literature and conference proceedings over the past 40 years . Studies included in the analysis were categorized based on type of survey ( national , household and hospital studies ) and location whether conducted in urban or rural areas; with the latter representing 95% of envenoming . The pooled incidence rates , amputation rates and mortality rates were obtained and applied to the population size to derive the mortality and amputation estimates ( see S1 Annex ) [4] . For each country in Western Africa the annual number of snakebite deaths and mean age at time of envenoming was obtained from the analysis and from the literature respectively . The corresponding Years of Life Lost ( YLL ) was derived for each of the countries , following the methodology outlined in the latest global burden of diseases report [9] , which applies a standard loss function specifying the years of life lost due to death at a specific age . The standard loss function is based on projected frontier period life expectancy at birth for Japan and South Korea in the year 2050 estimated at 91 . 9 years and is not discounted [9] . Thus , we defined the YLL due to SBE in each country as 91 . 9 years minus the mean age at the time of envenoming . The mean ages of SBE were not available for all countries included in our analysis , but were reported for Chad at 25 . 2 years , Niger at 29 years , Nigeria at 26 years and Mali at 28 years [10 , 11 , 12 , 13] . So , since SBE consistently occurs in victims at a mean age in the late twenties , we made the simplifying assumption that SBE occurs in the 25–29 year age bracket and applied the standard loss function that corresponds to the this age bracket for all countries in our analysis , which is 64 . 6 years [9] . We then multiplied the number of SBE-related deaths in each country ( Table 1 , column 2 ) by 64 . 6 years to calculate the YLL . Similarly , the Years of Life Lived with Disability ( YLD ) were estimated by multiplying the number of amputations ( Table 1 , column 3 ) by the respective disability weight of 0 . 13 and applying this disability weight for the remainder of undiscounted local life expectancy [9 , 14] . In this age group , the remaining local life expectancies for the 16 countries ranged from 37 years in Sierra-Leone to 45 years in Ghana and Senegal ( Table 1 , column 4 ) . The sum of YLL and YLD then defined the total DALY burden for each country .
Using sub-regional level alternative data that reported low and high estimates of 1504 and 18654 annual snakebite deaths for WA by Kasturiratne et al 2008 [6] yielded burden of YLL from SBE deaths of 97 , 158 DALYs and 1 , 205 , 048 DALYs for low and high estimates respectively . The derived high estimate is 3 . 77 times higher than that obtained using data from Chippaux 2011 [4] . Similarly , using recent alternative estimates of annual snakebite deaths reported in a WHO document for Benin Republic 650 , Burkina Faso 200 and Togo 199 yielded alternative YLL values of 41 , 990 DALYs , 12 , 920 DALYs and 12 , 855 DALYs for those countries respectively [15 , 16] .
In the current reappraisal of data from WA , SBE accounted for 320 , 000 DALYs although using higher mortality estimates the YLL could be as high as 1 . 2 million DALYs [6] . Our estimate of 0 . 32 million DALYs is higher than the worldwide burden estimated for Buruli ulcer , Echinococcosis , Leprosy , Trachoma , Yaws and Yellow Fever . The estimate is also higher than the burden of African Trypanosomiasis , Leishmaniasis and Onchocerciasis within the 16 countries in WA region [17] . It is also higher than that of Podoconiosis the only other non-communicable disease in the expanded WHO NTD list . Compared to NTDs reported in the Global Health Estimates ( GHE ) for 2012 , SBE has the fourth highest burden in the 16 WA countries , ranking below Schistosomiasis , Lymphatic Filariasis and Rabies [17] ( Fig 1 ) . Despite these estimates , SBE remains under-recognized . The resources allocated are not commensurate with its burden . In a study that evaluated funding for developing world health from 42 major donors ( comprising industrialized countries 23 , international financial institutions 5 , multinational pharmaceutical companies 6 and philanthropic foundations 8 ) , annual donor dollar direct funding for 8 of the ten NTDs ( Fig 1 ) ranged from $3 . 30 per DALY for Intestinal Nematode Infections to $146 . 96 per DALY for Onchocerchiasis [18] . There is no evidence that any amount was provided for SBE by these donors during the period of the survey . The difference in burden estimates from the two studies might have arisen from their methodologic approaches [4 , 6] . The SBE data reported by Chippaux 2011 [4] was obtained using a meta-analytic approach as described above . In contrast the study of Kasturiratne et al 2008 [6] modeled data from electronic databases , indexed and grey literature from 1985 . They provided lowest and highest SBE estimates and rates when more than one source was available from a country . This led to a very wide range and imprecise estimates . Furthermore , country-level estimates were not provided and data from Chad , Ghana , Guinea-Bissau , Liberia , Nigeria and Sierra-Leone were not used to derive the mortality estimates for the WA sub-region . They extrapolated from data in adjacent countries , ignoring the geographical variations in snake-bite incidence . Both studies have common limitations . The authors did not choose the survey sites , studied variables and analytical strategies of the data . Most of the collected data were incomplete and spotty , resulting in a questionable representativeness . Nevertheless , some extrapolations were corroborated by national health statistics of some countries , such as in Benin [19] . Disability Adjusted Life Years ( DALYs ) are the sum of two components: years of life lost ( YLLs ) and years lived with disability ( YLDs ) . The DALY represents one of the few metrics available that could estimate acute and chronic effects and allow for comparison of significance of burden of several conditions . With a few exceptions , notably Rabies with nearly 100% mortality , most of the NTDs currently listed by the World Health Organization ( WHO ) and those on the expanded list are disablers rather than killers . In contrast SBE is both a disabler and killer with DALYs accruing from both components . In WA , SBE is an important killer and most of the DALYs ( over 90% ) accrued from early deaths . These deaths are partially driven by envenoming from saw-scaled or carpet vipers ( Genus Echis ) which cause a high mortality of about 12–20% without antivenom therapy . However , our analysis has been conservative given only amputation was used as the main disability . Several important but rare sequelae ( e . g . , blindness , malignant ulcers , fetal loss , cognitive and pyschological impairment ) were not considered . In WA the frequency of venom ophthalmia and blindness from cobra spits is <0 . 01% although blindness rarely may result from carpet viper induced ocular bleeding [20 , 21] . We have also observed 1 case of fetal loss out of 1800 SBE cases or <0 . 1% but no reports of cognitive/psychological impairment have been made from WA in contrast to Asia [22 , 23] . This underestimates the total burden and the contribution of DALYs accrued from YLD . Globally , the public health significance of SBE is generally neglected and underappreciated . It is not among the WHO’s 17 major NTDs although it is mentioned among the ‘other neglected conditions’ ( http://www . who . int/neglected_diseases/diseases/en/ . However , there is no official WHO program for its prevention or treatment . For the first time , the GBD 2010 provided disease burden estimates for these ‘other NTDs’ , i . e . , amoebiasis , cryptosporidiosis , trichomoniasis , scabies , fungal skin infections , and venomous animal contact including snakebite , although they are not listed under the NTD and Malaria category . Out of the approximately 48 million DALYs ascribed to both groups of NTDs , venomous animal contact was projected to account for 2 . 72 million DALYs in the GBD 2010 [24 , 25] . Interestingly , the annual deaths from SBE in India alone was estimated at 45 , 900 in the rigorously conducted Million Death Study [26] . While there may be certain minor differences between WA and India , using the approach in this study will translate to 2 . 97 million DALYs from SBE related YLL in India alone . Thus , estimates from WA and India when combined with the burden from Latin America , Papua New Guinea , the rest of Africa and Asia would be very substantial and much more than the current gross underestimation . Indeed , global burden , using reported high mortality estimates of 93 , 945 annual deaths worldwide by Kasturiratne et al [6] , would result in 6 . 07 million DALYs . Effective antivenom therapy has been shown to prevent death from SBE by at least 75% and is a very cost-effective intervention with an incremental cost-effectiveness ratio of $100/DALY averted [8 , 27] . With expanded access to appropriate and affordable antivenom therapy , the burden of SBE will be considerably curtailed . About $33 . 61 million ( 95% Confidence Intervals: $25 . 85-$43 . 03 million ) will be required annually to control SBE in WA . This analysis is subject to a number of limitations , including data scarcity , variability and inherent difficulties in accurately estimating the number of incident cases reported in SBE studies . We used the approach adopted by WHO in 2012 and the GBD 2010 in computing DALYs , i . e . , with a time discount rate of 0% and no age-weighting [9 , 24 , 25] . This is now the standard way to assess disease burden but compared to the previous method it leads to a substantial increase in the absolute number of DALYs lost and a relative increase in the share of DALYs at the extremes of life . In conclusion , SBE is a major public health problem with a burden higher than that of most other NTDs in the WA sub-region . Commensurate efforts and funding compared to its burden should be made available for control globally and in the sub-region . | Snakebite envenoming ( SBE ) is a major problem in rural West Africa ( WA ) . However , despite the high incidence of SBE in this region , government funding for the prevention or treatment of SBE is generally limited . In this analysis , we attempted to estimate how the public health burden of SBE compares to other more widely recognized Neglected Tropical Diseases ( NTD ) . To this end , we estimated the impact of SBE mortality and morbidity based on the methodology outlined in the global burden of disease and reported our results in Disability Adjusted Life Years ( DALYs ) for 16 countries in WA . We calculated the total burden of SBE in WA at 320 , 000 DALYs ( 95% CI: 248 , 000–403 , 000 DALYs ) per year with the least and highest burdens in Guinea-Bissau and Nigeria accounting for 0 . 5% and 43% , respectively . The vast majority of the public health burden ( 91% ) is attributed to early mortality . We conclude that the public health burden of SBE in WA is substantial and similar to , and in some cases even exceeds , other more widely recognized NTDs such as Buruli ulcer , Echinococcosis , Intestinal Nematode Infections , Leishmaniasis , Onchocerchiasis , Trachoma and Trypanosomiasis . Efforts and funding commensurate with its public health burden should be made available for the control of snakebite . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Snakebite is Under Appreciated: Appraisal of Burden from West Africa |
Identification of cellular receptors and characterization of viral tropism in animal models have vastly improved our understanding of morbillivirus pathogenesis . However , specific aspects of viral entry , dissemination and transmission remain difficult to recapitulate in animal models . Here , we used three virologically identical but phenotypically distinct recombinant ( r ) canine distemper viruses ( CDV ) expressing different fluorescent reporter proteins for in vivo competition and airborne transmission studies in ferrets ( Mustela putorius furo ) . Six donor ferrets simultaneously received three rCDVs expressing green , red or blue fluorescent proteins via conjunctival ( ocular , Oc ) , intra-nasal ( IN ) or intra-tracheal ( IT ) inoculation . Two days post-inoculation sentinel ferrets were placed in physically separated adjacent cages to assess airborne transmission . All donor ferrets developed lymphopenia , fever and lethargy , showed progressively increasing systemic viral loads and were euthanized 14 to 16 days post-inoculation . Systemic replication of virus inoculated via the Oc , IN and IT routes was detected in 2/6 , 5/6 and 6/6 ferrets , respectively . In five donor ferrets the IT delivered virus dominated , although replication of two or three different viruses was detected in 5/6 animals . Single lymphocytes expressing multiple fluorescent proteins were abundant in peripheral blood and lymphoid tissues , demonstrating the occurrence of double and triple virus infections . Transmission occurred efficiently and all recipient ferrets showed evidence of infection between 18 and 22 days post-inoculation of the donor ferrets . In all cases , airborne transmission resulted in replication of a single-colored virus , which was the dominant virus in the donor ferret . This study demonstrates that morbilliviruses can use multiple entry routes in parallel , and co-infection of cells during viral dissemination in the host is common . Airborne transmission was efficient , although transmission of viruses expressing a single color suggested a bottleneck event . The identity of the transmitted virus was not determined by the site of inoculation but by the viral dominance during dissemination .
Morbilliviruses are enveloped , non-segmented , negative strand RNA viruses that belong to the family Paramyxoviridae [1] . They are highly contagious , spread via the respiratory route , cause profound immune suppression but also elicit lifelong immunity in surviving hosts , and have a propensity to cause large outbreaks associated with high morbidity and mortality in previously unexposed populations . Measles virus ( MV ) is the prototype morbillivirus and remains a significant cause of childhood morbidity and mortality in the developing world . Measles is characterized by fever , skin rash , cough and conjunctivitis , followed by a transient immune suppression [2] . The resulting increased susceptibility to secondary infections can lead to life-threatening complications [3] . In spite of the availability of safe and effective live-attenuated MV vaccines , measles outbreaks continue to occur in the industrialized world due to inadequate vaccination coverage and importations of this highly transmissible virus from endemic regions [4 , 5] . The narrow host range and long incubation period of MV have restricted the characterization of its pathogenesis since patients are not recognized as having measles until onset of rash , and animal studies predominantly rely on experimental infections of non-human primates ( NHPs ) . A surrogate model for MV pathogenesis is infection of ferrets with canine distemper virus ( CDV ) , a morbillivirus that can infect a wide range of carnivores [6 , 7] . However , CDV in carnivores is highly neurotropic and often leads to fatal disease [8–12] , which is in sharp contrast to MV infection of humans and NHPs . Morbilliviruses are amongst the most contagious viruses known and are primarily transmitted by aerosols or respiratory droplets . Once inhaled , virions establish primary infection by receptor-dependent fusion at the plasma membrane [13] . Two cellular receptors involved in morbillivirus infection have been identified: signaling lymphocyte activation molecule family member 1 ( SLAM/F1 , or CD150 ) , expressed by subsets of thymocytes , dendritic cells ( DCs ) , hematopoietic stem cells , macrophages , T- and B-lymphocytes [14] , and nectin cell adhesion molecule 4 ( nectin-4 , previously known as poliovirus receptor-related 4 ) , expressed at the adherens junction complex of epithelial cells [15 , 16] . Both receptors play a crucial role in viral pathogenesis ( reviewed in [17] ) , with CD150-mediated infection being critical for entry and dissemination [18 , 19] and nectin-4-mediated infection critical for virus transmission [20 , 21] . A number of aspects of morbillivirus pathogenesis remain unresolved . Studies in mice and NHPs have shown that MV initially infects alveolar macrophages and DCs in the lungs , instead of epithelial cells of the upper respiratory tract [22–24] . Even though this is a possible entry route , it seems unlikely that a highly contagious virus with an R0 of 12–18 [25] and of which infection with one 50% tissue culture infectious dose ( TCID50 ) is sufficient to cause productive infection in macaques [26] , exclusively depends on infection of target cells in the alveoli . Additional routes of entry into a susceptible host have been postulated , including infection of CD150+ immune cells within the epithelium of the respiratory tract [18 , 27] . As DCs are abundant within this epithelium , and DC-SIGN was previously identified as an attachment receptor for MV [28 , 29] , it is suggested that DCs could play a crucial role in this entry route [30] . Additionally , damage to the upper respiratory tract epithelium by mechanical injury [31] or respiratory co-infections potentially exposes nectin-4 as a cellular entry receptor . Another potential route of entry for morbilliviruses involves CD150+ and/or DC-SIGN+ cells present in the conjunctiva of the eye [32 , 33] . However , direct in vivo evidence from animal models for these alternative entry strategies is lacking . Recombinant ( r ) viruses expressing fluorescent reporter proteins have allowed sensitive assessment of morbillivirus entry and dissemination in vivo [18 , 34 , 35] . Multicolored viruses have been used to demonstrate MV polyploidy [36] , ribonucleoprotein trafficking [37] , superinfection immunity [38] , potential of segmenting the genome [39] and a cross-genomic cooperation modulating phenotype [40] . Recognizing the power of this approach for in vivo competition studies , we simultaneously administered three rCDVs expressing green ( Venus ) , red ( dTom ) or blue ( TagBFP ) fluorescent proteins to ferrets to assess viral entry , intra-host dissemination and inter-host transmission . We show that CDV enters the host efficiently if delivered to the nose or lung , and that infection of the host through conjunctival administration , although less efficient , is also possible . In vivo competition showed that the route of entry had no influence on viral dissemination . However , exclusive single-color virus transmission of the systemically dominant virus to recipient ferrets was observed , suggesting a bottleneck during transmission .
Plasmids containing full-length CDV strain Snyder-Hill ( CDVSH ) antigenomes were modified to encode rCDVs expressing fluorescent reporter proteins from an additional transcription unit ( ATU ) . Three fluorescent reporter proteins ( Venus , dTom and TagBFP ) were selected on basis of spectral discernibility , brightness , photo-stability , lack of oligomerization and potential to be detected by in-house flow cytometer and confocal microscope . These viruses , rCDVSHVenus ( 6 ) , rCDVSHdTom ( 6 ) and rCDVSHTagBFP ( 6 ) were rescued and grown on Vero cells modified to express canine ( c ) CD150 ( Vero-cCD150 ) in which they produced high levels of fluorescence in multinucleated syncytia ( Fig 1A ) . Infection of concanavilin A ( ConA ) -stimulated white blood cells ( WBC ) isolated from CDV-naive ferrets resulted in large numbers of fluorescent cells , as detected by confocal laser scanning microscopy ( CLSM , Fig 1B ) and flow cytometry . Six donor ferrets ( D1 –D6 ) were simultaneously inoculated in three consecutive experiments via the ocular ( Oc ) , intra-nasal ( IN ) and intra-tracheal ( IT ) routes with a low dose of the indicated rCDVSH ( 3 . 3 x 103 TCID50/route ) , depositing viruses in the eyes , nose or lungs . Even though the viruses had been engineered to have identical genome lengths to prevent subtle differences in fitness impacting the outcome of the in vivo experiment , the rCDVs were alternated over the three route combinations to mitigate any unforeseen effect on pathogenesis ( Fig 1C ) . All donor ferrets developed fever with a biphasic pattern , with temperature peaks around 5–7 and 12–14 days post-inoculation ( DPI ) ( Fig 2A ) . The first fever peak corresponded with the onset of lymphopenia: peripheral lymphocyte counts were strongly reduced by the end of the first week after infection , and did not recover during the second week ( Fig 2B ) . All donor ferrets became lethargic and were euthanized 14–16 DPI . Virus could be isolated from WBC of all ferrets from 4 DPI , with peak viremia levels observed around 6–8 DPI ( Fig 2C ) . The decline in viral load in WBC during the second week was likely due to the virtual absence of lymphocytes in peripheral blood , resulting in exhaustion of susceptible CD150-positive cells . Virus was isolated from eye , nose , throat , and rectal swabs , in most ferrets with progressively increasing virus loads during the second week after inoculation ( Fig 2D ) . Expression of fluorescent proteins within WBC throughout the course of the experiment , or in single cell suspensions prepared from lymphoid tissues collected at necropsy , was assessed by flow cytometry ( Fig 3A ) . In one ferret ( D5 ) only one fluorescent protein was detected , in three ferrets ( D1 , D3 , D4 ) a combination of two fluorescent proteins was detected and in two ferrets ( D2 , D6 ) all three inoculated viruses had initiated productive infections as shown by simultaneous detection of red , green and blue fluorescent proteins . Interestingly , in ferrets showing systemic infection with two or three viruses , all fluorescent proteins were already detectable in WBC collected at 6 DPI . This demonstrates that even though viruses were administered via multiple inoculation routes , parallel onset of viremia occurred within one week after inoculation . In all donor ferrets IT inoculation resulted in viremia , confirming that direct inoculation into the lower respiratory tract is a highly efficient entry route for morbilliviruses ( Fig 3A ) . In five out of six donor ferrets , IN inoculation also resulted in viremia , demonstrating that morbilliviruses can also efficiently invade a host when 3300 TCID50 in a low volume ( 25μl/nostril ) was deposited in the upper respiratory tract ( Fig 3A ) . Finally , in two out of six ferrets the morbillivirus directly inoculated into the conjunctival sac behind the lower eyelid ( again using a low volume inoculum ) caused viremia , demonstrating this is a functional route for morbillivirus entry , although apparently less effective than entry through the respiratory route ( Fig 3A ) . Next we focused on the period between onset of viremia and euthanasia of the donor ferrets to assess morbillivirus dissemination . In one ferret ( D1 ) the virus inoculated via the IN route became dominant , while in all other ferrets the virus given by IT inoculation became dominant . Likewise , viruses that predominated in WBC were in most cases also present at the greatest level in single cell suspensions from lymphoid tissues ( Fig 3A ) . Detection of fluorescent protein expression in WBC ( Fig 3A ) showed a good correlation with virus isolation from WBC ( Fig 3B and 3C , left panel ) . Ferrets D1 and D2 illustrate this since these animals represented two extremes of the study . In ferret D1 two viruses were detected systemically ( rCDVSHVenus ( 6 ) and rCDVSHdTom ( 6 ) ) , however one virus ( rCDVSHdTom ( 6 ) ) became dominant . In ferret D2 all three viruses replicated at comparable levels . Virus isolation from WBC in Vero-cCD150 cells resulted in predominantly red plaques for D1 , and in multicolored plaques for D2 . Photomicrographs in the left panels of Fig 3B and 3C show a high resolution CLSM image of a representative well , while the large pie charts show the relative color distribution produced by viruses isolated from WBC collected at 8 DPI . The smaller pie charts show the color distribution of virus isolated from other samples , including swabs of the conjunctivae , nose , throat and rectum collected at 8 DPI ( see also Fig 2D ) , and cerebrospinal fluid ( CSF ) and broncho-alveolar lavage ( BAL ) collected at necropsy . Interestingly , the proportion of green fluorescent virus in ferrets D1 and D2 was higher in BAL than in all other tissues , corresponding to IT inoculation of these two ferrets with rCDVSHVenus ( 6 ) . In ferret D1 , low systemic detection of green fluorescent cells , was contrasted by a relatively high percentage of green fluorescent cells in the tracheo-bronchial lymph node ( TB LN ) which drains the lungs ( Fig 3A ) . At necropsy , CLSM analysis of respiratory tract tissues and lymph nodes of ferret D1 revealed a predominance of red fluorescent cells ( Fig 3B , right panels ) , whereas the same tissues collected from ferret D2 contained a mixture of different colored fluorescent cells ( Fig 3C , right panels ) . Macroscopic fluorescence produced by rCDVSHVenus ( 6 ) and rCDVSHdTom ( 6 ) was imaged both in living ferrets and at necropsy . Macroscopic detection of blue fluorescence was not technically possible using the available apparatus . Both green and red fluorescence were detected in the head of ferret D2 ( Fig 3D , fluorescence was detected mainly at the nose , eyes , mouth and skin ) . Use of the light emitting diode ( LED ) lamp to detect green fluorescence , allowed detection of separate green and red foci of infected cells ( Fig 3D , inset in second panel at ( # ) ) . Macroscopic imaging of tissues during necropsies is illustrated by photos of the tongue , lungs and Peyer’s patches ( PP ) of ferret D2 , again showing a clear separation of green and red fluorescent foci of infection ( Fig 3D , inset in third panel at ( # ) ) . Morbillivirus viremia is mediated by circulation of infected cells rather than cell-free virions . As a consequence , morbilliviruses predominantly disseminate by cell-to-cell spread within the host . It has been hypothesized that superinfection immunity is a significant barrier to dual infections of cells . Unexpectedly , during virus isolation procedures from WBC we not only observed green , blue or red syncytia , but also syncytia that contained two or three fluorescent proteins , pseudo-colored as yellow ( Venus / dTom ) , purple ( dTom / TagBFP ) , cyan ( Venus / TagBFP ) or white ( Venus / dTom / TagBFP ) following image acquisition by CLSM ( Fig 4A ) . Since it may be possible that double- and triple-positive syncytia could have been caused by fusion of single-infected cells , thus not representing true double or triple infections of single cells , we performed direct CLSM of tissues collected during necropsy . This confirmed the presence of double-infected cells in the epithelium of the trachea ( Fig 4B , upper panel , Venus / TagBFP cell shown as cyan [*] and Venus / dTom cell shown as yellow [#] ) and in the spleen ( Fig 4B , lower panel ) . In the spleen , single-infected cells were observed throughout the section in red , green and blue , whereas many double-positive cells were observed in cyan , yellow and purple . Finally , double and triple infections were confirmed by flow cytometry . As an example , WBC obtained from ferret D2 at 8 DPI were gated for positive events for a single reporter protein ( Fig 4C , left panels ) , for which subsequently the other two reporter proteins were plotted ( Fig 4C , right panels ) . This demonstrated the presence of single-infected cells ( lower-left quadrant ) , but double- ( lower-right and upper-left quadrant ) and triple-infected ( upper-right quadrant ) lymphocytes were also commonly detected . Although triple-infected cells were only detected in 2/6 animals , double-infected cells were more common and were detected in 5/6 animals . To confirm these in vivo observations , we performed in vitro competition experiments in canine B-lymphoblastoma ( CLBL-1 ) cells [41] , and confirmed that double infections were also achieved in vitro ( Fig 4D ) . Moreover , in vitro double infections were also observed when the second virus infection was performed six hours after the first infection ( S1 Fig ) . Since airborne transmission of respiratory viruses is assumed to be associated with virus shedding from the upper respiratory tract [42 , 43] , we assessed the distribution of CDV-infected cells in the nasal cavity of ferrets D4 ( Fig 5 ) and D5 ( S2 Fig ) . Since CDVSH is highly neurotropic , we performed immunohistochemical analysis of slides containing a sagittal section of the entire ferret head which permitted assessment of infected cells in both the nasal cavity and brain . CDV-infected cells were mainly detected in the nasal cavity of ferret D4 ( Fig 5A ) , and few positive cells were observed in the meninges . Cells surrounding the nerve twigs of the olfactory nerve were CDV-positive on both sides of the cribriform plate ( dotted line ) , on both the side of the nasal cavity ( # ) and the olfactory bulb ( * ) ( Fig 5B ) . Very few cells within the olfactory epithelium were CDV positive ( Fig 5C ) , whereas the respiratory epithelium contained many CDV-infected cells ( Fig 5D ) , including ciliated epithelial cells and macrophage-like cells ( Fig 5D , macrophages indicated by arrow and shown in inset ) . Throughout the nasal cavity the majority of CDV-positive cells was found in the submucosa and included fibroblasts , large macrophage-like cells , and lymphocyte-like cells . In the nasal-associated lymphoid tissues ( NALT ) the majority of lymphoid cells were CDV positive ( Fig 5E ) . In addition , CDV- infected glands were occasionally observed in the nasal cavity , mainly in the tip of the nose ( Fig 5F ) . CDV-infected squamous epithelial cells were observed in the soft palate ( Fig 5G ) , mimicking similar observations in MV-infected macaques . In ferret D5 , rCDV was detected in the nasal cavity and CNS ( S2A Fig ) . In the nasal cavity , the number of CDV positive cells was lower in comparison to ferret D4 , and consisted predominantly of fibroblasts and macrophage-like cells in the submucosa . In the CNS , CDV-infected cells were frequently observed in the meninges , predominantly in the meninges surrounding the cerebellum . Positive ependymal cells were detected in the choroid plexus , responsible for the production of CSF ( S2B Fig ) , which corresponds to the isolation of virus from the CSF from this ferret . In the cerebellum , meninges surrounding the cerebellum and brainstem , endothelial cells and cells adjacent to blood vessels were occasionally CDV positive ( S2C Fig ) . This suggests hematogenous spread of CDV into the CNS . In both the olfactory bulb and cerebrum foci of CDV positive cells , including neurons and glial cells , were detected ( S2D and S2E Fig ) . Finally , CDV-positive cells were observed within the bone marrow . To assess airborne transmission , CDV-naive recipient ( R1 –R6 ) ferrets were placed in transmission cages at 2 DPI . Donor and recipient ferrets were sampled every other DPI and followed for a maximum of 16 or 22 DPI respectively ( Fig 6A and 6B ) . Ferrets were housed in pairs , meaning that recipient ferret R1 was placed in a cage adjacent to donor ferret D1 . Body temperature was measured in 4 out of 6 recipient ferrets . Fever was not observed in the recipient ferrets tested up to the time point of euthanasia ( Fig 6C ) . However , the majority of recipient ferrets showed decreased lymphocyte counts shortly before euthanasia ( Fig 6D ) . Airborne transmission was confirmed in all donor-recipient pairs and infectious rCDV was isolated from WBC ( Fig 6E ) , nose , throat , eye and/or rectal swabs collected from recipient ferrets ( Fig 6F ) . By using flow cytometry , rCDV replication was detected in WBC of 5 out of 6 ferrets ( Fig 6G ) . For recipient ferret ( R4 ) in which CDV-infected cells were not detected by flow cytometry , rCDVSHdTom ( 6 ) was isolated from throat and eye swabs . In all cases , only a single colored rCDV transmitted to the recipient ferret , which was also found in the lymphoid tissues at euthanasia and always was the virus that predominated in the corresponding donor animal ( Fig 3A ) .
We have performed in vivo competition and transmission studies in ferrets with virologically identical but spectrally distinct rCDVs administered simultaneously via multiple routes . Our aim was to study the temporal and spatial interplay of viruses during the early , intermediate and late stages of CDV infection . These “rainbow CDV” studies are the first to show that morbilliviruses can use multiple entry routes in parallel . Detection of circulating or lymphoid tissue-derived lymphocytes expressing multiple fluorescent reporter proteins was common , demonstrating that in vivo superinfection immunity is not a restrictive phenomenon . Airborne transmission to recipient ferrets was detected in all animal pairs , underpinning the highly infectious nature of morbilliviruses . Animal models of human viral diseases provide the bedrock of much of the current understanding of tropism and pathogenesis , and are essential for the development and licensure of drugs and vaccines . Ideally the human pathogen under study should not need to be adapted to the animal and the disease process should recapitulate the full clinical spectrum in people . This is challenging and oftentimes either the pathogen or host must be genetically altered to produce disease . Therefore , related animal pathogens infecting natural host species provide useful surrogates in the pathogenesis toolkit . For example , Sendai virus infection of mice [44] , bovine respiratory syncytial virus infection of calves [45] and simian immunodeficiency virus infection of NHPs [46] have provided important insights into the pathogenesis of closely related human viruses . Likewise , experimental CDV infections of ferrets have provided important insights into the pathogenesis of MV and other morbilliviruses [47–50] . Both measles [51] and canine distemper [47] are recognized as highly infectious diseases that are spread via the respiratory tract . The availability of rMVs expressing fluorescent proteins allowed us to identify alveolar macrophages and DCs in NHPs as early target cells following aerosol inhalation [24] . In that study , substantial numbers of MV-infected cells were not detected in the upper respiratory tract even though minuscule numbers of macrophages and DCs were detected in the deep lung at early time points after inoculation . This fitted well with our experience of IT inoculation as a highly reliable and standardized route of experimental MV infection [26] . The majority of CDV pathogenesis studies in ferrets has used IN inoculation , delivering the virus to the upper respiratory tract . Although it is important to note that IN inoculation of relatively large volumes can easily result in deposition of virus in both the upper and lower respiratory tracts [52] , we also hypothesized that it was unlikely that morbilliviruses exclusively used the lower respiratory tract as a portal of entry [30] . Therefore , we simultaneously inoculated ferrets with multicolor rCDVs via the IN and IT routes , ensuring delivery to only the upper and lower respiratory tract , respectively . Since the conjunctivae have also been suggested as a portal of entry for many respiratory viruses [33] , including MV [32] , ocular inoculation was used as a third possible route of entry . IN and IT delivery resulted in viremia in 5 out of 6 and 6 out of 6 ferrets , respectively , within a week after inoculation . The fact that the IT-delivered virus became dominant in 5 out of 6 ferrets suggests that this is the most efficient port of entry , confirming previous observations with MV infection of NHPs [26] . This could be explained by the fact that in the lungs the potential target cells ( alveolar macrophages and/or DCs ) are directly accessible and not shielded by an epithelial barrier . However , since virus delivered by IN inoculation in a low volume also resulted in viremia in the majority of the ferrets , this demonstrated that multiple routes of entry can be used in parallel . Inoculation of the virus onto the conjunctivae resulted in infection in only 2 out of 6 ferrets , demonstrating that this is a legitimate , albeit less efficient entry portal . However , since there is a direct connection between the eyes and the upper respiratory tract via the nasolacrimal duct , transport of virus particles to the upper respiratory tract after conjunctival delivery cannot be excluded . Moreover , it is important to note that this study was not designed to assess statistically significant differences between these routes of entry . CDV dissemination is mediated by infected circulating and tissue-resident B- and T-lymphocytes [35] , and results in lymphopenia and fever . During dissemination the infection remains highly cell-associated , and few virions are detected in plasma . Spread is mediated mostly by direct cell-to-cell transmission , e . g . by the formation of virological synapses [34 , 53 , 54] . Here , we show that regardless of the route of entry , all donor ferrets developed a similar course of fatal disease , and severity was independent of the number of circulating rCDVs . Interestingly , significant numbers of lymphocytes co-expressed two or three fluorescent reporter proteins , demonstrating that they were infected with viruses inoculated at different sites . Although it has been shown that morbillivirus infection induces superinfection immunity in vitro [38] , the incredibly rapid dissemination of CDV ( with percentages of infected WBC rising from undetectable to more than 50% within 2 days in some animals ) leads to lymphocytes being double- or triple-infected . We consider serial , direct cell-to-cell transmission of rCDVs from different donor cells to single acceptor cells the most likely explanation of the existence of double or triple infections . In fact , double infection was readily reproduced in vitro by exposing a B-lymphoblastic canine cell line to two rCDVs , and was also reproduced when the second infection was performed six hours after the first . Multi-route delivery of virologically identical but phenotypically distinct viruses is a powerful approach to dissect the complex interplay in evolving pathogenesis . None of the data suggest that the expression of different fluorescent reporter proteins had an effect on virus fitness and we have done our utmost to ensure that rCDVs were genetically identical in terms of genome length and ATU design . However , we cannot exclude the possibility that genes encoding Venus , dTom and TagBFP contain secondary or tertiary RNA structures or immune-activating sequences . Alternating the rCDVs and administration routes in three subsequent experiments further mitigated this risk . Although epidemiological observations have demonstrated that CDV is a highly contagious virus , surprisingly few experimental studies have examined transmission . In 1926 , Dunkin and Laidlaw meticulously described precautions taken to perform experimental CDV infections of ferrets and keep their breeding stock of animals free of canine distemper [47] . Three transmission pathways: direct contact between sick and healthy animals , housing a healthy animal in a cage from which a moribund animal had been removed several hours earlier , and airborne transmission were described [47] . The timespan between onset of disease in the donor and recipient animals was around ten days . Subsequent direct transmission studies documented a period of 6 to 11 days for development of disease in recipient animals [55] and experimental CDV outbreak studies in ferrets support a role for airborne transmission [56] . The observed time period required for CDV transmission in our experiment was in good accordance with these previous reports [47 , 56] . Even though we could not determine the exact tissue origin of transmitted virus due to widespread dissemination of different colored viruses throughout the donor ferrets , the dominant virus of the donor always was the only virus transmitted to the recipient . The added value of our study was that the transmission of a single color rCDV , even when two or three rCDVs were detected in donor ferrets , suggested a bottleneck event during airborne transmission: apparently only one or a few infectious units were transmitted from donor to recipient ferret . This mirrors influenza airborne transmission between ferrets , in which virus populations in recipient ferrets proved to be genetically much more homogeneous than those of the donor ferrets [43 , 57 , 58] . However , in this model transmission was restricted to airborne transmission , so the situation could be different for indirect and direct contact transmission . Overall , this illustrates the utility of gaining a comprehensive understanding of transmission in this important animal model , which is also being developed for Ebola virus and Nipah virus [59–61] . Here , we have performed in vivo competition and transmission studies with rCDVs discernable on basis of their fluorescent reporter proteins . We believe that this model will prove its use in the future for in vivo competition studies to identify factors associated with viral fitness .
Animal experiments were conducted at Erasmus MC , in strict compliance with European guidelines ( EU directive on animal testing 2010/63/EU ) and Dutch legislation . The study protocol was approved by Stichting Dier Experimenten Commissie Consult ( DEC Consult , permit number EMC3043 ) , a Dutch independent animal experimentation ethics review board . The manuscript was prepared in accordance with the ARRIVE guidelines [62] . CDV-naive ferrets were housed in groups prior to rCDV infection , received standard feed on a daily basis and had access to water ad libitum . Cages contained several sources of environmental enrichment . During the infection and transmission studies , ferrets were housed in transmission cages [63] . Briefly , ferrets were housed individually in perspex cages , with paired donor ( D ) and recipient ( R ) ferrets being separated by two stainless steel grids . The bottom of the cages was covered with carpet and cages were not cleaned during the course of the experiment to prevent aerosolizing bodily excretions . Transmission cages were placed in HEPA-filtered , negatively pressurized biosafety level 3 ( BSL-3 ) isolators . Animal welfare was checked on daily basis , and all animal handling was performed under light anesthesia using ketamine and medetomidine . After handling , atipamezole was administered to antagonize the effect of medetomidine . Vero cells stably expressing the CDV receptor canine SLAM ( Vero-cCD150 ) ( kind gift of Dr . Y . Yanagi , Kyushu University , Fukuoka , Japan ) were cultured as described previously [64] . To obtain primary ferret white blood cells ( WBC ) , small-volume blood samples were collected from CDV-naive ferrets in Vacuette tubes ( Greiner ) containing K3EDTA as an anticoagulant . Red blood cells in blood were subsequently lysed with red blood cell lysis buffer ( Roche , Basel , Switzerland ) , washed and resuspended in complete RPMI 1640 medium ( Gibco Invitrogen , Carlsbad , CA , USA ) supplemented with 2 mM L-glutamine , 10% ( V/V ) heat-inactivated fetal bovine serum ( FBS ) , penicillin ( 100 U/ml ) and streptomycin ( 100 μg/ml ) . The canine B-cell lymphoma cell line CLBL-1 [41] ( kind gift of Dr . Barbara Rütgen , University of Veterinary Medicine , Vienna , Austria ) was grown in complete RPMI-1640 medium supplemented with 10% ( V/V ) FBS . Recombinant CDV strain Snyder-Hill ( SH ) viruses were generated as described previously [10] . Genes encoding the fluorescent reporter proteins Venus , dTom or TagBFP were added as an ATU at the sixth ( 6 ) position of the genome ( between H and L ) ( Fig 1A ) . Importantly , TagBFP and dTom were modified by the addition of six ( GGSGSG ) and five ( GSGSG ) amino acids , respectively , to the carboxyl terminus to make them identical in size to Venus ( 239 amino acids ) . This ensured that the viral genome lengths were identical from the perspective of replication and transcription by the RNA-dependent RNA polymerase . The three viruses were designated rCDVSHVenus ( 6 ) , rCDVSHdTom ( 6 ) and rCDVSHTagBFP ( 6 ) . Fluorescence produced in cells infected with these viruses can be readily discerned by flow cytometry , UV epifluorescence microscopy and CLSM . Virus stocks were grown in Vero-cCD150 cells ( Fig 1A ) and tested negative for contamination with Mycoplasma species . Virus titers were determined by endpoint titration in Vero-cCD150 cells and expressed in TCID50/ml . Susceptibility of ferret WBC with the reporter viruses was determined in vitro . ConA stimulated ferret WBC were inoculated in quadruplicate with rCDVSHVenus ( 6 ) , rCDVSHdTom ( 6 ) or rCDVSHTagBFP ( 6 ) at a multiplicity of infection ( MOI ) of 3 for 1 hour , washed and subsequently cultured for 48 hours . Susceptibility of ferret WBC was analyzed directly by detection of fluorescent reporter proteins by CLSM with a LSM700 system fitted on an Axio Observer Z1 inverted microscope ( Zeiss ) ( Fig 1B ) and by flow cytometry on a FACS Canto II ( BD Biosciences ) . CLBL-1 cells were seeded in 96-well V-bottom plates ( Greiner ) at 2x105 cells per well . After centrifugation ( 5 minutes , 350g ) supernatants were removed , and cells were resuspended in 150μl culture medium or ( combinations of ) rCDV ( s ) diluted to 5x104 TCID50 per well in the absence or presence of 10μg/ml of infection-enhancing lipopeptide Pam3CSK4 [65] . After 1 hour at 37°C , the plate was centrifuged , the medium was discarded , and the cells were resuspended in 100μl culture medium ( without lipopeptide ) and cultured for 23 hours in 96-well flat bottom plates . Infection percentages were determined by flow cytometry ( Fig 4D ) . In a second experiment , CLBL-1 cells were seeded in 96-well V-bottom plates ( Greiner ) at 1 . 2x105 cells per well . After centrifugation ( 5 minutes , 350g ) supernatants were removed , and cells were resuspended in 100μl culture medium or ( combinations of ) rCDV ( s ) diluted to 1 . 2x105 TCID50 per well in the absence or presence of 10μg/ml of infection-enhancing lipopeptide PHCSK4 [65] . After 1 hour at 37°C , the plate was centrifuged , the medium was discarded , and the cells were resuspended in 100μl culture medium ( without lipopeptide ) . Five hours later ( i . e . six hours after infection 1 ) , cells were centrifuged again and a second infection was performed in the presence or absence of PHCSK4 . After 1 hour at 37°C , the plate was centrifuged , the medium was discarded , and the cells were resuspended in 100μl culture medium ( without lipopeptide ) , and cultured for 23 hours in 96-wells flat bottom plates . Infection percentages were determined by flow cytometry ( S1 Fig ) . Twelve CDV-seronegative ferrets ( Mustela putorius furo ) were used for the rCDV infection and transmission studies . A temperature probe was implanted intraperitoneally 2 weeks before the beginning of the experiments to monitor body temperature noninvasively . Six donor ferrets ( randomly selected , D1-6 ) were inoculated with 104 TCID50 rCDV , divided in three equal parts of rCDVSHVenus ( 6 ) : rCDVSHdTom ( 6 ) : rCDVSHTagBFP ( 6 ) ( each 3 . 3 x 103 TCID50 ) . Each reporter virus was administered via a different route , which were alternated over three experiments . Ferrets D1 and D2 received rCDVSHTagBFP ( 6 ) ocularly ( Oc ) , rCDVSHdTom ( 6 ) intra-nasally ( IN ) and rCDVSHVenus ( 6 ) intra-tracheally ( IT ) ; ferrets D3 and D4 received rCDVSHVenus ( 6 ) Oc , rCDVSHTagBFP ( 6 ) IN and rCDVSHdTom ( 6 ) IT; ferrets D5 and D6 received rCDVSHdTom ( 6 ) Oc , rCDVSHVenus ( 6 ) IN and rCDVSHTagBFP ( 6 ) IT ( Fig 1C ) . Oc administration was performed by pipetting virus suspension ( 50μl ) directly onto each of the conjunctivae , IN inoculation by pipetting virus suspension ( 50μl ) into the nostrils while the ferret was held on its back to prevent spread to the trachea and IT inoculations by direct instillation of virus suspension ( 1ml ) into the lower respiratory tract after intubation with a flexible catheter . Following inoculation , the six donor ferrets were placed individually in purpose built cages specifically designed to allow airborne transmission over a 10 cm divide . Donor ferrets were sampled every other day and were euthanized at 14–16 DPI . Six CDV-seronegative recipient ferrets ( R1-6 ) were placed in the transmission cages ( Fig 6A ) at 2 DPI of the donor ferret . Recipient ferrets were sampled every other day and were euthanized at 21/22 DPI ( Fig 6B ) . The animal protocol specified that recipient animals had to be euthanized no later than 22 days after inoculation of the donor animals , which made it impossible to assess the full spectrum of disease in the recipient ferrets . Small-volume blood samples were collected in Vacuette tubes ( Greiner Bio-One , Kremsmünster , Austria ) containing K3EDTA as an anticoagulant every other DPI of donor ferrets , or every other day after placement of recipient ferrets in transmission cages . Recipient ferrets were always sampled first to prevent direct contamination of recipient animals by sampling of the donor animals . Total WBC and lymphocyte counts were obtained using an automated counter ( pocH-100iV; Sysmex ) . WBC were obtained by lysis of whole blood with red blood cell lysis buffer ( Roche , Basel , Switzerland ) , washed and resuspended in complete RPMI 1640 medium as described above . Cells were counted using a hemocytometer and used directly for flow cytometry and virus isolation . The percentages of WBC infected by different reporter viruses were determined by detection of fluorescent reporter proteins by flow cytometry . Isolation of rCDV was performed on Vero-cCD150 cells using an infectious center test as previously described [66] . Virus isolations were monitored for cytopathic effect ( CPE ) by microscopy after co-cultivation with Vero-cCD150 cells for 3 to 6 days and results were expressed as the number of virus-infected cells/106 total cells . Relative contribution of the different reporter viruses to the number of virus-infected cells was determined by screening virus isolations on Vero-cCD150 cells for Venus , dTom and TagBFP expression by CLSM . Throat and rectal swabs ( cytobrush plus; Medscand Medical ) and nose and eye swabs ( polyester-tipped minitip urethral swab; Copan ) were collected every other DPI from donor ferrets , or every other day after placement from recipient ferrets , in transport medium ( Eagle's minimal essential medium [EMEM] with Hanks' salts , supplemented with lactalbumin enzymatic hydrolysate , penicillin , streptomycin , polymyxin B sulfate , nystatin , gentamicin , and glycerol ) and frozen at −80°C . After being thawed , samples were vortexed , the swab was removed , and the remaining transport medium was used for virus isolation . Isolation of rCDV was performed on Vero-cCD150 cells using an infectious center test as previously described [66] . Virus isolations were monitored for CPE by microscopy after co-cultivation with Vero-cCD150 cells for 3 to 7 days and results were expressed as TCID50/ml . Relative contribution of the different reporter viruses to the number of virus-infected cells was determined by separately screening virus isolations for Venus , dTom and TagBFP by CLSM . Ferrets were euthanized by exsanguination under deep ketamine/medetomidine anesthesia . Macroscopic detection of Venus and dTom was performed with an LED lamp and the appropriate filters as described previously [10 , 18] . Post-euthanasia , CSF was obtained by lumbar puncture . Virus isolation from CSF was performed by direct titration on Vero-cCD150 cells . A broncho-alveolar lavage ( BAL ) was performed postmortem by direct infusion of phosphate-buffered saline ( PBS; 5 ml ) into the right-hand side of the lung . BAL cells were resuspended in culture medium with supplements as described above , counted , and used directly for flow cytometry and virus isolation . The infection percentages of BAL cells were determined by detection of fluorescent reporter proteins by flow cytometry . Virus isolation was performed on Vero-cCD150 cells as previously described for MV on Vero cells expressing human CD150 [18] . During necropsy , multiple tissues including brain , trachea , primary bronchus , lungs and spleen were harvested and screened directly for expression of fluorescent reporter proteins by CLSM . The left lung was inflated with 2% ( W/V ) low-melting-point agarose before being screened , as described previously [24 , 67] . After screening , non-lymphoid tissues were transferred to 10% neutral-buffered formalin ( FA ) . From two ferrets ( D4 and D5 ) , the complete head was stored in 10% neutral-buffered FA for immunohistochemistry . Lymphoid tissues were collected in PBS for preparation of single-cell suspensions using cell strainers with a 100 μm pore size ( BD Biosciences , Erembodegem , Belgium ) and directly used for flow cytometry . The infection percentages of single cell suspensions by different reporter viruses were determined by flow cytometry . After fixation in 10% formalin , ferret heads were decalcified in 10% EDTA ( pH 7 . 4 ) for at least a month . After decalcification heads were embedded in paraffin . CDVSH was detected using a monoclonal antibody ( VMRD Inc . , Pullman , WA , USA ) . Briefly , 3μm paraffin sections were deparaffinized and antigens were retrieved by boiling slides for 15 minutes in citric acid buffer ( 10mM , pH 6 . 0 ) . Sections were incubated with the anti-CDV antibody for 1 hour at RT . Binding of the primary antibody was detected using a biotinylated rabbit-anti-mouse Ig ( DAKO ) , after which tissue sections were incubated with ABComplex-HRP ( DAKO ) for 30 minutes . Peroxidase was revealed using 3-Amino-9-ethyl-carbazole ( AEC , Sigma ) resulting in a bright red precipitate . In each staining procedure an isotype control was included as a negative control . | Canine distemper virus ( CDV ) infection of ferrets is a tractable animal model for measles . Ferrets are highly susceptible to CDV , and inoculation with a low dose leads to lethal disease . We performed in vivo competition experiments to study virus entry , dissemination and transmission . Ferrets were simultaneously inoculated with CDV via the conjunctival , intra-nasal and intra-tracheal routes . The viruses were identical except for the fluorescent reporter protein encoded by the viral genome . By detecting cells expressing the different fluorescent reporter proteins at various sites in the host , we determined that CDV can enter the host in parallel at multiple sites . Virus spread in the ferret occurred via infected lymphocytes , which often turned out to be double- or triple-infected . Sentinel ferrets , placed in physically separated adjacent cages , became infected by airborne transmission . Transmission of the dominant single color despite replication of multicolor viruses in the upper respiratory tract suggested a bottleneck event . | [
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] | 2017 | Delineating morbillivirus entry, dissemination and airborne transmission by studying in vivo competition of multicolor canine distemper viruses in ferrets |
The rarity of beneficial mutations has frustrated efforts to develop a quantitative theory of adaptation . Recent models of adaptive walks , the sequential substitution of beneficial mutations by selection , make two compelling predictions: adaptive walks should be short , and fitness increases should become exponentially smaller as successive mutations fix . We estimated the number and fitness effects of beneficial mutations in each of 118 replicate lineages of Aspergillus nidulans evolving for approximately 800 generations at two population sizes using a novel maximum likelihood framework , the results of which were confirmed experimentally using sexual crosses . We find that adaptive walks do indeed tend to be short , and fitness increases become smaller as successive mutations fix . Moreover , we show that these patterns are associated with a decreasing supply of beneficial mutations as the population adapts . We also provide empirical distributions of fitness effects among mutations fixed at each step . Our results provide a first glimpse into the properties of multiple steps in an adaptive walk in asexual populations and lend empirical support to models of adaptation involving selection towards a single optimum phenotype . In practical terms , our results suggest that the bulk of adaptation is likely to be accomplished within the first few steps .
The rate and extent of adaptive evolution over long time periods depends ultimately on the sequential substitution of beneficial mutations by natural selection , a process termed an adaptive walk . Although recent work with microbial populations has shed light on the properties of a single bout of adaptation [1]–[7] , no empirical data exist on the properties of multiple steps in an adaptive walk . Here , we present what is , to the best of our knowledge , the first comprehensive empirical results on the distribution of fitness effects among beneficial mutations for multiple steps of an adaptive walk and confront these with theoretical predictions concerning the number and fitness effect of mutations involved in adaptation . The dominant view championed since the 1930s by Fisher through his geometric model of adaptation has been that adaptive walks are driven by the substitution of many mutations with exceedingly small fitness effects [8] . More recent theoretical work gives a substantially different picture . Adaptation can be modeled as a sequence of moves or steps in either phenotype [9]–[12] or DNA sequence [8] , [13]–[17] space , via mutation , that increase fitness relative to the current wild type . Adaptation stops once a population becomes fixed for a genotype where all neighboring genotypes accessible by mutation have lower fitness . Provided the wild type is fairly well adapted to current conditions , beneficial mutations in both models can be viewed as draws from the right tail of a distribution of fitness effects [12] , and extreme value theory ( EVT ) can be used to make predictions about the distribution of fitness effects among beneficial mutations available to and fixed by selection [18]–[21] . Note that although these models allow for genetic drift through the stochastic loss of mutations when rare , they assume sufficiently strong selection that deleterious mutations are always effectively removed and “weak” mutation , meaning that beneficial mutations occur sufficiently rarely that clonal interference , the competition that results from beneficial mutants arising in different lineages that have escaped drift but have yet to fix , can be neglected . These so-called “strong selection–weak mutation” assumptions are no longer strictly appropriate when the mutation supply rate , which is the product of population size ( N ) and mutation rate ( μ ) , is high , because clonal interference becomes increasingly important [4] , [22]–[24] . Nevertheless , these models provide a useful starting point for thinking about the properties of adaptive walks . With these caveats in mind , models of adaptation make three compelling predictions about the nature of the distributions of mutational effects over the course of an adaptive walk . First , the combined effects of genetic drift , which tends to eliminate small-effect mutations , and clonal interference transforms the typically L-shaped distribution of fitness effects available to selection into a distribution of fitness effects among fixed mutations that is unimodal with a substantially higher mean [9] . Second , the pattern of fitness gains over the course of an adaptive walk depends on the distribution of fitness effects among available mutations . If this distribution is exponential ( Gumbel domain of attraction in EVT ) , or nearly so , the mean fitness effect of each successive mutation fixed will be identical throughout an adaptive walk . Alternatively , if the distribution is truncated on the right ( the Weibull domain in EVT ) , for example as predicted in an adaptive landscape—which describes how fitness changes as a function of either genotype or phenotype—involving selection towards a single phenotypic optimum [21] , adaptation is characterized by a pattern of decreasing fitness gains at each step [17] . Third , adaptive walks tend to be short , relative to the total number of beneficial mutations available . Precise predictions of the number of steps taken are difficult to make because they depend on the ruggedness of the adaptive landscape and the stringency of selection [15] . A lower bound on the expected number of mutations substituted on rugged landscapes ( technically , a random landscape with no autocorrelation ) when the fittest beneficial mutation available always fixes has been suggested to be 1 . 7 mutations on average [14] , [25] . This number is expected to be only modestly higher when landscapes are smoother and selection is less stringent , such as when drift can lead to the loss of even large-effect mutations on occasion; simulations suggest the number of substitutions under these conditions is on the order of three to five [26] . A comprehensive empirical test of these predictions requires following the fitness trajectories of a large number of replicate evolving lineages over many generations and estimating the number and effect size of the mutations fixed along the way . What experimental data exist focus exclusively on the number of mutations fixed , but not their effect size during adaptive walks in a limited number ( usually one to three ) of replicate lineages [27]–[32] . We developed a novel maximum likelihood ( ML ) framework ( see Box 1 and Text S1 ) to infer the number and size of adaptive mutations that occurred during adaptation over 800 generations in 118 replicate lineages founded by a single genotype of the filamentous fungus Aspergillus nidulans . This species forms spatially structured colonies on solid surfaces , making it relatively easy to detect beneficial mutations ( Figure 1 ) . We founded all lineages from a single ancestral genotype and propagated replicate lineages by serial transfer every 5 d ( which corresponds to ∼80 generations ) to fresh medium , bottlenecking half the lineages to approximately 50 , 000 nuclei and the other half to approximately 500 nuclei at each transfer . The ancestral genotype contains a mutation conferring resistance to the fungicide fludioxonil that is known to be costly under the growth conditions used here , and previous work has shown that a variety of genetic routes to adaptation are available to this genotype [33] , [34] . The results of the ML procedure were validated experimentally by estimating the number of segregating loci in the F1 progeny of sexual crosses between selected evolved lineages and the ancestor using the Castle-Wright estimator [30] , [35] .
The fitness trajectories of all 118 lineages are shown in Figure 2 . Mean fitness through time takes the form of a diminishing returns curve in both treatments , with the large bottleneck lineages reaching a higher mean fitness ( ±standard deviation [s . d . ]; 1 . 48±0 . 23 ) on average than the small bottleneck lineages ( 1 . 38±0 . 22 ) by the end of the experiment ( t110 = 2 . 10 , p = 0 . 038 ) . The variance in fitness among lineages follows a similar diminishing returns pattern but accumulates faster in the small bottleneck treatment ( interaction term between time , t , and bottleneck size from an analysis of covariance for t>0: F1 , 10 = 4 . 97 , p = 0 . 05 ) , reaching comparable levels in both treatments by the end of the experiment ( F54 , 60 = 1 . 09 , p = 0 . 73 ) . These results lend support to the idea that natural selection in populations of different size tends to lead to different effective fitness plateaus [36] and that drift is exaggerated in small populations , the effect of which is to cause more rapid fitness divergence among lineages in the early stages of adaptive evolution [37] . Occasional fitness decreases occurred in both treatments although these were modest in size relative to the initial fitness increase at the first transfer and not sustained over multiple transfers . This variation is likely due to uncontrollable microenvironmental variation in environmental conditions arising during the selection experiment . Alternative explanations include frequency-dependent selection or drift causing a transient increase in the frequency of mildly deleterious mutations . Frequency-dependent selection is a priori unlikely because spatial structure , which facilitates frequency dependent selection by allowing persistent interactions among genotypes [38] , [39] , was destroyed at each transfer by washing an entire colony off the plate and reinoculating from a well-mixed culture . Furthermore , we did not observe sustained coexistence of distinct colony morphotypes within a population , as would be expected if frequency-dependent selection acted to maintain diversity through mechanisms such as cross-feeding or allelopathy . We can also exclude drift as an explanation , as decreases in fitness between consecutive time points were no more common in the small bottleneck treatment than in the large ( small = 13 , large = 15; based on pairwise t-tests for all lineages between times t and t+1 using a false discovery rate criterion of α = 0 . 3 to determine significance ) , as would be expected if mildly deleterious mutations had a higher probability of increasing in frequency following each transfer . In previous work , the fungicide-sensitive strain from which the founder of our experiments was derived showed little evidence of adaptation to the same growth conditions used here , suggesting that the sensitive strain resides close to a fitness optimum [40] . Notably , a substantial number of lineages in our experiment have evolved fitness values that remain stable at levels either above or below that of the sensitive ancestor for much of the experiment ( Figure S1 ) , implying that these lineages have reached distinct fitness optima . Instead of relying on the fitting of fitness trajectories by step functions [28] , [31] , [33] , [41] , we developed a rigorous ML method to infer both the number and fitness effect of beneficial mutations segregating at high frequencies in all 118 evolving lineages ( Box 1; Text S1 ) . The fitness trajectory of each lineage was used to fit models sequentially assuming 1 , 2 , … , n clones with fitness values r1 , r2 , … , rn substituting in the population . Comparing the fit of the models to the data allowed us to estimate how many beneficial mutations arose in each lineage and the fitness effects associated with each new mutation . This procedure assumes an exponential model of population growth , and we provide experimental evidence to support this assumption in Figure S2 and Text S2 . Independent measures of the number of segregating loci in several evolved lineages derived from experimental crosses [42] ( Materials and Methods ) with the ancestor independently confirmed the results of the ML analysis ( see Table S1 and Text S3 ) . Note that the model does not assume any nesting relationship between clones , meaning that clone i+1 does not necessarily arise in the genetic background of clone i . Figure 3 depicts the distribution of fitness effects among mutations fixed at each step in our experiment . Theory has not been explicit about how the shape of the distribution among fixed mutations is expected to change over the course of an adaptive walk ( but see [11] ) , except to say that the combined effects of drift and clonal competition transform the typically L-shaped distribution of fitness effects among newly arisen mutations into a bell-shaped distribution of fixed fitness effects [4] , [9] , [19] , [43] . Our results shed some empirical light on this issue . All distributions from the large bottleneck treatment are unimodal and positively skewed , as observed previously for a single step [4] , [6] , [44] , however , the shape of the distributions from the small bottleneck treatment are more variable . In particular , the first step differs markedly between treatments ( compare the left-most panels of Figure 3A and 3B; permutation test following reference [45]; n = 100 , 000 permutations comparing the absolute value of the difference in coefficients of variation , d , between two distributions: d = 0 . 387 , p<0 . 0001 ) , the small bottleneck treatment appearing more L-shaped than in the large bottleneck treatment . This result suggests that weaker clonal competition arising from smaller population sizes leads to the fixation of more small-effect mutations and the fixation of mutations with a wider range of effects . Interestingly , the differences between treatments at steps 2 and 3 are not significant ( step 2: d = 0 . 059 , p = 0 . 250; step 3: d = 0 . 008 , p = 0 . 460 ) , implying that clonal competition becomes less important in shaping the distributions of fixed effects as the population adapts . Further support for this interpretation comes from the fact that significant differences were observed between the first and second steps in the large bottleneck treatment ( d = 0 . 353 , p<0 . 001 ) , but not between the second and third steps ( d = 0 . 149 , p = 0 . 098 ) , nor between any of the steps in the small bottleneck treatment ( step 1 vs . 2: d = 0 . 026 , p = 0 . 380; step 2 vs . 3: d = 0 . 200 , p = 0 . 058 ) . Inspection of Figure 3A suggests that this is due to the fixation of smaller-effect mutations in the second step compared to the first step of the large bottleneck treatment , as would be expected if clonal competition becomes weaker as fitness increases . Figure 4A shows that adaptive walks tend to be short , with a mean of 2 . 20 steps for the entire experiment . Large bottleneck lineages fixed more mutations on average ( ±s . d . ; 2 . 39±0 . 53 ) than small bottleneck lineages ( 2 . 00±0 . 74; Wilcoxon rank sum test: Z = 3 . 15 , p = 0 . 0016 , nlarge = 58 , nsmall = 60 ) , consistent with the higher probability of losing beneficial mutations by drift in smaller populations . Mutations restoring sensitivity to fludioxonil were rare in our experiment , being observed in just five of the 118 lineages . In two lineages from the small bottleneck treatment , sensitivity was restored by a single mutation . The remaining three fludioxonil-sensitive lineages ( one from the large and two from the small bottleneck treatments ) substituted multiple mutations that resulted in restored sensitivity . Removing these lineages from the analysis does not change our results . Thus , the short walks we observed are not merely due to the predominance of back mutations of the resistance mutation ( fldA1 ) . Moreover , we were able to detect significant genetic ( VG ) and genotype-by-environment interaction ( VGE ) variation among evolved lineages in both bottleneck treatments ( large: VG: F57 , 340 = 5 . 16 , p<0 . 0001 , VGE: F57 , 340 = 9 . 33 , p<0 . 0001; small: VG: F59 , 342 = 5 . 53 , p<0 . 0001 , VGE: F59 , 342 = 4 . 80 , p<0 . 0001 ) when grown across a concentration gradient of fludioxonil ( Materials and Methods ) . Thus , although we do not know in detail the identity of the molecular changes responsible for fitness increases , we can be confident that they were achieved through a variety of genetic routes . The marginal increase in fitness becomes smaller with each successive mutation fixed , the largest steps taken first , followed by consecutively smaller steps ( Figure 4B ) . This relationship is highly significant and independent of bottleneck size ( Table 1 ) . Formally , we cannot reject an exponential model as an adequate description of our data ( exponent ± 95% confidence interval: −0 . 309±0 . 170 ) , although we cannot reject a linear model either ( slope ± 95% confidence interval: −0 . 038±0 . 028 ) . Inspection of the variance explained by each model ( adjusted R2 in Table 1 ) indicates that the exponential model provides a modestly better fit to the data; however , no stronger inference can be made due to the lack of statistical power associated with observing just three steps . Such a pattern of diminishing fitness effects is consistent with the distribution of fitness effects among beneficial mutations available to selection being right-truncated , that is , it derives from the Weibull domain of attraction of the generalized Pareto distribution [17] . Alternatively , this pattern could arise if large fitness increases are due to the substitution of multiple mutations arising in the same genome and small fitness increases are due to single mutations [46] , [47] . Two lines of evidence argue against this hypothesis . First , reducing the supply rate of beneficial mutations should lead to a shallower relationship between fitness increase and number of mutations fixed; however , this was clearly not the case: the relationship between fitness increase and number of mutations fixed in the small bottleneck treatment was negative and indistinguishable from the large bottleneck treatment , despite a mutational supply rate at least two orders of magnitude less . Second , the ML procedure did not consistently underestimate the number of mutations segregating in crosses between evolved lineages and the founder , as would be expected if multiple mutations often hitchhiked together on the same genetic background ( see model selection in Text S1and S3 , and Table S1 ) . The observed short adaptive walks and the negative relationship between the number and size of steps taken are expected if the supply of beneficial mutations declines as a population adapts [10] , [13] . We tested this idea directly by characterizing the relationship between the fraction of beneficial mutations available to selection and mean fitness of the genotype from which these mutations were derived . We collected mutants from the original genotype used to start the experiment and from genotypes taken from multiple time points over the course of an adaptive walk in two evolved lineages that differed in the number of mutations fixed and final mean fitness ( see Materials and Methods ) . Multiple beneficial mutations often arise independently as sectors in different locales during a single growth cycle ( Figure 1B ) . Thus , sampling a colony from predetermined positions at the same radius on a plate and assaying fitness of these colony isolates allows us to estimate the distribution of fitness effects among mutations that arise and escape initial stochastic loss but have yet to fix . A representative distribution of fitness effects among 210 mutants collected from the founding genotype is shown in Figure 5A . The distribution is modal near zero with a substantially smaller mean ( ±s . d . ) among beneficial mutations ( 0 . 13±0 . 076 ) than for the first mutation fixed in both treatments ( large bottleneck: 0 . 21±0 . 085; t-test assuming unequal variance , t105 . 2 = 6 . 23 , p<0 . 0001; small bottleneck: 0 . 20±0 . 16 , t73 . 8 = 3 . 28 , p = 0 . 0016 ) and a long right tail representing putatively accessible beneficial mutations . More striking is the relationship between mean fitness of the evolving population and the fraction of beneficial mutations available to selection , which is negative ( Figure 5B ) even after accounting for multiple comparisons ( Materials and Methods and Figure S3 ) . This result implies that the supply of beneficial mutations is depleted as a population adapts in both lineages .
We have experimentally studied properties of adaptive evolution in mutation-limited populations by measuring fitness through evolutionary time . Previous work has provided insight into the properties of the first step of an adaptive walk , from the distribution of mutations prior to selection to those fixed [2] , [4] , [7] , [9] , [44] . Here , we have focused on the properties of multiple steps in an adaptive walk by monitoring 118 replicate evolving populations and inferring the number and the fitness effects of new mutations in each lineage . Our main findings can be summarized as follows . First , the distribution of fitness effects among mutations fixed by selection remains approximately bell-shaped at each step in an adaptive walk ( Figure 3 ) . Second , the adaptive walks we have studied ( specifically , those involved in the recovery of fitness due to a costly resistance mutation ) tend to be short , ranging between one and three steps ( Figure 4 ) . Third , both the supply of beneficial mutations and the fitness gains associated with each step of an adaptive walk decline as mean fitness increases ( Figure 5 ) . Taken together , these results imply that the gradualist view of evolution is incorrect; rather , the bulk of adaptation in mutation-limited populations is likely to be achieved by the first few mutational steps . Interestingly , our conclusions are not much altered by the bottleneck size used between serial transfers . This result is attributable to the opposing effects of drift and clonal competition in small and large populations . In small populations , only mutations of moderate to large effect are likely to escape stochastic loss when rare , and those that do are nearly guaranteed to fix due to the absence of clonal competition . In large populations , more mutations of small effect will reach appreciable frequencies but are eventually lost due to clonal competition . The result is that the mean fitness effect of mutations that fix under different population sizes remains approximately the same at each step throughout the course of an adaptive walk . By contrast , population size does affect the mean fitness among lineages achieved by the end of the experiment , with small populations having lower fitness than large populations by the end of the experiment . This observation is consistent with our finding of fewer fixed mutations in small compared to large populations , which likely stems from changes in the mutation supply rate , Nμ ( where N is population size and μ mutation rate ) . Importantly , even in our small bottleneck treatment , N is unlikely to be so low as to prevent sampling of a large number of single-step mutations , as required by theory [14] , [41] . As with most selection experiments , we have little direct information about the topography of the adaptive landscape; however , two results suggest that it may be fairly rugged . First , fitness tends to plateau at different levels in most lineages by about generation 500 , and these fitness differences are maintained throughout the experiment [48] . Second , the brevity of the adaptive walks observed here is consistent with that expected under a rugged landscape; smoother landscapes containing fewer , but higher , fitness peaks lead to longer adaptive walks [15] . The shortness of the adaptive walks is a striking feature of our results that stands in contrast to modestly longer walks of three to six steps documented in bacteria [28] , [31] , yeast [27] , [30] , a virus [32] , and a single example of an extremely long walk of 14 steps in a virus [29] . Our result is likely to be robust because it is based on statistical inferences that have been independently validated through experiment and that come from the analysis of between one and two orders of magnitude more populations than previous experiments . Nevertheless , the generality of our results may be questioned on two grounds . First , adaptive walks may appear short if selection is negatively frequency-dependent , as can often arise in spatially structured environments [39] , [49] , [50] . As we have pointed out , this explanation seems unlikely because our transfer procedure destroyed any spatial structure that may have arisen during colony expansion , and there were no obvious indications that distinct morphotypes coexisted for multiple transfers . Thus , although we cannot exclude the possibility that frequency-dependent selection may have gone undetected in some lineages , we have no compelling reason to suspect that it is widespread in our experiment . Second , it may be argued that short adaptive walks are expected a priori because we are studying compensatory evolution , that is , the recovery of fitness due to the presence of a costly resistance mutation [51] , [52] . However , there seems little reason to believe that compensatory evolution should differ qualitatively from more “open-ended” instances of adaptive evolution . All adaptive evolution is compensatory to a degree , in the sense that wild-type fitness declines initially either due to the fixation of a costly mutation , as in classic compensatory evolution , or due to a change in the environment . Indeed , this view is central to mutational landscape models [8] , [27] that see adaptation being initiated by a change in environment that causes a drop in the rank of wild-type fitness: the lower the fitness rank of the wild type , the larger the variety of mutational routes to adaptation available . Although our experiment did not test this prediction directly , we do know that a wide range of mutational routes to adaptation were taken by our evolved lineages , as evidenced by the observation of very few back mutations restoring sensitivity to fungicide ( just five out of 118 lineages ) and substantial genotype-by-environment interaction for fitness among all evolved populations . Furthermore , several evolved populations achieved a significantly higher fitness by the end of the experiment than the fungicide-sensitive ancestral strain ( see , for example , Figure S1 ) , suggesting that compensatory evolution need not constrain the founding population to exploring a single fitness peak . We thus expect our results to apply with equal force to any situation where the drop in fitness of our founding strain is of comparable magnitude . Why did we observe shorter adaptive walks than previous experiments ? One possibility is that our founding strain was already well adapted to the conditions of growth and so had only a few beneficial mutations available to it . This explanation is difficult to reconcile , however , with the observation of substantial genotype-by-environment interaction mentioned above , the apparently large fraction of beneficial mutations available to the ancestor ( Figure 5A ) and a gain in mean fitness of approximately 48% in the large bottleneck treatment . Notably , such a fitness increase is comparable in magnitude to that observed in experiments with yeast [27] , [30] and exceeds that observed in much longer-duration experiments with bacteria [28] , [31] . ( Note that low ancestral fitness is likely the explanation for the long walk observed in the viral experiment [29] , where the fitness of the evolved lineages increased by approximately 364% over the ancestor ) . A second possibility is that we did not run our experiment long enough , and adaptive walks monitored here did not yet reach a peak . However , the observation that the vast majority of populations have reached a fitness plateau by 500 generations strongly suggests we have captured the bulk of adaptation , and the few additional adaptive mutations that could occur if we ran the experiment longer would not qualitatively alter our conclusions . Finally , the observation that adaptive evolution in most lineages ceases by about generation 500 suggests that the underlying adaptive landscape remains relatively constant in our experiments . It is notable that , in at least two experiments where longer walks have been noted , adaptive landscapes have been observed to change over the course of the experiment , as evidenced by declines in mean fitness , relative to the ancestor , in evolved populations [27] and the emergence of negative frequency-dependent selection [53] . That the relationship between the mean fitness increases with each successive step in an adaptive walk is negative suggests two important insights into the mechanics of adaptive evolution . First , the distribution of fitness effects among beneficial mutations prior to selection is expected to lie in the Weibull domain of attraction [17] , a decreasing distribution with a truncated right-hand tail . In other words , there are more small-effect than large-effect mutations , and there is an upper limit to mutation size . It is notable that the prediction of a right-truncated distribution of fitness effects among beneficial mutations is one possible outcome of heuristic models based on EVT as well as the biologically more realistic Fisher's geometric model [21] . Second , adaptive evolution in our experimental system is best viewed as involving the successive substitution of single mutations . This conclusion derives from the fact that the relationship between mean fitness increase and number of steps remains negative regardless of bottleneck size , a result that can only occur if multiple co-occurring mutations make little or no contribution to the fitness increases associated with adaptation . Our results have two important practical implications . First , if the environment changes regularly , populations may be expected to adapt perpetually , and it will be difficult to make strong predictions as to the outcome , for example , of long-term changes to the environment such as those expected to occur under many climate change scenarios . Second , compensatory evolution in response to losses in fitness due to mutations conferring resistance to fungicides , antibiotics , or other chemotherapies , is likely to be fast , requiring only a few mutational steps . More fundamentally , the picture emerging from these experiments is that adaptive evolution in mutation-limited populations can be readily understood from rather simple models that , because they are largely based on the statistical properties of extreme events , are likely to be robust to biological details .
We used A . nidulans strain WG615 ( wA3 , fldA1 , pyroA4 , veA1 ) for the selection experiment , kindly provided by Fons Debets and Marijke Slakhorst at Wageningen University , the Netherlands . This strain is resistant to the fungicide fludioxonil ( fldA1 ) . Resistance confers a cost of around 46% relative to the sensitive strain from which it was derived when growing in the absence of fungicide [33] . Colony diameter of WG615 after 5 d of growth at 37°C ( MGR ) was 41 . 0 mm ( s . d . : 2 . 60 ) . For comparisons with a fungicide-sensitive strain , we used strain WG638 ( yA1 , veA1 ) , which has the same genetic background as WG615 . In the selection experiment , spores and mycelium were washed from the plate using 5 ml of saline-Tween ( water containing NaCl 0 . 8% and Tween-80 0 . 05% ) , reserving 5 µl for transfer to fresh medium and storing 0 . 8 ml of the mixture mixed with 0 . 3 ml 80% glycerol at −80°C . Bottleneck sizes were adjusted by dilution . For all experiments , we used solid Complete Medium ( CM ) set at pH 5 . 8 , consisting of NaNO3 6 . 0 g/l; KH2PO4 1 . 5 g/l; MgSO4 . 7H2O 0 . 5 g/l; NaCl 0 . 5 g/l; 0 . 1 ml of a saturated trace element solution containing FeSO4 , ZnSO4 , MnCl2 , and CuSO4; tryptone 10 g/l; and yeast extract 5 g/l and ( added after autoclaving ) glucose 4 . 0 g/l . Cultures were incubated at 37°C . We initiated 120 replicate populations from A . nidulans strain WG615 by placing 5 µl of a dense spore suspension ( >10 , 000 spores ) in the center of a Petri dish containing solid CM medium . We propagated these replicate lineages by serial transfer for a total of 800 generations . Every 5 d , ( ∼80 mitotic generations [33] , [34] ) , we transferred a 5-µl random sample of a mature colony containing either 50 , 000 or 500 nuclei to fresh medium ( large and small bottlenecks , respectively ) obtained from a final population size of approximately 109 nuclei . An aliquot of the transferred sample was stored at −80°C . Our bottlenecking scheme ensures that effective population size in our treatments differs by at least two orders of magnitude . Two evolving lineages from the large bottleneck treatment were lost due to infection . Note that the number of generations elapsed between transfers depends primarily on cell-cycle duration rather than bottleneck size because our populations expand radially at a constant rate and so lack a stationary phase [33] . We measured fitness of the ith genotype ( wi ) as the colony diameter ( mycelial growth rate; MGR ) after 5 d of incubation , which is widely used as a measure of fitness and is highly correlated with other fitness measures such as total spore production and biomass [33] , [54] , [55] , as well as competitive fitness ( based on competitions between seven strains displaying substantial variation in MGR and a genetically marked ancestor , see Text S4: r = 0 . 81 , t5 = 3 . 09 , p = 0 . 027 ) . Selection coefficients were calculated as s = ( wi−wancestor ) /wancestor . Each lineage was assayed in triplicate at multiple time points ( 0 , 80 , 160 , 240 , 320 , 480 , 640 , and 800 generations ) in a single assay . Two lineages from the large bottleneck treatment were eliminated from the maximum likelihood analysis due to missing data . For 26 evolved strains that had a high relative fitness , we reassayed the fitness at 800 generations using at least seven replicates together with the fungicide-sensitive strain WG638 . We estimated fitness of all lineages at the end of the selection experiment in CM supplemented with 0 , 0 . 05 , 0 . 2 , or 0 . 4 ppm of fludioxonil in duplicate and used an analysis of covariance to test the main effect of lineage ( genetic variance ) and the interaction between lineage and fludioxonil concentration ( genotype-by-environment interaction variance ) . We performed sexual crosses [42] to assess the number of loci fixed by crossing evolved genotypes derived from WG615 with strain WG561 ( fldA1 , lysB5 , veA1 ) . We estimated the fitness of at least 50 progeny for each of 15 crosses and used the Castle-Wright estimator [35] modified for haploids [30] ( see Table S1 and Text S3 ) . Strain WG638 , which has high fitness , was included in all fitness assays as a reference strain . We initiated replicate populations of genotypes of interest by placing 5 µl of a dense spore suspension in the center of a Petri dish . After 5 d of colony expansion , we sampled approximately 3 mm2 of mycelium including spores at three preassigned locations on the edge of the colony . Fitness was estimated for each spore sample , and the fraction of samples with fitness greater than that of the parent genotype was calculated for each time point . We assayed fitness in blocks and used strains WG615 and WG638 as reference strains to account for variation between blocks . Logistic regressions were performed in R version 2 . 6 . 1 using generalized linear models with binomial errors . We controlled for false positives , which would inflate our estimates of the fraction of beneficial mutations , using a false discovery rate analysis [56] . Using a range of false discovery rates reduces the number of mutations identified as beneficial in all lineages ( see Figure S3 ) , but does not qualitatively change our results . | Adaptation is one of the least understood processes in biology because it relies on beneficial mutations , which are often too rare to study . We developed a method to infer the number and size of beneficial mutations substituted during adaptation , a process called an adaptive walk , and used this to test predictions about the properties of adaptive walks in experimental populations of fungus . Our work shows that , in contrast to the gradualist view of adaptation dominant since the 1930s , adaptive walks tend to be fast and short , with beneficial mutations of large effect substituted first , followed by those of smaller effect . | [
"Abstract",
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"Methods"
] | [
"evolutionary",
"biology",
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] | 2009 | The Properties of Adaptive Walks in Evolving Populations of Fungus |
Human antibody 10E8 targets the conserved membrane proximal external region ( MPER ) of envelope glycoprotein ( Env ) subunit gp41 and neutralizes HIV-1 with exceptional potency . Remarkably , HIV-1 containing mutations that reportedly knockout 10E8 binding to linear MPER peptides are partially neutralized by 10E8 , producing a local plateau in the dose response curve . Here , we found that virus partially neutralized by 10E8 becomes significantly less neutralization sensitive to various MPER antibodies and to soluble CD4 while becoming significantly more sensitive to antibodies and fusion inhibitors against the heptad repeats of gp41 . Thus , 10E8 modulates sensitivity of Env to ligands both pre- and post-receptor engagement without complete neutralization . Partial neutralization by 10E8 was influenced at least in part by perturbing Env glycosylation . With unliganded Env , 10E8 bound with lower apparent affinity and lower subunit occupancy to MPER mutant compared to wild type trimers . However , 10E8 decreased functional stability of wild type Env while it had an opposite , stabilizing effect on MPER mutant Envs . Clade C isolates with natural MPER polymorphisms also showed partial neutralization by 10E8 with altered sensitivity to various gp41-targeted ligands . Our findings suggest a novel mechanism of virus neutralization by demonstrating how antibody binding to the base of a trimeric spike cross talks with adjacent subunits to modulate Env structure and function . The ability of an antibody to stabilize , destabilize , partially neutralize as well as alter neutralization sensitivity of a virion spike pre- and post-receptor engagement may have implications for immunotherapy and vaccine design .
Advances in both vaccine development and immunoprophylaxis are needed to combat HIV/AIDS [1]–[3] . Both of these strategies target the viral envelope glycoprotein spike ( Env ) , which is a trimer of gp120-gp41 heterodimers . HIV-1 Env is functionally labile [4] , [5] , heterogeneously glycosylated [6]–[9] and phylogenetically diverse [www . hiv . lanl . gov] . The membrane proximal external region ( MPER ) of HIV-1 is an important target on the transmembrane subunit gp41 as it is linked to a highly conserved sequence motif and epitopes of several broadly neutralizing antibodies [10]–[12] . However , a general inability to elicit broadly neutralizing antisera to these and other conserved epitopes on HIV-1 Env by vaccination has led to deeper investigation of the relevant Env-antibody interactions [1]–[3] , [13] . Models of the MPER typically focus on peptide monomers , either on micelles , lipid bilayers or in solution [14]–[17] . Broadly neutralizing MPER antibodies , 2F5 , 4E10 , Z13e1 , and the extremely potent 10E8 antibody have helped characterize the native MPER . Crystal structures of these antibodies in complex with MPER monomers have revealed distinct local conformations while detailed structural information of the MPER on HIV-1 Env trimers is currently lacking [10] , [18]–[22] . Hydrophobic CDR H3s seem to be crucial for MPER antibody neutralization [18] , [23]–[25] . In sequential binding models , the hydrophobic H3s of 2F5 and 4E10 first engage the viral membrane leading to binding of a membrane-embedded MPER monomer [15] , [25] . A somewhat different model shows the H3 of MPER antibodies dipping between the membrane and a six-helix bundle form of gp41 [26] , while a precise role for membrane in neutralization by 2F5 has been challenged [27] . Remarkably , 10E8 neutralizes HIV-1 with ≥10-fold greater potency than previously described MPER antibodies [10] . Although 10E8 seems to show weak binding to membranes the relationship between this activity and neutralization is incompletely understood [28] , [29] . Although antibodies can reach an occupancy level of three per Env spike [30] , [31] , studies have suggested that a single antibody is sufficient for HIV-1 neutralization [32] , [33] . Limits to occupancy are also possible , as antibody PG9 binds to just one gp120 protomer of the spike in an asymmetric manner [34] . MPER antibodies are the most potent of the described neutralizing antibodies to gp41 , and can bind to unliganded Env of sensitive isolates , but not typical neutralization-resistant isolates [35]–[39] . Engagement of host CD4 by Env stabilizes a site on gp120 for coreceptor ( i . e . CCR5 or CXCR4 ) and also reveals elements of gp41 , including the MPER , N-heptad repeat ( NHR ) and C-heptad repeat ( CHR ) regions [40]–[42] . Antibody stoichiometry following receptor engagement is poorly understood , but a short kinetic time window , steric blocks and flexibility in gp41 together appear to affect the potency of 2F5 , 4E10 , Z13e1 and certain fusion inhibitors post-receptor engagement [19] , [38] , [42]–[44] . Models have depicted the MPER at the base of unliganded spikes where it might interact with other elements of gp41 [45] . Indeed , mutations to the MPER can destabilize Env spikes [4] , and both 2F5 and 4E10 can cause spikes to shed gp120 [39] . The extreme potency of neutralization by 10E8 is not adequately explained by its affinity for MPER peptide , which is comparable to that of less potent neutralizers , 2F5 , 4E10 and Z13e1 [10] . Moreover , whereas some MPER mutations diminish antibody binding to peptide and abrogate neutralization ( e . g . W672A with 4E10 ) , others diminish peptide binding but enhance neutralization ( e . g . I675A with Z13e1 ) [46] , [47] . These and other findings have led to the conclusion that neutralizing MPER epitopes involve elements besides current crystallographic defined linear epitopes [11] , [12] , [48] . We wished to gain new insight into MPER mediated neutralization using 10E8 . We discovered an unexpected mechanism in which HIV-1 becomes partially neutralized by 10E8 wherein potency is high against a ‘neutralizable’ fraction of virus infectivity . Whereas 10E8 readily occupies all three protomers of wild type unliganded spikes it partially and inefficiently occupies MPER mutant unliganded spikes , indicative of a hindrance to further occupancy by 10E8 on adjacent subunit ( s ) . Here , 10E8 seems to bind but remarkably not fully inhibit HIV-1 spikes from mediating infection of target cells . Moreover , with the ‘non-neutralizable’ fraction of virus infectivity we find that 10E8 can kinetically alter stability and ligand-binding properties of Env spikes pre-receptor engagement as well as post-receptor engagement in distinct ways . These features define a novel mechanism of HIV-1 neutralization involving the MPER , accessibility by antibody to the MPER , and interactions between MPER and adjacent elements of Env . The novel mechanism described for 10E8 conceivably might also influence HIV-1 facing 10E8-like antibodies in vivo , and so has relevance to immunotherapy and vaccine approaches .
MPER mutants of JR2 were shown previously to be incompletely neutralized at high concentrations of 10E8 [10] . A molecular basis for partial neutralization by 10E8 has not been described so we decided to investigate . These mutants , and some newly engineered mutants with naturally occurring MPER polymorphisms [49] , [50] , were tested in neutralization assays against 10E8 , 4E10 and 2F5 ( Table S1 ) . Of 21 Ala mutants covering positions 660–680 of the MPER , only mutants W672A , F673A , W680A and K683A were partially neutralized by 10E8 , i . e . neutralization curves plateaued with less than full neutralization ( Figure 1A; data not shown ) . One mutant , N671A , was less sensitive to 10E8 but became fully neutralized by 10E8 at high concentration ( Figure 1B ) . Mutants containing natural polymorphisms F673L , W680G and K683Q also showed partial neutralization by 10E8 with plateaus at 30–80% maximum neutralization and relatively shallow curves ( Figure 1B ) . We note that at very high concentrations of 10E8 ( >50 µg/ml ) the plateau of some curves occasionally inflected and showed a downward slope; however this effect was not reproducible between experiments and plateaus were also observed with no downward slope ( see Figure S1 and below ) . Importantly , in all cases partial neutralization was consistently reproducible . The nature of amino acid substitution also had an effect on 10E8 neutralization . Mutants containing conservative substitutions of aromatics for aromatics ( F673W/Y and W672Y/F ) were fully neutralized whereas substitutions from hydrophobics to hydrophilics ( F673R/Q ) were partially neutralized with maximum plateaus at 50–60% ( Figure S2 ) . Antibody 2F5 to an MPER epitope upstream of 10E8 neutralized all of the mutants completely and more potently than wild type virus ( Figure 1B ) , as previously reported [47] . With 4E10 we were unable to determine whether full neutralization was achievable at high concentration due to limiting antibody reagent . Nevertheless , 4E10 neutralized mutant F673A with a shallow slope indicating that as antibody concentration increases neutralization becomes less efficient ( Figure 1B ) . The natural polymorphism F673L notably conferred almost complete resistance to 4E10 at both IC90 and IC50 . Remarkably , no single mutation tested imparted complete resistance to 10E8 . Having verified partial neutralization by 10E8 of JR2 , a Tier 2-like primary isolate , we decided to test a more sensitive strain , SF162 , for which direct access by MPER antibodies to unliganded Env has been reported [35] . We put particular focus on the naturally occurring mutation , F673L , as it has been observed in multiple HIV-1-infected individuals [49] , [50] , and shows clear partial neutralization by 10E8 in the JR2 background . Notably , F673L is observed in 0 . 97% of 4009 reported sequences of Envs among primary isolates [http://www . hiv . lanl . gov/content/sequence/QUICK_ALIGN/QuickAlign . html] . In most models of the MPER , F673 is found buried , either in the paratopes of 10E8 and 4E10 [10] , [51] , in peptide-embedded micelles [14] , in lipid bilayers [15] or within a MPER peptide homotrimer [52] . F673A also nearly knocks-out 10E8 binding to MPER peptide [10] . Hence , we envisioned that the mechanism behind partial neutralization of MPER mutants by 10E8 could be most readily elucidated using substitutions of F673 . 10E8 partially neutralized mutants F673L and F673A in the hypersensitive SF162 Env background , which indicated that partial neutralization by 10E8 was not JR2 specific and can also occur with a Tier 1 strain ( Figure 1C ) . Partial neutralization using dose-saturating concentrations of 10E8 conceivably might be due to an MPER mutant having enhanced fusion kinetics that limits the time in which 10E8 can act post-receptor engagement . We therefore tested the sensitivity of mutant F673L to fusion inhibitors C34 and 5-Helix , which act post-receptor engagement on NHR and CHR regions of the gp41 pre-fusion intermediate , respectively . We found that IC50s of JR2 F673L against C34 and 5-Helix ( IC50 = 0 . 9 µg/ml and IC50 = 4 . 1 µg/ml , respectively ) were very similar to that of wild type HIV-1 for these inhibitors ( IC50 = 1 . 2 µg/ml and IC50 = 6 . 1 µg/ml , respectively ) ; this was also true in the SF162 Env background ( data not shown ) . In fact , the F673L mutants were hypersensitive to MPER antibodies 2F5 and Z13e1 ( Figure 1B and 2 ) . Hence , fusion kinetics are unlikely to be accelerated by the gp41 mutation F673L , although exposure of the MPER may be increased . We also found no correlation between infectivity of virus stocks and partial neutralization , as well as no obvious correlation between reported affinities for MPER mutant peptides and neutralization of cognate mutant viruses by 10E8 [10] ( data not shown ) . Moreover , 10E8 partially neutralized mutant F673L using target cells bearing FcγRI receptors ( Figure S3A and D ) . FcγRI improves on-rates of antibodies against receptor-activated gp41 , particularly MPER antibodies [53] . The above results suggest that partial neutralization by 10E8 is not a result of the pre-fusion intermediate of gp41 having generally altered fusion kinetics . We considered that characteristics specific to TZM-bl target cells , or heterogeneity or molecular size of 10E8 IgG might be causing partial neutralization with 10E8 . However , incomplete neutralization of HIV-1 mutant F673L was also observed using U87 . CD4 . CCR5 cells and HOS . CD4 . CCR5 cells with plateaus similar to that of TZM-bls ( Figure S3B , C , and D ) . We also used 10E8 IgG produced both transiently in 293 cells and using a stable CHO-K1 cell line as well as Fab 10E8 prepared by enzymatic digestion . Both in-house 10E8 IgG preparations , a sample from the NIH ARRRP and the Fab 10E8 molecule all showed partial neutralization against the F673L mutant , however the potency with the Fab was found to be lower ( Figure 1D and E; data not shown ) . 10E8 IgG can aggregate at concentrations above ∼0 . 7 mg/ml [29] . However , the neutralization plateaus are observed at 10E8 concentrations 100-fold lower than this aggregation point; moreover , we found that insoluble aggregates of 10E8 also produced partial neutralization curves that were similar to that of soluble 10E8 ( Figure S1B ) . Hence , partial neutralization by 10E8 appears to occur independently of target cell type , antibody format or solubility state of the antibody Partial neutralization could indicate differences in glycosylation that result in sensitive and resistant viral subpopulations . The crystallographic defined epitope of 10E8 has only protein elements , but glycans conceivably could affect MPER accessibility . We therefore produced virions in GnTI-/- ( 293S ) cells and in 293T cells treated with kifunensine ( Kif ) , which results in relatively homogeneous Man5-Man9 and Man9 glycan residues , respectively [6] , [54] . MPER mutant viruses produced in GnTI-/- or Kif-treated cells were tested in a neutralization assay and were still partially neutralized by 10E8 , but plateaus were shifted from 42% ( 293T , no Kif ) to 82% and 88% neutralization , respectively ( Figure 3; Figure S4A and C ) . Kif treatment also slightly reduced viral infectivity and cleavage efficiency ( by ∼3-fold , and from >95% to 80–85% , respectively ) , while it caused gp120 and gp41 to run slightly faster on SDS-PAGE ( Figure S4 and data not shown ) . However , these effects of Kif on processing and function of Env were equal against wild-type and mutant so the changes in maximum neutralization by 10E8 are not a simple function of diminished infectivity or cleavage . To see if glycans on gp41 were responsible for limiting neutralization by 10E8 , we individually ablated the four N-linked glycosylation sequons ( NGS ) in gp41 on an F673L mutant background and tested these double mutants in a neutralization assay . None of the NGS mutations affected the sensitivity of the F673L mutant to control antibodies 2F5 or 4E10; however , N625Q increased the maximum level of 10E8 neutralization from 41% to 65% ( Figure 3; Figure S4B ) . The other three NGS knockouts were no more or less sensitive to 10E8 neutralization . Whereas contributions from other glycans or factors besides glycosylation cannot be ruled out , the results above suggest that complex glycan on Env and the glycan at N625 of gp41 , can significantly affect the maximum neutralization achieved by 10E8 . To further investigate partial neutralization by 10E8 , we considered whether 10E8 might somehow occupy MPER mutant Env trimers of the neutralization resistant fraction of virus without fully blocking their ability to mediate infection . We speculated that the presence of 10E8 might also affect neutralization at epitopes beside that of 10E8 . Focusing on the F673L mutant in both JR2 and SF162 Env backgrounds , we chose a fixed saturating concentration of 10E8 IgG that was within the maximum plateau of neutralization , and varied that of several fusion inhibitors ( e . g . 5-Helix and C34 ) as well as antibodies to gp41 ( e . g . 2F5 , Z13e1 , 8K8 and DN9 ) or gp120 ( e . g . sCD4 , b12 , b6 , VRC01 , 2G12 , PGT121 , F425-B4e8 , 447-52D , and 17b ) . For comparison , we similarly fixed a somewhat lower sub-neutralizing concentration of 10E8 IgG to use against wild type JR2 and wild type SF162 viruses . Indeed , the results confirmed our speculation . Remarkable differences were observed in the potency of a number of different inhibitors and antibodies against Env due to the presence of 10E8 , seen most significantly against the F673L mutants ( Figure 4 ) , but notably also to a lesser extent against wild type SF162 , but not at all with wild type JR2 ( Figure 2 , 4 , 5 and 6 ) . To our knowledge , this is the first example in which an antibody binds to an infectious viral spike and specifically alters its neutralization sensitivity involving a variety of different epitopes and sites of fusion inhibition . There are several notable observations to make on the neutralization experiments performed in the presence and absence of 10E8 . First , the effect of 10E8 on the IC50 of certain ligands can be very significant , i . e . over an order of magnitude with 2F5 ( Figure 2 and 4 ) , which cannot be explained from the 40–50% change in relative viral infectivity after treatment with 10E8 alone ( Figure 1 ) . Second , the presence of 10E8 decreases sensitivity to MPER antibodies , as might be expected with overlapping epitopes ( Figure 2 ) . Third , the presence of 10E8 hyper-sensitizes the virus to fusion inhibitors C34 and 5-Helix , as well as antibodies 8K8 and DN9 , all of which target sites on the heptad repeats of a receptor-activated , pre-bundle form of gp41 that do not overlap with that of 10E8 ( Figure 4 and 5 ) . Fourth , still other antibodies like 2G12 and PGT121 show no change in potency in the presence of 10E8 ( Figure 4 ) . Fifth , 10E8 decreases the apparent potency of soluble CD4 ( sCD4 ) , as well as CD4 binding site antibody b6 and coreceptor site antibody 17b against the otherwise sensitive MPER mutant of SF162 , suggesting that 10E8 restricts conformational changes required for binding by sCD4 , b6 and 17b ( Figure 4 and 5; see below ) . This effect was highly pronounced for weakly neutralizing antibodies 17b and b6 ( i . e . >10-300-fold decreases in neutralization of SF162 F673L ) that might be particularly sensitive to increases in Env rigidity . Finally , the presence of 10E8 can even affect the IC50 of antibodies against wild type Tier 1A isolate SF162 in the absence of the F673L mutation , and the directionality of the shift in IC50s with wild type SF162 caused by 10E8 is the same as with the F673L mutant for cognate antibody , though the magnitude in the shift is less ( Figure 2 and 4 ) . To determine if other inhibitors altered MPER recognition , we tested neutralization of mutant F673L by MPER and CD4bs antibodies in the presence of sub-neutralizing concentrations of 4E10 , b12 or sCD4 . No significant change was observed under these conditions either in the potency of these antibodies or in the magnitude of partial neutralization by 10E8 ( Table S2; data not shown ) . Importantly , the presence of human serum ( e . g . 20% or equivalent to ∼3 mg/ml IgG ) did not affect the level of partial neutralization of the F673L mutant by 10E8 suggesting that the effects we observed may also occur in vivo ( data not shown ) . The results above show that the presence of 10E8 ( i ) neither makes Env globally neutralization sensitive nor globally resistant but has more specific effects on both gp41 and gp120 , as well as ( ii ) modulates both receptor-naïve and receptor-activated spikes since 10E8 antagonizes ligands like sCD4 but potentiates ligands like C34 , which exclusively target unliganded and receptor-activated spikes , respectively . Moreover , 10E8 modifies neutralization sensitivity of wild type HIV-1 ( e . g . SF162 ) . Notably implicit with the strongest of the observed effects of 10E8 on neutralization profiles of HIV-1 is the perhaps contraintuitive notion that 10E8 may be binding to most , if not all , Env spikes with at least partial subunit occupancy and without fully inhibiting their function . We speculated that partial neutralization of MPER mutants by 10E8 might relate to stoichiometry of 10E8 binding to Env trimers . Addressing antibody occupancy post-receptor engagement is not straightforward . However , blue native ( BN ) PAGE can be useful for addressing stoichiometry with unliganded Env [33] . We used BN-PAGE to separate wild type and F673A Env in complex with 10E8 and probed Western blots using Env-specific antibodies [9] , [55] . Fab 10E8 was used for these experiments since it partially neutralizes but cannot crosslink spikes which can confound measurements . JR-FL E168K was used for Env because it forms homogeneous trimers , and can be probed using the trimer specific antibody PG9 [9] , [56] . Fab 10E8 caused a quantitative shift of the entire visible band corresponding to JR-FL trimers for both wild-type and F673 mutant virions ( Figure 7 ) . We found no evidence of 10E8-unreactive Env trimers . Wild type JR-FL spikes were shifted farther on the gel and also at lower concentrations of 10E8 than mutant F673A spikes ( Figure 7 ) . Similar results were seen with F673 mutants in the SF162 background ( data not shown ) . Fab Z13e1 readily shifted F673A trimers , consistent with its ability to efficiently neutralize this mutant , while stoichiometry of 4E10 binding to F673A appeared to be reduced similar to 10E8 , consistent with 4E10's ability to achieve an IC50 but not an IC80 against F673A mutant HIV-1 [47] ( Figure S5 ) . Compared to control Fabs PG9 and b12 , which bind to one and three gp120 subunit ( s ) on Env [30] , [34] , respectively , 10E8 shifted wild type Env spikes to the same degree as b12 . However , 10E8 shifted the F673A trimer band by more than PG9 and by less than b12 . These data would suggest that two subunits of MPER mutant Env would be occupied by 10E8 at a concentration of ∼10 µg/ml that is near its local maximum in neutralization ( Figure 1A ) . The same concentration by contrast would fully neutralize and saturate all three subunits of wild type virus . It should be cautioned however that BN-PAGE analyses measure binding to unliganded trimers while 10E8 neutralizes in large part post-receptor engagement . Notwithstanding , the above results suggest that 10E8 binds to wild type trimers with both higher stoichiometry and higher apparent affinity than F673 mutant spikes . Because 10E8 can bind to Env before or after detergent-solubilization , or both , we performed a washout step prior to adding detergent so that only Fab already bound to native trimers would remain . This procedure had a modest effect on 10E8 binding to wild type JR-FL spikes , verifying that 10E8 can bind to unliganded Env on virions , although it likely binds more efficiently post-solubilization ( Figure 7 ) . However , 10E8 occupancy of F673A Env was clearly reduced from two to one Fab per spike by the washout step ( equivalent shift to PG9 ) . This result implies that 10E8 binds inefficiently to mutant spikes in the membrane , although the lower affinity interaction may also allow Fab 10E8 molecules to fall off during solubilization and the PAGE procedure . We note that the washout step also reduced Fab b12 occupancy suggesting that binding affinity may be limited for at least one of the b12 molecules on the trimer . Nevertheless , it appears from both neutralization assays and BN-PAGE analyses that binding of 10E8 molecules to unliganded MPER mutant Env is blocked from reaching full subunit occupancy , either directly or indirectly , but that no such limitation to 10E8 occupancy exists with wild type Env . Ruprecht et al previously showed that 2F5 and 4E10 cause Env spikes to shed gp120 subunits , gradually inactivating HIV-1 in an irreversible process that takes several hours [39] . We anticipated that 10E8 would also alter Env stability , particularly as we found it alters the neutralization sensitivity profile of partially neutralized virus . First , we incubated wild type HIV-1 ( JR2 ) at physiological temperature in the presence or absence of 10E8 or 2F5 over a time course and then measured infectivity . A sub-saturating concentration of 10E8 ( i . e . 0 . 1 µg/ml ) decreased the half-life of wild type JR2 from 13 . 6 h to 8 . 7 h , indicating that it too destabilizes functional Env spikes over time ( Figure 8A ) . Antibody 2F5 decreased the half-life of JR2 as anticipated , from 13 . 6 h to 8 . 9 h ( data not shown ) . JR2 stability was also evaluated using a thermostability assay that determines the temperature ( T90 ) at which an Env variant of HIV-1 loses 90% of its infectivity in one hour [4] . In line with the physiological decay results , 10E8 strongly reduced the T90 of wild type JR2 from 49°C to 43°C in a dose dependent manner , indicating that 10E8 decreases wild type Env stability at both physiologic and elevated temperatures ( Figure 8B ) . In contrast to its effect on wild type JR2 , 10E8 altered the functional stability of F673L with a different pattern . Thus , at a low concentration range of 10E8 ( ∼0 . 01–1 . 0 µg/ml ) infectivity decay of F673L at physiologic temperature remained relatively constant but at high concentrations ( ∼500 µg/ml ) 10E8 reproducibly increased its half-life slightly from 3 . 6 h to 4 . 4 h ( Figure 8C ) . This was in contrast to 2F5 that decreased the already short half-life of the mutant virus from 7 . 1 h to 5 . 8 h ( data not shown ) . Surprisingly , in the thermostability assay , 10E8 significantly increased the thermostability ( T90 ) of F673L from 45°C to 50°C in a dose dependent manner ( Figure 8D ) . Hence , the change in T90 caused by 10E8 with mutant F673L shows a strongly significant inverse correlation with that of wild type JR2 ( p = 0 . 0034; Figure 8F ) . To see whether stabilization by 10E8 of the MPER mutant was restricted only to F673L , we assayed other MPER mutants that were partially neutralized by 10E8 . The presence of 10E8 strongly increased the stability ( T90 ) of mutants W672A , W680A and K683A by 5–6°C ( Figure 9A ) . Controls 4E10 ( 50 µg/ml ) and DEN3 ( 100 µg/ml ) had no effect on thermostability of JR2 F673L whereas the presence of Z13e1 ( 50 µg/ml ) decreased the thermostability of F673L ( Figure 9B ) , the latter result being consistent with Z13e1's ability to fully neutralize this mutant . The presence of 10E8 also increased the thermostability of several MPER mutants in an SF162 background ( Figure 9C ) . Hence , while 10E8 decreases the thermostability of wild type JR2 it increases the thermostability of Envs disrupted by mutations at different positions along the MPER . Stabilization of functional Env by a neutralizing antibody is to our knowledge unprecedented . We next assessed whether effects of 10E8 on Env function directly relate to effects on the oligomeric state of Env . First , wild type JR-FL and F673A mutant virions were subjected to a heat gradient in the presence or absence of 10E8 Fab and Env was analyzed using BN-PAGE . Unexpectedly , we found that binding of 10E8 stabilized a fraction of both wild type and MPER mutant Env trimers against temperatures that caused dissociation of Env spikes in the absence of 10E8 ( Figure 10A and B ) . However , in the presence of 10E8 the band corresponding to the Env trimer did grow fainter after exposure to higher temperatures . Fading of the Env trimer band after 57°C treatment was observed to a similar degree when the Western blot was stained using different combinations of antibodies against multiple epitopes on gp41 or gp120 ( Figure S7; data not shown ) . Hence , when 10E8-Env complexes bound by 10E8 are exposed to elevated temperatures they are stabilized , but may become altered to be less prominent on BN-PAGE analysis , perhaps through aggregation , while unbound spikes dissociate into individual subunits . To explore stability effects further , virions were also incubated at physiological temperature and then analyzed using BN-PAGE . Here , for up to 22 hours there was no detectable decay of JR2 spikes or MPER mutant spikes , as seen previously [4] . However , when 10E8 was present most of the trimer band faded at 37°C over time with some trimer remaining; decay products were again not visible ( Figure 10C ) . Similar fading on addition of 10E8 was observed when virion-associated Env was incubated at 37°C in detergent ( DDM ) , except that the trimer band disappeared more rapidly ( Figure 10D ) . Thus , 10E8 alters physical stability of functional Env trimers over time at physiological temperature , perhaps causing them to aggregate , but a fraction of 10E8-bound Env remains relatively stable on membrane . We conclude that at physiological temperature 10E8 kinetically stabilizes F673 mutant Env into one population that is active and one that is inactive , but into inactive conformations only for wild type spikes . We also conclude that effects of 10E8 on Env trimer stability depend on level of subunit occupancy as well as temperature and time of incubation . MPER antibodies neutralize HIV-1 in large part by binding to Env post-receptor engagement [37] , [42] , [57]–[59] , whereas neutralization pre-attachment varies between HIV-1 isolates [35] . Partial neutralization of F673 mutants by 10E8 must be limited both pre and post attachment to host cells as it occurs in saturating amounts of 10E8 maintained throughout the assay . However , the relative limits to 10E8 neutralization prior to and following receptor engagement might be different . In time course experiments , we found that 10E8 neutralization increases ( IC50 decreases ) roughly 10-fold with SF162 and JR2 wild type HIV-1 when virus is pre-incubated with 10E8 up to 20 hours rather than the standard 1 hour prior to adding to target cells ( Figure 11A , left panel; data not shown ) . This result is consistent with Env destabilizing properties ( pre-attachment ) previously reported for 2F5 and 4E10 ( Figure 11A , middle panels ) [39] , and as demonstrated above for 10E8 ( Figure 8A and B ) . However , a different effect was observed with F673L mutant Env . In this case , rather than 10E8 neutralization becoming more potent over time , both potency and the maximum percentage of F673 mutant virus neutralized were maintained during the 20-hour pre-incubation ( Figure 11B , left panel ) . The potency of fusion inhibitors 5-Helix ( data not shown ) and C34 , which only act post attachment [41] , [42] , remained unchanged throughout the assay as expected ( Figure 11 , rightmost panel ) . We also found that viral stock age had no effect on the level of partial neutralization ( Figure S7 ) , which is consistent with our findings above that mobility on BN-PAGE of 10E8-bound F673A spikes did not change with incubation times of up to 22 hours prior to running the gel ( Figure 10C ) . We conclude that whereas 10E8 readily occupies and gradually inactivates wild type Env , it inefficiently occupies and stabilizes infectivity of MPER mutant Env over time . 10E8 neutralization of JR-FL is reportedly relatively resistant to pre-attachment washout [10] , and our experiments using JR2 concurred with this ( Figure S8 ) . However , neutralization of F673L virus by 10E8 could be completely washed away prior to adding virions to target cells ( Figure S8 ) . This agrees with our BN-PAGE data that showed that the apparent affinity of 10E8 on the MPER mutant spike is relatively low and is readily washed off ( Figure 7 ) . Because 10E8 partially neutralizes JR2 F673 mutant in standard assay format we conclude that neutralization of the mutant primarily occurs post-attachment . MPER polymorphisms such as L673 occur naturally in different individuals infected with clade C isolates ( Table S1 ) [49] , [50] . L673 mutations have been used herein with well-characterized clade B envelopes , JR2 and SF162 , in which MPER mutations including F673L are destabilizing ( Figure 8 ) [4] . We speculated that isolates that naturally incorporate a Leu at position 673 might have co-evolved to compensate for instability in the MPER and might therefore respond differently to 10E8 . We first tested two clade C isolates that contain L673 , TM20 . 13 [50] and M20490 BMR 211 [49] , which have previously been shown to be resistant to 4E10 and Z13e1 ( Table S1; Figure 12A ) . Antibody 10E8 showed rather weak neutralizing activity against these isolates . However , at high concentrations of 10E8 ( ∼10–50 µg/ml ) partial neutralization was observed plateauing at 14% and 20% for the two isolates; the effect was specific to 10E8 as no such effect was seen using 4E10 ( Figure 12A ) [49] , [50] . In addition , consistent with our speculation that these Envs may have adapted to the presence of L673 and therefore might respond differently to 10E8 , 10E8 had little to no effect on the thermostability ( T90 ) of TM20 . 13 or M20490 BMR 211 Env spikes ( Figure 12B ) . However , the presence of 10E8 did show specific effects on sensitivity to neutralizing ligands with these two isolates ( Figure 12D ) . Neutralization of TM20 . 13 by 2F5 was hindered in the presence of 10E8 ( 2F5 does not neutralize M20490 BMR 211 ) , whereas neutralization by both 8K8 and/or DN9 was enhanced when 10E8 was present . Hence , as with the clade B MPER mutants , the pre-fusion intermediate of gp41 with these clade C isolates is being stabilized by 10E8 in a conformation favorable to the gp41 inhibitors but without being fully inactivated , while access to the MPER by further antibodies is blocked . Furthermore , the presence of 10E8 did not alter sensitivity of the clade C isolates to the pre-attachment inhibitor , sCD4 , which may relate to 10E8's lack of effect on the thermostability ( T90 ) of the clade C Envs , as both sCD4 binding and thermostability are properties of Env in its unliganded ( pre-attachment ) state . To further determine whether 10E8 would alter stability or ligand recognition of clade C viruses , we tested F673 mutants of two other clade C Envs . Thus , an F673A mutant of an otherwise 4E10 sensitive clade C isolate , COT6 [50] , showed evidence of partial neutralization by 10E8 with a shallow slope and noticeable plateau in the high 98% range that was absent with its wild type Env counterpart ( Figure 12C ) . Furthermore , an F673L mutant was generated for the 4E10-sensitive clade C isolate , M27390 PL 1706 [49] , which was partially neutralized by 10E8 with a plateau at 44% , closer to what was observed with the clade B MPER mutants ( Figure 12C ) . Further characterization of the pseudotyped Envs was more problematic as they are considerably more heterogeneous in BN-PAGE relative compared to the homogeneous JR-FL trimers [36] while Env M27390 PL 1706 also showed low infectivity . Sensitivity of the COT6 F673A mutant to ligands targeting the gp41 pre-fusion intermediate was also specifically enhanced by the presence of 10E8 , whereas thermostability was not affected ( Figure 12B and D ) . Thus , effects of 10E8 on pre-receptor engaged Env ( i . e . effects of 10E8 on thermostability and sensitivity to sCD4 ) can vary and be distinct from effects of 10E8 on receptor-activated Env ( i . e . effects of 10E8 on sensitivity to fusion inhibitors ) . Taken in sum , our analysis shows that 10E8 can alter ligand recognition properties of functional clade C Env spikes with both naturally occurring and introduced MPER mutations .
HIV-1 Env has evolved to sequester its most conserved surfaces from recognition by neutralizing antibodies of the host . With the MPER this likely involves steric limitations imposed by Env and viral membrane both pre- and post-engagement . However , molecular details are lacking on how MPER antibodies overcome these limitations . Here , we reveal novel mechanisms of 10E8 activity not accounted for by prior models . First , we confirmed that antibody 10E8 causes unusual partial neutralization with certain Envs . We showed that 10E8 occupied an apparent maximum of two gp41 subunits of an MPER mutant trimer instead of three subunits observed with its wild type counterpart that was fully neutralized . Clade C isolates with natural MPER polymorphisms were also partially neutralized by 10E8 suggesting that this phenotype could evolve during natural infection under antibody pressure . Second , we found that 10E8 functionally destabilizes unliganded Envs while functionally stabilizing mutant Env counterparts , the latter activity of which is unprecedented for a virus-neutralizing antibody . Third , we found that the presence of 10E8 can significantly alter the sensitivity of Env to neutralization by antibodies and inhibitors to gp120 and gp41 . A quaternary model incorporating behavior of each MPER on trimeric Env we think provides for a superior account of these observed effects of MPER recognition by 10E8-like antibodies . There has been uncertainty as to whether MPER antibodies act on a pre-hairpin intermediate [57] or on a late six-helix bundle form of gp41 [26] . In our studies , the presence of 10E8 enhanced sensitivity of HIV-1 to fusion inhibitors C34 and 5-Helix , which must act prior to six-helix bundle formation . We conclude from these results that , at a minimum , 10E8 acts on a pre-hairpin intermediate of gp41 . While it also remains possible that 10E8 can fall off during conformational changes caused by receptor engagement , dose-saturating concentrations of 10E8 were maintained throughout the entry process making this possibility less likely . In the unliganded state , 10E8 can functionally stabilize mutant Envs to heat and physiological decay , and can also inhibit neutralization by sCD4 and CD4bs antibodies . However , these pre-attachment effects were limited to certain unstable Envs . In contrast , the clade C isolates in which L673 occurred naturally were not stabilized to heat in the presence of 10E8 and only ligands that bind post-attachment had activities affected by 10E8 binding . MPER antibodies bind to unliganded Env better with variants that adopt a more open conformation , but with many primary isolates can only bind post-attachment [35] . Although mutation F673L in JR2 and SF162 backgrounds did not make the Envs globally hypersensitive to neutralization to every ligand , F673L did amplify effects of 10E8 both pre- and post-attachment . Meanwhile , clade C Envs that were less reliant on the MPER for stability only appeared to be accessible to 10E8 following CD4 engagement . Whereas MPER mutations can be disrupting [4] , [47] , [60] , compensatory mutations could have developed in these clade C Envs that uphold fitness and stability of Env in the presence of 10E8 . Considering the lack of sequence homology between the isolates and that individual mutations often destabilize Env , a molecular basis for the differences in observed effects of 10E8 on different Envs will be difficult to isolate [4] , [9] , [61] . Perhaps longitudinal studies that follow Env mutations in face of 10E8-like antibody selection pressure in different individuals might provide insight . Since Ala mutations to residues W672 , F673 , W680 and K683 in JR2 all caused similar partial neutralization as well as altered sensitivity to heat and ligands in presence of 10E8 , it seems that a more general disruption of the MPER is sufficient for these effects . These mutations would affect recognition of the CDR H3 and adjacent residues of 10E8 based on existing structural data [10] . Thus , 10E8 binding to wild type Env may stabilize the MPER in a conformation that is incompatible with membrane fusion . The above mutations would decrease affinity of 10E8 for unliganded mutant Env as our washout experiments indicated , and as they also diminish 10E8 binding to MPER peptides ( e . g . 102-106-fold drop in IC50 ) [10] . Weak 10E8 binding may fail to fully inactivate Env , and instead may stabilize at least a portion of the Env population into conformations capable of mediating viral entry . Interestingly , conservative mutations to the MPER ( Figure S2 ) such as W672F and F673W destabilize Env JR2 but do not lead to partial neutralization presumably because high affinity of 10E8 for the MPER is maintained [62] . One antibody is typically sufficient to neutralize one HIV-1 spike [32] , [34] . However , 10E8 significantly altered functional properties of Envs at concentrations in which our BN-PAGE analyses showed all observable trimeric Env was bound by 10E8 . These results contra-intuitively suggest that 10E8 can occupy Env without abrogating its function . Heterogeneity in the Env population could provide explanations for how this might occur . However , experiments that perturbed glycosylation of Env showed that glycan heterogeneity can contribute to but not fully account for partial neutralization . Another explanation relates to the asymmetric nature of Env occupied by one or two 10E8 antibodies . We speculate that whether an Env spike is blocked or not by 10E8 may depend on the spatial relationship between the gp41 subunit ( s ) bound by 10E8 and the gp120 subunit ( s ) that engage host cell receptors . The MPER acts at a late step during fusion ( e . g . expansion of the fusion pore [60] ) in which gp41 subunits participate in a monomer-trimer equilibrium [63] . Thus , MPERs on adjacent subunits may serve partially redundant functions [64] , so that occupancy by antibody under certain conditions might only retard and not fully block fusion . Better tools and atomic-level structural information on relevant conformational states of Env are needed before firmer conclusions can be drawn . How 10E8 can diminish neutralization by other MPER antibodies cannot be completely clear without detailed structural information . However , recent structures of disulfide-stabilized soluble gp140 trimers omit the MPER [21] , [22] , and the structure of the MPER following receptor activation is also unknown . But the structures do show a considerable distance between points where MPERs join the trimer ( ∼30 Å ) . We therefore prefer an allosteric , or “trimer constraining” model , to explain neutralization interference between MPER antibodies and 10E8 mediated alteration of ligand sensitivity more generally ( Figure S9 ) . This would also explain why saturation of the mutant spike by 10E8 does not fully block 2F5 neutralization and why the effect also occurs with ligands that bind distal to 10E8 . Flexibility of the MPER [19] would presumably allow propagation of conformational changes to other regions of Env upon antibody binding; functional links between the MPER , NHR and DSL regions have also been described [65] , [66] . MPER mutations might also lead to an exchange of the MPER between adjacent subunits and membrane . On binding to the spike , 10E8 may stabilize conformations in which unoccupied subunits have diminished affinity for certain ligands and increased affinity for others . The propagation of conformational changes from bound to unbound protomers might explain why binding of 10E8 to one gp41 protomer reduces apparent affinity of additional MPER antibodies to other protomers as opposed to a model in which 10E8 itself is the steric block to further antibodies . Our results are most consistent with a model in which antibody and MPER interact and function in the specific context of trimeric Env . First , there is a known lack of correlation between neutralization and antibody binding to monomeric MPER peptides [10] , [17] , [47] , [67] . Our own attempts to correlate IC50s or maximum neutralization percentages of 10E8 to 10E8-peptide affinity data by Huang et al produced no obvious relationships ( unpublished observations ) . Second , the epitopes of 2F5 and 4E10 are not well exposed on resting primary spikes suggesting an unmasking of elements of Env upon CD4 engagement that allows antibody recognition [35]–[37] , [57] . Third , the MPER is enriched with hydrophobic residues that are typically found in the hydrophobic interior of proteins . Fourth , even conservative mutations to hydrophobic residues in the MPER destabilize some Envs as if they engaged in specific protein-protein rather than protein-lipid interactions [4] . Fifth , MPER disrupting mutations can enhance sensitivity of HIV-1 to MPER antibodies [4] , [46] , [47] , [65]; conversely , selective tightening of subunit interactions diminishes neutralization by MPER antibodies [9] . Sixth , quaternary interactions between hydrophobic elements at the base of the spike could help explain why a hydrophobic tip on CDR H3 seems to be required for neutralization by MPER antibodies as its insertion would be energetically favorable and likely disruptive [10] , [23]–[27] , [39] . Seventh , that MPER antibodies promote gp120 shedding suggests an opposite force on the MPER keeping gp120 on the unliganded spike . Eighth , sCD4 enhances MPER exposure , which shows reciprocal links between gp120 and the MPER . Examination of the literature turned up no equivalent mechanism to 10E8 partial neutralization . Although several different neutralization mechanisms have been described for MPER antibodies , including antibody-induced shedding of gp120 [39] , pre-attachment and post-attachment antibody binding [35] , [37] , [42] , [58] , [59] , [68] , these describe complete neutralization and have also been observed with non-MPER antibodies [13] , [39] . Antibody PG9 binds to one gp120 subunit on HIV-1 Env and occasionally partially neutralizes virus due to heterogeneity in glycan that forms part of its epitope [34] . However , PG9 has not been shown to occupy spikes that remain infectious , and 10E8 has no reported dependency on glycan . We did find reference to partial neutralization involving antibodies to respiratory syncytial virus surface glycoprotein [69] , [70] , however a basis for the effect was not proposed or further investigated . 10E8 has a reported weak affinity for membranes [29] . We also found evidence for weak binding of 10E8 to bald viral particles and cells that may warrant further investigation ( unpublished results ) . However , autoreactivity has no clear correlation with neutralization potency [15] , [25] , [28] , [71] . Importantly , our results show that quaternary structure and stability of HIV-1 Env also affects neutralization as well as antibody occupancy at the MPER . For vaccine design , partial occupancy of Envs by MPER antibodies or B cell receptors ( BCRs ) elicited early in a primary response could alter the structure and immunogenicity of Env . Trimeric immunogens could be identified that promote or discourage specific quaternary features of the native MPER . Neutralizing antibodies that saturate all three MPERs of the HIV-1 spike in a clash free manner may be the most potent and therefore most desirable to elicit . Approaches to enhance immunogenicity of the MPER on native spikes are also desired , including prime-boost strategies using MPER specific immunogens equipped with compatible T cell epitopes [8] , [9] , [11] , [12] , [47] . Vaccination , immunotherapy and immunoprophylaxis are becoming increasingly attractive approaches to combat HIV/AIDS [1]–[3] , [72] , so 10E8 clearly warrants further investigation considering its extreme potency and breadth of neutralization . Our results raise , however , a potential caveat for monotherapy using 10E8 ( or single epitope vaccines based on the 10E8 epitope ) due to the potential for partial escape mutants to be functionally stabilized by 10E8 or 10E8-like antibodies . However , we also show that 10E8 partial-resistant mutants are hypersensitive to certain gp41 antibodies and fusion inhibitors in 10E8-bound form ( Figures 2 and 5 ) . Targeting multiple sites of vulnerability on gp41 and other conserved regions of Env will best capitalize on this heightened sensitivity and limit the possibility for neutralization escape . In conclusion , our work shows that in order to gain a full picture of neutralization at the base of the trimeric spike , that consideration be given not only to the interaction antibody makes with a single MPER but also to the stability and recognition properties of adjacent , unoccupied MPERs and subunits of trimeric Env both pre- and post- receptor engagement .
HIV-1 backbone plasmids pSG3ΔEnv and pNL4-3 . Luc . R- . E- were obtained through the NIH AIDS Research and Reagent Program ( ARRRP ) , contributed by J . Kappes and X . Wu and by N . Landau , respectively . Env complementation plasmid pSVIIIexE7pA−YU2 was kindly provided by J . Sodroski ( Harvard ) and the envelope genes JR2 [47] and SF162 [73] were cloned in pSVIIIexE7pA− using the KpnI and XhoI sites as described previously [47] . Molecular clones of JR-FL , JR2 and SF162 were produced by subcloning into plasmid pLAI . 2 as described previously [9] . Env plasmids COT6 and its MPER Ala mutants and TM20 . 13 were kindly provided by E . Gray and L . Morris ( National Institute of Communicable Diseases , Johannesburg ) . Env plasmids M27390 PL 1706 and M20490 BMR 211 were kindly provided by G . Aldrovandi ( Children's Hospital of Los Angeles ) [49] . Quikchange mutagenesis was performed on JR-FL , JR2 , SF162 , COT6 and M27390 PL 1706 according to the manufacturer's protocol ( Agilent ) . HIV-1 antibodies were obtained from the following sources ( target epitope and subunit in parentheses ) : 10E8 ( MPER , gp41 ) IgG heavy and light chain DNA expression vectors were kindly provided by M . Connors ( VRC , NIH ) and IgG was produced in house , Fab 10E8 was prepared using Endoproteinase Lys-C ( Promega ) digestion according to the manufacturer's protocol . IgGs 2F5 and 4E10 ( MPER , gp41 ) were purchased from Polymun ( Vienna ) . IgGs Z13e1 ( MPER , gp41 ) , 8K8 ( NHR , gp41 ) , and DN9 ( NHR , gp41 ) [40] as well as 5-Helix ( CHR , gp41 ) [74] were produced in house . IgGs PG9 ( V2 , gp120 ) [56] , b12 ( CD4 binding site , or CD4bs , gp120 ) [75] and b6 ( CD4bs , gp120 ) [76] were generously provided by D . Burton ( Scripps ) . PGT121 ( N332 supersite , gp120 ) [77] was a gift from P . Poignard ( Scripps ) . IgGs VRC01 ( CD4bs , gp120 ) [78] , and F425 B4e8 ( V3 crown , gp120 ) [79] , were obtained through the ARRRP , contributed by J . Mascola , and by M . Posner and L . Cavacini , respectively . IgG 17b ( CD4bs , gp120 ) [80] was kindly provided by J . Robinson ( Tulane ) . IgGs 2G12 ( glycan , gp120 ) [81] and 447-52D ( V3 crown , gp120 ) [82] were purchased from Polymun ( Vienna ) . Soluble CD4 was purchased from Progenics ( Tarrytown ) , and C34 peptide [83] was obtained through the ARRRP . HEK-293 cells were from the ATCC , and 293 GnTI- cells were a gift from H . G . Khorana ( MIT ) [84] . TZM-bl cells ( CD4+CXCR4+CCR5+ ) , TZM-bl FcγRI cells [53] , U87 cells ( CD4+CCR5+ ) , and HOS cells ( CD4+CCR5+ ) were obtained through the ARRRP , contributed by J . Kappes and X . Wu , by D . Montefiori and G . Perez , by H . Deng and D . Littman , and by N . Landau , respectively . TZM-bl cell lines were maintained in DMEM supplemented with 10% FBS , 2 mM L-glutamine , 100 U of penicillin/ml , and 100 µ g/ml of streptomycin . U87 . CCR5 cells lines were maintained in DMEM supplemented with 15% FBS , 2 mM L-glutamine , 100 U/ml of penicillin , and 100 µg/ml of streptomycin , 1 µg/ml puromycin , and 1 µg/ml G418 . HOS cells were maintained in the same medium as TZM-bl but with the addition of 1 µg/ml puromycin . Pseudotyped viruses were produced by transfection of HEK-293 cells . DNA comprising Env plasmid and pSG3ΔEnv or pNL4-3 . Luc . R- . E- at a mass ratio of 1 3 . 5 was mixed with transfection reagent polyethylene imine ( PEI 25K , Sigma-Aldrich ) . Kifunensine ( Cayman Chemical Co . ) was added to HEK-293 cells 30 min prior to transfection [6] . Virus was alternatively produced in 293 GnTI- cells as described previously [85] . Virus containing supernatant was harvested 72 hours post transfection and 0 . 2 µm filtered to remove cellular debris . Viral supernatants were aliquoted and stored at −80°C . Single cycle viral entry neutralization assays were performed using TZM-bl cells , unless otherwise indicated . TZM-bl FcγRI cells , U87 . CCR5 cells , and HOS cells were also used , as indicated . Cells ( 105 per well ) were seeded in 96 well plates 24 hours prior to assay . The virus and inhibitor mixture was incubated at 37°C , and then added to TZM-bl cells . Infectivity was measured 48 h post infection using a luciferase assay system ( Promega ) and a Synergy HT Microplate Reader ( Bio-Tek ) . Data was processed using Prism 5 . 0 software ( Graphpad ) . Washout neutralization assays were performed as described previously [35] . Time course neutralization assays were performed by allowing the virus and inhibitor mixture to incubate for 1 , 8 or 20 hour ( s ) at 37°C . Maturation neutralization assays were performed using virus that had been pre-incubated at 37°C for 20 hours prior to usage . Neutralization assays in the presence of 10E8 were performed by adding to the virus and inhibitor mixture a constant concentration of 10E8 IgG , typically 10 µg/ml for MPER mutants and 0 . 1 µg/ml and 0 . 01 µg/ml for wild type JR-FL and SF162 , respectively . The mixture of virus , 10E8 and inhibitor were incubated at 37°C for 1 hour and then added to TZM-bl cells . Modified assays were processed just as the infectivity assay described above . To test the effect of 10E8 aggregation on neutralization activity , 10E8 IgG was deliberately aggregated through concentration with Amicon Ultra centrifugal filters ( Millipore ) in PBS . Aggregate was pelleted by centrifugation at 22 , 000×g for 5 min and the soluble fraction of 10E8 in the supernatant was saved . The pellet was washed 4 times in PBS by vortexing and vigorous pipetting throughout which time the aggregate remained visible and insoluble . The amount of 10E8 in the pellet was estimated by subtracting the amount recovered in the soluble fraction . Neutralization assays using 10E8 visibly aggregated in suspension were performed as described above . Temperature gradient infectivity assays were determined using a gradient PCR block ( Mastercycler , Eppendorf ) as previously described [4] . Briefly , virus samples were incubated over a thermal gradient range from 37°C to 56°C for 1 hour in parallel using a 96 well PCR plate . Thermally treated virus samples were cooled to room temperature and added to TZM-bl cells . Luciferase activity was determined 48 hours post infection as described above . The temperature at which 10% of infectivity remained ( T90 ) was determined using Prism 5 . 0 software ( Graphpad , La Jolla ) . In half-life infectivity decay experiments , virus and antibody were co-incubated at 37°C for various time intervals and infectivity of virus was determined using TZM-bl indicator cells . Data was plotted using a non-linear , one phase exponential decay equation ( plateau constraint = 0 ) and t1/2 was determined using Prism 5 . 0 software . Virions were produced by transfection using molecular clone plasmid pLAI , pelleted in an Optima ultracentrifuge ( Beckman; 60 , 000×g at 4°C ) and resuspended 100-fold concentrated in PBS . For gel mobility shift assays , virions were pre-incubated with antibodies for 30 min before preparation for BN-PAGE . In some cases virus was pelleted in a microcentrifuge for 45 min at 4°C and the buffer was exchanged to remove unbound antibody prior to detergent treatment . For heat gradient BN-PAGE , virions were exposed to a temperature gradient for 1 hour , as detailed above , prior to detergent solubilization . BN-PAGE was performed as previously described [4] . Briefly , samples were treated with 1% DDM for 20 min on ice . Samples were then electrophoresed on 3-12% NativePAGE Bis-Tris gels ( Invitrogen ) according to the manufacturer's instructions . Proteins in the gel were then transferred to a PVDF membrane; membranes were blocked in 5% non-fat dry milk and blotted overnight at 4°C using a cocktail of antibodies to gp120 ( 2 µg/ml each of b12 , 2G12 and 447-52D ) and to gp41 ( 1 µg/ml each of 2F5 , 4E10 and Z13e1 ) combined . Membranes were washed , probed for 30 min at room temperature with a HRP conjugated goat anti-human Fc antibody ( Jackson ) , and peroxidase activity was assayed using Super Signal West Pico Chemiluminescence ( Pierce ) . Relevant exceptions to this protocol are noted in figure legends . In order to quantify antibody stoichiometry , the distance between the midpoints of Fab-shifted vs untreated bands on BN-PAGE blots was measured using ImageJ software ( NIH ) , and divided by the distance shifted by Fabs b12 and PG9 that are assumed to bind three and one Fab ( s ) per trimer , respectively . To estimate antibody affinity for the detergent-solubilized Env trimer , BN-PAGE blots were again analyzed using ImageJ software and the percentage of trimer that remained unshifted at each concentration was calculated by comparing the band intensity to that of samples with no antibody added . | As vaccination , immunoprophylaxis and immunotherapies are becoming increasingly feasible approaches to combat HIV/AIDS , understanding the activity of relevant anti-HIV antibodies is crucial . Antibody 10E8 defines a key vulnerability on the envelope spikes of a vast majority of HIV isolates but mechanisms of resistance to this neutralizing antibody are incompletely understood . Our findings show how partial neutralization of HIV can occur through apparent partial occupancy by 10E8 of HIV spikes that is accompanied by specific , antibody mediated effects on spike stability , infectivity and sensitivity to various inhibitors of HIV . We reveal a previously unappreciated mechanism of spike-antibody recognition where consequences on viral infectivity by 10E8 binding are dependent on interactions between subunits of the virion spike that modulate its stability and recognition properties . HIV vaccine development and immunoprophylaxis involving 10E8-like antibodies and their target , the gp41 MPER , may have to consider functional relationships involving the MPER and antibody occupancy at the base of trimeric spikes . | [
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] | 2014 | Antibody to gp41 MPER Alters Functional Properties of HIV-1 Env without Complete Neutralization |
Fes1 is a conserved armadillo repeat-containing Hsp70 nucleotide exchange factor important for growth at high temperature , proteasomal protein degradation and prion propagation . Depleting or mutating Fes1 induces a stress response and causes defects in these processes that are ascribed solely to disruption of Fes1 regulation of Hsp70 . Here , we find Fes1 was essential for degradation of gluconeogenic enzymes by the vacuole import and degradation ( Vid ) pathway and for cell wall integrity ( CWI ) , which is crucial for growth at high temperature . Unexpectedly , Fes1 mutants defective in physical or functional interaction with Hsp70 retained activities that support Vid and CWI . Fes1 and the Fes1 mutants bound to the Vid substrate Fbp1 in vitro and captured Slt2 , a signaling kinase that regulates CWI , from cell lysates . Our data show that the armadillo domain of Fes1 binds proteins other than Hsp70 , that Fes1 has important Hsp70-independent roles in the cell , and that major growth defects caused by depleting Fes1 are due to loss of these functions rather than to loss of Hsp70 regulation . We uncovered diverse functions of Fes1 beyond its defined role in regulating Hsp70 , which points to possible multi-functionality among its conserved counterparts in other organisms or organelles .
Fbp1 and other gluconeogenic enzymes that are highly expressed when cells are starved of glucose are rapidly inactivated and degraded when glucose is restored . Restoring glucose after starving one day causes Fbp1 to be degraded by the proteasome [1 , 2] , but when cells are starved three days before restoring glucose it is imported into specialized vesicles that transit to the vacuole in a process called vacuole import and degradation ( Vid ) [3–8] . Import of Fbp1 into Vid vesicles requires the cytosolic Hsp70 Ssa2 [9] . The nearly identical Ssa1 cannot substitute for Ssa2 in this process , but swapping one non-conserved amino acid ( Ala83/Gly83 ) between them is enough to switch their ability to function in the Vid pathway [10] . Hsp70s act in protein folding and transport by binding and releasing exposed hydrophobic surfaces of proteins in a two-step ATP-regulated cycle . ATP-bound Hsp70 is in an "open" substrate-accessible state . ATP hydrolysis causes a lid-like structure to close over bound substrates , effectively trapping them . Release of ADP and ensuing rebinding of ATP facilitates return to the open state and release of substrates . The low intrinsic rate of this cycle primes Hsp70 for rigorous regulation by many co-chaperones . In particular , J-proteins and nucleotide exchange factors ( NEFs ) are key Hsp70 partners that regulate the nucleotide hydrolysis and exchange steps , respectively [11] . J-proteins recruit Hsp70 to various locations , present substrates to Hsp70 and promote ATP hydrolysis . NEFs accelerate dissociation of ADP to facilitate release of substrates . Yeast have twenty-two J-proteins and four cytosolic NEFs [12 , 13] that cooperate to regulate this Hsp70 cycle , and the many possible combinations of these and other regulators provide both specificity and extensive versatility to Hsp70 function . Ssa1/Ssa2 residue 83 is near a region where NEFs interact [14] , so we hypothesized the specificity of this residue for Vid function was mediated by a difference in physical or functional interaction with NEFs . Here we tested if Hsp70 NEFs dictated specificity of Ssa2 for Vid function and found the NEF Fes1 [15] was itself required for Vid degradation of Fbp1 . Yet , differences in physical interaction of Fes1 with Ssa1 and Ssa2 did not seem significant enough to account for the specificity in Vid function . Unexpectedly , when we tested whether Fes1 affected ability of Ssa1 or Ssa2 to bind Fbp1 , we found Fes1 itself bound Fbp1 , showing it can bind a protein other than Hsp70 . Fes1 , which is related to human NEF HspBP1 , plays a role in helping Hsp70 deliver substrates to the proteasome and cells lacking Fes1 display reduced proteasome activity , a constitutively activated stress response and temperature sensitivity [15 , 16] . Fes1 has an armadillo repeat domain important for binding Hsp70 to facilitate release of ADP and a "release domain" that occupies the Hsp70 substrate-binding pocket to ensure released substrates do not rebind [14 , 16 , 17] . Because mutants of Fes1 defective in either of these functions failed to provide Fes1 function in vivo , the role of Fes1 is thought to be limited to its regulation of Hsp70 . Our initial findings prompted us to re-evaluate cellular functions of such Fes1 mutants . We found they not only retained Fes1 functions important for Vid and for growth at elevated temperature , but also bound to Fbp1 and provided functions important for cell wall integrity ( CWI ) and growth under various stresses . Our findings uncover unanticipated Fes1 substrate-binding activity and imply that Fes1 can perform important Hsp70-independent functions in cells .
