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Chronic soil-transmitted helminth ( STH ) infections are associated with effects on systemic immune responses that could be caused by alterations in immune homeostasis . To investigate this , we measured the impact in children of STH infections on cytokine responses and gene expression in unstimulated blood . Sixty children were classified as having chronic , light , or no STH infections . Peripheral blood mononuclear cells were cultured in medium for 5 days to measure cytokine accumulation . RNA was isolated from peripheral blood and gene expression analysed using microarrays . Different infection groups were compared for the purpose of analysis: STH infection ( combined chronic and light vs . uninfected groups ) and chronic STH infection ( chronic vs . combined light and uninfected groups ) . The chronic STH infection effect was associated with elevated production of GM-CSF ( P = 0 . 007 ) , IL-2 ( P = 0 . 03 ) , IL-5 ( P = 0 . 01 ) , and IL-10 ( P = 0 . 01 ) . Data reduction suggested that chronic infections were primarily associated with an immune phenotype characterized by elevated IL-5 and IL-10 , typical of a modified Th2-like response . Chronic STH infections were associated with the up-regulation of genes associated with immune homeostasis ( IDO , P = 0 . 03; CCL23 , P = 0 . 008 , HRK , P = 0 . 005 ) , down-regulation of microRNA hsa-let-7d ( P = 0 . 01 ) and differential regulation of several genes associated with granulocyte-mediated inflammation ( IL-8 , down-regulated , P = 0 . 0002; RNASE2 , up-regulated , P = 0 . 009; RNASE3 , up-regulated , p = 0 . 03 ) . Chronic STH infections were associated with a cytokine response indicative of a modified Th2 response . There was evidence that STH infections were associated with a pattern of gene expression suggestive of the induction of homeostatic mechanisms , the differential expression of several inflammatory genes and the down-regulation of microRNA has-let-7d . Effects on immune homeostasis and the development of a modified Th2 immune response during chronic STH infections could explain the systemic immunologic effects that have been associated with these infections such as impaired immune responses to vaccines and the suppression of inflammatory diseases .
An estimated two billion people worldwide are infected with soil-transmitted helminth ( STH or intestinal helminth or geohelminth ) parasites of which Ascaris lumbricoides and Trichuris trichiura are the most prevalent [1] . STH infections are associated with significant morbidity largely related to the nutritional effects of a chronically infected state among pre-school and school age children [2] . STH infections have been associated with impaired immunity to vaccines [3] , [4] and mucosal pathogens [5] , and the modulation of inflammatory diseases such as asthma [6] . The mechanisms by which chronic human STH infections modulate immune responses are poorly understood . For STH parasites to have modulatory effects at distal sites ( e . g . lung ) to non-parasite antigens ( e . g . aeroallergens ) , they would be expected to influence systemic immunity . Such influences could be mediated by alterations in immune homeostatic mechanisms that may include the enhanced production of immune modulatory cytokines . Alternatively , alterations in immune homeostasis could occur through effects on mRNA and/or microRNA expression . MicroRNAs ( miRNAs ) are small non-coding RNAs that play an important role in the post-transcriptional regulation of gene expression and are considered to be critical for the regulation of innate and adaptive immunity [7] . In the present study we investigated the impact of chronic STH infections on immune homeostasis by measurement of the production of a range of cytokines and chemokines by unstimulated peripheral blood mononuclear cells and on mRNA and microRNA expression in unstimulated peripheral blood from children .
The study design was cross-sectional . Children aged 7–12 years attending 6 schools that served rural communities in the District of Eloy Alfaro in Esmeraldas Province in Ecuador , were eligible for inclusion . Schools were visited and a general assembly of parents and children was called in which the study was explained . Parents wishing to enrol their children into the study then underwent a process of informed written consent individually with study investigators . Minor assent was obtained from the children . Parental consent and minor assent was obtained for 676 children ( 95% of those eligible ) aged 7–12 years . Children were asked to provide a total of 4 serial stool samples and a blood sample was collected into tubes containing sodium heparin as anticoagulant ( Vacutainer , BD Diagnostics ) for measurement of anti-A . lumbricoides IgG ( measure of exposure to STHs ) and IgG4 ( measure of chronic infections with STHs ) antibodies . Stool samples were examined by two highly experienced laboratory technicians ( SB and NB ) using the modified Kato-Katz and formol-ethyl concentration techniques [8] . Children were selected into 3 groups: 1 ) uninfected ( n = 41 ) - absence of STH infections in all 4 stool samples and the absence of anti-A . lumbricoides IgG and IgG4 antibodies; 2 ) light infections ( n = 45 ) – presence of STHs in at least one of 4 stool samples , presence of anti-A . lumbricoides IgG but no anti-A . lumbricoides IgG4 antibodies; and 3 ) chronic infections ( n = 61 ) – presence of STHs ( both A . lumbricoides and T . trichiura ) in all 4 stool samples and presence of anti-A . lumbricoides IgG and IgG4 antibodies . Children who did not provide all stool samples or did not satisfy the study group criteria with respect to the presence of STH infections and the presence or absence of anti-A . lumbricoides IgG and IgG were excluded . A second blood sample was obtained from the 147 children selected into the 3 study groups . Blood samples were collected into Paxgene tubes ( PreAnalitiX GmbH , BD Qiagen ) for gene expression and into tubes containing sodium heparin as anticoagulant ( Vacutainer , BD Diagnostics ) for cell culture . Blood samples for culture were analyzed within 5 hours of collection . Paxgene tubes were maintained at ambient temperature in a polystyrene box for 4 hours , then transferred to a −20°C freezer for 24 hours and stored at −80°C before shipping to the Wellcome Trust Sanger Institute on dry ice for analysis . The final selection of 60 children ( 20 in each infection group ) for analysis , a sample size restricted by cost considerations , was based on RNA quantity ( >50 ng/µl ) and integrity ( absence of degradation using the electrophoretic profile provided by Bioanalyzer ) after extraction . Sample collection was performed between May and October 2008 . The parents of each child received written results for all stool ( parasites ) and blood samples ( blood count and anemia ) and treatment where appropriate was provided by the study physician ( MR ) . The ethics committee of the Universidad San Francisco de Quito approved the study protocol . Parasite-specific IgE and polyclonal IgE antibody levels were analyzed using the UNI-CAP assay ( Pharmacia Biotech , Uppsala , Sweden ) . Anti-A . lumbricoides IgG and IgG4 antibodies were measured as described [9] . Positive values were defined as >3 SD above mean values of a pool of 10 uninfected non-endemic control sera . Control sera were collected from health professionals living in the town of Quinindé , Esmeraldas Province . All had four negative stool samples for STH parasites after examination using the modified Kato-Katz and formol-ethyl concentration techniques [8] . PBMCs were isolated by density gradient centrifugation over Histopaque ( Sigma ) . Cells were cultured at a concentration of 1×106 cells/well in 48-well tissue culture plates in duplicate ( Greiner-Labortechnik ) with supplemented RPMI 1640 medium ( i . e . no immunologic stimulation ) . Plates were incubated at 37°C and 5% CO2 for 5 days . Supernatant fluids were collected at 5 days , stored at −70°C and shipped to St George's University of London for analysis . Cytokine/chemokine protein was measured in supernatant fluids from PBMC cultures using R&D Fluorokine MAP kits ( for MCP-2 , GM-CSF , IL-2 , IL-5 , IL-10 , IL-12 , IFN-γ , and TGF-β ) following the manufacturer's instructions and the plates were read on a BioRad Luminex reader . Samples were run in duplicate blind to infection group . Kits of the same lot were used and all assays were run on the same day . Sensitivities for these assays were 35–70 pg/mL and 1 . 6 pg/mL for IL-10 . RNA extraction was performed using miRNeasy Mini Kit ( Qiagen ) , co-purifying total RNA and microRNA . cDNA and cRNA synthesis and labelling was done using the Illumina TotalPrep 96 kit ( Ambion , Austin , TX ) . Quantity and quality of the extracted RNA and cRNA was determined using Bioanalyzer ( Agilent technologies , Palo Alto , CA ) and Nanodrop ND1000 ( Nanodrop Technologies , Wilmington , DE ) and was comparably high between all subjects included in the analysis . Biotinylated cRNA ( or microRNA ) was hybridized to the arrays using Bead Station ( version 3 . 1 ) blind to infection group . Illumina Human WG-6 v2 Beadchips ( Illumina , San Diego , CA ) were used to generate expression profiles of more than 48 , 000 transcripts . MicroRNA levels were measured using a microRNA expression panel ( Illumina ) . After hybridization , chips were scanned on an Illumina BeadArray Reader and raw intensities were extracted using Illumina BeadStudio Gene Expression Module . Expression intensities were log base 2-transformed and quantile normalized using median expression intensity . 36798 probes out of 48701 were called present after background correction and normalization . Genespring software ( Agilent ) was used to perform quality control . Cytokine production was compared between the 3 infection groups and for two different infection effects: a STH infection effect ( combined chronic and light vs . uninfected groups ) and a chronic STH infection effect ( chronic vs . combined light and uninfected groups ) . Non-parametric tests were used to compare cytokine and antibody levels between groups: Mann-Whitney for two-group and Kruskall-Wallis for three-group comparisons with the Bonferroni correction for multiple comparisons . Principal components analysis ( PCA ) is a widely used data reduction method used to reduce often highly correlated variables such as cytokines into a smaller number of uncorrelated variables called principal components . PCA was used to summarize multiple cytokine parameters to a small number of principal components as described elsewhere [10] . PCA was done unscaled on the logarithm of the values . Mean values of the first principal component were then compared using Student's t test between infection groups by bootstrap with 100 , 000 replicates [11] . Statistical analyses were done using STATA , version 10 ( Statacorp , College Station , TX ) . Genespring software ( Agilent ) was used for analysis of microarray data . Comparisons were made for each of the STH infection and chronic STH infection effects . Because of the effects of averaging of gene expression across groups , the component comparisons for each infection effect were calculated . Differentially expressed ( DE ) genes were defined by a corrected P value<0 . 05 ( adjusted for multiple test correction using the Benjamini and Hochberg method [12] ) and a fold change ( FC ) cut off value of 1 . 5 for at least one of the comparisons . miRanda algorithm was used to scan miRNA sequences against 3′ UTR sequences on Illumina Human WG-6 chip searching for maximal local complementarity alignments and yielding scores that reflect the total miRNA vs . UTR alignment . Microarray and miRNA array data were uploaded onto the MIAME-based database ArrayExpress ( accession numbers E-TABM-938 and E-TABM-939 , respectively ) .
Details of the 60 children studied are provided in the Methods section and their baseline characteristics are summarized in Table 1 . Frequencies of potential confounders were not significantly different between the groups with the exception of crowding ( P = 0 . 001 ) . All children in the chronic infection group and 65% of children in the light infection group were co-infected with T . trichiura . Levels of polyclonal and anti-Ascaris IgE increased across the groups from uninfected to chronic infection groups ( Table 1 ) . Comparisons between the three groups of production of cytokines and the chemokine MCP-2 , by unstimulated PBMCs showed significant heterogeneity of effect for GM-CSF ( P = 0 . 01 ) , IL-2 ( P = 0 . 01 ) , IL-5 ( P = 0 . 04 ) , and IL-10 ( P = 0 . 0007 ) ( Figure 1A–D ) . No significant differences were observed between the three groups for the other cytokines and MCP-2: MCP-2 ( uninfected , median 170 pg/mL , interquartile range [IQR] 158-188; light , median 165 , IQR 70-199; chronic , median 170 , IQR 156-363 ) , IL-12 ( uninfected , median 35 pg/mL , IQR 35-35; light , median 35 , IQR 35-35; chronic , median 35 , IQR 35-148 ) , IFN-γ ( uninfected , median 70 pg/mL , IQR 70-155; light , median 70 , IQR 70-143; chronic , median 70 , IQR 70-382 ) , and TGF-β ( uninfected , median 265 pg/mL , IQR 160-465; light , median 285 , IQR 235-427; chronic , median 241 , IQR 201-427 ) . Post-hoc comparisons showed significantly elevated production of GM-CSF ( P = 0 . 005 ) , IL-2 ( P = 0 . 008 ) , and IL-5 ( P = 0 . 03 ) in the chronic infection compared to uninfected groups and elevated production of IL-5 ( P = 0 . 03 ) and IL-10 ( P = 0 . 0008 ) in the chronic versus light infection groups ( Figures 1A–D ) . The STH infection effect ( combined light and chronic infection groups ) was associated with greater production of GM-CSF ( P = 0 . 02 ) and IL-2 ( P = 0 . 004 ) ( Figures 1E & F ) and the chronic STH infection effect with greater levels of GM-CSF ( P = 0 . 007 ) , IL-2 ( P = 0 . 03 ) , IL-5 ( P = 0 . 01 ) , and IL-10 ( P = 0 . 01 ) ( Figures 1 I–L ) . PCA yielded a single component that accounted for 70% of total variation with coefficients of −0 . 119 for MCP-2 , −0 . 950 for IL-5 , and −0 . 272 for IL-10 ( i . e . a component representing an immune response dominated by IL-5 and IL-10 ) . All other cytokine loadings for this component were small ( i . e . <0 . 1 or >−0 . 1 ) . The remaining components , that each accounted for less than 20% of variance , were not analysed further . Comparisons between mean values of PC1 for infection groupings showed significant differences for the chronic ( P = 0 . 004 ) and STH infection ( P = 0 . 007 ) effects . Means for PC1 were different between chronic and light groups ( P = 0 . 02 ) or uninfected groups ( P = 0 . 001 ) . The gene expression profiles in peripheral blood of the three different infection groups were analysed using enriched RNA on microarrays covering 48 , 000 human transcripts . Hierarchical cluster analysis showed clustering of gene expression by infection group – there was evidence of differences between the three groups in terms of their gene expression profiles with the light infection group representing a transitional profile between uninfected and chronic infection groups ( Figure 2 ) . Overall relatively few genes were significantly differentially expressed for the STH infection ( uninfected vs . light/chronic infection groups ) and chronic STH infection ( chronic vs . light/uninfected groups ) effects after controlling for multiple comparisons . Findings with differential expression of at least 1 . 5-fold change ( FC ) and corrected P value of ≤0 . 05 are shown in Table 2 . The corrected P value for each of the two comparisons ( STH infection effect - combined light and chronic infection vs . uninfected groups; chronic STH infection effect - chronic vs . combined light and uninfected groups ) and FCs for each of the component comparisons ( STH infection effect – light vs . uninfected and chronic vs . uninfected; chronic STH infection effect: chronic vs . uninfected and chronic vs . light ) are shown . Differentially regulated genes were: CCL23 ( chemokine ligand 23 ) , CREB5 ( cAMP responsive element binding protein 5 ) , CYP4F3 ( cytochrome P450 polypeptide ) , HBE1 ( hemoglobin , epsilon 1 ) , HES4 ( hairy and enhancer of split 4 ) , HLA-DRB3 ( MHC class II , DR beta 3 ) , HLA-DRB4 ( MHC class II , DR beta 4 ) , HRK ( Harakiri , BCL interacting protein ) , IL-1R2 ( interleukin-1 receptor type II ) , IL-8 ( interleukin-8 ) , IDO ( indoleamine 2 , 3-dioxygenase ) , LILRA3 ( leukocyte immunoglobulin-like receptor , subfamily A member 3 ) , LOC128977 ( chromosome 22 open reading frame 39 ) , LOC645284 ( hypothetical protein LOC645284 ) , LOC652726 ( similar to ankyrin repeat domain 36 ) , LOC731682 ( HLA class II , DQ ( 1 ) alpha chain precursor-like ) , MME ( membrane metalloendopeptidase ) , PBEF1/NAMPT ( nicotinamide phosphoribosyltransferase ) , PI3 ( peptidase inhibitor 3 ) , PMP22 ( peripheral myelin protein ) , PRKAG1 ( protein kinase , AMP activated , gamma 1 ) , PROK2 ( prokineticin 2 ) , QPCT ( glutamyl-peptide cyclotransferase ) , RNASE2 ( eosinophil derived neurotoxin ) , RNASE3 ( eosinophil cationic protein ) , RPS23 ( ribosome protein S23 ) , SHANK2 ( SHANK2 SH3 and multiple ankyrin repeat domains 2 ) , SOS1 ( son of sevenless homolog 1 ) , UCP2 ( uncoupling protein 2 ) , VNN2 ( vanin 2 ) , and ZFAT1 ( zinc finger and AT hook domain containing ) . Three genes were differentially regulated for the chronic but not the STH infection effects ( up-regulated - HLA-DRB3 , LILRA3; down-regulated - ZFAT1 ) . Within the chronic STH infection effect , there was evidence of relatively greater effects on gene expression of chronic than light infection groups when compared to the uninfected groups . For example , in the chronic vs . uninfected group comparison there was a 2 . 1-fold up-regulation of CCL23 expression , while expression was upregulated 1 . 6-fold in light vs . uninfected and 1 . 3-fold in chronic vs . light groups . Overall , the chronic infection effect was associated with genes regulating: 1 ) inflammation: ZFAT1 [down regulated , anti-apoptotic regulator] , IL-1R2 [down , inducer of cell migration]; LILRA3 [up-regulated , potential anti-inflammatory molecule]; UCP2 [up-regulated , negative regulator of reactive oxygen species production by macrophages]; 2 ) neutrophil-mediated inflammation: IL-8 [down , neutrophil chemotaxis and activation] , VNN2 [down , transendothelial migration of neutrophils] , PBEF1 [down , inhibition of neutrophil apoptosis] , and CCL23 ( up , inhibition of production by and release of neutrophils from the bone marrow]; 3 ) antibacterial immunity ( down , skin antimicrobial peptide PI3 ) ; 4 ) immune homeostasis , a ) the induction of homeostatic mechanisms during infection ( up , IDO ) , b ) chemotaxis of resting T cells and monocytes ( up , CCL23 ) , and c ) activation of cell apoptosis ( up , HRK ) ; and 5 ) the capacity of eosinophils to kill helminth parasites ( up , RNASE2 and RNASE3 ) . CYP4F3 , an inactivator of inflammatory mediator leukotriene B4 , was downregulated and MHC class II antigen gene expression ( HLA-DRB3 and HLA-DRB4 ) were upregulated . The purified RNA populations were also analysed on a specific microarray for monitoring miRNA expression profiles . Both infection effects were associated with the significant differential expression of a single microRNA , hsa-let-7d ( STH infection effect , FC: −20 . 7 , corrected P = 0 . 012; chronic STH effect , −21 . 3 , corrected P = 0 . 010 ) . Data for differential expression of microRNAs for both infection effects with −1 . 5<FC>1 . 5 and uncorrected P values of ≤0 . 05 are provided in the online archive ( Table S1 ) . We searched for putative targets for hsa-let-7d using the miRBase targets database to see if it could be responsible for the regulation of either the differentially expressed genes or cytokine production observed during chronic infection . Because miRNA would be expected to be inversely associated with such targets , we looked for putative gene targets that had expression profile opposite to this microRNA . Hsa-let-7d was inversely associated with CCL23 ( score 17 . 2 , P base = 0 . 03 ) . Results of this analysis for the whole data set are provided in the online archive ( Table S2 ) . Of the 4 cytokines whose production was increased during chronic infection , IL-2 is a putative target for hsa-let-7d .
An important limitation was the small sample size that limited the power to detect relatively small changes in the expression of a large number of genes in peripheral blood . The analysis was exploratory and our data provide evidence for the differential expression of several potentially relevant genes such as IL-8 and the microRNA hsa-let-7d . The sample size was limited by the high cost of these analyses . We had to screen a relatively large population of school children to select our study population using strict selection criteria based on the presence or absence of STH infection in serial stool samples and anti-Ascaris IgG and IgG4 in plasma samples . Although this selection process allowed us to identify well-defined sub-groups of children that were of specific interest for our study objectives , our findings cannot necessarily be generalized to all children with STH infections , particularly as the prevalence of our STH infection sub-groups as defined will be extremely variable between populations . We defined chronic infection on the basis of repeated stool samples being positive for A . lumbricoides and the presence of anti-A . lumbricoides IgG4 antibodies . The presence of parasite-specific IgG4 is a well-recognised feature of chronic helminth infections [45] including those with STH parasites [19] , [46] . The use of anti-A . lumbricoides IgG4 to define chronic STH infection could have biased the results with respect to IL-10 production because this cytokine differentially induces IgG4 in B cells [47] . However , elevated spontaneous IL-10 has been reported in children with STH infections previously [19] , [48] , [49] . We defined homeostasis by the measurement of basal or unstimulated cytokine production by PBMCs and gene expression in unstimulated whole blood [18] , [50] . Such a definition may be subject to significant limitations: 1 ) Measurements were taken at a single time point . Prospective studies conducted from the time of first exposures to STH infections in infancy will be required to indicate more strongly a causal association between the development of chronic STH infection and such effects on immune responses . 2 ) The measurement of the accumulation of cytokines over 5 days by unstimulated PBMCs may be complicated by other processes such as the differentiation of monocytes into macrophages and cell death . Such in vitro artefacts would be expected to occur in all cultures irrespective of study group and would mask rather than emphasize inter-group differences . Further , our findings are consistent with those of previous studies of helminth-infected individuals in which unstimulated whole blood or PBMCs were cultured for variable periods of one to five days [18] , [19] , [22] , [48] , [49] , [51] , [52] . Recently the cells producing IL-10 spontaneously in helminth-infected individuals have been identified as CD4+ T regulatory type 1 cells [50] . Further , we observed also elevated production of IL-10 ( P = 0 . 04 ) and a trend of increased IL-5 in chronic versus non-chronic infections in PBMC cultures stimulated with A . lumbricoides antigen ( data not shown ) , an observation that is consistent with the findings from unstimulated cultures . Because the study was cross-sectional and we had no data on previous parasite infections , we cannot exclude heavy previous infections in the light infection group or past infections in the uninfected group although , in the case of the latter , the children were unlikely to have been infected recently because of the absence of A . lumbricoides-specific IgG antibodies . However , such misclassification would be expected to reduce the size of inter-group differences . The comparisons were not controlled for potential confounders and there were differences , albeit non-significant , between the groups in nutritional factors such as the prevalence of anemia . Other unmeasured confounding factors may have differed between the groups . Important co-infections with powerful effects on the immune response such as HIV , malaria , and tuberculosis were of very low prevalence in our study population . However , we cannot exclude confounding as an alternative explanation for our study findings . The present study has provided evidence that PBMCs of children with chronic STH infections produce elevated levels spontaneously GM-CSF , IL-2 , IL-5 , and IL-10 , the latter two cytokines being suggestive of a modified Th2 response . We were able to detect the differential regulation of several genes during STH infection including genes suggestive of the suppression of neutrophil-mediated inflammation and perhaps also the up-regulation of immune homeostasis . Both STH and chronic STH infection effects were associated with the down-regulation of microRNA has-let-7d possibly indicating a role for this gene during infection . The data from the present study underlie the complexity of the molecular processes associated with the development of chronic STH infections . A better understanding of the mechanisms by which STHs modulate anti-parasite inflammatory responses may lead to the development of new therapies for inflammation . CCL23 ( NM_145898 . 1 ) , CREB5 ( NM_182898 . 2 ) , CYP4F3 ( NM_000896 . 1 ) , HBE1 ( NM_005330 . 3 ) , HES4 ( NM_021170 . 2 ) , HLA-DRB3 ( NM_022555 . 3 ) , HLA-DRB4 ( NM_021983 . 4 ) , HRK ( NM_003806 . 1 ) , IL1R2 ( NM_173343 . 1 ) , IL-8 ( NM_000584 . 2 ) , IDO ( NM_002164 . 3 ) , LILRA3 ( NM_006865 . 2 ) , LOC128977 ( NM_173793 . 3 ) , LOC645284 ( XM_932788 . 1 ) , LOC645416 ( XM_928457 . 1 ) , LOC652726 ( XM_942351 . 2 ) , LOC731682 ( XM_001129369 . 1 ) , MME ( NM_000902 . 3 ) , PBEF1/NAMPT ( NM_005746 . 2 ) , PI3 ( NM_002638 . 2 ) , PMP22 ( NM_153321 . 1 ) , PRKAG1 ( NM_002733 . 3 ) , PROK2 ( NM_021935 . 2 ) , QPCT ( NM_012413 . 3 ) , RNASE2 ( NM_002934 . 2 ) , RNASE3 ( NM_002935 . 2 ) , RPS23 ( NM_001025 . 4 ) , SOS1 ( NM_005633 . 2 ) , UCP2 ( NM_003355 . 2 ) , VNN2 ( NM_004665 . 2 ) , and ZFAT1 ( NM_001029939 . 1 ) . | Soil-transmitted helminth ( STH or intestinal worm ) infections are extremely common infectious diseases of childhood in developing countries . Infections tend be chronic and may last for many years . Chronic STH infections are associated with modulation of the immune response , a consequence of which may be a reduced prevalence of allergic inflammatory diseases such as asthma . The mechanisms by which STH infections suppress inflammatory responses are poorly understood . In this study , we hypothesized that STH infections may affect immune responses through alterations of immune homeostasis ( or the steady-state adjustments of the immune system that maintain equilibrium ) . We investigated the capacity of blood from children classified as having no , light , or chronic STH infections to produce cytokines at rest ( i . e . no immunologic stimulation ) and the expression of genes in blood samples . Our data show that blood cells of children with chronic STH infections have an altered immune response that is likely to be associated with less allergic inflammation ( the modified Th2 response ) and that the expression of some inflammatory genes are reduced . Our findings provide insights into the mechanisms by which STH infections suppress immune responses in children to ensure the survival of the parasite and reduce inflammation . | [
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"Introduction",
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] | [
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] | 2011 | Effects of Chronic Ascariasis and Trichuriasis on Cytokine Production and Gene Expression in Human Blood: A Cross-Sectional Study |
Multiple PIP2 dependent molecular processes including receptor activated phospholipase C activity occur at the neuronal plasma membranes , yet levels of this lipid at the plasma membrane are remarkably stable . Although the existence of unique pools of PIP2 supporting these events has been proposed , the mechanism by which they are generated is unclear . In Drosophila photoreceptors , the hydrolysis of PIP2 by G-protein coupled phospholipase C activity is essential for sensory transduction of photons . We identify dPIP5K as an enzyme essential for PIP2 re-synthesis in photoreceptors . Loss of dPIP5K causes profound defects in the electrical response to light and light-induced PIP2 dynamics at the photoreceptor membrane . Overexpression of dPIP5K was able to accelerate the rate of PIP2 synthesis following light induced PIP2 depletion . Other PIP2 dependent processes such as endocytosis and cytoskeletal function were unaffected in photoreceptors lacking dPIP5K function . These results provide evidence for the existence of a unique dPIP5K dependent pool of PIP2 required for normal Drosophila phototransduction . Our results define the existence of multiple pools of PIP2 in photoreceptors generated by distinct lipid kinases and supporting specific molecular processes at neuronal membranes .
The detection and conversion of external stimuli into physiological outputs is a fundamental property of neurons and depends on intracellular signal transduction pathways . Phosphoinositides , the seven phosphorylated derivatives of phosphatidylinositol are key signalling molecules and of these the most abundant PIP2 has multiple roles in neurons . Several neuronal receptors ( such as the metabotropic glutamate , growth factor and sensory receptors ) transduce stimuli into cellular information using the hydrolysis of PIP2 by phospholipase C enzymes . Additionally , within the context of neuronal cell biology PIP2 has several roles including cytoskeletal function [1] [2] and several ion channels and transporters ( eg: Kir , TRP and Na+/Ca2+ exchanger ) require PIP2 for their activity [3] . At the pre-synaptic terminal , a regulated cycle of PIP2 turnover is essential to regulate synaptic vesicle cycling . Thus PIP2 plays multiple roles at the plasma membrane of neurons; hence not surprisingly , changes in phosphoinositide metabolism have been linked to several inherited diseases of the human nervous system [reviewed in [4]] . Finally , one of the molecular targets of lithium , used in the treatment of bipolar disorders , is inositol monophosphatase a key regulator of PIP2 turnover in neurons [5] . Given the multiple functions of PIP2 at the plasma membrane , it is unclear if a common pool of PIP2 supports all these functions . Alternatively , if there are distinct pools , it is unclear how these are generated and sequestered on the nanoscale structure of the membrane . In principle , PIP2 can be generated by the activity of two classes of phosphatidylinositol phosphate kinase ( PIPK ) enzymes , designated PIP5K and PIP4K; PIP5K phosphorylates PI4-P at position 5 of the inositol ring , whereas PIP4K phosphorylates PI5-P at position 4 [[6]] . Although PIP4K and PIP5K synthesize the same end product , they are not functionally redundant [7] and studies of the mammalian enzymes has defined the molecular basis of substrate specificity [8] . Genes encoding PIP5K are present in all sequenced eukaryotes; however PIP4K appears to be a feature of metazoans; mammalian genomes contain three distinct genes for each of these two activities . However , the functional importance of these two classes of enzymes in generating plasma membrane PIP2 has remained unclear . Drosophila photoreceptors are a well-established model for analyzing phosophoinositide signaling in-vivo [9] . In these cells , the absorption of photons is transduced into neuronal activity by G-protein coupled , phospholipase Cβ ( PLCβ ) mediated PIP2 hydrolysis [10] . Thus , during phototransduction , PIP2 needs to be resynthesized to match consumption by ongoing PLCβ activity . PIP2 turnover is tightly regulated in photoreceptors; mutants in molecules that regulate PIP2 turnover show defects in phototransduction [11] . However the role of PIPK enzymes in regulating PIP2 synthesis during phototransduction is unknown . In this study we have analyzed each of the three PIPK encoded in the Drosophila genome that could generate PIP2 in the context of phototransduction . Our analysis defines three pools of PIP2 supporting distinct molecular processes in photoreceptors .
In silico analysis of the Drosophila genome sequence revealed that there are four distinct genes that encode open reading frames that include the Interpro domain IPR00002498 which is the “PIP kinase catalytic domain” . These include CG6355 , CG3682 , CG9985 and CG17471 . Of these CG6355 encodes a FYVE domain containing protein that is the single ortholog of yeast Fab1 , a protein with 1-phosphatidylinositol 3—phosphate 5 kinase activity [12][13] . CG17471 ( dPIP4K ) has recently been shown to encode a PIP4K activity that can generate PIP2 by 4 kinase activity using PI5P as a substrate [14] . The remaining two genes namely CG9985 ( sktl ) and CG3682 could encode putative PIP5K activity . sktl has been proposed to encode a Drosophila PIP5K [15][16] . CG3682 is an independent gene that also encodes a putative PIP5K activity . Previous studies have shown that the activation loop region of PIPKs contains specific residues that are conserved among PIP5K and are distinct from PIP4K enzymes [8] . A multiple alignment of PIP5K and PIP4K proteins from mammals and Drosophila reveals that sktl and CG3682 have activation loop residues that are highly diagnostic of those seen in mammalian PIP5K enzymes ( Fig . 1A ) . Both SKTL and dPIP5K show high level of sequence similarity with all the isoforms of mammalian PIP5K . In catalytic domain the identity is more than 80% , whereas the overall sequence homology is from 55–65% with different mammalian isoforms . SKTL is ubiquitously expressed in all organs ( Fig . 1B ) suggesting its function in many/all cell types . In mammals this kind of expression pattern is evident for α and β isoforms of the PIP5K [17] . By contrast , the γ isoform of PIP5K is mostly expressed in neuronal tissues [18 , 19] an expression pattern recapitulated by dPIP5K . In addition dPIP5K has multiple splice variants with a conserved catalytic domain and variable C-terminal extensions [19] . The splicing pattern and protein isoforms of dPIP5K so generated as well as its expression pattern ( enriched in the adult head Fig . 1B ) recapitulates that seen for mammalian PIP5Kγ . The functional significance of the splice variants of dPIP5K remains to be established . In summary it is very likely that the Drosophila genome contains two genes that encode PIP5K activity namely sktl and CG3682 . We have named CG3682 as dPIP5K . Thus , collectively there are three phosphoinositide kinases ( PIPK ) , sktl , dPIP5K and dPIP4K all of which could generate PIP2 . In order to identify the PIPK that would generate PIP2 in adult Drosophila photoreceptors , we studied the expression of all three genes . We found that while sktl and dPIP4K were ubiquitously expressed in adult Drosophila , dPIP5K expression was mainly restricted to the head ( Fig . 1B ) . All three genes were expressed in the Drosophila retina; while sktl RNA was present at very low levels and showed no eye-enrichment , dPIP5K and dPIP4K , showed some degree of enrichment in the eye ( Fig . 1C ) . In order to reveal the function of dPIP5K in vivo , a loss-of-function mutant was generated using ends-out homologous recombination [20] . This results in the insertion of a dominant selection marker ( Pw+ ) flanked by multiple stop codons within the gene such that the kinase domain of dPIP5K was disrupted and the mutant allele should produce no protein . A total of eight independent knock-out alleles were isolated by following phenotypic markers , genetic mapping and molecular screening . Two of these namely dPIP5K18 and dPIP5K30 were studied in detail and are described in this study . All eight alleles were semi-lethal; very few homozygous mutant flies emerged as viable adults . Using a polyclonal antibody generated against a relatively unique C-terminal region of dPIP5K , we found that dPIP5K18 and dPIP5K30 were protein null alleles of dPIP5K ( Fig . 2A ) . In this study we also used a protein null allele of dPIP4K ( dPIP4K29 ) that has already been described [14] . Homozygous deletions in sktl ( eg: sktlΔ20 ) are larval lethal [15] and analysis using mitotic clones revealed that loss of sktl is also cell lethal in the developing eye . Thus for the analysis of sktl we have used an allelic combination sktlΔ20 / + and over expression of a kinase dead version of SKTL . These are not protein null alleles but represent the most severe alleles of sktl that give viable eyes . Since phototransduction in Drosophila involves rapid G-protein coupled PIP2 hydrolysis , it might be predicted that loss-of-function mutants in a PIPK that generates the PIP2 ( which is the substrate for photoreceptor PLCβ ) might also show a defective electrical response to light . We studied the response to light of mutants in all three genes encoding PIP kinases that can in principle generate PIP2 , namely dPIP5K , sktl and dPIP4K . A widely accepted way to study the electrical response to light is the electroretinogram ( ERG ) , an extracellular recording of light-induced electrical changes in the eye . Using ERG , we examined the electrophysiological responses of dPIP5K18 photoreceptors to light . Since very few homozygous mutant adults were obtained , FLP/FRT mediated mitotic recombination was used to obtain mosaic animals in whom the whole eye was homozygous mutant for dPIP5K18[21 , 22] . ERGs were performed on day 0 ( < 24hrs post-eclosion ) flies . Characteristically , wild type photoreceptors respond with a large depolarization associated with on and off-transients . By contrast , photoreceptors from dPIP5K18 produced a much smaller receptor potential in response to a stimulus of equivalent intensity associated with a very slow activation kinetics and response termination . Sample traces of voltage changes in response to a 2s stimulus of green light from wild type and mutants are shown ( Fig . 2B ) . This phenotype was seen in all eight knock-out alleles of dPIP5K that we isolated . Responses in dPIP5K18 displayed abnormal kinetics: the time of rise to the peak of the response was substantially prolonged ( Fig . 2F ) and the time for decay back to baseline following the end of the light stimulus was also increased ( Fig . 2E ) . dPIP5K18 photoreceptors did not display any on or off transients that represent synaptic activity at the first synapse between photoreceptors and the brain . An intensity response function analysis using flies of matched eye colour showed that dPIP5K18 photoreceptors have a reduction in response sensitivity when measured over several log units of light intensity ( Fig . 2C , D ) . Introduction of a genomic rescue transgene in dPIP5K18 flies was able to largely correct the peak amplitude , response termination as well as restore both “on” and “off” transients completely ( Fig . 2B , E , F ) . These results demonstrate that dPIP5K is required to support a normal electrical response to light in Drosophila photoreceptors . By contrast light responses were unaffected in dPIP4K29 [protein null mutant of dPIP4K [14]] photoreceptors ( Fig . 2G , H ) . Although the amplitude of ERG responses from dPIP4K29 look smaller than controls , this is because these flies are smaller than controls due to a growth defect during larval development [14] . However the kinetics of the light response were only marginally different in dPIP4K29 . Finally we studied the most severe allele of sktl that gives rise to adult photoreceptors , namely sktlΔ20/+ ( S1A Fig . ) ; these flies gave normal response to light . Further overexpression of a kinase dead version of sktl had no effect on the electrical response to light as measured by ERGs ( S1B Fig . ) . Together these results suggest that dPIP4K and sktl are most likely dispensable for a normal electrical response to light in Drosophila photoreceptors . A number of mechanisms could account for the abnormal light response in dPIP5K18 photoreceptors . PIP2 is known to be an allosteric regulator of a number of proteins involved in both vesicular transport as well as the cytoskeleton . Thus , loss of dPIP5K function might impact photoreceptor structure through defects in these processes and the abnormal light response may be a consequence of abnormal ultrastructure as seen in the case of mutants such as rdgA and rdgB [23] . To test this hypothesis , we studied photoreceptor ultrastructure using transmission electron microscopy ( TEM ) . This revealed that photoreceptors R1-R7 from 0 day old flies were normal in dPIP5K18 ( Fig . 3A ) . Microvilli were completely intact and showed no vesiculation or blebbing and only minimal defects were seen at the base of the microvilli; however these changes did not increase with age or illumination and rhabdomere structure remained completely intact ( Fig . 3B ) . A reduced light response could also result from reduction in the levels of key proteins required to support the phototransduction cascade . Thus the abnormal light response in dPIP5K18 photoreceptors could be due to altered levels of any one of the key proteins required for phototransduction such as Gq and Rh1 . Gαq1 , a severe hypomorph of dGq expressing less than 1% of the wild type protein levels shows more than 1000-fold reduction in sensitivity to light [24]and ninaE mutants characterized by large decrease in the level of Rh1 also show reduced sensitivity to light . Alternatively , the abnormal ERG could also be the consequence of reductions in the level of proteins like NORPA , TRP , INAD which act downstream of photon absorption to generate a normal electrical response to light . This led us to check the protein level of all the key components of the phototransduction cascade in dPIP5K18 . Western blot analysis ( Fig . 3C ) showed that neither the levels of Rh1 , Gαq , Gβ and Gγ , nor the levels of NORPA , TRP , TRPL , INAD and INAC were appreciably reduced compared to controls . This result suggests that the abnormal light response observed in dPIP5K18 is not due to reduced levels of any of the key protein required for phototransduction . Further the subcellular localization of Rh1 and TRP were also studied and found to be not different between control and dPIP5K18 ( Fig . 3D ) . Together , these findings suggest that ultrastructural defects or changes in the levels and localization of the major transduction proteins cannot explain the abnormal electrical response in dPIP5K18 photoreceptors . dPIP5K is a PIP kinase that is predicted to convert PI4P into PIP2 . To test its requirement in regulating the dynamics of light induced PIP2 turnover at the rhabdomeral membrane we used a live fly preparation in which a fluorescent biosensor consisting of the PH domain of PLCδ fused to GFP ( hereafter called PIP2 biosensor ) is expressed in photoreceptors . When eyes are illuminated with bright light ( λmax 488 nm ) high rates of PLC activation result in the hydrolysis of PIP2 and as a consequence the fluorescent biosensor is detached from the membrane and diffuses out of the microvillar cytoplasm resulting in the loss of the fluorescent pseudopupil signal . Under red light illumination that converts metarhodopsin to rhodopsin thus terminating PLCβ activity , PIP2 levels recover as a consequence of ongoing PIP2 resynthesis and the PIP2 biosensor signal in the pseudopupil recovers ( Fig . 4A , B ) . The kinetics of the fluorescent pseudopupil are blocked in norpAP24 which lacks appreciable PLCβ activity ( Fig . 4B ) . Using this approach we studied the kinetics of light induced PIP2 turnover in dPIP5K18 and compared it to wild type controls . This analysis revealed a clear delay in the kinetics of PIP2 resynthesis in dPIP5K18 compared to wild type controls and suggests that dPIP5K activity is required to support normal PIP2 resynthesis following phototransduction ( Fig . 4B ) . We also tested the effects of overexpressing dPIP5K in adult photoreceptors; the level of protein overexpression was established by Western blots of retinal extracts using an antibody to dPIP5K ( Fig . 4D ) . Overexpression of dPIP5K in photoreceptors resulted in a marked acceleration in the recovery of the PIP2 biosensor following stimulation with light ( Fig . 4E , F ) . This result implies that dPIP5K is able to regulate the rate of PIP2 resynthesis following illumination in photoreceptors . PIP5K has been shown to have a role in actin remodeling in both yeast and mammalian systems [25] . In dPIP5K18 , photoreceptor ultrastructure was largely normal by TEM analysis and growing flies under conditions of bright light illumination did not result in disruption of microvillar ultrastructure . This suggests that the actin cytoskeleton is unaffected by the absence of dPIP5K activity . Further , phalloidin staining suggested that the actin cytoskeleton was largely unaffected in dPIP5K18 ( Fig . 5A ) . The Ezrin/Radixin/Moesin ( ERM ) family of proteins are regulated by PIP2 and act as cross-linkers between cortical actin and plasma membrane thus playing a key role in maintaining membrane projections , such as microvilli and filopodia [26][27] . ERM proteins are regulated by PIP2 and in the presence of PIP2 the active , phosphorylated form of the protein is attached to the membrane; on PIP2 hydrolysis the proteins are dephosphorylated and the inactive form is released to cytosol [28] . Drosophila has only a single ERM protein , dMoesin , which is required for morphogenesis and maintenance of microvillar structure in photoreceptors . Moesin localizes to the base of the rhabdomere in wild-type flies in the dark [29] , but is dephosphorylated and translocates to the cytosol under bright illumination . Using immunolabelling we studied the distribution of p-Moesin in photoreceptors and found this to be no different between controls and dPIP5K18 ( Fig . 5B ) . Collectively these findings suggest that dPIP5K activity is not required to support cytoskeletal function in adult Drosophila photoreceptors . In Drosophila photoreceptors during illumination rhodopsin , the G-protein coupled receptor for light is endocytosed into a vesicular compartment called rhodopsin loaded vesicles ( RLV ) [30] , a key compartment in the turnover of rhodopsin . PIP2 is an important regulator of multiple steps in the endocytic cycle [31] . To test if dPIP5K supports the synthesis of the PIP2 pool regulating endocytosis , we observed the number of RLV in photoreceptors from dPIP5K18 photoreceptors; these were very similar to those in wild type ( Fig . 5C ) . We also tested the effect of dPIP5K18 on the retinal degeneration phenotype of norpA this has previously been shown to depend on endocytosis . norpA mutants undergo light dependent retinal degeneration due to the accumulation of excessive amounts of metarhodopsin-arrestin2 ( Arr2-Rh ) complexes stabilised at the microvillar membrane [32 , 33] . This process has been shown to depend on clathrin-mediated endocytosis , a process requiring a number of PIP2 dependent steps . To test if this was affected in dPIP5K18 , we generated double mutants of norpAP24;dPIP5K18 and studied the time-course of light dependent retinal degeneration in comparison with norpAP24 alone . If the PIP2 pool produced by dPIP5K was required to mediate endocytosis then one might expect reduced endocytosis of Arr2-Rh complexes in dPIP5K18 photoreceptors thus suppressing the retinal degeneration phenotype of norpAP24 . Such an effect is seen when dynamin function is removed using the shibire mutant that results in suppression of degeneration in norpA [32] . However we found that light dependent retinal degeneration was not suppressed in norpAP24; dPIP5K18 , although the time course of degeneration was marginally slower ( Fig . 5D , E ) . Additionally the number of RLVs in norpAP24 and norpAP24; dPIP5K18 were not significantly different ( Fig . 5F ) . These results suggest that dPIP5K function is not required to support the endocytic turnover of rhodopsin . Given our prediction that dPIP5K generates PIP2 that is used as a substrate for light induced PLCβ activity , it is likely that the enzyme is localized to the microvillar plasma membrane where PLCβ is localized . Initial fractionation experiments showed that almost all of the dPIP5K is localized to a membrane fraction ( Fig . 6A ) where it co-fractionates with key phototransduction proteins such as INAD . We attempted to establish the localization of dPIP5K expressed at endogenous levels using immunocytochemistry; under these conditions the dPIP5K antibody was able to detect the protein localized at the microvillar membrane ( Fig . 6B ) ; this was abolished in retinae from dPIP5K18 photoreceptors that are protein null alleles for this gene . Double labeling experiments showed that dPIP5K co-localizes with Rh1 at the microvillar membrane ( Fig . 6C ) . We exploited a genetic tool [34] that allowed us to elevate the expression level of untagged endogenous dPIP5K in photoreceptors , expressed from the endogenous gene locus . Under these conditions too , we found dPIP5K clearly localized to the microvillar membrane ( Fig . 6D ) . By contrast a dPIP4K::GFP transgene was excluded from the microvillar plasma membrane ( Fig . 6E ) . Photoreceptors of the Drosophila rdgB mutant show defects in the electrical response to light as well as light dependent degeneration . rdgB encodes a large multi domain protein including an N-terminal phosphatidylinositol transfer protein ( PITP ) domain [reviewed in [35]] . In vitro the PITP domain can bind and transfer phosphatidylinositol ( PI ) between two membrane bound compartments and it is presumed , though not demonstrated that the PI delivered to the acceptor compartment is the substrate for phosphorylation by PIPKs that generate phosphorylated versions of PI . In the case of PIP2 this would include the sequential phosphorylation of PI and PI4P by PI4K and PIP5K respectively . Although the precise molecular function of RDGB in photoreceptors in unknown , it has previously been shown that rdgB mutant photoreceptors have a defect in restoring the level of microvillar PIP2 following transduction triggered by a bright flash of light [36] . Thus rdgB mutants represent an opportunity to test the importance of a potential PIP5K that might generate microvillar PIP2 required for phototransduction . To test the relevance of dPIP5K in generating PIP2 required for G-protein coupled PLCβ activity , we generated photoreceptors that are double mutant rdgB9; dPIP5K18; importantly we used the rdgB9 allele that is a strong hypomorph and expresses a small amount of this protein and therefore has a residual response to light . We compared the light response of rdgB9 photoreceptors with those of rdgB9; dPIP5K18 ( Fig . 7A ) . Under similar conditions , while rdgB9 photoreceptors have peak ERG amplitudes of ca . 1 . 5 mV ( Fig . 7A ) , rdgB9; dPIP5K18 photoreceptors respond with a amplitude of only 0 . 4 mV ( Fig . 7B ) . This observation suggests that dPIP5K function is required to support the residual light response in rdgB9 photoreceptors . We also studied a second phenotype of rdgB9 namely light dependent retinal degeneration and found that , rdgB9; dPIP5K18 photoreceptors degenerated faster than rdgB9 alone ( Fig . 7C , D ) . By contrast loss of dPIP4K or sktl did not exacerbate the electrical response to light or the retinal degeneration phenotype of rdgB9 .
The hydrolysis of PIP2 by PLC in response to receptor activation is a widespread mechanism of signalling at the plasma membrane . In some cells such as neurons , activation of cell surface receptors by neurotransmitter ligands ( e . g glutamate , Ach ) or sensory stimuli triggers high rates of PLC activation and rapid consumption of PIP2 . Under these conditions , it is essential that levels of PIP2 , the substrate for PLC are maintained as failure to do so would likely result in desensitization . In mammalian cells , multiple classes of PIPK , the enzymes that resynthesize PIP2 have been described; yet the contribution of these enzymes to PIP2 resynthesis following PLC activation during cell signalling in vivo remains unclear . Broadly two classes of PIPK can synthesize PIP2 have been described; PIP5K that phosphorylates PI4P at position 5 [37] or PIP4K that can phosphorylate PI5P at position 4 [38] . In this study we have analyzed the consequence of loss of each of these two classes of PIPK to resynthesis following PLC mediated PIP2 depletion during Drosophila phototransduction . Loss of dPIP5K function results in profound defects in the light activated electrical response as well as slower recovery of plasma membrane PIP2 levels . Conversely overexpression of dPIP5K was able to substantially accelerate the recovery of PIP2 levels following stimulation with a bright flash of light . We found that dPIP5K is localized to the microvillar plasma membrane , the site at which PIP2 needs to be produced to support ongoing light induced PLC activity . Finally , we found that loss of dPIP5K enhances the ERG defect in a hypomorphic allele of rdgB , a gene with a well-established defect in the response to light . Collectively these observations strongly suggest that dPIP5K activity underlies the conversion of PI4P to PIP2 at the microvillar membrane where it is then available as a substrate for light induced PLCβ activity ( Fig . 7E ) . By contrast loss of the only PIP4K enzyme in the Drosophila genome has minimal effects on phototransduction and this enzyme is not targeted to the microvillar plasma membrane . Our findings also imply that dPIP4K activity ( and hence the conversion of PI5P into PIP2 ) is dispensable for maintaining PIP2 levels during Drosophila phototransduction . This is consistent with a previous study which found no reduction in the levels of PIP2 in flies lacking dPIP4K function [14] . Our observations validate the conclusion from biochemical studies in mammalian cells that the levels of PI5P are substantially lower than those of PIP2 and hence it is unlikely to be the source of the majority of PIP2 in cells [38] . The identity of the PI4K isoform that generates the substrate , PI4P used by dPIP5K remains unknown although a recent study in mammalian systems suggests that PI4KIIIα is likely to be the relevant isoform [39] . Although the ERG response is severely affected in dPIP5K18 , it is not abolished as seen in null mutants of PLCβ ( norpA ) that are not able to hydrolyse PIP2 . Additionally , the resting levels of PIP2 as detected by the PIP2 biosensor are comparable to wild type and following a bright flash of light that depletes PIP2 , its levels do recover albeit at a slower rate than in wild type photoreceptors . Given that dPIP5K18 is a protein null allele , these observations imply that there must be a second pool of PIP2 in dPIP5K18 cells that is able to support phototransduction and microvillar PIP2 re-synthesis albeit with lower efficiency ( Fig . 7F ) . This second pool of PIP2 is likely available with low efficiency for PLC activity in the absence of the dPIP5K dependent pool thus accounting for the residual light response and observed PIP2 dynamics in dPIP5K18 photoreceptors . We found that the ultrastructure of dPIP5K18 photoreceptors was essentially normal . This was particularly surprising given that in addition to phototransduction , PIP2 at the microvillar membrane is also expected to regulate multiple processes required to maintain normal microvillar structure including dynamin dependent endocytosis [40][32] as well as cytoskeletal function [41] . However , using multiple readouts we found that molecular readouts of endocytosis and cytoskeletal function were unaffected in dPIP5K18 photoreceptors ( Fig . 5 ) . These observations imply that the PIP2 required for these processes is not dependent on dPIP5K activity; rather PIP2 generated by a separate PIPK supports these processes . Thus far , dPIP4K has not been detected on the microvillar plasma membrane , dPIP4K29 photoreceptors show normal ultrastructure on eclosion and do not undergo light dependent microvillar degeneration; thus dPIP4K is unlikely to be the critical enzyme that generates the PIP2 required to support dynamin dependent endocytosis , p-Moesin localization or phototransduction . The Drosophila genome encodes an additional PIP5K activity , sktl that is expressed at low levels in the adult retina but is localized to both the microvillar and basolateral membrane and hence could synthesize PIP2 at both these locations . Complete loss of sktl function is cell lethal and overexpression of sktl in developing photoreceptors results in a severe block in rhabdomere biogenesis [42] whereas overexpression of sktl results in light dependent retinal degeneration in post-development photoreceptors . These findings presumably reflect an essential and non-redundant role for SKTL in supporting fundamental PIP2 dependent cellular processes such as endocytosis and cytoskeletal function that are not dependent on PIP2 hydrolysis by PLC . This model is consistent with the cell-lethal phenotype of photoreceptors that are null for sktl and previous studies showing a role for sktl in supporting cytoskeletal function and endocytosis in other Drosophila tissues and processes such as spermiogenesis [43] and oogenesis [44] . Collectively , our observations imply that there are at least two pools of PIP2 in photoreceptors; one generated by dPIP5K that is required to support a normal electrical response to light but is dispensable for non-PLC dependent functions of PIP2 in photoreceptors and another that is generated by enzymes other than dPIP5K ( most likely SKTL ) that is also capable of supporting PIP2 synthesis during the light response albeit with reduced efficiency . In summary the PIP2 pool synthesized by dPIP5K is unique in that it is required for a normal light response and apparently dispensable for other PIP2 dependent functions/processes . It also reflects the existence of distinct/segregated pools of PIP2 on the same microvillar plasma membrane that are maintained by distinct kinases . A number of previous studies have shown that in multiple eukaryotic cell types , plasma membrane PIP2 levels are remarkably stable , undergoing transient fluctuations despite ongoing PLC mediated PIP2 hydrolysis [36 , 45–47] . However the reasons for this remarkable finding have remained unclear although pharmacological studies have suggested the importance of PIP2 resynthesis in this process [47 , 48] . One potential explanation for this idea is the existence at the plasma membrane of two pools of PIP2 , a larger but less dynamic pool of that is not normally accessed by PLC and supporting non-PLC dependent functions of this lipid and a second , quantitatively smaller but more dynamic pool that is the substrate for PLC activity . What underpins such pools of PIP2 ? The existence of separate enzymes that generate unique pools of PIP2 has been previously suggested but there have been limited experimental studies to support this model . In murine platelets where thrombin induced PIP2 hydrolysis appears to be dependent on PIP5K1β but not PIP5Kγ [49]; since both these enzymes are expressed in platelets this implies the existence of two pools of PIP2 in these cells of which the PIP5K1β dependent pool is available for thrombin dependent PIP2 turnover . This finding together with our study in Drosophila photoreceptors implies that the plasma membrane in general may contain a specific pool of PIP2 dedicated for the use of receptor dependent PLC signalling and synthesized by a specific PIPK . It is possible that given the high rates of PLC activated PIP2 turnover at the plasma membrane ( such as the microvillar membrane in photoreceptors ) eukaryotic cells have evolved a mechanism to generate distinct PIP2 pool for this purpose so that other PIP2 dependent functions at the plasma membrane remain unaffected by ongoing receptor activated PIP2 hydrolysis . It is likely that dPIP5K and mammalian PIP5K1β represent PIP5K enzymatic activities required to support such a pool of PIP2 at the plasma membrane . It is presently unclear what properties might make dPIP5K more suitable for generating PIP2 in the context of receptor triggered PLC activity . One possibility is that the kinetic properties of the enzyme encoded by dPIP5K is distinct from that encoded by sktl allowing it to function in the context of high rates of PIP2 turnover . Alternatively ( or additionally ) within the nanoscale organization of the microvillar plasma membrane , it is possible that dPIP5K is segregated such that PIP2 generated by this enzyme is available within molecular distances of the phototransduction machinery . Interestingly , Drosophila photoreceptors contain within their microvillar membrane a macromolecular signalling complex organized by the PDZ domain protein INAD . It is presently not known if dPIP5K is part of a similar complex but the existence of such mechanisms has been previously shown for mammalian PIP5K1γ in the context of focal adhesion function [50 , 51] . Interestingly , it has been reported that the INAD protein complex that includes PLCβ is recruited to detergent resistant membranes during light stimulation [52] which themselves have been previously implicated in the formation of PIP2 microdomains and receptor activated PIP2 turnover [53][54] . It is possible that the two PIPKs , SKTL and dPIP5K show differential localization to such domains thus generating and segregating such pools of PIP2 and further studies in this direction are likely to provide insight into this issue . Nevertheless our study provided evidence for the concept of distinct PIPK enzymes as the basis for functionally distinct pools of PIP2 at the plasma membrane . Further analysis in this system is likely to reveal the molecular basis for the organization of PIP2 pools at cellular membranes .
Flies ( Drosophila melanogaster ) were reared on medium containing corn flour , sugar , yeast powder , and agar along with antibacterial and antifungal agents . Flies were maintained at 25°C and 50% relative humidity . There was no internal illumination within the incubator and the flies were subjected to light pulses of short duration only when the incubator door was opened . When required , flies were grown in an incubator with timed illumination from a white light source ( Intensity mentioned in the figure legends of each experiment ) . The wild-type strain was Red Oregon-R . The following fly alleles and insertions were obtained for the experiments described here: soD , norpAp24 ( Bloomington Stock Center ) , sktlΔ20 ( Hugo Bellen ) , rdgB9 ( R . C . Hardie , Cambridge University ) , dPIP5K overexpression line- GS200386 ( DGRC-Kyoto ) . In order to generate an antibody to dPIP5K , the antigenic fragment ( an ∼ 250 amino acid unique sequence at the C-terminus of dPIP5K ) was expressed as a recombinant protein and purified by affinity chromatography . Polyclonal antibodies were generated in rats using standard immunization protocols . A knockout of dPIP5K was generated using ‘ends-out’ homologous recombination [20] . A 5 . 4 kb sequence of dPIP5K genomic sequence was used to generate the donor construct . It consisted of two pieces of genomic dPIP5K cloned as insert 1 ( 3 . 17 kb ) and insert 2 ( 2 . 3 kb ) separated by a marker gene white ( Pw+ ) which was flanked by stop codons . These targeting sequences were cloned into the vector pW25 [55] . Transgenic flies carrying this construct were generated and used to perform homologous recombination as previously described [20] . Potential recombinants , which were mapped onto chromosome II , were subjected to molecular analyses using a PCR-based method . Finally , eight individual mutant alleles of dPIP5K ( termed as PC4 , PC5 , PC8 , PC18 , PC30 , PC33 , PC60 , and PC62 ) were confirmed and one of these dPIP5K18 was characterized in detail and used in all experiments described in this study . Since homozygous mutants in dPIP5K are semi-lethal during pupal development we recombined dPIP5K18 onto a chromosome with an FRT site at 42B . This allele was used to generate mosaic animals in which only adult retinae were homozygous mutant [21 , 22] . A BAC clone encompassing dPIP5K , CH321-03B05 ( in attB-Pacman-CmR vector ) was obtained from p[acman] resource [56] . This BAC clone was 57 . 178 Kb long and included the dPIP5K gene with extended 5’ and 3’ regions having the promoter and most of the regulatory regions of the gene . The presence of dPIP5K in the clone was verified by PCR using specific primers . This clone was microinjected into embryos and inserted via ΦC31 integration into the VK22attP docking site in the fly genome to generate the wild-type dPIP5K genomic transgene {Bac[dPIP5K]} . Classical genetic crosses were used to move Bac[dPIP5K] into the dPIP5K18 mutant background . Protein expression from Bac[dPIP5K] was verified using Western blotting using a dPIP5K specific antibody . Heads from 1 day old flies were homogenized in 2× SDS-PAGE sample buffer followed by boiling at 95°C for 5 min . Samples were separated using SDS-PAGE and electro blotted onto nitrocellulose membrane ( Hybond-C Extra; GE Healthcare ) using semidry transfer assembly ( Bio-Rad ) . Following blocking with 5% Blotto ( Santa Cruz Biotechnology , CA ) , blots were incubated overnight at 4°C in appropriate dilutions of primary antibodies [anti-α-tubulin ( 1:5000 dilution; E7 DSHB ) , anti-Gαq ( 1:1000 dilution ) , anti-TRP ( 1:5000 dilution ) , anti-Rh1 ( 1:200 , 4C5 DSHB ) , anti-INAD ( 1:2000 ) and anti-NORPA ( 1:1000 ) ] . Protein immunoreacted with the primary antibody was visualized after incubation in 1:10 , 000 dilution of appropriate secondary antibody coupled to horseradish peroxidase ( Jackson Immuno Research Laboratories ) for 2 h at room temperature . Blots were developed with ECL ( GE Healthcare ) and imaged using a LAS 4000 instrument ( GE Healthcare ) . Flies were immobilized on ice , decapitated using a blade and fixed on a glass slide using a drop of colorless nail varnish . It was imaged using 40× oil objective of Olympus BX43 microscope . Quantitation of degeneration was done as previously described [57] Pure preparations of retinal tissue were collected using previously described methods [58] . Briefly , 0- to 12-hr-old flies were snap-frozen in liquid nitrogen and dehydrated in acetone at −20°C for 48 hr . The acetone was then drained off and the retinae dried at room temperature . They were cleanly separated from the head at the level of the basement membrane using a scalpel blade . RNA was extracted from Drosophila head using TRIzol reagent ( Invitrogen ) . Purified RNA was treated with amplification grade DNase I ( Invitrogen ) . cDNA conversion was done using SuperScript II RNase H–Reverse Transcriptase ( Invitrogen ) and random hexamers ( Applied Biosystems ) . Quantitative PCR ( QPCR ) was performed with the Applied Biosystem 7500 Fast Real Time PCR instrument . Primers were designed at the exon-exon junction following the parameters recommended for QPCR . Transcript levels of the ribosomal protein 49 ( RP49 ) were used for normalization across samples . Three separate samples were collected from each genotype , and duplicate measures of each sample were conducted to ensure the consistency of the data . The primers used for QPCR were as follows: RP49 fwd: CGGATCGATATGCTAAGCTGT;RP49 rev: GCGCTTGTTCGATCCGTA; dPIP4K fwd: CATCCGTACGTTGTGGAGAG; dPIP4K rev: AGATCCACATCGTTGCTCAG; sktl fwd: CTCATGTCCATGTGTGCGTC; sktl rev: TTAATGGTGCTCATCAGTG; dPIP5K fwd: AGCAGAGAAAACCGCTTAGG; dPIP5K rev: GGCGATTCACTGACTTATTCC Fractionation was performed as described in [52]; in short frozen fly heads were homogenized in lysis buffer ( 20 mM Hepes , 30 mM NaCl , 5 mM EDTA ) with protease inhibitors ( Roche ) at 4°C . Homogenate was centrifuged at 600×g for 3 min to remove chitonous material . The supernatant was spun at 55 , 000 rpm ( ca . 100K g ) for 30 minutes at 4°C ( Beckman Ultracentriguge , Optima LE-80K ultracentrifuge , using a SW 50 . 1 rotor ) to separate membrane fraction from cytosol . Equal volume of each fraction were subjected to SDS PAGE and analyzed by Western immunoblotting . For immunofluorescence , retinae from flies ( 0–12 hour post eclosion ) were dissected under low red light in phosphate buffered saline ( PBS ) and then fixed in 4% paraformaldehyde in PBS with 1mg/ml saponin in fixing solution for 30 min at room temperature . Fixed eyes were washed 3 times in PBST ( PBS with 0 . 3% TritonX-100 ) for 10 minutes . The tissues were then incubated in blocking solution [5% fetal bovine serum ( FBS ) in PBST] for 2 hours at room temperature , after which the tissues were incubated with primary antibodies diluted in blocking solution [anti-p-Moesin-1:200[29]] , anti-Rh1–1:50 ( 4C5 , Developmental Studies Hybridoma Bank ) , anti-TRP-1:250 , anti-dPIP5K-1:100 , anti-GFP-1: 200 ( Abcam ) ] overnight at 4°C . Appropriate secondary antibodies conjugated with a fluorophore were used at 1:300 dilutions [Alexa Fluor 633/568 IgG , ( Molecular Probes ) ] and incubated for 2 hours at room temperature . Wherever required , during the incubation with secondary antibody , Alexafluor 568–phalloidin ( Invitrogen ) was also added to the tissues to stain the F-actin . After three washes in PBST , the tissues were washed in PBS for 10 min , mounted in mounting medium ( 70% glycerol in PBS ) . The whole-mounted preparations were viewed under Olympus FV1000 laser scanning confocal microscope . Flies were anesthetized and immobilized at the end of a disposable pipette tip using a drop of low melt wax . Recordings were done using glass microelectrodes filled with 0 . 8% w/v NaCl solution . Voltage changes were recorded between the surface of the eye and an electrode placed on the thorax . Following fixing and positioning , flies were dark adapted for 6 min . ERG was recorded with 2 second flashes of green light stimulus . Stimulating light was delivered from a LED light source to within 5 mm of the fly’s eye through a fiber optic guide . Calibrated neutral density filters were used to vary the intensity of the light source . Voltage changes were amplified using a DAM50 amplifier ( WPI ) and recorded using pCLAMP 10 . 2 . Analysis of traces was performed using Clampfit ( Axon Laboratories ) . To monitor PIP2 dynamics in live flies , transgenic flies expressing PH-PLCδ::GFP ( PIP2 biosensor ) were anesthetized and immobilized at the end of a pipette tip using a drop of low melt wax and fixed by clay on the stage of an Olympus IX71 microscope . The fluorescent deep pseudopupil ( dpp , a virtual image that sums rhabdomere fluorescence from ∼20–40 adjacent ommatidia ) was focused and imaged using a 10× objective . Time-lapse images were taken by exciting GFP using a 90ms flash of blue light and collecting emitted fluorescence . The program used for this purpose was created in Micromanager . Following preparation , flies were dark adapted for seven minutes after which the eye was stimulated with a 90 ms flash of blue light . The blue light used to excite GFP was also the stimulus to rapidly convert the majority of rhodopsin ( R ) to metarhodopsin ( M ) thus activating the phototransduction cascade and triggering depletion of rhabdomeric PIP2 . Between the blue light stimulations , photoreceptors were exposed to long wavelength ( red ) light that reconverts M to R . The resurgence in dpp fluorescence with time indicates translocation of the probe from cytoplasm to rhabdomere membrane upon PIP2 re-synthesis . The dpp intensity was measured using ImageJ from NIH ( Bethesda , MD , USA ) . Cross sectional areas of rhabdomeres of R1-R6 photoreceptors were measured and the mean intensity values per unit area were calculated . Samples for TEM were prepared as mentioned in Ref . 44 . Briefly eyes were bisected in ice-cold fixative ( 2 . 5% glutaraldehyde in 0 . 1 M PIPES buffer [pH 7 . 4] ) . After 10hrs of fixation at 4°C , eyes were washed with 0 . 1M PIPES , post-fixed in 1% OsO4 ( 30min ) , and stained en bloc in 2% uranyl acetate ( 1 hr ) . Eyes were dehydrated in ethanol series and embedded in epoxy . Ultrathin sections ( 50 nm ) were cut and viewed on a Tecnai G2 Spirit Bio-TWIN electron microscope . | PIP2 has been implicated in multiple functions at the plasma membrane . Some of these require its hydrolysis by receptor-activated phospholipase C , whereas others , such as membrane transport and cytoskeletal function , involve the interaction of the intact lipid with cellular proteins . The mechanistic basis underlying the segregation of these two classes of PIP2 dependent functions is unknown; it has been postulated that this might involve unique pools of PIP2 generated by distinct phosphoinsoitide kinases . We have studied this question in Drosophila photoreceptors , a model system where sensory transduction requires robust phospholipase C mediated PIP2 hydrolysis . We find that the activity of phosphatidylinositol-4-phosphate 5 kinase encoded by dPIP5K is required to support normal sensory transduction and PIP2 dynamics in photoreceptors . Remarkably , non-PLC dependent functions of PIP2 , such as vesicular transport and the actin cytoskeleton , were unaffected in dPIP5K mutants . Thus , dPIP5K supports a pool of PIP2 that is readily available to PLC , but has no role in sustaining other non-PLC mediated PIP2 dependent processes . These findings support the existence of at least two non-overlapping pools of PIP2 at the plasma membrane , and provide a platform for future studies of PIP2 regulation at the plasma membrane . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | A dPIP5K Dependent Pool of Phosphatidylinositol 4,5 Bisphosphate (PIP2) Is Required for G-Protein Coupled Signal Transduction in Drosophila Photoreceptors |
The antiviral factor tripartite interaction motif 5α ( Trim5α ) restricts a broad range of retroviruses in a species-specific manner . Although human Trim5α is unable to block HIV-1 infection in human cells , a modest inhibition of HIV-1 replication has been reported . Recently two polymorphisms in the Trim5 gene ( H43Y and R136Q ) were shown to affect the antiviral activity of Trim5α in vitro . In this study , participants of the Amsterdam Cohort studies were screened for polymorphisms at amino acid residue 43 and 136 of the Trim5 gene , and the potential effects of these polymorphisms on the clinical course of HIV-1 infection were analyzed . In agreement with the reported decreased antiviral activity of Trim5α that contains a Y at amino acid residue 43 in vitro , an accelerated disease progression was observed for individuals who were homozygous for the 43Y genotype as compared to individuals who were heterozygous or homozygous for the 43H genotype . A protective effect of the 136Q genotype was observed but only after the emergence of CXCR4-using ( X4 ) HIV-1 variants and when a viral load of 104 . 5 copies per ml plasma was used as an endpoint in survival analysis . Interestingly , naive CD4 T cells , which are selectively targeted by X4 HIV-1 , revealed a significantly higher expression of Trim5α than memory CD4 T cells . In addition , we observed that the 136Q allele in combination with the −2GG genotype in the 5′UTR was associated with an accelerated disease progression . Thus , polymorphisms in the Trim5 gene may influence the clinical course of HIV-1 infection also underscoring the antiviral effect of Trim5α on HIV-1 in vivo .
The susceptibility to HIV-1 infection and subsequent disease progression is highly variable between individuals . Host genetic variations have previously been demonstrated to account for at least part of these differences . Polymorphisms in chemokine receptors that serve as HIV-1 coreceptors , or in their natural ligands , have been associated with reduced susceptibility to infection as well as disease progression [1–4] . Furthermore , certain HLA types have been correlated with the clinical course of infection [5–8] . Variations in genes involved in innate immunity may also contribute to the differential susceptibility of humans to HIV-1 infection and the highly variable outcome of the disease . Recently , the tripartite interaction motif 5α ( Trim5α ) has been identified as part of the intrinsic immunity that protects human and non-human primates against retroviral infection [9 , 10] . Trim5α targets the capsid of the incoming retrovirus in the cytoplasm directly after entry and interferes with viral replication at an early post-entry step most likely at the poorly understood uncoating process [11–19] . Species-specific variations in Trim5α account for the restriction pattern of specific retroviruses [20–26] . For example , HIV-1 replication is blocked efficiently by Trim5α of rhesus macaques and African green monkeys , whereas SIV-mac is only restricted by Trim5α from African green monkey . Human Trim5α efficiently blocks N-tropic MLV and equine infectious anaemia virus , but is much less efficient in restricting HIV-1 replication . This indicates that HIV-1 has at least partially adapted to the human variant of this restriction factor . Recently , we observed that Trim5α escape variants developed late in infection in a proportion of the HIV-1 infected individuals [27] . The emergence of the escape variants was preceded by a prolonged asymptomatic phase , indicating that Trim5α mediated suppression of viral replication indeed plays a role in HIV-1 pathogenesis . The potential role of polymorphisms within the Trim5 gene on HIV-1 susceptibility has recently been studied [28–31] . Of the eight nonsynonymous polymorphisms that have been identified in the Trim5 gene , two have been reported to have functional consequences with regard to the antiviral activity of Trim5α ( H43Y and R136Q ) [28 , 30] . The H43Y is located in the RING domain of Trim5α and may impair its putative E3 ligase activity [28 , 30] . Indeed , the 43Y variant of Trim5α was less efficient in restricting HIV-1 replication in vitro [28 , 30] . The R136Q polymorphism has been associated with a slightly higher anti-HIV-1 activity of Trim5α [30] . In agreement , the R136Q polymorphism was more frequently observed in high risk seronegative as compared to HIV-1 infected individuals in a cohort of African Americans [30] , although not confirmed in other study populations [29 , 30] . So far no significant association between Trim5 polymorphisms and HIV-1 disease progression have been demonstrated [29 , 31 , 32] . Here we studied the effect of the Trim5α H43Y and R136Q polymorphisms on the clinical course of HIV-1 infection in participants of the Amsterdam Cohort studies . In addition , we analyzed whether the R136Q genotype in combination with a SNP in the 5′UTR of Trim5 ( −2G/C ) was associated with susceptibility to HIV-1 infection or disease progression .
The prevalence of Trim5α polymorphisms H43Y and R136Q was studied in 327 HIV-1 positive participants of the Amsterdam Cohort studies . For the H43Y polymorphism a minor allele frequency of 0 . 115 was observed . Of the 327 HIV-1 positive participants , 61 ( 18 . 7% ) were heterozygous and 7 ( 2 . 1% ) were homozygous for the 43Y allele . The R136Q polymorphism was observed at a minor allele frequency of 0 . 379 . Of the 327 participants , 156 ( 47 . 7% ) were heterozygous and 46 ( 14 . 1% ) were homozygous for the 136Q allele . Six mutually exclusive haplotypes were observed for the combination of R136Q and H43Y polymorphisms: 43HH/136RR ( n = 88 ) , 43HH/136RQ ( n = 125 ) , 43HH/136QQ ( n = 46 ) , 43HY/136RQ ( n = 31 ) , 43HY/136RR ( n = 30 ) and 43YY/136RR ( n = 7 ) . The 43Y polymorphism was not observed in the group homozygous for the 136QQ genotype and the 136Q polymorphism was never observed in combination with a homozygous 43YY genotype , also confirming that the 43Y polymorphism and the 136Q polymorphism are not located on the same allele [29 , 31] . No significant differences in the H43Y or R136Q minor allele frequencies were observed between the HIV-1 seropositive individuals and healthy controls ( allele frequencies of 0 . 106 and 0 . 389 for H43Y and R136Q , respectively ) . Kaplan Meier and Cox Proportional Hazard analysis with clinical AIDS ( Definition CDC 1987 and 1993 ) , CD4 T cell counts below 200 cells/μl blood , and plasma viral RNA load above 104 . 5 copies per ml plasma were used as end points to determine the effect of polymorphisms in the Trim5 gene on disease progression . We observed an accelerated disease progression in the group homozygous for the 43Y allele relative to the 43 HH wild type genotype , with a relative hazard ( RH ) of 3 . 1 ( p = 0 . 006 ) and 2 . 8 ( p = 0 . 007 ) for AIDS diagnosis according to the 1987 or 1993 CDC definition , respectively ( Figure 1A and 1B; Table 1 ) . An accelerated progression rate was also observed when CD4 T cell counts below 200 cells per μl blood were used as end point ( Figure 1C; Table 1 ) . The median viral RNA load of participants of the Amsterdam cohort progressing to AIDS has previously been determined at 104 . 5 copies HIV-1 RNA per ml plasma [1] . When viral RNA load above 104 . 5 copies per ml plasma was used as endpoint in the survival analysis , we again observed an accelerated disease progression for individuals homozygous for the 43Y genotype ( Figure 1D; Table 1 ) . The heterozygous genotype ( 43HY ) was not associated with delayed disease progression ( Figure 1 ) . Development of CXCR4 using HIV-1 variants ( X4-variants ) has previously been associated with an accelerated disease progression [33] . The ability of HIV-1 variants to use CXCR4 as a coreceptor and replicate in MT2 cells was analyzed routinely during follow-up in the cohort studies in 319 of 327 individuals from our study population . During the course of infection X4-variants developed in 126 individuals . No association between the prevalence of X4-variants and the H43Y genotype could be observed ( data not shown ) . However , X4-variants did develop more rapidly in individuals who were homozygous for the 43Y genotype ( Figure 1E; Table 1 ) . Our study population consisted of 130 participants who seroconverted for HIV-1 antibodies during follow-up and 197 seroprevalent participants with an imputed seroconversion date [34] . Inclusion of seroprevalent participants in our analysis did not bias our data and Cox regression analysis stratifying for seroconvertors and seroprevalent participants gave similar results ( data not shown ) . Next we examined a potential role for the R136Q polymorphism in Trim5 on the clinical course of HIV-1 infection . Using clinical AIDS ( Definition CDC 1987 and 1993 ) , CD4 T cell counts below 200 cells/μl blood or viral RNA load above 104 . 5 copies per ml plasma as endpoint in Kaplan Meier and Cox proportional hazard analysis , no significant associations between the 136RQ or 136QQ genotype and the clinical course of infection were revealed ( data not shown ) . The R136Q polymorphism also had no effect on the time to first detection of X4-variants ( data not shown ) . In addition , the prevalence of CXCR4 using HIV-1 variants was not associated with the R136Q genotype . Next we analyzed whether a potential effect of the R136Q Trim5 genotype was dependent on the coreceptor usage of the virus present . The R136Q genotype had no significant effect on the clinical course of infection when only CCR5 using HIV-1 variants ( R5-variants ) were present irrespective the end point used in the survival analysis ( data not shown ) . However , a significant protective effect on disease progression associated with the R136Q genotype was observed after X4-variant development using the median viral RNA load of participants of the Amsterdam cohort progressing to AIDS ( 104 . 5 copies per ml ) as an end point in survival analysis , with a RH of 0 . 44 ( p = 0 . 008 ) and 0 . 26 ( p = 0 . 030 ) for the heterozygous 136RQ and homozygous 136QQ genotype , respectively as compared to the 136RR genotype ( Figure 2; Table 2 ) . At the moment of X4-development the viral load of individuals who were homozygous ( QQ ) , heterozygous ( RQ ) or wild type ( RR ) for the amino acid residue at position 136 was similar ( data not shown ) . The R136Q polymorphism was not associated with disease progression after X4-variant development using clinical AIDS or CD4 cell counts below 200 cells μl blood as end points ( Table 2 ) . The protective effect of the 136Q Trim5α variant on disease progression only after emergence of X4 variants may imply that Trim5α has a stronger effect on X4 variants than on R5 variants . Previously we demonstrated that R5 and X4 HIV-1 variants partially reside in different CD4 T cell compartments due to differential expression of coreceptors CCR5 and CXCR4 [35 , 36] . R5 variants were selectively isolated from CD4 memory T cells , whereas X4 variants were isolated from memory and naïve CD4 T cells . Here we analyzed whether differences of the Trim5α mRNA levels in naïve and memory CD4 T cell populations could contribute to the differential effect of the 136Q variant on X4 and R5 variants in vivo . Naïve and memory CD4 T cells were isolated from PBMC from 12 healthy controls by FACS sorting based on CD45RO and CD27 expression and Trim5α mRNA levels were analyzed by quantitative real time PCR . To correct for differences in input , Trim5α mRNA levels were normalized for β-actin mRNA levels . Trim5α mRNA levels were significantly higher in naïve ( CD45RO−CD27+ ) CD4 T cells as compared to memory ( CD45RO+ ) CD4 cells ( p = 0 . 019 ) ( Figure 3A ) . In addition , Trim5α mRNA levels in naïve and memory CD4 T cells were analyzed in HIV-1 positive individuals early and late in the course of infection ( 23 PBMC samples from 11 individuals ) . Although no differences in Trim5α mRNA levels were observed during the course of infection , we again observed a significantly higher Trim5α mRNA level in naïve CD4 T cells as compared to memory CD4 T cells ( p = 0 . 003 ) ( Figure 3B ) . Recently a G to C polymorphism at position −2 in the 5′UTR of the Trim5 gene ( −2G/C; rs3824949 ) in combination with the R136Q polymorphism has been associated with enhanced susceptibility to HIV-1 infection ( 136Q/−2G haplotype ) and accelerated disease progression ( 136R/−2G haplotype ) [29] . To analyze whether the combined R136Q and −2G/C genotype was also associated with HIV-1 susceptibility or disease progression in our study population , we genotyped our study population for the G to C polymorphism at position −2 ( −2G/C ) . The −2C allele frequency was 0 . 486 and 0 . 418 in our HIV-1 positive individuals and healthy controls , respectively . When the −2G/C genotype was analyzed in combination with the R136Q genotype no significant differences in the distribution of the combined genotypes was observed between the HIV-1 infected individuals and the healthy controls . The −2GG genotype was not observed in combination with the homozygous 136Q genotype in both study populations . Next we analyzed the effect of the combined R136Q and −2G/C genotypes on disease progression using clinical AIDS ( Definition CDC 1993 ) as an end point . Individuals carrying the 136Q allele ( 136RQ and 136QQ ) in combination with the −2GG genotype showed an accelerated progression to disease in comparison to the −2GC ( p = 0 . 009; RH 2 . 6; 95% CI 1 . 3–5 . 2 ) and −2CC genotype ( p = 0 . 056; RH 2 . 1; 95% CI 1 . 0–4 . 3 ) ( Figure 4 ) . In contrast to the study by Speelmon et al . [29] , no significant effect of the −2G/C genotype on disease progression was observed in the group with the 136RR genotype . Uni- and multivariate relative hazard analysis were used to determine the predictive value of the Trim5 H43Y genotype ( 43YY ) in combination with previously established prediction markers such as CD4 T cell count , plasma viral RNA load , the presence of X4-variants , and CCR5-genotype [1] . Univariate analysis indicated that homozygosity for the H43Y polymorphism , CD4 T cell counts below 500 cells per μl , viral RNA load above 104 . 5 copies per ml plasma and the presence of X4-variants at 18–30 months after seroconversion were predictive for more rapid progression to AIDS , whereas heterozygosity for the CCR5-Δ32 genotype had a protective effect ( Table 3 ) . Multivariate analysis at 2 years after seroconversion indicated that CD4 T cell counts below 500 cells per μl blood , viral RNA load above 104 . 5 copies per ml plasma , the presence of X4-variants and homozygosity for the Trim5 H43Y genotype ( 43YY ) were independent predictors for progression to AIDS ( Table 3 ) . In our study population the homozygous H43Y genotype was not observed in combination with a heterozygous CCR5-Δ32 genotype excluding this parameter from the multivariate analysis .
Old World monkey Trim5α very efficiently blocks HIV-1 infection at an early step in the viral replication cycle , immediately after cellular entry . Human Trim5α is also able to interfere with HIV-1 infection , albeit less efficiently . Although this may suggest that HIV-1 has at least partially adapted to human Trim5α , we have recently provided the first evidence that Trim5α might still play a role in HIV-1 pathogenesis [27] . We observed that HIV-1 variants containing a H87Q mutation in the cyclophilin A binding region of capsid , which has previously been associated with escape from Trim5α [11–16] , developed during the late phase of infection in a proportion of the HIV-1 infected individuals [27] . The emergence of these Trim5α escape variants was preceded by a prolonged asymptomatic phase implying that Trim5α contributed to control of virus replication in vivo and concomitantly selected for Trim5α resistant variants . Two genetic polymorphisms in the human Trim5 gene have recently been described to affect the antiviral activity of Trim5α on HIV-1 . The H43Y polymorphism has been associated with an impaired anti-HIV-1 activity of Trim5α in vitro [28 , 30 , 32] . In agreement , we here observed that a 43YY homozygous genotype was predictive for an accelerated progression to AIDS , independent of CD4 cell counts , viral RNA load in plasma , and HIV-1 coreceptor usage at 18–30 months after seroconversion . In previous studies , the H43Y genotype had no effect on HIV-1 disease progression [29–32] . Speelmon et al . observed no significant difference in viral RNA load in the period from 100 days until 2 years post infection between individuals who were homozygous ( YY ) , heterozygous ( HY ) or wild type ( HH ) for amino acid residue 43 [29] . In agreement , we also did not observe a difference in viral RNA load at 2 years after seroconversion between the H43Y genotypic groups ( data not shown ) . However , the studies by Speelmon et al . and by Goldschmidt et al . did also not show significant differences in CD4 cell decline between the H43Y genotypic groups [29 , 31] . Due to the low minor allele frequency for H43Y in the study population by Speelmon et al . , which had a size of only 90 individuals , the number of individuals homozygous for the 43Y genotype might have been to low to observe significant difference between the genotypes . The discrepancy between our results and those of Goldschmidt et al . may lie in their relatively short average follow-up time of 3 . 2 years as compared to the average follow-up of 7 . 9 years on patients in our study . Nakayama et al . observed similar frequencies of homozygous 43YY and heterozygous 43HY genotypes in progressors and LTNP in a study population of Japanese HIV-1 infected individuals [32] . In our Amsterdam cohort however , none of the individuals homozygous for the 43YY genotype had an asymptomatic follow up of 10 years or more . Javanbakht et al . observed no significant differences in progression to CD4 T cell counts below 200 cells per μl blood , AIDS defining events or AIDS related deaths associated with the different H43Y genotypes in two large cohorts of African Americans and European Americans [30] . In the African American population the frequency of individuals homozygous for the 43YY genotype is however very low which might account for the discrepancy with our data . However , the minor allele frequency for H43Y in the European American cohort is similar to the frequency in the Amsterdam cohort ( 0 . 114 and 0 . 115 respectively ) . Unfortunately , lack of details on their European American cohort and their analyses make it difficult to bring up potential explanations for the inconsistency in results . The R136Q polymorphism has been associated with a slight increase of the anti-HIV-1 activity of Trim5α in vitro [30] , although not confirmed by others [28 , 29 , 31] . In agreement with earlier reports [29 , 31] , we did not observe an effect of the R136Q polymorphism on disease progression . A protective effect of the 136Q variant was only evident in the phase of infection when X4-variants were present , where a delayed rise in viral load above the median load during progression to disease ( 104 . 5 copies per ml ) was observed in individuals who were homozygous ( QQ ) or heterozygous ( RQ ) for the 136Q genotype as compared to individuals with the wild type genotype ( RR ) . The protective effect of the 136Q Trim5α variant on disease progression only after the emergence of X4-variants , may imply that Trim5α in vivo affects replication of X4-variants more efficiently than R5-variants . X4-variants develop in 50% of the HIV-1 infected individuals during the natural course of infection after which R5- and X4-variants coexist [35–37] . While co-existing R5- and X4-variants infect memory CD4 T cells that co-express CCR5 and CXCR4 , X4-variants have the unique ability to additionally infect naive CD4 T cells that selectively express CXCR4 [35 , 36] . Here we observed that naïve CD4 T cells expressed higher levels of Trim5α as compared to memory CD4 T cells . It is tempting to speculate that high Trim5α expression levels in naive T cells in combination with a more potent antiviral activity associated with the 136Q polymorphism provide prolonged control of HIV-1 replication in carriers of X4-variants . Recently a G to C polymorphism at position −2 in the 5′UTR of the Trim5 gene ( −2G/C; rs3824949 ) in combination with the R136Q polymorphism has been associated with HIV-1 susceptibility and disease progression [29] . Speelmon et al . observed an enrichment of the 136Q/−2G haplotype in the HIV-1 positive population [29] , however we were unable to confirm this and observed an equal distribution of the −2G/C polymorphism in combination with the 136Q allele between the HIV-1 positive population and the control group . However , the 136Q allele in combination with the −2GG genotype was associated with accelerated disease progression in our study population . Speelmon et al . observed an association between a faster CD4 T cell decline and the 136R/−2G haplotype [29] , but we were unable to confirm this . In our study population we observed a −2C allele frequency of 0 . 486 and 0 . 418 in the HIV-1 positive study population and the control group , respectively , which is similar to the frequencies in the European American population reported by Javanbakht et al . [30] . However the frequencies for the −2C allele frequencies in the study populations of Speelmon et al . were much lower ( 0 . 38 in exposed seronegatives and 0 . 31 in HIV-1 infected population ) [29] . Therefore , it cannot be excluded that differences in the distribution of the −2G/C polymorphism in different study populations account for the observed discrepancies in results . Our data confirm a role of Trim5α in the clinical course of infection . In addition , they show that different genetic variants in Trim5α are associated with a differential clinical course of infection . Overall , these results may encourage exploiting the possibility of using Trim5α or alike derivatives in antiviral strategies .
The study population , 364 Caucasian , homosexual men enrolled in the Amsterdam Cohort studies ( ACS ) on the natural history of HIV-1 infection between October 1984 and March 1986 , was previously described [1] . The censor date of our study was set at the first day of effective antiretroviral therapy of the participant . Of the 364 participants , 131 seroconverted during the study . The remaining 233 men were positive for HIV-1 antibodies at entry between October 1984 and April 1985 . In previous epidemiological studies , the time since seroconversion of these prevalent cases has been estimated based on the incidence of HIV-1 infection amongst homosexual participants of the Amsterdam Cohort and was on average 1 . 5 years before entry into the cohort studies [34] . For analysis , we combined the 131 participants with documented seroconversion and 233 seroprevalent participants with an imputed seroconversion date as one study group , since previous studies have not revealed differences in AIDS-free survival between the two groups [4] . The ACS has been conducted in accordance with the ethical principles set out in the declaration of Helsinki and written informed consent is obtained prior to data collection . The study was approved by the Amsterdam Medical Center institutional medical ethics committee . DNA samples of 327 out of 364 participants of the Amsterdam Cohort studies were available for Trim5 genotyping . For analysis of the Trim5 R136Q polymorphism ( rs10838525 ) , DNA samples were amplified by PCR using Taq DNA polymerase ( Invitrogen ) and primer pair Trim5-F ( 5′-ATGGCTTCTGGAATCCTGGTTAATG-3′ ) and Trim5-R136Q-R ( 5′-CCCGGGTCTCAGGTCTATCATG-3′ ) . The following amplification cycles were used: 5min 95°C; 35 cycles of 30s 95°C , 30s 50°C , 90s 72°C; 5min 72°C . Subsequently PCR products were purified and subjected to a restriction digest with 1U Ava1 ( 1 . 5 hour 37°C; NEB ) and analyzed on a 1% agarose gel . A PCR product containing an R at position 136 will result in digestion of the PCR product into a 405bp and 121bp product . A PCR product containing a Q at position 136 will result in a 526bp ( undigested ) product . For conformation , 15 samples ( 5 homozygous 136R , 5 homozygous 136Q and 5 heterozygous 136QR ) have been sequenced with the ABI prism BigDue Terminator kit V1 . 1 ( Applied Biosystems ) using primers Trim5-F and Trim5-R136Q-R ) . Sequences were analyzed on an ABI 3130XL Genetic Analyzer . For analysis of the Trim5 H43Y polymorphism ( rs3740996 ) , DNA samples were amplified by PCR using Taq DNA polymerase ( Invitrogen ) and primer pair Trim5-F and Trim5-H43Y-R ( 5′-GGCTGGTAACTGATCCGGCAC-3′ ) . For analysis of the −2GC polymorphism ( rs3824949 ) , DNA samples were amplified by PCR using Taq DNA polymerase ( Invitrogen ) and primer pair Tr5−2GC ( 5′-GCAGGGATCTGTGAACAAGAGG-3′ ) and Trim5-H43Y-R . The following amplification cycles were used: 5min 95°C; 35 cycles of 30s 95°C , 30s 55°C , 90s 72°C; 5min 72°C . Subsequently PCR products were purified and sequenced with the ABI prism BigDue Terminator kit V1 . 1 ( Applied Biosystems ) using primers Trim5-F and Trim5-H43Y-R for H43Y and Tr5−2GC and Trim5-H43Y-R for −2GC . Sequences were analyzed on an ABI 3130XL Genetic Analyzer . Cryopreserved PBMC were stained with antibodies against CD4 ( tricolor conjugated; Caltag Laboratories ) , CD45RO ( FITC conjugated; BD Biosciences ) and CD27 ( phycoerythrin conjugated; Caltag Laboratories ) , and sorted using a MoFlo cell sorter ( Cytomation Inc . ) . Cells were sorted in two different cell populations: naïve ( CD45RO-CD27+ ) CD4 T cells and memory ( CD45RO+ ) CD4 T cells . Total RNA was isolated from naïve and memory CD4 cells from HIV-1 infected individuals or healthy donors , using the RNeasy mini kit ( Qiagen , Hilden , Germany ) . Subsequently , cDNA was prepared using the SuperScript™ First-Strand Synthesis System for RT-PCR ( In Vitrogen ) . Trim5α mRNA levels were analyzed by SYBR green qPCR using the LightCycler ( Roche ) . The reaction mix contained 20 mM Tris-HCl ( pH 8 . 4 ) , 50 mM KCl , 3 mM MgCl2 , 200 μM dNTP , 250 μg/ml BSA , 500 nM primers , SYBR green I nucleic acid gel stain 40 , 000× diluted in water , and 0 . 6 U platinum Taq DNA polymerase ( In Vitrogen ) . The following primer sets were used for detection of Trim5α cDNA: Trim5α -RNA-F 5′-ccaggatagttccttccatac-3′ and Trim5α-R 5′-agagcttggtgagcacagagtc-3′ . Serial dilution of plasmid DNA containing cDNA of Trim5α were used as a standard curve . To correct for differences in the cDNA input , levels of β-actin cDNA were analyzed by a SYBR green qPCR using the following primer set: BA-RNA-F 5′-ggcccagtcctctcccaagtccac-3′ and BA-RNA-R 5′-ggtaagccctggctgcctccacc-3′ . A serial dilution of 8E5 cells was used as a standard curve for β-actin . SYBR green qPCR was performed using the following program on the LightCycler: ( 1 ) preincubation and denaturation: 50°C for 2 min , 95°C for 2 min; ( 2 ) amplification and quantification: 45 cycles of 95°C for 5 sec , 55°C for 15 sec , 72°C for 15 sec; ( 3 ) melting curve: 95°C for 0 sec , 65°C for 15 min , 95°C for 0 sec with a temperature transition rate of 0 . 1°C/sec . Specificity of the PCR products measured using the SYBR green method was confirmed by a melting curve . Kaplan Meier and Cox proportional hazard analysis were performed to study the relation between the R136Q and H43Y polymorphisms in the Trim5 gene and disease progression . The following endpoints were considered for analysis: ( 1 ) AIDS according to the 1987 Centers for Disease Control and Prevention ( CDC ) definition [38]; ( 2 ) AIDS according to the 1993 CDC definition [39]; ( 3 ) CD4 T cell counts below 200 cells/μl blood; ( 4 ) viral RNA load above 104 . 5 copies per ml blood plasma; ( 5 ) detection of X4-variants by coculture of patient PBMC and MT2 cells [37] . Fisher's exact test was used to analyze an association between the R136Q , H43Y polymorphisms and prevalence of X4-variants . Sequential Bonferroni correction ( Simes-Hochberg method ) was used to correct for multiple comparisons [40 , 41] . Univariate and multivariate relative hazards were calculated at 2 years after seroconversion for the H43Y genotype , CCR5 genotype , presence of X4-variants at 18–30 months after seroconversion , CD4 T cells at 18–30 months after seroconversion and viral RNA load at 18–30 months after seroconversion . Trim5α mRNA levels in naïve and memory CD4 T cells were compared using the Students T test . | The clinical course of HIV-1 infection is highly variable between individuals , and host genetic variations may at least account for part of these differences . Recently two single nucleotide polymorphisms in the tripartite interaction motif 5 gene ( Trim5 ) have been reported to affect the antiviral activity of the Trim5α protein . Here we analyzed the effect of these polymorphisms on the clinical course of HIV-1 infection in participants of the Amsterdam Cohort studies . We observed an accelerated disease progression for individuals who were homozygous for the 43Y genotype that has been associated with a decreased antiviral activity of Trim5α in vitro . The 136Q genotype has in vitro been associated with a slightly higher anti-HIV-1 activity . We observed a protective effect of the 136Q genotype only after the emergence of CXCR4-using HIV-1 variants using viral load above 104 . 5 copies per ml plasma as an endpoint in survival analysis . These results suggest that genetic variations in the Trim5 gene may influence the clinical course of HIV-1 infection and confirm a role of Trim5α on HIV-1 in vivo . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"viruses",
"homo",
"(human)",
"infectious",
"diseases",
"virology"
] | 2008 | The Effect of Trim5 Polymorphisms on the Clinical Course of HIV-1 Infection |
Free-ranging nonhuman primates are frequent sources of zoonotic pathogens due to their physiologic similarity and in many tropical regions , close contact with humans . Many high-risk disease transmission interfaces have not been monitored for zoonotic pathogens due to difficulties inherent to invasive sampling of free-ranging wildlife . Non-invasive surveillance of nonhuman primates for pathogens with high potential for spillover into humans is therefore critical for understanding disease ecology of existing zoonotic pathogen burdens and identifying communities where zoonotic diseases are likely to emerge in the future . We developed a non-invasive oral sampling technique using ropes distributed to nonhuman primates to target viruses shed in the oral cavity , which through bite wounds and discarded food , could be transmitted to people . Optimization was performed by testing paired rope and oral swabs from laboratory colony rhesus macaques for rhesus cytomegalovirus ( RhCMV ) and simian foamy virus ( SFV ) and implementing the technique with free-ranging terrestrial and arboreal nonhuman primate species in Uganda and Nepal . Both ubiquitous DNA and RNA viruses , RhCMV and SFV , were detected in oral samples collected from ropes distributed to laboratory colony macaques and SFV was detected in free-ranging macaques and olive baboons . Our study describes a technique that can be used for disease surveillance in free-ranging nonhuman primates and , potentially , other wildlife species when invasive sampling techniques may not be feasible .
The World Health Organization designated the assessment of the burden of zoonoses as a strategic area for action in their global plan to combat neglected tropical diseases [1] . Both domestic and wild animals contribute to the burden of zoonotic disease [2] . Viruses originating in wild animals however account for over 70% of emerging zoonotic infectious diseases in humans including viruses that have caused pandemics such as HIV/AIDS , epidemics such as Ebola hemorrhagic fever and yellow fever , as well as smaller outbreaks such as Marburg hemorrhagic fever [3–8] . Free-ranging nonhuman primates ( hereafter referred to as primates ) are of particular concern as sources or carriers of zoonotic viruses because of their close phylogenetic and physiologic relationship and , in many geographic regions , frequent and close contact with humans [9 , 10] . Human and primate contact is common in equatorial Africa with human encroachment into forest and savannah habitats [10] and in parts of Asia where urban-dwelling primates are flourishing [11 , 12] . Surveillance of free-ranging primates at these high-risk interfaces is critical and will facilitate improved understanding of disease ecology , identify human communities at risk for pathogen transmission , and can enable the detection of zoonotic pathogens before their spillover into humans [13–15] . However , sample collection from free-ranging primates is logistically difficult . Collection of invasive samples , such as blood and oral swabs , requires chemical immobilization , which can impose risk for both primates and human handlers . Field anesthesia , in the rough terrain typical of most remote habitats where free-ranging primates live is especially challenging . Primates are also highly intelligent and quickly learn to evade capture or darting making it difficult to sample multiple individuals in a group or to sample a particular individual at more than one time point . Moreover , handling primates may not be permitted , particularly for endangered and threatened species . In these scenarios , non-invasive sampling methods are often the only practical option [16 , 17] . Various non-invasive methods have been used to sample free-ranging primates , primarily for the collection of feces [16 , 17] and urine [18] . However , many zoonotic pathogens are shed in the oral cavity and spread of pathogens through bite wounds and discarded food is an important route of transmission at the primate-human interface . Furthermore , PCR inhibitors often pose a diagnostic challenge for fecal samples and commercial nucleic acid extraction kits demonstrate varying efficiencies for their removal [19] . Oro-pharyngeal swabs , which sample a combination of saliva and mucosal cells are useful for detecting orally shed viruses as well as viruses infecting the respiratory tract , which may be coughed up and recoverable from the oro-pharyngeal cavity . Oral samples have been used for the detection of viruses in primates including some with frequent cross species transmission , such as Ebola , herpes B , and simian immunodeficiency virus [20–22] . Oral samples have also been used for the detection of viruses in humans and could be applied to primate samples , such as dengue fever , Ebola , hepatitis A , Marburg , and measles [23–27] . Additionally , oral samples have been used for the detection of antibodies to bacteria and parasitic infections , such as leptospira , leishmania and trypanosoma cruzi , which could be applied to understanding the potential roles primates play in the zoonotic transmission routes of these diseases [28–30] . Non-invasive collection of oral samples using distributed ropes was developed for virus detection in domestic pigs and has been used for swine surveillance in the U . S . A . [31] . Non-invasive collection of oral samples has also been reported previously in primates through collection of partially chewed plants and distributed ropes . Chewed plants dropped by mountain gorillas have been used to detect gorilla DNA [32] and wadged plant material dropped by chimpanzees has been suggested as a sample for respiratory pathogens [33] . Saliva has been recovered using distributed rope devices for the detection of salivary cortisol , alpha amylase , and/or host genomic DNA from captive squirrel monkeys , rhesus macaques , baboons , bonobos , and eastern gorillas in addition to free-ranging rhesus and Tibetan macaques [32 , 34–40] . However the suitability of samples obtained by ropes distributed to primates has not yet been evaluated with respect to detection of pathogens . The goals of this study were to ( 1 ) evaluate rope distribution as a non-invasive oral sampling technique for free-ranging terrestrial and arboreal primate species and ( 2 ) evaluate oral samples from ropes for detection of both DNA and RNA viruses . Viruses were targeted for method evaluation because of their fragility in the environment , especially in tropical areas , and their susceptibility to sample handling , compared to bacteria and antibodies . Additionally , optimizing this technique for RNA viruses is particularly relevant to zoonotic disease surveillance because RNA viruses have higher mutation rates and are more likely to shift hosts resulting in disease transmission from animals to people [41] . To evaluate this sample collection method , we offered ropes to captive laboratory rhesus macaques ( Macaca mulatta ) and tested samples for the presence of two ubiquitous pathogens , rhesus cytomegalovirus ( RhCMV ) , a DNA herpes virus and simian foamy virus ( SFV ) , an RNA retrovirus [42 , 43] . We then evaluated the technique in field settings with partially habituated free-ranging primates in Uganda and Nepal . By optimizing this non-invasive sample collection technique for detection of viruses in the oral cavity , we provide an important method for sampling free-ranging primates at high-risk interfaces where spillover of pathogens from primates to humans is likely .
The Institutional Animal Use and Care Committee ( IAUCC ) of the California National Primate Research Center approved all laboratory colony macaque study protocols ( #16031 ) . The IAUCC of the University of California , Davis ( #17504 ) , USA the Uganda Wildlife Authority ( Uganda ) , Department of National Parks and Wildlife Conservation ( Nepal ) , Pashupati Area Development Fund , Swoyambhu Management and Conservation Committee , and local residents of the Thapatali temple complex ( Nepal ) approved all free-ranging primate study protocols . Initial optimization of non-invasive sample collection and virus detection was performed with primates housed in outdoor colonies at the California National Primate Research Center ( CNPRC , Davis , CA ) . Seventeen male and 6 female rhesus macaques ranging in age from 3 to 5 years were included in this study . On the day of sample collection , macaques were brought indoors and pair-housed in cages with a divider placed to physically separate the animals while allowing for visual and audio contact . To increase the likelihood of sampling during an episode of viral shedding , each macaque was sampled at least twice , with a minimum 1-week interval between sampling events . Each macaque was given one of three chewing devices: 1 ) a 6-inch piece of cotton dental rope designed for saliva collection in human dental procedures ( Salimetrics LLC , State College , PA ) ; 2 ) a six-inch piece of one half inch diameter nylon rope ( ACE Hardware Corp . , Oak Brook , Il ) , each with 3-feet of string sewn to the end of the rope for retrieval ( Fig 1 ) ; or 3 ) 3-feet of nylon rope . For all three devices , the length of rope +/- attached string was sufficient to enable the macaques to chew on the end of the rope while an animal technician maintained a grip on the other end for retrieval . Cotton and nylon material , as well as two different lengths , were tested to assess virus recovery and macaque behavioral preference for the two materials . Nylon ropes were soaked in de-ionized water to remove any particulate matter and autoclaved before distribution; non-sterile cotton ropes were not modified . Ropes were dipped in fruit jam or banana baby food as an attractant and placed inside enclosures . Macaques were allowed to chew on the rope until they either discarded it or a maximum of 3 minutes had passed , at which point , the rope was retrieved by the technician pulling the end of the attached string or rope . Chewed 6-inch ropes , with retrieval strings removed were compressed and placed directly into empty swab storage tubes ( 1 . 7 x 10 cm ) containing a separate compartment allowing for liquid flow through upon centrifugation ( Salimetrics ) . Immediately following rope collection , macaques were anesthetized with an intramuscular injection of ketamine ( 5–30 mg/kg IM ) . Once anesthetized , a sterile dacron swab ( Becton Dickson and Co , Franklin Lakes , NJ ) was rubbed inside the lower lip , into the buccal pouch , and along the gingiva . Swabs were placed into 15 ml conical tubes containing 1ml phosphate buffered saline ( PBS ) . Evaluation of behavioral acceptance of distributed ropes was also performed in a free-ranging setting with partially habituated primate species in Uganda and Nepal . Twenty-two olive baboons ( Papio Anubis ) were sampled from villages outside Queen Elizabeth National Park , Uganda , 20 red-tailed guenons ( Cercopithecus ascanius ) and 10 l’hoest’s monkeys ( Cercopithecus lhoesti ) from villages outside the Bwindi Impenetrable Forest , Uganda , and 65 rhesus macaques in the Pashupati , Thapatali and Swoyambhu temple complexes , Kathmandu , Nepal . Trials using six-inch nylon and cotton ropes were performed with the addition of 6-inch nylon oral swab ropes ( Salimetrics ) ( Fig 1 ) . Ropes were distributed both with and without retrieval strings attached by throwing the ropes to individual monkeys ( Figs 2; 3 and 4 ) . Strawberry jam was used as an attractant for all primate species with the exception of baboons whom were given ropes disguised inside bananas with no retrieval string attached ( Figs 5 and 6 ) . Primates were allowed to chew on the ropes until they voluntarily discarded them and retrieved by locating them in the surrounding terrain or pulling the retrieval string if used . Laboratory colony macaque rope and swab storage tubes were placed on ice during sample processing ( approximately 2 hours ) . Tubes were centrifuged at 3 , 000 rpm for fifteen minutes at 4°C ( Sorvall RC-5B ) . The sample volume eluted was measured by marking the side of the tube . One milliliter of PBS was then added to the rope compartment of the storage tube and centrifuged again at 3 , 000 rpm to remove any additional sample material . Sample eluted from the ropes was transferred to a sterile 2 ml cryovial tube . Swab samples were pulse-vortexed for 15 seconds , and extracted liquid was transferred into a sterile 2 ml cryovial tube . All extracted samples were stored at –80°C until testing . Free-ranging primate rope samples were collected similarly with the exception of pipetting 1 ml of MicroTest M6 viral transport media ( Remel , Lenexa , KS ) over the rope in the field instead of washing with PBS . All samples were collected within 5 minutes of being discarded by a primate , and swab storage tubes were immediately placed in coolers on ice packs for between 1 and 2 hours during transport . In Uganda , primate rope samples were eluted in the field using a portable centrifuge machine . In Nepal , samples were eluted at the Center for Molecular Dynamics Nepal . For macaque samples , DNA was extracted from rope and swab samples using QIAamp Blood kits ( QIAGEN , Valencia , CA ) . Each sample was processed according to the manufacturer’s instructions with a final elution volume of 200 μl . RNA was extracted from rope and swab samples using QIAamp Viral RNA Mini kits ( QIAGEN ) . Each sample was processed according to the manufacturer’s instructions with a final elution volume of 60 μl . Laboratory colony macaque samples were treated with DNase ( Turbo DNA-free kit; Applied Biosystems , Foster City , CA ) and cDNA was synthesized using VILO cDNA kits ( Invitrogen , Carlsbad , CA ) according to the manufacturer’s instructions with the exception of an extended incubation at 42°C for 2 hours . Free-ranging macaque sample cDNA was synthesized using SupescriptIII First Strand Synthesis cDNA kits ( Invitrogen ) according to manufacturer instructions . For Ugandan primate samples , nucleic acid was extracted using the NucliSENS minMag platform ( BioMerieux , Durham , NC ) and cDNA was synthesized using SuperscriptIII First Strand Synthesis cDNA kits ( Invitrogen ) . Real-time PCR was used to quantify RhCMV DNA copies of the glycoprotein B gene ( RhUL55 ) in laboratory colony macaque rope and swab samples according to published methods [44] . An Applied Biosystems 7900HT real-time PCR machine was used for all assays . A standard curve was generated by using 10-fold serial dilutions of a plasmid ( 106 to 100 copies per reaction ) containing the gB amplicon . Results were analyzed with the Sequence Detection System software ( version 2 . 4; Perkin-Elmer ) . Samples were analyzed in triplicate and considered to be positive for RhULgB when two of the three replicate wells exceeded 10 times the baseline fluorescence . Real-time PCR was used to quantify copy numbers of the pol gene of SFV in cDNA from laboratory colony and free-ranging rope and swab samples . The forward and reverse primer sequences were 5’-CTT CAG GTC AAA ATG GAT CCT CTA C-3’ and 5’-ATC CCA GTG GGC TTT TAA TTT AGT TC-3’ , respectively . The probe sequence ( 5’-CCT CCA GCC TCT GGA AGC GGA AAT-3’ ) contained 5’ FAM as the reporter dye and 3’ TAMRA ( 6-carboxytetramethylrhodamine ) as the quencher dye ( PE Applied Biosystems ) . Real-time PCR was performed according to previously published techniques [45] using 2x Taqman universal PCR master mixture ( PE Applied Biosystems ) , 4 . 5 μl of each primer ( 900 nmole/L ) , 1 . 25 μl probe , 4 . 75 μl 1x Tris-EDTA , and 10 μl cDNA in a 50 μl reaction volume . cDNA was amplified ( 1 cycle of 50° C for 2 minutes and 95° C for 10 minutes , followed by 55 cycles of 95° C for 15 seconds and 60° C for 1 minute ) using an AB 7900HT real-time PCR machine . As an internal control to ensure the presence of amplifiable genetic material , a real-time PCR was run simultaneously for the macaque OSM gene [46] . Samples were analyzed in duplicate and interpreted as positive when amplification was observed in one of the sample and standard control wells . Samples were interpreted as negative when amplification was not observed in either of the duplicate sample wells but amplification was observed in the OSM control well . This assay is available as a routine testing service provided by the pathogen detection core laboratory at the CNPRC ( http://pdl . primate . ucdavis . edu ) . All free-ranging macaque samples were tested by real-time PCR for simian foamy virus as described above and positive samples were subjected to confirmatory testing and sequencing by conventional PCR for the pol gene ( 632 bp ) according to previously described methods [47] . Ugandan primate samples were subjected to testing and sequencing by conventional PCR for the simian foamy virus LTR gene ( 357 bp ) according to previously described methods [47] . PCR products were cloned using Topo TA cloning kits ( Invitrogen ) , and sequencing was performed at the University of California Davis Sequencing Laboratory . To evaluate field procedure effects on sample quality , a beta-actin PCR assay was performed on all free-ranging primate samples according to previously described techniques [48] . Cohen’s kappa values , along with prevalence and bias-adjusted kappa values ( PABAK ) , were used to compare laboratory colony macaque swabs and each type of rope to evaluate agreement among sample collection methods . Sensitivity and specificities were estimated for rope and swab sample collection methods in order to determine their performance in relation to each other , considering neither method as a gold standard . Swab samples from primates are not considered to be a gold standard for oral pathogen detection , and therefore traditional sensitivity and specificity calculations were not used . Bayesian statistical approaches that do not require designation of a gold standard provide an estimate of test accuracy and can address bias that occurs in estimates of sensitivity and specificity if the test under evaluation is compared with an imperfect reference standard . This approach allows the combination of prior information on the test characteristics in the form of prior modes and their probability intervals , described as prior beta distributions , with information obtained through observed data to give a posterior distribution of the test characteristics . The results from the rope samples were modeled against swab samples using a “2 dependent tests , 1 population , no gold standard” Bayesian model as described by Branscum et al . [49] in WinBUGS version 1 . 4 [50] . The tests were considered conditionally dependent because both were testing the same individual and biological route of oral shedding using the same real-time PCR assays . Prior mode values estimating the hypothesized sensitivity , specificity , and prevalence of viral oral shedding were incorporated into the model based on expert opinion , data on oral shedding of RhCMV in macaques [21] , and a model estimating risk of SFV transmission from free-ranging temple macaque bites [51] ( Table 1 ) . Significant differences in estimated sensitivities and specificities calculated using this model were determined by evaluating 95% confidence intervals . Sensitivity analyses for the overall model were performed by changing the values of hypothesized prior values for the parameters with the largest confidence intervals , which included RhCMV shedding , SFV shedding , and rope type sensitivity .
Distributed ropes were accepted and chewed by primates in both laboratory and free-ranging settings . Among the laboratory colony trials , macaques accepted and chewed on the ropes 45 of the 55 times when the ropes were 6-inches in length , in contrast to 9 of 30 times when the ropes were 3-feet in length . These latter 9 macaques had however , already been exposed to 6-inch ropes . Among the free-ranging behavioral trials , oral samples were successfully collected from primates using ropes for 18 of 20 macaques , 18 of 22 olive baboons , 16 of 20 red-tailed guenons , and 8 of 10 l’hoest’s monkeys . All species accepted the ropes with fruit jam applied as an attractant ( Figs 2 and 3 ) except baboons; the rope had to be completely disguised inside a banana in order for them to chew on it ( Fig 4 ) . Ropes with retrieval strings attached were not as effective due to macaques , baboons and l’hoest’s monkeys being fearful and/or distracted by the strings . With respect to undiluted sample volume , an average of 353 . 1μl ( 95% CI 210 . 6–495 . 7 ) and 467 . 5 μl ( 95% CI 339 . 5–595 . 5 ) sample was eluted from chewed cotton and nylon ropes collected from laboratory colony macaques , respectively . A crude average of 400 μl sample was eluted from ropes collected from free-ranging macaques , red-tailed guenons , and l’hoest’s monkeys , with 800 μl recovered on average from olive baboons . Laboratory analysis of free-ranging macaque samples showed that beta-actin was detectable in 49 out of 65 samples ( 75% ) . SFV was detected by real-time PCR in 12 out of 65 samples ( 18% ) . Confirmatory sequencing of positive free-ranging samples determined that virus sequences were macaque simian foamy virus ( GenBank accession no . KP861860 ) . Analysis of free-ranging olive baboon samples showed that beta-actin was detectable in 17 out of 18 samples ( 94% ) . SFV was detected by conventional PCR in 8 out of 18 samples ( 44% ) . Confirmatory sequencing determined that the virus sequences were baboon simian foamy virus ( GenBank accession no . KP896160 ) . Analysis of free-ranging red-tailed guenons and l’hoest’s monkeys showed that beta-actin was detectable in 100% of samples . SFV was not detected by conventional PCR in any of these samples . All macaque and baboon samples positive for SFV were also positive for beta-actin . Of the 54 paired rope and swab samples collected from laboratory colony macaques , RhCMV DNA was detected in the same number of oral samples collected using ropes as swabs , although the measure of agreement was moderate ( K = 0 . 19 , PABAK = 0 . 48 ) ( Table 2 ) . Simian Foamy Virus cDNA was detected in a greater number of oral samples collected using swabs than ropes and the measure of agreement was fair ( K = 0 . 23 , PABAK = 0 . 33 ) ( Table 2 ) . Significant differences were not detected among estimated sensitivities and specificities for RhCMV detection in rope and swab samples ( Table 3 ) . For SFV , ropes had a significantly lower estimated sensitivity ( Se = 0 . 59; 95% CI 0 . 49–0 . 69 ) compared to swabs ( Se = 0 . 83; 95% CI 0 . 71–0 . 93 ) and significant differences were not detected among estimated specificities ( Table 3 ) . When assessing model fit , changing hypothesized parameters did not influence trends in outputs with respect to performance of non-invasive rope collection compared to swabs and showed minor variability with the estimated sensitivity and specificity .
Non-invasive oral samples were successfully collected from arboreal and terrestrial dwelling free-ranging primate species for detection of DNA and RNA viruses . To use this technique , primates targeted for sampling must be willing to chew on ropes . We found that the optimal technique for recovering oral samples from laboratory colony macaques was to use ropes 6-inches in length with a retrieval string attached . Laboratory colony macaques were not willing to chew on the ropes when they were longer than 6-inches . We speculate that 3-foot length ropes resemble snakes , as fear behaviors are common among various primate species [52] . Similarly , the optimal technique for free-ranging primates was to use ropes 6-inches in length but with no retrieval strings attached . Baboons and l’hoest’s monkeys were fearful of any form of an attached string and macaques were more likely to become aggressive towards the handler . When strings were removed , primates were less distracted by the strings and spent more time chewing on ropes . In addition , macaques from the Pashupati Temple Complex were observed placing the ropes into cheek pouches where prolonged contact with oro-pharyngeal mucosa could occur , potentially increasing the opportunity for viral sampling . Free-ranging primates targeted for sampling in this study were partially habituated to local communities and willing to allow a researcher to approach within an appropriate distance to distribute ropes . This technique is most practical for sampling semi-habituated primates at high-risk disease transmission interfaces , such as forest buffer zones in equatorial Africa where human and primate communities share habitats and in parts of Asia where urban-dwelling primates are flourishing . This technique would be more difficult to deploy in situations where primates do not allow researchers to approach within a feasible distance to distribute ropes . In addition , special consideration should be taken when providing food to free-ranging primates on a repeated basis because this can lead to further human-primate contact and associated public health risks to local communities as well as increase risks to primates by exposing them to added hunting and hazing pressure . In this study , primates did not swallow the ropes . Primates adapted to scavenging human materials were adept at discarding non-food items , and all ropes were dropped after investigation/chewing . Not all ropes could be recovered , however , as some were dropped into an area of terrain that could not be accessed . This pattern was more prevalent with arboreal species , as discarded ropes landed on tree branches . An estimated ten percent of targeted individuals should be added to sample size calculations to adjust for some ropes being irretrievable under rugged field conditions . Both RhCMV and SFV were detectable in samples collected from laboratory colony macaques . Estimated sensitivities were not significantly different between ropes and swabs for the detection of RhCMV; however , swabs were more sensitive than ropes for the detection of SFV . We evaluated the utility of this approach for detection of an RNA virus because RNA viruses are more likely to shift hosts and emerge as zoonoses . Given the need to assess detectability of RNA viruses across multiple primate species , SFVs were selected for this study because they are ubiquitous , non-pathogenic retroviruses that widely infect old world primates [53] . While low levels of proviral SFV DNA or endogenous retroviral DNA can be detected in tissues , viral RNA , indicative of viral replication is abundant in differentiated superficial oral mucosal cells that are shed into saliva [54 , 55] . With regard to more fragile RNA viruses , particularly in a field setting , rope collection sample processing could cause greater virus degradation . RNA was therefore extracted from free-ranging primate samples and tested for beta-actin as an overall indicator of the quality of sample collectable in the field . Positive beta-actin PCR results from 75% of the free-ranging macaque , 94% of the olive baboon , and 100% of the red-tailed guenon and l’hoest’s monkey samples indicated that mammalian host RNA was recoverable in a rigorous field setting and detection of SFV indicated that viral RNA was recoverable . Together , these results demonstrate that collection of oral samples from distributed ropes is effective with primates in the laboratory and field and could be used for the detection of DNA and RNA viruses . As with any new technique , this sample collection method should be evaluated for each new host species and new target viruses . In addition , spiking experiments could be performed to evaluate target virus recovery in the presence of different proposed attractants . This study evaluated cotton ropes because the only commercially available rope designed for oral use in humans is made of cotton and would be easily accessible for future field studies . Cotton however , is not optimal for the recovery of some pathogens , including herpes viruses [56] and raw cotton has been shown to contain PCR inhibitors [57] . In this study , samples from cotton ropes were estimated to yield less viral DNA when compared with nylon , and estimated sensitivity appeared to be lower for cotton than nylon although the difference was not significant . In future studies , the targeted virus could be considered in selecting the type of rope collection material . No significant differences in estimated specificities for rope or swab oral sample collection methods were detected . A moderate to fair level of agreement for positive and negative samples was observed for detection of RhCMV and SFV . These findings can be explained by several reasons that could result in differential detection; including increased handling of rope samples , ropes being allowed to contact the bottom of the cage prior to collection where they could have been cross-contaminated by virus shed in saliva , urine or feces , and because ropes were collected before swabs , where virus may have become saturated in ropes and less available for collection in swabs . Moderate agreement may also support current and intensive wildlife surveillance findings conducted by the investigators , which are showing inconsistent results from duplicate swabs taken from the same animal , which may have been targeting different areas of the mouth . We have demonstrated that non-invasive oral sampling using distributed ropes is a simple and effective technique that can be used for disease surveillance in semi-habituated free-ranging primates and , potentially , other wildlife species when invasive sampling techniques may not be possible or appropriate . This technique provides opportunity for monitoring endemic diseases of wildlife , viruses that may have been introduced from humans , as well as zoonotic viruses that are of significance for spillover into humans . Many high-risk human-primate interfaces globally have not yet been monitored for endemic and zoonotic viruses that could pose a risk to nearby human communities . Furthermore outbreaks of zoonotic disease in humans are rarely investigated with simultaneous sampling of suspected primate spillover hosts , particularly in resource-constrained situations where activities are focused on control of human cases . Implementing this low cost , relatively simple to deploy , non-invasive sampling technique in wildlife surveillance activities and outbreak response efforts could greatly enhance our understanding of wildlife sources of zoonotic diseases at important interfaces where zoonotic diseases are affecting human health . | Wild nonhuman primates are frequent sources of pathogens that could be transmitted to humans because they are closely genetically related and have intimate contact with humans in many parts of the world . Sampling primates to screen for zoonotic pathogens is logistically challenging because standard invasive sampling techniques , such as the collection of a blood sample or an oral swab , requires field anesthesia . This research describes a non-invasive oral sampling technique that involves distributing a rope for primates to chew on that can be retrieved and screened for pathogens . Oral samples were successfully collected from multiple wild primate species in remote field settings and viruses were detected in those samples . This non-invasive sampling method has the potential for future applications in disease studies examining primates as sources of diseases that could affect humans in remote tropical settings . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Optimization of a Novel Non-invasive Oral Sampling Technique for Zoonotic Pathogen Surveillance in Nonhuman Primates |
Cervical cancer is a multi-stage disease caused by human papillomaviruses ( HPV ) infection of cervical epithelial cells , but the mechanisms regulating disease progression are not clearly defined . Using 3-dimensional organotypic cultures , we demonstrate that HPV16 E6 and E7 proteins alter the secretome of primary human keratinocytes resulting in local epithelial invasion . Mechanistically , absence of the IGF-binding protein 2 ( IGFBP2 ) caused increases in IGFI/II signalling and through crosstalk with KGF/FGFR2b/AKT , cell invasion . Repression of IGFBP2 is mediated by histone deacetylation at the IGFBP2 promoter and was reversed by treatment with histone deacetylase ( HDAC ) inhibitors . Our in vitro findings were confirmed in 50 invasive cancers and 79 cervical intra-epithelial neoplastic lesions caused by HPV16 infection , where IGFBP2 levels were reduced with increasing disease severity . In summary , the loss of IGFBP2 is associated with progression of premalignant disease , and sensitises cells to pro-invasive IGF signalling , and together with stromal derived factors promotes epithelial invasion .
Metastasis involves multiple steps , so defining the processes which regulate cancer cell invasion are crucial for understanding the initiation of the metastatic process . In particular , it will be important to monitor the molecular events that occur in the transition from a hyper-proliferative epithelium to an invasive epithelium and determine their functions . High-risk human papillomavirus ( HPV ) types are responsible for the transformation of the cervical epithelium and subsequent cervical cancer . Expression of the ‘early’ HPV genes E6 and E7 has been identified to be sufficient to immortalise primary human keratinocytes [1 , 2] and are required for continued proliferation of infected cells however whether this is sufficient to transform cells into a malignant form is still disputed [2–4] . E6 and E7 proteins immortalize epithelial cells through their ability to inactivate the cell cycle checkpoints regulated by the retinoblastoma protein ( pRb ) and p53 resulting in enhanced proliferation and loss of differentiation [5–7] . If not cleared , the HPV infection can persist resulting in progression to invasive disease [8] . However , not all HPV infections of the cervix lead to progressive disease and so knowledge of the alterations during transition from low grade , CIN 1 , to high grade disease , CIN 3 and eventual invasive disease may yield novel molecular biomarkers that distinguish lesions with a propensity to progress to invasive disease from lesions that will remain premalignant [9] . During the development of cervical cancer , numerous molecular events have been described , including: altered viral gene expression [10 , 11] , regulation of immune-response [12] , activation of proliferative signalling pathways [13–15] , modification of chromatin [16–19] , and regulation of pro-invasive genes , such as matrix metalloproteases ( MMPs ) [11 , 20] . In the present study , we have investigated the factors and mechanisms which influence the invasive behaviour of the epithelium . We have examined the ability of the high-risk HPV16 E6 and E7 genes to transform primary human foreskin keratinocytes ( HFKs ) into an invasive epithelium and have identified a crucial role for the IGF ( Insulin-like growth factor ) signalling pathway in the progression to invasive growth . The invasive potential of E6/7 expressing keratinocytes is enhanced following dramatic down-regulation of insulin-like growth factor binding protein 2 ( IGFBP2 ) , resulting from enhanced histone deacetylase 3 activity at the IGFBP2 promoter . IGFBP2 has been shown to have both pro-tumourgenic properties and tumour suppressive functions , although the former tend to be independent of IGF/IGF receptor signalling [21] . In this study , we have found that IGFBP2 acts to suppress IGFI/II stimulation of the IGF receptor 1 ( IGF1R ) but in its absence , IGFI/II signalling , in conjunction with the stromal derived growth factor , keratinocyte growth factor ( KGF ) , stimulates the AKT pathway leading to invasion . Significantly , we have observed that IGFBP2 expression is inhibited in high-grade pre-malignant cervical lesions infected with HPV16 and propose that this down-regulation is a required step in the initiation of the invasion process .
The high-risk HPVs are a causal factor for cervical and infection is observed in a proportion of head and neck cancers . Whilst E6 and E7 expressing keratinocytes are immortalized , we have observed in three-dimensional organotypic cultures that they do not possess the ability to invade into the stroma [1 , 2] . The stromal compartment also regulates the invasive behaviour of the epithelium [22–24] , and we have recently demonstrated that pRb-depleted human foreskin fibroblasts ( HFFs ) promote epithelial invasion , i . e . breakdown of the basement membrane and growth into the underlying collagen layer ( S1 Fig ) . This invasion is driven through altered secretion of the keratinocyte growth factor [22] . Organotypic cultures generated using early-passage ( passage 3–10 ) HPV16 E6/7 expressing HFKs are refractory to the pro-invasive signals from pRb-depleted HFFs . However , with continued passage ( late passage ) ( i . e . >passage 14 ) , these cells acquire the ability to invade ( Fig 1A ) . We hypothesized that following extended passage . E6/7-HFKs may secrete growth factors or cytokines that could alter invasive behaviour of the epithelium . To test this hypothesis , conditioned medium ( CM ) from monolayer cultures of invasive cells , normal HFKs and early-passage E6/7-HFKs was transferred daily to organotypic cultures containing non-invasive early E6/7-HFKs . Medium taken from late-passage E6/7-HFKs was sufficient to induce invasion of the previously non-invasive cells , although not to the same level as late passage E6/7-HFKs ( Fig 1B and 1C ) , suggesting invasive late-passage E6/7-HFKs were generating a pro-invasive environment . Subsequently , conditioned medium from normalised numbers of early and late-passage E6/7-HFKs were subjected to growth factor array analysis , which measured the levels of 41 growth factors . Surprisingly , the pro-invasive late-passage E6/7-HFKs did not secrete additional growth factors/cytokines but produced significantly lower levels of the insulin-like growth factor binding protein , IGFBP2 , and the granulocyte-macrophage colony-stimulating factor ( GM-CSF ) at the protein ( Fig 1D and 1E ) and mRNA level ( Fig 1F and 1G ) in cell extracts . This result suggested that an inhibitor of invasion was lost as cells acquired invasive behaviour , indeed , when media from non-invasive , early pass E6/7-HFKs was transferred to invasive late-passage E6/7-HFKs , we observed a complete inhibition of invasion ( Fig 1H and 1I ) . Since IGFBP2 expression was the most dramatically altered factor ( >90% loss , p<0 . 001 ) , we wanted to investigate its role in the invasive phenotype of late passage E6/7-HFKs . Western blot and real-time analysis from three independently generated E6/7-HFK lines confirmed that late-passage E6/7-HFKs produced very low levels of IGFBP2 in comparison to early-passage E6/7-HFKs as well as primary HFKs ( Fig 2A and 2B , and S2A Fig ) . Down-regulation of IGFBP2 also occurred with continued passage of E6/7-HFKs maintained in co-culture with J2-3T3 fibroblasts in E-medium ( S2B Fig ) . Further examination of the IGFBP family identified that there was a modest increase in IGFBP3 but only IGFBP2 was significantly regulated following continued passage of E6/7-HFKs ( Fig 2C ) . These results implied that IGFBP2 may be down-regulated either as a consequence of long term culture or prolonged HPV16 E6/7 expression . The expression of IGFBP2 was therefore monitored in primary human foreskin keratinocytes and immortalised keratinocytes ( immortalised either by hTERT or through co-culture with J2-3T3 mouse fibroblasts and the Rock inhibitor Y27632 [23] ) . IGFBP2 was expressed at high levels in immortalized cells relative to late passage E6/7 keratinocytes ( S2C and S2D Fig ) . Furthermore , we established that HPV16 E6/7 were able to mediate down-regulation of IGFBP2 when introduced into these immortalised cells whereas control cells transfected with vector only did not ( S2E and S2F Fig ) . This implied that the down-regulation of IGFBP2 was a result of prolonged HPV16 E6/7 expression . To test this we targeted E6 and E7 in late passage E6/7-HFKs with siRNA , which resulted in re-expression of p53 , pRb , and IGFBP2 and concomitantly inhibited invasion in organotypic cultures ( S3A–S3D Fig ) . There is also a correlation between IGFBP2 levels and HPV in publicly available microarray datasets from various cervical cancer cell lines [24] where HPV positive cervical cancer cell lines were shown to have substantially reduced IGFBP2 expression compared to HPV negative cervical cell lines ( Fig 2D ) . We independently confirmed this at protein and RNA levels in C33a , Caski and Hela cell lines ( Fig 2E and 2F ) . To establish a role for IGFBP2 in the invasion process , recombinant IGFBP2 was added to organotypic cultures containing invasive late-passage E6/7-HFKs . Addition of physiological quantities of IGFBP2 to the cultures resulted in inhibition of epithelial invasion in a dose dependent manner ( Fig 3A and 3B , and S4B and S4C Fig ) . As IGFBP3 was found to be elevated in late passage cultures and is thought to promote invasion , we assessed whether IGFBP3 addition further induced epithelial invasion in late passage E6/7-HFKs ( S4D and S4E Fig ) , but found no further effect of IGFBP3 addition . This was also established in a modified organotypic raft culture containing the HPV16 positive cervical cancer cell line , Caski ( S4F and S4G Fig ) , where IGFBP2 , but not IGFBP3 , significantly inhibited invasion of the epithelial cells . To further assess the effects of IGFBP2 loss on the invasive potential of the epithelium , IGFBP2 levels were stably depleted in early-passage non-invasive E6/7-HFKs using two different shRNA molecules . IGFBP2 knockdown was confirmed by Western blot and real-time PCR analysis ( Fig 3C and 3D ) , and resulted in enhanced invasion ( Fig 3E and 3F ) . IGFBP2 was also depleted in primary HFKs ( Fig 3G ) , however , this did not result in the generation of an invasive epithelium ( Fig 3E and 3F ) suggesting that IGFBP2 acts as a brake to pro-invasive signalling mediated by E6 and E7 and following continued cell expansion , this brake is lost . IGFBP2 expression is regulated by a variety of factors , including the IGF system itself [25] and insulin [26] , however in E6/7 expressing keratinocytes , we found this was not the case ( S4H Fig ) . Epigenetic mechanisms have been associated with regulation IGFBP2 expression [27–29] , so next we wanted to determine if one or more of these mechanisms played a role in IGFBP2 regulation and if manipulation of the epigenetic factors would restore the IGFBP2 levels in late pass E6/7-HFKs . We have identified that as E6/7 immortalised keratinocytes are passaged , there is acquisition of methylation marks at CpG islands close to the transcriptional start of IGFBP2 in late passage cells only ( S5A and S5B Fig ) . However addition of 5-aza-C was unable to restore expression of IGFBP2 in the invasive cells ( S5C and S5D Fig ) suggesting DNA methylation may not be the critical regulator of IGFBP2 . HDAC inhibitors have been shown to elevate expression of IGFBP2 in cells which readily express the protein [28 , 29] , so to determine if this had occurred in late passage cells , which exhibit low levels of IGFBP2 , we added the pan-HDAC inhibitors sodium butyrate ( SB ) or trichostatin A ( TSA ) to invasive E6/7-expressing keratinocytes and Hela cells . The inhibitors restored both IGFBP2 mRNA and protein ( Fig 4A and 4B ) in late E6/E7 keratinocytes , as well as in Hela cells ( S6 Fig ) . Since these results suggest that there is increased histone deacetylation in the invasive keratinocytes resulting in reduced expression of IGFBP2 , we determined the expression of HDACs during the transition to an invasive epithelium . HDAC 1 , 2 , 3 , 5 and 6 were elevated in the invasive epithelial cells ( Fig 4C ) , and cell survival assays suggested that these invasive cells are sensitive to HDAC inhibition ( S7 Fig ) . Interestingly , addition of low doses ( IC25 ) of the HDAC inhibitors TSA , SAHA ( both pan inhibitors ) and romidepsin ( Romi , a class I inhibitor , which inhibits HDACs 1 , 2 , 3 but also HDAC4 and 6 ) , which do not inhibit proliferation , was sufficient to reduce invasive frequency of the late passage E6/7-HFKs ( Fig 4D and 4E ) . To further evaluate the mechanism through which IGFBP2 expression is regulated by histone modifications , selective HDAC inhibitors ( proprietary inhibitors of HDAC6 ( HDAC6i ) , HDAC1 and 2 ( HDAC1/2i ) and HDAC3 ( HDAC3i ) ) and entinostat , which is another class I inhibitor , were employed . Inhibitors of HDAC3 , the class I inhibitor , entinostat and to a lesser extent the HDAC1/2 inhibitor were sufficient to restore IGFBP2 RNA and protein expression ( Fig 5A and 5B ) . Using a commercially available HDAC3 selective inhibitor , RGFP966 , IGFBP2 expression was also restored ( S8 Fig ) . To further confirm a specific role for HDAC3 in the regulation of IGFBP2 expression , HDAC1-3 were individually depleted using siRNA and results showed that depletion of HDAC3 was sufficient to restore both mRNA and protein expression of IGFBP2 ( Fig 5C and 5D ) . To assess the function of HDAC3 in regulating IGFBP2 expression we utilised publicly available data , which examined histone modifications around the IGFBP2 locus in primary keratinocytes [30] ( Fig 5E ) . Three histone 3 lysine 9 ( H3K9 ) acetylation sites , which can be altered by HDAC3 [31] , were identified . Using CHIP-qPCR with anti-H3K9Ac CHIP-tested antibodies [30] , we observed that the acetylation at these three sites is lost in invasive cells , which do not express IGFBP2 ( Fig 5F ) , suggesting a mechanism for the loss of expression of IGFBP2 in late passage cells . In addition , the IGFBP2 promoter is bivalent , containing both active and repressive histone modifications within the promoter region , with the activating modifications proposed to predominate over the repressive elements [32] . Our results suggest that the loss of the activating marks allows the repressive elements to predominate leading to repression of the gene expression as previously suggested [33] . To support this suggestion , we showed , using ChIP-qPCR , that there is an increase in the presence of HDAC3 at the transcriptional start site , but no significant enrichment of HDAC3 at other sites within the IGFBP2 locus ( Fig 5G ) . Furthermore , HDAC3 resides in a repressive complex with NcoR1 ( NcoR1 ) and NcoR2 ( SMRT ) [34] resulting in an active repressive complex [35] . Analysis of NcoR1/2 at the protein and transcriptional levels showed marked elevation in invasive cells ( Fig 5H and 5I ) and also an enrichment at the Transcriptional Start Site ( TSS ) of IGFBP2 ( Fig 5J ) . Having established that IGFBP2 expression is lost in late passage E6/7-HFKs we next wanted to identify the significance of this loss and how it affected downstream signalling in the invasive cells . Since IGFBP2 has been shown to function through preventing IGF1 and IGF2 from binding to the IGF1-receptor [36–38] , we treated E6/7-HFKs with IGFBP2 prior to IGF1/2 treatment and established that IGFBP2 is acting to block IGF1/2 induced AKT and ERK activation ( Fig 6A ) . However , primary human foreskin keratinocytes ( HFK ) and early-passage E6/7-HFKs were unresponsive to IGF1 and IGF2 treatment ( Fig 6B ) . These results implied that IGF-signalling is enhanced in the invasive cells , as a result of the loss of IGFBP2 . The expression of the IGF-receptors 1 and 2 ( IGF1R and IGF2R ) in invasive versus non-invasive E6/7-HFKs was assessed by real-time and Western blot analysis and shown to be elevated in invasive cells whereas the related insulin receptor was unaltered ( Fig 6C and 6D ) . This also mirrors observations that IGF1R expression is elevated in CIN3 cervical lesions [13 , 39] . Previous work has demonstrated that invasion of the E6/7-HFKs relies on the secretion of keratinocyte growth factor ( KGF or FGF7 ) from stromal fibroblasts acting on the Fibroblast growth factor receptor 2b ( KGFR/FGFR2b ) on epithelial cells [22] . Therefore , we next investigated whether IGFBP2 could alter these effects . We treated late-passage E6/7-HFKs with KGF in the presence or absence of IGFBP2 and showed that IGFBP2 was sufficient to inhibit KGF induction of ETS2 and MMP1 , known modulators of the invasive process ( Fig 6E ) [40 , 41] . This implied a crucial role for the IGF pathway in the regulation of invasion . To test this hypothesis , IGF1R levels were depleted by siRNA in invasive late-passage E6/7-HFKs and depletion confirmed at the protein and mRNA levels ( Fig 6F and 6G ) . Depletion of IGF1R in the epithelium significantly reduced the frequency of invasions in organotypic rafts , suggesting that the IGF signalling pathway is pro-invasive ( Fig 6H and 6I ) . We also tested whether IGF1R-depleted cells respond to the KGF pro-invasive stimulus , and similar to IGFBP2 treatment of these cells , KGF was unable to activate ETS2 and MMP1 in the absence of IGFR1 ( Fig 6J ) . The ability of IGF signalling to alter the pro-invasive signalling of KGF implied that the two pathways were connected and it has recently been reported that KGF functions in a protease dependent manner , specifically activating A Disintegrin And Metalloprotease 17 ( ADAM17 ) [42] . We also confirmed in our cells that KGF activates downstream signalling events in a protease dependent manner , using the protease inhibitor GM6001 ( Fig 7A ) . Furthermore , we found that by depleting IGF1R in the late-passage E6/7-HFKs the activation of AKT was inhibited following KGF treatment , suggesting that KGF-induced activation of AKT is both a protease-dependent and IGF1R-dependent process ( Fig 7B ) . Late-passage E6/7-HFKs were compared to early-passage cells in terms of their expression of the ADAM family of proteins and ADAM17 expression was found to be elevated in these cells ( Fig 7C ) . We then tested whether KGF induced activation of AKT is ADAM17 dependent using siRNA . ADAM17 was efficiently depleted ( Fig 7D and 7E ) and this prevented activation of the AKT pathway ( Fig 7F and 7G ) . ADAM17 has been shown to shed various growth factors from cells , including IGF [43] so we determined if IGF is secreted from E6/7-HFKs following KGF treatment . IGF was found to be secreted from invasive E6/7-HFKs on KGF treatment , however , following knockdown of ADAM17 this secretion was inhibited ( Fig 7H ) , implying that KGF induced ADAM17 activation leads to enhanced shedding of IGF from invasive cells and drives activation of the AKT pathway through activation of IGF1R . In order to establish the clinical relevance of our findings , expression of IGFBP2 was assessed in pre-malignant cervical intraepithelial neoplasia’s ( CIN ) by immunohistochemistry and dual immunofluorescence , utilising p16INK4A ( p16 ) staining to distinguish premalignant cells from normal cells . IGFBP2 was readily detected in uninfected normal cervical epithelium ( Fig 8A , p16 negative region , white arrow ) , however in regions where HPV16 had infected the epithelium , IGFBP2 was dramatically reduced in CIN3 lesions ( Fig 8A , p16 positive regions , red arrow ) . Following our initial observations we evaluated IGFBP2 expression in a blinded manner in 40 CIN1 lesions and 39 CIN3 lesions , all of which had previously been identified as HPV16 positive [44] . IGFBP2 was found to be down-regulated in 43% of CIN1 lesions whilst in CIN3 lesions , 85% of the samples showed down-regulation ( Fig 8B and 8C ) . There was a significant difference in the expected ratio of samples with IGFBP2 loss when comparing CIN1 and CIN3 lesions using the Fischer’s exact test , Chi-square test and Z-test ( p<0 . 001 ) . IGFBP2 was also found to be down-regulated in invasive disease ( Fig 8B and 8C ) , although there was no difference in the proportions of tumours with reduced IGFBP2 at the various stages examined ( Fig 8D ) . We had follow-up data for 13 patients with CIN1 where IGFBP2 was reduced . From this group , 10 patients progressed to CIN3 , the other 3 patients either regressed ( 1 case ) or remained CIN1 ( 2 cases ) ( Fig 8E ) . The results imply that IGFBP2 is commonly down-regulated at advanced stages of infection , correlating with the effects of prolonged HPV16 E6 and E7 expression and reduced levels of IGFBP2 in CIN1 disease may indicate a propensity to progress to a high grade . We have also investigated whether IGFBP2 expression is regulated in other cancers associated with HPV infection . HPV infection has been observed in head and neck cancers , where it is associated with between 30–60% of oro-pharyngeal cancers [45] . We have utilised publically available gene array datasets to assess the expression of IGFBP2 in oro-pharyngeal cancers [46] . In these studies HPV infection was detected by immunohistochemistry of the surrogate marker p16 and the samples were sub-divided into p16 positive and p16 negative groups and microarray datasets were analysed for IGFBP2 and p16 expression . In p16 positive cancers , IGFBP2 expression was significantly reduced ( Fig 8F ) , suggesting that down-regulation of IGFBP2 expression is likely associated with HPV infection in oro-pharyngeal cancers . In summary , we have shown that IGFBP2 is reduced in an E6/7 dependent manner over the passage of human keratinocytes leading to invasion in our 3-dimensional model system . Our in vivo studies with cervical premalignancies show that IGFBP2 expression is reduced with severity of disease from CIN1 to CIN3 . The reduced IGFBP2 expression leads to activation of the IGF/IGFR pathways , which through cross talk with the KGF/FGFR2b complex can drive invasion ( Fig 9 ) . The results suggest that the IGF/IGFR/IGFBP2 axis would make a logical target for further investigation for potential treatment of cervical cancers .
Invasion of the hyper-proliferative cervical epithelium into the surrounding stroma is an important event in progressive disease and here we have identified a crucial role for IGFBP2 in controlling this invasion process . Our results suggest that the prolonged expression of E6/7 proteins can generate an invasive epithelium through depletion of IGFBP2 expression , which in turn leads to signalling through the IGF1R in cross-talk with the FGFR2b . These results are also in keeping with previous results which demonstrated that HPV16 E7 can transform fibroblasts , in an IGF1R dependent manner [47] . We propose that IGFBP2 functions as a brake preventing the HPV-infected epithelium from invading into the underlying stroma . Our in vitro findings are mirrored in cervical cancer specimens where IGFBP2 expression is commonly lost in 85% of CIN3 lesions , which progress to invasive disease with high incidence , if left untreated [48] , while CIN1 lesions do not . We did however observed that 43% of CIN1 lesions have reduced IGFBP2 , and our preliminary data indicates that a significant proportion of patients with CIN1 disease who later progressed to a higher grade lesion , had reduced levels of IGFBP2 in the HPV infected epithelium of the original CIN1 biopsy . As this has only been examined in a limited number of cases , future studies are required to confirm that IGFBP2 levels may indicate patients at risk of progression . If the reduction of IGFBP2 levels identifies a sub-group of CIN1 lesions that have the propensity to progress , this could be useful clinically , as these patients could be monitored more closely to detect disease progression . There is a possibility that the CIN1 lesions where IGFBP2 were down regulated were mis-classified , however , grading of lesions was conducted by two pathologists with overall agreement in each case . Mechanistically our results demonstrate a critical role for IGF-signalling in driving the invasive process . The IGF pathway is well known to be modulated in cancer and is known to promote neoplastic growth [13 , 39] . The IGF receptors are expressed in a variety of cancers , and in vivo studies have demonstrated that cancer cells have a dependency for IGF1 . Following prolonged expression of HPV16 E6 and E7 the IGF pathway becomes activated i ) through loss of IGFBP2 and ii ) through enhanced expression of the IGF-receptors . Here we demonstrate that re-addition or re-expression of IGFBP2 through HDAC inhibitor treatment , blocks IGF-signalling and is sufficient to inhibit epithelial invasion , while reciprocal knockdown of IGFBP2 in non-invasive E6/7-HFKs resulted in enhanced invasion , demonstrating a critical role for the pathway in the invasion process . We have further demonstrated that the loss of IGFBP2 allows pro-invasive signals derived from the stroma to enhance epithelial invasion , and this is conducted via IGF1R . It has previously been demonstrated that the keratinocyte growth factor functions via activating the metalloprotease ADAM17 [42] and here we show this is also the case , and there is preferential activation of the AKT pathway . We have previously demonstrated that the AKT pathway is activated in cervical cancer specimens [6] and have demonstrated it as a key component of epithelial invasion [22] . Here we show that the activation of AKT by KGF was dependent on IGF1R ( Fig 9 ) , and this can be modulated by IGFBP2 and ADAM17 . Signalling via the IGF1R pathway has been proposed to be an important determinant of cervical cancer progression , since elevated expression of IGF1R has been observed in cervical specimens which positively correlated with stage of the CIN lesions [13 , 39] and further highlights the importance of the IGF-axis in HPV infection and incidence of CIN lesions [49] . This , together with our data , suggests that an activated IGF1R pathway promotes a pro-invasive phenotype in the cervical epithelium . An important caveat is that our in vitro model utilises fibroblasts which promote epithelium invasion [22] , and reduction of IGFBP2 in the epithelium alone , may not be sufficient to drive invasion in situ . As CIN lesions take a number of years to progress to invasive disease during this time the stroma may be ‘activated’ which in combination with loss of IGFBP2 can drive epithelium invasion . In line with this hypothesis detection of cancer associated myofibroblasts has been observed in the stroma of cervical cancers and is correlated with poor prognosis [50] . IGFBP2 has been demonstrated to have both tumour suppressive and oncogenic properties in different cancer types . Our data show IGFBP2 as an inhibitor of the invasion process in our 3-D model of cervical pre-cancer and in the main , IGFBP2 tumour suppressive functions are those which antagonise IGF signalling [38 , 51 , 52] , although IGF-independent pro-apoptotic functions of IGFBP2 have also been described [53] . In examples where IGFBP2 functions in an oncogenic manner , these functions appear to be independent of IGF and are mediated via integrin alpha 5 [54 , 55] which leads to inhibition of PTEN and ultimately activates the AKT pathway [56] . These oncogenic functions of IGFBP2 were not observed in our studies which may be due to functions of the HPV E6 and E7 , which have been shown to down-regulate various integrins , including alpha 5 [57] . Whilst the HPV vaccine will ultimately reduce the incidence of cervical cancer if administered universally , there still remains a generation of women who will require intervention . Since IGFBP2 is itself a potential target for therapeutic intervention [58] , and has been shown to inhibit the growth of breast cancer cells in vivo [59] , we propose that since HDAC3 is involved in reducing expression of IGFBP2 , HDAC inhibitors maybe a useful tool to treat patients with progressive disease .
Ethical approval for human materials used in this study was from the Northern Ireland Tumour Bank ( NIB11-0001 ) . Primary human foreskin keratinocytes , derived from neonatal foreskins were maintained in Epilife containing growth supplement ( Life-Technologies ) . Primary human foreskin fibroblasts ( Cascade Biologics ) , the cervical cancer cell lines: C33a and Caski ( from existing laboratory stocks , frozen at low passage ) , and Hela’s ( American Type Culture Collection ) were maintained in DMEM with 10% FBS . hTERT immortalised HFKs were obtained from the Rheinwald lab and maintained in E-medium with mitomycin C arrested J2-3T3 feeders . Immortalisation of HFKs using J2-3T3 and Rock inhibitor , Y27632 , was conducted as previously described [23] ( n = 2 ) , to compare IGFBP2 expression levels all cells were grown in E-medium with J2-3T3s . Recombinant IGFBP2 , IGFBP3 , IGF-I , IGF-II , KGF ( Peprotech ) and protease inhibitor , GM6001 ( Millipore ) were prepared according to manufacturer’s protocols . Short term treatments were conducted in confluent monolayers generated following 2 days growth of 200 , 000 HFKs in 6 well plates and medium was replaced with Epilife without growth supplement for 24 hours before treatment with individual growth factors . To assess the effects of IGFBP2 on IGF1/2 signalling , HFKs were grown as above then switched to low glucose , serum free DMEM for 24 hours and samples pre-treated for 1 hour with IGFBP2 prior to IGF1/2 treatment . Samples were lysed in RIPA buffer for 20 minutes on ice . To assess the impact of KGF on ETS2 and MMP1 induction confluent monolayers grown on 100 μg/mL collagen I before commencing KGF treatment in fresh Epilife with growth supplement . Proteins were harvested using urea buffer . Organotypic cultures were grown in the presence of pRb-depleted fibroblasts in E-medium as previously described [5] , stained with haemotoxylin and eosin and invasions per cm of raft were counted . In Fig 1B and 1H media was changed each day . A modified organotypic system was used for the Caski cell line as described previously [60] with the exception that matrigel was replaced with an equivalent volume of egg-white , as previously described [61] . Real-time PCR was conducted using Roche Lightcycler480 , primers are described in S1 Table and gene expression data was normalised to RPLPO . E6/7-HFKs and pRb-depleted HFFs were generated as previously described [6] , [22 , 62] . shIGFBP2#1 and #2 were from the pRSC retrovirus shRNA library ( UCL ) with the following target sequences , GTGGAGAACCACGTGGACA and CGGAGCAGGTTGCAGACAA . siRNA sequences used in this study were; siE6/7: GCACACACGUAGACAUUCGdTdT as previously described [63] . siHDAC1 and 2 were from Qiagen . siHDAC3: GAUGCUGAACCAUGCACCUTT as previously described [64] . siIGF1R: CAAUGAGUACAACUACCGCdTdT as previously described [65] , siRNA targeting ADAM17 was purchased from Dharmacon siADAM17 smartpool . siRNA transfections were conducted using RNAiMAX ( Life-Technologies ) . Immunofluorescence ( IF ) and immunohistochemistry staining procedures were previously described [22] . Cervical intraepithelial neoplasia samples ( CIN1 and CIN3 ) were from New Mexico and invasive cervical cancers were from Belfast Health and Social trust and a TMA containing tumour and matched normal tissue ( Abcam , Ab178142 ) . IGFBP2 staining intensity and scored as- , + , ++ , +++ , for normal and p16 positive regions . If IGFBP2 scores decreased by a factor of 2 or more between p16 positive compared to normal regions then samples were identified as reduced IGFBP2 . Antibodies used for IF were as follows: IGFBP2 ( Cell-Signaling Technologies #3922 , 1:200 ) , p16 ( BD-Biosciences , 554079 , 1:200 ) . Western blot analysis utilised the following antibodies: Cell-Signaling Technologies: IGFBP2 ( #3922 , 1:1000 ) , phospho-AKTser473 ( #4060 , 1:2000 ) , total AKT ( #2920 , 1:1000 ) HDAC1 ( #5356 , 1:1000 ) and HDAC2 ( #2545 , 1:1000 ) , Santa-Cruz: p53 ( sc-126 , 1:1000 ) , pERK1/2 ( sc-7383 , 1:1000 ) , ERK2 ( sc-154-G , 1:1000 ) , IGF1R ( sc-713 , 1:1000 ) , INS-R ( sc711 , 1:1000 ) , Ets2 ( sc-351 , 1:2000 ) , MMP1 ( sc-58377 , 1:1000 ) and HDAC3 ( sc-11417 , 1:1000 ) , Sigma-aldrich: beta-actin ( A2228 , 1:10 , 000 ) and ADAM17 ( SAB3500367 , 1:1000 ) NcoR1 ( Bethyl laboratories A301-145A , 1:1000 ) and NcoR2 ( Abcam Ab24551 , 1:1000 ) . Human Growth factor array from RayBiotech was conducted according to manufacturer’s protocols and arrays were analysed using densitometry software , Fluorchem SP . Human IGF-1 ELISA was from RayBiotech . CHIP-qPCR experiments were conducted as previously described [66] using 3 x106 cells per immunoprecipitation . Chip antibodies used in this study as stated above , HDAC3 ( 2μg per CHIP ) , NcoR1 ( 2μg per CHIP ) , NcoR2 ( 10μL per CHIP ) and H3K9Ac ( 2 . 5μg per CHIP ) . Real-time PCR for CHIP-qPCR was conducted using Roche Lightcycler480 , primers are described in S2 Table . | The human papillomaviruses ( HPV ) are the etiological agents of cervical cancer and the disease progresses through the pre-malignant phases of cervical intraepithelial neoplasia I , II and III ( CINI-III ) , before becoming an invasive carcinoma . Therefore identifying factors , which regulate the transition through the premalignant phases and onto invasive cancer would be of importance clinically , to identify patients at risk of progressing from CIN I to CIN III . We show that expression of E6 and E7 proteins from the high risk HPV16 , causes reduced expression of the IGF binding protein 2 ( IGFBP2 ) and this correlates with progression from CIN I to CIN III . By modulating IGFBP2 levels in epithelial cells , we have demonstrated that reduction of IGFBP2 levels is a driving event in epithelial invasion . We have gone on to show that de-regulation of expression of IGFBP2 is due to histone deacetylation of the promoter , which can be reversed by histone deacetylase inhibitors . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | HPV16 Down-Regulates the Insulin-Like Growth Factor Binding Protein 2 to Promote Epithelial Invasion in Organotypic Cultures |
The Kato-Katz technique is the most widely used diagnostic method in epidemiologic surveys and drug efficacy trials pertaining to intestinal schistosomiasis and soil-transmitted helminthiasis . However , the sensitivity of the technique is low , particularly for the detection of light-intensity helminth infections . Examination of multiple stool samples reduces the diagnostic error; yet , most studies rely on a single Kato-Katz thick smear , thus underestimating infection prevalence . We present a model which estimates the sensitivity of the Kato-Katz technique in Schistosoma mansoni and hookworm , as a function of infection intensity for repeated stool sampling and provide estimates of the age-dependent ‘true’ prevalence . We find that the sensitivity for S . mansoni diagnosis is dominated by missed light infections , which have a low probability to be diagnosed correctly even through repeated sampling . The overall sensitivity strongly depends on the mean infection intensity . In particular at an intensity of 100 eggs per gram of stool ( EPG ) , we estimate a sensitivity of 50% and 80% for one and two samples , respectively . At an infection intensity of 300 EPG , we estimate a sensitivity of 62% for one sample and 90% for two samples . The sensitivity for hookworm diagnosis is dominated by day-to-day variation with typical values for one , two , three , and four samples equal to 50% , 75% , 85% , and 95% , respectively , while it is only weakly dependent on the mean infection intensity in the population . We recommend taking at least two samples and estimate the ‘true’ prevalence of S . mansoni considering the dependence of the sensitivity on the mean infection intensity and the ‘true’ hookworm prevalence by taking into account the sensitivity given in the current study .
Soil-transmitted helminthiasis ( STH ) and schistosomiasis are two of the most prevalent neglected tropical diseases with more than 1 billion and over 250 million people affected worldwide , respectively [1 , 2] . Their collective global burden is 6 million disability-adjusted life years [3] , with school-aged children at the highest risk of associated morbidity . Control efforts have intensified over the past 15 years , with preventive chemotherapy serving as the main pillar [4–6] . Information about the spatial and temporal distribution of STH and schistosomiasis are important to guide interventions . Moreover , it is necessary to know which age groups contribute most to transmission , in terms of helminth egg output , in order to effectively use the available resources . There are two approaches to obtain this information . First , large-scale , standardized studies including all age groups with intense sampling which , however , is difficult to pursue due to the high cost . Second , reanalysis of data from previously published studies . Inference is hampered by the paucity of quality data [7–9] . Individual-level data are usually not reported; instead , only the number of participants tested positive for a specific helminth infection is considered . In addition , diagnosis relies on the Kato-Katz technique [10] , i . e . , counting of helminth eggs in a small amount of stool . This approach , however , has a low , setting-dependent sensitivity , which is governed by variation in day-to-day production of eggs per worm , non-random distribution of eggs within a stool sample , decay of eggs in the sample due to methods and duration of the experimental procedure , transportation , and storage [11–15] . Collecting multiple stool samples over consecutive days increases the accuracy but there are no guidelines on the optimal number of samples [16 , 17] . Consequently , the comparison of studies that employed different sampling efforts , which is necessary for monitoring progress of control programs , is hampered . Statistical modeling can help studying the age-prevalence and its dependence on the diagnostic error but has been restricted by the aforementioned limitations that compromise the quality of the data [7 , 18] . Although the qualitative shape of helminthiasis age-prevalence curves is known , there has been little progress in the application of quantitative transmission models , especially for STH infections [19–22] . Furthermore , the dependency of the intensity of the infection on the diagnostic sensitivity has been largely neglected . The negative binomial distribution has commonly been used to fit helminth egg count data . For example , De Vlas and Gryseels [12] and Levecke et al . [23] proposed models that separate the measurement process from the true underlying infection intensity distribution . However , none of the preceding models are able to infer on the dependence of the sensitivity of the Kato-Katz method for repeated stool sampling on infection intensity . We developed a model for fecal egg-count ( FEC ) data , which quantifies the relation between sampling effort , infection intensity , and diagnostic sensitivity . The model separates the infected from the non-infected individuals and the measurement process from the infection status . Variability due to egg output , experimental conditions , and aggregation within a population are taken into account . We calculate the ‘true’ prevalence and other biological and transmission-related parameters based on the probability of false-negatives . Our model improves estimation of the age-related disease burden and provides inputs for mathematical transmission models .
This study consists of a secondary analysis of published data . Ethics approval , written informed consent procedures , and treatment of infected individuals have been described elsewhere [24–27] . We tested our model performing a secondary analysis of individual level FEC data from three separate studies in medium and high transmission settings in Côte d’Ivoire conducted in 1998 , 2002 , and 2011 , respectively . All data used were from baseline surveys with no previous mass drug administration in the area . The studies took place in Fagnampleu [24 , 25] , Zouatta [26] , and Azaguié [27] . Based on the Kato-Katz assay , hookworm prevalence varied from 11 . 4% to 59 . 0% , and mean or infected population based infection intensity from 280 eggs per gram of stool ( EPG ) to 396 EPG . For S . mansoni , prevalence varied from 35 . 6% to 76 . 3% , and infection intensity from 152 EPG to 307 EPG . Between two and four stool samples were collected and analyzed on consecutive days of a total of 1423 participants . Azaguié and Zouatta were surveys performed in the full age range from 0 to 90 years , while Fagnampleu only included school-aged children . Prevalence of Ascaris lumbricoides and Trichuris trichiura were too low to be analyzed . Summary measures are included in Table 1 , a more detailed description can be found in S1 Appendix , and the individual level data used is included in S1 Table . We utilized a hierarchical Bayesian model to address the objectives given in the introduction . Let Yij be the FEC , i . e . , the number of helminth eggs found in sample j in individual i , ki the number of stool samples from individual i , and xi the age of individual i . We assumed that a population consists of a proportion of infected individuals p , i . e . , people that carry at least one pair of worms , and that of uninfected individuals . Thus p is interpreted as the prevalence . Each infected individual has a characteristic infection intensity λi , measured in units of mean eggs per sample and assumed to be distributed within the population according to a shifted gamma distribution , given by λ i = v i + μ m v i ∼ Gamma ( μ f · α , α ) = α μ f · α Γ ( μ f · α ) v i μ f · α - 1 exp ( - α · v i ) ( 1 ) with a mean number of eggs μf + μm in an infected individual , variance μ f α that corresponds to the aggregation of infection intensities , and hence , the aggregation of worms within the population . The shift parameter μm is the mean number of eggs per sample that can be expected from an individual carrying exactly one female worm and thus the minimal possible infection intensity . Direct inference on the worm load is not possible in this frame as the dependence on mean egg output is non-linear and not well known [28] . The process of taking ki samples from an infected individual i with infection intensity λi is modeled by a negative binomial distribution with mean λi and a variance given by λ i + λ i 2 / r . r reflects the additional variation , due to changes in the day-to-day helminth eggs output , the aggregation of eggs in stool , and the precise experimental procedure but not the within-population variation which is given by α . If a perfectly random distribution of the eggs and perfect measurement is assumed r → ∞ , the measurement process becomes a Poisson process . By including the uninfected , the model is written as Y i ∼ { ( 1 - p ) + p · NB ( 0 , λ i , r ) k i , if I i = 0 p · ∏ j = 1 k i NB ( Y i j , λ i , r ) , if I i = 1 ( 2 ) which corresponds to a zero-inflated negative binomial model with p corresponding to the mixing proportion . NB is the negative binomial distribution and Ii the result of the Kato-Katz test over all samples from an individual . They are defined as follows: Ii={0 , ifmax ( Yi ) =01 , ifmax ( Yi ) ≠0NB ( y , λ , r ) = ( y+r−1y ) ( λλ+r ) y ( rr+λ ) r ( 3 ) False-negatives are included in the model as repeated zero measurements for an infected individual . Thus , the sensitivity depending on the number of repeated measurements becomes si[ ki ]=1−NB ( 0 , λi , r ) ki=1− ( rλi+r ) ki·r ( 4 ) where s is the sensitivity , and k and λ vary for each individual . Low-rank thin-plate splines are used to study the age dependence of p , μf , r , and α . For a detailed derivation of the spline model , see Crainiceanu et al . [29] . The representation of p is logit ( p i ) = β 0 + β 1 x i + ∑ m = 1 M u m ( x i - κ m ) 3 ( 5 ) where Θ1 = ( β0 , β1 , u1 , … , uM ) T is the vector of regression coefficients and κ1 < ⋯ < κM are the fixed knots . The other parameters can be represented analogously using logarithmic or linear spline models . The spline regression makes only a few very general assumptions about the shape of the curve , e . g . , continuity and differentiability , and is therefore able to infer without requiring prior knowledge about the biology of the process , i . e . , the transmission model . The minimum eggs per sample μm is fixed to the average egg output of a worm divided by an average amount of feces per day , multiplied by the weight of a sample . For hookworm , μm is 5 eggs in a sample which corresponds to 120 EPG and for S . mansoni , μm is 0 . 03 eggs which corresponds to 0 . 72 EPG [30 , 31] . We choose the following semi-informative priors for the model: gamma for r with mean 1 and variance 1; normal for log ( α ) with mean 0 and variance 1; gamma for μf with mean 2 and variance 4; normal for β0 , 1 with mean 0 and variance 1; normal for u1 , … , M with mean 0 and variance τ , where τ is distributed as a gamma with mean 2 and variance 4 . The results were not sensitive to the specific shape of the priors . Bayesian inference was performed using Markov chain Monte Carlo ( MCMC ) simulations implemented in Stan [32] . Validity of the model was checked using simulated data . Models with splines on p , μf , r , and α were run to check for age dependence . μf , r , and α showed no significant age dependence and were set as independent of age for the simulations presented in the results section . μm was varied from 1 egg to 6 eggs for hookworm and from 0 . 01 eggs to 0 . 1 eggs for S . mansoni , which also showed no significant influence . The lower limit of 0 . 01 eggs per slide for S . mansoni corresponds to roughly 100 eggs in 500 g of stool . The upper limit of 0 . 1 eggs per slide corresponds to 1000 eggs per 500 g of stool therefore any value larger than 0 . 1 is most likely unrealistic for a single worm pair . The model was run with a total of 25 chains , with 20 , 000 iterations each , of which 2 , 000 where used as warm up and adaption , for each study and for each of the two infections separately . Convergence was achieved , and assessed using Gelman + Rubin diagnostics and visual inspection of the chains [33] .
The three studies are from different hookworm transmission settings with observed prevalence of 11 . 4% and mean infection intensity of an infected individual of 396 EPG for Azaguié , 35 . 4% and 331 EPG for Zouatta , and 59 . 0% and 283 EPG for Fagnampleu . Based on our model , we estimated the ‘true’ hookworm prevalence at 14 . 3% ( 95% BCI 10 . 9–18 . 5% ) , 43 . 7% ( 95% BCI 38 . 6–49 . 2% ) , and 62 . 2% ( 95% BCI 56 . 6–67 . 6% ) , for Azaguié , Zouatta , and Fagnampleu , respectively . The estimated mean infection intensity does not significantly differ from one study to another and mean estimates ranged from 220 EPG to 262 EPG ( see Table 1 ) . Age-prevalence curves in Fig 1 from the three studies show similar features such as a steep increase from birth till an equilibrium is reached at ages of around 20 years for Zouatta , and 45 years for Azaguié . The prevalence stays constant till an age of about 60 years from where the rate of infection declines . For Fagnampleu only the initial steep increase is visible due to the fact that no individuals older than 15 years were included . For S . mansoni , the Azaguié and Zouatta studies show similar transmission levels with an observed prevalence of 35 . 6% and 40 . 8% and observed mean infection intensity of 179 EPG and 152 EPG , respectively . In contrast , the study in Fagnampleu had a prevalence of 76 . 3% and a mean infection intensity of 307 EPG . We estimated a ‘true’ prevalence of 49 . 3% ( 95% BCI 40 . 4–61 . 2% ) and a mean infection intensity of 132 EPG ( 95% BCI 101–167 EPG ) for Azaguié , 59 . 6% ( 95% BCI 50 . 7–69 . 3% ) and 104 EPG ( 95% BCI 84–128 EPG ) for Zouatta , and 83 . 8% ( 95% BCI 78 . 3–89 . 3% ) and 282 EPG ( 249–321 EPG ) for Fagnampleu . The estimated age-prevalence curves displayed in Fig 2 show similar qualitative features . The prevalence increases up to a peak between the ages of 15 and 20 years , and subsequently declines slowly up to an age of 60 years , followed by a stronger decrease . The lower prevalence after the peak is not significant but it appears both in the Azaguié and Zouatta data . For hookworm , the day-to-day variation given by r is consistent across study sites , ranging from 0 . 15 ( 95% BCI 0 . 13–0 . 17 ) to 0 . 25 ( 95% BCI 0 . 15–0 . 37 ) ( see Table 1 ) , indicating strong overdispersion . The aggregation of egg output within the population is also consistent across the studies with α estimates ranging from 0 . 19 ( 95% BCI 0 . 05–0 . 68 ) to 0 . 32 ( 95% BCI 0 . 06–1 . 23 ) . For S . mansoni the day-to-day variation is consistent across studies and significantly different from hookworm with values ranging from 0 . 83 ( 95% BCI 0 . 67–1 . 02 ) to 1 . 10 ( 95% BCI 0 . 80–1 . 46 ) . The aggregation of infections within the population shows no significant differences between studies with α ranging from 0 . 05 ( 95% BCI 0 . 04–0 . 07 ) to 0 . 09 ( 95% BCI 0 . 05–1 . 13 ) , which indicates a significantly higher variance than for hookworm . For hookworm , the estimates of the diagnostic sensitivity of Kato-Katz did not vary between locations . Based on a single Kato-Katz thick smear , sensitivity estimates were in the range of 47% to 57% , for two samples obtained from different days from 72% to 81% , for three samples estimates were within the range of 85% to 90% , and for four samples around 95% . For S . mansoni , data from Azaguié and Zouatta revealed similar sensitivity estimates within the range of 48% to 59% for one Kato-Katz thick smear , 62% to 73% for two samples , and 69% for three samples . Fagnampleu has a higher sensitivity of 70% , 84% , 88% , and 91% for one , two , three , and four samples , respectively ( see Table 1 ) . The sensitivity of the Kato-Katz technique for different infection intensities was calculated using eq 4 and is plotted in Fig 3 . For hookworm the dependence on infection intensity is weak , e . g . , only increasing from 40% to 55% from a very light infection of 120 EPG to a still light infection of 500 EPG . For moderate and heavy infections ( >2000 EPG ) the sensitivity did not significantly improve with infection intensity . However , the sensitivity can be greatly increased by examining several stool samples , e . g . , for an infection intensity of 360 EPG the sensitivity can raise from 50% based on a single sample to 75% for two samples , and 92 . 5% for three samples . The sensitivity was strongly associated with S . mansoni infection intensity . In particular , for very light infections ( <5 EPG ) , it was below 50% even after three samples . For light infections ( <100 EPG ) , it was still heavily dependent on infection intensity . For moderate infections ( 100–399 EPG ) , two samples gave a high sensitivity above 90% . Heavy infections ( >400 EPG ) were reliably detected ( i . e . >99% ) by testing two samples . Fig 4 shows the overall sensitivity in a population with a day-to-day variation of r = 1 . 0 and a population aggregation of α = 0 . 07 as a function of the mean infection intensity in the population . For lower transmission settings with 100 EPG comparable to Zouatta , the sensitivity after four samples is still below 75% . However , sensitivity rose to more than 95% for a setting with a mean infection intensity of over 300 EPG .
The proposed model succeeds in predicting the intensity-dependent sensitivity of the Kato-Katz technique directly from the day-to-day variation in helminth egg output . Hence , the model is able to explain the differences between the sensitivity of hookworm and S . mansoni . The sensitivity of Kato-Katz for hookworm is dominated by a high day-to-day variation . We recommend collecting at least two stool samples over subsequent days combined with the given sensitivity values to estimate ‘true’ prevalence . For S . mansoni infection the sensitivity is largely driven by light infections that are hard to detect by a single Kato-Katz thick smear . We also recommend collecting two samples due to almost perfect sensitivity for moderate and heavy infections and low benefit of additional samples for light infections . We predict that improving the sensitivity for S . mansoni can be achieved more cost effectively by increasing the number of Kato-Katz thick smears from the same stool sample instead of increasing the number of samples taken . Additionally , it is necessary to take into account the infection intensity-dependent sensitivity of Kato-Katz for S . mansoni when comparing data from several studies . Including the infection dependence becomes more important when close to elimination due to the larger changes in sensitivity of Kato-Katz with infection intensity . A further consequence of the results is due to the fact that the guidelines of WHO are defined in terms of observed prevalence . An observed prevalence of e . g . 10% for S . mansoni , which is the lower limit for yearly MDA , is indicative of a ‘true’ prevalence of roughly 14% , 20% , and 29% for 200 EPG , 100 EPG , and 50 EPG , respectively . Hence , the observed prevalence is a measure of both , the ‘true’ prevalence and the infection intensity . We advise the disentanglement of these two components by defining thresholds separately for ‘true’ prevalence and infection intensity . The results also suggest that the current disease burden estimates underestimate the true prevalence . The spline model for age-dependence used in this study can be replaced by appropriate transmission models to determine which age groups should be treated and how frequently that has to happen to increase the intervention effectiveness . The model can be further extended to analyze studies with multiple Kato-Katz thick smears performed per stool sample and thus separate day-to-day from within-sample variation . This would enable us to address the question of how repeated testing of the same sample compares to taking several samples in order to reduce cost and increase compliance . | The World Health Organization ( WHO ) has defined a roadmap for schistosomiasis and soil-transmitted helminthiasis morbidity control and interruption of transmission with targets to be reached by 2025 . Control efforts require reliable estimates of at-risk populations , number of infections , and disease burden estimates in population subgroups in terms of age and location . Intervention guidelines are based on insensitive diagnostic techniques , such as the Kato-Katz method and do not take into account the effect of sampling effort and infection intensity . Our proposed methodology estimates the infection intensity-dependent sensitivity and the ‘true’ age-prevalence of the blood fluke Schistosoma mansoni and hookworm . We also provide recommendations on the number of stool samples required and the methodology to be used to reliably estimate the ‘true’ prevalence of parasitic worm infections . | [
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] | 2017 | Estimating sensitivity of the Kato-Katz technique for the diagnosis of Schistosoma mansoni and hookworm in relation to infection intensity |
Taenia solium is known to cause human cysticercosis while T . saginata does not . Comparative in vitro and in vivo studies on the oncosphere and the postoncospheral ( PO ) forms of T . solium and T . saginata may help to elucidate why cysticercosis can occur from one and not the other . The aim of this study was to use in vitro culture assays and in vivo models to study the differences in the development of the T . solium and T . saginata oncosphere . Furthermore , this study aimed to evaluate the expression of cytokines and metalloproteinases ( MMPs ) in human peripheral blood mononuclear cells ( PBMCs ) , which were stimulated by these oncospheres and PO antigens . T . solium and T . saginata activated oncospheres ( AO ) were cultured in INT-407 and HCT-8 intestinal cells for 180 days . The T . solium began to die while the T . saginata grew for 180 days and developed to cysticerci in INT-407 cells . Rats were inoculated intracranially with AO and PO forms of either T . saginata or T . solium . Rats infected with T . solium AO and PO forms developed neurocysticercosis ( NCC ) , while those infected with the T . saginata did not . Human PMBCs were stimulated with antigens of AO and PO forms of both species , and the production of cytokines and metalloproteinases ( MMPs ) was measured . The T . solium AO antigen stimulated a higher production of IL-4 , IL-5 , IL-13 , IFN-γ , and IL-2 cytokines compared to T . saginata AO . In the PO form , the T . saginata PO antigen increased the production of IL-4 , IL-5 , IL-13 , IFN-γ , IL-1β , IL-6 , IL-10 , TNF-α and IL-12 cytokines compared to T . solium , suggesting that this global immune response stimulated by different forms could permit survival or destruction of the parasite depending of their life-cycle stage . Regarding MMPs , T . solium AO antigen stimulated a higher production of MMP-9 compared to T . saginata AO antigen , which may be responsible for altering the permeability of intestinal cells and facilitating breakdown of the blood-brain barrier during the process of invasion of host tissue .
Taenia solium and T . saginata are two taeniid cestodes that cause the diseases taeniasis and cysticercosis [1] . These are zoonotic diseases , and swine and bovine act as intermediate hosts , causing porcine and bovine cysticercosis , respectively . Humans act as the definitive hosts in both T . solium and T . saginata infection leading to taeniasis . In the case of T . solium , humans can also act as accidental intermediate hosts causing human cysticercosis [2] . However , only T . solium causes human cysticercosis , while T . saginata does not [3] . When cysticercosis involves the central nervous system in humans , it is called neurocysticercosis ( NCC ) . NCC is common throughout Latin America , sub-Saharan Africa , most of Asia , and parts of Oceania . Human NCC is believed to be the leading cause of acquired epilepsy worldwide [4 , 5] . The eggs of T . solium and T . saginata contain a six-hooked larva called the oncosphere [6] . When the eggs hatch , this oncosphere is released into the intestine . Intestinal fluid dissolves the oncospheral membrane , releasing and activating the oncosphere . The T . solium activated oncosphere can then penetrate the intestinal wall . Once in the tissue , usually in the muscle or the central nervous system , the oncosphere can transform into a postoncospheral form , and completely develop into cysticerci—a larval stage that consists of a fluid-filled sac containing an invaginated scolex . When this happens , the parasite produces a variety of molecules , which modulate the host immune response in order to evade parasite destruction [7] . The postoncospheral ( PO ) form is an intermediate stage between an oncosphere and a fully developed cysticercus in tissue [8] . The PO form of T . solium and T . saginata can be obtained in vitro by co-culture of oncospheres with a monolayer of mammalian feeder cells [9 , 10] . T . solium oncosphere and in-vitro generated PO forms can develop into cysticercus in rats causing NCC [9 , 11] . However , little is known regarding the in vitro development of the oncosphere to PO form of T . solium and T . saginata , specifically the immunological events that occur at the host/parasite interface . Also , it is not known if the development of the T . saginata oncosphere to PO form could cause NCC in the rat model as T . solium does . In vitro and in vivo models could lead to a better understanding of host-parasite relationships . Host immune cells such as macrophages , lymphocytes , and polymorphonuclear leukocytes can produce cytokines and metalloproteinases ( MMPs ) in order to prevent the development of the parasite [12 , 13] . Because of this , the parasite has developed mechanisms to evade or modulate the host immune response . Comparative studies on the oncosphere and the PO form of T . solium and T . saginata are limited . This study focused on the in vitro development of the oncosphere to the PO form for T . solium and T . saginata . These in vitro-developed larvae were then tested for infectivity in rats . Later , we obtained antigens from the T . solium and T . saginata oncosphere and PO forms to stimulate the production of cytokines and MMPs in healthy human peripheral blood mononuclear cells ( PBMCs ) .
HCT-8 and INT-407 cells , obtained from the American Tissue Culture Collection ( ATCC , Manassas , VA ) , were used to obtain T . solium and T . saginata PO forms . Cells were incubated at 37°C in 5% CO2 and grown in a specific medium as recommended by ATCC ( EMEM media for INT-407 , and RPMI for HCT-8; all medium was supplemented with 10% fetal bovine serum ) . The medium was changed every two days . Once cell confluency was obtained , cells were harvested using trypsin-EDTA ( Sigma Chemical Co ) . Cells were placed into 24-well plates ( 1x105 cells per well ) for maturation assay . The assays described below were performed when cells formed a monolayer . Tapeworms were collected after medical treatment of newly diagnosed patients , as described by Jeri et al [14] . Hatching of eggs and oncosphere activation were performed , as described by Verastegui et al [15] . The eggs were obtained from gravid proglottids of adult tapeworms by gentle homogenization in a 2 . 5% potassium dichromate solution ( Sigma , St . Louis , Missouri ) . Eggs were then washed three times in distilled water with centrifugation steps to collect the eggs between washes in 2500g for 5 minutes . The eggs were hatched , and the oncospheres were released using a solution of 0 . 75% sodium hypochlorite in water for 10 minutes ( Mallinckrodt Baker , Inc , Phillipsburg , NJ ) . Oncospheres were then washed three times in RPMI medium ( Sigma , St . Louis , Missouri ) , and activated by incubation at 37°C for 45 minutes ( in the case of T . solium ) or 90 minutes ( in the case of T . saginata ) with artificial intestinal fluid ( 1 g pancreatin ( Sigma Chemical Co . , St . Louis , MO ) , 200 mg Na2CO3 , and 1 ml of fresh porcine bile ( for T . solium ) , or 1 ml of fresh bovine bile ( for T . saginata ) , with enough RPMI 1640 medium ( pH 8 . 04 ) to make 100mL ) . After activation , the oncospheres were washed three times with RPMI medium and counted using a Neubauer chamber . Parallel in vitro maturation assays with INT-407 and HCT-8 monolayer cells , using T . solium and T . saginata activated oncospheres , were conducted in order to compare the morphological characteristics during development of each species using the methodology reported by Chile et al [9] . Ten thousand activated oncospheres were cultured in confluent INT-407 and HCT-8 monolayer cells for two weeks . During that time , the medium was changed every three days . At day 15 of culture , the postoncospheral forms were collected and rinsed twice with fresh medium , then transferred to another well containing a confluent of monolayer cells . This process was repeated every three days for up to six months to allow the postoncospheral forms to continue to develop . Cultures were inspected daily using an inverted microscope ( Leitz labovert FS ) . Parasites were collected at 15 , 30 , 60 , 120 , and 180 days of incubation . To determine if T . saginata AO and PO forms can develop into viable cysts in vivo , 15-day old Holtzman rats , purchased from Universidad Peruana Cayetano Heredia , Lima , Peru , were infected intracranially ( in the bregma ) with oncospheres and 15-day old PO forms from either species following the methodology reported by Verastegui et al . , 2015 [11] . Rats were anaesthetized with ketamine ( 100 mg/kg body weight ) and xylazine ( 5 mg/kg body weight ) before infection . Six rats were inoculated with 180 T . solium AO in 100 μL of saline solution; seven rats were inoculated with 180 T . saginata AO in 100 μL of saline solution . The negative control was 2 rats inoculated with saline solution . Eight rats were inoculated with ten T . solium 15-day PO forms in 100 μL of saline solution , eight rats were inoculated with ten T . saginata 15-day PO forms in 100 μL of saline solution , and five rats were inoculated with 100 μL of saline solution as a control . A 24-gauge syringe needle was used . After four months , necropsy was performed . Rats were anaesthetized with ketamine ( 100 mg/kg body weight ) and xylazine ( 5 mg/kg body weight ) . Anaesthetized rats were perfused with 200 ml of PBS and then with 100 ml of 4% paraformaldehyde in PBS . Brains were carefully removed , post-fixed for 24 hours at 4°C with 4% paraformaldehyde in PBS , and stored in 70% ethanol . Brains were observed macroscopically to identify extraparenchymal cysticerci . Five millimeter coronal brain sections were cut until the intraparenchymal cysticerci were observed . Antigens were obtained from AO and 30 day-PO forms of both parasites . AO and PO were obtained as described above . Parasites were rinsed three times with PBS buffer , sonicated , and centrifuged at 10 , 000g for 15 min at 4°C . The supernatant ( total soluble antigens ) was separated , and proteins were quantified using Bradford Protein Assay ( Bio-Rad ) and stored at -70°C until ready for use . Healthy volunteers ( n = 13 ) with negative serology for NCC were invited to participate in this study . After the volunteers signed an informed consent form , 10 mL of venous blood was collected from each non-infected donor . PBMCs were collected from 10 ml EDTA blood . The blood samples were centrifuged at 400g on a Ficoll-Hypaque gradient ( Ficoll–Paque TM PLUS , GE Healthcare ) for 10 minutes at room temperature for the separation of mononuclear cells . Cells were again suspended in RPMI medium plus 5% of inactivated human serum . Cell viability was evaluated with trypan blue and counted in a Neubauer chamber . Each well contained 2x105 group of cells was cultured in a 96-well plate at 37°C with 5% CO2 for 48 hours in an RPMI medium containing 5% of inactivated human serum . Cells were stimulated with 5 μg/ml of phytohemaglutinin ( PHA ) as the positive control , 20 μg/ml of AO antigens ( both species , separately ) , and 20 μg/ml of antigens from PO forms at 30 days of maturation ( both species , separately ) . At the end of the incubation period , PBMCs were harvested and centrifuged for 10 min at 400g , and supernatants were collected and stored at −70°C until tested for cytokine and metalloproteinase content by multiplex analysis . MILLIPLEX MAP kit High Sensitivity Human Cytokine Magnetic Bead Panel ( Millipore ) was used to measure cytokines ( IFN-γ , IL-1β , IL-2 , IL-4 , IL-5 , IL-6 , IL-10 , IL-12p70 , IL-13 and TNF-α ) , and Fluorokine MAP ( R&D system , Minneapolis USA ) was used to measure MMP ( MMP-2 and MMP-9 ) in the supernatant of stimulated PBMCs following the manufacturer’s instructions . The cytokines and MMP were detected by the Bio-plex 200 system ( Bio-Rad Laboratories , Hercules , CA ) using Luminex xMAP technology . Experiments were done according to the Guide for care and Use of Laboratory Animals of the National Institutes of Health . The protocol was approved by the Committee on the Ethics Animals of the Universidad Peruana Cayetano Heredia , Lima , Peru ( Permit Numbers: 61242 ) . For cytokines and MMP assays , the median fluorescent intensities of the beads with the cytokines or MMP now bound were converted to concentrations ( pg/ml ) , using a five-parameter logistic model using the Bio-Plex Manager™ 6 . 0 software ( Bio-Rad Laboratories , Hercules , CA ) . Each MMP´s or cytokine’s concentration was normalized by subtracting the data of medium alone ( control ) . The Mann Whitney test was used to compare two unpaired groups , and ANOVA followed by Tukey’s post-test was used for three or more group using the Prism V 6 . 0 statistical program ( GraphPad ) . A P-value of <0 . 05 was considered statistically significant .
T . saginata and T . solium developed from the oncosphere to the PO form in both cell lines , INT-407 and HCT-8 cells ( Table 1 ) . In HCT-8 cells , both species of PO forms developed until 60 days of culture , and then began to die . However , after 60 days in INT-407 cells , the T . saginata PO forms continued to develop to cysticerci , while the T . solium PO forms began to die . At 180 days of culture 0 . 25% of T . saginata oncospheres developed to cysticerci . The morphological characteristics of the T . saginata and T . solium PO forms obtained from culture in INT-407 monolayer cells were compared . At 15 days of incubation , both species were morphologically similar ( Fig 1A ) , ranging between 60 to 250 μm in size . Both had an oval form without hooks and shared characteristic movement . For both species at 30 days of incubation , PO forms increased in size ( between 200 to 1000 μm ) , maintained their oval form , and formed a protuberance at one end of the body ( Fig 1B ) . At day 60 of culture , the T . solium PO form had a spherical shape , similar to a cyst without a scolex , and cells accumulated in the protuberance of one end of the body ( Fig 1C ) . The T . saginata PO form had similar characteristics but also showed a neck-like prolongation at one end of the body ( Fig 1C ) , reaching up to 3000 μm . Additionally , at day 60 , T . solium PO forms began to die while T . saginata PO forms continued growing until 180 days of culture . At day 120 of culture , T . saginata developed into a cysticercus with a spherical form and an evaginated pre-scolex containing four characteristic suckers and measuring 4 mm in diameter ( Fig 2A ) . At day 180 of culture , T . saginata cysticerci increased in size compared to 120 day , reaching up to 6 mm and also displayed a well-defined scolex and suckers ( Fig 2B ) . We infected rats with T . saginata oncosphere and PO forms in order to evaluate if T . saginata can develop into cysticercus in vivo , as T . solium does . Table 2 shows rats that were infected with T . solium oncosphere or 15-days PO form developed NCC , while rats that were infected with T . saginata oncosphere or 15-days PO form did not develop NCC . Given that T . saginata does not develop to the cysticerci form in humans , we investigated the differences in host immune response to infection by T . solium and T . saginata . To determine this , we evaluated the production of cytokines during stimulation with AO and 30-day PO antigens of both species . When compared by stages ( AO and PO form ) species , both T . solium and T . saginata PO forms stimulated a higher production of the majority of cytokines evaluated than the AO forms ( S1 Fig ) . Nevertheless , when compared by species stages ( AO and PO form ) , we observed a higher production of IL-4 , IL-5 , IL-13 , IFN-γ and IL-2 cytokines stimulated by the T . solium AO form ( Fig 3A and 3B ) compared to the T . saginata AO; but in PO form , the T . saginata 30-day PO form stimulated a higher production of a variety of cytokines , including , IL-4 , IL-5 , IL-13 , IFN-γ , IL-1β , IL-6 , IL-10 , TNF-α and IL-12 compared to the T . solium 30-day PO form ( Fig 4A , 4B and 4C ) . To evaluate whether MMPs are involved in the host immune response against the parasites , we stimulated PBMCs with T . solium and T . saginata forms . The T . solium AO stimulated higher MMP-9 production compared to the T . saginata AO , while in PO form there was no significant difference between both species ( Fig 5 ) . There was no significant difference in the production of MMP-2 by PBMCs stimulated with different antigens of both parasites ( S2 Fig ) .
The present study demonstrates that T . saginata activated oncospheres can develop into cysticerci in vitro in a human cell line , while T . solium AO do not . In contrast , in the rat model , the T . saginata oncosphere and PO forms did not develop to cysticerci in vivo as T . solium does . We suspect the differences in development between the two species in the in vitro model are due in large part to the environment of the growth media and the specific cell line , whereas in vivo it is the host immune response that plays a predominant role in regulating parasite development and survival . For instance , Heath and Smyth noted that serum used for in vitro culture contains factors unique to each host-parasite system , which can stimulate development of the parasite [16] . In our study , both species were cultured in media containing fetal bovine serum ( FBS ) . FBS is known to contain large amounts of α- and β-globulin , proteins which have been shown to stimulate the development of the T . saginata oncosphere but not the development of other parasites like T . taeniaformis [17] . Additionally , the cell line used for culture likely plays an important role as T . saginata is able to develop to a cysticercus in vitro using cell line INT-407 , while T . solium does not . On the other hand , T . saginata and T . solium do not develop into cysticercus using the HCT-8 cell line . INT-407 cells originate from the duodenum while the HCT-8 cell line originates from the colon . Furthermore , it is known that INT-407 cells are contaminated with HeLa cells that express different surface molecules that could promote the development of T . saginata . To our knowledge , this is the first study to achieve in vitro development of T . saginata PO forms to cysticerci . In the in vivo , T . solium oncosphere and PO forms developed to cysticerci in brain while T . saginata forms did not . This finding is likely due to T . solium’s ability to evade the host immune response , either through binding of host plasma proteins or by synthesizing surface proteins that are antigenically similar to those of the rat [18] . Previous work has demonstrated that T . taeniaeformis oncospheres are able to initiate PO development after acquiring a component of rat serum on their surface , a component that presumably protects the oncosphere in the newly invaded host from recognition as a foreign antigen [17] . In the same study , T . saginata failed to bind this component and was subsequently attacked by the host immune system . A similar observation has been noted in pigs after infection with T . saginata eggs [19] . Another explanation may be genetic factors that play a role in the rat’s resistance to infection from T . saginata [20] . Although the rat is not a natural host of T . solium , we observed in previous studies that the cysticerci that develop in the rat brain are morphologically equal to those that develop in humans , as is the observed inflammatory response and subsequent pathology [11 , 21] . Therefore , we believe the rat , like the human , has molecules that prevent the development of T . saginata . The cytokines response plays an important role in survival of the oncosphere at the time of initial infection . We suspect T . solium stimulates a different cytokines profile than T . saginata allowing the T . solium oncosphere to survive , while T . saginata is destroyed by the host . To probe this hypothesis , we stimulated cytokine and MMP production in healthy human PBMCs using antigens of oncosphere and PO forms from both species . We observed that T . solium oncospheres stimulate a higher production of IL-4 , IL-5 , IL-13 , IFN- γ and IL-2 compared to T . saginata oncospheres . The IL-4 , IL-5 , IL-13 , are cytokines typically associated with a T helper 2 ( TH2 ) response , the predominant protective immune response against helminthic infections [22] . On the other hand , IFN- γ and IL-2 are associated with a T helper 1 ( TH1 ) response , which is thought to be responsible for destruction of the parasite [23] . The TH2 response is important in protecting against extracellular helminthic parasites through suppression of the TH1 response , neutralization of toxins , and defense of the host against damage [22 , 24] . Inflammatory reactions are dependent on a delicate balance between TH1 and TH2 type responses . In the case of the T . solium oncosphere , it appears a mix immune reaction of TH1/ TH2 type response that aids survival of the parasite . Perhaps in doing so , T . solium oncospheres are able to migrate from the vasculature to the brain , where they develop into the PO form . Similar mixed TH1/TH2 phenotypes have been observed in patients with NCC [7 , 13] whereas a predominantly TH2-type response is associated with asymptomatic disease [25 , 26] . In both species , the 30-day PO form generated an overall greater inflammatory response than the AO form . Levels cytokines increased when exposed to equal concentrations of PO vs AO antigen , suggesting that antigen composition changes as the parasite is maturing [9] . However , the T . saginata 30-day PO form stimulated a profile of pro-inflammatory cytokines ( IL-1β , IL-6 , IL-12 , TNF-α ) , plus a mix of TH1 and TH2 related cytokines ( IL-4 , IL-5 , IL-13 and IFN-γ ) that was stronger than the response produced by the T . solium 30-day PO form . IL-6 , a pro-inflammatory cytokine , plays a role in the death of microorganisms by stimulating the behavior of neutrophils [27]; and TNF-α is strongly expressed at the sites of parasite and cell destruction [28] . Together with the overproduction of IL-4 , IL-5 , and IL-13 , these cytokines may mount a response that could destroy the T . saginata PO form and prevents development of the cyst in vivo . Although these cytokines do have the suggested properties mentioned , they also could be involved in complex networks with mixed effects with respect to inflammation , for example IL-6 has been shown to exhibit anti-inflammatory properties [29] . T . solium oncospheres stimulated increased MMP-9 production in PBMCs compared to production by T . saginata oncospheres . MMP-9 is an endopeptidase produced by neutrophils , macrophages , monocytes , and intestinal epithelial cells [30 , 31] . It can degrade components of the blood brain barrier ( BBB ) as well as the extracellular matrix , increasing intestinal epithelial permeability [30 , 32 , 33] . MMP-9 has been associated with the breakdown of the BBB in a murine model of NCC [34 , 35] and is present in high concentrations in sera of symptomatic NCC patients [36] . We hypothesize that T . solium stimulates the production of MMP-9 as a means of enhancing epithelial permeability in order to pass restrictive biological barriers like the intestine and the BBB during early stages of infection . In conclusion , we have found novel evidence to suggest that T . saginata PO forms are capable of developing into cysticerci in the human cell line INT-407 while not in HCT-8 cells . In vivo , T . saginata fails to develop into cysticerci in the rat brain , suggesting there are factors in the host immune system ( that are not present in the in vitro culture ) that destroy the parasite . In the oncosphere stage , T . solium stimulated a strong mix of TH1 and TH2-related cytokines and MMP-9 production in healthy PBMCs , which may mediate the inflammatory response and promote oncosphere survival in the vasculature , aiding the entrance of oncospheres into the brain . In the PO form , T . saginata stimulated a strong pro-inflammatory and mix of TH1/TH2-related cytokines , responses that could be causing destruction of the parasite in the tissue . These differences between both species of Taenia found in vitro and in vivo could explain why the larval stage of T . saginata does not develop in the human host , while T . solium does , despite having similar life cycles . | Taenia solium and Taenia saginata are two parasites that cause the tissue infection cysticercosis in their intermediate hosts , pigs and cows , respectively . One major difference between them is that T . solium can also cause neurocysticercosis in the human brain , while T . saginata cannot . Neurocysticercosis is thought to be the major cause of adult-onset seizures in developing countries . It is not well understood why only T . solium can survive in human tissue; however , the host inflammatory response likely plays an important role . The authors found that human immune cells stimulated with T . solium in the early stages of the parasite life cycle produced a more robust cytokine response than T . saginata . However , in the mature stage , which occurs once T . solium reaches the brain , T . solium antigens stimulated a lower inflammatory response compared to T . saginata , suggesting the parasite is able to manipulate the host immune response in some way to evade destruction . These findings may support the differences in growth observed by the authors when rat brains were inoculated with either parasite species . This study provides new insights into the different ways T . solium and T . saginata activate the immune response to survive and develop within the host . | [
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] | 2019 | In vitro model of postoncosphere development, and in vivo infection abilities of Taenia solium and Taenia saginata |
Constraint-based metabolic modeling methods such as Flux Balance Analysis ( FBA ) are routinely used to predict the effects of genetic changes and to design strains with desired metabolic properties . The major bottleneck in modeling genome-scale metabolic systems is the establishment and manual curation of reliable stoichiometric models . Initial reconstructions are typically refined through comparisons to experimental growth data from gene knockouts or nutrient environments . Existing methods iteratively correct one erroneous model prediction at a time , resulting in accumulating network changes that are often not globally optimal . We present GlobalFit , a bi-level optimization method that finds a globally optimal network , by identifying the minimal set of network changes needed to correctly predict all experimentally observed growth and non-growth cases simultaneously . When applied to the genome-scale metabolic model of Mycoplasma genitalium , GlobalFit decreases unexplained gene knockout phenotypes by 79% , increasing accuracy from 87 . 3% ( according to the current state-of-the-art ) to 97 . 3% . While currently available computers do not allow a global optimization of the much larger metabolic network of E . coli , the main strengths of GlobalFit are already played out when considering only one growth and one non-growth case simultaneously . Application of a corresponding strategy halves the number of unexplained cases for the already highly curated E . coli model , increasing accuracy from 90 . 8% to 95 . 4% .
Metabolism is the best understood large cellular system . Genome-scale metabolic models that largely rely on constraints for mass balance ( i . e . , all internal metabolites that are produced must also be consumed ) are routinely applied to predict a wide range of metabolic phenomena [1] . The most widely-used of these constraint-based methods , Flux Balance Analysis ( FBA ) , has been successfully applied to predict a range of biological phenomena such as gene knockout effects [1] and the evolutionary adaptation of microbial strains [2–4] , and has been employed to predict drug targets [5] and to design microbial strains for bioengineering [6] . Network models are reconstructed by supplementing genomic annotation with information from biochemical characterizations and the organism-specific literature [7] . The resulting draft reconstructions often contain gaps: the modeled organism or its gene knockout strain can grow in vivo , while the model is unable to produce biomass in silico in the same metabolic environment ( false-negative predictions , FNp ) . Gap filling methods have been introduced to resolve individual FNp through a minimal number of network changes , making irreversible reactions reversible or adding reactions from a database [8–11] . A second type of inconsistencies is the erroneous prediction of growth where the experiment shows no growth ( false-positive predictions , FPp ) . Such cases can be rectified by deleting reactions , making reversible reactions unidirectional , or adding metabolites to the biomass ( all reactions necessary for the production of a given metabolite become essential once this metabolite is added to the biomass ) . GrowMatch [12] , the current state-of-the-art in automatic network refinement , uses bi-level optimization to identify reactions that must be deleted or modified for each FPp . GrowMatch also allows to add to the biomass products and/or substrates of reactions that are experimentally essential but are blocked in the model [12] . All currently available methods for network refinement based on growth data are greedy algorithms , solving one inconsistency between model and experiment at a time [8–15] . While each individual set of network changes is minimal , the union of these sets can become larger than a minimal set of changes that solves all inconsistencies simultaneously . Reactions considered essential or model changes introduced early may make the reconciliation of FNp or FPp considered later impossible ( for an example , see our application to Mycoplasma genitalium below ) . Furthermore , experimental errors that happen to be consistent with the initial model can severely bias the results . Moreover , previous methods only alter the biomass equation independently of other network modifications [12 , 16] and may miss solutions that combine biomass and network changes .
We present GlobalFit , a novel bi-level optimization method capable of comparing flux-balance analysis ( FBA ) [17] model predictions to measured growth across all tested environments and gene knockouts ( or subsets thereof ) simultaneously . Allowed model changes are ( i ) removals or ( ii ) reversibility changes of existing reactions; ( iii ) additions of reactions to the model from a database of potential reactions; ( iv ) removals of metabolites from the biomass; and ( v ) additions of metabolites to the biomass . GlobalFit does not change gene-protein-reaction associations ( GPRs ) , and thus isoenzymes should be identified and included in the model as a preprocessing step . The algorithm is first formulated as a bi-level linear problem , where each condition is represented by separate metabolites and fluxes ( see the detailed method description in Methods ) . To ensure in silico growth for conditions with experimentally demonstrated growth , the biomass production for these conditions must be greater than a predefined threshold . For non-growth phenotypes , the inner optimization problem maximizes the biomass production to check whether it stays below a non-growth threshold . The outer optimization problem jointly minimizes the number of model changes and the number of experiments that are incorrectly predicted by the final model . The penalties for individual network changes can be set independently . This allows , for example , to prefer reversibility changes over reaction additions , to preferentially remove reactions not associated with a gene , or to preferentially include additional reactions from metabolic network reconstructions of close relatives ( see some suggestions for setting these penalties in the S1 Table ) . The bi-level problem can be re-formulated as a single-level optimization problem [18]; a corresponding implementation of GlobalFit , integrated with the sybil toolbox for constraint-based analyses [19] , is freely available from CRAN ( http://cran . r-project . org/web/packages/GlobalFit/ ) . While GlobalFit is designed to find globally optimal network modifications by considering all experimental data simultaneously , the corresponding MILP problem rapidly becomes prohibitively large when considering high-throughput gene knockout data . For example , simultaneously considering all possible 1366 E . coli knockouts [20] with 4000 allowed network modifications would result in a matrix with 13 million columns by 37 million rows , a problem size not addressable with current computing infrastructures . However , when searching for model changes that rectify a FPp , trivial but unhelpful solutions such as the deletion of essential reactions are already avoided by simultaneously requiring growth in one or more specified true positive cases . When searching for model changes that rectify a FNp , overly generous changes ( such as the removal of metabolites from the biomass ) are avoided by simultaneously requiring non-growth in one or more specified true negative cases . Thus , while a globally optimal solution is only guaranteed when simultaneously considering all experimental growth data , a good approximation may be found by solving subsets of inconsistencies . We explore this “subset strategy” below in our application to the E . coli genome-scale model . We suggest contrasting each individual FPp with a wild-type growth case ( or , if growth was assayed on different media , with a small set of wild-type growth cases ) . FNp may first be solved alone . However , if a suggested solution for a FNp or a FPp converts other previously correct predictions to false predictions ( TPp to FNp or TNp to FPp ) , the originally considered case should be solved again , this time contrasting it with the complete set of these conflicting cases . This last step must be repeated until no more additional false predictions occur ( or until no solution is found ) . The runtime of MILP solvers depends crucially on the number of binary variables . Importantly , this number depends only on the number of allowed changes ( plus a single binary variable for the inclusion/exclusion of each growth/non-growth case ) . Thus , a MILP strategy that considers n possible model changes for a single growth/non-growth case solves a problem with n binary variables . In comparison , the number of binary variables in a GlobalFit run that considers n possible model changes and contrasts m growth and non-growth cases is n+m . The number of binary variables can be further reduced by a set of preprocessing steps ( Methods ) . When reconciling a metabolic network with experimental data , the most parsimonious network modifications are not always those that best describe the true metabolic system . GlobalFit can also provide a specified number of alternative optimal or sub-optimal solutions ( using the integer cut method ) . Thus , users can choose the solution ( s ) that best agree with available evidence , or design additional experiments that distinguish between competing network modifications . In cases where all suggested alternatives appear excessive or unrealistic , users may also consider modifying individual GPR rules . The runtime for n alternative solutions is approximately n times the runtime for a single optimum . In the test cases reported below , we only examined a small range of alternative solutions and did not consider manual modifications . We first applied GlobalFit to the genome-scale metabolic network of Mycoplasma genitalium [21] , using the same gene knockout essentiality data [22] as the initial reconstruction with GrowMatch ( reported by [21] to have a global accuracy of 87 . 3% , corresponding to a Matthew’s correlation coefficient , a more balanced measure of classification quality [23] , of MCC = 0 . 56; Table 1 ) . The growth medium used for the knockout experiments was chemically undefined [22] . When applying GlobalFit , we thus allowed the uptake of all nutrients for which transport reactions are included in the model . All other FBA parameters were set to the values used in [21] . The initial network obtained from [21] was not able to produce biomass; to rectify this problem , we had to convert three irreversible reactions ( ZN2t4 , INSK , LYSt3 ) to reversible reactions . With these modifications , the original model [21] has an accuracy of 85% and a Matthews’ correlation coefficient MCC = 0 . 44 . False predictions mainly occurred in the form of FPp , i . e . , by incorrectly establishing growth in silico where a lethal phenotype was observed in vivo ( Table 1 ) . To construct a database of potential additional reactions , we started from all reactions contained in metabolic networks provided by the BiGG database [24] . We removed globally blocked reactions , i . e . , those reactions of the database that were not able to carry any flux in a supernetwork containing all reactions . Reversible reactions were represented as two independent irreversible reactions , corresponding to forward and backward directions . The database is provided as S2 Database of the supplementary material . In our first analysis , we used a very restrictive , conservative set of potential network changes: ( i ) addition of reactions from other network reconstructions that are catalyzed by enzymes with significant sequence similarity to the M . genitalium genome ( BLAST e-value <10−13 ) ; ( ii ) conversion of irreversible to reversible reactions for reactions that are at least classified as reversible with uncertainty in the E . coli model [25]; ( iii ) removal of reactions ( separately for individual reaction directions for reversible reactions ) ; ( iv ) removal of biomass components; and ( v ) addition of biomass components that occur in the biomass of other network reconstructions [16 , 20 , 24] . In this application , we assigned the same penalty ( 1 . 0 ) for all changes . However , as the growth medium used in the knockout experiments was undefined , we assigned a lower penalty ( 0 . 1 ) for the removal of exchange reactions . Thus , removal of a metabolite from the representation of the undefined medium ( corresponding to the removal of an exchange reaction ) was preferred to the removal of the corresponding transporter . To test the applicability of GlobalFit’s subset strategy to larger models , we next applied it to the most recent genome-scale metabolic reconstruction for E . coli , iJO1366 [20] . Again , we employed the same gene knockout essentiality data [30 , 31] as used in the initial reconstruction . For all FBA simulations , we used the same parameters as described in [20] . The maximal influx of all nutrients in the defined growth media was set to 10 mmol gDW-1h-1 . The lower bound of the non-growth associated maintenance reaction ( ATPM ) was set to 3 . 15 mmol gDW-1h-1 . Gene essentiality was then calculated by FBA , considering any flux larger than 5% of the optimal biomass core reaction as growth . For the published iJO1366 model , we obtained the same accuracies as reported originally [20]: a combined global accuracy of 90 . 8% calculated across knockout experiments on glucose and on glycerol media , corresponding to a Matthew’s correlation coefficient MCC = 0 . 67 ( Table 3 ) . In the application of GlobalFit to the iJO1366 model , we only allowed conservative network modifications ( as defined for the M . genitalium model ) . However , as the growth medium used in the E . coli experiments was chemically defined , we did not allow the removal of exchange reactions . We constructed a database of potential new reactions as for M . genitalium ( S2 Database ) . The knockout data for E . coli includes growth data on two different media that contained either glucose or glycerol as carbon sources [30 , 31] . Accordingly , we solved all FPp against two wild-type growth cases , one on glucose and one on glycerol . While this increases the number of continuous variables compared to using only a single wild-type growth case , the number of binary variables is still the same as in algorithms that only consider a single non-growth case at a time [12] ( note that we don’t allow the exclusion of any growth/non-growth case in this application ) . We tested if the order in which false growth/non-growth predictions are considered in GlobalFit’s subset strategy affects the final result; this was not the case . By applying the network modifications suggested by GlobalFit , we could strongly increase the quality of predictions for growth on both glycerol and glucose ( Table 3 ) ; for the experiments on glucose and on glycerol combined , accuracy increased from 90 . 8% to 95 . 4% , while Matthew’s correlation coefficient increased from 0 . 67 to 0 . 84 . The detailed model changes are outlined below .
In this work , we describe and implement a novel algorithm to automatically modify metabolic network models based on growth/non-growth data . The algorithm can utilize data from different growth environments and/or different gene knockouts . In contrast to previous approaches , the “global” mode of GlobalFit does not reconcile the network model with inconsistent experiments iteratively , but finds a globally minimal set of network changes that resolves all inconsistencies simultaneously ( in so far as the inconsistencies are resolvable with the allowed model modifications ) . To make GlobalFit applicable to large metabolic network reconstructions , we also explored a subset strategy , where individual false predictions are solved simultaneously with small subsets of growth/non-growth cases . We demonstrate the utility of these approaches through applications to the previously published network models of M . genitalium [21] ( optimizing model predictions for gene knockout data from Ref . [22] ) and E . coli [20] ( utilizing gene knockout data from Ref . [30 , 31] ) . Allowing only highly conservative network changes ( e . g . , only adding reactions catalyzed by enzymes that are homologous to genes of the species studied ) , we were able to halve the number of false growth predictions in each case . Overall , GlobalFit improved the accuracy of growth/non-growth predictions for M . genitalium from 87 . 3% to 93 . 6% ( MCC from 0 . 56 to 0 . 68 ) and for E . coli from 90 . 8% to 95 . 4% ( MCC from 0 . 67 to 0 . 84 ) . If we allow a much wider range of possible network modifications—which is routinely done in alternative approaches [12 , 21]–even higher accuracies can be achieved . Importantly , GlobalFit can enumerate alternative optimal or sub-optimal solutions , such that expert knowledge or additional experiments can help select the biologically most realistic modifications . For some inconsistencies , we found solutions that improved accuracy on one medium while decreasing accuracy on the other . For example , adding selenium to the biomass reaction of E . coli would resolve three FPp on glycerol , while converting four TPp to FNp on glucose . Thus , the accuracy achievable for one growth medium could be further improved by sacrificing the accuracy for the other medium , albeit at a likely loss of biological correctness . This observation emphasizes the utility of combining gene knockout data across different nutritional environments to avoid problems of overfitting . In other cases , several genes whose products act together in a protein complex had contradictory experimental results: in the same medium , some were found to be essential , while the rest was declared non-essential . Such contradictions may be caused either by experimental errors , by erroneous assignment of genes to reactions ( incorrect GPRs ) , or by a residual function of the enzyme complex even with some of its components missing . GlobalFit may suggest a solution in this case , but this will simultaneously distort one or more true predictions . For example , the FPp for the E . coli gene b3560 ( the α-subunit of glycine tRNA synthetase ) could be resolved by adding the charged and uncharged glycine tRNA to the biomass reaction as substrate and product , respectively . This modification would at the same time transform the TPp of b3559 ( the β-subunit ) to a FNp , and would thus not improve accuracy . In the applications of GlobalFit , we adopted the in silico growth cutoffs used in the original model publications , i . e . , one third of the mean growth rate for M . genitalium [21] and 5% of the optimal biomass core reaction for E . coli [20] . A more general way to resolve FPp would be to treat the cutoff that distinguishes in silico growth from non-growth as an additional variable in the optimizations . For example , the knockout of E . coli ATPS4rpp reduced the biomass yield in glycerol below 10% of the wild-type yield . Such a substantial reduction in growth rate may explain why 6 out of 8 knockouts for the genes involved in the corresponding enzyme complex were labeled as essential in the experiment; however , following [20] in considering 5% biomass production as growth , we regarded these knockouts as FPp in this study . An adjustable growth threshold might have rectified these FPp cases without any model changes . It is not clear a priori which in silico cutoff corresponds best to a given set of experimental data . Thus identifying the cutoff value that minimizes the necessary model changes seems most appropriate . In this paper , we have explored the application of GlobalFit to the improvement of existing metabolic network reconstructions and showed that it can substantially reduce the number of false growth predictions even when restricted to conservative network changes . It is conceivable that GlobalFit can also be employed for other tasks related to metabolic model refinement . One possible such application is the initial reconstruction of a metabolic network model starting from a computer-generated template that is based on genome annotation ( such as provided , e . g . , by the SEED algorithm [33] ) . GlobalFit might also be used to remove thermodynamically impossible energy-creating cycles , which sometimes plague initial network reconstructions . While we only score growth and non-growth , GlobalFit could also be applied using yield data by choosing appropriate thresholds . Finally , we envisage future usage of GlobaFit for strain optimization in metabolic engineering applications that combine gene knockouts [34] with gene additions .