Restoring glucose to cells starved of glucose for three days causes rapid degradation of Fbp1 by Vid [18] . After Vid activation , the abundance of Fbp1 in our wild type cells was noticeably reduced after an hour , and by two hours it was barely detectable ( Fig 1A ) . As seen before [9 , 10] , this degradation depended on a function of Ssa2 that cannot be provided by Ssa1 . Our earlier findings suggested that this difference between Ssa1 and Ssa2 in the Vid pathway could be due to differential interaction of the Hsp70s with NEFs [10] . The yeast cytosolic Hsp70 NEFs are Fes1 , Snl1 , Sse1 and its paralog Sse2 . We used strains lacking individual NEFs to test if any of them have a role in Vid . Because deleting SSE1 causes pleiotropic effects , deleting SSE2 has no overt phenotype and deleting both is lethal [19–21] , we used cells lacking only SSE1 to test depletion of Sse NEF function . We found degradation of Fbp1 by Vid was normal in cells lacking Snl1 or Sse1 , but was severely impaired in fes1Δ cells ( Fig 1B and 1D ) . Thus , Fes1 , but not the other NEFs , has an essential role in degradation of Fbp1 by the Vid pathway . We then monitored proteasomal degradation of Fbp1 by doing similar experiments using cells that were starved for only one day before restoring glucose ( Fig 1C and 1E ) . This proteasomal degradation of Fbp1 was rapid and did not depend on Ssa2 or on Fes1 , as seen by others [9 , 16] . Fes1 is known only as a regulator of Hsp70 , so these results suggested that Fes1 underlies the requirement of Ssa2 for Vid . We first tested this idea by looking for differences in interaction of Fes1 with Ssa1 and Ssa2 using purified proteins . After mixing GST-Fes1 with the individual Hsp70s we pulled down Fes1 on glutathione resin and assessed its ability to capture Hsp70 ( Fig 2A ) . As ATP and ADP bind Hsp70 and regulate its substrate-binding cycle , we included these nucleotides separately in the reactions . To test if Fes1 affected interactions of Ssa1 or Ssa2 with Fbp1 , we performed a similar set of reactions that also contained His6-Fbp1 . Fes1 captured similar amounts of Ssa1 and Ssa2 in reactions without Fbp1 ( Fig 2A , lanes 1–4 ) or with Fbp1 ( Fig 2A , lanes 8–11 ) . Fes1 bound slightly more Hsp70 in reactions with ADP than in those with ATP , as seen by others [15] . This difference was observed consistently in our pull-down reactions . In the reactions that included Fbp1 , both Fbp1 and the Hsp70s were captured , which we presumed could occur in a ternary complex with Fes1 binding Hsp70 as NEF and Fbp1 being bound to Hsp70 as substrate . Unexpectedly , however , Fes1 bound Fbp1 in control reactions without Hsp70 ( Fig 2A , lanes 13 and 14 ) . Only traces of the Hsp70s ( Fig 2A , lanes 6–7 ) or Fbp1 ( lane 12 ) bound to the column when Fes1 was omitted . Together these results suggest that the specificity of Ssa2 for Vid is not determined by a difference in the way Fes1 physically interacts with Ssa1 and Ssa2 , and show that Fes1 can bind Fbp1 directly . To substantiate these conclusions , we performed similar experiments with the same proteins , but instead pulled down His6-Fbp1 on metal affinity resin and assessed its ability to capture Hsp70 and Fes1 ( Fig 2B ) . Although ATP and ADP can bind Fbp1 and inhibit its activity , AMP is recognized as a primary allosteric inhibitor of Fbp1 [22 , 23] , raising the possibility that it might influence binding of Fes1 to Fbp1 . We therefore included additional reactions containing AMP . In agreement with our results above , His6-Fbp1 captured Fes1 when Hsp70 was absent ( Fig 2B , lanes 1–4 ) , which confirms Hsp70-independent binding of Fes1 to Fbp1 . Compared with reactions lacking nucleotide , addition of AMP did not affect this binding , but ATP and ADP each enhanced it . This nucleotide dependency of Fes1-Fbp1 interaction was consistent in all our pull-down experiments , regardless of which protein was pulled down . His6-Fbp1 also bound Hsp70 ( Ssa1 and Ssa2 ) when Fes1 was omitted ( Fig 2B , lanes 13–20 ) , and this binding also occurred best when ATP or ADP was present . When all three proteins were mixed ( Fig 2B , lanes 5–12 ) His6-Fbp1 bound both Hsp70 and Fes1 in similar proportions and with similar nucleotide dependencies as when they were in separate reactions . These complementary experiments show that Fes1 and Fbp1 interact directly with Hsp70 and with each other . We then repeated the experiment using GST-Fes1 , as in Fig 2A , but added reactions with AMP or without nucleotides ( Fig 2C ) . In agreement with the way Fbp1 pulled down Fes1 ( Fig 2B ) , capture of Fbp1 by GST-Fes1 was more effective when ATP or ADP was present ( Fig 2C , lanes 1–4 ) , confirming that ATP and ADP enhanced binding of Fes1 to Fbp1 . Also in line with our initial observations in Fig 2A , GST-Fes1 bound Hsp70 ( both Ssa1 and Ssa2 ) in the presence of ATP and ADP ( Fig 2C , lanes 13–20 ) . Unexpectedly , Fes1 captured more Hsp70 in reactions with AMP or without nucleotides . Use of these binding conditions has not been reported previously , presumably because AMP has no known role in Hsp70 function and it is unclear why Fes1 would bind a nucleotide-free state of Hsp70 . The physiological relevance of these results is unclear , but we observed this pattern of nucleotide influence on Hsp70 capture by GST-Fes1 consistently in reactions with or without Fbp1 . In reactions containing all three proteins , GST-Fes1 captured Hsp70 best with AMP or without nucleotides and captured Fbp1 best with ATP and ADP ( Fig 2C , lanes 5–12 ) , which is consistent with the way it bound Hsp70 and Fbp1 in separate reactions . As additional controls for non-specific binding we repeated the pull down using GST alone in place of GST-Fes1 and found that only traces of Hsp70 and Fbp1 bound to the column and that the presence or absence of nucleotides did not affect the amounts of proteins bound ( Fig 2D ) . Therefore , the Fbp1 and Hsp70 that was captured by GST-Fes1 was binding primarily to Fes1 and not to GST . The first 12 amino acids of Fbp1 and other gluconeogenic enzymes , particularly a conserved proline at position 2 , are needed for their degradation by both the Vid and proteasome pathways [7 , 24 , 25] . Additionally , serine at position 12 of Fbp1 is phosphorylated , although this modification is not essential for Fbp1 degradation by Vid , and threonine residue 13 is a potential phosphorylation site [7 , 26 , 27] . Fes1 is dispensable for proteasomal degradation of Fbp1 ( Fig 1 and [16] ) , but this region of Fbp1 might contribute to the Vid requirement of Fes1 or Ssa2 . We tested if these Fbp1 residues were important for it to bind Fes1 or Hsp70 by deleting or mutating them . Deleting P2 , mutating it to alanine ( P2A ) , or combining P2A with S12A and T13A all had little effect on ability of purified His6-Fbp1 to capture Ssa1 , Ssa2 or Fes1 in vitro ( S1A and S1B Fig ) . His6-Fbp1Δ2–12 , which lacks amino acid residues 2–12 , also captured both Fes1 and Hsp70 ( S1C Fig ) . In a complementary experiment , GST-Fes1 captured both Fbp1Δ2–12 and Hsp70 ( S1D Fig ) . These results suggest that the role of residues 2–12 for the degradation of Fbp1 by Vid is not to mediate an interaction with Hsp70 or Fes1 . Fes1 residues A79 and R195 are needed for physical interaction of Fes1 with Hsp70 [14] and it was shown that the Fes1A79R , R195A double mutant does not interact with Hsp70 in vitro or provide Fes1 function in vivo [14 , 16 , 28 , 29] . In repeating the in vitro experiments , we found both Ssa1 and Ssa2 were clearly captured by wild type GST-Fes1 ( Fig 3A , lanes 1–6 ) , but these Hsp70s were observed in only trace amounts from reactions pulling down GST-Fes1A79R , R195A ( lanes 10–15 ) . Differences in quantified ratios of GST-Fes1A79R , R195A/Hsp70 in this gel and those of the background control GST/Hsp70 in Fig 2D lanes 5–12 were negligible , indicating that any Hsp70 detected in these reactions was likely not from binding to Fes1A79R , R195A . These results agree with the earlier conclusion that Fes1A79R , R195A does not bind Ssa1 in vitro and here we show it similarly fails to bind Ssa2 . In contrast , His6-Fbp1 captured GST-Fes1A79R , R195A in reactions without Hsp70 ( Fig 3B , lanes 1–4 ) , or with Hsp70 ( lanes 5–12 ) showing again that the combined A79R and R195A mutations do not prevent binding of Fes1 to Fbp1 . In all reactions containing His6-Fbp1 and GST-Fes1A79R , R195A , binding of His6-Fbp1 to GST-Fes1A79R , R195A showed similar nucleotide dependence as with wild type GST-Fes1 ( compare Fig 3B with Fig 2B ) . As in reactions with wild type GST-Fes1 , His6-Fbp1 again captured more Hsp70 in reactions with ATP and ADP compared with those containing AMP or without nucleotides . Thus , Fbp1 bound to Fes1A79R , R195A in a pattern similar to that of wild type Fes1 . When comparing relative amounts of Fes1A79R , R195A and wild type Fes1 that were pulled down by His6-Fbp1 in reactions with Hsp70 , however , it was apparent that Fbp1 captured more Fes1A79R , R195A than wild type Fes1 ( compare differences in amounts of Fes1 and Hsp70 in Fig 3B , lanes 5–12 with those in Fig 2B , lanes 5–12 ) . This visually evident difference was confirmed by quantifying relative amounts of Fes1 and Hsp70 in these gels ( Table 1 ) . This difference could be explained simply by there being more Fes1A79R , R195A available to bind His6-Fbp1 because it is not bound to Hsp70 . The first 34 amino acids of Fes1 is defined as a release domain ( RD ) important for ensuring substrate release by Hsp70 [17] . After Fes1 promotes nucleotide exchange and substrate dissociates from Hsp70 , the RD occupies the Hsp70 substrate-binding pocket to prevent rebinding of substrates . Fes1ΔRD , which lacks residues 2–34 , still binds Hsp70 and has NEF activity in vitro , but it lacks this substrate mimic function and Fes1ΔRD does not complement growth or proteasome defects of fes1Δ cells [17] . We repeated the in vitro pull-down reactions using GST-Fes1ΔRD and found it captured Hsp70 and Fbp1 in relative amounts and with nucleotide dependencies that were similar to those of wild type GST-Fes1 ( Fig 3C , compare lanes 1–4 with lanes 5–8 , and lanes 9–24 with Fig 2C , lanes 5–20 ) . These results indicate that the armadillo domain of Fes1 is enough to confer the wild type pattern of interactions with both proteins . If the essential role of Fes1 in Vid were mediated by its ability to regulate Hsp70 , then degradation of Fbp1 should be impaired in cells expressing Fes1A79R , R195A in place of wild type Fes1 . We found , however , that Vid degradation of Fbp1 in strain SKY207 , which expresses Fes1A79R , R195A from the FES1 genomic locus , was as efficient as that in wild type cells ( Fig 3D and 3E ) . These results suggest that the requirement of Fes1 for Vid does not depend on interaction of Fes1 with Hsp70 and that Fes1A79R , R195A retains Fes1 function needed for Vid that is separate from its NEF regulation of Hsp70 . We also constructed a strain ( 1853–35 ) expressing Fes1ΔRD from the native FES1 locus and found Fbp1 was degraded effectively by Vid in this strain ( Fig 3E and 3F ) . Thus , the RD function of Fes1 was also dispensable for Vid . The earlier observations that temperature sensitivity of fes1Δ cells can be rescued by plasmid-based expression of wild type Fes1 , but not by Fes1A79R , R195A , led to the conclusion that Fes1 NEF activity was essential for Fes1 function in vivo [14 , 16] . As with the earlier strain , growth of our fes1Δ mutant was near normal at optimal temperature ( 30°C ) , but severely compromised at 37°C ( Fig 4A , Table 2 ) . We found Fes1 also was important for growth at the sub-optimal 23°C . However , although the rate of growth of our fes1A79R , R195A cells at 37°C was three times slower than wild type cells , they grew three times faster than the fes1Δ mutant ( Table 2 ) . When grown on plates at 37°C , fes1Δ cells did not form colonies at all , while the difference in growth between fes1A79R , R195A and wild type cells was apparent , but much more subtle . Additionally , unlike cells lacking Fes1 , those expressing Fes1A79R , R195A were viable at 39°C , which is the upper limit for growth of most S . cerevisiae strains . They grew much more slowly at 39°C than wild type cells , which reflects reduced Fes1 activity , but they still formed colonies upon extended incubation ( Fig 4A ) . Together these results show that Fes1A79R , R195A retains substantial Fes1 function in vivo . The fes1ΔRD cells failed to grow at 37°C , which agrees with earlier work [17] , and they grew noticeably more slowly than fes1Δ cells at optimal temperature ( Fig 4A , Table 2 ) . These results indicate that interaction of Fes1 with Hsp70 is not enough to provide Fes1 functions needed for growth at non-optimal temperature and suggest that expression of Fes1ΔRD is toxic . To determine if growth differences of these strains could be due to differences in expression , we compared steady-state abundance of the Fes1 proteins ( Fig 4B ) . Fes1 encodes a longer splice variant ( Fes1L ) containing a C-terminal nuclear localization signal that is not needed for high temperature growth and other Fes1 functions [29] . Our chromosomal fes1A79R , R195A allele does not produce this long form because it has URA3 inserted just after the termination codon of Fes1 . Fes1A79R , R195A was a bit less abundant than that of wild type Fes1 and , as expected , the Fes1L version of Fes1A79R , R195A was absent . Fes1ΔRD was expressed at levels much higher than wild type Fes1 . Thus , reductions in growth are not explained simply by reductions in expression . To try and resolve differences in Fes1A79R , R195A phenotypes we see with those of the earlier work , we repeated those earlier experiments using the same strain and plasmid-based expression of Fes1 and Fes1A79R , R195A regulated by the weak ADH1 promoter [14 , 30 , 31] . As controls we included our fes1Δ strain and we repeated the experiments in both strains with alleles regulated by the FES1 promoter that is activated by stress . The FES1 promoter improved complementation by Fes1A79R , R195A , but not as effectively as integrating the fes1A79R , R195A allele at the native FES1 chromosomal locus ( S2A–S2C Fig ) . Here again the results are consistent with Fes1A79R , R195A functioning less well than wild type Fes1 , but retaining substantial Fes1 activity . Apparently , regulation of Fes1 expression in its native context is important for its functions in vivo , which could be related to maintaining a balance of interacting factors whose expression is co-induced by the same environmental conditions . Temperature sensitivity that is associated with defects in cell wall integrity ( CWI ) can be suppressed by osmotic support in the growth medium . We added 1M sorbitol to the medium used for growth assays to provide such support and found growth of fes1Δ and fes1ΔRD cells was restored even at 39°C ( Fig 4C ) . We tested our strains for other characteristics of cell wall defects and found fes1Δ and fes1ΔRD cells were hypersensitive to SDS and they leak alkaline phosphatase , even at 30°C where a growth defect is not so pronounced ( Fig 4D ) [32 , 33] . Cells expressing Fes1A79R , R195A showed no indication of SDS hypersensitivity or cell wall leakage at 30°C , suggesting the role of Fes1 in CWI does not require NEF function . Moreover , the temperature sensitivity of fes1Δ cells was not suppressed by elevating expression of Sse1 ( Fig 4E ) , which is consistent with the CWI defect of fes1Δ cells not being due simply to a loss of NEF function . Thus , the loss of Fes1 causes several phenotypes diagnostic of CWI defects that can be overcome by Fes1A79R , R195A , which lacks Hsp70-binding and NEF function , but not by Fes1ΔRD . As it is unlikely that sorbitol helps Hsp70 release substrates , we suspect the CWI defect in fes1Δ cells is related to loss of a Fes1 activity that is retained by Fes1A79R , R195A rather than the loss of Hsp70 NEF activity . Accordingly , we found that while fes1Δ and fes1ΔRD cells were hypersensitive to hydrogen peroxide , which is an oxidative stress not specific to cell wall damage , Fes1A79R , R195A also suppressed this sensitivity , but 1M sorbitol did not ( Fig 4F ) . Thus , sorbitol is not a general suppressor of phenotypes caused by lack of Fes1 , and Fes1 can perform PQC functions important for growth under stress beyond CWI that do not require its NEF regulation of Hsp70 , but do require its RD . [URE3] prions are composed of self-assembling amyloid aggregates of the transcriptional regulator Ure2 [34 , 35] . The replication of these aggregates that is necessary for their continued distribution among dividing cells depends on their fragmentation by the protein disaggregation machinery composed of Hsp104 , Hsp70 , Hsp40 and NEF [36] . Fes1 is required for propagation of [URE3] prions [37 , 38] and this dependence provides another measure of Fes1 function in vivo . We monitor [URE3] in our strains using ADE2 regulated by the DAL5 promoter , which is repressed by Ure2 so cells are Ade– . When [URE3] is present , Ure2 is depleted into amyloid aggregates and cannot maintain this repression so cells are Ade+ . To test if Fes1A79R , R195A or Fes1ΔRD could promote prion propagation , we monitored [URE3] among meiotic progeny of [URE3] diploids heterozygous for wild type FES1 and fes1A79R , R195A or fes1ΔRD ( Fig 4G ) . Among progeny of 20 tetrads for each diploid , all those expressing wild type Fes1 propagated [URE3] stably , while all of those expressing Fes1A79R , R195A or Fes1ΔRD did not . Thus , neither Fes1A79R , R195A nor Fes1ΔRD supported [URE3] propagation . These results agree with the conclusion made by others that these Fes1 mutants do not cooperate with Hsp70 in vivo . They also align with the requirement that all other Hsp70 co-chaperones known to be important for [URE3] propagation , including NEFs Snl1 and Sse1 , interact functionally with Hsp70 [16 , 37 , 39–42] . Cell wall stress activates a mitogen-activated protein kinase ( MAPK ) signaling pathway that is controlled by MAP kinase Slt2 [43 , 44] . Activation of Slt2 depends on the Hsp90/Hsp70 chaperone system and this pathway can be activated 2-3-fold in cells lacking Fes1 [45 , 46] . In light of our findings that Fes1 is essential for Vid and can bind Fbp1 , we tested if the relationship between Fes1 and defective cell walls might involve an interaction between Fes1 and Slt2 . We purified Fes1 from lysates of fes1Δ cells that express wild type or mutant versions of Fes1-GST from plasmids and looked for co-purification of Slt2 ( Fig 5A ) . We also repeated these experiments using the same cultures of cells treated with the cell wall-specific stressor calcofluor white . Slt2 co-purified with wild type Fes1 , Fes1A79R , R195A and Fes1ΔRD from lysates of both treated and untreated cells . For all strains , more Slt2 co-purified from cells exposed to calcofluor white , which corresponded to an increased amount of Slt2 in the treated strains . Thus , Fes1 and Slt2 interact in vivo and the ΔRD or combined A79R and R195A mutations do not disrupt this interaction . We were unable to purify Slt2 in a soluble form , so we cannot confirm whether this interaction could be direct . Nevertheless , together with our other data these results suggest that this interaction is important for CWI signaling , that it does not require Hsp70 binding or NEF function of Fes1 , that the armadillo domain mediates the interaction , and that the functional output of the interaction requires the RD . We further found Vid degradation of Fbp1 was normal in cells lacking Slt2 ( Fig 5B ) , indicating that Slt2 is not important for Vid and that any interaction between Fes1 and Slt2 is unrelated to this protein degradation pathway . Our findings that the RD is needed for CWI , but not Vid , are in line with Fes1 acting differently in these two processes . Wild type Fes1 and Fes1A79R , R195A maintained cell wall integrity , but Fes1ΔRD did not , which suggest that the RD has a function in CWI unrelated to its role in regulating Hsp70 . If so , then deleting the RD from Fes1A79R , R195A ( creating Fes1A79R , R195AΔRD ) should impair ability of Fes1A79R , R195A to support CWI . Alternatively , if the phenotypes of cells expressing Fes1ΔRD are due to impairment of Hsp70 function by non-productive binding of Fes1ΔRD to Hsp70 , then Fes1A79R , R195AΔRD , which should not bind Hsp70 , should support CWI like Fes1A79R , R195A . We found that a strain expressing Fes1A79R , R195AΔRD in place of Fes1 from its genomic locus grew somewhat faster than fes1Δ or fes1ΔRD mutants at 23°C ( Fig 6A ) . Otherwise , it grew more slowly than fes1Δ cells at optimal temperature ( 30°C ) , was more sensitive to high temperature and to the cell wall stressors SDS and calcofluor white , and leaked alkaline phosphatase at 30°C . These phenotypes resemble those of cells expressing Fes1ΔRD rather than those expressing Fes1A79R , R195A , which shows growth under these conditions depends on a function of the RD and that the RD has a role in CWI beyond its acting to prevent rebinding of substrates released by Hsp70 . Not surprisingly , Fes1A79R , R195AΔRD did not support propagation of [URE3] , as seen for both Fes1A79R , R195A and Fes1ΔRD ( S3 Fig ) . At 30°C the Fes1A79R , R195AΔRD protein was expressed at a level much lower than that of Fes1ΔRD ( Fig 6B ) , which suggests that negative effects on growth caused by expressing only the armadillo domain of Fes1 do not require high expression or interaction with Hsp70 . Additionally , unlike Fes1A79R , R195A , the abundance of Fes1A79R , R195AΔRD was not increased after a shift to elevated temperature , which suggests the RD and an interaction of Fes1 with Hsp70 can combine to influence Fes1 stability or expression .
Although all functions of Fes1 are thought to be mediated by its role in helping Hsp70 release ADP and substrates , we find that Fes1 mutants lacking these activities retain important functions in different cellular processes , which shows that Fes1 performs functions that are separate from its regulation of Hsp70 . We show Fes1 interacts with proteins involved in these processes , which reveals a non-Hsp70 protein-binding activity of Fes1 and suggests Fes1 could act in its roles by binding to these proteins . Although Fes1 is not needed for proteasomal degradation of Fbp1 after short-term starvation , we find Fes1 is needed for efficient degradation of Fbp1 when cells undergo prolonged starvation . Thus , Fes1 is not required for cells to switch away from the proteasome pathway , but it is needed for Vid after cells have committed to the switch . The specific role of Fes1 in Vid remains to be determined , but our results showing Fes1 binds similarly to Ssa1 and Ssa2 and that Fes1A79R , R195A and Fes1ΔRD supported Vid function imply that this role does not involve Fes1 regulation of Ssa2 or underlie the functional differences between Ssa1 and Ssa2 . That Fes1A79R , R195A and Fes1ΔRD also retain ability to bind Fbp1 suggests the role of Fes1 in Vid could be linked to this interaction and we suspect Fes1 might bind other Vid substrates . Our data do not rule out the formal possibility that Fes1A79R , R195A retains ability to bind and regulate Hsp70 in cells at some obviously reduced level . However , ample evidence provided here and by others implies that Fes1A79R , R195A does not function as a NEF for Hsp70 in a physiologically meaningful way . Based on crystal structure data of human Fes1 homolog HspBP1 bound to Hsp70 , the A79R and R195A mutations in Fes1 were designed to disrupt Hsp70 binding and then shown to have that effect on binding Ssa1 in vitro and in vivo [14 , 16 , 28 , 29] . We confirmed the in vitro findings and extended them to Ssa2 , which is more abundant in vivo . We further found Fes1A79R , R195A did not support propagation of [URE3] , which exemplifies the expected loss of Hsp70-NEF activity . We see differences in phenotypes of Fes1A79R , R195A with those reported earlier that apparently are due to differences in gene expression and growth conditions used . As in the earlier work [16 , 17] , we find plasmid-based expression of Fes1A79R , R195A gave generally reduced ( and variable ) complementation , in particular when using the weak ADH1 promoter . Integrating alleles into native chromosomal loci avoids uncontrollable variations in expression and resulted in the strongest complementation . In agreement with earlier data , we do see considerably reduced ability of Fes1A79R , R195A to support growth at the extreme temperature of 39°C , which clearly exposes functional deficiencies of this mutant . At the universally applied stringent temperature of 37°C , however , growth differences of wild type and fes1A79R , R195A cells were much less notable . Additionally , whereas depleting Fes1 abolished Vid function and caused readily identifiable growth defects under other stress conditions , cells expressing Fes1A79R , R195A natively from its chromosomal locus were for the most part phenotypically similar to wild type cells . Thus , loss of Fes1 NEF function is much less physiologically detrimental than loss of Fes1 , which implies that fes1Δ cells suffer from something greater than loss of Fes1 NEF activity . Accordingly , the temperature sensitivity of fes1Δ cells is not suppressed by elevating expression of the Hsp70 NEF Sse1 , which also is important for cell wall integrity [33] . In contrast , the CWI defect of sse1Δ cells is due to reduced NEF activity that can be suppressed by elevating Fes1 [33] . Thus , Fes1 contributes to CWI in a way that Sse1 does not and the need of Fes1 for CWI can be accomplished by a non-NEF function . Overall our data indicate Fes1A79R , R195A retains Fes1 functions that are important for cellular fitness . In contrast , cells expressing Fes1ΔRD , which binds Hsp70 and retains NEF function , had normal Vid function , but otherwise were even less fit than cells lacking Fes1 . It was proposed that growth defects associated with inability of Fes1ΔRD to ensure release of substrates from Hsp70 are caused by persistent binding of proteasome-targeted substrates to Hsp70 , which reduces both availability of Hsp70 and delivery of the substrates to proteasomes [17] . The conclusion that Fes1ΔRD is not only defective , but also could interfere with cellular processes that depend on Hsp70 by binding Hsp70 non-productively is in line with its high expression and the growth defects of fes1ΔRD cells being more pronounced than those of fes1Δ cells . Our findings that Fes1 A79R , R195AΔRD was expressed at much lower abundance and still behaved like Fes1ΔRD , however , indicate that negative effects of Fes1ΔRD on growth do not require its elevated expression or interaction with Hsp70 . We emphasize , moreover , that sorbitol , an osmotic stabilizer that is a widely recognized suppressor of temperature sensitivity caused by cell wall defects , very effectively overcame sensitivity of both fes1Δ and fes1ΔRD cells to even extreme temperature . It is difficult to envision how the relatively inert sorbitol could overcome severe and classic CWI phenotypes specifically by helping Hsp70 release ADP or substrates , especially as we show sorbitol is not a general suppressor of Fes1 deficiency . We presume the major growth defects of Fes1 mutants are more likely due to loss of an Hsp70-independent function of Fes1 in cell wall integrity rather than to lost or damaged ability of Fes1 to regulate Hsp70 . Our findings establish an important role for Fes1 in cell wall integrity and imply this role does not require its NEF function . We suppose the interaction of Fes1 with Slt2 is likely an important part of this role . This interaction was detected in cell lysates , so we cannot rule out that it is indirect . Fes1A79R , R195A also bound Slt2 , however , implying that any indirect interaction does not occur through binding of Fes1 to Hsp70 in a ternary complex . Additionally , Fes1ΔRD bound Slt2 , but failed to maintain CWI , showing that this interaction alone is not enough to maintain cell wall integrity . Together with our results showing Fes1A79R , R195AΔRD is also defective in CWI , these findings suggest the RD contributes importantly to such a role whether or not Fes1 binds Hsp70 . In addition to its role in cell wall integrity , Slt2 ( Erk5 in humans ) acts in a separate conserved MAPK signaling pathway that regulates proteasome abundance [47 , 48] . Although proteasome defects in cells with depleted or mutated Fes1 are attributed to inability of Fes1 to facilitate release of substrates from Hsp70 to the proteasome , our data suggest that altered Slt2 signaling caused by mutating Fes1 could contribute to the reduced proteasome function of Fes1 mutants . Fes1 is in the armadillo repeat family , which has members that can interact with multiple partners and have diverse functions [49] . It is not entirely surprising , then , to find that Fes1 can bind proteins other than Hsp70 . That it does so even when carrying mutations that disrupt its binding to Hsp70 reveals a specificity in binding of Fes1 to unrelated proteins . Our pull-down data showing Fbp1 binds more Fes1A79R , R195A than wild type Fes1 in reactions with Hsp70 ( Table 1 ) suggest that binding of Fes1 to Hsp70 could reduce its availability to bind other proteins . Working out the details of how the binding of Fes1 to non-Hsp70 proteins is regulated , how it might influence functions of such proteins and whether non-NEF activities of Fes1 are evolutionarily conserved are intriguing areas for future work .
Strains are listed in Table 3 . Standard methods were used to construct strains with mutant alleles [50] . The fes1A79R , R195A allele in strain SKY207 has URA3 with its promoter inserted immediately after the termination codon . It was created by integrative transformation of strain 1075 with the allele excised from a plasmid and selecting for transformants on -Ura plates . The fes1ΔRD strain 1853–35 is identical to wild type strain 1075 except it lacks codons 2–34 of FES1 . Strain 1890 is identical to strain 1853–35 except it also has the A79R and R195A mutations in FES1 . Both were created by co-transforming a [URE3] version of strain 1075 using pRS316 and PCR products containing the mutant alleles and selecting transformants on -Ura medium . Ura+ transformants were then screened for loss of [URE3] . Presence of [URE3] was monitored by ability to grow on adenine ( see text ) . Mutant strains generated by integrative transformation were verified by PCR , sequencing and western analysis . Except for strain 1890 , expression of only the desired Fes1 variant was verified further by mass-spectrometry . Standard yeast media and growth conditions were used [50] . Cells were grown at 30°C unless indicated otherwise . Glucose-rich YPAD contains 1% yeast extract , 2% peptone , 0 . 04% adenine and 2% dextrose . Glucose-limiting YPKAG is the same except it contains 0 . 5% dextrose and 1% potassium acetate . Synthetic media contain 2% glucose , 7 gm/L Yeast Nitrogen Base ( Difco ) and complete supplement mix ( Sunrise Science Products ) lacking only nutrients needed to maintain selection of plasmids or prions . Plasmids used are described in Table 4 . All plasmids generated in this study were constructed using standard recombinant DNA methods . Plasmid p315FES1 contains FES1 coding region with 437 bp of 5' and 256 bp of 3' flanking DNA . Plasmids with mutant versions of Fes1 are identical except where indicated in the FES1 coding region . Non-tagged Ssa1 and Ssa2 were purified from E . coli Rosetta 2 ( DE3 ) as described [51] . GST-Fes1 was purified as described [15] . His6-Fbp1 was expressed in Rosetta 2 ( DE3 ) pLysS and purified by standard metal affinity methods . Proline at position 2 of Fbp1 is often referred to as amino acid residue P1 because the initiator methionine of Fbp1 is removed . We refer to it as P2 because the methionine is present in purified Fbp1 and to avoid confusion regarding names of Fbp1 with N-terminal deletions . Strains grown in YPKAG for 3 days at 30°C were shifted to YPAD . Samples were collected immediately and 1 h , 2 h and 3 h post shift and treated immediately with 10 mM sodium azide . Cells were washed with water , suspended in lysis buffer ( 50 mM Tris . HCl-7 . 5 , 150 mM NaCl , 0 . 1% Triton-X100 and proteinase inhibitors ) and lysed by agitation with glass beads . Proteins ( 20 μg ) were separated on 12% SDS-PAGE gels and transferred to PVDF membranes , which were processed by standard immunoblotting techniques using antibodies against Fbp1 . Six μg each of His6-Fbp1 ( 1 . 6 μM ) , Hsp70 ( Ssa1 or Ssa2 , each 0 . 9 μM ) and GST-Fes1 ( 1 . 09 μM ) were incubated with or without ATP , ADP or AMP ( 2 . 5 mM ) in buffer PD ( 50 mM Tris-HCl pH 7 . 5 , 150 mM NaCl , 2% glycerol , 5 mM MgCl2 ) for 30 min at 4°C . His6-Fbp1 was purified using Talon resin pre-equilibrated with buffer PD . Resin was washed twice with wash buffer ( PD buffer + 5 mM imidazole ) and proteins were eluted in PD buffer containing 250 mM imidazole . Pull-downs using GST-Fes1 were performed as described [15] . Proteins were separated on 12% SDS-PAGE gels and stained with PageBlue ( ThermoScientific , cat . no . 24620 ) . All variants of Fbp1 have N-terminal His6 tags and all variants of Fes1 have N-terminal GST tags . Cells grown overnight in YPAD were diluted in fresh medium to OD600 = 0 . 1 , grown to OD600 = 0 . 7 and diluted to OD600 = 0 . 2 . Eight μl of a 10-fold dilution series was plated on YPAD supplemented and incubated as indicated . Colony sizes qualitatively reflect rates of growth . Growth rates were quantified by diluting overnight cultures to OD600 = 0 . 05 in 24 well plates ( Corning Costar ) and incubated with continuous shaking at 30°C or 37°C for 24 hr on an automated plate reader ( SPECTROstar Omega , BMG labtech ) with readings taken at 10 min intervals . Cell wall defects were assessed by using BCIP as described [33] . Briefly , cells were diluted to OD600 = 0 . 02 and 8 μl of this and further 10-fold dilutions were plated on YPAD . Plates were incubated as indicated , overlaid with 5 ml of 1% agar containing 10 mM 5-bromo-4-chloro-3-indolyl phosphate ( BCIP , Sigma cat . no B6149 ) in 0 . 05 M glycine buffer ( pH 9 . 5 ) and then incubated at room temperature for up to 2 hr . | Fes1 , a yeast homolog of human nucleotide exchange factor HspBP1 , binds and regulates Hsp70 , a universally conserved protein that helps maintain health of proteins in cells . Fes1 is believed to function only by helping Hsp70 release ADP and substrates and cells lacking Fes1 are sick . We find Fes1 is essential for protein degradation by a vacuolar pathway ( Vid ) and for cell wall integrity ( CWI ) , and it interacts with a Vid substrate and a regulator of CWI . Fes1 mutants that cannot regulate Hsp70 can still support Vid and CWI , interact with proteins involved in these processes and restore cell health . Thus , Fes1 binds proteins other than Hsp70 and has important functions beyond regulating Hsp70 that are needed for optimal cell fitness . | [
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] | 2019 | Hsp70-nucleotide exchange factor (NEF) Fes1 has non-NEF roles in degradation of gluconeogenic enzymes and cell wall integrity |
The entropy metric derived from information theory provides a means to quantify the amount of information transmitted in acoustic streams like speech or music . By systematically varying the entropy of pitch sequences , we sought brain areas where neural activity and energetic demands increase as a function of entropy . Such a relationship is predicted to occur in an efficient encoding mechanism that uses less computational resource when less information is present in the signal: we specifically tested the hypothesis that such a relationship is present in the planum temporale ( PT ) . In two convergent functional MRI studies , we demonstrated this relationship in PT for encoding , while furthermore showing that a distributed fronto-parietal network for retrieval of acoustic information is independent of entropy . The results establish PT as an efficient neural engine that demands less computational resource to encode redundant signals than those with high information content .
We are constantly required to perceive , distinguish , and identify signals in our acoustic environment . A critical first stage of these processes is the encoding of the information into a robust neural code that allows efficient subsequent processing in the auditory system [1] . We investigated the properties of such a robust neural code at the level of the cortex by varying the amount of information—or entropy—in the acoustic signal . In the context of information theory [2 , 3] , entropy ( H ) denotes the uncertainty associated with an event and thus provides a metric to quantify information content: a rare—or uncertain—event carries more information than a common—or predictable—event . The properties of many information transmitting systems can be characterised in terms of entropy . Indeed , Shannon originally applied information entropy to describe transitional probabilities in language [2]: in English , less common letters ( e . g . , “k” ) have a lower probability ( or higher uncertainty ) than more common letters ( e . g . , “e” ) , and therefore carry higher information and entropy . Similarly , entropy can be used to characterise pitch transition probabilities in simple musical melodies [4 , 5] . We used entropy to quantify the information content of pitch sequences . “Fractal” pitch sequences based on inverse Fourier transforms of f–n power spectra [6 , 7] provide a means to control directly the entropy of the sequence via the exponent n ( Figure 1 ) . For n = 0 , the excursion of the pitch sequence is equivalent to fixed-amplitude , random-phase noise and thus is completely random ( high entropy ) . In the context of information theory , the high degree of randomness in this signal does not correspond to noise that must be removed by the system , but rather to a low predictability of the stimulus that results in each individual element of the sequence making a high degree of contribution to the information in the sequence . As n increases , a single stream gradually dominates the local pitch fluctuations and successive pitches become increasingly predictable ( low entropy ) . Such stimuli are more predictable so that each element of the sequence makes little contribution to the overall information in the stimulus . These families of pitch sequences with different values of n are statistical “fractals” [8] in the sense that their statistical properties are scale-independent [7] . For present purposes , the critical property of these pitch sequences that we exploit here is not their fractal behaviour , but the variation of entropy that is produced as n varies , whilst pitch range , tempo , and pitch probability remain largely constant ( however , it is inherent to the system that for large exponents n > 4 , the pitch distribution approaches a sinusoid and consequently is tilted toward the extremes of the pitch range and also that the average interval size between successive pitches decreases for increasing exponents n ) . Entropy for pitch sequences generated with a given value of exponent n can be determined by computing the sample entropy ( HSampEn ) [9] . Intuitively , HSampEn is based on the conditional probability that two subsequences of length m that match within a tolerance of r standard deviations remain within a tolerance r of each other at the next point m + 1 . Explicitly , for a signal or time series of length N , HSampEn is defined as: where Ar ( m ) ( or Ar ( m + 1 ) ) denotes the probability that two subsequences of length m ( or m + 1 ) match within a tolerance r . Two sequences “match” if their maximum absolute point-by-point difference is within a tolerance of r standard deviations . That is , sample entropy is essentially a measure of self-similarity , where highly self-similar time series signify high redundancy and therefore low entropy , and time series with low self-similarity represent a high degree of uncertainty and therefore high entropy . Furthermore , sample entropy is a nonparametric measure in the sense that it does not require a priori knowledge of the true probability density function of the underlying time series . In the present case , the parameters were chosen as m = 2 , r = 0 . 5 , and N represents the number of tones of the pitch sequence . By varying information theoretic properties of pitch sequences , we address encoding mechanisms applied to sounds at a level of generic processing that is not specific to any semantic category . Even before such encoding mechanisms are engaged , the auditory system must represent spectrotemporal features of the stimulus in sufficient detail such that a number of different aspects of the stimulus can be encoded , in order to allow different types of subsequent categorical and semantic processing . In the current context , encoding constitutes the stage of analysis between the detailed representation of the spectrotemporal structure of the stimulus and the subsequent categorical analysis of abstracted acoustic forms . A single sound may be associated with more than one abstracted form: for example , we might obtain vowel , speaker , and position from a single sound , where each feature can undergo subsequent categorical and semantic processing . Here we use information theory to demonstrate encoding mechanisms in the brain that result in the abstraction of a form of the stimulus . We hypothesise that if such encoding mechanisms are efficient , they will use less computational resource for stimuli that have a low information content compared with stimuli that have high information content . This hypothesis is tested by measuring the functional MRI ( fMRI ) blood oxygenation level–dependent ( BOLD ) signal as an estimate of neural activity and computational resource during the encoding of auditory stimuli in which the information content is systematically varied . We further hypothesise that processing in primary auditory cortex in the Heschl's Gyrus ( HG ) corresponds to a stage at which the detailed spectrotemporal structure of sounds is represented [10–12] and where such a relationship will not be observed . Instead , such a relationship is expected to be observed in distinct auditory association cortex in the planum temporale ( PT ) , which we have previously characterised as a “computational hub” [13] that is required to convert spectrotemporal representations into “templates”—sparse symbolic neural representations that are the basis for categorical , semantic , and spatial processing . For example , the spectral envelope of a sound would represent such a template for vowel processing [14] . The model was developed to account for the involvement of PT in the analysis of a variety of complex sounds that can be processed categorically ( speech , music , and environmental sounds ) as well as different spatial attributes ( for a review , see [13] ) . Here we investigate the encoding of pitch sequences that can be like melodies in their structure , but in which the structure and information content is determined by statistical rules . We sought brain areas that display a positive relationship between the information content or entropy of pitch sequences and neural activity as assessed by the BOLD signal during encoding . Specifically , we hypothesised that such a relationship exists in PT but not in earlier auditory areas .
Participants were presented with pure-tone pitch sequences that were based on f–n power spectra with n ranging from n = 0–1 . 5 in five steps of 0 . 3 . In a behavioural experiment before scanning , we acquired full psychometric functions demonstrating that all of the 22 participants could reliably distinguish a nonrandom pitch sequence from a random ( n = 0 ) reference in a two-interval , two-alternative , forced-choice ( 2I2AFC ) paradigm ( see Materials and Methods ) . Perceptual thresholds for discriminating nonrandom from a random pitch sequence lay between n = 0 . 6 and n = 0 . 9 for the majority of participants . In a sparse fMRI paradigm [15 , 16] , participants listened to pitch sequences of a given value for n and indicated whether it was random or not . A parametric regressor based on the mean sample entropy [9] value at each of the six levels of n ( Table 1 ) was used to probe for cortical areas that increased their activity with increasing entropy . The fMRI analysis revealed a BOLD signal increase in PT as a function of increasing entropy at a significance level of p < 0 . 001 ( uncorrected for multiple comparisons , see Figure 2 and Table 2 ) and using a small volume correction for the anterior part of PT at a significance level of p < 0 . 05 ( see Materials and Methods ) . No area increased its activity as a function of decreasing entropy , i . e . , increasing predictability or redundancy . These results suggest a greater computational and energetic demand for encoding in PT as the information content of acoustic sequences ( as assessed by entropy ) increases . However , the present study has three potential confounds , which we addressed in a second study . First , we considered whether the effect of entropy in PT might reflect adaptation of the sensory cortical representation of frequency , as the pitch sequences were based on pure tones: for low values of exponent n , the frequency excursions are greater on average , so that the signal moves more between specific frequency representations , and PT might adapt less and thus produce a greater local activity . Such a mechanism would also be expected to occur in primary and secondary auditory cortex within HG . We therefore explored the specific relationship between fractal exponent and local activity in HG and PT by extracting the first eigenvariate of the BOLD signal in left and right HG as well as the local maxima in PT ( see Materials and Methods ) . No significant difference across entropy levels was demonstrated in HG ( 2 Hemisphere ( left , right ) × 6 Entropy Level ( 1–6 ) repeated measures analysis of variance ( ANOVA ) : no main effect of Entropy Level ( F ( 5 , 17 ) = 1 . 11 , p > 0 . 1 ) ; Figure 2 ) . Furthermore , a 2 Area ( PT , HG ) × 6 Entropy Level ( 1–6 ) × 2 Hemisphere ( left , right ) repeated measures ANOVA demonstrated a significant difference in the relationship between BOLD signal across entropy levels in PT versus HG: Area × Entropy Level interaction ( F ( 5 , 17 ) = 4 . 86 , p < 0 . 001 ) . The existence of the effect in auditory association cortex in PT , the absence of an effect in HG , and a significant interaction between effects in the two areas are indirect evidence against an explanation of the results based on sensory adaptation . Nevertheless , we addressed a putative sensory explanation in a second study by using regular-interval noise , where sounds have identical passband regardless of their pitch [17–19] . Second , we also considered whether the effect of entropy might reflect perceptual adaptation at the level of the representation of pitch . Again , such an effect would not be expected in association cortex , but in a proposed “pitch centre” in lateral HG [20–22] . The second study therefore incorporated a more suitable design to detect a potential differential response to the entropy of the acoustic stimuli in cytoarchitectonic [23] and functional [20] subdivisions of HG in medial , central , and lateral HG . Finally , we controlled for the fact that , in the first study , participants were explicitly required to assess whether the sequences were random or not . This made it possible that the results reflected a category judgment rather than a fundamental encoding mechanism . To test this , the second study differentially examined encoding and retrieval components as a function of entropy but independent of any other stimulus-related classification task . In a sparse fMRI paradigm [15 , 16] , participants were presented with fractal pitch sequences based on f–n power spectra , with n ranging from n = 0–1 . 2 in four steps of 0 . 3 . The separate pitches corresponded to regular-interval noise [17–19] ( see Materials and Methods ) . By using broadband stimuli and an increased number of silent trials , the second study used a more suitable design to allow disambiguation of the medial functional area in HG that corresponds to the primary auditory cortex and areas in lateral HG that correspond to secondary cortices , including the area within which activity corresponds to pitch salience [20 , 21] . The second paradigm also enabled the disambiguation of encoding and retrieval mechanisms . Participants were scanned ( 1 ) after being required to encode a pitch sequence with a particular entropy value and ( 2 ) after listening to a second pitch sequence that was either identical to the first sequence or different from the first sequence but with the same entropy value . Activity during the first scan reflects the energetic demands of encoding the first sequence , whereas activity during the second scan reflects encoding of the second sequence , retrieval of the first , and comparison of the two . In order to decorrelate the two scans [24] , we introduced a delay of one , two , or three scans between the pitch sequences ( see Material and Methods and Figure 3 ) . In contrast to the first study , participants were not informed about the nature of the pitch sequences and instead were only told that they would hear pairs of pitch sequences and that their task would be to say whether the second was same or different . Participants' behavioural performance in the scanner was assessed via hits ( hit ) and correct rejections ( cr ) percent scores ( see also Figure S2 ) . Both mean hit ( 74 . 25% ± 3 . 14 standard error of the mean [SEM] ) and mean cr ( 73 . 42% ± 3 . 31 SEM ) scores were significantly above chance ( 50% ) ( one-sample t-test , hit: t23 = 7 . 73; cr: t23 = 7 . 08 , both p < 0 . 001 ) . Furthermore , a 2 Response ( hit , cr ) × 5 Entropy Level ( 1–5 ) × 3 Delay ( 1–3 ) repeated measures ANOVA showed no main effect in any of the three factors ( F ( 23 , 1 ) = 0 . 33; F ( 20 , 4 ) = 1 . 1; F ( 22 , 2 ) = 0 . 53; all p > 0 . 05 , for Response , Entropy Level and Delay , respectively ) . There was no Response × Entropy Level interaction ( F ( 20 , 4 ) = 1 . 01 , p > 0 . 05 ) , indicating that participants' performance was not influenced by the entropy level of the pitch sequences . Participants had higher cr than hit scores for delay 3 , whereas there were more hits than cr for delays 1 and 2 ( Response × Delay interaction; F ( 22 , 2 ) = 7 . 91 , p = 0 . 001 ) . An Entropy Level × Delay interaction ( F ( 16 , 8 ) = 2 . 14 , p < 0 . 05 ) showed a performance increase for delay 1 from entropy level 1 to entropy level 5 , but there was no such systematic effect for delay 2 or delay 3 . There was no Response × Entropy Level × Delay interaction ( F ( 16 , 8 ) = 0 . 45 , p > 0 . 1 ) . The imaging results replicate the findings of the first study , demonstrating that activity in PT for encoding ( as assessed by both the first and second scan of each pair ) increased significantly as a function of entropy for the same significance thresholds as in the first study ( Figure 4 and Table 2 ) . We examined in detail the effect at the level of primary and secondary auditory cortex by extracting the BOLD signal in medial , central , and lateral HG [20 , 23] ( Figure 4 and Figure S1 ) : three separate 5 Entropy Level ( 1–5 ) × 2 Hemisphere ( left , right ) repeated measures ANOVAs showed no main effect of Entropy Level ( F ( 4 , 20 ) = 0 . 85 , F ( 4 , 20 ) = 0 . 77 , F ( 4 , 20 ) = 1 . 83 , all p > 0 . 1 , for medial , central , and lateral HG , respectively ) . Furthermore , the relationship between entropy and BOLD signal was significantly different between PT and all three subdivisions of HG: three separate 2 Area ( PT , ( medial , central , or lateral ) HG ) × 5 Entropy Level ( 1–5 ) × 2 Hemisphere ( left , right ) repeated measures ANOVAs carried out for medial , central , or lateral HG showed an Area × Entropy Level interaction ( F ( 4 , 20 ) = 2 . 61 , p < 0 . 05; F ( 4 , 20 ) = 3 . 31 , p < 0 . 05; F ( 4 , 20 ) = 5 . 55 , p < 0 . 001 , for medial , central , and lateral HG , respectively ) . The cardiac gated image acquisition in Study 2 furthermore allowed an examination of a potential effect of stimulus entropy in subcortical auditory structures . We examined the relationship between entropy and the activity in the medial geniculate body ( MGB ) and inferior colliculus ( IC ) using a smaller smoothing kernel ( 4 mm full width at half maximum [FWHM] ) that is appropriate for these subcortical structures ( Figure 5 ) . This analysis showed no main effect of entropy on the BOLD response in these areas ( two separate 5 Entropy Level ( 1–5 ) × 2 Hemisphere ( left , right ) repeated measures ANOVAs: F ( 4 , 20 ) = 0 . 35 , p > 0 . 1 , for IC; F ( 4 , 20 ) = 1 . 32 , p > 0 . 1 , for MGB ) . Due to the different spatial smoothing , no meaningful interaction with the response in cortical structures can be computed . A second analysis based on the contrast between the second and first scans sought areas involved in retrieval and comparison , but not encoding . This contrast highlighted activity within a bilateral fronto-parietal network , including the anterior insulae and frontal opercula , inferior parietal sulci , medial superior frontal gyri , and dorsolateral prefrontal cortex ( p < 0 . 05 , family-wise error ( FWE ) corrected for multiple comparisons; Figure 6 and Table S1 ) . A further contrast was carried out to identify an effect of entropy on retrieval and comparison , but not encoding . No effect of entropy on retrieval and comparison was demonstrated .
We have demonstrated an increase in the local neural activity as a function of the entropy of encoded pitch sequences in PT but not in HG . The results are consistent with a computational process in PT that requires increasing resource and energetic demands during encoding as the entropy of the sound stimulus increases . In the first study , the use of pure tones could not exclude a possible alternate explanation of the data in terms of sensory adaptation within cortical frequency representations . The existence of the relationship in PT , but not in HG , was indirect evidence against such sensory adaptation . However , in the second study we used broadband stimuli that continually activate a broad range of cortical frequency representations irrespective of pitch , rendering explanations based on sensory adaptation untenable . Another interpretation of these results could be based on perceptual adaptation within cortical correlates of pitch ( as opposed to sensory adaptation of the stimulus representation ) . Previous studies have demonstrated mapping of activity within secondary auditory cortex in lateral HG as a correlate of the perceived pitch salience , whether the stimulus mapping was in the temporal domain [20] or frequency domain [21] . An explanation of the results of either study might therefore be based on adaptation within the pitch centre in lateral HG for pitch sequences with higher fractal exponent n . In the second study , we were able to identify separate activations in medial , central , and lateral HG . Contrary to an interpretation based on adaptation in pitch-sensitive channels , there was no relationship between the entropy and local activity in any of the subregions of HG that would have supported such an explanation . Furthermore , the interaction between HG and PT provides additional evidence for an effect of entropy that is specific to PT . The most compelling explanation of these results is in terms of greater computational activity ( and therefore local synaptic activity and BOLD signal [25] ) as a function of the information content or entropy of the encoded sound . This is the first explicit demonstration of such a relationship . The results suggest an efficient form of encoding within PT , whereby sequences are encoded by a mechanism that demands less computational resource for sequences carrying low information content and high redundancy ( due to the predictability of the sequence ) than that required to encode sequences with little or no redundancy . “Sparse” [26–28] and “predictive” [29–31] coding both constitute such mechanisms and bases for PT acting as a computational hub [13] . In contrast , retrieval and comparison do not depend on entropy in the same way , which we propose reflects the decreased computational and energetic demands of retrieving and comparing stimuli at symbolic levels beyond stimulus encoding . The initial encoding process depends on a computationally expensive process that must abstract features from a complex spectrotemporal structure . Beyond this stage , the subsequent categorical retrieval and comparison mechanism does not depend on the detailed spectrotemporal structure . Indeed , the computational hub model [13] states that PT gates its output towards higher-order cortical areas that perform analysis at a symbolic and semantic level . We suggest that at least part of the function of PT is to compress the neural code corresponding to the initial acoustic signal ( e . g . , via sparse or predictive coding ) , and that subsequent processing is not dependent on stimulus entropy . That PT might even perform this type of analysis in more general or supra-modal terms is suggested by work in the visual domain [32] , demonstrating activation in Wernicke's area and its right-hemisphere homologue as a function of the entropy within a sequence of visually presented squares , irrespective of whether or not participants were aware of an underlying sequence . However , later studies using similar visual stimuli did not replicate this finding [33 , 34] . The retrieval and comparison phase highlighted a fronto-parietal network consisting of the anterior insulae and frontal opercula , inferior parietal sulci , medial superior frontal cortex , and dorsolateral prefrontal cortex . This activation pattern is common in the retrieval and comparison phase of ( auditory ) delayed match-to-sample tasks ( e . g . , [35 , 36] ) . The anterior insula in particular has been proposed as an additional auditory processing centre that allocates auditory attention , specifically with respect to sound sequences ( see [37] for a review ) . Similarly , the parietal cortex is generally regarded as being important for attention to and binding of sensory information [38] , whereas activity in the prefrontal cortex is often associated with response preparation and selection [39] . Our main aim was to study generic neural mechanisms of sound encoding as a function of entropy , and the range of pitch sequences we used included those approximating f−1 ( “one-over-f” ) power spectra , which resemble many naturally occurring acoustic phenomena [40] . Notably , music and speech display f−1 power spectra characteristics , reflecting the relative balance of “surprises” ( e . g . , musical transitions ) and predictability in such signals [41 , 42] . Pertaining specifically to the signals used here falling in the range of f−1 , two recent electrophysiological studies demonstrated preference within primary sensory cortices for f−1 signals [43 , 44] . We did not demonstrate any “tuning” to particular values of exponent in HG ( no main effect of Entropy Level; Figures 2 and 4 and Figure S1 ) . Although we do not dismiss the possibility of neuronal preference for particular natural sequence categories at the level of HG in humans , the current studies addressed the computational and energetic demands of the perceptual encoding of sounds , rather than their sensory representation . We have used entropy to characterise pitch sequences , but the information theoretic approach could be used to characterise sequences containing rhythm or more complex natural sound sequences . The hypothesised mechanism in PT is not a specific pitch mechanism and also predicts a similar relationship between information content and the encoding of more natural stimuli . In summary , the present data implicate PT as a neural engine within which the computational and energetic demands of encoding are determined by the entropy of the acoustic signal .
Participants . 30 right-handed human participants ( aged 18–43 y , mean age = 24 . 9 y; 19 females ) with normal hearing and no history of audiological or neurological disorders provided written consent prior to the experiment . None of the participants was a professional musician . The experiment was approved by the Institute of Neurology Ethics Committee , London . Eight participants had to be excluded due to excessive head movements ( more than 5 mm translation or 5° rotation within one session ) or not meeting the psychophysical assessment criteria ( see below ) , leaving a total of 22 participants ( aged 18–40 y , mean age = 24 . 2 y; 12 females ) . Stimuli . All stimuli were created digitally in the frequency domain using Matlab ( http://www . mathworks . com ) . Stimuli were fractal sine tone sequences based on inverse Fourier transforms of f–n power spectra [6 , 7] for six levels of n ( 0 , 0 . 3 , 0 . 6 , 0 . 9 , 1 . 2 , and 1 . 5 ) , where pitch sequences ranged from totally random ( n = 0; high entropy ) to more coherent or predictable ( n = 1 . 5; low entropy ) . By randomising the phase spectrum , each exemplar is unique while at the same time displaying the same characteristic correlational properties of a given level . The pitch range spanned two octaves from 300–1 , 200 Hz , with each octave split into ten discrete equidistant pitches . Pitch sequences were presented at a tempo of five notes per second , with a total duration of 7 . 6 s for each pitch sequence ( 38 notes per sequence ) . There were 60 exemplars for n = 0 and 30 exemplars for the remaining five levels of n . We calculated the mean entropy for each level of exponent n using the sample entropy HSampEn [9] measure , as described in the Introduction: Ar ( m ) denotes the probability that two subsequences of length m match within a tolerance r , i . e . , Ar ( m ) is the ratio of [all pairs of subsequences of length m that match] divided by [all possible pairs of subsequences of length m]; the same applies to Ar ( m + 1 ) . Guided by Lake and colleagues [45] , we chose tolerance r = 0 . 5 and length of subsequence m = 2 as parameter values . As Eke et al . [8] point out , taking a subset of data points from a fractal time series essentially introduces noise into the resulting time series , leading to lower n and consequently higher entropy estimates relative to the original values . Table 1 therefore lists the mean sample entropy values for the time series of the 38 notes in each pitch sequence . Experimental design . In a behavioural experiment prior to scanning , we acquired full psychometric functions from participants discriminating the nonrandom pitch sequence against a random reference ( n = 0 ) in a 2I2AFC paradigm . Participants were not given feedback . Stimuli were not the same as in the subsequent imaging paradigm and there were 72 trials ( 12 trials per level ) . Psychometric functions and 75% correct thresholds were estimated via a Weibull boot-strapping procedure [46] . Participants who did not reach at least 80% performance for levels 5 or 6 were not included in the fMRI analysis . In the functional imaging paradigm , participants were asked to categorise whether or not the pitch sequence was random by pressing the corresponding button at the end of each pitch sequence , bearing in mind that pitch sequences of intermediate levels ( n = 0 . 6–0 . 9 ) are neither completely random nor completely coherent ( in these cases , participants should nevertheless indicate their predominant percept ) . Stimuli were presented via custom-built electrostatic headphones at 70 dB sound pressure level ( SPL ) using Cogent software ( http://www . vislab . ucl . ac . uk/Cogent/ ) . Gradient weighted echo planar images ( EPI ) were acquired with a 3-T Siemens Allegra MRI system ( Erlangen , Germany ) , using a sparse temporal sampling technique [15 , 16] ( time to repeat/time to echo , TR/TE = 10 , 530/30 ms ) . A total of 246 volumes ( 42 slices , 3 × 3 × 3 mm voxel resolution ) were acquired over three sessions ( 82 per session ) , including 60 volumes for n = 0 and 30 volumes for the other levels of n , as well as 30 silent control trials ( the first two volumes of each session were discarded to allow for saturation effects ) . To correct for geometric distortions in the EPI images due to B0 field variations , Siemens fieldmaps were acquired for each participant [47 , 48] . A structural T1 weighted scan was acquired for each participant [49] . Image analysis . Imaging data were analysed using statistical parametric mapping software ( SPM2 , http://www . fil . ion . ucl . ac . uk/spm ) . Volumes were realigned and unwarped using the fieldmap parameters , spatially normalised [50] to standard stereotactic space , and smoothed with an isotropic Gaussian kernel of 8 mm FWHM . Statistical parametric maps were generated using a finite impulse response ( FIR ) box-car function in the context of the general linear model [51] . The six conditions were parametrically modulated based on the average sample entropy [9] value for each level of n ( Table 1 ) , statistically evaluated using a random-effects model and thresholded at p < 0 . 001 ( uncorrected for multiple comparisons across the brain ) for areas where we had an a priori hypothesis , i . e . , in auditory cortex and specifically in PT . In addition , we carried out a volume-of-interest analysis controlling for multiple comparisons within PT by centering a sphere with 1-cm radius around the centroid of the triangular anterior part of PT that is situated within the superior temporal plane as opposed to the more posterior part that abuts the parietal lobe ( Montreal Neurological Institute ( MNI ) [x , y , z] coordinates [–56 , –28 , 6] and [58 , –24 , 8] for left and right PT , respectively ) . Our choice of volume was based on the identification of the anterior part of PT in the studies that suggested the computational hub model [13] . For areas that were not predicted a priori , we adopted a statistical threshold of p < 0 . 05 after FWE correction . We investigated in detail a potential effect of adaptation in frequency bands at an earlier sensory level . Study 1 did not allow disambiguation of the three cytoarchitectonically [23] and functionally [20] distinct areas in HG , namely medial , central , and lateral HG ( see Study 2 below for further discussion ) . Therefore , we identified single coordinates based on local maxima of a sound minus silence contrast for left [–46 , −24 , 6] and right [50 , –24 , 8] HG that are most similar to central HG as defined by references [20 , 23] and extracted the first eigenvariate of the BOLD signal at these coordinates ( see Figure 2 ) . The BOLD signal was extracted using a standard procedure in SPM: the time series of a given voxel ( e . g . , the peak activation voxel for the entropy effect ) is provided by SPM via a voxel-of-interest ( VOI ) routine . At the second-level statistical analysis , this results in a time series for each contrast where each data point corresponds to a participant . The routine is executed for each contrast , in the current case either six ( Study 1 ) or five ( Study 2 ) [Level–Silence] contrasts , resulting in a 22 × 6 or 24 × 5 matrix ( 22 or 24 participants , respectively ) , where each row corresponds to a participant and each column to a contrast . The threshold at which the BOLD signal was extracted was p < 0 . 05 ( uncorrected for multiple comparisons ) . The values are then normalised to the maximum value . Note that the interaction described here between the BOLD signal in HG and PT across levels assumes that the coupling between neuronal response and the haemodynamic BOLD signal is identical in the two brain regions . While we have no reason to assume the contrary , it has also not been proven that this is indeed the case . Participants . 30 right-handed participants ( aged 20–44 y , mean age = 28 . 0 y; 16 females ) with normal hearing and no history of audiological or neurological disorders provided written consent prior to the experiment . The experiment was approved by the Institute of Neurology Ethics Committee , London . Six participants had to be excluded because of excessive head movements ( more than 5-mm translation or 5° rotation within one session ) , leaving a total of 24 participants ( aged 20–44 y , mean age = 28 . 58 y; 12 females ) . Stimuli . Similar to Study 1 , pitch sequences were again based on f–n power spectra for five levels of n ( 0 , 0 . 3 , 0 . 6 , 0 . 9 , and 1 . 2 ) . Each pitch was based on regular-interval noise [17–19] with 16 iterations . The pitch range spanned two octaves from 150–600 Hz , with each octave split into ten discrete equidistant pitches . Pitch sequences were presented at a tempo of four notes per second , with a total duration of 6 s for each pitch sequence ( 24 notes per sequence ) . The mean entropy values for each level of n are depicted in Table 1 and are slightly different from Study 1 , because each pitch sequence had 24 notes instead of 38 . There were 30 exemplars for each level of n , and stimuli were presented via custom-built electrostatic headphones at 70 dB SPL using Cogent software ( http://www . vislab . ucl . ac . uk/Cogent/ ) . Experimental design . In a sparse imaging paradigm [15 , 16] , participants were scanned ( 1 ) after being required to encode a pitch sequence with a particular entropy value and ( 2 ) after listening to a second pitch sequence that was either the same sequence or a different sequence from the same entropy level and indicating whether this was the same pitch sequence or different ( see also Figure 3 ) . To de-correlate [24] activations due to the first and second pitch sequence , the second pitch sequence followed the first pitch sequence either immediately in the next TR , or with two or three TR's delay ( within-trial delay ) . Similarly , the first pitch sequence of the next pair could follow the second pitch sequence of the previous pair immediately , or with one or two TR's delay ( between-trial delay ) . There were 20 pitch sequence pairs for each level , amounting to 100 encoding and 100 retrieval stimuli across the five levels of exponent n . In addition , there were a total of 100 within-trial volumes and 100 between-trial rest volumes . For each level of exponent n , 10 out of 20 pairs were identical , and 10 were different . Stimuli were counterbalanced between participants . To guide participants , a “1” was displayed at the centre of the screen from the start of the first pitch sequence until the start of the second pitch sequence , when a “2” was displayed . At the end of the second pitch sequence , participants briefly saw a “ ? ” to indicate they should now give their response as to whether they thought the second pitch sequence was the same as or different from the first pitch sequence . Participants received immediate feedback . During the rest period between trials , participants saw a fixation cross “+” at the centre of the screen and were instructed to relax . Gradient-weighted EPIs were acquired with a 3-T Siemens Allegra MRI system ( Erlangen , Germany ) , using a sparse temporal sampling technique [15 , 16] , where each volume was cardiac gated to reduce motion artefacts ( TR/TE = ∼8 , 800/30 ms ) . A total of 404 volumes ( 42 slices , 3 × 3 × 3 mm voxel resolution ) were acquired over two sessions ( the first two volumes of each session were discarded to allow for saturation effects ) . Subsequent to the functional paradigm , a structural T1 weighted scan was acquired for each participant [49] . Image analysis . Imaging data were analysed using statistical parametric mapping software ( SPM5 , http://www . fil . ion . ucl . ac . uk/spm ) . Volumes were realigned and unwarped , spatially normalised [50] to MNI standard stereotactic space , and smoothed with an isotropic Gaussian kernel of 8-mm FWHM . Statistical parametric maps were generated by modelling the evoked haemodynamic response to the stimuli and the delay period in the context of the general linear model [51] . To probe for an effect of entropy on encoding , a contrast was carried out to identify areas in which the BOLD signal in the first and second scans increased as a function of a parametric regressor based on the mean sample entropy value at each level ( see Table 1 ) . A second contrast investigated the effect of retrieval and comparison independent of encoding by subtracting the effect of encoding of the first stimulus only ( corresponding to the first scan ) from that to encoding of the second stimulus , retrieval of the first , and comparison of the two ( corresponding to the second scan ) . A third contrast examined the effect of entropy on retrieval by subtracting [first scan entropy increase] from [second scan entropy increase] . Statistical results are based on a random-effects model and thresholded at p < 0 . 001 ( uncorrected for multiple comparisons across the brain ) for areas where we had an a priori prediction , i . e . , PT , in addition to the same small volume correction ( p < 0 . 05 corrected for multiple comparisons ) as in Study 1 . For areas that were not predicted a priori , we adopted a more conservative statistical threshold of p < 0 . 05 after FWE correction . The second study was better suited to identify the three cytoarchitectonically [23] and functionally [20] distinct areas within HG based on the sound minus silence contrast because of ( 1 ) the greater number of silent trials and ( 2 ) the use of broadband stimuli . Three activations were identified in HG in either hemisphere , primarily to locate the lateral area previously implicated in perceptual pitch analysis [20 , 21] and to allow a comparison of the effect of entropy on activity here with that in PT ( for individual coordinates see Table 2 for PT , Figure 2 for central and Figure S1 for medial and lateral HG ) . Cardiac gating in Study 2 produced a reliable signal in subcortical structures IC and MGB ( Figure 5 ) . We reanalysed the data with a 4-mm FWHM smoothing kernel that is appropriate to these structures . Local maxima based on a sound minus silence contrast were identified in left IC ( [–6 , −34 , −12] ) and right IC ( [6 , –34 , –10] ) and left MGB ( [–14 , −26 , −8] ) and right MGB ( [12 , –24 , –8] ) . For further analysis considerations see Text S1 , Figures S3 and S4 , and Table S2 . | Understanding how the brain makes sense of our acoustic environment remains a major challenge . One way to describe the complexity of our acoustic environment is in terms of information entropy: acoustic signals with high entropy convey large amounts of information , whereas low entropy signifies redundancy . To investigate how the brain processes this information , we controlled the amount of entropy in the signal by using pitch sequences . Participants listened to pitch sequences with varying amounts of entropy while we measured their brain activity using functional magnetic resonance imaging ( fMRI ) . We show that the planum temporale ( PT ) , a region of auditory association cortex , is sensitive to the entropy in pitch sequences . In two convergent fMRI studies , activity in PT increases as the entropy in the pitch sequence increases . The results establish PT as an important “computational hub” that requires less resource to encode redundant signals than it does to encode signals with high information content . | [
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] | 2007 | An Information Theoretic Characterisation of Auditory Encoding |
The hemolytic uremic syndrome ( HUS ) is characterized by hemolytic anemia , thrombocytopenia and renal dysfunction . The typical form of HUS is generally associated with infections by Gram-negative Shiga toxin ( Stx ) -producing Escherichia coli ( STEC ) . Endothelial dysfunction induced by Stx is central , but bacterial lipopolysaccharide ( LPS ) and neutrophils ( PMN ) contribute to the pathophysiology . Although renal failure is characteristic of this syndrome , neurological complications occur in severe cases and is usually associated with death . Impaired blood-brain barrier ( BBB ) is associated with damage to cerebral endothelial cells ( ECs ) that comprise the BBB . Astrocytes ( ASTs ) are inflammatory cells in the brain and determine the BBB function . ASTs are in close proximity to ECs , hence the study of the effects of Stx1 and LPS on ASTs , and the influence of their response on ECs is essential . We have previously demonstrated that Stx1 and LPS induced activation of rat ASTs and the release of inflammatory factors such as TNF-α , nitric oxide and chemokines . Here , we demonstrate that rat ASTs-derived factors alter permeability of ECs with brain properties ( HUVECd ) ; suggesting that functional properties of BBB could also be affected . Additionally , these factors activate HUVECd and render them into a proagregant state promoting PMN and platelets adhesion . Moreover , these effects were dependent on ASTs secreted-TNF-α . Stx1 and LPS-induced ASTs response could influence brain ECs integrity and BBB function once Stx and factors associated to the STEC infection reach the brain parenchyma and therefore contribute to the development of the neuropathology observed in HUS .
The epidemic form of hemolytic uremic syndrome ( HUS ) , has been associated with enterohemorrhagic infections caused by Shiga toxin ( Stx ) -producing Escherichia coli ( STEC ) [1] . HUS is the most common cause of acute renal failure in children and is related to endothelial damage of kidney glomeruli and arterioles and epithelial cell damage induced by Stx , through the interaction with its globotriaosylceramide ( Gb3 ) receptor [2] . Although Stx is the main pathogenic factor for HUS development , the inflammatory response is able to potentiate Stx toxicity . In fact , both bacterial lipopolysaccharide ( LPS ) , and polymorphonuclear neutrophils ( PMN ) play an important role in the full development of HUS [3] . In severe cases of HUS , endothelial cell ( ECs ) damage is not limited to the kidney but extends to other organs , such as the brain . Central nervous system ( CNS ) complications are observed in about 30% of infant population with HUS and brain damage is the most common cause of death in this disease [4] . Brain ECs are part of the blood brain barrier ( BBB ) , they restrict the entry of potentially harmful substances and leukocytes from the bloodstream . In fact , brain ECs damage is thought to be involved in the disruption of the BBB integrity observed in HUS . However , the pathogenesis of CNS impairment is not yet fully understood . Although human brain ECs are relative resistant to Stx effects in vitro , inflammatory stimuli markedly increase their sensitivity towards Stx toxicity by increasing Gb3 expression on these cells [5] . ASTs are inflammatory cells found throughout the CNS and are surrounding almost entirely the brain endothelium by terminal processes [6] . The interaction of ASTs with brain ECs determines the BBB function [7] , as soluble factors released by ASTs can mediate not only the induction but also the maintenance of BBB properties in brain ECs [8] , [9] . In response to brain injury , ASTs become activated and release inflammatory mediators altering the integrity and permeability of the BBB which can affect neuronal survival and tissue integrity [10] , [11] . In addition , ASTs derived cytokines and chemokines can stimulate the peripheral immune system and attract peripheral inflammatory leukocytes to the site of injury [12] . We have recently demonstrated that Stx1 exerts a direct effect on ASTs although sensitization with LPS potentiates the effects induced by Stx1 . Stx1 induces activation of ASTs and development of an inflammatory response characterized by the secretion of NO , TNF-α and chemokines that promote PMN attraction [13] . Given the critical anatomical disposition of ASTs and the influence they exert over ECs from BBB , we hypothesized that the effects induced by LPS and Stx1 on ASTs may contribute to the brain ECs damage observed in severe cases of HUS . Thus , the aim of this study was to evaluate the effects of soluble factors released by ASTs treated with Stx1 alone , or in combination with LPS on ECs with brain endothelium properties . Here we demonstrate that rat ASTs treated with LPS and Stx1 release factors that affect the permeability of brain-like ECs and increase their susceptibility towards Stx1 cytotoxicity . Additionally , these factors activate ECs , PMN and platelets inducing a proinflammatory and prothrombic state that promotes PMN and platelet adhesion to ECs . Together , our results suggest that ASTs could influence brain ECs integrity and BBB function once Stx in combination with bacterial factors reach the brain parenchyma .
In order to accurately model the human BBB , purified ECs obtained from human umbilical cord veins ( HUVEC ) were differentiated into ECs with properties of cerebral endothelium ( HUVECd ) by incubating them with conditioned media from ASTs ( CM-ASTs ) . We assessed the induction of several properties that characterize brain ECs after 24 h incubation of HUVEC with CM-ASTs . Figure 1A shows a significant increase in the activity of alkaline phosphatase ( AlkP ) in HUVECd in comparison to HUVEC maintained in control medium ( DMEM ) . The activity of this enzyme remained high during 72 hours , even though the CM-ASTs was removed after 24 hours of incubation ( data not shown ) . Moreover , as shown in Figure 1B , the permeability to the passage of horse radish peroxidase ( HRP ) was decreased in HUVECd . Similarly , basal expression of tight junction ( TJs ) and TJs-associated proteins , occludin and ZO-1 respectively , were also increased in HUVECd ( Figure 1C and 1D ) . Additionally , photomicrographs depicted in Figure 1E corroborated these results , where an intense expression of ZO-1 and its typical localization at junctional regions of the cell surface were only evident in HUVECd . Finally , as ZO-1 is associated with actin filaments , we investigated the pattern of distribution of F-actin on HUVECd . Figure 1E shows structural changes in F-actin on cells cultured with CM-ASTs . While HUVEC maintained in DMEM showed an intense pattern of F-actin at the cell periphery ( “belt” like distribution ) , the arrangement of F-actin in HUVECd is mainly organized in transcytoplasmatic parallel bundles of intense stress fibers , with a lower degree of peripheral distribution as expected for brain ECs . Together , these results support the fact that soluble factors released by ASTs under basal conditions are able to transdifferentiate non-neural ECs into ECs with brain properties that simulates the endothelium from BBB . ASTs response to Stx1 entering the brain parenchyma may include the secretion of toxic factors for brain ECs located in close proximity . In order to test this hypothesis , we determined the toxicity on HUVECd induced by untreated CM-ASTs ( CM-Control ) or CM-ASTs treated with LPS and/or Stx1 ( CM-LPS , CM−Stx1 and CM−LPS+Stx1 ) . After 24 h HUVECd confluent cultures were observed by optical microscopy to register any change in the cellular appearance associated with toxicity ( shape , refringency or cellular detachment ) . Although no apparent changes were observed during the first 48 h , signs of toxicity were clearly evident at 72 h . Figure 2 shows a slight but significant toxicity induced on HUVECd incubated with CM−LPS compared with CM-Control . CM−Stx1 induced even more toxicity on HUVECd . However , maximal toxicity was observed when HUVECd were incubated with CM−LPS+Stx1 . These results suggest that Stx1 induces in LPS-sensitized ASTs the release of toxic factors that contribute to a long-term damage of endothelial cells with cerebral properties . As inflammatory mediators can modulate the expression of the Stx receptor , the effect of released factors from ASTs treated with LPS and/or Stx1 on Gb3 expression was evaluated on HUVECd after a 24 h exposure . Figure 3A shows that the maximum increase of Gb3 was obtained when HUVECd were treated with CM−LPS+Stx1 . As expected , HUVECd directly treated with LPS showed an up regulation of the Stx receptor . To determine whether the enhanced Gb3 expression correlates with an increased toxicity induced by Stx1 , HUVECd treated with the different CM-ASTs were exposed to a sublethal dose of Stx1 . After 18 h , cell death was evaluated by Annexin V ( AV ) and propidium iodide ( PI ) staining ( Figure 3B ) . In accordance to the results observed for the Gb3 expression , the percentage of total cell death ( AV−PI+ plus AV+PI− plus AV+PI+ ) induced by Stx1 was increased in HUVECd exposed to CM−LPS . Moreover , the cultures treated with CM−LPS+Stx1 showed the highest susceptibility to Stx1 . The analysis of the quadrants depicted in Figure 3B shows that the number of cells mapping in the necrotic quadrant ( AV−/PI+ ) is relatively small and constant for all treatments , whereas most cell death is present in the early apoptotic ( AV+/PI− ) and late apoptotic ( AV+/PI+ ) quadrants , indicating that apoptosis is the preferential mechanism accounting for HUVECd cell death in response to CM−LPS+Stx1 . Overall , these results indicate that Stx1 induces on LPS-sensitized ASTs the release of factors that sensitize HUVECd to the toxic effects of Stx1 , and this correlates with an increased expression of Gb3 in HUVECd . Several lines of evidence suggest that alterations in TJs , or proteins associated with them , could act as a possible mechanism leading to an increased BBB permeability [14] . Therefore , we evaluated the effect of CM-ASTs treated with LPS and/or Stx1 in the expression of proteins required for tight junction maintenance and BBB permeability . Figure 4A and 4B depicts that both CM−LPS and CM−Stx1 induced a significant decrease in the expression of ZO-1 and occludin on HUVECd compared to cells exposed to CM-Control . Nevertheless , this reduction was even more evident when HUVECd were stimulated with CM−LPS+Stx1 . In agreement with these results , micrographs depicted in Figure 4C show the reduced ZO-1 expression . In this sense , the intense staining of ZO-1 observed in the periphery of adjacent HUVECd treated with CM-Control was significantly reduced in those cells exposed to CM-LPS or CM−Stx1 , where only a few areas of low ZO-1 expression were evidenced . Furthermore , ZO-1 was absent in HUVECd stimulated with CM−LPS+Stx1 , even at the contact areas between adjacent cells . In addition , micrographies shown in Figure 4C illustrate that F-actin in HUVECd exposed to CM-Control were more prominently arranged defining transcytoplasmatic stress fibers . On the contrary , in HUVECd stimulated with CM−LPS and/or Stx1 , a peripheral localization of F-actin was observed . In addition , a cellular retraction was observed , with more pronounced intercellular spaces . Factors released by ASTs may increase HUVECd permeability allowing the passage of Stx1 across the endothelium . To test this hypothesis we determined the translocation of Stx1 across HUVECd treated with the different conditioned media . HUVECd in transwell upper chambers were stimulated for 24 h with CM−LPS and/or Stx1 . Then CM-ASTs were removed , cells were washed , and Stx1 was added . After 30 min . the medium in the lower chamber was recovered and the presence of Stx1 was determined by the Vero toxicity bioassay , a cell line highly sensitive to the toxin . Figure 4D shows a significant increase in the percentage of Vero cytotoxicity mediated by Stx1-translocated through HUVECd monolayer pretreated with CM−LPS or CM−Stx1 . This increase was even higher for HUVECd incubated with CM−LPS+Stx1 . These results indicate that factors released by ASTs sensitized with LPS and treated with Stx1 altered the expression of TJs proteins and TJs-associated proteins on HUVECd , and induced an increase of HUVECd permeability , allowing the translocation of Stx1 across the endothelium . In order to determine whether factors released by ASTs treated with LPS and/or Stx1 induce activation of HUVECd , we evaluated the expression of adhesion molecules such as ICAM-1 and E-selectin , and the release of procoagulant molecules , such as the von Willebrand Factor ( vWF ) . Figures 5A and B show that the expression of ICAM-1 and E-selectin was significantly increased exclusively on HUVECd stimulated with CM−LPS+Stx1 . The expression of these molecules was similar to that obtained for HUVECd stimulated directly with LPS . Figure 5C shows an increase in vWF secretion induced by CM−LPS . Nevertheless , the highest secretion was observed when HUVECd were stimulated with CM−LPS+Stx1 . These results suggest that Stx1 causes the release of factors on LPS-sensitized ASTs that induce the activation and a prothrombotic state on HUVECd . Numerous studies have suggested that activation of leukocytes is critical for endothelial damage . Therefore , we analyzed whether factors released by ASTs treated with LPS and/or Stx1 induce PMN activation . Purified PMN were incubated with the different CM-ASTs and the expression of PMN-activation markers was evaluated . An increase in the expression of CD11b ( Figure 6A ) and CD66b ( Figure 6B ) was found on PMN stimulated with CM−LPS+Stx1 compared with CM-Control . The production of cytokines and chemokines by brain ECs and ASTs can account for both recruitment and activation of leukocytes [15] , [16] . Therefore , we assessed whether factors released from ASTs treated with LPS and/or Stx1 were able to promote PMN migration across HUVECd . For this purpose , HUVECd placed in transwell upper chambers were stimulated for 24 h with CM−LPS and/or Stx1 . Afterward , PMN were added in the upper chamber and the number of migrated PMN in the lower chamber was determined after 1 . 5 h . Results depicted in Figure 6C reveal that the concentration of PMN that migrated through the endothelial monolayer increased significantly with the CM−LPS or CM−Stx1 compared with CM-Control . However , CM−LPS+Stx1 induced the maximal PMN transmigration . As the passage of leukocytes through ECs may contribute to the disruption of the cellular monolayer , HUVECd were vigorously washed in order to eliminate non-migrated PMN and then monolayers were stained . Micrographies depicted in Figure 6D show that the monolayer architecture of HUVECd was disrupted by transmigration of PMN on those cultures treated with CM−LPS or CM−Stx1 . However , disruption of HUVECd architecture as a result of PMN transmigration was higher when HUVECd were stimulated with CM−LPS+Stx1 . These effects were not observed in HUVECd stimulated with the different CM-ASTs in the absence of PMN , dismissing the possibility that the CM-ASTs alone could cause these effects ( data not shown ) . In summary , these results suggest that Stx1 induces on LPS-sensitized ASTs the release of factors that activate PMN and promote their transmigration through endothelium , causing in turn , the disruption of the endothelial monolayer . PMN-mediated endothelial damage can seriously compromise vasculature and associated tissue functions . The adhesion of PMN to endothelium and the consequent cytotoxicity is magnified by the expression of endothelial ICAM-1 and E-selectin . Therefore , we tested whether ASTs exposed to the toxin could promote PMN adhesion to HUVECd and damage . HUVECd were stimulated with CM−LPS and/or Stx1 . After removal of the stimulus , purified PMN were added and non-adhered PMN were removed 3 h later by vigorous washing . The percentage of PMN-derived alkaline phosphatase activity ( AlkP ) in the remaining attached cells was measured in order to evaluate PMN adhesion to HUVECd . On the other hand , to assess PMN-mediated cytotoxicity , the co-cultures were washed out after 8 h and stained with crystal violet . The percentage of cytotoxicity was determined microscopically by counting the remaining attached HUVECd . HUVECd were easily distinguishable from PMN because of their differences in shape and staining intensity . Figure 7A shows an increase in the percentage of PMN-derived AlkP activity when HUVECd were stimulated with CM−LPS+Stx1 . In addition , Figure 7B shows that the CM-LPS or CM−Stx1 sensitize HUVECd to PMN-mediated toxicity in comparison to HUVECd stimulated with CM-Control . However , this effect was further induced when HUVECd were stimulated with CM−LPS+Stx1 . In order to determine whether PMN-mediated cytotoxicity is dependent on the direct interaction with HUVECd , we performed the same experiment but seeding PMN in the upper chamber of a transwell . Figure 7C shows that under this condition PMN-mediated cytotoxicity was avoided . To sum up , these results indicate that Stx1 induces on LPS-sensitized ASTs the release of factors that promote PMN adhesion to HUVECd and increase their susceptibility to PMN-mediated damage , which depends on PMN contact with HUVECd . Under physiological conditions the endothelium produces many substances that prevent platelets activation and blood clots of fibrin [17] . In this sense , ASTs surrounding compromised endothelium could contribute to platelet activation/adhesion and subsequent brain microthrombi generation . In order to test this hypothesis , we determined whether factors released by ASTs treated with LPS and/or Stx1 induce platelets activation and adhesion to endothelium . As shown in Figure 8A expression of P-selectin and the percentage of activated platelets were increased by the CM−LPS+Stx1 . To assess the platelets adhesion to endothelium , HUVECd were stimulated with CM−LPS and/or Stx1 for 24 h , and platelets were seeded on HUVECd . After 1 . 5 h , free platelets were removed by repeated washings , and the remaining adherent platelets were assessed by measuring acid phosphatase activity ( AcP ) . The figure 8C shows an increase in the percentage of AcP activity induced by CM−LPS+Stx1 . Results indicate that in response to Stx1 , LPS-sensitized ASTs released factors that activate platelets and promote their adhesion to HUVECd . We have previously determined that inhibiting NF-kB with BAY 11-7082 or blocking secreted TNF-α activity with Etanercept prevented the activation and the inflammatory response on LPS-sensitized ASTs exposed to Stx1 [13] . Therefore , we investigated whether the effects observed in HUVECd , PMN and platelets were dependent on NF-κB activation , and particularly on the production of TNF-α by ASTs stimulated with LPS and Stx1 . Thus , the experiments were conducted with CM-ASTs of ASTs that were pretreated with Etanercept or BAY 11-7082 before the addition of LPS and Stx1 . As shown in Figure 9 , under these conditions neither the increment in Gb3 expression ( Figure 9A ) nor the increased sensitivity to the toxin ( Figure 9B ) was observed in HUVECd . Likewise , the declined expression of ZO-1 or occludin were not detected ( Figure 9C ) . A similar behavior was found when endothelial activation was analyzed through ICAM-1 and E-selectin expression ( Figure 9D ) and the release of vWF ( Figure 9E ) . Furthermore , neither PMN ( Figure 9F ) nor platelet ( Figure 9G ) activation was observed . Overall , the results indicate that NF-κB activation on LPS+Stx1-treated ASTs is necessary for the secretion of factors that induce activation of HUVECd , PMN and platelets , as well as for the increased expression of the Stx1 receptor and sensitivity to the toxin in HUVECd . Moreover , TNF-α seems to mediate these effects .
CNS complications are recognized as a major determinant of morbidity and mortality in the acute phase of STEC infections . Although , the pathogenesis of CNS involvement is not yet fully understood , the disruption and/or increased permeability of the BBB are central events in the CNS complications observed during the acute phase of HUS [18] . Stx is a macromolecule , and although in normal condition it should not be able to enter to brain parenchyma , studies in animal models have demonstrated that the toxin crosses the barrier . Moreover , the toxin have been found associated not only to brain ECs but also on parenchymal cells close to perivascular spaces including neurons and ASTs [19] , [20] , [21] suggesting that during STEC infection the BBB is altered . In addition , several in vivo studies found Stx in spinal cord fluid during the initials hours after toxin systemic inoculation , whereas pathological changes of blood vessels are noted at later stages [22] . In line with this , neuronal abnormalities appear before any vascular affection , suggesting the importance of events happening on the neuronal side on the outcome of the vascular pathology observed in HUS . Even though neurological commitment is epidemiologically related to Stx2 variant [23] , growing experimental evidence demonstrated the neurotoxicity of Stx1 [13] , [24] , [25] , [26] , [27] , [28] , [29] . Moreover , Stx1 have been shown to be in some models , even more neurotoxic than Stx2 [30] . Astrocytes ( ASTs ) are the most abundant inflammatory cells [31] , [32] , they are surrounding the cerebral endothelium and their interaction with ECs determines the BBB phenotype and function [8] , [9] , [33] , [34] . ASTs are therefore in a critical position to influence brain ECs integrity and the BBB function , once Stx and factors associated to the STEC infection reach the brain parenchyma . Although the astrocytic inflammatory response elicited by Stx in vivo or in HUS patients has not been studied until the moment , there are two reports in the literature using animal models that demonstrated ASTs activation and alteration after Stx inoculation [33] , [34] . In addition , local production of TNF-α has been described in mouse brains after STEC infection [35] . We have recently demonstrate that Stx1 exerts a direct action on rat ASTs , although sensitization with LPS potentiates Stx1-induced effects , by means of increasing Gb3 expression , revealing activation of ASTs and the development of an inflammatory response characterized by the secretion of nitric oxide ( NO ) , TNF-α and chemokines that promote PMN attraction . Moreover , ASTs derived TNF-α is a pivotal effector molecule that amplifies the Stx1 effects on LPS-sensitized ASTs [13] . Therefore , it is highly probable that mediators released by activated AST in response to Stx ( and LPS ) are influencing ECs functionality in vivo . In the present work we seek to obtain new knowledge for the role of ASTs on brain endothelial dysfunction in an attempt to further address the contribution of the local elicited inflammatory response to the neurophatology of HUS . Our model uses conditioned-media ( CM ) from rat ASTs ( that express the Stx receptor Gb3 ) , whereas ASTs from human biopsies were found negative for Gb3 expression [36] . However , activated human ASTs/astrocytoma cells do express Gb3 [37] . On the other hand , the LPS receptor ( TLR4 ) has been shown to be expressed in human ASTs [38] , [39] , [40] . Therefore , although experiments using human primary ASTs are necessary to confirm our current results , we can speculate that during the disease process activation of ASTs by LPS , systemic inflammatory mediators , or other bacterial products , may lead to the induction of Gb3 in ASTs , triggering a local inflammatory response that will be , in turn , amplified by Stx . In this context , the results originated from the treatments with CM−LPS and CM−LPS+Stx1 may most likely represent the HUS scenario . Peripheral endothelial cells can be induced to differentiate into brain capillary ECs by soluble factors released by ASTs [7] , [36] , [37] , [38] , [39] . Here , we differentiated HUVEC into ECs that adopt characteristics that coincide with those present in human brain ECs ( HUVECd ) , which allowed us modeling more accurately human BBB . In this regard , we observed an increase in AlkP activity , an increased expression of ZO-1 and occludin and a reorganization of F-actin . These observations are consistent with those reported by others [38] , [39] . In addition , we observed a decreased permeability to horse radish peroxidase ( HRP ) in HUVECd when compare to non-differentiated HUVEC , similar to the decrease in permeability observed in a comparative study using brain microvascular ECs and HUVEC [40] . Although we are aware of the limitations of our model , since HUVECd are not exactly ECs from brain origin , the use of human primary brain cells is restricted by the unavailability of experimental material , which is usually obtained from surgical material and often cannot be considered as “healthy” tissue [41] , [42] . Additionally , some characteristics of the in vivo BBB are lost in culture and this is compensated by co-culturing with ASTs or their CM [8] , [43] . On the other hand , the use of immortalized brain endothelial cell lines does not assure an exactly similar behavior in culture as in the brain . An advantage of using HUVECd primary cultures obtained from HUVECs of different donors is that the heterogeneity of responses that exist among different individuals is maintained , and the variability obtained in the results better represents the variability found in HUS patients , in contrast of using a cell line with the same genetic background . Taken all this considerations into account , we consider that brain properties elicited in HUVECd , by incubation of HUVEC with CM of ASTs , are enough representative of brain ECs . Then , in order to evaluate if the astrocytic response to LPS and/or Stx1 could altered properties of ECs forming the BBB , we studied the effects of soluble factors released by ASTs exposed to LPS and/or Stx1 on HUVECd . We found that TNF-α released from LPS-sensitized ASTs treated with Stx1 was able to increase Gb3 expression on HUVECd and this correlated with a higher susceptibility towards Stx1 toxic effects . These findings are in agreement with other reports using human brain ECs [27] , [44] . On the other hand , LPS-sensitized ASTs stimulated with Stx1 released factors that , only in the long term , turn out to be toxic for HUVECd . Although astrocytic factors responsible for HUVECd's toxicity were not determined in this work , TNF-α has been proposed as the molecule responsible for ASTs mediated cytotoxicity on oligodendrocyte [45] and neurons [46] . However , in vitro , TNF-α is not toxic for brain ECs cultures by its own , but it resulted in a surprising synergism when combined with reactive species [47] . In this respect , NO liberated in response to LPS and Stx1 may act in combination with TNF-α inducing long term HUVECd death . Another plausible explanation is that the protein synthesis inhibiting activity of Stx1 prevents the expression of a host response factor by ASTs that is necessary for maintenance of EC viability [48] , [49] , [50] . In this sense , down-regulation of cell survival factors , in addition to the release of cytotoxic factors , may also contribute to long term ECs death . The commitment of the TJs is a distinctive characteristic in neuroinflamatories diseases [51] . Numerous inflammatory substances modulate the permeability of the BBB , including TNF-α , NO and LPS [52] , [53] . In vivo studies have demonstrated that an increment in the barrier permeability is associated with low levels of expression of ZO-1 , occludin and actin filaments [54] . Here we determined that factors released by ASTs in response to LPS or Stx1 , and especially by the combination of LPS and Stx1 , induced an important decrease in occludin and ZO-1 expression in HUVECd . Moreover , this correlated with a significant peripheral localization of the ZO-1 protein , which has been shown to impact on the function of the BBB . Even though there are no concrete evidences that determine how this redistribution happens , it is believed to be associated with the reorganization of actin filaments , which are connected to the proteins of the TJs complex through ZO-1 [55] , [56] . The results shown in this work indicate , for the first time , that ASTs inflammatory response induced by LPS and Stx and particularly ASTs-derived TNF-α , alters these molecules contributing to the increased endothelial permeability observed in HUS . Although in vivo studies must be performed to corroborate this hypothesis , studies on other cerebral pathologies support this possibility [53] , [57] . In this sense , Álvarez et al . demonstrated in a murine model of cerebral inflammation that activated ASTs expressing BBB regulatory cytokines were juxtaposed to blood vessels exhibiting increased permeability [58] . An additional factor that can contribute to the loss of integrity of the BBB is the migration of PMN induced by chemotactic stimuli [59] . Migration across the BBB of PMN may initiate pathogenic events , leading to microvascular plugging , stasis , and thrombosis [60] , [61] . PMN may migrate across the endothelial monolayer at cell-cell junctions by a paracellular route and/or by a transcellular pathway that involves migration of PMN at non-junctional locations . Here , we found an augmented migration of PMN across HUVECd treated with CM−LPS+Stx1 . Given the increased permeability observed in HUVECd treated with CM−LPS+Stx1 , and in concordance with a markedly reduction of tight junction proteins ( ZO-1 and occludin ) , we can speculate that PMN more likely take a paracellular route to transmigrate , since this route is highly dependent on barrier function deregulation . Moreover , it has been recently demonstrated that PMN preferentially migrate across the BBB via the transcellular route when the barrier function is intact [62] Although PMN transmigration is not usually associated with disruption of the endothelial linearity , the presence of additional factors that induce PMN degranulation , such as TNF-α , can cause destruction of the vascular endothelial architecture due to the release of proteolytic enzymes [63] , [64] . The results obtained here indicate that , in addition to the increased endothelial permeability , PMN transmigration induced by factors secreted by ASTs in response to LPS and Stx1 was associated to the destruction of the endothelial monolayer linearity , compromising even more the integrity of the BBB . Additionally , factors released by treated ASTs induced PMN-mediated endothelial toxicity , and in agreement with other reports [65] , [66] , this was dependent on the close interaction/contact between HUVECd and PMN . All together , these results indicate that the systemic and local ( from perivascular ASTs ) inflammatory responses increase the BBB permeability , and at the same time , potentiate the damage of ECs triggered by Stx and factors associated to the infection . Endothelial dysfunction is crucial for the development of microangiopathic injuries in HUS [67] , [68] , and a vast bibliography suggests that the interaction between activated leukocytes ( especially PMN ) , platelets and ECs amplifies and extends renal damage [66] , [69] , [70] . However , so far no work regarding HUS-associated neuropathology contemplates the contribution of the intracerebral inflammation and its effect on ECs that comprise the BBB . Among the critical events commonly involved in CNS affection in HUS , edema , microthrombi and ischemic changes are found [71] , [72] . Autopsy material from patients with HUS reveals thrombosis of capillaries in kidney , lung , liver and brain . Both damage and stimulation with inflammatory mediators suppress the anticoagulant properties of endothelium , promoting a procoagulant condition . We determined that factors secreted by ASTs treated with LPS and Stx1 directly induced HUVECd , PMN and platelet activation , and increased both PMN and platelet adhesion to activated HUVECd . Therefore , these events may contribute to the cerebral inflammation in HUS triggering the formation of thrombi and altering ECs integrity . Moreover , activation of HUVECd , PMN and platelets was not observed when ASTs were pretreated with BAY 11-7082 and exposed to LPS and Stx1 , suggesting that inflammatory factors secreted by means of NF-kB activation were responsible for the effects observed in these cells . Furthermore , the blockade of ASTs released TNF-α by Etanercept also prevented these effects . The results presented herein suggest that NF-κB and TNF-α could be target molecules to prevent or diminish the CNS complication observed in HUS patients . Several in vivo studies of different CNS pathologies demonstrated that suppression of ASTs activation and their inflammatory response resulted in reduced disease severity and improved functional recovery [73] . In addition , recent reports showed clinical improvement in patients with Alzheimer and related disorders following the perispinal administration of Etanercept , suggesting that Etanercept has the ability to penetrate into the cerebral spin fluid in the brain at a therapeutically effective concentration [74] , [75] . Further in vivo studies should clarify whether inhibition of NF-κB signaling or TNF-α production results in protective effects , and if the NF-κB pathway results a convenient new target for the development of therapeutic strategies for the treatment of CNS commitment in HUS patients . Given the narrow interaction between ASTs and brain ECs , local concentration of secreted astrocytic factors in response to Stx is extremely relevant to understand the role of cerebral inflammation and its relation with the microvascular injury and BBB alterations in HUS . Primary alteration of the BBB after STEC infection as a consequence of the systemic inflammation and/or bacterial-derived factors may leave the brain unprotected to the entry of Stx and LPS . Thereafter , ASTs inflammatory response creates an amplification loop that potentiates the initial endothelial damage affecting even more the integrity of the BBB . Results from this work and the bibliographical precedents , stimulate the accomplishment of a more detailed in vivo study to determine the contribution of brain inflammatory response , and particularly of perivascular ASTs to understand the neuropathology of HUS .
Human normal samples were obtained from voluntary donors . This study was performed according to institutional guidelines ( National Academy of Medicine , Buenos Aires , Argentina ) and received the approval of the institutional ethics committee and written informed consent was provided by all the subjects . Human normal blood samples were obtained from voluntary donors by venipuncture and drawn directly into plastic tubes containing 3 . 8% sodium citrate . Human umbilical cords were obtained from normal placentas , and placed in a sterile container filled with a transfer buffer . Stx1 was kindly provided Dr Sugiyama Junichi ( Denka Seiken CO Ltd , Nigata , Japan ) . Purity was analyzed by the supplier by high performance liquid chromatography ( HPLC ) . Stx1 preparation was checked for endotoxin contamination by the Limulus amoebocyte lysate assay and contained <40pg lipopolysaccharide ( LPS ) /µg of pure protein . Human PMN were isolated by Ficoll-Hypaque gradient centrifugation ( Ficoll Pharmacia , Uppsala; Hypaque , Wintthrop Products , Buenos Aires , Argentina ) and dextran sedimentation , as previously described [76] . Viability was assessed by trypan blue exclusion and purity was determined by Turk's solution staining . Only fractions containing at least 80% of PMN were used . Platelet rich plasma ( PRP ) was obtained by centrifugation of the blood samples ( 180×g for 10 min ) . For washed platelet suspensions , PRP was centrifuged in the presence of prostacyclin ( PGI2 , 75 nM ) , and the platelets were then washed in washing buffer ( 140 mM NaCl , 10 mM NaHCO3 , 2 . 5 mM KCl , 0 . 5 mM Na2HPO4 , 1 mM MgCl2 , 22 mM sodium citrate , 0 . 55 mM glucose , 0 . 35% BSA , pH 6 . 5 ) washed platelets were resuspended in Tyrode's buffer and the platelet number was adjusted . ASTs were prepared from rat cerebral tissue cortex as previously described [77] . Briefly , cerebral hemispheres were dissected out from newborn rats , free of meninges , and dissociated by gentle pipetting on DMEM/Ham's F12 media ( GIBCO , Invitrogen ) ( 1∶1 v/v ) containing 5 µg/ml streptomycin and 5 U/ml penicillin , supplemented with 10% fetal calf serum ( FCS ) ( GIBCO ) . The cell suspensions were seeded into poly-L-lysine-coated 75 cm2 tissue culture flasks ( Corning ) . After 14 days in culture , ASTs were separated from microglia and oligodendrocytes by shaking twice , for 24 h each , in an orbital shaker . The purity of ASTs cultures was 90–95% , as assessed by GFAP immunostaining by flow cytometry . ASTs ( 7×104 ) were seeded into 24 well-plates and cultured in DMEM media containing 10% FCS and supplemented with 5 µg/ml streptomycin and 5 U/ml penicillin ( complete DMEM ) . Cultures were maintained at 37°C in a humidified 5% CO2 atmosphere . After 24 h , ASTs were mock-treated ( control ) or treated with LPS ( 0 . 5 µg/ml ) ; Stx1 ( 10 ng/ml ) or LPS+Stx1 ( Stx1 added 18 h after LPS ) . Purified LPS derived from E . coli O111:B4 ( Sigma ) was used . The inhibitor ( E ) -3-[4-methylphenylsulfonyl]-2-propenenitril ( BAY 11-7082; Biomol ) was used for suppressing NF-kB activation . BAY 11-7082 ( 40 µM ) was added to ASTs cultures 30 min before treatment . A soluble tumor necrosis factor receptor ( Etanercept; Enbrel , Wyeth Inc . ) was used to block ASTs secreted TNF-α action . Etanercept ( 5 ng/ml ) was added to ASTs cultures 10 min before treatment . Conditioned medium from confluent cultures of ASTs ( CM-ASTs ) grown in complete DMEM was collected every 24 h . CM-ASTs from untreated ASTs ( CM-Control ) or CM-ASTs treated with LPS and/or Stx1 ( CM−LPS , CM−Stx1 and CM−LPS+Stx1 ) were collected 18 h after Stx1 treatment . The CM-ASTs were centrifuged to eliminate cells and debris and sterilized by filtration . The CM-ASTs were aliquoted and stored at −80°C until used . Before CM-ASTs were employed in experimental cultures , 7 µg/ml of Polymyxin B ( Px , Sigma ) and an antibody ( Ab ) against Stx ( anti-Stx; final dilution 1∶100 Toxin Technology , Catalog Number STX1-9C9; anti-STX1 alpha subunit , mouse monoclonal antibody IgG ) were added to the CM-ASTs . Proper doses of the Px and anti-Stx to block the effects of remnant traces of LPS and/or Stx1 have been previously tested and validation tests are summarized in Figure S1 [13] . Human umbilical vascular endothelial cells ( HUVECs ) were obtained by collagenase digestion according to the method of Jaffe et al [78] . Cells were seeded until confluence on 1% gelatin-coated 25-cm2 tissue culture flasks and identified by their cobblestone morphology and von Willebrand factor ( VWF ) antibody ( Immunotech ) binding . Cells were grown in RPMI 1640 medium ( HyClone ) supplemented with 10% fetal bovine serum ( FBS , Gibco ) , heparin ( 100 µg/ml ) , endothelial cell growth factor supplement ( 50 µg/ml ) , sodium pyruvate ( 2 mM ) , L-glutamine ( 2 mM ) , penicillin ( 100 U/ml ) and streptomycin ( 100 µg/ml ) ( Sigma Chemical ) at 37°C in a humidified 5% CO2 incubator . HUVECs used for experiments were between first and third passages . Cultured cells were identified as endothelial by their morphology and VWF antibody binding . CM-ASTs was used to induce a cerebral endothelium phenotype in HUVEC ( HUVECd ) . HUVEC ( 1×105 cells ) were seeded into 24 well-plates and maintained in complete DMEM during 24 h . Then , HUVEC were differentiated into HUVECd by incubating them with CM-ASTs . After 24 h , HUVECd already exhibited properties of cerebral endothelium and these features lasted for up to 72 h even after removal of CM-ASTs . Endothelial Alkaline phosphatase ( AlkP ) was determined as previously described with some modifications [79] . Briefly , HUVEC and HUVECd were washed twice with PBS and 100 µl of 1% p-nitrophenyl phosphate disodium ( p-NPP ) in buffer containing 1 mM MgCl2 and 100 mM 2-amino-2-methyl-1-propanol ( Sigma ) was added to each well , and incubated at 37°C for 1 h . Then , 50 µl of stop solution ( 2 N NaOH ) was added and optical density was measured at 405 nm in Asys UVM340 Microplate Reader ( Biochrom Ltd . ) . Enzymatic activity , defined as dephosphorylated substrate produced in a given well during one hour at 37°C was expressed in arbitrary enzymatic units ( EU ) and calculated with the following equation: Similar procedure was applied for PMN adhesion with some modifications [80] . Briefly , purified PMN were seeded onto CM-ASTs treated HUVECd in a PMN/HUVECd ratio of 20∶1 and cultured for 3 h . Then , non-adherent PMN were removed by vigorous washing with PBS and AlkP assay was performed by incubating adherent cells for 1 h with 100 µl of 1% p-NPP in diethanolamine buffer ( 1 M , pH 9-8 ) . PMN-specific AlkP activity was calculated subtracting basal AlkP activity from HUVECd alone treated with the respective CM-ASTs . Data expressed as percentages of AlkP enzymatic activity ( %EA ) was calculated as follow:where AlkPtreatment was calculated as:and AlkPtotal PMN is the total AlkP from unwashed control PMN . The cellular distribution of ZO-1 was evaluated microscopically on the HUVECd grown in glass coverslips . After 24 h treatment with DMEM or the different CM-ASTs , cells were pre-extracted with 0 . 2% Triton X-100 in buffer containing 100 mM KCI , 3 mM MgCl2 , 1 mM CaCl2 , 200 mM sucrose , and 10 mM Hepes ( pH 7 . 1 ) for 2 min on ice and were immediately fixed with 2% paraformaldehyde in PBS ( 30 min on ice ) . The fixed cells were blocked for 30 min in 3% BSA , 0 . 25% Tritón X100 in PBS for 1 h . Primary antibodies ( rabbit IgG anti-ZO-1 , Invitrogen ) were diluted in the same solution and incubated with the cells for 1 h at room temperature . Then cells were washed with PBS and secondary antibody ( FITC-labeled mouse anti-rabbit ( 1∶200 final dilution , Vector ) was incubated during 1 h and finally washed . Images were acquired using a FluoView FV1000 confocal microscope ( Olympus ) equipped with a Plapon 60×/1 . 42 objective lens and processed using FV10-ASW software ( Olympus ) and Adobe Photoshop CS3 using only linear adjustments . The expression levels of ZO-1 and occludin was determined by intracellular flow cytometry ( FACS ) using a FACScan cytometer ( Becton Dickinson ) following the labeling procedure described above . For ZO-1 expression , the same antibodies were used . For occludin , mouse IgM anti-Occludin ( 1∶200 final dilution , Invitrogen ) with PE-labeled rabbit anti-mouse IgG ( 1∶200 final dilution , DAKO ) secondary antibody was used . Isotype matched control immunostaining was performed in parallel . FCS Express program ( De Novo Sofware ) for data analysis was employed . The analysis was made on at least 30 , 000 events per sample . HUVEC grown in glass coverslips were treated with DMEM or the different CM-ASTs and 24 h later cells were fixed with 1% paraformaldehyde in PBS during 20 min then washed and permeabilized with 0 . 25% Triton X-100 . F-actin was stained using 10 µM TRITC-labeled phalloidin ( Sigma ) for 1 h at room temperature . F-actin distribution was evaluated by confocal microscopy using the above equipment by considering maximum fluorescence intensity projections along the whole Z-stack . HUVEC ( 2×104 cells ) were seeded onto the upper chamber of a transwell system ( 0 . 3 mm pore size , Corning ) and treated with DMEM or CM-ASTs for 24 h to differentiate them into HUVECd . In some experiments , HUVECd were additionally stimulated with the different types of CM-ASTs ( CM−Stx1 , CM−LPS or CM− Stx1+LPS ) for another 24 h . The transendothelial passage of the 44 kD glycoprotein horse radish peroxidase ( HRP ) ( Figure 1B ) and the bioactive ∼75 kD multimeric protein Stx1 ( Figure 4D ) were evaluated respectively as follow: HUVECd ( 1×105 cells ) grown in 24 well-plates were stimulated with the different CM-ASTs during 24 , 48 , and 72 h . After incubation , dead cells were washed away with PBS and remnant cells were fixed and stained with crystal violet . Cytotoxicity on treated HUVECd was determined microscopically using the following equation: Globoside Gb3 ( CD77 ) expression was evaluated on HUVECd stimulated by 24 h with the different CM-ASTs . As a positive control , cells were stimulated with 0 . 5 µg/ml LPS . After incubation , HUVECd were harvested , blocked with 3% BSA for 20 min and exposed to rat anti-CD77 monoclonal antibody ( clone 38-13; final dilution 1∶100; Immunothech ) for 45 min at 4°C . Then , cells were washed and incubated with FITC-conjugated secondary goat antibody F ( ab ) ′2 anti-rat μ chain ( final dilution 1∶200 , Jackson ImmunoResearch Lab ) in the dark for 30 min at 4°C . Immunostained cells were then washed and immediately analyzed by FACS as indicated above . Isotype matched control immunostaining was performed in parallel . The cell death analysis of HUVECd treated 24 h with the different CM-ASTs was performed 24 h after stimulation with a sub-toxic dose of Stx1 ( 2 ng/ml ) . Detached dead cells were carefully collected along with undetached cells . The Annexin V-FITC Apoptosis Detection Kit ( Calbiochem ) was used according to manufacturer's instructions . Briefly , ASTs were incubated for 30 min with media binding reagent and fluorescein isothiocyanate ( FITC ) -labeled annexin V ( AV , 1 µg/ml ) in the dark at room temperature . Cells were washed , resuspended in media binding buffer , and incubated for 10 min with propidium iodide ( PI , 15 µg/ml ) . The percentage of total death PI+ AV+/− ( PI+/AV− plus PI+/AV+ ) and PI− AV+ cells was determined by FACS as indicated above . To evaluate HUVECd , PMN and platelet activation , ICAM-1 , E-selectin and CD11b , CD66b and P-selectin expression were determined respectively by direct immunofluorescence using conjugated anti-human monoclonal antibodies and the surface expression of these activation markers was evaluated by FACS . HUVECd were stimulated with the CM-ASTs during 12 h or 6 h at 37°C for ICAM-1 and E-selectin , respectively . Cells were washed and resuspended in PBS 3% BSA and stained with a mouse monoclonal antibody phycoerythrin ( PE ) -labeled anti-human CD54 ( final dilution 1∶50 , BD Pharmingen ) for ICAM-1 determination or with a mouse monoclonal antibody fluorescein isothiocyanate ( FITC ) -labeled anti-human CD62E ( final dilution 1∶150 , Immunotech ) for E-selectin determination . PMN were stimulated with the different CM-ASTs for 90 min or 40 min for CD11b or CD66b determination , respectively . Then PMN were collected in PBS 3% BSA and stained with PE-CD11b ( final dilution 1∶250 , Immunotech ) and FITC-CD66b ( final dilution 1/150 , Immunotech ) . Platelets were stimulated with the different CM-ASTs for 1 . 5 h at 37°C , washed and resuspended in PBS 3% BSA and stained with a FITC-anti CD62P ( anti-P-selectin antibody , final dilution 1∶50 BD Biosciences ) . Samples were incubated with the specific antibody for 30 min at room temperature . Then , cells were washed and suspended in 0 . 2 ml of ISOFLOW . In all cases , controls of isotype-matched antibody were assayed in parallel . Data were acquired and processed according the above indications . Cells were identified and gated according to their respective forward and light scattering ( FSC/SSC ) dot-plot profiles . Purified PMN were seeded onto CM-ASTs treated HUVECd in a PMN/HUVECd ratio of 20∶1 and cultured for 3 h . Then , non-adherent PMN were removed by vigorous washing with PBS . Alkaline phosphatase ( AlkP ) assay was performed in order to measure PMN adhesion [80] . Briefly , adhered cells were incubated for 1 h with 100 µl of 1% p-NPP in diethanolamine buffer ( 1 M , pH 9-8 ) and PMN AlkP activity was determined as indicated above subtracting basal AlkP activity from HUVECd alone treated with the respective CM-ASTs . Data expressed as percentages of AlkP enzymatic activity ( % EA ) was calculated as follow:where AlkPtreatment was calculated as:and AlkPtotal PMN is the total AlkP from unwashed control PMN . PMN were seeded onto CM-ASTs treated HUVECd or onto a transwell coculture system to avoid contact ( 20∶1 PMN/HUVECd ratio ) . After 8 h , non-adherent PMN and detached dead HUVECd were removed by vigorous washing . Remnant cells were stained with violet crystal . To assess PMN-mediated HUVECd cytotoxicity , 3 random fields per treatment , were photographed under 600×magnifications ( Nikon , Coolpix 4500 digital camera; Nikon , Eclipse TE2000-S , inverted microscope ) . Cell counts were performed as previously described [13] using ImageJ software ( U . S . National Institutes of Health , Bethesda , Maryland ) . Percentage of cytotoxicity was calculated as: Washed platelet ( Pt ) were seeded over treated HUVECd in a Pt/HUVECd ratio of 40∶1 and cultured for 3 h . Then , non-adherent Pts were removed by vigorous washing with warm PBS . Acid phosphatase ( AcP ) assay was performed in order to measure Pt adhesion [82] Briefly , adherent Pts were incubated for 1 h with 150 µl of 5 mM p-NPP in citrate buffer ( 0 . 1 M , pH 5 , 4 ) containing 0 . 1% Triton X-100 and enzymatic activity was determined as indicated above . Arbitrary AcP units ( EU ) were normalized to total EU present in unwashed control wells seeded with equal number of Pts ( EUtotal Pt ) according to the following equation: % EA = ( EU/EUtotal Pt ) ×100% von Willebrand factor ( vWF ) release was determined by ELISA on supernatants from HUVECd after 24 h stimulation with the different CM-ASTs following the manufacturer instructions ( Dako , Glostrup , Denmark ) . Results are expressed in ng/ml using normal pooled plasma as standard ( National Institute for Biological Standars and Control , UK ) . HUVECd grown in the upper chamber of transwell were treated 24 h with the different CM-ASTs and then PMN were added ( 20∶1 PMN/HUVECd ratio ) . After 1 . 5 h the number of PMN that migrated across the HUVECd monolayer were collected in the lower chamber of the transwell and counted microscopically using a Neubauer chamber . Statistical differences among treatments were determined using paired t-test for two groups comparison and paired one-way analysis of variance ( anova ) followed by Bonferroni post test for multiple comparison . A p value<0 . 05 was considered significant . All tests were carried out using Prism 5 . 0 ( Graph Pad Software , La Jolla , CA ) . TNF-α P16599 ( TNFA_RAT ) Reviewed , UniProtKB/Swiss-Prot NF-kB Q63369 ( NFKB1_RAT ) Reviewed , UniProtKB/Swiss-Prot Actin P68133 ( ACTS_HUMAN ) Reviewed , UniProtKB/Swiss-Prot Occludin Q16625 ( OCLN_HUMAN ) Reviewed , UniProtKB/Swiss-Prot ZO-1 Q07157 ( ZO1_HUMAN ) Reviewed , UniProtKB/Swiss-Prot Stx1 A subunit A8B1H9 ( A8B1H9_ECO57 ) Unreviewed , UniProtKB/TrEMBL Stx1 B subunit C8UEK0 ( C8UEK0_ECO1A ) Unreviewed , UniProtKB/TrEMBL von Willebrand factor 04275 ( VWF_HUMAN ) Reviewed , UniProtKB/Swiss-Prot ICAM-1 P05362 ( ICAM1_HUMAN ) Reviewed , UniProtKB/Swiss-Prot E-selectin P16581 ( LYAM2_HUMAN ) Reviewed , UniProtKB/Swiss-Prot CD11b P11215 ( ITAM_HUMAN ) Reviewed , UniProtKB/Swiss-Prot CD66b P31997 ( CEAM8_HUMAN ) Reviewed , UniProtKB/Swiss-Prot P-selectin P16109 ( LYAM3_HUMAN ) Reviewed , UniProtKB/Swiss-Prot Alkaline phosphatase P09923 ( PPBI_HUMAN ) Reviewed , UniProtKB/Swiss-Prot Etanercept P20333 ( TNR1B_HUMAN ) Reviewed , UniProtKB/Swiss-Prot | Hemolytic-uremic syndrome ( HUS ) is generally caused by Shiga toxin ( Stx ) -producing Escherichia coli but bacterial lipopolysaccharide ( LPS ) and neutrophils ( PMN ) contribute to the pathophysiology . Acute renal failure is the main feature of HUS , but in severe cases , patients develop neurological complications , which are usually associated with death . Although the mechanisms of neurological damage remain uncertain , alterations/injury of brain endothelial cells ( ECs ) which constitute the blood-brain barrier ( BBB ) is clear . Astrocytes ( ASTs ) are inflammatory cells enclosing ECs and are responsible of the normal function of the barrier . We have recently demonstrated that Stx1 , one of the most common types of Stx , induce an inflammatory response in LPS-treated ASTs . We then study the effects of factors released by ASTs in response to LPS and/or Stx1 on brain-like ECs . We demonstrate that Stx1 induces in LPS-treated ASTs the release of factors that alter brain properties in ECs , including the permeability; turning them more susceptible to Stx1 toxic effects . Furthermore , they activate ECs , neutrophils ( PMN ) and platelets and render ECs into a proagregant state promoting PMN and platelet adhesion . Our results suggest that ASTs could influence brain ECs integrity and BBB function once Stx in combination with bacterial factors reach the brain parenchyma . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"bacterial",
"diseases",
"infectious",
"diseases"
] | 2012 | Shiga Toxin 1 Induces on Lipopolysaccharide-Treated Astrocytes the Release of Tumor Necrosis Factor-alpha that Alter Brain-Like Endothelium Integrity |
BRIT1 protein ( also known as MCPH1 ) contains 3 BRCT domains which are conserved in BRCA1 , BRCA2 , and other important molecules involved in DNA damage signaling , DNA repair , and tumor suppression . BRIT1 mutations or aberrant expression are found in primary microcephaly patients as well as in cancer patients . Recent in vitro studies suggest that BRIT1/MCPH1 functions as a novel key regulator in the DNA damage response pathways . To investigate its physiological role and dissect the underlying mechanisms , we generated BRIT1−/− mice and identified its essential roles in mitotic and meiotic recombination DNA repair and in maintaining genomic stability . Both BRIT1−/− mice and mouse embryonic fibroblasts ( MEFs ) were hypersensitive to γ-irradiation . BRIT1−/− MEFs and T lymphocytes exhibited severe chromatid breaks and reduced RAD51 foci formation after irradiation . Notably , BRIT1−/− mice were infertile and meiotic homologous recombination was impaired . BRIT1-deficient spermatocytes exhibited a failure of chromosomal synapsis , and meiosis was arrested at late zygotene of prophase I accompanied by apoptosis . In mutant spermatocytes , DNA double-strand breaks ( DSBs ) were formed , but localization of RAD51 or BRCA2 to meiotic chromosomes was severely impaired . In addition , we found that BRIT1 could bind to RAD51/BRCA2 complexes and that , in the absence of BRIT1 , recruitment of RAD51 and BRCA2 to chromatin was reduced while their protein levels were not altered , indicating that BRIT1 is involved in mediating recruitment of RAD51/BRCA2 to the damage site . Collectively , our BRIT1-null mouse model demonstrates that BRIT1 is essential for maintaining genomic stability in vivo to protect the hosts from both programmed and irradiation-induced DNA damages , and its depletion causes a failure in both mitotic and meiotic recombination DNA repair via impairing RAD51/BRCA2's function and as a result leads to infertility and genomic instability in mice .
The repair of DNA double-strand breaks ( DSBs ) is critical for maintaining genomic integrity [1] , [2] . DSBs can arise from exogenous agents such as ionizing radiation ( IR ) [3] and endogenous factors such as stalled replication forks [4] . In addition , DSBs can form in a programmed manner during development including meiosis and immunoglobin rearrangements [5] , [6] . During meiosis , DSBs are generated to initiate recombination between homologous chromosomes which leads to the reciprocal exchange of genetic materials between parental genomes . The inability for hosts to respond properly to the breaks or to repair them may trigger physiological defects such as infertility or cause genomic instability . DNA damage response ( DDR ) pathways activated as a result of DSBs conceptually have three components , some with overlapping functions: sensors , signal transducers , and effectors [7] , [8] . Damaged DNA is recognized by sensors; the signal is brought to transducers , which then in turn activate or inactivate the effectors that trigger cell cycle checkpoints , DNA repair or apoptosis . In response to DNA damage , many proteins involved in DDR pathway , including ATM [8] , MDC1 [9] , H2AX [10] , NBS1 [11] , 53BP1 [12] , [13] , RAD51 [14] , BRCA1 [15] , and BRCA2 [16] , quickly accumulate to damage sites and form large nuclear aggregates that appear as IR-induced nuclear foci ( IRIF ) observed microscopically . A variety of evidence suggests that IRIF are required for precise and efficient DSB repair in the context of chromatin . Recent studies suggest that BRIT1 ( BRCT-repeat inhibitor of hTERT expression ) is a key regulator for DNA damage response pathways [17] , [18] . The sequence of BRIT1 was derived from a hypothetical protein that was later matched to a putative disease gene called microcephalin ( MCPH1 ) , one of at least six loci implicated in the autosomal recessive disease primary microcephaly [19] . BRIT1 protein contains three BRCT ( BRCA1 carboxyl terminal ) domains , one in N-terminal and two in C-terminal . Many DDR and DNA repair proteins such as BRCA1 , BRCA2 , MDC1 and NBS1 contain BRCT domains , suggesting that BRIT1 may also play a role in DDR . In fact , knockdown of BRIT1 by specific siRNA in cells showed that BRIT1 was required for DNA damage-induced intra-S and G2/M checkpoints [17] , [20] , [21] . BRIT1 is also a chromatin-binding protein that forms IRIF , which co-localize with ATM , γ-H2AX , MDC1 , NBS1 and 53BP1 , and depletion of BRIT1 by its siRNA impairs the IRIF formation of ATM , MDC1 , NBS1 , and 53BP1 [17] , [18] , indicating that BRIT1 may exert a direct role in transmitting DNA damage signals . In addition , expression levels of BRIT1 are decreased in several types of human cancer including breast and ovarian cancers [18] , suggesting that BRIT1 may function as a novel tumor suppressor gene . To better define its physiological role , here we generated BRIT1−/− mice and demonstrated BRIT1's essential role in regulation of both programmed and IR-induced DNA damage responses .
To characterize the physiological function of BRIT1 , we generated BRIT1−/− mice by gene targeting ( Figure 1A ) . The mice with germ-line transmission of the targeted conditional allele were crossed with Flp mice to eliminate the Neo cassette . To generate the global knockout mice , these mice were bred with transgenic mice carrying a Cre gene under the control of β-actin promoter to eventually generate BRIT1−/− mice , where exon 2 of BRIT1 was deleted , leading to out of reading frame mutation of BRIT1 . We confirmed the loss of both BRIT1 alleles in BRIT1−/− mice by Southern blot analysis ( Figure 1B ) . RT-PCR with primers flanking exon 2 followed by DNA sequencing also revealed the disruption of BRIT1 transcript in BRIT1−/− mice ( Figure 1C ) . We also developed a rabbit antibody specifically against the C-terminal fragment of mouse BRIT1 and anti-BRIT1 Western blot confirmed the loss of BRIT1 in BRIT1−/− mice ( Figure 1D ) . BRIT1−/− mice were able to survive to adulthood , but they were growth-retarded ( Figure 1E ) . The weight of BRIT1−/− mice at postnatal days 56 ( P56 ) was only 80% compared to wild type ( WT ) . In addition , birth rate of the BRIT1−/− mice was ∼10% among the offspring of self-cross of heterozygous mice ( Figure 1F ) , which was much lower than normal Mendelian ratio ( ∼25% ) , suggesting that BRIT1 deficiency may affect early development in mice . One of the hallmarks of defective DNA damage response is increased radiation sensitivity . To assess whether the loss of BRIT1 expression renders mice hypersensitive to IR , we irradiated BRIT1+/+ , BRIT1+/− and BRIT1−/− mice with the dose of 7 Gy . All BRIT1−/− mice died within 9 days after irradiation , while 80% of BRIT1+/− or BRIT1+/+ mice were still alive 4 weeks after irradiation ( Figure 2A ) . We also examined BRIT1−/− MEFs' sensitivity to IR . Passage 2 primary MEFs were used to expose to different dosages of IR . The surviving cells were counted at the 6th day after IR . BRIT1−/− MEFs were more sensitive to IR as compared to BRIT1+/+ cells ( Figure 2B ) . Thus , both BRIT1−/− mice and the MEFs derived from those mice are more sensitive to irradiation . To explore if genomic instability occur in BRIT1−/− cells , we performed metaphase spread assay to examine chromosome aberrations in both BRIT1+/+ and BRIT1−/− MEFs shortly ( 3 h ) and longer time ( 20 h ) after IR . Metaphase spread showed that most of BRIT1−/− MEFs had DNA breaks , while only fewer of BRIT1+/+ cells had breaks at as early as 3 h after IR ( Figure 2C and 2D ) . Remarkably , at 20 h after IR , we still observed more chromosomal aberrations in BRIT1−/− than BRIT1+/+ MEFs ( Figure 2C ) . To assess if BRIT1 plays a role in regulating spontaneous DNA damage , we isolated T cells from both wild-type ( WT ) and mutant mice spleens and compared their genomic stability by metaphase spread , and found more chromosomal aberrations in the BRIT1−/− T cells than in WT ( Table S1 ) . While other types of chromosomal aberrations ( e . g . translocations , polyploidy ) also occurred in IR-treated BRIT1−/− MEFs or T lymphocytes , the majority aberration in these cells was chromatid breaks , a phenomenon associated with defective homologous recombination ( HR ) [22] , [23] . Together with our previous studies [24] , [25] , these results indicate that loss of BRIT1 leads to defective DNA repair in homologous recombination , and eventually cause genomic instability . Thus , BRIT1 is involved in regulating both spontaneous and IR-induced DNA damage responses . BRIT1−/− male mice failed to yield any pregnancies when crossed with the WT female mice , suggesting that BRIT1−/− mice are infertile . Consistently , we found that BRIT1−/− testes were much smaller than WT , especially in the mutant mice at the age of 3- week or older ( Figure 3A and Figure S1 ) . The testicular tubes in BRIT1−/− mice were significantly smaller and thinner than those in WT , and much fewer spermatocytes were produced in BRIT1−/− seminiferous tubules ( Figure 3C , compared with Figure 3B ) , suggesting spermatogenesis is dysregulated due to loss of BRIT1 . In addition , BRIT1−/− female mice are also infertile , and consistently , these mice harbored the smaller ovaries with none of ovarian follicles ( Figure S2 ) . In the male mice , spermatogenesis is divided into several distinct stages: mitotic proliferation of spermatogonia , meiotic division of spermatocytes , and spermiogenesis of spermatids [26] , [27] . To determine which stage of spermatogenesis is disrupted in BRIT1−/− mice , we collected the BRIT1+/+ and BRIT1−/− testes at various developmental stages for histological analysis . At postnatal days 7 ( P7 ) , major cells were Sertoli or Sertoli-like cells ( the supporting cells for testes ) and spermatogonia , but no spermatocytes were found in either BRIT1+/+ or BRIT1−/− testes ( Figure 3D , H&E panel ) , consistent with the fact that spermatocytes do not form in mouse testes until around P10 [26] , [27] . Testes sections at P7 were also double-immunostained with anti-Tra98 and anti-Sox9 antibodies to detect spermatogonia and its surrounding Sertoli cells , respectively . The number of both spermatogonia ( Figure 3D , Tra98 panel ) and Sertoli cells ( Figure 3D , Sox9 panel ) was comparable between BRIT1−/− and BRIT1+/+ testes , indicating that BRIT1 deficiency does not impair spermatogonia or Sertoli cell proliferation . We next examined the development of spermatocytes using the mice at the age of 2- week or older . In testes at P14 , P21 and P28 , although spermatocytes had taken place in seminiferous tubules in both BRIT1−/− and BRIT1+/+ testes , there were considerably fewer spermatocytes in BRIT1−/− as compared to BRIT1+/+ , especially after around P21 ( panels c and d in Figure 3E ) . At P56 , in addition to much smaller seminiferous tubules , remarkably fewer spermatocytes and no spermatids were found in BRIT1−/− testes ( panels e and f in Figure 3E ) . In contrast to seminiferous tubules in WT with a full spectrum of spermatogenic cells ( panels a , c and e in Figure 3E ) , BRIT1−/− tubules just contained two or three layers of darkly stained zygotene-like germ cells and exhibited a complete lack of pachytene spermatocytes and postmeiotic germ cells ( panels b , d and f in Figure 3E ) , suggesting that meiosis in BRIT1−/− spermatocytes may arrest prior to the pachytene stage . Moreover , TUNEL assay revealed dramatically increased apoptosis in BRIT1-deficient tubules ( Figure S1 ) , suggesting degenerated spermatocytes in BRIT1−/− testes may be eliminated through apoptosis . Together , these data indicate spermatocyte meiosis is impaired due to loss of BRIT1 , as a result , leads to the male infertility . To determine the cause of meiotic arrest in BRIT1−/− spermatocytes , we sought to define the actual meiotic stage that was defected in the mutant by examining the assembly of the synaptonemal complex ( SC ) . SC morphology in spermatocyte nuclei can be assessed by immunostaining of synpatonemal complex protein 3 ( SCP3 ) , an integral component of the axial/lateral elements in the SCs [28] , [29] . SCP3-immunostaining of the spermatocyte nuclei spreads showed that all the substages of spermatocytes in meiotic prophase I were detected in WT mice such as leptotene ( Figure 4A ) , zygotene ( Figure 4C ) , pachytene ( Figure 4E ) . In BRIT1−/− mice , spermatocytes at leptotene ( Figure 4B ) or zygotene ( Figure 3D ) stage were also detected . However , no typical pachytene chromosome morphology was detected though there were aberrant pachynema in BRIT1−/− spermatocytes ( Figure 4F ) . In addition , there were much more zygotene spermatocytes found in the mutant testes as compared to those in WT ( Figure 4G ) , indicating that loss of BRIT1 leads to meiotic arrest prior to the pachytene stage . Furthermore , in contrast to WT spermatocytes in which synapsis initiated typically at the distal ends ( or in subtelomeric regions ) of the acrocentric chromosomes [30] ( Figure 4C ) , many mutant spermatocytes in mid- and late- zygotene were characteristic of interstitial initiation of synapsis with asynapsis on either side of the contact ( Figure 4D and 4F ) . Also , the bivalents in the late zygotene or aberrant pachytene were prone to be fragmented ( Figure 4F ) . To confirm the synapsis defects in BRIT1−/− spermatocytes , we performed anti-SCP1/SCP3 double-staining assay . SCP1 mediates synapsis via uniting homologous chromosomes during zygotene and pachytene stages . The accumulation of SCP1 on/around the axial/lateral elements of the whole SCs ( indicated by SCP3 ) is a hallmark for complete synapsis [31] . In the mutant spermatocytes , SCP1/SCP3 double-staining assay showed incomplete , dashed-line like SCP1 pattern occurred in many individual homologs at the late zygotene or zygotene/pachytene transition stage ( panel b in Figure 4H ) while in WT , the intact accumulation of SCP1 protein around the whole SCs was detected ( panel a in Figure 4H ) , revealing a defective synapsis existed in BRIT1-deficient spermatocytes . Collectively , these results indicate that meiosis in BRIT1−/− spermatocytes is arrested prior to the pachytene stage and BRIT1 is required for completing chromosomal synapsis during male meiosis . To further assess BRIT1's role in meiotic recombination , we examined BRIT1 expression pattern in seminiferous tubules as well as its foci formation in response to DSB during meiosis . We found that BRIT1 protein was expressed both in spermatogonia and spermatocytes ( Figure S3A ) . Spermatocyte-chromosome spreading assay showed that BRIT1 foci were formed on the meiotic chromosomes during both leptotene and zygotene . In contrast , during pachytene where synapsis is completed , BRIT1 foci were not found on the chromosomes except in telomeres and non-synapsed sex body ( Figure S3B ) . These data together indicate that BRIT1 is involved in repair of the DSBs occurring at leptotene and zygotene stages during meiotic recombination . To investigate the mechanism mediating the meiotic defect due to loss of BRIT1 , we examined if DSB formation and meiotic recombination repair was impaired . During meiosis , DSBs are introduced by SPO11 at leptotene for initiating meiotic recombination in spermatocytes [32] , and γ-H2AX is an important protein involved in recognition and signaling of the DSBs [33] , [34] . In mice , ∼300 DSBs are generated by SPO11 at leptotene/zygotene in each nucleus [35] . As shown in the Figure 5A , SPO11 foci had the similar pattern in WT and mutant spermatocytes , suggesting DSBs were normally formed in BRIT1-deficient testes . In response to the DSBs , γ-H2AX foci responded equally well and appeared at the damage sites during the leptotene stage of both the WT ( panel a in Figure 5B ) and the BRIT1 mutant ( panel b in Figure 5B ) testes with the same staining pattern and comparable intensity . In consistent with previous report [36] , at late zygotene/pachytene stage of WT spermatocytes , γ-H2AX staining disappeared from synapsed autosomal chromosomes though it still resided in the largely asynapsed sex chromosomes of the XY body ( panel c in Figure 5B ) . However , γ-H2AX staining in the BRIT1-mutant spermatocytes was sustained in asynapsed autosomal homologs at late zygotene ( Figure 5B and 5C ) . Thus , these data suggests that DSBs are normally formed , while DSBs cannot be properly repaired without functional BRIT1 . We next analyzed the homologous recombination process to address why DSBs are not repaired in BRIT1−/− spermatocytes . RAD51 is the homolog of E . coli RecA which binds to DSBs and plays a critical role in both mitotic and meiotic recombination DNA repair [37] , [38] . In response to DNA damages , BRCA2 can facilitate RAD51's loading to the damages sites via their physical interaction and these two proteins form nuclear foci at the DSB sites and execute the DNA repair process [16] . We examined RAD51 foci formation on meiotic chromosomes using by immunostaining of chromosomes from spermatocytes . Interestingly , in BRIT1−/− leptotene/zygotene spermatocytes , the number of RAD51 foci was greatly decreased as compared to the number in WT ( Figure 5D and 5E ) . We found that 80% of leptotene/zygotene spermatocytes in WT contained ∼100–250 RAD51 foci ( Figure 5D and 5E ) . In contrast , 60% of the mutant spermatocytes at these stages exhibited no or only a few RAD51 foci ( Figure 5D and 5E ) . In addition , RAD51 protein level was not changed in BRIT1−/− testes ( Figure S4 ) . Thus , reduction of RAD51 foci on chromosomes in BRIT1-deficient testes might be attributed to decreased localization of RAD51 onto the DSB sites . Furthermore , we found that BRCA2 foci formation in spermatocytes was also disrupted due to BRIT1 deficiency ( Figure 5F and 5G ) . Collectively , these data indicate that depletion of BRIT1 disrupts meiotic recombination repair through abolishing the recruiting and the function of RAD51/BRCA2 , and as a result leads to the catastrophic meiosis failure in BRIT1−/− spermatocytes . To further explore how BRIT1 regulates DNA repair , we assessed the ability of RAD51/BRCA2 to form nuclear foci and their chromatin association in BRIT1−/− MEFs . We first analyzed the IR-induced nuclear foci ( IRIF ) formation of RAD51 and BRCA2 in BRIT1+/+ and BRIT1−/− MEFs . As shown in Figure 6A , we observed typical RAD51 and BRCA2 foci in the BRIT1+/+ MEFs , whereas only diffused pattern of RAD51 and BRCA2 were seen in the BRIT1−/− MEFs , indicating the IRIF formation of RAD51 and BRCA2 was abolished in the BRIT1 null background . In addition , in comparison to WT , the amount of chromatin-bound RAD51 was markedly reduced with or without IR treatment , indicating the requirement of BRIT1 for the basal and IR-induced Rad51 chromatin association . The basal chromatin-binding of BRCA2 was also significantly reduced in the BRIT1 null background while the IR-induced BRCA2 association to the chromatin was modest reduced ( Figure 6B ) . The protein levels of RAD51 and BRCA2 were not altered due to lack of BRIT1 ( Figure 6C ) . These data indicate that BRIT1 is required for increased basal chromatin affinity and the physical assembly of RAD51 and BRCA2 to the DNA damage loci though there is different degree of BRIT1 dependency in terms of IR-induced chromatin binding between RAD51 and BRCA2 . Notably , we found that BRIT1 physically associated with RAD51 and BRCA2 , suggesting BRIT1 being directly involved in DNA repair ( Figure 6D ) . During the course of our study , a very recent report also shows that BRIT1 binds to BRCA2/RAD51 complex and disruption of the interaction between BRIT1 and BRCA2 leads to substantially reduced BRCA2/RAD51 at the DNA repair sites [39] . Altogether , these data indicate that BRIT1 may be directly involved in DNA repair via mediating RAD51/BRCA2's recruitment to the damage sites .
In this report , we generate a BRIT1 knockout ( BRIT1−/− ) mouse model and clearly demonstrate that BRIT1 is crucial for maintaining genomic stability in vivo to protect the hosts from both programmed and irradiation-induced DNA damages . Our studies on BRIT1 null mice also provide convincing evidence to identify a novel and important function of BRIT1 in meiotic recombination DNA repair . BRIT1−/− mice showed significant genomic instability , exemplified by chromosomal aberrations in T lymphocytes and MEFs . In addition , BRIT1−/− mice exhibited growth retardation , male infertility , and increased radiation sensitivity . These phenotypes are virtually identical to those of ATM−/− , MDC1−/− and H2AX−/− mice [40]–[42] , suggesting that these molecules integrate closely in the DDR pathway . Importantly , this is the first reported evidence demonstrating that BRIT1 indeed plays a crucial physiological role in programmed DNA damage response and HR DNA repair in vivo . Our data presented here clearly reveal that BRIT1 is essential for meiotic recombination DNA repair in spermatocytes . We first demonstrated that the male infertility in null mice was caused by catastrophic meiosis failure in spermatocytes and accordingly , no spermatids could be generated ( Figure 3E ) . We also showed that BRIT1−/− spermatocytes exhibited aberrant chromosomal synapsis , and meiosis was arrested before or at the transition of zygotene to pachytene of meiotic prophase I ( Figure 4 ) . In addition , we found that localization of RAD51/BRCA2 to meiotic chromosomes was severely impaired in the mutant spermatocytes while their protein levels were not altered due to loss of BRIT1 ( Figure 5 and Figure S4 ) . Thus , these data together indicate that the DSB generated by SPO11 at leptotene can not be repaired properly and as a result , leads to aberrant chromosomal synapsis and meiosis arrest at late zygotene stage . Interestingly , unlike MDC1 or H2AX whose deficiency only leads to male infertility [41] , [42] , loss of BRIT1 also lead to female infertility with much smaller ovary ( Figure S2 ) , suggesting that BRIT1 may also function as a key regulator in oocyte meiotic recombination . During meiosis , DSB is generated by SPO11 that leads to the initiation of meiotic recombination ( HR DNA repair ) [32] . In the null mice , BRIT1 deficiency only affected foci formation of RAD51/BRCA2 , but not those of SPO11 and γ-H2AX , at leptotene/zygotene stages , Thus , our data support a model in which BRIT1 functions downstream of the SPO11-mediated DSB formation but upstream of RAD51/BRCA2-midiated DSB repair during meiotic recombination . In addition to meiosis , we also found that in response to DSBs induced by IR , the association of RAD51/BRCA2 to chromatin and their foci formation was impaired in MEFs with BRIT1 deficiency while their protein levels were not altered ( Figure 6 ) . All these in vivo and in vitro studies together therefore demonstrate that Brit1 is critical for DNA repair during both meiosis and mitosis . The impaired RAD51 foci formation in both BRIT1−/− MEFs and spermatocytes is not due to the changes of RAD51 protein levels ( Figure 5 and Figure 6 ) . Although BRIT1 has been reported to regulate RAD51 expression [43] , we observed no altered RAD51 protein levels in either BRIT1−/− MEFs or the BRIT1−/− mouse testes which is consistent with our previous studies in BRIT1 knockdown human cells [25] . The mechanisms mediating BRIT1's function on DNA repair may be through multiple levels . Firstly , BRIT1 can be indirectly involved in DNA repair process via regulation of chromatin structure . We recently found that BRIT1 was associated with Condensin II , which modulates BRIT1-mediated HR repair [24] . In addition , we demonstrated BRIT1 being involved in chromatin remodeling via interacting with SWI/SNF , and this interaction relaxes the chromatin structure and increases the access of the repair proteins , including RAD51 , to the DNA damage sites [25] . Indeed , this chromatin remodeling function of BRIT1 may also contribute to the increased accessibility of many other DNA damage responsors , such as ATM , ATR , NBS1 , MDC1 , 53BP1 , RPA as we previously observed [18] . In addition to the generally increased affinity of DNA damage responsors and DNA repair proteins to chromatin , BRIT1 may further directly participate into DNA repair via interacting and recruiting RAD51/BRCA2 complex to the damage sites . Here , we observed that BRIT1 can physically associate with RAD51 or BRCA2 and in the absence of BRIT1 , recruitment of RAD51 and BRCA2 to chromatin was remarkably reduced while their protein levels were not altered , suggesting that BRIT1 being directly involved in DNA repair ( Figure 6 ) . Consistently , in the course of our study , a very recent report also shows that BRIT1 binds to BRCA2/RAD51 complex and this binding is required for recruitment or retention of BRCA2/RAD51 complex at the DNA repair sites [39] . Thus , BRIT1 also functions directly in DNA repair via directing BRCA2/RAD51 foci to the DSBs . In consistent with the role of BRIT1 in regulating the DNA repair function of BRCA2/RAD51 , the meiotic phenotypes in BRIT1−/− mice are virtually the same as those observed in mice with deficiency of BRCA2 [44] and DMC1 ( a homologue of RAD51 ) [45] , [46] . In these mice , spermatocytes are also arrested before or at the transition of zygotene to pachytene with aberrant chromosomal synapsis . In fact , like BRIT1−/− spermatocytes , BRCA2−/− spermatocytes also form DSBs without the consequent recruitment of RAD51 to the meiotic chromosome [44] . Our previous studies show that BRIT1 deficiency is correlated with genomic instability and breast cancer development [18] . Although our BRIT1 knockout mice within one and half years old did not develop any tumor , when we crossed BRIT1 knockout mice to the p53 null background , we found a significant effect of BRIT1 deficiency in enhancing cancer susceptibility ( unpublished data ) . Notably , our very recent preliminary data indicate that low dosage of irradiation can readily induce breast tumors in the mice with conditional knockout of BRIT1 in the mammary gland but not in the control littermates . It will be very interesting to investigate if and to what extent BRIT1 deficiency may contribute to the initiation and progression of cancer with the existence of oncogenic or genotoxic stress . For example , we can assess whether crossing an activated Ras or HER2 allele into the BRIT1 deficient background will result in increased genomic instability and tumorigenesis compared to either mouse strain alone . Thus , our BRIT1 null mouse will be a very valuable model for further assessing BRIT1's role in genome maintenance and tumor suppression in the future .
We isolated BRIT1 BAC clones from a 129/SvEv genomic library to construct the conditional targeting vector ( Figure 1A ) . A 0 . 5 kb fragment consisting of exon 2 ( E2 ) , 3′ end of intron 1 and 5′ end of intron 2 was inserted between two loxP sites in the targeting vector , which allows to remove E2 after introduction of Cre in mice . A 3 . 0 kb fragment from intron 2 was cloned into the same vector as the 3′ homologous arm . A 5′ homologous arm ( a 3 . 0 kb fragment containing E1 and 5′ of intron 1 ) was subsequently cloned into the vector . The Neo selection marker can be excised via the recombination of the Frt sites after introduction of Flp recombinase , which could avoid any unexpected effect of Neo cassette on the normal splicing of BRIT1 . The targeting vector was linear zed by PacI and electroporated into AB2 . 2 ES cells . Neomycin-resistant colonies were selected with Geneticin and analyzed for the expected homologous recombination by EcoRV digestion followed by Southern blot analysis using a 5′ flanking probe . The targeted clones were then confirmed using a 3′ flanking probe after BamHI digestion of the genomic DNA . To generate the mice with BRIT1 conditional targeting allele , two targeting ES cells were injected into C57BL/6J mouse blastocysts . The injection was carried out by the Engineered Mouse Core in department of Molecular and Human Genetics at Baylor College of Medicine [47] . Male chimeras with 95% agouti color were bred with C57BL/6J females and germ-line transmission of the BRIT1 targeting allele was confirmed by agouti coat color in F1 animals and by Southern blot analysis on mouse tail DNA using the two flanking probes for BRIT1 . These heterozygous mice were crossed with Flp mice to generate the mice carrying one BRIT1 conditional allele with the excision of the Neo cassette ( BRIT1+/co ) . These mice were then bred with transgenic mice carrying a Cre gene under the control of β-actin promoter to generate BRIT1+/− mice . BRIT1+/− mice were further bred to generate BRIT1−/− mice . Primary MEFs were obtained from embryonic days E14 . 5 by a standard procedure . MEFs were grown in DMEM supplemented with 10% FBS , 1% penicillin/streptomycin and 0 . 1% Fungazone and kept at a low passage for future use . Three plates of BRIT1+/+ or BRIT1−/− MEFs were exposed to IR ( 0 , 2 , 4 , or 6 Gy ) , and survival rates were calculated 6 days after IR . Whole-body γ-irradiation of mice was carried out as described previously [48] . Ten pairs of BRIT1+/+/BRIT1+/− and BRIT1−/− littermates were irradiated with 7 Gy of whole body IR , and survival rates were calculated every two days after irradiation . Mice were sacrificed at postnatal day P42 , and splenocytes were crushed though a mesh into a collection tube , and lymphocytes were separated using Lymph-M method ( Accurate Chemical & Scientific Corp ) . The separated lymphocytes were activated with ConA and IL-2 . MEFs were γ-irradiated with a dose of 1 Gy , and then collected for metaphase spread 3 hr or 20 hr post-IR . Cytological preparation of the activated T cells and γ-irradiated MEFs was made as described previously [49] . From each sample , at least 30–35 metaphase spreads were analyzed . MEFs on coverslips were treated with or without IR , and underwent immunoflurorescent ( IF ) staining at the indicated time points as described previously [18] . The primary antibodies used here were anti-RAD51 ( BD pharmingen ) and anti-γ-H2AX ( Bethyl ) . The coverslips were finally mounted onto glass slides with VectaShield antifade ( Vector Laboratories ) and visualized by using a Zeiss Axiovert 40 CFL fluorescence microscope . Testes were obtained from BRIT1+/+ and BRIT1−/− mice at different ages ( P7 , P14 , P21 , P28 , and P56 ) , fixed in 4% paraformaldehyde at 4°C , and routinely embedded in paraffin . Haematoxylin-eosin ( H&E ) staining was performed according to the standard procedure . Slides were de-paraffinized and rehydrated , and antigen retrieval was carried out by incubating the slides in 0 . 01 M sodium citrate buffer ( pH 6 . 1 ) in a hot water bath at 95°C for 30 min . If necessary , endogenous peroxidase activity was quenched with 3% hydrogen peroxide in methanol for 10 min at room temperature . Primary antibodies used here are anti-SOX-9 , anti-Tra98 ( gifts from Drs . Pumin Zhang and Xingxu Huang , Baylor College of Medicine , Houston , TX ) . Sections were finally visualized using a Zeiss Axiovert 40 CFL microscope , and images were captured with a Zeiss AxioCam MRc5 digital camera . Seminiferous tubules were collected from 2- to 4-month-old mice , and tubule segments were isolated in 1×DPBS . Chromosome spread of spermatocytes was made following the protocol elsewhere [50] , [51] . The following primary antibodies were used for immunofluorescent analyses: rabbit anti-SPO11 ( Santa Cruz ) , rabbit anti-γ-H2AX ( Bethyl ) , rabbit anti-RAD51 ( Sigma ) , rabbit anti-SCP1 ( GeneTex ) , rabbit anti-SCP3 ( GeneTex ) and guinea pig anti-SCP3 ( gift from Dr . Ricardo Benavente , University of Würzburg , Würzburg , Germany ) . Alexa 488-coupled anti-rabbit IgG and/or Alexa 594-conjugated anti-guinea pig IgG were used as secondary antibodies . The slides were finally counterstained and mounted with antifade mounting medium with DAPI ( Vector Laboratories ) . Foci were visualized microscopically with oil-immersed objectives , captured with a digital camera ( Zeiss AxioCam MRc5 ) , and processed with Photoshop ( Adobe ) . Chromatin isolation assay were performed as previously described [18] . Briefly , MEFs ( totally 5×106 cells ) were treated with or without IR ( 8 Gy ) , collected 1 h later , and then washed with PBS . The cells pellets were resuspended in 200 µl of solution A ( 10 mM HEPES [pH 7 . 9] , 10 mM KCl , 1 . 5 mM MgCl2 , 0 . 34 M sucrose , 10% glycerol , 1mM dithiothreitol , 10m MNaF , 1mM Na2VO3 , and protease inhibitors ) . Triton X-100 was added to a final concentration of 0 . 1% , and the cells were incubated for 5 min on ice . Cytoplasmic proteins were separated from nuclei by low-speed centrifugation . The isolated nuclei ( P1 ) were washed once with solution A and then lysed in 200 µl of solution B ( 3 mM ethylenediamine tetraacetic acid , 0 . 2 mM EGTA , 1 mM dithiothreitol , and protease inhibitors ) . Insoluble chromatin was collected by centrifugation , washed once in solution B , and centrifuged again for 1 min . The final chromatin pellets ( P3 ) were digested by resuspending nuclei in solution A containing 1 mM CaCl2 and 50 units of micrococal nuclease and incubated at 37°C for 1 min , after which the nuclease was stopped by addition of 1 mM EGTA . The chromatin pellets ( MNase-digested P3 ) were resuspended in 2× Laemmli buffer and boiled 10 min at 70°C . Following centrifugation at high speed ( 13 , 000 rpm ) , the chromatin associated proteins in the supernatant were analyzed by SDS-PAGE/Western blotting assay . | The repair of DNA breaks in cells is critical for maintaining genomic integrity and suppressing tumor development . DNA breaks can arise from exogenous agents such as ionizing radiation ( IR ) or can form during the process of germ cell ( sperm and egg ) generation . BRIT1 protein ( also known as MCPH1 ) is a recently identified DNA damage responding protein , and its mutations or reduced expression are found in primary microcephaly ( small brain ) patients , as well as in cancer patients . To investigate BRIT1's physiological functions and dissect the underlying molecular mechanism , we used a genetic approach ( gene targeting technology ) to delete BRIT1 gene in mice and generated a mouse model with BRIT1 deficiency ( called BRIT1-knockout mice ) . Here , we showed that BRIT1 knockout mice are more sensitive to IR due to their inability to repair the IR-induced DNA breaks . These mice are also infertile , and their DNA repair during the process of germ cell generation was impaired substantially . Thus , in this study , we generated a novel mouse model ( BRIT1 knockout mice ) with striking phenotypes related to defective DNA repair and clearly demonstrated the essential role of BRIT1 in DNA repair at organism level . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"genetics",
"and",
"genomics/gene",
"function",
"genetics",
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"genomics/cancer",
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] | 2010 | BRIT1/MCPH1 Is Essential for Mitotic and Meiotic Recombination DNA Repair and Maintaining Genomic Stability in Mice |
Epigenetic gene silencing plays a critical role in regulating gene expression and contributes to organismal development and cell fate acquisition in eukaryotes . In fission yeast , Schizosaccharomyces pombe , heterochromatin-associated gene silencing is known to be mediated by RNA processing pathways including RNA interference ( RNAi ) and a 3’-5’ exoribonuclease complex , the exosome . Here , we report a new RNA-processing pathway that contributes to epigenetic gene silencing and assembly of heterochromatin mediated by 5’-3’ exoribonuclease Dhp1/Rat1/Xrn2 . Dhp1 mutation causes defective gene silencing both at peri-centromeric regions and at the silent mating type locus . Intriguingly , mutation in either of the two well-characterized Dhp1-interacting proteins , the Din1 pyrophosphohydrolase or the Rhn1 transcription termination factor , does not result in silencing defects at the main heterochromatic regions . We demonstrate that Dhp1 interacts with heterochromatic factors and is essential in the sequential steps of establishing silencing in a manner independent of both RNAi and the exosome . Genomic and genetic analyses suggest that Dhp1 is involved in post-transcriptional silencing of repetitive regions through its RNA processing activity . The results describe the unexpected role of Dhp1/Rat1/Xrn2 in chromatin-based silencing and elucidate how various RNA-processing pathways , acting together or independently , contribute to epigenetic regulation of the eukaryotic genome .
In eukaryotic cells , DNA coils around histones to form nucleosomes , which are packaged into chromatin . Various post-translational modifications ( PTMs ) of histones , histone variants , and nucleosome remodeling factors confer distinct chromatin states on genes and facilitate the organization of large chromatin tracts into domains [1–3] . Euchromatic domains ( euchromatin ) contain actively transcribed genes and are enriched with hyperacetylated histones , while heterochromatic domains ( heterochromatin ) contain highly repetitive elements that are transcriptionally silenced and are associated with hypoacetylated histones [4–7] . In addition to its primary role in transcriptional gene silencing , heterochromatin is crucial for centromere-mediated chromosome segregation , cell fate determination , and the silencing of repetitive DNA elements [4] . In fission yeast , Schizosaccharomyces pombe ( S . pombe ) , its formation requires both histone hypoacetylation and histone H3 methylation at lysine 9 ( H3K9me ) , which provides a binding site for HP1 family proteins [8–10] . Heterochromatin is first nucleated at specific repetitive loci and subsequently spread for up to hundreds of kilobases ( kb ) into surrounding regions [4 , 11 , 12] . Once established , these silenced heterochromatic domains are heritable , and can be stably maintained through successive cell divisions [4 , 13 , 14] . Epigenetic silencing includes both transcriptional ( TGS ) and post-transcriptional gene silencing ( PTGS ) . In general , heterochromatin limits the access of RNA polymerase II ( RNAPII ) machinery to the DNA template and can therefore mediate transcriptional gene silencing ( TGS ) by preventing unwanted transcription from a given genomic region [15 , 16] . PTGS employs RNA processing machinery to rapidly degrade nascent RNAs to repress gene expression or to protect the genome from foreign genetic elements such as retroviral RNA or transposable DNA [17–20] . RNA processing machineries ensure the maturation and packaging of RNAs from longer precursors into mRNA/protein particles ( mRNPs ) before they are exported to the cytoplasm for translation [21] . Most of these processing events , such as addition of the 5’ cap , removal of introns , and polyadenylation at the 3’ end , occur while the RNA is still attached to RNAPII and chromatin , and are therefore referred to as transcription-coupled RNA processing events [22 , 23] . For example , RNA endocleavage at a polyadenylation ( polyA ) site is commonly required for transcriptional termination because the 5’ to 3’ exoribonucleolysis of the exposed 3’ fragment downstream of the polyA site facilitates RNAPII release from chromatin [24 , 25] . Besides their roles in RNA maturation and RNAPII termination , RNA-processing enzymes act as quality control systems , screening partly or fully transcribed products and degrading abnormal RNAs [22 , 26] . RNA processing pathways play an active role in epigenetic silencing , especially PTGS [27] , as many nuclear processes rely on the fine balance between RNA maturation and destruction to regulate gene expression [28] . The best understood RNA processing pathway in PTGS is the RNA interference ( RNAi ) -mediated formation of heterochromatin at centromeric regions in S . pombe [27 , 29 , 30] . In RNAi , RNAPII transcripts originating from repetitive DNA regions are converted to double-stranded RNA by the RNA-Dependent RNA Polymerase Complex ( RDRC ) [31] . They are then processed by Dicer into small interfering RNAs ( siRNAs ) [32 , 33] and loaded onto the Argonaute-containing RNA Induced Transcriptional Silencing ( RITS ) complex , which targets the repeat regions through the homology of the siRNA sequence [34] . RITS associates with chromatin by direct interaction with H3K9me [35] , then recruits the Clr4 complex to initiate chromatin remodeling [36–39] . Recently , several studies reported an RNAi-independent RNA-processing pathway in heterochromatin assembly at the centromeric region and some heterochromatic islands in euchromatic regions [40–43] . This new pathway is mediated by the exosome complex , which degrades unwanted RNAs via its 3’-5’ exoribonuclease activity [26] . Although both RNAi and exosome pathways are RNA-mediated and involved in processing long noncoding RNAs ( ncRNAs ) into small RNAs , how the exosome pathway contributes to heterochromatin assembly is not well understood . In addition , it is not known whether other RNAi-independent RNA processing pathways participate in epigenetic silencing . Here we report a new epigenetic silencing pathway involving Dhp1 , a conserved 5’→3’ exoribonuclease , the ortholog of budding yeast Rat1 and metazoan Xrn2 known to promote termination of RNA polymerase II ( RNAPII ) transcription [44–46] . We show that Dhp1-mediated heterochromatic silencing is independent of Din1 , an ortholog of budding yeast Rai1 that has been shown to stabilize Dhp1/Rat1 exoribonuclease activity [47] . In addition to maintenance of gene silencing , Dhp1 contributes to de novo establishment of heterochromatin at the centromeres and the silent mating type region . It also plays a role in the transcriptional-dependent spreading of heterochromatin . Importantly , Dhp1 interacts with heterochromatic factors and its catalytic activity is required for its role in silencing . Further genetic analyses indicate that Dhp1 operates in a distinct pathway parallel to RNAi and the exosome to mediate heterochromatic gene silencing . Finally , RNAPII localization and transcriptome-wide maps of RNAs associated with RNAPII revealed that Dhp1 likely acts at the post-transcriptional level to affect gene silencing . We propose that , in addition to RNAi and exosomes , Dhp1 constitutes a distinct RNA-processing pathway that enforces post-transcriptional gene silencing across the fission yeast transcriptome .
Dhp1/Xrn2 is an essential gene required for transcriptional termination and RNA quality control [22 , 45 , 46] . A recent study reported that impairment of transcription termination is sufficient to induce the formation of heterochromatin at protein-coding genes by trans-acting siRNAs in S . pombe [48] . However , it is not clear whether impaired transcription termination would alter epigenetic silencing at the major heterochromatic regions such as centromeric regions and the mating type locus . We therefore tested whether silencing at these regions is affected in dhp1 mutant cells . Because its loss is lethal , we utilized a conditional temperature-sensitive ( ts ) allele , dhp1-1 , which codes a truncated carboxyl-terminal form of Dhp1 that is partially replaced by a ura4+ transgene and is lethal at 37°C ( dhp1-1>>ura4+ ) [47] . We generated an independent dhp1-2 mutant , which carries the same carboxylic terminal truncation but is replaced with a nourseothricin resistance gene ( NatN2 ) ( S1 Table ) . This allele has a less severe ts phenotype compared to dhp1-1 , and is not lethal at 37°C ( S1A Fig ) . dhp1-2 is not fused with ura4+ , allowing us to investigate the silencing of the centromeric region by analyzing the expression of a ura4+ reporter gene inserted at the outer centromeric region ( otr∷ ura4+ ) ( Fig 1A , top panel ) . Wildtype cells carrying the reporter otr∷ura4+ grew well on counter-selective medium containing 5-Fluoroorotic Acid ( 5-FoA ) indicating otr∷ura4+ was silenced ( Fig 1A , bottom panel ) . The growth of dhp1-2 was greatly inhibited in the presence of 5-FoA indicating defective silencing of the reporter gene . Surprisingly , din1-null ( din1Δ ) cells do not have a severe growth or centromeric silencing defect , suggesting that Dhp1 is involved in a silencing pathway separate from its Din1-related activity ( Fig 1A , bottom panel ) . To further examine the observed silencing defect , an ade6+ reporter gene was inserted into the silenced mating type region ( Fig 1A , top panel ) . We can easily observe the silencing status of the ade6+ based on color; cells that cannot express the normal level of the reporter gene accumulate a red pigment due to blocked adenine biosynthesis . In wildtype cells , the reporter gene is silenced by heterochromatin , resulting in red/sectored colonies on low adenine media at 30°C . Cells lacking Clr4 , the sole histone H3K9 methyltransferase in S . pombe [49] , form white colonies due to loss of heterochromatin ( Fig 1A ) . Similar to clr4Δ , dhp1-2 but not din1-null ( din1Δ ) cells form white colonies , indicating a silencing defect at the mating type locus unique to the dhp1 mutant . Since dhp1-1 has a more severe silencing defect than dhp1-2 as evaluated by silencing assay and quantitative Reverse Transcription Polymerase Chain Reaction ( qRT-PCR ) ( S1 Fig ) , the rest of our studies focused on using the dhp1-1 allele . We next assessed whether loss of reporter gene silencing in the dhp1 mutant is correlated with the expression of heterochromatic repeats . Transcript analysis by qRT-PCR revealed substantially unregulated expression of repeats associated with pericentromeric heterochromatin and the silent mating type locus in dhp1 but not din1 mutant cells ( Fig 1B ) . Further analysis by expression profiling using a tiling microarray on both DNA strands showed increased expression throughout both heterochromatic regions in dhp1-1 , well above the increase observed in din1Δ cells ( Fig 1C ) . These results indicate that Dhp1 plays a previously unrecognized , Din1-independent function in epigenetic silencing . To avoid potential pleotropic effects caused by dhp1 mutation , we performed all our experiments at 30°C . This is a permissive temperature for dhp1-1 , in which silencing defects but no obvious growth deficiency are observed ( Fig 1A ) , suggesting that most of transcription-related functions of Dhp1 are retained . In addition , we carefully analyzed the transcriptional levels of all known heterochromatic factors in dhp1-1 cells using expression data . All coding transcripts of these proteins are affected less than 1 . 6-fold ( a typical threshold difference for microarray data ) compared to that of wildtype ( S2 Table ) . Further , a plasmid-borne wildtype copy of dhp1+ rescues the ts phenotype of dhp1-1 ( S2A Fig ) and a diploid heterozygous strain carrying a wildtype and a dhp1-1 allele showed the same phenotype as a wildtype diploid strain ( S2B and S2C Fig ) , demonstrating that dhp1-1 has no dominant negative effects . Altogether , these findings suggest that the loss of heterochromatic silencing in the dhp1 mutant is likely a direct consequence of impaired function of Dhp1 at heterochromatin rather than reduced transcription of heterochromatic factors . The silencing defect in dhp1-1 is unexpected because impaired transcription termination would reduce RNAPII transcription and the subsequent release of the RNA from the site of transcription , which may enhance the assembly of heterochromatin through induction of RNA-mediated chromatin modification such as H3K9 methylation ( H3K9me ) [48 , 50] . In addition , many reported Dhp1 functions are associated with Din1 , which contributes to the generation of the proper substrates for Dhp1’s exoribonuclease activity [46 , 51] . Because we did not observe a silencing defect in din1Δ cells , we wondered whether Din1 , like Dhp1 is involved in transcription termination . According to the “Torpedo model” [24 , 25] , Dhp1-mediated exonucleolysis of the cleaved 3’ fragment downstream of the mRNA polyA site facilitates RNAPII release from chromatin . Deficiency in Dhp1/Din1 will cause RNAs to accumulate at the 3’ end of genes , due to an RNAPII transcription termination defect [24 , 25] . To confirm this reported role of Dhp1/Din1 , we analyzed the transcriptomes of dhp1-1 and din1Δ cells at euchromatic regions . We detected a genome-wide increase of RNA levels at the 3’ end of genes compared to wildtype in both mutants , with a larger fraction of genes exhibiting transcription termination defects in dhp1-1 ( S3 Fig ) . While the role of Dhp1 is more dominant than that of Din1 , these results support earlier studies indicating that both Dhp1 and Din1 participate in RNAPII transcription termination . As an interacting protein of Rat1/Xrn2 , Rtt103 also contributes to transcription termination in yeast and humans [52–54] . Rhn1 , the S . pombe ortholog of Rtt103 , has a reported role in the suppression of meiotic mRNAs during vegetative growth [54] . However , whether it plays a role in heterochromatic silencing has not been reported . To further examine whether defective transcription termination is crucial for Dhp1-mediated epigenetic silencing , we compared the expression of repeat elements in wildtype and rhn1Δ cells using qRT-PCR and found that , like Din1 , loss of Rhn1 did not cause a silencing defect ( S4 Fig ) . Taken together , our data argue that Dhp1 plays a novel role in epigenetic silencing , which cannot be explained by its established function in transcription termination . Cells with mutations in factors that contribute to epigenetic silencing often exhibit defects in chromosome segregation as determined by their sensitivity to the microtubule-destabilizing drug thiabendazole ( TBZ ) [55] . Because heterochromatin formation has been linked to centromere function in various organisms including S . pombe [56–58] , we tested whether the dhp1 mutants are sensitive to TBZ , which would indicate impaired function of centromeric heterochromatin , resulting in a chromosome segregation defect . As expected , deletion of clr4 abolishes heterochromatin and causes severe TBZ sensitivity ( Fig 2A ) . Our assay clearly shows that dhp1 , but not din1 , mutant cells are sensitive to TBZ , suggesting a chromosome segregation defect specific to dhp1 mutants ( Fig 2A ) . To further examine the role of Dhp1 in chromosome segregation , we sporulated wildtype and mutant h90 strains to follow the segregation of chromosomes in tetrads using a fluorescence-based analysis ( S5 Fig ) . To sporulate , two haploid cells with opposite mating types conjugate to form a zygote which then enters meiosis . During meiosis , cells undergo two consecutive rounds of chromosome segregation . A normal meiosis results in an ascus in which each of four spores contain relatively equal amounts of DNA ( DAPI dots ) . Abnormal meiotic segregation within a tetrad will show an uneven distribution of DAPI staining in each spore , resulting in less than or greater than four dots . We found that meiotic chromosome segregation is severely perturbed in the dhp1-1 , but not in din1Δ cells , with nearly 50% of tetrads containing abnormal numbers of DAPI dots ( ≤ 3 or ≥ 5 ) ( S5 Fig ) . To determine whether the chromosome segregation defect seen in the dhp1 mutant is linked to its role in epigenetic silencing at major heterochromatic domains , we assessed the status of H3K9me-associated heterochromatin by a Chromatin-Immunoprecipitation ( ChIP ) assay . Although no reduction of H3K9me2 was seen at the endogenous repetitive regions ( Fig 2D and 2E ) , the levels of H3K9me2 at the reporter genes embedded in these regions were substantially reduced at these loci in cells deficient in dhp1 ( Fig 2B and 2C ) . Loss of din1 has no negative effect on the enrichment of the H3K9me mark at either the endogenous repetitive regions or the reporter genes ( Fig 2B–2E ) . These results suggest that Dhp1’s role in chromosome segregation is linked to its requirement to maintain functional heterochromatin at the centromeres . We next wondered whether Dhp1 interacts with heterochromatic proteins , which would support a direct role of Dhp1 in facilitating heterochromatin assembly . We purified Dhp1 and Din1 through two-step affinity purification ( S6 Fig ) and identified the co-purified proteins by mass spectrometry analysis ( S3 Table ) . Strains used for purification carry a functional Dhp1 or Din1 fused with FTP , a modified TAP tag comprising a protein A motif and a FLAG tag separated by a TEV protease cleavage site . Since Dhp1 and Din1 are associated with transcribing RNAs and chromatin , we performed all purifications in the presence of Benzonase to avoid indirect protein-protein interactions mediated by nucleic acids . Din1 is the major Dhp1-interacting protein as recovered Din1 peptides were found to be approximately 50% as abundant as those of the bait protein , Dhp1 . As expected , Dhp1 also co-purified with many RNAPII-related factors , consistent with its role in transcriptional termination . In particular , it is associated with several heterochromatic proteins , including Clr4 methyltransferase complex ( ClrC ) subunit Rik1 and exosome subunit Rrp6 . These data are consistent with interactions recently identified in a parallel study [59] . Notably , these heterochromatic proteins were not present in those fractions when Din1 was used as the bait , supporting a distinct role of Dhp1 in heterochromatic formation . Our data indicates that Dhp1 interacts with heterochromatic proteins and is likely directly involved in heterochromatin assembly . Multiple pathways are utilized to initiate epigenetic silencing including both RNA and DNA sequence-dependent mechanisms [9 , 60 , 61] . Several studies have shown that in S . pombe , both RNAi and the exosome contribute to the initiation of silencing at the centromere by processing RNAs transcribed from repetitive regions [13 , 41 , 61] . We next sought to determine whether Dhp1 also contributes to this process through examination of reporter gene expression following the reintroduction of functional clr4+ into dhp1-1 clr4Δ double mutant cells ( Fig 3A ) . Deletion of clr4 results in the abolition of H3K9me and the loss of heterochromatin . However , reintroduction of functional clr4+ is sufficient for de novo heterochromatin formation as previously reported [50] ( Fig 3B ) . One of the key members in RNAi machinery , Dcr1 , is the sole Dicer-family endoribonuclease in S . pombe [62] . dcr1Δ cells lose the ability to initiate heterochromatin formation de novo at repeat regions [13] . Consistent with previous findings , silencing at the centromeric region cannot be efficiently established without Dcr1 [13] . Reintroduction of clr4+ into clr4Δ cells shows a complete alleviation of TBZ sensitivity , while clr4+ reintroduction into dcr1Δ clr4Δ double mutant cells has no effect ( Fig 3B ) . Additionally , complementation of clr4+ has little effect on the relative expression of centromeric- and mating type locus-specific repeats in dcr1Δ clr4Δ cells , however silencing of these repeats is fully resumed in clr4Δ single mutants ( Fig 3B ) . Reintroduction of clr4+ in dhp1-1 clr4Δ double mutant cells partially resumed the silencing at the centromeric region as indicated by qRT-PCR ( Fig 3B ) . At the mating type locus , silencing is barely restored in dhp1-1 clr4Δ , having 10-fold more expression than dhp1-1 alone ( Fig 3B ) . H3K9me2 ChIP analysis further demonstrated that without functional Dhp1 , H3K9me2 is partially re-established at the centromeric region , but only a low level of H3K9me2 can be found at the mating type locus after complementation of clr4+ ( Fig 3C and 3D ) . These results indicate that Dhp1 is essential for efficient de novo heterochromatin assembly at peri-centromeres and the silent mating type locus . Spreading of heterochromatin from the nucleation sites enables the establishment of a heterochromatin domain spanning many kbps [12] . Although the mechanism is poorly understood , it depends on the oligomerization of chromatin modifiers such as HP1 and Tas3 , a RITS component , and the actions of Swi6-recruited histone deacetylases ( HDACs ) on adjacent nucleosomes [63–66] . While the polymerization of chromatin modifiers indeed constitutes a major part of heterochromatin spreading , a role for RNAPII in transcription-mediated spreading is currently being explored . The effect of spreading on the silencing of reporter genes inserted into centromeric repeat regions has been shown to vary with position relative to the RNAPII promoter; reporter genes downstream of the promoter are more effectively silenced than those inserted upstream [67 , 68] . While the molecular details remain unclear , transcription-mediated spreading appears to require transcription of the 3’ untranslated region as well as degradation of these transcripts by RNAi machinery [67] . Decreased enrichment of H3K9me at the reporter genes in dhp1-1 suggests that Dhp1 may be involved in the spreading of H3K9me ( Fig 2B and 2C ) . Because of its role in transcriptional termination , we next examined whether Dhp1 is required for RNAi-dependent spreading of heterochromatin , which partially relies on RNAPII transcription [68] . To this end , we adopted a spreading assay [13 , 69] ( Fig 4A ) . Nucleation of heterochromatin at cenH is dependent on RNAi and the cenH sequence itself [13 , 69] . Inserting the cenH sequence into a euchromatic locus causes ectopic establishment followed by spreading of heterochromatin and subsequent silencing of proximal genes [13] . By coupling ectopic cenH with an adjacent reporter gene ( ade6+ ) , we can directly observe the effects of spreading of silencing to the proximal reporter gene ( Fig 4A ) . In the wildtype background , about 35% of cells form pink/sectoring colonies , indicating the spreading of heterochromatin assembled at ectopic cenH to the ade6+ reporter gene . Cells lacking ago1 , a critical factor in RNAi form white colonies at 100% efficiency showing that the ade6+ reporter gene cannot be silenced . This is consistent with previous results indicating that RNAi machinery is required for the transcriptional-dependent spreading of heterochromatin assembled at cenH region [13 , 68] . Similar to ago1Δ , no pink colonies were formed in dhp1-1 background ( Fig 4B ) . H3K9me2 ChIP using multiple primers along ade6+ and its surrounding regions shows moderate reduction of this heterochromatic mark in dhp1-1 cells compared to wildtype cells ( Fig 4C ) . Altogether , these results suggest that Dhp1 plays a role in the transcription-related spreading of the H3K9me mark . While RNAi is critical to the establishment and spreading of heterochromatin , it is dispensable for the maintenance of a previously assembled chromatin state at the mating type locus and sub-telomeric regions [64 , 70] . To investigate the role of Dhp1 in heterochromatin maintenance , we introduced the dhp1 mutation into cells that lack part of the K region in the mating type locus , but continue to repress a proximal ade6+ reporter gene ( KΔ∷ade6+off ) ( Fig 5A ) . Deleting the K region results in loss of heterochromatin establishment within the mating type locus , but because heterochromatin is stably inherited through cell division , derepression is rarely seen without concomitant loss of the maintenance machinery [71] . Loss of maintenance machinery will result in derepression of ade6+ ( KΔ∷ade6+off will switch to KΔ∷ade6+on ) . While the molecular mechanisms which mediate maintenance remain unclear , Clr4 and Swi6 have been implicated [13 , 38 , 72] . We detected partial loss of repression of ade6+ in dhp1-1 , an intermediate phenotype between wildtype and swi6Δ ( Fig 5B and 5C ) . In the wildtype background , nearly 80% of cells form dark red colonies and only about 20% of cells form pink colonies . Unlike swi6Δ , which form 100% white colonies , about 75% of dhp1-1 cells form pink colonies , although no dark red colonies were ever observed . We further analyzed the ade6+ RNA level by qRT-PCR ( Fig 5D ) . Indeed , we observed a more than 20-fold increase the amount of ade6+ transcripts in dhp1-1 cells compared to that of wildtype cells . Consistent with previous studies , loss of Swi6 abolishes the enrichment of H3K9me2 at KΔ∷ade6+ ( Fig 5E ) [72] . Interestingly , mutation of dhp1 does not reduce this histone modification at the same region ( Fig 5E ) , suggesting that Dhp1 plays a role in effective maintenance of epigenetic silencing downstream of H3K9me . We consistently observed a stronger silencing defect in dhp1-1 at the mating type locus than the pericentromeric region ( Figs 1–3 ) . RNAi is known to play a major role in silencing centromeric repeats but only partially contributes to silencing at the mating type locus [64 , 65] . These results suggest that Dhp1-mediated silencing might be distinct from that of RNAi . To test this , we combined dhp1-1 with a deletion of ago1 , the sole Argonaute protein in S . pombe [62] , and analyzed the silencing defect at the centromeric region and the mating type locus by qRT-PCR . Consistent with previous findings , loss of Ago1 caused an upregulation of centromeric repeat transcripts ( Fig 6A ) and did not show an obvious silencing defect at the mating type locus ( Fig 6B ) . Whereas dhp1-1 exhibited a modest increase in transcription at the centromere and the mating type locus , a double mutant dhp1-1 ago1Δ showed a large increase beyond the cumulative effects of either single mutation ( Fig 6B ) , indicating that Dhp1 contributes to heterochromatic silencing in a pathway parallel to RNAi . Our conclusion was also supported by the expression data shown in Fig 3B: we consistently observed more relative expression of repeats in dhp1-1 dcr1Δ double mutant cells compared to that of dcr1Δ or dhp1-1 single mutant cells from both the centromeric region and mating type locus , further indicating an RNAi-independent role of Dhp1 . Many transcripts degraded by RNAi are also targets of Rrp6 [42] , the catalytic subunit of the nuclear exosome required for rapid elimination of cryptic unstable transcripts ( CUTs ) [73–75] . Its RNA degradation activities act in parallel with RNAi to promote heterochromatin assembly [43 , 50] . Since Dhp1 is an exoribonuclease and plays an independent role from RNAi , we next wondered whether Dhp1 has overlapping function with Rrp6 in the silencing of repeat elements . Indeed , qRT-PCR showed that dhp1-1 rrp6Δ double mutant cells have stronger silencing defects at both the centromeric region and mating type locus ( Fig 6C and 6D ) , although the effect is less than that observed in dhp1-1 ago1Δ . We next investigated whether the accumulated silencing defects in double mutants of dhp1 with ago1Δ or rrp6Δ are resultant from additive deficiencies of H3K9me2 . ChIP experiments show that , except for dhp1-1 ago1Δ at the centromeric repeats , none of the double mutants exhibit further reduction of H3K9me2 compared to single mutants ( Fig 6C and 6D ) , suggesting the role of Dhp1 in epigenetic silencing does not rely on H3 K9 methylation at repeat regions . Notably , combining dhp1-1 and rrp6Δ mutations enhances H3K9me both at the centromeric region and the mating type locus ( Fig 6C and 6D ) . A recent study reported that Rrp6 is required for RNAPII termination at specific targets [73] . Our observation of enhanced H3K9me occurring in dhp1-1 rrp6Δ double mutant cells suggests that transcription termination defects impair RNAPII transcription and favor the induction of RNA-mediated chromatin modification such as H3K9 methylation . In dhp1-1 rrp6Δ , the compounded silencing defect must be overcompensating for the increased silencing effect of the additively enhanced H3K9me ( S7 Fig and discussion ) . Collectively , these results show that the Dhp1-mediated silencing mechanism is independent of both RNAi and the exosome , and is likely downstream of H3K9me . Dhp1 is a conserved 5’-3’ exoribonuclease [44 , 46] . Previous studies of Xrn1 in Kluyveromyces lactis ( K . lactis ) indicated that switching the acidic aspartate at position 35 or glutamate at position 178 to neutral residues , such as alanine ( D35A ) or glutamine ( E178Q ) , completely abolished enzymatic activity [76] . In S . pombe , Dhp1D55 and E207 are conserved residues corresponding to K . lactis Xrn1D35 and E178 ( Fig 7A ) . To test whether the RNA processing activity of Dhp1 is important for its role in epigenetic silencing , we generated plasmids carrying a copy of dhp1 with both D55 and E207 mutated ( dhp1-D55A E207Q ) , which abolishes the catalytic activity of Dhp1 . A plasmid carrying a wildtype allele of dhp1+ can rescue both the ts phenotype and the silencing defect of dhp1-1 analyzed by dilution assays ( Fig 7B ) and qRT-PCR ( Fig 7C and 7D ) , suggesting that the wildtype allele can completely complement the C-terminal truncated form of dhp1 , further supporting that there is no dominate negative effect of dhp1-1 . However , the plasmid carrying the catalytic mutant could not rescue the ts phenotype ( Fig 7B ) or the silencing defect of dhp1-1 at the centromeric region and the mating type locus ( Fig 7C and 7D ) , indicating that the catalytic activity of Dhp1 is essential for its role in epigenetic silencing . In S . pombe , epigenetic silencing requires cooperation between the TGS and PTGS pathways [15 , 62 , 64] . As a classic example of PTGS , RNAi allows processing of RNAs transcribed from these regions to facilitate or reinforce heterochromatin assembly in a RNAPII-dependent manner [15 , 64] . In this process , siRNAs maintain the feedback loop and propagate heterochromatin . RNAPII activity is required for generating precursors of siRNA and thereby is crucial for heterochromatin assembly [68 , 77] . Additionally , RNAPII may have a more direct role in epigenetic silencing because mutation of RNAPII subunits , splicing factors , and RNA processing machineries impair heterochromatin [68 , 77–79] . TGS relies on heterochromatin which is mediated by histone modifications that recruit silencing effectors [4] . In addition to the H3K9 methyltransferase Clr4 and HP1 family proteins , HDACs are critical mediators of all three phases of heterochromatin formation [80–82] . Especially , deletion of class II HDACs clr3 or sirtuin sir2 cause marked reduction of H3K9me across the centromeric regions and mating type locus [80 , 82] . To gain further insight into the function of Dhp1 in TGS or PTGS , we compared the localization of Mit1-Myc , one of the core subunits of SHREC ( Clr3 complex ) at the centromeric regions and the mating type locus in wildtype , dhp1-1 , din1Δ , and clr4Δ cells ( Fig 8A and 8B ) . Mit1-Myc is a fully functional allele of Mit1 , and has been employed in previous studies [80] . Unlike clr4Δ , which abolishes the localization of Mit1 , dhp1-1 does not show any reduction of Mit1 localization at these regions ( Fig 8A and 8B ) , indicating that the localization of SHREC is not reduced . Next , we combined dhp1-1 with either clr3Δ or sir2Δ , and examined the silencing of repeat regions in wildtype , single and double mutant cells ( S8 Fig ) . RT-PCRs show that all double mutant cells have enhanced silencing defects compared to single mutants suggesting overlapping functions between Dhp1 and these HDACs ( S8 Fig ) . These results suggest that Dhp1 likely has a major role in PTGS and acts in a distinct pathway parallel to SHREC-mediated TGS . To further investigate the role of Dhp1 in TGS or PTGS , we analyzed the relationship between Dhp1 and RNAPII in heterochromatin formation . First , we attempted to combine dhp1-1 with rpb7-G150D , which carries a mutation on the RNAPII subunit Rpb7 and has a specific defect in centromeric pre-siRNA transcription [68] . Surprisingly , combined mutation of dhp1-1 with rbp7-G150D is lethal , suggesting the presence of a compensatory mechanism between Dhp1 and RNAPII to ensure proper regulation of the transcriptome . We next combined dhp1-1 with rpb2-m203 , a mutant of the second largest subunit of RNAPII [77] . This mutation does not affect the global transcriptional activity of RNAPII [77] . Instead , it specifically influences the generation of siRNA [77] . Our data indicates that Dhp1 plays a role in a pathway parallel to RNAi in the silencing of repetitive regions ( Fig 6 ) . Therefore , the Dhp1-mediated silencing defect is unlikely to be linked through rpb2-m203 . Indeed , rpb2-m203 dhp1-1 double mutant cells are viable , and have a stronger silencing defect at the centromere region than either single mutant ( S9 Fig ) , indicating independent , parallel functions in epigenetic silencing . Heterochromatic regions commonly exclude RNAPII as a mechanism of TGS , but PTGS mechanisms occur downstream of RNAPII recruitment . We wondered whether , like RNAi and the exosome , Dhp1 plays a major role in PTGS . Therefore , RNAPII inclusion or exclusion from chromosomal regions will serve as an indicator to elucidate the function of Dhp1 in transcriptional and/or post-transcriptional actions . We mapped RNAPII occupancy in wildtype and dhp1-1 by ChIP using clr4Δ as a control ( Fig 8C and 8D ) . Loss of Clr4 completely abolishes heterochromatin , thereby shows a strong TGS defect as indicated by dramatically increased RNAPII occupancy at the repetitive regions . However , no difference of RNAPII occupancy was observed between dhp1-1 and the wildtype control at repetitive regions , suggesting the role of Dhp1 is not in TGS , but rather PTGS ( Fig 8C and 8D ) . Decreased transcription termination demonstrated in dhp1 mutants may reduce the level of available RNAPII complexes for initiation of transcription and could mask the true extent of silencing . Additionally , protracted RNAPII association at a given locus due to stalling might confound ChIP results . To ensure that the true activity of RNAPII was measured , we performed a genome-wide survey of RNAPII targets using Cross-linking and analyses of cDNA ( CRAC ) in wildtype and dhp1-1 cells ( Fig 9 ) . This assay mapped the genome-wide distribution of RNAPII and also monitored the RNAPII complexes actively synthesizing RNAs [83] ( Fig 9A ) . A genome-wide study is necessary in this case as Dhp1 may serve distinct roles in euchromatin and heterochromatin , as genome-wide expression profiling suggested ( Fig 1 and S3 Fig ) . In euchromatic regions , defects in terminating RNAPII transcription caused by the dhp1 mutation led to an accumulation of unreleased RNAPII complexes at the 3’end of genes in dhp1-1 ( Fig 9B ) . However , the same phenotype was not observed in clr4Δ cells , indicating that loss of clr4 causes no transcription termination defect . In heterochromatic regions , although clr4Δ dramatically enhanced RNAPII-RNA associations at the centromeric region and mating type locus compared to that of wildtype cells due to complete loss of TGS and partial loss of PTGS , such a difference was not detected upon mutation of dhp1 ( Fig 9C ) . Given the fact that mutation of dhp1 leads to substantial upregulation of repeat transcripts ( Fig 1C ) without reduction of H3K9me at repetitive regions ( Figs 2 and 6 ) , and only marginally affects RNAPII occupancy and its association with repeat transcripts ( Figs 8C , 8D and 9 ) , the results support a primary role of Dhp1 in PTGS .
In spite of the crucial role for RNAi in heterochromatin assembly , heterochromatin is not completely abolished in RNAi mutants indicating that other pathways are involved [50 , 65 , 84] . These pathways are mediated by DNA-binding factors , RNA or RNAi-independent RNA processing factors [50 , 61] . In Arabidopsis , the flowering repressor gene FLC is thought to provide links between RNA processing activities and chromatin regulation in gene silencing [85] . In S . pombe , recent studies reveal the nuclear exosome , which governs RNA quality control and ensures the elimination of unwanted RNAs , exists as an RNAi-independent silencing mechanism [42 , 43 , 50 , 61] . Co-activators of the exosome , including TRAMP and MTREC , which help to recognize and degrade its substrates , are also connected to epigenetic silencing without affecting H3K9me , thereby play a major role in PTGS [61 , 86] . Additional studies on Triman , a 3’-5’ exonuclease in S . pombe , show that it generates Dicer-independent primal RNAs and is required for initiation of heterochromatin assembly via a mechanism requiring Ago1 [87] . In this study , we described a novel pathway involving Dhp1 , a conserved RNA 5’ to 3’ processing enzyme that contributes to PTGS ( Fig 10A ) . We propose that three RNA processing activities , RNAi , the exosome , and Dhp1/Xrn2 degrade repetitive transcripts to mediate the post-transcriptional gene silencing of repeat transcripts ( Fig 10B ) . Heterochromatin assembly is a dynamic process with distinct steps [4] . It is nucleated at genomic regions containing highly repetitive DNA elements and spread to surrounding regions [88] . Its structure is recaptured during DNA replication and maintained through cell division [14] . Silencing factors often participate at discrete step ( s ) rather than throughout the process . In particular , RNA-mediated silencing pathways are often required to nucleate heterochromatin formation [9] . Once silencing is established , these factors are dispensable [13]; the heterochromatic state persists in the absence of the initial stimulus . For example , at the mating type locus of S . pombe , RNAi machinery cooperates to nucleate heterochromatin assembly but is dispensable for its inheritance [13] . The re-establishment assay clearly indicates that Dhp1 is indispensable for efficient establishment of silencing at heterochromatic repeat regions ( Fig 3 ) . RNAi is well-known as the major nucleation pathway at centromeric regions but not at the mating type locus [64 , 65] . Interestingly , dhp1-1 ago1Δ double mutant cells have cumulative defects at the mating type locus indicating separate functions of these two pathways ( Fig 6 ) . Unlike Triman , which requires Argonaute to be loaded on longer RNA precursors [87] , Dhp1 has an Argonaute-independent role , although we cannot rule out the possibility that the slicer activity of Ago1 may also contribute to the generation of the substrates for Dhp1 . Since RNAi itself can initiate heterochromatin formation , we observed re-establishment of heterochromatin at repetitive elements in dhp1-1 cells following clr4+ complementation , although the restoration was not complete ( Fig 3 ) . These results suggest that Dhp1 plays a unique but overlapping role in heterochromatin nucleation in concert with RNAi . It is possible that the Dhp1-mediated degradation of heterochromatic repeat transcripts is required for de novo assembly of heterochromatin through recruiting silencing effectors , similar to RITS [36 , 38 , 39] . It is also possible that the processing activity of Dhp1 is involved in generating the primary small RNAs that contribute to initiation of epigenetic silencing as suggested for the role of the exosome in heterochromatin assembly [61] . In addition to defective nucleation , the H3K9me mark in dhp1 mutants is reduced in the reporter genes embedded at the repetitive regions , suggesting a spreading defect ( Fig 2B and 2C ) . The assay analyzing the spreading of H3K9me from an ectopic nucleation center to the surrounding regions indicates that Dhp1 facilitates the spreading of the heterochromatic mark ( Fig 4 ) . Although it is unclear how transcription-mediated spreading of heterochromatin occurs , it is possible that impaired transcription termination in the dhp1 mutant affects the rate of histone turnover during transcription and thereby impedes the spreading of H3K9me . In addition to its role in initiation and spreading , we provide evidence to show that Dhp1 functions in the maintenance of pre-established silencing ( Fig 5 ) . How does Dhp1 function in the maintenance of silencing ? It is known that heterochromatin maintenance relies on the binding of Swi6 and Clr4 to methylated H3K9 , which facilitates recapitulation of the specific chromatin configuration following DNA replication [38 , 70] . In addition , Swi6 and HP1 proteins work as binding platforms , recruiting other histone modifiers and with the factors that are involved in replication-coupled heterochromatin assembly , such as chromatin assembly factor 1 ( Caf1 ) [65 , 80 , 89 , 90] . Although the levels of H3K9me2 at the repeat regions are not decreased upon mutation of dhp1 , the dynamic binding of Swi6 could still be affected . In addition to H3K9me , Swi6 is also reported to bind “repellent” RNAs that antagonize the heterochromatic silencing [91] . Thus , Dhp1-mediated elimination of RNA may facilitate the dynamic binding of Swi6 to heterochromatin , and thereby ensure the maintenance of the silenced chromatic domains . Although the centromeric regions and the mating type locus are assembled by heterochromatin , they occupy different chromosomal contexts and use distinct strategies to target heterochromatin [4] . Notably , the effects in double mutants of dhp1 with ago1Δ or rrp6Δ are different at centromeres and the mating type region ( Fig 6 ) . At the centromeric region , RNAi is the major pathway [64] . Therefore , as expected , the ago1Δ single mutant exhibits a severe silencing defect and decreased H3K9me mark ( Fig 6 and S7 Fig ) . Compared to the already radically impaired silencing phenotype in the ago1Δ single mutant , the dhp1-1 ago1Δ double mutant shows even higher levels of repeat transcripts and lower levels of H3K9me2 ( Fig 6 ) , suggesting that transcripts produced from repeat regions in RNAi-deficient cells , are likely targets of Dhp1 . In contrast , without Rrp6/exosome , RNAi machinery is still functional . Therefore , we only observe a moderate silencing defect at the centromeric region in the dhp1-1 rrp6Δ double mutant ( Fig 6 ) . At the mating typing locus , at least three pathways initiate heterochromatin assembly and target H3K9me [4 , 27] . It is not surprising that the dhp1-1 ago1Δ double mutant maintains a high level of H3K9me2 ( Fig 6 ) ; other pathways may compensate for loss of function for both Dhp1 and RNAi at the mating type locus [4] . In addition , transcription termination defects caused by rrp6 and dhp1 mutation may contribute to the increased level of H3K9me seen at both centromere and mating type locus ( S7 Fig ) . Interestingly , cells containing the dhp1 mutation consistently show a stronger silencing defect at the mating type locus even in the presence of higher levels of H3K9me ( Figs 1–3 and 6 and S7 Fig ) , suggesting that Dhp1-mediated silencing occurs primarily downstream of H3K9me , likely as a mechanism of PTGS . In S . pombe , TGS and PTGS are intertwined . In TGS , heterochromatin greatly limits the access of RNAPII , allowing only a low level of transcription from highly repetitive DNA regions . RNAs transcribed from these regions are subject to PTGS by RNAi machinery , in which they are processed into siRNAs in order to feedback on chromatin to facilitate the assembly and propagation of heterochromatin [27 , 88] . The silencing defect in dhp1-1 is unexpected considering that compromised transcription termination would weaken RNAPII transcription and delay the release of RNA from the site of transcription , which may then enhance the assembly of heterochromatin mediated by RNA as suggested by previous studies [48 , 50] . To elucidate whether Dhp1 plays a major role in TGS , we used ChIP analysis to map H3K9me and SHREC ( Figs 2 , 8A and 8B ) , which have well-studied functions in TGS at repeat regions . If Dhp1 plays a role in TGS , we would expect to observe reduced enrichment of H3K9me and SHREC at the endogenous repetitive regions in dhp1-1 . No reduction of enrichment occurred however for either H3K9me2 or SHREC at endogenous repetitive regions in dhp1 mutants , suggesting that the role of Dhp1 in gene silencing is primarily associated with PTGS rather than TGS ( Figs 2 , 8A and 8B ) . We further investigated RNAPII occupancy and the levels of actively transcribing RNAPII at repeat regions in wildtype and mutant cells using ChIP and CRAC respectively ( Figs 8C , 8D and 9 ) . A role in TGS for Dhp1 would be suggested by increased RNAPII occupancy occurring at repetitive regions in dhp1-1 , as RNAPII in the context of impaired TGS would associate more frequently with heterochromatic transcripts . In contrast , no increase would implicate a role for Dhp1 in PTGS . Our RNAPII ChIP results clearly show no difference between dhp1-1 and wildtype , implicating a PTGS role for Dhp1 ( Figs 8C and 8D ) . Additionally , we showed that the catalytic activity of Dhp1 is required for its role in epigenetic silencing , providing strong evidence to support that the RNA processing role of Dhp1 is associated with PTGS ( Fig 7 ) . Although our results pinpoint the primary role of Dhp1 in epigenetic silencing through PTGS , completely discounting a function of Dhp1 in TGS is a challenge as Dhp1/Rat1/Xrn2 has well-established activity that is linked to RNAPII . RNAPII transcription and its associated activities are required for heterochromatin assembly . As a result , loss of silencing was reported to correlate with defective RNAPII transcription [68 , 77 , 92–94] . Is the RNAPII-linked function of Dhp1 related to epigenetic silencing ? In agreement with reported termination defects upon mutation of Dhp1 and Din1 , our expression profiling showed accumulation of 3’ untranslated transcripts at many genes in these mutants ( S3 Fig ) . To execute its function in RNAPII transcription termination , Dhp1/Rat1 exonucleases target the downstream fragments produced by cleavage at the polyA site during 3’ end processing [44–46] . The processed mRNAs are packed into nuclear RNA transporting cargos and exported to the cytoplasm for translation . Since this action of Dhp1/Rat1 in transcription termination lies downstream of mRNA processing and packaging , defects of Dhp1/Rat1 are unlikely to dramatically influence the amount and the quality of coding mRNAs . Indeed , at least at the permissive temperature , we did not observe significant alterations of coding transcripts in the dhp1 mutant ( S2 Table ) . Rather , the remarkable differences observed in the transcriptome were seen at non-coding regions ( S3 Fig ) . Recently , Rat1 in budding yeast was reported to maintain the balance of RNAPII CTD phosphorylation , and therefore plays a role in transcription elongation [95] . This finding suggests that Rat1 may have more complex roles in transcription than previously thought . In addition , neither loss of Rnh1 nor Din1 causes growth defects or silencing defects as seen in the dhp1 mutant ( Figs 1 and 2 and S4 Fig ) , raising the question about which role of Dhp1 , transcription or RNA quality control , is essential for cell growth and silencing . In this study , we indeed observed higher enrichment of H3K9me2 at the endogenous repetitive regions in the dhp1 mutant ( Figs 2 and 6 ) . This observation is in agreement with a study showing that an impaired Paf1 complex is sufficient to induce RNAi-mediated epigenetic silencing in trans at euchromatic loci , likely through its termination defect [48] . While a parallel study reported significant reductions of H3K9me2 in dhp1 mutants at all major heterochromatic regions [59] , we only observed reduced H3K9me at reporter genes but not at the repeat regions . It is likely that the discrepancies in the H3K9 methylation data are due to differences in culturing conditions . To minimize the pleiotropic impacts caused by dhp1 mutation and avoid the antagonistic effect of high temperature ( 37°C ) for heterochromatin formation [96] , we collected data at a permissive condition ( 30°C ) without shifting cell cultures to 37°C , the restrictive condition applied in the parallel study [59] . Therefore , our results are more likely to accurately represent the true effect of Dhp1 in epigenetic silencing . In addition , we provided evidence to show that Dhp1-mediated silencing is independent of RNAi ( Fig 6 ) . Overall , the role of Dhp1 in epigenetic silencing at major heterochromatic regions cannot be explained by its known function in transcriptional termination . It is possible that RNAPII may couple repeat transcription with its degradation by Dhp1 . A second possibility is that RNAPII may help “discriminate” noncoding pericentromeric repeat RNAs from general pre-mRNAs so that the former can be degraded by Dhp1 . The basis for this selection may be the aberrant ( double-stranded or abnormally capped ) structure of the transcribed RNA . Alternatively , the chromatin structure of the transcribed repeat region may somehow determine the fate of the transcripts , feeding into RNAi- , exosome- , or Dhp1-mediated silencing . The catalytic activity of Dhp1 is required for its role in epigenetic silencing ( Fig 7 ) . By what mechanism are the substrates for Dhp1-mediated silencing produced ? Due to the strong conservation of the active site , it is likely that the mechanisms of Xrns are very similar [97] . The crystal structure of Drosophila XRN1 indicates that substrates are limited to 5’ monophosphate RNAs because larger structures , such as m7G Cap or triphosphorylated RNAs , do not fit into the pocket [98] . Hence , the RNA pyrophosphohydrolase activity of Din1 seems necessary for the generation of monophosphorylated RNA substrates for Dhp1 , especially for decapping and RNA quality control . However , only Dhp1 , not Din1 , is essential for viability and epigenetic silencing . In addition , unlike Dhp1 , Din1 and its orthologs are not widely conserved [47 , 99] . Since Din1 is not essential and is not necessary for epigenetic silencing , an endoribonuclease or an extra RNA pyrophosphohydrolase likely produces the substrates for Dhp1 in silencing . In yeast , abnormal pre-mRNAs are degraded rapidly from both 5’ and 3’ ends by Rat1/Xrn2 and the nuclear exosome , respectively , with the exosome playing a dominant role [100] . In human cells , XRN2 appears to be more crucial for degradation of abnormal pre-mRNAs than the exosome [101] . Given the fact that Xrn2 is conserved from yeast to humans , our results may yield insights broadly applicable to the gene silencing field , including mammals . Dhp1/Xrn2 may represent a more generalized mechanism of an RNA-based form of silencing . Future studies identifying additional Dhp1/Xrn2 interacting proteins may help to address these questions .
S . pombe strains used in this study are listed in S1 Table . Cells were cultured using standard procedures for growth and manipulation [102] . Epitope-tagged and deletion mutant strains were engineered using standard PCR methods as described previously [103] . Double mutants were constructed via genetic crossing followed by tetrad dissection . For dilution assay , liquid cultures were diluted in series ( 1:10 ) and plated using a pin transfer tool on YEA media ( Rich , N/S ) , low adenine YE media , or YEA media containing either 20 μg/ml TBZ or 850 μg/ml 5-FoA . All cultures were grown at 30°C ( or 37°C where indicated ) . The strains used for cross-linking and analyses of cDNA ( CRAC ) carry a carboxyl-terminal HTP-tagged subunit of RNAPII , Rpb2-HTP . An HTP tag contains a 6X- His epitope and a protein A epitope separated by a Tobacco Etch Virus ( TEV ) protease cleavage site [83] . Strains carrying either KΔ∷ade6+off or KΔ∷ade6+on were isolated and saved as previously described [71] . To generate pdhp1+ , a PCR fragment amplified using oligos Dhp1-BamHI-Fw and Dhp1-Pst1-RV contains a wildtype dhp1+ gene including promoter , open reading frame , and 5’ and 3’ untranslated regions . The PCR fragment was digested by BamHI and PstI and ligated into a pREP41 digested with the same restriction enzymes ( BamHI/PstI ) . After BamHI/PstI digestion , pREP41 lost its nmt promoter . The resulting pdhp1+ expresses the wildtype dhp1+ driven by its endogenous promoter . pdhp1D55A E207Q was generated using a QuickChange Site-Directed mutagenesis kit ( Stratagene ) based on pdhp1+ . pclr4+ is a plasmid carrying a DNA fragment containing a wildtype clr4+ driven by its endogenous promoter as previously described [50] . Total RNA was prepared using the MasterPure Yeast RNA Purification Kit ( Epicentre ) . First-strand cDNA was produced with M-MLV Reverse Transcriptase ( Promega ) using site-specific primers following manufacturer protocols . Real-time PCR was performed on a 7500 Fast Real-Time PCR System ( Applied Biosystems ) with SYBR Select Master Mix ( Applied Biosystems ) . First-strand cDNA synthesis without reverse transcriptase was performed for negative controls . At least two biological repeats were performed for all experiments . Statistical analysis was performed using a student’s t test ( two-tailed distribution ) . Error bars represent standard error of mean ( s . e . m ) . Primers are listed in the S4 Table . Mating-type switching-competent ( h90 ) mid-log phase cells ( wildtype , dhp1-1 , or din1Δ ) were plated on solid sporulation medium ( SPA ) . Cells grew at 30°C for 6 hr , then switched to 37°C for 2 hr , and finally finished the sporulation at 30°C for 12hr . Cells were washed 3X with water . Ten microliter cells in water were spread on a glass slide , and fixed by heat at 70°C . The slides were then covered by 5μl of mounting buffer with DAPI ( VECTOR , H1500 ) and 13mm coverslips . The stained cells were imaged by a confocal microscope . Sample preparation for the expression array and array design were reported previously [104] . The expression profiling is performed as previously described [105] . The composite plot was generated using GenomicRanges R-package ( version 1 . 20 . 5 ) , from the high-resolution part of the microarray ( 2320 genes ) . The genes were aligned at the transcriptional termination site TTS ( S . pombe 2007_April annotation ) and the geometric means of the ratios ( Mutant/wt ) were plotted . Flag-TEV-protein A ( FTP ) -tagged purification and mass spectrometry were performed as previously described [105] . ChIP experiments were performed as described previously using antibodies against histone H3 ( di-methyl K9 ) ( Abcam , Ab1220 ) , RNAPII ( Abcam , Hab5408 ) , or Myc ( Santa Cruz , A-14 ) [106] . Real-time PCR was performed on a 7500 Fast Real-time PCR System ( Applied Biosystems ) with SYBR Select Master Mix ( Applied Biosystems ) . At least two biological repeats were performed for all ChIP experiments . Statistical analysis was performed using a Student’s t test ( two-tailed distribution ) . Error bars represent s . e . m . In vivo CRAC was performed as described with modifications [83] . Two-liter yeast cultures were grown to an OD600≈2 at 30°C . Cells were harvested by centrifugation and cell pellets were resuspended in 2 . 5L Phosphate-Buffered Saline ( PBS ) followed by UV-irradiation in a “Megatron” UV-cross-linker ( 254 nm ) for 3 min before cells were pelleted and frozen in liquid nitrogen . The pellets were then lysed by grinding in liquid nitrogen ( Resch , MM400 ) and resuspended in 10 ml of 1x TN150 lysis buffer ( 10x TN150: 0 . 5 M Tris-HCl ( pH 7 . 8 ) , 1 . 5 M NaCl , 1% NP-40 ) . Extracts were clarified by centrifugation ( 10 min at 4000 rpm and 45 min at 15 , 000 rpm at 4°C ) and incubated with 150 μl of equilibrated IgG Sepharose beads ( GE Healthcare ) for 1h at 4°C . After two washes with TN1000 buffer ( 100 mM Tris-HCl ( pH 7 . 8 ) , 2 M NaCl , 0 . 2% NP-40 ) and two washes with TN150 lysis buffer , the beads were incubated with GST-TEV protease for 2h at 16°C . The TEV eluates were collected by centrifugation and incubated with 10U of Turbo DNase ( Ambion ) for 8 min at 37°C followed by incubation with RNase Cocktail Enzyme Mix ( Ambion; 0 . 005 U RnaseA , 0 . 2 U Rnase T1 ) for 2 min at 37°C . Guanidine-HCl ( 0 . 4g ) was dissolved in 500 μl of TEV eluates . NaCl and Imidazole were added to final concentrations of 300 mM and 10 mM , respectively . Samples were incubated with 50 μl of nickel agarose beads ( Macherey-Nagel ) over night at 4°C . All washes , alkaline phosphatase treatment and 3’ linker ligation were carried out as described except that 40U T4 RNA ligase 2 truncated K227Q ( NEB ) was used instead of T4 RNA ligase . The beads were incubated in 80 μl phosphorylation mix ( 16 μl 5x PNK buffer ( 250 mM Tris-HCl ( pH 7 . 8 ) , 50 mM MgCl2 , 50 mM β-mercaptoethanol ) , 200 mM ATP ( Sigma , A6559 ) , 20U T4 polynucleotide kinase ( NEB ) , 80U RNase Inhibitor ) for 40 min at 37°C . For the ligation of the 5’ linker the beads were resuspended in 80 μl of 5’ ligation mix ( 16 μl 5x PNK buffer , 80U RNasin , 40U T4 RNA ligase , 100 pmol 5’linker , and 80 mM ATP ) and incubated at 16°C . After two washes with wash buffer II ( 50 mM Tris-HCl ( pH 7 . 8 ) , 50 mM NaCl , 10 mM Imidazole , 0 . 1% NP-40 ) the material was eluted with elution buffer ( 10 mM Tris ( pH 7 . 8 ) , 50 mM NaCl , 150 mM Imidazole and 0 . 1% NP-40 ) . The final eluate was incubated with 2 M EDTA , 20 μl 20% SDS and 100 μg proteinase K ( Ambion AM2548 ) for 2 hours at 50°C and the RNA was extracted using Phenol-Chloroform followed by ethanol precipitation . Reverse transcription with SuperScript III was performed following the manufacturer’s instructions ( Invitrogen ) followed by RNase H ( NEB ) digest ( 10U ) for 30 min at 37°C . The cDNA was amplified and the PCR-product was purified with the Agencourt AMPure XP PCR purification beads ( Beckman Coulter ) following the manufacturer’s instructions . The quality of the library was verified with the Bioanalyzer 2100 ( Agilent ) and the Agilent High Sensitivity DNA Kit ( Agilent ) . The amplified library was subject to high-throughput sequencing at BGI-Hong Kong Co . Ltd . The datasets were mapped to the mating type locus of S . pombe ( 41249 bp from chromosome 2 ) and to the full genome of S . pombe ( ASM294v1 . 17 ) using Tophat2 software . ( Tophat-2 . 0 . 14; [107] ) . Downstream data analysis was performed using R Bioconductor packages . The mean coverage over mRNA loci was normalized to 20 in the datasets . The difference plot was generated from all protein coding ORFs ( 5115 genes ) , aligning them at the annotated transcriptional termination site ( TTS ) ( S . pombe EF2 annotation ) . The plot is showing the ratio between the normalized Mutant/wt coverage . Two biological duplicates have been performed for genome-wide analysis for wildtype and dhp1-1 strains . All microarray and CRAC data sets are available at NCBI GSE77291 , GSE77289 and GSE77290 . | Epigenetic mechanisms regulate when , where , and how an organism uses the genetic information stored in its genome . They are essential to many cellular processes , such as the regulation of gene expression , genome organization , and cell-fate determination . They also govern growth , development , and ultimately human health . Heterochromatin constitutes silenced chromatic domains , in which gene silencing occurs through epigenetic mechanisms . RNA processing pathways , such as RNA interference ( RNAi ) and the exosome , are known to mediate the silencing of genes via degradation of unwanted or aberrant transcripts . In this study , we describe a new RNA processing mechanism in epigenetic silencing using fission yeast , a premier model for studying these processes . With genetic , cell biology , and genomic approaches , we uncovered a previously unrecognized function of Dhp1 , a highly conserved 5’-3’ exoribonuclease and ortholog of budding yeast Rat1 and metazoan Xrn2 . We show that Dhp1 mediates a novel RNA processing mechanism in epigenetic silencing which occurs independently of both RNAi and the exosome . Our results clarify how multiple RNA processing pathways are involved in the regulation of eukaryotic gene expression and chromatin organization . | [
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] | 2016 | A Novel Epigenetic Silencing Pathway Involving the Highly Conserved 5’-3’ Exoribonuclease Dhp1/Rat1/Xrn2 in Schizosaccharomyces pombe |
In a murine model of repeated exposure of the skin to infective Schistosoma mansoni cercariae , events leading to the priming of CD4 cells in the skin draining lymph nodes were examined . The dermal exudate cell ( DEC ) population recovered from repeatedly ( 4x ) exposed skin contained an influx of mononuclear phagocytes comprising three distinct populations according to their differential expression of F4/80 and MHC-II . As determined by gene expression analysis , all three DEC populations ( F4/80-MHC-IIhigh , F4/80+MHC-IIhigh , F4/80+MHC-IIint ) exhibited major up-regulation of genes associated with alternative activation . The gene encoding RELMα ( hallmark of alternatively activated cells ) was highly up-regulated in all three DEC populations . However , in 4x infected mice deficient in RELMα , there was no change in the extent of inflammation at the skin infection site compared to 4x infected wild-type cohorts , nor was there a difference in the abundance of different mononuclear phagocyte DEC populations . The absence of RELMα resulted in greater numbers of CD4+ cells in the skin draining lymph nodes ( sdLN ) of 4x infected mice , although they remained hypo-responsive . Using mice deficient for IL-4Rα , in which alternative activation is compromised , we show that after repeated schistosome infection , levels of regulatory IL-10 in the skin were reduced , accompanied by increased numbers of MHC-IIhigh cells and CD4+ T cells in the skin . There were also increased numbers of CD4+ T cells in the sdLN in the absence of IL-4Rα compared to cells from singly infected mice . Although their ability to proliferate was still compromised , increased cellularity of sdLN from 4x IL-4RαKO mice correlated with reduced expression of Fas/FasL , resulting in decreased apoptosis and cell death but increased numbers of viable CD4+ T cells . This study highlights a mechanism through which IL-4Rα may regulate the immune system through the induction of IL-10 and regulation of Fas/FasL mediated cell death .
Schistosomiasis is a debilitating disease that develops following percutaneous infection with the parasitic helminth Schistosoma spp [1 , 2] . The disease affects approximately 230 million people worldwide with infection occurring when the skin is exposed to the free-swimming tissue invasive cercariae [3] . Since the infective stage of the parasite is often present in water used for domestic purposes , individuals living in areas endemic to schistosomiasis are at risk of repeated infections . In order to investigate the effect of repeated infection with schistosome cercariae on the immune response , we developed an experimental model whereby mice were exposed via their pinnae once ( 1x ) , or repeatedly ( 4x ) , to doses of infective S . mansoni cercariae [4] . It was found that after 4x , compared to 1x , exposures there were major changes in the cell populations within the skin site of infection such that eosinophils , macrophages , dendritic cells ( DCs ) , neutrophils , mast cells , CD4+ T cells and keratinocytes were all increased after 4x infections [4–7] . Moreover , repeated infections resulted in the development of CD4+ T cell hypo-responsiveness in the skin-draining lymph nodes ( sdLN ) , as well as decreased immunopathology in the liver generated in response to eggs released by adult worms [4] . CD4+ T cells in the sdLN from 4x mice had reduced ability to proliferate and secrete cytokines in response to larval schistosome antigens , and this was shown to be IL-10 dependent [6 , 8] . Exposure of the skin to repeated doses of schistosome cercariae caused major changes in the local cytokine environment , particularly the levels of IL-4 , IL-13 and IL-10 , and it was proposed that immune responses in the skin involved mononuclear phagocytes that were alternatively activated [4] . The term alternative activation conventionally describes macrophages under the influence of IL-4 and IL-13 [9–12] . Alternatively activated macrophages have been given the term M2 , or subgroupings thereof M2a-c [13] , whilst the term alternative activation has also been used in the context of DCs [14] . Parasitic helminth infections are often associated with the development of alternatively activated cells and polarized Th2-type immune responses [15] . Together , these cell populations are linked to the development of wound healing , which accompanies the response to tissue invasive helminths [16 , 17] . Therefore , it is likely that there will be substantial wound healing following the repeated percutaneous invasion of the skin by schistosome cercariae . In the current study , we set out to investigate mononuclear phagocyte cell populations in the skin infection site following repeated exposure to schistosome larvae , and found evidence for a gene expression profile associated with alternative activation . Subsequently , we focused our investigation upon the impact of IL-4Rα which is a receptor for both IL-4 and IL-13 essential for the alternative activation of macrophages [9 , 10] , and Resistin-like molecule α ( RELMα ) which is a key marker of alternative activation and is abundant during Th2 immune responses in allergic lung inflammation and helminth infection [18–21] . Consequently , we hypothesized that IL-4Rα and RELMα might play an important role in the response following repeated exposures to infectious S . mansoni cercariae .
Experiments were carried out in accordance with the United Kingdom Animals Scientific Procedures Act 1986 and with the approval of the University of York Ethics Committee ( PPL 60/4340 ) . C57BL/6 wild-type ( WT ) , IL-4Rα deficient ( Il-4rα-/-; IL-4RαKO ) [22] , and Resistin-like molecule α deficient ( Retlnα-/-; RelmαKO ) [23] , mice were bred and housed at the University of York . Both IL-4RαKO and RelmαKO mice on C57BL/6 background were kind gifts from Professor Judith Allen , University of Edinburgh . Age and sex-matched mice ( between 6 and 10 weeks ) were used for all experiments . The life cycle of a Puerto Rican strain of S . mansoni was maintained at the University of York . Both pinnae of mice were percutaneously exposed to 150 S . mansoni cercariae either once only ( 1x ) , or four times in total ( 4x ) on a once-a-week basis between day 0 to day 21 , as described previously [4 , 24] . Pinnae and skin-draining lymph nodes ( sdLN; auricular lymph nodes ) were harvested 4 days after the final infection . Skin infection via the pinnae results in a 50% penetration rate [24] , therefore a dose of approximately 75 cercariae per pinna is achieved . Pinnae inflammation was measured using a dial gauge micrometer ( Mitutoyo , Japan ) . Pinnae were collected and briefly exposed to 70% ethanol to sterilize before being air-dried . They were then split along the central cartilage into two halves , and floated on the surface of complete RPMI media ( RPMI-1640 ( Gibco , Paisley , UK ) containing 10% heat inactivated FCS ( Biosera , Uckfield , UK ) , 2 mM L-Glutamine , 1% Pen/Strep ( both Gibco ) and 50 μM 2-mercaptoethanol ( Sigma-Aldrich , Gillingham , UK ) overnight at 37°C 5% CO2 , to obtain the dermal exudate cells ( DEC ) as previously described [4 , 24 , 25] . After overnight in vitro culture , the tissue samples were discarded , while the culture supernatants were spun at 1000 xg for 7 minutes at 4°C to recover the DEC . The skin biopsy culture supernatants were frozen at -20°C prior to subsequent analysis by ELISA . DEC were re-suspended in complete RPMI , numbers determined by trypan blue exclusion , and then subjected to either analysis by flow cytometry , or separated into groups by fluorescence-activated cell sorting ( FACS ) . The amounts of released IL-4 , IL-10 , IL-12p40 present in the skin biopsy culture supernatants were determined using DuoSet enzyme-linked immunosorbent assay ( ELISA ) kits ( R&D Systems , Abingdon , UK ) . Mice received 1 mg bromodeoxyuridine ( BrdU; Sigma-Aldrich ) via daily intraperitoneal ( i . p . ) injection for the final 4 days prior to harvest of the sdLN in order to determine in vivo CD4+ cell proliferation [8] . Cells recovered from the sdLN were initially blocked with anti-CD16/32 monoclonal antibodies ( mAbs; eBioscience , Hatfield , UK , ) in goat serum ( Sigma-Aldrich ) , and later labelled using anti-CD3 and anti-CD4 mAbs ( both eBioscience ) in PBS supplemented with 1% FCS ( FACS Buffer ) . Cells were then washed in FACS Buffer , incubated in 1x Fixation/Permeabilization buffer ( eBioscience ) for one hour at 4°C , washed again , and then incubated at 37°C in 100 μg DNase ( Sigma-Aldrich ) for 1 hour . After a final wash in FACS buffer , cells were labelled for 45 minutes at room temperature with anti-BrdU mAb , or rat IgG1 isotype control mAb ( both eBioscience ) , in 1x permeabilization buffer , according to the manufacturer’s instructions . DEC were first incubated using Fixable Live/Dead Aqua stain ( Life Technologies , Paisley , UK ) , blocked with anti-CD16/32 mAbs ( eBioscience ) in goat serum ( Sigma Aldrich ) , and subsequently labeled with the following mAbs conjugated to fluorescent labels: anti-CD45 , anti-F4/80 , anti-MHC-II ( IA-IE ) , anti-Fas , anti-FasL , anti-CD3 and anti-CD4 ( all eBioscience ) . Flow cytometry data was acquired on the Cyan ADP , or the BD LSR Fortessa analyzer ( Beckman Coulter , London , UK ) . Data was analyzed using FlowJo Software v7 . 6 . 5 ( Tree Star Inc , Oregon Bio , Oregon US ) . After surface staining for anti-CD3 and anti-CD4 , sdLN cells were washed in cold PBS supplemented with 1x annexin V binding buffer ( eBioscience ) , and incubated for 15 minutes with anti-annexin V FITC at room temperature . Cells were then washed in annexin V binding buffer and resuspended for analysis in annexin V binding buffer . Propidium iodide ( PI ) ( eBioscience ) was added directly before acquiring the data . DEC obtained from infected pinnae in three independent experiments ( 12–18 mice each ) recovered on day 4 after infection with S . mansoni cercariae were pooled and labelled with anti-MHC-II ( IA-IE ) ( clone # M5/114 ) and anti-F4/80 ( clone # BM8 ) mAbs . Cell populations which were F4/80+MHC-IIhigh , F4/80+MHC-IIlo , or F4/80-MHC-IIhigh were recovered by FACS ( MoFlo Astrios , Beckman Coulter ) and RNA extracted from sorted DEC populations using TRIzol ( Life Technologies ) . RNA was quantified using a Nanodrop ( Thermo Scientific , Waltham , USA ) and quality checked using a Bioanalyzer ( Agilent Technologies , Santa Clara , USA ) . Staff at the Technology Facility at the University of York , York , UK , prepared and carried out microarray analysis of purified RNA , including sample labelling and hybridisation , using the Agilent SurePrint system ( Agilent Technologies ) . GeneSpring ( Agilent Technologies ) was used to normalize microarray data , calculate fold differences , prepare dendrograms of cell populations and establish significant ( p< 0 . 05 ) differentially expressed genes . Statistical analyses were performed using a one-way analysis of variance ( ANOVA ) and Tukey’s multiple comparisons test using GraphPad Prism v6 software ( GraphPad Software Inc , San Diego , California . USA ) .
Dermal exudate cells ( DEC ) were recovered from in vitro cultured skin biopsies after exposure to either a 1x dose , or 4x doses , of S . mansoni cercariae in order to characterize genes that were differentially regulated four days after the final infection . Cells which were F4/80+MHC-II- , shown to be SiglecF+ eosinophils [4 , 12] were excluded from the analysis as we previously showed that the absence of these cells had no effect on the development of CD4+ cell hypo-responsiveness in the sdLN [7] . Therefore , our microarray analysis focused upon cells of the mononuclear phagocyte system ( i . e . DCs and macrophages ) . These cells distributed into three discrete populations: F4/80-MHC-IIhigh cells ( denoted ‘R4’ ) were classed as DCs , F4/80+MHC-IIhigh cells ( denoted ‘R4A’ ) were termed tissue resident macrophages , whilst F4/80+MHC-IIint cells ( denoted ‘R3’ ) were classified as macrophages [6] as shown in Fig 1A . Clustering analysis of microarray data obtained from biological replicates of the three sorted DEC populations is shown as dendrograms with corresponding selected heat maps for cells from 1x and 4x infected groups of mice ( Fig 1B and 1C ) . The heat maps displayed highlight a section from the start of the entire microarray heat map , whilst the dendrograms are based upon analysis of all identified genes to yield an overall comparison . This revealed clear clustering patterns within each of the defined cell populations ( i . e . R4 , R4A , and R3 ) validating our gating strategy . Based on the dendrogram analysis of the microarray data from the 1x and 4x DEC populations , gene expression profiles within the R4A tissue resident macrophages were more closely associated to R3 macrophages than to R4 DCs ( Fig 1B and 1C ) . Transcriptional distinctions between the R4A and R3 populations were reduced after 4x infection ( Fig 1C ) . Many identified genes found in the three sorted DEC populations were differentially up-regulated in the 4x compared to the 1x samples , and were linked to alternative activation ( e . g . retnla , chi3l3 , chi3l4 , ccl24 , cd209 and cd163 [11 , 26–28] ) ( Fig 1D ) . Retnla ( encoding RELMα ) was one of the most highly up-regulated genes in 4x DCs ( R4; x121-fold ) , 4x tissue macrophages ( R4A; x16-fold ) and 4x macrophages ( R3; x80-fold ) , compared to their 1x counterpart cell populations . Similarly , chi3l3 was up-regulated in both 4x tissue macrophages ( R4A; x31-fold ) and 4x macrophages ( R3; x31-fold ) , whilst ccl24 was up-regulated in all three cell populations 24-33-fold . The gene for IL-4 was up-regulated in 4x macrophages ( R3; x22-fold ) , whilst il4ra was also up-regulated in these cells ( R3; x6-fold ) . Other genes which featured in the top 20 up-regulated genes included those associated with tissue destruction/wound healing such as igf1 ( x8-30-fold ) and fn1 ( x14-23-fold ) . Given the significant changes in gene expression associated with IL-4Rα and RELMα , their roles in the early stage immune response were investigated using Il-4rα-/- , or Retlnα-/- mice . After 4x exposures to infective S . mansoni cercariae , pinnae thickness of WT mice , as well as those deficient in IL-4Rα , was significantly increased compared to 1x exposure ( Fig 2A; p<0 . 0001 and p<0 . 05 respectively ) . However , the thickness of the pinnae infection site was comparable between WT and IL-4RαKO mice , irrespective of the infection regime ( Fig 2A; p>0 . 05 ) . Similarly , whilst RelmαKO mice exposed to 4x doses of cercariae displayed significantly enhanced levels of skin inflammation compared to naive and 1x RelmαKO animals ( all p<0 . 0001 ) , the levels of inflammation were comparable with those of their 4x WT cohorts ( Fig 2B; all p>0 . 05 ) . Increased levels of IL-4 , IL-12p40 and IL-10 released by skin biopsies cultured in vitro in the absence of added antigen were detected in the pinnae of 4x compared to 1x WT mice ( Fig 2C–2H; p<0 . 05–0 . 0001 ) . In the absence of IL-4Rα , the levels of IL-4 in 4x IL-4RαKO mice were comparable to those in 4x WT samples ( Fig 2C ) , on the other hand , the levels of pro-inflammatory IL-12p40 in 4x IL-4RαKO mice were significantly increased compared to 4x WT samples ( Fig 2D; p<0 . 001 ) . The levels of regulatory IL-10 were low in 4x IL-4RαKO mice ( Fig 2E ) , resulting in a significant reduction in the quantities of IL-10 being released compared to 4x WT mice ( Fig 2E; p<0 . 0001 ) . The absence of RELMα had no effect on the production of IL-4 , or IL-12p40 in 1x and 4x mice compared to their WT cohorts ( Fig 2F and 2G; p>0 . 05 ) , however similar to 4x IL-4RαKO mice , IL-10 production was significantly lower in 4x RelmαKO skin compared to 4x WT skin ( Fig 2H; p<0 . 05 ) . Therefore , although IL-4RαKO and RelmαKO mice produce similar amounts of IL-4 after 1x and 4x infections , IL-4RαKO mice have a more pro-inflammatory phenotype ( i . e . increased IL-12p40 ) and both KO strains had decreased IL-10 production compared to WT mice . As expected , significantly greater numbers of DEC were recovered from 4x WT compared to 1x WT mice ( Fig 3A and 3B; p<0 . 01 ) , although as with pinnae thickness , DEC numbers in 1x and 4x infected IL-4RαKO mice were similar compared to their WT cohorts ( Fig 3A; p>0 . 05 ) . The number of DEC recovered from pinnae biopsies of 4x RelmαKO compared to 4x WT mice was also not significantly different ( Fig 3B; p>0 . 05 ) . The abundance of F4/80+ antigen presenting cells may be critical to the development of CD4+ T cell hypo-responsiveness , particularly as it has been shown that F4/80+ alternatively activated macrophages depress CD4+ cell responses following infection with filarial parasites [29] . Here , we show there were no significant changes in the number of F4/80-MHC-IIhigh ( R4 ) , F4/80+MHC-IIhigh ( R4A ) , or F4/80+MHC-IIint ( R3 ) cells in 4x WT compared to 1x WT mice ( Fig 3C–3E; all p>0 . 05 ) . However , in the absence of IL-4Rα signaling , there were large increases in the numbers of these three cell types in 4x IL-4RαKO compared to 1x IL-4RαKO mice ( Fig 3C–3E; p<0 . 05–0 . 01 ) . Moreover , the number of R4 and R4A cells , which express high levels of MHC-II , were significantly greater in 4x IL-4RαKO than in 4x WT cohorts ( Fig 3C and 3D; p<0 . 05–0 . 01 ) , although the change in the number of R3 macrophages which express intermediate levels of MHC-II in 4x IL-4RαKO and 4x WT mice was not statistically different ( Fig 3E; p>0 . 05 ) . In RelmαKO mice , the numbers of R3 , R4 and R4A DEC were similar to those in WT cohorts , and there was no significant difference in the number of these two cell types after 4x compared to 1x infection ( Fig 3F–3H; p>0 . 05 ) . Increased numbers of MHC-IIhigh R4 and R4A cells in 4x infected IL-4RαKO mice could support a stronger T cell response , both in the skin site of infection and down-stream in the draining lymphoid tissue [30 , 31] . This might be particularly relevant when coupled with low levels of IL-10 production in the skin , a cytokine that we recently showed to be fundamental in regulating CD4+ T cell numbers [6 , 8] . Indeed , in the absence of IL-4Rα signaling , significantly greater numbers of CD3+CD4+ T cells were present in the skin infection site of 4x IL-4RαKO , compared to 4x WT mice ( Fig 3I; p<0 . 0001 ) . Therefore , we subsequently investigated the changes in the numbers , viability , and/or responsiveness of CD4+ cells in the sdLN . After 4x infections , the sdLN of WT mice had significantly fewer cells compared to 1x WT mice ( Fig 4A; p<0 . 05 ) . However , in the absence of IL-4Rα , cellularity was increased , resulting in comparable cell numbers between 1x and 4x infected IL-4RαKO mice which were not significantly different ( Fig 4A; p>0 . 05 ) . Similarly , total sdLN cellularity was also increased in 4x RelmαKO compared to 4x WT mice ( Fig 4B; p<0 . 01 ) , resulting in the number of sdLN cells from 4x and 1x RelmαKO mice being not significantly different ( Fig 4B; p>0 . 05 ) . There were also decreased numbers of CD4+ T cells in the sdLN of 4x compared to 1x WT mice ( Fig 4C; p<0 . 05 ) , although the number of CD4+ T cells in 4x compared to 1x IL-4Rα KO mice was not significantly reduced ( Fig 4C; p>0 . 05 ) . Therefore , in comparison there were significantly more CD4+ T cells in 4x IL-4RαKO than in their 4x WT counterparts ( Fig 4C; p<0 . 05 ) . The numbers of CD4+ T cells in the sdLN of 1x and 4x RelmαKO mice was also similar ( Fig 4D; p>0 . 05 ) leading to significantly greater numbers of CD4+ T cells being detected in the sdLN of 4x RelmαKO compared to 4x WT mice ( Fig 4D; p<0 . 05 ) . This showed that the absence of either IL-4Rα , or RELMα , leads to increased number of CD4+ T cells in the sdLN following repeated infection comparable to the levels seen in 1x mice . It was proposed that the increased number of CD4+ T cells in sdLN of 4x IL-4Rα and 4x RelmαKO mice could be due to a reversal in the development of hypo-responsiveness normally seen in 4x WT mice . In fact , the total number of BrdU+CD4+ cells was slightly greater in 4x IL-4RαKO mice compared to the 4x WT cohort group ( Fig 4E; p<0 . 05 ) , although the number in 4x RelmαKO versus 4x WT mice was not significantly different ( Fig 4F; p>0 . 05 ) . While ~15–20% of the CD4+ T cells in the sdLN from 1x WT mice were BrdU+ and had therefore proliferated in vivo , only ~7% from 4x WT mice were BrdU+ ( Fig 4G; p<0 . 0001 ) confirming the establishment of CD4+ T cell hypo-responsiveness . However , although there were a slightly greater number of BrdU+ cells in 4x IL-4RαKO mice ( Fig 4E ) , the proportion of BrdU+ cells in 4x IL-4RαKO mice remained significantly lower than in 1x IL-4RαKO mice ( Fig 4G; p<0 . 05 ) demonstrating that the absence of IL-4Rα does not restore proliferation of CD4+ T cells in the sdLN of 4x mice to the levels seen in 1x WT mice . A similar situation was observed in 4x RelmαKO mice , where the proportions of BrdU+ cells in 4x RelmαKO compared to 4x WT mice were not significantly different ( Fig 4H; p>0 . 05 ) and both exhibited CD4 T cell hypo-responsiveness in vivo compared to their 1x cohorts ( Fig 4H; p<0 . 0001 ) . This shows that the absence of RELMα also does not restore CD4+ T cell proliferation in the sdLN after repeated exposures of the skin to S . mansoni cercariae . Previously , we reported that CD4+ T cell hypo-responsiveness in the sdLN observed after repeated infection is dependent on IL-10 [8] . This was due to increased CD4+ T cell activation accompanied by decreased death and apoptosis of the CD4+ T cell population in the sdLN . In the current study , we show that both IL-4RαKO and RelmαKO mice had decreased IL-10 production in the skin and increased cellularity in the sdLN after 4x infection . As RELMα expression is dependent on IL-4 signaling [32] , we restricted further analysis of the CD4+ cell population to cells in the sdLN of 1x versus 4x IL-4RαKO mice . We observed that surface protein expression of both Fas and FasL increased in CD4+ T cells recovered from 4x WT compared to 1x WT mice but was significantly decreased in 4x IL-4RαKO compared to 4x WT mice ( Fig 5A and 5B; p<0 . 05 and p<0 . 0001 ) . In addition , whereas in WT mice , there was a significant decrease in the proportions of AnnV-PI- viable CD4+ T cells in 4x compared to 1x mice ( Fig 5C; p<0 . 01 ) , the absence of IL-4Rα signaling resulted in significantly greater proportions of AnnV-PI- viable CD4+ T cells in 4x IL-4RαKO compared to 4x WT mice ( Fig 5C; p<0 . 01 ) . This caused there to be no significant difference between the viability of CD4+ T cells from 4x IL-4RαKO mice compared to either 1x WT , or 1x IL-4RαKO mice ( Fig 5C; p>0 . 05 ) . The increase in the proportion of AnnV-PI- viable CD4+ T cells in 4x IL-4RαKO mice was accompanied by the detection of significantly fewer AnnV+PI- apoptotic CD4+ T cells ( Fig 5D; p<0 . 05 ) , as well as fewer AnnV+PI+ dead CD4+ T cells , compared to 4x WT mice ( Fig 5E; p<0 . 01 ) . Thus , it appears that a reduction in IL-4Rα signaling facilitates CD4+ T cell survival . This increased survival could explain why the number of CD4+ T cells in the sdLN of 4x IL-4RαKO mice is not significantly reduced after repeated exposure to schistosome cercariae which contrasts with the situation in 4x WT mice where there is a significant reduction in number of CD4+ T cells when compared to their 1x WT counterparts .
Here we demonstrate that three discrete mononuclear phagocyte cell populations were present in DEC recovered from the skin infection site of mice exposed to repeated doses of schistosome cercariae . Moreover , all three populations exhibited an alternatively activated phenotype as judged by microarray analysis of DEC . For example , Retnla ( encoding RELMα ) which is a key marker of alternatively activated macrophages [33 , 34] was one of the most highly up-regulated genes in 4x compared to 1x DCs , tissue macrophages , and macrophages . Several other genes also linked to alternative activation were up-regulated in the three discrete 4x DEC populations including chi3l3 and chi3l4 ( encoding chitinase-like molecules YM-1 and YM-2 [12 , 33] ) . The gene for IL-4 was also up-regulated in the macrophage DEC population after repeated infections , and IL-4 has been shown to be produced by alternatively activated/type-II macrophages using both human and mouse cells [35 , 36] . Other up-regulated genes linked to alternative activation were mrc1 and cd209 which encode the C-type lectins for the mannose receptor CD206 and DC-SIGN CD209 [9] , whilst expression of cd163 encoding the scavenger receptor CD163 when used in combination with chi3l3 and Retnla [11 , 12 , 37] also supports the identification of the three discrete 4x DEC populations as being alternatively activated . Conversely , Arg1 which we had previously reported to be up-regulated in DEC from 4x mice [4] , was not >2 fold up/down-regulated in the any of the 3 sorted cell populations , whilst Clec10a of the C-type lectin/C-type lectin-like domain ( CTL/CTLD ) superfamily , associated with alternative activation [9] was only 2 . 2 fold up-regulated in the DC population . On the other hand , wound healing associated genes such as igf-1 encoding Insulin-like growth factor 1 suggested to be involved in resolving tissue damage following helminth infection [38] , and fn1 encoding fibronectin produced during the resolution of tissue damage [39] were both highly up-regulated . This provides evidence supporting an evolutionary link between alternative activation in mononuclear phagocytes and the resolution of tissue damage caused by helminth infection [16 , 17 , 38] . Indeed , we have shown that schistosome cercariae cause significant tissue disruption as they invade through the skin [3] , and therefore tissue repair of the skin following repeated exposure to invasive schistosome cercariae should be expected . The discrete DEC mononuclear phagocyte populations all express genes typically associated with alternative activation [9–12] . This provides a more in-depth analysis of DEC from 4x mice in which mononuclear phagocytes appeared to switch from classically-activated to alternatively-activated commensurate with up-regulated mRNA transcripts for Ym1 and RELMα but only low levels of iNOS and IFNγ [4] . Nevertheless , whilst the R4 , R4A , and R3 mononuclear phagocytes all appear to be alternatively activated , subtle qualitative differences in the expression of specific genes between the three DEC populations underline the likely heterogeneity/plasticity of these mononuclear phagocytes which may have different and/or overlapping functional roles in vivo [40–42] . In the context of repeat infection of the skin with S . mansoni cercariae , DEC recovered from the site of infection had a pronounced increase in the expression of RELMα but its absence had no effect on the extent of skin inflammation ( i . e . pinnae thickness ) , or on the numbers of mononuclear phagocyte DEC populations recovered from the skin infection site ( Fig 3F–3H ) . The absence of RELMα did however result in a slight reduction in IL-10 production in the skin alongside an increase in the numbers of CD4+ cells in the sdLN such that they were as abundant as in 1x RELMαKO mice and were more numerous than in 4x WT cohorts . However , the absence of RELMα did not affect the development of CD4+ T cell hypo-responsiveness in the sdLN of 4x mice . In contrast , a separate study showed that RELMα KO mice infected with Nippostrongylus brasiliensis exhibit enhanced intestinal and pulmonary pathology accompanied by expulsion of the parasite indicating a regulatory role for RELMα by dampening normally protective strong Th2-dependent responses [32 , 43] . Moreover , pulmonary immune granulomas to injected S . mansoni eggs were enhanced in the absence of RELMα [44] , as were hepatic immune granulomas formed at the chronic phase of schistosome infection [32] . Nonetheless , these previous studies were performed when the immune response was skewed towards a Th2 phenotype [21] . In contrast , the response investigated in our study occurs at an early phase of infection , prior to egg-induced Th2 biased immunopathology , and is accompanied by cytokines with a mixed Th1 and Th2 type response [4] . Therefore , RELMα may only have a regulatory role during strong Th2 responses during the chronic phase of infection and we suggest that it has less of a role when the immune response has a mixed Th1/Th2 phenotype . Signaling through IL-4Rα is well defined as being important in the development of alternative activation [10 , 12] and in the maintenance of Th2 responses [45] . Whilst the absence of IL-4Rα did not have an impact on inflammation at the skin site of infection , nor on the number of DEC , its absence significantly increased the numbers of cells expressing high levels of MHC-II . In addition , increased release of pro-inflammatory IL-12p40 in the absence of IL-4Rα was accompanied by decreased levels of regulatory IL-10 . Therefore , we considered it possible that the increased numbers of cells with antigen presenting potential in the skin might lead to enhanced CD4+ cell activity downstream in the sdLN . However , whilst the absence of IL-4Rα resulted in increased numbers of CD4+ cells in the skin infection site and the sdLN , the cells in the sdLN remained hypo-responsive in vivo to stimulation with parasite antigen similar to 4x WT mice . The use of cell specific gene deficient mice , such as those expressing a Cre recombinase from the lysozyme M-encoding locus , which has been widely used in the context of IL-4Rα function in macrophages and in the context of schistosome infection [22] , would have been desirable to fully interrogate the role of particular genes on mononuclear phagocytes . However , such mice were not available during the current project . In addition , it has been reported that Il4rα excision in these mice is incomplete during inflammatory conditions [46] , raising doubts about interpretation of data obtained using these mice and suggests that alternative cell specific gene animals should be sought . Further analysis of CD4+ cells in the sdLN revealed that there was a significant elevation in the expression of Fas and FasL on the CD4+ T cells in the sdLN in 4x infected WT mice , whereas in the absence of IL-4Rα the expression levels of these two molecules was not elevated and was not different from those in 1x IL-4Rα mice . We also showed that skin biopsies from 4x IL-4RαKO mice , in contrast to 4x WT cohorts , released negligible quantities of IL-10 suggesting a possible role for IL-4Rα in the promotion of IL-10 production . Indeed , IL-4Rα signaling is required for the production of IL-10 derived from Th2 cells following infection with N . brasiliensis , thereby resulting in increased levels of regulation via IL-10 [47] , although others show that IL-10 production following chronic schistosome infection can be IL-4Rα-independent [48] . Here , in our study we found that IL-10 production in the skin was significantly reduced in the absence of IL-4Rα , potentially resulting in decreased regulation via IL-10 . Several studies have identified a link between IL-10 and Fas/FasL expression . For example in systemic lupus erythematosus ( SLE ) , IL-10 is directly able to induce the expression of Fas on the surface of T cells resulting in increased levels of apoptosis [49–51] . Moreover , we recently showed that in IL-10KO mice , there was reduced Fas expression on CD4+ T cells in the sdLN following repeated schistosome infection which led to a reduction in CD4+ T cell death in the sdLN and consequently may have contributed to the alleviation of CD4+ T cell hypo-responsiveness in the absence of IL-10 [8] . Here , we suggest a novel mechanism for the regulation of the immune response through IL-4Rα , which impacts both IL-10 production and antigen presenting cells numbers , which would subsequently regulate Fas and FasL expression on CD4+ T cells in the sdLN of 4x infected mice . Thus , IL-4Rα signaling results in increased IL-10 production , increased levels of apoptotic and/or dead T cells in 4x mice and a dampening of the immune response . This link between IL-4Rα and IL-10 and Fas/FasL-induced apoptosis could be a potential novel mechanism through which IL-4Rα regulates the immune system . | In areas endemic for schistosomiasis , repeated exposure to infective cercariae is a frequent occurrence , and repeated exposure of murine skin to Schistosoma mansoni resulted in CD4+ T cells becoming hypo-responsive . Here potential contributory mechanisms were investigated . In the skin infection site , three mononuclear phagocyte populations were identified ( tissue macrophages , dendritic cells , and macrophages ) which exhibited up-regulation of genes associated with alternative activation , in particular the gene encoding RELMα . However , in repeatedly infected mice deficient in RELMα , there was no change in the abundance of mononuclear phagocytes in the skin , and CD4+ cells in the skin draining lymph nodes remained hypo-responsive . In mice deficient for IL-4Rα , required for alternative activation , levels of dermal regulatory IL-10 were reduced and there was an increase in the abundance of antigen presenting MHC-IIhigh cells , which was accompanied by increased numbers of CD4+ T cells . Although the absence of IL-4Rα did not translate into increased CD4+ cell responsiveness , they exhibited lower expression of Fas/FasL , resulting in decreased apoptosis/cell death and increased cell viability . This study highlights a mechanism through which IL-4Rα may regulate the immune system through the induction of IL-10 and regulation of Fas/FasL mediated cell death . | [
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] | 2016 | Alternatively Activated Mononuclear Phagocytes from the Skin Site of Infection and the Impact of IL-4Rα Signalling on CD4+T Cell Survival in Draining Lymph Nodes after Repeated Exposure to Schistosoma mansoni Cercariae |
Parasitic protozoa , such as Leishmania species , are thought to express a number of surface and secreted nucleoside triphosphate diphosphohydrolases ( NTPDases ) which hydrolyze a broad range of nucleoside tri- and diphosphates . However , the functional significance of NTPDases in parasite virulence is poorly defined . The Leishmania major genome was found to contain two putative NTPDases , termed LmNTPDase1 and 2 , with predicted NTPDase catalytic domains and either an N-terminal signal sequence and/or transmembrane domain , respectively . Expression of both proteins as C-terminal GFP fusion proteins revealed that LmNTPDase1 was exclusively targeted to the Golgi apparatus , while LmNTPDase2 was predominantly secreted . An L . major LmNTPDase1 null mutant displayed increased sensitivity to serum complement lysis and exhibited a lag in lesion development when infections in susceptible BALB/c mice were initiated with promastigotes , but not with the obligate intracellular amastigote stage . This phenotype is characteristic of L . major strains lacking lipophosphoglycan ( LPG ) , the major surface glycoconjugate of promastigote stages . Biochemical studies showed that the L . major NTPDase1 null mutant synthesized normal levels of LPG that was structurally identical to wild type LPG , with the exception of having shorter phosphoglycan chains . These data suggest that the Golgi-localized NTPase1 is involved in regulating the normal sugar-nucleotide dependent elongation of LPG and assembly of protective surface glycocalyx . In contrast , deletion of the gene encoding LmNTPDase2 had no measurable impact on parasite virulence in BALB/c mice . These data suggest that the Leishmania major NTPDase enzymes have potentially important roles in the insect stage , but only play a transient or non-major role in pathogenesis in the mammalian host .
Leishmania parasites cause a spectrum of diseases in humans , ranging from localized cutaneous lesions to disseminated mucocutaneous and lethal visceral infections . It is estimated that 1 . 5 to 2 million new cases of leishmaniasis occur annually and that more than 350 million people are at risk worldwide . Current first-line drug treatments are suboptimal due to high toxicity , cost , requirement for hospitalization and/or the emergence of drug-resistant strains , highlighting the need for the development of more effective therapeutics [1] . Leishmania parasites develop as extracellular promastigote stages in the digestive tract of the sandfly vector [2] . Following injection into the mammalian host during a sandfly bloodmeal , promastigotes are phagocytosed by a range of host cells ( neutrophils , dendritic cells and macrophages ) before differentiating to obligate intracellular amastigote stages that primarily proliferate within the phagolysosome compartment of macrophages . A number of surface molecules , including an abundant lipophosphoglycan ( LPG ) and several GPI-anchored glycoproteins , have been shown to be important for promastigote survival during these initial stages of infection [3] . In particular , LPG is thought to form a continuous surface glycocalyx that protects the promastigote stages of most Leishmania species from complement-mediated lysis and macrophage-induced oxidative stress during phagocytosis [3]–[5] . However , expression of LPG is down-regulated in amastigote stages and neither LPG nor GPI-anchored proteins are required for the long term growth and survival of this stage in macrophages . The potential role of other promastigote and amastigote secreted and surface proteins in the initiation and establishment of infection is less well defined . A number of protozoan parasites have been shown to express nucleoside triphosphate diphosphohydrolase activities on their cell surface or in the extracellular milieu [6]–[9] , and it has been suggested that hydrolysis of nucleotides may play a role in parasite pathogenesis [10]–[12] . Nucleoside triphosphate diphosphohydrolases ( NTPDases , CD39_GDA1 protein superfamily ) are a family of enzymes defined by the presence of five apyrase conserved regions ( ACRs ) and the ability to hydrolyze a wide range of nucleoside tri- and di-phosphates [13] . In mammals , surface-expressed NTPDases function in inflammation and immunity , vascular hemostasis and purine salvage [14] , while in the intracellular bacterial pathogen , Legionella pneumophila , a secreted NTPDase is required for full virulence in a mouse model of disease [15] , [16] . In Leishmania species , enzyme activity consistent with the presence of one or more surface-located NTPDases has been observed in both L . amazonensis and L . tropica , two species responsible for cutaneous leishmaniasis [17]–[19] . A number of lines of indirect evidence suggest that this surface NTPDase activity is important for virulence in the mammalian host . Specifically , surface NTPDase activity is elevated in virulent Leishmania strains and in the intracellular amastigote form of the parasite [17]–[19]; inhibition of surface NTPDase activity with chromium ( III ) adenosine 5′-triphosphate complex , reduced promastigote attachment and entry into mouse macrophages [20]; treatment of parasites with an antibody to the human NTPDase CD39 also reduced the interaction of Leishmania with mouse macrophages [19]; finally , polyclonal antibodies raised against synthetic peptides derived from the amino acid sequences of a putative L . braziliensis NTPDase caused significant cytotoxicity in cultured L . braziliensis promastigotes [21] . While these studies suggest roles for NTPDases in parasite nutrition , surface/secreted NTPDases could also contribute to pathogenesis by inducing host cell purinergic receptors . Purinergic receptors are upregulated in macrophages infected with L . amazonensis and these receptors display increased sensitivity to activation by nucleoside triphosphates ( NTPs ) . As changes in the levels of extracellular NTPs and NDPs have been shown to alter purinergic receptor activity and the immune response [22] , [23] , it has been speculated that hydrolysis of host nucleotides by parasite ecto-NTPDases may restrict the immune response and facilitate parasite proliferation . While these studies suggest NTPDases may function in Leishmania virulence and/or be essential for normal growth and development , they have relied heavily on techniques such as anti-NTPDase antibodies and/or chemical inhibition of enzyme activity to investigate the role of NTPDases in host-parasite interaction . Definitive genetic evidence of a relationship between a parasite NTPDase and parasite virulence is lacking . In this study , we show that L . major encodes two NTPDases , termed LmNTPDase1 and LmNTPDase2 ( abbreviated to NTPD1 and NTPD2 ) , and we generate null mutants in order to investigate their function during infection of mammalian cells . Our findings suggest that NTPD1 is primarily located to the Golgi apparatus , and plays an important role in regulating both the maturation of surface LPG and the capacity of L . major promastigotes to initially establish lesions . In contrast , NTPD2 was secreted , and was not required for lesion development , suggesting that its primary role is in the sandfly vector .
Use of mice in this study was approved by the Institutional Animal Care and Use Committee of the University of Melbourne ( ethics number 1212647 . 1 ) . All animal experiments were performed in accordance with the Australian National Health Medical Research council guidelines ( Australian code of practice for the care and use of animals for scientific purposes , 8th Edition , 2013 , ISBN: 1864965975 ) . Putative NTPDases were identified by BLAST [24] searching of the available Leishmania genomes , with subsequent manual identification of the conserved ACRs [25] , [26] . Protein sequence alignments were performed using ClustalW [27] , [28] . SMART [29] , [30] was used to identify motifs within the protein sequences . L . major substrain MHOM/SU/73/5-ASKH was used to create all mutant and transfected lines . Parasites were routinely cultured as axenic promastigotes in Medium-199 ( M199 , Gibco , Invitrogen , Australia ) supplemented with 10% heat-inactivated foetal bovine serum ( FBS , Invitrogen ) at 27°C or , prior to mouse infection and LPG purification , in SDM-79 medium supplemented with 10% FBS . G418 ( Invitrogen , 100 µg mL−1 ) or nourseothricin ( Werner BioAgents , Germany , 100 µg mL−1 ) was used as appropriate to maintain selection pressure on parasites transfected with pXGFP+-derived plasmids or pIR1SAT-derived and pXGSAT-derived plasmids , while puromycin ( Invitrogen , 20 µg mL−1 ) , hygromycin ( Boehringer Mannheim , 100 µg mL−1 ) and bleocin ( Calbiochem , 10 µg mL−1 ) were used to select transformants during mutagenesis . Lesion amastigotes were isolated by disrupting murine lesions ( diameter 5–10 mm ) by passage through a 70 µm plastic sieve , followed by passage through a 27 G needle to lyse macrophages and release parasites [31] . Cell debris was removed by slow speed centrifugation ( 50×g , 10 min , 4°C ) and the supernatant centrifuged ( 2000×g , 10 min , 4°C ) to collect amastigotes . Amastigotes were washed once in PBS and counted using a haemocytometer prior to use in mouse infections . Primer sequences used in genetic manipulation are detailed in supporting information ( S1 Table ) . L . major NTPDase null mutants were created via sequential homologous gene replacement in a manner similar to that previously described [32] , [33] . All L . major PCR products described below were obtained by amplification from genomic DNA . To delete ntpd1 , an 854 bp 5′ untranslated region ( UTR ) containing a 5′ Asp718 site and a 3′ XhoI site was amplified , and a 805 bp 3′ UTR region containing a 5′ BamHI and a 3′ SacI site was amplified . These products were then sequentially cloned into the pBluescript II SK vector ( Stratagene , CA , USA ) . Puromycin or hygromycin resistance cassettes were then excised from pXG-PAC and pXG-HYG [34] respectively and cloned into the XhoI/BamHI sites . To functionally delete ntpd2 a 688 bp fragment of the 5′ gene end was amplified with a 5′HindIII site and a 3′ BamHI/EcoRI/linker region , and an 1156 bp 3′ UTR region containing a 5′ BamHI/EcoRI/linker region and 3′ NotI site was amplified . An overlap PCR was then performed using these PCR products as template and the resultant product cloned into the HindIII/NotI sites of the pBluescript II SK vector ( Stratagene , CA , USA ) . Puromycin and bleocin resistance cassettes were excised from pXG-PAC and pXG-PHLEO [34] respectively using BamHI and EcoRI , and cloned into the engineered BamHI/EcoRI sites . Deletion mutant constructs were verified by restriction digest profiles and DNA sequencing . Targeting constructs were then excised by KpnI/SapI ( ntpd1 ) or HindIII/NotI ( ntpd2 ) digest , gel purified and 5 µg of each sequentially electroporated into L . major as described previously [35] . Clonal transfectants resistant to both selection drugs were chosen and deletion of the target gene and integration of resistance cassettes confirmed via triplicate PCR . To generate the pIR1SAT-ntpd1 construct used in chromosomal complementation , full-length ntpd1 was excised from pXG-LmNTPDase1-GFP using BamHI and cloned into the BglII site of the pIR1SAT vector [36] , [37] . SwaI digest was used to excise 5 µg of targeting DNA for electroporation into L . major Δntpd1 . Clonal transformants were selected on basis of resistance to nourseothricin and incorporation into the ssu locus confirmed by PCR . To create the LmNTPDase-GFP fusion proteins , full length ntpd genes were individually cloned into pXG-GFP+ [38] . To express the LPG1-mCherry fusion protein , mCherry from pEGFP-mCherry-N1 [39] was amplified with a 5′SmaI/BglII site and 3′BamHI site and cloned into the SmaI/BamHI sites of pXGSAT , generating pXGSAT-mCherry . lpg1 [40] was amplified and then cloned into SmaI/BglII of pXGSAT-mCherry , creating pXG-LPG1-mCherry . The resulting constructs were confirmed via DNA sequencing and electroporated into wild type L . major as previously described [35] . Promastigotes were incubated in serum-free media for 24 hours before harvesting by high speed centrifugation ( 16000×g , 5 min ) . Supernatants were filtered through a 0 . 45 µM filter to remove intact parasites before supernatant proteins were precipitated with 10% trichloroacetic acid . The pellet and supernatant fractions were analyzed by standard SDS-PAGE and immunoblotting techniques , with LmNTPDase-GFP fusion proteins detected using anti-GFP antibody ( clones 7 . 1 and 13 . 1 , Roche , Germany ) at 1∶1000 dilution . For microscopy studies live cells were immobilized on poly-L-lysine coated coverslips . Cells were visualized and images acquired using a Deltavision Elite fluorescent microscope and SoftWorx software . Stationary phase promastigotes grown in SDM-79 supplemented with 10% FBS were harvested by centrifugation and LPG extracted from de-lipidated cells and purified using octyl-Sepharose chromatography , as described previously [41] , [42] . The molecular weight of LPG was assessed via SDS-PAGE and silver staining using standard techniques . LPG was depolymerised with 40 mM trifluoroacetic acid ( 8 min , 100°C ) and dephosphorylated with calf intestinal alkaline phosphatase . The repeat units were desalted by passage over a small column of AG 50-X12 ( H+ ) over AG 4-X4 ( OH- ) ( 200 µL of each resin , Biorad ) and chromatographed by high performance anion-exchange chromatography ( HPAEC ) . The HPAEC system was equipped with a Dionex GP-50 gradient pump , a Carbo Pac PA-1 column ( 4×250 mm ) , with a PA-1 guard column and an ED50 integrated pulsed amperometric detector . The system was controlled and data analyzed by Chromeleon version 6 . 50 software ( DIONEX ) . The eluents used in the system were 75 mM NaOH ( E1 ) and 75 mM NaOH in 250 mM NaOAc ( E2 ) . Elution was performed by the following gradient: T0 = 0% ( v/v ) E2; T5 = 0% ( v/v ) E2; T40 = 100% ( v/v ) E2 , T60 = 100% ( v/v ) E2 , at a flow rate of 0 . 6 mL/minute . The phosphatidylinositol moiety of purified LPG was released by nitrous acid deamination ( 0 . 25 M sodium nitrite in 0 . 05 M sodium acetate buffer , pH 4 . 0; incubated at 40°C for 2 . 5 h ) , recovered by partitioning into water-saturated 1-butanol and analyzed using liquid chromatography mass spectrometry ( LC/MS ) . Washed stationary phase parasites ( 107 mL−1 ) were incubated with varying concentrations of peanut agglutinin ( PNA ) in PBS with 1% bovine serum albumin for 30 minutes at room temperature , and non-agglutinated parasites were counted using a haemocytometer ( adapted from [43] ) . Serum sensitivity assays were performed in a similar manner to those previously described [5] . Stationary phase promastigotes were washed and resuspended in PBS ( 107 cells in 500 µL PBS with 1 µg mL−1 propidium iodide ) and incubated with varying concentrations of human sera for 30 minutes . Fluorescence ( indicating cell lysis ) was then measured by flow cytometry . Virulence in mice was assessed using the tail base model of cutaneous leishmaniasis , as described previously [31] . Female BALB/c mice ( 6–8 week old , age-matched ) were injected subcutaneously at the tail base . Lesion size was assessed weekly and scored 0–4 , as described previously [44] . All parasite cell lines were passaged previously in mice to ensure no loss of virulence unrelated to the known genetic mutations . Parasites were re-isolated from mice as described in the “Parasite strains and culture conditions” section . Unpaired , two-tailed t-tests were performed using Prism GraphPad software ( version 6 ) and a P value less than 0 . 05 was considered significant . The exception was when more than two parasite strains were compared , in which case a two-way ANOVA , also using Prism GraphPad software , was performed to simultaneously compare the three different groups . A P value less than 0 . 05 was considered significant when comparing the differences between the three groups .
The L . major genome contains two putative NTPDase genes ( LmjF15 . 0030 and LmjF10 . 0170 ) , which are predicted to encode proteins with five ACR domains , the defining feature of all prokaryotic and eukaryotic NTPDase [45] . These genes are conserved amongst all sequenced Leishmania species , with homologues present in L . infantum , L . braziliensis , L . donovani and L . mexicana [46] . Importantly , a number of residues necessary for enzymatic activity of either CD39 or NTPDase3 , the two best characterized mammalian NTPDases [47] are absolutely conserved within the Leishmania proteins ( Fig . 1A ) . Using the nomenclature that we previously proposed for the parasite NTPDases [25] , we refer to LmjF15 . 0030 as LmNTPDase1 , and Lmj10 . 0170 as LmNTPDase2 ( abbreviated to NTPD1 and NTPD2 in this study for succinctness ) . Homologues for NTPD1 and NTPD2 are present in T . brucei , but only NTPD2 exists in T . cruzi ( Fig . 1B ) . Phylogenetic comparison with NTPDases found in other protozoa , mammals and yeast indicates that the trypanosomatid NTPDases are most closely related to mammalian NTPDase5 and NTPDase6 , which are usually located intracellularly but can undergo secretion , and to the Golgi-located yeast NTPDase GDA1 . Interestingly , the trypanosomatid NTPDases seem evolutionarily distinct from the NTPDases found in a range of apicomplexan parasites and Trichomonas protozoa ( Fig . 1B ) , perhaps indicating divergent functions . ntpd1 encodes for a protein ( 432 amino acids ) with a putative N-terminal transmembrane domain ( residues 17–36 ) , while ntpd2 encodes for a longer protein ( 685 amino acids ) with an N-terminal signal sequence ( residues 1–20 ) . To establish whether the two L . major NTPDases are secreted or targeted to the cell surface/intracellular compartment , wild type parasites were transfected with plasmids encoding NTPD1 and NTPD2 as fusion proteins containing C-terminal GFP . Western blot analysis of parasite cell pellets and culture supernatant showed that full-length proteins were expressed in each parasite line ( Fig . 2A ) . Interestingly , while the NTPD1-GFP fusion protein was exclusively associated with the cell pellet , NTPD2-GFP fusion protein was secreted ( Fig . 2A ) . The absence of detectable NTPD1 in the supernatant indicated that the presence of NTPD2 in the culture supernatant was not due to parasite lysis during culture , but represented active secretion ( Fig . 2A ) . Furthermore , live cell fluorescence microscopy of promastigotes expressing NTPD2-GFP did not detect significant cell surface or intracellular fluorescence , consistent with NTPD2 being primarily a secreted protein . Interestingly , Western blot analysis detected a small pool of NTPD2-GFP within the cell pellet fraction ( Fig . 2A ) , which is likely to represent newly synthesized NTPDase in transit to the cell surface , but below the level of detection of fluorescence microscopy . Because of the low abundance of this intracellular pool we can also not discount the possibility that NTPDase2 is directed to other intracellular organelles , such as the lysosome . In contrast , L . major promastigotes expressing NTPD1-GFP displayed a single , highly fluorescent punctate stain , at the anterior end of the parasite , proximal to the kinetoplast/flagellar pocket ( Fig . 2B ) . This location is highly characteristic of the Golgi apparatus . L . major parasites expressing NTPD1-GFP were therefore co-transfected with a second plasmid encoding the known Golgi protein LPG1 [40] fused to mCherry . Parasites expressing both NTPD1-GFP and the Golgi marker displayed overlapping fluorescence indicative of co-localization ( Fig . 2B ) . This co-localization was not seen in parasites transfected with either mCherry or GFP ( both of which display cytoplasmic localization ) , indicating that NTPD1 is primarily located in the Golgi apparatus . Although yeast NTPDases have been localized to the Golgi apparatus [48] , [49] , this is the first time a parasite NTPDase has been identified in the Golgi apparatus , rather than being secreted from the parasite or located on the cell surface . Previous transcript profiling studies have suggested that ntpd1 and ntpd2 are constitutively transcribed in both major developmental stages [50] , [51] , providing little information on potential stage-specific differences in function . To investigate the function of these enzymes we generated null mutants for each NTPDase gene , by sequential replacement of the two chromosomal alleles with drug resistance cassettes . ntpd1 was replaced with hygromycin and puromycin resistance cassettes , with gene deletion and correct integration of the resistance cassettes confirmed by triplicate PCR ( S1 Fig . ) , demonstrating that ntpd1 is not essential under rich culture conditions . In a similar manner ntpd2 was replaced with puromycin and bleomycin cassettes , with PCR confirmation performed in triplicate ( S1 Fig . ) , indicating that ntpd2 is also not essential in vitro . Both strains grew normally in routine culture medium . To investigate whether LmNTPDase1 or 2 is required for virulence in the mammalian host , we tested the ability of L . major Δntpd1 and Δntpd2 to induce lesions in susceptible BALB/c mice . Promastigote stages of the L . major NTPD1 null mutant exhibited a marked and highly reproducible delay in lesion development . This delay was largely abrogated by complementation of the null mutant by insertion of a full-length ntpd1 gene in the highly-transcribed ribosomal ssu locus [52] . Interestingly , no delay in lesion development was observed when amastigote stages of the NTPD1 null mutant were used to initiate the infection ( Fig . 3A–C ) . Together , these studies demonstrate that NTPD1 is required during the early stages of promastigote infectivity , but has limited function in production of lesions following amastigote infection . In contrast to the NTPD1 null mutant , the NTPD2 null mutant exhibited a virulence phenotype in BALB/c mice that was indistinguishable from wild type parasites , regardless of whether promastigotes or amastigotes were used to initiate infection ( Fig . 3D and 3E ) . Infections were repeated a number of times and it is possible that these parasites have adapted to loss of NTPD2 . Regardless , these results suggest that NTPD2 is not required for virulence in the mammalian host . Lesion development within the mouse reflects both parasite replication and the host response , and our results do not rule out an alteration in parasite replication levels between wild type and the NTPD2 null mutant . However the ability to cause disease , as measured by lesion size , was unchanged between the two strains . By analogy with the function of the Golgi-located yeast NTPDase , we predicted that NTPD1 may be involved in regulating the recycling of sugar-nucleotides in the Golgi lumen and hence glycosylation pathways [48] , [49] . This hypothesis was further supported by the delayed lesion virulence phenotype of the NTPD1 null mutant , which is reminiscent of that seen previously for L . major mutant parasites that lack the major surface glycoconjugate , LPG [5] , [53] . While LPG has multiple roles in the sandfly vector , it is only required for the early stages of promastigote infectivity in the mammalian host . LPG is not required for survival or growth of intracellular amastigotes , and LPG mutant parasites that survive the innate immune responses of the mammalian host can subsequently induce normal lesions [4] , [5] , as observed for the NTPD1 null mutant . To assess whether the L . major NTPD1 null mutant was defective in LPG biosynthesis , the de-lipidated wild type and mutant promastigotes were extracted in 9% 1-butanol and the lipoglycoconjugates purified by octyl-Sepharose chromatography [41] . The NTPD1 null mutant produced comparable levels of LPG as wild type parasites ( Fig . 4A ) . As expected , both LPG preparations were visualized as smears on SDS-PAGE gels , reflecting heterogeneity in the length of the phosphoglycan chains that comprise the major portion of the LPG [42] . However , the LPG isolated from null mutant promastigotes reproducibly exhibited a lower average molecular weight on the SDS-PAGE gels ( Fig . 4A ) and eluted later from the octyl-Sepharose column ( Fig . 4B ) , indicating shorter average chain length and/or reduced side chain branching . To distinguish between these possibilities , the LPG prepared from wild type and Δntpd1 promastigotes was depolymerized with mild acid treatment ( 40 mM TFA , 100°C , 8 min ) and dephosphorylated prior to analysis by HPAEC . Both LPG preparations had essentially identical oligosaccharide repeat unit profiles ( Fig . 4C ) . Furthermore , LC/MS analysis of the released PI lipid moieties showed that both wild type and mutant LPG contained identical very long chain ( C24:0 , C26:0 ) alkylglycerol moieties . Collectively , these structural analyses suggest that the faster SDS-PAGE mobility of LPG isolated from the NTPD1 null mutant reflects decreased phosphoglycan chain elongation , rather than altered side chain additions or increased hydrophobicity in the lipid anchor . Expression of shorter LPG chains on the surface of the NTPD1 null mutant would be expected to lead to increased surface binding by the lectin , peanut agglutinin ( PNA ) . PNA binds terminal β-Gal residues in the LPG side chains and intensity of binding is regulated by the abundance of β-Gal side chain , the extent to which these side chains are capped with arabinose and the overall length of the LPG [43] . Paradoxically , promastigotes expressing long LPG chains form surface aggregates in which LPG epitopes become cryptic and therefore bind less PNA . NTPD1 null mutant promastigotes were more effectively agglutinated than wild type promastigotes when harvested at the same stationary growth phase ( Fig . 5A ) . Given that both wild type and mutant produce LPG with essentially identical side chain compositions ( Fig . 4C ) , these results are consistent with the NTPD1 null promastigotes having a defect in LPG elongation . To assess whether the defect in LPG chain elongation was physiologically significant , stationary phase wild type and NTPD1 null promastigotes were incubated with increasing concentrations of human serum . The complement resistance of L . major promastigotes has previously been shown to be highly dependent on LPG chain length and the formation of a thick protective surface glycocalyx [5] . NTPD1 null mutant promastigotes were significantly more sensitive to serum lysis than wild type parasites ( Fig . 5B–D ) . In particular , FACS analysis of PI-stained parasites , showed ∼2-fold increased sensitivity at 5% serum concentrations ( Fig . 5B ) . Collectively , these results provide strong evidence that loss of Golgi NTPDase results in less efficient elongation of LPG in virulent stationary phase promastigotes , leading to increased susceptibility to complement lysis and a marked delay in lesion development .
The genomes of many parasitic protozoa encode one or more NTPDases , which have been implicated in various host-parasite processes [6]–[9] , [19] . However , the function of these enzymes in pathogenesis has not been rigorously defined using genetic approaches . In this study we have defined the subcellular localization and function of two clearly defined NTPDase enzymes in L . major . Both proteins are predicted to contain the five ACR domains that characterize NTPDases and to be constitutively transcribed in the two major life cycle stages . Based on analysis of GFP fusion proteins , we provide evidence that NTPD1 is primarily targeted to the Golgi apparatus , while NTPD2 is secreted into the extracellular milieu . We propose that NTPD1 has an important role in regulating glycosylation pathways in the Golgi apparatus as loss of NTPD1 resulted in a defect in LPG elongation in stationary phase promastigotes . Although the overall decrease in LPG chain length in the NTPD1 null mutant was modest , it was associated with significantly increased sensitivity to complement lysis and a conspicuous delay in lesion development when promastigotes were used to initiate infection . A similar lag in lesion development was not observed when NTPD1 null mutant amastigotes were used to initiate infection , consistent with the defect being associated with a promastigote-specific virulence factor such as LPG . The similarity between the virulence phenotype of the NTPD1 null mutant and previously generated L . major LPG mutants in which assembly of the entire phosphoglycan chain has been disrupted is striking [4] , [53] , and strongly suggests that LPG chain elongation during stationary phase is both critical for promastigote virulence , and likely to underlie the major function of this glycoconjugate during the early stages of infection in the mammalian host . S . cerevisiae expresses two NTPDases , GDA1 and YND1 , that are targeted to the Golgi apparatus with their catalytic domains orientated into the lumen [48] , [49] , [54] . These enzymes have been shown to hydrolyze NDP nucleotides to the corresponding NMP nucleotide , which is then used as the counter ion to import sugar nucleotides from the cytoplasm into the Golgi lumen . NTPDase-mediated hydrolysis of NDPs is thus critical for maintaining luminal levels of a range of sugar nucleotides that are used by Golgi glycosyltransferases [55] . In Leishmania , the Golgi apparatus contains enzymes required for the assembly and elongation of complex phosphoglycans on GPI anchor precursors , as well as a number of cell surface and secreted proteophosphoglycans ( PPGs ) . All of these phosphoglycans contain the biosynthetic repeat unit , Galβ1-4Manα1-PO4 , which is assembled by sequential transfer of Manα-1phosphate and galactose to the growing phosphoglycan chain by GDP-Man and UDP-Gal-dependent Golgi glycosyltransferases , respectively . The reactions catalyzed by the UDP-Gal dependent galactosyltransferases generate UDP , which would need to be converted to UMP by a NTPDase activity in order to sustain continued import of UDP-Gal into the Golgi lumen ( Fig . 6 ) . In contrast , the GDP-Man dependent Man-1-PO4-transferase ( s ) generate GMP , rather than GDP , and this NMP could be used to drive import of GDP-Man independent of the NTPDase activity . Thus the Golgi NTPDase is likely to be exclusively required for the galactosyltransferase-mediated reactions and not the GDP-Man-dependent Man-1-PO4 reactions . The fact that we see a specific defect in LPG chain elongation , but not in side chain modifications in the NTPDase mutant implies that β1-4-galactosyltransferase involved in assembly of the repeat unit backbone is more sensitive to depletion of UDP-Gal in the Golgi lumen than the β1-3galactosyltransferases that add additional galactose residues to the repeat unit backbone . At present , essentially nothing is known about the mechanisms that regulate LPG elongation , notwithstanding the importance of this process during the differentiation of rapidly dividing promastigotes to non-dividing , hypervirulent metacyclic promastigotes in culture and in the sandfly vector . Our findings raise the possibility that the changes in the availability of sugar nucleotides , either through changes in the activity/expression levels of Golgi membrane transporters or the luminal orientated NTPD1 , could play an important role in this respect . In contrast to NTPD1 , deletion of NTPD2 had no measurable impact on the growth of L . major promastigotes in vitro or in vivo . As NTPD2 was secreted into the medium , it is unlikely that the absence of a detectable LPG or virulence phenotype in the NTPD2 mutant reflects redundancy between the two NTPDases . One possibility is that secreted NTPDase2 is primarily required for salvage of extracellular purines . Leishmania are purine auxotrophs but express a number of surface nucleotidases , acid phosphatases , nucleotide/nucleoside/purine base transporters , as well as intracellular enzymes involved in interconverting different purine intermediates [56] . This robust network of redundant purine salvage pathways could account for the absence of a conspicuous phenotype in the NTPD2 null mutant . A recent study has suggested that L . braziliensis LbNTPDase1 is localized on the cell surface of promastigotes [21] , and that opsonization with a polyclonal antibody directed to this protein was cytotoxic . Using this antibody , the authors also suggested that LbNTPDase1 may be additionally targeted to the mitochondria , cytoplasmic vesicles , kinetoplast and nucleus . It is possible that the Leishmania NTPDase1 homologues are targeted to different subcellular localizations in a species-specific manner and perform different functions . Further work to validate the specificity of the LbNTPDase1 polyclonal antibodies and/or determination of tagged proteins would be of interest . Previous work demonstrated variation in the level of ecto-nucleotidase activity between Leishmania species [57] . Activity in L . major was lower than that observed for L . amazonensis , which was also more virulent in the mouse model used in the study , suggesting that the role of NTPDases in the disease process could differ between species of Leishmania . However , this study did not demonstrate that the observed ecto-nucleotidase activity was linked to ntpd gene expression , and the activity may relate to other enzymes . The same study also utilised Western blot analysis , using polyclonal antibody against T . cruzi NTPDase , to detect a band corresponding to the predicted size of NTPDase1 in L . amazonensis , but failed to identify a similar band in L . major . This may be due to failure of the antibody to recognize the L . major NTPDase , but could also suggest the natural level of expression of NTPDase1 in L . major is lower . However , in light of our findings that LmNTPDase1 localises to the Golgi apparatus , it is unlikely that lower expression of LmNTPDase1 would result in lower ecto-nucleotidase activity of L . major . Future studies taking defined genetic approaches to study NTPDases in other species of Leishmania would be extremely valuable in both defining their function , and in elucidating the value of this class of enzymes as a potential therapeutic target in Leishmania . It is also important to recognize that a number of studies have implicated general surface-located hydrolysis of ATP , ADP ( and sometimes other NTPs and NDPs ) in the virulence of both Leishmania and a number of other parasites [18] , [19] , [58]–[62] . This observed activity has often been assumed to be due to the presence of NTPDases . However , our data raise the possibility that other classes of parasite enzymes are responsible for the observed activity and play a role in pathogenesis themselves . For example , a known NTPDase inhibitor , ARL67156 , only inhibits 30% of observed ecto-ATPase activity of T . cruzi [6] , suggesting that investigation of other classes of enzymes would also be worthwhile . It may be that a combinatorial approach is required , and that inhibition of two or more surface enzymes could be successful in treating disease . In conclusion , this work considerably expands our knowledge of the role of Leishmania NTPDases in host-parasite interactions . We show for the first time that parasite NTPDases can be targeted to the Golgi , and play an important role in regulating the assembly of surface virulence factors . Unexpectedly , and notwithstanding previous studies suggesting that secreted NTPDases may have essential roles in purine acquisition , and/or host or parasite purinergic signalling , loss of the secreted NTPD2 had no discernible affect on promastigote or amastigote infectivity in mice . These studies highlight the importance of exploiting genetic approaches whenever possible in investigating the function of these enzymes in host-parasite interactions . | Nucleoside triphosphate diphosphohydrolases ( NTPDases ) are a family of enzymes expressed in many eukaryotes , ranging from single-celled parasites to mammals . In mammals , NTPDases can have an immunomodulatory role , while in pathogenic protists cell-surface and secreted NTPDases are thought to be important virulence factors , although this has never been explicitly tested . In this study we have investigated the function of two NTPDases , termed LmNTPDase1 and LmNTPDase2 , in Leishmania major parasites . We show that LmNTPDase 1 and LmNTPDase 2 are differentially targeted to the Golgi apparatus and secreted , respectively . A Leishmania major mutant lacking the Golgi LmNTPDase1 exhibited a delayed capacity to induce lesions in susceptible mice when promastigote ( insect ) stages were used to initiate infection , but not when amastigote ( mammalian-infective ) stages were used . Loss of promastigote infectivity in the LmNTPDase1 null mutant was associated with the synthesis and surface expression of lipophosphoglycan ( LPG ) , with shorter glycan chains and increased sensitivity to complement-mediated lysis . In contrast , a null mutant lacking the secreted LmNTPDase2 did not exhibit any difference in virulence . Our results suggest that Leishmania major NTPDases have specific roles in regulating Golgi glycosylation pathways , and nucleoside salvage pathways in the insect stages , but do not appear to be required for virulence of the mammalian-infective stages . | [
"Abstract",
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"Results",
"Discussion"
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] | 2014 | Golgi-Located NTPDase1 of Leishmania major Is Required for Lipophosphoglycan Elongation and Normal Lesion Development whereas Secreted NTPDase2 Is Dispensable for Virulence |
Dosage compensation ensures similar levels of X-linked gene products in males ( XY or XO ) and females ( XX ) , despite their different numbers of X chromosomes . In mammals , flies , and worms , dosage compensation is mediated by a specialized machinery that localizes to one or both of the X chromosomes in one sex resulting in a change in gene expression from the affected X chromosome ( s ) . In mammals and flies , dosage compensation is associated with specific histone posttranslational modifications and replacement with variant histones . Until now , no specific histone modifications or histone variants have been implicated in Caenorhabditis elegans dosage compensation . Taking a candidate approach , we have looked at specific histone modifications and variants on the C . elegans dosage compensated X chromosomes . Using RNAi-based assays , we show that reducing levels of the histone H2A variant , H2A . Z ( HTZ-1 in C . elegans ) , leads to partial disruption of dosage compensation . By immunofluorescence , we have observed that HTZ-1 is under-represented on the dosage compensated X chromosomes , but not on the non-dosage compensated male X chromosome . We find that reduction of HTZ-1 levels by RNA interference ( RNAi ) and mutation results in only a very modest change in dosage compensation complex protein levels . However , in these animals , the X chromosome–specific localization of the complex is partially disrupted , with some nuclei displaying DCC localization beyond the X chromosome territory . We propose a model in which HTZ-1 , directly or indirectly , serves to restrict the dosage compensation complex to the X chromosome by acting as or regulating the activity of an autosomal repellant .
Many species- such as humans , mice , flies , and worms- utilize a chromosome-based mechanism to establish sex . This results in a difference in sex chromosome number between the sexes that , left uncorrected , puts one sex at a great selective disadvantage . In order to combat this , these organisms employ a second mechanism to ensure that the same amount of sex chromosome-linked gene expression occurs in both sexes . This mechanism is called dosage compensation [1]–[4] . In flies and mammals , specific posttranslational histone modifications and/or replacement of core histones with variants are key features of the dosage compensated X chromosomes [5] . Dosage compensation in flies is brought about by the MSL ( male-specific-lethal ) complex that localizes to the single X chromosome in males resulting in a two-fold increase in gene expression [6] , [7] . The MSL complex is made up of at least five proteins ( MSL1 [8] , MSL2 [9] , MSL3 [10] , MLE [11] , and MOF [12] ) , and one of two non-coding RNAs ( roX 1 and roX2 , RNA on the X [13] , [14] ) . The hypertranscribed male X is enriched for histone H4 lysine 16 acetylation ( H4K16ac ) [15] . MOF ( males-absent on the first ) places the H4K16ac mark on the male X and this function is essential for dosage compensation [16]–[18] . In mammals , one of the two female X chromosomes is transcriptionally inactivated [3] . The inactive X is targeted for silencing by the non-coding RNA , Xist in mice [19] , [20] , XIST in humans [21] , that coats the inactive X chromosome [22] . This is followed by chromosome-wide histone H3 lysine 27 tri-methylation ( H3K27me3 ) by Polycomb repressor complex 2 ( PRC2 ) [23] , and histone H2A and H2A . Z mono-ubiquitylation by Polycomb repressor complex 1 ( PRC1 ) [24] , [25] . On the inactive X chromosome , there is also an enrichment of histone H3 lysine 9 dimethylation ( H3K9me2 ) [26] and an enrichment of the histone variant macroH2A [27] . However , other modifications and variants are specifically under-represented on the inactive X: di- , and trimethylation of histone H3 lysine 4 ( H3K4me ) [26] , [28] , dimethylation of histone H3 arginine 17 ( H3R17me2 ) and H3 lysine 36 ( H3K36me2 ) [29] , acetylation of the N-terminal tails of histones H2A , H3 and H4 [30]–[32] , and the phosphorylated form of macroH2A1 [33] . These and other modifications are thought to be vital for the resulting essential change in X-linked gene expression in male flies and female mammals . In C . elegans , dosage compensation is achieved by the dosage compensation complex ( DCC ) , which binds both X chromosomes in hermaphrodites to downregulate gene expression two-fold [2] . DPY-27 [34] , MIX-1 [35] , DPY-26 [36] , DPY-28 [37] , and CAPG-1 [38] form a condensin-like complex , condensin IDC . SDC-2 [39] , SDC-3 [40] , and DPY-30 [41] are thought to be responsible for recruitment of the condensin-like complex , as well as DPY-21 [39] and SDC-1 [42] , [43] , to the X chromosomes in hermaphrodites . All DCC proteins , except for SDC-2 , are supplied maternally to the oocyte , and are initially present in both male and hermaphrodite embryos [2] . SDC-2 is not contributed maternally , expressed only in hermaphrodites and is thought to confer both sex-specificity and X-chromosome specificity to dosage compensation [39] . The DCC initially binds to rex ( recruitment elements on the X ) sites , which represent sites of DCC enrichment on the X chromosome , and spreads in cis along the lengths of both X chromosomes in the hermaphrodite [44]–[47] . As a result , gene expression from the two hermaphrodite X chromosomes is down-regulated by half , thus limiting X-linked gene products to levels produced in XO males [48] . Condensin complexes are well known for their roles in affecting chromosome architecture during mitosis and meiosis [49] , so it is believed that the DCC may be altering the overall organization of the X chromosomes to dampen gene expression during interphase . A chromosome-wide architectural change by the DCC condensin may require or lead to specific modifications to the basic organizational unit of chromatin , the nucleosome . However , no nucleosomal changes , such as posttranslational modification of histones or histone variants , have been previously implicated to play a role in C . elegans dosage compensation . While in somatic cells of hermaphrodites the X chromosome is subject to dosage compensation , in the postembryonic germ line of both sexes the X is subject to a distinct form of chromosome-wide regulatory process: global repression throughout meiosis in males and during early meiosis in hermaphrodites [50] . The maternal effect sterility ( MES ) proteins mediate silencing of the germ line X chromosome and their function is required for germ line viability [50]–[52] . Three of the mes genes ( mes-2 , mes-3 , and mes-6 ) encode proteins that function together in a PRC2-like complex , which localizes to the germ line X-chromosome ( s ) and leads to enrichment of H3 lysine 27 trimethylation on the X [53]–[56] . By contrast , an additional MES protein , MES-4 , localizes only to the autosomes and not X , and its function is necessary for germ line X silencing [51] , [57] . Additionally , the silenced germ line X chromosomes show a significant depletion of activating marks such as acetylation of the N terminal tail of histone H4 and methylation of lysine 4 on H3 [50] , [58] . From studies of dosage compensation in other organisms and of germ line X chromosome silencing in C . elegans , there are many well-documented links between different forms of chromosome-wide gene regulation and specific nucleosome characteristics . This led us to explore whether we might find a similar link between C . elegans dosage compensation and nucleosome composition . We were interested to see if any histone modifications or histone variants play a functional role in dosage compensation in worms . In this paper we report on the role of the C . elegans histone H2A . Z variant ( HTZ-1 ) . The histone variant H2A . Z is conserved from yeast to humans and has been implicated in diverse biological processes . Interestingly , depending on its histone partner in the nucleosome core particle , H2A . Z can either stabilize or destabilize the nucleosome [59] . When partnered with histone H3 , the H2A . Z-containing nucleosome becomes more stable , but when partnered with the histone variant H3 . 3 , the nucleosome becomes destabilized . Unstable H2A . Z/H3 . 3 . nucleosomes may function to poise genes for activation . Consistently , studies in several organisms implicate H2A . Z in various aspects of transcription activation . In Tetrahymena , hv1/H2A . Z associates with the transcriptionally active macronucleus [60]–[62] . Genome-wide localization studies in yeast [63]–[66] , worms [67] , flies [68] , plants [69] , and humans [70] , revealed that H2A . Z preferentially localizes to 5′ ends of genes , consistent with a role in transcription activation . Loss of Htz1 has been shown to diminish RNA Pol II binding to promoters , slow the activation of regulated genes , or prevent rapid reactivation of recently repressed genes [66] , [71] , [72] . A role of HTZ-1 to poise genes for rapid activation has also been observed in a study of the C . elegans H2A . Z homolog , HTZ-1 [73] . However , H2A . Z also localizes to regulatory regions not corresponding to promoters to exert other functions . In budding yeast , Htz1 also functions at boundary elements to protect genes from heterochromatinization by antagonizing the spread of silencing complexes [74] . This antisilencing functions at the global level , not just locally [75] . Consistent with an antisilencing role , in plants , H2A . Z antagonizes DNA methylation [69] . H2A . Z also localizes to insulator elements in chicken [76] , and to functional regulatory elements in human cells [70] . It has been proposed that in this context , the presence of an H2A . Z/H3 . 3 labile nucleosome prevents the spreading of heterochromatic marks [59] . On the other hand , H2A . Z also plays a role in heterochromatin formation . In this context , H2A . Z most likely partners with H3 to form stable nucleosomes [59] . In mammals and in flies , H2A . Z associates with pericentric heterochromatin and interacts with heterochromatin protein HP1 [77]–[80] . Mammalian H2A . Z also becomes incorporated into the inactive XY body following meiosis [81] . However , H2A . Z is significantly underrepresented and differentially modified on the mammalian inactive X chromosome in somatic cells , indicating that H2A . Z enrichment is not a general feature of all heterochromatin [25] , [79] , [82] . Consistent with that , H2A . Z is not enriched at heterochromatic chromocenters in plants [69] , [83] . Here we show that in C . elegans the histone variant H2A . Z/HTZ-1 functions in dosage compensation . Consistent with previous reports [67] , we find that HTZ-1 is under-represented on the dosage compensated X chromosomes in somatic nuclei of hermaphrodites . However , we do not observe HTZ-1 depletion on the non-dosage compensated X chromosome in male somatic nuclei . We also see an underrepresentation of HTZ-1 on the silent X chromosomes of both male and hermaphrodite germ nuclei . Partial depletion of HTZ-1 does not lead to an overall decrease in DCC protein levels . Instead we see mislocalization of the DCC away from the X chromosomes and onto autosomes . These results reveal an HTZ-1-dependent activity that serves to repel the DCC away from autosomes . We propose that HTZ-1 plays a role in dosage compensation by directly or indirectly restricting binding of the DCC to the X chromosomes .