GlobalFit compares flux-balance analysis ( FBA ) [17] model predictions to measured growth across all tested environments and gene knockouts simultaneously . Allowed model changes are ( i ) removals or ( ii ) reversibility changes of existing reactions; ( iii ) additions of reactions to the model from a database of potential reactions; ( iv ) removals of metabolites from the biomass; and ( v ) additions of metabolites to the biomass . We thus solve the following bi-level problem: min→δ ( ∑y∈M ( δyRF+δyRB ) ×wyR+∑x∈IδxI×wxI+∑z∈Dδzadd×wzadd+∑j∈ASδjAS×wjAS+∑k∈APδkAP×wkAP+∑l∈BSδlRS×wlRS+∑m∈APδmRP×wmRP+∑g∈GδgG×wgG+∑h∈NδhN×whN ) ( 1 ) subject to: ∀g∈GS×vg=0 ( 2 ) ∀h∈GS×vh=0 ( 3 ) ∀y∈M , g∈G∪N vymin× ( 1−δyRB ) ≤ vyg≤ vymax× ( 1−δyRF ) ( 4 ) ∀x∈I , g∈G∪N−1000×δxI≤vxg ( 5 ) ∀z∈D , g∈G∪N 0≤ vzg≤1000×δzadd ( 6 ) ∀y∈M , g∈G∪N∑l∈BS ( 1−δlRS ) ×clRS+∑j∈ASδjAS×cjAS→vBiog∑m∈BP ( 1−δmRP ) ×cmRP+∑k∈APδkAP×ckAP ( 7 ) ∀g∈G ( vBiog+1000× δBioiG≥ Tg ) ( 8 ) ∀h∈N ( v^Bioh−1000× δBioiN≤ Th ) ( 9 ) with: Inner Problem:v^Bioh∶= maxv→hvBioh , ( 10 ) subject to: Eqs ( 3 ) – ( 7 ) and to the definitions following below . Line ( 7 ) defines the flux through the biomass reaction , vBiog , for condition g . The sets used in this system of equations are listed in Table 9 , while the parameters are defined in Table 10 . For binary variables , 1 corresponds to TRUE ( i . e . , a model change is executed ) , while 0 corresponds to FALSE ( no change compared to the initial network ) . What is the purpose of each of the lines in the above system of equations ? The network must be in a steady state ( i . e . , no concentration changes to internal metabolites ) in all conditions g ∈ G Eq ( 2 ) and h ∈ N Eq ( 3 ) that are to be solved simultaneously . Lines ( 4 ) – ( 6 ) convert the binary variables for the removal or reversibility change of existing reactions , and for the addition of new reactions from the database , into constraints for the respective fluxes . In Eq ( 4 ) , if δyRB=0 ( i . e . , no change ) , then the lower limit for reaction y in all conditions g ( vyg ) remains at the predefined limit vymin; setting δyRB=1 instead sets the lower flux limit to 0 , i . e . , removes the backwards reaction . Similarly , setting δyRF=0 keeps the upper flux limit for reaction y at the predefined limit vymax , while setting δyRF=1 sets the upper flux limit to 0 , i . e . , removes the forward reaction . Line ( 5 ) sets the lower flux limit to -1000 for reaction y in all conditions g if δxI=1 , i . e . , it makes an irreversible reaction ( with flux vxg≥0 ) reversible in this case . Line ( 6 ) allows non-zero ( positive ) flux for reactions that are not part of the original ( input ) model if δzadd=1 . Note that in the database of additional potential reactions , we consider bidirectional reactions as two separate reactions corresponding to forward and backward directions ( both with fluxes ≥0 ) . Metabolites can be removed from both sides of the biomass reaction ( flux vBiog ) , and additional metabolites can be added Eq ( 7 ) with pre-specified stoichiometric coefficients c . To ensure in silico growth for conditions with experimentally demonstrated growth , the biomass flux for these conditions must be greater than a predefined threshold Tg in all conditions g ∈ G Eq ( 8 ) . Conversely , to ensure in silico non-growth for conditions with experimentally demonstrated non-growth , the biomass flux for these condition must be less than a predefined threshold Th in all conditions h ∈ N Eq ( 9 ) . The thresholds Tg and Th can be set separately for each phenotype , e . g . , to account for estimates of experimental errors . For non-growth phenotypes , a simple condition that forces the biomass production to be lower than a threshold is not sufficient , though , as a trivial solution with v→h=0 would satisfy this condition . To overcome this problem , the inner optimization problem maximizes the biomass production of non-growth cases Eq ( 9 ) , and this maximum is compared against the non-growth threshold . Line ( 1 ) describes the outer optimization problem . GlobalFit aims to find a solution that is able to correctly predict all growth and non-growth cases with a minimal number of network changes ( indicated by values 1 for the binary variables ) : δyRF , δyRB , δxI , δzadd , δjAS , δkAP , δlRS , δmRP , δgG , δhN The penalties for each type of network change , and even for each individual change , can be set independently . This allows , for example , to prefer reversibility changes over reaction additions , or to preferentially include new reactions with stronger genomic evidence , or reactions from metabolic network reconstructions of close relatives . Users should choose appropriate penalties based on the details of the network reconstruction and the proposed changes . As a starting point , we include a list of suggested penalty values in S1 Table ) . To guarantee a feasible solution , even if inconsistent growth cases are used , we implemented additional binary variables that allow the exclusion of individual growth ( δgG Eq ( 8 ) ) and non-growth cases ( δhN Eq ( 9 ) ) from the growth threshold conditions . In our application to the M . genitalium network , we penalize these condition exclusions with very high values wgG and whN; thus , any network modification that explains additional cases is preferred over the exclusion of conditions , regardless of the number of required changes . Instead , the penalties can be set to smaller values , so that the exclusion of potentially erroneous experiments is preferred over excessive network changes . Metabolic network reconciliation with large-scale experimental data usually incorporates a manual curation stage , where experts for the physiology and biochemistry of the organism under study review network changes suggested by automated methods . To support this process , GlobalFit can put out not just one best solution , but , e . g . , the five best solutions that can then be reviewed to identify the changes most compatible with existing knowledge . To speed up the calculations , network changes can also be limited to a maximal number . No efficient software tools for general bi-level optimization problems are available . Solving the inner problem for each possible combination of network changes would be computationally too slow . We adapt the “Reduction Ansatz” of Section 4 . 3 . 4 in [18] to eliminate the inner problem in line ( 9 ) . In this approach , the optimality conditions of the inner optimization problem are expressed as equality and inequality conditions using additional “dual” variables . For fixed δ→ and h , the inner problem is simply a linear program; thus , the assumptions in [18] are trivially satisfied . Because of the use of binary variables , algorithms to solve this type of optimization problem are termed mixed integer linear programming ( MILP ) . MILP is NP hard [35]; while no known algorithms can guarantee to find a solution efficiently , algorithms that work well for many practical problems exist in software solvers . We used the solver of IBM ILOG CPLEX 12 . 5; to avoid trickle flow , we implemented indicator constraints . Alternatively , our implementation of GlobalFit also allows using the GUROBI solver . Academic users can obtain both CPLEX and GUROBI free of charge . The search for a globally minimal set of network changes is a computationally very intensive task . To speed up this process , it is advisable to restrict the examined conditions to a maximal consistent ( “feasible” ) set , i . e . , a maximal set of conditions that can all be correctly predicted with the same modified metabolic network ( regardless of the type and number of modifications ) . To identify such feasible condition sets , GlobalFit provides a simple mode , which only minimizes the number of erroneous predictions of growth regardless of the number of network changes . To speed up the calculation of a feasible condition set , it is possible to first solve individual wrong predictions against a “control” condition , thereby identifying conditions that cannot be reconciled with the network with the allowed modifications . We applied this strategy for the pre-processing of the M . genitalium data ( see Results ) . Furthermore , the number of binary variables can be reduced by a set of additional preprocessing steps . First , binary variables for changes to the network not allowed ( such as reversibility changes to reactions strictly considered irreversible ) should be constrained to zero . Second , we can consider a “supermodel” that encompasses the input model with all allowed reactions converted to reversible reactions and all reactions from the database of potential additional reactions . We can then reduce the number of binary variables further by ( i ) excluding all reactions that are blocked in this supermodel , ( ii ) constraining to zero the binary variables for the removal of reactions that are essential in this supermodel . GlobalFit can optionally calculate a user-defined number n of alternative optimal or suboptimal solutions . The search for alternative solutions is executed using the integer cuts method . Thus , the complexity for each additional alternative solution is only increased through a single linear constraint . Consequently , the runtime for n alternative optimal or suboptimal solutions is approximately n times the runtime for a single optimum . We provide an implementation of GlobalFit , integrated with the sybil toolbox for constraint-based analyses [19] , which runs in the R environment for statistical computing [36] . The source code and documentation is available free of charge from CRAN ( http://cran . r-project . org/web/packages/GlobalFit/ ) . The optimized models for E . coli and M . genitalium are provided as SBML files that can be read , e . g . , by sybil [19] and the COBRA toolbox [37] . | Mathematical models that aim to describe the complete metabolism of a cell help us understand cellular metabolic capabilities and evolution , and aid the biotechnological design of microbial strains with desired properties . Draft models are frequently improved through adjustments that increase the agreement of growth/non-growth predictions with observations from gene knockout experiments . Automated methods for this task typically correct one erroneous prediction after the other . We present GlobalFit , a novel method that can consider all experiments and all possible changes simultaneously to identify model modifications that are globally optimal ( i . e . , that correct the largest possible number of wrong predictions while introducing sets of changes that are most compatible with existing knowledge ) . This becomes computationally very hard when considering large metabolic models; however , a reduced application of GlobalFit that only looks at small subsets of experiments simultaneously works very well in practice . Allowing only changes that are conservative ( e . g . , introducing new reactions only if supported by significant genomic evidence ) , GlobalFit halves the number of wrong growth/non-growth predictions for the state-of-the-art metabolic models of E . coli and Mycoplasma genitalium , increasing prediction accuracy to 95 . 4% and 93 . 0% , respectively . By additionally allowing less conservative changes , we are able to improve accuracy further to 97 . 3% for the M . genitalium model . | [
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] | 2016 | Improved Metabolic Models for E. coli and Mycoplasma genitalium from GlobalFit, an Algorithm That Simultaneously Matches Growth and Non-Growth Data Sets |
Among all species of Bartonella , human-restricted Bartonella bacilliformis is the most virulent but harbors one of the most reduced genomes . Carrión’s disease , the infection caused by B . bacilliformis , has been afflicting poor rural populations for centuries in the high-altitude valleys of the South American Andes , where the pathogen’s distribution is probably restricted by its sand fly vector’s range . Importantly , Carrión’s disease satisfies the criteria set by the World Health Organization for a disease amenable to elimination . However , to date , there are no genome-level studies to identify potential footprints of B . bacilliformis ( patho ) adaptation . Our comparative genomic approach demonstrates that the evolution of this intracellular pathogen is shaped predominantly via mutation . Analysis of strains having publicly-available genomes shows high mutational divergence of core genes leading to multiple sub-species . We infer that the sub-speciation event might have happened recently where a possible adaptive divergence was accelerated by intermediate emergence of a mutator phenotype . Also , within a sub-species the pathogen shows inter-clonal adaptive evolution evidenced by non-neutral accumulation of convergent amino acid mutations . A total of 67 non-recombinant core genes ( over-representing functional categories like DNA repair , glucose metabolic process , ATP-binding and ligase ) were identified as candidates evolving via adaptive mutational convergence . Such convergence , both at the level of genes and their encoded functions , indicates evolution of B . bacilliformis clones along common adaptive routes , while there was little diversity within a single clone .
Bartonellae can serve as powerful models to study the evolution of intracellular , Gram-negative bacteria transmitted through the bite of hematophagous arthropods . As a genus , Bartonella currently includes 31 host-adapted species with varying degrees of pathogenicity [1] . While human-restricted Bartonella bacilliformis represents the most virulent Bartonella species [2] , with fatality rates as high as 88% in untreated cases [3 , 4] and a distribution restricted to South America [5] , other bartonellae have a much wider geographical range and show significantly lower virulence potential in their corresponding mammalian hosts ( e . g . , Bartonella quintana causes human trench fever while Bartonella henselae causes asymptomatic feline infections and human cat-scratch disease world-wide ) [6] . In addition , while core genes of different Bartonella species are found to be mostly syntenic , indicating a host-integrated metabolism [7] , dynamic genome evolution is reflected at the species level; ranging from substantial genome expansion in Bartonella tribocorum , via gene duplications as well as lateral acquisition of prophages and genomic islands , to extensive genome reduction in species like Bartonella clarridgeiae and B . bacilliformis ( PATRIC; http://www . patricbrc . org/ ) [8] . Conserved attributes of pathogenesis during human bartonelloses include bacteremia , erythrocyte parasitism ( hemotrophy ) , infection of vascular endothelial cells , and pathological angiogenesis . Interestingly , B . bacilliformis represents the sole ancestral lineage in phylogenetic reconstructions of the genus , and lacks a number of virulence factors that are common to other Bartonella species ( e . g . , the type IV secretion system and corresponding substrate effector proteins used to subvert host cells ) [9–11] . This conspicuous disparity and the marked virulence of B . bacilliformis suggest that the bacterium employs infection strategies that are distinct from other bartonellae , however , its virulence factors and pathogenomics are relatively under-characterized . Carrión’s disease , the name given to the entire spectrum of clinical manifestations during a B . bacilliformis infection , affects an endemic population of about 1 . 7 million people limited to a defined altitudinal zone ( 600 to 3 , 200 m ) of the Andean mountain valleys of Peru , Colombia and Ecuador , with reports of over 10 , 000 cases annually ( Peruvian Ministry of Health , http://www . minsa . gob . pe/ ) . High-risk populations include children ( < 5 years old ) and recent immigrants to endemic areas , although relatively recent reports document the spread of the disease into larger , non-endemic areas ( e . g . , in the Andean highlands and the Amazonian region east of the Andean mountains ) [5 , 12–15] . Little progress has thus far been achieved in understanding the molecular basis for host adaptation , differential disease presentations ( e . g . , Oroya fever , verruga peruana and chronic bacteremia ) and the evolutionary mechanisms underlying the population diversity of B . bacilliformis . Previous comparative genomic approaches detected a set of genes orthologous to potential virulence genes of other Bartonella species ( e . g . , B . tribocorum ) , implying a similar role in B . bacilliformis [16] . However , to date , no study has been undertaken to delineate evolutionary forces that could affect genome-wide variations and potential pathoadaptation in B . bacilliformis . In this study , we compared publicly-available genomes of B . bacilliformis strains to construct pan-genomic profiles in order to decipher the relative contributions of horizontal gene transfer , recombination and mutation in shaping the bacterium’s evolution . While this study confirms a suspected sub-species structure in B . bacilliformis , we found mutation to be the primary force in both sub-species divergence and adaptive convergence within sub-species .
For a set of 53 total strains ( including 13 completely sequenced genomes ) , the maximum-likelihood based phylogeny was reconstructed using concatenated internal fragments of 7 housekeeping genes–bvrR , flaA , ftsZ , groEL , ribC , rnpB and rpoB–used previously [17] for multilocus sequence typing ( MLST ) . We used MEGA6 [18] to calculate the sub-species diversity of B . bacilliformis based on internal fragments of 4 housekeeping genes ( gltA , groEL , ribC and rpoB ) used for assessing the sub-species diversity of Bartonella vinsonii [19] . Since only the genome of strain KC583 was annotated and the remaining 12 strains had draft genomes ( with the number of contigs ranging from 4 to 20 ) , we first used MAUVE [20] to order the contigs of each draft genome using the annotated KC583 genome as a reference . The protein-coding genes of these draft genomes were then annotated using a combination of the RAST annotation server [21] and our recently developed software , PanCoreGen [22] . Using PanCoreGen , we constructed the pan-genomic profile at different nucleotide sequence identity threshold values ( 75% , 80% , 85% , 90% and 95% ) keeping a constant 95% threshold for gene length-coverage in the process of identifying orthologous genes . For phylogenomic reconstruction and selection analysis , we used stringent cut-off values ( 95% ) for both sequence-identity and gene length-coverage to determine the set of core genes , thereby eliminating highly diverse genes and minimizing the influence of non-homologous recombination or gene-shuffling . The phylogenomic tree was generated by MEGA6 [18] based on maximum parsimony . Potential genes affected by homologous recombination events were detected using PhiPack software [23] that included three recombination-detection statistics: pairwise homoplasy index ( Phi ) , maximum χ2 ( MaxChi ) , and neighbor similarity score ( NSS ) . If P values for all of the 3 statistics were <0 . 1 , the gene was designated as recombinant [24] . Using DnaSP [25] , we performed a McDonald-Kreitman test [26] to detect any positive selection footprints during versus after the divergence of a sub-species . Phylogenetic analysis of each core gene and detection of convergent structural mutations were performed by TimeZone software [27] . In all 13 genomes , prophage regions were identified using PHAST ( http://phast . wishartlab . com/ ) Web Server [28] by uploading the GenBank-formatted files for each genome . We considered genes from all regions designated as “intact” , “incomplete” or “questionable” by PHAST as prophage genes in our analysis . EvolveAGene 3 [29] was used for gene-by-gene simulation of mutations under neutrality . Ten rounds of simulation were performed per gene . In each round , a random tree topology ( with every branch having equal probability of leading to a terminal node or to an internal node ) was generated considering the allele of the KC583 strain as the root sequence . Mutation rate , average branch lengths and average selection on amino acid replacements ( i . e . , dN/dS ) were estimated from the phylogeny of a real data set for the corresponding gene . Neutrality was achieved by setting a constant default modifier value of 1 for the selection over sequence , and also over all branches in the phylogenetic tree . No indels were allowed in simulated datasets , since real datasets of core genes analyzed did not have any indels . Clustering of candidate genes based on protein functions was done by DAVID software [30] . For the analysis , classification stringency was set to ‘medium’ . Clusters were chosen to minimize the redundancy of genes representing encoded proteins in each functional group . An enrichment score higher than 0 . 5 and a P value less than 0 . 05 were used to assign a cluster as enriched or overrepresented .
The phylogenetic relationship of 13 sequenced strains ( S1 Fig ) was established based on 7 housekeeping genes used previously for MLST analysis [17] . While there was an overall average pairwise nucleotide diversity ( π ) of 0 . 011±0 . 001 , we determined Ver097 to be distinct from the other strains . The π value of 0 . 044±0 . 004 between Ver097 and CAR ( closest relative of Ver097 in the sample set ) was significantly higher ( P<0 . 0001 ) than the average π value ( 0 . 005±0 . 0004 ) for the other 12 strains . Also , we detected no correlation between evolutionary relationships among the strains and their geographical locations of isolation . Next , to assess the diversity of sequence types ( ST ) , we combined our MLST dataset for 13 strains with that of 43 B . bacilliformis strains analyzed in earlier work [17] , including strains KC583 , CUSCO5 and Cond044 for which we had genome sequences in our analyzed set ( Fig 1 ) . We discovered 4 new ST groups represented by Ver075 ( ST9 ) , Peru38 ( ST10 ) , VAB9028 ( ST11 ) and Ver097 ( ST12 ) . Interestingly , while previous work suggested that ST8 was a genospecies of B . bacilliformis , being well-distant from the rest [17] , we found ST12 to have directly descended from ST8 . In contrast , ST9 , ST10 and ST11 remained closely-related to ST1 through ST7 with inter-ST nucleotide diversity ( π ) ranging from 0 . 0003 to 0 . 0102 . Importantly , the π value between ST8 and ST12 was 0 . 0003±0 . 00033 , i . e . , identical to the lower limit of the diversity range among the other STs . This implied that ST8 and ST12 belong to a single clade well distant from the rest of the STs , thereby strengthening the possibility of multiple sub-species of B . bacilliformis . To test this , we compared B . bacilliformis diversity with the sub-species diversity of B . vinsonii based on 4 housekeeping genes , as previously described [19] . No significant differences ( P>0 . 10 ) were detected in the diversity values between Ver097 and rest of B . bacilliformis ( π = 0 . 039±0 . 004 ) and the values between B . vinsonii sub-species ( arupensis-berkhofii π = 0 . 053±0 . 005; arupensis-vinsonii π = 0 . 053±0 . 005; berkhofii-vinsonii π = 0 . 036±0 . 004 ) . This strongly suggests that , similar to B . vinsonii , B . bacilliformis includes multiple sub-species . Combining information from Chaloner et al . [17] , it appeared that , while ST1 through ST7 and ST9 through ST11 were members of one sub-species ( referred to here as sub-species I ) , ST8 and ST12 joined to form another sub-species ( designated as sub-species II ) ( Fig 1 ) . ST8 strains ( LA6 . 3 in 1990 and Luc-Uba in 1999 ) as well as ST12 representative Ver097 were isolated from the Ancash region of Peru , implicating this geographical area as a possible point of emergence for the sub-species . The MLST phylogeny of B . bacilliformis evolution using other Bartonella species as outgroups ( S2 Fig ) displayed deep branches coalescing to a common ancestor of the two sub-species . Such a phylogenetic structure suggests that the two B . bacilliformis sub-species might have diverged long ago from a common ancestor , rather than a recent emergence of sub-species II . We next wanted to determine to what extent the MLST phylogeny would reflect the genomic scenario of 13 sequenced strains based on both gene content and diversity . We therefore performed a pan-genomic profiling of B . bacilliformis . In the first step , we chose a low , 75% cut-off value for nucleotide sequence identity while using a 95% threshold for gene-length coverage to identify orthologous genes . We found that 79% of the genes of the pan-genome were core , i . e . , present in all 13 strains ( Fig 2A ) . In the accessory fraction , 14% of genes were mosaic ( i . e . , present in multiple but not all strains ) and 6% were strain-specific ( i . e . , unique to one of the 13 strains analyzed ) . Out of 74 strain-specific genes in sub-species I , 56 genes ( 76% ) were harbored by Cond044 , and 44 of these genes represented an intact prophage region of 36 . 3 kb ( S1 Table ) . This suggested an increased frequency of horizontal gene transfer events in Cond044 . While the prophage cluster in Cond044 was unique across all analyzed B . bacilliformis , a BLASTn search detected a short fragment ( ~7 . 5 kb ) of this region in B . vinsonii subsp . berkhoffii at an 82% nucleotide identity level . Interestingly , 82% of this Cond044-specific region was also found in B . tribocorum ( strains BM1374166 and CIP 105476 ) in different-sized fragments , each with multiple copies , ranging from 193 bp to 10 . 6 kb long ( at 77–82% identity levels ) and distributed throughout the genome . This supports the previous explanation of genome expansion in B . tribocorum via horizontal gene transfer and duplication events [8] . In contrast to Cond044 , Ver097 harbored only 37 strain-specific genes ( of which 65% were annotated as hypothetical ) , even though Ver097 showed up as an outlier based on the diversity of housekeeping genes ( Fig 1 ) . Therefore , it was evident that in separating Ver097 from the remaining 12 strains , unique gene content was not a major contributor , suggesting a limited role for horizontal transfer events in sub-speciation of B . bacilliformis . As a result , we hypothesized that high nucleotide diversity might have been acquired in an otherwise same set of genes , which ultimately led to the sub-species structure . To test this , we re-analyzed the pan-genomic profiling of B . bacilliformis at higher nucleotide identity cut-offs using the same 95% threshold for gene-length coverage ( Fig 2A ) . We found a drastic drop in core genes to 43% and a rise in Ver097-specific genes ( 364 genes ) at 95–95 cut-offs ( i . e . , 95% threshold levels for both nucleotide sequence identity and gene-length coverage ) . Importantly , the genomic locations of 327 Ver097-specific genes ( i . e . , the difference between Ver097-specific genes in 95–95 and 75–95 cut-offs ) were distributed throughout the genome and were not limited to a few specific gene clusters ( S3 Fig ) . This observation suggests accumulation of high nucleotide diversity in genes over the course of sub-species divergence , ruling out any significant contribution of gene mosaicism through horizontal gene transfer to sub-speciation . The pan-genomic profile of B . bacilliformis , excluding Ver097 , showed little variation in core , mosaic and strain-specific fractions at different identity thresholds ( Fig 2B ) . For example , we detected only a 10% decrease in the core fraction as we increased cut-off values from 75–95 ( 86% core genes ) to 95–95 ( 76% core genes ) , as opposed to a 36% decrease in core genes for the pan-genome based on all 13 genomes . Such a low fraction of accessory genes ( 14% using 75–95 cut-offs ) suggested a limited contribution by horizontal transfer events , most of which affected a single strain , Cond044 , as described above . On the other hand , the core fraction comprised more than 75% of the genome . To gain an understanding of how sequence diversity impacted the phylogenomic relationship of the 13 strains , we reconstructed a phylogeny based on core genes . For orthologous gene identification , we used high thresholds ( 95–95 ) for nucleotide sequence identity and gene-length coverage , respectively , to essentially exclude highly-diverse genes resulting from non-homologous recombination or gene-shuffling . We identified 862 core genes that were then tested for the possible presence of homologous recombination . We identified 25 core genes as recombinants and excluded them from further analysis ( S2 Table ) . We also excluded a total of 33 prophage genes ( S3 Table ) along with 50 annotated/un-annotated pseudogenes with truncation mutations ( S4 Table ) from the set of core genes . Finally , a B . bacilliformis phylogenomic tree was reconstructed using 754 core genes of possible mutational origin ( S4 Fig ) . While Ver097 , similar to the MLST tree ( Fig 1 ) , remained distant from the rest and re-confirmed the sub-species structure , we detected the presence of 4 distinct clades in the remaining 12 strains: ( clade 1 ) KC583 , INS , San Pedro600-02; ( clade 2 ) Peru38 , Ver075 , Peru-18 , CUSCO5; ( clade 3 ) VAB9028 , Hosp800-02 , CAR600-02 , Heidi Mejia; and ( clade 4 ) Cond044 . The within-clade diversity ( π ) ranged from 0 . 0002±0 . 00007 in clade 1 to 0 . 029±0 . 0005 in clade 2 . However , between-clade diversity ( π ) was significantly higher ( P<0 . 001 ) , ranging from 0 . 034±0 . 001 ( between clades 1 and 2 ) to 0 . 129±0 . 002 ( between clades 2 and 4 ) . Altogether , results of MLST diversity , pan-genomic profiling and phylogenomic structure confirmed the existence of two sub-species of B . bacilliformis , where sub-species I maintained a tight clonal structure among 12 strains . Extrapolating the tightness of ST8 and ST12 in MLST phylogeny ( Fig 1 ) , we anticipate that sub-species II will show a similar picture . Also , we demonstrate that the emergence of new sub-species primarily resulted from enormous nucleotide divergence in common genes , rather than by horizontal transfer of phage genes and non-phage clusters of non-B . bacilliformis origin . The presence of a small number ( 3% of core genes ) of recombinant genes suggests that nucleotide diversity predominantly arose via mutations in the core genomic fraction . In the set of 754 non-recombinant core genes , we wanted to assess the footprints of positive selection within and between sub-species . For this , we utilized evolutionary convergence of structural mutations , i . e . , repeated independent ( phylogenetically unlinked ) mutations at the same amino acid positions of the encoded proteins . Positively-selected genes , i . e . , ( patho- ) adaptive mutations that modify or inactivate functions of the encoded ( virulence ) proteins resulting in better fitness to the organism , are likely to be repeated when strains compete for survival under similar environmental conditions [31 , 32] . Therefore , non-random accumulation of convergent structural mutations represents strong footprints of positive selection [33–37] , especially in highly clonal populations as we determined here for B . bacilliformis . We detected a set of 152 genes ( 20% of the non-recombinant core fraction ) that acquired convergent structural mutations . There were 61 ( 40% ) genes that showed mutational convergence restricted to only the strains of sub-species I . In contrast , the remaining 91 ( 60% ) genes were found to share at least one position that accumulated convergent mutations in Ver097 ( the sole sub-species II representative ) and the strains from sub-species I . Convergent mutations can be of two types , including parallel ( identical mutations at the same amino acid positions ) and coincidental ( different mutations at the same amino acid positions ) . Surprisingly , we detected distinct features of convergence between sub-species in the set of 91 genes , compared to convergence within sub-species I in the set of 61 genes ( Fig 3 ) . The frequency of parallel convergent mutations ( 1 . 84±0 . 19 ) was ~2 . 7 times higher ( P<0 . 0001 ) than coincidental mutations ( 0 . 69±0 . 14 ) in genes of the within sub-species set . In contrast , in genes of the between sub-species set , the frequency of coincidental mutations ( 2 . 03±0 . 16 ) was significantly higher ( P = 0 . 0012 ) than parallel mutations ( 1 . 25±0 . 18 ) . Also , there were significant differences in frequencies of parallel ( P = 0 . 0244 ) and coincidental mutations ( P<0 . 0001 ) between the two sets . Interestingly , if mutations were allowed to accumulate under neutrality , the frequency of coincidental convergent mutations would always be much higher than the parallel ones simply by random chance . On the other hand , previous work [38 , 39] demonstrated the predominance of parallel convergence under positive selection , possibly responding to the need for precisely-tuned functional modification of proteins . By the same token , different replacements at specific positions ( coincidental convergent mutations ) critical for functional or structural integrity of encoded proteins can lead to possible immune escape or protein inactivation under adaptive pressures . While parallel convergent changes may result from recombination , the coincidental ones ( resulting in different changes ) are always mutational events . Although we detected less than 3% of genes potentially affected by recombination , it is possible that the presence of homologous recombination might have been overlooked owing to a lack of sensitivity by the tools used . At this point , based on the predominance of coincidental convergent mutations between sub-species , we can confirm a mutational , non-recombinational origin of high nucleotide diversity that separated the two sub-species . Consistent with the MLST ( Fig 1 ) and phylogenomic ( S4 Fig ) trees , we observed that Ver097 , the only representative of sub-species II , remained widely distant from sub-species I strains in almost every core gene phylogeny . To assess the evolutionary forces leading to the high mutational diversity that gave rise to the sub-species structure of B . bacilliformis , we performed simulation of mutations under the null hypothesis of no selection to examine two contrasting possibilities . It could be that sub-species II ( Ver097 ) emerged under positive selection pressures leading to coincidental convergent changes . Alternatively , the separation of Ver097 from the rest by a long branch incorporating a large number of substitutions caused some pressures towards random acquisition of coincidental changes . Indeed , for the set of 91 genes showing convergence between sub-species , we detected no significant difference ( P = 0 . 12 ) between the frequency of coincidental mutations in simulated data ( 1 . 74±0 . 10 ) and that of the real dataset , while the parallel mutation frequency in simulated data ( 0 . 30±0 . 03 ) was significantly lower ( P<0 . 0001 ) than that in the real dataset ( Fig 3A ) . The absence of positive selection pressures in the divergence of two sub-species was also supported by a McDonald-Kreitman test [26] with a P value of 0 . 74 . The scenario of excess coincidental convergent changes , equivalent to the expected frequency under neutrality , during sub-species divergence suggests a different possibility for the way sub-speciation might have occurred . Emergence of sub-species II could be a recent evolutionary incident wherein the sub-species was derived directly from sub-species I via hypermutation . This is an alternative to the divergence of two sub-species long ago in the evolution of B . bacilliformis , as suggested by MLST topology ( S2 Fig ) . The recent emergence could be attributed to a mutator phenotype via certain defects in the DNA repair system . It is known that a low mutation rate leads to high metabolic costs in non-recombining microbial populations , and selection pressures sporadically drive allelic variations in genetic systems that dictate the precision of DNA replication and repair , thereby leading to an increased rate of mutation [40–42] . Importantly , we detected that recF , encoding a DNA replication and repair protein , was inactivated in Ver097 via a premature stop codon ( S4 Table ) . It is known that restoration of a non-mutator phenotype is one of the primary roles of the DNA repair machinery [43] . Also , inactivation of RecF in intracellular microbes was previously shown to be linked to the loss of recombinational gene conversion [44] . Interestingly , we found that in Ver097 , mutations predominantly targeted selected , mutable sites in specific genes across the genome ( as evidenced by a high frequency of coincidental convergence , even higher than the simulated frequency under neutrality ) . If a mutator strain was the basis for the emergence of sub-species II , on one hand , the close relationship between ST8 and ST12 ( Fig 1 ) indicates a massive reduction in mutation rate at some point after the rise of the mutator phenotype . On the other hand , similar to Ver097 , the isolation of LA6 . 3 and Luc-Uba happened during 1990–1999 from Oroya fever patients in the Ancash region of Peru [45] , depicting their stability and circulation in an otherwise endemic region . Models of clonal populations suggest that favorable mutants can arise in non-mutator genetic backgrounds from a mutator genotype [46] , which in essence would lead to the fixation of genotypes ( that we currently see in the environment ) acquiring adaptive mutations on a fast track via hypermutation [47–49] . Since a trade-off due to continuous accumulation of deleterious mutations always exists , a reduction in mutation rate can be explained by the state where the fitness cost due to deleterious mutations exceeds the fitness gain level in metabolic activities [41 , 50 , 51] . We therefore hypothesize that natural selection might have favored the rise of a mutator phenotype in B . bacilliformis sub-species II with an aim to gain metabolic fitness . However , a more comprehensive understanding of the emergence will not be possible until genomes of more strains representing sub-species II are sequenced . On the other side , we extracted all 67 core genes ( S5 Table ) that accumulated convergent mutations shared by the strains of sub-species I: ( i ) 61 genes showing convergence exclusively within sub-species I , and ( ii ) 6 genes with convergent mutations both within and between sub-species I . In the set of 12 strains of sub-species I , we detected a significant ( P = 0 . 0002 ) predominance of parallel convergence ( 1 . 31±0 . 23 ) over the coincidental type ( 0 . 31±0 . 13 ) ( Fig 3B ) . The simulated values for the corresponding dataset were extremely low ( P<0 . 0001 ) , with parallel and coincidental mutation-frequencies of 0 . 02±0 . 01 and 0 . 06±0 . 03 , respectively ( Fig 3B ) . Such non-random acquisition of convergent mutations strongly suggests the presence of positive selection in the evolution of B . bacilliformis sub-species I . Therefore , the convergent changes , both of parallel and coincidental types , acquired in the course of sub-species I evolution cannot be explained by neutrality . Also , we detected that the mutations were converged exclusively between clades and not within a clade ( i . e . , not between the tightly-linked strains ) . This suggests the possibility of common fitness goals of the four clades that could lead to convergent adaptive pressures in sub-species I . Our next step was to determine whether the convergent adaptation we detected among clades in sub-species I was restricted to the level of genes targeted by convergent mutations , or whether adaptive convergence happened at the functional level of the encoded proteins . Functional enrichment analyses of 67 candidate genes ( S5 Table ) revealed the presence of 10 non-redundant functional clusters representing Gene Ontology ( GO ) categories of Biological Process , Molecular Function , and Domain ( Fig 4 ) . Interestingly , 4 of these clusters—DNA repair , glucose metabolic process , ATP-binding and ligase—were found to be enriched or overrepresented in our candidate gene-set . Seven DNA repair genes encoding RecA , two DNA helicases ( AddA , RuvA ) , a nuclease involved in lesion repair ( UvrC ) , the endonuclease used to cleave the cruciform structure at the Holliday junction ( RuvC ) , DNA ligase ( LigA ) and a base excision repair glycosylase ( MutM ) were identified as core genes under positive selection in sub-species I ( S5 Table ) . The DNA repair functional cluster was previously shown to be under positive selection in ionizing-radiation-resistant-bacteria , a group renowned for its extraordinary ability to withstand both ionizing radiation and desiccation [52] . While B . bacilliformis is not normally exposed to extremes in water availability or ionizing radiation , the bacterium must nevertheless contend with disparate environments imposed by the sand fly vector’s midgut and the bloodstream of the human host . Moreover , as a facultative intracellular pathogen of humans , the bacterium would commonly encounter a variety of potentially DNA-damaging , immune-response effectors , such as reactive oxygen species . Thus , mutational convergence in genes representing this functional cluster would be clearly adaptive . Notably , experimental studies have identified recA as one of the pathogenicity genes in B . tribocorum [16] . Six genes involved in glucose metabolism were identified as candidates under positive selection . These included genes for phosphoglycerate kinase ( pgk ) , transaldolase , glucose-6-phosphate isomerase ( pgi ) , phosphopyruvate hydratase ( eno ) , 2-oxoglutarate dehydrogenase ( sucA ) , and glyceraldehyde-3-phosphate dehydrogenase ( gap ) . These results suggest that the Emden-Meyerhof glycolytic pathway is undergoing “fine-tuning” in B . bacilliformis to accommodate disparate carbon sources and availability within the vector and/or human host cells . Indeed , altered regulation of carbon metabolism has previously been reported in strains of Pseudomonas aeruginosa isolated from cystic fibrosis patients , in support of this hypothesis [53] . The vast majority of genes identified as positively-selected in S5 Table are involved in metabolic processes . In addition to the six glucose metabolic genes described above , several genes involved in ATP synthesis ( atpG , atpD ) , electron transport ( nuoM , nuoD , BARBAKC583_0816 ) , vitamin B synthesis ( cobT , folC ) , amino acid metabolism ( proB , gcvP , lys1 , aroK ) and nucleotide biosynthesis ( pyrG , guaA , BARBAKC583_0178 ) were identified . These data suggest that metabolic pathways of B . bacilliformis are under considerable selective pressure , possibly reflecting the pathogen’s evolution towards optimizing its ability to reside and survive in both sand flies [54] and humans , where conditions are quite dissimilar . It is tempting to speculate that these genes may contribute to the enhanced virulence of B . bacilliformis relative to other pathogenic bartonellae . Interestingly , recent work examining pathoadaptive evolution of Salmonella Typhimurium during chronic infections of mice identified a known metabolic regulator ( KdgR ) , where a single nucleotide polymorphism significantly enhanced bacterial transmission between littermates and colonization of the intestines relative to a wild-type parental strain [55] . Positively-selected genes involved those encoding five potential transporters , including an amino acid ABC transporter ( BARBAKC583_0595 ) , a glucan ABC transporter potentially involved in osmoregulation , a Bcr/Cfla subfamily drug resistance transporter ( BARBAKC583_0866 ) , a RND membrane fusion protein ( MFP ) family efflux transporter ( BARBAKC583_0305 ) and a potential macrolide-specific efflux protein MacA ( BARBAKC583_0096 ) . In addition , the PhoU regulator for the phosphate transport system and a transporter facilitator ( BARBAKC583_0895 ) were found to be positively-selected . Of particular interest are the potential drug efflux pumps , Bcr/Cfla , the RND MFP and MacA . Bcr/Clfa homologs have demonstrated resistance activity against bicyclomycin , chloramphenicol and florfenicol in various Gram-negative bacteria , while both RND and MacA homologs include transporters for bacterial hemolysins and drug efflux . Of possible relevance is that acute manifestations of Carrión’s disease ( Oroya fever ) have been treated for many decades with chloramphenicol , while macrolides have been used as second-line antimicrobials to treat both acute and chronic forms ( verruga peruana ) of disease [56] . Thus , selective pressure to acquire resistance to antimicrobials would be prevalent in endemic areas . A handful of genes involved in translation were found to be under positive selection in B . bacilliformis , including the beta subunit of glycyl tRNA synthetase ( glyS ) , two ribosomal proteins ( rplQ , rpsF ) and ribosome recycling factor ( frr ) . Typically , evolution of translation-related genes is relatively slow , to ensure maintenance of structural and functional integrity of the essential gene products [57 , 58] . Presumably , these genes are under strong selective pressure to maintain or optimize overall translational efficiency in B . bacilliformis . The cell wall constitutes an essential protective barrier against the extracellular environment and an interface between the bacterial cell and its niche . Four genes were identified as positively-selected in this category . The first , lolA ( BARBACK583_0094 ) encodes an outer membrane lipoprotein chaperone . The second gene , mraY , encodes a protein involved in the transfer of peptidoglycan precursors from the cytosol to bactoprenol for transport across the cell membrane . Third , murD , encodes a ligase for production of peptidoglycan precursors in the cytosol . Finally , a peptidoglycan recognition protein ( PRP ) amidase ( BARBACK583_0904 ) was identified . Interestingly , no obvious membrane proteins with potential virulence function [56]–such as Bartonella repeat proteins ( Brps ) , hemin-binding proteins ( Hbps ) , flagellin ( FlaA ) and invasion-associated locus A ( IalA ) –were found to be under selection for accumulation of convergent mutations . Although reasons for this are unclear , it could be that immunological pressure for these proteins is minimal , despite their physical location in the cell and potential exposure to acquired and innate immune effectors . Alternatively , these membrane proteins provide essential virulence functions in the context of the sand fly vector and/or human host and are therefore indispensable to bacterial fitness and survival . In B . tribocorum , signature-tagged mutagenesis studies detected 97 genes linked to pathogenesis , of which 66 genes had orthologs in B . bacilliformis [16] . Only four of these genes , including recA discussed above , were found to undergo adaptive evolution via mutational convergence . Three other genes included BARBAKC583_0153 ( hypothetical ) , BARBAKC583_0335 ( encoding sugar isomerase of KpsF/GutQ family ) and BARBAKC583_1364 ( hypothetical ) , although in B . tribocorum these genes were annotated as either putative regulators or signal peptide proteins . This insignificant overlap , along with complete absence of several virulence genes found in B . tribocorum and other Bartonella species , indicates markedly different pathoadaptation strategies for B . bacilliformis . Indeed , the highly reduced genome of B . bacilliformis relative to other species such as B . tribocorum , with its expanded genome arising from horizontal transfer and duplication events [8] , underscores these differences . With recent advances in high-throughput sequencing technologies , population genomics studies are warranted to associate genetic variation with a pathogen’s disease potential , and convergence-based association is a powerful approach [37 , 59] , especially in clonal populations of species like B . bacilliformis . Our study maps , for the first time , convergent adaptive evolution of protein-coding genes across the highly-reduced genome of a facultative intracellular pathogen . Candidate genes and the potential adaptive mutations therein , should serve as an important resource for functional studies leading to a better understanding of B . bacilliformis pathogenesis as well as possible common virulence characteristics of intracellular pathogens harboring reduced genomes . As we re-confirm the sub-species structure of B . bacilliformis , we hypothesize that the sub-speciation event might be evolutionarily recent—triggered by the emergence of a mutator strain , followed by the fixation of adaptive variants in a non-mutator background . However , the possibility that two sub-species diverged from a common ancestor cannot be ruled out . To decipher how , why and when sub-speciation happened , additional genome sequencing and more information regarding strains and clinical history of the patients from which they were isolated is crucial . We believe this study provides a stepping stone toward future translational research , such as clinical diagnostics , epidemiology , and environmental control of B . bacilliformis , thereby facilitating elimination of Carrión’s disease in poor , rural , mountain communities of South America . | How host-restriction , intracellularity and genome reduction interplay to exert or maintain virulence is poorly characterized . The fact that B . bacilliformis is the most pathogenic Bartonella and has a highly reduced genome makes it an attractive model to gain insights into ( patho ) adaptive evolution of intracellular pathogens . Also , B . bacilliformis is known to lack many virulence genes present in other Bartonella , indicating unique strategies of ( patho ) adaptation . Our study reveals a prevalent nature of mutational force in B . bacilliformis evolution with two distinct outcomes: ( a ) mutational divergence leading to sub-speciation , possibly recently , via accelerated accumulation and fixation of favorable mutations mediated by a mutator phenotype; and ( b ) mutational convergence between clones of a sub-species exhibiting shared functional trajectories of adaptive evolution . Our findings highlight positions accumulating adaptive mutations in candidate genes , offering future functional studies to elucidate B . bacilliformis virulence evolution , and of broad application to intracellular pathogens with a reduced gene repertoire . | [
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] | 2016 | Mutation-Driven Divergence and Convergence Indicate Adaptive Evolution of the Intracellular Human-Restricted Pathogen, Bartonella bacilliformis |
Phyllotaxis , the arrangement of leaves on a plant stem , is well known because of its beautiful geometric configuration , which is derived from the constant spacing between leaf primordia . This phyllotaxis is established by mutual interaction between a diffusible plant hormone auxin and its efflux carrier PIN1 , which cooperatively generate a regular pattern of auxin maxima , small regions with high auxin concentrations , leading to leaf primordia . However , the molecular mechanism of the regular pattern of auxin maxima is still largely unknown . To better understand how the phyllotaxis pattern is controlled , we investigated mathematical models based on the auxin–PIN1 interaction through linear stability analysis and numerical simulations , focusing on the spatial regularity control of auxin maxima . As in previous reports , we first confirmed that this spatial regularity can be reproduced by a highly simplified and abstract model . However , this model lacks the extracellular region and is not appropriate for considering the molecular mechanism . Thus , we investigated how auxin maxima patterns are affected under more realistic conditions . We found that the spatial regularity is eliminated by introducing the extracellular region , even in the presence of direct diffusion between cells or between extracellular spaces , and this strongly suggests the existence of an unknown molecular mechanism . To unravel this mechanism , we assumed a diffusible molecule to verify various feedback interactions with auxin–PIN1 dynamics . We revealed that regular patterns can be restored by a diffusible molecule that mediates the signaling from auxin to PIN1 polarization . Furthermore , as in the one-dimensional case , similar results are observed in the two-dimensional space . These results provide a great insight into the theoretical and molecular basis for understanding the phyllotaxis pattern . Our theoretical analysis strongly predicts a diffusible molecule that is pivotal for the phyllotaxis pattern but is yet to be determined experimentally .
Living organisms often form periodic patterns with spatial regularity in a self-organizing manner [1 , 2] . One such well-known example is phyllotaxis , the arrangement of leaves on a plant stem . The phyllotaxis exhibits various types of patterns depending on the plant species and this has attracted many scientists because of its beautiful geometric configuration [3] . Phyllotaxis is originated at the shoot meristem , in which leaf primordia are periodically formed by maintaining a constant distance from each other [4–7] . This spatial regularity is established by the mutual interaction between a mobile plant hormone auxin and its efflux carrier membrane protein PIN1 , which cooperatively generate small regions with high auxin concentrations called auxin maxima that are involved in leaf primordia . In the process of the auxin maxima formation , auxin accumulates at the presumptive position of a future primordium while PIN1 is polarized toward the center position [8–11] . According to this experimental observation , the auxin maxima pattern is often explained by the concept of “up-the-gradient” in which auxin is transported by PIN1 against its own gradient while PIN1 is polarized toward higher auxin [11–13] . Based on this concept , a class of mathematical models ( corresponding to Model O in this paper ) has been proposed , in which PIN1 is localized to a cell membrane depending on the auxin concentration of neighboring cells . Because these models can successfully reproduce the spatial regularity of auxin maxima and various phyllotaxis types , they are excellent models for understanding the nature of phyllotaxis pattern formation [11 , 12 , 14–16] . On the other hand , they are highly simplified and abstract models , and could have various problems when considering the molecular mechanism . With respect to spatial structure , these can be distilled into two major points . First , extracellular space ( i . e . , apoplast space ) is absent in these models , in which auxin moves directly between cells by PIN1 and diffusion . However , in plant tissues , auxin is transported between cytoplasm and apoplast because cells do not contact each other but are separated by apoplast space . Second , it remains unclear how cells sense auxin concentrations of neighboring but separated cells for PIN1 polarization . Sahlin et al . [16] showed that , despite the extracellular space , self-organized patterns can be generated by the “up-the-gradient” concept that PIN1 polarization depends on the auxin concentration of neighboring cells . However , it has still not been clarified how the information on auxin concentration is transmitted between neighboring cells . Conversely , although the apoplast space is also considered by Webnik et al . [17] and Cieslak et al . [18] , these reports describe the canalization pattern generated by the “with-the-flux” concept , which is different from auxin maxima by the “up-the-gradient” concept . In this paper , therefore , we investigated how the spatial regularity of phyllotaxis pattern is controlled under realistic conditions , that is , in the presence of extracellular space . We first confirmed that the introduction of extracellular space has a disruptive effect on the spatial regularity in the conventional model ( Model O ) , even in the presence of direct auxin diffusion between cytoplasm or apoplast spaces . This result strongly suggests that an unknown molecular mechanism is required for phyllotaxis pattern formation . We also found that the spatial regularity can be restored by assuming a diffusible molecule that mediates the feedback signaling from auxin to PIN1 polarization . This theoretical analysis strongly predicts a diffusible molecule that is critical for phyllotaxis pattern but remains to be found .
Change of auxin concentration of cell i ( ai ) is described by daidt=Ga ( A−ai ) −∑jfi , j+∑jDa ( aj−ai ) ( 1 ) fi , j=Ep ( pi , jai−pj , iaj ) ( 2 ) where cell j is a neighbor of cell i , A is related to the synthesis rate , Ga is the degradation rate , Da is the diffusion coefficient , Ep is the efficiency of PIN1 efflux carrier , and fi , j ( = −fj , i ) is the net flow of auxin by PIN1 from cell i to cell j , consisting of auxin efflux and influx . Auxin is constantly synthesized and degraded at a constant rate ( the first term of the right-hand side of Eq 1 ) , is transported by PIN1 ( the second term ) , and diffuses between neighboring cells ( the third term ) . On the other hand , change of PIN1 density ( pi , j ) is described by dpi , jdt=Gp ( Kpφ0 ( aj ) ∑jφ0 ( aj ) −pi , j ) ( 3 ) where Gp is the degradation rate , K is the number of neighboring cells , p is a constant related to PIN1 density , and φ0 ( aj ) is the regulatory function for PIN1 polarization ( Fig 1A ) . PIN1 is localized to cell membrane depending on the auxin concentration of neighboring cells and is degraded at a constant rate . The total PIN1 amount of cell i , Pi ≡ ∑jpi , j , satisfies dPi/dt = Gp ( Kp − Pi ) , indicating that Kp is the stable equilibrium of Pi . Thus , equilibria of ai and pi , j are given respectively by aeq=Aandpeq=p ( 4 ) When Gp is sufficiently large , pi , j quickly approaches equilibrium: pi , j=Kpφ0 ( aj ) ∑jφ0 ( aj ) ( 5 ) Therefore , Eq 5 can be used instead of Eq 3 in a simplified version of Model O . Model O in this study is equivalent to models reported previously in references [11] and [12] . Model equations of the simplest form in [11] can be described by daidt=−∑jT ( pi , jai−pj , iaj ) +∑jD ( aj−ai ) ( 6 ) pi , j=Paj∑jaj ( 7 ) where T , D , and P are constants . Eqs 6 and 7 are identical to the simplified version of Model O ( Eqs 1 , 2 and 5 ) with Ga = 0 , Da = D , Ep = T , p = P/K , and φ0 ( aj ) = aj . Conversely , equations used in [12] are somewhat complicated compared to those in [11] . However , phyllotactic patterns can be generated under the condition of fixed total PIN1 concentration ( i . e . , [PIN]i is constant in Eq 2 of [12] ) , no saturation of auxin synthesis ( i . e . , κIAA = 0 in Eq 5 ) , and the linear dependence of the flux on auxin concentration ( i . e . , replacement of [PIN]i2 and [PIN]j2 by [PIN]i and [PIN]j , respectively , in Eq 3 ) . In addition to these conditions , by considering a regular cell lattice ( i . e . , cell side length li→j is constant in Eq 2 ) , model equations can be simplified by daidt=ρIAA−μIAAai−∑jfi , j+∑jD ( aj−ai ) ( 8 ) fi , j=T ( pi , jai1+κTaj−pj , iaj1+κTai ) ( 9 ) pi , j=Pbaj∑jbaj ( 10 ) where ρIAA , μIAA , D , T , κT , P , and b are constants . Eqs 8–10 are identical to the simplified Model O ( Eqs 1 , 2 and 5 ) with Ga = μIAA , A = ρIAA/μIAA , Da = D , Ep = T , p = P/K , and φ0 ( aj ) =baj , except for the saturation effect of auxin from neighboring cells on the flux in Eq 9 . This effect would negatively affect the pattern formation in a manner that the saturation effect becomes strong and accordingly the pattern tends to disappear as κT increases . Therefore , this effect is not essential for generating a phyllotactic pattern , indicating that Eqs 8–10 are equivalent to Model O . We used one-dimensional arrays of N = 200 , 50 , or 40 cells and two-dimensional sheets of 20 × 20 or 14 × 14 hexagonal cells in the numerical simulations . Initial values of auxin , PIN1 , and molecule X are given by their equilibrium with 1 . 0% fluctuation . The numerical simulations were performed using the Euler method with time step Δt = 0 . 001 under the periodic boundary condition . Equations and regulatory functions used are summarized in S1 Table . Parameter values used are described in figure legends . To evaluate the spatial scale of auxin patterns generated by the numerical simulations , we used wavelength of auxin maxima ( L1 ) and average size of auxin maximum ( L2 ) as indices of the spatial scale ( Fig 1G ) . In a one-dimensional array of N cells in the periodic boundary condition , auxin concentration of cell n or apoplast ( n , n+1 ) is denoted here by cn , where n = 0 , 1 , ⋯ , N and c0 ≡ cN .
The phyllotaxis pattern of auxin maxima has been explained by a class of simplified mathematical models based on the feedback dynamics between auxin and PIN1 . Because these models do not consider extracellular region ( i . e . , apoplast ) , auxin moves directly between cells by PIN1-dependent directional transport and passive diffusion to change its distribution ( Fig 1A ) . On the other hand , PIN1 is asymmetrically localized to the cell membrane , preferentially toward neighboring cells with high auxin concentrations . We used Model O ( Eqs 1–3 ) , which is one of the simplest representations of such dynamics , to examine the spatial regularity control of auxin maxima pattern . As in previous reports [11 , 12] , we confirmed that Model O can form auxin maxima patterns with spatial regularity , an essential characteristic of phyllotaxis , focusing on its spatial scale .
In phyllotaxis pattern formation , auxin maxima involved in leaf primordia are formed in a self-organizing manner while maintaining a constant distance from each other . However , the molecular mechanism generating the spatial regularity remains unclear . This spatial regularity has been explained by simple mathematical models ( Model O; Figs 1A , 2 and 3A ) , in which PIN1 is polarized preferentially toward neighboring cells with higher auxin concentrations [11–13] . But these models have two major problems concerning spatial structure: one is the absence of extracellular space and the other is how cells perceive auxin concentrations of neighbors for PIN1 polarization . In this study , therefore , we intensively investigated how the spatial regularity of the phyllotaxis pattern is controlled under appropriate conditions for plant cells . We showed theoretically and numerically that auxin maxima patterns with large spatial scale are completely eliminated by introducing extracellular space ( Model A; Figs 1B , 3B , 4 and 5 ) . This strongly suggests that phyllotaxis pattern requires an unknown molecular mechanism as well as auxin–PIN1 mutual interaction . Furthermore , we found that regular patterns can be restored by the simple and plausible assumption that a diffusible molecule mediates the feedback from auxin to PIN1 polarization ( Model B6; Figs 3C and 8 ) . Although we mostly investigated in one-dimensional space , the same can be applied to the case of two-dimensional space . Model O can generate regular patterns of auxin maxima ( Fig 3A ) . This spatial regularity is completely disrupted by considering extracellular space in Model A ( Fig 3B ) , but is restored by introducing a diffusible molecule in Model B6 ( Fig 3C ) . This diffusible molecule plays the role of transmitting auxin concentration to neighboring cells . Auxin reportedly enhances the PIN1 localization at the cell membrane [27–29] . AUXIN-BINDING PROTEIN 1 ( ABP1 ) might act as an apoplastic auxin receptor in the signaling pathway of PIN1 polarization although the function of ABP1 has recently contended [28 , 30–32] . However , ABP1 probably makes no contribution to the phyllotaxis pattern formation regardless of whether it is an actual auxin sensor or not , because our study strongly suggests that the auxin maxima pattern cannot be established by the direct regulation of auxin on PIN1 polarization ( Figs 4–6 and S1–S3 ) . Although our study showed that a diffusible factor can restore regular patterns that are disrupted by the presence of extracellular space , this finding does not rule out other possibilities for the spatial communication between cells . One such possible mechanism is mechanical force , including stress and strain , which affects the morphogenesis of plants and animals [33–36] . Mechanical force could stabilize the outgrowth of leaf primordia by feedback mechanism in which mechanical stress induces alignment of microtubules , enhancing cell elongation and primordial outgrowth , which reinforces the stress field [37] . In contrast , experimental evidence that mechanical force is involved during auxin maxima formation has not yet been obtained [4 , 7] . However , auxin could alter the mechanical properties of the extracellular matrix by inducing cell-wall loosening [5 , 6 , 38 , 39] , suggesting that mechanical force may contribute to the pattern formation . Our model predicts that the spatial scale of generated patterns ( L* ) becomes large by increasing ν* , which follows ν* ∝ ( 1 + Ra ) /aeq|θ′ ( aeq ) | , where Ra ≡ Da/Epp and θ ( ai ) is the regulatory function of auxin on X synthesis ( Model B6; S1 Text , Eq 44 ) . Therefore , if molecule X predicted in this paper exists , L* is affected by the regulatory activity of auxin on the expression of X as well as by the amount of PIN1 . That is , under the conditions of a constant amount of PIN1 and constant activity of PIN1 recycling between cytosol and cell membrane , it is expected that , as the gene expression control by auxin becomes weak , the spacing between auxin maxima gradually increases and patterns suddenly disappear under a threshold of the control strength . This prediction could be used to experimentally validate whether or not our model is correct . Auxin affects the expression of many genes by cooperating with the TRANSPORT INHIBITOR RESISTANT 1/AUXIN SIGNALING F-BOX ( TIR1/AFB ) F-box proteins , the AUXIN/INDOLE-3-ACETIC ACID ( Aux/IAA ) transcriptional coregulators , and sequence-specific binding proteins called AUXIN RESPONSE FACTORs ( ARFs ) [40–42] . Because these factors are possible candidates that control the expression of molecule X , our model could be verified using plants showing various expression activities by genetically manipulating these factors . Our study could predict a diffusible factor that is essential for phyllotaxis pattern but remains to be found . This factor ( s ) X must satisfy the following requirements: Although such factors like X are not yet known , several diffusible molecules affecting PIN1 polarization have been reported . Strigolactone is a mobile plant hormone and cooperates with auxin to control shoot branching of plants . Auxin positively regulates the transcript of strigolactone biosynthesis genes and , in turn , strigolactone signaling triggers PIN1 depletion from the plasma membrane [43–45] . On the other hand , GOLVEN ( GLV ) genes encode small secretory peptides that are involved in root gravitropic responses and meristem organization in Arabidopsis . Transcription of GLV genes is rapidly induced by auxin , and the GLV peptide treatment stimulates the localization of auxin efflux carrier PIN2 at the cell membrane [46 , 47] . Another mobile plant hormone cytokinin plays important roles in various developmental events through crosstalk with other plant hormones including auxin [48–50] . For example , in vascular differentiation , cytokine affects the orientation of PIN proteins in cell membrane while auxin regulates cytokinin signaling [51 , 52] . Besides , also during lateral root organogenesis , cytokinin enhances the PIN1 depletion from cell membrane to affect PIN1 polarization [53–55] . Furthermore , it is reported that the localization of PIN proteins is affected by diffusible molecules such as jasmonate and narciclasine [56 , 57] . It is not yet clear whether these molecules are involved in the phyllotaxis pattern or not . However , in near future , we hope that a molecule predicted theoretically in this study will be revealed experimentally . | Self-organization of spatially regular patterns is critical for development and differentiation in multicellular organisms . Phyllotaxis , the arrangement of leaves on a plant stem , shows diverse patterns depending on plant species , which attracts many people because of its beautiful geometric configuration . In particular , it is well known that the spiral phyllotaxis is closely related to mathematical concepts such as the golden ratio and Fibonacci sequence . The phyllotaxis pattern is established by the mutual interaction between a diffusible plant hormone auxin and its efflux carrier PIN1 , but its molecular mechanism is still largely unknown . To understand how phyllotaxis the pattern is controlled , we have theoretically and numerically investigated mathematical models based on the auxin–PIN1 interaction . Our theoretical analysis predicts a diffusible molecule that is critical for the phyllotaxis pattern but is yet to be determined experimentally . Furthermore , we predict the molecular mechanism of this molecule , which must mediate the feedback signaling from auxin to PIN1 polarization . Our study provides a detailed insight into the theoretical and molecular basis for understanding the phyllotaxis pattern , leading to the creation of plant varieties with various phyllotaxis patterns in the future . | [
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] | 2018 | Spatial regularity control of phyllotaxis pattern generated by the mutual interaction between auxin and PIN1 |
Filoviruses such as Ebola virus ( EBOV ) cause outbreaks of viral hemorrhagic fevers for which no FDA-approved vaccines or drugs are available . The 2014–2016 EBOV outbreak in West Africa infected approximately 30 , 000 people , killing more than 11 , 000 and affecting thousands more in areas still suffering from the effects of civil wars . Sierra Leone and Liberia reported EBOV cases in every county demonstrating the efficient spread of this highly contagious virus in the well-connected societies of West Africa . In communities , canines are often in contact with people while scavenging for food , which may include sickly bush animals or , as reported from the outbreak , EBOV infected human bodies and excrement . Therefore , dogs may serve as sentinel animals for seroprevalence studies of emerging infectious viruses . Further , due to their proximity to humans , they may have important One Health implications while offering specimens , which may be easier to obtain than human serum samples . Previous reports on detecting EBOV exposure in canines have been limited . Herein we describe a pilot project to detect IgG-responses directed against multiple filovirus and Lassa virus ( LASV ) antigens in dogs from EBOV affected communities in Liberia . We used a multiplex Luminex-based microsphere immunoassay ( MIA ) to detect dog IgG binding to recombinant filovirus antigens or LASV glycoprotein ( GP ) in serum from dogs that were old enough to be present during the EBOV outbreak . We identified 47 ( 73% ) of 64 dog serum samples as potentially exposed to filoviruses and up to 100% of the dogs from some communities were found to have elevated levels of EBOV antigen-binding IgG titers . The multiplex MIA described in this study provides evidence for EBOV IgG antibodies present in dogs potentially exposed to the virus during the 2014–16 outbreak in Liberia . These data support the feasibility of canines as EBOV sentinels and provides evidence that seroprevalence studies in dogs can be conducted using suitable assays even under challenging field conditions . Further studies are warranted to collect data and to define the role canines may play in transmission or detection of emerging infectious diseases .
Ebola virus ( EBOV ) was first identified in the Democratic Republic of the Congo ( DRC ) in 1976 [1] and has caused multiple outbreaks , resulting in 30–90% fatality rates since then [2] . EBOV , a member of the Filoviridae family , causes hemorrhagic fevers , and currently no FDA approved drugs or vaccines are available to prevent or treat the disease . The current EBOV outbreak in the Democratic Republic of the Congo has claimed more than 1 , 000 lives and is the largest EBOV outbreak in the nations’ history according to the CDC . A new EBOV strain has been implicated as the cause for the outbreak with the proposed name “Tumba” [3] . The 2013–2016 outbreak in West Africa was caused by the Makona variant of EBOV and affected nearly 30 , 000 people , killing more than 11 , 000 in West Africa . Countries such as Sierra Leone and Liberia reported EBOV cases in every county with many , including Montserrado County where Monrovia is located , counting between 501–4 , 000 cases [4] . The severity and global spread of this outbreak has significantly increased countermeasure efforts [5] and has focused more attention on interactions within the affected communities during these outbreaks . The World Health Organization ( WHO ) has developed social mobilization and community engagement campaigns for intense transmission areas [6] . These efforts highlight the broadening net of investigative measures to control and understand this disease . Despite intense efforts , no reservoir host for the virus has yet been identified . However , epidemiologic studies have demonstrated that gorilla , chimpanzee , and duiker carcasses may in the past have been the significant sources of initial human infection [7] . The swift emergence , virulence , and disappearance of EBOV highlight the need to fully investigate the animal reservoirs that may be harboring the virus which includes both wild and domesticated animals . While vaccine candidates and antibody-based immunotherapeutics are in development to protect against or clear Ebola virus disease ( EVD ) [8 , 9] , EBOV animal reservoirs continue to remain elusive . Furthermore , the role domesticated animals may play in the community during outbreaks has remained understudied . Some evidence suggests that dogs may be infected with EBOV , however remain asymptomatic [10] . While dogs may be susceptible to infection , EBOV replication has been shown to be restricted in canine and feline cells [11] , further explaining the apparent asymptomatic display of canines . This suggests that dogs are not likely to be reservoirs but may serve as sentinels as they may mount an immune response when exposed to EBOV . As dogs scavenge for food sources , they are potentially exposed to high amounts of EBOV particles present on dead animals or humans , thus they could mount a detectable IgG response against EBOV even if infection is low and unproductive . Classically , anti-EBOV IgG has been measured using ELISA . This method was used to screen dog serum samples for anti-EBOV IgG after the 2001–2002 Gabon outbreak [10] . While ELISA is a reliable method for sero-surveillance , it is not as sensitive as the microsphere immunoassay ( MIA ) [12] . Additionally , ELISA is limited in that it can only detect one antigen at a time . In contrast , MIA allows for multiplexing several antigens to be detected in a single assay . Therefore , we developed a multiplex MIA to detect dog IgG reactivity to EBOV glycoprotein ( GP ) , viral matrix protein 40 ( VP40 ) , and nucleoprotein ( NP ) as well as GPs of Sudan virus ( SUDV ) , Marburg virus ( MARV ) , and an unrelated , but endemic , Lassa virus ( LASV ) . Using this assay we screened serum collected from domesticated dogs over 4 years of age in neighborhoods affected by the 2013–2016 EBOV outbreak in Montserrado County , Liberia . Our goal was to demonstrate that MIA-based sero-surveillance assays could be employed in resource and infrastructure poor environments in a reliable manner . The MIA-based assay described in this report provides evidence that domesticated canines may serve as unintentional sentinels for EBOV outbreaks in West Africa .
Sixty-four dog serum samples were collected during routine examinations by staff of the Ledlum Central Veterinary Diagnostic Laboratory from communities in Montserrado County , Monrovia , Liberia . Serum samples were collected between April and May 2018 from dogs within a roughly 40-mile radius around Monrovia in Montserrado County ( Fig 1 ) . All dogs were identified as being at least four years old by their owner from communities affected by the 2014–2016 EBOV outbreak . No stray dogs were used in this study . Samples were collected via cephalic vein bleeds to a volume less than 1 mL . Blood samples were allowed to clot for a minimum of 30 minutes before centrifugation at room temperature ( 20°C to 25°C ) at 1200 x g for 10 minutes . Sera were removed from the collection tubes and transferred to cryopreservation tubes . Samples were stored under refrigeration at the Ledlum Central Veterinary Diagnostic Laboratory . The coupling of individually addressable microspheres with EBOV VP40 , NP , GP , as well as SUDV GP , MARV GP , and LASV GP proteins was conducted as described previously [13 , 14] . Microspheres dyed with spectrally different fluorophores were also coupled with bovine serum albumin , and PBS as controls . Internally dyed , carboxylated , magnetic microspheres ( MagPlexTM-C ) were obtained from Luminex Corporation ( Austin , TX , USA ) . Detection of NHP and dog IgG was conducted as previously described [15] . Briefly , microspheres coupled with EBOV VP40 , NP , GP , SUDV GP , MARV GP , LASV GP , and control beads coupled to PBS and BSA were combined and diluted in PBS-1% BSA at a dilution of 1/200 . Fifty μL ( containing approximately 1 , 250 beads of each type ) of the microsphere suspension were added to each well of black-sided 96-well plates . Serum samples were diluted 1:1000 ( NHP ) or 1:200 ( dog ) in PBS-BSA and dog samples were serially diluted two-fold for titrations . Fifty μL of diluted serum were added to the microspheres in duplicate and incubated for 30 min on a plate shaker set at 700 rpm in the dark at room temperature . The plates were then washed twice with 200 μL of PBS-BSA using a magnetic plate separator ( Millipore Corp . , Billerica , MA ) . Fifty μL of red-phycoerythrin ( R-PE ) conjugated F ( ab’ ) 2 fragment rabbit anti-dog IgG ( H+L ) ( Rockland Immunochemicals , Inc ) were added at 1 μg/mL to the plates and incubated on a 96-well plate shaker for another 45 min . The plates were washed twice , as above , and microspheres were then resuspended in 100 μL of sheath fluid and analyzed on the MAGPIX Instrument ( MilliporeSigma ) . Data acquisition detecting the median fluorescence intensity ( MFI ) was set to 50 beads per spectral region . Antigen-coupled beads were recognized and quantified based on their spectral signature and signal intensity , respectively . Assay cutoff values were calculated first by taking the mean of technical duplicate values using the average MFI ( indicated as a solid black line ) . Cut-offs were generated by determining the mean of 1/3rd serum samples ( 22 ) showing the lowest MFI values plus three standard deviations as determined by Microsoft Office Excel program . Serum samples showing MFI values greater than the cutoff value were considered positive . If the cut-off value fell below the internal control BSA cutoff , the highest BSA-bead reading of 45 MFI was used ( indicated by a dotted line ) . Graphical representation of the data was done using Prism , Graphpad Software ( San Diego , CA ) .
Serological studies in West Africa have been conducted across a broad range of species and provide much insight into zoonotic diseases . This is the first study to report on the potential exposure of canines to filoviruses in Liberia . Furthermore , we report what appears to be a sustained level of detectable IgG up to four years after the 2014–2016 EBOV outbreak . This study is the first to implicate dogs as potential sentinels to zoonosis in West African communities . In this study , the primary goal was to establish a robust serological assay to test for filovirus antigen-reactive immunoglobulins using dog serum samples collected within a roughly 40 mile radius around Monrovia in Montserrado County , Liberia ( Fig 1 ) . Before deploying the assay to Liberia , reactivity and specificity was confirmed for all six viral antigens by testing serum samples from a rhesus macaque that survived infection with 1000 pfu of EBOV ( Kikwit strain ) . The assay background was low in the pre-infected sample while the post-infection sample displayed elevated IgG titers to EBOV antigens GP , VP40 and NP as expected ( Fig 2 ) . In contrast , there was no increase in MARV GP and only minimal increase in SUDV GP antigen reactivity , demonstrating the specificity of the assay . Interestingly , the serum reactivity for VP40 post-infection with EBOV was higher than that against GP . This phenomenon may be related to the low amount of glycoprotein compared to VP40 present on each virion [16] . This observation corroborates previous studies which indicate that EBOV GP is not as immunogenic as VP40 [17] . Overall , the assay contained minimal background and displayed EBOV specificity . As the focus of the study was to determine if dogs could be used as sentinels of infectious diseases in Liberia , age appropriate dogs were selected from communities affected by the 2013–2016 EBOV outbreak . Detectable levels of IgG to several antigens offered evidence that the assay was working in the field ( Fig 3 ) . Specifically , 53 . 1% of the samples showed antibodies binding to EBOV-VP40 , 26 . 6% reacted to EBOV-NP , 17 . 2% to EBOV-GP , 48 . 4% to SUDV-GP , and 23 . 4% to MARV-GP . In contrast , only 15 . 6% of samples reacted with LASV-GP ( Fig 3A ) , showing a lower seroprevalence rate for this arenavirus known to be endemic in Liberia . Upon analyzing dogs that showed reactivity with more than one antigen , we found that dogs 58 , 47 , 42 , 38 , and 35 were the most highly reactive with multiple antigens ( Fig 3B ) . Interestingly , one of these high titer dogs was from New Krew Town , one was from White Plains , and three were from Jerusalem community , respectively . This data may indicate hotspots of repetitive and/or early contact into Montserrado County , as the virus further spread into Monrovia and the community surrounding Redemption Hospital . Whether or not this indicates recent exposure in these communities is beyond the scope of this study but is important to consider nonetheless . Dogs 58 and 38 displayed high reactivity with EBOV-VP40 and SUDV-GP . Sample 42 appears highly positive for EBOV-VP40 and MARV-GP , whereas dogs 47 and 35 appear to display high reactivity with multiple antigens , having MARV and SUDV-GP in common . Collectively this data indicates that , in the field , canine serum samples appeared to show IgG reactivity with multiple filovirus antigens and almost no reactivity to LASV . Lack of LASV titers may be related to the sample origins’ proximity to the capital city of Monrovia . Recurrent studies of similar nature may help reveal if epidemiological information can be drawn from this type of surveillance . While titers observed are overall relatively low , it is important to note that these samples were collected from animals 3–4 years after the height of the Ebola crisis , so it is not surprising that IgG titers may have waned . Samples from rural villages or dogs of a younger age may show different reactivity . Interestingly , the dogs also displayed the most reactivity to EBOV-VP40 and SUDV-GP similar to what was seen in the sample from an experimentally infected rhesus macaque ( Fig 2 ) . The overall relatively low EBOV-GP titers in the dogs may be due to the amount of GP antigen present on EBOV virions , or a sign that they may have been exposed to another , closely related virus . The low titers may also have resulted from animals being only exposed to infectious material , but not productively infected with any filovirus . This highlights a limitation of this study in that we were not able to assess viral status of these animals . Still , this data highlight the sensitivity of the multiplex assay , which may have impeded previous ELISA-based seroprevalence studies , such as those conducted in bats . Earlier work from Gabon also reported seropositivity in dog sera [10] tested by ELISA with irradiated EBOV as the coating antigen . This study has experimentally-derived limitations . First , the origin and age of the animals from which samples were collected made it impossible to establish a negative baseline as no sera from confirmed unaffected dogs were available in the region and age-matched dog sera from other regions would not be a proper control due to different diet and health status . Therefore , our cutoff value for all analytes was based on the mean of 1/3rd of dog serum samples showing the lowest MFI values plus three standard deviations . Furthermore , due to unavailability of confirmed EBOV positive canine sera we cannot assess the magnitude of infection these animals may have experienced . Despite these limitations , we identified 47 ( 73% ) of 64 dog serum samples as potentially exposed to filoviruses . The most common antigen reactivities observed were EBOV-VP40 , SUDV-GP , and MARV-GP . Reactivity against SUDV-GP was generally higher than either EBOV , or MARV-GP . Considering that EBOV and MARV GP are rather distinct from SUDV GP among filoviruses [18] , this finding could warrant further investigation . Since no SUDV cases have been reported in Liberia , this could also be due to the presence of related , yet unknown viruses ( such as the recently reported Bombali virus [19] ) . Several dogs with apparent high IgG titers to one antigen proved to have high titers to multiple , but not all , antigens . The non-filovirus antigen reactivity remained relatively low for all dogs . To determine the robustness and specificity of the IgG reactivity , serial dilutions were conducted on several dog sera that appeared to have high titers to the recombinant filovirus antigens ( Dogs #58 , 47 , 38 , 35 ) as well as on samples from two dogs with no apparent IgG reactivity ( Dogs #27 & 7 ) . As expected , the reactivity ( shown as MFI ) reduces proportionally to the increase in dilution factor in the strongly IgG positive dogs whereas no change is observed in the dilutions from dogs appearing to be negative for filovirus antigen-specific IgG ( Fig 4 ) . This dose-dependent response further supports the multiple-reactivity data . Collectively , these phenomena may be an indicator of exposure to one or more filovirus particles acquired by canines from the environment . The results obtained using the multiplex filovirus assay were also analyzed specific to different communities by determining the numbers of dogs in each town that were positive for IgG binding to at least one filovirus antigen . Interestingly , towns such as Blaton , White Plains , Dixville , New Kru Town , and Seonkpa had detectable IgG to the filovirus antigens in 100% of the samples tested ( Table 1 ) . White Plains , Dixville , and New Kru Town had the highest percentage of multi-positive dogs: 80% , 75% , and 80% , respectively . We recognize that some towns are less represented than others in this study and further targeted sampling of other townships would benefit future studies . We also observed a slightly higher proportion of females ( 78 . 7% ) to males ( 62 . 1% ) , which displayed filovirus reactivity to one or more antigens . Thus , much information about the prevalence and impact of filovirus antigens in the environment can be gathered with this assay . Collectively these data reveal that the specificity and robustness of the developed MIA allow an efficient analysis of serum samples directly in endemic regions of Africa . The implications of animals testing positive for filovirus antigens other than EBOV surrounding Monrovia are perplexing . It has peaked our interest that the assay was specific in a controlled setting ( Fig 1 ) yet broad reactivity was observed with greater than expected frequency in the field . Since this is four years post-outbreak , it is difficult to speculate what may be causing broad reactivity . We suspect that humoral responses to antigen acquired through a contaminated meal , instead of virus being injected Intramuscularly as in animal disease models , may be one explanation for these variances . Future work may include IgA titer analysis as well to elucidate mucosal responses . Overall , this assay should also be applied to places with recent or ongoing EBOV outbreaks so that we may be able to determine how broad the reactivity profile is in such settings . Of importance would be to determine the utility of this method on human sera as well . In conclusion , we observed high numbers of dogs positive for filovirus-specific antibodies throughout Liberian communities in Montserrado County . Our data indicate that the multiplex MIA is specific and robust at detecting filovirus-reactive IgG in dogs . Further , these data support the feasibility of MIA to detect anti-filovirus IgG in a field setting , and suggest that dogs may serve as accidental EBOV sentinels . Previous studies have used ELISAs , which are limited not only in sensitivity but also by the number of antigens that can be tested for in a single assay . In contrast , the multiplexed MIA allows for rapid detection of immunoglobulins against a variety of antigens in a single run . This has the advantage of reducing the sample volume and assay materials , thereby easing transportation while allowing deployment to the field . Collectively , these advantages allow this assay to be effectively executed in resource- and infrastructure-poor regions of the world . We hope these data can provide a foundation for further filovirus-specific sero-epidemiology research in domesticated and wild animals in West Africa . | Ebola Virus ( EBOV ) and its related species cause hemorrhagic fevers for which there are no FDA- approved treatments . The 2014–2016 EBOV outbreak in West Africa infected over 30 , 000 people , killing more than 11 , 000 . This was the largest outbreak to date and Liberia was the unfortunate epicenter . In Liberia , EBOV cases were reported in every county . While preventative and therapeutic agent developments have received much attention , prophylactic measures involving Liberian communities have seen much less attention . In Liberia , dogs may warrant surveillance as they routinely interact with animals of the forest and people within communities . Despite scavenging the excrements and even bodies of infected individuals during the outbreak , dogs reportedly remained asymptomatic for EBOV . In collaboration with the University of Liberia and the Leon Quist Ledlum Central Veterinary Diagnostic Laboratory of Liberia , our team used a multiplex Luminex-based assay to detect dog antibodies ( IgG ) binding recombinant filovirus antigens or LASV glycoprotein in samples from animals that were present during the EBOV outbreak . We identified several communities in which 100% of dogs showed IgG responses reactive to one or more filovirus antigens . This preliminary report establishes the feasibility of conducting EBOV seroprevalence studies in resource poor outbreak sites in Africa using modern and economical serological assay techniques . | [
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] | 2019 | Serological evidence of Ebola virus exposure in dogs from affected communities in Liberia: A preliminary report |
The identification of a causal mutation is essential for molecular diagnosis and clinical management of many genetic disorders . However , even if next-generation exome sequencing has greatly improved the detection of nucleotide changes , the biological interpretation of most exonic variants remains challenging . Moreover , particular attention is typically given to protein-coding changes often neglecting the potential impact of exonic variants on RNA splicing . Here , we used the exon 10 of MLH1 , a gene implicated in hereditary cancer , as a model system to assess the prevalence of RNA splicing mutations among all single-nucleotide variants identified in a given exon . We performed comprehensive minigene assays and analyzed patient’s RNA when available . Our study revealed a staggering number of splicing mutations in MLH1 exon 10 ( 77% of the 22 analyzed variants ) , including mutations directly affecting splice sites and , particularly , mutations altering potential splicing regulatory elements ( ESRs ) . We then used this thoroughly characterized dataset , together with experimental data derived from previous studies on BRCA1 , BRCA2 , CFTR and NF1 , to evaluate the predictive power of 3 in silico approaches recently described as promising tools for pinpointing ESR-mutations . Our results indicate that ΔtESRseq and ΔHZEI-based approaches not only discriminate which variants affect splicing , but also predict the direction and severity of the induced splicing defects . In contrast , the ΔΨ-based approach did not show a compelling predictive power . Our data indicates that exonic splicing mutations are more prevalent than currently appreciated and that they can now be predicted by using bioinformatics methods . These findings have implications for all genetically-caused diseases .
Tremendous progress has been made in recent years in high-throughput technologies enabling fast and affordable massive parallel DNA sequencing . These methods are now being implemented both in molecular diagnostic settings and in basic research laboratories and hold great promise for discovering the genetic bases of rare and complex diseases [1] . However , even if next-generation sequencing has greatly improved the detection of nucleotide changes in the genome of each individual , the biological and clinical interpretation of most variants remains challenging , representing one of the major hurdles in current medical genetics [2 , 3] . Several reasons account for the difficulty in distinguishing which variants may cause or contribute to disease [4 , 5]: ( i ) the number of single-nucleotide variants ( SNVs ) detected in each genome is very high , ( ii ) many disorders are genetically heterogeneous and often caused by rare variants , ( iii ) access to clinical and family data , such as pedigrees , is frequently limited , ( iv ) in silico tools aiming at identifying deleterious changes are still imperfect , and ( v ) biological samples from variant carriers are rarely available for functional analysis . There is obviously a great need for overcoming these limitations , and considerable efforts are now deployed for developing strategies allowing prioritization of variants for functional testing [4 , 6] . In general , scrupulous attention is given to exonic SNVs mapping to protein coding regions , especially to those producing missense changes . Indeed , one of the most widely used strategies for narrowing down the number of variants susceptible of causing disease is to use a combination of in silico tools that focus on protein features ( such as PolyPhen-2 , SIFT and Mutation Assessor , among others ) [7–9] . Yet , such protein-centric view of the exome landscape merely represents a fraction of the “expression code” underlying each gene sequence . Current knowledge clearly indicates that , besides their protein coding potential , exonic sequences can play an important role in RNA splicing ( reviewed in [10 , 11] ) . Notably , ( i ) the first and the last 3 exonic positions are an integral part of 3’ and 5’splice site ( 3’ss and 5’ss ) consensus sequences , and ( ii ) exons may also contain splicing regulatory elements ( ESRs ) , such as exonic splicing enhancers ( ESEs ) and exonic splicing silencers ( ESSs ) . ESEs and ESSs usually correspond to 6–8 nucleotide stretches that serve as landing pads for splicing activator or splicing repressor proteins , respectively , thereby influencing the recruitment and activity of the splicing machinery . Whereas 3’ss and 5’ss consensus sequences have been extensively characterized leading to the development of in silico tools that reliably predict alterations in splice site strength ( such as MaxEntScan , SpliceSiteFinder-like and Human Splicing Finder , among others ) , ESRs are still poorly understood and generally regarded as difficult to predict by using bioinformatics approaches [12–15] . Lately , three new in silico approaches were described as promising tools to predict variant-induced ESR alterations . The first approach relies on ESRseq scores established through experimental assessment of the ESR properties of all possible 6-nucleotide motifs [16] . Calculation of total ESRseq score changes ( ΔtESRseq ) , taking into account overlapping hexamers and variant-induced changes , was then implemented by our group as a tool to predict the impact on splicing produced by exonic variants [17] . The second approach is based on ZEI scores derived from a RESCUE-type analysis that computed the relative distribution of hexamer motifs in exons and introns [18] . According to this study , the value of total HZEI score changes ( ΔHZEI , also corresponding to overlapping hexamers ) can be applied for predicting the impact on splicing of any exonic variant . The third and last approach relies on ΔΨ values ( Ψ , percent spliced in ) that were bioinformatically established upon compilation of RNAseq data obtained from different tissues , and integration of a large set of pre-established sequence features [19] . The ΔΨ-based approach was developed to predict splicing aberrations induced by any sequence change , either intronic or exonic , including variants affecting splice sites or splicing regulatory signals . Today , it is estimated that ~15% of all point mutations causing human inherited disorders disrupt splice-site consensus sequences , particularly at intronic positions [20] . Yet , it is now speculated , based on in silico data , that disease-causing aberrant RNA splicing may be more widespread than currently appreciated , with up to 25% of exonic disease-associated variants being expected to disturb ESRs [11 , 21] . Here , we decided to use the exon 10 of the MLH1 gene as a paradigm to experimentally evaluate these assumptions . MLH1 exon 10 was selected as model system for 3 main reasons: ( i ) MLH1 is the major gene implicated in Lynch syndrome , one of the most frequent forms of hereditary cancer worldwide , formerly known as hereditary nonpolyposis colorectal cancer ( HNPCC ) , ( ii ) this gene exhibits a large mutational spectrum , with at least 30% of variants being currently classified as variants of unknown significance and for which large national and international efforts persist in bringing clarification [22 , 23] , and ( iii ) alterations of potential ESRs were already reported for 3 SNVs in this exon [13 , 24] . We retrieved all SNVs identified in MLH1 exon 10 and analyzed their impact on splicing by resorting to both minigene-based assays and analysis of patient’s RNA when available . Moreover , we used our experimental data to perform a comparative analysis of the 3 newly developed in silico tools aiming at predicting variant-induced ESR alterations . Our results revealed an unexpected high number of splicing mutations in MLH1 exon 10 , most of which affecting potential ESRs , thus corroborating our initial hypotheses . Moreover , we confirmed the predictive power of ΔtESRseq- and ΔHZEI- based approaches , but not that of ΔΨ , for pinpointing this type of mutations .
We have recently shown that a high number of variants ( 15 out of 36 , i . e . 42% ) identified in the exon 7 of the BRCA2 gene have a negative impact on splicing [17] , a finding strongly suggesting that either BRCA2 exon 7 is exceptionally sensitive to exonic splicing mutations , or that this type of mutations is more frequent than currently estimated . To test these hypotheses , we decided to study the impact on splicing of SNVs identified in another gene , more specifically in MLH1 , a gene implicated in Lynch syndrome . Our approach was to use the exon 10 of MLH1 as a model system . We began by interrogating National and International public databases in order to retrieve all single substitutions reported in this exon ( Table 1 and Fig 1A ) . As a result , we found a total of 22 SNVs , most of which identified in cancer patients suspected of Lynch syndrome , including 15 missense , 3 nonsense and 4 synonymous variants . Only 9 of these SNVs are currently classified as clearly pathogenic , and 1 as clearly not pathogenic ( Table 1 ) . To assess the impact of all 22 variants on MLH1 exon 10 splicing , we performed an ex vivo splicing assay with pSPL3m-M1e10-derived minigenes ( Fig 1B ) . As shown on Figs 1C and S1 , the wild-type pSPL3m-M1e10 minigene predominantly generated transcripts containing exon 10 ( 79% exon inclusion ) , and a minority of transcripts without exon 10 . These results are in agreement with those previously reported by using the same minigene system [13] , and indicate that wild-type pSPL3m-M1e10 mimics , at least in part , the alternative splicing pattern of endogenous MLH1 transcripts [25–27] ( ~35% to 67% exon 10 inclusion in normal blood samples , according to a study from Charbonnier and collaborators [25] ) . Importantly , the minigene assay results revealed that 17 out of the 22 SNVs ( 77% ) altered the splicing pattern of exon 10 relative to wild-type ( Figs 1C and S1 ) . More precisely , 13 variants increased exon skipping , 4 variants increased exon inclusion , and only 5 variants showed no effect on splicing . Of note , 8 of the 13 exon-skipping mutations ( 5 missense , 1 synonymous , and 2 nonsense ) are currently classified as pathogenic ( Table 1 ) . The 13 variants increasing exon 10 skipping can be separated into three categories according to the severity of the splicing defect observed in the pSPL3m-M1e10 minigene assay: a first category consisting of 6 variants causing near-total exon skipping ( 7% to 9% exon inclusion: c . 793C>A , c . 882C>T , c . 883A>C , c . 883A>G , c . 884G>A and c . 884G>C; S1 Table and S3B Fig ) , a second category consisting of five variants inducing moderate skipping ( 26% to 67% exon inclusion: c . 793C>T , c . 794G>A , c . 845C>G , c . 851T>A and c . 882C>G , S1 Table ) and a third category consisting of 2 variants ( c . 840T>A and c . 842C>T ) that led not only to increased exon 10 skipping ( 51% and 18% exon inclusion , respectively , S1 Table ) but also to deletion of the last 48 nucleotides of the exon in a small fraction of the minigene transcripts ( S1B Fig , right panel ) . A bioionformatics analysis using splice site-dedicated algorithms revealed that both variants , c . 840T>A and c . 842C>T , create a 5’splice site at exonic position +46 ( MLH1 c . 836 , S1C Fig ) , which explains the 48-nucleotide deletion at the end of the exon but not the predominant exclusion of the entire exon . To gain further insight into the severity of the splicing defects of the 13 mutants that induced exon 10 skipping in pSPL3m-M1e10 , we tested these variants in the context of another splicing reporter minigene , pCAS2-M1e10 ( S2 Fig ) , previously shown to be less sensitive to splicing mutations than pSPL3m-M1e10 [13] . Our results indicate that , in contrast to pSPL3m-M1e10 and as expected , wild-type pCAS2-M1e10 exclusively generated transcripts containing exon 10 . We also observed that 5 out of 6 variants from the first category exhibited a behavior similar to the one observed with the pSPL3m minigenes , i . e . near-total exon 10 skipping; the exception being c . 882C>T that induced partial , though strong , exon 10 skipping in the pCAS2 system . Four out of 5 variants from the second category showed splicing defects less severe than the ones observed with pSPL3m-M1e10 and , in one case , c . 851T>A , no splicing anomaly was detected . One should note that the level of exon skipping induced by this last variant was borderline noticeable in the pSPL3m-derived minigene ( Fig 1C and S1 Table ) . As for the third category group , we observed that the splicing defects detected in the pSPL3m minigene were faithfully reproduced in pCAS2 for one of the variants ( c . 842C>T , which predominantly yielded exon-skipped products ) but not for the other ( c . 840T>A ) . The fact that , in the pCAS2 system , c . 840T>A predominantly produced transcripts containing exon 10 deleted of its last 48 nucleotides and almost no exon-skipped products ( S2 Fig ) suggests that the type and severity of the splicing defect caused by this variant depends on surrounding nucleotide context . We surmise , given their position at the termini of the exon , that 7 out of the 17 splicing mutations detected in exon 10 directly affect the definition of the reference splice sites , either at the level of the 3’splice site ( YAG│G , first exonic position underlined ) or of the 5’splice site ( CAG│GURAGU , 3 last exonic positions underlined ) [10] . Accordingly , the effects produced by these 7 variants ( c . 791A>G , c . 882C>G , c . 882C>T , c . 883A>C , c . 883A>G , c . 884G>A and c . 884G>C ) could have been predicted by algorithms commonly used to predict the strength of splice sites , such as Splice Site Finder , MaxEntScan , and Human Splice Finder ( HSF-ss ) . As shown on S3 Fig , in silico data derived from these programs strongly suggest that c . 791A>G improves exon inclusion by directly increasing the strength of the 3’splice site , and that the variants at the last three positions of the exon ( c . 882 to c . 884 ) , induce exon skipping by decreasing , to different extents , the strength of the 5’splice site . Moreover , according to this bioinformatics analysis , especially with MaxEntScan ( MES ) , one could have correctly predicted that the splicing defect produced by c . 882C>G is less severe than the one induced by c . 882C>T . Our data further indicates that a decrease in 5’ss MES score of ≥19% predicts very drastic exon 10 skipping . Because the 10 remaining splicing mutations detected in MLH1 exon 10 are located outside the positions directly defining the splice sites , we strongly suspect that they interfere with exon recognition by altering ESRs . This type of splicing mutations includes variants that map within the first two thirds of exon 10 and are responsible for inducing either exon skipping ( c . 793C>A , c . 793C>T , c . 794G>A c . 840T>A , c . 842C>T , c . 845C>G and c . 851T>A ) or exon inclusion ( c . 814T>G , c . 815T>C and c . 855C>T ) . To better understand how MLH1 exon 10 splicing is regulated and how certain SNVs disturb this process , we decided to functionally characterize different regions of the exon in the context of pcDNA-Dup . This three-exon minigene contains a middle exon particularly sensitive to alterations of splicing regulatory elements [13 , 17 , 28] . For this purpose , four partially overlapping segments covering the entire exon 10 of MLH1 ( R1 to R4 , ~30 bp-long each ) were individually inserted into the middle exon of this construct and then tested in the context of an ex vivo splicing assay ( Fig 2A and 2B ) . As shown in Fig 2C , our results revealed that region R1 ( MLH1 c . 791-819 ) strongly contributed to the recognition of the middle exon . Regions R2 ( c . 809-c . 840 ) , R3 ( c . 828-c . 859 ) and R4 ( c . 856-c . 884 ) had a positive effect on exon inclusion as well , although more moderate for R2 and R3 ( in comparison to R1 ) , and weak for R4 . Next , we tested all MLH1 exon 10 splicing mutations suspected of altering splicing regulatory elements ( n = 10 , Fig 2A ) . Importantly , we found that 8 out of these 10 variations altered the splicing efficiency of the middle exon relative to wild-type ( Fig 2D ) . Variants c . 793C>A and c . 793C>T ( both tested in the context of the R1 segment ) induced exon skipping , with c . 793C>A having an effect stronger than c . 793C>T and thus faithfully recapitulating the defects initially detected in pSPL3m-M1e10 and pCAS2-M1e10 minigene assays . The impact on splicing of variants c . 814T>G and c . 815T>C was also reproduced in the context of pcDNA-Dup as both variants led to an increase in middle exon inclusion ( R2 segment ) . Finally , variants c . 840T>A , c . 842C>T , c . 845C>G and c . 851T>A induced middle exon skipping ( R3 segment ) , with c . 842C>T having the more pronounced effect , reminiscent of the effects observed in the pSPL3m-M1e10 and pCAS2-M1e10 assays . Results obtained with c . 793C>T and c . 842C>T agree with those previously reported by using the pcDNA-Dup system [13] . In contrast , we found that the splicing alterations induced by c . 794G>A and c . 855C>T in the context of pSPL3m-M1e10 minigene could not be reproduced in the pcDNA-Dup assay . As shown on Fig 2D , c . 794G>A did not induce middle exon skipping ( R1 segment ) , and c . 855C>T did not lead to an increase in exon inclusion ( R3 segment ) , on the contrary , it slightly increased middle exon skipping . We hypothesize that the effect on splicing of these particular variants depends on surrounding nucleotide context . In conclusion , these experiments point to an apparent asymmetry in the distribution of ESRs in MLH1 exon 10 , with a potential ESE-enrichment at the 5’ portion , corresponding to about the first 2/3 of the exon , and/or an ESS-enrichment towards the 3’end . Moreover , the evidence of portability , i . e . that the splicing defects caused by most of the analyzed SNVs can be reproduced in a completely heterologous system , further supports the hypothesis that these variants modify bona fide splicing regulatory elements . Three independent in silico approaches based on the calculation of either ΔtESRseq [17] , ΔHZEI [18] , or ΔΨ [19] values were recently described as promising tools for predicting variants that potentially modify splicing regulatory elements . Because it remained to be determined if these tools could be applied to new cases and how they compare to each other , we next decided to evaluate their performance by using the output of the pSPL3m-M1e10 minigene assay as a new dataset . We started by separating the fifteen MLH1 exon 10 SNVs located at distance from the splice sites into three groups based on the minigene results ( S1 Table ) . The first group included mutations that increased exon 10 skipping ( n = 7 ) , the second group included variations with no effect on exon 10 splicing ( n = 5 ) and the third group included those that increased exon 10 inclusion ( n = 3 ) . Then , score differences ( ΔtESRseq , ΔHZEI , and ΔΨ ) were calculated for each variant and plotted in a parallel fashion in order to easily confront the discriminating power of the three bioinformatics approaches ( Fig 3 ) . Visual inspection of the plots revealed a striking distribution of ΔtESRseq clearly distinguishing the 3 groups of variants . More specifically , most variants with no effect on exon 10 splicing seemed to display ΔtESRseq values higher than those increasing exon skipping and lower than those increasing exon inclusion . This feature was not so clearly noticeable for ΔHZEI or ΔΨ . A statistical analysis further confirmed that ΔtESRseq values were overall concordant with the separation of variants in 3 groups according to minigene data ( ANOVA test , p-value of 0 . 03 ) , whereas ΔHZEI and ΔΨ were not ( ANOVA test , p-values of 0 . 15 and 0 . 59 , respectively ) ( Fig 3 and S1 Table ) . To better assess the performances of the three in silico approaches , we set preliminary upper and lower thresholds for predicting variant-induced exon skipping and variant-induced exon inclusion: -0 . 5 and +0 . 5 for ΔtESRseq , -20 and +20 for ΔHZEI , and -0 . 05 and +0 . 05 for ΔΨ [19] , respectively ( Fig 3 ) . As a consequence , score differences ( Δ ) smaller than the pre-established negative thresholds were considered indicative of increased exon skipping , whereas those higher than the positive thresholds were considered indicative of increased exon inclusion . We then determined the relative number of true calls produced by each in silico approach giving a particular attention to predictions of exon skipping , the most dreaded variant-induced splicing defect . As shown in Fig 3 and S1 Table , we observed that the ΔtESRseq approach produced the highest number of true calls , outperforming ΔHZEI and ΔΨ in discriminating variants that induce exon skipping from those that do not . More specifically , one can reckon 13 ( 87% ) true calls for ΔtESRseq against 9 ( 60% ) true calls for both ΔHZEI and ΔΨ . Overall , our results revealed a better sensitivity ( 86% versus 57% ) and specificity ( 88% versus 63% ) for ΔtESRseq than for ΔHZEI ( S1 Table ) . In spite of displaying a level of specificity similar to ΔtESRseq ( 88% ) , ΔΨ showed a very high false negative rate ( 29% sensitivity ) . Finally , a statistical analysis further confirmed that ΔtESRseq values could discriminate variants that increased MLH1 exon 10 skipping from those that did not , whereas ΔHZEI and ΔΨ could not ( t-test , p-values of 0 . 01 , 0 . 11 and 0 . 42 , respectively ) . Results discordant with experimental data , such as the ΔtESRseq values obtained for MLH1 c . 845C>G and c . 855C>T ( outliers in Fig 3 ) may be due to features not taken into account by the in silico approach such as the RNA secondary structure , the chromatin structure , the presence of ESRs longer than six nucleotides , or to a crosstalk with other splicing regulatory elements located nearby . Next , we wondered if these bioinformatics methods could predict the severity of the splicing defects , i . e . if there was a correlation between the level of exon inclusion detected in the pSPL3m-M1e10 assay and the score differences produced by the ΔtESRseq , ΔHZEI and ΔΨ approaches . To answer this question , we plotted the minigene data against the score differences generated by the in silico approaches , and performed a regression analysis with both variables . As shown on Fig 4 and S1 Table , our results revealed a linear distribution of the in silico score differences as a function of exon inclusion levels for ΔtESRseq and ΔHZEI but not for the ΔΨ approach ( Pearson correlation , p-values of 0 . 001 , 0 . 004 and 0 . 93 , respectively ) . Then , we decided to compare the performance of these three new ESR-dedicated in silico tools with that of three previously used methods , more precisely EX-SKIP [29] , ESEfinder [30 , 31] and HSF-SR [32] . As shown in S2 Table , EX-SKIP displayed relatively high specificity ( 75% ) but low sensitivity ( 43% ) in predicting exon-skipping mutations . Statistical analyses further revealed that EX-SKIP could not significantly distinguish MLH1 exon 10 variants that had an effect on splicing from those that did not ( t-test and ANOVA test , p-values of 0 . 22 and 0 . 26 , respectively ) . Still , we found that there was a correlation between the level of exon 10 inclusion and EX-SKIP values ( Pearson correlation , p-value of 0 . 02 ) . Data derived from ESEfinder , and especially HSF-SR , were difficult to interpret because of the presence of conflicting calls ( S2 Table ) . Moreover , given their lack of comprehensiveness and global quantitation , the performance of these two in silico tools could not be statistically analyzed , nor properly compared to that of ΔtESRseq , ΔΔHZEI , ΔΨ or EX-SKIP . To further evaluate the performance of the recently developed ΔtESRseq , ΔHZEI and ΔΨ-based approaches , we extended our study to the dataset that first revealed the predictive potential of ΔtESRseq [17] . This dataset includes a total of 32 BRCA2 exon 7 variants located outside the reference splice sites , some of which cause exon skipping ( n = 11 , suspected of altering ESRs ) and some do not ( n = 21 ) , as determined experimentally . As shown in S5 Fig and S3 Table , we found again by using this dataset that ΔtESRseq displayed a slightly better sensitivity and specificity than ΔHZEI ( in this case , 100% versus 91% , and 86% versus 76% , respectively ) whereas ΔΨ showed very low sensitivity but high specificity ( 18% and 94% , respectively ) . Interestingly , not only ΔtESRseq but also ΔHZEI could distinguish variants that increased exon skipping from those that did not ( t-test , p-values of 3 . 5 e-6 and 5 . 7 e-6 , respectively ) . Again , ΔΨ could not discriminate these 2 groups of variants ( t-test , p-value of 0 . 56 ) . Moreover , we observed a statistically significant correlation between the level of BRCA2 exon 7 inclusion and the score differences produced by the ΔtESRseq and ΔHZEI approaches ( Pearson correlation , p-values 1 . 1 e-6 and 0 . 9 e-3 , respectively ) , whereas ΔΨ showed no correlation ( Pearson correlation , p-value = 0 . 15 ) ( S6 Fig and S3 Table ) . Finally , we completed our study by analyzing three additional datasets experimentally characterized by other laboratories , namely: ( i ) a set of 42 BRCA1 exon 6 variants [29] , ( ii ) a set of 41 CFTR exon 12 variants [33 , 34] and ( iii ) a set of 24 NF1 exon 37 variants [35] . As shown in S4 , S5 and S6 Tables , here again , we found that ΔtESRseq and ΔHZEI . had better sensitivity than ΔΨ for predicting which variants induce exon skipping ( 67–100% and 68–100% versus 0–33% sensitivity , respectively , depending on the dataset ) . Statistical analyses further highlighted the good performance of ΔtESRseq and especially ΔHZEI , but not that of ΔΨ for discriminating variants that lead to exon skipping and for predicting the severivity of the splicing defect within these 3 additional datasets ( S7 Table ) . We surmise that out of the three new in silico approaches expected to predict ESR-mutations [17–19] , ΔtESRseq and ΔHZEI show the best performance at least for the five datasets analyzed in our study . Indeed , these two approaches displayed a better balance between sensitivity and specificity than ΔΨ , or the prior in silico method EX-SKIP , for predicting exon skipping-mutations ( S8 Table ) . Importantly , we found that ΔtESRseq and ΔHZEI can be used to predict not only the direction but also the severity of the induced splicing defects , more negative score differences being indicative of higher exon skipping levels ( S7 Table ) . To evaluate the physiological pertinence of the MLH1 exon 10 minigene assays and the above described in silico predictions , we set to compare our results with data derived from the analysis of RNA obtained from carriers of MLH1 exon 10 variants , especially of those located outside splice sites . Patients’ RNA being rarely available , we had the opportunity to obtain patient RNA samples for only two SNVs of interest: ( i ) MLH1 c . 793C>T ( Patient P793CT . 1 ) and ( ii ) MLH1 c . 842C>T ( Patient P842CT . 1 ) . First , peripheral blood RNA of patient P793CT . 1 ( heterozygous for MLH1 c . 793C>T ) was analyzed by RT-PCR , by using primers targeting exons 8 and 12 , and compared to those of three control individuals . Our results revealed a complex splicing pattern involving MLH1 exons 9 , 10 and 11 in all individuals ( Figs 5A and S4 ) . These data are concordant with previous studies describing exon 10 as an alternative exon that is naturally partially skipped , either alone or in combination with exon 9 and/or exon 11 [25–27] . We also observed that , as compared to controls , the sample derived from patient P793CT . 1 showed a lower amount of full-length transcripts ( FL ) , and a higher amount of transcripts lacking exon 10 ( Δ10 ) , indicative of aberrant splicing . Sequencing of the FL products revealed the presence of WT ( c . 793C ) exon 10 only ( Fig 5A , right panel ) , which suggests that in this sample the exon 10-skipped products mostly derive from the mutant allele ( c . 793T ) , and that the variant-associated splicing defect is very severe . Importantly , the observation that c . 793C>T is associated with a drastic splicing defect in endogenous MLH1 transcripts agrees with the data obtained in the minigene assays ( Figs 1 , 2 and S2 ) , especially in the context of pSPL3m-M1e10 c . 793C>T , which showed a very high level of exon skipping . To better evaluate the consequences of c . 793C>T on MLH1 expression , and because Sanger sequencing is known to have low detection sensitivity , we then decided to measure the relative contribution of the WT and mutant alleles to the production of the full-length ( FL ) transcripts by using an allele-specific primer extension method . Our findings , shown on Fig 5B and 5C , indicate that the FL transcripts expressed from the mutant allele were in fact present in the blood cells of patient P793CT . 1 but at very low level as compared to the WT allele ( ~10% ) . Given the results of our minigene assays , and RT-PCR analysis of patient’s RNA , we conclude that this allelic imbalance is mostly due to c . 793C>T-induced exon 10 skipping . Moreover , if one assumes that , in the blood cells of Patient P793CT . 1 , both alleles are transcribed in equal amounts , then one can deduce that the pSPL3m-M1e10 assay closely reflects the effects detected in vivo in patient’s peripheral blood , at least for this variant . We then analyzed LCL RNA from patient P842CT . 1 ( heterozygous for MLH1 c . 842C>T ) by comparing to those from 5 controls , including: 3 healthy individuals , and 2 Lynch syndrome patients ( P791-5TG . 1 and P882CT . 1 ) carrying MLH1 c . 791-5T>G and c . 882C>T mutations directly altering the 3’ss or 5’ss of exon 10 , respectively [36] . Results shown on S7 Fig indicate that patient P842CT . 1 has a splicing pattern similar to that of patients P791-5TG . 1 and P882CT . 1 , i . e . an apparent decrease in the amount of FL MLH1 transcripts , a mild increase in exon 9–10 skipping ( Δ9–10 ) and a drastic increase in exon 10 skipping ( Δ10 ) , as compared to healthy controls . Δ10 transcripts were detected in LCLs treated with puromycin , strongly suggesting that the aberrant Δ10 out-of-frame transcripts ( S4B Fig ) are degraded by the NMD pathway in these cell lines . As expected , the level of Δ9–10 in-frame transcripts did not increase in the presence of the NMD-inhibitor puromycin . Sequencing of the FL RT-PCR products of patients P842CT . 1 and P882CT . 1 revealed the absence of c . 842T and c . 882T mutant FL transcripts ( S7C Fig ) , indicating that c . 842C>T and c . 882C>T cause very severe splicing defects . These in vivo results clearly agree with the pSPL3m and pCAS2 minigene assays , which revealed predominant exon 10 skipping for both c . 842C>T and c . 882C>T ( Figs 1 , 2 and S2 ) . Importantly , these results highlight the physiological pertinence of the in silico predictions produced by the ΔtESRseq and ΔHZEI approaches . Indeed , these methods accurately predicted the variant-induced splicing aberrations observed in vivo in patients carrying exonic SNVs , as shown here for MLH1 variants c . 793C>T and c . 842C>T , and ( ii ) BRCA2 c . 520C>T or c . 617C>G [17 , 37] . As of note , the ΔtESRseq approach also accurately predicted the physiological effect of MLH1 c . 794G>A [13] . We conclude that ΔtESRseq and ΔHZEI , but not ΔΨ , are promising tools for prioritizing exonic variants for splicing assays .
The present study was initiated to follow up on our observation that a large number of variants in the exon 7 of BRCA2 induce exon skipping [17] . Our hypothesis was that exonic splicing mutations were also underestimated in other exons and genes . We thus decided to analyze the impact on splicing of all SNVs identified in the exon 10 of MLH1 ( n = 22 ) , a gene implicated in Lynch syndrome . Before this study , only 5 SNVs in MLH1 exon 10 had been reported as causing aberrant splicing; more specifically , they were all shown to increase exon 10 skipping [13 , 24 , 36] . Our work not only confirmed those initial findings but , importantly , uncovered 12 new splicing mutations ( 8 exon skipping- , and 4 exon inclusion-mutations ) , bringing the number of MLH1 exon 10 splicing mutations to a total of 17 . Hence , our results revealed a striking high proportion of splicing mutations in MLH1 exon 10 ( 77% ) , largely exceeding the fraction of splicing mutations detected in BRCA2 exon 7 ( 42% ) [17] . Moreover , we found that the majority of MLH1 exon 10 splicing mutations ( ~60% ) map outside the reference splice-site consensus sequences , indicating an important contribution of variant-induced ESR alterations in this exon . Importantly , among the 12 new splicing mutations in MLH1 exon 10 , we identified 4 SNVs causing increased exon inclusion . To our knowledge , this is the first report of SNVs having a positive impact on MLH1 splicing . Besides full-length transcripts , MLH1 is known to normally produce a fraction of transcripts lacking exon 10 , such as Δ10 , Δ9/10 , Δ10/11 and Δ9/10/11 [25 , 26] , with Δ9/10 being one of the most frequently reported alternative MLH1 isoforms [27] . It is possible that the 4 variants that increased exon 10 inclusion in our minigene assays also disturb MLH1 physiological alternative splicing leading to a higher production of FL transcripts and lower amount of Δ10 , Δ9/10 and Δ9/10/11 . Because the role of alternative MLH1 isoforms is still unknown [27] , it is difficult to predict the biological and clinical consequences of these splicing alterations . Of note , a few cases of variant-induced exon inclusion have already been described in other genes . Particularly , previous studies have shown that mutations that increase exon inclusion can have a significant clinical impact . For instance , they can behave as protective factors , as is the case of SMN2 c . 859G>C ( p . Gly287Arg ) , a variant that attenuates the severity of spinal muscular atrophy ( SMA ) by increasing inclusion of SMN2 exon 7 [28 , 38] . Moreover , mutations inducing exon inclusion can also be harmful , as is the case for the majority of mutations identified in the exon 10 of the Microtubule Associated Protein Tau gene ( MAPT ) ( reviewed in [39] ) . It has been shown that dysregulation of MAPT exon 10 splicing disrupts normal tau isoform ratio and leads to neurodegeneration and dementia: increased MAPT exon 10 skipping causes Pick disease , whereas increased inclusion typically causes FTDP-17 ( frontotemporal dementia with parkinsonism linked to chromosome 17 ) . Given the challenging need in medical genetics for stratifying exonic variants for functional analyses , we decided to use the experimental data generated in this study to evaluate the predictive power of in silico tools at discerning splicing mutations . We found that the impact on splicing of the seven MLH1 exon 10 variants mapping within the splice-site consensus sequences ( potential splice site-mutations ) were correctly predicted by splice site-dedicated in silico tools ( SSF , MES and HSF-ss ) . These findings confirm and extend previous studies that highlighted the good reliability of these algorithms for predicting exon skipping-mutations [12–15] , further pinpointing their interest as filtering tools in variant stratification strategies . As for the variants located outside the reference splice sites , our minigene data revealed 10 ESR-mutations and 5 variants with no impact on splicing . We took advantage of these results and selected four additional experimental datasets previously described in other genes [17 , 29 , 33–35] to evaluate the discriminating power of 3 bioinformatics approaches recently described as suitable for predicting variant-induced ESR alterations [16–19] . Our findings revealed that the ΔtESRseq and ΔHZEI ESR-dedicated tools show the best performance in identifying ESR-mutations , outperforming the previous bioinformatics method EX-SKIP , whereas ΔΨ did not show compelling predictive power . Our results further indicate that both ΔtESRseq- and ΔHZEI- based approaches can predict the severity of the variant-induced splicing defects , underlining the quantitative character of these methods . It is possible that the ΔtESRseq- and ΔHZEI-based approaches produced somewhat similar results because they both rely on the appreciation of hexamer sequences as ESRs . Interestingly , a correlation between ESRseq and ZEI scores has already been reported [18] suggesting that most ESRs are indeed defined by 6-nucleotide stretches and that ESE sequences are more frequently represented in exons than in introns . Contrary to our initial expectations , ΔΨ displayed the weakest predictive power out of the 3 new in silico tools dedicated to identifying ESR alterations . This was unexpected as this method had been validated by using a large set of previously published splicing data , such as data on a plethora of MLH1 nucleotide variants [19] . Close inspection of the validation set used on that study revealed a large excess of intronic variants relative to exonic changes , and also a considerable under-representation of known ESR-mutations relative to the total number of variants included in the dataset . Most of the positive predictions reported for ΔΨ [19] thus correspond to intronic mutations directly affecting splice sites . This may explain why we detected an excess of true negative calls relative to true positive calls when using ΔΨ-values for predicting ESR-mutations in the five datasets analyzed in this study , and suggests that the ΔΨ-based method may be overall suitable for predicting mutations directly modifying splice sites but not entirely reliable for predicting those affecting ESRs . In sum , our findings suggest that both ΔtESRseq- and ΔHZEI- , based approaches can be used to stratify exonic variants for functional testing , and that this strategy may help identifying disease-causing variants . We cannot exclude that ΔΨ-values may be useful in particular conditions and , conversely , that the ΔtESRseq- and ΔHZEI- , based approaches may not be suitable for the analysis of certain exons or genes . In the case of MLH1 , it is clear that severe exon 10 skipping causes Lynch syndrome ( reviewed in [27] ) . Skipping of MLH1 exon 10 leads to a shifted reading frame , resulting in a premature stop codon in exon 11 ( MLH1 p . His264Leufs*2 ) and probable degradation of the aberrant transcripts by NMD . Variants inducing total exon 10 skipping cause a drastic loss in FL MLH1 protein and are therefore considered deleterious . In contrast , the clinical significance of variants inducing partial exon 10 skipping is still unknown . First , it is unclear if the amount of FL transcripts produced in the presence of such variants is enough to fulfill MLH1 function . Second , it is possible that remaining FL MLH1 transcripts carrying missense variants lead to production of nonfunctional proteins . Additional analyses are thus necessary to determine the biological and clinical significance of partial exon skipping-variants , including protein assays , assessment of patient clinical history and family data . Given that exon 10 codes for part of an important domain of the MLH1 protein ( interaction with MUTSα ) [40] , we suspect that SNVs increasing exon 10 inclusion can either have a protective impact if co-occurring with variants showing the opposite effect , or a deleterious effect if introducing a missense change that severely impairs protein function . Thus , in the absence of further information , MLH1 missense SNVs inducing exon 10 inclusion , as well as those not affecting splicing , should be considered as variants of unknown significance ( VUS ) . In contrast , the clinical classification of synonymous substitutions not affecting RNA splicing can eventually evolve from VUS to likely not pathogenic depending on expert panel assessment [22 , 23] . In conclusion , our results revealed an unexpected high number of splicing mutations in the exon 10 of MLH1 , most of which affecting potential ESRs , and confirmed the predictive power of ΔtESRseq- and ΔHZEI-based approaches for pinpointing this type of mutations , at least in MLH1 exon 10 , BRCA2 exon 7 , BRCA1 exon 6 , CFTR exon 12 and NF1 exon 37 . In principle , the bioinformatics methods described in our study are amenable to automation and , as such , have the potential to be used as filtering tools for identifying disease-causing candidates among the large number of variants detected by high-throughput DNA sequencing .
Written informed consent was obtained from all individuals . We collected all SNVs reported in the exon 10 of MLH1 , until January 2013 , by interrogating the following public databases: UMD-MLH1 ( Universal Mutation Database-MLH1 , http://www . umd . be/MLH1/ ) [22] , LOVD ( Leiden Open Variation Database , http://chromium . liacs . nl/LOVD2/colon_cancer/variants . php ? select_db=MLH1 ) , dbSNP ( the Single Nucleotide Polymorphism database http://www . ncbi . nlm . nih . gov/SNP/ ) , and UniProtKB/Swiss-Prot ( the European protein sequence database , http://swissvar . expasy . org/cgi-bin/swissvar/result ? global_textfield=MLH1 ) ( Table 1 and Fig 1A ) . Nucleotide numbering is based on the cDNA sequence of MLH1 ( NCBI accession number NM_000249 . 3 ) , c . 1 denoting the first nucleotide of the translation initiation codon , as recommended by the Human Genome Variation Society . In order to evaluate the impact on splicing of each MLH1 exon 10 variant , we performed functional assays based on the comparative analysis of the splicing pattern of wild-type and mutant MLH1 reporter minigenes . These minigenes were prepared by using two different vectors: pSPL3m and pCAS2 . The pSPL3m plasmid , a modified version of the exon-trapping vector pSPL3 ( Invitrogen ) which in turn derives from pSPL1 [41] , carries two chimeric exons ( here named I and II , both containing rabbit β-globin and HIV Tat sequences ) separated by an intron containing BamHI and MluI cloning sites ( Fig 1B ) [13] . Expression of the pSPL3m minigene is driven by the SV40 promoter . The pCAS2 vector carries two exons ( here named A and B ) with a sequence derived from the human SERPING1/C1NH gene , separated by an intron with BamHI and MluI cloning sites ( S2A Fig ) . Expression of the pCAS2 minigene is under the control of a CMV promoter . The pCAS2 is a modified version of the previously described pCAS1 plasmid [13 , 42] . Two modifications were introduced into the exon A of pCAS2 relative to pCAS1: ( i ) the first 114 bp of exon A were deleted , and ( ii ) the SERPING1/CINH translation initiation codon was disrupted by replacing the sequence GATG ( initiation codon underlined ) by TCAC . The wild-type genomic fragment MLH1 c . 791-168_c . 884+187 ( MLH1 exon 10 and flanking intronic sequences ) was inserted into the BamHI and MluI cloning sites of the reporter plasmids pSPL3m and pCAS2 , yielding the three-exon hybrid minigenes pSPL3m-M1e10 and pCAS2-M1e10 , respectively ( Figs 1B and S2A ) . Minigenes carrying MLH1 exon 10 variants were prepared by site-directed mutagenesis by using the two-stage overlap extension PCR method [43] and the wild-type pSPL3m-M1e10 construct as template . Then , the mutant amplicons were digested with BamHI and MluI , and introduced into BamHI and MluI cloning sites of the pSPL3m-M1e10 minigene to replace the wild-type fragment . The inserts of all constructs were sequenced to ensure that no other mutations had been introduced during the cloning process . In some cases , as indicated ( S2 Fig ) , mutant inserts were digested from the pSPL3m-M1e10 minigene with BamHI and MluI and then subcloned into pCAS2 . Next , wild-type and mutant minigenes ( 1μg/well ) were transfected in parallel into HeLa cells grown at ~60% confluence in 6-well plates using the FuGENE 6 transfection reagent ( Roche Applied Science ) . HeLa cells were cultivated in Dulbecco’s modified Eagle medium ( Life Technologies ) supplemented with 10% fetal calf serum in a 5% CO2 atmosphere at 37°C . Total RNA was extracted 24 hours after transfection using the NucleoSpin RNA II kit ( Macherey Nagel ) according to the manufacturer’s instructions . Then , the minigene transcripts were analyzed by semi-quantitative RT-PCR ( 30 cycles of amplification ) in a 25 μl reaction volume by using the OneStep RT-PCR kit ( Qiagen ) , 100 ng total RNA , and pSPL3m- or pCAS2-appropriate forward and reverse primers ( SD6 and SA2 or pCAS-KO1-F and pCAS-2-R , respectively , as described in [13] and [37] ) . RT-PCR products were separated by electrophoresis on 2 . 5% agarose gels containing ethidium bromide and visualized by exposure to ultraviolet light under non-saturating conditions using the Gel Doc XR image acquisition system ( Bio-RAD ) . Semi-quantitative analysis , gel extraction and sequencing of the RT-PCR products were carried out as previously described [42] . MLH1 exon 10 fragments ( ~30 bp-long ) were analyzed for their splicing enhancer properties by performing a functional assay based on the splicing pattern of the pcDNA-Dup minigene [13] . This vector contains a β-globin-derived three-exon minigene with a middle exon particularly sensitive to the presence of exonic splicing regulatory signals . Expression of the minigene is under the control of the CMV promoter ( Fig 2B ) . The exonic fragments of interest , wild-type or mutant , were obtained by annealing complementary 5’-phosphorylated oligonucleotides carrying 5’-EcoRI and 3’-BamHI compatible ends . Then , the exonic segments were inserted into the EcoRI and BamHI cloning sites of the middle exon of pcDNA-Dup to produce hybrid pcDNA-Dup-M1e10-R minigenes . All constructs were sequenced to ensure that no other mutations were introduced into the middle exon during the cloning process . Transfection , RNA extraction and RT-PCR analysis were performed as described for the splicing minigene reporter assay except that RT-PCR reactions were performed with 150 ng total RNA as template , T7-Pro ( 5’-TAATACGACTCACTATAGG-3’ ) and Dup-S4-Seq-3R ( 5’-CGTGCAGCTTGTCACAGTGC-3’ ) as forward and reverse primers respectively , and 28 cycles of amplification . RT-PCR products were analyzed by electrophoresis as described above for the splicing minigene reporter assay . Three in silico tools were used to predict variant-induced alterations in 3’ and 5’ splice site strength , namely: SpliceSiteFinder-like ( SSF , http://www . interactive-biosoftware . com ) , MaxEntScan ( MES , http://genes . mit . edu/burgelab/maxent/Xmaxentscan_scoreseq . html; Maximum Entropy Model ) and the splice site module of Human Splicing Finder ( here named HSF-ss for splice site-dedicated HSF , http://www . umd . be/HSF/ ) . These algorithms were interrogated simultaneously by using the integrated software tool Alamut ( Interactive Biosoftware , France , http://www . interactive-biosoftware . com ) , as previously described [17] . Three newly developed in silico approaches were used to predict variant-induced alterations in exonic splicing regulatory elements ( ESRs ) : ( i ) calculation of total ESRseq score changes ( ΔtESRseq ) by using the method previously described by our group [17] with a small modification ( here , only exonic positions were taken into account ) , ( ii ) calculation of ΔHZEI values by using the HEXplorer method [18] , and ( iii ) assignment of ΔΨ values based on the Splicing Regulatory Model ( http://tools . genes . toronto . edu ) recently described by Xiong and co-workers [19] . Moreover , as indicated , we also resorted to three previously established ESR-dedicated in silico tools: ( i ) EX-SKIP ( http://ex-skip . img . cas . cz/ ) [29] in which we took into account the full nucleotide sequence of the exon of interest , ( ii ) ESEfinder ( http://rulai . cshl . edu/cgi-bin/tools/ESE3/esefinder . cgi ? process=home ) [30 , 31] , and ( iii ) the ESR module of Human Splicing Finder ( here named HSF-SR for ESR-dedicated HSF , http://www . umd . be/HSF3/ ) [32] . Results are presented as the mean ± SD of three independent experiments . Data derived from comparisons of experimental and in silico analyses were compared by using either the one-way ANOVA test or the Student’s t-test , and the Pearson’s correlation coefficient , as indicated . More specifically , the ANOVA test was used for assessing the performance of the bioinformatics tools in discriminating 3 groups of variants ( i . e . variants that increase exon skipping versus those with no effect on splicing versus those that increase exon inclusion ) , whereas Student’s t-test was used when only 2 groups of variants were taken into account ( i . e . variants that increase exon skipping versus those that do not ) . Correlation between exon inclusion levels and in silico predictions was measured by calculating Pearson correlation coefficients ( r ) . p-values and r are indicated in the figures . Results were considered significant when p-value <0 . 05 . Statistical tests were performed by using BiostaTGV ( http://marne . u707 . jussieu . fr/biostatgv/ ) . The power to distinguish mutations that induce exon skipping from those that do not was further assessed , for each ESR-dedicated in silico method , by calculating sensitivity and specificity values ( true positives x 100/ ( true positives + false negatives ) and ( true negatives x 100/ ( true negatives + false positives ) , respectively ) . Sensitivity and specificity were determined by taking into account the following thresholds: -0 . 5 for ΔtESRseq ( arbitrary threshold ) , -20 for ΔHZEI ( arbitrary threshold ) , and -0 . 05 for ΔΨ ( threshold previously established by the authors , [19] . Peripheral blood samples were directly collected into PAXgene Blood RNA Tubes ( Qiagen ) from which total RNA was extracted by using the PAXgene Blood RNA kit , according to the manufacturer’s instructions . EBV-immortalized lymphoblastoid cell lines ( LCLs ) were cultivated in RPMI medium ( Life Technologies ) supplemented with 2 mM of L-glutamine and 10% fetal calf serum , at 37°C in a 5% CO2 atmosphere . Before RNA extraction , LCLs were transferred into 6-well plates , at 2 . 5x106 cells/well , and incubated for 5 . 5 hours with/without 200 μg/ml puromycin . Then , total RNA was extracted by using the NucleoSpin RNA II kit ( Macherey Nagel ) . Written informed consent was obtained from all individuals . The splicing pattern of MLH1 transcripts expressed in peripheral blood and in LCLs was analyzed by semi-quantitative RT-PCR using the OneStep RT-PCR kit ( Qiagen ) in 25 μl-final volume reactions containing 100 ng of total RNA , a forward primer located in MLH1 exon 8 ( MLH1-RT-8Fbis , 5’-AAGGAGAGACAGTAGCTGATGTT-3’ ) and a reverse primer located in exon 12 ( MLH1-12R , 5’-TGCTCAGAGGCTGCAGAAA-3’ ) . To ensure that the assay was in the linear range , RT-PCR reactions were performed with 34 cycles of amplification ( S4 Fig ) . Then , RT-PCR products were separated by electrophoresis on a 2% agarose gel , gel-purified and sequenced . Allele specific expression ( ASE ) was measured by performing a SNaPshot assay ( ABI Prism SNaPshot , Fig 5B ) . First , RT-PCR products spanning MLH1 exons 8 to 12 were obtained , as described above , from a peripheral blood RNA sample of a patient carrying the heterozygous c . 793C>T substitution in MLH1 exon 10 ( Patient P793CT . 1 ) . In parallel , a segment encompassing MLH1 exon 10 was amplified by PCR from the genomic DNA of the same patient by using the Multiplex PCR kit ( Qiagen ) according to the manufacturer’s instructions . Briefly , PCR reactions ( 35 cycles of amplification ) were performed in a final volume of 25 μl with 100 ng of genomic DNA as template , a forward primer in MLH1 intron 9 ( MLH1-10-Bam-F , 5’-GACCGGATCCTTGGAAAGTGGCGACAGG-3’ ) and a reverse primer in intron 10 ( MLH1-10-Mlu-R , 5’-GACCACGCGTAATTAGTGAATAAATGAAGGAAAA-3’ ) . Then , 5 μl aliquots of RT-PCR and PCR products were treated with one unit of Shrimp Alkaline Phosphatase ( SAP , USB ) and 8 units of Exonuclease I ( Thermo Scientific ) in the presence of SAP buffer in a final volume of 10 μl . After 1 hour at 37°C , the reactions were terminated by incubating at 75°C for 15 minutes . Next , 2 μl aliquots of treated RT-PCR and PCR products were subjected to SNaPshot reactions , in a final volume of 10 μl , by using the SNaPshot Multiplex Kit ( Applied Biosystems ) and a reverse primer targeting the sequence immediately downstream MLH1 c . 793C>T ( SNAP-M1 . 793-R , 5’-GGAAGTTGATTCTACCAGAC-3’ ) . SNaPshot reactions were carried out by performing 25 cycles of primer extension ( denaturation at 96°C for 10 sec , annealing at 50°C for 5 sec and elongation at 60°C for 30 sec ) . Next , the reactions were incubated with 1 unit of SAP at 37°C for 1 hour , and terminated at 75°C for 15 minutes . Finally , the extension products were separated by electrophoresis and analyzed quantitatively by using an ABI PRISM-3100 Genetic Analyzer ( Applied Biosystems ) . SNaPshot results obtained from patient cDNA were normalized by those obtained from gDNA . | The biological interpretation of most disease-associated variants has become a real challenge , especially with the implementation of next-generation sequencing . Particular attention is typically given to protein-coding changes often neglecting the potential impact of exonic variants on RNA splicing . Here , we used the exon 10 of MLH1 , a gene implicated in hereditary cancer , as a model system to assess the prevalence of RNA splicing mutations among all single-nucleotide variants identified in a given exon by using minigene-based assays . Our study revealed an unexpected high proportion of splicing mutations in MLH1 exon 10 mostly affecting potential exonic splicing regulatory elements ( ESRs ) , which are typically difficult to predict by using in silico tools . We then used five experimental datasets ( MLH1 , BRCA1 , BRCA2 , CFTR and NF1 ) to evaluate the predictive power of 3 in silico approaches recently described for pinpointing ESR-mutations . What’s more , besides predicting which exonic variants affect splicing , ΔtESRseq and ΔHZEI values also indicated the direction and severity of the induced splicing defects . In contrast , the ΔΨ-based approach did not show a compelling predictive power . Our data indicates that exonic splicing mutations are more prevalent than currently appreciated and that they can now be predicted by using bioinformatics methods . These findings have implications for all genetically-caused diseases . | [
"Abstract",
"Introduction",
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"Methods"
] | [] | 2016 | Exonic Splicing Mutations Are More Prevalent than Currently Estimated and Can Be Predicted by Using In Silico Tools |
Evolutionary pressures act on protein complex interfaces so that they preserve their complementarity . Nonetheless , the elementary interactions which compose the interface are highly versatile throughout evolution . Understanding and characterizing interface plasticity across evolution is a fundamental issue which could provide new insights into protein-protein interaction prediction . Using a database of 1 , 024 couples of close and remote heteromeric structural interologs , we studied protein-protein interactions from a structural and evolutionary point of view . We systematically and quantitatively analyzed the conservation of different types of interface contacts . Our study highlights astonishing plasticity regarding polar contacts at complex interfaces . It also reveals that up to a quarter of the residues switch out of the interface when comparing two homologous complexes . Despite such versatility , we identify two important interface descriptors which correlate with an increased conservation in the evolution of interfaces: apolar patches and contacts surrounding anchor residues . These observations hold true even when restricting the dataset to transiently formed complexes . We show that a combination of six features related either to sequence or to geometric properties of interfaces can be used to rank positions likely to share similar contacts between two interologs . Altogether , our analysis provides important tracks for extracting meaningful information from multiple sequence alignments of conserved binding partners and for discriminating near-native interfaces using evolutionary information .
Protein-protein interactions are of fundamental importance in biological systems , and understanding the principles underlying these interactions is currently a major biological challenge [1] , [2] . Two complementary sources of information about protein complexes are available . High throughput techniques deliver abundant information about protein-protein interaction networks . For every node of these networks , a number of homologous sequences can be aligned to highlight slowly evolving regions and pinpoint putative binding sites at the surface of proteins [3] . On the other hand , a smaller but significant and rapidly growing number of protein complex 3D structures provide high resolution data , available in the Protein Data Bank [4] . The general purpose of the present work is to explore the possibility of using the available structural information to improve our understanding and interpretation of sequence alignments . To combine these two approaches , we focused on the perspectives provided by evolutionary information . Indeed , in the course of evolution , multiple selective pressures occur at protein surfaces in order to preserve interactions between partners , so that protein interfaces are more constrained and evolve more slowly than the rest of the protein surface [5] , [6] . However , these constraints are not specific enough to enable straightforward prediction of interfaces: in particular , most proteins have more than one possible interaction partner and their surface can contain several interface regions [7] . Building up on these evolutionary trends , the conservation of the global structure and architecture of complexes has been investigated . Above 30% sequence identity , the global quaternary structure of complexes was shown to be conserved [8] , as was the binding mode for inter-molecular domain-domain interactions [9] . To capture the molecular principles determining common binding modes , there is a need for more detailed investigations of “interface structure conservation” [10] . This is precisely the approach that we adopt in the present study . The evolutionary rate within the interface significantly depends on the degree of residue burial upon complexation [11]–[13]: evolution slows down in buried regions of the interface . Conserved residues also tend to be clustered in interfaces [14] . However , interface coevolution is a complex phenomenon . Correlated interface mutations are very difficult to pinpoint , in particular in transient interactions with an intrinsic need for fast adaptation [15] , [16] . Identification of direct residue contacts from sequence alone through pairwise correlation analysis requires a large number of aligned sequences [17] , [18] . Alternative methods exist to study more directly the coevolution of a given interface , but require a considerable amount of experimental effort [19] . The difficulty inherent in coevolution studies may be explained by the necessity to consider the local context of interface contacts [20] , [21] . Relying on sequence analysis , the SCOTCH method revealed a great versatility in the way interface physicochemical complementarity is maintained across evolution and underlined the importance of local compensations [22] . Molecular aspects of evolution such as epistasis also revealed possible mechanisms for the observed variability in the way proteins achieve binding and interaction specificity [23] , [24] . As mutual information is hard to extract from sequence alone , structure can be used as a complementary source of information to shed light on complex molecular coevolution events . Protein-protein interactions rely on a global architecture which can be described at the geometric or the physico-chemical level . Complexes between proteins that have different global folds can share similar binding sites [25] and it appears that a limited number of protein interface architectures likely cover the whole range of cellular functions [26] , [27] . The complementarity of interfaces is also based on a mosaic of physico-chemical properties at interface [28] , [29] . Different binding strategies have been observed [30] and case studies illustrated the importance of salt bridges and hydrogen bond networks [31]–[33] . Medium scale studies showed that , although hydrophobic interactions are central to binding , especially in obligate interactions [34] , some polar residues are also conserved [35] . Among these properties , it is still unclear which are the most relevant and how the underlying physico-chemical constraints can best be extracted from multiple sequence alignments . Such an issue is particularly critical when challenging a predicted docked model of complex against its evolutionary history . The in-depth structural analysis of homologous interfaces offers a unique opportunity to address this question in a quantitative manner . Our objective is to use as many structures of homologous interfaces as possible to understand the fate of deleterious mutations at the interface of complexes and to capture the most likely mechanisms buffering the destabilization of interfaces through the rewiring of contact networks . To tackle this challenge on a large scale , we relied on the InterEvol database [36] which we recently designed to explore the structure and evolution of protein complexes . In particular , InterEvol provides 1 , 024 non-redundant heteromeric structural interologs corresponding to conserved interactions between pairs of homologous protein chains [37] . We analyzed the conservation of interface contacts between these interologs , distinguishing between atomic , polar and apolar contacts . We found that overall , the conservation of polar contacts using usual descriptors is surprisingly low , rarely exceeding 30% . We thus explored whether alternative criteria may help to extract interface features correlated to a higher level of conservation . We show that anchor residues and apolar patches are of particular interest since they exhibit a significant increase in contact conservation . We also propose a hierarchy of interface properties which can be used to predict the likelihood for each residue to conserve its contact environment . These findings provide essential guidelines to read multiple sequence alignments and account for molecular plasticity at complex interfaces .
In order to analyze the conservation of interface contacts between pairs of residues , a large dataset of 1 , 024 pairs of non-redundant heteromeric structural interologs ( Dataset S1 ) was derived from the InterEvol database [36] , going as far as possible in sequence divergence while retaining structurally similar binding modes ( see section 15 in Text S1 and Figure S1 in Text S2 ) . In particular , the interface root-mean square deviation between any two interologs is always below 8 Å and in most cases ( 82% of interolog couples ) below 4 Å . The correlation between interface area within each couple of interologs is very good ( correlation coefficient 0 . 98 ) . As a first step , we analyzed the global number of atomic contacts for each interface ( see Methods and Figure 1B ) . We found that , on average , an interface contains 230 atomic contacts and the number of atomic contacts is roughly the same for any pair of interologs ( the numbers differ by 15% on average between two interologs ) . Over an average of 61 atomic contacts when grouped into residue-residue contacts , each interface has on average 2 salt bridges , 9 hydrogen bonds ( excluding backbone-backbone hydrogen bonds ) and 35 apolar contacts ( also grouped into residue-residue contacts ) . These distributions together with the composition of the interface and interface sub-regions is fully consistent with previous statistical analyses performed on smaller sets of heteromeric complexes [28] , [29] ( see section 1 of Text S1 and Table S1 in Text S2 for details ) . We then compared the specific positions involved in each contact , weighting their contribution by the number of atomic contacts they were involved in . Corresponding residues between interologs were defined from a structural alignment , as illustrated in Figure 1A . It is important to keep in mind that the nature of the amino acid can vary between corresponding residues in two interologs ( see Methods ) . Surprisingly , only 59 . 3% of contacts are conserved on average , meaning that between two homologous complexes , a given position in the interface of one monomer will likely interact with different positions in the binding partner . The corresponding mean of contact conservation is represented in light pink in Figure 2A . This corresponds to a range of situations depending on sequence divergence between the two interologs as displayed in Figure 2B . In order to have sufficiently populated subsets , the minimum interface sequence identities were binned into four categories , from very divergent to very similar interfaces: 0–30% , 30–50% , 50–70% , and 70–100% . A large spread in all distributions of contact conservation , represented in Figures S2A and S2B in Text S2 , shows that the conservation of atomic contacts is heterogeneous among interologs . As expected , the higher the sequence identity at interface , the more contacts are conserved . In particular , contact conservation increases sharply between the very divergent interologs ( below 30% sequence identity ) and the less divergent interfaces . The choice of looking at contacts to characterize plasticity implies to be careful about the different possible reasons for contact variation , which is why we defined several control datasets ( details about the datasets are given in section 15 of Text S1 , Dataset S1 and Figure S1 in Text S2 ) . To ensure that the positions of side-chains are well defined , a higher-resolution dataset interologs2 . 5 was constructed from the full dataset by restriction to X-ray structures with a resolution better than 2 . 5 Å . In interologs2 . 5 , the average proportion of conserved contacts is 61 . 6% , very similar to the whole dataset . We also built a redundant , non-exhaustive dataset redundant95 , containing 387 pairs of complexes with at least 95% overall sequence identity between both pairs of chains . In redundant95 , we obtain on average 84 . 8% of conserved contacts . The results for redundant95 are considered as an estimation of the experimental heterogeneity between near identical structures [38] . This heterogeneity explains part , but not all of the non-conserved contacts . Three sub-regions of the interface were defined depending on residue burial: core , support and rim ( see Methods and Figure S3A in Text S2 ) . A number of studies previously underscored the specificities of these sub-regions in terms of composition and evolutionary properties [11] , [28] , [39] . We found that atomic contacts in the core and support regions of the interface are significantly more conserved than the contacts in the rim region ( see Figure S3B in Text S2 ) . In particular , the atomic contact conservation for contacts involving at least one residue from the core and support regions ( in any of the two interologs ) is on average 63 . 2% while the atomic contact conservation for contacts involving at least one residue from the rim region ( in any of the two interologs ) is on average 48 . 9% ( p-value<2 . 2e-16 using a non-parametric Wilcoxon rank sum test ) . The individual evolutionary rates of interface residues were also found to modulate the conservation of the contacts they participate in: when the analysis is restricted to the most conserved residues in each interface ( i . e . those with a normalized Rate4Site score [3] of more than 80 , see Methods ) , the average contact conservation is 73 . 7% , significantly higher than the 59 . 3% average conservation over all residues ( p-value<2 . 2e-16 using a non-parametric Wilcoxon rank sum test ) . As could be expected , we thus identified the core and support regions as well as the slowly evolving positions as markers of contact conservation . However , various other conventional descriptors of the interface ( types of secondary structure , obligate or non-obligate nature of the interaction , orthologous or paralogous relationship between interologs ) were not found to influence significantly the conservation of atomic contacts ( see section 2 of Text S1 and Figures S4A–B in Text S2 ) . When the non-conserved contacts are examined more closely , a striking proportion ( around 39% ) actually corresponds to cases where at least one of the two residues is no longer at interface in the interolog . This phenomenon , which we call “switching out of the interface” , is a major and non obvious cause of non-conservation . In the whole dataset , on average , 26 . 4% of all interface residues “switch out” of the interface in the interolog . The percentage of “switching out” is lowered to 23 . 7% for interologs2 . 5 . In redundant95 , 11 . 4% of residues still switch out of the interface . Therefore , heterogeneity in the local structure of interfaces explains part , but not all of the fluctuations in the contours of the interface . Most fluctuations actually occur in the rim region: the “switching out” concerns mostly the interface periphery ( see section 3 in Text S1 and Figures S4C–D in Text S2 ) . Accordingly , 39% of rim residues switch out of the interface while 30% of support and only 7% of core residues switch out . Apart from the switching out effect , contacts involving rim residues are also intrinsically less conserved than for core and support residues . This double effect amplifies the versatility of contacts in the rim . Support residues switching out generally correspond to hydrophobic residues already quite buried in one monomer , which become even more buried in the interolog , to the point that they are no longer surface accessible . Looking in more details at the intriguing cases of core residues switching out , we observed that they generally correspond to residues which are further away from the geometric center of the interface or are not involved in a secondary structure element ( see section 3 in Text S1 for further details ) . To further investigate the relative contribution of different interface features to the probability for a residue to switch out , we used a logistic regression model . Details of the procedure are provided in the Methods . Briefly , from all the features tested in the logistic regression , six descriptors reported in Table 1 were found to significantly improve the prediction of residues switching out . These parameters come from two sources , relying either on sequence or on interface geometry . The logistic regression coefficients reported in Table 1 were estimated using a training dataset composed of the interface residues of one third of the 1 , 024 interolog couples selected at random . The other two thirds were used to test the predictive efficiency of the approach and the random splitting procedure was repeated ten times . For all the residues of the test dataset , the predicted probabilities to switch out were calculated . Residues were ranked and were progressively included in a ROC curve representing the fraction of true positives obtained versus the fraction of false positives . The resulting area under the curve reached 0 . 79 ( Figure S5A in Text S2 ) . The relative importance of the six features in Table 1 was analyzed by rating their impact for the reduction in the deviance of the logistic regression model . Detailed values are provided in Text S1 ( section 14 ) and Table S2 in Text S2 . Table 1 only summarizes that geometrical features are the most important , with the number of contacts of the residue and its location in the interface sub-regions ( support , rim , core ) as major contributors . When considering the structure of a complex and a sequence alignment of interologs , the equation provided in Text S1 ( section 14 ) can thus be used to predict which residues are most likely to switch out from the interface . We next analyzed the conservation of interface polar contacts: salt bridges and hydrogen bonds . On average , only 22 . 1% of salt bridges are conserved between interologs ( Figure 2A , red bar ) . The spread in the distribution of salt bridge conservation for each pair of interfaces , represented in Figure S2A in Text S2 ( red ) , is very high as there are few salt bridges per interface . The conservation of salt bridges is 23% in interologs2 . 5 and 54 . 6% in redundant95 . These values are surprisingly low for datasets of highly similar interfaces and high resolution: this is partly due to the restrictive distance cutoff ( 3 . 5 Å ) applied when defining salt bridges . If we consider charged contacts with a distance threshold between charged atoms of 5 . 5 Å ( dark red bar in Figure 2A ) , conservation reaches 86% in redundant95 , while it remains below 35% on average in interologs2 . 5 . This relaxed threshold was used in previous studies [11] , [40] and provides a means to get indirect access to potentially water-mediated interactions , showing that such interactions could explain a much larger proportion of the missing salt bridges in redundant95 than in the whole interolog dataset . Taken together , these results also show that experimental structural heterogeneity is largely insufficient to explain the very low conservation of salt bridges . Figure 2B shows the mean values of polar contact conservation as a function of minimum interface sequence identity ( in light red for salt bridges and dark red for longer distance charged contacts ) . Even at high sequence identity , the conservation of salt bridges is extremely low . However , the core and support regions and the evolutionary rate remain markers of higher salt bridge conservation . For instance , salt bridge conservation is 45 . 6% on average among residues with a normalized Rate4Site conservation score over 80 , and this increase is significant for sequence identities above 30% ( p-value<4 . 7e-3 in a Wilcoxon rank sum test ) . The distributions of polar contact conservation depending on interface sub-regions are represented in Figure S3E in Text S2 . Interestingly , cases of charge exchange between two positions ( which are counted as conserved contacts ) represent only 1% of the conservation cases , showing that binary substitutions of charged positions are extremely rare events in evolution . Therefore , artificial design strategies which search for direct effects of compensatory charge substitution to assess the physiological relevance of an interaction [41] , [42] are not representative of events which happen in the course of evolution . What happens in the cases of non-conserved salt bridges ? 38% of non-conserved salt bridges correspond to at least one residue switching out of the interface , 15% correspond to a charged contact at longer range in the interolog and 40% correspond to the mutation of at least one of the two residues into an uncharged residue . In interfaces where salt bridges are lost , other intra- or inter-molecular salt bridges or charged contacts generally occur so that charged residues do not remain isolated . In 75% of the situations where one of the two residues is mutated into a neutral or opposite-charge residue or switches out of the interface , but the other remains charged and at interface , the latter residue stays involved in at least one intra- or inter-molecular salt bridge or charged contact . This means that although the residues involved in salt bridges are frequently mutated , there remain significant charge compensation constraints and the energetic frustration created by a lost salt bridge or charged contact seems to be easily released through local plasticity , as illustrated in Figure 3A–C . The type of plasticity events expected for charged residues in an interface can be quantified from this analysis ( see Figure 3D and Figure S6 in Text S2 ) , which provides guidelines in interpreting their substitutions in multiple sequence alignments . The conservation of hydrogen bonds is also low: 27 . 8% on average for the whole dataset ( blue bars in Figure 2A–B ) . Hydrogen bond determination is particularly sensitive to resolution as it relies on precise geometry and orientation of atoms . We thus checked if the conservation was still low in a context of good resolution and high redundancy . The conservation is 62 . 4% ( respectively 65 . 6% ) in the subsets of redundant95 with resolution better than 2 . 5 Å ( respectively 2 . 0 Å ) , 29 . 7% in interologs2 . 5 and 30 . 0% in the subset of interologs with resolution better than 2 . 0 Å . This shows that hydrogen bonds are extremely versatile ( see sections 5 and 6 of Text S1 and Figure S4C–D in Text S2 for a detailed analysis of the cases of non-conserved hydrogen bonds ) . Overall , plasticity in hydrogen bonds occurs up to high sequence identities , although it is not as pronounced as for salt bridges . However , there are strong constraints on each interface so that most polar residues satisfy their hydrogen bonding potential , as detailed in section 6 of Text S1 . Apolar contacts were calculated as all atomic contacts involving apolar surface atoms ( C or S ) from any type of interface residue ( see Methods ) . On average , 51 . 7% of interface apolar contacts are conserved between interologs , as displayed in golden yellow in Figure 2A . The apolar contact conservation is 54 . 9% in interologs2 . 5 and 80 . 8% in redundant95 . A significant part of the non-conservation is thus due to the heterogeneity in the local structure of the interface . In Figure 2B , the average apolar contact conservation ( in golden yellow ) as a function of minimum sequence identity at interface clearly shows that apolar contacts are much more conserved than polar contacts for any range of interface sequence identity . Non-conserved apolar contacts correspond to at least one residue switching out of the interface in 25% of all non-conservation cases . More interestingly , in 48% of the non-conservation cases , the residues are no longer in pairwise apolar contact , but contact can be recovered through their involvement in apolar regions of the interface called apolar patches . Hydrophobic patches were previously characterized on protein surfaces and protein-protein interfaces [43] , [44] and shown to be meaningful in the perspective of prediction or protein design [45] . Here , apolar patches are identified as contiguous regions of the interface connected through at least four interface atoms from at least two different interface residues and sharing a delocalized property of apolarity ( see Figure S7A in Text S2 ) . Among the interfaces of the whole interolog dataset , on average 41% of all interface residues are involved in an apolar patch through at least one of their atoms . Apolar patches are well conserved between interologs , as illustrated in Figure 4 . 82% of patches correspond to an equivalent patch in the interolog and fluctuations in the patches occur mostly at the periphery of the patch . We first distinguished the behavior of residue pairwise apolar contacts with respect to their location in an apolar patch . We considered 3 situations , depending whether i ) both residues in contact are in a patch , ii ) only one is in a patch , iii ) none of them is in a patch . There is a significant difference in the conservation trend between the three , supporting the view that apolar patches coincide with a location in which apolar contacts are more conserved ( Figure 2A , three bars on the far right in shades of yellow ) . In particular , apolar contacts between two residues involved in apolar patches ( mean conservation of 57% ) are much more conserved than apolar contacts between two residues not involved in apolar patches ( mean conservation of 39% ) ( p-value<2 . 2e-16 in a Wilcoxon rank sum test ) . Rather than looking at apolar contacts between two specific residues , we also considered the possibility that apolar patches may conserve their contacts at the patch level , irrespective of the specific residues involved in the contacts . Under these conditions , we measured that the average conservation of the apolar contacts when clustered into bundles of contacts between two apolar patches reaches 84% . This high level of contact conservation holds for the different ranges of sequence identity ( see Figure S7B in Text S2 ) . The level of conservation of apolar contacts between patches is not directly comparable to atomic contacts since they involve clusters of residues , which makes them inherently more robust to mutations . To better assess the significance of such an increased conservation , we further probed what would be the conservation of apolar contacts between two randomly selected patches in contact . Random patches were generated with a distribution of patch size and distributions within core , support and rim as close as possible to the distributions for the real apolar patches ( see detailed procedure in Text S1 , section 10 ) . We also controlled that random patches faced each other in a manner similar to that occurring between naturally observed apolar ones . The average conservation of apolar contacts when they are considered between random patches ( clusters of residues ) rather than at the residue level ranges from 58% to 63% depending on the level of constraint that we applied to define the random patches . Whatever the conditions these values are significantly below ( p-value<2 . 2e-16 in Wilcoxon rank sum tests ) the average conservation of 84% observed for real apolar patches ( Figures S7D and S7E in Text S2 ) . Therefore , although apolar contacts appear moderately conserved at the pairwise residue level , apolar patches have a reservoir of plasticity for the contacts they engage in which can buffer the way contacts are rewired during evolution . For two apolar patches in contact , on average 65% of the residues involved in the patch participate actively in the contact and this proportion is similar in the two interologs . We wondered whether the property of apolar contact conservation between patches was mainly observed for obligate complexes and whether it would hold true if we consider the most transient interactions contained in the dataset . From a close inspection of the functions associated with the non-obligate interfaces , we extracted a non-exhaustive subset of 60 pairs of interologs that we could confidently assign to the transient category ( flagged in Dataset S1 , see section 15 in Text S1 for details ) . This subset was sufficient to consider meaningful statistics . Remarkably , we found that the distribution of apolar contact conservation between apolar patches obtained for this subset of interactions was undistinguishable from the rest of the dataset ( Figure S7C in Text S2 ) . “Anchor residues” were initially identified as the surface residues burying the largest solvent-accessible surface area upon binding ( usually one residue with ΔrASA≥100 Å2 , but sometimes two or three residues with a slightly lower ΔrASA ) and shown to generally coincide with functionally or kinetically important positions or energetic hot spots [46] . Compared to hot spots , the notion of anchor has the advantage that it can be defined on a geometric basis without thermodynamic analysis . Interface anchoring was also previously used in design [47] , [48] . For each interface in the interolog dataset , we identified the three residues from the core and support regions that bury the most accessible surface area upon binding ( and in any case , over 80 Å2 ) as “anchor residues” . Details about anchor residues are provided in Text S1 ( sections 11 and 12 ) and Figure S8 in Text S2 . In particular , anchor residues make more interface contacts than other core residues: on average an anchor residue makes 24 atomic contacts ( with a standard deviation of 10 ) and other core residues make 10 atomic contacts ( with a standard deviation of 7 ) . The distributions of the number of atomic contacts are significantly different between anchor residues and other core residues ( p-value<2 . 2e-16 in a Wilcoxon rank sum test ) . The proportion of anchor residues which are aligned with another anchor residue in the interolog is on average 53% . As a control , in redundant95 , 74% of anchor residues are aligned with another anchor residue in the interolog . The conservation of contacts involving at least one anchor residue was assessed . The average atomic contact conservation is raised from 63 . 2% for contacts involving core and support residues to 67 . 7% for contacts involving anchor residues ( p-value = 3 . 7e-8 in a Wilcoxon rank sum test ) . This is illustrated in Figures S8E and S8F in Text S2 and by the dark pink bar in Figure 2 . The conservation of anchor neighbors and atomic contacts is illustrated in Figure 5 in the case of an anchor which conserves 76% of its contacts although the substitution of a phenylalanine by a lysine residue represents a drastic physico-chemical change . The average conservation of contacts involving at least one anchor residue whose structural equivalent in the interolog is also an anchor residue is raised to 83% . Therefore , in comparison to the properties of the core , the relative increase in anchor conservation is moderate but significant . The general trend is globally that the more contacts a residue make across an interface , the more its contacts tend to be conserved . This is true in particular for anchor residues , which concentrate many interface contacts . This is consistent with the results we observed in developing the contact conservation predictor discussed below . The large scale analysis of interolog structures reveals a significant versatility in the way contacts redistribute among interfaces . We wondered whether several of the features that were analyzed might be combined to improve the prediction of the residues likely to conserve their contacts . We explored two distinct strategies either using the information contained in the set of redundant complexes from the redundant95 or following the logistic regression approach as developed before in the switching out section . First , we asked whether the structural flexibility observed in the set of redundant95 complexes might correlate with residues likely to change their contact networks during evolution . This idea follows the observations in [49] where the authors observed an overall relation between protein dynamics and conformational changes enabling sequence changes in evolution . From the InterEvol database , we could extract 52 cases of interfaces where for a given complex , we had the structures of both a redundant complex ( with over 95% sequence identity ) and at least one structural interolog . Relying on these 52 cases ( listed in Table S3 in Text S2 ) , we compared the structural flexibility ( contact conservation between a complex of interest and its redundant complex ) and the evolutionary flexibility ( contact conservation between the same complex of interest and its interolog ( s ) ) . Concerning salt bridges for example , we found that when a salt bridge is conserved in the redundant complex , then it is also present in the interolog in 35% of cases , a percentage significantly higher than the average 22% observed over the whole database . More details are provided in Text S1 ( section 13 ) for salt bridges and apolar contacts . Overall , the conservation of contacts in the redundant complexes provides some information about the potential conservation of contacts in the interolog . The second predictive approach aimed at developing a logistic regression model following the same procedure as in the switching out section . We searched which parameters would best discriminate the residues likely to retain most of their contacts between two interologous interfaces . A third of the 1 , 024 couples of interologs was randomly selected and used as the training dataset for the estimation of the logistic regression coefficients . The same six features as those identified in the switching out section were actually found to best predict the contact conservation of interface residues . The hierarchy of their importance is however different as shown by the impact of each of the six features on the reduction in deviance ( see Table 1 and details in section 14 of Text S1 ) . The sequence similarity scores of the residues contacting a residue of interest ( its “environment” ) now appears as the predominant variable followed by the number of contacts a residue is involved in . In contrast , the contribution of the different sub-region categories ( core/support/rim ) appears less prominent . From the coefficients reported in Table 1 , it is possible to use the equation in Text S1 ( section 14 ) to predict the residues most likely to conserve their contacts . When testing the predictive power of the logistic regression on the test dataset ( the remaining two thirds of the interolog couples ) , the area under the ROC curve reached 0 . 75 ( Figure S5B in Text S2 ) . It is particularly interesting that the number of contacts in which a residue is involved plays such a significant role since it is directly related to our observations that many anchors exhibit significant contact conservation with respect to other residues of the core . The property of anchors can be seen as the salient feature of the more generic “number of contacts” descriptor . Accordingly , there is also a significant enrichment of anchor residues among the best residues predicted from the logistic regression model supporting their ability to concentrate a conserved network of contacts ( see section 14 in Text S1 ) .
In this study , we have observed that although the binding modes between structural interologs are globally well conserved , there is a great diversity in the details of the interface arrangements at the residue level . Among the variety of the properties analyzed through the 1 , 024 couples of interologs , contact conservation was found significantly increased in two structural contexts: ( i ) between apolar patches and ( ii ) around anchor residues . These notions are defined based on the geometry and composition of each interface , independently of the concept of structural interology . We show that if we restrict the analysis to a few residues per interface which are actively involved in the binding ( anchor residues ) , contact conservation is significantly higher than for core and support residues ( Figure S8 in Text S2 ) . Even more strikingly , apolar patches are maintained between interologs and apolar contacts between these patches are especially well conserved . This is critically important when interpreting multiple sequence alignments under the assumption that a set of positions maintained their mutual contacts throughout evolution . Anchors can change their physico-chemical status as illustrated in Figure 5 ( the distribution of anchor amino acid types is given in Table S4 in Text S2 ) while apolar patches maintain their hydrophobic character distributed over a number of positions . It is important to underscore that non-hydrophobic residues also have to be considered to build these clusters , revealing such an invariant property . Hydrophobic residues represent 71% of the contributors to the apolar patches and significant contribution is also brought by arginine and to a lesser extent lysine side-chains ( see Table S4 in Text S2 ) . The conservation of both anchors and apolar patches holds true for obligate and non-obligate interfaces as predicted by NOXclass [50] . Among the non-obligate complexes , we isolated a subset of 60 pairs that we confidently assigned to the class of transient interactions and found that the conservation properties were the same , underscoring the robustness of our findings to multiple types of interfaces ( Figures S2A and S7C in Text S2 ) . We observed that polar contacts are much more versatile than apolar contacts , both in obligate and non-obligate interfaces . This is particularly intriguing since every interface satisfies its constraints on charge compensation ( although sometimes at longer distances ) and hydrogen bond saturation . In particular , salt bridges are very versatile , in agreement with a recent analysis of charged residue pairs performed on another set of homologous interactions [51] . Swapped charges almost never occur and strict conservation of charged residue pairs is rather rare . We go several steps further in tracking the fate of positions with apparent non-conserved salt-bridges or polar contacts . For every scenario of non-conservation , such as the loss of one charged residue in a salt-bridge pair or a mutation from a polar to an apolar residue , we quantified the mechanisms by which favorable interactions may be recovered ( Figure 3 , sections 6 and 7 of Text S1 and Figure S6 in Text S2 ) , laying the basis for a probabilistic framework on how to infer the type of plasticity events which may occur depending on the sequence divergence . Our systematic study thus opens several key perspectives regarding the mechanisms of interface evolution for heteromeric complexes . So far evolution of homo-oligomers has been particularly scrutinized underscoring the importance of symmetry [8] , [52] , [53] , the relative simplicity of tuning oligomeric states by introducing large hydrophobic amino acids at interfaces [54] and the role of insertions and deletions in enabling or disabling specific binding modes [55] . Here , we have more specifically investigated the plasticity at the position and side-chain levels in hetero-oligomers . The high degree of interface plasticity is probably tolerated through conformational epistasis allowing for mutations with little consequence to accumulate and change locally the environment until more drastic changes occur [24] . In this evolutionary scheme , our observations suggest that although epistatic events can significantly induce sequence drifts , apolar patches in contact do not dissolve during the evolution of homologous interfaces . While conservation of apolar patches may ensure a basal affinity between members of two protein families , variation in the nature of the anchors may switch binding specificities as a first order effect . Such an effect was observed and experimentally challenged [23] , [56] in the case of the colicin-IM complex in which we found the apolar patches to be conserved , while the anchors drastically switch their physico-chemical nature and drive major specificity changes , as illustrated in Figure 5 . Further mutations outside the anchoring sites can then shape more exquisitely the specificity profiles . The evolutionary constraints we have unraveled bring crucial information on how to improve scoring potentials for predictive docking using evolutionary information . Protein-protein interface prediction methods have been developed by combining evolutionary conservation and structural similarity , such as PRISM , which relies on the architecture of a template protein complex [57] . Our analysis provides very complementary insights since it aims at evaluating the likelihood of a protein-protein interface model by integrating as much evolutionary information as we found that was reliable , but without the use of a template interface . The predictors that we developed can be directly used to weight the contribution of various positions of a multiple sequence alignment to assess the likelihood of a structural model over the course of evolution . We propose that a hierarchy of rules should be applied when analyzing a structural interface model using the information brought by multiple sequence alignments . First , strong constraints should be applied to ensure that positions corresponding to anchors and apolar patches maintain a high complementarity in every homologous sequence . Outside these positions and given the prevalence of positions likely to switch out of interfaces , most concern should be directed toward core and support regions . Polar contacts were found to evolve in a versatile manner; yet , our quantification of the possible recovery scenarios can be used to rate the compatibility between a predicted interface and its evolutionary history . Altogether , these guidelines will provide highly valuable clues to better exploit the wealth of information contained in multiple sequence alignments towards prediction , as is the case for protein monomeric folds .
Several procedures for the calculation of a protein-protein interface have been used in previous studies . The most popular definition relies on the variation in solvent-accessible surface area ( rASA ) upon binding [58]–[60] , but there are alternative definitions based on residue environment [61] or interface contacts [11] . Following the latter definitions , interface residues were detected as those which either gain at least one structural neighbor upon complexation or make at least one contact with a residue in the other chain . Two residues are considered structural neighbors if their Cβ atoms ( Cα for glycine ) are within 8 Å of one another . Three interface sub-regions were defined depending on the number of neighbors a residue had and the number of neighbors it gained upon binding [11] , [39] . Two residues are defined as extended structural neighbors if their Cβ atoms are within 10 Å of one another; for glycine residues , we consider Cα atoms instead of Cβ . For each residue , the numbers of extended structural neighbors in the monomer and in the complex are calculated , and a burial index is then calculated for both the monomer and the complex as 0 if the residue has 15 neighbors or less , 1 if the residue has 24 neighbors or more , and a fraction in between ( 1/9 for 16 neighbors , 2/9 for 17 neighbors and so on ) . Then , if the difference in burial index between the monomer is over 0 . 5 , the residue is considered “core” ( it becomes significantly more buried upon complexation ) ; else , if the average of the monomer and complex burial indices is over 0 . 6 , the residue is considered “support” ( significantly buried in both the monomer and the complex ) , otherwise it is considered “rim” ( not very buried even in the complex ) . Details are available in Figure S3 in Text S2 . For each pair of interfacial residues in contact in one interolog , the corresponding pair of residues in the other interolog was identified thanks to the structural alignment ( performed using MATRAS [62] ) and we assessed whether this other pair of ( structurally equivalent ) residues was also in contact ( Figure 1A ) . Only those residues with a structural equivalent in the interolog ( determined on the basis of the structural alignment ) were retained in the definition of interface contacts and their properties . For each type of contact , the conservation is calculated as the ratio of the number of conserved contacts over the sum of the numbers of conserved and non-conserved contacts . This corresponds to the Jaccard index ( similarity coefficient ) between the graphs of contacts in each interolog . For atomic contacts and apolar contacts , if a contact between two positions exists in both interologs , the corresponding edge of the graph is weighted by the average number of atomic contacts between the two positions; if the contact exists in only one of the two interologs , the edge is weighted by the number of atomic contacts in the interolog where the contact exists . In order to avoid detecting non-conservation effects due to gaps in the structural alignment of a pair of interologs , interfaces were restricted to residues which were structurally aligned with another residue in the interolog . We measured the sequence divergence of each pair of interologs using the minimum sequence identity at interface [9] . Atomic contacts were calculated on the basis of an α-shape representation of the interface [63] . To compare contact conservation between two interologs , atomic contacts were grouped depending on the residues they involved . Charged contacts were defined as contacts between a N atom from arginine , lysine or histidine and an O atom from aspartate or glutamate within a 6 Å distance , salt bridges as charged contacts with a distance threshold restricted to 3 . 5 Å . Hydrogen bonds were defined by HBexplore with standard criteria [64] . Apolar contacts were calculated using the α-shape representation of atomic contacts , including all C and S surface interface atoms belonging to interface residues and using a van der Waals radius expansion of 0 . 7 Å ( half the standard probe size ) for polar atoms [43] , [44] . Apolar patches for each side of the interface were defined as contiguous regions ( based on the α-shape connections between surface interface C and S atoms and using a van der Waals radius expansion of 1 . 4 Å i . e . one standard probe size for polar atoms ) containing at least four atoms from at least two different residues . To enable comparison between interologs , patches were iteratively merged so that one patch corresponded to a maximum of one patch in the interolog . Two patches ( one on each side of the interface ) were considered in contact if there was at least one apolar contact between them . Details are given in Text S1 ( sections 8 and 18 ) and Figure S7 in Text S2 . The notion of anchor provides a simple way to identify important residues for the interaction using geometric criteria [46] . In the present study , for each interface , we picked anchor residues as up to three residues from the core and support regions which bury the most solvent-accessible surface area ( rASA ) upon binding and in any case , more than 80 Å2 . rASA values were assessed for each residue using NACCESS [65] . Details are given in section 11 of Text S1 and Figure S8 in Text S2 . For each pair of structural interologs , a pair of multiple sequence alignments was generated using InterEvolAlign [36] . The evolutionary rates of interface residues were computed using the Rate4Site algorithm [3] . The conservation scores were normalized between 0 and 100 for each chain . The analysis was restricted to evolutionary rates calculated from multiple sequence alignments containing more than 10 sequences . Details are given in section 19 of Text S1 . The R package was used to perform statistical tests , as well as to build and analyze the logistic regression models and assess the importance of the various parameters [66] . All the p-values presented in this paper were calculated using non-parametric Wilcoxon rank sum tests . The confidence intervals in Figure 2 and Figures S7D and S8F in Text S2 were obtained by performing a bootstrap on the population of 1 , 024 interolog couples . This consisted in randomly drawing one half of the dataset ( without replacement ) one thousand times , calculating the mean value of contact conservation in each of the 1 , 000 resampled populations and extracting the intervals containing 95% of the calculated mean values . Two predictors , one for the switching out of the interface , and another one for the conservation of atomic contacts , were built on the basis of the various interface features described in the Results section . Both predictors relied on a simple logistic regression to fit the coefficients corresponding to the chosen parameters and their quality was assessed on the basis of a ROC curve . The interface residues were split into a training dataset ( interface residues from one third of the interolog couples drawn at random ) and a test dataset ( interface residues from the remaining two thirds of the interolog couples ) . This random partition of interfaces was repeated ten times . Each time , the logistic regression was performed on the training dataset and each residue in the test dataset was scored on the basis of the coefficients obtained in the regression . The residues in the test dataset were then ordered from the best score to the worst score and a ROC curve was drawn by progressively including all residues from all interolog couples , starting from residues with the best score towards residues with the worst score . The relative importance of all 6 parameters in both predictors as well as their significance were assessed on the basis of standard tests of deviance ( see section 14 in Text S1 and Table S2 in Text S2 ) . | Unraveling how interfaces of protein complexes coevolved is of major importance to improve our ability to predict their structures and design novel binders . Proteins whose interaction was maintained throughout evolution generally have their homologs binding in a similar manner while their sequences can have significantly diverged . Constraints holding proteins together should be captured from the growing body of available multiple sequence alignments . However , it remains unclear which features of the interfaces provide most tolerance to mutations and it is unknown whether any invariant properties may help to extract meaningful signals from sequence alignments . To solve this issue , we tackled an unprecedented large scale analysis of more than 1000 non-redundant couples of structural interologs . Structural interologs are pairs of complexes of known structure whose chains are homologs . We quantitatively measured how the networks of contacts varied between two interfaces . Although highly versatile , we found that contact networks were more conserved for residues acting as anchors and for apolar contacts when they are clustered into surface patches . Altogether , our results provide major guidelines for exploiting the wealth of evolutionary information contained in the sequences of binding partners . On those bases we developed a method to predict which residues most likely conserve their contacts . | [
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] | 2012 | Versatility and Invariance in the Evolution of Homologous Heteromeric Interfaces |
While hippocampal and cortical mechanisms of memory consolidation have long been studied , their interaction is poorly understood . We sought to investigate potential interactions with respect to trace dominance , strengthening , and interference associated with postencoding novelty or sleep . A learning procedure was scheduled in a watermaze that placed the impact of novelty and sleep in opposition . Distinct behavioural manipulations—context preexposure or interference during memory retrieval—differentially affected trace dominance and trace survival , respectively . Analysis of immediate early gene expression revealed parallel up-regulation in the hippocampus and cortex , sustained in the hippocampus in association with novelty but in the cortex in association with sleep . These findings shed light on dynamically interacting mechanisms mediating the stabilization of hippocampal and neocortical memory traces . Hippocampal memory traces followed by novelty were more dominant by default but liable to interference , whereas sleep engaged a lasting stabilization of cortical traces and consequent trace dominance after preexposure .
Memory traces of episodic-like events are encoded in parallel by the hippocampus and neocortex throughout the day , but their retention over time is often transient . Traces subject to consolidation are retained , whereas later memory retrieval is unsuccessful when consolidation fails or is insufficient . Consolidation in both the hippocampus and neocortex is , however , now recognised as a complex set of processes involving both “cellular” mechanisms that operate largely within individual neurons and “systems” mechanisms that include network interactions across brain areas [1–4] . An additional mechanism called “reconsolidation” enables consolidated traces to be updated , indicating that stabilization need not imply fixation [5–7] . The distinction between cellular and systems consolidation is therefore not a sharp one , for the enactment of systems consolidation ( involving interactions between hippocampus and neocortex ) will necessarily involve the mechanisms of cellular consolidation as well . This overlap of mechanisms contributes to the challenge of studying of how hippocampal and cortical consolidation interact . The overarching aim of this study was to investigate the interaction of hippocampal and cortical consolidation with respect to the retention of two potentially incompatible associations . Consider the following hypothetical situation . An experimental subject , be it human or an animal model , is required to learn first one thing and then later something different that may even contradict the first thing . In the procedural domain , it is important that the new skill overrides the first one and is then expressed in isolation ( e . g . , learning new balancing skills when riding a bicycle ) . However , in the episodic domain , it can be beneficial for the subject to remember both things even when they contradict one another ( as in , “I used to think that John liked Mary but I now know it is only Mary that likes John” ) . This raises the conceptually deep issue of when new knowledge should interfere with and so “overwrite” earlier knowledge and when two items of ostensibly contradictory knowledge should both be retained . Morris and Doyle [8] trained rats in a hippocampal-dependent watermaze task over many days to find a hidden escape platform in the northeast corner of the pool ( in practice , this location was geometrically counterbalanced ) . Once this memory was well established , a “reversal” procedure was instituted such that , over eight trials , the platform was hidden in the opposite southwest corner . The key variable manipulated in the experiment was the interval of time between these eight trials ( 30 s or 24 h ) . In the 24-h condition , the animals learned the reversal and thereafter always searched for the platform in the southwest corner in successive memory tests over several weeks until the memory was lost . The animals also learned the reversal in the 30 s condition and first searched persistently in the southwest corner during an initial posttraining memory test , but , without any further training , they switched to searching preferentially in the northeast corner during a memory test conducted 12 d later . The amount of training on the reversal ( eight trials ) was exactly matched , but arguably the opportunity for engaging hippocampal ( fast ) and cortical ( slow ) consolidation mechanisms differed as a function of the short versus long intertrial intervals , respectively . In the former case , two incompatible traces were retained; in the latter , the first memory was overwritten . The present study builds on this preliminary observation as a first step towards a systematic analysis of the intriguing issue of interference versus mutual retention . In the hippocampus , protein synthesis–dependent cellular consolidation acts soon after encoding as a selective filter to enable traces to be retained for longer [9–12] , a process now known to be enhanced by postencoding novelty [13] , possibly via a ventral tegmental area-hippocampal formation feedback loop [14] . Separately , cortical consolidation can occur ( especially during overnight sleep ) to guide and stabilize network interactions between the hippocampus and neocortex [15–22] . This likely operates using overlapping mechanisms ( i . e . , both cellular and systems mechanisms ) but with the passage of sleep activating distinct neural mechanisms to enable either stable , episodic-like memory traces in the cortex , the potential loss of contextual associations , and/or the successful assimilation of new information with prior knowledge [23–27] . Cortical consolidation is widely thought to be a slower process [15] , but there is growing evidence that it can sometimes be initiated soon after learning and act relatively quickly , such as during sleep [2 , 20 , 27 , 28] . We therefore sought to create two conflicting memory traces and then identify manipulations that would favour interference and loss or dominant behavioural expression of one contradictory memory without loss of the other . Postencoding novelty in the waking state or the opportunity for sleep soon after training were two distinct behavioural manipulations used to potentiate hippocampal or neocortical consolidation , respectively . These were supplemented by pretraining to assist assimilation with prior knowledge ( cortical ) or an interference protocol ( likely to operate preferentially in the hippocampal domain ) . One complication was that the novelty condition necessitated the simultaneous use of brief sleep deprivation to distinguish it from the sleep condition , and this necessitated an additional control study to check that brief sleep deprivation itself did not alter memory performance . Another unavoidable concern was that it is unlikely that novelty or sleep act exclusively on the hippocampus or cortex , respectively . Nonetheless , while less clear-cut than would be optimal , there are grounds for believing that postencoding novelty will have a preferential impact in the hippocampus [13 , 29] , whereas sleep has a preferential impact on the interactions between the hippocampus and neocortex [16 , 17] . In the context of our experimental design , we can think of the two competing memory traces as occupying each side of a children’s “seesaw . ” The relative dominance of one or the other trace is then “flipped” by changing behavioural parameters of training that likely affect hippocampal and cortical consolidation preferentially [30] . These manipulations being behavioural , it was incumbent upon us to identify whether potential neural markers of consolidation , such as immediate early gene ( IEG ) expression , were activated differentially at these two relevant anatomical sites . Our aim here was not to compare detailed patterns of expression across hippocampal subregions or cortical brain regions , nor to conduct a comprehensive comparison of expression patterns as a function of time [31] , but rather to secure preliminary measures of the impact of these manipulations in the hippocampus and a specific region of the cortex . We predicted that novelty would lead to a learning-independent increase in immediate early gene ( IEG ) mRNA expression in the hippocampus related to the production of plasticity-related proteins implicated in synaptic tagging and capture [32] and the consequent consolidation of hippocampal traces [29] . In contrast , sleep should trigger a relatively selective increase in cortical consolidation after learning but against the background of a time-dependent down-regulation of IEG expression unrelated to memory consolidation , resembling findings recently reported for firing-rate changes [16 , 17 , 33–35] .
In separate experiments , rats ( n = 337 ) were randomly assigned for training in a spatial memory task , with brain tissue from a subset of animals analysed ( blind ) with respect to the expression of IEGs . An initial cohort ( n = 32 ) was given brief spatial learning ( four blocks of two trials per block ) in a watermaze to one escape location , followed—after 7 . 5 h—by equivalent training to an opposite location ( Fig 1A ) . These two sessions of training deliberately set up two competing memories such that memory tested much later could of be of one memory , the other , or of both . The animals learned each location in a comparable manner across the two training sessions within a day ( Fig 1B ) and showed , during a memory test 7 d later , significantly above chance swim time in predefined zones centred on the two platform locations ( t = 2 . 45 , df 26 , p = 0 . 022; Fig 1C ) . While there was a trend favouring the more recently trained location , there was no significant difference in acquisition of memory associated with the two sessions ( p > 0 . 7; Fig 1C ) . Two conditions , ( 1 ) sleep and ( 2 ) novelty + sleep deprivation ( N + SD ) , were scheduled in a counterbalanced and within-subjects manner after the first and second ( competing ) sessions of spatial learning . In the N + SD condition , the animals were placed in a novel environment with the repeated presentation of novel objects and other items and repeated gentle handling to prevent the animals from going to sleep . To control that the effects seen here were due to novelty and not sleep deprivation , we repeated the experiments but only with sleep deprivation by gentle handling and excluded novelty ( see S11 Fig ) . We used a doubly counterbalanced design ( with respect to both the order of platform location and of the N + SD versus sleep conditions; Fig 1D; S1 Fig ) . Accumulating data across animals and conditions required us to “rotate” the data matrix of half of the datasets by 180° for averaging , statistical , and graphical purposes . The swim paths in the 7 d probe test ( memory retrieval ) showed swim paths that moved back and forth between the two trained locations but revealed preferential search in the trained location that was followed by N + SD ( representative animal in Fig 1E ) . The time searching in a virtual zone around the escape location was above chance for the N + SD condition but did not differ from chance in the sleep condition ( N + SD t = 2 . 31 , df 26 , p = 0 . 03; sleep t = –0 . 13 , df 26 , p > 0 . 8 , Fig 1F , separated for sequence [see S8 Fig] ) . Thus , under “baseline” conditions , and despite the opportunity for 6 further days of the animals’ routine sleep/waking cycle in the absence of further training , weak spatial learning followed by conditions favouring cellular consolidation in the hippocampus dominates the expression of memory in behaviour . We then examined two further cohorts ( n = 32 in each ) in which we sought to flip the “seesaw” in one direction or the other . Experiment 1 can be thought of as having established a “default” situation in which the competing memory trace followed by N + SD is dominant over the memory trace followed by sleep ( Fig 2A , Base condition ) . One behavioural method of flipping the seesaw was extensive preexposure of the animals to the context . Context preexposure would create prior knowledge of the extramaze cues of later watermaze learning and , we predicted , should enhance the speed and effectiveness of its cortical consolidation with relatively little effect on the trace , followed by N + SD ( Fig 2A Pre-E ) . Context preexposure consisted of 3 d of 8 min exploration of the training context , achieved by placing the animals on a solid floor located within the watermaze ( without water , but at the same height as the water would normally be , and with all extramaze cues visible ) . This allowed the animals to explore the environment and should have enabled them to learn about the relative location of extramaze cues . Subsequent training in the watermaze might then trigger learning in which the location of the hidden platform is rapidly assimilated within a previously established context representation . Specifically , we predicted “fast” systems consolidation [36] , in much the same manner as can happen when animals have previously learned schematic knowledge [27] . The other method of influencing trace dominance was interference by removing access to the hidden platform during a test of memory 24 h after training . This involved a 120 s swim trial in the watermaze with no platform present , a procedure that would be likely to have the greatest effect on the “dominant” trace [30] . The consequences of such additional postacquisition learning should serve to diminish the capacity of the earlier trained N + SD trace to dominate the sleep memory with which it is competing ( Fig 2A Int ) and might even alter permanently the hippocampal representation of where escape may be possible . The results confirmed these predictions ( Fig 2B ) . Context preexposure enhanced performance after watermaze training to a level at which postencoding sleep enabled rapid assimilation of new information about the escape location during the four blocks of swim trials ( Fig 2B ) . Conversely , postlearning interference had a greater impact on information that had been subject to strengthening by the prior N + SD condition ( Fig 2B ) . Analysis of all three conditions—baseline , pre-exposure and interference—revealed a significant conditions x consolidation type interaction ( F = 3 . 2 , df 2/83 , p = 0 . 043 , d = 3 . 37 controlling for sequence of consolidation type ) . This statistical interaction justified deeper analyses of the data—specifically , to compare the impact of masking ( through trace dominance ) and erasure ( through interference or new learning ) . From both the baseline study and the additional experiments , we derived “dwell time” ( heat ) maps ( Fig 2C ) and a related but statistically distinct “cluster analysis” ( Fig 2D ) . Dwell time maps show a summated occupancy of locations across the pool , with the “hot” colours reflecting greatest time . The cluster analysis , in contrast , identifies local maxima of occupancy even when these occur at levels well below the absolute maximum , signified by the hottest colour in the dwell time map . Using a gap-statistic method [37] , the optimal number of clusters of occupancy could be calculated from the spread of all identified local maxima locations ( see Methods ) . The cluster analysis is important as it has the potential to reveal the existence of a spatial memory ( a focused cluster ) even in circumstances in which its behavioural expression is masked by a separate dominant memory; similarly , it can reveal its absence . For graphical purposes , the sleep condition is displayed as northwest while the N + SD condition is shown as southeast ( but this was fully counterbalanced , S1 Fig ) . For the baseline condition , the dwell time map ( Fig 2C left ) showed most searching in the N + SD location , complementing the previous zone-analysis , but the cluster-analysis ( Fig 2D left ) revealed memory for two separate locations . This indicated that the dominant behavioural expression of the memory consolidated by posttraining N + SD only masked the other memory with which it was in direct competition with respect to the control of behaviour . The presence of a significant negative correlation between swim time in the respective zones for N + SD and sleep offers further evidence for memory competition rather than erasure ( S9 Fig ) . The preexposure condition revealed a more symmetrically balanced heat map and detectable clusters at or near both escape platform locations ( Fig 2C and 2D , middle ) . In contrast , giving the animals 24 h interference trial , in which they could learn that neither platform was available , reduced the intensity of “hot” colours in the dwell time map and resulted in a complete loss of any clustering around the platform location followed by N + SD ( Fig 2C and 2D , right ) . In this case , however , there are grounds for suspecting a loss of the N + SD location rather than masking because the cluster analysis identifies only one cluster centred on the sleep location with no local maxima for the N + SD being detectable . Our manipulations have had differential effects . The distinction between “masking” and “erasure” is subtle but important . To further substantiate this putative dissociation , a third analysis was performed based on the swim time in the zones surrounding the platform locations ( Fig 2E ) . For each experiment and group , we divided the animals into good performers above 20% swim time in zone and poor performers below it . The number of good and poor performers did not differ between the sleep and N + SD conditions for the Base and Pre-E experiment . In contrast , after interference , significantly more good performers were present in the sleep condition , and more poor performers were present in the N + SD condition ( Fisher’s exact test p = 0 . 019 ) . This supports the idea that the masking of memory traces takes place in the baseline experiment , but when the opportunity is given to learn that no escape is possible , memory erasure can both occur and occur selectively . N + SD animals were placed in a novel environment and subjected to the repeated presentation of novel objects and items , coupled with gentle handling to prevent them going to sleep . To check whether the effects of N + SD were due to novelty , rather than sleep deprivation , two further cohorts ( n = 32 in each ) were run that repeated the Base and Int experiments but with sleep deprivation by gentle handling and explicitly excluding novelty ( see Fig 2F and S11 Fig ) . In contrast to the N + SD experiments , sleep deprivation by gentle handling did not lead to the interaction effects seen across experiments and conditions . In fact , when compared with the main experimental condition , there was a significant novelty ( N + SD/SD ) x experiment ( Base/Int ) x condition ( sleep/sleep deprivation ) interaction ( F = 3 . 7 , df 1/116 , p = 0 . 033 ) . The next important step was to complement these behavioural observations with measures of likely markers of consolidation , namely immediate early genes . If the account we have offered so far in terms of differential impact of hippocampal versus cortical consolidation is valid , there should be analogous changes at the IEG level following these same manipulations . We therefore sought to observe the impact of N + SD and sleep on genes likely relevant to neuronal plasticity . We focused on selected markers to represent activity-related and plasticity-related processes: immediate early gene expression of cFos mRNA as an indicator of activity [38] and Zif-268 and Arc mRNA as an indicator of plasticity [1] . These were all monitored in both the hippocampus and medial prefrontal cortex ( mPFC ) . There were two key decisions about the experimental design . First , the experimental method involved in measuring mRNA expression in association with memory encoding and consolidation was real-time quantitative PCR analysis . We chose qPCR in contrast to immunocytochemistry as we sought to achieve an exact and quantitative measure of the extent and time course of gene transcription . This enabled more complex statistical models with multiple within- and between-subject comparisons at the cost of being unable to compare and contrast different subregions within the hippocampus or areas of the cortex . Second , behavioural training was necessarily to a single escape location in the watermaze because the “trace-competition” design could not be used unambiguously . Additionally , it was important to measure IEG activation at defined time points soon after encoding , and this also precluded the use of training to two escape platforms several hours apart . The consolidation-specific effects of the two conditions ( sleep and N + SD ) were investigated as well as general effects of the condition . To achieve this aim , specific contrasts in our analysis were chosen . For consolidation-specific effects , the contrast was between animals that did or did not experience the watermaze , but we controlled for their condition with both groups having either sleep or N + SD ( Fig 3 ) . In contrast , for general condition effects , the contrast was between animals that experienced the watermaze ( and had either sleep or N + SD ) with awake , home cage control animals ( Fig 4 ) . The study design focused on comparing IEG expression during the course of the respective postencoding N + SD or sleep manipulations in brain tissue from animals that had all learned the watermaze in a single session with tissue from animals that had not been subject to training ( Fig 3 ) . For all experiments , the brains were extracted with the hippocampus ( HPC ) ( yellow ) and mPFC ( grey ) immediately dissected and then snap-frozen for later analysis ( Fig 3A ) . We compared brain tissue from animals that had experienced a learning session in the watermaze ( WM ) with animals that did not ( NoWM; Fig 3B and 3C ) . This was done either directly after encoding ( 0 . 5 h ) or 5 h into the consolidation period in association with postencoding sleep or N + SD ( n = 30 ) . The graphs are plotted such that positive values indicate higher gene expression in WM than in NoWM ( negative values , vice versa ) . We chose a neutral wake control condition ( NoWM ) because possible alternative control conditions such as swimming in the watermaze without a platform can display alterations in IEG expression in association with stress or with incidental learning about the environment through exploration [39–41] , and these confounding factors can hinder interpretation of results [42] . Furthermore , for present purposes , the critical results are the contrasts between N + SD and sleep with respect to spatial learning or its absence . For the 0 . 5 h encoding condition , without subsequent behavioural manipulations , there was a significant and equivalent increase gene expression in both the HPC and mPFC relative to NoWM ( Fig 3B and 3C , left ) . There was a significantly larger increase in cFos compared to the plasticity-associated genes ( F = 19 . 8 , df 1 . 1/6 . 6 , p = 0 . 01 , post hoc simple , contrasts cFos versus Arc F = 10 . 3 , df 1/4 , p = 0 . 033 and cFos versus Zif F = 56 . 5 , df 1/4 , p = 0 . 002 ) . This is an important finding , as it points to substantial and rapid gene up-regulation in both brain regions at or soon after learning . The comparison of WM and NoWM after 5 h of N + SD or sleep revealed different patterns of gene expression in hippocampus versus cortex ( Fig 3B and 3C , right ) . In the hippocampus , the pattern was for higher gene expression with WM after N + SD and lower after sleep; in the cortex , the opposite pattern prevailed . The ANOVA showed a significant brain area x condition interaction at the 5 h time point ( F = 6 . 9 , df 1/24 , p = 0 . 015 , post hoc linear contrast p = 0 . 015 ) . This interaction is important and most easily illustrated by focusing on the effect of time ( 0 . 5 and 5 h ) on cFos . Note that the relative level of gene expression in HPC after 5 h of N + SD ( at approximately 20% greater for the animals had been trained ) was not significantly less than that observed at 0 . 5 h ( t = 2 . 0 , df = 8 , p > 0 . 08 ) ; a much larger and now significant decrease in relative gene expression over time was observed for sleep with gene expression being approximately 35% less in animals that had been trained ( t = 4 . 0 , df = 8 , p = 0 . 004; Fig 3B ) . In contrast , if one looks at the relative impact of N + SD and sleep in cortex , the decrease in relative cFos expression across training conditions from +120% to –60% for N + SD was significant ( t = 3 . 1 , df = 8 , p = 0 . 015 ) , whereas for sleep the corresponding difference did not reach significance ( t = 1 . 1 , df = 8 , p > 0 . 3; Fig 3C ) . The same pattern was also seen across all genes ( HPC: sleep: F = 12 . 2 , df 1/8 , p = 0 . 008 , d = 2 . 48; N + SD: F = 1 . 6 , df 1/8 , p > 0 . 2 , d = 0 . 90; mPFC: sleep: F = 0 . 3 , df 1/8 , p > 0 . 5 , d = 0 . 42; N + SD: F = 5 . 6 , df 1/8 , p = 0 . 046 , d = 1 . 67 ) . The differential effects of N + SD and sleep on gene expression , offset by consolidation-associated changes , warranted further study into the more general effects of N + SD and sleep . Specifically , in a separate cohort of 35 animals , IEG expression was measured in hippocampus ( yellow ) or prefrontal cortex ( grey ) at 2 h , 4 h , or 6 h after encoding ( Fig 4A ) , during which postencoding N + SD ( white ) or sleep ( black ) were occurring in different subgroups of animals ( conditions ) . This time , these were referenced to a neutral , awake home cage control ( HCC ) to enable comparison of the general change in gene expression across both behaviours ( n = 5 for each “con” subgroup and n = 5 for the “HCC” control ) . In the separate cohort of trained and untrained animals ( n = 25 ) , the time-point 5 h was examined only . Animals allowed to sleep for varying time periods showed a monotonic , time-dependent decrease in gene expression relative to HCCs at the neocortical but not the hippocampal site ( for an explanation of the “fold-change” measure , see Methods RT-qPCR ) . In contrast , the impact of recurring exposure to novelty coupled to sleep deprivation was associated with positive changes in gene expression in both brain regions that did not change over time . An overall ANOVA of gene expression—including condition ( Sleep , N + SD ) , time ( 2 , 4 , and 6 h ) , brain area ( HPC , mPFC ) , and gene ( cFos , Zif-268 and Arc ) —revealed significant interactions for condition x time and condition x brain area ( F = 13 . 1 , df 1/24 , p = 0 . 001 with post hoc linear contrast p < 0 . 001; F = 6 . 1 , df 2/24 , p = 0 . 007 , respectively ) . The same general pattern was seen for all genes , but Arc expression drove the result in the cortex as indicated by a significant condition x time x brain area x gene interaction . Ongoing novelty for 2 , 4 , or 6 h was associated with an apparently stable up-regulation of IEG expression with no relevant interactions . In an additional control analysis , we also compared the 5 h WM/NoWM and sleep/N + SD conditions to a neutral wake home cage control and could replicate the general decrease in gene expression in sleep and increase in N + SD , which was thus shown to be learning independent and mainly reflected the current experience ( sleep or novelty ) of the animal ( Fig 4 right ) .
We begin by clarifying the distinction between hippocampal versus cortical consolidation on the one hand and cellular versus systems consolidation on the other . Cellular consolidation was initially identified by monitoring the impact of inhibitors of protein synthesis soon after training [43] , with many later studies focusing on its expression in the hippocampus . Interestingly , consolidation within the hippocampus has been shown to be enhanced by postencoding novelty [13] . In contrast , systems consolidation was initially identified using perturbing interventions such as lesions via the phenomenon of retrograde amnesia [44] , with more recent evidence indicating that this process may occur primarily during sleep [2] . Cortical consolidation likely involves both systems consolidation ( interactions with the hippocampus ) and cellular consolidation ( stabilising synaptic changes within cortical networks ) . The hippocampal and cortical processes are generally held to act in sequence , with the hippocampal process setting the stage for the hippocampal–neocortical interactions that follow [26] . However , an alternative possibility supported by our data is that these two processes can occur in parallel and interact dynamically using both cellular and systems mechanisms , even if their respective time courses are not fully overlapping . The novel approach here was to search for interactions between hippocampal and cortical consolidation in situations in which one or another competing memory trace might be rendered more dominant . This approach utilises the important concept of “dominance of the trace , ” derived from studies of reconsolidation [30] , which is particularly pertinent in situations involving potentially incompatible memory traces . With respect to the observations of Morris and Doyle [8] , we can see that reversal training across days would have maximally enhanced cortical consolidation and lasting stabilisation , whereas within-day training would have enhanced a hippocampal trace with only minimal impact on cortical consolidation . The latter conditions would have been permissive for the spontaneous reversal during memory retention that was observed . One classic perspective on memory formation is that the hippocampus and cortex engage in “parallel encoding” [45–47] and that neocortical traces fade rapidly unless subject to a stabilisation signal from information retained within the hippocampus [48] . In contrast , the complementary learning systems theory initially suggested that rapid hippocampal encoding is sequentially followed by a slower neocortical “interleaving” process [26] , a theory that has now been revised to recognise the possibility of “fast” consolidation [25] . The qPCR results showing similar up-regulation of IEGs in both structures within 30 min of memory encoding also support the “parallel encoding” concept , as they point to molecular events that are indirect markers of consolidation happening soon after memory encoding in both brain regions . IEG activation is triggered by memory encoding in both structures . Their interaction alters as a function of distinct behavioural manipulations such as postencoding novelty or sleep . Others have proposed that the medial prefrontal cortex may not be a storage site for memory but , rather , responsible for memory integration and control [3 , 45–47] . Interestingly , the mPFC has to be active for reconsolidation to occur in the watermaze [48] , and lesions in this area seem to especially affect memory retrieval under partial cueing conditions [49 , 50] . Here , sleep led to immediate early gene activation in the mPFC that , if the mPFC is for memory integration and behavioural control , could be associated with mPFC neural activity , leading to better integration of the two distinct experiences in the watermaze . This could explain our results in conditions with multiple experiences in the watermaze such as Pre-E and Int , in which the memory encoded prior to sleep was shown to dominate the control over behaviour . Interestingly , the opposite was the case in the Base condition , in which N + SD had the dominant control of behaviour . Independent of if the prefrontal cortex is the actual storage site or facilitates memory integration and control , in our case , plasticity processes occur in this region after learning and , more importantly , after sleep . N + SD led to increased IEG expression in the hippocampus . According to the synaptic-tagging and capture- and clustered-plasticity models of consolidation [29 , 32 , 51] , an increase of mRNA expression and translation caused by a novelty experience would enable newly synthesized , plasticity-related proteins to be captured by not only the initiating synapse ( in this case , the one encoding the novel experience ) but also by other synapses ( in this case , the watermaze memory encoded 30 min earlier ) . The procedure we used of gentle handling of the animals and their frequent exposure to new objects in a novel environment likely sustained the relevant activation of protein synthesis over time , as evidenced by our IEG results showing sustained up-regulation over 6 h . Our interpretation is that these novelty-induced plasticity proteins would have triggered cellular consolidation of the watermaze memory in the hippocampus . This would have enabled the hippocampal trace to last long enough to be still present during a memory test conducted 24 h later in the case of our “Int” manipulation ( Fig 2 ) . With this trace retained and “active” in the hippocampus over this duration , the opportunity for training-associated updating would have prevailed [52] . The memory test would have enabled the animals to alter their hippocampal trace to indicate the escape platform was no longer available . Importantly , the trace consolidated in the cortex by sleep ( see below ) would have been relatively unaffected . In contrast to novelty , the impact of sleep is that an initial learning-related up-regulation of relevant gene expression is sustained over time in the cortex ( Fig 3 ) , but this is in the context of a learning-independent decrease that becomes larger with time ( Fig 4 ) . During sleep , activated synapses especially in the cortex are thought to be potentiated by intermittent replay events , putatively reflected in our sustained , learning-specific up-regulation of gene expression in the cortex [17 , 34 , 53–57] . However , sleep is also associated with a general time-dependent decrease in gene expression relative to home cage controls . Our study is the first to reveal extended time-dependent effects on IEG expression after longer periods of sleep , as earlier experiments have only investigated sleep for 1–2 h after learning [58 , 59] . This time-related decrease—possibly a “wash-out” of gene expression–related products—may be related to “downscaling” [34 , 60] . Downscaling is a type of cortical resetting process that is not yet well understood mechanistically [35 , 61–64] . The important idea is that within engram cells with potentiated synapses , synapses that are recently inactive are selectively downscaled , while the potentiated synapses of a new memory trace may be left intact [33 , 34 , 60] . In our case , downscaling was putatively reflected in the time-dependent decrease of gene expression that was learning independent . However , selectively after learning , a bimodality of cortical IEG expression was revealed as a sustained relative increase in gene expression during sleep , analogous to reported firing rate changes [35] . These bimodal changes are consistent with the concept of a selective strengthening of synapses involved in engrams—which most likely includes only a small percentage of neurons—and the general downscaling of all other synapses in mPFC during sleep , with a consequential enhancement of signal-to-noise ratio in the cortex as a signature of systems consolidation [16 , 17] . Preexposure to the environment enhanced the effectiveness of systems consolidation during sleep; in contrast , behavioural interference ( a memory test ) acted directly on the memory trace that had been enhanced soon after encoding by novelty with the effect of erasing it . The impact of 3 d of context preexposure would likely have created a network-level representation of the training context in the neocortex ( and possibly in the hippocampus in parallel [65 , 66] ) . With that prior knowledge on the part of the animals , brief training was shown to be sufficient for rapid assimilation of information about the location of the hidden platform into the existing cortical representation , analogous to what happens in formal studies of knowledge assimilation into schemas [27 , 67] . In support , our behavioural analyses began with the demonstration of two search zones in animals with context preexposure who were trained to visit both escape platform locations in the pool; the cluster analysis quantitatively confirmed the existence of two separate clusters of memory traces at these two escape locations . Thus , while we present no evidence on the occurrence of rapid hippocampal independency as seen in Tse et al . [27] , we did show a behavioural effect of the preexposure with the selective strengthening of the memory trace that was followed by sleep . In contrast , in the case of behavioural interference , as discussed above , the memory test acted directly on the memory trace that had been enhanced soon after encoding by novelty with the effect of erasing it . Here , the other memory whose consolidation was augmented by sleep was now the only one detectable . There are certain caveats to our approach . One is that the second session of learning in the watermaze might be thought to constitute “interference” trials for what was acquired in the first session of learning . Interestingly , our heat map and cluster analysis data establish that this did not occur . Instead , one or another trace “dominated” the control of behaviour , and the sequence in which they were learned had no statistically significant effect . Alterations to the stored memory representation of a hidden platform did occur when an interference test was conducted 24 h after training , but such a trial consisted of a long period of time swimming in the pool with multiple opportunities to learn that escape was no longer possible at a previously learned location . This new learning primarily affected what we suspect may be the hippocampal but not the cortical trace , but our data allows only that it was the memory trace that , in the original training the day before , was followed by novelty rather than by sleep . Second , dissociating a putative hippocampal and cortical trace is not straightforward; a complication is that spatial learning in the watermaze does not follow the usual temporal parameters of retrograde amnesia associated with posttraining hippocampal damage [68–70] . The reason is that the integrity of the hippocampus is required for the expression of spatial navigation . For example , “reminding” of a latent but inactive cortical spatial memory trace that is then expressed during a second retrieval trial requires the integrity of the hippocampus [68] . Accordingly , our group introduced an alternative way of investigating systems consolidation of watermaze learning in which the hippocampus functions normally at retrieval . Starting soon after training was completed , bilateral osmotic minipumps were used to infuse an AMPA receptor antagonist into the hippocampus for 7 d [71] . When the animals were tested 14 d after the end of training , the hippocampus was shown electrophysiologically to be working normally . Nonetheless , the impact of shutting down the hippocampus for 7 d was that spatial memory had been lost . On the basis of this and other earlier evidence [8 , 72] , there are grounds for believing that the hippocampus and cortex interact normally for the consolidation of spatial learning in the watermaze . A third caveat concerns the possibility that the novelty manipulation has its effects because it is novelty and sleep deprivation . The procedural difficulty was that it is impossible to sustain a novelty manipulation for the same 6 h duration as the sleep manipulation we used without coupling it to sleep deprivation in order to ensure frequent access to novel stimuli . Further , because of the innate curiosity of the animals , novel stimuli act as natural agents of sleep deprivation . For this reason , we have throughout the results referred to our manipulation as “N + SD” and intend to investigate the potentially dissociable parameters of this protocol in future work . As an initial control experiment , we did repeat the interference experiment but this time with sleep deprivation by gentle handling but excluding novelty ( see supplementary S11 Fig ) . This did abolish the differential effect of interference on the two memory traces , further indicating that our findings were primarily due to the effect of novelty and not sleep deprivation . Fourth , we recognise that the behavioural protocol differed for the qPCR study than for the earlier parts of the study , but as explained above ( see Results ) , this was necessary for timing reasons and to avoid ambiguity . Marr proposed that sleep may be the ideal state for systems consolidation to occur [23 , 24] . However , the cyclical and unavoidable nature of this state in living animals makes exacting experimental designs to test causality very difficult to realise . Consequently , convincing tests of his ideas have been lacking . A growing body of data nonetheless points to the possibility that the hippocampal–neocortical interactions that reflect systems consolidation can occur more rapidly than others after Marr have generally considered [3 , 20 , 73] . An interventional approach inhibiting one or the other brain area during sleep or novelty would be ideal to test necessity or sufficiency , but the putative interdependence of the underlying processes limits the ability to achieve selective interventions . Nonetheless , our observations showing that hippocampal and cortical consolidation systems can interact offers further evidence for the dynamic nature of postencoding memory processing and their modulation by such factors as event-associated novelty , context preexposure , and postencoding sleep . The competitive memory study design sets the stage for future , more detailed investigations into the mechanisms of the hippocampal and cortical memory systems and their interactions .
The subjects were adult male Lister hooded rats ( Charles River , United Kingdom [UK] ) , aged 8–10 wks at the start of experimentation and weighing ~250 g . They were housed in groups of four rats per cage . They had free access to food and water at all times and were kept on a delayed day–night cycle ( behavioural experiments 12 p . m . –12 a . m . light on; qPCR experiments 10 a . m . –10 p . m . light on ) . After arrival , the animals adapted to the environment for at least a week and were handled across 3 d for at least 5 min per day before progressing to the watermaze habituation . A total of 305 rats were used ( behavioural experiments: n = 160; qPCR experiments: n = 155 ) . All procedures were compliant with national ( Animals [Scientific Procedures] Act , 1986 ) and international ( European Communities Council Directive of 24 November 1986[86/609/EEC] ) legislation governing the maintenance of laboratory animals and their use in scientific experiments . We used the minimal number of rats for the necessary statistical power , with random assignment to groups , and there was minimal suffering associated with any of the experimental procedures . The experiments were approved by the UK home office under the project licence number 60/4566 and the Experimental Request Forms by the Edinburgh University division of the National Veterinary Service . The animals were habituated with a visual-cue version of the watermaze ( diameter = 2 m ) for 3 d prior to the main experimental day . During the four trials per day habituation , the rats had to find the submerged platform in the watermaze , indicated by a visual cue placed on top of the platform ( diameter = 12 cm ) , while curtains surrounding the pool hid any extramaze cues . If the animals did not go to the platform within 120 s , they were guided to it by the experimenter ( this occurred very rarely ) . After reaching the platform , the animals had to wait on the platform for 30 s before being picked up and the next trial began . All animals passed this habituation period with success , and with an average escape latency of <10 s on day 3 . Thus , the animals were familiar with the procedure before spatial training was begun . To reduce further any experiment-associated stress , the animals were also habituated to the sleep cages ( two times each for ~60 min ) and the wake arena without any objects ( 15 min ) . We used two basic designs for watermaze training: a two-session design for the behavioural experiments and a one-session design for the qPCR experiments . The main training day for the behavioural experiments consisted of two sessions in the watermaze separated by 7 . 5 h; each session was composed of four consecutive training blocks ( each training block contained two trials , with a 15-s intertrial interval ) to one platform location with randomized , counterbalanced , and varied starting positions ( north , east , south , and west ) . If the animals did not reach the platform by 120 s , they were guided to the location . After each session , the animals underwent one of the two conditions ( e . g . , sleep , N + SD—see below ) in a counterbalanced matter . Further , the platform position ( northeast , southwest ) was also counterbalanced across session and condition ( see S1 Fig ) . Only animals that learned both platform locations successfully ( block four escape latency < 55 s ) were included in the zone analysis of the later probe trial . The probe trial was performed 7 d after the training day , and the animals were placed for 120 s in the watermaze without any platform present while their swim paths were tracked by automated software ( Watermaze , Watermaze Software , Edinburgh , UK; [74] ) . Starting positions were counterbalanced and of equal distance to both platform locations ( southwest , northeast ) . It should be noted that this software computes path parameters , including zone and dwell-time analyses , in a fully automatic manner—and is thus “blind” to the assignment of animals to groups and conditions . For the qPCR experiments , a similar behavioural protocol was used for training , but with only one session to one platform location . A between-subject design was employed , with the single session of training ( four blocks of two trials ) to the single platform location followed by either N + SD or sleep before humane killing at varying intervals and RT-qPCR analysis of brain tissue . In addition to the Base , Pre-E , and Int experiments of the behavioural study described in the main text , we performed an additional control experiment ( S2 Fig ) . To investigate if the effects seen in the N + SD condition were indeed caused by novelty , we repeated the Int experiment using animals that had been sleep deprived with gentle handling rather than novelty exposure ( Int-SD ) . In the sleep condition , the animals were kept in individual cages with video monitoring . Because of the high number of animals ( >300 ) , we were unable to perform implanted EEG recordings; instead , with the tracking data from the video monitoring of these animals , we calculated estimated sleep periods , defining sleep as at least >5 s with no movement [75] . We verified that total sleep time ( M = 292 . 3 min ± 4 . 8 ) as well as length ( M = 1 . 58 min ± 0 . 03 ) and number ( M = 187 . 8 ± 3 . 1 ) of sleep bouts were similar to polysomnographic verified recordings . Novelty with sleep deprivation was undertaken in individual compartments in an arena over a period of 6 h , with one experimenter assigned per four animals , their role being to monitor the rats and introduce novel objects as soon as the animal showed signs of tiredness ( e . g . , curling up ) . This procedure required considerable concentration by the experimenters and thus was done in shifts but was conducted in a conscientious manner ( S4 Fig , left ) . Sleep deprivation ( SD ) without novelty ( for the control studies ) was performed in the home cages by gently tapping the cage or removing the cover as soon as the animal showed signs of tiredness ( S4 Fig , right ) . We analysed the watermaze probe trial data in two ways: zone analysis of swim time and dwell time map–based cluster analysis . For each analysis , the data of 3–60 s of the probe trial was used to avoid the bias added by starting position . | Memories are initially stored in a hippocampal–cortical network; however , which brain area is important for long-term storage depends on what happens after learning . For example , replay of recent memories during sleep is thought to lead to consolidation in the cortex . In contrast , postlearning novelty is thought to strengthen hippocampal memory traces via a mechanism that depends on dopamine . Here , we show that indeed sleep leads to cortical consolidation , whereas novelty leads to hippocampal consolidation . Further , the memories followed by sleep or novelty differed in their behavioural expression and in the factors that could influence them . Memory traces followed by novelty were more dominant by default and showed stronger expression than those followed by sleep . This came at a cost: these memories were susceptible to interference that decreased their behavioural expression . In contrast , memories followed by sleep were more resistant to interference and benefitted from preexposure to the training context . In sum , we showed that events that follow learning can influence the future expression of a memory trace . | [
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"science"
] | 2017 | The Yin and Yang of Memory Consolidation: Hippocampal and Neocortical |
Investigating the pleiotropic effects of genetic variants can increase statistical power , provide important information to achieve deep understanding of the complex genetic structures of disease , and offer powerful tools for designing effective treatments with fewer side effects . However , the current multiple phenotype association analysis paradigm lacks breadth ( number of phenotypes and genetic variants jointly analyzed at the same time ) and depth ( hierarchical structure of phenotype and genotypes ) . A key issue for high dimensional pleiotropic analysis is to effectively extract informative internal representation and features from high dimensional genotype and phenotype data . To explore correlation information of genetic variants , effectively reduce data dimensions , and overcome critical barriers in advancing the development of novel statistical methods and computational algorithms for genetic pleiotropic analysis , we proposed a new statistic method referred to as a quadratically regularized functional CCA ( QRFCCA ) for association analysis which combines three approaches: ( 1 ) quadratically regularized matrix factorization , ( 2 ) functional data analysis and ( 3 ) canonical correlation analysis ( CCA ) . Large-scale simulations show that the QRFCCA has a much higher power than that of the ten competing statistics while retaining the appropriate type 1 errors . To further evaluate performance , the QRFCCA and ten other statistics are applied to the whole genome sequencing dataset from the TwinsUK study . We identify a total of 79 genes with rare variants and 67 genes with common variants significantly associated with the 46 traits using QRFCCA . The results show that the QRFCCA substantially outperforms the ten other statistics .
As of February 6th , 2017 , a catalog of published Genome-Wide Association Studies ( GWAS ) had reported significant association of 26 , 791 SNPs with more than 1704 traits in 2 , 337 publications [1] . It is reported that more than 4 . 6% of the SNPs and 16 . 9% of the genes were significantly associated with more than one trait [2] . These results demonstrate that genetic pleiotropic effects , which refers to the effects of a genetic variant affecting multiple traits , play a crucial role in uncovering genetic structures of correlated phenotypes [3–10] . Most genetic analyses of quantitative traits have focused on a single trait association analysis , analyzing each phenotype independently [11] . Less attention has been paid to comprehensive analysis of pleiotropic effects [12] . However , multiple phenotypes are correlated due to shared genetic and environmental effects [13] . The integrative analysis of correlated phenotypes which tests the association of a genetic variant with multiple traits often increases the statistical power to identify genetic associations and increases the precision of genetic effect estimation [13–16] . It is increasingly recognized that the genetic effect can be detected only when the association of the genetic variant with the multiple traits are jointly tested [17] . It is also noted that directional pleiotropy indicating that the genetic effects of the variant on the multiple traits are in the same direction ( all positive or all negative ) widely exists [18] . Changes of one trait may cause undesired changes of other traits . Investigation of pleiotropy provides a tool for designing the effective treatment with fewer side effects . Two types of approaches can be used for genetic pleiotropic analysis . One approach is to utilize summary statistics for estimating genetic correlations and testing association of genetic variants with multiple traits [17–22] . An alternative approach is to use individual genotypic information for association analysis of multiple correlated traits [23] . The focus of this paper is to use individual genotypes for pleiotropic analysis . Three major types of methods are commonly used to explore the association of genetic variants with multiple correlated phenotypes: multivariate techniques including multivariate linear models [15 , 24–33] , linear mixed models [11 , 16 , 33 , 34] and functional linear models [35] , the combinations of univariate association measures for different phenotypes [36–39 , 43] , and dimension reduction methods including principal component analysis ( PCA ) [14 , 40–43] , and canonical correlation analysis [30 , 44–47] . Statistical methods for testing the association of common variants with multiple traits have been well developed and successfully applied [2] . The methods for pleiotropic analysis of rare variants are still under development [25 , 48] . Next-generation sequencing and modern biosensing techniques have generated dozens of millions of SNPs and large numbers of clinical and intermediate phenotypes . The current multiple phenotype association analysis paradigm lacks breadth ( the number of phenotypes and genetic variants jointly analyzed at a time ) and depth ( hierarchical structure of phenotype and genotypes ) . Most approaches perform analysis on the subsets of the full data space that are often missing , but now available . A key issue for high dimensional pleiotropic analysis is to effectively extract rich correlation information from extremely high dimensional genotypic and phenotypic data . The statistical power of the methods that do not efficiently explore dimension reduction of both phenotype and genotype data will be limited . Despite their wide applications to the pleiotropic analysis , the current pleiotropic analysis methods share the same drawbacks . These methods , particularly multivariate analysis methods , either do not use data dimension reduction or ignore the rich linkage disequilibrium structure of genomic data when data dimension reduction is used . The most widely used methods for pleiotropic analysis are originally designed for analyzing a small number of phenotypes and common variant data . Due to the lack of efficient analytic platforms , the current pleiotropic analysis methods have not been applied to large-scale real genetic pleiotropic analysis with a large number of phenotypes and next-generation sequencing ( NGS ) data . To overcome these limitations and fully take the advantages of the rich linkage disequilibrium information across a genomic region , we combine two approaches: ( 1 ) functional data analysis and ( 2 ) quadratically regularized CCA to develop a novel statistical method that is referred to as a quadratically regularized functional canonical correlation analysis ( QRFCCA ) for testing the association of genomic regions with multiple traits . The QRFCCA first transforms the high dimensional correlated discrete genotype data across the genes or genomic regions to a few regularized functional principal components in the low orthonormal eigenfunctional space by functional principal component analysis ( FPCA ) . Then , the QRFCCA will further utilize the quadratically recognized matrix factorization to project both the phenotype data and compressed genomic data by FPCA to low dimensional space with much fewer number of bases ( components ) than the traditional matrix factorization or PCA and changed distribution of eigenvalues in which the proportion of top eigenvalues substantially increases . The QRFCCA dramatically reduces the dimensions of both genotype and phenotype data while fully retaining the original genotypic and phenotypic information . To evaluate the performance of the developed QRFCCA for association analysis of multiple phenotypes , we conduct large-scale simulations comparing QRFCCA to ten statistics: Sparse CCA ( SCCA ) [49] , MSKAT [50] , GAMuT [25] , FCCA [51] , kernel CCA ( KCCA ) [52] , CCA , A Unified Score-Based Association Test ( USAT ) [53] , PCA ( applying to both phenotypes and genotypes ) , MANOVA ( multivariate ANOVA applied to multiple phenotypes and SNPs ) , and minP ( minimum of P-values for testing the association of single SNP with multiple phenotypes ) and demonstrate that the QRFCCA has a much higher power than other competing statistics while retaining the correct type 1 error rates . Finally , the QRFCCA and ten other statistics are applied to the whole genome sequencing dataset from the TwinsUK study where 756 individuals with 33 , 746 genes and 46 traits in 13 major phenotype groups are included in the analysis . We find that the QRFCCA for pleiotropic analysis substantially outperforms the ten other statistics . A program for implementing the developed QRFCCA for association analysis of multiple phenotypes can be downloaded from our website https://sph . uth . edu/research/centers/hgc/xiong/software . htm and https://cran . r-project . org/web/packages/ . The UK10K data can be downloaded from http://www . uk10k . org .