To search for chromatin modifiers involved in worm dosage compensation , we utilized two RNAi-based assays in a genetic background sensitized for detecting disturbances in dosage compensation . We tested genes encoding C . elegans homologs of histone variants , genes implicated in modifying chromatin via posttranslational histone modifications ( such as acetylation or methylation ) or chromatin remodeling [84] , as well as genes annotated to contain chromo- , bromo- or SET domains ( Wormbase [http://www . wormbase . org] , release WS201 ) . The first assay was completed in the sex-1 ( y263 ) mutant background . sex-1 functions genetically as an X signal element by repressing xol-1 , the master switch regulating both sex-determination and dosage compensation [85] , [86] . In addition , sex-1 plays a role downstream of xol-1 , promoting dosage compensation in hermaphrodites [87] . In sex-1 ( y263 ) mutant hermaphrodites , dosage compensation is partially impaired , resulting in 15–30% embryonic lethality . In these worms , partial loss-of-function due to feeding RNAi of a gene important for dosage compensation leads to increased lethality [87] . A second genetic assay was based on the rescue of males that inappropriately turn on dosage compensation due to a xol-1 ( y9 ) mutation . Expression of xol-1 in males is essential to prevent dosage compensation of the single X chromosome [88] . Mutations in xol-1 are male lethal due to ectopic dosage compensation , leading to abnormally low levels of X-linked gene expression . The sex-1 ( y263 ) mutation partially weakens dosage compensation , as described above . xol-1 ( y9 ) sex-1 ( y263 ) males die , but they can be rescued by feeding RNAi of dosage compensation genes [38] , [87] . To ensure a consistent proportion of males in our test strain , we perform these assays in a strain that also carries the him-8 ( e1489 ) allele . Mutations in him-8 cause X chromosome nondisjunction in meiosis and results in a predictable 38% of XO progeny each generation [89] . RNAi of DCC components show near complete sex-1 lethality and results in 33–60% rescue of him-8 ( e1489 ) ; xol-1 ( y9 ) sex-1 ( y263 ) males in these two assays [38] . One candidate , the histone variant htz-1 ( C . elegans H2A . Z homolog ) showed a similar genetic interaction . RNAi in the wild type background leads to little to no lethality , while htz-1 RNAi in the sex-1 ( y263 ) background leads to near complete embryonic lethality ( Figure 1A ) . In the him-8 ( e1489 ) ; xol-1 ( y9 ) sex-1 ( y263 ) background , RNAi of the histone variant htz-1 resulted in over 15% rescue ( Figure 1B ) . To ensure that these phenotypes are not caused by general disruption to chromatin , we also tested two genes encoding H3 . 3 histone variants ( his-71 and his-72 ) [90] , and genes encoding linker histones [his-24 ( H1 . 1 ) , hil-3 ( H1 . 3 ) , hil-4 ( H1 . 4 ) , hil-5 ( H1 . 5 ) , hil-6 ( H1 . 6 ) , and hil-7 ( H1 . Q ) ] [91] . RNAi of these genes did not show similar genetic interactions . RNAi of many other chromatin factors also failed to result in significant male rescue ( for a complete list , see Table S1 ) . The chromatin remodeling enzyme isw-1 , and the histone deacetylase let-418 are shown as examples ( Figure 1B ) . We conclude that depletion of HTZ-1 leads to disruption of dosage compensation . A previous study found that in worms HTZ-1 preferentially localizes to promoters , as in other organisms [67] . Furthermore , fewer peaks of HTZ-1 incorporation were found on the X chromosome , as compared to autosomes . The authors attribute this difference to the relative lack of developmentally important genes on the X chromosome , rather than a direct role in dosage compensation [67] . Our RNAi data above indicates that HTZ-1 function is needed for wild type levels of dosage compensation , but does not address whether this role is direct or indirect . That is , htz-1 may directly regulate some aspect of DCC function , or htz-1 may indirectly affect dosage compensation by regulating expression of known or unknown dosage compensation genes . To begin to distinguish between these possibilities , we analyzed the distribution of HTZ-1 in male and hermaphrodite nuclei . We reasoned that if HTZ-1 functions in dosage compensation , its distribution in the nucleus may be different in males ( dosage compensation inactive ) and hermaphrodites ( dosage compensation active ) . To analyze HTZ-1 distribution , we took advantage of a strain expressing a YFP-HTZ-1 fusion protein , or used an HTZ-1 specific antibody . The specificity of our HTZ-1 antibody is demonstrated by recognition of a protein of the predicted size on western blots and reduction of signal after HTZ-1 depletion on both western blots and by immunofluorescence ( IF ) ( Figure S1 ) . We marked the X-chromosome territory with an antibody specific to DPY-27 ( marks the X chromosomes in hermaphrodites only ) or X-paint fluorescent in situ hybridization ( FISH ) ( to mark the X chromosomes in both sexes ) . Consistent with a previous report [67] , we observed reduced HTZ-1 staining on the dosage compensated X chromosomes in mid-to-late stage hermaphrodite embryos after the onset of dosage compensation by DPY-27/HTZ-1 IF ( Figure 2A and 2B ) , and combined X-Paint FISH/HTZ-1 IF ( Figure 2C ) . We also observed reduced levels of YFP-HTZ-1 in the territory of the X-chromosomes in transgenic hermaphrodite embryos ( Figure S2 ) . However , in males we did not observe a decrease in HTZ-1 staining intensity in the X chromosome territory of somatic nuclei ( Figure 2C ) . These results indicate that reduced HTZ-1 levels are specific to dosage compensated X chromosomes and not a general feature of X chromosomes in both sexes in adult animals . The results of the genetic assays and localization assays appeared contradictory: reduced htz-1 expression disrupts dosage compensation , yet the protein itself is depleted on the dosage compensated X chromosomes . Therefore , we wanted to explore how dosage compensation is affected in htz-1 depleted animals . If HTZ-1 functions in dosage compensation indirectly ( by regulating expression of dosage compensation genes ) we would predict to see a decrease in DCC protein levels upon HTZ-1 depletion . We analyzed worms carrying the htz-1 deletion allele tm2469 that removes 345 of 885 base pairs from htz-1 and likely represents a null allele . htz-1 ( tm2469 ) homozygous progeny of heterozygous mothers ( m+z− ) develop into healthy adults but are sterile , as reported [67] . However , the tm2469 deletion appears to affect expression of not just htz-1 , but the neighboring gene as well ( Figure S3 ) . It was therefore important to obtain HTZ-1-depleted worms using an alternate method and to confirm that phenotypes are due to HTZ-1 depletion , and not depletion of the neighboring gene product . As an alternative method , we depleted HTZ-1 levels by feeding worms bacteria expressing double stranded RNA corresponding to htz-1 . As a control , worms were fed bacteria carrying an empty vector . Feeding RNAi in wild type animals greatly reduced HTZ-1 as detected both by immunofluorescence and quantitative Western blot analyses ( 89% reduction ) ( Figure 3B and Figure S1 ) . To investigate the possibility that HTZ-1 depletion leads to a decrease in DCC protein levels , we quantified protein levels by western blotting of HTZ-1 depleted and control animals . Although HTZ-1 levels were clearly reduced after htz-1 RNAi , we did not observe a dramatic change in DCC protein levels ( Figure 3A and 3C ) . Levels of DPY-27 and CAPG-1 show a very slight decrease while MIX-1 , DPY-26 , and DPY-28 show very slight increases after htz-1 RNAi . Our results suggest that , HTZ-1 reduction does not lead to a significant defect in overall DCC protein levels . However , we cannot exclude the possibility that the timing of DCC gene expression is changed ( delayed ) in HTZ-1 depleted cells , as was observed for genes involved in foregut development [73] . It is also possible that a small amount HTZ-1 that remains after feeding RNAi is sufficient for DCC gene expression , but more complete HTZ-1 depletion would result in a significant decrease in DCC protein levels . SDC-2 , the primary determinant of hermaphrodite fate , is the only DCC protein whose expression in the zygote is essential [39] . The remaining DCC proteins are maternally loaded into the oocyte and this maternal load is sufficient to carry out dosage compensation in the developing embryo . Therefore , it was important to determine whether sdc-2 transcript levels are affected after HTZ-1 depletion . We analyzed sdc-2 mRNA levels in HTZ-1 depleted and control animals by reverse transcription followed by quantitative polymerase chain reaction ( RT-qPCR ) and observed no significant change in sdc-2 expression ( Figure 3D ) . We conclude that the changes observed in DCC protein and RNA levels are not likely to be sufficient to explain the observed requirement for HTZ-1 to maintain wild type levels of dosage compensation . An alternative possibility is that HTZ-1 has a more direct role in dosage compensation by affecting DCC localization or function . To explore this possibility , we used immunofluorescence to observe DCC localization in HTZ-1-depleted worms . The DCC was clearly present in nuclei of htz-1 ( RNAi ) animals , again suggesting that HTZ-1 depletion does not lead to a significant reduction in DCC protein levels . However , the territory occupied by the DCC in these nuclei was significantly more diffuse in appearance than in wild type nuclei ( Figure 4 ) . We hypothesized that the diffuse appearance of the DCC reflected mislocalization of the DCC away from the X chromosome . To observe DCC localization relative to X chromosomes , we combined DCC immunofluorescence with X-paint FISH in htz-1 ( RNAi ) ( Figure 4 ) and mutant ( Figure 5 ) animals . We used intestinal nuclei because they are 32-ploid , allowing for easier visualization of sub-nuclear regions by FISH [92] . To determine the degree of colocalization between X-Paint and DPY-27 signals we determined Pearson's correlation coefficient ( Rr ) values ( see Materials and Methods ) . An Rr value of +1 indicates a complete and positive correlation between two signals within a region of interest while a value of 0 indicates no linear relationship between the two signals . In vector control RNAi animals we observed that the DCC was highly restricted to the X chromosomes and the mean Rr was 0 . 65±0 . 14 . Rr was greater than 0 . 5 in the vast majority of nuclei observed ( 88% ) , and only a minority of nuclei had Rr values between 0 . 5 and 0 . 2 ( ∼12% ) . No correlation values of less than 0 . 2 were observed in these animals . Representative nuclei and corresponding Rr are shown in Figure 4A . By contrast , after htz-1 RNAi , the mean Rr for htz-1 RNAi nuclei was 0 . 44±0 . 20 , significantly lower than control ( p = 5 . 79E-8 ) . The majority of nuclei ( 58% ) had DPY-27/X-paint correlation values below 0 . 5 , and 27% of nuclei had values below the lowest value observed in the control . Representative htz-1 ( RNAi ) nuclei and corresponding Rr values are shown in Figure 4B . A summary of DPY-27/X-Paint colocalization quantification after vector and htz-1 RNAi is shown in Figure 4C . DCC mislocalization was also observed in intestinal nuclei of homozygous htz-1 ( tm2469 ) hermaphrodite progeny of heterozygous mothers ( m+z− ) ( Figure 5 ) and in HTZ-1-depleted embryos ( Figure S4 ) . The mislocalization phenotype observed in htz-1 ( tm2469 ) ( Figure 5 ) mutant animals was very similar to the observations made after htz-1 RNAi . In wild-type hermaphrodites only 5% of nuclei observed had Rr values below 0 . 5 , but a majority of htz-1 ( tm2469 ) nuclei ( 59% ) had values below 0 . 5 . Additionally , 45% of nuclei observed had values below the lowest value observed in wild-type nuclei ( Figure 5B and 5C ) . We also analyzed HTZ-1-depleted embryos after the 50-cell stage in development ( after the onset of dosage compensation ) . We observed 16% of htz-1-depleted embryos with a diffuse nuclear DCC localization pattern ( as opposed to 2% of vector RNAi control embryos ) , confirming that DCC mislocalization is not tissue specific ( Figure S4 ) . Finally , we analyzed DCC distribution in nuclei of ssl-1 ( n4077 ) mutant animals . ssl-1 encodes a homolog of Swr1 , the catalytic subunit of Swr1-com , the complex responsible for exchanging H2A for H2A . Z [93]–[96] . Consistent with this function , ssl-1 ( n4077 ) m+z− homozygous animals have reduced HTZ-1 staining ( Figure 5A ) . In ssl-1 ( n4077 ) hermaphrodites , 43% of nuclei observed had Rr values below 0 . 5 , similar to what we observe after htz-1 RNAi and in htz-1 ( tm2469 ) animals . Also , 33% of ssl-1 nuclei had Rr values below the lowest value observed in wild type nuclei , confirming that reduced HTZ-1 disrupts the localization of DCC to the X chromosomes ( Figure 5B and 5C ) . Together , these results strongly suggest that HTZ-1 , a protein more abundant on autosomes , is important for restricting localization of the DCC to the X chromosomes . The portion of DCC which is not associated with the X chromosome appears nonetheless bound to chromatin . When we combined X-Paint FISH/DPY-27 IF with a protocol previously shown to extract nucleoplasmic proteins [97] , we were unable to remove the non-X associated DCC within intestinal nuclei of htz-1 RNAi animals ( Figure 6A ) . This suggests that the non-X associated DCC is associated with autosomal chromatin . To confirm DCC association with autosomes , we analyzed prophase chromosomes in both vector control and htz-1 depleted embryos . We reasoned that individualized mitotic chromosomes would allow for more conspicuous visualization of DCC localization . To mark mitotic nuclei , embryos were co-stained with α-Phospho-Histone H3 Serine 10 . In control embryos , DCC localization was largely restricted to two chromosomes in prophase nuclei . After htz-1 RNAi , however , 32% of prophase nuclei had low-level DCC staining on more than two chromosomes ( Figure 6B ) . These data indicate that the DCC associates not only with the X chromosome , but also with autosomes in HTZ-1-depleted animals . Taken together , these results suggest the existence of an HTZ-1 dependent autosomal repellent activity . In wild type animals , this activity restricts localization of the DCC to the X chromosome . Loss of htz-1 reduces the efficiency of this repellant , allowing the DCC to bind other chromosomes . In different organisms , H2A . Z has been observed to be either enriched in silent chromatin , ( such as mammalian and Drosophila centromeres [77]–[80] , or the XY sex body in the mammalian germ line [81] ) or depleted in silent chromatin ( such as heterochromatic chromocenters in plants [69] , [83] , or the transcriptionally inactive micronucleus in Tetrahymena [60]–[62] ) . Dosage compensation in worms is thought to involve two-fold downregulation of gene expression , but not complete silencing [2] . To explore whether HTZ-1 localizes to silent chromatin in worms , we examined its distribution in the germ line , where the X chromosomes are subject to chromosome-wide silencing by a mechanism unrelated to dosage compensation [50] . In the male germ line , the single X chromosome is subject to meiotic silencing of unpaired chromatin and is silent throughout meiosis . In the hermaphrodite germ line , the paired X chromosomes are silent during early meiosis , but become transcriptionally active in later stages [50] . To test HTZ-1 levels on the silent X chromosome in the germ line , we performed immunofluorescence experiments on dissected male and hermaphrodite gonads . To distinguish the X from autosomes we used antibodies specific to MES-4 , H3K27me3 , or H4K16ac , all of which have been used in previous studies to distinguish the X from autosomes in the germ line . MES-4 , a SET-Domain protein , is enriched on autosomes and markedly depleted from the X chromosome in the germ line [51] , [57] . Conversely , H3K27me3 is enriched on the silent X chromosomes in the germ line [53] . In the male germ line , H4K16ac is present on autosomes but absent from the unpaired X chromosome [50] . We found that HTZ-1 levels are much lower on the X chromosomes than on autosomes in both male and hermaphrodite germ lines ( Figure 7 ) . Thus , underrepresentation of HTZ-1 appears to be a general feature of both types of chromosome-wide repression in the worm: two-fold downregulation by dosage compensation and complete meiotic silencing . The possible involvement of HTZ-1 in germ line X chromosome silencing will be explored elsewhere .
One of the intriguing challenges in the study of dosage compensation is to understand how the DCC machinery is able to specifically target the X chromosomes for regulation . Our studies indicate that when HTZ-1 is depleted , the DCC appears to be no longer targeted correctly to the X chromosome . Rather than binding solely to the X chromosomes , the complex now binds autosomes as well . These results reveal that the normal function of HTZ-1 ( or an HTZ-1 regulated factor ) includes keeping the DCC away from autosomes . Previous studies indicated that specific DCC binding sites on the X chromosome , so-called rex sites ( recruiting element on X ) , are important for attracting the DCC to the X chromosome ( [44]–[47] ) . Taken together , these data suggest that positive forces ( X-specific recruitment elements that attract the DCC ) and negative forces ( autosomal chromatin that repels the DCC ) cooperate to discriminate the X from autosomes ( Figure 8 ) . The mechanism of how HTZ-1 restricts DCC localization is unclear . We will consider three possible models . First , HTZ-1 may serve as a direct regulator of DCC binding . Targeting of the DCC to the X chromosome is believed to be a two-step process . The complex initially binds to an estimated 200 rex sites , followed by dispersal to numerous so-called dox sites ( dependent on X ) or “way stations” [44] , [46] , [47] , [98] . Rex sites coincide with the highest peaks of DCC binding and are characterized by the presence and clustering of short sequence motifs called MEX motifs [46] , [47] , [99] . MEX motifs are slightly enriched on the X chromosome , but are also present on autosomes [46] . In principle , HTZ-1 can affect either DCC targeting to rex sites , or dispersal to dox sites , or both . It should be pointed out that this model is different from the interpretation of the HTZ-1 localization data presented in [67] . Using high-resolution analysis of HTZ-1 binding , the authors showed that a subset of DCC peaks on the X chromosome coincide with HTZ-1 peaks . One way to reconcile their data and ours is to point out that the DCC/HTZ-1 overlap tends to be at promoters ( DCC dox sites ) , and less so at the highest peaks of DCC binding ( DCC foci or rex sites ) [67] . Therefore , if HTZ-1 is a negative regulator of DCC binding , it is more likely that HTZ-1 affects the targeting step to rex sites , but not the dispersal step to dox sites . Another way to reconcile the data in the two studies is to suggest that HTZ-1 at dox sites is modified posttranslationally ( see below ) in such a way that permits DCC binding . According to this model , MEX motifs attract DCC to the X chromosome , whereas HTZ-1 negatively regulates DCC recruitment to rex sites . If a MEX motif-containing sequence is not bound by HTZ-1 the DCC will be recruited . However , if a MEX motif-containing sequence is bound by HTZ-1 , the DCC will be prevented from binding . From sites of entry , the DCC then may be dispersed to dox sites in a sequence and HTZ-1 independent manner . When HTZ-1 levels are reduced by RNAi or mutation , the DCC will be able to bind all MEX motif containing sites , whether they are on the autosomes or on the X chromosome . Ectopic DCC binding to autosomes will reduce the amount of DCC binding to the X chromosomes , and dosage compensation will be impaired as a result . To test this model , it will be important to observe DCC binding patterns genome-wide at high resolution upon htz-1 depletion and to determine whether ectopic DCC binding sites contain a DNA sequence motif similar to MEX motifs . An alternative possibility is that changes in the higher order chromatin organization imposed by HTZ-1 determine whether the DCC is able to bind the chromosome . H2A . Z has been reported to alter the nucleosome surface , affect recruitment of other chromatin components , and thereby modulate higher order features of the chromatin fiber [77] . High levels of HTZ-1 on the autosomes may result in alterations in the overall structure of the chromatin fiber , which preclude DCC binding . Low levels of HTZ-1 on the X chromosome would allow DCC binding . Upon reduction of HTZ-1 levels , general disruption of higher order chromatin folding would allow the DCC to bind both the X and the autosomes . According to this model , the changes in chromatin fiber folding are a direct consequence of HTZ-1 levels on the chromosome . However , the model does not require complete mutually exclusive binding of the DCC and HTZ-1 at high resolution , and therefore does not conflict with the data in [67] . Finally , HTZ-1 may regulate expression of a DCC component , or another gene needed for proper DCC localization . While HTZ-1 is an obvious candidate for the DCC-repelling activity , it should be noted that in principle another HTZ-1-regulated protein could also perform this function . Our evidence , as yet , does not support this model , as we do not observe a change in DCC protein levels or sdc-2 RNA levels when htz-1 expression is reduced . In addition , most DCC proteins are loaded into oocytes , and this maternal load of DCC proteins is sufficient for healthy development . Therefore , it is unlikely that the observed dosage compensation defects in m+z− htz-1 mutant animals are due to defects in transcription of DCC genes . However , it remains possible that HTZ-1 plays more subtle roles in regulating the exact levels and timing of expression of dosage compensation genes . Nonetheless , the difference in HTZ-1 levels in male and hermaphrodite X chromosomes ( Figure 2C ) argue for a more direct role for HTZ-1 in the hermaphrodite specific-process of dosage compensation . High-resolution analysis of HTZ-1 binding to the male X chromosome may help distinguish between the models presented above . A question that remains unanswered is how HTZ-1 is specifically targeted to autosomes , or conversely , how the X chromosomes become depleted of HTZ-1 . The small number of developmentally important genes on the X chromosome relative to autosomes can certainly contribute to this difference [67] . However , this model does not explain why the male X in adult animals does not appear to be depleted of HTZ-1 at the chromosomal level ( Figure 2C ) . The X chromosome in the both the male and hermaphrodite germ lines is subject to silencing [50]–[52] , and it is possible that the chromosome maintains some memory of this silencing after fertilization . Such effects have been seen on the sperm-derived X chromosome [100] . The differences between the sperm derived X ( which only hermaphrodite embryos receive ) and the oocyte-derived X ( which both males and hermaphrodites receive ) may contribute to the sex-specific differences in observed X-linked HTZ-1 levels . Comparison of HTZ-1 dynamics in male and hermaphrodite embryos in early development will be an important future area of investigation . It is important to keep in mind that dosage compensation is a chromosome-wide gene regulation mechanism that is super-imposed on the unique transcriptional programs of individual X-linked genes . While HTZ-1 levels on the dosage compensated X chromosomes are reduced overall , the protein is not completely absent . Indeed , HTZ-1 binds to the promoter of an X-linked dosage compensated gene , myo-2 , and is needed for its proper temporal activation [73] . It is possible that a pool of HTZ-1 functions at promoters , including promoters on the X chromosome , to promote timely regulation of gene expression . Superimposed on that is the global repression of the X chromosome by the DCC . Thus , HTZ-1 may perform a double role: it regulates genes both individually ( by binding to promoters ) and chromosome-wide ( by regulating DCC binding ) . Different pools of HTZ-1 may differ in their histone partners and/or the level of posttranslational modifications . Unlike yeast ( where the only histone H3 is most similar to H3 . 3 ) , worms possess both H3 and H3 . 3 [90] . Therefore , in principle , one population of HTZ-1 in worms is able to form labile nucleosomes , while another population can form stable nucleosomes . Furthermore , both populations can be modified by various posttranslational modifications , increasing the number of potentially different ways in which HTZ-1 can affect genome activity . Consistent with this idea , acetylation of the N-terminal tail of Htz1 is necessary for the anti-silencing property of Htz1 in S . cerevisiae , as unacetylatable Htz1 shows no change in localization to anti-silenced genes , but Sir complex spreading and decreased expression of anti-silenced genes is observed [101] . Htz1 is also subject to C-terminal SUMOylation . SUMO-Htz1 is implicated in directing chromosomes with persistent double-strand breaks to re-localize to the nuclear periphery in budding yeast [102] . Posttranslational modification of H2A . Z has also been observed in mammalian dosage compensation [25] . H2A . Z is under-represented on the inactive X in female mouse nuclei , but the small population of remaining H2A . Z is specifically mono-ubiquitylated by the Ring1b E3 ligase as part of the Polycomb repressor complex 1 ( PRC1 ) . Ring1b is also responsible for mono-ubiquitylation of histone H2A in X inactivation [24] . Although it is not currently understood how mono-ubiquitylation of H2A and H2A . Z function in X inactivation , the fact that this modification is largely specific to the inactive X suggests an important role . It is highly likely that C . elegans HTZ-1 is subject to posttranslational modification and it will be important to address how these modifications affect its role both in dosage compensation and in other processes . The proposed role of HTZ-1 in dosage compensation is similar to that of two proteins shown to function in germ line X-chromosome silencing in C . elegans . The gene encoding MES-4 was originally identified in a forward genetic screen with several other genes whose mutations led to the same mes phenotype ( maternal effect sterility ) [51] . MES-2 , MES-3 and MES-6 , are the protein products of the other genes identified , and these form a Polycomb repressor-like complex that is responsible for enriching the X chromosomes with the silencing H3K27me3 mark [53] . Surprisingly , MES-4 , a histone H3 lysine 36 methyltransferase ( HMT ) , localizes to autosomes , not the X , and yet it has been shown to be important for germ line X-chromosome silencing [51] , [57] . MRG-1 , an ortholog of the mammalian mortality factor related protein MRG15 , is the second autosome-enriched protein that has been shown to play a role in germ line X chromosome silencing [103] . In both mes-4 and mrg-1 mutants , de-silencing of X-linked genes is observed . It has been proposed that the activities of MES-4 and MRG-1 on autosomes prevent the binding of a repressor protein or complex and help limit repressor binding to the X chromosomes . The proposed mode of action of MRG-1 and MES-4 in germ line X chromosome silencing is similar to the model of HTZ-1 function in dosage compensation we have proposed . Our model describing HTZ-1 as an autosomal DCC barrier is also similar to the role of Htz1 in yeast in blocking the spread of silencing complexes into euchromatic regions adjacent to telomeres [74] . In htz1Δ cells , the Sir proteins spread into these regions , leading to silencing of genes . Recent evidence has shown that loss of Htz1 leads to ectopic Sir complex localization that is not limited to immediate anti-silenced regions , but , rather , is found throughout the genome [75] . Thus , Htz1p in yeast may serve a global , not just a local , anti-silencing function , similar to our proposed model of HTZ-1 action in worms . Furthermore , in Arabidopsis , H2A . Z also plays a global antisilencing role by protecting DNA from methylation [69] . When H2A . Z incorporation is compromised , DNA methylation expands into regions once protected by H2A . Z-containing nucleosomes . Therefore , a function for H2A . Z in the protection against transcriptional repression may be a widely conserved role for this histone variant . Htz1 functions in parallel with other nucleosomal elements to prevent heterochromatic spreading . The Set1 complex is responsible for histone H3 lysine 4 methylation ( H3K4me ) and also has an anti-silencing function [104] . A recent study found that Set1 and Htz1 cooperate to mediate global antisilencing in yeast [75] . This raises the possibility that there are other nucleosomal modifications or elements that might function in X-chromosome DCC restriction in parallel with HTZ-1 in C . elegans .
All strains used were maintained as described [105] . Strains include: N2 Bristol strain ( wild type ) , TY2384 sex-1 ( y263 ) X; TY4403 him-8 ( e1489 ) IV; xol-1 ( y9 ) sex-1 ( y263 ) X; EKM11 htz-1 ( tm2469 ) IV/nT1 ( qIs51 ) IV , V; MT12963 ssl-1 ( n4077 ) III/eT1 ( III;V ) ; SM1353 cha-1 ( p1182 ) IV; pxEx214 ( HTZ-1promoter::YFP::HTZ-1 + HTZ-1promoter::CFP::LacI +pRF4 ) [73] . E . coli HT115 bacteria expressing double stranded RNA for htz-1 , dpy-27 , capg-1 , his-71 ( coding region ) , his-24 , hil-3 , hil-4 , hil-5 , hil-6 , hil-7 , isw-1 or vector ( polylinker ) , were used for feeding RNAi using the Ahringer feeding RNAi clones [106] . To generate RNAi vectors for let-418 and the 3′ UTR of his-71 and his-72 , the regions were PCR amplified , digested with Bam HI and Bgl II ( let-418 ) or Bgl II and Not I ( his-71 and his-72 ) , and cloned into the DT7 vector as described [106] . The following primers were used for amplification: his-71 3′-UTR cgaagatctcgtgcataaacgttgagctg and gagcggccgccatgcacgctgttcaaaaac his-72 3′-UTR cgaagatctagctccatcaccaattctcg and gagcggccggcgtggaatatagttgct let-418 catgggatccttgccgctcctcattcaact and gtacagatctgacgatgtgcacgagagaaa RNAi in N2 was initiated at the L1–L2 stage . Adults were then transferred to new RNAi plates to produce progeny for 24 hours . For IF/FISH , western , and RT-PCR analysis , RNAi progeny were processed 24 hours post-L4 . To score embryonic lethality in the sex-1 strain , adult animals were allowed to lay eggs for 24 hours and the number of embryos laid was counted . The next day the number of dead embryos and larvae were counted and the percentage of embryonic lethality was calculated by dividing number of dead embryos by the total number of embryos laid . To score male rescue in him-8 ( e1489 ) IV; xol-1 ( y9 ) sex-1 ( y263 ) X , adult animals were allowed to lay eggs for 24 hours . When adult animals were removed from the RNAi plates , the number of embryos laid was counted . Three days later the number of male progeny on each plate was counted . Male viability was calculated by dividing the number of male progeny observed by the expected number of males . The him-8 ( e1489 ) mutation reproducibly results in 38% male self-progeny [89] , so the expected number of males was determined to be 38% of total embryos laid . Male rescue = Number of males/ ( total embryos×0 . 38 ) . All RNAi was conducted at 20°C . Rabbit and rat α-HTZ-1 antibodies were raised against the C-terminal 19 amino acids ( NKKGAPVPGKPGAPGQGPQ ) and affinity purified . Polyclonal rat α-HTZ-1 was used at a dilution of 1∶500 , polyclonal rabbit α-HTZ-1 at a dilution of 1∶100 for immunofluorescence . Other primary antibodies used are: polyclonal rabbit α-DPY-27 at a dilution of 1∶100 [38] , polyclonal rabbit α-MES-4 ( Susan Strome [UC Santa Cruz] , [57] ) at 1∶100 , rabbit antiserum α-acetyl-histone H4 ( Lys16 ) ( Upstate ) at 1∶100 , rabbit polyclonal α-trimethyl-histone H3 ( Lys27 ) ( Upstate ) at 1∶500 , and mouse monoclonal α-phospho-histone H3S10 ( 6G3 ) ( Cell Signaling Technology ) at 1∶500 . Secondary antibodies used are: Fluorescein ( FITC ) conjugated donkey α-rabbit ( Jackson ImmunoResearch ) and Cy3 conjugated donkey α-rabbit IgG ( Jackson ImmunoResearch ) both at a dilution of 1∶100 . Embryos were stained as described [34] . Adult animals were dissected and stained as described [44] . In adults , somatic non-intestinal nuclei near the cut site ( vulval area ) were observed . Images were captured with a Hamamatsu ORCA-ERGA CCD camera mounted on an Olympus BX61 motorized X-drive microscope using a 60× oil immersion objective . Captured images were deconvolved using 3i Slidebook imaging software . Projected images were taken at 0 . 2 µm intervals through samples . Adobe Photoshop was used for assembling images . FISH probe templates were generated by degenerate oligonucleotide primed PCR to amplify purified yeast artificial chromosome DNA . The labeled X-paint probe was prepared and used as described [44] . Hybridization was performed on adult animals ( 24 hours post-L4 ) with or without previous RNAi treatment . For X-paint hybridization followed by DPY-27 immunostaining , sample and probe were denatured at 95°C for 3 minutes . For X-paint hybridization followed by HTZ-1 immunostaining , sample and probe were denatured at 78–80°C for 8–10 minutes in a Hybaid OmniSlide in situ Thermal Cycler System ( Thermo Scientific ) . Imaging was conducted as described above . 3i Slidebook imaging software was used to measure colocalization of DPY-27 ( FITC ) and X-Paint ( Cy3 ) signals on images obtained as described above . A FITC mask was set for each nucleus z-stack and the correlation between signals was calculated within this mask by the software . The FITC∶Cy3 correlation coefficient was recorded and used as an indication of colocalization between DPY-27 and X-Paint . Detergent extraction of nucleoplasmic protein from dissected nuclei was performed by dissecting animals in 1× sperm salts plus 1% Triton detergent [97] . Dissected animals were then processed for either Fluorescent in situ hybridization or immunofluorescence . For each treatment described , 100 animals ( all 24 hours post-L4 ) were picked into 1XM9 , washed , and incubated for ten minutes at 95°C in 19 µl SDS-PAGE loading dye ( 0 . 1 M Tris-HCl pH 6 . 8 , 75 M Urea , 2% SDS , Bromophenol Blue for color ) plus 1 µl β-mercaptoethanol . The treated samples were then loaded into either 6% acrylamide ( for detection of DPY-27 , MIX-1 , DPY-26 , DPY-28 , and CAPG-1 ) or 15% acrylamide gels ( for detection of HTZ-1 ) . SDS-PAGE was performed and protein was transferred onto nitrocellulose . The following antibodies and dilutions were used: rabbit α-HTZ-1 at 1∶500 , rabbit α-DPY-27 at 1∶500 , rabbit α-CAPG-1 at 1∶500 [38] , rabbit α-DPY-28 ( gift of K . Hagstrom ) at 1∶500 , rabbit α-DPY-26 ( gift of K . Hagstrom ) at 1∶5000 , rabbit α-MIX-1 ( gift of R . Chan ) at 1∶500 , mouse monoclonal α- α-Tubulin ( Sigma ) at 1∶1000 , LI-COR IRDye 800CW Conjugated Goat ( polyclonal ) α-Mouse IgG at 1∶10000 , LI-COR IRDye 800CW Conjugated Goat ( polyclonal ) α-Rabbit IgG at 1∶10000 . Blots were scanned and band intensities were quantified using an Odyssey Infrared Imaging System ( LI-COR Biosciences ) . Protein levels for DCC proteins and HTZ-1 were normalized to α-tubulin . Relative protein levels after htz-1 RNAi were calculated by dividing the normalized htz-1 RNAi level by normalized vector RNAi level . Trizol ( Invitrogen ) was used to extract RNA from all samples . Worms were washed from RNAi plates or normal OP50 plates 24 hours post L4 , washed with M9 and stored at −80°C until extraction . For RNA extraction , samples were thawed on ice and tissue was homogenized by grinding using a microcentrifuge tube pestle . Tissue was ground in three 60-second intervals and re-frozen in liquid nitrogen between each interval . During the final 60-second interval , 250 µl of Trizol was added to the tube , and when completed , another 250 µl was added for a total volume of 500 µl Trizol and the standard protocol was used to extract RNA from the homogenized samples ( Invitrogen ) . DNA-Free kit ( Applied Biosystems ) was used to digest remaining DNA contamination . Reverse transcription ( RT ) reactions were performed utilizing the High Capacity cDNA Reverse Transcription Kit with RNase Inhibitor ( Applied Biosystems ) . 1 µl of DNase-treated RNA was used in each RT reaction . PCR was used to observe relative levels of htz-1 , R08C7 . 10 , and act-1 ( actin ) expression levels . The following primers were used with a 60°C annealing temperature: act-1: gctatgttccagccatccttc and aagagcggtgatttccttctg htz-1: tggctggaggaaaaggaaag and aacgatggatgtgtgggatg R08C7 . 10: gtagaccaaaccagccagca and agcgccttgacgatacttttt Real-time PCR analysis was conducted as described [98] . The following primers were used with an annealing temperature of 59°C: act-1: same as above htz-1: gcgctgccatcctcgaat and gggctcccttcttgttcatc sdc-2: ggaaacaagaccgacaggaa and gatgcaatagtacacgccaaatc Relative htz-1 and sdc-1 expression levels were calculated using the Pfaffl method [107] incorporating the PCR efficiency for each primer set as determined by a 10-fold dilution series for each primer set in each reaction . Reactions were conducted in triplicate per experiment . Data shown are resulting averages from three experiments . | In organisms where females have two X chromosomes and males only have one , a mechanism called dosage compensation ensures that both sexes receive the same amount of information from their X chromosomes . Disruption of dosage compensation leads to lethality in the affected sex . While the precise mechanisms of dosage compensation differ between organisms , changes to the structure of the X chromosomes are involved in each case . The DNA of all chromosomes is packaged into a complex protein–DNA structure called chromatin . The most basic level of packaging involves wrapping DNA around a group of small proteins called histones . In both mammals and flies , dosage compensation is associated with specific changes to the histones on the dosage compensated X chromosome . Until now , no such change has been associated with dosage compensation in worms . Here we present evidence that the histone variant HTZ-1/H2A . Z plays a role in dosage compensation in the worm . Specifically , we suggest that HTZ-1 functions to ensure that only the X chromosomes , and not the other chromosomes , are subjected to dosage compensation . This suggests that , despite different mechanisms , one common theme of dosage compensation is a change at the level of the histones associated with the chromosomal DNA . | [
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] | 2009 | Restricting Dosage Compensation Complex Binding to the X Chromosomes by H2A.Z/HTZ-1 |
Over 200 cryptosporidiosis outbreaks have been reported , but little is known if other enteric pathogens were also involved in some of these outbreaks . Recently , an outbreak of cryptosporidiosis linked to poor hygiene by two Cryptosporidium hominis subtypes occurred in a pediatric hospital ward ( Ward A ) in China , lasting for more than 14 months . In this study , the concurrence during the outbreak of three other enteric pathogens with a similar transmission route , Giardia duodenalis , Enterocytozoon bieneusi , and Clostridium difficile , was assessed . The occurrence of G . duodenalis , E . bieneusi , and C . difficile in 78 inpatients from Ward A and 283 and 216 inpatients from two control wards ( Wards C and D ) in the same hospital was examined using molecular diagnostic tools . Significantly higher infection rates were found in children in Ward A for all study pathogens than in Wards C and D ( P<0 . 01 ) : 9 . 5% versus 1 . 4% and 0% for G . duodenalis , 10 . 8% versus 2 . 8% and 3 . 7% for E . bieneusi , and 60 . 8% versus 37 . 8% and 27 . 8% for C . difficile , respectively . These differences were mostly seen in children ≤12 months . Enteric pathogen-positive children in Ward A ( 31/58 or 53 . 4% ) were more likely to have mixed infections than those in Ward C ( 4/119 or 3 . 4% ) or D ( 5/68 , 7 . 4%; P<0 . 01 ) . Having cryptosporidiosis was a risk factor for G . duodenalis ( OR = 4 . 3; P = 0 . 08 ) , E . bieneusi ( OR = 3 . 1; P = 0 . 04 ) , and C . difficile ( OR = 4 . 7; P<0 . 01 ) infection . In addition , a lower diversity of G . duodenalis , E . bieneusi , and C . difficile genotypes/subtypes was observed in Ward A . Data from this study suggest that multiple pathogens were concurrently present during the previous cryptosporidiosis outbreak . Examination of multiple enteric pathogens should be conducted when poor hygiene is the likely cause of outbreaks of diarrhea .
Cryptosporidium is a significant cause of diarrhea in humans worldwide [1] . Humans can acquire Cryptosporidium infections through the fecal-oral route via direct person-to-person or animal-to-person contact , or ingestion of contaminated water or food [2] . Thus far , over 200 waterborne , foodborne , person-to-person , and zoonotic cryptosporidiosis outbreaks have been reported [3] , [4] . However , whether other co-pathogens were involved in some of these outbreaks remains largely unexamined . Similar to Cryptosporidium , pathogens like Giardia duodenalis , Enterocytozoon bieneusi , and Clostridium difficile are also significant causes of diarrhea in humans worldwide and can be transmitted from persons to persons by the same fecal-oral route involved in cryptosporidiosis occurrence [1] , [5] , [6] . All of these pathogens are major causes of healthcare-associated infections , especially Clostridium difficile [7]–[9] . Despite their wide occurrence , the epidemiology of these enteric pathogens is largely unclear in developing countries . Only limited data exist on the molecular epidemiology of these pathogens in China [10]–[16] . In one recent molecular epidemiologic study on Cryptosporidium in in-patients from three pediatric hospitals , P . R . China , we identified an extended outbreak of cryptosporidiosis in a pediatric hospital ward ( Ward A , Hospital I ) , with more than 50% ( 38/74 ) children affected by two C . hominis subtypes ( IaA14R4 and IdA19 ) during a 14-month period ( Sep . 2007–Oct . 2009 ) [17] . The infection rate in Ward A was significantly higher than the overall rates in Hospitals I ( 2 . 8% ) , II ( 0 . 6% ) and III ( 0 . 4% ) . The diversity of Cryptosporidium species and C . hominis subtypes were significantly lower in Ward A than in other wards/hospitals , with only one species ( C . hominis ) and two C . hominis subtypes ( IaA14R4 and IdA19 ) being found in 38 patients in Ward A while four species of Cryptosporidium and six C . hominis subtypes being found in 62 patients in other wards and hospitals [17] . Because concurrent infections of multiple pathogens are sometimes involved in gastroenteritis in hospitalized children [18] , [19] , in the present study , we retrospectively compared the infection rates and subtype distribution of G . duodenalis , E . bieneusi , and C . difficile in hospitalized children in Ward A with those in two control wards in the same hospital: Ward C for patients having hemophilia , anemia , and neurological diseases , and Ward D for patients having general surgeries . This was the first study to use genotyping and subtyping tools to investigate the transmission of multiple enteric pathogens during a cryptosporidiosis outbreak .
Written informed consent was obtained from the parents or guardians of the children . This study was approved by the Ethics Committee of the East China University of Science and Technology . All specimens for this study were collected from in-hospital children during September 2007–October 2009 as described [17] . These children were hospitalized mostly due to non-gastrointestinal illness: Ward A for patients with various congenital or inherited diseases from a local welfare institute; Ward C for children attending the Department of Endocrinology , Hematology and Neurology; and Ward D for children attending the Department of General Surgery . In this study , Ward A ( Cryptosporidium infection rate = 51 . 4% ) , where the cryptosporidiosis outbreak occurred , was regarded as the case ward , while two other wards ( Wards C and D; Cryptosporidium infection rates = 1 . 8% and 2 . 3% , respectively ) in the same hospital ( Hospital I in Shanghai , China ) without cryptosporidiosis outbreak were regarded as the control wards . Overall , 573 children , including 74 from Ward A ( age range: 1–192 months; mean age: 20 . 7 months ) , 283 from Ward C ( age range: 1–168 month; mean age: 41 . 3 months ) , and 216 from Ward D ( age range: 1–216 months; mean age: 43 . 8 months ) , were examined for the occurrence and genotype/subtype distribution of G . duodenalis , E . bieneusi , and C . difficile . In addition , 2 , 672 children from other known or unknown wards in Hospital I ( age range: 0–228 months; mean age: 46 . 9 months ) , 489 children from Hospital II ( age range: 0–192 months; mean age: 37 . 2 months age ) , and 311 children from Hospital III ( age range: 1–159 months; mean age: 40 . 4 months ) in the same city , were also examined for G . duodenalis . Information on age , gender , and the occurrence of diarrhea as defined by the attending physicians was collected for each patient as previously described [17] . Genomic DNA was extracted from 0 . 2 ml of fecal materials using a FastDNA SPIN Kit for Soil ( BIO 101 , Carlsbad , CA ) . To detect G . duodenalis , a 532-bp fragment of the triosephosphate isomerase ( tpi ) gene was amplified by nested PCR [20] . A 511-bp fragment of the β-Giardia ( bg ) and a 530-bp fragment of the glutamate dehydrogenase ( gdh ) gene were further amplified from DNA of the tpi-positive specimens [21] , [22] . Giardia duodenalis genotypes and subtypes were determined using the established nomenclature system based on multilocus sequence data [22] . A ∼392-bp fragment of the rRNA gene containing the entire internal transcribed spacer ( ITS ) was amplified and sequenced to detect and identify E . bieneusi genotypes [23] . Genotypes of E . bieneusi were named according to established nomenclature [23] , [24] . A PCR based on the tcdB gene was used to detect C . difficile [25] . Clostridium difficile in tcdB-positive specimens was subtyped by sequence analysis of the slpA gene as previously described [9] . All positive PCR products generated in the study were directly sequenced using Big Dye Terminator v3 . 1 Cycle Sequencing Kits ( Applied Biosystems , Foster City , CA ) and an ABI 3130 Genetic Analyzer ( Applied Biosystems ) . Sequences were assembled using ChromasPro ( version 1 . 5 ) software ( http://technelysium . com . au/ ? page_id=27 ) . The accuracy of the sequencing reads was confirmed by bidirectional sequencing . The nucleotide sequences of G . duodenalis , E . bieneusi , and C . difficile genotypes/subtypes obtained were aligned with reference sequences of each genetic locus downloaded from GenBank using ClustalX ( http://www . clustal . org/ ) . A neighbor-joining analysis of the aligned sequences was performed with the program Mega 5 ( http://www . megasoftware . net/ ) . Unique nucleotide sequences generated from the study were deposited in GenBank under accession numbers JX994231-JX994292 . The χ2 test was used to compare infection rates between Ward A and the control wards . The same method was used to analyze the association between infection and age , gender , or diarrhea status . The strength of the association was measured using the odds ratio ( OR ) . Differences were considered significant at P≤0 . 05 . All statistical analyses were performed using the SPSS Statistics 17 . 0 ( SPSS Inc , Chicago , IL ) .
Only four parasites including Cryptosporidium , G . duodenalis , E . bieneusi and C . difficile were analyzed in this study and no examinations of bacteria or viruses were conducted . The Cryptosporidium infection rates were 51 . 4% ( 38/74 ) in case Ward A while 1 . 8% ( 5/283 ) and 2 . 3% ( 5/216 ) in control Wards C and D , respectively [17] . Among the 74 specimens from the case Ward A , seven ( 9 . 5% ) were positive for G . duodenalis at the tpi locus ( Figure 1 ) . In contrast , only 4 of 283 ( 1 . 4% ) and 0 of 216 ( 0% ) specimens from the control Wards C and D were positive ( Figure 1 ) . The difference in G . duodenalis infection rates between the case ( A ) and control wards ( C and D ) was significant ( P<0 . 01; Table 1 ) . In addition , 4 of 1 , 019 ( 0 . 4% ) children from other wards in Hospital I , 17 of 1 , 653 ( 1 . 0% ) children from unknown wards in Hospital I , 3 of 489 ( 0 . 6% ) children from Hospital II , and 5 of 311 ( 1 . 6% ) children from Hospital III were also positive for G . duodenalis . The prevalence of giardiasis in children from these locations was significantly lower than the prevalence in Ward A ( P<0 . 01 ) . Among the 573 specimens examined , 24 ( 4 . 2% ) were positive for E . bieneusi at the ITS locus , with eight positives in each ward . The infection rate of E . bieneusi in Ward A ( 10 . 8% ) was significantly higher than those in Ward C ( 2 . 8% ) and D ( 3 . 7% ) ( P = 0 . 01; Figure 1; Table 1 ) . Altogether , 212 of the 573 specimens were positive for C . difficile at the tcdB locus , with 45/74 ( 60 . 8% ) , 107/283 ( 37 . 8% ) , and 60/216 ( 27 . 8% ) in Wards A , C , and D being positive , respectively ( Figure 1 ) . The infection rate of C . difficile was significantly higher in Ward A than in Wards C and D ( P<0 . 01; Table 1 ) . Concurrent infections of multiple pathogens , including Cryptosporidium , G . duodenalis , E . bieneusi , and C . difficile , were detected in both the case and control wards . Comparing with the control Wards C and D , Ward A had a significantly higher overall infection rate of enteric pathogens ( 58/74 or 78 . 4% versus 119/283 or 42 . 0% and 68/216 or 31 . 5%; P<0 . 01 ) . Over half of children with enteric pathogens in Ward A ( 31/58 or 53 . 4% ) were concurrently infected with two or more pathogens , while only a small number of children with enteric pathogens in Ward C ( 4/119 or 3 . 4% ) or D ( 5/68 , 7 . 4% ) were infected with multiple pathogens ( P<0 . 01 ) . In this study , children who had cryptosporidiosis during the outbreak were more likely to be infected with other enteric pathogens ( Table 1 ) . Among the 573 children examined for all four pathogens , 48 were previously diagnosed as having cryptosporidiosis . These Cryptosporidium-positive children had higher infection rates of G . duodenalis ( 6 . 3% versus 1 . 5%; P = 0 . 08 ) , E . bieneusi ( 10 . 4% versus 3 . 6%; P = 0 . 04 ) , and C . difficile ( 70 . 8% versus 33 . 9%; P<0 . 01 ) than Cryptosporidium-negative children ( Table 1 ) . The age distribution of G . duodenalis , E . bieneusi , and C . difficile infections in 573 children from Wards A , C , and D is shown in Table 2 . Infection rates of G . duodenalis were similar among all age groups ( P>0 . 05 ) . In contrast , children ≤6 months were more likely infected with E . bieneusi ( 11/99 or 11 . 1% versus 12/473 or 2 . 5% for other age groups , P<0 . 01 ) , and children ≤12 months were more likely infected with C . difficile ( 124/277 or 44 . 8% versus 88/295 or 29 . 8% for other age groups , P<0 . 01; Table 2 ) . Among children under 12 months , infection rates of all three study pathogens were significantly higher in Ward A than in control wards ( 5/59 or 8 . 5% versus 1/218 or 0 . 5% , P<0 . 01 for G . duodenalis; 7/59 or 11 . 9% versus 8/218 or 3 . 7% , P = 0 . 03 for E . bieneusi; 38/59 or 64 . 4% versus 86/218 or 39 . 4% , P<0 . 01 for C . difficile ) . In contrast , in children older than 12 months , only G . duodenalis was significantly more prevalent in Ward A than in the controls ( 2/15 or 13 . 3% versus 3/280 or 1 . 1% , P = 0 . 01 for G . duodenalis; 1/15 or 6 . 7% versus 7/280 or 2 . 5% , P = 0 . 88 for E . bieneusi; 7/15 or 46 . 7% versus 81/280 or 28 . 9% , P = 0 . 14 for C . difficile; Table 2 ) . No gender difference was seen in the occurrence of G . duodenalis , E . bieneusi , and C . difficile infections in this study ( P>0 . 05; Table 2 ) . The distribution of G . duodenalis multilocus subtypes was different between case and control wards . In Ward A , six of the seven specimens positive for G . duodenalis at the tpi locus were also positive at the bg and gdh loci , and all of them belonged to the multilocus subtype AII ( Table 3; Figure 2 ) . In contrast , both multilocus subtype AII and subtypes belonging to the assemblage B ( 2 cases each ) were found in Ward C . Similarly , both AII and B were detected in other known or unknown wards in Hospital I , and in Hospitals II and III ( Table 3 ) . Four known genotypes of E . bieneusi were found in this study , with Peru 11 as the dominant one ( in 6 cases ) . The other three includes EbpC ( 1 case ) , EbpA ( 2 cases ) , and D ( 1 case ) . Twelve novel genotypes ( SH1–12 ) were found in this study , with SH2 in three cases and all other genotypes in one case each ( Table 3 ) . All 16 E . bieneusi genotypes except SH5 belonged to Group 1 phylogenetically , while genotype SH5 belonged to Group 2 ( Figure 3 ) . Higher occurrence of E . bieneusi genotype Peru 11 was seen in Ward A ( 4/8 genotyped ) than in Wards C and D ( 1/8 genotyped each; Table 3 ) . Among the 212 C . difficile-positive specimens based on PCR analysis of the tcdB gene , 160 specimens were subtyped at the slpA locus successfully . In total , 20 slpA subtypes were obtained , including 8 novel ones ( Table 3 ) . Most of the novel subtypes were genetically close to subtypes previously reported , although two of them , sh-01 and sh-02 , had very different sequences and formed an independent clade in the phylogenetic tree ( Figure 4 ) . The most common subtype in Ward A was fr-01 ( 15/40 slpA-positive cases ) , compared to kr-03 in control Wards C ( 23/74 slp-A positive cases ) and D ( 18/46 slp-A positive cases; Table 3 ) . A significantly higher diarrhea rate was observed in Ward A than in control Wards C and D ( 43/74 or 58 . 1% versus 180/499 or 36 . 1% , P<0 . 01 ) . Infection with Cryptosporidium was significantly associated with the occurrence of diarrhea ( OR = 1 . 95 , p = 0 . 002 ) . However , a large number of asymptomatic G . duodenalis , E . bieneusi , and C . difficile infections were observed in both case and control wards in this study ( Table 2 ) . None of the three pathogens were significantly associated with the occurrence of diarrhea in the pediatric inpatients ( P>0 . 05; Table 2 ) . None of the dominant genotypes/subtypes of the three study pathogens were significantly associated with the occurrence of diarrhea ( P>0 . 05; data not shown ) . In addition , in Ward A , the difference in diarrhea rates between children with multiple infections and children with single infection was not significant ( 17/31 or 54 . 8% versus 17/27 or 63 . 0% , P = 0 . 51 ) . This was also the case in the control Wards C and D ( 3/9 or 33 . 3% versus 56/178 or 31 . 5% , P = 1 . 0 ) .
Molecular epidemiological investigations have improved our understanding of the transmission of enteric pathogens , including those examined in the present study [2] , [7]–[9] , [14] . They are especially useful in identifying the occurrence of outbreaks , linking seemingly un-associated cases , and tracking infection sources . In the present study , using genotype and subtype tools , we retrospectively identified concurrent transmission of G . duodenalis , E . bieneusi , and C . difficile during a cryptosporidiosis outbreak previously identified in Ward A of Hospital I in Shanghai , China . This is reflected by higher infection rates and lower genetic diversity of these enteric pathogens in Ward A than in control wards . The low infection rates of G . duodenalis in wards other than Ward A in Hospital I ( 0–1 . 4% ) , Hospitals II ( 0 . 6% ) , and III ( 1 . 6% ) are similar to those reported in out-patients and inpatients ( 0 . 2–0 . 6% ) and the general population ( 2 . 5% ) in China [11] , [26]–[29] . Likewise , infection rates of E . bieneusi in hospitalized children in Wards C ( 2 . 8% ) and D ( 3 . 7% ) are also very low . In contrast , significantly higher infection rates of G . duodenalis ( 9 . 5%; P<0 . 01 ) and E . bieneusi ( 10 . 8%; P = 0 . 01 ) were seen in Ward A , indicating that these pathogens were transmitted frequently within this ward during the cryptosporidiosis outbreak . Although a high carriage of C . difficile was found in control Wards C ( 37 . 8% ) and D ( 27 . 8% ) , this is similar to the 16–35% carriage of C . difficile in hospital inpatients in other countries [30] , and significantly lower than the infection rate of C . difficile in Ward A ( 60 . 8%; P<0 . 01 ) . Previously , no data existed on the prevalence of C . difficile in hospitalized children in China , although an infection rate of 9 . 5% was reported in adults in Shanghai [14] . The low genetic diversity of G . duodenalis , E . bieneusi , and C . difficile found in Ward A versus in control wards also supports the hypothesis of concurrent transmission of these enteric pathogens during the cryptosporidiosis outbreak . For G . duodenalis , AII was the only subtype seen in Ward A , although assemblage B and both AI and AII subtypes of assemblage A are commonly found in humans around the world ( 7 ) , and they were all found in other wards and hospitals in the present study ( Table 3 ) . For E . bieneusi , Peru 11 was the dominant genotype in Ward A , being found in half of the genotyped specimens , while it was only found in one of eight specimens each genotyped in Wards C and D ( Table 3 ) . For C . difficile , the fr-01 subtype was dominant in Ward A and accounted for 1/3 of all C . difficile infections in this ward , whereas the most prevalent genotype kr-03 in control Wards C and D accounted for less than 1/5 of all C . difficile infections ( Table 3 ) . Outbreaks involving multiple enteric pathogens have been infrequently reported and , in the investigations of the few such outbreaks , sewage contamination of water or food was often the main cause for concurrent transmission of multiple enteric pathogens . For example , a waterborne outbreak of Shigella sonnei , Giardia , and Cryptosporidium infections on a Lake Michigan dinner cruise was caused by contamination of potable water with diluted sewage as the result of storm runoff in the cruise ship [31] . Another waterborne outbreak of gastroenteritis with multiple etiologies in resort island visitors and residents in Ohio in 2004 was caused by sewage contamination of groundwater [32] . Likewise , a national multi-pathogen outbreak of diarrheal illness in Botswana in 2006 was caused by sewage contamination of the environment during heavy rains in late 2005 and early 2006 [33] . Similarly , contact with manure from calves was responsible for two multi-pathogen outbreaks at a farm day camp in Minnesota [34] . In the present study , over half of the patients with enteric pathogens ( 31/58 ) in Ward A were infected with more than one pathogen , compared to a very limited number of cases ( 9/187 ) in control wards ( P<0 . 01 ) . Of note is the significant association of the three enteric pathogens examined in this study and occurrences of cryptosporidiosis in these children ( Table 1 ) . As suggested in our previous investigation of cryptosporidiosis in these children [17] , poor diaper changing and hand washing practices by caregivers were probably responsible for this multi-pathogen outbreak among pediatric inpatients in Ward A , Hospital I . Children in Ward A were orphans from a welfare institute . They were taken care of by hired caregivers . In contrast , children in other wards were primarily from the general community and cared for by their parents [17] . Considering the fact that most infections in Ward A occurred in children younger than 12 months ( Table 2 ) , who mostly stayed in cribs and beds , hired caregivers in Ward A might have acted as vehicles for the disease transmission among pediatric inpatients . This is also supported by the finding that in children under 12 months , Ward A had significantly higher infection rates of all study pathogens than Wards C and D , but in children older than 12 month , Ward A had only significantly higher infection rates of G . duodenalis than Wards C and D . Very few studies have been conducted on molecular epidemiology of G . duodenalis , E . bieneusi , and C . difficile in China [10]–[16] . The occurrence of both assemblages A and B of G . duodenalis in non-outbreak children is in accordance with previous findings of near equal distribution of the two genotypes in 18 Giardia-positive humans in Henan [11] and 8 in Anhui [10] . In contrast , the dominance of Group 1 E . bieneusi genotypes in children in this study is different from the dominance of Group 2 genotypes in children in Jilin [13] , although we also detected a novel Group 2 genotype in a child from Ward C ( Figure 3; Table 3 ) . The high diversity of known and novel E . bieneusi genotypes reported in this study and previous studies [12] , [13] suggests that there is a need for more studies to examine the characteristics of E . bieneusi transmission in humans in China . Ribotypes 027 and 078 are recognized as leading causes of nosocomial outbreaks of C . difficile infection in the world [9] , [15] . However , neither has been reported in China thus far [14]–[16] . Interestingly , the dominant C . difficile slpA subtypes fr-01 in Ward A was previously characterized as toxin A-negative and toxin B-positive ( A−B+ ) , whereas the dominant subtype kr-03 in control wards was toxin A-positive and toxin B-positive ( A+B+ ) [9] . In a previous study , A−B+ strains were the dominant ones ( 24 . 0% ) in patients in three hospitals in Beijing , Shandong and Guangzhou in China [16] . The high prevalence of A−B+ strains in China indicates that toxin B , rather than toxin A , is probably a key virulence determinant as previously suggested [35] . Nevertheless , in the present study , no significant association was found between any of the C . difficile subtypes and the occurrence of diarrhea , although we previously showed a link between cryptosporidiosis and diarrhea in these children [17] . A new group of slpA subtypes including sh-01 and sh-02 were found in many children from all three wards ( Figure 3; Table 3 ) . Further studies are needed to better understand the public health importance of this new group of subtypes . The results of this study and our previous study [17] showed that although Cryptosporidium infection was associated with the occurrence of diarrhea , single-pathogen infection with G . duodenalis , E . bieneusi , or C . difficile was not . None of the dominant genotypes/subtypes of G . duodenalis , E . bieneusi , and C . difficile were significantly associated with the occurrence of diarrhea , and concurrent infections of multiple pathogens were not more associated with occurrence of diarrhea than infections with single pathogens . The lack of differences in occurrence of diarrhea between children with single-pathogen infection and children with mixed infections in this study was probably attributable to the already high diarrhea rates in Ward A ( 58 . 1% ) and low occurrence of mixed infections in control Wards C and D ( 9 cases of mixed infections versus 178 cases of single-pathogen infection ) . In addition , with the exception of Cryptosporidium , none of the other pathogens examined in this study were among the recently identified major pathogens for moderate-to-severe diarrhea in the Global Enteric Multicenter Study [1] . This has probably also made it difficult to use attributable fraction calculation in estimating the role of mixed infections in the occurrence of diarrhea in a hospital study setting with high occurrence of diarrhea . In conclusion , using genotyping and subtyping tools we retrospectively identified a multi-pathogen outbreak in a pediatric hospital ward . As reported previously [17] , this outbreak lasted ≥14 months , with ∼60 inpatient children affected by cryptosporidiosis . Most of the Cryptosporidium-positive children were co-infected with G . duodenalis , E . bieneusi , or C . difficile . The young age of affected children and concurrent infections with multiple enteric pathogens clearly implicated poor diaper changing and hand washing by hired caregivers as the cause of the outbreak . Thus , better training of caregivers on hygienic practices such as hand washing and proper use of disposable gloves and disinfectants is needed to reduce the risk of pathogen transmission in healthcare facilities . Results of this study also highlight the importance of molecular epidemiologic investigations in understanding the transmission of enteric pathogens in hospitals . | The transmission of Giardia duodenalis , Enterocytozoon bieneusi , and Clostridium difficile is poorly understood in developing countries despite their wide occurrence . Because they are transmitted by the same fecal-oral route as Cryptosporidium , in this study , we have examined the occurrence of these enteric pathogens in children during a cryptosporidiosis outbreak in a pediatric hospital in China . Using molecular diagnostic tools , we have detected significantly higher infection rates of these enteric pathogens in the outbreak ward than in two control wards in the same hospital . We have also shown a much higher occurrence of these pathogens in children having cryptosporidiosis than those having no cryptosporidiosis . We have demonstrated that the genetic diversity of enteric pathogens is much lower in the outbreak ward than in control wards . Therefore , other enteric pathogens are concurrently present during the cryptosporidiosis outbreak , and examinations for multiple enteric pathogens should be conducted when poor hygiene is considered the likely cause of outbreaks of diarrhea . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2013 | Concurrent Infections of Giardia duodenalis, Enterocytozoon bieneusi, and Clostridium difficile in Children during a Cryptosporidiosis Outbreak in a Pediatric Hospital in China |
Mycobacterium species are grown using specific media that increase laboratory cost , thus hampering their diffusion in resource-limited countries . Preliminary data suggested that versatile blood agar may be also used for mycobacterial culture . We examined the growth of 41 different Mycobacterium species on 5% blood agar . Over a 24-month period we analysed isolation of mycobacteria after parallel inoculation of clinical specimens into both a reference automated system ( BACTEC 9000 MB broth ) and 5% blood agar slant tubes , after NaOH decontamination , and compared the cost of performing 1 , 000 analyses using these two techniques . Mycobacterium reference species cultured on blood agar , with the exception of Mycobacterium ulcerans . Inoculation of 1 , 634 specimens yielded 95 Mycobacterium isolates . Blood agar performed significantly more efficiently than BACTEC 9000 MB broth ( 94 vs 88 isolates , P = 0 . 03 ) . Decontamination of Candida albicans in 5 specimens by addition of amphotericin B in blood agar yielded one more M . tuberculosis isolate that could not be isolated in BACTEC broth . Uneven distribution of time to culture positivity for M . tuberculosis had a median ( range ) of 19±5 days using blood agar and 26±6 days using BACTEC 9000 MB broth . Cost for 1 , 000 analyses in France was estimated to be of 1 , 913 euros using the blood agar method and 8 , 990 euros using the BACTEC 9000 MB method . Blood agar should be regarded as a first-line medium for culturing Mycobacterium species . It saves time , is cost-effective , is more sensitive than , and at least as rapid as the automated method . This is of particular importance for resource-limited countries in which the prevalence of tuberculosis is high .
Specific media , such as egg-based media ( e . g . Lowenstein Jensen medium ) , agar-based media ( e . g . Middlebrook media ) and liquid media ( e . g . Middlebrook and BACTEC broths ) are recommended for culturing Mycobacterium species [1] . Such requirements pose logistic and economic problems , especially in resource-limited areas where bacteriological culture facilities are few and the prevalence of mycobacterial infections , notably tuberculosis , is high . The cost of automatic detection using reference automated mycobacterial culture systems is high . The rationale for using specific media for culturing mycobacteria was to ensure the growth of Mycobacterium species without supporting the growth of contaminants . However , effective decontamination procedures have been developed for non-sterile samples [1] . We therefore questioned the utility and cost-effectiveness of an alternative medium , blood-agar , for the isolation and growth of Mycobacterium species as it is relatively commonly available and inexpensive . Moreover , it may save time and expense by avoiding duplicate inoculation for sterile specimens when other microorganisms are suspected . Sporadic papers have reported the isolation of Mycobacterium tuberculosis on standard blood-agar incubated for 3 weeks or more [2] , [3] and proof-of-principle studies have been published [4] , [5] . However , it has not been evaluated whether blood agar is useful for the isolation of opportunistic non-tuberculous Mycobacterium species , what the contamination frequency is for non sterile samples , and what the cost-effectiveness of such a diagnostic approach is . The data presented herein show that ordinary blood agar medium can support the growth of Mycobacterium species other than Mycobacterium ulcerans , in a cost-effective way and that contamination is rarely a problem if decontamination procedures are strictly followed .
Forty-one reference strains of Mycobacterium species ( Institut Pasteur , Paris , France and American Tissue Culture Collection , Rockville , Maryland ) were tested ( Table 1 ) . An aliquot of 10 µl of stock culture ( concentration 102 colony-forming units/ml ) was streaked simultaneously onto Middlebrook 7H10 agar ( Becton Dickinson Diagnosis Systems , Le Pont de Chaix , France ) and blood-agar slant tubes containing 21 g/l peptones , 1 g/l starch , 5 g/l NaCl , 12 g/l agar and 5% defibrinated sheep blood , pH 7 . 3 ( Bio Technologie Appliquée , Dinan , France ) . The agar slants so inoculated were incubated for 28 days . Each culture was performed in triplicate . The study was approved by the local ethical committee according to French laws . Between January 2005 and December 2006 , the sensitivity and contamination rate of cultured Mycobacterium species from clinical specimens on blood agar media was prospectively evaluated by parallel inoculation of eligible specimens into BACTEC 9000 MB broth . Eligible specimens comprised Ziehl-Neelsen positive respiratory tract specimens and all other specimens except cerebrospinal fluid , blood and bone marrow regardless of the Ziehl-Neelsen staining result . Prior to inoculation , respiratory and faecal samples were decontaminated as described previously [6] . In brief , an average 1 ml of contaminated specimen was mixed with 1 ml of decontamination solution ( NaOH , 2% w/vol final concentration; N-acetyl-L-cystein , 0 . 5% w/vol final concentration ) by gentle vortexing for 15 seconds and incubated at room temperature for 15 min . After gentle vortexing , 48 ml of phosphate buffered saline ( PBS ) pH 6 . 8 ( BioMérieux , La Balme les Grottes , France ) were added and the suspension was centrifuged at 2 , 8449 g for 20 minutes . The supernatant was discarded and the pellet was resuspended into 5 ml sterile PBS; 2 ml were inoculated into a BACTEC 9000 MD broth bottle and 0 . 2 ml were inoculated onto blood agar . The samples obtained from cutaneous and lymph node biopsies were crushed aseptically into Penta mixture ( Becton Dickinson Diagnostic Systems ) and sterile isotonic water , respectively . All samples were inoculated in parallel into BACTEC 9000 MD broth ( Becton Dickinson Diagnostic Systems ) as well as onto 5% sheep blood agar in slant tubes incubated at 37°C and 30°C for skin biopsy specimens . Time of positive detection in the BACTEC system and time to grow colonies on blood agar as monitored by naked eye examination of tubes three times a week , were observed for 8 weeks . Isolates were identified after Ziehl-Neelsen staining by phenotypic analyses and partial rpoB gene sequence analysis [7] , [8] . We compared the cost of inoculation onto sheep blood agar in tube and into the BACTEC system by incorporating the cost of blood agar in tube , the cost of either BACTEC system or incubator with a 5-year period for depreciation and the averaged cost of labour on the basis of 2006 salary in France . We also evaluated the cost of labour for blood agar-based technique in Algeria , Brazil , India , Laos and Malawi using 2006 salaries . Numerical variables were compared using the Fisher's exact test .
M . ulcerans grew only on Middlebrook 7H10 agar and M . haemophilum grew only on blood agar media . All other Mycobacterium species tested grew equally well on both blood agar and Middlebrook 7H10 agar media ( Table 1 ) . Compared to those observed in blood agar media , Mycobacterium gordonae , Mycobacterium szulgai , Mycobacterium xenopi , and Mycobacterium intracellulare grew more rapidly on Middlebrook 7H10 agar . Mycobacterium chelonae grew more rapidly on blood agar . Results were identical in triplicate experiments . A total of 7 , 419 clinical specimens submitted for the isolation and culture of mycobacteria during this 24-month period yielded 156 Mycobacterium isolates ( prevalence = 2 . 1% ) comprising 118 M . tuberculosis complex isolates and 38 non-tuberculous isolates . 1 , 634 clinical specimens eligible for and included in the present study yielded 95 Mycobacterium isolates ( prevalence = 5 . 8% ) ( Table 2 ) including 84 M . tuberculosis organisms isolated from 48 samples obtained from the respiratory tract , 27 from lymph nodes , 7 from biopsies and 2 from stools . Sixteen M . tuberculosis ( 19% ) isolates resistant to streptomycin included two multi-drug resistant ( MDR ) ( 2 . 4% ) isolates and no extensively drug-resistant isolate . A total of 5 M . avium isolates were cultured from 3 samples obtained from the respiratory tract , 1 from lymph node and 1 from stools . M . xenopi was cultured from the respiratory tract of one patient and a knee prosthesis abscess in another patient . M . marinum was cultured from a skin biopsy in one patient . M . fortuitum was cultured from a bone biopsy in one patient . M . chelonae and Mycobacterium massiliense were cultured from respiratory tract specimens in one patient each . Blood agar inoculation missed one M . tuberculosis complex isolate ( 1% ) which grew from a lymph node after 32-day inoculation in BACTEC broth only . BACTEC broth inoculation missed 7 Mycobacterium organisms ( 7 . 3% ) which were isolated on blood agar only and included 6 M . tuberculosis complex and 1 M . marinum organisms . The number of missed M . tuberculosis isolates was significantly higher with the BACTEC than the blood-agar technique ( P = 0 . 03 ) . M . tuberculosis complex isolates missed by the BACTEC inoculation included 4 isolates from lymph nodes , 1 from a lung biopsy and 1 from a respiratory tract sample contaminated with Candida albicans . Indeed , 5 respiratory tract specimens were found to have been contaminated ( contamination rate of respiratory specimens , 1 . 5% ) with Candida albicans on both tested media . One of these specimens was inoculated again on blood agar media in the presence of amphotericin B ( 16 mg/ml ) and grew M . tuberculosis . The median time to culture positivity of M . tuberculosis was 19±5 days ( range: 3–45 ) using blood-agar and 26±6 days ( range: 7–39 ) using reference automated mycobacterial culture ( P = 0 . 1 ) . No difference was observed in culturing streptomycin-resistant , MDR and antibiotic-susceptible M . tuberculosis isolates . In France the annual cost for performing 1 , 000 BACTEC analyses including incubator/alert machine for 2 , 092 € , reagents for 6 , 090 € and labour for 78 € gave a total of 8 , 990 € . For the blood-agar method the annual cost for performing 1 , 000 analyses included one incubator for 57 € , reagents for 1 , 076 € and labour for 780 € gave a total of 1 , 913 € . In other countries , the estimated labour cost for performing 1 , 000 analyses using blood agar ranged from 37 € in Algeria and Brazil , 40 . 5 € in Malawi , 45 € in India and Laos and 50 € in Romania . The cost of reagents is variable , in many laboratories ( such as in Laos ) , farm animals are blood sampled by technicians to generate blood agar at very low cost .
This study extends the findings of previous anecdotal [2] , [3]–[9] and proof-of-principle studies [4] , [5]–[13] and demonstrates that blood-agar in slant tubes outperforms the reference automated method for isolation of mycobacteria from clinical specimens . Most Mycobacterium species encountered in the clinical microbiology laboratory readily grew onto 5% sheep-blood agar , except for M . ulcerans , a fastidious organism which is rarely isolated from diseased tissues in patients with Buruli ulcer [14] . The characterisation of adverse factors preventing this species culture on blood agar was beyond the scope of this study . These data extend previous information to a number of Mycobacterium species routinely encountered in microbiological laboratory practice , regardless of whether the species were slow or rapid growers . Previous study indeed demonstrated that blood-agar was at least equivalent to egg-based medium for the isolation of M . tuberculosis from respiratory and lymph node specimens [5] . In present study , the sensitivity of blood agar for culturing Mycobacterium isolates from clinical specimens was 98 . 9% ( 1/95 isolate was cultured in the BACTEC bottles only ) . This M . tuberculosis isolate had been recovered from a diseased lymph node and there was no obvious reason for the lack of growth on blood agar . No clinical material was left in order to reproduce the parallel inoculation . Seven M . tuberculosis isolates made on blood-agar medium failed to grow in BACTEC bottles , thus giving a significantly lower sensitivity ( 92 . 6% ) of the BACTEC medium compared to blood agar for the isolation of mycobacteria . M . marinum grew in blood agar incubated at 30°C and not in BACTEC incubated at 37°C . This observation agrees with the reported optimal growth temperature of this species and that some M . marinum isolates grow better on blood than on Middlebrook agar [15] . Blood agar slants offer the possibility of incubation at different temperatures whereas all automatic systems , including BACTEC , do not allow for such modulation . We suggest that the M . tuberculosis complex isolates which did not grow in BACTEC bottles , failed to grow as the inocula were too small to promote growth in the 40-ml volume of broth but large enough to promote minute growth on solid medium . The contamination frequency of 1 . 5% on blood agar for respiratory tract specimens was low and warrants further evaluation in other laboratories . We used amphotericin B to decontaminate samples inoculated on blood agar , whilst amphotericin B cannot be used in BACTEC system as it interferes with the detection system . Accordingly , one respiratory tract M . tuberculosis isolate was made on blood agar after amphotericin B decontamination . The median time to culture positivity for M . tuberculosis was shorter , albeit non-significantly , for blood-agar than for the automated mycobacterial culture . In our experience , the slant tube format prevented the otherwise rapid desiccation of blood-agar and allowed long-term incubation of the clinical specimen at 37°C . It also minimized the risk of infection for the laboratory workers [16] . These data indicate that blood-agar in slant tubes is a suitable alternative medium for the routine isolation of mycobacteria for clinical specimens . Our economic analysis demonstrated that , at least in France , blood-agar was 4 . 7 times less expensive than a reference automated mycobacterial culture ( BACTEC broth ) system . We estimated a cost of 1 . 9 € per specimen for the blood-agar method . For developing countries we estimated a labour cost ranging from 37 € to 50 € for 1 , 000 analyses . Demonstration of the cost-effectiveness of blood-agar slants for the secure isolation of mycobacteria is of particular importance for the management in resource-limited countries where mycobacterial infections , chiefly tuberculosis , are highly prevalent . The microscopic-observation drug-susceptibility ( MODS ) assay has recently been demonstrated to be a cost-effective assay for M . tuberculosis isolation in Peru , costing an estimated 2 US$ per specimen [17] . The MODS assay simply relies on low-power microscopic examination of inoculated liquid medium and median time to culture positivity was significantly shorter for MODS than for the automated mycobacterial culture . MODS , unlike blood agar slants , however requires specific training [17] . Beyond standard laboratory equipment , automated mycobacterial culture requires computer-linked automated culture incubators and the servicing of sophisticated equipment which may not be easily achieved in resource-limited countries . Indeed , as for exemple , CSF specimens only are routinely cultured for M . tuberculosis using egg-based medium in Laos ( P . Newton , personnal communication ) . The blood-agar method requires no specific equipment and the same tube allows for the isolation of non-mycobacterial bacteria and mycobacteria , thus even reducing the cost per analysis that we estimated . Contrary to the MODS assay , labour costs were not found to be significantly higher for the blood-agar method than the automated BACTEC method . Blood-agar method requires only an incubator , basic equipment in clinical laboratory and aseptically collected mammalian blood and NaOH which can be readily obtained in any country . This is of concern for resource-limited countries where blood-agar , but not specialized media , are available and mycobacterial infections are highly prevalent . In addition , developed countries require simplified procedures for the broadest spectrum of isolation and growth of organisms . Blood agar is a versatile medium routinely used in the laboratory for the isolation of both rapidly growing and fastidious bacteria from most clinical specimens . Prolonged incubation on blood agar has been advocated for the isolation of Bartonella species from clinical specimens such as lymph nodes and mycobacteria including M . tuberculosis appeared to be the first group of organisms to be isolated from such diseased specimens [18] . This study suggests that the isolation of mycobacteria on blood agar is not an anecdotal finding and that contamination of culture is not a frequent problem . We submit that if incubated for sufficiently long time ordinary blood agar is a cost-effective method for culturing Mycobacterium species from various clinical specimens . Blood agar was not only inexpensive , but was also more sensitive and as rapid as the reference automated method used in industrialised countries . | Mycobacteria are organisms responsible for animal and human infections comprising tuberculosis due to Mycobacterium tuberculosis and other opportunistic infections . Such infections require specific antibiotic treatment and prevention of secondary cases in the occurrence of pulmonary tuberculosis . The accurate diagnosis of mycobacteria infection is therefore of prime importance . Isolation and culture of mycobacteria from diseased clinical specimens is the gold standard for diagnosis . It relied for decades on the use of specific isolation media , resulting in most laboratories not attempting such diagnosis . Alternatively , specific automates and culture broths are available only in developed countries . We herein demonstrate that blood agar , a basic medium widely and routinely used in laboratories worldwide , is suitable for the isolation and culture of mycobacteria encountered in human pathology , including tuberculosis . It performed at least as well as reference culture broth . Morever , using blood agar was cost-effective . Blood agar should be recommended as a routine medium for the isolation of most pathogenic organisms , including mycobacteria , both in developing and developed countries . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"infectious",
"diseases/bacterial",
"infections"
] | 2007 | Cost-Effectiveness of Blood Agar for Isolation of Mycobacteria |
Many important protein–protein interactions are mediated by the binding of a short peptide stretch in one protein to a large globular segment in another . Recent efforts have provided hundreds of examples of new peptides binding to proteins for which a three-dimensional structure is available ( either known experimentally or readily modeled ) but where no structure of the protein–peptide complex is known . To address this gap , we present an approach that can accurately predict peptide binding sites on protein surfaces . For peptides known to bind a particular protein , the method predicts binding sites with great accuracy , and the specificity of the approach means that it can also be used to predict whether or not a putative or predicted peptide partner will bind . We used known protein–peptide complexes to derive preferences , in the form of spatial position specific scoring matrices , which describe the binding-site environment in globular proteins for each type of amino acid in bound peptides . We then scan the surface of a putative binding protein for sites for each of the amino acids present in a peptide partner and search for combinations of high-scoring amino acid sites that satisfy constraints deduced from the peptide sequence . The method performed well in a benchmark and largely agreed with experimental data mapping binding sites for several recently discovered interactions mediated by peptides , including RG-rich proteins with SMN domains , Epstein-Barr virus LMP1 with TRADD domains , DBC1 with Sir2 , and the Ago hook with Argonaute PIWI domain . The method , and associated statistics , is an excellent tool for predicting and studying binding sites for newly discovered peptides mediating critical events in biology .
Protein–protein interactions are vital for all cellular processes , including signaling , DNA repair , trafficking , replication , gene-expression and metabolism . These interactions can vary substantially in how they are mediated . What perhaps most often comes to mind are interactions involving large interfaces , such as those inside the hemoglobin tetramer , however , many important protein interactions , particularly those that are transient , low-affinity or related to post-translational modification events like phosphorylation , are mediated by the binding of a globular domain in one protein to a short ( e . g . , 3–10 amino acid ) peptide stretch in another [1] . These stretches often reside in the non-globular and/or disordered parts of the proteome , including many of the disordered interaction hubs [2] , [3] , thus helping to explain many of the emerging functional roles for such regions . Peptide regions binding to a common protein , or domain , often conform to a sequence pattern , or linear motif that captures the key features of binding [4] . For instance , SH3 domains bind PxxP motifs , WW domains bind PPxY or PPLP motifs , and SH2 , 14-3-3 and PTB domains bind phosphorylated peptides [1] . Since they are generally held to be more chemically tractable than interactions involving larger interfaces , protein–peptide interactions also represent an important new class of drug targets , and there are a growing number of small molecules that are designed to target them [5] . The discovery of new peptides and motifs mediating interactions has been of intense interest in recent years ( e . g . , [6]–[8] ) . Several techniques have been developed to uncover new variants of peptides that bind to known partners . For instance , phage display and peptide array technologies have been applied to uncover new peptide partners for many proteins or domains , including SH3 [9] , WW [10] and PDZ [11] domains . Several computational approaches have also been developed that use protein–peptide complexes of known 3D structure to find additional peptides that are likely to bind ( e . g . , [12]–[16] ) , and recently , probabilistic interaction networks have been used to predict peptide regions corresponding to kinase substrate [17] . The common thread to all of these approaches is that they rely on prior knowledge of the type of peptide binding to a domain and often require further knowledge of the peptide binding site on the globular protein . They are thus generally only effective for finding new variants of known peptides , and cannot directly uncover new protein–peptide interaction types . Protein–protein docking is currently the only widely used technique that can be applied to this problem generally , however this approach has limited application for peptides longer than 4 residues largely owing to the high degree of flexibility that one must consider when docking a typical peptide of 5–10 residues or the need for a known peptide conformation which is only rarely available [18] . Moreover , docking methods are very sensitive to conformational changes and require very high-resolution structures to perform well . Determining new protein–peptide interaction types is problematic experimentally , mostly because it is difficult in advance to know the regions in larger proteins responsible for binding , necessitating painstaking experiments such as deletion mutagenesis coupled to binding assays ( e . g . , [6] , [19] ) . To address this , several computational methods have been developed to discover new protein–peptide-motif pairs using the principle of sequence over-representation in proteins with a common interacting partner [6]–[8] . These methods , together with much conventional work focused on understanding interactions , have identified or predicted hundreds of new peptide-motifs mediating interactions with particular protein domain families . However , these discoveries rarely provide information about where the peptide binds the protein . Knowing these details can suggest further experiments and help ultimately to design chemical modulators of the interaction . Structures of protein–peptide complexes for all newly discovered interactions will require substantial time to become available , though the rapid increase in structural data for single proteins means that very often 3D structures are available ( or readily modeled ) for at least part of a protein in isolation . There is thus a widening gap between proteins of known structure that are known or predicted to bind to a particular peptide and available 3D complexes that would foster a deeper understanding of mechanism and afford the discovery of additional peptides . Here we present a method that attempts to bridge this gap by predicting the binding site for peptides on protein surfaces . We used a dataset of protein–peptide complexes of known 3D structure extracted from the Protein Data Bank ( PDB ) [20] to define spatial position specific scoring matrices ( S-PSSMs ) capturing preferences for how each amino acid binds to protein surfaces . Three dimensional position specific scoring matrices have been used in the past to predict protein folding [21] , to assess the quality of structural models [22] or to predict the function of proteins based on the matches of these position specific scoring matrices to a new protein structure [23] and to identify protein surface similarities [24] . However , to the best of our knowledge , they have not been used to predict interactions in this way . For a new protein–peptide pair , we identify candidate peptide binding sites by linking predicted sites for each residue on the protein surface according to peptide-deduced distance constraints ( Figure 1 ) . We developed statistics to determine the confidence of a prediction to estimate whether or not a putative peptide binds . When applied to a benchmark in a cross-validated fashion , we obtained excellent sensitivity and specificity , which allowed us to apply the approach to several new interactions , such as the interaction of the viral oncoprotein latent membrane protein 1 ( LMP1 ) with the tumor necrosis factor receptor 1-associated death domain protein ( TRADD ) [25] offering suggestions of binding sites for further investigation .
We created spatial position specific scoring matrices ( S-PSSMs ) for each of the 20 standard amino acids and three phosphorylated variants to capture their preferred binding environment . We superimposed the binding sites for each type of amino acid and quantified the protein atom preferences in a 3D grid ( see Materials and Methods ) . Comparing S-PSSMs between amino acids shows that those with similar properties are often bound to similar binding sites , as might be expected ( Figure S1 ) with certain exceptions ( e . g . , Trp/Gly ) . For example , S-PSSMs for phosphorylated amino acids are similar to glutamate or asparate , but differ from that for positively charged arginine ( Figure S1 ) . We then used the S-PSSMs to scan protein surfaces to predict binding sites for amino acids and , based on distance constraints between them , binding sites for peptides ( see Materials and Methods ) . Figure 1 shows an overview of how the S-PSSMs are generated and how searches for binding sites are performed . To assess the performance of the method in its ability to identify the correct binding sites for peptides , we constructed a large benchmark of 405 known protein–peptide complexes ( Dataset S1 ) , from our training set , where at least one structure of the protein not bound to the peptide was available ( see Materials and Methods ) . We then predicted binding sites for all peptides in the set to all corresponding un-bound protein structures using leave-one-out cross validation to ensure that no information derived from identical or homologous proteins was used to compute the parameters . Additionally we predicted the binding sites of random peptides of variable length to random chains from the structure database assuming this to be our negative dataset . For an additional benchmark , we extracted a smaller dataset of 18 protein–peptide complexes that were deposited in the PDB after we had constructed our training dataset ( i . e . , after 1st March 2007 ) , and for which we could find the corresponding un-bound protein , and where these did not have a sequence similar to any protein used in the larger benchmark . The rationale was that this would provide a true test of the approach , since none of the development of the method could be biased in any way by exposure to these new complexes . The ROC curve ( Figure 2 ) shows the false positive rate ( x-axis ) versus the true positive rate ( y-axis ) when varying the p-value cutoff and testing whether a peptide , predicted to bind to a site was correct ( i . e . , a true positive ) or incorrect ( a false positive ) . The ratio of the true positive predictions to the total number of predictions made , represents the prediction accuracy of the method at different p-value cutoffs , i . e . , it shows what fraction of the predictions made are actually correct and this corresponds to the statistical measure of positive predictive value ( PPV ) . We used the top 5 scoring predictions for both the positive ( 1109 scores – we used only correct predictions ) and negative dataset ( 2455 scores ) . The ROC curve , for the cross-validation tests on the large benchmark , also shows that the method performs well . For instance , predictions with p-values below 0 . 1 give a false positive rate ( fraction of non-binding events wrongly predicted ) of 0 . 1 and a true positive rate ( fraction of known binding sites predicted correctly ) of approximately 0 . 3 ( 295/1109 ) , i . e . , a PPV of 75% , while even a very low false positive rate of 0 . 01 ( p-values below 0 . 003 ) still has a true positive rate of approximately 0 . 1 ( 94/1109 ) , which represents a PPV of 89 . 9% . The Matthews correlation coefficient suggests the optimal p-value cut-off to be 0 . 04 , which gives a false positive rate of 0 . 03 , a true positive rate of 0 . 17 ( 186/1109 ) , and a PPV of 85% . We obtain a similar result for the smaller benchmark , with statistically significant predictions ( p<0 . 04 ) of the correct binding site for 2/18 ( true positive rate of 0 . 11 ) complexes . Overall , the ROC analysis suggests that the approach will correctly identify whether a peptide binds and where it binds for a reasonable number of peptide binding sites with significance . The curve resembles those for remote homology detection by techniques like PSI-blast [26] or threading when tested on difficult benchmarks consisting of structurally similar but sequence dissimilar proteins ( e . g . , [27] ) . This suggests that the problem of identifying binding sites in this way is a difficult one , but that the method can often nevertheless make useful predictions . Although the tests above show a coverage of only about 11% for the optimal p-value , it is very important to emphasize that the ROC analysis tests the most difficult scenario , whereby one knows neither if a peptide binds nor where it binds to the protein surface . This neglects the common situation where one has identified a protein–peptide binding event , and does not know where on the surface it binds . To test this situation one must simply ask how often the correct binding site is found with any p-value . For the large benchmark this greatly increases the coverage: the correct site was found for 60% ( 241/405 ) of the complexes , which corresponds to the accuracy of the binding site prediction , with a similar fraction for the small benchmark ( 11/18 ) . Our leave-one-out cross-validation , which removed any detectable ( BLAST [28] E-value<0 . 1 ) sequence homologues before evaluation , still leaves a possibility that remote homologues could in some way lead to S-PSSMs over-learning peptide binding sites . To remove this possibility , we repeated the benchmark process using stricter definitions of homology by taking single representatives from groups as defined in the Structural Classification Of Proteins ( SCOP [29] ) database ( family , superfamily and fold ) . This gave results similar to those seen in the original benchmark: all redundancy reductions ( SCOP family , superfamily and fold gave similar datasets ) led to 56% ( 192/342 ) correct peptide binding site predictions and a ROC performance similar to that for the original dataset , which suggests that there was no real bias in the creation of the S-PSSMs even with the sequence only reduction . The similarity between family , superfamily and fold reduced sets is due to the fact that the vast majority of the remotely homologous relationships involving similar peptide binding sites are removed at the family level ( e . g . , all SH3 or WW domains are in the same SCOP family ) . For very low p-values the method does not perform as well; this is because there are very few high scoring predictions left after removing proteins lacking SCOP assignments ( i . e . , the newest structures ) , and is not statistically significant . For example for p-values<0 . 003 only 6 of the 23 complexes that scored high in the original dataset are left in the stricter dataset thus reducing the true positive rate while not changing the false positive rate . Remote homologues can play some role in defining binding sites for each other—for instance in creating the original cross-validated S-PSSMs for proline , three distantly related WW domains ( i . e . , PDB IDs 1i5h , 1djyI and 1f8a ) were present—but this effect does not appear to bias the overall performance . For most unsuccessful predictions within the benchmarks ( i . e . , where the binding site was not predicted even with poor p-values ) there are explanations for failure . For 32 ( ∼20% ) out of the 165 incorrect predictions the peptide was bound via augmentation of a beta-sheet [30] , with a strong influence of backbone interactions that are not currently considered because they are not based on the specificity of particular residues for specific binding sites which is the assumption the method is based on . In principle , this binding mode could be accommodated by considering a backbone profile and stricter distance constraints to enforce this conformation . For 25/165 ( ∼15% ) , the peptides adopted a helical or circular structure , making distance constraints less effective , and for 20/165 ( ∼12% ) the peptide contained heteroatoms or modified residues ( e . g . , biotinylated lysines , etc . ) that the method had not been trained on . There are currently too few examples of known structure to derive effective S-PSSMs for rare modifications . For 33/165 ( ∼20% ) of the wrong predictions , we could see no obvious trends , but we noticed upon inspection that some were likely correct binding sites not seen in the complex structure . For example we predicted a different binding site for the peptide GPAGPPGA from that found in a complex with the human matrix metalloproteinase 2 ( MMP2; PDB ID: 1eak ) . This unpublished structure appears to be a complex between MMP2 and a fragment of collagen/gelatin , the natural substrate ( e . g . , [31] ) . Our binding site does not agree with that in the complex , which resides in a central cavity of the protein , but is instead inside an exposed aromatic surface on a fibronectin domain ( Figure 3A ) . This surface resembles that for many other proline-rich peptide binding proteins ( e . g . , SH3 , WW , etc . ) . This was originally suggested to be the binding site for gelatin , based on an early single domain structure [32] and alanine scanning mutagenesis in MMP9 [33] and subsequent studies in MMP2 itself [34] , showed that residues equivalent to our prediction were important for gelatin binding . Though no current approaches focus specifically on the problem of generally predicting peptide binding sites on protein surfaces , it is possible to predict binding sites generally by looking for patches of conservation on protein surfaces , an approach that has been under much focus for the past ten years [35] . Though not directly comparable , we applied one readily available algorithm , rate4site [36] to the same dataset for comparison . When considering the best predictions , conservation alone identifies 51% of peptide binding sites compared to 60% for our approach . However , the ROC curves show that conservation alone performs poorly in terms of specificity , owing largely to the fact that the approach identifies additional binding sites that do not bind peptides ( Figure 2 ) . Inclusion of the conservation of the binding sites in our predictive method results in a slight reduction of the coverage of binding sites being predicted with an improvement of only 2% in the true positive rate . Additionally it is computationally costly and it is only applicable for proteins that have a sufficient number of homologous sequences . We sought recently published examples of protein complexes distinct from those used in the benchmark to test the approach in a more real-world situation . Several recent protein–peptide complexes lack a 3D structure , but the location of the binding site has been partially determined by other means . For instance , the conserved linear motif termed the “Ago hook” , which was determined to bind to the PIWI domain of the Argonaute protein at the site where the 5′ end of an siRNA normally binds [37] . The interaction is important for transcriptional gene silencing and miRNA-mediated translational silencing , as well as for the recruitment of Ago proteins to specific cellular locations such as P-bodies . There were no available structures of Eukaryotic PIWI domains , so we predicted binding of the peptide PDNGTSAWGEPNESSPGWGEMD to Archaeal structures , either in isolation or bound to RNA ( PDB IDs: 1ytu [38]; 1w9h [39] ) . Most of the best predictions lie near to the site of RNA binding ( Figure 3B ) . A similar example is found in the tudor domain of the protein SMN , which plays a role in assembly of the spliceosomal ribonucleoprotein complexes by interacting with RG rich C-terminal tails of Sm proteins . NMR titration showed that these repeats bind on the tudor domain in a particular region rich in aromatic residues [40] . Our prediction for the binding of an RGRGRGRG peptide to the human SMN tudor domain ( PDB ID: 1mhn [40] ) matches the NMR mapped binding site ( Figure 3C ) . A recent example of a known protein–protein interaction delineated to a region in one protein binding another , but lacking a 3D structure , is the binding of the leucine zipper domain from Deleted in Breast Cancer-1 ( DBC1 ) to the catalytic domain of the mammalian protein deacetylase Sir2 [41] . The predicted binding site on Sir2 ( PDB ID: 1m2g [42] ) lies in the same region as a p53 peptide ( PDB ID: 1ma3 [43]; Figure 3D ) , and is thus consistent with the finding that DBC1 blocks the ability of Sir2 to deacetylate p53 [41] . A similar picture emerges for the binding by Tumor necrosis factor-receptor-1-associated death domain protein ( TRADD ) to Latent membrane protein 1 ( LMP1 ) of the Epstein-Barr virus [44] . The 16 C-terminal residues of the LMP1 ( GDDDDPHGPVQLSYYD ) bind to the TRADD protein and cause the blockage of the apoptotic pathway , and induce the NF-kappaB pathway by recruiting and activating I-kappaB kinase beta [25] . The predicted binding sites on the TRADD N-terminal domain ( PDB ID: 1f2h [45] ) for 3 overlapping 10 residue peptides from this segment ( GDDDDPHGPV , DDPHGPVQLS , and HGPVQLSYYD ) are roughly in the same site ( Figure 3E ) as the TRAF2 protein ( PDB ID: 1f3v [46] ) , which suggests the virus might affect apoptosis and other processes by mimicking the TRADD/TRAF2 association and subsequent binding to the kinase [25] . When a protein–protein interaction is known , but the regions involved are either not delineated , or are too long to be considered short peptides , our approach can be used to scan for putative binding peptides , by searching for significant scores among overlapping predictions within a region ( or the entire protein ) . We demonstrate this for the interaction of Sec23/Sar1 with Sec31 which occurs as part of the COP II Coat Nucleation complex formation process [19] . A fragment of Sec31 ( residues 850–1175 ) was initially identified to interact with full length Sec23 in a two-hybrid analysis [47] . This region largely overlaps with a proline-rich , disordered region that was subsequently revealed to contain a 40-residue segment responsible for the interaction , and confirmed by X-ray studies [19] . We scanned the region 770–1100 from human Sec31 ( Uniprot O94979 ) using a 12 residue window for peptides that were predicted bind the Sec23/Sar1 complex ( un-bound PDB ID: 1m2o [48] ) . The plot of averaged p-values ( Figure 4B ) shows the best peptides to be near to those known to bind Sec23/Sar1 , and overlap with the most conserved region of the 40 residue region of Sec31 ( Figure 4A ) .
This approach will be of benefit to researchers investigating the structural basis of protein–protein interactions . It can be applied to structures known to bind a peptide , and is likely to be informative about the site of interaction , and thus readily suggest further experiments to test the interaction . Although a lack of data currently prevents many modified residues from being studied we expect that the steady growth in structures will permit additional residues to be considered in the near future , and that new structures will continue to improve each residue profile and the approach . Additional data will ultimately permit more sensitive S-PSSMs , such as residue pairs , which we expect will greatly increase the performance . We are also currently developing modifications to account for the limitations mentioned above , such as the special case of peptides that bind via beta-sheet augmentation . The method has advantages over many others that predict protein–peptide interactions . First , it does not require a known binding site , such as those approaches specifically tailored to predict SH3 or MHC binding peptides , and can thus be applied to any protein for which a structure is available and ideas about binding peptides or proteins . Second , it does not require that a substantial number of interactions be known for predictions to be made , but can in principle work on a single known or predicted peptide sequence . Most importantly , the method is accompanied by a statistic measure to estimate the reliability of predictions , which means it can be applied to many structures systematically to identify the strongest predictions , and to make predictions as to whether binding occurs at all . Our approach partly systemizes what structural biologists often do when trying to guess a binding site from a protein surface ( e . g . , [49] ) by trying to match properties of a binding peptide with complementary properties on the protein surface . However , it has the advantage that these inferences are coupled to rationally derived knowledge of how amino acids in peptides bind proteins , and a measure of the probability that such a predicted binding site might occur by chance . As such , it provides a more reliable starting point for site-directed mutagenesis , or other studies designed for finding true binding sites experimentally . It also provides an excellent starting point for protein docking approaches , which always fare better when applied to restricted binding regions instead of the entire protein surface . The fact that several sites are also found by a surface conservation method is perhaps not surprising , since proteins that bind peptides will undoubtedly often show conservation of the peptide binding site , as is generally true for all sites of molecular recognition . However , the improved performance over such approaches indicates that this method offers a more precise , and specific way to study peptide binding sites as distinct from general functional sites . Moreover , despite the fact that when attempting to directly combine the two approaches the improvement in accuracy is marginal and the cost in coverage is high , they can still be complementary: if a predicted binding site is also conserved this can provide additional evidence to increase confidence in a prediction . As the number of known protein–protein interactions grows , so do the number of instances for which a peptide stretch is discovered to mediate an interaction of importance . At the same time , the increased pace of structure determination of single proteins or domains , means that it is now rare to find globular domains lacking structural information . Taken together , this suggests that techniques like that described here will be of growing importance to those interested in understanding , targeting and modifying protein interaction networks involved in critical biological processes .
To train and test our method we created a manually-curated , non-redundant set of protein–peptide complex 3D structures . We first extracted 5055 complexes from the Protein Data Bank [20] , in which peptide stretches of 3–20 residues were in contact with globular domains . Inspection showed that many complexes were due to non-specific crystal contacts . We corrected for this by manually inspecting a smaller subset of 386 highly non-redundant complexes ( permitting only one member of any family from the SCOP database [29] ) , and classified these as one of: ( 1 ) true protein–peptide complexes; ( 2 ) protein–protein interactions mediated by a peptide stretch in one partner; and ( 3 ) probable crystal contacts . Within the first two categories , 85% of complexes had more than 18 protein atoms within 6 Å of those in the peptide , compared to only 20% in the crystal contacts set ( Figure S2 ) . We then applied this cutoff to the larger dataset to leave 2970 complexes . We grouped the remaining complexes into 23 overlapping sets according to the amino acids contained in the peptide . Each set contained all complexes of proteins with a peptide stretch containing at least one of each particular amino acid ( including the 20 standard plus phosphorylated serine , threonine and tyrosine ) . To derive spatial position-specific scoring matrices ( S-PSSMs ) for each amino acid ( see below ) we required the set of complexes corresponding to each amino acid to be non-redundant in order to avoid any bias due to homology . In principle , this could have been done across the entire dataset , however this lead to too few data points for the rarer amino acids ( e . g . , Trp , Met , phosphorylated-Tyr ) , owing to single complexes containing an amino acid in a peptide being removed because of homology to other complexes lacking it . To make each set non-redundant , we performed an all against all BLAST sequence comparison [28] within each set and kept one representative of group of homologues sharing pairwise E-values< = 0 . 1 . We selected preferably recently determined , refined X-ray structures , with the best resolution . These were then manually inspected to remove complexes that were due to crystal packing effects not captured by the filter above or that had missing residues , or instances where a presumed peptide was actually part of the original chain . This left a total of 553 complexes belonging to 364 SCOP families , in 23 sets for each amino acid . The number of non-redundant complexes per amino acid set varied from 13 for phosphorylated threonine to 288 for leucine ( Table S1 ) . Given that the redundancy reduction was performed inside each of the residue sets the full set of 553 inevitably contained some redundancy . For leave-one-out cross-validation , we thus removed a particular complex and its homologous representatives ( as defined above ) from every set in which it was contained , meaning that no similar complex would be used to construct the S-PSSMs . We also repeated the procedure using three stricter levels for the definition of homologous representatives , i . e . , we removed all members of the same SCOP family , superfamily and fold for the leave-one-out cross-validation . Note , however , that all three levels gave almost identical datasets , since all cases of proteins binding peptides were similar at the family level , even if homology was remote ( thus explaining a single curve in Figure 2 ) . We created S-PSSMs for each of the 23 residues , capturing their preferred binding environment when present in a peptide . We first computed the solvent accessible atoms for each protein , having first removed the complexed peptide , using NACCESS [50] with default parameters , and kept only atoms with accessibility scores above zero . Our reasoning was that peptide stretches bind mostly to the surface of the protein and thus the solvent accessible surface should be sufficient to create robust matrices . We then superimposed each of the residues found in the peptides , along with their associated protein environments . The superimposition is made in such a way that the residue side-chains are oriented the same way ( Figure 1B ) . This way we could observe and quantify preferences for parts of each residue to be near to particular protein atoms in three dimensions . We first defined active parts of each side-chain as those most commonly involved in side-chain functions ( i . e . , the active center of each residue side-chain ) . We then performed superimpositions of the active part of each side chain using the PINTS [51] & STAMP packages [52] . For simplicity we did not consider all atoms of each side chain for the superimposition , but instead defined a subset ( Table S2 ) that was sufficient for the PINTS & STAMP packages to obtain reasonable superimpositions . To quantify the protein atom preferences for the space around each peptide residue r we created a grid over each superimposed residue environment . We placed the center of mass of the active part of each residue in the centre of the grid , and divided the space +/− 6 Å around it using a cell spacing of 3 Å ( Figure 1C ) . We then studied the types of atoms found in each grid point , and computed a score for the preference of each atom type ( as we have defined them based on their properties in Table S3 ) as:where nc is the number of atoms in cell c , navg is the average number of atoms in a cell of this grid , ni is the number of atoms of type i in cell c , fsi is the background frequency of atom type i on protein surfaces and dsi is the standard deviation of the frequency of atom type i on protein surfaces . In theory these values can range from very negative , where the environment is very different to the one favored by the particular residue , to very positive , which represent a good match for the residue's binding site . For the best 10 sites on the protein surface that we define as hot spots ( see below ) , the values are between −2 and 83 ( Table S4 ) . To predict binding sites for a given peptide on a protein surface we first identify potential binding sites for each residue ( hot-spots ) by scanning and scoring the whole protein surface using the S-PSSMs ( Figure 1D ) . To do this we place the corresponding S-PSSM at a specific distance from multiple planes defined on the protein surface , and oriented so that the active centre of the side chain faces the surface as if it were bound as a peptide residue on that protein site . This is accomplished by placing the centre of a grid on a vector perpendicular to a local plane centered at the surface atom and searching for the appropriate orientation of the grid . The distance is defined from our training dataset as the average of the minimum distances for each residue from the protein surface ( Table S5 ) . The planes are defined by two vectors starting at the atom for which we are calculating the score and ending at the previous ( vector 1 ) or the next atom ( vector 2 ) in the coordinate file . We assume that these atoms are close enough to the central atom to be able to define a valid local plane for the score calculation . In practice , this means that each amino acid is placed thousands of times on a structure in many different relative orientations ( i . e . , using each atom on the surface of the structure ) , and whilst it does not amount to a full 3D search , we found that it is more than adequate to sample the orientations actually found in known protein–peptide complexes . The procedure is roughly equivalent conceptually to rotating the protein with respect to the S-PSSM ( Figure S3 ) . In combination with the flexibility provided by the size of the S-PSSM cells ( 3 Å ) , this ensures that an effective sample of S-PSSM/protein orientations is considered when scanning the protein surface . It is important to underscore that the method is not designed to detect precise atomic details of protein–peptide binding sites , but to offer approximate locations . This purpose is well served using this approximation , with the advantage that it saves on the computational time needed for an exhaustive 3D search of orientations of the peptide residue on every possible site of the protein . The score for each orientation is calculated as:where K is the number of atom types that have been matched in the grid that was placed locally , N ( = 64 ) is the number of cells in the grid and scorerci is the value from the S-PSSM of the particular residue r in cell c for atom type i . It is important to note that this procedure , i . e . , orienting the S-PSSM appropriately and scoring the protein surface site , is performed for all atoms of the protein surface , thus ensuring a complete search of the space of possible surface/residue orientations . After scoring each site on the protein surface for each of the 23 amino acid S-PSSMs , we use the top ten scoring sites as potential binding sites for these residues ( Figure 1E ) . It is possible to marginally improve the sensitivity of the approach by including more sites ( i . e . , down to a statistical significance threshold ) , but in practice this slowed the approach and hindered usage . We then search for combinations of amino acid hot-spots that are spaced such that they satisfy the constraints deduced from the peptide sequence . To derive the constraints we analyzed all peptides inside the training dataset to compute average distances between C-alpha atoms ( DCal ) at particular sequence separations ( i ) and average distances from C-alpha atoms to the residue active centers ( Dr ) . Combinations of predicted residue sites are kept if all distances lie within DCal±Dr . In practice , these constraints are very flexible , with a slight preference for extended peptide conformations , since they grow as a function of sequence separation , and thus slightly disfavor helical or circular peptide conformations . For each potential peptide match ( Figure 1F ) we calculated the overall score as:Where scoreα is calculated using the formula above . We then computed a statistical significance p-value for each score as 1−Φ ( x ) where Φ ( x ) is the cumulative distribution probability that represents the probability that a random variable V with that distribution is less than or equal to x . Therefore the p-value represents the opposite , i . e . , the probability of the event that a random variable V with that distribution is more than or equal to x . We calculated a background score distribution defining random scores as those for peptides selected randomly from our training dataset having fewer than 2 residues ( in any position ) in common with that seen to bind a particular protein . We selected 5 random peptides for each of the 405 un-bound proteins ( see above ) . We cannot rule out that some of these random peptides will , in fact , bind to the proteins , but the statistics hold ( and indeed will be conservative ) even if a small fraction of random values correspond to positives . We defined correct binding site predictions as those where the average distance between predicted and known amino acid locations was less than a threshold ( between 6 and 10 Å ) . Visual inspection of several dozen predictions suggested 10 Å to be a reasonable upper limit , allowing for typical deviations in side-chain placements that occur after structural rearrangements upon binding , but not counting wildly different binding sites as correct . We compared our method to the rate4site [36] program that predicts functional sites on proteins by finding clusters of conserved residues . To do this we ran PSI-Blast [26] using the sequence of the bound structure against the NCBI non-redundant databases . For those with at least 3 significant sequence matches , we created alignments of the best 50 sequences ( which is the default for Consurf , the web version of rate4site ) using ClustalW [53] and gave these ( and the structure ) as input to rate4site . We defined correct predictions in a lenient fashion as those where at least one of the top 5 conserved positions was within 10 Å of the bound peptide . For the ROC analysis we defined negatives as all other sites on the proteins . The results were very similar when using conservation scores calculated only for the solvent accessible residues and when using those for the full protein sequence . We therefore used the full protein sequence since this is the way the program is actually used . A server to run predictions using the PEPSITE approach is available at http://pepsite . embl . de . | An important class of protein interactions in critical cellular processes , such as signaling pathways , involves a domain from one protein binding to a linear peptide stretch of another . Many methods identify peptides mediating such interactions but without details of how the interactions occur , even when excellent structural information is available for the unbound protein . Experimental studies are currently time consuming , while existing computational methods to predict protein–peptide structures mostly focus on interactions involving specific protein families . Here , we present a general approach for predicting protein–peptide interaction sites . We show that spatial atomic position specific scoring matrices of binding sites for each peptide residue can capture the properties important for binding and when used to scan the surface of target proteins can accurately identify candidate binding sites for interacting peptides . The resulting predictions are highly illuminating for several recently described protein–peptide complexes , including RG-rich peptides with SMN domains , the Epstein-Barr virus LMP1 with TRADD domains , DBC1 with Sir2 , and the Ago hook with the Argonaute PIWI domain . The accurate prediction of protein–peptide binding without prior structural knowledge will ultimately enable better functional characterization of many protein interactions involved in vital biological processes and provide a better picture of cellular mechanisms . | [
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] | 2009 | Accurate Prediction of Peptide Binding Sites on Protein Surfaces |
Buruli ulcer , the third mycobacterial disease after tuberculosis and leprosy , is caused by the environmental mycobacterium M . ulcerans . There is at present no clear understanding of the exact mode ( s ) of transmission of M . ulcerans . Populations affected by Buruli ulcer are those living close to humid and swampy zones . The disease is associated with the creation or the extension of swampy areas , such as construction of dams or lakes for the development of agriculture . Currently , it is supposed that insects ( water bugs and mosquitoes ) are host and vector of M . ulcerans . The role of water bugs was clearly demonstrated by several experimental and environmental studies . However , no definitive conclusion can yet be drawn concerning the precise importance of this route of transmission . Concerning the mosquitoes , DNA was detected only in mosquitoes collected in Australia , and their role as host/vector was never studied by experimental approaches . Surprisingly , no specific study was conducted in Africa . In this context , the objective of this study was to investigate the role of mosquitoes ( larvae and adults ) and other flying insects in ecology of M . ulcerans . This study was conducted in a highly endemic area of Benin . Mosquitoes ( adults and larvae ) were collected over one year , in Buruli ulcer endemic in Benin . In parallel , to monitor the presence of M . ulcerans in environment , aquatic insects were sampled . QPCR was used to detected M . ulcerans DNA . DNA of M . ulcerans was detected in around 8 . 7% of aquatic insects but never in mosquitoes ( larvae or adults ) or in other flying insects . This study suggested that the mosquitoes don't play a pivotal role in the ecology and transmission of M . ulcerans in the studied endemic areas . However , the role of mosquitoes cannot be excluded and , we can reasonably suppose that several routes of transmission of M . ulcerans are possible through the world .
Buruli ulcer , which is caused by M . ulcerans , is a neglected tropical disease affecting mostly poor rural communities in West and Central Africa . In 2013 , 75% of all new cases of Buruli ulcer worldwide were declared by Ivory Coast , Ghana and Benin . This skin disease , which mostly affects children , causes large ulcerative lesions often leading to permanent disabilities [1 , 2 , 3] . The cutaneous lesions are caused by a M . ulcerans toxin called mycolactone with cytotoxic , immunomodulatory and analgesic effects [4] . At early stages , Buruli ulcer can be treated with a combination of streptomycin and rifampin for eight weeks; at later stages , antibiotic therapy is associated with extensive surgery [5 , 6 , 7 , 8] . Buruli ulcer occurs mostly in low-lying swampy areas [9 , 10] . Epidemiological studies have shown that the aquatic environment is the main reservoir of M . ulcerans , with many aquatic vertebrates and macro-invertebrates harboring this bacillus . The exact ecological features and mode of transmission of M . ulcerans to humans remain to be identified . In recent decades , several studies have suggested that water bugs and mosquitoes may play a role in M . ulcerans transmission [11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25] . Water bugs have been implicated as potential hosts and vectors of the bacillus in laboratory experiments and field ecology studies in Africa [26 , 27 , 28 , 29 , 30] . Outside the aquatic environment , adult mosquitoes tested positive for M . ulcerans DNA in an area of endemic Buruli ulcer in Australia , leading to the suggestion that these insects might transmit the bacterium to humans [26 , 28 , 29 , 30] . However , this hypothesis was not confirmed by laboratory experiments , and , surprisingly , no study has investigated the possible involvement of mosquitoes in M . ulcerans ecology in Africa , the continent with the highest level of endemicity for Buruli ulcer . The objective of this study was to investigate the presence of M . ulcerans DNA in flying insects , including mosquitoes , in an area of Buruli ulcer endemicity in Benin . We monitored , in parallel , the levels of M . ulcerans in the aquatic environment , as a marker of the presence of the bacterium in the study area .
The study was carried out in the Oueme administrative area in South-East Benin , where Buruli ulcer has been endemic for several decades [31 , 32 , 33 , 34 , 35 , 36] . Sampling was carried out in three districts crossed by the Oueme River ( Bonou , Adjohoun and Dangbo ) . The districts were selected for study because they are accessible throughout the year ( including the rainy season ) and because data were available for relevant epidemiological studies . Flying insects were sampled at four sites and aquatic sampling was carried out at nine sites ( Fig 1 ) . The Oueme River originates in the Taneka hills in the Atacora Mountains and flows into the Atlantic Ocean close to Cotonou . The study area is characterized by a subequatorial climate with two rainy seasons . The first rainy season extends from April to July and the second extends from October to November . Mean annual precipitation is 1122 mm , and temperatures range from 22°C to 26°C . There are two main types of soil: alluvial soils , which are fertile but liable to flooding , and sandy soils , which are less fertile but suitable for growing coconut , palm , and other tropical trees . Most of the population in this area is engaged in farming ( rice , maize , cassava , cowpeas , market garden crops , etc . ) , fishing and trade . The natural vegetation consists of grassy savannah and swampy mangrove forest . This study focused on the adult stage of mosquitoes and other flying insects and the immature stages of mosquitoes . Insects were collected during four surveys in June , July , November , and December 2013 , at four sites in the Bonou Centre , Kode , Gbada and Houeda areas ( Fig 1 ) . The collection periods correspond to the start , middle and end of the rainy season and the dry season , respectively . Flying insects were collected with Centers for Disease Control ( CDC ) light traps . A CDC light trap consists of a 150 mA incandescent light bulb and a fan , powered by 6 V batteries . At each survey , once consent had been received from the heads of household , insects were trapped from two selected houses in each village , over a period of two days . Traps were placed both indoors and outdoors at each house , from 6:00 pm to 6:00 am , corresponding to the period from dusk to dawn . The indoor traps were suspended from the ceiling , about 2m above the ground . The outdoor traps were hung on trees at about the same height . The insects collected were identified in the field in two steps . In the first step , mosquitoes were separated from the other insects . All mosquitoes were identified to species level under stereoscopic microscopes , according to morphological criteria in dichotomous keys [37 , 38 , 39] . They were counted and stored , in pooled groups of up to 15 individuals of the same species , in 70% ethanol for transport to the laboratory . In the second step , the remaining flying insects were identified to order level on the basis of their morphology under a stereoscopic microscope , with the appropriate keys [40 , 41] . They were stored in 70% ethanol , in pooled groups of up to 15 individuals from the same order , and were transported to the laboratory for PCR analysis ( Fig 2 ) . During each survey , mosquito larvae were collected throughout the selected area by dipping with a 350 ml ladle . Samples were collected from various temporary and permanent bodies of water constituting potential habitats for the development of populations of mosquito larvae . All larvae were transported in clean water , in plastic containers , to the field laboratory . Larvae were identified to genus level with appropriate morphological keys [37 , 38 , 39] . The larvae of each genus were then separated into two groups . The larvae of the first group were preserved in 70% ethanol , in pools of 20 individuals for each genus . The larvae of the second group were reared to emergence . The resulting adults were then stored in 70% ethanol , in pools of up to 15 individuals . Exuviae were also preserved in 70% ethanol , in pools of 20 , for laboratory analysis ( Fig 2 ) . Samples were collected from the principal sources of water for domestic washing , bathing , fishing and recreation . The sampling sites were located in nine villages in the three districts: Bonou Centre , Agbonan , Agbomahan , Agonhoui , Gbame , Kode , Assigui , Houeda , and Mitro ( Fig 1 ) . Aquatic sampling was carried out with the same methods at each site , at least twice , between January 2013 and December 2013 . Invertebrates and fish were captured with a square net ( 32 x 32 cm and 1 mm in mesh size ) , from the surface down to a depth of 0 . 2 to 1 m , over a distance of 1 m . A sample was considered to correspond to all the insects collected in 10 such sweeps with the net . All insects were preserved in 70% ethanol for laboratory identification . For the detection of M . ulcerans DNA , the insects were sorted into pooled groups , each including no more than 20 specimens from the same family . For each body of water , we collected plant samples from the predominant and the second most frequent types of living plant . Each of these plant samples consisted of one to five plant leaves , stems or roots , depending on the size of the plant sample . They were placed directly in a clean 100 ml bottle containing 70% ethanol ( Fig 2 ) . Pooled insect bodies were ground and homogenized in 50 mM NaOH . Tissue homogenates were heated at 95°C for 20 min . The samples were neutralized with 100 mM Tris-HCl , pH 8 . 0 . DNA was extracted from the homogenized insect tissues with the QIAquick PCR purification kit ( Qiagen ) , according to the manufacturer’s recommendations . Negative extraction and purification controls were included in each series of manipulations . The homogenizers were decontaminated by incubation overnight in 1 M NaOH , to eliminate any traces of DNA . For each aquatic plant sample , the material was cut into small pieces with a scalpel and then ground in 50 mM NaOH . The extract was heated and neutralized and the DNA was purified with the Mobio purification kit , according to the manufacturer’s recommendations . Oligonucleotide primer and TaqMan probe sequences were used for detection of the IS2404 sequence and the ketoreductase B ( KR ) domain of the mycolactone polyketide synthase ( mls ) gene from the plasmid pMUM001 [13 , 42 , 43] . PCR mixtures contained 5 μl of template DNA , 0 . 3 μM of each primer , 0 . 25 μM probe , and Brilliant QPCR Master Mix ( Agilent Technologies ) in a total volume of 25 μl . Amplification and detection were performed with a Thermocycler StepOne ( Applied Biosystems ) , using the following program: heating at 95°C for 10 min , followed by 40 cycles of 95°C for 15 s and 60°C for 1 min . DNA extracts were tested at least in duplicates , and negative controls were included in each assay . Quantitative readout assays were set up , based on an external standard curve generated with five tenfold serial dilutions of M . ulcerans ( strain 1G897 ) DNA . Samples were considered positive only if both the IS2404 sequence and the gene sequence encoding the ketoreductase B domain ( KR ) were detected , with threshold cycle ( Ct ) values strictly < 35 cycles . An inhibition control was performed as previously described [44] and 10% negative controls ( water alone ) were included in each assay . Mosquito abundance was compared between sites and between seasons in nonparametric Kruskal–Wallis tests .
We collected 7230 flying insects from nine orders: Coleoptera , Diptera , Heteroptera , Homoptera , Hymenoptera , Lepidoptera , Nevroptera , Odonate , Tricoptera . At all sites , Diptera was by far the most frequent order of flying insects caught , accounting for 84% of all insects trapped . Heteroptera was the least abundant order at each site and was not detected at Gbada and Houeda ( Table 1 ) . The 6047 dipteran specimens collected during the four surveys included 4322 mosquitoes from 10 species . Mansonia africana ( 50% ) , Culex nebulosus ( 27% ) , and Culex quinquefasciatus ( 22% ) were the most abundant species , accounting for 98% of all the mosquitoes trapped . The four least represented species were Anopheles pharoensis , Aedes vittatus , Culex decens , and Culex fatigans , with no more than four individuals each ( S1 Table ) . No significant differences in the abundance of the mosquitoes and other flying insects caught were found between sites ( p>0 . 05 ) . Flying insects were significantly more abundant ( p<0 . 05 ) in the wet season than in the dry season , whereas no significant difference in mosquito densities was observed within seasons ( p>0 . 05; S2 Table ) . During the surveys , we collected a total of 5407 mosquito larvae . These larvae were identified as Culex spp . , Anopheles spp . and Aedes spp . In total , 3146 mosquito larvae belonged to the genera Culex and Anopheles . Culex spp . were the most abundant , accounting for 66 . 35% of the mosquito larvae collected . For the adults emerging in the laboratory following the rearing of larvae collected in the field , 2261 individuals belonging to the genera Culex , Anopheles and Aedes were identified . Culex was the most abundant genus , accounting for 79 . 08% of the sample ( S3 Table ) . During the survey , we collected 3377 aquatic vertebrates and macro-invertebrates from various bodies of water in the Oueme administrative area ( Table 2 ) . Insecta accounted for 72% of the animals collected , with a majority of Hemiptera . The bodies of water studied were of various natures ( flooded land , river and swamp ) and were scattered around the Oueme , making it possible to sample diverse types of specimens from different ecological niches . In total , 95 plants were collected from the various bodies of water . They were identified as belonging to the Poaceae , Lemnaceae , Nymphaeaceae , Araceae and Potamogetonaceae families . We tested flying insects , larvae , aquatic vertebrates and invertebrates , and plants collected in 2013 from various sites in Oueme for the presence of M . ulcerans DNA . We found that 942 pools of flying insects ( corresponding to the 7230 captured flying insects and the 5407 collected larvae ) tested negative for M . ulcerans DNA by PCR . Positive PCR results were obtained for 8 . 7% ( 28/322 ) of aquatic animal sample pools from the various bodies of water . No positive specimens were obtained at two sites , and 5 . 5 to 25% of the sample pools at the other seven sites tested positive ( Table 2 and S4 Table ) . Decapoda was the invertebrate family with the highest level of mycobacterial contamination ( 26% ) . We performed 295 PCR analyses on the 95 plants collected . These analyses were carried out on leaves , stems and roots , and three samples tested positive for M . ulcerans DNA by PCR: a leaf pool and a stem pool from the same plant from a water body in Kode and a leaf pool from Mitro ( S4 Table ) . Both plants concerned belonged to the Poaceae plant family .
The ecological characteristics and mode of transmission of M . ulcerans are not entirely understood , and several fundamental questions remain unanswered . One key concern relates to the routes by which M . ulcerans crosses the human skin barrier . There are currently two main hypotheses: ( i ) direct contact between an existing wound and water containing M . ulcerans; ( ii ) the inoculation of M . ulcerans into the skin [2 , 45] . Comparisons with the modes of transmission of other environmental mycobacteria in immunocompetent humans ( e . g . M . fortuitum , M . chelonae , M . xenopy ) and recent studies of M . ulcerans [46] have suggested that direct inoculation into the skin is the most likely mode of transmission . In this context , the two most likely scenarios for the inoculation with the bacterium are either inoculation by an active vector harboring M . ulcerans , as described for various microorganisms , including parasites ( e . g . Leishmania sp . or Plasmodium sp . ) , arboviruses ( e . g . the Dengue and Chikungunya viruses ) , and bacteria ( e . g . Yersinia pestis and Borrelia sp . ) , or inoculation by a mechanical vector , such as aquatic plant thorns or sharp leaves , biting or sucking insects ( bacilli present on the outside of the insects ) [13 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 25 , 26 , 27 , 28 , 29 , 30 , 47 , 48 , 49 , 50 , 51 , 52] . M . ulcerans ecology is highly complex . It is therefore possible for these scenarios to co-exist , and their importance or significance is dependent on a number of different criteria ( e . g . human behavior , including access to drinking water , rural or urban life and work , fauna and flora biodiversity , presence of permissive species , season ) . Several experimental studies have explored the role of aquatic hemipterans as passive or active vectors of M . ulcerans . These approaches were supported by various environmental and epidemiological studies conducted in Africa . However , the importance ( unique , major , or marginal ) of this transmission route has yet to be established and other transmission routes should therefore be explored . For instance , it has been suggested that mosquitoes act as vectors of M . ulcerans in Australia , but , surprisingly , this possibility has never been explored in Africa . In this context , the aim of our study was to assess the role of mosquitoes in M . ulcerans ecology . We carried out an extensive field study in an endemic area in Benin , involving temporal and spatial monitoring of the presence of M . ulcerans in mosquitoes and other flying insects , used as a control for the distribution of M . ulcerans in aquatic flora and fauna . M . ulcerans DNA was detected in various aquatic macro-invertebrates and vertebrates , and some aquatic plants . The global rate of detection was about 9% , consistent with the findings of other environmental studies [26 , 27 , 28 , 29 , 30] . M . ulcerans DNA was not detected in any of the flying insects collected in CDC light traps inside and around houses over the same period ( including mosquito families in which M . ulcerans DNA was detected in Australia ) . As only one type of sampling method was used to collect flying insects ( CDC light traps ) , it is possible that this introduced a bias in terms of species diversity . Nevertheless , in a recent study performed in the same area with three other types of sampling method for mosquito collection , the three most abundant mosquito species were the same as in our study , and eight of the 14 species identified were common to our study [53] . Our results suggest that mosquitoes and non-aquatic flying insects are not involved in the ecology and dissemination of M . ulcerans in an area of South-East Benin in which Buruli ulcer is highly endemic , and confirm that the aquatic environment is the main environmental reservoir of the bacillus . However , a role for mosquitoes in other areas , including Australia , cannot be definitively excluded . The ecology and mode of transmission of micro-organisms may differ between geographic locations , with biological diversity affecting bacterial adaptation and human activities . This concept could be applied to M . leprae , a mycobacterium that also causes a dermatosis . Indeed , a recent study has suggested that the ecological features , reservoirs and transmission routes of M . leprae may differ between continents . It has been shown that , in North America , wild armadillos harbor the same strain of M . leprae as leprosy patients . Leprosy may thus be a zoonosis in this region [54] . This situation cannot be transposed to other continents in which leprosy is highly endemic such as Africa and Asia , where there are no armadillos and no other mammal is known to harbor the bacillus . A similar situation may apply to M . ulcerans . In Australia , mammals such as possums have been shown to be hosts of M . ulcerans and may play a key role in its dissemination , together with mosquitoes . However , there are no possums in Africa , and M . ulcerans has never been detected in the tissues of any mammal other than humans in Africa . Based on the results of various studies performed in recent decades and aiming to decipher the ecological characteristics of M . ulcerans , it seems likely that M . ulcerans can be transmitted via several routes , potentially differing between locations in different parts of the world . | Buruli ulcer is a neglected tropical disease due to M . ulcerans , an environmental mycobacteria . Modes of transmission to human remain unclear and water bugs and mosquitoes had been incriminated with more or less experimental laboratory evidences and filed studies . In this context , we have investigated the presence of M . ulcerans DNA in mosquitoes and other flying insect in a highly endemic area of Buruli ulcer in Benin . No trace of the bacteria was found in mosquitoes and other flying insects , while 8 , 7% of aquatic insects , including water bugs , caught in the same area and in the same period were found positive to M . ulcerans DNA . Our results support the hypothesis that mosquitoes don’t play a major role in ecology of M . ulcerans in our research area and is in favor of a transmission from the aquatic environment . | [
"Abstract",
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] | [] | 2015 | A Field Study in Benin to Investigate the Role of Mosquitoes and Other Flying Insects in the Ecology of Mycobacterium ulcerans |
Regulatory and developmental systems produce phenotypes that are robust to environmental and genetic variation . A gene product that normally contributes to this robustness is termed a phenotypic capacitor . When a phenotypic capacitor fails , for example when challenged by a harsh environment or mutation , the system becomes less robust and thus produces greater phenotypic variation . A functional phenotypic capacitor provides a mechanism by which hidden polymorphism can accumulate , whereas its failure provides a mechanism by which evolutionary change might be promoted . The primary example to date of a phenotypic capacitor is Hsp90 , a molecular chaperone that targets a large set of signal transduction proteins . In both Drosophila and Arabidopsis , compromised Hsp90 function results in pleiotropic phenotypic effects dependent on the underlying genotype . For some traits , Hsp90 also appears to buffer stochastic variation , yet the relationship between environmental and genetic buffering remains an important unresolved question . We previously used simulations of knockout mutations in transcriptional networks to predict that many gene products would act as phenotypic capacitors . To test this prediction , we use high-throughput morphological phenotyping of individual yeast cells from single-gene deletion strains to identify gene products that buffer environmental variation in Saccharomyces cerevisiae . We find more than 300 gene products that , when absent , increase morphological variation . Overrepresented among these capacitors are gene products that control chromosome organization and DNA integrity , RNA elongation , protein modification , cell cycle , and response to stimuli such as stress . Capacitors have a high number of synthetic-lethal interactions but knockouts of these genes do not tend to cause severe decreases in growth rate . Each capacitor can be classified based on whether or not it is encoded by a gene with a paralog in the genome . Capacitors with a duplicate are highly connected in the protein–protein interaction network and show considerable divergence in expression from their paralogs . In contrast , capacitors encoded by singleton genes are part of highly interconnected protein clusters whose other members also tend to affect phenotypic variability or fitness . These results suggest that buffering and release of variation is a widespread phenomenon that is caused by incomplete functional redundancy at multiple levels in the genetic architecture .
The relationship between genotype and phenotype is a central concern of many fields , from developmental biology to human genetics to evolutionary biology . Although in most cases this relationship is poorly understood , some general properties do seem to be shared across diverse systems . Chief among these is robustness to genetic and environmental variation [1] . That is , most species maintain abundant genetic variation and experience a wide range of environmental conditions , yet phenotypic variation is relatively low [2] . Because of its ubiquity , phenotypic robustness , also termed canalization or buffering , is worthy of study in its own right [3] . It also presents an apparent contradiction: if biological systems are so robust , how do they diverge and adapt through evolutionary time ? The contradiction might be resolved if the robustness itself were to be modulated by particular mutations or environmental conditions . The robust system would accumulate conditionally neutral , or “cryptic , ” genetic variation . A genetic or environmental perturbation that impaired the system's robustness would then reveal the cryptic variation in the form of greater phenotypic diversity . The modulation of robustness would not only allow evolutionary divergence , but it might also accelerate it relative to the slow , step-wise fixation of fitness-increasing alleles that is normally considered within the Neodarwinian paradigm [3–5] . It is therefore essential to investigate mechanisms that contribute to the robustness of biological systems , and to understand how such mechanisms determine the phenotypic effects of different sources of variation . A model for the buffering and release of variation is provided by the molecular chaperone Hsp90 , which targets a large set of signal transduction proteins . In both Drosophila and Arabidopsis , compromised Hsp90 function results in diverse morphological changes that exhibit strong dependence on the genetic background [6 , 7] . This implies that Hsp90 normally contributes to phenotypic robustness to genetic variation . Because Hsp90 function allows stores of genetic variation to build up , and Hsp90 impairment releases this variation to have phenotypic effects , it is termed a “phenotypic capacitor” [7] . Controversy surrounds the evolutionary relevance of Hsp90-mediated capacitance and any similar mechanisms that might exist . One issue is whether any fraction of the phenotypic variation revealed by an impaired capacitor is adaptive , or instead whether the variants consist entirely of hopeful , but ultimately unfit , monsters [8 , 9] . The major morphological defects seen originally in flies [7] support the latter conclusion , yet selection for one such defect did not cause correlated fitness costs , suggesting that the pleiotropic effects of Hsp90 impairment are modular and not unconditionally deleterious [10] . The variation seen in Arabidopsis also supports the potential adaptive value of capacitance , in that this variation is considerably less monstrous than that seen in flies [6] . A more systematic analysis of the phenotypic and fitness effects of capacitor impairment is needed to resolve this issue . A second debate concerns the ultimate evolutionary reason that capacitance exists . One view is that the ability to modulate evolvability is itself an adaptive trait , and that natural selection has therefore favored capacitor function [11] . This view generally meets with great skepticism , as do similar views on the evolutionary benefits of mechanisms that alter mutation or recombination rates [9] . Nonetheless , a population-genetic model has shown that an allele that modifies the rate of revelation of cryptic genetic variation can invade a population under a realistic range of parameter values [12 , 13] . Although adaptive evolution of capacitance therefore remains a formal possibility , many favor an alternative view that considers capacitance a side effect of other selected properties . One possibility is that natural selection favors mechanisms that buffer against environmental variation , with environmental variation taken to mean both large macro-environmental differences and stochastic fluctuations in the external micro-environment or internal cellular environment . The buffering of genetic variation then results from a hypothesized mechanistic congruence between the impacts of allelic variation and environmental variation on regulatory networks [14] . Another possibility is that regulatory networks inherently attenuate variation because they contain thresholds and other nonlinearities that allow them to respond properly to internal or external cues [8 , 15] . Indeed , our own simulations of evolving regulatory networks predicted that many gene products should act as phenotypic capacitors , contributing to phenotypic robustness when present and producing greater phenotypic diversity when absent [16] . The above considerations motivate the development of an experimental system in which many phenotypes can be precisely measured in many individuals , multiple gene products can be screened for capacitor function , and sources of variation can be precisely controlled and partitioned . Here we present such a system , using single-cell morphological phenotypes in the yeast S . cerevisiae . We focus here on the robustness of these phenotypes to environmental variation caused by stochastic fluctuations in a constant macro-environment . While the study of robustness to environmental variation is critical to understanding the development and physiology of organisms , it also lays the foundation for future work that will rigorously test the congruence between mechanisms of environmental and genetic buffering and that will investigate the impact of capacitors on evolutionary trajectories . To identify gene products that contribute to robustness to environmental variation , we take advantage of data from high-dimensional quantitative morphological phenotyping of 4 , 718 haploid yeast single-gene knockout ( YKO ) strains [17] . Phenotyping was performed by growing cells in rich media to logarithmic growth phase and triply staining them for the cell surface , actin cytoskeleton , and nuclear DNA . Digital micrographs of ∼200 cells per strain were processed using automated image analysis [17] , yielding means and variances for 220 diverse quantitative phenotypes for each YKO ( Figure 1A ) . The phenotypes include measures of the size and shape of mother and bud cells and their nuclei , the number and size of actin patches , the position of nuclei or actin patches in reference to other cell landmarks , and relationships between the mother and bud , such as the bud angle ( for a complete list , see [17] ) . Using these data we identify more than 300 gene products required for robustness to environmental variation . We find that these capacitors are involved in a number of critical cellular processes and that they are highly connected , in terms of both physical and genetic interaction networks . Despite this centrality , capacitor deletions result on average in decreases in growth rate that could allow these mutants to persist for many generations in the presence of wild-type cells , suggesting that capacitor impairment need not produce unfit monsters . Capacitors encoded by a member of a duplicate gene pair differ in their functional and network properties from those encoded by singletons , suggesting that these two classes of capacitors are likely to buffer environmental variation by different mechanisms .
We validated our identification of capacitors by repeating the phenotyping of 50 C1 strains and 50 control strains in our lab . Haploid mutants in the YKO library used for the original phenotyping were passaged for an unknown number of generations . Because some knockouts might increase mutation rates and thereby cause phenotypic variability that is not associated with loss of environmental buffering , we used instead a haploid-convertible heterozygous diploid YKO library [19] and kept the number of generations between sporulation and fixation to a maximum of ∼50 . C1 strains exhibited more phenotypic variability than control strains ( Figures 2 and S8 ) . Phenotypes also appeared to be highly heterogeneous among C1 strains , suggesting that knockouts are not all disrupting a small number of high-level processes that result in a limited set of phenotypes . With the phenotype means and standard deviations from only the 100 haploid-converted strains , we repeated the calculation of phenotypic potential as described above , using the 70 already identified phenotypic medoids ( Figure 2 , lower right ) . Our expectation was that this repeated analysis would have reduced power because the proportionally large number of C1 strains that exhibit high variance would bias the lowess regression to decrease the magnitude of residuals . Even with this limitation , we found the phenotypic potential scores to be clearly higher in the C1 strains ( p < 9 . 8 × 10−10 , Wilcoxon-Mann-Whitney test ) . For a less conservative measure of phenotypic potential , we sampled data from capacitor and control strains in rough proportion to their numbers in the original analysis ( five to 42 ) , calculated scores on 1 , 000 repeated samplings , and averaged across all the 1 , 000 samples to calculate final phenotypic potentials ( Figure S9 ) . While this analysis still results in a conservative bias because of a reduction of residuals at the edges of the lowess regression due to fewer data points , capacitors exhibit ∼2-fold higher phenotypic potentials than controls ( p < 2 . 7 × 10−10 , Wilcoxon-Mann-Whitney test ) . A notable exception is RAD27 , which has a lower phenotypic potential than all but three of the control strains in the more conservative first analysis and all but nine in the less conservative sampling analysis . RAD27 deletion causes a strong spontaneous mutator phenotype [20] , which suggests that the high phenotypic variability observed in the original YKO might have been due to mutation accumulation . We also sampled ten capacitors in the C2 or C3 classes and these too exhibited significantly higher phenotypic potential scores than control strains ( p < 0 . 002 , Wilcoxon-Mann-Whitney test ) . As additional validation , we looked for evidence in the literature of capacitors causing increased cell-to-cell variability . Indeed , knockouts of the capacitors CCR4 , CLN3 , and SWI6 have each been shown to result in increased variability in diploid cell size in liquid media [21] . Knockout of CCR4 also causes irregular colony morphology on solid medium , a finding consistent with increased cell-to-cell variation [22] . Two other members of the CCR4-NOT core complex , NOT5 and POP2 , are identified as capacitors in our screen suggesting that disruption of this transcriptional regulatory complex is likely to result in cellular heterogeneity . Additionally , two of three knockouts that increase intrinsic expression noise of the PHO5 promoter are the capacitors ARP8 and SNF6 [23] . Lastly , knockout of the capacitor FUS3 has been shown to increase cell-to-cell variation in response to a pheromone signal [24] . We next investigated the entire set of 502 capacitors for enrichment in Gene Ontology ( GO; http://www . geneontology . org/ ) process terms ( Table S2 ) . Phenotypic capacitors are highly enriched in numerous processes , most of which can be broadly categorized into DNA maintenance and organization , cell cycle and cell organization , response to stimuli such as stress , RNA elongation , or protein modification . This diverse set of enriched terms is likely to represent processes that can result in broad cellular changes when disrupted . Specifically , capacitors contain 123 of 565 ORFs annotated with “chromosome organization and biogenesis” and 80 of 275 ORFs annotated with its daughter term , “telomere organization and biogenesis” ( p < 2 . 9 × 10−27 and p < 2 . 1 × 10−25 , respectively , hypergeometric distribution with Bonferroni correction ) . These include all genes annotated with recombinase activity , the entire telomerase holoenzyme complex , the entire RecQ helicase-Topo III complex , all but one gene from the homologous recombination module , all genes involved in postreplication repair , the entire CTF18m complex involved in sister chromatid cohesion and DNA-replication check-point signaling , the entire MMS22m module thought to be involved in double strand break repair , and both genes of the HEX3m module [25] . Notably , however , other modules that are likely to cause DNA instability are absent , including the nucleotide excision repair module , the DNA damage checkpoint module RAD9m , the MUS81m module involved in cleaving branched DNA , and the TOF1m module involved in promoting sister chromatid cohesion to repair DNA damage [25] . Capacitors also include 28 transcriptional regulators; numerous gene products involved in global mRNA production , including all members of the carboxy-terminal domain protein kinase complex; most members of the THO complex , which is thought to couple transcriptional elongation with mRNA metabolism and export; numerous nuclear pore-associated proteins , including both members of the mRNA export SAC3/THP1 complex; at least seven gene products involved in mRNA splicing; approximately half of the gene products annotated to the cytoplasmic mRNA processing ( P ) body; genes involved in protein transport and degradation , including three members of the Golgi-localized alpha-1 , 6-mannosyltransferase complex and eight gene products involved in vacuolar acidification; and genes involved in the control of actin ( nine genes ) and microtubule ( 12 genes ) organization and in bud emergence or selection . Protein–protein interaction ( PPI ) and synthetic lethal interaction ( SLI ) networks have a small number of highly connected nodes ( hubs ) and many more poorly connected nodes [26 , 27] . In PPI networks , deletion of a hub is more likely to be lethal than deletion of other nodes [26] . This finding suggests that genetic properties are , at least in part , traceable to global network architecture . Our numerical simulations of transcriptional networks implied that robustness is likely to be an emergent property of complex networks , and that in some cases network architecture constrains functional and evolutionary properties [15 , 16 , 28] . Others have predicted that SLIs are crucial to understanding buffering [29] or that phenotypic capacitors are likely to be hubs [30 , 31] . Thus , we asked here if a gene's phenotypic potential is traceable to network position . First , we used networks derived both from curated literature citations [32] and from the nearly complete set of affinity-capture mass spectrometry interactions [33 , 34] to determine if the average PPI degree ( number of interactions ) of capacitors is different than that of other genes . We found that capacitors have more physical interactions than other nonessential gene products but fewer than essential gene products ( Figure 3A ) . Because capacitors are enriched in select GO categories , one potential explanation for the high degree of capacitors is that they fall into GO categories whose members tend to be highly connected . However , in most cases , capacitors have significantly more interactions than GO-matched nonessential genes ( Figure S10 ) . Exceptions to this rule appear to occur mostly in GO categories where essential and nonessential genes do not differ in PPI degree . A plot of phenotypic potential versus binned PPI degree shows that gene products with a higher connectivity have on average a higher phenotypic potential ( Figure 3B ) ; this relationship is almost entirely explainable by an increased proportion of capacitors in the highly connected bins ( Figure S11 ) . Interestingly , the proportion of capacitors precipitously drops at PPI degrees above 30 , with similar proportions in the highest ( >80 PPI ) and lowest ( <3 PPI ) bins . The vast majority of genes in this highest bin have a duplicate in the genome , including 28 ribosomal proteins , three histones , and , of particular note , the homologues of Hsp90: HSP82 and HSC82 . While surprising , the finding that Hsp90 homologues do not act as capacitors in S . cerevisiae according to our definition is consistent with a previous study that found that Hsp90 activity , not impairment , allowed new mutations to have immediate phenotypic consequence [35] . However , we do find a homologue of the chaperone Hsp70 , SSE1 , to be a capacitor in yeast , consistent with studies on other Hsp70 family members [36] . Next , we used all SLIs from curated literature citations [32] and , again , find that capacitors have a higher degree than other nonessential genes ( Figure 3C , solid bars ) . Because genome-scale SLI assays have only been completed for a subset of genes ( ∼267 used as bait in synthetic genetic array [SGA] experiments ) , one potential explanation for the high SLI degree of capacitors is that they were more likely to be used as bait . To control for this possibility , we generated a subnetwork derived only from SGA experiments and divide genes in this network into those that had been used as bait and those that had not . For each class , we found that capacitors have higher SLI degree than other nonessential genes ( Figure 3C , striped bars ) . The higher SLI degree of capacitors is maintained even when the number of physical interactions of a gene is controlled for ( Figure S12 ) or when capacitors are compared to other nonessential genes within GO categories ( Figure S13 ) . A plot of phenotypic potential versus binned SLI degree ( Figure 3D ) shows that genes with a higher connectivity have on average a higher phenotypic potential; this relationship is almost entirely explainable by an increased proportion of capacitors in the highly connected bins ( Figure S11 ) . We have shown that capacitors are likely to be PPI and/or SLI hubs , that they might be less likely to be completely functionally redundant , and that they are involved in a number of central processes in the cell . At this point , one might ask if disruption of capacitor function has too drastic of an effect on fitness to play a role in adaptation . To investigate this possibility , we asked if capacitors are less dispensable than other nonessential genes by comparing the growth rates of haploid [37] or diploid [38] mutants . Whereas capacitors appear to have no effect on growth rate in the heterozygous diploid , they are less dispensable in haploid and homozygous diploid knockouts , with a larger rate decrease seen in the haploid ( Figure 3E ) . This effect is maintained even when we controlled for the PPI degree ( Figure S12 ) or when capacitors were compared to other nonessential genes within GO categories ( Figure S14 ) . Gene knockouts that result in drastically reduced growth rates ( less than 70% of wild-type ) on average have higher phenotypic potentials ( Figure 3F ) ; this relationship is entirely explainable by an increased proportion of capacitors that cause low growth rates ( Figure S11 ) . However , in most cases , capacitors cause decreases in growth rate that are not as severe , with 79% and 95% of capacitor YKOs having a growth rate exceeding 0 . 80 of wild-type in the haploid and homozygous diploid , respectively . Indeed , 124 capacitor YKOs have an increased growth rate over the wild type . The finding that many of the most highly connected PPI hubs are duplicates but not capacitors ( Figure 3B ) suggests a relationship between functional redundancy and buffering . We investigated this further by asking how capacitor genes distribute among the 1 , 425 unambiguous duplicates and 2 , 375 unambiguous singletons that have been identified in the yeast genome [39–41] . We found that capacitor genes are more likely to be singletons than other nonessential genes ( Table 1 , p < 0 . 026 , G-test ) . Capacitor singletons and capacitor duplicates tend to be enriched in different GO process categories . Capacitor singletons are enriched in the categories of DNA maintenance and organization , response to stimuli , and RNA transcription and localization ( Table S3 ) . Capacitor duplicates , while more heterogeneous overall , tend to be most enriched in the categories of protein metabolism and endocytosis . We next investigated if capacitor singletons differ from capacitor duplicates in any network or dispensability properties . Both duplicate and singleton capacitor genes tend to have a high number of SLIs and to cause decreases in growth rate when knocked out in the haploid or homozygous diploid ( Figure S15 ) . However , only capacitor duplicates tend to be PPI hubs ( Figure 4A ) . The above finding presents an apparent contradiction: high PPI degree is strongly associated with capacitor identity in duplicates ( Figure 4A ) yet many highly connected duplicates are not capacitors ( Figure 3B ) . The resolution of this contradiction appears to be that capacitor duplicate pairs are older and have less functional redundancy than other hub duplicate pairs . Using the synonymous substitution rate ( Ks ) between members of a duplicate pair as a rough estimate of age of the duplication event [39 , 42] , we found that duplicate pairs that contain at least one capacitor are on average less ancient than duplicate pairs that contain at least one essential gene and appear to be more ancient than duplicate pairs that contain only nonessential noncapacitor genes ( Figure 4B ) . Because substitution rates correlate negatively with expression level of a gene [43 , 44] , one explanation for the differences in Ks is that duplicates that contain at least one capacitor have a different distribution of expression levels than other duplicate pairs . However , we found the pattern persists even when we control for mRNA expression level ( Figure S16 ) . The average age difference between capacitor duplicate pairs and nonessential duplicate pairs appears to be due to recent duplicates ( Ks < 1 ) , of which there are fewer capacitor duplicate pairs than nonessential noncapacitor duplicate pairs ( p < 0 . 002 , G-test , Figure S17 ) . The same relation holds when comparing only hub duplicate pairs , which we defined as duplicate pairs where at least one paralog has a PPI degree ≥ 20 . To calculate this difference , we separated hub duplicate pairs into those that contain at least one capacitor and those that do not . The average PPI degree of gene products in these two categories is approximately the same ( p = 0 . 53 , Wilcoxon-Mann-Whitney test ) . Comparing these two categories , we found that hub capacitor duplicates appear to be older on average due to an absence of recent duplication events . The greater average age of capacitor duplicate pairs raises the possibility that they are predominantly ohnologs , tracing their origin to the whole genome duplication in yeast ∼100 million years ago [45] . However , we did not find ohnologs to be overrepresented among capacitor duplicates relative to noncapacitor duplicates ( p = 0 . 47 , G-test ) . The greater synonymous-site divergence of capacitor duplicate pairs does not appear to be matched by greater divergence , as measured by the nonsynonymous substitution rate ( Ka ) . For this comparison , we restricted our analysis to ohnologs to control for differences in duplication time that might introduce errors in Ka/Ks . Capacitor ohnologs have Ka values that are not significantly different from nonessential ohnologs , whereas essential ohnologs show marginally greater divergence ( Figure S18 ) . A more striking difference between capacitor duplicate pairs and other duplicate pairs was evident in the correlations in expression between paralogs across many experimental conditions [39] . Duplicate pairs that contain at least one capacitor have a lower expression similarity on average than duplicate pairs that contain an essential gene or noncapacitor nonessential duplicate pairs ( Figure 4B ) . Hub duplicate pairs that contain at least one capacitor also have a lower expression similarity than other nonessential hub duplicate pairs . These findings suggest that capacitor duplicate pairs are less functionally redundant than other duplicate pairs . To further dissect this difference in correlated expression , we examined the 64 capacitor-containing duplicate pairs in which both members of the pair have only one paralog in the genome . From these pairs , we excluded two pairs where both copies encode capacitors ( the ribosomal proteins RPL8A and RPL8B and the mannotransferases HOC1 and OCH1 ) and two pairs where a capacitor gene is paired with an essential gene ( the cyclins CDH1 and CDC20 and the UDP-glucose phosphorylases YHL012W and UGP1 ) , yielding 60 capacitor genes paired with a nonessential noncapacitor gene . Comparing only these 60 capacitors to their paralogs , we found that the capacitor is likely to have a higher mRNA and protein abundance than its noncapacitor duplicate ( Figure 4C ) . Perhaps because of these expression profile differences , the capacitor gene has on average approximately three times as many SLIs as its paralog ( Figure 4C ) . The higher expression of the capacitor in the pair suggests that perhaps the noncapacitor paralog also has an effect on robustness but a smaller one that did not surpass our threshold for capacitor identification . However , the noncapacitor paralogs do not have elevated phenotypic potentials when compared to all nonessential genes ( Figure 4D , p = 0 . 38 , Wilcoxon-Mann-Whitney test ) . Indeed , analysis of noise in protein abundance [46] suggests that the noncapacitor paralogs might be the targets rather than the sources of buffering: nonessential duplicates have on average greater variability in protein abundance than nonessential singletons ( p < 2 . 2 × 10−3 , Wilcoxon-Mann-Whitney test; Figure S15 ) . A partially redundant duplicate ( the capacitor gene ) might buffer this variability , whereas its deletion might expose this variability at the level of the phenotype . If one assumes that incomplete functional redundancy is also causing high phenotypic variability in the case of deleted singleton capacitors , understanding the mechanism of this process poses a greater challenge because there are no obvious candidate genes that overlap with singleton function . One hypothesis is that redundancy is achieved not at the level of the single gene as is the case for duplicates , but rather at the level of the protein module . Indeed , many singleton capacitors are part of functionally overlapping modules in the DNA integrity network [25] . To test the hypothesis that this is a more general property of singleton capacitors , we examined further their network characteristics . The clustering coefficient is a measure of local network interconnectivity . Singleton capacitors have on average higher clustering coefficients in the PPI network than all nonessential singletons , suggesting that they are interacting with more tightly knit groups of proteins that might represent functional modules ( Figure 5A ) . Despite their high local connectivity , capacitor singletons occupy less central positions in the overall PPI network than do other nonessential singletons , as measured by betweenness centrality ( Figure 5A ) . This is in stark contrast to capacitor duplicates , which tend to be more central when compared to other nonessential duplicates ( Figure S15 ) . The clustering of singleton capacitors into modules suggests that their other immediate PPI partners , or first-degree neighbors ( FDN ) , might have special network and dispensability characteristics . Because neighbor parameters are not likely to be independent of PPI degree , we first generated FDN scores for each gene product that describe the properties of a gene product's neighbors while controlling for the PPI degree of that gene product ( see Materials and Methods ) . Using these measures , we found that the FDNs of singleton capacitors are more likely to be capacitors , essential genes , or genes with a high SLI degree when compared to all nonessential singletons ( Figures 5B and S19 ) . We also found that FDNs of singleton capacitors are more likely to cause decreases in growth rate in haploid or homozygous diploid knockouts . Taken together , these results suggest that singleton capacitors are acting in highly interconnected modules whose other members are likely to disrupt robustness or be deleterious when knocked out . Approximately one-fourth of identified capacitors are annotated to be involved in maintaining chromosome stability . While this class of genes still meets all of the criteria for singleton capacitors discussed above , one alternative mechanism to explain the increased phenotypic variability in these YKOs is that they are causing mutations or chromosomal aberrations [47] . Because YKOs that cause drastic increases in the spontaneous mutation rate are relatively well defined and rare ( see Protocol S1 ) , we concerned ourselves with YKOs that may be causing chromosomal aberrations , most easily measured by rates of gross chromosomal rearrangements ( GCRs ) [48] . We found that a high rate of GCRs is neither necessary nor sufficient for high phenotypic potential . For example , focusing on the eight genes in the homologous recombination module [25] , we found YKOs that cause both high phenotypic potentials and high GCR rates ( RAD50 , RAD52 , RAD57 , XRS2 ) , high phenotypic potentials but relatively low GCR rates ( RAD51 , RAD54 ) , and low phenotypic potentials but high GCR rates ( MRE11 ) [48] . Additionally , we estimated that measured GCR rates are likely to be too low to explain the phenotypic heterogeneity observed in the haploid-converted strains ( see Protocol S1 ) . Thus it is unlikely that GCR events alone explain high phenotypic variance . Another source of genetic variation in these lines could be caused by increased insertion rates of transposable elements such as Ty1 [49 , 50] . However , we again found that increased Ty1 insertion rates are neither necessary nor sufficient for high phenotypic potential ( see Protocol S1 ) . It is also possible that these same disrupted processes are changing the mutational spectrum at microsatellites [51 , 52] . Although not related to environmental buffering , this last possible mechanism is of great evolutionary interest , given recent findings in yeast [53] and dogs [54] of phenotypic variation driven by tandem-repeat length changes . Another possibility is that DNA stability knockouts are causing changes at the telomere [55 , 56] or elsewhere that heterogeneously activate cell cycle checkpoints [51 , 57] . Thus , the mechanism by which this subset of capacitors produces phenotypic variability warrants further study . If , however , mutational mechanisms can be excluded , our findings might provide new insight into human malignancies . Many cancers are associated with mutations in genes involved in DNA stability , including orthologs of the capacitors RAD50 [58] , RAD54 [59 , 60] , and YAF9 [61] , or with genes with a large number of interactions , such as the transcription factor p53 [62] . Our results suggest that the first advantage mutations in these genes might provide to developing malignancies is phenotypic heterogeneity by way of network-associated loss of robustness .