We first briefly introduce the smooth functional principal component analysis ( FPCA ) for genetic variant data [54] . We first review the definition of genetic variant profiles . Let t be the position of a genetic variant within a genomic region and T be the length of the genomic region being considered . For convenience , we rescale the region from [0 , T] to [0 , 1] . We can view t as a continuous variable in the interval [0 , 1] because the density of genetic variants is high . We define the genotype function of the i-th individual as xi ( t ) ={2MM1Mm0mm , i=1 , ⋯ , n ( 1 ) where M is an allele at the genomic position t and n is the number of sampled individuals . Although the FPCA can also be applied to function-valued phenotypes , for example , RNA-seq data , this paper will focus on genotype function , assuming that the phenotypes are scale variables . A key step of the FPCA is that the functional data are projected into a finite-dimensional space of FPCs or eigenfunction [51 , 54] . Let βj ( t ) , j = 1 , 2 , … be a set of FPCs which can be obtained from solving integral eigenequation ( details are referred to the book [51] or paper [54] ) . Similar to Fourier series or wavelet expansions , the genotype profile function xi ( t ) can be expanded in terms of orthogonal FPCs where FPCs were taken as basis functions: xi ( t ) =∑j=1Jξijβj ( t ) , ( 2 ) where ξij is the FPC score of the ith individual which can be estimated by ξij=∫Txi ( t ) βj ( t ) dt , ( 3 ) where integral was calculated numerically [51] . FPCs were constructed from genotype functions for each gene and contained linkage disequilibrium information . Each individual has a number of FPC scores . The FPC scores can represent the original genotype functions . FPCs can efficiently compress the data . For example , in the TwinsUK data set , 2 , 633 , 479 common SNPs and 2 , 249 , 090 rare SNPs and 33 , 746 genes were included in the FPCA analysis . The FPCA analysis was performed for each gene . On average , 2 . 5 FPCs each gene can account for 90% of common variant variation and 2 . 7 FPCs each gene can account for 90% of rare variant variation . Consider a data matrix A ∈ Rn×q consisting of n samples with q features ( variables ) . The data matrix A represents the genotype data , the FPC scores or the phenotype data . The phenotype data include both continuous and discrete values . The ith row of A is a vector of q features for the ith sample , and the jth column of A is a vector of the jth feature across the set of n samples . Matrix factorization is used as a general framework to embed the genetic and phenotype data into the low dimensional vector space to reduce the data dimension and remove anomalous or noise data points [55] . To accomplish this , we first seek the best rank-l approximation to the matrix A by factorizing it into a product of two low rank matrices . Let G ∈ Rn×l and H ∈ Rl×q . Assume that the rank of A is r . Therefore , r ≤ min ( n , q ) . Matrix factorization attempts to minimize the approximation error: minG , H‖A−GH‖F2 , ( 4 ) where ‖ . ‖F denotes the Frobenius norm of a matrix . A solution to problem ( 4 ) can be found by truncating the singular value decomposition ( SVD ) of Aij [55] . Let the SVD of A be given by A=UΛVT , ( 5 ) where U = [u1 , … , ur] ∈ Rn×r , V = [v1 , … , vr] ∈ Rq×r , UTU = Ir×r , VTV = Ir×r , and Λ = diag ( λ1 , … , λr ) ∈ Rr×r with λ1 ≥ λ2 ≥ … ≥ λr > 0 . The columns of U and V are referred to as the left and right singular vectors of A , respectively , and λ1 , … , λr are referred to as the singular values of A . Let Λl = diag ( λ1 , … , λl ) , Ul = [u1 , … , ul] and Vl = [v1 , … , vl] . Define G=UlΛl1/2 and H=Λl1/2Vl . The best rank-l approximation to the matrix A or the l-rank matrix factorization of A is then given by [55] A≈GH . ( 6 ) The matrix factorization compresses the q features ( variables ) in the original data set to l < q new features and hence reduces the data dimension . An alternative to multivariate linear regression analysis , the CCA is a popular analytic platform for genetic pleiotropic analysis . The goal of CCA is to seek optimal correlation between linear combinations of two sets of variables: the set of traits and the set of SNPs . The CCA measures the strength of association between the multiple SNPs and the traits . The pairs of linear combinations are called canonical variates and their correlations are called canonical correlations [56] . Consider a phenotype matrix Y = [Y1 , … , Yk] with k traits and genotype matrix X = [X1 , … , Xp] with p SNPs or FPC scores . We assume that p + k variables Z = [XT , YT]T jointly have the covariance matrix Σzz=[ΣxxΣxyΣyxΣyy] . Let R2=Σyy−1/2ΣyxΣxx−1ΣxyΣyy−1/2 ( 7 ) and K=Σxx−1/2ΣxyΣyy−1/2 . The SVD of K is K=UΛVT , ( 8 ) where Λ = diag ( λ1 , … , λq ) and q = min ( p , k ) is the smaller number of variables in the two genotype-phenotype datasets . It is well known that the canonical vectors are A=Σxx−1/2U , B=Σyy−1/2V , ( 9 ) and the vector of canonical correlations are CC=[λ1 , … , λq]T . ( 10 ) A squared canonical correlation measures the proportion of variance linearly shared by the two sets of canonical variates derived from the input genotype-phenotype data sets . Canonical correlations between the genotype and phenotypes measure the strength of their association . The CCA produces multiple canonical correlations . But we wish to use a single number to measure the association of the genetic variation with the multiple traits . We propose to use the summation of the square of the singular values as a measure to quantify the association of the genetic variation within a gene or genomic region with the multiple traits: r=∑i=1qλi2=Tr ( Λ2 ) =Tr ( R2 ) . ( 11 ) To test the association of the genetic variation in a gene or genomic region is equivalent to test independence between the two genotype-phenotype datasets X and Y or to test the hypothesis that each variable in the set X is uncorrelated with each variable in the set Y . The null hypothesis of no association of the genotype data X with the phenotype dataset Y can be formulated as H0:Σxy=0 . The likelihood ratio for testing H0: ∑xy = 0 is Λr=|Σzz||Σxx||Σyy|=∏i=1q ( 1−λi2 ) , ( 12 ) which is equal to the Wilks’ lambda Λ defined in the multivariate linear regression model . This demonstrates that testing for association using multivariate linear regression can be treated as special case of CCA [57] . We usually define the likelihood ratio test statistic for testing the association as: TCCA=−N∑i=1qlog ( 1−λi2 ) . ( 13 ) For small λi2 , TCCA can be approximated by N∑i=1qλi2=Nr , where r is the measure of association of the genetic variation in the gene or genomic region with the multiple traits . The stronger the association , the higher the power that the test statistic can test the association . Under the null hypothesis H0: ∑xy = 0 , TCCA is asymptotically distributed as a central χpk2 . When sample size is large , Bartlett ( 1939 ) suggests using the following statistic for hypothesis testing: TCCA=−[N− ( q+3 ) 2]∑i=1qlog ( 1−λi2 ) . ( 14 ) The power of the test statistics in CCA depends on the squared canonical correlations or eigenvalues of the matrix R2 . We wish to increase the power via changing distribution of the canonical correlations and data reduction . In matrix factorization , for fixed rank l , we want to approximate the matrix A by the product of two factor matrices G and H as accurately as possible . However , the Frobenius norm of the matrices G and H may be large . We need to balance the approximation accuracy and the Frobenius norm of the factor matrices . Specifically , we add the Frobenius norm of the factor matrices to the objective in Eq ( 4 ) . The optimization problem ( 4 ) is now transformed to the quadratically regularized matrix factorization problem: minG , HF=‖A−GH‖F2+μ‖G‖F2+μ‖H‖F2 . ( 15 ) From Eq ( 5 ) , the matrices G and H have the forms: G=UlΛl1/2andH=Λl1/2Vl , ( 16 ) where Λl = diag ( τ1 , … , τl , 0 , … , 0 ) . The matrices G and H are determined by τj . Seeking the matrices G and H to optimize the objective function in Eq ( 15 ) is equivalent to finding solutions τj to minimize the objective function in Eq ( 15 ) . Using techniques in ( 55 ) , we can show that the solution to optimization problem ( 15 ) is τj= ( λj−μ ) + , ( 17 ) where ( a ) + = max ( a , 0 ) and λj is defined in Eq ( 8 ) . In practice , a singular value is selected as a penalty parameter μ such that the sum from the selected singular value to the smallest singular value accounted for 20% of total singular values . Define the matrix Λl as Λl=diag ( ( λ1−μ ) + , … , ( λl−μ ) + ) . Then , factor matrices G=UlΛl1/2 and H=Λl1/2Vl are the solution to the minimization problem ( 15 ) . We use truncation of the SVD to keep only the top l singular values and soft-thresholding on the singular values to change distribution of the singular values . When μ increases beyond some singular values λm , l − m + 1 singular values of GH will disappear . Analytically , we can easily show that λ1−μλ1−μ+λ2−μ+…+λl−μ>λ1λ1+λ2+…+λl . In other words , increasing μ will move up the proportion of the first singular value in the total of singular values . The phenotype data consist of 756 samples with 46 traits . The initial largest singular value and total singular values of the phenotype data are 73 . 97 and 1030 . 14 respectively . S1 Fig shows that the proportion of the first singular value in the total of singular values is an increasing function of threshold μ . This clearly demonstrates that adding quadratic regularization results in changing the distribution of the singular values of the factor matrices . Therefore , we can expect that regularized matrix factorization for data reduction will increase the power to detect association of the genetic variation with the traits . Quadratically regularized matrix factorization and CCA have broad applications . There are a number of ways to use the quadratically regularized matrix factorization for data reduction in the association analysis which are briefly summarized as follows . Quadratically recognized matrix factorization can also be applied to the K or R2 matrix in the CCA . The test statistics then use the singular values of the reduced K or R2 matrix to test association of genetic variation with a trait . To adjust for covariates , we first regress phenotypes on the covariates . If the covariates are the same for all traits , then the multivariate regression will be used to simultaneously regress all the phenotypes to the covariates . Otherwise , we regress each trait to the covariates individually . The residuals are then taken as one set of variables ( similar to phenotypes ) for CCA . Next we study the relationships between the association of the genetic variation within a gene or genomic region with the multiple traits and the heritability of quantitative traits . For simplicity , we assume that the genetic variation is the only major contribution to the phenotypic variation and we will not consider the covariates . In Supplemental note A , we show that the narrow heritability is equal to the measure of association r of the genetic variation within a gene or genomic region with the multiple traits defined in Eq ( 11 ) . In supplemental note B , we use reproducing kernel Hilbert spaces ( RKHS ) as a general framework and the covariance operator as a general tool for unifying CCA , kernel CCA , functional CCA and other association analyses including GAMuT . Many multivariate and functional statistical methods such as regression , CCA , kernel regression , kernel CCA , functional regression and functional CCA can be used to test the association of genetic variants with the phenotypes . In supplemental note B , we develop a unified framework for association tests to reveal the relationships among various multivariate and functional association tests . In Supplemental Note B , we define two kernels Kx = ( K ( Xi , Xj ) ) m×m , Ky = ( K ( Yi , Yj ) ) m×m , G=Im−1m1m , and centered kernels: K˜x=GKxG and K˜y=GKyG . Using the centered kernels we can define the dependence measure as ( N39 ) : 1m2Trace ( K˜xK˜y ) , ( 18 ) which is the basis for the GAMuT test [25] . In Supplemental note B , we show that the KCCA is quite similar to the kernel independent test and that the association measure in the KCCA is exactly equal to the dependence measure . Finally , we consider the FCCA . In Supplemental note B , we unify multivariate association tests and functional association tests . Suppose that the FPC scores form a feature space . In supplemental note B , we define the feature maps from the original functional data to the FPC score feature space . We show that the dependence measure in the FPC score-based kernel analysis is asymptotically equal to the association measure of the FCCA . This implies that the FCCA is a specific kernel analysis that uses the FPC score to define the kernels instead of directly using the genotype data to define the kernels .
To examine the null distribution of test statistics for association analysis of multiple traits , we performed a series of simulation studies to compare their empirical levels with the nominal ones . We calculated the type I error rates for rare alleles , and both rare and common alleles . We first assumed the model for multiple traits: Yi=μ+εi , i=1 , … , n , where Yi = [yi1 , … , yik] , k is the number of traits , μ is a vector of overall means , and εi is distributed as N ( 0 , Σ ) , where ∑ is a k × k residual correlation matrix . We similarly model the correlation matrix as in Broadaway et al [25] . We also consider three scenarios of low residual correlation among phenotypes with pair-wise correlation selected from a uniform ( 0 . 1 , 0 . 2 ) distribution , moderate residual correlation with pair-wise correlation selected from a uniform ( 0 . 2 , 0 . 4 ) distribution , and high residual correlation with pair-wise correlation selected from a uniform ( 0 . 4 , 0 . 7 ) distribution . We randomly generated 1 , 000 , 000 haplotypes with gene C16orf62 from 659 samples of European origin in The 1000 Genome Project . 1 , 000 SNPs with 600 rare variants ( frequencies ranging from 0 . 0005 to 0 . 01 ) and 400 common variants ( frequencies larger than 0 . 01 ) were randomly selected from C16orf62 gene . The number of sampled individuals for type 1 error simulations from populations of 500 , 000 individuals ranged from 500 to 2 , 000 . A total of 10 , 000 simulations were repeated . The type 1 error rates were estimated as the proportion of the datasets under the null distribution in which the P-values were less than or equal to the significance level . Tables 1 and 2 summarized the type 1 error rates of the eleven statistics: QRFCCA , Sparse CCA ( SCCA ) [49] , GAMuT [25] , MSKAT [50] , FCCA , Kernel CCA ( KCCA ) , CCA , A Unified Score-Based Association Test ( USAT ) [53] , PCA ( applying to both phenotypes and genotypes ) , MANOVA ( multivariate ANOVA applied to multiple phenotypes and multiple SNS ) and minP ( minimum of P-values for testing the association of single SNP with multiple phenotypes ) for testing the association of rare variants , and both rare and common variants , within a genomic region with 15 high correlated traits , respectively , at the nominal levels α = 0 . 05 , α = 0 . 01 , α = 0 . 001 , α = 0 . 0001 , and α = 0 . 00001 . Tables S1-S16 showed type 1 error rates of the eleven statistics for testing the association of rare variants , and both rare and common variants with 5 , 10 and 15 traits under three scenarios: low , moderate and high correlations . These tables showed that the estimated type 1 error rates of the QRFCCA across a range of assumptions were not appreciably different from the nominal levels α = 0 . 05 , α = 0 . 01 , α = 0 . 001 , α = 0 . 0001 , and α = 0 . 00001 . We also observed that the type 1 error rates of other ten statistics , in most scenarios , were appropriate . To evaluate the performance of the QRFCCA in association analysis , we used simulated data to estimate power of eleven statistics for testing the association of a gene or a genomic region with the traits . We simulated 5 , 10 and 15 traits with low , moderate and high correlations . An additive genetic model was used to summarize all genetic effects of causal variants in the gene or genomic region . For each individual , 5 , 10 , 15 quantitative traits were simulated by the summation of genetic effects and the residual correlation between the traits . Let hk2 be the narrow heritability of the kth trait . Assume that each SNP had a 2% chance to be associated with a trait and its genetic effect on the kth trait was equal to the hk2MAF multiplied by the number of minor alleles where MAF denoted the frequency of the minor allele . This indicates that the genetic effect of causal variants was inversely proportional to its minor allele frequency . We did not assume that the gene of interest was associated with all traits . For each of five traits , ten traits and fifteen traits , we consider three scenarios: ( 1 ) the gene of interest was truly associated with three of five assessing traits , six of ten assessing traits and eight of fifteen assessing traits ( the gene was associated with 53 . 3% of traits ) ; ( 2 ) the gene of interest was truly associated with two of five assessing traits , four of ten assessing traits and six of fifteen assessing traits ( the gene was associated with 40% of traits ) ; and ( 3 ) the gene of interest was truly associated with one of five assessing traits , two of ten assessing traits and three of fifteen assessing traits ( the gene was associated with 20% of traits ) . We consider two significant levels: α = 0 . 05 and α = 0 . 00001 . The residual correlation was simulated from a multivariate distribution with mean zero and covariance matrix [1−h12r12 ( 1−h12 ) ( 1−h22 ) ⋯r1K ( 1−h12 ) ( 1−hK2 ) r12 ( 1−h12 ) ( 1−h22 ) 1−h22⋯r2K ( 1−h22 ) ( 1−hK2 ) ⋮⋮⋮⋮rK1 ( 1−h12 ) ( 1−hK2 ) rK2 ( 1−h22 ) ( 1−hK2 ) ⋯1−hK2] , where the correlation between traits rij was randomly generated with uniform distribution: low correlation [0 . 1–0 . 2] , moderate correlation [0 . 2–0 . 4] and high correlation [0 . 4–0 . 7] . In summary , the genetic model for power evaluation is given by [y1⋯yK]=[x1⋯xq]×[ ( α11⋯αK1⋮⋱⋮α1q⋯αKq ) ∘ ( b11⋯bK1⋮⋱⋮b1q⋯bKq ) ]× ( t1⋯0⋮⋱⋮0⋯tK ) × ( h12⋯0⋮⋱⋮0⋯hK2 ) +[ε1⋯εK] , where yi represented phenotypes , xj was an indicator variable for coding the genotype of the jth SNP in the gene , taking values 0 , 1 , 2 to represent the number of minor alleles at the SNP . αij denoted the genetic effect that followed a normal distribution with N ( 0 , hi2MAFj ) , where hi2 denoted the narrow heritability of the ith trait , MAFj denoted the frequency of the minor allele at the jth SNP , bij in the matrix represented the probability of the jth SNP being the causal variant for the ith trait and followed a binomial distribution B ( 1 , 0 . 02 ) . Notation ∘ denoted an element-wise matrices multiplication , ti ∼ B ( 1 , 0 . 6 ) , ti ∼ B ( 1 , 0 . 4 ) , ti ∼ B ( 1 , 0 . 2 ) represented the probability of the gene being tested contributing the genetic effect to the ith trait for scenarios 1 , 2 and 3 , respectively , and hi2 denoted the heritability of the ith trait and followed a uniform distribution U ( 0 . 005 , 0 . 015 ) , εi , i = 1 , … , K denoted residuals and followed a multivariate normal distribution as defined above . The genotype data in type 1 error calculations were also used for power evaluation . A total of 10 , 000 simulations were repeated for the power calculations . We first compared the power of QRFCCA with ten other competing statistics that are described in type 1 error rate calculations for testing the association of rare variants with multiple continuous traits . Power was estimated as a function of sample sizes . Figs 1–3 plotted the power of the curves as a function of sample sizes of the eleven statistics for collectively testing the association of all rare variants in the gene with 10 low , moderately and highly correlated traits for scenario 1 , respectively , at the significance level α = 0 . 05 . The power curves of eleven statistics for testing the association of the gene including only rare variants with 5 and 15 low , moderately and highly correlated traits for scenario 1 , respectively , were plotted in Fig S2–S4 , Fig S5–S7 . The power of the curves as a function of sample sizes of the eleven statistics for collectively testing the association of all rare variants in the gene with 10 low , moderately and highly correlated traits for scenarios 2 and 3 , respectively , were plotted in Fig S8–S13 . We observed several remarkable features . First , we clearly observed that the QRFCCA had highest power among all eleven statistics , followed by SCCA for most scenarios considered . Second , in general , the power of the FCCA was higher than that of the KCCA and the GAMuT , but their differences were very small . Third , we often observed that the sparse CCA had higher power than the classical CCA . Fourth , the power of the MSKAT was often higher than that of GAMuT . Next we investigated whether the power pattern of the eleven statistics for testing the association of the gene with only rare variants would still hold when testing the association of the gene with both rare and common variants . Figs 4–6 presented the power curves of eleven statistics for testing the association of the gene including both rare and common variants with 10 low , moderately and highly correlated traits for scenario 1 at the significance level α = 0 . 05 , respectively . Fig S14-S16 and Fig S17-S19 showed the power curves of the eleven statistics for testing the association of the gene including both rare and common variants with 5 low , moderately , and highly correlated traits , and 15 low , moderately , and highly correlated traits for scenario 1 , respectively . We also presented the power curves of the eleven statistics for testing the association of the gene including both rare and common variants with 10 low , moderately , and highly correlated traits for the scenarios 2 and 3 , respectively , in Figure S20-S25 . We first observed that the power of the QRFCCA in any cases was much higher than that of all other ten statistics . Then , we observed that differences in power between the QRFCCA and other ten statistics for the common variants were much higher than for the rare variants and their differences increased as the correlation between traits increased or the number of the traits which the gene was associated with increased . We also observed that the power of all statistics for testing the association of common variants was higher than that of all statistics for testing the association of rare variants . Finally we observed the power of the classic CCA , manova , PCA and min P was very low . To show that the dimension reduction of the QRFCCA for the phenotype will also improve the power of the test , we presented Fig S26 that showed the power of eleven statistics for testing the power of a single common variant with 15 low correlated traits as a function of sample sizes . We observed that the QRFCCA still had higher power than all other ten statistics for testing the association of the single common variant with multiple traits due to its efficient dimension reduction , followed by kernel CCA . Since the MSKAT and GAMuT did not provide tools for efficiently reducing the dimension of the phenotypes , the power of the MSKAT and GAMuT were much smaller than that of the QRFCCA , and even smaller than that of kernel CCA . Finally , we presented Fig S27-S32 showing the power curves of the eleven statistics for testing the association of the gene including rare variants only , and both rare and common variants with 15 highly correlated traits in scenarios 1 , 2 and 3 , at the significance level α = 0 . 00001 , respectively . Again , we still observed that the power of the QRFCCA was the highest among eleven statistics . However , when the significance level was reduced from α = 0 . 05 to α = 0 . 00001 the power of all statistics was reduced . We also observed that when the significance level was reduced , the simulations were unstable . In this case , we need to increase the number of simulations . These figures demonstrated that the QRFCCA substantially outperformed the ten other statistics and the difference in power between the QRFCCA and other statistics for the both rare and common variants was much larger than that for the rare variants only . This demonstrated that the regularization in singular vectors plays a more important role in association analysis of both rare and common variants than that in association analysis of only rare variants . Investigation of the contribution of the entire allelic spectrum of genetic variation to multiple traits are still at its infancy . The systematic searching for both common and rare variants associated with large number of traits is essential for unraveling the genetic architecture of complex diseases . To further evaluate the performance , the QRFCCA and ten other statistics were applied to the UK-10K dataset . The UK-10K Cohorts project used a low read depth whole-genome sequencing ( WGS ) to assess the contribution of the genetic variants to the sixty-four different traits [58] . However , missing phenotypes were found in many individuals . To ensure no missing phenotypes in individuals , we included 765 individuals with 2 , 240 , 049 SNPs in 33 , 746 genes , and shared 46 traits in 13 major phenotypic groups which covered a wide range of traits ( Table S17 ) in the analysis . We took the rank-based inverse normal transformation of the phenotypes [59] as trait values . Principal components ( PCs ) can be used for covariates to adjust for the impact of population structure . We first studied the association of genes with only rare variants ( MAF ≤ 0 . 01 ) . The total number of genes with only rare variants tested for association was 33 , 746 . A p-value for declaring significant association after applying the Bonferroni correction for multiple tests was 1 . 48 × 10−6 . To examine the behavior of the test statistics , we plotted the QQ plot of the QRFCCA with one FPC , FCCA with one FPC , PCA and GAMuT using a linear kernel in Fig 7 and the QQ plot of the MSKAT , KCCA , SCCA , CCA , MANOVA and USAT in Fig S33 , assuming no PC adjustment . The QQ plots showed that the false positive rate of the QRFCCA and FCCA for testing the association of the gene with 46 traits in some degree was controlled . However , the behavior of the QQ plot of KCCA and SCCA was weird . The total number of genes consisting of only rare variants significantly associated with the 46 traits with and without PC adjustment using QFCCA , FCCA , PCA , SCCA , KCCA , MSKAT , GAMuT , CCA , USAT and MANOVA , were shown in Table 3 . A list of P-values of top 25 genes with rare variants only significantly associated with 46 traits using QRFCCA was summarized in Table 4 . A list of P-values of 54 remaining genes significantly associated with 46 traits using QRFCCA were summarized in Table S18 . We observed that the list of 79 significant genes identified by QFCCA included all 59 significant genes using FCCA , all 8 significant genes using SCCA , all 3 significant genes using KCCA , 19 significant genes using MSKAT , and 8 significant genes using PCA . The Manhattan plot showing genome-wide p-values of association with 46 traits calculated using QRFCCA is presented in Fig 8 . To further assess the performance of the QFCCA and GAMuT , we presented Tables S19 and S20 . Table S19 summarized the top ten genes ranked using GAMuT where p-values calculated by both GAMuT and QRFCCA were also listed . None of ten genes reached genome-wide significance levels by the GAMuT . However , we noticed that 7 of top ten genes ranked by GAMuT were significantly associated with the 46 traits identified by QFCCA . Although we observed that the p-value of RNU6-1229P calculated by GAMuT was smaller than that calculated by QRFCCA , we did not find any significant SNPs within RNU6-1229P ( Fig S34 ) . This may imply that association was spurious . In Table S20 , we listed p-values of all SNPs within gene ADAM19 . We observed that QRFCCA identified significance of ADAM19 with a p-value less than 6 . 07 × 10−11 and at least 4 SNPs in ADAM19 had very small p-values and two additional SNPs had p-values that were close to the threshold p-value of genome-wide significance ( Fig S34 ) . However , the GAMuT missed to identify significance of ADAM19 . To characterize the pleiotropic pattern , we presented the heat map showing the pattern of cross phenotype association of genes with rare variants only and the most important pleiotropic effects of the genes ( Fig 9 ) . Table S21 summarizes the number of traits which a single gene was associated with ( p-value ≤ 0 . 05 ) . In Table S21 , we also listed the p-values for testing the association of the gene with all 46 traits . All p-values in Fig 9 and Table S21 were calculated using QRFCCA . We observed two remarkable features . First , we observed that 5 genes were significantly associated with 3 traits , 10 genes were significantly associated with 2 traits , and 39 genes were significantly associated with one trait at the genome-wide significance level after Bonferroni correction . The remaining 25 genes did not reach the genome-wide significance with any trait . However , we observed that these genes still showed mild association with multiple traits . Second , we observed that multiple genes were significantly associated with single phenotype ( Table S22 ) . For example , 345 genes were significantly associated with creatinine , 108 genes with HOMA-IR , 20 genes with HOMA-B , 72 genes with HsCRP , 21 genes with glucose , 15 genes with insulin , 14 genes with GGT , and 11 genes with VLDL . Some results can be confirmed in the literature . Throughout this section , all p-values were calculated using QRFCCA . We found that 30 out of 79 genes ( 38 . 0% ) with rare variants significantly associated with 46 traits were reported association with some of them in the literatures . For example , PNOC which was associated with the 46 traits ( p-value ≤ 1 . 63 × 10−12 ) , HOMA-IR ( p-value ≤ 1 . 2 × 10−7 ) and triglycerides ( p-value ≤ 9 . 15 × 10−9 ) was reported to be associated with insulin resistance [60] and triglycerides [61] . MIR409 which was associated with the 46 traits ( p-value ≤ 2 . 77 × 10−10 ) , weight ( p-value ≤ 1 . 65 × 10−6 ) , Hip ( p-value ≤ 7 . 19 × 10−7 ) and total lean mass ( p-value ≤ 2 . 15 × 10−6 ) was used as a weight loss biomarker [62] . GAPDH which showed an association with the 46 traits ( p-value ≤ 2 . 96 × 10−8 ) and specifically with creatinine ( p-value ≤ 3 . 45 × 10−8 ) was reported to be associated with creatinine [63] . MLN that demonstrated association with the 46 traits ( p-value ≤ 9 . 47 × 10−8 ) and showed a strong association with glucose ( p-value ≤ 6 . 24 × 10−11 ) played an important role in controlling the rise rate of glucose level [64] . JUN , which was associated with the 46 traits ( p-value ≤ 1 . 25 × 10−7 ) and showed a strong association with creatinine ( p-value ≤ 3 . 43 × 10−20 ) , was reported to be correlated with the serum creatinine level [65] . COMP which showed significance with the 46 traits p-value ≤ 4 . 83 × 10−7 ) and a strong association with specific trait creatinine ( p-value ≤ 9 . 58 × 10−15 ) was also reported an association with creatinine [66] . Next we studied the association of genes with only common variants ( MAF ≥ 0 . 05 ) . The total number of genes with only common variants tested for association was 33 , 166 . The p-value to declare the significant association after applying Bonferroni correction for multiple tests was 1 . 51 × 10−6 . To examine the behavior of the test statistics , we plotted the QQ plot of the QRFCCA , FCCA , PCA and GAMuT using a linear kernel in Fig 10 and the QQ plot of the KCCA , MSKAT , SCCA , CCA , MANOVA and USAT in Fig S35 . Similar to rare variants , the QQ plots for common variants showed that the false positive rate of the QRFCCA and FCCA for testing the association of the gene with 46 traits in some degree was controlled . However , the behavior of the QQ plot of KCCA and SCCA was not satisfied . The total number of genes significantly associated with the 46 trait that were identified using ten statistics with and without PC adjustment were listed in Table 5 . Table 6 listed P-value of top 25 genes with common variants only significantly associated with 46 traits that were discovered using QRFCCA . Table S23 summarized remaining 42 genes with common variants only significantly associated with 46 traits that were identified using QRFCCA . Similar to rare variants , we observed that a list of 67 significant genes identified by QRFCCA included all the 55 significant genes identified using FCCA . However , unlike rare variants , only one significant gene was shared between the QRFCCA and KCCA . We observed from Table 5 that the QRFCCA , FCCA substantially outperformed other seven statistics and that the impact of population structure on the QRFCCA , FCCA and KCCA was small . To assess whether the QRFCCA for testing the association of genes including common variants only with multiple traits was appropriate or not , we presented Fig S36 which shows the p-values of all SNPs within gene REG1B . We observed in Fig S36 that more than 11 SNPs in REG1B with p-values ≤ 0 . 0001 jointly made contributions to the strong association of REG1B with the 46 traits with p-value ≤ 1 . 65 × 10−116 . The QRFCCA can catch the features of genetic variation . In Fig S36 we listed the p-values of all SNPs within the gene XXbac-BPG154L12 . 4 . From Fig S36 , we also observed that although the GAMuT treated XXbac-BPG154L12 . 4 as associated with the 46 traits ( p-value ≤ 2 . 55 × 10−6 ) , none of SNPs were even weakly associated with the 46 traits . We should point out that the QRFCCA did not find any , even mild association ( p-value ≤ 0 . 3175 ) . The Manhattan plot showing genome-wide p-values of association of genes consisting of only common variants with the 46 traits calculated using QRFCCA was presented in Fig 11 . To unravel the genetic pleiotropic structure of common variants , we presented the gene/phenotype association heat map that demonstrated the most important pleiotropic relations between a single gene and multiple traits ( Fig 12 ) and summarized the number of traits a single gene affected in Table S24 . All p-values in Fig 12 and Table S24 were calculated using QRFCCA . We observed that one gene significantly influenced 6 traits; 2 genes , 5 traits; 4 genes , 4 traits; 6 genes , 3 traits; 17 genes , 2 traits and 20 genes , one trait . The remaining 17 genes did not reach the genome-wide significance with any trait . However , we observed that these genes still made genetic contributions to multiple traits . The significant association of the remaining 17 genes with the 46 traits was due to summation of the mild genetic effects on multiple traits of a single gene . We also analyze the association of all common SNPs in one gene with one trait for all the 46 traits . The results were summarized in Table S25 . We observed that 34 genes were significantly associated with creatinine , 29 genes with HsCRP , 23 genes with HOMA-IR , 23 genes with HOMA-B , 9 genes with glucose , 8 genes with insulin , 7 genes with GGT , and 6 genes with VLDL . The distributions of the number of genes consisting of only common variants associated with traits were similar to that of rare variants although the number of genes associated with common variants was smaller than the number of genes associated with rare variants . Finally , we reported computation times of whole genome association testing for 10 test statistics as in Table 7 where Intel ( R ) Xeon ( R ) CPU E7- 4870 @ 2 . 40GHz was used for calculations . We observed from Table 7 that less than 2 and half hours to complete whole exome association analysis of 46 traits were needed for QRFCCA . We also observed that the computational times of QRFCCA were much less than that of SCCA , USAT , MANOVA , GAMuT and CCA , but larger than that of PCA , KCCA and MSKAT . Throughout this section , all genes included common variants only in the analysis . The literature confirmed many gene-trait associations which were identified in this study . We found that 58 . 2% of identified genes ( 39 out of 67 genes ) with common variants only were reported association with some of 46 traits in the literatures . For example , REG1B , which showed the most significant association with the 46 traits ( p-value ≤ 1 . 65 × 10−116 ) , has been reported to be associated with glucose [67] ( our analysis identified an association with p-value ≤ 1 . 31 × 10−21 ) , the production of insulin [68] ( our analysis identified an association with insulin with p-value ≤ 1 . 81 × 10−24 and HOMA-IR with p-value ≤ 5 . 02 × 10−94 ) , and triglyceride whose increment has deleterious effects on the function of islet beta cells [69] ( our analysis showed an association with triglyceride with p-value ≤ 1 . 81 × 10−24 ) . LEF1 , which was associated with the 46 traits ( p-value ≤ 7 . 44 × 10−83 ) , has been related to insulin resistance [70] ( the analysis demonstrated an association with HOMA-B with p-value ≤ 7 . 17 × 10−52 ) and heart failure [71–73] ( the analysis showed an association with HsCRP with p-value ≤ 1 . 22 × 10−122 and Homocysteine with p-value ≤ 2 . 24 × 10−14 ) . DYNC1H1 , which was associated with the 46 traits ( p-value ≤ 3 . 46 × 10−58 ) , HOMA-IR ( p-value ≤ 4 . 76 × 10−108 ) , insulin ( p-value ≤ 2 . 83 × 10−28 ) , glucose ( p-value ≤ 1 . 07 × 10−10 ) and creatinine ( p-value ≤ 2 . 57 × 10−9 ) , has been associated with hyperinsulinemia , hyperglycemia , the progress of glucose intolerance [74] , and the blood creatinine level [75] . DOCK7 , which presented associations with the 46 traits ( p-value ≤ 4 . 42 × 10−57 ) , HsCRP ( p-value ≤ 3 . 66 × 10−53 ) and BMI ( p-value ≤ 9 . 36 × 10−7 ) , has been associated with heart disease and ischemic stroke [76] and overweight and obesity[77] . Gene GBF1 , which was associated with the 46 traits ( p-value ≤ 6 . 30 × 10−28 ) , HOMA-B ( p-value ≤ 2 . 91 × 10−17 ) , has been reported to be involved in insulin resistance and type 2 diabetes [78] . METAP2 , which showed strong association with the 46 traits ( p-value ≤ 2 . 62 × 10−20 ) , HOMA-B ( p-value ≤ 3 . 14 × 10−32 ) , HOMA-IR ( p-value ≤ 3 . 17 × 10−17 ) and insulin ( p-value ≤ 8 . 61 × 10−10 ) , has demonstrated associations with insulin resistance and insulin levels [79] . Gene GRN , which presented associations with the 46 traits ( p-value ≤ 1 . 63 × 10−16 ) , HOMA-IR ( p-value ≤ 2 . 28 × 10−29 ) and insulin ( p-value ≤ 7 . 89 × 10−10 ) , has been reported to associate with insulin resistance in type 2 diabetes patients [80] and the blood insulin levels [81] . Finally , gene USP44 , which showed associations with the 46 traits ( p-value ≤ 3 . 49 × 10−14 ) , HsCRP ( p-value ≤ 1 . 81 × 10−31 ) and HOMA-IR ( p-value ≤ 7 . 75 × 10−11 ) , has been reported to associate with congenital heart disease [82] , the increment of the HsCRP in congenital heart disease patient and insulin resistance [83] .
Investigating the pleiotropic effects of the genetic variants can provide important information to allow a deeper understanding of the complex genetic structures of health and disease . However , the identification of complete pleiotropic structures of high dimensional genotype-phenotypes poses great statistical and computational challenges . To meet these challenges , we have addressed several issues to overcome the critical barriers in advancing the development of novel statistical methods and computational algorithms for genetic pleiotropic analysis . The first issue is to explore deep architectures of genotype-phenotype data in cross-phenotype association analysis . The traditional single trait and multiple trait analysis usually use genotype data in their raw form . These methods do not transform the raw data into a suitable internal representation in which association analysis can be used for distinguishing disease patterns from health patterns . DNA sequences and genetic variants are highly correlated and hierarchically organized in the genome . Exploring multiple levels of representation of the genetic variants and efficiently using correlation information in the data can increase the power to detect the association of the genetic variants with phenotypes . Multiple levels of representation of genetic variants consist of several steps: ( 1 ) FPCA , ( 2 ) matrix factorization , ( 3 ) quadratic regularization , and ( 4 ) CCA . The FPCA changes the raw genetic variants to the functional principal component representation that captures the linkage disequilibrium features . The matrix factorization is to embed the functional principal component scores into the low dimensional vector space . It compresses the functional principal component score data to a few new features that are another level of representation of genetic variants . Quadratic regularization further compresses the data and changes the representation of functional principal component scores . Finally , CCA is used as an effective tool for two-view dimension reduction . QRFCCA combines dimension reduction in different levels of data representations . Multiple levels of representation of genetic variants are effective at leveraging data structure . This can be evidenced from the analysis of less derived traits . Our results showed that the significance decreased when the derived traits were removed from the analysis . Utilizing the data structure is a key component of the proposed QRFCCA method . Using less derived traits will decrease the dependence among traits and hence the QRFCCA analysis will use less correlation information among the traits and hence slightly reduce the significance . Multiple levels of representation of genetic variants are a philosophy . We borrow this concept from deep learning methods that are representation methods with multiple levels of representations , each transferring the representation at one level into a representation at another level . Specifically , to fully utilize the linkage disequilibrium information of genetic variants across the genomic region and efficiently reduce the dimension of the data , we proposed a new paradigm of association analysis that consists of three steps to combine multilevel data reduction and CCA . The first step is to apply FPCA to the original data for dimension reduction . The FPCA decomposes the genetic data into several functional components . Each functional component contains functional information of genetic variants across the genomic region , preserves the orders of genetic variants along the genomic and returns all possible pair-wise and high order linkage disequilibrium . If the phenotypes are function-valued physiological traits or RNA-seq data , the FPCA can also be applied to the phenotype data . The second step is to use quadratically regularized matrix factorization for further compressing the FPC scores into low rank representation and removing noisy data points . As a result , the FPCA and matrix factorization extracted useful genetic and phenotype information and deeply learned the internal genetic and phenotype representation . The third step is to apply CCA to the extracted FPC scores . Large scale simulations and real data analysis demonstrated that QRFCCA substantially outperformed all ten other statistics and FCCA outperformed some of multivariate statistics . The second issue is to develop a general framework for unifying association analysis , which provides a theoretic basis to evaluate various statistical methods for association analysis and design the guidance for developing novel statistics for testing the association of genetic variants with phenotypes . We used reproducing kernel Hilbert spaces ( RKHS ) as a general framework and the covariance operator as a general tool for unifying CCA , kernel CCA , functional CCA , dependence measure-based independence tests and other association analyses including GAMuT . We showed that multivariate linear regression are equivalent to the classical CCA . Covariance is a key measure to assess linear association . Its extension to covariance operator provides a tool for quantifying the nonlinear association and derives kernel-based dependence measures and independence tests which form the basis for the GAMuT test . We also show that the KCCA is quite similar to the kernel independent test . Finally , we considered the FCCA . To unify multivariate association tests and functional association tests , we used RKHS as a general framework for the formulation of the functional CCA . We showed that the dependence measured in the FPC score-based kernel analysis is asymptotically equal to the association measure of the FCCA . The FCCA also use the kernel-based dependence measure to develop association tests . Unlike the KCCA and GAMuT test where the kernels are selected by users , the FCCA uses the FPC scores as the feature space and derives the kernels from the data . This was why large-scale simulations and real data showed that in general , the FCCA outperformed the GAMuT , MSKAT and KCCA . Our FPC scores were generated via two steps . The first step used Fourier or wavelet expansions to derive eigenfunctions . The second step was to generate the FPC scores from the eigenfunctions . In the literature , some authors used one step FPCA to directly derive FPC scores from the Fourier or wavelet expansions . Our experiences showed that the number of functional principal components using two step FPCA was , in general , smaller than that directly derived from the Fourier or wavelet expansions . Therefore , two step FPCA had higher power than the one step FPCA . The third issue is how to reveal pleiotropic structure of the genetic variants and quantify the degree of pleiotropy . Pleiotropy is a widely used word to indicate that a gene affects multiple traits . However , the nature and extent of pleiotropy is less precisely defined . Recently , Schaid et al , 2016 [84] gave the formal testing for pleiotropy . They proposed that formal test of pleiotropy should assume a null hypothesis that one or fewer traits are associated with a genetic variant . Unlike the definition of Schaid et al . our null model of the pleiotropy of a gene is the absence of any traits which the gene was associated with . If Schaid et al ( 2016 ) definition of a formal test of pleiotropy is used the proposed test is not a pleiotropy tool . The fourth issue is the cross-phenotype association analysis with next-generation sequencing . The popular methods for cross-phenotype association analysis is to assess the influence of a single variant on multiple distinct phenotypes . These methods work very well for cross-phenotype association analysis of common variants , but are not suitable for testing the association of rare variants with multiple phenotypes . To illustrate the urgent need to develop gene-based statistical methods for cross-phenotype association analysis of rare variants , we searched the variants across the genome for significant associations with the multiple phenotypes . We found that 21 , 272 rare variants were significantly associated with the 46 traits at the genome-wide significance level after Bonferroni correction . It is highly unlikely that so many rare variants affected the 46 traits . To overcome this limitation , we developed the QRFCCA for gene-based cross-phenotype association analysis . The QRFCCA can be applied to both multivariate phenotypes , function-valued phenotypes and NGS genotype data . Since the genotype profiles of the common variants and rare variants have different patterns , to increase the power of the tests , we take the association tests of common variants and rare variants separately . We found that the significant genes with common variants only were not overlapped with the significant genes with rare variants only . In pleiotropic analysis , we should conduct cross-phenotype analysis for both common and rare variants and separately . The QRFCCA provides a powerful tool to accomplish this task . To provide a guidance for cross phenotype association studies , we comprehensively evaluated the current existing statistics for cross-phenotype association by using large-scale simulations and real data analysis . In all simulated cases , when the sample size reached 2 , 000 , the power of the QRFCCA varies between 80% and 90% . We found that the proposed QRFCCA not only substantially outperformed all other widely used competing statistics , but also was very flexible . The QRFCCA can be used for association analysis of both common variants and rare variants , and any phenotypes including quantitative or qualitative , multivariate or function-valued phenotypes . We performed cross-phenotype association analysis of a largest number of traits with NGS data up to the present time . We identified 79 genes including rare variants only which were significantly associated with the 46 traits and 67 genes including common variants only which were significantly associated with the 46 traits . These two sets of genes were not overlapped . Some of gene-phenotype association can be confirmed in the literature . We found that the largest number of the traits which a gene significantly affected at the genome-wide significance level was six and three in the cross-phenotype association analysis of common and rare variants , respectively . We also discovered that the largest number of traits which a gene affected with the P-value < 0 . 05 was 18 and 16 in the cross-phenotype association analysis of common and rare variants , respectively . In the single trait association analysis , we found that a large number of genes significantly affected creatinine ( genes with rare variants: 345 , genes with common variants: 34 ) , HsCRP ( genes with rare variants: 72 , gene with common variants: 29 ) and HOMA-IR ( genes with rare variants: 108 , genes with common variants: 24 ) . The results presented in this paper are preliminary . The greatest lengths of the genes that were significantly associated with the 46 traits for rare and common variants in the real data analysis were 131Kb and 42Kb , respectively . The proposed methods may not have power to detect the association of the genes with lengths longer than these numbers . The number of basis functions for genotype profile expansion is an important factor for the power of the FPCA-based tests . We have not performed theoretical analysis to determine the appropriate number of basic functions for genotype profile expansions . We resort to ad hoc approaches to select the number of basis function in the expansions . The current pleiotropic analysis cannot identify the global causal structure of pleiotropy , which will decrease our power to unravel mechanisms underlying complex traits . To overcome this limitation , causal inference tools should be explored for cross-phenotype association analysis . The purpose of this paper is to stimulate further discussions regarding the great challenges we are facing in the pleiotropy analysis of high dimensional phenotypic and genomic data produced by modern sensors and next-generation sequencing . | Association analysis of multiple phenotypes will unravel the genetic pleiotropic structures of multiple phenotypes , provide a powerful tool for developing drug with fewer side effects . To increase the power of the tests for high dimensional association analysis of multiple phenotypes with next-generation sequencing data , a key issue is to develop novel statistics that can effectively extract informative internal representation and features from high dimensional data . However , the current paradigm of association analysis of multiple phenotypes does not efficiently utilize the rich correlation structure of the genotype and phenotype data . To shift the paradigm of association analysis from shallow multivariate analysis to comprehensive functional analysis , we proposed a new general statistical framework referred to as a quadratically regularized functional canonical correlation analysis ( QRFCCA ) for association test which explores rich correlation information in the genotype and phenotype data . Large-scale simulations demonstrate that the QRFCCA has a much higher power than that of the many existing statistics while retaining the appropriate type 1 errors . To further evaluate the new approach , the QRFCCA are also applied to the TwinsUK study with 46 traits and sequencing data . The results show that the QRFCCA substantially outperforms the other statistics . | [
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] | 2017 | A quadratically regularized functional canonical correlation analysis for identifying the global structure of pleiotropy with NGS data |
Acute regional ischemia in the heart can lead to cardiac arrhythmias such as ventricular fibrillation ( VF ) , which in turn compromise cardiac output and result in secondary global cardiac ischemia . The secondary ischemia may influence the underlying arrhythmia mechanism . A recent clinical study documents the effect of global cardiac ischaemia on the mechanisms of VF . During 150 seconds of global ischemia the dominant frequency of activation decreased , while after reperfusion it increased rapidly . At the same time the complexity of epicardial excitation , measured as the number of epicardical phase singularity points , remained approximately constant during ischemia . Here we perform numerical studies based on these clinical data and propose explanations for the observed dynamics of the period and complexity of activation patterns . In particular , we study the effects on ischemia in pseudo-1D and 2D cardiac tissue models as well as in an anatomically accurate model of human heart ventricles . We demonstrate that the fall of dominant frequency in VF during secondary ischemia can be explained by an increase in extracellular potassium , while the increase during reperfusion is consistent with washout of potassium and continued activation of the ATP-dependent potassium channels . We also suggest that memory effects are responsible for the observed complexity dynamics . In addition , we present unpublished clinical results of individual patient recordings and propose a way of estimating extracellular potassium and activation of ATP-dependent potassium channels from these measurements .
The heart is an electromechanical pump , where contraction is triggered and synchronized by electrical activation originating from the sinoatrial node . Abnormal initiation or conduction of the electrical impulses could result in a cardiac arrhythmia . Cardiac arrhythmias are an important cause of sudden and premature death in the industrialized world . In many cases the lethal event is ventricular fibrillation ( VF ) . During VF , rapid and self-sustaining electrical activity in the ventricles acts to suppress the natural pacemaker , resulting in uncoordinated , weak and rapid contractions , which lead to death within several minutes [1] . VF often occurs as a result of acute regional cardiac ischemia , which is a condition when blood flow to part of the heart is substantially decreased , for example by reduced flow through a coronary artery [2] . In addition , global ischemia unavoidably accompanies VF , because the abrupt fall in cardiac output resulting from VF also results in compromised myocardial perfusion . Thus an episode of spontaneous VF will result in a progressively ischemic heart . The effect of this secondary ischemia on electrical activity during VF is important clinically , because defibrillation typically occurs several minutes after the onset of VF , and thus the mechanism is likely to have been modified by ischemia [3] , [4] . It is known that ischemia profoundly affects the electrophysiological properties of cardiac cells and tissue [5] . During VF , the rapidly changing patterns of electrical activity in the ventricles are sustained by re-entry , in which waves of electrical activation continually propagate into regions of recovered tissue [6] . Re-entry is seen as a spiral wave on the surface of the heart , and a scroll shaped activation wave in 3D cardiac tissue [7] . The question of how ischemia influences the behaviour of re-entrant activity during VF is important , and has been addressed by clinical , experimental and modeling studies . Two important characteristics of VF are the frequency and spatiotemporal complexity of activation patterns , and these have been studied in animal heart experiments . Experiments on canine hearts [6] have shown that both activation rate and pattern complexity ( measured as the number of wave fronts ) increase slightly during the first minute of VF , followed by a decrease over periods of up to 10 minutes , associated with progressive global cardiac ischemia . Studies in porcine hearts , [8] , demonstrated a monotonic decrease of the activation rate of VF during the first 5 minutes of ischemia , with a decrease in complexity for the first two minutes followed by a rapid increase . In spite of the significance of these animal studies , the most valuable question for clinical practice is how global ischemia modulates the mechanism of VF in the human heart . Both clinical and modelling studies have established that the organization of VF in the human heart is quantitatively different to that in canine and porcine hearts [9] , [10] , with VF in the human heart shown to be characterized by a much lower VF complexity compared with animal hearts of similar size . For this reason , experimental studies on human hearts are extremely important for understanding underlying mechanisms . Recently this gap has partially been filled by studies in isolated myopathic hearts [11] , [12] , and studies in the in-situ human heart [13] . In the in-situ study [13] , electrical activity was mapped on the heart surface during VF in ten patients undergoing routine cardiac surgery with cross-clamp fibrillation . Following the commencement of cardiopulmonary bypass to support the systemic circulation , the following protocol was used: ( 1 ) VF was induced by burst pacing , ( 2 ) after 30 s , global cardiac ischemia was initiated by applying an aortic cross-clamp , ( 3 ) after 2 . 5 min . ischemia , cardiac perfusion was restored by release of the cross-clamp , ( 4 ) recording continued for 30 s during reperfusion . During all 3 . 5 minutes of VF , electrical activity was recorded using 256 unipolar electrodes sewn into an elasticated sock placed over the entire ventricular epicardium by the surgeon . Consistent with studies in animal hearts , the activation rate of fibrillation gradually decreased during global ischemia and increased abruptly after reperfusion . In contrast with animal hearts , the complexity of fibrillation patterns ( measured as the number of phase singularities ) continued to increase gradually during ischemia , although the data showed large variation . The above mentioned study [13] had several important limitations , in common with other studies in human and animal hearts . First , electrical activity was recorded only on the epicardial surface , whereas electrical activation patterns in the ventricles are 3-dimensional . Second , ischemia is a complex process which involves several separate mechanisms including hyperkalemia , hypoxia and acidosis , and each of these components has its own time course in ischemia [5] . However , it is not generally possible to measure the relative contribution of these individual components to the excitation patterns in vivo . Because it is difficult to measure all of the quantities of interest , even in animal hearts and tissue , detailed mechanistic and multi-scale models of cardiac electrophysiology at cell , tissue , and whole organ scales are becoming important research tools for the study of arrhythmia mechanisms . It was shown previously that the combination of modeling with experimental and clinical studies can provide valuable insights into arrhythmia mechanisms [9] , [10] , [14] . In this paper , we describe a detailed and comprehensive modeling study , which seeks to establish mechanisms that are consistent with the changes in activation patterns observed during VF in the human heart with global ischemia [13] . In addition , we present previously unpublished clinical results from [13] , namely the changes in dominant frequency ( DF ) for individual patients . The model we use is a representation of human ventricular tissue , with cellular electrophysiology described by the TNNP06 model [15] . This model was adapted to enable us to investigate the effects on VF of three components of ischemia [5] both separately and in combination . Briefly the effects of these three components on the eletrophysiological processes can be characterized as follows . Hyperkalemia is elevation of the extracellular potassium concentration , which leads to a shift of the resting potential of cardiac cells . Acidosis is an increase in intracellular pH , which results in smaller fast sodium and L-type calcium currents during depolarization . Hypoxia is a reduced oxygen supply , which impairs cellular metabolism and changes the ratio of ADP to ATP concentration inside the cell . In turn , this change results in the opening of specific ATP-dependent potassium channels . To describe them in our simulations , we developed a new model of the human channel based on experimental data from human cardiac cells [16] . We first determined the effect of each of the three components of ischemia on action potential duration ( APD ) and conduction velocity ( CV ) restitution in 1D models , along with the effects on re-entry in 2D models . Then we performed simulations of VF using a 3D anatomical model of the human ventricles [10] , where the dependency of the activation rate of VF on each component of ischemia was determined . Next , we used these dependencies to interpret the clinical recordings for individual patients . We showed that the predominant factor responsible for the change in activation rate during ischemia is hyperkalemia , and estimated the magnitude of this effect in each individual patient recording . Then we studied how the three ischemic components affect the complexity of VF expressed by the number of scroll wave filaments . Although there was a big variation , the results we obtained were qualitatively similar to the clinical recordings and we concluded that the main factors important for the observed dynamics were memory effects . We also proposed a simple algorithm to estimate the dynamics of hyperkalemia and the activation of the current using individual patient recordings . Overall , we demonstrated that taking into account the electrophysiological changes caused by hyperkalemia and hypoxia , the results of [13] can be fully explained , indicating that acidosis has only a minor effect on VF activation during the first few minutes of ischemia .
Cardiac cellular electrophysiology was modeled using the TP06 model of human ventricular cardiomyocyte [15] , [17] coupled into a monodomain model for cardiac tissue . In this model the main equation determining the propagation of transmembrane voltage is given by: ( 1 ) where is transmembrane voltage , a diffusion tensor and is the sum of ionic currents: ( 2 ) where all currents except were described by the equations from [18] , [15] , and was a new current that we introduced to take the effects of hypoxia into account . For most parameters we used the values listed in Table 1 and Table 2 of [15] corresponding to epicardial cells . For the five parameters listed in Table 2 of [15] we used the values corresponding to “Slope 1 . 8” for 3D simulations , and “Slope 1 . 1” in 2D simulations for finding the restitution curves . We used parameters corresponding to all three different slopes: 1 . 1 , 1 . 4 and 1 . 8 , for analyzing the stability of re-entry in 2D . Ischemia was introduced to the model by changing the parameters corresponding to each of the three ischemia components described above . To model hyperkalemia we varied in the range 5–10 mM . Acidosis was taken into account by decreasing the maximum conductivity of sodium and L-type calcium currents within the limits of 20–100% . The effect of hypoxia was represented by a novel description of the ATP dependent potassium current in the human heart . Table 1 summarizes the changes to the TP06 model due to ischemia . We developed our model of the current based on the results of in vitro experiments [16] . Figure 1 shows measured current-voltage dependencies of for two different extracellular potassium concentrations . We fitted these data using functions with a power dependency on concentration and exponential functions of voltage , similar to functions used in [19] , [20] for . Our expression for human was: ( 3 ) where is in mM , is transmembrane voltage measured in mV , and is the Nernst potential for potassium also in mV . Our function reproduces well the current-voltage dependency in a range between and mV . We also see some small deviations for lower and higher values of voltage , which , however , should not be essential as they are outside the important physiological range . The fraction of open channels ( or the probability for a channel to be open ) is expressed by the factor . It depends on the energy capabilities of the cell . For our model we left this dependency unspecified , changing the value directly to induce hypoxia . The scaling coefficient was fitted following the approach of [20] , where the value of APD decreases twofold if of channels are open , which led to . For 1D and 2D simulations we solved equation ( 1 ) for homogeneous and isotropic tissue with the diffusion tensor where was taken to be 1 . 54 and is the Kronecker delta . For these parameter values , the velocity of propagating plane waves at a stimulation frequency of 1 Hz was 72 . For 3D whole heart simulations we used an anatomically based model of human ventricles presented in [21] . This model takes anisotropy into account by reconstructing the fiber direction field described in [22] and assuming that the diffusion coefficient across ( transverse ) the fibers is 4 times less than the diffusion coefficient along fibers , which was set to 1 . 54 . For 3D simulations the components of the diffusion tensor were given by:where are the coordinates of a normalized vector oriented along the fibers . For all types of the media we used ‘no flux’ boundary conditions:where are the coordinates of a vector that is normal to the boundary . To solve the differential equations we used a finite difference approach . For both 2D and 3D simulations we introduced a rectangular mesh of about one million points . To approximate the diffusion term we used a stencil of 5 grid points for 2D and 17 points for 3D . We used an explicit first order Euler method to solve the discretized system , which for 2D tissue was: ( 4 ) where the time step ms , was the space step , and weights corresponding to the diffusion tensor at location . The space step was 0 . 25 mm for 2D simulations , and 0 . 5 mm for 3D simulations [23] . The gating variables in the TP06 model were integrated using the Rush and Larsen approach [24] . To cache the results returned by functions that were only voltage dependent we used pre-computed look-up tables . The model was implemented using the C and C++ programming languages with OpenMP extensions for parallelization . We mainly used the Intel ICC compiler toolkit . The code for 2D was run on an Intel Core i7-3930K ( 3 . 20 GHz ) machine , and the 3D code for human ventricles was run on dual-processor Intel Xeon E5-2650 ( 2 . 0 GHz ) machines .
In a model of a thin strip of human cardiac tissue , we studied how the different components of ischemia influence APD and CV restitution , a dynamic property of cardiac tissue important for the onset of re-entry and for the stability of re-entrant waves [25] , [26] . We followed the same approach as in [14] , but with a different formulation of . Restitution and dispersion curves were obtained using an S1S2 protocol , in a cm sheet of 2D simulated tissue . Superthreshold stimuli were delivered along one short edge of the sheet , and measurements of APD were made at a distance of 2 . 5 cm from the stimulated edge . The basic cycle length ( BCL ) for S1 stimuli was 1000 ms . The ten S1 stimuli were followed by a single S2 stimulus . The duration between the last S1 and S2 was decremented from 1000 ms until there was no response to the S2 stimulus at the point of measurement . The restitution curves are shown in Figure 2 . In Figure 2A the effect of hyperkalemia on APD restitution is shown , indicating that APD decreases with increasing . This effect is due to and currents , which depend directly on and become larger once is increased , accelerating the repolarization process . The effect of hyperkalemia on conduction velocity is shown in Figure 2D . We see that the conduction velocity decreases with increasing . This is because an increase in shifts the resting potential to more positive values suppressing the sodium current and , consequently , reducing the excitability of the cell . Figures 2A and 2D also show how the minimal APD——value ( the left most point of the restitution curve ) depends on . increases substantially with hyperkalemia . This fact can be important as is considered as one of the main factors determining the complexity of pattern of excitation during fibrillation [21] , [27] . Figures 2B and 2E illustrate the effect of acidosis ( modeled as reduction of and ) on APD and CV restitution . Both APD and CV decrease with acidosis , because acidosis diminishes the depolarizing currents available to the cell . However , the value of the showed almost no dependency on acidosis , unless the conductance of the and channels was reduced to 20% of their default values . Figures 2C and 2F show the effect of hypoxia , modeled as activation of . Both APD and become substantially shorter since hypoxia activates a strong depolarizing current . The value of CV showed almost no dependency on hypoxia because hypoxia does not affect . Figure 2 shows that each component of ischemia acted to reduce the slope of restitution . Steep restitution is considered as one of the main mechanisms contributing to the breakup of re-entry in VF [25] , [26] . According to the restitution hypothesis , flattening restitution prevents possible spiral wave breaks from happening , and therefore prevents formation of new arrhythmia sources . We studied how the different components of ischemia influence the dynamics and stability of re-entrant waves in 2D—spiral waves . In these simulations we varied the slope of the restitution curve for non ischemic conditions by changing five parameters of our model , as described in the Methods . We used three different sets of parameters , which correspond to a slope of 1 . 1 , 1 . 4 and 1 . 8 under normal conditions . For initiation of a spiral we used an S1S2 protocol: at first , the S1 stimulus was delivered along one side of the medium , then the S2 stimulus was applied after the wave had passed half of the medium . Figure 3 shows the resulting patterns of transmembrane voltage after 10 s of simulated activity for each parameter set and under each component of ischemia . For tissue with a restitution slope of 1 . 1 under normal conditions , the spiral remained stable regardless of ischemia . For a restitution slope of 1 . 4 ( the second row ) , a complex fibrillatory pattern developed under normal conditions resulting from breakup of the initial spiral wave , however each ischemia factor prevented breakup . The third row gives an example for a relatively high restitution slope of 1 . 8 under normal conditions . In this case the spiral wave broke into fibrillation under both normal and ischemic conditions . However , it was still possible to stabilize the spiral rotation by further increasing the ischemia parameters . The bottom row in Figure 3 corresponds to that case: as ischemia became more severe the activation pattern did not evolve into fibrillation . Overall , all three components of ischemia acted to stabilize re-entry , and to prevent the breakups that can happen under normal conditions . This is in line with the prediction that we derived from analyzing the restitution curves in the previous section , which was based on flattening of the restitution curves due to ischemia . Our next step was to understand the change of the main dynamical characteristics of cardiac tissue with ischemia and to compare these with the activation patterns observed in the human heart [13] . Therefore , we performed simulations using an anatomically detailed model of the human ventricles developed in [21] . To explain our results we shall use information on 1D and 2D wave propagation collected in the previous sections . Our results allowed us to estimate the extent of hyperkalemia and hypoxia for each patient involved in the clinical study [13] . We used the dependency of how DF ( 1/period ) changes throughout experimental VF for individual patients . One of these recordings is shown in Figure 6 . The red region corresponds to normal perfusion of the heart , blue to global ischemia and the green to reperfusion . As one can see , DF gradually decreases during ischemia and then increases abruptly after reperfusion to an even higher level that it was initially . For each patient we used three points on this graph: DF at the beginning of ischemia ( 30 s , point A ) , DF at the end of ischemia ( 180 s , point B ) and DF at the end of the experiment ( 210 s , point C ) . Ischemia takes place in between points A and B , thus and are increasing there . At point B we expect to have the highest values for these quantities . At point B reperfusion starts , resulting in a rapid elevation of DF . Figure 5 indicates that hypoxia acts to shorten the VF period , thus it is likely to be responsible for this change . Therefore we assumed that the recovery of hypoxic channels during reperfusion is slower than the recovery of extracellular potassium concentration . We assumed that had returned to a normal value by the point C whereas remains at the same level as at point B . Thus , knowing DF at point C , we can find the fraction of open channels at that moment . Using this value and the value of DF we can estimate at point B . The results we obtained using this algorithm are given in first three columns of Table 2 . The results shown in columns 4–7 of the table deal with a possible partial recovery of and will be described in the discussion section . The results in the last two columns are based on the assumption that channels open even before the beginning of ischemia . This will also be described in the discussion section . We see that the estimated concentrations for different patients vary in the range from 6 . 5 to 8 . 0 mM , while the fraction of activated channels does not go beyond 0 . 1% . We also estimated how hypoxia and hyperkalemia change in the course of ischemia . This problem of fitting does not have a unique solution because the period of fibrillation depends on two parameters , while we have only one period dependency for each patient . To account for that we assumed that the dependencies of and are monotonic with time , and these values can never decrease during ischemia . Then we wrote the relation between the rate of change of these values: ( 5 ) where is the period of fibrillation over time ( from the clinical results ) and is the dependency of VF period on the ischemia components , obtained from Figure 5 . Our goal was to determine the patient specific functions and , based on known , and boundary conditions from Table 2 . To solve ( 5 ) we needed to impose an additional constraint on our functions . Thus we assume that ( 6 ) where and are the values we fitted for point B . This constraint ensured the values of hypoxia and hyperkalemia tended to reach those values at point B . The results we obtained using this approach for two patients are shown in Figure 7 . The rest of the results for all 10 patients are available in Figures S1–S10 . We see that we can fit the clinical data with smooth monotonic functions using our approach . However , as our constraint ( 6 ) cannot be justified from biological background , these fits can be considered as a conjecture rather than established results . The study of [13] describes how the number of epicardial phase singularities and the number of wave fronts change during the course of ischemia . These values have approximately the same dynamics and to understand the results we studied the change in the number of phase singularities . Phase singularities are points where filaments intersect the surface of the heart [7] , [10] . As was shown in [10] , the number of filaments obtained from simulations in an anatomically detailed model of human ventricles provide a good estimate of the number of phase singularities on the surface of the heart . We counted the number of filaments in our model in normal conditions and under different factors of ischemia . Figure 8 shows four examples of how the number of filaments depends on time under conditions that we expect to correspond to those observed for the first 2 . 5 minutes of ischemia . Figure 8A shows filament numbers during normal conditions . We see dynamics similar to that reported in [21] . Figures 8B and C correspond to hyperkalemia mM and hypoxia , respectively . We see that compared to normal condition there is a tendency for decreasing of the number of filaments . Finally , Figure 8D gives an example where simulated VF terminated spontaneously , although the number of filaments stayed relatively high until immediately prior to termination . For that simulation we used mM and . The mean number of filaments in the two parameter space—hyperkalemia and hypoxia—for a wider range of parameters is given in Figure 9 . The error bars in this figure correspond to the standard deviations of root mean square . The point with mM and corresponds to normal conditions . We can see that for values of hypoxia the complexity of the pattern slightly decreases . However , it increases for larger values of hypoxia . All these results differ substantially from our observation from the 2D patterns ( see Figure 3 ) . In 2D , we observed that every single component of ischemia suppressed breakup , which would correspond to a decrease of filament numbers and termination of fibrillation in our whole heart simulations . However , this figure shows that for 3D an increase in fibrillation complexity as the degree of ischemia increases . This is a surprising result that can be explained in the following way . The 2D simulations always started from a single spiral , and breakup was self induced . In 3D , however , we started from an initial pattern with multiple re-entrant waves . This complex pattern was able to maintain itself in spite of the presence of simulated ischemia . To confirm this observation , we performed simulations in 2D , starting from a developed VF pattern . These simulations showed that even in 2D , the complex pattern can persist compared with initiation with the S1S2 protocol of Figure 3 . The results of the clinical research are presented in Table 3 . The minimal and maximal value of the number of phase singularities on the surface of the heart are given there for both the beginning and the end of ischemia . As shown , that there are large deviations in PS numbers making it difficult for us to give conclusive statements from these data . However , they do indicate relative conservation on PS numbers during ischemia . If we now compare these results with results on filament numbers shown in Figure 9 we can conclude the following . In the range of ischemic components which we estimated for the individual patients ( mM , ) we see some tendency for a decrease in the filament numbers in our simulations . However , this decrease is minimal and to some extent can be considered as conservation of filament numbers in a way similar to that we see in clinical data . However , our simulations indicate that we can expect an increase in filament numbers if ischemia is more prolonged or if channels open to a greater extent .
There are several factors that were not taken into account in this study . We used a homogeneous model of the heart which does not take into account the differences between epicardial , endocardial and M-cells . However , as it is shown in [38] the differences between these cell types decrease substantially at the high frequency of excitation typical for VF patterns . Note that the differences between different cell types are not that pronounced in tissue due to the electrotonic effect . Next , we used a monodomain model , to represent the cardiac tissue . Bidomain models are of considerable importance when describing defibrillation phenomena but in the absence of applied external currents the difference between these two types is extremely small [39] . Due to numerical limitations , we were not able to reproduce full 3 . 5 minutes of the clinical study in our model and limited ourselves by only 20 seconds . We did not check if the effects we studied are also presented in other detailed electrophysiological models of cardiomyocyte , such as [40] and [41] , nor did we take into account effects due to mechanical activity of the heart during VF . We did not consider the change in the gap junction conductance caused by ischemia . There are several experimental studies on animal models that demonstrate that the effect on gap junction uncoupling is relatively small in first minutes of ischemia . The investigations in a rabbit papillary muscle [42] show that under ischemic conditions the intracellular resistance stays constant within the first 10–15 minutes , whereas the extracellular resistance slightly changes immediately following the onset of ischemia . However this change was not associated with a significant decrease of the conduction velocity within the first 4 minutes of ischemia . Another result obtained in isolated rat hearts [43] indicates that the absolute value of the impedance of the tissue changes for about 3 with the initial value of about 150 . Finally , another set of experiments on rabbit papillary muscle [44] also shows that the tissue resistance stays remarkably stable for the first 10 minutes of acute ischemia . These data allow us to assume that the process of gap junction uncoupling does not have a significant effect on the conduction velocity for the time scale of 2 . 5 minutes of ischemia . We did not consider either the heterogeneity of the tissue response to ischemia nor heterogeneity in ionic channels distributions . As shown in other studies [45] , heterogeneities in distribution can lead to a transmural gradient of the DF in myocardium . There are several studies that show that this gradient occurs in experimental models [11] , [12] , [46] . Furthermore , global ischemia may lead to different depths of ischemia in different regions of the heart , since the endocardium is in contact with a large volume of oxygenated blood in the ventricular cavities , at least in the early stages of VF . We expect that these heterogeneities can lead to a different fibrillation mechanism ( to so-called mother rotor fibrillation ) as well as increase in the number of filaments [47] , [48] . This factor may also contribute to increasing the number of phase singularities localized at the surface of the heart . The role of Purkinje system was not considered in this study . There are several experimental studies in dog hearts [46] , [49] , which show that the Purkinje system can play an important role in early termination of VF because it acts to increase the transmural activation rate gradient . However , a computer modeling study [45] shows that this effect is not that prominent in comparison with the effect of heterogeneity in distribution . Finally , we omitted several other effects that may occur during ischemia . In our case , depletion of ATP results only in activation of the channel . We did not consider the effect of ATP on other important processes , such as functioning of the pump , the processes occurring at the mitochondria membranes and their effect on calcium dynamics . We did not take into account any changes of cell size or extracellular medium size occurring as a result of osmosis , and possible changes of the ionic concentrations due to it . It would be valuable to extend existing cell models to incorporate a more detailed description of ischemia , and to study the effects of ATP depletion using such a model . | Cardiac arrhythmias are an important cause of death in the industrialized world . The most dangerous type of cardiac arrhythmias is ventricular fibrillation . If left untreated , it leads to death within just few minutes . In most of the cases ventricular fibrillation occurs as a result of cardiac ischemia , which is a shortage of blood supply to the heart muscle . Futhermore ventricular fibrillation leads to decreased cardiac output , which in turn results in secondary ischemia . A recent clinical study investigated the effect of secondary ischemia on the organization of ventricular fibrillation in the human heart . However , in the clinical study it was not possible to obtain the whole picture of activation of the heart and to separate the relative roles of different processes induced by ischemia in the alterations to the cardiac electrical activity . In this study we use computer modeling to address these problems and to complement the results of the clinical study . Our results allow us to explain the change of electrical activation pattern in the heart during the first minutes of ischemia and to estimate the relative rates of those ischemia-induced physiological processes . We also present previously unpublished data on individual patient recordings from the clinical study . | [
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] | 2014 | Effect of Global Cardiac Ischemia on Human Ventricular Fibrillation: Insights from a Multi-scale Mechanistic Model of the Human Heart |
Natural selection favors efficient expression of encoded proteins , but the causes , mechanisms , and fitness consequences of evolved coding changes remain an area of aggressive inquiry . We report a large-scale reversal in the relative translational accuracy of codons across 12 fly species in the Drosophila/Sophophora genus . Because the reversal involves pairs of codons that are read by the same genomically encoded tRNAs , we hypothesize , and show by direct measurement , that a tRNA anticodon modification from guanosine to queuosine has coevolved with these genomic changes . Queuosine modification is present in most organisms but its function remains unclear . Modification levels vary across developmental stages in D . melanogaster , and , consistent with a causal effect , genes maximally expressed at each stage display selection for codons that are most accurate given stage-specific queuosine modification levels . In a kinetic model , the known increased affinity of queuosine-modified tRNA for ribosomes increases the accuracy of cognate codons while reducing the accuracy of near-cognate codons . Levels of queuosine modification in D . melanogaster reflect bioavailability of the precursor queuine , which eukaryotes scavenge from the tRNAs of bacteria and absorb in the gut . These results reveal a strikingly direct mechanism by which recoding of entire genomes results from changes in utilization of a nutrient .
Because the genetic code maps the 64 possible nucleotide-triplet codons to only 20 amino acids and three stop signals , proteins can be coded in multiple ways in the genome using different sets of synonymous codons . Despite specifying the same amino-acid sequence , the particular coding employed can alter fitness , sometimes dramatically [1] , resulting in the highly nonrandom codings found in extant genomes [2] , [3] . Although selection has long been thought to only weakly shape variation in synonymous codings , very recent evidence from Drosophila indicates a much stronger potential role for selection [4] . Selection acts in a host of different ways to constrain the evolutionarily viable set of protein codings , with most constraints imposed on aspects of gene expression . The charged tRNA molecules that physically embody the genetic code , bearing a triplet anticodon on one end and an amino acid at the other , read codons with differing speed and accuracy [5] , [6] arising from their cellular abundances and kinetic properties . Recent work has uncovered an ever-multiplying panoply of potential mechanisms by which codon choice alters fitness . Codon choice influences the stability of mRNA secondary structures [7] , [8] and reduced stability associates with higher protein production , consistent with higher rates of translational initiation [9] , [10] . Slowly translated codons may induce ribosomal pauses necessary for proper protein folding and targeting [11] , or regulate the entry of ribosomes into coding sequences in ways which limit jamming [12] . Adding to the complexity are mechanisms which constrain synonymous codon choice due to pressures on other processes , such as mutational biases and selection for efficient splicing [13] . All of these effects remain limited in their ability to explain the biased use of certain codons over their synonyms at the genome scale [14] . Two mechanisms remain dominant: selection on translational speed , and selection on translational accuracy . Across widely diverged bacterial species , shorter generation times correlate with increases in total tRNA and ribosomal RNA ( rRNA ) copy number and elevated preferential usage of particular codons in high-expression genes [15]–[17] . These trends constitute evidence for selection acting to speed ribosomal transit across transcripts . Increased speed reduces the density of ribosomes on transcripts , thus raising the proportion of unbound ribosomes , which accelerates the translation initiation rate and , finally , overall protein production rate [18] . Consequently , selection for increased growth rate favors coding sequences that cause rapid elongation rates . Evidence for selection on speed remains sparse in multicellular organisms [19] , and recent work has failed to find systematic codon-dependent ribosome velocity differences correlated to codon usage [19]–[21] . Selection on speed may be of reduced importance for animals [17] , whose developmental processes sharply reduce the coupling between fitness and the cell doubling rate . By contrast , natural selection to improve translational accuracy has been demonstrated in organisms ranging from bacteria to humans [22]–[24] . Amino acid errors at the ribosome , estimated to occur in roughly one out of every five average-length proteins [25] , may cause loss of function [22] or cytotoxic misfolding [24] , [26] . Consequently , coding sequences which reduce such errors , and reduce their impact on folding and function , will be favored by selection . Selection against mistranslation-induced misfolding suffices to generate major patterns of accuracy-driven codon usage observed from bacteria to humans [24] . Akashi introduced a clever method to isolate selection on translational accuracy [22] , which has since been widely applied [23] , [24] , [27] . Akashi's test quantifies the tendency of particular codons , such as those corresponding to abundant tRNAs , to be found encoding amino acid sites that are sensitive to substitution , such as those conserved over evolutionary time , where errors in translation are likely to be most costly [22] . The use of tRNA abundance estimates to predict which codons will be most efficiently translated has become commonplace . A standard approach predicts tRNA abundances from modestly correlated but more readily measurable genomic tRNA gene copy numbers [12] , [28] , [29] , and designates codons “optimal” or “preferred” if they are predicted to be read by the most-abundant tRNAs . However , tRNAs are heavily chemically modified , often in the anticodon [30] , making assignments of which tRNA reads which codon nontrivial . As a relatively well-known example , a eukaryotic tRNA with a genomically encoded anticodon 5′-AGC-3′ might be naively predicted to bind and read the alanine codon GCU more readily than the synonym GCC . Instead , such tRNAs generally have their 3′-adenine modified post-transcriptionally to inosine ( I ) by tRNA-adenosine deaminases , yielding 5′-IGC-3′ , which binds GCC more strongly than GCU [31] . Accounting for these modifications substantially improves the correlation between genomic codon usage and levels of corresponding tRNA [17] , [32] . Codons corresponding to the most-abundant tRNAs are often assumed to be read more rapidly and more accurately . Consideration of the kinetics of translation , however , indicates that this need not be true [17]: codons read by high-abundance tRNAs may also be misread by high-abundance near-cognate tRNAs , reducing their accuracy [33] . These studies reveal the surprising richness of selection on protein coding across the tree of life . Both speed and accuracy selection play substantial roles , although major questions remain about the relative strength of selection on these traits [20] . Developing a mechanistic answer to the question of what determines genome-wide protein coding requires synthesizing translation kinetics , tRNA biochemistry , mutational processes , gene expression , population genetics , organism life-history traits , and systems-level pressures on organism fitness . Changes in coding between orthologous proteins are easy to find , but only between organisms that have diverged on many if not all of these contributing factors . Here we report the discovery and mechanistic illumination of whole-genome recoding restricted to the well-studied drosophilids ( Drosophila melanogaster and its relatives ) , in which codon choice has been thought to be highly conserved [34] . We develop a novel measure for selection on translational accuracy , based on Akashi's insight , which reveals a large-scale , phylogenetically coherent reversal in the relative accuracy-driven fitness benefit of multiple codons over their synonyms . To explain this reversal , we hypothesize that levels of a known tRNA modification in the anticodon , guanine to queuosine , change across species . We detect this quantitative change in queuosine modification directly in tRNAs of four species by electrophoretic separation , finding that modification levels vary exactly opposite published predictions . We then predict , and verify , that because queuosine modification levels change throughout D . melanogaster development , the accuracy-driven codon usage of genes expressed at different developmental stages should covary with the modification level much as they do across the phylogeny . We propose a kinetic model to explain how changes in queuosine modification suffice to reverse the relative accuracy of synonymous codons , while preserving their relative speed . Surprisingly , queuosine modification is known to be largely determined by intake of the precursor nutrient queuine , which animals solely acquire from bacteria , providing a remarkably simple pathway for nutrient availability to alter genome-scale protein coding .
In pioneering work , Powell and colleagues hypothesized that elevated Q modification might explain the unusual codon usage they observed in D . willistoni under the assumption that Q-modified tRNA preferentially reads U-ending codons [46] . Measurements of TGT gene expression levels as proxies for Q modification levels produced ambiguous results [47] . We therefore employed a method to quantify Q modification levels directly starting from total RNA . Cis-diol moieties , such as the 3′ ribose of every tRNA , slow migration through gels composed of polyacrylamide covalently linked with N-acryloyl-3-aminophenylboronic acid ( APB ) [48] . Consequently , queuosine's additional ribose moiety ( Figure 2B ) slows Q-tRNA migration relative to G-tRNA , producing two bands on an APB gel [48] . This differential migration can be eliminated by oxidizing the ribose cis-diols with periodate , producing a single faster-running band [48] . We confirmed these expected effects by Northern blotting of total RNA from D . melanogaster with a probe specific to tRNATyr ( Figure 3A ) . Subsequent quantification of Q modification in D . melanogaster tRNAHis , tRNATyr , and tRNAAsn confirmed that Q-tRNA abundance is low in third-instar larvae and rises until roughly half of these tRNAs are modified in adult flies , consistent with results of a previous study using an independent , chromatography-based method to quantify Q modification ( Figure 3B ) [38] . We were not able to isolate separate Q- and G-tRNAAsp bands on APB gels , likely due to a secondary mannosyl-queuosine modification [49] . We then quantified Q- and G-tRNATyr , -tRNAAsn , and -tRNAHis in third-instar larvae and adult flies of D . pseudoobscura , D . willistoni , and D . virilis , species which span the drosophilid phylogeny . Substantial differences were apparent , with D . willistoni and D . virilis showing lower levels of Q modification than D . pseudoobscura and D . melanogaster for each tRNA species . Modification levels and between-species differences were greater in adults than in larvae ( Figure 3D ) . TGT gene expression poorly predicted modification levels ( Figure S2 ) . Queuosine tRNA modification in adult flies , but not larvae , shows a significant and positive correlation with an accuracy-driven selective advantage favoring C- over U-ending codons , quantified by the Akashi selection score , across all codons and species ( adults , Spearman r = 0 . 61 , p<0 . 05 , Figure 3D; larvae , r = 0 . 05 , p = 0 . 86 , not shown ) . Importantly , the relationship between Q modification and Akashi selection score in adults is positive not just in aggregate , but also within each codon family ( minimum Spearman r = 0 . 6 for His , Asn , and Tyr , with p>0 . 05 due to small sample size ) . Indeed , variation in the characteristic level of modification in , and selection upon , each synonymous family will tend to spuriously reduce the observed relationship between Q modification and Akashi selection score . After subtracting means from each family , the overall Spearman correlation across all families is r = 0 . 73 , p<0 . 01 , indicating that Q modification suffices to explain more than half the variation in selection scores across species . Based on these cross-species correlational results , we hypothesized that Q modification alters relative codon accuracies , creating a signature of selection that we can observe using Akashi selection scores . A serendipitous opportunity to test this causal hypothesis arises from the observation that levels of Q modification vary across development within a species ( Figure 4A , results from White and colleagues [38] ) . If the level of Q modification alters relative codon accuracy , then genes expressed at their highest levels in a particular developmental stage should experience accuracy selection modulated primarily by the level of Q modification at that stage . That is , we predict that specific codon substitutions—the ones which reverse their Akashi selection scores across species as adult-stage Q modification drops—will change their selection coefficients in the same direction during D . melanogaster developmental stages where Q modification drops . To test this prediction , we determined the Akashi selection scores using non-overlapping sets of genes maximally expressed at several D . melanogaster developmental stages similar to those in the White and colleagues' study ( early/late embryo , larva , pupa , adult male/female ) [50] , [51] . We performed statistical tests on gene sets pooled into four categories: maximal expression in embryo , larva , pupa , or adult flies . We focused on the seven synonymous codon pairs showing the strongest changes in Figure 1: four pairs encoding amino acids read by Q-modified tRNAs , and three pairs showing shifts at least as strong ( Figures 1A and 4B ) . As predicted , these seven pairs showed a systematic shift in Akashi selection scores from moderately positive in favor of the C-ending codon during the embryonic stage , where Q modification is elevated , to near zero or negative ( favoring the U-ending codon ) during the larval and pupal stage , where Q modification is lowest , rising again to strongly positive in adults , where Q modification is highest ( Figure 4; Table 1 ) . As with variation across species ( Figure 3 ) , variation in Akashi selection scores for Asp , His , Asn , and Tyr codons over development correlates with the modification levels of the corresponding tRNA species ( Figure 4C , three tRNAAsn isoacceptor levels averaged ) with r = 0 . 61 , p<0 . 02 . As noted before , differing mean levels of selection and modification in each family add unwanted noise , and also as before , subtracting means from each family yields a stronger correlation , r = 0 . 63 , p<0 . 01 . Wilcoxon signed-rank tests indicated significant reductions in Akashi selection scores in genes expressed when Q is lowest ( larva , pupa ) compared to genes expressed when Q is highest ( embryo , adult ) ( all four comparisons p<0 . 05 ) . Comparisons between larva and pupa , and between embryo and adult , were not significant . Most pairs individually follow the predicted pattern , though there are some exceptions . The benefit of CAC over CAU ( His ) , for example , rises slightly from embryonic to larval stages before dropping markedly in pupa and rising again in the adult stages . Remarkably , though , we even observe reversals of relative codon accuracy selection during development: all Akashi selection scores are positive during the embryonic stage ( mirroring the genome-wide average ) , yet more than half turn negative in the larval stage , and all but one ( for Asp codons ) switches sign twice between the embryo and adult stages . Asp , while always showing a benefit in favor of GAC over GAU , shows the predicted U-shaped change in selection scores mirroring the change in Q modification . As a control , we examined the selection scores for seven codon pairs chosen to be as similar as possible to the seven shifting pairs examined above . Each of these pairs differed from one of the shifting pairs by a single nucleotide substitution , in the wobble position wherever possible ( Figure 4B ) . These control pairs showed no significant changes across any of the developmental stages ( Wilcoxon signed-rank test p>0 . 2 for all comparisons ) , demonstrating the specificity of the observed shifts in selection scores linked to Q modification . Together , these results show the predicted shift in accuracy-driven codon usage during D . melanogaster development corresponding to changes in Q modification of tRNA . Higher levels of Q modification correspond to increasing use of C-ending over U-ending codons at sites encoding conserved amino acids . Because these predictions were made on the basis of cross-species changes in tRNA modification , and there is no other known connection between the developmental progression of D . melanogaster and the divergence of species across the phylogeny , we conclude that Q modification is likely to be the major cause of the changes in codon usage observed in both situations . That Q modification correlates with usage of C-ending codons is perplexing because previous work in D . willistoni made the opposite prediction [47] . The idea behind that prediction was simple: assuming that G-tRNA translates C-ending codons more rapidly than U-ending codons , and observing that U-ending codons are used more frequently in D . willistoni , it makes sense to guess that Q-tRNA preferentially translates U-ending codons , and therefore that Q modification should rise in D . willistoni . So how is it possible for both G-tRNA and Q-tRNA to preferentially translate C-ending codons , but for selection to switch to favoring U-ending codons ? Moreover , why do some codons that are not read by Q-modified tRNAs shift in their relative accuracy when Q modification changes ? We argue that substantial insight into these questions can be gained by examining the kinetic effects that modification may have on translational fidelity . In what follows , we present a model in which changes in Q modification alone suffice to cause the observed changes in relative accuracy , which selection would then act upon by recoding genes . The selective changes observed in Figures 1 and 4 arise because of translational accuracy . Experimental work has established that accuracy is determined by tRNA competition [5] , which can be quantified by the fraction of time a codon is translated by a cognate tRNA bearing the proper amino acid ( the “right” tRNA ) rather than a near- or non-cognate competitor tRNA bearing another amino acid ( the “wrong” tRNA ) . Translation has many identifiably distinct kinetic steps , from initial binding to accommodation to proofreading to translocation , offering several places in which right and wrong tRNAs might differ and so alter their competition . Because it is not yet known in detail how queuosine tRNA modification alters any one of these steps , we concentrate on the overall rate of translation of a codon by a tRNA , which is a complex function of all kinetic steps . The essential idea in the model detailed below is that the focal C-ending codons are translated rapidly , and mistranslated rapidly , by both right and wrong tRNAs , respectively , making their translation inaccurate unless the right tRNA is altered such that it overpowers the competitor . Q modification confers such strength . In the absence of Q modification , the U-ending codon , while read more poorly by the right tRNA , is more accurate because the competition from the wrong tRNAs is yet weaker . Thus , the presence of the modification alone is sufficient to determine which codon will be more accurately translated , and therefore favored by selection on accuracy . To determine whether Q modification is sufficient to explain the reversal in relative codon accuracy within synonymous families , we constructed a simplified kinetic model of translation of a focal codon in which all tRNAs have the same molecular abundance ( Methods ) ( Figure 5A; Listing S1 ) [52] . We focus on the asparagine codons AAU and AAC and their cognate tRNAAsn ( anticodon 5′-GUU-3′ , for G-tRNA , or QUU , for Q-tRNA ) . The competitor near-cognate tRNA is threonine tRNAThr ( IGU ) , where I denotes inosine ( see Introduction ) ; we refer to this species as I-tRNA . The model assumes that G-tRNA , Q-tRNA , and the near-cognate I-tRNA all have higher first-order rate constants for reading C-ending codons than for U-ending codons ( Figure 5A ) , consistent with in vitro binding studies [31] . Q-tRNA is assumed to bind more rapidly than G-tRNA to any given codon , consistent with a higher affinity of Q-modified tRNA for ribosomes [45] . Finally , the relative rate of Q-tRNA reading C-ending over U-ending codons is assumed higher than for G-tRNA . Under these assumptions , the identity of the most accurately translated ( lowest error rate ) synonymous codon in a family can switch from C-ending to U-ending solely as a function of changes in queuosine modification ( Figure 5B and 5C ) . This model generates error rates ( between 10−4 and 10−3 ) and translation speeds ( 1–10 amino acids per second ) matching physiological estimates ( Figure 5C ) [53] , [54] . While the model's precise parameters are surely inaccurate , its value lies in showing that tRNA modification alone is capable of inducing an accuracy reversal under biologically plausible conditions . The kinetic competition model offers a unique and intuitive explanation for why codons that are not normally read by a Q-modified tRNA nonetheless shift in accuracy when Q modification levels change: these codons are misread by Q-tRNA . If Q modification primarily increases tRNA affinity for ribosomes , then increased Q modification will reduce the accuracy of near-cognate codons due to misreading by Q-tRNA ( Figure 5D ) . This accuracy reduction is detectable as a reduced Akashi selection score . Kinetically , accuracy reduction arises when we consider the inverse of the above problem: misreading of threonine ACC codons by G/Q-tRNAAsn ( which would properly read AAC/AAU codons ) . Given the apparent codon preferences of Q-modified tRNA , we can predict that ACC and ACU will be misread more often by Q-tRNAAsn than will ACG , which is read by a separate tRNA , tRNAThr ( CGU ) . Consistent with this prediction , ACC is deleterious relative to ACG in the melanogaster subgroup where Q modification is highest , and beneficial in D . virilis where Q modification is nearly absent ( Figure 1A; Table S2 ) . Indeed , the relative benefits of A- or G-ending codons compared to U- or C-ending synonym change similarly for six amino acids ( Gly , Thr , Val , Pro , Ser , and Leu ) ( Figure 1A; Table S2 ) . Most but not all observed codon-usage shifts can be explained by this kinetic model . The major exception is isoleucine , for which the A-ending codon AUA has an accuracy benefit over AUC/AUU in virilis but a cost in melanogaster . The isoleucine codon AUA is costly relative to AUU in every developmental stage except for larva , the lowest-Q stage , where the fitness cost becomes insignificant . This change mirrors the changes in accuracy benefit of these two codons in D . virilis , the lowest-Q species in our measurements , suggesting a link to the modification which is not captured by our kinetic model . The kinetic model predicts that , unlike accuracy , the relative speed of codons always favors C-ending codons regardless of the level of Q modification ( Figure 5B and 5C ) . That is , speed and accuracy selection can come into conflict dependent on the modification level , where one codon is more accurately translated but less rapidly translated than its synonym . If selection for speed were strong enough for a set of genes , those genes would show little or no accuracy-driven shift . A previous analysis found that genes encoding ribosomal proteins show consistent use of C-ending codons for His/Asn/Tyr across the phylogeny , but Asp codons shift in usage from C-ending to U-ending [18] . Because selection on speed favors increased production of ribosomes , ribosomal proteins may be expected to bear strong signatures of speed selection in addition to accuracy selection , making them unusually subject to speed/accuracy conflicts . We hypothesize that in most cases the speed benefit overwhelms the accuracy cost of C-ending codons in low-Q conditions for His/Asn/Tyr , but that accuracy costs outweigh speed benefits for Asp—perhaps because Asp codon mistranslation yields products that are particularly disruptive to ribosomal assembly or function . Our hypothesis illustrates the larger principle that the outcome of speed/accuracy conflicts can be amino-acid-specific , depending upon the consequences of speed and accuracy differences for each synonymous codon .
We find that entire genomes , under pressure for both accurate and rapid translation , have been recoded to maintain translational accuracy dependent on a tRNA modification . This modification varies across development , and the coding in genes expressed at different stages depends on the stage-specific modification level . Contrary to the common assumption that certain codons are “optimal” for translational speed and accuracy , we show how particular pairs of codons can reverse their relative accuracies while preserving their relative speeds . Our results provide evidence for multiple such speed/accuracy conflicts , building on the kinetic distinction between the translational accuracy and speed of codons articulated in previous studies [17] , [33] . Going further , we show that a similar modification-dependent shift occurs during the developmental process of a single species , a striking example of the plasticity of translational fidelity . These results indicate that , if a codon is to be denoted optimal for translation , it is necessary to specify what aspect of translation the codon is optimal for , and under what biological circumstances . Many previous studies have attempted to provide explanations for why certain codons are used more frequently than others within a genome , or in particular genes . Here , we have examined related but distinct questions: why do closely related species use different codons , and use them preferentially at evolutionarily conserved sites in proteins ? And how does this site-specific usage change across the developmental program ? Our results do not conflict with the well-established influence of gene expression levels or tRNA abundances on codon usage bias ( to choose two of several causal factors ) , but do indicate that existing models are incomplete in important ways . Our study provides molecular and mechanistic insights that must be incorporated into any large-scale integrated attempt to explain the evolution of codon usage within and between species . Why were such clear , systematic , multi-species shifts in codon usage not found in previous analyses ? Close examination of results in a previous study reveals that , using one analytical approach , virtually all of the same shifts we report are apparent , but were passed over in favor of other approaches to yield the conclusion that the preferred set of codons is quite constant across Drosophila [35] ( cf . their Figure 2C ) . The approach in which results most closely match ours—analysis of relative synonymous codon usage ( RSCU ) in the top 10% most-biased genes as determined by their effective number of codons ( ENC ) [35]—has no particular mechanistic or evolutionary interpretation that differentiates it from similar approaches that gave different results . An analysis of codon usage in 69 ribosomal proteins across the 12 species reported a reversal of the most frequently used Asp codon , but not others , and argued that this change was minor and likely to be unimportant [36] . This result may reflect the restricted size or unusual constraints on the ribosomal protein-coding gene set; we argue that speed/accuracy conflicts may also explain the apparent differences between this analysis and ours . A systematic codon-usage shift in D . willistoni is well-documented [35] , [47] , [55] , but this species appears in most analyses to be a strong outlier in its codings . Mutational biases appear to contribute to , but not fully explain , changes in codon usage in willistoni [36] , [55] , a conclusion our data support . A major advantage of the within-gene comparison introduced by Akashi , and exploited here , is that it controls for mutational biases that vary over large genomic regions and between chromosomes . That willistoni behaves much like related species in our analyses is consistent with the idea that mutational biases contribute to its outlier appearance , but not its codon-usage shift . Overall , it appears that previous studies have seen signs of the shifts we report , but without a mechanism-specific analytical approach , a strong control for confounding biases , and experimental knowledge of the tRNA modification , these signs failed to coalesce into a coherent picture . Outside of the examples above , only one additional shift in codon usage has been identified in the drosophilids , a preference shift from UCC to AGC ( serine ) between D . melanogaster and D . virilis . This reflects small relative differences between three codons ( including UCG ) , of six , that are all roughly equally preferred over their counterparts [35] , such that the apparent change in preference is analogous to front-running athletes edging each other out rather than a fundamental change in the race . Why these changes have occurred remains unclear . Our study indicates that in terms of accuracy selection detectable using Akashi selection scores , serine codons remain quite stable , with a slight shift in the benefit of UCA relative to UCU . We identify several other accuracy-related shifts , most linked to changes in queuosine modification of tRNA , others ( such as in isoleucine ) less clearly so . Our findings have implications for the recent discovery that selection on synonymous sites in the drosophilids is far stronger than previously appreciated [4] . This study concluded that standard explanations for selection on codons , such as translational speed and accuracy , could not account for this strong selection . To reach this conclusion , codons were designated optimal or non-optimal , and these assignments were assumed constant across the phylogeny ( excepting D . willistoni ) and over the course of development . The results here suggest all three assumptions overlook key features of codon usage in these animals: different codons can be optimal for different selective mechanisms , and the relative selective benefit of codons is not constant across the phylogeny nor across development . It may prove useful to revisit the causes of strong selective constraint on synonymous sites with a more nuanced model for how selection has acted on translation in the drosophilids . The tRNA modification studied here , guanine to queuosine in the anticodon , has been studied for decades yet still has an unknown primary function . Given the many modifications targeting tRNA anticodons [30] , we conjecture that this modification is only one of many which regulate the speed , fidelity , and possibly other aspects of translation in ways that leave evolutionary fingerprints . Our results expose multiple shifts in accuracy-driven codon usage coupled to changes in queuosine modification , many but not all of which our kinetic model can explain as consequences of the modification . We do not claim that all such shifts arise from Q modification; other factors may well contribute . However , other coordinated shifts in accuracy ( such as those in isoleucine codons ) may be linked to queuosine modification in ways we do not yet grasp . The parallel changes in relative accuracy of isoleucine codons between species and across development provide some evidence to suggest that our understanding of the effects of the tRNA modification is far from complete . We anticipate that further studies , population-genetic and biochemical , will deepen our understanding of the genomic upheavals exposed here . What causes between-species and developmental variation in queuosine modification levels ? Two forces are likely to be at work: regulation of Q modification by the TGT enzyme or upstream factors , and bioavailability of the precursor nutrient queuine . Several lines of evidence suggest that bioavailability provides the dominant selective force . If reduced Q modification is a regulatory effect , it should be largely independent of substrate availability . Contrary to this prediction , supplementation of free queuine to D . melanogaster third-instar larvae ( the lowest-Q phase [38] ) at nanomolar levels suffices to increase Q modification of tRNA several-fold [40] . Micromolar queuine supplementation leads to near-complete modification [40] . Free cellular queuine concentration is strongly positively correlated with tRNA modification level , a hallmark of a substrate-limited process [41] . Thus , substrate limitation , rather than regulation , appears to be the primary determinant of Q modification levels . Whether other species are substrate-limited for queuine like D . melanogaster remains an open question . Species-wide variation in Q modification may stem from differences in gut microbiota , consistent with the wide variation we observe in species reared on identical diets , or from host variation , such as differences in expression of the enzyme ( s ) responsible for liberating queuine for absorption . Limiting queuine provides a simple explanation for the dip in Q modification during larval stages , and indeed the longstanding but poorly understood association between mitotic activity and reduced levels of queuosine tRNA modification , which is also observed in rapidly dividing cancer cells [56] . During rapid growth , such as larval development when mass ( and thus tRNA content ) increases more than 200-fold [40] , [51] , queuine intake must increase just to keep modification levels constant . If TGT is substrate-limited and the microbial sources of queuine multiply less rapidly than the growing organism , the exponential increases in tRNA abundance during rapid growth will result in transiently reduced Q modification , as observed in D . melanogaster [40] . Our results illuminate a surprising interplay between microbially acquired compounds , the fidelity of an organism's translational apparatus during development , and the evolutionary fate of its genome . Application of the general approaches introduced here to diverse taxa will likely yield more and deeper insights into this and similar novel modes of coevolutionary change .
Akashi selection scores for the 12 drosophilid species , and for genes maximally expressed at D . melanogaster developmental stages , may be accessed from the Dryad repository ( datadryad . org ) at doi:10 . 5061/dryad . 1jn88 [57] . Assuming weak selection , free recombination , and evolutionary steady-state , the log proportion of codon I relative to codon J is given by ln pIC/pJC = MIJ+SIJ where MIJ is the mutational bias ( the log-ratio of mutation rates from J to I ) and SIJ is the population-scaled additive selective ( fitness ) advantage of codon J over codon I ( SIJ = NesIJ with Ne the effective population size ) [58] . Let the proportion of sites with codon I that encode amino acids which are conserved across all 12 species be pIC , and at unconserved ( variable ) sites be pIV . At sites within the same protein , then , where SIJAkashi is the Akashi selection score quantifying the population-scaled difference in selective advantage resulting from a change from reference codon J to codon I at a conserved site relative to that at a variable site in the same gene . This difference is attributable to translational accuracy . The left-hand side is the log-odds ratio for a 2×2 contingency table ( conserved versus variable , codon I versus codon J ) which can , given codon counts n , be estimated by ψ ˆ = ln nIC/nJC−ln nIV/nJV . Akashi pointed out that such log-odds ratios can be estimated using the Mantel-Haenszel procedure [59] , which allows 2×2 tables to be computed for each gene separately and then combined into a single estimate , which , by construction , controls for all between-gene differences ( such as levels of gene expression , structure , function , between-gene variation in mutational biases , and so on ) which can distort other estimates of selection . With genes indexed by g and ng = nIgC+nJgV+nIgV+nJgC , the Mantel-Haenszel estimate is ψ ˆ = Σg ( nIgC nJgV/ng ) /Σg ( nIgV nJgC/ng ) with variance given by the Robins-Breslow-Greenland estimator [60] . These estimates are only approximately additive . Selection coefficients quantify the fitness advantage of a genotype over a reference , and we take as a reference the lowest-relative-fitness codon in D . melanogaster in each synonymous family . That is , we choose the reference codon such that all synonymous changes from that codon are ( in this analysis ) beneficial in D . melanogaster . We quantify Akashi selection scores for all possible pairs of synonyms ( no score for single-codon families , one pair for two codons , three pairs for three codons , six pairs for four codons , and 15 pairs for six codons ) . Coding sequence alignments for 12 drosophilid species were obtained for 9 , 855 transcripts from FlyBase [61] ( ftp://ftp . flybase . org/12_species_analysis/clark_eisen/alignments/all_species . guide_tree . cds . tar . gz ) , and filtered to include a single transcript per gene , aligned with 1∶1 orthologs in all 12 species ( ftp://ftp . flybase . org/12_species_analysis/clark_eisen/homology/GeneWise . revised . homology . tsv . gz ) , with a minimum fraction alignable of 50% and at least 50 codons , yielding 5 , 182 alignments used for all analyses . Maximal developmental-stage expression was evaluated using published data [51] , which , among the alignments above , yielded 1055 , 675 , 885 , 502 , 891 , and 301 genes with maximal expression in early embryo ( E0 ) , late embryo ( E3 ) , larva ( L ) , pupa ( P ) , adult male ( M ) , and adult female ( F ) flies , respectively . Embryo ( E ) genes were those with maximal expression in either E0 or E3 , and adult ( A ) genes were those with maximal expression in M or F . All drosophilid species were reared in bottles on standard yeast-glucose media at room temperature ( approximately 23°C ) . RNA was extracted from third instar larvae and 2-week-old adults using the standard TRIzol ( Invitrogen ) protocol . For larval collection , adults were placed on fresh food for 24 hours , after which great care was used to ensure that all flies were removed . We noted when larvae first started the roaming stage . 24 hours later larvae were collected from the bottles by pouring enough 5 M NaCl to cover the media and allowing the resulting mixture to set for 5 minutes . Floating larvae were poured onto mesh and washed in water before snap-freezing in liquid nitrogen . To age adults , newly eclosed flies were transferred to fresh bottles and every few days transferred to new bottles . After two weeks the flies were snap-frozen in liquid nitrogen . This method was based on the protocol developed by Igloi and Kössel [48] . 2 . 5 µg of total RNA was deacylated by incubating in 100 mM TrisHCl ( pH 9 ) for 30 min at 37°C . The deacylated RNA was combined with an equal volume of denaturing gel loading buffer containing 8 M urea , 5% glycerol , 0 . 05% bromophenol blue , and 0 . 05% xylene cyanol . Samples were loaded onto denaturing 10% polyacrylamide gels containing 5% 3-aminophenylboronic acid ( Boron Molecular ) and gel electrophoresis was run at 4°C in TAE . RNA was transferred under vacuum by layering 3MW blotting paper ( MIDSCI ) , Hybond-XL membrane ( GE Healthcare ) , gel , and plastic wrap on a gel dryer for 2 h at 80°C . After transfer the gel was removed from the membrane by soaking in distilled water . The membrane was washed twice for 30 min each in hybridization buffer ( 20 mM phosphate , pH 7 , 300 mM NaCl , 1% SDS ) , followed by incubation with 5′ 32P-labeled DNA oligonucleotide probes in the hybridization buffer for 16 h at 60°C . Membranes were washed three times for 20 min each in a solution containing 20 mM phosphate ( pH 7 . 2 ) , 300 mM NaCl , 2 mM EDTA , and 0 . 1% SDS and exposed to phosphor-imaging plates . Band intensity was quantified using software from the PhosphorImager manufacturer ( Fuji Medicals ) . Total RNA was first deacylated as described above . The deacylated RNA was incubated in 100 mM NaOAc/HOAc ( pH 4 . 5 ) and 50 mM freshly prepared periodate ( NaIO4 ) at room temperature for 30 min . 100 mM glucose was added and the mixture incubated for another 5 min . The mixture was run through a pre-equilibrated G25 column ( GE Healthcare ) to remove periodate followed by ethanol precipitation . Sample was then dissolved in the denaturing gel loading buffer . 7 µg of total RNA was incubated at 55°C for 15 min in 7% formaldehyde , 50% formamide , and 0 . 5× running buffer ( 1× running buffer is 200 mM MOPS , pH 7 , 80 mM NaOAc , 10 mM EDTA ) . Samples were then combined with an equal volume of gel loading buffer ( 5% glycerol , 0 . 05% bromophenol blue , and 0 . 05% xylene cyanol ) , and loaded onto 0 . 8% agarose gels ( 0 . 8% agarose , 1× running buffer , 2% formaldehyde ) . After electrophoresis , the gel was washed for 15 min in distilled water and 15 min in 10× SSC . RNA was transferred by capillary blotting overnight . After transfer the RNA was cross-linked to the membrane at 70 , 000 µJ/cm2 . The membrane was washed , hybridized , and exposed in the same manner as described for the acryloyl aminophenylboronic acid gel shift assay . Oligonucleotide probe sequences were: tRNAHis: 5′-TGCCGTGACCAGGATTCGAACCTGGGTTACCACGGCCACAACGTGGGGTCCTAACCACTAGACGATCACGGC; tRNATyr:5′-TCCTTCGAGCCGGASTCGAACCAGCGACCTAAGGATCTACAGTCCTCCGCTCTACCARCTGAGCTATCGAAGG; tRNAAsn:5′-CGTCCCTGGGTGGGCTCGAACCACCAACCTTTCGGTTAACAGCCGAACGCGCTAACCGATTGCGCCACAGAGAC; TGT mRNA:5′-CGATCCACCCARCGWATDGTVCGCTCCATRGCCTC; Actin mRNA:5′-CTTCTCCTTGATGTCRCGNACRATTTCACGCTCAGCSGTGGTGGTGAA We observe that the proportion of Q-tRNA rises ( and G-tRNA correspondingly decreases ) as C-ending codons rise in inferred accuracy relative U-ending codons . To understand this shift , we assume that ( 1 ) relevant tRNAs read C-ending codons more rapidly than U-ending codons; ( 2 ) a competitor tRNA bearing another amino acid , such that mistranslation would occur if this tRNA were accepted , also reads C-ending codons more rapidly than U-ending codons; ( 3 ) ribosomes translate cognate codons using Q-tRNA more rapidly than using G-tRNA . We adapt a previously introduced framework to build a kinetic model [52] . Let the first-order rate constant of X-tRNA for reading NAY ( Y = C or U ) codons be kXY and the concentration of X-tRNA be [X] . Then , for example , the rate of translation of NAC codons by Q-tRNA is kQC[Q] . For simplicity , we model competitor tRNAs using a single “effective” tRNA with concentration [M] and reading rate constants kM* . The rate of translation of Y-ending codons is rY = kQY[Q]+kGY[G]+kMY[M] , and the proportion of mistranslated Y-ending codons is ϵY = kMY[M]/rY . We denote the proportion of Q-modified tRNA as q = [Q]/[T] with total cognate tRNA concentration [T] = [Q]+[G] , and further assume that the competitor tRNA is present at a concentration α times that of the cognate tRNA , [M] = α[T] . In our simulations , we choose α = 1 for simplicity . Then the error rate of a Y-ending codon ( Y = C or U ) , as a function of the proportion of Q-modified tRNA , is ϵY ( q ) = kMYα/[kMYα+kQYq+kGY ( 1−q ) ] . R source code to reproduce the graphs in Figure 5 is included as Listing S1 . | Ribosomes translate mRNA into protein using tRNAs , and these tRNAs often translate multiple synonymous codons . Although synonymous codons specify the same amino acid , tRNAs read codons with differing speed and accuracy , and so some codons may be more accurately translated than their synonyms . Such variation in the efficiency of translation between synonymous codons can result in costs to cellular fitness . By favoring certain coding choices over evolutionary timescales , natural selection leaves signs of pressure for translational fidelity on evolved genomes . We have found that the way in which proteins are encoded has changed systematically across several closely related fruit fly species . Surprisingly , several of these changes involve two codons both read by the same tRNA . Here we confirm experimentally that the anticodons of these tRNAs are chemically modified—from guanine to queuosine—in vivo , and that the levels of this modification in different species track the differences in protein coding . Furthermore , queuosine modification levels are known to change during fruit fly development , and we find that genes expressed maximally during a given developmental stage have codings reflecting levels of modification at that stage . Remarkably , queuosine modification depends upon acquisition of its precursor , queuine , as a nutrient that eukaryotes must obtain from bacteria through the gut . We have thus elucidated a mechanism by which availability of a nutrient can shape the coding patterns of whole genomes . | [
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] | 2014 | A Nutrient-Driven tRNA Modification Alters Translational Fidelity and Genome-wide Protein Coding across an Animal Genus |
Structural colors are generated by scattering of light by variations in tissue nanostructure . They are widespread among animals and have been studied most extensively in butterflies and moths ( Lepidoptera ) , which exhibit the widest diversity of photonic nanostructures , resultant colors , and visual effects of any extant organism . The evolution of structural coloration in lepidopterans , however , is poorly understood . Existing hypotheses based on phylogenetic and/or structural data are controversial and do not incorporate data from fossils . Here we report the first example of structurally colored scales in fossil lepidopterans; specimens are from the 47-million-year-old Messel oil shale ( Germany ) . The preserved colors are generated by a multilayer reflector comprised of a stack of perforated laminae in the scale lumen; differently colored scales differ in their ultrastructure . The original colors were altered during fossilization but are reconstructed based upon preserved ultrastructural detail . The dorsal surface of the forewings was a yellow-green color that probably served as a dual-purpose defensive signal , i . e . aposematic during feeding and cryptic at rest . This visual signal was enhanced by suppression of iridescence ( change in hue with viewing angle ) achieved via two separate optical mechanisms: extensive perforation , and concave distortion , of the multilayer reflector . The fossils provide the first evidence , to our knowledge , for the function of structural color in fossils and demonstrate the feasibility of reconstructing color in non-metallic lepidopteran fossils . Plastic scale developmental processes and complex optical mechanisms for interspecific signaling had clearly evolved in lepidopterans by the mid-Eocene .
Structural color has long been of interest to biologists . It is phenotypically significant in many organisms [1] , forms the basis of diverse inter- and intra-specific communication strategies [2] , and is implicated in pivotal evolutionary transitions [3] . Evidence of structural color has been reported from some fossil biotas [3]–[6] , but has received little attention . This limits our ability to reconstruct the origins of activity patterns , habitat preferences , and social and sexual signaling mechanisms [7] . This is particularly problematic in the case of Lepidoptera ( butterflies and moths ) , which exhibit the most complex and diverse structural colors of any living group of organisms [8] . Structural colors in extant lepidopterans are generated by modification of one or more components of the basic scale architecture ( longitudinal ridges and transverse crossribs upon a basal lamella that is supported by columnar trabeculae in the scale lumen ) into a biophotonic nanostructure of chitin and air [9] . Such color-generating multilayer structures can arise via specialization of the ridges and their ridge-lamellae , crossribs , or the scale lumen; the lumen can also exhibit various other modifications , including complex three-dimensional photonic crystals . The various color-producing nanostructures in lepidopteran scales may be related developmentally [9] and all generate color via interference of scattered light [1] , although the overall visual effect can be influenced by other optical mechanisms at the level of ultra- and macrostructure [10] . Attempts to reconstruct the evolution of color-producing nanostructures using phylogenetic and/or structural evidence have hypothesized that multilayer structures in the scale lumen are evolutionarily primitive [11] , [12] , but these conclusions are not widely accepted [1] , [10] . Fossils provide direct evidence of stages in the evolution of biological structures and can be used to test evolutionary hypotheses . The lepidopteran fossil record extends from the Early Jurassic to the Recent and includes representatives of numerous extant lepidopteran families [13] , [14] . Fossil specimens of adult macrolepidopterans often exhibit light- and dark-toned areas on their wings [14] and can retain ultrastructural details of their scales [15]; preservation of pigmentary or structural colors has not been reported . Most fossil lepidopterans occur as inclusions in amber and within fine-grained sediments [14]; Baltic and Dominican amber ( Eocene-Oligocene ) , the lacustrine sediments of Florissant ( Eocene , Colorado ) , and the offshore marine sediments of the early Palaeocene Fur Formation ( Denmark ) are especially rich sources [14] . Fossil lepidopterans have also been reported ( but not described ) from the mid-Eocene Messel oil shale of Germany , which is celebrated for preserving a diverse paratropical ecosystem with remarkable fidelity [16]; the biota includes mammals , reptiles , amphibians , abundant fish and insects , and plants [17] , the last represented by leaves , fruits , and seeds [18] . Messel fossils are typically well preserved: animals are often well-articulated and many show evidence of soft tissues ( including stomach contents ) ; insects ( especially beetles ) may exhibit metallic coloration [16] . Here we use scanning- and transmission electron microscopy ( SEM and TEM ) , reflectance micro-spectrophotometry , and 2-D discrete Fourier analysis [1] , [19] to demonstrate that metallic color in the fossil lepidopterans from Messel is structural in origin and to reconstruct their original color .
The gross morphology of the scales is difficult to determine as they overlap and are typically fractured . Ultrastructural evidence demonstrates that four types are present ( Figures 1 , 2 , Figures S3 , S4 ) . Type A scales , the most common , are the primary contributor to the observed color . They are cover scales and occur over the dorsal and ventral surface of the forewing ( Figures 1D–J , 2 ) . The abwing surface of these scales , as in extant lepidopterans [22] , exhibits prominent longitudinal ridges connected by orthogonal crossribs ( typical spacing 1 . 8–2 . 5 µm and 510–600 nm , respectively ) ( Figure 1D ) . The ridges are up to 1 µm high ( Figure 2C ) . They comprise overlapping lamellae ( each 1 . 2–3 . 1 µm long and 110–150 nm wide ) inclined at 10–12° to the scale surface and exhibit short lateral microribs ( typical spacing 122–170 nm ) ( Figure 1D–G ) . The ridges and crossribs frame a series of windows that are typically perforated ( Figure 1E–G ) ; the lamina perforation factor ( p ) [23] increases from the proximal ( p = 0 . 05 ) ( Figure 1F ) to distal ( p = 0 . 32 ) parts of a scale ( Figure 1G ) . The scale lumen contains 3–5 perforated internal laminae that differ in their structure and thickness ( Figures 1E , H , 2 ) . The uppermost lamina ( 93–124 nm thick ) exhibits densely packed , bead- to rod-like spacers ( 60 nm wide and 60–500 nm long ) ( arrow in Figure 1E ) . The next two to four laminae exhibit less densely packed , bead-like , spacers ( typically 60 nm×60 nm ) ( Figure 1I ) and decrease progressively in thickness ( from 74–110 nm to 55–63 nm ) towards the adwing scale surface ( Figure 2A , B ) . In the proximal parts of a scale , the lowermost lamina in the stack is the basal lamina of the scale . In medial and distal parts of a scale , however , the stack is supported by additional pillar-like trabeculae ( each 0 . 6–1 µm high ) above a lamina with a distinctive reticulate texture ( 55–65 nm thick ) , which forms the base ( Figure 1J ) . The ultrastructure of Type A scales ( including the spacing of laminae , which is known to control color in living lepidopterans ) varies according to their color and location on the wing ( see Table S2 ) . Similar variation occurs in extant lepidopterans [22] , [24] . Even non-metallic brown scales in the fossils preserve ultrastructural details , including the laminar ultrastructure in the scale lumen ( Figures S3 , S5 ) . Scale types B , C ( “satin-type” [9] ) , and D are rare and do not contribute significantly to the observed color in the fossils ( see Text S1 ) . Three-dimensional structures in fossils are vulnerable to compaction during burial of the host sediments . It is not assumed a priori that the preserved structure of the laminar array in the fossil lepidopteran scales is identical to that in vivo , especially as the trabeculae are typically fractured and now orientated parallel to , and superimposed upon , the basal lamina of the scale ( Figure 2G ) . There is , however , no evidence that the laminar array has been similarly affected . Successive laminae are not superimposed and the vertical spacers between them are neither fractured nor flexed . Preferential fracturing of the trabeculae may have been promoted by their wider spacing and greater height . There is no evidence ( e . g . , dessication cracks ) that the geometry of the laminar array was affected by shrinkage of the scales during diagenetic dehydration of the organic tissue . Nor is there evidence for diagenetic expansion of the scale structure: spacers are continuous between adjacent laminae . Collectively , these observations indicate that diagenetic processes had little or no impact upon the preserved structure of the laminar array . The preserved ultrastructure is therefore considered to be extremely similar , if not identical , to that originally present in vivo . The ultrastructure of the laminar array was not modified during fossilization and is therefore a reliable basis for reconstructing the original colors of the fossil scales . 2-D Fourier analysis of longitudinal TEM images of the ultrastructure in the lumen of scales from the basal part of the dorsal forewing reveals two points of high values aligned above and below the origin ( Figure 3A , B ) . The dominant periodicity is in the vertical direction; that is , the preserved structure is highly laminar . Fourier power spectra of transverse TEM images show a wider distribution of Fourier power peaks above and below the origin ( Figure 3C , D ) . This results from the concave geometry of the laminar array in transverse section and consequent increase in the range of angles over which the observed color maintains the same peak hue [1] . Radial averages of the Fourier power spectra demonstrate that the preserved laminar nanostructure is a multilayer reflector: the peak spatial frequencies in refractive index lie within the range capable of producing visible colors by scattering of light ( Figure 3E ) . The visual properties of extant lepidopteran scales can be influenced by scale tilt ( the angle between the scale and the wing membrane ) [10] , scale curvature [2] , the number and thickness of laminae [25] , the degree of overlap of ridge lamellae [21] , the spacing of the ridges [21] , microribs [21] , and crossribs [26] , and the lamina perforation factor [23] . Scale tilt and curvature are not preserved in the fossils . The number ( up to five ) of laminae and their different thicknesses indicate that the fossil multilayer reflector is non-ideal ( i . e . reflects much less than 100% of incoming light ) [27] . The ridge lamellae in the fossils do not overlap sufficiently [28] to have a significant impact on the observed color . Closely spaced microribs or crossribs in satin-type scales can also generate diffraction [26] and play a secondary role in the generation of blue and violet colors in lepidopteran scales [19] , [23] . The spacing of the microribs ( 140 nm ) in the fossil satin-type scales , however , is significantly less than the wavelength of visible light ( approx . 350–700 nm ) ; conventional diffraction theory indicates that zero-order diffraction ( i . e . specular ( directional ) reflectance ) will be produced . Further , the satin-type scales are restricted to the inner margin of the forewing in the fossils , precluding their having a significant impact on the observed color . Collectively , these observations indicate that the primary color-producing nanostructure in the fossils is the multilayer reflector in the scale lumen . The optical properties of the fossil scales are , however , influenced by the perforation factor and concave geometry of the laminae , and by the spacing of the ridges . In extant lepidopterans , iridescence , spectral bandwidth , and total reflectance are reduced at higher perforation factors ( between 0 . 2 and 0 . 4 ) relative to scales with lower perforation factors [23]; this generates a purer ( albeit less intense ) color that is visible over a wider range of angles . In the fossils the exposed ( medial and distal ) parts of the overlapping scales typically have perforation factors of 0 . 32 . Concave distortion of laminar arrays also reduces iridescence [1]; the arcuate geometry of the fossil multilayer reflector in transverse section would have enhanced the iridescence-reducing effect of the perforated laminae . Multilayer reflectors typically generate directional ( specular ) color that flashes at specific observation angles [29]; this effect can be modified by diffraction . Ridge periodicities of between 0 . 85 µm and 4 µm generate diffraction [21] , [30] , [31]; a strong diffractive effect has been reported for periodicities of ca . 1 . 3 µm [31] and 1 . 7 µm [30] . The ridges in the fossils are spaced 1 . 8–2 . 5 µm apart and therefore probably constitute diffraction elements that render the color generated by the multilayer reflector visible over a wide range of observation angles , but do not contribute to the observed hue [31] . Scales from the dorsal surface of the basal part of the forewing exhibit a measured reflectance peak of 473 nm ( Figure 3F ) that corresponds to their blue color in air . The predicted peak of reflectance ( with λmax = 565 nm ) calculated from the radial averages ( using refractive index values of 1 . 56 and 1 . 0 for the high- and low-index layers , respectively ) , however , indicates that the dorsal surface of the basal part of the forewing was originally yellow-green . The color in air today and the measured reflectance peak are artefacts , probably a result of alteration of the biomolecular composition of the scale cuticle , and thus its refractive index , during fossilization; most fossil arthropod cuticles are chemically altered during diagenesis [32] . Furthermore , recent experiments using extant butterfly scales demonstrated that alteration of the original organic material results in a shift in the reflectance peak without altering the scale ultrastructure significantly [33] . Calculation of reflectance peaks for scales from other parts of the wings ( see Figure S5 ) allows the original colors of the fossil lepidopterans to be reconstructed ( Figure 4; Table S3 ) . Scales in postdiscal to submarginal wing zones have predicted reflectance peaks λmax≈515 nm and λmax≈440 nm , respectively; scales along the wing margins have a predicted reflectance peak λmax≈750 nm , and scales from the abdomen have a predicted reflectance peak λmax≈550 nm ( Figure S5 ) . The fossil lepidopterans therefore originally exhibited yellow-green hues in basal and discal to postdiscal zones of the wing; the color graded to green-cyan and then blue in postdiscal to submarginal wing zones , and was brown along the outer wing margin . Scales on the abdomen were yellow to yellow-green . Structural colors in extant butterflies function primarily in species and mate recognition [29]; the function of structural colors in extant moths , however , has not been investigated . The fossil moths described here are colored most highly on the dorsal surfaces of the forewings ( the surfaces which are exposed in most extant moths , including zygaenids , when they are at rest [34] ) , suggesting that the Eocene moths , like extant zygaenids , were diurnal . The visual ecology of the structural color in the fossil moths can therefore be compared with those in extant diurnal lepidopterans . The fossil moths were characterized by a yellow-green dorsal coloration that was visible over a wide range of angles but not highly reflective . The visual signal lacked certain properties , e . g . strong iridescence , brightness , and color contrast within the wing , that are important in conspecific communication [30] . Instead , the optical characteristics of the fossil scales , notably their original yellow-green hue and suppression of iridescence , indicate a primary defensive function . In extant lepidopterans , reduced iridescence enhances presentation of visual signals for protective purposes [35] . Structural green coloration functions cryptically in extant butterflies [24] , [29] and beetles [36] , [37] . In particular , a combination of a structural green hue with reduced iridescence provides particularly efficient color matching with a diffuse leafy background [36] , [37] . A cryptic function for the structural color in the fossil lepidopterans is consistent with the ecology of extant zygaenid moths: many Procridinae species with green scales are cryptic except when feeding on flowers [38] , when they can be highly conspicuous ( Gerhard Tarmann ( Tirolier Landesmuseen , Austria ) , personal communication ) [39] . The latter feature is inconsistent with cryptism: high chromatic contrast with the background environment is characteristic of an aposematic ( warning ) signal [40] . However , an aposematic function for the structural color while feeding does not necessarily conflict with a cryptic function in a foliaceous environment: dual-purpose visual signals are known in extant lepidopterans [41]–[43] . The visual signal generated by the structurally colored scales in the fossil lepidopterans probably served two functions: cryptic when specimens were at rest , and aposematic during nectaring . It is possible that this dual function is evolutionarily conserved in Procridinae and that aposematism and diurnality are ancestral traits of zygaenids . Further , defensive behavior in the fossil moths is consistent with the use of chemical defense: extant zygaenids , including taxa that are largely cryptic [28] , can synthesize cyanide for defense by enzymatic breakdown of cyanoglucosides [24] , [44] , [45] . The discovery of structural color in Messel lepidopterans constrains the timing of the origin of several important evolutionary novelties . Different scale types in extant lepidopterans arise via subtle modifications of a common membrane-folding developmental process dominated by self-assembly [9] , [22] , [46] . The presence of different scale types in the fossils confirms that such plastic developmental processes had evolved in moths by the mid-Eocene . The complexity of the iridescence-reducing nanostructure in the fossil moths indicates that sophisticated optical mechanisms for interspecific signaling were in use at this time . Predator-prey interactions are recognized as a major stimulus in insect evolution [47]; the use of cryptic and aposematic signals by the fossil moths described here supports the evidence of other fossils from Messel [48] that sophisticated mechanisms for avoiding detection by visually hunting predators had evolved in insects by the mid-Eocene . The striking resemblance of the fossil moths to some extant zygaenids and the cryptic/aposematic function of their structural color suggest that dual-purpose visual signals , and especially aposematism , may be evolutionarily conserved in this group of moths , originating early in the history of the group and persisting to the present day . Preservation of ultrastructural detail in all scales in the fossils , even non-metallic brown examples , offers the possibility of reconstructing the original colors and patterning of even lepidopteran fossils that lack obvious structural color .
Specimens are held by the Senckenberg Forschungsinstitut und Naturmuseum , Forschungsstation Grube Messel , Germany . Small ( 2–3 mm2 ) tissue samples were removed using sterile tools and , for TEM , placed in the following ethanol∶glycerine mixtures , each for 24 h under rotation: 10% , 25% , 50% , 75% , 100% ethanol . For SEM , samples were dehydrated using HMDS or under vacuum , mounted on aluminum stubs , carbon- or gold-coated , and examined using a FEI XL-30 ESEM-FEG microscope equipped with an EDAX energy disperse X-ray spectrometer . Observations were made at an accelerating voltage of 15 kV , with acquisition times of 60 s for EDS spectra of carbon-coated samples . For TEM , samples were washed in propylene oxide twice , each for 1 h , and impregnated with Spurr's resin under vacuum in the following resin∶ethanol mixtures , each for 24 h: 10% , 20% , 30% , 40% , 50% , 60% , 70% , 80% , 90% , 100% resin . To ensure optimal orientation for sectioning , a 10 mm3 block of resin containing the sample was extracted , re-orientated , and re-embedded in 100% resin . Ultrathin ( 80–90 nm thick ) microtome sections were placed on formvar-coated Cu grids , stained using uranyl acetate and lead citrate , and examined using a Zeiss EM900 TEM at 80 kV with an objective aperture of 90 µm diameter . Reflectance spectra were recorded from samples in 100% glycerine , 100% ethanol , and in air ( the latter from only the basal part of the dorsal forewing to minimize damage due to drying ) using an epi-illumination Nikon Optiphot 66 microscope , an Ocean Optics HR2000+ spectrophotometer , and a tungsten-halogen light source; spectra were collected from a 70 µm spot . All recorded spectra were normalized against the spectrum of the light source recorded from a white standard . Nanoscale spatial periodicity in the refractive index of a material results in constructive interference of scattered light; structural color is generated where such scattering occurs in the visible part of the spectrum . Herein we use an established analytical method [13] of analyzing the periodicity and optical properties of structurally colored biological tissues using the discrete Fourier 2-D transform . Digital TEM micrographs of scales from the fossil lepidopterans were analyzed using MATLAB ( version 7 . 11 . 0 ) and a 2-D Fourier tool freely available as a series of MATLAB commands ( http://www . yale . edu/eeb/prum/fourier . htm ) . Variation in the refractive index of nanostructures in the fossil scales was analyzed using the procedure described in ref . [1] . The reconstruction of the original colors of the fossil lepidopterans is based upon the preserved ultrastructure of the multilayer reflector in the scale lumen and the assumption that the original refractive index of the fossil scale cuticle was similar to that in modern lepidopterans ( i . e . ∼1 . 56 ) . Only the original colors of the dorsal surface of the specimens were reconstructed: ( 1 ) only this surface is exposed at rest and ( 2 ) the ventral surface of the hindwing is not visible in any specimen . TEM images of the multilayer reflector preserved in Type A scales of different colors were analyzed using 2-D Fourier analysis . Predicted wavelength values scales from different locations on the wing are based on 2–4 replicate analyses . Wavelength data for predicted reflectance peaks were converted to RGB values . Calculation of precise RGB values for a specific wavelength , however , is difficult [49] . RGB values for predicted wavelength data were therefore calculated using three different methods: ( 1 ) using the “Wavelength to RGB” application available from http://miguelmoreno . net/sandbox/wavelengthtoRGB/ ( downloaded December 28 , 2010 ) , ( 2 ) using the “Spectra” application available from www . efg2 . com/lab ( downloaded December 28 , 2010 ) , and ( 3 ) using the “Wavelength to RGB” converter available online at www . uvm . edu/~kspartal/Physlets/Lecturedemo/LambdaToRGB . html ( accessed December 28 , 2010 ) . The three methods yield similar RGB values; the colors depicted in the reconstruction are based on the averages of the values obtained . | Biological structural colors are generated when light is scattered by nanostructures in tissues . Such colors have diverse functions for communication both among and between species . Structural colors are most complex in extant butterflies and moths ( lepidopterans ) , but the evolution of such colors and their functions in this group of organisms is poorly understood . Fossils can provide insights into the evolution of biological structures , but evidence of structurally colored tissues was hitherto unknown in fossil lepidopterans . Here , we report the preservation of structurally colored scales in fossil moths with striking metallic hues from the ∼47-million-year-old ( Eocene ) GrubeMessel oil shales ( Germany ) . We identify the color-producing nanostructure in the scales and show that the original colors were altered during fossilization . Preserved details in the scales allow us to reconstruct the original colors and show that the dorsal surface of the forewings was yellow-green . The optical properties of the scales strongly indicate that the color functioned as a warning signal during feeding but was cryptic when the moths were at rest . Our results confirm that structural colors can be reconstructed even in non-metallic lepidopteran fossils and show that defensive structural coloration had evolved in insects by the mid-Eocene . | [
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] | 2011 | Fossilized Biophotonic Nanostructures Reveal the Original Colors of 47-Million-Year-Old Moths |
Dot1 is an evolutionarily conserved histone methyltransferase specific for lysine 79 of histone H3 ( H3K79 ) . In Saccharomyces cerevisiae , Dot1-mediated H3K79 methylation is associated with telomere silencing , meiotic checkpoint control , and DNA damage response . The biological function of H3K79 methylation in mammals , however , remains poorly understood . Using gene targeting , we generated mice deficient for Dot1L , the murine Dot1 homologue . Dot1L-deficient embryos show multiple developmental abnormalities , including growth impairment , angiogenesis defects in the yolk sac , and cardiac dilation , and die between 9 . 5 and 10 . 5 days post coitum . To gain insights into the cellular function of Dot1L , we derived embryonic stem ( ES ) cells from Dot1L mutant blastocysts . Dot1L-deficient ES cells show global loss of H3K79 methylation as well as reduced levels of heterochromatic marks ( H3K9 di-methylation and H4K20 tri-methylation ) at centromeres and telomeres . These changes are accompanied by aneuploidy , telomere elongation , and proliferation defects . Taken together , these results indicate that Dot1L and H3K79 methylation play important roles in heterochromatin formation and in embryonic development .
Histones are subject to a variety of post-translational modifications , including acetylation , phosphorylation , ubiquitination , and methylation . These modifications dictate chromatin structure by affecting the recruitment of nonhistone proteins and/or the interactions between nucleosomes [1] , [2] . Heterochromatin is associated with high levels of methylation at H3K9 , H3K27 , and H4K20 and low levels of acetylation , whereas actively transcribed euchromatin is typically enriched with acetylation and methylated H3K4 , H3K36 , and H3K79 . Most histone H3 modifications occur on residues within the N-terminal tail . In contrast , H3K79 is located in a loop within the globular domain , exposed on the nucleosome surface . The yeast Dot1 and its homologues in other species are the only known H3K79 methyltransferases [3]–[5] . Unlike other histone lysine methyltransferases , Dot1 family members do not have a SET domain [3]–[5] . Instead , their catalytic domain contains conserved sequence motifs characteristic of class I methyltransferases such as DNA methyltransferases ( DNMTs ) and the protein arginine methyltransferase PRMT1 [6] . Dot1 was initially identified as a disruptor of telomeric silencing in Saccharomyces cerevisiae [3] . Subsequent studies showed that both overexpression and inactivation of Dot1 as well as mutations at H3K79 all lead to loss of telomeric silencing [7]–[9] . Although the mechanisms by which Dot1 affects telomere structure and function are not fully understood , it is believed that H3K79 methylation plays an important role in restricting the Sir proteins at heterochromatic regions [7] , [8] , [10] . Dot1-dependent H3K79 methylation has also been shown to be involved in meiotic checkpoint control and in G1 and S phase DNA damage checkpoint functions of Rad9 in yeast [11] , [12] . H3K79 methylation is also a widespread histone modification in mammalian cells [4] . Abnormal H3K79 methylation has been linked to leukemogenesis in humans [13] , [14] . However , the biological function of H3K79 methylation in mammals remains largely unknown . Here we generated a mouse line containing a null mutation in Dot1L , the murine Dot1 homologue , and investigated the role of Dot1L and H3K79 methylation in embryonic development and cellular function . We provide evidence that Dot1L is required for embryogenesis and for the integrity of constitutive heterochromatin at the cellular level .
To target the Dot1L gene , we constructed a targeting vector in which a 2 . 3-kb genomic region containing exons 5 and 6 and a promoterless β-geo selection cassette were flanked , respectively , by three loxP sites ( Figure 1A ) . Exons 5 and 6 encode 108 amino acids that form several conserved motifs in the Dot1L catalytic domain , including the SAM-binding motif and motifs X , I , and II [6] . Since mutations of conserved residues within motif I abolish the methyltransferase activity of Dot1L [4] , we predicted that deletion of exons 5 and 6 would inactivate Dot1L . ES cells were transfected with the targeting vector and selected in G418-containing medium . Clones with homologous recombination were identified by Southern blot analysis with a 5′ external probe ( Figure 1B ) . Three of these clones , referred to as Dot1L3lox/+ , were injected into blastocysts to generate chimeric mice , which subsequently transmitted the mutant allele to their offspring . Deletion of the β-geo cassette plus exons 5 and 6 was achieved by breeding the Dot1L3lox allele into mice expressing Cre recombinase in the germ line . The resulting null allele is referred to as Dot1L1lox ( Figure 1A ) . Genotypes were determined using PCR ( Figure 1C ) . We first determined the expression of Dot1L during embryonic development , taking advantage of the fact that cells containing the Dot1L3lox allele express lacZ under the control of the endogenous Dot1L promoter . We conducted X-gal staining on Dot1L3lox/+ heterozygous embryos and wild-type littermates at different stages of development . Dot1L expression is ubiquitous as early as 7 . 5-dpc ( the earliest time point tested , Figure 2A ) . At 9 . 5-dpc , Dot1L expression remains ubiquitous and areas of elevated expression are apparent . Tissues that demonstrate high levels of lacZ staining include the optic vesicle , the first branchial arch , the limb buds , the heart , the otic pit , and the neural ectoderm ( Figure 2B ) . Dot1L is also expressed at high levels in extra-embryonic tissues , including the visceral endoderm and visceral mesoderm of the yolk sac , and in primitive erythrocytes ( Figure 2C ) . Similar lacZ staining patterns are observed in embryos harvested at 10 . 5-dpc , 11 . 5-dpc , and 12 . 5-dpc ( data not shown ) , suggesting that Dot1L is broadly expressed during embryonic development . Dot1L1lox/+ mice were grossly normal and fertile . However , intercrosses of Dot1L1lox/+ mice produced no viable Dot1L1lox/1lox homozygous offspring , suggesting embryonic lethality ( Table 1 ) . Dot1L1lox/1lox embryos harvested at 8 . 5-dpc were indistinguishable from wild-type and Dot1L1lox/+ littermates ( data not shown ) . At 9 . 5-dpc , Dot1L1lox/1lox embryos were smaller than littermates , had enlarged hearts and stunted tails on gross observation when viewed under a dissecting microscope ( Figure 2D , center ) . Approximately 15% of the Dot1L1lox/1lox embryos demonstrated a severe phenotype , exhibiting developmental arrest at E8 . 5 ( Figure 2D , right ) . Histological examination of 9 . 5-dpc Dot1L1lox/1lox embryo sections revealed focal areas of extensive apoptosis , but no obvious structural defects ( Figure S1 ) . At 10 . 5-dpc , the percentage of viable Dot1L1lox/1lox embryos was substantially below the expected Mendelian ratio ( Table 1 ) , suggesting that many of the Dot1L1lox/1lox embryos die during this time interval . The few that survived to this stage exhibited developmental arrest at E9 . 5 and severe cardiac dilation ( Figure 2E ) . No Dot1L1lox/1lox embryos survived beyond 10 . 5-dpc ( Table 1 ) . As stunted growth and enlarged heart are phenotypes that often occur as a result of defects in extraembryonic tissues , we examined the yolk sac and placenta of 9 . 5-dpc Dot1L1lox/1lox embryos . While the placenta showed no obvious defects , the yolk sac exhibited abnormal vascular morphology . The yolk sac vasculature was present and contained primitive erythrocytes , but was frequently underdeveloped and disorganized when compared to control littermates ( Figure 2F ) . These observations indicate that , in the absence of Dot1L , vasculogenesis took place in the yolk sac but angiogenesis was defective . To investigate the cellular function of Dot1L , we derived Dot1L mutant ES cells from blastocysts produced from intercrosses of Dot1L1lox/+ mice . Two Dot1L1lox/1lox and multiple Dot1L1lox/+ and Dot1L+/+ lines were established . As expected , H3K79 di- and tri-methylation was greatly reduced in Dot1L1lox/1lox cells compared to Dot1L+/+ cells ( Figure 3A ) . Dot1L1lox/+ cells had intermediate levels of H3K79 di- and tri-methylation , indicating haploinsufficiency of Dot1L ( Figure 3A ) . Surprisingly , Western blot analysis using a “mono methyl H3K79” antibody ( ab2886 , Abcam ) detected no change in signal intensity in Dot1L mutant ES cell lines ( data not shown ) . To verify the results , we carried out mass spectrometry . In wild-type ES cells , ∼11% of histone H3 showed K79 methylation , among which mono- , di- , and tri-methylation accounted for ∼70% , ∼30% , and less than 1% , respectively . In Dot1L1lox/1lox ES cells , H3K79 mono- and tri-methylation was absent although trace amount of di-methylation was detected ( Figure 3B and Figure S2 ) . We therefore concluded that the Western blot result showing no alteration in H3K79 mono-methylation in the absence of Dot1L was an artifact due to nonspecific recognition of histone H3 by the “mono methyl H3K79” antibody . The low level of H3K79 di-methylation detected in Dot1L1lox/1lox samples could be from feeder cells present in the culture or due to incomplete inactivation of Dot1L . Taken together , these results indicated that Dot1L is most likely the sole H3K79 methyltransferase in mice . Dot1L mutant ES cells maintained an undifferentiated state , as judged by morphology and ES cell markers such as Oct4 and Nanog ( data not shown ) . We investigated whether Dot1L deficiency affects ES cell growth . We plated 3×105 Dot1L+/+ , Dot1L1lox/+ , and Dot1L1lox/1lox ES cells in standard ES cell medium and monitored proliferation . By 24 hours , the number of Dot1L+/+ ES cells ( 7 . 3×105 ) was significantly higher than the number of Dot1L1lox/+ or Dot1L1lox/1lox cells ( 4 . 2×105 and 3 . 6×105 respectively , P<0 . 05 , Figure 3C ) . Over the 5 days examined , Dot1L+/+ , Dot1L1lox/+ , and Dot1L1lox/1lox ES cells had average doubling times of 16 , 22 , and 26 hours , respectively . The fact that both Dot1L1lox/+ and Dot1L1lox/1lox cells exhibited growth defects showed the importance of Dot1L gene dosage in cellular function . The reduction of H3K79 methylation in Dot1L1lox/+ cells ( haploinsufficiency ) suggested that Dot1L level is relatively low . Interestingly , Dot1L1lox/+ mice were apparently normal despite the defects in Dot1L1lox/+ ES cells . It is possible that 50% of Dot1L can barely maintain normal cellular function under ideal conditions ( e . g . in vivo ) but is not sufficient to do so under suboptimal conditions ( e . g . in culture ) . We next examined apoptosis and cell cycle status of the Dot1L mutant ES cells . Annexin V staining revealed that 4 . 0% of the Dot11lox/1lox ES cells and 3 . 1% of the Dot1L1lox/+ ES cells were annexin V positive , while only 1 . 2% of the Dot1L+/+ ES cells were annexin V positive ( Figure 3D ) . This indicates that more than twice as many of the Dot1L mutant ES cells were undergoing apoptosis compared to wild-type ES cells . Furthermore , cell cycle analysis by propidium iodide staining revealed an elevated percentage of G2 cells and a reduced percentage of G1 cells among the Dot1L mutant ES cells when compared to the wild-type ES cells ( Figure 3E ) . These results suggest that both elevated apoptosis and G2 cell cycle arrest contribute to the reduced growth rate of Dot1L mutant ES cells . Dot1-deficient S . cerevisiae show telomere elongation and defects in telomere silencing [3] . We therefore evaluated the effect of Dot1L inactivation on telomere length . First , we used Southern blot terminal restriction fragment ( TRF ) analysis to estimate telomere length in two ES cell lines of each Dot1L genotype . Both Dot1L1lox/1lox lines and one of the Dot1L1lox/+ lines showed telomere elongation , as evidenced by the presence of high molecular weight ( MW ) TRFs and the increase in the lengths of bulk TRFs compared to wild-type controls ( Figure 4A ) . Next , we carried out quantitative fluorescence in situ hybridization ( Q-FISH ) using a telomere-specific probe to determine the mean telomere length ( mtl ) and the distribution of telomere lengths for each cell line ( Figure 4 , B and C ) . Consistent with the TRF results , both Dot1L1lox/1lox lines had higher mtl , greater percentages of elongated ( >100 kb ) telomeres , and reduced percentages of short ( <50 kb ) telomeres compared to Dot1L+/+ lines ( Figure 4C ) . The heterozygous ES cells again showed an intermediate phenotype ( Figure 4C ) . Examination of the Q-FISH samples revealed frequent aneuploidy in Dot1L-deficient cells ( Figure 4B ) . To further investigate this phenotype , we prepared metaphase chromosome spreads from Dot1L1lox/1lox and Dot1L+/+ ES cells and examined them for chromosomal defects . Dot1L+/+ cells were karyotypically stable , as the vast majority had 40 chromosomes . In contrast , over 40% of metaphase Dot1L1lox/1lox cells were aneuploid . Most of the aneuploid cells showed gain of chromosomes and some ended up being tetraploid ( Figure 4 , B and D ) . Aside from aneuploidy , no obvious chromosomal abnormalities were frequently observed in Dot1L-deficient cells ( Figure 4B ) . These results point to defects in chromosome segregation in the absence of Dot1L . Our data suggested a role for Dot1L in the homeostasis of telomere length . Two main mechanisms have been described for the maintenance of mammalian telomeres: the addition of telomeric repeats by telomerase and the so-called alternative lengthening of telomere ( ALT ) mechanism that relies on homologous recombination between telomeric sequences [15] , [16] . Dot1L mutant ES cells showed increased telomere heterogeneity ( Figure 4 ) , which is a hallmark of ALT cells [17] . To determine whether the ALT pathway is activated in Dot1L-deficient cells , we assessed the presence of ALT-associated PML bodies ( APBs , colocalization of PML and telomeres ) , another hallmark of ALT [17] . Dot1L+/+ , Dot1L1lox/1lox , as well as Dnmt3a−/−3b−/− ( positive control ) ES cells were immunostained with antibodies against TRF1 ( a telomere-binding protein ) and PML . In the absence of Dot1L , both the frequency of cells showing APBs and the number of APBs per cell were significantly increased compared to wild-type cells ( Figure 5 , A–C , χ2 tests , P<0 . 001 ) , suggesting that aberrant elongation of telomeres in Dot1L-deficient cells was due , at least in part , to activation of the ALT pathway . Aneuploidy and telomere elongation can result from defects in the chromatin structure at centromeres and telomeres , respectively [18]–[23] . To evaluate changes in chromatin structure in Dot1L mutant cells , we used chromatin immunoprecipitation ( ChIP ) to examine histone modifications at major satellite repeats ( present at pericentric regions ) , minor satellite repeats ( present at centromeric regions ) , telomeric repeats , and subtelomeric regions ( Figure 6 ) . H3K79 di-methylation was detected in all these heterochromatin regions in Dot1L+/+ cells ( Figure 6 , A and B ) . As expected , this modification was reduced in Dot1L1lox/+ cells and almost absent in Dot1L1lox/1lox cells ( Figure 6 , A and B ) , validating our experimental procedures . As further controls , the levels of centromere- and telomere-bound histone H3 were similar in wild-type and Dot1L mutant cells ( Figure 6 , A and B ) , and the telomere-binding protein TRF1 associated with telomeric repeats , but not with major satellite repeats ( Figure 6B ) . In Dot1L1lox/1lox cells , H4K20 tri-methylation , a hallmark of constitutive heterochromatin , was greatly reduced at minor satellite repeats and sub-telomere regions , and moderately reduced at major satellite repeats ( Figure 6A ) . Consistent with this observation , immunofluorescence analysis revealed the loss of enrichment of H4K20 tri-methylation at pericentric heterochromatin in the absence of Dot1L ( Figure S3 ) . H3K9 di-methylation , but not H3K9 tri-methylation , was reduced in all regions examined in Dot1L1lox/1lox cells ( Figure 6 , A and B ) . Concomitantly , H3K9 mono-methylation showed marked increases at major satellite repeats and minor satellite repeats ( Figure 6A ) , and H3K9 acetylation , a mark of euchromatin , was elevated in all regions examined ( Figure 6 , A and B ) . These changes appeared to be heterochromatin-specific , as all histone modifications examined , except H3K79 methylation , showed no global changes in Dot1L mutant cells ( Figure S4 ) . Dot1L deficiency did not cause alterations in DNA methylation at major satellite repeats and minor satellite repeats as well as other genomic regions such as the intracisternal A-particle ( IAP ) retroviral elements ( Figure S5 ) . Altogether , these results suggest that loss of H3K79 methylation results in a less compacted ( or more open ) chromatin state at centromeres and telomeres .
In this report , we provide genetic evidence that Dot1L and , by implication , H3K79 methylation are essential for mammalian development and normal cellular function . We show that loss of Dot1L results in yolk sac angiogenesis defects and embryonic lethality . Furthermore , our characterization of Dot1L-deficient ES cells reveals that , like in yeast , H3K79 methylation plays a critical role in heterochromatin structure in mammalian cells . Considering the differences in chromatin structure between yeast and mammals , the phenotypic similarities in mutants of these organisms are both striking and surprising . For example , both mutant organisms exhibit telomere elongation , but mammalian telomeres contain H3K79 methylated histones , while S . cerevisiae telomeres contain no histones at all . Furthermore , while ∼7% of the budding yeast genome is packaged as heterochromatin ( including rDNA ) , ∼55% of the mammalian genome is composed of heterochromatin [24] . Dot1L recruitment is coupled with gene transcription [25] and H3K79 methylation is enriched in euchromatin , which seem to be counterintuitive to the heterochromatin phenotype of Dot1L-deficient cells . One possible explanation is that Dot1L inactivation alters the expression of specific factors involved in heterochromatin assembly . Alternatively , global loss of H3K79 methylation may result in redistribution of heterochromatin factors , thereby reducing their relative abundance at constitutive heterochromatin . Indeed , loss of Dot1 in yeast leads to mislocalization of the Sir proteins , which promote heterochromatin formation and telomere silencing [7] , [8] . H3K9 tri-methylation and H4K20 tri-methylation are hallmarks of constitutive heterochromatin , such as that at centromeres and telomeres [20] , [21] , [23] , [26] , [27] . Based on the observation that H3K9 tri-methylation by the Suv39h methyltransferases is required for the induction of H4K20 tri-methylation by the Suv4-20h methyltransferases at pericentric heterochromatin , a sequential model of chromatin assembly at constitutive heterochromatin has been proposed in which Suv4-20h enzymes act downstream of the Suv39h enzymes [26] . Dot1L-deficient cells show loss of H4K20 tri-methylation at telomeres and centromeres , suggesting that Dot1L functions upstream of the Suv20h enzymes . Given that H3K9 tri-methylation shows no obvious alterations in Dot1L-deficient cells , it is possible that Dot1L acts in parallel or downstream of the Suv39h enzymes . Interestingly , despite the relatively normal levels of H3K9 tri-methylation , H3K9 di-methylation is severely reduced at constitutive heterochromatin in the absence of Dot1L . Because the total levels of H3K9 di-methylation and of several H3K9 methyltransferases ( Suv39h1 , ESET , and G9a ) are not altered in Dot1L-deficient cells ( Figure S4 ) , we speculate that Dot1L deficiency may affect the targeting of one or more H3K9 methyltransferases or demethylases to constitutive heterochromatin . Further studies will be required to elucidate the mechanisms by which Dot1L and H3K79 methylation regulate heterochromatin . Perturbation of epigenetic marks at constitutive heterochromatin has been shown to cause chromosome instability and telomere elongation [20]–[23] . Therefore , aberrant changes in chromatin at centromeres and telomeres most likely underlie the aneuploidy and telomere elongation observed in Dot1L-deficient ES cells . How the observed alterations in chromatin structure and cellular function contribute to the developmental abnormalities in Dot1L-deficient embryos is less clear . The requirement of Dot1L for normal cellular function does not appear to be ES cell-specific , as RNAi-mediated Dot1L knock-down in somatic cell lines also leads to growth arrest and cell death [13] . It is thus probable that intrinsic defects in cellular proliferation and viability , which themselves are likely the result of heterochromatin alterations , contribute to the growth defects and apoptosis observed in Dot1L mutant embryos . However , we believe that yolk sac defects are a major cause of embryonic lethality . In the absence of Dot1L , yolk sac angiogenesis is severely impaired . As both the endoderm and mesoderm cell layers of the visceral yolk sac are critical for blood vessel development [28] , [29] and both express Dot1L , we speculate that , in the absence of Dot1L , aberrant changes in gene expression and chromatin structure in one or both cell layers may underlie the yolk sac vascular defects . Some embryonic abnormalities , such as cardiac dilation , could be secondary to yolk sac vascular defects . Although Dot1L is also expressed in primitive erythrocytes , loss of Dot1L does not appear to have an obvious impact on erythropoiesis . It remains to be determined , however , whether the erythrocyte function is impaired .
The Dot1L conditional targeting vector , in which a 2 . 3-kb genomic region containing exons 5 and 6 was flanked by loxP sites , was constructed by sequentially subcloning Dot1L genomic fragments and a floxed βGeo cassette into pBluescript SK ( Stratagene ) . The Dot1L genomic fragments were generated by PCR using mouse genomic DNA as the template . The primer pairs used were: 5′-TTC ACT AGT CCC CAC CTT TGG ATT G-3′ and 5′-GGC ACT AGT GTC ACA CAC CTT TA-3′ for the 5′ arm , 5′-CAT GTC GAC ACC GTG TAG TCC TGG TGG GA-3′ and 5′-CTC GGC CGG CCT TGC CTG TGG CTG ACG-3′ for the 3′ arm , and 5′-GAC ACC GGT GCC TGG CAA CCT TTT GG-3′ and 5′-CTG GGC GCG CCA CCA GGA ACA CAC AGG TAC-3′ for the floxed region ( underlined are the restriction sites used for cloning ) . The identity of the vector was verified by DNA sequencing . The Dot1L conditional targeting vector was transfected into ES cells via electroporation , and transfected cells were selected with G418 . Clones with homologous recombination ( Dot1L3lox/+ ) were identified using Southern blot . Genomic DNA was digested with EcoRI and hybridized with a 5′ external probe ( The probe was generated by PCR using the following primers: 5′-CTC TGG TAC CTT TGT TGT TAT ACA G-3′ and 5′-CTC TCA AGT CGA CTG TAA GAT GAA G-3′ ) . Multiple Dot1L3lox/+ clones were used to generate chimeric mice and F1 heterozygotes . Deletion of exons 5 and 6 as well as the βGeo cassette was achieved by crossing Dot1L3lox/+ mice with Zp3-Cre transgenic mice , which express the Cre recombinase in the germline . Mutant mice were maintained on a C57BL/6 inbred or a C57BL/6-129Sv hybrid background . Primers used for PCR genotyping were: DF1: 5′-GGA ACT CAA GCT ATA GAC AG-3′ , DR1: 5′-CAC TGC CCA GGT CGA CAA ACA G-3′ , and DR2: 5′-ATC CTC TCT CCT GAG GAG GCA GC-3′ ( Figure 1 ) . Female mice in Dot1L1lox/+ intercrosses were examined for plug formation to establish the timing of copulation . Deciduas were isolated from euthanized females at various time points following copulation , and embryos were examined under a dissecting microscope . DNA from the yolk sac was used for genotyping by PCR using the primers described above . X-gal staining was performed on 7 . 5- to 12 . 5-dpc Dot1L3lox/+ embryos and littermates as previously described [30] . Embryo , yolk sac , and placental tissue specimens , which were harvested at 9 . 5-dpc , were fixed in Bouin's solution , washed extensively in 70% ethanol , processed routinely for paraffin embedding , sectioned at 5 µm , stained with hematoxylin and eosin , and then evaluated by bright field microscopy . Dot1L mutant ES cells were derived from blastocysts produced from intercrosses of Dot1L1lox/+ mice , as previously described [31] . Established ES lines were maintained in ES cell medium [32] . Apoptosis was analyzed using an Annexin V-PE apoptosis detection kit ( BD Pharmingen ) . Cell cycle analysis was done using a PI/RNase Staining Buffer ( BD Pharmingen ) . Immunoblot and indirect immunofluorescence analyses were carried out using standard procedures . The following antibodies were used: anti-H3K79Me1 ( Abcam ) , anti-H3K79Me2 ( Abcam ) , anti-H3K79Me3 ( Abcam ) , anti-H3 ( Millipore ) , anti-H3K4Me2 ( Millipore ) , anti-H3K4Me3 ( Millipore ) , anti-H3K9Me1 ( Millipore ) , anti-H3K9Me2 ( Millipore ) , anti-H3K9Me3 ( Millipore ) , anti-H3K27Me1 ( Millipore ) , anti-H3K27Me3 ( Millipore ) , anti-H3K9Ac ( Millipore ) , anti-H4K20Me3 ( Millipore ) , anti-H4Ac ( Millipore ) , anti-Suv39h1 ( Upstate ) , anti-ESET ( Upstate ) , anti-G9a ( Cell Signaling ) , anti-TRF1 ( Abcam ) , anti-PML ( Chemicon ) , Alexa 488-conjugated goat anti-rabbit IgG ( Molecular probes ) , Alexa 555-conjugated goat anti-mouse IgG ( Molecular Probes ) , and peroxidase-conjugated goat anti-rabbit and goat anti-mouse IgG ( Jackson ImmunoResearch Laboratories ) . Histone H3 purified from ES cells was digested with trypsin , and the resulting peptides were analyzed using a LTQ-FT mass spectrometer ( Thermo Fisher Scientific Inc . ) hyphenated with an Agilent 1200 HPLC system ( Agilent ) . Identification of the peptides was performed by searching the MS/MS fragmentation data against the histone H3 sequence using MASCOT search software ( Matrix Science , version 2 . 1 ) . All identifications were manually inspected for correctness . The abundance of each identified and validated peptide was calculated from its peak intensity using extracted ion chromatogram ( XIC ) of LC/MS spectra . Relative quantification of different forms of H3K79 methylation was performed by comparing the signal intensities of the tryptic peptide EIAQDFKTDLR at m/z 668 . 35 ( [MH2]2+ ) , EIAQDFKmeTDLR at m/z 675 . 36 ( [MH2]2+ ) , EIAQDFK2meTDLR at m/z 682 . 35 ( [MH2]2+ ) , and EIAQDFK3meTDLR at m/z 689 . 35 ( [MH2]2+ ) . To analyze telomere length , we performed Q-FISH and TRF analyses according to procedures described previously [22] . To prepare metaphase spreads , cells were incubated with 0 . 1 µg/ml of colcemid for 4 hours and then harvested and resuspended in 200 µl PBS . 10 ml of 75 mM KCl solution was added dropwise with constant gentle agitation . Cells were fixed by slow addition of 3∶1 methanol/acetic acid solution , and then dropped onto a microscope slide . Slides were washed in 70% acetic acid , stained with DAPI and mounted . Chromosome spreads were observed using a Zeiss fluorescence microscope . ChIP was performed using 20×106 ES cells as described in the online protocol provided by Upstate . Antibody sources are described above . Purified DNA was either analyzed with quantitative real-time PCR ( qPCR ) using Applied Biosystems SYBR PCR mastermix or used in a dot blot assay as described [22] . qPCR primers used were specific for major satellite repeats , minor satellite repeats [33] , and subtelomeric regions of chromosome 1 ( forward: 5′-TTA GGA CTT CTG GCT TCG GTA G-3′ , reverse: 5′-AGC TGT GGC AGG CAT CGT GGC-3′ ) and chromosome 2 ( forward: 5′-GAA TCC TCC CTG TAG CAG GG-3′ , reverse: 5′-GTA CAT AAC CGA TCC AGG TGT G-3′ ) . Relative enrichment was calculated as 2ˆ ( CT ( control CHIP ) - CT ( experimental CHIP ) ) , where CT is equal to the CT ( immunoprecipitated sample ) - CT ( input ) and normalized so that the wild-type value was 1 , with the exception of H3K9Me1 at major satellite repeats where the Dot1L1lox/1lox value was 1 . Each sample used in the dot blot contained DNA precipitated from 2 . 5×106 cells . Probes used were 32P-labelled oligonucleotides specific for telomeric repeats ( ( TTAGGG ) x11 ) and major satellite repeats ( 5′-TAT GGC GAG GAA AAC TGA AAA AGG TGG AAA ATT TAG AAA TGT CCA CTG TAG GAC GTG GAA TAT GGC AAG-3′ ) , respectively . Genomic DNA isolated from ES cells was digested with methylation-sensitive restriction enzymes and analyzed by Southern hybridization using probes specific for the major satellite repeats , the minor satellite repeats , and the intracisternal A particle retrovirus [32] , [34] . | Histone methylation plays a critical role in the regulation of gene expression and chromatin structure . Among the sites of histone methylation , lysine 79 of histone H3 ( H3K79 ) is unique in that it is not located within the H3 N-terminal tail but in the globular domain . Our knowledge about H3K79 methylation comes primarily from studies in yeast . This study focuses on the role of H3K79 methylation in mammalian development and cellular function . We show that genetic disruption of Dot1L , the only known H3K79 methyltransferase gene in mouse , results in embryonic lethality . At the cellular level , Dot1L deficiency leads to alterations in constitutive heterochromatin , accompanied by telomere elongation , aneuploidy , and proliferation defects . Our work represents a key step toward understanding the function of H3K79 methylation in mammals . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"genetics",
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"genomics/epigenetics",
"developmental",
"biology/embryology",
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] | 2008 | The Histone H3K79 Methyltransferase Dot1L Is Essential for Mammalian Development and Heterochromatin Structure |
Copy number polymorphism ( CNP ) is ubiquitous in eukaryotic genomes , but the degree to which this reflects the action of positive selection is poorly understood . The first gene in the Plasmodium folate biosynthesis pathway , GTP-cyclohydrolase I ( gch1 ) , shows extensive CNP . We provide compelling evidence that gch1 CNP is an adaptive consequence of selection by antifolate drugs , which target enzymes downstream in this pathway . ( 1 ) We compared gch1 CNP in parasites from Thailand ( strong historical antifolate selection ) with those from neighboring Laos ( weak antifolate selection ) . Two percent of chromosomes had amplified copy number in Laos , while 72% carried multiple ( 2–11 ) copies in Thailand , and differentiation exceeded that observed at 73 synonymous SNPs . ( 2 ) We found five amplicon types containing one to greater than six genes and spanning 1 to >11 kb , consistent with parallel evolution and strong selection for this gene amplification . gch1 was the only gene occurring in all amplicons suggesting that this locus is the target of selection . ( 3 ) We observed reduced microsatellite variation and increased linkage disequilibrium ( LD ) in a 900-kb region flanking gch1 in parasites from Thailand , consistent with rapid recent spread of chromosomes carrying multiple copies of gch1 . ( 4 ) We found that parasites bearing dhfr-164L , which causes high-level resistance to antifolate drugs , carry significantly ( p = 0 . 00003 ) higher copy numbers of gch1 than parasites bearing 164I , indicating functional association between genes located on different chromosomes but linked in the same biochemical pathway . These results demonstrate that CNP at gch1 is adaptive and the associations with dhfr-164L strongly suggest a compensatory function . More generally , these data demonstrate how selection affects multiple enzymes in a single biochemical pathway , and suggest that investigation of structural variation may provide a fast-track to locating genes underlying adaptation .
A spate of studies over the past five years have described widespread copy number variation ( CNP ) within the genomes of humans , mice , Drosophila and other eukaryotes [1]–[5] . The existence of large regions of the genome that vary in copy number between individuals has lead to a reconsideration of the importance of structural variation for our understanding of genetic and phenotypic variation [6] . However , it is unclear whether CNP evolution is predominantly neutral , or whether positive or negative selection play significant roles in shaping the patterns observed [1] . The fact that CNPs tend to be enriched for particular gene classes , and for genes showing evidence for positive selection at the nucleotide level , strongly suggests the action of positive selection [7] , although the alternative explanation of purifying selection against CNP in particular gene classes cannot be discounted . Furthermore , that CNPs explain ∼20% of variance in transcript abundance in humans suggests that they have the potential to make a significant contribution to disease susceptibility and adaptive evolution [8] . However , despite these indirect lines of evidence for positive selection , adaptive copy number evolution has been demonstrated or hypothesized in only a few cases . In humans there are two notable examples . Gonzales et al [9] showed that protection from HIV is associated with CNP at the CCL3L1 gene . This CNP shows extreme geographical variation which further supports the action of selection by HIV ( or , more likely , by an older human pathogen ) [10] . Similarly , Perry et al [11] showed higher copy number of the amylase gene in populations with high starch diets . CNP is also widespread in the malaria parasite genome [12] , [13] . Malaria parasites are exposed to strong selection from the human immune response and treatment with antimalarial drugs . They have relatively small genomes ( 23 Mb ) and haploid genetics , and can be grown and genetically manipulated in the laboratory , so provide a useful eukaryotic organism for investigating the functional role of CNP . One CNP on chromosome ( chr . ) 5 is known to underlie a multidrug resistance phenotype: chromosomes carrying this CNP have risen to high frequencies in Southeast Asia [14] , [15] and manipulation of copy number alters response to multiple drugs [16] . However , this one example of adaptive copy number variation in P . falciparum has been regarded as an exceptional case , and SNP based approaches have been prioritized as the primary tool for mapping functional genes in Plasmodium [17] . The first cGH study of P . falciparum in 16 laboratory isolates revealed a particularly interesting CNP containing GTP-cyclohydrolase I ( gch1 ) [12] . This gene encodes the first and rate limiting enzyme in the folate metabolism pathway ( Figure 1 ) [18] , [19] . Two key enzymes in later stages of this pathway–dihydrofolate reductase ( dhfr ) ( chr . 4 ) and dihydropteroate synthase ( dhps ) ( chr . 8 ) –are targets of the antifolate drugs pyrimethamine and sulfadoxine , which are combined in the drug Fansidar ( Roche ) . This drug replaced chloroquine as the first-line treatment against malaria in many countries , but resistance has spread rapidly where it has been deployed . Specific point mutations ( N51I , C59R , S108N , I164L ) in parasite dhfr alter the binding of pyrimethamine to the enzyme's active site [20] . In addition to causing resistance , mutations in dhfr reduce enzyme efficacy and carry adverse fitness effects [21] , [22] , [but see 23] . Similarly , mutations in dhps ( S436A/F , A437G , K540E , A581G , and A613T/S ) underlie resistance to sulfadoxine [20] . Kidgell et al [12] speculated that increased gene dosage might play a compensatory role in antifolate resistance by increasing flux in the pathway to compensate for reduced efficacy of dhfr and/or dhps genes bearing resistance mutations . This study was designed to determine whether CNP at gch1 is a consequence of adaptive evolution . To do this , we examined the population genetics of gch1 CNP in Laos and Thailand . These two neighboring countries in SE Asia have contrasting selection histories with antifolate drugs . In Western Thailand antifolate drugs were the first line treatment from 1970–80 , and resistance mutations at both dhps and dhfr are fixed or at high frequency [24] , [25] . By contrast , in Laos antifolate drugs were the second line drug until 2006 , but were seldom used [26] , and resistance mutations are at lower frequencies in both dhfr and dhps [25] , [27] . We provide compelling evidence–from patterns of population differentiation , hitchhiking , and amplicon structure–that the gch1 CNP ( chr . 12 ) in P . falciparum results from recent adaptive evolution that is most likely associated with antifolate treatment . Furthermore , we show strong association between gch1 CNP and a critical mutation in dhfr , indicating functional linkage between genes on different chromosomes in the same biochemical pathway .
We collected 5 mL blood samples from P . falciparum infected patients visiting the malaria clinic at Mawker-Thai on the Thai-Burma border between December 2000 and September 2003 . These patients had not visited the malaria clinic within the past 60 days , and had no history of prior malaria treatment during this time . The clinic serves people on both sides of the border; 90% of patients travel from within a 10 km radius around the clinic ( François Nosten , personal communication ) . We collected samples from Phalanxay ( Savannaket ) in Laos between June 2002 and Sept 2003 from patients involved in clinical drug efficacy trials . Collection protocols were approved by the Ethical Committee of the Faculty of Tropical Medicine , Mahidol University , Bangkok , and by the Institutional Review Board at the University of Texas Health Science Center at San Antonio . Parasite DNA was prepared by phenol/chloroform extraction of whole blood , following removal of buffy coats as described previously [28] . Malaria parasites are obligately sexual protozoans . Haploid blood-stage parasites replicate asexually within the human host and differentiate into male and female gametocytes . Male and female gametes fuse in the mosquito midgut generating a short lived diploid stage ( ookinete ) . Meiosis then gives rise to haploid infective stages ( sporozoites ) that are transmitted to humans during mosquito feeding . Self-fertilization predominates in regions of low transmission such as Thailand and Laos , because most infected humans , and therefore mosquito blood meals , contain a single parasite clone . The recombination rate is high ( 1 cM = 17 kb ) , but the effective recombination rate is modified by the rate of selfing in natural parasite populations . The effective rate of recombination ( r′ ) is given by r′ = r ( 1-F ) where r is the recombination rate and F is the inbreeding coefficient [29] , [30] . In Thailand the inbreeding coefficient is estimated to be between 0 . 6–0 . 9 [31] . The generation time ( from mosquito to mosquito ) is variable , but is estimated to be ∼8 weeks [31] . We used microsatellite genotyping to identify infections containing >1 parasite clone . We genotyped seven di- or tri-nucleotide markers distributed on different chromosomes: ARA2 ( chr . 11 ) , POLYα ( chr . 4 ) , TA1 ( chr . 6 ) , C2M1 ( chr . 2 ) , C3M54 ( chr . 3 ) , TA60 ( chr . 13 ) , and C4M30 ( chr . 4 ) . Primers and protocols for amplifying these loci are described in [32] . We amplified microsatellites using fluorescent end-labeled oligos , ran products on an ABI 3100 capillary sequencer , and scored alleles using GENESCAN and GENOTYPER software . Multiple clone infections were defined as those in which one or more of the seven loci showed multiple alleles . Minor alleles were scored if they were >33% the height of predominant alleles [33] . We genotyped 33 dinucleotide microsatellites on chr . 12 flanking gch1 to examine expected heterozygosity and LD . These included 10 markers situated within a 14 kb window containing gch1 . Oligos and locus details are listed in Table S1 . These were genotyped using the methods described above . However , for 17/33 loci we used the M13 tailing method [34] in which the fluorescent labeled M13 oligo ( tgtaaaacgacggccagt ) was added to the 5′ of the forward oligo to label PCR products . We used a real-time PCR assay to measure copy number of gch1 relative to single copy gene ( PFL0770w: Seryl-T synthetase ) . Assay details are provided in Table S2 . We measured amounts of gch1 or Seryl-T synthetase PCR products using Minor Groove Binding ( MGB ) probes ( Applied Biosystems , Foster City , CA ) . We used the ΔΔCT method to measure copy number relative to a standard calibrator sample . We initially used the sequenced parasite strain 3D7 as the calibrator . However , initial trials indicated that this parasite line has multiple copies of gch1 , as observed previously [12] . We therefore measured gene copy number relative to a field sample with a single copy of gch1 . All assays were run in quadruplicate in 10 µl volumes on 384-well plates on an ABI 7900HT real-time PCR machine . We excluded real-time PCR data when the CT was >32 for either test or reference gene , or if the upper 95% confidence interval around the copy number estimate is >0 . 4 , or if the estimated copy number was <0 . 5 . For some analyses the continuous real-time estimates were made discrete , by rounding to the nearest integer value . To ensure data comparability we run real-time assays for samples from Thailand and Laos on the same 384-well plates and included parasite 3D7 ( which had 4 . 5±0 . 36 ( 1 s . d . ) copies in 8 replicate assays ) as a positive control on each plate . We also used real time assays for genes flanking gch1 on chr . 12 to identify the size and gene content amplified chromosomal regions ( table S2 ) while we amplified and sequenced breakpoint specific PCR products ( table S3 ) to identify the precise position of the chromosomal breakage points ( Figure S1 ) using methods previously described [14] . We sequenced gch1 from 24 parasites with a single copy of gch1 from Laos and from 24 parasites from Thailand ( 8 with single copies and 16 with multiple copies ) . 1205 bp sequences were PCR amplified using the oligos TTCATTTAATGGACTGGAAA and GGCTAATTTAAATTTTCCAC . Amplified products were sequenced directly on both strands using the BigDye Terminator v3 . 1 cycle sequencing kit ( Applied Biosystems , Inc . , Foster City , CA ) and internal oligos CATTACTTTTATTTCCTTCC , CATCTTTTACCTTTTGAAGG , GAACAAATAGAAGATATGCTG , and CTTCAAAAGGTAAAAGATGG . BigDye products were cleaned using Sephadex G-50 ( Sigma-Aldrich Co . , St . Louis , MO ) prior to sequencing . We used primer extension to determine point mutations in dhfr and dhps associated with antifolate resistance in P . falciparum using protocols described previously [28] . Genotyping was performed by primer extension using the ABI PRISM SNaPshot Multiplex Kit ( Applied Biosystems ) and the products of the SNaPshot reactions were scored on an ABI 3100 capillary sequencer using GENESCAN and GENOTYPER software . This method allows simultaneous genotyping of all five mutations in dhfr or dhps in single reactions [28] . We used the Illumina BeadXpress platform to genotype 96 synonymous SNPs . Targeted SNPs were chosen using the query system in www . plasmodb . org . We searched for synonymous sites that were polymorphic in the genome sequence data from laboratory parasites Dd2 , FCC-2 , FCB , K1 , VI/S . These were selected from genes showing low levels of variation and dN/dS ratios <1 . We also excluded surface expressed genes and transporters from our searches , as well as genes in telomeric regions ( 100 kb from chromosome ends ) because these are enriched for antigenic loci . Only SNPs that were deemed designable by Illumina ( with SNP scores>0 . 4 ) were included ( table S4 ) . Genotyping was carried out according to the Illumina BeadXpress instructions , except we used 25 ng DNA rather than 250 ng starting DNA . We included parasites FCC-2 , FCB , K1 , VI/S as controls . Two SNPs were not scoreable , while 4 others gave high levels ( >5% ) of missing data . Five loci were monomorphic in the controls suggesting that they result from errors in the existing genome sequence data . Of the remaining 85 loci , 74 ( 87% ) were polymorphic in at least one sample , while 73 had minor allele frequencies >1% in at least one of the two populations examined . We compared gch1 gene expression in 6 parasite isolates that varied in gch1 copy number , to determine if expression is a reflection of gene dosage . Parasites were cultured at 90% N2 , 5% Co2 5% O2 in complete media containing albumax , synchronized using sorbitol , and aliquots were preserved in TriReagent ( Molecular Research Center Inc . ) for RNA extraction . We treated samples with DNase 1 ( Invitrogen ) to ensure no genomic DNA was present and confirmed absence of DNA by PCR . mRNA was transcribed using RNA PCR core kit ( Applied Biosystems ) , to generate cDNA . We quantified levels of cDNA with the Taqman assay run on the ABI PRISM 7900 using the oligos listed in Table S2 . We used t-tests and Mann-Whitney U-tests to investigate association between SNPs and CNP at gch1 . Copy number was log transformed for parametric analyses . We measured expected heterozygosity ( He ) at each locus using the formula He = [n/ ( n−1 ) ][1−Σpi2] , where n is the number of infections sampled and pi is the frequency of the ith allele . For haploid blood stage malaria parasites this statistic measures the probability of drawing two different alleles from a population . The sampling variance of He was calculated as 2 ( n−1 ) /n3{2 ( n−2 ) [Σpi3− ( Σpi2 ) 2]} [a slight modification of the standard diploid variance [35]] . To examine patterns of LD surrounding gch1 we measured Extended Haplotype Homozygosity ( EHH ) [36] where EHH at a distance x from the gene of interest is defined as the probability that two randomly chosen haplotypes are homozygous for all microsatellites in this region . These statistics ( ±1 SD ) were measured using the EHH Calculator [37] . We calculated FST following [38] using FSTAT2 . 9 . 3 [39] .
Initially , we sampled P . falciparum from infected patients visiting a single clinic on the Thailand-Burma border . Following removal of multiple clone infections 140 samples were available . We found between 1 and 11 copies of gch1 by taqman PCR with 72% of parasites sampled carrying >1 copy . To determine the arrangement and size of these genome amplifications we measured copy number in genes surrounding gch1 ( Figure 2a ) and designed PCR assays to identify chromosome breakpoints [14] ( Table S3 , Figure S1 ) . We found 5 different amplicon types containing one to >six genes ( Figure 2b ) . Breakpoint specific PCR assays indicate that these were arranged in tandem . However we note that duplicative transposition of some amplicons would not be detectable using this assay . The most common of these amplicons contained only gch1 and accounted for 48% of all amplicon types , while the largest amplicon ( >11 kb ) , was found in only one sample . We sequenced the chromosome breakpoints for 4 of the 5 amplicons . In each case breakpoints were found in between genes in microsatellite sequences or monomeric tracts ( Figure S1 ) . The amplicon size data provides a natural mapping experiment . Gch1 was the only gene observed in all amplicons . Hence , if CNP results from selection , then gch1 is clearly the gene that is targeted . Comparing patterns of geographical differentiation in neutral and putatively selected loci provides a powerful approach to identify loci that underlie adaptation [25] , [40] , [41] . This approach is based on the premise that allele distribution at neutrally evolving loci will be determined by mutation and drift alone , while selection will influence patterns observed at loci involved in adaptation . We measured gch1 copy number in parasites from Phalanxay ( Southern Laos ) for comparison with Thailand . These neighboring countries differ considerably in history of antifolate treatment [25] , [27] . In Thailand , there was intensive selection with antifolates from 1970–1980 [42] , whereas in Laos antifolates were the official second line treatment for malaria until 2006 , but in reality they were very seldom used [26] . Differences in treatment policies and antifolate selection are evident from patterns of polymorphism at dhfr and dhps . All parasite samples ( n = 139 ) examined from Thailand carried between 2 and 4 mutations at dhfr , and 80% of parasite isolates carried the dhfr-164L mutation , while at dhps all Thai parasites carried 2 or 3 mutations conferring resistance ( Figure 3 , Table S5 ) . In contrast , in Laos the dhfr-164L mutation was absent , and 23% of parasites carried wild type dhfr alleles , while 92% of parasites carried wild-type alleles at dhps ( Figure 3 , Table S5 ) . While 72% of Thai parasites carry >1 copy of gch1 , in Laos we found just 2/122 ( 1 . 6% ) parasites with >1 gch1 copy ( Figure 3a ) . Hence , there is strong differentiation ( FST = 0 . 67 [grouping chromosomes with single or multiple copies of gch1] ) between parasites sampled ∼500 km apart . The real-time copy number data is supported by PCR assays of chromosome breakpoints for 4 of the 5 amplicon types ( Table S3 ) . Using these assays , we detected tandem repeats in 72% of Thai samples , but in only 1% of samples from Laos . One of the two parasites from Laos with >1 copy of gch1 carried the 1 . 8 kb amplicon , while the other had the >11 kb amplicon real time PCR profile ( Figure 2 ) . To compare differentiation at this gch1 CNP with neutral expectations , we genotyped 96 synonymous SNPs ( sSNPs ) situated on all 14 chromosomes ( Table S4 ) in the same population samples . These sSNPs were located in genes with dN/dS ratios <1 and were polymorphic in genome sequence data from SE Asian parasites [43] , [44] . The gch1 CNP shows greater differentiation than all 73 polymorphic sSNPs ( defined as those loci with >1% frequency of the minor allele in at least one population ) consistent with strong local adaptation ( Figure 3d ) . Caution is needed when comparing FST at SNPs and CNPs because both the rate and the directionality of mutation are likely to be radically different . However , we suggest that comparing SNPs with CNP will provide a conservative test , because copy number mutations typically occur at higher rates than SNPs [45] and may show frequent reversions to the single copy state . Hence , homoplasy is expected to reduce geographical differentiation at CNPs . The geographical distribution of gch1 CNP is suggestive of strong local adaptation , but on its own does not constitute proof . Furthermore , the high frequency of gch1 CNP in Thailand is consistent with the hypothesis that amplification is driven by antifolate treatment . If chromosomes carrying gch1 CNP have been under strong recent selection in Thailand , we would predict that genetic diversity would be reduced and LD increased in the vicinity of gch1 on chr . 12 . We measured length variation at 33 di-nucleotide microsatellite markers ( Table S1 ) distributed across chr . 12 in parasites from the Thai-Burma border and Laos . These included a cluster of 11 markers within 14 kb of gch1 . Expected heterozygosity ( He ) was high across chr . 12 in Laos ( mean = 0 . 85 , s . d = 0 . 08 ) and at markers situated far from gch1 in Thai parasites . However , for the 11 markers flanking gch1 variation was halved in Thai parasites ( He ( ±s . d . ) = 0 . 83 ( ±0 . 07 ) in Laos vs He = 0 . 41±0 . 16 in Thailand ) ( Figure 4 ) and variation was reduced in Thailand for 600–950 kb ( 40–63 cM ) . These data are consistent with strong recent selection acting on gch1 CNP . Comparison of this selective event with selective sweeps around known drug resistance genes provides a means to assess the strength of this selective event . We have previously described patterns of microsatellite variation around dhfr ( chr . 4 ) , the chloroquine resistance transporter ( pfcrt ( chr . 7 ) using parasites collected from the same clinic [27] . We observed significant reduction in variation for 98–137 kb ( 6–8 cM ) around dhfr , and for 195–268 kb ( 11–16 cM ) around pfcrt . The window of reduced variation is considerably larger around gch1 , emphasizing that selection on this locus was strong and recent . Examination of extended haplotype homozygosity ( EHH ) also reveal striking differences in LD between parasites from Thailand and Laos . In Laos , haplotype blocks are short and EHH decays to <0 . 02 within 3 kb on either side of gch1 . In contrast , haplotype blocks extend largely unbroken for 63 kb ( 4 cM ) around gch1 in Thai parasites with multiple copies of each of the three predominant amplicon types bearing gch1 . These data are consistent with rapid recent spread of this CNP in Thailand ( Figure 5 ) . We used the amplicon sizes and microsatellite haplotypes in markers flanking gch1 to infer evolutionary origins of these gene amplification events . Amplification of gch1 CNP has occurred at least three times in Thailand . The 2 . 2 kb , 8 . 7 kb , and >11 kb amplicon share identical or similar backgrounds , suggesting that they share a common origin . We counted the mean proportion of shared alleles between all pairwise combinations of parasites carrying the 2 . 2 kb , 8 . 7 kb , and >11 kb amplicon for the 10 markers ( 12_970980–12_984557 , Table S1 ) immediately flanking gch1 . 94 . 6–98 . 1% of alleles were shared between chromosomes bearing these three amplicon types ( Table 1 ) . In contrast , the markers flanking the 1 . 7 and 7 . 3 kb amplicons were divergent from one another ( 42 . 0% shared alleles ) and from the 2 . 2 and 8 . 7 and >11 kb amplicons ( 33 . 8–38 . 0% alleles shared ) . The simplest explanation is that the 2 . 2 kb , 8 . 7 kb , and >11 kb amplicon share the same origin , and that the 1 . 7 kb and 7 . 3 kb amplicons have evolved independently . The observation that three amplicons share the same flanking markers suggests that initially large amplicons have been progressively reduced in size . Such amplicon size reduction has been observed previously in E . coli [46] . In this case , deletion of genes from large amplicons results in increased growth rates and higher fitness . We expect that similar deleterious fitness effects of large amplicons may lead to progressive reduction in amplicon size in this system . Interestingly , chr . 12 bearing a single copy of gch1 from Thailand show surprisingly strong LD compared with single copy chromosomes from Laos . Furthermore , haplotypes surrounding gch1 on single copy chromosomes are similar to those seen in chromosomes bearing the 2 . 2 kb , 8 . 7 kb , and >11 kb amplicons ( Figure 5b ) . These observations suggest that chromosomes carrying multiple copies of gch1 frequently revert to single copy status . Reversion is predicted to be common in the evolution of tandem amplifications [10] and contrasts with SNPs where reversion is rare . Importantly , such reversion events will limit the power of within-population long range haplotype tests [36] to detect evidence for selection and will reduce LD between CNP and flanking SNPs reducing the power of SNP based genome wide association studies [1] . In the previous paragraph we argued that three of the five amplicons have a common origin and reversion to single copy status is frequent . However we cannot prove this scenario with the current data and less parsimonious alternative evolutionary scenarios are possible . For example , one haplotype could have spread to high frequency prior to copy number amplification . The three different amplicon types may have then evolved independently on the same genetic background . We have assumed so far that CNP itself is adaptive . However , it is conceivable that CNP is linked to SNPs in gch1 , which are the true target of selection . We therefore sequenced 48 gch1 alleles . These sequences ( 940 bp ) were from 24 Thai samples , representing all five amplicon types as well as single copy gch1 , and from 24 single copy gch1 alleles from Laos . Sequence polymorphism can be difficult to detect in samples with high gch1 copy number . However , of 16 samples with multiple copies of gch1 sequenced only 3 carried >4 copies , so we do not believe our ability to detect sequence variants was severely impaired . We found just one non synonymous SNP ( G→T , M169I ) in one each of the Thai and Lao samples , demonstrating low levels of nucleotide variation in this gene . This sequence homogeneity supports the argument that copy number , rather than associated coding SNPs , are targeted by selection . The demonstration that a derived gch1 CNP has rapidly spread to high frequency within Thai parasite populations , but not in neighboring populations from Laos , provides strong evidence that gch1 is adaptive , but provides few clues about the nature of the selection involved . We reasoned that if gch1 CNP is involved directly or indirectly in resistance to antifolate drugs , we might expect to see genetic evidence for interactions with genes involved in resistance downstream in the folate biosynthesis pathway . We therefore examined associations between gch1 copy number and known mutations that underlie antifolate resistance in dhfr ( chr . 4 ) and dhps ( chr . 8 ) . In Thailand parasites bearing dhfr-164L carried significantly higher copy number of gch1 ( t = −4 . 313 , p = 0 . 000026 ) than those bearing dhfr-164I ( Figure 6a ) . More marginal associations were also observed for dhfr-N51I ( t = −1 . 964 , p = 0 . 051 ) and two sites ( A436S and A581G ) in dhps ( t = −2 . 184 , p = 0 . 033 ) in perfect LD . To empirically test the significance of the dhfr-164L result we compared associations between gch1 copy number and 55 sSNPs with minor allele frequency >5% in Thailand . The dhfr-164L association was the strongest observed and remained significant after correction for multiple tests , arguing that this association cannot be explained by population structure ( Figure 6b ) . These results reveal the genetic signature of functional interaction ( epistasis for fitness ) between two physically unlinked genes in the same biochemical pathway , providing strong evidence that gch1 CNP results either directly or indirectly from antifolate selection . In Thailand , antifolate drugs were abandoned as first line therapy ∼25 years ago , and are rarely used in neighboring Burma [24] , although low levels of indirect selection may result from use of a related antifolate drug ( Cotrimoxazole ) used for treatment of bacterial infections [47] . Because reassortment and recombination during meiosis breaks down LD between genes , there must be strong selection favoring association of gch1 CNP and dhfr-164L in the absence of sustained antifolate selection . Hence , these data are consistent with the hypothesis that gch1 CNP compensates for reduced efficiency of dhfr-164L [12] . Selection could increase gch1 copy number within infections carrying dhfr-164L . Alternatively , recombinants bearing dhfr-164L/wild-type gch1 or dhfr-164I/gch1 CNP may suffer fitness costs and fail to establish infections . Compensatory evolution involving amplification of initiator tRNA genes has previously been observed during laboratory evolution of Salmonella . In this case , amplification mitigates fitness costs of point mutations conferring resistance to deformylase inhibitors [48] . The associations observed between dhfr-164L and elevated gch1 copy number in Plasmodium are all the more remarkable , because they are maintained in the face of recombination . The strength of selection required to maintain multilocus allelic combinations depends on levels of inbreeding , because this determines the rate at which recombination breaks up such combinations . In the Thai population ∼40% of infections contained multiple parasite clones , consistent with high levels of inbreeding ( f = 0 . 6–0 . 90 [31] . Therefore , selection must be sufficiently strong to overcome breakdown of favorable allele combinations in 10–40% of the parasite population during each generation . LD is rarely observed between unlinked genes in recombining organisms: it is possible in this situation because outcrossing is rare and selection is strong . The data presented provides strong evidence that gch1 CNP is adaptive and associated with antifolate drug selection . An important assumption made is that gene dosage is reflected in increased transcription of amplified genes . To test this assumption directly , we grew six parasites in the laboratory that vary in gch1 copy number , and harvested mRNA at 4 different time periods in the asexual cycle . We observe strong correlations between copy number and expression in all stages of the cycle validating this assumption ( Figure 7 ) . Hence , increased expression of gch1 has the potential to increase flux in the folate pathway . However , detailed dissection of protein flux through the pathway will be required to fully understand the impact of copy number of the dynamics of folate biosynthesis in Plasmodium . The evolution of resistance to antifolate drugs has been assumed to have a simple genetic basis , because a small number of point mutations are involved and resistance can be selected in the laboratory [49] . However , molecular genetic data from regions flanking dhfr demonstrate surprisingly few origins and intercontinental movement of high level resistance alleles [31] , [50] . Furthermore , while parasites carrying the three mutations in dhfr are now widespread in Africa , the I164L mutation , which signals the end of the useful life of pyrimethamine-sulfadoxine treatment , has not spread on this continent . Our results demonstrate that adaptation to antifolate selection is more complex than has previously been recognized , with involvement of three different components of the folate pathway ( dhfr , dhps and gch1 ) . This result parallels recent identification of multiple selected genes within metabolic pathways involved in skin pigmentation , hair and exocrine development , and lassa fever susceptibility in humans [51] . Hence , involvement of gch1 CNP is consistent with a multilocus model for antifolate resistance evolution . The precise manner in which antifolate treatment selects for increased copy number at gch1 remains unclear . There are two possible explanations . First , gch1 amplification may be directly selected by antifolate treatment . In this case parasites bearing multiple copies of gch1 might be expected to show higher levels of resistance than those carrying a single copy . We have not tested this hypothesis , which we believe is unlikely . If this were the case then we would not expect to see associations with dhfr in the absence of drug selection . Second , as envisaged by Kidgell et al [12] , gch1 amplification may compensate for reduced efficacy of dhfr and/or dhps enzymes bearing resistance mutations downstream in the biosynthesis pathway . Kidgell et al [12] favored compensation for fitness effects of mutations in dhps . However , the associations we observe between dhfr-164 and gch1 CNP argues strongly for involvement with dhfr . Ultimately , manipulation of gch1 copy number will be required to determine the phenotypic impact of gene amplification . We caution that the effects of copy number manipulation may be strongly dependent on parasite genetic background . Hence transfection experiments designed to better understand gch1 amplification will need to carefully consider background mutations present in dhfr and dhps and measure fitness in both the presence and absence of drug treatment . There are now six genes known that are involved in adaptation to drug treatment in P . falciparum . These include pfcrt ( Chloroquine/quinine resistance ) , dhfr ( pyrimethamine resistance ) , dhps ( sulfadoxine resistance ) , pfmdr1 ( resistance to multiple drugs ) , mtDNA cytochrome b ( atovaquone resistance ) [52] and gch1 . CNP is involved in two ( pfmdr1 and gch1 ) of these six genes ( 33% ) . We suggest that high frequency CNPs will be enriched for genes involved in adaptation , as is the case for high frequency derived SNPs [51] . Such CNPs can easily be located by cGH surveys of different parasite populations . The observation that CNP is involved in resistance to drug treatment in other pathogenic protozoa , bacteria , and cancers [53]–[55] , provides further empirical evidence that CNP may be a common evolutionary response to strong selection . There is currently a strong community-wide effort to generate a high density SNP map for P . falciparum to help identify the genetic determinants of drug resistance and virulence by genome wide association [17] . Such SNP based screens may succeed in detecting functional CNPs . However , SNP maps in humans tend to be sparse in regions of CNP and LD between CNP and flanking SNPs is often minimal , so it is much better to determine CNP directly [1] , [56] . Fortunately , this is now relatively easy using either microarray or “next generation” sequence technology . We suggest that the combination of direct assessment of CNP with dense genome-wide SNP data [6] is likely to provide a particularly powerful approach to understand the genetic determinants of adaptation to drug treatment and other selective forces in malaria parasites and other pathogens . | Recent comparative genomic hybridization studies have revealed extensive copy number variation in eukaryotic genomes . The first gene in the Plasmodium folate biosynthesis pathway , GTP-cyclohydrolase I ( gch1 ) , shows extensive copy number polymorphism ( CNP ) . We provide compelling evidence that gch1 CNP is adaptive and most likely results from selection by antifolate drugs , which target enzymes downstream in this pathway . Gch1 CNP shows extreme geographical differentiation; hitchhiking reduces diversity and increases LD in flanking sequence , indicating recent rapid spread within Thailand , while amplicon structure reveals multiple origins and parallel evolution . Furthermore , strong association between elevated copy number and a critical mutation dhfr-I164L that underlies high-level antifolate resistance indicates functional linkage and fitness epistasis between genes on different chromosomes . These data reveal hidden complexity in the evolutionary response to antifolate treatment and demonstrate that analysis of structural variation can provide a fast-track to locating genes that underlie adaptation . | [
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] | 2008 | Adaptive Copy Number Evolution in Malaria Parasites |
The loss of dopaminergic neurons is a hallmark of Parkinson’s disease , the aetiology of which is associated with increased levels of oxidative stress . We used C . elegans to screen for genes that protect dopaminergic neurons against oxidative stress and isolated glit-1 ( gliotactin ( Drosophila neuroligin-like ) homologue ) . Loss of the C . elegans neuroligin-like glit-1 causes increased dopaminergic neurodegeneration after treatment with 6-hydroxydopamine ( 6-OHDA ) , an oxidative-stress inducing drug that is specifically taken up into dopaminergic neurons . Furthermore , glit-1 mutants exhibit increased sensitivity to oxidative stress induced by H2O2 and paraquat . We provide evidence that GLIT-1 acts in the same genetic pathway as the previously identified tetraspanin TSP-17 . After exposure to 6-OHDA and paraquat , glit-1 and tsp-17 mutants show almost identical , non-additive hypersensitivity phenotypes and exhibit highly increased induction of oxidative stress reporters . TSP-17 and GLIT-1 are both expressed in dopaminergic neurons . In addition , the neuroligin-like GLIT-1 is expressed in pharynx , intestine and several unidentified cells in the head . GLIT-1 is homologous , but not orthologous to neuroligins , transmembrane proteins required for the function of synapses . The Drosophila GLIT-1 homologue Gliotactin in contrast is required for epithelial junction formation . We report that GLIT-1 likely acts in multiple tissues to protect against 6-OHDA , and that the epithelial barrier of C . elegans glit-1 mutants does not appear to be compromised . We further describe that hyperactivation of the SKN-1 oxidative stress response pathway alleviates 6-OHDA-induced neurodegeneration . In addition , we find that mutations in the canonical apoptosis pathway and the calcium chaperone crt-1 cause increased 6-OHDA-induced dopaminergic neuron loss . In summary , we report that the neuroligin-like GLIT-1 , the canonical apoptosis pathway and the calreticulin CRT-1 are required to prevent 6-OHDA-induced dopaminergic neurodegeneration .
Dopamine acts as a neurotransmitter to control attention , cognition , motivation and movement [1] . The progressive loss of dopaminergic neurons in the substantia nigra of the midbrain is a hallmark of Parkinson’s disease , the second most common neurodegenerative disorder ( for review [2 , 3] ) . The etiology of the disease is largely unknown and there is no causative treatment for Parkinson’s disease to date ( for review [4] ) . Less than 10% of Parkinson’s patients report a positive family history while the majority of instances are sporadic [3] . Most Parkinson’s disease cases appear to be evoked by the interplay of one or multiple genetic defects and environmental factors [4] . A major risk factor for both genetic and sporadic forms of Parkinson’s disease is oxidative stress , the imbalance between the production of reactive oxygen species and the antioxidant response ( for review [5] ) . Reactive oxygen species can damage cellular components and ultimately cause cell loss [5] . However , it is not clear which pathways mediate dopaminergic cell death after oxidative stress exposure ( for review [6] ) . The gradual loss of dopaminergic neurons can be modelled by exposure to the oxidative stress-inducing drug 6-hydroxydopamine ( 6-OHDA ) ( for review [7] ) . 6-OHDA is an hydroxylated dopamine analogue which is specifically taken up into dopaminergic neurons by the dopamine transporter and which inhibits complex I of the mitochondrial electron transport chain [7] . The resulting formation of reactive oxygen species is assumed to trigger 6-OHDA-induced neurodegeneration [7] . 6-OHDA was found in human brain samples [8] and in higher concentrations in the urine of Parkinson’s patients [9] . 6-OHDA exposure of C . elegans leads to the selective loss of dopaminergic neurons [10] . The components of dopaminergic synaptic transmission are highly conserved in C . elegans ( for review [11] ) . The nematodes’ dopaminergic neurons are not required for viability and overt movement but they mediate subtle motor behaviours [11] . C . elegans hermaphrodites possess eight dopaminergic neurons , all of which have mechanosensory roles [12]: four CEP ( cephalic sensilla ) and two ADE ( anterior deirids ) neurons in the head , and two PDE ( posterior deirids ) neurons in the midbody . These neurons can be visualised by expression of a fluorescent protein from the promoter of the dopamine transporter [11] . Upon 6-OHDA exposure , dopaminergic neurons display blebbed processes , dark and rounded cell bodies and chromatin condensation , and eventually degenerate [10] . The pathways mediating 6-OHDA-induced dopaminergic cell death are unknown . Interference with the core apoptosis machinery does not prevent 6-OHDA-induced dopaminergic neurodegeneration; however , affected cells do not show typical morphological features for necrotic cell death either [10] . We used the C . elegans 6-OHDA model of dopaminergic neurodegeneration to screen for genes that protect from oxidative stress . Previously , this approach led to the characterisation of the tetraspanin TSP-17 which acts in dopaminergic neurons and which alleviates 6-OHDA-induced neurodegeneration [13] . In this study , we report that the previously uncharacterised neuroligin-like gene glit-1 prevents 6-OHDA-induced dopaminergic cell death . glit-1 likely acts in the same genetic pathway as tsp-17: glit-1 and tsp-17 mutants are short-lived , exhibit similarly increased expression of oxidative stress reporters and show basically identical sensitivity upon exposure to oxidative stress-inducing compounds . We further found that the apoptosis pathway and the calcium chaperone CRT-1 protect dopaminergic neurons from 6-OHDA-induced cell death .
Animals carrying mutations in neuroprotective genes are expected to exhibit increased cell death when exposed to a low dose of a neurodegenerative drug . To find genes that prevent dopaminergic neuron death , a C . elegans strain with GFP-labelled dopaminergic neurons ( Fig 1A ) was mutagenised with ethyl methanesulfonate . The F2 progeny of these mutagenised animals was exposed to the neurodegenerative drug 6-hydroxydopamine ( 6-OHDA ) at a dose that does not induce cell death in wild-type animals . We treated C . elegans synchronised at the first larval stage ( L1 ) , the first developmental phase after egg hatching at which the ADE and CEP dopaminergic head neurons are already fully differentiated . 72 hours after 6-OHDA treatment , the now adult population was screened for mutant animals exhibiting excessive loss of dopaminergic head neurons . We isolated the gt1981 mutant line , which exhibited highly increased 6-OHDA-induced dopaminergic neurodegeneration when L1 , L2 or L3 stage larvae were exposed to 6-OHDA ( Fig 1B ) . Later stages were not severely affected by gt1981 mutation ( Fig 1B ) . We further found that the general healthiness of gt1981 mutants is not compromised following 6-OHDA exposure as determined by measuring animal development: gt1981 mutants exhibited slightly slower growth than wild-type animals , but this delay was not exacerbated following 6-OHDA treatment ( S1A Fig ) . Thus , the gt1981 mutation affects dopaminergic neuroprotection , but not the overall development of C . elegans larvae after 6-OHDA exposure . Combining whole-genome sequencing and single nucleotide polymorphism mapping [14 , 15] , we determined that gt1981 animals carry an amino acid-changing mutation in glit-1 ( gliotactin ( Drosophila neuroligin-like ) homologue ) ( Fig 1E ) . The glit-1 ( gt1981 ) 6-OHDA hypersensitivity phenotype was confirmed by the glit-1 ( gk384527 ) splice-site mutation and the glit-1 ( ok237 ) promoter deletion ( Fig 1C and 1E ) . The glit-1 ( gk384527 ) allele did not complement gt1981 as glit-1 ( gk384527 ) /gt1981 transheterozygous mutants were still hypersensitive to 6-OHDA ( S1B Fig ) . To determine if the glit-1 ( gt1981 ) mutation behaves in a dominant or in a recessive fashion , glit-1 ( gt1981 ) /glit-1 ( gt1981 ) homozygous mutants were crossed to wild-type ( + ) animals . We found that the glit-1 ( gt1981 ) /+ heterozygous F1 progeny did not exhibit increased 6-OHDA-induced neurodegeneration ( S1C Fig ) . We conclude that loss-of-function of the neuroligin-like gene glit-1 causes increased degeneration of dopaminergic neurons after exposure to 6-OHDA . Neuroligins are postsynaptic cell adhesion proteins required for the correct function of synapses ( for review [16] ) . The neuroligin protein family is defined by an extracellular carboxyesterase-like domain that is enzymatically inactive [16] . The neuroligin-like GLIT-1 is predicted to contain this conserved extracellular carboxyesterase-like domain , as well as a single-pass transmembrane domain and a short intracellular segment ( S2 Fig ) . Phylogenetically , GLIT-1 groups with invertebrate esterases and its Drosophila homologue Gliotactin ( S3 Fig ) . In the predicted carboxyesterase-like domain of GLIT-1 , two aspartates and an arginine replace the characteristic catalytic triad of acetylcholinesterases ( Fig 1D , S4 Fig , S5B–S5D Fig ) . Furthermore , the glit-1 ( gt1981 ) mutation that causes 6-OHDA sensitivity results in a proline to leucine amino acid substitution in the GLIT-1 carboxyesterase-like domain ( Fig 1D , S5A Fig ) . This proline is likely important for protein function as it is conserved in neuroligins and acetylcholinesterases alike ( S5A Fig ) . In summary , the gt1981 mutation affects a conserved residue in GLIT-1 , a previously uncharacterised C . elegans neuroligin-like protein . Inhibition of neuronal 6-OHDA uptake by mutation of the C . elegans dopamine transporter DAT-1 prevents 6-OHDA-induced dopaminergic neurodegeneration in wild-type animals [10] . We thus tested if DAT-1 is also required for 6-OHDA hypersensitivity conferred by glit-1 mutation and found that this is the case as glit-1;dat-1 double mutants were resistant to 6-OHDA ( S1D Fig ) . To determine where GLIT-1 is expressed , we generated GFP reporter constructs . A Pglit-1::gfp transcriptional construct revealed that GLIT-1 is expressed in the pharynx , the intestine , and in several cells in the head including dopaminergic neurons ( S6F–S6H Fig ) . N-terminal GFP-tagging of GLIT-1 protein confirmed this expression pattern ( Fig 2A and 2B and S6A–S6E Fig ) . GFP::GLIT-1 expression could be clearly detected in dopaminergic neurons in L4 stage larvae and adult animals , but such expression could not be ascertained at earlier developmental stages , possibly due to low levels of GLIT-1 expression ( Fig 2C–2F , S6C–S6E Fig , S1 Movie ) . We note that N-terminal GFP-tagging appears to render GLIT-1 non-functional , likely by compromising the N-terminal membrane targeting sequence: GFP::GLIT-1 localised to the cytoplasm and not the plasma membrane . Accordingly , the construct did not rescue the 6-OHDA hypersensitivity of glit-1 ( gt1981 ) mutants ( S7A Fig ) . Furthermore , a targeted chromosomal GFP insertion at the GLIT-1 C-terminus did not reveal any expression . In summary , transcriptional and translational reporter constructs show that GLIT-1 is expressed in the pharynx , the intestine and in several cells in the head , including dopaminergic neurons . We next aimed to determine in which tissues GLIT-1 acts to protect against toxicity conferred by 6-OHDA . An integrated , single-copy construct driving glit-1 expression under its own promoter almost completely prevented dopaminergic neuron loss ( Fig 3A ) . In contrast , single-copy expression of glit-1 in dopaminergic neurons , the pharynx and the intestine , respectively , did not alleviate 6-OHDA-induced neurodegeneration of glit-1 mutants ( Fig 3B and 3C ) . We conclude that GLIT-1 likely acts in several tissues to protect against dopaminergic neurodegeneration after 6-OHDA exposure . To test if GLIT-1 overexpression protects wild-type animals against dopaminergic neuron loss after 6-OHDA exposure , we analysed two independent transgenic lines carrying Pglit-1::glit-1 extrachromosomal arrays . The extent of neurodegeneration in these transgenic animals was comparable to that of wild-type animals , suggesting that overexpression of glit-1 does not confer protection against 6-OHDA ( S7B Fig ) . Previously , we have shown that mutation of the C . elegans tetraspanin tsp-17 renders dopaminergic neurons hypersensitive to 6-OHDA [13] . To determine if GLIT-1 and TSP-17 act in the same genetic pathway , we generated the glit-1 ( gt1981 ) ;tsp-17 ( tm4995 ) double mutant and found that it exhibits a similar extent of 6-OHDA-induced dopaminergic neurodegeneration as the glit-1 and tsp-17 single mutants ( Fig 3D ) . Hence , GLIT-1 and TSP-17 are likely to function in the same genetic pathway to protect from dopaminergic neuron loss after exposure to 6-OHDA . We used qRT-PCR to determine if expression of tsp-17 and glit-1 is induced after 6-OHDA treatment . However , we did not find altered tsp-17 and glit-1 transcript levels in wild-type and tsp-17 and glit-1 mutant larvae ( S7C–S7F Fig ) , suggesting that tsp-17 and glit-1 provide constitutive protection against oxidative stress . This hypothesis is consistent with the fact that we did not determine a protective effect of glit-1 overexpression ( S7B Fig ) . The glit-1 Drosophila homologue Gliotactin is localised at tricellular junctions and necessary for the development of septate junctions , an invertebrate analogue of tight junctions in epithelial cells [17 , 18] . To address if glit-1 mutation might compromise the epithelial barrier function of the C . elegans intestine , we tested if a blue dye ( ‘Smurf’ assay ) or fluorescein leak into the body cavity after ingestion [19 , 20] . We did not find an increased intestinal permeability in glit-1 mutants as compared to wild-type animals ( S8 Fig and S9 Fig ) . We note that the molecular weight of fluorescein ( Mw = 376 . 2 g/mol ) , the smaller one of the two dyes , is still slightly higher than the molecular weight of 6-OHDA ( Mw = 205 . 64 g/mol ) . Thus , we cannot fully exclude the possibility of increased organismal 6-OHDA uptake in glit-1 mutants , albeit this appears unlikely . C . elegans possesses two neuroligin genes: The neuroligin-like glit-1 , which forms the centre of this study , and the neuroligin nlg-1 . NLG-1 has been shown to mediate oxidative stress protection [21 , 22] and is transcriptionally induced by the oxidative stress ‘master regulator’ SKN-1 ( skinhead ) /Nrf [22] . To test for genetic interactions between glit-1 and nlg-1 , we scored dopaminergic neurodegeneration in single and double mutants after treating animals with 0 . 75 mM 6-OHDA , a concentration that leads to a loss of approximately 50% dopaminergic head neuron in glit-1-mutants ( S10A Fig ) and thus allows for assessing increased or decreased neurodegeneration . We found that nlg-1 mutation does not influence 6-OHDA-induced dopaminergic neurodegeneration in wild-type or glit-1 mutant animals ( S10B and S10C Fig ) . As neuroligins generally modulate signalling by contacting pre-synaptic neurexins [16] , we further asked if glit-1 genetically interacts with the C . elegans neurexin nrx-1 . We tested two different neurexin deletions; nrx-1 ( wy778 ) affecting long and short isoform of the gene , and nrx-1 ( ok1649 ) affecting only long neurexin isoforms . We found that while nrx-1 ( wy778 ) mutation does not influence 6-OHDA-induced neurodegeneration , nrx-1 ( ok1649 ) mutation slightly alleviated dopaminergic neuron loss in glit-1 mutants and wild-type animals alike ( S10B–S10D Fig ) . These results indicate that there are no specific genetic interactions between the neuroligin-like glit-1 and the neuroligin nlg-1 or between glit-1 and neurexin that affect 6-OHDA-induced dopaminergic neuron loss . As glit-1 and tsp-17 are both expressed in dopaminergic neurons , we aimed to determine if the hypersensitivity of glit-1 mutants to 6-OHDA is altered by mutations affecting dopamine metabolism ( simplified cartoon in S11F Fig , for review [23] ) . However , we did not find that dopamine receptor mutations specifically alter 6-OHDA-induced dopaminergic neurodegeneration in the glit-1 mutant background ( S11A , S11D and S11E Fig , S1A Text ) . We further found that some mutants defective in dopamine synthesis can lead to increased 6-OHDA-induced neurodegeneration specifically in glit-1 mutants ( S11B–S11E Fig ) . At present , we cannot explain the partly contradictory effects we found ( S1B Text ) . To assess in more detail if the signalling of dopaminergic neurons is altered by glit-1 and tsp-17 mutation , we tested several behaviours that are mediated by dopamine . We firstly found that dopamine signalling appears to be functional in the mutants [13] ( S12A Fig ) by analysing the basal slowing response , the dopamine-mediated slowing of C . elegans when encountering a lawn with bacterial food [24] . Secondly , we found that glit-1 mutants ( S12B Fig ) , unlike tsp-17 mutants [13] , do not exhibit premature swimming-induced paralysis , a behaviour indicative of increased extracellular dopamine levels [25] . Thirdly , we tested dopamine-induced paralysis: Overstimulation of C . elegans with exogenous dopamine eventually leads to animal paralysis [26] . We found that glit-1 and tsp-17 mutants stopped moving at much lower dopamine concentrations than wild-type animals ( Fig 4A ) . This increased sensitivity to exogenous dopamine depends on dopamine signalling as it was rescued by mutation of the dopamine receptors dop-2 and dop-3 ( Fig 4B ) . We conclude that glit-1 mutants , in contrast to tsp-17 mutants , do not exhibit signs of altered intrinsic dopamine signalling . However , both tsp-17 and glit-1 mutants exhibit increased dopamine-induced paralysis . As oxidative stress elicited by 6-OHDA led to increased dopaminergic neurodegeneration in glit-1 and tsp-17 mutant animals , we aimed to determine if these mutants are also hypersensitive to oxidative stress at an organismal level . To this end we exposed animals for 1 hour to paraquat , an oxidative stress-inducing herbicide linked to the development of Parkinson’s disease [3] . Paraquat , unlike 6-OHDA , is not known to be specifically taken up into dopaminergic neurons . We found that after incubation with different concentrations of paraquat , glit-1 and tsp-17 mutants exhibit a very similar developmental delay compared to wild-type animals ( Fig 5A ) . To follow up on this result , we also analysed the second glit-1 allele glit-1 ( gk384527 ) , as well as the tsp-17 ( tm4995 ) ;glit-1 ( gt1981 ) double mutant after exposure to very low concentrations of paraquat . We found that tsp-17 and glit-1 single and double mutants exhibit an almost identical developmental delay phenotype ( Fig 5B ) . Furthermore , glit-1 and tsp-17 mutants also developed much slower than wild-type animals after incubation with hydrogen peroxide ( H2O2 ) , another oxidative stress-inducing compound ( Fig 5C ) . In summary , glit-1 and tsp-17 mutants are hypersensitive to oxidative stress at the organismal level and likely act in the same pathway . Increased oxidative stress has been implicated with premature aging ( for review [27] ) , thus we asked if the increased oxidative stress sensitivity exhibited by glit-1 and tsp-17 mutants is linked to a shorter lifespan . We found that glit-1 and tsp-17 mutants die earlier than wild-type animals ( Fig 5D , S13 Fig ) , indicating that the overall fitness of the mutants is decreased . We aimed to determine if glit-1 and tsp-17 mutants exhibit an altered oxidative stress response . Using qRT-PCR , we measured transcript levels of the oxidative stress reporter gst-4 ( glutathione S-transferase ) , gcs-1 ( gamma glutamylcysteine synthetase ) and gst-1 ( glutathione S-transferase ) in wild-type and mutant L1 stage larvae after 1 hour of incubation with 10 mM 6-OHDA in comparison to mock treatment . GST-1 was previously shown to mildly inhibit the drug-induced degeneration of C . elegans dopaminergic neurons [28] . We found that gst-4 transcripts were approximately 1 . 5-fold upregulated in wild-type animals after 6-OHDA treatment in comparison to approximately 5-fold in tsp-17 and glit-1 mutants ( Fig 6A and S15A Fig ) . Furthermore , gcs-1 and gst-1 transcripts were not induced by 6-OHDA in wild-type larvae , but an approximately 2-3-fold induction occurred in tsp-17 and glit-1 mutants ( Fig 6B and 6C and S15B and S15C Fig ) . Comparing the basal level of gcs-1 and gst-1 and gst-4 expression in wild-type and tsp-17 and glit-1 mutants , we found that there is no difference in gcs-1 and gst-1 expression , while gst-4 appeared upregulated in tsp-17 and glit-1 mutants . ( S14 and S15D–S15F Fig ) . In addition , we measured reporter expression after exposure to paraquat and found a modest level of hyperinduction of the reporters in tsp-17 and glit-1 mutants ( S16 Fig ) . In summary , we found increased levels of oxidative stress signalling in glit-1 and tsp-17 mutants , indicating that the two genes protect from various oxidative-stress inducing compounds . Both gst-4 and gcs-1 are targets of SKN-1 [29 , 30] , an orthologue of the mammalian Nrf ( nuclear factor E2-related factor ) transcription factor family ( for review [31] ) which promotes oxidative stress resistance and longevity in C . elegans [32] . Thus , we aimed to determine if SKN-1 is involved in the protection against 6-OHDA-induced dopaminergic neurodegeneration . In C . elegans , SKN-1 abundance is negatively regulated by the CUL-4/DDB-1 ubiquitin ligase substrate targeting protein WDR-23 ( WD Repeat protein ) [33] . We found that hyperactivation of SKN-1 activity–conferred by a gain-of-function ( gof ) mutation in skn-1 or by loss of the SKN-1 repressor WDR-23– led to reduced dopaminergic neurodegeneration in glit-1 mutants ( Fig 6D ) and wild-type animals ( Fig 6E ) . However , a skn-1 ( zu67 ) loss-of-function mutant did not exhibit increased dopaminergic neuron loss ( Fig 6F ) . Thus , SKN-1 hyperactivation generally improves the defence against 6-OHDA-induced neurodegeneration , but it is unlikely that SKN-1 malfunction underlies 6-OHDA hypersensitivity observed in glit-1 and tsp-17 mutants . We tested additional C . elegans stress response pathways for their involvement in 6-OHDA protection . However , we found that mutation of the DAF-2/DAF-16 insulin/insulin-like growth factor signalling pathway ( for review [34] ) , the JNK-1 and KGB-1 JNK ( Jun N-terminal kinase ) pathways and the PMK-1 and PMK-3 p38 MAPK ( mitogen-activated protein kinase ) pathways ( for review [35] ) did not overtly alter 6-OHDA-induced dopaminergic neuron loss ( S17 Fig , S18 Fig ) . In addition , we tested if 6-OHDA induces the expression of reporters for the endoplasmic reticulum unfolded protein response , hsp-4 , and the mitochondrial unfolded protein response , hsp-6 . While hsp-4 transcript levels were slightly elevated after 6-OHDA , we did not detect a change in hsp-6 transcript levels ( S19A–S19D Fig ) . Furthermore , we did not find a difference in hsp-4 and hsp-6 transcript levels between wild-type , and tsp-17 and glit-1 mutant larvae ( S19A–S19D Fig ) . Thus , of the stress pathways we tested , only hyperactivation the skn-1 pathway leads to detectable protection from neurotoxicity conferred by 6-OHDA . It is not known which cell death pathway mediates 6-OHDA-induced neurodegeneration , so we revisited the role of the canonical apoptosis pathway . The conserved core apoptosis pathway is required for the vast majority of C . elegans apoptotic cell deaths and comprised of proapoptotic EGL-1 BH3-only domain protein , the Apaf1-like protein CED-4 and the CED-3 caspase . The apoptosis pathway also includes the Bcl2-like protein CED-9 , which protects from apoptosis . CED-13 in contrast solely contributes to apoptosis induced by DNA damaging agents such as ionising irradiation[36] ( simplified overview in Fig 7G , for review [37] ) . In addition to mediating apoptosis , CED-3 , CED-4 and CED-13 were also reported to be part of a hormetic response mediating lifespan extension upon limited mitochondrial oxidative stress [38] . We found that compared to wild-type animals , egl-1 , ced-13 , ced-4 and ced-3 loss-of-function ( lof ) mutants and the ced-9 gain-of-function ( gof ) mutant exhibit increased 6-OHDA-induced dopaminergic neurodegeneration ( Fig 7A–7F ) . Thus , the apoptosis pathway appears to protect dopaminergic neurons after 6-OHDA exposure . In contrast , we found that increased neurodegeneration in glit-1 mutants is not further enhanced by ced-3 , ced-4 and ced-13 mutations ( Fig 7B–7D , S20 Fig ) . It is conceivable that the increased neurodegeneration conferred by apoptosis gene mutations becomes undetectable in the glit-1 mutant background . Alternatively , it is possible that those apoptosis genes and glit-1 act in the same genetic pathway to protect dopaminergic neurons . In summary , our results show that the apoptosis pathway helps to prevent 6-OHDA-induced dopaminergic neurodegeneration . Since dopaminergic neurons do not undergo apoptosis after 6-OHDA exposure , we aimed to determine if they die via a necrosis-like cell death . Mutation of the C . elegans calreticulin CRT-1 , a calcium-binding and calcium-storing chaperone in the endoplasmic reticulum , prevents several necrosis-type cell deaths [39 , 40] . In contrast , we found that crt-1 mutation led to a highly increased loss of dopaminergic neurons after exposure to 6-OHDA in an otherwise wild-type background ( Fig 8A–8C ) . We further found that the crt-1;glit-1 and the crt-1;tsp-17 double mutants are synthetic lethal . We used a fluorescently labelled balancer chromosome to keep crt-1 in a heterozygous state in a glit-1 homozygous background . In the next generation , genetic segregation of the crt-1 and the fluorescent balancer chromosome is expected . However , we did not find any non-fluorescent animals , confirming that crt-1 homozygosity is lethal in the glit-1 mutant background . We furthermore found that synthetic lethality occurs when tsp-17 ( tm4995 ) and crt-1 ( ok948 ) are combined . Although it is possible that this synthetic lethality is related to the function of CRT-1 in calcium-induced necrosis , synthetic lethality might also be caused by developmental roles of crt-1: crt-1 mutation was shown to be synthetic lethal with vab-10 and other genes required for mechanical resilience of the epidermis [41] . Using qRT-PCR , we also tested if crt-1 expression is altered by 6-OHDA but we found crt-1 transcript levels remain unchanged after 6-OHDA treatment in wild-type , and in tsp-17 and glit-1 mutant animals ( Fig 8D , S21B Fig ) . crt-1 expression also remains constant after paraquat treatment ( S16 Fig ) . Furthermore , basal expression of crt-1 is not altered in tsp-17 or glit-1 mutants as compared to wild-type ( Fig 8E , S21C Fig ) . To follow up on a more general role of calcium homeostasis in 6-OHDA-induced dopaminergic neurodegeneration , we also analysed itr-1 ( inositol triphosphoate receptor ) and cnx-1 ( calnexin ) mutants . ITR-1 and CNX-1 were proposed to play similar roles as CRT-1 in the calcium-induced necrotic cell death of C . elegans PLM ( posterior lateral microtubule cells ) neurons [39] . However , we found that unlike crt-1 mutation , itr-1 and cnx-1 mutations do not alter the extent of dopaminergic neuron loss after 6-OHDA exposure ( S21A Fig ) . In summary , we report that the calreticulin orthologue CRT-1 is required for protecting against 6-OHDA-mediated dopaminergic neurodegeneration .