We show that: ( 1 ) there are in excess of 300 phenotypic capacitors of environmental variation in S . cerevisiae; ( 2 ) capacitors are highly enriched in GO processes that are likely to result in broad cellular changes when disrupted; ( 3 ) capacitors tend to be SLI hubs; ( 4 ) most capacitor knockouts result in decreases in growth rate that are not severe; ( 5 ) capacitor duplicates tend to be PPI hubs that have undergone a relatively ancient duplication event and diverged in expression from their paralogs; and ( 6 ) capacitor singletons tend to be part of highly interconnected protein clusters whose members are likely to disrupt robustness or be deleterious when knocked out . Taken together , these findings strongly suggest that loss of phenotypic robustness is a widespread phenomenon that is a consequence of disrupted physical or genetic-interaction networks . The mechanism of this disruption appears to be different in cases of duplicate and singleton capacitors , although in both cases incomplete functional redundancy might be causing phenotypic variability when the capacitor is absent ( Figure 6 ) . For duplicate capacitors , this partial functional redundancy appears to operate at the level of the paralogous gene pair . Indeed , two properties of duplicate capacitors , high PPI degree and high expression divergence , are found in common with paralogous pairs that are likely to have some redundancy [39] . For singleton capacitors , functional redundancy appears to operate at a higher level of protein modules with partially overlapping function . One common trait shared by both classes of capacitor is a high number of SLIs . The particularly striking finding that nearly 60% of genes with over 100 SLIs are capacitors ( Figures 3D and S11 ) suggests that genetic interactions might be highly predictive of capacitor identity in other organisms [29 , 30] . A second hallmark of capacitors in other organisms might be the existence of a paralogous gene that has diverged in its regulation , as identified by expression studies or perhaps comparative genomics of cis-regulatory regions . With the increasing availability of automated quantitative phenotyping [63] , these predictions might soon be possible to test . While previous studies have suggested that phenotypic capacitor function must be responsive to changes in the environment to influence evolutionary trajectories [6 , 7] , the finding that many capacitors cause decreases in growth rate that are not severe offers another plausible mechanism in yeast: A loss-of-function mutation in a capacitor could be maintained indefinitely in a heterozygous diploid with little or no impact on fitness . Sporulation would be promoted under harsh environmental conditions [64] resulting in haploids with phenotypic variability that is dependent on the ( previously cryptic ) underlying genotype . Because capacitor loss-of-function usually causes decreases in haploid growth rate that are not severe , these cells could persist for many generations without being out-competed by wild-type counterparts . Some genotypes might provide a selective advantage , which , upon successive rounds of sporulation and mating , could become fixed in the population even in the absence of the capacitor loss-of-function mutation [6 , 7 , 16 , 65] . Alternatively , phenotypic heterogeneity in the absence of genotypic variation might produce epigenetic “persistent” phenotypes with increased fitness in some environments , analogous to those described in models of stochastic phenotype switching [66] . The estimate that approximately 30% of wild S . cerevisiae strains persist for some time as haploids ( i . e . , are heterothallic ) [67 , 68] suggests that these are plausible mechanisms . Several challenges remain , however , to understanding mechanistically how phenotypic robustness impacts evolutionary trajectories . One major challenge is characterizing the relationship between environmental and genetic buffering . Phenotypic capacitors identified here buffer environmental variation . Some have predicted that the same mechanisms that buffer environmental sources of variation will also act to buffer genotypic variation [14] . Evidence in support of this hypothesis is mixed and mostly stems from studies of the molecular chaperone Hsp90 , a known capacitor of genetic variation in flies and plants [6 , 7] . Using fluctuating asymmetry in isogenic Drosophila lines as a measure of robustness to environmental perturbation , Hsp90 was found to buffer environmental variation in some traits [69] but not others [70] . In Arabidopsis , Hsp90 appeared to buffer environmental variation for every trait tested [71] . Results described here provide an experimental system with which to formally test the congruence between the mechanisms of genetic and environmental buffering . Because variability of many quantitative phenotypes can be determined in a high-throughput manner and because we have identified many capacitors of environmental variation , experiments that precisely control and partition different sources of variation can be performed to test if these same gene products contribute to genetic robustness . Another major challenge is determining if the phenotypic heterogeneity that results from disrupted capacitor function in yeast could be advantageous under natural or artificial selection . One indication that this is likely comes from the finding that disrupted capacitor function might provide an advantage in environments that require invasive growth in yeast . A genetic screen found that mutations in 35 genes can promote haploid invasive growth in the ∑1278 genetic background , 27 of which were considered in our study and 12 of which are phenotypic capacitors [72] . Additionally , we found at least one capacitor YKO that promotes robust haploid invasive growth in the S288C background ( SWI6 , unpublished data ) . Stronger evidence comes from a study of stress-sensitive deletion mutants grown in varying concentrations of heavy metals or pro-oxidants [73] . Six deletions , four of which we identify as capacitors ( CTR1 , CUP5 , VMA6 , VMA7 ) , resulted in a fitness disadvantage at moderate toxin concentrations but a clear heterogeneity-dependent fitness advantage over the wild-type at high toxin concentrations . Also of note , genome-wide examination of genes under positive selection in S . cerevisiae [74] finds nine capacitors out of 72 genes . Because of the relatively short generation time of yeast , it is now possible to formally test if disrupted capacitor function can provide a fitness advantage in some environments . Although our study has focused on the effects of deletions of nonessential genes , it might be more relevant to common evolutionary trajectories to ask if subtler allelic changes , such as those that result in altered transcription or protein sequence , could cause loss of robustness . Our prediction is that the allelic changes that affect phenotypic heterogeneity are most likely to alter network architecture or dynamics . One possibility is that such mutations would occur in essential genes because , like capacitor genes , they tend to encode highly connected network hubs . Encouraging results come from a recent study that used the progeny from a cross between a yeast lab strain and a wild isolate to map the variances of 35 quantitative phenotypes to 14 quantitative trait loci , one of which is a single nucleotide polymorphism in the essential G-protein alpha subunit GPA1 [75] . The recent availability of a yeast library with decreased expression of essential genes through mRNA perturbation [76] now makes this possible to test on a genome-wide scale .
Haploid convertible diploid BY4743 YKO magic marker deletion strains ( MATa/α ura3Δ0/ ura3Δ0 leu2Δ0/leu2Δ0 his3Δ1/ his3Δ1 lys2Δ0/LYS+ met15Δ0/MET15+ can1Δ::LEU2+-MFA1pr-HIS3/CAN1+ xxx::kanMX/XXX+ ) were purchased from Open Biosystems . All data analysis was performed using the open-source R statistical computing package ( http://www . r-project . org/ ) . Lowess regression was performed using the “lowess” function with a smoother span of 0 . 2 ( 944 YKOs ) and three iterations for the genome-wide analysis ( Figure 1C ) , and with a smoother span of 0 . 4 ( 40 YKOs ) and five iterations for the repeated analysis ( Figure 2 ) . PAM and silhouette plots were performed using the “pam” and “silhouette” functions from the cluster library . A major problem we faced in dealing with a dataset of 220 phenotypes is removing those phenotypes that might be biologically or physically redundant . Principal components analysis , a common means by which to reduce the dimensionality in a matrix ( remove redundant phenotypes ) , transforms the data to a new coordinate system such that the greatest variance of any projection of the data lies on the first coordinate or principal component , the second greatest variance on the second coordinate , etc . This transformation , however , does not preserve the directionality or syntax of the initial dataset because the coordinates are drawn to maximize the overall variance ignoring whether these values are positive or negative . In other words , loadings of the initial data into a principal component may be negative . Thus , a high value in a principal component may represent a high or low variance in the underlying phenotypes . Because we are interested in identifying genes that only result in high variance , principal components analysis was not appropriate , and so we used a strategy based on clustering instead . PAM [77] was selected for dimensional reduction because it has several advantages to k-means clustering with respect to our question: ( 1 ) resultant medoid cluster centers are real phenotypes from our residuals of standard deviation matrix ( rather than difficult-to-interpret linear combinations of residuals of many phenotypes as in k-means ) ; ( 2 ) using medoids as cluster centers rather than average cluster centers makes the procedure more robust to outliers; and ( 3 ) an “average silhouette” strategy [78] utilized here allows for estimation of the appropriate number of distinct clusters . To estimate the number of nonredundant clusters , the average silhouette strategy [78] was used: we performed PAM over a range of 20 to 100 clusters and generated silhouette plots for each . We then plotted the average silhouette width versus the number of clusters chosen ( Figure S2 ) . Although the average silhouette width peaks around 80 clusters , significant gaps ( silhouette widths close to 0 ) are more likely to appear in silhouette plots when greater than 70 clusters were chosen , suggesting that noninformative clusters are added beyond 70 . Thus , we estimated 70 nonredundant clusters . However , using a range of 50 to 80 clusters for PAM to identify phenotypic capacitors results in an extremely similar set of genes ( Figure S3 ) . Using the reduced matrix of 4 , 718 YKOs by 70 phenotype medoids , we next calculated a single measure of the overall phenotypic variance resulting from a gene's deletion , which we term the phenotypic potential . We generate this score for each YKO by averaging the top 35 ( of 70 ) residuals of standard deviation . The rationale for using 35 of 70 medoids is as follows: ( 1 ) A gene deletion that results in a high variance in only one or a few phenotypes does not meet the pleiotropy requirement of our definition of a phenotypic capacitor . Thus , we sought YKOs that have both a large number of high variance phenotypic medoids and high magnitudes in those medoids ( i . e . , as many medoids should be averaged as possible to capture the overall phenotypic potential ) . ( 2 ) Only 24 YKOs result in high variance ( >1 SD ) in greater than 35 phenotype medoids , all of which were subsequently identified as phenotypic capacitors . Thus , phenotypic potential scores that rely on greater than 35 medoids are likely to increase noise in the scoring procedure . Although we chose to score 35 phenotype medoids to identify phenotypic capacitors , alternative procedures result in an extremely similar set of genes over a broad range of number of medoids scored ( Figure S4 ) . First , we generated 100 randomized phenotypic medoid by YKO matrices by permuting elements within each phenotype column of the original matrix . These 100 matrices are then used to calculate phenotypic potentials . We then generated a reference distribution by averaging the top ranking phenotypic potential for each of the 100 trials , the second top ranking phenotypic potential , etc . This reference distribution was used to estimate the expected false positive rate under the null hypothesis . At a given expected false positive rate , the number of true positives was estimated by subtracting the number of false positives ( i . e . , expected false positive rate × 4 , 718 ) from the number of genes in the actual distribution that have a higher phenotypic potential than the reference distribution ( all positives ) . The maximum number of true positives occurs at an expected false positive rate of 0 . 036 with 333 true positives estimated for 502 positives ( a FDR of 34% ) . YKO magic marker deletion haploid convertible diploid strains ( Open Biosystems ) were grown on YPD agar for ∼48 h , spread on GNA ( 5% D-glucose , 3% Difco nutrient broth , 1% Difco yeast extract , 2% bacto agar ) plates and grown for 24 h . A single medium-sized colony was added to 5 ml of sporulation medium ( 10% potassium acetate , 0 . 005% zinc acetate + Ura + His + Leu ) and incubated at 30 °C for 5–7 d . One to 10 μl of the sporulated cells were spread onto magic media plates ( SC-Leu-His-Arg + canavanine + G418 ) and grown until medium sized colonies appeared ( ∼20 generations and for no more than 72 h when possible ) . Single colonies were frozen at this point for later processing . Cell stocks were streaked out on YEPD agar and grown at 30 °C for a maximum of ∼48 h when possible ( ∼20 additional generations ) . Cells were subsequently grown overnight in 3 ml YEPD at 30 °C with shaking . Because many of the haploid YKO strains were expected to include heterogeneous morphologies , direct cell counts using a hemocytometer were relied upon rather than optical density readings . Cells were counted , then 1 × 108 cells were added to 20 ml YEPD and grown for 3–3 . 5 hrs at 30 °C ( early logarithmic phase ) . Fixation and straining was performed as described in the CalMorph manual with modifications ( http://scmd . gi . k . u-tokyo . ac . jp/datamine/calmorph/ ) . Briefly , cells were fixed in 3 . 7% formaldehyde , 100 mM potassium phosphate , and triply stained for cell-surface manno-protein , actin cytoskeleton , and nuclear DNA using fluorescein isothiocyanate-Con A ( Sigma ) , rhodamine-phalloidin ( Molecular Probes ) , and 4′ , 6-diamidino-2-phenylindole ( Sigma ) , respectively . Cells were mounted in vectashield ( Vector Laboratories ) , and visualized by epifluorescent microscopy on a Nikon Eclipse 90i automated microscope using a 100× objective and a Roper 1K CCD camera . For each YKO , a minimum of 40 micrographs was captured to yield a minimum of 200 ( and usually in excess of 300 ) phenotyped cells that are not classified as “complex . ” Captured micrographs were analyzed for quantitative morphological traits using the CalMorph software package . For all YKOs processed , ∼50 generations is estimated to have passed from sporulation to fixation and phenotyping: ∼40 generations on agar plates and ∼10 generations in liquid media . Control strains are defined as those knockouts with a phenotypic potential that ranked below 1 , 000 in the original analysis of the haploid knockout library . The full GO term hierarchy was downloaded from the GO website on May 11 , 2007 ( http://www . geneontology . org/ ) . To determine GO process term enrichment , the hierarchy was first trimmed by removing GO terms that did not annotate three or more yeast ORFs . Additionally , the highly annotated process GO terms “physiological process , ” “cellular process , ” “biological process , ” and “cellular physiological process” were removed because they are too general to be meaningful . Trimmed hierarchies resulted in 775 process terms . Significance of GO term enrichment of the 502 putative phenotypic capacitors was calculated using the hypergeometric distribution and Bonferroni corrected using the trimmed hierarchy . The May 1 , 2007 release of interaction data ( BIOGRID-ORGANISM-Saccharomyces_cerevisiae-2 . 0 . 27 . tab . txt ) was downloaded from the BioGRID ( http://www . thebiogrid . org/ ) [32] . For analysis of physical interactions , two networks were constructed from the BioGRID: ( 1 ) every physical interaction from the hand-curated literature citation interaction database was included ( including affinity capture-mass spectrometry , affinity capture-western , affinity capture-RNA , cofractionation , colocalization , copurification , fluorescence resonance energy transfer ( FRET ) , two-hybrid , biochemical activity , cocrystal structure , far western , protein–peptide , protein–RNA , reconstituted complex; 5 , 192 nodes , 70 , 900 edges ) ; or ( 2 ) only interactions from the higher confidence affinity capture-mass spectrometry ( 3 , 686 nodes , 47 , 774 edges ) . Because nearly every gene has been used as bait for the affinity capture-mass spectrometry network [33 , 34] and because of the higher consistency and lower rate of false positives compared to other methods , this network might represent the most unbiased view of the PPI network . Thus , this smaller network was used to calculate network properties of genes such as betweenness and clustering coefficient , to control for PPIs when estimating other gene properties such as synthetic lethality and dispensibility , and to estimate the properties of immediate neighbors in the physical interaction network . All interactions in the BioGRID annotated with “Synthetic Lethality” were used to generate the SLI network ( 2330 nodes , 18 , 550 edges ) . Because the SLI network is incomplete , we hand curated synthetic-lethal entries to create a subset of this network that only includes interactions derived from SGAs where both genes are completely functionally compromised and where the experiment contains ten or more total interactions ( 1 , 326 nodes , 11 , 934 edges ) . The subset of genes involved in SLIs discovered in SGA experiments was further separated into those genes that have been used as “bait” and those that have not , “prey . ” Double knockouts for the 267 bait genes and all other nonessential genes have been performed and thus the complete SLIs for bait genes have likely been discovered; however , double knockouts for the 1 , 060 prey genes have only been performed with the 267 genes used as bait and thus represent an incomplete , although likely representative , interaction set . Genes without any interactions were excluded from the interaction networks . Betweenness [79] was calculated using the “betweenness” function from the “sna” library in R . The clustering coefficient for each gene was calculated , as described [80] . Essential genes are those genes that were determined to be essential in the systematic deletion project [81] except for the following genes which were deemed nonessential based on subsequent synthetic-lethal analysis: YJL174W , YJR057W , YLR103C , YOR326W , YPL153C , YBR234C , YDL102W , YDL029W , YDL017W , YDL003W , YDR052C [19 , 82] . Unless otherwise noted , nonessential genes refers to the 4 , 718 genes that were knocked-out and phenotyped in the high-dimensional quantitative morphological phenotyping experiment including any identified phenotypic capacitors [17] . All putative phenotypic capacitors ( those with the top 502 phenotypic potential scores ) were compared to all essential genes by Wilcoxon-Mann-Whitney test using PPI degrees derived from both the literature citation and mass spectrometry networks ( p < 2 . 2 × 10−16 and p < 5 . 2 × 10−13 , respectively ) . A similar comparison was made using only capacitors in the C1 and C2 classes ( p < 2 . 0 × 10−6 for the literature citation network , and p < 7 . 8 × 10−7 for the mass spectrometry network ) . Genome-wide datasets were acquired from the following sources: PPIs and SLIs [32] , haploid growth rates [37] , heterozygous and homozygous diploid growth rates [38] , duplicate and singleton identity [39] , ohnolog identity [45] , Ks of duplicate pairs [39] , mRNA length [43] , mRNA abundance [83] , protein abundance [84] , noise in protein expression in permissive ( YEPD ) and restrictive ( SD ) media [46] , Ka/Ks [43 , 85] . The expression similarity between duplicate pairs was acquired from Kafri et al . [39] and was determined using correlations of expression over several reported expression array experiments ( Ran Kafri , personal communication ) . The Ka for duplicates resulting from the whole genome duplication ( ohnologs ) was calculated as follows: alignments of the S . cerevisiae ohnologs and an ortholog from Kluyveromyces waltii , a related species whose divergence precedes the whole genome duplication event , have been previously performed [86] . Ka was calculated from these alignments in PAML [87] using the Yang and Nielsen method [88] with all settings set to default except for “icode , ” which was set to 2 to reflect the yeast genome . Because Ks and Ka are correlated with the expression level [43] , we performed analyses of covariance ( ANCOVAs ) to estimate evolution rate differences . ANCOVAs were performed as described with modifications [43] . In one case we used Ks instead of dN , and we used the average mRNA expression level of the two paralogs as the continuous variable . Only duplicate pairs for which the expression level of each paralog has been measured were used for the analysis ( Figures S16 and S18 ) . Parameters ( such as phenotypic potential , dispensability , physical , or synthetic-lethal degree ) for the immediate PPI neighbors of a given gene were calculated as follows: first , each gene is given a neighbor score in the parameter by averaging the scores of all of its neighbors . For example , a gene with three neighbors with haploid growth rate scores of 0 . 5 , 0 . 9 , and 1 . 0 will get a neighbor haploid growth rate score of 0 . 8 . Second , the effect of PPI degree on the neighbor parameter score is removed by plotting the neighbor parameter score for each gene versus its PPI degree , fitting a lowess regression to this plot , and taking residuals of the curve fit as a measure of the neighbor parameter controlled for the number of PPIs . Thus , the gene described above with three neighbors and a neighbor haploid growth rate score of 0 . 8 will only have a low neighbor growth rate ( and a negative residual ) if it is low relative to other genes with a similar number of PPIs . | Most species maintain abundant genetic variation and experience a wide range of environmental conditions , yet phenotypic differences between individuals are usually small . This phenomenon , known as phenotypic robustness , presents an apparent contradiction: if biological systems are so resistant to variation , how do they diverge and adapt through evolutionary time ? Here , we address this question by investigating the molecular mechanisms that underlie phenotypic robustness and how these mechanisms can be broken to produce phenotypic heterogeneity . We identify genes that contribute to phenotypic robustness in yeast by analyzing the variance of morphological phenotypes in a comprehensive collection of single-gene knockout strains . We find that ∼5% of yeast genes break phenotypic robustness when knocked out . The products of these genes tend to be involved in critical cellular processes , including maintaining DNA stability , processing RNA , modifying proteins , and responding to stressful environments . These genes tend to interact genetically with a large number of other genes , and their products tend to interact physically with a large number of other gene products . Our results suggest that loss of phenotypic robustness might be a common phenomenon during evolution that occurs when cellular networks are disrupted . | [
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] | 2008 | Network Hubs Buffer Environmental Variation in Saccharomyces cerevisiae |
Discriminating the causative disease variant ( s ) for individuals with inherited or de novo mutations presents one of the main challenges faced by the clinical genetics community today . Computational approaches for variant prioritization include machine learning methods utilizing a large number of features , including molecular information , interaction networks , or phenotypes . Here , we demonstrate the PhenomeNET Variant Predictor ( PVP ) system that exploits semantic technologies and automated reasoning over genotype-phenotype relations to filter and prioritize variants in whole exome and whole genome sequencing datasets . We demonstrate the performance of PVP in identifying causative variants on a large number of synthetic whole exome and whole genome sequences , covering a wide range of diseases and syndromes . In a retrospective study , we further illustrate the application of PVP for the interpretation of whole exome sequencing data in patients suffering from congenital hypothyroidism . We find that PVP accurately identifies causative variants in whole exome and whole genome sequencing datasets and provides a powerful resource for the discovery of causal variants .
Since the first successful identification of disease-causing variation From whole exome sequencing in 2010 [1] , impressive advances have been made in the field of next generation sequencing and its related analysis , with the aim of fulfilling the promise of whole exome ( WES ) and whole genome ( WGS ) sequencing for personalized medicine . Such approaches have revolutionized our ability to identify the genetic underpinnings of disease as well as improve our capacity to stratify patient populations and diagnose them in a more accurate and timely manner [2] . A recent critical study provided some objective estimates of the efficiency of diagnoses by traditional medical genetics diagnostic approaches , with 54% of referred patients undiagnosed [3] . The introduction of next generation sequencing ( NGS ) technologies in clinical settings is anticipated to improve diagnosis efficiency , and between 13% [4] to 50% of those remaining undiagnosed are likely to receive a molecular diagnosis following WES or WGS [5] . Nevertheless , the success rate of the state-of-the-art tools for identifying causative variants using WES data range between 22% to 25% [6 , 7] , and WGS data in a similar range [8] depending on the disease type and the availability of sequence data from family members . The identification of the causative disease mutations in an individual patient remains a challenge due to the complexity and scale of the task . An individual exome might contain 20 , 000-30 , 000 variants with respect to the reference genome; a third of which might comprise non-synonymous variation [9] . Many thousands of variants in an average genome might be unique , and on average 20 genes may have complete loss of function ( LOF ) mutations [10] whose physiological consequences for the bearer are unpredictable [11] . Adding to the complexity of analysis are contingencies such as oligogenicity and haploid insufficiency . Oligogenicity is the phenomenon where additional genes modify the phenotypic effect of a variant in a primary gene , so that the overall disease phenotype is the consequence of multiple variants in the same genome . Haploid insufficiency describes a situation where loss of function of one allele of a gene in a normal diploid cell or individual results in an abnormal phenotype . For many genes , loss of function of one allele is not significant , but for some genes , dosage is critical and phenotypic effects are seen with the loss of one allele . Consequently , in haploid insufficiency , a heterozygote with a loss of function allele may develop an abnormal phenotype [12] . Given these phenomena , it is clear why finding the “needle in a stack of needles” [13] remains one of the key challenges in fully utilizing WES and WGS data for personalized medicine . The main approaches taken to prioritize the pathogenic consequences of genomic mutations involve variant calling to identify variants from raw sequencing data , filtering by variant quality , filtering by minor allele frequency , and then successive assessment of variant properties based on its potential to affect protein integrity and function , for example , by the insertion of nonsense codons or indels , compromising the function of active sites , protein-protein interactions , dominant or recessive inheritance , physico-chemical properties , sequence conservation [14] , or analysis of changes in the DNA regulatory domains [15] . Although the majority of the methods currently used to assess pathogenicity of a variant are focused on exonic variation , there are also methods that examine non-coding sequences , notably GWAVA , CADD , DANN , FATHMM-MKL , and others [16–20] . However , many of these methods alone are not able to identify the causative variants underlying a patient’s phenotype and require additional investigation , such as analysis of additional family members , to look for de novo variants , identification of shared rare variants in unrelated individuals with similar diseases [21] , and identity-by-descent inference [2] . Prioritizing disease candidates by using phenotypic similarity to known diseases and characterized non-human disease models can potentially add an additional layer of discrimination to gene prioritization , but until recently the ability to computationally establish formal phenotypic relatedness at scale was not possible . Two crucial developments have enabled the computational integration and comparison of phenotypes: the systematic application of the PATO framework [22 , 23] and the development of the cross-species anatomy ontology Uberon [24] . While PATO provides a uniform way of describing phenotypes , Uberon can be used to systematically describe and relate anatomical structures between species . In 2011 , PhenomeNET [25] was developed to exploit phenotype-genotype associations observed in humans and model organisms and prioritize candidate causal genes based on patient phenotypes . PhenomeNET makes use of axioms and formal definitions in the major phenotype ontologies using the PATO ontology [22] to formally integrate species-specific phenotypes [26–30] . It gathers phenotype data from model organism and human genotype-phenotype databases , applies measures of phenotypic similarity and then systematically compares them across species . PhenomeNET has been demonstrated to provide a high degree of predictive accuracy for the discovery of animal models of human disease [31] , novel pathways [32] , gene function [33] , and druggable therapeutic targets [34] . Since the introduction of PhenomeNET , several further methods have been been developed that take advantage of this approach and utilize phenotypic similarity between patients and gene-phenotype associations in public databases to improve variant prioritization for WES datasets [35–37] . We developed PhenomeNET Variant Predictor ( PVP ) to prioritize causal variants based on comparing patient phenotypes with gene-phenotype associations made in humans and model organisms . PVP combines two main sources of information: molecular and phenotypic . We use molecular information from multiple pathogenicity prediction tools to identify the pathogenicity of a variant and the phenotypic information to determine whether a variant is causative . PVP facilitates a highly accurate identification of causative variants from both WES and WGS datasets , and we demonstrate the performance of PVP on a set of synthetic and real whole exome and whole genome sequences . Our results demonstrate that PVP significantly outperforms other state of the art tools revealing that phenotypic similarity can provide a powerful approach for prioritizing causal variants .
PVP has been developed to facilitate the identification of causative variants in genomic data ( whole exome or whole genome ) . We consider a variant to be causative if it is both pathogenic ( evaluated based on molecular information ) and involved in developing the patient’s phenotype ( evaluated based on the gene–disease similarities provided by PhenomeNET ) . Variants may be pathogenic but not causative if they are not involved in the pathogenesis of the patient’s phenotype [11] , whilst non-functional , benign variants are generally not causative . In PVP , we combine methods to determine whether a variant is pathogenic ( i . e . , functional ) with information about the phenotypes in which a gene is known to be involved to identify candidate causative variants in WES and WGS data . For predicting pathogenicity , we utilize tools that can provide a pathogenicity score for every variant within a genome , i . e . CADD [17] , DANN [18] , and GWAVA [16]; for the latter , we use an improved version of the PhenomeNET framework to match a patient’s phenotypes with a database of gene-phenotype associations derived from human , mouse and fish resources . The full list of features used for prediction in PVP is provided as S1 Table . PhenomeNET consists of a repository of gene-phenotype associations from human and model organisms , an ontology that integrates phenotypes across species , and a semantic similarity measure that determines the similarity between two sets of phenotypes . It provides a score that measures the similarity between a set of patient phenotypes and sets of phenotypes in the PhenomeNET repository . Depending on the intended application , the choice of gene-phenotype associations can strongly affect the performance of PhenomeNET [31] . Here , we utilize two overlapping sets of gene-phenotype associations; we include gene-phenotype associations observed in zebrafish and mouse ( marked “Model” for Model Organism Databases ) , and additionally include human phenotypes propagated from known gene-disease and disease-phenotype associations ( marked “Human” in our experiments ) . We also use both genotype-phenotype associations together . We represent variants by their pathogenicity scores , the scores provided by the PhenomeNET system to measure similarity between the patient’s phenotype and known phenotypes associated with the gene affected by the variant , a small set of high-level phenotypes observed in a patient , as well as mode of inheritance of the disease ( if known ) and zygosity of the variant . We use these as features to train a random forest classifier that separates variants into causative variants and non-causative variants . Initially , we use 80% of the pathogenic variants available from the ClinVar database [38] to train our model , keeping 20% of the ClinVar variants for further testing . In 10-fold cross validation on these 80% , our model achieves an area under the receiver operating characteristic curve ( ROC AUC ) of up to 0 . 994 and F-measure of up to 0 . 963 ( S2 Table ) . To test the performance of this model in identifying causal variants in sequencing data , we generated a synthetic dataset of 11 , 251 whole genomes sequences ( one for each of the 20% variants in ClinVar that were not used to train the model ) . The synthetic dataset was created by randomly choosing one of the WGS samples from the 1 , 000 Genomes Project ( 1KGP ) [39] and inserting a single causative variant in each of these . 8 , 746 causative variants were inserted in exonic regions and 2 , 505 in non-exonic regions . Next , we mark the synthetic individual as having the disease and use the phenotypes associated with the disease in the HPO database [40] as the patient phenotypic profile before trying to recover the inserted pathogenic variant using our PVP-based models . Before applying our PVP models , we apply a filter to remove variants with ≤ 1% global minor allele frequency from 1KGP on each variant . We perform two experiments to test the performance of PVP , PVP-Human and PVP-Model . First , we remove all non-exonic variants from the synthetic genomes to simulate a WES dataset and employ the resulting WES dataset to assess our recovery rate of causative variants located in an exonic region . We identify 45 . 82% of the candidate causative variants as the top ranked and 72 . 64% of the causative variants in the top 10 ranked variants for WES data using only model organism phenotypes to determine phenotypic similarity , 79 . 21% of variants top-ranked and 87 . 94% variants in the top 10 ranks when using only human phenotypes , and 78 . 80% top-ranked and 89 . 50% within the top 10 when using both human and model organism phenotypes together . As second experiment , we apply our approach to all variants in the whole genome sequences , and recover 12 . 62% of the variants at first rank and 23 . 75% within the first 10 ranks using only model organism phenotypes , 75 . 10% variants top-ranked and 89 . 36% in the top 10 ranks using only human phenotypes , and 76 . 47% top-ranked and 88 . 61% within the top 10 when using both model organism and human phenotypes . Tables 1 and 2 summarize these results . We compare our method against several state of the art variant prioritization tools , namely CADD [17] , DANN [18] and GWAVA [16] , as well as the phenotype-based tools Exomiser/Genomiser [41 , 42] , Phevor [35] and eXtasy [37] . Our results and the comparison with state of the art tools is summarized in Tables 1 and 2 as well as Figs 1 and 2 , demonstrating that PVP outperforms the other methods in our experiments . We further assess how well our method performs on identifying causative variants for diseases with different mode of inheritance ( MOI ) in WES data . The percentage of cases in which the causal variant is ranked first is shown in Table 3 . We find that , unsurprisingly , our models perform better on recessive diseases as the variants have to be homozygous , which can be used as an additional filter , while a dominant mode of inheritance may be caused by either heterozygous or homozygous variants , and complicated by haploid insufficiency , and hence cannot be used to discriminate between causative and non-causative variants . To evaluate the importance of the “depth” of phenotyping [43] for predicting candidate variants , we compared the predictive accuracy of PVP with the information content in the disease ( or patient ) description . Information content of a phenotype class is measured by its depth in the PhenomeNET ontology and the number of diseases in our sample that contain this phenotype . For diseases associated with multiple phenotypes , we sum the information content of the individual phenotype classes . We evaluate the correlation between the rank of the causative variant in our set of 8 , 746 synthetic exome sequences and the information content associated with the disease , and find a negative correlation ( Spearman’s rank correlation ρ = −0 . 54 ) , i . e . , if the information content of the phenotypes used to characterize the disease ( or patient ) is higher , PVP can provide better predictions . The set of phenotypes observed in patients is not always complete , or patients may suffer from multiple co-morbidities that can affect our phenotype-based analysis . To determine the effect of noise on our analysis , we focus on a subset of 8 , 522 out of 8 , 746 synthetic whole exome sequences for which the disease is characterized phenotypically ( the remaining cases were imputed by our algorithm , see Materials and Methods ) , and we perform two experiments ( see S3 Table ) : first , we randomly add the phenotypes of a second disease to the phenotypes of the patient to simulate co-morbidity; and second , we randomly remove each phenotype used to characterize the patient’s disease with a probability of 1/3 ( i . e . , on average , 1/3 of the phenotype annotations for each disease are removed ) . Using the PVP-Human model , we find that in the first experiment , only 3 , 547 ( 41 . 62% ) variants are ranked first and 4 , 315 ( 50 . 63% ) in the top 10 , compared to over 75% ranked first with phenotypes matching the disease exactly . In our second experiment , removing phenotypes with probability 1/3 results in 3 , 963 ( 46 . 50% ) of causative variants ranked first and 4 , 921 ( 57 . 74% ) in the top 10 . We further investigated how well PVP can distinguish between variants that are causative for closely related diseases . For this purpose , we insert a second causative variant v2 to the whole exome sequence of the synthetic patients ( each containing a single causative variant v1 ) . The second variant v2 is chosen to be causative for the most phenotypically similar disease ( within our test dataset ) . We then use the phenotypes associated with v1 and test at which rank v1 and v2 are predicted by PVP . Using PVP-Human , we find v1 ranked first in 62 . 38% of the cases , while v2 is ranked first in 15 . 36% of the cases , demonstrating that PVP can also discriminate between closely related diseases . Combining the phenotypes associated with v1 and v2 , we predict both v1 and v2 with equal probability of 37% on the first rank ( see S3 Table ) . To make PVP available as a tool for diagnostic support , we re-train all our models using the whole ClinVar dataset and combine the phenotype similarity computation using PhenomeNET with annotation of pathogenicity into the PVP tool . PVP can analyze WES or WGS datasets using the VCF file and a set of observed patient phenotypes as input and then outputting a list of variants ranked by the likelihood they are causative for the observed phenotypes . We evaluate the performance of PVP on a series of real exomes from individuals diagnosed as having Congenital Hypothyroidism ( CH ) , included in the UK10K dataset [44] ( see Methods ) , to assess how well we could recover potentially pathological variants in genes already associated with the disease . Congenital hypothyroidism is one of the most frequent endocrine disorders of the neonate with a frequency of up to 1/1 , 500 births [45] , although some forms and molecular etiologies can be much more rare , such as Central Congenital Hypothyroidism ( CCH ) [46] estimated at around 1/16 , 000 . Historically , most cases were thought to be due to thyroid gland dysgenesis comprising ectopias , hypoplasia and complete agenesis [47] . However , recently , an increase in diagnosis of CH in the presence of apparently anatomically normal glands ( gland-in-situ ) has been reported [45] . The pathophysiology of such cases may include organisational and functional defects ( dyshormonogenesis ) within the glands leading to compromised or absent function . A range of genes has been implicated in these processes which include thyroid transcription factors , genes involved in thyroid hormone biosynthesis , and the Thyroid Stimulating Hormone receptor ( TSHR ) [48] . Mutations in known genes are implicated in less than 5% of thyroid dysgenesis cases , whereas dyshormonogenesis is usually associated with mutations in components of the thyroid hormone biosynthetic machinery [47] . We analyze 43 individuals from the UK10K rare disease cohort of patients and relatives with congenital hypothyroidism , using PVP . The dataset includes 11 confirmed cases of thyroid dysgenesis ( DG ) , 30 CH with gland-in-situ ( GIS , likely involving dyshormonogenesis ) , and two with CCH , in addition to 80 individuals that do not show any phenotypes but have a family relation to the affected individuals . We use a common set of phenotypes from the HPO for the whole cohort , comprising hypothyroidism ( HP:0000821 ) , congenital hypothyroidism ( HP:0000851 ) , TSH excess ( HP:0002925 ) , thyroid hypoplasia ( HP:0005990 ) , and TSHR defect ( HP:0011791 ) ; these are the most relevant phenotypes in HPO . We analyze the individual cases independently and do not account for the relationships between individuals . Thirty six of these show variants in genes already associated with CH within the top 20 hits , filtered for a minor allele frequency ( MAF ) of 1% ( S4 Table ) while the remainder do not show known CH-associated disease genes above this rank . We do not , in the current study , further analyze the likelihood that high ranking genes in these 7 individuals might represent novel genes in this disease or differential diagnoses . Of the 11 cases of thyroid dysgenesis , 9 show homozygous or heterozygous alleles of genes already implicated in dysgenesis-associated CH within the first five ranked hits . All were assessed as deleterious or possibly deleterious by SIFT [49] , PolyPhen [50] , or both . These genes include GLIS3 [51] , NKX2-1 [52] , and PAX8 [53] . One case shows a predicted deleterious allele of LHX3 normally associated with CCH through an effect on pituitary development [46] . Of the cases with GIS all but 9 show deleterious alleles in DUOX2 [54] , TG [55] , or TPO [56] , and in some cases predicted pathogenic variants of two or three of these genes are found together in the highest ranks in our analysis . The remainder show variants in NKX2-1 , LHX3 , and , in one case , PAX8 . Homozygous alleles in DUOX2 and TPO are present in 15 individuals . One homozygous variant has been previously reported in ClinVar to be pathogenic and affects iodotyrosyl coupling ( NM_003235 . 4 ( TG ) :c . 638+5G>A ) [57] . In five cases of GIS , homozygous mutations of TG are found in the same individual as deleterious heterozygous DUOX2 alleles . In one case , a homozygous DUOX2 allele is found with a compound heterozygote in TG . While our analysis of the complete dataset provides hypotheses about the most likely disease-causing variants , confirmation requires detailed analysis and re-sequencing . Of the 43 cases we analyze , 15 individuals with CH were previously subjected to Sanger sequencing of candidate variants , confirming the association with the disease [58] . In 9 of these 15 cases , PVP correctly implicates the likely causative alleles as the first hit . In six of the cases , potentially deleterious mutations are found in two genes , and in five of these six cases , PVP correctly identifies the second gene within the first 10 ranks . Additionally , multiple mutations in TG are found in three cases , and in two of these , PVP identifies the second variant as the second rank ( S5 Table ) . The unexpected involvement of oligogenic and triallelic loss of function/hypomorphic mutations in the genesis of congenital thyroid disease is discussed in [58] . We also test PVP with diseases displaying different sets of phenotypes . We utilize data available from the Personal Genomes Project ( PGP ) [59] and examine if we can predict disease-associated variants consistent with the information that patients that participate in the PGP have declared . We analyze two patients from the PGP , one patient ( PGP:hu92FD55 ) with a disease in mental functioning ( Asperger’s Syndrome ) the other ( PGP:hu432EB5 ) with hemostasis abnormalities ( Von Willebrand disease ) . For the individual associated with Asperger Syndrome ( OMIM:300494 ) , the top variant predicted by our approach is in PLCB1 , phospholipase C beta 1 , located at 20p12 . 3 . PLCB1 , which is involved in extracellular signal transduction in the phosphoinositol pathway , has been implicated in GWAS analysis for autism spectrum associated phenotypes in the ALSPAC study [60] and a homozygous deletion in a single case of malignant migrating partial seizures in infancy ( MMPEI ) [61] . Rare mutations associated with autistic spectrum disorders , largely small deletions and duplications , have been reported within and around the gene [62] . The variant seen here is predicted to be pathogenic , heterozygous , and has not been previously reported , suggesting that this is not a simple LOF mutation as seen in MMPEI , and may warrant further research . For the case of the patient associated with von Willebrand disease ( OMIM:193400 ) [63] , VWF is the top hit in our analysis , identifying the variant ( chr12:6143978G>A ) , already described as pathogenic . This individual is heterozygous , consistent with the known pathogenesis of type 1 von Willebrand disease . PVP provides a system for prioritization of causative genomic variants . While other systems have previously used phenotypes for variant prioritization [35 , 37 , 41 , 42] , key novelties of PVP are a novel cross-species phenotype ontology and the way in which gene-phenotype information is used for variant prioritization . The choice of gene-phenotype associations strongly determines the performance of the system and possible application scenarios . In particular , in contrast to systems such as Phevor or Exomiser , we explicitly provide PVP with the option to ignore human phenotype information and rely only on independent data from model organisms . Human phenotypes , provided by the HPO project [40] , are derived from disease phenotypes by identifying causative genes for a disease and propagating the phenotypes associated with the disease to the known disease genes . While we observe a strong increase in performance when using these propagated human phenotypes , methods that are trained using them will likely over-emphasize known disease genes and therefore only provide limited performance in identifying variants in novel disease genes . Another observation from our experiments is that the type of evaluation has a strong impact on the reported performance . We evaluate PVP and related variant prioritization systems using ClinVar variants , and , since PVP was trained using this dataset , we specifically evaluate PVP and the other systems using a 20% holdout set that we have not used for training our models so that we can determine its performance on unseen variants . While we find that PVP performs comparably to , or better than , other systems in our experiments using WES data , we also observe a striking difference in performance to previously reported results for some variant prioritization systems . For example , Exomiser has been reported to identify up to 97% of causative variants on the first rank in prior experiments using WES data [41] , and over 70% of causative variants on the first rank in WGS data [42] . The main difference between our experiments and those performed to evaluate Exomiser/Genomiser is the use of a different evaluation dataset which only partially overlaps with the dataset used to evaluate Exomiser/Genomiser . Additionally , the results reported in the evaluations of Exomiser and Genomiser [41 , 42] that found up to 97% of variants to be predicted correctly were performed on the model’s training data , i . e . , using an overfitted model [41] . Such a strategy will be able to accurately find known variants ( i . e . , variants on which the model has been trained ) , but , as demonstrated by our results , will perform with lower accuracy on previously unseen or novel data . In PVP , we chose to focus on two different application scenarios that should be among the most useful in the task of interpretation of variants in a clinical setting: identification of causative variants in known disease genes ( using PVP-Human ) , and identification of causative variants in potentially novel genes ( using PVP-Model or PVP ) . Use of phenotypic similarity of experimental mouse models to human diseases has been shown to guide the discovery of the associated human gene . For example the mouse “hairless” mutation was first described in 1859 and the gene identified in 1994 [64] . On the basis of phenotypic similarity to alopecia universalis , the human gene was identified as the human homologue of mouse “hairless” in 1998 [64] . In PVP , phenotype data from mouse and fish models is particularly useful when no human phenotypes are available for a gene , i . e . , when a variant is in a gene not previously implicated in a disease . Currently ( 23 Jan 2017 ) , mouse phenotypes are available for 9 , 045 mouse genes with human orthologs , but only 3 , 698 genes are associated with phenotypes in OMIM , and we evaluated the effect of using mouse phenotype data for variants in genes without available human phenotypes ( see S6 Table ) . In our analysis , we can identify a variant ( rs766783183 ) in the keratin 25 ( KRT25 ) gene at rank 8 for Hypotrichosis 8 ( OMIM:278150 ) in our analysis based on a strong concordance between mouse phenotypes ( all of which are associated with hair and nail morphology and hair growth ) and the phenotypes associated with the human disease . Using PVP without model organism phenotypes results in rank 172 for the same variant . Similarly , we can improve the rank of a variant ( rs764239923 ) in the Gliomedin ( GLDN ) gene as causative for lethal congenital contracture arthrogryposis-11 ( OMIM:617194 ) from rank 342 without model organism phenotype to rank 7 using model organism phenotypes based on matching nervous system abnormality phenotypes in the mouse . However , in some cases , the model organism phenotypes add noise to the results , especially where there are discordant phenotypes , either for reasons intrinsic to the disease , due to differences in human and mouse physiology , or because the scope of phenotyping in the model organism is distinct from that carried out on humans . For example , a variant ( rs121908425 ) in the collapsin response mediator protein 1 ( CRMP1 ) gene would be prioritized at rank 1 for the disease Ellis-van Creveld syndrome ( OMIM:225500 ) without relying on any phenotypes and based on pathogenicity of the variant alone . All phenotypes associated with the mouse ortholog Crmp1 are associated with abnormal nervous system physiology and morphology , while the phenotypes associated with the human disease relate to a wide range of morphological abnormalities . Consequently , when relying on PVP-Mod that uses phenotypic similarity to model organism phenotypes , prediction of the causative variant drops to rank 65 . In our quantitative evaluation , predictive performance including mouse phenotypes is slightly less than performance relying on human phenotypes alone , demonstrating ( unsurprisingly ) that model organism phenotypes are overall less similar to a human disease than phenotypes observed in humans . However , in particular in cases where no human phenotypes are available or causative variants occur in genes not previously implicated in a disease , model organism phenotypes may aid in identifying causative variants . In the future , methods should be developed that can determine automatically whether the phenotypes observed in a model organism are of sufficient quality and depth to contribute to prioritization of causative variants . Mobilizing the volume and richness of genotype-phenotype associations From human and model organism databases provides a powerful resource with which potential disease candidates can be discriminated . Data From large scale mutagenesis efforts and hypothesis-driven science have created sufficient genotype-phenotype association data . PhenomeNET [25] was developed as a framework that exploits these phenotypes in a computational approach , using phenotypes as surrogates for their underlying genes . By identifying relations between phenotypes , PhenomeNET identifies the similarity between the underlying molecular processes and their components . We have developed PVP as a computational method to prioritize variants , and we demonstrate here using synthetic and real patients’ genomic data that PVP is a system for highly accurate genome-scale identification of causative variants involved in human disease . PVP on its own relies only on model organism phenotypes and is particularly useful when variants in potentially novel genes should be found; PVP-Human emphasizes variants in known disease genes and should be used when variants are suspected in genes already known to be involved in the pathogenesis of a disease .
Changes in the HPO , MP and other ontologies , as well as improved OWL reasoning technologies [65] , allowed us to improve upon the method originally used to build the PhenomeNET [25] to generate a more comprehensive phenotype ontology spanning zebrafish , mouse and human . PhenomeNET includes all classes contained in the HPO , MP , but is formalized primarily based on the structure of anatomy and physiology ontologies [66] . All our experiments are based on ontology versions downloaded from the AberOWL ontology repository [67] on 10 June 2016 , and all ontologies included in the PhenomeNET ontology are from this date . The PhenomeNET ontology includes UBERON [24] , GO [68] , BSPO [69] , ZFA [70] , PATO [22] , CL [71] , NBO [72] , but removes all disjointness axioms from these ontologies prior to inclusion due to possible inconsistencies arising from these . Furthermore , the PhenomeNET ontology includes the CHEBI [73] and MPATH [74] ontologies as imports . Within the PhenomeNET ontology , axioms are rewritten to follow the phene pattern [66] so that phenotypes are primarily organized by anatomical structure or physiological process . In particular , within HPO and MP , we identify axioms for a phenotype class P by identifying a class E and Q , and reformulate the formal definition of P as P EquivalentTo: has-part some ( E and has-quality some Q ) . We initialize E and Q with owl:Thing and then generate axioms from the definition of P provided by HPO or MP using the following rules: These axioms are intended to reformulate axioms in the HPO and MP so that each phenotype class characterizes a whole organism that has an entity E as part which is further characterized by its qualities and relations to other entities . Furthermore , the axioms aim to enforce a taxonomic structure that closely resembles anatomy ( from Uberon ) and physiology ( from GO ) . Specifically , if X is a subclass of part-of some Y in either Uberon or GO , the axioms aim to force X phenotype to become a subclass of Y phenotype . To completely resemble parthood relations , we further generate an additional phenotype class S for each unique E that we identify , using the axiom S EquivalentTo: has-part some ( part-of some ( E and has-quality some owl:Thing ) ) . This class serves as additional class that is not usually present in either HPO or MP , and enforces the taxonomic structure of the PhenomeNET ontology to follow both the taxonomic structure and parthood structure of the GO and Uberon . Zebrafish phenotypes are not represented using a dedicated phenotype ontology but rather annotated using E and Q classes directly . Within the PhenomeNET ontology , we generate one class for each unique combination of E and Q found in annotations to zebrafish models . If two entities are used to annotate a zebrafish model ( i . e . , E1 and E2 , we generate the axiom P ≔ has-part some ( E1 and has-quality some ( Q and towards some E2 ) ) . The ontology structure is not manually created but must be inferred using deductive reasoning . We rely on the ELK reasoner [65] to infer the ontology structure . The PhenomeNET ontology is updated regularly , is freely available and can be queried using the ELK reasoning in the AberOWL ontology repository [67] . We collected the mutant model organism phenotypes for mouse from the MGI database [75] on 14 December 2015 , human phenotypes From the HPO database [40] on 14 December 2015 , and zebrafish phenotypes from the ZFIN database [70] on 13 December 2015 . We compute semantic similarity between a patient phenotype and the collection of model organism and human phenotypes using Resnik’s measure [76] with the Best Matching Average ( BMA ) strategy for combining pairwise similarities . We use Resnik’s information content measure [76] computed over the corpus of gene-phenotype associations ( from human , mouse and zebrafish ) as specificity measure for each class in the phenotype ontology . Semantic similarity is computed using the Semantic Measures Library [77] . We normalize semantic similarity values to the range of [0 , 1] for the annotation of variants by dividing each similarity value by the maximum similarity observed for each patient phenotype profile . To train our models , we used the set of variants from ClinVar [38] . ClinVar is a public archive of human variations with their corresponding clinical significance . Clinical significance information in ClinVar is provided based on the American College of Medical Genetics and Genomics ( ACMG ) guidance in describing variants identified in genes that cause Mendelian disorders . We used ClinVar ( dated 05 January 2016 ) using the reference genome of GRCh37 . p13 as our main set . Within the 120 , 509 records in this dataset , we identified two sets of variants that we use for training , a set of pathogenic variants ( ClinVar significance code 5 ) and a set of benign variants ( ClinVar significance code 2 ) . Additionally , for each pathogenic variant , we obtain the disease that the variant causes , identified through its OMIM identifier [78] . By default , ClinVar grouped a variant with multiple alleles into a single record . By using the VCF2TSV parser script from VCFLIB ( https://github . com/vcflib ) we converted the VCF format file of ClinVar to a tab-delimited format file and split the variants with multiple alleles into multiple records . We further split variants that are associated with multiple diseases into multiple records . Next , we downloaded the mode of inheritance ( MOI ) for diseases in OMIM From the HPO phenotype database . We obtained a total of 5 , 864 MOI records which were classified as “Dominant” , “Recessive” , “Multifactorial” , “Others” , “Sporadic” , “X-linked” and “Y-linked” . We combined this information with the variants from ClinVar to generate candidate disease-causing genotypes; if the MOI of the disease associated with a ClinVar variant is “Recessive” , we generate a single homozygote genotype using the variant; in all other cases , we generate a heterozygote as well as a homozygote genotype based on the variant . The results are 43 , 236 genotypes classified as pathogenic and 52 , 084 genotypes classified as benign . This set includes 12 , 783 pathogenic non-coding variants ( i . e . , variants that do not lie in an exonic region , including intronic and intergenic variants ) . So that we can quantitatively evaluate our method , we generated 11 , 251 synthetic whole genome sequences corresponding to our hold-out test sets . To generate this test set , we inserted a single pathogenic variant into a randomly selected whole genome sequence from the 1000 Genomes Project , hg19 . In 8 , 746 of these sequences we inserted an exonic causative variant and in 2 , 505 we inserted a non-exonic causative variants . 46 exonic and 7 non-exonic variants from our holdout set were excluded as they have a MAF higher than our cutoff of 1% . We generated synthetic exome sequences by removing non-exonic variants from the 8 , 746 WGS files that include an exonic variant . We use these synthetic whole exome and whole genome sequences to test the performance of our method . We split the set of 43 , 236 pathogenic variants randomly into 80% for training and 20% for testing . We annotated all variants in these sets with methods that can predict pathogenicity of both coding and non-coding variants . We used the Combined Annotation Dependent Depletion ( CADD ) [17] , Genome Wide Annotation of VAriants ( GWAVA ) [16] and a deep neural network approach ( DANN ) [18] to obtain three pathogenicity prediction scores for each of the variants . Additionally , we used the genotype ( homozygote or heterozygote ) of a variant as feature . For each variant , we also added features related to the disease the variant is involved in according to ClinVar . In particular , we added as features the mode of inheritance of the disease , using only “Dominant” , “Recessive” , “X-linked” , and “Other” as features , and a binary vector of 54 high-level phenotypes of the disease based on our PhenomeNET ontology combining HPO and MP . Finally , we added the normalized semantic similarity between the disease phenotypes and the gene in which the variant is located as a feature . If a variant is non-exonic , we used the gene that is closest to the variant in genomic coordinates as the gene for which similarity was computed . In total , each variant is represented as 60 features ( see S1 Table ) . Based on these 60 features , we trained a random forest classifier to classify variants into causative and non-causative ( given a set of phenotypes observed in a patient ) . We understand a causative variant as a variant that is both pathogenic and involved in the pathogenesis of the disease phenotypes observed in the patient . For training , we represented the patient’s disease phenotypes by the phenotypes associated with the disease in the HPO database . A variant may be pathogenic but not causative for a set of patient phenotypes [11] . We simulated this case by randomly selecting another disease from the OMIM database and assigning these phenotypes as patient phenotypes in the feature representation of the variant . We called these variants pathogenic non-causative variants . We treated all variants identified as benign in ClinVar as non-causative and selected the phenotypes of a random OMIM disease to represent them . For training , missing values were imputed using the C4 . 5 method [79] . We use pathogenic causative variants as positives , but have two different types of negatives: pathogenic non-causative variants and benign non-causative variants . We train three models that emphasize the negative variants differently: a first model uses only pathogenic non-causative variants as negatives , a second model uses only benign variants as negatives , and a third model uses 50% pathogenic non-causative and 50% benign non-causative variants as negatives . Since the first model cannot distinguish variants by their pathogenicity prediction scores ( since both positive and negative variants are pathogenic and only differ in the disease for which they are causative ) , it is trained to under-emphasize pathogenicity of a variant and rely primarily on the phenotype similarity . The second model can clearly distinguish pathogenic variants from non-pathogenic based on pathogenicity prediction scores and will not have to rely heavily on the phenotype similarity scores; therefore , it is trained to under-emphasize phenotype similarity and predict primarily based on pathogenicity of a variant . The third model aims to achieve a balance between both . For each model , we train a random forest binary classifier ( using the pre-selected 80% of the variants in ClinVar [38] while keeping 20% of the variants as holdout set for final validation ) and evaluate the results using stratified 10-fold cross-validation . We trained the models using the Random Forest implementation in Weka [80] using 100 trees , unlimited depth of trees , and constructing each tree considering 6 random features . Random forests are trained to output probability estimates of class assignment , which we use as prediction score to rank variants . We report cross-validation evaluation results in S2 Table . The trained models are then applied to our synthetic exomes and genomes . Each synthetic whole exome or whole genome sequence is taken randomly from one of the 1 , 000 Genomes project sequences , with one causal variant from our holdout set artificially inserted . We use the phenotypes associated with the disease for which this variant is causal as patient phenotypes and use our models to compute a prediction score for each variant in the synthetic sequences . We then evaluate the ranks on which we recover the causal variant and compare the results against Exomiser version 7 . 2 . 1 , Phevor version 2 , eXtasy version 0 . 1beta ( for whole exome sequences only ) , and CADD version 1 . 3 , DANN version 1 , GWAVA version 1 , and Genomiser version 7 . 2 . 1 ( for whole genome sequences ) . For evaluation , none of our models were trained on the variants we inserted in these sequences . We report the area under the receiver operating characteristic curve ( ROC AUC ) and the top ranks and top 10 ranks obtained by applying each method . We analyze the synthetic whole exome sequences with the Exomiser [41] using the same sets of phenotypes and mode of inheritance as input and using its variant prioritization mode . For comparison with Phevor , we first rank variants based on their CADD score and submit the ranked list to the Phevor web interface using the same phenotypes used in our analysis . Phevor provides a ranked list of genes , not variants , and we assign variants the Phevor rank of the gene in which it is located . We performed the analysis with eXtasy using its default parameter settings with imputation of missing values , and combining multiple phenotypes . eXtasy was not able to utilize all HPO phenotype classes in our analysis and we omitted the phenotypes that were not available to eXtasy . In all tools besides PVP , we remove variants for which no rank is assigned from the analysis . For DANN and GWAVA , this includes all insertions and deletions as they are not scored by these tools . In PVP , we remove all variants that are not clearly identified as homozygote or heterozygote ( e . g . , genotypes that were not confidently called ) . Moreover , if the mode of inheritance of the disease is known to be recessive , we filter out variants associated with 0/1 genotype call as the disease will require a variant with a 1/1 genotype call in order to be present . MAF is also used as a filtering option for some of the experiments we conducted . MAF data were obtained from the 1000 Genomes Project corresponding to all populations ( release August 2015 ) using the Annovar tool [81] . The source code of PVP is freely available at https://github . com/bio-ontology-research-group/phenomenet-vp . Use of UK10K data for this project was approved by the UK10K Data Access Committee at the European Genome-phenome Archive for GVG , RH , MK , IB , and RBMR . Access to UK10K data and analysis was limited to GVG , RH , MK , IB , RBMR . Source code developed for this project is available at https://github . com/bio-ontology-research-group/phenomenet-vp , and analysis results at http://www . cbrc . kaust . edu . sa/onto/pvp/ . Data to UK10K samples is available from the European Genome-Phenome Archive through the UK10K Data Access Committee ( datasharing@sanger . ac . uk , https://www . uk10k . org/data_access . html ) for researchers who meet the criteria for access to confidential data . | We address the problem of how to distinguish which of the many thousands of DNA sequence variants carried by an individual with a rare disease is responsible for the disease phenotypes . This can help clinicians arrive at a diagnosis , but also can be instrumental in improving our understanding of the pathobiology of the disease . Many methods are currently available to help with the problem of determining causative variant , using information about evolutionary conservation and prediction of the functional consequences of the sequence variant . We have developed a novel algorithm ( PVP ) which augments existing strategies by using the similarity of the patients phenotype to known phenotype-genotype data in human and model organism databases to further rank potential candidate genes . In a retrospective study , we apply PVP to the interpretation of whole exome sequencing data in patients suffering from congenital hypothyroidism , and find that PVP accurately identifies causative variants in whole exome and whole genome sequencing datasets and provides a powerful resource for the discovery of causal variants . | [
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] | 2017 | Semantic prioritization of novel causative genomic variants |
The complexity in composition and function of the eukaryotic nucleus is achieved through its organization in specialized nuclear compartments . The Drosophila chromatin remodeling ATPase ISWI plays evolutionarily conserved roles in chromatin organization . Interestingly , ISWI genetically interacts with the hsrω gene , encoding multiple non-coding RNAs ( ncRNA ) essential , among other functions , for the assembly and organization of the omega speckles . The nucleoplasmic omega speckles play important functions in RNA metabolism , in normal and stressed cells , by regulating availability of hnRNPs and some other RNA processing proteins . Chromatin remodelers , as well as nuclear speckles and their associated ncRNAs , are emerging as important components of gene regulatory networks , although their functional connections have remained poorly defined . Here we provide multiple lines of evidence showing that the hsrω ncRNA interacts in vivo and in vitro with ISWI , regulating its ATPase activity . Remarkably , we found that the organization of nucleoplasmic omega speckles depends on ISWI function . Our findings highlight a novel role for chromatin remodelers in organization of nucleoplasmic compartments , providing the first example of interaction between an ATP-dependent chromatin remodeler and a large ncRNA .
ISWI , the catalytic subunit of several ATP-dependent chromatin remodeling complexes , is highly conserved during evolution and is essential for cell viability [1] . ISWI-containing complexes play central roles in DNA replication , gene expression and chromosome organization [2] . ISWI uses ATP hydrolysis to catalyze nucleosome spacing and sliding reactions [1] . Loss of ISWI function in Drosophila causes global transcription defects and dramatic alterations in higher-order chromatin structure , including decondensation of chromosomes [3] , [4] . In vitro and in vivo studies in several model organisms have also shown the involvement of ISWI-containing complexes in other nuclear functions like telomere silencing , stem cell renewal , neural morphogenesis and epigenetic reprogramming during nuclear transfer in animal cloning [2] , [5] , [6] . The diverse functions associated with ISWI depend upon the ability of other cellular and nuclear factors to regulate its ATP-dependent chromatin remodeling activity [7]–[9] . In order to identify new regulators of ISWI function , we developed in vivo assays to identify genetic interactors of ISWI in D . melanogaster [10] , [11] . Using an eye-based assay to identify factors antagonizing ISWI activity , we recovered , among other genes , a genetic interaction between ISWI and hsrω [10] . The hsrω gene is developmentally expressed in almost all cells types and is one of the most strongly induced heat shock genes in flies [12]–[14] . The hsrω locus encodes multiple non-coding RNAs ( ncRNA ) , of which the large >10 kb nuclear species ( hsrω-n ) is essential for the assembly and organization of the hnRNP-containing omega speckles [14] . These specialized nuclear compartments are distinct from other nuclear speckles and are localized in the nucleoplasm close to chromatin edges [14] . Omega speckles play essential roles in storage and sequestration of members of the hnRNP family and other proteins involved in RNA processing and maturation in normal as well as environmentally or genotoxically stressed cells ( for a list of hsrω interactors see [13]–[15] . Here we show that the hsrω ncRNA interacts in vivo and in vitro with ISWI to directly regulate its ATPase activity . Additionally , we provide in vivo data showing that omega speckle nuclear organization depends on ISWI function . Our work thus suggests that ISWI and the omega speckle associated hsrω ncRNAs interact and reciprocally affect each other's activities . Our findings highlight a novel role for chromatin remodelers in organization of nuclear speckles .
Loss of hsrω function by RNAi [15] results in a striking amelioration of morphological defects in eyes exclusively composed of ISWI-null mitotic clones ( Figure 1A , 1B and Figure S1A–S1D , S1J; Table S1A ) . Mutations in the sqd gene , which encodes the Squid hnRNP , a component of omega speckles , also suppresses ISWI mutant eye defects ( Figure S1F–S1I and S1K; Table S1A ) [10] . Absence of ISWI in larval salivary gland cells causes a dramatic decondensation of the male X polytene chromosome [4] . Remarkably , hsrω-RNAi suppresses the ISWI null male X chromosome condensation defects as well ( Figure 1C , 1D ) . Tissue-specific mis-expression of the catalytically inactive ISWIK159R transgene also produces eye phenotypes and global chromosome decondensation [3] , [4] , [11] . Silencing of hsrω-n activity not only suppresses ISWIK159R eye phenotypes ( Figure 1E–1H ) but also restores normal polytene chromosome condensation ( Figure 1I , 1J ) . In agreement with the above observations , the larval lethality of ISWI-null mutants is also partially suppressed by hsrω-RNAi ( Figure 1K; Table S1B ) , strongly indicating that reduction of hsrω-n transcripts improves ISWI activity . On the other hand , over-expression of hsrω through the hsrωEP93D allele [15] antagonizes ISWI activity , resulting in enhanced chromosome condensation defects and eye phenotypes in ISWI-null background ( Figure 1L and Figure S1E; Table S1A ) . The suppression of chromosome condensation and eye defects in ISWI nulls by hsrω-RNAi is not due to a reduction in the efficiency of the GAL4/UAS driving system used to produce the ISWI-null and ISWIK159R mutant phenotypes ( Figure S2A , S2B ) . Furthermore , the effect of hsrω-RNAi is highly specific for the loss of ISWI function ( Figure S2C , S2D ) . Given the role played by omega speckles in nuclear RNA processing [13] , we also checked if the levels of ISWI or ISWIK159R proteins and their corresponding mRNA were affected by hsrω-RNAi , which could account for the suppression of ISWI-null or ISWIK159R defects . However , depletion of hsrω transcripts by RNAi does not detectably affect ISWI protein or mRNA levels in either of these cases ( Figure S3 ) . In order to understand the molecular basis of the specific suppression of ISWI phenotypes by hsrω-RNAi , we examined the distribution and organization of omega speckles in the ISWI mutant third instar larval Malpighian tubule nuclei , which show abundant omega speckles using either RNA∶RNA in situ hybridization to hsrω-n ncRNA or immunostaining for some of the omega speckle associated hnRNPs [14] . Interestingly , the organization and distribution of omega speckles in ISWI mutants is profoundly altered when compared with wild type cells . Instead of typical speckles , the hsrω-n transcripts form “trail”-like structures in ISWI-null mutant nucleoplasm , indicating a severe defect in their maturation or organization ( Figure 2A , 2B ) . Interestingly , Squid , NonA and other omega speckle associated hnRNPs also form “trail”-like structures in ISWI mutants ( Figure 2C–2F , Figure 3A–3D , and Figure S4 ) , which shows that distribution of not only the hsrω-n ncRNA but also of the omega speckle-associated hnRNPs is compromised in ISWI mutant nuclei . As shown earlier [15] , the omega speckles do not form in the absence of hsrω-n transcripts and the omega speckle-associated hnRNPs remain diffused in the nucleoplasm ( Figure 3E–3F ) . Interestingly , when the ISWI as well as hsrω-n ncRNA are absent , omega “trails” are not formed ( Figure 3G–3H ) , strongly indicating that ISWI mutant specific omega “trails” are dependent on the presence of the hsrω-n ncRNA . Analysis of live cells expressing a SquidGFP transgene [16] clearly identifies the GFP-positive “trails” in live ISWI mutant cells similar to those observed in fixed cells ( Figure S5 ) . This shows that the ISWI omega “trails” are not a fixation artifact . Significantly , comparable hsrω RNA “trails” were not seen ( Figure S6 ) in the presence of other mutants like jil1 , ada2 and gcn5 which also display chromosome condensation defects similar to those observed in the ISWI mutants [17] , [18] . This excludes the possibility that the omega “trails” in ISWI mutant nuclei result from a “squeezing” effect of the nucleoplasm due to a massive “fallout” of chromatin associated proteins following global chromosome decondensation . Studies in several model organisms have shown that ISWI plays a global role in transcriptional activation as well as repression [1] , [3] , [4] . Therefore , we examined if ISWI mutation altered the levels of hsrω-n ncRNA or the omega speckle-associated proteins . However , no significant difference in their levels was found between ISWI mutant and wild type cells ( Figure S7 ) . The >10 Kb hsrω-n ncRNA that organizes the omega speckles contains a small 0 . 7 Kb intron [14] , [19] . It has been recently observed [20] that a spliced form of the hsrω-n transcript is also associated with the omega speckles . Therefore , we checked if the ISWI mutant condition affects splicing of this RNA which may result in the “trail”-like organization . RT-PCR and Northern blot analyses clearly indicate that ISWI mutation does not affect splicing of the hsrω-n ncRNA ( Figure S8A–S8C ) . In light of the significant role played by ISWI in gene expression , we checked whether an engulfment of the nuclear RNA export machinery in ISWI mutants affected RNA transport from nucleus , which in turn could modify the omega speckles into “trails” . In situ hybridization to cellular RNA with poly-dT probe did not reveal any difference in the nuclear vs cytoplasmic distribution of poly-A RNAs between wild type and ISWI mutant cells ( Figure S8D ) . Thus , ISWI mutant nuclei do not seem to have a general RNA export defect , which could have been responsible for the observed omega “trails” . Omega speckles are thought to provide a dynamic system to sequester and release specific RNA processing factors in normal as well as stressed cells [13] . Following heat shock , hsrω is one of the most highly transcribed genes [13] , [21] and omega speckles coalesce into bigger growing clusters that finally get restricted to the hsrω gene locus , providing a dynamic sink for proteins that need to be transiently withdrawn from active nuclear compartments under stress conditions [14] . As already noted above , ISWI mutant condition causes the omega speckles to form nucleoplasmic “trails” in unstressed cells ( Figure S9A , S9B ) . Although heat shock caused clustering of the omega speckles or “trails” in wild type and ISWI mutant cells , respectively , the numbers of clusters in the latter cells were much less ( Figure S9C , S9D ) , suggesting that speckle dynamics under heat shock is also compromised because of ISWI mutant background . Finally , the “trail”-like organization of hsrω ncRNA and its associated proteins in ISWI mutants is not limited to Malpighian tubule or salivary gland polytene cells ( Figure 2A , 2B and Figure S10A , S10B ) , but is also observed in ISWI mutant diploid cells ( ) , indicating that disorganization of omega speckles is a general consequence of loss of ISWI function . Unlike the association of ISWI with different bands and interbands on polytene chromosomes [4] , [11] , the hsrω-n ncRNA localizes in the nucleoplasm in proximity or at the edges of chromosome spreads , without any apparent overlap with the chromatin associated ISWI ( Figure 4A , 4B ) . However , examination of confocal images of intact nuclei revealed some chromosome-nucleoplasm sites where ISWI and the hsrω-n ncRNA are adjacent and seem to form connecting bridges between nucleoplasm and chromatin ( Figure 4C , 4D ) . Barring a few exceptions , Squid and other omega speckles associated hnRNPs also showed no overlap with ISWI on polytene chromosome spreads ( Figure 4E , 4F and Figure S11A , S11B ) . Significantly , like the hsrω ncRNA they too were found to partially overlap with ISWI in several nucleoplasmic foci in intact nuclei ( see Figure 4G , 4H and Figure S11C , S11D ) , suggesting that ISWI may indeed partially interact directly or indirectly , at least transiently , with omega speckles in the three-dimensional nuclear space . In order to directly investigate whether the chromatin remodeling factor ISWI physically interacts with omega speckles , we used an affinity purified ISWI antibody [4] to conduct classic RNA immunoprecipitation . Our semi-quantitative RT-PCR analysis revealed the presence of hsrω-n ncRNA in larval nuclear extracts immunoprecipitated with ISWI antibody ( Figure 5A , 5B ) . To rule out a non-specific association of ncRNAs with ISWI , we used the same immunoprecipitate to detect U4 and Rox1 [22] ncRNAs by RT-PCR . Significantly , neither of these two otherwise abundant ncRNAs were detectable ( Figure 5A , 5B ) in the mmunoprecipitate . This confirms the specificity of the physical interaction between ISWI and hsrω-n RNA in native larval extracts . Further , to exclude the possibility that the physical association observed between ISWI and hsrω was due to fortuitous interactions occurring during nuclear extract preparation , we conducted the CLIP assay ( Cross-Linking & Immuno Precipitation ) using the affinity purified anti-ISWI antibody [4] on fixed larval nuclear extracts . The CLIP data confirmed a highly specific interaction between ISWI and the hsrω ncRNA in the nucleus ( Figure 5C ) , as observed with the native extracts ( Figure 5B ) . Moreover , as shown in Figure 5D , RNA pull down assay confirmed that ISWI is also specifically pulled down by immobilized hsrω-n ncRNA along with the other known omega speckles associated hnRNPs [13] while a control generic RNA does not pull down ISWI or the other hnRNPs ( Figure 5D ) . Classic gel shift assay using in vitro transcribed hsrω-n ncRNA repeat unit ( 280b ) and full length recombinant ISWI clearly shows that ISWI effectively retards hsrω-n ncRNA mobility , but that of a generic control RNA is retarded poorly ( Figure 5E ) . Moreover , the mobility shift of the hsrω-n RNA by ISWI binding is specifically competed by hsrω-n but not by a generic RNA ( Figure 5F ) . This further confirms the specific nature of ISWI/hsrω physical interaction in vitro . A functional significance of the physical interaction between ISWI and hsrω-n ncRNA is indicated by the stimulation of ISWI ATPase activity . Remarkably , as also reported previously [23] , while the generic control RNA very poorly stimulates the ISWI ATPase activity , the hsrω-n ncRNA was found to specifically stimulate the ISWI ATPase activity to levels greater than those normally seen with DNA but lower than nucleosome-stimulation ( Figure 5G ) [23] . The 280b hsrω-n nuclear ncRNA repeat unit used for the binding and ATPase assays is predicted to organize into a stable double stranded RNA molecule containing a few loops ( Figure S12 ) . This secondary organization is common to many RNAs , but this structure is also reminiscent of a double stranded DNA molecule . Therefore , it remained possible that the recognition of a double stranded nucleic acid ( RNA or DNA ) may provide a basis for the observed binding and stimulation of ATPase activity of ISWI by the hsrω-n ncRNA . When we checked the ability of the double stranded DNA sequence encoding the hsrωncRNA to elicit ISWI ATPase activity , we found that ISWI was stimulated to levels similar to those reported for other generic linear double stranded DNA molecules [23] ( Figure S13A ) . Furthermore , co-presence of hsrω-n ncRNA and nucleosomes in a classic ATPase assay with ISWI clearly shows that both substrates compete for ISWI binding and its ATPase activity stimulation ( Figure S13B ) . The ISWI protein has two functional domains ( Figure 6A ) , the N-terminal ( ISWI-N ) ATPase domain and the C-terminal ( ISWI-C ) nucleosomal DNA recognizing domain [24] . Results presented in Figure 6B and 6C , show that the hsrω-n binds with the ISWI-N fragment and stimulate its ATPase activity , suggesting that ISWI could interact with hsrω-n ncRNA through its ATPase domain . Therefore , we further checked if the presence of ATP , ATP-γ-S ( a non-hydrolizable form of ATP ) or ADP could affect ISWI binding or determine a conformational change in the ISWI/hsrω complexes resolved by gel shift . Our data show that all the three nucleotides have no effect on ISWI binding ( Figure 6D ) , probably suggesting that the ATPase activity of ISWI may not be necessary for physical interaction between ISWI and hsrω RNA .
Factors that coordinate nuclear activities occurring on chromatin and the nucleoplasmic compartments remain unidentified and uncharacterized . Therefore , an important open question in nuclear organization field is how nuclear speckles localize and organize themselves near transcriptionally active genes to cross talk with chromatin factors for processing of the nascent RNAs . Our data indicate that ISWI may provide a functional ‘bridge’ between chromatin and nuclear speckle compartments . Indeed , ISWI can directly or indirectly contact the omega speckles in intact nuclei , through hsrω-n ncRNA or some of the associated hnRNPs . Our confocal analysis suggested a functional ‘bridge’ between a chromatin factor ( ISWI ) and nucleoplasmic omega speckle components ( hsrω ncRNA and hnRNPs ) . However , not all omega speckles show partial overlap with ISWI . Indeed , these molecular “bridges” between chromatin and nucleoplasm are probably transient , since time-lapse movies on live cells with fluorescently tagged chromatin and omega-speckle components clearly show very high mobility of these speckles ( see Video S1 ) , which probably may explain the absence of classic co-localization between ISWI and omega speckle components . The observed direct physical interaction between ISWI and hsrω-n ncRNA together with the stimulation of ISWI-ATPase activity in light of the partial overlap revealed by confocal microscopy suggests that ISWI may interact with hsrω-forming speckles only transiently , probably to help the hsrω ncRNA to properly associate with or release the various omega speckle-associated hnRNPs . Loss of ISWI may impair the correct maturation , organization or localization of omega speckles resulting in the observed omega “trail” phenotype . Our data also provide a possible explanation for the suppression of ISWI defects by hsrω-RNAi . In ISWI mutants carrying normal levels of hsrω transcripts , the limited maternally derived ISWI [3] is shared between chromatin remodelling and omega speckle organization reactions so that its sub-threshold levels in either compartments severely compromises both functions ( see Video S2 ) . However , when hsrω transcript levels are reduced by RNAi in ISWI null background , most of the maternal ISWI may become available for chromatin remodelling reactions , so that a minimal threshold level of chromosome organization can be achieved . This would permit initiation of close to normal developmental gene activity programs resulting in suppression of the ISWI eye and chromosome defects or in the postponement of the larval lethality to pupal stage . Additionally , it is known that when hsrω ncRNA is down regulated through RNAi , levels of free hnRNPs and other chromatin factors ( i . e . CBP ) are also elevated [25] . Therefore , we cannot formally exclude the possibility that these changes may also counteract ISWI defects by as yet unknown mechanisms . Our work provides the first example of modulation of an ATP-dependent chromatin remodeler by a ncRNA , and to our knowledge the first in vivo and in vitro demonstration of a role of chromatin remodeler in organization of a nuclear compartment . However , the mechanism underlying stimulation of the ATPase activity of ISWI by the hsrω-n ncRNA , which may facilitate the organization of omega speckles , remains to be understood . Given the evolutionary derivation of the ISWI ATPase-domain from RNA-helicase-domains [1] , a provocative hypothesis is that ISWI could “remodel” speckles by structurally helping the assembly or release of specific hnRNPs with the hsrω-n ncRNA to generate mature omega speckles . Chromatin remodelers , nuclear speckles and their associated long ncRNAs are emerging as essential components of gene regulatory networks , and their deregulation may underlie complex diseases [15] , [25]–[27] . The functional homology of the human noncoding sat III transcripts with the Drosophila hsrω ncRNA [13] , [27] , highlights the relevance and translational significance of studies unraveling the functional connections between ncRNA-containing nuclear compartments and chromatin remodelers .
Flies were raised at 22°C on K12 medium [28] . Unless otherwise stated , strains were obtained from Bloomington , Szeged or VDRC ( Vienna Drosophila RNAi Center ) . Genetic tests for dominant modifier ( enhancement or suppression ) of ISWI-EGUF and ISWIK159R phenotypes were conducted as previously described [10] , [11] . The tissue specific expression of the UAS-ISWIK159R [4] , the UAS-hsrωRNAi3 and the EP93D transgenic lines [15] was obtained with ey-GAL4 ( for eyes and larval salivary glands ) or Act5C-GAL4 driver ( for larval Malpighian tubules and testis ) . The surface architecture of adult eyes was examined by the nail polish imprint method [26] . For the larval lethality assay , numbers of larvae of different genotypes that pupated and the numbers of pupae emerging as flies in a given cross were separately counted . Mouse monoclonal antibodies against the following proteins were used at the indicated dilutions: Hrb87F ( P11 ) [14] dilution 1∶5 for IF and 1∶100 for WB; Squid ( 1B11 ) [29] dilution 1∶100 for IF and 1∶2000 for WB; NonA [30] dilution 1∶50 for IF and 1∶1000 for WB; PEP [31] dilution 1∶2000 for WB . Affinity purified rabbit ISWI antibody [4] was diluted 1∶200 for IF and 1∶2000 for WB . FITC- and Rhodamine- conjugated anti-mouse and anti-rabbit secondary antibodies ( Jackson Immuno Research ) were diluted 1∶200 for IF and 1∶2000 for WB , respectively . The biotin-labeled anti-sense hsrω-n RNA 280b riboprobe was generated from the pDRM30 plasmid [32] and used for FRISH . For gel mobility assays the sense hsrω-n RNA riboprobe was generated from the same plasmid . Single and double immunofluorescence on polytene chromosome spreads were conducted as previously described [11] . Larval tissues ( salivary glands , Malpighian tubules and testis ) were dissected from third-instar larvae grown at 22°C . Fully or partially squashed tissue preparations were used for FRISH and Immuno-FRISH assays as previously described [14] with some modifications ( Text S1 ) . Total proteins from salivary glands and Malpighian tubules were extracted as previously described [11] . The SDS-PAGE separated proteins were transferred onto nitrocellulose membrane ( Whatman Schleicher & Schuell ) for Western detection using SuperSignal West Femto substrate ( Pierce ) . Chemiluminescent signals were acquired with the ChemiDoc XRS imager ( BioRad ) . Native larval nuclear protein extracts from third instar w1118 larvae were prepared as previously described [11] and RNA-immunoprecipitations were conducted as published earlier [33] with small modifications ( Text S1 ) . Recombinant full length ISWI or ISWI-N or ISWI-C proteins [23] , [24] were incubated with in vitro transcribed sense 280b tandem repeat unit of the hsrω-n ncRNA or a generic RNA of the same size ( RNActr , Roche ) as a control , in increasing ratios of 1∶1 , 5∶1 , 10∶1 and 20∶1 nmoles . The hsrω-n ncRNA or the RNActr were incubated with the desired protein for 30 min at 25°C in RB2 buffer ( 20% Glycerol , 0 . 2 mM EDTA , 20 mM Tris-HCl pH 7 . 5 , 1 mM MgCl2 , 150 mM NaCl , 1 mM DTT and RNAsin ) . After incubation , the RNA/protein complexes were resolved on 1 . 4% agarose gel in 0 . 5× TBE at 4°C for 105 minutes at 70 volts . RNA molecules were visualized by ethidium bromide staining . ATP , ATP-γ-S and ADP ( Roche ) were added in the gel shift assay at a final concentration of 100 µM . Excess of cold hsrω-n repeat unit or a generic RNActr transcript was used as competitor for ISWI/hsrω binding detected by gel mobility shift using P33 radiolabeled hsrω280b sense repeat unit and recombinant ISWI . RNA/protein complexes were resolved as above . After gel drying , RNA/protein complexes were visualized using the BioRad Phosphoimager system . ATPase assay was conducted as previously described [23] . Extent of ATP hydrolysis was calculated with the following formula [P33/ ( P33+AMP-P−P33 ) ]*100 ( Figure 5G ) . The ATPase activity of 4 nmoles of full length ISWI was assayed for 1 hour; 4 nmoles of ISWI-N and ISWI-C were assayed for 30 minutes in the presence of 2 nmole of either supercoiled plasmid DNA , 280 bp hsrω-repeat unit encoding double stranded DNA , hsrω-n 280 bp tandem repeat ncRNA or a 300 bp generic RNA ( RNActr; Roche ) as a control . | Chromatin structure and function are regulated by the concerted activity of covalent modifiers of chromatin , nucleosome remodeling factors , and several emerging classes of non-coding RNAs . ISWI is an evolutionarily conserved ATP-dependent chromatin remodeler playing essential roles in chromosome condensation , gene expression , and DNA replication . ISWI activity has been involved in a variety of nuclear functions including telomere silencing , stem cell renewal , neural morphogenesis , and epigenetic reprogramming . Using an in vivo assay to identify factors regulating ISWI activity in the model system Drosophila melanogaster , we recovered a genetic interaction between ISWI and hsrω . The hsrω gene encodes multiple non-coding RNAs ( ncRNAs ) , of which the >10 kb nuclear hsrω-n RNA , with functional homolog in mammals , is essential for the assembly and organization of hnRNP-containing nucleoplasmic omega speckles . These special nuclear compartments play essential roles in the storage/sequestration of hnRNP family and other proteins , thus playing important roles in mRNA maturation and other regulatory processes . Here we show that the hsrω-n ncRNA interacts in vivo and in vitro with ISWI to directly regulate its ATPase activity . We also provide in vivo data showing that omega speckle nuclear organization depends on ISWI function , highlighting a novel role for chromatin remodelers in nuclear speckles organization . | [
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] | 2011 | The ISWI Chromatin Remodeler Organizes the hsrω ncRNA–Containing Omega Speckle Nuclear Compartments |
Meeting the increasing food and energy demands of a growing population will require the development of ground-breaking strategies that promote sustainable plant production . Host-induced gene silencing has shown great potential for controlling pest and diseases in crop plants . However , while delivery of inhibitory noncoding double-stranded ( ds ) RNA by transgenic expression is a promising concept , it requires the generation of transgenic crop plants which may cause substantial delay for application strategies depending on the transformability and genetic stability of the crop plant species . Using the agronomically important barley—Fusarium graminearum pathosystem , we alternatively demonstrate that a spray application of a long noncoding dsRNA ( 791 nt CYP3-dsRNA ) , which targets the three fungal cytochrome P450 lanosterol C-14α-demethylases , required for biosynthesis of fungal ergosterol , inhibits fungal growth in the directly sprayed ( local ) as well as the non-sprayed ( distal ) parts of detached leaves . Unexpectedly , efficient spray-induced control of fungal infections in the distal tissue involved passage of CYP3-dsRNA via the plant vascular system and processing into small interfering ( si ) RNAs by fungal DICER-LIKE 1 ( FgDCL-1 ) after uptake by the pathogen . We discuss important consequences of this new finding on future RNA-based disease control strategies . Given the ease of design , high specificity , and applicability to diverse pathogens , the use of target-specific dsRNA as an anti-fungal agent offers unprecedented potential as a new plant protection strategy .
According to the FAO [1] , more than half of the world’s harvested area is allotted to cereals such as rice , maize and wheat ( ca . 2 . 3 billion tons in 2010 ) . Diseases of cereal crops such as Fusarium head blight ( FHB ) and Fusarium seedling blight ( FSB ) , caused by necrotrophic fungi of the genus Fusarium , exert a particularly great economic and agronomic impact on global grain production and the grain industry [2 , 3] . Food safety can be compromised by contamination of agricultural products with mycotoxins , which are produced during FHB and FSB development [4] and represent a serious threat to human and animal health . Currently , the major strategies to control Fusarium diseases include resistance breeding , crop rotation , and biological control along with the application of DMI ( demethylation inhibitors ) fungicides [5] . DMI fungicides , such as tebuconazole , triadimefon , and prochloraz inhibit ergosterol biosynthesis by binding to cytochrome P450 lanosterol C-14 α-demethylase ( CYP51 ) , thereby disrupting fungal membrane integrity [6] . However , heavy reliance on DMI fungicides since their discovery in the mid-1970s holds a risk of the emergence of DMI-tolerant strains of plant pathogens . Conventional plant breeding strategies have been only partly successful , as the quantitative nature of FHB and FSB resistance does not allow straightforward breeding programs . Since the discovery in 1998 that exogenous double-stranded ( ds ) RNA triggers suppression of gene activity in a homology-dependent manner [7] , along with the identification of small RNAs ( sRNAs ) as a new class of regulatory molecules [8] that functions via RNA interference ( RNAi ) , our understanding of the essential cellular function of gene silencing has increased considerably [9–10] . Mobile RNA silencing signals are capable of translocating from the host to its interacting organism , and vice versa [11–14] . Recent evidence supports the significant contribution of sRNAs and RNAi to the communication between plant hosts and a pathogenic fungus [15] . Exploiting the RNAi mechanism in plants also has a strong potential for agriculture . Indeed , expression of inhibitory dsRNAs in the corresponding host plant conferred protection from predation or infection by targeted gene silencing [16–18] , a phenomenon that has been termed host-induced gene silencing ( HIGS ) . Recently , we demonstrated that in Arabidopsis ( Arabidopsis thaliana ) and barley ( Hordeum vulgare ) , transgenic expression of CYP3-dsRNA , a 791 nt long dsRNA targeting the three fungal CYP51 genes involved in ergosterol biosynthesis , confers resistance to infection with Fusarium graminearum [19] . While these results provided proof-of-concept that RNAi-based plant protection is an effective strategy for controlling diseases caused by devastating necrotrophic pathogens , the broad applicability of this transgenic method remains questionable due to the persisting weak acceptance of GMO strategies for food and feed production in many countries . More important , a broad application of this transgenic approach is hampered by the lack of transformability of various crop plants and the missing genetic stability of the silencing trait . Here we investigate the potential and the mechanism of an RNAi-based crop protection strategy using direct spray applications of CYP3-dsRNA to target F . graminearum . We show that the 791 nt long dsRNA is taken up by the plant and transferred in an unmodified form via the vascular system to fungal infection sites where it is processed by the fungal RNAi machinery as a prerequisite for its antifungal activity . We show a strong correlation between accumulation of CYP3-dsRNA at infection sites , silencing of CYP51 expression , and fungal inhibition .
To provide a proof of concept , we conducted an experiment targeting the expression of the jellyfish green fluorescent protein ( GFP ) in the GFP-expressing F . graminearum strain Fg-IFA65GFP [20] by using a GFP-specific 720 nt long dsRNA ( GFP-dsRNA , S1 Fig ) . Detached barley leaves were locally sprayed with 20 ng μL-1 GFP-dsRNA or Tris-EDTA buffer ( TE , control ) and drop-inoculated 48 h later with Fg-IFA65GFP in the distal ( non-sprayed ) leaf segment . Confocal microcopy showed strong GFP fluorescence associated with fungal mycelia on TE-treated control leaves at six days post inoculation ( dpi ) ( Fig 1A ) . In contrast , fluorescence ( Fig 1B ) and GFP transcripts ( Fig 1C ) were largely absent in mycelia grown on leaves that were locally sprayed with GFP-dsRNA , although mycelial growth was unrestricted as evidenced by light microscopy . This observation clearly demonstrates the possibility of targeting a gene of an attacking microbe via SIGS . To further explore the potential of SIGS , we assessed the silencing efficiency of CYP3-dsRNA , which targets the three Fusarium genes CYP51A , CYP51B , and CYP51C . The 791 nt long CYP3-dsRNA contains complementary fragments of these genes starting with N-terminal CYP51B , followed by CYP51A and CYP51C [19] . Leaves were sprayed with CYP3-dsRNA and 48 h later drop-inoculated directly onto the sprayed area with Fg-IFA65 . At six dpi , CYP3-dsRNA-treated leaves developed brownish lesions that were substantially smaller than those on TE- or GFP-dsRNA-sprayed leaves that served as control in this experiment ( Fig 2A ) . Quantitative real-time PCR ( qPCR ) analysis of fungal DNA levels , based on the ratio between fungal tubulin and plant ubiquitin , confirmed reduced fungal growth on CYP3-dsRNA-treated leaves ( Fig 2B ) . To confirm that inhibition of Fusarium growth by CYP3-dsRNA was provoked by sequence-specific gene silencing , expression of all the three fungal CYP51 genes was assessed . At six dpi , total RNA was isolated from infected leaves and the levels of CYP51A , CYP51B and CYP51C transcripts were measured by qPCR and normalized to the expression of the fungal ß-tubulin gene . Consistent with the concept of spray-induced gene silencing , we found that the relative amounts of CYP51 transcripts were reduced on average by 58% ( CYP51A ) , 50% ( CYP51B ) , and 48% ( CYP51C ) in leaves sprayed with CYP3-dsRNA vs . the GFP-dsRNA control ( Fig 2C ) . Mobile cell non-autonomous inhibitory RNAs that spread gene silencing into adjacent cells and tissues have been observed in various plants [21–23] . Encouraged by the observed reduction in GFP fluorescence in Fg-IFA65GFP upon infection of leaf segments that did not receive a direct GFP-dsRNA spray ( see Fig 1 ) , we tested whether locally sprayed CYP3-dsRNA confers gene silencing in Fusarium infecting distal , non-sprayed segments of barley leaves . To this end , the upper part of detached leaves ( local tissue ) was sprayed with 20 ng μL-1 CYP3-dsRNA , GFP-dsRNA , or TE , while the lower part ( distal tissue ) was covered by a plastic tray to prevent direct dsRNA contamination . After 48 h , the distal , non-sprayed part of the leaves was drop-inoculated with Fg-IFA65GFP; six days later , resistance to fungal infection was assessed . Distal leaf areas of CYP3-dsRNA-treated leaves developed substantially smaller lesions as compared to leaves sprayed with GFP-dsRNA or TE ( S2 Fig ) indicating that the silencing signal was basipetally transported . Consistent with this finding , the amount of fungal DNA as determined by qPCR was greatly reduced in the distal leaf area as compared to the control treatments ( Fig 3A ) . The relative amounts of fungal CYP51A , CYP51B and CYP51C transcripts were strongly reduced on average by 72% ( CYP51A ) , 90% ( CYP51B ) , and 71% ( CYP51C ) as compared with control ( GFP-dsRNA ) treatment ( Fig 3B ) . Confocal microscopy of fungal inoculation sites in distal leaf areas confirmed that , on TE-treated leaves , Fg-IFA65GFP conidia had germinated and colonized tissue next to the inoculation site ( Fig 3C ) . In contrast , fungal mycelia on CYP3-dsRNA-treated leaves were only visible at the inoculation sites , and the surrounding leaf tissue was free of infection hyphae ( Fig 3D ) . Consistent with this , the large number of fungal conidia with very short germ tubes at the inoculation sites of CYP3-dsRNA-treated leaves indicated that fungal germination was strongly impaired . We also tested whether the silencing signal was transported in the acropetal direction . Segments were sprayed with 20 ng μL-1 CYP3-dsRNA and subsequently drop-inoculated in the distal leaf area . Fusarium infections were also reduced in the acropetal experimental set up ( S3A Fig ) as shown by macroscopic inspection ( S3B Fig ) and qPCR quantification of fungal DNA ( S3C Fig ) . To further explore the SIGS mechanism , we investigated whether the spray-applied long CYP3-dsRNA is translocated in the plant tissue and/or processed by the plant’s silencing machinery independent of fungal infections . Following CYP3-dsRNA treatment , local ( sprayed ) and distal ( non-sprayed ) leaf segments were harvested separately at 24 , 48 , 72 , or 168 h after spraying . Northern blot analysis detected unprocessed 791 nt CYP3-dsRNA in both local and distal tissue ( Fig 4A ) , showing that the long dsRNA is systemically translocated within the plant . In the local ( sprayed ) segment , CYP3-dsRNA was detected over the full time range , while it accumulated only transiently at early time points ( 24 h ) after spraying in the distal ( non-sprayed segments ) . This accumulation profile is consistent with the idea that the vast bulk of the CYP3-dsRNA fraction was absorbed via the cut surface of the detached leaf . Moreover , CYP3-dsRNA-derived 21 nt and 22 nt small interfering ( si ) RNAs also accumulated over the whole time range after spraying in the local leaf segments , demonstrating that CYP3-dsRNA was partly processed by the plant ( Fig 4B ) . In this experiment , Northern analysis could not detect siRNAs in distal leaf parts , probably because the technique was not sensitive enough . To further investigate uptake and transport of sprayed CYP3-dsRNA , it was labeled with the green fluorescent dye ATTO 488 ( CYP3-dsRNAA488 ) and sprayed onto barley leaves . The biological activity of CYP3-dsRNAA488 was indistinguishable from non-labeled CYP3-dsRNA as evidenced by reduced fungal infection and strong silencing of fungal CYP51 genes upon spray application ( S4A–S4C Fig ) . Moreover , using confocal laser scanning microscopy , a green fluorescent signal was detected in the vascular tissue at 24 hours after spraying leaves with 20 ng μl-1 CYP3-dsRNAA488 . In leaf cross-sections , fluorescence was seen in the xylem ( Fig 5A–5C ) . Inspection of longitudinal leaf sections revealed that the fluorescence was not confined to the apoplast but also was present in the symplast of phloem parenchyma cells , companion cells , and mesophyll cells , as well as in trichomes and stomata ( Fig 5D–5I ) . When CYP3-dsRNAA488-sprayed leaves were inoculated with Fg-IFA65 , the fluorescent signal also was detectable inside fungal conidia and germ tubes ( Fig 5H ) and fungal mycelium ( Fig 5J and S5 Fig ) . Together these data show that CYP3-dsRNA is taken up by the plant and is transferred via the plant vascular system . Systemic translocation within the plant and accumulation by the fungus also raised the possibility that CYP3-dsRNA is processed by the fungus into inhibitory siRNAs to eventually target fungal CYP51 genes . To test this possibility , we first profiled CYP3-dsRNA-derived siRNAs in infected and non-infected leaves . Small RNA sequencing ( RNAseq ) analysis revealed distinctly different CYP3-dsRNA-derived siRNA profiles in mock- vs . Fg-IFA65-infected local and distal ( non-sprayed ) leaf segments ( Fig 6A and 6B ) with higher numbers of reads of CYP3-dsRNA-derived siRNAs in infected leaves , and highest numbers of reads in locally-inoculated vs . distally-inoculated leaves . These data suggest that CYP3-dsRNA also is processed by the fungus and that the fungal silencing machinery is involved in SIGS and reduced fungal infections . Detection by RNAseq of CYP3-dsRNA-derived siRNA in the distal ( non-sprayed ) part of leaves also supported our interpretation that northern analysis failed to detect low amounts of these siRNAs due to sensitivity problems . To further substantiate involvement of the fungal silencing machinery , we generated a fungal dcl-1 mutant ( Fg-IFA65Δdcl-1 ) that is deficient for DICER-LIKE 1 ( S6 Fig ) , a critical component of the fungal silencing machinery that produces siRNA from long dsRNA stretches . Fg-IFA65Δdcl-1 and the wild type Fg-IFA65 were indistinguishably virulent on TE-sprayed barley leaves ( Fig 7A ) , showing that fungal DCL-1 is not required for successful leaf infections . However , in contrast to Fg-IFA65 , the mutant Fg-IFA65Δdcl-1 also heavily infected distal areas of CYP3-dsRNA-treated barley leaves ( Fig 7B ) , suggesting that the mutant strain is not amenable to SIGS . We concluded that the fungal silencing machinery appears to be indispensable for CYP3-dsRNA-mediated SIGS at systemic areas in the barley-Fusarium graminearum pathosystem . To further confirm that FgDCL-1 is required for CYP51 target gene silencing , levels of CYP51A , CYP51B and CYP51C transcripts were compared by qPCR in the wild type vs . the dcl-1 mutant on infection of CYP3-dsRNA sprayed leaves . The relative amounts of transcripts were reduced in Fg-IFA65 on average by 50% ( CYP51A ) , 70% ( CYP51B ) , and 40% ( CYP51C ) as compared with TE ( control ) treatment . In contrast , expression of CYP51 targets was not reduced in the Fg-IFA65Δdcl-1 mutant ( Fig 7C ) . We additionally conducted an in vitro experiment to further demonstrate the requirement of FgDCL-1 for CYP3-dsRNA-mediated silencing of fungal CYP51 genes . Mycelia of axenic cultures of Fg-IFA65 and Fg-IFA65Δdcl-1 were treated with CYP3-dsRNA . Subsequently , expression of CYP51 genes was recorded . Consistent with the leaf assay , the relative amounts of fungal CYP51A , CYP51B and CYP51C transcripts were reduced in the wild type Fg-IFA65 but not in the Fg-IFA65Δdcl-1 mutant ( Fig 7D ) . Confirmatory total sRNAs profiling by RNAseq in axenically-grown Fg-IFA65 revealed a range of sRNAs originating from CYP3-dsRNA ( Fig 7E–7G and S2 Table ) , further proving that the fungus can process CYP3-dsRNA . Suspiciously , the majority of siRNA species mapped to sites in the CYP51A gene fragment of the CYP3-dsRNA . Further work must show if this profile is a result of the physical structure of the dsRNA . The failure to detect CYP3-dsRNA-derived siRNA in the distal area of CYP3-dsRNA-sprayed leaves by northern analysis along with the compromised SIGS phenotype of the mutant Fg-IFA65Δdcl-1 suggested that the concentration of siRNA in the distal leaf parts was too low to mediate silencing of CYP51 genes in the fungus . Alternatively , Fusarium is generally unable to absorb siRNA from barley leaves . To address these possibilities , we sprayed barley leaves with high concentration of CYP3-dsRNA-derived siRNAs ( 20 ng μl-1 ) and subsequently inoculated local ( sprayed ) and distal ( non-sprayed ) leaf segments with Fg-IFA65 . We found that the fungus was strongly inhibited by these siRNAs both in the local ( S7A and S7C Fig ) and distal leaf segments ( S7B and S7C Fig ) as compared with a control ( GFP-dsRNA-derived siRNAs ) . Consistent with this , CYP3-dsRNA-derived siRNA also reduced the expression of CYP51 genes of the fungus growing on local ( S7D Fig ) and distal leaves segments ( S7E Fig ) , which shows that F . graminearum also can ingest inhibitory siRNAs from plant tissue . In clear support of this notion and consistent with the finding that CYP3-dsRNA-derived siRNA accumulated to higher concentration in leaf areas directly sprayed with CYP3-dsRNA , mutant Fg-IFA65Δdcl-1 was not compromised in SIGS when drop-inoculated directly to the sprayed leaf area ( S8 Fig ) . In mammalian cells , perception of certain dsRNAs via toll-like receptors triggers an inflammation response [24 , 25] . Therefore , we assessed whether CYP3-dsRNA elicits an innate immune response called pattern-triggered immunity ( PTI ) [26] , when sprayed onto barley leaf segments . To this end , expression of barley genes that are indicative of the canonical defense-related salicylate- and jasmonate-dependent pathways [27] was evaluated . Expression of salicylate-responsive pathogenesis-related 1 ( HvPR1 ) and jasmonate-responsive S-adenosyl-l-methionine:jasmonic acid carboxyl methyltransferase ( HvJMT ) in TE-treated leaves was strongly induced by Fg-IFA65 , but not by CYP3-dsRNA treatment ( Fig 8 ) . Furthermore , Fg-IFA65-induced expression of either gene was much lower in CYP3-dsRNA-treated leaves as compared with TE-treated leaves . This result strongly suggests that CYP3-dsRNA does not induce PTI in barley , and that the SIGS mechanism does not rely on activation of canonical defense pathways . The finding also is relevant when considering the fitness cost , and thus yield , of the SIGS strategy .
In this study , we show that delivery of long noncoding double-stranded RNA targeting the three CYP51 genes of the necrotrophic ascomycete fungus Fusarium graminearum via spray application effectively reduces the development of the pathogen on barley leaves . Thus , our work further supports the idea that RNA could be used as a chemical treatment to control plant diseases . Next to the economic and ecologic consideration about the deployment of antimicrobial RNAs as a new plant protection measure , elucidating the molecular mechanisms of RNA-based disease control is a key for successful future implementation . While plant-derived transgene-mediated silencing of target genes in plant pathogens and pests ( a mechanism known as host-induced gene silencing [HIGS] ) has been frequently reported [12 , 14 , 19] , few examples have demonstrated the efficiency of exogenous RNA delivery to kill plant attackers . HIGS virtually is based on the plant’s silencing machinery , whereas the mechanism of gene silencing by exogenously delivered dsRNA constitutes a more complex situation . For instance , diverging questions are possible involvement of the silencing machineries of host and/or the pathogen/pest , the requirement of local and remote transport of channeled dsRNA molecules , and the problem of dsRNA transport at the apoplast–symplast interface . Using the F . graminearum—barley pathosystem as a model to study the mechanism of exogenously applied inhibitory dsRNA was motivated by the fact that Fusarium Head Blight and Crown Rot cause serious problems worldwide including food and feed safety issues due to mycotoxin contamination of cereals and maize . Focusing on fungal CYP51 genes as targets for silencing was reasonable because our previous work provided proof-of-concept that transgene-mediated silencing of these genes effectively reduced fungal development in Arabidopsis and barley . More than that , direct treatment of F . graminearum with inhibitory dsRNA matching CYP51 gene sequences had been demonstrated to inhibit fungal development in axenic cultures [19] . The previous finding of impaired fungal growth in axenic cultures , when treated with a 791 nt dsRNA ( CYP3-dsRNA ) targeting the three fungal genes CYP51A , CYP51B , and CYP51C , let us speculate that the fungus can process CYP3-dsRNA into siRNA that interfere with the expression of CYP51 genes . Gene annotation of F . graminearum’s genome ( http://www . broadinstitute . org ) predicted genes coding for two ARGONAUTE-like proteins , two DICER-like proteins and five RNA-dependent RNA Polymerases ( RDR ) , suggesting that the RNAi pathway is functional [28] . Consistent with these findings , RNAseq analysis of axenically grown F . graminearum , treated with CYP3-dsRNA , showed high numbers of reads of CYP3-dsRNA-derived siRNA ( Fig 7 ) . Together these data showed that F . graminearum possesses a functional gene silencing system , which is a prerequisite for disease control by SIGS . To test the antifungal activity of CYP3-dsRNA and their siRNA derivatives , we used a detached leaf assay that enabled us to assess fungal growth in local ( directly sprayed ) and distal ( semi-systemic , non-sprayed ) leaf segments . Using this approach , we could demonstrate that inhibitory dsRNA translocated via the plant vascular system and eventually was absorbed by the pathogen from leaf tissue ( Fig 5 ) . The profile of inhibitory dsRNA accumulation , as demonstrated by northern blot analysis ( Fig 4 ) and RNAseq ( Fig 6 ) , showed that both long CYP3-dsRNA and plant-processed CYP3-dsRNA-derived siRNA accumulate in the plant vascular system , though translocation of siRNA seems to be less efficient and thus siRNA concentration at the remote infection sites probably was not high enough to induced SIGS . Consistent with this notion , in planta produced CYP3-dsRNA-derived siRNAs was detected in the distal leaf parts only by the more sensitive RNAseq technique but not by northern analysis . Nevertheless , spraying high concentrations of CYP3-dsRNA-derived siRNA ( 20 ng μL-1 ) induced the SIGS process ( S7 Fig ) , demonstrating that the fungus is able to absorb siRNAs from barley leaves . Because of the less efficient movement of siRNAs , transport and translocation of the unprocessed 791 nt dsRNA has a critical role in the SIGS process , demonstrated by a compromised SIGS phenotype of the Fusarium mutant Fg-IFA65Δdcl-1 at distal leaf segments ( Fig 7 ) but not at local , directly sprayed leaf segments ( S8 Fig ) . Compromised DICER activity resulted in the fungus inability to cleave CYP3-dsRNA into siRNA , thus interrupting the RNA interference mechanism in case the concentration of CYP3-dsRNA-derived siRNA is not sufficiently high . Our finding that the unprocessed long dsRNA could be absorbed from leaf tissue has further implications for future disease control strategies using dsRNA . There are good arguments that application of longer dsRNAs might be more efficient than application of siRNAs given there more efficient translocation . Long dsRNA would be processed into many different inhibitory siRNA by the fungus , which not least also could be an issue when considering the risk of compound resistance emerging in a pathogen under field test conditions . Thus , based on our findings , further research is required to establish rules for optimal dsRNA structures , considering e . g . molecule lengths , combinatorial order of gene fragments , target sites in a given gene target , and the number of genes targeted by one dsRNA . Supporting the requirement for more information as to the design of dsRNA probes , RNAseq analysis revealed that most of the CYP3-dsRNA-derived siRNAs , accumulating in the axenic fungal mycelium , were not equally distributed at the CYP3-dsRNA scaffold , but accumulated at the CYP51A gene fragment ( Fig 7 and S2 Table ) . Further analysis is required to explain this bias in the production of siRNAs from CYP3-dsRNA . Our results are also consistent with the view that inhibitory dsRNA is more effectively absorbed by the fungus through infection hyphae that have intimate contact to plant tissue ( compare CYP51 gene expression in Figs 2 and 3 ) . How these hyphae differ from the fungal germ tubes and extracellular hyphae is however yet unclear although distinct biochemical modifications of fungal hyphae that penetrate host plants and are involved in nutrient uptake have been demonstrated [29 , 30] . Thus it is likely that these specialized , leaf tissue colonizing hyphae show dsRNA uptake that is superior over germ tubes . Our data are consistent with reports showing that silencing signals in plants are mobile [31 , 32] . The design of our experiments based on the previous finding that sRNAs , just as viroids [33] , preferably move via the vascular system in the source-to-sink direction although some reports discussed transport in the opposite route ( for review see [21] ) . Source-to-sink movement is one reason why the phloem rather than the xylem is generally considered as the conduit for movement of the silencing signal . This hypothesis is supported by the finding that the xylem sap , which transports water and ions , commonly is free of RNA [34] . However , spray application of dsRNA onto detached leaves cannot be compared with the situation in an intact leaf . Exogenously applied dsRNA first reached the apoplast , including the xylem ( Fig 5 ) , and subsequently translocated into the symplast by a yet unknown mechanism . Consistent with this , we could also demonstrate acropetal movement of the silencing signal that resulted in the inhibition of the fungus in distal not sprayed leaf areas ( S3 Fig ) . Apoplastic movement of RNA has been proposed , e . g . to explain how maternally expressed siRNAs could be transferred from the endosperm of developing seeds into the symplastically isolated embryo [35] . Regardless of how target-specific inhibitory RNAs are applied–by transgene delivery ( HIGS ) or spray ( SIGS ) –the use of target-specific inhibitory RNAs to confer plant protection potentially is an alternative to conventional chemicals because they are i ) highly specific and solely depending on their nucleotide sequence and ii ) can be developed against an unlimited range of pathogens provided that the RNAi machinery is in place . Given the accumulation of dsRNA in the plant phloem ( Fig 5 ) , sucking insects also are realistic SIGS targets as their efficient control by HIGS has been largely demonstrated [36 , 37] . Certainly , many questions have to be addressed in the future to eventually judge the agronomical potential of SIGS , including the costs of RNA applications and their stability under field conditions . Hence , more research is required to develop application strategies , including improved uptake by compound design and use of chemical formulations . Another yet unassessed issue is the risk that microbial strains become insensitive to a commercial dsRNA product . Such scenario could probably be resolved by application of dsRNA mixtures that target different regions in one gene or even different genes . Moreover , a commercial dsRNA product should be designed not to have off-target effects in other organisms that might be relevant in the respective agroecosystem , including beneficial fungi and bacteria . In this respect , it is important to understand that both plants [12] and fungi [38] support the production of secondary siRNAs , meaning there is a potential for transitivity and amplification . It is therefore possible that low abundance inhibitory dsRNA sprayed onto the plant triggered a large silencing effect in Fusarium via these secondary RNAs . Importantly , when considering the regulatory issue of RNA-based plant protection it is crucial to emphasize that the principles of SIGS and HIGS rely on the mechanisms found for trans-kingdom communication in mutualistic and parasitic interactions , and thus is a natural phenomenon [12 , 13 , 14] . Apart from the dsRNAs prospects in future plant protection strategies , there is an additional technological potential in developing new pesticides . The simple phenotyping adopted by the SIGS screens renders them a powerful tool for genetic studies to assess compound targets with high efficiency and low costs . Thus , the present data provide essential scientific information on a fundamentally new plant protection strategy , thereby opening novel avenues for improving crop yields in an environmentally friendly and sustainable manner .
The spring barley ( Hordeum vulgare ) cultivar ( cv . ) Golden Promise was grown in a climate chamber under 16 h light photoperiod ( 240 μmol m-2 s-1 photon flux density ) at 18°C/14°C ( day/night ) and 65% relative humidity . The wild type Fusarium graminearum strain Fg-IFA65 was described earlier [20] . Plates were incubated at room temperature under constant illumination from one near-UV tube ( Phillips TLD 36 W/08 ) and one white-light tube ( Phillips TLD 36 W/830HF ) . Fungal strains were cultured on synthetic nutrient-poor agar ( SNA ) -medium [39] . Generation of transgenic F . graminearum ( Fg-IFA65GFP ) , expressing a jellyfish green fluorescence protein ( GFP ) gene under the Neurospora crassa isocitrate lyase gene promoter ( PCII ) [40] , was performed as described [41] . For generation of the DICER LIKE-1 Fg-IFA65Δdcl-1 knock-out mutant , a homologous recombination strategy was used . The two homologous recombination segments USS and DSS ( ~1 kb each ) , representing promoter and termination regions of the FgDCL-1 gene , were selected based on the sequence information available at the Fusarium graminearum genome database ( http://www . broadinstitute . org ) , and were PCR amplified . Primers used for USS ( DCL_1_USS_KpnI_F and DCL_1_USS_KpnI_R ) , and DSS ( DCL_1_DSS_HindIII_F and DCL_1_DSS_HindIII_R ) are listed in S1 Table . The USS and DSS were cloned into flanking sites of the hph cassette of the pPK2 binary vector [38] using USER enzyme mix ( New England Biolab , Inc . , Ipswich , MA , USA ) in Escherichia coli . The resultant plasmid was confirmed for proper orientation of cloned inserts in the vector by PCR conducted using USS/DSS and vector-specific primers and then by sequencing the PCR products . The pPK2::ΔFgdcl-1 binary plasmid containing two Fgdcl-1 USS and DSS was transformed into Agrobacterium tumefaciens strain LBA4404 by electroporation , and transformants were analyzed by conducting restriction analysis . The ATMT of F . graminearum was based on a modified protocol [42] . Briefly , A . tumefaciens LBA4404 containing the pPK2::ΔFgdcl-1 plasmid was grown overnight in LB medium at 28°C ( 25 μg mL-1 kanamycin , 25 μg mL-1 rifampicin , and 5 μg mL-1 tetracycline ) . The next day , 10 ml of LB medium supplemented with above mentioned antibiotics and 200 μM acetosyringone was inoculated with 100 μl of the A . tumefaciens culture . This A . tumefaciens cell suspension with an OD600 of 0 . 5 to 0 . 7 was mixed with F . graminearum conidial suspensions ( 105−106 mL-1 ) in liquid SNA medium in equal proportions [1:1 ( v/v ) ] . Aliquots of 200 μl of the mixture were spread on black filter paper circles ( Grade 551; Whatman Inc . , Piscataway , NJ , USA ) , which were overlaid on SNA plates containing 200 μM acetosyringone and incubated for 2 days in the dark at RT until mycelial growth was observed on the filter paper . Transformants were selected on SNA medium supplemented with 50 μg mL-1 of hygromycin B ( Sigma , St . Louis , MO , USA ) and 300 μg mL-1 ticarcillin ( Fisher Scientific , Pittsburgh , PA , USA ) . The stacked clone ( CYP51 B-A-C ) [19] covering sequences of the three cytochrome P450 lanosterol C-14α-demethylase genes CYP51A ( FGSG_04092 ) , CYP51B ( FGSG_01000 ) , and CYP51C ( FGSG_11024 ) from F . graminearum was used as template for the synthesis of a 791 nt long CYP3-dsRNA [19] . The pLH6000-Ubi::sGFP plasmid [43] was used as template for the synthesis of a 720 nt long GFP-dsRNA ( S1 Fig ) . dsRNA was generated using MEGAscript RNAi Kit ( Invitrogen ) following MEGAscript protocols . Primer pairs T7_F and T7_R with T7 promoter sequence at the 5`end of both forward and reverse primers were designed for amplification of dsRNA ( S1 Table ) . sRNAs were generated using PowerCut Dicer ( Thermo Scientific ) . Following the PowerCut Dicer protocol , CYP3-dsRNA or GFP-dsRNA was used as template for Dicer cleavage . Detached leaf assay: Detached leaves of three-week-old barley plants were transferred into square Petri dishes ( 120 x 120 x 17mm ) containing 1% agar . For spray application , dsRNA was diluted in 500 μL water to a final concentration of 20 ng μL-1 , corresponding to 10 μg dsRNA per plate . For sRNA application , the reaction mixture of DICER-cleaved dsRNA was used at a final concentration of 10 μg siRNA diluted in 500 μL-1 water per plate . Leaves were sprayed using a spray flask ( 10 mL capacity ) . Each dish containing six detached leaves was evenly sprayed . For the semi-systemic setup , detached leaves were covered before spraying with a plastic tray leaving only the upper part ( approximately 1 cm ) uncovered . After spraying , dishes were kept open until the surface of each leaf was dried ( approximately 2 h ) . After an indicated lag time , leaves were drop-inoculated with 20 μL of 2 × 104 fungal conidia mL−1 . Closed dishes were incubated for 6 d at approximately 20°C on the lab bench . Alternatively , one-week-old barley seedlings were sprayed in the first leaf stage with 20 ng μL-1 dsRNA , and spray-inoculated three weeks later with 2 × 104 conidia mL−1 of Fg-IFA65 . Inoculated plants were grown for three weeks in a growth chamber before evaluating the infection symptoms . Fg-IFA65 and Fg-IFA65Δdcl-1 were cultured on synthetic nutrient SNA-medium . Plates were incubated at room temperature under constant illumination from one near-UV tube ( Phillips TLD 36 W/08 ) and one white-light tube ( Phillips TLD 36 W/830HF ) . Conidia were harvested from one-weak-old cultures with a sterile glass rod and sterile water [19] . CYP3-dsRNA was added to the fungal samples . Plates were incubated at room temperature . Gene expression studies were performed 24 h post CYP3-dsRNA treatment . The relative amount of fungal DNA was measured using qPCR to quantify fungal infection . DNA was extracted using the CTAB method [44] . Expression analysis of the three fungal CYP51 genes as well as plant defense marker genes PR1 and JMT , respectively , was performed using qPCR . RNA extraction from infected leaves was performed with TRIzol ( Invitrogen ) following the manufacturer’s instructions . Freshly extracted mRNA was used for cDNA synthesis using QuantiTect Reverse-Transcription kit ( Qiagen ) . For qPCR 10 ng of cDNA was used as template in the Applied Biosystems 7500 FAST realtime PCR system . Amplifications were performed in 7 . 5 μL of SYBER green JumpStart Taq ReadyMix ( Sigma-Aldrich ) with 0 . 5 pmol oligonucleotides . Each sample had three repetitions . To quantify the amount of fungal DNA , primers were used for assessing expression of the fungal β-tubulin gene ( FGSG_09530 ) with reference to barley ubiquitin gene ( S1 Table ) . Primers were used for assessing expression of target CYP51 genes with reference to β-tubulin gene ( S1 Table ) . After an initial activation step at 95°C for 5 min , 40 cycles ( 95°C for 30 sec , 57°C for 30 sec , 72°C for 30 sec ) were performed . Ct values were determined with the 7500 Fast software supplied with the instrument . Transcript levels of β -tubulin gene were determined via the 2-Δ Δ Ct method [45] by normalizing the amount of target transcript to the amount of reference transcript . RNA enriched for the sRNA fraction was purified from plant and fungal samples using the mirVana miRNA Isolation Kit ( Life Technologies ) . Indexed sRNA libraries were constructed from these enriched sRNA fractions with the NEBNext Multiplex Small RNA Library Prep Set for Illumina ( New England Biolabs ) according to the manufacturer’s instructions . Indexed sRNA libraries were pooled and sequenced on the Illumina HiSeq and NextSeq 500 platforms and the sequences sorted into individual datasets based on the unique indices of each sRNA library . The adapters and indices were trimmed using Trimmomatic [46] version 0 . 2 . 2 and the reads were mapped to the CYP3-dsRNA vector sequence using bowtie2 [47] version 2 . 1 . 0 . to identify sRNAs with a perfect match . Each library contained at least 5 million total reads . For Northern blot analysis , 8 ng of total RNA from local region and 80 ng of systemic region or negative control ( TE-mock ) and 10 pg of in vitro transcribed CYP3-dsRNA was loaded onto a 6% denaturing polyacrylamide gel with DNA Molecular Weight Marker VIII ( Roche ) , transferred to a nylon membrane . CYP3-dsRNA and U2 snRNA were detected using the DIG Labeling and Detection System ( Roche ) following the manufacturer’s instructions . Chemiluminescence was detected using X-ray films . The CYP3-dsRNA probe was created by PCR with CYP3-dsRNA forward and reverse primer ( S1 Table ) on the stacked clone ( CYP51 B-A-C ) [19] using PCR DIG Labeling Mix ( Roche ) . U2 snRNA loading control was amplified from cDNA created from total RNA using qScript Flex cDNA Kit ( Quanta BioSciences ) and primers U2 forward ( TACCTTTCTCGGCCTTTTGG and U2 reverse ( CAGCAGCAAGCTACTGTGGT ) . Gel purified probes were hybridized in NorthernMax Prehybridization/Hybridization Buffer ( Ambion ) at 45°C over night . Northern blots for the detection of CYP3-dsRNA-derived siRNA were performed as described [19] using a 791 nt [α-32P]-dCTP labeled CYP3-dsRNA as probe . Twenty-four h after spraying fluorescing dsRNA were imaged using a Leica TCS SP2 ( Leica Microsystems , Wetzlar , Germany ) equipped with a 75-mW argon/krypton laser ( Omnichrome , Chino , CA ) and a water immersion objective ( HCX APO L40x0 . 80 W U-V-l objective ) . Fluorescing dsRNA were imaged using a LSM 880 ( Zeiss Microscopy GmbH , Jena , Germany ) with the 488 nm laser line of an argon multiline laser ( 11 . 5 mW ) and a HeNe 594 nm ( 1 . 3 mW ) laser . Images were taken with a 20x objective ( Plan-Apochromat 20x/0 . 8 ) . Lambda stacks were created using the 32 channel GaAsP detector . Reference spectra with each pure fluorescence dye were recorded . The sample was inspected in Online Fingerprinting mode . Specific areas of the sample were imaged in lambda mode followed by Linear Unmixing with ZEN software ( Zeiss , Jena , Germany ) . Fluorescent labeling of the dsRNA was performed using the Atto 488 RNA Labeling Kit ( Jena Bioscience , Jena , Germany ) following the manufacturer’s instructions . Leaves were sprayed with the labeled dsRNA and 24 h later drop-inoculated with 2 × 104 Fg-IFA65 conidia mL−1 . To assess whether dsRNA has an effect on fungal morphology , leaves were inoculated with Fg-IFA65GFP and infected leaves were analyzed at 6 dpi . For observation of phloem tissue , cortical cell layers were removed down to the phloem from the lower side of the main vein of a mature leaf . The leaf surface and longitudinal- as well as cross sections were stained with 4 . 3 μM of the membrane dye RH-414 ( -N- ( 3-triethylammoniumpropyl ) -4- ( 4- ( 4- ( diethylamino ) phenyl ) butadienyl ) pyridiniumdibromid ) and/or with 5 μg mL-1 of the fungal hyphae dye wheat germ agglutinin ( WGA ) Alexa Fluor® 594 conjugate ( Invitrogen ) for at least 10 min . RH-414 , WGA Alexa Fluor 594 , and the autofluorescence of cell walls and chloroplasts were excited by the 564-nm line of the argon/krypton laser , while GFP and ATTO 488 were excited with the 488-nm line . For observation at the 590 nm and 510 nm wave lengths , respectively , a long pass filter was used . Digital images were processed with Adobe Photoshop to optimize brightness , contrast , and color and to enable an overlay of the photomicrographs . Analyses were performed in SigmaPlot 12 ( Systat Software ) using Student´s t-tests after data were tested for normality distribution ( Shapiro-Wilk test ) . FgCYP51A ( FGSG_04092 ) ; FgCYP51B ( FGSG_01000 ) ; FgCYP51C ( FGSG_11024 ) ; FgDCL1 ( FGSG_09025 ) ; ß-tubulin ( FGSG_09530 ) ; HvPR1 ( X74940 ) ; HvJMT ( BAD33074 . 1 ) ; HvUBQ ( M60175 ) | RNA interference has emerged as a powerful genetic tool for scientific research . The demonstration that agricultural pests , such as insects and nematodes , are killed by exogenously supplied RNA targeting their essential genes has raised the possibility that plant predation can be controlled by lethal RNA signals . We show that spraying barley with a 791 nt long dsRNA ( CYP3-dsRNA ) targeting the three fungal ergosterol biosynthesis genes ( CYP51A , CYP51B , CYP51C ) , whose respective proteins also are known as azole fungicide targets , efficiently inhibited the necrotrophic fungus Fusarium graminearum in directly sprayed and systemic leaf tissue . Strong inhibition of fungal growth required an operational fungal RNA interference mechanism as demonstrated by the fact that a Fusarium DICER-LIKE-1 mutant was insensitive to CYP3-dsRNA in systemic , non-sprayed leaf areas . Our findings will help in the efficient design of RNAi-based plant disease control . We provide essential information on a fundamentally new plant protection strategy , thereby opening novel avenues for improving crop yields in an environmentally friendly and sustainable manner . | [
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] | 2016 | An RNAi-Based Control of Fusarium graminearum Infections Through Spraying of Long dsRNAs Involves a Plant Passage and Is Controlled by the Fungal Silencing Machinery |
Metastasis is a complex process utilizing both tumor-cell-autonomous properties and host-derived factors , including cellular immunity . We have previously shown that germline polymorphisms can modify tumor cell metastatic capabilities through cell-autonomous mechanisms . However , how metastasis susceptibility genes interact with the tumor stroma is incompletely understood . Here , we employ a complex genetic screen to identify Cadm1 as a novel modifier of metastasis . We demonstrate that Cadm1 can specifically suppress metastasis without affecting primary tumor growth . Unexpectedly , Cadm1 did not alter tumor-cell-autonomous properties such as proliferation or invasion , but required the host's adaptive immune system to affect metastasis . The metastasis-suppressing effect of Cadm1 was lost in mice lacking T cell–mediated immunity , which was partially phenocopied by depleting CD8+ T cells in immune-competent mice . Our data show a novel function for Cadm1 in suppressing metastasis by sensitizing tumor cells to immune surveillance mechanisms , and this is the first report of a heritable metastasis susceptibility gene engaging tumor non-autonomous factors .
Metastatic disease remains a major problem for the clinical treatment of many different malignancies . Metastases can appear years after treatment of the primary tumor and is frequently refractory to therapy [1] . It has been estimated that approximately 90% of cancer-related deaths are directly attributable to the development of metastatic disease , rather than the primary tumor [2] . In order for a tumor cell to form a clinically-relevant metastatic lesion , it must undergo a highly complex process termed the invasion-metastasis cascade , which includes escaping from the primary tumor , entering the circulation , evading the immune system , seeding the secondary organ , and adapting to growth in this foreign environment [3] . Evidence suggests that the invasion-metastasis cascade is driven by a complex interplay of tumor cell-autonomous properties and host derived factors [3] . There is also accumulating evidence that germline polymorphism modifies tumor cell metastatic capability , indicating that heritable genetic variability can predetermine a tumor cell's propensity to metastasize [4]–[6] . In this study , we employ a complex genetics screen that exploits the differential heritable metastatic susceptibility observed among strains of inbred mice to identify tumor-autonomous expression of Cadm1 as a germline modifier of metastatic susceptibility . We demonstrate that over-expression of Cadm1 by as little as 1 . 5-fold can specifically suppress metastasis without any resultant difference in primary tumor growth . In addition to tumor-autonomous cellular phenotypes , metastatic efficiency is also impacted by tumor non-autonomous , host-derived factors including the immune system [3] . However , mechanisms by which tumor cells interact with the immune system remain poorly understood . Here , we show that the metastasis suppressive effects of Cadm1 are lost in mice lacking functional T cell–mediated immunity , an effect which is partially phenocopied by the depletion of CD8+ T cells in immune-competent mice , suggesting that Cadm1 sensitizes tumor cells to immune-surveillance mechanisms by CD8+ T cells . Since differences in expression of Cadm1 are inherited in mice , our data links the contribution of the genetic background in determining metastatic risk to the adaptive immune system , suggesting that individuals with higher levels of Cadm1 expression may be more resistant to metastasis .
Previous work from our laboratory demonstrated that the progeny of FVB-MMTV-PyMT , a mouse model of metastatic breast cancer , and NZB/B1NJ or C58/J mice have significantly reduced pulmonary metastasis relative to the parental FVB-MMTV-PyMT [7] . Preliminary genetic mapping in an NZBxFVB backcross ( Figure 1A ) suggested the presence of a metastasis susceptibility gene on chromosome 9 [8] which was subsequently validated by the development of a chromosomal substitution strain [9] . Reproducible association of a metastasis susceptibility locus on proximal chromosome 9 was obtained by analysis of a 226 animal C58 x FVB backcross . Linkage analysis of the C58 backcross replicated the association with metastasis susceptibility for the proximal half of mouse chromosome 9 ( Figure 1B and Figure S1 ) . Despite the reduction of metastasis in the C58xPyMT or the NZBxPyMT F1 progeny , inheritance of either the C58 or NZB proximal chromosome 9 was associated with increased metastasis in the backcrosses . These data are consistent with the possibility that NZB and C58 harbor a common metastasis promoting allele whose presence was unmasked by segregation of unlinked compensatory metastasis-resistance genes in the backcross populations . To further resolve the segment of chromosome 9 carrying this susceptibility locus , three congenic strains were developed that contained overlapping segments of NZB chromosome 9 , spanning from 16–67 megabases ( Figure 1C ) . Each congenic was bred to the PyMT transgenic animal and the effect of the NZB chromosomal segments on metastatic progression was assessed . No significant difference was observed for primary tumor burden . Only the congenic strain containing NZB chromosome 9 from 46–67 megabases was found to significantly increase metastatic susceptibility relative to FVB ( p = 0 . 022 , Figure 1D ) , consistent with the original quantitative trait locus ( QTL ) analysis . These results indicate that the gene ( s ) of interest resided within 46 Mb and 67 Mb of chromosome 9 and therefore subsequent analysis focused on this region . To filter the gene list in the chromosome 9 metastasis modifier interval for potential candidate genes , haplotype analysis was performed . Based on the hypothesis that NZB and C58 shared a common metastasis susceptibility gene , screening for genes with identical haplotypes in NZB and C58 that were distinct from the FVB haplotype identified 80 potential candidate genes . Since the majority of polymorphisms occur in non-coding sequence it is hypothesized that the majority of quantitative traits are due to subtle changes in gene expression rather than non-synonymous amino acid substitutions . Based on this assumption the candidate gene list was further filtered by RNA expression microarray analysis . Tumors from PyMT-FVB or NZB chromosome 9 substitution ( NF9 ) strains were subjected to microarray analysis to identify differentially expressed transcripts . Three genes within the candidate interval , Cadm1 , Zbtb16 and Pias1 , had the appropriate haplotype structure and were differentially expressed between the two genotypes . Of the three Cadm1 had the greatest magnitude of differential expression on microarray . Since aberrations in Cadm1 expression have previously been associated with poor prognosis in numerous malignancies [10]–[14] , Cadm1 was selected for further characterization . Zbtb16 and Pias1 are under investigation in separate studies . Exon sequencing of the Cadm1 protein-coding exons was performed to confirm the predicted haplotype differences between NZB and FVB and to screen for undescribed polymorphisms . Sequence analysis confirmed that NZB and FVB have distinct haplotypes at the Cadm1 locus and validated one synonymous polymorphism in exon 2 ( rs32721609 , Figure S2 ) . Since no non-synonymous polymorphisms were observed , subsequent efforts were focused on the possible effect of expression levels on the metastatic phenotype . To validate microarray data that Cadm1 was differentially expressed in FVB and NZB , quantitative real time PCR ( qRT-PCR ) was conducted on reverse transcribed RNA extracted from PyMT-FVB and NF9 tumors . The results showed a trend toward increased Cadm1 expression in NF9 tumors relative to FVB that was of borderline significance ( p = 0 . 073 , Figure 1E ) . To rule out the possibility that differential expression might have resulted from somatic alterations , expression was assessed in normal lung tissue as a representative epithelial sample . qRT-PCR in normal lung tissue from high-metastatic FVB and low-metastatic ( NZB x FVB ) F1 transgene-negative females showed a statistically significant increase in Cadm1 expression in NZB F1 mice of about 1 . 5-fold ( Figure 1E ) . These results , in combination with linkage and haplotype data , were consistent with the hypothesis that Cadm1 expression is likely to be a germline modifier of metastasis . Cadm1 , also known as Tslc1 , Necl2 , Ra175 , IgSF4a and SynCAM is an immunoglobulin superfamily adhesion molecule reported to be involved in homotypic and heterotypic cell-cell interactions [15] . It has also been identified as a tumor suppressor in lung adenocarcinoma [16] . To test whether variation in Cadm1 expression had an impact on tumor growth and metastasis , two independent mouse mammary tumor cell lines stably expressing Cadm1 were generated . Mvt-1 and 6DT1 cells [17] were transduced with Pol2-driven empty vector or Cadm1 expression vector lentivirus particles and selected with blasticidin . Stable expression of exogenous Cadm1 was confirmed by Western Blot and qRT-PCR ( Figure 2A and 2D ) and quantification revealed that 6DT1 cells over-expressed Cadm1 by approximately 1 . 5 fold , similar to the difference observed in NF9 tumors and lung relative to FVB ( Figure 1E and Figure 2D ) . One hundred thousand cells stably expressing Cadm1 or empty vector were then injected into the fourth mammary fat pad of syngeneic mice . Thirty days post-injection mice were euthanized and primary tumors were resected and weighed to assess the effect of Cadm1 on primary tumor growth . A statistically significant reduction in primary tumor growth was observed for the Mvt-1 cell line ( Figure 2B ) . In contrast , no significant difference in primary tumor growth was observed 6DT1 cells . The effect of ectopic Cadm1 expression on metastasis was then assessed by pulmonary surface metastasis counts . To account for the reduction of primary tumor growth for the Mvt-1/Cadm1 cell line , metastasis counts were also normalized by primary tumor burden to allow better comparisons between cell lines . As shown in Figure 2C , 2E , and 2F , Cadm1 expression significantly reduced the metastatic capability of both Mvt-1 and 6DT1 relative to controls , consistent with a metastasis susceptibility role for Cadm1 . The metastatic nodule size was measured on lung sections of orthotopically injected mice and did not differ significantly between control and Cadm1 expressing cells ( Figure S3 ) . Significant reductions in pulmonary metastasis were also observed by intravenous injection of tumor cells into the tail-vein , a model that assays tumor cell colonization of the lung ( Figure 2G ) , suggesting that the effect of Cadm1 on metastasis operates downstream of local invasion and intravasation . Expression of Cadm1 in 6DT1 cells suppressed metastasis without significantly impacting primary tumor growth , suggesting that , Cadm1 might be functioning purely as a metastasis suppressor in this cell line . To test whether Cadm1 had metastasis-specific effect on metastatic progression , knockdown of the endogenous gene in 6DT1 cells was performed as Cadm1 expression in 6DT1 cells reproducibly has no effect on primary tumor growth . Due to the potentially confounding effect on the primary tumor the Mvt-1 cell line was excluded from this analysis . 6DT1 cells were transduced with two independent shRNA lentivirus constructs targeting Cadm1 and were selected with puromycin , achieving 44% and 81% reduction of total Cadm1 protein , respectively ( Figure 3A and 3D ) . These cells were subsequently injected orthotopically into immune-competent syngeneic mice for in vivo analysis . Cells expressing shRNA-14 showed a statistically significant reduction in primary tumor mass while those expressing shRNA-15 showed no change in primary tumor mass ( Figure 3B ) . The tumor suppression observed with shRNA-14 is likely to be an off-target effect since it was only observed with one of two shRNAs . Although only shRNA-14 significantly promoted metastasis ( Figure 3C ) , both constructs showed significant increases in pulmonary metastasis after normalization for primary tumor burden ( Figure 3E ) . Since increases in metastases were observed for both constructs Cadm1 is likely to have a metastasis-specific function in this model system . To assess if the reduction in metastasis observed in Cadm1 over-expressing Mvt-1 cells was due to a tumor suppressor or metastasis-specific effect , immunohistochemistry was performed . Matched tumor and lung specimens were sectioned and staining was conducted against the V5 epitope to determine if pulmonary metastases from Cadm1 positive tumors retained expression of V5-tagged Cadm1 . As shown in Figure 4A–4F , the primary tumors ( Figure 4A–4B ) stained positive for the V5 epitope , however , pulmonary metastases ( Figure 4C–4D ) from matched tumors did not stain for V5 , indicating that these metastases had lost expression of the Cadm1 transgene . Further , gross dissection with subsequent qRT-PCR of metastatic nodules from the lungs of mice injected intravenously with Mvt-1/Cadm1 cells revealed that exogenous Cadm1 mRNA expression was attenuated in all four mice relative to the levels expressed in those cells upon injection ( Figure 4G ) and total Cadm1 expression was not significantly different between Mvt-1/Vector and Mvt-1/Cadm1 bearing mice ( Figure 4H ) . Since these cells were injected directly into the venous circulation and likely seeded the lungs immediately after injection , they had little time to lose Cadm1 expression suggesting that tumor cells with lower overall Cadm1 expression upon injection were selected for colonization . Together , this data suggests that low Cadm1 expression may be required for successful lung colonization by Mvt-1 cells , consistent with a specific metastasis-suppressing role in this cell line . Previous studies demonstrated that expression of Cadm1 could affect in vitro phenotypes associated with metastatic potential [18] . To examine the mechanism by which Cadm1 influences metastasis in breast cancer , proliferation , migration and invasion assays were performed . Ectopic expression of Cadm1 had no significant impact on proliferation rates in either 2-D or 3-D culture , and no difference in trans-well migration or invasion ( Figures S4 , S5 and S6 ) . Though Cadm1-expressing 6DT1 cells demonstrated a slight , marginally significant reduction in motility relative to control cells by scratch wound assay , Mvt-1 cells showed no difference in motility ( Figure S7 ) . These data would suggest that the effects of Cadm1 are likely distinct from events leading to the initial escape from the primary tumor , which is supported by the observed suppression of metastasis upon tail vein injection ( Figure 2G ) . Cadm1 has been shown to engage in heterotypic interactions with class-I restricted T cell adhesion molecule ( Crtam ) , a marker of activated CD8+ T cells and NK-cells [19] . This interaction has been shown to induce interferon-γ production by CD8+ T cells , enhance CD8+ T cell and NK-cell cytotoxicity in vitro , and tumor rejection in vivo [20] , [21] . In order to test the role of cell-mediated immunity in Cadm1-mediated metastasis suppression , control and Cadm1-expressing cells were orthotopically implanted into athymic nude mice . As illustrated in Figure 5 , the metastatic phenotype of Cadm1-expressing cells was rescued in immunocompromised mice . In addition , the tumor suppressive effect of Cadm1 in Mvt-1 cells was also lost . This suggests that T cell mediated immunity may be critical for the tumor and metastasis suppressive effects of Cadm1 . It has been reported that Crtam is present on activated natural killer ( NK ) and CD8+ T cells . Since athymic mice possess functional NK cells , we hypothesized that the loss of Cadm1-mediated metastasis suppression might be mediated by CD8+ T cells . Thus we orthotopically injected Mvt-1 cells expressing Cadm1 in immune-competent mice in which CD8+ lymphocytes were depleted with anti-CD8 antibody ( Figure 6A and Figure S8 ) . Unexpectedly , Cadm1-expressing Mvt-1 did not suppress tumor growth in this experiment as previously observed ( Figure 2C ) . However , subsequent protein-level expression analysis of the cells used in this experiment revealed that the level of Cadm1 over-expression was lower than in previous studies ( Figure S9 ) and similar to the levels observed in the 6DT1 cell line , suggesting that higher Cadm1 expression is required to suppress primary tumor growth than to achieve metastasis suppression . Although pulmonary metastases were significantly reduced in both control IgG treated and CD8+ lymphocyte-depleted mice , this effect was lost specifically in CD8+ lymphocyte-depleted mice when metastases were normalized by matched primary tumor mass ( Figure 6B , 6C , and 6D ) . This suggests that CD8+ lymphocytes contribute to Cadm1-mediated metastasis and implicates the role of cell-mediated immunity in metastasis suppression by Cadm1 . The engagement of Cadm1 on tumor cells and Crtam on CD8+ T cells has been reported to induce interferon-γ production of CD8+ T cells [20] , [21] . We hypothesized that enhanced interferon-γ may mediate an inflammatory response to induce tumor cytotoxicity . To test whether interferon-γ secretion was enhanced in mice bearing Cadm1-expressing tumors relative to those bearing control tumors , we performed an ELISPOT assay . Cells derived from the draining lymph nodes contralateral to the tumor were used because the tumors frequently enveloped the ipsilateral lymph nodes . We observed a marked increase in interferon-γ secretion in lymphocytes derived from the lymph nodes of Cadm1+ tumor bearing mice ( Figure 6E and 6F ) , indicating that an interferon-γ-mediated immune response local to the primary tumor may be involved in Cadm1-mediated metastasis suppression . The down-regulation of Cadm1 in invasive breast cancer , primarily by promoter hypermethylation , has been reported in at least four studies [22]–[25] . This loss or reduction in Cadm1 expression is associated with increased tumor grade , stage , and local invasiveness [22]–[25] . To determine whether levels of Cadm1 expression in breast tumors correlated with survival in patients , we searched three publicly available datasets from Oncomine , two of which have associated publications [26] , [27] and the Gene expression-Based Outcome for Breast cancer Online ( GOBO , http://co . bmc . lu . se/gobo/ ) , a web-based meta-analysis tool containing microarray-based tumor expression data on 1881 patients from 11 public datasets [28] . With the exception of the Boersma set , high Cadm1 expression consistently correlated with significantly improved survival in estrogen receptor ( ER ) positive tumor datasets , ( Figure 7A , 7B , 7C , 7E , and Table S1 ) . In contrast , high Cadm1 levels only significantly correlated with improved survival in ER negative patients in the Boersma dataset ( Figure 7D and 7F ) , suggesting that any effect Cadm1 levels may have on tumor progression are primarily relevant to ER positive tumors . Since metastasis is the primary determinant of overall survival in cancer , this suggests that high tumor cell Cadm1 expression may also be protective against metastasis in human breast cancer .