We show that GLIT-1 protects C . elegans dopaminergic neurons from 6-OHDA-induced degeneration , likely in a similar way as the previously identified tetraspanin TSP-17 [13] . We found that glit-1 and tsp-17 mutants are equally hypersensitive to 6-OHDA and paraquat , and that double mutants do not exhibit an enhanced phenotype . We previously showed that tsp-17 mutants display phenotypes consistent with excessive dopaminergic signalling and we hypothesised that TSP-17 protects dopaminergic neurons by inhibiting the uptake of 6-OHDA into dopaminergic neurons [13] . In this study , we uncovered that both TSP-17 and GLIT-1 protect against oxidative stress at an organismal level . Consistent with increased oxidative stress levels in glit-1 and tsp-17 mutants , we find increased transcription of oxidative stress reporters and hypersensitivity after exposure to oxidative stress-inducing compounds . In addition , using a reverse genetics approach , we found that the calcium chaperone CRT-1 , as well as canonical apoptosis pathway and CED-13 , a protein previously implicated in DNA damage-induced apoptosis , are also required to protect dopaminergic neurons from 6-OHDA-mediated degeneration . We are considering several possibilities as to how the GLIT-1 and TSP-17 transmembrane proteins might confer the various phenotypes we observe . glit-1 and tsp-17 are both expressed in dopaminergic neurons . However , in contrast to TSP-17 , which also appears to partially function in dopaminergic neurons [13] , GLIT-1 expression is likely required in multiple tissues to confer protection against 6-OHDA . Furthermore , tsp-17 mutants , but not glit-1 mutants display subtle defects in a behaviour mediated by dopamine , the swimming-induced paralysis ( this study and [13] ) . Both the neuroligin-like glit-1 mutant and the tetraspanin tsp-17 mutant exhibit increased dopamine-induced paralysis . This dopamine sensitivity phenotype could point towards an increased level of intrinsic dopamine signalling , possibly accounting for an increased level of 6-OHDA uptake via the DAT-1 transporter . It is also conceivable that the increased sensitivity to dopamine-induced paralysis and oxidative stress is due to a higher organismal drug uptake , which might be caused by decreased endothelial barrier function . The GLIT-1 homologue in Drosophila , Gliotactin , localises to tricellular junctions and is necessary for the development of septate junctions , an invertebrate analogue of epithelial tight junctions [17 , 18] . However , using dye leakage assays , we did not find indications for increased intestinal permeability in glit-1 mutant animals . We consider it likely that both glit-1 and tsp-17 protect constitutively against oxidative stress at an organismal level , as several oxidative stress reporters are hyper-induced in glit-1 and tsp-17 mutants . The specific sensitivity of dopaminergic neurons to 6-OHDA is likely due to the specific uptake of 6-OHDA via the dopamine transporter . In line with this , we never observed dopaminergic neurodegeneration in wild-type or mutant animals after treatment with paraquat and H2O2 , even at doses at which animal development is severely delayed . Both TSP-17 and GLIT-1 are homologous to vertebrate tetraspanins and neuroligin-like molecules , but do not have clear mammalian orthologues . As the genetic screen for factors that protect dopaminergic neurons from oxidative damage is far from saturation , we consider it likely that further studies like ours will help to uncover additional pathways that protect neurons from oxidative damage . Our data clearly indicate that the apoptosis pathway protects dopaminergic neurons from 6-OHDA-induced degeneration . The increased neuronal loss in egl-1 loss-of-function ( lof ) , ced-9 gain-of-function , ced-3 ( lof ) and ced-4 ( lof ) mutants could be ascribed to a defect in the core apoptosis pathway defined by these genes . However , the ced-13 ( lof ) mutant also displays higher dopaminergic neurodegeneration . CED-13 has previously been found to mildly affect radiation-induced germ cell apoptosis , but not to alter developmental apoptosis as mediated by the core apoptosis pathway [36] . In addition , the core apoptosis pathway and CED-13 were reported to form a protective , lifespan-prolonging pathway that senses mild oxidative stress ( conferred by the partial inhibition of mitochondrial respiration ) [38] . Remarkably , glit-1 expression was increased under these oxidative stress conditions ( supplementary information in [38] ) . It is still not known how apoptosis proteins sense oxidative stress [38] , and it certainly remains to be established how they confer protection of dopaminergic neurons after exposure to 6-OHDA ( our observations ) . There is mounting evidence that ‘apoptosis proteins’ adopt functions that are not related to apoptosis . For instance , it was demonstrated that ced-4 is required for DNA damage-induced cell cycle arrest [42] and we previously showed that CED-4 and CED-9 are expressed in both dying and surviving cells , consistent with roles beyond apoptosis [43] . We further found that the calreticulin CRT-1 protects against 6-OHDA-induced dopaminergic neurodegeneration , in contrast to the calnexin CNX-1 and the putative inositol phosphate receptor ITR-1 . CRT-1 is a calcium-binding and calcium-storing chaperone of the endoplasmic reticulum ( ER ) which is predominantly expressed in the C . elegans intestine and induced by organismal stress [44] . CRT-1 is required for neuronal cell death induced by hyperactivated MEC-4 ( d ) ( mechanosensory abnormality ) and DEG-1 ( d ) ( degeneration of certain neurons ) ion channels , as well as by a constitutively active Gαs subunit [39] . Furthermore , crt-1 mutation alleviates dopaminergic cell death caused by gain-of-function mutations of the TRP-4 ( transient receptor potential ) channel [40] . ITR-1 and CNX-1 are less well characterised than CRT-1 , but mutation of itr-1 also reduces neuron loss as induced by hyperactivity of MEC-4 ( d ) and TRP-4 , albeit to a lesser extent than mutation of crt-1 [39 , 40] . cnx-1 mutation only suppressed MEC-4 ( d ) , but not TRP-4-induce cell death [39 , 40] . CRT-1 , ITR-1 and CNX-1 are thought to be required for calcium release from the ER that is needed for necrotic cell death [39] . When necrosis is linked to increased calcium influx across the plasma membrane , as conferred by the hyperactivated DEG-3 ( d ) channel , necrosis is not blocked by crt-1 mutation [39] . Finally , CRT-1 also appears to influence the regeneration of axons after laser cut for ALM neurons [45] , but not PLM neurons [46] . In the ALM axon regeneration model , the CED-3 and CED-4 apoptosis proteins also promote neuronal repair [45] . Is it possible to reconcile the differential influence of crt-1 mutation on different types of necrosis and axonal repair with the increased sensitivity to 6-OHDA-induced dopaminergic neurodegeneration ? It has been speculated that depending on the levels and duration of the calcium signal , distinct pathways driving neuronal regeneration or cell death can be triggered [47]: Low calcium transients would promote neuronal repair , whereas high levels of increased calcium over a longer period of time would trigger necrosis . 6-OHDA might thus induce a different injury as compared to locally applied laser cuts or the aforementioned necrosis models . The apoptosis pathway and calreticulin are conserved between C . elegans and humans . It is thus conceivable that both apoptosis and necrosis play a role in neurodegeneration in Parkinson’s disease . To the best of our knowledge , apoptosis and necrosis genes have not been defined as Mendelian Parkinson’s loci , and have not been associated with an increased risk of Parkinson’s disease based on genome-wide association studies . However , it is noteworthy that the inheritance of Parkinson’s disease is considered to be comparably low , and that the vast majority of disease cases appear to be defined by environmental factors . Thus , apoptosis or oxidative stress signalling linked to apoptosis proteins , as well as necrosis , could well have an important role in protecting dopaminergic neurons in idiopathic Parkinson’s disease .
Strains were grown at 20°C . wdr-23 ( tm1817 ) and tsp-17 ( tm4995 ) mutants were generated and kindly provided by Shohei Mitani of the National Bioresource Project ( NBRP ) ( http://shigen . nig . ac . jp/c . elegans/ ) . Additional information about alleles in this study is provided by WormBase ( www . wormbase . org ) . Strains from the Caenorhabditis Genetics Center ( CGC ) or the NBRP were outcrossed to the BY200 strain to eliminate unlinked mutations and to introduce a green fluorescent protein ( GFP ) marker for dopaminergic neurons . The isolated gt1981 mutant was outcrossed six times to the BY200 wild-type strain . L4 and young adult staged BY200 animals were mutagenised with 25 mM ethyl methanesulfonate ( EMS ) in M9 buffer for 4 hours at 20°C , washed and grown at 15°C overnight on NGM plates seeded with OP50 E . coli . The animals were bleached once they started to lay eggs and the F1 progeny let hatch overnight in M9 . This synchronised F1 population was left to develop on seeded plates until reaching the early adult stage . The F1 adults were then washed off in M9 again to lay a synchronised population of F2 L1 larvae which was treated with 10 mM 6-OHDA and screened for dopaminergic neuron loss after 72 h . Candidates were transferred to separate plates , backcrossed at least three times and re-tested for their hypersensitivity to 6-OHDA . The genomic DNA was sent for whole-genome sequencing and SNP mapping was performed as previously described [14 , 15] . For generation of transgenic constructs , genomic DNA was amplified using primers containing additional 8 base pair restriction sites for insertion into the pCFJ151 vector [48] . In the following primer sequences , restriction sites are highlighted in italics , translation start and translation stop codons in bold and codons are separated by a space . Primers for the transcriptional reporter construct AscI_Pglit-1_NotI_ GFP_FseI_3’glit-1_PacI: AscI_Pglit-1 –GCTAggcgcgccGTATCTGGCATTGGCTCG; Pglit-1_NotI–GCTA gcg gcc gca CATTCCATGTGACGCGAT; NotI_GFP—GCTAgc ggc cgc AGT AAA GGA GAA GAA CTT TTC ACT GG; GFP_FseI—AAGTTA ggc cgg ccc CTT GTA TGG CCG GCT AG; FseI_3'glit-1 –AC AAG ggg ccg gcc TAACTTTCAAAGTTTGTAAATAATGTATAATTTA; 3'glit-1_Pac–GCTAttaattaaCCAGTTGCAGTGTTTTTTTG . Further primers for the GFP-tagged translational construct AscI_Pglit-1_NotI_GFP_FseI_glit-1_3’glit-1_PacI: FseI_glit-1_3’glit-1—GG CCA TAC AAG Ggg ccg gcc ATG TTC ACC GGC ACA ATT; glit-1_3’glit-1_PacI–TAGGGCCCTCAAttaattaaGACAATACATGTTTTCTTTTTGAAAATG . Additional primers for the untagged translational constructs AscI_Pglit-1_FseI_glit-1_3’glit-1_PacI and AscI_Pdat-1_FseI_glit-1_3’glit-1_PacI:AscI_Pglit-1 –AGATTAggcgcgccGTATCTGGCATTGGCTCG; Pglit-1_FseI—AGAAAAggc cgg cca CATTCCATGTGACGCGAT; AscI_Pdat-1 –ATTAggcgcgccAATGTTTCTAGTCGTTTTTGTATTTTAAAG; Pdat-1_FseI—AGAAAAggc cgg cca CATGGCTAAAAATTGTTGAGATTCG . The glit-1 coding sequence was amplified from cDNA ( the AT-rich glit-1 introns rendered cloning unfeasible ) . The vector was adapted to include the following restriction site architecture: AscI_SgfI_NotI_FseI_PacI . The strains were generated by microinjections of unc-119 ( ed3 ) mutants . For microscopy , animals were transferred into a drop of 25 mM levamisole solution on top of a 2 . 5% ( w/v ) agar pad on a microscope slide . Images were acquired using a DeltaVision microscope ( Applied Precision ) and analysed with the softWoRx Suite ( Applied Precision ) and ImageJ [49] . All C . elegans assays were performed in a blinded manner . To obtain synchronized L1 larvae for oxidative stress assays , 1–10 adult animals were incubated to lay eggs in 70 μl M9 without food on a benchtop shaker at 20°C , 500 rpm for 24–30 h . Due to the swimming-defect of tsp-17 ( tm4995 ) , all L1 larvae for experiments that included this strain were obtained by filtering a mixed population of animals . For 6-OHDA assays , 10 μL 200 mM ascorbic acid and 10 μL of the respective 6-OHDA 5 x stock concentration were added to 30 μL of L1-stage larvae in M9 . 6-OHDA concentration was chosen such that differential sensitives of various strains could be efficiently analysed . After 1 hour incubation at 20°C and shaking at 500 rpm , 150 μL M9 buffer was added to oxidise and inactivate the 6-OHDA . The animals were pipetted to one half of an NGM plate containing a stripe of OP50 bacteria on the opposite half of the plate and adult animals and eggs were removed to prevent growth of animals that were not treated at L1 stage . Plates were incubated at 20°C before blind scoring of the 6-OHDA-induced degeneration using a Leica fluorescent dissecting microscope . Unless indicated otherwise , animals were scored 72h after treatment in technical duplicates . For treatment of different developmental stages , the respective stages were selected from a mixed population of animals after 6-OHDA treatment . Replicates for strains shown in the same graph were performed in parallel . For paraquat and H2O2 assays , 10 μL of the respective 5x stock concentration was added to 40 μL of L1-stage larvae in M9 . After 1 hour incubation in the shaker , 150 μL M9 buffer was added and the L1 larvae were counted after pipetting them to a seeded NGM plate . Surviving animals were scored again after 24 h . Dye leakage assays were performed as described previously [19 , 20] . A mixed , well-fed population of animals was washed off the plate and incubated for 3 hours in 2 . 5% ( wt/vol ) Brilliant Blue FCF ( Sigma ) and 2 . 5% ( wt/vol ) fluorescein sodium salt ( Sigma ) , respectively . The dyes were solubilised in a saturated solution of OP50 E . coli in LB ( Lysogeny Broth ) medium . After incubation , the animals were pipetted onto NGM plate seeded with OP50 E . coli . For microscopy , single animals were transferred immediately into a drop of 25 mM levamisole solution on top of a 2 . 5% ( w/v ) agar pad on a glass slide . For the blue dye ( ‘Smurf’ ) assay , images were taken using a camera mounted to a Leica stereo microscope . To determine fluorescein uptake , a bright-field image and a fluorescence image using the GFP channel were taken with a Zeiss Axio Scope . Bright-field and fluorescence image channels were merged using ImageJ [49] . For lifespan assays , 50 L4 stage animals were selected per strain and transferred to new plates using a platinum wire every day during the first 6 days and then only to avoid mixing with the progeny . Animals that did not move after touch with a platinum pick were scored as dead . Dead animals were counted and removed every day . Bag of worms , dry animals and burst animals were censored . The data was analysed using OASIS ( Online application for Survival Analysis , http://sbi . postech . ac . kr/oasis/ ) [50] . Dopamine paralysis assays were carried out as previously described [51] . Staged adult animals were incubated for 20 minutes on a plate containing dopamine and assessed for their ability to complete body movement through minimum and maximum amplitude during a 5 second observation period . Two plates containing 25 animals each were assessed for each condition . For the swimming-induced paralysis ( SWIP ) assay 5–15 L4 stage larvae were transferred into a glass well in 40 μL water . The number of paralysed animals scored using a Leica dissecting microscope every minute for 30 minutes [25] . Basal slowing assays were performed as previously described [52] . NGM plates were prepared and half of them seeded with HB101 bacteria before incubating them at 37°C overnight . Also , mid-L4 larvae were transferred on separate plates . The next day , these staged young adult animals were put in 40 μL M9 for 2 minutes to clean them from bacteria and then transferred to the centre of a plate . 6 animals each were put on a plate with and without bacteria , respectively . After a 2 minutes adaptation time , the locomotion rates of each animal was quantified by counting the number of body bends completed in five consecutive 20 s intervals . The experiment was performed twice on different days . L1 stage animals were filtered from two 9 cm plates containing mixed stage animals . Animals were washed twice and then split in half for control treatment ( addition of ascorbic acid and water only ) and 6-OHDA treatment . After treatment , RNA extraction was performed based on He , F . ( 2011 ) . Total RNA Extraction from C . elegans . Bio-protocol Bio101: e47 . DOI: 10 . 21769/BioProtoc . 47 . The following changes were introduced: The chloroform extraction was repeated once more and 0 . 1 volumes of 3 M NaAc were added to the propanol extraction to help RNA precipitation . The RNA pellet was resuspended in 15 μL H2O . The cDNA was digested using the Turbo DNA-free kit ( Ambion Life Technologies ) in 18 μL total volume . 1 μg of RNA was reverse transcribed to cDNA with 15 bp oligo dT primers using the M-MLV Reverse Transcriptase ( Promega ) according to manufacturers’ instructions . The cDNA was analysed by qRT-PCR using the StepOnePlus Real-Time PCR System ( Applied Biosystems ) applying melting temperature of 62°C . The following primers were designed using QuantPrime [53]: K08F4 . 7_F: ATGGTCAAAGCTGAAGCCAACG and K08F4 . 7_R: ACTGACCGAATTGTTCTCCATCG , F37B12 . 2 . 1_F: GTTGATGTGGATACTCGGTGTACG and F37B12 . 2 . 1_R: ATCTCTCCAGTTGCTCGTTTCG , R107 . 7 . 1_F: CGTCATCTCGCTCGTCTTAATGGG and R107 . 7 . 1_R: TGGTGTGCAAATCACGAAGTCC , Y38A10A . 5 . 1_F: GTGATCCAATACACCGTCAAGCAC and Y38A10A . 5 . 1_R: AGCATCAGCTCTCATGACCTTAAC , C02F12 . 1b_F: AGACAATGGAGACACGCAGTGG and C02F12 . 1b_R: AGGCAAATTCCAAGCATCATGGTC , F55D10 . 3_F: TCTAGGAGTGCCCTATGCAGAACC and F55D10 . 3_R: AAGCTGCTGTGGTGGCTTAAATC , F43E2 . 8 . 1_F: GCGTCTGATTGGGCGTTTCTAC and F43E2 . 8 . 1_R: ACTTGTCGACGATCTTGAACGG , C37H5 . 8_F: GAACCGGAAAGGAACAACAGATCG and C37H5 . 8_R: TTTGGTCCTTGGAAAGTCCTCCAG . The PCR mixture consisted of 0 . 2 μM primers , cDNA ( diluted 1:50 ) and 1× Power SYBR Green PCR Master Mix ( Thermo Fisher Scientific ) . All reactions were run in triplicates . Relative abundance was calculated using the ΔΔCt method [54] . To control for template levels , values were normalised against the control genes Y45F10D . 4 and pmp-3 [55 , 56] . GLIT-1 extra- and intracellular and transmembrane domains and signal peptides were predicted with Phobius ( http://phobius . sbc . su . se/ ) [57] . GLIT-1 protein structure model was calculated in SWISS-MODEL ( swissmodel . expasy . org ) [58] and visualised with Swiss-PdbViewer 4 . 1 . 0 ( http://www . expasy . org/spdbv/ ) [59] . For phylogenetic analysis of GLIT-1 , NCBI Protein BLAST ( Basic Local Alignment Search Tool ) was used to search the ‘reference proteins’ database of the indicated organism against the GLIT-1 protein sequence . A multiple alignment was performed using COBALT with the 250 best-matching sequences from all organisms . Protein alignments to visualise conservation of GLIT-1 domains and residues alignment of protein sequences was performed in CLC workbench Version 7 . 6 . 4 using the default parameters: Gap open cost 10 . 0 , Gap extension cost 1 . 0 , End gap cost as any other , Alignment very accurate ( slow ) and create a the tree ( Tree construction method: Neighbour Joining , Protein distance measure: Jukes-Cantor , Bootstrapping: 100 Replicates ) . The two-tailed t-test and were performed with the Analysis ToolPak Add-In in Microsoft Excel 2010 . To choose a t-test assuming equal or unequal variances , respectively , the same Add-in was used to conduct an F-test . To compare 6-OHDA treatment data , a G-Test was performed with R Studio 1 . 0 . 44 DescTools descriptive statistics tools package . p-values were calculated for each biological replicate and for the pooled biological replicate data , and the least significant of these p-values is indicated in the graphs . Unless otherwise indicated , data is only marked as significant if all replicates and the pooled replicate data were found to be significant . | Neurons use dopamine as a chemical messenger to mediate diverse behaviours . The gradual loss of dopaminergic neurons in specific brain areas is a hallmark of Parkinson’s disease . The increased occurrence of highly reactive oxygen radicals , also called oxidative stress , is assumed to contribute to the demise of dopaminergic neurons . In this study , we searched for genes that protect dopaminergic neurons against oxidative stress . We used the nematode C . elegans , a well-characterised model organism whose dopamine signalling system is very similar to that of humans . When C . elegans is exposed to 6-hydroxydopamine , an oxidative stress-inducing compound , dopaminergic neurons gradually die . Our major findings include: ( i ) absence of the neuroligin-like gene glit-1 causes highly increased cell death of dopaminergic neurons after 6-OHDA exposure; ( ii ) GLIT-1 acts in a similar manner as the previously identified tetraspanin TSP-17; ( iii ) GLIT-1 and TSP-17 also protect C . elegans from other types of oxidative stress; and ( iv ) the programmed cell death pathway and a calcium chaperone protect dopaminergic neurons as well . Collectively , this study shows that apoptosis proteins , the calcium chaperone CRT-1 and the neuroligin-like GLIT-1 protect against neurodegeneration after oxidative stress exposure . | [
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] | 2018 | Mutations in Caenorhabditis elegans neuroligin-like glit-1, the apoptosis pathway and the calcium chaperone crt-1 increase dopaminergic neurodegeneration after 6-OHDA treatment |
Stress responses are crucial processes that require activation of genetic programs that protect from the stressor . Stress responses are also energy consuming and can thus be deleterious to the organism . The mechanisms coordinating energy consumption during stress response in multicellular organisms are not well understood . Here , we show that loss of the epigenetic regulator G9a in Drosophila causes a shift in the transcriptional and metabolic responses to oxidative stress ( OS ) that leads to decreased survival time upon feeding the xenobiotic paraquat . During OS exposure , G9a mutants show overactivation of stress response genes , rapid depletion of glycogen , and inability to access lipid energy stores . The OS survival deficiency of G9a mutants can be rescued by a high-sugar diet . Control flies also show improved OS survival when fed a high-sugar diet , suggesting that energy availability is generally a limiting factor for OS tolerance . Directly limiting access to glycogen stores by knocking down glycogen phosphorylase recapitulates the OS-induced survival defects of G9a mutants . We propose that G9a mutants are sensitive to stress because they experience a net reduction in available energy due to ( 1 ) rapid glycogen use , ( 2 ) an inability to access lipid energy stores , and ( 3 ) an overinduced transcriptional response to stress that further exacerbates energy demands . This suggests that G9a acts as a critical regulatory hub between the transcriptional and metabolic responses to OS . Our findings , together with recent studies that established a role for G9a in hypoxia resistance in cancer cell lines , suggest that G9a is of wide importance in controlling the cellular and organismal response to multiple types of stress .
The ability of an organism to sense and adapt to changes in the environment is essential for survival . In particular , harmful environmental challenges require acute responses to avoid cellular and organismal damage [1] . The coordinated regulation of stress response genes coupled with a reallocation of cellular energy use is required to implement effective cellular stress responses [2 , 3] . In extreme cases , translation of non-stress-related proteins is repressed , and cellular energy stores become primarily dedicated to the stress response , at the expense of other normal cellular processes [4] . Little is known about the regulators that safeguard an appropriate amplitude of stress response and ensure sufficient cellular resources to execute an effective defense . Conserved defense mechanisms have evolved to counteract stress such as heat shock , DNA damage , and oxidative stress ( OS ) [1 , 5] . Exposure to xenobiotics such as paraquat or hydrogen peroxide ( H2O2 ) can lead to increased accumulation of reactive oxygen species ( ROS ) . Increased ROS triggers multiple signaling pathways , which activate key transcription factors such as c-Jun N-terminal kinase ( JNK ) [6] , activator protein 1 ( AP-1 ) ( D-Fos/D-jun ) [7] , forkhead box O ( FoxO ) [8] , and activating transcription factor 3 ( Atf-3 ) [9] . These transcription factors induce the expression of ROS scavengers ( superoxide dismutases [SODs] , catalases , glutathione peroxidases , glutathione S-transferases ) and genes involved in the repair of ROS-mediated damage ( peroxiredoxins , proteasomal components , DNA repair machinery ) [10 , 11] . Recent studies have indicated that chromatin regulators are critical factors in mediating the cellular response to stress [12] . Chromatin modifiers of the evolutionarily conserved G9a/euchromatin histone methyltransferase ( EHMT ) protein family mediate histone H3 lysine 9 dimethylation ( H3K9me2 ) within euchromatic regions the genome [13] . Recently , it has been shown that G9a is required for hypoxia resistance during the rapid proliferation of cancer cells in culture [14 , 15] . At the organismal level , G9a is important in mediating responses to various environmental insults and stimuli , including viral infection [16] , starvation [17 , 18] , cocaine [19 , 20] , and learning [21 , 22] . Previously , we characterized putative genomic H3K9me2 target sites of G9a in Drosophila larvae . These G9a target sites were enriched at genes that are regulated in environmentally induced processes requiring immediate responses , including memory , immune response , and response to OS [22] . Whereas the former were demonstrated to be predictive for defects in learning and memory [22] and immune responses to virus infection [16] of G9a-null mutant flies , the biological relevance of Drosophila G9a OS-related targets has remained elusive . In the present study , we demonstrate an essential role for G9a in OS tolerance . Our data suggest that G9a mutants experience an overactivated stress response , elevated glycogen use , and an inability to access lipid energy reserves , resulting in premature death due to reduced net energy availability . This defines G9a as an important regulator of transcriptional and metabolic homeostasis that is required for an optimal metabolic response to stress .
To investigate whether G9a is required for OS response , we exposed G9a-null mutants ( G9aDD1 and G9aDD2 ) and an isogenic control strain [22] to paraquat , a potent inducer of OS [23] . Scoring survival of the three genotypes over time , we found that G9a mutants showed reduced survival upon paraquat exposure , dying dramatically faster than the controls . In contrast , untreated G9a-null mutants and controls showed full viability over the time course of the experiment ( Fig 1A and S1A Fig ) . To independently verify this finding , we also generated G9a knockdown flies and exposed them to paraquat . Knocking down G9a using the ubiquitous actin-Gal4 driver and a previously validated G9a RNA interference ( RNAi ) line [24] also led to a significant reduction in median survival time during exposure to paraquat compared to controls . Untreated G9a-knockdown and control flies were viable over the time course of the experiment ( Fig 1B ) . The observation that two G9a-null alleles and G9a-knockdown flies show reduced survival in response to paraquat suggests that G9a may play a role in OS response . G9a mutant flies were also sensitive to the OS-inducing agent menadione sodium bisulfide ( MSB ) ( S1B Fig ) , demonstrating that the sensitivity to OS is not specific to paraquat . Previous chromatin immunoprecipitation sequencing ( ChIP-seq ) profiling of H3K9me2 , the epigenetic mark deposited by G9a , in G9a mutants versus control larvae revealed that genes implicated in OS are enriched among putative G9a target genes . Based on this finding and the increased susceptibility of G9a mutants to OS , we hypothesized that G9a may be required for an appropriate transcriptional response to OS . To address this hypothesis and to uncover specific mechanisms underlying OS sensitivity of G9a mutant flies , we assessed gene expression changes in G9a mutants over a time course of OS exposure . For this , we generated transcriptome profiles by RNA sequencing ( RNA-seq ) of G9a mutant and control heads at 0 , 6 , and 12 h after paraquat exposure . We mapped reads to the Drosophila reference genome and created normalized count data ( see Materials and methods and S1 Data ) . Euclidean sample-to-sample distance ( S2A Fig ) showed that ( 1 ) biological duplicates cluster together , ( 2 ) controls and G9a mutants with no OS exposure cluster apart from each other , ( 3 ) samples with OS exposure cluster apart from samples with no OS exposure , and ( 4 ) G9a mutant samples after 6 and 12 h OS exposure cluster apart from the control samples with OS exposure . These findings suggest that there are differences in the global transcriptional response to OS in G9a mutants compared to controls . Principal component analysis confirms that sample duplicates cluster closely together . It also illustrates that transcriptional changes upon OS are more dramatic in the mutants than in the controls ( S2B Fig ) . To identify differentially expressed ( DE ) genes , we used DESeq [25] , with cutoffs of ≥1 . 5-fold change and p-adj ≤ 0 . 05 . We performed four pairwise comparisons: 0 h versus 6 h OS exposure and 0 h versus 12 h OS exposure in controls and in G9a mutants . We found 2 , 731 genes to be differentially expressed in at least one of the four pairwise comparisons ( S2A Data ) . To reveal patterns in global gene expression changes among the two genotypes during OS exposure , we used the partitioning around medoids ( PAM ) R package [26] to identify 12 clusters of genes with similar expression changes in response to OS ( Fig 2A ) . Some of these 12 clusters showed a similar pattern across the four conditions , varying mostly by the amplitude of the changes . These were pooled , resulting in five principal groups ( Fig 2B and S2A Data ) . Genes in group 1 ( 271 genes , cluster 1 ) were up-regulated upon OS exposure in both G9a mutants and controls to a similar extent . Group 2 ( 384 genes , cluster 2 ) represents genes that were down-regulated in both G9a mutants and controls . Group 3 ( 1 , 139 genes , clusters 3–7 ) , the largest group , includes genes that were induced by OS in control and mutant conditions but to a larger extent in G9a mutants . Group 4 ( 285 genes , clusters 8–10 ) represents genes that were up-regulated in the controls but down-regulated in G9a mutants . Group 5 ( 652 genes , clusters 11 and 12 ) contains genes that were down-regulated in the control and mutant conditions but to a larger extent in G9a mutants . Next , we performed gene ontology ( GO ) enrichment analysis to obtain a global understanding of the biological function associated with the genes in the five principle groups described in Fig 2B ( complete GO statistics are shown in S3A Data ) . We identified several GO terms related to stress response in the large group 3 ( Fig 2C , highlighted in yellow , e . g . , response to toxic substances , response to stimulus , regulation of translation , immune system process , protein folding , response to stress ) , which contained genes that were up-regulated in response to OS but more up-regulated in G9a mutants ( Fig 2A and 2B ) . Thus , G9a mutants show an augmented transcriptional response to OS . In addition , many metabolic terms were enriched ( Fig 2C , highlighted in brown ) . This was especially evident for group 5 genes ( Fig 2C , e . g . , lipid metabolic processes , carbohydrate metabolic processes , carbohydrate transport , steroid metabolic processes , generation of precursor metabolites , and energy and cellular amino acid metabolic processes ) , which were down-regulated in response to stress but more down-regulated in G9a mutants ( Fig 2A and 2B ) . We also investigated an alternative RNA-seq data analysis approach by performing pairwise comparisons of transcriptional changes in G9a mutants versus control at each of the three time points: 0 , 6 , and 12 h of OS exposure ( cutoffs of ≥1 . 5-fold change and p-adj ≤ 0 . 05 ) . Using this approach , we identified 2 , 600 DE genes ( S2B Data ) . By PAM clustering of DE genes , as described above , we again identified five principle gene expression groups ( S3 Fig ) . We found similar patterns of expression changes and comparable representation of biological process within five different principal groups ( S3 Fig and S3B Data ) . For example , group 3 of this alternative analysis contained many stress genes that were up-regulated in G9a mutants only at 6 and 12 h after OS but not at 0 h in steady-state conditions . Group 5 of the alternative analysis contained many metabolic genes that were down-regulated in G9a mutants after 6 and 12 h of OS exposure but not at 0 h in steady-state conditions ( S3 Fig ) . Taken together , we consistently see augmented activation of stress response genes and reduced expression of metabolic genes in G9a mutants after exposure to OS . We initially hypothesized that the strongly reduced survival of G9a mutants in response to OS may be due to the inability to initiate the transcriptional defense mechanisms protecting against OS . It was thus surprising to identify stress response genes to be enriched in group 3 , characterized by an exaggerated transcriptional response in G9a mutants ( Fig 2 ) . We therefore further analyzed specific genes encoding proteins that function in neutralizing ROS and oxidative damage ( Fig 3A ) . SOD catalyzes transformation of oxygen radicals ( •O−2 ) into H2O2 . Catalase , glutathione peroxidases , peroxiredoxins , and thioredoxins help to reduce H2O2 to water . Examining OS-induced expression changes of these enzymes , we found similar expression in G9a mutants and controls at steady state . Upon OS exposure , we observed an increase in mRNA levels after 6 h and even more after 12 h of OS exposure , a response that was augmented in G9a mutants ( Fig 3B ) . The most striking examples for augmented induction by OS are glutathione S-transferase E1 ( GstE1 ) ( fold increase at 12 h after OS = 1 . 8 in controls versus 22 . 1 in G9a mutants , p = 0 . 017 , Fig 3B , bottom left ) and peroxiredoxin 2540–1 ( Prx2450-1 ) ( fold increase at 12 h after OS = 2 . 2 in controls versus 10 . 5 in G9a mutants , p = 0 . 025 , Fig 3B , middle right ) . These expression differences were validated in independent quantitative real-time PCR ( RT-qPCR ) experiments ( S4A Fig ) . GstE1 , Prx2450-1 , and Catalase ( Cat ) were previously predicted to be G9a targets by H3K9me2 ChIP-seq [22] , suggesting that G9a might be required to buffer the stress-induced induction of some OS response genes . These data show that G9a mutants are not defective in their ability to induce expression of OS response genes but rather show increased induction . Cellular ROS can react with and cause damage of lipids , DNA , and proteins . We therefore further surveyed the expression of protein groups that counteract ROS-mediated damage . These included glutathione S-transferases , which are responsible for detoxifying peroxidized lipids ( Fig 3C ) , DNA damage repair machinery ( Fig 3D ) , and genes encoding components of the proteasome complex , a key organelle in clearing damaged proteins ( Fig 3E ) . In general , genes involved in these processes are not differentially expressed in G9a mutants under steady-state conditions ( Fig 3C–3E , G9a null versus control after 0 h OS ) . At 6 h and 12 h OS , glutathione S-transferase genes show a trend toward increased expression in controls , which is somewhat augmented in G9a mutants ( Fig 3C ) . DNA repair genes show no induction after OS in controls or G9a mutants , with the exception of some outliers ( Fig 3D ) . For genes encoding components of the proteasome , we observed a trend toward increased expression in controls after OS exposure , and this increase was significantly higher in G9a mutants ( Fig 3E , G9a null versus controls after 6 h OS: p = 0 . 0012 , G9a null versus control after 12 h OS: p = 0 . 0011 ) . Together , our data indicate that G9a mutants show an intact and even exaggerated transcriptional response of genes implicated in OS defense . Having identified that the transcription of OS defense genes was enhanced in G9a mutants , we set out to measure markers of OS and oxidative damage in G9a mutants and controls upon OS induction . We measured H2O2 levels , an intermediate ROS metabolite used as a marker for ROS levels . G9a mutants already had significantly higher levels of H2O2 at steady state ( 0 h OS ) compared to controls ( Fig 4A ) . However , H2O2 remained unchanged for up to 18 h after OS exposure , which was the latest time point at which we were able to collect sufficient living G9a mutant animals to perform measurements . We also estimated lipid peroxidation as a marker for oxidative damage , by quantifying malondialdehyde ( MDA ) levels in G9a mutants and controls ( Fig 4B ) . We observed similar levels of MDA in both genotypes at steady state ( 0 h OS ) and no changes in MDA levels upon OS induction . This indicates that G9a mutants are not defective in clearing ROS or eliminating molecules that are damaged by ROS , in agreement with the transcriptional data . In addition , we tried to rescue the G9a mutant OS sensitivity by feeding the antioxidants vitamin E and glutathione at concentrations that have previously been shown to counteract harmful effects of OS [27 , 28] . However , we did not see any beneficial effects of these antioxidants in either G9a mutants or controls ( S5 Fig ) , suggesting that ROS clearance is not limiting for survival under these conditions . The results obtained above strongly suggested mechanisms other than an impaired stress response to underlie reduced survival of G9a mutants in response to OS . We therefore turned to the second predominant biological theme of misregulated genes: genes involved in metabolism . We examined OS-induced expression changes for genes encoding the main enzymes that drive energy use and storage ( Fig 5A and S6 and S7 Figs ) . In G9a mutants at steady state , metabolic genes showed very little misregulation ( Fig 5B–5M and S7 Fig; G9a mutants versus controls after 0 h OS ) , with the exception of a few genes involved in the regulation of fatty acid beta oxidation and triglyceride synthesis ( S6 Fig ) . Upon OS induction , there were no global changes in expression of genes involved in glycogen metabolism , gluconeogenesis , glycolysis , pyruvate dehydrogenases , citric acid cycle , ketogenesis , ketolysis , or mitochondrial oxidative phosphorylation ( Fig 5B–5L ) . There were also no significant differences in expression of these genes in G9a mutants compared to controls after OS induction ( S7 Fig ) . However , we noticed that specific genes regulating fatty acid beta oxidation showed highly diverging expression changes ( in either direction , up and down ) in response to OS in the G9a mutants compared to the controls ( Fig 5H and S6 and S7 Figs ) . Genes involved in triglyceride synthesis showed a clear trend toward augmented down-regulation in G9a mutants upon both 6 h and 12 h of OS ( Fig 5K and S6 and S7 Figs ) . The most dramatic effect was observed for the Lactate dehydrogenase ( Ldh ) gene , the sole enzyme responsible for the conversion of pyruvate to lactate , which was significantly up-regulated in controls upon OS induction and dramatically overinduced in G9a mutants ( fold increase at 12 h after OS = 17 . 91 for controls , 95 . 83-fold for G9a mutants , p = 0 . 012 , Fig 5M ) . Like the hyperactivated genes operating in OS defense ( GstE1 , Prx2450-1 , and Cat ) , Ldh was previously identified as a potential direct target gene of G9a [22] . We validated the identified expression changes for several metabolic genes , including Ldh , in an independent RT-qPCR experiment ( S4 Fig ) . Having observed misregulation of several genes involved in fat metabolism ( see fatty acid beta oxidation and triglyceride synthesis , Fig 5 , S6 and S7 Figs ) and energy use ( Ldh , Fig 5M ) in G9a mutant heads at steady state and upon OS induction , we asked whether G9a mutants show altered energy use during OS exposure . In controls , glycogen stores were gradually depleted at a rate of −0 . 29 normalized glycogen units ( ngu ) /h over the time course of OS exposure ( Fig 6A ) . Although glycogen levels were 2 . 5-fold higher in G9a mutants at steady state compared to controls ( G9a mutants = 19 . 75 ngu , controls = 7 . 73 ngu , p < 0 . 0001 , Fig 6A ) , glycogen stores got depleted at a much higher , exponential rate ( −0 . 99 ngu/h ) in G9a mutants , reaching values lower than in controls after 12 h of OS . These differences in glycogen metabolism might be mediated through posttranslational modifications rather than transcriptional changes , since mRNA levels of glycogen regulators were overall not significantly different in G9a mutants versus controls ( Fig 5 and S7 Fig ) . Similar to glycogen energy stores , triglyceride levels were also highly increased in G9a mutants at steady state when compared to controls ( G9a mutants = 462 . 81 normalized triglyceride units [ntu] , controls = 150 . 06 ntu , p < 0 . 0001 , Fig 6B ) . Upon OS exposure , triglyceride levels gradually decreased in control flies at a rate of −4 . 68 ntu/h ( controls 0 h versus 18 h , p < 0 . 001 ) . In contrast , triglyceride levels in the G9a mutants did not decrease during OS exposure ( Fig 6B ) . In agreement with the measured high triglyceride levels in G9a mutants , histological sectioning revealed that G9a mutant heads , in comparison to controls , presented with a striking increase in overall pericerebral fat body size and in lipid droplet diameter ( Fig 6C ) . Thus , G9a mutant heads showed a dramatic increase in steady-state energy stores and showed altered use of these stores during the OS response . This is consistent with the observed misregulation of genes involved in lipid metabolism in G9a mutants at steady state and during OS exposure ( Fig 5 , S6 and S7 Figs ) . We asked whether the observed differences of energy stores between G9a mutants at steady state and during OS exposure are due to differences in feeding . We found that G9a mutants have normal food intake; they eat neither more at steady state nor less upon OS exposure , as could be expected in light of increased steady-state energy stores and quickly declining glycogen ( S8 Fig ) . Taken together , these data show that G9a mutants rapidly used up glycogen during the OS response , and they failed to mobilize triglycerides . The data also indicate that the observed differences in energy stores result from transcriptional and metabolic dysregulation that occurs despite normal food intake . Because the results above revealed abnormal energy consumption and metabolic dysregulation in G9a mutants , we investigated the role of G9a in tissues regulating energy homeostasis . We performed tissue-specific knockdown of G9a in fat body tissue using the lsp2-Gal4 driver , in insulin-secreting cells using the dilp2-Gal4 driver , and in all neurons using the elav-Gal4 driver ( Fig 7A and 7B and S9 Fig ) . The Drosophila fat body is the main organ for energy storage and important for energy homeostasis during growth and development as well as in immediate energy release in response to environmental stressors such as starvation , infection [16] , and OS [3 , 29] . G9a knockdown in the fat body reduced survival upon OS , demonstrating a crucial function of G9a in fat body tissue during OS induction by paraquat ( Fig 7A ) . Drosophila insulin-like peptide 2 ( dilp2 ) neurons have a neuroendocrine function and are responsible for orchestrating hormonal regulation of energy storage and release via secretion of dilps , which act on fat body , muscles , gut , and other organs involved in uptake , storage , synthesis , and release of energy [30] . Decreasing G9a levels in dilp2-expressing neurons also resulted in reduced OS resistance ( Fig 7B ) . In contrast , panneuronal knockdown using elav-Gal4 did not have an effect ( S9 Fig ) , possibly because elav-driven Gal4 is not as strongly expressed as dilp2-driven Gal4 in dilp2-expressing neurons . Taken together , the data suggest that G9a safeguards energy homeostasis during OS in tissues with a known role in mediating organismal energy homeostasis . Finally , we asked whether energy could be a limiting factor for survival in our paraquat-induced OS regime and whether abnormal energy consumption in G9a mutants underlies their premature death in response to OS . We addressed these questions by manipulation of glucose availability in G9a mutant and control animals . First , we provided a high dose of glucose as an immediately accessible energy source ( Fig 8A ) . This high-sugar diet restored survival of G9a mutants to control levels ( G9a null versus G9a null high sugar , p < 0 . 0001; controls versus G9a null high sugar , p = 0 . 885 ) while also extending the survival of controls ( controls versus controls high sugar , p > 0 . 0001 ( Fig 8A ) . In contrast , when flies were provided a high-protein diet , no improvement of survival in G9a mutants or controls was observed ( Fig 8B ) . Improved OS resistance in response to a high-sugar diet in G9a mutants and controls suggests that energy availability is generally a limiting factor in OS resistance . To directly address if limiting energy availability affects OS resistance , we determined survival of flies with ubiquitous knockdown of glycogen phosphorylase ( GlyP ) , the rate-limiting enzyme responsible for glycogen breakdown into glucose . Ubiquitous GlyP RNAi knockdown resulted in a 66% reduction in GlyP mRNA ( Fig 8C ) and caused partial lethality . The surviving GlyP-knockdown adults recapitulated the reduced OS tolerance of G9a mutants ( Fig 8D ) . Taken together , these data show that access to energy stores is a limiting factor in resistance to OS exposure . G9a mutants , which are characterized by a perturbed metabolic response to OS resulting in a net reduction of available energy , show reduced OS resistance .
We propose that three mechanisms may contribute to increased OS susceptibility in G9a mutants: ( 1 ) inaccessible fat stores , ( 2 ) inefficient use and thus accelerated wasting of glycogen , and ( 3 ) an overactivated transcriptional response to OS that will consume additional energy . At steady state , expression of the lipid phosphatases CG11437 and CG11438 , which are important for triglyceride synthesis , are up-regulated in G9a mutants ( Fig 5K and S4B , S6 and S7 Figs ) . Furthermore , expression of heimdall and bubblegum , two important enzymes in charge of breaking down triglyceride long-chain fatty acids , are down-regulated in G9a mutants ( Fig 5H and S4B , S6 and S7 Figs ) . These findings may account for the identified high triglyceride stores and increased abundance and size of fat body lipid droplets in G9a mutants at steady state ( Fig 6B and 6C ) . Heimdall and bubblegum expression levels remain low in G9a mutants during OS exposure , whereas in controls heimdall is strongly induced , which could explain the defect in lipid mobilization during the OS response . Three transcription factors are known to bind to both heimdall and bubblegum cis-regulatory elements ( dorsal , kruppel , and medea [31] ) , but none of them has been linked to G9a , and in general little is known about G9a’s sequence-specific cofactors . Whereas triglycerides remain inaccessible in G9a mutants , we observed a rapid exhaustion of glycogen stores , suggesting that there is a high energy demand during the OS response . Depletion of glycogen stores during OS exposure has been reported previously [32] . Energy is needed for the rapid production and activity of ROS protective enzymes . In the absence of G9a , ROS protective enzymes are overactivated ( Fig 3 ) . Our data argue that this inappropriate scaling of the transcriptional response to OS causes an additional energy demand in G9a mutants that is exacerbated by their deficiency in triglyceride breakdown , resulting in early death due to lack of available energy . Some of the overactivated genes , such as GstE1 , Prx2450-1 , Cat , and Ldh , were previously identified as targets for G9a-mediated H3K9me2 . This suggests that this epigenetic modification might serve to buffer stress-induced gene activation , as we have previously observed in the response to viral infection [16] . However , these epigenetic differences were identified originally in whole larvae , and it remains to be seen if chromatin changes are also present in G9a mutant heads or in other specific tissues/cells that we identified to be important , such as the fat body or dilp2-expressing neurons . Our data further indicate that G9a mutants may suffer from an OS-induced metabolic shift resulting in energy wasting , known as the Warburg effect or aerobic glycolysis [33] . The rapid use of glycogen ( Fig 6A ) and the highly increased expression of Ldh in G9a mutants under stress conditions ( Fig 5M ) are consistent with this metabolic state that is often seen in tumors , in which all energy is derived from glycolysis rather than from mitochondrial oxidative phosphorylation , despite the presence of oxygen . Under these conditions the main product of glycolysis , pyruvate , is converted to lactate by Ldh , a process that produces energy quickly but less efficiently than mitochondrial oxidative phosphorylation . Recently , it was suggested that G9a is required for optimal survival in response to starvation in Drosophila [17] . An and colleagues [17] proposed that the role of G9a is highly specific for this type of stress , as they were unable to detect susceptibility of G9a mutants to heat stress and observed no difference in survival time upon exposure to 10 mM paraquat when compared to controls . In contrast , we demonstrate the importance of G9a in the transcriptional and physiological response to OS and have previously provided evidence for G9a’s role in the response to virus infections [16] . In addition , we have observed that G9a-deficient flies are sensitive to several stresses in addition to paraquat , including the alternative potent OS-inducing agent MSB ( S1B Fig ) and other stressors such as heat stress ( S10A Fig ) and cold shock ( S10B Fig ) . Our observations are supported by several recent cell culture studies indicating the importance of G9a in hypoxia resistance during the rapid proliferation of cancer cell lines [14 , 15] . Thus , in contrast to previous conclusions [17] , it appears that Drosophila G9a has a protective function in response to multiple different types of stress . Another recent study has shown that G9a mutants show increased resistance to starvation on agar media [34] , which is consistent with our own findings ( S10C Fig ) and makes sense in light of the increased steady-state energy stores . The inconsistencies with An and colleagues [17] might arise from methodological differences that were employed by the authors , such as the use of high temperature ( 29°C ) in combination with using filter paper during starvation assays , as opposed to agar media . It is conceivable that these conditions might result in confounding factors such as dehydration . Here , we report reduced survival of two G9a-null mutants and G9a RNAi-mediated knockdown flies upon exposure to 50 mM paraquat , which induces lethality within 48 h ( Fig 1 ) . An and colleagues concluded that G9a is not important for OS resistance . Their applied level of OS using 10 mM paraquat appears to be mild , as flies survived for up to 20 d [17] . This may suggest that G9a is particularly essential to defeat high levels of OS , which may be supported by our observation that G9a was not required for OS resistance when exposed to 5% H2O2 ( S10D Fig ) , a milder OS agent than paraquat . It is also compatible with our finding that G9a mutants are well able to combat ROS and oxidative damage , even under high OS as tested in our study , and that decreased OS resistance results from energy shortage . An and colleagues also observe that G9a mutants have a higher level of glycogen in steady-state conditions , which is rapidly depleted in response to starvation . They did not observe increased triglycerides in G9a mutant flies , nor did they observe an inability to access these stores in response to starvation , findings for which we provided evidence both in histology as well as in metabolite assays ( Fig 6 ) . Although the discrepancy at steady state may relate to the different G9a mutant that they have utilized , triglyceride usage in G9a mutants may also be specific to the type of stress . Taken together , these studies suggest that G9a is involved in a complex metabolic response to multiple types of environmental stress . Recent studies have shown that G9a dampens expression of target genes regulating hypoxia , tumor suppression , autophagy , and angiogenesis , making it an attractive target to interfere with cancer progression [14 , 15] . Of note , mutations in the G9a orthologue EHMT1 in humans cause Kleefstra syndrome , a neurodevelopmental disorder that is characterized by intellectual disability and autism [24 , 35] and that also shows neurodegenerative features [36] . Whether the disorder is characterized by increased cancer resistance and/or metabolic defects is unknown , but increased frequency of obesity has been reported as a feature of Kleefstra syndrome [37 , 38] . Combined loss of Drosophila heimdall and bubblegum , the two key enzymes of triglyceride long-chain fatty acid breakdown that we found to be highly down-regulated in G9a mutants , causes neurodegeneration [39] , raising the possibility that equivalent metabolic mechanisms could underlie neurodegenerative features of Kleefstra syndrome [36] . Our data propose metabolic dysregulation as a novel hypothesis about the mechanisms underlying physiological and pathological aspects of Kleefstra syndrome , meriting novel lines of clinical investigation . In conclusion , we have identified a role for Drosophila G9a in the transcriptional and metabolic response to systemic OS exposure . These findings were obtained by feeding adult Drosophila a lethal dose of paraquat . Paraquat has been used as a herbicide in agriculture and adverse effects on health due to chronic exposure are well studied in humans [40] . Although our experimental conditions do not mimic the environmental exposure in human populations , it has allowed us to identify the mechanistic role of G9a as a generally protective factor in the organismal response to environmental stress .
Flies were reared on standard medium ( cornmeal/sugar/yeast ) at 25°C and 70% humidity on a 12 h light/dark cycle . Flies were reared at 28°C and 60% humidity for tissue-specific G9a RNAi-mediated knockdown . G9aDD1 and G9aDD2 mutants were generated previously by P-element excision [22] . Although both mutants show undetectable G9a protein by western blot , G9aDD2 shows slightly milder phenotypes than G9aDD1 [22] , indicating that G9aDD2 may be a strong hypomorph rather than a G9a-null allele . A precise transposon excision line has been generated in the same genetic background and served as a control for G9a-null mutants in all experiments . After verification of OS survival in G9aDD1 and G9aDD2 ( Fig 1A and S1A Fig ) , we continued further experiments using G9aDD1 , referred to as G9a-null mutant throughout the paper . The following driver lines were obtained from the Bloomington Drosophila stock center ( Indiana University ) actin-Gal4 , lsp2-Gal4 , dilp2-Gal4: yw; actin-Gal4/CyO ( BL4414 ) , yw;; lsp2-Gal4 ( BL6357 ) , w1118; dilp2-Gal4/CyO ( BL37516 ) . The driver w1118; UAS-Dicer-2/CyO GFP; elav-Gal4/TM6C was assembled in house . To generate the G9a- and GlyP-knockdown progenies and their isogenic controls , the drivers were crossed to the UAS-G9a-RNAi ( w1118; UAS-G9a-RNAi/CyO [VDRC25474] ) and UAS-GlyP-RNAi ( w1118; UAS-GlyP-RNAi/CyO [VDRC27928] ) lines and to the isogenic background of the two RNAi lines ( w1118 [VDRC60000] ) . VDRC25474 , VDRC27928 , and VDRC60000 lines were obtained from the Vienna Drosophila Resource Center ( VDRC ) . Flies were collected after eclosion and allowed to recover from CO2 exposure for 5 d prior to paraquat exposure . Paraquat ( Methyl viologen dichloride hydrate 98%; Sigma 856177 ) was mixed into the fly food at 40°C to a final concentration of 50 mM . For OS induction , 5–9 d old flies were transferred to paraquat-containing food and incubated at 25°C and 70% humidity . At each time point , flies were flash frozen in liquid nitrogen followed by vortexing and filtering through a series of sieves to isolate heads from other body parts . Fly heads were used for RNA extraction and metabolic measurements . TriKinetics Drosophila Activity Monitors ( DAM2 ) were used to quantify survival during paraquat exposure . Flies ( 5–9 d old ) were allowed to recover from CO2 exposure for 5 d . They were transferred by aspiration into 5 mm diameter tubes containing normal food or food supplemented with 50 mM paraquat and incubated at 25°C and 70% humidity . Raw activity monitor files were processed using DAMFileScan110 software ( TriKinetics ) . Monitor counts were binned per hour , and time of death was determined when counts reached zero . Survival curves showing mean survival over time were plotted using Graphpad Prism . p-Values were obtained using the Gehan-Breslow-Wilcoxon test . Experiments were repeated at least three times . Food intake was quantified using the automated , high-resolution behavioral-monitoring system flyPAD [41] . Fully fed individual male flies were placed in flyPAD arenas for 1 h with standard food containing 1% agarose and 50 mM paraquat , following 0 , 6 , or 12 h of paraquat treatment . Total number of sips , previously shown to be the most reliable measure of food intake [41] , was used . For heat stress , two groups of 20 flies in standard food vials were put in a water bath at 37°C . For cold shock , five groups of 20 flies in empty vials were put in a salt brine ice bath at −5°C for 1 h and moved to standard food vials for recovery . For MSB ( Sigma M5750 ) , flies were exposed to 75 mM MSB-containing food . For starvation , 16 individual flies per genotype were transferred into vials containing 1% agar . For H2O2 , five groups of 20 flies were put on fly food containing 5% H2O2 . Survival was monitored manually or using DAM activity monitors ( Materials and methods subsection Paraquat survival assay ) . During all stress assays except heat stress , survival was monitored at 25°C and 70% humidity . All experiments were repeated at least twice . Survival curves showing percent survival over time were plotted using Graphpad , and p-values were obtained using the Gehan-Breslow-Wilcoxon test . Food was prepared with four-times-increased sugar dose ( 440 g/liter ) or two-times-increased dry baker’s yeast ( 56 g/liter ) to obtain a high-sugar or high-protein diet , respectively . Flies were transferred to vials containing either of these diets and 50 mM paraquat and monitored for survival . Survival curves showing percent survival over time and SE were plotted using Graphpad , and p-values are obtained using the Gehan-Breslow-Wilcoxon statistical test . Experiments were repeated three times . Standard food was prepared , and paraquat was added to a final concentration of 50 mM . Then , 0 . 5 mM vitamin E ( α-Tocopherol , Sigma 258024 ) and 0 . 25 mM reduced glutathione ( L-Glutathione , Sigma G4251 ) were added to paraquat-containing food , and flies were subsequently monitored for survival . Survival curves showing percent survival over time were plotted using Graphpad , and p-values are obtained using the Gehan-Breslow-Wilcoxon statistical test . Experiments were repeated two times . RNA was extracted from 200 fly heads per sample using QIAGEN lipid mini tissue kit . The TruSeq RNA Sample Preparation Kit ( Illumina ) was used to prepare adapter ligated PCR fragments for sequencing . In brief , mRNA was purified from total RNA and fragmented . The cleaved mRNA was primed with random hexamers and reverse transcribed into first-strand cDNA . The RNA template was then removed , and a replacement , complementary strand was generated . The ends of the double-stranded cDNA were repaired and adenylated . Then , sequencing adapters were ligated to the prepared cDNA . PCR was used to selectively enrich the fragments containing the adapters . The PCR fragments were validated using Agilent 2200 TapeStation . Single indexed samples were multiplexed and sequenced on an Illumina HiSeq 2500 sequencing system in single-end mode with a read length of 30 bp . Quality of sequenced reads was assessed with FastQC . The RNA-seq experiments were conducted on two biological duplicates for each condition . Sequenced reads were aligned with the Burrows-Wheeler algorithm ( BWA ) [42] to the Drosophila reference genome ( BDGP 5 ) , and per-gene read counts were generated with HTSeq count [43] . A total of 25–30 million reads with high-quality alignment were obtained for each sample and used for differential expression analysis ( S1 Data ) . DESeq [25] was used to obtain library size–normalized read counts and to generate heatmap and principle component plots . DE genes ( fold change ≥ 1 . 5 , adjusted p-value ≤ 0 . 05 , Benjamini-Hochberg ) were identified using DESeq in seven pairwise comparisons: control 0 h versus 6 h , control 0 h versus 12 h , G9a mutant 0 h versus 6 h , G9a mutant 0 h versus 12 h , G9a mutant versus control 0 h , G9a mutant versus control 6 h , and G9a mutant versus control 12 h after OS exposure ( Fig 2 , S3 Fig and S2 Data ) . The RNA-seq data are available at the NCBI Gene Expression Omnibus under series accession number GSE110240 . RNA was isolated from fly heads in triplicate using the RNeasy Lipid Tissue Mini Kit ( QIAgen ) , with DNase treatment , and cDNA synthesis was performed using iScript Reverse Transcription Supermix or the SensiFAST cDNA Synthesis Kit ( Bioline ) . RT-qPCR was performed using a 7500 Fast Real-Time PCR System ( Applied Biosystems ) and the BioRad CFX 384 with the GoTaq Green Master Mix ( Promega ) or SensiFAST SYBR No-Rox kit . Expression of target genes was normalized to transcript levels of the reference genes betacop , gamma-tubulin 23C , and eIF2 . Detections of GlyP RNAi-mediated knockdown was done as previously described [44] . All primers ( S4 Data ) were validated for efficiency according to standard procedures . Clustering was performed using the PAM algorithm [26] . DE genes were clustered based on log2 fold changes in the four different pairwise comparisons . GO analysis was performed on the five principle groups ( Fig 2C ) using Panther ( http://pantherdb . org/ ) [45] with all Drosophila genes as the background and with Bonferroni-corrected p-values . Annotation of enzymes involved in ROS ( Fig 3 ) and metabolic pathways ( Fig 5 ) is based on GO as well as manual annotation of known enzymes involved in these processes . A complete list of genes and gene expression values for the different ROS and metabolic pathway groups shown in Figs 3 and 5 is provided as S3 Data . Groups of 20 fly heads in triplicate were used for metabolic measurements . H2O2 , lipid peroxidation , and triglyceride and glycogen levels were measured using H2O2 colorimetric assay ( K265 ) , lipid peroxidation ( MDA ) colorimetric assay ( 739 ) , triglyceride quantification colorimetric assay ( K622 ) , and glycogen colorimetric assay kit II ( K648 ) , respectively , according to the manufacturer’s protocols ( BioVision ) . Protein level was measured in parallel for each sample using Pierce BCA Protein Assay Kit , and metabolite levels were normalized to total protein content . Bar graphs showing mean values and standard error of the means ( SEMs ) were generated using Graphpad , and p-values were obtained using multiple t tests followed by FDR correction according to Benjamini , Krieger , and Yekutieli . Fly heads were collected in 2% glutaraldehyde buffered with 0 . 1 M sodium cacodylate ( pH 7 . 4 ) , postfixed in 1% osmium tetroxide in Palade buffer ( pH 7 . 4 ) with 0 . 5% potassium hexacyanoferrate ( III ) -trihydrate and after dehydration in ethanol and propylene oxide . Drops of 5% triton X-100 in PBS were added to decrease surface tension . Heads were embedded in EPON epoxy resin and fixed at 50°C overnight . Embedded fly heads were cut transversally with a microtome blade to the plane where optical lobes connect to the central brain , for consistency between sections . For light-microscopy imaging , semithin slices ( 1 μm ) were cut and subsequently stained with toluidine blue , and images were captured at 10× magnification using an Axioskop 2 plus microscope . For scanning electron microscopy images , ultrathin sections ( ±80 nm ) are made and contrasted with 6% uranyl acetate and lead citrate solutions . Images were captured on a JEOL 6310 SEM . | Stress responses require proper activation of genetic programs to protect the organism from the stressor . However , the mechanisms controlling energy consumption during stress responses are not well understood . Here , we investigate the role of epigenetic modifier G9a in regulating metabolism and gene transcription during oxidative stress responses in Drosophila . Flies lacking G9a show a shift in the metabolic and transcriptional responses to oxidative stress , leading to decreased stress tolerance despite intact oxidative stress defense mechanisms . During oxidative stress exposure , G9a mutants show overactivation of stress response and many other genes , rapid depletion of glycogen energy stores , and an inability to access lipid energy stores . The increased susceptibility of G9a mutant flies to oxidative stress can be rescued simply by providing extra sugar . This suggests that G9a mutants are sensitive to stress because of reduced access to immediately available energy . Wild-type flies also become more tolerant to oxidative stress when they are fed extra sugar , whereas blocking energy access by genetically reducing a key metabolic enzyme leads to oxidative stress sensitivity . Though the genetic response to oxidative stress has long been appreciated , our study emphasizes the importance of energy metabolism for stress tolerance and identifies the histone methyltransferase G9a as an important player regulating both . | [
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] | 2019 | The histone methyltransferase G9a regulates tolerance to oxidative stress–induced energy consumption |
ClinicalTrials . gov NCT02397772
Periodic large-scale administration of anthelmintic medication to populations at risk can effectively reduce the disease burden of soil-transmitted helminth ( STH ) infections [1] , which together are responsible for an estimated 4 . 98 million years lived with disability each year [2] . For this reason , the World Health Organization ( WHO ) has set the goal for STH as—control of morbidity attributable to infection , with a treatment coverage target of 75% of pre-school and school-age children living in areas of risk by 2020 [3] . Countries are rapidly progressing towards this goal: by 2016 , preventive chemotherapy programmes were delivering a reported 947 . 9 million benzamidazole treatments globally , either as part of LF elimination programmes ( 495 . 6 million treatments ) or as school-based deworming ( 452 . 3 million treatments ) [1] . The global community is increasingly looking beyond these targets to ask whether transmission interruption is possible [4–7] , with several large-scale impact evaluations currently examining the impact of mass deworming targeting all ages [8–10] . However , there are few comprehensive contemporary evaluations of STH epidemiology in communities that have undergone repeat rounds of mass drug administration , with the majority of studies focusing on school children . In SSA , for example , only 10% of the STH surveys available through the Global Atlas of Helminth Infection included adults and the majority of these were conducted prior to large-scale intervention [11 , 12] . A similar pattern is seen for Asia [13] . To understand how best to reduce STH transmission , it is essential to have age-stratified epidemiological data , as these can highlight age groups among whom infection remains common , most intense and therefore serve as a reservoir to re-infect the treated age groups . In this study , we present data from a large-scale cross-sectional survey of STH infection and intensity conducted in 2015 in Kwale county , Kenya , which has benefitted from consecutive , annual rounds of deworming implemented by the Ministry of Health and Ministry of Science Education and Technology through the National School Based Deworming Programme ( NSBDP ) since 2012 [14] . The dataset used represents the baseline parasitological survey for the TUMIKIA trial , an evaluation of school- versus community-wide annual and biannual deworming conducted between 2015 and 2017 [8 , 15] . The aim of the present analysis is to describe the population distribution of STH species infection and investigate associated environmental , household and individual risk factors .
The survey was conducted in Kwale County , Kenya . Kwale is an environmentally heterogeneous area , comprising four subcounties , with a total population of approximately 760 , 897 [16] . As a county , Kwale demonstrates high levels of income inequality and low levels of access to water and sanitation . The region is primarily rural , with many communities living on subsistence farming of maize and cassava . The predominant STH species is hookworm , with T . trichiura and A . lumbricoides found to a lesser extent [17 , 18] . The county has received a number of anthelmintic interventions . Annual school-based deworming with albendazole has been implemented successfully since 2012 ( with a prior round in 2009 ) with programmatic coverage of 82% of pre-school and school-age children ( 2–14 years ) in 2014 [19 , 20] . Additionally , there have been four rounds of community-based mass drug administration ( MDA ) for lymphatic filariasis ( LF ) , in which albendazole and diethylcarbamazine ( DEC ) were delivered to individuals aged 2 years and above . These LF MDA rounds were implemented in Kwale County in 2003 , 2005 , 2008 and 2011 . For the two most recent MDA rounds prior to this survey , 2008 and 2011 , programmatic coverage levels of 62 . 7% and 58 . 3% were reported [11 , 20 , 21] . For the ten years prior to this survey , Child Health Days ( Malezi Bora ) have been implemented on a biannual basis , targeting children under five years with benzimidazoles—either albendazole or mebendazole—and vitamin A [22] . However , coverage has been low , with surveys indicating as few as 19 . 6% of young children ( aged 12–59 months ) were dewormed through this programme in 2012 [22] . Finally , national guidelines state pregnant women are to receive benzimidazoles during their antenatal clinic visits from the second trimester . This survey constitutes the baseline survey for the TUMIKIA trial , a cluster randomised evaluation of the impact of alternative treatment strategies on the prevalence and intensity of STH infection . The sample size determination for the trial outcomes is detailed by Brooker et al ( 2015 ) and resulted in the selection of 225 individuals in each of 120 clusters [8] . An initial census of households in Kwale County was used to delineate community clusters , broadly synonymous with Ministry of Health ( MoH ) Community Units ( CU ) , a health-service delivery structure covering a population of approximately 1 , 000 households . Of 130 clusters delineated across Kwale County , 120 were included in the survey ( Figs 1 and 2a ) comprising a population of approximately 540 , 000 . Sample clusters comprised between 1 and 24 villages ( median 7 ) , and cluster size varied between 267 households ( an island ) and 1676 households ( an urban setting ) with a median of 851 . In each cluster , 225 households were selected using simple random sampling , regardless of cluster size , with an additional 75 households selected to replace refusals or migrated households . This sample size and approach was based on the trial design , powered to compare the primary outcome across arms . At each household , structured questionnaires were conducted with heads of household or primary caregiver on smartphones using SurveyCTO software ( Dobility , Inc . , dobility . com ) . The questionnaire manual , pdf and electronic form can be downloaded via this link: https://www . lshtm . ac . uk/research/centres-projects-groups/laser#tools--training . Household locations were collected in World Geodetic System 1984 datum using the smartphones’ Global Positioning System ( GPS ) . In each sampled household , all household members were first enumerated along with their demographic information and education status , and a randomisation function then selected an individual to provide a stool sample . Eligibility criteria included: individuals 2 years old and above , who provided informed consent ( and assent where appropriate ) . If the first sampled individual was not present and could not be located or was unwilling or unable to provide a stool sample , the form selected a replacement individual , up to a total of three per household . Observed household construction materials and reported household assets were collected to derive a measure of socioeconomic status ( SES ) . Information on reported household access to water , sanitation , and hygiene ( WASH ) facilities was collected , and structured observations were made of sanitation and hygiene facilities . Individual-level information collected from the member providing the stool sample included WASH-related behaviour , observed shoe wearing and reported history of deworming . Questionnaire data were uploaded from the smartphones daily . Assembled questionnaire data were linked to parasitology results using a unique identifier . Discrepancies and queries during linkage were verified against the original laboratory books . Stool samples were transported to a nearby health facility laboratory and examined in duplicate within one hour of processing , using the quantitative Kato-Katz ( KK ) thick smear method ( 41 . 7mg templates ) . Duplicate slides prepared from the single day stool samples were read by independent microscopists and a 10% quality control check was performed by a supervisor . Egg counts were enumerated for each species separately . The outcomes of interest were presence and intensity of infection with hookworm , T . trichiura and A . lumbricoides . Infection was defined based on presence of at least one egg across the duplicate slide readings , and intensity was expressed as the arithmetic mean of eggs per gram ( epg ) of faeces across the two slides . Infection intensities were categorised according to the WHO classification; for hookworm light ( 1–1 , 999 epg ) moderate ( 2 , 000–3 , 999 epg ) and heavy ( >4 , 000 epg ) ; for T . trichiura light ( 1–999 epg ) moderate ( 1 , 000–9 , 999 epg ) and heavy ( >10 , 000 epg ) ; for A . lumbricoides light ( 1–4 , 999 epg ) moderate ( 5 , 000–49 , 999 epg ) and heavy ( >50 , 000 epg ) [23] . A suite of environmental and topographic datasets were explored as potential environmental drivers of STH in the study area . Further details are provided in supplementary information ( S1 Text , S1 and S2 Figs ) but in brief , maps of Enhanced Vegetation Index ( EVI ) and Land Surface Temperature ( LST ) were produced by processing satellite images provided by the Moderate Resolution Imaging Spectroradiometer ( MODIS ) instrument operating in the Terra spacecraft ( NASA ) at a resolution of 250m [24 , 25] . The fortnightly continuous gridded maps were aggregated by calculating the mean for the period of study . We obtained raster datasets of elevation and aridity at 1km2 from the Consortium for Spatial Information ( CGIAR-CSI ) [26] . Estimates of soil acidity ( pH KCl ) and sand content were extracted from soilgrids . org at a resolution of 250m [27] . Estimates of population density for 2015 were obtained from the WorldPop project , which was used to classify areas as urban , peri-urban or rural areas [28] . The range of environmental and topographic data were extracted using point-based extraction for each household using ArcGIS 10 . 3 ( Environmental Systems Research Institute Inc . Redlands , CA , US ) . Households without GPS coordinates were given the village mean or mode value for continuous and categorical environmental measures , respectively . Continuous variables were categorised by tertiles for analysis . Data management and analyses were performed using STATA version 14 . 0 ( STATA Corporation , College Station , TX , USA ) . Prevalence and intensity descriptive analyses for the three STH species were calculated using robust standard errors to allow for clustering . A factor analysis was used to determine the relative SES of households surveyed , using dichotomous indicators of ownership of a motorbike , bicycle , mobile phone , radio , television , electricity , sofa set and household wall and roof materials . Factor analyses were performed separately for rural and urban/peri-urban households , because of the difference in distribution of these variables in relation to SES between these distinct settings . The indices were divided into quintiles within each setting and subsequently the second , third and fourth quintiles were combined ( middle ) prior to analysis , with the first ( poorest ) and fifth ( least poor ) indicating the relative wealth extremes . During analysis the poorest and least poor groups were compared against the middle group , taken as the reference group . Risk factor analyses were conducted for hookworm and T . trichiura infection . A . lumbricoides infection was not modelled due to very low infection levels found in the study population . Population-averaged models were used to estimate associations for both presence and intensity of infection . Univariable associations between presence of infection and individual- , household- and environmental-risk factors were assessed using generalised estimating equations ( GEE ) , assuming a binomial probability distribution , and an exchangeable correlation structure , accounting for clustering at the community unit ( cluster ) level [29] . Associations between STH intensity ( epg ) and risk factors were modelled using a zero-inflated negative binomial regression [30] of egg counts , with quantity of stool assessed per sample included as an offset . Quantity of stool assessed was calculated according to the template size ( 41 . 7mg ) and number of slides prepared and read per sample . The model accounted for clustering at the community unit ( cluster ) level using a clustered sandwich estimator . Both eggs per sample and egg output per person were well described by the negative binomial distribution with variance much greater in value than the mean across faecal samples and across people . Variables in the inflation model were decided a priori as age , sex and aridity , with aridity being used as an indicator of environmental suitability for STH transmission [31] . A priori interactions between age and sex were investigated in the prevalence models for both species . We used a backwards stepwise strategy to build separate multivariable models for prevalence and intensity of both species . Separate subgroup analyses were conducted to estimate the relationship between both hookworm and T . trichiura infection and observed sanitation conditions in those households with facilities , using GEE , assuming a binomial probability distribution , and an exchangeable correlation structure , accounting for clustering at the community unit ( cluster ) level and adjusting for SES . The study was approved by the Kenya Medical Research Institute ( KEMRI ) Scientific and Ethics Review Unit ( SERU No . 2826 ) and the London School of Hygiene & Tropical Medicine ( LSHTM ) Ethics Committee ( #7177 ) . Prior to the survey , stakeholders’ meetings were held at the national , county and sub-county levels , after which sensitization meetings were held with chiefs and assistant chiefs from all locations and sub-locations . The 880 villages included in the TUMIKIA Project were sensitized through a total of 583 village meetings . Written informed consent was sought from the household head or adult answering the household-level questionnaire , and consent was also sought from the individual selected to provide the stool sample and complete the individual-level questionnaire . Parental consent was sought for participants between 2 and 17 years and written assent was additionally obtained from children aged 13 to 17 years . All information and consent procedures were conducted in Kiswahili .
Of the 25 , 448 households initially approached for interview , 661 ( 2 . 6% ) households refused and 1 , 373 ( 5 . 4% ) were unavailable at the time of the household visit ( Fig 1 ) . Among households where a survey was conducted , in 626 ( 3 . 0% ) households the individuals selected to provide samples did not provide consent . The individuals for whom a sample could not be matched ( n = 3104 ) were very similar to the final individuals included in the analyses with respect to demographic and economic characteristics ( S3 Table ) . A total of 19 , 684 individuals aged 2 years and above gave consent ( and assent where required ) and provided stool samples , which were linked with household- and individual-level information , and are included in the analyses . Median age was 22 years ( inter-quartile range , IQR: 9–40 ) and the male:female ratio was 0 . 67 , due to under-sampling of adult males , particularly in the 20–40 year age group ( Fig 3a ) . Overall , 21 . 5% of sampled individuals were infected with an STH infection ( 15 . 6% in under 5s , 20 . 9% in 5–14 years and 22 . 5% in ≥15 years ) and 2 . 2% had moderate-high intensity ( MHI ) infections ( 1 . 8% in under 5s , 1 . 6% in 5–14 years and 2 . 5% in adults ) . Only 7 . 1% ( n = 301 ) of infected individuals harboured multiple STH species . Initial observations of graphical outputs highlighted two outliers , one adult female with a hookworm intensity of 137 , 460 epg and one male <5 years with a T . trichiura intensity of 99 , 804 epg . These were subsequently excluded for the following descriptions and risk analyses . Prevalence of hookworm infection was 19 . 1% ( 95% confidence interval [CI]: 16 . 4–21 . 7% ) ; 12 . 5% in under 5s , 17 . 4% in 5–14 years and 20 . 7% in adults; with over 90% ( 3 , 394/3 , 750 ) of infected individuals exhibiting light infections ( < = 1 , 999 epg ) . The arithmetic mean hookworm intensity was 161 epg ( standard deviation [SD]: 1 , 083 ) , and 843 epg ( SD: 2 , 363 ) in those infected . Hookworm-infected individuals were found in 119 of the 120 clusters although considerable heterogeneity in prevalence by cluster was observed ( Fig 2b ) ; 16 clusters exhibited a hookworm prevalence below 5% , whereas hookworm prevalence exceeded 50% in three clusters . A total of 710 ( 3 . 6% ) individuals were infected with T . trichiura , with prevalence of infection varying markedly by cluster . In the 93 clusters in which infection was observed , prevalence ranged from 0 . 5% to 34 . 1% . T . trichiura infection was focal , localised to the coast , with the highest infection prevalence found on an island ( Fig 2c ) . Again , infections were predominantly low intensity , with an arithmetic mean intensity of 12 epg ( SD: 219 ) and a mean intensity of 329 epg ( SD: 1 , 109 ) in those infected . A . lumbricoides infections were rare ( 0 . 4% ) across the study area , with only 78 infections found across 33 clusters ( Fig 2d ) , and of moderate intensity in those infected ( on average 9 , 387 epg ( SD: 18 , 542 ) . Hookworm prevalence increased with age across both sexes , and was higher in males ( Fig 3a ) . Infection intensity remained fairly constant through childhood , before increasing at age twenty and above in males , and thirty and above in females . T . trichiura infection prevalence and intensity were greatest in the younger age groups , and again in males . A . lumbricoides displayed no clear age or sex patterns in prevalence ( Fig 3b ) and given the very low levels was not examined further . Fig 4a and 4b depict the relationships between community-level hookworm infection indicators for both pre-school and school-aged children ( 2–14 years ) and adults ( ≥15 years ) separately for the 120 clusters . Correlation between prevalence of hookworm infection and arithmetic mean infection intensity were ρ = 0 . 75 , p<0 . 001 and ρ = 0 . 71 , p<0 . 001 for adults and children respectively ( Fig 4a ) . A higher proportion of the MHI infections were found in adults ( ρ = 0 . 77 , p<0 . 001 ) ( Fig 4b ) , consistent with patterns shown in Fig 3a . Whereas the relationship between prevalence of infection and MHI infection in school-aged children was substantially lower ( ρ = 0 . 61 , p<0 . 001 ) , emphasising how the relationship between these two indicators may be perturbed after treatment ( Fig 4b ) . In fact , in 23 clusters , prevalence of MHI infection exceeded the 1% threshold , despite the overall infection prevalence in these clusters lying below 20% . In contrast for T . trichiura the vast majority of MHI infections were found in children . The correlations between both prevalence and prevalence of MHI infections and the arithmetic mean infection intensity was much greater in children ( ρ = 0 . 77 , p<0 . 001 ) than adults ( ρ = 0 . 42 , p<0 . 001 ) ( Fig 4c and 4d ) . Child-level prevalence of MHI infection exceeded the 1% threshold where overall infection prevalence was below 20% in 10 clusters . The majority of individual , household and community-level factors investigated were marginally associated with hookworm infection in the univariable analysis ( S1 Table ) . Adjusting for other associated factors , many of these associations remained , with males and older age groups exhibiting greater odds of infection ( Table 1 ) . Age was found to significantly modify the association of sex with hookworm infection ( p<0 . 0001 ) . The difference in odds of infection between males and females was more pronounced across increasing age groups . Wearing shoes ( Adjusted odds ratio [AOR] 0 . 71 ( 95% CI: 0 . 64–0 . 78 ) , attending school and being dewormed in the last 12 months were all found to be protective behaviours for hookworm infection . Individuals from the least poor households had reduced odds of hookworm infection , whilst those in the poorest were at increased risk ( Table 1 ) . Several household WASH factors were also associated with reduced odds of infection , including access to a private latrine ( AOR: 0 . 80 ( 95% CI: 0 . 68–0 . 94 ) and to an improved water source , as classified according to the WHO/UNICEF Joint Monitoring Programme for Water Supply , Sanitation and Hygiene ( JMP ) indicators [32] . A covered ( man-made ) floor halved the odds of infection compared to an earthen one . The influence of the community environment was observed , with positive associations between hookworm infection and high vegetation coverage , and humid conditions . There was a strong association between hookworm infection intensity and socio-economic status , with those in the poorest households having the heaviest infections and those in the highest wealth quintile the lightest ( Table 1 ) . Improved water sources were also related to lighter infections . Similarly , as with presence of infection , wearing shoes and being dewormed in the last year were protective against heavier infections , whereas being male and older in age were associated with heavier infections . In relation to environmental factors , only high levels of vegetation was associated with infection intensity . Several individual , household and environmental factors were associated with the presence of T . trichiura infection at the univariable level ( S2 Table ) . After adjusting for other associated characteristics , only a few risk factors for infection remained . Males exhibited significantly higher odds of infection than females , and school-age children had substantially greater odds of infection than either preschool-age children or adults ( Table 2 ) . No evidence of an age-sex interaction was observed . Individuals in the poorest and least poor SES quintiles had increased and reduced odds of infection , respectively , when compared with the central three quintiles . Private latrine access was protective against both presence and intensity of infection . An arid environment and higher elevation were found to be protective against T . trichiura infection . A seven-fold increase in odds of infection was observed in more humid environments ( Table 2 ) as these infections were almost exclusively restricted to the coast ( Fig 2c ) . Similarly , low-lying locations were associated with substantially higher intensity T . trichiura infections . While attending school was associated with the higher intensity infections , having been treated in the last 12 months was associated with lower intensity infections . To explore further the relationship between STH and sanitation , we conducted a sub-analysis of toilet facility type , construction , and cleanliness among households that reported access to a toilet facility and permitted us to conduct an observation of the sanitation facilities ( n = 7791 ) . After adjusting for socio-economic status , access to a latrine constructed with man-made materials and/or a washable slab was associated with reduced odds of hookworm infection . Whilst the latrine type did not appear to be associated with the odds of hookworm infection , cleanliness did , as those with visible faeces round the edge of the latrine , had a 30% increased odds of infection ( Table 3 ) . Access to a handwashing station with water and soap was protective , with a 30% reduction in odds of hookworm infection , compared with no handwashing station or one with only water available . In contrast , sanitation conditions and handwashing facilities in those households with access did not appear to be associated with the presence of T . trichiura infection .
This study of almost 20 , 000 individuals in coastal Kenya provides a detailed analysis of the community-level epidemiology of STH within the context of ongoing school-based treatment . Given the rapid uptake of national school-based deworming programmes across much of the continent , findings presented here are likely to be broadly comparable to large parts of SSA where the STH epidemiological and demographic profiles are similar . The observed hookworm prevalence of 19 . 1% was lower than the prevalence of 42% observed in a convenience sample of adults in the same coastal region of Kenya in 2010 [17] but was in line with a community-wide parasite survey conducted in four sentinel villages in the region between 2009 and 2011 , which suggested a hookworm prevalence of 21% [18] . The NSBDP reported a substantially lower prevalence of 9 . 7% , 2 . 4% and 0 . 4% for hookworm , T . trichiura and A . lumbricoides respectively in school children in Kwale in February 2015 [33] . The increase in hookworm prevalence and intensity with age across both sexes , although higher in males , is a pattern similar to that observed in the majority of published age-structured cross sectional surveys [6 , 12 , 34] . Taken together , these observations suggest that despite notable declines in STH infection in school-going children following three years of school-based deworming , the programme appears to have had less of an impact on prevalence of hookworm in adults and non-school-going children . More recently , results of a national evaluation in 2017 , following five years of school-based deworming in Kenya , indicate that despite substantial reductions in STH prevalence , complementary interventions may be required in some settings moving forward [35] . This conclusion is in line with the epidemiology of hookworm and modelling results , which show that in most settings school-based deworming alone is insufficient to reduce hookworm transmission within communities [6 , 36] . The WHO-stated aim is to reduce STH infection in school-age children to less than 1% MHI infections , under the assumption that STH-related morbidity is no longer a public health problem at this level [23] . The results presented here demonstrate that after three consecutive years of school-based deworming , Kwale sits just above this 1% threshold , with substantial heterogeneity between clusters . However , the sizeable burden of MHI infections is in adults ( 2 . 4% , males 3 . 4% and females 1 . 9% ) , who remain vulnerable to the anaemia associated with harbouring heavy hookworm burdens , especially women of childbearing age [34 , 37] . This suggests that targeting adults for deworming may have important consequences for both morbidity and transmission control in areas where STH transmission is dominated by hookworm . Conversely , our results suggest that children remain the most important targets for control and surveillance when T . trichiura is the dominant species . Understanding the nuanced relationship between prevalence and intensity of infection can be helpful when evaluating the relevance of these indicators for monitoring and surveillance of control programmes [1] . It is widely recognised that measures of infection intensity are better indicators of transmission and public health impact in communities than prevalence alone [38–40] . Control measures such as repeated MDA tend to alter the pattern of aggregation of the parasites in communities ( as measured by the negative binomial parameter k ) and concomitantly significantly alter the relationship between prevalence and intensity [41] . For low values , the prevalence is roughly constant across a wide range of mean intensity values . Prevalence in such circumstances is a poor predictor of the number of high intensity infections . The weak associations between prevalence and intensity observed here , especially for hookworm in school-aged populations , are a reflection of this pattern . This study emphasises the large degree of heterogeneity in STH infection risk across a relatively small geographic area , and confirms a number of well-known risk factors for STH infection such as the protective effect of shoe wearing in relation to hookworm . In addition to highlighting established environmental risk factors which are known to affect suitability for transmission including elevation , aridity , vegetation and soil pH [42 , 43] , we demonstrate how the relationship between infection and poverty is enduring across species , even after accounting for associated proximal risk factors such as sanitation . This finding further underlines how the poorest in endemic communities continue to experience the highest infection burden after years of control , emphasising the importance of developing equitable strategies that reach the very poorest community members . The poorest people in communities may also have low adherence to repeated rounds of deworming and low coverage at each round . For example , 78% of the children included in our survey reported attending school , although this drops from 86% in the highest quintile to 72% in the poorest quintile whom also experience the greatest infection risk . New strategies are therefore required to reach non-school-attending children in a sustainable and equitable manner . It would seem most feasible to target these individuals alongside the wider population through a community-wide treatment approach , as currently employed by the lymphatic filariasis and onchocerciasis elimination programmes . It would be essential to try and ensure high MDA coverage and good compliance over repeated rounds of MDA in such individuals . Latrine access has commonly been reported as associated with reduced risk of STH infection [44–46] , and as can be seen from these results , access to a toilet facility was associated with reduced odds of both hookworm and Trichuris infection . However , our evidence suggests that only access to a private toilet is associated with reduced infection and that shared access confers no benefits in preventing infection . This finding lends support to the guidelines that shared sanitation access be considered as basic sanitation , with private household access as improved sanitation [47 , 48] . Shared latrines are argued to be unimproved for reasons of reduced quality and cleanliness , and the finding in this setting of decreased hookworm infection in individuals with better constructed and cleaner latrines lends support to the theory that poorly constructed and poorly maintained sanitation facilities can be harmful [46 , 49] . Poorly maintained toilet facilities may concentrate faeces in an environment that individuals repeatedly come into contact with , thereby increasing harmful exposure . Similar associations between A . lumbricoides and latrine structure and cleanliness have been found in surveys of school WASH in Kenya [46] . The highly focal distribution of T . trichiura infection concentrated in the communities along the coast , although consistent with previous findings , warrants further investigation [50] . It is widely accepted that the current benzamidazoles ( albendazole and mebendazole ) demonstrate limited efficacy against T . trichiura , with reported cure rates as low as 28% [51 , 52] . This reduced efficacy would appear to be supported by our findings , as there is a notable absence of any reduction in infection risk in those individuals reporting dewormed in the last 12 months , in direct contrast to hookworm , although deworming was found associated with reduced T . trichiura intensity . However , treatment efficacy does not fully explain the highly clustered nature of this infection . Possible explanations could relate to uncaptured environmental characteristics , unmeasured behavioural or hygiene-related practices along the shoreline , or a localised history of poor treatment coverage . Interestingly , we observed an absence of association between T . trichiura infection and handwashing and toilet facility conditions in those with access , which is perhaps surprising but suggests that other hygiene-related factors may be more influential in this setting . Despite the unique scope of this robust survey , there are several important limitations . First , we encountered challenges inherent in community-based stool sampling that may have led to some bias in our final sample . We recruited participants in their homes by randomly selecting from all listed household members . Selected members whom we could not find at home or at a nearby location were replaced by another randomly selected household member . This replacement approach may have contributed to the gender imbalance observed in our sampled population . Such imbalances are frequently encountered in community-based surveys , as women and young children are more likely to be found at home during the day , while the population engaged in activities outside the home is absent . [18 , 53] . The use of a population census sampling frame—as opposed to the household census sampling frame used here—could have potentially reduced any bias introduced by on-the-spot randomisation and limited replacement from the selected households . Additionally , Kato-Katz thick smear method ( a commonly used field assessment tool for detection of STH infection presence and intensity ) is known to have low sensitivity at low levels of infection [33 , 41 , 54 , 55] . Consecutive day sampling for assessment with Kato-Katz would likely have increased the diagnostic accuracy , with evidence from western Kenya indicating greater than 20% increase in sensitivity of Kato-Katz when assessing consecutive samples [56] . However , duplicate stool collection was not logistically feasible for such a large sample in this study . An improved understanding of the distribution of STH infections and factors associated with increased risk in endemic communities is vital if we are to fully understand the impact of current and proposed STH control and elimination strategies . The data reported here show that even after repeated rounds of school-based deworming , hookworm remains prevalent throughout Kwale County , with highest prevalence and intensity observed in adult men . This suggests that school-based deworming has been insufficient to control community-wide hookworm infection , and we instead need to evaluate the impact and cost-effectiveness of community-wide treatment strategies . Our results also confirm that to have greatest impact , treatment strategies not only need to reach a wider age-range , but also those most vulnerable to infection , including the very poorest households and those without access to sanitation . An increased focus on ensuring high coverage and good compliance in the poorest families must be a priority in future efforts to control STH infection . | Soil-transmitted helminth ( STH ) infections , including Trichuris trichiura , Ascaris lumbricoides and the hookworms Ancylostoma duodenale and Necator americanus , remain endemic in many regions of sub-Saharan Africa ( SSA ) , including parts of Kenya . The current WHO-recommended treatment strategy focuses on morbidity control , and comprises periodic deworming of population groups at particular risk , including pre-school and school–age children . Consequently , the majority of epidemiological descriptions of STH have focused on infection in this age group , and are conducted using a school platform . There is therefore a notable lack of age-stratified data ( that includes adults ) from communities . We present data from a community-wide , cross-sectional survey of STH infection across 19 , 684 individuals and investigate associated risk factors at the household and individual level . We demonstrate highest prevalence and intensity of hookworm ( the predominant species in this setting ) in adults and males—a group not routinely included in deworming activities . There was marked geographic variation in infection risk across the study area , and an enduring relationship between infection risk and factors associated with poor access to sanitation and hygiene . Conducted after three years of ongoing , annual school-based deworming , the findings presented here are likely to be representative of many regions of SSA , with similar epidemiological and demographic profiles , implementing the current WHO-recommended STH control strategy . | [
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] | 2019 | Community-level epidemiology of soil-transmitted helminths in the context of school-based deworming: Baseline results of a cluster randomised trial on the coast of Kenya |
Strongyloides seroprevalence is hyper-endemic in many Australian Aboriginal and Torres Strait Islander communities , ranging from 35–60% . We report the impact on Strongyloides seroprevalence after two oral ivermectin mass drug administrations ( MDAs ) delivered 12 months apart in a remote Australian Aboriginal community . Utilizing a before and after study design , we measured Strongyloides seroprevalence through population census with sequential MDAs at baseline and month 12 . Surveys at months 6 and 18 determined changes in serostatus . Serodiagnosis was undertaken by ELISA that used sonicated Strongyloides ratti antigen to detect anti-Strongyloides IgG . Non-pregnant participants weighing ≥15 kg were administered a single 200 μg/kg ivermectin dose , repeated after 10–42 days if Strongyloides and/or scabies was diagnosed; others followed a standard alternative algorithm . A questionnaire on clinical symptoms was administered to identify adverse events from treatment and self-reported symptoms associated with serostatus . We surveyed 1013 participants at the baseline population census and 1060 ( n = 700 from baseline cohort and 360 new entrants ) at month 12 . Strongyloides seroprevalence fell from 21% ( 175/818 ) at baseline to 5% at month 6 . For participants from the baseline cohort this reduction was sustained at month 12 ( 34/618 , 6% ) , falling to 2% at month 18 after the second MDA . For new entrants to the cohort at month 12 , seroprevalence reduced from 25% ( 75/297 ) to 7% at month 18 . Strongyloides positive seroconversions for the baseline cohort six months after each MDA were 2 . 5% ( 4/157 ) at month 6 and 1% at month 18 , whilst failure to serorevert remained unchanged at 18% . At 12 months , eosinophilia was identified in 59% of baseline seropositive participants and 89% of seropositive new entrants , compared with 47%baseline seronegative participants and 51% seronegative new entrants . Seropositivity was not correlated with haemoglobin or any self-reported clinical symptoms . Clinical symptoms ascertained on the day of treatment and 24–72 hrs after , did not identify any adverse events . Two community ivermectin MDAs delivered 12 months apart by trained Aboriginal researchers in collaboration with non-Indigenous researchers resulted in a sustained and significant reduction in Strongyloides seroprevalence over 18 months . Similar reductions were seen in the baseline cohort and new entrants .
Strongyloides is a neglected tropical disease which infects an estimated 100 million people worldwide . [1] Three species are known to parasitize humans , Strongyloides stercoralis , S . fuelleborni and S . kellyi . [1 , 2] In tropical Australian Aboriginal and Torres Strait Islander communities infection with S . stercoralis is hyper-endemic[3] with seroprevalence ranging from 35–60% . [4 , 5] Strongyloidiasis can present as an acute infection with diarrhoea[6 , 7] , bloating and intestinal distention ( “pseudo-obstruction” ) , [8] hypokalaemia in children and wasting[3] however , many infections are asymptomatic . [3 , 9] Anaemia and eosinophilia have been reported in some studies[10 , 11] but not others . [12] Because of an auto infective cycle , asymptomatic carriage of S . stercoralis can persist for decades . [13] Complicated strongyloidiasis occurs when carriage of enteric bacteria by autoinfective larvae results in secondary sepsis or meningitis , or when the auto-infective cycle becomes uncontrolled , resulting in a large number of larvae disseminating to lungs and other organs . [1] Such dissemination is associated with high mortality[14] and most documented cases are in immunosuppressed individuals on corticosteroid therapy , or those with HIV and HTLV-1 infections . [3 , 15 , 16] Globally there are no public health programs targeting Strongyloides infections however , ivermectin ( the recommended treatment for Strongyloides ) has been used in mass drug administration ( MDA ) programs for other parasitic infections ( lymphatic filariasis and onchocerciasis ) for more than 20 years . [17 , 18] In Australia , standard treatment guidelines target symptomatic individuals , [19] as there have been no studies providing evidence that MDA for Strongyloides has any impact on primary health care ( PHC ) presentations or in reducing population morbidity; nor is there any evidence that ivermectin MDA for chronic Strongyloides is effective in preventing ongoing transmission and eliminating the disease . There is however , a strong emphasis on preventing disseminated strongyloidiasis by use of pre-emptive ivermectin therapy for immunosuppressed individuals from remote Indigenous communities where Strongyloides is endemic . [20] We were invited by a remote Aboriginal community to deliver an oral-ivermectin MDA targeting both Strongyloides and scabies . A three year regional skin health program reported no impact on scabies prevalence in children , [21] and the PHC service identified Strongyloides in ~25% of 300 adults screened in this community . Our aim was to determine if MDA was an effective public health measure to reduce the prevalence of both endemic infections . [22] We have previously reported the outcomes of the MDA on the prevalence of scabies;[22] here we report the outcomes against Strongyloides after two MDAs implemented in 2010 and 2011 .
The project was registered with the Australian New Zealand Clinical Trial Register ( ACTRN– 12609000654257 ) [32] and received ethical approval from Human Research Ethics Committee of the Northern Territory Department of Health and Menzies School of Health Research ( EC00153—project 09/34 ) . Following consultation with residents from a remote island community we designed a staged roll-out of two population census and MDAs delivered 12 months apart . A survey was conducted six months after each population census to follow-up participants with equivocal or positive Strongyloides results and those diagnosed with scabies . The project was evaluated in a before and after study design that has been described previously . [22] The remote tropical island community is located 550km from Darwin , Australia , with an estimated population of 2000 . [23] . The dry season is from March-September and the wet season from October-April with an average annual rainfall of 1400mm . [24] Temperatures range from 15–35°C and humidity from 20–95% . [24] The main language spoken is Djambarrpuyngu however , there are up to 12 other languages used in this community . [25] Most residents live in the main community however , ~200–400 resided in one of 10 associated “homelands” ( small satellite settlements , five of which were accessible only by air or water ) . In 2010 there were 159 houses in the community at the start of the project and 165 at the second population census and MDA in 2011 . The project team in the community comprised of a Project Manager , Parasitologist , local Aboriginal Health Practitioners ( AHPs ) , local Aboriginal Community Workers ( ACWs ) , third year pharmacy students and a PhD student . The AHPs and ACWs completed a nationally accredited training program ( Certificate II in Child Health Research 70131NT ) delivered in community to provide them with the knowledge and skills to understand research methods relevant to the project and screen for Strongyloides , scabies and skin sores . Additional accredited training was provided to the ACWs so they could perform phlebotomy ( units HLTPAT304B , HLTPAT306B , HLTPAT308B ) and assist the AHPs and pharmacy students to administer the study medications ( units HLTAP301A , CHCCS305A ) . The project commenced in the dry season and all residents were eligible to enrol . Using the Aboriginal Resource and Development community education model , [26] the ACWs and a non-Aboriginal educator visited homes and work places to provide information on Strongyloides , scabies , and the research project . In a subsequent visit to each house , the ACWs and/or AHPs sought and obtained written informed consent using a pictorial flipchart that incorporated a culturally appropriate process to explain the project , [27] and to also establish a household occupancy list . Parents or legal guardians provided written informed consent for children aged <18 years . Portable workstations were erected at consenting participant’s homes to screen for Strongyloides and scabies and administer the eligible drug regimen . Implementation was over an extended period in accordance with community preference that encompassed house to house consultation , screening and treatment by the locally trained ACWs and AHPs in conjunction with pharmacy students and the project manager . At participant’s homes , venous blood for Strongyloides serology was collected in a 5ml SST vacutainer , stored in insulated containers and kept cool with an ice brick . Specimens were taken to the provisional testing facility twice a day , centrifuged for 10 minutes at 3000 rpm and refrigerated overnight at 2–8°C . The SST tubes were then transported by air ( 2hrs ) to Darwin and sorted at a commercial laboratory ( Western Diagnostic Pathology ) before being sent by air ( 4hrs ) the following day to the regional reference laboratory ( PathWest Laboratory Medicine ) in Perth . At PathWest the samples were batched and tested weekly with the quantitative Australian Strongyloides ELISA in house test that used sonicated S . ratti antigen to detect anti-Strongyloides IgG ( sensitivity 93% and specificity 95% ) . [28 , 29] The results were reported as an optical density ( OD ) and interpreted as seronegative ( 0-<0 . 25 ) , equivocal ( 0 . 25–0 . 45 ) or seropositive ( >0 . 45 ) . Specimens were stored at PathWest for 12 months for parallel testing of subsequent specimens . Strongyloides was also identified by microscopy from fresh faecal specimens within four hours of collection for those not consenting to a venous blood ( mostly children ) . Approximately 0 . 005–0 . 01 ml of faeces was put onto a slide with normal saline under two 22mm2 coverslips side by side and the wet preparation examined to identify parasites . Approximately 0 . 2g of faeces was also inoculated onto a Mueller Hinton agar plate for culture and transported by air to the Menzies School of Health Research laboratory the following day or the next working day if collected over the weekend . Specimens were maintained between 20–27°C during transport . The agar plates once inoculated were held at room temperature for 5 days and examined on days 2 , 3 , 4 and 5 post-collection . On day five , the agar plate was washed with 4% formaldehyde , and examined for parasite larvae . Faecal results were reported separately ( n = 80 ) and not included in the analysis of seroprevalence . From the 6 month survey onwards , venous blood was extracted into a 4ml EDTA tube to measure haemoglobin ( Hb ) and eosinophil counts . After extraction the EDTA tube was inverted several times , stored in insulated containers and kept below 8°C with an ice brick . Twice a day the insulated containers were taken to the provisional testing facility where the EDTA tubes were refrigerated overnight at 2–8°C before being transported to Darwin . Anaemia and eosinophilia were defined using the WHO haemoglobin criteria . [30] and The Royal College of Pathologists of Australasia ( RCPA ) reference intervals for leucocyte differential counts . [31] ( Table 1 ) . An allocated drug regimen for Strongyloides was delivered based on weight and pregnancy status ( Table 2 ) . Participants were excluded from the ivermectin MDA and treatment of Strongyloides if they weighed <15 kg , were pregnant or females aged 12–45 years who declined a urine hCG test , had an allergy to any components of the allocated drug regimen or had received the eligible study medication in the previous seven days . Children ineligible for ivermectin received albendazole 200 mg if weight was 6-<10 kg or 400mg if weight was 10-<15 kg . All non-pregnant participants who weighed ≥15 kg were administered a single dose of ivermectin 200 μg/kg at baseline and at month 12 . Oral drug administration was directly observed by the researchers and given with 200mL of full-cream flavoured milk to enhance the absorption of ivermectin and albendazole . Pregnancy testing and medication administration was undertaken in portable work stations ensuring individual privacy . Treatment was repeated 10–42 days after the MDA if Strongyloides was diagnosed . At the month 6 and 18 surveys there was no MDA , only those diagnosed with Strongyloides and/or scabies and household contacts of scabies cases were treated . Participants with equivocal Strongyloides serology followed a treatment algorithm based on previous results ( Table 3 ) . Participants were asked a series of questions before receiving the MDA and being screened for Strongyloides to identify any associated symptoms , and 24–72 hrs post MDA to ascertain if any participants had experienced adverse reactions . Two surveys were conducted during the wet season that were six months after each population census and MDA ( month 6 and 18 ) to: a ) follow-up participants with an equivocal or positive Strongyloides result and/or were positive for scabies in the population census six months prior , b ) screen a computer-generated random sample of participants who were negative for both Strongyloides and scabies in the population census six months prior and c ) follow-up household contacts of participants diagnosed with scabies at month 6 or 18 . The staged roll-out ensured subsequent visits to households were scheduled to comply with the planned 6–12 month follow-up timeline outlined in the study protocol . [32] We estimated that a random sample of 160 participants without evidence of either disease would have a 90% power to detect an increase in the proportion of Strongyloides from 0 to 8% . Data were analysed using Stata 13 ( StataCorp LP ) . Strongyloides seroprevalence at baseline and month 12 was calculated as a proportion of those seen who were seropositive . At month 6 and 18 surveys , seroprevalence was determined as a weighted average of ( i ) the failed sero-reversion rate which was calculated as the seroprevalence in participants who were seropositive at the survey and who had been seropositive at the population census six months prior , and ( ii ) the positive seroconversion rate which was calculated as the seropositive rate at the survey for those who were seronegative at the population census six months prior ( Tables S1 , S2 and S3 ) . The OD ratio was calculated from the OD value taken six months after treatment divided by the OD value prior to treatment , as not all samples had been tested in-parallel An OD ratio >0 . 6 was a considered a positive seroconversion . [33] Regression to the mean was determined through random number simulations using the same mean and standard deviation at month 0 , assuming a normal distribution on the transformed scale ( log of the optical density +0 . 01 ) with no decline in time irrespective of treatment , and 0 . 5 correlation between the optical density at month 0 and 12 as seen in the actual data . Data entry was validated by double entering 15% of the records . The data entry error rate for variables used in the analysis was <5% .
The baseline population census and MDA was conducted over six months from March-August 2010 . There were 1256 residents at home at baseline of which 1013 ( 81% ) consented to participate ( Fig 1 ) . At the month 6 survey conducted from August 2010-March 2011 , 395 participants from the baseline cohort were followed up . At month 12 , ( March-November 2011 ) , participation increased from 1013 to 1060 ( n = 1163 residents , 91% participated ) of whom 700 were participants seen at baseline and 360 were new entrants to the cohort . At the month 18 survey ( October 2011-August 2012 ) , 388 participants from month 12 were followed-up , 235 from the baseline cohort and 153 new entrants . At baseline , 859 ( 85% ) participants were screened for Strongyloides , 41 by faecal microscopy/culture and 818 by serology ( Table 4 ) . Of the 859 participants screened , 175 ( 21% ) were seropositive , four ( 11% ) were faecal microscopy/culture positive and 121 ( 15% ) equivocal . Per protocol MDA was given to 938 ( 93% ) participants that included 786 ( 92% ) screened for Strongyloides . Ivermectin was administered to 853 ( 84% ) participants and albendazole to 85 ( 8% ) participants . At month 12 , Strongyloides screening increased by 6% to 954 ( 90% ) participants ( 636 from the baseline cohort and 318 new entrants ) , 39 by faecal microscopy/culture and 915 by serology . There were 34 ( 6% ) from the baseline cohort who were seropositive at month 12 , one ( 6% ) positive by faecal microscopy/culture and 63 ( 10% ) equivocal . For new entrants at month 12 , 75 ( 25% ) were seropositive , none were faecal microscopy/culture positive and 41 ( 14% ) had an equivocal result . Per protocol MDA was given to 989 ( 93% ) month 12 participants that included 885 ( 91% ) screened for Strongyloides . Ivermectin was administered to 920 ( 87% ) participants and albendazole to 69 ( 7% ) participants . The median age of baseline seropositive participants was 21 ( IQR 12–31 ) that was not significantly different to that of the new entrants at month 12 , ( 16 , IQR 11–31 , p = 0 . 11 ) . There were no significant differences in the median age or gender of new participants at month 12 and those not seen from the baseline cohort . There were 154 participants at baseline with a median age of five ( IQR 3–9 ) and 106 at month 12 with a median age of four ( IQR 2–8 ) for whom Strongyloides status was unknown as we were unable to obtain a blood or faecal sample . More males were positive than females ( 25% vs 18% , p = 0 . 015 ) at baseline however , this difference was not evident at month 12 for the baseline cohort ( 6% vs 5% ) or the new cohort seen for the first time ( 26% vs 23% ) . At month 12 there was no evidence that Strongyloides seropositivity had any impact on Hb when stratified by gender and age group ( excluding one male aged 0–4 years ) or when comparing anaemia rates between the baseline cohort and the new entrants ( 23% vs 17% respectively ) . At month 12 , eosinophilia was identified in 66 ( 80% , 95% CI 69% , 88% ) seropositive participants and 277 ( 48% , 95% CI 44% , 52% ) who were seronegative ( difference 32%; 95% CI 22% , 41% ) . There were significantly more new seropositive entrant participants with eosinophilia ( 89% ) than participants seropositive from the baseline cohort ( 59% ) , difference of 30% ( 95% CI 10% , 50% ) . From the questionnaire collected at baseline , month 12 and 18 , the participant-reported symptoms on the day of MDA were not significantly associated with being seropositive , seronegative or equivocal for Strongyloides when stratified by age groups or analysed collectively ( p>0 . 05 for all symptoms ) ( Fig 2 ) . No adverse events after the MDAs were reported based on the self-reported questionnaire review 24–72 hrs post MDA . Strongyloides seroprevalence reduced from 21% at baseline to 5% at month 6 after the first MDA ( Fig 3 and Table A in S1 Data ) . For participants from the baseline cohort this reduction was sustained at month 12 , then after the second MDA reduced further to 2% at month 18 ( Table B in S2 Data ) . For new participants to the cohort at month 12 , seroprevalence reduced from 25% to 7% at month 18 ( Table C in S3 Data ) . The percentage of faecal specimens positive for Strongyloides reduced from 10% at baseline to 6% at month 12 for the baseline cohort however , the difference was not significant ( p = 1 ) . There was a high prevalence of positive Strongyloides serology in all age groups except those aged 0–4 years ( Fig 4 ) . Children aged 0–4 years had 23 serology tests that were all seronegative and four ( 7% ) faecal samples that were positive by microscopy/culture . The peak serology age group at both baseline and month 12 was school children aged 5–14 years who were being seen for the first time ( Fig 4 ) . For participants seen at baseline and month 12 ( n = 618 ) , the seroprevalence reduced across all age groups . At month 6 , we were to follow-up 460 participants , 179 that were positive for Strongyloides ( 175 seropositive and four faecal positive ) , 121 with equivocal results and 160 that were randomly selected participants who were negative for Strongyloides and scabies at baseline ( Table A in S1 Data ) . Of the 460 participants , we followed-up 363 ( 79% ) , 133/175 ( 76% ) that were seropositive , 1/4 ( 25% ) that was faecal positive , 88/121 ( 73% ) that were equivocal and 141/160 ( 88% ) that were negative for Strongyloides and scabies . We also screened an additional 32 participants of which 16 had scabies but were negative for Strongyloides at baseline , eight had scabies but an unknown Strongyloides status and eight were household contacts of scabies cases diagnosed at month 6 . At month 18 , there were 374 participants that required follow-up , 110 that were positive for Strongyloides ( 34 seropositive and 1 faecal positive from the baseline cohort and 75 seropositive from new entrants ) 104 participants with equivocal results ( 63 from baseline cohort and 41 from new entrants ) and 160 that were negative for Strongyloides and scabies at month 12 ( Table 4 ) . Of the 374 participants , 296 ( 79% ) were seen ( 178 from the baseline cohort and 118 from the new entrants ) , 83/110 ( 75% ) that were seropositive , 81/104 ( 78% ) with equivocal results and 132/160 ( 83% ) that were negative for Strongyloides and scabies . We also screened an additional 63 participants , 49 ( 30 from the baseline cohort and 10 new entrants ) that were scabies positive and Strongyloides negative at month 12 , two that had scabies but were negative for Strongyloides and 20 household contacts of scabies cases diagnosed at month 18 . At month 6 , the positive seroconversion rate was 2 . 5% ( 4/157 ) and the failed seroreversion rate 17% ( 23/134 ) for the baseline cohort ( Table A in S1 Data ) . Of the 134 participants seen at month 6 that were previously seropositive at baseline , 23 ( 17% ) failed to serorevert however , all but three had an OD ratio >0 . 6 ( S1 and S2 Figs ) . For the three participants that had an OD ratio <0 . 6 at month 6 , and would be classified as failed seroreversions , the basline antibody level was only slightly above the 0 . 45 threshold for positive ( 0 . 47 , 0 . 48 , 0 . 51 respectively ) . Of the three participants with an OD ratio <0 . 6 at baseline who reverted to equivocal at month 6 ( OD 0 . 31 , 0 . 36 , 0 . 42 ) , two had received two doses of ivermectin at baseline and one had received one dose . Of the 23 participants who were seropositive at baseline and month 6 , 19 ( 83% ) had received two doses of ivermectin at baseline and the remaining four had received only one dose . The median time between ivermectin treatments for those participants failing to sero-revert was 15 days ( IQR 12–21 ) . Failure to serorevert for males was almost double ( 21% , n = 16 ) that of females ( 12% , n = 7 ) ( p = 0 . 15 ) . At month 18 , of the 12 that had an OD ratio >0 . 6 , five had an increase in OD and the other seven still had an OD in the positive range ( >0 . 45 ) . At month 18 , the positive seroconversion rate for the baseline cohort was 1% ( 1/127 ) and 4% ( 2/46 ) for the new entrants ( Table B in S2 Data and Table C in S3 Data ) . The failed seroreversions for the baseline cohort was 32% ( 9/28 ) and 18% ( 9/50 ) for new entrants . Of the 18 that failed to serorevert , nine ( 50% ) were new entrants of which five ( 56% ) had an OD <0 . 6 ( S3 and S4 Figs ) . Fifteen ( 83% ) participants had received two doses of ivermectin at month 12 and the remaining three had received only one dose . The median time between ivermectin treatments for participants failing to serorevert was 23 days ( IQR 17–28 ) . The median time interval from baseline to the survey at month 6 was 5 . 8 months ( IQR 5–7 ) and 7 . 6 months ( IQR 6–9 ) from the second population census at month 12 to the survey at month 18 . After excluding participants that were seen at all four time points ( baseline , month 6 , 12 and 18 ) there was no difference in the failed seroreversion rate at month 6 ( 7/38 , 18% ) and month 18 ( 9/50 , 18% ) . There were 504 participants with paired serology from baseline and month 12 who did not receive ivermectin at month 6 . Of the 504 participants , there was minimal difference in the median OD when comparing the number of ivermectin doses ( 1dose v’s 2 doses ) ( Fig 5 ) . For seropositive participants receiving either one or two doses of ivermectin the median difference in the change in OD was 0 . 07 ( p = 0 . 76 ) . The median OD for those negative at baseline remained unchanged at month 12 for the majority of participants with the exception of 21 ( 7% ) who had received only one dose and had an increase in OD above the negative cut-off of 0 . 25 . Regression to the mean simulations showed that this phenomenon explains 50% of the reduction in OD , inferring the other 50% is attributable to MDA , under the assumption that if we had not given ivermectin there would be no change in the OD .
In our study , the ivermectin MDA led to a substantial reduction in Strongyloides seroprevalence from 21% at baseline to 5% at month 6 . The lower prevalence was maintained by the baseline cohort at month 12 when there was a second MDA which further reduced prevalence to 2% at month 18 . The 360 new entrants to the study at month 12 had a similar substantial reduction in seroprevalence on follow-up at month 18 . In a region where temporary relocation for cultural activities is common , [34] the introduction of new entrants at month 12 highlighted the significant contribution both a repeated MDA and community ownership had in sustaining a low prevalence for the baseline cohort . The replication of baseline prevalence in the new cohort at month 12 , and subsequent rapid reduction from 25% to 7% after MDA , supports the concept of incorporating a multi-faceted control program with ongoing surveillance and repeated annual MDAs at least initially , to achieve a sustainable reduction in seroprevalence . Diagnosis of Strongyloides is problematic as there is no gold standard test and detection rates vary between diagnostic tools used . [15] Coprological examination tends to underestimate the prevalence of the parasite in population-based studies , and whilst serology gives a higher prevalence , [13] it often does not detect acute or hyperinfections . [29] The ELISA test was a suitable serological test for our epidemiological study where prevalence of chronic Strongyloides infection was high and response to treatment could be monitored through the change in optical density . [5] At month 6 , 15 ( 65% ) participants that failed to serorevert to negative ( OD <0 . 25 ) had an OD >1 . 0 at baseline and five ( 28% ) at month 18 . Kobayashi et al . ( 1994 ) demonstrated that OD results with extremely high antibody levels failed to serorevert to negative despite being coprologically negative and postulated that an OD ratio <0 . 6 was an accurate measure of failed seroreversion . [33] Twenty ( 87% ) participants from month 6 and 12 ( 67% ) from month 18 had an OD ratio >0 . 6 and would be considered positive serocoversions using Kobayashi’s ratio . Over 80% of participants who failed to serorevert had received two doses of ivermectin , with no difference in the median time interval between doses when compared to those who did serorevert . The shorter median time interval for follow-up at month 6 of 5 . 1 months , may have overestimated those who failed to serorevert ( 17% ) , as serology can take six months or longer to revert to negative . [33 , 35] Nevertheless , there was no significant difference in the seroreversion failure rate at month 6 ( 18% ) compared with that at month 18 ( 18% ) where the median follow up time interval was 7 . 9 months , suggesting that the shorter interval of 5 . 1 months was not a contributing factor for those that failed to serorevert at month 6 . At a population level we found there was no difference in the reduction in OD for seropositive participants who had received one dose of ivermectin compared to those who had received two doses . Other studies have reported cure rates of 68% and 70% from serology and 83% and 87% from faecal analysis with a single dose of ivermectin . [5 , 36–39] The product information from the manufacturer recommends only one dose for uncomplicated Strongyloides infection however , one and two dose ivermectin regimens for the treatment of Strongyloides have been reported in other studies with cure rates increasing for those administered two doses . [5 , 39 , 40] The importance of Strongyloides infections in the Australian Indigenous population is contentious among health professionals and benefits of MDA programs for this infection remain to be elucidated as the degree of morbidity from chronic Strongyloides has not been established . The risk of disseminated strongyloidiasis and its association with high mortality[14] however , is not contentious and is managed by hospital and primary health care practitioners implementing the pre-emptive Top End ivermectin therapy guideline for those being prescribed immunosuppressive therapy of a defined nature . [20] From our questionnaire , symptoms reported on the day that medication was administered showed no correlation with Strongyloides results . Notably , non-specific self-reported symptoms of strongyloidiasis ( diarrhoea , abdominal pain , skin and respiratory symptoms ) on the day medication was administered were not more common in those seropositive . An electronic health record audit of participants from our study is currently being conducted to review the reasons for and frequency of PHC presentations 12 months prior to the study commencing for participants that were seropositive compared with those matched by age and sex that were seronegative at baseline . This may help further elucidate the clinical relevance of Strongyloides seropositivity in community members and enable assessment of any relevant findings to compare with the myriad of other community health issues that contribute to the poorer health experienced overall by Aboriginal and Torres Strait Islander people . [41–43] . At baseline and month 12 , over 50% of children enrolled aged <5 years were not tested for Strongyloides as a faecal specimen was not provided . For children aged 5-<15 years , Strongyloides screening increased from 75% at baseline to 89% at month 12 after parents requested we include serological testing of children as well as coprological examination in the protocol . Haemoglobin and eosinophils were not collected at baseline due to budget constraints . However , from month 6 onwards sufficient funds were made available . Not all specimens could be tested for eosinophilia if the sample arrived at WDP after 24–36 hrs of being collected . A field testing station to prepare the blood samples for transportation and perform coprological examinations on faecal specimens was established in the community . This field testing station did not have the resources or funds to be able to perform coprological examination on all enrolled participants . Eosinophilia was associated with seropositivity however , almost half of those seronegative also had an eosinophilia . Scabies was present in approximately one fifth of participants at month 12 that were seronegative with eosinophilia ( baseline cohort 43/198 , 21% and new entrants 16/79 , 20% ) but we were unable to determine if other helminth infections were contributing to the eosinophilia as faecal specimens were only collected for testing children for whom we did not get a venous blood . Worldwide , eosinophilia is most commonly caused by helminth infections , [44] which are still prevalent in the region where this study was conducted . [45 , 46] However , over 40% of the baseline cohort and 11% of new entrants that were seropositive had no eosinophilia , indicating that the absence of eosinophilia does not exclude the possibility of a helminth infection . The significantly reduced eosinophilia in the baseline cohort at month 12 compared to the new entrants may have been attributable to the MDA at baseline however , as eosinophilia was not tested for at baseline this cannot be confirmed . In Central Australia , where the link between endemic HTLV-1 infection and clinical disease from strongyloidiasis has been studied , [16] the introduction of an ivermectin MDA is likely to have clinical benefits . Whilst an MDA for Strongyloides may not in itself be a priority for the Top End of the Northern Territory , the success of this ivermectin MDA in reducing and sustaining a low Strongyloides seroprevalence over 18 months provides evidence that it has the potential to be an effective public health measure . Assessment of the impact of such a program on the regional prevalence of Strongyloides and its sustainability could take into consideration the potential for regional eradication and also the impact of ivermectin on other infections such as scabies . The use of dried blood spots to define the antibody response to S . stercoralis recombinant antigen NIE provides a non-invasive collection method to accurately determine seroprevalence , particularly in children . [47] Of note , the role of MDAs for endemic Strongyloides and of individual therapy for non-immunosuppressed asymptomatic individuals in endemic communities has recently been challenged by a study suggesting Strongyloides may have a protective benefit against development of type 2 diabetes ( T2DM ) and metabolic syndrome in the Australian Indigenous population . [48] This is supported by a limited body of evidence on animal models[49] and humans[50] that suggest helminth infections are able to attenuate the development of metabolic disorders such as T2DM . [51] The reduction in Strongyloides prevalence in this study was reassuring in confirming that a program built on community engagement and education in combination with an ivermectin MDA can have a positive impact on reducing prevalence . The study also supported the need for MDAs to be repeated , in this case yearly , when there is substantial movement of untreated people into the community . The logical extension of the findings from this study is MDAs involving larger populations to encompass whole regions within which population movements occur . In summary , two community ivermectin MDAs delivered 12 months apart by trained Aboriginal researchers in collaboration with non-Indigenous researchers resulted in a sustained and significant reduction in Strongyloides seroprevalence over 18 months . Ongoing studies are required to clarify the benefits and any potential harms of MDAs for the differing epidemiological circumstances seen globally for Strongyloides and other geohelminth infections . | We were invited by one community in East Arnhem Land to develop and deliver an ivermectin MDA to reduce the prevalence of Strongyloides and scabies . We demonstrated a sustained reduction in Strongyloides seroprevalence following the ivermectin MDA . Strongyloides is endemic in many Australian Aboriginal and Torres Strait Islander communities with seroprevalence ranging from 35–60% . Utilizing a before and after study design , we measured Strongyloides seroprevalence by ELISA through population census with sequential MDAs at baseline and month 12 . Strongyloides seroprevalence reduced from 21% at baseline to 5% at month 6 after the first MDA . For the baseline cohort this reduction was sustained at month 12 , falling to 2% at month 18 after the second MDA . For new entrants to the cohort at month 12 , seroprevalence reduced from 25% to 7% . | [
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] | 2017 | Strongyloides seroprevalence before and after an ivermectin mass drug administration in a remote Australian Aboriginal community |
Yeast mating type is determined by the genotype at the mating type locus ( MAT ) . In homothallic ( self-fertile ) Saccharomycotina such as Saccharomyces cerevisiae and Kluveromyces lactis , high-efficiency switching between a and α mating types enables mating . Two silent mating type cassettes , in addition to an active MAT locus , are essential components of the mating type switching mechanism . In this study , we investigated the structure and functions of mating type genes in H . polymorpha ( also designated as Ogataea polymorpha ) . The H . polymorpha genome was found to harbor two MAT loci , MAT1 and MAT2 , that are ∼18 kb apart on the same chromosome . MAT1-encoded α1 specifies α cell identity , whereas none of the mating type genes were required for a identity and mating . MAT1-encoded α2 and MAT2-encoded a1 were , however , essential for meiosis . When present in the location next to SLA2 and SUI1 genes , MAT1 or MAT2 was transcriptionally active , while the other was repressed . An inversion of the MAT intervening region was induced by nutrient limitation , resulting in the swapping of the chromosomal locations of two MAT loci , and hence switching of mating type identity . Inversion-deficient mutants exhibited severe defects only in mating with each other , suggesting that this inversion is the mechanism of mating type switching and homothallism . This chromosomal inversion-based mechanism represents a novel form of mating type switching that requires only two MAT loci .
Many yeast species have a sexual cycle as well as an asexual proliferation cycle . Sexual reproduction in yeast is initiated by the recognition of a mating partner and cell fusion , followed by nuclear fusion to form diploid cells that undergo meiosis and produce haploid progeny . In most ascomycetous yeast , cell-cell recognition only occurs between opposite mating types that are dictated by a single mating type locus , the MAT locus [1] , which encodes transcriptional regulators that function in various combinations to regulate the expression of genes that confer a sexual identity to cells . Because mating type in Ascomycota is predominantly bipolar , there are two possible DNA sequences for the MAT locus , which are referred to as idiomorphs rather than alleles due to a lack of overall DNA sequence homology [2] ( Figs . 1 and S1 ) . In Saccharomyces cerevisiae , haploid a or α cells are competent to mate with cells of the opposite mating type while diploid a/α cells are non-mating . The S . cerevisiae MAT locus carries one of two idiomorphs , MATa or MATα that encodes one or two proteins , a1 or α1 and α2 , respectively . The α1 protein induces the expression of α-specific genes , while α2 represses a-specific genes . In contrast , the expression of a-specific genes does not require any of the MAT genes and occurs by default as long as α2 is absent [3] . This has resulted from the evolutionary loss of a2 , another protein found in MATa idiomorphs of several other Saccharomycotina species . In Candida albicans and Candida lusitaniae , a2 activates a-specific genes [4] . In diploid S . cerevisiae cells , α2 forms a complex with a1 to repress haploid-specific genes , which results in the loss of mating capability and gain of the ability to initiate meiosis [4] . Communication through mating pheromones is important in yeast mating [5] . In S . cerevisiae , pheromone and receptor genes are regulated by MAT [3]; the α-factor receptor , Ste2 , and a-factor are expressed only in a cells and the a-factor receptor , Ste3 , and α-factor only in α cells . Therefore , pheromone/receptor pairs can only be formed between a and α cells and mating can only occur between a and α cells . When bound by pheromone , both receptors activate the same downstream target molecules [6] , and the signal is transmitted through the mitogen-associated protein kinase ( MAPK ) cascade—comprising Ste11 , Ste7 , and Fus3—to ultimately activate downstream effectors including the transcription factor Ste12 , which then activates the expression of mating-specific genes [7] . The pheromone signal transduction pathway is highly conserved across fungi even beyond Ascomycota [8] , [9] . Sexual reproduction can be heterothallic ( cross-fertility ) , where mating occurs between individuals with compatible MAT idiomorphs , or else homothallic ( self-fertility ) , where mating occurs within a population of the same strain . Two types of homothallism are known in yeast: in one , genetically identical cells mate with each other [10] , while in the other , cells switch from one mating type to another , producing a cell population with two cell types that differ only in terms of MAT and are compatible to mate . The best characterized example of the latter is in S . cerevisiae which , in addition to the MAT locus , has silent copies of both idiomorphs at different locations on the same chromosome ( HMLα and HMRa ) [3] , [11] , [12] . Cells switch mating type during the mitotic cycle and become sexually compatible with neighboring cells . Mating type switching is a gene conversion event that copies information from silent cassettes to the MAT locus and is initiated by a double-strand break generated by the HO endonuclease . While species related to S . cerevisiae such as C . glabrata , Saccharomyces castellii , and Zygosaccharomyces rouxii have silent mating type cassettes and the HO endonuclease gene , the silent cassette is absent in the most distantly related Saccharomycotina such as C . albicans or Yarrowia lipolytica [13] ( Fig . 1 ) . In more closely related yet still relatively distant yeasts such as Kluveromyces lactis , there are two silent cassettes but the HO endonuclease is absent . As in S . cerevisiae , mating type switching in K . lactis is mediated by mitotic gene conversion , but the initiating DNA lesion is evoked by a transposase homolog encoded by the MATα locus [14] , [15] . Hansenula polymorpha is a more distantly related yeast used for genetic analyses , but the genetic and molecular details of its life cycle remain unknown . The species is predominantly haploid , but diploid cells can be isolated and maintained [16] . Because it is homothallic , haploid cells can mate with each other , followed by meiosis and sporulation under conditions of nutrient limitation . Diploid cells also efficiently undergo meiosis to form four ascospores [16] , [17] . Mating type was suggested to be bipolar and the switching induced by nitrogen deprivation [16] . However , it was also claimed to be tetrapolar [16] . The genome sequence revealed the presence of the MAT locus but not silent cassettes or the HO gene . The MAT locus contains a unique combination of mating type genes—α2 , α1 , and a1—adjacent to each other on the same chromosome in that order and all in the same orientation [13] . However , it is not known how mating type is determined and whether and how the mating type switch occurs in this organism [13] . Here we report a functional analysis of mating type genes in H . polymorpha . Mutational analyses revealed that the previously reported MAT locus corresponds to MATα , while MATa is encoded by a second MAT locus located close to MATα . Only one MAT locus was transcribed mitotically while the other was repressed . The chromosomal location determined which MAT was active . During mating , the chromosomal region between the two MAT loci became inverted , which resulted in the switching of the MAT locus that was expressed . Preventing the inversion severely perturbed the mating of cells with each other , suggesting that this is the major mechanism of homothallism in H . polymorpha .
The MAT locus of H . polymorpha has been previously described as containing both MATa and MATα information on the same idiomorph , i . e . , the α2 , α1 , and a1 genes in that order [13] ( Figs . 2 , S1 , and S2 ) . In addition , the draft genome sequence of BY4329 ( originally named SH4329 ) revealed a second a1-like gene , together with the C-terminal half of the SLA2 gene , about 18 kb upstream of α2 in the opposite orientation ( Fig . 2 ) . The predicted amino acid sequences of the two a1-like proteins were identical except for the N-terminal 24 amino acids ( Fig . S3 ) . Amino acid similarity to S . cerevisiae a1 was detected only within the identical sequences ( Fig . S3 ) . A similar genome structure was reported for the closely related yeast Ogataea parapolymorpha DL-1 [18] . Hereafter , the a1 gene of the previously reported MAT locus and the second a1-like open reading frame ( ORF ) are referred to as a1* and a1 genes , respectively , and mating type loci containing them are referred to as the MAT1 and MAT2 loci , respectively , since both are expressed and function in the sexual cycle ( Fig . 2 , see below ) . To elucidate the molecular mechanism of homothallism in H . polymorpha , the contribution of each mating type gene to the sexual cycle , i . e . mating and meiosis/sporulation , was investigated . To this end , we first sought cells that behaved like heterothallic a and α cell type strains in mating and meiosis . H . polymorpha genome sequences contain ORFs homologous to S . cerevisiae STE2 and STE3 genes encoding α- and a-factor receptors , respectively [19] , [20] . Ste2Δ and ste3Δ strains were generated that were expected to behave as heterothallic α and a cell types , respectively , and therefore unable to self-mate , while cross-mating was possible . The mating capability of the strains was determined by a semi-quantitative mating assay . When H . polymorpha mate successfully , the resulting diploid cells ( zygotes ) immediately undergo meiosis and sporulation , provided that nutrients remain limited . However , if nutrients are supplied after mating and before the commitment to meiosis , cells return to the proliferative state as diploids . We took advantage of this life cycle to evaluate mating efficiency based on the number of diploid colonies formed after return to growth . Although Ste2Δ and ste3Δ cells produced comparable numbers of diploids when crossed with wild-type cells or with each other , no diploids were observed from the Ste2Δ × Ste2Δ and ste3Δ × ste3Δ crosses ( Fig . 3A ) . These results suggest that mating is bipolar in the homothallic laboratory strain derived from NCYC495 , and that Ste2Δ and ste3Δ cells can undergo mating only as α and a cells , respectively . Genetic and phenotypic analyses of mating type gene deletion mutants were carried out to determine the functional roles of the a1 , a1* , α1 , and α2 transcription factors . Mating capability was evaluated by the semi-quantitative mating assay and meiosis/sporulation was determined by microscopy . Although mating efficiency was generally low ( <∼2% after 24 h ) and varied widely among strains , deleting the α1 gene nearly abolished mating with cells of the same genotype ( i . e . , α1Δ × α1Δ; Fig . 3B ) . There were no signs of mating such as zygotes and altered cell morphology ( i . e . , mating projections ) detected by microscopy . In contrast , a1*Δ , a1Δ , and α2Δ cells exhibited normal mating behavior and produced homozygous diploids in crosses with cells of the same genotype ( i . e . , α2Δ × α2Δ , a1*Δ × a1*Δ , and a1Δ × a1Δ; Fig . 3B ) , although the efficiency was lower for the a1Δ × a1Δ cross than for other combinations . Interestingly , α1Δ cells were able to mate with Ste2Δ cells , but did not produce diploids when mated with ste3Δ ( Figs . 3C and S4A ) . Furthermore , α1Δ ste2Δ cells did not mate with wild-type cells ( Figs . 3D and S4B ) . In contrast , Ste2Δ cells could mate with all mutants of mating type genes ( Figs . 3C and S4A ) . Thus , α1 but not α2 determines the α cell identity and is indispensable for mating . The a cell identity may be established by default , as is the case in S . cerevisiae , because neither the a1* nor the a1 gene was essential for mating . Support for this conjecture comes from the observation that constitutive expression of the α1 gene strongly inhibited mating with Ste2Δ ( α cell-like ) but not with ste3Δ ( a cell-like ) ( Fig . S5 ) . Although a1 and α2 were not required for mating , homozygous diploids of a1Δ or α2Δ ( a1Δ/a1Δ and α2Δ/α2Δ ) did not undergo meiosis nor did they produce spores ( Fig . 3E , F ) . In contrast , a1*Δ/a1*Δ diploid cells exhibited normal meiosis/sporulation ( Fig . 3F ) . Since the amino acid sequences of a1* and a1 are identical except for the N-terminal 24 amino acids ( Fig . S3 ) , the possibility of functional redundancy was examined . Meiotic deficiency of a1Δ/a1Δ diploid cells was not suppressed by expressing the a1* gene from the constitutive HpTEF1 promoter[21] , while a1 expression restored normal meiosis and sporulation ( Fig . 3E ) , suggesting that the two genes have distinct functions . Thus , α1 has an essential role in mating while a1 and α2 are indispensable for meiosis and sporulation , in a manner analogous to S . cerevisiae . Because a1* was not involved in sexual differentiation , we concluded that MAT1 and MAT2 represent α and a mating types , respectively . The sequences 2049 bp downstream of MAT1 and upstream of MAT2 ( referred to as IR1 and IR2 , respectively ) are identical ( Fig . 4A ) . Since PCR amplification of the region spanning IR1 or IR2 often yields ambiguous results ( Fig . S6A ) , Southern blot analysis was used to verify genome sequences surrounding the two MAT loci . Genomic DNA was prepared from the laboratory wild-type strains HPH22 ( derived from BY4329 ) and BY4330 ( originally named SH4330 ) , and DNA fragments encompassing MAT1 and in close proximity to MAT2 were used as probes A and C , respectively . Results for BY4330 matched our draft genome sequences , but for HPH22 , a match was observed only if the sequences between IR1 and IR2were presumed to be inverted ( Fig . 4A , B ) . To investigate whether the orientation of this region differed in the two strains , two PCR reactions were carried out in which only one orientation was amplified ( Fig . 4C ) . A PCR product was observed for only one reaction using BY4330 and the other reaction using HPH22 ( Fig . 4C ) , indicating that there are two distinct genomic structures surrounding the MAT loci . The conservation of gene order flanking the MAT1 locus has been previously noted [13] . The presence of the SLA2 and SUI1 genes downstream of the MAT locus is conserved among yeast species distantly related to S . cerevisiae such as Saccharomyces kluyveri , K . lactis , and Y . lipolytica [13] ( Fig . S1 ) . Furthermore , the DIC1 gene is located on the other side of MAT in S . kluyveri . Based on this conserved gene order , BY4330 likely reflects the ancestral type . Therefore , the BY4330 and HPH22 types are hereafter referred to as ancestral ( A ) - and inverted ( I ) -type , respectively ( Fig 4C ) . In addition , the ancestral chromosomal location of MAT and the 2nd location are referred to as positions 1 and 2 , respectively ( Fig . 2 ) . After an additional 5–10 amplification cycles , specific products often appeared in both PCR reactions ( Fig . S6A ) . Furthermore , although most single colonies isolated from HPH22 maintained the I-type orientation , some isolates such as HPH22i became A-type ( Fig . 4C ) . Further isolates obtained from HPH22i ( 15 out of 16 ) remained as A-type ( Fig . S6B ) . These results suggest that the switch between I- and A-types can occur in mitotically growing cells , albeit at a low frequency . Moreover , once inversion takes place , the new orientation is stably maintained . Given that information for both MATa and MATα co-exist in a single cell but cells are nonetheless competent for mating , the possibility that the transcription of mating type genes are differentially regulated was investigated . Reverse transcriptase PCR ( RT-PCR ) analysis of mitotically growing HPH22 cells revealed that the a1 gene but not genes at the MAT1 locus ( α1 , α2 , and a1* ) are expressed ( Fig . 4D ) . In contrast , three genes at MAT1 were expressed while the a1 gene at MAT2 was repressed in BY4330 cells ( Fig . 4D ) . The differences in MAT gene expression patterns were not due to different genetic backgrounds , but were instead dependent on the chromosomal arrangement surrounding MAT loci ( A- or I- type ) , because HPH22i exhibited the same type of expression as BY4330 ( Fig . 4D ) . This suggests that both MAT1 and MAT2 are transcriptionally active at position 1 , but are repressed at position 2 . Although α1 , α2 , a1* RNA was detected by RT-PCR , it is unclear whether these are transcribed individually . The α2 and α1 ORFs are separated only by a 5-bp gap , while a 19-bp overlap exists between α1 and a1* ( Fig . S2 ) . Indeed , we detected RNA species that carry both α1 and a1* ORFs ( Fig . S7 ) . Because MAT1 and MAT2 represent α and a mating types , respectively , and the mating type identity of cells was determined by the chromosomal arrangement of MAT loci ( A- or I- type ) , it was predicted that mating efficiency would be higher when the A- and I-types were mixed than for either type alone . However , combining A- or I- types had no effect on mating efficiency ( Fig . 5A ) . This might suggest that the mating type identity of cells frequently switches under mating conditions regardless of the mating type during mitotic growth . Next , the expression of mating type genes under mating conditions was investigated . In addition to α1 , the expression of a1 was induced in the A-type strain BY4330 after a 10-h incubation on the mating medium , MEMA ( Fig . 5B , C ) . Similarly , the α1 transcript was upregulated in the I-type strain HPH22 during mating although the induction was weaker than that of a1 in BY4330 ( Fig . 5B , C ) . Thus , all mating type genes were expressed during mating , providing an explanation for the self-mating observed in all examined strains . The transcriptional activation of the MAT locus at position 2 after transfer to the mating medium could be due to de-repression of the repressed MAT locus . Alternatively , the inversion of the MAT intervening region could bring the repressed MAT locus to a transcriptionally active location . In the latter instance , the inversion would be frequently observed under starvation conditions . To investigate this possibility , logarithmically growing HPH22 , HPH22i , and BY4330 cells were transferred to mating medium and chromosome orientation was evaluated by PCR ( Fig . 5D ) . In all three strains , the inverted orientation became apparent under starvation conditions . The inversion might be more efficient in BY4330 than in HPH22 , which could explain the stronger induction of a1 mRNA in BY4330 as compared to α1 mRNA in HPH22 under these conditions ( Fig . 5B , C ) . These results support the notion that the inversion of the MAT intervening region is responsible for transcriptional induction . The above results do not exclude the possibility that de-repression of the MAT locus at position 2 contributes to mating . In this scenario , the resulting diploids would harbor two chromosomes of the same type; therefore , chromosome types in diploid clones were examined . All 146 diploids isolated from all combinations of crosses had one I- and one A-type chromosome ( Table 1 ) . Thus , it is unlikely that transcription from the MAT locus at position 2 contributes significantly to mating . To further confirm the transcriptional status at position 2 , meiosis was examined in diploid cells heterozygous for a1Δ ( a1Δ/+ ) . Diploid cells carrying the a1Δ allele on an A-type chromosome would be expected to express meiotically indispensable a1 protein from the a1 gene at position 1 on an I-type chromosome . As predicted , such diploid cells underwent efficient meiosis and sporulation ( HPH824; Fig . 5E ) . On the contrary , diploid cells carrying a1Δ on an I-type chromosome would only be capable of meiosis if the a1 gene at position 2 were expressed . Indeed , meiosis was severely perturbed in these cells ( HPH825; Fig . 5E ) . These results suggest that mating type genes at position 2 are not transcribed or else activated at subthreshold levels that are insufficient to induce meiosis . The aforementioned data strongly suggests that the A- and I- inversion types correspond to α and a mating types , respectively , and that inversion of the MAT intervening region is the major mechanism of mating type switching . To test this model , inversion-deficient mutants were generated . Because IR sequences likely play an important role in the inversion , an IR2 deletion was introduced ( Fig . 6A ) , which abolished inversion in A-type ( α ) cells after transfer to the mating medium ( Figs . 6B and S8 ) . Mating with each other and with Ste2Δ cells was almost abolished in these cells , while mating with ste3Δ was unaffected ( Fig . 6C ) . These results suggest that inversion after nutrient starvation is necessary for mating type switching , and is responsible for homothallism in H . polymorpha .
Mutational analyses revealed that mating and meiosis are regulated by the distinct functions of four mating type gene products in H . polymorpha ( Fig . 7A ) . The activation of haploid-specific genes is likely to be regulated in a manner similar to what is presumed for mating type genes of S . cerevisiae , although genes that are expressed specifically in a- , α- , or haploid cells have not yet been identified in H . polymorpha . In S . cerevisiae haploid cells , α1 is essential for α-specific gene expression , while a-specific genes are expressed by default and do not require any mating type genes . Thus , in this species , a cell identity is established by default unless α2 represses a-specific genes [1] . Establishment of α identity requires the activation of α-specific genes by α1 in addition to the repression of a-specific genes . However , in H . polymorpha , α2 is not involved in the repression of a-specific genes , and it is therefore unclear how these are repressed in α cells . It is currently unknown whether the repression of a-specific genes is necessary in α cells . One possibility is that α1 contributes to this repression , as was suggested in C . lusitaniae [4] . Alternatively , there may be no mechanism to repress a-specific genes , in which case the intrinsic noise of gene expression may create different populations that express variable levels of α1 and a-specific genes . Cells may therefore exhibit α identity during a time window during which α1 level is high and a-specific gene expression is low . The MAT loci in H . polymorpha—MAT1 and MAT2—are transcriptionally active at position 1 , the ancestral location . However , their transcription became repressed at position 2 after the inversion of the MAT intervening region . The promoter sequences of mating type genes were not responsible for the repression , since sequences upstream of mating type genes were unaltered; instead , the orientation of mating type genes and others within the MAT intervening region was reversed . However , this was unlikely to repress transcription . Indeed , the expression of the FAS2 gene located in the middle of the MAT intervening region was independent of A- or I-type arrangement and nutrient starvation ( Fig . S9 ) [22] . The most plausible explanation is that position 2 is in a silent configuration . This is supported by the fact that there are no ORFs in the >12 kb region next to IR2 distal to position 1 , except for one encoding the polyprotein-like protein of the Ty/Copia retrotransposon . It may also explain why IR2 deletion could not be rescued by a DNA fragment containing a selection marker of similar size . Whether the repression at position 2 depends on heterochromatin structure is unknown . However , it is worth noting that , like other Saccharomycotina , there are no Heterochromatin Protein 1 family members in H . polymorpha , nor any clear homologs of S . cerevisiae trans-acting silencing proteins such as Sir1 , Sir3 , and Sir4 [23]-[26] , although a histone deacetylase homologous to S . cerevisiae Sir2/Hst1 is present . H . polymorpha may have a silencing mechanism in which the Sir2 homolog plays a critical role and the Orc1 homolog possesses a Sir3-like silencing function as in K . lactis [27] . A Sir4 homolog may be too diverse to detect based on amino acid sequence similarity [28] . Mating and meiosis are distinct programs in S . cerevisiae but are integrated in H . polymorpha . A similar sexual cycle occurs in the Saccharomycotina species C . lusitaniae and the distantly related Taphrinomycotina species S . pombe [4] . A recent study on the mechanism of sexual programs in C . lusitaniae has revealed the co-regulation of mating- and meiosis-specific gene expression programs [29] . In S . cerevisiae , the pheromone-associated transcription factors Ste12 and Ime2 are specifically involved in mating and meiosis , respectively . In contrast , C . lusitaniae Ste12 and Ime2 orthologs are required for efficient progression through both mating and meiosis . The absence of α2 , which prevented expression of haploid-specific genes , including MAPK genes , was proposed to facilitate MAPK signaling and confer a meiotic role to Ste12 . The coupling of mating and meiosis may have evolved to ensure the return of diploids to the haploid state to satisfy the preference for haploidy [29] . The same argument could be applied to the sexual cycle of the predominantly haploid H . polymorpha . Nonetheless , there are species differences in the expression of components essential for sexual regulation . In both C . lusitaniae and S . pombe , the transcription of genes encoding pheromone receptors and pheromone-associated transcription factors is induced during mating , but these are constitutively expressed in mitotically growing H . polymorpha cells ( Fig . S10 ) [4] , [30]–[32] . The evolution of this constitutive expression and the mechanisms involved in its regulation will be a focus of future studies . Mating type switching has been best studied in S . cerevisiae , K . lactis , and S . pombe [14] , [15] , [33] . These species all harbor silent cassettes in their genomes and their switching events are mitotic recombination-dependent , although the molecular details differ . Homothallism in H . polymorpha involves two independent regulatory processes: transcriptional repression of one MAT locus , and inversion of the chromosomal region between the two MAT loci—MAT1 and MAT2—that reside ∼18 kb apart on the same chromosome and are idiomorphs for the α and a mating types , respectively ( Fig . 7B ) . Both MAT loci are active in the ancestral chromosomal position ( position 1 ) while the other locus ( at position 2 ) is repressed . The inversion of the MAT intervening region is induced under mating conditions , resulting in a chromosome that harbors the formerly repressed mating type genes at the active location and establishes the opposite mating type identity . Because this system differs from those of S . cerevisiae and K . lactis , it is likely to have evolved independently after H . polymorpha branched out from Saccharomycetaceae . Interestingly , the organization of MAT1 is similar to that observed in homothallic Pezizomycotina such as Sclerotiniasclerotiorum and some Cocliobolus species [34] , [35] . In the former , the inversion of part of the MAT locus leads to mating type switching [36] . Thus , the fusion of two MAT idiomorphs of the heterothallic ancestor followed by the acquisition of mitotic recombination to differentiate the two transcriptional profiles likely occurred multiple times during fungal evolution . In H . polymorpha , the insertion of a retrotransposon found in close proximity to position 2 may have caused the duplication of the IR region that contains most of the a1*/a1 ORF and then initiated an inversion event between the two IR regions . The two MAT loci in H . polymorpha may therefore represent an intermediate state preceding the acquisition of a set of silent cassettes . Comparative studies in other fungal species would be required to evaluate this possibility . The molecular mechanism underlying the inversion in H . polymorpha is currently unknown . Well-studied examples of inversion-dependent phenotypic switching include phase variation systems in bacteria , such as Type 1 fimbrial phase variation in Escherichia coli and flagellar phase variation in Salmonella enterica , where the inverting regions contain a promoter for adjacent genes that determine the phenotype , with inversion therefore resulting in transcriptional on/off switching . In these cases , nonhomologous , site-specific serine or tyrosine families of recombinases act on inverted repeats , which leads to the inversion of the intervening sequence [37] , [38] . In S . cerevisiae , the site-specific FLP tyrosine recombinase is an essential part of the 2-µm plasmid amplification system [39] . It will be interesting to determine whether inversion in H . polymorpha depends on site-specific recombination . However , there were no serine or tyrosine recombinases in the genome . Given that long homologous sequences ( >2 kb ) are in inverted orientations ( IR1 and IR2 ) , homologous recombination between IR regions is another possible mechanism leading to inversion of the MAT intervening region . Although inversion is observed at low frequency during mitotic growth , it is strongly induced upon nutrient starvation in H . polymorpha . It is interesting that mating type switching is induced and mating is initiated in response to harsh environmental conditions such as nutritional starvation in K . lactis [40] . Elucidating the molecular mechanisms and regulation of mating type switching in H . polymorpha can provide deeper insight into how mating type switching evolved .
Strains and plasmids used in this study are listed in Table S1 . Unless otherwise indicated , yeast strains were derived from NCYC495 [41] and were generated by PCR-based methods [42] , [43] . Gene deletion alleles were generated in ku80Δ or ku70Δ cells and then crossed with either HPH22 or BY4330 to obtain KU80+ or KU70+ cells carrying the deletion allele . Primers used to amplify cassettes are listed in Table S2 . H . polymorpha cells were transformed by electroporation [44] . pSC6cen103a is a newly developed plasmid stably maintained in H . polymorpha , the construction of which will be described elsewhere . The HpURA3 DNA fragment containing 800 bp upstream and 500 bp downstream sequences was amplified by PCR and inserted into AatII/SacI sites in pRS305 to generate pHM821 . The 500-bp sequences up- and downstream of the HpTEF1 ORF were used as the HpTEF1 promoter and terminator , respectively [21] . Yeast strains were grown in yeast extract , peptone , and dextrose medium containing 200 mg/l adenine , leucine , and uracil ( YPDS ) [45] . Diploid cells were grown in synthetic/defined ( SD ) medium supplemented with appropriate amino acids and nucleotides . Cells were grown at 30°C unless otherwise indicated . Mating and meiosis were induced on 2 . 5% maltose and 0 . 5% malt extract medium ( MEMA ) plates at 30°C . Yeast cells were fixed with 70% ethanol , washed with phosphate-buffered saline ( PBS ) , and incubated in PBS containing 1 µg/ml 4′6 , -diamidino-2-phenylindole ( DAPI ) to visualize DNA . Images were acquired using the DeltaVision Personal system ( Applied Precision , Issaquah , WA , USA ) . A Z series in 0 . 4-µm steps was acquired for DAPI images , and ImageJ ( National Institutes of Health , Bethesda , MD , USA ) was used to generate projected images . Adobe Photoshop ( Adobe Systems , Inc . , San Jose , CA , USA ) was used to process and produce merged images . The BY4329 genome was sequenced using a Genome Sequencer FLX System ( Roche Diagnostics , Basel , Switzerland ) and Genome Analyzer GAIIx ( Illumina Inc . , San Diego , CA , USA ) . The paired-end library for the former was prepared according to the Paired-End Library Preparation Method Manual −20 kb and 8 kb Span ( Roche Diagnostics ) , and a genome library for the latter was prepared with a TruSeq DNA Sample Preparation v2 Kit ( Illumina Inc . ) according to the manufacturer's protocol . All reads were assembled into contigs and then ordered into scaffolds using GS De Novo Assembler version 2 . 6 ( Roche Diagnostics ) . The draft sequence data is submitted to DNA Data Bank of Japan ( DDBJ ) and its BioProject ID is PRJDB3035 . To determine the orientation of the region between IR1 and IR2 , PCR primers specific for the sequence to the left of IR2 ( Primer_I ) , the intervening region ( Primer_D and Primer_E ) , and the sequence to the right of IR1 ( Primer_A ) were designed . Primer_I and Primer_D were used in the I reaction , which yielded an I-type chromosome-specific 3-kb PCR product , while Primer_A and Primer_D were used in the A reaction , which was A-type chromosome-specific . Primer_A and Primer_E were used in the Ai reaction , which gave an I-type-specific product . A total of 10 ng genomic DNA was used in each reaction . I- or A- type was judged after 20 cycles of amplification with PrimeSTAR Max DNA polymerase ( Takara Bio Inc . , Shiga , Japan ) . For Southern blotting , H . polymorpha genomic DNA was prepared using a standard protocol [46] . Briefly , 3 µg DNA was digested with EcoRI , XhoI , PstI , and BamHI restriction enzymes before electrophoresis . A standard protocol was used for blotting and hybridization [46] . DNA probes were prepared and detection was performed using the AlkPhos Direct Labeling and Detection System with CDP-Star ( GE Healthcare , Pittsburgh , PA , USA ) . Yeast strains of leu1-1 or ura3-1 genotypes were grown at 30°C in YPDS until the optical density at 663 nm ( A663 ) was between 0 . 5 and 1 . 5 . Cells were washed with PBS and diluted to A663 = 1 . 0 , and a 10-µl cell suspension of the two strains was mixed on a nitrocellulose membrane filter that was placed on a MEMA plate and incubated for 24 h at 30°C . Cells were re-suspended in PBS and dilutions were plated on SD plates supplemented with leucine or uracil or on unsupplemented SD plates that were incubated for 2 days at 37°C . The mating percentage was calculated as the number of colonies on unsupplemented plates divided by the number on leucine- or uracil-supplemented plates ( i . e . , whichever had fewer colonies ) . It should be noted that the mating percentage does not represent overall mating efficiency because meiosis and sporulation proceed immediately after mating in H . polymorpha . Total RNA was isolated from H . polymorpha as previously described [47] , treated with DNase I , and then further purified using the RNeasy Plus Kit ( Qiagen , Valencia , CA , USA ) . A total of 1 µg RNA was used to synthesize cDNA with SuperScriptIII ( Invitrogen , Carlsbad , CA , USA ) according to the manufacturer's protocol , and 1 µl cDNA reaction mixture was used in a PCR reaction with the primers listed in Table S2 . The QuantStudio 3D Digital PCR system ( Thermo Fisher Scientific Inc . , Waltham , MA , USA ) was used to quantify RNA copy number . Forward , reverse , and TaqMan primers are listed in Table S2 . | The mating system of Saccharomycotina has evolved from the ancestral heterothallic system as seen in Yarrowia lipolytica to homothallism as seen in Saccharomyces cerevisiae . The acquisition of silent cassettes was an important step towards homothallism . However , some Saccharomycotina species that diverged from the common ancestor before the acquisition of silent cassettes are also homothallic , including Hansenula polymorpha . We investigated the structure and functions of the mating type locus ( MAT ) in H . polymorpha , and found two MAT loci , MAT1 and MAT2 . Although MAT1 contains both a and α information , the results suggest that it functions as MATα . MATa is represented by MAT2 , which is located at a distance of 18 kb from MAT1 . The functional repression of MAT1 or MAT2 was required to establish a or α mating type identity in individual cells . The chromosomal location of MAT1 and MAT2 was found to influence their transcriptional status , with only one locus maintained in an active state . An inversion of the MAT intervening region resulted in the switching of the two MAT loci and hence of mating type identity , which was required for homothallism . This chromosomal inversion-based mechanism represents a novel form of mating type switching that requires two MAT loci , of which only one is expressed . | [
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] | 2014 | Inversion of the Chromosomal Region between Two Mating Type Loci Switches the Mating Type in Hansenula polymorpha |
The glucocorticoid receptor ( GR ) associates with glucocorticoid response elements ( GREs ) and regulates selective gene transcription in a cell-specific manner . Native GREs are typically thought to be composite elements that recruit GR as well as other regulatory factors into functional complexes . We assessed whether GR occupancy is commonly a limiting determinant of GRE function as well as the extent to which core GR binding sequences and GRE architecture are conserved at functional loci . We surveyed 100-kb regions surrounding each of 548 known or potentially glucocorticoid-responsive genes in A549 human lung cells for GR-occupied GREs . We found that GR was bound in A549 cells predominately near genes responsive to glucocorticoids in those cells and not at genes regulated by GR in other cells . The GREs were positionally conserved at each responsive gene but across the set of responsive genes were distributed equally upstream and downstream of the transcription start sites , with 63% of them >10 kb from those sites . Strikingly , although the core GR binding sequences across the set of GREs varied extensively around a consensus , the precise sequence at an individual GRE was conserved across four mammalian species . Similarly , sequences flanking the core GR binding sites also varied among GREs but were conserved at individual GREs . We conclude that GR occupancy is a primary determinant of glucocorticoid responsiveness in A549 cells and that core GR binding sequences as well as GRE architecture likely harbor gene-specific regulatory information .
The great challenge of metazoan transcriptional regulation is to create specialized expression pathways that accommodate and define myriad contexts , i . e . , different developmental , physiological , and environmental states in distinct organs , tissues , and cell types . This is achieved by a network of transcriptional regulatory factors , which receive and integrate signaling information and transduce that information by binding close to specific target genes to modulate their expression . For example , the glucocorticoid receptor ( GR ) associates selectively with corticosteroid ligands produced in the adrenal gland in response to neuroendocrine cues; the GR-hormone interaction promotes GR binding to genomic glucocorticoid response elements ( GREs ) , in turn modulating the transcription of genes that affect cell differentiation , inflammatory responses , and metabolism [1 , 2] . Expression profile analyses have identified glucocorticoid responsive genes in different cell types [3 , 4] , and it is striking that there is only modest overlap in glucocorticoid-regulated gene sets between two cell types . The mechanisms by which GR selectively regulates transcription in cell-specific contexts are not well established . An intriguing feature of GREs and other metazoan response elements is that their positions relative to their target genes are not tightly constrained [5 , 6] . Although certain metazoan response elements have been described that operate from long range , most searches for such regulatory sequences have nevertheless focused for technical reasons on restricted zones just upstream of promoters , where prokaryotic and fungal elements reside . Thus , the GRE for interleukin-8 ( IL8 ) is just upstream of the promoter [7] , whereas the tyrosine aminotransferase GRE resides at −2 . 5 kb [8] . Recent , more systematic searches for response elements have revealed dramatic examples , such as an estrogen response element 144 kb upstream from the promoter of the NRIP gene [9] , and an intragenic region 65 kb downstream from the Fkbp5 promoter that appears to serve as an androgen response element [10] . It has been suggested that long-range regulatory mechanisms are likely to facilitate and promote regulatory evolution [11] . However , it has not been determined whether the position of a response element relative to its target gene is functionally significant . Evidence from numerous anecdotal , gene-specific studies indicates that native response elements are typically composite elements that encompass distinct sequence motifs recognized by two or more regulatory factors . In turn , the bound factors recruit non-DNA binding coregulatory factors , forming functional regulatory complexes that remodel chromatin and modify the activity of the transcription machinery . In this scheme , the structure and activity of the regulatory complex at a given response element would be specified by at least three determinants: the sequence motifs comprising the response element; the availability of those sequences for factor binding; and the availability and activity levels of regulatory factors present in the cell . For example , primary GREs , defined as those at which GR occupancy is required for glucocorticoid-responsive regulation , are a diverse family of elements that bind GR together with an array of additional factors defined by the above three determinants . Such composite response elements provide a powerful driving force for combinatorial regulation [2 , 12] , vastly increasing the capacity of a single factor to assume multiple regulatory roles . Indeed , the mere presence of GR in a regulatory complex is not sufficient for glucocorticoid regulation [7] . It is not known , however , whether such “nonproductive” binding by GR is common , or if instead GR occupancy is a strong indicator of GRE function . GR binds to a family of related sequences that defines a consensus motif: an imperfect palindrome of hexameric half sites separated by a three-bp spacer [13–15] . Within those 15-bp core GR binding sequences , a few positions are nearly invariant , whereas a substantial proportion can be altered with little effect on GR binding affinity [16] . However , the functional consequences of such “permitted” sequence variations are unknown . GR can mediate a range of regulatory processes within a single cell type , including activation and repression of specific genes [4 , 17] . These findings , together with the results of biochemical and structural studies , raise the possibility that the core GR binding sequences might themselves serve as distinct “GR ligands , ” allosterically affecting GR structure to produce distinct GR functions [18 , 19] . Studies of other regulatory factors have led to similar conclusions [20 , 21] . If different core GR binding sequences indeed produce GRE-specific ( and therefore target gene-specific ) regulatory activities , we could expect that the core GR binding sequence associated with a given target gene would be strongly conserved through evolution , whereas the collection of core GR binding sequences across different genes would vary substantially . Analogously , if the architecture of composite GREs , i . e . , the arrangements of additional sequence motifs surrounding the core GR binding site , are also important for gene-specific regulation , we would expect flanking sequences surrounding the core GR binding site to also be evolutionarily conserved in a GRE-specific manner but not across GREs within a single genome . Neither of these notions has been examined . In the present work , we sought to define and characterize a set of GREs in A549 human alveolar epithelial cells . Thus , we determined in A549 cells the presence of GR at specific GREs close to genes that are steroid regulated across a range of cell types . We assessed whether the GR-occupied GREs were limited mainly to genes that are GR regulated in A549 and measured within and between species the conservation of GRE sequences , architecture , and genomic positions .
To assess the correlation of GR occupancy with glucocorticoid responsiveness , we examined GR binding at three classes of genes in A549 human lung carcinoma cells: first , genes regulated by GR in A549 cells; second , genes regulated by GR in U2OS human osteosarcoma cells but not in A549; third , genes regulated by GR or the androgen receptor ( AR ) in cells other than A549 or U2OS . The AR-responsive genes were of interest because AR is closely related to GR and shares similar DNA-binding specificity in vitro [14 , 16 , 22] . The first two classes of genes were identified in our lab using expression microarrays , whereas the third class was compiled from our own microarray data and from published reports of others [3 , 4 , 23 , 24] . Both positively and negatively regulated genes were included; together the three classes comprised 548 candidate GR target genes . By examining these genes for GR binding in A549 cells , we could determine if GR occupancy in vivo is restricted only at genomic sites of genes actually regulated by glucocorticoids in A549 cells; alternatively , GR might also bind at genes that are not under glucocorticoid control in A549 , but are regulated by GR or AR in other cells . To identify GR binding regions ( GBRs ) , we used chromatin immunoprecipitation-microarray ( ChIP-chip ) to interrogate 100-kb genomic segments centered on the transcription start sites ( TSSs ) of our set of 548 genes . This ~55-Mb sample of the genome also included or impinged upon an additional 587 genes not previously reported to be regulated by GR; thus , we assessed GR occupancy in the vicinity of more than 1 , 000 genes . Immunoprecipitated chromatin samples from A549 cultures treated for one hour with the synthetic glucocorticoid dexamethasone ( dex ) ( 100 nM ) or ethanol were hybridized onto the ChIP-chip tiling arrays . Independent biological replicates were hybridized onto two separate arrays , and GBRs were identified using the SignalMap detection program; we detected a 3 . 4% false positive rate for the GBRs found in both arrays as assessed by conventional ChIP and quantitative PCR ( qPCR ) analysis . Importantly , we did not detect GR occupancy at 22 regions that showed no GR binding in the arrays ( unpublished data ) . The ChIP-chip experiments revealed a total of 73 GBRs adjacent to 61 genes ( Table 1 ) , which were validated by GR ChIP and qPCR analysis ( Figure 1A ) . In addition to identifying GBRs previously detected by conventional ChIP , our experiments revealed novel GBRs in regions not searched in prior studies . For example , two known promoter proximal GBRs at SCNN1A [25] and SDPR [3] were confirmed in the ChIP-chip arrays as well as newly observed GBRs +3 kb and −20 kb from the SCNN1A and SDPR TSSs , respectively ( Figure 1B ) . Of the 73 A549 GBRs identified in the present study , 64 ( 88% ) were associated with genes regulated by GR in those cells ( Table 1 ) . Although the remaining nine GBRs may be nonfunctional , they may mediate responses under different biological conditions . Notably , 27% of the genes that were glucocorticoid responsive specifically in A549 but not in U2OS cells were associated with a GBR , whereas only 1 . 9% of the genes responsive to glucocorticoids in U2OS but not in A549 contained A549 GBRs ( Figure 2 ) . Similarly , only 1 . 8% of the genes that were glucocorticoid or androgen responsive in other cells and only 0 . 3% of the genes that were sampled by the ChIP-chip arrays but were not steroid regulatory targets were associated with A549 GBRs ( Figure 2; Table S1 ) . Thus , GR occupancy in A549 cells is generally restricted to genes that are actually regulated by glucocorticoids in those cells; specifically , GR is rarely bound in A549 cells at genes responsive to glucocorticoids in other cells . We conclude that GR occupancy is a major determinant of glucocorticoid responsiveness in A549 cells at the genes assessed in this study . To test whether the A549 GBRs can confer glucocorticoid-directed transcriptional responses , we cloned 500-bp DNA fragments encompassing the GBRs into luciferase reporter plasmids . Of the 20 GBRs randomly selected from the 73 GBRs identified in this study , 19 were dex responsive in A549 cells as assessed by reporter analysis ( Figure 3A ) . We define primary GREs ( denoted here simply as GREs ) as genomic regions that are occupied in vivo by GR and confer glucocorticoid-regulated transcription in transfected reporters . Although the reporter analyses do not prove that the identified elements are functional in their native contexts ( see Discussion ) , they establish that the 500-bp fragments tested harbor sufficient information for GR to regulate transcription . Thus , we shall refer to the GBRs henceforth as GREs . We determined the positions of the A549 GREs relative to TSSs of their respective target genes ( Figure 4A ) . For this analysis , the GREs were assigned to the nearest gene responsive to dex in A549 cells . Surprisingly , we found that 45% of the GREs were located downstream of the TSSs , suggesting that GR exhibits transcriptional regulation without a significant preference for regions upstream or downstream of TSSs ( Table 1 ) . Figure 4B summarizes the distribution of promoter proximal ( within 5 kb from the TSS ) and distal GREs ( farther than 10 kb from the TSS ) . Strikingly , 63% of the GREs were distal , whereas only 31% of them were promoter proximal ( Figure 4B ) . Mammalian response elements are commonly thought to reside upstream and proximal to their cognate promoters; thus , identification of GREs and response elements in general have mainly focused on these regions . Importantly , Figure 4B demonstrates that only a small fraction of the GREs ( 17% ) identified in this study was positioned within these regions . These results indicate that GREs are just as likely to be located downstream of the TSSs and that the majority operate remotely from their target promoters , at least by linear DNA distance . Our finding concerning GRE distribution is supported by two indirect analyses using nuclease sensitivity and sequence conservation . Sabo et al . found that DNAse I hypersensitive sites , indicative of chromatin-bound factors , are broadly distributed with a majority located >10 kb from the nearest TSS [26] . Furthermore , Dermitzakis et al . showed that conserved nongenic sequences ( CNGs ) , ungapped 100-bp fragments with at least 70% identity between human and mouse that are presumed factor-binding regions , have no significant preference for promoter proximal regions [27 , 28] . As expected [29] , we found that GR occupancy was correlated with DNAse I-hypersensitive cleavage at both promoter proximal ( 1 . 3 , 1 . 5 , 12 . 1 , and 16 . 1 ) and distal GREs ( 2 . 4 , 5 . 1 , 6 . 1 , 6 . 3 , 7 . 3 , and 20 . 2 ) ( Figure 5A ) . In addition , by aligning the human GRE sequences with the corresponding regions in the mouse genome , we found that 23 of the GREs correspond to CNGs ( Figure 5B ) . Moreover , GR occupancy and glucocorticoid responsiveness for several of these GREs/CNGs ( 6 . 4 , 12 . 1 , 5 . 1 , 6 . 1 , 6 . 2 , 10 . 5 , X . 1 , and X . 2 ) were maintained in mouse cells ( see Figure 6A , 6B ) . Thus , by testing the GREs identified in our study , we were able to provide direct support for the notion that DNAse I hypersensitive sites and CNGs serve as regulatory elements [26 , 28] . Native GREs , defined as naturally evolved genomic elements that confer glucocorticoid regulation on genes in their chromosomal contexts , are likely to be “composite elements , ” made up of binding sites for GR together with multiple nonreceptor regulatory factors [2] . To assess whether we could detect such complex architecture , we used computational approaches ( Bioprospector and MobyDick ) to survey the 500-bp GRE-containing fragments for sequences related to known regulatory factor binding sites [30–32] . The most prominent motif found , present in 68% of the GRE sequences , was a series of imperfect palindromes similar to known core GR binding sites ( Figure 3B ) . Potentially , GR may interact with the remaining 32% of GREs through other recognition motifs or through tethering to other factors [7] . Mutagenesis of computationally predicted core GR binding sites decreased or completely abolished dex stimulation for each of 13 randomly tested sites , validating this approach for identifying functional core GR binding sequences ( Figure 3A ) . Some GREs , such as 6 . 2 , 7 . 2 , and 7 . 3 , contained multiple GR binding sites; we found that reporters mutated at only one of those sites retained residual dex inducible activity . These experiments imply that most of the core GR binding sites identified in our computational analysis are functional . In addition , we found that motifs similar to AP-1 , ETS , SP1 , C/EBP , and HNF4 binding sequences were enriched in the 500-bp GRE fragments ( Figure 3B ) . For example , motifs resembling AP-1 and C/EBP binding sites were identified in the GRE of the IL8 gene . Importantly , the AP-1 binding site is known to be crucial for regulation of IL8 by the AP-1 factor [33]; similarly , C/EBPα enhances transcription of a reporter spanning the IL8 GRE region [34] . Thus , as with GR binding sequences , our computational analysis was capable of discovering functional nonreceptor binding sites . Detection of multiple factor binding sites within the GRE sequences is consistent with the hypothesis that native GREs are typically composite response elements that recruit heterotypic complexes for combinatorial control [2] . To estimate the extent of GRE conservation , we measured sequence identity in human and mouse across 4-kb regions centered on the core GR binding sites ( see Figure 3C legend and Materials and Methods ) averaged across 50-bp windows; a similar ( albeit higher resolution ) pattern was obtained with 15-bp windows ( unpublished data ) . Strikingly , we found that flanking sequences roughly 1 kb surrounding the core GR binding sites were conserved relative to background ( Figure 3C ) . This elevated evolutionary conservation implies that these segments are biologically functional , not only in reporter constructs ( Figure 3A ) , but also in their native chromosomal contexts , further supporting the view that native GREs are composite elements . We next sought to examine in detail the extent of sequence conservation of some of the individual core GR binding sequences and GREs that we had identified in our study . We chose a subset of 12 human GREs that are occupied by GR both in another species , mouse , and in another cell type , C3H10T1/2 mesenchymal cells ( Figure 6A ) . Consistent with the correlation between GR occupancy and glucocorticoid responsiveness ( Figure 2; Table 1 ) , we confirmed that several of these genes ( Fkbp5 , Ddit4 , Gilz , MT2A , and Sgk ) were indeed dex inducible in the C3H10T1/2 cells ( Figure 6B ) . These 12 GREs resided at very different locations relative to the TSSs of their human target genes ( ranging from 0 . 1 kb to 86 kb ) ( Table S2 ) ; remarkably , however , each locus was approximately maintained in the mouse genome ( Table S2 ) . This finding suggests that the positions of individual GREs may be integral to their regulatory functions . We then examined the extent of conservation of the 15-bp core GR binding sites within the GRE set defined above . As anticipated , the 12 core GR binding sites from the different human GREs differed substantially , with only five invariant positions across the 15-bp sequences ( Figure 6C ) ; for example , the binding sites of human GRE 5 . 1 and human GRE 10 . 3 match at only seven positions . In striking contrast , we found that the core GR binding site sequences within the individual GREs were highly conserved among human , mouse , dog , and rat ( Figure 6C ) ; for example , the core GR binding sequence at GRE 10 . 5 is identical in all four evolutionarily distant species . Finally , we compared in human and mouse the patterns of conserved sequences flanking the core GR binding sites , which provide “architectural signatures” of individual GREs . We found that the patterns of sequence conservation differed dramatically among the different GREs ( Figure 6D; Figure S3 ) . For example , GRE X . 1 contains conserved sequence elements at −900 , −500 , and +600bp , whereas GRE X . 2 displays no conservation at those positions ( Figure 6D ) . Although the functional significance of the conserved regions has yet to be tested ( for example , we have not ruled out incidental overlaps with conserved noncoding expressed regions ) , the conserved regions are likely to correspond to regulatory or structural motifs . As predicted by these findings , pair-wise calculations of sequence identity of different human GREs ( using a 15-bp window centered on the core GR binding sites ) demonstrated that sequences flanking the core GR binding sites varied extensively among human GREs ( Figure S4 ) . Thus , the overall family of GREs is broadly divergent in sequence and organization , but each individual GRE retains a distinctive signature of conserved sequences , suggesting that each corresponds to a composite GRE that is functionally distinct .
We set out to examine the organization and function of genomic elements responsible for transcriptional regulation by GR . Our study yielded five conclusions: ( 1 ) GR occupancy at a GRE is generally a limiting determinant of glucocorticoid response in A549 cells; ( 2 ) the core GR binding sequences conform to a consensus that displays substantial GRE-to-GRE variation as anticipated , but the precise binding sequences at individual GREs are highly conserved through evolution; ( 3 ) GREs appear to be evenly distributed upstream and downstream of their target genes; ( 4 ) most GREs are positioned at locations remote from the TSSs of their target TSSs; and ( 5 ) native GREs are commonly composite elements , comprised of multiple factor binding sites , and they are individually conserved in position and architecture yet very different from each other . We shall consider the implications of these conclusions in turn . We began by surveying more than 1 , 000 genes , with half of them candidates for steroid regulation , and a specific subset known to be GR-regulated in A549 cells . We found that GR occupancy of A549 GREs correlated strongly ( nearly 90% ) with genes that are glucocorticoid responsive in A549 , suggesting that GR binding is generally a limiting determinant for response in these cells . In a small number of cases , we observed GR occupancy close to genes that were GR-unresponsive in A549 cells , but were steroid regulated in other cells [4] ( E . C . Bolton and K . R . Yamamoto , unpublished results ) . This implies that GR occupancy at these genes likely reflects bona fide response element binding , but that GR binding is not a limiting factor for glucocorticoid regulation of this minority class of genes in A549 cells . Collectively , our data suggest that restriction of GR occupancy in A549 cells may be responsible for much of the cell-specific GR-mediated regulation in these cells . The mechanisms of occupancy restriction could be positive or negative mechanisms , such as accessory factors that stabilize GR binding , or chromatin packaging that precludes it . Although the strong correlation between GR occupancy and glucocorticoid responsiveness in A549 cells seems likely to hold in other cell types , it is conceivable that responsiveness may be determined differently in other cell types . Thus , it will be interesting to examine cell-specific GR regulation in other cells to complement the observations made in A549 cells . It is intriguing that one component , GR , within such varied and complex machineries would so strongly predominate as a determinant of transcriptional regulation in A549 cells . It will be interesting to examine regulatory complexes that mediate other types of responses ( e . g . , heat shock and DNA damage ) to assess whether response element occupancy by a single factor in each class is a dominant determinant of responsiveness . We examined sequence conservation of a set of GREs that are occupied by GR both in human lung epithelial cells and in mouse mesenchymal stem cells . We found that the 15-bp core GR binding sequences varied greatly among the different GREs ( Figure 3B ) , whereas the sequences of the individual binding sites were nearly fully conserved across four mammalian species ( Figure 6C ) . Crystallographic studies demonstrate that GR makes specific contacts with only four bases of the 15-bp core binding sequence [35] , yet every position , including the “spacer” between the hexameric half sites , appears to be equivalently conserved . This indicates that the binding sequences serve functions in addition to merely localizing GR to specific genomic loci and instead may carry a regulatory code that affects GR function . Leung et al . reported similarly strong evolutionary conservation of individual κB binding sequences [36] . Indeed , Luecke and Yamamoto showed that GR directs distinct regulatory effects when tethered to NFκB at two κB response elements that differ by only one base pair [7] . Thus , one interpretation of our data findings is that factor binding sites may serve as allosteric effectors [19] in which individual binding sequences convey subtle conformational differences to specify distinct factor functions . Conceivably , this hypothesis might also explain why GR predominates as a limiting determinant of responsiveness , because factors that read allosteric regulatory codes might specify the rules for assembly of GRE-specific and thus gene-specific regulatory complexes . To characterize the architecture of GREs , we took several approaches . In unbiased computational analyses , we identified enriched sequence motifs within 500-bp segments encompassing core GR binding sites . Sequence motifs resembling binding sites for GR , AP-1 , ETS , SP1 , C/EBP , and HNF4 were overrepresented relative to a background of unbound GR regions , consistent with the notion that native GREs are composite elements . For most of these GREs , the role of these factors in GR transcriptional regulation remains to be tested , but it is notable that ETS-1 , SP1 , and HNF4 have been shown at other genes to augment glucocorticoid responses [37–39] . Moreover , Phuc Le et al . [40] described motifs resembling AP1 and C/EBP binding sites within certain mouse GREs and showed that nearly half of the GREs predicted to encompass C/EBP binding sites did indeed bind C/EBPβ [40] . These findings further the view that our computational analysis can infer factors that potentially interact with GR at GREs . Using a similar approach , Carroll et al . [9] and Laganiere et al . [41] have interrogated estrogen response elements and identified FOXA1 as a factor playing an important role for both estrogen receptor binding and transcriptional activity . Thus , we anticipate that the factors that occupy the GR composite elements may interact physically , functionally , or both , thereby affecting binding as well as regulatory activity . Indeed , an averaged comparison of human and mouse sequences flanking core GR binding sites revealed that a region of approximately 1 kb was conserved above the background level ( Figure 3C ) , suggesting that native composite GREs are extensive and typically may contain numerous factor binding sites . Interestingly , individual GREs displayed distinctive patterns of sequence conservation extending from the core GR binding sites ( Figure 6D; Figure S3 ) . These GRE signatures likely reflect conservation of various sequence motifs at different positions within each element , producing GRE-specific ( and therefore gene-specific ) architecture that likely creates distinct regulatory effects . To investigate the distribution of regulatory elements relative to their target genes , we monitored GR occupancy across 100 kb regions centered on the TSSs of glucocorticoid responsive genes . We found that GREs were evenly distributed upstream and downstream of their target genes with the majority located >10 kb from their target promoters; other metazoan regulatory factors , such as estrogen receptor ( ER ) and STAT1 , have similarly been reported to act from sites remote from their target genes [9 , 42–45] . In contrast to these factors , E2F1 was shown to mainly bind promoter proximal regions [42]; others have used computational approaches to infer factor binding sites close to promoters , but these have not been experimentally confirmed [46] . In parallel with our findings , Carroll et al . reported that only 4% of estrogen receptor ER binding regions was mapped within −800 bp to +200 bp from TSS of known genes from RefSeq [43] . Our data demonstrated that 9% of GBRs were positioned at this location . These studies together imply that steroid receptors , which include estrogen receptor and GR , in general regulate transcription from remote locations . Interestingly , we found that the positions of individual GREs were generally conserved across species ( Table S2 ) , implying that GRE position may be functionally important for target gene regulation . In any case , our findings differ dramatically from those in prokaryotes and fungi , where transcriptional regulatory elements are promoter proximal . It has been suggested that these two broad classes of regulatory mechanisms , so-called long range and short range , are mechanistically and evolutionarily related , and that long range control might facilitate regulatory evolution [11] . As predicted by that model , distal elements , far from target genes as measured by linear DNA distance , may operate in close proximity with their target promoters in 3-D space . For example , Carroll et al . detected an interaction between the NRIP-1 promoter and its distal estrogen response element [9] . It will be interesting to determine whether response element location ( i . e . , promoter proximal versus distal ) is somehow related to mechanism or to physiological network . Remote response element locations can complicate assignment of cognate target genes . An extreme example is olfactory receptor gene expression , which is governed by a regulatory element that can operate on target genes located on different chromosomes [47] . In this study , we assigned the GREs to the nearest RefSeq gene responsive to dex in A549 cells . In other contexts , these GREs may be nonfunctional or may operate on genes other than those assigned in A549 cells ( Table 1 ) . Clearly , unequivocal assignment of a GRE to a given target gene will require genetic manipulations not readily accessible in mammalian cells at present . It is encouraging , however , that GR occupancy of GREs correlated strongly with glucocorticoid responsiveness of adjacent genes , supporting the view that these are bona fide direct GR targets ( Figure 2; Table 1 ) . In fact , when these genes were subjected to Gene Ontology analysis , we found that they were enriched in cell growth and immune responses ( unpublished data ) , two biological processes regulated by GR in A549 cells [48 , 49] . We found GR occupancy at genes up- and down-regulated in response to dex , consistent with GR serving either as activator or repressor in different contexts . At present , we cannot assess the significance of the finding that GR was detected at GREs adjacent to activated genes versus repressed genes at a 6:1 ratio in A549 cells; whether this difference reflects differences in GRE occupancy , epitope accessibility , crosslinking efficiency , or other variables has not been determined . Genomic response elements orchestrate transcriptional networks to mediate cellular processes for single- and multicellular organisms . The present study advanced our understanding of the organization , evolution , and function of GREs and at the same time raised a series of interesting questions . Among the more intriguing: How is GR occupancy restricted to a small subset of potential GREs in a given cell context ? What is driving the strong conservation of virtually every base pair within the core GR binding sequence at individual GREs ? Addressing these and other questions raised in our study will contribute additional new insights about gene regulation by GR and by other regulatory factors .
A549 and C3H10T1/2 cells were grown in DMEM supplemented with 5% or 10% FBS , respectively , in a 5% carbon dioxide atmosphere . Before hormone treatment , media was replenished with DMEM containing charcoal stripped FBS , which depletes endogenous steroids . Plasmid PGL4 . 10 E4TATA ( generously provided by Yuriy Shostak ) was created by insertion of the E4TATA minimal promoter into pGL4 . 10 vector ( Promega , http://www . promega . com ) . The 20 reporters tested ( Figure 3A ) represent randomly chosen GRE fragments . The QuikChange kit ( Stratagene , http://www . stratagene . com ) was used for reporter mutagenesis . The 13 core GR binding sites that were mutated in the reporters ( Figure 3A ) were also randomly chosen based on success of mutagenesis . GBR-containing DNA fragments ( 500 bp ) were amplified by PCR and subcloned into pGL4 . 10 E4TATA using KpnI and XhoI sites ( see Table S3 for primer sequences ) . A549 cells were grown in a 48-well plate and cotransfected with 19 ng of the reporter constructs , 10 ng pRL Luc ( Promega ) , and 38 ng pCDNA3 hGR ( human GR expression vector ) using Lipofectamine 2000 ( Invitrogen , http://www . invitrogen . com ) . After overnight transfection , cells were treated with hormone , harvested , and luciferase activity was measured as described for the dual luciferase reporter system ( Promega ) using a Tecan Ultra Evolution plate reader ( Tecan , http://www . tecan . com ) . ChIP assays were performed as described [7] with the following modifications . The chromatin samples were extracted once with phenol-chloroform and purified using a Qiaquick column as recommended by the manufacturer ( Qiagen , http://www1 . qiagen . com ) . The ligation-mediated PCR ( LMPCR ) process was adapted from Oberley et al . [50] . We used 3 . 5–20 ng of amplicon for real-time qPCR analysis , and data were normalized to Hsp70 ( see Table S4 for primer sequence ) . Human and mouse DNA sequences were retrieved from University of California Santa Cruz ( UCSC ) Genome Browser ( http://genome . ucsc . edu ) NCBI Build 35 , and qPCR primers were designed using Primer3 [51] . For the array , ~50 kb upstream and downstream regions were tiled with isothermal 50 mer oligos ( spaced on average of every 54 bp apart ) relative to the TSSs of the investigated target genes . Where 100-kb regions overlapped , the surrounding genomic region was tiled further bidirectionally . ChIP samples from ( final concentration , 0 . 01% ethanol ) or dex-treated A549 cells were labeled with Cy3 or Cy5 , hybridized onto the arrays , and relative signal intensities were measured by NimbleGen ( http://www . nimblegen . com ) . SignalMap was utilized to find peak enrichments with both window threshold detection ( 500-bp peak window size , 25% of Peak Threshold ) and second derivative peak detection ( 500-bp peak window size , 20 bp smooth step , 25% peak threshold ) ( NimbleGen ) . The RNA isolation , reverse transcription , and qPCR steps were performed as previously described [4] . Primers for cDNA amplification are displayed in Table S4 . The experiments were adapted from previous described protocol [52] with the following modifications . Briefly , nuclei from A549 cells treated with vehicle or dex for 1 h were treated with 6 . 25–200 units/ml of DNAse I ( Qiagen ) for 5 min at room temperature . The reaction was stopped and treated with Proteinase K for 1 h at 65 °C . The DNA samples were extracted once with 1:1 phenol-chloroform and further purified using MiniPrep columns ( Qiagen ) . The samples were subjected to qPCR analysis to determine the relative amount of cleaved product ( see Table S4 for primer sequences ) , which was converted to percent DNAse I cleavage . For computational analysis of enriched motifs , all repeat-masked DNA sequences were downloaded from the UCSC genome browser ( NCBI Human Build 35 ) . BioProspector analysis was initially performed using nucleotide widths ( w ) 14 and 16 on GREs to identify GR binding sites and the top motifs were masked to identify other motifs [32] . For MobyDick analysis , both the human and the human/mouse aligned sequences were used as inputs to identify enriched motifs [30 , 31] . Similar motifs were clustered using CAST [53–55] . All p-values for enrichment were Bonferroni corrected to identify putative factor binding sites [55] . The top Bioprospector w14 position weight matrix ( PWM ) was used to score GREs for putative GR binding sites with a false positive rate of less than 10% . This upper bound was calculated from randomly sampling unbound GR regions ( Figure S2 ) . We built position weight matrices ( PWMs ) of those motifs with p-values less than 0 . 05 , which were used to measure similarity to known binding sites in TRANSFAC [56] . We measured the distance between the PWMs and those representing binding sites for known regulatory factors using relative entropy ( Kullback-Liebler divergence ) with a cutoff of less than 6 . 0 to associate motifs with putative regulatory factor binding sites . The known binding site matrices were obtained from TRANSFAC professional version 9 . 3 . The human–mouse conservation score was calculated as described [9] using a 50-mer window for 50 sequences containing a putative GR binding site based on our computational and experimental analysis ( Figure S2 and Figure 3A ) . The conservation score was calculated as number of bp matches minus the number of bp deletions or insertions divided by the bp window size . We centered each alignment based on the highest scoring putative GR binding site in human and expanded equally on each side of the binding site to a total length of 4 kb . The background level was calculated by taking the average of all conservation scores across the 4-kb region . The human ( hg ) /mouse ( mm ) genome alignments were downloaded from Vista ( http://pipeline . lbl . gov/cgi-bin/gateway2 ) . | The glucocorticoid receptor ( GR ) regulates a myriad of physiological functions , such as cell differentiation and metabolism , achieved through modulating transcription in a cell- and gene-specific manner . However , the determinants that specify cell- and gene-specific GR transcriptional regulation are not well established . We describe three properties that contribute to this specificity: ( 1 ) GR occupancy at genomic glucocorticoid response elements ( GREs ) appears to be a primary determinant of glucocorticoid responsiveness; ( 2 ) the DNA sequences bound by GR vary widely around a consensus , but the precise sequences of individual GREs are highly conserved , suggesting a role for these sequences in gene-specific GR transcriptional regulation; and ( 3 ) native chromosomal GREs were generally found to be composite elements , comprised of multiple factor binding sites that were highly variable in composition , but as with the GR binding sequences , highly conserved at individual GREs . In addition , we discovered that most GREs were positioned far from their GR target genes and that they were equally distributed upstream and downstream of the target genes . These findings , which may be applicable to other regulatory factors , provide fundamental insights for understanding cell- and gene-specific transcriptional regulation . | [
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Zwitterionic capsular polysaccharides ( ZPS ) of commensal bacteria are characterized by having both positive and negative charged substituents on each repeating unit of a highly repetitive structure that has an α-helix configuration . In this paper we look at the immune response of CD8+ T cells to ZPSs . Intraperitoneal application of the ZPS Sp1 from Streptococcus pneumoniae serotype 1 induces CD8+CD28− T cells in the spleen and peritoneal cavity of WT mice . However , chemically modified Sp1 ( mSp1 ) without the positive charge and resembling common negatively charged polysaccharides fails to induce CD8+CD28− T lymphocytes . The Sp1-induced CD8+CD28− T lymphocytes are CD122lowCTLA-4+CD39+ . They synthesize IL-10 and TGF-β . The Sp1-induced CD8+CD28− T cells exhibit immunosuppressive properties on CD4+ T cells in vivo and in vitro . Experimental approaches to elucidate the mechanism of CD8+ T cell activation by Sp1 demonstrate in a dimeric MHC class I-Ig model that Sp1 induces CD8+ T cell activation by enhancing crosslinking of TCR . The expansion of CD8+CD28− T cells is independent , of direct antigen-presenting cell/T cell contact and , to the specificity of the T cell receptor ( TCR ) . In CD8+CD28− T cells , Sp1 enhances Zap-70 phosphorylation and increasingly involves NF-κB which ultimately results in protection versus apoptosis and cell death and promotes survival and accumulation of the CD8+CD28− population . This is the first description of a naturally occurring bacterial antigen that is able to induce suppressive CD8+CD28− T lymphocytes in vivo and in vitro . The underlying mechanism of CD8+ T cell activation appears to rely on enhanced TCR crosslinking . The data provides evidence that ZPS of commensal bacteria play an important role in peripheral tolerance mechanisms and the maintenance of the homeostasis of the immune system .
Humans are colonized by multitudes of commensal organisms the importance of which is now recognized for good health . Capsular polysaccharides of the physiologic human bacterial flora are immunogenic components that first encounter the immune system during their initial colonization , and , at the time the immune system is developing and maturing . As opposed to common negative charged polysaccharides , the biologic activity of polysaccharides from certain commensal bacteria is unique in their ability to stimulate CD4+ T cells in vivo and in vitro [1] , [2] , [3] , [4] , [5] . Examples of such bacteria are , Bacteroides fragilis the ubiquitous anaerobic member of the gut flora , Staphylococcus aureus a temporary member of the skin and mucosal flora , and , Streptococcus pneumoniae of the upper respiratory tract flora . CD4+ T cell activation induced by these polysaccharides depends on their unique electrical charge . Each repeating unit has a minimum of one positive and one negative charge leading to their common three-dimensional configuration characterized by a right-handed helix with repeating negatively charged grooves , the positive charges being on the outer surface of the lateral boundaries [1] , [4] , [6] , [7] . Elimination of their charged groups abrogates T cell-dependent immune responses in vitro and in vivo [1] . Introduction of a zwitterionic charge motif into a non-ZPS converts a T cell-independent polysaccharide into a CD4+ T cell-dependent antigen [1] , [8] . Presentation of the so-called zwitterionic polysaccharide ( ZPS ) from S . pneumoniae serotype 1 ( Sp1 ) by MHC class II molecules requires its retrograde transport from lysosomes to the cell surface within tubules as a ZPS/MHC class II complex and also , the DM molecule [9] , [10] . Binding studies suggest that ZPS binds to the antigen binding cleft of the MHCII molecule [11] . ZPS directs the development of the systemic cellular immune response by correcting CD4+ T cell deficiencies and TH1/TH2 imbalances towards a TH1 immune response [12] . Intraperitoneal application of ZPS , such as PS A1 from B . fragilis or Sp1 from S . pneumoniae , with adjuvant , promotes CD4+ T cell-dependent intra-abdominal abscess pathology [5] , [13] , whereas subcutaneous ZPS application without adjuvant induces regulatory IL-10-secreting CD4+ T cells that protect from abscess formation [14] . Recently , it was shown that oral application of ZPS prevents inflammatory bowel disease ( IBD ) [15] . The ZPS PS A1 protects from IBD through a functional requirement for interleukin ( IL ) -10-producing CD4+ T cells . A significant number of potentially self-reactive lymphocytes leave the primary lymphoid organs and are kept under control by “peripheral tolerance mechanisms” [16] . Probably the most important of these mechanisms is assured by regulatory CD4+ and CD8+ T lymphocytes capable of suppressing adaptive and also innate immune-responses [17] , [18] . Regulatory T cells are known to control immune responses to self-antigens but also to nonself-antigens ( e . g . upon infection [19] , [20] ) and to innocuous ( probably non-self ) antigens in intestinal mucosa [21] . “Naturally” occurring regulatory/suppressive CD8+ T lymphocytes ( Ts ) constitute an endogenous long-lived population of T cells that develop in the thymus [22] , [23] . In mice , CD8+CD122+ T cells have received special attention . As naturally occurring Ts , they are regarded as a functional T cell subset that affects immunity through the release of the anti-inflammatory cytokine IL-10 [24] , [25] . They are believed to have specificity for self antigens and are poised to prevent autoimmunity [26] . Mice genetically deficient for CD122 ( the IL-2/IL-15 β-receptor chain ) , spontaneously develop severe hyperimmunity . Furthermore , adoptive transfer of CD8+CD122+ Ts ameliorates established experimental autoimmune encephalomyelitis [27] . Another population of described Ts is termed “adaptive” or “induced” because these Ts are induced after one time or several rounds of stimulation with cytokines , antibodies , allogeneic or xenogeneic stimulator cells , antigen-pulsed APCs , or antigens mostly of an artificial synthetic peptide ( overview in [28] ) . However , data from in vitro induced CD8+CD28− Ts after priming with specific antigens suggests that the Ts do not respond to the priming antigens [29] . No natural antigen either self , or tumor , or bacterial , has ever been demonstrated to induce the differentiation of CD8+CD28− T cells or any other subset of CD8+ Ts in vitro or in vivo . CD8+CD28− Ts were shown to suppress autologous and heterologous CD4+ T cell proliferation by rendering APC tolerogenic through the induction of receptors that transmit negative signals . Specifically , upregulation of immunoglobulin-like transcripts ( ILT ) 3 and ILT4 [30] . CD8+CD28− Ts have been attributed to be players in autoimmunity [31] . IL-10 producing CD8+CD28− Ts infiltrate tumors , circulate in the peripheral blood of the majority of cancer patients , and inhibit both proliferative and cytotoxic T cell responses in this setting [32] . Moreover , CD8+CD28− Ts seem to regulate immune-responses in inflammatory disease [33] . CD8+CD28− Ts are also involved in the control of mucosal immune responses . In an IBD model [34] , in contrast to CD8+CD28+ , CD8+CD28− T cells freshly isolated from the spleen or gut of naïve mice , being rather “natural” Ts , effectively prevent the development of colitis . CD8+CD28− T cells derived from IL-10–deficient mice lack the functional ability to prevent colitis . Moreover , IBD induced with colitogenic T cells incapable of responding to TGF-β is not prevented with CD8+CD28− Ts . These data suggest that in addition to IL-10 , TGF-β also seems to play a role in the suppressive activity of CD8+CD28− Ts . Other “naturally” and “adaptive” murine and human Ts populations have also been described [35] , [36] , [37] , [38] , [39] , [40] . Various cell surface markers such as , CTLA-4 , CD25 , CD62L , CD122 and the lack of expression of CD28 , CD44 , the synthesis of the cytokines TGF-β and IL-10 , and the expression of Foxp3 have been reported in Ts [28] . Not all cell surface markers are expressed on one subset of Ts , and not all cytokines are produced by one particular Ts subset . It is currently unknown whether the different types of Ts are distinct cell populations , overlapping , or , are essentially derived from one source . They seem to emerge after T cell receptor ( TCR ) stimulation , and exhibit their down-regulatory function by impairing the responsiveness of other T cells . TCR is the primary trigger for antigen-mediated T cell stimulation . In principal three different mechanisms of antigen recognition by the TCR are known [41] . Firstly , conventional peptide antigens are loaded onto MHC molecules within the binding groove for antigen recognition by the TCR . Secondly , superantigens polyclonally activate T cells by bridging the outer surface of the MHC and the TCR molecule . Thirdly , mitogens and lectins induce T cell activation by crosslinking T cell surface molecules such as the TCR and CD3 . For all three mechanisms , engagement of the TCR by an antigen translates into an intracellular signal by inducing a phosphorylation cascade resulting in the activation of ζ-associated protein of 70 kDa ( Zap-70 ) . Activated Zap-70 phosphorylates scaffold proteins resulting in the activation of transcription factors such as NFκB that initiate gene programs promoting proliferation , differentiation , and effector actions of T cells . TCR- and co-stimulatory CD28-mediated signaling effect the susceptibility to apoptosis . Previous investigations have exclusively focused on the role of effector and regulatory CD4+ T cells in ZPS-mediated immune responses . However , the role of CD8+ T cells in ZPS-mediated immunity has not been addressed to date . Here , we analyzed the ability of a ZPS to regulate CD4+ T cell immune responses through CD8+ T cells . We found that Sp1 induces the expansion of regulatory CD8+CD28− T lymphocytes in vitro and in vivo and we assessed their function and activation mechanism .
To study CD8+ T cell immunity to ZPS , we first employed a well characterized experimental model of CD4+ T cell-dependent immune response , which is the mouse model of Sp1-mediated abscess formation [10] . Whole ZPS-positive commensal bacteria or their ZPS , such as Sp1 , induce intraperitoneal abscesses when applied intraperitoneally with sterile cecal content ( SCC ) adjuvant [1] . Application of adjuvant alone and chemically modified Sp1 ( mSp1 ) plus adjuvant , as negative controls , do not induce abscesses ( Fig . 1A ) . In contrast , challenging C57BL/6 WT mice with intraperitoneal Sp1 plus adjuvant results in the formation of sterile abscesses six days later . The median abscess size of Sp1-challenged mice is 2 mm ( one abscess/mouse ) . CD4+ T cell-dependency of this immune response is confirmed , as CD4+ T cell depletion leads to significant inhibition of abscess formation ( no abscesses observed ) . In contrast , CD8+ T cell depletion results in the formation of significantly larger abscesses with a median abscess size of 7 mm ( one abscess/mouse ) . Immunohistochemical analysis of Sp1-mediated intraperitoneal abscesses shows that CD8+ T cells participate in the formation of the organized wall of abscesses ( Fig . 1B ) . Intraperitoneal Sp1 challenge results in a significant increase of the total cell count in the peritoneal cavity peaking at 24 h and 36 h following Sp1 challenge [42] . The cellular influx mainly consists of neutrophils and macrophages ( not shown ) . DCs ( about 7% ) and CD4+ T cells ( about 1% ) also are attracted by Sp1 into the peritoneal cavity [10] , [42] . Here , we analyzed the quantity and phenotype of CD8+ T cells migrating into the peritoneum after Sp1 application . To establish the distinction of CD28− and CD28+ CD8+ T cells fluorescent staining of CD28 was performed with native spleen CD8+ T cells that contained a mixture of CD28− and CD28+ CD8+ T cells ( data not shown ) . As no clear CD8+CD28− population could be distinguished CD8+CD28− cells are therefore defined as those expressing CD28 at background isotype levels . The thus defined CD28− population represents about 30% of CD8+ splenocytes as previously described [34] . Flow cytometry analysis of the CD28 phenotype of CD8+ T cells migrating into the peritoneal cavity 24 h and 36 h after Sp1 challenge reveals that almost all CD8+ T cells are CD8+CD28− cells ( Fig . 1C ) . We conclude that Sp1 attracts CD8+C28− T cells into the peritoneal cavity that modulate CD4+ T cell-dependent abscess formation . To further characterize the phenotype of the Sp1-induced CD8+CD28− T lymphocytes , we first investigated whether intraperitoneal Sp1-challenge also induces CD8+CD28− T cells in the spleens of C57BL/6WT mice which had been challenged the day before with intraperitoneal Sp1 . Compared to SCCA- and mSp1-challenged mice , Sp1 induces an expansion of CD8+CD28− T cells by 27% and 24% , respectively ( Fig . 2A ) . Different CD8+CD28− T cell subpopulations have been characterized by means of the appearance of the surface markers expressed and secretion of cytokines . Sp1-induced CD8+ T cells expressed CD122 , albeit mainly at low levels ( Fig . 2B ) . Whereas all CD8+CD28− cells were CD122low , a fraction of CD8+CD28+ cells expressed high levels of CD122 . To analyze the expression of markers that allow the distinction of naïve , activated , and memory T cells , we stained the Sp1-induced CD8+CD28− T cells and CD8+CD28+ T cells as controls with CD44 , CD25 , and CD62L ( Fig . 2B ) . Amongst CD28+ cells , a population of CD44high activated/memory CD8+ T cells was found . In contrast , CD28− cells homogenously expressed CD44 at a low level ( 50% positive cells ) that implies a naïve phenotype . Further phenotyping by determination of the CD25 and CD62L surface markers of Sp1-induced CD8+CD28− T cells showed a CD25low and CD62Lhigh pattern which confirms them as naïve quiescent CD8+ T cells [34] , [43] , [44] , [45] . We also determined the expression of CTLA-4 and CD39 . CD39 is an ectonucleotidase that had been found positive in CD4+ regulatory T cells [46] . CD8+CD28− T cells show an increased expression of CTLA-4 ( 22% ) and CD39 ( 19% ) when compared to CD8+CD28+ T lymphocytes . Immunomodulation by several regulatory T cell populations often involves TGF-β and IL-10 . We therefore investigated if CD8+CD28− cells can produce these cytokines . CD8+CD28− T cells were isolated from spleens of mice challenged the day before with Sp1 , and mSp1 , or SCC alone as controls . Latency Associated Peptide ( LAP ) is a proteolytic product of the pro TGF-β1 protein and its surface expression is therefore limited to TGF-β1 expressing cells [47] . As shown in Fig . 2C , in comparison to mice challenged with SCC and mSp1 plus SCC , a substantial proportion ( 34% ) of CD8+CD28− cells expressed LAP . We observed that a substantial proportion ( 21% ) of CD8+CD28− cells produced IL-10 . All groups showed background levels which is likely due to basic cytokine expression caused by the adjuvant SCC . However , the detection of IL-6 , that has not been described to play a role in immunosuppressive CD8+ T cell and thus was used as a negative control , was low at 5% . To address the function of Sp1-induced CD8+CD28− T cells , we investigated their effect on CD4+ T cell responses . First , we examined the effect of CD8+CD28− T cells on ZPS-induced abscess formation . In contrast to the adoptive transfer of Sp1-induced CD8+CD28+ T cells that together cause intraperitoneal abscesses with a median abscess size of 4 mm , transfer of Sp1-induced CD8+CD28− T lymphocytes significantly inhibited the abscess formation ( no abscess detected ) ( Fig . 3A ) . The intraperitoneal application of Sp1 , as a positive control , induced abscesses with a median abscess size of 4 mm ( one abscess/mouse ) , and the application of SCC alone and SCC plus mSp1 as negative controls , did not induce abscess formation . Transfer of CD8+CD28− T lymphocytes from naïve mice also suppressed abscess formation ( data not shown ) . We also addressed the functional role of Sp1-induced CD8+CD28− T lymphocytes in a mixed leukocyte reaction . DCs and CD4+ T cells of different haplotypes were co-incubated to provoke a mixed leukocyte reaction ( MLR ) . Proliferation of the CD4+ T lymphocytes was visualized by their CFSE staining and flow cytometry . Fig . 3B demonstrates that in the absence of CD8+ T cells a proliferative response of CD4+ T lymphocytes takes place . When CD8+CD28+ T cells are added , CD4+ T lymphocytes proliferate to a similar degree . However , the co-incubation of DCs and CD4+ T cells in the presence of CD8+CD28− T lymphocytes results in an efficient reduction of CD4+ T cell proliferation that is comparable to the control with non-proliferating CFSE-labeled CD4+ T cells measured on day 0 of incubation . These investigations show that Sp1-induced CD8+CD28− T lymphocytes efficiently suppress a Sp1-mediated CD4+ T cell-dependent immune response in vivo and a strong non-antigen-specific CD4+ T cell response in vitro . To address the mechanism of the induction of CD8+CD28− T cells by the Sp1 antigen , we first investigated the induction of CD8+CD28− T lymphocytes by Sp1 in vitro . We stimulated spleen cells of C57BL/6 WT mice with Sp1 , and mSp1 or medium alone as controls in vitro , and quantified the CD8+CD28− population by flow cytometry . In comparison to medium- and mSp1-treated spleen cells of which 34% or 33% are of the CD8+CD28− phenotype , respectively , 59% of spleen cells stimulated with Sp1 are characterized as CD8+CD28− T cells ( Fig . 4A ) . To investigate whether direct contact between APC and CD8+ T cells is required for the induction of CD8+CD28− T cells , purified CD8+ T cells separated from spleen cells via a membrane with a pore size that did not allow APC/T cell contact , were treated with Sp1 , mSp1 , and medium . In contrast to medium- or mSp1-treated cells , Sp1 treatment induced an increase of the CD8+CD28− T cell population in 21% and 23% respectively . This result left open the question , do cytokines secreted by the APCs induce CD8+CD28− Ts ? We therefore performed an assay with purified CD8+ T cells . 34% and 35% of CD8+ T lymphocytes stimulated with medium alone or mSp1 , respectively , are CD28− , whereas 68% of the Sp1-treated purified CD8+ T cells are of the CD28− phenotype . This result indicates that Sp1-mediated induction of CD8+CD28− T lymphocytes is independent of direct contact between CD8+ T cells with APCs . Finally , we demonstrate that the induction of CD8+CD28− T lymphocytes relies on expansion of this cell population ( Fig . 4B ) . CFSE-labeled CD8+ T cells were incubated with Sp1 , mSp1 and medium as controls , overnight and analyzed for their proliferation capacity . Fig . 4B demonstrates that in contrast to 7% of CD8+CD28+ T cells , 28% of CD8+CD28− T cells undergo cell division more than three times in response to Sp1 . Proliferation of T cells involves the engagement of TCR by an antigen . We investigated whether CD8+CD28− T lymphocyte activation by Sp1 involves the TCR signaling cascade , resulting in T cell activation and proliferation . Following TCR engagement by an antigen , Zap-70 is rapidly phosphorylated which in turn results in enhanced Zap-70 kinase activity and downstream signaling events ultimately leading to NF-κB activation . We therefore incubated CD8+ , CD8+CD28− , and CD8+CD28+ T cells in the presence of Sp1 , mSp1 or medium alone and analyzed Zap-70 phosphorylation by western blotting . As shown in Fig . 5A , in contrast to medium alone or incubation with mSp1 , treatment with Sp1 results in increased phosphorylation of Zap-70 in CD8+ T cells . Analysis of the CD28− and CD28+ subpopulations revealed that CD8+CD28− T lymphocytes are significantly more activated by Sp1 than CD8+CD28+ T lymphocytes . We next investigated NF-κB translocation activation . In correlation with the Zap-70 phosphorylation , Sp1 leads to significant NF-κB activation in CD8+ and CD8+CD28− T lymphocytes , but not in CD8+CD28+ T cells ( Fig . 5B ) . It is important to note that CD8+ T cells treated with mSp1 and PBS as controls were not activated . One of the central features of TCR-induced NF-κB action is to promote the expression of several anti-apoptotic proteins which enable cells to survive and to proliferate . To examine whether Sp1-induced T cell activation simultaneously prevents apoptosis , we treated CD8+ T cells with polysaccharide antigens in the presence or absence of staurosporine , a potent initiator of apoptosis [48] . We performed FACS analyses of CD8+CD28− and CD8+CD28+ T lymphocytes and examined the extracellular exposure of annexin V as an indicator for apoptosis , and the uptake of the cell death marker propidium iodide ( PI ) . Fig . 6A demonstrates that CD8+CD28− T lymphocytes stimulated with Sp1 are protected from apoptosis and apoptosis-mediated cell death induced by staurosporine . Within the CD8+CD28− T cell population stimulated with Sp1 , only 5% of cells were apoptotic , and 1% were double-positive for the apoptosis and cell death marker - similar to the cells without staurosporine treatment . In contrast , non- and mSp1-treated CD28− T cells are significantly susceptible to staurosporine-mediated apoptosis and cell death . 27% and 28% of CD8+CD28− T cells , respectively , stained positive for annexin V and 8% and 9% , respectively , stained double-positive for annexin V and PI . Strikingly , Sp1 treatment does not confer any anti-apoptotic effect in the CD8+CD28+ T cell population . In this population , the percentage of apoptotic cells was similar to Sp1- , mSp1- , and non-treated cells ( 36% , 30% , and 32% , respectively ) . The same is true for the apoptosis and cell death double-positive cells ( 12% , 8% , and 11% , respectively ) . Furthermore , the anti-aoptotic effect of Sp1 was demonstrated by examination of executioner caspase 3 activation in CD8+CD28− T cells treated with staurosporine . In resting and non-apoptotic cells caspase 3 is a p32 proenzyme which is proteolytically processed in apoptotic cells and forms an active p19 mature enzyme . The irreversible proteolytic processing of caspase 3 serves as an indicator for ongoing apoptotic process . These analyses underscore the anti-apoptotic potential of Sp1 and significantly demonstrated that Sp1 treatment specifically inhibits staurosporine-induced caspase 3 activity in CD8+CD28− T cells but not in CD8+CD28+ T cells ( Fig . 6B ) . In summary , we show by these analyses that Sp1 induces the activation and expansion of CD8+CD28− T cells via TCR engagement and down-stream signaling which eventually results in inhibition of apoptosis in CD8+CD28− T cells . Activation of CD8+CD28− T lymphocytes is mediated through TCR signaling . To test whether the induction of the CD8+CD28− T cell population depends on peptide specificity and TCR specificity , we incubated spleen cells from OT-1 mice carrying a transgenic TCR on their CD8+ T cell surface for the specific recognition of the ovalbumin sequence SIINFEKL with Sp1 , mSp1 , or medium alone as controls , in the presence of the SIINFEKL peptide ( 10−9 M ) . Fig . 7A demonstrates that irrespective of the TCR specificity , Sp1 induces an increase of the CD28− T cell population within the SIINFEKL-stimulated CD8+ T lymphocytes ( 84% versus 30% and 31% of medium- and mSp1-treated cells , respectively ) . We further analyzed Ts induction of CD8+ T cells with an artificial APC system that lacks APCs but contains MHC class I molecules loaded with SIINFEKL peptide . Compared to the negative control , Sp1 induced an increased expression in the CD28− phenotype in 24% of CD8+ T cells . We also tested whether the induction of the CD8+CD28− T cells requires MHC class I molecules at all and cultured purified CD8+ OT-1 T cells in the presence of SIINFEKL in medium alone , and in medium containing mSp1 or Sp1 . Compared to the medium and mSp1 control , Sp1-treated CD8+ T cells show an CD8+CD28− T cell increase of 32% and 27% , respectively . These results demonstrate an APC- and MHC class I-independent induction of the CD28− phenotype by Sp1 in peptide-specific CD8+ T cells . At the same time , they allowed us to study the effect of Sp1 on the TCR membrane organisation using a MHC-Ig dimer model . Quantification of the TCR membrane organisation was achieved by fitting the binding data into a model of equilibrium dimerization of homogeneous monovalent receptors by divalent ligand [49] ( Fig . 7B; upper right box ) . In this model , the first monovalent binding binds with single site affinity [50] . If there is another receptor nearby then the dimer may bind with both “arms” creating an “apparent binding affinity , ” i . e . , the avidity . Since the receptors are identical , increases in avidity can be attributed to increases in local concentration of receptors . In this model the single site dissociation constant , Kd , characterizes the binding of one site on the MHC to one TCR and the dimensionless cross-linking constant , KxRt , characterizes the ability of the MHC/TCR complex to recruit another TCR . The binding avidity , Kv , is approximately the ratio Kd/KxRt [51] . When there is minimal cross-linking potential , KxRt is close to 1 and Kd and Kv are similar . If the cross linking potential Kx is high , there is an enhancement of binding due to dimerization , resulting in a stronger measured avidity than the intrinsic single site affinity . Analysis of the binding data indicated that the overall avidity , Kv , of the dimeric MHC in the presence of Sp1 was ∼2 . 5-fold higher than in the absence of Sp1 ( Fig . 7B ) . The increased avidity of MHC-Ig binding is due to an increased cross-linking constant ( Kx = 0 . 58 cells/# in the absence of Sp1 vs . Kx = 1 . 25 cells in the presence of Sp1 ) , rather than an increased intrinsic dissociation constant ( Kd = 1 . 7 nM vs . Kd = 1 . 2 nM ) , or to changes in the total amount of receptors ( Rt = 9 . 9 # mean channel fluorescence ( MCF ) vs . Rt = 11 . 9 # MCF ) . Thus , the enhanced avidity results from an increase in the cross-linking potential in the presence of Sp1 .