Numerous studies have implicated down-regulation of Cadm1 with poor patient prognosis [10] , [12]–[14] , [29] , [30]; however , to the best of our knowledge no studies have investigated the mechanism responsible . Here we demonstrate that expression of Cadm1 in two independent cell lines resulted in either specific metastasis suppression or tumor suppression accompanied by a reduction in metastasis . As metastasis is the primary cause of cancer-induced mortality , these findings imply a direct role for Cadm1 in metastatic progression in breast cancer and provide a link between diminished Cadm1 expression and reduced progression-free survival . Two key observations suggest a direct role for Cadm1 in metastasis suppression . First , in the Mvt-1 cell line , Cadm1 ectopic expression was lost in all metastases , suggesting that down-regulation of Cadm1 may be a prerequisite for completion of the metastatic cascade . Second , manipulation of Cadm1 in 6DT1 cells specifically affected the ability to form pulmonary metastases , without any significant effect on primary tumor growth . Thus , in the 6DT1 cell line , Cadm1 appears to function as a metastasis susceptibility gene , based on variations in functional levels . Together , the results suggest that Cadm1 may have both tumor suppressive and metastasis suppressive activities which may depend on the level of Cadm1 expressed . The identification of Cadm1 as an inherited metastasis suppressor gene was not predicted by the initial linkage analysis . Genetic analysis of both backcross populations and the congenic mouse strains indicated that inheritance of the C58 or NZB chromosome 9 candidate region was associated with an increase in pulmonary metastasis . In contrast , the up-regulation seen for both the C58 and NZB Cadm1 alleles was associated with a decrease in pulmonary metastasis in the orthotopic transplant studies . A likely explanation for this discrepancy lies with the complexity of the genome . Every individual carries numerous susceptibility and resistance factors and the final phenotype is dictated by the balance of these genes . The pro-metastatic chromosome 9 interval that was investigated in this study contains more than one gene that fit the criteria for a metastasis susceptibility gene . In addition , the strategy followed here does not capture candidates based on non-synonymous amino acid substitutions . We speculate the paradox between the identification of Cadm1 as a metastasis-suppressing gene in a metastasis-promoting genomic region is explained by the presence of one or more metastasis-promoting genes within the interval . When inherited together the metastasis-promoting factors dominate , masking the effect of Cadm1 . Finally , our results potentially implicate Cadm1 in cancer immunoediting , a process by which the adaptive immune system protects the host from developing cancer and , in turn , alters tumor progression by driving the outgrowth of tumor cells with decreased sensitivity to immune attack [31] , [32] . We have shown that Cadm1 may regulate metastasis by sensitizing tumor cells to surveillance by the immune system . Our findings show the metastatic potential of Cadm1-expressing cells was fully rescued in mice deficient of T cell mediated immunity , indicating that Cadm1-mediated metastasis suppression specifically involves the adaptive cell mediated immunity rather than the innate immunity , as the innate immune system is intact in athymic mice . However , we cannot rule out the possibility that thymus-dependent , Cadm1-sensitive immune cells are indirectly associated with metastasis suppression by recruiting innate immune cells , including NK-cells , to eliminate tumor cells . Since the effect of CD8+ T cells depletion did not result in a full rescue , we hypothesize that NKT cells , which also express the Cadm1 receptor , Crtam , [33] and are absent in athymic mice [34] may also contribute to tumor cell cytotoxicity and clearance . Further , the partial rescue of the metastatic phenotype by depletion of CD8+ T cells suggests that Cadm1 may promote the detection of tumor cells by an immune surveillance mechanism which potentially results in the CD8+ T cell–mediated cytotoxicity and clearance of Cadm1 expressing cells from circulation and secondary sites . While we have not directly shown that Cadm1 expression resulted in tumor cell death by T cells , this notion is supported by previous studies [20] , [21] showing Cadm1 expression on target cells enhances T cell cytotoxicity in an MHC-dependent manner . Thus loss or reduction in Cadm1 expression may contribute to the final step of cancer immunoediting: tumor cell “escape” from detection by the immune system [31] . Reports that the Crtam-Cadm1 interaction induces interferon-γ secretion [20] , [21] combined with our observation that lymph nodes draining Cadm1+ tumors contain lymphocytes that secrete higher levels of interferon-γ than controls suggest that the interaction with the immune system may be mediated by engaging Crtam on cytotoxic lymphocytes . Further , the role of Cadm1 as a tumor-autonomous component of immune surveillance is consistent with our observations that 1 ) knockdown of endogenous Cadm1 potentiates metastatic capability , and 2 ) pulmonary metastases have lost expression of exogenous Cadm1 . Together with the numerous [10]–[14] reports in the literature that Cadm1 expression is lost in more invasive tumors , our data implicates Cadm1 loss as an important step in cancer immunoediting that enhances the immune evasive and metastatic properties of tumor cells .
The NZB backcross has been described previously [8] . NZB chromosome 9 subcongenics were generated by breeding the NF9 chromosomal substitution line [9] to FVB/NJ , backcrossing the F1 progeny to FVB/NJ and screening the N2 backcross progeny for retention of NZB chromosomal segments . Chromosomal segments of interest were made homozygous by breeding to FVB/NJ followed by brother-sister mating of animals containing the segment of interest . Genotyping was performed by using a combination of microsatellite and SNP genotyping . The C58 backcross was generated by crossing PyMT male animals with C58/J females to generate PyMT-positive F1 males , which were subsequently bred to FVB/NJ females to generate N2 backcross animals . Tumor phenotyping was performed as previously described [35] . Genotyping was performed using the Illumina Mouse Medium Density Linkage array at the Center for Inherited Disease Research ( CIDR ) using spleen DNA isolated by the Wizard DNA purification kit . Quantitative trait mapping ( QTL ) was performed using J/QTL [36] . Exons were amplified from genomic DNA then run on a 2% agarose gel . Amplified bands were purified by the Qiagen Gel Extraction Kit and cloned into TOPO-TA vectors ( Invitrogen ) and transformed into One-shot Top10 chemically competent E . Coli ( Invitrogen ) as per manufacture's protocol . Plasmid was purified using the Mini Prep kit ( Qiagen ) . Sequencing reactions were conducted on ∼350 ng of purified plasmid using Big Dye Terminator reaction mix ( Applied Biosystems ) following the manufacturers protocol and sequenced by the NCI Sequencing Facility . All experiments were conducted using FVB/NJ background mouse mammary tumor explants cell lines 6DT1 and Mvt-1 kindly provided by Dr . Lalage Wakefield [17] . Cell lines were cultured in Dulbecco's Modified Eagle Medium ( DMEM ) ( Gibco ) supplemented with L-Glutamate ( Gibco ) , 9% fetal bovine serum ( FBS ) ( Gemini BioProducts ) , and 1% Penicillin and Streptomycin ( P/S ) ( Gemini BioProducts ) . pDonr253 is a Gateway Donor vector modified from pDonr201 ( Invitrogen ) . pDonr253 replaces the kanamycin resistance gene with a gene encoding spectinomycin resistance , and contains several sequencing primer sites to aid in sequence verification of Entry clones . The following oligonucleotides ( Eurofins MWG Operon ) were used in this study: L10379: 5′-GGGGACAACTTTGTACAAAAAAGTTGGCACCATGGCGAGTGCTGTGCTGCCGAGCGGATC L10383: 5′-GGGGACAACTTTGTACAAGAAAGTTGAGATGAAGTACTCTTTCTTTTCTTC The sequences of short hairpin RNA pLKO . 1 vectors for Cadm1 knockdown were shRNA14: CCGGCGGACTGGTTTGTAAAGGAAACTCGAGTTTCCTTTACAAACCAGTCCGTTTTTG shRNA15: CCGGCCTGTTCATCAATAACCTAAACTCGAGTTTAGGTTATTGATGAACAGGTTTTTG Murine Cadm1 was cloned into a Gateway Entry clone by PCR from a cDNA template ( BC095986 , RefSeq NM_018770 . 3 ) . The Entry clone contains the complete ORF preceded by a Kozak translation initiation sequence and an ATG start codon , and lacking a stop codon at the 3′ end to allow C-terminal fusions to be generated . DNA was amplified using specific primers L10379 and 10383 containing the Gateway recombination sequences , and PCR was carried out using Pfusion polymerase ( New England Biolabs ) . The final PCR product contains the gene of interest flanked on the 5′ side with a Gateway attB1 site and on the 3′ side with a Gateway attB2 site . The PCR product was cleaned using the QiaQuick PCR purification kit ( Qiagen ) , and recombined into pDonr253 using the Gateway BP recombination reaction ( Invitrogen ) by the manufacturer's protocols . The subsequent Entry clone was sequence verified throughout the entire cloned region . A lentiviral vector expressing a C-terminal V5 epitope-tagged fusion of Cadm1 was generated using Multisite Gateway recombination . An entry clone using the murine Pol2 promoter was recombined with the Cadm1 entry clone and a C-terminal entry clone encoding the V5 epitope tag ( GKPIPNPLLGLDST ) into a Gateway destination vector pDest-659 . This vector is a modified version of the pFUGW lentiviral vector which contains the enhanced polypurine tract ( PPT ) and woodchuck regulatory element ( WRE ) to provide higher titer virus . In addition , it contains an antibiotic resistance gene for blasticidin resistance . Entry clones were subcloned by Gateway Multisite LR recombination using the manufacturer's protocols ( Invitrogen ) . Expression clones were transformed into E . coli STBL3 cells to minimize unwanted LTR repeat recombination , and verified by agarose gel electrophoresis and restriction digest . Transfection-ready DNA for the final clones was prepared using the GenElute XP Maxiprep kit ( Sigma ) . A control vector ( 8166-M24-658 ) was generated by standard Gateway LR recombination of a stuffer fragment made up of a non-coding DNA into the pLenti6-V5-DEST vector ( Invitrogen ) . All lentivector constructs and lentivirus particles were generated by the Protein Expression Laboratory and the Viral Technology Group in NCI , Frederick . Two milliliter suspensions of 5×104 cells were incubated at 37°C in 5% CO2 overnight . Cells were then infected with 50 uL of concentrated lentivirus suspension , and selected 30 hours post-infection with 5 mg/mL blasticidin for over-expression ( Invitrogen ) constructs or 10 ug/mL ( shRNA ) puromycin for shRNA constructs . Protein was extracted by cell lysis in 400 uL of Pierce lysis buffer , vigorous homogenization , and incubation on ice for one hour . Twenty micrograms of protein extract per sample in NuPage LDS Sample Buffer and NuPage Reducing Agent ( Invitrogen ) were used for western blotting . PVDF membrane ( Millipore ) containing transferred proteins was incubated overnight with the primary antibodies: mouse anti-V5 ( Invitrogen ) , chicken IgY anti-SynCAM/TSLC1/Cadm1 ( MBL International ) , or mouse anti-β-actin ( Abcam ) . The membrane was then incubated with horse-radish peroxidase linked anti-mouse ( GE Healthcare ) or anti-chicken IgY secondary antibodies ( Abcam ) . Immunoblot was visualized using Amersham ECL Prime Western Blotting Detection System and Amersham Hyperfilm ECL ( GE Healthcare ) . Densitometry data were obtained and analyzed with a ChemiDoc-It Imaging System and VisionWorksLS software ( UVP ) . RNA was isolated from tumors and cell lines using RNeasy kit ( Qiagen ) and reverse transcribed using iScript ( Bio-Rad ) . Real-Time PCR was conducted using QuantiTect SYBR Green PCR kit ( Qiagen ) . See Table S2 for primer sequences . One-milliliter suspensions of 6×105 cells were seeded in triplicate into each well of 24-well Essen ImageLock ( Essen Bioscience ) plates in selective media and incubated at 37°C and 5% CO2 overnight . The following day , the cells washed twice with PBS and incubated at 37°C and 5% CO2 for 3 hours in 10 ug/mL mitomycin C . Scratch wounds were made using Essen 4-channel scratch instrument loaded with Eppendorf 10 uL micropipette tips . Cells were subsequently washed three times with PBS , placed in selective media and placed in Incucyte ( Essen Bioscience ) . Incucyte was programmed to image each well at 2-hour intervals . Data analysis was conducted using Incucyte 2011A software . One-milliliter suspensions of 5×103 cells were seeded in six replicates into each well of 24-well cell culture plates ( Corning , Inc . ) in selective media and placed in Incucyte . Incucyte was programmed to image each well at 3-hour intervals . Data analysis was conducted using Incucyte 2011A software . Matrigel inserts were hydrated for 2 hours in 500 uL serum-free DMEM then suspensions of 7 . 5×104 cells were seeded in triplicate into each well of the 24-well plate format BD BioCoat Control and Matrigel Invasion Chambers ( BD Biosciences ) and incubated for 20 hours at 37°C in 5% CO2 . Enriched media was used as chemoattractant . Membranes were subsequently fixed with methanol , stained with crystal violet , and cells present on the underside of the membrane were counted . Cultrex 3-D culture matrix ( Trevigen , Inc . ) was injected into 8-chamber slides and allowed to solidify at 37°C for 30 minutes . Cells were trypsinized and 5 , 000 counted then resuspended in 400 uL DMEM , 9%FBS , 5 ug/mLBlasticidin , and 2% Cultrex and incubated at 37°C with 5% CO2 for 3 days prior to microscopy . Female FVB/NJ mice from Jackson Laboratories were injected at 6–8 weeks of age . Two days prior to orthotopic injections , cells were placed in non-selective media . On the day of injection , 1×105 cells were injected orthotopically into the fourth mammary fat pad of age-matched virgin females . After 30 days the mice were euthanized by intraperitoneal injection of 1 mL Tribromoethanol with subsequent cervical dislocation . Primary tumors were resected , weighed , and snap frozen in liquid nitrogen . Lungs were resected , surface metastases were counted; lungs were inflated with 10% nitrate-buffered formalin and sent for sectioning and staining . For tail vein injection , 7×105 were injected into the lateral tail vein , mice were euthanized 22 days post-injection . For the CD8-antibody treatment study , orthotopic transplantation was conducted but mice were treated with 0 . 5 mg of either control rat IgG or rat monoclonal anti-CD8 IgG ( Harlan Bioproducts , kindly provided by Dr . Lalage Wakefield ) on days −4 , −3 , −2 , +3 , +10 , +17 , +24 , and +30; cells were injected on day 0 and mice were euthanized on day 37 . All procedures were performed under the Animal Safety Proposal ( LCBG-004 ) and approved by the NCI-Bethesda Animal Care and Use Committee . Immunohistochemical staining was performed on LeicaBiosystems' Bond Autostainer on paraffin embedded tissue sections with biotinylated rabbit anti-Goat IgG; primary antibody , anti-V5 rabbit polyclonal antibody ( Abcam Catalog #: ab95038 ) ; LeicaBiosystems Intense R Detection Kit . All histological analysis including paraffin embedding , sectioning , hematoxylin and eosin staining and immunohistochemistry were conducted by the Pathology/Histotechnology Laboratory , Laboratory Animal Sciences Program , SAIC , Frederick . For IFN-γ production by T cells in either Cadm1-expressing or control tumor bearing mice: Single cell suspensions from the lymph nodes of tumor bearing mice were prepared . 1 . 6 million cells were loaded into each well of an IFN-γ ELISPOT plate , stimulated with anti-CD3 ( 0 . 5 ug/ml , eBioscience , San Diego , CA ) , and cultured overnight . The procedure was done according to recommendations from the manufacture ( BD ) . Three to four mice for each experimental group , and triplicates for each sample were examined . The ELISPOT plate was scanned in ImmunoSpot ( Cellular Technology Ltd . Shaker Heights , OH ) and quantification was assessed using the CTL Scanning and CTL counting 4 . 0 . Statistical analysis comparing two samples were conducted using the Mann-Whitney test on Prism Version 5 . 03 ( GraphPad Software , La Jolla , CA ) . Multiple-comparison data was analyzed by Kruskal-Wallis test with post-hoc Conover-Inman correction for multiple analyses by R-script . Survival data was conducted with the Mantel-Cox test on Prism . | Metastasis , the dissemination and growth of tumor cells in organs distinct from which they originated , is the most common cause of cancer-related death . Accumulating evidence indicates that an individual's genetic background , the heritable complement of genetic variations that distinguish individuals , not only contributes to overall cancer risk , but also specifically influences metastatic potential . Using a mouse model of metastatic breast cancer and complex genetic analysis , we have identified Cadm1 as a metastasis susceptibility gene . Cadm1 was previously identified as a tumor suppressor in lung adenocarcinoma , and reductions in its expression have been associated with poor survival in numerous cancer types . In this manuscript , we use in vivo modeling to show that high expression of Cadm1 inhibits pulmonary metastasis , while knockdown of Cadm1 promotes the metastatic capability of tumor cells . We further show that the metastasis-suppressive effect of Cadm1 expression is lost in mice lacking T cell–mediated immunity and that this effect is partially mediated by CD8+ T-lymphocytes . Our data suggest that the inverse correlation between Cadm1 expression and disease-free survival in humans is a result of a metastasis-suppressive interaction of Cadm1 with the cell-mediated immunity . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"animal",
"genetics",
"cancer",
"genetics",
"immune",
"cells",
"genetic",
"screens",
"genetics",
"t",
"cells",
"immunology",
"biology",
"genetics",
"and",
"genomics"
] | 2012 | Cadm1 Is a Metastasis Susceptibility Gene That Suppresses Metastasis by Modifying Tumor Interaction with the Cell-Mediated Immunity |
Epigenetic changes can be induced by adverse environmental exposures , such as nutritional imbalance , but little is known about the nature or extent of these changes . Here we have explored the epigenomic effects of a sustained nutritional change , excess dietary methyl donors , by assessing genomic CpG methylation patterns in isogenic mice exposed for one or six generations . We find stochastic variation in methylation levels at many loci; exposure to methyl donors increases the magnitude of this variation and the number of variable loci . Several gene ontology categories are significantly overrepresented in genes proximal to these methylation-variable loci , suggesting that certain pathways are susceptible to environmental influence on their epigenetic states . Long-term exposure to the diet ( six generations ) results in a larger number of loci exhibiting epigenetic variability , suggesting that some of the induced changes are heritable . This finding presents the possibility that epigenetic variation within populations can be induced by environmental change , providing a vehicle for disease predisposition and possibly a substrate for natural selection .
Epigenetic modifications lie at the interface between genes and the environment , and thus have the potential to create functional diversity in response to environmental cues . There is mounting evidence that the establishment of epigenetic states during mammalian development can be influenced by the gestational and neonatal milieu , resulting in lifelong phenotypic changes . Epigenetic changes have been observed after early exposure to a variety of insults including environmental toxins [1] , variations in maternal care [2] , in vitro culture [3] and nutritional stressors [4]–[12] . In some cases the epigenetic effects are heritable , giving rise to environmentally-induced phenotypes in subsequent , unexposed generations [1] , [5] . The epigenetic response to altered nutrition is of great interest because it may explain how nutritional stress during gestation can have health effects beyond the neonatal period . Suboptimal nutrition or exposure to environmental toxins or stress during gestation increases the susceptibility of offspring to a number of adult-onset diseases , a phenomenon known as fetal programming [13] . It has been widely speculated that epigenetic changes underlie the phenotypic response to early nutritional stress [14]–[17] , but the genes responsible for the phenotypic changes are not known , and few studies have examined the magnitude and extent of epigenetic changes in response to altered nutrition . Perhaps the best-studied model of epigenetic response to nutrition is the effect of methyl donor supplementation on the murine Avy allele . Supplementation of pregnant dams with methyl donors influences the epigenetic state of the Avy allele in offspring , resulting in suppression of the obese yellow phenotype characteristic of Avy mice [4]–[5] , [9] . We have previously shown that this environmentally-induced epigenetic change can be passed from one generation to the next [5] . However , there is no reason to suppose that the Avy allele is the only locus whose epigenetic state is susceptible to dietary influence . Epigenetic changes have been observed at various individual loci after exposure to general nutritional deprivation or excess [7] , [18]–[21] and more recent genome-wide screens in cases of intrauterine growth restriction have suggested that changes may occur at loci throughout the genome [22]–[23] . We have investigated the extent of epigenetic changes induced by methyl donors , by assessing cytosine methylation at CpG island promoters across the genome in mice exposed to methyl donors for one or six generations . We find that methyl donors induce stochastic changes in methylation at thousands of loci throughout the genome , leading to an increase in epigenetic variability among individuals that is more pronounced in mice exposed for multiple generations . While affected genes differed among individual mice , similar functional groups were affected: genes involved in gene expression and transcription , organogenesis , and cellular development were highly overrepresented , suggesting that these genetic programs may be more susceptible to environmental influence .
Methyl donors participate in an arm of one-carbon metabolism that creates methyl groups for donation to various molecules , including DNA , via the conversion of S-adenosylmethionine to S-adenosylhomocysteine . The observed effect of methyl donors on the Avy allele – epigenetic silencing of the IAP element that drives ectopic expression of the agouti gene [4]–[5] , [9] – has been supposed to result from increased cytosine methylation due to an increase in the availability of methyl groups [9] . To determine if methyl donor supplementation leads to a global increase in the level of cytosine methylation , we assessed 5-methylcytosine ( m5C ) levels in genomic DNA from the livers of supplemented and unsupplemented mice by high-performance liquid chromatography ( HPLC ) . We find that the m5C content of DNA from supplemented mice is not increased , even after six generations of supplementation ( Figure 1 ) . The absence of gross changes in genomic m5C levels does not preclude changes at some loci in supplemented mice . Methyl donors have been reported to induce epigenetic changes in at least two discrete loci ( Avy and AxinFu ) [5] , [12] but it is not known if other genomic loci are also affected . To determine whether methyl donors exert epigenetic changes at other loci , and to resolve the extent of any changes , we compared genomic methylation patterns of supplemented and unsupplemented mice using a recently described method that combines enrichment of the unmethylated fraction of DNA with promoter microarray analysis [24] . Enrichment of the unmethylated fraction gives a better signal-to-noise ratio than other methods based on enrichment of methylated DNA , because removal of most repetitive sequences reduces the size of the DNA pool; moreover , since unmethylated CpG dinucleotides are less abundant in the genome than methylated CpG dinucleotides , this method is considerably more sensitive to DNA methylation changes at CpG islands [25] . We constructed libraries enriched for the unmethylated fraction of genomic DNA from liver using sequential HpaII and McrBC digestion and ligation-mediated PCR [24] , and hybridised them to Agilent Mouse CpG Island 105K arrays representing approximately 16 , 000 CpG islands . We chose to examine CpG islands for two reasons: first , methylation changes at CpG islands are more likely to reflect regulatory changes than methylation changes at low-CpG density loci [26]; second , the enzymatic enrichment method we used preferentially targets CpG islands . We compared libraries from five F1 and five F6 supplemented mice to those from five unsupplemented controls; pooled libraries from 10 unsupplemented controls acted as the reference sample for each array . We analysed normalised array data using Partek Genomics Suite software . To view the overall distribution of array data from each group of mice , we performed a principal component analysis ( PCA ) . PCA is a variable reduction procedure by which data with many variables is reduced to a few artificial variables , called principal components , which together account for most of the variance in the actual variables . The first three components of our data accounted for 38 . 7% of the variability and are visualized as a pseudo three-dimensional score plot in Figure 2A . In this visualization , array datasets from control mice cluster more closely than datasets from supplemented mice , suggesting that there is less variability between datasets from control animals than between those from supplemented animals . But control datasets do not overlap each other entirely , showing that there is some variability between controls . This variability cannot be attributed to technical variation between arrays , as principal component scores from array replicates were highly similar , so it is most likely due to methylation differences between control animals . This suggests that isogenic mice exposed to the same environment exhibit intrinsic epigenetic variation . To confirm that the inter-individual epigenetic variation we observed was indeed biological in origin and not due to some intrinsic variability in probe signal , we measured the intrinsic variability of each probe by calculating the standard deviation of the signals from the reference pool across all 15 arrays . We compared this value with the probe's array signal standard deviation in each group . We found no correlation between reference pool standard deviation and array signal standard deviation ( Figure S1 ) . We also find no correlation between array signal standard deviation and probe GC content , which is the primary source of intrinsic variation in probe hybridization behavior [27] ( Figure S1 ) . This data indicates that the inter-sample variation we observe is due not to technical variation , but rather to methylation differences between animals . Array datasets from supplemented mice show a broader range of principal component scores than those from controls ( Figure 2A ) , indicating that array data from supplemented mice are more variable . Datasets from supplemented mice are also spatially distinct from control datasets in the PCA . Together , this suggests that supplemented mice have methylation patterns that are both more variable than , and different from , unsupplemented mice . Principal component scores from F6 supplemented animals show even greater dispersal than those from F1 animals , suggesting that the increased variability in methylation patterns seen in methyl donor supplemented animals is amplified with multigenerational exposure . Datasets from long-term supplemented mice are also more distant from controls than those from short-term supplemented mice . This suggests that in addition to increasing methylation variability , long-term supplementation may cause mice to become progressively more epigenetically distinct from mice that have never been supplemented . As a second measure of overall variability in the array data , we calculated the range of probe signal standard deviations within each treatment group ( Figure 2B ) . The average standard deviation was significantly higher for both F1 and F6 supplemented mice than for controls ( p<0 . 001 , unequal variance t-test ) , consistent with greater variability in methylation patterns between individual supplemented mice than between individual controls . Third , we analysed each probe to determine whether it was more variable in one treatment group than another ( Bartlett's test ) : this revealed significantly more variability in short term supplemented mice than control mice , and in long term than short term supplemented mice ( Figure 2C ) . Finally , consistent with the idea that methyl donor supplementation increases epigenetic variability , histogram plots of array signals show an increased frequency of very low and very high signals in exposed mice ( Figure 3A ) . Taken together , these results indicate that supplemented mice harbor many loci that carry more or less methylation relative to control mice . The measures that we performed indicated variability in methylation at individual CpG island loci in the genomes of both unsupplemented and supplemented mice . To identify candidate changes at individual loci induced by methyl donor supplementation , the conventional approach would be an analysis of variance ( ANOVA ) . But candidate identification by ANOVA relies on within-group variance being lower than between-group variance , and our measures of overall variability indicated high within-group variance ( particularly within the supplemented groups ) . Thus an ANOVA of our datasets yielded very few candidate loci , which when subjected to validation by extensive bisulphite sequencing showed no change in methylation ( data not shown ) . We therefore took a different approach and first attempted to identify where methylation variability occurs , regardless of the treatment group: to do this , we interrogated the array probes that showed the most variable signals between mice of the same group , rather than between groups . We identified probes with standard deviation values above the 95th percentile of the control group and mapped them to their respective CpG islands; we arbitrarily defined these loci as “methylation-variable” . We find 2110 methylation-variable loci in the control group , 2606 in F1 and 3640 in F6 ( Figure 3B; for a list of all methylation-variable loci , see Table S1 ) . There were 1490 methylation-variable loci in common between the short-term and long-term supplemented groups; 800 of these were also methylation-variable in the controls . A considerable proportion of methylation-variable loci were unique to each treatment group: long-term supplemented animals display the most ( 1752 or 48% of all this group's methylation-variable loci ) and control animals the least ( 601 or 28% ) . Thus , not all the loci that are methylation-variable in control animals were affected by methyl donors in our sample supplemented population; this may be a reflection of the small sample size . Representative methylation-variable loci are illustrated in Figure 3C . The variable regions are tightly defined and are flanked by sequence that is methylation-invariant among animals . Consistent with our finding that methyl donors do not alter global levels of m5C , we find that methylation-variable loci in supplemented animals are as likely to lose methylation as to gain it ( Figure 3A and 3C ) . This challenges the assumption that methyl donors exert epigenetic effects via an increase in cytosine methylation [7] , [9] , and is consistent with our previous finding that methyl donors increase the probability of silencing at Avy without increasing the level of cytosine methylation [28] . At any given methylation-variable region , differences invariably occur in the same direction , although the amplitude differs among mice . Four loci interrogated by bisulphite allelic sequencing are shown in Figure S3 . We found that just over half of validated loci ( 5/9 ) showed small methylation changes in the direction indicated by the array; the verification rate ( FDR ∼0 . 55 ) , and the small magnitude of changes we observe , are comparable to that of previous studies using this array strategy [29]–[30] . Taken together these results show that methylation variability occurs at many loci across the genomes of isogenic mice , and that the number of loci that exhibit variability increases with exposure to dietary methyl donors . Methylation changes in response to methyl donors are therefore stochastic and act to increase the epigenetic variability extant in an isogenic population . We find significantly more methylation-variable loci that are common to the three groups than expected by chance ( 800 vs 150; p<0 . 0001 , χ2 test , 6 degrees of freedom ) ; this suggests that methylation variability does not occur randomly , but rather that some genes are more epigenetically “plastic” than others . We performed a gene ontology ( GO ) analysis of the methylation-variable loci using two independent methods ( Ingenuity Pathways Analysis ( IPA ) and GOstat [31] ) , to determine whether genes associated with these loci had functions in common . Both methods showed that genes involved in transcription , development and organogenesis are significantly overrepresented in methylation-variable loci , and that this is independent of dietary intervention ( Figure 4 and Table S2 ) . This applied to the loci that were common among groups as well as those unique to a group; thus , although genes may be idiosyncratically methylation-variable from one individual to the next , the variations appear to occur in common pathways . We considered the possibility that the methylation variability we observed was conditioned by the underlying genetic sequence , and so compared the sequence composition of the promoter regions ( −1000 bp to +500 bp relative to the TSS ) associated with the 100 most variable probes in the control group to that of the promoters associated with the 1000 least variable probes . We found no difference in GC content between methylation-variable and methylation-invariant promoters ( Figure S2 ) . We ran a de novo motif prediction pipeline ( GimmeMotifs ) to uncover any DNA motifs common to variable promoters , then compared the frequency of these motifs between the methylation-variable and methylation-invariant promoters . We identified nine motifs in the promoters of variable genes , but none of these were enriched relative to the methylation-invariant set ( data not shown ) . Finally , given the known role of repetitive elements in affecting the epigenetic state of nearby genes , we examined the frequency and relative location of genomic repeat elements ( LINE , SINE , LTR retrotransposons , simple repeats , low complexity repeats , microsatellites and DNA transposons ) in the same promoter regions as above . We found no evidence for a difference in either repeat frequency or distribution between methylation-variable and methylation-invariant promoters ( Figure S2 ) . Taken together , these results indicate that local sequence context is unlikely to account for the methylation-variable regions that we have observed .
We have conducted a genomewide DNA methylation analysis to investigate the epigenomic consequences of a sustained nutritional change , methyl donor supplementation . The epigenetic effect of dietary methyl donors has been well documented at the retrotransposon-derived murine Avy allele , but the extent to which the genome as a whole is affected by any sustained dietary intervention is largely unexplored . We found that methyl donor supplementation has widespread effects which increase epigenetic variation and are exacerbated by long-term exposure . The increase in epigenetic variation induced by methyl donors occurred on a background of inter-individual epigenetic variation already extant in C57BL/6J mice . DNA from different control mice did not give identical array signals; these differences cannot be attributed to technical variation or genetic differences , and indicate epigenetic variation between isogenic mice reared in the same environment . The methylation-variable regions we defined usually do not span entire CpG islands , but are restricted to a subset of probes within each affected island , with surrounding probes showing no variability . Since the CpG islands on the array were chosen using computational ( rather than functional ) criteria , the methylation-variable regions we have identified may represent functional components within CpG islands . Our finding of well-defined methylation-variable loci in a control population of isogenic individuals is consistent with previous observations of variably methylated regions ( VMRs ) in the genomes of inbred mice by Feinberg and Irizarry [32] . Although the two studies used different methods of analysis , they identified methylation-variable regions that show striking overlap in gene ontology . It would be interesting to examine whether the widespread epigenetic differences that have been observed between human monozygotic twins [33]–[34] occur in genes from the same ontologies . While several independent studies ( including this one ) now suggest that epigenetic variation persists in the absence of any genetic or environmental change , this study provides the first indication that additional epigenetic variation can be induced by environmental exposure . Methyl donor supplementation resulted in an increase in the number of methylation-variable loci: the epigenetic changes induced by dietary methyl donors were small in magnitude but widespread throughout the genome . Importantly , changes were stochastic , occurring at different loci in different individuals . Long-term exposure to excess methyl donors further increased the epigenetic variability within the population . That the effect becomes more pronounced with multigenerational exposure suggests that at least some of the induced changes are heritable . If so , phenotypic diversity created by an environmentally-induced increase in epigenetic variability might be acted upon by natural selection independently of genotype ( Figure 5 ) . This could enable rapid ( within a few generations ) adaptation to new environments [35]–[37] , and because no genetic change is required , the acquired phenotypes would potentially be reversible if environmental conditions reverted . A sustained environmental change over a longer period might eventually result in a permanent epigenetic change which can in turn facilitate genetic mutation through the increased mutability of 5-methylcytosine [32] , [38]–[39] . The idea that nutritional perturbations result in epigenetic changes throughout the genome , as opposed to at a few key regulatory genes , is consistent with the findings of several recent studies investigating the epigenetic contribution to fetal programming . Most candidate-approach studies report small , subtle methylation changes ( typically <10% ) [7] , [19] , [21]–[23]; reports of larger changes are less common [40]–[41] . An immediate question that arises is whether such small methylation changes are likely to exert any significant effect on phenotype . The VMRs identified by Feinberg and Irizarry were associated with gene expression variability [32] , so small methylation changes may well have the potential to alter phenotype . Small differences in the methylation level of a locus , such as we have detected by array , could be due to a small methylation change in many cells , or a large methylation change in a small subset of cells . A large methylation change would likely be reflected in a change in gene expression within those particular cells; small changes in methylation might be considered less likely to be associated with a change in gene expression . However , the methylation status of critical CpG dinucleotides at some loci ( e . g . within transcription factor binding motifs ) can be tightly linked to gene expression [2]; changes at these CpGs could alter gene expression without large methylation changes across the locus . It is also possible that small , widespread changes in methylation induced by a poor intrauterine environment may become magnified over a lifetime and hence accelerate age-associated epigenetic decline [15]; this may go some way to explaining why fetal programming effects are observed later in life . Fetal programming consistently increases the risk of the metabolic syndrome , despite being induced by a variety of environmental insults . This raises the question of whether specific metabolic genes are targeted by altered nutrition . In our model , methylation changes do not always occur at the same loci in different animals , but affected loci cluster in common gene ontologies . Metabolic ontologies are notable by their absence: rather , the most significant enrichment is seen in gene expression , organ development and cellular development . The fact that control animals ( both in our study , and that of Feinberg and Irizzary ) also show epigenetic variation within these ontologies suggests that genes in these pathways are “normally” epigenetically plastic; their increased epigenetic variability after supplementation implies that this plasticity ( or “metastability” ) renders the genes more susceptible to environmental influence . If so , even opposing environmental insults such as gestational undernutrition and overnutrition could produce epigenetic changes in these same pathways . The absence of metabolic ontologies does not necessarily preclude the generation of metabolic phenotypes: changes in organ development , for example , could have indirect metabolic consequences [42] . It has been proposed that adaptation though intrinsic epigenetic diversity may rely ultimately on genetic change within a species [32] , but there is no reason to suppose that altered epigenetic states might not become stable in a population ( or a subset of a population ) without leading to a genetic mutation . The Lcyc epimutation of Linaria vulgaris represents one example of a potentially adaptive ( and reversible ) phenotypic change that is purely epigenetic [43]; the epimutation allows the plant to alter its floral symmetry , perhaps in response to environmental cues , and has remained in this species for centuries without effecting a permanent genetic change . Evaluating the heritability of more subtle epigenetic alterations induced by environmental changes , such as those induced by dietary methyl donors in mice , will be key to understanding the impact of early environment on the epigenetic contribution to complex disease risk .
All animals were handled in strict accordance with good practice as defined by the NHMRC ( Australia ) Statement on Animal Experimentation , and the requirements of NSW State Government legislation . All animal work was approved by the St Vincents/Garvan Animal Ethics Committee ( animal research authorities #06/12 and #09/12 ) . C57BL/6 mice were fed ad libitum on either ( control ) NIH-31 diet or ( methyl donor supplemented ) NIH-31 diet supplemented with ( per kg ) 15 g of choline , 15 g of betaine , 7 . 5 g of L-methionine , 150 mg of ZnSO4 , 15 mg of folic acid and 1 . 5 mg of vitamin B12 ( Specialty Feeds , Glen Forrest , Western Australia ) . Supplementation was commenced two weeks prior to mating founder pairs and continued for six generations; mice to be tested were sacrificed at 5 weeks of age for DNA collection . We extracted DNA from liver tissue , chosen because of its relative cellular homogeneity and high DNA yield . Genomic 5-methylcytosine ( m5C ) levels in supplemented and unsupplemented mice were assessed using high performance liquid chromatography ( HPLC ) . 1 µg liver genomic DNA was denatured , digested into single nucleotides and dephosphorylated as previously described [44] . HPLC was performed using a method modified from Kovacheva et al . [45] with an Atlantis dC18 column ( 5 µm , 4 . 6×150 mm ) and a 2 . 5%–16% methanol gradient in 50 mM K3PO4 ( pH 4 . 5 ) . For CpG island microarray , genomic DNA from supplemented and unsupplemented mice was enriched for the unmethylated fraction as previously described [25] . Briefly , 250 ng liver genomic DNA was subject to HpaII digestion and adaptor ligation followed by a second digestion with McrBC and adaptor-specific PCR . Library preparation was performed in triplicate and replicate libraries pooled for microarray analysis . Libraries were subject to two quality control steps . First , a fraction of each amplified library was analysed by gel electrophoresis and any libraries showing anomalous amplification ( low amplicon quantity or unusual size range ) were discarded . Second , in vitro methylated pCMV DNA and unmethylated pIRES DNA were spiked in to each sample before the McrBC digestion step . After library construction , the control plasmids were PCR amplified and amplicons quantified by densitometry; any libraries showing significant amplification of pCMV ( >10% of an unmethylated control sample ) or poor amplification of pIRES were discarded . The DNA libraries were hybridized to Agilent 105K Mouse CpG Island microarrays . Before analysis of microarray data , outliers and low signal intensity features ( within 2 . 6 standard deviations of background ) were removed . Data was analysed using Partek Genomics Suite with LOESS normalization and median scaling to zero . We chose to use LOESS normalization because both test and reference samples underwent enrichment , and signals would thus be expected to center around 0 , as required by LOESS normalization . A Shapiro Wilks test in R 2 . 11 . 1 [46] was used to confirm that normalized probe signals were normally distributed . Differences in the variance of probe signals between groups were assessed using a Bartlett's test in R 2 . 11 . 1 , with a post hoc analysis comparing the magnitude of probe standard deviation used to identify probes with increased variability . Allelic methylation patterns of selected methylation-variable loci were assessed by bisulphite allelic sequencing [47] . For bisulphite PCR , 2 µg liver genomic DNA was treated with sodium bisulphite using the Epitect Bisulphite kit ( Qiagen ) and 10% of the reaction was used in each PCR . Amplicons were cloned into pGEM-T and transformed into DH5-α E . coli cells , and plasmid DNA from individual colonies was sequenced . For each of the 100 most variable probes in the control samples , we defined the genomic location of the closest known gene's promoter region as 1000 bp upstream and 500 bp downstream of the transcription start site using Galaxy [48] and the mm9 build of the UCSC Genome Browser [49] . As a control we used the 1000 least variable promoters in the control samples . We used GimmeMotifs [50] ( version 0 . 61 , using default options and medium motif size , with a randomized genomic background ) to discover sequence motifs common to methylation-variable loci . The program Clover ( version Jun 12 2006 , with default options , and 1000 randomizations and a p-value threshold of 0 . 05 ) [51] was used to interrogate whether any of the motifs discovered were enriched in the methylation-variable dataset relative to the 1000 least variable . Using the same promoter regions as described above , we obtained the GC content of each promoter using the geecee tool from Galaxy , the genomic location of the microsatellites from the microsat track , and the LINE , SINE , LTR , Simple_repeat , Low_complexity , and DNA repeats from the RepeatMasker track , all at UCSC Genome Browser . We compared the distribution of the distance from the TSS to the midpoint of each element for variable versus control promoters using a two-sample unpaired t-test , and compared the frequency of these elements using a χ2 test , in R 2 . 11 . 1 [46] . To identify genes associated with methylation-variable probes , the list of array probes with intra-group standard deviation above the 95th percentile of control standard deviations was matched to overlapping annotated genes using Ingenuity Pathways Analysis ( IPA ) software . Functional analysis of the resulting gene list was performed independently in both IPA and GOStat ( http://gostat . wehi . edu . au/ ) , using the array genes and all RefSeq genes ( mm9 ) as reference sets for both analyses . | Epigenetic changes to gene expression that do not involve changes to DNA sequence can be influenced by the environment and provide one candidate mechanism by which early nutrition can influence adult disease risk . Here , we examined epigenetic changes across the genome in response to short- and long-term exposure to a dietary supplement in genetically identical mice . We find that the supplement induces small but widespread epigenetic changes in exposed mice . These changes increase the epigenetic variability among exposed mice , and this effect is magnified in mice exposed long-term . The epigenetic changes are overrepresented in gene functions involved in cell and organ development and in gene expression . Our data is consistent with the external environment having pervasive effects on the epigenome and suggests that some genetic pathways may be more susceptible to environmental influence than others . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"genetics",
"and",
"genomics/epigenetics",
"nutrition"
] | 2011 | A Sustained Dietary Change Increases Epigenetic Variation in Isogenic
Mice |
A phylogenetically diverse subset of bacterial species are naturally competent for transformation by DNA . Transformation entails recombination of genes between different lineages , representing a form of bacterial sex that increases standing genetic variation . We first assess whether homologous recombination by transformation is favored by evolution . Using stochastic population genetic computer simulations in which beneficial and deleterious mutations occur at many loci throughout the whole genome , we find that transformation can increase both the rate of adaptive evolution and the equilibrium level of fitness . Secondly , motivated by experimental observations of Bacillus subtilis , we assume that competence additionally entails a weak persister phenotype , i . e . , the rates of birth and death are reduced for these cells . Consequently , persisters evolve more slowly than non-persisters . We show via simulation that strains which stochastically switch into and out of the competent phenotype are evolutionarily favored over strains that express only a single phenotype . Our model's simplicity enables us to derive and numerically solve a system of finite- deterministic equations that describe the evolutionary dynamics . The observed tradeoff between the benefit of recombination and the cost of persistence may explain the previously mysterious observation that only a fractional subpopulation of B . subtilis cells express competence . More generally , this work demonstrates that population genetic forces can give rise to phenotypic diversity even in an unchanging and homogeneous environment .
Bacteria mainly reproduce asexually , which has strong implications for the degree and patterns of intraspecific genetic diversity . However , three quasi-sexual mechanisms operate to combine genetic information between different lineages: conjugation , transduction , and transformation . Among these , transformation is unique in that the genes responsible for it are natively present on the chromosome , suggesting that it is favored by natural selection . Cells capable of this act are said to be competent for genetic transformation , or “competent” for short . In this article , we consider only natural competence , as opposed to that induced artificially in the laboratory by electroporation , etc . For a review of competence in bacteria , see [1] and references therein . The source of extracellular DNA during transformation in wild populations is not entirely clear . Detritus from cell lysis probably contributes to this pool , although active secretion from intact cells is also a possibility [2] . Perhaps more importantly , extracellular DNA can originate from the same or from different species . However , sequence similarity between the host chromosome and the incoming fragment increases the probability of integration [1] . This suggests that homologous gene recombination ( HGR ) of DNA from conspecifics occurs more often than horizontal transfer of novel genes between species . Although interspecific transfer is known to play an important role in microbial evolution [3] , here we focus exclusively on homologous recombination ( HGR ) . Besides transformation of DNA , a secondary property of competence observed in Bacillus subtilis is reduced rates of metabolic activity [4] and cell division [5] , [6] . The increased time between cell divisions may be necessary to perform the chromosomal manipulations required for HGR without causing DNA damage [5] . Furthermore , perhaps because of reduced metabolic rates , competent B . subtilis cells also die more slowly when exposed to antibiotics , as compared to non-competent cells [5]–[7] . Reduced birth and death are the hallmark of the “persistence” phenotype [8] , [9] . In E . coli , persisters are known to stochastically switch back and forth from the usual growth ( i . e . “vegetative” ) state [10] . Following recent work by Johnsen et al . [6] , we describe competence in terms of both recombination and persistence . With few exceptions , competence is regulated in naturally transformable species [11] . Here , we focus on the most thoroughly studied example , B . subtilis , in which competence is considered a stress response [12] . Although competence in this species is just one aspect of a more complicated survival strategy , notably including sporulation , here we focus exclusively on competence . Under normal laboratory conditions ( e . g . growth in LB broth ) , expression of competence genes or their associated phenotype cannot be detected . However , certain “competence media” [13] induce noisy activation by the regulatory circuit and a differentiation process in which merely of cells express competence while the remaining continue vegetative growth or perhaps sporulate [11] . There are no known conditions that induce all cells to simultaneously become competent in B . subtilis . Furthermore , recent single cell experiments dramatically show that this ratio is a dynamic equilibrium: over timescales hours , a single cell lineage may enter , exit , and then re-enter competence [14]–[16] . As those authors note , the statistics of competence initiation are consistent with a simple memory-less model of phenotypic switching . The observed phenotypic differentiation originates not from genetic differences , but rather from noisy fluctuations in the key transcription factor [14]–[18] . Here , we ask “why” only a fraction of B . subtilis cells become competent , i . e . why the population exhibits phenotypic diversity . Previous studies [10] , [19]–[21] interpret phenotypic diversity as “bet hedging” against an uncertain , fluctuating environment . By contrast , in this article we demonstrate that phenotypic diversity for the competence phenotype can result from natural selection even in an unchanging , homogeneous environment . The fact that competence is intimately related to the ability to create genetic changes suggests that the road to understanding this mysterious phenotypic diversity goes through population genetics/evolutionary dynamics . To this end , we developed evolutionary computer simulations that include both aspects of competence: HGR and persistence . Our in silico populations consist of an approximately constant number of vegetative and competent cells . HGR is not assigned an a priori advantage , but it turns out to be evolutionarily favored for indirect reasons related to the evolution of sex and recombination . These findings can be readily understood in relation to previous studies of HGR [22]–[25] ( see Discussion for elaboration ) . Similarly to HGR , persistence is not assigned an a priori fitness effect and is , to a first approximation , evolutionarily neutral in our simulations . However , a closer analysis reveals that persistence incurs an indirect cost during adaptive evolution . We conclude that populations face a tradeoff when “deciding” what fraction of cells express competence ( HGR is “good , ” but persistence is “bad” ) . An alternative interpretation of this decision is that ( lineages of ) cells must decide how to allocate their time spent between the competent and vegetative phenotypes . During competence , novel recombinant genotypes are created by HGR , but these recombinants maximize their evolutionary success when they are later expressed in rapidly growing vegetative cells . This tradeoff could plausibly explain the phenomenon of heterogeneous competence expression in B . subtilis .
We model the bacterial chromosome as loci , each of which has either a more fit ( one ) or less fit ( zero ) allele ( figure 1 ) . For simplicity , we do not represent the genes responsible for competence , nor do we allow mutations to change a cell's competence properties . A cell's intrinsic birth rate ( ) simply equals the fraction of “ones” in the genome ( 1 ) For our continuous time dynamics ( see below ) , the additive structure of equation 1 corresponds to independent contributions across loci . If discrete ( e . g . Wright-Fisher ) dynamics were used instead , then a multiplicative function would correspond to independent loci . Synergistic or antagonistic epistasis can easily be included by choosing a different functional form of . The carrying capacity represents limited nutrients and/or space , which decreases the actual birth rate ( ) from the intrinsic value ( ) : ( 2 ) In continuous time ( overlapping generations ) , one of the following simulation steps is stochastically chosen according to the well known “Gillespie algorithm” [26]: C++ code for our simulations is available upon request . Homologous recombination by transformation ( HGR ) occurs with rate between a living cell ( the acceptor ) and a pool of extracellular DNA ( the donor ) derived from recently lysed conspecific cells . The allele at ( exactly ) one randomly chosen locus in the acceptor is replaced by a homologous allele chosen randomly from the extracellular pool . Since , for simplicity , we do not explicitly represent the genes that enable recombination , these genes obviously cannot be transferred by an HGR event in our model . This implies that a recombining cell cannot transform itself into a non-recombining cell in our model . Further , for simplicity , the allele frequencies in the extracellular pool are assumed to be identical to those in the population of living cells . This assumption may not be true for real populations because cells carrying deleterious/lethal mutations could lyse more often than fit cells ( see figure S1 and text S1 ) . Unlike other models [6] , [23] , we do not explicitly consider how depends on population density ( cells per volume ) . Rather , we assume that density is constant regardless of the census size , and lump density effects within the parameter ( see parameter estimation below ) . For “shuffled” and clonal initial conditions , populations were founded by cells all having the same number of “ones” ( ) in their genomes . The initial number of cells was chosen as so that , i . e . so that the population as a whole was neither growing nor shrinking . For “shuffled” initial conditions , the “ones” in each genome were independently assigned random positions in each founding cell , thus maximizing the genetic diversity consistent with fixed initial fitness . By contrast , each cell in the clonal initialization scheme had exactly the same genotype . The populations were then allowed to evolve up a significant portion of the fitness landscape ( 40 beneficial mutations for the data presented ) so as to minimize the influence of initial conditions . We then calculated the average velocity during the interval in which the mean number of “ones” increased from to . These velocities were averaged over either or replicates ( see figure captions ) . For runs with “natural” initial conditions , the procedure was slightly more complicated . First , populations were “burned in” to an equilibrium configuration ( see below ) while recombining with rate ( ) . Next , 70 of the 100 loci were chosen randomly . Each cell then had its allele “flipped” at each of these 70 loci . This had the effect of reducing the number of “ones” in each genome by an amount ≲70 , while maintaining the level of genetic diversity obtained during a long period in equilibrium . This is equivalent to a sudden change in the fitness function ( so that many loci which were beneficial ( deleterious ) under the old fitness function are deleterious ( beneficial ) under the new fitness function ) that would result from a sudden environmental change . After this complicated initialization step , was unchanged from its value during the “burn in” phase , and the procedure was identical to that for the clonal and shuffled cases outlined above . One of “burn in” populations was randomly chosen for the initialization procedure for each of the replicate velocity measurements . Populations were founded with a clone having a perfect genome ( ) and a given recombination rate . The initial number of cells was . The simulation dynamics then proceeded for time units , which , for , and corresponds to approximately “generations , ” i . e . birth events . Since had clearly reached equilibrium ( figure 3 , left ) and generations is known to be a very long population genetic timescale , we have reasonable confidence that these populations reached an equilibrium level of genetic diversity . The equilibrium fraction of “ones” in figure 3 , right was determined by averaging over the final generations and replicate trials . We performed competitions both when the resident was adapting up the fitness peak and also when it had reached mutation/selection/drift equilibrium . In the adapting case , we allowed replicate populations to adapt from to “ones , ” starting from a single clone , then saved the population . For each competition , one of the saved populations was randomly chosen , from which ( between 1 and 100 ) cells were selected at random and changed to the invader type ( either purely competent or stochastically switching ) . Thus , the new invaders occur in randomly sampled genetic backgrounds and compete in populations that contain a semi-natural level of diversity . The procedure for the equilibrium case was similar , but only 5 initial populations were saved , after first evolving for ≥ generations , as described above . Each data point in figure 5 was derived from at least competitions . was computed by multiplying the slope of the least-squares linear fit by the average number of cells ( ) present when the invader was introduced . Most of our simulations shared core set of parameters , which we now discuss and relate to experiment . The switching and growth parameters are based on data from [14] , [15] . These authors observed a median cell division time of minutes , excluding competence events . In our constant population size scenario , this quantity must equal the death rate , so . The mean duration of competence events was hours , i . e . . Based on the supplementary movies from [14] , upon escape from competence , a cell fragments into vegetative cells . Our model treats this sudden burst of offspring from a competent cell as continuous growth . Thus , , which implies that . We used loci which each represent recombining segments of base pairs [1] . This implies a genome length of base pairs , which can be compared to and for B . Subtilis and S . pneumoniae , respectively . We also note that competence initiation was observed during of vegetative cell division events [15] , [16] . This implies that . If the equilibrium fraction ( ) of competent cells depended only on the switching rates ( ) , and not on selection for or against cells expressing competence , then , in fair agreement with experimental observations . This is consistent with our quasi-neutral treatment of competence expressing cells . Our simulations used idiosyncratic time units ( TU ) that can easily be related to hours . In all simulations , we used , which can be equated to , yielding the conversion factor . Multiplying those parameters values from simulations which have dimensions ( see figure captions ) by reproduces the experimental estimates above . The deleterious mutation rates for B . Subtilis and S . pneumoniae are likely roughly similar to that of E . coli , which has been experimentally estimated as [27] per genome , which is times smaller than our value of . However , our value could plausibly correspond to a mutator strain ( see , e . g . [28] ) and thus is not entirely unrealistic . Experimental estimates of the beneficial mutation rate range widely from [29] to [30] in E . coli , depending on the environmental conditions and which mutations , exactly , are being measured . Thus , , which equals the ratio of beneficial to deleterious mutation rates , experimentally lies in the range to . Our value of is thus on the high side , but not unreasonable . See figures S4 , S5 , S10 for some results using 100-fold lower mutation rates . Our reason for using such large mutation rates is that we are interested in the regime in which many beneficial mutations are spreading simultaneously , which occurs when [31] . This is a plausible biological scenario because real microbial population sizes can easily exceed . However , this is prohibitively large for simulations like ours which are fully stochastic and must potentially track any of possible genomic variants . Thus , our only recourse is to make as large as computationally feasible , as small as possible , and , while keeping . We believe that our choices of population size , , and are reasonable for these purposes , although they are certainly not equivalent to actual biological parameters . Our assertion that they capture the qualitative behavior of the real , experimental parameter set is bolstered by solutions to the finite- deterministic model ( see figure S9 ) , which can handle arbitrary parameters values . Furthermore , in figures S4 , S5 , S10 we explore some consequences of using biologically realistic , fold lower mutation rates in simulations . To the best of our knowledge , the recombination rate relevant to our model has not been directly measured . However , a related estimate was recently obtained for B . subtilis by fitting a model of bacterial growth and transformation to the experimentally observed kinetics of transformation for a particular antibiotic resistance marker [6] with partial homology to the acceptor strand . In order to quantitatively relate our recombination parameter with theirs ( call it ) , it is necessary to discuss three important differences between the two models . First , those authors modeled transformation with second order kinetics , such that the total rate of recombination in the population equals . By contrast , our description uses first order kinetics , such that the overall rate of recombination equals . This suggests an identification of , where is the population size in their experiments . However , a second difference between our description and theirs is that they only observe transformants at a particular antibiotic resistance locus , whereas our model refers to each of the loci throughout the genome . Thus , using their values of and , we obtain . In relation to the total mutation rate per genome ( per cell division [32] ) , the results from [6] imply recombination events per mutation event . This value roughly agrees with estimates derived from multi-locus sequence typing ( MLST ) analyses of several bacterial species including S . pneumoniae ( e . g . [33] , [34] . MLST estimates are obtained by comparing the number of fixed substitutions among populations that likely arose via mutation to those that likely arose via recombination . Since these two kinds of genetic changes probably achieve fixation with different probabilities , the MLST approach does not directly reflect our parameter . Nevertheless , those studies were designed to minimize this effect by examining only housekeeping genes , and their estimates serve as a valuable experimental reference point . In any case , both the MLST approach and that adopted by [6] likely underestimate the true recombination rate because they cannot detect “null” exchanges among members of the same local population , in which the donor and acceptor alleles are identical . Thus , the experimental value of our parameter is probably somewhat larger than . Given our values for the remaining set of parameters , in order for phenotypic switching to be optimal ( or , for that matter , for HGR to have a significant effect ) during adaptation requires . This certainly stretches experimental bounds but is not entirely implausible . However , according to figure 3 , HGR can confer increased equilibrium fitness even for .
Organisms are usually well adapted to their environment , i . e . their genomes reside near a local fitness peak in sequence space . However , if the environment suddenly changes , then the fitness peak can shift beneath the population , effectively displacing it to a slope in the fitness landscape . In this case , the population will likely evolve up this slope , toward a new locally optimal sequence . We first demonstrate that HGR can increase the speed of evolution up a smooth fitness peak , i . e . the speed of positive selection . For simplicity , we consider neither subsequent environmental changes nor the possibility that the population crosses a valley and jumps to a separate fitness peak [35] . Previous articles based on similar models [22] , [23] , [36] , [37] have demonstrated that HGR increases the speed of adaptation up a smooth fitness peak . Figure 2 confirms and elaborates upon these findings in the context of our current model and parameter values . We see that the speed of adaptive evolution ( “ones”/dt ) increases with the rate of HGR ( ) and eventually reaches an asymptotic value when there are HGR events per mutation event ( for these parameter values ) . The essential conclusion that HGR increases the speed of adaptation was also made experimentally in the case of transformation in Helicobacter pylori [38] and the distinct but conceptually similar mechanism of conjugation in Escherichia coli [39] . However , two other studies [40] , [41] do not support this conclusion . See [23] for further discussion and interpretation of these experiments . The influence of HGR depends crucially on the population's level of genetic diversity . To explore how initial conditions impact this effect , we initialized our simulations with varying levels of diversity ( figure 2 ) . When populations were founded by a single clone , was relatively small . On the opposite extreme , we also founded populations with many randomly generated sequences compatible with the initial fitness level , which resulted in a much larger . As a third , perhaps more realistic alternative , we initialized simulations by suddenly displacing a population in mutation/selection/drift equilibrium from the fitness peak ( see Methods ) . These “natural” initial conditions resulted in an intermediate level of diversity and a correspondingly intermediate rate of adaptation ( ) . This “natural” level of diversity depends in an unknown and complicated way on the population size , the mutation rate , and the fitness effect of mutations . The observed influence of initial conditions on the speed of adaptation is likely due to a long transient period during which neutral genetic diversity accumulates . Although computationally inconvenient , this transient is likely important in laboratory experiments and natural populations whose size fluctuates . In figures S2 , S3 , S4 we explore how the aforementioned benefit of HGR depends on various parameters including population size , strength of mutations , and mutation rate . Generally , we find that HGR accelerates adaptation over a broad range of parameters . However , the magnitude of the advantage is reduced in smaller populations with low mutation rates . Conceptually , this is because these conditions decrease the genetic diversity upon which HGR acts . In fact , we see that under those conditions , increasing the level of HGR to extremely high levels can begin to decrease the rate of adaptation . The likely explanation for this effect is related to the non-reciprocal nature of bacterial transformation– a unique beneficial mutation can be lost by an HGR event at that locus ( figures S2 , S3 , S4 ) . Besides the rate of adaptation , evolutionary success can also be measured by the fitness level achieved in mutation/selection/drift balance [42] . In order to explore this effect we founded populations with a perfectly adapted clone , which was then evolved for generations ( figure 3 ) . The average fitness declined initially due to the overwhelming number of deleterious mutations , reflecting the mutational load , the “fixed drift load” [43] , [44] , and a limited form of “Muller's ratchet” [45] . As deleterious mutations accumulated , more beneficial mutations became available , eventually leading to an equilibrium fitness level . This level increases monotonically with , eventually reaching an asymptotic value when there are HGR events per locus per time , corresponding to HGR event per mutation event . Thus , the rate of HGR needed to maximize equilibrium fitness is times less than that required to maximize the rate of adaptive evolution ( for these parameter values ) . Our measured equilibrium fitness should be compared to the standard deterministic ( ) calculation [46] which neglects beneficial mutations . That classical method predicts that the number of “zeros” carried by a genome follows a Poisson distribution with mean , where is the mutation rate per genome and is the selection coefficient of ( deleterious ) mutations . For the parameters in figure 3 ( ) , this formula implies that cells will be perfectly fit and that the mean fitness is . In stark contrast , figure 3 shows that , if HGR is not strong enough , stochastic fluctuations and finite population size reduce equilibrium fitness below by an additional . For higher levels of HGR , populations achieve , but do not substantially exceed , the deterministic prediction ( the tiny excess beyond seen for large is likely due to the presence of back mutations in our model ) . These findings agree with and extend previous theoretical work [24] , [25] , [47] which showed that , when epistasis is absent , as it is in our model , recombination cannot increase equilibrium mean fitness beyond the deterministic prediction . However , figure 3 clearly shows that HGR can increase equilibrium fitness beyond that which is actually achievable in finite , noisy populations . This finding is broadly and conceptually important because it shows that epistasis is not necessary for HGR to confer an equilibrium advantage to moderately sized ( for our parameters ) populations , in agreement with recent theoretical studies oriented toward eukaryotes [48]–[50] . Importantly , the benefits of HGR on equilibrium fitness are parameter dependent . For example , when we lowered the mutation rate by a factor of and kept , mean fitness in moderate size populations was independent of HGR and well described by the classical theory ( see figure S5 ) . We expect that the equilibrium benefits of HGR will be greatest in small populations with relatively high mutation rates and weak selection coefficients ( so that is large ) . Quantifying these dependencies remains a challenge for future work . As mentioned in the Introduction , competence is characterized by two distinct properties: recombination and persistence . Having presented our preliminary results showing the effects of HGR , we now turn our attention to persistence . We assume that when a cell switches to the persister phenotype , the reduction in birth rate is accompanied by a proportional reduction in death rate . In terms of our parameters , competent cells have The second and third properties taken together comprise persistence . Traditional population genetics models , such Wright-Fisher sampling [51] or Moran's process [52] , require that all individuals have the same death rate and thus these models cannot easily model persistence . This is why we resorted to a less traditional logistic model in which birth and death are decoupled ( see Methods ) . Ignoring recombination and mutation for the moment , under growth conditions in which the total number of cells is increasing ( ) , the frequency of persister cells will decline exponentially with rate proportional to . By contrast , when the total number of cells is decreasing under severe stress conditions ( ) , the frequency of persister cells will rise exponentially . In conditions intermediate between boom and bust , when the population size is stable , the frequency of persisters will remain approximately constant as long as their birth and death rates are each reduced by the same factor . These dynamics can be thought of as a form of “ vs . selection” [53] , [54] . For a comprehensive treatment of these dynamics , see [55]–[57] . See Discussion for a comparison between ours and previous models of persistence . Before proceeding , it is important to point out some consequences of this persistence model . As mentioned above , our persisters are competitively neutral compared to vegetative cells ( though see [56] for a small stochastic correction ) . However , the situation becomes more subtle when spontaneous mutations are considered . Since most mutations occur during DNA synthesis and cell replication , the mutation rate of persisters is reduced by a factor . Additionally , beneficial ( deleterious ) mutations will expand ( decline ) more rapidly by a factor when expressed in vegetative cells . In other words , selection operates more quickly on vegetative cells than competent cells because selection is ultimately a consequence of birth and death events , both of which occur times more frequently among vegetative cells . Indeed , birth , mutation , and death , and therefore the entire asexual dynamics , are all reduced by the common factor , effectively slowing down time for persisters . These effects follow from persister cells' increased generation time , and they combine to impose an indirect cost to the persister phenotype during periods of adaptive evolution . Based on observations from [14]–[16] ( see Introduction ) , we model phenotypic switching by allowing cells to stochastically , and without memory , enter ( exit ) competence with rate ( ) per unit time . In principle , the fraction ( ) of cells expressing competence at any particular time is governed both by the switching rates ( ) and the strength of selection for one phenotype over another . For our persistence model ( see above ) and experimentally motivated parameter values ( see Methods ) , we find that selection only negligibly affects in our simulations ( see figures S6 and S7 ) . Thus , after a brief transient period , is well approximated by ( 3 ) This relationship roughly holds experimentally in B . subtilis [14]–[16] ( see Methods ) . We measured the relationship between the fraction of competent cells ( ) and the speed ( ) of adaptive evolution by varying while holding all other parameters constant . Figure 4 ( left ) shows that when the rate of HGR ( ) is large enough , is fastest when a finite fraction ( for these parameters ) of cells express competence at a particular time . This supports our hypothesis that phenotypic diversity for competence can be favored by natural selection , even in an unchanging environment . This striking result has a simple conceptual explanation . Increasing causes both an increased effective level of HGR , which accelerates adaptation ( figure 2 ) , and an increased generation time , which acts to slow down adaptation . The fact that these forces oppose one another presents the possibility that there exists a nontrivial ( i . e . ) that strikes the optimal balance between cost and benefit . An additional piece to the puzzle , evident in figure 4 , is that only when is large enough . This makes sense in light of the shape of figure 2 , which becomes flatter with increasing . We can think of the parameter as tuning the effective rate of HGR ( ) between the values zero and . When is small , only the steep portion of figure 2 is “accessible” as varies between zero and one , and the marginal benefit of increasing is large . Consequently , for small ( figure 4 ) . By constrast , for larger , figure 2 becomes flat , and the marginal benefits of HGR become overwhelmed by the indirect cost of persistence for some . The hypothesis that increased generation time is the relevant counterweight to the positive effects of HGR is supported by figure 4 ( right ) , which considers the speed of adaptation per generation ( ) . This measure naturally masks all generation time effects . We see that by this measure , i . e . the optimal strategy is for all cells to express competence . Of course , real competitions depend on fitness changes that occur in real time , and therefore , not is the better measure of success . Above , we discussed the speed of adaptation , which measures evolutionary success at the population level . We now turn our attention toward direct competitions which measure evolutionary success at the individual level . We placed an initially rare “invader” in the context of a much larger “resident” population . For simplicity , we did not allow invaders to mutate into residents or vice versa . We did not observe stable coexistence between invaders and residents . Rather , after a sufficiently long period of time elapsed , the invader's lineage either went extinct or , occasionally , conquered the entire population ( i . e . went to fixation ) . The probability of fixation ( ) was then compared to the expectation under selective neutrality , which is simply the initial fraction of invaders ( ) . Figure 5 ( left ) shows the results of competitions initiated as the resident population adapted up the fitness peak ( see Methods ) . One set of invaders fully committed to competence whereas another ( favored ) set stochastically switched between the two phenotypes . We see that the fully competent invaders are favored , but the invaders that switch are even more highly favored . This supports our previous conclusion that stochastic switching is optimal during adaptation . We also initiated competitions in which the population was not climbing the fitness peak but , rather , had already reached mutation/selection/drift equilibrium ( see Methods ) . Figure 5 ( right ) shows that stochastically switching populations were more likely to conquer a vegetative resident than were purely competent cells . Unlike the adaptive case , mixed invaders never beat competent residents in equilibrium , which is unsurprising since competent populations have higher equilibrium fitness ( figure 3 ) . The competitive success of stochastically switching invaders can be rationalized by their optimal speed of adaptation ( figure 4 ) . However , we do not have a corresponding simple explanation for the advantage of stochastic switching in equilibrium . We hope to pursue this topic in future work . To put the data from figure 5 in perspective , consider the expected number of descendants ( ) left by each invader , which equals divided by the initial frequency of invaders . A neutral allele , of course , produces just one descendant ( ) . During adaptation and invasion of vegetative residents , stochastic switchers have , whereas for purely competent invaders , . Stochastic switchers have when directly invading competent residents . In the equilibrium case , and , and so again . Since each of these values is much larger than unity , they can be compared to the scaled selection coefficient usually denoted “Ns” in population genetics . We anticipate that HGR will also confer a competitive advantage in other parameter regimes . Gordo and Campos [50] showed recently , via simulation , that in a mutation/selection/drift equilibrium context eukaryotic sex is most favored for mutations of intermediate strength– must be small enough to promote genetic diversity but not so small as to be invisible to selection . We expect this same logic to apply to our somewhat different model of bacterial transformation . Those authors , as well as others [48] , also found an increasing advantage to sex as population size increased , but only in the regime where . For we expect the opposite trend to occur , since the effect of Muller's ratchet is strongest in this regime . In figure S10 we explore the case of fold smaller mutation rates . In that case , mixed cells continue to invade adapting populations . However , in equilibrium populations with small mutation rates , we observed no fixations of invaders of any type , from which we conclude that the fixation probability of those invaders was less than or comparable to . To summarize , at the large mutation rates in figure 5 , stochastically switching cells are competitively superior to purely competent cells in every scenario tested except when switchers invade competent cells in mutation/selection/drift equilibrium . These results suggest an interesting dynamic in which a purely competent population displaced from its fitness peak ( by e . g . an environmental change ) becomes susceptible to invasion by switching cells , but then later becomes susceptible to reversion to the purely competent strategy once adaptation ceases and equilibrium is regained . As a final point , we consider whether our invaders exhibit “frequency-dependent selection , ” as was recently reported in a simulation study [23] for the related case of invaders that perform HGR but not persistence . In order to determine whether frequency-dependent selection is operating , it is first necessary to articulate the behavior of a null model of “frequency-independent selection . ” In the null case , each of invaders would have an independent probability of fixating , and thus if . By contrast , frequency-dependent selection means that depends on . Figure 5 shows a clear linear increase of with , consistent with the null expectation . This linear trend continued for up to ( for clarity , data not shown ) . The authors from [23] concluded that frequency-dependent selection was present , based ( only in part ) on their observation that out of 50 replicate trials , was large when competitions were initiated with a ratio of resident to invader , but small or zero when , say , a ratio was used . Our model would almost certainly exhibit this same qualitative behavior , but we emphasize that both our data and theirs is consistent with a simpler explanation , and neither data requires frequency-dependent selection . In order to fully explore frequency dependence in this system , one would need to measure for all and examine whether this function can be fit by the frequency-independent model . In that regime , where invaders are initially abundant , HGR may well display some frequency dependence . Our proceeding results were based exclusively on computer simulations whose underlying Markov process cannot be solved analytically . Below , we develop a set of equations that approximate these simulations . The solutions display impressive qualitative agreement with the key conceptual findings discussed above , but the quantitative agreement is often weak ( compare symbols and solid curves in figures 2 , 4 ) . The utility of these equations is that they can be rapidly solved numerically , for arbitrary parameter values . This allows a qualitative exploration of regions in parameter space that are biologically relevant but consume prohibitively large amounts of CPU time . Below , we briefly present the finite- deterministic approach , the basis of which is treated in detail elsewhere [22] , [36] . Table 1 summarizes the notation used . More detail can be found in Methods . The basic goal of our finite- determistic equations is to dynamically describe the number of cells carrying a given fraction ( ) of “ones” in its genome . This is determined by the processes of birth , death , mutation , and HGR , which we will consider in turn . The birthrate ( ) depends on both and the total number of cells ( ) , via a simple logistic factor ( see Methods ) . By contrast , the death rate ( ) is a constant in our model . Thus , if we neglect stochastic fluctuations and temporarily omit mutation and HGR , then the number ( ) of vegetative ( asexual ) cells with a given fraction of “ones” ( ) is given by ( 4 ) where is the “growth operator” acting on the population . The term is the Heaviside step function which simply equals one if and zero otherwise . The purpose of this “cutoff factor” is to heuristically incorporate finite number fluctuations and prevent fractional numbers of very fit individuals from growing extremely quickly and dominating the dynamics [58] , [59] . Now let us consider mutation in isolation from birth , death , and HGR . Upon birth , each of loci can be “flipped” from or vise versa . Deleterious mutations occur at a rate at each of the loci carrying a “one . ” Similarly , beneficial mutations occur with rate at each of the loci carrying a “zero , ” where reflects the preponderance of deleterious over beneficial mutations . These mutations result in a flux of cells between fitness level and its neighboring fitness levels ( ) . Thus , mutation acting in isolation can be represented by as , where the mutation operator is given by ( 5 ) This equation merely expresses single beneficial and deleterious mutations occurring between neighboring “fitness classes . ” We neglect the chance of more than one mutation occurring during a single replication event in these equations ( but not in the simulations ) . The growth and mutation operators can be combined in a single equation that has proven qualitatively successful in describing asexual evolution dynamics [59]: . This is essentially a quasi-species equation [60] , [61] , except for two non-traditional features: ( i ) distinct sequences are binned according to their fitness value ( ) and , ( ii ) the presence of the cutoff factor . We now include recombination , which is much more difficult to model . When an HGR event occurs , a consequential genomic change happens only if the donor allele differs from that of the acceptor . The frequency of such events is deeply related to the probability that two randomly chosen cells differ at a particular locus under selection , often referred to as the “heterozygosity . ” Since we are unaware of any rigorous method to calculate this quantity in our finite , multi-locus setting , we invoke a strong but useful approximation . Specifically , we assume that every genotype containing ones is uniformly represented in the population at all times . In other words , we assume a maximal level of genetic diversity within each fitness class [62] . Under this assumption , the probability that a “one” is chosen as the donor allele at a particular locus is simply the probability that a “one” is the donor at any locus , i . e . the mean fraction of “ones” in the population ( ) . We assume that this is true regardless of . Thus , we can construct a recombination operator : ( 6 ) where denotes the number of competent cells with fraction of “ones . ” In figure S1 and text S1 we construct a modified recombination operator that represents an external DNA pool loaded with excess deleterious mutations . This modification does not change our essential results . After assembling these dynamical ingredients , including the persistence factor , and allowing for phenotypic switching between and , we obtain the coupled set of equations ( 7 ) ( 8 ) Solutions to equations 7 , 8 are plotted as solid curves in figure 2 , 4 . We see that the finite- deterministic equations reproduce the qualitative behavior of simulations , but , on a quantitative level , they always overestimate the speed of adaptive evolution ( ) . Quantitative agreement is strongest when “shuffled” initial conditions were used , because the recombination operator ( equation 6 ) assumes this extreme amount of neutral diversity within each fitness class . However , this degree of diversity cannot be maintained by finite populations , resulting in an overestimate of the effects of HGR and hence the speed as well . The quantitative disagreement is large when simulations were founded with a single clone because , over the timescale of the population climbing the fitness peak , the population is unable to generate the neutral diversity assumed by the recombination operator . However , our most important qualitative conclusion– that stochastic switching in and out of competence is evolutionarily optimal– remains true even if clonal conditions are used ( see figure S8 ) . In the artificial case that that the carrying capacity , every possible genetic sequence exists for all , and recombination confers only a very small advantage . Consequently , the optimal strategy in this case is for no cells to express competence ( pure vegetative growth ) ( see figure S9 ) . Two very recent studies [37] , [63] develop a stochastic analytic approach to the dynamics of adapting sexual populations . Those authors calculate the fixation probability of new beneficial mutations as they continually recombine into different genetic backgrounds , then relate this fixation probability to the speed of adaptation . The stochastic nature of these calculations is an improvement on our essentially deterministic equations , although , unlike our study , they neglect deleterious mutations . In addition to eukaryotic recombination , Neher et al . [37] also address a bacterial transformation model similar to ours . They consider an infinitely long genome where the number of segregating mutations is set by the balance between random drift , the rate of recombination , and the strength and production rate of beneficial mutations . Recombination is assumed to shuffle these segregating mutations into all possible combinations , consistent with a Gaussian fitness distribution . These assumptions are superficially reminiscent of those we employ in constructing our recombination operator ( equation 6 ) . However , the assumptions are in fact quite different . Whereas they assume the occupation of all fitness classes consistent with combinations of newly segregating beneficial mutations , we assume the reverse– namely the occupation of all genotypes consistent with a given fitness distribution . Whereas our methods overestimate the speed of adaptation from simulations , theirs yields an underestimate to the eventual steady state velocity . Generally , their assumptions are much more reasonable than ours for the problem of steady-state adaptation , though ours may better approximate scenarios following a sudden environmental change , in which previously neutral or mildly deleterious polymorphisms are initially present [62] ( e . g . our “natural initial conditions” ) . Furthermore , it is worth reiterating that the eventual steady state considered in those studies is reached only after an extremely long transient , as evidenced by the sensitivity to initial conditions in figure 2 .
Our birth and death dynamics assume that the overall population size is ( at least approximately ) constant . By contrast , previous models of persistence [6] , [21] , [64] focus on cycles of boom and bust resulting from environmental changes . During booms , the population expands and non-persisters exponentially outgrow persisters . Conversely , during busts , non-persisters die exponentially while the persisters remain intact . A modified version of these dynamics was recently applied to competitions between strains of B . subtilis that possess normal competence genes ( ) and a strain with a disabled competence system ( ) [6] . Since a subpopulation of cells expresses competence , which includes persister effects , this strain is favored over the strain during busts ( mediated by antibiotics ) but disfavored during booms ( access to fresh media ) . This observation was supported by experiments those authors performed on B . subtilis . In stark contrast to our model , theirs does not include homologous recombination ( HGR ) . Rather , they include the effect of recombination only by allowing the occasional acquisition of strongly beneficial genes ( e . g . antibiotic resistance ) from a truly exogenous source , e . g . another species . Furthermore , they do not address the potential optimality of mixed competence expression . Nevertheless , boom and bust dynamics could , in principle , be relevant to understanding mixed competence expression via a “bet-hedging” mechanism [19] , [21] , [64] , [65] . One straightforward way for a population to cope with uncertainty is by sensing the environment and then responding with the appropriate phenotype . A different strategy , known as “bet-hedging , ” is to blindly and simultaneously express diverse phenotypes . The obvious cost to this strategy is that some cells invariably express the inappropriate phenotype . Interestingly , this cost is minimized by a level of diversity , and underlying switching rates , that mirror the frequency of environmental change [21] , [64] . Bet-hedging can be favored over a sense-and-response strategy when environments change infrequently and the sensing apparatus imposes a large enough cost [21] . In the context of B . subtilis , bet-hedging implies that the cell is blind regarding whether the environment is suitable for competence expression . However , this seems inconsistent with the well known fact that competence in B . subtilis is a tightly regulated stress response [12] to particular environmental cues . Thus , bet-hedging seems unlikely to explain diverse competence expression because B . subtilis pays both the diversity cost and the sensing cost . Furthermore , the competence system involves a large number ( ) of genes [66] , suggesting that it did not evolve primarily as a persistence system , which would presumably require far fewer genetic components . The apparent failure of bet-hedging explanations in this context motivates the central hypothesis of this article– that diversity in competence expression is itself optimal in a population-genetic sense . A recent study [16] used the bet-hedging framework to address a related but distinct aspect of competence expression in B . subtilis . In particular , they investigated the optimal distribution of competence duration times , finding that a broad distribution is best able to hedge against uncertain concentrations of extracellular DNA . Their underlying assumption is that the ideal strategy for the cell is to remain competent for long enough to encounter sufficient DNA , and then return to vegetative growth . The purpose of our article is precisely to understand the basis of this assumption . Our results have some bearing on the evolutionary advantage of sex and recombination . There is an enormous amount of literature covering this topic , most of which is oriented toward diploid eukaryotes . Although there are non-trivial differences between meiotic crossing over and bacterial transformation , models of the former provide insight to competence . Below , we touch upon some of this literature . The essential effect of recombination is to reduce the correlations between alleles at different loci . Without these correlations , recombination can have at most a tiny effect . Correlations among loci are known as “linkage disequilibrium” ( LD ) , which can have several origins . One source of LD , known as the “Fisher-Muller effect , ” [45] , [51] occurs in adapting populations in which more than one beneficial spreads ( i . e . “segregates” ) simultaneously . Our parameter values correspond to this regime . In asexual populations , these concurrently spreading beneficial mutations most likely originate ( and remain ) in different backgrounds . Therefore , in the absence of recombination , the presence of one beneficial mutation is anti-correlated with the presence of the other , i . e . LD is negative . Recombination brings the mutations together in a common chromosome , which pushes LD closer to zero and accelerates adaptive evolution . The Fisher-Muller effect underlies the advantage to recombination seen in figures 2 and 5 ( left ) , and also in previous studies of HGR [22] , [23] . When beneficial mutations are not common enough to generate the Fisher-Muller effect , recombination can be favored in infinite populations if “synergistic epistasis” is present [67]–[70] . Synergistic ( also called “negative” ) epistasis means that each additional deleterious mutation has a larger effect than those which preceded it . Consequently , sequences carrying multiple deleterious mutations are under-represented in the population , as compared to the case with no epistasis . Thus , synergistic epistasis generates negative LD between deleterious mutations . Recombination decreases the extent of this LD and , under certain restrictions on the strength of epistasis [69] , [70] , can be favored . Importantly , each of these studies predict that if epistasis is absent or “antagonistic , ” LD will not be negative and recombination will not be favored by evolution . While correct in the infinite limit , this prediction does not apply to moderately size populations , as can be seen in figures 3 and 5 ( right ) and previous studies [48] , [50] . In fact [48] , [50] , show an increasing advantage to recombination as increases from to ( when ) . We expect the prediction to hold when the number of cells is much larger than the number of combinations of loci under consideration ( ) . When the number of genotypes is large ( e . g . in our simulations ) , the theory [67]–[70] may not be a good approximation for any realistic value of . Three previous studies [23]–[25] explicitly consider HGR in bacteria , but not phenotypic switching into competence . Redfield and co-workers studied the equilibrium level of fitness achieved by infinite populations [24] , [25] , finding that synergistic epistasis is required in order to confer an equilibrium advantage to HGR , in accord with ( and subject to the same limitations as ) the aforementioned theory . However , they do not consider the important case in which beneficial mutations are available and the population is adapting ( i . e . positive selection ) . A major strength of that study is that they consider interesting issues that our work largely neglects , such as recombination of genes responsible for HGR and the possibility that alleles in the extracellular pool may tend to be loaded with deleterious mutations ( although see figure S1 and text S1 ) . Future work could reconsider these important complications in our stochastic , finite context . Recently , Levin and Cornejo [23] devised an HGR model that bears many similarities with ours , although they do not consider phenotypic switching . In rough agreement with our results , those authors found that HGR accelerates adaptation and that HGR can invade asexual residents ( although , see commentary surrounding frequency-dependent selection in Results and also figure 5 ) . The most important difference between our approach and theirs is that they included only five loci with small fitness effects . Each of these loci had a very small mutation rate ( ) , suggesting that they represent perhaps nucleotides each . Thus , their approach neglects the vast majority of genotypic diversity present throughout the rest of the genome . This is especially important in the context of recombination because the frequent mutations in this region generate sequence diversity upon which recombination will act . A prominent feature of our model is cell death during competence inducing conditions . Cell death is usually not explicitly measured during laboratory experiments unless killing agents ( e . g . antibiotics ) are applied . However , in natural populations , it seems quite reasonable to assume that birth and death balance , in what has been referred to as “long-term stationary phase” [71] . Additionally , in B . subtilis , there are complications that interrelate competence , sporulation , and cell death . Competence and sporulation are distinct stress responses in B . subtilis , but they are often activated during the same conditions [12] , [72] . Spore formation involves asymmetric cell division in which the eventual products are a spore and a lysed non-competent cell ( see , e . g . [73] for a review of sporulation in B . subtilis ) . Also , recent experiments [74] , [75] demonstrate “cannibalism” in which sporulating cells secrete factors that kill non-sporulating cells . The related phenomenon of “fratricide” occurs during competence induction in S . pneumonia . Together , these observations suggest that cell death , particularly among non-competent cells , is both important and commonplace under conditions relevant to the evolution of competence . Careful treatment of these phenomena , and their interrelationships , is beyond the scope of the relatively simple model presented here , but could be pursued in future work . In this article we make several assumptions that could be relaxed in future work . First , our model neglects sporulation , which may be inherently coupled to the competence system in B . subtilis [72] . Secondly , we have not directly represented the genes responsible for recombination in cells ( i . e . a “modifier locus” ) . Since bacterial transformation is non-reciprocal , this modifier locus can exchange itself for a non-functional homologue in the extracellular pool , thereby becoming . However , the reverse process required to replenish the number cells cannot occur , and thus the number of cells should decrease under this influence . This process cannot alter our results concerning the rate of adaptation ( figure 2 , 4 ) because all cells in those populations carry the modifier locus . However , this effect will to some extent impact our results concerning competition experiments ( figure 5 ) . This issue should lead to an effective selection coefficient against cells . Based on our parameter estimation ( see Methods ) , this implies a – effective disadvantage to . Since we have estimated for recombining invaders ( figure 5 ) , we can very roughly estimate a selection coefficient of in favor of . This indirect benefit may or may not be sufficient to overcome the – decay caused by non-reciprocal exchange . Of course , it is important to remember that genes enabling a phenotype are obviously somehow maintained in many real bacterial populations . It has been pointed out by other researchers that many genes enabling have other important functions , and that the capacity for HGR might only be maintained as a by-product ( see [76] for a review ) . In this article , we do not take a position on whether HGR is the dominant reason that these genes exist . Exploration of that topic requires detailed experimental knowledge of the pleiotropic effects of these genes , as well as estimates of their fitness consequences . Rather , we have merely isolated , quantified , and attempted to deepen understanding of the population genetic aspects to competence . Relative to some previous studies [24] , [25] , our stochastic treatment reveals that HGR can be favored strongly and broadly ( e . g . without epistatic effects ) . Thirdly , we have assumed that competence does not entail a direct fitness ( dis ) advantage . Although we suspect that our broad qualitative conclusions will remain true if competent invaders are assigned a sufficiently small direct birth/death penalty , future work could quantify the maximum size of this handicap . Along these same lines , one could allow the overall population size to either grow ( directly favoring the vegetative phenotype ) or shrink ( directly favoring the persistence phenotype ) . Fourthly , we have assumed that all mutations have the same effect on fitness , which is obviously not true in real populations . A step toward greater realism could be made by incorporating a set of loci that become lethal if mutated . Fifthly , our fitness function ( equation 1 ) is non-epistatic; in other words , different loci make independent contributions toward organismal fitness . This is a significant assumption , given the prominent role of epistasis in theories of the evolution of sex/recombination . However , our non-epistatic assumption is conceptually neutral in that it does not “automatically build in” the result that HGR is indirectly favored by evolution . Indeed , as discussed above , population genetic theory predicts neither an advantage nor a disadvantage to recombination in the absence of epistasis . Additionally , the experimental data is mixed and inconclusive ( e . g . [77]–[79] ) regarding whether synergistic , antagonistic , or no epistasis predominates between loci in real bacterial genomes . Given this set of facts , our assumption of no inter-locus epistasis seems fair . Nevertheless , future work could investigate our model under various epistatic fitness functions . Finally , we point out an empirical shortcoming of our theory: Figure 4 predicts that the speed of evolution is optimized when of cells express competence , which is significantly larger than the observed in the laboratory . This quantitative discrepancy could be due to any or all of the limitations listed above . A fundamental prediction of our theory is that , if total population size is approximately constant , cells which stochastically switch between the competent and vegetative phenotypes will prevail in competition experiments against otherwise isogenic cells that are either or that fully commit to competence . Süel et al . demonstrated experimentally that the switching rates ( ) can be independently tuned by manipulating the basal expression rates of and , respectively [15] . Thus , in principle , the fraction of competent cells can be experimentally adjusted ( equation 3 ) while holding constant the time spent in competence . Although conceptually straightforward , there are potential complications to these experiments . First , both serial-passage and chemostat protocols discard excess cells indiscriminately , resulting in the same “death” rate for both the competent and vegetatively growing phenotype . An alternative that circumvents this problem is long-term batch culture [71] . However , in this case metabolic waste products accumulate and the environment is not constant . A second complication to this suggested experiment involves the possibly bizarre behavior of cells engineered to fully commit to competence . Recall that normal B . subtilis cells elongate while expressing competence , then fragment into daughter cells only upon exit from competence ( [14] , supplementary movies ) . Thus , if exit never occurs , these cells might not ever divide . In this case , our simple , continuous growth model completely misrepresents the strange behavior of the engineered cells . However , this does not at all change our central conclusion that phenotypic diversity for competence is evolutionarily favored over total commitment to competence , since non-dividing cells will obviously lose the competition . Although we constructed our model with the behavior of B . subtilis in mind , our assumptions and conclusions may also be appropriate for a broader class of naturally transformable bacteria . First , we followed Johnsen et al . [6] in assuming that competent cells replicate slowly compared to vegetatively growing cells . As those authors point out , this effect has been observed in B . subtilis [4]–[6] and S . pneumoniae [80] but has not been observed , nor specifically investigated , in other species . Along with those authors , we predict that in other naturally transformable species , phenotypically competent cells will also replicate slowly compared to vegetatively growing cells . Additionally , we predict that , in a range of naturally competent species , phenotypically competent cells will die more slowly than vegetatively growing cells under natural competence stimulating conditions . More strikingly , we also predict that , in other species as well as B . subtilis , observations of single cells over relatively long time-scales will reveal phenotypic switching to a faster growing , non-recombining phenotype . Evolutionary modeling on a genome-wide scale necessarily entails many rough and abstract assumptions . However , the staggering complexity of real biological systems does not necessarily preclude insightful contributions from abstract population-genetic models . In this article we have included many ubiquitous features such as multiple loci , genetic linkage , and both beneficial and deleterious mutations . All of our parameter values are based on experimental estimates . The conceptual basis of our conclusions is rather simple: Homologous recombination ( HGR ) by itself is evolutionarily favored , but this advantage is offset by a reduced replication rate . Slower replication may be unavoidable if a cell is to avoid DNA errors introduced by the HGR process . The optimal balance between cost and benefit is achieved by allowing the novel recombinant genotypes created by HGR during competence to cycle back and later be expressed in rapidly growing vegetative cells . Because competence initiation and exit are stochastic and asynchronous , these cycles result in heterogeneous competence expression throughout the population . The link between persistence and competence plays a crucial role in our model . Besides B . subtilis , there is also evidence for this link in S . pneumoniae [80] . To our knowledge , this connection has not been observed , nor specifically investigated , for other bacterial species . In all likelihood , there are many counterexamples to the competence-persistence link– these signal violations of our simple model assumptions and could point toward interesting biological questions . | In certain environmental conditions , populations of the bacterium Bacillus subtilis split into two physiologically distinct phenotypes . While some cells continue to grow and divide , a minority become “competent” for transformation by extracellular DNA . This differentiation process is driven not by genetic differences among cells , but rather by noisy molecular fluctuations . Although the differentiation process is thought to confer an evolutionary advantage , the basis of this advantage has remained elusive until now . We developed computer simulations of the joint dynamics of cell replication , cell death , mutation , and the quasi-sexual transfer of genes through the extracellular DNA pool . We find that bacterial sex via DNA transformation is indirectly favored by evolutionary forces . However , the indirect benefits of sex are counterbalanced by a reduced replication rate . We find that these opposing forces present an evolutionary dilemma best solved when the population splits into the two experimentally observed phenotypes . These results present a mechanism that selects for phenotypic diversity , even in an unchanging and homogeneous environment . | [
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] | 2010 | Optimal Strategy for Competence Differentiation in Bacteria |
MIcrophthalmia-associated Transcription Factor ( MITF ) regulates melanocyte and melanoma physiology . We show that MITF associates the NURF chromatin-remodelling factor in melanoma cells . ShRNA-mediated silencing of the NURF subunit BPTF revealed its essential role in several melanoma cell lines and in untransformed melanocytes in vitro . Comparative RNA-seq shows that MITF and BPTF co-regulate overlapping gene expression programs in cell lines in vitro . Somatic and specific inactivation of Bptf in developing murine melanoblasts in vivo shows that Bptf regulates their proliferation , migration and morphology . Once born , Bptf-mutant mice display premature greying where the second post-natal coat is white . This second coat is normally pigmented by differentiated melanocytes derived from the adult melanocyte stem cell ( MSC ) population that is stimulated to proliferate and differentiate at anagen . An MSC population is established and maintained throughout the life of the Bptf-mutant mice , but these MSCs are abnormal and at anagen , give rise to reduced numbers of transient amplifying cells ( TACs ) that do not express melanocyte markers and fail to differentiate into mature melanin producing melanocytes . MSCs display a transcriptionally repressed chromatin state and Bptf is essential for reactivation of the melanocyte gene expression program at anagen , the subsequent normal proliferation of TACs and their differentiation into mature melanocytes .
MIcrophthalmia-associated Transcription Factor ( MITF ) is a basic helix-loop-helix leucine zipper ( bHLH-Zip ) factor playing an essential role in the differentiation , survival , and proliferation of normal melanocytes , and in controlling the melanoma cell physiology [1–4] . Quiescent murine adult melanocyte stem cells ( MSCs ) residing in the bulge region of the hair follicle do not express MITF , but its expression is induced in the proliferating transit amplifying cells ( TACs ) that are generated at anagen [5–7] . MITF expression persists as TACs migrate towards the bulb to form terminally differentiated melanin producing melanocytes [8 , 9] . Following malignant transformation of melanocytes , cells expressing low or no MITF are slow cycling and invasive , displaying enhanced tumour initiating properties , high MITF activity is characteristic of proliferative melanoma cells , and even higher MITF activity is associated with terminal differentiation of melanocytes [10] . MITF silencing in proliferative melanoma cells leads to cell cycle arrest and entry into senescence [11 , 12] . These and other observations gave rise to the proposed ‘rheostat’ model postulating that the level of functional MITF expression determines many biological properties of melanocytes and melanoma cells [13 , 14] . MSCs and slow cycling melanoma cells express low or no MITF , while TACs and proliferative melanoma cells express higher levels . High MITF activity induces terminal melanocyte differentiation and can also induce cell cycle arrest of melanoma cells [15] . This property has been exploited to derive drugs that induce terminal differentiation of melanoma cells as a therapy for melanoma [16] . MITF is both an activator and a repressor of transcription and functions through a host of cofactors . A comprehensive analysis of the MITF ‘interactome’ in 501Mel melanoma cells revealed that the NURF ( Nucleosome Remodelling Factor ) complex associates with MITF [17] . NURF was first identified in Drosophila Melanogaster [18 , 19] and comprises NURF301 ( in mammals , BPTF , Bromodomain , PHD-finger Transcription Factor ) , the ISWI-related SNF2L ( SMARCA1 ) ATPase subunit , NURF55 ( RbAp46 , RBBP7 ) and NURF38 [20–23] . NURF promotes ATP-dependent nucleosome sliding and transcription from chromatin templates in vitro [24–26] . Mammalian NURF comprises BPTF , RBBP4 and SNF2L and may further comprise the SNF2H ( SMARCA5 ) ATPase subunit , BAP18 and HMG2L1 [27 , 28] . The 450 kDa BPTF is the defining and only unique subunit of NURF and binds active promoters via the interaction of its PHD ( plant homeodomain ) domain with trimethylated H3K4 and of its bromodomain with acetylated H4K16 [21 , 29 , 30] . Despite extensive characterisation of the biochemical properties of the NURF complex and its BPTF subunit [31] , much less is known about their biological functions in mammals . Bptf inactivation in mouse leads to embryonic lethality shortly after implantation [32 , 33] . Bptf loss is not however cellular lethal as it is possible to isolate viable Bptf-/- ES cells , but in vitro they show defective differentiation into mesoderm , and endoderm lineages [33] . Somatic inactivation of Bptf in CD4-CD8 double negative thymocytes has shown that it is required for their subsequent maturation [34] . Furthermore , BPTF may also be involved in maintaining human epidermal keratinocyte stem cells in an undifferentiated state in vitro [35] . The interaction of MITF with NURF prompted us to investigate its role in melanoma cells and in the melanocyte lineage . BPTF is selectively required in melanoma cells and melanocytes in vitro . Inactivation of Bptf in developing melanoblasts ( Bptfmel-/- ) shows that it acts during their embryonic proliferation and migration , and we observed a unique phenotype in neonatal mice where the second post-natal anagen coat is devoid of pigment resulting in rapid loss of pigmentation and a lasting white pelage . Contrary to previous models where premature post-natal greying results from loss of MSCs , in Bptfmel-/- mice an MSC population persisted throughout the life of the animal , but at anagen , Bptf is required for normal TAC proliferation , for expression of melanocyte markers and hence for terminally differentiation into mature melanin producing melanocytes .
We previously described tandem affinity purification of N-terminal FLAG-HA epitope tagged MITF from the soluble nuclear and chromatin associated fractions of 501Mel cells [17] . Amongst the identified proteins were the BPTF , SMARCA5 , SMARCA1 and RBBP4 subunits of the NURF complex . Multiple peptides for these proteins were detected in the chromatin-associated fraction from the cells expressing tagged MITF , whereas no peptides for these factors were found in immunoprecipitations from the control extract ( S1A Fig ) . Interaction of these NURF components with MITF was confirmed in western blot experiments showing that BPTF , SMARCA1 and SMARCA5 all specifically precipitated with tagged MITF in the chromatin-associated fraction ( S1B Fig ) . MITF therefore associates , either directly or indirectly , with the NURF complex on chromatin in 501Mel cells . We next interrogated transcriptome data [36] to assess expression of the BPTF , SMARCA1 and SMARCA5 subunits of NURF in a collection of human melanoma cells . The three subunits were expressed at comparable levels in all of the tested cell lines , whether they expressed high ( 501Mel , 888-mel ) low ( SK-Mel-28 , LYSE ) or no ( 1205Lu , WM852 ) MITF ( S1C Fig ) . The expression of BPTF , SMARCA1 and SMARCA5 proteins was also assessed in extracts from a subset of these lines . Again , the levels of each protein were comparable in the different lines ( S1D Fig ) . NURF is therefore present in all types of melanoma cells irrespective of MITF expression and their tumourigenic properties . To address the function of BPTF in 501Mel cells , we performed both siRNA and shRNA knockdown experiments ( Fig 1A ) . SiBPTF led to prominent morphological changes in 501Mel cells similar to those observed following siRNA silencing of MITF ( Fig 1B ) . In both cases , cells showed an enlarged , flattened and irregular morphology with extensive cytoplasmic projections . Similar results were observed following infection with lentiviral vectors expressing two different shRNAs directed against BPTF both of which led to diminished BPTF levels whereas MITF expression was unaffected ( Fig 1A ) . ShBPTF silencing also led to strongly diminished SMARCA5 and SMARCA1 protein levels ( Fig 1A ) , although the expression of the corresponding genes was not reduced ( see below ) . As BPTF is so far believed to be exclusive to the NURF complex [20] , the loss of SMARCA5 and SMARCA1 suggests that a large fraction of these proteins is associated with BPTF in the NURF complex that is destabilized by BPTF silencing . ShBPTF knockdown therefore leads to a loss of NURF function possibly through its chromatin-remodeling activity . After 5 days of shBPTF silencing , cells displayed marked morphological changes analogous to those seen following siBPTF silencing and si/shMITF silencing ( Fig 1C and 1D ) . These morphological changes were characteristic of those observed when 501Mel cells enter senescence [12 , 37] and up to 90% of shBPTF or shMITF silenced cells showed staining for senescence-associated β-galactosidase ( Fig 1C and 1D ) . BPTF silencing therefore induced senescence in 501Mel cells . Analogous results were observed in several other MITF-expressing melanoma cell lines . ShRNA-mediated BPTF silencing in SK-Mel-28 cells led to reduced cell number and marked morphological changes with many bi-nucleate and multi-nucleate cells ( S2A , S2B and S2C Fig ) . In melanoma MNT1 cells , shBPTF silencing led to a spindle-like bipolar morphology ( S2B and S2C Fig ) , while in 888-Mel cells , BPTF knockdown led to strongly reduced cell numbers with the remaining cells again showing a spindle-like bipolar morphology ( S2C Fig ) . We also investigated BPTF function in MITF-negative 1205Lu cells . In this cell line also , BPTF silencing induced an enlarged , flattened more rounded senescence-like morphology ( S2D , S2E and S2F Fig ) . On the other hand , shMITF silencing had no effect on these cells consistent with the fact that they do not express MITF . In contrast to the above , shBPTF silencing in a variety of non-melanoma cells such as HeLa ( cervical cancer ) , HEK293T ( human embryonic kidney ) had no effect on either proliferation or morphology ( S3A , S3B and S3C Fig ) . These observations show that BPTF plays a selective and essential role in melanoma cells that is not seen in non-melanoma cells . Moreover , as BPTF is essential in 1205Lu cells , it may have both MITF-dependent and independent functions in melanoma . As BPTF and MITF silencing in 501Mel cells generated similar phenotypes , we performed RNA-seq following shRNA-mediated silencing and compared the de-regulated gene expression programs . Following BPTF knockdown , 494 genes were down-regulated ( Fig 1E ) , enriched in ontology terms associated with regulation of transcription , kinase signalling , pigmentation and cell cycle ( Fig 1F and S1 Dataset ) . 593 genes were down-regulated by MITF knockdown , enriched in cell cycle , in particular in mitosis , consistent with previous observations that MITF silencing leads to severe mitotic defects [12 , 17] . Comparison of the two data sets identified 191 common repressed genes associated with transcription regulation and kinase signalling ( Fig 1E and 1F ) . Thus , 39% of genes down-regulated by BPTF silencing were also down-regulated by MITF silencing indicating a large overlap between the programs regulated by each factor . To determine the statistical significance of this overlap , we used hypergeometric probability to calculate the representation factor ( RF ) that determines whether the number of genes in the overlap is higher than expected by chance taking into account the number of genes regulated by MITF and BPTF with respect to the total number of expressed genes . For the common down-regulated genes the RF was 9 . 3 ( p < 6 . 048e-141 ) showing the high statistical significance of the overlap . Some examples of co-regulated genes are listed in Fig 2 . Moreover , the number of co-regulated genes was highest in the top quartiles of shMITF-regulated genes ( 44% in quartile 1 , S4A Fig ) . Genes with strongest dependency on MITF were therefore more often co-regulated by BPTF than those of the bottom quartiles whose expression is least affected by MITF loss . Following BPTF knockdown , 669 genes were up-regulated ( Fig 1E and 1F ) , enriched in cell adhesion , morphology and motion as well as a set of secreted cytokines and growth factors that constitute the senescence associated secreted phenotype ( SASP ) . 748 genes were up-regulated by MITF knockdown , enriched in terms analogous to those of BPTF including an extensive SASP [17 , 38] . Comparison of the two data sets identified 278 common induced genes , with 41% of genes up-regulated by BPTF silencing also induced by MITF silencing . For the common up-regulated genes the RF was 7 . 9 ( p < 3 . 495e-191 ) again showing the statistical significance of this overlap . As noted above for down-regulated genes , the number of co-regulated genes was highest in the top quartiles of shMITF-regulated genes ( 57% in quartile 1 , S4A Fig ) . Consistent with the similar morphological changes , the common regulated genes were involved in morphology and adhesion as well as the SASP . None of the genes repressed by MITF knockdown were activated by BPTF knockdown and only 8 genes repressed by BPTF knockdown , showed an opposite regulation , being activated by MITF knockdown . These data show a large and significant overlap between the gene expression programs controlled by BPTF and MITF . Together these two factors positively regulate genes required for proliferation and negatively regulate genes involved in modulating cell morphology and motility . We determined which up and down-regulated genes are associated with MITF-occupied sites . As described [17] , MITF occupies >16000 sites in 501Mel cells . Using a window of +/-30kb with respect to the TSS , taking into account potential regulation by MITF from distant enhancers , identified up to 5694 potential targets ( S4B Fig ) . Of these 176 genes are down-regulated upon MITF silencing consistent with them being directly activated by MITF . Several cell cycle regulators such as CCND1 , ANLN and CIT were associated with multiple MITF binding sites ( S2 Dataset ) . Up to 56 genes associated with MITF binding sites were co-regulated by BPTF , including BIRC7 SHB , and BCL2A1 critical regulators of proliferation , apoptosis and survival , NPM1 an important cell cycle regulator involved in chromosome congression , spindle and kinetochore-microtubule formation required for normal centrosome function [39 , 40] and PPARGC1A ( PGC1α ) implicated in resistance to oxidative stress and mitochondrial function in melanoma [41 , 42] ( S4C Fig ) . MITF and BPTF therefore co-regulate these critical MITF target genes . Similarly , up to 187 genes were potentially directly repressed by MITF including several SASP components such as SERPINE1 , IL8 , IL24 and PDGFB [see also [17]] . Of these 67 were co-regulated by BPTF such as SASP components IL8 and IL24 . BPTF and MITF therefore appear to co-regulate gene expression in a positive and negative manner . We next investigated the role of BPTF in untransformed human melanocytes by shBPTF silencing in the Hermes 3A cell line . As previously shown , MITF silencing in these cells led to proliferation arrest , morphological changes and entry into senescence [[17] and Fig 3A and 3C] . Fewer cells were also detected following BPTF knockdown , but the cells had a more bipolar morphology compared to the expanded and flattened morphology of the shMITF cells ( Fig 3A , 3B and 3C ) . Almost 85% of shBPTF cells showed senescence-associated β-galactosidase staining ( Fig 3D ) . RNA-seq showed that BPTF silencing repressed 1356 genes and up-regulated 1139 genes ( Fig 3E and S3 Dataset ) . The effects of BPTF loss on gene expression were therefore more extensive in these cells than in 501Mel . Down-regulated genes were strongly enriched in cell cycle , mitosis and pigmentation functions ( Fig 3F ) . BPTF is therefore a major regulator of genes required for proliferation of Hermes 3A cells . The up-regulated genes on the other hand were involved in transcription regulation , cell-cell signalling , including many secreted and membrane associated proteins . The gene expression programs regulated by BPTF and MITF overlapped significantly ( RF = 5 . 1 , p < 8 . 086e-162 ) as overall 44% of genes down-regulated by shMITF knockdown were also down-regulated by shBPTF ( Fig 3E ) . As seen with 501Mel cells , the genes most strongly regulated by MITF were most often co-regulated by BPTF ( 62% in the first quartile , S4A Fig ) . Co-regulated genes were associated with cell cycle/mitosis , cell adhesion and apoptosis ( Figs 3F and 2 ) . For example , several critical regulators of cell cycle and mitosis such as AURKB , CDCA2 and CCND1 , genes associated with MITF occupied sites in 501Mel cells , were repressed under both conditions ( Fig 2 ) . Similarly , 37% ( RF = 5 , p < 1 . 259e-112 ) of genes up-regulated by shMITF were also induced by shBPTF ( Fig 3E and 3F ) including a plethora of signalling molecules and transcriptional regulators . We next compared the gene expression programs regulated by silencing of BPTF , MITF and BRG1 in Hermes 3A cells . BPTF and BRG1 silencing commonly down-regulated 277 genes . Of these 122 were also regulated upon MITF silencing ( S4D Fig ) . In all , 58% of MITF-regulated genes were co-regulated by either BRG1 or BPTF and 16% regulated by both . BPTF and BRG1 silencing commonly up-regulated 208 genes of which 126 were also up-regulated by MITF silencing ( S4D Fig ) . Overall , 56% of genes up-regulated by MITF were co-regulated by either BRG1 or BPTF and 19% regulated by both . A large fraction of MITF regulated genes are therefore co-regulated by these remodellers in Hermes 3A cells . A similar comparison could not be performed in 501Mel cells were BRG1 regulated a very large number of genes . The transcriptional programs regulated by MITF in 501Mel and Hermes3A cells were somewhat different as only 172 genes ( 22% ) were down-regulated and only 130 genes ( 19% ) up-regulated in both lines ( S4E Fig ) . Nevertheless , the common down-regulated genes comprised regulators of cell cycle and mitosis defining a critical set of MITF-regulated cell cycle genes and up-regulated genes were principally involved in signalling and cell motion/morphogenesis . An analogous comparison of BPTF regulated genes showed 46% of common down-regulated genes and 34% of up-regulated genes . Common down-regulated genes comprised regulators of cell cycle and up-regulated genes comprised genes involved in signalling and cell motion/morphogenesis ( S4F Fig ) . A small number of genes were also co-regulated by MITF and BPTF in both cells lines ( shaded area in Fig 2 ) . The essential role of BPTF in melanoma and melanocyte cells in vitro prompted us to investigate its role in the murine melanocyte lineage in vivo . Interrogation of transcriptome data from purified melanoblasts ( GFP+ cells , see [43] ) and the GFP- cells ( mainly keratinocytes ) from E15 . 5 mouse embryos indicated that Bptf and Smarca5 were expressed in both cell types to levels comparable to those of the Smarca4 ( Brg1 ) and Pbrm1 subunits of the PBAF complex that is essential for melanoblast development [44] , ( S1C Fig ) . However , no significant Smarca1 expression was seen at this stage . We crossed mice with a floxed Bptf gene ( Bptflox/lox ) with those expressing Cre recombinase under the control of the Tyrosinase enhancer that allows selective inactivation in the developing melanocyte lineage at E9 . 5-E10 . 5 [45] . The resulting Tyr-Cre/°::Bptflox/lox mice were further crossed with animals expressing the LacZ reporter gene under the control of the Dct promoter that marks cells of the melanocyte lineage ( Tyr-Cre/°::Bptflox/lox::Dct-LacZ/° ) . Following crosses of Tyr-Cre/°::Bptflox/+ mice , Tyr-Cre/°::Bptflox/lox animals in which Bptf was inactivated in the melanocyte lineage ( Bptfmel-/- ) displayed a pigmentation phenotype characterised by a grey belly , and grey extremities of the paws , ears and tail compared to wild-type mice and heterozygous Tyr-Cre/°::Bptflox/+ ( Bptfmel+/- ) littermates ( Fig 4A and S5A , S5B and S5C Fig ) . This phenotype was confirmed upon growth of the first hair at P14 that was grey on the belly with a small and variable white belly spot ( Fig 4B and S5B Fig ) . Otherwise , the dorsal coat was almost indistinguishable from wild-type . Melanoblasts lacking Bptf are therefore viable , but BPTF regulates their proliferation and/or migration . To better characterise this phenotype , we used the Tyr-Cre/°::Bptflox/lox::Dct-LacZ/° mice to monitor melanoblast development . The number of Dct-LacZ positive melanoblasts was counted at E15 . 5 on 4 mice of the Bptfmel-/- and Bptfmel+/- genotypes . The migration front on the belly and the paws was similar in both genotypes ( S6A and S6B Fig ) . In contrast , the number of melanoblasts was reduced by around 10% in the Bptfmel-/- foetuses ( S6C Fig ) . By E16 . 5 , clear differences in the ventral and limb migration fronts were observed ( Fig 5A ) . In Bptfmel+/- , clusters of melanocytes were clearly visible on the trunk that correspond to melanocytes colonising the nascent hair follicles [46] ( Fig 5B ) . Although such clusters were less prominent in the Bptflox/lox animals , the number of melanoblasts was now reduced by around 20% in the Bptfmel-/- foetuses ( Fig 5C ) , perhaps accounting for their diminished prominence . Futhermore , Bptf also regulated melanoblast morphology as those lacking Bptf were less dendritic and much more rounded . Bptf therefore acts during embryogenesis to regulate melanoblast proliferation , migration and morphology . Pigmentation of the first coat is provided by the embryonic derived melanoblasts that colonise and terminally differentiate in the hair follicles [6] . As the number of melanoblasts is lower in the ventral region than in the dorsal region even in wild-type mice , the further reduction in melanoblast numbers in the Bptfmel-/- animals during the course of development could partially account for the observed greyer phenotype of the first ventral hair coat . As BPTF regulates expression of the melanin synthesis enzymes in human melanocytes in vitro ( see above ) , it is also possible that a reduction in melanin production by the mutant melanocytes would also contribute to the greyer ventral phenotype . As the Bptfmel-/- animals grew older , their pelage showed progressive greying such that by 3–6 weeks both the ventral and dorsal coat became grey and then finally white ( Fig 4C and S5D Fig ) . The animals maintained this completely white pelage throughout their lifespan ( Fig 4D ) . This premature greying phenotype was fully penetrant and most animals became completely white indicating complete recombination of the Bptf alleles during embryogenesis , although some animals showed spotting ( for example , animal on the right of Fig 4D ) by rare melanocytes derived from melanoblasts that escaped recombination . Recombination of the Bptf allele was confirmed by PCR-based genotyping on both neonatal mouse-tail DNA and purified E15 . 5 melanoblasts ( S5E Fig ) . Greying was accelerated by depilation of 3-week animals , following which the newly grown hair was white ( Fig 4E and S5F Fig ) . These observations revealed that Bptfmel-/- animals were unable to pigment the pelage from the second post-natal anagen , growth phase of the hair cycle when the new hair follicle is generated , that requires the generation of mature melanin producing melanocytes from the post-natal MSC population . Bptf is therefore required for establishment , maintenance and/or functionality of the MSC population or its derivatives . To investigate this , we performed immunostaining of hair shafts from the Bptfmel-/- and Bptfmel+/- genotypes at different post-natal stages using antibodies against Dct , staining the MSCs , the TACs and mature melanocytes , against Sox10 , labelling TACs and mature melanocytes and against the cell cycle marker Ki67 labelling all proliferating cells in the bulb [6 , 47 , 48] . Control staining of a section from an adult wild-type mouse indeed confirmed that Dct stained the mature melanocytes in the bulb along with MSCs in the bulge and TACs ( Fig 6A ) . Staining of dorsal hair shafts from P7 and P10 animals revealed equivalent numbers of Dct-Sox10 stained melanocytes in the bulb region ( Fig 6B and 6C ) . However at 3 weeks the number of Dct stained cells in the bulb strongly decreased in the Bptflox/lox animals and almost half the shafts were devoid of Dct-stained cells ( Fig 6D ) . In 1-year animals , no Dct-stained cells were detected in the bulb ( Fig 6E ) . The loss of mature bulb melanocytes is in accordance with the progressive loss of pigmentation in the mutant animals and the sustained white coat in older animals . In previous mouse models , premature greying was associated with a loss of the MSC population [49–51] . To determine the fate of the MSC population upon Bptf inactivation , we stained sections from the epidermis of the Tyr-Cre/°::Bptflox/lox::Dct-LacZ/° and Tyr-Cre/°::Bptflox/+::Dct-LacZ/° animals at different ages for the presence of LacZ to visualize melanocytes . At P10 , DCT-LacZ stained melanocytes were seen in the bulbs of the Bptfmel-/- and Bptfmel+/- genotypes and the presence of melanin was clearly visible ( Fig 7A ) . Dct-LacZ stained cells were also seen in the bulge regions at this stage . By six weeks , strong staining for melanin persisted in the Bptfmel+/- animals along with the presence of Dct-LacZ-stained melanocytes in both the bulge and bulb regions ( Fig 7B ) . In the Bptfmel-/- animals , many shafts devoid of melanin were visible and only rare Dct-LacZ-stained melanocytes were seen in the bulb . However , Dct-LacZ-positive cells were visible in the bulge region , clearly seen in short telogen/early anagen shafts ( Fig 7B , right panel ) . Staining of one year-old animals that were white , devoid of melanin and Dct-LacZ-stained bulb melanocytes also revealed Dct-LacZ-positive cells in the bulge region . Thus , an adult MSC population is established and maintained in the absence of Bptf , but these cells are unable to give rise to differentiated pigment producing melanocytes . To ask whether MSCs were able to proliferate and differentiate in response to anagen stimuli , we depilated 4 month-old white animals and monitored the presence of Dct-LacZ-stained cells after 1 , 3 , 6 , 10 and 15 days . In Bptfmel+/- animals , short telogen shafts remain in wild-type animals at 1 day following depilation where Dct-LacZ stained cells were observed in the bulge region ( Fig 8A ) . By 3 days post-depilation , many TACs could now be seen along with melanocytes that begin to colonise the future bulb ( Fig 8B ) . By 6 days post-depilation , the bulb was filled with mature and pigment-producing melanocytes as seen by staining for Dct-LacZ and melanin and numerous TACs could still be observed ( Fig 8C ) . After 10 and 15 days strong Dct-LacZ-staining was maintained in the bulb and the newly grown hair was pigmented ( Fig 8D ) . Immediately after depilation of Bptfmel-/- animals , Dct-LacZ stained cells were observed in the bulge region , but after 3 days fewer TACs and bulb melanocytes were visible compared to Bptfmel+/- animals ( Fig 8E and 8F ) . Similarly , at 6 days post-depilation , while Dct-LacZ stained cells were visible in the bulge , staining of the bulbs was less prominent and more variable with many bulbs showing only few melanocytes and very few residual TACs were observed ( Fig 8G ) . By 10 and 15 days , Dct-LacZ stained cells were no longer seen in the bulb region ( Fig 8H ) . Moreover , no melanin was made from the bulb melanocytes and the out-growing hair was white in accordance with the fact that these animals were white both before and after depilation . We also used immunostaining to detect endogenous Dct after depilation . At day 3 , Dct labelled cells could be seen in the bulge region of Bptfmel+/- animals ( Fig 9A ) as well as in migrating TACs and cells colonising the bulb ( Fig 9B ) . Moreover , Dct-labelled cells expressing Pax3 , Mitf and Sox10 were also observed at day 3 ( Fig 9B and 9C ) . In contrast , no expression of Dct or of any of the other markers was detected in the Bptfmel-/- animals ( Fig 9A ) . At days 6 and 10 , antibody staining showed endogenous Dct in the Bptfmel+/- bulb melanocytes that were also labelled for Sox10 ( Fig 9C and S7 Fig ) . Melanocytes in bulbs from these animals also expressed Pax3 , Mitf and the HMB45 melanosome marker ( S7C Fig ) . In contrast , no staining for any of these proteins was seen in the Bptfmel-/- bulbs . Thus , in absence of Bptf , the Dct-LacZ-labelled cell population did not express endogenous Dct and their expansion at anagen was not associated with detectable expression of Mitf , Pax3 and Sox10 . In addition , the few cells that migrated to the bulb by day 6 also did not express these melanocyte markers . These observations indicate that Bptf acts at an early stage in the generation of differentiated mature melanocytes from the adult MSC population . It was previously reported that adult MSCs display a down-regulation of RNA polymerase II ( Pol II ) transcription witnessed by diminished staining for phosphorylated serine 2 of the C-terminal domain ( CTD ) of the largest subunit and for Cdk9 [52] . During the course of this study , we tested several antibodies for their ability to detect Bptf in the hair follicle . None of the tested antibodies detected Bptf in immunostaining of hair follicles , whereas a commercial antibody gave a strong nuclear staining for Smarca5 in the keratinocyte population as well as the Dct-expressing bulb melanocytes ( Fig 10A ) . Interestingly however , Dct-expressing MSCs in wild-type mice selectively showed strongly diminished Smarca5 staining suggesting its down-regulation in these transcriptionally silent cells ( Fig 10B ) . A similar result was seen for the Smarca4 ( Brg1 ) chromatin remodeller . To further investigate the transcriptionally repressed state , we stained hair follicles for marks of active chromatin ( H3K4me3 , H3K9ac and H3K27ac ) and the repressive mark H3K27me3 . All of these antibodies gave strong nuclear staining in keratinocytes and Dct-expressing bulb melanocytes ( Fig 10A ) . In contrast , Dct-expressing MSCs showed strongly diminished staining for all of the active marks , but not for the repressive H3K27me3 mark ( Fig 10B ) . These data extend the previous observations showing not only diminished phosphorylation of the Pol II CTD and Cdk9 , but also reduced expression of the Brg1 and Smarca5 chromatin remodellers and diminished active chromatin marks . Together these observations support the idea that MSCs enter into a transcriptionally silent state . Emergence from this state to properly reactivate expression of the melanocyte differentiation genes and generate mature melanocytes requires Bptf .
We show that peptides for the NURF components BPTF , SNF2H , SNF2L and RBBP4 were found in the MITF interactome . As previously described no peptides for these proteins were found in the control FLAG-HA immunoprecipitations from 501Mel cells with native MITF [17] . We did not detect peptides for the smaller BAP18 and HMG2L1 subunits . Whether this is because the small number of peptides from these proteins was missed in the mass-spectrometry or whether they are not part of the complex in 501Mel cells remains to be determined . Mass-spectrometry and immunoblot showed that NURF subunits were detected only in the chromatin-associated fraction indicating that MITF and NURF preferentially interact on chromatin . Immunoprecipitation showed that endogenous NURF was co-precipitated only in the cells expressing tagged MITF demonstrating the specificity of the interaction with MITF . Lack of ChIP-grade BPTF antibodies has hampered our attempts to identify sites on the genome where MITF and BPTF co-localize . It has previously been shown that BPTF localizes almost uniquely to the active TSS by virtue of the interactions between its PHD and bromodomain with the histone modifications at the TSS [29] . However , these experiments were performed with a truncated epitope tagged protein containing only the PHD and bromodomains and thus it remains possible that BPTF may be recruited to other regions of the genome not by interaction with chromatin marks , but via interactions with transcriptional activators such as MITF as is seen with the PBAF complex [17] . NURF components are expressed in all tested melanoma cell lines and shRNA-mediated BPTF silencing showed its essential role in a variety of MITF-expressing melanoma cell lines , including 501Mel , MNT1 , SK-Mel-28 , and 888-Mel as well as the MITF-negative 1205Lu cell line where it must act independently of MITF . In contrast , BPTF silencing in a series of non-melanoma cell lines such as HeLa , H293T had no detectable effect , in accordance with previous observations showing Bptf-/- ES cells and MEFs proliferate almost normally in vitro , and that thymocytes do not exhibit any proliferation or survival defects in vivo [33 , 53] . There is therefore no general requirement for BPTF for proliferation , but rather a specific requirement in melanoma cells that is both MITF-dependent and independent . Upon BPTF silencing , 501Mel cells adopted a morphology similar to that seen upon MITF knockdown , developed a SASP and showed senescence-associated β-galactosidase staining . Comparative analyses showed that 39% of shBPTF down-regulated and 41% of up-regulated genes were regulated in an analogous manner by shMITF . Genes such as BIRC7 , BCL2A1 , and NPM1 that have roles in survival and/or in cell cycle regulation were identified as potential direct MITF targets with multiple MITF-occupied sites often close to the transcription start sites , whose expression is co-regulated by BPTF . Similarly , MITF and BPTF co-repress SASP genes like SERPINE1 , IL24 , PDGFB , and CYR61 as well as ZEB1 that has a crucial role in melanoma progression [54 , 55] . The above observations support the idea that MITF and BPTF positively co-regulate expression of genes involved in proliferation , but co-repress genes controlling cell motility and invasive properties . Nevertheless , BPTF likely acts as a cofactor for other transcription factors in MITF-negative melanoma cells and there are clearly genes regulated by BPTF , but not MITF , in MITF-expressing lines . Acting as a cofactor for MITF is therefore only one facet of BPTF function in melanoma cells . While this manuscript was in preparation , Dar et al [56] reported the implication of BPTF in human melanoma . In agreement with our results they showed that BPTF silencing in MITF-negative 1205Lu cells arrested their proliferation . More importantly , they showed that BPTF expression is increased in human melanomas , where the corresponding gene is often amplified and that these increases are predictive of poor outcome . While these observations identified BPTF as a prognostic factor for melanoma progression , they did not provide a molecular basis for BPTF function in melanoma . We show here that BPTF acts as a cofactor for MITF in regulating critical cell cycle , invasion , motility and apoptosis genes thus providing a molecular mechanism by which BPTF promotes melanoma growth and progression . Inactivation of Bptf in melanoblasts does not impair their viability . Instead , Bptf regulates melanoblast proliferation and migration with by E16 . 5 , a 20% reduction in the number of melanocytes , an alteration in their morphology and a less advanced migration front . The altered melanoblast morphology with reduced dendriticity may further reflect their impaired migration . Although expression of RAC1 , an important regulator of melanoblast migration [57] is not affected in shBPTF melanocytes in vitro , expression of RAC2 is down-regulated along with PREX1 another important regulator of melanoblast migration [58] . Moreover , MITF and BPTF co-regulate PREX1 expression in melanocytes in vitro and the corresponding gene locus comprises multiple MITF-occupied sites [44] . PREX1 is therefore a direct MITF target gene co-regulated by BPTF in melanocytes in vitro and controlling melanoblast migration in vivo . While these in vivo effects recapitulate to some extent the function of BPTF in regulating melanocyte cell cycle and morphology in vitro , Bptf is clearly not essential as a cofactor for Mitf-driven melanoblast development as could have been implied from the observation that Bptf is essential in melanoma/melanocyte cells in vitro . The first cycle of hair growth is pigmented by embryonic melanoblasts that colonize the developing hair follicles and differentiate into mature melanocytes . The almost normal first black dorsal coat of the Bptfmel-/- animals reflects the presence of abundant melanoblasts rendering this region less sensitive to the reduction in melanoblast numbers . The ventral region on the other hand , normally comprises less melanoblasts and hence is more sensitive to the reduced number and migration of melanoblasts in the mutant animals . Nevertheless , by 3–4 weeks as the first coat is discarded and regenerated by the second anagen phase , the mutant animals show progressive greying of both the dorsal and ventral coats . Greying is accentuated by 5–6 weeks when almost all the first coat has been exchanged . Depilation of 3 week-old animals results in the outgrowth of white hair indicating that mutant animals are unable to pigment the hair of the second anagen . In contrast to the first anagen where pigmentation comes from terminally differentiated embryonic melanoblasts [6] , the melanocytes pigmenting the second anagen are derived from post-natal MSCs . The inability of the mutant mice to pigment the hair from the second anagen phase suggests either , the lack of the MSC population , MSCs that are unable to respond to signals that induce their proliferation and/or differentiation at anagen , or a defective proliferation and differentiation of the TACs . The presence of Dct-LacZ-positive cells in the bulge region of the hair follicle at P10 and at all subsequent stages including as late as one year when the mice have been completely white for more than nine months shows that Bptf is not required to establish and maintain a MSC population . However , diminished numbers of TACs and bulb melanocytes are observed following depilation-induced anagen of mutant adult animals . These TACs do not express endogenous Dct , Mitf , Sox10 or Pax3 and by 6 days after depilation reduced numbers of Dct-LacZ-positive cells are present in the bulb while by 10 days , Dct-LacZ expressing cells can no longer be detected . The lower numbers of Dct-LacZ-stained TACs and bulb melanocytes may be accounted for by their premature death or by switching off of the Dct-LacZ reporter . We cannot exclude the possibility that cells previously stained by Dct-LacZ remain in the hair follicle in an undifferentiated state with no expression of melanocyte markers . It is also interesting to note that Dct , and Sox10 staining was also strongly reduced in three week old animals . While it is possible that the negatively stained shafts represent very early second anagen phase shafts , it is more probable that these represent first anagen shafts where there is premature death of the melanocytes or a loss of their melanocyte marker expression . Bptf may therefore be required to maintain the viability and/or the terminally differentiated character of these melanocytes . These observations show that Bptfmel-/- MSCs are stimulated to proliferate at anagen and fulfill the events necessary to maintain the MSC population throughout the multiple anagens in the life of the animal . Nevertheless , MSCs established just after birth in absence of Bptf , inactivated at an early stage of melanoblast development , are abnormal as they do not express detectable levels of endogenous Dct . This discrepancy with Dct-LacZ staining highlights a differential requirement for Bptf for expression of the endogenous Dct locus compared to the exogenous transgene reflecting the fact that these two genes are in different chromosomal localizations ( chromosome 4 for Dct-LacZ and 14 for Dct ) and chromatin environments and hence show a differential requirement for Bptf/NURF for their expression . The results reported here together with previous studies show that MSCs undergo a down-regulation of Pol II transcription [52] and display strongly diminished levels of chromatin remodellers and active chromatin marks . All of these observations are consistent with the entry of MSCs into a transcriptionally repressed chromatin state , between the anagen phases . Bptf , presumably via the ATP-dependent chromatin-remodelling activity of NURF , is essential for reactivation of the melanocyte gene expression program at anagen , the subsequent normal proliferation of TACs and their differentiation into mature melanocytes . This is the major defect seen in vivo , it is a unique and complex physiological situation that cannot be easily mimicked by cell lines in vitro . A specific requirement for Bptf upon reactivation of the MSCs would also explain why Bptf is not essential for differentiation of embryonic melanoblasts that have not undergone a prolonged period of stem cell associated quiescence . It is also noteworthy that the BPTF and SMARCA5 subunits of NURF were identified in an siRNA screen as factors required to maintain keratinocyte stem cells in an undifferentiated state in vitro [35] . BPTF is not required to maintain MSCs in an undifferentiated state in vivo , but rather is required for their differentiation . BPTF may therefore play opposing roles in keratinocyte and melanocyte stem cells . Wnt signaling is believed to play an important role in reactivation of MSCs at anagen , [59] . Deletion of Ctnnb1 in melanocytes results in a loss of differentiated progeny and hair greying , but the MSC population is maintained , a phenotype similar to that observed here . Bptf may therefore act downstream of Wnt prior to Mitf induction an observation reminiscent of the situation in Drosophila where NURF is required for Wnt signaling [60] . The role of BPTF/NURF in melanocytes differs from that of BRG1/PBAF that also interacts with MITF [17] . While BRG1 and BPTF are essential in melanocytes and melanoma cells in vitro , they regulate overlapping but distinct gene expression programs . Furthermore , mice lacking Brg1 in melanocytes are born with a complete absence of pigmentation and no identifiable melanocytes in the hair follicles showing an essential role for Brg1 in melanoblast development , whereas Bptf is essential only for reactivation of the MSC population . Our results therefore define specific and distinct roles for the PBAF and NURF chromatin remodelling complexes in epigenetic regulation of gene expression in melanocytes and melanoma . The phenotype observed here is unique and distinct from previous mouse mutants where premature greying was ascribed to a progressive loss of the stem cell population [61] . For example , targeted ablation of Bcl2 in the melanocyte lineage results in loss of the MSC population showing that it is required for their survival [51] . Similarly , melanocyte-specific knockout of Notch signalling components shows the requirement of this pathway to maintain the MSC population [49 , 62 , 63] . Bptf knockout on the other hand does not deplete the MSC population that persists throughout life , but plays an essential role in their differentiation .
Cell extracts were prepared essentially as previously described and subjected to tandem Flag-HA immunoprecipitation [17 , 64] . MITF was detected by antibody ab-1 ( C5 ) from Neomarkers , BPTF by a rabbit polyclonal antibody generated and kindly donated by Dr . J . Landry as described [33] , and SMARCA1 and SMARCA5 were detected using antibodies generated and kindly donated by Dr P . Becker as described [65] . Mice were kept in accordance with the institutional guidelines regarding the care and use of laboratory animals and in accordance with National Animal Care Guidelines ( European Commission directive 86/609/CEE; French decree no . 87–848 ) . All procedures were approved by the French national ethics committee . Mice with the following genotypes have been described elsewhere: conditional Bptflox/lox [33] , Tyr-Cre [45] and Dct-LacZ [66] Genotyping of F1 offspring was carried out by PCR analysis of genomic tail DNA with primers detailed in the respective publications . E15 . 5 and E16 . 5 embryos were washed in PBS and fixed in 0 . 25% gluteraldehyde in PBS for 45 min at +4°C , after which they were washed with PBS for 15 min at +4°C . Embryos were incubated with permeabilization solution ( 100 mM phosphate buffer pH 7 . 4 , 2mM MgCl2 , 0 . 01% sodium deoxycholate , 0 . 02% NP40 ) for 30–45 min at room temperature ( RT ) . Staining was performed overnight at 37°C with permeabilization buffer containing 5mM potassium ferricyanide and potassium ferrocyanide ( Sigma ) and 0 . 04% X-gal solution ( Euromedex ) . The samples were post-fixed for 3 h in 4% paraformaldehyde at RT and washed in PBS overnight at +4°C . To count embryonic melanoblasts , photos were taken with a Nikon AZ100 Multizoom microscope ( Nikon , Tokyo , Japan ) and defined regions were analyzed with Photoshop grid counter . For epidermal samples , skin biopsies at the indicated stages were isolated , cut into small pieces ( 4mm x 2mm ) and treated with the same protocol as the embryos , with the exceptions of overnight staining at RT and an overnight post-fixation in 4% paraformaldehyde . For further immunohistochemical analysis , samples were dehydrated , embedded in paraffin and sectioned at 10 μm . Sections were subsequently stained with nuclear fast red ( Abcam ) and , when indicated , the Fontana Masson kit ( Abcam ) and pictures were taken with a brightfield microscope . Biopsies of dorsal skin were isolated , cut into small pieces , fixed overnight in 4% paraformaldehyde , washed with PBS , dehydrated , paraffin imbedded and sectioned at 5 μm . For antigen retrieval , the sections were incubated with 10mM sodium citrate buffer , within a closed plastic container placed in a boiling waterbath , for 20 min . Sections were permeabilised with 3x5 min 0 . 1% Triton in PBS , blocked for 1h in 5% skin milk in PBS , and incubated overnight in 5% skin milk with primary antibodies . The following antibodies were used: goat anti-Dct at dilution of 1/1000 ( Santa Cruz Biotechnology , sc-10451 ) , rabbit anti-Ki67 , at 1/500 ( Novocastra Laboratories , NCL-Ki67p ) , rabbit anti-Sox10 , at 1/1000 ( Abcam , ab155279 ) , H3K4me3 ( 04–745 Millipore ) , H3K9ac ( 07–352 Millipore ) , H3K27me3 ( CS200603 Millipore ) , H3K27ac ( 39133 Active motif ) SMARCA5 ( Abcam ab72499 ) . Sections were washed 3x5 min 0 . 1% Triton in PBS , and incubated with secondary antibodies , Alexa 488 donkey-anti-goat , and Alexa 555 donkey-anti-rabbit ( Invitrogen ) for 1 h . Sections were subsequently incubated with 1/2000 Hoechst nuclear stain for 10 min . Sections were washed 3x5 min in PBS , dried , mounted with Vectashild , and coverslip immobilized with nail polish . Melanoblasts were isolated according to a protocol adapted from Van Beuren and Scambler [67] . Briefly , the trunk epidermis of E14 . 5-E15 . 5 Dct-LacZ::Tyr-Cre::Bptflox/+ embryos was dissociated into a single cell suspension and LacZ-positive cells were labelled using the DetectaGene Green CMFDG LacZ Gene Expression kit ( Molecular Probes ) and isolated by FACS prior to genotyping . Melanoma cell lines SK-Mel-28 , 501Mel , MNT1 , 888Mel and 1205Lu were grown in RPMI 1640 medium ( Sigma , St Louis , MO , USA ) supplemented with 10% foetal calf serum ( FCS ) . HEK293T , HeLa and fibroblast cell lines were grown in Dulbecco’s modified Eagle’s medium supplemented with 10% FCS and penicillin/streptomycin ( 7 . 5 μg/ml ) . Hermes-3A cells were grown in RPMI 1640 medium ( Sigma ) supplemented with 10% FCS , 200nM TPA , 200pM cholera toxin , 10ng/ml human stem cell factor ( Invitrogen ) , 10 nM endothelin-1 ( Bachem ) and penicillin/streptomycin ( 7 . 5 μg/ml ) . Hermes 3A cells were obtained from the University of London St Georges repository . All lentiviral shRNA vectors were obtained from Sigma ( Mission sh-RNA series ) in the PLK0 vector and virus was produced in HEK293T cells according to the manufacturers protocol . Cells were infected with the viral stocks and after 5 days ( or as indicated in the Figure legends ) of puromycin selection ( 3 μg/ml ) , cells were photographed and collected for preparation of cell lysates or isolation of RNA . In each case between 5X105-1X106 cells were infected with the indicated shRNA lentivirus vectors and all experiments were performed at least in triplicate . The following constructs were used shBPTF ( TRCN0000319152 , TRCN0000319153 ) , shMITF ( TRCN0000019119 ) . For lentivirus infection , virus was produced in 293T cells according to the manufacturers protocol described . The siRNA knockdown of BPFT was performed with ON-TARGET-plus human SMARpool ( L-010431-00 ) purchased from Dharmacon Inc . ( Chicago , Il . , USA ) . Control siRNA directed against luciferase was obtained from Eurogentec ( Seraing , Belgium ) . siRNAs were transfected using Lipofectamine RNAiMax ( Invitrogen , La Jolla , CA , USA ) . The senescence-associated β-galactosidase staining kit from Cell Signaling Technology ( Beverly , MA , USA ) was used according to the manufacturer’s instructions to histochemically detect β-galactosidase activity at pH 6 . mRNA isolation was performed according to standard procedure ( Qiagen kit ) . qRT-PCR was carried out with SYBR Green I ( Qiagen ) and Multiscribe Reverse Transcriptase ( Invitrogen ) and monitored using a LightCycler 480 ( Roche ) . Actin gene expression was used to normalize the results . Primer sequences for each cDNA were designed using Primer3 Software and are available upon request . RNA-seq was performed essentially as previously described [68] . Gene ontology analyses were performed using the functional annotation clustering function of DAVID ( http://david . abcc . ncifcrf . gov/ ) . | The melanocytes pigmenting the coat of adult mice derive from the melanocyte stem cell population residing in the permanent bulge area of the hair follicle . At each angen phase , melanocyte stem cells are stimulated to generate proliferative transient amplifying cells that migrate to the bulb of the follicle where they differentiate into mature melanin producing melanocytes , a processes involving MIcrophthalmia-associated Transcription Factor ( MITF ) the master regulator of the melanocyte lineage . We show that MITF associates with the NURF chromatin-remodelling factor in melanoma cells . NURF acts downstream of MITF in melanocytes and melanoma cells co-regulating gene expression in vitro . In vivo , mice lacking the NURF subunit Bptf in the melanocyte lineage show premature greying as they are unable to generate mature melanocytes from the adult stem cell population . We find that the melanocyte stem cells from these animals are abnormal and that once they are stimulated at anagen , Bptf is required to ensure the expression of melanocyte markers and their differentiation into mature adult melanocytes . Chromatin remodelling by NURF therefore appears to be essential for the transition of the transcriptionally quiescent stem cell to the differentiated state . | [
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] | [] | 2015 | Chromatin-Remodelling Complex NURF Is Essential for Differentiation of Adult Melanocyte Stem Cells |
In mammalian oocytes , three actin binding proteins , Formin 2 ( Fmn2 ) , Spire , and profilin , synergistically organize a dynamic cytoplasmic actin meshwork that mediates translocation of the spindle toward the cortex and is required for successful fertilization . Here we characterize Fmn2 and elucidate the molecular mechanism for this synergy , using bulk solution and individual filament kinetic measurements of actin assembly dynamics . We show that by capping filament barbed ends , Spire recruits Fmn2 and facilitates its association with barbed ends , followed by rapid processive assembly and release of Spire . In the presence of actin , profilin , Spire , and Fmn2 , filaments display alternating phases of rapid processive assembly and arrested growth , driven by a “ping-pong” mechanism , in which Spire and Fmn2 alternately kick off each other from the barbed ends . The results are validated by the effects of injection of Spire , Fmn2 , and their interacting moieties in mouse oocytes . This original mechanism of regulation of a Rho-GTPase–independent formin , recruited by Spire at Rab11a-positive vesicles , supports a model for modulation of a dynamic actin-vesicle meshwork in the oocyte at the origin of asymmetric positioning of the meiotic spindle .
In mouse meiosis I , translocation of the spindle toward a cortical site that defines polar body extrusion is the first step in establishment of oocyte polarity [1] , [2] . This process is driven by assembly of cytoplasmic actin filaments in which formin 2 ( Fmn2 ) plays a pivotal role [3]–[8] . Loss of Fmn2 prevents correct positioning of the metaphase spindle and causes pregnancy loss and infertility [9] . The mechanism of actin-based translocation of the spindle is an important issue in cell biology [7] . Fmn2 is required for assembly of an isotropic , dynamic cytoplasmic network , but the mechanism by which actin assembly drives asymmetric spindle positioning is not understood [3] , [7] , [10] . Local myosin-dependent pulling on the actin meshwork in the spindle pole region has been proposed [5] , [11] . Other studies suggest that the spindle is pushed by Fmn2-induced insertional assembly of filaments around the spindle [4] . Other actin-based mechanisms seem posssible considering the very slow rate of spindle translocation . A recent report indicates that in mouse oocytes , actin nucleators are clustered on Rab11a-positive vesicles associated with myosin Vb and that Rab11a and myosin Vb are also required for asymmetric positioning [12] . Fmn2 cooperates with two other actin binding proteins , Spire and profilin . Genetic interactions between Spire , formin Cappuccino ( the ortholog of Fmn2 in Drosophila ) , and profilin were first revealed in polarity axis patterning of the Drosophila oocyte [13]–[15] . In the mouse oocyte , overexpression studies suggest that Spire and Fmn2 cooperate in a functional unit to achieve spindle translocation [6] . Fmn2 and Spire also display nearly identical expression patterns in developing and adult nervous tissues [16] . Fmn2 and Cappucccino are members of the Fmn family of Rho-GTPase–independent formins . The autoregulatory DAD domain of Diaphanous-related formins ( DRFs ) is replaced by a short FH2 tail sequence that makes an inhibitory contact with the N-terminal region in Cappuccino [17] . Spire is a modular protein . The N-terminal region ( Nt-Spire ) consists of a kinase-like noncatalytic domain ( KIND ) followed by four consecutive WH2 domains that bind actin . The C-terminal moiety contains a Spir box and a FYVE-related domain , potentially interacting with Rab GTPases and membranes [18] . Nt-Spire nucleates actin assembly in vitro in the absence of profilin [19] . Under physiological conditions where profilin-actin ( PA ) complex is the main form of polymerizable actin , the binding of Nt-Spire to filament barbed ends blocks assembly from PA [20] , [21] . Spire and Fmn2 directly interact [22] via association of the C-terminal tail of the FH2 domain of Fmn2/Capu with the KIND domain of Spire [23]–[26] . Binding of KIND to the isolated FH2 domain of Cappuccino inhibits FH2-induced stimulation of actin assembly [23] , [25] . The synergy observed in vivo between Spire and Fmn2 contrasts with in vitro evidence for opposite effects of Nt-Spire and Fmn2 , taken individually , on filament barbed end assembly and for the inhibition of FH2 by KIND . To understand the molecular mechanism by which Spire and Fmn2 act in synergy to promote actin assembly and spindle translocation , here we perform bulk solution and single filament assays of the interplay between Nt-Spire , Fmn2 , and profilin in actin assembly . We find that Nt-Spire binding to barbed ends facilitates the recruitment of Fmn2 via direct interaction between the KIND domain of Spire and the C-terminal region of Fmn2 , called Formin-Spire Interacting ( FSI ) region , followed by release of Nt-Spire and fast processive filament growth . In the presence of Nt-Spire , Fmn2 , and PA , filaments display rapid processive growth interrupted by pauses due to the alternating barbed end occupancy by Fmn2 and Nt-Spire , acting in an original “ping-pong” mechanism . In vitro data , validated by the effects of injected proteins in the mouse oocyte , lead to a comprehensive model of coupled dynamics of actin filaments and Rab11a vesicles .
We purified constructs of human Nt-Spire comprising the N-terminal KIND domain and the 4 WH2 domains , of the isolated KIND domain , of the FH2 and FH1-FH2 domains of mouse Fmn2 , and the more soluble truncated FH1t-FH2 and mDia1-chimeric FH1D-FH2 ( Figure 1A , Materials and Methods ) . The FH2 includes the C-terminal region of interaction with KIND , called “tail” or “FSI . ” A FSI-deleted construct FH1D-FH2ΔFSI was purified as well . The FSI peptide was chemically synthesized . As demonstrated along the paper , FH1t-FH2 and FH1D-FH2 behaved quantitatively identical to the original FH1-FH2 domain of Fmn2 . This result indicates that the original FH2 domain of Fmn2 , but not the nature and proline content of the FH1 domain , is essential in the activity and regulation of formin 2 by Spire . Most quantitatively detailed data were collected with FH1D-FH2 . We further checked that all main properties resulting from interactions between Nt-Spire and FH1D-FH2 , were reproduced with FH1-FH2 of Fmn2 . FH1D-FH2 stimulates filament assembly from MgATP-G-actin ( in the absence of profilin ) more efficiently than the isolated FH2 domain ( Figure 1B ) . The FSI peptide did not affect assembly of actin alone , nor Nt-Spire-nucleated actin assembly , in contrast with a previous report [23] . The isolated KIND domain did not affect assembly of actin alone but inhibited FH2- or FH1D-FH2-stimulated polymerization . The FSI peptide abrogated the inhibitory effect of KIND , as reported with the mammalian proteins and their Drosophila orthologs Dm-Spir-KIND , Capu-CT , and Capu tail [24] , [25] . Profilin , the FH2 domain of formins , and Spire ( via WH2 domains ) all bind the barbed face of actin individually . The mutually exclusive binding of the three proteins to actin is at the heart of the puzzling mechanism by which they act in synergy . This issue was thus addressed in a straightforward fashion by monitoring spontaneous assembly of filaments from PA in the presence of either Fmn2 , or Spire or both together . Profilin by itself strongly inhibits actin nucleation ( Figure 2A , black line ) . FH1D-FH2 ( Figure 2A , blue line ) , but not FH2 ( Figure 2A , red line ) , promoted filament assembly from PA , like other formins [27]–[29] , albeit much less efficiently . The FH1-FH2 of mDia1 showed the same nucleation activity at a one order of magnitude lower concentration ( unpublished data ) . Nt-Spire did not support assembly from PA ( Figure 2A , green line ) , consistent with the known capping of barbed ends by Nt-Spire [20] . In this experiment ( 2 . 5 µM actin , 6 µM profilin ) the concentration of PA is 2 . 44 µM , and 0 . 06 µM actin is unliganded . Since no nucleation was observed , in the absence of FH1D-FH2 or in presence of FH2 only , over at least 1 h , and since FH1D-FH2 does not nucleate assembly of 0 . 06 µM actin , we conclude that FH1D-FH2 most likely nucleates and assembles filaments from PA . Remarkably , in this physiological situation , where PA is the polymerizing form of actin , Nt-Spire greatly enhanced FH1D-FH2–induced nucleation ( Figure 2A , purple line ) and promoted filament assembly by FH2 ( Figure 2A , magenta line ) . Both FH1D-FH2 and FH1t-FH2 nucleated assembly from PA and were stimulated by Spire quantitatively identically to the original FH1-FH2 of Fmn2 ( Figure S1A , B , C ) . The KIND domain and the FSI peptide each abolished the synergistic effect of FH1D-FH2 and Nt-Spire , indicating that enhanced promotion of actin assembly results from the direct interaction between Nt-Spire and Fmn2 , as observed in vivo ( Figure 2B ) . The inhibition by KIND developed in a substoichiometric fashion , suggesting that only one KIND polypeptide bound per FH2 dimer greatly alters the activity of the dimer ( Figure S1D ) . To confirm that synergistic asssembly results from the direct interaction between Spire and FH2 , we tested the ability of FH1D-FH2ΔFSI to stimulate actin assembly in synergy with Nt-Spire . Although deletion of the FSI greatly diminished the stimulation of actin assembly , as observed with the Capu-CT construct [25] , KIND did not inhibit the residual activity of FH1D-FH2ΔFSI and Nt-Spire failed to stimulate it ( Figure S1E ) . The inhibition of assembly by Nt-Spire was attributed to its competitive displacement of FH1D-FH2ΔFSI from barbed ends . We conclude that ( 1 ) the C-terminal region of Fmn2 , like Cappuccino , plays a functional role in actin assembly and ( 2 ) both the inhibition by KIND and the stimulation by Nt-Spire of the activity of FH1D-FH2 are mediated by the direct interaction of the C-terminal region of Fmn2 with the KIND domain of Spire . Puzzlingly , interaction of the FH2 domain of FH1D-FH2 with the isolated KIND domain of Nt-Spire makes an abortive complex for nucleation , while this interaction , in the context of Nt-Spire comprising its four WH2 domains , is required for enhanced filament assembly from PA . The opposite behaviors of KIND and Nt-Spire thus reveal that the interaction of the WH2 domains of Nt-Spire with the barbed face of actin is involved in the synergy between Nt-Spire and FH1D-FH2 . Since in the polymerization assay G-actin is 97 . 5% saturated by profilin , the main candidate left for WH2 binding is an F-actin subunit at the filament barbed end . The FH1 domain of FH1D-FH2 or FH1-FH2 is dispensable , but improves the synergy . In the absence of profilin , FH1D-FH2– or FH2-nucleated filament assembly is also stimulated by Nt-Spire , however since both formin and Nt-Spire individually nucleate actin , no clear evidence distinguishes synergistic from simple additive effects ( Figure S2 ) . We then measured the rate of assembly in the presence of profilin , FH1D-FH2 , and increasing concentrations of Nt-Spire ( Figure 2C ) . The assembly rate first increased with Nt-Spire up to a maximum of 5-fold . At higher Nt-Spire concentrations , the assembly rate and the amount of F-actin assembled at steady state both decreased . The increase in unassembled actin at steady state is consistent with increasing capping of the barbed ends Nt-Spire [20] . Indeed PA complex does not assemble at pointed ends; thus , profilin becomes a G-actin sequestering protein when all barbed ends are capped . The amount of PA at steady state , [PASS] , then is expressed as follows [30] , [31]:where [Ptotal] represents the total concentration of profilin , ACP the critical concentration for actin assembly at pointed ends , and KP the dissociation constant of PA complex . The decreased amount of F-actin upon addition of Nt-Spire thus reflects the gradual saturation of barbed ends by Spire dominating over FH1-FH2 . The superimposed increases in the rate of assembly at a series of FH1D-FH2 concentrations are suggestive of a titration of FH1D-FH2 by Nt-Spire in an assembly-productive complex , whereas the competitive antagonism between Nt-Spire and FH1D-FH2 at barbed ends appears when Nt-Spire dominates over FH1D-FH2 ( Figure 2D ) . A similar behavior was displayed by FH2 and Nt-Spire ( Figure S3 ) . Spontaneous filament assembly from a large amount of monomeric actin is not a physiologically relevant process . In vivo , the steady state levels of assembled and unassembled actin vary via relaxation processes linked to regulatory signaling . To address the synergy between Nt-Spire , profilin , and FH1D-FH2 under such cellular conditions , we monitored the amount of F-actin assembled at steady state in the presence of profilin , Nt-Spire , and increasing amounts of FH1D-FH2 . In the absence of FH1D-FH2 , Nt-Spire caused a decrease in the amount of F-actin at steady state , due to the accumulation of PA , subsequent to barbed end capping by Nt-Spire ( see above ) . Addition of FH1D-FH2 restored the amount of F-actin measured in absence of Nt-Spire ( Figure 2E ) . Thus , FH1D-FH2 reversed the dominant barbed end capping effect of Nt-Spire by generating actively polymerizing barbed ends from PA . The relative amounts of unassembled and assembled actin at steady state are controlled by the Nt-Spire∶FH1D-FH2 molar ratio . In spontaneous assembly assays , both nucleation and barbed end growth contribute in the global polymerization rate . To understand whether only nucleation or also barbed end growth from PA is affected by FH1D-FH2 and Nt-Spire , seeded barbed end growth assays were performed ( Figure 3A , B ) . Barbed end growth from PA was blocked by Nt-Spire alone ( Figure 3A , black line ) , in agreement with previous work [20] , but not detectably affected by FH1D-FH2 alone up to 200 nM ( single filament studies described later in the text explain why ) . Strikingly , addition of FH1D-FH2 in the range 0 to 30 nM to Nt-Spire-capped filaments ( 90 nM Nt-Spire ) restored barbed end growth to a defined level . Note that in the absence of seeds , controls show a very low level of nucleation ( dotted lines in Figure 3A , blue line in Figure 3B ) , demonstrating that the main effect measured in the presence of seeds is on seeded barbed end growth . The FH1D-FH2 concentration dependence of the increase in initial rate displays a saturation behavior ( Figure 3B ) . The very low concentration at half-effect ( Kd = 2 nM ) of FH1D-FH2 for Nt-Spire–bound barbed ends at largely saturating amounts of Nt-Spire is not consistent with the competitive displacement of Nt-Spire from barbed ends by FH1D-FH2 . A more plausible explanation is that enhanced barbed end growth results from high affinity direct binding of FH1D-FH2 to barbed end-bound Nt-Spire , contrasting with its absence of effect on free barbed ends . In agreement with this interpretation , both KIND and FSI inhibited the stimulating effect of Nt-Spire on barbed end growth by FH1D-FH2 ( Figure 3C ) . These bulk solution assays reveal the synergy between Nt-Spire and Fmn2 at barbed ends , but only provide an averaged measure of barbed end growth . They do not specify the number of re-growing filaments nor their individual growth rates and they do not provide information on Fmn2 processive parameters . While ADP-actin [31] , [32] and AMPPNP-actin [29] are both competent for filament assembly and profilin binding , FH1D-FH2 did not nucleate assembly of actin filaments from profilin-ADP-actin nor from profilin-AMPPNP-actin , and Nt-Spire did not stimulate filament assembly in either case ( Figure S4 ) . The data extend conclusions established for ADP-actin [33] , [34] . Bulk solution studies demonstrate that Nt-Spire and FH1D-FH2 not only antagonize by competing with each other , but also bind together at barbed ends to enhance filament assembly from PA . These studies were essential in outlining the mechanistic issues and designing the appropriate conditions of assays conducted using TIRF microscopy of individual filaments , to understand how Nt-Spire and FH1D-FH2 , individually and together , affect barbed end nucleation and assembly dynamics . Filament nucleation was monitored by TIRF in the presence of PA alone and with addition of Nt-Spire , or FH1D-FH2 , or both together ( Figure 4A ) . Nucleation was stimulated by FH1D-FH2 and enhanced by addition of Nt-Spire . In the presence of PA alone , filaments grew slowly ( 8 . 8±1 . 3 subunits per second , N = 20 ) . Upon addition of FH1D-FH2 ( 20 nM ) , rare very fast elongation events ( 53 . 8±6 . 5 subunits per second , N = 20 ) over periods of up to 2 min were observed ( Figure 4B , Movie S1 ) , while 95% of filaments grew slowly at the rate characteristic of free barbed ends . Hence , by itself FH1D-FH2 is processive , but rarely binds to free barbed ends . In the presence of PA , 10 nM Nt-Spire and 20 nM FH1D-FH2 , 47% of filaments displayed fast sustained growth with the same rate ( 63 . 6±6 . 3 subunits per second , N = 20 ) as with FH1D-FH2 alone ( Figure 4C ) . Some of these filaments showed alternating periods of fast growth ( 63 . 8±11 . 7 subunits per second , N = 7 ) and arrested growth ( green traces , Figure 4C and Movie S2 ) . Thus , Nt-Spire facilitates FH1D-FH2–induced fast processive events . The mutual interplay of the two proteins at individual barbed ends was quantified by kinetic experiments using microfluidics-assisted TIRF microscopy ( Figures 5 and 6 ) . This method allows to monitor changes in filament growth rate within 1 s delay following a change in solution conditions [34] , [35] . The rate of association of Nt-Spire to barbed ends was revealed by the time taken for filaments to switch from slow growth in the presence of PA to arrested growth ( growth rate = 0 ) , following addition of Nt-Spire to the flowing PA solution ( Figure 5A , Movie S3 ) . A kymograph of the capping of one filament by Spire ( 5 nM ) is shown in central frame ( Figure 5A ) . The apparent first order rate constant for Spire binding to barbed ends was measured at different concentrations ( Figure 5A , right frame ) . The rate constant for Spire association to barbed ends was derived from the linear dependence of the pseudo–first order rate constant on Spire concentration . Conversely , dissociation of Nt-Spire from capped barbed ends was revealed by the switch from arrested growth to restored slow growth of free barbed ends from PA upon changing the flowing solution from PA+Nt-Spire to PA alone . Values of 2 . 7 µM−1 s−1 and 0 . 0101 s−1 were found for the association ( k+S ) and dissociation ( k−S ) rate constants of Nt-Spire at free barbed ends ( Figure 5A ) from which the equilibrium dissociation constant of Nt-Spire for barbed ends is KS = k−S/k+S = 3 . 8 nM . This value is in reasonable agreement with our previous bulk solution measurements demonstrating capping of barbed ends by Spire [20] , further documented here , ( Figure 7 ) . The association of FH1D-FH2 to free barbed ends , revealed by the switch from slow to fast growth , was addressed using the same protocol ( Figure 5B ) . The association of FH1D-FH2 to free barbed ends was so slow that very few fast growing filaments were recorded over a period of 10 min , in contrast with mDia1 ( our unpublished observations ) and Capping Protein [36] . The measured association rate constant of FH1D-FH2 to free barbed ends was k+F = 7 . 4 10−3 µM−1 s−1 ( Figure 5B ) . The off rate constant of FH1D-FH2 derived from the duration of processive growth was k−F = 3 . 17 10−3 s−1 , consistent with an average dwell time of FH1D-FH2 at barbed ends of 3 to 4 min at 1 µM PA ( corresponding to processive assembly of a 37 µm long filament ) . The rate of fast growth increased linearly with PA concentration , leading to a rate constant of 63±4 µM−1 s−1 for processive assembly by FH1D-FH2 from PA ( Figure 5B ) , compared with the value of 48 µM−1 s−1 for mDia1 , so far the fastest known formin [37] . Quantitatively identical data were obtained with FH1-FH2 ( Fmn2 ) , indicating that the FH2 domain of formin 2 , not the FH1 domain , is responsible for its intrinsic processive behavior ( open symbol in Figure 5B , central panel , inset of Figure 6G , and table in Figure S5D ) . In more complex assays , filaments first capped by Nt-Spire were switched to the same solution of PA containing FH1D-FH2 either in absence or presence of Nt-Spire ( kymographs in Figure 6A , B and Figure S5 ) . These assays revealed major striking features of the synergy between Nt-Spire and FH1D-FH2 . Remarkably , each of the two proteins associated with a barbed end occupied by the other . Binding of Nt-Spire to FH1D-FH2–bound , rapidly growing barbed ends caused arrest of fast growth . Binding of FH1D-FH2 to Nt-Spire–arrested barbed ends promoted fast growth . Nt-Spire associated to a FH1D-FH2–bound barbed end more slowly than to a free barbed end , with a rate constant k′+S = 0 . 396 µM−1 s−1 ( Figure 6C , D , red lines; Figure S5A ) , as might be anticipated from the partial occupancy of barbed end subunits by structural elements of FH1D-FH2 , hindering WH2 binding sites . In contrast , association of FH1D-FH2 ( as well as FH1-FH2 ) to Nt-Spire–precapped barbed ends was 30-fold faster than to free barbed ends , leading to k′+F = 0 . 29 µM−1 s−1 , conspicuously similar to the association rate constant of Nt-Spire to FH1D-FH2–bound barbed ends . Ninety percent of precapped filaments displayed fast processive growth within 2 min following addition of 40 nM FH1D-FH2 ( Figure 6F , G , red lines; Figure S5B ) . Identical rates of fast growth were recorded when FH1D-FH2 associated to a Nt-Spire–bound barbed end ( 57 . 6±6 . 1 subunits per second , N = 106 ) and to a free barbed end ( 55 . 5±5 . 9 subunits per second , N = 40 ) as in the absence of flow . Filament barbed ends were capped by Nt-Spire in the presence of FSI peptide at the same rate as without FSI ( Figure S5D ) . However , FH1D-FH2 binding to barbed ends capped by Nt-Spire in the presence of FSI was strongly reduced ( Figure S5C ) . These results establish that direct interaction between barbed end–bound Nt-Spire and Fmn2 , via the KIND-FSI contact , is required to facilitate binding of Fmn2 to barbed ends and resumed fast growth . The data rule out the possibility that the synergy results only from an indirect effect of Spire binding to barbed ends . However , they do not exclude the possibility that the structure/reactivity of barbed ends is affected by the WH2 domains of Spire in a way that facilitates binding of Fmn2 . Filaments growing in the presence of both FH1D-FH2 and Nt-Spire displayed alternating phases of fast growth and arrested growth , visualized by staircase-like kymographs ( Figure 6B ) . No slow growth periods were observed , suggesting that the barbed ends were never free . Arrests of growth and switches to fast growth were indicative of barbed end ocupancy by Nt-Spire and FH1D-FH2 , respectively . Do Nt-Spire and FH1D-FH2 remain bound to each other at the same barbed end , though in functionally different configurations , during the alternating periods of fast growth and arrested growth ? The identical rates of FH1D-FH2–catalyzed processive assembly in absence or presence of Nt-Spire already argue against this possibility . We also figured that Nt-Spire ( respectively FH1D-FH2 ) would dissociate from barbed ends at different rates whether it was or was not bound to FH1D-FH2 ( respectively , Nt-Spire ) . Measurements of the dwell times of FH1D-FH2 at filaments precapped by Nt-Spire and of Nt-Spire at filaments previously in the fast growth phase before arrest unambiguously show that FH1D-FH2 and Nt-Spire dissociate from these preoccupied ends at the exact same rates as from free barbed ends ( Figure 6E , H ) . Kinetic parameters are summarized in Figure S5D . These results altogether convey the view that Nt-Spire associates directly to barbed end–bound FH1D-FH2 , and FH1D-FH2 associates to barbed end–bound Nt-Spire , in transient ternary complexes . Thus , in the presence of Nt-Spire and FH1D-FH2 , filaments switch rapidly from a pausing , Nt-Spire–capped state to a fast-growing FH1D-FH2–bound state , the two proteins kicking off each other to occupy their genuine binding sites at the barbed ends . In filament growth assays in ATP , the nucleotide bound to barbed end subunits is ATP or ADP-Pi [32] , [38] . Dilution-induced filament disassembly assays were performed to know how FH1D-FH2 and Nt-Spire interact with ADP-bound barbed end subunits in the absence ( Figure 7A , B ) and presence ( Figure 7C , D ) of profilin . In the absence of profilin in the depolymerization buffer , FH2 and FH1D-FH2 identically slowed down filament disassembly by 50% , corresponding to about 60% inhibition of barbed end disassembly ( Figure 7A ) . The inhibition of depolymerization occurred within 5 s mixing dead time . The formin concentration dependence of the depolymerization rate was consistent with high affinity binding of FH2 or FH1D-FH2 to barbed ends ( KD = 6±1 nM ) causing a slow dissociation of ADP-actin . The rapid , high affinity binding of FH1D-FH2 to ADP-bound barbed ends contrasts with its slow association with growing ATP-bound barbed ends ( Figure 7A ) . When barbed ends were saturated by FH1D-FH2 , KIND blocked barbed end disassembly , again indicating that it bound to FH1D-FH2 barbed ends with a KD of 20 nM and the FH1D-FH2-KIND complex acts as a barbed end capper ( Figure 7B , dashed blue curve ) . Strikingly , KIND had the opposite effect on disassembly of FH2-bound barbed ends and restored the fast rate of disassembly of free barbed ends ( Figure 7B , dashed red curve ) . Thus , binding of KIND to barbed end–bound FH2 weakens FH2 interaction with barbed end terminal subunits and promotes its dissociation from barbed ends in an inactive KIND-FH2 complex , allowing the free barbed ends to depolymerize ( Figure 7B , dashed lines ) . KIND in itself does not affect barbed end disassembly ( Figure 7B , grey curve ) . The binding of Nt-Spire to barbed ends ( with a KD of 9 nM ) slows down barbed end disassembly by about 70% ( Figure 7B , green curve , and [20] ) . In the presence of saturating amounts of FH1D-FH2 or FH2 in depolymerizing buffer , which slow down disassembly by 60% , addition of Nt-Spire promoted complete blockage of barbed ends ( Figure 7B , solid blue and red curves , and expanded inset ) . The dependence of the decrease in depolymerization rate on Nt-Spire concentration reflects the binding of Nt-Spire to FH1D-FH2– or FH2-bound barbed ends with 10-fold enhanced affinity ( Kd = 0 . 5 to 1 nM ) as compared to its binding to free barbed ends . Thus , Nt-Spire and FH1D-FH2 bind together to ADP-bound barbed ends in a configuration in which filament disassembly is blocked . When profilin was present in the depolymerization buffer , FH1D-FH2 again slowed down filament disassembly . The dependence of the disassembly rate on FH1D-FH2 concentration shows that FH1D-FH2 binds to barbed ends with a higher affinity ( Kd = 1 to 2 nM ) in the presence than in the absence of profilin ( Figure 7C , Figure S6A ) . In contrast , the affinity of the FH2 domain for barbed ends was lowered by profilin ( Kd = 20 nM , Figure 7C , Figure S6B ) . Thus profilin strengthens the binding of FH1D-FH2 at barbed ends , presumably via the known interaction of profilin with the FH1 domain [39] . The effects of Nt-Spire and KIND observed in Figure 7B were conserved in the presence of profilin ( Figure 7D ) . In conclusion , the strong interaction of FH1D-FH2 and Nt-Spire at ADP-bound barbed ends involves contacts between the WH2 domains of Nt-Spire and barbed end terminal subunits , in addition to the contacts between the KIND domain of Nt-Spire and the FH2 C-terminus . Profilin enhanced the rate of disassembly from free , FH2-bound , or FH1D-FH2–bound barbed ends ( Figure S6C ) , as previously observed at free barbed ends [32] , [33] , [40] . At saturation by profilin , slower maximal rates of depolymerization were observed in the presence than in the absence of FH2 or FH1D-FH2 . Values of equilibrium dissociation constants of all proteins with barbed ends are summarized in the table in Figure 7E . To investigate whether the direct interaction between Nt-Spire and FH1D-FH2 also leads to synergistic actin assembly in vivo , the Nt-Spire or the isolated KIND domain , or FH1D-FH2 , or the FSI peptide , were injected into mouse oocytes ( Figure 8 ) . Injection of Nt-Spire or FH1D-FH2 induced a large increase in the mass of cytoplasmic F-actin and 50% increase in intensity of fluorescent phalloidin staining as compared to the control , whereas injection of the KIND domain had the opposite effect and depressed by 2-fold the intensity of phalloidin staining indicative of cytoplasmic F-actin . Thus , constitutively active Nt-Spire and FH1D-FH2 recapitulate the effects of overexpression of full-length Spire and Fmn2 [6] . In the oocyte , only a fraction of the Spire and Fmn2 molecules may be bound to each other; hence , addition of constitutively active Nt-Spire or FH1D-FH2 may stimulate further actin assembly . In contrast , injection of KIND prevents the synergistic effect of Spire and Fmn2 on barbed end nucleation and growth . Thus , existing filaments disassemble . In agreement with our in vitro data showing that FH2 cannot promote processive filament assembly from PA , injection of FH2 depresses actin assembly . This result validates the concept that profilin is a player in the synergy between Spire and Fmn2 .
Bulk solution studies and single filament analysis of actin assembly provide mechanistic insight into the reported genetic interactions between Spire , Fmn2/Cappuccino , and profilin in oogenesis . The data reveal how Nt-Spire and FH1D-FH2 both cooperate and antagonize in filament assembly from PA , and establish that replacing the FH1 of Fmn2 by FH1D of mDia1 or deleting a few proline regions does not affect the function of Fmn2 nor its synergy with Nt-Spire . Thus , the conclusions of this work apply to FH1-FH2 ( Fmn2 ) . FH1-FH2 is highly processive in itself , but binds filament barbed ends inefficiently . Capping of barbed ends by Nt-Spire kinetically facilitates barbed end association of FH1-FH2 . All data emphasize that the faster binding of FH1-FH2 is due to the direct interaction between the two proteins at barbed ends rather than to only an indirect effect of the WH2 domains of Nt-Spire on the conformation of the barbed end ( Figure 9A ) . Spire and FH1-FH2 control filament assembly using a “ping-pong” [41] ( or “tag-team” ) mechanism that has no precedent in the regulation of formin-mediated actin assembly . Filaments display alternate phases of fast processive growth and arrested growth , in which barbed ends bind in turn FH1-FH2 or Nt-Spire , respectively . Each protein kicks off the other via formation of transient complexes in which they interact together at the barbed end . The dwell time in each phase , as well as the relative amounts of F-actin and G-actin at steady state , are governed by the relative concentrations of Nt-Spire and FH1-FH2 . The control of actin assembly dynamics by the Nt-Spire∶FH1-FH2 ratio may extend to the synergy between Nt-Spire and Cappuccino in Drosophila mid-oogenesis . The following minimal scheme describes the data without making any mechanistic hypotheses . ( 1 ) ( 2 ) ( 3 ) ( 4 ) B , BS , and BF represent the barbed ends in free , Nt-Spire–bound , and FH1-FH2–bound states , respectively . BFS and BSF are transient states in which Nt-Spire and FH1-FH2 interact directly together as well as with terminal subunits at the barbed end . When Nt-Spire and FH1-FH2 coexist with PA in solution , because association of FH1-FH2 to barbed ends or prenuclei is extremely slow , a likely sequence of events ( Figure 9A ) is the initial rapid capping of barbed ends or prenuclei by Nt-Spire , followed by rapid association of FH1-FH2 in a low affinity transient complex BSF , leading to dissociation of Nt-Spire and formation of BF . In other words , FH1-FH2 is firmly saddled on a barbed end nucleus or filament by Nt-Spire . Spire thus assists Fmn2 , in agreement with genetic data [15] . Note that the origin of the synergistic action of Nt-Spire and FH1-FH2 derived from the present data contrasts with the anticipated mechanism within the alternate view that both Spire and Fmn2 are nucleators individually , and that their interaction leads to inhibition of actin assembly [23] , [25] , [42] . The mutual kick off of Nt-Spire and FH1-FH2 from barbed ends implies that the transient complexes BSF and BFS differ structurally/chemically , so as to lead to BF and BS , respectively . Thus , the present data , illustrated by this scheme , raise structural and mechanistic issues regarding the possible conformations of the FH2 domain of Fmn2 and the WH2 domains of Spire interacting with the terminal barbed ends subunits of the actin filament , individually and together . A “kick off” process may imply that each protein interacts with the barbed end with at least two subsites , which in the present case may be facilitated by the fact that two actin subunits are exposed at the filament barbed end . For instance , uncapping of capping protein ( CP ) from barbed ends by VopF is possible because the ß-tentacle of CP occupies the main WH2 binding site only on the terminal barbed end subunit , leaving the homologous site on the subterminal subunit available for one WH2 domain of the dimeric VopF [43] . Similarly , the crystal structure of the FH2 domain of Bni1 in complex with TMR-actin shows that the “knob” of FH2 occupies the WH2 binding site only on the subterminal subunit , leaving the barbed face of the terminal subunit exposed in the “closed” state [44]–[46] . Assuming that a large fraction of the FH2 of Fmn2 shares the actin binding mode of Bni1 , it is tempting to suggest that one WH2 domain of Nt-Spire binds to the terminal subunit in the “closed” FH2-actin state , following association of the KIND domain with the C-terminal region of FH1-FH2 . We find that the isolated KIND domain causes destabilization of FH2 from the barbed end , which implies that the C-terminus of FH2 , which is specific to Fmn2 , participates in the interaction of the FH2 domain with terminal subunits and processive walk , in agreement with Vizcarra et al . [25] . Therefore , when Nt-Spire binds to an FH1-FH2–bound barbed end , the structural change linked to FH2-KIND interaction may be involved in the kick off of FH1-FH2 coupled to tightening of Nt-Spire binding to terminal subunits . The proposed rapid equilibrium of FH2 between the “closed” and “open” states during processive assembly may be affected by Spire and may allow FH1-FH2 and Nt-Spire to adopt different conformations in BFS and BSF states as well . The nature of the nucleotide bound to the two actin barbed end subunits may be important in the binding of FH1-FH2 and the kick off mechanism . The fact that FH1-FH2 associates very slowly to barbed ends in a regime of growth in ATP , while it binds rapidly and with high affinity to ADP-bound barbed ends , may suggest that FH1-FH2 has a higher affinity for ADP-actin , which is not frequently present at barbed ends growing from profilin-ATP-actin . Alternatively , FH1-FH2 association to ATP-bound barbed ends may occur as a two step reaction , formation of a rapid equilibrium low affinity complex being followed by a structural change strengthening the binding of FH1-FH2 and allowing processive assembly . The observation that no stimulation of filament assembly by either FH1-FH2 or FH1-FH2+Nt-Spire takes place in AMPPNP nor ADP further suggests that ATP hydrolysis plays some role in Fmn2 function as well as in its cooperation with Spire . While evidence has been provided for processive tracking of barbed ends by formins mDia1 and Bni1 in a growth regime in ADP and in a depolymerization regime [36] , [46] , [47] , thus demonstrating that ATP hydrolysis is not required for tracking of barbed ends by formin , the very fast processive assembly from PA is oberved only in ATP [29] , [33] , and pauses in growth are observed upon addition of CrATP that does not release Pi following cleavage of ATP [32] . Moreover , processive assembly can be modeled without involving ATP hydrolysis only if the affinity of profilin for ATP-actin is assumed to be 50-fold lower than its acknowledged value [48] . Two other formins , INF2 and FMNL3 , use WH2 domains and FH2 domains in the same polypeptide chain , to regulate actin assembly . Remarkably , in this case , the WH2 domain affects nucleation using a different mechanism [49] , [50] , in which interaction of the WH2 domain with G-actin relieves the auto-inhibition [51] . Most formins promote processive filament assembly in a Rho GTPase-mediated , site-directed fashion . In the mouse oocyte , Fmn2 , which is not regulated by Rho GTPases , is recruited via Spire to Rab11a positive vesicles . Both the high dynamics of filament assembly and the action of myosin Vb , linked to Rab11a vesicles , are required for spindle translocation [12] , [52] . Myosin Vb , together with Nt-Spire and Fmn2 , controls the global dynamics of this coupled vesicle-filament system leading to outward movement of vesicles and slow spindle translocation toward the cortex [52] . We tentatively propose that the ping-pong mechanism integrates this context as follows ( Figure 9B ) . Association of Nt-Spire to Rab11a vesicles leads to barbed end binding of filaments or prenuclei , triggering Fmn2 association to the Nt-Spire–attached barbed ends , displacement of Nt-Spire from the transient BSF state , and fast barbed end growth . The presumed presence of ADF/cofilin ensures rapid pointed end disassembly of the filaments , which creates a stationary large pool of PA , which feeds fast barbed end processive assembly and fosters rapid treadmilling at the scale of individual filaments [29] . The shortened filaments then either release Fmn2 spontaneously and again get capped by Nt-Spire at the surface of the vesicles , or directly bind Nt-Spire vesicles into the BFS state , then release Fmn2 . For simplicity , the cycle of filament nucleation release at Rab11a vesicles organized by Spire and Fmn2 is illustrated at the level of an individual filament in Figure 9B . At the collective level , dynamic links between the filaments are imposed in part by the ping-pong mechanism and in part by the clustering of the players Nt-Spire , Fmn2 , and myosin Vb at Rab11a-positive vesicles . These connections organize the formation and maintenance of a dynamic gel in a rapid renewal state that controls the plasticity of the oocyte cytoplasm and facilitates break of symmetry and the first slow step in directional migration of the spindle [53] . This process appears hampered in a gel in which filaments do not undergo rapid turnover , as demonstrated by the failure of spindle to translocate in jasplakinolide-treated oocytes [12] . The very slow migration rate of the spindle toward the cortex actually argues for a mechanism in which actin assembly in the oocyte is not directly applied to a surface to develop a pushing force . Microrheological studies of actin solutions in the presence of Nt-Spire , FH1-FH2 , profilin , and ADF , mimicking cellular media , may reveal how the Nt-Spire∶FH1-FH2 balance affects the properties of this gel . A confined environment may further affect rheological properties [54] . In Drosophila oocytes , massive actin assembly at midoogenesis , resulting from the synergy between formin Cappuccino and Spire , is required to avoid premature cytoplasmic streaming and failure in axis patterning . The rescue of Spir mutants by expression of SpirD [15] , which is identical to the Nt-Spire protein studied here , further establishes the in vivo relevance of the present biochemical data . Completion of oogenesis requires the subsequent disappearance of the actin meshwork . Our work shows that an excess of Nt-Spire over FH1-FH2 causes capping of barbed ends by Nt-Spire that leads to depolymerization of F-actin by profilin . Monitoring the evolution of the Spire∶Fmn2 ratio during oogenesis and manipulating it genetically may validate or rule out this potential regulatory mechanism .
The following constructs of human Spire 1 ( accession number NP_001122098 ) , mouse Formin 2 ( accession number NP_062318 . 2 ) , and mDia1 ( accession number NP_031884 ) were designed as follows . FH1D-FH2 ( P854-T1578 ) and truncated FH1t-FH2 ( P854-T1578Δ ( 912–967 ) ) constructs , FH2 ( F1128-T1578 ) and KIND ( G35-S257 ) cDNA were cloned between BamH1 and Xho1 cloning sites of a modified pGEX-6P1 expression vector containing a N-terminal histidine thioredoxine tag in place of the GST tag and a C-terminal Streptag II . The cDNA of the chimeric FH1 ( mDia1 ) -FH2 ( Fmn2 ) , called FH1D-FH2 , was chemically synthetized from the amino acid sequence obtained by juxtaposing the FH1 amino acid sequence of mDia1 ( S568-P747 ) to the FH2 amino acid sequence of Fmn2 ( F1128-T1578 ) and back-translating it to a nucleotide sequence optimized for expression in E . coli . The FH1D-FH2ΔFSI construct was subcloned from the FH1D-FH2 cDNA sequence down to S1558 ( thus deleting the last 20 residues of the FH2 domain ) into the modified pGEX-6P1 expression vector . The Nt-Spire cDNA sequence corresponding to ( M1-S443 ) was cloned in an unmodified pGEX-6P1 vector between the BamH1 and Xho1 cloning sites . All constructs were expressed in E . coli Rosetta ( DE3 ) ( Novagen ) , in LB medium . Cultures were induced by 1 mM IPTG at 16°C overnight . Bacteria pellet were resuspended in lysis buffer ( 20 mM potassium phosphate buffer pH 7 . 4 , 900 mM NaCl , 15 mM imidazole , 3 mM DTT , 5% sucrose , 0 . 1 mM EDTA , 1 mM PMSF , 5 µM benzamidine , protease inhibitor cocktail , 1% Triton X100 , and lyzozyme ) and sonicated on ice . Ultracentrifuged cell lysates were loaded on HisTrap FF crude column ( GE Healthcare ) . The HisTrap resin was equilibrated with binding buffer 1 ( 20 mM phosphate buffer pH 7 . 4 , 900 mM NaCl , 15 mM imidazole , 3 mM DTT , 5% sucrose , 0 . 1 mM EDTA ) , then washed with 4% of elution buffer 1 ( binding buffer 1 except for 250 mM imidazole ) . Proteins were eluted with a 60% elution buffer gradient step . The Fmn2-enriched fraction was then diluted with a suitable volume of 100 mM Tris pH 7 . 5 to decrease NaCl concentration to 300 mM and loaded to a Strep Trap HP ( GE Healthcare ) . The resin was then washed with binding buffer 2 ( 100 mM Tris pH 7 . 5 , 300 mM NaCl , 1 mM EDTA , 3 mM DTT , 5% sucrose ) , and bound proteins were eluted with elution buffer 2 ( binding buffer 2 supplemented with 4 mM desthiobiotin ) . Eluted fractions were pooled and concentrated with a Vivaspin ( 10 kDa cutoff ) and injected on a Superdex 200 16/60 ( GE Healthcare ) pre-equilibrated with 100 mM Tris pH 7 . 5 , 300 mM NaCl , 3 mM DTT , 5% sucrose . Fractions corresponding to pure Fmn2 constructs were pooled , concentrated , flash frozen in liquid nitrogen , and stored at −80°C . The very low level of expression and poor solubility of Fmn2 FH1-FH2 precluded extensive biochemical characterization . Truncation of two proline-rich regions of the FH1 domain or its replacement by the FH1 domain of mDia1 yielded over one order of magnitude higher level of expression of soluble constructs , respectively called FH1t-FH2 and FH1D-FH2 . The Stokes radii of FH2 , FH1D-FH2 and FH1D-FH2ΔFSI derived from gel filtration revealed their dimeric structure . Concentrations of FH2 and FH1D-FH2 are expressed in molarity of the protomer . FH2 and KIND were expressed and purified similarly to FH1D-FH2 constructs up to the HisTrap purification step . Prior to the Strep Trap purification step , the histidine thioredoxine tag was cleaved using Prescission Protease ( 5 U/mg fusion protein ) overnight at 4°C . Digested protein was then loaded to a Strep Trap HP ( GE Healthcare ) . The resin was then washed with binding buffer 2 , and bound proteins were eluted with elution buffer 2 . Eluted fractions were pooled , concentrated , and loaded on a Superdex 200 16/60 ( GE Healthcare ) pre-equilibrated with 20 mM Tris pH 7 . 5 , 75 mM KCl , 1 mM DTT for FH2 , or 20 mM Tris pH 7 . 5 , 100 mM KCl , 1 mM DTT for KIND . Fractions corresponding to pure FH2 or KIND were pooled and concentrated . FH2 was stored at 4°C . KIND was flash frozen in liquid nitrogen and stored at −80°C . Nt-Spire was expressed and purified similarly to FH1D-FH2 constructs up to the HisTrap purification step . The concentrated His Trap eluted material was loaded onto a desalting Hiprep 10–26 column pre-equilibrated with a desalting buffer ( 50 mM Tris pH 7 . 5 , 400 mM NaCl , 1 mM DTT , 1 mM EDTA ) . The GST tag was cleaved by overnight incubation at 4°C of the concentrated fusion protein solution with Prescission Protease ( 5 U/mg fusion protein ) . Nt-Spire was eventually purified by gel filtration in 15 mM Tris pH 7 . 5 , 250 mM KCl , 1 mM DTT , 1% sucrose buffer , and was kept frozen at −80°C . The FSI peptide comprising the 27 C-terminal residues of human Fmn2 ( NP_064450 . 3 ) ( E1549-T1578 ) was chemically synthetized ( Proteogenix ) . We dissolved 10 mg of peptide in 500 µL Tris 20 mM , KCl 100 mM , and DTT 1 mM , and loaded it on a pre-equilibrated PD-10 desalting column . The eluted peptide fractions were stored frozen at −80°C . Actin was purified from rabbit muscle and isolated in monomeric form in G-buffer ( 5 mM Tris-Cl− , pH 7 . 8 , 0 . 1 mM CaCl2 , 0 . 2 mM ATP , 1 mM DTT , 0 . 01% NaN3 ) . Profilin and spectrin-actin seeds were purified as described [31] . Spectrin-actin seeds ( 0 . 1 µM ) , equilibrated in 0 . 3 mM NaPO4 pH 7 . 6 , were reacted with 20 µM sulfoNHS-LC-LC-Biotin ( Pierce ) for 2 h at room temperature , then dialysed against 0 . 3 mM NaPO4 pH 7 . 6 , 1 mM DTT buffer . Biotinylated spectrin-actin seeds were supplemented with 50% ethylene glycol and stored at −20°C . ADP-actin was prepared by treatment of ATP-G-actin with hexokinase and glucose [31] . Briefly , Ca-ATP-G-actin ( 10 µM ) in G buffer was supplemented with 20 µM MgCl2 , 0 . 2 mM EGTA , 1 mM glucose , 10 µM Ap5A as an inhibitor of myokinase , and 15 U/ml hexokinase ( Sigma ) . Polymerization assays were performed in the presence of ADP and Ap5A . AMPPNP-actin was prepared from ADP-actin as above , followed by addition of 1 mM AMPPNP and gel filtration on Sephadex G25 ( PD10 columns , GE Healthcare ) equilibrated in GX buffer ( G buffer containing 1 mM AMPPNP instead of ATP , 10 µM MgCl2 , 1 mM glucose , and 5 U/ml hexokinase to ensure the absence of contaminating ATP in the commercial AMMPNP [55] ) . It was checked that 100% of G-actin was AMPPNP-bound by equilibrating the initial ATP-G-actin solution with [3H]-ATP ( NEN ) , and measuring the absence of [3H]-ADP in the fractions of AMPPNP-G-actin eluted from the PD-10 column in GX buffer . Solutions of ADP-G-actin and AMPPNP-G-actin were kept on ice and used within 6 h . Actin polymerization/depolymerization kinetic experiments were based on fluorescence change of pyrenyl-labeled G- or F-actin ( λexc = 366 nm , λem = 407 nm ) . All experiments were carried out at 20°C , on a Safas Xenius FLX spectrofluorimeter ( Safas , Monaco ) , using a multiple sampler device . Polymerization assays were performed in F-buffer ( 5 mM Tris-Cl pH 7 . 8 , 0 . 2 mM ATP , 0 . 1 mM CaCl2 , 1 mM DTT , 0 . 05 M KCl , 1 mM MgCl2 ) . Prior to each experiment , a stock solution of CaATP-G-actin ( 10 µM , 5% pyrenyl-labeled ) was converted into MgATP-G-actin by addition of 20 µM MgCl2 and 0 . 2 mM EGTA and kept on ice . We added 24 µM Profilin to this stock solution for polymerization assays at final concentrations of 2 . 5 µM G-actin and 6 µM profilin . Dilution-induced depolymerization assays were performed by quickly diluting 4 µL of 2 . 5 µM 50% pyrenyl-labeled F-actin into 196 µL F buffer containing the proteins of interest . The initial rate of depolymerization was measured and normalized with respect to the initial depolymerization rate in control samples . Measurements of F-actin asembled at steady state were performed as described [29] using 2% pyrenyl-labeled actin . Samples were incubated at 4°C overnight in the dark before fluorescence measurements . Standard TIRF assays were performed using a flow chamber assembled by placing two parallel strips of double-sided tape ( 26×10×0 . 1 mm ) spaced by 8 mm onto a cleaned glass slide ( 76×26 mm ) , surmounted with a PLL-PEG passivated coverslip . Chambers were sequentially washed with G buffer , 5% BSA , Fluo F buffer ( 5 mM Tris-Cl− pH 7 . 8 , 150 mM NaCl , 1 mM MgCl2 , 0 . 2 mM EGTA , 0 . 2 mM ATP , 10 mM DTT , 1 mM DABCO , 0 . 01% NaN3 ) . Assays were performed in Fluo F buffer supplemented with 0 . 3% methylcellulose ( Sigma Cat . No . M-0262 , 400 cP for a 2% aqueous solution at 20°C ) and with actin , profilin , Nt-Spire , and FH1D-FH2 or FH1-FH2 at indicated concentrations . Microfluidics-assisted TIRF microscopy assays were performed using PDMS flow cells , with three inlets [34] . Prior to flowcell assembly , coverslips are first extensively cleaned by sequential sonication in pure water , ethanol , and 1 M KOH for 20 min each , then dried with air and exposed to a plasma discharge for 2 min . The microchambers were placed on the microscope stage and connected to the microfluidic system ( MFCS and Flowell , from Fluigent ) . The coverslip is then functionalyzed by absorption of PLL-PEG/PLL-PEG-biotin ( 20% ) ( from SuSoS ) to minimize nonspecific protein binding and achieve specific anchoring of biotinylated spectrin-actin seeds via a streptavidin sandwich . Actin was labeled with Alexa488 succimidyl ester [34] . The fraction of labeled actin was 10% . Assays were performed in FluoF buffer without methylcellulose . TIRF observations were carried out on an Olympus IX71 inverted microscope , with a 60× TIRF objective , and a 473 nm laser ( Cobolt ) . The experiment was controlled using the Metamorph software . Images were acquired using a cascade II EMCCD camera ( Photometrics ) , with a frame interval of 10 s for all experiments . Images are further analyzed by ImageJ to obtain kymographs and to determine the times at which filaments experience transitions from one to another of the three possible states: slow elongation ( “free barbed-end” ) , rapid elongation ( “FH1D-FH2–bound barbed end” ) , or capped ( “Nt-Spire–bound barbed end” ) . Single exponential curve fitting of the data points is done using Gnuplot . On the kymographs , slopes of elongation phases give us the elongation rates in presence or absence of FH1D-FH2 . We considered that each actin subunit contributes to 2 . 7 nm of the filament length . All mice were maintained in a specific pathogen-free environment according to UK Home Office regulations . Oocytes were isolated from ovaries of 8-wk-old FVB mice , cultured , and microinjected as described in detail [53] . BSA ( Sigma ) or recombinant Nt-Spire , KIND , FH2 , and FH1D-FH2 protein fragments were microinjected into oocytes in buffer supplemented with 0 . 05% NP-40 Alternative ( Calbiochem ) . Final protein concentrations were calculated by dividing the total amount of injected protein by the total volume of the oocyte . These were 1 to 3 µM for each protein , 8 µM for KIND , and 163 µM for FSI . At 4–5 h after resumption of meiosis using previously detailed methods [46] , oocytes were fixed for 30 min at 37°C with 100 mM HEPES , 50 mM EGTA , 10 mM MgSO4 , 2% formaldehyde , and 0 . 2% Triton X-100 and extracted in PBS supplemented with 0 . 1% Triton X-100 at 4°C overnight . Actin staining was performed for 1 h in PBS , 3% BSA , and 0 . 1% Triton X-100 with Alexa Fluor-488 Phalloidin ( Molecular Probes; 1∶20 ) . Single optical sections in the equatorial region of oocytes were acquired with a Zeiss LSM710 confocal microscope equipped with a ×63 C-Apochromat 1 . 2 NA water-immersion objective as described previously [56] . Images in control and perturbed situations were acquired with identical imaging conditions . Care was taken that images were not saturated during acquisition . To quantify the density of the cytoplasmic actin network , the mean intensity of Alexa Fluor-488 phalloidin was measured in the cytoplasm and in a region outside the oocyte for background subtraction using ImageJ . Average ( mean ) , standard deviation , and statistical significance based on Student's t test ( always two-tailed ) were calculated in OriginPro ( OriginLab ) . | Mammalian reproduction requires successful meiosis , which consists of two strongly asymmetric cell divisions . In meiosis I , movement of the spindle ( the subcellular structure that segregates chromosomes during division ) toward the oocyte cortex ( the outer layer of the egg ) is essential for fertility . This process requires that actin filaments assemble in a dynamic mesh , driven by three actin binding proteins , profilin , formin 2 , and Spire . To date the molecular mechanisms by which these three proteins cooperate are not known . We now explore this in vitro by a combination of bulk solution and single actin filament assembly assays in the presence of profilin , Spire , and formin 2 . Individually , Spire binds to actin filament ends to block their growth , and by itself , formin 2 associates poorly with filament ends , promoting fast processive assembly from the profilin-actin complex . However , when present together , Spire and formin 2 interact with one another ( the formin 2 C-terminal binds to the N terminal Spire KIND domain ) , forming transient complexes at filament ends from which each binds alternately to the filament ends to regulate actin assembly by a ping-pong mechanism . Our in vitro observations are validated by injection studies in mouse oocytes . In oocytes , the additional interaction of Spire and formin 2 with Rab11a-myosin Vb vesicles couples high actin dynamics to vesicle traffic . | [
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] | 2014 | Spire and Formin 2 Synergize and Antagonize in Regulating Actin Assembly in Meiosis by a Ping-Pong Mechanism |
Bloodstream-form African trypanosomes encode two structurally related glycosylphosphatidylinositol ( GPI ) -anchored proteins that are critical virulence factors , variant surface glycoprotein ( VSG ) for antigenic variation and transferrin receptor ( TfR ) for iron acquisition . Both are transcribed from the active telomeric expression site . VSG is a GPI2 homodimer; TfR is a GPI1 heterodimer of GPI-anchored ESAG6 and ESAG7 . GPI-valence correlates with secretory progression and fate in bloodstream trypanosomes: VSG ( GPI2 ) is a surface protein; truncated VSG ( GPI0 ) is degraded in the lysosome; and native TfR ( GPI1 ) localizes in the flagellar pocket . Tf:Fe starvation results in up-regulation and redistribution of TfR to the plasma membrane suggesting a saturable mechanism for flagellar pocket retention . However , because such surface TfR is non-functional for ligand binding we proposed that it represents GPI2 ESAG6 homodimers that are unable to bind transferrin—thereby mimicking native VSG . We now exploit a novel RNAi system for simultaneous lethal silencing of all native TfR subunits and exclusive in-situ expression of RNAi-resistant TfR variants with valences of GPI0–2 . Our results conform to the valence model: GPI0 ESAG7 homodimers traffick to the lysosome and GPI2 ESAG6 homodimers to the cell surface . However , when expressed alone ESAG6 is up-regulated ~7-fold , leaving the issue of saturable retention in the flagellar pocket in question . Therefore , we created an RNAi-resistant GPI2 TfR heterodimer by fusing the C-terminal domain of ESAG6 to ESAG7 . Co-expression with ESAG6 generates a functional heterodimeric GPI2 TfR that restores Tf uptake and cell viability , and localizes to the cell surface , without overexpression . These results resolve the longstanding issue of TfR trafficking under over-expression and confirm GPI valence as a critical determinant of intracellular sorting in trypanosomes .
Many eukaryotic secretory proteins such as surface antigens , adhesion proteins , and receptors are attached to the external leaflet of the plasma membrane by glycosylphosphatidylinositol ( GPI ) anchors [1 , 2] . GPI anchors function as vesicular transport signals for ER export , for post-Golgi sorting , and for subsequent delivery to the plasma membrane . For example , in yeast , inhibition of GPI attachment leads to delayed ER exit of the major GPI-AP , Gas1p [3]; and GPI-APs exit the ER in cargo vesicles that are distinct from other secretory and plasma membrane proteins [4] . In mammalian cells , GPI-anchors serve as cell surface targeting signals by specific association with sterol/sphingolipid-rich detergent-insoluble membranes ( a . k . a . lipid rafts ) at the trans-Golgi network [5 , 6] . Likewise at the plasma membrane , GPI-APs preferentially cluster in lipid raft microdomains [7] . Ultimately cell surface GPI-APs play critical roles in cell adhesion in fungi , inhibition of complement lysis in erythrocytes , and in defence against host immunity in parasitic protozoa like African trypanosomes [2 , 8] . African trypanosomes ( Trypanosoma brucei ssp ) , parasitic protozoa responsible for human ( Sleeping Sickness ) and veterinary ( nagana ) trypanosomiases , have two cell surface GPI-APs that are critical to the pathogenic bloodstream ( BSF ) stage: variant surface glycoprotein ( VSG ) and transferrin receptor ( TfR ) . VSG is a homodimer ( GPI2 ) that forms a dense surface coat covering the contiguous cell body and flagellar membranes [9 , 10] . It acts as a macromolecular barrier for host-derived antibodies targeting underlying invariant surface proteins . BSF trypanosomes avoid elimination by host anti-VSG immune responses by switching monoallelic expression of antigenically distinct VSGs from a repertoire of >1500 genes . VSG transcription is from a promoter distal position in a telomeric expression site ( ES ) ( Fig 1A ) ; there are ~15 such ESs , only one of which is active at a time [11] . TfR , which is structurally related to VSG , is a heterodimer of ESAG6 ( E6 ) and ESAG7 ( E7 ) ( Expression Site Associated Genes ) . They are expressed from promoter proximal sites in the active ES ( Fig 1A ) , but up to 20% of all TfR transcripts come from background transcription of the other ‘silent’ ESs [12] . E6 and E7 are highly similar from N-termini to the C-terminus of E7 , but E6 is longer and has a C-terminal GPI attachment peptide [13 , 14] . Native TfR is thus a GPI1 protein . In addition to functional E6:E7 heterodimers , each can form homodimers , but these cannot bind Tf [13] . At steady state TfR localizes in endosomal compartments and in the flagellar pocket , where it binds and internalizes holotransferrin ( Tf ) for iron acquisition; an essential nutrient for survival in the mammalian host [13 , 15] . Internalized Tf is stripped of iron in acidic endosomes and the receptor is recycled to the flagellar pocket . Eventually TfR is degraded ( t1/2 ~1 . 5 hr ) in the lysosome [16] . Although GPIs were first characterized in trypanosomes [17] , investigation of their role ( s ) in intracellular trafficking has lagged behind other model systems . However , studies in our laboratory indicate that GPIs are positive forward signals for ER exit in both BSF and procyclic insect stage ( PCF ) parasites . And in BSF cells at least , ER exit of GPI-APs is mediated by a distinct subset of COPII vesicle coat proteins [18] . GPIs are also critical for post-Golgi sorting of VSG via the flagellar pocket to the cell surface in BSF cells . [Throughout this report we will make a distinction between the flagellar pocket , a small invagination of the plasma membrane specialized for exo- and endocytic trafficking , and the contiguous outer cell surface comprised of the flagellum and cell body] Expression of VSG without the GPI addition signal ( VSGΔgpi , GPI0 valence ) leads to delayed ER exit followed by rapid mis-targeting to the lysosome and subsequent degradation ( t1/2 ~45 min ) , and this holds for other GPI0 reporters as well [16 , 19] . In contrast , native VSG ( GPI2 valence ) is rapidly delivered to the cell surface ( t1/2 ~15 min ) and is highly stable [20–23] . Any single VSG molecule is endocytosed and recycled repeatedly , turning over with a population half-life of >30 hr . Interestingly , a series of GPI1 reporters based on endogenous secretory proteins have phenotypes intermediate to GPI0 and GPI2 VSG , parsing between lysosomal targeting/degradation and transport to the cell surface [16] . Those reporters that do reach the cell surface are shed due to a quirk of GPI synthesis in BSF trypanosome—the penultimate GPI precursor is specifically remodeled to contain dimyristoyl ( C14 ) glycerol , which alone is not sufficient to maintain long-term membrane association of a GPI1 protein [24 , 25] . These findings have led us to propose that GPI valence controls progression within the secretory/endosomal system of BSF trypanosomes: GPI2-APs progress to dynamic cell surface expression; GPI1-APs have transient endosomal/flagellar pocket localization with ultimate parsing between lysosomal turnover and surface shedding; and GPI0-APs traffick by default to the lysosome for degradation . Native TfR is unusual in this regard . At normal expression levels it is located in endosomes and the flagellar pocket , but is barely detectable in shed extracellular fractions indicating that it rarely escapes onto the cell surface . However , under conditions that BSF cells perceive as iron starvation , including altered transferrin source and hypoxia , TfR expression is dramatically upregulated and receptor is readily detectable on the cell surface , suggesting a saturable retention mechanism in the flagellar pocket [26 , 27] . However , we found that upregulated surface receptor is not functional for Tf binding , nor is it shed from cells , as would be expected for a normal GPI1 TfR heterodimer [16] . Based on these findings , and in concordance with the GPI valence concept , we proposed that surface TfR actually represents GPI2 homodimers of E6 , which would be expected to behave essentially as homodimeric VSG . In this study we further explore these alternative possibilities , and in so doing challenge the GPI valence model . Using an RNAi cell line to eliminate background expression of TfR from silent ESs [28] , we have engineered in situ expression of RNAi resistant ( RNAiR ) versions of wild type and modified E6 and E7 subunits from the active ES . This functional complementation of RNAi approach [29] allows controlled analyses of the trafficking , localization , and turnover of TfRs of GPI0–2 valence . Our results are fully consistent with the valence hypothesis and provide strong supportive evidence for our model for surface localization of over-expressed TfR .
Controlled genetic manipulation of TfR genes has been impossible because ~20% of TfR transcripts derive from ‘silent’ ESs [12] . Therefore , to study the behavior of TfR subunits without background interference , we created a parental RNAi cell line targeting all native TfR transcripts ( both E6 and E7 ) regardless of source [28] . The native E6 and E7 ORFs ( E6N and E7N ) in the active ES were then replaced with recoded RNAiR ORFs ( E6R and E7R ) taking care to preserve the native 3’ UTRs so that normal expression levels remained unaltered ( Fig 1B ) . As previously reported [28] , TfR silencing was lethal over a period of 3 days ( Fig 2A , Par ) , although cells remained viable after 24 hours of induction due to excess iron stores [30 , 31] . The TfR RNAi-mediated growth phenotype was completely rescued by co-expression of both E6R and E7R genes from the active ES ( Fig 2A , E6:E7 ) . All subsequent analyses were performed at 24 hours post-silencing since cells remained viable with excellent morphology at this time point . TfR silencing led to specific depletion ( ~80% ) of E6N and E7N transcripts in both parental ( Fig 2B , left ) and RNAi resistant E6R:E7R cell lines ( Fig 2B , right ) , while E6R and E7R transcripts remained unaffected in E6R:E7R cells ( Fig 2B , right ) . Pull-down experiments with metabolically labeled cells were performed to assess the effect of silencing on TfR biosynthesis . Stoichiometric amounts of metabolically labeled E6 and E7 were captured by pull down with anti-TfR and Tf ligand in both parental and E6R:E7R cells ( Fig 2C , tet- ) . Induction of dsRNA almost completely eliminated E6 and E7 synthesis in the parental cells ( Fig 2C , left ) , but subunit synthesis was unaffected in E6R:E7R cells ( Fig 2C , right ) . Loss of TfR predictably abolished Tf uptake in parental cells ( Fig 2D , left ) , but was restored to wild-type levels in the RNAiR cells ( Fig 2D , right ) . In each case uptake of tomato lectin ( TL ) , a surrogate for receptor-mediated endocytosis [32 , 33] , was normal confirming that general endocytosis was unaffected . Finally , native TfR localizes normally to the flagellar pocket and endocytic compartments ( Fig 2E , Par , red ) . In the absence or presence of native TfR silencing , a similar TfR staining pattern was seen in RNAiR cells ( Fig 2E , E6:E7 , red ) indicating that trafficking of E6R:E7R TfR is normal . These results show that co-expressed E6R and E7R form functional TfR heterodimers , which was confirmed by BN-PAGE ( S2 Fig ) . Taken together these findings fully validate our experimental system . Subsequent experiments involve expression of E7R , E6R and E7G ( Fig 1C ) alone or in combination in the TfR RNAi cell line . E7R was inserted into BES1 leaving the native E6 gene intact . Without silencing , growth was normal indicating formation of functional E6N:E7R heterodimers ( Fig 3A ) , which was confirmed by pull down experiments with Tf-beads ( Fig 3C ) and uptake experiments ( Fig 3D ) . However , when native E6 was ablated by RNAi ( Fig 3B , ~80% ) E7R alone was insufficient to maintain cell growth . This correlated with complete loss of E6 synthesis ( Fig 3C ) , and of Tf binding ( Fig 3C ) and endocytosis ( Fig 3D ) . Interestingly , in the absence of E6 there was a marked up-regulation in steady-state E7R transcripts ( ~8 fold ) and in E7R synthesis . We interpret this phenomenon as an iron starvation response in the absence of functional TfR , similar to the up-regulation of TfR observed when cells are deliberately starved for transferrin [16 , 26 , 27] . In control cells , TfR localization was identical to functional native TfR—endosomal and flagellar pocket ( Fig 3E , tet- , red ) . In contrast , the TfR signal ( E7R only ) dramatically increased in silenced cells , and overlapped markedly with BiP , consistent with ER localization ( Fig 3E , tet+ , yellow ) . ER accumulation could be due to the absence of GPI anchors as forward ER exit signals on E7R homodimers [18 , 19] , and/or to improper folding/dimerization [28] . BN-PAGE indicates that E7R is present primarily as dimers with a significant amount of low mobility smearing consistent with both possibilities ( S2 Fig ) . Overall these results are in general agreement with the valence model . E6R alone was inserted into the active expression site of TfR RNAi cells , and all phenotypic analyses were performed as described above for E7R cells . Without silencing , E6R cells grew normally ( Fig 4A ) , and functional E6R/E7N heterodimers were detected by Tf pull-down ( Fig 4C ) and uptake ( Fig 4D ) assays . TfR silencing ablated all native E6 and E7 transcripts ( Fig 4B ) and synthesis of native E7 subunit ( Fig 4C ) . Depletion of E7N also resulted in up-regulation of E6R transcript levels ( ~8-fold ) and synthesis . As with the discrete expression of E7R , we interpret this as a response to perceived iron starvation . TfR ( E6R only ) was still localized in endosomal compartments after silencing of E7N , but a prominent signal of surface and flagellar staining became apparent ( Fig 4E , tet+ ) . RNAi-dependent surface expression was confirmed by flow cytometry of non-permeablized cells ( S3 Fig ) . Interestingly there was a 3-fold increase in uptake of tomato lectin in TfR silenced cells ( Fig 4D , TL ) . We attribute this increase to elevated expression and surface localization of the E6R protein , which is known to have glycan epitopes reactive with this lectin [34 , 35] . Finally , BN-PAGE confirms that E6R forms GPI2 homodimers in TfR silenced cells ( S2 Fig ) . Collectively these data , homodimerization and surface expression , are fully consistent with the valence hypothesis . However , because surface expression was only detected under conditions of E6R up-regulation , we cannot completely eliminate a saturable retention mechanism for exclusion of TfR from the cell surface . In order to rule out up-regulation as a confounding factor , a cell line expressing functional GPI2 TfR was generated . The C-terminus of E6 , with GPI attachment signal , was fused to E7 ( E7G ) , and this construct was co-expressed with E6R generating the RNAiR E6R:E7G cell line . Growth was normal under TfR silencing ( Fig 5A ) , suggesting formation of functional GPI2 heterodimers . qRT-PCR and pull down analyses confirmed loss of E6N/E7N transcripts ( ~80% ) , and of E7N protein . However , E6R and E7G transcripts , and corresponding protein levels , were unaffected , ( Fig 5B and 5C respectively ) , suggesting that cells were not iron deprived . In agreement , Tf binding ( Fig 5C ) and uptake ( Fig 5D; Tf ) were unaffected . The formation of GPI2 heterodimers was confirmed by BN-PAGE ( S2 Fig ) . Finally , in addition to endosomal localization , IFA in both control and silenced cells showed low intensity cell surface TfR staining in permeabilized cells ( Fig 5E , perm ) , and more prominently in non-permeabilized cells ( Fig 5E , non-perm ) . Surface localization was confirmed by flow cytometry ( S3 Fig ) . These results indicate that E6R:E7G form functional GPI2 heterodimers that rescue growth , mediate Tf binding and uptake , and localize to the cell surface . Most importantly , surface accumulation occurs without over-expression , arguing against a saturable retention mechanism and demonstrating that GPI2 valence can override any restrictions on TfR trafficking that may be imposed by flagellar pocket architecture . These results are fully consistent with the GPI valence model . We first confirmed that the E6R and E6R:E7G dimers were indeed GPI anchored using reactivity with anti-Cross Reacting Determinant ( CRD ) antibodies following GPI hydrolysis by endogenous GPI-phospholipase C ( S4 Fig ) . We then investigated the ultimate fates of the various RNAiR TfRs by quantitative turnover analyses ( Fig 6 ) . In agreement with our previous work [16] , normal TfR heterodimer turns over with a half-life of ~1 . 5 hr in both the parental ( Fig 6A ) and RNAi resistant E6R:E7R cell lines ( Fig 6B ) . In each case loss of both E6 and E7 subunits is completely rescued by treatment with the lysosomal cathepsin L ( TbCatL ) inhibitor , FMK024 . Inhibition revealed accumulation of E6 as a larger mature form presumably due to glycan processing during transit of the Golgi . The E7 cell line , which contains both homodimers and aggregates ( S2 Fig ) presents a more complex decay profile ( Fig 6C ) . The apparent overall loss rate in untreated cells is similar to that of normal TfR heterodimers , but is only ~70% rescued by FMK024 , representing lysosomal degradation of GPI0 homodimers . The remaining portion could represent turnover by ER-associated degradation ( ERAD ) , but it cannot be rescued with the proteasomal inhibitor MG132 , as would be expected for misfolded secretory proteins in trypanosomes [28] . Turnover of GPI2 E6R homodimers was markedly delayed relative to normal TfR ( t1/2 ~4 hr ) , but was unaffected by inhibition of TbCatL ( Fig 6D ) . MG132 also had no effect , nor was E6 detected in the media fraction during the chase period . Turnover of GPI2 E6R:E7G was also delayed ( ~2-fold ) relative to normal TfR , but unlike E6 homodimers , was more fully rescued by FMK024 indicating lysosomal degradation . Generally these results are consistent with a correlation of increased GPI valence with increased stability—with two caveats . First , as there are aggregates in E7R cells ( S2 Fig ) , and as degradation is not completely rescued by FMK024 , we cannot be certain of the true turnover rate for bona fide GPI0 E7 homodimers . Second , we can offer no explanation for the actual mode of turnover of GPI2 E6 homodimers , and why it differs from that of GPI2 E6R:E7G heterodimers . Finally it is worth noting that while more stable than GPI0 or GPI1 TfRs , both GPI2 TfRs are still much less stable than native VSG ( t1/2 >30 hr; discussed below ) . The functionality of surface E6R:E7G was investigated by assaying direct binding of fluorescent Tf . All attempts with cells freshly harvested from culture failed , presumably because surface TfR was already saturated with Tf from complete medium . However , preincubation in serum-free media to generate newly synthesized non-ligated TfR on the cell surface allowed detection of direct binding by flow cytometry ( Fig 7A ) . Surface labeling was blocked when cycloheximide was included during the preincubation , confirming the need for ongoing protein synthesis , and binding was inhibited by excess unlabeled transferrin . Fluorescent imaging revealed prominent flagellar staining , with diffuse staining over the cell body ( Fig 7B ) . Again binding was dependent on synthesis of new receptor and was blocked by excess Tf . Importantly , binding was observed even when native TfR subunits were ablated by RNAi silencing confirming that the signal is specific for E6R:E7G heterodimers . To investigate at higher resolution , SEM was performed on cells that were pre-labeled with Tf-conjugated colloidal gold ( Fig 8 ) . Consistent with fluorescent imaging , gold particles were prominently detected in close proximity to the flagella and flagellar attachment zone , but also to a lesser degree over the cell body . Binding was blocked by excess transferrin , and no binding was observed in the parental RNAi cell line . These results conclusively demonstrate that cell surface E6R:E7G heterodimer is functional for Tf binding .
We have investigated two interrelated aspects of GPI function in African trypanosomes , the role of GPI valence in post-Golgi sorting , and the localization of TfR at normal and elevated expression levels . GPI valence broadly correlates with secretory progression and stability [16 , 19] . Native VSG ( GPI2 ) is a super-abundant surface protein that is constantly endocytosed and recycled to the cell surface . It is slowly shed from cells ( t1/2 >30 hr ) by a combination of exocytic vesicles and GPI hydrolysis [21 , 23 , 36] . Any lysosomal degradation , if it does occur , is below the limits of detection . However , GPI-minus VSG , as well as other GPI0 reporters , are rapidly ( t1/2 <1 hr ) delivered to the lysosome and degraded by resident thiol proteases . In contrast , a series of GPI1 reporters engineered on native secretory proteins have a continuum of intermediate behaviors . When GPI anchored , the ATPase domain of the ER chaperone BiP is at one extreme , being overwhelmingly delivered to the cell surface followed by shedding into the medium [one dimyrstoylglycerol-GPI freely dissociates from membranes] . Native TfR is at the other extreme , essentially being all degraded in the lysosome [t1/2 ~1 . 5 hr , [16] and this work] . In between are insect stage procyclin , which parses evenly between these two fates , and the lysosomal glycoprotein p67 , which is mostly delivered to the lysosome ( ~85% ) when GPI-anchored . However , TfR is a special case in that it does escape to the cell surface when over-expressed . Originally it was proposed that escape results from saturation of a flagellar pocket retention mechanism [26 , 27] . Later , because such surface TfR is not shed , as would be expected for a GPI1 heterodimer , and is non-functional for ligand binding , we proposed that surface TfR represents GPI2 E6 homodimers [16] . To challenge our valence model , and to resolve the issue of TfR surface expression , we used a novel system for exclusive expression of TfR subunits [28] , the critical features of which are the conditional ablation of all native TfR transcripts , regardless of source , and the expression of recoded RNAiR E6 and E7 genes from endogenous loci within the active expression site . Silencing completely abrogates TfR synthesis , and consequently Tf uptake and cell viability . Importantly , all essential functions are restored by co-expression of RNAiR TfR subunits , fully validating our approach for independent expression of E6R ( GPI2 ) and E7R ( GPI0 ) homodimers , and the special case of E6R:E7G ( GPI2 ) heterodimer . All three behave largely as predicted by the valence model—E7 homodimers are degraded in the lysosome and E6 homodimers are delivered to the cell surface . However , in each instance these subunits are dramatically over-expressed ( 7–8 fold ) , presumably in response to perceived iron starvation in the absence of functional TfR . We have also found that E7G localizes to the cell surface when expressed alone , but again with significant over-expression . Consequently , one might still argue that surface localization of GPI2 TfR dimers results from saturation of a flagellar pocket retention mechanism . However , the E6R:E7G heterodimer , which is functional for Tf uptake , is not up-regulated and yet is still found on the cell surface . Collectively , these results argue compellingly for GPI valence as the critical determinant for cell surface localization of TfR when expressed at either normal or elevated levels . They do not however , sensu stricto , prove that GPI2 valence is sufficient , rather than merely necessary , to achieve surface expression as there may be other feature ( s ) of E6/E7 dimers that are necessary for egress from the flagellar pocket . However , this does seem unlikely given that TfR has evolved not to be surface exposed . Although our results are broadly consistent with the valence model , one detail does not quite conform—turnover . VSGs are very stable and we expected that GPI2 E6R and E6R:E7G would be equally long lived . However , while both are twice as stable as GPI1 E6R:E7R and GPI0 E7R hetero/homodimers ( t1/12 ~4 hr vs ~2 hr ) , neither is nearly as stable as VSG . Thus , while our work indicates that there is nothing special about VSG in terms of accessing the cell surface , other than two GPI anchors , there is something unique in terms of avoiding degradation . This may be related to the additional membrane proximal C-terminal domain in VSG that is absent in TfR . This domain , which is less variable than the larger N-terminal domain , might have conserved features that enhance stability , either by favoring recycling to the cell surface or by resisting sorting to the lysosome . Whatever the explanation , the higher turnover rate likely explains the failure of our several attempts to replace the resident VSG221 gene in the BES1 expression site with homodimeric E6 . The finding that E6R:E7G forms functional TfR heterodimers is remarkable . Direct binding requires de novo receptor synthesis in the absence of Tf ligand , indicating that all pre-existing cell surface TfR must be saturated during in vivo growth in 10–20% serum . Binding to E6R:E7G in the context of the densely packed VSG surface coat is consistent with mutagenesis and molecular modeling studies that place the Tf ligand binding site distal to the plasma membrane [37 , 38] . Furthermore , because TfR is smaller than VSG , the structural model dictates that Tf must gain access to a ligand-binding site that is recessed within the surrounding surface coat . Our results confirm this as a realistic model . TfR is thought to be evolutionarily derived from VSG [39 , 40] , and the fact that it is functional with two GPI anchors begs the question of what selection pressure drove the truncation of ESAG7 and the loss of one anchor , or conversely why VSG is a GPI2 homodimer ? The answer to the later question is clearly that dimerization is necessary because a single dimyristoyl GPI anchor is unstable in the plasma membrane of BSF trypanosomes . The simplest answer to the first is that in the face of the host adaptive immune response it would be detrimental to have an invariant antigen on the cell surface . Having one GPI anchor assures that any TfR that might exit the flagellar pocket onto the cell surface will be rapidly shed . This may be true , but trypanosomes have other trans-membrane invariant antigens that cannot be shed and that are modeled to protrude from the surrounding VSG coat [40] . Clearly there are other as yet undetermined factors that contribute to immune evasion by trypanosomes . We have previously proposed a simple mechanism for GPI-dependent post-Golgi trafficking [for a more extensive treatment see [16]] . GPI0 cargo trafficks by default to the lysosome as we have seen for many reporters lacking specific retention or targeting signals [16 , 19 , 32 , 34 , 41] . GPI2 cargo , of which VSG is the exemplar , trafficks rapidly to the flagellar pocket and then diffuses laterally out to the cell surface . Clearly this is facilitated by valence since native TfR ( GPI1 ) does not exit the flagellar pocket , while the nearly identical E6R:E7G ( GPI2 ) does . GPI1 cargoes , which parse between the lysosome and the cell surface , are free to dissociate from internal membranes at any time during intracellular transport , but are just as likely to re-associate . This is also true once they reach the flagellar pocket , as indicated by EM studies that consistently show both membrane-bound and lumenal pools of TfR [13 , 15 , 16 , 42] . In either state , GPI1 reporters can then be endocytosed and we propose that if membrane associated in endosomal compartments they are likely to be recycled back to the pocket . If they are dissociated from the membrane they will eventually reach a point in endosomal trafficking where subsequent delivery to the lysosome is committed , much as for other soluble fluid phase cargo . Alternatively , GPI1 cargo can exit the flagellar pocket by lateral diffusion , at which point dissociation will be essentially irreversible . Exit from the pocket in the soluble state is less likely as we have consistently found that secretion of bona fide soluble secretory reporters is severely constrained in bloodstream trypanosomes , presumably due to pocket architecture [16] . We propose that it is the physical properties of each molecule that ultimately determines the fate of any given GPI1 reporter . For instance , native TfR is a large and highly glycosylated dimeric protein that is unable to exit the pocket . Conversely , BiPN:GPI is a small globular non-glycosylated reporter that is mostly shed into the media ( ~80% ) . It should be noted that the secretion rate of soluble BiPN is ~50% , confirming that even one GPI anchor can enhance exit from the pocket if the correct reporter is used . This is perhaps a simplified model , but it does account for all our observations . However , whatever the mechanism for post-Golgi sorting of GPI anchored cargo in bloodstream trypanosomes , it is unlikely to be mediated by sterol/sphingolipid-rich rafts , as in polarized epithelial cells [5] , since VSG does not enter into Triton X100 insoluble complexes [43] , nor does inhibition of sphingolipid synthesis impact its normal transport [44 , 45] . Finally , how well do other endogenous GPI anchored proteins conform to the valence model for post-Golgi sorting ? One such protein is the haptoglobin-hemoglobin receptor ( HpHbR ) , an essential nutrient receptor for heme acquisition , and the portal of entry for the innate primate immune factor , trypanolytic factor [40 , 46 , 47] . HpHbR is a monomeric GPI1 protein that localizes predominantly to the flagellar pocket . Nothing is known about its turnover , but based on localization alone it apparently fits our model . Another is the serum resistance associated protein , SRA , which confers resistance to trypanolytic factor in human infective trypanosome species . Like TfR , SRA is VSG related and localizes to endosomal compartments , but unlike TfR is modeled to be a homodimer [48 , 49] . However , its quaternary structure has never been empirically confirmed , thus its GPI valence is uncertain . One might predict based on localization alone that SRA will be either a GPI1 monomer or heterodimer , but further investigation will be required to determine if it fits the model or is an exception . And undoubtedly other GPI anchored proteins will be discovered and characterized in trypanosomes . A cautious scientist would assume that these will not all adhere strictly to the model , but we are confident that our work with VSG , TfR and other engineered GPI reporters lays a general foundation for understanding post-Golgi trafficking of GPI anchored proteins in bloodstream form trypanosomes .
All experiments were carried out with the tetracycline-responsive single-marker ( SM ) derivative of bloodstream form Lister 427 strain T . brucei brucei ( MITat1 . 2 expressing VSG221 ) [50] , grown at 37°C in HMI9 medium [51] . For experiments , cells were harvested at mid-late log phase ( 0 . 5x106 to 106 ) . Generation of the TfR RNAi cell line using SM cells as the parental cell line has been described in [28] . Cells were grown under antibiotic selection as appropriate . Induction of anti-TfR double-stranded RNA was achieved by addition of 1 μg/ml of tetracycline . All constructs are schematically represented in Fig 1B & 1C . RNAi resistant ( RNAiR ) ESAG7 ( E7R ) , ESAG6 ( E6R ) or ESAG7-GPI ( E7G –fusion of E6 C-terminus to C-terminus of E7 ) constructs were cloned into our pXS6 vector [32] . All TfR segments were PCR amplified from H25N7 BAC DNA containing the BES1 expression site [clone H25N7 , [52] , gift of Gloria Rudenko] as template . Briefly , the E7 genomic replacement construct was assembled as follows ( 5’-3’ ) : 5’ upstream targeting regions ( nts -489 to 1; relative to the E7 ORF ) ; puromycin resistance cassette; βα-tubulin intergenic region; the E7 ORF including the native signal sequences ( nts 1–1023 , codons 1–341 ) ; 3’ downstream targeting region ( nts 1–524; relative to E7 stop codon ) . All segments were confirmed by sequencing . To recode the E7 reporter for RNAi resistance the N-terminal region from the start codon ( SnaBI ) to an internal BamHI site ( nt 739 ) was chemically synthesized ( Integrated DNA Technologies , Coralville , IO ) , synonymously altering all codons to the next most frequently used codon in T . brucei housekeeping genes [53 , 54] . This synthetic DNA was placed in the E7 construct using SnaBI/BamHI and is referred to hereafter as E7R . The E6 genomic replacement construct was created as described above for E7 except for the following: 5’ upstream targeting regions ( nts -484 to 1; relative to the E6 ORF ) ; hygromycin resistance cassette; βα-tubulin intergenic region; the E6 ORF including the native signal sequences ( nts 1–1206 ) , codons 1–402 ) ; 3’ downstream targeting region ( nts 1–601; relative to E6 stop codon ) . The synthetic recoded E6 reporter from start codon ( SnaBI ) to the internal BamH1 site ( nt 742 ) was cloned ( SnaBI/BamHI ) into the E6 construct to generate E6R . To alter the GPI status of E7 , the E6R construct was digested with SacI/MfeI ( internal SacI to the stop codon ) and cloned into the corresponding E7R construct with the same restriction enzymes creating E7-GPI ( denoted as E7G ) . Alignment of wild type and RNAiR full length sequences are presented in S1 Fig . The resultant RNAiR reporters ( E6R , E7R , and E7G ) were linearized with ClaI/FseI for homologous replacement of the endogenous respective genes in the active ES1 expression site of the TfR RNAi cell line ( Fig 1B ) [28] . Transfection and clonal selection with appropriate antibiotics was performed was described in [28] . The following antibodies have been described in our prior publications [28 , 32]: rabbit anti-VSG221 , mouse anti-BiP , and anti-HSP70 . Rabbit anti-TfR ( ES1 specific ) was a generous gift of Dr . Piet Borst ( Netherlands Cancer Institute , Amsterdam ) . Secondary reagents for western blotting were IRDye680- and IRDye800-conjugated goat anti-rabbit and anti-mouse IgG ( Li-Cor , Lincoln NB ) . Secondary reagents for immunofluorescent imaging were species-specific Alexa-conjugated goat anti-IgG as appropriate ( Molecular Probes , Eugene , OR ) . Pulse-chase radiolabeling of log-phase cultured BSF trypanosomes with [35S]methionine/cysteine; Perkin Elmer , Waltham , MA] , and subsequent immunoprecipitation of labeled polypeptides were performed as described previously [55 , 56] . As indicated , cells were pre-treated ( 15 min ) and radiolabeled as described above in the continued presence of the thiol protease inhibitor FMK024 ( morpholinourea-phenylalanine-homophenylalanine-fluoromethyl ketone; 20 μM; MP Biomedicals , Aurora , OH ) . Pulse and chase times are indicated in the figure legends . For TfR dsRNA and RNAiR subunit expression , control and tetracycline-induced cells ( 0 or 24 hrs ) were radiolabeled for 1 hr . Radiolabeled TfR ( native or RNAiR ) polypeptides were subjected to pull-downs with transferrin-conjugated beads ( Tf-beads ) , anti-TfR or anti-HSP70 . All pull-downs were fractionated by 12% SDS-PAGE , and gels were analyzed by phosphorimaging using a Molecular Dynamics Typhoon FLA 9000 system with native ImageQuant Software ( GE Healthcare , Piscataway , NJ ) . Endocytosis was assayed by flow cytometry as generally described in [32] . Washed log-phase cells ( 106/ml ) were pre-incubated ( 10 min , 37°C ) in serum free HMI9 medium with 0 . 5 mg/ml BSA . Ligands ( Alexa488 conjugated bovine transferrin or tomato lectin , 5 μg/ml , Molecular Probes ) were added and incubation was continued for 30 minutes . Cells were then processed for flow cytometry . Gels were transferred to Immobilon-P membranes ( Millipore Corp . , Bedford , MA ) using a Trans-Blot Turbo apparatus ( BioRad , Hercules , California ) . Membranes were blocked and probed with appropriate dilutions of primary and secondary antibodies in Odyssey Blocking Buffer ( Li-Cor ) . All washes were with PBS , 0 . 5% Tween20 . Quantitative fluorescent signals were scanned on an Odyssey CLx Imager ( Li-Cor ) . Specific transcript levels were determined using quantitative RT-PCR ( qPCR ) . Total RNA was isolated from log phase cultures using RNeasy Mini kit ( Qiagen , Valencia , CA ) . RNA was treated with DNAse1 on-column using RNase-Free DNase Set ( Qiagen ) and cDNA synthesized using iScript cDNA synthesis kit ( BioRad , Hercules , CA ) . qPCR was performed using diluted cDNAs and Power SYBR green PCR Master Mix ( Life Technologies , Carlsbad , CA ) with oligonucleotide pairs specifically targeting transcripts for native E6N and E7N , and RNAiR E6R and E7R . The positions of these primers are indicated in the sequence alignment presented in S1 Fig . TbZFP3 ( Tb927 . 3 . 720 , nts 241–301 ) was used as the control amplicon . Amplification was performed using an Applied Biosystems StepOne Real-Time PCR System ( Life Technologies , Carlsbad , CA ) . For each transcript post-amplification melting curves indicated a single dominant product . All calculations and normalizations were done using StepOne software , version 2 . 2 . 2 . Reactions were performed in technical triplicates , and means ± standard errors of the means ( SEM ) for three biological replicates are presented . Immunofluorescence ( IFA ) microscopy was performed with formaldehyde fixed/detergent permeablized cells as described in [55] . Cells were also stained with DAPI ( 0 . 5 μg ml-1 ) to reveal nuclei and kinetoplasts . Serial image stacks ( 0 . 2 micron Z-increment ) were collected with capture times from 100–500 msec ( 100x PlanApo , oil immersion , 1 . 46 na ) on a motorized Zeiss Axioimager M2 stand equipped with a rear-mounted excitation filter wheel , a triple pass ( DAPI/FITC/Texas Red ) emission cube , differential interference contrast ( DIC ) optics , and an Orca ER CCD camera ( Hamamatsu , Bridgewater , NJ ) . Images were collected with Volocity 6 . 1 Acquisition Module ( Improvision Inc . , Lexington , MA ) and individual channel stacks were deconvolved by a constrained iterative algorithm , pseudocolored , and merged using Volocity 6 . 1 Restoration Module . Unless otherwise stated all images presented are summed stack projections of merged channels . The xyz pixel precision of this arrangement has been validated in [18] ( see S1 Fig therein ) . Cells grown in HMI9 medium were harvested , washed with HEPES buffered-saline ( HBS ) supplemented with 1% w/v glucose [57] , and incubated at 37°C ( 2 hr , 5x106 cells/ml ) in HMI9/BSA . This treatment was necessary to replace existing ligated surface TfR with newly synthesized unligated TfR . During the pre-incubation period , the cells were untreated ( control ) , or treated with cycloheximide ( CHX , 100 μg/ml ) to block protein synthesis . For flow cytometry and epifluorescence microscopy cells were then washed with ice cold PBS with 1% w/v glucose ( PBSG ) and incubated with Tf488 ( Molecular Probes , 2 μg/ml , 1 hr , 4°C ) without or with 100x excess holotransferrin as competitor . Cells were then processed for either flow cytometry or microscopy as described above . For scanning electron microscopy , Tf-colloidal gold ( Cytodiagnostics , Burlington , ON , Canada , 100 nm ) was concentrated by centrifugation and added directly to cells ( final 3 . 75 mg/ml , 7 . 5 OD ) following the preincubation step . Incubation was continued an additional 1 hr at 4°C and cells were processed directly for electron microscopy as described below . For electron microscopy , cells were stained with Tf:gold as described above . The fixation/dehydration protocol is described in [58] with the following modifications . Cells were fixed in HMI9/BSA ( 2 hrs , 4°C ) in 2 . 5% EM grade glutaraldehyde . Post fixation , cells were collected by syringe-passage onto 0 . 2 μm pore polycarbonate filters ( Whatman Nucleopore , 25 mm dia . , SIGMA-ALDRICH , St . Louis , MO ) keeping fluid in the upper filter chamber ( Whatman Swin-Lok Cartridge , 25 mm , SIGMA-ALDRICH ) in all subsequent steps until final air drying . Washing and fixation were done through the filter as follows: 5 ml 2 . 5% glutaraldehyde in PBS allowing rest of 10 mins; 10 ml PBS rest 10 mins; 5 ml 30% v/v , 50% v/v , 70% v/v , 90% v/v ethanol in water 5 mins each; 5 ml 100% ethanol twice 5 mins each . Samples were then dried with hexamethyldisilazane ( HMDS , 5 mls , 5 mins ) . Filters were removed , air dried , and coated with evaporated carbon at high vacuum ( Denton 502 evaporator ) . Cells were imaged with a Hitachi SU70 FESEM at 20 KeV using combined signals from a conventional Everhart-Thornley detector ( adjusted to maximize backscattered electron component ) and in-lens secondary electron detector . The combined signal showed gold nanoparticles as bright dots superimposed on cell surface morphology . BN-PAGE was performed using the NativePAGE Bis-Tris Gel System ( Thermo Fisher Scientific , Waltham , MA ) . Briefly , cells were harvested , washed with HBS and solubilized in NativePAGE Sample Buffer supplemented with 10% glycerol , 1% DDM ( n-dodecyl-β-D-maltoside ) , 1X protease inhibitor cocktail and 100 μg/ml DNaseI . The samples were incubated in the solubilization buffer on ice for 30 min , centrifuged ( 13000g at 4°C , 1 hr ) , and the resulting supernatants were either untreated or treated with 4M urea to denature protein complexes . Samples were then fractionated on precast 4–16% BN gradient gels ( Thermo Fischer Scientific ) . After electrophoresis , proteins were transferred to PVDF membranes ( Millipore Corp . , Bedford , MA ) and detected by our standard immunoblotting protocol with anti-TfR or anti-VSG221 . Phosphorimages and fluorescent blot scans were quantified with ImageJ software ( http://imagej . nih . gov/ij/ ) . For analysis of specific band intensities , signals were corrected by subtraction of the signal from equivalent unlabeled areas of each lane . All subsequent data management was performed with Prism4 software ( GraphPad Software , Inc . , San Diego CA ) . | African trypanosomes , protozoan parasites that cause African Sleeping Sickness , have two structurally related secretory proteins that are critical for their success as pathogens: variant surface glycoprotein ( VSG ) , which is responsible for evasion of host immune responses , and transferrin receptor ( TfR ) , which is responsible for acquisition of essential iron for nutritional purposes . Both are dimers and both are attached to cell membranes by glycolipid anchors . VSG has two anchors and is found on the outer plasma membrane; TfR has just one anchor and is found in the flagellar pocket , a small restricted invagination of the plasma membrane that is the portal for transport in and out of the cell . These locations are critical to the function of each protein . To test the hypothesis that number of anchors , or valence , controls the localization of these proteins we have genetically engineered a trypanosome cell line that allows controlled expression of TfR with 0 , 1 , or 2 glycolipid anchors . Detailed studies of the localization and intracellular trafficking of these reporters confirm that glycolipid valence controls ultimate localization , and thus is critical to the essential functions of both VSG and TfR . | [
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] | 2017 | Controlling transferrin receptor trafficking with GPI-valence in bloodstream stage African trypanosomes |
Alternative splicing of messenger RNA can generate a wide variety of mature RNA transcripts , and these transcripts may produce protein isoforms with diverse cellular functions . While there is much supporting evidence for the expression of alternative transcripts , the same is not true for the alternatively spliced protein products . Large-scale mass spectroscopy experiments have identified evidence of alternative splicing at the protein level , but with conflicting results . Here we carried out a rigorous analysis of the peptide evidence from eight large-scale proteomics experiments to assess the scale of alternative splicing that is detectable by high-resolution mass spectroscopy . We find fewer splice events than would be expected: we identified peptides for almost 64% of human protein coding genes , but detected just 282 splice events . This data suggests that most genes have a single dominant isoform at the protein level . Many of the alternative isoforms that we could identify were only subtly different from the main splice isoform . Very few of the splice events identified at the protein level disrupted functional domains , in stark contrast to the two thirds of splice events annotated in the human genome that would lead to the loss or damage of functional domains . The most striking result was that more than 20% of the splice isoforms we identified were generated by substituting one homologous exon for another . This is significantly more than would be expected from the frequency of these events in the genome . These homologous exon substitution events were remarkably conserved—all the homologous exons we identified evolved over 460 million years ago—and eight of the fourteen tissue-specific splice isoforms we identified were generated from homologous exons . The combination of proteomics evidence , ancient origin and tissue-specific splicing indicates that isoforms generated from homologous exons may have important cellular roles .
Studies have estimated that alternative splicing can produce differently spliced messenger RNA ( mRNA ) transcripts for practically all multi-exon human genes [1 , 2] . These mRNA variants have the potential to expand the cellular protein repertoire far beyond the one gene–one protein model that formed part of the central dogma for many years [3 , 4] . The number of alternatively spliced transcripts annotated in reference human gene sets has grown steadily in recent years and manual genome annotation projects such as GENCODE [5] are identifying ever more alternative variants . The current version of the GENCODE gene set annotates more than 93 , 000 protein-coding variants , a number that has increased by 10 , 000 since 2009 . Theoretically all these transcripts could be translated into functional protein isoforms and could greatly diversify the cellular functional repertoire . However , although we have a limited understanding of the function of a small number of these alternative isoforms , there is a general lack of knowledge about the functional roles of the vast majority of annotated splice isoforms in the cell . All we can say is that most of the annotated splice variants in the human genome will produce isoforms with substantially altered 3D structure and consequent drastic change of biological function , if translated to protein [6 , 7] . There is considerable supporting evidence for the generation of multiple alternative mRNA transcripts from the same gene . EST and cDNA sequence evidence [8] , microarray data [9] and RNAseq data [10] strongly support alternative splicing at the mRNA transcript level . In spite of the overwhelming evidence of alternative splicing at the transcript level , there is limited support for the translation of these alternative transcripts into protein isoforms . Individual experiments can provide evidence for the expression of isoforms for single genes [11] . At the genome level large-scale antibody tagging [10] holds promise for the detection of alternative isoforms , but the broad specificity of most antibodies makes their discrimination almost impossible at present . For antibody tagging to be of use in distinguishing alternative isoforms , they should be designed from the beginning with this purpose in mind , and each antibody should be only capable of detecting one splice event in one protein . Ribosome profiling experiments [12] have been used in recent years as a proxy for protein coding potential [13 , 14] , but ribosome-profiling data should be used with caution [15] not least because ribosome-profiling methods require transcript reconstruction algorithms to predict splicing variants . The reliability of these transcript reconstruction algorithms has recently been thrown into doubt [16–18] . These factors may have lead research groups to reach two entirely different and opposing conclusions from the same ribosome profiling data [19 , 20] . High-throughput tandem mass spectrometry ( MS ) -based proteomics [21] is the main source of peptide evidence . Reliable proteomics data can confirm transcript coding potential even where there is little other supporting evidence . MS-based proteomics has become an increasingly important tool in genome annotation thanks to advances over the last two decades and a number of groups have demonstrated how proteomics data might be properly used to validate translation of protein coding genes [22–24] . On a larger scale , the Human Proteome Project [11] is attempting to identify at least one protein product for each human gene . Several groups have now identified small numbers of alternative protein isoforms in species ranging from human [22] to mouse [23] , Drosophila [25] , Arabidopsis [26] and Aspergillus flavus [27] . Recently two large-scale analyses produced similar results . Our group [24] detected the expression of multiple splice isoforms for 150 of 7 , 597 human genes from an analysis of spectra from the GPM [28] and PeptideAtlas [29] databases , while Low et al . [30] identify 83 alternative splicing events and 13 , 088 genes in rat . By way of contrast , a number of recent proteomics studies claim to have found substantially more cases of alternative splicing at the protein level . Menon et al [31] identified 420 alternative isoforms from 1 , 278 mouse genes , but at the time the mouse genome was not well annotated and it is not clear whether this study required peptides to identify both constitutive and alternative splice isoforms . Recently the numbers of identified splice isoforms have escalated substantially . In two papers published in the same issue of Nature , Kuster and co-workers [32] identified 1 , 279 alternative proteins for more than 18 , 097 human genes , while Pandey and colleagues found “isoform-specific peptides” for 2 , 861 protein isoforms from more than 17 , 294 genes [33] . As we have shown [34] , the main problem with these studies is that they dramatically overestimate the number of reliable peptide identifications . At the extreme end of the scale Ly et al claim to have found evidence for 33 , 575 separate protein isoforms from just 12 , 000 human genes [35] , suggesting that they identified more than 21 , 000 alternative isoforms , an order of magnitude greater than any previous study . Here the authors did not use discriminating peptides , but instead chose to infer the expression of different isoforms based on peptide abundances in an analogous way to the protocols used for transcript level estimation in RNAseq studies [16 , 17] . This form of identifying alternative protein isoforms is wholly inappropriate in proteomics studies because of the low peptide coverage typical of these experiments and because of the non-uniform distribution of the peptides detected . Given the wide variety in the numbers of splice isoforms reported in what are essentially similar , large-scale proteomics experiments , we felt that it was important to carry out a rigorous study of alternative splicing at the protein level . To accomplish this we produced as reliable a set of peptides as possible from eight high-throughput MS analyses . These analyses were carried out on a wide range of cell types . We identify alternative splice isoforms for 246 genes from the reliable peptide evidence from the eight data sets . We demonstrate that this is far below what would be expected if the main and alternative splice isoforms were produced in comparable quantities in the cell , suggesting that most genes have a single main protein isoform . We found that homologous exons substitutions , consecutive exons that are homologous and are spliced in a mutually exclusive manner , were highly enriched among the splicing events that we did identify and we show that remarkably few of the events we identified affected the composition of functional domains .
Over the 8 experiments we identified splicing events from 246 genes . This is 60% more than our previous study , in which we reported peptide evidence for 150 genes [24] . We identified splicing events for 77 of these 150 genes in this analysis . Twenty genes had evidence for more than one alternative splice isoform , and three genes ( PLEC , TPM1 and UGT1A ) had evidence for five or more different splice isoforms . Here the UGT1A gene cluster is defined as a single gene , even though it is annotated as a cluster of nine independent genes in the GENCODE gene set . These “genes” differ in a set of mutually exclusive 5’ exons that are joined to a common set of 3’ exons by alternative splicing , so we have treated them , and four similar GENCODE 20 gene clusters , as splice variants of a single gene ( see Materials and Methods ) . There was peptide evidence for 8 different protein isoforms from the UGT1A gene ( S1 Fig ) . To take into account multiple splice isoforms from the same gene , we carried out our analysis on alternative splicing events rather than genes . Alternative splicing events are those that differentiate the alternative isoform from the main isoform ( the isoform for which we identify most discriminating peptides ) . Alternative splicing events identified in the analysis are referred to as identified splicing events ( ISE ) . We found peptide evidence for 282 different ISEs from the 246 genes ( S2 Table ) . The ISE were classified by their effect on the protein since this is more relevant for a study at the protein level . Events were classified as ( i ) indels–insertion or deletion of more than 4 amino acid residues ( S2 Fig ) ; ( ii ) NAGNAG splicing [41] , defined as the insertion or deletion of up to four amino acid residues ( Fig 1 ) ; ( iii ) homologous substitutions ( Figs 1 and S3 ) : ( iv ) other C-terminal substitutions ( S4 Fig ) ; ( v ) other N-terminal substitutions ( S5 Fig ) ; ( vi ) internal non-homologous substitutions ( S6 Fig ) ; and ( vii ) generating two non-homologous proteins ( S7 Fig ) . The majority of the ISE were indels ( 109 ISE ) ; this is to be expected since most annotated alternative splicing events are indels [42 , 43] . The second most common alternative splicing events were homologous substitutions ( 60 ISE ) , followed by non-homologous C-terminal substitutions ( 43 ISE ) . There were also numerous alternative splice events involving GYNGYN donors or NAGNAG acceptors ( 39 ISE ) . These result in small insertions and are referred to in the paper as NAGNAG splicing events . There were fewer non-homologous N-terminal substitution events ( 24 ISE ) , while internal non-homologous substitutions ( 2 ISE ) and events that generated distinct proteins ( 5 ISE ) were less frequent ( Fig 2 ) . We carried out a similar analysis with mouse . We would expect to identify fewer alternative splice variants because the mouse genome is annotated with fewer alternative isoforms ( see the Materials and Methods section ) and because we only interrogated three analyses , the equivalent peptides from the NIST and PeptideAtlas databases , and an in-house analysis of the mouse spectra in the PeptideAtlas and GPM databases . As with the human analysis we required all peptides to have been identified in at least two analyses ( a more stringent requirement because in this case there were only three proteomics analyses ) . We identified splice isoforms for 56 genes and 68 splicing events . We detected the identical splice event in the orthologous human gene for 35 of the 68 mouse ISEs . We also detected another 29 mouse events that were equivalent to human ISEs but that were only supported by peptides from a single analysis . Twenty-one of the 68 mouse ISE ( 30 . 1% ) we detected were generated from homologous exons and all but one of these 21 splicing events also had peptide evidence in the orthologous human genes . Hence , homologous substitutions are particularly highly represented among those splice events detected in both human and mouse ( Fig 2 ) , and make up almost 60% of the orthologous splicing events we detected in both human and mouse experiments . We previously found that substitution by homologous exons was among the least common annotated splicing events [24] , so at first glance the number of homologous exon events detected in the human and mouse analyses is surprisingly high . In order to test whether there were significantly more homologous exon substitution ( HES ) events than expected in the human analysis , we hand-curated the results from BLAST [44] searches against the GENCODE 20 gene set ( see Materials and Methods section ) to generate a set of 157 genes with HES events that was independent of the events we detected in the proteomics experiment . We found peptide evidence for 33 of these 157 genes ( 21% ) in our proteomics analysis . We carried out a Fisher test to determine whether the 21% detection rate for the HES genes was significantly different from the 0 . 01% AS event detection rate for the remaining 19 , 850 annotated genes: the p-value was close to zero ( < 2 . 2e-16 ) . The set of identified AS events is significantly enriched in homologous exon events . In pure theoretical terms , the greater the differences between the two isoforms , the easier it should be to identify peptides for both sides of the event and the fewer peptides we should need to detect that event . The easiest AS events to detect should be the longest substitutions ( of any type ) , the largest indels and those cases where there are two completely different proteins . By way of contrast the hardest events to find ought to be substitutions that are almost identical , such as highly similar homologous exon events ( there are events that change just a single residue ) short indels , or NAGNAG splicing events ( for NAGNAG-type splicing events , that generally result in a single residue indel , only a single peptide can identify each side of the splice event ) . Of course the detection of peptides is not totally random and there are other factors that influence , such as the number of lysines and arginines around the spliced region and how easy it is to detect the individual discriminating peptides by mass spectrometer . However , it is important to bear in mind that all these factors are rendered irrelevant if the protein isoform containing the peptide is not expressed in sufficient quantities to be detected in proteomics experiments . It ought to be easier to find splicing events for those proteins that are more abundant . In order to identify a pair of splice isoforms for a gene , we need to detect at least two peptides for that gene . We binned genes by protein abundance ( here protein abundance is measured as the number of peptides identified for each gene ) and plotted the distribution of the AS genes against the background ( the remaining annotated genes ) . As expected , there is a relation between peptide abundance in proteomics experiments and AS detection ( see S8 Fig ) . If the detection of different types of splicing events is determined by purely theoretical considerations , we should need fewer peptides per gene to identify those splice events that are easier to detect ( two distinct proteins ) and require more peptides per gene to identify those that are harder to detect ( NAGNAG splicing events ) . However , it turns out that there are few significant differences in the numbers of peptides identified per gene for each of the different splice types ( Fig 3A ) . Wilcoxon rank tests between each group show that the only significant differences were between the “two protein” type and the other types ( fewer peptides were needed to detect two distinct proteins as ought to be expected ) , and between the C-terminal substitution events ( fewer peptides ) and indel events . We detected substantially fewer peptides in genes in which we identified NAGNAG-type splicing events than in genes where we identified indels when would expect to need to identify more peptides per gene to identify NAGNAG events . Even though NAGNAG splicing events are theoretically the hardest splicing events to confirm , 13 . 5% of the alternative splicing events we detected were generated from NAGNAG splicing . We can infer from these results that there is not a strong bias in protein abundance towards any one of different types of splicing event , which implies that the abundance of HES within the set of detected AS events ( ISE ) is not due simply to their being easier to detect . We next wanted to determine whether the enrichment of homologous exon events was simply because they are mostly found in highly expressed genes . In principle the results from our study suggest that this bias is unlikely; if we were detecting HES events only because they were in highly expressed genes , we would also expect to detect evidence for other events in the same genes ( HES genes are all annotated with other types of splice event in addition to the HES event ) . In fact we only detected non-HES splice events in one HES gene , LDB3 . To test this we used the 157 HES genes identified with BLAST . We compared the numbers of peptides detected for these genes to a background set of the 13 , 157 genes that are annotated with multiple splice isoforms . We used these counts to bin the genes according to the number of peptides that were detected and compared the proportions for each of the two populations of genes ( Fig 3B ) . The two distributions ( HES genes and background ) are very similar , though there are a slightly higher proportion of the most highly expressed genes in the HES gene population . However , a t-test ( two tailed , unequal variance , p = 0 . 191 ) shows that this is not significant . Hence , the enrichment of HES events among the AS events we identified is clearly not because HES genes are all highly expressed . Nor is the enrichment in HES events a consequence of combining the results from the eight analyses—the proportions of each type of splicing event are similar in all eight individual analyses as can be seen in S3 Table and S9 Fig . However , we do find that the HES events that we identified are generally more ubiquitous than the other major splice event types; 36 of the 114 events identified within at least four of the individual analyses ( 31 . 6% ) were HES events , while 34 were indels and just 12 were C-terminal substitutions ( S10 Fig ) . These results suggest that the abundance of homologous exons in the alternative splice isoforms identified at the protein level is a real biological phenomenon , and not an artefact of the methods employed in the analysis . We detected peptides that mapped to paralogous splice events in six human protein families . These splice events clearly predated gene duplication in these families and have remained conserved in at least some of the paralogous genes that make up the family members . We identified homologous events for members of the enigma ( LDB3/PDLIM3 ) , alpha-actinin ( ACTN1/ACTN4 ) , dynamin ( DNM1/DNM2 ) , plasma membrane calcium-transporting ATPase ( ATP2B1/ATP2B4 ) , reticulon ( RTN3/RTN4 ) , and tropomyosin ( TPM1/TPM2/TPM3/TPM4 ) families . According to EnsemblCompara 78 [45] , the gene duplication events date back to vertebrates in the case of the tropomyosins , reticulons and alpha-actinins and jawed vertebrates in the case of the dynamins and the plasma membrane calcium-transporting ATPases . In the case of the enigma family the duplication dates back to chordata phylum . The origin of the splice events we identified must have pre-dated these duplication events . This strongly suggests functional relevance for the splicing events in these genes . The alternative isoforms we detected in five of the six families ( enigma , alpha-actinin , dynamin , the plasma membrane calcium-transporting ATPases and tropomyosin ) were generated from mutually exclusive homologous exons . Since five of the six paralogous splice events were generated from HES events , we looked further into the evolutionary conservation of mutually exclusive homologous exons . We scanned the genomes of five distantly related aquatic vertebrates to identify long-standing conservation . We chose lamprey , spotted gar , fugu , zebrafish , and coelacanth as target species to date the origin of each splicing event . We found that every one of the 60 homologous exon splicing events that we identified in the proteomics analysis were present in at least one of these species , implying that they evolved in the ancestor jawed vertebrates or earlier , at least 460 million years ago [46] . As a comparison we calculated the proportion of alternative exons annotated in GENCODE 20 that were conserved between human and mouse . We found that just 19 . 3% of these alternative human exons were conserved in mouse . Human and mouse diverged close to 90 million years ago , so there is a notable difference in conservation between the HES events we identified and those annotated in the genome . In a previous paper we showed that the older and more conserved a gene , the more likely we were to identify it in proteomics experiments [39] . The same seems to be true here—the splice isoforms that we identify in proteomics experiments , in this case the isoforms generated from homologous exon events , are the oldest , most conserved splice isoforms . The analysis of peptide data from the Kim [33] experiments allowed us to investigate tissue-specific alternative splicing . Tissue or cell specific alternative splicing is difficult to detect . Many splice events can only be identified with a single peptide and if that peptide is only detected sporadically , its absence from a tissue does not necessarily imply that it is not expressed in that tissue . Ideally each experiment should have a number of replicates to increase the probability of catching a hard-to-detect peptide . In this regard the Kim study was particularly useful , since most tissues had a small number of replicates . The evidence for tissue-specific expression of splice isoforms principally came from four tissues: foetal and adult heart , adult cortex and foetal brain . This is not because these tissues had the most peptides ( while foetal heart found the most peptides , the other tissues found the 8th most , the 22nd most and the 24th most peptides ) . We found five tissue-specific isoforms in brain . FYN is a tyrosine protein kinase that has two isoforms generated by homologous exons . Isoform 1 ( FYN-B ) and 2 ( FYN-T ) are supposed to be highly expressed in the brain and in hematopoietic cells , respectively [47] . The Kim experiment identified tissue-specific splicing of FYN [33] and we confirmed that FYN-B was indeed present in adult and foetal brain samples , while there was evidence for FYN-T in blood cells . FYN regulates cytoskeletal remodelling and cell survival by phosphorylating a number of proteins . Curiously we identified brain-specific splice isoforms for two of these: MAPT ( Tau protein ) and AGAP2 . Isoform 1 of AGAP2 ( PIKE-L ) is known to be brain specific [48] and we found peptides for this isoform in adult and foetal brain in the Kim experiment . Isoform 2 ( PIKE-A ) is described as ubiquitous: we detected peptides in blood cells . MAPT isoform PNS-tau is expressed in the peripheral nervous system while other isoforms are expressed in the central nervous system [49] . We found peptides for PNS-tau in adult heart , while peptides mapping to other MAPT isoforms were found in both adult and foetal brain . The splicing events that distinguish the MAPT and AGAP2 isoforms are indels . We also found a brain specific alternative isoform for GLS ( gelsolin ) with different N-terminals , and heart and brain-specific isoforms for VDAC3 ( Voltage-dependent anion-selective channel protein 3 ) , a mitochondrial membrane protein with a NAGNAG splicing event ( Fig 1 ) . The other hotspot for tissue-specific splicing was the heart . We found heart-specific splice isoforms for a further nine genes and most of these genes locate to the thin filaments or Z-discs ( see Fig 4 ) . Peptides from the Kim analysis identify cardiac specific splice isoforms for three members of the ALP/enigma family [50] . All seven family members are cardiac expressed , but specific functions have only been found for PDLIM3 ( ALP ) , PDLIM5 ( ENH ) and LDB3 ( ZASP ) . All three are present in the Z-discs and interact with ACTN2 , a gene that is estimated to make up 20% of the Z-disc mass [51] . LDB3 and PDLIM5 have both been implicated in dilated cardiomyopathy DCM [50 , 52] . The data from the Kim analysis suggested that ZASP1 is the major LDB3 isoform expressed isoform in foetal heart , while ZASP2 and ZASP6 seem more highly expressed in adult heart . Isoform ZASP1 differs from isoforms ZASP2 and ZASP6 by the substitution of a remotely homologous exon . The two isoforms of PDLIM3 we identified in the analysis ( ALP-SK and ALP-H ) are formed from a splicing event paralogous to the one in LDB3 . The peptides from the Kim analysis locate ALP-H to adult and foetal heart and ALP-SK to oesophagus . The third gene , PDLIM5 , has a heart-specific isoform ( ENH1e ) that differs from the non-cardiac isoform by a large insertion . NEBL is one of the very few genes that generate splice isoforms with different Pfam domain compositions . The main isoform , nebulette , contains 14 nebulin repeats and a C-terminal SH3 domain . The shorter isoform LIM-nebulette swaps 12 of the N-terminal nebulin repeats for a LIM domain . Nebulette is a cardiac-specific isoform that is known to bind actin at the Z-discs [53] . At least four nebulette-specific amino acid variants [54] have been implicated in DCM . The data from the Kim analysis locates nebulette uniquely to adult and foetal heart tissue , while LIM-nebulette was found in frontal cortex , spinal cord , lung , kidney and prostate . TPM1 ( tropomyosin ) is one of just nine human genes that is annotated with multiple HES events . It is closely involved with nebulette in heart muscle and their interaction is important in the maintenance and stability of the thin filaments [55] . Again TPM1 is known to be important in DCM; a number of TPM1 single nucleotide variants that are likely to be pathological for DCM have been described in the literature [56] . We mapped these variants to the TPM1 splice isoforms and found that all twelve likely pathogenic variants mapped to isoform TPM1-002 ( TPM1alpha ) suggesting that this was the most important isoform in heart tissue . Data from the Kim analysis confirmed that TPM1alpha is preferentially expressed in heart tissues [57] . We identified more peptides for TPM1alpha than any other TPM1 isoform in every single one of the nine heart tissue experiments and fewer peptides for TPM1alpha in all other tissues . CAPZB , the beta subunit of F-actin-capping protein , generates two proteins via alternative 3’ homologous exons that localize to different regions in the heart [58] . Splice isoform CAPZB1 is located at the Z-discs where it caps the thin filaments ( see Fig 4 ) . We found more evidence for CAPZB1 in heart tissues in the Kim analysis , while the CAPZB2 isoform was identified in liver , kidney , foetal brain and all red blood cells . We also identified a known cardiac-specific isoform for TTN ( cardiac novex-3 ) and possible heart-specific isoforms for TPM2 and ITGA7 , both of which are known to be present in the heart but which are not annotated with cardiac-specific isoforms . The isoforms for all three of these genes were generated by HES events . In total we found evidence for tissue-specific alternative splicing for 14 genes . Many of these genes are known to interact with each other . FYN interacts with MAPT and AKAP2 in the brain , while seven of nine heart-specific isoforms are located to the Z-discs and at least five are known to be important in DCM ( Fig 4 ) . Though this is only a small sample , it is interesting to note that there seem to be tissue-specific protein-protein interaction networks involving AS isoforms . More than half of these 14 genes ( and 7 of the 9 genes with cardiac specific isoforms ) generate their tissue specific splice isoforms via alternative splicing of homologous exons . Very few structures of alternative isoforms have been crystallised in the PDB structural database [59] . Hegyi and colleagues [60] found just 15 cases with resolved 3D structures of splice isoforms in the PDB . It has been suggested that many splicing events ( in particular non-conserved splicing events ) will result in unstable conformations [61] . This may explain the lack of crystallised structures: proteins that do not fold in stable conformations will not form proper crystals and their structures will not be resolved . We looked for evidence of protein 3D structures for all 282 splice events detected in the analysis in the PDB , allowing the splice isoforms to come from any related species . We found ten pairs of alternative isoforms in which each isoform had a resolved 3D structure . Nine of the ten pairs were generated from homologous exons ( Table 1 ) , suggesting that structures generated from homologous exons might be easier to resolve . Among the 157 HES genes identified by BLAST searches , we found another 9 pairs of structures for alternative isoforms generated from homologous exons making a total of 18 pairs of structures for isoforms generated from homologous exons deposited in the PDB . The structures of the 9 pairs of alternative isoforms identified in our analysis are shown in Fig 5 and S11 , S12 , S13 , S14 and S15 Figs . The HES events have a range of effects on the structures . The HES region of the gene ACP1 is confined to one surface of the protein , while the homologous substitution in gene H2AFY will clearly affect the binding to the nucleotide substrate . In gene PFN2 the homologous region ( from the orthologous mouse protein , but 100% sequence identical ) may affect binding to the proline rich peptide substrate . In gene MASP1 the whole trypsin domain is generated from homologous exons that have less than 33% identity , but the two proteins maintain the same fold . The two structures resolved for the MASP1 gene illustrate an important principle regarding the effect of alternative splicing on protein structures . It is clear from Fig 5D that the overall structure of the alternative MASP1 trypsin domain is not affected even though the identity between the two alternatively spliced trypsin domains is only 33% . In fact this is to be expected since protein structures are very stable in the face of evolutionary change . Two proteins as little as 10% identity can have the same overall structure , as long as they are homologous [62] . By way of contrast indels and non-homologous substitutions that fall in regions with globular may produce completely different folds ( or unfolded proteins ) even though the two proteins have a relatively high identity over their whole structure . The final pair of resolved alternative isoforms comes from the well-studied CDKN2A gene where translation from two different ( conserved ) frames results in two completely different proteins ( S16 Fig ) . There are many structures for isoform p19INK , but just one for p16ARF and this structure is from mouse . We looked at the effect of splicing events on the domains annotated in the Pfam functional domain database [63] for the 282 splicing events we identified . Pfam A domains were mapped to all isoforms with the program Pfamscan [63] . We counted a Pfam domain as broken if the splice event would cause the domain to lose or gain five or more residues . The alternative splicing events identified in this study tended to not to have an effect on the composition of Pfam functional domains . Just 19 of the 282 splice events identified in the proteomics analyses ( 6 . 7% ) would break Pfam domains , while 20 more ( 7 . 1% ) would lead to the loss of one or more Pfam functional domains ( while leaving the remainder intact ) . Five ISE would result in a swap of one set of Pfam domains for a different set . The remaining 84 . 4% of identified splice events would leave Pfam domains untouched . In the case of the homologous exons where the HES event coincided with a Pfam domain , we considered the Pfam domain unbroken , except for the plasma membrane calcium-transporting ATPases ( ATP2B1/ATP2B4 ) , where the substitution event clearly broke a poorly defined Pfam domain ( see S17 Fig ) . We calculated the effect of splicing events on Pfam domains over the whole GENCODE 20 gene set and against 4 other subsets of genes as a comparison . We took the principal isoform for each gene from the APPRIS database [64] . The APPRIS principal isoforms have been shown to be a reliable means of predicting dominant isoforms at the protein level [18] . We then generated pairwise alignments between these principal isoforms and all alternative isoforms . We counted all unique splice events from the pairwise alignments between the principal isoform and each of the alternative isoforms for all genes . We mapped the splicing events to the Pfam-A annotations from Pfamscan . The four subsets of genes compared were: all 12 , 716 genes we detected peptides for , all genes that we detected at least 20 peptides for ( 2 , 271 genes , the median number of peptides for the HES genes ) , all genes that we detected at least 50 peptides for ( 385 genes ) , and all the 246 genes for which we detected splice isoforms ( Fig 6 ) . The GENCODE 20 gene set annotates 45 , 346 unique alternative splicing events; 37 . 3% of the splice events ( 16 , 937 ) occur inside Pfam functional domains and would generate alternative isoforms with damaged functional domains . Another 32% ( 14 , 766 ) of the splicing events would lead to the loss of one or more whole Pfam domains ( Fig 6 ) . The figures were similar for the other four gene subsets ( Fig 6 ) , even for the subset of 246 genes for which we detected splice isoforms . The 246 genes that we identified splice isoforms for are annotated with 1 , 650 splice events , 524 ( 31 . 8% ) would split Pfam domains , while 477 ( 28 . 9% ) would lead to the loss of one of more whole Pfam domains ( Fig 6 ) . In clear contrast , the splice events that we detected in the proteomics experiments tended not to lead to the loss or damage of Pfam functional domains . These results clearly show that most alternative isoforms with damaged or lost Pfam domains are not produced in quantities detectable in standard proteomics experiments . This strongly suggests that many variants that affect Pfam composition are produced in very low quantities or that there is some form of control at the level of translation , or post-translation , that protects the cell against protein isoforms with damaged domains . While we find peptides for 12 , 716 genes , we identify a mere 282 splice events ( 0 . 62% of the annotated events ) . Over the 8 experiments we identified just 0 . 02 alternative splice events per identified gene while the numbers of annotated splice events per gene is 2 . 28 . This seems to be a very small proportion . Not only do we identify very few splice isoforms , we also detect very few alternative peptides . If we were to use trypsin to digest the human proteome ( in this case the GENCODE 20 annotation of the human coding genes that we used to map the peptides from the eight analyses ) , we would generate 693 , 324 unique tryptic peptides of at least seven residues . A total of 11 . 21% of these 693 , 324 unique tryptic peptides would come exclusively from isoforms tagged as “alternative” by APPRIS ( those isoforms that were not principal isoforms , see Materials and Methods section ) . In contrast in our analysis just 0 . 38% of the 149 , 612 discriminating peptides that we detected mapped to alternative isoforms . The equivalent numbers for the alternative peptides , splice events and genes identified in the individual experiments can be found in the supplementary S1 Table . We attempted to estimate the number of AS genes we would have expected to identify from our data using a range of simulated null models . For the first model we generated tryptic peptides from the GENCODE 20 gene set by in silico lysis . Each peptide was represented just once in the database of peptides . We used just tryptic peptides for this simulation because all possible missed cleavage combinations would make the search space too large . For this first model we did not eliminate any redundancy from the GENCODE 20 annotation , so as to approximate a model in which all annotated transcripts were expressed equally . If we had only used tryptic peptides in our experiments we would have found evidence of alternative splicing for 226 genes ( 20 of the AS genes were identified via missed cleavages ) , while there would have been evidence of three or more isoforms for 14 genes . To simulate the expected numbers of AS genes we drew peptides at random from this database . In order to approximate the expression levels from the experiments , the number of tryptic peptides drawn for each gene was the same the number we identified in our analysis . We repeated the simulation 100 times and took the mean value to be the estimation of the number of AS genes we would have expected to find if genes were expressed at experimental levels and all isoforms of each gene were expressed equally . The numbers of AS genes from this in silico analysis were substantially larger than those in our experiment; in our model we found evidence of alternative splicing for 3 , 508 genes ( 15 times greater than the experiments ) , and evidence of at least three isoforms for 937 genes ( 67 times greater than the experiments ) . We repeated the simulation , but this time carrying out an in silico analysis that produced 50-times more peptides for the main isoform of each gene than for all the other isoforms . For this model we used the principal isoforms from the APPRIS database as a stand-in for dominant splice isoforms [64] . Again for each gene we randomly selected exactly the same number of tryptic peptides as we had identified in our experiment . We repeated the simulation 100 times . This simulation approximates a model in which genes have expression levels similar to the experimental expression and all peptides are equally detectable , but in which one isoform is produced 50 times more than the other isoforms . The simulation showed that we would have expected to detect 1 , 289 genes with evidence of AS at the protein level and another 152 genes with at least three distinct splice isoforms in this model . Again this is in clear contrast with the observed results and suggests that the majority of alternative isoforms may be produced at levels that are considerably lower than 2% of those of the main isoform . This evidence clearly supports a model in which most genes have one dominant isoform at the protein level [18] .
We generated a highly reliable set of peptides from eight large-scale proteomics analyses by applying rigorous filters . The rigorous quality controls on the peptide data allowed us to be confident that the isoforms we identified were expressed and present in high enough quantities to be detected in proteomics analyses . With these peptides we detected the expression of alternative isoforms for 282 distinct splice events from 246 distinct human genes . While the filters undoubtedly limited the number of splice events we detected , they do mean that this set of alternative splice isoforms can be regarded as a gold standard for what can be detected in large-scale proteomics experiments . Even with peptide data from eight large-scale analyses that cover a wide range of tissues , cell lines and developmental stages , we still detect many fewer alternative isoforms than would be expected from transcript data . We found peptides for almost 64% of annotated protein coding genes , but identified less than 0 . 6% of the annotated alternative splice events . In part this may be due to proteomics technology . Standard MS experiments generate relatively low coverage of the proteome and cannot detect peptides expressed at very low levels . This is a technical problem that is unlikely to be resolved in the short term . We found unexpectedly high numbers of isoforms generated by alternative splicing of homologous exons; more than 20% of the splice events we detected in the human proteomics experiments came from mutually exclusively spliced homologous exons and these homologous substitutions made up 60% of the orthologous splicing events detected in both mouse and human experiments . A surprisingly high proportion of isoforms from homologous exon substitutions had resolved 3D structures . The explanation for this may be simply that structures generated from homologous exons are easier to crystallize [61] . Homologous structures will maintain their 3D fold , while non-homologous exons that fall in structured regions may cause the 3D structures to become partially unfolded and therefore difficult to resolve . The recent publication of a large-scale tissue-based proteomics analysis with replicates [33] allowed us to carry out a study of alternative splicing at the level of tissues . We found evidence for tissue-specific expression of fourteen pairs of alternative isoforms . Curiously those genes for which we detected tissue-specific splicing isoforms had at least one isoform that was specifically expressed in either heart or brain and many of them are known to interact . The heart-specific isoforms were particularly interesting because the majority proteins coded by these variants are known to locate at the Z-discs and to be involved in dilated cardiomyopathy . For many of the remaining isoforms the data was inconclusive . Seven of the nine heart-specific isoforms we identified were generated from homologous exon splicing events . In 2001 , Kondrashov and Koonin [65] found evidence for 50 genes with homologous exon substitutions across a range of species . They reported that half of the 29 HES for which they identified an ancestor arose in the mammalian lineages . With the data now available , we find that all the HES detected in our experiments ( and 27 of the 29 HES identified by Kondrashov and Koonin ) , had their origins in the ancestor of jawed vertebrates or earlier , more than 460 million years ago . This is a remarkable level of conservation for alternatively spliced isoforms . In contrast Modrek and Lee [66] found that only 25% of what they termed “minor” alternative exons were conserved between human and mouse . We carried out our own analysis of alternative exons ( see Materials and Methods section ) and found a similar result , just 19 . 3% of the 3 , 626 alternative exons we analysed ( excluding homologous exons ) were conserved between human and mouse . The homologous exon substitution events we identified are clearly much more conserved than this . The ancient conservation of isoforms generated from homologous exon substitution events , taken together with the abundance of peptide evidence for these isoforms , their tissue-specific expression , and the fact that these events have a demonstrably subtle , non-disruptive , effect on protein structure , strongly suggests that alternative splice isoforms generated from mutually exclusively spliced homologous exons are likely to have important cellular roles that merit further investigation . Most of the splice events we identify in this analysis would have relatively modest effects on protein structure and function; many alternative isoforms were generated from homologous exons and even most indels were either short or fell in regions that are likely to be unstructured . In fact very few of the splice events we detected would damage or cause the loss of conserved Pfam functional domains . This is in sharp contrast to the splice variants annotated in the GENCODE gene set , where the majority of the splice events would be expected to have an effect on Pfam domains . The preference for splice events that do not disrupt Pfam functional domains and the analysis of evolutionary conservation strongly suggest that not all annotated alternative transcripts will be converted into stable proteins . One possible explanation for this finding is that alternative protein isoforms with damaged or lost functional domains are more likely to have a disruptive effect on cellular processes and their production may be subject to regulation by one of the many cellular quality control pathways [67–70] , to ensure that isoforms with damaged domains are not present in the call in large quantities . At the moment , it is still not clear how much of the alternative splicing observed in the transcriptome is functionally relevant . Our results suggest that , at the protein level at least , the diversity generated by alternative splicing may be smaller than most previous estimates . If true these findings will have important practical implications for variant-calling analyses that include potentially non-relevant transcripts [71] and will affect our understanding of how organisms and complexity evolve .
We wanted to make sure that the alternative isoforms that we detected were not identified from incorrectly mapped peptides , so we used a series of filters to remove as many false positive peptides as possible from each analysis . The peptides from the individual analyses were filtered as follows . The peptides from the Geiger [36] and Nagaraj [37] experiments were treated in identical fashion . The peptides in these studies had a peptide false discovery rate ( FDR ) of 1% , but for the purposes of this experiment we required the peptides identified in the two datasets also to have an Andromeda [73] score of 100 or more . It has been shown that using multiple search engines increases performance [74 , 75] and since peptides identified by Andromeda with scores of 100 or greater are almost always in agreement with those identified by Mascot [76] for the same spectra [73] , concentrating on the peptides with PSM above this score decreases the false positive rate . The Kim experiment [33] used the Mascot and Sequest [77] search engines and the peptides identified in their analysis came from the union of these two search engines . The Kim experiment described the peptide-spectrum match ( PSM ) FDR as being 1% . We have shown that this is likely to be an underestimate [34] . In order to be more rigorous for the purposes of our experiment , we only included the peptides from the intersection of the two search engines used in the Kim analysis , that is , those peptides that were identified by both the Mascot and Sequest search engines . The Wilhelm analysis [32] also used two search engines , this time Mascot and Andromeda . Again the peptides came from the union of the two search engines . The Wilhelm experiment had a 1% PSM FDR and a 5% peptide FDR , but again we found that this was likely to be an underestimate [34] . Upon analysis of the scores from the two search engines used we found a high number of dubious spectra in which Andromeda and Mascot agreed on a peptide match , but both search engines had very low scores . For the purposes of our experiment we treated the Wilhelm analysis in the same way as the Geiger and Nagaraj analyses; we only took those peptides identified with an Andromeda PSM score of 100 or more . The NIST database uses five different search engines ( Sequest , Andromeda , Mascot , X ! Tandem and OMMSA [78] ) to identify peptides from human spectra , and three search engines for spectra from mouse experiments . While the NIST database includes many peptides , the FDR is quite high . For the purposes of our experiment we filtered out those NIST peptide-spectrum matches identified by just one search engine . The peptides from the Ezkurdia analysis [39] were identified with X ! Tandem and had a peptide FDR of 0 . 1% , while the peptides from the Munoz [42] analysis were identified using Mascot and had a peptide FDR of 1% . PeptideAtlas peptides have a PSM FDR of 0 . 0002% . All peptide data sets were then subject to the following filters: we filtered out non-tryptic and semi-tryptic peptides and only allowed peptides with missed cleavages that were supported by at least one of the fully cleaved sub-peptides . We applied the equivalent rule to the peptides from the Wilhelm analyses for peptides cleaved with LysC and chymotrypsin , and to the peptides detected in the Nagaraj analysis that were cleaved by GluC or LysC enzymes . In the case of the Ezkurdia , PeptideAtlas and NIST analysis we did not know the digesting enzyme a priori , so we chose the conservative option of assuming that all peptides were cleaved by trypsin . Search engines do not easily distinguish leucine from isoleucine due to their identical mass , so leucine and isoleucine residues were allowed to map to either leucine or isoleucine in the GENCODE20 gene set . All peptides that mapped to more than one gene were disregarded in the analysis . The numbers of peptides and genes identified in the individual experiments can be seen in S1 Table . We only mapped peptides that were identified by two or more different experiments or databases in our analysis of alternative isoforms . We excluded peptides identified in just one of the eight peptide data sets , since , even after the filtering carried out in this analysis , a proportion of these peptides are likely to be false positives . For the human analysis the peptides were mapped to the protein isoforms annotated in the GENCODE 20 human gene set . The gene set was first filtered for pseudoautosomal genes and for read-through transcripts . Read-through transcripts are flagged in the GENCODE 20 annotation and are transcripts that are ( generally ) formed by skipping the last exon of a gene and reading through to the neighbouring gene or non-coding gene , or pseudogene . All these transcripts are highly unlikely to be translated into proteins and ( most importantly ) overlap with known coding transcripts . Read-through transcripts that overlap with coding transcripts would render these coding transcripts indistinguishable . The remaining GENCODE 20 gene set was annotated with 19 , 906 protein-coding genes and 92 , 341 protein isoforms; the manual GENCODE annotations are highly enriched in alternative isoforms [49] . 15 , 548 genes were annotated with more than one distinct splice isoform . For the mouse analysis we mapped peptides to the isoforms annotated in the GENCODE mouse M2 gene set ( equivalent to Ensembl74 ) . The M2 gene set had 22 , 645 protein-coding genes and 51 , 610 transcripts . In the mouse gene set just 10 , 607 genes were annotated with protein sequence distinct variants . We mapped isoform-discriminating peptides to the splice isoforms annotated in the GENCODE 20 gene set looking for peptides that mapped unambiguously to distinct splice isoforms . The initial set of splice isoforms was checked by hand by mapping the isoform-discriminating peptides to multiple alignments . Only those splice events for which we identified peptides that mapped to both sides of the events were included in the final set . There was peptide evidence for the expression of 7 different isoforms from the UGT1A gene cluster . Although UGT1A transcripts are annotated as independent genes in GENCODE 20 , we have treated them as splice variants of a single gene in this analysis . In the UGT1A gene cluster the individual genes/transcripts share a set of common 3’ exons and each has a unique ( but homologous ) 5’ exon ( S1 Fig ) . As with the Drosophila gene Lola ( where the variable exons are at the 3’ end rather than the 5’ end [79] ) , the different protein products are formed by joining one of a set of variable exons to the common exons , The selection of the 5’ exons is thought to be determined via alternative promoters . The use of alternative promoters is not a standard alternative splicing mechanism , but the end result is the alternative splicing of exons , just as it is in Lola in Drosophila , another non-standard alternative splicing mechanism , where variable and constant exons are joined by trans- rather than cis-splicing . There are three other similar gene clusters in the GENCODE 20 gene set ( UGT2A , PCDH-gamma and PCDH-alpha ) and we regarded all four gene clusters as single genes with alternatively spliced protein isoforms . Based on Ensembl version 78 of December 2014 [40] we compared human transcripts to automatically identify a set of genes with mutually exclusive homologous exons . We defined the transcript with the longest amino acid sequence as the reference against which compare other transcripts and looked for pairs or sets of exons that are mutually exclusive , i . e . that do not co-occur in the same transcript . For exons that were more than 30 bps long , we obtained their amino acid sequences and compared them with BLAST v2 . 2 . 25 [44] , setting an e-value threshold of 0 . 005 . To validate each of the resulting potential homologous exons we assessed whether the exons occupied equivalent positions within the corresponding alternative transcripts . In addition , we discarded those cases in which one of the alternative exons belonged to a paralogous neighbour gene or pseudogene . Finally , we included additional cases of homologous exons that were identified by BLASTP searches [39] , but missed in this automatic analysis . We visually inspected the alignments of all the potential cases . We ended with a set of 157 genes with ( mutually exclusive ) homologous exon substitutions . Although the proteomics analysis of alternative splicing revealed other additional cases of homologous exons beyond this set , we did not include them in this test set to avoid potential biases . To identify whether the homologous exons substitutions we detected were of ancient origin , here defined as those that originated more than 400 million years ago , we scanned the genomes of five distantly related vertebrates using TBLASTN [44] with the exons as amino acid query sequences , turning off low complexity filtering and setting an e-value threshold of 0 . 1 . These five taxa included lamprey , spotted gar , zebrafish , fugu , and coelacanth , all of which were retrieved from Ensembl v75 . We used bedtools v2 . 17 . 0 [80] to group sequence similarity hits based on the 95 percentile of gene lengths of each target species , then assigning these hits to annotated or new genes in each target species . For every target gene we determined whether it was orthologous or paralogous to the query human gene using EnsemblCompara phylogenetic trees [45] . Results were carefully revised to determine which genes from each target species had specific hits to each query exon , i . e . whether the human homologous exons were already present in that species . The origin of the homologous exons was inferred under the assumption that homologous exons have not been acquired independently in different species , i . e . we relied on Dollo parsimony [81] . We determined the constitutive exons to be those that were annotated as principal isoforms in APPRIS [64] . We defined as alternative exons all those protein-coding exons that did not overlap with the constitutive exons . We found 13 , 079 of these exons in the GENCODE 20 gene set . We improved the reliability of the annotation by filtering out those genes where the principal isoforms was not determined by the core modules or by unique CCDS identifier [82] . We further filtered these exons as follows . We removed exons that were too short to identify homology with mouse in the TBLASTN searches . These were defined as those with a BLAST e-value higher than 0 . 001 when compared against the whole human proteome , which includes the query exons ( these are exons for which we may expect a significant sequence similarity hit in the mouse genome if the exon is conserved ) . We also removed exons that were similar to exons in the APPRIS principal isoforms ( e-value threshold of 0 . 1 ) . This avoids complicating the interpretation of potential conservation and also excludes exons that can be defined as homologous . If exons overlapped with each other we took the longer of the exons as the representative . We also filtered out 15 genes presenting a large number of alternative exons caused by a gene model that was clearly not finished or had errors that were influencing the selection of the principal isoform by APPRIS ( for example , FRAS1 , where the gene model was unfinished in GENCODE 20 or FIP1L1 , which had an unannotated read-through transcript that APPRIS selected as the main isoform ) . After all the filtering steps , we ended up with a set of 3 , 626 alternative human exons . Using these exons as amino acid query sequences we searched the mouse genome with TBLASTN , turning off low complexity filtering and setting an e-value threshold of 0 . 1 . We defined the exon as conserved when a significant similarity was found within a mouse gene ( or close to the gene , we set a distance threshold based on the 95 percentile of gene lengths ) that is present in the same EnsemblCompara phylogenetic tree than the corresponding human gene . APPRIS [64] derives a principal splice isoform for each gene based on the presence of protein features , such as protein structure , functional domains and cross-species conservation . These features can discriminate between splice isoforms because they have generally been conserved over large evolutionary timeframes . Isoforms that have “lost” these features are unlikely to be the principal isoform . APPRIS maps protein structural information from the structural homologs in the PDB [60] to individual isoforms , annotates functional information from the Pfam domain database [63] and from the functionally important amino acid residues from firestar [83] and evaluates the cross-species conservation of every isoform . The isoform with the most conserved protein features is chosen as the principal splice isoform . Where the APPRIS annotations are not sufficient to distinguish a single principal isoform , such as genes TMPO [84] or MARVELD3 [85] , APPRIS uses external annotations , such as presence in the CCDS database [82] in order to make a decision . The variant chosen from the core APPRIS modules have been shown to be in almost complete agreement with the main proteomics isoform [18] . Here we selected a single isoform as the main isoform ( using APPRIS principal isoforms ) and defined an alternative splice event as being an event that changed the protein sequence between the main isoform and alternative isoforms . Alternative proteins that were simply truncated were not used to count splice events in order to avoid including annotated fragments of transcripts from unfinished gene models . We used PfamScan to annotate Pfam domains onto the transcripts and counted all those cases where the splice event would cause the Pfam domain to lose five or more amino acid residues ( a damaged Pfam domain ) , or the whole Pfam domain ( a lost Pfam domain ) . | Alternative splicing is thought to be one means for generating the protein diversity necessary for the whole range of cellular functions . While the presence of alternatively spliced transcripts in the cell has been amply demonstrated , the same cannot be said for alternatively spliced proteins . The quest for alternative protein isoforms has focused primarily on the analysis of peptides from large-scale mass spectroscopy experiments , but evidence for alternative isoforms has been patchy and contradictory . A careful analysis of the peptide evidence is needed to fully understand the scale of alternative splicing detectable at the protein level . Here we analysed peptides from eight large-scale data sets , identifying just 282 splice events among 12 , 716 genes . This suggests that most genes have a single dominant isoform . Many of the alternative isoforms that we identified were only subtly different from the main splice variant , and one in five was generated by substitution of homologous exons by swapping one related exon for another . Remarkably , the alternative isoforms generated from homologous exons were highly conserved , first appearing 460 million years ago , and several appear to have tissue-specific roles in the brain and heart . Our results suggest that these particular isoforms are likely to have important cellular roles . | [
"Abstract",
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"Methods"
] | [] | 2015 | Alternatively Spliced Homologous Exons Have Ancient Origins and Are Highly Expressed at the Protein Level |
Chlamydia trachomatis is the etiological agent of trachoma the world's leading cause of infectious blindness . Here , we investigate whether protracted clearance of a primary infection in nonhuman primates is attributable to antigenic variation or related to the maturation of the anti-chlamydial humoral immune response specific to chlamydial antigens . Genomic sequencing of organisms isolated throughout the protracted primary infection revealed that antigenic variation was not related to the inability of monkeys to efficiently resolve their infection . To explore the maturation of the humoral immune response as a possible reason for delayed clearance , sera were analyzed by radioimmunoprecipitation using intrinsically radio-labeled antigens prepared under non-denaturing conditions . Antibody recognition was restricted to the antigenically variable major outer membrane protein ( MOMP ) and a few antigenically conserved antigens . Recognition of MOMP occurred early post-infection and correlated with reduction in infectious ocular burdens but not with infection eradication . In contrast , antibody recognition of conserved antigens , identified as PmpD , Hsp60 , CPAF and Pgp3 , appeared late and correlated with infection eradication . Partial immunity to re-challenge was associated with a discernible antibody recall response against all antigens . Antibody recognition of PmpD and CPAF was destroyed by heat treatment while MOMP and Pgp3 were partially affected , indicating that antibody specific to conformational epitopes on these proteins may be important to protective immunity . Our findings suggest that delayed clearance of chlamydial infection in NHP is not the result of antigenic variation but rather a consequence of the gradual maturation of the C . trachomatis antigen-specific humoral immune response . However , we cannot conclude that antibodies specific for these proteins play the primary role in host protective immunity as they could be surrogate markers of T cell immunity . Collectively , our results argue that an efficacious subunit trachoma vaccine might require a combination of these antigens delivered in their native conformation .
The obligate intracellular bacterial parasite Chlamydia trachomatis is the causative agent of blinding trachoma and sexually transmitted diseases . C . trachomatis utilizes a unique biphasic developmental cycle alternating between infectious elementary bodies ( EB ) and metabolically active reticulate bodies ( RB ) . Multiple serovars exist within C . trachomatis . The ompA gene , coding for the immunodominant major outer membrane protein ( MOMP ) , differentiates these serovars [1] . Serovars A , B , Ba , and C are the etiological agents of trachoma [2] , the global impact of which is significant . Designated by the WHO as one of the major neglected tropical diseases [3] it is the world's leading cause of preventable blindness , primarily afflicting populations in developing nations [4] . Where endemic , trachoma infection is initiated at a very early age presenting as acute follicular conjunctivitis . However , prolonged repeated infection due to deficient protective immunity can trigger chronic pro-inflammatory immune responses leading to conjunctival scarring , trichiasis , and corneal opacity . Though chronicity of infection is frequently believed to relate to constant exposure and reinfection , the pathogenesis of trachoma is not fully understood . It is believed that an imbalance of host protective and pathological immune response is responsible for the pathophysiology of the disease . Poor natural immunity leads to multiple bouts of re-infection that serve as the antigenic stimulus for a sustained damaging inflammatory pathologic immune response [4] . Uncertainty remains however regarding the precise contribution of long-duration chlamydial infections and reactivations in trachoma pathology . The nonhuman primate ocular model is the most relevant animal model for studying trachoma . Not only is this ocular model appropriate in its ability to mimic the acute aspects of human trachoma infection but isolation of laboratory animals ensures infection exposure and disease are not related to reinfection . Previously , we used this model to examine infection with a recently isolated virulent Tanzanian clinical strain of C . trachomatis serovar A , A2497 [5] . We reported that following ocular infection of cynomolgus monkeys , an initial peak shedding period was followed by clearance and bouts of smaller reactivation peaks of infection that lasted for months . Clinical response scores of hyperemia and follicle formation remained high throughout the infection period and continued for weeks after complete absence of bacterial shedding . This experimental picture in NHP closely mimics the acute phase of the naturally occurring infection in hyperendemic trachoma regions . In this study , we investigate whether antigenic drift or maturation of the host C . trachomatis specific humoral immune response might explain the basis of the protracted period of time required to eradicate the primary infection and clinical disease . We found no evidence linking antigenic variation to delayed clearance of primary infection in these animals but observed convincing findings implicating gradual changes in the humoral immune response specific to a few chlamydial antigens as a possible mechanism .