In this paper we describe the first ever identification of a bacterial antigen that regulates CD4+ T cell immune responses through CD8+ T cells . A model ZPS antigen of commensal bacteria Sp1 induces CD8+CD28− T lymphocytes which exhibit a suppressive function on CD4+ T cells in intraperitoneal abscess formation and in an allogeneic CD4+ T cell proliferation assay . In the experimental model of abscess induction , CD8+CD28− Ts represent a small population with about 0 . 1% of cells migrating into the peritoneal cavity upon Sp1 challenge . A portion of these cells is finally incorporated into the abscess wall . Although there is a small number of Ts cells participating in abscess formation , their depletion results in a significant enlargement of the abscess size , suggesting that they have an important role in their ability to regulate CD4+ T cell responses . We provide evidence that the Sp1-induced CD8+CD28− T cells indeed act as suppressor cells . A regulatory/suppressive function of CD8+CD28− Ts has been demonstrated in various in vivo experimental models such as , experimental autoimmune encephalitis and inflammatory bowel disease [34] , [52] . However , this is the first ever demonstration that they can efficiently prevent intra-abdominal abscess formation . Abscess formation associated with secondary peritonitis , which is the most frequent type of peritonitis , causes severe morbidity and is often fatal with a mortality rate up to 30% [53] , [54] . Even with optimal therapy including the administration of antibiotics and surgical treatment , residual abscesses form in many patients resulting in substantial morbidity and mortality [55] . Combining the cited reports and the result presented here , strongly suggest that CD8+CD28− Ts cells play an important role in the physiological control of not only intestinal and cerebral , but also , of peritoneal immune responses . Intraperitoneal application of Sp1 not only leads to an influx of CD8+CD28− T cells into the peritoneum but also to an increase of these cells in the spleen , demonstrating a systemic effect of Sp1 on the immune system . In contrast , mSp1 without the positive charged substituent and therefore resembling a common negatively charged polysaccharide not only fails to induce CD4+ T cell-dependent abscesses but also CD8+CD28− T cells in the peritoneum and in the spleen . Phenotype analysis of the Sp1-induced CD8+ T cells distinguishes them from the previously reported “naturally” inhibitory CD8+CD122+ T cell population [24] , [25] . The CD8+CD28− T lymphocytes induced by Sp1 express CD122 at a low level whereas the CD8+CD122+ population described in these reports correspond to CD8+CD122high cells . Furthermore , it appears that the CD28+CD122high identified in our study are CD44highCD62Lhigh thus classifying them as central memory cells [56] . The Sp1-induced CD8+CD28− Ts cells are phenotype CD44lowCD25lowCD62Lhigh which categorizes them as naïve T cells , thereby , again clearly distinguishing them from CD8+CD122+ T cells . In conclusion , the induction of CD8+CD28− T lymphocytes by Sp1 reflects an expansion of “naturally” CD8+CD28− naïve T cells that had been isolated in spleens from naive C57BL/6 mice [34] . It is currently not understood whether the different types of regulatory CD8+ T cells described are distinct or overlapping cell populations because different markers have been employed for their characterization [28] . Given the need for a more coherent and broader phenotyping we expanded the description of the Sp1-induced CD8+CD28− T cells to the surface markers CTLA-4 and CD39 . In contrast to the CD8+CD28+ T lymphocytes , CD8+CD28− T cells are CTLA-4high . CTLA-4 interacts with CD80 and CD86 expressed by APCs and by effector T cells to suppress T cell activation [57] , [58] . CD4+CD25+ regulatory T cells inhibit T cell activation of effector cells in vitro . Interaction of CTLA-4 with CD80/CD86 is the only mechanism known to be involved in this regulatory process [59] . It is important to further study how and at what stage the CTLA-4 suppressor function intervenes in regulating immunity . In the immune system , extracellular ATP exhibits multiple pro-inflammatory effects that can be inactivated by the ectoenzyme CD39 ( nucleoside triphosphate diphosphohydrolase-1 ) . The catalytic activity of CD39 is strongly enhanced by T cell receptor ligation [59] , [60] . It was recently shown that CD39 is expressed primarily by immunosuppressive regulatory CD4+ T cells and that CD39 , together with CD73 , efficiently distinguishes T regulatory cells from other resting or activated T cells in mice [46] , [59] , [60] . Here , we observe a CD39high phenotype on Sp1-induced CD8+CD28− Ts . This result suggests that CD39 may not only be a marker on regulatory CD4+ but also on CD8+ Ts . It has been suggested that IL-10 protects against inflammation in numerous in vivo and in vitro systems [61] , [62] . A distinct population of CD4+CD25− T cells produces IL-10 in response to the ZPS Sp1 and is responsible for protection in an experimental model of surgical fibrosis [14] . Here , we observe that ex CD8+CD28− T lymphocytes stimulated in vivo with Sp1 also synthesize IL-10 . We conclude that Sp1-induced CD8+CD28− Ts belong to the group of naturally occurring CD8+ Ts that exhibit their regulatory function through IL-10 [24] , [25] , [26] , [34] , [39] , [63] , [64] , [65] . TGF-β blocks T cell proliferation , Th1 , and Th2 differentiation [47] . Our data show that the Sp1-induced Ts express TGF-β1 ( as assessed by analysis of cell surface LAP ) . TGF-β has been described to be secreted by various Ts subpopulations: a ) naturally occurring CD8+CD28− Ts that prevented colitis in an experimental IBD model , b ) induced CD8+ T cells that were generated by myelin basic protein and protected animals from EAE , c ) CD8+CD25+Foxp3+ that inhibited proliferation of responder T cells in vitro , and d ) CD8+Foxp3+ that prevented lupus [34] , [66] , [67] , [68] , [69] , [70] , [71] , [72] . Most of these TGF-β-secreting Ts also suppressed T and B cell responses in vitro . Sp1-induced CD8+CD28− Ts-derived IL-10 and TGF-β may not only contribute in controlling peritoneal immunity but additionally , modulate other inflammatory immune responses . It will be important to evaluate the precise mechanisms involved in the IL-10- and TGF-β-dependent prevention of intraperitoneal abscess formation , and other inflammatory processes of the host by Sp1-induced CD8+CD28− regulatory T cells , because , regulatory lymphocytes are an attractive target for the treatment of various inflammatory processes . The ZPS PS A2 from B . fragilis and Sp1 from S . pneumoniae are known to form a wide right-handed α-helix structure with the positively charged groups turned towards the outside of the molecule allowing facultative interactions with protein molecules or glycoproteins [6] , [7] . MHC class II molecules have open-ended antigen binding grooves [41] and therefore can accommodate antigen molecules that are larger than the groove , such as , the 15 kDa-size PS A1 or Sp1 saccharide . MHC class I has a closed antigen binding groove and therefore it is unlikely that a ZPS fragment is presented by MHC class I . We tested this hypothesis and demonstrated that the expansion of CD8+CD28− Ts is independent of direct contact of APC and T cell , and the MHC class I molecule . Consequently , activation of CD8+CD28− T cells due to presentation of Sp1 in the MHC class I binding groove or cross linkage of MHC and TCR - similar to the activation mechanism employed by superantigens – is excluded . Furthermore , APC-mediated NF-kB activation of T cells through TCR has been shown to be dependent on the engagement of CD28 co-receptor by its ligands B7-1 ( CD80 ) and B7-2 ( CD86 ) on APCs [73] . However , Sp1 is capable to initiate NF-kB activation in CD8+ T cells lacking CD28 co-receptor . Therefore , it is conceivable that Sp1 because of its pronounced repetitive and charged structure binds externally to T cell surface molecules such as the TCR that mediate an activation signaling cascade . We demonstrate that Sp1 predominantly induces Zap-70 phosphorylation of the CD8+CD28− subpopulation and ultimately leads to their NF-kB activation . Consequently , this cell population is protected from cell death . Several factors countering cell death cascades have been shown to be positively regulated by NF-kB activation . Inhibition of mitochondrial apoptotic cascade in CD4+CD28− T cells isolated from patients with rheumatoid arthritis has been shown to be the underlying molecular mechanism which gives rise to expansion and accumulation of CD4+CD28− T cells in these patients [74] . Here in CD8+CD28− T cells , Sp1 confers resistance to the broad range kinase inhibitor staurosporine which preferentially initiates mitochondrial apoptotic cascade [48] . Thus , Sp1 treatment promotes NF-kB activation in CD8+CD28− T cells which leads to blockade of mitochondrial apoptotic function . However , the nature of this specific modification of mitochondrial apoptotic machinery in CD8+CD28− T cells by Sp1-treatment remains to be determined . The proliferation of Ts does not depend on the specificity of its TCR which enabled us to approach investigations of the mechanism of TCR activation . Dimeric MHC-Ig complexes have been used as potential tools for studying T cell membrane organizations [50] , [75] . By means of Db-Ig MHC-Ig dimers we calculated the affinity , avidity , and the numbers of receptors on GP33–41-specific T cells , in the presence and absence of Sp1 during antigen-specific binding . Since we did not see any relevant changes regarding the affinity or numbers of the receptors , but a significant increase in the avidity , we conclude that Sp1 exerts its T cell activation via an enhanced cross-linking of TCRs . Data from in vitro induced CD8+CD28− T cells after priming with specific antigens showed that the CD8+CD28− Ts did not respond to the priming antigens [29] . Our data indicate that Sp1 modulates CD8+ T cell responses induced by different specific peptides through non-peptide- and non-TCR-specific enhancement of the TCR cross-linking . T cell responsiveness to an epitope is affected both by its affinity for the presenting MHC molecule as well as by the affinity between the MHC-peptide complex and the TCR . Low affinity interactions may result in weak T cell responses [76] . In this respect , ZPSs may enhance the stability and crosslinking of the SMAC and subsequently trigger CD8+ T cell immune functions , including suppressive properties of CD8+CD28− T cells . On the other hand , since avidity changes in the TCR represent a method of increasing the sensitivity of activated T cells it is also possible that ZPS-mediated cross-linking of the TCR sensitizes antigen-specific CD8+ cells , including CD8+CD28− suppressor cells , to lower levels of antigen [50] . We propose the following model regarding the mechanism of Ts induction by ZPS: ZPS crosslinks TCR molecules on CD8+CD28− T lymphocytes that are engaged in recognizing and binding to specific antigens of foreign or self origin , and , lead to their activation , proliferation , cytokine secretion and non-antigen-specific suppressive function on T cells . Interestingly , proliferation of Ts does not depend on the specificity of its TCR but clearly depends on the presence of the positive charge on the polysaccharide molecule . This observation implies that the zwitterionic charge that determines the three-dimensional α-helix configuration is the dominant feature in TCR-mediated T cell activation . Taken together , these data explain survival and expansion of the CD8+CD28− population . But , we do not yet have an answer to the question , why , almost exclusively the CD8+CD28− and not the CD8+CD28+ become activated by Sp1 . Various scenarios are possible and will need to be addressed in the future . 1 . The lack of expression of the CD28 co-stimulatory surface molecule might promote better binding of Sp1 to the TCR due to less steric hindrance . 2 . The lack of expression of the CD28 molecule on the T cell surface is associated with a phenotype and functionally unique cell biology . Consequently , other molecules that remain to be identified may be as relevant as the CD28 protein for the susceptibility of CD8+CD28− T cell activation by Sp1 . 3 . Post-translational modifications such as glycosylation of proteins play a key role in immune regulation [77] , [78] . Changes of the glycosylation state , mainly desialysation have been shown to enhance class I MHC-TCR interactions and T cell stimulation [78] . ZPSs are repetitive highly charged molecules . It seems conceivable that binding of ZPS enhances cross-linking of TCRs due to different charge states of the TCR or co-receptors on CD28− and CD28+ T cells . The enhanced cross-linking of TCR caused by a polysaccharide antigen represents an exciting novel mechanism in cellular immunology . Further investigations will be needed to explore in detail , the interaction of ZPS with the TCR of CD8+ effector and regulatory T cells . In conclusion , ZPS from commensal bacteria represent a unique class of antigens that exhibit multiple functions on different host cells and contribute to the maintenance of peripheral tolerance . Here we demonstrate that the ZPS Sp1 induces regulatory CD8+ T cells inhibiting CD4+ T cell immune responses in vitro and in vivo . The Sp1-induced CD8+CD28− Ts increase the knowledge of physiologic bacterial antigens and with it , the possibility to develop cell-based therapies against unwanted immune responses in human beings .
Animal experiments were performed in accordance with the German animal protection legislation guidelines ( article K07/05 and K16 . 5/06 ) and approved by the “Bezirksregierung Köln” , Germany . S . pneumoniae type 1 capsular polysaccharide complex was obtained from the American Type Culture Collection and further purified to obtain homogeneity as previously described [10] . Chemical modification of Sp1 by neutralization of the free amino group on the 2-acetamido-4-amino-2 , 4 , 6-trideoxygalactose by N-acetylation that creates a polysaccharide with a net negative charge was performed as previously described [1] . The capsular polysaccharide of group B streptococcus serotype III ( GBSIII ) was kindly provided by Dennis L . Kasper . High-resolution ( 500 MHz ) proton NMR spectroscopy [7] revealed that Sp1 and modified Sp1 ( mSp1 ) were free of contaminating protein and nucleic acids . Endotoxin was not detectable by the limulus test with a sensitivity of <8 pg LPS/mg Sp1 . The peptides ASN ( sequence H-ASNENMETM-OH ) and GP33–41 ( sequence H-KAVYNSATM-OH ) were obtained from Mimotopes , the SIINFEKL ( sequence H-SIINFEKL-OH ) from JPT . Wildtype C57BL/6 mice were obtained from Charles River Laboratories . OT-1 C57BL/6 mice expressing a transgenic receptor specific for the ovalbumin peptide sequence SIINFEKL were obtained from Jackson Laboratories . P14 TCR-tg C57BL/6 ( P14 ) mice expressing a transgenic receptor for the GP33–41 peptide sequence of the lymphocytic choriomeningitis virus were generously provided by J . P . Schneck [79] . Mice were kept in special pathogen-free environments . In the abscess induction studies , 6–8 week-old mice were injected intraperitoneally with Sp1 or modified Sp1 ( mSp1 ) ( 100 µg in PBS mixed with sterile cecal content ( SCC ) adjuvant; 1∶1 v/v , 0 . 2 ml total volume ) or SCCA alone , as control [10] . SCC is a required steril adjuvant in this model and mimics leakage of the intestinal flora during secondary peritonitis [1] . For the preparation of SCC , cecal content of non-treated mice is grinded through 100 µm-pore size meshes and autoclaved thereafter . The SCC dose not able to induce abscesses when applied alone is determined by titration in the experimental model of abscess formation . In order to block CD4+ or CD8+ T cells intraperitoneally , 24 h prior to challenge , mice were injected intravenously with a CD4-specific mAb ( clone YTS 169 , 500 µg per mouse ) or a CD8-specific mAb ( clone YTS 191 , 500 µg per mouse ) , or left untreated . Depletion of CD4+ T cells and CD8+ T cells was confirmed by flow cytometry analysis of whole blood and spleen cells 24 h and 48 h after injection . Analysis showed depletion of >95% of the CD4+ and CD8+ T cell population ( data not shown ) . For adoptive transfer studies , CD8+CD28− or CD8+CD28+ T cells ( 4×105 per mouse ) were injected intraperitoneally at the time of polysaccharide challenge . Six days after challenge , mice were macroscopically examined for the presence of abscesses within the peritoneal cavity by two double-blinded examiners . Abscesses were isolated and their diameter measured . In each experiment , four to six mice per group were tested . The experiments were performed three times in an independent manner . Snap-frozen abscesses were cryo-sectioned ( 5–6 µm ) , fixed in 4% buffered formalin for 1 min and then used for immunohistochemical analysis of CD8+ T cells . After blocking endogenous peroxidase by 0 . 3% H2O2 in methanol , and endogenous biotin with an avidin-biotin blocking kit ( Vector Laboratories ) for 30 min , sections were treated with normal goat serum then incubated overnight with an anti-CD8 antibody ( 1∶50; rat anti-mouse CD8 , clone Ly-2 , BD ) . Next , goat biotin-conjugated anti-rat antibodies ( 1∶100 , BD ) were applied and incubated for 1 h at room temperature followed by incubation with alkaline phosphatase-conjugated streptavidin-Ab complex ( Dako ) for 30 min . Immunostaining was achieved using Fast Red ( Dako ) as a substrate for alkaline phosphatase . Sections were then counterstained with hemalaun . Peritoneal cells were obtained at different times following intraperitoneal Sp1 , mSp1 , or PBS challenges . Mice underwent peritoneal lavage with 4 ml of ice-cold RPMI-1640 supplemented with 10% FBS and 1% penicillin/streptomycin . Total cell count was obtained with a hemocytometer after trypan blue staining . Each sample was then analyzed by flow cytometry . CD4+ and CD8+ T cells were enriched from spleens of C57BL/6 mice by , grinding the tissue through a 70 µm mesh , lysis of red blood cells , centrifuging in Ficoll-Hypaque gradients ( density 1 , 083 ) , and , immunomagnetic negative selection according to the manufacturer's instructions ( Miltenyi Biotec , R&D ) . The purity of the cell population was confirmed by flow cytometry ( >95% ) . CD8+CD28− and CD8+CD28+ T cells were isolated as follows . CD8+ T cells were incubated with PE-labeled anti-CD28 mAb for 20 min , washed , centrifuged , and incubated with anti-PE-microbeads for 15 min at 4°C ( Miltenyi ) . Alternatively , CD8+ T cells were incubated with a biotinylated anti-CD28 mAb for 10 min at 4°C , washed , centrifuged , and labeled with streptavidin-APC for 15 min at 4°C . PE- or APC-labeled CD28+ cells were enriched by positive selection using anti-PE- or anti-APC-labeled microbeads , respectively ( Miltenyi ) . Magnetic negative selection of CD28− was performed following the manufacturer's instructions ( Miltenyi ) . For adoptive transfer studies of CD28− and CD28+ T cells , CD8+ T lymphocytes were stained with FITC-labeled anti-CD8 and PE-labeled anti-CD28 for 20 min on ice . CD8+CD28− and CD8+CD28+ T lymphocytes were sorted electronically using a FACSVantage ( BD ) More than 93% purity was routinely obtained ( Fig . S1 ) . DCs were generated from mouse bone marrow by adapting a previously described method [80] . In brief , bone marrow cells from C57BL/6 or Balb/c mice were cultured in RPMI-1640 supplemented with 5% FBS , 500 U recombinant mouse granulocyte/macrophage-colony stimulating factor ( GM-CSF ) , 20 µg/ml gentamicin , and 50 µM 2-mercaptoethanol . The DC medium was exchanged at two-day intervals . DCs were isolated by magnetic cell sorting with a CD11c-specific mAb ( Miltenyi Biotec ) . Experiments involving overnight culturing of WT CD8+ T cells were performed in RPMI-1640 supplemented with 1% L-glutamine , sodium pyruvate , penicillin-streptomycin , non-essential amino-acids , 50 µM 2-ME , 10% FBS ( Life Technologies ) , and purified anti-CD3 mAb ( 5 ng/ml ) . Cell culture with abrogated contact between WT APCs and T cells were performed with the CD8+ T cells in the upper chamber of transwell inserts with a 0 . 4 µm pore size ( Corning ) and spleen cells in the lower chamber . In experiments with OT-1 CD8+ T cells , instead of purified anti-CD3 mAb , SIINFEKL was used at a concentration of 10−9 M . Co-cultering of purified CD8+ T cells from OT-1 mice with SIINFEKL-loaded H2Kb dimers was performed as followed: MHC class I-Ig dimer ( Kb-Ig; BD ) were loaded with peptide by incubation in a 40-fold molar excess of the specific peptide SIINFEKL as instructed by the manufacturer . 5×105 purified CD8+ T cells from OT-1 mice were incubated with SIINFEKL-loaded MHC class I-Ig dimers ( 8 µg ) in the absence or presence of Sp1 ( 100 µg/ml ) in 200 µl culture medium at 37°C overnight For staining surface markers , cells were stained with specific antibodies for 30 min on ice , washed , and then analyzed by flow cytometry . To analyze the cellular intraperitoneal influx , the absolute number of each respective cell type present in the peritoneal lavage was calculated by taking the proportion of each cell type as determined by flow cytometry and multiplying it by the total number of cells per ml of lavage obtained from each mouse . For intracellular cytokine staining ( ICS ) , cells were stimulated with anti-CD3 ( 5 ng/ml ) for 6 h at 37°C and 5% CO2 . After the first hour of incubation , Golgi Stop ( BD ) was added . Cells were stained for surface markers by 30 min on ice with specific antibodies , washed , fixed with Cytofix/Cytoperm ( BD ) for 20 min on ice , then permeabilized with Perm/Wash Solution ( BD ) for 10 min on ice and treated with interleukin-specific antibodies for 30 min at 4°C in Perm/Wash Solution . The apoptotic capability of Sp1-treated CD8+ T cells was analyzed after staurosporine treatment using Annexin V staining . 1×106 CD8+ T cells/ml isolated from the spleens of C57BL/6 mice were incubated in medium ( RPMI-1640 supplemented with 5% FCS ) containing 1 µM staurosporine ( Streptomyces sp . , Calbiochem-Novabiochem ) in the presence of Sp1 , mSp1 ( 100 µg/ml , respectively ) , or in medium alone . Cells were washed after 4 h to free cells from staurosporine . Thereafter , cells were incubated in fresh medium plus antigens for another 8 h . As a control , medium without staurosporine was used . After washing , cells were dissolved in binding buffer ( 0 . 1 M Hepes/NaOH ( pH 7 . 4 ) , 1 . 4 M NaCl , 25 mM CaCl ( BD ) ) and stained with FITC-conjugated Annexin V ( 5 µl/1×106 cells; BD Apoptosis Detection Kit ) for 15 min on ice . Cells were washed , and 5 µl propidium iodide ( PI ) ( BD ) was added to the cells for 2 min . The PI staining was stopped by the addition of 5 ml of ice-cold PBS . Cells were washed and prepared for flow cytometry . Purified , FITC- , PE- , or APC-labeled monoclonal antibodies specific to murine CD8 ( clone RM4-5 , BD ) , CD25 ( clone 7D4 , BD ) , CD28 ( clone 37 . 51 , BD ) , CD44 ( clone IM7 , BD ) , CD62L ( clone MEL-14 , BD ) , CD69 ( clone H1 . 2F3 , BD ) , CD39 ( clone TU66 , BD ) , CD122 ( clone TM-BETA 1 , Serotec ) , LAP ( clone 27232 , R&D ) , CTLA-4 ( clone UC10-4B9 , eBioscience ) , and the respective isotype controls were used for surface marker staining . For ICS , mIL-6 ( clone MP5-20F3 , BD ) , anti-mouse mIL-10 ( clone JES5-16E3 , BD ) , and the respective isotype controls were used . Cells prepared for flow cytometry were analyzed – after gating for viable cells by forward and side scatter - with FACS Calibur™ ( Becton Dickinson ) using CELLQuest™ software ( Becton Dickinson ) . The results were expressed as percentage ( % ) or mean fluorescence intensity ( MFI ) of fluorescence-labeled cells in a population . Experiments were performed a minimum of three times in an independent manner . Allogeneic mixed leukocyte reaction ( MLR ) was performed as described previously [81] . DCs from Balb/c mice ( H-2d haplotype ) were incubated with negatively selected ( Miltenyi ) and CFSE-labeled CD4+ T cells of C57BL/6 mice ( H-2b haplotype ) ( 1 . 5×105/well ) in the absence or presence of CD8+CD28− or CD8+CD28+ T cells isolated from C57BL/6 mice ( 5×105/well ) treated for 18 h with Sp1 ( 100 µg/ml ) . Cells were incubated for 5 days at 37°C , 5% CO2 . Proliferation of CFSE-labeled CD4+ T cells was evaluated by flow cytometry . For the analyses of Zap-70 and caspase 3 activity , magnetically separated CD8+ , CD8+CD28− and CD8+CD28+ were incubated in medium ( RPMI-1640 supplemented with 5% FCS ) in the presence of Sp1 , mSp1 ( 100 µg/ml , respectively ) , or in medium alone for 4 h at 37°C . For analysis of caspase 3 activity , cells were treated in medium containing 1 µM staurosporine . Whole cell extracts were denatured at 100°C for 10 minutes . Equal amounts of protein were separated by 14% Tris-glycine sodium dodecyl sulfate polyacrylamide gel electrophoresis ( SDS-PAGE ) . Western blotting was performed with purified mouse anti-Phospho-Zap-70 ( Tyr319 ) /Syk ( Tyr352 ) ( Cell Signaling Technology ) , mouse anti-Zap-70 kinase ( clone 29/ZAP70 kinase , BD ) , rabbit anti-caspase 3 ( clone 8G10 , Cell Signaling Technology ) , anti-ß-actin ( clone AC-15 , Sigma Aldrich ) , goat anti-mouse IgG HRP , and goat anti-rabbit IgG HRP ( R&D ) antibodies as described previously [48] . Magnetically separated CD8+ , CD8+28− and CD8+CD28+ T cells were treated with Sp1 , mSp1 ( 100 µg/ml , respectively ) or left untreated for 2 h at 37°C in medium ( RPMI-1640 supplemented with 5% FCS ) for 2 h . Cells were washed , counted , and incubated for 15 min in low salt buffer on ice before NP-40 was added . After sedimenting the nuclei , a high salt buffer was added slowly . The nuclear extract is separated from the nuclear envelop/DNA by centrifuging at maximum speed . A BCA assay was performed to determine the protein concentration . The NF-κB ELISA was performed according to the manufacturer's instructions ( Active Motif ) . As positive control the nuclear extract from Jurkat cells was used . MHC class I-Ig Dimer ( Db-Ig ) were prepared as described previously [50] , [82] . Db-Ig dimers were labeled with fluorescein isothiocyanate ( FITC; Molecular Probes ) at pH 7 . 4 and purified by gel column chromatography . Concentration of protein and dye was measured by assessing the absorbance at 280 nm and 496 nm respectively . For loading , FITC-labeled Db-Ig dimers were incubated in a 50-fold molar excess of the specific peptide GP33–41 and a two fold-molar excess of human β2-microglobulin for five days at 4°C . ASN was used as a control peptide to address nonspecific binding . For binding assays , CD8+ T cells were isolated from P14 mice . 1×105 cells were incubated with varying amounts of protein-loaded Db-Ig dimers in the presence or absence of Sp1 ( 100 µg/ml ) at 4°C in darkness for 90 min . Without any washing stages , binding was measured by flow cytometry . Subtraction of the nonspecific binding ( ASNDb-Ig ) from the total binding ( GP33–41Db-Ig ) determined the specific binding . The single site equilibrium dissociation constant ( Kd ) , the equilibrium cross-linking constant ( Kx ) and the total number of receptors available for binding ( Rt ) were calculated using Microcal Origin 6 . 1 software ( Origin Lab Corporation , Northampton , USA ) [50] . Results of peritoneal cytokine and cellular influx assays in the various groups were compared by Student's t-test . Comparison of groups with regard to abscess formation was made by chi-square analysis . Results of NF-κB ELISA were compared by Student's t-test . | One of the most difficult challenges for the mammalian immune system is to protect its host from pathogens and cancer while at the same time avoiding a self-destructive or overwhelming immune response . In addition to so-called central tolerance induced in the thymus , the immune system relies on peripheral control mechanisms . One of the most important brakes of the peripheral tolerance system is constituted by so-called regulatory T lymphocytes . The predominately investigated regulatory T lymphocytes belong to the CD4+ subset but CD8+ regulatory T lymphocytes are now also believed to play a major role in controlling immune responses . Herein , we describe for the first time a natural occurring saccharide antigen from a commensal bacterium which induces the accumulation of a defined population of CD8+ regulatory T lymphocytes . These CD8+ regulatory lymphocytes suppress inflammatory immune responses in vivo and in in vitro assays . We also describe how the bacterial antigen induces the activation of CD8+ T cells . Our findings not only describe a novel mechanism of saccharide-mediated T cell activation but also provide evidence that commensal bacteria play an important role in the induction of peripheral tolerance and maintenance of the mammalian immune system . | [
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] | 2009 | Streptococcus pneumoniae Serotype 1 Capsular Polysaccharide Induces CD8+CD28− Regulatory T Lymphocytes by TCR Crosslinking |
Colon cancer accounts for more than 10% of all cancer deaths annually . Our genetic evidence from Drosophila and previous in vitro studies of mammalian Atonal homolog 1 ( Atoh1 , also called Math1 or Hath1 ) suggest an anti-oncogenic function for the Atonal group of proneural basic helix-loop-helix transcription factors . We asked whether mouse Atoh1 and human ATOH1 act as tumor suppressor genes in vivo . Genetic knockouts in mouse and molecular analyses in the mouse and in human cancer cell lines support a tumor suppressor function for ATOH1 . ATOH1 antagonizes tumor formation and growth by regulating proliferation and apoptosis , likely via activation of the Jun N-terminal kinase signaling pathway . Furthermore , colorectal cancer and Merkel cell carcinoma patients show genetic and epigenetic ATOH1 loss-of-function mutations . Our data indicate that ATOH1 may be an early target for oncogenic mutations in tissues where it instructs cellular differentiation .
The Atonal ( Ato ) proneural transcription factors form a highly conserved group of key developmental regulators in multiple neural and neuroendocrine tissues . The mammalian Ato ( CG7508 ) ortholog , ATOH1 ( Ensembl accession number: ENSG00000172238 ) , is essential for cell fate commitment of mechanoreceptive Merkel cells in the skin [1] and the secretory goblet , Paneth , and enteroendocrine cells in the intestine [2 , 3] in addition to multiple neuronal lineages [4 , 5] . The functional conservation between the fly and mammalian proteins is underscored by the fact that Drosophila ato can fully rescue the Atoh1 ( Ensembl: ENSMUSG00000073043 ) null mutant mouse [6] . Genetic analyses in Drosophila [7] suggest that ato regulates the formation and progression of tumors in fly retina , where it acts as a master regulator of cell fate specification . In mammals , two aggressive human cancers derive from tissues where ATOH1 instructs cell fate commitment , namely Merkel cell carcinoma ( MCC ) and colorectal cancer ( CRC ) . MCC is a rare , but very aggressive , neuroendocrine cancer of the skin with approximately 40% mortality [8] . CRC is a highly prevalent cancer with high mortality ( 36% ) representing 11% of all cancer deaths annually [9] . Recent studies in colon cancer cell lines suggest that ATOH1 can inhibit tumor cell growth in vitro [10] . If the anti-oncogenic function of Drosophila ato is conserved in its mammalian counterparts , one would predict that the loss and gain of function of ATOH1 would enhance and suppress tumor formation , respectively , in MCC and CRC models . In addition , the ATOH1 should be subject to loss-of-function mutations in a significant number of human cancer patients . We tested this prediction in two different mouse models for colon cancer , as well as human MCC and CRC cell lines . In addition , we examined the status of the ATOH1 locus in primary tumor samples from human MCC and CRC patients . Our data show that loss of ATOH1 strongly enhances the formation and progression of tumors in mice and human cell lines . Conversely , gain of ATOH1 function strongly inhibits the oncogenic phenotypes in human cell lines . Furthermore , we find genetic and epigenetic loss-of-function mutations with very high frequency in primary human tumors derived from ATOH1-dependent tissues and provide biochemical insight into how these epigenetic mutations may arise . Finally , we describe a highly conserved anti-oncogenic molecular signaling pathway that links ATOH1 activity to the stress sensor Jun N-terminal kinase ( JNK ) pathway mediated via cell type–specific differential co-option of receptor tyrosine kinases ( RTKs ) . Together with the genetic analysis in Drosophila [7] , these data support a novel , highly conserved tumor suppressor function for the Atonal group of transcription factors .
In the mouse , Atoh1 is a master regulator of secretory cell fate commitment in the intestinal epithelium [2 , 11] . To investigate whether Atoh1 plays a role in intestinal tumors , we analyzed the function of the mammalian ato homolog , Atoh1 , in colon tumorigenesis . We assessed the tumor susceptibility of mice with an intestine-specific deletion of Atoh1 ( Atoh1Δintestine ) [3] in two different established mouse models of CRC . We first treated Atoh1Δintestine mice with azoxymethane ( AOM ) , a chemical carcinogen that preferentially induces colon tumors . We found a significant enhancement in polyp formation in the large intestines of Atoh1Δintestine compared to wild-type littermate mice , characterized by increased incidence ( 33% [4/12] vs . 100% [8/8] , p < 0 . 005; Figure 1A ) , multiplicity ( 0 . 75 vs . 10 . 3 polyps/colon , p < 0 . 0008; Figure 1C ) , and size ( 2 . 1 vs . 4 . 1 mm/polyp , p < 0 . 0004; Figure 1E ) when examined under the dissecting microscope . Histological analysis of colons from AOM-treated Atoh1Δintestine mice confirmed these findings and showed a range of histological phenotypes , from severely hyperplastic mucosa with embedded multifocal adenomas , to large , highly dysplastic adenomas , with one animal having invasive adenocarcinoma ( Figure 2A , 2A′ , and 2A′′ ) . In contrast , histological examination of AOM-treated Atoh1wt littermates showed none with adenomatous changes; with all polyp-like structures shown to be gut-associated lymphoid tissue ( GALT; Figures 2B and S1 ) . These data support the notion that loss of Atoh1 can be an initiating event in mammalian cancer formation . In previous work , we found that ablation of Atoh1 leads to an increase of proliferation but has no effect on apoptosis [3] . We therefore examined proliferation and apoptosis in the “preneoplastic” normal-appearing epithelium within the colons of AOM-treated Atoh1wt and Atoh1Δintestine mice . As in the Drosophila model , we find more proliferation of epithelial cells in Atoh1Δintestine crypts compared to nonrecombined wild-type crypts within the same animals , or compared to crypts from Atoh1wt littermates ( Figures 2E and S2D–S2F ) but no obvious difference in apoptosis ( Figures 2F and S2G–S2I ) . We extended our tumor analysis with an independent genetic mouse model for CRC by crossing Atoh1Δintestine mice to APCmin mice ( APC: ENSMUSG00000005871 ) , in which Wnt signaling is constitutively activated [12 , 13] . APCmin mice develop spontaneous adenomas primarily in the small intestine , with only occasional polyps in the colon [14] . Interestingly , Wnt signaling is thought to interact negatively with ato during sense organ formation in the developing Drosophila epithelium [15] . Comparing the large intestines of APCmin; Atoh1wt mice to APCmin; Atoh1Δintestine littermates at 16 wk , we find significantly more polyps in the colons of double-mutant mice , characterized by increased incidence ( 52% [14/27] vs . 100% [10/10] , p <0 . 007; Figure 1B ) , multiplicity ( 1 . 1 vs . 12 . 3 polyps/colon , p <0 . 0001; Figure 1D ) , and size ( 3 . 3 vs . 4 . 3 mm/colon , p <0 . 0002; Figure 1F ) . Histological examination confirmed these polyps as adenomas in both APCmin; Atoh1wt and APCmin; Atoh1Δintestine mice , with two polyps in APCmin; Atoh1Δintestine mice having progressed to invasive adenocarcinoma ( Figure 2C and 2D ) . The polyps in the APCmin; Atoh1Δintestine mice originated from the Atoh1 mutant crypts as indicated by the absence of goblet cells in the polyps ( Figure S3A and S3B ) . In summary , thus far , loss-of-function analysis of Atoh1 in two mouse models of colon tumorigenesis supports a role for Atoh1 as a key switch in tumor formation and progression . This role appears to be mediated by increased cell proliferation in the absence of Atoh1 . Importantly , the mouse tumors lack the secretory cell types that depend on Atoh1 for their formation . Thus , the role of Atoh1 in tumor formation is likely linked to its function as a master regulator of differentiation . Given the remarkable evolutionary conservation of ato/Atoh1 function in cancer development , we decided to analyze whether loss of the human ortholog , ATOH1 , might be selected for during malignant transformation in human cancer . To this end , we investigated the role of ATOH1 in CRC and MCC . In vitro overexpression experiments in CRC cell lines suggest that ATOH1 gain of function decreases the population growth potential of CRC cells [10] . Similarly , expression analysis in MCC cell lines suggests an inverse correlation between ATOH1 levels and the population growth of MCC cells ( Figure S4A–S4C ) [16] . These observations hint at a potential role for ATOH1 in these cancers . To test this possibility in vivo in primary human tumors , we began by asking whether the expression of ATOH1 is down-regulated in primary tumor samples from 42 CRC and four MCC patients . As MCC is a very rare cancer , only four samples were available for analysis . Seventy percent of the CRC samples show a significant decrease of ATOH1 mRNA expression compared to tissue-matched colon samples from normal controls as analyzed by quantitative reverse-transcriptase PCR ( RT-qPCR ) , suggesting that loss of ATOH1 expression is a highly common feature of CRC oncogenesis ( Figures 3F and S4 ) . Furthermore , ATOH1 mRNA levels are significantly lower in adenocarcinoma samples compared to adenomas ( t-test: p = 0 . 017 ) , indicating progressive loss of ATOH1 expression levels with increasing tumor severity ( Figure 3A ) . Similarly , in MCC , the two samples with lower ATOH1 expression levels were derived from the two patients showing metastases ( Figure S5 ) . One mechanism to explain loss of gene expression during oncogenesis is the deletion of the locus , which we tested for using quantitative PCR on genomic DNA in all 46 patient samples , one MCC cell line ( MCC14 . 2 ) , and one CRC cell line ( Ht29 ) . At least one deletion in the locus was detected in 57% of the samples ( Figure 3B and 3F ) . A second primer set yielded similar results ( 49% deletion rate , Figure 3B ) . One allele was also deleted in both Ht29 and MCC14 . 2 . Comparative genomic hybridization ( CGH ) array experiments [17] on three samples did not show a deviation of the clones flanking the ATOH1 locus ( Figures S5 and S6 ) , indicating that the deletions observed in patients are likely to be ATOH1-specific microdeletions . When only one copy is deleted , complete loss of gene function could be achieved by point mutations in the remaining allele . Sequencing of the ATOH1 open reading frame of 24 samples , however , did not reveal any mutations in any of the samples ( unpublished data ) . Together with the loss of ATOH1 mRNA expression in patients , these results suggest that an epigenetic silencing mechanism may be involved—a possibility we tested in further detail . To address a putative epigenetic transcriptional silencing mechanism , we began by asking whether transcription could , in principle , be initiated from the ATOH1 locus in cancer cells . Drosophila ato and mouse Atoh1 are both known to be autoregulatory [18 , 19] . This provides the opportunity to test whether transcription could be activated from the ATOH1 locus by ATOH1 itself in normal versus cancer cells . We took advantage of expressing the mouse ortholog in human cells to distinguish the expression of endogenous ATOH1 from that of Atoh1 . For the tests in human cancer cell lines , we used the MCC14 . 2 cell line , which is derived from MCC and has strongly reduced expression of ATOH1 . Surprisingly , Atoh1 overexpression failed to activate endogenous human ATOH1 expression in the MCC14 . 2 cell line ( Figure 3C ) , despite the virtually complete conservation of the two proteins . This may be due to a key difference between the two proteins , or to a possible disruption of the autoregulatory loop during oncogenesis . To test whether Atoh1 can activate ATOH1 expression in normal noncancerous skin cells , we transduced normal human primary keratinocytes with an Atoh1 expression vector and tested the expression of endogenous ATOH1 40 h after lentiviral transduction . In contrast to the cancer cell lines , endogenous ATOH1 is up-regulated 38-fold by Atoh1 ( Figure 3C ) . This indicates that the expressability of the ATOH1 locus is inhibited during oncogenesis . Genomic database searches show that all known ATOH1 orthologs , from human to Drosophila , reside in a CpG island covering at least the promoter and the transcription start site , suggesting that CpG methylation may be a mechanism of ATOH1 loss of function . We therefore assayed all patient samples and both cell lines for ATOH1 methylation using three independent assays: pull-down of methylated DNA , methylation-sensitive restriction digest , and methylation-specific PCR for bisulfite DNA modification . We found that up to 81% of the patients , as well as both cell lines , show methylation of the ATOH1 locus in both the coding sequence and the promoter sequences directly upstream of the ATG . In contrast , none of the control samples show ATOH1 methylation ( Figures 3D , 3F , and S7A–S7C ) . This suggests that methylation is likely causal to the transcriptional silencing of the locus during oncogenesis as this is accompanied by lower ATOH1 expression . In a few cases , however , the correlation was not observed . In two cases ( samples 27 and 30 ) , this may be due to the presence of a putative duplication , which is yet to be methylated . Alternatively , but not exclusively , it may be due to heterogeneity of the degree of methylation in different cells comprising the tumor samples analyzed . To provide evidence that methylation silences the ATOH1 locus , we inhibited DNA methyltransferases ( Dnmts ) with 5-azadeoxycytidine in the Ht29 cell line . This results in an approximately 8-fold increase in ATOH1 expression ( Figure 3E ) . Thus , the ATOH1 locus is methylated in cancer patients and derived cell lines , and this methylation can be reversed , resulting in the transcriptional reactivation of the locus . The autoregulatory function of ATOH1 combined with the methylation of its regulatory sequences might hint to the involvement of ATOH1 in the silencing of its own promoter . To gain some insight into whether this may indeed be the case , we asked whether Atoh1 can physically interact with Dnmt proteins . We tested the binding of Atoh1 to Dnmt1 ( ENSG00000130816 ) , Dnmt3a ( ENSG00000119772 ) , and Dnmt3b ( ENSG00000088305 ) using GST pull-down assays . We find that Atoh1 binds to all three Dnmts . This is further supported by the ability of GST-Atoh1 to pull down DNA methyltransferase activity from nuclear extracts , similar to the positive control ( EED: embryonic ectoderm development , ENSG00000074266 ) [20] . Focusing on Dnmt1 , the maintenance DNA methyltransferase , we confirmed this observation using coimmunoprecipitation experiments in 293T cells transfected with HA-tagged Atoh1 . Lastly , mapping experiments identified several regions of Dnmt1 as mediating the association with Atoh1 ( Figure S8A–S8D ) . In summary , deletions and epigenetic silencing via methylation combine to cause loss-of-function mutations of the human ATOH1 locus in primary human cancers . Together with loss-of-function evidence in five independent cancer models in human cells , Drosophila , and mouse , these data strongly support a role for the Ato/ATOH1 transcription factors as key modulators of oncogenic transformation . To better understand the role that ATOH1 plays in cancer , we sought to determine the molecular mechanism by which it acts to suppress the formation and progression of tumors . Gain- and loss-of-function analyses point to Atoh1-dependent regulation of proliferation in mouse colon tumors ( Figures 2E and S3D–S3F ) . Analysis of the role of Drosophila ato in fly retinal tumors [7] shows that this function is mediated by the JNK signaling pathway . We hypothesized that the Ato-JNK-p21 pathway may mediate the tumorigenic phenotype in our mouse models of CRC as well . If loss of ATOH1 were indeed an initiating event in tumor formation , we reasoned that the effects of Atoh1 function would have to be detectable in preneoplastic tissue as this is where oncogenesis begins . We therefore first examined the expression levels of Atoh1 , Cdkn1a ( p21 , ENSG00000124762 ) , Cdkn1b ( p27 , ENSG00000111276 ) , and Cdkn1c ( p57 , ENSG00000129757 ) and the JNK target cJun ( ENSG00000177606 ) in colon crypts isolated from Atoh1wt and Atoh1Δintestine mice ( Figure 4A ) . We observe a 6 . 8-fold reduction in Atoh1 expression and an approximately 2 . 8-fold reduction in expression of all three Cdkn1 isoforms in colon crypts from Atoh1Δintestine mice . We also observe a similar reduction in cJun mRNA levels . We further examined the Ato-JNK-Cdkn1 pathway by immunoblotting proteins from colon polyps and normal-appearing colon tissue from APCmin/+; Atoh1wt and APCmin/+; Atoh1Δintestine mice . Colon polyps show higher levels of cJun and p21waf protein compared to normal colonic tissue . Importantly , however , we find a reduction in cJun levels in APCmin/+; Atoh1Δintestine polyps compared to APCmin/+; Atoh1wt polyps , consistent with the mRNA analysis showing less cJun ( Figure 4B ) . Importantly , we find a reduction in p27 protein and a trend toward less p21 in Atoh1-mutant colon tissues and polyps , consistent with our analysis of mRNA levels ( Figure 4B ) . We also find a specific reduction in pJNK1 levels ( Figure 4C ) in APCmin/+; Atoh1wt versus APCmin/+; Atoh1Δintestine tissues , in agreement with earlier reports that JNK1 ( MAPK8: ENSG00000107643 ) may have anti-oncogenic activity in the mouse intestine [21] , whereas JNK2 ( MAPK9: ENSG00000050748 ) deficiency enhances tumorigenesis in other epithelia [22] . Finally , we examined the pattern of JNK activation in the colon of Atoh1wt and Atoh1Δintestine mice , and observe identical numbers of pJNK1/2-positive cells in Atoh1-null compared to control crypts ( Figure S9 ) . Together with western blot analysis demonstrating selective reduction in pJNK1 , but not pJNK2 , our data suggest that the level of pJNK1 in individual crypt cells is decreased upon loss of Atoh1 . These data are consistent with a preneoplastic function of Atoh1 in regulating proliferation , by a JNK-dependent induction of cell cycle inhibitors . Next we asked whether the molecular mechanism of ATOH1 function in human cancer is similar to the mouse . We took advantage of several established MCC cell lines [23 , 24] with either high ATOH1 expression ( MCC1 and MCC6 , derived from less aggressive tumors ) or low ATOH1 expression ( MCC13 , MCC14 . 2 , and MCC26 , derived from highly aggressive metastatic tumors; Figure S4A and S4C ) . We find that the growth rate of cell lines , measured as their doubling time , correlates inversely with levels of ATOH1 expression ( Figure S4B ) . To determine whether the reduction in ATOH1 levels might be causal to decreased doubling time , we restored ATOH1 function by creating stable cell lines expressing Atoh1 using lentiviral vectors ( Figure 5A and 5B ) . Stable Atoh1-expressing cell lines ( MCC14 . 2-Atoh1 . 1a , MCC14 . 2-Atoh1 . 1b , MCC14 . 2-Atoh1 . 2a , and MCC14 . 2-Atoh1 . 2b ) have a significantly slower population doubling time compared to control cell lines ( MCC14 . 2 and MCC14 . 2-GFP; p <0 . 0001; Figure 5C ) . To assess whether this increase in doubling time reflects decreased malignancy , we tested these cell lines for growth in soft agar . Cell lines with high levels of Atoh1 expression display a marked decrease in growth in soft agar compared to control cell lines ( Figure 5D , p < 0 . 001 ) . The change in population doubling time could be due to a slower cell cycle , an increased apoptotic rate , or both . Although the distribution of cells in the cell cycle appears unaltered ( Figure S10A ) , the speed of the cell cycle is 25% lower in Atoh1-expressing cells , as assayed by BrdU pulse-chase experiments ( Figure 5E , p <0 . 01 ) . In addition to slower proliferation , we find a specific and strong increase in apoptotic cell death , as measured by annexin-V and cleaved caspase-3 ( ENST00000308394 ) , in MCC ( MCC14 . 2 series ) and CRC ( Ht29 ) cell lines transduced or transfected with Atoh1 ( Figures 6A , 6B , and S10B ) . This increase in cell death is mediated by the intrinsic apoptosis pathway , as suggested by enhanced caspase-9 ( ENST00000333868 ) cleavage ( Figure 6A and 6B ) . Data from mouse models indicate an involvement of JNK-mediated regulation of cell proliferation for the tumor suppressor effect of Atoh1 . To assess whether the same mechanisms are operating in human cancer , we tested the expression levels of the caspase-3 and caspase-9 , p21waf1 , and p-JNK in MCC and CRC cells lines . We note a clear and specific up-regulation of p21waf1 and p-JNK levels upon Atoh1 expression ( Figures 6A , 6B , and S10C–S10H ) . Similarly , transfection of wild-type Drosophila Ato results in the up-regulation of cleaved caspase-3 , p21waf1 , and p-JNK ( Figure 6C ) , in contrast to a null-mutant form of the protein that completely fails to bind DNA [25] , indicating the conservation and specificity of the Ato/Atoh1 effect . Next , we tested the expression of these proteins in a loss-of-function setting . We generated and expressed the transcriptional repressor form of ATOH1 by fusing it to the engrailed repressor domain ( ATOH1ERD ) [26] , which specifically inhibits the Atoh1-induced effects on MCC cell lines ( Figure 6D ) . In the MCC1 cell line , which shows higher endogenous ATOH1 expression compared to MCC14 . 2 ( Figure S4C ) , expression of ATOH1ERD leads to a down-regulation of p21waf1 and p-JNK as well as inhibition of caspase-3 cleavage ( Figure 6E ) . In summary , gain- and loss-of-function studies in mouse colon cancer models and human cancer cells support a conserved antitumor function for ATOH1 mediated by JNK and p21 . How does ATOH1 expression lead to JNK activation in cancer cells ? ATOH1 and its orthologs are known to exert their developmental functions by modulating Notch; Atonal orthologs inhibit Notch signaling to induce differentiation during development , whereas Notch inhibits ato expression by its target gene Hes1 ( ENSG00000114315 ) [27–29] . Notch signaling has also been described to be upstream of JNK [30] . However , three lines of evidence suggest that the Atoh1-mediated effects are not Notch-dependent . First , Atoh1 expression levels do not influence expression of HES1 , a Notch signaling target gene ( Figure S11A ) . Second , we do not detect cleaved intracellular Notch in the MCC14 . 2 cell line , which expresses low levels of ATOH1 ( Figure S11B ) . Finally , blocking of Notch activation by selective inhibition of γ-secretase [31] has no effect on the growth of MCC cells ( Figure S11C ) . Other known upstream activators of JNK are RTKs [32] . As Atoh1 is a transcription factor , we checked mRNA expression of all 90 human RTKs upon Atoh1 expression in both the MCC14 . 2 and the Ht29 cell line compared to control cells . We observed a significant and specific Atoh1-dependent up-regulation of Neurotrophic tyrosine kinase receptor type 1 ( NTRK1: ENSG00000198400 ) , a hallmark for differentiation in Merkel cells [33 , 34] in MCC14 . 2 , and of FGF receptors in Ht29 ( Figure S11D ) . The increase in NTRK1 expression in MCC14 . 2 seen on the mRNA level was confirmed using RT-qPCR ( Figure S10E ) . We also observed elevated NTRK1 levels in the endogenously ATOH1-expressing MCC1 cells ( Figure S10E ) . This was accompanied by higher protein levels of both the NTRK1 receptor and one of its ligands , Neurotrophin-3 ( NT3: ENSG00000185652; Figure S10E–S10H ) , also a marker for Merkel cell differentiation . Therefore , Ato gain of function results in tumor type–specific elevation of RTK levels . To test whether the RTKs may be functionally linked with ATOH1 , we incubated the various MCC cell lines with K252a , a narrow-specificity RTK inhibitor ( NTRK , FGFR , PDGFR , and IGFR ) [35–37] . This results in a dose-dependent decrease in doubling time of the MCC14 . 2-derived , Atoh1-expressing cell lines , indicating that the Atoh1-induced change in doubling time is RTK-dependent ( Figure 7A ) . In addition , the induction of apoptosis by Atoh1 in the MCC14 . 2 and the Ht29 cell line is blocked by RTK inhibition ( Figure 7B and 7C ) . These effects are accompanied by RTK inhibitor–dependent decrease in the expression of p21waf1 and p-JNK ( Figure 7D ) , suggesting that both p21waf1 and p-JNK are downstream effectors of Atoh1-induced RTK signaling . Next , we asked whether p-JNK is required for p21waf1 up-regulation and apoptosis by treating MCC cells with SAPK Inhibitor II , a specific JNK inhibitor [38] . We observed a decrease in the Atoh1-induced expression of p21waf1 and cleaved caspase-3 ( Figure 7E ) . Similarly , transfection of dominant-negative c-jun ( TAM67 ) [39] leads to a significant increase in cell number compared to mock-transfected cells ( t-test: p = 0 . 04 ) , specifically in the MCC14 . 2-Atoh1 . 2a , which has high Atoh1 expression levels ( Figure 7F ) . In summary , taken together , evidence from mutation analysis in human patients , as well as gain- and loss-of-function analysis in mouse and human cells , support a model ( Figure 8 ) in which ATOH1 modulates JNK activity , possibly via co-option of context-specific RTK signaling , to induce apoptosis and up-regulate p21waf1 expression , keeping tumor growth in check . Loss-of-function mutations in ATOH1 prevent JNK-mediated apoptosis and p21-mediated cell cycle arrest , leading to enhanced tumor progression .
Our data support an evolutionarily conserved tumor suppressor role for ATOH1 in CRC and MCC . Loss of ATOH1 promotes tumor formation and progression , and mutations in the ATOH1 locus are found with relatively high frequency . Given the high deletion and methylation rate of ATOH1 in human tumor samples , loss of ATOH1 function is likely to be an early event in these tumors . We therefore propose that ATOH1 acts as a key switch regulating the transformation of pre-oncogenic epithelia to neoplastic and metastatic tumors . Genetic analysis of the function of Drosophila ato in fly eye tumor suggests that its anti-oncogenic function is linked to its activity as a regulator of cell fate commitment and differentiation [7] . This is similar to what we observe in the different mouse models , where the adenomas and adenocarcinomas do not contain secretory cells ( Figure 2 ) [3] . Interestingly , this also appears to be the case in human cancer , where the majority of human CRCs do not contain differentiated secretory cells . The loss of ATOH1 in most human CRC patients likely explains this observation . In addition , cell type–specific RTK differentiation genes are up-regulated upon overexpression of Atoh1 in CRC and MCC cell lines . Importantly , these markers of differentiation are necessary for the anti-oncogenic effect of ato/ATOH1 . It is tempting to speculate that in other tissues , similar loss of differentiation factors is involved in oncogenesis . In this sense , the loss of differentiation factors has already been implicated in late stages of tumor progression as with GATA-3 ( ENSG00000107485 ) in breast cancer [40] . Although loss of GATA-3 in early tumor leads to an inhibition of tumor formation , loss of GATA-3 in later stages leads to the acquisition of metastatic potential . We , therefore , wonder whether loss of other differentiation factors , perhaps bHLH proteins , might play a role in earlier stages of breast cancer development . Tumor suppressor genes are defined by the fact that ( 1 ) loss-of-function mutations make the cells more prone to malignant transformation , ( 2 ) overexpression leads to inhibition of the malignant phenotype , and ( 3 ) spontaneous somatic mutations are found in patients with cancer . “Classical tumor suppressor genes , ” such as p53 ( ENSG00000141510 ) , are special in the sense that when mutated , the cell is more prone to accumulate additional mutations , and thus actively drive malignant progression as opposed to just “taking away the brakes” [41] . It is notable that ato/Atoh1 shows most of the hallmarks of a tumor suppressor gene . Because silencing of ato/Atoh1 is not sufficient to drive oncogenesis , we suggest that ato/Atoh1 , and similar genes , are important brakes on malignant transformation . Therefore , the role that differentiation factors might play as key switches in malignant transformation in different tissues is not different from the classical definition of tumor suppressor in a functionally relevant sense . The RTK and JNK signaling pathways , which are essential for Atoh1's tumor suppressor activity , have been suggested as context-dependent oncogenes or tumor suppressors [42] . Our data indicate that Atoh1 is important in deciding this context by the up-regulation of cell type–specific RTKs . The activation and co-option of RTK and JNK signaling by Ato/ATOH1 in this context suggests that the status of differentiation of the tumor-initiating cell may be the key determinant of the specific role of various signaling pathways in cancer . This may have important clinical implications: treatment of CRC or MCC patients with RTK or JNK inhibitors might have an adverse effect on tumors where ATOH1 is still expressed . In this medical context , our data suggest that screening for ATOH1 expression , deletion , and methylation may be a useful diagnostic tool for early detection and treatment decision of MCC and CRC . Similarly , treatment of CRC and MCC patients whose tumors show epigenetic silencing of ATOH1 with DNA methyltransferase inhibitors might prove a powerful avenue for therapy , because it appears to be sufficient to restore ATOH1 expression and induce cancer cell death . Furthermore , such treatment in combination with Notch inhibitors may enhance re-expression of the ATOH1-driven differentiation program [43 , 44] and synergistically inhibit cancer growth . Therefore , elucidation of the basic mechanisms of ato/ATOH1 function , as well as their target genes and interacting proteins , might offer potential avenues for future therapeutic intervention .
The ATOH1 open reading frame was PCR amplified and fused in frame with the engrailed repressor domain after XhoI restriction digest . This fusion product was then blunt ligated in the MSCV-IRES-GFP vector ( Clontech ) , giving rise to the MSCV-ATOH1ERD-IRES-GFP construct . Atoh1Δintestine mice are a conditional deletion of Atoh1 and are described in more detail elsewhere [3 , 45] . These mice were generated using the loxP/cre system in which Cre-mediated deletion of Atoh1 is mosaic and is restricted to the distal ileum and large intestine ( 80%–90% deletion ) . APCmin mice were purchased from The Jackson Laboratory and mated with Atoh1Δintestine mice to generate APCmin; Atoh1Δintestine mice . Eight-week-old male Atoh1wt and Atoh1Δintestine littermates were injected intraperitoneally with AOM ( Midwest Research Institute of the National Cancer Institute's chemical carcinogen repository ) at 10 mg/kg body weight . AOM was injected weekly for a total of six injections , and the mice were sacrificed 20 wk after the first AOM injection . Two hours prior to sacrifice , mice were injected with 50 mg/kg BrdU . The large intestine was isolated and flushed with PBS and then fixed in 10% buffered formalin at room temperature for 16–24 h . Colons were placed in 70% ethanol before macroscopic analysis of polyps , and then embedded into paraffin blocks . Similarly , 16–19-wk-old APCmin and APCmin; Atoh1Δintestine colons were isolated and fixed as for the AOM-treated colons . For macroscopic analysis , the position and diameter ( millimeters ) of each polyp were recorded using a dissecting microscope . The protocol for use of animals was approved by the Cincinnati Children's Hospital Institutional Animal Care and Use Committee . The colons were imbedded in paraffin blocks and sectioned for hematoxylin and eosin staining by the University of Cincinnati pathology core facility . Tumor phenotypes were determined as in Boivin et al . ( 2003 ) [46] . Paraffin-embedded colons of AOM-treated mice were sectioned at 5 μm and used for BrdU and cleaved caspase-3 staining . Mouse anti-BrdU antibodies were obtained from the Developmental Studies Hybridoma Bank maintained by the Department of Biological Sciences of the University of Iowa . The sections were de-paraffinized , rehydrated , and antigen retrieval was performed in citric acid buffer ( pH = 6 ) using a microwave . Endogenous peroxidase activity was blocked with hydrogen peroxide/methanol solution , and Avidin/Biotin block was performed according to the manufacturer's recommendations ( Vector Laboratories ) . Endogenous immunoglobulins block and primary and secondary antibody incubations were performed using the M . O . M . kit following the manufacturer's recommendations ( Vector Laboratories ) . Anti-BrdU antibody was incubated for 16 h at 4 °C in a humidified chamber . Color was developed using the DAB peroxidase substrate kit ( Vector Laboratories ) followed with hematoxylin staining and dehydration of the tissues . Nuclei from well-oriented crypts in Atoh1wt and Atoh1Δintestine colons were counted at 40× magnification , followed by counts of BrdU-positive cells . The percent of BrdU-positive Atoh1wt or Atoh1-null cells was determined for each animal ( at least 1 , 000 cells per genotype were counted for each animal ) . Student two-tailed t-test was performed to measure significance . Cleaved caspase-3 staining and counting were performed similarly to BrdU staining and analysis . Polyclonal rabbit anti–cleaved caspase-3 antibodies ( 1:100 ) were obtained from Cell Signaling Technology . The Rabbit IgG VECTASTAIN ABC kit was used according to the manufacturer's recommendations for blocking , antibody incubations , and signal amplification ( Vector Laboratories ) . Crypts from wild-type and Atoh1Δintestine mice were isolated using a modification of the Evans method [47 , 48] . Mice were sacrificed and the colons removed and flushed with ice-cold PBS , opened flat , and then placed in cold PBS containing protease and phosphatase inhibitors . After a brief vortexing , the colon was cut into four pieces and placed into a 15-ml tube containing shaking solution ( 1 . 5 mM KCL , 96 mM NaCl , 27 mM Na Citrate , 8 mM KH2PO4 , 5 . 6 mM Na2HPO4 , 15 mM EDTA , and 1 mM dithiothreitol supplemented with protease and phosphatase inhibitors ) . The tubes were agitated at 4 °C on a vortexer holding the tubes at 180° until the solution appeared cloudy ( 5–8 min ) . The colon pieces were transferred into new tubes with shaking buffer and agitated until most of the crypts were released ( enrichment was assessed by phase contrast microscopy at different time points ) . The solution containing the crypts was filtered through a 100-μm cell strainer to isolate the crypts , and sorbitol ( 2% final concentration ) was slowly added and mixed immediately . The solution was centrifuged at 160g for 8 min at 4 °C . The supernatant ( containing single cells ) was removed , and the pellet ( containing purified crypts ) was snap frozen in liquid nitrogen , to be used for protein and RNA analyses . RNA was purified from colon crypts isolated from wild-type and Atoh1Δintestine mice using Trizol ( Invitrogen ) according to the manufacturer's recommendations . Trizol-purified RNA ( 100 μg ) was subjected to DNase digestion and further purification ( RNeasy Mini; Qiagen ) ; 2 μg of total RNA was reverse transcribed ( Superscript III , Invitrogen ) , and cDNA equivalent to 100 ng of RNA used for SYBR Green–based real-time PCR using an Mx3005 ( Stratagene ) . For each gene assessed , colon crypt RNA from nine wild-type and eight Atoh1Δintestine mice was compared using the standard curve method of relative quantification . All gene expression levels were normalized to the expression of GAPDH . t-Tests measured significant differences between the average normalized expression levels in wild-type versus Atoh1Δintestine mice . Cecal and colonic tissues from wild-type and Atoh1Δintestine mice were homogenized in lysis buffer ( 1× Phosphate Buffered Saline , 1% NP-40 , 0 . 5% sodium deoxycholate , 0 . 1% SDS , 0 . 7 mM EDTA supplemented with protease and phosphatase inhibitors ) , sonicated , and the lysates used for quantitation and western blots . Similarly , lysates of colonic tissues and polyps from APCmin , and APCmin; Atoh1Δintestine double-mutant mice were used for whole lysate preparation and western blots . The following antibodies were used for western blot analysis: p21waf1 mouse monoclonal antibody ( 1:500 , cat #556431; BD Pharmingen ) , actin mouse IgM antibodies ( 1:100 , JLA20; Developmental Studies Hybridoma Bank ) , mouse monoclonal p27kip1 antibody ( 1:250 , cat #610241; BD Transduction Laboratories ) , goat anti-p57 polyclonal antibodies ( 1:100 , cat # sc-1039; Santa Cruz Biotechnology ) , rabbit polyclonal c-Jun antibodies ( 1:1000 , cat # sc-1694; Santa Cruz Biotechnology ) , and pJNK antibody ( 1:1000 , cat #559309; EMD-Calbiochem ) . Formalin-fixed tissues from AOM-treated mice were used for phosphorylated-JNK ( pJNK ) immunohistochemistry . Rabbit polyclonal pJNK antibody was used ( 1:100 , cat #559309; EMD-Calbiochem ) . The sections were de-paraffinized , hydrated , and the antigen retrieval was performed in citric acid buffer using a microwave . Endogenous peroxidase activity was blocked with hydrogen peroxide/methanol solution , a short remobilization step was included ( 0 . 2% Triton X 100 in PBS ) , and Avidin/Biotin block was performed according to the manufacturer's recommendations ( Vector Laboratories ) . The Rabbit IgG VECTASTAIN ABC kit was used according to the manufacturer's recommendations for blocking and antibody incubations ( Vector Laboratories ) . Primary antibody was incubated at 4 °C for 14–16 h; color was developed using the DAB peroxidase substrate kit ( Vector Laboratories ) followed by hematoxylin staining and coverslipping . Cells of well-oriented crypts in wild-type and Atoh1Δintestine colons were counted at 40× magnifications , followed by counts of pJNK-positive cells . The total number of cells and pJNK-positive cell numbers were added for each animal , and the average numbers were compared across crypts and animals . A Student t-test was performed to measure significance . MCC cell lines were cultured in RPMI medium supplemented with 15% FCS ( Perbio ) . The Ht29 cell lines ( obtained from Deutsche Sammlung von Mikroorganismen und Zellkulturen [DSMZ] ) were cultured in McCoy medium supplemented with 10% FCS . Primary human keratinocytes were isolated and pooled from foreskins of three different donors ( less than 6 y ) . Fourth passage cells were used in the experiments . The procedure has been approved by the ethical committee of the University of Leuven . Experiments performed adhered to the Declaration of Helsinki Principles . Keratinocytes were seeded in serum-free and growth factor–containing medium ( Keratinocyte-SFM; Invitrogen ) , which contains several growth factors ( 5 μg/ml insulin , 74 ng/ml hydrocortisone , 6 . 7 ng/ml triiodo-l-thyronine , 50 μg/ml bovine pituitary extract , and 5 ng/ml human recombinant EGF ) . All culture experiments were done at 37 °C in 5% CO2 . A fixed amount of cells was seeded in standard culture conditions , and the number of cells was counted after 3 to 5 d . The doubling times ( T2X ) were calculated using the formula T2X = LN ( 2 ) / ( ( LN ( nΔt ) -LN ( n0 ) ) /Δt ) with Δt , time in culture , n0 , number of seeded cells , and nΔt the number of cells after Δt The lentiviral vector HIV-CMV-Atoh1-IRES-GFP was constructed by cloning a PCR fragment of Atoh1-IRES into the HIV-CMV-GFP vector [49] , which was used as control vector . The HIV-CMV-GFP vector was first restricted with XbaI and Age1 , and ligated to the Atoh1-IRES fragment , which was spanned between Xba1 and Age1 . The Atoh1 and GFP were expressed from a bicistronic vector under the control of the CMV promoter . These lentiviral vectors were produced as described in [50 , 51] . Stable cell lines were created by transducing the cells in normal medium , supplemented with 8 nM polybrene and a multiplicity of infection between 100 and 150 ) . Four stable cell lines were made with the lentiviral vectors expressing Atoh1 and eGFP from a bicistronic Atoh1-IRES-eGFP construct ( MCC14 . 2-Atoh1 . 1a , MCC14 . 2-Atoh1 . 1b , MCC14 . 2-Atoh1 . 2a , and MCC14 . 2-Atoh1 . 2b ) , and one with eGFP alone ( MCC14 . 2-GFP ) as a negative control , under the control of a CMV promoter ( Figure 5A ) . We failed to created stable cell lines from the Ht29 . Cells were heavily selected against , and FACS isolation did not yield surviving cells . A total of 2 , 500 cells/ml were resuspended in 0 . 6% agarose ( Invitrogen ) in culture medium . A 2-ml layer of 0 . 35% agarose in culture medium was added on top of the 2-ml 0 . 6% layer . After 1 wk , 2 ml of 0 . 35% agarose in culture medium was added . Cells were cultured for 2 wk in standard conditions , and the number of colonies was analyzed under an inverted microscope with a 10× magnification . The experiment was done in triplicate . Cells were grown up to 40% confluency under standard conditions and were trypsinized . Cells were fixed and permeabilized using 70% ice-cold ethanol for 2 h . Cells were washed in PBS , and cells were stained for 30 min using 0 . 1% ( v/v ) Triton X-100 ( Sigma ) in PBS and 0 . 2 mg/ml DNase-free Rnase A ( Sigma ) and 20 μg/ml PI ( Sigma ) . Cells were analyzed on a FACSCalibur cytometer ( BectonDickinson ) . To analyze the cell cycle progression , cells were pulsed for 20 min with 10 μM BrdU ( Sigma ) . Medium was then substituted with normal medium for 6 h . Cells were collected and fixed in 70% ice-cold ethanol . DNA was denatured using 2 M HCl for 20 min . Cells were stained using anti-BrdU and IgG-alexa555 and analyzed in a FACSCalibur ( BD Bioscience ) . Annexin-V staining was performed using Annexin-V-biotin ( Roche ) and streptavidin-PercP ( BD biosciences ) as described by the manufacturer . Cells were imaged with the Leica TCS Sp2 confocal microscope , and images were analyzed and quantified using LCS software . Lysates were obtained by scraping cells in medium and centrifuging the medium . The pellet was resuspended in lysis buffer containing PBS with 1 mM EDTA , 1 mM EGTA , 50 mM NaF , 1% TritonX , 5 mM Na3VO4 , 20 μM PAO , and Complete Protease Inhibitor . The crude extract was separated by SDS-PAGE in a 4%–12% NuPAGE novex bis-tris gel ( Invitrogen ) and electroblotted onto Hybond-ECL membrane ( Amersham ) . Antibodies were diluted in the appropriate concentrations in 5% BSA in TBS-tween20 . The antibodies used are beta-actin ( clone AC-15; Sigma ) , anti–cleaved caspase-3 ( Asp-175; Cell Signaling Technology ) , polyclonal antibody to caspase-9 ( active ) ( ALEXIS ) , chromogranin A+B ( Abcam ) , cyclinA1 ( Santa Cruz Biotechnology ) , pJNK ( Thr183/Tyr185; BioSource ) , p27 ( BD Bioscience ) , p21waf1 ( DCS60; Cell Signaling Technology ) , PCNA ( clone PC10; Sigma ) , TRK ( Santa Cruz Biotechnology ) , Atoh1 ( 1/50; Developmental Studies Hybridoma Bank ) , NT3 ( Santa Cruz Biotechnology ) , and Cleaved Notch ( Val1744; Cell Signaling Technology ) . All western blot analyses are quantified in Figure S11 . K252a diluted in DMSO , from VWR , and DMSO as control were used . Final concentrations ranged from 0 μM to 0 . 5 μM; when no concentration is mentioned , 0 . 33 μM was used . SAPK inhibitor II , from Calbiochem , was used at a concentration of 10 μM . We used inhibitor X as a γ-secretase inhibitor ( Calbiochem ) . Concentrations used are mentioned in the figure legend . mRNA was amplified using the Superscript II One-Step RT-PCR system with Platinum Taq-100 reactions ( Invitrogen ) and the ATOH1 primers CAGCCAGTGCAGGAGGAAAA and GAAAATTCCCCGTCGCTTCT , and the Hes1 primers GGACATTCTGGAAATGACAGTGAA and AGCGCAGCCGTCATCTG , according to the manufacturer's protocol . Quantitative RT-PCR was performed on a ABI prism 7000 ( Applied Biosystems ) . The primers were designed using Primer Express ( Applied Biosystems ) . The primers used are ATOH1: CAGCCAGTGCAGGAGGAAAA and GAAAATTCCCCGTCGCTTCT; Atoh1: GCTGTGCAAGCTGAAGGG and TCTTGTCGTTGTTGAAGG . Primers to check copy number of the ATOH1 locus: ATOH1 locus set 1: CCCCGGGAGCAATCTTG and GGGACCGAGGCGAAGTT; control locus set 1: TCTGGGACCTGAGCTAATGGA and GGCCATAATTAGGACCATGAAAGA; and ATOH1 locus set 2: GCCAGTGCAGGAGGAAAACA and GAAAATTCCCCGTCGCTTCT . Control locus set 2: GGGTTCAGCCTCAACTTGTATCC and CCCACCACCTGGCATCTCT . RNA was diluted to 300 μg/μl and treated with TURBO DNA-free ( Ambion ) following the manufacturer's protocol . cDNA was synthesized using random primers and SuperscriptII reverse transcription ( Invitrogen ) using the manufacturer's protocol . cDNA was amplified using gene-specific primers ( concentration 5 μM ) for the different human tyrosine kinases . Methylation of the DNA was detected using ApaI enzyme . DNA ( 2 μg ) was dissolved in 50 μl of the appropriate buffer and 10 U of ApaI , and incubated at 30 °C overnight . A mirror condition was done in which the ApaI was exchanged by glycerol as a control for unspecific degradation . The resulting DNA was analyzed using two primer sets: one set spanning the restriction site ( AATAAGACGTTGCAGAAGAG and TCGCAGAGCAAAAATTAAAGGGTGC ) and another set next to the restriction site ( CCCCGGGAGCATCTTGCAGCCA and TCGCAGAGCAAAAATTAAAGGGTGC ) . Pull-down of methylated DNA fragments ( restricted using EcoRI ) was performed using the Methylcollector kit ( Active Motif ) , and bisulfite modification of DNA was done using the EZ DNA Methylation-Gold kit ( Zymo Research ) according to the manufacturers' protocols . We carried out array CGH using Code Linked Slides ( AP Biotech ) containing the 3 , 527 BAC clones from the Wellcome Trust Sanger Institute 1 Mb Clone Set , a gift from N . P . Carter ( The Wellcome Trust Sanger Institute ) . Array CGH was performed as described [17] . The ATOH1 open reading frame was amplified with PCR using AATAAGACGTTGCAGAAGAG and TCGCAGAGCAAAAATTAAAGGGTGC and AmpliTaq Gold DNA polymerase and the GeneAmp PCR System 2400 ( Applied Biosystems ) . The PCR products were purified and sequenced in both directions on the ABI Prism BigDye ( Terminator Cycle Sequencing Kit version 1 . 1 ) on an ABI PRISM 3100 Genetic Analyser ( Applied Biosystems ) . | Like most cancers , colon cancer displays a loss of differentiation , and the stronger this property , the more aggressive the cancer . This suggests that the loss of the capacity to differentiate may be a critical and possibly early event during the formation of these tumors . The key gene instructing secretory cell fate differentiation in the epithelium of the colon , namely Atonal homolog 1 ( ATOH1 ) , is highly conserved in flies , mice , and humans . We asked whether ATOH1 could be a pivotal factor in causing colon cancer in mice and humans . Our studies show that colon-specific loss of ATOH1 in mice is sufficient to trigger colon cancer and that the majority of human colon cancers also have an inactivated ATOH1 . Reactivating ATOH1 in cultured human colon cancer cells causes these cells to stop dividing and to commit suicide . Since reactivation of this epigenetically silenced gene can be achieved using small chemical compounds , studying how ATOH1 acts may offer therapeutic avenues in the future . | [
"Abstract",
"Introduction",
"Results",
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"Methods"
] | [
"oncology",
"molecular",
"biology",
"genetics",
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"genomics"
] | 2009 | Atonal homolog 1 Is a Tumor Suppressor Gene |
Piwi proteins associate with piRNAs and functions in epigenetic programming , post-transcriptional regulation , transposon silencing , and germline development . However , it is not known whether the diverse functions of these proteins are molecularly separable . Here we report that Piwi interacts with Tudor-SN ( Tudor staphylococcal nuclease , TSN ) antagonistically in regulating spermatogenesis but synergistically in silencing transposons . However , it is not required for piRNA biogenesis . TSN is known to participate in diverse molecular functions such as RNAi , degradation of hyper-edited miRNAs , and spliceosome assembly . We show that TSN colocalizes with Piwi in primordial germ cells ( PGCs ) and embryonic somatic cells . In adult ovaries and testes , TSN is ubiquitously expressed and enriched in the cytoplasm of both germline and somatic cells . The tsn mutants display a higher mitotic index of spermatogonia , accumulation of spermatocytes , defects in meiotic cytokinesis , a decreased number of spermatids , and eventually reduced male fertility . Germline-specific TSN-expression analysis demonstrates that this function is germline-dependent . Different from other known Piwi interters , TSN represses Piwi expression at both protein and mRNA levels . Furthermore , reducing piwi expression in the germline rescues tsn mutant phenotype in a dosage-dependent manner , demonstrating that Piwi and TSN interact antagonistically in germ cells to regulate spermatogenesis . However , the tsn deficiency has little , if any , impact on piRNA biogenesis but displays a synergistic effect with piwi mutants in transposon de-silencing . Our results reveal the biological function of TSN and its contrasting modes of interaction with Piwi in spermatogenesis , transposon silencing , and piRNA biogenesis .
PIWI proteins are a subfamily of the PIWI/ARGONAUTE protein family . Piwi proteins associate with Piwi-interacting RNAs ( piRNAs ) and function in germline stem cell ( GSC ) self-renewal , germline development , epigenetic programming , post-transcriptional regulation , and transposon silencing [1–3] . The defining member of the Piwi/AGONAUTE family is the Piwi protein in Drosophila ( PIWI herein stands for the subfamily whereas Piwi specifically stands for the Drosophila Piwi protein ) , which is known to regulate GSC maintenance , germ cell proliferation , heterochromatin formation , and transposon silencing [4–8] . However , it is not known whether the diverse functions of these proteins are molecularly separable; nor it is known whether all Piwi functions are piRNA-dependent . Furthermore , although Piwi proteins are known to interact with multiple proteins , including Tudor-domain-containing proteins , no interactor is known to regulate Piwi expression or interacts with Piwi antagonistically , or only impact on only a subset of Piwi functions . Here we report that Tudor-SN ( Tudor staphylococcal nuclease , TSN ) , a member of the evolutionarily conserved Tudor protein family , as a novel and unique Piwi-interactor in Drosophila . TSN contains five staphylococcal nuclease-like domains ( SN1-SN5 ) and a methyl lysine/arginine recognizing Tudor domain [9–11] . TSN is highly conserved during evolution from fission yeast to mammals , and it exists as a single gene in a species with no close homologue [12] . TSN has been reported to participate in a variety of molecular functions , such as transcriptional activation through interacting with STATs , formation of RNA-induced silencing complex ( RISC ) and stress granules , degradation of A-to-I hyper-edited miRNAs , and assembly of spliceosome [13–19] . In mice and humans , TSN ( a . k . a . SND1 ) has been demonstrated to interact with mouse and human PIWI proteins MIWI and MILI via binding to the symmetrically dimethylated arginine ( sDMA ) resides in the PIWI proteins [20 , 21] , similar to other Tudor family proteins [20 , 21] . In Drosophila , although Tudor protein family members , including PAPI ( Partner of PIWIs ) have been shown to interact with PIWI proteins via sDMA residues [22] , TSN has not been demonstrated to interact with PIWI proteins . Moreover , the biological role of TSN has not been explored in any organism . Here we report the biological function of TSN in Drosophila , its interaction with Piwi , and the regulatory effect of such interaction . We show that TSN is highly expressed in embryos and adult gonads . In testes , tsn mutations result in abnormal spermatogenesis , including a higher mitotic index of spermatogonia , drastically increased number of spermatocytes , defects in meiotic cytokinesis , a reduction in spermatids , and consequently a decline in male fertility . Furthermore , the phenotype of tsn mutants is rescued by the mutations of piwi , indicating an antagonistic relationship of TSN and Piwi in the germline during spermatogenesis . In support of this , we show that TSN down-regulates the expression of Piwi at both protein and RNA levels . Finally , we demonstrate that tsn mutants display little impact on the piRNA biogenesis but have synergistic impact with Piwi on transposon repression . Our data suggest that TSN negatively regulates piwi expression in germline development while it may work with the Piwi protein in piRNA biogenesis and transposon silencing .
In an attempt to identify novel molecular interactors of Piwi , we previously reported the fractionation of cytoplasmic extracts of 0–12 h w1118 wild-type embryos using size-exclusion chromatography [23] . After the final chromatography column , Piwi migrated with an apparent molecular weight of ~150 kDa . We resolved the peak fraction for Piwi obtained from Superdex 200 chromatography on a 7 . 5% SDS polyacrylamide gel and stained it with silver stain . We obtained the identities of individual bands by excising bands from a gel stained with colloidal coomassie blue , followed by mass spectrometry [23] . Piwi-Hsp90 interaction was described in our earlier paper [23] . Here we report that TSN , an evolutionarily conserved Tudor protein family member , is another novel Piwi-interacting protein . TSN contains five staphylococcal nuclease-like domains ( SN1-SN5 ) and a methyl lysine/arginine-recognizing Tudor domain ( Fig 1A ) . To confirm Piwi and TSN interaction , we performed co-immunoprecipitation assays using 0–12 h wild-type embryos . Piwi was immunoprecipitated with TSN , and the interaction was further confirmed by reciprocal co-immunoprecipitations , suggesting that Piwi and TSN form a complex in vivo ( Fig 1B and 1C and S1 Fig ) . Moreover , immunofluorescence microscopy in 0–2 h wild-type embryos revealed that Piwi is co-localized with TSN in the cytoplasm and nuclei of PGCs as well as in the nuclei of somatic cells ( Fig 1D–1H ) . Taken together , our results suggest that Piwi and TSN physically interact during embryogenesis . To investigate the biological function of TSN , we first analyzed the expression of TSN in different tissues and at key stages of development . Western blot analysis indicated that TSN is widely expressed in most developmental stages with the highest expression levels in embryos , adult ovaries and testes ( Fig 2A ) . In addition , we used immunofluorescence microscopy to examine the expression pattern and subcellular localization of TSN in wild-type adult ovaries and testes . TSN is expressed in both the germline and somatic cells , from the germarium to advanced stage egg chambers ( Fig 2B–2B” ) , where TSN is mainly present in the cytoplasm ( Fig 2C–2D” ) . TSN is also expressed throughout spermatogenesis ( Fig 2E ) in both germ cells ( labeled by VASA staining in Fig 2E’ ) and somatic cells ( labeled by Tj staining in Fig 2E” ) , where TSN was also highly enriched in the cytoplasm ( Fig 2F–2F”” and 2G–2G‴ ) . As previously reported [5] , Piwi is expressed in the nuclei of hub cells , early germ cells and somatic cyst cells ( Fig 2G’ and 2G‴ ) . To determine the biological role of TSN , we first examined five ethyl-methane sulfonate ( EMS ) mutant alleles of TSN obtained from the Fly TILLING [24] for their molecular lesions . The tsn0536 and tsn0614 are point mutations ( 282Ile→Phe and 320 Glu→ Lys , respectively ) in the SN2 domain . The tsn0251 and tsn1236 are point mutations ( 368Gly→Glu and 501Gly→Arg , respectively ) in the SN3 domain . The tsn1120 mutation is a deletion from +3009 ( A ) to +3067 ( G ) ( per FlyBase coordinate of CG7008 ) that causes a frame shift from 437Ile on ( Fig 3A ) . To determine whether tsn mutant alleles affect protein stability , we analyzed the protein level of TSN using the testicular lysates from each mutant allele transheterozygous to a deficiency ( Df ) allele uncovering the tsn genomic region . We found that TSN protein levels were undetectable in tsn 1120/Df mutants ( Fig 3B ) . Moreover , antibodies recognizing epitopes in the N- or C-terminal region of TSN failed to recognize detectable protein levels in tsn 1120/Df testes , indicating that tsn 1120 is a protein-null allele . To further examine the role of TSN in spermatogenesis , we performed immunostaining of 2-day-old mutant testes . Testes in tsn0251/Df and tsn0536/Df mutants ( Fig 3D and 3E ) were similar to tsn+/tsn+ control testes ( Fig 3C ) , suggesting these mutations fail to disrupt TSN function . However , tsn0614/Df , tsn 1120/Df , and tsn1236/Df alleles resulted in the swollen apical tips of testes containing an excessive number of early spermatogenic cells , with 42% ( n = 84 ) , 86% ( n = 81 ) , and 36% ( n = 70 ) of the testes displaying this phenotype , respectively ( Fig 3F–3H ) . Notably , TSN immunostaining signal was undetectable in tsn 1120/Df testes ( Fig 3G ) , consistent with the result of our Western blot analysis ( Fig 3B ) . In addition , we analyzed 2-day-old tsn 1120/tsn0614 , tsn 1120/tsn 1236 , and tsn 0614/tsn1236 transheterozygous males . Interestingly , testes from all of the three transheterozygous mutants exhibited the swollen apical tips , with 81% ( n = 80 ) , 78% ( n = 79 ) , and 45% ( n = 71 ) of the testes displaying the phenotype , respectively . The knockdown of TSN using Actin5c-Gal4 driver ( S2 Fig ) also showed phenotype similar to tsn1120/tsn0614 transheterozygous mutant , though at a lower frequency ( 36% , n = 42 ) ( S2 Fig ) . The lower penetrance ( compared to that of tsn 1120/tsn 0614 transheterozygous mutant ) may due to the modest knockdown efficiency of TSN in testes ( S2 Fig ) . Although the knockdown efficiency of TSN in ovaries is much stronger , we did not observe any obvious defects in TSN knockdown ovaries ( S3 Fig ) or tsn1120/tsn0614 transheterozygous mutant ovaries ( S4C Fig ) . Together , these data indicate that tsn is required for normal progression of spermatogenesis but not oogenesis . To determine whether the function of tsn in spermatogenesis is germline-dependent , we expressed wild-type TSN in germ cells using nosVP16-Gal4 driver ( see Materials and Methods ) . This expression rescued the swollen testis apical tip phenotype of 68% of the tsn1120/tsn0614 transheterozygous mutants to WT-like ( n = 62; S5A Fig ) , indicating that the tsn function in spermatogeneis is germline-dependent . To further investigate the spermatogenic defects of tsn mutants , we immunostained mutant testes from 2-day-old tsn 1120/tsn 0614 transheterozygote males with VASA as a germ-cell marker , Hu-Li Tai Shao ( Hts ) as a fusome marker , and DAPI as a DNA marker . Fusome is a germline-specific organelle containing membrane skeletal proteins and connecting differentiating germ cells within a germline cyst [25] . In the testis , it connects spermatocytes derived from a single gonialblast ( the differentiated daughter cell of a spermatogonial stem cell ) . The majority of cells in the swollen apical tip were VASA positive ( Fig 4A , red ) with branched fusomes ( Fig 4A , arrow head ) and faint DNA staining signal , indicating that these excessive number of early spermatogenic cells were likely spermatocytes . In addition , we found a few bundles of elongated spermatids with their heads resided at the apical tip of the testis . 62% of the testes examined displayed this phenotype ( n = 42; Fig 4A , arrow ) , in contrast to their normal localization at the basal region of the testis . In order to assess whether the excess spermatocytes in tsn mutants are resulted from the defects in GSCs , we examine the number of GSC located next to the hub , the somatic niche of GSCs in testes . The average number of GSCs per tsn mutant testes was 6 . 5 ( n = 12 ) , which was similar to that in wildtype control testes ( 6 . 67 , n = 12 ) , even though all the mutant testes displayed the swollen tip phenotype ( S6 Fig ) . This result indicates that the spermatocyte expansion phenotype of tsn mutants is not caused by abnormal GSC number . To determine whether the excess spermatocytes are due to a higher proliferation rate of spermatogonial cells , we immunostained tsn mutant third instar larval testes with anti-phospho-histone-3 ( pH3 ) antibody to examine the mitotic index of spermatogonia . The mutant third instar testes show a higher mitotic index of spermatogonia and are already larger than tsn+/tsn+ wild-type larval testis , even though the mutant spermatogonia are similar in size to those in wild-type larval testes ( cf . Fig 4D and 4E ) . Quantification revealed that the wild-type testes exhibited an average of 2 . 56 pH3-positive spermatogonia per testis ( n = 27 ) , yet the mutant testes had an average of 3 . 44 pH3-positive spermatogonia per testis ( n = 27 ) ( Fig 4F ) . The higher mitotic index of spermatogonia can be caused by two possibilities: a reduced differentiation from spermatogonia to spermatocyte and/or higher proliferation rate of spermatogonia . In the former case , the number of spermatocytes would decrease because the germ cells would be arrested at the spermatogonial stage . However , we did not observe a reduction of spermatocytes in tsn mutant testes . Thus , the higher mitotic index of spermatogonia indicates that there is likely a higher proliferation rate of spermatogonia in tsn mutant testes . To further determine whether TSN regulates later stages of spermatogenesis , we used phase contrast microscopy to examine onion-stage spermatids in freshly squashed tsn mutant testes . In wild-type testes , each round spermatid contained a Nebenkern ( mitochondrial derivates , Fig 4B , left panel , dark sphere ) associated with a haploid nucleus ( Fig 4B , left panel , white sphere ) . However , in 24% ( n = 58 ) of tsn mutant testes , approximately 30% of round spermatids contain a single over-sized Nebenkern associated with two haploid nuclei ( Fig 4B , right panel ) , indicating that these tsn mutant spermatids have succeeded in nuclear division but fail in proper cytokinesis . To assess if the spermatogenic defects are more severe in aged files , we examined the spermatid bundle numbers of 2-week-old mutant testes . The average numbers of spermatid bundle in wild-type and tsn mutants were 12 . 33 ( n = 27 ) and 8 . 85 ( n = 27 ) , respectively . The reduction of spermatid bundles in the tsn mutants was significant ( p = 2 . 24E-09 , Fig 4C ) . This decreased instead of increased number of spermatid bundles in tsn mutant flies indicates that spermiogenesis ( the process of spermatid morphogenesis ) may be delayed and/or partially blocked in the mutants . Taken together , these results indicate that the tsn mutant testes likely have a higher proliferation of spermatogonia but a compromised differentiation of spermatocytes . To evaluate the impact of these spermatogenic defects on fertility , we analyzed the fertility of tsn mutant males by crossing them to w1118 virgin females . Initially , the mutant males showed relatively normal fertility , but their fertility was decreased faster over time as compared to the control tsn+/tsn+ wild-type males ( Fig 4G ) , causing a significantly reduced male fertility ( P = 4 . 38E-05 ) , as expected from their compromised spermatogenesis . Expression of one copy of wild-type tsn gene in germ cells using nosVP16-Gal4 driver in tsn mutant males rescued their fertility to 74% of wildtype males at 2wk-old age , as effective as introducing one copy of the endogenous wildtype tsn gene ( on the TM3 balancer ) into the tsn mutant background ( S5B Fig ) . These results indicate the germline-dependence of tsn function in the testis . In addition , these results indicate the dose-dependence of the TSN function in spermatogenesis . To further study the dose-dependence of TSN in spermatogenesis , we overexpressed TSN in either germline cells or all cells in wildtype testes and examined the potential phenotypical consequence ( S8 and S9 Figs ) . No obvious abnormality was observed , except that only a slight increase of hub cells number was observed in Actin-gal4>Flag-tsn testes ( S8 and S9B Figs ) . Hence , increasing TSN level to more than wildtype level does not generate more impact on germ cell differentiation . To investigate the biological significance of TSN and Piwi interaction in regulating spermatogenesis , we examined the phenotype of the piwi and tsn double mutants . We focused on piwi1 and piwi2 mutant alleles , which are strong recessive mutations of piwi [5 , 6] . Remarkably , we found that introducing one or two copies of either piwi1 or piwi2 mutant alleles into the tsn mutant background rescued tsn mutant phenotype in a dosage-dependent manner ( Fig 5A–5C ) . We categorized the phenotype into three classes based on the extent of swollen apical tip of the testis: severe ( as shown in Fig 5A ) , moderate ( as shown in Fig 5B ) , and WT-like ( as shown in Fig 5C ) phenotype . In tsn1120/tsn0614 mutant testes , only 5% of the testes showed WT-like morphology , 14% of the testes had moderate phenotype , and 81% of the testes displayed severe phenotype . However , introducing one copy of piwi1 allele resulted in 18% and 63% of the testes showing WT-like and moderate phenotype , respectively . Introducing piwi1/piwi2 alleles led to 54% and 41% testes showing WT-like and moderate phenotype , respectively ( Fig 5D ) . Moreover , the fertility of tsn mutants ( Fig 5E , red line ) was also restored by piwi mutations ( Fig 5E , green line ) . Interestingly , Western blot and quantitative RT-PCR ( qRT-PCR ) analyses revealed that the protein and mRNA expression levels of piwi were upregulated in tsn mutant testes ( Fig 5F and 5G ) . Immunostaining of Piwi in tsn mutant testes further showed that the expression pattern of Piwi did not change compared to that in the control ( S7 Fig ) . Therefore , the upregulation of Piwi is not due to ectopic expression of Piwi , but is mostly , if not completely , due to increased Piwi expression within its expressing cells . Consistently , overexpression of TSN with actin5c-Gal4 driver in testes leads to slight decrease in Piwi expression , with no effect in Piwi localization ( S9 Fig ) . The antagonistic interactions between Piwi and TSN at both genetic and molecular levels together define a functionally antagonistic relationship between TSN and Piwi in spermatogenesis . Interestingly , we observed similar regulatory relationship of Piwi and TSN in the ovary . Although tsn mutant ovaries did not show any obvious oogenesis defects ( S4C Fig ) , the germline-depletion phenotype of piwi mutants ( S4B Fig ) was partially restored by introducing the mutations of tsn ( S4D Fig ) . Furthermore , Piwi protein and mRNA expression levels were both significantly upregulated in the tsn mutant ovaries ( S10 Fig ) , suggesting the antagonistic interaction between Piwi and TSN is conserved in spermatogenesis and oogenesis . Because TSN is involved in RNA metabolism [14–18] , we performed TSN RNA immuneprecipitation using Flag antibody to examine if TSN binds to Piwi mRNA ( see Materials and Methods ) . Piwi mRNAs are enriched more than 7-fold using two different sets of piwi primers ( S10C Fig ) . These results together indicate that TSN regulate Piwi expression likely by directly binding to its mRNA . As Piwi is expressed in both germline and somatic cells , we next asked whether TSN antagonizes the germline or the somatic function of Piwi . To address this question , we used nosVP16-Gal4 ( germline ) and tj-Gal4 ( somatic ) drivers to knockdown Piwi tissue-specifically in the tsn mutant background . Germline knockdown of Piwi rescued tsn mutant phenotype ( 72% of the testes examined were WT-like , n = 75; Fig 6 ) ; however , somatic depletion of Piwi did not ( S11 Fig ) . These results suggest that TSN antagonizes the function of Piwi in germ cells to regulate spermatogenesis . Because Piwi plays an important role in the piRNA pathway [2] , we wanted to know whether TSN regulates piRNA biogenesis and functions . To address this question , we first sequenced small RNA libraries prepared from an equal quantity of two-day-old tsn+/tsn+ wild-type and tsn1120/tsn0614 mutant testes . The composition of small RNA libraries from wild-type and tsn mutant testes are shown in Fig 7A . The size distribution profiles of small RNAs ( excluding miRNAs and fragments of long cellular RNAs such as tRNAs and rRNAs ) from wild-type and tsn mutant testes are shown in Fig 7B . It revealed that the tsn mutant testes have essentially identical piRNA profiles ( Fig 7B ) . Moreover , the 1U-bias of piRNAs ( a strong U bias at the most 5’ position ) and the nucleotide composition were well maintained in the tsn mutants ( Fig 7C ) . We further mapped all of the small RNAs to known piRNA clusters , and only clusters 4 and 7 showed ~3-fold upregulation in tsn mutants ( Fig 7D ) . However , we noticed that relatively few deep sequence reads mapped to these two clusters , so the 3-fold increase could be due to or contributed by fluctuation in reads . These data indicate that , unlike Piwi , TSN either is not involved in piRNA biogenesis or plays only a very minor role in this process . To investigate whether TSN is involved in piRNA biogenesis in the ovary and to test whether the mild effect of tsn mutations on small RNAs in the testis is caused by altered abundance of different cell types in the mutant testis , we sequenced and analyzed small RNAs prepared from wild-type and tsn mutant ovaries . The total small RNA composition profiles were shown in S12A Fig . Similar to the results observed in testes , the size distribution profiles of small RNAs from wild-type and tsn mutant ovaries are not significantly affected ( S12B Fig ) . The 1U-bias and nucleotide composition were not affected in the mutant ovaries , either ( S12C Fig ) . We next examined the abundance of piRNAs generated from known piRNA clusters , and did not observe any strong effects of tsn mutations on these piRNA clusters ( S12D Fig ) . Together , these results indicate that TSN has a minor role , if any , in piRNA biogenesis in both testes and ovaries . To exclude the possibility that the compensation of other Piwi-interacting Tudor proteins may rescue the loss of TSN , we examine the expression level of Papi and Tudor [22] in tsn mutant testes , and did not detect significant difference in their expression ( S13 Fig ) . Therefore , TSN unlikely to be directly involved in piRNA biogenesis . Because a primary function of Piwi is to silence transposons [2] , we next examined whether tsn mutations results in any transposon desilencing and whether TSN interacts with Piwi in regulating transposons . To this end , we analyzed the expression of transposons in wild-type , tsn mutant , piwi mutant , and piwi and tsn double mutant testes by qRT-PCR . Consistent with the only slight reduction of piRNAs in tsn mutant testes shown in Fig 7B , transposons were only mildly upregulated ( ~2-6X change ) in the tsn mutants compared with the wild-type controls ( Fig 7E ) . In contrast , the upregulation of transposons in the piwi mutant is more significant ( ~4-18X for most transposons and 120X and 385X for gtwin and gypsy , respectively; Fig 7E ) . Remarkably , in tsn; piwi double mutants , the transposon upregulation is even much more drastic ( ~13-62X for most transposons and 280X and 620X for gtwin and gypsy , respectively ) . Similar results were observed in ovaries ( S12E Fig ) . These results indicate that TSN and Piwi interact synergistically in transposon silencing in both testes and ovaries .
Piwi is a multifunctional protein and its deficiency leads to abolishment of GSC self-renewal , abnormalities in germ cell proliferation and differentiation , defects in piRNA biogenesis , derepression of transposons , and aberrant epigenetic programming ( reviewed in [1 , 2] ) . However , it has not been reported that these functions are separable . In addition , no negative interactor of Piwi proteins has been identified . Here we have identified TSN as a novel Piwi interactor that interacts with Piwi antagonistically and demonstrated that this interaction is only for spermatogenesis but not for piRNA biogenesis or transposon silencing . The tsn mutant testis exhibits phenotypic defects , including germline overproliferation , meiotic cytokinesis abnormality , and a reduced number of spermatids , all of which contribute to a decrease in the fertility of these mutant males ( Fig 4 ) . The opposite phenotype of piwi and tsn mutants in germ cell proliferation , together with the negative regulation of Piwi by TSN , indicate an antagonistic relationship between Piwi and TSN that is essential for spermatogenesis . In support of this , the phenotype of tsn mutants in spermatogenesis and fertility is rescued by piwi mutations ( Fig 5A–5E ) . Because TSN negatively regulates the expression of piwi mRNA and Piwi protein in the testis ( Fig 5F and 5G ) , it is likely that the antagonistic interaction happens at level of Piwi expression . Furthermore , this regulation occurs mostly , if not exclusively , in the germline , since TSN function is germline-dependent ( S5B Fig ) and tsn phenotype is rescued by germline-specific piwi knockdown ( Fig 6 ) . It is worth noting that the TSN function in spermatogenesis is not only germline dependent but also dose-dependent . Tsn/+ testes show intermediate degrees of defects between tsn/tsn and +/+ testes . Furthermore , the nos-Gal4-driven expression of one copy of tsn gene rescues defects of tsn/tsn mutants to that of tsn/+ testes . However , over-expression of TSN in either the germline or the entire testis does not have detectable effect , which suggests that the dose-dependence is capped at the +/+ level . Our study indicates that the antagonistic regulatory relationship between Piwi and TSN is conserved between spermatogenesis and oogenesis . TSN also negatively regulates the expression of piwi mRNA and Piwi protein in the ovary ( S10 Fig ) . Furthermore , TSN binds to the piwi mRNA in the ovary ( S10C Fig ) . These results also support that TSN antagonizes Piwi function at least in part by directly binds to its mRNA and suppress its expression . Despite the strong interaction between TSN and Piwi , it is surprising that TSN does not have obvious effects on piRNA biogenesis ( Fig 7 ) . Although a simple explanation would be that spermatogenesis is more sensitive to the dose of Piwi than piRNA biogenesis . This is unlikely because Piwi expression is up-regulated in tsn mutant , yet Drosophila strains with four copies and even six copies of piwi are perfectly fertile and can be maintained as stocks [7] . Therefore , we favor an alternative possibility that the antagonistic protein-protein interaction between TSN and Piwi is needed for spermatogenesis but not for piRNA biogenesis , It is surprising to observe that , despite the antagonistic interaction between TSN and Piwi , they have synergistic effect on transposon silencing . This might reflect that TSN and Piwi act through independent pathways that silence transposons ( S14 Fig ) . In piwi mutants , transposons are significantly de-silenced , as previously reported by many . Interestingly , tsn mutants display a milder de-silencing effect . This is likely because Piwi is significantly up-regulated in tsn mutants , which partially compensated for the loss of TSN-mediated transposon repression . In tsn; piwi double mutants , both TSN and Piwi-mediated repression mechanisms are abolished , which leads to an even more drastic transposon repression that has not been reported . Thus , PIWI/piRNA-mediated mechanism is not the only mechanism involved in transposon repression . It would be important to investigate the TSN-mediated transposon silencing mechanism and to determine whether it depends on piRNA . Relevantly , the Piwi-TSN protein interaction may only affect piRNA-independent function of Piwi such as miRNA regulation [7] or other unidentified roles of Piwi . Both TSN and Piwi have been implicated in the miRNA pathway [7 , 26] . Since miRNAs play an important role during germline development , it would be intriguing to examine whether TSN interacts with Piwi to mediate miRNA regulation which in turn exerts different effects on spermatogenesis and transposon silencing . In any case , the above data allow us to conclude that the function of Piwi in spermatogenesis can be separated from piRNA biogenesis and transposon silencing . It has been shown that the expression of human TSN ( a . k . a . , SND1 ) is upregulated in various human cancers such as colon , prostate , breast , and hepatocellular cancers [27–30] . In prostate cells , SND1 recruits splicing factor SAM68 and other spliceosomal components on CD44 pre-mRNA , promotes the inclusion of CD44 variable exons , which correlates with increased proliferation , motility , and invasiveness of cancer cells [27] . Moreover , SND1 interacts with Metadherin ( MTDH ) to promote metastasis and was also shown to promote resistance to apoptosis and to regulate the expression of genes associated with metastasis and chemoresistance [28] . However , both the function of TSN during normal development and the molecular mechanism of TSN in cancer cells still remain elusive . Importantly , PIWI proteins are also implicated in cancers [31–33] . Therefore , our study of TSN and Piwi interaction provides a possible mechanism in tumorigenesis , and it may be due to the multifunctional nature and the broad interacting partners and targets of TSN .
All fly stocks were raised at 25°C on yeast-containing molasses/agar medium . Both yw and w1118 flies were used as the wild-type controls . The tsn EMS mutant alleles were generated and screened using Fly TILLING [24] . The deficiency allele that disrupts the tsn genomic region , w1118; Df ( 3L ) BSC125/TM6B , Tb1 , was obtained from Bloomington Stock Center . The tsn RNAi strain HMS00184 was obtained from TRiP at Harvard . The knockdown of tsn was induced by Actin5C-Gal4 driver obtained from Bloomington Drosophila stock center . UASp-driven Flag-tagged tsn transgenic strains , carrying a functional tsn gene , were generated with standard protocols using pVALIUM10-roe vector from TRiP at Harvard . Germline-specific expression of the Flag-tagged tsn transgene was achieved by introducing nosVP16-Gal4 ( Bloomington Drosophila stock center ) and Flag-tagged tsn transgene in the tsn1120/tsn0614 mutant background . piwi1 and piwi2 alleles were generated by P-element insertions as previously described [5 , 6] . The piwi RNAi strain w1118; P{GD11827}v22235 was obtained from Vienna Drosophila RNAi Center . The knockdown of piwi was induced by nosVP16-Gal4 driver ( Bloomington Drosophila stock center ) and tj-Gal4 driver ( Drosophila Genetic Resource Center ) . All chromatographic steps and mass spectrometry analysis were described previously [23] . Cytoplasmic extracts of w1118 embryos were collected as described previously [23] . All steps in extract preparation were performed at 4°C . A total of 1 mg of total protein in a volume of 1 ml H ( 0 . 15 ) buffer ( 25 mM HEPES-NaOH , pH 7 . 8 , 150mM NaCl , 0 . 5 mM EGTA , 0 . 1 mM EDTA , 2 mM MgCl2 , 0 . 02% NP-40 and 20% glycerol ) was used for each immunoprecipitation reaction . The lysates were pre-cleared using Protein A/G PLUS-Agarose ( Santa Cruz ) for 1h at 4°C . Pre-cleared lysates were incubated with the monoclonal mouse anti-Piwi antibody ( a gift from M . C . Siomi ) [34] or the monoclonal mouse anti-TSN antibody , a gift from O . Silvennoinen [35] , overnight at 4°C with gentle agitation . 60 μl of beads were then added to the lysate-antibody mixture and incubated further for 3 h at 4°C . Beads were then washed five times with 1 ml of H ( 0 . 15 ) buffer at 4°C and finally analyzed by Western blotting . For Western blot analysis , the following antibodies were used: monoclonal mouse anti-Piwi ( 1:200 ) , a gift from M . C . Siomi [34] , monoclonal mouse anti-TSN ( 1:5000 ) , a gift from O . Silvennoinen [35] , polyclonal rabbit anti-GAPDH antibody ( 1:5000 , Sigma ) , polyclonal Guinea Pig anti-Papi antibody ( 1:5000 ) and polyclonal mouse anti-β-tubulin ( 1:10000 , Developmental Studies Hybridoma Bank ) antibodies . Peptides corresponding to the 26 residues of the N-terminal and the 27 residues of the C-terminal of Drosophila TSN were synthesized and used for generating polyclonal rabbit anti-TSN-N and anti-TSN-C antibodies , respectively ( Cocalico Biologicals ) . Both antibodies were purified and used at 1:5000 dilution . The specificity of each anti-TSN antibody is shown in S1 Fig . The preparation of embryos , ovaries , and testes was performed as previously described [25 , 36] . For immunofluorescence staining , the following primary antibodies were used: monoclonal mouse anti—Flag M2 ( 1:500; Sigma-Aldrich ) , monoclonal mouse anti-Piwi ( 1:20 ) , a gift from M . C . Siomi [34] , monoclonal mouse anti-TSN ( 1:500 ) , a gift from O . Silvennoinen [35] , polyclonal rabbit anti-VASA ( 1:400 , Santa Cruz ) , polyclonal rabbit anti-Ser10-phospho-histone H3 ( 1:200 , Cell Signaling ) , polyclonal rabbit anti-TSN-N and anti-TSN-C ( both at 1:1000 dilution ) , polyclonal mouse anti-Hts ( 1:40 , Developmental Studies Hybridoma Bank ) , and polyclonal guinea pig anti-Tj ( 1:100 , a gift from D . Godt , University of Toronto ) antibodies . Alexa Fluor -488 , -568 , or -633-conjugated goat anti-rabbit , anti-guinea pig , and anti-mouse IgG secondary antibodies were purchased from Jackson Immunoresearch Laboratory and were used at 1:400 dilution . Immunofluorescently labeled samples were counterstained with DAPI with standard protocol . Images were taken using Leica TCS SP5 Spectral Confocal Microscope in the sequential scanning mode and then processed using Photoshop ( Adobe ) . Testes were dissected from 3-day-old adults in phosphate-buffered saline ( PBS ) and transferred into a 2-μL drop of PBS on a coverslip . The testes were then torn open using tungsten needles and slightly squashed by putting a slide over the coverslip . Phase contrast images of squashed testes were obtained using a Leica DM6000 microscope with a 40X objective . Individual 2-day-old males of the indicated genotypes were crossed with three 2-day-old virgin w1118 females . For every 3 days , each vial was visually examined to ensure eggs had been laid . The females were then discarded and each male was transferred to a new vial and mated with three 2-day-old virgin females . The number of adult progeny in each vial was counted on the 20th day after each mating . A minimum of 15 males for each genotype was used for testing . Low-molecular-weight RNAs were isolated from the adult testes of w1118 and tsn1120/tsn0614 mutant males ( approximately 200 pairs for each genotype ) using a mirVana miRNA isolation kit ( Life Technologies ) . Small RNAs ranging in size between 16 and 29 nucleotides ( nt ) ( below 2S rRNA ) were gel-purified , and small RNA libraries were prepared using a small RNA sample prep kit ( Illumina ) according to the alternative v1 . 5 protocol . The clones were sequenced using Genome Analyzer II . Only sequences perfectly matching the Drosophila melanogaster release 5 genome ( excluding Uextra ) were analyzed . As TSN is known to regulate miRNAs [17] , here we used the total reads instead of the reads of miRNAs to normalize the two libraries . After removal of miRNAs and fragments of long cellular RNAs such as tRNAs and rRNAs , we mapped 18–32-nt unique piRNAs to a subset of known piRNA clusters and to the complete collection of Drosophila melanogaster transposable elements ( Repbase ) [37] . Thirty pairs of testes were dissected from 2-day-old adults in ice-cold PBS . Total RNA was then extracted using TRIzol ( Invitrogen ) following manufacturers’ protocols . RNA was finally eluted in 30μl nuclease-free water and quantified by Nanodrop . 2 μg of eluted RNA was used to generate cDNA in a 20 μl reaction using High Capacity cDNA Reverse Transcription kit ( Applied Biosystems ) . Quantitative RT-PCR of the Piwi , TSN , and transposon transcripts was performed as previously described [38] . The following primer pairs were used- Piwi: forward ( 5’-TGCCATGAGCAGTTATACGC-3’ ) and reverse ( 5’-TGTCCA GTTCCATGTTCCAG-3’ ) ; TSN: forward ( 5’-GGCAACATGTGTTCGCTATC-3’ ) and reverse ( 5’- GTGGCGTTATCCTTCTTTGC-3’ ) ; 412: forward ( 5’-CACCGGTTTGGTCGAAAG-3’ ) and reverse ( 5’-GGACATGCCTGGTATTTTGG-3’ ) ; blood: forward ( 5’-TGCCACAGTACCTGATTTCG-3’ ) and reverse ( 5’-GATTCGCCTTTTACGTTTGC-3’ ) ; diver: forward ( 5’-GGCACCACATAGACACATCG-3’ ) and reverse ( 5’-GTGGTTTGCATAGCCAGGAT-3’ ) ; gtwin: forward ( 5’-TTCGCACAAGCGATGATAAG-3’ ) and reverse ( 5’-GATTGTTGTACGGCGACCTT-3’ ) ; gypsy: forward ( 5’-GTTCATACCCTTGGTAGTAGC-3’ ) and reverse ( 5’-CAACTTACGCATATGTGAGT-3’ ) ; HetA: forward ( 5’-CGCGCGGAACCCATCTTCAGA-3’ ) and reverse ( 5’-CGCCGCAGTCGTTTGGTGAGT-3’ ) ; invader1: forward ( 5’-GTACCGTTTTTGAGCCCGTA-3’ ) and reverse ( 5’-AACTACGTTGCCCATTCTGG-3’ ) ; max: forward ( 5’-TCTAGCCAGTCGAGGCGTAT-3’ ) and reverse ( 5’-TGGAAGAGTGTCGCTTTGTG-3’ ) ; mdg1: forward ( 5’-AACAGAAACGCCAGCAACAGC-3’ ) and reverse ( 5’-CGTTCCCATGTCCGTTGTGAT-3’ ) ; rt1a: forward ( 5’-CCACACAGACTGAGGCAGAA-3’ ) and reverse ( 5’-ACGCATAACTTTCCGGTTTG-3’ ) ; rp49: forward ( 5’-CCGCTTCAAGGGACAGTATCTG-3’ ) and reverse ( 5’-ATCTCGCCGCAGTAAACGC-3’ ) . Adult testes from wildtype or actin5c-Gal4>UASp Flag-TSN were dissected in ice-cold PBS and homogenized in H ( 0 . 15 ) buffer . Testes lysates were collected by centrifugation at 10 , 000g for 30 min at 4°C . Immunoprecipitation was performed using mouse anti-Flag ( Sigma ) with Protein G magnetic beads ( Invitrogen ) . Immunoprecipitates were washed five times with H ( 0 . 15 ) buffer at 4°C . Immunoprecipitated RNAs were then isolated from the immunopurified complexes with Trizol and precipitated with ethanol . Reverse transcription and qRT-PCR were then done following the protocol described above . Primer pairs used are the same as described above except piwi #2: forward ( 5’- TGCCATGAGCAGTTATACGC-3’ ) and reverse ( 5’- TGTCCAGTTCCATGTTCCAG-3’ ) . | Piwi proteins bind to a large class of small noncoding RNAs called Piwi-interacting RNAs ( piRNAs ) . These proteins have emerged as major players in germline development , stem cell self-renewal , transposon silencing , and gene regulation . However , it is not known whether these functions of Piwi proteins represent separate molecular mechanisms . Furthermore , although multiple Piwi interactors have been identified , including Tudor-domain-containing proteins , none of them regulates Piwi expression or interacts with Piwi antagonistically , or only impact on a subset of Piwi functions . Here we show that Drosophila Piwi interacts with a special Tudor-domain-containing protein called Tudor-SN ( Tudor staphylococcal nuclease , TSN ) . TSN is drastically different from the known Piwi interactors because it represses Piwi mRNA and protein expression and interacts with Piwi antagonistically in spermatogenesis but synergistically in transposon silencing . However , this interaction is not required for piRNA biogenesis . Our study represents the first demonstration that different functions of Piwi are mediated by different molecular mechanisms . In addition , this is the first in vivo study that reveals the biological function of TSN protein in an organism . | [
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] | [] | 2016 | Tudor-SN Interacts with Piwi Antagonistically in Regulating Spermatogenesis but Synergistically in Silencing Transposons in Drosophila |
Cartilage and bone are formed into a remarkable range of shapes and sizes that underlie many anatomical adaptations to different lifestyles in vertebrates . Although the morphological blueprints for individual cartilage and bony structures must somehow be encoded in the genome , we currently know little about the detailed genomic mechanisms that direct precise growth patterns for particular bones . We have carried out large-scale enhancer surveys to identify the regulatory architecture controlling developmental expression of the mouse Bmp5 gene , which encodes a secreted signaling molecule required for normal morphology of specific skeletal features . Although Bmp5 is expressed in many skeletal precursors , different enhancers control expression in individual bones . Remarkably , we show here that different enhancers also exist for highly restricted spatial subdomains along the surface of individual skeletal structures , including ribs and nasal cartilages . Transgenic , null , and regulatory mutations confirm that these anatomy-specific sequences are sufficient to trigger local changes in skeletal morphology and are required for establishing normal growth rates on separate bone surfaces . Our findings suggest that individual bones are composite structures whose detailed growth patterns are built from many smaller lineage and gene expression domains . Individual enhancers in BMP genes provide a genomic mechanism for controlling precise growth domains in particular cartilages and bones , making it possible to separately regulate skeletal anatomy at highly specific locations in the body .
The vertebrate skeleton is constructed of cartilage and bone tissues that are formed into highly specific shapes , sizes , and repeating arrays during normal development . Individual bones can show striking morphological specializations in different species , suggesting that separate genetic mechanisms must exist for regulating the growth of skeletal tissue at highly specific anatomical sites in the body [1] , [2] . Despite the importance of skeletal structures for support , protection , eating , breathing , and movement , the detailed genetic mechanisms controlling the shape and growth of individual bones are still poorly understood . Over fifty years ago , Bateman proposed that characteristic skeletal shapes are determined by varying patterns of differential growth and erosion that occur in stereotyped positions along the surfaces of each bone [3] . Localized growth at ends of a bone results in long straight structures . Uniform deposition around a bone produces uniform circumferential growth . Preferential deposition and erosion on opposite surfaces of a bone generates lateral displacement or curvature . Localized patches of deposition and erosion may also produce many of the specific processes , ridges , foramina , and articular surfaces that are characteristic of each bone in the body . Although highly localized patterns of deposition and erosion have long been proposed or visualized in the skeleton [4]–[6] , little is known about how such stereotyped patterns may be encoded in the genome . Previous studies demonstrate that secreted signaling molecules in the bone morphogenetic protein ( BMP ) family play a key role in both formation and repair of skeletal structures [7] . These molecules are expressed both in early skeletal precursors , and in the surface perichondrium and periosteum layers that surround growing cartilage and bone [8]–[13] . Pure recombinant BMPs can induce cartilage and bone formation when implanted at ectopic sites in animals [14] , [15] . Conversely , mouse mutants missing members of the BMP family show defects in subsets of bone and cartilage elements . The classical mouse short ear locus encodes one of the mammalian BMP molecules ( BMP5 ) [16] . Mutations at this locus reduce outer ear growth by disrupting the formation and activity of the surface perichondrium surrounding outer ear cartilage [17] . The same locus also controls the presence or absence of processes on specific vertebrae and the fibula , the morphology of the xiphoid process at the end of the sternum , the number of ribs along the vertebral column , and the total volume of the thoracic cavity [18]–[21] . A large number of spontaneous and induced short ear mutations suggest that the Bmp5 locus is surrounded by large regulatory regions required for developmental expression patterns in bones and other tissues [22]–[24] . Here we carry out detailed enhancer surveys to test the regional specificity of regulatory sequences controlling Bmp5 expression in skeletal tissues . Our studies suggest that stereotyped growth patterns along the surface of both ribs and nasal cartilages are controlled by highly specific “anatomy” elements in the Bmp5 gene . These modular enhancers in BMP genes may provide a flexible basis for encoding the detailed growth and form of specific bones in the vertebrate skeleton .
Previous regulatory scans of the Bmp5 locus identified several large regions that could drive expression of a lacZ reporter gene in developing skeletal structures [23] , [24] . Expression in developing ribs was observed when two different bacterial artificial clones ( BACs ) covering non-overlapping regions of the gene ( Figure 1A , E , H ) were coinjected with a minimal heat shock-lacZ expression construct [24] . BAC199 includes most of the exons and introns of the Bmp5 gene . In contrast , BAC178 includes sequences from a large region three prime ( 3′ ) of all Bmp5 coding exons . Chromosome rearrangements in this 3′ region have generated two regulatory alleles of the Bmp5 locus ( Bmp5se38DSD and Bmp5se4CHLd , Figure 1A ) . These alleles confirm that extensive 3′ sequences are required for normal expression and function of the endogenous Bmp5 gene [23] . To compare the lacZ expression in ribs driven by different BACs spanning the Bmp5 locus , we examined a series of coronal sections taken along the dorsoventral axis of the ribs of transgenic embryos beginning near the vertebral column and ending near the sternum . In dorsal sections from a BAC199-lacZ transgenic embryo , β-galactosidase activity was surprisingly restricted to a lateral domain within the rib perichondrium ( Figure 1F ) . This pattern changed as sections progressed ventrally . In later sections , β-galactosidase activity was found in both the lateral and medial rib perichondrium ( Figure 1G ) . Interestingly , the pattern of lacZ expression controlled by the distal BAC clone , BAC178 , was complementary to that seen with proximal BAC199 . In dorsal sections from a BAC178-lacZ transgenic embryo , β-galactosidase activity was found in anterior , medial and posterior domains of the rib perichondrium ( Figure 1I ) . More ventral rib sections showed loss of medial expression but retained β-galactosidase activity predominantly in anterior and posterior rib perichondrium ( Figure 1J ) . Thus , BAC178-lacZ rib expression complements BAC199-lacZ rib expression as it changes along the dorsoventral axis ( Figure 1F , I and G , J ) . Taken together , these results suggest that gene expression in different domains of the rib perichondrium is controlled by distinct regulatory elements in the Bmp5 locus . Notably , the complementary rib regulatory regions are separated by over 100 kilobases ( kb ) ( Figure 1A ) . Analysis of endogenous Bmp5 expression in wild-type and Bmp5se38DSD regulatory mutants confirms the existence of distinct control regions for different domains of the rib perichondrium . The Bmp5se38DSD regulatory mutation derives from a chromosomal rearrangement whose breakpoint lies near the Bmp5 coding exons [22] . Therefore , this rearrangement is predicted to remove all distal rib control sequences ( Figure 1A ) . In situ hybridization analysis of Bmp5 transcripts in dorsal rib sections shows reduction of anterior , medial and posterior rib domain expression within Bmp5se38DSD ribs as compared to wild-type ( Figure 1C , D ) . In contrast , strong Bmp5 expression is still seen in the lateral rib perichondrium ( asterisk in Figure 1D ) , as expected given the location of lateral control elements upstream of the Bmp5se38DSD breakpoint . Therefore , general Bmp5 expression in rib perichondrium appears to be a composite of smaller , independently regulated expression domains . To further characterize and localize Bmp5 regulatory sequences , we used sequence alignment programs PipMaker and LAGAN/VISTA to compare human and mouse Bmp5 loci [25]–[27] . This approach revealed numerous evolutionarily conserved non-coding regions ( ECRs ) scattered across the Bmp5 locus ( Figure 2 , Figure S1 ) . We then cloned multiple small genomic fragments containing single or multiple ECRs upstream of a minimal heat shock-lacZ reporter cassette , injected them into fertilized mouse eggs , and scored expression patterns in transgenic embryos . The survey of putative enhancer regions extended across the entire 400 kb interval detailed in Figure 1A ( see Figure 2A and Figure S1 ) . A 6 . 2 kb clone from the BAC199 region including 4 ECRs surrounding Bmp5 exon 4 ( Ex4r ) drove reproducible expression in nasal cartilages , distal limbs , and ribs ( Figure 2B ) . As with the BAC transgenics , a series of coronal sections was taken along the ribs of Ex4r-lacZ transgenics . Dorsal rib sections again revealed expression in a restricted domain along the lateral surface of the developing rib ( Figure 2C ) . To further characterize this peripheral surface domain we hybridized adjacent sections with molecular markers for perichondrium ( type I collagen , Col1a1 ) , chondrocytes ( type II collagen , Col2a1 , and type X collagen , Col10a1 ) , and developing muscle ( MyoD1 ) [28]–[30] . The lacZ-positive region corresponds to a particular sector of the surface perichondrium surrounding the ribs , which otherwise extends in a continuous circle around the developing rib cartilage ( Figure 2C–F ) . Unlike the BAC199-lacZ transgene , the Ex4r sequence did not drive discrete localized expression in a medial perichondrium domain in more ventral rib sections ( data not shown ) . These results demonstrate that anatomical control sequences for lateral perichondrium expression map within the exon 4 region , and that additional sequences are required for the medial rib expression seen with BAC199 . To further narrow the region required for lateral perichondrium expression , we tested a series of smaller genomic fragments and deletions of conserved ECRs from within the Ex4r subclone ( Figure S2 ) . This analysis demonstrated that the core sequences necessary for lateral perichondrium expression reside in a 1069 bp peak of conservation at the 3′ end of the Ex4r region , and that other sequences in the Ex4r construct are required for expression in limbs and nasal cartilages . Bmp5 is expressed in the perichondrium surrounding many other skeletal structures , including the nasal septum and the shelf-like turbinates that project into the nasal cavity [11] . In addition , new micro computerized tomography ( MicroCT ) analysis of wild-type and Bmp5 mutant skulls shows that the Bmp5 gene is also required for normal development of turbinates in the anterior nasal region ( Figure 3A , B ) , and for normal branching patterns in more posterior nasal regions ( Figure 3C , D ) . To determine whether Bmp5 expression in nasal cartilages is also controlled by separable regulatory sequences , we examined the nasal region in both BAC199-lacZ and Ex4r-lacZ transgenic embryos . The larger BAC199 clone showed widespread β-galactosidase activity throughout the nasal cartilages , including multiple turbinates and the nasal septum ( data not shown ) . In contrast , Ex4r-lacZ transgenics showed activity restricted to a small arc-like domain located on the inner surface of nasal cartilage between turbinate shelves in the anterior nasal cavity and along the neck of the developing turbinates ( Figure 3E ) . Like the lateral rib expression , turbinate expression was seen predominantly in subregions of the surface perichondrium ( Figure S3A , C ) . No expression was seen in the nasal septum , in posterior cartilages or at the tips of the shelf-like projections of the turbinates themselves . Testing fragments of the Ex4r clone demonstrated that sequences directing restricted nasal cartilage expression and restricted lateral rib perichondrium expression are distinct ( Figure S2 ) . Examination of other regions of the Bmp5 locus known to contain skeletal enhancers identified an additional non-overlapping sequence that also gives expression in nasal cartilages . A 17 kb clone ( Phage 7 in Figure 1 ) previously reported to give thyroid cartilage expression [23] also showed strikingly specific nasal cavity expression . β-galactosidase activity was seen at the dorsal tips of the expanding turbinate shelves , colocalizing with Col2a1 in proliferating chondrocytes , but was absent from the ventral tips , the turbinate necks , and the cartilages between turbinate shelves ( Figure 3F , Figure S3B , F ) , a pattern partially complementary to that driven by the Ex4r construct . The sequences included in Phage 7 are located approximately 100 kb 3′ of the chromosome breakpoint in the Bmp5se38DSD regulatory mutation ( Figure 1A ) . Endogenous Bmp5 expression is dramatically reduced in the dorsal tips of turbinate shelves in Bmp5se38DSD mice compared to wild-type ( Figure 3G , H ) , as well as in the cribriform plate , the structural roof of the nasal cavity . These data confirm that 3′ regulatory sequences are required for Bmp5 expression in the tips of turbinate shelves , but not in the surrounding neck and inter-turbinate perichondrium . Bmp5se38DSD mutant mice also show defects in the cribriform plate and branching alterations in nasal turbinates ( data not shown ) . Thus , in both ribs and nasal cartilage , an apparently continuous layer of perichondrium consists of distinct expression domains controlled by separate regulatory elements in the Bmp5 gene . Ribs are derived from somitic mesoderm [31] . Previous chick-quail lineage tracing experiments have shown that rib cells arise from different portions of developing somites: the head , neck , and the inner surface of ribs are derived from the posterior compartment of somites ( white regions in Figure 4A ) , while the lateral surface of the mid shaft of the rib arises from the anterior compartment of somites ( blue regions of Figure 4A ) [32] . We noticed that lacZ expression driven by the Ex4r construct begins some distance from the vertebral column , and is strongest along the midshaft of ribs ( Figure 4B ) , a pattern reminiscent of the anatomical domain thought to arise from anterior somites . To analyze rib enhancer activity at additional developmental stages , we generated stable transgenic lines for the Ex4r-lacZ construct and collected embryos beginning at embryonic day 10 . 5 . At this early stage of development , the Ex4r-lacZ construct is expressed in the anterior halves of developing somites ( Figure 4C ) . Examination of lacZ localization in rib sections at later stages showed that Ex4r-driven expression was largely missing from the head and neck region of ribs , was present in the lateral perichondrium along the rib shaft , and became symmetric around the rib in sternal portions ( Figure 4D–F ) . Both the somitic expression and the changes in patterns along the length of ribs suggest that the lateral rib expression reflects the dual origin of ribs from separate somite compartments . Multiple BMP family members are expressed in overlapping patterns in the developing ribs [12] . To test the biological effects of localized increase or decrease in BMP signaling in subdomains of the rib perichondrium , we used the Ex4r sequence to drive the expression of either a constitutively active ( caBmprIb ) or dominant negative ( dnBmprIb ) version of BMP receptor IB [33] . We chose BmprIb because it is widely expressed throughout the developing skeleton , including rib perichondrium , and is known to be used by multiple BMP ligands [33]–[38] . Each receptor construct was coinjected with the original Ex4r-lacZ clone to generate transgenic embryos . Both Ex4r-caBmprIb and Ex4r-dnBmprIb transgenic embryos showed gross changes in rib development at E14 . 5 when examined by whole-mount skeleton preparations ( Figure 5A–C ) . Increased BMP signaling in the lateral rib domain caused an overgrowth of alcian blue-positive cartilage , beginning midway along the rib shaft ( Figure 5B arrow ) . Sections through Ex4r-caBmprIb embryos that were assayed for β-galactosidase activity showed that rib expansion was accompanied by an excess of lacZ-positive cells in the lateral rib ( Figure 5E ) . This lateral expression marked the outer edge of the rib deformation ( Figure S4A , C ) and overlays a cartilaginous mass of cells made up predominantly of hypertrophic chondrocytes expressing Col2a1 and Col10a1 ( Figure S4E , G ) [28] , [29] . In contrast , decreased BMP signaling caused a marked deflection in rib trajectory ( Figure 5C bracket ) . Ribs in Ex4r-dnBmprIb transgenic embryos emerged normally from the vertebral column but were deflected inwards along the central region of the rib shaft , producing a more constricted upper thorax . This deformation in trajectory was not accompanied by changes in rib cross section ( Figure 5F , Figure S4 ) . Neither construct affected the head or neck of the ribs ( Figure 5B , C ) , as expected from the restricted expression domain of Ex4r control sequences along the rib shaft ( Figure 4B ) . The highly localized domains of Bmp5 expression in rib perichondrium are reminiscent of previous models suggesting that rib growth occurs by differential activity on the lateral and inner surfaces of the rib [3] . To visualize in vivo patterns of bone deposition in growing ribs , we injected mice twice , at 6 and 7 weeks , with calcein , a fluorescent dye that specifically incorporates into newly formed bone ( Figure 6 ) . Dorsal rib cross-sections showed two major growth domains labeled with dye; one visible along the lateral periosteal surface ( D1 ) , and a second predominantly along the anterior , medial , and posterior endosteal surfaces of the rib ( D2 ) . Each bone deposition front is represented by two calcein labelings , reflecting the two separate injections ( Figure 6A ) . Injections with different dye colors demonstrate that the bone fronts labeled by the initial injection ( arrows in Figure 6A ) become embedded in bone after a week of growth , and that the new bone fronts labeled by the second injection are found near surfaces ( arrowheads in Figure 6A , data not shown ) . These deposition patterns show striking asymmetry , with bone deposition occurring preferentially in the lateral domain of the outer surface periosteum and in the anterior , medial , and posterior domains of the inner endosteum . To compare patterns of bone deposition and bone resorption , dorsal rib cross-sections were also examined for tartrate-resistant acid phosphatase activity , an osteoclast marker [39] . Bone resorption was also highly asymmetric , and complementary to the areas of bone deposition ( Figure 6B ) . In the outer periosteum , osteoclast activity was most intense on the anterior , medial , and posterior surfaces of the rib; and was nearly absent along the lateral surface where major bone deposition was occurring . Likewise , along the inner endosteum , osteoclast activity was most intense on the lateral wall , and nearly absent from the anterior , medial and posterior surfaces . During growth , these highly asymmetric patterns of bone deposition and resorption would result in the net lateral displacement of ribs and the expansion of the intrathoracic cavity , while preserving marrow space . Bmp5 mutant mice are known to have a smaller thoracic volume than wild-type animals [21] . To further characterize detailed bone deposition patterns in Bmp5 mutants , we performed dual calcein injections on Bmp5 null and Bmp5 regulatory mutants , and measured the amount of bone deposition in the different rib domains described above ( Figure 6C ) . Mice with null mutations in the Bmp5 gene show a significant reduction in bone deposition in both major ossification domains , D1 and D2 ( Figure 6C ) . In contrast , regulatory mutant mice missing anterior , medial and posterior but not lateral rib control sequences ( Bmp5se4CHLd , Figure 1A ) show significantly reduced bone deposition in D2 , but not in D1 domains . The Bmp5 gene is thus required for normal rates of bone growth on both the outer and inner surface of the rib , and these two growth domains are controlled independently by different regulatory regions of the Bmp5 locus .
It has long been recognized that cartilage and bone can be molded into a remarkable range of different shapes and sizes . Previous genetic studies show that the morphology of different skeletal elements is controlled by multiple independent genetic factors [2] , [40] , [41] . Based on studies of jaw and limb morphology in mice , Bailey previously suggested that different subregions of a single bone must be controlled by a large number of independent “morphogenes” , each active in small patches along the surface of a single bone [2] . Despite recent progress identifying genes that regulate formation of all cartilage or all bones , or genes that control skeletal formation in different subdomains along the body axis , little is known about the fine-grained mechanisms that control detailed growth patterns of individual skeletal elements [42] . Here we show that highly defined growth domains in particular bones are controlled by remarkably specific enhancers in the Bmp5 gene ( Figure 7 ) . We propose that anatomy-specific enhancers in BMP genes provide a genomic mechanism for independent developmental control of local growth along discrete domains of individual cartilages and bones in the vertebrate skeleton . When BMP genes were first discovered and assayed for expression in vertebrates , individual members of the family were initially proposed to promote general steps in the differentiation of all skeletal tissue [8] . Although Bmp5 is expressed in a continuous fashion in the perichondrial layer surrounding many developing skeletal structures [11] , [12] , our enhancer surveys do not show evidence for general enhancers in the Bmp5 gene that drive expression around the surface of all cartilage or all bones . Instead , distinct Bmp5 enhancers regulate expression in individual skeletal structures . Furthermore , separate enhancers also exist for discrete domains around the surface of individual bones , including lateral , anterior , medial , and posterior domains of the rib perichondrium , and tip versus neck and inter-turbinate domains in the nasal cartilages ( Figures 1 , 3 ) . This remarkably fine control of gene expression is clearly sufficient to alter skeletal morphology at specific locations ( Figure 5 ) . Null and regulatory mutations also show that the Bmp5 gene is necessary for normal bone deposition rates along particular surfaces of growing ribs ( Figure 6 ) . These results confirm that detailed growth patterns in an individual bone can be encoded by highly specific anatomy enhancers in genes for bone morphogenetic proteins . Previous studies of HOX genes have shown that expression and function at particular anatomical locations in the body are related to the physical location of genes along the chromosome [43]–[45] . The overall correlation between anatomy and gene position may arise from progressive changes in chromatin structure during body axis development; or from proximity to enhancers that map outside the HOX complex , which have decreasing effects on genes that map at increasing physical distances from the enhancer [45] , [46] . In contrast , the Bmp5 skeletal enhancers we have identified to date show no obvious relationship between anatomical position in the body and physical location within the Bmp5 locus . The regulatory elements for discrete surface domains around a single bone clearly map to different regions of the Bmp5 gene ( Figure 1 ) . In addition , rib and nose enhancers are interspersed with each other ( Figure 7 ) and with other separate enhancer regions previously identified controlling expression in the sternum , thyroid cartilage , lung , and genital tubercle [23] , [24] . The dispersed enhancer pattern seen in Bmp5 may reflect the different roles of BMP and HOX genes in skeletal patterning . Nested sets of HOX gene expression are evolutionarily ancient programs used to pattern basic body axes in both vertebrates and invertebrates [44] , [45] , [47] . In contrast , both cartilage and bone are more evolutionarily recent , vertebrate-specific tissues that vary widely in form from species to species [1] . For example , respiratory nasal turbinates are thought to have arisen separately in bird and mammals to help conserve water during breathing [48]–[50] . They vary widely in branching structure within mammals , and are reduced or absent in fish , amphibians , and reptiles [48] , [51] . Since a variety of studies suggest that BMPs are the endogenous signals used to induce cartilage and bone in vertebrates [7] , formation of nasal turbinates and other species-specific skeletal structures presumably occurs through cis- or trans-acting alterations that produce local changes in BMP expression at particular sites in the body . Therefore , the complex architecture of skeletal enhancers in the Bmp5 gene may reflect a historical process of piecemeal gain and loss of regulatory elements controlling local domains of BMP expression . How are the remarkably specific domains of Bmp5 expression generated along the surface of ribs or nasal cartilages ? A variety of data suggests that mechanical forces can give rise to highly localized patterns of bone deposition and erosion [52] , [53] . For example , rib cages and skulls both enclose rapidly growing tissues . Outward pressure from soft tissue growth may lead to bone deposition on skeletal surfaces under mechanical tension ( the convex outermost surface of ribs or cranial bones ) , and bone erosion on surfaces under compression ( the innermost surface of ribs or cranial bones ) . Although mechanical tension and compression are clearly coupled to bone remodeling , we do not think that the restricted patterns of expression we observe for Bmp5 enhancers are simply responding to the distribution of mechanical forces on growing skeletal structures . First , there is no obvious relationship between mechanical forces and the contrasting tip and neck expression patterns seen in nasal cartilages . Second , the Bmp5 enhancer that drives expression along the outer surface of ribs is not similarly expressed along the outer surface of either the sternum or the skull , although these bones should be subject to similar mechanical forces from the rapid expansion of underlying tissue . Third , the Ex4r-lacZ construct that drives highly localized patterns of expression in growing ribs also drives compartmentalized expression in developing somites ( Figure 4 ) . These results suggest that the remarkably specific Bmp5 domains in ribs are related to the dual origin of ribs from different somite compartments , rather than to simple mechanical forces acting during later growth and expansion of the thoracic cavity . Previous lineage tracing experiments have shown that the lateral edges of rib shafts are derived from cells in the anterior half of somites [32] ( Figure 4A ) . Response elements for anterior somite transcription factors could provide a simple mechanism for controlling mid-shaft Bmp5 expression in the lateral perichondrial domain . Conversely , response elements for posterior somite expression could provide another simple mechanism for regulating Bmp5 expression in rib head and necks , and in the anterior , medial , and posterior perichondrial domains along the rib shaft , similar to the patterns seen with BAC178 ( Figure 1 ) . The current sizes of Bmp5 rib enhancers are still too large to identify particular binding sites for upstream factors . However , future narrowing of the minimal sequences capable of driving rib domain expression may make it possible to link specific somite transcription factors with the different domains of rib expression identified in this study . The dual origin of axial structures from anterior and posterior halves of adjacent somites produces vertebrae and ribs that form one half segment out of register with the original metameric pattern seen in somites . The functional significance of this shift has been debated for over a hundred years [31] , [54]–[56] . Resegmentation causes axial muscles , and many of their origin and insertion points on adjacent vertebrae and ribs , to all be derived from a single somite . Our studies suggest resegmentation also plays a key role in establishing detailed growth patterns in developing ribs ( Figure 7 ) . Although ribs are usually thought of as simple tubular structures , they can be extensively modified in different organisms to produce the diverse cross-sectional shapes , as well as the varied curvatures seen in wide- and narrow-bodied animals [1] , [57] . It has long been recognized that differential deposition on the lateral surface of ribs must underlie the expansion and ultimate shape of ribs and thoracic cavities [3] . We suggest that resegmentation helps establish the lineage domains that make it possible to independently control cartilage and bone growth in specific rib surface domains . The multiple enhancers present in BMP genes provide an elegant mechanism for linking such lineage domains to actual sites of bone growth , leading to highly detailed patterns of deposition that can be independently controlled along the length and around the circumference of a single bone . While lineage domains may be used to produce separate lateral versus medial domains of gene expression in developing ribs , we think additional mechanisms must be operating to produce other highly localized patches of Bmp5 expression . For example , our comparison of BAC199 and BAC178 expression suggests at least four different expression domains may exist at certain positions along the ribs ( lateral , medial , anterior , posterior; Figure 1 ) . Control sequences for the lateral domain have been mapped to a single 1069 bp peak of sequence conservation within the Bmp5 Ex4r region , but additional sequences responsible for expression in the other domains remain to be identified in the larger regions covered by BAC199 and BAC178 . Highly localized expression patterns are also seen in multiple spatially restricted patches along the necks and tips of nasal cartilages ( Figure 3 ) . The elements controlling these patches are distinct from those controlling rib expression . In addition , nasal cartilage development is quite different from rib morphogenesis ( Figure 7 ) . For example , the facial bones and cartilages are derived from cranial neural crest that migrates from positions in the developing brain [58]–[60] . HOX genes are not expressed in this cranial region , and transplantation studies have demonstrated a remarkable degree of plasticity in the cranial neural crest populations [61] , [62] . Patterning signals are thought to emerge from the local endoderm and ectoderm to control the shape and size of individual facial skeletal structures [61] , [63] . Therefore , unlike ribs , we currently know of no lineage compartments that can account for the various separate tip and shelf domains seen during the branching morphogenesis of nasal cartilages . In Drosophila , branching morphogenesis takes place during tracheal airway development , and is controlled by numerous local patches of breathless/FGF expression . The specific enhancers controlling breathless expression near tips of growing trachea branches have not been isolated , but may respond to different combinations of transcription factors that are themselves expressed in local or intersecting patterns [64] . Multiple , locally acting enhancers in BMP and FGF genes may thus represent a common molecular strategy for molding skeletal tissue or trachea airways into particular shapes in different animals [7] , [64] . Further studies of anatomy-specific elements in BMP genes should lead to a molecular understanding of the type of “morphogenes” that have long been postulated to control local growth decisions in different subdomains of particular bones [2] . In addition , gain and loss of regulatory elements in BMP genes may provide a simple genomic mechanism for evolutionary modification of skeletal structures . While null mutations in BMP genes often have pleiotropic defects , adaptive changes in specific regulatory sequences could localize effects to particular skeletal structures , making it possible to alter vertebrate anatomy while preserving viability and fitness [7] . This possibility has taken on renewed interest in light of studies linking changes in BMP expression to different beak shapes in naturally occurring bird species [65]–[67] , and to different jaw morphologies in African cichlids [68] . Regulatory lesions are difficult to identify , and it has not yet been possible to track particular bird or fish anatomical changes to specific DNA sequence alterations in BMP genes . Nonetheless we think the kind of modular regulatory architecture we have found for the Bmp5 gene probably exists around many other members of the BMP family [69] , [70] . Isolation and characterization of additional anatomy elements from BMP genes will make it possible to test whether anatomical changes in naturally occurring species result from structural and functional modifications in the type of modular enhancer regions identified in this study .
Regulatory ( Bmp5se38DSD , Bmp5se4CHLd ) and null ( Bmp5null ) alleles were described previously [11] , [22] , [23] . All strains used for bone growth assays are on the C57Bl/6J background . The generation of BAC199-lacZ , BAC178-lacZ and Phage7-lacZ transgenics was reported in [23] , [24] . All new DNA constructs were prepared for microinjection as previously described [24] . The Ex4r-caBmprIb and Ex4r-dnBmprIb plasmids were coinjected with the Ex4r-lacZ clone at a 4∶1 molar ratio . Pronuclear injection into FVB embryos was carried out by the Stanford Transgenic Facility and Xenogen Biosciences in accordance with protocols approved by the Stanford University Institutional Animal Care and Use Committee . BAC426K2 ( Genbank accession #AC079245 ) and BAC343K17 ( Genbank Accession #AC079244 ) were isolated from the RPCI-23 Female ( C57Bl/6J ) Mouse BAC library ( Invitrogen ) using a 1334 bp EcoRI probe and/or a 591 bp HaeIII probe located 123 , 536 bp and 225 , 112 bp , respectively , from the Bmp5 transcriptional start site . Sequences were compiled following designation of BAC426K2 and BAC343K17 as clones of high biomedical interest by the National Human Genome Research Institute and sequencing by the Advanced Center for Genome Technology at the University of Oklahoma , Norman . 5′ mouse sequences were added from BAC429A10 ( Genbank accession #AC144940 ) as they became available . Human Bmp5 genomic sequence was compiled from the following clones: Genbank accession numbers AL589796 , AL137178 , AL133386 , AL590290 , AL590406 and AL592426 . The human and mouse Bmp5 sequences were masked using RepeatMasker ( A . F . A . Smit , R . Hubley and P . Green , unpubl . ; http://www . repeatmasker . org/ ) . ECRs were identified using global sequence alignment programs as previously described [69] . The Ex4r-lacZ plasmid was generated by amplifying a 6221 bp fragment corresponding to mouse sequences 93 , 656–99 , 876 bp in Figure 2 using primers 622: 5′GGATTGCGGCCGCTATGGACAGCTTTGAAGAGCTTTGGTA3′ and 624: 5′GGATTGCGGCCGCTATTCTAGCCTCTCCTGTAGGATTATG3′ . Following NotI digestion , the fragment was cloned into the Not5'hsplacZ vector [23] . To generate Ex4r-caBmprIb and Ex4r-dnBmprIb constructs , the constitutively active ( ca ) or dominant negative ( dn ) form of BmprIb was amplified using primers lpf21: 5′CATGCCATGGCCATGCTCTTACGAAGCTCTGGAAAAT3′ and lpf22: 5′GCTCTAGAGCTTAGATCCCCCCTGCCCGGTTATTATTATCAGAGTTTAATGTCCTGGGACTCTG3′ . The PCR products were digested with NcoI/XbaI and cloned into the Ex4r-lacZ plasmid that had been digested with NcoI and XbaI ( partial ) , replacing the lacZ cassette . To generate plasmid Ex4rCD-lacZ , a 3 kb fragment was amplified from the Bmp5 BAC426K2 using primers 624 ( above ) and 627: 5′GGATTGCGGCCGCTATTCTAGGCTGTTGGAAAGCAAGTCTA3′ . The PCR product was digested with NotI and cloned into Not5'hsplacZ . Construct Ex4rΔC-lacZ was generated using primers 750: 5′ATGTGGCCAAACAGGCCTATTAATGGTCAACCAGATGAATACAGCA3′ and 751: 5′ATGTGGCCTGTTTGGCCTATTATAGAACACATAGAGGCATACCAGG3′ to amplify directly from the Ex4r-lacZ plasmid using the Expand Long Template PCR system ( Roche #1681834 ) . The 12 . 9 kb product was digested with SfiI and the free ends were ligated together . The 5488 bp insert with a 733 bp deletion of ECR C from Ex4r was removed by NotI digestion and recloned into an unamplified Not5'hsplacZ vector . The Ex4rΔD-lacZ plasmid was generated by amplifying 4677 kb and 454 bp products from BAC426K2 using primer 622 ( above ) with primer 754: 5′ATGTGGCCTGTTTGGCCTATTCCTTTTGAGAATCTCGGCTTCTAGA3′ and primer 752: 5′ATGTGGCCAAACAGGCCTATTGGCAGGTTAGAGAAAGTAATGATAG3′ with primer 624 ( above ) , respectively . The PCR products were digested with SfiI and ligated together . The resulting 5 . 1 kb product containing a 1069 bp deletion of ECR D from Ex4r was digested with NotI and ligated into the Not5'hsplacZ vector . Plasmid ECRD-lacZ was generated by amplifying a 1127 bp fragment from BAC426K2 using primers 690: 5′ATGTGGCCTGTTTGGCCTATTCTTTCTCTAACCTGCCTCTACCCTG3′ and 736: 5′ATGTGGCCAAACAGGCCTATTGAAGCCGAGATTCTCAAAAGGTGGA3′ . The PCR product was digested with SfiI and ligated into pSfi-hsplacZ [69] . Embryos collected by Xenogen Biosciences were fixed for 1 hour in 4% paraformaldehyde in 1× PBS at 4°C , placed in cold 1× PBS and shipped overnight on ice . Whole-mount staining for β-galactosidase activity was performed as described [69] with the following modifications: Embryo fixation times varied with age ( E10 . 5 for 30 minutes , E13 . 5 for 75 minutes , E14 . 5 for 90 minutes ) . E13 . 5-E14 . 5 embryos were hemisected after 1 hour . Rib and nasal cartilage cryosections from lacZ whole-mount embryos were collected and counterstained as described [69] . Prior to embedding , samples were equilibrated in embedding solution ( 15% sucrose , 7 . 5% gelatin ( 300 Bloom , Sigma #G2500 ) in 1× PBS ) for 1 hour at 42°C . Ex4r-caBmprIb and Ex4r-dnBmprIb transgenic embryos were frozen in OCT compound ( Tissue Tek ) , cryosectioned at 25 microns and counterstained with Nuclear Fast Red ( Vector labs , #H-3403 ) . β-galactosidase activity on cryosections was assayed by fixing samples in 4% paraformaldehyde in 1× PBS for 5–8 minutes at room temperature . Slides were rinsed 3 X 5 minutes with 1× PBS , washed in lacZ wash buffer ( 0 . 1 M sodium phosphate buffer ( pH 7 . 3 ) , 2 mM MgCl , 0 . 01% deoxycholate , 0 . 02% Nonidet P-40 ) for 10 minutes and incubated in lacZ stain ( wash buffer supplemented with 4 mM K3Fe ( CN ) 6 , 4 mM K4Fe ( CN ) 6⋅3 H2O , 0 . 1M Tris ( pH 7 . 4 ) and 1 mg/mL X-gal ( Sigma #B4252 ) ) at 37°C for at least 24 hours . Stained sections were rinsed with 1× PBS , fixed for an additional 10 minutes in 4% paraformaldehyde in 1× PBS , and counterstained with Nuclear Fast Red . The Bmp5 , Col10a1 , and Col2a1 probes used were described [23] , [29] , [71] . The MyoD1 probe was generated from a clone ordered from Open Biosystems ( clone id 372340 ) . The Col1a1 probe was generated using the pMColI-Bam plasmid ( a gift of Dr . Ernst Reichenberger ) . Timed matings were performed to collect wild-type ( C57Bl/6J ) , Ex4r-lacZ , Phage7-lacZ , and Bmp5se38DSD mutant heads and/or torsos at E13 . 5-E15 . 5 . Ex4r-caBmprIb and Ex4r-dnBmprIb embryos generated by Xenogen Biosciences were collected at E15 . 5 and fixed for 1 hour in 4% paraformaldehyde in 1× PBS , bisected , and one half embryo embedded in OCT and one half analyzed for β-galactosidase activity to identify transgenic embryos . 12 micron sections were collected from samples frozen in OCT compound and analyzed for gene expression as previously described [72]; except that the color reagent BM purple ( Roche #1442074 ) was used in place of NBT/BCIP . E14 . 5 skeletons were prepared as described [73] , with the following modifications: Embryos were placed directly into staining solution after ethanol dehydration . Following potassium hydroxide treatment , embryos were cleared in 50% glycerol overnight , and then stored in 100% glycerol . All steps were done at room temperature . Two successive intraperitoneal injections of calcein ( Sigma # C0875 , 2 . 5 mg/ml in 1× PBS ) were performed at postnatal day 43 ( p43 ) and p51 on C57Bl/6J males ( 10 mg injected/kg body weight ) . Whole rib cages were collected at p53 and dehydrated in ethanol for at least 1 week at 4°C , then embedded in methylmethacrylate and ground sectioned to obtain 50 micron coronal sections by HMAC ( Birmingham , AL ) . To quantify levels of bone deposition in wild-type and mutant animals , calcein labeled rib cages from six males of each category ( C57Bl/6J , Bmp5se4CHLd and Bmp5null ) were equilibrated overnight in 15% sucrose in 1× PBS and at least 24 hours in 30% sucrose in 1× PBS , all at 4°C . Rib cages were bisected , and the right half was embedded in OCT . Six 50 micron coronal cryosections were taken approximately 1 mm apart , beginning at the growth plate and moving dorsally . Each section was digitally photographed , and pixel areas between labeled bone deposition fronts were measured with Photoshop . All measurements were taken on the fifth rib . Data are expressed as mean areas±s . e . m . relative to wild-type mice . Differences between groups were evaluated using Student's t-test . C57Bl/6J male rib cages were collected at p53 into cold 1× PBS , fixed in 4% paraformaldehyde in 1× PBS for 3 days at 4°C , and washed 3 times for 30 minutes in cold 1× PBS . The right halves were embedded in paraffin , sectioned , and stained by HMAC [74] . Scans from 4 wild-type and 5 Bmp5null mutant skulls , aged 4 weeks postnatally , were generated using a Scanco MicroCT-40 operated at a tube potential of 45 kV and tube current of 177 microA using a 0 . 30 second integration with 2× averaging . All samples had undergone skeletal preparation prior to scanning . | Every bone in the skeleton has a specific shape and size . These characteristic features must be under separate genetic control , because individual bones can undergo striking morphological changes in different species . Researchers have long postulated that the morphology of individual bones arises from the local activity of many separate growth domains around each bone's surface . Differential growth within such domains could modify size , curvature , and formation of specific processes . Here , we show that local growth domains around individual bones are controlled by independent regulatory sequences in bone morphogenetic protein ( BMP ) genes . We identify multiple regulatory sequences in the Bmp5 gene that control expression in particular bones , rather than all bones . We show that some of these elements are remarkably specific for individual subdomains around the surface of individual bones . Finally , we show that local BMP signaling is necessary and sufficient to trigger highly localized growth patterns in ribs and nasal cartilages . These results suggest that the detailed pattern of growth of individual skeletal structures is encoded in part by multiple regulatory sequences in BMP genes . Gain and loss of anatomy-specific sequences in BMP genes may provide a flexible genomic mechanism for modifying local skeletal anatomy during vertebrate evolution . | [
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] | 2008 | Shaping Skeletal Growth by Modular Regulatory Elements in the Bmp5 Gene |
L . tropica can cause both cutaneous and visceral leishmaniasis in humans . Although the L . tropica-induced cutaneous disease has been long known , its potential to visceralize in humans was recognized only recently . As nothing is known about the genetics of host responses to this infection and their clinical impact , we developed an informative animal model . We described previously that the recombinant congenic strain CcS-16 carrying 12 . 5% genes from the resistant parental strain STS/A and 87 . 5% genes from the susceptible strain BALB/c is more susceptible to L . tropica than BALB/c . We used these strains to map and functionally characterize the gene-loci regulating the immune responses and pathology . We analyzed genetics of response to L . tropica in infected F2 hybrids between BALB/c×CcS-16 . CcS-16 strain carries STS-derived segments on nine chromosomes . We genotyped these segments in the F2 hybrid mice and tested their linkage with pathological changes and systemic immune responses . We mapped 8 Ltr ( Leishmania tropica response ) loci . Four loci ( Ltr2 , Ltr3 , Ltr6 and Ltr8 ) exhibit independent responses to L . tropica , while Ltr1 , Ltr4 , Ltr5 and Ltr7 were detected only in gene-gene interactions with other Ltr loci . Ltr3 exhibits the recently discovered phenomenon of transgenerational parental effect on parasite numbers in spleen . The most precise mapping ( 4 . 07 Mb ) was achieved for Ltr1 ( chr . 2 ) , which controls parasite numbers in lymph nodes . Five Ltr loci co-localize with loci controlling susceptibility to L . major , three are likely L . tropica specific . Individual Ltr loci affect different subsets of responses , exhibit organ specific effects and a separate control of parasite load and organ pathology . We present the first identification of genetic loci controlling susceptibility to L . tropica . The different combinations of alleles controlling various symptoms of the disease likely co-determine different manifestations of disease induced by the same pathogen in individual mice .
Leishmaniasis is endemic in 98 countries on 5 continents , causing 20 , 000 to 40 , 000 deaths per year [1] . In the past decade the number of endemic regions have expanded , prevalence has increased and the number of unrecorded cases must have been substantial , because notification has been compulsory in only 32 of the 98 countries where 350 million people are at risk [1] , [2] . Infection represents an important global health problem , as no safe and effective vaccine currently exists against any form of human leishmaniasis , and the treatment is hampered by serious side effects [3] . The disease is caused by obligate intracellular vector-borne parasites of the genus Leishmania . In the vertebrate host organism , Leishmania parasites infect so-called professional phagocytes ( neutrophils , monocytes and macrophages ) [4] , as well as dendritic cells [5] , immature myeloid precursor cells , sialoadhesin-positive stromal macrophages of the bone marrow , hepatocytes and fibroblasts [6] . Leishmaniasis includes asymptomatic infection and three main clinical syndromes . In the dermis , parasites cause the cutaneous form of the disease , which can be localized or diffuse; in the mucosa , they cause mucocutaneous leishmaniasis , and the metastatic spread of infection to the spleen and liver leads to visceral leishmaniasis ( also known as kala-azar or black fever ) . Parasites can also enter other organs , such as lymph nodes , bone marrow and lungs , and in rare cases , can even reach the brain [4] . One of the major factors determining the type of pathology is the species of Leishmania [7] . However , the transmitting vector , as well as genotype , nutritional status of the host , and environmental and social factors also have a large impact on the outcome of the disease [4] , [7] . That is why even patients infected by the same species of Leishmania develop different symptoms [7] and may differ in response to therapy [3] . The basis of this heterogeneity is not well understood [8] , but part of this variation is likely genetic [4] . The search for loci and genes controlling leishmaniasis included candidate-gene approach , genome-wide linkage and association mapping . Genotyping of candidate genes , which have been chosen on the basis of previous immunological studies ( hypothesis-driven approach ) detected influence of polymorphism in HLA-Cw7 , HLA-DQw3 , HLA-DR , TNFA ( tumor necrosis factor alpha ) , TNFB , IL4 , IFNGR1 ( interferon gamma receptor 1 ) [reviewed in [4]] , TGFB1 ( transforming growth factor , beta 1 ) [9] , IL1 [10] , IL6 [11] , CCL2/MCP1 ( chemokine ( C-C motif ) ligand 2 ) [12] , CXCR1 ( chemokine ( C-X-C motif ) receptor 1 ) [13] , CXCR2 ( chemokine ( C-X-C motif ) receptor 2 ) [14] , FCN2 ( ficolin-2 ) [15] and MBL2 ( mannose-binding lectin ( protein C ) 2 ) [16] on response to different human leishmaniases . Hypothesis-independent search for susceptibility genes included genome-wide linkage and association mapping . Bucheton and coworkers [17] performed a genome-wide linkage scan , identified a major susceptibility locus that controls the susceptibility to L . donovani on chromosome 22q12 [17] and found that polymorphism in IL2RB ( interleukin 2 receptor , beta chain ) in this chromosomal region is associated with susceptibility to visceral leishmaniasis [18] . Genome-wide search with the subsequent analysis of a putative susceptibility locus on chromosome 6q27 revealed that polymorphism in DLL1 ( delta-like 1 ( Drosophila ) ) , the ligand for NOTCH3 ( Neurogenic locus notch homolog protein 3 ) [19] is associated with susceptibility to visceral leishmaniasis caused by L . donovani and L . infantum chagasi . Delta1-Notch3 interactions bias the functional differentiation of activated CD4+ T cells [20] . GWAS ( genome-wide association study ) established that common variants in the HLA-DRB1-HLA-DQA1 HLA class II region contribute to susceptibility to L . donovani and L . infantum chagasi [21] . Genome-wide linkage in mouse revealed susceptibility genes Nramp1 ( Natural resistance-associated macrophage protein 1 ) /Slc11a1 ( solute carrier family 11 ( proton-coupled divalent metal ion transporters ) , member 1 ) [22] and Fli1 ( Friend leukaemia virus integration 1 ) [23] and the role of these genes has been also established in humans [13] , [24] , [25] . NRAMP1 , which controls susceptibility to L . donovani and L . infantum functions as a divalent metal pH-dependent efflux pump at the phagosomal membrane of macrophages and neutrophils [26] . It is also expressed in dendritic cells and influences major histocompatibility complex class II expression and antigen-presenting cell function [27] . Susceptible mouse allele carries a “null” mutation that abolishes gene function ( it is a natural knockout ) [28] , whereas polymorphisms in the promoter , exon3 and the intron of human SLC11A1 [24] , are expected to have a smaller impact on gene function . The Friend leukaemia virus integration gene , linked with wound healing , influences cutaneous leishmaniasis caused by L . major in mouse [23] and by L . braziliensis in human [25] . It remains to be tested , whether natural polymorphisms detected in mouse genes bg ( beige ) /Lyst ( lysosomal trafficking regulator ) [29] and cationic amino acid transporter Slc7a2 ( solute carrier family 7 ( cationic amino acid transporter , y+ system ) , member 2 ) [30] influencing response to L . donovani [31] and L . major [30] , respectively , plays role also in humans . However , nothing is known about genes controlling L . tropica-induced disease in humans . L . tropica causes cutaneous leishmaniasis in humans , but it can also visceralize . Although cutaneous disease due to L . tropica is known for a long time , its potential to visceralize in humans has been recognized only relatively recently [32] . Visceralized L . tropica was also identified as the cause of an initially not understood systemic illness in veterans returning from endemic areas in the Middle East [33] . This finding stimulated interest in less typical symptoms induced by this parasite . It was found that L . tropica caused visceral disease in Kenya [34] , as well as classical visceral leishmaniasis ( kala-azar ) in India [35] , [36] and in Iran [37] , and disseminated cutaneous leishmaniasis accompanied with visceral leishmaniasis in Iran [38] . L . tropica was also implicated in development of mucosal leishmaniasis in Iran [39] . The reasons of this variability are not known . A suitable animal model for study of this parasite would therefore contribute to genetic dissection of the functional and clinical manifestations of infection . Golden hamsters ( Mesocricetus auratus ) have been considered to be the best model host for L . tropica infection , but this host is not inbred and therefore not suitable for genetic dissection . Fortunately , several L . tropica strains from Afghanistan , India [40] , and Turkey [41] have been reported to cause cutaneous disease in inbred BALB/c mice . Extension of analysis to the strains C57BL/6J , C57BL/10SgSnAi and gene-deficient mice on their backgrounds indicated role of IL-10 and TGFβ in regulation of parasite numbers in ears of infected mice [42] . We studied susceptibility to L . tropica using BALB/c-c-STS/A ( CcS/Dem ) recombinant congenic ( RC ) strains [43] , which differ greatly in susceptibility to L . major [44] , [45] . Parental strains BALB/c , STS and RC strains CcS-3 , CcS-5 , CcS-11 , CcS-12 , CcS-16 , CcS-18 , and CcS-20 were infected with L . tropica and skin lesions , cytokine and chemokine levels in serum , splenomegaly , hepatomegaly , and parasite numbers in organs were measured [46] . These experiments revealed that manifestations of the disease after infection with L . tropica are strongly influenced by genotype of the host . We have found that females of the RC strain CcS-16 that contains 12 . 5% genes of the resistant donor strain STS and 87 . 5% genes of the susceptible strain BALB/c [43] , [47] developed the largest skin lesions and exhibited a unique systemic chemokine reaction , characterized by additional transient early peaks of CCL3 and CCL5 , which were present neither in CcS-16 males nor in any other tested RC strain [46] . In order to establish the genetic basis of these differences , we prepared F2 hybrids between BALB/c and CcS-16 , infected them with L . tropica and measured their skin lesions , splenomegaly , hepatomegaly , parasite numbers in spleen , liver and inguinal lymph nodes , and serum level of CCL3 , CCL5 and CCL7 during the transient early peak . The strain CcS-16 carries STS-derived segments on nine chromosomes . They were genotyped in the F2 hybrid mice and their linkage with pathological symptoms and systemic immune responses was determined , which revealed eight controlling genes .
Females of strains BALB/c ( 16 infected , 16 uninfected ) and CcS-16 ( 15 infected , 11 uninfected ) were 8 to 19 weeks old ( mean age 12 weeks , median age 12 weeks ) at the time of infection . When used for these experiments , strain CcS-16 was in more than 90 generations of inbreeding . The parts of its genome inherited from the BALB/c or STS parents were defined [48] . 247 female F2 hybrids between CcS-16 and BALB/c ( age 9 to 16 weeks at the time of infection , mean age 13 weeks , median 13 weeks ) were produced at the Institute of Molecular Genetics AS CR , v . v . i . . Mice were kept in individually ventilated cages ( Ehret , Emmendingen , Germany ) and tested in two experimental groups . Both groups of F2 hybrids were derived from the same F1 parents; second experiment started seven weeks after the first . 2 mice died shortly after inoculation and were excluded from experiments . Among analyzed F2 hybrids , first experiment consisted of 111 mice , of which 51 mice originated from a cross ( BALB/c×CcS-16 ) F2 ( mean age 11 . 9 weeks , median 12 weeks; 3 mice died before the end of an experiment ) , 60 mice originated from a cross ( CcS-16×BALB/c ) F2 ( mean age 12 . 6 weeks , median age 13 weeks; 1 mouse died before the end of an experiment ) . According to the nomenclature rules , the first strain listed in the cross symbol is the female parent , the second the male . The second experiment contained 134 mice , of which 64 mice originated from a cross ( BALB/c×CcS-16 ) F2 ( mean age 12 . 6 weeks , median 16 weeks; 2 mice died before the end of an experiment ) , 70 mice originated from a cross ( CcS-16×BALB/c ) F2 ( mean age 13 . 4 weeks , median age 13 weeks; 6 mice died before the end of an experiment ) . The numbers of mice analyzed for individual phenotypes are given in Supplementary Table S1 . All experimental protocols utilized in this study comply with the Czech Government Requirements under the Policy of Animal Protection Law ( No . 246/1992 ) and with the regulations of the Ministry of Agriculture of the Czech Republic ( No . 207/2004 ) , which are in agreement with all relevant European Union guidelines for work with animals and were approved by the Institutional Animal Care Committee of the Institute of Molecular Genetics AS CR and by Departmental Expert Committee for the Approval of Projects of Experiments on Animals of the Academy of Sciences of the Czech Republic ( permission Nr . 37/2007 ) . Leishmania tropica from Urfa , Turkey ( MHOM/1999/TR/SU23 ) was used for infecting mice . Amastigotes were transformed to promastigotes using SNB-9 [49] , and 1×107 stationary phase promastigotes from subculture 2 were inoculated in 50 µl of sterile Phosphate Buffer Saline ( PBS ) s . c . into the tail base , with promastigote secretory gel ( PSG ) collected from the midgut of L . tropica-infected Phlebotomus sergenti females ( laboratory colony originating from L . tropica focus in Urfa ) . PSG was collected as described [50] . The amount corresponding to one sand fly female was used per mouse . The size of the skin lesions was measured every second week using the Profi LCD Electronic Digital Caliper Messschieber Schieblehre Messer ( Shenzhen Xtension Technology Co . , Ltd . Guangdong , China ) , which has accuracy 0 . 02 mm . Blood was collected every 2 weeks in volume from 60 to 180 µl , and serum was frozen at −30°C for further analysis . The mice were killed 43 weeks after inoculation . Blood , spleen , liver and inguinal lymph nodes were collected for later analysis . Parasite load was measured in frozen lymph nodes , spleen , and liver samples using PCR-ELISA according to the previously published protocol [51] . Briefly , total DNA was isolated using a TRI reagent ( Molecular Research Center , Cincinnati , USA ) standard procedure ( http://www . mrcgene . com/tri . htm ) . For PCR , two primers ( digoxigenin-labeled F 5′-ATT TTA CAC CAA CCC CCA GTT-3′ and biotin-labeled R 5′-GTG GGG GAG GGG CGT TCT-3′ ( VBC Genomics Biosciences Research , Austria ) were used for amplification of the 120-bp conservative region of the kinetoplast minicircle of Leishmania parasite , and 50 ng of extracted DNA was used per each PCR reaction . For a positive control , 20 ng of L . tropica DNA per reaction was amplified as a highest concentration of standard . A 30-cycle PCR reaction was used for quantification of parasites in lymph nodes; 33 cycles for spleen , and 40 cycles for liver . Parasite load was determined by analysis of the PCR product by the modified ELISA protocol ( Pharmingen , San Diego , USA ) . Concentration of Leishmania DNA was determined using the ELISA Reader Tecan and the curve fitter program KIM-E ( Schoeller Pharma , Prague , Czech Republic ) with least squares-based linear regression analysis . Levels of GM-CSF ( granulocyte-macrophage colony-stimulating factor ) , CCL2 ( chemokine ligand 2 ) /MCP-1 ( monocyte chemotactic protein-1 ) , CCL3/MIP-1α ( macrophage inflammatory protein-1α ) , CCL4/MIP-1β ( macrophage inflammatory protein-1β ) , CCL5/RANTES ( regulated upon activation , normal T-cell expressed , and secreted ) and CCL7/MCP-3 ( monocyte chemotactic protein-3 ) in serum were determined using Mouse chemokine 6-plex kit ( eBioscience , Vienna , Austria ) . The kit contains two sets of beads of different size internally dyed with different intensities of fluorescent dye . The set of small beads was used for GM-CSF , CCL5/RANTES and CCL4/MIP-1β and the set of large beads for CCL3/MIP-1α , CCL2/MCP-1 and CCL7/MCP-3 . The beads are coated with antibodies specifically reacting with each of the analytes ( chemokines ) to be detected in the multiplex system . A biotin secondary antibody mixture binds to the analytes captured by the first antibody . Streptavidin-phycoerythrin binds to the biotin conjugate and emits a fluorescent signal . The test procedure was performed in the 96 well filter plates ( Millipore , USA ) according to the protocol of manufacturer . Beads were analyzed on flow cytometer LSR II ( BD Biosciences , San Jose , USA ) . Lyophilized GM-CSF and chemokines ( CCL2/MCP-1 , CCL3/MIP1α , CCL4/MIP1β , CCL5/RANTES , CCL7/MCP-3 ) supplied in the kit were used as standards . Concentration was evaluated by Flow Cytomix Pro 2 . 4 software ( eBioscience , Vienna , Austria ) . The limit of detection of each analyte was determined to be for GM-CSF 12 . 2 pg/ml , CCL2/MCP-1 42 pg/ml , CCL7/MCP-3 1 . 4 pg/ml , CCL3/MIP-1α 1 . 8 pg/ml , CCL4/MIP-1β 14 . 9 pg/ml , and for CCL5/RANTES 6 . 1 pg/ml . DNA was isolated from tails using a proteinase procedure [52] with modifications described in [51] . The strain CcS-16 differs from BALB/c at STS-derived regions on nine chromosomes [48 and unpublished results] . These differential regions were typed in the F2 hybrid mice between CcS-16 and BALB/c using 23 microsatellite markers ( Generi Biotech , Hradec Králové , Czech Republic ) : D2Mit156 , D2Mit389 , D2Nds3 , D2Mit257 , D2Mit283 , D2Mit52 , D3Mit25 , D3Mit11 , D4Mit153 , D6Mit48 , D6Mit320 , D10Mit67 , D10Mit103 , D11Mit139 , D11Mit242 , D11Nds18 , D11Mit37 , D16Mit126 , D17Mit38 , D17Mit130 , D18Mit35 , D18Mit40 and D18Mit49 ( Supplementary Table S2 ) . The maximum distance between any two markers in the chromosomal segments derived from the strain STS or from the nearest BALB/c derived markers was 14 . 16 cM . The DNA genotyping by PCR was performed as described elsewhere [53] . The genotyping for microsatellite markers with fragment length difference of less than 10 bp was performed by using ORIGINS Elchrom Scientific electrophoresis ( Elchrom Scientific AG , Cham , Switzerland ) according to manufacturer's instruction . Briefly , DNA was amplified as described in [53] . Each PCR product was mixed with 5 µl of loading buffer and electrophoresed using Spreadex EL300 gel and Spreadex EL400 gel ( Elchrom Scientific AG , Cham , Switzerland ) for product with size of less than 150 bp or more than 150 bp , respectively . The best gel and proper running time was selected using ElQuantTM Software ( Elchrom Scientific AG , Cham , Switzerland ) . 30 mM TAE buffer was used as a running buffer . Running temperature was set to 20°C and to 50°C , when voltage was set to 120 V and 100 V , respectively . After finishing the electrophoresis gel was stained by ethidium bromide and the results were read by GENE bio-imaging system ( Syngene , Cambridge , UK ) . The role of genetic factors in control of the tested pathological and immunological symptoms was examined with ANOVA using the program Statistica for Windows 8 . 0 ( StatSoft , Inc . , Tulsa , Oklahoma , USA ) . Marker , grandparent-of-origin effect and age were fixed factors and the experiment was considered as a random factor . In order to obtain normal distribution of the analyzed parameters , the obtained values were transformed , each as required by its distribution , as shown in the legends of the Tables . Markers and interactions with P<0 . 05 were combined in a single comparison . To obtain whole genome significance values ( corrected P-values ) the observed P-values ( αT ) were adjusted according to Lander and Schork [54] using the formula:where G = 1 . 75 Morgan ( the length of the segregating part of the genome: 12 . 5% of 14 M ) ; C = 9 ( number of chromosomes segregating in cross between CcS-16 and BALB/c , respectively ) ; ρ = 1 . 5 for F2 hybrids; h ( T ) = the observed statistic ( F ratio ) . The percent of total phenotypic variance accounted for by a QTL and its interaction terms was computed by subtracting the sums of squares of the model without this variable from the sum of squares of the full model and this difference divided by the total regression sums of squares:
Differences in skin lesions development between strains BALB/c and CcS-16 are controlled by two loci , which are not dependent on or influenced by interaction with other genes ( main effects ) ( Table 1 , Figure 1 ) . Ltr2 ( Leishmania tropica response 2 ) linked to D2Nds3 ( Figure 1A ) and D2Mit389 influences lesion size at week 19 ( corrected P = 0 . 004 , Bonferroni corr . P = 0 . 049 ) , 21 ( corrected P = 0 . 0020 , Bonferroni corr . P = 0 . 024 ) and 31 ( corrected P = 0 . 0152 , Bonferroni corr . P = 0 . 18 ) after infection , Ltr3 that controls lesion size at week 21 after infection is linked to D3Mit11 ( corrected P = 0 . 042 , Bonferroni corr . P = 0 . 5 ) ( Figure 1B ) . STS allele of both Ltr2 and Ltr3 determines larger lesions . STS allele of Ltr4 marked by D4Mit153 ( which also controls parasite numbers in liver and in lymph nodes ) has an opposite effect on the studied trait; its STS allele is associated with smaller lesions at week 27 after infection . Figure 1C and Figure 1D show the strong additive effects of Ltr2 and Ltr3 , and Ltr2 , Ltr3 and Ltr4 , respectively . However , Ltr3 and Ltr4 effects on skin lesions ( nominal P value = 0 . 00048 and 0 . 00096 , respectively , corr . P value = 0 . 024 and 0 . 045 , respectively ) were not significant after Bonferroni correction for number of tested weeks of infection and for whole genome significance . Although lesions were larger in the second experiment , no significant interaction between experimental group and markers was observed . Genetic analysis of F2 hybrids has revealed identical genetic control of serum levels of CCL3 and CCL5 at week 7 after infection ( Table 5 , 6 ) . Ltr3 linked to D3Mit11 determines levels of both CCL3 ( corrected P = 0 . 0046 ) and CCL5 ( corrected P = 0 . 010 ) , its BALB/c allele is associated with higher chemokine levels ( Table 5 ) . Ltr3 has not only individual ( main ) effect on chemokines levels , but also influences levels of CCL3 ( corrected P = 0 . 014 ) and CCL5 ( corrected P = 0 . 0012 ) in interaction with Ltr7 linked to D17Mit130 . The largest effect is seen by Ltr3 when Ltr7 is SS . In that genotypic situation the Ltr3 CC alleles cause more than 300×higher levels of CCL3 and 28×higher levels of CCL5 than the Ltr3 SS alleles ( Table 6 ) . It is likely that this very large size of this effect in Ltr7 SS mice makes the Ltr3 effects visible as a main effect , although smaller , in F2 hybrids irrespective of their Ltr7 genotype . CCL7 level is controlled with two loci with an opposite effect on the studied trait . The homozygosity for the STS allele of Ltr2 ( SS ) determines higher CCL7 level ( corrected P = 0 . 002 ) , whereas homozygosity for the BALB/c allele of Ltr8 ( CC ) is associated with higher level of this chemokine ( corrected P = 0 . 013 ) ( Table 5 ) . No significant interaction between experimental group and marker was observed . Older mice had higher levels of CCL7 in serum than the younger ones , but we did not observe any interactions between marker and age ( nominal P ( Ltr2 ) = 0 . 80 , nominal P ( Ltr8 ) = 0 . 64 ) . Levels of CCL7 in serum of infected mice are also influenced by interaction of Ltr2 linked to D2Mit257 and Ltr6 linked to D11Mit37 ( corrected P = 0 . 016 ) , the highest CCL7 levels are observed in STS allele ( SS ) homozygotes in Ltr6 in combination with heterozygotes ( CS ) or STS allele ( SS ) homozygotes in Ltr2 ( Table 6 ) . Although chemokine levels were higher in the first experiment , no significant interaction between experimental group and markers was observed . No linkage was found for GM-CSF , CCL2/MCP-1 and CCL4/MIP-1β .
Our data show that interaction of mice with L . tropica parasites is complex and involves numerous genes and responses ( Table 7 ) . We have detected eight loci that in the strain CcS-16 control host-parasite interaction ( Table 7 , Figure 2 ) . All eight Ltr loci are involved in gene-gene interactions ( Figure 3 ) , four loci ( Ltr2 , Ltr3 , Ltr6 , Ltr8 ) have also individual effect , while effects of Ltr1 , Ltr4 , Ltr5 and Ltr7 are seen only in interaction with other Ltr loci . This is not surprising , as the average proportion of genetic variation explained by epistatic QTLs in mice in different systems was estimated to be 49% [86] and gene-gene interactions were observed also in response to other pathogens such as L . major [87]–[89] , Trypanosoma brucei brucei [53] , Salmonella enteritidis [90] , Plasmodium falciparum [91] and Mycobacterium leprae [92] . The loci described here have heterogeneous effects ( Table 7 ) . Ltr1 on chromosome 2 controls in interaction with Ltr4 only parasite numbers in lymph nodes , whereas the more distal Ltr2 on the same chromosome influences development of skin lesions , splenomegaly ( in interaction with Ltr3 ) , hepatomegaly , parasite load in liver and level of CCL7 in serum . Multiple functions are also exerted by Ltr3 on chromosome 3 , which controls splenomegaly ( in interaction with Ltr2 ) , parasite numbers in spleen , and levels of CCL3 and CCL5 in serum . We have analyzed genetic control of early levels of chemokines , as there is a unique early peak in the CcS-16 females [46] . However , comparison of genetic control of CCL3 and CCL5 levels with genetic control of development of skin lesions indicates that there is no simple correlation between the chemokines levels and manifestations of disease . Ltr4 on chromosome 4 controls in interaction with Ltr1 and Ltr8 parasite numbers in lymph nodes and in liver , respectively . Ltr5 on chromosome 10 influences in interaction with Ltr7 or Ltr8 splenomegaly . Ltr6 influences parasite numbers in spleens and level of CCL7 in serum ( in interaction with Ltr2 ) . Ltr7 controls splenomegaly ( in interaction with Ltr5 ) and in interaction with Ltr3 level of both CCL3 and CCL5 in serum . Ltr8 controls splenomegaly ( as a main effect gene and in interaction with Ltr5 ) , parasite numbers in liver ( in interaction with Ltr4 ) and level of CCL7 in serum . Ltr1 and Ltr5 control only one parameter , whereas other loci have multiple effects . Some multiple effects could reflect causal relationship – e . g . CCL7 influences recruitment of monocytes to spleen [93] , which could contribute to splenomegaly . The observed multiple effects of some Ltr loci might also suggest that some such loci might represent complexes of two or more closely linked Ltr genes . This issue will be resolved by future recombinational analysis . We have detected also loci that control symptoms , such as splenomegaly , in which the strains BALB/c and CcS-16 do not differ [46] . This is because in an inbred strain the final outcome of response is exerted by multiple genes , which often have opposite effects , masking each other . In the F2 hybrids these genes segregate and can be therefore detected . Reliability and validity of the described loci is supported by the fact that they have been detected by analysis of different phenotypes and their statistical significance was corrected for whole genome testing and where appropriate also by conservative Bonferroni correction . The relatively high proportion of variance explained by the mapped loci ( Table 1–6 ) might be partly due to a limited variability of the tested manifestations of the disease . Most inbred mouse strains that were produced without intentionally selectively bred for a specific quantitative phenotype ( like susceptibility to specific infections ) inherited from their non-inbred ancestors randomly susceptible alleles at some loci and resistant alleles at others , so that their overall susceptibility phenotype depends on the relative number of both . STS is resistant to L . tropica and does not develop skin lesions [24] , however some STS-derived segments carried by CcS-16 on chromosome 2 ( Ltr2 ) and possibly also on chromosome 3 ( Ltr3 ) are associated with larger lesions . Similarly , STS-derived alleles of Ltr2 and Ltr6 are associated with higher parasite load in liver and spleen , respectively . This finding is not unique as susceptibility alleles originating from resistant strains were found in studies of colon cancer [94] and L . major [95] susceptibility; a low-responder allele was identified in a strain exhibiting high response to IL-2 [96] or producing a high level of IFNγ [97] , whereas a high responder allele was found in a strain producing low level of IL-4 [98] . Loci Ltr3 and Ltr6 influencing parasite numbers in spleen ( Table 2 ) were significant only in the cross ( BALB/c×CcS-16 ) F2 , but not in the cross ( CcS-16×BALB/c ) F2 , hence the outcome in these crosses that are theoretically genetically identical depends on the strain of the female or male used originally to produce the F1 hybrids , which were then crossed with each other to produce the F2 hybrids for the tests . Thus , this is a special type of a transgenerational parental effect as the mothers and fathers of the F2 hybrids were genetically identical . Recently , examples of transgenerational parental effects have been described in several species [reviewed in [99]] and several possible mechanisms have been proposed . Our observation may reflect a parental effect due to modification of the developing immune system of fetuses or youngs by maternal environment , maternal nutritional effects , or epigenetic effects , and it offers a possibility to characterize the transgenerationally regulated functional pathways . Control of parasite elimination differs among organs: the loci Ltr1 and Ltr4 interact to control parasite numbers in inguinal lymph nodes , while Ltr4 in interaction with Ltr8 influences parasite load in liver ( Table 4 ) . Parasite load in liver is also controlled by Ltr2 ( Table 2 ) , whereas parasite burden in spleen is influenced by Ltr3 and Ltr6 ( Table 2 ) . These data show that parasite elimination in lymph nodes , liver and spleen are controlled differently , suggesting a predominantly organ specific control of parasite load . Mechanistic studies analyzing response to L . tropica in different organs are not yet available , but generally organ specific responses described here are compatible with the mechanistic studies of other parasites . The enzymes inducible nitric oxide synthase and phagocyte NADPH oxidase , which are required for the control of L . major , display organ- and stage-specific anti-Leishmania effects [76] , [100] . Inducible nitric oxide synthase has been shown to control resistance to parasites in skin and draining lymph nodes , but not in spleen of the resistant strain C57BL/6 [100] . On the other hand , activity of phagocyte NADPH oxidase is essential for the clearance of L . major in the spleen , but it is dispensable for the resolution of the acute skin lesions and it exerted only a limited effect on the containment of the parasites in the draining lymph node [76] . Similarly , bg/Lyst ( lysosomal trafficking regulator ) is involved in control of parasite numbers of L . donovani in spleen , but not in liver [31] . On the other hand VCAM-1 ( vascular cell adhesion molecule-1 ) and VLA-4 ( very late antigen-4 ) interactions influenced early L . donovani burden in liver , but not in spleen [82] . Comparison of genetic control of parasite numbers in spleen and splenomegaly , or parasite numbers in liver and hepatomegaly shows that control of parasites elimination and organ pathology overlap only partially . For example Ltr3 controls both parasite numbers in spleen and splenomegaly , but Ltr6 is involved in control of parasite numbers in spleen , but not in splenomegaly , whereas Ltr2 , Ltr8 , Ltr5 , and Ltr7 are involved only in control of splenomegaly ( Table 2 , 3 , 7 ) . Similarly , Ltr2 influences both parasite load in liver and hepatomegaly , but parasite load in liver is controlled also by interaction of Ltr4 with Ltr8 . The differences in genetic control of parasite numbers and organ pathology induced by the parasites are probably due to the fact that during a chronic disease the organ damage is a combined result of speed of elimination of parasite on one hand and changes caused by reaction to parasite ( such as influx of immune cells , inflammatory responses ) and healing processes on the other hand . It is therefore likely that these processes are regulated by different sets of genes . It is important to understand that as in any QTL study , failure to find a linkage between a phenotype and a marker does not rule out that such linkage may exist , although its phenotypic effect are likely smaller than in the detected linkages . So for a QTL , which affects several but not all parameters of a complex disease , this indicates that it has predominant effects on some parameters , although it might modify to a lesser extent other parameters as well . Usually , a standard inbred-strain mapping experiment using F2 hybrids will map a QTL into a 20- to 40-cM interval [105] . In the RC strains 54% of their donor strain genome reside in segments of medium length ( 5–25 cM ) [106] . However , RC strains can carry on some chromosomes very short segments of the donor strain origin . This feature of the RCS system allowed us previously to narrow the location of Lmr9 ( Leishmania major response 9 ) on chromosome 4 to a segment of 1 . 9 cM ( 6 . 79 Mb ) without any additional crosses [101] . The short length of this segment , which controls levels of serum IgE in L . major infected mice , enabled us to detect a human homolog of this locus on human chromosome 8q12 and show that it controls susceptibility to atopy [107] . In another study , we were able to precisely map Tbbr2 ( Trypanosoma brucei brucei response 2 ) to 2 . 15 Mb [53] . In the present F2 mapping experiment the shortest locus Ltr1 is 4 . 07 Mb long ( Figure 2 ) . Although most Ltr loci contain several possible candidate genes , here we list ( Figure 2 ) [10] , [12] , [55]–[83] only those that have been shown previously to influence infection with Leishmania ssp . . However , the effects of many of Ltr loci might be caused by genes that are at the present not considered as candidates . Currently we are producing mice with recombinant haplotypes that carry individual Ltr loci in a very short segment on chromosome . The testing of these strains will restrict the present number of the candidate genes to the most likely ones . We present the first description of genetic architecture of response to L . tropica in any species . We observed organ specific control of infection and distinct control of parasite load and organ pathology , the typical characteristics of immune response to many pathogens observed in all infections where multiple disease parameters were studied ( L . major [4] , L . donovani [4] , Borrelia burgdorferi [102] , Toxoplasma gondii [108] , Trypanosoma congolense [109] , and Chlamydia psittaci [110] ) . In addition , the genetic control of response to L . tropica exhibits heterogeneity of gene effects , gene-gene interactions , and trans-generational parental effects . These complexities of genetic control have been invoked [111] to explain the very large fraction of heritability that has not been detectable in genome-wide association studies ( GWAS ) [112] , a power deficiency that likely cannot be ameliorated by further increases of the number of tested SNPs or by whole genome sequencing . Identification of these complexities in the present study will open way to elucidation of their functional basis and detection of homologous processes in humans . | Leishmaniasis , a disease caused by Leishmania ssp . is among the most neglected infectious diseases . In humans , L . tropica causes cutaneous form of leishmaniasis , but can damage internal organs too . The reasons for this variability are not known , and its genetic basis was never investigated . Therefore , analysis of genes affecting host's responses to this infection can elucidate the characteristics of individual host-parasite interactions . Recombinant congenic strain CcS-16 carries 12 . 5% genes from the mouse strain STS/A on genetic background of the strain BALB/c , and it is more susceptible than BALB/c . In F2 hybrids between BALB/c and CcS-16 we detected and mapped eight gene-loci , Ltr1-8 ( Leishmania tropica response 1-8 ) that control various manifestations of disease: skin lesions , splenomegaly , hepatomegaly , parasite numbers in spleen , liver , and inguinal lymph nodes , and serum level of CCL3 , CCL5 , and CCL7 after L . tropica infection . These loci are functionally heterogeneous - each influences a different set of responses to the pathogen . Five loci co-localize with the previously described loci that control susceptibility to L . major , three are species-specific . Ltr2 co-localizes not only with Lmr14 ( Leishmania major response 14 ) , but also with Ir2 influencing susceptibility to L . donovani and might therefore carry a common gene controlling susceptibility to leishmaniasis . | [
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] | 2013 | Mapping the Genes for Susceptibility and Response to Leishmania tropica in Mouse |
Previously we have described the V2-directed CAP256-VRC26 lineage that includes broadly neutralizing antibodies ( bNAbs ) that neutralize globally diverse strains of HIV . We also identified highly mutated “off-track” lineage members that share high sequence identity to broad members but lack breadth . Here , we defined the mutations that limit the breadth of these antibodies and the probability of their emergence . Mutants and chimeras between two pairs of closely related antibodies were generated: CAP256 . 04 and CAP256 . 25 ( 30% and 63% breadth , respectively ) and CAP256 . 20 and CAP256 . 27 ( 2% and 59% breadth ) . Antibodies were tested against 14 heterologous HIV-1 viruses and select mutants to assess breadth and epitope specificity . A single R100rA mutation in the third heavy chain complementarity-determining region ( CDRH3 ) introduced breadth into CAP256 . 04 , but all three CAP256 . 25 heavy chain CDRs were required for potency . In contrast , in the CAP256 . 20/27 chimeras , replacing only the CDRH3 of CAP256 . 20 with that of CAP256 . 27 completely recapitulated breadth and potency , likely through the introduction of three charge-reducing mutations . In this individual , the mutations that limited the breadth of the off-track antibodies were predicted to occur with a higher probability than those in the naturally paired bNAbs , suggesting a low barrier to the evolution of the off-track phenotype . Mapping studies to determine the viral immunotypes ( or epitope variants ) that selected off-track antibodies indicated that unlike broader lineage members , CAP256 . 20 preferentially neutralized viruses containing 169Q . This suggests that this globally rare immunotype , which was common in donor CAP256 , drove the off-track phenotype . These data show that affinity maturation to counter globally rare viral immunotypes can drive antibodies within a broad lineage along multiple pathways towards strain-specificity . Defining developmental pathways towards and away from breadth may facilitate the selection of immunogens that elicit bNAbs and minimize off-track antibodies .
Antiretroviral therapy has transformed HIV from a progressive fatal infection to a manageable chronic disease [1] . However , a preventative vaccine is urgently needed as drug resistance , access to medication and adverse side effects hamper the utility of antiretroviral drugs . Despite intensive research , strategies to induce protective immune responses by vaccination have achieved little success [2] . While neutralizing antibodies are elicited during HIV infection , the response is typically strain-specific . However , ~20% of individuals develop broadly neutralizing antibodies ( bNAbs ) which potently neutralize diverse global viruses in vitro and protect non-human primates from infection [3–8] . Therefore , understanding the mechanisms behind the elicitation and evolution of bNAbs is central to the development of an HIV vaccine . During infection , the humoral response targets the HIV envelope ( Env ) glycoprotein which consists of three heavily glycosylated non-covalently linked gp41-gp120 protomers . While strain-specific antibodies recognize exposed and variable sites , bNAbs target relatively conserved and occluded sites , including the membrane proximal external region ( MPER ) , gp120-gp41 interface , CD4-binding site ( CD4bs ) , N332 supersite and a quaternary V1V2 epitope at the trimer apex [9] . Many V1V2 directed bNAbs have atypically long ( >24 amino acids ) third complementarity determining regions of the heavy chain ( CDRH3 ) which provides access to glycan shielded and buried epitopes [10–14] . Additionally , bNAbs frequently exhibit unusually extensive somatic hypermutation ( SHM ) , which suggests multiple rounds of affinity maturation . Understanding how these unusual features arise is critical to recapitulating this process by vaccination . The degree to which SHM and affinity maturation occurs is influenced by HIV diversity and antigen load [15] . A number of longitudinal studies describing the “arms race” between viral quasispecies and bNAb lineages have provided insights into the evolution of breadth [16] . These studies have identified bNAb-initiating Envs that engage antibody precursors as well as emerging viral variants , which select bNAb intermediates [12 , 17–23] . We have extensively studied virus/antibody co-evolution in the superinfected CAP256 donor from whom 33 members of the CAP256-VRC26 V2-targetting bNAb lineage were isolated [11 , 12 , 22] . The evolution of this lineage was elucidated through analysis of longitudinal viral and antibody deep sequences from 59–206 weeks post infection [11 , 12 , 22] . Viruses from six weeks post infection are sensitive to neutralization by CAP256 lineage members but by 59 weeks post infection most viruses are resistant . Similar to other V2-directed bNAbs , the CAP256-VRC26 lineage members have very long ( 35–37 amino acid , aa ) anionic and tyrosine-sulfated CDRH3s that recognize the electropositive V2 apex [24] . Modelling and mapping studies show that several residues within the CDRH3 are critical for epitope recognition , including the tyrosine sulfated YYD motif at the apex of the CDRH3 [24] . This motif is present in the unmutated common ancestor ( UCA ) of the CAP256-VRC26 lineage , 27 of the 33 lineage members as well as other V2-directed bNAbs such as PG9/PG16 [10 , 11] . In addition , the CDRH1 has been shown to stabilize the CDRH3 , and residues in CDRH2 have been implicated in glycan recognition [25 , 26] . Within strands B and C of the viral Env protein , the CAP256-VRC26 lineage’s footprint includes residues N160 , R166 , D167 , K168 and K169 and is similar to other V2 glycan bNAbs ( PG9/PG16 ) . The breadth , potency and extent of SHM of CAP256-VRC26 lineage members ranges from 2–63% , 0 . 003–5 μg/mL and 4 . 2–18% , respectively [11] . SHM , which is mediated by activation‐induced cytidine deaminase ( AID ) , is essential for breadth as bNAb germline revertants generally lack substantial binding and neutralization [10 , 27–30] . Mutations that are selected during affinity maturation may increase antibody-antigen affinity directly at the paratope or indirectly by improving stability [31 , 32] . SHM is a semi-random process as AID hot-spots ( WRCH/Y , W = A/T , R = A/G and H = A/C/T ) and cold-spots ( SYC , S = C/G and Y = C/T ) are distributed throughout the antibody variable regions . Consequently , mutations associated with breadth are unlikely to occur if they are in cold-spots and/or require more than one nucleotide change [33 , 34] . Additionally , neutral mutations can accumulate via co-selection and constitutive background mutational noise [32] . Although increased SHM is generally associated with neutralization breadth , exceptions exist within the CAP256-VRC26 and other bNAb lineages . We have previously described these non-broad but highly mutated CAP256 antibodies as “off-track” [22] . Such off-track antibodies have been recovered from bNAb lineages in several individuals . 1AZCETI5 is a clonal member of the CD4bs CH103 bNAb lineage , and shares VDJ genes , CDRH3 length and extent of SHM with bNAb CH103 , but the former only neutralizes autologous viruses [18] . The PGDM1400 bNAb , a member of the V2 glycan targeting PGDM lineage with 83% breadth has similar genetic properties to PGDM1406 , but the latter exhibits limited breadth ( 6% ) [35] . Similarly , within the PGT121 N332 targeting bNAb lineage are two heavy chains ( HC ) ( 22H and 6H ) that are related to broad members but exhibit no neutralization [36] . As monoclonal antibody isolation methodologies are designed to recover bNAbs , the proportion of off-track antibodies in bNAb lineages is unknown . However , the finding that 40% of related antibodies recovered by flow cytometry from the donor of the CD4bs bNAb , VRC-PG04 , were off-track , indicates that such antibodies may be common [37] . Collectively , studies into co-evolution and bNAb targets have identified candidate antigens to initiate antibody lineages and guide the development of breadth . Indeed , native-like Env trimers administered in combination or sequentially elicits tier 2 neutralizing antibodies in mice and rabbits , providing a strong rationale to test similar immunogens in people [38–42] . Importantly , the mutations crucial for breadth and potency as well as those that confer an off-track phenotype in bNAb lineages are largely unknown . Identification of these mutations will inform the design of vaccine antigens that elicit breadth but dampen the evolution of off-track responses . The high degree of sequence identity between CAP256-VRC26 bNAbs and their off-track relatives , the richly populated lineage and the availability of contemporaneous viral sequences is an ideal opportunity to compare off-track antibodies with bNAbs to determine why these antibodies lack breadth . Here we define key residues that restrict neutralization breadth and potency in two off-track antibodies within the CAP256-VRC26 lineage . We found that in one off-track/bNAb pair , residues in each of the three CDRHs were necessary for potency and that a key breadth-conferring mutation in the CDRH3 was highly improbable . In the second pair , we show that the CDRH3 was completely responsible for modulating neutralization and that the mutations leading to the off-track phenotype were relatively probable . Lastly , through epitope mapping we identified viruses that guided the evolution of an off-track antibody toward a globally rare immunotype . This study highlights the stochastic nature of SHM and shows that breadth-restricting mutations typically arise with a greater probability than breadth-conferring mutations . This work provides insights into the evolution of breadth and mature strain-specificity , informing the design of immunogens that minimize the elicitation of off-track mutations while guiding evolution to globally conserved sites .
To determine the genetic determinants that controlled the off-track phenotype , we selected two bNAbs with high sequence identity to two off-track antibodies . Off-track antibodies were defined as having low neutralization breadth despite their capacity to neutralize early autologous viruses [12] , extensive sequence maturation and identity to related bNAb lineage members . The CAP256 . 25 bNAb was paired with the off-track antibody CAP256 . 04 and bNAb CAP256 . 27 was paired with the off-track antibody , CAP256 . 20 . The two pairs of antibodies cluster together on a maximum likelihood tree ( Fig 1A ) , consistent with our previous observations that antibodies with considerably different breadth may differ by relatively few amino acids [22] . The HC and light chain ( LC ) of CAP256 . 04 ( 30% breadth , 0 . 27 μg/mL potency , 9% SHM ) both share 92% sequence identity with the HC and LC of bNAb CAP256 . 25 ( 63% breadth , 0 . 003 μg/mL potency , 12% SHM ) ( Fig 1A ) . Within the second pair , the HC and LC of CAP256 . 20 ( 2% breadth , 1 . 87 μg/mL potency , 16% SHM ) shares 93% and 96% sequence identity with the CAP256 . 27 HC and LC ( 59% breadth , 0 . 047 μg/mL potency , 16% SHM ) , respectively ( Fig 1A ) . A total of eighteen amino acids differ between the HC and 11 amino acids between the LC of CAP256 . 04 ( orange ) and CAP256 . 25 ( brown ) which are displayed as yellow spheres in an alignment of the two antibodies ( Fig 1B ) . Seventeen amino acids ( yellow spheres ) differentiate the HCs and four the LCs of CAP256 . 27 ( purple ) and CAP256 . 20 ( light purple ) ( Fig 1B ) . As neutralization by members of the CAP256 bNAb lineage has been largely attributed to the HC [12] , we first confirmed that the LC was not responsible for the off-track phenotype of CAP256 . 04 and CAP256 . 20 . We generated chimeras between the HC and LC of each off-track/bNAb pair ( 25HC+04LC , 04HC+25LC and 27HC+20LC , 20HC+27LC ) and tested their neutralization against both CAP256 infecting viruses ( the primary and superinfecting viruses ) and 14 heterologous viruses . The titers did not differ significantly ( Wilcoxon signed rank test ) between the chimeric antibodies and the natural antibody from which the HC was donated ( Fig 1C ) . These data indicate that the LC has no impact on the neutralization breadth within these pairs , consistent with structural analyses showing no light chain contacts with the viral epitope [26] , and we therefore focused on the HC in subsequent studies . To determine which regions of the HC restrict the breadth of CAP256 . 04 , we constructed several chimeras and point mutants between CAP256 . 25 and CAP256 . 04 , focusing initially on the CDRHs . The sequence variation between the CDRHs is shown in Fig 2A and includes charge and hydrophobicity differences between the two antibodies ( R28S , D30N , G31R , H53Y , K57D , A100rR and N100ddF ) and an insertion ( G100w ) in CAP256 . 25 that is absent from CAP256 . 04 ( Fig 1B ) . We first transplanted all the CAP256 . 25 CDRHs ( 12 amino acid changes in total ) into CAP256 . 04 , creating CAP256 . 0425H123 ( Fig 2B ) . Antibodies were tested against a panel of 16 viruses that were sensitive to CAP256 . 25 ( geometric mean potency of 0 . 002 μg/mL against this panel ) , of which 8 were neutralized by CAP256 . 04 ( with a 230-fold lower geometric mean potency of 0 . 17 μg/mL ) ( Fig 2B ) . The neutralization breadth and potency of the CAP256 . 0425H123 chimera ( which neutralized all 16 viruses with a geometric mean potency of 0 . 008 μg/mL ) matched that of CAP256 . 25 ( 0 . 002 μg/mL ) ( Fig 2B ) . In the reverse experiment , introducing the framework ( FR ) regions from CAP256 . 25 into CAP256 . 04 ( CAP256 . 0425FR123 ) had no effect on breadth ( Fig 2B ) . These data suggest that the mutations in the CDRs , rather than the FW regions restrict the breadth of CAP256 . 04 . To dissect the role of individual CDRHs , each was individually swapped between the two parental antibodies . CAP256 . 0425H1 neutralized two additional viruses ( ZM214 and ZM249 ) that were resistant to CAP256 . 04 ( Fig 2B ) . In addition , we observed virus-specific increases in potency for some sensitive viruses ( with a 6-18-fold improvement for Du156 , Q259 , and Du422 and a 250-fold increase in potency for CAP45 ) . In contrast , CAP256 . 0425H2 neutralized the same number of viruses as CAP256 . 04 ( Fig 2B ) , though again slight virus-specific differences in potency were observed , with CAP45 becoming resistant to CAP256 . 0425H2 while PVO gained sensitivity ( Fig 2B ) . The largest effect of a single CDRH swap was observed for the CDRH3 which differed by six amino acids . CAP256 . 0425H3 neutralized six additional viruses ( ZM109 , PVO , ZM214 , ZM249 , Q461 and Q842 ) ( Fig 2B ) although with similar potency to CAP256 . 04 ( 0 . 39 μg/mL and 0 . 17 μg/mL , respectively ) . This was confirmed in the reverse experiment , where the CAP256 . 04 CDRH3 was introduced into CAP256 . 25 ( CAP256 . 2504H3 ) resulting in reduced breadth ( Fig 2B ) . Overall , these data suggest that the breadth of the CAP256 . 04 off-track phenotype is modulated by the CDRH3 , but potency is attenuated by a combination of all three CDRHs . As the CAP256 . 25 CDRH3 conferred additional breadth into CAP256 . 04 , we assessed which residue ( s ) were responsible for this effect ( Fig 2A ) . Mutating CDRH3 residues that altered the charge ( K102N ) or aromaticity ( F100ddA ) of the CDRH3 failed to improve the neutralization of CAP256 . 04 ( S1 Fig ) . In contrast , the R100rA mutation , which removed a positive charge from the CDRH3 , improved the breadth of CAP256 . 04 from 50% to 94% of this panel , with seven viruses resistant to CAP256 . 04 becoming sensitive to CAP256 . 04 R100rA ( Fig 2B ) . Interestingly , this single mutation recapitulated the breadth introduced into CAP256 . 04 by the entire CAP256 . 25 CDHR3 . The reverse mutant , CAP256 . 25 A100rR similarly resulted in a substantial loss of breadth ( S1 Fig ) . The combination of R100rA with the CAP256 . 25 CDRH1 did not confer additional breadth beyond that introduced by R100rA ( Fig 2B ) and , surprisingly , the addition of the CDRH2 to either CAP256 . 04 R100rA or CAP256 . 0425H1 R100rA reduced the breadth of these antibodies ( Fig 2B ) . To model the interactions between V1V2 and CAP256 . 04 or CAP256 . 25 , we fitted the sequence of CAP256 . 04 to the crystal structure of CAP256 . 25 in complex with the CAP256-34 week trimer which shares key contact residues for the CAP256-VRC26 lineage , including N160 , R166 , D167 , K168 and K169 with CAP256 . 15-wk SU ( Fig 3A ) , and which likely triggered the lineage [22] . The model showed that the N130 glycan projects from the trimer and is situated proximal to the CDRH1 . The CAP256 . 25 R28 residue was predicted to hydrogen bond with the N130 glycan while CAP256 . 04 S28 was not predicted to interact with this glycan . Surprisingly , the introduction of S28R to CAP256 . 04 R100rA and CAP256 . 0425H3 ( S1 Fig ) decreased the breadth of these antibodies by five and four viruses , respectively , although the potency remained unchanged . Together with R100rA , the combination of S28R and N30D increased the breadth compared to S28R alone , but two viruses ( PVO . 04 and AC10 . 29 ) remained resistant to neutralization ( S1 Fig ) . The conformation of the CDRH3 is partly supported by the intermolecular interactions between this loop and the CDRH1 and CDRH2 [25 , 26] . Consequently , the interaction between R28 and the CDRH3 may shift the CDRH1 toward the N130 glycan , which may be countered by the N30 residue ( Fig 3A , top panel ) . Furthermore , the model showed that 100r was positioned in close proximity to the viral residue K168 , and that K168 was hydrogen bonded to the CDRH3 residue E100c ( Fig 3A , bottom panel ) . Therefore , due to electrostatic repulsion , R100r likely displaces K168 , disrupting the interaction between K168 and E100c . In contrast , CAP256 . 25 has a small , uncharged Ala at position 100r which would not obstruct the adjacent bond . This suggests that an R100rA mutation may reduce electrostatic repulsion and therefore increase antigen affinity , providing a mechanism for the enhanced breadth associated with this mutation . To estimate the probability of the occurrence of mutations prior to selection that are associated with the off-track phenotype , we utilized the ARMADiLLO software [34] . We first assessed the probability of the CDRH1 T28 residue ( found in the CAP256 . UCA ) mutating to either 28R ( as in CAP256 . 25 ) or 28S ( in CAP256 . 04 , Fig 2A ) . We found that the CAP256 . 25 T28R mutation was improbable ( 0 . 41% ) , requiring two base changes ( ACC to AGG ) , whereas T28S ( in CAP256 . 04 ) was ten times more probable ( 5 . 1% ) , requiring only one change ( ACC to TCC , Fig 3B ) . Similarly , two base changes were necessary for the improbable ( 1 . 1% ) mutation S30D ( in CAP256 . 25 ) , whereas S30N ( in CAP256 . 04 ) was a probable ( 14 . 7% ) event as it occurred in an AID hot-spot . The combination of T28R and S30D ( in CAP256 . 25 ) , which was necessary for neutralization when transplanted into CAP256 . 04 , was a highly improbable event ( 0 . 004% , Fig 3B ) . Furthermore , the CDRH3 Q100rA mutation , that confers breadth to CAP256 . 25 , was predicted to occur very rarely with a 0 . 019% probability , requiring two base changes ( CAA to GCA ) , one of which was in an AID cold-spot ( Fig 3B ) . In contrast , we found that Q100rR that results in the off-track phenotype was predicted to occur with a much higher probability of 4 . 7% , requiring only a single nucleotide change that was not located within an AID cold-spot ( Fig 3B ) . Furthermore , the improbability of the evolution of T28R , N30D and Q100rA was reflected in next-generation sequences of the CAP256 lineage [22 , 43] , where T28R , S30D and Q100rA together accounted for <2% of the sequences . Altogether , this suggests that a single relatively probable mutation in the CDRH3 primarily limits the neutralization breadth of CAP256 . 04 . Next , we sought to determine which sites were responsible for the off-track phenotype of CAP256 . 20 compared to the related bNAb , CAP256 . 27 ( Fig 4A ) . Of the 6 amino acids that distinguish these CDRH3s , several were charge changes , with an overall CDRH3 charge of -7 and -3 in CAP256 . 27 and CAP256 . 20 , respectively . First , we transplanted the CDRH3 from CAP256 . 27 into CAP256 . 20 ( CAP256 . 2027H3 ) and tested this chimera for neutralization breadth . In contrast to the CAP256 . 04/ . 25 pair , transfer of only the CAP256 . 27 CDRH3 into CAP256 . 20 introduced both bNAb-like breadth and potency into the off-track antibody , increasing breadth from 1/16 to 14/16 viruses and potency from 2 . 92 to 0 . 05 μg/mL ( Fig 4B ) . In the reverse experiment , replacing the CDRH3 of CAP256 . 27 with that of CAP256 . 20 ( CAP256 . 2720H3 ) almost completely abrogated neutralization ( Fig 4B ) . To determine which of the six residues within the CDRH3 of 256 . 20 limited breadth , we focused on three residues which altered the electronegativity of the loop , R100d , H100j and V100n ( Fig 4A ) , and mutated these sites alone and in combination . The V100nE mutation , which slightly decreased the charge of the CDRH3 only marginally improved neutralization breadth by one additional virus ( the autologous superinfecting virus , CAP256 . 15-wk SU ) ( Fig 4B ) . R100dW also decreased the charge and increased breadth by an additional three viruses ( CAP256 . 15-wk SU , CAP210 and ZM197 ) . Similarly , introduction of an H100jD mutation conferred neutralization of the same three additional viruses but with greater potency than R100dW ( geometric mean potency of 0 . 04 compared to 0 . 33 μg/mL , respectively ) . The combination of R100dW and H100jD increased potency and breadth to a total of 12/16 viruses , while V100nE , R100dW and H100jD together introduced the same bNAb-like broad neutralization and potency as the entire CDRH3 transplant ( Fig 4B ) . In the reverse experiment , mutating positions 100d , 100j and 100n in the CDRH3 of CAP256 . 27 to match the sequence of CAP256 . 20 knocked out neutralization against previously sensitive viruses ( S2 Fig ) . Therefore , of the 17 residues that differentiate the HC of CAP256 . 27 and CAP256 . 20 only three amino acids , all within the CDRH3 , are responsible for limiting the breadth of CAP256 . 20 . Comparison of the probabilities of the breadth-conferring and restricting mutations between CAP256 . 27 and the CAP256 . UCA showed that the G100nE bNAb mutation was improbable ( 0 . 89% ) , since two base changes were required ( Fig 5A ) . In contrast , the W100dR , D100jH and G100nV mutations that took CAP256 . 20 off-track are highly probable ( 10% , 14% and 3% , respectively ) , as only single base changes were necessary , with the H100j mutation in an AID hot-spot ( Fig 5A ) . The probability of retaining all three mutations that confer breadth ( W100d , D100j and G100n ) in the absence of selection is 0 . 33% . Overall , the relatively higher probability of these mutations which restrict breadth highlights the potential ease with which members of bNAb lineages may mature along undesirable pathways . To explore the basis of these results , we fitted the amino acid sequences of CAP256 . 20 and CAP256 . 27 HCs to the crystal structure of CAP256 . 25 HC in Swiss-PdbViewer ( v4 . 1 . 0 ) , and then aligned this to a CAP256-34 week trimer structure ( Fig 5B , [26] ) . The YYD motif of CAP256 . 27 was predicted to hydrogen bond with two K166 residues on two protomers and R166 and K169 on the remaining protomer ( Fig 5B , top left ) . The mutations in CAP256 . 20 likely disrupt these interactions by placing a positive charge ( H100j ) proximal to K169 and possibly preventing sulfation of the preceding Tyr [44] , which would knock out contacts with K166 ( Fig 5B , top right ) . Furthermore , the hydrogen bond between E100n , present in CAP256 . 27 , and the N160 glycan was disrupted by the CAP256 . 20 V100n substitution ( Fig 5B , bottom left ) . In addition , the CAP256 . 20 R100d mutation , places a positive charge next to K169 resulting in electrostatic repulsion ( Fig 5B , bottom right ) and possibly preventing the backbone interactions between these residues . Therefore , key interactions between the highly electronegative CDRH3 of CAP256 . 27 ( -7 ) and the electropositive V2 epitope are abrogated by the more electropositive CDRH3 of CAP256 . 20 ( -3 ) , resulting in the off-track phenotype . We were interested in determining which viral immunotypes ( or epitope amino acid variants ) drove CAP256 . 20 away from breadth . The K169 immunotype , which forms part of the CAP256 epitope , is fairly conserved within subtype C viruses ( 65 . 9% ) ( www . hiv . lanl . gov ) ( Fig 6A ) [12] . In contrast , the 169Q immunotype is present in only 6 . 5% of subtype C viruses ( Fig 6A ) but dominated the viral population in donor CAP256 across 21 of 28 time points from six to 206 weeks post infection ( Fig 6B and S3A Fig ) . We hypothesized that this globally rare immunotype , which predominated in CAP256 , contributed to the evolution of CAP256 . 20 . Introduction of 169Q mutations into eleven heterologous viruses slightly improved the neutralization of four viruses by CAP256 . 20 ( S3B Fig ) , suggesting a preference of CAP256 . 20 for 169Q , though other viral determinants clearly contribute to neutralization sensitivity/resistance . As autologous viruses , rather than heterologous viruses , select mutations during SHM , we determined the sensitivity of autologous viruses containing either a 169Q or 169K immunotype to neutralization by CAP256 . 20 and CAP256 . 27 . We identified two viruses that naturally contained a 169Q ( CAP256 . 42-wk 5 and CAP256 . 48-wk 10 ) , and were sensitive to CAP256 . 20 and CAP256 . 2720H3 . Introduction of Q169K mutations into these viruses knocked out neutralization by these antibodies , and their matched CDRH3 chimeras ( Fig 6C ) . In the reverse experiment , introduction of a 169Q into three autologous viruses ( CAP256 . 34-wk 80 , CAP256 . 59-wk 10b and CAP256 . 15-wk SU , containing 169K/I and resistant to CAP256 . 20 and CAP256 . 2720H3 ) resulted in increased sensitivity to these antibodies ( Fig 6C ) . In all five viruses , the 169K immunotype was associated with increased neutralization potency by CAP256 . 27 , CAP256 . 2027H3 and the additional broad CAP256 . 20 mutants and chimeras ( S3C Fig ) , consistent with previous studies [11 , 12] . These data indicate a CDRH3-mediated preference of CAP256 . 20 for the 169Q immunotype , which limited the evolution of breadth in this antibody .