Healthy adult naïve cynomolgus macaques ( Macaca fascicularis ) maintained at the Rocky Mountain Laboratories were cared for under standard practices implemented by the Rocky Mountain Veterinary Branch/NIAID/NIH . Monkeys were housed separately when being used for experimental studies . All handling procedures were reviewed and affirmed by the Animal Care and Use Committee at Rocky Mountain Laboratories and work was conducted in full compliance with the Guide for Care and Use of Laboratory Animals . The facilities are fully accredited by the American Association for Accreditation of Laboratory Animal Care . Conjunctival chlamydial infections can cause transient local inflammation in the eye and are not associated with pain or significant discomfort; discomfort is usually limited to minor photophobia . During the experimentations we were prepared to treat any animals determined to be under stress or in significant discomfort with appropriate analgesics and antibiotics . We observed no stress or significant discomfort during the entire experiment . At the end of the nonhuman primate re-challenge experiment animals were treated with antibiotics and returned to their colony unharmed . C . trachomatis strains A2497 ( serovar A ) and Ba/Ap-2/OT ( serovar Ba ) were cultured in either HeLa 229 or McCoy cells ( ATCC ) and elementary bodies ( EBs ) were purified by density gradient centrifugation [6] . All NHP ocular chlamydial infections and re-infections were done using 2×104 IFU per eye directly applied on the conjunctival surface of the upper and lower lids of both eyes with strain A2497 . Chlamydial culture and ocular clinical disease scoring was done as previously described [7] . Briefly , swabs were taken from the upper and lower conjunctiva of each eye , and chlamydiae were cultured on HeLa cells to monitor infection . Each eye was scored for disease based on both the intensity of conjunctival hyperemia and follicle formation . Hyperemia and follicular scoring were combined to produce an aggregate clinical disease score shown graphically on a scale of 0–12 with 12 being the maximum score . Blood was collected at the same time as culturing and clinical scoring . Three male cynomolgus monkeys were infected ocularly with the A2497 trachoma strain and the infections were allowed to run their natural self-limiting course ( 4–6 months ) . Approximately three months following spontaneous clearance of infection and disease ( 269 days post primary infections ) monkeys were similarly re-challenged and again the infections were allowed to run their natural course . At weekly intervals during the entire experiment , ocular disease was evaluated and scored and samples were collected . The primary infection of these animals has been described before in the Journal of Infectious Diseases [7] . At various times post-infection chlamydiae cultured from the conjunctivae of all three monkeys were plaque cloned using McCoy cells [8] . For each isolate 20–24 individual plaques were selected and consecutively passed twice in McCoy cells . DNA was isolated from chlamydial cells 48 hours post infection ( hpi ) by lysis in 0 . 2 ml of 0 . 5 M NaOH and then neutralized by the addition of 0 . 2 ml of 1M Tris-HCl pH 8 . 0 . ompA PCR and sequencing was performed as previously described [7] . Sequencing data , in FASTA format , was compiled for each original clone with the CAP3 Sequence Assembly Program ( http://pbil . univ-lyon1 . fr/cap3 . php ) . Using the NCBI website ( http://www . ncbi . nlm . nih . gov/blast/bl2seq/wblast2 . cgi ) , assembled sequences were compared against the previously reported A2497 ompA sequence [7] . Plaque clones from monkey RML126 were passed in HeLa 229 cells to obtain sufficient organisms to infect six well culture plates . Six 6-well culture plates of HeLa cells were infected with each clone at an MOI of 1 . At 48 hpi , chlamydiae were harvested and partially purified on 30% MD-76R ( Merry X-Ray Corporation ) density gradients [6] . Genomic DNA was isolated from 108 organisms as previously described [9] . Ten µg of DNA for each clone was used for genome sequencing by NimbleGen Systems as previously described [7] . McCoy cells grown in 6 well tissue culture plates were infected with C . trachomatis serovar A or serovar Ba at a MOI of 2 . 5 and fed with RPMI ( supplemented with 1% L-glutamine , 0 . 33% glucose , 10% dialyzed FBS , and gentamicin ) containing 4 µg/ml of emetine . Twelve hpi cells were washed twice with 1 ml of RPMI minus L-methionine and L-cysteine and then incubated with 300 µCi of 35S L-methionine and L-cysteine in 3 ml of RPMI . At 50 hpi the monolayers were washed 3 times with 5 ml PBS and then lysed in 1 ml of cold RIPA lysis buffer ( 25 mM Tris-HCl pH 7 . 6 , 150 mM NaCl , 1% NP-40 , 1% sodium deoxycholate , and 0 . 1% SDS ) containing protease inhibitor ( Roche Protease Inhibitor Cocktail Tablet ) . The cell lysate was gently passed through a 25 gauge needle 10 times to shear DNA and centrifuged at 19 , 000× g at 4°C for 30 min . Intrinsically radiolabelled antigen supernatants were collected , aliquoted , and stored at −20°C , with aliquots used in immunoassays . Fifty µl Protein A and 50 µl Protein G magnetic beads ( Invitrogen ) were used for each RIP reaction according to the manufacturer's protocol . Briefly , beads were washed several times with Ab Binding and Wash Buffer . Intrinsically radiolabeled chlamydial antigen lysates were pre-cleared by incubation with 100 µl of beads for 1 hr at RT with continuous rotation . Beads were then re-suspended in Ab Binding and Wash Buffer . NHP sera were added to the suspension to achieve a final dilution of 1∶80 . Tubes were rotated for 1 hr at RT and the supernatants removed . Beads were washed and then incubated with 100 µl of pre-cleared antigen lysate for 2 hours by rotating at RT . Supernatants were removed and the beads were washed four times and then transferred to a clean tube . Beads were suspended in 30 µl of 2× Laemmlie sample buffer ( BioRad ) and boiled for 10 min at 100°C . Supernatants were removed from the beads , loaded onto 4–15% precast SDS-PAGE gels ( BioRad ) , and electrophoresed at 70 V for 30 min and then 150 V for 70 min . Gels were rinsed in 5 ml of dH2O for 10 min and then placed on filter paper , vacuum dried and exposed to high performance chemiluminescence film ( Amersham Hyperfilm ECL ) . McCoy cells were infected with chlamydiae and chlamydial antigen lysates were prepared as described above for intrinsically radiolabeling experiments , except no 35S L-methionine and L-cysteine was added to the culture media . The immunoprecipitation ( IP ) procedure used was also identical with the exception that the final elution of antigen bound to beads employed the Invitrogen non-denaturing elution protocol using 20 µl elution buffer for 5 min at RT . The elution supernatant was transferred to a new tube and 20 µl of 2× sample buffer was added to each tube . Samples were boiled for 10 min at 100°C and electrophoresed as described above . Proteins were transferred to PVDF ( BioRad ) membranes for 4 hr at 350 mA . Following transfer , the membranes were blocked overnight , washed twice with diH20 , and incubated with mouse monoclonal or rabbit monospecific primary antibody specific to chlamydial proteins at RT for 3 hours . The membrane was washed , incubated with anti-mouse or anti-rabbit secondary antibody at RT for 1 hr , washed repeatedly , and then incubated with 1 ml of chemiluminescent developing solution for 5 mins at RT ( Invitrogen ) . Gels were exposed to high performance chemiluminescence film ( Amersham Hyperfilm ECL ) .
In our previous study three cynomolgus monkeys were infected ocularly with C . trachomatis strain A2497 , a recent trachoma clinical isolate with enhanced virulence characteristics for the nonhuman primate ( NHP ) eye [7] . Primary ocular infection continued for up to 14 weeks post infection ( wpi ) and then resolved spontaneously ( RML134: 13 wpi; RML124: 8 wpi; RML126: 14 wpi ) . Monkeys were partially immune to re-challenge 3 months following clearance of the primary infection and disease . Re-challenged monkeys shed significantly less chlamydiae for a much shorter duration ( RML134: 3 wpi; RML124: 5 wpi; RML126: 7 wpi ) accompanied by reduced ocular disease that was also of shorter duration than that observed for the primary infections ( 11 wpi versus 30 wpi , respectively ) . Interestingly , the initial 4–5 weeks of the primary infection was characterized by higher levels of bacterial shedding , whereas later time points ( 6–14 wpi ) were characterized by fluctuating periods of culture positive and negative results with much less organism being shed from the conjunctivae . Intense ocular disease was present in two of the three animals ( RML 134 and RML 126 ) during this period of low infectious burden and an additional 15 weeks was required for disease resolution despite being culture negative for chlamydiae . To examine the possible role of antigenic drift in delayed clearance , chlamydia positive ocular swabs from time points following culture negative periods were cultured and chlamydiae were plaque cloned ( RML134: week 9 and 12; RML124: week 8; RML126: week 8 , 11 and 14 ) . We initially looked for antigenic variation in ompA , the gene encoding the major outer membrane protein ( MOMP ) , an immunodominant neutralizing target and the serotyping antigen of chlamydiae [1] , [6] . We examined over 200 re-isolated clones but no ompA sequence differences were found ( data not shown ) . To look for antigenic variation beyond ompA , 4 clones from the final reactivation period ( week 14 ) and 2 clones from the initial peak shedding period ( week 1 ) in one monkey ( RML 126 ) underwent comparative genomic sequencing . As with ompA , no genetic variation was observed between the genomes of the plaque clones isolated from early and late time points ( data not shown ) . To investigate the possible role of an evolving antibody mediated immune response we collected sera from the three monkeys during primary infection and following re-challenge . These sera were analyzed by radioimmunoprecipitation ( RIP ) with intrinsically radiolabelled labeled chlamydial proteins prepared from non-denaturing detergent lysed infected cells . Despite the complex composition of the lysates , we observed a surprisingly simple antibody recognition profile by RIP that was consistent among the three nonhuman primates ( Figure 1 ) . All three monkeys recognized the MOMP , a 40 kDa protein , and nine other polypeptides . The molecular weights of these proteins were 166 , 90 , 72 , 60 , and approximately 29 , 25 , 23 , 16 and 15 kDa . There was a noticeable temporal and recall recognition pattern of the antigens by all three monkeys . The dominant MOMP was recognized early in infection ( 2–4 weeks ) with sustained response through spontaneous clearance of the primary infection . Antibody recognition of other proteins occurred later during primary infection and peaked at the time of spontaneous resolution . We also observed a marked increase in recognition of all proteins following ocular re-challenge . In order to investigate whether NHP antibodies recognize conserved or variable chlamydial antigens , RIP was performed using lysates made from C . trachomatis serovars A2497 and serovar B ( Ba/Ap-2 ) , strains differentiated by ompA antigenic variation that are representative of the C and B complex serogroups , respectively . For this experiment we used sera from a single monkey ( RML 134 ) obtained at four representative timepoints: at the days of infection and re-challenge , and at the peak of anti-MOMP reactivity post infection and re-challenge ( Figure 1 ) . This monkey recognized the A2497 MOMP strongly but reacted poorly to the serovar B MOMP ( Figure 2 ) . In contrast , antibodies from this monkey reacted equally well to the 166 , 90 , 72 , 60 , 29 kDa and lower MW proteins demonstrating that they are conserved antigens shared between these two serologically distinct strains . Serum antibodies against these proteins diminished significantly four months following resolution of the primary infection but were clearly recalled following re-challenge and correlated with the partial immune status of the animal . To identify both high and low molecular mass antigenically conserved proteins shown in Figure 2 we employed a panel of monoclonal or monospecific polyclonal antibodies of known specificity to probe IP antigens recognized by NHP sera . Immunoprecipitation for this study was performed using sera from monkey RML 134 obtained during primary infection and following re-challenge using unlabeled chlamydial proteins prepared from non-denaturing detergent lysed infected McCoy cells . Using this procedure we identified some of the high and low molecular weight antigenically conserved proteins identified in Figure 2 . Western blots of the IP proteins using a panel of PmpD anti-peptide antibodies identified full length PmpD and its two proteolytically processed fragments , the 82 kDa translocator domain and a 73 kDa passenger domain ( Figure 3A ) [10] . We also identified the 60 kDa polypeptide as Hsp60 ( data not shown ) . We surmised that the low molecular weight proteins might be two previously described immunogenic chlamydial antigens , chlamydial protease-like activity factor ( CPAF ) and the plasmid encoded gene protein 3 ( Pgp3 ) . Both proteins have been shown to be secreted , antigenically conserved , and immunogenic by other investigators [11]–[15] . We identified the 29 kDa polypeptide as the C-terminal fragment of CPAF and the 25 kDa polypeptide as Pgp3 ( Figure 3B ) . The other three polypeptides remain to be identified . Next we studied whether antibody recognition of chlamydial proteins was sensitive to heat denaturation as an indirect parameter to differentiate between native conformational and contiguous epitopes . Sera from all three infected monkeys were incubated with unheated or heated ( 70°C for 30 minutes ) RIP antigen ( Figure 4 ) . Antibody recognition of MOMP was significantly decreased which is consistent with our previous observation that NHP anti-MOMP antibodies are at least partially directed against conformational epitopes of the heat-labile MOMP trimer [5] . All three of the highest MW polypeptides were destroyed by heating . The 60 kDa polypeptide antigenicity was not noticeably affected by heat treatment . Two of the 4 low MW polypeptides were heat sensitive . To presumptively identify the heat sensitive proteins , IP were similarly performed on heated and unheated non-labeled A2497 lysates followed by Western blotting using antibodies against PmpD , Hsp60 , CPAF and Pgp3 ( Figure 4B ) . Antibody recognition of PmpD , its proteolytic fragments and CPAF was destroyed after heat treatment . To a lesser extent Pgp3 recognition was also destroyed , consistent with previous findings describing the heat stable nature of Pgp3 trimers and the observation that anti-Pgp3 antibodies reactive with the trimer react poorly with the denatured monomer [16] . In contrast , antibody recognition of Hsp60 was resistant to heat treatment .
In this study we investigated whether a long-duration but self-limiting ocular C . trachomatis infection in NHP primates was related to antigenic drift or whether gradual maturation of the host antibody mediated immune response was required to eradicate the infection . We detected no signs of antigenic drift in ompA or other chlamydial genes . This finding is consistent with epidemiological studies showing MOMP does not change rapidly even in trachoma endemic areas [17] , [18] . However , we found that a slow maturation of the humoral immune response specific to multiple chlamydial antigens strongly correlates with reduction in early infection burdens , complete eradication of infection , and partial immunity to re-challenge . In a study similar to ours , Caldwell et al . examined the temporal serum antibody response in cynomolgus macaques infected ocularly with C . trachomatis serovar B by western blotting [19] . In general , they found a simple temporal relationship in antigen recognition with a uniform and predominant response against the MOMP at approximately 21 days post-infection . A more variable and later appearance of antibody specific to chlamydial HSP60 and lipopolysaccharide was also detected; however antibody recognition was limited to these three antigens . Those findings agree with ours in part , but there are noteworthy differences between the studies both in experimental design and methodology . The Caldwell et al . study infected macaques ocularly with C . trachomatis serovar B , a laboratory reference strain of unknown virulence . The strain produced a self-limiting acute conjunctivitis that resolved spontaneously at approximately 8 weeks post-infection without delayed clearance or episodes of reactivation; no culture positivity was seen after week 6 . The animals were not re-challenged following clearance of the primary infection so their protective immune status was unknown . Finally , western blotting was used to define anti-chlamydial antibody specificity over the infection period , a method that detects antibodies primarily against denatured protein antigens . In contrast , we employed a recent serovar A trachoma clinical isolate ( A2497 ) , a strain shown to have enhanced virulence characteristics for macaques compared to a laboratory adapted C . trachomatis serovar A strain ( HAR-13 ) [7] . Ocular infection with A2497 resulted in a long-duration primary infection that did not spontaneously resolve until approximately 9–15 weeks post-challenge . Notably , A2497 long-duration infections produced repeated episodes of infection reactivity , modulating from culture negative to culture positive periods over the course of a primary infection that was accompanied by severe to moderate clinical disease . Collectively these results argue that A2497 infection of NHPs more closely mimics trachoma in humans [7] . We also re-challenged A2497 infected macaques after spontaneous clearance to evaluate protective immunity . Finally , and importantly , we employed RIP assay rather than Western blotting to study the humoral immune response to A2497 infection . The RIP assay utilizes intrinsically radiolabeled chlamydiae prepared from infected cells under non-denaturing conditions . This is a valid methodological difference as Western blotting measures only denatured antigen found in purified chlamydial EBs , whereas RIP assay is capable of detecting all chlamydial antigens associated with infected cells; these include antigens associated with both the EB and RB developmental forms of the organism and chlamydial secreted antigens . Thus , the larger spectrum and diversity of antigenic targets extracted under non-denaturing condition provides a unique opportunity to comprehensively study infection induced humoral immunity to conformationally dependent epitopes of these antigens . We argue that this is relevant and important to trachoma vaccine design . This hypothesis is supported by the findings that native MOMP is a superior immunogen for eliciting protective immunity in both the mouse and NHP [7] , [20] . In another more recent study , Lu et al . investigated the antibody response in trachoma patients in a genome-wide scale [21] . Lu et al . used GST fused antigens expressed in a heterologous system and they describe recognition of multiple potentially important chlamydial proteins . However , their experiments failed to recognize both PmpD and MOMP , the two most immunogenic proteins in chlamydiae , likely because recognition of these antigens in humans is dependent on native confirmation . Our findings imply that an efficacious trachoma vaccine might require multiple chlamydial antigens expressed in their native confirmation . A logical cocktail of recombinant antigens based on our results would consist of MOMP , PmpD , CPAF , and Pgp3 , although other low molecular weight antigens associated with infection eradication that were not identified in this study may also be needed . Mechanistically , the kinetics of antibodies specific for these proteins infers that antibody specific to MOMP , a surface exposed and antibody neutralizing target , likely function in reducing early chlamydial burdens , whereas antibodies to CPAF , Pgp3 and PmpD , another surface exposed neutralizing target , are required to eradicate infection . This hypothesis is consistent with previous reports using these antigens as single subunit vaccines in both NHP and murine models . Kari et al . immunized NHP with native-MOMP extracted from C . trachomatis EBs that resulted in a significant 70 fold reduction in infectious ocular burdens early post infection [5] . Interestingly , native-MOMP immunization had no effect on the duration of the infection suggesting that complete clearance of ocular chlamydial infection requires additional non-MOMP related antigens . In numerous studies employing recombinant CPAF and Pgp3 as immunogens in mice , protection was repeatedly limited to later time periods post-challenge with no to minimal effect on reducing early chlamydial burdens or shedding [13] , [22]–[27] . A caveat of our study is that we cannot conclude antibodies are the primary protective mechanism as they might simply be surrogate markers of protective T cell immune responses . It is unclear why the C . trachomatis specific humoral immune response takes so long to fully develop . It is possible that recognition of certain potentially protective epitopes requires long-term antigen stimulation . This would be similar to the natural protective immunity that older individuals develop in hyper endemic areas after years of long-term exposure through repeated reinfection episodes . Nevertheless , we believe a vaccine containing a cocktail of these antigens might generate protective immunity capable of functioning at early and late time points during chlamydial infection . A vaccine with these protective properties would be beneficial in preventing or reducing chlamydial transmission and re-infection , both of which are driving forces in the pathology of blinding trachoma . The difficulty however with this approach is if the native conformation of these proteins is critical to protective immunity , as our findings imply , producing these immunogens as recombinant proteins in heterologous expression systems will be a major yet worthy challenge . An alternative vaccine approach that would negate this requirement is the use of a live-attenuated trachoma vaccine , such as the recently described live-attenuated plasmidless vaccine [28] . The advantage of a live-attenuated vaccine is that it naturally presents multiple antigens to the mucosal immune system in their native conformation . This not only allows appropriate recognition by B cells to generate antibody against native antigens but confers the ability of the host to process and present these antigens in association with class I and II HLA molecules that may be equally important for T cell immunity . If successful , a multivalent subunit vaccine or an efficacious live attenuated vaccine could ultimately prevent hundreds of thousands cases of conjunctival scarring and blindness . | Chlamydia trachomatis is the etiological agent of trachoma the world's leading cause of infectious blindness . In this study , we investigated whether delayed clearance of a primary infection in nonhuman primates was attributable to antigenic variation or related to gradual changes in the humoral immune response specific to chlamydial antigens . We found that antigenic variation was not related to the inability of monkeys to efficiently resolve their infection . However , exploring changes in the immune response as a possible reason for delayed clearance revealed that antibody recognition was restricted to the antigenically variable major surface protein and a few conserved polypeptides . Antibody recognition of the major antigenically variable surface protein correlated with the initial reduction in infectious burdens while recognition of conserved chlamydial antigens occurred late and correlated with infection eradication . These findings suggest that delayed clearance of chlamydial infection is not the result of antigenic variation but a consequence of a gradually evolving humoral immune response specific to different chlamydial antigens . Antibody recognition was at least partially directed against conformational epitopes , indicating that an efficacious subunit trachoma vaccine might require a combination of antigens delivered in their native conformation . | [
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] | [
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] | 2013 | Antibody Signature of Spontaneous Clearance of Chlamydia trachomatis Ocular Infection and Partial Resistance against Re-challenge in a Nonhuman Primate Trachoma Model |
The emergence of infectious diseases of zoonotic origin highlights the need to understand social practices at the animal-human interface . This study provides a qualitative account of interactions between humans and wild animals in predominantly Mende villages of southern Sierra Leone . We conducted fieldwork over 4 months including participant and direct observations , semi-structured interviews ( n = 47 ) , spontaneously occurring focus group discussions ( n = 12 ) , school essays and informal interviews to describe behaviours that may serve as pathways for zoonotic infection . In this region , hunting is the primary form of contact with wild animals . We describe how these interactions are shaped by socio-cultural contexts , including opportunities to access economic resources and by social obligations and constraints . Our research suggests that the potential for exposure to zoonotic pathogens is more widely distributed across different age , gender and social groups than previously appreciated . We highlight the role of children in hunting , an age group that has previously not been discussed in the context of hunting . The breadth of the "at risk" population forces reconsideration of how we conceptualize , trace and monitor pathogen exposure .
Recent occurrences of infectious disease outbreaks involving pathogens such as Lassa virus , Ebola virus and simian retroviruses have led to increasing concern about emerging zoonoses [1] . The probability of a zoonotic infection depends in part upon the frequency and nature of contact between animal hosts and humans [2] . Thus , in addition to the biological aspects of pathogen transmission , zoonotic diseases must be understood as resulting from social processes . Social science approaches are therefore an essential component in the study of infectious diseases [3] . More specifically , the environmental , social , cultural and economic aspects of animal-human interactions must be studied alongside human and animal behaviours to determine pathways for infections [2] . As a socially dense , gendered and sometimes secretive activity , hunting is a prime topic for in-depth social scientific analysis . Hunting and butchering wild animals poses a significant risk for transmission because such activities expose humans to animal secretion and fluids through bites , scratches and handling organs [4] . Outbreaks of Ebola Virus Disease ( EVD ) , for instance , have been directly attributed to handling various wild mammal species during hunting or as carrion [5] . Hunting has been a major topic in disciplines such as anthropology , including in West and Central Africa [6–8] . However , the public health dimensions of these animal-human interactions are only beginning to be subject of sustained ethnographic consideration [9–12] . The multifaceted nature of animal-human interactions can pose considerable methodological challenges for research , particularly when such practices are hidden or secretive . Hunting , for instance , can be forbidden by law or custom; it can be associated with disease; and it can involve practices or knowledge that amplify social status or satisfy social requirements . The 2013–2016 EVD outbreak in West Africa heightened these ambiguities following a ban on hunting , sale and consumption of meat from wild animals . Using questionnaire surveys to investigate sensitive topics may introduce systemic bias [13–15] . In particular , children are more difficult to study through quantitative survey techniques and their role as a potential group at risk from zoonotic infection remains largely unrecognized . Such difficulties can be alleviated by immersive qualitative and open-ended study based on building trusting relationships , developed over lengthy periods of time . Long-term qualitative studies allow researchers to build a rapport with informants that can reveal information not accessible through other methods . Open-ended approaches , with a strong observational component , facilitate understanding of behaviours at the animal-human interface that are routinized and/or controversial [16] . Anthropological studies of animal-human interactions such as hunting and butchering practices can offer a critical entry point to understanding zoonotic risk dynamics [17 , 18] . Ethnographic approaches help to frame public health understanding of the ways different social groups engage with animals and can inform the design of disease surveillance measures . Understanding the drivers of animal-human interactions is important when designing risk prevention strategies . Further , a fuller appreciation of such interactions can help to contextualize research in zoonotic disease ecology . This is of particular use in West Africa following the renewed interest in zoonotic disease ecology in the region with the presence of numerous wild animal reservoirs for zoonotic pathogens , including Lassa virus [19] and ebolavirus , which has possibly been circulating in West Africa for decades [20–22] . The aim of this study was to provide a finely grained description of human actors and behaviours that may serve as pathways for zoonotic infection from wild animals , and to understand the drivers behind these behaviours .
The fieldwork was conducted in the Southern ( Bo , Pujehun and Moyamba districts ) and Eastern ( Kenema district ) Province of Sierra Leone ( Fig 1 ) . We conducted fieldwork in urban and rural locations . Bo City is the second largest city of Sierra Leone and its inhabitants are involved in a range of economic activities including small-scale trading and salaried employment . Bo City borders swamps and grasslands merging into a mosaic of swidden farmland and secondary forests . In rural locations , three villages were chosen based on previous fieldwork and familiarity with the field researchers . These villages ( between 6 and 12km from the outskirts of Bo City ) were visited at a minimum twice weekly during the fieldwork and provided the core of the data collected . Six other villages , identified through snowball sampling , were chosen to represent more isolated areas ( up to 40–50km from a major town ) but were only visited between 1 and 4 times . Villagers depend on fishing , hunting , swidden farming , cultivation of small plots and small-scale trade for subsistence and income . Fieldwork was conducted for a total of 4 months in 2015 ( August , September , November , and December ) , overlapping the rainy ( May–October ) and dry seasons ( October–May ) . We also draw on interviews and observations collected in May and June 2014 from the same study site . Some of the fieldwork took place during the EVD outbreak , although we worked in districts without active cases during fieldwork . Given the sensitive nature of our research during the EVD epidemic , we began by visiting informants known to us through previous fieldwork . Transects through villages , forests and swidden served to identify people engaging in behaviours of interest . We conducted semi-structured interviews and informal discussions until data saturation was achieved . The discussions were conducted in English , Mende or Krio ( creole English ) . Interviews lasted between 30 and 60 minutes . The questions were pre-determined and covered food security , local gastronomy and forms of interactions with wild animals , which included their practical and symbolic significance . A separate question set covered the impact of the EVD epidemic and is discussed elsewhere . Photos of wild animal species were used to determine vernacular names and ensure accuracy of translation . Although the interview guides were pre-defined , questions were posed in an informal manner to encourage discussion . The observation guides used for direct and participatory observations covered forms of direct and indirect contact between humans and wild animals . Participatory observations were mainly done with trusted informants . Informants were given the opportunity to ask questions about the link between wild animals and EVD . Our answers covered risk factors for zoonotic infection and current hunting regulations . Thereafter , no attempt was made to challenge the activities observed , except to encourage basic biosecurity measures when handling animal carcasses . We set two simple written essay questions for children aged 14 to 16 years attending the school of village A . The questions asked children to describe an animal that lives in the bush and to describe the last hunt in which they participated . Recordings and field notes were immediately transcribed into English by the field researchers ( JB and MK ) using Word 2011 ( Microsoft Corp . , Redmond , WA ) . The data was rendered anonymous from the onset and shared online with the research team . Analysis was carried out continuously and interview and observation guides were amended iteratively . Triangulation was obtained with three field researchers ( JB , MK and MD ) and multiple methods of data collection . Coding was done in MS Word 2011 using a thematic analysis . A priori codes included forms of interactions with wild animals , use of wild animals and food security . Inductive codes were applied to understand the social , cultural and economic context of these interactions . The study was approved by the ethics committee of the Government of Sierra Leone and the University of Exeter . Participants were provided with information sheets that were read out . We emphasized that participants did not have to answer questions and could end their participation at any time without consequences . Written and oral consent was obtained from the respondent or a parent for participants under 18 years .
The Mende classify animals into three broad categories: livestock , pets ( dogs and cats ) and wild animals ( “bush animals” ) . The term “bush animals” refers to species that live in or outside of villages but are not domesticated . Villagers discuss these primarily as a crop pest ( e . g . rodents ) or as resource to be exploited . “Bush animals” have individual vernacular names in Mende . In the following text , we group species according to their size , ranging from small ( small rodents , squirrel , mongoose , bat , bird , amphibians , reptiles ) , medium ( Gambian pouched rat , cane rat , brush tailed porcupine , genet cat , small non-human primates ) , and large species ( forest antelope and forest hog ) . We set out with about 7 adult hunters and a dozen children ( aged around 6–12 ) , most of whom carried nets on their head . Everyone brought their own cutlass ( I brought one to fit in , ) and dogs obediently followed their owners’ steps . You could tell that both dogs and people were excited by the hunt , and as we made our way through the bush , everyone became progressively quieter . The first hunt was unsuccessful and we moved to a second area ( about a kilometer from the previous one ) , again unsuccessful . To get there , we passed along a long fence with many traps set along it . The third hunt was successful; a grass cutter ( cane rat ) got tangled in the net and was jumped upon by the hunters . They kept it alive until I got there and then killed it by punching his head in ( you cannot used a cutlass as it destroys the net ) . Relatively fast to unconsciousness , no more than 7 seconds . Blood everywhere . The kill was immediately handed to a boy ( who was very proud of it ) and ran away in the bush with it on his shoulders . The hunt continued again and we moved twice more until the hunt was declared over ( field notes from a communal hunt DO-04A ) . The only preparation methods for meat that we observed involved singing the hair , followed by gutting and butchering ( Fig 5 ) . When selling meat to traders , hunters usually sell the entire carcass because of the higher price it will receive . In this instance , the market seller butchers the carcass . Only certain parts of animals are not eaten ( nails , hooves , and horns ) . The gut and the gall bladder are the only viscera that are consistently removed , as they are deemed to taste bad and be poisonous . Some people choose to remove the genital organs because of the smell . Any unwanted organs are thrown away , fed to dogs , or kept for use as bait in fish and crustacean traps . Bones are eaten entirely unless too big , in which case they are broken and the marrow is sucked out , which is particularly prized by some . Preparation is not gender specific . Cooking is done over a fire . It involves grilling meat over the fire , frying it in palm oil , boiling it in water , or more often , a combination of all . Leftover meat can be conserved by smoking it over a fire . Palm oil brought to boiling point is widely believed to kill pathogens and other impurities in food , such as rodent poison , which is occasionally used to protect swidden . While the majority of people prefer eating well-cooked meat and gag at the suggestion of eating raw meat , two people stated that they prepared a soup from boiled meat that retains raw blood , as it is considered more “nourishing” in terms of protein , as well as better tasting: “[We] let the blood just escape a little and then ( laughs ) we start eating it [the meat] . Sometimes we do not cook it , we do not cook , we only put it on the fire , make it look fine then we eat it . And then you will really eat and enjoy it” ( urban farmer , IDI-05B ) . During our observations , carcasses were always handled with bare hands and blood was rinsed off with water or sometimes with chlorine water , which is present in villages since the EVD outbreak .
The most common activity placing humans in contact with wild animals observed in our study was hunting and slaughtering , which are associated with zoonotic disease infections and disease emergence [24] . Understanding how differences in demographic , socio-cultural and economic characteristics influence such activities is important to inform pathogen surveillance and prevention measures . Our research suggests that the “conventional” narrative of hunting and its role in pathogen transmission is incomplete . Previous research on hunting in Western and Central Africa commonly describes an activity conducted by adult males , while butchering and trading wild meat is done by women who are exposed to fluids through cuts and scratches [25–27] . This narrative of the “cut hunter” attributes pathogen emergence to “bushmeat hunters” who are invariably assumed to be adult males [11] . In addition , children are rarely thought of as being in contact with wild animals despite being presumed index cases in at least three EVD outbreaks [28–30] . Further , to our knowledge , questionnaire surveys looking at exposure to wild animals do not recruit subjects below 15 years of age [25–27 , 31] , yet we frequently recorded hunting among children below this age group . One study on animal-human contacts in Uganda suggests that children from the age of 3 years are exposed to non-human primates , however these results were derived from adults responding on behalf of their children [32] . We previously showed that hunting of small rodents is more widely distributed across age , gender lines and social groups than previously appreciated [14] . In our present study , we sought to determine whether such observations were also pertinent to other species of wild animals , in particular those species that are not present in domestic spaces ( as small rodents are ) and might be associated with different hunting norms . While our research confirms that among the Mende , hunting is , indeed , considered a traditional adult male activity—the respective roles of hunting and fishing among men and women reflecting divisions of activities that mark gender identity [6 , 8]—we find that children and women are significant actors in complex collaborative practices for catching and preparing wild animals . With the exception of large species that are deemed physically dangerous and are associated with witchcraft ( buffalo , forest hogs , leopards ) , the participation of women and children does not conform to assumed gender and age-related roles . Rather , hunting , slaughter , consumption and trade of wild animals are determined by individual circumstances and practicalities . Crucially , contact with wild animals often involves children who , compounded by traditional family hierarchy related to food access , frequently engage in high-risk practices during hunting and preparing meat from wild animals . Thoroughly cooking meat is considered sufficient to inactivate EVD in blood , but consuming undercooked meat , which was reported by children and adults for different reasons , is likely to present a risk of infection [33] and a similar degree of risk may exist when consuming bone marrow . Not all species of wild animals present the same risk of transmitting zoonotic pathogens . For example , certain species of fruit bats are suspected reservoirs for ebolavirus [34] and although we did find some villages organizing bat hunts , we did not find any evidence of systematic bat trade . This could however be specific to ethnic groups or villages and requires further investigation . Other species of mammals including duiker antelopes and NHPs are susceptible to ebolavirus [35 , 36] and hunting sick animals or scavenging carrion is a major risk for ebolavirus infection [5] . We did not identify any particular taboos against eating species that are known to pose a risk for zoonotic diseases , or against collecting fresh carrion , however we did not consistently ask whether people would eat sick wild animals . The process of trapping does not allow trappers to monitor the health of animals before killing them . Further , raw meat is widely distributed across commercial and social networks , with the potential to spread pathogens , with limited possibility for monitoring or traceability . Species , and their associated pathogens , are distributed according to criteria related to market value . Many of the taxa associated with zoonotic pathogens , such as small rodents [37] and bats , have little market value , and are mostly kept for personal consumption and inter-village trade . Children privilege such small sized taxa for their ease of hunting and their low market value , an observation also reported in a nutritional survey of animal species consumed among children in the Democratic Republic of the Congo [38] . We documented occurrences of urban hunting in fringe sites of Bo City , which suggests that such anthropogenic ecotones should be targeted in disease prevention strategies . Although such zones have previously been associated with pathogen emergence [39] , our findings stand in contrast to common intervention designs which assume , incorrectly , that there is little contact between humans and animals in urban zones , as has recently been described in Uganda [32] . Sierra Leone has one of the highest rates of malnutrition and child under-nutrition in the world [40] . In this context of chronic food insecurity , disposing of hunted or trapped game—an important source of nutriments for growth [38 , 41]—is rarely an option , especially where access to alternative sources ( fish or domestic animals ) is scarce or expensive . Family hierarchies prioritise protein consumption among adults , which compounds the difficulty faced by children in obtaining animal protein , encouraging them to hunt . We previously reported how the consumption of rodents is strongly linked to food security [14] and extend this observation to other wild animals that are considered a threat to crops , on which the Mende are highly dependent . The link between crop protection and species hunted has been illustrated in the Eastern Province of Sierra Leone , where cacao farmers were observed to commonly eat monkeys ( a cacao pest ) hunted on their farms [6] . Adult informants also discussed wild meat in terms of taste , perceived therapeutic and nutritional value , and as a source of income generation , as previously reported in Western and Central Africa [42 , 43] . Social , political and economic processes can influence host-pathogen dynamics , for example through changes in reservoir abundance and contact with reservoir hosts [44] . Comparing current practices with accounts from older informants , we described how social changes have modified interactions between humans and wild animals . Communal hunting was discouraged in post-civil war policies because it had been used as a means for village chiefs to impose their authority upon subjects and test for political dissent [8] . This coincided with an increase in fast reproducing , resilient species such as rodents that thrive in a modified agricultural landscape [45] . Previous studies have shown how changes in agricultural practices can influence biodiversity and lead to adaptions in hunting practices , for example “garden hunting” near domestic spaces [46] and trade of wild meat [47] . Such observations support our data that the increasingly small size of animals hunted no longer justify sacrificing time for communal hunting and could explain the reported increase in the use of traps and focus on trapping smaller species , with the potential for changes in zoonotic pathogen ecology . Post-war policies also directly influenced hunting practices by imposing a firearm ban , making bats more difficult to hunt in Sierra Leone compared to Guinea where shotguns are common , and cartridges are loaded with grit to kill large numbers of bats ( Bonwitt , J . ; pers . obs . ) . Our fieldwork was affected by the EVD epidemic . Sensitization messages erroneously emphasized the risk of infection through contact with wild animals and hunting was penalized . These measures raised the degree of respondent anxiety on the topic of hunting . The quality of discussions often considerably improved when we refrained from recording interviews . For ethical concerns , the research team answered frequent questions about the risks of ebolavirus infection from wild animals , which arose during discussions and may have affected our results . Our presence initially generated suspicion; however this was minimized thanks to our work in the area prior to the epidemic . Through observations , discussions and participatory observations , we learned to discern the subtle traces of hunting and trapping activities , such as people with hunting nets or concealed rifles , concealed traps or a cleaned village ( a punishment imposed for letting prey escape ) . Despite these reassurances , we cannot exclude the possibility that we underestimated the frequency of certain behaviours of interest or missed some altogether . Although we describe behaviours occurring among women and children , the majority of our semi-structured interviews were conducted with males ( 70% ) . However , much of our data was obtained , and indeed strengthened from spending time in villages , conducting participatory observations and informal interviews with women and children . Our research could have benefited from more interviews with women , for which a female field researcher would have been beneficial . Our study could have been enriched by quantitative data . However , we sought to address the paucity of qualitative data on hunting as explored from a public health perspective . In providing a finely grained description on hunting practices we hope that our results will broaden the scope of future quantitative research on this topic . Our observations corroborate previous studies of hunting throughout West and Central Africa [6 , 11 , 25–27 , 31 , 32] but emphasize the social nuances of the practice by expanding on the diversity of actors , social norms and motivations involved . The “cut hunter” narrative which assumes most hunters to be adult males has underpinned disease intervention strategies , and remains a subject of debate and research [48] . Previous research has shown the need to expand beyond the “bushmeat paradigm” to include other forms of animal-human contacts as risks for zoonotic infections and that are unrelated to hunting practices [32] . Yet even within the much studied “bushmeat paradigm” , we find that the diversity of actors hunting wild animals and the breadth of the "at risk" population forces reconsideration in how we conceptualize , trace and monitor pathogen exposure . These results also underscore the challenges of interventions , surveillance , research and sensitization campaigns . To address such complexity , intervention strategies should become more diversified and context-specific . In particular the role of children should be recognised; specific intervention strategies should be tailored to children’s specific hunting practices . Finally , our findings provide a base for further investigations to determine risk factors for zoonotic infections in the West African region . A better understanding of the interactions between humans and reservoir hosts can help to elucidate the mechanisms of disease spillover into human populations in Sierra Leone [49] by linking epidemiological , ecological and ethnographic data . | Studying how and why humans interact with animals is important to understand the transmission of zoonotic diseases ( infectious diseases transmitted from animals to humans ) and how to prevent and control them . We conducted a qualitative study to understand how and why people come into contact with wild animals in the Southern province of Sierra Leone , a region with numerous wildlife species known to carry zoonotic diseases . Previous studies on hunting in sub-Saharan Africa principally describe adult men as hunters and adult women as retailers of meat from wild animals . Based on our results , we seek to broaden the category of people deemed “at risk” of zoonotic diseases through hunting by including women and children . In particular , because of their limited physical abilities and social position , children hunt under different circumstances than those of adults . Our results have implications for zoonotic disease research and prevention , for example by ensuring children are integrated in health interventions and that their unique reasons to hunt are taken into account during such processes . | [
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] | 2017 | Participation of women and children in hunting activities in Sierra Leone and implications for control of zoonotic infections |
RNAi screens have , to date , identified many genes required for mitotic divisions of Drosophila tissue culture cells . However , the inventory of such genes remains incomplete . We have combined the powers of bioinformatics and RNAi technology to detect novel mitotic genes . We found that Drosophila genes involved in mitosis tend to be transcriptionally co-expressed . We thus constructed a co-expression–based list of 1 , 000 genes that are highly enriched in mitotic functions , and we performed RNAi for each of these genes . By limiting the number of genes to be examined , we were able to perform a very detailed phenotypic analysis of RNAi cells . We examined dsRNA-treated cells for possible abnormalities in both chromosome structure and spindle organization . This analysis allowed the identification of 142 mitotic genes , which were subdivided into 18 phenoclusters . Seventy of these genes have not previously been associated with mitotic defects; 30 of them are required for spindle assembly and/or chromosome segregation , and 40 are required to prevent spontaneous chromosome breakage . We note that the latter type of genes has never been detected in previous RNAi screens in any system . Finally , we found that RNAi against genes encoding kinetochore components or highly conserved splicing factors results in identical defects in chromosome segregation , highlighting an unanticipated role of splicing factors in centromere function . These findings indicate that our co-expression–based method for the detection of mitotic functions works remarkably well . We can foresee that elaboration of co-expression lists using genes in the same phenocluster will provide many candidate genes for small-scale RNAi screens aimed at completing the inventory of mitotic proteins .
RNA interference ( RNAi ) in Drosophila cell cultures is a powerful tool for the identification of proteins involved in mitotic cell division . The addition of a double stranded RNA ( dsRNA ) to the cell medium leads to rapid downregulation of the corresponding mitotic protein , resulting in a specific and penetrant phenotype [1]–[10] . Identification of mitotic genes/proteins by RNAi has thus far relied on two general approaches . The first involved genome-wide screens to detect gross changes in cell and nuclear morphology [3] , [6] , [8] , defects in cytokinesis [7] , [8] or in spindle and centrosome structure [10] . Most of these screens were performed using automated microscopy [3] , [6] , [8] or the visual analysis of a very simple phenotype [7] . In a second approach , RNAi experiments were performed on selected gene groups , such as those encoding kinesins , actin-binding proteins , kinases or phosphatases [2] , [4] , [5] , [9] . Cells depleted for these proteins were examined by standard fluorescence microscopy that allowed detection of a wide spectrum of mitotic abnormalities . Although genome-wide and gene-specific approaches have identified many mitotic functions , the inventory of such proteins is likely to be largely incomplete . For example , RNAi has never been used to detect genes involved in establishing proper mitotic chromosome morphology or required to maintain mitotic chromosome integrity . We present here a novel approach for the identification of mitotic proteins by RNAi . Using a co-expression-based bioinformatic procedure , we generated a list of 1000 Drosophila genes highly enriched in mitotic functions . We then performed RNAi experiments for each of these 1000 genes , and examined both mitotic chromosome structure and spindle morphology in the treated cells . This screen has led to the identification of 142 mitotic genes , 70 of which have not been previously implicated in mitosis .
Numerous studies indicate that genes involved in the same biological process tend to be transcriptionally co-expressed ( see , for example , [11]–[13] . We thus exploited extant microarray data [14] to rank the complete set of annotated Drosophila genes according to their co-expression with six well-characterized genes representative of different aspects of mitosis: gluon ( glu ) encodes a condensin [15]; ida/APC5 , specifies a subunit of the anaphase promoting complex ( APC/C [16] ) ; cid/CenpA is the gene for the centromere-specific histone H3 variant required for kinetochore assembly [17] , [18]; Eb1 encodes a microtubule ( MT ) -associated protein required for spindle assembly [19]; zw10 specifies a component of the RZZ complex that helps target cytoplasmic dynein to the kinetochore and that is involved in the spindle checkpoint [20]; and finally sti ( Citron kinase ) encodes a serine/threonin protein kinase required for the completion of cytokinesis [21]–[23] . Using the Pearson correlation coefficient , the expression of these prototype genes was correlated with the expression levels of most Drosophila genes across 89 different microrray experiments [14] . The 13 , 166 probesets contained in this dataset were separately ranked for their co-expression with each prototype gene . We then generated a ranked consensus co-expression list by combining the six gene-specific lists ( Table S1 ) . To validate our bioinformatic approach , we determined the rank in the consensus co-expression lists of 164 Drosophila mitotic genes; these genes represent most of the Drosophila mitotic genes so far identified but do not include the genes identified in the recent genome-wide screen performed by Goshima et al . [10] . As shown in Figure 1A and Tables S2 and S3 , the first 1000 genes of our consensus co-expression list include 46% of the 164 known mitotic genes . This implies that the first 1000 genes of the same list should contain roughly half of all mitotic genes , including those that are currently unknown . To identify new mitotic genes , we synthesized a dsRNA for each of the first 1000 genes in our consensus co-expression list . In designing the primers for such RNAs , we minimized gene overlap to avoid off-target effects of dsRNAs [24] , [25] ( Table S4 ) . Each dsRNA was then added to S2 cells grown in 3 milliliters ( ml ) of culture medium . After a 96 h treatment with dsRNA , the cells were split into two aliquots . 2 ml were fixed with formaldehyde and then stained for both tubulin and DNA . The resulting preparations were then blindly scored by at least two independent observers for abnormalities in spindle morphology and chromosome segregation . The remaining 1 ml of cell suspension was incubated with colchicine for two hours , hypotonically swollen , fixed with methanol/acetic acid , and stained with DAPI . The metaphase chromosomes obtained in this way were then blindly examined by at least two observers for abnormalities in chromosome structure and/or the presence of chromosome aberrations . We performed two independent RNAi experiments for each gene . In most of these experiments , we examined 50 colchicine-arrested metaphases and 50 tubulin-stained mitotic figures . If the results of the two experiments were significantly different , we performed additional experiments to define the RNAi phenotype . An RNAi phenotype was considered positive only when the frequency of affected cells was significantly different from controls with p<0 . 001 ( using the χ2 contingency test; see Methods ) . Our screen identified 155 genes whose inactivation by RNAi causes a strong mitotic phenotype . Based on phenotypic analysis , these genes can be grouped in seven broad categories that we further subdivided in 18 phenoclusters ( PHCs ) [26]: 13 genes required for progression through the cell cycle , identified by dsRNAs that result in a complete ( or nearly complete ) absence of mitotic figures ( PHC: NM ) ; 44 genes required for chromosome integrity , identified by dsRNAs that cause chromosome aberrations ( PHC: CA ) ; 11 genes required for proper mitotic chromosome condensation ( PHCs: CC1–CC3 ) ; 41 genes required for regular chromosome segregation ( PHCs: CS1–CS5 ) ; 33 genes required for spindle assembly ( PHCs: SA1–SA4 ) ; 7 genes required for cytokinesis ( PHCs: CY1 and CY2 ) ; and 6 genes required for multiple mitotic functions ( PHCs: SC1 and SC2 ) ( Figures 1B , 2 , 3 , and 4C; Table S5; a synopsis on the functions of these genes can be found in Table S6 ) . Remarkably , the distribution of these mitotic genes in our co-expression list was clearly nonrandom: their frequency decreased with an increase of their rank , further validating our co-expression approach ( Figure 1C ) . We identified 13 dsRNAs that result in the absence of dividing cells at 96 h after treatment initiation ( Table S5 ) . Six of these genes ( cdc2c , cyclinA , cyclinE , geminin , ran and string ) are well-known cell-cycle regulators . Two genes , RpII140 and RpII215 , encode the 140 and 215 kDa subunits of RNA polymerase II , respectively . Three genes are involved in RNA metabolism and encode either canonical splicing factors ( Prp8 and SF1 ) or the small DebB ribonucleoprotein , which is also likely to be involved in RNA splicing . Defects in PRPF8 , the human homolog of Prp8 , are one cause of Retinitis pigmentosa . Of the remaining two genes , CG9273 encodes a protein with similarity to a subunit of DNA replication factor A , and Bx42 specifies a protein involved in Notch signal transduction ( Table S6 ) . Although the 1000 genes in our list were selected for their co-expression with mitotic genes , our screen uncovered several functions required for chromosome integrity ( Figures 1 and 4; Tables S5 and S6 ) . As shown in Figure 4 , we found 44 dsRNAs that significantly increase the frequency of spontaneous chromosome aberrations . Of the 44 genes identified by these dsRNAs , 38 have apparent human orthologs ( Figure 4C ) yet only 4 have previously been implicated in the maintenance of chromosome integrity ( Table S6 ) . These genes can be subdivided in several broad classes , based on their putative functions: ( 1 ) genes required for DNA replication , including Ribonucleotide reductase ( RnrS ) , DNA primase ( DNAprim ) , DNA polymerase alpha ( DNApol ) , Orc5 , RfC40 , Rpa70/RPA1 and peterpan ( ppan ) ; ( 2 ) genes involved in both DNA replication and repair , such as mus209/PCNA , cul-4 , thymidilate synthetase ( Ts ) and CG6854/CTP synthase; ( 3 ) genes that mediate different aspects of DNA repair but are not known to participate in DNA replication , such as DDB1 , okra/RAD54L , CG6197/XAB2 and CG7003/MSH6; ( 4 ) genes involved in transcription and RNA maturation , including Dp/TFPD2 , CG10354/XRN2 , Taf6 , l ( 2 ) NC136/CNOT3 , noi/SF3A3 , CG7757/PRPF3 ) , without children ( woc ) , CG6480/FRG1 and CG6686/SART1 ( Table S6 ) . Chromosome aberrations were also induced by RNAi against 8 genes whose diverse functions are not easily classified into the four groupings above . These include BEAF-32 , that encodes a chromatin insulator factor; dnk , that specifies a deoxyribonucleotide kinase similar to the human mitochondrial kinase TK2; Su ( var ) 2-10 , whose product is an E3 SUMO ligase; the H3 . 3B histone variant gene; SMC1 , that encodes a conserved cohesin involved in the Cornelia de Lange syndrome in humans; Dcp-1 , that specifies a caspase precursor; Megator ( Mtor ) that encodes a component of the putative spindle matrix; and CG17446 , whose product is homologous to a subunit of the mammalian Set1 histone methyltransferase complex ( Table S6 ) . Our screen also identified 12 chromosome stability genes without any assigned putative functions , 7 of which are conserved in humans . Together , these results indicate that the maintenance of chromosome stability requires a large number of functions , many of which remain to be identified . The analysis of tubulin-stained mitotic figures revealed an interesting phenotype associated with the presence of chromosome aberrations . We observed many metaphase figures with the centric portions of broken chromosomes aligned at the metaphase plate and the acentric fragments near the cell poles ( Figure 4B ) . Immunostaining for the kinetochore marker Cenp-C [27] verified that most chromosome fragments at the poles of these metaphases were indeed devoid of centromere ( Figure 4B ) . This phenotype suggests that chromosome fragments severed from their kinetochores are transported to the cell poles . A similar phenomenon has been observed in plants ( Hemanthus ) and in crane fly spermatocytes . In both systems , when a metacentric chromosome is cut with the laser , the resultant acentric fragment moves to the closest cell pole at the same velocity as anaphase chromosomes [28] , [29] . To explain this phenomenon , it has been suggested that the acentric chromosomes fragments adhere to the lateral surfaces or plus ends of microtubules and are transported poleward by the microtubule flux [29] . We believe that this mechanism also occurs in Drosophila S2 cells . Strong support for this view comes from observations on RPA70-depleted cells , which exhibit extreme chromosome fragmentation but form regular spindles . In these cells , most acentric fragments accumulate at the poles of ana/telophase figures , suggesting that they are driven poleward by microtubule-based forces ( Figure 4B ) . We identified 41 genes required for regular chromosome segregation . These genes are not required for spindle formation , as cell depleted for their products do not exhibit defects in late prophase/early prometaphase spindles . However , metaphase and ana/telophase spindles are often highly abnormal with respect to spindle morphology and the distribution of chromosomes along the spindle . The genes required for chromosome segregation ( CS ) can be subdivided into five phenoclusters ( CS1 , CS2 , CS3 , CS4 and CS5 ) based upon differences and similarities in the RNAi phenotypes ( Figure 2 ) . The CS1 group includes only the doubleparked ( dup ) gene . In most dup RNAi metaphase-like figures , the chromosomes are not replicated and have the appearance of single chromatid ( Figure 5B ) . This is likely to results in a merotelic attachment of the spindle fibers to the kinetochore , leading to an impairment of chromosome movement during anaphase ( Figure 5B ) . This phenotype has been previously observed in embryonic cells of dup mutants , suggesting that dup is required for both DNA replication and the checkpoint that prevents mitosis until completion of S-phase [30] , [31] . RNAi for the 5 genes in the CS2 group resulted in precocious sister chromatid separation , lack of chromosome congression to the cell equator at metaphase , and unequal or otherwise abnormal sister chromatid segregation ( Figures 5C and S1 ) . Four of the genes included in this CS2 phenocluster ( bub1 , bub3 , zw10 and rod ) are well known components of the spindle checkpoint machinery ( Table S6 ) . The other gene , dalmatian ( dmt ) has never been implicated in this checkpoint . However , since studies in C . elegans have clearly shown that genes with similar RNAi phenotypes are often required for a common process [26] , [32]–[34] , we propose that dmt might play a role in the spindle checkpoint . Inactivation of the 18 genes in the CS3 phenocluster ( Figure 2 ) resulted in a peculiar mitotic phenotype . The chromosomes of metaphase-like figures were not connected to the spindle poles by bundles of kinetochore microtubules ( MTs ) and thus never congressed to the equator of the spindle . In addition to metaphase-like spindles , the RNAi cells of the CS3 phenocluster also showed many elongated ana/telophase spindles . However , these spindles contained chromosomes with unseparated sisters chromatids; these chromosomes usually appeared to segregate to the poles at random ( Figures 6 , 7 and S2–S4 ) . Some of these peculiar ana/telophase-like figures displayed both a central spindle and an actin-based contractile ring ( Figure S5 ) . However , most of these structures were morphologically irregular and were thus probably unable to mediate cytokinesis . To define their phenotype in more detail , RNAi cells for the CS3 genes were stained for the checkpoint proteins ZW10 and BubR1 and also for the cell cycle marker Cyclin B . In most ana/telophase-like figures , Cyclin B was still high , whereas in control cells it was degraded during anaphase and absent from telophases ( Figure 6B ) . In the metaphase-like RNAi figures , ZW10 did not exhibit any streaming towards the cell poles as occurs in normal metaphases ( Figure 7A ) , consistent with a defect in microtubule attachments to the kinetochore [35] . Moreover , the ana/telophase-like figures showed strong ZW10 and BubR1 centromeric signals; these signals were mostly absent from control ana/telophase chromosomes ( Figure 7A and data not shown ) . Finally , the chromosomes of the ana/telophase-like cells displayed two centromeric spots after staining for the kinetochore marker Cenp-C ( Figure 7B ) . These findings confirm that the chromosomes at the poles of the ana/telophase spindles seen in the CS3 phenocluster are indeed comprised of both sister chromatids . The CS3 phenocluster includes the CG9938/Hec1/Ndc80 , CG8902/Nuf2 and CG1558/Nsl1 genes , which encode interacting components of the Drosophila kinetochore [36] , [37] , as well as cid/Cenp-A , that encodes the Drosophila centromere-specific histone H3 variant [17] , [18] . Of the remaining 14 genes in the CS3 group , one specifies a conserved product of unknown function ( CG8233 ) and 13 encode highly conserved splicing factors ( Table S6 ) . The RNAi phenotypes of the genes in the CS3 phenocluster suggest that their products are required for proper kinetochore-microtubule interactions . We propose that in the absence of these interactions , the spindle checkpoint remains engaged and sister chromatid separation does not occur . The high levels of Cyclin B and the lack of ZW10 streaming in CS3 RNAi cells are both consistent with this hypothesis [38] . We further posit that the chromosomes are driven to the spindle poles by the same forces that act on the acentric chromosome fragments . As the chromosomes move towards the poles , the spindle elongates so as to resemble an ana/telophase spindle; some of these spindles manage to assemble a defective central spindle and attempt to undergo cytokinesis . Collectively , these results provocatively indicate that in S2 cells typical telophase events , such as central spindle assembly and initiation of cytokinesis , can occur in the absence of sister chromatid separation . RNAi for the 9 genes in the CS4 group resulted in a pseudo metaphase-arrest phenotype ( Figures 2 , 8A , and Figure S6 ) . Most dsRNA-treated cells with spindles of metaphase shape displayed apparently normal kinetochore fibers and normal chromosome congression . However , we also observed many mitotic figures with elongated ana/telophase-like spindles and unsegregated chromosomes at the center of the cell . In these peculiar mitotic figures , the centromeres of most chromosomes had congressed to the middle of the spindle , while the chromosomes arms were parallel to the spindle axis with the telomeres pointing towards the spindle poles . In addition , in many cells with long telophase-like spindles , the chromosomes stuck at the cell equator displayed variable degrees of decondensation , as through they were undergoing the decondensation process that occurs during normal telophase ( Figure 8A and Figure S6 ) . Finally , in most ana/telophase-like figures , Cyclin B remained high , as observed in RNAi cells for the CS3 genes ( data not shown ) . The CS4 phenocluster includes the Separase and three rows ( thr ) genes , which encode interacting proteins required for sister chromatid separation at anaphase ( Figure 2 and Table S6 ) . Previous studies have shown that embryonic cells of thr mutants display metaphase arrest with congressed chromosomes , followed by an irregular extension of the spindle without chromosome segregation and by chromosome decondensation [39] , [40] . This phenotype is fully comparable to that we observed in S2 cells after thr downregulation by RNAi . The CS4 group also includes the CyclinB gene and Otefin , a gene encoding a non-conserved protein that may interact with lamin ( Table S6 ) . All the remaining genes in the group are involved in RNA metabolism: one specifies a putative transcription factor while the others encode conserved splicing factors ( Table S6 ) . The genes included in the CS4 phenocluster are likely to be required for sister chromatid separation at the anaphase onset . We propose that upon inactivation of these genes , the opposing forces exerted by the MTs attached to the sister kinetochores keep the centromeres aligned at the metaphase plate . At the same time , however , the same forces that mediate the poleward motion of acentric fragments act on the chromosome arms , orienting them parallel to the spindle axis . Our observations also suggest that the latter forces can occasionally prevail over those exerted by the kinetochore fibers , so that some chromosomes leave the metaphase plate and move towards the poles with unseparated chromatids . The finding that RNAi cells for the CS4 genes undergo spindle elongation and chromosome decondensation while arrested in a metaphase-like state provides further support for the view that in S2 cells telophase events do not require sister chromatid separation . RNAi for the 9 genes in the CS5 phenocluster resulted in defective chromosome congression at metaphase and abnormal chromosome segregation at anaphase ( Figure 2 ) . Knockdowns of the expression of most of these genes caused a partial metaphase arrest characterized by extremely contracted chromosomes . However , even though sister chromatid separation did occur in most of the cases , ana/telophases were severely defective . The segregating chromatids were highly contracted and the two chromatid sets remained close to each other in many cells ( Figure 8B and Figure S7 ) . These unusual ana/telophases resemble very early anaphase figures , which are quite rare in untreated cells . These observations suggest that chromosome movement towards the poles is partially impaired in RNAi cells , resulting in delayed and irregular chromosome segregation . The CS5 phenocluster includes ida/APC5 and CG11419/APC10 , that encode two subunits of the APC complex; and fizzy ( fzy ) /Cdc20 , whose product regulates APC/C activity ( Table S6 ) . This phenocluster also includes Pros26 . 4 , that specifies a proteasome subunit; Klp3A , that encodes a kinesin-like protein; CG4266 and kin17 , whose products are conserved proteins implicated in RNA metabolism and the stress response , respectively; and CG3221 , that encodes a poorly conserved product of unknown function . The finding that the phenotype elicited by depletion of the APC components is substantially different from that caused by Separase inhibition strongly suggests that the APC/C is required not only for Securin and Cyclin B degradation , but also for the regulation of other aspects of spindle dynamics and spindle-kinetochore interactions . Inactivation of the genes in the CS1–CS5 groups often resulted in very elongated ana/telophase spindles ( Figure S8 ) ; in some cases , these spindles were twice as long as their counterparts in control cells . Long spindles were often bent or S-shaped , probably due to mechanical constraints imposed by the plasma membrane ( Figures S1 , S2 , S3 , S4 , S5 , S6 , S7 , and S8 ) . In addition , we observed that the degree of spindle elongation correlates with the presence of scattered chromosomes between the spindle poles . Long spindles have been observed previously in both Drosophila and mammalian cells with defective kinetochores [2] , [10] , [37] , [41] , and have been attributed to a misregulation of tubulin addition at the plus ends of kinetochore MTs [2] , [41] . We observed long ana/telophase-like spindles in cells containing chromosomes with either functional or nonfunctional kinetochores . Thus , spindle elongation may depend on factors other than kinetochore dysfunction . For example , the chromosomes scattered within the aberrant ana/telophase figures may induce MT growth and/or stabilization [42] , leading to the formation of particularly long spindles . We identified 11 dsRNAs that cause defects in chromosome structure without affecting spindle assembly . The phenotypes produced by these RNAs can be grouped into three phenoclusters we call CC1 , CC2 and CC3 ( Figure 2 ) . The CC1 group includes Minichromosome maintenance 3 ( Mcm3 ) , Mcm7 and cap . Mcm3 and Mcm7 encode the orthologs of two components of the human ( MCM ) 2-7 helicase complex ( Table S6 ) , while Cap encodes a protein orthologous to the SMC3 cohesin whose mutant form is responsible for a mild variant of the Cornelia de Lange syndrome [43] . RNAi for these genes resulted in loss of sister chromatid cohesion in the heterochromatic regions of the chromosomes and defective chromosome congression and segregation ( Figure 9A and Figure S9 ) . A similar phenotype was previously observed in mutants in the wings-apart like ( wapl ) Drosophila gene [44]; the human ortholog of Wapl interacts with cohesin and regulates its association with chromatin [45] , [46] . The CC2 phenocluster includes 5 genes that encode well-known condensins: SMC2 , Gluon/SMC4 , CapD2 , Cap-G and Barren/CAP-H ( Figure 2 and Table S6 ) . RNAi for these genes resulted in very similar phenotypes . In all cases , chromosomes displayed an abnormal mitotic condensation: although their longitudinal axis was shortened normally , their sister chromatids were swollen and fuzzy . In addition , ana/telophase figures displayed frequent lagging chromosomes and chromatin bridges , consistent with a strong defect in sister chromatid resolution during anaphase ( Figure S10 ) . In contrast , in RNAi cells for either gene in the CC3 group , Topoisomerase II ( Top2 ) , greatwall ( gwl ) ( encoding a conserved kinase; see Table S6 ) and Orc-2 , metaphase chromosomes were abnormally elongated and irregularly condensed , suggesting a defect in chromosome shortening . In RNAi cells for these genes , chromosome congression and segregation were also affected , consistent with previously published results ( Figure 9B; Table S6 ) . RNAi for 33 genes caused defects in spindle structure that were apparent as early as prophase or the beginning of prometaphase . Most of these genes ( 29/33 ) can be grouped in three broad phenoclusters ( SA1 , SA2 and SA3 ) ; although the phenotypes associated with the remaining 4 genes do not resemble each other , we assign them to a single miscellaneous group ( SA4 ) for convenience ( Figure 3 ) . Inactivation of the 18 genes in the SA1 group resulted in the formation of bipolar spindles that were significantly shorter than control spindles . Knockdowns of most of these genes also caused poorly focused spindle poles , monopolar spindles , hypercontracted chromosomes and defects in chromosome congression and segregation ( Figures 3 , 10; Figures S11 and S12 ) . The monopolar spindles observed in these RNAi cells might not reflect defective centrosome separation at prophase , but instead be a consequence of the instability of short bipolar spindles . This is suggested by previous observations of Orbit/Mast-depleted S2 cells . Live imaging of these cells has shown that the centrosomes of bipolar minispindles often collapse towards each other during prometaphase to form a monopolar spindle [47] . The excessive chromosome contraction is the likely outcome of a delayed progression through mitosis and could be responsible for a partial impairment of kinetochore function , resulting in defective chromosome congression and segregation . The SA1 phenocluster includes the β-tubulin gene β-tub56B , 4 genes that encode MT-interacting proteins [Map60/CP60 , Eb1 , Minispindles ( Msps ) and Mars/HURP] , the mitotic kinase gene ik2 , and 3 genes ( CG4865 , CG14781 and CG17293 ) that encode proteins of unknown function ( Table S6 ) . The remaining 9 genes of the SA1 group are involved in either transcription or translation . CG8950 encodes a PolII transcription factor; tho2 specifies a component of the conserved THO complex , which couples splicing and mRNA export ( Table S6 ) . Trip1/eIF3-S2 , CG8636/eIF3-S4 , Int6/eIF3-S6 , eIF3-p66 and eIF3-S10 encode different subunits of the highly conserved eukaryotic translation initiation factor 3 , while Nnp-1 and CG1234 are involved in ribosome biogenesis or maturation ( Table S6 ) . The Int6 gene is a frequent integration site of the MMTV virus in mouse mammary tumors and its silencing leads to mitotic defects in human cells ( Table S6 ) . In addition to short spindles , Int6-depleted cells displayed two unusual phenotypic traits: a severe undercondensation of the pericentric regions of the chromosomes and abnormally long astral MTs in telophase figures ( Figure 10A ) . Horse tail-like telophase asters were also observed in msps and CG8950 RNAi cells ( Figure 3 and Figure S11 ) . The SA2 phenocluster includes only 5 genes: CG11881 and CG16969 , that encode proteins of unknown function; Grip75 and γ tubulin 23C , that specify components of the gamma tubulin ring complex; and NippedA , that encodes a subunit of the conserved TRRAP complex implicated both in transcriptional regulation and DNA repair ( Table S6 ) . Interestingly , loss of the TRRAP complex affects gene expression at mitotic stages [48] . Downregulation of the genes in the SA2 group results in spindles with a low MT density and poorly focused poles ( Figure 11 and Figure S13 ) . Aberrant spindles with low MT density have previously been observed in S2 cells depleted of gamma tubulin ring components [49] . RNAi cells for the SA2 genes were also defective in chromosome congression and sister chromatid separation , just as those of the CS3 phenocluster ( Figure 11 ) . This phenotypic profile suggests that the products of the SA2 genes are required for the stability of spindle MTs and for their interaction with the kinetochores . RNAi for the 6 the genes in the SA3 phenocluster resulted in anastral and poorly focused spindles with normal MT density ( Figure 12 and Figure S14 ) . These genes encode the DSas-4 protein required for centriole duplication; the PCM component Centrosomin ( Cnn ) required for MT nucleation; the CG17826 product homologous to the C . elegans centrosomal protein Spd2; and Abnormal spindle ( Asp ) , a protein that associates with both the centrosomes and the MT minus ends ( Table S6 ) . The other two genes in the SA3 group encode the NiPp1 inhibitor of protein phosphatase type 1 ( PP1 ) ; and the CG6937 product , which is homologous to the human MKI67IP protein that contains an RNA recognition motif and interacts with the Ki-67 mitotic protein ( Table S6 ) . The phenotypic differences between DSas-4 , cnn or CG17826 RNAi cells ( SA3 phenocluster ) and those depleted of either Dgrip75 or γ tubulin ( SA2 ) are intriguing; they support the view that the latter proteins are not only involved in MT nucleation from the centrosomes but are also required for either MT stability or chromatin and/or kinetochore-induced MT growth [50] . We have included in the SA4 group 4 genes that are essential for spindle assembly but elicit different phenotypic profiles when inactivated by RNAi . Consistent with previous results , RNAi of Klp61F and ncd resulted in monopolar spindles and disorganized bipolar or multipolar spindles , respectively , while Klp67A downregulation led to abnormally long MTs that are unable to interact properly with the kinetochores ( Figure 3 and Figure S15; Table S6 ) . The phenotype of cells treated with dsRNA for cdc2 is reminiscent of that of the CS3 phenocluster , with chromosomes that remain congressed in a metaphase plate even when the spindle assumes an ana/telophase configuration . However , cdc2 RNAi cells often show an additional phenotype in which the centrosomes/asters are detached from the spindle poles ( Figure 3 and Figure S15 ) . We identified 6 genes required for both chromosome condensation and spindle formation , which can be subdivided into two phenoclusters ( SC1 and SC2 ) . The SC1 phenocluster includes the three components of the chromosome passenger complex ( Incenp , Ial/Aurora B and Borealin ) , as well as chromatin assembly factor 1 ( Caf1 ) . Consistent with previous studies , downregulation of these proteins resulted in elongated and poorly condensed chromosomes , disorganized spindles , defective chromosome congression and segregation , and frequent failures in cytokinesis ( Figure 3 and Figure S16; Table S6 ) . The SC2 group includes Polo kinase and the Drosophila homolog of the Myb transcriptional activator ( Figure 3 and Table S6 ) . In polo RNAi cells , the chromosomes were fuzzy and irregularly condensed , while in Myb-depleted cells the chromosomes were overcontracted and swollen with no resolution between sister chromatids . Dowregulation of either of these two proteins disrupts MT-kinetochore interactions , leading to failures of chromosome congression and sister chromatid separation ( Figure 3 and Figure S16 ) . The RNAi phenotypes of the 7 genes required for cytokinesis ( Figure 3 ) have been described previously in greater detail ( Table S6 ) . They can be subdivided into two phenoclusters ( CY1 and CY2 ) . Inactivation of the genes in the CY1 group [fascetto ( feo ) , racGAP50 , pavarotti ( pav ) and pebble ( pbl ) ] results in early cytokinetic defects in both the central spindle and the contractile ring . In contrast , ablation of the CY2 genes [anillin ( ani ) , citron kinase ( sti ) and twinstar ( tsr ) ] does not affect either central spindle or contractile ring assembly , but it does disrupt the final stages of cytokinesis ( Figure 3 and Table S6 ) .
Our RNAi screen for mitotic genes differs from those previously performed in two important ways . First , we used a bioinformatic approach to focus our experiments on a group of genes that was enriched in mitotic functions . Second , we analyzed potential mitotic phenotypes not only by examining cells stained for tubulin and DNA , but also by looking at colchicine-treated chromosome preparations . Since this latter technique allows the analysis of well spread metaphase chromosomes with excellent cytological resolution , we were able to identify 44 genes required to prevent spontaneous chromosome breakage , most of which have not previously been implicated in the maintenance of chromosome integrity . The human orthologs of some of these genes may play roles in carcinogenesis , as shown for many genes required for chromosome stability [51] , [52] . In addition , examination of colchicine-arrested metaphases led to the detection of phenotypes such as precocious sister chromatid separation ( CS2 phenocluster ) and a lack of sister chromatid cohesion in the heterochromatic regions ( CC1 phenocluster ) . These phenotypic traits allowed us to distinguish between genes required for proper chromosome segregation , permitting their assignment to different functional groups . Although previous RNAi screens were not designed to detect subtle changes in chromosome structure , they identified many genes involved in spindle assembly , chromosome segregation and cytokinesis [1]–[10] . Of particular interest is a comparison between our screen and a recent genome-wide screen performed by Goshima and coworkers in S2 cells [10] ( see Text S1 and Table S7 for details ) . Goshima et al . used automated microscopy to identify 189 genes required for spindle assembly and chromosome alignment at metaphase . Remarkably , 38% of these 189 genes are included in the first 1000 genes of our consensus co-expression list and 50% in the first 2000 . We identified 98 genes involved in the same processes , 30 of which were not found in the Goshima et al . screen . However , we failed to detect 17 genes that elicited RNAi phenotypes in their screen . Together , these results further validate our co-expression-based method for the identification of mitotic genes by RNAi . We believe that elaboration of consensus co-expression lists using genes in the same phenocluster will provide many candidate genes for small-scale RNAi screens aimed at completing the inventory of proteins involved in specific mitotic processes . One striking and unanticipated finding among our results merits special attention . We identified 17 highly conserved splicing factors that are required for sister chromatid separation at anaphase . RNAi for the genes encoding these factors resulted in two types of aberrant mitotic figures . Downregulation of the genes in the CS3 phenocluster resulted in mitotic cells showing scattered chromosomes without apparent kinetochore-spindle connections . This phenotype was identical to that caused by downregulation of genes encoding well-known kinetochore proteins such as cid , CG9938/Ndc80/Hec1 , CG8902/Nuf2 or l ( 1 ) G023/Nsl1 . In RNAi cells for genes in the CS4 phenocluster , most chromosomes showed regular connections with the spindle fibers and remained at the center of the cell , a phenotype similar to that produced by downregulation of Separase . However , RNAi for all of the CS4 genes and some of the CS3 genes produced a fraction of cells with an intermediate CS3/CS4 phenotype . These observations raise the possibility that inactivation of the splicing factor genes of both phenoclusters causes the same primary defect in centromere/kinetochore organization . One can envisage that when this defect is strong , both sister chromatid separation and kinetechore MT-interaction are affected; when the defect is weak , sister chromatid separation would be disrupted with little effect on kinetochore function . Splicing factors have previously been implicated in mitosis in fission yeast , Drosophila and human cells [10] , [53]–[55] . However , the precise mitotic function of these splicing factors has never been described . We have clearly shown here that splicing factors are required for sister chromatid separation . However , the mechanisms by which splicing factors regulate centromere/kinetochore function remain unclear . It is possible that these factors mediate the splicing of one or more pre-mRNAs , whose protein products play crucial roles for proper centromere or spindle function . Alternatively , the splicing factors may be involved in the production and/or stabilization of spindle- or centromere-associated structural RNAs . Recent studies have shown that RNA associates with the mitotic spindle and plays a translation-independent role in spindle assembly [56] . Moreover , there is evidence that maize and human kinetochores are enriched in single-stranded RNAs encoded by centromeric DNA sequences . It has been suggested that these RNAs may facilitate proper assembly of centromere-specific nucleoprotein complexes [57] , [58] . Deciphering the precise role of splicing factors in centromere/kinetochore assembly and functioning will be a challenging task for future studies .
Co-expression analysis was perfomed on a previously described gene expression dataset [14] . This comprised 267 GeneChip Drosophila Genome Arrays ( Affymetrix , Santa Clara , CA , USA ) that covered 89 different embryonic and adult experimental conditions and contained expression data for 13 , 166 probesets ( corresponding to 12 , 229 genes in the current FlyBase release ) . Pearson correlation coefficients ( PCC ) were calculated on log2 transformed expression values , averaged for each experimental condition [14] . For each of the six prototype mitotic genes , we obtained a ranked coexpression list by calculating the PCC of the corresponding probeset with all the other probesets of the gene expression matrix . In the case of two or more probesets referring to the same gene , we considered only those showing the highest ranks . To obtain a ranked consensus co-expression list , we first scored every probeset for its presence in the upper 3000 ranks of the single co-expression lists and calculated the average PCC; we then ordered the probesets for decreasing values of these parameters ( Table S1 ) . The first 3 , 000 genes in this consensus co-expression list are reported in Table S1 . 56 of the putative genes in the first 1000 positions of the list were either too small ( less than 300 bp ) or otherwise could not be amplified by PCR using at least two different pairs of primers . These putative genes ( highlighted in grey in Table S1 ) were not assayed in our RNAi screen . Thus , to total 1000 genes , we performed RNAi for 56 additional genes that ranked from 1001 to 1056 on the consensus coexpression list ( Table S1 ) . To ascertain the predictive value of our coexpression lists , we determined the ranks of 164 known mitotic genes in each list . These genes were selected because their ablation or knockdown by either mutation or RNAi has previously been shown to result in a strong mitotic phenotype . These 164 genes represent most of the Drosophila mitotic genes so far identified , but they do not include new mitotic genes detected in a recent RNAi screen performed by Goshima et al . [10] ( see Text S1 and Table S7 for a comparison with this screen ) . As shown in Tables S2 and S3 , 46% of these 164 genes are included in the first 1000 genes of our consensus coexpression list , which contains in total more than 13 , 000 genes . This non-random clustering indicates that there is both a strong tendency for mitotic genes to be transcriptionally coexpressed , as well as a strong positive correlation between the rank of a gene in the list and the probability of its involvement in mitosis . Examination of Tables S2 and S3 shows that the mitotic genes that are most tightly coexpressed are those required for proper chromosome structure and/or condensation . In contrast , the genes implicated in cytokinesis exhibit the broadest variation in expression patterns . This variation might reflect the complexity of the cytokinetic process , which requires several functions that are not specific for mitosis . For example , functions involved in regulation of the actin cytoskeleton or membrane trafficking are likely to be required in many cellular processes in addition to cell division . Recent work has raised the issue of possible off-target effects ( OTEs ) associated with the use of long dsRNAs for RNAi screens . It has been suggested that short homology stretches of 19 base pairs ( bp ) within a long dsRNA can target a gene other than the intended one [24] , [59] , [60] . In designing the primers for dsRNA synthesis , we consistently tried to obtain RNAs longer than 600 bp with a minimum content of OT sequences . The average length of the RNAs used in our screen was 750 bp; none of these RNAs carried strings of CAR triplets that are known to result in OTEs [25] , but 54% of our dsRNAs contained at least one OT sequence ( Table S4; OT sequences were identified using the program developed by Flockhart et al . [61]: http://flyrnai . org/RNAi_find_frag_free . html ) . However , we have several reasons to believe that very few , if any , of these OT sequences contributed to the production of the observed phenotypes . First , except for the “no dividing cells” ( NDC ) phenotype , we looked at very specific , strong and reproducible mitotic phenotypes , which are unlikely to be elicited by an OT sequence highly diluted by the excess of sequences homologous to the target gene . Second , we never found homology between any OT sequences present in the genes of the same phenocluster . Third , we found that where the data are available , the mitotic phenotypes observed in our screen match those seen in fly mutants and/or in previous RNAi experiments , regardless of whether OT sequences were present in the dsRNAs ( 31% of the dsRNAs that resulted in expected phenotype contained OT sequences ) . Finally , examination of 100 randomly chosen RNAs that did not elicit mitotic phenotypes showed that 40% of them contain OT sequences . Individual gene sequences were amplified by PCR from a pool of cDNAs obtained from 5 different libraries: 4 embryonic libraries from 0–4 , 4–8 , 8–12 and 12–24 hr embryos; and an imaginal disc library , all kindly provided by N . Brown [62] . If this cDNA pool did not provide the desired PCR product , DNA was amplified from genomic DNA . The primers used in the PCR reactions were 35 nt long and all contained a 5′ T7 RNA polymerase binding site ( 5′-TAATACGACTCACTATAGGGAGG-3′ ) joined to a gene-specific sequence . The primers used to amplify the 155 mitotic genes detected in the screen are reported in Table S4 . dsRNA synthesis and analysis were performed as previously described [1] . S2 cells were cultured at 25°C in Shields and Sang M3 medium ( Sigma ) supplemented with 10% heat-inactivated fetal bovine serum ( FBS , Invitrogen ) . RNAi treatments were carried out according to Somma et al . [1] . 1×106 cells were plated in 1 ml of serum-free medium in a well of a six-well culture dish ( Sarstedt ) . Each culture was inoculated with 15 µg of dsRNA . After a 1 hr incubation at 25°C , 2 ml of medium supplemented with 15% FBS were added to each culture . Control cultures were prepared in the same way but without addition of dsRNA . Both RNA-treated and control cells were grown for 96 hr at 25°C , and then processed for cytological analyses . RNAi treatments were performed using six-well plates . In a typical experiment , cells in 16 wells from three plates were treated with dsRNAs , while cells in the remaining two wells served as control . After four-day incubation with dsRNA , cells from 3 ml cultures were resuspended and processed in two ways . 2 ml of this suspension were centrifuged at 800 g for 5 min , washed in 10 ml PBS , and fixed for 7 min in 3 ml 3 . 7% formaldehyde in PBS . Fixed cells were spun down by centrifugation , resuspended in 500 µl PBS , and cytocentrifuged onto a clean slide using a Shandon cytocentrifuge at 900 rpm for 4 min . The slides were immersed in liquid nitrogen , washed in PBS , and incubated in PBT ( PBS+0 . 1% TritonX-100 ) for 15 min , and then in PBS containing 3% BSA for 20 min . These preparations were immunostained using the following antibodies , all diluted 1∶100 in PBS: anti-α tubulin monoclonal DM1A ( Sigma ) , rabbit anti-ZW10 [20] , rabbit anti-cyclin B and anti-CENP-C ( gifts of Christian Lehner , University of Bayreuth , Germany ) , and chicken anti-CID [18] . These primary antibodies were detected by incubation for 1 hr with FITC-conjugated anti-mouse IgG and Cy3-conjugated anti-rabbit IgG ( Jackson Laboratories ) . All slides were mounted in Vectashield with DAPI ( Vector ) to stain DNA and reduce fluorescence fading . To obtain metaphase chromosome preparations , 1 ml of cell suspension was left in its well and treated for 2 hr with colchicine ( final concentration 10−5 M ) . Colchicine-treated cells were then centrifuged at 800 g for 5 min . Pelleted cells were washed in 10 ml PBS , spun down by centrifugation and resuspended in 5 ml hypotonic solution ( 0 . 5 M Na citrate ) for 7 min . After further centrifugation , pelleted cells were fixed in 5 ml of methanol: acetic acid ( 3∶1 ) , spun down again , and resuspended in the small volume of fixative left after the removal of supernatant . 10 µl of this suspension was dropped onto a microscope slide and air-dried . All slides were mounted in Vectashield with DAPI ( Vector ) to stain DNA . All images were captured using a CoolSnap HQ CCD camera ( Photometrics; Tucson , AZ ) connected to a Zeiss Axioplan fluorescence microscope equipped with an HBO 100W mercury lamp as described previously [63] . Gray scale digital images were collected separately , converted to Photoshop format , pseudocolored , and merged . We considered 18 major phenotypic traits indicated in the headings of Figures 2 and 3 , and Table S5 . In addition , we observed 13 relatively rare phenotypes that are reported under the OSD ( other spindle defects ) and OMD ( other mitotic defects ) headings of Figures 2 and 3 , and Table S5 . Each individual phenotypic trait was considered as genuine when its frequency in a dsRNA-treated sample was significantly higher than the frequency of that trait in controls with p<0 . 001 , using the χ2 contingency test . The same trait was considered strong when its frequency was at least threefold ( in the case of chromosome aberrations ) or fivefold ( in all the other cases ) the control frequency . A phenotypic trait was considered weak when its frequency was significantly different from the control ( with p<0 . 001 ) , but below the above thresholds . Cells from control wells without dsRNA were systematically compared with cells treated with dsRNAs that do not elicit mitotic phenotypes . We never observed phenotypic differences between any of these cells , indicating that none of the aberrant traits we detected was due simply to the addition of random dsRNA sequences to the culture . Most of the mitotic genes identified in our screen are highly conserved and have putative human orthologs . The known function of these Drosophila genes and their human counterparts are reported in Table S6 , together with the supporting references . | Mitosis is the evolutionarily conserved process that enables a dividing cell to equally partition its genetic material between the two daughter cells . The fidelity of mitotic division is crucial for normal development of multicellular organisms and to prevent cancer or birth defects . Understanding the molecular mechanisms of mitosis requires the identification of genes involved in this process . Previous studies have shown that such genes can be readily identified by RNA interference ( RNAi ) in Drosophila tissue culture cells . Because the inventory of mitotic genes is still incomplete , we have undertaken an RNAi screen using a novel approach . We used a co-expression–based bioinformatic procedure to select a group of 1 , 000 genes enriched in mitotic functions from a dataset of 13 , 166 Drosophila genes . This group includes roughly half of the known mitotic genes , implying that it should contain half of all mitotic genes , including those that are currently unknown . We performed RNAi against each of the 1 , 000 genes in the group . By limiting the number of genes to be examined , we were able to perform a very detailed phenotypic analysis of RNAi cells . This analysis allowed the identification of 70 genes whose mitotic role was previously unknown; 30 are required for proper chromosome segregation and 40 are required to maintain chromosome integrity . | [
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] | 2008 | Identification of Drosophila Mitotic Genes by Combining Co-Expression Analysis and RNA Interference |
During meiosis , repair of programmed DNA double-strand breaks ( DSBs ) by recombination promotes pairing of homologous chromosomes and their connection by crossovers . Two DNA strand-exchange proteins , Rad51 and Dmc1 , are required for meiotic recombination in many organisms . Studies in budding yeast imply that Rad51 acts to regulate Dmc1's strand exchange activity , while its own exchange activity is inhibited . However , in a dmc1 mutant , elimination of inhibitory factor , Hed1 , activates Rad51's strand exchange activity and results in high levels of recombination without participation of Dmc1 . Here we show that Rad51-mediated meiotic recombination is not subject to regulatory processes associated with high-fidelity chromosome segregation . These include homolog bias , a process that directs strand exchange between homologs rather than sister chromatids . Furthermore , activation of Rad51 does not effectively substitute for Dmc1's chromosome pairing activity , nor does it ensure formation of the obligate crossovers required for accurate homolog segregation . We further show that Dmc1's dominance in promoting strand exchange between homologs involves repression of Rad51's strand-exchange activity . This function of Dmc1 is independent of Hed1 , but requires the meiotic kinase , Mek1 . Hed1 makes a relatively minor contribution to homolog bias , but nonetheless this is important for normal morphogenesis of synaptonemal complexes and efficient crossing-over especially when DSB numbers are decreased . Super-resolution microscopy shows that Dmc1 also acts to organize discrete complexes of a Mek1 partner protein , Red1 , into clusters along lateral elements of synaptonemal complexes; this activity may also contribute to homolog bias . Finally , we show that when interhomolog bias is defective , recombination is buffered by two feedback processes , one that increases the fraction of events that yields crossovers , and a second that we propose involves additional DSB formation in response to defective homolog interactions . Thus , robust crossover homeostasis is conferred by integrated regulation at initiation , strand-exchange and maturation steps of meiotic recombination .
During meiosis , haploid gametes are formed from diploid precursor cells via two successive rounds of chromosome segregation . By a program of events unique to meiosis , parental chromosomes ( homologs ) associate into homologous pairs and then disjoin from one another at the first division of meiosis ( MI ) . In most organisms , the process of homologous recombination mediates both the pairing and disjunction of homologs [1] . Meiotic recombination initiates with the formation of numerous DNA double-strand breaks ( DSBs; [2] ) . Nuclease processing of DSB-ends generates single-stranded tails , which then assemble into nucleoprotein filaments comprising RecA-family proteins , Rad51 and Dmc1 , and their accessory factors [3] , [4] , [5] . These filaments mediate DNA homology search and strand invasion of a homologous template chromosome to form joint molecule ( JM ) intermediates [6] , [7] , [8] . In this way , recombinational interactions promote the pairing of homologs and their end-to-end connection by zipper-like structures called synaptonemal complexes ( SCs; [9] , [10] , [11] , [12] ) . A subset of recombination sites then form crossovers resulting in the stable interhomolog connections called chiasmata that facilitate homolog bi-orientation on the spindle and thereby promote accurate disjunction at meiosis I [13] , [14] . The cell-to-cell variation in crossover numbers is much lower than the variation seen for DSB numbers [15] . This homeostatic regulation has been shown to buffer against stochastic and experimentally-induced variation of DSB numbers [16] , [17] , [18] , [19] , [20] , [21] . Crossover homeostasis is inferred to reflect two key regulatory processes that define the upper and lower limits for crossover numbers [15]: ( i ) crossover assurance – each homolog pair obtains a minimum of one crossover , as required for accurate disjunction [22]; and ( ii ) crossover interference – adjacent crossovers are widely separated [23] , [24] , [25] . The mechanisms that underlie crossover homeostasis have not been defined , but potential for regulation at each step of meiotic recombination has been inferred . For example , at the initiation stage , DSB numbers appear to be modulated by signaling pathways involving the PI3-kinase-like kinases , ATM/Tel1 and ATR/Mec1 [26] , [27] , [28] , [29] , [30] , [31] . During DNA strand exchange , template choice is biased to favor homologs over the sister chromatid , which is normally the preferred template for recombinational repair in mitotically cycling cells [32] , [33] , [34] , [35] . Following initial strand exchange , crossing-over involves the formation of metastable JM intermediates , including double-Holliday junctions [36] , while non-crossovers can arise from nascent D-loop intermediates [37] , [38] , [39] , [40] . Finally , resolution of double-Holliday junctions is inferred to produce primarily crossovers , but alternative modes of resolution may also result in non-crossovers [40] , [41] , [42] . How regulation at each of these steps contributes to crossover homeostasis remains unclear . In this study , specific relationships between Rad51 , Dmc1 , and their accessory factors are defined . Regulation of the strand-exchange step of meiotic recombination , especially template choice , has been a major focus of studies of meiotic recombination [32] , [33] , [43] , [44] , [45] . This includes understanding the functional relationships between the meiosis-specific RecA-homolog , Dmc1 , and its counterpart , Rad51 . Although both proteins are essential for meiosis , recent work has inferred that Rad51 does not participate directly in strand exchange , but facilitates Dmc1-mediated DNA strand-exchange [46] . In this study , specific relationships between Rad51 , Dmc1 , and their accessory factors are defined . Unexpectedly , Hed1 , an inhibitor of Rad51's DNA strand exchange activity [47] , [48] , is shown to be required for efficient homolog template bias . Moreover , this function of Hed1 does not require the meiotic kinase , Mek1 [49] , [50] , [51] . Our analysis also reveals an unanticipated function for Dmc1 as an inhibitor of Rad51's strand exchange activity . The hed1 mutation also provides a means to test the inference that Rad51 can effectively substitute for Dmc1 during meiosis [47] . Contrary to this idea , severe defects in inter-homolog bias , homolog pairing , SC morphogenesis , and crossover assurance are observed in dmc1 hed1 cells , where Rad51 catalyzes DNA strand exchange . We show that efficient crossover assurance requires homolog template bias and that even a mild reduction in interhomolog bias causes defective SC morphogenesis and renders cells sensitive to modest reductions in DSB numbers . Remarkably , and despite the profound primary defects associated with Rad51-catalyzed meiotic recombination , crossing-over occurs at nearly wild-type frequencies . We show that most cells are able to compensate for defective interhomolog bias by biasing the outcome of recombination to favor crossing-over; and by a second feedback process that we propose involves additional DSB formation at sites that normally experience relatively low levels of recombination .
In the budding yeast SK1 strain background , dmc1 mutants arrest in meiotic prophase with unrepaired DSBs and profound defects in homolog pairing and SC formation [7] , [52] . Previous studies have shown that activation of Rad51-mediated strand exchange allows dmc1 mutants to progress through meiosis and form viable spores [10] , [45] , [47] . One interpretation of these observations is that Rad51 can effectively substitute for Dmc1 [10] , [47] . To explore this possibility , the effects of activating Rad51's strand-exchange activity by deleting Hed1 were studied in detail at the HIS4::LEU2 DSB hotspot in cell cultures undergoing synchronous meiosis ( Figure 1 , Figure S1; [53] , [54] , [55] ) . At HIS4::LEU2 , restriction fragment polymorphisms between parental ( Mom and Dad ) chromosomes allow parental , DSB and crossover molecules to be resolved and monitored by Southern analysis ( Figure 1A ) . DSBs appeared at the same time in cultures of wild type , hed1 , dmc1 and hed1 dmc1 cells ( Figure 1B , C ) . In wild type and hed1 single mutants , DSBs peaked at 3 . 5 hours and were almost undetectable by 6 hours . As seen previously , DSBs formed in dmc1 single mutants accumulated to very high levels and persisted for the duration of the experiment ( Figure 1B , C ) . Abnormally extensive 5′-resection of DSB-ends was evident as a time-dependent increase in the heterogeneity and average mobility of DSB fragments ( Figure 1B ) . Also consistent with previous work [10] , [47] , in the dmc1 hed1 double mutant , DSB signals persisted much longer than in wild-type cells , but ultimately disappeared with a delay of about 2 hours . Crossing-over within the 4 . 3 kb XhoI fragment surrounding the HIS4::LEU2 DSB site plateaued at ∼19% of total DNA in wild-type cells ( Figure 1A , B , C ) . Crossover formation was delayed in both the hed1 single and dmc1 hed1 double mutants relative to wild type . Furthermore , final crossover levels were reduced 1 . 4-fold in the hed1 single mutant and 3 . 5-fold in the hed1 dmc1 double mutant ( see Figure S1E , F for additional measurements of final crossover levels ) . MI was also delayed in hed1 and , to a greater degree , dmc1 hed1 cells . However , more than 90% of cells executed MI . To summarize , our analysis confirmed previous observations , which showed that meiotic DSBs are efficiently repaired in dmc1 hed1 cells and can be converted into crossovers , albeit with significantly reduced efficiency [40] . Unexpectedly , absence of Hed1 alone also causes a significant decrease in crossing-over at the HIS4::LEU2 recombination hotspot . To examine the ability of Rad51 to substitute for Dmc1 in more detail , we analyzed JM formation in dmc1 hed1 cells [47] . At the HIS4::LEU2 locus , a variety of JM species can be monitored using 2D gels and Southern analysis ( Figure 2A , B; [53] , [54] , [55] ) . These include single-end invasions ( SEIs ) ; double-Holliday junctions formed between homologous ( IH-dHJs ) or sister chromatids ( IS-dHJs ) ; and complex structures comprising three and four interconnected chromatids ( ternary and quaternary multi-chromatid joint molecules , mcJMs ) . Representative 2D gel images are shown in Figure 2B and quantitation of individual JM species is presented in Figure 2C . Consistent with previous data , JM formation is almost completely blocked in dmc1 single mutants . Strikingly , dmc1 hed1 cells form high levels of JMs , but the majority involve sister chromatids not homologs . Whereas wild-type cells form >5-fold more IH-dHJs than IS-dHJs , the IH-dHJ/IS-dHJ ratio is reduced from 5 . 7 to 0 . 22 in dmc1 hed1 cells ( Figure 2C; also see Figure S1 ) . This 25-fold difference in the IH-dHJ/IS-dHJ ratio indicates that the processes responsible for interhomolog bias are severely defective in dmc1 hed1 mutants . To rule out the possibility that the inverted template bias seen in dmc1 hed1 cells reflects differential formation and/or resolution of IH-dHJs relative to IS-dHJs , we also determined IH/IS ratios in the ndt80 mutant background , in which JM resolution is blocked [40] ( Figure S2 ) . JMs were analyzed at 7 and 8 hrs after induction of meiosis , when they have accumulated to maximum levels . As in NDT80+ cells , the IH/IS dHJ ratio was 25-fold lower in dmc1 hed1 cells than in wild type , when examined in the ndt80 mutant background ( Figure S2B ) . This result confirms that the low IH/IS dHJ ratio seen in NDT80+ dmc1 hed1 cells results from defective template choice . Additional changes in JM processing can also be discerned in dmc1 hed1 mutants . Peak levels of both SEIs and mcJMs are up to 1 . 5-fold higher than those in wild type and all JM species remain detectable at late times ( Figure 2B , C ) . Accumulation of SEIs may indicate a reduction in the efficiency with which the “second end” is engaged following formation of a stable interhomolog JM by the “first” or leading end . Alternatively , increased SEI levels in dmc1 hed1 could be a consequence of higher than normal DSB formation ( as discussed further below ) . Increased levels of mcJMs provide more specific support for defective coordination between DSB ends . This defect could result from enhanced strand-invasion activity of the “second” end when Dmc1 is absent , or a defect in the helicase-dependent mechanism that normally acts to resolve mcJMs [55] . Further evidence that coordination of DSB ends is defective in dmc1 hed1 cells comes from the observation that non-allelic crossing-over between HIS4::LEU2 and the native LEU2 locus [56] is increased 3 . 2-fold ( from 0 . 47% ( ±0 . 24 S . E . ) in wild-type to 1 . 51% ( ±0 . 35 S . E . ) in dmc1 hed1; non-allelic recombination bands are indicated by an asterisk in Figure 1B ) . In summary , analysis of JMs demonstrates that recombination is severely abnormal in dmc1 hed1 cells . Most notably , strand-exchange now occurs primarily between sister-chromatids instead of homologs . To demonstrate that results obtained at HIS4::LEU2 , an artificial hotspot , were representative , we also examined dHJ intermediates and crossover products at ERG1 ( YGR175c ) , the site of a natural DSB hotspot on chromosome VII [57] ( Figure 2D ) . In wild type cells , dHJ formation is biased to occur between homologs at the ERG1 hotspot , with a IH/IS ratio of 4 . 5 , similar to that seen at HIS4::LEU2 ( Figure 2E , F ) . This bias is again inverted in dmc1 hed1 double mutants ( IH-dHJ/IS-dHJ ratio of 0 . 15; Figure 2E , F ) . Crossover levels were also reduced at ERG1 in dmc1 hed1 cells , by 2 . 1-fold ( Figure 2G , H ) . Thus , two recombination hotspots show dramatic reductions in homolog bias , but more modest reductions in final crossover levels in the dmc1 hed1 double mutant . Recombination and spore viability in dmc1 hed1 was shown previously to be Rad51-dependent [47] . Further , Hed1 is known to inhibit Rad51-mediated strand-exchange via direct interaction [47] , [48] . Using DNA physical assays at HIS4::LEU2 , we confirmed that rad51 mutation is fully epistatic to the hed1 mutation indicating that Hed1 does not have a detectable influence on recombination in the absence of Rad51 ( Figure S3 ) . Unexpectedly , the IH-dHJ/IS-dHJ ratio was also reduced in the hed1 single mutant , in which Rad51 strand-exchange activity is activated in the presence of Dmc1 . However , the effect was relatively mild compared to the dmc1 hed1 double mutant ( Figure 2B , C; Figure S1 ) . In hed1 cells , the IH-dHJ/IS-dHJ ratio was 2 . 1 , down from 5 . 7 in wild type , but still much higher than the 0 . 22 ratio seen in dmc1 hed1 . The less severe template choice defect of a hed1 single mutant , compared to the dmc1 hed1 double mutant , suggests that Dmc1 is , in effect , inhibitory to Rad51-mediated strand exchange ( discussed further below ) . To further investigate the idea that Dmc1 is inhibitory to Rad51-mediated strand-exchange , we examined the effect of removing Hed1 when both Dmc1 and Rad51 have been incorporated into the recombination complex , but strand exchange is blocked . This phenotype is seen in the absence of the Hop2-Mnd1 complex [58] , [59] , [60] , [61] , [62] , [63] . Similar to dmc1 single mutants , mnd1 mutants arrest during meiotic prophase with unrepaired DSBs ( Figure 3A , B; Figure 1 ) . However , the mnd1 and dmc1 phenotypes differ . First , arrest of mnd1 mutants is more robust: 0% of mnd1 cells have divided after 24 hrs compared to 3% of dmc1 cells ( Figure 3B; also see Figure 1C ) . Second , the extensive smearing of DSB signals seen in dmc1 cells ( indicative of extensive 5′-strand resection ) is not observed in mnd1 mutants ( Figure 3A; also see Figure 1B ) . Third , crossover bands at HIS4::LEU2 are not detected in mnd1 cells while they reach 3% ( ∼15% of wild-type levels ) in the dmc1 mutant ( Figures 3B; also see Figure 1C ) . Like the dmc1 mutant , arrest of mnd1 cells is alleviated by hed1 mutation , but much less efficiently ( Figures 3B ) . By 11 hrs , 73% of dmc1 hed1 cells have undergone one or both meiotic divisions , while only 13% of mnd1 hed1 cells have divided . Crossover products in mnd1 hed1 are also significantly reduced compared to dmc1 hed1 , 1 . 7% versus 5 . 4% , respectively ( Figure 3A , B ) . Strikingly , analysis of JM intermediates in mnd1 hed1 cells reveals an even stronger defect in template choice than that seen in dmc1 hed1 ( Figure 3C ) . JM formation is delayed by an additional 2 hrs in mnd1 hed1 cells compared to dmc1 hed1 cells and although IS-dHJs eventually form , IH-dHJs are barely detectable resulting in an IH-dHJ/IS-dHJ ratio of ≤0 . 12 ( at least a 42-fold difference from wild type; Figure 3D ) . Thus , the presence of Dmc1 causes a general delay to Rad51-catalyzed JM formation and specifically blocks Rad51 filaments from catalyzing strand-exchange between homologs . Further support for this inference comes from analysis of an mnd1 dmc1 hed1 triple mutant , which more closely resembles the dmc1 hed1 double mutant than the mnd1 hed1 double mutant with respect to JM timing as well as levels of IH-dHJs and crossovers ( Figure 3C–D ) . The meiotic recombination checkpoint pathway is important for normal template choice [32] , [45] , [56] , [64] , [65] , [66] , [67] . The phospho-kinase cascade that underlies this pathway leads to activation of the serine-threonine effector kinase , Mek1 [65] , [67] . Absence of Mek1 kinase activity allows dmc1 mutants to progress through meiosis and repair DSBs . However , both mek1 and dmc1 mek1 cells show severe defects in template choice , with recombination occurring primarily between sister chromatids [44] , [66] , [67] . To understand the relationship between Hed1-mediated repression of Rad51 and Mek1-mediated checkpoint signaling , we compared template choice in mek1 , hed1 , and mek1 hed1 strains ( Figure 4 ) . Consistent with previous data [44] , [66] , JMs in mek1 null mutants form primarily between sister chromatids resulting in an IH-dHJ/IS-dHJ ratio of 0 . 36 ( Figure 4A–C ) . In the mek1 hed1 double mutant , a further reduction in IH-dHJs is observed ( Figure 4A ) , to the point where they are barely detectable above background , resulting in an IH-dHJ/IS-dHJ ratio of ≤0 . 12 ( Figure 4C ) . Analysis of the ATP analog-sensitive mek1-as allele [68] gave results almost identical to those obtained with mek1 null cells ( Figure S4 ) . These data indicate that , despite a severe template choice defect , mek1 is not fully epistatic to hed1 , i . e . Hed1 and Mek1 can function independently to promote interhomolog recombination . Given the importance of interhomolog recombination for chromosome pairing and synapsis , we monitored these processes in hed1 and dmc1 hed1 cells ( Figure 5 ) , which show , respectively , moderate and severe defects in interhomolog template choice ( Figure 2 ) . To monitor pairing , surface-spread meiotic nuclei were immunostained for the central kinetochore protein , Ctf19 ( Figure 5A , B ) . When the 32 homologs of diploid budding yeast are completely paired , 16 Ctf19 foci are detected [69] , whereas unpaired chromosomes result in nuclei with >16 foci . At 7 hrs , 28% of prophase wild-type nuclei display evidence of incomplete pairing compared to 63% of dmc1 hed1 nuclei ( Figure 5B ) . Furthermore , 46% of prophase dmc1 hed1 nuclei have incompletely paired chromosomes even at 10 hours , a time when >95% of wild type and hed1 single mutant cells have exited prophase ( Figure 5B ) . These data indicate that chromosome pairing is inefficient in dmc1 hed1 cells . In contrast , Ctf19 immunostaining of hed1 single mutant cells indicates little or no defect in the efficiency of pairing ( Figure 5B ) . SC morphogenesis was monitored by immunostaining spread nuclei for Red1 , a component of the Mek1 signaling pathway that localizes along SC axial/lateral elements , and for Zip1 , a major component of the SC central region ( Figure 5C ) [70] , [71] . Nuclei were assigned to one of three structural classes [70] ( Figure 5D ) . Class 1 nuclei , corresponding to the stage called leptonema , contain only Zip1 puncta , but no elongated Zip1 structures; Class 2 nuclei ( zygonema , partial synapsis ) have at least one elongated Zip1 structure together with multiple Zip1 puncta; and Class 3 ( pachynema , full synapsis ) defines nuclei in which the majority of Zip1 staining forms elongated structures . Nuclei were also analyzed for the presence of extra-chromosomal aggregates of Zip1 called polycomplexes , a typical manifestation of defective SC assembly ( highlighted by arrows in Figure 5C ) [12] , [72] . Although the kinetics of SC formation were similar for both wild type and hed1 strains , the hed1 mutant showed unexpected defects in SC morphogenesis . Polycomplexes , while rarely seen in wild type ( ≤4% of cells at any single time point ) , were present in up to 40% of hed1 nuclei ( Figure 5D ) . Furthermore , the average intensity of Zip1 staining along SCs was 1 . 4 fold lower than that of wild type , while Red1 staining intensity along SCs was 1 . 4 fold higher ( Figure 5E; Table S1 ) . In addition , the pattern of Red1 and Zip1 abundance along SCs was altered ( Figure 5C , F ) . Although SCs appear rather uniform when viewed by metal staining and electron microscopy , immunostaining for individual components reveals alternating domains of high and low abundance [73] , [74] . This domain substructure is observed for a number of chromosomal proteins including Zip1 , Red1 and its associated axial protein Hop1 , and the meiosis-specific cohesion component , Rec8 . Domains of abundant Zip1 and Rec8 alternate with domains of abundant Hop1 and Red1 [73] , [74] . Consistent with published data , in wild-type nuclei , Red1 and Zip1 form alternating domains of relative abundance ( [73]; Figure 5F ) . In hed1 nuclei , the Red1-Zip1 domain structure was less pronounced . This appears to be a consequence of the overloading of Red1 at regions where it would normally be found in low abundance ( Figure 5C–F ) . These data link inhibition of Rad51-mediated inter-sister recombination to normal axis morphogenesis . This domain “blurring” is reminiscent of the SC defects seen in cells lacking the AAA+ ATPase , Pch2 [73] , [74] . To examine Red1 localization in more detail , we employed stimulated emission depletion microscopy ( STED ) , a method of light microscopy that can achieve 50 nm resolution compared to the 200 nm resolution provided by standard widefield epifluorescence microscopy [75] . As a proof-of-principle , we first used a mixture of antibodies against Red1 and Rec8 to achieve a uniformly dense staining pattern along SC axial/lateral elements . This approach revealed parallel rows of densely-spaced foci separated by ∼100 nm ( Figure 5G , white arrows ) . Thus , Red1 and Rec8 remain closely associated with SC lateral elements during the spreading procedure and parallel lateral elements can be resolved by STED microscopy . STED imaging of nuclei stained for Red1 alone resolved the 30–40 abundance domains of Red1 per nucleus , into 217±32 staining foci ranging from simple puncta to elongated structures with lengths of up to 600 nm . Thus , abundance domains observed in spread preparations are composed of clusters of about 5–7 discrete Red1-containing complexes rather than a single axis-associated structure or a more uniformly dispersed distribution of protein molecules along axial/lateral elements ( Figure 5F , G; Figure S5; Table S2 ) . Consistent with results of conventional microscopy , the density of Red1 staining foci along SCs detected via STED was higher in hed1 single mutants than in wild type . Indeed , the increased density of Red1 puncta resulted in regions where the parallel configuration of the underlying lateral elements was evident ( as observed in the Red1+Rec8 mixed staining experiment ) again demonstrating that Red1 is a component of axial/lateral elements ( Figure 5F , G; Table S2 ) . To address whether activation of Rad51 restores synapsis to dmc1 mutants , we monitored SC formation in dmc1 and dmc1 hed1 cells ( Figure 5C , D ) . Primarily Class 1 nuclei are observed in dmc1 nuclei , 68% of which contain a large Zip1 polycomplex . This severe SC defect is partially suppressed by hed1 mutation , with 62% of dmc1 hed1 nuclei progressing to Class 2 , and 8% to Class 3 ( Figure 5C , D ) . However , despite the large fraction of Class 2 nuclei , the average number of elongated Zip1 structures in this class was 2 . 4 fold lower in dmc1 hed1 than in wild-type nuclei ( Table S1 ) . Furthermore , dmc1 hed1 nuclei show a unique morphological abnormality , with decreased Red1 staining intensity along SCs and a dramatic “blurring” of the alternating high and low abundance domains of Red1 and Zip1 ( Figure 5E , F; Table S1 ) . Analysis of STED images confirmed that the density of Red1 puncta is lower in the dmc1 hed1 double mutant than in wild type ( Figure 5F , G; Figure S5; Table S2 ) . Thus , Dmc1 influences the composition of axial/lateral elements . Because Zip1 can form elongated structures that are not associated with chromosomes [72] , it was important to confirm that the Zip1 structures formed in dmc1 hed1 nuclei are bona fide SCs . To this end we showed that the Zip1 structures colocalize with both centromeres ( by co-staining with the centromere-associated protein , Ctf19 ) and the crossover-associated protein , Zip3 ( Figure 5A; Figure S6 ) . More definitively , electron microscopy of silver-stained chromosome spreads from dmc1 hed1 revealed the typical SC tripartite structure ( Figure S6 ) . Thus , SCs visualized by metal staining and electron microscopy can appear quite normal despite having an abnormal composition with respect to the abundance and arrangement of Red1 and Zip1 . We conclude that activation of Rad51's strand invasion activity can promote assembly of SCs in the absence of Dmc1 . However , the composition of axial/lateral elements is abnormal and SC morphogenesis is delayed and , typically , incomplete . Although crossing-over at the HIS4::LEU2 and ERG1 hotspots is reduced , respectively , by 3 . 5 and 2 . 1-fold , ( Figures 1 and 2 ) in dmc1 hed1 cells , spore viability remains relatively high ( ∼70%; see below; [47] ) . A possible explanation is that despite a >2-fold reduction in crossing-over , crossover assurance remains efficient in dmc1 hed1 cells . To address this possibility we used tetrad analysis to determine crossover frequencies and distributions for eight linked intervals spanning the entire length of chromosome III ( Figure 6A ) . Surprisingly , and despite the defects in template choice , homolog pairing and synapsis , genetic map distances for the hed1 single mutant and the dmc1 hed1 double mutant were very similar to those of wild type ( Figure 6B ) . In the hed1 mutant , minor fluctuations in map distances were detected in some intervals , but the overall map distance for chromosome III was nearly identical to that of wild type ( Figure 6B , C ) . In the dmc1 hed1 double mutant , small but significant decreases ( 13–25% ) in map distances were detected in three of eight intervals , but overall map distance of chromosome III was reduced by only 11% , from 131 . 6 cM ( 2 . 6 crossovers per meiosis ) in wild type to 116 . 8 cM ( 2 . 3 crossovers per meiosis ) in dmc1 hed1 ( Figure 6B , C ) . Thus , with respect to crossover frequency measured by tetrad analysis , Rad51 is ostensibly capable of efficiently substituting for Dmc1 . Spore viability of dmc1 hed1 cells is only 70% compared to 96% for both wild type and the hed1 single mutant [47] . The spore viability pattern of the dmc1 hed1 strain is indicative of meiosis I nondisjunction , with a preponderance of tetrads containing two and zero viable spores ( Figure 6D ) . Allelic , centromere-linked URA3 and LYS2 markers on chromosome III were used to confirm an elevated rate of meiosis I nondisjunction . Disomic spores carrying both CEN3::URA3 and CEN3::LYS2 were observed in only 0 . 4% of wild-type tetrads ( 5/1179 ) compared to 1 . 5% of dmc1 hed1 tetrads ( 45/3052 ) . Reasoning that the reduced spore viability of dmc1 hed1 mutants may be caused by defective crossover assurance , we determined the frequency of tetrads in which chromosome III failed to undergo any crossing-over ( Figure 6E ) . If crossovers between chromosome III were randomly distributed in wild-type cells , 8% of tetrads would have zero crossovers between chromosome III . On the contrary and as expected , crossover assurance is efficient in wild type , with only 0 . 9% ( 9/1039 ) of tetrads lacking a crossover between chromosome III ( for a random distribution , the probability of ≤9 non-exchange tetrads occurring given a sample size of 1039 is <0 . 0001 ) . Remarkably , even though the average crossover frequency in dmc1 hed1 cells is very similar to wild type ( 2 . 3 versus 2 . 6 crossovers per chromosome III per meiosis ) , 15% ( 210/1376 ) of tetrads lack a crossover between chromosome III ( Figure 6E ) . Not only is this level of crossover failure much higher than that of wild-type cells ( P<0 . 0001 ) , but it is also significantly higher than that predicted for a Poisson distribution ( 10% of tetrads; P<0 . 0001 ) . Thus , despite a high level of crossing-over in the population , crossover assurance is profoundly defective in dmc1 hed1 cells . The frequency of non-exchange tetrads seen in hed1 single mutants ( 2% ) may by slightly higher than that of wild type ( 0 . 9% ) , but the difference is not significant in this data set . Consistent with the possibility that crossover failures in dmc1 hed1 cells results from defective homolog pairing ( Figure 5 ) , gene conversion events were almost undetectable in non-exchange tetrads . Only 0 . 5% of tetrads that lack an exchange between chromosome III ( 1/210 ) had a detectable gene conversion for markers along that chromosome . By comparison , 8% of all dmc1 hed1 tetrads had a detectable gene conversion between chromosome III ( 94/1185; P<0 . 0001 ) . A second aspect of crossover control , interference , was analyzed by a new approach that took advantage of the ability to purge the data of tetrads in which chromosome III did not undergo a crossover event . This approach avoids the diminution of the genetic signature of interference that is associated with defective crossover assurance [76] ( see Supplemental Materials , Figure S7 , for a comparison of this approach with that of conventional analysis of interference; see Table S5 , Figure S8 and Text S1 for further validation of the approach ) . Interference was first assessed by analyzing frequencies of non-parental di-type ( NPD ) tetrads . NPDs arise from a double crossover event within a single genetic interval that involves all four chromatids . The expected number of NPD tetrads is calculated assuming the absence of crossover interference [77] . Ratios of observed/expected NPDs that are significantly <1 indicate positive crossover interference . In wild-type tetrads , NPD ratios were <1 for all seven intervals tested and significant positive crossover interference was inferred in four intervals ( Figure 6F; Table S3 ) . In contrast , NPD ratios in dmc1 hed1 tetrads showed a general increase relative to wild type and significant interference was detected in only one of the seven intervals indicating substantially weaker crossover interference ( Figure 6F; Table S3 ) . Mutation of hed1 alone did not cause a detectable change in crossover interference relative to wild type . Weakened interference in dmc1 hed1 cells was confirmed by analysis of coincident crossovers in adjacent genetic intervals [78] . For a given reference interval , tetrads are first divided into two subsets: those with a crossover ( AdjCO ) in the interval and those without ( AdjPD ) . These two subsets are then analyzed for map distances in an adjacent interval ( test interval ) and interference is expressed as the ratio of these two distances , which is indicative of the strength of interference ( cMAdjCO/cMAdjPD in Figure 6G ) . By this method , significant interference ( cMAdjCO/cMAdjPD<1 ) was detected for 5 of 6 intervals in wild type , hed1 , and dmc1 hed1 indicating that significant interference can occur in the absence of both hed1 and dmc1 . However , dmc1 hed1 shows significant decreases in the strength of interference in 4 of the 6 intervals tested ( Figure 6G; Table S4 ) . We conclude that crossover interference is reduced , but not eliminated in dmc1 hed1 tetrads . Paradoxically , even though homolog bias and chromosome pairing/synapsis are severely defective in dmc1 hed1 cells , the overall crossover rate is similar to that of wild-type cells ( Figure 6 ) . A possible explanation is that a larger fraction of interhomolog recombination events develop into crossovers rather than non-crossovers in dmc1 hed1 cells . To test this possibility , we measured the ratios of interhomolog crossovers and non-crossovers at the HIS4::LEU2 and ERG1 loci ( Figure 7 ) . Restriction site polymorphisms at these two sites allow crossover and non-crossover products to be distinguished in Southern blot assays [41] . At HIS4::LEU2 , the ratio of crossovers to non-crossovers in wild-type cells is 1 . 4 , while in dmc1 hed1 cells the crossover/non-crossover ratio is increased to 1 . 8 ( Figure 7C ) . More dramatic results were obtained at the ERG1 hotspot , where the crossover/non-crossover ratio is 1 . 0 in wild-type cells and 4 . 9 in dmc1 hed1 cells ( Figure 7D–F ) . These data suggest that the crossover versus non-crossover outcome of meiotic recombination can be dynamically regulated in response to fluctuations in the number of interhomolog interactions . However , given the severity of the homolog template bias defect , the observed increases in the crossover/non-crossover ratios do not fully account for the relatively minor crossover defect seen in dmc1 hed1 cells ( see Supplementary Text for relevant calculations ) . This suggests that enhancement of the crossover versus non-crossover outcome is not the only mechanism that compensates for the homolog bias defect of dmc1 hed1 ( see Discussion ) . Martini et al . previously demonstrated that global reductions in DSB levels , caused by hypomorphic alleles of SPO11 , are compensated for by increases in crossover/non-crossover ratios [18] . This study led to the concept that crossover frequency is under homeostatic control such that cell-to-cell variance is low despite relatively high variance in DSB numbers [15] , [17] , [21] . We reasoned that homeostatic control responds to the number of interhomolog recombination interactions and not the number of DSBs per se . Thus , we predicted that dmc1 hed1 cells would be unable to compensate for even mild reductions in DSB levels . To test this prediction , we determined the effects of combining dmc1 hed1 with hypomorphic spo11 mutations . Crossover/non-crossover ratios were determined using a previously described random spore assay that measures the crossover frequency associated with intragenic recombination at the ARG4 locus [18] . We constructed sets of dmc1 hed1 mutant strains and DMC1+ HED1+ control strains that were also homozygous for SPO11+ ( wild type ) , or spo11-HA , or heterozygous for spo11-HA/spo11-yf; these combinations of SPO11 alleles result in 100% , ∼80% , and ∼30% of normal DSB levels , respectively [18] . In the DMC1+ HED1+ control strains , the fraction of recombination events that result in crossing-over increases with decreasing DSB levels [18]; the crossover/non-crossover ratios in SPO11+ , spo11-HA , and spo11-HA/spo11-yf strains are 1 . 3 ( 56% crossovers ) , 1 . 6 ( 61% ) , and 1 . 7 ( 63% ) , respectively ( Figure 8 ) . Consistent with our analysis at the HIS4::LEU2 and ERG1 hotspots ( Figure 7 ) , the crossover/non-crossover ratio in the dmc1 hed1 SPO11+ strain is already very high ( 2 . 3; 70% crossovers ) . This ratio may increase slightly in the spo11-HA background ( 2 . 6; 72 . 5% crossovers ) , but no increase was seen for the most severe spo11 allele combination , spo11-HA/spo11-yf ( 2 . 3; 70% crossovers ) . Thus , homeostatic mechanisms that modulate the crossover/non-crossover ratio appear to be already operating at maximum capacity in dmc1 hed1 cells and therefore are unable to further compensate for reduced DSB levels . Consistent with this interpretation , in the dmc1 hed1 background , spo11-HA caused a 2-fold drop in spore viability ( from 41% to 20% viable spores; n = 140 and 180 tetrads , respectively ) and the spo11-HA/spo11-yf combination resulted in a 4 . 3-fold decrease ( 9 . 5% viable spores; n = 100 tetrads; Figure 8C ) . In sharp contrast , the spore viabilities of DMC1+ HED1+ control strains were not significantly impacted by the spo11-HA and spo11-HA/spo11-yf mutations ( spore viabilities of 97% , 97% , and 93% for SPO11 , spo11-HA , and spo11-HA/spo11-yf strains , respectively; n = 100 tetrads for all three strains; Figure 8C ) . In the hed1 single mutant background , crossover/non-crossover ratios are slightly higher than those of wild type and increase with decreasing DSB levels . The crossover/non-crossover ratio was 1 . 4 ( 59% crossovers ) in hed1 SPO11 and increased to 1 . 8 ( 64% crossovers ) in hed1 spo11-HA and 2 . 1 ( 68% crossovers ) in hed1 spo11-HA/spo11-yf ( Figure 8B ) . Surprisingly , spore viability decreased from 93% in hed1 SPO11+ to 53% in hed1 spo11-HA and only 6 . 5% in hed1 spo11-HA/spo11-yf ( Figure 8C ) . These data indicate that Hed1 helps cells compensate for suboptimal DSB levels and imply that wild-type levels of interhomolog bias are important for efficient crossover homeostasis . Although chromosome-wide crossover levels in dmc1 hed1 cells are similar to wild type ( Figure 6 ) , crossing-over at both the HIS4::LEU2 and ERG1 hotspots is substantially reduced [47] ( Figures 1 , 2 ) . A trivial explanation for this disparity is that dissected tetrads are enriched for crossovers relative to the cell population as a whole . This possibility was addressed by measuring crossover levels by both Southern blot and tetrad analysis in cells sampled from the same meiotic cultures ( Figure 9 ) . The two selected cultures underwent meiotic divisions with nearly identical efficiencies and formed similar fractions of mature asci containing four spores ( Figure 9A and data not shown ) . Similar to the data in Figure 1 , Southern analysis of crossovers within the 4 . 3 kb XhoI fragment surrounding the HIS4::LEU2 DSB site ( “interval 1” ) were reduced 2 . 7-fold from 17 . 3% in wild-type cells to 6 . 5% in dmc1 hed1 cells ( Figure 9A , B , C ) . By tetrad analysis , crossing-over within the larger 12 kb URA3 to HIS4::LEU2 interval surrounding the HIS4::LEU2 DSB site ( “interval 2” ) was reduced 2 . 2-fold , from 31 . 0 cM to 13 . 8 cM ( Figure 9D ) . At 130 kb , the adjacent interval , from HIS4::LEU2 to MAT ( “interval 3” ) , is physically just over ten times larger than interval 2 , but shows a very similar crossover rate ( 27 . 7 cM in wild type ) . Strikingly , crossing-over in interval 3 is not reduced in dmc1 hed1 cells ( Figure 9E ) . In fact , the map distance is significantly increased relative to wild type ( 35 . 6 cM in dmc1 hed1 ) . Thus , in dmc1 hed1 cells , interhomolog interactions at DSB hotspots appear to be diminished to the point where homeostatic mechanisms are unable to maintain local crossover levels . However , crossover levels measured over larger distances remain similar to wild type . These data suggest that a second homeostatic mechanism operates over large chromosomal regions to help maintain crossover levels in dmc1 hed1 cells ( discussed below ) .
Previous work showed that activation of Rad51's strand-exchange alleviates the meiotic arrest of dmc1 cells and results in near normal levels of crossing-over and high spore viability [45] , [47] . A possible interpretation of these data is that Rad51 normally contributes the bulk of strand exchange activity to the meiotic recombination process while Dmc1 acts as a regulatory factor [47] . On the contrary , more recent work indicates that Rad51's strand exchange activity is dispensable for meiotic recombination [46] , but Rad51 plays an essential supporting role for Dmc1-mediated strand-exchange [1] , [32] , [46] , [79] , [80] . Consistent with these conclusions , the current work shows that Rad51-mediated meiotic recombination is profoundly abnormal , but near normal levels of crossing-over still form by virtue of compensatory mechanisms ( discussed below ) . Rather than catalyzing homology search and strand invasion , current and previous analyses indicate that Rad51 complexes normally play multiple regulatory roles during meiotic prophase , including: ( i ) facilitating the assembly of Dmc1 nucleoprotein filaments [4] , [46] , [81]; ( ii ) limiting the extent of DSB resection [32] , [81]; ( iii ) enhancing Dmc1-dependent interhomolog template bias [32] , [46]; and ( iv ) promoting normal SC morphogenesis . The precise spatial arrangement of Rad51 and Dmc1 on DSB-ends remains unclear . Cytological experiments argue against mixed filaments of Rad51 and Dmc1 [80] , [82] . Instead , immunostaining patterns suggest that homo-filaments of the two strand exchange proteins are assembled as immediately adjacent pairs . One interpretation of side-by-side foci is that Dmc1 assembles on one DSB-end and Rad51 on the other [78] . This idea has proven attractive and is suggested to account for a variety of observations [1] , [44] , [54] , [82] , [83] . Under this scenario , while the Rad51-associated end is rendered inactive for strand invasion , it still functions in a regulatory capacity to block access to the sister-chromatid template by the Dmc1-associated end and/or acts to antagonize the inhibitory effects of cohesin on interhomolog recombination [44] . However , the demonstration that Rad51 enhances the assembly and strand exchange activity of Dmc1 makes it likely that Dmc1 and Rad51 can assemble on the same DNA end [46] , [79] . Thus , paired Rad51-Dmc1 foci may represent a single DSB-end . This configuration can accommodate the unexpected observation that Dmc1 complexes are generally inhibitory to Rad51-mediated strand exchange ( Figure 3; see model in Figure 10A ) . We suggest that assembly of Dmc1 at a DSB-end modulates the adjacent Rad51 filament in such as way that its strand-exchange activity is inhibited . Defining the precise arrangement of Rad51 and Dmc1 on DSB ends in vivo remains a key objective for understanding meiotic recombination . Consonant with the fact that Hop2-Mnd1 is absent from the same evolutionary lineages that lack Dmc1 [84] , we show that Hop2-Mnd1 is specifically required for Dmc1-mediated strand exchange in budding yeast meiosis and does not facilitate or modulate Rad51-dependent strand exchange in vivo . Moreover , Hop2-Mnd1 is required even when Dmc1 catalyzes strand exchange between sister chromatids [85] . In vitro and in vivo studies provide clear evidence that Hed1 inhibits Rad51's strand exchange activity , which blocks meiotic DSB-repair when Dmc1 is absent [47] , [48] , [86] . However , the physiological role of Hed1 during unperturbed meiosis has remained uncertain . Our analysis reveals that Hed1 facilitates Dmc1-dependent interhomolog bias , the efficient incorporation of Zip1 into SCs , and the organization of alternating domains of Red1 ( +Hop1 ) and Zip1 ( +Rec8 ) protein abundance by limiting Red1 loading ( Figures 2 , 5 and 10A ) . The most straightforward interpretation of Hed1's role in promoting homolog bias is that it is solely a consequence of its ability to inhibit the strand exchange activity of Rad51 . However , it is also possible that Hed1 modulates the Rad51 ensemble in a positive way to help promote interhomolog partner choice via Dmc1-mediated strand-exchange . In this context , it is worth noting that Hed1 has been shown to stabilize Rad51 filaments [86] . Such stabilization could , in principle , enhance the regulatory roles of Rad51 during meiosis . A hed1 mutation also renders cells unable to compensate for modest reductions in DSB levels , indicating that Hed1-dependent interhomolog bias is especially important in cells with suboptimal DSB levels . These data also raise the possibility that the ∼5-fold interhomolog bias seen in wild-type cells reflects a physiologically important “set point” that enables individual cells to compensate for stochastic variation in DSB numbers . Finally , we note that the phenotypes conferred by hed1 mutation are similar to those of cells lacking the AAA+ ATPase , Pch2 , and may reflect a common defect in enforcing interhomolog template choice [73] , [74] , [87] . Together with previous studies , our data indicate that the concerted action of at least four components promote interhomolog bias catalyzed by Dmc1-dependent DNA strand exchange ( summarized in Figure 10A ) : ( i ) a strand exchange-independent activity of Rad51 and its accessory factors [32] , [46]; ( ii ) phospho-kinase signaling via the Red1-Hop1-Mek1 pathway [32] , [88] , [89]; ( iii ) inhibition of Rad51-dependent strand exchange by Hed1; and ( iv ) inhibition of Rad51-dependent strand exchange by Dmc1 . When Dmc1 is absent , both Hed1 and Mek1 are required to inhibit Rad51-dependent recombination , which occurs primarily between sister chromatids [32] , [66] , [67] . Thus , one aspect of Mek1's role in homolog bias is inferred to be inhibition of Rad51-catalyzed inter-sister strand exchange [43] , [90] . However , once Dmc1 is incorporated into the recombination complex , interhomolog bias is largely independent of Hed1 ( Figure 2; Figure 3 ) . The above considerations imply that the relatively strong interhomolog bias defect seen in mek1 mutants compared to hed1 mutants is not explained by Rad51 inhibition alone . Moreover , in mutants lacking factors required for assembly of Rad51 filaments ( mediator complexes Rad55-Rad57 and Psy3-Csm2-Shu1-Shu2 , as well as Rad51 itself; [32] , [85] ) Dmc1 catalyzes recombination between sister-chromatids indicating that the interhomolog bias function of Mek1 requires the presence of a complete Rad51 ensemble . These considerations invoke a model in which Mek1's kinase activity cooperates with Rad51 complexes to direct Dmc1-dependent strand exchange between homologs ( Figure 10 ) . In this context , we note that Mek1 and Rad51 are also proposed to play positive roles in homolog bias by locally removing inhibitory effects of sister chromatid cohesion on formation on interhomolog interactions [44] . Tsubouchi and Roeder [47] suggested that one role of Hed1 may be to inhibit Rad51 while Dmc1 filaments are assembled , such that the activities of Dmc1 and Rad51 are coordinated . Our data are in agreement with this proposal and further suggest that the requirement for Hed1 in Rad51 inhibition is largely alleviated once Dmc1 filaments are assembled . The ability of Dmc1 to inhibit Rad51-mediated intersister recombination also suggests a possible explanation for how organisms that lack obvious Hed1 orthologs might limit Rad51-mediated inter-sister recombination; the transcriptional programs in such organisms may ensure induction of sufficiently high Dmc1 levels prior to DSB formation such that Rad51 can be immediately inhibited as recombinosomes assemble . Although elongated SCs can form in dmc1 hed1 cells , their formation is inefficient and the composition of these structures is highly abnormal . Most notably , the density of Red1 along lateral elements is significantly lower in dmc1 hed1 than in wild type ( Figure 5 ) . Thus , a Dmc1-dependent process promotes assembly or stabilization of Red1 in local abundance domains along the SCs . It is possible that Dmc1 influences Red1 localization prior to synapsis , although technical limitations prevent this possibility from being tested at present . If Dmc1 does promote Red1 localization prior to synapsis , this activity will concentrate Red1 around developing recombinosomes , which will locally enhance Mek1-kinase activity and thereby enhance inter-homolog bias ( Figure 10A ) . Although previous analyses showed that high levels of crossing-over occur when Rad51 is activated in the absence of Dmc1 [10] , [45] , these studies could not assess the efficiency of crossover assurance . Here we show that despite near wild-type frequencies of crossing-over in the cell population , crossover assurance is very inefficient in dmc1 hed1 cells . This defect is most readily explained by the stochastic failure of homologs to stably pair due to insufficient interhomolog interactions . In this regard , we can infer that Dmc1-dependent interhomolog bias is an essential precondition for efficient crossover assurance . We note that although chromosome III fails to crossover in ∼15% of dmc1 hed1 cells , nondisjunction of this chromosome is detected in only 1 . 5% of tetrads , much lower than the ∼7 . 5% expected for random segregation of achiasmate homologs . It can be inferred that the well-characterized back-up segregation system [91] , [92] , [93] , [94] makes a major contribution to accurate achiasmate disjunction and spore viability in dmc1 hed1 cells , further belying the severity of the recombination defect in this strain . Published studies indicated that crossover interference is diminished when Rad51 catalyzes meiotic recombination [10] , [45] . However , population heterogeneity could have contributed to the apparent reduction in interference in these experiments [95] . Specifically , cell-to-cell heterogeneity in recombination activity may have arisen from variable copy number of the RAD51 and RAD54 plasmids used to activate Rad51-dependent recombination in these studies . Moreover , defective crossover assurance will exacerbate population heterogeneity and dilute interference signals ( [76]; Figure S7 ) . Our analysis allows these effects to be separated to more definitively assess interference between Rad51-dependent crossovers . Although interference is significantly weakened in dmc1 hed1 cells , we can conclude that the mechanisms responsible for interference retain significant activity . Reduced interference could arise from changes caused by defective interhomolog bias in dmc1 hed1 cells . One possibility is that adjacent recombinational interactions arise less synchronously in dmc1 hed1 cells than in wild type ( discussed further below ) . Early arising recombination events may not interfere with late events ( and vice versa ) resulting in dilution of the overall intensity of crossover interference . In support of this idea , delayed , asynchronous , and inefficient chromosome pairing can perturb interference in C . elegans [96] , [97] . In dmc1 hed1 cells , 5-fold defects in template choice are observed at the HIS4::LEU2 and ERG1 hotspots ( Figure 2 ) , but final crossover levels at these loci are reduced by only 3 . 5- and 2 . 1-fold , respectively ( Figure 1 ) . Moreover , the chromosome-wide crossover frequency is only marginally lower than wild type ( 1 . 1 fold reduction; Figure 6 ) . Our physical and genetic analyses indicate that feedback processes respond specifically to low levels of interhomolog interactions to enhance processes that eventually lead to formation near normal crossover frequencies ( Figure 10B ) . Two distinct feedback processes are indicated:
Strain information is listed in Supplemental Table S6 . All strains are derivative of strain SK-1 . Synchronous yeast cultures were induced to undergo meiosis by transfer to sporulation media as described previously [4] . Samples were taken over time to monitor the events of meiotic recombination and SC assembly . Meiotic progression was monitored by counting the number of DAPI staining bodies per cell for at least 100 cells per time point . Haploid strains were mated for 4–24 hrs on YPD plates and sporulated on plates containing 1% potassium acetate and 0 . 02% raffinose at 30°C for 2 days . Alternatively , asci were taken from meiotic time course after 48 hrs . Asci were digested with zymolyase and dissected on YPD plates supplemented with adenine , uracil , methionine , lysine , and threonine . Only tetrads producing four viable spores and showing Mendelian segregation of markers were used to calculate genetic map distance . Map distances were determined using the Perkins equation: ( 100 ( 6NPD+TT ) ) / ( 2 ( PD+NPD+TT ) ) [102] . Standard errors were calculated using Stahl Lab Online Tools ( http://www . molbio . uoregon . edu/~fstahl/ ) . The NPD ratio for interference analysis is calculated as the fraction of NPDs observed/fraction of NPDs expected . NPD expected is calculated under the assumption where there is no interference using the Papazian equation: NPD expected = 0 . 5[ ( 1-fT ) − ( 1- ( 3fT/2 ) ) 2/3] , where fT denotes the observed frequency of tetratypes [77] . The diploid strain used for JM analysis at ERG1 was engineered by a series of two-step gene replacements . On one copy of chromosome VII , SacII restriction sites were engineered into intragenic regions at Saccharomyces Genome Database ( SGD ) ( S288c genome assembly ) coordinate 844276 ( YGR173w ) and coordinate 854464 ( YGR179c ) ; and a SalI site was engineered between ERG1 and YGR177c at coordinate 848724 . For the SacII site at coordinate 844276 the region between coordinates 843173 and 845291 was PCR amplified ( 5′ and 3′ primers contain HindIII restriction sites ) , digested with HindIII , and cloned into pRS306 . The SacII site was introduced by Quickchange Site-Directed Mutagenesis ( Invitrogen ) with the primers 5′-GTTGTTGCCACAGCAAGGACCGCGGATCTAGTATTAATGG and 5′-CCATTAATACTAGATCCGCGGTCCTTGCTGTGGCAACAAC ( A→G ) . This plasmid was subsequently linearized using a unique MscI restriction site . For the SacII site at coordinate 854464 , the region between coordinates 853473 and 855252 was PCR amplified ( 5′ and 3′ primers contain HindIII restriction sites ) , digested with HindIII , and cloned into pRS306 . The SacII site was introduced by site-directed mutagenesis with the primers 5′-GCTTTAATTCATAATTCCGCGGCAACCTTTCTCTATACTCAGC and 5′-GCTGAGTATAGAGAAAGGTTGCCGCGGAATTATGAATTAAAGC ( G→C ) . This plasmid was linearized using a unique EcoNI restriction site . For the SalI restriction site at coordinate 848724 , the region between coordinates 847679 and 849776 was PCR amplified ( 5′ and 3′ primers contain HindIII restriction sites ) , digested with HindIII , and cloned into pRS306 . The SalI site was introduced by site-directed mutagenesis with the primers 5′-GCAGCCACGGCATGCGTCGACTACGAGCGTATTGTG and 5′-CACAATACGCTCGTAGTCGACGCATGCCGTGGCTGC ( A→G ) . This plasmid was linearized using a unique AgeI restriction site . On the other copy of chromosome VII , SacII restriction sites were engineered at SGD coordinate 845470 ( intergenic ) and coordinate 852145 ( YGR178c ) and a SpeI site was engineered between ERG1 and YGR177c at coordinate 848683 . For the SacII restriction site at coordinate 845470 , the region between coordinates 844520 and 846322 was PCR amplified ( 5′ and 3′ primers contain HindIII restriction sites ) , digested with HindIII , and cloned into pRS306 . The SacII site was introduced by site-directed mutagenesis with the primers 5′-GGTTTAGATCCAAGATTCCGCGGTTCCACCATTTAATATG and 5′-CATATTAAATGGTGGAACCGCGGAATCTTGGATCTAAACC ( C→G ) . This plasmid was linearized using a unique NruI restriction site . For the SacII restriction site at coordinate 852145 , the region between coordinates 851037 and 853268 was PCR amplified ( 5′ and 3′ primers contain SalI restriction sites ) , digested with SalI , and cloned into pRS306 . The SacII site was introduced by site-directed mutagenesis with the primers 5′-CCTCTGGCGCACCTGCTGCCGCGGGAGTAGAGGTATCCG and 5′-CGGATACCTCTACTCCCGCGGCAGCAGGTGCGCCAGAGG ( T→C ) . This plasmid was linearized using a unique BglII restriction site . For the SpeI restriction site at coordinate 848683 , the region between coordinates 847679 and 849776 was PCR amplified ( 5′ and 3′ primers contain HindIII restriction sites ) , digested with HindIII , and cloned into pRS306 . The SpeI site was introduced by site-directed mutagenesis with the primers 5′-CATGCGAGGTAAGACTAGTGTCTGAGACTTATACCCGACC and 5′-GGTCGGGTATAAGTCTCAGACACTAGTCTTACCTCGCATG ( T→G ) . This plasmid was linearized using a unique AgeI restriction site . Recombination events at HIS4::LEU2 and ERG1 loci were similarly monitored by gel electrophoresis . The HIS4::LEU2 assay system contains XhoI restriction site polymorphisms between parental homologs producing fragments diagnostic for parental and recombinant chromosomes . A BamHI/NgoMIV polymorphism immediately at the DSB site allows detection of non-crossover products . Non-crossovers were analyzed by double digestion of genomic DNA with XhoI and NgoMIV and separation on one-dimensional gels . The ERG1 locus contains SacII restriction site polymorphisms that produce fragments diagnostic for JMs and recombinant products . To analyze relative amounts of crossover and non-crossover products at ERG1 , genomic DNA was doubly digested with SacII and SalI and analyzed by one-dimensional gel and Southern hybridization . The polymorphic SalI site located at the DSB site is diagnostic for non-crossover products . Samples were treated with psoralen and UV to crosslink DNA and stabilize JM intermediates using previously described methods [36] , [54] , [103] . DNA extraction and Southern blot analysis were carried out using methods that have been described in detail previously [100] . Yeast cells were fixed and spread as described previously [4] , [103] . For STED microscopy ProLong Gold ( Invitrogen Molecular Probes , catalog # P36930 ) was used as an anti-fade reagent instead of vectashield . Spreads were stained with primary antibodies followed by incubation with fluorochrome-conjugated secondary antibodies ( Invitrogen Molecular Probes , 1∶1000 dilution ) . Images were acquired using Zeiss Axiovision 4 . 6 at 100×magnification . For STED microscopy images were acquired using a Leica SP5 II STED-CW at 100×magnification . Images were adjusted for brightness and contrast using NIH ImageJ . NIH ImageJ was utilized to measure intensities of staining structures . The rabbit anti-Ctf19 antibody was a gift from Phil Heiter and was used following 1 to 1000 dilution . The rabbit anti-Zip3 and rabbit anti-Rec8 antibodies were gifts from Akira Shinohara and were both used diluted 1∶500 . The rabbit anti-Red1 antibody used was a gift from Shirleen Roeder and was used at a 1∶500 dilution . The Goat anti-Zip1 antibody is commercially available via Santa Cruz ( catalog # sc-15632 ) . Random spore analysis was performed as described [18] . Briefly , strains were induced to undergo sporulation , harvested , and asci were digested with zymolyase , diluted in 0 . 1% Tween-20 , sonicated to produce single spores , and plated onto solid media lacking arginine . ARG+ colonies were then tested for growth on plates lacking uracil and threonine . Nuclei were first fixed and spread followed by silver staining to visualize the synaptonemal complex [104] , [105] . To prepare spheroplasts , 10 ml of meiotic culture was collected and spun for 2 . 5 min at 1 , 600 rpm , the pellet was resuspended in 2 ml of ZK buffer ( 25 mM Tris pH 7 . 5 , 0 . 8 M KCl ) , and 40 µl of 1 M DTT was added . After a 2 min incubation at room temperature , cells were spun for 2 . 5 min at 1 , 600 rpm , the pellet was then resuspended in 2 ml ZK buffer , 15 µl zymolyase solution ( 50 mM Tris pH 7 . 5 , 2% glucose , 20 mg/ml zymolyase ) was added , and cells were incubated for 20 min with gentle rotation at 30°C . Cells were then spun for 2 . 5 min at 1 , 300 rpm , washed in 5 ml MES ( 1 M sorbitol , 0 . 1 M MES pH 6 . 5 , 1 mM EDTA , 0 . 5 mM MgCl2 ) , and pelleted as before . Next , cells were resuspended in 1 ml MES and spun for 1 min at 200 rpm , followed by aspiration of the supernatant . To release nuclei from the spheroplasts , 5 µl of the pellet was placed into 50 µl MEM/protease inhibitor ( 0 . 1 M MES pH 6 . 8 , 1 mM EDTA , 0 . 5 mM MgCl2 ) with freshly added 0 . 1 M PMSF ( 10 µl of PMSF stock to 1 ml MEM ) , then gently resuspended by pipetting . To fix the released nuclei , 50 µl of PFA was added and mixed well . 50 µl of the final mixture was spread across microscope slide precoated with polystyrene plastic and incubated at room temperature in a humid chamber . After 5 min , an additional 400 µl of PFA was pipetted onto the slide . After 5 min more , preparations were rinsed with 4 ml of 0 . 4% Photoflo ( Kodak ) and allowed to dry completely before proceeding to silver staining . 200 µl of colloidal developer and 200 µl silver nitrate were mixed on a 24×50 coverslip and the mixture was put on top of a slide that had fixed nuclei . The preparation was placed on a 60°C hotplate for 2–5 min , then gently rinsed with water to remove the coverslip and stop the reaction . Once dry the polystyrene membrane was floated off on a water surface and clean EM grids were placed on the membrane . The membrane and grids were removed from H2O and allowed to dry . Images were taken at 3 , 500×magnification using a Hitachi H-7600 electron microscope housed in the Oklahoma Medical Research Foundation Core Facility for Imaging . | Meiosis is the specialized cell division that produces gametes by precisely reducing the chromosome copy number from two to one . Accurate segregation of homologous chromosome pairs requires they be connected by crossing-over , the precise breakage and exchange of chromosome arms that is carried out by a process called recombination . Recombination is regulated so each pair of homologous chromosomes becomes connected by at least one crossover . We studied the roles of two recombination proteins , Rad51 and Dmc1 , which can act directly to join homologous DNA molecules . Our evidence supports the idea that Dmc1 is the dominant joining activity , while Rad51 acts indirectly with other proteins to support and regulate Dmc1 . Furthermore , Hed1 , an inhibitor of Rad51's DNA joining activity , is also shown to enhance the efficiency of crossing-over . Cells in which Rad51 is activated to promote DNA joining in place of Dmc1 have unregulated and inefficient crossing-over that often leaves chromosome pairs without the requisite crossover . Despite these defects , most cells that use Rad51 in place of Dmc1 complete meiosis and produce high levels of crossovers . Our results indicate that compensatory processes ensure that meiotic cells accumulate high levels of crossover intermediates before progressing to the first round of chromosome segregation . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Meiotic Crossover Control by Concerted Action of Rad51-Dmc1 in Homolog Template Bias and Robust Homeostatic Regulation |
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