A major focus of HIV vaccine design is based on a deep understanding of the development of bNAbs during HIV infection . Studies of HIV/antibody co-evolution have provided a template for the maturation of breadth that is now the basis of B-cell lineage vaccine strategies [12 , 17–23] . However , much less is known about the maturation of antibodies within bNAb-containing lineages that fail to acquire breadth . We have previously shown that these include both “dead-end” antibodies ( that fail to acquire breadth and exhibit low SHM ) , and “off-track” antibodies that acquire substantial SHM , but little breadth , and are the focus of this study . Here we used previously identified strain-specific “off-track” antibodies that are closely related to broad members of the CAP256-VRC26 bNAb lineage to probe the genetic and viral contributors to their maturation [22] . In two pairs , we identified key breadth-restricting mutations , and defined the probabilities of their occurrence . Furthermore , we show the preferential neutralization of a globally rare immunotype by CAP256 . 20 impeded the evolution of breadth , providing a mechanism for the development of off-track responses in infection . Together these data provide insights into the challenges associated with driving maturation of antibodies towards breadth , a central question in HIV vaccine design . Studies of bNAb lineages have highlighted the substantial plasticity in their maturation , enabling the development of breadth through multiple pathways [11 , 12 , 21 , 43] . This suggests that immunization strategies promoting high levels of SHM along diverse pathways may enhance bNAb development . Our data suggests that similarly , the development of off-track antibodies can occur by multiple pathways , and through the acquisition of very few mutations . Indeed , within the CAP256 . 20/27 pair , only three mutations were sufficient to divert CAP256 . 20 away from both breadth and potency . Furthermore , the enhanced breadth of CAP256 . 04 R100rA , which contains a single CDRH3 mutation , in contrast to the six mutations in CAP256 . 0425H3 , suggests substantial mutational “noise” . Additionally , we found an increase in breadth when the CAP256 . 25 CDRH2 or single CDRH1 mutations were introduced into CAP256 . 04 . These data confirm that extensive SHM does not necessarily result in breadth and suggests that many mutations do not impact or negatively affect breadth . The maturation of bNAbs towards breadth includes a requirement for functionally relevant but improbable mutations [34] . Consistent with previous studies , we find that the key breadth-conferring mutations in the CAP256-VRC26 lineage , such as Q100rA and G100nE , are highly improbable [34] and that mutations that restricted breadth were in many cases relatively probable . Interestingly , the two pairs of CAP256 antibodies provide distinct examples of how the probability of mutations could shape on- versus off-track maturation . In CAP256 . 20/27 , the potential for high probability breadth-limiting mutations ( e . g . W100dR , D100jH and G100nV ) is evident in pulling the B-cell off-track . In contrast , in the CAP256 . 04/25 pair , an important improbable mutation associated with breadth , Q100rA , represents a potential bottleneck that CAP256 . 25 has overcome to achieve breadth , but CAP256 . 04 has not . The more probable R100r mutation is also present in broader lineage members , suggesting compensatory mechanisms and that multiple pathways to breadth exist . Together these data indicate that , in some instances , the pathway to the off-track phenotype offers less “resistance” compared to the requirement for highly improbable mutations associated with breadth , and will require careful selection of immunogens to avoid this phenotype [34] . Several studies , including our previous work in the CAP256-VRC26 lineage , have shown how exposure to diverse viral variants contributes to the development of breadth [11 , 12 , 22] . This study extends this work to define the mechanisms that select the off-track phenotype within a single lineage . The enrichment of the globally rare 169Q immunotype in CAP256 and the preferential neutralization of this immunotype by CAP256 . 20 , implicates 169Q autologous viral variants in the evolution of this off-track antibody . Early CAP256-VRC26 lineage members ( CAP256 . 01 and CAP256 . 24 ) and other off-track antibodies ( CAP256 . 12 and CAP256 . 13 ) are unable to neutralize 169Q viruses . However , most lineage members , especially those with breadth , are able to neutralize this immunotype , though to a lesser extent than K169 viruses . This indicates that whereas most lineage members tolerate 169Q , CAP256 . 20 is uniquely reliant on the globally rare Q169 immunotype , explaining the strain-specificity of this antibody . Structurally , this may be a consequence of the reduced dependence of bNAbs on specific side chain residues within the epitope , compared to an increased dependence of off-track antibodies such as CAP256 . 20 on such side chains . These data highlight the fact that evolution towards breadth , a desirable outcome from a vaccinology perspective , is distinct from antibody maturation to counter circulating autologous viral variants . This study provides evidence that affinity maturation to counter globally rare viral immunotypes can drive antibodies within a broad lineage away from breadth . As with the maturation of breadth , off-track antibodies can develop through multiple evolutionary pathways . Furthermore , limited breadth despite high levels of SHM can occur by the introduction of few , but relatively probable , mutations . The inherently stochastic nature of affinity maturation may make avoiding off-track antibodies challenging . Furthermore , additional research is needed to determine if the expansion of ‘off-track’ B-cell lineages prevents or limits the maturation of bNAbs . However , defining pathways towards and away from breadth will facilitate the selection of immunogens that elicit bNAbs and minimize off-track antibodies . Our data suggests that by selecting sequential immunogens that present globally conserved epitopes , the elicitation of off-track antibodies can be minimized . As immunization strategies improve to allow targeting of specific antibody residues , these data inform the design of immunogens to elicit V2-directed bNAbs .
CAP256 is a participant enrolled in the CAPRISA 002 Acute Infection study , established in 2004 in Kwa-Zulu Natal , South Africa . The CAPRISA 002 Acute Infection study was reviewed and approved by the research ethics committees of the University of KwaZulu-Natal ( E013/04 ) , the University of Cape Town ( 025/2004 ) and the University of the Witwatersrand ( MM040202 ) . CAP256 , an adult , provided written informed consent . The specificity of her plasma has been described , monoclonal antibodies isolated and autologous Env evolution characterized [11 , 12 , 22 , 45 , 46] . Exchanging the CDRH3s between monoclonal antibodies was achieved with a two-step overlapping PCR . The CDRH3s were amplified with the AccuPrime Pfx DNA Polymerase and reaction mix ( Thermo ) with primers that were complementary to the recipient antibody . The product was gel extracted ( 1% , 1x TAE ) and purified with the QIAquick Gel Extraction Kit ( Qiagen ) and used as the mutagenesis primer for the second PCR with the QuikChange Lightning Multi Site-Directed Mutagenesis Kit ( Stratagene ) . Genes with CDRH1 , CDRH2 and CDRH3 exchanges were synthesized ( GenScript ) and excised with Age1 and Sal1 ( Thermo ) according to the manufacture’s recommendation . Heavy chain fragments were separated in an agarose gel ( 1% 1x TAE ) and ligated ( Roche ) into the CMVR expression vector as per the manufacturer’s protocol . Mutations in both antibody and viral Env genes were introduced by site-directed mutagenesis with the QuikChange Lightening Multi Kit ( Stratagene ) per the manufacturer’s instructions and plasmid sequences were confirmed by Sanger sequencing with the ABI PRISM Big Dye Terminator Cycle Sequencing Ready Reaction kit ( Applied Biosystems , Foster City , CA ) and resolved on the 3500 genetic analyzer . The TZM-bl cell-line was obtained from the AIDS Research and Reference Reagent Program and the 293T cell-line was obtained from Dr . George Shaw ( University of Pennsylvania , Philadelphia , PA ) . The cell-lines were cultured at 37°C ( 5% CO2 ) in Dulbecco’s Modified Eagle Medium ( DMEM ) supplemented with 10% heat-inactivated foetal bovine serum ( FBS ) , 50 μg/mL gentamicin ( Sigma ) and 25 mM HEPES , at confluency monolayers were disrupted with 0 . 25% trypsin and 1 mM EDTA ( Sigma ) . The 293F cell-line ( Life Technologies ) was maintained in serum and antibiotic free FreeStyle 293 Expression media at 37°C ( 10% CO2 ) in an orbital shaker ( 130 rpm ) . Cloned Envs and pSG3ΔEnv backbone plasmids ( NIH AIDS Research and Reference Reagent Program ) were co-transfected into 293T cells with 1:3 PEI MAX transfection reagent ( Polysciences ) . Following 48 hours , filtered pseudovirus supernatants were adjusted to 20% FBS and stored at -80°C . Equal quantities of antibody heavy and light chain plasmids were co-transfected into 293F cells using PEI MAX . Monoclonal antibodies were purified from cell-free supernatants after six days using protein A affinity chromatography . Concentration and buffer exchange ( 1x PBS ) was completed with Vivaspin concentrators ( Sartorius ) . Neutralization was measured as previously described by a reduction in luciferase gene expression after single-round infection of TZM-bl cells with Env-pseudotyped viruses [47 , 48] . Titers were calculated as the reciprocal antibody dilution ( IC50 ) causing 50% reduction of relative light units ( RLU ) . The crystal structures of CAP256 . 04 ( PDB: 4ORG ) was aligned to the crystal structure of CAP256 . 25 and CAP256-34 week trimer in PyMOL 2 . 0 . 2 . No structures were available for CAP256 . 20 and CAP256 . 27 , therefore the amino acid sequences of these antibodies were fitted to the crystal structure of CAP256 . 25 in Swiss-PdbViewer 4 . 1 . 0 and aligned in PyMOL . Protein model representations were created with Swiss-PdbViewer v4 . 1 . 0 and PyMOL v2 . 0 . 2 . Graphs were created in GraphPad Prism 6 , sequence alignments were made in BioEdit v7 . 2 . 5 and phylogenetic trees were constructed in MEGA v6 . 06 . The Antigen Receptor Mutation Analyzer for Detection of Low Likelihood Occurrences ( ARMADiLLO ) program estimates the probability of amino acid substitutions prior to antigenic selection [34] . Briefly , 104 simulated mature sequences per site of interest are generated with the S5F mutability and substitution models through comparison of the nucleotides sequences of the CAP256 . UCA and a given mature lineage member [33] . The probability of particular amino acid substitutions is then estimated by the frequency of the observed site in the set of simulated sequences . Here , a mutation of <2% probability is classified as “improbable” as this reflects a frequency ≤2 B-cells per germinal centre harbouring that particular mutation [34] . Probabilities of combinations of mutations are derived from the products of individual probabilities . | Broadly neutralizing antibodies ( bNAbs ) develop in some HIV infected individuals , partly due to their complex evolutionary pathways that are characterized by extensive somatic hypermutation ( SHM ) . Furthermore , bNAbs within a lineage may form a minor subset , amidst many strain-specific “siblings” , indicating that minor sequence differences between lineage members can significantly affect neutralization . Here , we define mutations that limit breadth in two “off-track” members of the CAP256-VRC26 bNAb lineage , and show that these occur with relatively high probability . A dominant autologous virus with a globally rare V2 sequence appears to have selected for an off-track antibody , providing a mechanism for the development of this antibody during infection . These data highlight the complex interdependencies between high levels of SHM and breadth , as mutations that neutralize autologous viruses may limit heterologous breadth . Consequently , strategies to increase SHM by repeated vaccinations will require careful antigen selection to focus the humoral response to globally common epitopes , limiting off-track responses . | [
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] | 2019 | Somatic hypermutation to counter a globally rare viral immunotype drove off-track antibodies in the CAP256-VRC26 HIV-1 V2-directed bNAb lineage |
Leptospirosis is an extremely widespread zoonotic infection with outcomes ranging from subclinical infection to fatal Weil's syndrome . Despite the global impact of the disease , key aspects of its pathogenesis remain unclear . To examine in detail the earliest steps in the host response to leptospires , we used fluorescently labelled Leptospira interrogans serovar Copenhageni to infect 30 hour post fertilization zebrafish embryos by either the caudal vein or hindbrain ventricle . These embryos have functional innate immunity but have not yet developed an adaptive immune system . Furthermore , they are optically transparent , allowing direct visualization of host–pathogen interactions from the moment of infection . We observed rapid uptake of leptospires by phagocytes , followed by persistent , intracellular infection over the first 48 hours . Phagocytosis of leptospires occasionally resulted in formation of large cellular vesicles consistent with apoptotic bodies . By 24 hours , clusters of infected phagocytes were accumulating lateral to the dorsal artery , presumably in early hematopoietic tissue . Our observations suggest that phagocytosis may be a key defense mechanism in the early stages of leptospirosis , and that phagocytic cells play roles in immunopathogenesis and likely in the dissemination of leptospires to specific target tissues .
Though traditionally thought of as a tropical disease , leptospirosis is endemic worldwide due to widespread infection of urban and sylvatic rodents and other animal reservoir hosts . In areas of the world with high levels of rodent exposure , human infection is common and frequently progresses to serious disease or death [1] , [2] . Although much has been learned about the biology and transmission of Leptospira species , the mechanisms of their pathogenesis and host colonization remain largely unknown . Leptospires colonize the renal tubules of reservoir hosts , from where they are shed in the urine and infect new hosts via mucosal surfaces and abraded skin . In the reservoir host , there is transient low-level hematogenous dissemination , followed by chronic infection limited to the kidney [1] , [3] , [4] . In contrast , susceptible hosts experience a heavy burden of infection in the bloodstream and multiple organs . The eventual antibody response precipitates an intense inflammatory reaction associated with hepatorenal failure . A key difference between reservoir and susceptible hosts is the ability of the TLR4 innate immune receptor to recognize leptospiral lipopolysaccharide ( LPS ) [5] , [6] . Murine peritoneal macrophages are strongly stimulated by purified leptospiral LPS , while human macrophages are unable to respond to leptospiral LPS via the TLR4 pathway [5] . Taken together , these studies suggest that early containment of infection via innate mechanisms , including recognition of leptospiral antigens and phagocytosis by macrophages , is essential for effective immune defense [7] . Previous in vitro studies have demonstrated that macrophages are capable of phagocytosing leptospires [8] , [9] . A variety of animal models of leptospirosis have been established , each with unique advantages and drawbacks . Guinea pigs [10] and hamsters [11] , [12] are the primary models of hosts susceptible to acute disease , while several animals including mice [4] , rats [3] , monkeys [13] , dogs [14] and skunks [15] can be experimentally infected and seem variously plausible as models of reservoir hosts . It is not certain to what degree these various model hosts retain features of natural infection and colonization . The zebrafish is increasingly used as a model organism for bacterial pathogenesis , with published studies of adult infection with pathogens including mycobacteria [16] , streptococci [17] , and Edwarsiella [18] . The ability to conduct forward genetic screens , along with the economy of infecting large numbers of animals are key advantages to this model [19] , [20] . Beyond these , the zebrafish embryo allows unparalleled in vivo microscopy and tracking of host-pathogen interactions involving fluorescently labeled bacteria . Minute details of the early steps of bacterial pathogenesis have been published using zebrafish embryos infected with Mycobacterium marinum [21] , [22] , [23] , Salmonella enterica [22] , [24] and Pseudomonas aeruginosa [25] . By 32 hours post fertilization a zebrafish embryo has a circulatory system and a fully functional innate immune system , along with a variety of distinct tissue types ( Figure 1A ) , making it a self-contained ‘laboratory’ for the study of bacterial infection . In this work we have investigated the earliest events in leptospirosis by inoculating the developing zebrafish with L . interrogans sv . Copenhageni . During the first 36 hours of infection , L . interrogans produces persistent infection in the zebrafish embryo , with phagocytes playing a central role in the initial host response to infection and possibly in the localization of leptospires to target tissues .
Wild-type AB zebrafish embryos were maintained and infected by injection into the caudal vein or hindbrain ventricle as described previously [22] , [23] , [26] at 24–30 hours post fertilization unless otherwise noted [22] , [23] , [26] . All animals were handled in strict accordance with good animal practice as defined by the relevant national and/or local animal welfare bodies , and all animal work was approved by the University of Washington Institutional Animal Care and Use Committee . Leptospira interrogans serovar Copenhageni strain Fiocruz L1-130 was isolated from a patient in Salvador , Brazil [27] . Virulent leptospires isolated from infected Golden Syrian hamsters were grown in EMJH medium supplemented with 1% rabbit serum and 100 ug/mL 5-fluorouracil at 30°C [28] . Staining was performed in a 1∶1000 dilution of SYTO-83 ( Invitrogen ) for 30 minutes , followed by rinsing with PBS to remove unbound dye . Inoculum was estimated based on fluorescence microscopy after injection . Widefield microscopy was performed on a Nikon E600 compound microscope equipped with DIC optics and 100 W Mercury lamp for epifluorescence . Objectives used included 10× Plan Fluor , 0 . 3 NA , 20× Plan Fluor , 0 . 5 NA , and 60× Water Fluor , 1 . 0 NA . Widefield fluorescence and DIC images were captured on a CoolSnap CF CCD camera ( Photometrics ) using MetaMorph 7 . 1 ( Molecular Devices ) . Dataset analysis and visualization was performed using MetaMorph 7 . 1 ( Molecular Devices ) . Movies were produced from stacks compiled in MetaMorph . Additional movie compilation and formatting was performed in Adobe Premiere 6 . 0 and QuickTimePro 7 . 4 ( Apple ) . Figure processing and assembly were performed in Adobe Photoshop CS2 .
To determine the effect of Leptospira interrogans infection on developing zebrafish , we injected doses of roughly 10 to 100 organisms into 30 hour post fertilization zebrafish by either the caudal vein or hindbrain ventricle ( Figure 1A ) . Inoculation by either route resulted in no lethality or gross pathology over 48-hours although organisms were detectable by DIC microscopy as long as 24 hours post-infection . Extracellular organisms were observed immediately after injection ( Figure 1B ) and video microscopy revealed that they exhibited the flexing , bending and spinning motility characteristic of these organisms in vitro ( Video S1 ) . These organisms appeared to be phagocytosed rapidly; within the first two hours post intravenous infection we found many macrophages in the blood contained leptospira . To ascertain that we were observing intracellular Leptospira by DIC microscopy , we stained L . interrogans cultures with SYTO 83 to render them red fluorescent before injection , and visualized infection with both DIC and fluorescence microscopy . Again by four hpi we were unable to detect extracellular bacteria at any location in the zebrafish embryos and all organisms were visualized within phagocytes , presumably macrophages based upon their morphology ( Figure 1C ) . While the SYTO 83 stain confirmed the rapid intracellular localization of the organisms , we found that it diminished motility of stained bacteria in vitro . reducing motility from 100% immediately after staining to 10% at six hours post staining . Therefore , we performed all subsequent experiments using both stained and unstained bacteria to ensure that the observed infection phenotypes were not simply an artifact of bacterial compromise due to staining . To examine the capacity of leptospires to attract macrophages , we injected similar doses of L . interrogans into the hindbrain ventricle at 30 hours post infection , a time in development when very few if any macrophages reside in this compartment [29] . Macrophages were rapidly recruited to the ventricle and took up the bacteria within the first four hours ( Figure 1D ) . This result showed that the uptake of leptospires by macrophages did not require blood flow to bring the two together , and that macrophages actively migrated to the site of infection . After encountering and taking up leptospires in the bloodstream or the hindbrain ventricle , the macrophages that contained organisms took on a distinct morphology . Although the bacteria appeared to be contained within compartments separate from the cytoplasm ( Figure 1B , D ) , the macrophages generated numerous small vesicles which moved rapidly about the cytoplasm ( Figure 1E and Video S2 ) . Occasional membrane blebbing was also visible ( Video S3 ) . Again we note that both stained and unstained leptospira produced similarly unaffected embryos with the same characteristic-looking phagocytes during the first day of infection . By 24 hours post infection , there was no gross pathology although bacteria were still plentiful . In experiments where fluorescent bacteria were used , all fluorescence correlated with intracellular clusters , always found in cells of a similar phenotype as the day before—many subcellular vesicles were present , often moving throughout the cytoplasm ( Figure 2A , Video S4 ) . Cells of similar morphology were found in embryos infected with unstained leptospires . The affected cells found in the brain also contained several larger vesicles consistent with apoptotic bodies ( Figure 2B ) . Such cells were common in embryos infected via hindbrain , found occasionally in embryos infected intravenously , but not in uninfected controls ( data not shown ) . The most striking feature of embryos 24 hours after intravenous infection was the localization of fluorescent bacteria . While some fluorescent clusters were present in the caudal vein ( Figure 2C , arrowheads ) , the majority were dorsolateral to the dorsal aorta in the trunk ( Figure 2D–E , Video S5 ) , a location which has been shown to play a part in early hematopoiesis [30] . Because of the location deep within the tissues , it was not possible to verify that the same localization occurred after infection with unstained bacteria . Previous experimental infections of zebrafish embryos with other organisms have not demonstrated such localization , suggesting that this accumulation is specific to infection with L . interrogans . To confirm this suggestion , we compared leptospiral infection to infection with fluorescent Pseudomonas aeruginosa over the same time course . Infection with P . aeruginosa produces either overwhelming infection or clearance over the first 36 hours of infection , depending upon dosage [25] . At 24 hours post infection with a non-lethal dose of P . aeruginosa , we found that the remaining bacteria were similar in number to L . interrogans remaining at 24 hours . Despite the fact that both bacterial types were apparently contained within phagocytes at this time , there was no accumulation of P . aeruginosa-infected cells in the region dorsolateral to the dorsal aorta ( Figure 2F ) . While this phenomenon could represent a general mechanism whereby dead or compromised organisms are transported to this location , we note that we have not seen such a localization with heat-killed fluorescent M . marinum . Therefore , we suspect that this localization is the result of specific host-bacterial interaction .
We undertook the study of leptospiral infection of zebrafish embryos to assess the usefulness of zebrafish as a general model host for infection , as well as to examine the details of early pathogenesis directly in vivo . While Borrelia burgdorferi , another spirochete , has been observed in vivo during early pathogenesis [31] , [32] , this is the first in vivo visualization of leptospirosis of which we are aware . At least in embryos , infection of zebrafish with L . interrogans appears to be asymptomatic for the first 48 hours . It is not clear if this trend is inherent to the host-pathogen interaction or perhaps due to the lack of a functional adaptive immune system at this point in zebrafish development [33] . Also , it is possible that more damaging effects of infection require more than 48 hours to develop . At any rate , the immediate response of zebrafish embryos to L . interrogans infection is as follows . In contrast to B . burgdorferi in mice , which directly migrate out of the vasculature [31] , Leptospires are taken up by phagocytes within a few hours of injection . The lack of antibody of any kind at this early stage in development demonstrates that it is not required for phagocytosis , as has been suggested [9] , [34] , [35] . The zebrafish complement system appears to be quite functional by this time [36] , so it may be that complement-based opsonization is all that is required . Phagocytosis does not appear to rely upon accidental encounters with phagocytes , but may instead involve chemotactic mechanisms , as injection into the hindbrain , which normally contains very few if any phagocytes [29] , results in active migration of macrophages to the site of infection . Proposed models for leptospiral pathogenesis mostly describe extended periods of leptospiremia , with extracellular bacteria finding their way into target tissues [4] , [37] , [38] . Defense against extracellular organisms , particularly in the early phases of infection , is likely to involve the innate defense mechanism of complement-based opsonophagocytosis . Supporting the relevance of the observations we report here , intracellular leptospires have been observed within splenic phagocytes by immunohistochemistry in the hamster model of leptospirosis [39] . It is possible that the infecting dose used in our experiments was too low to simulate a pathogenic infection . Barring this , however , our results suggest that leptospires are intracellular from very early in infection . Observations of zebrafish embryonic macrophages that have ingested leptospires suggest that this phagocytosis may result in adverse cellular events . The appearance of small to medium sized vesicles moving about the cytoplasm takes place within one or two hours of the encounter , and infected cells with this characteristic morphology are still visible 24 hours later . It is not certain if this represents the persistence of the same affected cells , gradually gaining more vesicles , or the death of the initial macrophage followed by re-uptake of bacteria by another cell . Indeed , there has been evidence of apoptotic effects on infected host cells [40] , [41] , [42] , [43] , and we report here the blebbing appearance of affected cells after hindbrain infection . In our observations this blebbing was relatively rare , and so further observations are required to learn how relevant it is to pathogenesis . It has been shown that some of the macrophages within the yolk circulation valley at the advent of circulation actually migrate into the brain , change their gene expression profiles , and become microglia [29] . These cells then collect and dispose of apoptotic bodies of neurons [29] , [44] , although they are also capable of fighting infection [22] . By 24 hours after injection of L . interrogans into the hindbrain ventricle , these cells are often seen to contain clusters of multiple apoptotic bodies , strikingly similar to microglia made incapable of digesting their cargo by knockdown of v0-ATPase a1 [44] . To our knowledge , functional impairment of macrophages after leptospiral infection has never been reported . When combined with the experimental approaches for detection and perturbation of phagolysosome fusion of Peri et al [44] , the zebrafish model provides an ideal opportunity to explore the mechanisms of leptospiral effects on macrophages . At 24 hours post infection , leptospira were conspicuously located dorsolateral to the dorsal aorta . This location corresponds to that of early hematopoietic cells populating a tissue analogous to the ‘aorta-gonad-mesonephros’ ( AGM ) hematopoietic tissues in developing mammals [30] . Blood cell precursors migrate from this area to the caudal hematopoietic tissue ( CHT ) in the ventral tail , starting around 24 hours post fertilization . While some infected cells were indeed found in the CHT ( Figure 2C ) , there were consistently more at or near the AGM . It should be noted that due to technical limits of DIC microscopy , this localization was noted only when injecting leptospires stained with SYTO 83 , which impairs bacterial motility . Since all earlier features of the infection appear to be unaffected by the stain , however , we consider it likely that this localization is not an artifact of staining but this will need to be verified with intrinsically fluorescent strains . The developmental timing of our observation of infected cells here corresponds with the later times of AGM to CHT migration ( which ends around 72 hours post fertilization ) [30] , so the trunk tissue could still be acting as a hematopoietic site . The fate of this tissue , after its period as a hematopoietic zone , is unknown , and from our studies it is not clear whether the infection is within cells destined to depart or within other more permanent cells . The strikingly specific delivery of leptospires to this tissue by phagocytes provides insights into pathogenesis by suggesting a novel mechanism for targeting of organs during leptospiral dissemination . | Leptospirosis is a common bacterial infection in many tropical regions of the world that causes serious and often fatal disease in humans . The infection is transmitted by carrier animals , especially rats and other rodents , that release the leptospire bacteria from their kidneys into their urine . Humans are infected through exposure of broken skin or mucous membranes to contaminated water . Little is known about how or why the bacteria traffic from these sites specifically to the kidneys . The zebrafish embryo is a popular model organism for studying embryonic development , in part because of the ease with which living cells within the transparent embryos can be studied under the microscope . In this study , we use leptospire-infected zebrafish embryos to examine early leptospirosis by microscopy . In the first days of infection , the embryos appear normal . We find that leptospires are readily ingested ( but not killed ) by white blood cells called phagocytes . Later , infected cells are found specifically in a tissue near the dorsal aorta . This site may be a tissue that produces new blood cells and may represent a conduit for subsequent tissue targeting of the organisms . Our findings suggest that the zebrafish model may be useful for studying the pathogenesis of leptospirosis . | [
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"immunology/cellular",
"microbiology",
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] | 2009 | Leptospira interrogans Stably Infects Zebrafish Embryos, Altering Phagocyte Behavior and Homing to Specific Tissues |
A common yet poorly understood evolutionary transition among flowering plants is a switch from outbreeding to an inbreeding mode of mating . The model plant Arabidopsis thaliana evolved to an inbreeding state through the loss of self-incompatibility , a pollen-rejection system in which pollen recognition by the stigma is determined by tightly linked and co-evolving alleles of the S-locus receptor kinase ( SRK ) and its S-locus cysteine-rich ligand ( SCR ) . Transformation of A . thaliana , with a functional AlSRKb-SCRb gene pair from its outcrossing relative A . lyrata , demonstrated that A . thaliana accessions harbor different sets of cryptic self-fertility–promoting mutations , not only in S-locus genes , but also in other loci required for self-incompatibility . However , it is still not known how many times and in what manner the switch to self-fertility occurred in the A . thaliana lineage . Here , we report on our identification of four accessions that are reverted to full self-incompatibility by transformation with AlSRKb-SCRb , bringing to five the number of accessions in which self-fertility is due to , and was likely caused by , S-locus inactivation . Analysis of S-haplotype organization reveals that inter-haplotypic recombination events , rearrangements , and deletions have restructured the S locus and its genes in these accessions . We also perform a Quantitative Trait Loci ( QTL ) analysis to identify modifier loci associated with self-fertility in the Col-0 reference accession , which cannot be reverted to full self-incompatibility . Our results indicate that the transition to inbreeding occurred by at least two , and possibly more , independent S-locus mutations , and identify a novel unstable modifier locus that contributes to self-fertility in Col-0 .
Sexual reproduction may have evolved because it can combine different sequence variants through recombination [1] and because it can remove deleterious mutations linked to advantageous ones [2] , [3] . However , approximately 20% of flowering plants are self-fertilizing and engage in sexual reproduction without obtaining either of these benefits [4] . It has been proposed that inbreeding plant lineages represent evolutionary “dead ends” [5] that evolved from outbreeding ancestors [4]–[6] . In this view , mating system switches from an outbreeding to inbreeding mode may have been selected for by pollinator scarcity or population bottlenecks [7] , with inbreeding providing the benefits of reproductive assurance and increased potential for colonization , and in some cases possibly representing a survival mechanism used as a last resort to perpetuate a species . Because the outbreeding mode of mating is typically associated with the accumulation of recessive deleterious alleles that cause inbreeding depression , self-fertile taxa can only become established if this genetic load is purged . Theoretical models of the evolution of selfing have shown that inbreeding depression can indeed be overcome and selfing alleles can spread when the advantage of reproductive assurance outweighs the reduction of fitness [8] . However , mechanistic studies of switches from outbreeding to self-fertility have rarely been performed , and the genetic basis of these switches is poorly understood . In the crucifer ( Brassicaceae ) family , switches to inbreeding have occurred frequently and entailed loss of self-incompatibility ( SI ) . Self-incompatibility is a barrier to self-fertilization that is determined by variants of a single highly polymorphic locus , called the “S locus” . In self-incompatible plants , pollen is prevented from hydrating , germinating , and producing pollen tubes at the stigma surface if the same “S-locus” variant is expressed in pollen and stigma , whether these structures are located within the same flower or derived from different flowers on the same plant or different plants ( for recent review , see [9] ) . As a result , self-incompatible plants are largely but not completely self-sterile , and autonomous seed set is typically less than 5% that set by self-compatible plants . In all self-incompatible crucifer species investigated to date , the “S locus” is not a single gene , but rather consists of two polymorphic genes , allelic forms of which together constitute a unique S-locus haplotype ( hereafter S haplotype ) that defines a unique recognition specificity . One gene encodes the S-locus Receptor Kinase ( SRK ) [10] and the second gene encodes the small S-locus Cysteine-Rich protein ( SCR ) , which is the ligand for SRK . SRK is expressed in stigma epidermal cells , and its product is anchored via a single transmembrane domain in the plasma membrane of these cells . SCR is expressed in the anther tapetum , a cell layer that lines the sacs in which pollen grains develop , from which its SCR product is secreted and becomes incorporated into the outer pollen coat [11] . SCR proteins are delivered to the stigma surface upon pollen-stigma contact , but an SCR will bind to the extracellular domain of SRK and activate its cytoplasmic kinase domain , thus triggering the SI response , only if the SRK and the SCR proteins are encoded by the same S-locus haplotype [12] , [13] , i . e . when stigmas are pollinated with pollen derived from the same plant or from plants expressing the same S haplotype . In view of this S haplotype-specific interaction , recombination events that disrupt the genetic linkage of matched SRK and SCR alleles will cause loss of SI . Consequently , there is strong selection for maintaining the tight linkage of these genes . Recombinants between SRK and SCR are rare in self-incompatible plants , either because self-compatible genotypes that might arise do not persist in nature ( due to their genetic load ) or because recombination is actively suppressed in the S-locus region [14]–[17] . Similar to other genomic regions exhibiting low effective recombination rates [18]–[20] , the S haplotypes of self-incompatible Brassica and A . lyrata strains have been shown to accumulate haplotype-specific sequences due to divergent evolutionary trajectories and independent degeneration of non-coding sequences , and these features no doubt limit recombination in the region [14] , [17] , [21]–[23] . The model dicot plant Arabidopsis thaliana is a highly self-fertile crucifer that is thought to have had a self-incompatible ancestor based upon phylogenetic inference [24] and rescue of the SI trait by transgenic complementation with a functional SRK-SCR allelic pair from its close self-incompatible relative A . lyrata [25] , [26] . However , despite several recent studies and much debate [27]–[31] , the nature and number of mutational events that caused the switch to self-fertility in the A . thaliana lineage have not been established . Consistent with the expectation that selective pressures for maintaining the integrity of the S locus and its genes would be relaxed subsequent to the switch to self-fertility , all A . thaliana accessions analyzed to date harbor a non-functional S locus , referred to as pseudo-S ( ΨS ) , which carries inactivating mutations in the SRK and/or SCR genes [23] . Analysis of SRK and SCR sequence divergence in various accessions identified three distinct ΨS haplotypes , designated ΨSA , ΨSB , and ΨSC [23] , [28] , [29] , [32] . These three A . thaliana ΨS haplotypes are inferred to be orthologous , respectively , to the S37 , S16 , and S36 haplotypes of A . lyrata . This conclusion is based on the observation that SRK or SCR sequences in the A . lyrata S37 , S16 , and S36 haplotypes share much higher sequence similarity with the ΨSRK or ΨSCR sequences of the A . thaliana ΨSA , ΨSB , and ΨSC haplotypes , respectively , than with other A . lyrata S haplotypes [30] . Despite clear evidence for inactivating mutations in the SRK or SCR sequences of many A . thaliana accessions [23] , [27] , [28] , it is not possible to conclude that inactivation of the S locus was the primary cause of the switch to self-fertility in all A . thaliana populations . Indeed , the species also harbors mutations at other genes required for SI , as indicated by differences among accessions in the ability to express SI upon transformation with A . lyrata SRKb-SCRb ( AlSRKb-SCRb ) genes [26] , [33] . Among seven accessions analyzed by inter-specific complementation experiments , only C24 yielded a developmentally-stable SI response identical to that of A . lyrata Sb plants ( <5 pollen tubes/self-pollinated stigma at all stages of stigma development ) , demonstrating unequivocally that a non-functional S locus is the only cause of self-fertility in this accession [26] , [27] . By contrast , in other accessions , SI was transient [starting strong ( <5 pollen tubes/self-pollinated stigma ) in young flower buds , and later breaking down ( >100 pollen tubes/self-pollinated stigma ) in older flower buds and flowers] , weak ( 25–50 pollen tubes per self-pollinated stigma ) , or absent ( large numbers of pollen tubes/self-pollinated stigma at all stages of stigma development , similar to wild type untransformed A . thaliana ) . These phenotypes indicate the presence of mutations not only at the S locus , but also at “SI modifier” loci required for SI [26] , [33] . Indeed , one such SI modifier was identified in a cross between a C24::AlSRKb-SCRb transformant , which expresses a robust and developmentally-stable SI response , and a plant from the ΨSA-containing RLD accession , which expresses transient SI [33] . Molecular genetic analysis of this cross determined that transient SI is associated with reduced SRK transcript levels in older flowers caused by sequences upstream of the Col-0 allele of PUB8 ( Plant U-Box 8 ) , a gene tightly-linked to the S locus [33] . A comprehensive understanding of the switch to self-fertility in A . thaliana requires analysis of the S locus and of SI modifier loci , because any of these loci might have been targets of selection for self-fertility . Accordingly , we used a two-pronged approach to elucidate the genetic events that accompanied the evolution of self-fertility in A . thaliana . Firstly , we transformed several A . thaliana accessions with the AlSRKb-SCRb genes in an attempt to identify accessions like C24 , which express a robust and developmentally-stable SI response , and would therefore harbor mutations at the S locus but not at SI modifier loci . We reasoned that only in such accessions might it be possible to determine if the transition from outbreeding to inbreeding in A . thaliana occurred by a single mutational event or by multiple independent events . Secondly , we performed a Quantitative Trait Loci ( QTL ) analysis of SI modifier loci that differentiate AlSRKb-SCRb transformants of the reference Columbia ( Col-0 ) accession , which express transient SI , from those of the C24 accession .
To identify additional A . thaliana accessions , which , like C24 , might express a robust and developmentally-stable SI phenotype , we transformed several previously-untested accessions with AlSRKb-SCRb . In selecting accessions for transformation , we excluded accessions that carry the ΨSA haplotype [27] and its closely-linked PUB8 allele previously associated with transient SI [33] , because AlSRKb-SCRb transformants of these accessions are not expected to express stable SI . For each selected accession , independent AlSRKb-SCRb transformants were generated and tested for SI by pollination assays at different stages of stigma development ( Table 1 ) . AlSRKb-SCRb transformants of four accessions , Sha , Kas-2 , Hodja , and Cvi-0 , were found to express a developmentally-stable SI phenotype identical to that observed in C24::AlSRKb-SCRb transformants and in A . lyrata Sb plants [26]: immature floral buds were self-compatible , and strong inhibition of self pollen was first detected in stage-13 buds and persisted in older flowers . In addition , there was very little seed set on these plants , either by open pollination ( Table 1 ) or following manual self-pollination of mature floral buds and flowers . Significantly , these self-incompatible phenotypes are stably transmitted to subsequent transgenic generations , as determined by analysis of pollination phenotype over 20 generations in C24 , 10 generations in Sha , and two generations in each of Cvi-0 , Kas-2 , and Hodja . Our successful complementation of the Sha , Kas-2 , Hodja , and Cvi-0 accessions suggests that self-fertility in these accessions is due to a non-functional S locus , as in the C24 accession . It is therefore of interest to determine if the ΨS-haplotypes in these five accessions are the same or different ( i . e . are likely to be derived from the same ancestral mutant ΨS-haplotype or from independently-derived ancestral ΨS-haplotypes ) . At present , detailed descriptions are available only for the Col-0 , C24 , and Cvi-0 ΨS haplotypes . The Col-0 reference accession was shown to harbor a ΨSA haplotype containing aberrant SRK and SCR sequences . Its ΨSRKA allele contains a frameshift mutation that introduces a premature stop codon within the fourth of seven exons found in SRK genes . Its SCR sequences consist of several truncated ΨSCR sequences , the longest of which is designated ΨSCR1 [23] . In contrast , the C24 ΨS haplotype was shown to have been produced by recombination between ΨSA and ΨSC haplotypes [27]: it contains rearranged remnants of ΨSRKA exon 1 [which encodes the SRK extracellular domain ( ΨeSRK ) ] , a truncated version of ΨSRKC consisting of exon 7 , and two copies of ARK3 ( At4g21380 ) , a polymorphic gene located at one flank of the S locus in Arabidopsis species [23]: one copy consists of an ARK3SC allele characteristic of ΨSC haplotypes located at its normal location and an additional chimeric ARK3 copy located between the ΨSRKA and ΨSRKC sequences , which resulted from recombination between an ARK3SC allele and an ARK3SA allele characteristic of ΨSA haplotypes . As for the Cvi-0ΨSB haplotype , its complete DNA sequence ( accession number EF637083 [29] , [32] ) revealed the presence of a ΨSRKB allele containing a splice-site mutation at the end of intron 2 [28] and a convergently-oriented ΨSCRB allele lacking obvious inactivating mutations [29] , [32] . Information on the molecular events associated with the transition from out-crossing to selfing in A . thaliana may also be gleaned by genetic analyses of crosses between accessions that differ in expression of SI . In previous studies , genetic analysis of a relatively small C24::AlSRKb-SCRb x Col-0 F2 population [26] had inferred the segregation of two loci affecting pollination phenotype and identified a major modifier causing breakdown of SI in close linkage to the Col-0 ΨS locus [33] . In this study , we raised a larger F2 population of 300 plants derived by selfing an F1 plant , and we performed a cursory analysis to confirm the hypothesis that two loci with dominance of SI-conferring alleles segregated in this cross . Individual plants were classified into four phenotypic groups based on autonomous seed set: plants producing empty fruits with only an occasional fruit containing seed , similar to the C24::AlSRKb-SCRb parent ( 1 in 80 fruits measured ) ; plants with a full seed set similar to wild-type untransformed plants; plants producing few fruits with seed ( 1–3 for every 10 fruits measured ) ; and plants producing many fruit with seed ( 4–8 for every 10 fruits counted ) . Subsequent manual self-pollination of these plants determined that the number of pollen tubes formed at the stigma surface was consistent with fruit set . Plants in the empty-fruit group exhibited the SI response in all self-pollination assays , while plants with full fruit set exhibited a self-compatible pollination phenotype similar to untransformed plants . Plants that produced few or many fruits with seed exhibited variable pollination phenotypes , in which breakdown of SI occurred in an apparently random fashion in individual flowers , and these plants are classified as being partially self-compatible . In addition , loss of SI was stigma specific as determined by reciprocal pollinations of self-compatible plants with the C24::AlSRKb-SCRb parent . The results of a chi-squared test based on the proportions of the phenotypic categories were consistent with segregation of two loci with dominance of SI-conferring alleles ( X2 = 3 . 38; p = 0 . 3 ) . A scan of the genome with molecular markers distributed on all five chromosomes confirmed the presence of a Col-0-derived modifier locus with strong effect located on chromosome 4 near the ΨS locus , which corresponds to the previously-identified S-locus-linked modifier on chromosome 4 [33] . It also determined that a second Col-0-derived modifier locus responsible for partial self-compatibility was located on the bottom of chromosome 3 . The strong-effect S–locus-linked modifier [33] can mask the effects of weak-effect modifiers . Therefore , to ensure detection of weak-effect loci , a QTL mapping population was generated that subtracted the genetic effects of this major modifier ( see Methods ) . This population segregated for self-fertility , as expected . Manual self-pollination of a developing series of stigmas from two representative self-compatible plants revealed weakening of SI and some pollen tube growth in the most mature flowers . In contrast , self-pollination of a developing series of stigmas from two representative self-incompatible plants detected no pollen tubes in mature stigmas . Furthermore , reciprocal pollinations of self-compatible plants with C24::AlSRKb-SCRb transformants confirmed that the modifier alleles segregating in this population have stigma-specific effects as in the original C24::AlSRKb-SCRb x Col-0 cross . However , the self-compatible trait exhibited low penetrance in this population . On any given self-compatible plant , some flowers would not develop fruits with seeds , due to the SI response , while other flowers would develop into fruits filled with seeds . There was also great variability as to where on the stem SI would break down , the number of flowers that exhibited breakdown of SI , and the strength of the breakdown for each individual flower . In view of this variability , manual self-pollinations of a small number of randomly-selected individual flowers , as is usually done in pollination assays , cannot reflect overall plant phenotype . Consequently , standard pollination assays are not useful for phenotypic classification of plants in the QTL mapping population . Therefore , we used the size of mature fruit produced by autonomous self-pollination as a measure of the extent of SI breakdown in individual flowers . We reasoned that fruit size was a valid proxy for pollination phenotype because of the known strong correlations between fruit size and number of seeds per fruit ( as described previously [34] and confirmed in our study ) , and between number of seed in a fruit and strength of SI ( as observed in our F2 population ) . QTL analysis was performed using a total of 186 individuals ( see Methods ) . For phenotypic classification , it was important to distinguish between empty fruits and fruits with few seeds . Based on dissection of 25 of the smallest fruits in this population , it was determined that a mature fruit containing at least one seed had a width of at least 0 . 6 mm . Therefore , fruits that were narrower than 0 . 6 mm were classified as being empty and indicative of a self-incompatible response , while fruits that had a width of 0 . 6 mm or greater were classified as containing seed and indicative of a breakdown of SI . Similar measurements of mature fruit produced by self-incompatible plants in the QTL mapping population gave an average fruit length of 0 . 42 cm±0 . 05 ( n = 912 , with only one fruit in 25 having a width of 0 . 6 mm ) , a value very similar to that of the C24::AlSRKb-SCRb parental strain , in which average mature fruit length was 0 . 48 cm±0 . 07 ( n = 80 , with only one fruit having a width of 0 . 6 mm ) . By comparison , the average length of seed-filled mature fruit in the self-compatible parent of the QTL population was 1 . 33 cm±0 . 4 ( n = 59 ) , while average fruit lengths in untransformed plants of the C24 and Col-0 accessions were 1 . 54 cm±0 . 19 ( n = 80 ) and 1 . 38 cm±0 . 07 ( n = 80 ) , respectively . As shown in Figure 4 , the trait value distribution for the mapping population was continuous and approximately normal , suggesting the involvement of several genes in the control of fruit length . Individual plants were genotyped using 24 markers , microsatellites , and single nucleotide polymorphisms in chromosomal regions that segregated for Col-0-derived sequences . As shown in Figure 5 and Table 3 , four QTL underlying the observed differences in fruit length were found: two QTL ( QTL3 . 1 and QTL3 . 2 ) on chromosome 3 , one QTL ( QTL5 ) on chromosome 5 , and one QTL ( QTL1 ) on chromosome 1 , which accounted respectively for 25% , 24% , 15% , and 16% , of the observed variation in fruit length . All of the QTL regions were well above the significance threshold , and none corresponded to “minor QTL” with peaks near the threshold line . Nearly isogenic lines ( NIL ) were generated for each QTL region . Among these , only one NIL exhibited a breakdown of SI , as determined by manual self-pollination of flowers over the course of stigma development and by observation of autonomous seed set ( Table 3 ) . This NIL , NIL3 . 2 , incorporates QTL3 . 2 and likely corresponds to the chromosome-3 modifier that was associated with partial self-compatibility in the original C24::AlSRKb-SCRb x Col-0 F2 population . Epistasis between QTL1 , QTL3 . 1 , and QTL5 was assessed by crossing the corresponding NILs to generate “double NILs” . However , none of the “double NILs” showed a breakdown of SI based on observations of seed set . A possible explanation for this result is that these QTL do contribute to breakdown of SI , but their effect may only be detected when all three are combined with QTL3 . 2 . Another possibility is that QTL1 , QTL3 . 1 , and QTL5 control SI-independent variation in fruit length . Although the SI response exerts the major influence on seed number and consequently on fruit size in the populations we analyzed , modifier loci affecting differences in fruit size and seed number per fruit between wild-type Col-0 and C24 may also be segregating , similar to the loci uncovered in a previous analysis of natural variation for various fruit parameters [34] . Interestingly , this earlier study of fruit length differences between the Cvi and Landsberg accessions had identified a QTL in the QTL3 . 1 region [34] , but not in the other QTL regions identified in this study . In an attempt to fine-map QTL3 . 2 , an F2 mapping population was generated by crossing an NIL3 . 2 plant with a wild type ( untransformed ) C24 plant . This population segregated for the 1-megabase Col-0 introgression encompassing QTL3 . 2 ( Table 3 ) . F2 plants exhibiting recombination within the QTL3 . 2 region were identified by screening 2 , 016 individual plants , both phenotypically for seed set and genotypically with markers “NGA12” and “intron2” located just inside the introgressed region ( Table S1 ) . Three phenotypic groups were observed among recombinant plants: self-incompatible , self-compatible , and surprisingly , partially self-compatible . The occurrence of partially self-compatible plants in the recombinant pool was not expected because the gene underlying QTL3 . 2 was determined to be recessive in the Col-0 background . Also unexpectedly , these recombinants did not show a tight correlation between genotype and phenotype under the assumption of complete dominance of the SI-conferring C24 allele ( Table S2 ) . Nevertheless , F3 families were generated from self-compatible NIL3 . 2 F2 plants . Analysis of nine such NIL3 . 2 F3 families failed to identify self-compatible plants in six of those families , indicating that the self-compatibility phenotype can be completely erased from one generation to the next ( Table S2 ) . In view of this result , the genotype-to-phenotype correlations inferred for the self-incompatible class of NIL3 . 2 F2 plants become questionable . Nevertheless , with this caveat in mind and considering only the unambiguous self-compatible NIL3 . 2 F2 plants , QTL3 . 2 is tentatively mapped to a region of approximately 105 , 000 base pairs between genes At3g60440 and At3g60730 ( Table S2 and Table S3 ) .
Our results have extended our understanding of the genetic events at the S locus and at modifier loci that accompanied the switch to self-fertility in A . thaliana . The identification of four accessions , in addition to C24 , in which self-fertility may be clearly attributed to a non-functional S locus is significant for several reasons . From a practical point of view , the availability of several strains with diverged genetic backgrounds that do not contribute SI modifier alleles in crosses to laboratory-generated mutants will greatly facilitate the mapping of these mutants and the eventual cloning of genes required for SI . From an evolutionary perspective , the finding demonstrates that rather than being unique , the C24 accession is only one of potentially many accessions whose self-fertile phenotype may be fully reverted to SI by transformation with the AlSRKb-SCRb genes . Interestingly , these accessions are not confined to one geographical region: C24 is a southern-European accession originally isolated in Portugal , whereas Kas-2 , Hodja , and Sha are all central Asian accessions from Kashmir ( Kas-2 ) or Tajikistan ( Hodja and Sha ) , and Cvi-0 is restricted to the Cape Verdi Islands . A genome-wide polymorphism study in which 876 loci spread across the genome were surveyed in 96 accessions [35] had indicated that all accessions isolated from Tajikistan are genetically very similar to one another ( although Hodja was not included in the study ) , that Sha and Kas-2 are very closely related to each other , and that both are significantly diverged from C24 and Cvi-0 , which in turn are also highly diverged from each other . Our analysis of the C24 , Cvi-0 , Kas-2 , Hodja , and Sha accessions has illuminated the genetic events that likely caused loss of SI in these accessions and potentially others with similar ΨS-loci , genome-wide polymorphisms , and provenance . Keeping in mind that the ΨSA , ΨSB , and ΨSC haplotypes were derived from distinct ancestral functional S haplotypes , the four haplotypic structures observed in C24 , Cvi-0 , Kas-2 , and the Hodja/Sha group ( Figure 2 ) are consistent with independent origins of these ΨS haplotypes . The Cvi-0 ΨSB haplotype , which lacks ΨSA and ΨSC sequences was clearly independently derived . The Sha and Hodja ΨS haplotypes are highly-decayed versions of the ancestral SA haplotype also found in Col-0 , and it is possible that the S haplotypes in these three accessions might have been derived from the same ΨSA haplotype . In contrast , the C24 and Kas-2 ΨS haplotypes are both recombinant haplotypes generated by illegitimate recombination between ancestral SA and SC haplotypes . It is possible that the C24 ΨS haplotype was derived from a Kas-2-like ΨS haplotype via a complex series of restructuring events . Alternatively , based on the extensive genome-wide divergence inferred for the C24 and Kas-2 accessions [35] , their recombinant ΨS haplotypes might have arisen independently , as illustrated in Figure 6 . Our data thus demonstrate that the ability to express a developmentally-stable transgenic SI response is not restricted to one group of highly-related accessions or to accessions harboring one ΨS haplotype . Additionally , the divergence of ΨS haplotypes harbored by these accessions provides further evidence for the lack of a single selective sweep at the A . thaliana ΨS locus [27] , [29] . Rather , the results support the hypothesis that the switch to self-fertility in this species occurred by recurrent selection of distinct S-locus loss-of-function mutations . Such a process involving selection of adaptive mutations of independent origins has been referred to as a “soft sweep” [36] . Notably , soft sweeps are not restricted to the switch to self-fertility described here , and evidence of their occurrence is suggested by studies of polymorphisms in a variety of systems and organisms ranging from protozoa to human [36] . For example , in three-spine stickleback fish , selection for reduced body-plate armor in isolated European and Japanese populations has apparently resulted in the fixation of different alleles of ectodysplasin , a factor required for epithelial cell morphogenesis [37] , [38] . Possible scenarios for the generation of the observed ΨS haplotypes are shown in Figures 7 and 8 . It should be noted however , that the exact nature of the inactivating mutation and sequence of events that produced these ΨS haplotypes cannot be inferred from our data . A major difficulty in charting the history of the A . thaliana S locus is distinguishing a primary inactivating mutation from subsequent decay of the non-functional haplotype by further mutation , sequence loss , and rearrangement . For example , it is impossible to know whether the recombination events that produced the C24 and Kas-2 S haplotypes caused S-locus inactivation by disrupting the physical linkage between functional allelic SRK-SCR pairs , or if they occurred between already-mutated SA and/or SC haplotypes . There is also uncertainty as to whether the Kas-2 primary mutation is the same as that of Hodja and Sha . Although all three accessions have closely-related genomes and originate from close geographical locations , their ΨS loci differ in allele content and extent of decay . Furthermore , in contrast to the ΨSA haplotypes and the ΨSB haplotype of Cvi-0 , for which both ΨSRK and ΨSCR sequences as well as their A . lyrata orthologues are known , only an incomplete picture of ΨSC haplotypes is available because neither A . thaliana ΨSCRC sequences nor the orthologous A . lyrata SCR36 ( AlSCR36 ) sequences have as yet been isolated . Identification of AlSCR36 is likely to be particularly informative . Just as AlSCR37 sequences allowed a resolution of the Col-0 ΨSCR1 structure in this study , AlSCR36 sequences may be used to investigate the fate of the SCRC allele in A . thaliana and to determine if , and in what form , these sequences were maintained in Kas-2 , C24 , or other ΨSRKC-carrying accessions . The structures of the ΨS haplotypes observed for Kas-2 and C24 as well as Nok-3 ( Table 2 ) reveal an important role for recombination in shaping extant S-locus structure in A . thaliana . The ΨSA-ΨSC recombinant haplotypes of these accessions provide clear evidence for the occurrence of inter-haplotype recombination events in geographical areas where the SA and SC haplotypes were both present [27] , as in southwestern Europe for the C24 ΨS haplotype and in central Asia for the Kas-2 ΨS haplotype ( Figure 6 ) . Only the ΨSB haplotype , which is restricted to the Cape Verdi Islands , did not participate in inter-haplotype recombination ( Figure 6 ) . Thus , recombination between S haplotypes that encode different SI specificities can occur , despite the extensive structural heteromorphism and sequence divergence that typically distinguish these S haplotypes . It is possible that DNA crossover might occur in small regions of sequence similarity , such as regions containing the many transposon-like sequences present within the locus [27] . The contrast between the occurrence of inter-haplotype recombination events inferred in this study and the very low effective rate of recombination that typically characterizes the S-locus region in self-incompatible species [15] , [17] suggests that purifying selection against recombinants actively maintains low rates of recombination in the region , as previously discussed [17] . The switch to self-fertility is expected to have caused relaxation of this selective pressure , leading to further restructuring of the S-locus region . Thus , it is interesting to consider whether current recombination rates at the ΨS locus of A . thaliana are consistent with this expectation . The potential for recombination certainly exists despite high levels of self-fertility , as gene flow via pollen dissemination has been shown to contribute to genetic variability in local populations of the species [39] . Furthermore , the S-locus region was identified as a recombination hotspot in a cross between the Col-0 and Ler-0 accessions [40] . However , these two accessions harbor highly similar if not identical ΨSA haplotypes [27] , and much lower recombination rates are expected in crosses involving structurally-divergent ΨS haplotypes . This expectation was confirmed by a recent analysis of 3 , 210 plants derived from a cross between C24 and RLD , an accession that carries the same ΨSA haplotype as Col-0 ( Figure 2 ) . Using the S-locus flanking markers PUB8 ( At4g21350 ) and ARK3 ( At4g21380 ) , which are separated by 34 kilobases in RLD , only 1 recombinant was recovered , and this recombinant was produced by a cross-over event within the promoter region of PUB8 , not within the S locus proper [33] . Thus , the likelihood of further S-locus restructuring by recombination between structurally-diverged ΨS haplotypes is low , despite relaxed selection on the locus . The acquisition of a robust and developmentally-stable SI response by accessions that harbor independently-derived ΨS haplotypes provides the strongest evidence to date that A . thaliana evolved from an obligate out-crosser to a predominantly selfing species through multiple S-locus inactivating mutations in distinct outbreeding individuals . One interpretation of our data is that self-fertility in A . thaliana arose at least twice: once in an SA or SC haplotype ( producing the Hodja/Sha , C24 , and Kas-2 ΨS haplotypes ) and once in an SB haplotype ( producing the Cvi-0 ΨSB haplotype ) . A less conservative interpretation would invoke three origins of self-fertility if the C24 and Kas-2 S haplotypes are assumed to have arisen independently ( Figure 6 ) . When and how frequently mutations at SI modifier loci occurred in A . thaliana must await the molecular cloning of these loci . At least one such SI modifier was uncovered in our QTL analysis of differences in expression of SI between AlSRKb-SCRb transformants of the C24 and Col-0 accessions . This previously-unidentified recessive modifier , defined by QTL3 . 2 , was associated with self-fertility in Col-0 and was mapped to chromosome 3 . However , phenotypic instability , low heritability , and erasure of the self-compatibility trait in advanced mapping populations precluded further fine mapping and isolation of the underlying gene ( s ) . The cause of this instability is not known . One intriguing possibility is that it might reflect an epigenetic component in control of the self-compatibility trait in these populations . Indeed , phenotypic instability is a hallmark of epigenetically-controlled traits in various organisms [41]–[44] . Furthermore , examples of naturally-occurring epialleles have been reported in plants [43] , [45] , and widespread epigenetic natural variation has been noted among accessions of A . thaliana [46]–[48] . Similar to other epialleles that display unpredictable patterns of instability , the instability of QTL3 . 2 might be due to the loss of an unlinked trans-acting “maintainer” locus through segregation in NIL populations . In any case , our identification of an unstable modifier of SI has relevance for theoretical modeling and mechanistic studies of switches to self-fertility in A . thaliana and other plant species . Clearly , approaches more suited to the identification of unstable alleles than traditional QTL analysis and association mapping [49] will be required to clone at least some of the genes associated with self-fertility . Future molecular genetic analysis of polymorphisms at SI modifier loci , as well as investigation of S-locus structure in additional accessions that might express developmentally-stable SI upon transformation with the AlSRKb-SCRb genes , will undoubtedly determine if switches to self-fertility occurred exclusively by inactivation of the S locus in the A . thaliana lineage .
A . thaliana plants were typically grown at 22°C and a photoperiod of 16 hours . Plants that were used for transformation by the floral dip method [50] were grown under a 24-hour photoperiod . All accessions used in this study were obtained from the Arabidopsis Biological Resource Center in Columbus , Ohio . The Kashmir ( Kas-2; CS22638 ) , Shahkdara ( Sha; CS929 ) , and Hodja-Obi-Garm ( Hodja; CS6178 ) accessions were transformed with the p548 plasmid ( here designated AlSRKb-SCRb ) , a previously-described pBIN-PLUS derivative containing the A . lyrata SRKb and SCRb genes [26] . DNA gel blot analysis was used to confirm the independent origin of transformants and to identify transformed lines carrying single integrations of the transgene pair: genomic DNA was isolated from individual plants by the CTAB method [51] , digested with EcoR1 , transferred to Hybond H+ membrane ( Amersham Biosciences , Piscataway , NJ ) , and hybridized according to the Hybond H+ membrane instruction manual with a probe specific for the Neomycin PhosphoTransferase ( NPTII ) gene that was labeled with 32P using the Random Priming kit ( Roche , Indianapolis , IN ) . Hybridized membranes were washed at 65°C first in a solution containing 2× SSC and 0 . 5% SDS and subsequently in a solution containing 0 . 2× SSC and 0 . 5% SDS . Blots were exposed to phosphor screens , scanned using a GE Healthcare STORM phosphorimager ( Piscataway , NJ ) , and analyzed with the ImageQuant software package purchased as a bundle with the phosphorimager . In all cases analyzed , each transformant was found to exhibit a unique transgene pattern ( data not shown ) , consistent with independent transgene integration events and demonstrating that each of the analyzed transformants was or independent origin . Pollination responses were tested on pollen-free stigmas just before anthesis , when the stigmas are receptive to pollen but before the pollen grains are mature and released from the anthers . Using a stereomicroscope , stigmas were manually pollinated with hundreds of pollen grains from the dehisced anthers of mature flowers . Two hours after pollination , flowers were fixed for 10 minutes in a 3∶1 mixture of ethanol and acetic acid at 65°C , softened for 10 minutes in 1N NaOH at 65°C , washed two times in water , stained in decolorized aniline blue , and transferred to a slide for observation by epifluorescence microscopy [52] . Under these conditions , a pollination is scored as strongly incompatible if no or fewer than 5 pollen tubes are observed per pollinated stigma , as fully compatible when more than 50 pollen tubes are observed per pollinated stigma , and as partially self-compatible ( or weakly self-incompatible ) when intermediate numbers of pollen tubes are observed . Genomic DNA gel blot analysis with probes derived from different ΨSRKs was used to assess the composition of the S locus in various accessions of A . thaliana . This method is more suitable than amplification by the polymerase chain reaction ( PCR ) for our study because of the known or expected sequence divergence of the loci under study . Indeed , previous applications of this method to analysis of S-locus polymorphisms in A . thaliana have demonstrated that it can identify homologous sequences that are missed by PCR ( 27 ) . Under low-stringency hybridization and washing conditions , DNA gel blot analysis can detect sequences that share as little as 50% overall similarity with the probe but not small stretches of sequence similarity or sequences that have decayed to below the 50% sequence similarity threshold . The probes for this analysis were fragments corresponding to the first exon and the seventh or last exon of A . thaliana ΨSRKA ( At4g21370 ) from Columbia ( Col-0; CS1092 ) , to the first intron of ΨSRKB and ΨSCRB from the Cape Verdi Islands accession ( Cvi-0; CS1096 ) , and to the first intron of ΨSRKC from the Ibel Tazekka accession ( Ita-0; CS1244 ) . Fragments were amplified from genomic DNA using specific primers ( Table S1 ) , labeled with 32P , and used in sequential hybridizations of EcoRI-digested genomic DNA isolated from various accessions , as described above . An insertion/deletion polymorphism in ARK3 [27] , a gene tightly linked to the S locus in Arabidopsis species , was also assessed by PCR using specific primers ( Table S1 ) to distinguish between the ARK3SC allele ( characteristic of ΨSC haplotypes ) , which has the deletion , and the ARK3SA allele ( characteristic of ΨSA haplotypes ) , which lacks the deletion . Accessions used in this analysis included Kashmir ( Kas-2; CS1264 ) , Shahkdara ( Sha; CS929 ) , Hodja-Obi-Garm ( Hodja; CS6178 ) , C24 ( CS906 ) , Col-0 , Lezoux ( Lz-0; CS22615 ) , Noordwijk ( Nok-3; CS22643 ) , Randan ( Ra-0; CS22632 ) , Ita-0 , Monte ( Mr-0; CS22640 ) , and Cape Verdi Islands ( Cvi-0; CS902 and CS1096 ) . Standard PCR reagents were used with 35 cycles of the following: 94°C for 30 seconds , 55°C for 30 seconds , and 72°C for one minute or longer . The accessions were also assayed for previously-unidentified AtΨSCR1 exon 2 sequences , which were isolated in this study as follows . A recently-reported partial sequence of the A . lyrata SCR37 ( AlSCR37 ) gene , the ortholog of A . thaliana ΨSCR1 in Col-0 [30] , was used as anchor to clone the remainder of AlSCR37 using the “DNA Walking SpeedUp Premix Kit II” ( Seegene , Rockville , MD ) and gene-specific primers ( Table S1 ) . Amplification of AlSCR37 genomic DNA ( kindly provided by Dr . Jesper Bechsgaard ) was performed according to the manufacturer's directions and amplified products were cloned into pGemT-easy ( Promega , Madison , WI ) . Inserts were sequenced at the Cornell University Life Sciences Core Laboratories Center ( Ithaca , NY ) using SP6 and T7 universal primers . A BLAST search of the A . thaliana Col-0 genome using the newly-identified A . lyrata SCR37 second exon located the corresponding portion of A . thaliana ΨSCR1 , and primers were designed ( Table S1 ) to screen for the presence of an intact AtΨSCR1 second exon in 96 accessions of A . thaliana [35] using A . lyrata S37 DNA as positive control . SI prevents self pollen from reaching and fertilizing the ovule , and thus precludes fruit expansion . A breakdown or absence of SI allows self pollen to fertilize the ovules , resulting in fruit expansion and elongation . Consequently , for QTL analysis , fruit size was used as a proxy for self-pollination phenotype . Data used to calculate the phenotype values for individual plants were collected by sampling three inflorescence stems , scanning them using a flat-bed scanner , and measuring the length and width of each fruit using ImageJ software ( http://rsb . info . nih . gov/ij/ ) . An average of 80 fruits were scanned and measured for each plant , and on average across the population , one-fourth of those fruits contained seeds and were used in the average length calculation . Each of these fruits was the result of autonomous self-pollination , because they were grown in the absence of pollinators . A flower was deemed self-compatible , if the fruit width was greater than 0 . 6 mm , i . e . the minimal width of one fully-developed seed . Because of variability in fruit development , the trait values reported here were calculated for each plant as the average length of fruits with at least one seed . The QTL mapping population was generated using a self-fertile F4 plant derived from the C24::AlSRKb-SCRb x Col-0 cross , which was homozygous for the PUB8C24 allele and for the Col-0 allele at the chromosome-3 modifier . The F2 parent of the selected F4 plant displayed a transient SI phenotype as determined by seed set and pollination assays ( <5 pollen tubes/stigma in young buds and >50 pollen tubes/stigma in older buds and flowers ) . The F4 plant also produced abundant seed , although some flowers remained self-incompatible throughout development and did not produce seeds . It was homozygous over most of its genome , with Col-0-derived DNA occurring in large stretches on chromosomes 1 , 3 , and 5 , and in a small region on chromosome 4 . This plant was back-crossed to C24 , producing F4BC progenies that were self-incompatible , similar to the original C24::AlSRKb-SCRb x Col-0 F1 hybrid . The F4BC was subjected to forced selfing in immature floral buds ( i . e . before stigmas acquire the ability to reject self pollen ) to generate an F4BCF2 population for QTL analysis , which we refer to as the QTL mapping population . Since the C24 accession was not completely sequenced when this study was undertaken , a search for markers that showed co-dominant polymorphisms between C24 and Col-0 was done by PCR screening of publicly available microsattelite markers designed for other pairs of accessions and of random amplification of repetitive elements found in the Col-0 genome ( www . arabidopsis . org ) . In addition , a limited number of dominant SNP markers were designed to detect differences as small as one base pair between the two parents . Twenty-four marker loci ( Table S1 ) were found to be polymorphic between the two accessions and were scored on 186 individuals in the QTL mapping population . Markers were amplified using forward primers with M13 adapters to enable large scale genotyping [53] . A linkage map and mapping files containing genotype and phenotype data were produced using MapManager for analysis in MapManager and also exported into WinQTL Cartographer ( http://statgen . ncsu . edu/qtlcart/ ) . All recombination distances , measured in centiMorgans ( cM ) , were co-linear with physical distances ( data not shown ) . QTL interval mapping and composite interval mapping methods were applied to the genotype and marker data using both software programs . The various analyses and programs all produced similar results . A 0 . 05 significance threshold of LOD 2 . 8 was determined in WinQTL ( http://statgen . ncsu . edu/qtlcart/ ) by creating a random distribution of the data through 1000 permutations . | The mating system adopted by a species has a profound influence on extent of polymorphism , population structure , and evolutionary potential . In flowering plants , the switch from outbreeding to inbreeding has occurred repeatedly , yet little is known about the underlying genetic events . This is true even for the model species A . thaliana , a highly self-fertile member of the crucifer family . In this family , outbreeding is enforced by a self-incompatibility system controlled by the S locus , which involves the recognition of pollen by the stigma to prevent self-fertilization and familial inbreeding . We recently demonstrated that A . thaliana accessions may be reverted to full or partial self-incompatibility by transformation with S-locus genes isolated from its close self-incompatible relative A . lyrata . Despite much recent debate , however , we still do not know how A . thaliana became self-fertile . Here , we use our recently established A . thaliana transgenic self-incompatible experimental model to address these issues . Analysis of the S locus in accessions that can be reverted to full self-incompatibility demonstrates that self-fertility in A . thaliana arose by at least two independent S-locus mutations . Furthermore , analysis of an accession that expresses only partial self-incompatibility shows that self-fertility is associated with an unstable allele at a locus unlinked to the S locus . | [
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] | 2009 | Independent S-Locus Mutations Caused Self-Fertility in Arabidopsis thaliana |
The Caenorhabditis elegans inner nuclear envelope protein matefin/SUN-1 plays a conserved , pivotal role in the process of genome haploidization . CHK-2–dependent phosphorylation of SUN-1 regulates homologous chromosome pairing and interhomolog recombination in Caenorhabditis elegans . Using time-lapse microscopy , we characterized the movement of matefin/SUN-1::GFP aggregates ( the equivalent of chromosomal attachment plaques ) and showed that the dynamics of matefin/SUN-1 aggregates remained unchanged throughout leptonene/zygotene , despite the progression of pairing . Movement of SUN-1 aggregates correlated with chromatin polarization . We also analyzed the requirements for the formation of movement-competent matefin/SUN-1 aggregates in the context of chromosome structure and found that chromosome axes were required to produce wild-type numbers of attachment plaques . Abrogation of synapsis led to a deceleration of SUN-1 aggregate movement . Analysis of matefin/SUN-1 in a double-strand break deficient mutant revealed that repair intermediates influenced matefin/SUN-1 aggregate dynamics . Investigation of movement in meiotic regulator mutants substantiated that proper orchestration of the meiotic program and effective repair of DNA double-strand breaks were necessary for the wild-type behavior of matefin/SUN-1 aggregates .
During the first meiotic division , homologous parental chromosomes must accomplish numerous tasks that eventually result in their connection via homologous recombination . They must recognize one another , align , synapse via the tripartite proteinaceous synaptonemal complex ( SC ) , and repair programmed double-strand breaks ( DSBs ) ; a subset of DSBs is repaired using the homologous partner as a template [1] . During this period , the chromosomes are connected to the nuclear envelope at one or both ends [2] . The highly conserved protein interaction module of SUN/KASH domain proteins has emerged as a core element for the attachment of chromosomal ends to the nuclear envelope , and for telomere-led chromosomal movement . The mechanism for moving chromosomes during early prophase I inside the nucleus via the SUN/KASH bridge , which provides a connection to various cytoskeletal forces in the cytoplasm , appears to be a general , evolutionarily conserved phenomenon ( for reviews see [3]–[5] ) . Studies in Saccharomyces cerevisiae [6] , [7] , Schizosaccharomyces pombe [8] , and maize [9] discovered differences among organisms with regard to which factors are employed to build the connection of the chromosome ends to the SUN-domain proteins in the inner nuclear envelope , and which cytoskeletal forces drive the movement . For example , in S . cerevisiae , telomere-led chromosome movement has been observed during meiotic prophase I , from leptotene to pachytene [6] , [7] , [10] . Interference with prophase chromosome movement in S . cerevisiae results in delayed pairing and DSB processing , aberrant crossover formation , and loss of crossover interference [6] , [7] , [11]–[16] . In many organisms , formation of the synaptonemal complex requires the formation of programmed meiotic DSBs; however , in C . elegans , synapsis is independent of DSBs [17] . In C . elegans , the SC is comprised of the lateral element components HTP-1 to 3 and HIM-3 , and the central region components SYP-1 to SYP-4 [18] , [19] . HTP-1 , in addition to being part of the lateral element , also plays a role in licensing synapsis [20] , [21] . Another characteristic of C . elegans is that the pairing of homologs involves homolog recognition regions ( HRRs ) , also called pairing center ( PC ) regions , which are enriched in heterochromatic repeats localized at one end of each chromosome . HRRs were shown to be required to initiate the subsequent key features of meiosis I , recombination and disjunction [22]–[24] . The PC proteins ZIM-1 to ZIM-3 and HIM-8 bind to HRRs , and specifically localize to either one or two chromosomes [25]–[27] . When extrachromosomal arrays of the heterochromatic repeats found in HRRs are introduced into C . elegans germline cells , they recruit PC proteins and the arrays localize to the nuclear periphery [27] . In C . elegans , the protein matefin/SUN-1 ( referred to as SUN-1 from this point forward ) and its interacting partner ZYG-12 bridge the nuclear membranes and play a central role in the pairing of homologous chromosomes and the licensing of synapsis [28] , [29] . The PC proteins colocalize with SUN-1 , and are thought to connect chromosome ends to SUN-1 [28] , [29] . A point mutation in the SUN domain of C . elegans SUN-1 revealed that prophase movement is required for chromosomes to find each other and to prevent nonhomologous synapsis [29] , [30] . Recently , it was found that in worms , microtubules and dynein motors act through the nuclear envelope bridge formed by matefin/SUN-1 and ZYG-12 , and that these components are at the core of licensing synapsis only for properly aligned bivalents [29] . Progression of meiosis is tightly regulated in C . elegans , and involves the checkpoint protein kinase CHK-2 at the meiotic entry . CHK-2 is responsible for the polarization of the chromatin , which is characteristic of the transition zone ( TZ ) [31] , and is involved in induction of DSBs and proper SC polymerization , as well as the phosphorylation of SUN-1 [28] . PROM-1 , an F-box–containing protein , controls progression of early meiosis; its depletion leads to an extended meiotic entry zone followed by nonhomologous synapsis [32] . The newly identified meiotic regulator HIM-19 is involved in chromatin polarization , formation of DSBs , and elongation of the SC . It encodes a protein with an RNA helicase domain , as determined by metastructure analysis [33] . In cra-1 mutants , central-region components of the SC first fail to localize extensively to chromosomes; later , they instead polymerize along chromosomal axes , leading to unconnected bivalents . The tetratricopeptide repeat domain–bearing protein CRA-1 uncouples the polymerization of the central region components of the SC from the repair of DSBs [34] . In this study , we used live imaging microscopy to show that chromosome ends , highlighted as SUN-1 aggregates , were highly dynamic and contributed to chromosome movement in the leptotene/zygotene stages of C . elegans prophase I; they came together , coalesced and dispersed . Disruption of the SUN/KASH interaction in mtf-1/sun-1 ( jf18 ) mutants resulted in an absence of motion of the SUN-1 aggregates . We also analyzed the SUN-1-GFP aggregates in different mutant backgrounds affecting phosphorylation of SUN-1 and structural components of the lateral and central region of the SC , the DSB inducing enzyme SPO-11 and a group of genes that play a regulatory role during prophase I in C . elegans . Abrogation of synapsis led to a deceleration of SUN-1 aggregate movement . Aggregate behavior was influenced by recombination . Quantification of SUN-1 aggregates in meiotic regulators suggested that mere chromosome collisions driven by movement of nuclear envelope–attached chromosomes were insufficient for successful homologous pairing .
C . elegans gonads recapitulate the progression of nuclei through meiotic prophase I in a spatial manner [35] . The mitotic zone is followed by the TZ , with its characteristically polarized chromatin ( corresponding to leptotene/zygotene ) . In the next stage ( pachytene ) , chromatin is redistributed and forms parallel tracks of DNA . Later , chromosomes condense ( diplotene ) and connected bivalents become apparent at diakinesis . SUN-1 forms foci and patches ( the equivalent of the chromosomal attachment plaque seen in vertebrates ) in the TZ [28]–[30] . We followed the movement of a functional SUN-1::GFP transgene integrated into the corresponding deletion background sun-1 ( ok1282 ) using in vivo imaging microscopy [28] . By additionally applying Hoechst 33342 , we recorded both the motion of chromatin and the movement of SUN-1::GFP ( Figure 1A , Video S1 ) . SUN-1::GFP formed both large and small aggregates that moved in an erratic manner on one half of the nuclear periphery . Small and large aggregates met , fused and dispersed during the leptotene/zygotene stage of prophase I . Small aggregates , termed foci , likely define single chromosome end attachments , whereas large aggregates , termed patches , likely define multiple chromosome ends that are locally enriched . Indeed , only a single patch was visible at t = 425 s ( Figure 1A ) , whereas at an earlier timepoint ( t = 140 s ) , one patch and five foci were observed . Later ( t = 555 s ) , we observed three foci and one patch . The coalescence of foci and patches were often very transient: in less than 130 s , we observed three foci driven out from the patch . Once two foci or one focus and a patch fused , they dispersed within a short time . SUN-1::GFP aggregates , via cytoplasmic forces , also triggered protrusion of the chromatin , deforming the nuclear membranes . At t = 140 s , the chromatin was pulled outward by the SUN-1::GFP patch , which moved at the periphery of the nucleus ( Figure 1A , white arrow; t = 140 s ) . A point mutation in the SUN domain of SUN-1 results in the absence of a defined TZ , and disturbs the interaction of SUN-1 with ZYG-12 [30] . In the cytoplasm , ZYG-12 interacts with the cytoskeleton via a dynein motor [29] , [36] . We performed a time-lapse analysis of the mutated SUN-1 ( G311V ) ::GFP transgenic line in the sun-1 ( ok1282 ) deletion background and found that , on average , 7 . 8±2 . 3 ( SD ) aggregates inside a nucleus exhibited restrained movement ( Table 1 , Figure 3A and 3B , and Video S3 ) . The distribution of the projected speed of these aggregates was a sharp bell , with 95% of the aggregates moving within a range of 10–100 nm/s ( Figure 3C ) ; the projected speed was significantly reduced compared to wild-type aggregates ( Mann-Whitney test , p<0 . 001 ) . The displacement tracks indicated weak local oscillatory movement ( Figure 3B ) , and the distances traveled were significantly reduced . The arcs varied between 27° and 59° ( Figure 3D ) . No exchange of SUN-1 aggregates was seen ( Figure 2A ) . A functional SUN/KASH bridge mediating the connection to the cytoskeleton was , thus , necessary for the movement of SUN-1 aggregates and correlated with the absence of polarized chromatin . The SC is a “zipper-like” structure that stabilizes the close pairing of parental chromosomes . This complex is composed of lateral elements on chromosome axes bridged by central region components [18] . We explored the behavior of SUN-1 aggregates in the SC mutants him-3 ( gk149 ) , htp-1 ( gk174 ) , syp-2 ( ok307 ) , and syp-3 ( me42 ) . HIM-3 is a major constituent of the lateral element , and HTP-1 , in addition to being part of the lateral element , licenses synapsis [20] , [21] . The central region components of the SC , SYP-1 , SYP-2 , SYP-3 and SYP-4 zip up paired homologs [37] , [38] , [19] . An allele of the central region component SYP-3 revealed that SYP-3 plays a role in meiotic repair pathway decisions , in addition to its role in SC formation [39] . The him-3 ( gk149 ) null allele displays loss of the polarized conformation of chromatin in the TZ of the gonad and lacks presynaptic alignment [40] . Depleting HIM-3 resulted in 1 . 1±0 . 3 ( SD , n = 53 ) SUN-1 aggregates moving inside a nucleus ( Table 1 ) , and , on rare occasions , up to two aggregates ( Figure 4Ai and 4Aii ) unable to fuse ( Figure 2A , Video S4 ) . The projected speed distribution of the aggregates was “Gaussian-shaped , ” with 95% of the aggregates moving within a range of 10–100 nm/s ( Figure 4Aiii ) ; this was significantly reduced compared to wild type ( Mann-Whitney test , p<0 . 001 ) . Depletion of HIM-3 also reduced the distance traveled by the SUN-1 aggregates: arc values only varied between 23° and 91° ( Figure 4Aiv ) . HIM-8 is one of the four PC proteins , and binds specifically to the X chromosome [25] . In him-3 ( gk149 ) , HIM-8 always colocalizes with SUN-1::GFP , as shown by immunostaining [28] . Thus , the single SUN-1 aggregate moving with a reduced projected speed corresponds to the X chromosome and HIM-3 is , therefore , required for the formation of autosomal chromosome attachment plaques ( [28] and this study ) . To test whether the formation of SUN-1 aggregates was impaired in him-3 ( gk149 ) due to mislocalization of the PC proteins , we stained him-3 ( gk149 ) with the PC protein ZIM-3 ( marker for chromosomes I and IV ) . In him-3 ( gk149 ) mutant worms , the PC protein ZIM-3 did not colocalize with SUN-1 aggregates , although chromatin-associated signals were observed ( Figure S4 , Text S2 ) . Therefore , we hypothesize that defective lateral elements of the SC impeded functional attachment plaque formation . htp-1 mutants precociously synapse with the wrong partner despite proper loading of HIM-3 [20] , [21] . In htp-1 ( gk174 ) , the average number of aggregates was reduced ( Mann-Whitney test , p<0 . 05 ) . The maximum number of aggregates was comparable to the average number of SUN-1 aggregates in wild-type worms ( Table 1 ) . The displacement tracks of SUN-1::GFP in this background recapitulated the polarization of the chromatin characteristic of the TZ ( Figure 4Bi and 4Bii; Video S5 ) . The projected speed distribution within a nucleus was comparable to that in him-3 ( gk149 ) . For most SUN-1 aggregates ( >95% ) , the projected speed varied between 10 and 100 nm/s ( Figure 4Biii ) . The distance traveled was significantly reduced compared to him-3 ( gk149 ) ( Mann-Whitney test , p<0 . 001 ) : arc values ranged from 20° to 62° ( Figure 4Biv ) . The precocious synapsis in htp-1 ( gk174 ) mutant worms likely inhibits the mobility of SUN-1 aggregates ( see below ) . This interpretation was reinforced by a significant decrease in the number of fusion/splitting events ( Figure 2A and Table S1 ) and a significant increase in the time without exchange ( Figure 2B and Table S2 ) . Indeed , SUN-1 aggregates coalesced for longer than 1 min in 60% of cases . Although ZIM-3 loading was defective in him-3 ( gk149 ) , it was unaffected in htp-1 ( gk174 ) , with 2–4 ZIM-3 signals overlapping with SUN-1 aggregates ( Figure S4 ) . We conclude that htp-1 ( gk174 ) mutant worms readily formed single chromosomal attachment plaques . The SC central region component mutant syp-2 ( ok307 ) has an extended TZ , characteristic of SC mutants [38] . We compared the dynamics of SUN-1 aggregates in distal ( first half of the TZ , Video S6 ) and proximal ( second half of the TZ , Video S7 ) positions in the prolonged TZ . In the distal part , on average , 2 . 8±1 ( SD , n = 30 ) SUN-1 aggregates showed reduced movement ( Figure 4Ci and 4Cii ) ; in addition , the maximum number of SUN-1 aggregates was reduced ( 3 . 5±1 . 1; SD , n = 30 ) compared to wild type ( Table 1; Mann-Whitney test , p<0 . 05 ) . Although the projected speed distribution of 95% of SUN-1 aggregates was 10–100 nm/s ( Figure 4Ciii ) , comparable to him-3 ( gk149 ) , the distribution was more Maxwellian-shaped . The distance traveled by SUN-1::GFP aggregates was reduced ( from 16° up to 90° , with the exception of one track that went up to 163°; Figure 4Civ ) compared to wild type . Depletion of SYP-2 also reduced the number of SUN-1::GFP fusion/splitting events in the distal part of the TZ ( Figure 2A and Table S1 ) , with mostly 1–5 fusion/splitting events occurring within 15 min . The time of coalescence between SUN-1 aggregates was increased . Indeed , only 50% of SUN-1 aggregates coalesced for less than 1 min , while 22% of them coalesced for more than 3 min ( Figure 2B and Table S2 ) . syp-2 ( ok307 ) SUN-1::GFP aggregates in the proximal part of the TZ traveled longer distances . The arcs were between 20° and 150° ( Figure 4Civ' ) , which is in accordance with a broader projected speed distribution . The projected speed distribution for 95% of the aggregates ranged between 15 and 140 nm/s ( Figure 4Ciii' ) , and adopted a more Maxwellian-shaped distribution than in the early TZ , with the distribution shifted towards the higher speed . In the proximal part of the TZ , the displacement tracks were more reminiscent of the crescent shape of the chromatin than in the distal part of the TZ ( compare Figure 4Ci and 4Ci' ) . In the proximal part of the TZ , the maximum number of SUN-1 aggregates went up to 4 . 2±1 . 2 ( SD , n = 45 ) ( Table 1 ) ; nevertheless , their average number remained decreased compared to wild type ( Mann-Whitney test , p<0 . 05 ) . The number of SUN-1::GFP fusion/splitting events also differed from those seen in wild-type worms; there were less than ten exchanges within 15 min ( Figure 2A and Table S1 ) . The periodicity of the exchanges was similar to wild type ( Figure 2B and Table S2 ) . Disruption of synapsis resulted in a lack of long tails in the speed distribution and a decrease in the number of SUN-1 aggregates exchanged in both the distal and proximal parts of the TZ . The syp-3 allele me42 is special because it has a shortened TZ and appears to use a different repair pathway for the repair of meiotic DSBs [39] . In contrast to SC-deficient mutants , in me42 , the central region component SYP-1 was found to be polymerized on univalents . The displacement tracks of SUN-1::GFP in syp-3 ( me42 ) followed a half-moon shape ( Figure 4Di and 4Dii; Video S8 ) . The number of SUN-1 aggregates was significantly decreased compared to wild-type values ( Table 1; Mann-Whitney test , p<0 . 001 ) . The distribution of the projected speed was more Maxwellian-shaped and comparable to the distribution of SUN-1::GFP aggregates in the distal part of the syp-2 ( ok307 ) mutants ( Figure 4Diii ) . The distance traveled was greater than in syp-2 ( ok307 ) mutant worms , and covered angles from 37° to 100° ( Figure 4Div ) . The number of SUN-1::GFP fusion/splitting events was comparable to those in wild-type worms ( Figure 2A and Table S1 ) , but the frequency of the exchanges was significantly decreased ( Figure 2B and Table S2 ) . To test the idea that restricted aggregate behavior in htp-1 ( gk174 ) was due to nonhomologous synapsis , we depleted SYP-1 in htp-1 ( gk174 ) mutants . Surprisingly , in htp-1 ( gk174 ) ; syp-1 ( RNAi ) SUN-1 aggregates were extended and not restricted to the first cell row where chromatin is strongly polarized ( Figure S5 ) . SUN-1 aggregates were still detectable in nuclei with more loosely clustered chromatin . We divided the zone with aggregates into distal ( Video S9 ) and proximal ( Video S10 ) for analysis . In the distal part of htp-1 ( gk174 ) ; syp-1 ( RNAi ) the average number of SUN-1 aggregates ( 3 . 5±0 . 6 , SD , n = 23 , Table 1 ) was close to wild type , but their maximum number was significantly reduced ( Mann-Whitney test , p<0 . 05 ) . Displacement tracks adopted a circular form , in contrast to the crescent shape seen in wild type ( Figure 5A and 5B , 11 out of 23 nuclei ) , where movement pushes the nucleolus to one side of the nucleus . Here the tracks are found at the periphery , with chromatin likely rotating around the nucleolus . SYP-1 depletion in htp-1 ( gk174 ) significantly increased the projected speed of SUN-1 aggregates when compared to htp-1 ( gk174 ) , nevertheless the percentage of long tails ( 4% ) was significantly below wild-type values ( Figure 5C , Mann-Whitney test , p<0 . 05 ) . The distance traveled was also significantly increased in htp-1 ( gk174 ) ; syp-1 ( RNAi ) , with arc values reaching 156° ( compare Figure 4Biv and Figure 5D , Mann-Whitney test , p<0 . 05 ) . Depletion of SYP-1 in htp-1 ( gk174 ) resulted in an increase of exchanged aggregates ( Figure 2A , Table S1 ) and restored the periodicity of these exchanges to the wild type value ( Figure 2B , Table S2 ) . In the proximal region of htp-1 ( gk174 ) ; syp-1 ( RNAi ) gonads the number of SUN-1 aggregates was significantly reduced compared to the distal part ( Mann-Whitney test , p<0 . 05 ) with an average of 2 . 8±0 . 6 ( SD , n = 24 , Table 1 ) . Of the 23 nuclei analyzed , 16 nuclei displayed circular displacement tracks ( Figure 5A' and 5B' ) . The distribution of the projected speed was significantly increased compared to the distal part ( 6% long tails , Figure 5C' , Mann-Whitney test , p<0 . 05 ) , but still below wild-type values ( Mann-Whitney test , p<0 . 05 ) . SUN-1 aggregates traveled longer distances in the proximal part than in the distal ( Figure 5D' , Mann-Whitney test , p<0 . 05 ) , but the distances traveled , the exchanges , and the frequencies were still below wild-type values ( Mann-Whitney test , p<0 . 05 ) . ( Figure 2A and 2B , Tables S1 and S2 ) . Our analysis of the behavior of SUN-1 aggregates in mutants defective in SC formation confirmed that intact lateral elements of the SC were necessary for the formation of functional SUN-1 aggregates . We showed that SC components played a role in the exchange of SUN-1 aggregates . HTP-1 has an inhibitory influence on the exchange of aggregates as we observed reduced exchanges in htp-1 ( gk174 ) and syp-2 ( ok307 ) single mutants , whereas exchanges were increased to wild type values in htp-1 ( gk174 ) ; syp-1 ( RNAi ) ) . Strikingly , defects in SC polymerization reduced the projected speed of aggregates , whereas decreasing synapsis in the htp-1 mutant increased the projected speed of SUN-1 aggregates . The persistent aggregates in the syp-1 htp-1 double mutant formed circular displacement tracks concomitant with only loosely clustered chromatin . To date , known early meiotic regulators with a role in chromosome pairing in C . elegans include CHK-2 , HIM-19 , PROM-1 , and CRA-1 . Depletion of CHK-2 results in a lack of SUN-1 aggregates in chk-2 [28]; therefore , no live imaging can be shown . HIM-19 is a newly identified meiotic regulator . In him-19 ( jf6 ) mutants , meiotic defects are aggravated with age . In 2-day-old ( 2-d-old ) him-19 ( jf6 ) mutant worms , the TZ is not defined , DSB formation is likely defective , and elongation of the SC is restricted and nonhomologous [33] . PROM-1 is involved in the progression of meiosis I , and its depletion elicits nonhomologous synapsis . prom-1 ( ok1140 ) mutant worms lack a defined TZ and instead display dispersed nuclei with a polarized conformation after a prolonged meiotic entry zone [32] . CRA-1 regulates SC formation . Unlike chk-2 , prom-1 , and him-19 , cra-1 mutant worms display an extended TZ . In addition , cra-1 ( tm2144 ) mutant worms are defective in the formation of the SC central region [34] . In dispersed nuclei with a TZ-like appearance in prom-1 ( ok1140 ) mutant worms , SUN-1::GFP aggregates were movement competent ( Video S11 ) , and their tracking reconstructed the crescent shape of the chromatin ( Figure 6Ai and 6Aii ) . The number of SUN-1 aggregates in prom-1 ( ok1140 ) was reduced compared to wild type ( Mann-Whitney test , p<0 . 05 ) , despite a similar average number of aggregates ( Table 1 ) . In this background , SUN-1 aggregates displayed a Maxwellian-shaped projected speed distribution and lacked long tails ( Figure 6Aiii; Mann-Whitney test , p<0 . 05 compared to wild type ) , although the distance traveled by SUN-1::GFP aggregates was unaffected ( from 19° up to 143°; Figure 6Aiv; Mann-Whitney test , p>0 . 05 ) . Depletion of PROM-1 also reduced the number of exchanged aggregates ( Figure 2A ) . Indeed , in a significant number of nuclei , SUN-1 aggregates were unable to fuse or split , and the number of nuclei showing 6–10 fusion/splitting events was considerably reduced ( Table S1 ) . The time period without exchange was significantly increased in prom-1 ( ok1140 ) compared to wild type: 47% of SUN-1::GFP aggregates were found to coalesce for less than 1 min and 23% for more than 3 min ( Figure 2B and Table S2 ) . In prom-1 ( ok1140 ) , it is likely that the delayed orchestration of the meiotic program led to a reduction in the speed of SUN-1 aggregates without affecting the distance traveled . In addition , the number and frequency of exchanges were reduced in prom-1 ( ok1140 ) mutants . The formation of SUN-1 aggregates in 2-d-old him-19 ( jf6 ) mutants was restricted to the few dispersed nuclei with a polarized chromatin conformation , but could be augmented by γ-irradiation ( Figure 6B and [28] ) . In aged him-19 ( jf6 ) worms , a few SUN-1::GFP aggregates were movement competent ( Video S12 ) , and their displacement tracks resembled a crescent shape ( Figure 6Bi and 6Bii ) . Both the average number of SUN-1 aggregates ( 2 . 8±0 . 8; SD , n = 8 ) ) and the maximum number of aggregates was decreased ( 4 . 0±1 . 3; SD , n = 8 ) ( Table 1; Mann-Whitney test , p<0 . 05 ) . In addition , the projected speed distribution of SUN-1::GFP aggregates was reduced compared to wild type ( Mann-Whitney test , p<0 . 05 ) : it was similar to the speed of SUN-1::GFP aggregates in htp-1 ( gk174 ) , with 95% of the aggregates moving from 10 to 130 nm/s ( Figure 6Biii ) . The distance covered was also reduced , as demonstrated by arc values ranging from 9° up to 137° ( Figure 6Biv; Mann-Whitney test , p<0 . 05 ) . In him-19 ( jf6 ) mutants , the number of SUN-1 fusion/splitting events was wild type ( Figure 2A and Table S1 ) , whereas the time without exchange increased , with 38% of SUN-1 aggregates coalescing for less than 1 min and 21% for more than 3 min ( Figure 2B and Table S2 ) . Two hours after γ-irradiation , the average number of SUN-1 aggregates reached wild-type levels in 2-d-old him-19 ( jf6 ) worms ( Table 1 ) ; however , the number of SUN-1 aggregates was significantly reduced ( Mann-Whitney test , p<0 . 05 ) . The displacement tracks of SUN-1::GFP in 2-d-old irradiated him-19 ( jf6 ) worms recapitulated the polarized conformation of the chromatin ( Figure 6Bi' and ii'; Video S13 ) . The distribution of the projected speed of SUN-1::GFP aggregates was shifted towards the higher speed after irradiation and became more Maxwellian-shaped . Nevertheless , long tails were absent from the distribution ( Figure 6Biii' ) . The projected speed of SUN-1 aggregates was reduced in aged irradiated him-19 ( jf6 ) compared to wild type ( Mann-Whitney test , p<0 . 05 ) . In contrast , irradiation-induced SUN-1::GFP aggregates in him-19 ( jf6 ) moved similar to those in wild-type worms in terms of the distance traveled ( Figure 6Biv'; Mann-Whitney test , p>0 . 05 ) , whereas the number of exchanges was significantly reduced . The class of 1–5 fusion/splitting events represented more than 50% of nuclei , and the exchange was abrogated for a representative number of nuclei ( Figure 2A and Table S1 ) . The frequency of these exchanges was reduced compared to wild type ( Figure 2B and Table S2 ) , and there was no significant increase compared to nonirradiated him-19 ( jf6 ) worms . We conclude that γ-irradiation restored the formation of SUN-1 aggregates to wild-type levels with respect to their numbers and distances traveled , whereas the dynamics of SUN-1 aggregates remained impaired both in terms of the distribution of the projected speed and the number and frequency of exchanges . To ensure that γ-irradiation had no side effects , we irradiated 2-d-old worms solely expressing SUN-1::GFP and performed the same analysis . The appearance of the displacement tracks of SUN-1::GFP aggregates was circular in 12 of the 24 nuclei analyzed whereas the other ones recapitulated the crescent shape of the chromatin ( Figure 6Ci and 6Cii; Video S14 ) . The distribution of the projected speed of SUN-1::GFP aggregates was shifted markedly towards lower values , with only 3% long tails ( >160 nm/s ) ( Figure 6Ciii; Mann-Whitney test , p<0 . 05 ) . Nonetheless , after γ-irradiation , SUN-1::GFP aggregates move faster in wild-type worms than in irradiated him-19 ( jf6 ) ( Mann-Whitney test , p<0 . 05 ) . In addition , the maximum number of SUN-1 aggregates was reduced ( Table 1; Mann-Whitney test , p<0 . 05 ) compared to nonirradiated wild type . γ-irradiation also reduced the distance traveled by SUN-1 aggregates; arc values ranged between 19° and 128° ( Figure 6Civ; Mann-Whitney test , p<0 . 05 ) . γ-irradiation had no impact on the number of SUN-1::GFP fusion/splitting events ( Figure 2A and Table S1 ) . However , it significantly increased the time without exchanges ( Figure 2B and Table S2 ) . FISH analysis with a probe specific for chromosome V revealed that pairing was affected after γ-irradiation ( Figure S6A and S6B ) . To ascertain whether the decrease in the speed distribution might be due to an SC defect , we stained for SYP-1 in nonirradiated and irradiated wild-type worms . No gross irregularities in SYP-1 polymerization were evident 2 h after γ-irradiation ( Figure S6C , Text S2 ) . γ-irradiation clearly had an impact on the dynamics of SUN-1 aggregates in wild-type gonads . Nonetheless , the behavior of restored SUN-1 aggregates in irradiated him-19 ( jf6 ) mutants adopted a more wild-type-like behavior compared to the sparse aggregates in non-irradiated him-19 ( jf6 ) gonads . In cra-1 ( tm2144 ) mutants , SUN-1 aggregates were movement competent ( Video S15 ) , and their displacement tracks adopted a rather circular form ( 19 out of 22 ) , like in htp-1 ( gk174 ) ; syp-1 ( RNAi ) ( Figure 6Di and 6Dii ) . Likewise , in cra-1 ( tm2144 ) chromatin loosely clustered following a short stretch of nuclei with strong chromatin polarization ( Figure S5 ) . The number of SUN-1 aggregates was similar to that of wild type ( Table 1; Mann-Whitney test , p>0 . 05 ) . However , the distribution of the projected speeds of SUN-1 aggregates increased towards higher speeds ( 12% long tails ) ( Figure 6Diii; ( Mann-Whitney test , p<0 . 05 ) . This was also the case for the distance traveled , with arcs ranging from 15° up to 173° ( Figure 6Div; Mann-Whitney test , p<0 . 05 ) . Except for the class of 1–5 fusion/splitting events , which was significantly reduced compared to wild type , the number of SUN-1 fusion/splitting events was similar to that of wild type in the cra-1 ( tm2144 ) background ( Figure 2A and Table S1 ) . The frequency of SUN-1 aggregate exchanges was unaffected in cra-1 ( tm2144 ) ( Figure 2B and Table S2 ) . In cra-1 ( tm2144 ) , the kinetics of SUN-1 aggregates were increased ( speed and distance traveled ) compared to wild type , whereas the number and frequency of fusion/splitting events was unaffected . Impairment of meiotic regulators showed that DSBs could be involved in the formation of functional SUN-1 aggregates , and that γ-irradiation nonetheless had some impact on SUN-1 aggregate movement . Improper orchestration of the meiotic program disturbed SUN-1 aggregate dynamics . In order to test whether recombination impacts SUN-1 aggregate dynamics , we followed SUN-1 movement in the spo-11 ( me44 ) mutant ( Video S16 ) . The average number of SUN-1 aggregates was significantly reduced ( 3 . 0±0 . 6 , SD , n = 18 , Table 1 ) compared to wild type . Surprisingly , the appearance of SUN-1 displacement tracks was circular in 9 out of the 18 nuclei analyzed ( Figure 7Ai and 7Aii ) . The distribution of the projected speed of SUN-1 aggregates in spo-11 ( me44 ) showed only 5% long tails ( Figure 7Aiii ) , and was reduced in terms of speed and distance traveled ( Mann-Whitney test , p<0 . 05 ) ( Figure 7Aiv ) . In spo-11 ( me44 ) , the number and frequency of exchanges ( Figure 2A and 2B , Tables S1 and S2 ) were wild type . Next we tested whether introduction of artificial DSBs by γ-irradiation in spo-11 ( me44 ) ( Video S17 ) could restore the properties of SUN-1 aggregates to wild-type values . The average number of SUN-1 aggregates ( 3 . 6±0 . 6 , SD , n = 23 , Table 1 ) in spo-11 ( me44 ) mutants increased to wild-type levels after irradiation . The displacement tracks were circular in 11 of the 23 nuclei analyzed ( Figure 7Bi and 7Bii ) . The distribution of the projected speed of SUN-1 aggregates displayed 3% of long tails , like wild type 2 hours after irradiation , but SUN-1 aggregates tended to move faster ( Mann-Whitney test , p<0 . 05 ) . γ-irradiation had no impact on the distance traveled ( Figure 7Biv , Mann-Whitney test , p>0 . 05 ) . The frequency of exchanges was significantly reduced when compared to wild type ( Figure 2B , Table S2 ) , but no effect could be detected in the number of SUN-1 aggregates exchanged ( Figure 2A , Table S1 ) . The properties of SUN-1 aggregates in spo-11 ( me44 ) after irradiation were close to wild-type values after irradiation . Intrigued by the fact that less SUN-1 aggregates can be found in spo-11 ( me44 ) , we compared the ratio of foci and patches in the gonad of both irradiated and non-irradiated wild type and spo-11 ( me44 ) worms . We found a significant increase in the number of foci in spo-11 ( me44 ) compared to wild type ( Figure 7C , Fisher's exact test , p<0 . 05 ) . The ratio of foci to patches was significantly reduced after irradiation ( Figure 7C , Fisher's exact test , p<0 . 05 ) with an increase in the fraction of patches for wild type and spo-11 ( me44 ) . All together , these results confirm that formation of DSBs is necessary for the generation of wild-type numbers of SUN-1 aggregates and patches . The appearance of circular tracks in spo-11 ( me44 ) confirms loose chromatin clustering in spo-11 ( me44 ) ( not shown ) .
Wild-type numbers of SUN-1 aggregates require HIM-3 , a component of the lateral elements of the SC . The sole movement-competent aggregate in the him-3 ( gk149 ) mutant colocalizes with the X chromosome [28] . The “autosomal SUN-1 aggregates” are , therefore , missing . Lateral elements of the SC are possibly required for their formation; alternatively , they may affect their stability . The fact that formation of attachment plaques at the nuclear periphery is independent of the lateral element of the SC in rat testis [41] provides support for the latter explanation . The reduced mobility of the SUN-1 aggregate in him-3 ( gk149 ) could be explained by the ability of the lateral element of the SC to rigidify the chromosomes , thereby supporting resolution of chromatin entanglements that otherwise might slow down movement . Formation of functional SUN-1 aggregates also requires a functional SUN-domain , enabling the movement of SUN-1 aggregates . We showed that disruption of the SUN/KASH bridge abrogated the movement of SUN-1 aggregates , leading to nonhomologous synapsis in SUN-1 ( G311V ) ::GFP . The movement of SUN-1 aggregates , thus , exerted an inhibitory action on synapsis with the wrong partners . In addition , SUN/KASH-mediated movement exerted a positive effect on synapsis by mixing chromosomes in the nucleus , thereby positively reinforcing homologous synapsis . The movement of SUN-1 aggregates also elicited the polarized appearance of the chromatin , which required more than one aggregate to be moving , as exemplified in him-3 ( gk149 ) , where chromatin did not cluster . Chromosome ends move along the inner surface of the nuclear envelope and come together in areas where patches of SUN-1 are seen . At the same time , SC polymerization is present , implying that once parental partner chromosomes have met , they engage in synapsis [37] . At this point , successful synapsis did not lead to dissolution of the aggregate . There was no difference in terms of the number of SUN-1 aggregates during the progression of leptotene/zygotene ( as assessed by dividing this stage into three substages ) ; instead , the average number of SUN-1 aggregates fluctuated at around four . We propose that duplets/multiplets of chromosome ends are linked to SUN-1 patches . These duplets/multiplets of chromosome ends attached to SUN-1 patches continue to move and meet other SUN-1 patches or foci . While the newly met SUN-1 aggregates coalesce , homology is assessed; how homology is assessed is still an open question . Chromosome ends are then shuffled through patches of SUN-1 , and when the right partner is met , synapsis can take place . The shuffling of chromosome ends through the patches continues until all of the chromosomes have found their homolog . This process is dynamic , as most SUN-1 aggregates coalesce for less than 1 min . The shuffling of the chromosome ends during SUN-1 aggregate coalescence is , thus , one of the driving forces for homology search ( Figure 8A and 8B ) . This allows homologous chromosome ends to meet and nonhomologous chromosomes to separate . Chromosome ends coalescing in SUN-1 patches might not solely reflect shuffling chromosome ends but also interactions caused by recombination repair intermediates . Indeed , in spo-11 fewer patches are formed than in wild type and introduction of DSBs significantly increases the formation of patches . These two processes ( assessing homology ) and ongoing repair of DSBs ( up to the strand invasion step ) might be the source of the long tails observed in the speed distribution of SUN-1 aggregates ( see below ) . In addition interlocked chromosomes could contribute to the long tails . During the leptotene/zygotene stage , SUN-1 aggregates in C . elegans moved more slowly and did not accelerate abruptly , similar to what was reported for telomere ends in budding yeast and maize [6] , [7] , [9] . Nonetheless , SUN-1 aggregates reached relatively high speeds ( >160 nm/s ) , as demonstrated by the long tails in the distribution of the projected speed of SUN-1 aggregates . Two nonexclusive explanations could account for the formation of the long tails in the projected speed distribution of SUN-1 aggregates: first , “a model of tension” [29] , where assessment of homology ( Figure 8A and 8B ) , a nascent repair intermediate ( Figure 8C ) or chromosome entanglements ( Figure 8D ) leads to the formation of tension between chromosome ends . These interactions are counteracted by cytoplasmic forces ( generation of tension ) . When cytoplasmic forces overcome these interactions , the two chromosome ends are disjoined ( loss of accumulated tension ) , leading to an increase in the speed distribution of SUN-1 aggregates . Absence of DSBs significantly reduces the formation of long tails . Indeed other factors , such as chromosome interlocks , could contribute to the formation of the tension . Recently , the repair protein mlh-1 in S . macrospora had been assigned a critical role in resolution of interlocks [42] . When SYP-1 is depleted in htp-1 , long tails reappear in the speed distribution despite their absence in htp-1 , and fewer DSBs are made , as previously observed in htp-1;syp-2 [21] . This suggests that entanglements can also contribute to the generation of tensions . A second explanation for the formation of high speed could be that a patch containing paired homologs could move faster than a patch of nonpaired homologs . In the mutant SUN-1 ( S12E ) , pairing is fairly effective in the proximal part of the TZ [28] . The distribution of the projected speed of SUN-1 aggregates in this area was significantly shifted towards higher speeds ( 16% of long tails ) , supporting the idea that patches containing paired homologs could move faster ( see Text S1 , Videos S18 and S19 , and Figures S7 , S8 , and S9 ) . This is in contradiction with our finding that SUN-1 dynamics remained unchanged during the leptotene/zygotene stage despite processing of pairing . In fact , paired homologs in patches cannot account for the shift towards higher speeds , because we showed that patches ( paired or unpaired homologs ) and foci ( most likely single chromosome ends ) were both able to reach high speeds ( Text S1 ) . Patches are only faster than foci in the middle segment of the speed distribution . Addressing this paradox will require live imaging of the tagged end of a single chromosome . It has been shown that an unpaired chromosome keeps chromatin loosely clustered once stable strand invasion is established [43] . Similarly , we found that in htp-1; syp-1 , chromatin was also loosely clustered concomitant with circular displacement tracks . Circular displacement tracks are likewise found in cra-1 where partial synapsis is coupled with accumulation of DSB intermediates [34] . The fact that in htp-1; syp-2 DSBs do not accumulate [21] could be an explanation why circular tracks were less frequent than in cra-1 . Previously , we proposed that exit from polarized chromatin requires a certain recombination intermediate and/or full synapsis [28] . Loose chromatin clustering in spo-11 , together with circular displacement tracks , could be explained by the absence of the particular recombination intermediate required to dissolve chromosomal attachment plaques . The absence of SUN-1 aggregates in chk-2 ( me64 ) confirms that this gene plays an indispensible role in pairing [28] . The triggers to activate CHK-2 are unknown , but DSBs could be one such trigger , as shown by the analyses of SUN-1 aggregates in aged him-19 ( jf6 ) irradiated worms [28] . In the present study , γ-irradiation significantly rescued some aspects of the behavior of SUN-1 aggregates in aged him-19 ( jf6 ) . Despite their different roles during meiosis , htp-1 and prom-1 mutants display nonhomologous synapsis [20] , [21] , [32] . Deletion of either of these two proteins results in different phenotypes ( precocious synapsis or delayed progression of meiosis ) . These mutants highlight the necessity of coordinating sub-events right after meiotic entry for the generation of functional SUN-1 aggregates . In both mutants , the speed distributions of SUN-1 aggregates lacked long tails; perhaps when chromosomes nonhomologously synapse , the tension-generating process cannot take place ( assessment of pairing ) . The maximum number of aggregates was five in both cases , because when aggregates coalesce into a patch , chromosome ends could not be shuffled , because synapsis had already taken place . The two mutants were also similar in terms of the number of fusion/splitting events and of periodicity of coalescence time . The analysis of these two mutants strongly suggests that effective pairing of homologs requires more than just chromosome end movement . Other prophase I events are also necessary for successful pairing . In this study , we showed that instead of single chromosome ends looking for their homologous partner , duplets or multiplets and single chromosome ends were brought together in groups ( patches ) during the leptotene/zygotene stage by SUN-1 aggregate movement . Chromosome ends shuffled through these patches in search of the correct partner . Simultaneous in vivo imaging of specific chromosome ends and SUN-1 aggregates should be the next step to substantiate our finding . Second , these patches might represent ongoing repair of DSBs . Deciphering the regulation of synapsis initiation while chromosome ends are being shuffled in a tightly regulated process is one of the challenges for the future .
All C . elegans strains were cultured using standard techniques [44] . The following C . elegans strains were used: N2 Bristol , sun-1 ( ok1282 ) ; sun-1::GFP [28] , sun-1 ( ok1282 ) /nT1[let- ? qIs51]; sun-1::GFP ( G311V ) [28] , chk-2 ( me64 ) /unc-51 ( e369 ) rol-9 ( sc148 ) ; sun-1::GFP [28] , syp-2 ( ok307 ) /nT1[qIs51]; sun-1::GFP [28] , him-3 ( gk149 ) /nT1[let- ? qIs51]; sun-1::GFP [28] , htp-1 ( gk174 ) /nT1[let- ? qIs51]; sun-1::GFP [28] him-19 ( jf6 ) ; sun-1::GFP [33] , syp-3 ( me42 ) /hT2[bli-4 ( e937 ) let- ? ( q782 ) qIs48]; sun-1::GFP , prom-1 ( ok1140 ) ; sun-1::GFP , cra-1 ( tm2144 ) /hT2 ( GFP ) ; sun-1::GFP , spo-11 ( me44 ) /nT1[unc- ? ( n754 ) let- ? qIs50]; sun-1::GFP [45] . Nematode strains were provided by the Caenorhabditis Genetics Center , which is funded by the NIH National Center for Research Resources ( NCRR ) . him-19 ( jf6 ) ; sun-1::GFP and sun-1 ( ok1282 ) ; sun-1::GFP worms were γ-irradiated with a dose of 50 Gy for 10 sec using a 137Cs source and the time lapse microscopy was recorded 2 hrs after irradiation . syp-1 ( RNAi ) was done as described in [46] . For time lapse acquisitions , adult hermaphrodites preselected at the L4 stage 16 hrs before were mounted in a drop of 10 mM Levamisol on a 2% agarose pad and covered with a coverslip . For him-19 ( jf6 ) , worms were preselected 2 days before irradiation . The coverslip was sealed with melted Vaseline . Images were acquired at room temperature every 5 sec over a 15 min time period as stacks of optical sections at 1 µm intervals using a Deltavision deconvolution microscopy system ( Applied Precision , Inc , 1040 12th Avenue , Northwest Issaquah , Washington 98027 , USA ) under the following conditions: FITC channel , 32%ND , bin 1×1 , exposure time 200 msec , objective 60x . To assay the effect of Levamisole on the worms , worms were mounted as described above in a drop of Levamisole or M9 and then the hatch rate of the filmed worms evaluated under the two conditions . No significant decrease of the hatch rate was observed ( Levamisol hatch rate: 93% ( >500 eggs laid ) , M9 hatch rate: 96% ( >200 eggs laid ) , Fisher's exact test: p>0 . 05 ) . Maximum intensity projection was done using Softworx software ( Applied Precision , Inc , 1040 12th Avenue , Northwest Issaquah , Washington 98027 , USA ) and the collection of files saved . The collection of pictures was opened with Metamorph Offline ( Molecular Devices , 402 Boot Rd . , Downingtown , PA 19335 , USA ) and saved as stack files . To support the plotting process , the background of the stack pictures was removed using Autoquant X2 ( AutoQuant Imaging , Troy , NY , USA ) and the slices realigned using the first slice as a reference . With the help of Metamorph Offline , the positions of SUN-1 aggregates were manually followed . When two aggregates split , instead of starting to record two new tracks , tracking of the larger aggregate was continued . Then movement of the second aggregate was recorded . Subsequently positions of SUN-1 aggregates were plotted using Gnuplot software ( Thomas Williams , Colin Kelley et al . 2004 , http://www . gnuplot . info ) . Frequency and number of fusion/splitting events were computed using the number of aggregates as a function of time . Automated plotting of the movement of SUN-1 aggregates was done using Image J ( NIH , http://rsbweb . nih . gov/ij/ ) and the plug-in MTrack2 ( author: Nico Stuurman , http://valelab . ucsf . edu/people/p-stuurman . htm ) ( see Figure S9 ) . The speed of the object between two successive data points was calculated as the distance covered divided by the time required to cover the distance . Arcs were computed as described in Figure S3 . Three-dimensional reconstruction of SUN-1 aggregate movement was done using the relationship x2+y2+z2 = r2 , where x , y , z are the coordinates of a point situated on a sphere of radius r . The z-coordinates were computed from the x- and y-coordinates of the nuclei viewed from the top as follows: z = √ ( r2−x2−y2 ) , and when x2+y2>r2 , the previous z-coordinate was used . Statistical analysis was done using the software R ( R Development Core Team , http://www . R-project . org ) . | During meiosis , homologous chromosomes from each parent must pair , synapse , and recombine before being assorted to the gametes . In Caenorhabditis elegans , to find the correct pairing partner , telomere-led chromosome movement occurs in a restricted subvolume of the nucleus . This feature is comparable to the widely conserved meiotic bouquet , a configuration where telomeres cluster in a limited area at the nuclear periphery . Chromosomes are moved by cytoskeletal forces transmitted via the SUN/KASH bridge across the nuclear envelope , and abrogation of movement leads to precocious nonhomologous synapsis . Using live cell imaging , we followed the movement of matefin/SUN-1 aggregates , which highlight chromosome ends . Instead of single chromosome ends looking for their homologous partners , we observed that duplets/multiplets and single chromosome ends were brought together into “patches” by the ongoing movement during the leptotene/zygotene stages of meiosis . Chromosome ends then shuffled through these patches in search of the correct partner . This study was a comprehensive analysis of matefin/SUN-1 aggregate dynamics in wild type , known Caenorhabditis elegans pairing mutants , and the recombination mutant spo-11; and it examined the contributions of these genotypes to leptotene/zygotene chromosome movement . | [
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] | 2010 | Leptotene/Zygotene Chromosome Movement Via the SUN/KASH Protein Bridge in Caenorhabditis elegans |
NF449 , a sulfated compound derived from the antiparasitic drug suramin , was previously reported to inhibit infection by enterovirus A71 ( EV-A71 ) . In the current work , we found that NF449 inhibits virus attachment to target cells , and specifically blocks virus interaction with two identified receptors—the P-selectin ligand , PSGL-1 , and heparan sulfate glycosaminoglycan—with no effect on virus binding to a third receptor , the scavenger receptor SCARB2 . We also examined a number of commercially available suramin analogues , and newly synthesized derivatives of NF449; among these , NF110 and NM16 , like NF449 , inhibited virus attachment at submicromolar concentrations . PSGL-1 and heparan sulfate , but not SCARB2 , are both sulfated molecules , and their interaction with EV-A71 is thought to involve positively charged capsid residues , including a conserved lysine at VP1-244 , near the icosahedral 5-fold vertex . We found that mutation of VP1-244 resulted in resistance to NF449 , suggesting that this residue is involved in NF449 interaction with the virus capsid . Consistent with this idea , NF449 and NF110 prevented virus interaction with monoclonal antibody MA28-7 , which specifically recognizes an epitope overlapping VP1-244 at the 5-fold vertex . Based on these observations we propose that NF449 and related compounds compete with sulfated receptor molecules for a binding site at the 5-fold vertex of the EV-A71 capsid .
Enterovirus A71 ( EV-A71 , formerly named enterovirus 71 ) is a non-enveloped single-stranded RNA virus that belongs to the enterovirus A group of human picornaviruses ( for a general review of EV-A71 see [1] ) . EV-A71 most often causes a mild childhood illness , hand-foot-mouth disease . However , some infected children suffer severe complications , which include flaccid paralysis , brainstem encephalitis , and cardiorespiratory failure . Although EV-A71 was first isolated in California , its major impact is now felt in the Asia-Pacific region . In an ongoing epidemic in mainland China , nearly 7 million cases of EV-A71 disease have occurred since 2008 , with more than 80 , 000 severe cases and over 2 , 400 deaths [2] . Several inactivated vaccine candidates show promising efficacy and safety profiles [3–5]; however , it is not clear when EV-A71 vaccines will be introduced for widespread use or whether they will provide protection against multiple EV-A71 genotypes [6] . At present , there are no specific therapies for EV-A71: treatment is entirely supportive , with severe cases requiring intensive management in critical care units [7–9] . One potential target for antiviral therapies is the interaction between EV-A71 and receptor molecules on host cells . EV-A71 has been reported to bind to several different receptors , including scavenger receptor class B member 2 ( SCARB2 ) [10] , P-selectin glycoprotein ligand-1 ( PSGL-1 , a molecule primarily expressed on blood cells ) [11] , and heparan sulfate glycosaminoglycans [12]; virus interactions with annexin II [13] , vimentin [14] , and nucleolin [15] have also been reported to promote infection , although their importance is less clear . We have shown that EV-A71 interaction with PSGL-1 on leukocytes requires the presence of sulfated tyrosine residues near the N-terminus of PSGL-1 [16] , and depends on two highly conserved lysine residues , VP1-244K and VP1-242K , near the 5-fold vertex of the viral capsid [17] . Another residue near the 5-fold vertex , VP1-145 , determines whether or not a particular isolate binds PSGL-1 ( with G or Q in isolates that bind PSGL-1 , E in those that do not ) , by influencing the orientation of VP1-244K [17] . In addition to their role in PSGL-1 binding , the positively-charged lysine residues at the 5-fold vertex have been proposed—although not yet confirmed—to be important for virus interaction with heparan sulfate [12] . We previously identified NF449 , ( 4 , 4' , 4'' , 4‴- [carbonylbis[imino- 5 , 1 , 3- benzenetriylbis ( carbonylimino ) ]]tetrakis- 1 , 3- benzenedisulfonic acid ) , as an inhibitor of EV-A71 infection in a screen of a compound library [18]; NF449 inhibited EV-A71 infection , but not poliovirus infection , and showed no detectable cellular toxicity . Inhibition was seen when NF449 was added at the start of infection , but not after 2 hrs , suggesting that the drug acts at an early stage in the virus life cycle . We isolated an NF449 escape mutant that had undergone two mutations within the viral capsid—one involving VP1-244K , the other involving VP1-98E , a neighboring residue at the 5-fold vertex . Based on these observations , we hypothesized that NF449 , which contains sulfonated aromatic rings closely resembling sulfotyrosines , binds to EV-A71 near the 5-fold vertex and interferes with virus attachment to PSGL-1 and other sulfated cellular receptors .
We first confirmed the earlier observation [18] that NF449 inhibits EV-A71 infection . EV-A71-1095-EGFP , a PSGL-1-binding isolate engineered to express enhanced green fluorescent protein ( EGFP ) in infected cells , was incubated with NF449 or with control medium , then added to RD cells . After 16 hours , infection was measured by flow cytometry to detect GFP expression . NF449 had a strong inhibitory effect at 4–32 μM ( Fig 1A ) ; in contrast , a control compound , the synthetic heparin analogue fondaparinux , whose molecular weight , net negative charge , and degree of sulfation are similar to those of NF449 ( structures are shown in Fig 2A ) , had little effect . NF449 markedly inhibited attachment of 35S-labeled EV-A71-1095 to RD cells ( Fig 1B , left panel ) , at a concentration similar to that required for inhibition of infection; in contrast , NF449 had no effect on attachment of radiolabeled coxsackievirus B3 , a control enterovirus that binds to receptors distinct from those recognized by EV-A71 [19] ( Fig 1C ) . These results suggest that NF449 interferes specifically with EV-A71 attachment to the cell surface—by interacting with the virus , with receptors on the cell surface , or with both . NF449 is derived from suramin [20] , a compound known to bind extensively to serum proteins [21] . We observed that NF449 was much more effective in inhibiting virus attachment in the absence of serum ( Fig 1B , right panel , and see below ) . To assess the avidity of compounds for the virus and/or receptors , without complications introduced by the presence of serum proteins , further binding experiments ( but not experiments testing inhibition of infection ) were performed in serum-free medium . To begin to understand the structural basis of NF449's antiviral activity , we compared the inhibitory effects of NF449 to those of a number of commercially-available compounds with related structures ( Fig 2A ) . At 4 μM concentration several of the commercial compounds—including NF449 and suramin—effectively blocked virus attachment ( Fig 2B ) ; however , when tested at 0 . 4 μM , only NF449 and NF110 showed a marked inhibitory effect . None of the compounds showed cytotoxicity at the doses used to inhibit virus attachment ( S1 Fig ) . When we measured the effect of NF110 and NF449 on virus replication ( experiments performed in serum-containing medium ) , NF110 inhibited replication of EV-A71-1095 in RD cells at a somewhat lower concentration than did NF449 [NF110 50% inhibitory concentration ( IC50 ) , 1 μM; NF449 IC50 , 4 μM] ( Fig 2C ) ; both NF449 and NF110 inhibited infection more effectively than the parent compound , suramin ( IC50 32–64 μM ) . NF110 is very similar in structure to NF449 , but it has only 4 sulfonate groups , exclusively at the para positions of the aromatic rings , whereas NF449 has 8 sulfonate groups , at both ortho and para positions . The results thus suggested that activity was not strictly related to the number of sulfonate groups or the net negative charge , but more likely related to to the specific orientation of sulfonate groups and to other features of the molecule . With the idea of identifying a more effective compound than NF110 , we synthesized a number of new analogues ( Fig 3 ) . Our working hypothesis was that NF110 binds to the capsid surface , with electrostatic interactions between its sulfonate residues and capsid lysines , stabilized by hydrophobic and hydrogen bonding interactions involving other parts of the molecule . Based on this , we changed the orientation , number , and spacing of the sulfonate groups ( NM1-3 and NM13-15 ) ; we altered potential hydrophobic and hydrogen bonding properties by alkylating amide and urea NH bonds ( NM4 and 5 ) and by replacing the urea oxygen with sulfur ( NM7 ) ; and we reduced the molecule’s flexibility by confining the urea moiety in a 6-membered ring ( NM6 ) . We also produced an NF110-like molecule in which the peripheral aromatic rings were modified to resemble more closely a sulfotyrosine moiety ( NM16 ) . When these new compounds were tested for their effect on virus attachment , several showed no activity even at 4 μM , and at 0 . 4 μM only NM7 and NM16 showed activity comparable to that of NF110 and NF449 ( Fig 4A ) . At lower concentrations , NM16 was more active than NF110 and NF449 ( Fig 4B ) , reducing attachment of EV-A71-1095 to RD cells by 50% even at 0 . 05 μM . None of the new compounds showed cytotoxicity at the doses used to inhibit virus attachment and infection ( S1 Fig ) . Some , but not all , EV-A71 isolates bind to PSGL-1 [11 , 17] , a receptor molecule expressed largely on haematopoietic cells , and not expressed on RD cells . These PSGL-1-binding ( PB ) isolates , which include EV-A71-1095 , infect Jurkat T-cells in a PSGL-1-dependent manner [11 , 17] . We found that NF110 and NF449 inhibited infection of Jurkat cells by EV-A71-1095 ( Fig 5A ) , at concentrations slightly higher than those required to block infection of RD cells ( Fig 2C ) whereas suramin and fondaparinux had no effect ( Fig 5A ) . NM16 also blocked infection of Jurkat cells , but was less effective than NF110 and NF449 . However , in virus binding assays , performed in the absence of serum , NF449 , NF110 , and NM16 all inhibited attachment of radiolabeled EV-A71-1095 to Jurkat cells at sub-micromolar concentrations ( Fig 5B ) . NF449 was previously shown to inhibit infection by a number of EV-A71 isolates [18] , although one isolate ( BrCr ) showed partial resistance . We subsequently determined that , unlike the other isolates tested , BrCr does not bind PSGL-1 [11 , 17] . This raised the possibility that non-PB viruses are relatively resistant to NF449 and related compounds . To test this , we examined another non-PB isolate , EV-A71-02363 . Infection of RD cells by EV-A71-02363 was inhibited by NF449 , NF110 , and NM16 ( Fig 6A ) , and all three compounds inhibited attachment of radiolabeled EV-A71-02363 ( Fig 6B ) . However , whereas NF110 was more effective than NF449 in inhibiting infection by EV-A71-1095 , NF449 was more effective than NF110 against EV-A71-02363 . These results suggest that at least some non-PB isolates are sensitive to inhibition by NF449 , NF110 , and NM16 , although particular inhibitors may more effective against particular viral isolates . EV-A71-1095 bound to SCARB2-Fc and PSGL-1-Fc fusion proteins fixed to magnetic beads ( Fig 7A and 7B ) , but not to a control Fc fusion protein . EV-A71-1095 also bound beads coated with heparin ( a form of heparan sulfate , Fig 7C ) , but not to beads coated with mannan , a nonsulfonated polysaccharide . NF449 and NF110 blocked virus binding to both PSGL-1 and heparin at low micromolar concentrations , but had no effect on virus binding to SCARB2 . Both PSGL-1 and heparan sulfate are sulfonated molecules , which may interact directly with positively-charged lysine residues clustered near the 5-fold capsid vertex . In contrast , current evidence suggests that SCARB2 , which is not sulfonated , binds in or near the viral canyon , at some distance from the 5-fold axis [22] . To confirm that residues near the 5-fold axis influence EV-A71 susceptibility to NF449 , we introduced specific mutations into EV-A71-1095 , and determined whether the mutant viruses were inhibited by NF449 ( Fig 8 ) . NF449 protected RD cells from infection by wild-type virus , but showed a reduced effect against viruses with an arginine ( R ) residue replacing lysine ( K ) at VP1-244 , or with glutamine ( Q ) replacing glutamate ( E ) at VP1-98; the virus with both mutations was not inhibited by NF449 at any concentration tested . The results indicate that VP1-244 , which is critical for virus attachment to PSGL-1 , and the nearby residue VP1-98 , are both important for inhibition by NF449 , and they suggest that NF449 interferes with receptor access to a site near the 5-fold vertex . A neutralizing monoclonal antibody raised against EV-A71-1095 , MA28-7 , binds specifically to the 5-fold vertex , with a footprint ( defined by cryo-electron microscopy [23] ) encompassing VP1-98E and VP1-244K ( Fig 9A ) . MA28-7 immunoprecipitated EV-A71-1095 capsids from solution ( Fig 9B ) as did another EV-A71 antibody , 10F0 , which recognizes an epitope within VP2 , remote from the 5-fold axis [24] . NF449 inhibited precipitation of virus by MA28-7 , but not by 10F0 . Pirodavir , an antiviral compound that is likely to interact with the viral canyon [25 , 26] had no effect on virus interaction with either antibody . These results suggest that NF449 specifically competes with attachment of MA28-7 to its epitope at the 5-fold vertex . When we repeated these experiments with radiolabeled virus , which is purified by sedimentation in sucrose gradients , interaction of 160S mature particles with MA28-7 was inhibited by sub-micromolar concentrations of NF449 and NF110 ( Fig 9C ) . Surprisingly , we found that both 10F0 and MA105 , an antibody that recognizes an undefined epitope within VP1 , failed to immunoprecipitate purified 160S mature particles ( S2 Fig ) although ( Fig 9D , S2 Fig ) , they did effectively precipitate purified 80S procapsids . NF110 inhibited interaction of MA28-7 with procapsids ( Fig 9D ) , just as it inhibited MA28-7 interaction with mature virions . In contrast , NF110 had no effect on procapsid interaction with 10F0 or MA105 . Taken together , the results support the idea that NF449 and NF110 interact with the 5-fold vertex and block virus attachment to MA28-7 .
The results we report here indicate that NF449 and related compounds interact with the 5-fold capsid vertex to block attachment of enterovirus A71 to receptors on the cell surface . NF449 inhibited attachment of radiolabeled EV-A71 both to RD cells and to Jurkat cells , and it inhibited attachment both to PSGL-1 and to heparin , a form of heparan sulfate . Both PSGL-1 [17] and heparan sulfate [12] have been proposed to interact with positively-charged lysine residues at the 5-fold vertex of the EV-A71 capsid , and our results suggest that NF449 inhibits receptor interaction by binding to an overlapping site . NF449’s inhibitory effect was markedly reduced by mutation of residues near the 5-fold vertex , VP1-98E as well as VP1-244K— a highly conserved residue to be important for virus interaction with PSGL-1 . Further , we found that NF449 blocked attachment of a monoclonal antibody specific for the 5-fold vertex . While this work was in progress , other investigators reported that suramin , NF449’s parent compound , inhibits EV-A71 infection of RD cells and blocks virus attachment to the RD cell surface [12 , 27 , 28]; suramin was further shown to interact with the EV-A71 capsid ( as determined by STD NMR ) , and to inhibit EV-A71 replication in mice and in non-human primates [28] . Because it has previously been approved for use in humans , suramin will likely be tested for clinical efficacy against EV-A71 infection . However , suramin has a number of toxic effects [29] , some of them dose-related [30] , and treatment of infected children may require development of new agents with greater antiviral activity at lower doses . We found that NF449 , NF110 , and NM16 consistently inhibited EV-A71 infection at concentrations lower than those required for inhibition by suramin . We attempted to modify the NF110 structure to produce a more potent antiviral compound: although NM16 , the tetra-sulfotyrosine analog was somewhat more effective than NF110 and NF449 in preventing EV-A71-1095 attachment to RD cells , none of the other novel analogs showed greater potency than the parent compounds . Nonetheless , our work provides several insights into structure-activity relationships among this series of compounds . First , relocation of the sulfonate groups to other positions in the NF110 structure ( NM2 , NM3 ) led to partial loss of activity , and truncated structures—lacking one or more of the aromatic rings and accompanying sulfonate groups ( NM8-NM12 ) —had markedly reduced activity . These results suggest that antiviral activity depends on sulfonate groups in specific orientations . Further , activity was lost when N-methyl groups replaced the N-H groups of the amide ( NM4 ) or urea ( NM5 ) functionalities , which suggests that the hydrogen bonding capability of secondary amide groups is also likely to be important . VP1-244K and the adjacent lysine residue at VP1-242 are present in virtually all EV-A71 isolates [17] , suggesting that a concentration of positive charge at the 5-fold vertex serves an important function in the virus life cycle , and that it may provide an attractive target for antiviral agents . We do not yet know precisely how NF110 , NF449 , or other sulfonated inhibitors bind to the virus surface . However , using a molecular docking program , we found that NF449’s size , and the spacing between its sulfonate groups , may permit it to bridge multiple VP1-244K and -242K residues around the 5-fold vertex ( S3 Fig ) . The capacity to bind multiple lysine residues simultaneously may be important for activity , and may explain why the more active compounds have sulfate or sulfonate residues at particular positions . Particular inhibitors may vary in efficacy in blocking particular virus-cell interactions , so that understanding structure-function relationships will require consideration of how each compound interacts with the surface of a particular virus isolate , as well as how effectively it blocks interaction with a particular receptor . Taken together , our results provide evidence that the 5-fold vertex is important for PSGL-1-dependent virus attachment to leukocytes as well as for attachment to cells that do not express PSGL-1 . Further , they identify a series of prototype compounds that bind the 5-fold vertex to block EV-A71 interaction with specific cell surface receptors , most likely because their sulfonate residues mimic tyrosine sulfate moieties near the N-terminus of PSGL-1 , and sulfated/sulfonated components of heparan sulfate or other cell surface molecules . We believe that these results provide insights that—combined with ongoing structural studies of NF449 analogs bound to virus—will facilitate the design of potent antiviral compounds .
RD cells obtained from the US Centers for Disease Control were maintained in DMEM ( Life Technologies ) supplemented with 10% heat-inactivated FBS ( HI-FBS ) . Jurkat cells were obtained from the Riken Cell Bank and cultured in DMEM without phenol red ( Life Technologies ) supplemented with 10% heat-inactivated fetal bovine serum . HeLa cells obtained from the ATCC were maintained in MEM ( Sigma-Aldrich ) supplemented with 5% HI-FBS , 1x non-essential amino acids ( Life Technologies ) and 100 U/ml penicillin and 100 μg/ml streptomycin ( Life Technologies ) . EV-A71-1095 ( PB ) and EV-A71-02363 ( non-PB ) were used . EV-A71-02363 was generated from an infectious viral cDNA clone ( pBREV71-02363-KE ) as described previously [17] . Both viruses were propagated in RD cells . Viral titers were determined by a microtitration assay using 96-well plates and RD cells as previously described [31] . Briefly , 10 wells were used for each viral dilution and the viral titers were expressed as 50% cell culture infectious dose ( CCID50 ) . Coxsackievirus B3 ( RD strain ) [32] was used as a control . EV-A71 modified to express EGFP in infected cells ( pBREV71-1095-EGFP-EG ) was generated as described previously [10] with modifications . EV-A71 cDNA ( pBREV71-1095-EG , [17] ) and pEGFP ( Clontech ) were used as templates for overlap extension PCR . DNA encoding EGFP was amplified , with the addition of an EV-A71 protease 2A recognition sequence ( AITTLGS; 2A cleaves between TL and GS ) at the 3'-end , and inserted into EV-A71 VP4 , between residues 3 and 4 ( MGS-QVS… ) . Protease 2A releases EGFP ( with the N-terminal extension MGS and the C-terminal extension AITTL ) as well as VP4 ( GSQVS… ) ; the initial methionine of VP4 is normally cleaved to permit myristylation of the N-terminal glycine residue [33] . Mutations were introduced into the pBREV71-1095-EG plasmid [17] by site directed mutagenesis using PCR . For mutagenesis from E to Q at VP1-98 , the primers 5'-ccctcttcaaggcacaaccaacccgaatgg-3' and 5'-gtgccttgaagagggaggtctatctctcca-3' were used . For mutagenesis from K to R at VP1-244 , the primers 5'-tcgaaatcccgttacccattagtggtcaggattt-3' and 5'-tgggtaacgggatttcgaggtccctacagtccgca-3' were used . The plasmid with the VP1-E98Q mutation was named pBREV71-1095-QG . The plasmid with the VP1-K244R mutation was named pBREV71-1095-EG-K244R . The plasmid with both VP1-E98Q and VP1-K244R mutations was named pBREV71-1095-QG-K244R . The anti-EV-A71 mAbs MA28-7 ( mouse IgG1 ) [11] and MA105 ( mouse IgG2b ) [23] , were generated from mice immunized with EV-A71-1095 . The anti-EV-A71 mAb 10F0 ( IgG1 ) was purchased from Abcam . Anti-DAF antibody ( IF7 , mouse IgG2b ) [34] was used to inhibit Coxsackievirus B3 binding to HeLa cells . For negative controls , mouse IgG1 ( MOPC-21 ) and IgG2b ( MOPC-141 ) were purchased from Biolegend and Sigma-Aldrich , respectively . Soluble recombinant forms of human proteins fused to the Fc region of human IgG1 ( PSGL-1-Fc , SCARB2-Fc , and CTLA-4-Fc ) were purchased from R&D Systems . CTLA-4-Fc was used as a negative control Fc protein . Suramin hexasodium salt , NF023 , NF110 , NF157 , NF279 , NF340 , NF449 , PSB0739 , and MRS2578 were purchased from Tocris Bioscience . Fondaparinux sodium ( Arixtra ) was purchased from GlaxoSmithKline . Pirodavir was a kind gift from Dr . John Lambert , Biota Pharmaceuticals . MRS2578 and Pirodavir were dissolved in DMSO and used for the experiments . The synthesis of all compounds was based on the work of Kassack , et . al . [35] . Compounds were purified by silica gel chromatography , and purity was assessed by HPLC or NMR ( 1H and 13C ) . Structures were analyzed by NMR , infrared , and high resolution mass spectrometry . All compounds were synthesized as sodium salts , but Na+ ions are not indicated in figures . Experimental details ( S1 Appendix ) for all new compounds are included in the Supplementary Material Drug cytotoxicity was evaluated by measuring ATP as a marker of metabolically active cells . RD cells ( 5x103 ) were cultured with diluted drugs ( total 25 μl/well , triplicate ) in a 384-well plate ( Corning ) at 37°C for 16 h . ATP levels were measured using a CellTiter-Glo 2 . 0 luminescent cell viability assay kit ( Promega ) according to the manufacturer’s instructions . EV-A71-EGFP ( 105 CCID50/100 μl , 400 μl ) was incubated with diluted inhibitors ( 400 μl ) in DMEM supplemented with 10% HI-FBS ( 800 μl total ) on ice for 1 h . The virus-inhibitor mixture ( 200 μl: 105 CCID50 ) was then added to RD cells ( 5x104 cells per well in a 48-well plate: 2 CCID50/cell ) in triplicate . The plate was incubated at 4°C with gentle agitation for 1 h , then incubated in a CO2 incubator at 37°C for 16 h . The cells were trypsinized , fixed with 4% paraformaldehyde and analyzed using a FACSCalibur ( Becton-Dickinson ) . EV-A71-1095 or EV-A71-02363 ( 4x105 CCID50/100 μl , 400 μl ) was incubated with diluted inhibitors ( 400 μl ) in DMEM supplemented with 10% HI-FBS ( 800 μl total ) on ice for 1 h . The virus-inhibitor mixture ( 200 μl; 4x105 CCID50 ) was added to RD cells ( 5x104 cells per well in a 48-well plate; 8 CCID50/cell ) in triplicate . The plate was incubated at 4°C for 1h with gentle agitation , then incubated in a CO2 incubator at 37°C for 16 h . RD cells were trypsinized , fixed , permeabilized with 1X permeabilization buffer ( eBioscience ) , stained with MA105 conjugated with Alexafluor 488 [11] and analyzed by FACSCalibur . For Jurkat cell infection , EV-A71-1095 ( 4x105 CCID50/100 μl , 400 μl ) was incubated with diluted inhibitors ( 400 μl ) in DMEM without phenol red supplemented with 10% HI-FBS ( 800 μl total ) on ice for 1 h . The virus-inhibitor mixture ( 800 μl: 1 . 6x106 CCID50 ) was added to Jurkat cells ( 2x105 cells: 8 CCID50/cell ) in a 1 . 5 ml tube and incubated on ice for 1 h . The Jurkat cells with the virus and inhibitor ( 200 μl: 5x104 cells; 8 CCID50/cell ) were added to a well in a 96-well plate in triplicate and incubated in a CO2 incubator at 37°C for 16 h , then washed , fixed , permeabilized , stained and analyzed without trypsinization . Radiolabeled virus was prepared as described with minor modifications [36] . Briefly , HeLa cells ( 1x107 cells ) were infected with EV-A71-1095 ( 2x109 CCID50 ) . For non-PB virus , RD cells ( 5x106 cells ) were incubated with EV-A71-02363 ( 8x109 CCID50 for 45 min at room temperature , and then washed and incubated at 37°C in methionine/cysteine-free medium ( Life Technologies ) . After 5 h , the medium was replaced with 4 ml of methionine/cysteine-free medium containing 100 μCi of 35S-methionine/cysteine per ml and incubation continued overnight . Cells were lysed by freezing and thawing three times , and then lysates were made in 0 . 5% Triton X-100 and clarified by centrifugation . Sodium dodecyl sulfate ( 1% ) was added , and then the virus was pelleted through a 30% sucrose cushion using an SW55Ti rotor ( Beckman Coulter ) ( 45 , 000 rpm , 16°C , 90 min ) . The virus pellet was resuspended in phosphate buffered saline without calcium or magnesium ( PBS ) and purified by sedimentation through 15 to 35% sucrose gradients using a SW55Ti rotor ( 45 , 000 rpm , 16°C , 60 min ) . Twenty-four 0 . 2 ml fractions were collected from the top of each gradient and 5 microliter samples were analyzed for radioactivity: procapsids were detected in fractions 12–16 ( peak , fraction 13 ) and mature virions in fractions 18–23 ( peak , fraction 19 ) . 35S-labeled EV-A71 ( 35S-EV-A71 ) ( 5x103 CPM , unless indicated ) was incubated with diluted inhibitors in DMEM without FBS on ice for 1 h . RD cells ( 105 cells per well in a 48-well plate ) were washed once with DMEM without FBS and incubated with the mixture of 35S-EV-A71 and inhibitors with gentle agitation at 4°C for 1 . 5 h . Jurkat cells were washed once with DMEM without FBS and incubated with the mixture of 35S-labeled EV-A71 and inhibitors on ice for 1 h . Unbound virus was removed with at least two washes with DMEM without FBS . RD cells were lysed with Solvable detergent ( Perkin-Elmer ) , and cell-bound radioactivity was assessed . 35S-CVB3 was prepared as described [32] . The EV-A71-receptor binding assay was performed as in [11] , with minor modifications . Briefly , 5 μl of Dynabeads protein G ( Life Technologies ) and 0 . 5 μg of chimeric Fc proteins were diluted in 100 μl of DMEM without phenol red supplemented with 0 . 01% Tween 20 ( DMEM-T ) and incubated using a rotary mixer for 1 h at 4°C . The beads were washed once . 35S-EV-A71 ( 5x103 CPM , unless indicated ) were incubated with diluted inhibitors in DMEM-T on ice for 1 h . Then the virus-inhibitor mixture was added to Dynabeads protein G with chimeric Fc proteins and incubated using a rotary mixer for 1 h at 4°C . Unbound virus was removed with two washes with DMEM-T . Then Dynabeads-bound radioactivity was assessed . To detect EV-A71 binding to heparin , heparin-agarose ( Sigma-Aldrich ) was used . As a sulfonate-negative control , mannan-agarose ( Sigma-Aldrich ) was used . For 35S-EV-A71–antibody binding inhibition assays , anti-EV-A71 mAbs ( 0 . 5 μg ) were bound to Dynabeads instead of chimeric Fc proteins . RD cells infected with EV-A71-1095 were lysed by freezing and thawing three times . The medium with virus and cell debris was clarified by centrifugation . Virus in the supernatant was pelleted through a 30% sucrose cushion using an SW28 rotor ( Beckman Coulter ) ( 27 , 000 rpm , 16°C , 3h ) . The virus pellet ( mixture of procapsid and mature virion ) was resuspended in PBS and used for receptor binding inhibition assays . Dynabeads protein G and chimeric Fc proteins were prepared as described above . Viruses purified by ultracentrifugation ( 0 . 5 μg of VP1 protein in SDS-PAGE analysis ) and inhibitors were incubated in 100 μl of DMEM-T for 1 h on ice . Then the virus-inhibitor mixture was added to Dynabeads protein G bound to chimeric Fc proteins and incubated using a rotary mixer for 1 h at 4°C . We washed the beads and subjected the immunoprecipitates to 12 . 5% SDS-PAGE . For western blotting , proteins were transferred onto nitrocellulose membranes and blotted with anti-EV-A71 VP1 mAb MA105 . For EV-A71-antibody binding inhibition assay , anti-EV-A71 mAbs were used instead of chimeric Fc proteins to bind to Dynabeads . The Molecular Operating Environment ( MOE ) Software Package [37] was used to draw the molecular surface of EV-A71 ( PDB 4AED ) [38] and to simulate molecular docking of NF449 . The Site Finder function was used to identify potential binding sites around the 5-fold vertex , then the Pharmacophore setting was used to impose the constraint that at least one sulfonate group of NF449 must interact with at least one VP1-244K residue . Fifty-six potential docking sites were identified . For each of two possible conformations of NF449 , using the GBVI/WSA dG scoring function for binding free energy , we selected three sites with the highest ( negative ) S scores to represent in S3 Fig . All infection and binding assays were carried out in triplicate , and the mean values were compared using Student’s t-test ( two-tailed ) . P values <0 . 01 were considered statistically significant . The following sequences are deposited in GenBank: EV-A71-1095 ( AB550332 ) ; EV-A71-02363 ( AB747375 ) ; and pBREV71-1095-EGFP-EG ( LC053680 ) . | Enterovirus A71 is epidemic in the Asia-Pacific region , and has been responsible for thousands of cases of fatal neurological disease in young children . There are no specific therapies available . We previously identified NF449 as a compound with anti-EV-A71 activity , although its mechanism of action was uncertain . In the current work we found that NF449 and related molecules prevent virus attachment both to PSGL-1 , a receptor molecule important for virus interaction with white blood cells , and to heparan sulfate , a receptor that may be important for virus interaction with a variety of other cell types . In contrast , we found that NF449 had no effect on virus attachment to another proposed receptor , SCARB2 . We also found that NF449 and related compounds interact with a specific site on the viral capsid , remote from the binding site for another major receptor , SCARB2 . Our work provides information that may facilitate development of improved antiviral compounds that block the attachment of EV-A71 to cellular receptors . | [
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] | [] | 2015 | The Suramin Derivative NF449 Interacts with the 5-fold Vertex of the Enterovirus A71 Capsid to Prevent Virus Attachment to PSGL-1 and Heparan Sulfate |
Vpr is a conserved primate lentiviral protein that promotes infection of T lymphocytes in vivo by an unknown mechanism . Here we demonstrate that Vpr and its cellular co-factor , DCAF1 , are necessary for efficient cell-to-cell spread of HIV-1 from macrophages to CD4+ T lymphocytes when there is inadequate cell-free virus to support direct T lymphocyte infection . Remarkably , Vpr functioned to counteract a macrophage-specific intrinsic antiviral pathway that targeted Env-containing virions to LAMP1+ lysosomal compartments . This restriction of Env also impaired virological synapses formed through interactions between HIV-1 Env on infected macrophages and CD4 on T lymphocytes . Treatment of infected macrophages with exogenous interferon-alpha induced virion degradation and blocked synapse formation , overcoming the effects of Vpr . These results provide a mechanism that helps explain the in vivo requirement for Vpr and suggests that a macrophage-dependent stage of HIV-1 infection drives the evolutionary conservation of Vpr .
HIV-1 Vpr is conserved in all primate lentiviruses . However , decades of research have not revealed a functional explanation for its evolutionary conservation . CD4+ T lymphocytes are the most abundant cellular target of HIV-1 in vivo and are widely regarded as the main drivers of viremia , persistence and progression to acquired immunodeficiency syndrome [1] . While Vpr enables robust T lymphocyte infection and rapid disease progression in vivo [2 , 3] and in ex vivo human lymphoid tissue [4] , Vpr is dispensable and may actually be detrimental to HIV-1 replication in T lymphocytes in vitro [5–7] . Recent work using transformed cell lines has defined a molecular mechanism by which Vpr limits immune detection of HIV-1 through modulation of host cellular ubiquitin ligase pathways and activation of a cellular nuclease [8] . Vpr modulates these pathways at least in part through its interaction with its cellular co-factor DCAF1 ( also known as VprBP ) [9 , 10] . Vpr utilizes this pathway to counteract a macrophage-specific restriction of HIV-1 Env glycoprotein expression [11] . However , in T lymphocytes , there is no defect in Env expression in the absence of Vpr [11] and it remains unclear how Vpr enhances HIV-1 replication in CD4+ T lymphocytes in vivo [12 , 13] . In this study , we describe cell culture conditions in which HIV-1 infection of primary T lymphocytes depended entirely on contact-dependent spread from monocyte-derived macrophages ( MDM ) ; a mode of spread that evaded neutralization by some antibodies . Under these conditions , Vpr enhanced the formation of virological synapses ( VS ) between infected MDM and primary T lymphocytes . Mechanistic studies revealed that Vpr functioned to prevent an innate immune response that dramatically reduced HIV-1 Env expression , normal virion trafficking and VS formation in MDM-T lymphocyte co-cultures . The addition of exogenous interferon-α ( IFN ) effectively counteracted the ability of Vpr to promote spread from MDM to T lymphocytes . Our results highlight the importance of macrophages in HIV-1 pathogenesis and explain a requirement for Vpr in HIV-1 infection of T lymphocytes , providing a previously elusive explanation for Vpr’s strong evolutionary conservation .
To evaluate a role for Vpr in T lymphocyte infection that explained in vivo observations , we developed an assay to measure HIV-1 spread from primary MDM to autologous CD4+ T lymphocytes . As outlined in Fig 1A , we inoculated primary MDM with HIV-1 and allowed infection to establish for two days before co-cultivation with activated autologous CD4+ T lymphocytes for an additional two days to enable viral spread . MDM-T lymphocyte co-cultures produced an average of six-fold more HIV-1 than infected MDM alone , suggesting that co-cultivation resulted in efficient spread between MDM and T lymphocytes ( Fig 1B ) . To measure the frequency of infection in each cell type , we used flow cytometry to distinguish MDM from T lymphocytes by expression of surface markers and measured infection by intracellular Gag staining ( S1A Fig ) . Detection of Gag+ cells was dependent on retroviral integration , demonstrating that our assay measures productive HIV-1 replication ( S1B Fig ) . Although HIV-1 infects and depletes CD4+ T lymphocytes to cause acquired immunodeficiency syndrome in vivo , infection of primary CD4+ T lymphocytes by cell-free virus was inefficient in vitro after two days of continuous culture ( Fig 1C and S1A Fig ) using an inoculum comparable to the amount of virus present in MDM-T lymphocyte co-cultures . In comparison , co-cultivation of activated T lymphocytes with infected MDM increased T lymphocyte infection by thirty-fold ( Fig 1C ) . The capacity for MDM to efficiently infect autologous primary CD4+ T lymphocytes depended on direct cell-to-cell contact because infection was not detected when the cells were separated by a virus-permeable transwell insert ( Fig 1D and 1E ) . Direct cell-to-cell transmission of HIV-1 across virological synapses between infected and target cells has been previously described and is known to be highly resistant to antibody neutralization [14 , 15] . Consistent with this mode of spread , we observed that MDM-dependent spread to autologous primary CD4+ T lymphocytes was highly resistant to a subset of neutralizing antibodies ( b12 , Z13E1 and SIM2 ) that inhibited greater than 95% of infection by free virus at the same antibody concentration ( Fig 1F , compare left and right panels ) . In contrast , the monoclonal antibody 2G12 , which is capable of disrupting cell-to-cell spread [16] , was able to efficiently neutralize MDM-dependent T lymphocyte infection at 1 μg/ml ( Fig 1F , right panel ) . Consistent with a previous report , a ten-fold higher concentration of b12 was also able to neutralize cell-to-cell spread ( Fig 1F , right panel ) [17] . Previous studies have demonstrated that uninfected dendritic cells and MDM can infect T lymphocytes through a “trans” mechanism in which virions bound to lectin receptors are transferred to T lymphocytes ( S1C Fig ) [18 , 19] . This contrasts with “cis” infection that requires HIV-1 replication in MDM . To determine the mode of infection that was active in our system , we used the protocol described in Fig 1A but substituted an HIV-1 molecular clone that can infect T lymphocytes but not MDM ( NL4-3 ) . Similar to HIV-1 89 . 6 , cell-free HIV-1 NL4-3 did not efficiently infect primary T lymphocytes ( S1D Fig ) . Consistent with previous reports [20] , however , NL4-3 infected a high percentage of T lymphocytes upon spinoculation ( S1D Fig ) . As expected , NL4-3 did not infect MDM ( S1D Fig ) and MDM treated with NL4-3 as outlined in Fig 1A did not spread infection to primary CD4+ T lymphocytes ( S1D Fig ) . Thus , spread of infection from MDM to primary CD4+ T lymphocytes required productive HIV-1 replication in MDM under the conditions of our assay . In summary , efficient infection of primary CD4+ T lymphocytes required contact-dependent , neutralizing antibody-resistant , cis-mediated virus transfer from HIV-1 infected MDM . The HIV-1 Vpr protein is necessary for optimal infection and spread in MDM cultures but can actually be detrimental to spread of infection in actively replicating cells due to its inhibitory effects on cell cycle progression [7 , 21 , 22] . Because CD4+ T lymphocytes are the main target of HIV-1 in vivo , Vpr’s role in HIV-1 infection and its evolutionary conservation across lentiviral species targeting a wide range of primates has remained enigmatic [23] . We hypothesized that the mode of spread we describe here , in which efficient T lymphocyte infection was dependent on infected MDM , might reveal a crucial role for Vpr in enabling efficient T lymphocyte infection . To address this , we co-cultivated activated primary CD4+ T lymphocytes with autologous MDM infected by HIV-1 89 . 6 containing or lacking Vpr ( Fig 2A ) . Indeed , we observed a striking enhancement of infection by Vpr in our co-culture assay as measured by virion production ( seven-fold , Fig 2B ) and frequency of T lymphocyte infection ( three-fold , Fig 2C ) . We observed similar results with the CCR5-tropic HIV-1 AD8 ( three-fold , S2A Fig ) . Because Vpr stimulates HIV-1 spread among MDM ( Fig 2C ) [11 , 24] , it was possible that the stimulation of T lymphocyte infection we observed may result from an increase in the number of infected MDM that could amplify virus production . To address this , we measured spread of HIV-1 from infected MDM to T lymphocytes under conditions in which HIV-1 could only infect MDM for a single round and subsequent spreading infection could only occur in T lymphocytes . This was accomplished by using T-lymphotropic HIV-1 NL4-3 pseudotyped with macrophage-tropic YU-2 Env ( Fig 2D ) . This virus utilizes YU-2 Env protein to efficiently infect MDM for one round of viral replication . However , de novo virions produced by the infected MDM express only NL4-3 Env and thus can only infect T lymphocytes . As previously reported [11] , this virus initially infected MDM equally in the presence or absence of Vpr expression ( Fig 2E ) . Remarkably , however , Vpr significantly enhanced spread of HIV-1 from infected MDM to T lymphocytes ( four-fold , Fig 2E ) . In contrast , Vpr did not stimulate direct infection of primary T lymphocytes via spinoculation with cell-free virus ( Fig 2E ) , or by spread of virus between T lymphocytes ( S2B–S2E Fig ) , consistent with previous studies [5] . These data indicate that Vpr promotes the directional spread of HIV-1 from MDM to T lymphocytes and that this activity of Vpr is conserved in diverse HIV-1 isolates . Vpr interacts with the cellular protein DDB1-and-CUL4-associated factor 1 ( DCAF1 , also known as VprBP ) to modulate ubiquitylation and proteasomal degradation pathways [9 , 25–27] . Recent work has demonstrated that DCAF1 is an essential co-factor for Vpr to evade the induction of a type I IFN response [8] , and thereby counteract macrophage restriction of Env and virion production [11] . To determine whether this pathway was required for spread of HIV-1 from infected MDM to primary T lymphocytes , we employed the Vpr Q65R mutant of 89 . 6 that is deficient at interacting with DCAF1 and relatively defective at inducing DCAF1-dependent cell cycle arrest [11 , 28] . Using the co-culture assay described in Fig 2A , we found that Vpr Q65R was proportionally defective at enhancing HIV-1 spread from MDM to CD4+ T lymphocytes ( Fig 3A ) . To more directly address the requirement of DCAF1 for Vpr-dependent spread , we silenced DCAF1 in infected MDM ( Fig 3B ) and co-cultured these cells with autologous T lymphocytes . Remarkably , we found that DCAF1 silencing abrogated the ability of Vpr to stimulate transmission of HIV-1 from MDM to CD4+ T lymphocytes ( Fig 3C ) . While DCAF1 is required for Vpr to stabilize Env [11] , its silencing also induces IFN in HeLa cells [8] , raising the possibility that MDM silenced for DCAF1 produce IFN that may reduce T lymphocyte permissivity . To examine this , we used quantitative RT-PCR to measure IFN induction in MDM treated with control shRNA or shRNA directed against DCAF1 . As shown in S3A Fig , there was no significant difference in IFNA1 and MXI induction between these two conditions , indicating that DCAF1 silencing does not stimulate an IFN response in MDM . To extend these results , we also examined whether soluble factors produced by MDM silenced for DCAF1 could contribute to reduced HIV-1 transmission . We found that conditioned medium from MDM silenced for DCAF1 did not suppress infection of activated primary T lymphocytes ( S3B Fig ) . These results are consistent with a prior study that did not observe induction of IFN-stimulated genes in primary myeloid cells silenced for DCAF1 [29] . Collectively , these data demonstrate that Vpr requires DCAF1 to promote MDM-to-T lymphocyte spread of HIV-1 and that this requirement for DCAF1 is not due to soluble factors induced by DCAF1 silencing . MDM infected by HIV-1 lacking Vpr restrict Env expression by accelerating lysosomal degradation of Env , and Vpr counteracts this pathway via a DCAF1-dependent mechanism [11] . Because DCAF1 was also required for Vpr-dependent MDM-T lymphocyte spread of HIV-1 ( Fig 3A–3C ) , it is possible that restriction of Env expression leads to reduced spread from MDM to T lymphocytes . As a first step to address this possibility , we analyzed co-culture whole-cell lysates for steady-state Env expression by quantitative immunoblot in the presence or absence of Vpr ( Fig 3D ) . Indeed , we observed a loss of Env gp160 , gp120 and gp41 relative to the HIV-1 Gag precursor pr55 in the absence of Vpr in co-cultures ( Fig 3E ) , similar to what was previously reported in HIV-1 infected MDM [11] . Remarkably , however , a similar analysis of HIV-1 protein expression from the non-adherent T lymphocyte fraction of the co-culture did not reveal a Vpr requirement for Env expression ( S3C Fig ) , consistent with our model that Vpr counteracts an MDM-intrinsic restriction of Env . Because Vpr and DCAF1 are required for Env stability and virion incorporation [11] , we sought to address whether Vpr increases T lymphocyte infection by increasing virion infectivity . To examine this , we harvested virus from MDM infected with wild type or Vpr-null HIV-1 and used these MDM-derived virions to infect activated primary T lymphocytes via spinoculation . Consistent with our prior observations [11] , we found that there was no significant difference in infection frequency when T lymphocytes were infected with equal mass amounts of cell-free virus collected from wild type and Vpr-null-infected MDM ( S3D Fig ) . Thus , under the conditions of our assay , Vpr acts primarily by counteracting a cell-intrinsic pathway in MDM that restricts efficient transfer of virions to T lymphocytes rather than by increasing virion infectivity . Because Vpr and DCAF1 cooperate to counteract induction of a type I IFN response [8 , 11] , we also sought to determine whether reduced MDM-dependent T lymphocyte infection in the absence of Vpr may be mediated by soluble IFN produced by Vpr-null-infected MDM . To this end , we neutralized the type I IFN receptor ( IFNAR2 ) at the time of co-culture , but still observed a Vpr requirement for T lymphocyte infection ( S3E Fig ) . Additionally , pretreatment of T lymphocytes with conditioned supernatants from MDM infected in the presence or absence of Vpr did not block HIV-1 infection by spinoculation ( S3F Fig ) . Thus , infection activates an intrinsic antiviral pathway in MDM that primarily acts to restrict viral spread rather than to release soluble antiviral factors that influence T lymphocyte permissivity . To further characterize how viral spread is restricted , we sought to determine the mechanism by which MDM restricted efficient transfer of virions to T lymphocytes in the absence of Vpr . Our prior studies have demonstrated that: ( 1 ) Vpr prevents degradation of Env in lysosomes , ( 2 ) Env is required for Vpr-dependent changes in virion release , and ( 3 ) that there are significantly fewer cell-associated virions in MDM infected with Vpr-null HIV-1 based on immunoblot analysis of Gag p24 [11] . Thus , we hypothesized that in the absence of Vpr , Env-containing virions are targeted for lysosomal degradation in MDM . To test this , we examined the co-localization of mature virions ( Gag MAp17+ ) with LAMP1 , a marker of lysosomes . Because HIV-1-infected cells form syncytia , infected MDM are frequently multinucleated , which we also observed ( Fig 4A ) . Remarkably , in the absence of Vpr , mature virions ( magenta puncta in Fig 4A , right panels ) frequently co-localized with LAMP1 . In comparison , expression of Vpr reduced co-localization of mature virions with lysosomal markers ( Fig 4A and 4B ) . In addition , we observed more virions present in LAMP1+ compartments when lysosomal acidification was blocked by NH4Cl treatment , but not when proteasomal degradation was inhibited by MG132 treatment , indicating that colocalization with LAMP1 represents bona fide lysosomal targeting that results in significant degradation ( S4A Fig ) . We observed similar results when co-staining was performed with the lysosome marker LAMP2 , but not with the endoplasmic reticulum membrane marker calnexin ( S4B and S4C Fig ) . Remarkably , we also observed that lysosomal targeting of virions depended on expression of Env from the integrated provirus ( Fig 4B and S4D Fig ) . These studies reveal that in the absence of HIV-1 Vpr , MDM restrict HIV-1 by targeting Env-containing virions for lysosomal degradation . Because restriction of Env expression and virion release by wild type infected MDM is inducible by type I IFN [11] , we treated MDM with exogenous IFN to assess its effects on virion localization . Interestingly , IFN stimulated lysosomal targeting of virions even in MDM expressing Vpr , but only if Env was present ( Fig 4B ) , providing further support for the model that Vpr functions to prevent an IFN-inducible restriction of Env and Env-containing virions in MDM . Infection of T lymphocytes in our culture system occurs by direct cell-to-cell spread , which requires formation of transient VS between an infected cell and its target . Formation of VS requires interactions between HIV-1 Env on infected cells and CD4 on target cells [30] . Upon VS formation , high concentrations of mature virions localize to VS to mediate cell-to-cell spread [31] . Because Vpr rescues Env and Env-containing virions from lysosomal degradation , we hypothesized that Vpr would also enable the formation of VS in the co-culture system . To determine whether Vpr affects VS formation between MDM and primary T lymphocytes , we used laser-scanning confocal microscopy to visualize areas of co-localization between surface CD4 on T lymphocytes and mature virions in MDM . We pre-stained T lymphocytes with an anti-CD4 antibody ( DK4003 ) that does not disrupt the ability of CD4 to bind Env , and co-cultured these cells with infected MDM briefly to allow formation of cellular contacts . We then washed away unbound cells and stained with an antibody against Gag MAp17 to visualize mature virions , as previously described [16 , 32 , 33] . Virological synapses were identified as regions of co-localization between CD4 ( green puncta in Fig 5A ) on T lymphocytes and mature Gag on MDM ( red puncta in Fig 5A ) . We identified similar numbers of MDM infected with wild type and mutant virus , and infected MDM were frequently multi-nucleated syncytia ( Fig 5A ) . However , we consistently observed significantly more VS per infected MDM in the presence of Vpr ( Fig 5A and 5B ) . These results explain why spread of HIV-1 from MDM to T lymphocytes is dramatically enhanced by Vpr . As has been shown for other types of cell-to-cell spread [30] , we observed that VS between MDM and primary T lymphocytes did not form in the absence of de novo Env expression ( YU-2 Env-pseudotyped HIV-1 89 . 6env− ) ( Fig 5C ) . Furthermore , consistent with a previous report [17] , VS formation was efficiently blocked by treating infected MDM with a high concentration ( 10 μg/ml ) of the broadly-neutralizing anti-Env gp120 antibody b12 at the time of co-culture ( Fig 5C ) . Thus , VS formation between HIV-1 infected MDM and primary T lymphocytes requires HIV-1 Env expression and is markedly enhanced by expression of Vpr in MDM . Vpr enhances Env expression by counteracting a type I IFN-inducible restriction of Env expression [11] that targets Env-containing virions for lysosomal degradation ( Fig 4 ) . Therefore , we asked whether the addition of IFN to infected MDM affected VS formation with T lymphocytes . Indeed , we observed that IFN significantly reduced the number of VS detected per infected MDM even when Vpr was expressed ( Fig 5C ) . Because Vpr and DCAF1 cooperate to counteract type I IFN induction , we also sought to determine whether DCAF1 is required for Vpr-dependent VS formation . To test this , we silenced DCAF1 in MDM from two donors ( Fig 5D ) and no longer observed significant Vpr-dependent VS formation with T lymphocytes ( Fig 5E ) . In sum , these results are consistent with a model in which Vpr and DCAF1 cooperate to increase HIV-1 infection of T lymphocytes by counteracting a type I IFN-inducible restriction of Env-dependent VS formation in MDM that reduces efficient transfer of virions from MDM to autologous primary T lymphocytes .
Vpr is a highly conserved HIV-1 protein that is required for full pathogenesis in vivo by a mechanism that is poorly understood . Here we show that under conditions in which efficient CD4+ T lymphocyte infection required contact-dependent VS formation with infected MDM , Vpr promoted VS-mediated transmission of HIV-1 . Moreover , we provide evidence that Vpr promoted infection by counteracting an IFN-inducible restriction of HIV-1 Env expression in MDM . Although CD4+ T lymphocytes are the most abundant HIV-1-infected cell type in vivo and are responsible for much of its pathogenesis , T lymphocytes are relatively refractory to infection by cell-free HIV-1 in vitro . In contrast , we observed significantly more HIV-1 infection of activated primary CD4+ T lymphocytes when T lymphocytes were co-cultured with autologous infected MDM , despite similar amounts of free virus in the co-culture supernatant . These results are consistent with research from other investigators showing cell-to-cell spread is much more efficient than infection of T lymphocytes by cell-free virus [32 , 34] . We also observed that once low-level initial infection of T lymphocytes by cell-free virus was established , subsequent spread within the culture became highly efficient and Vpr-independent . Thus , in the in vitro co-culture system , Vpr and macrophages help the virus overcome a bottleneck to initial infection , accelerating infection of T lymphocytes . In this respect , the co-culture system recapitulated the in vivo requirement for Vpr for maximal T lymphocyte infection and provides a mechanism that helps explain its evolutionary conservation . As reported by others [16 , 32] , we demonstrate that HIV-1-infected MDM efficiently spread HIV-1 to T lymphocytes across Env-dependent VS , and that this mode of spread is resistant to neutralization by some antibodies . Furthermore , we show that productive infection of MDM was required for spread to T lymphocytes; passive trans-infection of T lymphocytes by uninfected MDM was not observed under the conditions of our assay . These results reveal a critical role for macrophage infection in maximal HIV-1 infection of T lymphocytes . Our previous work indicates that Vpr increases MDM infection by preventing lysosomal degradation of Env and amplifying release of Env-containing virions [11] . We report herein that in the absence of Vpr , virions containing Env were targeted to macrophage lysosomes and fewer virions were localized to Env-dependent VS between MDM and T lymphocytes . Indeed , our results illustrate that Vpr from multiple HIV-1 isolates promoted efficient macrophage-dependent T lymphocyte infection by this mechanism . This conserved function of Vpr provides a mechanistic explanation for its evolutionary conservation . Finally , we provide confirmatory evidence that Vpr prevents the activation of an innate immune restriction of HIV-1 in MDM . Vpr activates the SLX4 endonuclease complex through its adaptor protein , DCAF1 , allowing HIV-1 to evade the induction of a type I IFN response [8] . This pathway is active in MDM and may explain how Vpr prevents macrophage-specific restriction of Env [11] . Consistent with this , we demonstrated that treatment of infected MDM with exogenous IFN increased Env-dependent lysosomal targeting of virions and impaired Env-dependent VS formation with T lymphocytes . While the involvement of DCAF1 and IFN in Vpr-dependent HIV-1 spread from MDM to T lymphocytes supports a potential role for SLX4-mediated immune evasion , this has not yet been directly demonstrated . IFN has several well-documented antiviral effects and likely acts through multiple mechanisms to inhibit HIV-1 infection and spread . While we cannot exclude the possibility that IFN affects VS formation through additional mechanisms , our results suggest that the Env-dependent restriction observed in MDM in the absence of Vpr is inducible by exogenous IFN treatment . Whether the restriction observed in Vpr-null-HIV-1-infected MDM requires secreted IFN is an interesting possibility that requires further study . Restriction of HIV-1 by IFN is of particular interest in light of recent evidence that IFN treatment may shrink the HIV-1 reservoir [35 , 36] . Further elucidation of this pathway , including the mechanism by which HIV-1 is detected and the identity of the IFN-stimulated macrophage restriction factor are important areas for future investigation . In sum , we report a novel role for Vpr in promoting VS-mediated HIV-1 infection of T lymphocytes by counteracting IFN-inducible restriction of Env in MDM . These results underscore the importance of macrophages in HIV-1 pathogenesis and antiviral immunity , and provide a compelling explanation for the in vivo function and evolutionary conservation of Vpr .
Antibodies to CAp24 ( KC57-FITC , Beckman Coulter ) , CD3 ( OKT3-Pacific Blue , BioLegend ) and CD14 ( HCD14-APC , BioLegend ) were used for flow cytometry . Antibodies to the following proteins were used for immunoblot analysis: DCAF1 ( 11612-1-AP Proteintech ) , GAPDH ( Santa Cruz Biotech ) , Gag pr55 ( HIV-Ig ) , Env gp160/120 , Env gp41 , and Vpr ( AIDS Reagent Program , Division of AIDS , NIAID , NIH: Catalog 288 from Dr . Michael Phelan [37] , 11557 from Dr . Michael Zwick [38] , 3951 from Dr . Jeffrey Kopp , and 3957 from NABI and NHLBI ) . Antibodies to the following proteins were used for microscopy: CD4 [DK4003 ( Centre for AIDS Reagents , NIBSC , contributed by Dr . D Healey ) ] , Gag MAp17 [4C9 ( Centre for AIDS Reagents , NIBSC , contributed by Drs . R B Ferns and R S Tedder ) ] , LAMP1 ( H4A3 ) , LAMP2 ( H4B4 ) and calnexin ( AF18 ) from Abcam . Secondary antibodies were FITC-conjugated goat anti-mouse IgG ( H+L ) and AlexaFluor 647-conjugated goat anti-mouse IgG2a ( BD Biosciences ) . Neutralizing antibodies 2G12 , b12 , SIM . 2 , and Z13E1 ( AIDS Reagent Program , Division of AIDS , NIAID , NIH: Catalog 1476 from Dr . Hermann Katinger [39] , 2640 from Dr . Dennis Burton and Carlos Barbas [40] , 723 from Dr . James E . K . Hildreth [41] ) were used at a 1 μg/ml for neutralization studies at the time of co-culture , and b12 was used at 10 μg/ml to block VS formation and cell-to-cell spread . Anti-human IFNAR2 ( MMHAR-2 , PBL Assay Science ) was used at 1 μg/ml for neutralization where indicated . p89 . 6 and pNL4-3 were obtained through the AIDS Reagent Program , Division of AIDS , NIAID , NIH: catalogs 3552 and 114 from Dr . Ronald G . Collman and Dr . Malcolm Martin , respectively [42–44] . p89 . 6vpr- , p89 . 6env- , p89 . 6vprQ65R , pNL4-3env- , pNL4-3vpr- , and pNL4-3vpr-env- were constructed as previously described [11] . pSIV3+ , psPAX2 , pAPM-1221 ( shNC ) and pDCAF-APM . 1-3 ( shDCAF1 ) were obtained from Dr . Jeremy Luban [45] . pYU-2env was obtained from Joseph Sodroski [46] . pAD8 and pAD8vpr- were obtained from Vicente Planelles [47] . Virus stocks were obtained by transfection of 293T cells with virus expression plasmids using polyethylenimine , as described [11 , 48] . Pseudotyped virus was produced by co-transfecting 293T cells with provirus and Env expression plasmid , as described [11] . Viral supernatants were collected at 48h and centrifuged at 1500 rpm to remove cell debris . Virus was stored at -80°C and quantified by CAp24 ELISA , as described [11] . Leukocytes isolated from anonymous donors by apheresis were obtained from New York Blood Center Component Laboratory . Peripheral blood mononuclear cells ( PBMC ) were purified by Ficoll density gradient separation , as described [49] . CD14+ monocytes and CD4+ T lymphocytes were isolated as previously described [11] . Briefly , monocytes were isolated by positive selection with an EasySep magnetic sorting kit ( StemCell Technologies ) . Monocyte-derived macrophages ( MDM ) were obtained by culturing monocytes in R10 [RPMI-1640 with 10% Certified endotoxin-low fetal bovine serum ( Gibco , Invitrogen ) ] , penicillin ( 10 Units/ml ) , streptomycin ( 10 μg/ml ) , L-glutamine ( 292 μg/ml ) , carrier-free M-CSF ( 50 ng/ml , R&D Systems ) and GM-CSF ( 50 ng/ml R&D Systems ) for seven days . MDM were incubated with 5 μg HIV-1 for six hours and cultured in fresh medium for two to four days . CD4+ T lymphocytes were isolated by CD8 negative selection ( DynaBeads , Life Technologies ) , cultured in R10 for several days and activated with 5 μg/ml phytohaemagglutinin ( PHA-L , Calbiochem ) overnight before addition of 500 IU/ml recombinant human IL-2 ( R&D Systems ) . T lymphocytes were infected with 5 μg or 50 μg HIV-1 by spinoculation at 2500 RPM for 2–3h with 8 μg/ml polybrene ( Sigma ) 72h following PHA stimulation , as described [49] , or incubated with virus for two days , where indicated . For co-culture experiments , HIV-1-infected MDM were co-cultured with autologous CD4+ T lymphocytes 72 hours after PHA activation for two days . Infected T lymphocyte monocultures or co-cultures were maintained in R10 and IL-2 until analyzed . Where indicated , control cells were treated at the time of infection with 4 μM raltegravir ( Selleck Chemical ) to block retroviral integration . Surface staining for CD3 and CD14 was performed before fixation and intracellular staining for Gag CAp24 was performed as described previously [11 , 50] . Flow cytometric data was acquired using a FACSCanto instrument with FACSDiva collection software ( BD ) or a FACScan ( BD , Cytek ) with FlowJo software ( TreeStar ) and analyzed using FlowJo . Cell cycle analysis of 293T cells was performed previously [11] . Where indicated , cells were labeled with CMTMR fluorescent dye ( Life Technologies ) following the manufacturer’s protocol . MDM or MDM-T lymphocyte co-cultures were lysed in Blue Loading Buffer ( Cell Signaling ) , sonicated with a Misonix sonicator ( Qsonica , LLC . ) and clarified by centrifugation at 13000 RPM . Lysates were analyzed by SDS-PAGE immunoblot and protein levels were quantified using Adobe Photoshop as described [11 , 49] . CAp24 ELISA was performed as previously described and quantitation of mass is based upon commercial standards ( ViroGen ) [11] . Short hairpin RNA-mediated knockdown of DCAF1 was performed as previously described [11 , 45] . Briefly , we spinoculated primary monocytes with VSV-G-pseudotyped SIV3+ for 2 hours with 10 μg/ml polybrene to allow Vpx-dependent downmodulation of SAMHD1 . Cells were then incubated overnight in R10 with M-CSF ( 50 ng/ml ) and GM-CSF ( 50 ng/ml ) plus 20 μg VSV-G-pseudotyped lentivirus containing a shRNA cassette targeting luciferase ( Control ) or DCAF1 . Following an overnight incubation , the cells were cultured for 3 days in fresh medium before addition of 10 μg/ml puromycin for 3 additional days prior to HIV-1 infection . LSCM of MDM or MDM-T lymphocyte VS was performed as described previously [16 , 32] , with modifications . Briefly , MDM were differentiated on Nunc Lab-Tek 4-well chambered borosilicate cover glass ( Thermo Fisher ) . For VS visualization , autologous , PHA/IL-2-activated CD4+ T lymphocytes were pre-stained for surface CD4 for one hour with primary antibody plus 30 minutes with secondary antibody and co-cultured for four hours at room temperature with MDM before gentle washing with warm RPMI . For experiments using exogenous IFN , infected MDM were treated with 500 U/mL recombinant IFN-α ( Calbiochem ) two days before harvest . For LAMP1 staining , infected MDM were treated with 20 mM ammonium chloride for the final eight hours to prevent lysosomal acidification unless otherwise noted . Cells were fixed in 4% paraformaldehyde for one hour at room temperature and permeabilized with 0 . 1% saponin ( Sigma ) in 10% pooled human AB and goat sera for FC-receptor blocking for one hour at room temperature , and endogenous biotin was blocked using endogenous biotin-blocking kit ( Life Technologies ) before staining for Gag p18 and/or LAMP1 for one hour primary and 30 minutes secondary using the antibodies listed above . Actin cytoskeleton was visualized by Phalloidin-TRITC ( Sigma ) and nuclei were stained using DAPI ( Fisher Scientific ) . Cells were preserved in ProLong Gold anti-fade ( Life Technologies ) and visualized on a Leica SPX5 inverted confocal microscope at the University of Michigan Microscopy and Image-Analysis Laboratory . Images of optical sections of approximately 1 μm depth were captured at 20X dry or 100X oil-immersion objective magnification . Images were processed using ImageJ ( NIH ) and co-localization was quantitated by automated spots analysis using Imaris ( BitPlane ) . Each Gag MAp17+ puncta with signal 2-fold or greater above background based on a raltegravir-inhibited infected MDM control was identified in an automated manner , and fluorescence intensity in each channel was quantitated for each Gag+ spot . Co-localization was defined as the number of Gag+ spots that were also positive for LAMP1 or CD4 ( VS ) two-fold or greater above isotype staining controls , per Gag+ cell imaged . RNA was collected with RLN buffer ( 50 mM Tris-Cl , pH 8 . 0 , 140 mM NaCl , 1 . 5 mM MgCl2 , 0 . 5% ( v/v ) Nonidet-P-40 ) and spun at 300 x g for 2 minutes . Supernatant was transferred to a new microcentrifuge tube and resuspended in RLT buffer . RNA was isolated from MDMs using RNeasy Kit ( Qiagen ) with on-column DNase I digestion . RNA was reverse transcribed using iScript Advanced cDNA Synthesis Kit ( Bio-Rad ) . cDNA was amplified with SsoAdvanced Universal SYBR Green Supermix ( Bio-Rad ) on an Applied Biosystems 7300 Real-Time PCR System using commercially available IFNA1 primers ( Prime PCR , qHsaCED0020782 , Bio-Rad ) , synthesized MX1 primers ( Forward: 5’-TTG AGA CAA TCG TGA AAC AGC AA-3’ , Reverse: 5’-TCC GTC ACG GTG TGT AGC ATA-3’ ) , or with TaqMan Gene Expression Master Mix with β-actin primers and FAM-MGB probes ( TaqMan Gene Expression , Hs99999903_m1 , Life Technologies ) ( Applied Biosystems ) . Reactions were quantified using ABI Sequence Detection software compared to serial dilutions of a single-stranded DNA oligo spanning the IFNA1 amplicon , MX1 amplicon , or cDNA from mock-treated cells . Calculated copies from the no-RT controls were subtracted from the calculated copies of the cDNA samples , then normalized for input measured by β-actin . Vpr ( Q73369 ) , DCAF1 ( Q9Y4B6 ) , LAMP1 ( P11279 ) , CD4 ( P01730 ) , Env ( Q73372 ) , Gag ( Q73367 ) , CD14 ( P08571 ) , CD3 ( P07766 ) , IFN-α ( P01562 ) , IFNAR2 ( P48551 ) , LAMP2 ( P13473 ) , Calnexin ( P27824 ) , MX1 ( P20591 ) . | Human immunodeficiency virus ( HIV-1 ) , the leading infectious killer worldwide , dysregulates the immune system primarily through infection and depletion of CD4+ T cells . The conserved HIV-1 Vpr protein has been previously shown to promote T cell infection and disease progression in an animal model; however , infection of primary CD4+ T cells in culture does not require Vpr , and its mechanism of action remains undefined . Here we show that Vpr promoted HIV-1 infection of CD4+ T cells by counteracting an antiviral restriction in infected primary macrophages . This restriction degraded HIV-1 in macrophages and impaired the formation of virological synapses–intercellular contact sites that facilitate efficient and immunoevasive viral transmission to T cells . Treatment of infected cells with the antiviral cytokine interferon-alpha induced this restriction even in the presence of Vpr , suggesting that Vpr prevents induction of an antiviral state in macrophages with consequences for viral spread to T cells . Our study provides mechanistic insight into the function of Vpr and the role of macrophage infection in HIV-1 pathogenesis , with implications for the development of improved treatment strategies . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | Vpr Promotes Macrophage-Dependent HIV-1 Infection of CD4+ T Lymphocytes |
The F-type ATP synthase complex is a rotary nano-motor driven by proton motive force to synthesize ATP . Its F1 sector catalyzes ATP synthesis , whereas the Fo sector conducts the protons and provides a stator for the rotary action of the complex . Components of both F1 and Fo sectors are highly conserved across prokaryotes and eukaryotes . Therefore , it was a surprise that genes encoding the a and b subunits as well as other components of the Fo sector were undetectable in the sequenced genomes of a variety of apicomplexan parasites . While the parasitic existence of these organisms could explain the apparent incomplete nature of ATP synthase in Apicomplexa , genes for these essential components were absent even in Tetrahymena thermophila , a free-living ciliate belonging to a sister clade of Apicomplexa , which demonstrates robust oxidative phosphorylation . This observation raises the possibility that the entire clade of Alveolata may have invented novel means to operate ATP synthase complexes . To assess this remarkable possibility , we have carried out an investigation of the ATP synthase from T . thermophila . Blue native polyacrylamide gel electrophoresis ( BN-PAGE ) revealed the ATP synthase to be present as a large complex . Structural study based on single particle electron microscopy analysis suggested the complex to be a dimer with several unique structures including an unusually large domain on the intermembrane side of the ATP synthase and novel domains flanking the c subunit rings . The two monomers were in a parallel configuration rather than the angled configuration previously observed in other organisms . Proteomic analyses of well-resolved ATP synthase complexes from 2-D BN/BN-PAGE identified orthologs of seven canonical ATP synthase subunits , and at least 13 novel proteins that constitute subunits apparently limited to the ciliate lineage . A mitochondrially encoded protein , Ymf66 , with predicted eight transmembrane domains could be a substitute for the subunit a of the Fo sector . The absence of genes encoding orthologs of the novel subunits even in apicomplexans suggests that the Tetrahymena ATP synthase , despite core similarities , is a unique enzyme exhibiting dramatic differences compared to the conventional complexes found in metazoan , fungal , and plant mitochondria , as well as in prokaryotes . These findings have significant implications for the origins and evolution of a central player in bioenergetics .
Mitochondrial F-type ATP synthase complexes are remarkable molecular machines that link proton-motive force generated by respiration to the synthesis of ATP , the currency of energy economy in biology . The eukaryotic enzyme is made up of two structural sectors , the Fo and the F1 ( hence , the complex is often called the FoF1 or F1Fo complex; complex V is another common designation , referring to the fifth and final complex of the oxidative phosphorylation pathway ) . The membranous Fo sector consists of a subunit c oligomer , subunit a , the peripheral stalk subunits b , d , F6 ( h ) , and OSCP , as well as additional associated subunits depending on the species . The globular catalytic sector F1 is made up of subunits α3 , β3 , and the central stalk subunits γ , δ , and ε [1] , [2] . The movement of protons through a channel constituted by the a and c subunits provides the energy required for the clockwise rotation of the c ring , which in turn causes the central stalk to rotate because of its close contact with the c ring . The rotation of the central stalk subunit γ creates a conformational change in the catalytic subunits β and α , which are in contact with the upper portion of γ , leading to the synthesis of ATP from bound ADP and phosphate [1] , [3]–[6] . When the central stalk rotates , it is critical that α3β3 subcomplex is held in position , and this is accomplished by the peripheral stalk that acts as a bearing and a stator [7] , [8] . The origin of proton-driven ATP synthesis by the FoF1 complex can be traced to the Eubacteria . Because of the critical nature of interactions between the Fo and F1 sectors that underlie the functioning of this complex [2] , the subunit proteins that form the essential core of the complex are highly conserved , and the genes encoding them are usually readily identified in complete genomic sequences of prokaryotes and eukaryotes . When we searched the genome sequences of apicomplexan parasites [9]–[12] , we were intrigued by the apparent absence of genes encoding the Fo sector subunits that form the peripheral stalk ( except OSCP ) as well as the subunit a of ATP synthase , although F1 sector subunits and the Fo subunit c were readily detected . Clearly , a functional ATP synthase complex cannot be assembled without these subunits . We initially reasoned that the parasitic existence of these organisms might underlie the loss of a functional ATP synthase , possibly through a greater reliance on hosts for energy generation . However , publication of the macronuclear genome sequence of the ciliate T . thermophila [13] revealed that the same set of proteins apparently missing in the apicomplexans was also undetectable in this ciliate . The ciliates and the apicomplexans ( along with dinoflagellates ) belong to a “crown group” of thousands of organisms called alveolates that is phylogenetically distant from metazoans , fungi , and plants [14]–[16] . It is possible that during evolution , these subunits may have diverged in these lineages beyond the point of identification using current bioinformatics tools , although such subunits are readily detectable by the same tools in evolutionarily more distant prokaryotic genomes . Alternatively , novel proteins may have been recruited to fulfill functions of the missing subunits . It is also possible that the retained ATP synthase subunits ( i . e . , those forming the F1 sector ) may serve functions other than ATP synthesis in these organisms . However , several studies done in the 1970s showed Tetrahymena mitochondria to be capable of oxidative phosphorylation [17]–[19] . Therefore , it seemed more likely that novel or highly divergent subunits may have replaced the conventional a and peripheral stalk subunits in T . thermophila , leading to a unique but fully functional enzyme . Further , such novel subunits might be shared by the members of the whole clade of alveolates , if they were adopted by an early common ancestor of the ciliates , dinoflagellates , and apicomplexans . The ease with which Tetrahymena can be grown , the size of the cells , the abundance of mitochondria in each cell , and availability of standardized techniques to isolate mitochondria made Tetrahymena an attractive model to study the ATP synthase of alveolates . Although Tetrahymena has served as a model eukaryote and has been the subject of many seminal studies that have resulted in numerous important insights in biology [20]–[22] , its ATP synthase has not been investigated . We show in this report that Tetrahymena's ATP synthase possesses an unusual structure , with similarity in the F1 headpiece morphology , but significant differences are seen in its dimer shape and in protein mass on the intermembrane side of the complex compared to previously studied ATP synthases from a variety of other organisms . In addition to readily identifiable F1 subunits , the enzyme appears to contain several subunits that have no known orthologs in other organisms . The absence of orthologs to these novel subunits even in apicomplexans and dinoflagellates suggests that the ciliate ATP synthase is truly unique .
Previous studies on oxidative phosphorylation in Tetrahymena were carried out in the 1970s using strains that were not always defined . Since our goal was to take advantage of the sequenced T . thermophila genome to identify ATP synthase subunits , for all our studies we decided to use the same strain ( SB210 ) for which the macronuclear genomic sequence has been published [13] . To confirm that mitochondria from this strain were comparable to those used in previous studies , we assessed the in situ capability of the mitochondria in digitonin-permeabilized T . thermophila cells to carry out oxidative phosphorylation , which is indicative of a functional ATP synthase and electron transport chain , in respirometry experiments . A typical oxygen consumption trace is shown in Figure 1A , in which respiration was dependent on the presence of mitochondrial substrate , succinate , and stimulated 2 . 4-fold by the addition of ADP . Similar results were obtained in earlier studies of mitochondria from various isolates of T . pyriformis [23] . Stimulation of the rate of respiration in this type of experiment is due to increased utilization of the proton gradient by the ATP synthase to drive the synthesis of ATP from the added ADP; the rate of respiration increases in response to the reduction of the proton gradient . Under appropriate conditions , FoF1 ATP synthases are capable of the reverse reaction , i . e . , ATP hydrolysis . Indeed , in a number of organisms the reverse reaction is important for maintenance of the proton electrochemical gradient under specific growth conditions or life stages [24]–[27] . A coupled spectrophotometric assay ( see Materials and Methods ) was used to assess the ATP hydrolase activity in T . thermophila mitochondrial preparations . Reaction traces ( Figure S1 ) show that T . thermophila SB210 ATP hydrolase exhibits time- and ATP-dependent activation , as seen in other FoF1 ATP synthases/hydrolases [28] . T . thermophila mitochondria had a somewhat lower specific ATPase activity compared to yeast ( unpublished data ) ; however , it is possible that the measured activity represents only a fraction of the ATP synthase complexes present in the mitochondrial membranes , since isolated dimeric complexes exhibited negligible hydrolase activity ( see below ) . The measurable ATPase activity also showed unusual resistance to the classical FoF1 ATP synthase inhibitors oligomycin and sodium azide ( Figure 1B and 1C ) . Similar resistance to oligomycin , as well as other inhibitors , was previously reported in mitochondria from T . pyriformis [23] , [29] . ATP synthase generally forms the second largest complex after complex I and runs as high molecular weight bands in blue native ( BN ) polyacrylamide gel electrophoresis ( PAGE ) [30] . T . thermophila mitochondria were solubilized with digitonin or dodecyl maltoside and separated on a 3%–10% gradient BN gel to resolve high molecular weightweight complexes ( Figure 2A ) . We assessed the ATP hydrolase activity of the sample bands using an in-gel ATPase assay that generates a white precipitate . In digitonin-solubilized fractions , the principal regions of ATPase activity were found lower down in the gel ( below band 3 ) , and thus may be due to monomers and/or separate catalytic F1 head pieces ( Figure 2B ) . Even after overnight incubation ( 8–12 h ) , we saw only a very limited amount of precipitate in the top two bands , which contain dimeric ATP synthase complexes on the basis of single particle electron microscopy results ( see below ) , indicating very weak ATPase activity , in contrast to active dimeric and higher oligomeric forms of ATP synthase complexes previously reported in other species [30]–[33] . The complexes from the highest molecular weight bands ( Figure 2A , bands 1 , 2 , and 3 ) were electroeluted under gentle conditions that largely preserved their structure and analyzed by single particle electron microscopy . In the samples from bands 1 and 2 , we observed structures resembling dimeric ATP synthase complexes ( complex V2 ) , as well as apparent supercomplexes of complex I ( NADH dehydrogenase ) , and a dimer of complex III ( complex I–III2 ) . Band 3 appeared to contain complex III dimers ( Figure S2 ) . Since the electroeluted particles from these bands were quite uniform , without breakdown products , we were able to select homogenous datasets of 40 , 000 single particle images obtained after digitonin or dodecyl maltoside solubilization and used them to generate averaged 2-D projection maps . Analysis of the projections indicated that subsets of projections from digitonin and dodecyl maltoside comprised the same types of projections . Hence , we combined the data to improve the quality of final images . Side-view projection maps of T . thermophila dimeric ATP synthase showed particles attached in a parallel and flat position on the carbon support film ( Figure 3A–3C ) or in a slightly tilted position ( Figure 3D ) . Some dimers appeared to have a large protein attached to the Fo sector ( Figure 3E , blue arrowhead ) . As estimated from its surface area , the mass of this domain could be as much as 200 kDa . In addition , we also obtained top views of the dimers ( Figure 3F and 3G ) . The best maps had a resolution of about 1 . 5 nm , which permitted recognition of specific known and novel features as depicted in a schematic model ( Figure 3H ) . The projection maps indicated that the all structural elements of mammalian and Escherichia coli enzyme were present , including the F1 headpiece consisting of α3β3 subunits [34] , the rotor composed of the subunit c ring , as well as the central stalk ( rotor ) consisting of the γ , δ , and ε subunits . In addition , OSCP , the uppermost stator component ( Figure 3B and 3C , see green arrowheads ) was present , although it was apparently lost from a substantial number of projections ( Figure 3A ) . The headpieces are separated by at least 2 nm , and there is protein density present in the dimer interface region between the two Fo parts ( marked red in H ) that appears structurally similar to that previously observed in the alga and yeast ATP synthases [35] , [36] . All other visible densities ( marked blue ) appear to be unique to T . thermophila dimeric ATP synthase since they have not been observed in any other species . There were two domains attached at the interface of the monomers . A large domain , estimated to be at least 100 kDa , was attached to the bottom side of the complex; another was at the matrix-exposed side close to the F1 head pieces and seemed to be connected to the catalytic F1 part ( Figure 3C , orange arrowhead ) . The latter density could represent novel subunits that help the two monomers associate with each other . Interestingly , the dimer also had distinct novel membrane-bound densities at the extreme left and right position of the c subunit rotors ( Figure 3A , blue arrowheads ) . Some dimers appeared to have a large protein attached to the Fo sector ( Figure 3E , dark-blue arrowhead ) . As estimated from its surface area , the mass of this domain could be as much as 200 kDa . Furthermore , the two monomers appeared to be parallel to each other , rather than forming an acute angle as seen in the other species examined thus far . This finding was dramatically different from projections of the yeast , Polytomella , and bovine complexes [35]–[39] . A final question is the position of the two stators of the dimer . This question is difficult to answer because the stators are strongly overlapping with the F1 headpieces . One possibility is that they are at the extreme periphery . In some views there is a faint connection between the headpiece and the membrane ( Figure 3B , yellow arrowhead ) . This connection becomes stronger upon tilting ( Figure 3D ) . On the other side , there is ample space in the center of the dimer where an extensive structure resides in between the F1 headpieces . This structure is connected to the headpiece ( Figure 3C , orange arrowhead ) and may hide the stator . The latter position may be considered more likely by reason of structural homology . The yeast , Polytomella , and bovine ATP synthase complexes have one stator per monomer [35] , [37]–[39] , but fully lack the peripheral domains marked light blue ( Figure 3C ) . In addition to dimeric ATP synthase , bands 1 and 2 contained complex I–III2 supercomplex and projection maps of its side and top view were analyzed ( Figure S2 ) . These maps resemble their counterparts in Arabidopsis and other organisms [37] , [40] , [41] . Complex III2 was located at the tip of the membrane arm of complex I ( Figure S2A , white arrowhead ) . A small number of complexes lacked a part of the hydrophilic arm ( Figure S2C , black arrowhead ) , which has been observed in many complex I preparations . The assignment of the position of complex III2 in the supercomplex was confirmed by a structural analysis of single dimeric complex III , eluted from band 3 ( Figure S2D ) . Features of the matrix-exposed domain , which are part of the subunits 1 and 2 of complex III , were similar in both types of particles ( Figure S2A , D white arrowheads ) . Complex III2 from T . thermophila was structurally comparable to its counterpart in Arabidopsis ( Figure S2E ) , but not identical [40] . Overall , the I–III2 supercomplex and the dimer of complex III were structurally similar to those of many other organisms , suggesting conservation of these respiratory complexes in T . thermophila . The three high molecular weight bands identified by BN-PAGE were excised from gels for analysis by liquid chromatography-mass spectrometry-mass spectrometry ( also known as liquid chromatography-tandem mass spectrometry or LC/MS/MS ) . Samples excised from gel runs were divided and separately digested with trypsin or chymotrypsin to improve the chances of detecting hydrophobic proteins . The digests were subjected to LC/MS/MS analyses as described in the Materials and Methods . Overall , peptides originating from 59 proteins were identified in band 1 . The main annotated proteins in band 1 were subunits of respiratory complexes I , II , III , and V , as well as some additional proteins . In band 2 we detected peptides originating from 50 proteins , including subunits of complexes I , III , and V . In band 3 , there were 21 protein hits , including subunits of complex III . There were also many unannotated proteins and some apparent contaminating proteins ( i . e . , proteins that are not known to be part of oxidative phosphorylation complexes ) in each of the three bands . Data for all these peptides are summarized in Table S1; LC/MS/MS data for peptides detected in bands 1–3 are given in Table S2 . The presence of multiple complexes in the BN-PAGE bands made it difficult to assign any of the observed hypothetical proteins to specific complexes . To achieve further separation of the complexes , we carried out 2-D BN/BN-PAGE . The presence of 0 . 03% dodecyl maltoside in the cathode buffer of the second dimension BN-PAGE was strong enough to dissociate Band 1 and Band 2 of the first dimension BN-PAGE into two individual spots ( Figure 2C , designated spot 1 and spot 2 from band 1 , and spots 3 and 4 from band 2 ) . Band 3 ran as a single spot , which was designated as spot 5 ( Figure 2C ) . The dissociation pattern observed in 2-D BN/BN-PAGE was reproducible . Samples were excised from the central portion of each spot for analysis . A set of spots from one 2-D gel was digested with trypsin and a set from a second was digested with chymotrypsin for LC/MS/MS analysis . The results revealed that Spot 1 , and to a lesser extent , spot 3 contained conventional ATP synthase subunits including α , β , γ , OSCP , and c ( ATP9 ) , whereas spots 2 , 4 , and 5 largely did not , but rather contained subunits normally found in complexes I and III , as well as other proteins . A summary of data from all five spots is given in Table S1 , and the LC/MS/MS data for peptides detected in spots 1–5 are given in Table S3 . In addition to the annotated ATP synthase subunits , spot 1 LC/MS/MS results included three additional proteins normally not associated with ATP synthases ( branched-chain amino acid aminotransferase family protein , lipid A-disaccharide synthase , and peptidase M16 inactive domain-containing protein; however the latter two may be contaminants as described below ) , and 15 hypothetical or uncharacterized proteins that have no obvious homology to any other proteins in the database ( Table 1 ) . As noted in a previous study of the T . thermophila mitochondrial proteome by Smith et al . [42] , many of the original gene models merit some corrections . In our analysis , we utilized appropriately corrected sequences for several of the proteins on the basis of data from Smith et al . , extant EST data , or comparison with data from the related ciliate , Paramecium tetraurelia . Among the corrected uncharacterized proteins we detected low , but significant , similarity to two additional ATP synthase subunits—118355322 to F1 subunit δ and 118360532 to Fo subunit d ( Table 2; evidence for these assignments is provided in Texts S1 and S2 ) . On the basis of the prevalence of peptides from known ATP synthase subunits in spot 1 data ( see Table 1 , “Unique Peptides” column ) , their near absence from spots 2 , 4 , and 5 , and the near absence in spot 1 of peptides from known subunits of other mitochondrial complexes ( Tables S1 and S3 ) , we considered it likely that the remaining uncharacterized proteins in Table 2 were authentic subunits of the complex V dimer . On the other hand , the peptides from two of the annotated proteins , peptidase M16 inactive domain-containing protein and lipid A-disaccharide synthase , were predominantly found in spot 5 and in spots 3 and 4 , respectively ( Tables 1 and S3 ) . These proteins may thus represent contaminants present in spot 1 because of trailing in the gels . While putative Fo subunits c , d , and OSCP were detected , subunits with sequence similarity to the structurally and mechanistically critical integral membrane subunits a and b were not found . However , there were proteins among the LC/MS/MS results with appropriately positioned predicted transmembrane segments that could be evaluated as possible highly divergent subunits or novel functional replacements for these subunits . Ymf66 is an integral membrane protein with approximately eight predicted transmembrane helices . Interestingly , the corresponding region in Paramecium mitochondrial DNA ( mtDNA ) is split into two open reading frames ( ORFs ) [43] . Ymf66 has several features that are characteristic of Fo subunit a in a general fashion: ( 1 ) it is encoded by mtDNA ( all known subunits a , with one exception , are mitochondrially encoded ) ; ( 2 ) it is a multispan membrane protein ( subunit a is the only Fo subunit with >>2 transmembrane helices ) ; ( 3 ) it has a conserved arginine residue embedded in a predicted transmembrane helix in the C-terminal region of the protein ( subunit a has a conserved and functionally essential arginine , located in the fourth transmembrane helix in the well-studied E . coli subunit ( Figures 4 and S2 ) ; and ( 4 ) the same transmembrane helix also contains another arginine residue that is conserved at a similar position in most ATP synthase a subunits ( except vertebrates where it is usually replaced by a glutamine ) . An examination of Ymf66 from five Tetrahymena species , as well as from Paramecium , revealed that all these features including appropriately placed arginines were absolutely conserved , providing additional support to our proposition that this protein substitutes for the ATP synthase a subunit in ciliates . We found five proteins ( 146185889 , 118398278 , 118366175 , 146161614 , 146180703 ) that could be considered candidates to functionally replace subunit b , on the basis that they contain one to two hydrophobic regions in the N-terminal half of the protein followed by a more hydrophilic C terminus , and fall roughly within the size range of this subunit ( predicted topologies of these proteins are given in Figure S4 ) . Secondary structure predictions were consistent with this possibility in the case of the first three proteins ( 146185889 , 118398278 , and 118366175 ) , which were predicted to have a predominance of alpha helical structure throughout the region C-terminal to the hydrophobic section ( Figure S4 ) . The known and predicted structure of this section of the bovine subunit b is almost entirely composed of an extended α-helix , allowing the matrix section of the subunit to reach from the membrane to near the top of the F1 subcomplex [8] . One or more of these candidate proteins could participate in forming the stator or be associated with one of the apparently novel membrane-associated domains observed in the Tetrahymena structure . The structural and proteomic analyses seem to suggest unique evolutionary history for many subunits of Tetrahymena ATP synthase . Whereas some of the subunits were clearly recognizable as orthologs of ATP synthase subunits from other organisms , there were many others that seem to be limited to ciliates . To understand evolutionary provenance and relationships of the recognizable subunits , we carried out phylogenetic analyses of these subunits . Alignments of the Tetrahymena ATP synthase subunits β , γ , δ , and c with orthologs from a broad range of other species were constructed and used to calculate their apparent phylogenetic relationships ( Figures 5 and S5; Text S1 ) . The sequences of the catalytic β subunits are well-conserved among all species , with numerous sequence positions that exhibit total amino acid identity . The F1 rotor subunit γ is somewhat less conserved but still has a high degree of similarity among species . The phylogenetic reconstructions that included ciliate and apicomplexan β subunit ( Figure S5 ) or γ subunit ( Figure 5A ) exhibit a similar relationship among the major groups ( metazoa , fungi , Viridiplantae , alveolates ) as that seen in many phylogenetic studies [44]–[46] . The relatively moderate branch lengths of the ciliate clade ( Figure 5A ) suggest a rate of genetic change similar to the average of other groups . The kinetoplastids , in contrast , exhibit very long branch lengths , suggesting they have experienced a period of rapid divergence . Proteomic analysis of Trypanosoma brucei ATP synthase has recently revealed divergence of this complex as well [47] . When we examined the less well-conserved F1 δ and Fo c subunits , we found a different pattern . The alveolates , especially the ciliates , exhibit very long branches indicating an accelerated rate of change ( Figure 5B ) . For these smaller and more divergent subunits , the overall phylogeny of the major groups is less well reproduced , probably owing to their greater divergence and shorter length , i . e . , there are a relatively small number of positions that can be reliably aligned , and in addition , a degree of saturation at some sites cannot be ruled out . Thus , the well-conserved subunits ( α , β , γ ) form a contiguous subset of the complex apparently undergoing modest evolutionary change in ciliates , while the δ and c subunits and the evidently even more divergent a-like subunit ( Ymf66 ) may represent a subset coevolving at a much more rapid rate . We assessed one possible specific instance of coevolution by comparing sequences from the interface regions of the δ and c subunits . This interface has been characterized in bacterial ATP synthase and , along with the γ-c and δ-γ interactions , is critical for the transfer of the rotational movement of the c subunit ring to the central stalk [48] , [49] . In a functionally essential protein–protein interface region , changes in one partner that affect the interface would normally be matched by compensating changes in the interacting partner that act to maintain function [50] , [51] . The divergence of the interface regions of the δ and c subunits ( Figure 5C ) can be construed as a result of coevolution that probably required a series of compensatory changes . One evident hypothesis , which could potentially be tested experimentally , is the interaction of acidic residues acquired in ciliate subunits c adjacent to the conserved loop residues ( RNP ) with the basic residue acquired in ciliate subunits δ next to the position of the otherwise conserved histidine ( Figure 5C ) .
There are three types of multiprotein complexes that link ion movement across the membrane with rotational catalysis of ATP synthesis/hydrolysis: the archaeal A-type and bacterial/mitochondrial F-type ATP synthases use H+ ( or sometimes Na+ ) ions to drive ATP synthesis , whereas the V-type ATPases hydrolyze ATP to pump H+ against its concentration gradient . Shared structural features of these molecular machines , such as distinct sectors that constitute the catalytic , ion transport , and stator functions , suggest a common evolutionary ancestry possibly dating back to the origin of cellular life . In general , the individual A- , F- , or V-type ATP synthases/hydrolases are highly conserved along vast evolutionary distances [52] , although some species-specific features/subunits are also seen . In contrast , we have described here a very unusual F-type ATP synthase in T . thermophila . The overall structure of this complex determined by single particle electron microscopy projections is dramatically different from any other ATP synthase examined . In organisms as divergent as E . coli , Saccharomyces cerevisiae , Polytomella , and the cow Bos taurus , the overall structures of ATP synthases are very similar [37] , [53] . The most obvious difference in T . thermophila is the parallel disposition of individual ATP synthase monomers compared to the angular arrangement seen in all other organisms . It has recently been suggested that the angular arrangement of ATP synthases in mitochondria may be important for the curvature of the cristae tips formed by the inner mitochondrial membrane [54] . Tetrahymena ( as well as other alveolate ) mitochondria , however , have tubular cristae that do not form the curved tips seen in mitochondria from other organisms [55] . We suggest it as a possibility that the parallel arrangements of the ATP synthase monomers might dictate tubular cristae arrangement in ciliate ( and perhaps in all alveolate ) mitochondria . A second unusual structural feature of the ATP synthase dimer is the presence of novel additional membrane-embedded domains that flank the dimer , and could be connected to the F1 headpiece . Such structures have not been observed in any ATP synthase thus far . This is in contrast to all other F-type and V-type ATPases . All studied F-type ATP synthases have just one stator , attached to subunit a . V-type ATP synthases have two to three stators [56] , [57] , but they merge together at one point where they connect to the c subunits ring via subunit a . A third unusual feature is the presence of a large domain attached to the intermembrane part of one of the monomers . Again , this has not been observed in any ATP synthase and the significance of which is unclear . Remarkably , prokaryotic ATP synthases are structurally more similar to their mitochondrial counterparts than is T . thermophila mitochondrial ATP synthase . We were able to resolve the large mitochondrial complexes through 2-D BN/BN-PAGE , which permitted a proteomic cataloguing of the subunit proteins that constitute T . thermophila ATP synthase . While it is possible that the proteomic analysis may have missed some of the component proteins , those that we did detect could be assigned with a reasonable degree of confidence as being subunits of the complex . Of the 24 proteins present in spot 1 , 22 are the likely constituents of the ATP synthase . Only six of these were annotated as subunits of ATP synthase . On the basis of our analysis , two hypothetical proteins could be assigned as subunit d and δ; that leaves 14 proteins with no assigned functions , one of which has homologues believed to be oxidoreductases in many other organisms . Thus , 13 proteins that seem to be part of mitochondrial ATP synthase complex in T . thermophila have no detectable orthologs in any organism other than ciliates . The proteomic data and even phylogenetic analyses of generally conserved subunits seem to confirm the notion from structural studies that ciliate ATP synthase is highly divergent from its mitochondrial or bacterial counterparts . This degree of divergence is also apparent when one examines ciliate mitochondrial DNA . The 47-kb mtDNA in Tetrahymena encodes 44 ORFs , 20 of which have no orthologs in any organisms other than ciliates and have no function assigned to them [43] , [58] , [59] . To put this in perspective , the protozoan Reclinomonas americana has 67 mitochondrial ORFs , the largest number known thus far , of which 66 have orthologs in other species with assigned functions [60] . This finding would suggest either that the unassigned ORFs in ciliates have undergone highly accelerated evolutionary divergence or that ciliate mtDNAs have acquired almost half of their genes from sources other than the α-proteobacterial ancestral endosymbiont that lies at the origin of all extant mitochondria . Our extensive sequence searches have failed to find homologous sequences to the unassigned ciliate mtDNA ORF proteins or the 12 nuclearly encoded subunits of T . thermophila ATP synthase in any of the currently available collections of ORFs , which include metagenomes as well as Genomic Encyclopedia of Bacteria and Archaea Genomes available at the Joint Genome Institute . Thus , the provenance of these ciliate-specific mitochondrial proteins remains obscure . A major motivation for our study was the apparent lack of a gene encoding the subunit a of the Fo sector in complete genomic sequences of any alveolate . Because this subunit , in association with the multimeric subunit c ring , forms the channel through which protons move and drive the catalytic rotation of the enzyme , its absence would be incompatible with proton motive force driven ATP synthesis . Through proteomic analysis of isolated ATP synthase complexes and careful sequence comparison we now propose that the function of subunit a could be served by the highly divergent or novel protein Ymf66 encoded by the mtDNA . This protein is predicted to have eight transmembrane helices , one of which has buried arginines in positions where they could form critical residues for the proton channel as reported in other ATP synthases . Other than this tenuous but potentially critical homology , Ymf66 bears no discernable similarities to any known subunit a from any organism , except for the fact that , like most other subunits a , it too is encoded by mtDNA and is predicted to be a polytopic membrane protein . Remarkably , as discussed above , Ymf66 has no discernable ortholog in any organism other than ciliates . Genes encoding the Fo subunit b were also not detected in any alveolate . Subunit b forms a crucial part of the stator that extends from the membrane to near the top of the globular F1 sector . The role of the stator is to stabilize α3β3 from rotation caused by the centrally positioned γ stalk . Again , the absence of a stator would be incompatible with ATP synthase function . Single particle electron microscopy projections , however , revealed the presence of not one but two stator structures in T . thermophila ATP synthase . Taking into consideration the requirement that subunit b has its N-terminal sequence buried in the membrane and rest of its amino acids forming extended mostly hydrophilic α-helical structure , we have identified three proteins detected in T . thermophila ATP synthase as candidate substitutes for the b subunit . It is not uncommon to have the stator structure formed by homo- or heterodimers of b subunits . Again , it was not possible to detect homologues of these proteins in any organisms other than ciliates . Dinoflagellates and Apicomplexa are two sister clades of ciliates that form the crown group alveolates . Therefore , it is intriguing that these related organisms seem to lack any of the unassigned proteins that are part of the ATP synthase complex in T . thermophila . Mitochondrial evolution in alveolates , however , is complicated [61] . Unlike the ∼44 ORFs encoded by the ciliate mtDNA [43] , [58] , [59] , dinoflagellate and apicomplexan mtDNAs encode just three proteins [61] , [62] . The massive loss of ORFs is also accompanied by unusual structural arrangements of mtDNA and scrambling of rRNA genes; some apicomplexans have actually lost the mitochondrial genome altogether [63] , [64] . However , all these organisms , including those without mtDNA , continue to encode at least α and β subunits of ATP synthase . It is not clear whether these proteins are assembled into a functional ATP synthase , but there are indications that mitochondria are capable of ATP synthesis in at least some of apicomplexans . The question as to what constitutes the functional ATP synthase in these organisms remains unanswered . If what we have reported here for the ciliates is an indication , answers to this question could prove interesting and important , for Apicomplexan pathogens extract an enormous toll from humanity . The unusual and highly divergent ATP synthases could form attractive targets for selective therapeutic approaches .
T . thermophila SB 210 cells were grown in proteose peptone media and mitochondria were isolated as previously described [19] , [65] , [66] . Briefly , 500 ml cultures were harvested at late log phase of growth by centrifugation at 1 , 000g for 5 min . The cells were washed with mitochondria isolation buffer ( MIB; 0 . 3 M sucrose , 1 mM EDTA , 0 . 1mM EGTA , and 12 . 5 mM HEPES ( KOH [pH 7 . 4] ) ; trehalose was substituted for sucrose on two occasions with no evident changes in properties of the mitochondrial preparation ) , and were resuspended in 5 volume of MIB . The suspension was homogenized in a 30 ml Kontes tight fitting glass hand homogenizer on ice until 80%–90% of the cells were broken . The whole homogenate was transferred to a 50-ml conical tube and centrifuged at 300g for 5 min at 4°C in an HS-4 Sorvall rotor . The supernatant was centrifuged at 7 , 000g for 10 min at 4°C . The resulting fraction consists of a hard brown pellet at the bottom followed by cream-colored layer of mitochondria and a loose whitish layer above it . The supernatant and most of the whitish layer was carefully removed . Five volume of MIB was added gently to the pellet and gently shaken to remove the creamy mitochondrial layer . The crude mitochondrial fraction was resuspended in 10 ml of MIB containing 10% percoll and was centrifuged at 5 , 300g for 5 min . The supernatant was removed and the pellet was washed with 10% percoll again . To remove Percoll , the pellet was washed with MIB and centrifuged at 5 , 300g for 5 min at 4°C . The resulting pellet was resuspended with 1 . 5 ml of MIB and was layered on top of a discontinuous sucrose gradient ( 3 ml of 30% [w/v] , 3 ml of 45% , and 3 ml of 60% sucrose ) and was centrifuged at 22 , 000 rpm for 2 h at 4°C in a Sorvall SW27 rotor . A cream-colored band formed at about the position of the 45%–60% sucrose junction and was collected as the purified mitochondrial fraction . This fraction was resuspended in 10 ml of MIB and was centrifuged at 5 , 300g for 5 min to remove excess sucrose . The step was repeated again , and the final pellet was resuspended in a small volume of MIB buffer . Protein concentration was estimated by Bradford assay . Mitochondria ( 1 mg protein ) were resuspended in water and pelleted by centrifugation at 10 , 000 rpm for 10 min at 4°C in a Sorvall SW 50 . 1 rotor . The pellet was resuspended in mitochondria solubilization buffer ( 50 mM Nacl , 50 mM Imidazole/HCl [pH 7 . 0] , 2 mM 6-aminohexanoic acid , and 1 mM EDTA , at 4°C ) . Detergent concentrations were adjusted to 5 µg digitonin per µg of mitochondrial protein , or 1 . 5 µg dodecyl maltoside per µg of mitochondrial protein by addition of 20% stock solutions of the respective detergent . After incubation for 30 min on ice , the sample was centrifuged for 30 min at 30 , 000 rpm in the SW 50 . 1 Sorvall rotor . Coomassie dye from a 5% G-250 stock suspension was added to the supernatant to give a detergent/dye ratio of 8 . The sample was loaded in a 3%–10% BN-PAGE gradient gel and the gel was run for 3–4 h with an initial constant voltage of 100 V , followed by a constant current of 15 mA , as described by Wittig et al . [67] . In-gel ATPase activity of the enzyme was measured by incubating the BN gel strips in a buffer containing 35 mM Tris . HCl ( pH 8 . 4 ) , 270 mM glycine , 14 mM MgSO4 , 0 . 2% Pb ( NO3 ) 2 , and 4 mM ATP at room temperature for overnight as described [68] . For electroelution , protein complexes from the bands were cut with scalpel and transferred into electroeluter chambers ( D-Tube Dialyzer , Novagen ) . The dialyzer tubes were pretreated with 1% ethanolamine and rinsed with ultrapure H2O . Electroelution was done overnight in the electroelution buffer ( 25 mM tricine , 7 . 5 mM Bis-Tris , 1 mM phenylmethylsulfonyl fluoride [pH 7 . 0] ) containing either 0 . 1% digitonin or 0 . 03% dodecyl maltoside at 150 V in 4°C as described [69] . 2-D BN/BN-PAGE was carried out as described by Sunderhaus et al . [70] with slight modifications . The 1-D gel strip was incubated with 0 . 03% dodecyl maltoside ( Anatrace ) for 10 min . After incubation , the gel strip was placed in between the glass plates and a 4%–12% gradient gel was poured . After polymerization , the space between the 1-D gel strip and 4%–12% gradient gel was filled with a 3 . 5% stacking gel . Dodecyl maltoside to a final concentration of 0 . 03% was added in the cathode buffer and the gel was run overnight at a constant current of 15 mA . The relative amount of digitonin required to permeabilize 99% of freshly harvested cells was determined immediately prior to the experiment by monitoring loss of trypan blue exclusion after a 5 min incubation of cells suspended in MIB plus digitonin , and was found to be 0 . 135 mg digitonin per mg cellular protein . Cells containing 315 mg protein were incubated for 5 min . with digitonin under the above conditions , then diluted 6-fold with MIB , recovered by centrifugation , washed once more with MIB , and resuspended at ∼15 mg/ml . Oxygen consumption by the permeabilized cells was measured with a microcathode oxygen electrode ( number 1302 , Strathkelvin Instruments ) in a closed respirometry cell ( MT200 , Strathkelvin Instruments ) with a 100-μl working volume maintained at 32°C . The system was calibrated the same day as each experiment per the manufacturer's instructions . The working solution was MIB containing 2 mM magnesium chloride and 2 mM potassium phosphate with additions as indicated in the figure caption . ATPase activity was determined using a coupled assay modified from Pullman et al . [71] , in which NADH oxidation is coupled to ATP hydrolysis using lactate dehydrogenase and pyruvate kinase . The assay was performed at 35°C in a stirred cuvette with a final volume of 1 ml containing 50 mM HEPES ( KOH [pH 7 . 5] ) , 2 mM MgSO4 , 3 mM phosphoenolpyruvate , 0 . 3 mM NADH , four units lactate dehydrogenase ( Sigma ) , four units pyruvate kinase ( Sigma ) , 0 . 6 mg/ml dodecylmaltoside , ∼200 µg protein of mitochondrial preparation and including inhibitors of adenylate kinase ( 10 µM P1 , P5-di ( adenosine-5′ ) pentaphosphate and 5 mM AMP ) , vacuolar ATPase ( 0 . 2 µM concanamycin A ) , complex IV ( 2 mM KCN ) , and complex I ( 34 µM rotenone ) . The oxidation of NADH was recorded with a modified SLM-AMINCO DW2C dual wavelength spectrophotometer ( On-Line Instrument Systems , Inc . ) in dual mode ( 341–401 nm ) . Dual wavelength spectroscopy ameliorates the effects of light scattering with turbid samples . The specific ATPase activity was quantitated by measuring the slopes of the linear postactivation ( steepest ) part of the assay traces . Electroeluted complexes were applied on carbon coated copper grids and negatively stained with 2% uranyl acetate by droplet method . Images were recorded on a CM12 electron microscope ( Philips ) operated at 120 kV with slow scan 4 k×4 k CCD camera ( Gatan ) at 78 , 000 magnification and pixel size 3 . 8 Å at the level of specimen . Single particle analysis was performed with the Groningen Image Processing ( GRIP ) software package as described by Dudkina et al . [35] , [40] . Bands or spots were excised from BN-PAGE gels and processed with either trypsin or chymotrypsin according to the Coomassie stained gel protocol described by Gundry et al . [72] . All samples were desalted with C18 Omix tips ( Varian ) according to manufacture's protocol . Peptides were analyzed using the LTQ ( ThermoFinnigan ) in gradient mode with the following gradients; 8 . 5%–30% of 0 . 1% formic acid/90% acetonitrile ( 30 min ) , 60% of 0 . 1% formic acid/90% acetonitrile ( 18 min ) , and to 100% of 0 . 1% formic acid/90% acetonitrile ( 22 min ) with a flow rate of 300 nl/min . The peptides were separated on a hand-packed 75-µm reversed phase column consisting of YMC ODS-AQ ( 5-µm particle size and 120-A pore size ) . Using an electrospray voltage of 2 . 2 kV , precursor scans were taken from m/z of 350–1 , 800 m/z and the top eight ions picked for MS/MS . The acquired MS/MS data were searched with Sorcerer 2-Sequest ( SageN Research Products ) , with postsearch analysis using Scaffold ( Proteome Software ) . Peak extraction was performed using Sorcerer 2 SEQUEST default settings . Data were searched using all species in the Trembl and National Center for Biotechnology Information ( NCBI ) databases as well as in the custom Smith et al . database [42] . The following criteria were used: a full trypsin or full chymotrypsin digestion , all species , and the variable modifications of carbamidomethyl and oxidation ( methionine ) . Peptide mass tolerance was set to 1 . 2 amu . All MS/MS spectra were manually examined using Scaffold and low quality spectra were removed . Protein redundancy was then removed by using the Blast tool to assess protein similarity . Sequences of representative species from a broad range of eukaryotic groups were collected from the NCBI refseq protein database for most of the ATP synthase subunits that were identified in T . thermophila . In a few cases , the sequence set was extended with one or two translations of complimentary DNA ( cDNA ) /expressed sequence tag data ( see Table S5 ) ; in these instances , we verified that the sequences used matched the relevant genomic data or were highly similar to sequence data from closely related species . Identifications of sequences used for alignment are given in Table S5 . Sequences were aligned using ClustalX [73] , TCoffee ( Expresso ) [74] , and MAFFT ( L-INS and/or E-INS strategy ) [75]; the alignments were compared and unambiguously aligned positions chosen for phylogenetic analyses . MrBayes [76] was used for Bayesian inference [77] simulations . The program was run with two chains for at least 1 . 2 million generations , sampled every 60 generations , and analysis continued if necessary until probable convergence was indicated by stability of the log likelihood values and the standard deviation of split frequencies for at least 0 . 6 million generations . A preliminary run using the “mixed” amino acid model was used to find the optimal amino acid model , which was the “WAG” model [78] with our datasets , and the final analyses were run using the WAG model and assuming invariable positions and a gamma-distributed substitution rate heterogeneity [79] , the “WAG+Γ+I” model . Probable convergence was verified postsimulation by the randomness of the plot of log likelihood values and potential scale reduction factor ( PSRF ) values of 1 . 00 . PhyML [80] was used for maximum likelihood phylogenetic analysis [81] using the WAG+Γ+I model and calculating support with 200 nonparametric bootstrap repeat calculations ( using α and proportion invariant parameters fixed at the values optimized for the real data to minimize computation time ) . Phylogenetic tree output was viewed and arranged for presentation using the Tree Explorer module in the MEGA 4 package [82] . To attempt to identify homologies for the unassigned protein sequences discovered in spot 1 from 2-D BN-PAGE by the LC/LC/MS analysis , comparative searches were conducted using multiple algorithms and protein databases: ( 1 ) BLAST search [83] , [84] repeated with all three available BLOSUM amino acid matrices at NCBI databases; also repeated at the CAMERA metagenomic database [85] available at http://camera . calit2 . net/ . ( 2 ) SSEARCH at EBI ( http://www . ebi . ac . uk/Tools/fasta33/index . html ? program=SSEARCH ) , which conducts a rigorous Smith-Waterman search [86] . ( 3 ) Sequence search at Pfam database version 23 and 24 [87] , which is based on the HMMER hidden Markov model program . ( 4 ) COMPASS , a generalized Psi-BLAST alignment profile to alignment profile query [88] available at http://prodata . swmed . edu/compass/compass . php . ) ; for this purpose , the T . thermophila proteins were aligned with their orthologs from P . tetraurelia ( and other ciliates if available ) . Significant similarities found are indicated in Tables 2 and S4 , but in the majority of cases , no additional significant matches were obtained . Texts S1 and S2 contain detailed examples of the results of many of these searches . The prediction of transmembrane helices in membrane proteins was carried out using TMHMM ( v2 ) ( http://www . cbs . dtu . dk/services/TMHMM/ ) , TMMOD ( http://liao . cis . udel . edu/website/servers/TMMOD/scripts/frame . php ? p=submit ) , and TOPCONS ( http://topcons . cbr . su . se/index . php ) [89]–[92] . Protein secondary structure predictions were made using the PSIPRED Protein Structure Prediction Server ( http://bioinf . cs . ucl . ac . uk/psipred/ ) [93] , [94] . | Synthesis of ATP , the currency of the cellular energy economy , is carried out by a rotary nano-motor , the ATP synthase complex , which uses proton flow to drive the rotation of protein subunits so as to produce ATP . There are two main components in mitochondrial F-type ATP synthase complexes , each made up of a number of different proteins: F1 has the catalytic sites for ATP synthesis , and Fo forms channels for proton movement and provides a bearing and stator to contain the rotary action of the motor . The two parts of the complex have to interact with each other , and critical protein subunits of the enzyme are conserved from bacteria to higher eukaryotes . We were surprised that a group of unicellular organisms called alveolates ( including ciliates , apicomplexa , and dinoflagellates ) seemed to lack two critical proteins of the Fo component . We have isolated intact ATP synthase complexes from the ciliate Tetrahymena thermophila and examined their structure by electron microscopy and their protein composition by mass spectrometry . We found that the ATP synthase complex of this organism is quite different , both in its overall structure and in many of the associated protein subunits , from the ATP synthase in other organisms . At least 13 novel proteins are present within this complex that have no orthologs in any organism outside of the ciliates . Our results suggest significant divergence of a critical bioenergetic player within the alveolate group . | [
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] | 2010 | Highly Divergent Mitochondrial ATP Synthase Complexes in Tetrahymena thermophila |
Many species of bacteria harbor multiple prophages in their genomes . Prophages often carry genes that confer a selective advantage to the bacterium , typically during host colonization . Prophages can convert to infectious viruses through a process known as induction , which is relevant to the spread of bacterial virulence genes . The paradigm of prophage induction , as set by the phage Lambda model , sees the process initiated by the RecA-stimulated self-proteolysis of the phage repressor . Here we show that a large family of lambdoid prophages found in Salmonella genomes employs an alternative induction strategy . The repressors of these phages are not cleaved upon induction; rather , they are inactivated by the binding of small antirepressor proteins . Formation of the complex causes the repressor to dissociate from DNA . The antirepressor genes lie outside the immunity region and are under direct control of the LexA repressor , thus plugging prophage induction directly into the SOS response . GfoA and GfhA , the antirepressors of Salmonella prophages Gifsy-1 and Gifsy-3 , each target both of these phages' repressors , GfoR and GfhR , even though the latter proteins recognize different operator sites and the two phages are heteroimmune . In contrast , the Gifsy-2 phage repressor , GtgR , is insensitive to GfoA and GfhA , but is inactivated by an antirepressor from the unrelated Fels-1 prophage ( FsoA ) . This response is all the more surprising as FsoA is under the control of the Fels-1 repressor , not LexA , and plays no apparent role in Fels-1 induction , which occurs via a Lambda CI-like repressor cleavage mechanism . The ability of antirepressors to recognize non-cognate repressors allows coordination of induction of multiple prophages in polylysogenic strains . Identification of non-cleavable gfoR/gtgR homologues in a large variety of bacterial genomes ( including most Escherichia coli genomes in the DNA database ) suggests that antirepression-mediated induction is far more common than previously recognized .
Temperate bacteriophages are major players in the evolution of bacterial genomes . Phages can act as vectors for gene transfer and , by virtue of their ability to integrate in the bacterial chromosomes , they can permanently modify the properties of the host cell . Such “lysogenic conversion” is particularly prominent in enteric bacteria presumably due to their promiscuous lifestyle . Enteric species like E . coli and Salmonella typically contain multiple resident prophages whose variability in number and assortment constitutes a major source of diversity between strains [1]–[3] . Some prophages express functions that contribute to pathogenicity . Lysogenization of E . coli by bacteriophages carrying Shiga-like toxin genes converts a harmless commensal into a dreadful enteric pathogen [4] . The toxin gene stx is repressed in the lysogenic state , but is activated under conditions that elicit prophage induction [5] , [6] . In Salmonella , the contribution of prophages to pathogenicity results from the synergistic action of multiple factors playing subtle and often redundant roles . The genes encoding such factors are expressed in the lysogenic state under the control of the regulatory circuitry of the host bacterium [7] , [8] . Gifsy-1 and Gifsy-2 are lambdoid prophages found in most strains of Salmonella enterica serovar Typhimurium and were originally identified genetically during a study of recB suppressor mutations in strain LT2 [9] . Both phages contain recET gene orthologs that , although repressed in the lysogenic state , can be activated by mutation , resulting in the suppression of recombination defects . A third Gifsy-related prophage found in another model strain , ATCC14028 , has been named Gifsy-3 [2] . All three prophages exhibit the typical modular organization of bacteriophage λ with two identifiable divergent transcription units originating from a site roughly one third away from the left end of the prophage map [8] , [10] . When induced , all three prophages form virions that closely resemble λ [11] . As the genome sequences from an increasing number of serovar Typhimurium strains have become available , it has become possible to compare the sequences of their resident Gifsy prophages . This analysis revealed that Gifsy-1 displays extensive polymorphism in the region surrounding the lysogenic repressor and other regulatory elements [7] , [8] . Conversely , Gifsy-2 is highly conserved throughout the serovar , while Gifsy-3 appears to be a specific acquisition of strain ATCC14028 [2] , [12] . Phage circulation among strains results from conditions that relieve lysogenic repression and elicit the developmental program of the virus . The paradigm for this induction process is set by widely studied phages such as λ and P22 . In both of these phages , induction results from the autocatalytic cleavage of a repressor triggered by the accumulation of RecA-DNA filaments [13] , [14] . λ and P22 repressor proteins , 237 and 216 amino acids ( aa ) , respectively , contain two domains: an N-terminal DNA-binding domain and a C-terminal oligomerization domain with the cleavage activity [15] . RecA-stimulated cleavage occurs at identical alanyl-glycil sequences near the center of both proteins [16] and is catalyzed by a highly conserved Lys/Ser dyad . Identification of the Gifsy-1/-2 phage repressor in strain LT2 revealed it to be significantly smaller ( 136 aa ) than the λ or P22 repressors and to lack the signature motif for autocatalytic cleavage [10] . Examination of the Gifsy prophage sequences from other strains showed them to have similar small sizes , raising the question of the mechanism responsible for repressor inactivation in these prophages . The work described in this paper was aimed at answering this question . We show that the induction of Gifsy prophages does not result from repressor cleavage , but rather from repressor inactivation consequent to the binding of antirepressor proteins . The genes encoding these antirepressors are located outside the immunity region and under direct control of the LexA protein . A similar regulatory mechanism was previously described in coliphages 186 and N15 [17] , [18] . Interestingly , some of the antirepressors identified here have the ability to act on non-cognate repressors , providing the basis for a molecular crosstalk that allows coordinating the induction of multiple prophages in polylysogenic bacteria .
In strain LT2 , an approximately 12 Kb portion of the Gifsy-2 prophage genome , including the immunity region together with replication and recombination functions , is duplicated at the corresponding position of the Gifsy-1 genome [10] . Conceivably , a recombination or conversion event homogenized the two sequences during the evolutionary history of this strain . As a result , the Gifsy-1 repressor of LT2 , GogR , is a perfect copy of the Gifsy-2 repressor , GtgR , and the two phages are homoimmune [7] , [10] . Sequence analysis of the Gifsy phages in strain ATCC14028 showed the immunity region of Gifsy-1 to differ extensively from that of Gifsy-1/-2 prophages of strain LT2 ( Figure 1A ) . In contrast , Gifsy-2 sequences are nearly identical in the two strains , while Gifsy-3 carries a different immunity region . The presumptive repressor genes of the Gifsy-1 , and Gifsy-3 prophages of strain ATCC14028 were named gfoR , and gfhR , respectively . GfoR ( Gifsy-1 ) and GfhR ( Gifsy-3 ) share 66 . 4% similarity in their amino acid sequences and are 32 , 6% and 33 , 8% similar to GftR ( Gifsy-2 ) , respectively ( Figure 1B ) . The sequence of the latter is 100% identical to that of LT2's GtgR . Finally , it is worth mentioning that Gifsy-3 repressor , GfhR , is 100% identical to the repressor of a prophage found at the site of Gifsy-1 in strain SL1344 , another Salmonella model strain [19] ( GenBank FQ312003 ) . These findings account for the original observation that ATCC10028's Gifsy-3 and SL1344's Gifsy-1 phages are homoimmune [7] and provide further evidence for extensive module shuffling between Salmonella phages . To monitor the fate of Gifsy prophage repressors under inducing conditions , variants of the gfoR , gftR and gfhR genes carrying carboxy-terminal 3xFLAG epitope tags were constructed in the ATCC14028 chromosome [20] . Tagged GfoR and GfhR remained competent to confer immunity against the corresponding phage ( data not shown ) , suggesting that presence of the tag did not adversely affect the function of the proteins . Similar epitope tag fusions were derived from two additional genes: a dinI homologue in the Gifsy-2 left operon , to serve as a control for the transcriptional response to the inducing treatment , and a gene presumed to encode the repressor of the Fels-1 prophage of strain LT2 [21] . Fels-1 putative repressor , hereafter referred to as FsoR , is a 231 aa protein similar to λ's CI repressor and thus expected to undergo cleavage during induction . Exponentially growing cells from strains carrying the 3xFLAG-tagged genes were exposed to Mitomycin C ( MitC ) and processed for Western blot detection using anti-FLAG antibodies . As shown in Figure 2 , none of the Gifsy repressors suffers detectable cleavage throughout the treatment , while the accumulation of the DinI protein ( Figure 2B ) , together with the appearance of cleavage products of the FsoR repressor ( Figure 2D ) , confirm that induction is taking place . In Gifsy-3 , the MitC treatment leads to the accumulation of a GfhR variant with an N-terminal extension ( marked GfhR* in Figure 2C ) . This protein originates from an upstream , in-frame AUG codon ( Figure S1 ) . A construct with the longer open reading frame fused to the PBAD promoter expressed the shorter version of GfhR in the absence of arabinose ( Figure S1 ) suggesting that the gfhR promoter lies within the interval between the two AUGs . GfhR* must therefore originate from a different promoter , located upstream from the primary promoter , and apparently activated upon induction . The role of GfhR* , if any , was not further investigated here . The results in Figure 2 suggested that induction of the Gifsy prophages in strain ATCC14028 occurs by a mechanism not involving cleavage of repressors . Experiments with lacZ fusions supported this conclusion . Fusions of lacZ to the recE gene , or to an homologue of λ's cII gene were no longer activated by MitC when combined with deletions that remove material to the right of the DNA replication genes ( see diagram in Figure S2 ) . Thus , the induction mechanism requires one or more genes located outside of , and relatively distant from , the immunity region . A previous report identified potential LexA binding sites in Gifsy prophage genomes [22] . To assess the role of LexA in Gifsy induction , we made use of the lexA3 allele , which produces a non-cleavable form of the LexA protein [23] . The mutation was introduced into strains with recE-lacZ fusions in either Gifsy-1 or Gifsy-2 and the resulting strains were tested for their response to MitC on X-gal indicator plates . As shown for Gifsy-2 in Figure 2E , lexA3 completely abolishes MitC-dependent induction . Thus , LexA cleavage appears to be required for Gifsy prophage induction . In contrast , the lexA3 mutation does not prevent the MitC-dependent activation of a lacZ fusion in the late operon of the Fels-1 prophage ( Figure 2F ) . The latter findings are consistent with the idea that Fels-1 induction results directly from cleavage of a CI-type repressor ( Figure 2D ) . The presumptive LexA binding site lies within the region of sequence identity between Gifsy-1 and Gifsy-2 prophages of strain LT2 . The site is located 1 . 3 Kb downstream from the replication genes and adjacent to the dinI gene homologue . The LexA box is also found at the corresponding position for all three Gifsy prophages of strain ATCC14028 , in all instances preceded by palindromic sequences resembling Rho-independent transcription terminators ( Figure 3A ) . To assess the requirement of the LexA binding motif for regulation , the segment between the putative terminator and the AUG translation initiation codon of the dinI homologue in the Gifsy-2 prophage was deleted and replaced with an araC-PBAD promoter module . The construct was combined with a recE-lacZ translational fusion in an LT2-derived strain cured for Gifsy-1 . Disk tests on Lac indicator plates showed that the promoter replacement completely abolishes MitC-dependent activation and renders the recE-lacZ fusion inducible by arabinose ( Figure 3B ) . Thus , these results confirmed the existence of an SOS locus within the Gifsy-2 genome and suggested that this locus includes one or more genes needed for induction . The analysis of nested deletions originating at the right end of the prophage allowed delimiting the minimal sequence required for recE-lacZ activation to the interval between the dinI and irsA homologues ( Figure S2C ) . The region could encode a small protein starting from a non-canonical GUG codon . To assess the role of this locus in prophage induction , the ORF sequence was moved under the control of the chromosomal PBAD promoter , starting with an AUG codon corresponding to the initiation codon of the araB gene . Exposure of the resulting strain to a paper disk soaked with arabinose produced a halo of bacterial killing around the disk concomitant to the release of ß-galactosidase activity from the lysed cells ( Figure 3C ) . Together , these effects suggested that the sequence being analyzed contained all the information needed to elicit prophage induction . Interestingly , a fortuitous single bp insertion near the 5′ end of the sequence completely abrogated arabinose-dependent killing and lac expression . Since the mutation alters the reading frame of the putative gene , these findings strongly suggested that the inducing molecule was a protein as opposed to an RNA . We postulated that this protein acts as an antirepressor and named it GftA ( Gifsy-two antirepressor ) . As seen in Figure 3C , colonies appeared in the area of bacterial lysis upon prolonged incubation . Characterization of a number of these arabinose-resistant isolates showed some of them to result from prophage deletions while others carried mutations linked to the ara locus . One class of mutants had changes in the araC gene . Presumably these mutations affect the ability of the AraC protein to bind arabinose thus preventing PBAD promoter activation . A second class of mutation fell within the gftA coding sequence and tentatively identified residues important for antirepressor function ( see below ) . The gftA gene lies within the region of sequence identity between the Gifsy-1 and Gifsy-2 prophages of strain LT2 ( see above ) and is 100% identical to the corresponding gene in the Gifsy-2 genome of strain ATCC14028 . Small ORFs initiating with UGG codons are found at the corresponding locations in ATCC14028's Gifsy-1 and Gifsy-3 prophages . These ORFs are 98% identical to each other but more distantly related to gftA ( Figure 4A ) . The Gifsy-1 sequence was moved into the ara operon as done for gftA ( see above ) . The resulting strain lysed and released high titers of phage when exposed to arabinose , consistent with the identification of this locus as an antirepressor gene . Significantly , removal of either Gifsy-1 or Gifsy-3 did not relieve the arabinose-induced lethality . Only the concomitant elimination of both prophages relieved the lethality , suggesting that the Gifsy-1 antirepressor can inactivate the Gifsy-3 repressor , GfhR , as well as GfoR . A plaque assay confirmed the presence of both phages in lysates from arabinose-treated cells ( data not shown ) . The antirepressor genes were named GfoA ( Gifsy-1 ) and GfhA ( Gifsy-3 ) . Derepression of Gifsy lytic transcription can also be monitored using lacZ fusions to a cII gene ortholog in the putative early right operon [10] . These fusions can be constructed by concomitantly deleting material in the right portion of the prophage ( including the antirepressor gene ) , making it possible to test for susceptibility to antirepressors encoded by unlinked prophages ( Figure S2 ) . Thus , a cII-lacZ fusion that removes Gifsy-2's gftA gene is still MitC-inducible in an LT2 background ( strain MA8363; Table 1 ) but not in a strain derived from ATCC14028 ( MA8361 ) . This difference might be ascribed to the presence of a duplicate copy of the gftA gene in the Gifsy-1 genome of LT2 and its absence in ATCC14028 ( see above ) . Surprisingly , however , the Gifsy-2-borne cII-lacZ fusion remained MitC-inducible in strain LT2 after removing Gifsy-1 , suggesting that yet another prophage could complement the gftA defect . Strain LT2 carries two other prophages , Fels-1 and Fels-2 . Fels-2 seemed the most likely candidate to encode such a function in light of its strong analogies with E . coli phage 186 , also regulated by an antirepressor mechanism [22] , [24] . Unexpectedly , however , removal of Fels-1 , and not Fels-2 , abolished Gifsy-2 induction . To confirm the presence of a gftA homologue in the Fels-1 genome , an ATCC14028 strain carrying the cII-lacZ ΔgftA Gifsy-2 construct was lysogenized with Fels-1 phage from strain LT2 . The resulting strain proved positive for lacZ expression when challenged with MitC . Deletion analysis localized the locus responsible for lacZ activation in the interval between loci STM0896 and STM0897 in Fels-1's left operon ( Figure 4B ) . When the presumptive antirepressor gene ( named fsoA ) was placed under PBAD promoter control , lacZ fusions to recE or to the cII ortholog in Gifsy-2 became derepressed in the presence of arabinose ( Figure 4C and data not shown ) . The FsoA protein shares a number of amino acid identities or similarities with both GftA and GfoA ( Figure 4A ) . Significantly , most of the GftA null mutations ( see above ) affect residues that are conserved in all three proteins . It seems conceivable that the most highly conserved residues ( red boxes ) fulfill general structural requirements for antirepressor function while identities restricted to GftA and FsoA ( green boxes ) might define residues contributing to the specificity of repressor recognition ( Figure 4A ) . From the slope of the induction curves in Figure 4C , it is apparent that FsoA is less effective than GftA in relieving GftR-mediated repression . Addition of the 3xFLAG epitope sequence to the C-termini of the two antirepressor proteins does not appear to impair their activities to any significant extent ( Figure 4C ) . Construction of epitope-tagged variants of the antirepressors allowed monitoring the regulation of these proteins by Western analysis . Figure 5 shows the results of such an experiment with cells exposed to MitC . GfoA and GftA , undetectable at the beginning of the treatment , accumulate in the presence of the drug . In contrast , as already shown in Figure 2 , the levels of 3xFLAG-tagged GfoR and GftR do not change significantly throughout the treatment . Furthermore , neither the repressors nor the antirepressors were significantly affected during a one-hour chase with chloramphenicol , indicating that none of these proteins is particularly susceptible to proteolytic turnover ( Figure 5 ) . Overall , these results strongly suggest that GfoA and GftA elicit prophage induction by affecting the activity , not the concentration , of the phage repressors . Finally , the data in Figure 5 confirm that Fels-1's FsoA protein is also induced in response to DNA damage . The ability of Gifsy antirepressors to interact with their corresponding repressors was assessed by a surrogate pulldown assay . Strains harboring chromosomal 3xFLAG tagged antirepressor genes fused to the PBAD promoter and carrying or lacking 6xHis-tagged cognate repressor genes on a plasmid , were grown in the presence or absence of arabinose . Cell-free extracts were incubated with nickel nitrilotriacetic acid agarose beads . Retained material was eluted and subjected to gel electrophoresis for direct visualization of proteins and Western blot analysis . As shown in Figure 6 , in extracts from cells expressing the antirepressor genes , proteins with the molecular weight predicted for the 3xFLAG-tagged derivatives of GftA ( panel A ) or GfoA ( panel B ) , were specifically retained along with the cognate repressors and revealed by the anti 3xFLAG monoclonal antibodies ( panels C and D , respectively ) . Curiously , the anti 3xFLAG antibodies appear to react with the His-tagged repressors as well ( Figure 6C ) . We considered that this reactivity might be due to the release of some antirepressor molecules from the membrane during the blotting procedure and their interaction with membrane-bound cognate repressors . To test this hypothesis , we asked whether antirepressor-repressor interactions could be detected by the “far Western” protocol [25] . Total proteins from a strain expressing GtgR were fractionated on an SDS gel , blotted on a Polyvinylidene fluoride ( PVDF ) membrane . The blot was split into two halves , one of which was incubated with a crude extract from a strain expressing 3xFLAG tagged GftA protein , prior to anti 3xFLAG antibody probing . The results in Figure 6E show that the GftR protein is only revealed in the membrane treated with the extract . This confirms that GftA and GtgR interact strongly with each other . Since the above analysis was carried out under denaturing conditions , the interaction must not require the proteins to be in their native conformation . We devoted considerable effort to determining the subunit structures of Gifsy repressors , antirepressors and their complexes by gel exclusion chromatography . This work was made difficult by a marked tendency of both proteins to form non-specific aggregates and to stick to various surfaces , particularly following buffer changes . Such problems could not be solved for the antirepressors . In contrast , using N-terminally tagged versions of the repressors , satisfactory elution profiles were eventually obtained with the repressors and their complexes . Results from a representative experiment are shown in Figure 7 . GfoR and the GfoR-GfoA complex elute from a G75 Sephadex column with an apparent molecular weight of about 40 kD and 60 kD , respectively . These sizes are consistent with the GfoR being a dimer and the GfoR-GfoA complex a heterotetramer ( A2B2 ) . Similar results were obtained for the GftR/GftA complex ( data not shown ) . Binding of repressors to the corresponding operator sites can be monitored by mobility shift assays in native gels . One such mobility shift is observed when a DNA fragment spanning the Gifsy-2 right operator is incubated with purified GtgR protein ( Figure 8 ) . Addition of increasing amounts of purified GftA protein to the preformed GtgR-DNA complex causes the operator fragment to be progressively released ( Figure 8 ) . Thus , these results suggest that GftA binding to the GtgR repressor causes the latter to lose affinity for DNA . No binding of GftA to DNA can be inferred from the data in Figure 8 or from a number of independent tests ( data not shown ) . This leads us to conclude that the antirepressor most likely exerts its action by inducing a conformational change in cognate repressor , as opposed to competing for DNA binding .
In the present study , we have characterized the induction mechanism of the Gifsy prophages of Salmonella . This mechanism differs from that used by model phages λ and P22 . In these phages , all information needed to elicit induction is contained within the repressor sequence . Binding of the repressors to RecA-DNA filaments formed during DNA damage , stimulates the self-catalytic proteolysis of the repressor and its inactivation . Cleavage occurs within a linker region , the “connector” that separates the N-terminal DNA-binding domain from the C-terminal dimerization domain of the protein [15] . In contrast , the regulation of Gifsy prophage induction involves two spatially separated modules: one containing the repressor gene and its sites of action ( the immunity region ) , the other carrying a transcription unit that encodes , among others , an antirepressor protein . During normal growth , this unit is repressed by LexA , the general repressor of the SOS regulon . LexA also undergoes RecA-stimulated cleavage in the presence of damaged DNA . Antirepressor is then synthesized , it binds to and inactivates the lysogenic repressor , thereby causing the induction of the lytic program . Gifsy repressor proteins GfoR and GtgR show sequence identity with the N-terminal domain of phage λ's CI repressor for nearly their entire lengths . This suggests that these proteins lack the bipartite structure of the CI repressor and are not susceptible to self-proteolysis . A survey of the bacterial genome sequence databases reveals the existence of gfoR/gtgR homologues in prophage-like elements from a large variety of bacterial species ( Figure S3 ) . Because of their relative small size ( in the 150 aa range ) these proteins are unlikely to undergo self-cleavage and are thus candidates for being regulated by an antirepressor . Besides being present in the Gifsy-like prophages of many Salmonella enterica serovars , gfoR/gtgR homologues are found in most Escherichia coli strains in the database ( 60 genes in a total of 42 strains ) as well as in Citrobacter , Klebsiella , Yersinia and Enterobacter strains ( Figure S3 ) . Two relevant members of the group are the DicA and RacR repressors of the Qin and Rac prophages respectively [26] . Previous examples of LexA-controlled antirepressors include the Tum protein of phage 186 [18] and more recently the AntC protein in phage N15 [17] . Like the GfoA and GftA proteins studied here , Tum binds to its cognate repressor and prevents its binding to the operator site [18] . Tum is nearly twice the size of the GfoA and GftA proteins ( 146 aa ) and shows significant identity to the DinI protein in the second half of its sequence . This suggests that the antirepressor and DinI sequences are fused into a single polypeptide . Consistent with this idea , the two phage 186 relatives , Salmonella Fels-2 and coliphage PSP3 have the tum coding region split into two halves by a stop codon [22] . In phage PSP3 the upstream gene encodes the antirepressor activity and the downstream gene encodes the DinI homologue ( G . E . Christie , personal communication cited in [22] ) . This order is reversed in the Gifsy prophages where the dinI homologue is the first gene of the LexA-regulated operon , followed by the antirepressor gene and by a homologue of the irsA gene . The DinI protein is thought to modulate the SOS response through its binding to RecA-DNA filaments; however , its exact role remains elusive [27]–[29] . This is also the case for irsA , a locus originally identified as the site of Tn10 insertions impairing Salmonella growth in host cells ( Chai and Heffron , unpublished ) . In the course of this study , non-polar deletions in the dinI or irsA homologues of Gifsy-1 and Gifsy-3 had no significant effects on the levels or on the rates of induction of recE-lacZ or cII-lacZ fusions ( data not shown ) . Still , the conservation of the dinI-irsA region in putative prophages from Salmonella , Escherichia , Citrobacter , Klebsiella and Enterobacter species in genome sequence databases , suggests that these genes play some role in regulation . Overall , these findings strongly suggest that lysogenic regulation by repressor/antirepressor pairs is far more common than previously recognized . Consistent with this idea , several homologues of GfoA and GftA can be found in protein databases ( Figure S4 ) . In the λ pathway of induction , proteolytic inactivation of the repressor makes the process irreversible . In contrast , the LexA/antirepressor-mediated mechanism can in principle be reversed if DNA damage is repaired . As LexA levels are replenished , the reduction in antirepressor synthesis will favor dissociation of the repressor-antirepressor complexes allowing the repressor to resume its function . This would probably limit viral replication and might promote reestablishment of lysogeny . One could envision the existence of a latency period during which the phage DNA undergoes limited replication before committing to the lytic pathway . The presence of chromosomal partitioning parA gene homologues in the left operons of the Gifsy-1 and Gifsy-3 prophages supports this idea . These properties inspire analogies with the induction pathway of Vibrio cholerae filamentous CTXφ phage . In this system , LexA directly modulates the levels of the phage repressor , RstR , by activating rstR transcription when bound to a site overlapping with the rstR promoter [30] , [31] . Reconstitution of the LexA pool during recovery from DNA damage was proposed to favor the reestablishment of lysogeny [31] , [32] . Interestingly , RstR is the target of an antirepressor made from CTXφ's satellite phage RS1 . This protein , RstC , is not required for prophage induction; however , it is made under inducing conditions and , by inactivating RstR , is thought to prolong the RS1 and CTXφ production period [33] , [34] . The latter findings highlight an interesting property of the antirepressor function , namely its potential to serve as basis for a molecular crosstalk between phages . This feature is clearly illustrated in the present study . We found that some antirepressors can inactivate repressors made by heteroimmune prophages and trigger induction of the latter . Particularly intriguing was the discovery that the FsoA protein of the Fels-1 prophage can act on the Gifsy-2 repressor GtgR . The fsoA gene lies in Fels-1 left operon and is derepressed under inducing conditions following auto-proteolysis of the FsoR repressor . By targeting GtgR , FsoA effectively uncouples Gifsy-2 induction from the SOS response and puts the Gifsy-2 regulatory circuitry under FsoR control . This regulatory hijacking is difficult to rationalize since the Fels-1 prophage is induced normally in a Gifsy-2-cured strain or when the fsoA gene is inactivated ( data not shown ) . However , subtle differences in induction rates or thresholds might have been missed in these experiments , and the possibility that one or more function ( s ) expressed from the Gifsy-2 genome positively affect ( s ) Fels-1 development cannot be completely ruled out . Similar effects might account for the reciprocal transactivation of Gifsy-1 and Gifsy-3 gene expression demonstrated in this study . It is also worth considering that synchronization of prophage induction in polylysogenic strains might be vital to prophages with delayed induction responses ( see above ) . “Slow-inducing” prophages are in danger of sharing the fate of host DNA and of being destroyed when present in a strain carrying prophages that are induced more rapidly . In this scenario , paradoxically , Gifsy-2 would be the one that hijacks Fels-1 functions through FsoA . The wide specificity of antirepressor action was first recognized in Salmonella phage P22 . The Ant protein of P22 , besides inactivating the phage's own repressor C2 , can act on the repressor of Salmonella phage L and coliphages λ and 21 [35] . The role of Ant in the P22 life cycle is not completely understood . The protein is not required for induction of the P22 prophage or for any steps of the lytic or lysogenic pathways [36] . To our knowledge , the only reported activity of Ant is its ability to transactivate early gene expression in P22 lysogens when expressed constitutively from a superinfecting P22 phage [37] . It is tempting to speculate that an important role of the Ant protein is to couple induction of P22 prophage to that of other prophages in polylysogenic strains .
All strains used in this study are derivatives of S . enterica serovar Typhimurium . Their genotypes are listed in Table 1 . The bacteria were cultured in LB broth [38] solidified by the addition of 1 . 5% Difco agar when needed . When appropriate , the LB medium was supplemented with 0 . 2% arabinose . Antibiotics ( Sigma-Aldrich ) were included at the following final concentrations: chloramphenicol , 10 µg mL−1; kanamycin monosulfate , 50 µg mL−1; sodium ampicillin , 75 µg mL−1; spectinomycin dihydrochloride , 80 µg mL−1; and tetracycline hydrochloride , 25 µg mL−1 . LB plates containing 40 µg mL−1 5-bromo-4-chloro-3-indolyl-β-D-galactopyranoside ( X-Gal ) ( Sigma-Aldrich ) were used to monitor lacZ expression in bacterial colonies . Prophages were induced using Mitomycin C ( Sigma-Aldrich ) at a final concentration of 1 µg mL−1 in liquid medium or by applying 5 µL from a 2 mg mL−1 stock solution on 5 mm diameter filter paper discs for plate tests . Liquid cultures were grown in New Brunswick gyratory shakers , and growth was monitored by measuring the optical density at 600 nm with a Milton-Roy Spectronic 301 spectrophotometer . Generalized transduction was carried out using the high frequency transducing mutant of phage P22 , HT 105/1 int-201 [39] . Typically , P22 lysates were used at a 1∶50 dilution , mixed with aliquots from overnight cultures of recipient bacteria in a 1∶2 ratio , and incubated for 30 min at 37°C prior to being plated on selective media . Transductant colonies were purified by two sequential passages on selective plates and verified to be free of phage by streaking on Evans Blue Uranine plates [40] . Chromosomal engineering was carried out by the λ Red recombination method [41]–[43] as previously described [20] . Donor DNA fragments were generated by PCR using plasmid or chromosomal DNA templates . A complete list of the oligonucleotides used as primers in these experiments is in Table S1 . Amplified fragments were electroporated into appropriate strains harboring the conditionally replicating plasmid pKD46 , which carries a λ red operon under the control of the PBAD promoter [41] . Bacteria carrying pKD46 were grown at 30°C in the presence of ampicillin and exposed to arabinose ( 10 mM ) for 3 hours prior to preparation of electrocompetent cells . Electroporation was carried out using a Bio-Rad MicroPulser under the conditions specified by the manufacturer . Recombinant colonies were selected on LB plates containing the appropriate antibiotic . Constructs were verifed by PCR and/or DNA sequencing . When needed , the antibiotic resistance cassette was excised by transforming strains with plasmid pCP20 , which encodes the Flp recombinase [44] . Plasmids used as PCR templates for λ Red-mediated gene disruptions included pKD3 , pKD4 and pKD13 [41] . Plasmid pSUB11 was the template in the construction of 3x-FLAG epitope fusions [20] . Additional plasmid templates constructed in the present work were pSEB1 , which carries the spectinomycin-resistance aadA cassette , and pSEB3 , carrying an aph-araC-PBAD module . For the construction of pSEB1 , the aadA gene of plasmid pBT22 [45] was amplified by PCR with primers pp411 and pp412 ( Table 2 ) ; the resulting fragment was digested with EcoRI and ligated into EcoRI-cleaved plasmid pSUB2 . The latter is a derivative of pGP704 [46] lacking the BamHI segment spanning the RP4 tra operon . Plasmid pSEB3 was derived from a chromosomal construct carrying the kanamycin-resistance aph gene immediately downstream from araC gene ( strain MA7794; Table 1 and Table S1 ) . The aph-araC-PBAD segment was amplified from MA7794 chromosomal DNA with primers le41 and le42 ( Table 2 ) , cleaved with EcoRI , and cloned into pSUB2 . A second set of recombinant plasmids was constructed to overproduce and purify phage repressor and antirepressor proteins . Repressor genes gftR and gfoR were amplified from wild-type ATCC14028 chromosomal DNA with primer pairs le146/le147 and le144/le145 , respectively ( Table 2 ) . In both cases , the forward primer contained a 5′ extension designed to produce an N-terminal 6xHis tag fusion . The amplification products were doubly digested with SalI and EagI restriction endonucleases and ligated to SalI×EagI-cleaved pKTQ12 DNA [45] yielding plasmids pSEB10 ( gftR ) and pSEB11 ( gfoR ) . A different vector , pNFB28 , was used for cloning antirepressor genes . Plasmid pNFB28 is a derivative of Novagen's pET-16b plasmid modified so as to allow the construction of both N-terminal and C-terminal 7xHis tag fusions . The modification involved ligating a DNA fragment produced by annealing oligonucleotides pp849 and pp850 ( Table 2 ) to pET-16b DNA doubly digested with XbaI and XhoI endonucleases . gftA and gfoA genes were amplified from wild-type ATCC14028 chromosomal DNA with primer pairs pp864/pp865 and pp866/pp867 , respectively . The amplified fragments were doubly digested with NcoI and SacI ( gftA ) , and BspHI and SacI ( gfoA ) , and ligated to pNFB28 DNA digested with NcoI and SstI . In the resulting plasmids , pSEB12 and pSEB13 , the gftA and gfoA genes carry 7xHis-encoding sequences at their 3′ ends and are under the control of the T7 promoter . For the purification of repressors and repressor/antirepressor complexes , strains MA8567 ( PBAD-gftA-3xFLAG ) and MA8731 ( PBAD-gfoA-3xFLAG ) , carrying or lacking plasmids pSEB10 and pSEB11 , respectively , were grown to an OD600≈0 . 15 at 37°C and exposed to 0 . 1% L-arabinose ( Sigma-Aldrich ) or left untreated . Bacteria were cultivated at 37°C for additional 6 hours , Cells were harvested by centrifugation at 10000G and rinsed once in PBS ( 137 mM NaCl , 2 . 7 mM KCl , 100 mM Na2HPO4 and 2 mM KH2PO4 ) . Pellets were then submitted to several freeze-thaw cycles in a dry-ice/ethanol bath before being resuspended in IP buffer ( Tris-HCl 20 mM pH 8 , NaCl 500 mM , Igepal 0 . 1% ( Sigma-Aldrich ) , imidazole 20 mM ) and sonicated on ice until complete but gentle lysis . Cell debris was spun down and the supernatant applied to Nickel nitrilotriacetic acid ( Ni-NTA ) resin ( Qiagen ) . Incubation was continued for 2 hrs at 4°C . Liquid was removed and the resin was rinsed twice with 10 times the extract volume of buffer IP . Proteins still specifically bound to the resin were eluted in buffer IPE identical to buffer IP except for imidazole concentration ( 250 mM ) . Fractions were then adjusted to 15% glycerol and frozen at −80°C for storage . The purity as assessed from the repressor content was greater than 80% and concentration was in the range of 0 . 1–1 µg mL−1 . C-terminally 7xHis tagged GftA protein was purified from E . coli strain BL21 carrying plasmid pSEB12 . Cells were grown essentially as described above but induction was carried out with IPTG ( 0 . 1 mM final concentration ) for 3 hrs . Bacterial pellets were processed as described above , except that Igepal was omitted from IP and IPE buffers . A Sephadex G75 column ( Amersham ) previously calibrated with α-chymotrypsin , ovalbumin and bovine serum albumin purchased from Sigma-Aldrich was connected to an Äkta P-9000 HPLC apparatus and a Frac-950 fraction collector . To eliminate any aggregate that might have formed during storage , protein samples were systematically centrifuged at maximum speed in a micro centrifuge for 15 min prior to loading . About 10 µg of proteins were loaded onto a column pre-equilibrated with about 2 volumes of IPE buffer . After the passage of the void volume , 0 . 5 mL fractions were collected and vacuum dried . Samples were resuspended in loading buffer , boiled and separated on a 12% SDS-polyacrylamide gel . Western blotting was conducted essentially as previously described [47] . Briefly , bacteria from 2 ml overnight cultures were harvested by centrifugation and resuspended in 50–80 µL of Laemmli buffer . Cells were lysed by boiling 10 min and lysates loaded onto 15% SDS-polyacrylamide gels . Biorad's Precision Plus Kaleidoscope standards were included as migration markers . After the gel run , proteins were electro-transferred to a PVDF membrane , which was blocked with PBS containing 3% skimmed milk and 0 . 05% Tween 20 . The blocking buffer was then replaced with a similar buffer containing the primary anti-FLAG antibody ( anti-FLAG M2 from Sigma-Aldrich ) for 30 min . The membrane was rinsed thoroughly in PBS 0 . 05% Tween 20 before the secondary antibody ( anti-mouse peroxidase-labeled secondary antibodies from Sigma-Aldrich ) was applied . Finally , results were revealed with the ECL kit from Amersham and imaged on a Fuji LAS3000 apparatus . A DNA fragment spanning the binding site of the GftR was amplified by PCR from strain LT2 chromosomal DNA with primers le127 ( 5′-GTTCGCCGATGCTCATTT-3′ ) and le128 ( 5′-CCGTGAGAGGTCAGCCATA-3′ ) . The PCR product was then radioactively labeled with T4 polynucleotide kinase ( NEB ) and γ-32P-ATP as recommended by the manufacturer . Labeled DNA ( approximately 5 ng ) was mixed with 5 µL of 5× buffer BB ( Tris-HCl pH 7 . 5 100 mM , NaCl 250 mM , EDTA 1 mM , MgCl2 5 mM , glycerol 25% , PMSF 250 mM sonicated salmon sperm DNA 250 µg mL−1 ) and varying amounts of protein . The reaction volume was adjusted with water to a final reaction volume of 20 µL . When appropriate , GftA antirepressor was added to GftR∶DNA complexes formed during an initial 15 min incubation at room temperature . Incubation was continued at the same temperature for additional 30 min ( for samples with or without added GftA ) . Samples were loaded onto a 5% non-denaturing polyacrylamide gel . After electrophoretic separation , gels were fixed in 20% ethanol 10% acetic acid , dried , and imaged with a Storm 820 apparatus from Molecular Dynamics . | Many viruses that infect bacteria ( bacteriophages ) can direct the integration of their DNA into the bacterial chromosome . This condition , known as lysogeny , is relevant to bacterial evolution , as it is one of the main pathways leading to the incorporation of foreign DNA in nature . Indeed , bacteriophages often carry genes that escape lysogenic repression and benefit the bacterium . This symbiotic association can come to an end if bacteria suffer DNA damage . A mechanism mediated by the host's RecA protein causes the relief of repression , viral DNA excision , and replication . This process , known as prophage induction , kills the host and results in the release of viral particles . In this work , we have analyzed the mechanism responsible for induction in a large family of prophages naturally present in the genomes of Salmonella bacteria . We found that , unlike in best-studied model phages , the repressors of these Salmonella phages do not undergo RecA-mediated proteolysis; rather , they are inactivated by the binding of small antirepressor proteins . We show that some antirepressors can act on both cognate and non-cognate repressors , allowing separate prophages within a given strain to be induced simultaneously . We discuss evidence suggesting that antirepressor-mediated prophage induction is quite common in the bacterial world . | [
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] | 2011 | Bacteriophage Crosstalk: Coordination of Prophage Induction by Trans-Acting Antirepressors |
Monospermic fertilization is mediated by the extracellular zona pellucida composed of ZP1 , ZP2 and ZP3 . Sperm bind to the N-terminus of ZP2 which is cleaved after fertilization by ovastacin ( encoded by Astl ) exocytosed from egg cortical granules to prevent sperm binding . AstlNull mice lack the post-fertilization block to sperm binding and the ability to rescue this phenotype with AstlmCherry transgenic mice confirms the role of ovastacin in providing a definitive block to polyspermy . During oogenesis , endogenous ovastacin traffics through the endomembrane system prior to storage in peripherally located cortical granules . Deletion mutants of ovastacinmCherry expressed in growing oocytes define a unique 7 amino acid motif near its N-terminus that is necessary and sufficient for cortical granule localization . Deletion of the 7 amino acids by CRISPR/Cas9 at the endogenous locus ( AstlΔ ) prevents cortical granule localization of ovastacin . The misdirected enzyme is present within the endomembrane system and ZP2 is prematurely cleaved . Sperm bind poorly to the zona pellucida of AstlΔ/Δ mice with partially cleaved ZP2 and female mice are sub-fertile .
In mammals , gamete recognition initiates fertilization and the onset of development . Although an overwhelming number of sperm are deposited in the lower female reproductive tract at coitus , a limited number progress to encounter the one , or relatively few , ovulated eggs . For fertilization , sperm must bind and penetrate the extracellular zona pellucida to fuse with eggs in the ampulla of the oviduct . Equally important are mechanisms that limit fertilization to a single sperm to avoid polyspermy which is embryonic lethal . Following fertilization , there is an immediate block to gamete fusion at the egg plasma membrane [1 , 2] that is independent of cortical granule exocytosis [3 , 4] . Subsequent cortical granule exocytosis [5] blocks sperm binding to the zona pellucida [6] , but the underlying molecular mechanisms remain obscure and we have little understanding of these subcellular organelles in mice . Mammalian cortical granules , first described in hamster [7] , are detected in mice starting in unilaminar follicles where immature oocytes are surrounded by a single layer of cuboidal granulosa cells . During oocyte-growth , cortical granules continuously arise from the Golgi apparatus [8] and require microfilaments to migrate to the periphery [9] . Concomitant with oocyte growth , the number of cortical granules increases to 6 , 000–8 , 000 and are uniformly distributed in the subcortex of full-grown , 80 μm oocytes [10] . However , cortical granule exocytosis is not yet enabled in these germinal vesicle ( GV , nucleus ) intact oocytes [11] . During meiosis I , a substantial cortical granule free domain appears in mice ( 25–40% of surface area ) , the first polar body is extruded and cortical granules decrease to ~4 , 000 [12] . Rather than early exocytosis , this cortical granule free domain reflects a redistribution of granules that is triggered by the peripheral migration of chromatin during meiosis I and formation of an actin cap [13] . Cortical granules become fully competent for exocytosis after completion of the first meiotic division in MII eggs [11 , 14] . The egg is activated at fertilization and cortical granule exocytosis is triggered by release of Ca+2 from proximate , peripherally located stores in the endoplasmic reticulum [15] . Although precise details remain under investigation , cortical granule fusion with the egg plasma membrane involves SNARE-protein mediated pathways [16 , 17] . Unlike regulated secretory granules in somatic cells ( e . g . , synaptic vesicles of neurons , zymogen granules of pancreatic acinar cells ) , cortical granules in oocytes are not renewed after exocytosis . Cortical granules contain a discrete set of proteins , albeit varying by report [18–20] , including trypsin-like proteases [21–23] , ovoperoxidase [24] , N-acetylglycosaminidase [25] , a 75 kD protein of unknown function [26] and , most recently , an astacin-like metalloendoprotease encoded by Astl [27] . The only documented function ascribed to cortical granules is the post-fertilization zona block to polyspermy that prevents sperm binding and penetration through the zona pellucida [22 , 28] . The zona pellucida , composed of three ( mouse ) or four ( human ) glycoproteins ( ZP1-4 ) , surrounds growing oocytes , ovulated eggs and pre-implantation embryos [29 , 30] . The N-terminus of ZP251-149 has been defined as the zona ligand for sperm binding based on gain- and loss-of-function assays in transgenic mice [31 , 32] . Ovastacin ( 435 amino acids ) is an oocyte-specific member of the astacin-like family of Zn+2 metalloendoproteases that is synthesized as a zymogen with a signal peptide ( 1–23 aa ) to direct it into the endomembrane system for ultimate storage in cortical granules . For enzymatic activity , astacins require removal of an N-terminal prosegment ( 24–85 aa ) that runs in opposite direction to the future protein substrate [33] . The enzymatic active site of ovastacin is formed by glutamate and a single zinc atom complexed to three adjacent histidine residues ( underlined , 182HELMHVLGFWH192 ) . Following fertilization , ovastacin is exocytosed from egg cortical granules and cleaves ZP2 at 166LA↓DE169 after which sperm no longer bind . Ablation of Astl that encodes ovastacin , or mutation of the ZP2 cleavage site ( Zp2Mut ) , prevents post-fertilization cleavage of ZP2 and sperm bind to the surface of the zona pellucida of 2-cell embryos despite fertilization and cortical granule exocytosis [27 , 34] . Using mouse transgenesis , we rescue the AstlNull phenotype by ectopic expression of AstlmCherry and define a 7 amino acid motif required for ovastacin trafficking to cortical granules . Deletion of the cortical granule localization signal at the endogenous locus with CRISPR/Cas9 unexpectedly leads to premature modification of the egg’s zona pellucida and female sub-fertility .
The zona pellucida and ovastacin proteins transverse the endomembrane system during oogenesis during which passage ZP2 remains intact [27] . Secreted ZP2 is incorporated into the extracellular zona pellucida surrounding ovulated eggs and is cleaved in the zona matrix surrounding 2-cell embryos [28] . In addition to a rapid , cortical-granule independent , post-fertilization block to gamete fusion [1 , 2] , ZP2 cleavage provides a definitive block to polyspermy in that sperm that do not bind cannot penetrate the zona matrix nor fuse with the egg plasma membrane [34] . The single copy Astl gene encodes ovastacin ( 435 aa ) , an oocyte-specific metalloendoprotease [35] that has been located by immunohistochemistry in cortical granules at the periphery of growing oocytes and ovulated eggs . In AstlNull mice that lack ovastacin , ZP2 remains intact in the zona surrounding 2-cell embryos [27] . To confirm that these observations reflect the absence of ovastacin , we established AstlmCherry transgenic mice expressing ovastacin tagged with fluorescent mCherry at the C-terminus ( Fig 1a , S1a and S1b Fig ) and documented ovary-specific expression by RT-PCR ( S1c Fig ) . The biological authenticity of the AstlmCherry transgene in vivo was investigated by crossing AstlmCherry with AstlNull mice to generate a ‘rescue’ mouse line . The null phenotype was reversed in mice expressing the AstlmCherry transgene in the AstlNull background and ZP2 was cleaved in the zona pellucida surrounding 2-cell embryos ( Fig 1b ) . Sperm bind to the zona pellucida if ZP2 is uncleaved , independent of fertilization and cortical granule exocytosis [27 , 34] . Thus , sperm did not bind to the zona matrix surrounding wild-type embryos ( Fig 1c ) , but did bind to AstlNull derived embryos ( Fig 1d ) in which ZP2 remains intact . However , sperm did not bind to 2-cell embryos derived from ‘rescue’ female mice ( Fig 1e ) in which ZP2 was cleaved ( Fig 1b ) . The ability of AstlmCherry transgene to rescue the AstlNull phenotype is consistent with ovastacin acting as the cortical granule protease responsible for the post-fertilization cleavage of ZP2 that prevents sperm binding and provides a block to polyspermy . The AstlmCherry mice also offer a useful platform for live-imaging that should provide additional insights into cortical granule biology . Prior to germinal vesicle ( nucleus ) breakdown ( GVBD ) , cortical granules are uniformly present in the subcortex of fully grown oocytes [27] . To validate the intracellular trafficking of ovastacin to the cortex in vivo , intraovarian oocytes ( up to 70 μm diameter ) were isolated from AstlmCherry transgenic mice and placed in an environmental chamber where confocal microscopy was used to obtain live images during GVBD . Most of the fluorescent signal was detected in peripherally located cortical granules . However , by increasing the sensitivity , ovastacinmCherry was detected throughout the first 2 hr of culture in the peri-nuclear region where the endoplasmic reticulum ( ER ) is located ( Fig 2a , left panel ) . Subsequently , during the early phases of GVBD , ovastacinmCherry was present in close proximity to the dissolving nuclear membrane ( Fig 2a , middle panel ) , but by 4 hr it was primarily located in the subcortex in the periphery of oocytes ( Fig 2a , right panel ) . These observations were complemented in fixed samples in which ovastacinmCherry partially co-localized with markers for the endoplasmic reticulum ( GP73 ) and the Golgi apparatus ( calregulin ) in 70–75 μm growing oocytes ( S2a Fig ) which confirmed its presence in the endomembrane system . The absence of co-localization with the endosome pathway marker ( EEA1 ) indicates little diversion into degradation pathways . Thus , we conclude that ovastacin follows the normal progression from the endoplasmic reticulum to the Golgi apparatus and is then sequestered in peripheral cortical granules . OvastacinmCherry , not present in wild-type GV-intact oocytes , co-localized with anti-ovastacin antibody staining and LCA consistent with the localization of endogenous ovastacin and ovastacinmCherry in peripheral cortical granules ( Fig 2b ) . During meiotic maturation , chromosomes move to the cortex at metaphase I where a cortical granule free domain ( CGFD ) develops ( Fig 2b ) . The biological significance of CGFD is not understood , although it has been reported that sperm do not fuse with the egg plasma membrane in this region to protect the maternal genetic material [36] . However , whether cortical granules in this region are released or redistributed has not been fully resolved , although the latter has become the favored model [13] . To confirm that the presence of an actin cap correlated with the absence of cortical granules from the CGFD in the transgenic mice , cRNA encoding UtrCH-GFP , a green fluorescent protein tagged actin binding protein , was injected into oocytes from AstlmCherry mice . Live imaging for 4 hr during meiotic maturation documented that the appearance of the actin cap and CGFD formation temporally coincided with complementary formation of the GFP ( actin ) and mCherry ( ovastacin within cortical granules ) signals ( Fig 2c ) . To determine if formation of the CGFD was reversible , ovulated eggs from AstlmCherry mice were incubated with CK-666 to stabilize the inactive form of the arp2/3 complex and depolymerize the actin cytoskeleton [37] . Within 3 hr , the cortical granules became uniformly distributed in the AstlmCherry eggs and the metaphase plate migrates back towards the center of the cell ( S2b Fig ) . Thus , we conclude that formation of the actin cap excludes cortical granules and results in a cortical granule free domain which has been reported to be physiologically induced by chromatin signaling [13] . Following fertilization and cortical granule exocytosis , ovastacinmCherry was no longer detected in one-cell zygotes in fixed samples ( Fig 2b ) . To detail this process , cortical granules were live-imaged with confocal microscopy during the 5 hr following fertilization . Sperm fused to eggs and cortical granules released ovastacinmCherry 90–120 min after insemination . This process was complete by 150 min by which time the egg had extruded a second polar body ( 2nd PB ) to complete meiosis II . The sperm nucleus then decondensed ( 180 min ) leading to formation of male and female pronuclei within 5 hr of insemination which was indicative of successful fertilization ( S2c Fig ) . These observations are consistent with ovastacin being directed into the endomembrane system via its signal peptide and being transported to cortical granules in the periphery of growing oocytes . Ovastacin is rapidly released from cortical granules following fertilization and formation of the 1 cell zygote . However , the molecular basis for localization of ovastacin in cortical granules was unclear . To search a motif for cortical granule localization , cRNA encoding full-length ovastacin ( 1–435 aa ) fused at the C-terminus with mCherry was injected into the cytoplasm of growing oocytes and transiently expressed for 6 hr as a positive control ( Fig 3a ) . Subsequently , cRNA encoding deleted portions of ovastacin that retained the signal peptide , were injected into oocytes . Constructs encoding 1–89 aa , but not 82–435 aa ( Fig 3b ) , localized to cortical granules as did constructs encoding 1–64 aa , but not 61–435 aa ( Fig 3c ) . This initial delineation of a 41 aa motif ( 23–64 aa ) was further refined by the observation that constructs with 52–64 aa , but not 1–51 aa , were sufficient for cortical granule localization ( Fig 3d ) . Thus , 52DKDIPAINQGLIS64 is necessary and sufficient for the in vitro localization of a reporter protein to cortical granules ( Fig 3e ) . Although well conserved among mammalian ovastacins ( Fig 3f ) , the 13 amino acid motif was not identified in other proteins after a BLAST search of mouse databases . The 13 aa cortical granule localizing motif is encoded by exons 2 and 3 of the endogenous Astl gene . To avoid disruption of RNA splicing , we elected to delete the first 7 aa ( 52DKDIPAI58 ) encoded entirely by exon 2 with a protospacer adjacent motif ( PAM ) sequence targeted by CRISPR/Cas9 ( Fig 4a ) . To confirm the validity of this approach , cRNA encoding the signal peptide and ovastacin52-58 fused to mCherry were injected into oocytes . The localization of mCherry confirmed the sufficiency of the 7 aa to direct proteins to peripheral cortical granules ( Fig 3g ) . To delete DNA encoding 52DKDIPAI58 at the endogenous Astl locus , Cas9 cRNA , sgRNA and HDR ( homology directed repair ) oligonucleotides ( S3a and S3b Fig ) were injected into zygotes , cultured to blastocysts , and transferred to uteri of pseudopregnant foster mothers . Of 7 pups screened by PCR ( S3c Fig ) , two had bi-allelic mutations that were confirmed by DNA sequence ( S3d and S3e Fig ) . In each line , the CRISPR/Cas9 mutation had been modified by homology directed repair to produce the desired deletion on one of the two endogenous alleles . Each line was crossed to wild-type mice to remove the non-desired mutant allele ( either a single base pair insertion or deletion ) and bred to homozygosity . Each expressed AstlΔ alleles that encoded ovastacin lacking 52–58 aa ( Fig 4a , ovastacinΔ52–58 ) . Using a monospecific antibody to the C-terminal region of the metalloendoprotease [27] , ovastacin ( accumulated during oocyte growth ) was detected in cortical granules defined by staining with the LCA lectin in the periphery of wild-type GV oocytes ( Fig 4b ) . In contrast , ovastacin was detected as punctate loci throughout the endomembrane system of oocytes and eggs ( upper two rows of panels ) from AstlΔ/Δ female mice ( Fig 4c ) which could reflect either an inability to correctly traffic to cortical granules for storage or retrograde transport after reaching cortical granules . These results confirmed the in vitro transient assays in growing oocytes in which expression of mutant mCherry-tagged proteins was below the detection level of the antibody ( Fig 3 ) and defined the 7 aa motif ( ovastacinΔ52–58 ) as important for the correct intracellular trafficking of ovastacin . Following fertilization , cortical granule exocytosis was documented by the loss of LCA staining , but ovastacin remained diffusely present throughout the endomembrane system of zygotes and 2-cell embryos ( Fig 4c , lower two rows of panels ) . In oocytes and eggs from Astl+/Δ mice , ovastacin was present both in the endomembrane system and at the periphery , where it partially co-localized with LCA in cortical granules ( Fig 4d ) . To determine if the AstlΔ allele was co-dominant , the Astl+/Δ mice were crossed with AstlmCherry transgenic mice to produce Astl+/Δ; AstlmCherry transgenic mice . In these mice , ovastacinmCherry lacks the deletion mutation and provides a proxy for the wild-type allele . OvastacinmCherry was detected in cortical granules located in the subcortex of oocytes and eggs ( Fig 4e ) . Taken together , these observations define the ovastacinΔ52–58 protein as co-dominant with the wild-type protein and the absence of the cortical granule localization motif results in persistence of the mutant protein in the endomembrane system . Zonae pellucidae isolated from eggs ovulated by wild-type , Astl+/Δ and AstlΔ/Δ female mice as well as from 2-cell embryos after mating wild-type mice were analyzed by immunoblot . As expected , ZP2 was uncleaved in wild-type eggs and completely cleaved in wild-type 2-cell embryos ( Fig 5a ) . The partial cleavage of ZP2 observed in Astl+/Δ eggs became more pronounced in AstlΔ/Δ eggs and is similar to the ‘hardening’ reaction observed during in vitro fertilization in the absence of serum proteins [38] . Normally , sperm bind to the zona matrix surrounding eggs , but not 2-cell embryos . Compared to wild-type eggs , many fewer sperm bound in vitro to Astl+/Δ eggs ( 18 . 9 ± 1 . 7 vs . 47 . 2 ± 3 . 0 ) and virtually no sperm ( 3 . 9 ± 0 . 7 ) bound to the zona pellucida surrounding AstlΔ/Δ eggs ( Fig 5b and 5c ) . In vivo fertilization of wild-type , Astl+/Δ and AstlΔ/Δ mice reflected these in vitro observations . After natural mating , eggs/embryos were recovered from the oviduct of female mice: 87 . 7 ± 2 . 3% of wild-type , 22 . 5 ± 4 . 0% of Astl+/Δ , but only 0 . 8 ± 0 . 8% of AstlΔ/Δ eggs were fertilized ( S1 Table ) . To confirm that the block to fertilization was based on partially cleaved ZP2 , fertility was assessed in the presence and absence of zonae pellucidae . After in vitro insemination with capacitated sperm , zona-intact Astl+/Δ eggs had ~50% reduction in fertility compared to wild-type eggs and no AstlΔ/Δ eggs were fertilized in these experiments . However , after removal of the zona pellucida matrix , the fertilization rates of zona-free Astl+/Δ and AstlΔ/Δ eggs were comparable ( Fig 5d ) . Thus , we concluded that the partially cleaved ZP2 in the zona pellucida adversely affected fertilization . The homozygous AstlΔ allele had a profound effect on the fecundity of female mice . In vivo fertility of ovastacinΔ52–58 was assessed by co-caging wild-type , Astl+/Δ and AstlΔ/Δ females with male mice proven to be fertile . The number of litters from Astl+/Δ was decreased compared to wild-type female mice ( 9 vs . 13 ) and the number of pups/litter produced by Astl+/Δ was significantly lower than wild-type female mice ( 2 . 5 ± 0 . 8 vs . 9 . 2 ± 2 . 5 ) . A single litter of 2 pups was obtained from eight AstlΔ/Δ female mice ( Fig 5e and S1 Table ) . Taken together , we conclude that ovastacinΔ52–58 does not properly traffic to cortical granules leading to premature cleavage of ZP2 which prevents sperm binding to the zona pellucida and decreases female fertility .
ZP2 is a major component of the zona pellucida to which sperm bind prior to penetrating the zona matrix and fusing with the egg . Following fertilization and cortical granule exocytosis , ZP2 is cleaved by ovastacin after which sperm do not bind to the zona pellucida . Zp2Mut and AstlNull mice documented that sperm binding to the surface of the zona pellucida is dependent on the cleavage status of ZP2 , independent of fertilization and cortical granule exocytosis [27 , 34] . In each of these mutant lines , ZP2 is uncleaved in the zona pellucida surrounding ovulated eggs which can be fertilized in vitro and in vivo . After fertilization and cortical granule exocytosis , ZP2 remains uncleaved and sperm can bind de novo to the zona matrix surrounding zygotes derived from Zp2Mut and AstlNull female mice . Although at risk , the additional post-fertilization block to sperm fusing with the egg plasma membrane [1 , 2] and the relatively few sperm ( <10 sperm/egg ) that reach the egg in the ampulla of the oviduct [39 , 40] help protect Zp2Mut and AstlNull females from polyspermy . The observed partial cleavage of ZP2 in the AstlΔ/Δ is puzzling and could reflect a continuous release of low concentrations of constitutively secreted ovastacin during oocyte growth rather than the abrupt release of higher concentrations of the enzyme thought to accompany cortical granule exocytosis at fertilization . This formulation is consistent with earlier observations in which incubation of ovulated eggs in the absence of serum proteins caused a ‘hardening’ reaction that prevented sperm binding to the zona pellucida and fertilization [41] . The molecular basis of this phenomenon has recently been ascribed to fetuin-B , a member of the cystatin superfamily of protease inhibitors that are secreted by the liver and circulate as serum proteins . Fetuin was reported to inhibit premature cleavage of ZP2 [38] and this activity has been attributed more precisely to fetuin-B which is present at low levels in follicular fluid [42] . It has been proposed that a minimal amount of fetuin-B is sufficient to inhibit the small amount of secreted ovastacin that escapes sequestration in cortical granules and prevent premature cleavage of ZP2 . However , the ovastacin released from cortical granule exocytosis would overwhelm this fragile defense and cleave ZP2 to prevent post-fertilization sperm binding [43] . Similar to AstlΔ/Δ mice , ZP2 is only partially cleaved in the zona matrix surrounding ovulated eggs from FetubNull mice and yet female mice are sub-fertile . Thus , the timing of ZP2 cleavage by ovastacin is a critical determinant of whether or not sperm bind and fertilize eggs encased in the surrounding zona pellucida . Premature ZP2 cleavage ( AstlΔ/Δ or FetubNull ) prevents sperm binding and fertility , whereas delayed or absent ZP2 cleavage runs the risk of post-fertilization polyspermy . Eggs from AstlNull females do not accumulate ovastacin in their cortical granules and are unable to cleave ZP2 following fertilization and cortical granule exocytosis . The ability of AstlmCherry to rescue this phenotype and restore fertility confirms the role of ovastacin in the post-fertilization cleavage of ZP2 and provides a marker for future investigations into the biology of cortical granules . The cortical granule localization motif defined in vitro and confirmed in vivo by deletion of a 7 amino acids in the endogenous gene locus is highly conserved among mammals . Identification of binding partners to ovastacin should provide further insight into the molecular basis for the translocation of ovastacin to these unique subcellular organelles of female germ cells .
All mice were handled in compliance with the guidelines of the Animal Care and Use Committee of the National Institutes of Health under the Division of Intramural Research , National Institute of Diabetes and Digestive and Kidney Diseases approved animal study protocols . A summary of the transgenic mice used in these investigations is provided in S2 Table . To construct the transgene , bacterial artificial chromosome ( BAC ) DNA ( Life Technologies ) that included mouse Astl ( BMQ56H22 ) was transformed into SW102 bacterial cells containing the λ prophage recombineering system [44] . A PCR fragment ( 1331 bp ) containing the galK operon flanked by 50 bp homologous to the Astl gene that would insert the galK gene at the stop codon in the 3’ region of the gene was amplified using Long Amp Taq Polymerase ( New England Biolabs , Ipswich , MA ) . After digestion with DpnI and overnight gel purification ( 0 . 7% agarose , 15 V , 16 hr ) , the PCR fragment was electroporated into SW102 cells containing the BAC , and recombinants were selected by growth on minimal media with galactose . Using a clone from this first step , the galK cassette was replaced by recombineering with a second PCR fragment ( 880 bp ) encoding mCherry with 50 bp homology arms . Clones were selected on minimal media with 2-deoxy-galactose and confirmed by DNA sequence . Finally , a NotI fragment containing the AstlmCherry transgene was retrieved from the BAC with pl253 , and the fidelity of coding regions was confirmed by DNA sequence . After gel purification , the AstlmCherry transgene was injected into the male pronucleus of >200 one-cell zygotes which were transferred to pseudopregnant female mice . Offspring were genotyped ( S1b Fig ) using tail DNA and Astl exon 9 oligonucleotides ( S3 Table ) that distinguished between the normal allele ( 268 bp ) and the AstlmCherry transgene ( 967 bp ) . From 30 pups , 6 founders were identified that passed the transgene through the germline . Three lines were maintained and the analysis of FVB/N-Tg ( Astl-mCherry ) 1Dean is described herein . Tissue-specific expression of AstlmCherry was determined by RT-PCR using total RNA from various tissues to make cDNA with SuperScript® III First-Strand Synthesis System ( Life Technologies ) . PCR analysis of cDNA was performed using primers ( S3 Table ) designed to span an exon-intron boundary . pMLM3613 ( Addgene , Cambridge , MA #42251 ) expressing Cas9 was linearized by PmeI ( New England Biolabs ) , purified with a PCR clean-up kit ( Clontech Laboratories , Mountain View , CA ) and in vitro transcribed with mMESSAGE mMACHINE T7 ULTRA ( Life Technologies-Ambion , Carlsbad , CA ) . Double-stranded synthetic DNA targeting exon 2 of Astl ( 5’-GGACATCCCCGCAATTAACCAAGG-3’ ) was cloned into the pair of BsaI sites of pDR274 ( Addgene , #42250 ) expressing sgRNA . After linearization by digestion with DraI , the plasmid was purified with the PCR clean-up kit and in vitro transcribed using MEGAshortscript T7 ( Life Technologies-Ambion ) . After transcription , the Cas9 cRNA and the sgRNA were purified with MEGAclear kit ( Life Technologies-Ambion ) according to the manufacturer’s instruction and eluted in RNase-free water . For zygote injection , B6D2F1 ( C57BL/6 x DBA2 ) female mice were hormonally stimulated to ovulate ( see below ) and mated with B6D2F1 males . One-cell embryos were collected and injected with Cas9 cRNA ( 50 ng/μl ) , sgRNA ( 20 ng/μl ) and donor oligo ( 20 ng/μl ) . The injected embryos were cultured in KSOM ( Zenith Biotech , Guilford , CT ) until the blastocyst stage and transferred into pseudopregnant CD1 female mice . The sequence of injected oligonucleotide was: 5’-TCTGGAGTCTGCAGTACCAGTGTTCCAGAAGGCTTCACTCCTGAGGGAAGCCCGGTATTTCAGAACCAAGGTGAGAACACGGGGCCACACTCCAAAGCCATGCTGAATGTGGACATGCGGAAAAGA-3’ . The genotype of the AstlΔ allele was initially determined by DNA sequence of tail DNA and subsequently by PCR using an oligonucleotide primer that bridged the deleted sequence ( S3 Table ) . GV-intact oocytes from 4–6-wk-old female mice were collected by puncturing ovarian follicles in M2 medium ( Sigma-Aldrich , St . Louis , MO ) at 48 hr post-injection of 5 IU of equine gonadotropin hormone ( eCG ) . Ovulated eggs and embryos from 4–6-wk-old female mice were collected before and after mating , respectively , in M2 medium after injection of 5 IU of eCG followed by 5 IU of human chorionic gonadotropin ( hCG ) 46–48 hr later . Embryos were subsequently cultured in KSOM ( Zenith Biotech ) at 37°C in 5% CO2 to obtain 1- and 2-cell embryos . To inhibit actin polymerization , the medium was supplemented with 100 μM CK-666 ( Sigma-Aldrich ) , a cell permeable inhibitor of arp2/3 . For individual experiments , 20–30 cells were used from 3 different animals and representative images were included in figures . A rabbit polyclonal antibody that binds a C-terminal peptide of ovastacin395-408 and monoclonal antibody M2c . 2 that binds to the C-terminal region of ZP2 have been characterized previously [32][45] . The following antibodies and lectins were obtained commercially: LCA-FITC ( Sigma-Aldrich ) ; antibodies to GP73 , calregulin and EEA1 , Alex Fluor 488 goat anti-rabbit IgG ( H+L ) ( Life Technologies-Invitrogen , Carlsbad , CA ) ; Alexa Fluor 555 donkey anti-rabbit IgG ( H+L ) ( Life Technologies-Invitrogen ) ; DyLight 649 goat anti–rabbit IgG ( H+L ) ( Life Technologies-Invitrogen ) ; and goat anti-rat IgG-HRP ( Santa Cruz ) . pCS2+/UtrCH-EGFP ( Addgene , #26737 ) was linearized with NsiI and in vitro transcribed using SP6 mMESSAGE mMACHINE ( Life Technologies-Ambion , AM1340 ) . cRNA was purified by MEGAclear ( Life Technologies-Ambion ) . Full-length and truncated ovastacin open reading frames were inserted into the pmCherry-N1 vector ( Clontech Laboratories ) . Capped cRNAs were synthesized from PCR templates using T7 mMessage mMachine ( Life Technologies-Ambion ) , and purified with MEGAclear ( Life Technologies-Ambion ) . Microinjection was performed in M2 medium ( Zenith Biotech ) using a TransferMan NK2 micromanipulator ( Eppendorf , Hauppauge , NY ) . Typically , 10–12 pl ( 4% of the oocyte volume ) of 0 . 5–1 . 0 μg/μl cRNA was injected into oocytes . For each cRNA construct , 20–30 GV-intact oocytes from 3 mice were injected , incubated in M2 media ( 37°C , 5% CO2 ) for 6 hr prior to fixation and imaging by confocal microcopy . Variations in the intensity of the mCherry signal were noted among oocytes injected with the same construct and those with the strongest signals were selected for fixation and imaging by confocal microscopy using similar settings . GV-intact oocytes , ovulated eggs or embryos ( 20–30 ) were fixed in 4% paraformaldehyde ( PFA ) for 30 min , permeabilized in 0 . 5% Triton X-100 for 20 min , and blocked in SuperBlock ( Piercenet , Thermo-Fisher Scientific , Rockford , IL ) for 1 hr at room temperature . Samples were incubated with primary antibody ( 1:100 ) for 1 hr at room temperature or 4°C overnight . After three washes in 0 . 3% PVP/PBS containing 0 . 1% Tween 20 and 0 . 01% Triton X-100 for 5 min each , oocytes or embryos were incubated with secondary antibody ( 1:200 ) for 1 hr at room temperature . For LCA staining , eggs or embryos were stained with LCA-FITC ( 1:100 ) for 1 hr at room temperature . After three washes in 0 . 3% PVP/PBS containing 0 . 1% Tween 20 and 0 . 01% Triton X-100 for 5 min each , samples were mounted in PBS containing Hoechst 33342 ( 1μg/ml ) . Confocal laser-scanning images were obtained using similar settings within experiments on an LSM 510 confocal microscope ( Carl Zeiss AG , Jena , Germany ) , with a 63 x 1/2 W objective and exported as full-resolution TIF files and processed in Photoshop ( Adobe Systems , San Jose , CA ) to adjust brightness and contrast . Time-lapse imaging was obtained with the LSM 510 confocal microscope equipped with a Plan Apochromat 40× , 1 . 2 NA water immersion objective . mCherry was excited with a 561-nm laser line and detected with a 575–615-nm band pass . Ovulated eggs ( 10–20 from 3 animals ) were collected , followed by removal of zonae pellucidae ( see below ) , and placed in HTF medium supplemented with 5 ng/ml Hoechst 33342 ( Life Technologies-Molecular Probes , Eugene , OR ) on a gridded cover glass bottom dish ( MatTek , Ashland , MA Cat . No . P35G-1 . 5-7-C-grid ) . The dish was placed in a humidified chamber ( 5% CO2 , 37°C ) attached to the microscope and inseminated with 1 x 104 ml-1 capacitated sperm . GV-intact oocytes , ovulated eggs and two-cell embryos ( 10–15 ) were lysed in 4x LDS ( lithium dodecyl sulfate ) sample buffer with 10x reducing reagent ( Life Technologies-Invitrogen ) , separated on 12% Bis-Tris precast gels , transferred to nitrocellulose membranes ( Life Technologies-Invitrogen ) , blocked in 5% nonfat milk in TBS ( Tris buffered saline , pH 7 . 4 ) with 0 . 1% Tween 20 ( TBST ) for 1 hr at room temperature , and then probed with 1:500–1:1 , 000 dilution of primary antibodies at 4°C overnight . On the following day , blots were incubated with a 1:10 , 000 dilution of secondary antibodies conjugated to HRP ( horse radish peroxidase ) . Chemiluminescence was performed with ECL Plus ( Piercenet ) and signals were acquired by a Luminescent Image Analyzer LAS-3000 ( Fujifilm , Valhalla , NY ) or with BioMax XAR film ( Kodak , Rochester , NY ) . Caudal epididymal sperm were isolated from wild-type ICR mice and placed under oil ( EMD Millipore , Billerica , MA ) in human tubal fluid ( HTF ) medium ( Zenith Biotech ) previously equilibrated with 90% N2 , 5% O2 , 5% CO2 and capacitated by an additional 1 hr of incubation at 37°C . Sperm binding to ovulated eggs or two-cell embryos isolated from wild-type , AstlNull , AstlRescue , Astl+/Δ and AstlΔ/Δ mice was observed using capacitated sperm and wild-type 2-cell embryos as a negative wash control . Samples were fixed in 4% PFA for 30 min , stained with Hoechst 33342 . Bound sperm were quantified from z projections acquired by confocal microscopy [46] , and results reflect the mean ± s . e . m . from at least three independently obtained samples , each containing 6–12 mouse eggs/embryos . Statistical differences were determined by the 2-tailed Student’s T-test . The zona pellucida of eggs was removed after 5 min incubation in 100 μl of acid Tyrode's solution ( Sigma ) and then washed 3 times in fresh M2 medium . Cauda epididymides were lanced in a dish of HTF to release sperm that were capacitated for 1 hr ( 37°C with 90% N2 , 5% O2 , 5% CO2 ) and added to zona-intact or zona-free eggs ( 30 from 3 different animals ) at a concentration of 4 x 105 ml-1 sperm in 100 μl HTF for 5 hr at 37°C , 5% CO2 . The presence of two pronuclei was scored as successful fertilization . Statistical differences were determined by the 2-tailed Student’s T-test . | Monospermic fertilization is essential for the onset of development . Egg cortical granules exocytose their contents after fertilization to prevent polyspermy by modifying the extracellular zona pellucida ( ZP1 , ZP2 , ZP3 ) . Little is known about the biology of these subcellular organelles which are unique to oocytes . Ovastacin , a zinc metalloendoprotease that cleaves ZP2 to prevent sperm binding , is a pioneer marker of mammalian cortical granules . ZP2 remains uncleaved in transgenic mice lacking ovastacin and sperm bind to the zona matrix independent of fertilization and cortical granule exocytosis . After documenting the rescue of the null phenotype with transgenic mice expressing fluorescently-tagged ovastacin , we defined a unique , well conserved , cortical granule localization motif using cRNA deletion mutants microinjected into mouse oocytes . The importance of the motif for localization to cortical granules was confirmed in vivo by deleting DNA encoding 7 amino acids of the endogenous locus with CRISPR/Cas9 . Unexpectedly , mutant female mice were sub-fertile due to partial cleavage of ZP2 in the zona pellucida which prevented sperm from binding to ovulated eggs in vitro and in vivo . These observations offer unique insight into the molecular basis for translocation of proteins to cortical granules which is needed for successful , monospermic fertilization . | [
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] | 2017 | A Unique Egg Cortical Granule Localization Motif Is Required for Ovastacin Sequestration to Prevent Premature ZP2 Cleavage and Ensure Female Fertility in Mice |
The majority of life on Earth depends directly or indirectly on the sun as a source of energy . The initial step of photosynthesis is facilitated by light-harvesting complexes , which capture and transfer light energy into the reaction centers ( RCs ) . Here , we analyzed the organization of photosynthetic ( PS ) complexes in the bacterium G . phototrophica , which so far is the only phototrophic representative of the bacterial phylum Gemmatimonadetes . The isolated complex has a molecular weight of about 800 ± 100 kDa , which is approximately 2 times larger than the core complex of Rhodospirillum rubrum . The complex contains 62 . 4 ± 4 . 7 bacteriochlorophyll ( BChl ) a molecules absorbing in 2 distinct infrared absorption bands with maxima at 816 and 868 nm . Using femtosecond transient absorption spectroscopy , we determined the energy transfer time between these spectral bands as 2 ps . Single particle analyses of the purified complexes showed that they were circular structures with an outer diameter of approximately 18 nm and a thickness of 7 nm . Based on the obtained , we propose that the light-harvesting complexes in G . phototrophica form 2 concentric rings surrounding the type 2 RC . The inner ring ( corresponding to the B868 absorption band ) is composed of 15 subunits and is analogous to the inner light-harvesting complex 1 ( LH1 ) in purple bacteria . The outer ring is composed of 15 more distant BChl dimers with no or slow energy transfer between them , resulting in the B816 absorption band . This completely unique and elegant organization offers good structural stability , as well as high efficiency of light harvesting . Our results reveal that while the PS apparatus of Gemmatimonadetes was acquired via horizontal gene transfer from purple bacteria , it later evolved along its own pathway , devising a new arrangement of its light harvesting complexes .
Photosynthetic ( PS ) microorganisms play an important role in many of Earth’s ecosystems due to their ability to harvest light and convert it to metabolic energy [1] . So far , phototrophic species were found in 7 bacterial phyla: Cyanobacteria , Proteobacteria , Chlorobi , Chloroflexi , Firmicutes , Acidobacteria , and Gemmatimonadetes [2] . The conversion of light into metabolic energy occurs in reaction centers ( RCs ) that carry out charge separation . Based on the terminal electron acceptor , the RCs can be divided in two groups [3] . Type 1 RCs , which use Fe-S clusters , are present in Chlorobi , Firmicutes , and Acidobacteria . Type 2 RCs , which use quinones , are possessed by Chloroflexi , Proteobacteria , and Gemmatimonadetes . Cyanobacteria are the only phototrophic prokaryotes that can evolve oxygen and possess both RC types . The latest group found to contain phototrophic representatives is the phylum Gemmatimonadetes [4 , 5] . This phylum was formally established in 2003 , with G . aurantiaca as a type species [6] . Its only cultured phototrophic representative is G . phototrophica , which was recently isolated from a freshwater lake in the Gobi Desert [7 , 8] . G . phototrophica contains bacteriochlorophyll ( BChl ) a as a main light-harvesting pigment and a large quantity of carotenoids . Its photosynthesis genes are organized in a 42 . 3-kb photosynthesis gene cluster ( PGC ) whose organization closely resembles that of Proteobacteria [7] . Also , phylogenetic analysis of the PS genes confirmed their homology to Proteobacteria . Based on these facts , it was suggested that phototrophy in Gemmatimonadetes originated from an ancient horizontal gene transfer event of a complete PGC from a purple PS bacterium [7] . If true , G . phototrophica represents the first known example of horizontal gene transfer of a complete set of photosynthesis genes between phototrophic and nonphototrophic representatives of distant bacterial phyla [2 , 7] . The environmental significance and distribution of phototrophic Gemmatimonadetes is not completely clear . These organisms are photoheterotrophic species , which require organic carbon for their metabolism and growth , but they can supplement a large part of their energy requirements using light-derived energy . Based on the analyses of available metagenomes , the highest proportion of phototrophic Gemmatimonadetes was found in wastewater treatment plants , soils , lake waters and sediments , estuarine waters , biofilms , plant-associated habitats , estuaries , and intertidal sediments . In contrast , no sequences from phototrophic Gemmatimonadetes were found in marine waters [9] . Little is known about the PS apparatus of G . phototrophica . The presence of the puf operon in its genome indicates the presence of type 2 RCs homologous to RCs of phototrophic Proteobacteria . The in vivo absorption spectrum of G . phototrophica reveals 2 main bands ( 819 , 866 nm ) in the near infrared region ( NIR ) [7] . This resembles the spectra of many phototrophic Proteobacteria that possess two types of light-harvesting complexes , which serve to both increase cross-section and expand spectral range of the RCs [10 , 11] . The inner antenna LH1 subunits encircle the RC , forming together the LH1-RC core complex [12–14] . The outer antenna light-harvesting complexes 2 ( LH2 ) are organized in small rings placed in physical contact with the core complex . Interestingly , the genome of G . phototrophica does not contain any LH2 genes [7] , so the identity of its 2 NIR absorption bands is unknown . The second characteristic of G . phototrophica is the presence of a large amount of carotenoids responsible for a strong absorption in the blue-green spectral region [7] . The light harvesting role of these pigments is uncertain since the heterotroph G . aurantiaca contains a similar set of carotenoids . In order to elucidate the organization of the PS apparatus in G . phototrophica , we purified its PS complexes and performed a detailed biochemical and spectroscopic characterization .
The released PS membranes from G . phototrophica contained 2 clearly visible absorption bands in the NIR and a large amount of carotenoids ( S1 Fig ) . The PS complexes were purified by a combination of anion-exchange and size-exclusion chromatography ( for details see Materials and methods ) . During chromatography , the majority of carotenoids eluted differently from the PS complexes ( S2 Fig ) . This indicates that most of the carotenoids present in G . phototrophica’s membranes are not bound to PS complexes and do not serve for light harvesting . Based on the retention time during the size-exclusion chromatography and native gel electrophoresis , we estimated that the G . phototrophica PS complex has a molecular weight of approx . 800 ± 100 kDa ( S3A Fig ) , which is about 2 times larger than the LH1-RC core complex in R . rubrum ( approximately 400 kDa ) . Further separation of G . phototrophica PS complex by sodium dodecyl sulfate ( SDS ) -electrophoresis have identified only 6 main protein bands in the range between 2 and 40 kDa . The 2 most intense bands , with apparent molecular masses approximately 5 kDa , most likely originated from light-harvesting antenna subunits ( S3B Fig ) . The purified complex contained 62 . 4 ± 4 . 7 BChl a molecules ( mean ± SD , n = 4 ) . Both results showed that the PS complexes in G . phototrophica are much larger than the core complexes of R . rubrum . The activity of the purified complex was verified by flash photolysis . The flash-induced difference spectrum was highly similar ( S4 Fig ) to the spectra previously recorded in purple PS bacteria [15] , confirming that the RC of G . phototrophica is of the purple-bacterial type . The functionality was further confirmed using variable fluorescence measurements , which documented the high efficiency of primary charge separation ( FV/FM approximately 0 . 62 ) and active electron transfer ( S5 Fig ) . Interestingly , the isolated complex retained fully functional photochemistry up to 60°C , which far exceeded G . phototrophica’s growth optimum of 25−30°C [8] . Such high thermal stability indicates a robust architecture of the studied PS complexes . To obtain information about the overall structure of the light-harvesting systems of G . phototrophica , we analyzed the purified PS complexes using single particle analysis . The raw transmission electron microscopy ( TEM ) image revealed a large quantity of circular complexes ( S6 Fig ) . The averaged image of the PS complex revealed a roughly circular structure with an apparent outer diameter of 198 Å ( Fig 1 ) . Assuming a 10 Å layer of detergent , one can estimate the net dimension of the PS complex to approximately 18 nm . The side-view projection showed elongated structures , frequently with a bulge on 1 side of the complex ( thickness including the bulge of approximately 72 Å ) , probably representing the attached cytochrome ( pufC gene product ) . For comparison , we also performed single particle analysis of the purified RC-LH1 complex of R . rubrum , which has an outer diameter of 13 nm . This means that the PS complex of G . phototrophica occupies an approximately 2 times larger membrane area when compared to the RC-LH1 complex of R . rubrum . The isolated complexes were further characterized by steady-state spectroscopy ( Fig 2 ) . The UV part of the spectrum was dominated by the Soret band of BChl a peaking at 370 nm ( Fig 2A ) . Carotenoids cover an absorption range between 430–570 nm with the maximum at 515 nm . The vibrational sub-bands of the carotenoid spectrum were not well resolved . The minor absorption band at 575–595 nm seems to originate from the overlapping carotenoid 0–0 transition and the Qx transition of BChl a . In the NIR region , the spectrum was characterized by BChl a bands peaking at 816 and 868 nm; in the following , these spectroscopic species will be denoted as B816 and B868 , respectively . The ratio of amplitudes B816:B868 was approximately 1 . 7 . Lowering of the temperature to 77 K led to the narrowing of both BChl a absorption bands and a shift of their maxima to longer wavelengths ( Fig 2A ) . The shift was much more pronounced in the case of B868 ( 12 nm versus 2 nm of B816 ) . Such a large decrease of the transition energy upon cooling is characteristic of the excitonically coupled pigment pools ( B870 and B850 ) of LH1 and LH2 complexes [16] . At 77 K , the blue edge of the B816 resolved into a well-defined shoulder at approximately 800 nm . Linear dichroism ( LD ) spectrum of the PS complex embedded in the vertically compressed gel is shown in Fig 2B . Assuming that under compression , the preferential orientation of the plane of the flat , disk-like complex was horizontal ( normal vertical ) , the Qy transitions of both the B816 and B868 were oriented predominantly parallel to the plane of the complex . The larger value of the LD/Absorbance ratio of B816 compared to B868 suggested that the Qy dipole moments of the B816 BChls were slightly more in-plane than those of B868 in contrast to the B800 BChl a of LH2 and the B808 of B808-B866 from Chloroflexi [17–19] . The Qx peak was observed at 583 nm and suggested that the corresponding dipole was oriented along the complex normal ( vertically in the present geometry ) . The carotenoids exhibited very low LD . The circular dichroism ( CD ) spectrum was dominated by BChl a . Carotenoids contributed only a minor broad positive band in the 430–550 nm region ( Fig 2C ) similar to CD of whole PS membranes of R . rubrum [20] . In contrast , CD spectra of isolated antenna complexes ( LH1 , LH2 , B808-866 ) typically exhibit large , often nearly conservative carotenoid bands [19 , 21–23] . The BChl a contribution consisted of positive peaks at approximately 582 nm , 795 nm , and 855 nm , and negative bands peaking at 820 and 880 nm . Although the CD spectrum of the B868 region could be easily interpreted as a LH1-like BChl a aggregate , the B816 region deserves more attention . The large asymmetry between the positive and negative lobe of the CD spectrum , accompanied with a large , 13 nm blue-shift of the zero-crossing point with respect to the maximum of the absorption band are not typical of LH2 or B808-866 complexes [19 , 21 , 22] . However , both features are present in the CD spectrum of the structural unit of the LH1 complex , B820 , an excitonically coupled dimer of BChl a bound to α and β helices [20 , 24] . To explore energy transfer between the B816 and B868 nm bands , we excited the complex at 820 nm and recorded transient absorption spectra in the 700–970 nm spectral window . Fig 3A shows kinetics at the wavelengths corresponding to the ground-state bleaching of the both bands . The kinetics clearly demonstrate the energy transfer process: as the signal at 820 nm decays , the signal at 880 nm ( red-shifted with respect to steady-state absorption because of the contribution from the stimulated emission and overlap with excited-state absorption ) appears . Fig 3B shows the complementary transient absorption spectra , which provide information about the spectral evolution of the system . Global fitting of the whole spectro-temporal dataset revealed time constants of 2 ps and 210 ps ( S6 Fig ) . The first time constant obviously characterizes the energy transfer between the B816 and B868 bands because it is associated with the decay of the B816 band and concomitant rise of the B868 band ( Fig 3A and S7 Fig ) . The B816-B868 energy transfer time of 2 ps is slightly longer but comparable to the B800-B850 energy transfer in LH2 complexes: Rhodobacter sphaeroides ( 0 . 7 ps ) [25] , Rhodopseudomonas acidophila ( 0 . 8–0 . 9 ps ) [26 , 27] , Thermochromatium tepidum ( 0 . 8–0 . 9 ) [28] , Rhodospirillum molischianum ( 1 . 0 ps ) [29] . A similar situation was found in Chloroflexi , which contain type 2 RCs surrounded by a circular antenna , which in this case binds 2 different pools of pigments [19 , 21] . Here , the energy transfer times in the core complex of Chloroflexus aurantiacus [30] and Roseiflexus castenholzii [31] were almost the same as in G . phototrophica . The slower kinetics , observed in the B868 band , populated by energy transfer from B816 , have a lifetime of 210 ps ( S7 Fig ) , which thus characterizes B868-RC energy transfer ( Fig 3A , inset ) . The shape of transient absorption spectra can also provide some information about arrangement of BChl a molecules within the PS complex of G . phototropica . The ground-state bleaching signals of both B816 and B868 bands are accompanied by positive , blue-shifted excited-state absorption bands . This pattern is well-known from systems containing excitonically coupled BChls , such as LH1 [32] or LH2 [33] complexes of purple bacteria . The G . phototrophica light-harvesting complex is thus likely a system in which both B816 and B868 bands exhibit signatures of excitonic coupling . It is also worth mentioning that the zero-crossing point in the transient absorption spectrum at approximately 810 nm hardly moved with time ( Fig 3B ) . Essentially the same behavior was recorded for the B820 complex , whereas in the LH1 complex the zero-crossing point shifted over time due to equilibration among LH1 subunits [32] . Thus , as for the CD spectra described above , the dynamic behavior of the transient absorption spectra also points to the B816 band as being composed of BChl a dimers with no or slow energy transfer between them . All the collected structural and spectroscopic data provide evidence for some unique features of G . phototrophica’s PS complex . It is an approximately circular aggregate with an outer diameter of approximately 18 nm . The complex contains 62 . 4 ± 4 . 7 BChl a molecules per RC . This number is almost identical to the value determined previously from the whole cell extracts [7] , which indicates that the number of BChl a molecules in the complex is fixed and is not dependent on growth conditions . This number also far exceeds the pigment pools of 30–36 BChl a molecules per RC observed in LH1–RC complexes of Proteobacteria [14 , 34] . These considerations led to a double concentric ring organization of the G . phototrophica PS complex with a densely packed inner part , similar in dimension to LH1 for B868 and a loosely spaced outer shell of B816 . To determine the number of subunits , we analyzed the angular distances of the subunits observed on the peripheral part of the complex . The mean angular distance of the apparent subunits was 24 degrees , which corresponds to the 15-meric symmetry ( S8 Fig ) . We assume that the BChl a molecules are divided into 3 pools—4 molecules as a part of the RC , 30 molecules forming an inner ( LH1-like ) ring around the RC , and 30 molecules forming the second peripheral ring ( for details see the discussion in S1 Text ) . The structural unit of both pigment pools can be assumed to be a 2-helix–2BChl a complex . This predicted organization with 2 concentric rings composed of 15 dimers each harboring 2 BChl a molecules translates in total to 60 BChl a molecules , which is consistent with the number of BChl a determined by the liquid chromatography . To verify our theoretical prediction , we calculated the steady-state NIR spectra ( absorbance , CD , LD ) using a point-dipole approximation [35] for such pigment geometry and compared the simulated spectra with the measured ones ( Fig 4A–4C ) . The simulation started from a 2-helix–2BChl a building block ( Protein Databank Identifier:2FKW ) repeated so as to produce the required 2 rings with 15-meric symmetry , in a similar experiment to that done by Georgakopoulou et al . [23] . Parameters used in the cited work to simulate the LH1 spectra were used as a starting point . The dipole directions and transition energies were then adjusted to match the measured spectra of G . phototrophica PS complex , first manually and then fine-tuned using a genetic algorithm . The full set of parameters used to compute the steady-state spectra is presented in S1 Table . As seen in Fig 4A–4C , the majority of the features of the experimental steady-state spectra are quantitatively accounted for by the given parameters , including the blue-shift of the B816 crossing point ( Fig 4B ) , the slightly higher orientation of the B816 dipoles compared to B868 ( Fig 4C ) , and a decrease of polarization in the blue edge of the B816 band ( Fig 4C ) , due to the overlap of several excitonic components . On the other hand , the model failed to predict the extent of the nonconservative nature of the CD signals . However , this was expected because it was shown before ( e . g . , ref . [23] ) that the inclusion of the interaction of BChl a Qy with higher energy transitions , such as Soret bands , Qx and carotenoids is necessary to produce the required degree of asymmetry in the CD bands . The relative difference of the intensity of the B816 and B868 bands is accounted for by less than 20% of the relative increase of the transition dipole moment of BChl a bound to B816 compared to B868 , which is well within the values used to simulate spectroscopic properties of LH1 [23] . The above considerations led us to propose the model shown in Fig 4D . As expected , the dominant pigment–pigment interactions were found within the BChl a dimers ( 289 cm−1 and 220 cm−1 for B868 and B816 subunits , respectively ) . The strongest computed interaction between neighboring dimers was 55 cm−1 in the B868 ring . This is more than 5 times lower compared to typical inter-dimer interactions of both LH1 or LH2 complexes . This can be partially accounted for by the fact that the simulation was performed for a 15-meric ring with a diameter corresponding to the standard 16-meric LH1 , leading to the larger separation between closest BChls of neighboring dimers , but it also likely indicates a difference in the detailed geometry of the pigments . The strongest inter-dimer interaction within the B816 ring was less than 7 cm−1 due to the large distance between the dimeric subunits; hence , B816 consists effectively of isolated dimers . The strongest predicted coupling between B868 and B816 pigments was −12 cm−1 . This is lower but comparable to the theoretically predicted B800–B850 couplings in LH2 [36 , 37] and in agreement with the observed excitation transfer times . In addition , because the present model of the G . phototrophica PS complex assumes concentric arrangement of dimeric subunits with the B868 forming an ( approximately ) LH1-like core surrounded by an external B816 antenna , it is of interest to compare it also to the functioning of the PS unit of LH2-containing purple bacteria . Here , the fastest LH2-LH1 transfer times were found in the range 3–5 ps [38 , 39] for the theoretically predicted electronic coupling between donor and acceptor states in the range of approximately 2–10 cm−1 [40] . For completeness , in Fig 4E we suggest the organization of the protein helices corresponding to the above described pigment geometry . Light-harvesting complexes in G . phototrophica harbor approximately 60 BChl a molecules arranged in 2 concentric rings surrounding the type 2 RC . This unique and elegant organization offers high efficiency of light absorption and excitation transfer as well as high structural stability . Our results also demonstrate that while the PS apparatus of Gemmatimonadetes was likely acquired via horizontal gene transfer from purple bacteria , it later evolved along its own trajectory devising a novel organization for its light-harvesting complexes .
G . phototrophica strain AP64T was grown on modified R2A agar media in a Memmert INCO 108med incubator under 90% N2 , 10% O2 atmosphere at 28 ± 1°C , in the dark [8] . The medium was supplemented with 50 mg L−1 ampicillin to avoid bacterial contamination . The purity of the colonies was routinely checked using a custom made NIR microspectrometry system assembled from a Nikon SMZ800N stereomicroscope and a QE Pro-FL CCD spectrometer ( Ocean Optics Inc . , Largo , FL ) connected via fiber optics . The colonies were harvested approximately 1 month after inoculation , using a plastic scraper and stored at −20°C until needed . R . rubrum was grown in cap-closed bottles on complex medium [41] on an orbital shaker . The harvested cells ( collected from approximately 20 agar plates ) were resuspended in buffer A ( 50 mM Tris , pH 8 , 1 mM EDTA , 50 mM NaCl ) and centrifuged for 10 min at 10 , 000 × g . The cells were broken using an EmulsiFlex-C5 ( Avestin Inc . , Ottawa , Ontario , Canada ) at 140 MPa , and unbroken cells with cell debris were removed by centrifugation for 10 min at 5 , 000 × g . The released membranes were pelleted by ultracentrifugation ( 60 min , 110 , 000 × g ) and resuspended in 0 . 5 mL buffer A containing 1 mM phenylmethylsulfonyl fluoride . Subsequently , the membranes were solubilized with a mixture containing 2% of n-dodecyl β-D-maltoside ( β-DDM ) and 0 . 2% of Triton-X100 at room temperature in the dark for 30 min . The separation of solubilized membranes was carried out using a Pharmacia FPLC system equipped with a UnoQ-6 ion-exchange column ( Bio-Rad , Hercules , CA ) . The sample was loaded on top of the column and eluted in 20 mM HEPES , pH 8 . 0 , 0 . 06% β-DDM , with linearly increasing concentration ( from 0 to 0 . 5 M ) of MgCl2 at a flow rate of 1 mL min−1 over 60 min . The signal was detected using a Prominence SPD-20AV 2-wavelength UV/VIS detector ( Shimadzu Inc . , Kyoto , Japan ) . The fractions containing PS complexes were pooled and concentrated on 100-kD cutoff micro-concentrators ( Sartorius , Göttingen , Germany ) . The solubilized complexes were further purified by gel filtration using a Yarra SEC-3000 column ( Phenomenex , Torrance , CA ) and 20 mM HEPES , pH 8 . 0 , with 0 . 2% β-DDM at a flow rate of 0 . 2 mL min−1 at 10°C . The gel filtration was performed using an Agilent 1200 system equipped with a UV-VIS-NIR diode-array detector and fraction collector . The collected pigment–protein complexes were kept on ice in the dark to prevent sample degradation . Electron microscopy was performed on freshly prepared complexes ( same day of purification ) . Samples were deposited on glow-discharged carbon-coated copper grids and negatively stained with 1 . 5% uranyl acetate , and visualized using a JEOL JEM–2100F transmission electron microscope ( JEOL , Tokyo , Japan; using 200 kV at 20 , 000 × magnification ) . TEM images were recorded using a bottom-mounted Gatan CCD Orius SC1000 camera , with a resolution corresponding to 3 . 4 Å per pixel . Image analysis was carried out using RELION [42] . The selected projections were rotationally and translationally aligned , and treated by empirical Bayesian approach in combination with classification procedure to refine 2D class averages . Room temperature steady-state absorption spectra were recorded using a UV-VIS-NIR spectrometer UV2600 ( Shimadzu ) equipped with an integrating sphere . Low-temperature absorption was measured using an OptistatDN2 nitrogen cryostat . CD spectra at room temperature were recorded using a Jasco J-715 spectropolarimeter . LD spectra were recorded on samples embedded in 10% acrylamide gel [17]; cylinders of gel , 0 . 9 cm in diameter were vertically compressed to 60% of their original height in 1 × 1 cm cuvettes , leading to horizontal expansion of the gel . The femtosecond time-resolved spectroscopy was conducted using a modular laser system assembled from a Spitfire Ace-100F ultrafast Ti-sapphire regenerative amplifier ( Spectra-Physics , Santa Clara , CA ) seeded with the Mai Tai SP oscillator ( Spectra-Physics ) and pumped with an Empower 30 laser ( Spectra-Physics ) . The system produced pulses with a central wavelength of 800 nm , approximately 120 fs duration and a 1 kHz repetition rate . Part of the output power was used to prepare excitation ( pump ) pulses , another part to produce broad-band probe pulses . A gradually increasing delay between the 2 pulses was set by a computer-controlled delay line in the probe pathway . The desired excitation wavelength was tuned by means of an optical parametric amplifier ( TOPAS; Light Conversion , Vilnius , Lithuania ) . The generation of supercontinuum for the probe pulses was achieved in a 2-mm sapphire plate by applying 1 , 100-nm pulses derived from another TOPAS . The mutual polarization between pump and probe was set to the magic angle ( 54 . 7° ) . The probe beam was split into 2: one served as a reference , the other overlapped spatially with the pump beam at the sample . Both broadband pulses were then directed into the spectrograph , in which they were dispersed onto a double CCD array . Prior to the measurements , the sample was diluted in a buffer to reach an optical density of approximately 0 . 4 at 820 nm in a 2-mm path length quartz cuvette . A microstirrer was used to continuously mix the sample during the measurements . The intensity of the pump pulses was kept below 1013 photons pulse−1 cm−1 . The data were fitted globally using DAFit software ( Pascher Instruments , Lund , Sweden ) , which employs a sequential kinetic scheme with increasing lifetimes . Flash-induced absorbance spectra of purified PS complexes of G . phototrophica were measured using a laboratory-built kinetic spectrometer [43] . The spectrum was calculated as a light minus dark difference of absorption spectra recorded at 3 μs after xenon flash . The pigment-protein complexes were analyzed by CN electrophoresis . For native electrophoresis , the membranes from G . phototrophica or R . rubrum were concentrated ( Vivaspin 100K MW cut-off ) and resuspended in buffer B containing: 25 mM MES/NaOH , pH 6 . 5 , 10 mM MgCl2 , 10 mM CaCl2 , 25% glycerol . The buffer B was supplemented with 10% ( DDM ) in H2O [w/v] . The sample was mixed and spun down ( 18 , 000 × g , 10 min , 4°C ) and subsequently loaded on 4%–14% clear native gel [44] . Colored bands corresponding to PS complexes of G . phototrophica and R . rubrum were cut from the native gel , incubated for 30 min in 2% SDS and placed on the top of the 12%–20% gradient SDS gel [45] . Separated proteins were visualized by Coomassie blue staining . Pigments were analyzed using a Prominence-i HPLC system ( Shimadzu Inc . ) equipped with a Phenomenex Luna 3μC8 ( 2 ) 100 Å column using an ammonium acetate:methanol solvent system as described before [34] . The number of BChl a molecules per RC ( PS unit size ) was determined from the ratio of molar concentrations of BChl a and bacteriophaeophytin a multiplied by 2 ( for details see ref . [34] ) . | The majority of life on Earth depends directly or indirectly on the sun as a source of energy . Phototrophic organisms use energy from light to power various cellular and metabolic processes . The initial step of photosynthesis is facilitated by light-harvesting complexes , which capture and transfer light energy into the reaction centers where it is used to power proton gradients or to form new chemical bonds . Here , we analyzed photosynthetic complexes in Gemmatimonas phototrophica , the only known phototrophic representative of the bacterial phylum Gemmatimonadetes . Using a combination of biochemical and spectroscopic techniques , we show that the light-harvesting complexes of G . phototrophica are organized in 2 concentric rings around the reaction center . This organization is unique among anoxygenic phototrophs . It offers both structural stability and high efficiency of light harvesting . The structural unit of both antenna rings is a dimer of photosynthetic pigments called bacteriochlorophyll . The inner ring is populated by more densely packed dimers , while the outer ring contains more distant dimers with a minimal excitation exchange . Such an arrangement modifies the spectral properties of bacteriochlorophylls in the complex and ensures efficient capture of light in the near-infrared part of the solar spectrum . | [
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] | 2017 | Unique double concentric ring organization of light harvesting complexes in Gemmatimonas phototrophica |
Helminth parasites cause untold morbidity and mortality to billions of people and livestock . Anthelmintic drugs are available but resistance is a problem in livestock parasites , and is a looming threat for human helminths . Testing the efficacy of available anthelmintic drugs and development of new drugs is hindered by the lack of objective high-throughput screening methods . Currently , drug effect is assessed by observing motility or development of parasites using laborious , subjective , low-throughput methods . Here we describe a novel application for a real-time cell monitoring device ( xCELLigence ) that can simply and objectively assess anthelmintic effects by measuring parasite motility in real time in a fully automated high-throughput fashion . We quantitatively assessed motility and determined real time IC50 values of different anthelmintic drugs against several developmental stages of major helminth pathogens of humans and livestock , including larval Haemonchus contortus and Strongyloides ratti , and adult hookworms and blood flukes . The assay enabled quantification of the onset of egg hatching in real time , and the impact of drugs on hatch rate , as well as discriminating between the effects of drugs on motility of drug-susceptible and –resistant isolates of H . contortus . Our findings indicate that this technique will be suitable for discovery and development of new anthelmintic drugs as well as for detection of phenotypic resistance to existing drugs for the majority of helminths and other pathogens where motility is a measure of pathogen viability . The method is also amenable to use for other purposes where motility is assessed , such as gene silencing or antibody-mediated killing .
Billions of people are infected with helminths in developing countries , resulting in many thousands of deaths annually [1] , [2] . Helminths also plague livestock in developing and developed countries alike , with the global anthelmintic market for livestock and companion animals valued at $US 3 . 7 billion in 2002 [3] . While chemotherapy is available for most parasitic helminths , widespread use of anthelmintics in livestock has resulted in the emergence of drug-resistant parasites [4] , [5] . Mass drug administration campaigns to control human helminth infections are becoming more widespread and early data are emerging indicating the possible emergence of anthelmintic resistance , for example in river blindness caused by Onchocerca volvulus where ivermectin has been widely used , as well as in hookworm and schistosome infections [6]–[10] . Despite the impact of helminths on the health of humans and livestock , the anthelmintic pharmacopoeia is small . This is due in part to the high cost and limited financial return from drug development , particularly for human helminth infections . Another , often overlooked impediment to drug development is the lack of objective high throughput screening methods for assessing drug effectiveness [7] , [11] , [12] . The current gold standard for measuring drug effectiveness for most adult and larval helminth parasites is in vitro assessment of worm motility , as measured visually via microscopy and larval development assays for some larval stages . Such an approach is laborious , subjective and difficult to standardize [8] , [11] . For example , the cost and effort to standardise testing for larval anthelmintic resistance against four intestinal parasites of livestock across Europe was substantial [13] . In the 1980s an automated screen was developed , the micromotility meter [14] , [15] . The unit utilized light disruption to determine helminth movement . While successful in monitoring motility in both larval and adult stages of a range of parasites , the inherent limitations restricted its use to small scale studies [16] . Many research programs are underway to explore the genetic basis of anthelmintic resistance in order to develop molecular diagnostic assays for anthelmintic resistance . However , with the exception of the benzimidazole class of drugs [17] , [18] , the molecular basis of anthelmintic resistance is poorly understood , precluding development of widely applicable molecular diagnostics at the present time . Assays based on changes in egg output after drug treatment , the so-called fecal egg count reduction test ( FECRT ) , are useful only after resistance has become commonplace in the population ( at least 25% ) . This method , however , is confounded by density-dependent fecundity effects [19] . Furthermore , for some parasites eggs are not easily collected in quantity , or the only developmental stage present in feces is the larval stage ( eg . Strongyloides sp . ) . Assays have been developed in recent years that score worm migration , feeding and development [8] , [12] , [20] , [21] . While these approaches remove some of the subjectivity , they still require visual scoring by skilled operators , precluding the scale up that would for example be required for a drug discovery program . There are regular pleas in the peer-reviewed literature for high-throughput screening methods to facilitate drug development , and to detect emerging resistance [11] , [22]–[24] . Indeed , the Tropical Diseases Research network ( TDR ) of the WHO ( http://apps . who . int/tdr/ ) has developed an international resistance screening network , but due to the limitations of available techniques , the screening methods utilized have remained low- to medium-throughput [8] , [25] . And without a scalable , automated , objective assay for helminth viability , drug development and monitoring for drug resistance for neglected tropical diseases will be difficult [26] .
All animals used were maintained in accordance with the guidelines of the Animal Ethics Committee ( AEC ) of the Queensland Institute of Medical Research ( QIMR ) and James Cook University , or under the guidelines set out by the F . D McMaster Animal Ethics Committee , CSIRO Livestock Industries . All studies and procedures were reviewed and approved by the Animal Ethics Committees of QIMR or CSIRO ( Animal ethics approval number 09/16 ) . Feces were collected from H . contortus infected sheep that were housed at the McMaster Laboratory , CSIRO Livestock Industries , Armidale , New South Wales ( NSW ) , Australia , and then sent by overnight courier to the CSIRO laboratory in Brisbane , Queensland . The nematode isolates were as follows; [1] Kirby 1981 - isolated from the field at the University of New England Kirby Research Farm in Northern NSW in 1981 - these parasites are susceptible to ivermectin ( IVM ) and levamisole ( LEVA ) and thiabendazole ( TBZ ) [27]; [2] Wallangra 2003 - isolated from the Wallangra region of NSW [28] and resistant to LEVA , benzimidazoles , closantel and macrocyclic lactones . To ensure the resistance status of these parasites , sheep harbouring infections were treated with the recommended dose of a macrocyclic lactone 5 weeks after infection; [3] LAWES – an isolate from South East Queensland that is resistant to LEVA and benzimidazoles ( including TBZ ) [29] . To ensure the resistance status of these parasites , sheep harbouring infections were treated with the recommended dose of LEVA 5 weeks after infection . Nematode eggs were isolated from feces by filtration and sucrose density gradient centrifugation as previously described [30] , while L3 were collected as they migrated from fecal cultures . For real time cell assay ( RTCA ) experiments , 3 , 000 L3 were cultured per well of an E-plate ( Roche Inc . ) in 200 µl of 0 . 5× PBS ( 25 mM sodium phosphate pH 7 . 2 , 70 mM NaCl ) at 27°C . Strongyloides ratti L3 were obtained as described elsewhere [20] . For RTCA , 300 L3 were cultured per well of an E-plate in 200 µl of 0 . 5× PBS at 21°C . Adults of the canine hookworm , Ancylostoma caninum were collected from euthanized stray dogs and cultured in vitro at 37°C with 5% CO2 as described elsewhere [31] with a modification entailing the supplementation of 200 µl of medium per well with 10% fetal calf serum ( Invitrogen ) . For RTCA , culturing was performed using a single adult worm per well of an E-plate . Immobile worms used for dead background controls were determined by visual inspection . Adult Schistosoma mansoni pairs were collected from the mesenteric veins of mice by perfusion in PBS and then transferred to defined culture medium and cultured at 37°C with 5% CO2 as described elsewhere [32] . For RTCA , culturing was performed using one pair in 200 µl ( one coupled male and female worm ) per well of an E-plate . Immobile worms used for dead background controls were determined by visual inspection . The motility of all helminth species and developmental stages was assessed using an xCELLigence system ( Roche Inc . ) that monitors cellular events in real time without the incorporation of labels by measuring electrical impedance across interdigitated micro-electrodes integrated on the bottom of tissue culture E-Plates ( see http://www . roche-applied-science . com/sis/xCELLigence/ezhome . html ) . For all experiments the inter-well spaces of the E-plate were filled with PBS to reduce evaporation . The RTCA controller software ( Roche Inc . ) was used to determine how the information was gathered from the single plate RTCA unit ( Roche Inc . ) . The first step consisted of a background reading followed by regular user defined reads at 15 sec intervals for adult and L3 stages of all helminths tested ( now referred to as “worm tests” ) and 25 min intervals for H . contortus eggs ( now referred to as “egg tests” ) . For worm tests , helminths were cultured in 180 µl of their respective media per well of the E-plate and motility was monitored overnight to obtain a baseline motility reading prior to addition of 20 µl of a 10× solution of each anthelmintic drug . After addition of drugs ( see below ) , helminths were monitored for a further 3–5 days . For egg tests , E-Plate wells were first filled with 230 µl 0 . 5× PBS . Then a 96 well Multiscreen mesh filter plate ( 20 µm pore size , Millipore ) was aligned on top of the E-plate and filled with 200 µL of 0 . 5× PBS containing 3 , 000 eggs . Dilutions of TBZ ( see below ) were generated so that 100 µl of drug was added to 100 µl of eggs . Culture was undertaken for 48 hours at 27°C with a small fluorescent light placed 60 cm above the plate to encourage egg hatching . Drugs used were prepared as stock solutions in DMSO at the following concentrations: 5 mg/ml TBZ; 10 mg/ml IVM; 10 mg/ml LEVA , 5 mg/ml praziquantel ( PZQ ) . Drugs were diluted to 10× stocks in the respective tissue culture media for culturing of each parasite and pre-equilibrated for 1 h before addition of 20 µl of 10× drug to 180 µl of media containing helminths as described above . Final working concentrations of drugs were as follows: PZQ for schistosomes −1 . 6 µg/ml and two-fold serial dilutions from 400–50 ng/ml; TBZ for adult hookworms ( 100 , 20 , 10 and 1 µg/ml ) and H . contortus eggs ( three-fold dilutions from 9 µg/ml–0 . 037 µg/ml ) ; IVM for H . contortus L3 ( three-fold dilutions from 30–0 . 4 µg/ml ) and S . ratti L3 ( two-fold serial dilutions from 2–0 . 02 µg/ml ) ; LEVA for H . contortus L3 resistant and sensitive strains – two-fold serial dilutions from 50–0 . 4 µg/ml . Control worms were cultured in the presence of DMSO equivalent to that used for the highest drug concentration; this group was used to determine 100% motility . Motility index was used to determine IC50 values of drugs for adult and L3 stages of the helminths tested , and was calculated as the standard deviation ( SD ) over 800 data points ( i . e . 4 readings per min for 200 min ) of the cell index ( CI ) difference from the rolling average over 20 data points ( 10 proceeding and preceding CI values- 5 min total ) . One hundred percent motility was determined from the average motility index of the untreated wells , while 0% motility was determined as an average of when the lowest readings flatten out . The motility index averaged over 100 data points ( 25 min ) was converted to percent motility and this figure was used in Graphpad prism 5 . 0 to calculate and compare IC50 values . We used a log ( drug concentration ) vs normalised response ( 100%–0% ) formula with variable slope and automatic removal of outliers ( with default ROUT coefficient used: Q = 1 . 0% ) . For analyses where there were insufficient samples for a complete drug dilution series ( Haemonchus L3 vs IVM and hookworm ) a standard hill slope ( -1 ) was used with the previously described non-linear analysis . Determination of IC50 values for TBZ with H . contortus eggs utilized the raw cell index values that were converted to percent hatching from an average of 100% hatching ( no drug ) and 0% hatching ( 9 µg/ml TBZ ) . All other analyses were as stated above for adult worm and L3 stages . Statistical analyses were undertaken using Graphprism 5 . 0 . When data were sufficient to use the variable slope analysis ( all except hookworm and H . contortus L3 vs IVM ) the Hill Slope and the LogIC50 value were together compared for significant differences using an extra sum-of squares F-test . Hookworm and H . contortus L3 vs IVM were analysed with a set Hill Slope value of -1 ( described above ) and subsequently only the LogIC50 was compared with the F-test .
The Real Time Cell Assay ( RTCA ) unit can differentiate between live and dead parasites at multiple developmental stages for a range of different helminths ( Figure 1 ) . The gold electrodes embedded in the base of the wells ( Figure 1A ) monitor electrical resistance and generate an output presented as a cell index . Larval and adult helminth developmental stages were monitored every 15 sec and the resulting amplitude of the cell index output was proportional to the motility ( visual ) of the worms ( Figures 1B and C ) . When eggs were monitored using a modified version of the larval migration assay ( without the agar overlay ) [12] , [20] , [33] the cell index output was for the most part proportional to the number of hatched larvae that crawled through the nylon mesh and came into contact with the electrodes covering the base of the E-plate ( Figure 1D ) . For generation of IC50 values the cell index output was converted to a motility index ( Figure 2A ) which is a measure of the amplitude of the curve scatter . The optimal combination for helminth species and developmental stage was determined as the standard deviation ( SD ) over 800 data points of the cell index ( CI ) difference from the rolling average ( over 20 data points ) . The motility index was subsequently converted to a percentage of maximum motility to generate a dose response curve for traditional IC50 calculations ( Figure 2B ) . As data is continually monitored , any time point can be selected for IC50 analysis . To visualise the effects over time , numerous time points were selected for IC50 calculations ( Figure 3 ) . As evident from Figure 3 , each different helminth and developmental stage exhibited different responses to the drugs tested . For example , the IC50 of praziquantel ( PZQ ) for paired adult schistosomes increased over time and stabilised at 48 hrs ( Figure 3A ) . This is in contrast to the response of female adult hookworms to thiabendazole ( TBZ ) where the IC50 decreased over time but then stabilised at 24 h ( Figure 3B ) , and the response of H . contortus egg hatching to TBZ which did not significantly change ( Figure 3C ) . The motility index analysis clearly differentiates between resistant and sensitive strains of H . contortus ( Figure 4A and B ) . The IC50 values over time ( Figure 4C ) further demonstrate the differences between motility in levamisole ( LEVA ) -resistant versus -sensitive lines of H . contortus L3 . Twelve minutes after adding the drug significant ( P<0 . 01 ) differences were detected between motility of sensitive and resistant lines , and from 6 hours onwards the difference was highly significant ( P<0 . 0001 ) . The curves displaying the IC50 over time demonstrated that the LEVA-resistant strain became less motile in a consistent manner , while the motility of the LEVA-sensitive strain decreased after the first reading and then remained steady . The technique also allowed clear differentiation between ivermectin ( IVM ) -resistant and -sensitive H . contortus L3 ( Figure 4D ) , where the curves displayed different trends over time . Significant differences in the IVM IC50 values between sensitive and resistant lines were apparent over the first 12 hour period but thereafter lost significance . The data generated from the RTCA unit is summarised and compared to previously published drug sensitivity data in Table 1 . In each case the IC50 values for the RTCA were lower than those obtained by standard worm motility or egg hatch assays . The differences ranged from 4-fold up to 50-fold .
The RTCA unit was developed for automated monitoring of cell growth , from rapid responses over a few minutes to long term studies over a period of weeks [34] , [35] . With the ability to monitor adherent cells in a label-free fashion in real time , datasets containing substantially more information than previously obtainable are now being generated . While the system can measure growth of cells in suspension , it requires many more cells than it does for adherent cultures due to the requirement for contact with the electrodes in the bottom of the wells to generate a signal . In fact , any change in the conductivity across the gold electrodes , such as contact , will result in a change in the cell index reading . Live helminth parasites writhe in culture ( as they do in vivo ) , and constantly come into contact with the electrodes on the E-plate surface , making the RTCA system ideal for monitoring helminth motility for high-throughput studies . The initial purchase price of the unit might prove an impediment for some laboratories , but the wide ranging of cell based applications and the associated e reduction in manual labour to conduct medium- to high-throughput required will make the system an attractive proposition in the future . Additionally , once the initial RTCA unit and E-plates are purchased , the costs are no greater than those for conventional assays that are currently used for manual monitoring of parasite motility , as the plates are durable and readily reusable . After parasites have been killed by freezing the plates , they can be easily rinsed , sterilized with ethanol and reused many times with minimal reduction in sensitivity and less than 0 . 2% well failure ( data not shown ) . Because the RTCA system measures changes in worm motility with a high level of precision , it is widely applicable to a range of helminth species and developmental stages . While we have only tested this technique for the species tested herein ( Table 1 ) , it is highly likely that any motile developmental stage from any species that will rest at the bottom of a 96 well microtiter plate can be monitored using minor adaptations of the techniques that we describe here . The ability to directly assess multiple developmental stages for susceptibility to a drug or other intervention is a distinct advantage . For example , PZQ is much more effective against the adult stage of S . mansoni than it is against the schistosomulum , the developmental stage that is usually the focus of in vitro drug assessments [8] . H . contortus displays drug susceptibility differences between infective larval and adult stages , which poses a problem for drug screening and resistance detection that can be overcome by utilizing the RTCA assay for assessing motility of adult worms [36] , [37] . Defined skills and experience are generally required to assess worm motility by visual scoring using microscopy . The automated motility index method described herein lends itself to consistency and reproducibility between experiments , between researchers and between laboratories [38]–[40] , and thus obviate the requirement for challenging quality assurance programs [13] . The objective nature of the testing removes the subjectivity that afflicts that the majority of current testing methods . The IC50 values obtained by the RTCA were in all cases lower than those obtained from standard motility and egg hatch assays . This is most likely a reflection of the greater sensitivity of the RTCA unit in being able to detect subtle changes in motility that would be missed by the standard methods . The relative ability to detect resistance was mixed - the RTCA more readily detected LEVA resistance than a standard motility assay , while the latter more readily allowed quantification of IVM resistance levels . This highlights an issue which exists among the current suite of phenotypic assays , namely , that a single assay may not be the most suitable for resistance diagnosis for all drugs and helminth species ( for example , [39] ) . Importantly though , the real-time nature of the RTCA readout in Figure 4D does allow for discrimination in the responses to IVM , however the variability seen in the data at these time points would suggest that such an assay would require a deal of careful standardisation before it could adequately quantify IVM resistance levels . Recent programs to screen large libraries consisting of thousands of currently available drugs and other compounds have shown some promise for identifying new anthelmintics . For example , Abdulla et al . screened more than 2000 compounds in vitro against S . mansoni schistosomula and then progressed to screening 105 initial hits against adult stage parasites [8] . They used 200–300 schistosomula and 4–8 adult pairs per replicate and numerous additional screens when different time points were required . While robust data were generated , the program required a large scale effort . Even ignoring the time , effort and animal work required to produce the large number of worms , the screening alone took two full time researchers one month of training to identify phenotypes , three months to complete the primary screen with schistosomula and another month to screen the adult parasites . This laboratory and industry-based groups are developing automated video motility monitoring to improve scalability [41] . Initially developed for monitoring C . elegans sinusoidal movement the technique is now being adapted for parasites [42] . Currently , these systems require extensive mathematical modelling in the analysis programming that has to be customised by experienced personnel to each parasite and life cycle stage . While promising , this limits the applicability for the use of video monitoring for lab scale testing and development at this time . Microfluidic chips have also recently been developed and are showing great promise for screening of C . elegans . With innovative micro-channels to direct worms and micro-suction valves that trap individual worms , this device can sort whole worms depending on phenotype [43] , [44] . This live , whole worm sorting is combined with florescence and digital imaging and permits phenotypic screening down to sub-cellular resolution . The limitations are that the microfluidic chambers are limited by size and adult parasites of many species are too large to be screened . While currently behaviour and neural function of C . elegans have been the focus of microfluidics research , it is feasible that these units could be adapted to monitor drug effects on larval parasites [45] . As with video-based monitoring , all these new technologies will have a place with the RTCA unit at various stages of the drug screening and resistance detection pipeline in the future . As previously described , the E-plates contain 96 wells in a standard microtiter plate format , with up to 96 wells being monitored at any one time . The ease of experimentation enables the simultaneous monitoring of different species or developmental stages on the same plate . The RTCA unit that we used was the original single plate xCELLigence model ( RTCA SP instrument ) . However , Roche Inc . recently released a multi-plate unit that can monitor up to six plates ( 576 samples ) simultaneously . Additionally , a soon to be released 384 well model will assist scale up of larval assays , allowing for testing of additional samples with fewer larvae per well . These larger scale applications could be adapted to incorporate robotic handling for use with helminth eggs or larvae to streamline the scale-up in drug discovery programs . Post-genomic methods to determine the function of parasite genes and proteins are being developed [46] , [47] , and in time this will result in a suite of druggable targets . However , the lack of a high throughput objective tests for anthelmintic effectiveness represents a significant bottleneck that hampers the exploitation of this new post-genomic information [1] , [8] , [11] , [22]–[24] , [26] . Other xCELLigence models , such as the RTCA dual plate unit , are small , portable and powered by a laptop computer via USB connection . Such units may enable assessment of anthelmintic activity in field settings where drug efficacy studies are undertaken . The high sensitivity of this motility assay allows for detection of subtle differences following drug application with relative ease . Subtle drug effects are often overlooked when existing methods are used . For example , the effect of low PZQ concentrations on schistosomes we observe has until now gone unnoticed ( Figure 4A ) . The ability to measure parasite motility with enhanced sensitivity in a user-friendly manner will prove valuable in the detection of emerging drug resistance , a rapidly growing area of concern for human helminth infections [48]–[50] , thereby facilitating early intervention . A unique aspect to RTCA for monitoring helminth motility is its ability to continuously assess movement in real time . While the full analysis requires conversion of raw data into a motility index , effects on parasite motility can be easily monitored as the experiment progresses ( Figure 1 ) . Moreover , live data can be simply exported for motility index analysis during the experimentation period . This is particularly useful for experimental design using adult stage worms which are less amenable to long-term culture than are larval stages . The ability to measure motility ( and set baseline parameters ) prior to addition of drugs ensures that adequate replicates of healthy motile worms are recorded for each treatment condition , a consideration that assists data interpretation and statistical power . The added benefit of real time , intervention-free monitoring is that IC50 values can be generated for any number of time points within a single sample . Firstly , this allows fewer parasites to be used with less set-up time required . Secondly , this enables greater insight into defining the optimal time points for the detection of resistance ( for example , Fig 4C ) and timing of treatment . Thirdly , combination treatments can be more easily analysed , either with concurrent or successive applications . The real time nature of the assay allows multiple factors that affect resistance to be assessed , such as the kinetics of LEVA resistance [51]–[53] , or early and/or late effects that may be overlooked when defined time points are recorded . For example , when we cultured schistosomes in 50 and 100 ng/ml PZQ ( Figure 2A ) , there was an immediate effect on motility upon addition of drug , followed by a gradual recovery of motility from approximately 15–72 hours . A second example is the difference between the IC50 values of LEVA- and IVM-resistant Haemonchus L3 over time ( Figures 4C and 4D ) , where significant differences in motility were detected between resistant and sensitive lines until 12 hours following addition of both drugs . Thereafter the difference in motility between resistant and susceptible parasites was maintained for LEVA resistant L3 . In contrast , IVM-treated parasites showed similar motility between resistant and sensitive lines after 24 hours . Many anthelmintic drugs are metabolised within hours , so this data will be critical in designing treatment programs to maximise drug effectiveness and reduce costs . One drawback of monitoring slow acting drugs with this technique , such as IVM and TBZ ( Figures 3B and 4D ) , is that the IC50 95% confidence intervals can be substantial in the early period of the experiment . The reasons for this are unclear but we suspect that it reflects the slow induction of paralysis , hence the increased variability between samples . The versatility of this RTCA technique for measuring motility of microorganisms may result in a wide range of applications . It could be used to assess the effects on helminths of treatments other than drugs , including antibodies and other immune interventions , or gene silencing approaches where the phenotype affects motility [54] , [55] . Modification of the RTCA method for use with a range of other difficult to assess organisms is feasible . The free-living nematode Caenorhabditis elegans is widely used as a model for parasitic nematodes due to its functional and biotechnological tractability [56]–[59] . Adult C . elegans range from 1–2 mm in length , so it is likely that their motility in liquid culture could be easily measured using a modified RTCA approach [57]–[62] . The range of potential species that may be monitored with this technique is extensive , including agricultural , medical and veterinary pests and pathogens such as ticks , fleas , aphids , mites and dipteran larvae [63]–[65] . In conclusion , we present a novel use of a Real Time Cell Assay device ( xCELLigence ) that can simply and objectively assess the effectiveness of anthelmintic drugs in real time by measuring motility in a high-throughput , reproducible fashion with minimal effort and training required . While originally designed for real time measurement of cell growth , the device is amenable to high throughput screening of a range of developmental stages of different human and livestock helminth parasites . This method is envisaged to be applicable for the majority of helminth species and developmental stages where egg hatch assays or motility is accepted as a measure of worm viability . We predict that the method could be applied to other large pathogens or pests that can survive and be motile in liquid culture in a 96 well plate ( or smaller ) . Moreover , new models of the xCELLigence are soon to be released by Roche Inc , displaying improved sensitivity and increased scale-up potential . The widespread use of this device to screen for new therapeutics or emerging drug resistance will be an invaluable asset in the fight against the wide range of biomedical and veterinary helminths that plague our planet . | Parasitic worms cause untold morbidity and mortality on billions of people and livestock . Drugs are available but resistance is problematic in livestock parasites and is a looming threat for human helminths . Currently , new drug discovery and resistance monitoring is hindered as drug efficacy is assessed by observing motility or development of parasites using laborious , subjective , low-throughput methods evaluated by eye using microscopy . Here we describe a novel application for a cell monitoring device ( xCELLigence ) that can simply and objectively assess real time anti-parasite efficacy of drugs on eggs , larvae and adults in a fully automated , label-free , high-throughput fashion . This technique overcomes the current low-throughput bottleneck in anthelmintic drug development and resistance detection pipelines . The widespread use of this device to screen for new therapeutics or emerging drug resistance will be an invaluable asset in the fight against human , animal and plant parasitic helminths and other pathogens that plague our planet . | [
"Abstract",
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] | 2010 | A Novel High Throughput Assay for Anthelmintic Drug Screening and Resistance Diagnosis by Real-Time Monitoring of Parasite Motility |
The World Health Organization ( WHO ) currently recommends height or age-based dosing as alternatives to weight-based dosing for mass drug administration lymphatic filariasis ( LF ) elimination programs . The goals of our study were to compare these alternative dosing strategies to weight-based dosing and to develop and evaluate new height-based dosing pole scenarios . Age , height and weight data were collected from >26 , 000 individuals in five countries during a cluster randomized LF clinical trial . Weight-based dosing for diethylcarbamazine ( DEC; 6 mg/kg ) and ivermectin ( IVM; 200 ug/kg ) with tablet numbers derived from a table of weight intervals was treated as the “gold standard” for this study . Following WHO recommended age-based dosing of DEC and height-based dosing of IVM would have resulted in 32% and 27% of individuals receiving treatment doses below those recommended by weight-based dosing for DEC and IVM , respectively . Underdosing would have been especially common in adult males , who tend to have the highest LF prevalence in many endemic areas . We used a 3-step modeling approach to develop and evaluate new dosing pole cutoffs . First , we analyzed the clinical trial data using quantile regression to predict weight from height . We then used weight predictions to develop new dosing pole cutoff values . Finally , we compared different dosing pole cutoffs and age and height-based WHO dosing recommendations to weight-based dosing . We considered hundreds of scenarios including country- and sex-specific dosing poles . A simple dosing pole with a 6-tablet maximum for both DEC and IVM reduced the underdosing rate by 30% and 21% , respectively , and was nearly as effective as more complex pole combinations for reducing underdosing . Using a novel modeling approach , we developed a simple dosing pole that would markedly reduce underdosing for DEC and IVM in MDA programs compared to current WHO recommended height or age-based dosing .
Lymphatic filariasis ( LF ) is a disabling mosquito-borne parasitic disease that affects some 68 million people globally [1]; the WHO estimated more than 880 million people in 51 countries remained at risk for LF in 2017 [2] . In the year 2000 , the World Health Organization ( WHO ) implemented a strategic plan ( Global Programme to Eliminate Lymphatic Filariasis [GPELF] ) to eliminate LF as a public health problem by 2020 [3 , 4] . As part of GPELF , the WHO recommends using two-drug treatment combinations ( diethylcarbamazine [DEC] + albendazole or ivermectin [IVM] + albendazole ) in mass drug administration ( MDA ) programs that dose both “at risk” and infected individuals in LF-endemic areas [3 , 4] . Recent studies have shown that a triple-drug treatment combination ( IVM + DEC + albendazole , IDA ) is more effective [5] and as safe [6] as a standard two-drug LF treatment ( DEC + albendazole , DA ) . This has resulted in an updated WHO policy to include IDA as an option for LF treatment in certain settings [7] which will help to more rapidly achieve the GPELF goal of eliminating LF as a global public health problem . Key components of GPELF include the use of MDA to deliver treatment to infected people and to reduce parasite transmission by reducing the reservoir of parasites required for mosquito transmission . The WHO recommends weight-based dosing for IVM and DEC [3 , 4] . However , this is often not possible in remote , resource-limited areas . Consequently , when weight-based dosing is not feasible , the WHO has recommended alternatives such as the use of height-based dosing poles for IVM and age-based dosing for DEC . The current height-based IVM dosing pole recommended by the WHO was developed in the early 1990s [8–10]; it was originally employed in Nigerian communities based on 150 μg/kg and has a dosing range of 3 to 12 mg for four different height groupings ( 90 to 119 cm , 120 to 140 cm , 141 to 158 cm , and > 158 cm ) . The WHO age-based dosing for DEC ranges from 100 to 300 mg across three different age groupings ( 2 to 5 years , 6 to 15 years , > 15 years ) [9] . Although height and age-based dosing are recommended by the WHO for LF MDA programs , the maximum recommended doses using these methods are lower than those indicated by weight-based dosing ( 6 mg/kg for DEC and 200 μg/kg for IVM ) . Furthermore , having separate dosing methods for DEC and IVM increases the complexity and challenge of implementing the new 3-drug treatment for LF MDAs , and an alternative based on a single height pole for both DEC and IVM would increase the efficiency and feasibility of administering this new treatment option . The primary objectives of our study were to: ( 1 ) compare the WHO’s height- ( IVM ) and age- ( DEC ) based dosing recommendations for MDA programs to gold-standard weight-based dosing with data from a variety of LF endemic areas; ( 2 ) to use field data to develop and evaluate alternative height-based dosing poles for IVM and DEC; and ( 3 ) determine whether a single height-based dosing pole can be used to administer both IVM and DEC with the aim of reducing underdosing compared to current WHO recommended methods . To address these objectives , we analyzed height and weight data collected as part of a large LF drug-safety clinical trial [6] .
The study protocols were reviewed and approved by independent Federal-Wide Assurance ( FWA ) registered ethical review boards in each country and at institutions of research partners who participated in the studies . The de-identified data used in this study were limited to gender , height , weight , age and country . The data were collected during a community-based safety study of MDA for LF that enrolled more than 26 , 000 participants in 5 countries ( Haiti , India , Indonesia , Fiji , and Papua New Guinea ) . A 21 CFR Part 11 compliant electronic data capture system allowed deidentified data to be entered directly into a hand-held tablet via a mobile data management solution ( ‘App’ ) called CliniTrial ( CliniOps , Fremont , CA ) . Data were synchronized regularly over the internet from all study sites through a secured Amazon Virtual Private Cloud server and compiled into one complete dataset . Validation checks and automated alert checks were programmed into the electronic data capture system to maintain a high level of data quality at the points of entry . The current study included participants that met the inclusion/exclusion criteria and received treatment ( IDA or DA ) in the LF clinical trial [6] , and were ≥ 90 cm in height . Age , sex , country , height and weight were the primary variables of interest in the present study . Our modeling process involved three steps ( Fig 1 ) . The first step was to use quantile regression to predict weight based on height . Because of the myriad factors that influence weight as a person ages , weight and height become increasingly decoupled as individuals age . Quantile regression is a statistical approach that enables the data to be modeled across a range of quantiles [11 , 12] . Quantiles correspond to the proportion of observations below a threshold . For example , in the context of quantile regression , the 0 . 25 quantile corresponds to the point at which ~25% of the observations fall below the regression line and ~75% of the observations are above the regression line . For our height-weight quantile regressions , the estimates for lower quantiles emphasize the prediction of lighter individuals and estimates for higher quantiles emphasize predictions towards heavier individuals . This approach allowed us to create and evaluate many different dosing pole scenarios ( “dosing poles” ) and choose the pole ( s ) that best meets the study objectives . In our quantile-regression models we employed a range of quantiles ( 0 . 10 to 0 . 90 at intervals of 0 . 05 for a total of 17 quantiles ) to ensure that we captured a breadth of relationships between height and weight . In addition to Global models ( no stratification of data , 1 model for each of the 17 quantiles ) , we performed analyses for multiple strata to determine whether predictions improved when data were stratified by Country ( 5 countries x 17 quantiles ) , Sex ( 2 sexes x 17 quantiles ) , or Country x Sex ( 5 countries x 2 sexes x 17 quantiles ) , resulting in a total of 306 quantile-regression models . Weight outcomes were log transformed prior to analysis and model predictions were then back-transformed ( antilog [model prediction] ) to obtain predicted weights on the original scale of the data . This allowed us to capture the nonlinear association between height and weight . Likelihood-ratio test statistics were used to obtain P-values . P-values < 0 . 05 were considered significant . All quantile-regression analyses were conducted using PROC QUANTREG in SAS version 9 . 4 ( SAS Institute Inc . , Cary , NC ) . The second step was to use predicted weights from our step 1 models to create height-interval dosing poles . Our “gold standard” ( hereafter recommended dosage ) for weight-based dosing is based on the WHO GPELF recommendations and modified from the LF drug-safety clinical trial [6] so that the weight midpoints for the different dosages provided 6 mg/kg for DEC and 200 μg/kg for IVM ( Table 1 ) . The quantile-regression weight predictions were converted into tablet numbers using the weight ranges in Table 1 . The predicted number of tablets for a given height were then operationalized as “full-dose” DOLF dosing poles . The third step was to assess how well the DOLF dosing pole predictions ( step 2 ) , the WHO age-based DEC dosing [9] , and the WHO height-based ( dosing pole ) IVM dosing [8] corresponded to the recommended weight-based dosing ( Table 1 ) using the observed weights from the LF clinical trial dataset . We also created two “hybrid” dosing poles that combine criteria from the WHO IVM dosing pole and the full-dose DOLF ( IVM ) dosing pole providing in a single pole that can be used for both IVM and DEC dosing . The hybrid poles are identical to the full-dose DOLF IVM dosing poles , except the hybrid poles included a maximum number of 4 ( Hybrid 4 ) or 6 ( Hybrid 6 ) tablets , and all participants ≥ 90 cm receive at least 1 tablet ( participants < 90 cm were excluded from the dataset prior to analysis ) . Using a single pole for DEC and IVM and administering a small number of tablets that constitutes an adequate dose is desirable from both an implementation standpoint ( individuals may be more likely to adhere to treatment with fewer tablets and MDA is simplified by use of a single dosing pole ) and from a cost perspective . If the hybrid dosing poles perform similarly to the full-dose poles , then the hybrid poles would be the preferred option . The different dosing options ( WHO , and DOLF full-dose and hybrid poles ) were assessed by estimating the percentage of subjects that would have received below ( BRD ) , above ( ARD ) , or the recommended weight-based dosage .
Height and weight data from 26 , 821 individuals were included in this analysis . Sex of participants was balanced within and among countries with the percentage of males ranging from 47% to 53% . There was a 12-year range in median age , with Haiti having the youngest participants ( age: median [IQR] = 18 [11 , 30] years; 51 . 6% adults ) and Fiji having the oldest participants ( age: median [IQR] = 30 [12 , 49] years; 62% adults ) . There was substantial variability in height and weight across the different countries . Participants in Indonesia had the lowest mean height ( 144 . 5 cm ) and weight ( 38 . 7 kg ) , and participants in Fiji had the greatest mean height ( 159 . 8 cm ) and weight ( 69 . 5 kg ) ( Table 2 ) . Histograms that provide a graphical summary of the distributions for the variables in Table 1 are included as a supplement ( S1 Fig ) . Very different dosing recommendations were obtained with different dosing algorithms ( Fig 2 ) . The WHO dosing pole for IVM agreed with the weight-based dosing in about 56% of participants; it resulted in below the recommended dosage ( BRD ) for 27% of the participants and above the recommended dosage ( ARD ) for 17% of the participants . Both BRD and ARD were more frequent in people with heights > 140 cm . Dosing discrepancies were more frequent with age-based dosing for DEC; it agreed with weight-based dosing in 47% of participants , with ARD and BRD rates of 21% and 32% , respectively . The percentage of participants receiving ARD was greatest for the 6 to 15-year age group and the percentage receiving BRD was greatest for people older than 15 years . When the analysis was restricted to adult ( ≥ 18 years ) males , BRD percentages were 39% for IVM and 54% for DEC . The quantile regression analyses revealed substantial heterogeneity in model predictions across quantiles . For all 306 quantile-regression models , the slope estimates showed significant positive associations between height and weight , regardless of quantile . Slope estimates and/or intercept estimates generally increased with quantile indicating a greater predicted weight for a given height in higher quantiles ( S1 Table ) . Plots of the model predictions from the Global models ( without stratification by country , sex , or age ) showed that differences between model predictions increased with height , with the largest differences for the 10th and 90th quantile models for taller participants ( Fig 3 ) . For example , at a height of 95 cm the model predictions were relatively close for the 10th and 90th quantiles with predicted weights of 11 . 6 kg and 15 . 3 kg , respectively . However , at a height of 180 cm , the 10th quantile model predicted weight of 64 . 7 kg , while the 90th quantile model predicted a weight of 119 . 8 kg . The country-specific models revealed marked variability in height-weight relationships between countries . Fiji and Haiti generally had the highest predicted weights for a given height , and Indonesia had the lowest , with India and PNG falling between these extremes ( Fig 4 ) . Weight prediction differences between countries increased both with subject height as well as quantile . Country-specific height-weight slope estimates indicated that for all quantiles Fiji weight predictions had the largest increase in predicted weight with height ( S2 Fig ) . When we stratified the data by sex , we found that females were heavier for a given height than males , as reflected in both the parameter estimates and the model predictions . Similar to the other strata , the differences between the quantile-regression predictions increased with height , and the predicted weight differences by sex were greater in larger quantiles ( Fig 5 ) . Sex-specific slope estimates indicated that females had steeper slopes than males for all quantiles ( S3 Fig ) . Models stratified by both country and sex revealed some variability between sexes within a country , but generally the difference was small relative to inter-country differences ( S1 Table ) . Results from our weight predictions were used to make dosing poles based on observed subject heights . Compared to the WHO height-based IVM dosing pole and the WHO age-based DEC dosing , the “full dose” DOLF dosing poles had a greater range of dosages ( 0–6 tablets for DEC and 0–7 tablets for IVM ) . Results from our Global models ( combined data from all countries ) revealed that for both DEC and IVM , the minimum height required for a subject to receive any specific number of tablets was consistently lower for the higher quantile models ( Fig 6 ) . For example , at the 25th quantile the minimum height to receive a single tablet of IVM was 104 cm , whereas for the 90th quantile the minimum height to receive a single tablet of IVM was only 93 cm . For the WHO IVM dosing pole , the minimum height required to receive a specific dosage ( ≤ 4 tablets ) was lower than the DOLF models for smaller quantiles ( 0 . 25 and 0 . 50 ) and higher for larger quantiles ( 0 . 75 and 0 . 90 ) except for “one-tablet” . Results from the hybrid poles that can be used for dosing DEC or IVM with either a 4-tablet maximum ( Hybrid 4 ) or a 6-tablet maximum ( Hybrid 6 ) showed that , in contrast to the full-dose poles , all participants 90 cm or greater would receive at least one tablet . Furthermore , for the highest quantile model ( 0 . 90 ) there was a much wider range of participants that would receive four tablets or more ( 147–200 cm ) as compared to the WHO IVM pole ( 159–200 ) . Dosing poles varied somewhat according to strata , with the most dramatic changes attributed to Fiji , which was the country with the lowest tablet thresholds of any of the dosing poles ( S4 Fig ) . Application of the model-based dosing poles to the DOLF dataset revealed a marked improvement in dosing compared to the current WHO recommendations . Using dosing poles from the stratified analyses ( Country , Sex , Country x Sex ) produced only marginal improvements compared to unstratified Global models with regards to dosing ( See S5 Fig ) . Therefore , we decided to focus on results from the Global models here . For all models there was an inverse relationship between the percentage of participants above the recommended weight dosage ( ARD ) and below the recommended weight dosage ( BRD ) : % BRD declined with quantile reaching a minimum at quantile 0 . 90 whereas the minimum % ARD was at quantile 0 . 10 ( Fig 7 ) . The Global DOLF models resulted in lower % BRD values for quantiles ≥ 0 . 50 compared to WHO height-based IVM dosing , and greater than quantile 0 . 35 compared to the WHO age-based DEC dosing . The percentage receiving recommended dosing for the DOLF IVM models were approximately equal to the percentage receiving recommended dosing for the WHO-IVM dosing between quantiles 0 . 3 and 0 . 6 . For DEC , the percentage receiving recommended dosing for the DOLF model exceeded the percentage receiving recommended dosing for the WHO-DEC dose between quantiles 0 . 15 and 0 . 70 . The dosing pole that minimized the percentage receiving BRD occurred at the 0 . 90 quantile , with 5% or less of participants BRD for both IVM and DEC resulting in 22% and 27% improvements over the existing WHO dosing methods , respectively . This dosing pole resulted in 69% ( IVM ) and 64% ( DEC ) participants ARD with only 5 . 8% of participants receiving more than two tablets ARD for IVM , and only 2 . 5% receiving more than two tablets ARD for DEC . Importantly , the 0 . 90 quantile DOLF dosing poles dramatically reduced BRD for adult males , with estimates of the percentage receiving BRD less than 3% for IVM and DEC compared to 39% and 54% underdosing for the WHO IVM height pole and WHO DEC age-based dosing , respectively . The improvement ( compared to WHO ) in the percentage of participants receiving BRD for IVM with 4-tablet Global hybrid dosing poles was lower than that obtained with the full-dose DOLF IVM dosing pole . For DEC , the BRD percentage fell below the WHO DEC BRD at lower quantiles than the full-dose DOLF DEC dosing pole ( Fig 8 ) . The DEC 4-tablet hybrid pole performed better than the full-tablet pole ( with respect to BRD ) for many of the quantiles because we applied the DOLF IVM dosing pole to DEC which has lower weight thresholds for different dosing levels ( see Table 1 and Fig 6 ) . For IVM the hybrid 0 . 90 quantile dosing pole had 11% lower BRD compared to the WHO IVM pole , and for DEC the 0 . 90 quantile dosing pole had 22% lower BRD compared to the WHO age-based dosing . Similar to the 4-tablet hybrid pole , the 6-tablet hybrid percentage receiving BRD for DEC fell below the WHO BRD at small quantiles , and the percentage BRD was the lowest of any of the dosing poles based on Global models—reaching as low as 1 . 9% ( Fig 9 ) . The trade-off is that for the 0 . 90 quantile dosing pole , 80% of individuals were ARD for DEC with about 7 . 1% receiving three or more tablets above the recommended dosage . For IVM , the 6-tablet hybrid dosing pole at the 0 . 9 quantile performed similarly to the full-dose IVM pole with 6% of the participants BRD , 27% recommended , and 67% ARD . Estimates of the percentage of participants BRD from the Global dosing poles applied to specific countries indicated that at larger quantiles the DOLF dosing poles generally improved upon the current WHO age and height-based dosing ( Fig 10 , inset and lines ) . In India , Indonesia , and PNG the 4 and 6-tablet Global models % BRD estimates were approximately equal to the full-dose dosing poles for IVM . However , both hybrid poles resulted in lower estimates of the percentage receiving BRD as compared to the full-dose poles for DEC over all quantiles . In Haiti , the 4-tablet hybrid dosing pole had poorer performance for IVM than the full-dose and 6-tablet hybrid dosing poles for most quantiles , and the 4-tablet hybrid pole performed better than the full-dose DEC poles in all but the most extreme ( >0 . 70 ) quantiles . The 6-tablet hybrid pole outperformed the 4-tablet and full-dose poles for DEC across all quantiles in Haiti . In Fiji , the full-dose poles for DEC and IVM performed much better than the 4-tablet hybrid poles , especially for larger quantiles . The 6-tablet hybrid poles outperformed the full-dose and 4-tablet hybrid poles for DEC , and the estimates of the percentage receiving BRD for the 6-tablet hybrid pole were similar to the full-dose poles for IVM . We assessed whether use of the current WHO IVM dosing pole would have improved DEC dosing compared to age-based dosing . Our results indicated that the current WHO IVM pole would have resulted in 17% fewer participants receiving BRD compared to age-based dosing ( 15% vs 32% , Table 3 ) . The consequence for that improvement was a 12% increase in ARD ( 33% vs 21% , Table 3 ) . However , the WHO IVM pole did not perform as well as the Hybrid 4 and the Hybrid 6 DOLF dosing poles ( 0 . 90 quantile models ) which had 22% and 30% fewer participants receiving BRD than age-based dosing , respectively . As indicated previously for IVM , the Hybrid 4 and Hybrid 6 poles reduce the percentage of participants receiving BRD compared to the WHO IVM pole by 11% and 21% , respectively ( Table 3 ) .
Current WHO alternatives to weight-based dosing for mass treatment of LF are suboptimal . We have presented a modeling approach that offers an improved dosing method for administering IVM and DEC to LF-endemic populations . Our recommendation for mass treatment of LF is that a single 6-tablet maximum dosing pole from the 0 . 90 quantile should be used in all contexts . Areas with smaller individuals ( e . g . , in this study India and Indonesia ) may be able to employ the 4-tablet dosing pole without appreciable increases underdosing . Results from our modeling effort go beyond recommending a single dosing pole solution for LF treatment . Users of our models can take into account a variety of competing goals and objectives to choose the dosing pole ( s ) that best corresponds to their setting . Improved dosing may enhance the efficacy of MDA and accelerate LF elimination . | Lymphatic filariasis is a debilitating parasitic disease that affects over 65 million people globally . To eliminate this disease the World Health Organization ( WHO ) recommends treating entire communities with drug combinations that include ivermectin and diethylcarbamazine . Ideally , the amount of drug administered is determined by weight; however , obtaining accurate weight measurements in remote , resource limited areas is oftentimes not feasible . The alternatives currently recommended by the WHO based on height ( ivermectin ) and age ( diethylcarbamazine ) have maximal doses below that recommended by weight-based dosing . In this study we use statistical models , based on data from a large ( 5-country , >26 , 000 individuals ) lymphatic filariasis clinical trial , to develop model-based dosing poles and compare dosing based on our dosing models to WHO recommendations . Our results showed that the WHO methods would have resulted in 32% ( diethylcarbamazine ) and 27% ( ivermectin ) of individuals in the clinical trial dataset to receive below the recommended weight-based dosage . Dosing poles based on our statistical models showed that our dosing pole would markedly reduce underdosing with 2% and 6% receiving below the recommended dosage for diethylcarbamazine and ivermectin , respectively . The dosing pole we propose has the potential to dramatically improve dosing and facilitate the elimination of lymphatic filariasis globally . | [
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] | 2019 | Dosing pole recommendations for lymphatic filariasis elimination: A height-weight quantile regression modeling approach |
A model-based gating strategy is developed for sorting cells and analyzing populations of single cells . The strategy , named CCAST , for Clustering , Classification and Sorting Tree , identifies a gating strategy for isolating homogeneous subpopulations from a heterogeneous population of single cells using a data-derived decision tree representation that can be applied to cell sorting . Because CCAST does not rely on expert knowledge , it removes human bias and variability when determining the gating strategy . It combines any clustering algorithm with silhouette measures to identify underlying homogeneous subpopulations , then applies recursive partitioning techniques to generate a decision tree that defines the gating strategy . CCAST produces an optimal strategy for cell sorting by automating the selection of gating markers , the corresponding gating thresholds and gating sequence; all of these parameters are typically manually defined . Even though CCAST is optimized for cell sorting , it can be applied for the identification and analysis of homogeneous subpopulations among heterogeneous single cell data . We apply CCAST on single cell data from both breast cancer cell lines and normal human bone marrow . On the SUM159 breast cancer cell line data , CCAST indicates at least five distinct cell states based on two surface markers ( CD24 and EPCAM ) and provides a gating sorting strategy that produces more homogeneous subpopulations than previously reported . When applied to normal bone marrow data , CCAST reveals an efficient strategy for gating T-cells without prior knowledge of the major T-cell subtypes and the markers that best define them . On the normal bone marrow data , CCAST also reveals two major mature B-cell subtypes , namely CD123+ and CD123- cells , which were not revealed by manual gating but show distinct intracellular signaling responses . More generally , the CCAST framework could be used on other biological and non-biological high dimensional data types that are mixtures of unknown homogeneous subpopulations .
Understanding cancer heterogeneity is increasingly being regarded as critical in understanding cancer progression and overcoming therapeutic resistance [1]–[4] . Different types of heterogeneity are commonly observed among the cells composing a single tumor , including genetic [5] , [6] , epigenetic [7] , and phenotypic heterogeneity [3] , [4] . Although technological challenges have limited our ability to fully characterize intra-tumor heterogeneity , in recent years characterizing heterogeneous populations of cells at the single-cell level using multidimensional fluorescence and mass flow cytometric data , combined with novel computational tools , has greatly improved our understanding of the extent of cellular heterogeneity [8] , [9] . Moreover , by sorting out homogeneous subpopulations , researchers can measure and compare genomic and other functional properties of different subpopulations . However , in spite the high-throughput nature of these single cell measurements , current methods for sorting specific cell subpopulations rely on a low dimensional , often user-defined , process known as gating . Gating on a fluorescence-activated cell sorting ( FACS ) machine commonly refers to a manual process , performed by sequentially selecting regions from bivariate graphs that depict the expression of two markers at a time across all the cells . The gating strategy often relies on an expert's assessment of the choice of gating markers , the order of gating and cut points to identify each gated region; this assessment is often based on a subjective analysis using packages such as flowJo and FlowCore [10] . It is well documented that minor differences in gating strategy can lead to significantly different quantitative conclusions [11] , [12] . We present a gating strategy that is optimized for cell sorting . Because our gating strategy is data derived , we argue that is optimal compared to manually-derived gating strategy which can be biased and highly variable . In our work , we make a distinction between gating algorithms that are optimized for sorting single cells versus analyzing a heterogeneous population of single cell data . Even though our gating strategy is optimized for cell sorting , it also has value when used in analysis of population data at the single cell level . When analyzing a population of single cells , several gating algorithms have been developed to reduce the technical , biological and human sources of variation involved in identifying and analyzing clusters of similar cell subpopulations [8] , [9] , [13]–[17] . Bashashati and Brinkman provide a comprehensive overview of analysis tools for flow cytometry ( FCM ) data [18] . More recently , the FlowCAP-II project [12] compared the accuracy and reproducibility across several gating algorithms in terms of identifying cell clusters . All gating algorithms , including ours , have some form of a clustering algorithm , which is used to identify homogeneous subpopulations , as a major component . Many unsupervised clustering algorithms take into account the uncertainty in cluster assignments by modeling the data as mixtures of parametric distributions [18] . Although parametric mixture models have been developed to analyze FCM data [14] , computational , as well as estimation errors , in clustering could still arise from outliers and skewness in the data which may not reflect the underlying assumptions of the parametric model . As an alternative , we propose a modified version of the non-parametric multivariate mixture modeling approach by Benaglia et al . [19] for clustering FCM data , where our modification includes the use of silhouette measures . This clustering algorithm handles uncertainty regarding to which cluster an event should be assigned as well as the uncertainty in the number of underlying cell states in the heterogeneous parent population and makes little or no prior assumptions on the underlying model structure . In addition , we implement an alternative clustering algorithm , namely hierarchical clustering [20] , to show that the results from our gating strategy are independent of the particular clustering method used . The goal of our study is not to provide an optimal clustering strategy , but instead to provide an optimal gating strategy for sorting homogeneous cell subpopulations given any reasonable clustering algorithm . A commonly neglected area in studying populations of single cells is identifying an optimal gating strategy for cell sorting . Sorting cells for downstream analysis relies not only on the identifying the clusters but also on the gating strategy , which is defined by the gating markers , thresholds and sequence . For manual gating at the FACS machine , typical gating strategies are organized like a family tree . For example , from mature bone marrow cells , lymphocytes are gated from the parent cells and from that gate , T-cells or B-cells are gated , and from those gates , specific T-cell and B-cell types are gated [9] . In particular , sorting out T-cells is equivalent to isolating a CD4+/CD8+ population; the user would first isolate the lymphocytes , then derive the CD3+ cells and from there , would draw a gate around the CD4 positive and CD8 positive subpopulations . This approach assumes prior knowledge of the underlying set of markers that define cell types , the gating hierarchy and relative boundaries for isolating pure cell subpopulations of interest . Selecting these parameters based solely on literature and human perspective introduces bias and variability and could result in contamination among the cell subpopulations . We make this process data-driven and fully automated by applying a recursive partitioning technique that generates a decision tree representing a reproducible gating strategy for all subpopulations of interest . Recognizing the current reliance on human perspective and intuition in manual gating , Ray and Pyne [17] recently developed a gating framework which emulates the human perspective in FCM data analysis based on a mathematical map of the high dimensional data landscape . They propose flexible , sample-specific templates for extracting features of interest , which may have unusual shapes and distributions . An alternative approach by Lee et al [21] uses transfer learning technique combined with the low-density separation principle; this approach transfers expert knowledge on training FCM data sets to a new data . A more recent study by Aghaeepour et al . [22] developed a supervised learning computational framework that automatically reveals cell subsets that correlate strongly with clinical outcome and identifies their relevant set of markers for gating . In a follow-up study , Aghaeepour et al . [22] developed a computational tool , RchOptimyx [23] , that uses dynamic programming and optimization techniques from graph theory to construct a cellular hierarchy , providing a gating strategy to identify target populations to a desired level of purity . One might argue that our work is most similar to RchOptimyx . However , as will be shown later , RchyOptimyx provides multiple approaches for gating a specific subpopulation , whereas our approach aims to find a single , optimal gating strategy in a fully automated manner without relying on qualitative judgement . We present an algorithm , named CCAST for Clustering , Classification and Sorting Tree , to identify and sort homogeneous subpopulations from a heterogeneous parent population using a decision tree representation for a gating strategy that can be used to sort for homogeneous cell subpopulations . The gating strategy derived from CCAST is data-driven and fully automated and it does not rely on expert knowledge . While CCAST is optimized for cell sorting , CCAST also has value when applied to data analysis by filtering and retraining the decision tree to produce more homogeneous subpopulations . In addition , when used for data analysis , CCAST may identify new subpopulations among the initial clusters . We apply CCAST on populations of single cell measurements made on breast cancer and normal human bone marrow . On the breast cancer SUM159 cell line , CCAST reveals at least 5 distinct cell states based on two surface markers ( CD24 and EPCAM ) . When applied to normal bone marrow data , CCAST reveals an efficient strategy for gating T-cells . In addition , CCAST reveals two new mature B-cell subtypes , which were not found by manual gating but show distinct intracellular signaling behaviors .
To illustrate the basic properties of CCAST , we applied it to a simulated dataset of 850 single cells comprised of a mixture of 5 cell types , as illustrated in Figure 1A . On each single cell , 3 markers are measured; the distributions of marker values for each cell type are summarized in Supplement Table S1 . We sampled 100 , 300 , 150 , 100 , 200 cell vector expression values for each cell type respectively . Figure 1B shows the 3D scatter plot of the cell measurements with the 5 cell types color coded; from this figure , it is not automatically apparent how to optimally sort out these 5 clusters . Figure 1C shows the first CCAST-derived decision tree based on the entire dataset; this tree partitioned the data into 5 clusters as evidenced from the leaf nodes ( 5 , 6 , 9 , 10 and 11 ) of the tree . Nodes 9 , 10 and 11 represents pure subpopulations of clusters 4 , 3 and 5 , respectively; node 8 shows a mixture of clusters 1 and 4; nodes 5 and 6 are dominated by cells from clusters 2 and 1 respectively . After CCAST removed the contaminating cells from the subpopulations that have more than one cluster and re-ran the decision tree algorithm , it generated the final decision tree in Figure 1D . Note also that these subpopulations were gated using only two markers even though 3 markers were measured . Figure 1E shows the application of the final decision tree ( Figure 1D ) on the entire dataset . When this gating strategy was applied to the filtered dataset for downstream analysis , the resulting subpopulations are shown in Figure 1F , represented with bar plots of the markers' expression and labeled by their corresponding cell cluster . Figure 1G shows the application of the gating strategy using the estimated cut-offs on the entire data using hierarchical clustering instead of “npEM” clustering . The similar partitions on the 2D data imply that using different a clustering algorithm results in similar homogeneous subpopulations . We next demonstrate the applicability of CCAST on actual hematopoietic dataset obtained in the study by Bendall et al . [9] . This study analyzed normal bone marrow at the level of single cells using mass cytometry ( MCM ) , which is a recently developed high throughput technology for labeling single cells with metal-chelated antibodies that reduce auto fluorescence effect . An appeal of this particular study is that hematopoietic cells have a well-established set of lineage markers defining their differentiation stages . In this study , unstimulated and stimulated human peripheral blood mononuclear cells ( PBMCs ) from a healthy donor were analyzed using thirteen surface parameters , namely: CD45 , CD45RA , CD19 , CD11b , CD4 , CD8 , CD34 , CD20 , CD33 , CD123 , CD38 , CD90 , and CD3 . In addition , 18 intracellular signaling molecules were measured . The manual gating process and the characterization of the major cell populations are shown in Supplement Figure S5 of [9] . One part of this study focused on a T-cell subset that included naive CD4+ and CD8+ T-cells and mature CD4+ and CD8+ T-cells . The analysis of the induced intracellular signaling responses in these subpopulations , as compared with those of an unstimulated control , relied on a manually-defined gating process . To demonstrate the robustness of CCAST , we consider a subset of the data from the study by Bendall et al . [9] in order to assess both the error and reproducibility of our results in a transparent manner . We focus on a 20 , 000 cell T-cell subpopulation which had been manually gated into 4 subtypes ( see Figure 1 in Bendall et al . [9] for the manual gating scheme ) . Here we pool this manually gated T-cell data , then blind the data by removing all prior knowledge of cell types or marker labels . We then randomly separate this data into a training and test set of 10 , 000 cells each . Pairwise scatter plots across all 13 markers , unlabeled , are shown in Figure 2 . We apply CCAST on the training data to obtain the final decision tree shown in Figure 3A . These results indicate that the 4 distinct homogeneous cell states can easily be isolated using only 2 of the 13 measured markers , namely Marker 5 and Marker 2 . We next carried out a sensitivity analysis on the decision tree parameters , namely the optimal tree height , denoted as L , and the split points ( see Materials and Methods ) . First we ask the question: what happens to the purity of the homogeneous subgroups if we increase the level of pruning the decision tree , L ? Figure S1A in the supplement document shows exactly the same decision tree as in Figure 3A after increasing L to 3 or more levels . In fact , an L-sensitivity analysis with the simulated 3D data ( described above ) showed that increasing L above 4 produces the expected 5 homogeneous groups but decreases the expected number of cells per group ( results not shown ) . CCAST automatically determines L based on the homogeneity of the subpopulations ( Materials and Methods ) . Next , we performed a bootstrap analysis to assess the range of values for the split points in the optimal decision tree . More specifically , we performed a strata-sampling method with replacement to generate 200 bootstrap datasets of the same sample size as the training data . We ran CCAST on these samples to generate 200 decision trees with different split points . The hierarchy and selected markers for these bootstrap samples were exactly the same as shown in Figure 3A . We show the confidence intervals of the split points by minimum and maximum boundary estimates from our bootstrap analysis ( see range located beside split point estimates in Figure 3A ) . Note that we could not calculate the normal confidence intervals for these split point estimates due to the multi-modal nature of the split point distributions ( Figure 3B ) . To test the performance of CCAST , we applied CCAST on the test data using decision tree derived from the training set ( Figure 3A ) . After data filtering , the final decision tree on the test dataset is shown in Supplemental Figure S1B . Note that all split point estimates lie within the previously estimated confidence intervals shown in Figure 3A . In addition the hierarchy of the tree remains the same . This result demonstrates that CCAST yields robust split point estimates and can produce reproducible results . Finally , we compare the CCAST result before ( Figure 3C ) and after data filtering ( Figure 3D ) . Figure 3C show a 2D scatter plot of the 2 markers that partition the training data into clearly 4 clusters . Although there is a strong evidence of 4 clusters , it is apparent that sorting out the population in the yellow cluster without contaminating green cells would be challenging . Figure 3D show the results after applying CCAST on the training data for data analysis . Notice the pure subpopulations after applying the data-filtering step of CCAST . Hence , in addition to providing a gating strategy , CCAST can also produce a more homogenous representation of the original data for data analysis . Using the T-cell dataset described above , we show that our CCAST-derived gating strategy reproduces the manual gating results in Bendall et al . [9] without relying on expert knowledge . Figure 3E shows that CCAST isolates the 4 distinct T-cell states using only 2 of the 13 measured surfaces markers . These two markers turn out to be CD4 and CD45RA . Figure 3F shows the distribution of the 4 labeled T-cell subtypes based on CD4 and CD45A expression . This result demonstrates that CCAST can identify the 2 of 13 markers that are known to be most relevant to identifying the subtypes of interest without relying on prior knowledge of the subtypes or the markers that are best known to define them . Moreover , for data analysis , CCAST provides more homogeneous subpopulations by filtering out the contaminating cells; an analogous step was not performed in the manually gated analysis [9] . We next applied CCAST only on the manually gated B-cell subpopulations of the Bendall et al . study [9] . In this study , the manually gated B-cell subtypes were: early Pre-B I cells , late Pre-B II cells , immature B-cells , naive mature CD38mid B-cells and mature CD38low B-cells ( see Figure 1 in Bendall et al . [9] ) . To verify the existence of these 5 major B-cell subpopulations , we performed hierarchical clustering , with a cutoff of 5 clusters , on the pooled manually-gated B-cell data , which consisted of about 17 , 000 cells . The silhouette plot in Figure 4A shows strong evidence of 5 clusters . Figure 4B shows the CCAST-derived gating strategy as a decision tree whereby the 5 distinct cell types can be isolated using only 4 , of the 13 , surface markers ( namely CD45 , CD34 , CD38 and CD123 ) with only 3 levels of branching . A cross classification analysis between the CCAST-derived versus the manually gated subtypes is summarized as a heatmap in Figure 4C . Based on Figure 4C , we predict that subpopulations comprising CCAST-derived Cell-types 1 , 4 , 3 , and 5 are predominately immature B , mature CD38low B , Pre B II , and Pre B I cells , respectively . However , there is not a clear one-to-one mapping between the CCAST-derived and manually gated subtypes . In particular , Figure 4C shows strong evidence of a mixture of the mature B-cell subtypes in CCAST Cell-types 2 and 4 . The heatmaps in Figure 4D show evidence of two CCAST-derived distinct cell types corresponding to Cell-types 2 and 4 which were considered as one major population , namely mature CD38low B-cells , by manual gating . Based on surface marker expression , the most striking difference between Cell-types 2 and 4 is the expression of CD123 , a signaling molecule which promotes proliferation and differentiation within the hematopoietic cell lines and is associated with hairy cell leukemia [24] . Figure 5A provides the heatmaps of BCR , IFNa , FTL3 , IL3 , IL7 , and SCF induced intracellular signaling responses in the 5 CCAST-derived B-cell subtypes compared with an unstimulated control . For the purpose of comparing with the results of Bendall et al . [9] signaling induction was calculated using the difference of the mean scaled arcsinh value of unstimulated condition and the mean scaled arcsinh value of a stimulated condition; moreover , only the 13 surface markers were used to predict the cell types in the stimulated conditions using the decision tree from the unstimulated controls . The difference is calculated as a difference of absolute fold changes . BCR , IFNa , IL7 and SCF stimulations induce strong intracellular signaling across the B-cells across the different development stages . The heatmap in Figure 5B provides heatmaps of BCR , IFNa , FTL3 , IL3 , IL7 , and SCF induced intracellular signaling responses for various B-cell subtypes derived from the manual gating in [9] . In the manually gated cells , the strongest signaling differences are limited to mature B-cells particularly associated with P38 and Ki67 . In the CCAST-gated cells , BCR stimulation induces strong differences in PLC-gamma2 signaling , STAT3 , H3 , S6 , CREB; IL7 stimulation alters ERK1/2 and P38 signaling , INFalpha alters STAT3 signaling; and SCF induces changes in P38 signaling . Overall , compared to the manually gated cell types , the CCAST-derived cell types exhibit more differences in stimulated induced signaling , presumably because the CCAST-gated subpopulations are more homogeneous . Finally , as an aside , we note that CCAST produces 7 homogeneously gated subpopulations , 3 of which belong to Cell-type 3 , suggesting that this cell type may be more heterogeneous than suggested by the clustering algorithm . We applied CCAST on about 1 million cells of a SUM159 ( triple negative ) breast cancer cell line . We generated primary FACS analysis on SUM159 cell line for this study based on expression of EPCAM , CD24 and CD44 ( see Materials and Methods ) . To assess the likely number of cell clusters in SUM159 , we ran the “npEM” cluster algorithm , assuming 10 clusters , on a random subsample of about 3 , 000 cells and obtained 5 clusters . Using hierarchical clustering with a cut off of 5 clusters , on the entire SUM159 dataset , CCAST-derived the gating strategy that is shown in Figure 6 . CCAST identified 9 homogenous subpopulations denoted as P1–P9 at the terminal nodes of the tree in Figure 6 . A similar implementation on flowJo showing 9 homogeneous clusters is shown in Supplemental Figure S2 . Figure 7A summarizes the results for the estimation process for all the split point statistics on all the inner nodes of the CCAST decision tree . The root node corresponding to EPCAM shows one global maximum indicating a strong split point . Nodes 3 , 4 , 8 , 9 , 13 and 14 have clear natural maxima indicating optimal splits for the data into clearly 9 subpopulations , each corresponding to 9 single mode histograms in the leaf nodes of the tree . Corresponding barplots for all 9 subpopulations with standard deviation intervals for each marker are shown in Figure 7B . A multivariate Hotelling's T square test showed significant differences between group pairs ( p-value: 0 ) , indicating that these 9 nine subpopulations are statistically different from each other . Interestingly , CCAST splits cluster 1 into the subpopulations P5 , P6 and P8; it also splits cluster 3 into the subpopulations P3 , P4 and P7 . Next we compare the results of the CCAST-derived gating strategy on SUM159 to the manually-defined gating strategy by Gupta et al . [3] on the same cell line . Gupta et al . identified three cell states ( stem-like , basal-like and luminal-like cells ) in SUM159 based on the three markers ( EPCAM , CD24 and CD44 ) . Based on prior knowledge , the stem-like cells were defined as CD44-high , CD24-neg , and EPCAM-low; basal-like cells were defined as CD44-high , CD24-neg and EPCAM-neg; and luminal-like cells were defined as CD44-low , CD24-high , and EPCAM-high . Figure 7C reproduces the Gupta et al . [3] gating strategy on FCM file analyzed in Figure 6 . Gupta et al . strategy first gates the cells based on EPCAM high and low then gated the stem , luminal and basal like subpopulations based solely on CD24 low and high , as shown in Figure 7C . A cross classification table of our 9 subpopulations and the 3 Gutpa et al . cell states ( labeled as stem , luminal and basal like subpopulations ) is shown in Figure 7D . This analysis indicates that the basal-like subpopulation identified by the Gupta et al . gating is a combination of all the CCAST-derived cell states . Furthermore the analysis suggests that a mixture of basal- , stem- and luminal-cell like populations from the Gupta et al . sorting actually correspond to a single CCAST subgroup P9 . This results implies that the cell-type specific analysis provided by Gupta et al . may have reflected the behavior of a single cell type . The Gupta et al . analysis may have been more informative if it were to investigate the distinct subpopulations , such as P1 , P2 , P5 and P7 . Finally , for experimental validation , we applied our CCAST-derived gating strategy on a SUM159 cell line in real time at a FACS machine . Supplemental Figure S3 shows the sorting result from this independent replicate; we are able to recover 5 distinct CCAST-derived subpopulations in real-time . We compare the application of CCAST and RchyOptimyx algorithm on the FCM data of the SUM159 breast cancer cell line . As briefly described in the Introduction , RchyOptimyx provides a gating strategy to identify target populations at various levels of purity [23] . On SUM159 , RchyOptimyx initially generates 27 subpopulations for analysis . Because there is no clinical outcome variable to filter through these 27 predicted phenotypes using the RchyOptimyx algorithm , we selected only the phenotypes that correspond to a combination of CD24 and EPCAM for comparison to CCAST . Recall that CCAST resulted in 9 homogenous subpopulations that can be characterized in terms of these 2 markers alone . Based on use of EPCAM and CD24 alone RchyOptimyx yielded 12 subpopulations that can be targeted by a variety of gating strategies as shown in Supplement Figure S4 . In other words , RchyOptimyx provides several possible paths to a particular subpopulation; in comparison , CCAST offers only a single path to target homogenous subpopulations thereby circumventing any additional interpretation of the output from RchyOptimyx for choosing the gating strategy . The underlying formalism of RchyOptimyx and CCAST are different but a full description of those differences is beyond the scope of this analysis .
We presented a model-based gating strategy , CCAST , for sorting a homogeneous subpopulation from a heterogeneous population of single cells without relying on expert knowledge . To identify a hierarchical 2D gating scheme to sort out homogeneous cells , we propose CCAST as a new approach that addresses three key and often-neglected questions: ( 1 ) How do we select the optimal markers for gating ? ( 2 ) What is the optimal ordering of markers for sorting ? ( 3 ) How do we estimate the marker cut offs for drawing the gates ? The answers to these questions are usually decided in a subjective and bias manner making it very difficult to draw precise conclusions from the resulting sorted data . CCAST is an automated and unbiased strategy , requiring minimal human expertise , for optimizing gating of single cell data . While CCAST is optimized for cell sorting it can be applied for analysis of purified subpopulations among heterogeneous single cell data . In all applications of CCAST in the study , we show that it is possible to characterize and isolate cell types based on a subset of the measured markers . When applied to normal bone marrow data , CCAST reveals an efficient strategy for gating T-cells . CCAST also produced an alternative gating framework for B-cells that produced a new characterization of mature B-cells into CD123+ and CD123- cells . The ability to isolate important cell subpopulations based on limited markers is particularly important since high-throughput cytometry technologies are increasing the number of markers they can measure and one will need new approaches to optimally select important set markers for gating . Hence CCAST not only provides the relevant marker set , optimized gating scheme , and reduces the need for human expertise , it can also reduce the number of antibodies needed for cell sorting . We further motivated the need for CCAST as an automatically-generated gating scheme that does not rely on prior knowledge of cell states or marker relevance on the SUM159 breast cancer cell line . On this cell line Gutpa et al . tested the hypothesis that cancer cells can transition in any of the several possible cell states which exhibit important functional properties [3] . This study aimed to demonstrate the evidence of phenotypic switching between stem , basal and luminal breast cell states , which were defined by CD24 and EPCAM . Establishing strong evidence of cell state transitions would require pure cell states at onset , however , pure sorting is not evident by the manual gating scheme used in the study . In an independent study on the issue of phenotypic switching of cancer cell states , Zapperi and Porta [4] gave an alternative interpretation of the Gupta et al . based on an imperfect marker scenario . The CCAST analysis also infers nonhomogeneous subpopulations under the Gutpa et al . gating strategy and provided an alternative , more homogeneous cell states using an alternative gating strategy based on the same markers , namely CD24 and EPCAM . CCAST identifies at least 5 distinct breast cancer cell states in SUM159 and sorted out these pure cell states automatically ( Figure 6 ) using only two surface markers , namely EPCAM and CD24 . These subpopulations warrant further investigation to validate the notion of phenotypic switching in breast cancer cells as proposed by the Gupta et al . study . CCAST enables the possibility to sort out unique underlying unknown cell states from a heterogeneous parent population in an optimal and unbiased manner using a gating scheme based on a decision tree representation . CCAST identifies homogeneous cell subpopulations using a non-parametric mixture distribution . Although several other clustering algorithms can also be used , CCAST can handle the unknown number of true clusters without the mathematical optimization of a distribution function . Silhouette coefficients are used to optimize the cell subpopulations and a recursive partitioning technique on the complete data given the cell states is used to generate the optimal decision tree for isolating the various subpopulations of interest . The partitioning comes after a marker selection step , which depends on a non-parametric test statistic making it completely data driven . CCAST also provides a confidence interval for marker cut-offs taking into account possible variability in marker distributions . For future methodological improvement on CCAST to both the computational cost and the pruning level L , one might consider multi way splits at each node , instead of using binary splits . Another methodological direction could be to use the confidence intervals to further enhance the decision trees; in particular , methods proposed by Katz et al . [25] can be adapted for CCAST . In summary , CCAST is a fully automated model framework to identify a gating strategy to isolate subpopulations from single cell data with greater homogeneity compared to manual gating procedures . More generally , the CCAST framework could be used on other biological and non-biological high dimensional data types involving a mixture of unknown homogeneous subpopulations .
CCAST formalizes the gating process of single cells as a statistical model and provides a simple unbiased hierarchical 2D gating scheme with the relevant set of marker cut-offs for gating a homogenous cell subpopulation given FCM data . Following , we describe the various steps in the non-parametric model framework of CCAST when applied to single cell data . A typical FCM dataset comprises simultaneous quantitative signal measurements of multiple biomarkers of single cells . These measurements can be fluorescence or atomic mass based . The data are stored in flow cytometry standard ( FCS ) files as a data frame with rows representing the cells or events and the columns corresponding to the markers of interest . Currently , we assume that the data have already been compensated to correct for spectral overlap during data generation and preprocessed using standard preprocessing steps in analysis of FCM data to remove spurious events . The data is then transformed using the recommended Arcsinh function [9] which can handle both positive and negative expression values . Sum 159 cells were cultured in Ham F12 medium supplemented with 5% calf serum , insulin ( 5 ug/ml ) , hydrocortisone , Pen/Strep/L-Glutamine . Cells were grown at 37° C in a 5% CO2 incubator . Stock aliquots of cells were frozen in 10% DMSO and 90% FBS and stored in −80° C liquid nitrogen . The cells were thawed initially into T25 flasks and allowed to expand in culture for two weeks prior to sorting ( expanded into T75 flasks ) . The day of sort , cells were trypsinized , washed with PBS and stained with antibodies specific for the following human cell surface markers: EPCAM ( ESA ) -FITC ( AbD Serotec , MCA1870F ) , CD24-PE ( BD Biosciences ) , CD44-APC ( BD Biosciences ) , CD49f-PerCP/Cy5 . 5 ( Biolegend ) . Roughly 1×107 cells were incubated with antibody ( 20uL antibody per million cells ) for 15 min at room temperature in PBS with 1% BSA . Unbound antibody was washed off and cells were analyzed on a custom Stanford and Cytek upgraded FACScan ( Beckman Center , Stanford ) no more than one hour after staining . Cell sorting was performed on BD Aria II ( Beckman Center , Stanford ) . The raw data is available in supplement Dataset S1 as an FCS file . | Sorting out homogenous subpopulations in a heterogeneous population of single cells enables downstream characterization of specific cell types , such as cell-type specific genomic profiling . This study proposes a data-driven gating strategy , CCAST , for sorting out homogeneous subpopulations from a heterogeneous population of single cells without relying on expert knowledge thereby removing human bias and variability . In a fully automated manner , CCAST identifies the relevant gating markers , gating hierarchy and partitions that isolate homogeneous cell subpopulations . CCAST is optimized for cell sorting but can be applied to the identification and analysis of homogeneous subpopulations . CCAST is shown to identify more homogeneous breast cancer subpopulations in SUM159 compared to prior sorting strategies . When applied to normal bone marrow single cell data , CCAST proposes an efficient strategy for gating out T-cells without relying on expert knowledge; on B-cells , it reveals a new characterization of mature B-cell subtypes not revealed by manual gating . | [
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] | 2014 | CCAST: A Model-Based Gating Strategy to Isolate Homogeneous Subpopulations in a Heterogeneous Population of Single Cells |
Several plant species require microbial associations for survival under different biotic and abiotic stresses . In this study , we show that Enterobacter sp . SA187 , a desert plant endophytic bacterium , enhances yield of the crop plant alfalfa under field conditions as well as growth of the model plant Arabidopsis thaliana in vitro , revealing a high potential of SA187 as a biological solution for improving crop production . Studying the SA187 interaction with Arabidopsis , we uncovered a number of mechanisms related to the beneficial association of SA187 with plants . SA187 colonizes both the surface and inner tissues of Arabidopsis roots and shoots . SA187 induces salt stress tolerance by production of bacterial 2-keto-4-methylthiobutyric acid ( KMBA ) , known to be converted into ethylene . By transcriptomic , genetic and pharmacological analyses , we show that the ethylene signaling pathway , but not plant ethylene production , is required for KMBA-induced plant salt stress tolerance . These results reveal a novel molecular communication process during the beneficial microbe-induced plant stress tolerance .
Abiotic stresses like salinity , drought or heat negatively affect plant growth and yield and belong to the most limiting factors of agriculture worldwide [1 , 2] . For example , salinity , known to affect almost one fourth of arable land globally , is a two-phase stress composed of a rapid osmotic and a slower toxic stress , resulting from Na+ ion accumulation and loss of K+ in photosynthetic tissues [3] . Salt stress reduces the rate of photosynthesis , leading to a decrease of plant growth and crop yield [4] . However , in the context of global climate change and an increasing world population , abiotic stress tolerant crops and sustainable solutions in agriculture are urgently needed to respond to growing food demands [5] . One way to overcome these challenges is to take advantage of plant-interacting microbes [6–8] . Indeed , plants and their rhizosphere host diverse microbial communities , selected from bulk soil [9–11] , and beneficial bacteria , defined as plant growth-promoting bacteria ( PGPB ) , can establish symbiotic associations with plants and promote their growth under optimal growth conditions or in response to biotic and abiotic stresses [12–18] . Direct plant growth-promotion mechanisms include the acquisition of nutrients by nitrogen fixation , phosphate and zinc solubilization , or siderophore production for sequestering iron . The modulation of phytohormone levels , such as auxin , ethylene , cytokinin or gibberellin , also largely contributes to the beneficial properties of PGPB [19–21] . Indirect mechanisms comprise the production of antimicrobial agents against plant pathogenic bacteria or fungi , or inducing systemic resistance against soil-borne pathogens [18 , 22] . Arid regions cover about one quarter of the Earth’s land surface and encompass many of the challenges for increasing agricultural productivity [23] . In contrast to better known dryland farming , desert agriculture can function only when crop plants are irrigated–usually with underground water with various levels of salinity [24] . Those areas face extreme environmental conditions , characterized by high levels of radiation , low rainfall , extreme temperatures , coarse soil which retains very little moisture , as well as low nutrients and typically high natural salinity , which all strongly limit the yield of crops [25] . Although deserts appear to be hardly inhabitable , a wide diversity of organisms has adapted to these extreme conditions . Plants along with their interacting microbial partners have evolved sophisticated mechanisms such as the production of osmoprotectants , reactive oxygen species scavengers or late embryogenesis abundant proteins to monitor the environment and reprogram their metabolism and development [26 , 27] . Therefore , this particular environment is an ideal reservoir to isolate and identify beneficial bacteria enhancing plant tolerance towards environmental stresses such as drought , heat or salinity [7] . To identify and characterize stress tolerance-promoting bacteria that can increase plant tolerance to abiotic stresses and therefore could be used for improving desert agriculture , we previously isolated and sequenced a number of rhizosphere and endophytic bacterial strains from nodules of desert pioneer plants [28–30] . Here , we report that Enterobacter sp . SA187 , an endophytic bacterium isolated from root nodules of the indigenous desert plant Indigofera argentea [31] , significantly increased yield of the agronomically important crop alfalfa ( Medicago sativa ) in field trials under both normal and salt stress conditions , demonstrating that SA187 has a high potential to improve agriculture under desert conditions . To better understand the molecular mechanisms for conveying enhanced stress tolerance of plants , we studied its interaction with Arabidopsis thaliana . SA187 could enhance Arabidopsis tolerance to salt stress , and GFP-labeled SA187 colonized surface and inner tissues of Arabidopsis roots and shoots . Moreover , transcriptome analyses uncovered that SA187-induced plant tolerance to salt stress is due to maintenance of photosynthesis and primary metabolism and a reduction of ABA-mediated stress responses . Using different plant hormone related mutants , ethylene sensing was found to play a primary role in SA187-induced salt stress tolerance . Indeed , Arabidopsis mutants impaired in ethylene perception were compromised in their beneficial response to SA187 , while mutants deficient in ethylene synthesis remained unaffected . Gene expression analysis of SA187 indicated an upregulation of the methionine salvage pathway upon plant colonization , increasing the production of 2-keto-4-methylthiobutyric acid ( KMBA ) , which is known to be converted into ethylene in planta [32] . KMBA alone could mimic the beneficial effects of SA187 on plant salt stress tolerance and 2 , 4-dinitrophenylhydrazine ( DNPH ) , which specifically precipitates KMBA [33] , could abrogate SA187-induced plant stress tolerance . These results unravel a novel communication process during beneficial plant-microbe interactions under stress conditions .
Since SA187 was an outstandingly performing bacterial isolate in a previous screen using Arabidopsis as a model plant [31] , we evaluated the potential agronomic use of SA187 as a biological solution for agriculture . Therefore , we tested the beneficial activity of SA187 on different growth parameters of the crop plant alfalfa ( Medicago sativa ) , which is largely used as animal feed in different regions of the world . Alfalfa seeds were coated with SA187 and tested in parallel with mock-coated seeds at the experimental field station Hada Al-Sham near Jeddah , Saudi Arabia . A randomized complete block design with a split-split plot arrangement with different replicates was used over two subsequent growing seasons ( 2015–2016 and 2016–2017 ) . Using low saline water ( EC = 3 . 12 dS·m-1 ) for irrigation , SA187-inoculated alfalfa plants showed an increase of 16 and 12% of fresh weight and 14 and 17% of dry biomass in the two growing seasons , respectively ( Fig 1A ) . Using high saline water ( EC = 7 . 81 dS·m-1 ) for irrigation , a similar beneficial impact on plant growth was observed over the two growing seasons ( Fig 1B ) . However , the growth parameters in the second season were statistically not significant , most likely due to exceptional rainfall in that period ( S1 Fig ) . We concluded that SA187 can efficiently improve crop productivity under extreme agricultural conditions . To better understand the molecular mechanism by which SA187 confers stress tolerance to plants , we used the genetic model plant A . thaliana and first assessed the capacity of SA187 to affect the early stages of Arabidopsis development under normal conditions ( ½ MS agar medium , 22°C , 16 h of light ) . When compared to mock-inoculated plants , SA187 had no influence on the germination rate of Arabidopsis seeds ( Fig 2A ) , and apart from considerably longer root hairs ( Fig 2B and 2C ) , 5-day-old seedlings showed no morphological changes . Similarly , after transfer onto new ½ MS plates ( S2 Fig ) , no differences between 17-day-old mock- and SA187-inoculated seedlings were recorded , when measuring root length , lateral root density , shoot morphology , or root and shoot fresh and dry weight of seedlings ( Fig 2D–2F ) indicating that SA187 has no significant effect on Arabidopsis development under normal growth conditions . On the other hand , the stress tolerance and growth promoting capacity of SA187 on Arabidopsis was highlighted under salt stress . Five days after germination , SA187- and mock-inoculated seedlings were transferred onto ½ MS agar plates supplemented with 100 mM NaCl ( S2 Fig ) , and the same growth parameters as abobe were evaluated up to 12 days after the transfer to salt plates . SA187-inoculated plants showed stress tolerance promoting activity on salt stress: the shoot and root systems of SA187-inoculated plants were significantly more developed than those of mock-inoculated plants ( Fig 2E and 2F ) . While primary root length was similar between SA187- and mock-inoculated plants ( Fig 2D ) , lateral root density was significantly increased ( Fig 2F ) . Similarly to 5-day-old seedlings , SA187-inoculated plants at this stage had more than twice longer root hairs compared to the mock-inoculated ones under both normal and salt stress conditions ( S3 Fig ) . Moreover , we proved that the beneficial activity of SA187 was largely linked to living bacterial cells as heat-inactivated SA187 cells did not induce any beneficial activity ( S4A Fig ) . Overall , SA187 strongly enhanced Arabidopsis growth of both shoot and root under salt stress conditions , in contrast to normal conditions . The concentration of sodium ( Na+ ) and potassium ( K+ ) ions in shoots is an important parameter for salt stress tolerance [34] . Therefore , the Na+ and K+ contents were determined in Arabidopsis organs in the absence and presence of SA187 . Interestingly , both shoots and roots of SA187-inoculated plants accumulated similar levels of Na+ compared with mock-inoculated plants under normal and salt stress conditions ( Fig 3A and 3D ) . However , increased K+ levels were found in SA187-inoculated plants ( Fig 3B and 3E ) , resulting in significantly reduced shoot and root Na+/K+ ratios under saline conditions ( Fig 3C and 3F ) , which may help the inoculated plants to keep high growth rate . After recognition of the beneficial impact of SA187 on plant physiology , we wanted to characterize the interaction of SA187 with plants in more detail , and find whether SA187 is able to efficiently colonize Arabidopsis as its non-native host . SA187 cells were stably transformed to express GFP ( SA187-GFP ) , which did not affect their beneficial effect on Arabidopsis seedlings ( S4B Fig ) . Confocal microscopy revealed that SA187-GFP colonized both roots and shoots on ½ MS agar plates or in soil ( Fig 4 ) . On vertical ½ MS agar plates , the first colonies ( formed by a small number of cells ) were observed on the root epidermis in the elongation zone , preferentially in grooves between epidermal cell files ( Fig 4A and 4B ) . In the differentiation zone and older root parts , colonies were larger and proportional with the age of the region ( Fig 4C ) . A similar colonization pattern was observed in soil-grown seedlings , however , with a more random distribution of colonies ( Fig 4D ) . SA187-GFP colonies were also often found in cavities around the base of lateral roots ( Fig 4E ) . While it was rare to detect SA187-GFP cells inside root tissues in 5–7 days old seedlings , the apoplast of the root cortex and even of the central cylinder was regularly occupied by small scattered colonies in 3 weeks old seedlings ( Fig 4F ) . Indeed , in our initial plant assays , SA187 could be re-isolated from surface sterilized Arabidopsis roots , indicating that SA187 was proliferating inside root tissues . Inspecting shoots , SA187-GFP colonies were found deep inside the apoplast of hypocotyls , cotyledons and the first true leaves , and in several cases , bacterial cells were directly observed to penetrate through stomata of these organs ( Fig 4G–4I ) . Furthermore , we evaluated colonization of root systems by SA187 ( wild type strain ) under normal and salt conditions . Plants were germinated on ½ MS agar plates containing SA187 wild type strains , transferred to new ½ MS plates with or without 100 mM NaCl after 5 days ( S2 Fig ) , and parts of their root systems grown after the transfer were used for bacterial extraction after 5 more days . Interestingly , quantification based on counting of colony forming units ( CFU ) revealed that roots from salt conditions were twice more colonized than those from normal conditions ( S5 Fig ) , suggesting that in our experimental system plants can probably facilitate their accessibility to colonization by beneficial bacteria under stress conditions . To uncover how salt stress tolerance is achieved in SA187-inoculated Arabidopsis seedlings , we performed RNA-Seq analysis comparing the transcriptome of mock-inoculated to SA187-inoculated plants under non-saline ( Mock , SA187 ) , and salt stress conditions ( Salt , SA187+Salt ) . Compared to “Mock” conditions , 545 , 3113 and 1822 genes were found to be differentially expressed in the “SA187” , “Salt” and “SA187+Salt” samples , respectively ( S1 Table ) . To obtain a global overview , the transcriptome data were organized by hierarchical clustering into 8 groups and analyzed for gene ontology enrichment ( Fig 5 , S2 Table ) . Cluster 1 and 7 comprise the largest sets of differentially expressed genes with 1607 and 744 members , respectively , and consist of salt-stress regulated genes that were unaffected by the SA187 inoculation . Whereas Cluster 1 genes are strongly downregulated under salinity and are involved in water homeostasis , salicylic acid ( SA ) and defense response , those of Cluster 7 are highly upregulated and enriched in genes that are induced in response to water and salt stress or abscisic acid ( ABA ) . A specific effect of SA187 on the transcriptome of plants was found in Clusters 2 , 3 and 4 . Cluster 2 ( 354 genes ) represents genes that are upregulated by SA187 independently of the growth conditions . This cluster is significantly enriched in plant defense genes such as chitin responsive genes but also in ethylene and jasmonic acid ( JA ) signaling ( Fig 5 ) . Importantly , Cluster 3 genes ( 246 ) are strongly downregulated in mock-inoculated plants under salt stress conditions but remain unaltered upon SA187-inoculation . These genes have a role in the primary metabolism , such as photosynthesis , carbon and energy metabolisms . On the contrary , Cluster 4 genes ( 464 ) are enriched in ABA and abiotic stress response and are upregulated in salt-treated plants , but not when the plants were inoculated with SA187 . In summary , these data indicate that SA187 colonization triggers in Arabidopsis the expression of genes involved in defense response as shown by the significant enrichment for chitin responsive genes and ethylene and JA signaling . Moreover , under saline conditions , SA187-inoculated plants release themselves from the impact of abiotic stress ( ABA ) , maintain higher metabolic and photosynthetic activity , and can therefore grow better than mock-inoculated plants . Since our transcriptome analysis indicated possible roles of several hormone pathways in the SA187-induced growth promotion under salt stress , we measured the levels of salicylic acid ( SA ) , jasmonic acid ( JA ) and abscisic acid ( ABA ) in mock- and SA187-inoculated plants . SA187 did not significantly change plant SA levels in the absence or presence of salt ( Fig 6A ) . Plant ABA and JA concentrations remained also unchanged upon SA187 colonization under normal conditions , but their salt-induced accumulation was significantly lower in SA187-inoculated plants ( Fig 6B and 6C ) , indicating a partial attenuation of stress responses in these plants . To assess the level of ethylene in Arabidopsis roots and possibly confirm the activation of the ethylene signaling pathway observed in Cluster 2 , we used the ethylene-dependent pEBF2::GUS reporter [35] . In contrast to mock-inoculated seedlings , the reporter line showed strong GUS activity in root tips upon SA187-inoculation , similar to the treatment with the ethylene precursor aminocyclopropane-1-carboxylic acid ( ACC ) ( Fig 6D ) , indicating the activation of the ethylene signaling pathway . To substantiate the phytohormone quantifications , Arabidopsis hormone deficient or insensitive mutants were analyzed . The JA-receptor coi1-1 mutant [36] , the JA-insensitive jar1-1 mutant [37] , the ABA biosynthesis aba2-1 mutant [38] or the ABA receptor quadruple mutant pyr1-1 pyl1-1 pyl2-1 pyl4-1 ( named here as pyr1/pyl ) [39] maintained the SA187 beneficial activity upon salt stress , indicating that ABA or JA may not play a major role in this interaction ( Fig 7A , S6 Fig ) . However , the ethylene insensitive ein2-1 and ein3-1 mutants [40 , 41] , impaired in ethylene perception , were strongly compromised in the beneficial effect of SA187 , indicating that ethylene sensing could be of importance in SA187-induced tolerance of Arabidopsis to salt stress conditions . This result was confirmed by the up-regulation of the four ethylene-induced genes , ERF106 , ERF018 , RAV1 and SZF1 , upon colonization by SA187 ( Fig 7B ) . Moreover , application of 100 nM ACC during salt stress could largely mimic the beneficial activity of SA187 on plants ( Fig 7C , S7 Fig ) . In contrast , the heptuple ethylene-biosynthesis deficient mutant acs1-1 acs2-1 acs4-1 acs5-2 acs6-1 acs7-1 acs9-1 ( called acs in this study ) still showed full sensitivity to the beneficial activity of SA187 under salt stress ( Fig 7A ) . Additionally , the SA187 beneficial effect was maintained when plants were treated with amino-ethoxy-vinyl glycine ( AVG , 1 μM ) , an ethylene production inhibitor blocking ACC synthesis [42] ( Fig 7D ) . However , when plants were treated with silver nitrate ( AgNO3 , 1 μM ) , which interferes with ethylene perception [42] , SA187-inoculated plants did not exhibit any SA187-induced tolerance to salt stress ( Fig 7D ) . Altogether , these results indicate that the beneficial effect of SA187 may not be mediated by JA perception or the ABA pathway , but rather by the ethylene perception , as it was found to be necessary for SA187-induced salt stress tolerance on Arabidopsis plants . The previous results suggested that ethylene most likely originates from SA187 cells rather than from the canonical plant ACC synthase ( ACS ) pathway . To support the hypothesis that SA187 provides ethylene to promote plant growth , we searched for bacterial genes encoding ACS or ethylene forming enzymes ( EFE ) [43] in the genome of SA187 [31] . No ACS- or EFE-related genes were found in SA187 , which on the other hand , contains a conserved methionine salvage pathway ( also known as 5’-methyl-thioadenosine cycle ) , and one of its components , KMBA , is known to be an ethylene precursor [44] . While SA187 alone did not produce ethylene when grown on synthetic media ( S8 Fig ) , the expression level of most of the genes encoding proteins involved in the methionine salvage pathway were upregulated in SA187 upon plant colonization compared with bacteria incubated for 4h in liquid ½ MS with or without 100 mM NaCl in the absence of plants ( Fig 8A ) . To confirm that KMBA could function as an ethylene precursor during the beneficial plant-microbe interaction , we tested the effect of KMBA on Arabidopsis in comparison to SA187 inoculation . Under salt stress conditions , application of 100 nM KMBA induced a similar beneficial activity on Arabidopsis as SA187 resulting in a similar increase in both root and shoot fresh weight ( Fig 8B , S7 Fig ) . Finally , we took the advantage of 2 , 4-dinitrophenylhydrazine ( DNPH ) , a known interactor of KMBA in vitro that was previously shown to precipitate KMBA produced by Botrytis cinerea and consequently impairs the production of ethylene by photo-oxidation [33] . Here , we could show that when plants were cultivated with 3 μM DNPH , the beneficial impact of SA187 on Arabidopsis growth under salt stress was greatly reduced from 68% to 14% ( Fig 8C and 8D ) , showing the importance of KMBA in mediating SA187-induced plant tolerance to salt stress .
Enterobacter sp . SA187 was previously isolated from the desert pioneer plant Indigofera argentea Burm . f . ( Fabaceae ) [29 , 31] . In this work , we show that this bacterium promotes plant tolerance to salt stress , describing this strain as a stress tolerance-promoting bacterium . Indeed , under field conditions , using SA187 as an inoculum for alfalfa seeds and by monitoring growth parameters and yield over two different agriculture seasons , the inoculated plants showed a clear improvement in yield independently of the water regime applied ( high or low salt stress ) . The data show similar effectiveness of the SA187 inoculations in both years . However , the differences for high and low-saline conditions were reduced during the second year ( Fig 1 ) , which could be explained by the increased rainfall ( S1 Fig ) during the 2nd growing season that may have diluted the salinity effects . We conclude that SA187 can efficiently improve crop productivity under extreme agricultural conditions and could be a simple biological solution to grow plants under extreme adverse conditions . In order to understand the mechanisms underlying the beneficial plant interaction with SA187 , Arabidopsis was used as a model system . SA187 colonizes both surface and inner tissues of Arabidopsis roots and shoots , supporting a functional plant-bacterial interaction ( Fig 4 ) . Colonization of both above- and under-ground organs is in agreement with the observation that leaf and root microbial communities share an important portion of their bacterial species [11] . While the mechanism of entry of SA187 into roots occurs most probably via cracks and/or by active penetration between epidermal cells [45] , we observed that shoots were colonized through stomata , indicating that these apertures represent a major route of entry into plants not only by pathogenic but also by beneficial bacteria . The capacity of SA187 to enhance salt stress tolerance of Arabidopsis was analyzed in detail . While SA187 induced only negligible morphological and physiological changes in plants under non-stress conditions ( with the exception of longer root hairs ) , SA187 significantly enhanced root and shoot growth with increased fresh and dry weight under salt stress ( Fig 2E and 2F ) . In addition , SA187 increased lateral root density , and thus the overall root surface area ( Fig 2F ) under salt stress . Changes in the root architecture have been considered to be beneficial for adaptation to various abiotic stress conditions including salinity [46] , and very likely contribute to the SA187-induced salt tolerance in Arabidopsis . The effect of salinity on plants includes two components: an osmotic component , being the consequence of an altered osmotic pressure due to an increased salt concentration , and a toxic ion effect as a result of the high Na+ concentration in shoots [47 , 48] . The toxic effects of the Na+ accumulation result in premature senescence , leading to a decrease in photosynthesis efficiency and impaired metabolic processes . Na+ also competes with K+ in membrane transport and enzymatic functions , reducing plant growth . Most plant cells possess mechanisms to counteract the harmful effects of Na+ accumulation by retaining K+ and actively excluding Na+ in roots and/or sequestering Na+ in vacuoles in shoots [47–50] . Several studies have shown that an inoculation of commercial crops , such as maize , strawberry and wheat by PGPBs under salt stress results in a decrease of Na+ and an increase of K+ in their shoots and leaves [51–53] . The inoculation of Arabidopsis thaliana and Trifolium repens ( white clover ) by Bacillus subtilis GB03 induced a decrease in the Na+ content in shoots in both species accompanied by an increase or no change in the K+ content [54 , 55] . In our study , we found no differences in Na+ contents in shoots or roots between SA187-inoculated and mock-inoculated plants in response to salt stress . However , K+ ion levels in both roots and , to a lesser extent , shoots increased upon the SA187 inoculation , resulting in reduced Na+/K+ ratios ( Fig 3C and 3F ) , which might contribute to the higher salt tolerance of SA187-inoculated plants [56] . To analyze the interaction of SA187 with Arabidopsis at the molecular level , the transcriptome of Arabidopsis grown under salt and non-stress conditions in the absence or presence of SA187 was compared . The inoculation with SA187 dramatically reprogrammed the gene expression of plants grown either on ½ MS or on ½ MS with 100 mM NaCl . This was highlighted in Clusters 2 , 3 , and 4 of the RNA-Seq analysis ( Fig 5 ) . Cluster 3 genes , mostly related to photosynthesis and primary metabolism , were strongly downregulated under salt stress in mock-inoculated plants , confirming previously published reports which correlated such a downregulation with the inhibition of growth and development under salt stress conditions [57] . These results could therefore explain why SA187-inoculated plants grow better under stress conditions: SA187-inoculated plants only mildly reduce their photosynthetic capacity and maintain a functional metabolism allowing further growth in comparison to mock-inoculated plants . Cluster 4 genes are enriched in ABA-related stress genes and were induced upon salt stress in mock-inoculated plants , but not in SA187-inoculated plants . These results indicate that some salt stress-induced responses , including the enhancement of ABA levels , are dampened by SA187 . However , they do not explain why plants are more salt stress tolerant . Indeed , the ABA biosynthesis aba2-1 mutant or the ABA receptor quadruple mutant pyr1-1 pyl1-1 pyl2-1 pyl4-1 still exhibited a similar growth improvement by SA187 as wild-type plants when exposed to salt stress , indicating that ABA production and signaling are dispensable in the presence of these beneficial bacteria ( Fig 7A ) . Induced salt stress tolerance by SA187 could be elucidated by Cluster 2 , comprising genes specifically induced upon SA187-inoculation . This cluster is significantly enriched for genes involved in defense response to bacterium , and for chitin response . This latter GO term is not surprising in a plant-bacterial system , since pathogen associated molecular patterns ( PAMPs ) such as fungal chitin and bacterial flagellin are inducing a large set of common genes in plants , with more than 60% of overlap [58] . But the most interesting feature lies in the enrichment of the ethylene response pathway . Indeed , SA187 activates the ethylene perception pathway as shown by the qPCR analysis of ethylene-induced genes and by the ethylene reporter pEBF2::GUS ( Figs 6D and 7B ) . Moreover , ACC and KMBA as ethylene precursors largely mimicked the beneficial effect of SA187 on plants under salt stress ( Figs 7C and 8B ) . Finally , the involvement of ethylene was also supported by the observation of much longer root hairs ( Fig 1B and 1C; S3 Fig ) , as ethylene plays an important role in root hair elongation [59 , 60] . Although the role of ethylene in plant abiotic stress tolerance is controversial [61] , several pieces of evidence indicate that this phytohormone is important for plant adaptation to abiotic stresses . For example , the pre-treatment of Arabidopsis seedlings with ACC , or the use of the constitutive ethylene response ( CTR1 ) or the EIN3 gain-of-function mutants were shown to enhance salt stress tolerance [62 , 63] . Furthermore , an ethylene overproduction in the eto1 mutant lead to salinity tolerance due to improved Na+/K+ homeostasis through an RBOHF-dependent regulation of Na+ accumulation [64] . Importantly , ethylene-related Arabidopsis mutants revealed that the beneficial activity of SA187 is to a major extent mediated via the perception of externally produced ethylene . Although the ein2-1 and ein3-1 mutants were compromised in their beneficial response to SA187 , the disruption of the plant ethylene production in the heptuple acs mutant showed the same growth enhancement under salt stress when comparing SA187-inoculated plants to mock-inoculated plants ( Fig 7A ) . This was supported by a parallel pharmacological approach , demonstrating that inhibition of the ACS activity using AVG did not block the stress tolerance promoting activity of SA187 , while blocking the ethylene receptors by AgNO3 compromised the beneficial activity of SA187 on plants under salt stress ( Fig 7D ) . As plants were shown to perceive ethylene even without functional plant ethylene production , we suspect that SA187 could provide plants with ethylene or its precursor . Three main pathways for ethylene biosynthesis have been described in bacteria and other microbes . The mold Dictyostelium mucoroides and fungi Penicillium citrinum produce ethylene from methionine via S-adenosyl-methionine , through the sequential action of ACC synthases and ACC oxidases . S-adenosyl-methionine is first converted to ACC by ACC synthases , which is then oxidized by ACC oxidases to release ethylene and cyanide . The same pathway is well known to be responsible for ethylene biosynthesis in plants , where cyanide is converted to β-cyanoalanine to avoid toxicity [44 , 65] . Microbes can also produce ethylene from α-ketoglutarate and arginine by the action of the ethylene forming enzyme ( EFE ) , which has been found in several microbial species such as Pseudomonas syringae and Penicillium digitatum [44 , 66] . A third pathway has been identified in a variety of bacteria such as Escherichia coli and Cryptococcus albidus , or in fungi like truffle or pathogenic Botrytis cinerea , where ethylene is produced via oxidation of KMBA , an intermediate of the methionine salvage pathway [32 , 33 , 44 , 67] . KMBA can be spontaneously converted to ethylene by photo-oxidation or through the action of peroxidases [33] , which are abundantly present in the plant apoplast [68 , 69] . Based on P-BLAST homology searches , genome analysis of SA187 revealed that neither ACC synthase nor EFE genes are present in SA187 . Instead , SA187 contains the entire methionine salvage pathway , suggesting that KMBA is most likely the precursor of ethylene in SA187 . Interestingly , most of the methionine salvage pathway genes in SA187 are only actively expressed upon colonization of Arabidopsis ( Fig 8A ) . Moreover , the application of KMBA could mimic the beneficial effect of SA187 on plants when subjected to salt stress ( Fig 8B ) . Importantly , the SA187 beneficial activity towards plant was highly reduced when treated with DNPH , known to provoke KMBA precipitation and prevent thus its oxidation and ethylene release [33] . Taken together , the KMBA involvement in abiotic stress tolerance constitutes a novel mechanism in the field of plant-beneficial bacteria interaction . While the induction of the ethylene signaling pathway by PGPB has been reported in several studies to play an important role in the induced systemic resistance in plants [18 , 70 , 71] , PGPB activity in the context of abiotic stress has been commonly attributed rather to a reduction of the plant ethylene level through the activity of bacterial ACC deaminases [53 , 72–74] , or shown to be independent of the ethylene signaling pathway [75 , 76] . Several reports hypothesized the involvement of ethylene signaling in abiotic stress tolerance induced by rhizosphere bacteria , with evidences that were largely based on emissions of unidentified volatiles or by comparison with plant-fungal interactions [32 , 77 , 78] . Recently , it has been reported that the beneficial bacterium Burkholderia phytofirmans PsJN enhanced plant growth through an auxin/ethylene-dependent signaling pathway under optimal conditions , but in contrast to the present study , the authors hypothesized that the plant intrinsic ethylene production was fundamental in that interaction [79] . In conclusion , we provide evidence that the endophytic bacterium Enterobacter sp . SA187 induces salt stress tolerance in Arabidopsis via production of KMBA to activate the ethylene pathway . SA187 enhances plant salt stress tolerance under controlled conditions in the model plant Arabidopsis thaliana and under field conditions in the crop plant alfalfa . These results show the potential use of SA187 for bringing saline agriculture of current crops a step closer to reality .
To inoculate alfalfa ( Medicago sativa var . CUF 101 ) seeds , a slurry was prepared consisting of sterilized peat , a broth culture of SA187 , and sterilized sugar solution ( 10% ) in the ratio 5:4:1 ( w/v/v ) . Subsequently , alfalfa seeds were coated with the slurry at a rate of 50 mL·kg-1 . As a control , seeds were coated with a similar mixture without bacteria . Field trial was conducted at the experimental station in Hada Al-Sham ( N 21°47'47 . 1" E 39°43'48 . 8" ) , Saudi Arabia , in winter seasons 2015–2016 and 2016–2017 . The experiment was a randomized complete block design with a split-split plot arrangement of four replicates in the for season 2015–2016 season and three replicates in the 2016–2017 season , plots ( 2 × 1 . 5 m ) with seed spacing 20 cm row-to-row . The field was irrigated using groundwater with two different salinity levels: low salinity ( EC = 3 . 12 dS·m-1 ) , and high salinity ( EC = 7 . 81 dS·m-1 ) . The soil had an average pH 7 . 74 and salinity EC = 1 . 95 dS·m-1 . Agronomical data ( plant height , fresh biomass , and dry biomass ) were recorded every 25–30 days from each harvest; three harvests were done in the first season , four harvests in the second season . Field trials data were analyzed as a randomized complete block design using a Factorial ANOVA Model , followed by least significant difference ( LSD ) test for pairwise comparisons . Results with a p-value < 0 . 05 were considered significant . All statistical analysis was carried out using SAS/STAT software ( https://www . sas . com/ ) . Enterobacter sp . SA187 was previously isolated from root nodules of the leguminous pioneer plant Indigofera argentea in the Jizan region of Saudi Arabia [29 , 31] . Arabidopsis seeds were obtained from publicly available collections . The following mutant lines used in this study were published previously: the JA-receptor coi1-1 mutant [36] , JA-insensitive jar1-1 [37] , the ABA biosynthesis aba2-1 mutant [38] , the ABA receptor quadruple pyr1-1pyl1-1pyl2-1pyl4-1 mutant [39] , the ethylene insensitive ein2-1 [40] and ein3-1 mutants [41] , the heptuple ethylene-biosynthesis deficient mutant acs1-1acs2-1acs4-1acs5-2acs6-1acs7-1acs9-1 [80] , and the ethylene-dependent pEBF2::GUS reporter [35] . Prior to every experiment , A . thaliana seeds were surface sterilized 10 min in 70% ethanol + 0 . 05% sodium dodecyl sulfate on a shaker , washed 2 times in 96% ethanol and let to dry . To ensure SA187-inoculation , sterilized seeds were sown on ½ MS plates ( Murashige and Skoog basal salts , Sigma ) containing SA187 ( 2·105 cfu·ml-1 ) , stratified for 2 days at 4°C in the dark and then placed vertically to growth conditions for 5 days as shown as in S2 Fig . The ½ MS plates with SA187 were prepared by addition of 107 bacteria to 50 ml pre-cooled agar medium during plate preparation . Average length of root hairs was determined based on images of 5-day-old roots ( 1 image per root at constant distance from the root tip , 25 seedlings per condition ) or 16-day-old roots ( along the whole primary root length grown after transfer ) captured by a Nikon AZ100M microscope equipped with an AZ Plan Apo 2x objective and a DS-Ri1 camera ( Nikon ) . All root hairs in focus were measured using ImageJ ( https://imagej . nih . gov/ij/ ) . Average values and standard deviations were calculated from 10% longest root hairs to eliminate non-developed root hairs and describe the maximal elongation capacity of root hairs . For salt stress tolerance assays , 5-day-old seedlings were transferred onto ½ MS plates with or without 100 mM NaCl ( Sigma ) . Primary root length was measured every 2 days using ImageJ software after scanning the plates . Lateral root density was evaluated as detectable number of lateral roots under a stereo microscope divided by the primary root length . Fresh weight of shoots and roots was measured 12 days after transfer of seedlings . Dry weight was measured after drying shoot and shoots for 2 days at 70°C . Following Koch’s postulate , SA187 was re-isolated from Arabidopsis root system at the end of an initial experiment to confirm the genotype of the inoculated strain . To address the ethylene involvement in Arabidopsis adaptation to salt stress , ACC ( 1-aminocyclopropane-1-carboxylic acid , Sigma ) , KMBA ( 2-keto-4-methylthiobutyric acid , Sigma ) , AVG ( aminoethoxyvinylglycine , Sigma ) , AgNO3 ( silver nitrate , Sigma ) were added into pre-cooled ½ MS agar medium together with 100 mM NaCl . For DNPH ( 2 , 4-dinitrophenylhydrazine , Sigma ) , 5 mM solution was prepared by solubilizing DNPH into 2M HCl ( hydrochloric acid , Sigma ) as described previously [81] , then the solution was diluted until reaching 1 mM , and equilibrated to the same pH as MS medium ( pH 5 . 8 ) using 2M KOH ( potassium hydroxide , Sigma ) . DNPH was used at final concentration 3 μM . All plants were grown in long day conditions in growth chambers ( Percival; 16 h light / 8 h dark , 22°C ) . Each experiment was performed at least in three biological replicates . Dry rosettes and root systems were weighted . All samples were measured individually except for salt-treated root systems , whereby pools of three root systems were measured to ensure proper weight measurements . Sodium and potassium concentrations were prepared for shoot and root dry samples by adding 1 mL of freshly prepared 1% HNO3 ( nitric acid , Fisher Scientific ) to the pre-weighed samples . The concentrations of sodium and potassium were determined , using Inductively Coupled Plasma Optical Emission Spectrometer ( Varian 720-ES ICP OES , Australia ) . SA187 was genetically labeled with the GFP expressing cassette by taking advantage of the mini-Tn7 transposon system [82] . In order to specifically select for a bacterium carrying the GFP integration in the genome , a spontaneous rifampicin resistant mutant of the strain was obtained first [83]: an overnight-grown culture of SA187 was plated on LB plates supplemented with 100 μg·mL-1 of rifampicin , and the plates were incubated for 24 h at 28°C . At least 10 colonies , representing spontaneous rifampicin resistant ( RifR ) mutants of the strain were streaked twice on LB plates containing 100 μg·mL-1 of rifampicin and thereafter twice on LB plates supplemented with 200 μg·mL-1 of rifampicin . The GFP expressing cassette was introduced in the SA187 RifR strain by conjugation as described in Lambertsen et al . ( 2004 ) [84] . Briefly , 1010 cells of SA187 RifR strain were mixed with 109 cells of E . coli SM10λpir harboring the helper plasmid pUX-BF13 , the GFP donor ( a mini-Tn7 ) plasmid and mobilizer pRK600 plasmid . The mixed culture was incubated on sterile nitrocellulose filter for 16hrs . The conjugation culture of bacterial cells was resuspended in saline buffer ( 9 g/L NaCl ) and spread on selective media with a propitiate antibiotics to select transformed SA187 . The selected colonies were screened by fluorescence microscopy for GFP fluorescence and positive colonies were further subjected to genotype confirmation by 16S rRNA gene sequencing . GFP-labeled SA187 on Arabidopsis roots was imaged using an inverted Zeiss LSM 710 confocal microscope equipped with Plan-Apochromat 10x/0 . 45 , Plan-Apochromat 20x/0 . 8 , and Plan-Apochromat 40x/1 . 4 Oil objectives . Seedlings grown for 3–21 days on vertical ½ MS agar plates or in soil inoculated with SA187-GFP were washed gently in sterile distilled water and transferred on a sterile agar plate . A block of agar with several seedlings was immediately cut out and placed upside-down to a chambered cover glass ( Lab-Tek II ) with 30 μM propidium iodide ( PI ) in water as mounting medium . The GFP and PI fluorescence was excited using the 488nm laser line , and captured as a single track ( emission of 493–537 nm for the GFP channel , 579–628 nm for the PI channel , 645–708 nm for chloroplast autofluorescence ) . For 3D reconstructions , 1 μm-step Z-stacks were taken , and images were generated in the integral 3D view of the Zen software ( Zeiss ) . Col-0 seedlings were germinated on SA187-inoculated ½ MS agar plates and transferred to new ½ MS plates with or without 100 mM NaCl 5 days after germination ( 10 seedlings per plate ) . Parts of their root systems grown after the transfer were cut , gently washed by dipping in distilled water to remove non-attached bacterial cells , and then ground in Eppendorf tubes using Teflon sticks . Each sample was resuspended in 1 ml of extraction buffer ( 10 mM MgCl2 , 0 . 01% Silwet L-77 ) , sonicated for 1 min and subsequently vortexed for 10 min . Samples were diluted 10-fold , and then spread on LB agar plates , and colony forming units ( CFUs ) were counted after overnight incubation at 28°C . Calculated number of CFUs was normalized per centimeter of root length ( total root length was determined based on images of root systems before their harvest ) . The experiment was conducted in three biological replicates , each with three technical replicates per condition; each sample consisted of five roots . Total RNA was extracted from 5-day-old plants either or not inoculated with SA187 and transferred for 10 more days on ½ MS plates with or without 100 mM NaCl using the Nucleospin RNA plant kit ( Macherey-Nagel ) , including DNaseI treatment , and following manufacturer’s recommendations . RNA samples were analyzed by Illumina HiSeq deep sequencing ( Illumina HiSeq 2000 , Illumina ) . Three biological replicates were processed for each sample . Paired-end sequencing of RNA-Seq samples was performed using Illumina GAIIx with a read length of 100 bp . Reads were quality-controlled using FASTQC ( http://www . bioinformatics . babraham . ac . uk/projects/fastqc/ ) . Trimmomatic was used for quality trimming [85] . Parameters for read quality filtering were set as follows: Minimum length of 36 bp; Mean Phred quality score greater than 30; Leading and trailing bases removal with base quality below 3; Sliding window of 4:15 . TopHat v2 . 0 . 9 [86] was used for alignment of short reads to the A . thaliana genome TAIR10 , Cufflinks v2 . 2 . 0 [87] for transcript assembly and differential expression . To identify differentially expressed genes , specific parameters ( p-value: 0 . 05; statistical correction: Benjamini Hochberg; FDR: 0 . 05 ) in cuffdiff were used . Post-processing and visualization of differential expression were done using cummeRbund v2 . 0 . 0 [88] . Gene was considered as regulated if fold change > log2|0 . 6| and q-value < 0 . 05 compared to Mock condition . RNA-Seq data set can be retrieved under NCBI geo submission ID GSE102950 . For qPCR analysis , mock and SA187-inoculated plants were used for RNA extraction as described above . Samples were used for analysis of either plant or SA187 gene expression . For bacteria alone , SA187 incubated for 4h in liquid ½ MS or ½ MS with 100 mM NaCl at 28°C and dark were used for RNA extraction , using the RiboPure RNA Purification Kit ( Ambion ) , following manual instructions for Gram-negative bacteria , with the exception that no beads were added during bacterial lysis . RNA extraction was followed by DNAseI treatment . cDNAs were using SuperscriptIII ( Invitrogen ) : 1 μg of total RNA , oligo-dT as a primer , following manufacturer’s recommendations . For Arabidopsis gene expression analyses , ACTIN2 ( At3g18780 ) and UBIQUITIN10 ( At4g05320 ) were used as reference genes . For SA187 gene expression analyses , infB , rpoB and gyrB were used as reference genes . All reactions were done in a CFX96 Touch Real-Time PCR Detection System ( BIO-RAD ) as follows: 50°C for 2 min , 95°C for 10 min; 40× [95°C for 10 sec and 60°C for 40 sec]; and a dissociation step to validate PCR products . All reactions were performed in three biological replicates , and each reaction as a technical triplicate . Gene expression levels were calculated using the Bio-Rad CFX manager software . Primer sequences used in this analysis are listed in S3 Table . Arabidopsis regulated genes were used to generate HCL tree using Multi Experiment Viewer ( MeV 4 . 9 . 0 version , TM4 , https://sourceforge . net/projects/mev-tm4/files/mev-tm4/MeV%204 . 9 . 0/ ) . Raw data were normalized for every gene . Hierarchical clustering was performed using Euclidian distances , average linkage and leaf order optimization . Gene enrichment analyses were performed using AmiGO website ( http://amigo1 . geneontology . org/cgi-bin/amigo/term_enrichment ) . All clusters were analyzed using default parameter ( S2 Table ) . For each sample , 10 mg of freeze-dried powder were extracted with 0 . 8 mL of acetone/water/acetic acid ( 80/19/1 v:v:v ) . For each sample , 2 ng of each standard was added to the sample: abscisic acid , salicylic acid , jasmonic acid , and indole-3-acetic acid stable labeled isotopes used as internal standards were prepared as described previously [89] . The extract was vigorously shaken for 1 min , sonicated for 1 min at 25 Hz , shaken for 10 minutes at 4°C in a Thermomixer ( Eppendorf ) , and then centrifuged ( 8000 g , 4°C , 10 min ) . The supernatants were collected , and the pellets were re-extracted twice with 0 . 4 mL of the same extraction solution , then vigorously shaken ( 1 min ) and sonicated ( 1 min; 25 Hz ) . After the centrifugations , three supernatants were pooled and dried . Each dry extract was dissolved in 140 μL of acetonitrile/water ( 50/50; v/v ) , filtered , and analyzed using a Waters Acquity ultra performance liquid chromatograph coupled to a Waters Xevo Triple quadrupole mass spectrometer TQS ( UPLC-ESI-MS/MS ) . The compounds were separated on a reverse-phase column ( Uptisphere C18 UP3HDO , 100 × 2 . 1 mm , 3 μm particle size; Interchim , France ) using a flow rate of 0 . 4 mL·min-1 and a binary gradient: ( A ) acetic acid 0 . 1% in water ( v/v ) and ( B ) acetonitrile with 0 . 1% acetic acid . For ABA , salicylic acid , jasmonic acid , the following binary gradients were used ( time , % A ) : ( 0 min , 98% ) , ( 3 min , 70% ) , ( 7 . 5 min , 50% ) , ( 8 . 5 min , 5% ) , ( 9 . 6 min , 0% ) , ( 13 . 2 min , 98% ) , ( 15 . 7 min , 98% ) , and the column temperature was 40°C . Mass spectrometry was conducted in electrospray and multiple reaction monitoring scanning mode ( MRM mode ) , in the negative ion mode . Relevant instrumental parameters were set as follows: capillary 1 . 5 kV ( negative mode ) , source block and desolvation gas temperatures 130°C and 500°C , respectively . Nitrogen was used to assist the cone and desolvation ( 150 L·h-1 and 800 L·h-1 , respectively ) , argon was used as the collision gas at a flow of 0 . 18 mL·min-1 . Samples were reconstituted in 140 μL of 50/50 acetonitrile/H2O ( v/v ) per mL of injected volume . The limit of detection ( LOD ) and limit of quantification ( LOQ ) were extrapolated for each hormone from calibration curves and samples using Quantify module of MassLynx software , version 4 . 1 . Seedlings were vacuum infiltrated with the pre-fixation buffer [0 . 3% formaldehyde , 0 . 28% mannitol , 50 mM sodium phosphate buffer ( pH 7 . 2 ) ] , washed with phosphate buffer and incubated in staining solution [250 μM K3Fe ( CN ) 6 ( potassium ferricyanide ) , 250 μM K4Fe ( CN ) 6 ( potassium ferrocyanide ) , 2% Triton-X , 1 mM 5-bromo-4-chloro-3-indolyl-b-D-glucuronic acid ( X-GlcA; Duchefa ) , 50 mM sodium phosphate buffer ( pH 7 . 2 ) ] . Tissue was cleared with Visokol ( Phytosys ) overnight and observed with Axio Imager 2 ( Zeiss ) equipped with Plan-Neofluar 10x/0 . 45 objective . A fresh SA187 culture was prepared by inoculation of 50 mL of liquid LB medium with 1 mL of overnight-grown culture . Subsequently , 2 mL of fresh culture was transferred to 10 mL chromatography vials and sealed with a rubber plug and snap-cap ( Chromacol ) after 0 , 1 , 2 or 4 hours of growth on a shaker incubator ( 220 rpm , 28°C ) . The sealed vials were again transferred to the shaker incubator for another 2 hours to allow ethylene accumulation . Three biological replicates were prepared at each time point along with 3 controls to correct for background ethylene emanation . Ethylene emission was measured with a laser-based photo-acoustic detector ( ETD-300 ethylene detector , Sensor Sense , The Netherlands ) [90] . Immediately after the ethylene measurement , OD600 was determined with Implen NanoPhotometer NP80 ( Sopachem Life Sciences , Belgium ) to correct for the total amount of bacterial cells present in the samples . RNA-Seq data are available under the ID GSE102950 ( http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE102950 ) | Plants as sessile organisms are facing multiple stresses during their lifetime . Among them , abiotic stresses , such as salt stress , can cause severe crop yield reduction , leading to food security issues in many regions of the world . In order to respond to growing food demands , especially in the context of the global climate change and increasing world population , it then becomes urgent to develop new strategies to yield crops more tolerant to abiotic stresses . One way to overcome these challenges is to take advantage of plant beneficial microbes , defined as plant growth promoting bacteria ( PGPB ) . In this study , we report the beneficial effect of Enterobacter sp . SA187 on plant growth under salt stress conditions . SA187 increased the yield of the forage crop alfalfa when submitted to different saline irrigations in field trials . Moreover , using the model plant Arabidopsis thaliana , we demonstrate that SA187 mediates its beneficial activity by producing 2-keto-4-methylthiobutyric acid ( KMBA ) , which modulates the plant ethylene signaling pathway . This study highlights a novel mechanism involved in plant-PGPB interaction , and proves that endophytic bacteria can be efficiently used to enhance yield of current crops under salt stress conditions . | [
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] | 2018 | Ethylene induced plant stress tolerance by Enterobacter sp. SA187 is mediated by 2‐keto‐4‐methylthiobutyric acid production |
The parasite Trypanosoma brucei rhodesiense and its insect vector Glossina morsitans morsitans were used to evaluate the effect of parasite clearance ( resistance ) as well as the cost of midgut infections on tsetse host fitness . Tsetse flies are viviparous and have a low reproductive capacity , giving birth to only 6–8 progeny during their lifetime . Thus , small perturbations to their reproductive fitness can have a major impact on population densities . We measured the fecundity ( number of larval progeny deposited ) and mortality in parasite-resistant tsetse females and untreated controls and found no differences . There was , however , a typanosome-specific impact on midgut infections . Infections with an immunogenic parasite line that resulted in prolonged activation of the tsetse immune system delayed intrauterine larval development resulting in the production of fewer progeny over the fly's lifetime . In contrast , parasitism with a second line that failed to activate the immune system did not impose a fecundity cost . Coinfections favored the establishment of the immunogenic parasites in the midgut . We show that a decrease in the synthesis of Glossina Milk gland protein ( GmmMgp ) , a major female accessory gland protein associated with larvagenesis , likely contributed to the reproductive lag observed in infected flies . Mathematical analysis of our empirical results indicated that infection with the immunogenic trypanosomes reduced tsetse fecundity by 30% relative to infections with the non-immunogenic strain . We estimate that a moderate infection prevalence of about 26% with immunogenic parasites has the potential to reduce tsetse populations . Potential repercussions for vector population growth , parasite–host coevolution , and disease prevalence are discussed .
Insect vectors are essential for the transmission of malaria and African sleeping sickness , among many other diseases . Despite the high disease incidence in mammalian hosts , infection prevalence in insect vectors is typically low . For example , with the tsetse vectors of trypanosomes that cause African sleeping sickness , often only 1–3% of flies are infected in field populations ( reviewed in [1] ) . This is also reflected in laboratory experiments where although all flies are subjected to an infectious bloodmeal , only a few show established midgut infections [2] , [3] . Successful parasite infection of vectors likely reflects a balance between the effectiveness of the vector insect's immune response and the ability of the parasite to evade this response . The ability to resist parasitism has been shown to carry a fitness cost [4]–[7] . In several cases , a decrease in reproductive output has been shown to result from activation of the host's immune responses [8]–[10] . In a recent study , transgenic mosquitoes which expressed molecules that conferred parasite resistance were found to be more fit than wild type insects when exposed to malaria parasites [11] . Presumably the transgenic insects were able to eliminate infections prior to the activation of costly natural immune responses . In parasitized insects , the parasites likely compete for the insect's restricted nutritional resources . Here a balance also probably exists , since reductions in host survival or fitness would diminish the likelihood of parasite transmission . To compensate for nutrient competition while maintaining longevity , many insect vectors exhibit reduced or delayed reproduction as a trade-off during parasite infection ( reviewed in [10] ) . Understanding the effect of parasitism on vector fitness is fundamental to predicting the future trajectory of coevolution between parasites and their hosts and eventual disease transmission dynamics . Little has been reported on the impact of trypanosome infection outcomes on tsetse . Unlike most insects that are oviparous , tsetse females are viviparous producing one larva at a time , a process that results in few offspring produced over a lifespan . The pregnant females nurture their single larva in utero via specialized accessory gland ( milk gland ) secretions . It is likely that tsetse's viviparous character would result in different outcomes on lifetime fitness traits when compared to other insects where reproductive output is greatest and most sensitive to adverse occurrences in early adulthood [12] . In addition to reproductive fitness , vector longevity is especially relevant as African trypanosomes undergo multiple stages of differentiation in tsetse and require extensive developmental periods before transmission to their next mammalian host . During blood-feeding , adult tsetse can acquire bloodstream form ( BSF ) trypanosomes from mammalian hosts . During the early course of infection , BSF , which differentiate and replicate as procyclic forms in the midgut , undergo a massive attrition . In a small percentage of flies , parasites continue to proliferate and establish midgut infections . The mechanisms involved in parasite elimination are likely based on tsetse immune responses [13] and may include lectin agglutination [14] , [15] , other lectin-like activities [16] , [17] , antioxidant activity [18]–[20] and killing by antimicrobial peptides ( AMPs ) [2] , [18] . Both AMP transcripts and their encoded products are produced during parasite attrition in the midgut [2] , [18] , [21] . Additional support for a role of AMPs in resistance comes from studies where knock down of AMP expression in tsetse resulted in increased parasite prevalence in vivo [3] and where recombinant Attacin has been shown to exhibit trypanolytic activity in vivo [22] . In this study , we examined the cost to tsetse of resistance to trypanosome infections . We examined whether T . b . rhodesiense midgut infections can increase mortality and reduce reproductive fitness and cause delayed life-history effects on future progeny . We evaluated the cost of infections using two parasite lines , which differ in their ability to activate tsetse immune responses . We identified and discuss one putative mechanism by which tsetse reproductive physiology might be compromised upon parasite infection . Using a mathematical model , we then estimated the impact of fecundity cost on tsetse populations and discuss the putative effect of this cost on disease transmission .
BSF of the YTat1 . 1 parasites were derived from T . brucei rhodesiense stabilate TREU 164 ( TREU = Trypanosomiasis Research Edinburgh University ) . TREU 164 represents passage 21 in mice from parasites originating from a capsule of bovine blood on which Glossina pallidipes captured at Lugala , Busoga , Uganda , in 1960 , were allowed to feed . The pedigree of TREU 164 has been published [23] . The ETat3 variant derived from TREU 164 was triply cloned at Yale and referred to as YTat1 . ETat3 , and similarly , YTat1 clone1 ( YTat1 . 1 ) are both highly virulent to the mammalian host , in that mice , even if infected with only a single organism , invariably die during the first parasitic wave . Procyclic culture forms ( PCF ) of YTat1 . 1WT cells were derived from BSF taken from infected rat blood and maintained axenically at 28°C in SDM-79 medium supplemented with 10% heat inactivated fetal bovine serum and penicillin-streptomycin antibiotic cocktail [24] . For YTat1 . 1EP selection , YTat1 . 1WT PCF were grown to about 8×107 cells/ml and diluted with fresh medium ( 1:20 ) once a week . The selection of the YTat1 . 1EP phenotype of trypanosomes was reproducible in three different experiments and was observed after maintaining cells under high-density culture conditions for at least two months . For maintenance of YTat1 . 1WT and the selected YTat1 . 1EP line , parasite cultures were split three times per week , after they typically reached 6–8×106 cells/ml . The YTat1 . 1WT cells ( PCFs or the BSFs ) are not able to establish salivary gland infections in the fly . Glossina morsitans morsitans ( Westwood ) flies were maintained in the insectary at Yale University [25] . To initiate infections , newly emerged teneral flies were given a blood meal containing PCFs ( 105 cells/ml ) . Parasite infection prevalence in flies was confirmed by midgut dissection and microscopic viewing at designated times . Three independent experiments were performed . No significant difference was seen by arcsine transformation analysis , which allowed pooling of the groups . Chi-square analysis was used on the pooled data . Fat body was dissected from individual trypanosome-infected or uninfected flies and homogenized in Trizol ( Invitrogen , Carlsbad , CA ) to extract total RNA following the manufacturer's instructions . Total RNA ( 10 µg per sample ) was analyzed on a 1 . 5% agarose gel and UV crosslinked onto nylon membrane ( Hybond N+ , Amersham Pharmacia Biotech , NY ) . The membrane was hybridized with P32-labeled GmmAttA1 or GmmDef probes overnight as described [2] . The GAPDH housekeeping gene was used to normalize RNA input . The abundance of mRNAs was determined using a Phosphorimager ( PSI-Molecular Dynamics ) . One representative data set is shown from six independent replicates . In vitro maintained YTat1 . 1WT or YTat1 . 1EP PCF were used for immunoblotting . Parasites ( 5×103 ) were boiled in Laemmli sample loading buffer and their proteins were separated by SDS-PAGE . After transfer to polyvinylidene difluoride membranes , procyclins were detected with EP specific mAb 274 [26] and GPEET specific mAb 5H3 [27] , respectively . For analysis of procyclins in vivo , infections were initiated with YTat1 . 1EP and YTat1 . 1WT PCF and midguts from microscopically positive flies were dissected . Parasites were eluted from dissected midgut and proventriculus tissues by gentle homogenization and washed 5 times in serum-free SDM-79 medium . Cell numbers were determined with a hemocytometer and 2×103 cells were used for immunoblot analysis . The YTat1 . 1WT and YTat1 . 1EP PCF were transfected with pHD1034-GFP or pHD1034-RFP plasmid , respectively and the transformants were selected with 1 µg/ml puromycin [28] . The in vitro growth rate of each parasite line and parasite numbers in mixed cultures was measured over time by fluorescence microscopy using a hemocytometer . Parasite numbers in dissected and homogenized midgut extracts were similarly determined 14 days post acquisition . Infections were initiated either singly ( 106 cells/ml ) or as mixed infections ( 5×105 cells/ml of each parasite strain mixed ) . Although multiple experiments were performed the results from two representative experiments are shown . Female adults , 48 hours post emergence , received a single blood meal containing YTat1 . 1WT or YTat1 . 1EP parasites ( 105 cells/ml ) and those that fed were maintained on a normal blood meal diet . Mortality rates were determined for these two groups and an uninfected control group over a period of sixty days . To determine the effect of parasite infections on tsetse fecundity , all females were mated 96 hours post emergence and maintained in individual cages and monitored daily for larval deposition . Fly midguts were dissected and microscopically analyzed for infection status at the completion of the experiment . Data were analyzed using SAS system v . 8 . 02 for Windows [29] . Student's t-tests or Mann Whitney U-tests were used to determine whether larval deposition time intervals significantly differed between trypanosome infected ( YTat1 . 1WT or YTat1 . 1EP ) and control ( both resistant and non-challenged ) females . Student's t-tests were also employed to determine whether pupal weight and wing traits ( length and width ) significantly differed in the progeny . F-tests were applied to assess the homogeneity of variances . Total RNA from eight YTat1 . 1WT and YTat1 . 1EP parasite infected 24 day old female flies and their age-matched normal controls were prepared . Following treatment with RNase-free Turbo DNase I ( Ambion ) the absence of DNA was confirmed by PCR amplification in a PCT-200 Peltier Thermal Cycler . One µg of total RNA was used for each sample for cDNA synthesis ( Superscript II reverse transcriptase kit , Invitrogen , Carlsbad , CA ) . For qRT-PCR standard construction , inserts were cloned into the pGEM-T easy vector system ( Promega , Madison , WI ) [30] . Transcript quantification was performed on an iCycler Real-time detection system ( Bio-Rad ) and data were analyzed using software version 3 . 1 . qRT-PCR for triplicate samples was performed using primers GmmMGPF 5′-CTGGACTCTTGACCCGTGAAC-3′ and GmmMGPR: 5′-GGGGAAGTGATGTTCCTTGA-3′ for 36 cycles at 58°C . For normalization , G . m . morsitans tubulin was amplified using the primer pair GmmTubF: 5′-GACCATGACGTGGATCACAG-3′ and GmmTubR: 5′-CCATTCCCACGTCTTCACTT-3′ for 36 cycles at 58°C .
Exposure of flies to wild type T . b . rhodesiense ( Yale Trypanozoon antigenic type 1 . 1; YTat1 . 1WT ) cells results in the upregulation of tsetse immune responses . For this analysis , we evaluated as immune markers the expression of two AMPs , attacin and defensin , which we had previously described from trypanosome-infected tsetse flies [2] . Infections with YTat1 . 1WT trypanosomes activated the host immune system and induced the expression of attacin and defensin as early as 3 days post acquisition ( Figure 1 , lane 1 ) . This heightened response persisted ( when analyzed at day 10 ) in susceptible insects harboring midgut parasite infections ( lane 2 ) . The immune response was also evident in those resistant flies that lacked microscopically detectable parasite infections at day 10 ( lane 3 ) . Only later , at day 30 , had the immune response of resistant flies subsided to normal levels , indicating the long lasting impact of parasite recognition on host immune stimulation ( data not shown , [2] ) . By passaging procyclic culture forms at late log-phase , a cell line was selected and designated ( YTat1 . 1EP ) . This parasite line did not induce tsetse AMP expression when analyzed at day 3 and day 10 , following parasite acquisition ( lanes 5–6 , respectively ) . The phenotype of this trypanosome strain is described below . We first examined the expression of the cell surface procyclins on the immunogenic wild type parasites and the non-immunogenic strain that we had accidently selected in culture . We did this in order to understand the basis of the differential host immune activation , since the procyclic trypanosome surface coats are possible candidates for interaction with the fly's immune system . In T . brucei sspp . , procyclins exist in several forms that are defined by the amino acid sequences of their C-terminal repeat domains carrying extensive glutamic acid-proline ( EP ) dipeptide repeats of differing lengths or pentapeptide repeats ( GPEET ) . However , the expression of the procyclins is not static . It has been observed that the ratio of EP to GPEET procyclin expression in vitro can vary considerably between different parasite lines [27] or between different passages of the same culture [31] and that the composition of the coat may change in response to extracellular signals in vitro and during development in vivo [32] . Down-regulation of GPEET expression in trypanosomes has also been shown to be accelerated in vitro by hypoxia or be prevented by exogenous glycerol [32] . Procyclic culture forms of the non-immunogenic YTat1 . 1EP parasites expressed EP and not GPEET procyclins , whereas the immunogenic YTat1 . 1WT cells expressed both EP and GPEET procyclins , as determined using specific monoclonal antibodies ( mAbs ) by immunoblotting ( Figure 2 , panels A and B ) and by flow cytometry ( Figure 2 , panels C and D ) . We do not know how our specific culture conditions caused the selection of the phenotype that exhibited decreased GPEET expression . However , the procyclin repertoire of the trypanosome lines we investigated was remarkably stable and did not change over several months of culture . Indeed , after being frozen for more than 6 months , thawed parasites grown in log-phase for a month exhibited the same phenotype ( Figure 2C and D ) . Procyclic midgut trypanosomes purified from infected flies 12 days post parasite acquisition primarily expressed the EP procyclins with both parasite strains ( Figure 2 , D and F ) . Attempts to evaluate procyclin expression in epimastigotes in the proventriculus failed , likely due to the small number of parasites that could be obtained ( Lane 3 , Figure 2D and F ) . For these experiments , it is especially important to obtain pure parasite preparations given the cross reactivity EP monoclonal antibody exhibits with the unrelated tsetse EP proteins [33] . Nevertheless , these results suggest that in vivo YTat1 . 1WT parasite undergo a similar differentiation process as YTat1 . 1EP with respect to the expression of procyclins . It is of interest that the non-immunogenic trypanosomes originally fed to tsetse did not express GPEET whereas the immunogenic wild type parasites did . Both YTat1 . 1WT and YTat1 . 1EP parasites had similar growth rates with a doubling time of 12±0 . 15 hours under in vitro cultivation conditions . The parasite lines were labeled with green fluorescent protein ( GFP-YTat1 . 1WT ) or red fluorescent protein ( RFP-YTat1 . 1EP ) , respectively , to allow visual discrimination in mixed infection experiments . There was no statistically significant difference between the numbers of red and green trypanosomes in in vitro cultures that were initiated with single phenotype or mixed parasites ( Figure 3A ) . Prevalence of midgut infections with trypanosomes YTat1 . 1WT and YTat1 . 1EP were compared ( Table 1 ) , but showed no significant differences ( p = 0 . 726 . No significant difference in midgut infection intensity ( parasite numbers ) was found two weeks post infection acquisition with either parasite line ( Figure 3B ) . However , when fly infections were established by feeding equal numbers of a mixture of the tagged parasites , a significantly higher number of YTat1 . 1WT cells were seen in established midgut infections ( Figure 3B ) . Prior studies have shown that YTat1 . 1 cells exhibit high virulence in the mammalian host [34] . Since the YTat1 . 1 cells that we used have lost the ability to establish salivary gland infections in the fly , we were unable to compare the transmission potential of the two parasite lines and the potential virulence of YTat1 . 1EP for mammalian hosts . We thus limited our analysis to the impact of midgut parasite infections on tsetse fecundity and resulting population structure . We compared the mortality and fecundity of flies infected with each parasite line for up to 60 days . There was no significant difference in mortality rates between the YTat1 . 1WT parasite exposed group and the age-matched , unchallenged control group ( 30 . 6% versus 30% , respectively ) . We next compared the fecundity of fertile females infected with YTat1 . 1WT and YTat1 . 1EP trypanosomes to age matched , uninfected controls ( Table 2 ) . Females were monitored daily for larval deposition through their initial three reproductive cycles . The infection status of the females was microscopically confirmed by examination of dissected midguts at the conclusion of the experiment . Our experiments with YTat1 . 1WT and YTat1 . 1EP parasites were conducted during 2002 and 2006 , respectively . The difference in the larval deposition periods observed between these experiments can reflect environmental variations such as temperature and humidity conditions in the insectary . Hence , each experimental group was compared to its age-matched control group subjected to the same environmental conditions . For final analysis , the control group consisted of the nonchallenged and challenged but uninfected ( resistant ) females because of the lack of any significant differences between these two groups ( P>0 . 05 ) . All three larval deposition periods of flies infected with YTat1 . 1WT parasites were found to be significantly longer than those of the corresponding uninfected controls , while YTat1 . 1EP infected females had similar larval deposition periods to those of their control group . We also evaluated the expression of tsetse larvagenesis associated protein , ( milk gland protein; GmmMGP ) . GmmMGP is an abundant milk protein synthesized by the female accessory gland tissue [35] and supplied to the developing intrauterine progeny in the “milk” secretion [36] . Fertile females infected with the immunogenic YTat1 . 1WT parasite strain exhibited significantly decreased expression levels of GmmMGP in comparison to uninfected age-matched control females ( Figure 4A ) . A similar reduction in GmmMGP was not observed in flies infected with YTat1 . 1EP parasite strain ( Figure 4B ) , suggesting that the delayed larvagenesis process in immune stimulated flies may result from the decreased expression of the GmmMGP protein , which is necessary for larval growth . In addition to the cost on direct life-history traits , we obtained morphological data reflective of adult fitness from three sequential progeny produced by mothers infected with YTat1 . 1WT parasites and compared these to an uninfected cohort ( Table S1 ) . There was no difference in pupal weight or in hatch rate ( percent pupae that successfully emerged ) between the two groups of progeny . In addition , there were no significant differences in wing width and length of the hatched progeny between the two groups . Based on morphological findings , delayed larvagenesis does not apparently result in loss of fitness of future progeny despite reducing the reproductive output of the mother . We performed a mathematical analysis to translate the empirically measured times between larva depositions ( Table 2 ) into the relative differences in fecundity of tsetse infected with the immunogenic versus the non-immunogenic trypanosome strains . In our analysis , we assumed that a proportion sP of deposited larva survive pupation to become adults , while the expected duration of the pupation is lP . We let m be the proportion of offspring that are female . We also assumed a constant adult death rate , μA , resulting in the expected duration of the adult stage . The time to first deposition was denoted t1 and the time between subsequent depositions t2 . Thus , the second deposition occurs at time t1+t2 , the third deposition at t1+2t2 , and so on . The expected total number of female offspring of a female parent over her lifetime is then ( 1 ) which , when divided by the expected life span , lP+lA , gives the reproductive rate ( 2 ) Reproductive rates in the absence of trypanosome infection , rU , and in the presence of infection , rI , are related by ( 3 ) so that ε is the relative fecundity cost of infection . Then ( 4 ) Taking t1 to be the time to first deposition from our empirical measurements ( Table 2 ) , t2 to be given by the mean of the times to the second and third depositions ( Table 2 ) , sP = 0 . 82 , lP = 31 . 4 days , and μA = 0 . 0253 day−1 [37] gives ε = 0 . 3 . That is , trypanosome infection causes a reduction in fecundity of approximately 30% . We next converted the fecundity differences calculated at the level of the individual fly into differences for tsetse population growth depending on whether or not the tsetse are infected . The population growth rate , r , is the root of the Euler–Lotka equation [38] , [39] . ( 5 ) For the parameter values above and 50% of offspring being female ( m = 1/2 ) , equation ( 5 ) gives rU = 0 . 0008 day−1 for uninfected tsetse and rI = −0 . 0025 day−1 for a population composed entirely of infected tsetse . Thus , for uninfected tsetse , rU is slightly positive , indicative of the slow viviparous reproduction of tsetse , while rI is negative , which would lead to population collapse if all tsetse were infected . If the probability of surviving the pupal period is decreased to sP = 0 . 82 ( [37] for G . palpalis gambiensis ) , the growth rate is negative , rU = −0 . 0012 day−1 , while for infected tsetse , rI = −0 . 0044 . With sP = 1 , an increased death rate of μA = 0 . 030 day−1 has an even stronger effect on the basic reproduction number and growth rate , rU = −0 . 00046 day−1 and rI = −0 . 0082 day−1 . For a varying level of infection , if we assume no vertical transmission , the population growth rate for the overall population is given by ( 6 ) where p is the prevalence of antigenic virulent trypanosome infection in tsetse . With sP = 1 and μA = 0 . 022 day−1 , at around p = 0 . 26 , r = 0 . For infection prevalence below 26% , the tsetse population continues to grow , while prevalence above 26% gives declining tsetse populations ( Figure 5 ) . The relationship between population growth rate and infection prevalence is monotone but not linear . The prevalence of trypanosomiasis would be expected to be reduced with a decline in the vector tsetse population . These modeling results are not intended to be precise quantitative predictions , but indicate likely qualitative dynamics of the interaction between tsetse and trypanosomes . Thus , the main conclusion that infection with the immunogenic parasite strain has the potential to suppress the tsetse population should apply broadly . However , the precise prevalence of trypanosomiasis that result in negative tsetse population growth depends on the exact parameter values , which may vary seasonally and spatially .
We studied the tsetse-trypanosome system to evaluate the molecular and physiological aspects of host-parasite interactions and the consequences of parasite infections on host fecundity . Two trypanosome strains that differentially activate host immunity were employed to assess the cost of the ability to clear parasite infections ( resistance ) and the cost of midgut parasite infections in susceptible flies . In contrast to the dogma that expression of parasite resistance traits incurs a fitness cost to the insect host , we observed no significant fecundity cost associated with trypanosome resistance in laboratory reared tsetse . However , activation of tsetse immune responses by infecting flies with immunogenic trypanosomes reduced host reproductive output while infections with non-immunogenic trypanosomes did not . It is unusual that we did not detect a fecundity cost associated with parasite resistance in tsetse . This observation , that differs from what is seen in mosquitoes , may reflect the viviparous reproductive biology of tsetse where investments in reproductive output begin significantly later and continue throughout the lifetime of adult female flies . Female tsetse develop a single oocyte per gonotrophic cycle . Following ovulation and fertilization , the embryo develops within the uterus and hatches into a first instar larva , which molts through two more instars before being deposited as a fully developed larva which quickly pupates in the soil . From this point on , there is a continuous investment in nurturing the single larva produced one at a time , approximately every ten days , through the remainder of the female lifespan . This differs dramatically from mosquitoes , which have a high reproductive output as young adults and hence may be more sensitive to perturbations early in life . We cannot rule out however that our in vitro experiments conducted under uniform environmental conditions and ample nutritional supply may have skewed any potential fecundity cost that flies may experience upon expression of parasite resistance in the wild . Reduced host fecundity has been observed in various systems with parasitized insects ( reviewed in Hurd , 2003 ) . Our result is the first demonstration of a significant loss of fecundity in parasitized tsetse but only when infected with an obviously immunogenic trypanosome line . Loss of fecundity in tsetse may arise from the cost of prolonged immune activation since a similar fitness cost was not observed in flies infected with the non-immunogenic parasites . Our results suggest that the decreased expression of milk gland protein GmmMGP , the most abundant protein product in the “milk” secretion of modified accessory glands , may cause the observed delayed larvagenesis process [36] , [40] . We recently noted that in addition to GmmMGP , transferrin , which is also expressed in the female milk gland organ and is transported into the developing larva is down regulated by YTat1 . 1WT infections [41] . A similar reduction in the abundance of vitellogenin ( Vg ) mRNA and the titer of circulating Vg in the hemolymph has been reported in plasmodium infected mosquitoes during early oocyst development [42] . Later in the infection process , changes in the ovarian follicular epithelium have also been associated with a decrease in Vg uptake by the ovary and may also result in reduced hormone ecdysone production , which is needed for the transcriptional regulation of Vg expression [43] . Our results suggest several avenues for future research . It remains to be seen whether the regulation of host gene expression could result from an adaptive strategy that has evolved in response to parasitism , or that it reflects the manipulation of the host insect by the parasites . The regulation of milk-gland protein and transferrin transcription remains to be described , but may similarly be subject to hormonal regulation , which in turn may be influenced by parasite infections . One potential difference we identified between the trypanosome strains used to infect tsetse was in their EP and GPEET procyclin compositions . Under our culture conditions , the immunogenic YTATWT line expressed both major forms of procyclin , while the YTATEP line only expressed EP procyclins . Procyclin epitopes analyzed from parasites obtained from midgut infections indicated that both lines expressed the EP isoforms later in the infection process , similar to previous reports [44] . It is possible that the highly phosphorylated GPEET molecules present on YTATWT procyclic culture form parasites , upon acquisition in the blood meal , are recognized by the host's immune system early in the infection process and elicit the immune activation we observed in our study . Supporting this idea , when beads carrying different surface charges were introduced into mosquitoes , different physiological networks were induced resulting in varied vector cost outcomes [45] . It is possible that as a consequence of host-parasite co-evolutionary dynamics , trypanosomes have evolved to down regulate the expression of GPEET in order to avoid inducing host resistance mechanisms . It is entirely possible however , that variations in parasite molecules other than procyclins may be responsible for the observed host immune outcome . The expression of GPEET alone in the early infection process cannot explain the sustained induction of host immune effectors in the case of infections with the immunogenic line since this line also switches to express predominantly the EP procyclins in late midgut infections . As molecular data on tsetse immunity are accumulating , it would be of interest to conduct a global expression analysis of both RNA and protein using the two parasite lines to gain a broader appreciation of the host-parasite interactions in the tsetse system [46] . Multiple vector immune components are likely to play a role in parasite transmission and resistance phenotype . Although our previous results have identified an important role for antimicrobial peptides in parasite establishment in tsetse , lack of AMP expression does not result in greater parasite infection prevalence for YTATEP . It is possible that other trypanocidal effectors , such as lectins [14]–[17] and antioxidant activity in the midgut milieu at the time of parasite acquisition that are necessary for resistance . Alternatively , cascades in immunity pathways could result in the synthesis of different trypanolytic immune effectors , which are uncharacterized at the present time . The similar midgut infection intensities observed with both trypanosome lines may also suggest general regulatory processes whereby homeostasis of parasite density is maintained to prevent excessive harm on host physiology . Our mixed infection experiments in vivo however showed that the immunogenic parasites outcompeted the non-immunogenic parasites in midguts . It is plausible that tsetse immune products , in particular antimicrobial peptides , may have varying trypanolytic effect on parasites with initial differences in surface protein composition . It is then possible that infections with the immunogenic parasites , invoking host immune responses , could prevent the transmission of other strains that may be more sensitive to the host immune products . The prevalence of tsetse infections reported with T . brucei sspp . in the wild is low , typically in the order of 1–3% [47]–[49] , suggesting the potential for only a small impact on the whole tsetse population . However , the prevalence of infections with other trypanosome species such as T . congolense and T . vivax , can be significantly higher , for example 28% reported in Burkina Faso [50] and 24% in Tanzania [51] . Furthermore infections with mixed trypanosome species are common , comprising over 30% of infections reported in natural populations [52]–[54] . Our modeling results suggest that these higher infection rates could have substantial effect on the tsetse population dynamics if the presence of these parasite species were to cause similar reproductive delays . Such reproductive delays caused by immunogenic trypanosome strains could lead to a reduction in tsetse population size and consequently to less transmission of parasites to vertebrate hosts . In turn , the resulting reduced trypanosome prevalence in vertebrates would lead to a lower prevalence in tsetse and a corresponding rebound of the tsetse population until an equilibrium of trypanosome prevalence in tsetse and vertebrate hosts is reached at lower levels than would occur with an avirulent strain . The mechanisms of tsetse population regulation are not well understood [55] , so the magnitude of the impact of trypanosome-induced fecundity delays on trypanosome infections in vertebrates is difficult to predict . Assuming that both parasite lines we investigated have equal probability for maturation in the salivary glands for mammalian transmission , our co-infection dynamics would favor the spread of the immunogenic phenotype . Reducing its tsetse host's fitness is evolutionarily disadvantageous to the trypanosome , but only weakly compared to the possible benefits of increased virulence . Interestingly , YTat1 . 1WT is known to be highly virulent in the mammalian host , but we do not have a virulence phenotype for YTat1 . 1EP since we cannot obtain salivary gland mammalian transmissible infections with this line . Trade-offs between virulence and parasite transmissibility can maintain virulence , as can competition within a host between multiple strains of a parasite [56] . Co-evolution between pathogen and host could lead to more complicated dynamics , from the emergence of mutualism to an escalating cycle of virulence and host resistance . In fact , a field study demonstrated that the resistance of Anopheline mosquitoes to Plasmodium parasite development varied considerably between different combinations of parasite and vector isolates , suggesting that there are specific compatibilities between the insect and parasite genotypes [57] . If there is no cost of resistance against virulent trypanosomes , we would expect resistance to increase until the “virulent” trypanosome is driven extinct . In contrast , the “avirulent” trypanosome would not impose selection on its tsetse host , and would persist . If there is a cost of resistance however , there would be an equilibrium proportion of resistance genotype at which the cost of resistance is balanced by the cost and prevalence of virulent infection . In this case , if the avirulent trypanosome strain without any fecundity cost emerged and spread , genetically susceptible tsetse would be expected to completely replace resistant tsetse . Simultaneously , disease prevalence in both tsetse and mammalian hosts would rise . Future experiments will need to be conducted with flies in the field to understand the impact of natural parasite infections on host immunity , and the potential burden of host immune activation on fecundity in order to relate to disease epidemiology . Our laboratory findings coupled with our modeling studies now provide a framework to investigate the status of co-infections , host immune activation processes , fecundity outcomes , transmission dynamics and host virulence phenotypes in natural tsetse-trypanosome populations . | In many cases , parasites adapt to their hosts' biology over time and the extent of their harmful effects gradually diminishes . Insect-transmitted parasites such as African trypanosomes , however , are unusually pathogenic for their mammalian hosts because they rely on their invertebrate hosts for transmission to the next mammalian host . To ensure their maximum transmission , it is essential that parasite infections do not compromise insect host's fitness traits , including longevity and host-finding ability . Our results in tsetse indicate that , as theory predicts , trypanosome infections do not reduce host longevity . Instead , they divert host resources from reproduction and can reduce reproductive output by as much as 30% . Such loss of reproductive fitness occurs as a result of the induction of tsetse's immune responses . A closely related non-immunogenic parasite line does not induce host responses and does not compromise host fecundity . It is possible that host immune responses are needed in the case of the immunogenic line to control the parasite density to prevent excessive host damage . Because tsetse are viviparous and each adult female typically gives rise to only few progeny during their lifetime , even modest costs on reproduction can have a significant impact on host abundance . Our model predicts that if the prevalence of immunogenic parasite infections in tsetse populations reaches over 26% , they begin to have a negative impact on population growth rate . Infection rates as high as 30% have been reported with trypanosomes in the field . Our laboratory findings coupled with our modeling studies now provide a framework to investigate the status of co-infections , host immune activation processes , fecundity outcomes , transmission dynamics , and host virulence phenotypes in natural tsetse–trypanosome populations . | [
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] | 2008 | Infections with Immunogenic Trypanosomes Reduce Tsetse Reproductive Fitness: Potential Impact of Different Parasite Strains on Vector Population Structure |
Aggregation of alpha-synuclein ( ASYN ) in Lewy bodies and Lewy neurites is the typical pathological hallmark of Parkinson's disease ( PD ) and other synucleinopathies . Furthermore , mutations in the gene encoding for ASYN are associated with familial and sporadic forms of PD , suggesting this protein plays a central role in the disease . However , the precise contribution of ASYN to neuronal dysfunction and death is unclear . There is intense debate about the nature of the toxic species of ASYN and little is known about the molecular determinants of oligomerization and aggregation of ASYN in the cell . In order to clarify the effects of different mutations on the propensity of ASYN to oligomerize and aggregate , we assembled a panel of 19 ASYN variants and compared their behaviour . We found that familial mutants linked to PD ( A30P , E46K , H50Q , G51D and A53T ) exhibited identical propensities to oligomerize in living cells , but had distinct abilities to form inclusions . While the A30P mutant reduced the percentage of cells with inclusions , the E46K mutant had the opposite effect . Interestingly , artificial proline mutants designed to interfere with the helical structure of the N-terminal domain , showed increased propensity to form oligomeric species rather than inclusions . Moreover , lysine substitution mutants increased oligomerization and altered the pattern of aggregation . Altogether , our data shed light into the molecular effects of ASYN mutations in a cellular context , and established a common ground for the study of genetic and pharmacological modulators of the aggregation process , opening new perspectives for therapeutic intervention in PD and other synucleinopathies .
Alpha-synuclein ( ASYN ) is an abundant neuronal protein whose normal function is still elusive , but seems to be related to SNARE-complex assembly [1] . Misfolding and aggregation of ASYN in proteinaceous inclusions , known as Lewy bodies ( LBs ) , are associated with Parkinson's disease ( PD ) and other neurodegenerative disorders known as synucleinopathies [2] , [3] . PD is the second most common neurodegenerative disease , affecting approximately 1% of the population over 65 years of age [4] , and is therefore a growing problem in the aging population . Both point mutations [5] , [6] , [7] and multiplications [8] , [9] , [10] , [11] of the SNCA gene , encoding for ASYN , have been linked to autosomal-dominant forms of PD . More recently , GWAS studies identified the SNCA locus as a strong risk factor underlying PD [12] , [13] , and two additional familial mutations ( G51D and H50Q ) were recently identified [14] , [15] , [16] . The H50Q mutation is associated with late-onset parkinsonism , and the patients exhibit similar pathological features to those observed for patients carrying E46K or A53T mutations [17] . The G51D mutation is associated with early onset of disease [15] . Over the years , numerous in vitro and in vivo studies confirmed the toxic potential of both wild type ( WT ) and PD-linked ASYN mutants [18] , [19] . In vitro , these ASYN mutations alter the aggregation process and interfere with oligomerization , fibril formation , and subcellular distribution [20] , [21] , [22] . Upon overexpression , ASYN induces aggregation and cytotoxicity [23] , disrupts vesicular transport [24] , [25] , causes mitochondrial deficits , impairs autophagy [26] , increases sensitivity to oxidative stress [27] , impairs vesicle recycling , neuronal plasticity and synaptic integrity [28] as well as the folding/refolding of SNARE proteins [29] . Several animal models have also been generated based on the overexpression of either wild type or mutant ASYN , but the phenotypes reported are quite diverse [30] , [31] , [32] . Posttranslational modifications ( PTM ) in the context of PD are also controversial . The majority of ASYN in LBs isolated from PD patients is phosphorylated at serine-129 ( S129 ) but whether this is a cause or a consequence of aggregation is unclear [33] , [34] , [35] . Other ASYN residues like serine-87 ( S87 ) and tyrosines-125 , -133 and -136 ( Y125 , Y133 and Y136 ) can be also phosphorylated [36] . SUMOylation , another type of PTM that modulates protein-protein interactions , affects subcellular localization , stability and solubility of target proteins [37] , [38] . Engineered mutants of ASYN to prevent SUMOylation enhanced the tendency to aggregate in cell-based assays and increase cytotoxicity in dopaminergic neurons of the substantia nigra ( SN ) , in vivo [38] . Although aggregation of ASYN is recognized as a central process in synucleinopathies , it is still unclear whether inclusions are toxic or protective [39] . Actually , accumulating evidence suggests ASYN oligomers may constitute the toxic species , rather than mature aggregates [40] , [41] . To overcome these limitations , engineered mutation is a simple way to understand the putative effects impact of determine residues in the context of PD . Several artificial proline mutants of ASYN ( A56P , A30P/A76P double mutant , and A30P/A56P/A76P triple mutant ( TP ) display impaired propensity to fibrilize [40] . A similar effect was reported for mutants disrupting the formation of salt bridges between β-strands of ASYN ( E35K and E57K ) [41] , which increases the formation of oligomers when compared with WT ASYN . Nevertheless , conflicting results obtained in different cell and animal models , and the limited existence of systematic studies comparing the behaviour of WT and ASYN mutants in the same model systems , complicate our understanding of the molecular determinants of ASYN aggregation and toxicity . Here , we conducted a systematic comparison of the effects of PD-linked and engineered ASYN mutants in two established cell-based models of ASYN oligomerization [42] and aggregation [43] . Our findings establish the effects of the different mutants studied and pave the way for the identification of genetic and pharmacological modulators of the various processes studied , opening new perspectives for the design of therapeutic strategies aimed at targeting specific steps of the ASYN aggregation process .
To investigate the molecular determinants of ASYN oligomerization and aggregation in a cellular context , we used site-directed mutagenesis to generate a panel of 19 ASYN point mutants including five mutations associated with familial PD ( A30P , E46K , H50Q , G51D and A53T ) and others known to interfere with different aspects of ASYN biology ( Fig . 1 and Table S1 ) . Then , we analysed the behaviour of each mutant in established paradigms of ASYN oligomerization ( Fig . 2A ) or aggregation ( Fig . 3A ) . In order to assess the effect of ASYN mutations on oligomerization , we used a variant of the Bimolecular Fluorescence Complementation ( BiFC ) assay we previously described [42] , based on the reconstitution of functional Venus fluorescent protein promoted by the interaction between , at least , two ASYN molecules , that enables us to directly visualize the formation of ASYN dimeric/oligomeric species ( hereforth referred to as oligomeric species for simplicity ) in living cells ( Fig . 2A ) [42] . We have previously demonstrated that the efficiency of the ASYN BiFC assay is identical in different cell lines , including HEK cells [42] . Using epifluorescence microscopy we found that , as expected , all the ASYN variants formed oligomers in HEK cells ( Fig . 2B and C ) . One striking observation was that the PD-linked mutant A53T promoted the strongest increase ( ∼50% ) in the accumulation of ASYN oligomers in the nucleus ( Figure 2B and D ) . To compare the extent to which different ASYN variants promoted oligomerization we used flow cytometry to measure the fluorescence intensities of cells expressing the different mutants . We observed an increase in fluorescence intensity for proline and lysine mutants . This increase was around 16 . 3% for A56P mutant , and 16 . 5% for the TP mutant ( Fig . 2C ) . In the case of A30P/A76P double mutant ( DP ) and single A76P mutant we observed a trend towards an increase in fluorescence intensity , but the increase was not statistically significant . Likewise , we found a 28 . 4% increase in oligomerization for the E35K mutant , and 28 . 1% for the E57K mutant . In contrast , we found that the S87E mutant , mimicking phosphorylation at S87 , reduced oligomerization by ∼16% . To investigate whether these effects were explained by differences in the levels of ASYN , we performed immunoblot analysis . We found that the levels of VN-ASYN decreased for almost all mutants ( statistically significant for G51D , S129D and S87A ) and increased for H50Q , A30P/A76P and K96R/K102R ( Fig . 2E , F ) . Interestingly , there was no correlation between the levels of the mutants and the fluorescence signal , suggesting the effects observed were intimately correlated with the effects of the mutations on oligomerization , and not due to differences in the levels of expression of the various ASYN mutants . In parallel , we asked whether the selected mutations altered ASYN inclusion formation . For this , we took advantage of an established paradigm of ASYN aggregation based on the co-expression of SynT and synphilin-1 , an ASYN-interacting protein that is also present in LBs ( Fig . 3A ) [43] . As previously established , human neuroglioma cells ( H4 ) were co-transfected with plasmids encoding each of the SynT variants and synphilin-1 and inclusion formation was assessed 48 hours post-transfection . Since the inclusion pattern was heterogeneous , we defined four categories ( cells without inclusions , with <5 inclusions , with more than 5 and less than 9 inclusions , or ≥10 inclusions ) in order to obtain a more precise assessment of the effects of the mutations . For the PD-linked mutants , we found that A30P increased the percentage of cells without inclusions to ∼70% when compared to WT ASYN . In contrast , the E46K and G51D mutations dramatically increased the percentage of cells with inclusions to ∼90% and ∼80% , respectively ( Fig . 3B , C and Fig . S1 ) . In the case of the proline mutants , we observed that all four mutants reduced the percentage of cells displaying inclusions ( Fig . 3B , C and Fig . S1 ) . For the E35K and E57K mutants , the number and the size of the inclusions varied . Both mutants promoted an increase to ∼70–80% of cells with inclusions ( Fig . 3B , C and Fig . S1 ) . While we predominantly observed the presence of small inclusions with the E57K mutant , we detected a mix of small and larger inclusions with the E35K mutant ( Fig . 3B ) . We also investigated the effect of phosphorylation on ASYN inclusion formation . For this , we screened mutants that block ( S87A , Y125F , S129A and S129G ) or mimic ( S87E , Y125D and S129D ) phosphorylation . We found that mimicking phosphorylation on S87 resulted in a marked decrease in the number of inclusions per cell , with ∼80% of the transfected cells displaying no inclusions ( p<0 . 01 Fig . 3C , Fig . S1A ) . No significant effect was observed with the S87A mutant , suggesting the S87 may normally exist , at least in our cell model , mostly unphosphorylated . Also , no significant differences were observed for Y125D , S129A , S129G , or S129D mutants , when compared to WT SynT . However , we found the Y125F mutation and SUMOylation-deficient mutant ( K96R/K102R ) mutation induced an altered inclusion pattern , with the accumulation of inclusions of different sizes , similar to that observed with the lysine mutant . Immunoblot analysis showed that the levels of ASYN varied depending on the particular mutant being expressed , but we only found a significant increase in the levels of the S129G mutant ( Fig . 3D and E ) . Interestingly , we found a trend towards a decrease in the levels of mutants that promoted accumulation of inclusions or changed the size of the inclusions ( E46K , E57K and K96R/K102R ) . Based on the results obtained in the oligomerization and aggregation paradigms , we decided to focus on seven ASYN mutations ( A30P , E46K , A53T , E35K , E57K , TP and Y125F ) that had the most pronounced effects for subsequent analysis ( Table S2 and Fig . 4 ) . We examined these selected ASYN mutants using different assays , including toxicity measurements , biochemical analysis of ASYN , ASYN secretion , degradation pathways , and Golgi and ER stress ( Fig . 4 ) , in order to obtain detailed information on the cellular effects of specific types of ASYN accumulations . We started by investigating the toxicity of the selected variants of ASYN by taking advantage of the budding yeast as a model of synucleinopathies , as previously described [23] , [44] . In conditions where the expression of ASYN was induced , using galactose-containing media , we found that almost all mutations induced toxicity similar to WT ASYN ( Fig . 5A ) . In line with the observation in H4 cells , the TP mutant did not form inclusions and the A30P mutant strongly impaired inclusion formation ( Fig . 3B-C and 5B-C ) . Neither of these mutants was toxic in yeast ( Fig . 5A ) [23] , [44] . Importantly , we found no significant differences in the levels of expression of all variants tested , ruling out the possibility that toxicity and/or inclusion formation were due to differences in the levels of expression of ASYN ( Fig . 5D ) . To further assess the biochemical nature of the ASYN species visualized by the BiFC assay , we employed non-denaturing polyacrylamide gel electrophoresis ( native-PAGE ) . Immunoblot analysis showed a smear , which is indicative of the accumulation of oligomeric species of various sizes ( Fig . 6A ) . In order to characterize the structure of the ASYN inclusions formed in H4 cells , we used stimulated emission depletion ( STED ) super resolution microscopy ( Fig . 6B ) . STED provided unprecedented access to the fine structure of the ASYN inclusions in the cytoplasm . We focused on selected mutants that displayed extreme patterns of aggregation ( Fig . 3 ) and found that both smaller and larger inclusions are highly compact . For the TP mutant , only diffuse signal was detected , confirming the inability of this mutant to accumulate in inclusions that can be resolved by light microscopy techniques ( Fig . 6B ) . It is widely established that LBs are primarily composed of amyloid filaments of ASYN [2] . To determine whether the inclusions formed by the different ASYN mutants were composed of amyloid-like fibrils , we used thioflavin S ( thioS ) , a dye that binds specifically to amyloid-like structures [26] , [45] . We verified that large inclusions formed by WT or mutant ASYN stained positive for thioS , whereas small inclusions did not ( Fig . 6C ) . Also , thioS stained the inner part of the inclusions ( marked with arrow head ) ( Fig . 6C ) , suggesting the accumulation of mature amyloid-like structures in the inner part of the inclusions . To further characterize these different types of ASYN aggregates , we assessed the detergent solubility of the inclusions formed by the selected ASYN mutants . Interestingly , we observed that TP and E57K accumulated a smaller fraction of Triton X-100 insoluble ASYN species ( Fig . 6D and E ) . To assess the effect of the selected mutations on the distribution of ASYN oligomers inside and outside cells , we used a previously described bioluminescent protein complementation assay ( bPCA ) that enables the detection of oligomeric species with great sensitivity [46] . In this assay , reconstitution of Gaussia princeps luciferase activity upon ASYN oligomerization was used as a readout [47] ( Fig . 7A ) . Consistent with the results obtained with the Venus-based BiFC assay ( Fig . 2A ) , we detected reconstitution of luciferase activity with all mutants tested . However , we observed a strong increase in intracellular ( Fig . 7B ) and extracellular ( Fig . 7C ) luciferase activity with the TP and Y125F ASYN mutants when compared to WT ASYN . This indicates that not only these mutations are able to promote increased formation of oligomers inside cells , but also in the extracellular space . To determine if these mutants also promoted the release of oligomeric species we calculated the ratio of luciferase activity in the media compared to that in cells . Interestingly , we found that familiar mutants A30P and A53T showed an increased ratio of luciferase activity outside versus inside cells suggesting that these mutants also promote the secretion of ASYN oligomers ( Fig . 7D ) . We confirmed these differences were not simply due to the accumulation of increased levels of mutant ASYN , since the levels of expression were identical for all mutants tested ( Fig . 7E-F ) . ASYN is a cytosolic protein , but recent studies detected both monomeric and oligomeric forms of ASYN in human cerebrospinal fluid and plasma at low nanomolar concentrations , in both PD and control individuals [48] , [49] . To complement the observations with the bioluminescence complementation assay , we asked whether the selected ASYN mutants were differentially released from cells using the aggregation paradigm described above ( Fig . 3A ) . In this case , we measured the levels of ASYN in the culture medium using a highly sensitive electrochemiluminescence-based immunoassay [50] . We detected extracellular ASYN with all variants tested ( Fig 8A ) . Next , to demonstrate that the release of ASYN was not caused by membrane leakage from unhealthy or dying cells , we performed LDH toxicity measurements in the same media . In fact , in the aggregation paradigm , we observed an inverse correlation between ASYN release and cytotoxicity , where the TP and Y125F mutants appeared as the most toxic forms ( Fig . 8B and C ) . Knowing that ASYN is predominantly degraded by lysosomal pathways and , therefore , requires intact lysosomal function , we used lysosomal associated membrane protein 1 ( LAMP-1 ) as a marker to establish the relationship with aggregation formation . We observed that LAMP-1 partially co-localized with ASYN inclusions ( Fig . 9A ) , suggesting that at least some types of inclusions might be degraded in lysosomes . Interestingly , we observed that some inclusions formed by two of the mutations that primarily accumulated thioS-negative inclusions ( E57K and Y125F , Fig . 6B ) stained positive for LAMP-1 at the periphery ( Fig . 9B ) , reinforcing the idea that specific types of ASYN inclusions are degraded in lysosomes . Given that fragmentation of Golgi apparatus ( GA ) has been described in several neurodegenerative diseases [51] , [52] , [53] , we next investigated the cellular consequences of the accumulation of ASYN oligomers or inclusions on this organelle . For this , we examined the morphological integrity of the GA using fluorescence microscopy of cells immunostained for Giantin , an endogenous transmembrane protein of the cis and medial Golgi complex ( Fig . 10 ) . We defined three types of Golgi structures ( non-fragmented , diffuse and fragmented ) . In general , we observed that in the ASYN oligomerization model there was an increased percentage of cells displaying fragmented Golgi , in comparison to what was observed in the aggregation model ( Fig . 10 A-B and Fig . S2 ) . In particular , we found a statistically significant increase in the percentage of cells displaying fragmentation of the GA for the E35K and E57K mutants ( Fig . 10A and Fig . S2A ) . In the ASYN aggregation paradigm , the GA displayed normal compact morphology near the nucleus ( Fig . 10B and Fig . S2B ) . Recent studies showed that endoplasmic reticulum ( ER ) stress , together with deficient protein degradation , plays a crucial role in the death of dopaminergic cells [54] . Under ER stress conditions , BiP is upregulated and preferentially binds to misfolded proteins in the ER [55] . We observed that oligomeric forms of ASYN promoted an increase in the levels of BiP ( E46K and A53T mutants ) ( Fig . 10C and D ) whereas no differences were detected in the aggregation paradigm ( Fig . 10 E ) . Taken together , these results suggest oligomeric forms of ASYN are more capable of promoting Golgi fragmentation and ER stress than aggregated forms . Accumulating evidence implicates phosphorylation on ASYN aggregation and toxicity [56] . However , it is unclear whether mutations in ASYN affect the typical pattern of phosphorylation . Using the oligomerization assay , we investigated whether ASYN was differentially phosphorylated on S129 ( Fig . S3 ) . Interestingly , we detected a significant increase in S129 phosphorylation in the E35K mutant , and only a trend towards an increase for the familial mutants and E57K ( Fig . S3 ) . The results shows that phosphorylation of ASYN on S129 is altered in the context of specific mutations known to affect the aggregation of the protein .
Given the central role of ASYN in PD and other synucleinopathies , and the uncertainty about the precise molecular mechanisms leading to neurodegeneration , we sought to take advantage of various cellular systems in order to systematically compare a set of ASYN mutants according to their effects on different cell functions . Our systematic analysis enabled us to directly compare the effects of the selected mutations in terms of oligomerization and aggregation ( Fig . 11 ) . We observed that all 19 mutations tested enabled the formation of ASYN oligomers , as assessed by the BiFC system and biochemical methods . Interestingly , we found the different mutants resulted in the accumulation of different types of inclusions . While we did not detect significant differences in the oligomerization induced by familial PD mutations , the A30P mutant displayed a reduced propensity to form inclusions , in contrast to the E46K and G51D mutants , which enhanced inclusion formation when compared with WT ASYN . The fact that we observed a decrease in the number of inclusions with the A30P might be related to the long-range contacts between the N and C-termini , shielding the central domain , which is known to promote aggregation , and reducing the formation of the same types of inclusions observed with WT or other mutants . On the other hand , the robust increase of inclusion formation observed with the E46K mutant might be due to the location of the mutation within the KTKEGV repeats that are involved in alpha-helix formation [57] . The charge difference introduced by the mutation could enhance the destabilization of the protein structure , leading to an increased propensity to aggregate , as reported previously [57] . Recent studies showed that the G51D mutation attenuates aSyn aggregation in vitro [58] , [59] . However , we observed a different trend in our cell models , where G51D increased the number of aSyn inclusions per cell , as observed with the E46K mutant . This might be explained by differences in the cellular environment in the different models , but might also be due to the presence of synphilin-1 in the particular model we used for aSyn aggregation . The same might apply to the H50Q mutant since , in contrast to other studies , we did not observe any difference in comparison to WT aSyn [59] . For the A53T mutant we found increased presence of oligomeric species in the nucleus , but no differences in terms of the aggregation pattern . Thus , additional studies on the effect of ASYN in the nucleus will be important . The use of engineered mutants enables the exploration of the structural determinants of ASYN physiology in the cell . Artificial proline mutations were designed to impair fibrillization in vitro and promote the formation of soluble oligomers [40] . Indeed , in our cell models , the proline mutants resulted in increased oligomerization ( Fig . 2B and C ) and lowered the propensity to form inclusions . In particular , the TP mutant increased oligomerization as assessed by BiFC and bPCA , and reduced inclusion formation . These observations were also confirmed in yeast cells , where the A30P formed fewer inclusions , and the TP mutant completely blocked inclusion formation . However , in contrast to what was observed in primary neurons , worms , and in flies [40] , the proline mutants failed to promote increased cytotoxicity in yeast cells . One possibility is that yeast process the species formed by the proline mutants in a distinct manner , explaining the lower levels of toxicity . The lysine mutants ( E35K and E57K ) were designed to disrupt salt bridges between the β-strands of ASYN and to interfere with its binding to lipid membranes [41] . We found both mutants increased ASYN oligomerization and promoted distinct inclusion patterns . The lack of thioS staining suggests the inclusions may represent either off-pathway or immature species that cannot proceed towards the formation of mature amyloid-like inclusions . Interestingly , in cells where we could detect inclusions formed by the E57K mutant , these appeared surrounded by LAMP1 , suggesting they were targeted to lysosomal degradation . Post-translational modifications ( PTMs ) are important modulators of the structural and functional properties of proteins in health and pathological conditions . Several lines of evidence suggest that phosphorylation of ASYN may play an important role in regulating its aggregation , fibrillogenesis , Lewy body formation , and neurotoxicity in vivo . In addition , it appears that , in vivo , less than 5% of ASYN is normally phosphorylated [60] and that this occurs predominantly in the C-terminus ( S129 and Y125 ) but also in the NAC domain ( S87 ) . However , there is still no consensus on the effects of phosphorylation due to existing contradictory results [56] , [61] , [62] , [63] . S87 is located in the NAC region , which is crucial for ASYN aggregation and fibrillogenesis [64] and is also the region involved in interactions with other proteins [62] . Our study supports the importance the NAC domain in the formation of ASYN inclusions , as the S87E phosphomimic mutant induced different effects than those observed with the S87A mutant ( Fig . 3B and C ) . This is in line with in vitro experiments where the S87E mutant inhibits ASYN aggregation [62] . Again , this effect on the formation of cytoplasmic inclusions might be attributed to changes in the conformation when binding to membranes , as other studies suggested [63] . Moreover , a recent study showed that S87E ASYN reduces aggregation and is less toxic [62] . Detecting significant levels of Y125-P in ASYN in human brain tissues has proven difficult [65] . Our results indicate , in the context of living cells , Y125-P ASYN exhibits similar aggregation properties to WT ASYN , in accordance with what was observed in other systems [65] . We also observed the accumulation of small inclusions for the Y125F mutant that were similar to those formed by the lysine mutants . Recently , it was shown that when SUMO acceptor sites in ASYN ( K96R/K102R ) are modified , SUMOylation is strongly impaired , leading to increased inclusion formation and toxicity [38] . In our study , we observed only a 10% increase in the percentage of cells displaying inclusions and , interestingly , we observed the accumulation of smaller inclusions . These small inclusions promoted by several mutants tested ( E35K , E57K , Y125F and SUMOylation mutants , Fig . 3B ) might represent intermediate species in the aggregation process of ASYN that fail to mature and may , therefore , lead to proteasomal impairment . To gain insight into the cellular consequences of different types of ASYN accumulations , we selected representative mutations for additional studies . ASYN overexpression and/or aggregation can affect the secretory pathway . One of the consequences observed is the disruption of the Golgi and impairment ER-to-Golgi trafficking . Fragmentation of this organelle has been reported in neurodegenerative disorders [51] , [52] , including PD [53] , and it was shown that it occurs in the cells accumulating prefibrillar ASYN aggregates [66] . Indeed , we observed the same trend in our study , where increased Golgi fragmentation was observed in the oligomerization model , particularly with E35K and E57K ASYN . Furthermore , we observed increased levels of BiP with familial mutants of ASYN in the oligomerization model . This was not detected in the aggregation model , further supporting the concept that oligomers are detrimental and disturb proteostasis by affecting the normal intracellular trafficking . Autophagy is a catabolic process that is involved in the control of cellular damage in response to genetic perturbations , aging , and/or environmental toxins . Our study also underscores the interplay between ASYN inclusion formation and autophagy since we found the lysosomal marker LAMP-1 surrounding and concealing mature inclusions , as judged by thioS staining ( Fig . 9B ) . Again , this reinforces the idea that aggregates per se might not be directly harmful to cells but , instead , might constitute an effort for cells to remove abnormal proteins from the cytoplasm . Altogether , the systematic assessment of the effects of different ASYN mutations on its oligomerization and aggregation in cellular models allowed us to address , for the first time , the effect of the selected mutations on a panel of readouts that reflect important aspects of the biology/pathobiology of the protein . While additional studies using alternative models will be important to further dissect the effects of mutations , our study establishes the foundation for testing hypotheses that may open novel opportunities for the development of therapeutic strategies for synucleinopathies .
The primers were designed according to the manufacturer's instructions using the QuickChange Primer Design Program and the web-based program Primer X ( Table S1 ) . Site-directed mutagenesis using QuickChange II Site-Directed Mutagenesis Kit ( Agilent Technologies , SC , USA ) was performed following the manufacturer's instructions . Mutagenesis were performed in the plasmids encoding the ASYN-Venus BiFC system [42] or SynT [43] and confirmed by DNA sequencing . Also , fusion constructs ASYN-hGLuc1 ( S1 ) and ASYN-hGLuc2 ( S2 ) were generated as described previously [42] . Yeast plasmids expressing GFP ( pME3759 ) , human wild-type ASYN-GFP ( pME3763 ) , A30P-GFP ( pME3764 ) , A53T-GFP ( pME3765 ) and TP-GFP ( pME3942 ) from galactose-inducible promoter ( GAL1 ) were described previously [67] . Plasmids harboring E46K-GFP ( pME4085 ) , E35K-GFP ( pME4086 ) , E57K-GFP ( pME4087 ) and Y125F-GFP ( pME4088 ) were generated by site-directed mutagenesis using the same primers as above . Plasmid pME3763 was used as a template for generation of the desired amino acid substitutions . Human neuroglioma cells ( H4 ) were maintained in Opti-MEM I Reduced Serum Medium ( Life Technologies- Gibco , Carlsbad , CA , USA ) and Human Embryonic Kidney 293 ( HEK ) cells were grown in Dulbecco's Modified Eagle Medium ( DMEM , Life Technologies- Invitrogen , Carlsbad , CA , USA ) . Both media were supplemented with 10% Fetal Bovine Serum Gold ( FBS ) ( PAA , Cölbe , Germany ) and 1% Penicillin-Streptomycin ( PAN , Aidenbach , Germany ) . The cells were grown at 37°C in an atmosphere of 5% CO2 . For the bioluminescence complementation assay with ASYN-hGLuc1 ( S1 ) and ASYN-hGLuc2 ( S2 ) constructs , the cells were transfected as described above and conditioned media was collected 48 hours post-transfection and centrifuged for 5 minutes at 3000 g to eliminate floating cells before being used . Forty-eight hours after transfection , culture media was transferred to a new 96 well plate ( Costar , Corning , NY , USA ) . Cells were washed with PBS and replaced with serum- and phenol-red free media . Luciferase activity from protein complementation was measured for conditioned media and live cells in an automated plate reader at 480 nm with a signal integration time of 2 seconds following the injection of the cell permeable substrate , Coelenterazine ( 20 µM ) ( PJK , Kleinblittersdorf , Germany ) . Yeast strain W303-1a ( MATa; ura3-1; trp1Δ 2; leu2-3 , 112; his3-11 , 15; ade2-1; can1-100 ) was used for transformation performed by standard lithium acetate protocol . All strains were grown in Synthetic Complete medium lacking uracil ( SC-Ura ) , supplemented with 2% raffinose or 2% galactose . ASYN expression was induced by shifting yeast cells cultured overnight in raffinose to galactose medium ( OD600 = 0 . 1 ) . Overnight cultures of yeast strains were grown in SC-Ura medium containing 2% raffinose . For induction of the GAL1 promoter , cells were inoculated in SC-Ura medium containing 2% galactose to an OD600 = 0 . 1 and incubated for 6 h . Cell extracts were prepared and the protein concentrations were determined with a Bradford assay . Immunoblotting was performed following standard procedures using anti-ASYN monoclonal antibody ( AnaSpec , CA , USA ) or Cdc28 polyclonal antibody ( Santa Cruz Biotechnologies , Santa Cruz , CA , USA ) as a loading control . To analyse cell growth on solid media , cultures were grown to mid-log phase in SC-Ura medium containing raffinose . Cells were normalized to equal densities , serially diluted 10-fold starting with an OD600 of 0 . 1 , and spotted on SC-Ura plates containing either 2% glucose or 2% galactose . After 3 days incubation at 30°C the plates were photographed . Yeast cell cultures were grown in SC-Ura medium containing 2% raffinose until mid-log phase and transferred to SC-Ura medium supplemented with 2% galactose . Expression was induced for 6 h and fluorescent images were obtained with Zeiss Observer . Z1 microscope equipped with CSU-X1 A1 confocal scanner unit ( YOKOGAWA ) , QuantEM:512SC ( Photometrics ) digital camera and SlideBook 5 . 0 software package ( Intelligent Imaging Innovations ) . For quantification of aggregation at least 300 cells were counted per strain and per experiment . For each strain , the number of cells displaying cytoplasmic foci was reported to the total number of cells counted and displayed as percentage on a column chart . Twenty-four or forty-eight hours after transfection , cells ( HEK or H4 ) were washed with PBS and fixed with 4% paraformaldehyde ( PFA ) for 10 minutes at room temperature ( RT ) , followed by a permeabilization step with 0 . 5% Triton X-100 ( Sigma-Aldrich , St . Louis , MO , USA ) for 20 minutes at RT . After blocking in 1 . 5% normal goat serum ( PAA , Cölbe , Germany ) /DPBS for 1 hour , cells were incubated with primary antibody . Primary antibodies used were: mouse anti-ASYN ( 1∶1000 , BD Transduction Laboratory , New Jersey , USA ) or rabbit anti-ASYN ( 1∶1000 , Abcam , Boston , USA ) , rabbit anti-LAMP-1 ( 1∶1000 , Abcam , Boston , USA ) , anti-Giantin ( 1∶1000 , Abcam , Boston , USA ) for 3 hours or overnight and secondary antibody ( Alexa Fluor 488 donkey anti-mouse IgG and/or Alexa Fluor 555 goat anti rabbit IgG , ( Life Technologies- Invitrogen , Carlsbad , CA , USA ) for 2 hours at RT . Finally , cells were stained with Hoechst 33258 ( Life Technologies- Invitrogen , Carlsbad , CA , USA ) ( 1∶5000 in DPBS ) for 5 minutes , and maintained in PBS for epifluorescence microscopy . For STED microscopy , H4 cells were plated in 15 mm coverslips . Forty-eight hours after transfection , the cells were washed with PBS and fixed with 4% PFA for 30 minutes at RT . The cells were rinse with quenching buffer ( PBS +100 mM of Ammonium Chloride ( Sigma-Aldrich , St . Louis , MO , USA ) ) for 15 minutes . After washing , the cells were permeabilized and blocked ( 2% BSA and 0 . 1% Triton X-100 , in PBS ) for 20 minutes . Cells were then incubated with primary antibodies against ASYN ( 1∶2000 , BD Transduction Laboratory , New Jersey , USA ) in the permeabilization-blocking solution diluted 1∶2 with PBS for 1 hour at RT . Finally , we incubated the cells with a secondary goat anti-mouse antibody labeled with Atto 647N ( 5 µg/mL; Sigma-Aldrich , St . Louis , MO , USA ) . STED images were taken with a Leica TCS STED system ( Leica Microsystems ) with a 100X oil objective ( 1 . 4 numerical aperture , NA , 1003 HCX PL APO CS oil; Leica Microsystems ) . For excitation , we used a 635 nm diode laser , and for the depletion donut ( 130 mW with the 1003 objective ) we used a Spectra-Physics MaiTai multiphoton laser at 750 nm ( Newport Spec- tra-Physics ) . Scans were performed at 1 , 000 Hz and the final images represent an average of 96 scans ( STED ) ( average performed line-by-line ) with the pinhole set at 47 µm . STED images were obtained at 20 . 20×20 . 20 nm pixels . After staining with secondary antibody , cells were incubated with freshly prepared 0 . 05% Thioflavin S ( Sigma-Aldrich , St . Louis , MO , USA ) for 5 minutes . Cells were then washed with 80% EtOH for 5 minutes and , finally , stained with Hoechst 33258 , washed and maintained in PBS for fluorescence microscopy . Nuclear and cytoplasmic fluorescence intensities were quantified using ImageJ software ( http://rsbweb . nih . gov/ij/ ) . Using the freehand tool the nucleus and cytosol were selected and the respective intensities were measured . The results reflect the counting of 25 cells per experiment . Transfected cells were detected and scored based on the ASYN inclusions pattern and classified into four groups: cells without inclusions , less than five inclusions ( <5 inclusions ) , between five to nine inclusions ( ≥5–9 inclusions ) and more than ten inclusions ( ≥10 inclusions ) . Results were expressed as the percentage of the total number of transfected cells obtained from three independent experiments for each mutation . Transfected HEK and H4 cells were scored based on the morphology of the Golgi apparatus and classified into three groups: non-fragmented , diffused and fragmented . Results were expressed as the percentage of the total number of transfected cells . Three independent experiments were performed . HEK and H4 cells were lysed with Radio-Immunoprecipitation Assay ( RIPA ) lysis buffer ( 50 mM Tris pH 8 . 0 , 0 . 15 M NaCl , 0 . 1% SDS , 1% NP40 , 0 . 5% Na-Deoxycholate ) , 2 mM EDTA and a Protease Inhibitor Cocktail ( 1 tablet/10 mL ) ( Roche Diagnostics , Mannheim , Germany ) . To detect phosphorylated-ASYN was added Phosphatase Inhibitor Cocktail ( 1 tablets/10 mL ) ( Roche Diagnostics , Mannheim , Germany ) . Protein concentration was determined using the Bradford assay ( BioRad Laboratories , Hercules , CA , USA ) and the gels were loaded with 40 µg protein after denaturation for 10 minutes at 100°C in protein sample buffer ( 125 mM of 1 M Tris HCl pH 6 . 8 , 4% SDS 0 , 5% Bromphenol blue , 4 mM EDTA 20% Glycerol 10% β-Mercapto ethanol ) . The samples were separated on 12% SDS-polyacrylamide gels ( SDS-PAGE ) with a constant voltage of 110 V using Tris-Glycine SDS 0 . 5% running buffer ( 250 mM Tris , 200 mM Glycin , 1% SDS , pH 8 . 3 ) for 60 minutes . The transfer was carried out to nitrocellulose membrane ( Protran , Schleicher and Schuell , Whatman GmbH , Dassel , Germany ) for 90 minutes with constant current at 0 . 3 A using Tris-Glycine transfer buffer . Membranes were blocked with 5% ( w/v ) skim milk ( Fluka , Sigma-Aldrich , St . Louis , MO , USA ) in 1xTBS-Tween ( 50 mM Tris , 150 mM NaCl , 0 . 05% Tween , pH 7 . 5 ) for 60 minutes at RT . Membranes were further incubated with the primary antibody , either mouse anti-ASYN ( 1∶1000 , BD Biosciences , San Jose , CA , USA ) or rabbit anti-ASYN ( 1∶1000 , Santa Cruz Biotechnologies , Santa Cruz , CA , US ) , anti-BiP ( BD Biosciences , San Jose , CA , USA ) and 1∶1000 mouse anti-β-actin ( Sigma-Aldrich , St . Louis , MO , USA ) in 3% Albumin Bovine Fraction V ( BSA ) /TBS-Tween ( NZYTech , Lisbon , Portugal ) , at RT for 3 hours or overnight at 4°C . After washing three times in TBS-Tween for five minutes , the membranes were incubated for 1 hour with secondary antibody , anti-mouse IgG , or anti-rabbit IgG , horseradish peroxidase labeled secondary antibody ( GE Healthcare , Bucks , UK ) at 1∶10000 in 3% milk/TBS-Tween . Detection was done using Luminol Reagent and Peroxide Solution ( Millipore , Billerica , MA , USA ) and applied to the membrane 1 minute before scanning with in AlphaImager FluoroChem software ( AlphaInnotech ) . Protein levels were quantified using ImageJ and normalized to the β-actin levels . H4 cells transfected with ASYN-hGLuc constructs were washed with cold PBS to remove excess cell culture media . Cell lysis buffer ( 20 mM NaCl , 0 , 6% Deoxycholate , 0 , 6% Igepal , 25 mM Tris pH 8 . 0 , Protease Inhibitor Cocktail tablet , 1 tablet/10 mL ) was added on human H4 cells in 60 mm dishes and incubated on ice for 10 minutes . After cell scraping samples were centrifuged for 10 minutes at 13 , 000 g . Lysates were resolved by electrophoresis on a 4–12% Bis-Tris gradient gel ( NuPAGE Novex Bis-Tris Gel , Life Technologies , Darmstadt , Germany ) according to manufacturer's instructions using NuPAGE MOPS buffer . After transfer to nitrocellulose membrane ( Protran , Schleicher and Schuell , Whatman GmbH , Dassel , Germany ) membranes were blocked in 1x Roti Block ( Roth , Carlsbad , Germany ) for 1 hour at RT . The forward steps were similar as describe above . The blots were directly removed from the blotting chamber and fixed in 0 . 4% PFA in PBS for 30 minutes . The membranes were briefly boiled in PBS , washed very short in TBS-Tween with phosphatase inhibitor ( 25 mM B-Glycerolphosphat , 5 mM NaF , 1 mM Na3VO4 ) , and then were blocked in 5% BSA/TBS-Tween with phosphatase inhibitor for at least 1 hour ( in the cold room ) . Anti-S129 phosphorylation ASYN 1∶1000 , WAKO , Richmond , Virginia , USA ) was prepared in 5% BSA/TBS-Tween with phosphatase inhibitor and incubated overnight in the cold room . After washing tree times with TBS-Tween with phosphatase inhibitor for 10 minutes , the membranes were incubated with secondary antibody ( anti-mouse IgG ) for 1 , 5 hours at RT . Finally they were washed and revealed as previously described . For native PAGE , samples were lysed with detergent-free lysis buffer ( 50 mM Tris HCl pH 7 . 4 , 175 mM NaCl , 5 mM EDTA pH 8 . 0 and Protease Inhibitor Cocktail tablet , 1 tablet/10 mL ) . Native-PAGE was run with detergent-free Tris-Glycine running buffer ( 250 mM Tris and 200 mM Glycin ) and in protein sample buffer ( 1 M Tris HCl pH 6 . 8 , Glycerol 100% , 0 , 4% Bromophenol blue ) . The H4 cells were plated and transfected as previously describe . Using 80 µL lysing buffer 1 ( 25 mM Tris pH 7 . 5 , 150 mM NaCl , 1 mM EDTA , 1% Triton X-100 , ½ tablet of EDTA Protease Inhibitor ) the cells were harvested and centrifuged at 100 . 000 g for 30 minutes at 4°C . Supernatants were collected ( soluble fraction ) and the pellets ( insoluble fraction ) were washed with cold PBS and transferred to new tubes . Once again , the samples were centrifuged at 14 . 000 rpm for 10 minutes at 4°C and the pellets were resuspended in 50 µL lysing buffer 2 ( 75 mM Tris pH 6 . 8 , 3% SDS , 15% Glycerin , 3 . 75 mM EDTA pH 7 . 4 ) . Finally , the samples were sonicated ( 10 pulse/second ) and a western blot was run as previously described . The insoluble fraction was calculated as: and then normalized for ASYN WT . HEK cells were plated and imaged after twenty-four hours , as described above . Then , cells were trypsinized , neutralized with growth medium , centrifuged ( 1500 g at 5°C ) and the pellet was reconstituted in 7-aminoactinomycin D ( 7-AAD , Life Technologies- Invitrogen , Carlsbad , CA , USA ) prepared 1∶1000 in PBS . The fluorescence was measured using a microcapillary system ( GuavaeasyCyte HT system , Millipore ) . 25000 events were counted per sample . A detailed description of the assay procedure has been published before [50] . [50]96-well Multi-Array standard plates ( Meso Scale Discovery , Gaithersburg , MD , USA ) were coated with 30 µl of MJF1 clone 12 . 1 ( kindly provided by Liyu Wu , Epitomics , Burlingame , CA , USA ) as capture antibodies at 3 µg/ml dissolved in PBS buffer and incubated overnight at 4°C without shaking . All washing steps were done three times with 150 µl PBS-T ( PBS supplemented with 0 . 05% Tween-20 ) . After washing away the capture antibodies plates were blocked with 1% BSA/PBS-T for 1 h at RT with shaking at 300 rpm . Standards and biosamples collected from H4 cells were diluted in 1% BSA/PBS-T and applied in 25 µl volumes . Incubation was done for 1 h ( RT , 700 rpm ) . Plates were washed again and 25 µl Sulfo-TAG labelled ASYN ( BD Biosciences , Heidelberg , Germany ) were applied at 1 µg/ml . After a final washing 150 µl 2x Read Buffer T ( MSD ) were applied to the wells and plates were read in a Sector Imager 6000 ( MSD ) . For lactate dehydrogenase ( LDH ) cytotoxicity assay ( Roche Diagnostics , Mannheim , Germany ) the reaction mixture were prepared according to the manufacturer . The growth media from H4 cells were plated in triplicates in a 96 well plate , in a ratio 1∶1 with the reaction mixture . The absorbance measurements were performed in a TECAN Infinite 200 Pro plate reader at 490 nm . To determine the percentage cytotoxicity , the average absorbance values were subtracted with the average absorbance value obtained in the background control . The percentage of toxicity was calculated as indicated by the manufacturer . Data were analysed using GraphPad Prism 5 ( San Diego California , USA ) software and were expressed as the mean ± SEM of at least three independent experiments . The values of ASYN mutations from flow cytometry were normalized to WT ASYN and mean values for each experiment were determined . Statistical differences from WT ASYN were calculated using unpaired Student t-test . Significance was assessed for p≤0 . 05 , where * corresponds to p≤0 . 05 , ** corresponds to p≤0 . 01 and *** corresponds to p≤0 . 001 . | The accumulation of aggregated proteins in the brain is common across several neurodegenerative disorders . In Parkinson's disease ( PD ) , the protein alpha-synuclein ( ASYN ) is the major component of aggregates known as Lewy bodies . It is currently unclear whether protein aggregates are protective or detrimental for neuronal function and survival . The present hypothesis is that smaller aggregated species , known as oligomers , might constitute the toxic forms of ASYN . Several mutations in ASYN cause familial forms of PD . In the laboratory , artificial mutations have been designed to enable the study of the aggregation process . However , different studies relied on the use of different model systems , compromising the interpretation of the effects of the mutations . Here , we addressed this by ( i ) assembling a panel of 19 ASYN variants and ( ii ) by performing a systematic comparison of the effects of the mutations in mammalian cell models . Interestingly , our study enabled us to correlate oligomerization and aggregation of ASYN in cells . Altogether , our data shed light into the molecular determinants of ASYN aggregation , opening novel avenues for the identification of modulators of ASYN aggregation , which conceal great hopes towards the development of strategies for therapeutic intervention in PD and other synucleinopathies . | [
"Abstract",
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"Results",
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"Methods"
] | [
"molecular",
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] | 2014 | Systematic Comparison of the Effects of Alpha-synuclein Mutations on Its Oligomerization and Aggregation |
Parasites of the phylum Apicomplexa cause diseases that impact global health and economy . These unicellular eukaryotes possess a relict plastid , the apicoplast , which is an essential organelle and a validated drug target . However , much of its biology remains poorly understood , in particular its elaborate compartmentalization: four membranes defining four different spaces . Only a small number of organellar proteins have been identified in particular few proteins are known for non-luminal apicoplast compartments . We hypothesized that enlarging the catalogue of apicoplast proteins will contribute toward identifying new organellar functions and expand the realm of targets beyond a limited set of characterized pathways . We developed a bioinformatic screen based on mRNA abundance over the cell cycle and on phyletic distribution . We experimentally assessed 57 genes , and of 30 successful epitope tagged candidates eleven novel apicoplast proteins were identified . Of those , seven appear to target to the lumen of the organelle , and four localize to peripheral compartments . To address their function we then developed a robust system for the construction of conditional mutants via a promoter replacement strategy . We confirm the feasibility of this system by establishing conditional mutants for two selected genes – a luminal and a peripheral apicoplast protein . The latter is particularly intriguing as it encodes a hypothetical protein that is conserved in and unique to Apicomplexan parasites and other related organisms that maintain a red algal endosymbiont . Our studies suggest that this peripheral plastid protein , PPP1 , is likely localized to the periplastid compartment . Conditional disruption of PPP1 demonstrated that it is essential for parasite survival . Phenotypic analysis of this mutant is consistent with a role of the PPP1 protein in apicoplast biogenesis , specifically in import of nuclear-encoded proteins into the organelle .
Apicomplexa are a phylum of single-celled eukaryotes . All members of the phylum are parasites and a number of species are responsible for important human ( malaria and toxoplasmosis ) and livestock ( babesiosis , theileriosis and coccidiosis ) diseases . Of these , malaria is the most significant global health problem with a billion people at risk of infection and millions of cases annually . Treatment of malaria is constantly threatened by the ability of the parasite to rapidly develop drug resistance . Toxoplasma infections are even more common but do not usually result in overt disease . However , severe toxoplasmosis occurs in immunocompromised individuals and during congenital infection . Toxoplasma has also proven to be a reliable model for the study of those aspects of parasite biology that are shared among the members of the phylum . One of the current prime targets for the development of new anti-apicomplexan drugs is the apicoplast . The apicoplast is a unique chloroplast-like organelle present in most apicomplexans , including the agents of malaria and toxoplasmosis . While no longer photosynthetic , the apicoplast is a center of metabolic activity harboring several major anabolic pathways [1] , [2] . The particular importance of each of these pathways varies between parasite species and specific host cell niches . Apicoplast fatty acid synthesis , for example , is essential in T . gondii and in the liver stage of Plasmodium but dispensable in the erythrocytic phase of malaria [3]–[5] . In contrast , apicoplast isoprenoid synthesis appears to be more uniformly essential and may represent the primary function of the organelle [6]–[9] . Regardless of these specific differences , numerous aspects of apicoplast metabolism and its biogenesis and maintenance are essential for the parasite , and multiple enzymes have been validated as targets genetically and/or chemically . It is likely that many more such targets are yet to be discovered . The evolutionary history of the apicoplast is fascinatingly complex as it is derived from two independent endosymbiotic events . In the first step , endosymbiosis of a photosynthetic cyanobacterium in a eukaryotic auxotroph gave rise to primary plastids . These include the chloroplasts of plants and green and red algae . In the second step , a single-celled red alga was engulfed by a phagotrophic protist ( Figure 1A ) . This alga was retained and eventually transformed into a fully dependent endosymbiont organelle [10] . According to the chromealveolate hypothesis , a single secondary endosymbiotic event involving a red alga lies at the origin of a very large and tremendously diverse group of organisms . The chromealveolates span all the way from single-celled predators and parasites to complex multicellular organisms like the large kelps [11] . While this hypothesis has been challenged at various points ( reviewed in [12] ) , several independent phylogenetic analyses support a robust relationship between the apicoplast and the secondary plastids of other members of the Chromalveolata [10] , [13]–[16] . Each of the two consecutive endosymbioses was accompanied by gene transfer from the genome of the endosymbiont to the host genome . As a result the vast majority of apicoplast proteins are nuclear encoded . Importantly , nuclear encoded apicoplast proteins are of varied origin: some are derived from the cyanobacterium , others are of eukaryotic origin and trace back to the algal endosymbiont or the second eukaryotic host and represent a later invention or adaptation . The complex origin of the apicoplast is not only evident in its genetic makeup but also has morphological consequences , most visible in the presence of four surrounding membranes . The two inner membranes correspond to the chloroplast membranes , which themselves are thought to be homologous to the two membranes of the cyanobacterium [17] . The second outermost membrane , often referred to as the periplastid membrane , is considered a remnant of the algal plasma membrane . Finally the outermost membrane is believed to derive from the host endomembrane system [18]–[20] . Four membranes define four compartments: The inner most is the apicoplast lumen or stroma . This is the most voluminous and home to the organellar genome and most of the apicoplast metabolic pathways characterized to date . Information on the biology of the outer compartments is limited , as very few of their proteins have been discovered as of yet . Nuclear encoded proteins travel to the apicoplast using an elaborate and still poorly understood system of signals and import machineries . For most stromal apicoplast proteins , trafficking depends on a bipartite leader at their N terminus [21] , [22] . Not all peripheral proteins carry such a signal [13] , [23]–[26] . Apicoplast proteins are likely cotranslationally imported into the endoplasmic reticulum , and then travel to the apicoplast through the secretory pathway . This step is the least understood but may follow an endosomal route [27] , delivering proteins to the outermost compartment of the organelle . Three protein translocons have been recently described that translocate protein cargo across the remaining apicoplast membranes . Passage through the periplastid membrane was proposed to be accomplished using an endosymbiont-derived endoplasmic reticulum-associated degradation translocon [13] , [28]–[30] . Genetic evidence from T . gondii demonstrated that apicoplast import depends on one of the components of this complex , Der1 [13] . Transport over the two inner apicoplast membranes appears mediated by complexes homologous to the translocon of the outer and the inner chloroplast membranes ( TOC/TIC ) [20] . A small number of putative components of these complexes have been identified in apicomplexans , including TIC20 , TIC22 , and TOC75 [28] , [31] , [32] , and their critical role in import has been validated through mutant analysis . The import machinery is an obvious candidate for intervention . It employs a large number of potential target enzymes of divergent origin , and a block on import is bound to inhibit most apicoplast functions . We hypothesize that enlarging the catalogue of confirmed apicoplast proteins would provide a fuller understanding of apicoplast biology and ultimately lead to novel chemotherapeutic targets . To date , two major approaches have been used to identify apicoplast proteins . One is based on the identification of homologs of specific pathways or proteins previously described in the plastids of other organisms . Driven by the complete genome coverage of a large number of Apicomplexa , this has been very successful [1] . The second approach uses bioinformatics to search for bipartite targeting motifs in the N-terminus of proteins encoded in the P . falciparum genome [33] . Proteins that lack either the targeting signal or known plastid homologs remain undetected by both approaches . Here , we extended these efforts by taking advantage of functional genomic data to discover novel apicoplast proteins that may not be readily identifiable . We then prioritized candidates based on the phyletic distribution of their homologs . We experimentally tested a first set of genes in T . gondii ( Figure 1B ) . From 30 localized proteins we report the identification of 11 new apicoplast proteins found within different sub-compartments of the organelle . To address their function , we developed a robust system for rapid gene disruption , and demonstrate its efficiency by generating mutants for two essential genes , encoding one luminal and one peripheral plastid proteins .
To more fully understand the many functions of the apicoplast , we wished to establish a comprehensive catalog of organellar proteins . To this end , we developed a multi-step pipeline to prioritize potential candidates . In the first step , we paid particular attention to the timing of gene expression . Behnke and colleagues have recently discovered common periodic profiles of mRNA abundance for distinct organellar proteins in Toxoplasma . This cell cycle timing appears to coincide with the formation of the organelles . Similar time-ordered relationships leading to shared transcription patterns had previously been observed for organellar biogenesis during replication of Plasmodium merozoites in the red blood cell [34] . Thus , co-regulation of transcription might be used as one criterion to identify unknown components of organelles [35] , [36] . To explore whether timing of mRNA expression could be used to identify new apicoplast genes , we compiled a list of 20 proteins , for which apicoplast localization had been confirmed in T . gondii and Plasmodium ( Table S1 ) . The mRNAs encoding these proteins have similar cell cycle profiles with peak levels during the G1 to S phase transition [36] . A group of 369 candidate genes ( Figure 1B , Table S2 ) matched this specific periodic pattern with high correlation ( GeneSpring 11 . 0 , Pearson correlation at 0 . 05 FDR ) . In addition to the original 20 apicoplast proteins the expanded list captured at least 8 previously confirmed apicoplast proteins and proteins from secondary plastids of other organisms ( Table S1 ) demonstrating the predictive value of this approach . To assess the efficiency of this filter , we selected two sets of control genes . One set contained 200 genes whose periodic pattern is inverse to the median expression profile of the 369 genes ( R3 and R4 in [36] ) , none of the confirmed apicoplast genes was found here . Additionally , 200 genes were selected at random from ToxoDB in 3 independent experiments , those included 0 , 0 and 2 of the confirmed apicoplast proteins . This suggested significant enrichment in the list used . In order to further assess candidates and to derive a more focused list suitable for experimental validation , we applied a secondary screen based on molecular phylogeny . The rationale behind this second filter is the hypothesis that genes encoding apicoplast proteins follow a phyletic distribution that is consistent with the evolutionary path that led to the acquisition of the organelle ( Figure 1A ) . The predicted protein sequences of the first 200 candidates in the expression list were retrieved from the T . gondii genome database ( http://toxodb . org/toxo/ ) and used to perform BLAST searches against the NCBI nucleotide collection database ( http://blast . ncbi . nlm . nih . gov/ ) , and the results were systematically analyzed . A set of rules was developed by which we scored the presence or absence of homologs in different clades , and the degree of statistical confidence associated with that identification . Based on these rules we assigned positive or negative points to candidates ( see Materials and Methods for details ) . The parameters were iteratively adjusted such that a positive score ( 1 or more ) was obtained for all the 28 known apicoplast proteins ( Table S1 ) mentioned above . Accordingly only genes with positive scores were considered candidates for further characterization . Half ( 49 . 5% ) of the genes analyzed met this second requirement , and the first 57 genes ( Table S3 ) were selected as a manageable number to further test experimentally . The next step in our pipeline was to determine experimentally whether a candidate gene product indeed targets to the plastid . We introduced a DNA cassette encoding an epitope-tag and a selectable marker directly into the native gene locus via homologous recombination . The efficiency of in-locus tagging is dramatically increased in T . gondii mutants that lack TgKu80 , a key component of the non-homologous end-joining repair machinery [37] , [38] . In this mutant , stable transformation occurs predominantly through homologous recombination . Using the approach described by Huynh and coworkers we attempted 3' endogenous tagging for each of the 57 candidates . After manually reassessing the predicted gene models ( see Materials and Methods ) we designed primers to generate targeting plasmids through ligation independent cloning for all 57 genes ( Table S3 ) . Sequences for 48 genes were successfully amplified and cloned into p3HA . LIC . CATΔpac ( see Materials and Methods ) . Each of the resulting vectors was linearized using a unique restriction site contained within the gene specific sequence ( Table S3 ) . DNA was transfected into the newly engineered TATiΔKu80 line ( see below for details ) and chloramphenicol resistant parasites were established with all 48 constructs . In 30 of these polyclonal pools of stably transfected parasites , we detected unambiguous expression of the epitope-tag by immunofluorescence assay ( IFA ) ( Table S3 ) . 11 candidates ( Table 1 ) were localized to the apicoplast or its immediate proximity ( Figure 2A , B and Figure S1 ) . Other localizations observed included mitochondrion ( 2 gene products ) , nucleus ( 5 ) , Golgi apparatus ( 3 ) , cytosol ( 2 ) , endoplasmic reticulum ( 1 ) , vesicular pattern ( 4 ) and unknown compartments ( 2 ) ( Table S3 , Figure 2C and Figure S2 ) . Apicoplast-localized gene products therefore make up 36% of the total candidates localized in this study . Note that some of the non-apicoplast localized proteins , may nevertheless be involved in apicoplast biogenesis . Such may be the case for vesicular proteins . In that context TGME49_070070 is of particular interest . The predicted protein encoded by this gene contains a C-terminal transmembrane domain and share several domains with proteins known for their role in vesicle trafficking . The protein does not overlap with markers of the Golgi apparatus or the apicoplast ( Figure S2 ) , and its precise subcellular localization is yet to be determined . As documented by IFA , the 11 new apicoplast proteins showed variation in their staining pattern , suggesting that they might target to different sub-compartments within the apicoplast . We divided them into two initial groups: the first group consists of proteins that fully co-localize with the luminal apicoplast marker CPN60 [13] indicating that they likely are also luminal proteins . The second group consists of proteins that , while consistently in proximity , have little or no direct overlap with staining from CPN60 ( Figure 2A , B Figure S1 , Table 1 ) . To gain insight into the function of these proteins we subjected their amino acid sequences to a variety of bioinformatic analyses ( Table 1 ) . We found three to be predicted membrane proteins ( 110770 , 059230 and 039320; we refer here to genes and proteins by the number component of the corresponding gene ID in ToxoDB , so that TGME49_110770 becomes 110770 ) . We then identified several conserved functional domains ( Table 1 ) . The first of those is encoded by 021330 , a homolog of the subunit A of the bacterial type II topoisomerase , DNA-gyrase . The genomes of T . gondii and of the Plasmodium spp . each encode two gyrase subunits ( GyrA and GyrB ) , and both were studied in Plasmodium [39] , [40] . While the apicoplast targeting of 021330 was not surprising based on its Plasmodium homolog , other proteins found in the luminal group were less readily anticipated . Three of those are also likely involved in plastid genome maintenance: 00884 encodes a presumptive ATP-dependent DNA helicase that shows significant sequence similarity with the RecG protein of Gram-negative bacteria . RecG is a double-stranded DNA translocase that is involved in DNA recombination and repair [41] . 059230 encodes a domain found in phage-integrases and DNA break-rejoining enzymes , suggesting that this protein is also part of the machinery that mediates apicoplast DNA recombination events . The third protein , 091670 , contains domains typically found in RNA-helicases . The luminal group also includes 039320 , which encodes a protein with a Bol-A like domain at its C-terminus . The exact molecular function of BolA proteins is still under study . Escherichia coli BolA is suggested to be implicated in the tolerance towards different environmental pressures , since its expression is induced under stress conditions . This leads to the reduction of cell surface area , via switching between elongation and septation systems during cell division [42] . While these observations propose a potential role in transcription regulation , a recent study raises a second interesting possibility in the context of plastids . A component of the Arabidopsis thaliana plastid Iron-Sulfur cluster pathway , SufE , was reported that also contain a BolA domain in its C-terminus [43] . A second protein found in our screen is proposed to be involved in the same pathway: whereas the product of 021920 is annotated as a hypothetical protein , more careful recent analysis suggests its involvement in iron-sulfur cluster biogenesis as a NifU-like scaffold protein [2] . Taken together proteins from the first group show predicted domains that tie them to luminal functions of the plastid consistent with their observed localization . The last luminal apicoplast protein , 039680 ( Figure S1 ) , has no predicted domain from which a putative function could be inferred . The staining pattern of four of the new apicoplast proteins suggested a potential non-luminal localization within the apicoplast . The protein encoded by 002440 is found in close proximity to CPN60 but shows additional punctate staining around the plastid that has not been previously reported ( Figure 2A ) . The product of 001270 shows staining that is very similar to the previously described circum-plastid localization of ATrx [23] ( Figure 2A ) . Finally the proteins encoded by 110770 ( Figure 2B ) and 087270 ( Figures 2B , 3A ) show a patchy peripheral apicoplast localization reminiscent of the localization of Der1 , UfD1 and CDC48 proteins that are believed to reside in the periplastid compartment ( PPC ) – a compartment equivalent to the cytoplasm of the algal endosymbiont [13] . To follow this further we performed cryo-electron microscopy using the endogenously HA-tagged 087270 cell-line , as this protein appeared to be expressed at the highest level . Cryosections were stained with HA specific antibodies followed by gold labeled anti-immunoglobulin . We observed strong labeling of the periphery of the apicoplast , while gold beads were absent from the lumen of the organelle ( Figure 3B ) . Interestingly , we noticed a polarized distribution of the protein in the apicoplast periphery . Studying the two presumptive PPC proteins in greater detail we noticed a restricted distribution across the tree of life: tBLASTn searches against GenBank and various additional nucleotide databases ( including recent EST , GSS and environmental sampling efforts ) revealed homologs of these genes only in a relatively small collection of organisms . Specifically , these proteins are restricted to chromalveolates that maintain a plastid , and to red algae . We further noted that the homologs found in the cryptomonads Guillardia theta and Hemiselmis andersenii are encoded in the nucleomorph , the relict nucleus of the algal symbiont that resides in the PPC of some secondary plastids [44] . Both these nucleomorphs are of red algal origin , while the nucleomorph of the chlorarachniophyte Bigelowiella natans is of green algal origin , and does not encode a homolog of this protein . We performed multiple sequence alignments of the putative orthologs of each of the two putative T . gondii PPC proteins . In both cases we observed a core region conserved among all sequences ( Figure 4A and Figure S5B ) . For 110770 , this conserved region corresponded to a predicted thioredoxine-like domain ( Trx , superfamily 52833 , Figure S5B ) . The statistical support for a Trx-like domain is not equally strong in all homologs ( the E-value for the T . gondii protein is 4e–03 ) . However , we note the invariant presence of a C-X-X-C motif , the critical signature of Trx-domains [45] , in all sequences ( Figure S5B ) . These observations suggest 110770 to be a second plastid-resident thioredoxin-like protein in T . gondii , which we here name ATrx2 . For 087270 the high similarity block was found at the carboxy-terminal end and revealed a conserved glutamine ( Q ) rich patch ( Figure 4A ) . We subjected both protein families to phylogentic analyses ( see Materials and Methods ) . Figure 4B shows a neighbor-joining tree derived from 140 unambiguously aligned residues of 087270 . The apicomplexan proteins form a well-supported clade , diatoms and brown algae form a sister clade to the apicomplexans , and cryptomonads and red algae are the members most distant to apicomplexans . We note that while electron microscopy supported localization of 087270 to the apicoplast periphery , its resolution is generally not sufficient to distinguish between the various peripheral compartments . As described previously , the distances between these compartments are similar to the size of the antibodies used for labeling [13] , and this makes a firm assignment of a precise subcompartment in apicomplexans difficult . However , in diatoms , periplastid proteins appear to localize to a distinct and identifiable compartment referred to as the blob-like structure at the center of the usually bilobed chloroplast [46] . We identified a diatom homolog for 087270 , and decided to localize it in Thalassiosira pseudonana , a genetically tractable diatom . The coding sequence of the corresponding gene ( protein ID 267353 , JGI: Joint Genome Institute: www . jgi . doe . gov/ ) ) was amplified from cDNA and introduced into a diatom expression vector under the nitrate reductase ( NR ) promoter , resulting in a C-terminal GFP fusion [47] . The resulting construct was then used to transform T . pseudonana by microparticle bombardment . Stable transformants were selected in the presence of CloneNAT and cloned on agar plates . Clones were propagated under nitrogen starvation and were tested for expression upon induction by growth in nitrate medium for 24 h . As shown in Figure 3C we detected GFP fluorescence mainly in discrete spots at the center of the chloroplast just outside of the plastid lumen ( as detected by the red autofluorescence of chlorophyll ) . This localization pattern is consistent with the previously described “blob-like” structure in diatoms [46] , [48] . Taken together our analyses suggest that 087270 likely resides in the apicoplast PPC , we will subsequently refer to this protein as peripheral plastid protein 1 ( PPP1 ) . We next wanted to define the biological role of PPP1 using a reverse-genetic loss-of-function approach . We first attempted a direct gene knock-out . We engineered a targeting cosmid by recombineering [6] ( Figure S3 ) , replacing the coding sequence within TOXOW30 with a chloramphenicol acetyl transferase marker . The modified cosmid was transfected into a TgKu80 knock-out background ( generously shared by Huynh and Carruthers ) [38] . Whereas stable drug resistant parasites were established from each of 4 independent transfections , the PPP1 locus was not disrupted in any of the 43 clones that were screened by PCR ( Figure S3 ) . These observations suggested that PPP1 may be indispensable for parasite survival . Similarly , we targeted the loci of four additional newly identified apicoplast proteins , two of which we could disrupt ( Table 1 and discussion ) . We therefore next elected to construct conditional mutants . Several approaches have been developed to isolate conditional mutants [49]-[53] . Out of these the tetracycline-regulated transactivator system has been well suited for our studies of apicoplast biology [3] , [6] , [9] , [13] , [32] . We reasoned that we could further streamline this approach by engineering a parasite strain that combines high efficiency of homologous recombination with regulated gene expression . We recombineered cosmid TOXOY50 to replace the coding sequence of the TgKu80 locus with the phleomycin resistance marker BLE [6] . The resulting modified cosmid was then transfected into a line expressing a tetracycline-regulated transactivator ( TATi ) [51] and stable clones were established by phleomycin selection . A TgKu80 deletion clone was identified by PCR ( Figure 5A ) and successful deletion of the gene was further verified by Southern blot ( Figure 5C ) . In this background we tested the efficiency of an endogenous promoter replacement strategy . In this strategy , the native promoter is replaced with the tetracycline-regulated promoter [51] via double homologous recombination , resulting in a conditional knock-down mutant in a single step ( see scheme in Figure 6A ) . We assessed PPP1's gene model experimentally and identified its true start site ( at position 1 , 435 , 362 on chromosome TGME49_chrV ) . We then engineered a targeting plasmid that flanked a pyrimethamine resistance marker and the regulated promoter with sequences from the PPP1 locus to replace the native promoter with this cassette ( see Materials and Methods , and Figure 6A ) . The resulting vector was linearized with AvrII and transfected into a TATiΔKu80 background , where PPP1 was already endogenously tagged at the C-terminus as described above ( Figure 6A ) . Clones were isolated after pyrimethamine selection and tested for promoter replacement by PCR . Both the C-terminal tagging and the promoter replacement in this ( Tet ) PPP1 ( HA ) line were confirmed by Southern blot ( Figure 6B , and Figure S4 ) . We next tested whether the inserted promoter can be used to control PPP1 expression by addition of anhydrous tetracycline ( ATc ) to the growth medium . We measured PPP1 levels using the endogenous HA tag by Western blot and IFA ( Figure 7A , B ) . In the absence of ATc , we detected the two forms of the protein at a level comparable to the tagged parental strain in which expression is driven by the native promoter ( Figure 7A ) . ATc addition resulted in marked reduction of expression , and 48 hours after addition of ATc the protein was no longer detectable ( Figure 7A ) . In order to establish whether PPP1 is essential for T . gondii survival , parasite growth was examined by plaque assay . ( Tet ) PPP1 ( HA ) parasites , their parental ( ( PPP1 ( HA ) ) and grand-parental ( TATiΔKu80 ) lines were all capable of forming plaques when grown in normal medium ( Figure 7C ) . However , in the presence of ATc , no plaques were observed in the ( Tet ) PPP1 ( HA ) line after incubation times as long as 12 days ( Figure 7C ) , while both parental lines form plaques readily under these conditions . To confirm that this growth defect is solely due to depletion of the targeted gene , we re-introduced PPP1 as a Ty tagged minigene under the control of a constitutive promoter . We constructed a vector to target and disrupt the uracil phosphoribosyl-transferase ( UPRT ) locus and used 5-fluorodeoxyuridine ( FUDR ) to isolate stable transgenic clones . Integration of PPP1Ty was confirmed by PCR ( not shown ) , as well as by IFA using antiTy antibody ( Figure 7D ) . Complementation of ( Tet ) PPP1 ( HA ) with PPP1Ty rescued the growth phenotype observed by plaque assay upon ATc treatment ( Figure 7C ) . To time the onset of growth inhibition , an RFP-RFP transgene [32] was introduced into the ( Tet ) PPP1 ( HA ) line and a clone was isolated by cell sorting [3] . We measured the growth of parasites in the presence and absence of ATc using a real-time fluorescence assay [54] . Parasites cultured in the presence of ATc showed a pronounced growth defect compared with parasites grown without ATc , and this defect became detectable on day 3 ( Figure 7E ) . Taken together these observations demonstrate that PPP1 is essential for parasite growth , and that tightly regulated mutants can be constructed in the TATiΔKu80 strain . To test the reproducibility of the TATiΔKu80 approach , we next attempted a promoter replacement for 039680 , one of the new luminal plastid proteins ( Figure S1 and Figure 7F ) that we identified ( Table 1 ) . This is a protein of unknown function , yet is conserved among apicomplexans . After testing the gene model and identifying its start site ( at position 831330 on chromosome TGME49_chrVI ) , we targeted the promoter as described above for PPP1 , and the corresponding ( Tet ) 039680 clones were isolated after selection with pyrimethamine . Integration of the construct into the locus , and promoter replacement , were confirmed by PCR ( Figure 7G ) . We tested the effect of ATc on the growth of these parasites by plaque assay . ( Tet ) 039680 parasites formed plaques in the absence of ATc , but no plaques were observed upon growth in ATc ( Figure 7H ) . We conclude that 039680 has a critical role in the lumen of the apicoplast , and further that the TATiΔKu80 provides a reliable background for the rapid construction of conditional mutants . Having demonstrated that PPP1 is essential for growth , we were next interested to examine its specific biological role . Based on the putative PPC location we hypothesized that it may act in apicoplast biogenesis . To be able to follow organelle biogenesis , the luminal acyl carrier protein ( ACP ) was tagged with YFP directly in its locus in the mutant line , ( Tet ) PPP1 ( HA ) , to establish a native marker . Our western blot analysis suggested complete depletion of PPP1 between 24 h and 48 h of ATc treatment ( Figure 7A ) . We therefore imaged ACP-YFP parasites at 48 h of growth in ATc . We observed intact plastids of typical shape in essentially all parasites ( Figure 8A ) . This finding argues against a role of PPP1 in apicoplast division which produces aberrant plastid morphology and immediate loss of apicoplasts due to unequal segregation [55] . However , we noted that the intensity of the ACP-YFP signal in the organelle appeared to be reduced , and we observed an accumulation of the reporter protein outside of the plastid ( Figure 8A arrowhead ) . This phenotype was observed previously in mutants of import components and suggested a protein import defect . We therefore next studied the maturation of ACP in this mutant in the presence and absence of ATc . Like many apicoplast proteins , ACP is synthesized with a bipartite N-terminal signal , which is cleaved upon reaching the organelle lumen , and this modification depends on import of the protein into the lumen of the organelle [13] , [21] , [32] . In cells grown in the absence of ATc , two bands are detected by western blot corresponding to the premature and mature protein forms ( Figure 8B -ATc ) . In contrast , cells grown for 48 hours in ATc show a pronounced reduction of the mature form with concomitant accumulation of premature ACP-YFP ( Figure 8B , C 48 h ) . We no longer detected the mature form after 72 hours of ATc treatment ( Figure 8B , C 72 h ) . These observations suggested that loss of PPP1 results in an import defect . As an independent measure of protein import , we determined the level of lipoylation of the apicoplast pyruvate dehydrogenase E2 subunit ( PDH-E2 ) . This was measured in real time by pulse-chase labeling after ATc treatment . Proteins are then immunoprecipitated with an antibody specific for lipoic acid . Protein import is a prerequisite for lipoylation of PDH-E2 , as the process requires two apicoplast resident enzymes , and the precursor molecule octanoyl-ACP , which is synthesized de novo in the lumen of the organelle by the type II fatty acid synthesis system [3] , [13] , [32] , [56] . We observed lipoylated PDH-E2 in lysate of untreated cells ( Figure 8D , -ATc , C ) . After 24 hours under ATc treatment , the level of lipoylated PDH-E2 was dramatically reduced , and after 2 days was no longer detected ( Figure 8C , +ATc , C and 8D 24 h/48 h ) . At the same time , lipoylation of mitochondrial proteins remained unchanged ( Figure 8D mito-E2 bands ) . It was previously documented that ablation of proteins involved in apicoplast import will ultimately result in organelle loss [13] , [32] . Using the ACP-YFP marker , we scored the number of plastids at different time points of ATc treatment . At 24 hour and 48 hour time points , apicoplast numbers are comparable to the untreated control ( over 92% ) . In contrast , at the 72 hour time point , we detect plastids in 61 . 25% of parasites ( Figure 8E ) . This timing is similar to our previous observation for the TgDer1 mutant , a periplastid component of the organellar protein import machinery [13] . Comparing the timing of our various measurements we find that loss of import precedes the loss of the organelle and parasite death . We note that after 72 h growth in the presence of ATc , organelles other than the plastid ( such as the mitochondrion , micronemes and rhoptries ) were indistinguishable from untreated or parental parasites ( data not shown ) .
The apicoplast has exceptional potential as a target for anti-apicomplexan drugs . Its evolutionary history provides a large number of biochemical and cell biological mechanisms that are either absent from the human host or highly divergent . Many of these mechanisms are essential for parasite growth , either directly by providing an essential metabolite to the parasite cell , or indirectly by maintaining the organelle and its genome . To date , we have only scratched the surface by studying a small set of targets that were more readily identifiable due to the fact that they are well studied in chloroplasts . Many more targets are likely yet to be discovered . An important step on the way is to define the proteome of the apicoplast . Foth and coworkers took advantage of the presence of the bipartite signal found in many proteins destined to secondary plastids . They developed a prediction strategy for in silico identification of apicoplast proteins , which predicted 466 potential plastid proteins in P . falciparum [33] . More recently functional studies have shown that a significant proportion of apicoplast proteins lack such a signal , or feature a recessed signal peptide that prevents identification by this algorithm [9] , [13] , [24] . The genomes of several chromalveolates have been recently sequenced . This provides an important opportunity as many of these organisms harbor chloroplasts that share their ancestry with the apicoplast , and their targeting elements are more readily predicted [46] , [57]-[59] . In a recent study , Moog and coworkers used these resources together with evidence for functional conservation , gene duplication and targeting signal prediction to search for PPC proteins in the diatom P . tricornutum [48] . Similarly , we chose to intersect data from different sources , weighing the cell cycle expression profiles and the molecular phylogenies of candidate proteins . Our search led to the identification of eleven apicoplast proteins ( Table 1 ) . Of those , five have homologs in P . falciparum , for which a bipartite targeting signal can be computationally predicted using PlasmoAP [33] ( Table 1 ) , and one has a homolog in the recent study by Moog et al ( PPP1 ) . The remaining six are previously unpredicted apicoplast proteins , which reinforces the importance of the use of novel and varying strategies to enlarge the pool of known plastid components . Importantly , all bioinformatics avenues explored to date produced large numbers of both false positive and false negative assignments . Thus the importance of experimental validation cannot be overstressed at this point . In this study we focused on genes which have mRNA abundance peaks in the G1 phase of the tachyzoite cell cycle [36] . Our observation that apicoplast proteins are enriched in this group ( see Materials and Methods ) supports the predictive value of this approach for Apicomplexa , and suggests expression profiling as a potential approach for similar studies on other organelles , or in related organisms . This is also supported by the non-apicoplast localizations we observed . For example , identification of five nuclear proteins adds to the number of genes that support the original report of a G1 wave of DNA replication genes [36] . Moreover mRNAs coding for proteins destined to the organelles involved in invasion ( micronemes , rhoptries and dense granules ) are known to peak in the S/M phase [36] , and those were markedly absent from our dataset . A live imaging study by Nishi and coworkers also supports the notion of the ordered timing of organelle genesis in T . gondii , proposing a model of discontinuous synthesis and targeting of organellar proteins [60] . Following this idea further , we used the now enlarged pool of confirmed apicoplast proteins ( Table S1 ) to ask whether their mRNA expression profiles correlates with apicoplast biological features . We noticed two expression clusters: for 23 genes the expression was highly focused towards the end of G1 while 36 genes showed a broader wave across this cell cycle phase ( Table S1 ) . Interestingly , the putative promoters ( −2 kb to +0 . 1 kb ) of the focused set show a common DNA sequence motif by FIRE analysis ( Figure S6 ) . This motif ( GAGACA ) is near identical to motif #8 ( AGAGACA ) reported by Behnke and coworkers to be enriched in the G1 phase [36] . Such a consensus motif does not emerge with statistical significance from the genes encoding non-apicoplast proteins , or when 28 genes are picked at random from either the list of 369 genes or the entire genome . Identifying a potential common regulator of expression for apicoplast proteins could provide additional avenues to identify apicoplast proteins . One of our main goals was to identify truly novel apicoplast proteins , and the filtering pipeline was adjusted accordingly . As a result , 8 of the newly identified proteins are annotated as predicted hypothetical proteins , half of them are conserved among all apicomplexans . Now we are faced with the challenge of deducing their biological roles , and establishing those which may be suitable future drug targets . We attempted direct gene knock outs in the ΔKu80 background , using recombineered cosmids , and found that 4 of our new genes are likely indispensable for T . gondii , while 2 others are not essential for growth in vitro ( Table 1 ) . A robust and rapid system to generate conditional mutants was needed to pursue this further . We constructed a parasite strain that combines tight control of gene expression using the tetracycline inducible transactivator ( TATi ) system [51] with a low background of non-homologous recombination [37] , [38] . We confirmed the efficiency of locus manipulation in this strain by epitope knock in , and localization of 30 proteins in this study . This strain can also be used to reliably construct mutants by promoter replacement . In this study we isolated two conditional mutants , and we note that using the same reagents we were able to generate six additional mutants in independent studies on apicoplast protein import and genome maintenance ( Swati Agrawal , Sarah Reiff , LS , JD and BS unpublished ) . Promoter replacement not only allows for higher throughput , but also permits the study of genes that are difficult to express as ectopic copies ( either due to large size , or due to the loss of biological activity upon epitope tagging ) . Among the newly identified proteins , PPP1 stands out as a gene unique to the red algal lineage , and that in cryptomonads is encoded in the nucleomorph . Proteins encoded by the nucleomorph are destined to two main locations: the lumen of the plastid and the periplastid compartment [61] . Our light and electron microscopy demonstrated that PPP1 is a protein of the apicoplast periphery ( Figure 3 ) . Pinpointing a particular compartment of the periphery is difficult due to the minute size of the apicoplast . Efforts to use a split GFP assay [32] were hampered by our inability to construct suitably tagged versions of PPP1 . Our results for the T . pseudonana homolog ( Figure 3C ) , as well as the recent localization of a P . tricurnitum homolog [48] to the diatom plastid PPC , make residence in this compartment in apicomplexans very likely . PPP1 lacks a typical apicoplast leader sequence at its N-terminus . However , it possesses a hydrophobic sequence at amino acids 86-106 that could serve as a recessed signal . Such recessed signals were previously described for apicoplast proteins such as CDC48 Ap and Der1Ap [13] , both likely PPC residents . In diatoms , it is thought that a conserved phenylalanine in the leader of luminal proteins prevents cleavage prior to reaching the lumen . Proteins lacking this feature presumably lose their transit peptide in the PPC and thus remain in this compartment [57] . Such a residue is not obvious in apicomplexan plastid proteins , and further studies are required to dissect these targeting elements . In this context it is interesting to note that all T . gondii PPC proteins seem to manifest a patchy staining pattern ( Figure 2B , 3A and [13] ) , rather than the more uniform staining shown for other peripheral proteins ( [23] , [24] and 001270 here ) . Polarized accumulation is particularly evident on electron-micrographs labeled for PPP1 . Overall this is reminiscent of the blob-like structure of the diatom plastid , where PPC markers show a concentrated staining close to the center of the organelle , rather than an even circumferential distribution [46] . In a recent study , Tawk and coworkers showed that phosphatidylinositol 3-monophosphate ( PI3P ) is involved in the trafficking of ER derived vesicles to the apicoplast [27] . The use of a PI3-kinase inhibitor resulted in polarized accumulation of membranes in the plastid periphery [27] . These interesting observations together with the localization of PPP1 may point to a polarization of apicoplast protein import . How this particular structure serves the function of the organelle remains to be defined . Based on proteomic evidence PPP1 is one of the most abundant apicoplast proteins . We show here that it plays an important role in apicoplast and parasite biology . Depletion of PPP1 results in the rapid demise and death of T . gondii . Our studies link PPP1 function to protein import . We demonstrate the loss of transport-dependent post-translational modifications of luminal apicoplast proteins in the absence of PPP1 . The timing of the loss of import when compared to the loss of organelle ( the ultimate phenotype of all apicoplast biogenesis mutants [3] , [13] , [32] , [55] ) suggests import as the primary defect of the PPP1 mutant . Based on the current model for apicoplast protein import [62] , a role for PPP1 may be considered for three steps of the import process: it may interact with the recently described ERAD system to help cargo to enter the PPC , it could chaperone cargo proteins while they cross this compartment , or it might facilitate their interactions with the presumptive TOC complex in order to leave the PPC on the way to the lumen . The second likely PPC protein that we identify here is ATrx2 a conserved thioredoxin-like protein . Interestingly , the ATrx2 CXXC sequence differs from the canonical motif . The classic reductive-type Trxs share a C ( G/P ) PC motif , and protein disulfide isomerases and the oxidative type DsbA protein have conserved a C ( P/G ) HC sequence ( reviewed in [63] ) , whereas ATrx2 and its homologs contain a C ( E/D ) ( H/Y ) C sequence ( Figure S5 ) . Studies have shown that the central residues of the motif define the redox potential of Trx proteins , and control their ability to interact with substrate proteins and to isomerize disulfides [45] . What might be the role of Trx in this compartment , which is derived from the cytoplasm of the algal endosymbiont ? Several lines of evidences connect the redox control with chloroplast protein import ( reviewed in [64] ) . Specifically components of the TOC machinery appear to be subjected to regulation through disulfide bridge formation in domains exposed to the cytoplasm [65] , [66] , which , in the case of secondary plastids , is equivalent to the PPC . A family of non-canonical CXXC containing Trx proteins was recently reported in the chloroplast of Arabidopsis thaliana . At least two of them are distributed between the lumen and membranous fraction of the chloroplast [63] . It is therefore tempting to hypothesize that ATrx2 and PPP1 not only share their localization and peculiar phylogenetic distribution but that both may play a role in the same pathway of apicoplast protein import . Our initial study was focused on developing tools suitable for a large-scale interrogation of apicoplast function . We studied a subset of 50 genes , which produced 11 new apicoplast proteins including several with essential function . The computational and experimental pipeline assembled here has shown power and throughput and should allow us to assemble a comprehensive and prioritized list of potential apicoplast intervention targets .
Parasites were grown in hTERT-BJ1 ( clontech ) cells in supplemented Dulbecco's modified Eagle's medium [67] ) . Parasite cloning and plaque assays were performed in human foreskin fibroblasts ( HFF ) . For the selection of stable transgenic lines , drugs were added as follow: 1 µM pyrimethamine added one day after transfection for one week , 20 µM chloramphenicol added the day of transfection for three weeks , 5 µM FUDR added two days after transfection for one week . To repress the regulated promoter , parasites were grown in the presence of 0 . 5 µM anhydrotetracycline ( ATc ) . Thalassiosira pseudonana ( Hustedt ) Hasle et Heimdal CCMP1335 was grown in an artificial seawater medium ( EASW ) according to the North East Pacific Culture Collection protocol ( http://www3 . botany . ubc . ca/cccm/NEPCC/esaw . html ) at 18°C under constant light . Where indicated , NaNO3 was omitted from the medium ( nitrogen-free medium ) or replaced by 0 . 55 mM NH4Cl ( ammonium medium ) . The mRNA abundance dataset generated by [36] was interrogated using GeneSpring GX 11 . . 5 ( Agilent ) . We searched for genes with periodic expression patterns that matched each of the 20 “baits” ( Table S1 ) , using Pearson correlation at 0 . 05 FDR . A group of 369 candidate genes ( Table S2 ) was composed where each gene showed high correlation to at least one of the genes in the training group . We next screened the first 200 genes in this list using a scoring system to filter candidates based on molecular phylogeny . We used tBLASTn to compare each of our candidate amino acid sequences against translated nucleotide databases . Only hits with E-value scores smaller than 1×10−4 were considered , and the following scores were given to candidates whose homologs were found in the following organisms ( organellar genomes ) : Cyanobacteria ( 10 ) ; Cryptosporidium ( −1 ) ; No Cryptosporidium ( 1 ) ; Plasmodium ( 1 ) ; distribution of homologs among eukaryotes outside Archaeplastida ( −1 to -5 , depending on number of organisms and level of similarity and coverage ) ; none of the latter ( 1 ) ; distribution among Archaeplastida ( 1 to 5 ) ; nucleomorph genome ( 5 ) . We generated multiple sequence alignments using ClustalX . The pairwise alignment parameters included a gap opening penalty of 35 , and gap extension penalty of 0 . 75 . Sequences that were used for alignments were identified on publicly available databases ( see results section ) , and their accession numbers are provided in Table S5 . 140 ( PPP1 ) or 99 ( ATrx2 ) unambiguously aligned amino acid positions were used for analysis using Jalview ( full alignments available on request ) . For PPP1 the alignment presented in Figure 4 was used to generate a bootstrapped neighbor joining in ClustalX with 999 repetitions . The tree was visualized with TreeView . Prior to tagging , the computationally predicted gene models for each of the candidates were re-assessed manually , with particular attention paid to the predicted stop codon . We scrutinized expressed sequence tags ( ESTs ) , peptides identified through proteome-analyses , and the presumptive position of active promoters as identified by chromatin modification ( all accessed through ToxoDB ) . We determined alternative gene models for 4 of the 57 candidates . No in silico evidence was available at the time of assessment to dispute the predicted gene models for the remaining 54 genes . Data from deep sequencing mRNA became available at later stages of our study , and supported the predicted C-terminal assignment for 53 of our models ( Table S3 ) , As the size and processing pattern of internally tagged PPP1 detected by western blots was inconsistent with the gene model , we experimentally determined its N-terminus . We tested three alternative translation start sites for which the respective coding sequences were amplified from cDNA , cloned into an expression vector that fuses a C-terminal myc epitope tag [32] and transfected into parasites . Immunofluorescence showed that only the start codon at position 1 , 435 , 362 on chromosome TGME49_chrV resulted in apicoplast targeting ( data not shown ) . Western blot analysis of this strain resulted in bands of sizes indistinguishable to those detected from the endogenously HA-tagged protein ( not shown ) . Similarly , for 039680 we experimentally established an alternative start site from the predicted gene model at position 831330 on chromosome TGME49_chrVI . The new gene models are reported in user comments on the corresponding gene pages in ToxoDB and were submitted to genbank ( JN053049 , JN053050 ) . In both cases , the coding sequence was amplified by PCR from T . gondii RH cDNA using primers introducing flanking BglII or BclII and AvrII or XbaI restriction sites , and products were cloned into BglII and AvrII sites in pDT7S4myc [32] . To modify loci with a triple HA epitope tag , the vector p3HA . LIC . DHFR ( generously shared by Huynh and Carruthers [38] ) was modified to replace the DHFR ( Dihydrofolate reductase ) cassette ( HindIII and NotI ) with a chloramphenicol acetyl-transferase ( CAT ) expression cassette excised with the same enzymes from pTUB5CAT [68] . Next , the PacI site found within the CAT-ORF was mutated by site directed mutagenesis ( Stratagene ) to render the PacI site found within the LIC ( ligation independent cloning ) sequence unique , generating p3HA . LIC . CATΔpac . Targeting sequences were amplified by PCR from T . gondii RH genomic DNA and products were LIC-cloned into p3HA . LIC . CATΔpac as described previously [38] . Positive clones were isolated using primer 1595 in combination with the specific forward primer by PCR screen and confirmed by sequencing . pLIC ACP YFP was generated similarly in pLIC YFP DHFR [38] . For complementation and UPRT targeting , PPP1 was first sub-cloned from pDT7S4PPP1 . 1myc into pBTTgTPTty [6] using BglII and AvrII sites . Then the entire TUB_PPP1_Ty cassette was excised with BamHI and SpeI and cloned into the BglII and AvrII sites in pUPRT-KO ( a gift of Brooke and Gubbels ) . The resulting vector , pUPRT_ ( TUB ) PPP1Ty was transfected into ( Tet ) PPP1 ( HA ) and clones were isolated after FUDR ( 5 µM ) selection , and tested for expression of PPP1Ty by IFA . For promoter replacements pDT7S4myc [32] was modified such that the 3' targeting flank ( the coding sequence beginning with the initiation codon ) was cloned between BglII and AvrII , and the 5' flank ( upstream of predicted promoter region ) was cloned between two NdeI sites found in the 5'UTR of the DHFR selection cassette . Promoter regions were predicted based on the ChIP on chip data [69] viewed through ToxoDB . Accordingly , the 1 . 7 kb fragment found in positions 1 , 434 , 003 to 1 , 435 , 362 on TGME49_chrV was amplified with NdeI sites for PPP1 , and the 0 . 7 Kb upstream of position 831330 on chromosome TGME49_chrVI was amplified with MseI sites for 039680 . The resulting vectors were linearized with AvrII ( PPP1 ) and BstBI ( 039680 ) . All primers used are found in Tables S3 and S4 . Immunofluorescence assays were performed as previously described [70] . We used anti-HA antibodies ( Roche ) at a dilution of 1∶200 , anti-CPN60 [13] at 1∶1000 , anti-ATrx1 [23] at 1∶1000 , anti-Ty BB2 hybridoma supernatant [71] at 1∶5 , anti–Myc ( Pierce ) at 1∶100 , anti-GFP ( Roche ) at 1∶200 , anti-TgMys [72] at 1∶1000 , antiROM4 [70] at 1∶100 , anti-IMC3 [73] at 1∶500 . Fluorescence images were acquired using a Delta Vision microscope as described [13] . For cryo-electron microscopy , infected cells were fixed in 4% paraformaldehyde/0 . 05% glutaraldehyde ( Polysciences Inc . ) in 100 mM PIPES buffer . Samples were then embedded in 10% gelatin and infiltrated overnight with 2 . 3 M sucrose/20% polyvinyl pyrrolidone in PIPES at 4°C . Samples were frozen in liquid nitrogen and sectioned with a cryo-ultramicrotome . Sections were probed with anti-HA antibody followed by a rat secondary antibody conjugated to 18 nm colloidal gold , stained with uranyl acetate/methylcellulose , and analyzed by transmission EM as described previously [74] . Confocal fluorescence microscopy of T . pseudonana was performed using an inverted Zeiss LSM 510 laser scanning microscope ( Jena , Germany ) . Fluorescent signals were detected for GFP ( Argon laser , 488 nm ) using a 505/550-nm bandpass filter and chloroplast auto-fluorescence ( HeNe laser , 543 nm ) using a 585 nm long pass filter in the multitrack mode of the microscope . Cells were immobilized for microscopy by a thin slice of 1% Agarose dissolved in EASW medium . Western blotting was performed as previously described [32] . We used anti-HA antibodies ( Roche ) at a dilution of 1∶100 , anti-Tubulin [75] at 1∶1000 , anti-GFP ( Roche ) 1∶200 , anti-Myc ( Pierce ) 1∶100 . Pulse-chase analyses were performed as described previously [13] , [32] with the modification that experiments were performed in T25 flasks . | Apicomplexa are a group of parasites that cause important diseases , including malaria and several AIDS associated opportunistic infections . The parasites depend on an algal endosymbiont , the apicoplast , and this provides an Achilles' heel for drug development . We use Toxoplasma gondii as a model to characterize the biology and function of the apicoplast . In this study we apply a strategy to identify new apicoplast proteins and to prioritize them as potential targets through the analysis of genetic mutants . To aid this goal we develop a new parasite line and a protocol enabling the streamlined construction of conditional mutants . Using this new approach we discover numerous new apicoplast proteins , many of them have no assigned function yet . We demonstrate that function can be deduced using our genetic approach by establishing the essential role in apicoplast protein import for a new factor with intriguing localization and evolutionary history . | [
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] | 2011 | A Systematic Screen to Discover and Analyze Apicoplast Proteins Identifies a Conserved and Essential Protein Import Factor |
Breast cancer is the most common solid organ malignancy and the most frequent cause of cancer death among women worldwide . Previous research has yielded insights into its genetic etiology , but there remains a gap in the understanding of genetic factors that contribute to risk , and particularly in the biological mechanisms by which genetic variation modulates risk . The National Cancer Institute’s “Up for a Challenge” ( U4C ) competition provided an opportunity to further elucidate the genetic basis of the disease . Our group leveraged the seven datasets made available by the U4C organizers and data from the publicly available UK Biobank cohort to examine associations between imputed gene expression and breast cancer risk . In particular , we used reference datasets describing the breast tissue and whole blood transcriptomes to impute expression levels in breast cancer cases and controls . In trans-ethnic meta-analyses of U4C and UK Biobank data , we found significant associations between breast cancer risk and the expression of RCCD1 ( joint p-value: 3 . 6x10-06 ) and DHODH ( p-value: 7 . 1x10-06 ) in breast tissue , as well as a suggestive association for ANKLE1 ( p-value: 9 . 3x10-05 ) . Expression of RCCD1 in whole blood was also suggestively associated with disease risk ( p-value: 1 . 2x10-05 ) , as were expression of ACAP1 ( p-value: 1 . 9x10-05 ) and LRRC25 ( p-value: 5 . 2x10-05 ) . While genome-wide association studies ( GWAS ) have implicated RCCD1 and ANKLE1 in breast cancer risk , they have not identified the remaining three genes . Among the genetic variants that contributed to the predicted expression of the five genes , we found 23 nominally ( p-value < 0 . 05 ) associated with breast cancer risk , among which 15 are not in high linkage disequilibrium with risk variants previously identified by GWAS . In summary , we used a transcriptome-based approach to investigate the genetic underpinnings of breast carcinogenesis . This approach provided an avenue for deciphering the functional relevance of genes and genetic variants involved in breast cancer .
Breast cancer is the most common solid organ malignancy and the most frequent cause of cancer death among women worldwide [1] . Family history is among the strongest known risk factors for breast cancer . Individuals with a first-degree relative affected by the disease have a roughly two-fold increased risk of developing breast cancer themselves , and a more extensive family history or having relatives diagnosed at an earlier age confers yet greater risk [2–4] . A recent twin study estimated the heritability of breast cancer to be 31% [5] , but the combination of rare variants ( e . g . , in BRCA1 , BRCA2 ) identified from linkage studies ( summarized in [6] ) and common single nucleotide polymorphisms ( SNPs ) at roughly 100 loci identified from genome-wide association studies ( GWAS; summarized in [7] ) explain only one-third of the excess familial risk of disease [8] . Thus , a substantial gap remains in the understanding of the genetic factors that contribute to breast cancer risk . The National Cancer Institute’s Up for a Challenge ( U4C ) competition offered a unique opportunity to further elucidate the genetic basis of breast cancer . Seven ethnically diverse GWAS datasets were made available in dbGaP and participants were challenged to use innovative approaches to identify novel loci , genes , and/or genomic features involved in breast cancer susceptibility . Our group leveraged the U4C genotype data along with gene expression datasets to search for evidence of additional genes involved in breast cancer susceptibility . Recently , methods have been developed to leverage genotypic data toward imputing gene expression that can then be evaluated in association studies [9] . These methods are based on strong evidence that expression quantitative trait loci ( eQTLs ) , which contribute to regulating gene expression levels , account for a substantial portion of the risk of various disease phenotypes [10–12] . A reference dataset with genotype and gene expression data is used to derive a set of SNPs that optimally predicts the expression of each gene . These SNPs can then be used to impute genetically regulated gene expression in datasets without measured expression data , and these imputed values can then be tested for associations with a phenotype of interest . Evaluating gene expression with respect to breast cancer risk has the potential to offer insights distinct from those available from traditional GWAS . First , associations with gene expression have clear functional interpretations . In contrast , the functional relevance of SNPs discovered by GWAS usually remains unclear . Second , association testing for genes substantially reduces the multiple testing burden relative to single variant approaches . Third , association testing for gene expression allows for rational combination of multiple SNPs , which may help to enhance weak signals . We conducted a transcriptome-wide association study of gene expression and breast cancer risk by applying an innovative method called PrediXcan [9] that uses eQTL reference transcriptome datasets to impute genetically regulated expression . We used reference expression data from breast tissue and whole blood to identify the SNPs that predict gene expression . We then used the U4C datasets combined with data from the UK Biobank to search for genes for which predicted expression is associated with susceptibility to breast cancer . The approach provided an avenue for deciphering the functional relevance of both genes and SNPs involved in breast cancer development .
After splitting the GWAS of Breast Cancer in the African Diaspora ( African Diaspora ) , Breast and Prostate Cancer Cohort Consortium GWAS ( BPC3 ) , and Multiethnic Cohort GWAS in African Americans , Latinos , and Japanese ( MEC ) datasets into sub-populations , and excluding the Nurses’ Health Study ( NHS2 ) sub-population from the BPC3 ( because it was already included in the Cancer Genetic Markers of Susceptibility Breast Cancer GWAS [CGEMS] dataset ) , we imputed gene expression into 14 separate discovery studies with a total of 12 , 079 breast cancer cases and 11 , 442 controls . In addition , we used 3 , 370 cases and 19 , 717 controls from the publicly available UK Biobank cohort study as a replication population [13] . Additional details of the study populations , genotyping , and quality control ( QC ) process are provided in Table 1 and the Materials and Methods section . The developers of PrediXcan previously determined the cis-eQTL SNPs relevant to the estimation of gene expression in 44 distinct tissue types . The weights that should be applied to each SNP to impute transcript levels in other datasets are maintained in the publicly available database PredictDB . For our study , we elected to use the weights developed based on gene expression in breast tissue and , separately , in whole blood . We used the former for its direct relevance to breast cancer ( developed based on n = 173 samples ) and the latter because the weights were developed based on the largest number of samples among all tissues ( n = 922 ) . Weights for the prediction of breast tissue expression were available for 4 , 473 genes based on 102 , 762 unique SNPs . The mean expected correlation between imputed transcript levels and true gene expression across all transcripts was 0 . 097 . Regarding the prediction of whole blood expression , weights were available for 9 , 791 genes based on 249 , 696 unique SNPs . The mean expected correlation between imputed transcript levels and true gene expression across all transcripts was 0 . 145 . A meta-analysis of the U4C discovery datasets yielded 280 transcripts with imputed breast tissue levels nominally ( p-value < 0 . 05 ) associated with breast cancer risk ( S1A Table ) . We evaluated all of these genes for associations in the UK Biobank data . Our genomic inflation factor was 1 . 07 ( λ1000 = 1 . 01 ) . All genes with a p-value < 0 . 10 in this replication cohort and effect estimates in the same direction as the discovery effect were included in a combined meta-analysis of discovery and replication . Table 2 describes the three genes for which the combined meta-analysis showed evidence of an association with breast cancer . Decreased expression levels of RCCD1 ( p-value: 3 . 6x10-06 ) and DHODH ( p-value: 7 . 1x10-06 ) showed significant associations with breast cancer risk based on a Bonferroni-corrected significance threshold ( 0 . 05 / 4 , 473 = 1 . 1x10-05 ) , and higher expression levels of ANKLE1 demonstrated a suggestive association ( p-value: 9 . 3x10-05 ) . The DHODH association was largely driven by the discovery dataset ( p-value: 2 . 4x10-05 ) with little contribution from replication ( p-value: 0 . 056 ) . Estimates from each of the discovery datasets and the replication dataset are presented in S1 Fig for each of the three genes . While RCCD1 and ANKLE1 have been implicated by GWAS of breast cancer risk , DHODH has not been previously identified . The imputed expression of genes based on whole blood yielded no statistically significant associations with breast cancer risk after multiple testing correction ( Bonferroni significance threshold = 0 . 05 / 9 , 791 = 5 . 1x10-06 ) ( S1B Table ) . Our genomic inflation factor was 1 . 06 ( λ1000 = 1 . 01 ) . However , Table 2 shows results for three genes that showed suggestive evidence of an association ( p-value < 1 . 0x10-04 ) . Notably , decreased expression levels of RCCD1 in whole blood ( as in breast tissue ) were suggestively associated with breast cancer risk ( p-value: 1 . 2x10-05 ) . Furthermore , we found that higher expression levels of ACAP1 ( p-value: 1 . 9x10-05 ) and LRRC25 ( p-value: 5 . 2x10-05 ) were suggestively associated with an increased risk of breast cancer . Estimates from each of the discovery datasets and the replication dataset are presented in S2 Fig for each of the three genes . Neither ACAP1 nor LRRC25 have previously been implicated by GWAS of breast cancer risk . The volcano plots in S3 Fig depict the U4C and UK Biobank meta-analysis summary statistics for 4 , 469 breast tissue transcripts and 9 , 768 whole blood transcripts . Outliers with beta estimates outside three standard deviations from the mean were excluded from the plots–four for breast tissue and 23 for whole blood . The x-axis gives the beta effect sizes reflecting the fold change in gene expression between cases and controls , and the y-axis plots the corresponding -log10 ( p-value ) . S3 Fig is thus illustrative of the differential expression between cases and controls for genes across the transcriptome . For breast tissue expression ( S3A Fig ) , we saw few genes beyond those noted above showing any evidence of association . In contrast , the distribution of p-values for whole blood expression ( S3B Fig ) was slightly broader , albeit with a more stringent threshold for statistical significance . However , among those genes significantly or suggestively associated with breast cancer risk , the magnitudes of the effect sizes were larger for breast tissue expression ( |Beta| ≥ 0 . 15 ) than for whole blood expression ( |Beta| ≤ 0 . 11; Table 2 ) . For the 2 , 840 genes that overlapped , the correlation of the betas for the breast tissue and whole blood analyses was significant ( r2 = 0 . 32; p-value: 2 . 2x10-16 ) . We tested for heterogeneity of the associations across studies in the meta-analysis of the U4C datasets alone , and in the meta-analysis combined with the UK data . These analyses did not indicate any statistically significant heterogeneity ( p-values > 0 . 10 ) . Furthermore , we did not detect heterogeneity within ancestry groups ( p-values > 0 . 15 ) , except for ANKLE1 in the European only meta-analysis ( p-value: 0 . 022 ) . Upon restricting the analysis to women with ER negative breast cancer , however , we no longer found significant heterogeneity ( p-value: 0 . 32 ) . Table 2 indicates the number of SNPs identified by PredictDB for optimal prediction of the genetically regulated expression of each of the genes showing suggestive associations with breast cancer risk . PrediXcan uses an elastic net method to determine the best set of SNPs for predicting gene expression . Because elastic net allows for highly correlated variables in prediction models , some of the SNPs are in high linkage disequilibrium ( LD ) . We evaluated associations between each of the SNPs and breast cancer risk ( S2 Table ) ; those achieving nominal ( p-value < 0 . 05 ) significance in a meta-analysis of the U4C and UK Biobank data are displayed in Table 3 . The tables also indicate the proportion of total weight attributed to each SNP in the gene prediction models . The sum of the relative weights for all SNPs contributing to the prediction of any given gene always equals to one , and the SNP ranking remains static . Raw weights used for gene expression prediction can be found within the GTEx and DGN PredictDB databases . Fig 1 displays the location of eQTL SNPs for the genes for which breast tissue expression levels were associated with breast cancer risk . The y-axis indicates the strength of association between the SNPs and breast cancer risk and each point is sized based on the relative contribution of the variant to gene expression . Among the 24 SNPs predicting expression of RCCD1 , rs3826033 showed the strongest association with breast cancer risk ( joint p-value: 9 . 5x10-06 ) . It contributed 13% of the weight for predicting RCCD1 expression , third only to rs2290202 ( 24% ) and rs17821347 ( 16% ) . rs2290202 was also strongly associated with breast cancer risk ( p-value: 1 . 7x10-05 ) . It should be noted that rs3826033 and rs2290202 are in high LD ( r2 = 0 . 97 in 1000 Genomes Phase 3 European populations ) , and both SNPs are within close proximity of RCCD1 relative to the other eQTL SNPs . In contrast , rs17821347 is furthest away from RCCD1 among SNPs predicting RCCD1 expression and showed no evidence of an association with breast cancer risk ( p-value: 0 . 89 ) . Among the remaining RCCD1 eQTLs , only rs4347602 showed a nominal association ( p-value: 2 . 4x10-03 ) ; it has not previously been identified by GWAS . All three nominal associations that we identified for SNPs predicting DHODH expression in breast tissue have not been implicated by GWAS . rs3213422 showed the strongest signal ( p-value: 4 . 5x10-06 ) and also contributed the majority of the weight ( 56% ) among the seven SNPs predicting of DHODH expression . Both rs2240243 and rs12708928 ( r2 = 1 . 0 ) are in moderate LD with rs3213422 ( r2 = 0 . 50 for both variants ) and also showed evidence of associations with breast cancer risk ( p-values: 1 . 0x10-03 and 1 . 3x10-03 respectively ) . After rs3213422 , the second most weight was contributed by rs7190257 ( 16% ) , which showed no evidence of association ( p-value: 0 . 77 ) . We identified two SNPs out of six total eQTL SNPs predicting ANKLE1 expression in breast tissue that were associated with breast cancer; both have been previously associated with breast cancer risk [14–19] . The SNPs , rs34084277 ( p-value: 4 . 7x10-05 ) and rs8170 ( p-value: 6 . 3x10-05 ) , are in perfect LD ( r2 = 1 . 0 ) and both contributed substantial weight to the prediction of ANKLE1 expression ( 23% and 26% respectively ) . Notably , rs3745162 also contributed substantial weight ( 24% ) , but showed no evidence of an association with breast cancer risk ( p-value: 0 . 32 ) . Fig 2 depicts the genes for which whole blood expression levels were associated with breast cancer risk . Among the 20 RCCD1 eQTL SNPs , rs3826033 ( p-value: 4 . 1x10-03 ) and rs2290202 ( p-value: 5 . 3x10-03 ) contributed the most weight to prediction ( 33% and 29% respectively ) and were the most strongly associated with breast cancer risk . The other SNPs showing evidence of an association were rs7180016 ( p-value: 7 . 3x10-03 ) , rs11073961 ( p-value: 9 . 9x10-03 ) , rs11207 ( p-value: 0 . 016 ) , rs2285937 ( p-value: 0 . 023 ) , and rs3809583 ( p-value: 0 . 035 ) . rs3826033 , rs2290202 , and rs11207 were included in the both the breast tissue and the whole blood prediction models for RCCD1 expression . Only rs11073961 and rs3809583 have not been previously implicated in breast cancer GWAS . Among the 19 ACAP1 whole blood eQTL SNPs , five were nominally associated with breast cancer risk . Most noteworthy was rs35776863 , which not only had the strongest association with breast cancer risk ( p-value: 1 . 4x10-04 ) , but also contributed nearly half of the weight for predicting ACAP1 expression ( 49% ) . The other SNPs showing evidence of an association were rs9892383 ( p-value: 3 . 6x10-03 ) , rs5412 ( p-value: 8 . 0x10-03 ) , rs4791423 ( p-value: 0 . 018 ) , and rs35721044 ( p-value: 0 . 019 ) . None of these SNPs have been previously implicated in breast cancer GWAS . Out of 33 LRRC25 whole blood eQTL SNPs , five showed evidence of an association with breast cancer risk . Again , the SNP that contributed the most weight ( 25% ) , rs11668719 , also showed the strongest association signal with disease risk ( p-value: 1 . 2x10-05 ) . The next two strongest signals were for SNPs in moderate LD with rs11668719 , namely rs7257932 ( r2 = 0 . 39; p-value: 2 . 5x10-04 ) , which is the only SNP predicting LRRC25 expression previously implicated in breast cancer GWAS , and rs13344313 ( r2 = 0 . 43; p-value: 3 . 2x10-03 ) . Also suggestively associated with breast cancer risk , albeit contributing less than 0 . 1% of the weight for predicting LRRC25 expression , was rs3795026 ( p-value: 0 . 013 ) . The last SNP nominally associated with breast cancer risk was rs7251067 ( p-value: 0 . 041 ) .
In this transcriptome-wide association study , we identified five genes for which genetically regulated expression levels may be associated with breast cancer risk . We also found 23 unique SNPs contributing to the expression levels of these five genes that were associated with disease . Out of the 23 SNPs , seven in breast cancer genes identified by GWAS and one in a breast cancer gene previously unidentified by GWAS have been previously implicated in breast cancer or are in high LD ( r2 > 0 . 50 in 1000 Genomes Phase 3 populations ) with known risk variants . The remaining SNPs have not been previously associated with breast cancer risk . We found that lower predicted expression of RCCD1 ( i . e . , RCC1 domain containing 1 ) in both breast tissue and whole blood was associated with increased breast cancer risk . This finding supports limited existing evidence for the role of RCCD1 in breast cancer . A 2014 GWAS of East Asian women reported a genome-wide significant association for rs2290203 , which is 5 , 712 bp downstream of RCCD1 on 15q26 . 1 [20] . The authors then replicated the association in a European population . They also showed a correlation between rs2290203 and expression of RCCD1 [20] , which supported a previous eQTL analysis of human monocytes that indicated that rs2290203 is a cis-eQTL for RCCD1 [21] . A more recent study identified an association between rs8037137 , another 15q26 . 1 SNP in moderate LD with rs2290203 ( r2 = 0 . 59 in 1000 Genomes Phase 3 European populations ) , and both breast and ovarian cancer [7] . The effect alleles of both rs2290203 and rs8037137 decrease RCCD1 expression [7 , 20] , aligning with our finding that lower RCCD1 expression is associated with increased breast cancer risk . Neither rs2290203 nor rs8037137 was among the SNPs included in PredictDB for the prediction of RCCD1 expression . However , these SNPs are in LD with RCCD1 eQTL SNPs that were included in the prediction models , namely rs2290202 ( r2 = 0 . 59 for rs2290203 , r2 = 0 . 99 for rs8037137 ) and rs3826033 ( r2 = 0 . 57 , r2 = 0 . 96 ) . The PrediXcan breast tissue model explains approximately 30% of the variance in RCCD1 expression , and rs2290202 and rs3826033 account for approximately 37% of that variation . The histone demethylase complex formed by RCCD1 protein with KDM8 is important for chromosomal stability and fidelity during mitosis division [22] . It is thus plausible that lower expression of RCCD1 could lead to errors in cell division that could potentially increase the risk of breast cancer . Future studies should evaluate the specific mechanisms whereby reduced RCCD1 expression could be associated with breast cancer risk . ANKLE1 ( i . e . , ankyrin repeat and LEM domain containing 1 ) has been previously implicated in breast cancer . Both cis-eQTLs for ANKLE1 , rs8170 and rs34084277 , among several other SNPs in the 19p13 . 11 region , have been identified as breast cancer risk variants in several GWAS[8 , 14–19 , 23–25] . Little experimental evidence exists regarding associations between over- or under-expression of ANKLE1 and cancer risk . In our study , we found that higher expression levels of ANKLE1 were associated with an increased risk of breast cancer . Variants in the two SNPs positively associated with ANKLE1 expression in our study were also positively associated with breast cancer risk in previous work by Antoniou et al . [14] . With regard to the genotypic association with breast cancer risk , the effect estimates corresponding to the same risk allele were similar . Specifically , for rs8170 , the A allele was positively associated with breast cancer in the previous study ( OR = 1 . 28 among BRCA1 carriers ) and our study ( OR = 1 . 08 ) . Although the direction of effect was not previously reported for rs34084277 , this variant is in almost perfect LD with rs8170 and shares the same direction of effect in our study ( OR = 1 . 09 ) . ANKLE1 is an endonuclease involved in DNA damage repair pathways [26] . Its overexpression could therefore perturb the delicate balance required for DNA damage repair . That SNPs in the 19p13 . 11 locus have also been implicated in ovarian cancer [27 , 28] implies that ANKLE1 may also be involved in hormonally-mediated carcinogenic pathways . To the best of our knowledge , DHODH , ACAP1 , and LRRC25 have not been implicated in GWAS of breast cancer risk . Even though the imputation quality of DHODH ( i . e . , dihydroorotate dehydrogenase [quinone] ) , was lowest among the genes of interest in our study , we still identified a statistically significant association between decreased expression levels of DHODH in breast tissue and breast cancer risk . The existing literature regarding the directionality of association for DHODH and breast cancer is potentially inconsistent; deletion of the 16q22 . 2 locus has been associated with both better prognosis [29] and increased risk of metastasis [30] . Still , DHODH inhibition has been leveraged in the treatment of breast cancer . In particular , a DHODH inhibitor called brequinar has been shown to have modest activity in patients with advanced breast cancer [31] . It is thus difficult to reconcile our findings regarding disease risk with those of existing studies of disease progression . ACAP1 ( i . e . , ArfGAP with coiled-coil , ankyrin repeat and PH domains 1 ) has not been implicated in breast cancer risk , but it has been shown to potentially play a role in disease progression . Its protein product activates the Arf6 protein [32] , the expression of which has been shown to be higher in highly invasive breast cancer than in weakly invasive or noninvasive breast cancer and normal mammary epithelial cells [33] . ACAP1 also interacts with the third cytoplasmic loop of SLC2A4/GLUT4 . SLC2A4 encodes a protein that functions as an insulin-regulated facilitative glucose transporter; inhibition of this gene affects cell proliferation and cell viability , suggesting a potential biological hypothesis for how ACAP1 may be involved with breast cancer [34] . LRRC25 ( i . e . , leucine rich repeat containing 25 ) is more than one megabase away from ANKLE1 at 19p13 . 11 . It is located in a leukocyte-receptor cluster and may be involved in the activation of hematopoietic cells , which play a critical role in innate and acquired immunity [35] . If LRRC25 overexpression results in an elevated inflammatory response , then it could also increase the risk of breast cancer . In a study of the cis-eQTL activity of known cancer loci , the 19p13 . 11 breast cancer risk SNP rs4808801 was most significantly associated with the expression of LRRC25 ( p-value: 3 . 2 x 10-03 ) [36] . rs4808801 is in high LD ( r2 = 0 . 88 in 1000 Genomes Phase 3 European populations ) with the eQTL rs7257932 that we used to impute LRRC25 . It is our understanding that ours is the first study to use PrediXcan to impute eQTLs transcriptome-wide toward evaluating associations with cancer . It is important , however , that it be interpreted in the context of some limitations . The weights housed in PredictDB were largely developed based on Caucasian samples . However , no SNPs that were monomorphic in any of the 14 U4C ancestral populations were included in our analysis . Still , whether or not the weights are valid for application in non-Caucasian populations is unclear and requires further study . Furthermore , true gene expression was unmeasured . Rather , our study evaluated estimated genetically regulated gene expression , sometimes with low imputation quality . The mean expected correlation of imputed genetically regulated gene expression and true gene expression is 0 . 097 for breast tissue and 0 . 145 for whole blood . For most genes , we would not expect the correlation to approach one given that gene expression is regulated by factors other than germline genetics , but because PrediXcan was only recently developed , an appropriate threshold for usable imputation quality is not yet definitive . In the release of PredictDB used here ( dated 8/18/16 ) , the authors only included genes that had a false discovery rate ≤ 5% based on the elastic net models used to generate the SNP weights . With respect to our results , imputation quality seemed related to the number of SNPs included in the gene expression prediction model . It is interesting , however , that we were still able to detect signal for the genes in our study for which expression was predicted by the smallest number of SNPs ( ANKLE1 and DHODH ) . The imputation quality and included genes will likely change as updated versions of PrediXcan and PredictDB become available . How sensitive findings are to PrediXcan updates is an important consideration given that prediction is dependent on the reference panel . In summary , by employing a transcriptome-wide approach , we identified novel associations for gene expression with breast cancer risk that have not surfaced from traditional GWAS designs . The approach also allowed for the development of new hypotheses regarding biological mechanisms at play in breast carcinogenesis . Future research focusing on the downstream effects of imputed gene expression , such as gene-gene interactions and gene co-expression networks , may further advance the characterization of breast cancer etiology .
Discovery analyses used all seven dbGaP datasets provided for the purposes of U4C: African American Breast Cancer GWAS ( AABC ) ; African Diaspora; CGEMS [37 , 38]; BPC3 [19 , 39]; San Francisco Bay Area Latina Breast Cancer Study ( Latina Admixture ) ; MEC; and Shanghai Breast Cancer Genetics Study ( Shanghai ) . All of the U4C datasets provided case-control status , age , and principal components of race/ethnicity . Genotyping platforms varied by study as outlined in Table 1 . Imputed genotypic data were also made available for U4C , but we elected to impute each dataset to the same reference panel as described later on . We used the publicly available UK Biobank as a replication population . The UK Biobank is a cohort of 500 , 000 persons aged 40 to 69 recruited from across the United Kingdom between 2006 and 2010 . Its protocol has been previously described [13] . In brief , every participant was evaluated at baseline in-person visits during which assessment center staff introduced a touch-screen questionnaire , conducted a brief interview , gathered physical measurements , and collected both blood and urine samples . In an interim data release , UK Biobank has made typed genotypic data available for 152 , 736 individuals whose blood samples passed QC . Affymetrix genotyped 102 , 754 of these individuals' samples with the UK Biobank Axiom array [40] and 49 , 982 with the UK BiLEVE array [41] . The former array is an updated version of the latter; it includes additional novel markers that replace a small fraction of the markers used for genome-wide coverage . In all , the two arrays share over 95% of their marker content , and 806 , 466 SNPs that passed QC in at least one batch [41] . In addition to the typed data , UK Biobank has released imputed data for 152 , 249 samples that were not identified as outliers . Imputation was conducted based on a consolidation of the UK10K haplotype and the 1000 Genomes Phase 3 reference panels [42] . It resulted in a dataset of 73 , 355 , 667 SNPs , short indels , and large structural variants . From among the individuals in the UK Biobank with imputed data available , we identified 3 , 370 European ancestry women diagnosed with breast cancer according to ICD-9 ( 174 ) and ICD-10 ( C50 ) codes . Because non-breast cancers are unlikely to metastasize to breast tissue [43] , we assumed that all first diagnoses of cancers in the breast were primary malignancies and included women with prior non-breast cancer diagnoses . Of the 3 , 370 breast cancers included in the analysis , 171 ( 5 . 1% ) had a previous diagnosis of a separate cancer-related condition . A majority of these were nonmelanoma skin cancers ( n = 43 ) or in situ conditions ( n = 50 ) ; the number of cases with other malignancies was very low ( n = 78 , 2 . 3% of total cases ) , and including them was thus unlikely to materially alter our findings . We defined European ancestry individuals as those classified as British , Irish , or any other European background according to the baseline questionnaire . We randomly selected 19 , 717 controls frequency-matched to cases by five-year age groups from among European ancestry females in the UK Biobank cohort without an ICD9 or ICD10 code for any primary or secondary diagnosis of cancer and with imputed genotypic data . We excluded from controls any women with a previous cancer to limit the potential for bias arising from a shared genetic basis underlying different cancers . Age at the time of initial assessment was calculated by subtracting year of birth from year of assessment; month and day of birth were unavailable . The Institutional Review Boards of each project that made the data used here publicly available approved the research . Since these are non-identifiable data , we are exempt from Institutional Review Board approval at our home institution . For each of the seven U4C datasets and the UK Biobank case-control sub-study , we used the KING toolset to calculate pairwise kinship coefficients and remove subjects with up to second degree familial relationships . We found that all participants of the NHS1 were included in both the CGEMS and BPC3 U4C datasets . We thus excluded the NHS1 from the latter dataset . For related individuals , we retained one individual from the relationship pair for potential inclusion in our analyses . As a first QC step for the U4C datasets , we merged all dbGaP consent groups within each of the seven studies and then checked self-reported sex against genotypic data ( i . e . , the X chromosome ) . We excluded all individuals with sex discrepancies as well as any individuals with overall call rates < 0 . 95 . Next , we evaluated the rate of heterozygosity for all subjects . Of the seven U4C datasets , some included data from multiple sub-populations or cohorts ( i . e . , BPC3 , MEC , and African Diaspora ) . As a result , we split BPC3 , having already excluded the NHS1 , into six datasets ( Cancer Prevention Study II [CPSII] , European Prospective Investigation into Cancer and Nutrition [EPIC] , MEC—European , Nurses' Health Study 2 ( NHS2 ) , Polish Breast Cancer Study [PBCS] , and Prostate , Lung , Colorectal , and Ovarian Cancer Screening Trial [PLCO] ) , MEC into two datasets ( MEC—Japanese and MEC—Latina ) , and African Diaspora into two datasets ( African and African American / Barbadian ) . Within the four datasets that we did not split , and in each of the ten newly created split datasets ( 14 datasets total ) , we excluded individuals with a heterozygosity rate greater than three standard deviations from the mean rate . Regarding SNP QC , we excluded those with an array genotyping rate < 0 . 98 in each study , as well as those with a minor allele frequency < 0 . 02 . Our next step was to ensure that all 14 datasets mapped to the same human reference genome ( hg19 ) . We used liftOver to lift datasets mapped to hg18 over to hg19 as necessary . We then ran SHAPEIT for haplotype phasing of each dataset . Finally , we imputed all datasets to the Haplotype Reference Consortium using Minimac3 [44] . Before being made available , UK Biobank data had already undergone extensive individual- and SNP-level QC procedures as previously described [13] . We thus used the data as provided except as outlined in the section below . We also used the imputed data provided by UK Biobank as described in the Study Populations and Genotyping section above . We implemented principal component analysis to assess genetic ancestry in each of the 14 U4C datasets and in the UK Biobank case-control sub-study of unrelated individuals . To do so , we first LD pruned typed SNPs with r2 > 0 . 2 in PLINK . Then we excluded SNPs with > 0 . 2% missingness in the U4C datasets and > 1% missingness in the UK Biobank dataset . With the remaining data , we determined the principal components ( PC ) using EIGENSTRAT within smartpca [45] . Based on the PCs for the U4C datasets , we excluded any individuals outside six standard deviations along any one of the top ten principal components ( S3 Table ) . For the UK Biobank dataset , we first focused on the top two PCs to identify any clusters of individuals that may have comprised separate sub-populations . Upon identifying one such cluster , we excluded outliers with a PC eigenvector value greater than seven standard deviations from the mean; doing so excluded individuals in the identified cluster ( S3 Table ) . Details of the PrediXcan method have been previously described [9] . In brief , PrediXcan uses reference datasets in which both genomic variation and gene expression levels have been measured to train additive models of gene expression . The models are constrained using an elastic net method that allows for the inclusion of highly correlated variables . Estimates from the best fit models are stored in the publicly available database PredictDB . The application of PrediXcan to GWAS datasets entails imputing gene expression across the transcriptome using the weights stored in PredictDB and correlating transcript levels with the phenotype of interest . For these analyses , we accessed the sets of imputation weights referencing the breast tissue transcriptome from the GTEx Project and the set of weights referencing the whole blood transcriptome from the Depression Genes Network ( DGN ) [46 , 47] . The versions of PrediXcan and PredictDB used here were dated 6/29/16 and 8/18/16 , respectively . We used each set of weights to impute the transcriptome in each of our 14 discovery datasets and in our replication dataset based on the subset of SNPs with imputation quality ≥ 0 . 3 . In each dataset , we performed logistic regression to estimate the associations between imputed transcript levels and breast cancer risk , adjusted for the top ten PCs and age . Finally , we combined the results from the 14 discovery datasets and then included the replication dataset using inverse-variance-weighted fixed-effects meta-analyses . We assessed heterogeneity in the meta-analyses of the discovery U4C datasets , and in the joint meta-analyses with the UK data using Cochran’s Q-test as implemented by METAL [48] . When a joint meta-analysis indicated a suggestive association between expression of a particular gene and breast cancer risk , we evaluated associations between its cis-eQTLs and breast cancer risk . Again , we performed logistic regression adjusted for the top ten PCs and age in each dataset and then combined estimates via meta-analysis . | There is a clear genetic basis of breast cancer , and previous work has identified numerous genetic variants that increase risk of this common disease . However , much of the underlying genetic variation in breast cancer remains unexplained . To address this void , as part of the National Cancer Institute’s “Up for a Challenge” ( U4C ) competition , we undertook a large-scale study of genetically regulated gene expression and breast cancer . Specifically , we estimated gene expression levels based on germline genetics for subjects in the seven breast cancer studies provided by U4C and for subjects in the UK Biobank . We then evaluated associations between gene expression and breast cancer and detected three novel and two known breast cancer genes . These genes exhibit potential biological mechanisms for impacting breast carcinogenesis . Our work highlights the value of leveraging different sources of data to more thoroughly study the genetic basis of complex diseases . | [
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] | 2017 | Cis-eQTL-based trans-ethnic meta-analysis reveals novel genes associated with breast cancer risk |
A subset of human papillomavirus ( HPV ) infections is causally related to the development of human epithelial tumors and cancers . Like a number of pathogens , HPV entry into target cells is initiated by first binding to heparan sulfonated proteoglycan ( HSPG ) cell surface attachment factors . The virus must then move to distinct secondary receptors , which are responsible for particle internalization . Despite intensive investigation , the mechanism of HPV movement to and the nature of the secondary receptors have been unclear . We report that HPV16 particles are not liberated from bound HSPG attachment factors by dissociation , but rather are released by a process previously unreported for pathogen-host cell interactions . Virus particles reside in infectious soluble high molecular weight complexes with HSPG , including syndecan-1 and bioactive compounds , like growth factors . Matrix mellatoproteinase inhibitors that block HSPG and virus release from cells interfere with virus infection . Employing a co-culture assay , we demonstrate HPV associated with soluble HSPG-growth factor complexes can infect cells lacking HSPG . Interaction of HPV-HSPG-growth factor complexes with growth factor receptors leads to rapid activation of signaling pathways important for infection , whereas a variety of growth factor receptor inhibitors impede virus-induced signaling and infection . Depletion of syndecan-1 or epidermal growth factor and removal of serum factors reduce infection , while replenishment of growth factors restores infection . Our findings support an infection model whereby HPV usurps normal host mechanisms for presenting growth factors to cells via soluble HSPG complexes as a novel method for interacting with entry receptors independent of direct virus-cell receptor interactions .
Human papillomaviruses ( HPVs ) are small , DNA-containing viruses that infect mucosal and cutaneous epithelium to cause benign and malignant tumors , including many anogenital , oropharyngeal and some skin cancers [1] , [2] . HPVs demonstrate remarkable host restrictions and have strict tropism for stratifying squamous epithelium . HPV virions consist of 360 copies of the L1 capsid protein , 12–72 copies of the L2 protein and the circular viral genome ( ≈8 kb ) condensed by cellular histones . Like a number of other pathogens , HPV entry into target cells is a multistep process initiated by binding to cell surface attachment factors , the most common of which are glycosaminoglycan chains , especially heparan sulfate in proteoglycans ( HSPGs ) [3] , [4] . Binding to these negatively charged polysaccharides is usually electrostatic and relatively nonspecific . Many microbes like HPVs must transfer from HSPG to a distinct secondary receptor responsible for active pathogen internalization [5] . For HPVs this entry receptor has been elusive . Despite intensive investigation , the mechanism of HPV movement from primary HSPG attachment receptors to secondary high-affinity receptors has been unclear . Several studies suggest a role for HPV L2 protein in facilitating infection via interaction with a secondary receptor ( reviewed in ref . [6] ) . In this model , initial virus attachment to HSPG causes a conformational change in L1 that facilitates a critical proteolytic cleavage of L2 by furin , a proprotein convertase [6]–[8] . L2 cleavage is thought to expose the L2 binding site for the secondary cell receptor , lowering the affinity of L1 for HSPG binding and resulting in transfer to the entry receptor [8] . Many , but not all , of the accumulating experimental data support this attractive hypothesis . Although antibodies raised to L2 can neutralize infection [9] and in vitro synthesized L2 peptides and proteins can interact with the cell surface [10] , [11] , there is no direct evidence that L2 in the context of the virion has a function at the cell plasma membrane . Scatchard plot analyses indicate high affinity binding of HPV33 VLP to HeLa cells , with a Kd of ∼85×10−12 M [12] . This strong binding affinity of L1 VLP for cells makes it difficult to conceive how cleavage of L2 , which is not involved in primary binding , could change the affinity of L1 so dramatically as to cause particle dissociation from the HS chain . Moreover , a recent report shows that heparin binding does not induce obvious conformational changes in the HPV16 capsid structure in vitro , except for slight movements of the surface loops and the residues directly involved in oligosaccharide binding [13] . Additional observations that call into question a function for L2 in early entry steps include the fact that L1-only containing virus-like particles ( VLP ) are capable of normal internalization in cells [14]–[16] and PsVs containing a furin-resistant L2 mutant bind , enter , and uncoat in the endosome [7] . Finally , furin cleaved HPV particles can be rendered non-infectious by heparinase treatment , suggesting that furin does more than simply altering HPV L2 proteins [17] . These various observations illustrate the uncertainty of how HPV particles move from HSPGs to an internalization receptor . Syndecan-1 is the most abundant HSPG in keratinocytes and is an HPV attachment receptor [18] , [19] . Syndecans possess enormous molecular and functional diversity owing to modifications of their HS chains by sulfate groups that vary in sulfation degree , length , charge and sugar composition as well as by covalent attachment of chondroitin sulfate chains [20] . These modifications facilitate the interaction of syndecans and other HSPG with a variety of ligands including growth factors ( GFs ) , cytokines , chemokines , extracellular matrix ( ECM ) proteins , proteinases and their inhibitors , viruses from such Families as Retroviridae , Herpesviridae , Papillomavirdae and Flaviviridae , as well as several bacterial pathogens [21] , [22] . This range of ligand interactions allows HSPG and syndecans to participate in many different cellular activities , including organogenesis , GF and cytokine binding , cellular adhesion , and wound healing . By binding soluble GFs , syndecans are able to concentrate these ligands on or near cells and present them to their high affinity cell surface receptors ( depicted in Figure 1A ) [23] , [24] . A prominent characteristic of syndecans is that their extracellular domains can be cleaved to release intact HS-containing ectodomains decorated with bioactive molecules that act as soluble effectors [25] , [26] . All syndecan ectodomains are shed constitutively as a normal part of turnover , but this process is also regulated ( e . g . , certain GFs accelerate shedding ) . The enzymes responsible for syndecan shedding are the matrix metalloproteinase peptidases ( MMPs ) that cleave the syndecan core protein and release the ectodomains ( Figure 1A i ) . MMPs comprise a family of over 25 endopeptidases capable of cleaving all kinds of ECM proteins and cell surface receptors; MMPs also can process a number of bioactive molecules [27] . The HS moieties on syndecans also can be processed by heparinases , which can liberate the HS bound to GFs and bioactive compounds . The many biological functions of shedding syndecans have been summarized in several excellent reviews [21] , [23] , [28] . Because HPVs are known to interact with HSPGs like syndecan-1 at the cell surface and on the ECM , we investigated whether virus particles bound to these molecules could be released in association with HSPG complexes containing bioactive molecules like GFs . Although syndecan-1 HSPGs associate with a number of soluble biological mediators to present them to their high affinity binding receptors [21] , we chose to focus on epidermal growth factor ( EGF ) and fibroblast growth factor 7 ( FGF7 , also known as keratinocyte growth factor [KGF] ) and their cognate receptors EGFR and KGFR ( FGFR2IIIb ) . These receptors are abundant GFRs on human keratinocytes and play vital roles during wound healing [29] , an important mediator of HPV infection of epithelial surfaces [30] . Further , syndecan-1 interactions with EGFR and KGFR ligands are well characterized [21] . We hypothesized that the normal cellular mechanism involving HSPG-GF/bioactive complex release from cells might help explain how HPVs transfer to secondary internalization receptors . Herein we describe two novel findings with respect to initial HSPG binding by a pathogen and movement to specific uptake receptors on host cells . First , we show that HPV particles bound initially to cell surface HSPGs are released as soluble and infectious high molecular weight ( HMW ) complexes with HSPGs and GFs . Second , we provide evidence that the HPV-HS-GF complexes activate signaling cascades that are important mediators of HPV infection of human keratinocytes . The data support a model whereby HPVs bind to uptake receptors indirectly via a GF bridge between the virus and the cognate GFR .
Confocal microscopy and immunoprecipitation ( IP ) were used to verify HPV16 and HPV31 particles bind to syndecan-1 on HaCaT human keratinocytes ( Figure S1 ) . HSPGs including syndecans-1 are actively shed from epithelial cells via the activity of a variety of MMP sheddases including , but not limited to , MMP7 , MMP9 , MT1MMP , ADMTS1 , ADAM17 , and LasA ( Figure 1Ai ) [25] , [31] . Therefore , we hypothesized that HSPG-bound HPV particles would be released from cells in complex with HSPGs and syndecan-1 . To test this theory we collected media from HaCaT cells growing in complete medium ( CM; DMEM containing 10% FCS ) , including cells exposed to HPV16 PsV , and assayed for released syndecan-1 and HPV . Immunoblot analysis for syndecan-1 showed that CM itself contained substantial levels of syndecan-1 ( not shown ) . This finding complicated the determination of virus binding effect on syndecan-1 release . To avoid the issue of free syndecan-1 in CM , we used serum-free medium ( SFM ) or Tyrode's buffer solution ( see Materials and Methods ) . Cells starved in SFM were exposed to HPV16 at 4°C and analyzed for syndecan-1 release after 6 h maintenance in Tyrode's buffer at 37°C . Immunoblot showed that cells released a truncated form corresponding to the syndecan-1 ectodomain , whereas the monoclonal antibody detected the SDS-stable dimeric form of syndecan-1 form in cell lysates ( Figure 2A ) . HPV did not appear to accelerate syndecan shedding as reported for several bacterial pathogens [32] . To characterize HPV16 released from cells , media from virus-exposed cells were concentrated with Amicon 30 ultra-filters then applied on a Sepharose 4B column . This method is used widely to isolate and characterize differently sized complexes [33] . Size-exclusion chromatography fractions were analyzed by SDS-PAGE and immunoblot . As expected based on the large MW of the virus , HPV16 eluted in void volume fractions of this highly porous gel ( fraction 4 contains large complexes or particles with MW>107 Da ) [34] ( Figure S2 ) . Immunoblot analysis of void volume fractions revealed that the amount of HPV released into the medium increased with time ( Figure 2B ) . There are at least two explanations for release of bound HPV16 from the cell surface in CM . First , the non-covalent association of HPV to HSPG is dynamic and viral particles could dissociate from the cell and associate with soluble high concentrations of competing syndecan-1 in the serum-containing CM . Second , HPV could be released in complex with syndecan-1 or HS via the activity of MMP cleaving the anchored ectodomain of the HSPGs or by heparinases liberating HS . The second scenario is consistent with our finding that syndecan-1 and HPV16 are released in Tyrode's buffer , which is devoid of soluble syndecan ( Figure 2A and not shown , respectively ) . If HPV exposure leads to increased MMP activity , gelatin zymography analysis should reveal a higher level of gelatinases in experimental medium in the presence of virus . However , this sensitive and widely used method detects non-active latent MMP forms in addition to active forms [35] , and failed to show a change in MMP levels when virus was present ( Figure 2C ) . Therefore , to more specifically test the involvement of MMP activity in HPV release and infection , we investigated the effects of MMP inhibitors on virus release and infection . The large number of MMPs important for epithelial cell HSPG ectodomain shedding and fact that many posses overlapping substrates in vitro make genetic knockdowns unfeasible [36] . Since we wished simply to determine if HSPG release was related to HPV16 infectivity , we tested broadly active MMP inhibitors , batimastat ( BM ) marimastat ( MM ) , and tissue inhibitor of metalloproteinases 3 ( TIMP3 ) that are typically used to assay the functional consequences of inhibiting MMP activity and the release of their substrates [25] , [31] . Whereas TIMP3 broadly inhibits ADAM-TS4 and ADAM-TS5 and all MMPS tested to date , BM is specific to MMP -1 , -2 , -3 , -7 and -9; MM blocks function of MMP -1 , -2 , -7 , -9 , -14 . These widely used hydroxamic acid MMP inhibitors are well known to block the release of syndecans from cells when used at >1 µM concentrations [37]–[39] . Both BM and MM effectively prevented the release of HPV16 from cells ( Figure 2D , E ) and efficiently reduced HPV16 infection of HaCaT cells ( Figure 2F ) . TIMP3 also prevented HPV16 infection ( Figure 2F ) , but due to lower MMP specificity was not investigated in other assays . A dose-response analysis of BM and MM revealed HPV16 infection inhibition at an IC50 of 400 nM ( BM ) and 1 µM ( MM ) in the absence of visible toxicity ( Figure S3 ) . Thus , the actions of the inhibitors indicate that MMPs are involved the release of virus from cell membranes and that virus release plays an important role in infection . Syndecan HSPGs participate in assembling signaling complexes by accumulating biological mediators including GFs and presenting these factors to their high affinity receptors [40] . Therefore , we predicted that released HPV particles would be in complex with HS ( or HSPG ) of varied sizes along with assorted GFs . Solubilization of the Sepharose 4B void volume fraction ( MW>107 Da ) in SDS-mercaptoethanol sample buffer and boiling caused dissociation of virus resulting in a ∼55 kDa band of HPV16 L1 protein ( Figure 2B ) . We found temperature to be crucial for viral complex dissociation; without heating , HPV16 L1 in SDS-reducing buffer was detected only in a form >150 kDa ( Figure 3A ) . These results indicate the cell surface-released HPV is part of a detergent-resistant and temperature-sensitive HMW complex . To determine the role of HS in this complex , the Sepharose 4B void volume fraction was exposed to heparanase III . Treatment with heparanase III induced partial dissociation of HMW complexes and a considerable amount of soluble HPV16 L1 was detected at ∼55 kDa , indicating that HS is involved in formation of HMW virus-containing complexes . Under non-reducing conditions in HMW fractions , HPV16 L1 migrated well above 250-kDa ( Figure 3B ) demonstrating the reducing conditions caused dissociation of some complexes . This is in contrast to the fact that L1 proteins from purified mature HPV PsV appear as 125 kDa dimers and 195 and 215 kDa trimers under non-reducing SDS-PAGE conditions , but never migrate above 215 kDa [41] . Next we used the HMW void volume Sepharose 4B fraction for analysis of GFs and HS . Individually these molecules are low molecular weight and mainly elute from the column in later fractions ( >9 , Figure S2B ) . Fractionated media from mock exposed HaCaT cells was a control . Immunoblot revealed the presence of amphiregulin ( AREG ) , heparin binding epidermal growth factor ( HB-EGF ) , EGF , HS , and syndecan-1 but only in HMW fractions of media from cells exposed to HPV16 ( Figure 3B ) . Non-reducing SDS-PAGE of this void volume Sepharose 4B fraction showed all of these molecules were present , each appearing to be ≥250 kDa in size . To more specifically assess the direct association of these components , we performed an IP for HPV16 particles released into CM following virus binding to cells at 4°C and shift to 37°C for 6 or 24 h . Immunoblot for HB-EGF , EGF and syndecan-1 demonstrated these factors were in a complex with HPV16 released from cells ( Figure 3C ) . These findings indicate HPV particles released in HMW complexes from cells are “decorated” with syndecan-1 ectodomains , HS , and assorted GFs . Although post-attachment release of incoming virus has been reported for some retroviruses [42]–[45] , to our knowledge this is the first demonstration of an attached incoming non-enveloped virus being liberated from the cell surface into the experimental medium . Further , this is the first report of a mechanism , distinct from dissociation , by which bound virions are released from cells . To ascertain if released virus complexes were infectious , we designed a co-culture transwell system wherein unexposed ( “recipient” ) cells were cultured in chambers below an insert holding “donor” cells that separately had been exposed to HPV16 ( Figure 4A–D ) . As a proof-of-principle , HaCaT cells were tested as both donor cells and recipient cells ( Figure 4E ) . HaCaT donor cells were allowed to bind HPV16 , washed to remove unbound virus and placed atop recipient HaCaT cells where they were incubated with gentle rocking for 24 h . Comparable infection levels were detected between directly PsV-exposed HaCaT donor cells and the recipient HaCaT cells grown in the lower chamber demonstrating the infectivity of the released HPV16 material ( Figure 4E ) . To verify that HPV16 released from donor cells was in a complex with syndecan-1 , we used bead-attached anti-HPV16 antibody instead of recipient cells in the lower chamber . Following capture of the viral particles , non-reducing SDS-PAGE and immunoblot for syndecan-1 confirmed the co-IP of syndecan-1 with released HPV16 ( Figure 4F ) . Similar to when the material released into cell media was subjected to chromatography ( Figure 3 ) , the syndecan-1 plus L1 complex released from donor cells appeared as a HMW form ≥250 kDa . Conversely , only the 35-kDa monomeric form of syndecan-1 was detected via this rabbit antiserum in the cell lysate from cells not exposed to HPV ( Figure 4F ) . To determine the importance of HS in the infectious process following PsV release , we tested wild-type Chinese hamster ovary ( CHO-K1 ) cells and mutant CHO cells defective in HS biosynthesis ( pgsd-677 ) [46] . Consistent with previous reports [18] , [47] , we found the HSPG-defective cells could be infected by HPV16 PsV , but at levels reduced to only ≈5–8% of the wild-type CHO cells ( Figure 4G ) . Using our co-culture system , PsV-exposed CHO-K1 or pgsd-677 donor cells were placed atop of CHO-K1 or pgsd-677 cells grown as recipient cultures . Infections were assayed in paired donor and recipient cells from these co-cultures ( Figure 4G and H , respectively ) . Donor CHO-K1 cells exposed to HPV16 PsV could fully confer infection to recipient CHO-K1 cells ( Figure 4H , white bar ) . Importantly , recipient pgsd-677 cells were also fully able to support infection , but only when CHO-K1 cells were used as PsV donors ( Figure 4H , blue bar ) . These results demonstrate for the first time that HSPG attachment receptors are not required for recipient cell infection when HPV particles are released in complex with HSPG from donor cells that are able to express HSPG . These data show an essential infectious role for the released HMW complexes containing HPV16 decorated with HS on cells that lack HSPG . That donor pgsd-677 cells could confer limited infection to CHO-K1 cells ( Figure 4H , black bar ) may reflect low level dissociation or release of virus to the fully receptive HSPG-wild type CHO-K1 cells . We hypothesized that if specific GFs were present in association with HS-decorated virus , the very high affinity of GFs for their specific receptors ( KD≈10–100 pM ) might permit the GF to determine the fate of the virus-cell interaction prior to HPV entry . If supported , we should detect interaction of virus with GF receptors ( GFR ) . Co-localization of HPV16 with GFs and GFRs was assayed by confocal microscopy and physical associations were tested by co-IP . HaCaT cells exposed to HPV16 were either incubated with fluor-labeled EGF or immunostained for KGFR . Figure 5A , B shows the partial co-localization of HPV16 with EGF and KGFR on the cell plasma membrane . IP of HPV16 PsVs provided additional evidence of interactions with EGFR and KGFR following PsV binding to HaCaT cells . Immunoblot demonstrated the co-IP of EGFR and phospho-KGFR from HaCaT cells following the IP of HPV16 PsVs ( Figure 5C ) . These data confirm the interaction of HPV16 with EGFR and KGFR on the plasma membrane of human keratinocytes . The chromatography and IP data together support the idea that HPV particles become decorated with HS and bioactive molecules like GFs to interact with GFRs . The engagement of GFRs by their ligands induces rapid auto-phosphorylation and downstream signaling . To investigate the involvement of EGFR and KGFR activation and signaling in HPV infections , we analyzed phosphorylation levels of the GFR and mitogen-activated protein kinases ( MAPKs ) ERK1/2 , key enzymes of their pathways [29] , [48] . HaCaT cells starved in SFM for 4 h were incubated with low doses of HPV PsV ( 10–20 vge/cell ) to avoid non-specific events; phosphorylation of target proteins was determined by immunoblot analysis . Consistent with receptor-ligand kinetics , GFRs were rapidly activated within 10 min of treatment with ligands ( GFs or HPV16 ) inducing concomitant phosphorylation of the downstream effector ERK1/2 ( Figure 6A ) . Phospho ( p ) -EGFR ( Y1173 ) levels induced by HPV16 were considerably lower compared to the effect induced by EGF . The Y1173 site of EGFR is involved in MAPK signaling , and importantly , the phosphorylation levels of p-ERK1/2 induced by HPV16 were comparable to the effect of EGF ( Figure 6A ) . Treatment with a potent inhibitor of EGFR ( PD168393 ) , a pan-FGFR inhibitor ( PD173074 ) , or the general receptor tyrosine kinase ( RTK ) inhibitor , genistein , before exposure to GFs or HPV16 diminished the rapid phosphorylation of the target GFRs and downstream p-ERK1/2 . KGFR activation of ERK1/2 can involve EGFR cross talk and activation [49] , [50] , which may explain why EGFR inhibitor PD168393 fully blocks ERK1/2 activation by HPV16 when it also appears KGFR signaling is initiated by the virus . In contrast , daidzein , a genistein analog that lacks RTK blocking activity , did not inhibit HPV16-induced signals ( Figure 6A ) . To specifically query ligand-dependent EGFR activation by HPV16 , we investigated the effects of cetuximab , an EGFR-specific monoclonal antibody that binds to the EGFR extracellular domain with a higher affinity than ligands EGF or TGF-alpha . Cetuximab inhibits EGFR phosphorylation and activation and leads to receptor internalization and degradation [51] . We found that cetuximab fully abrogated EGF- and HPV16-induced phosphorylation of EGFR and p-ERK1/2 in this assay ( Figure 6B ) . GFs strongly activate ERK1/2 proteins [52] and upon stimulation , a significant population of these kinases moves from the cytoplasm into the nucleus [53] . P-ERK1/2-specific immunoblotting of nuclear protein fractions and confocal microscopy each revealed nuclear movement of p-ERK1/2 upon virus-induced activation ( Figure 6C , D ) . The timing of the p-ERK1/2 nuclear migration induced by HPV16 exposure reached maximum ≈10 min post exposure and indicates signaling pathways are activated as early as 5 min post virus-host interaction . These results agree with the report showing that even low-level EGFR activation can fully induced ERK1/2 signals in human keratinocytes [54] . To evaluate the importance of GFRs and tyrosine kinase activation in HPV infection , HaCaT cells were incubated with HPV PsV following pretreatment with and in the presence of a reversible ( AG1478 ) or an irreversible ( PD168393 ) EGFR-specific inhibitor , genistein , cetuximab , and an FGFR inhibitor ( PD173074 ) in CM . Both EGFR specific biochemical inhibitors substantially blocked infection by HPV16 ( ≥50% ) , while genistein almost completely inhibited infection ( Figure 7A ) . Treatment of HaCaT cells with an EGFR blocking antibody ( cetuximab ) or FGFR/KGFR inhibitor ( PD173074 ) reduced infectivity by 50 and 35% , respectively . Similar GFR signaling activation and response to inhibitors was observed with HPV31 PsV and with particles carrying the viral genome ( not shown; [55] ) , ruling out a luciferase-specific inhibition . The complete inhibition of HPV infection by preventing RTK signaling with genistein demonstrates the requirement for this class of receptors in HPV infection . Specific inhibitors of EGFR ( cetuximab , AG1478 , PD168393 ) or of KGFR ( PD173074 ) , while completely abrogating signaling from their respective RTK under brief starvation conditions described in Figure 6 , only partially reduced HPV infection under conditions in CM ( Figure 7A ) . These data show that no single RTK is essential for HPV16 infection of HaCaT keratinocytes; rather , EGFR , KGFR , and potentially other RTK are important mediators of HPV infection . A genetic approach using siRNA to inhibit EGFR expression gave complementary results . Typical transfection efficiency of HaCaT cells was ≈70% as monitored by fluorescein-labeled control siRNA . EGFR knockdown was assessed by immunoblot in four separate transfections at 48 h post transfection and ranged from remaining EGFR expression of 77% to 36% compared to cells transfected with a nonspecific control siRNA ( Figure 7B ) . HPV16 PsV infections were performed 24 h post transfection in matching replicates . Infection levels measured 24 h later were reduced in a dose-dependent manner that closely paralleled the level of EGFR knockdown ( Figure 7C ) . Because progression into early M-phase is needed for HPV infection [56] , it was important to assess whether the inhibitors prevented infection via cell cycle blockade . Therefore , we assayed the fraction of cells in each phase of the cell cycle during the inhibitor treatments under which infections were determined above . Although every condition affected the cell cycle distribution , in no case did an inhibitor arrest the cells in any one cell cycle phase . Further , we found no correlation between infection inhibition and cell cycle distribution under the assay conditions employed ( Figure S4 ) . For example , the distribution of cells in the G1 , S , or G2/M phases of the cell cycle were relatively similar whether cells were grown in CM and infected with HPV16 with no treatment or treated with batimastat , marimastat , PD173074 , PD168393 , or cetuximab . However infection levels ranged from 0% decrease with no inhibitor to nearly 90% reduction with marimastat ( Figure 2E ) . Specifically , the moderate changes observed in the number of cells in G2/M phase were not sufficient to account for the levels of infection inhibition demonstrated for each inhibitor tested . The most striking result was found when using monastrol , which increases the number of cells in late M-phase and promotes infection [56] ( Figure 7D ) . When PD168393 treatment was added with monastrol , a similar cell cycle profile was seen , yet infection was dramatically inhibited by ≈70% ( Figures S4 and 7D , respectively ) . These data indicate that cell cycle effects cannot account for the inhibition of early infection events by these various compounds . The cell binding and infectivity of some viruses are affected by medium composition [57] , [58] . We also found HPV infection of HaCaT cells to be dependent upon the nature of the experimental media . Equal doses of HPV16 were allowed to attach to serum-starved cells in SFM at 4°C and , after washing away unbound virus , cells were incubated at 37°C overnight in SFM or CM . As a positive control HaCaT cells were used where virus binding and infection were both performed in the presence of CM . As shown in Figure 7E , there was no difference in infection levels between the positive control and cells where virus was bound to cells in the presence of SFM and thereafter incubated with CM . This demonstrates that virus binding to initial attachment factors is unaffected by the nature of the media . The addition of serum ( containing various GFs and HSPGs ) to media significantly increased virus infection , indicating an important role for these molecules in virus uptake and infection . As we found that CM contains considerable amounts of syndecan-1 , likely in complex with GF [40] , we predicted depletion of syndecan-1 from the CM would remove a substantial level of components needed for infection . As expected , when the CM was stripped of syndecan-1 by IP , infection levels were reduced to levels close to those in SFM ( Figure 7E ) . Similarly , depletion of EGF from the serum also robustly reduced infection levels ( Figure 7E ) . Based on our finding that bound HPV particles become decorated with HS and are released from cells plus the fact that main constituents of serum include albumin and GF , we performed the reciprocal experiment and tested whether GFs facilitate infection . If GFs are responsible for bridging the soluble HMW HPV-HSPG complexes to secondary receptors , then reconstituting GF in SFM should restore infectivity . Although the addition of albumin did not enhance infectivity in SFM ( not shown ) , the addition of EGF and KGF in SFM dramatically restored infection in dose dependent manners . EGF was able to fully restore infection levels but KGF at the same concentrations was only able to partially restore infection levels to those seen in CM ( Figure 7F ) . Thus , we show syndecan-1 plus either EGF or KGF are required for HPV16 infection of human keratinocytes . Although infection in SFM increased the number of cells in G1 phase , the depletion conditions did not alter the cell cycle profiles significantly from that in CM or in SFM plus EGF ( Figure S4 ) , suggesting cell cycle changes alone could not account for infection inhibition .
Integrins , laminin 332 and syndecans have all been shown to interact with HPVs [19] , [65] , [79] , [80] . Each of these interactions may be primarily due to the association of HPV particles with HSPGs , which are direct modifiers of syndecan-1 and interaction partners with laminin 332 and alpha-6 integrin ( as shown in Figure 1 ) . We demonstrated HPVs associate with HS molecules bearing various GFs and interact with EGFR and KGFR . Together , our findings indicate that binding of HPV to a secondary receptor depends on the nature of active compounds decorating HPV . Reported entry half times for HPVs range from 4 h to 24 h [3] , [14] , [15] , [18] , [81] , [82] . Although we reported a 14 h internalization half-time for HPV31 in HaCaT cells [82] , we also detected HPV31 early transcripts by RT-PCR as early as 4 h post infection [83] . These observations suggest that some HPV particles are able to enter via an infectious route much more quickly than others . The findings in this current study and the normal biology of HS-GF complexes lead us to reason that the protracted and variable HPV entry timing is due to the multiple locations and ways that virions can become decorated with HS-GF complexes ( Figure 1B ) . Particles decorated with HS-GF during isolation or potentially associating with these soluble materials in serum ( Figure 1Bv ) may be readily able to directly engage the entry receptor , effectively bypassing the more time consuming steps of HSPG-GF interaction and subsequent enzymatic release of HMW complexes . Our data and reports from other labs showing RTK/GFR signaling can occur minutes after virus exposure also support this idea [65] , [66] , [84] . The preferential association of HPV with the ECM and basement membrane appears to be due to interactions with laminin 332 ( formerly named laminin 5; Figure 1Bii ) [17] , [80] , [85] . This is likely because laminin 332 is a depot for HS-GF complexes to which HPV can attach [86] , and these active complexes can be liberated by heparinases and sheddases [87] . Our co-culture assay does not differentiate between virus released from the cell surface or the ECM . We previously reported the disappearance of ECM-bound HPV over time [55] suggesting that the release of both ECM- and plasma membrane-bound HPV-HS-GF complexes could contribute to the infectious process . Thus , longer internalization kinetics would be expected if some HPV capsids associate with HS-GF by binding HSPG on the plasma membrane , or by associating with the HS-GF complexes that are normally sequestered on the ECM or the basement membrane . MMP- or heparinase-mediated release of these HWM HPV-HS-GF complexes would be required for subsequent engagement of the secondary receptor ( Figure 1Biv ) . We propose the spectrum and diversity of the active compounds ( e . g . , GFs ) with which HPV-HS could interact clarifies why a single secondary receptor responsible for virus internalization has not been identified . Various active compound-virus complexes bind to distinct receptors and consequently are internalized via different endocytic pathways , which explains the internalization of HPVs dependent on clathrin [15] or relying on caveolin [82] , [88] , as well as pathways independent of both clathrin and caveolin [89] . EGFR and KGFR internalization are typically clathrin-dependent . However , EGFR entry can also involve slower clathrin-independent modes and EGFR associates with caveolae and lipid microdomains , especially when coupled with alpha-6 beta-4 integrin [29] , [90] . Blocking ligand binding or the kinase activity of these receptors with specific inhibitors clearly shows significant roles for these GFRs in HPV infection . CHO cells lack EGFR ErbB1 , but are readily infected with HPVs , further demonstrating the ability of HPV to utilize multiple routes of infection . Similarly , vaccinia virus infection of HeLa cells is EGFR dependent , yet the virus also infects CHO cells using an undefined alternate mechanism [77] . Previously , we showed that organotypic ( raft ) epithelial tissue-derived HPV31 virions infect HaCaT cells in an HSPG-independent manner [91] , whereas HPV31 PsVs from the 293T system are HSPG-dependent in the same cells ( our unpublished data and [85] ) . We speculate that the differences are due to a high level of decoration occurring during virion isolation from the raft tissues , which then allows raft-derived virions to bypass the need for HSPG association on newly exposed naïve cells . This is based on our finding that viral particles extracted from raft tissues are substantially less pure relative to HPV particles obtained from the 293T expression system , likely due to the lower yields of virus particles per cell in the raft system compared to the 293T model [92] . It is probable that low-level HPV capsid decoration occurring during assembly and purification from 293T cells contribute to the basal levels of infection observed in the absence of HSPG or serum components ( Figures 4G , 7EF , refs . [18] , [47] ) . Differences in HPV particle decoration due to isolation techniques could result in quantitatively disparate phenotypes depending upon the assays . The possibility that other structural modifications with functional consequences occur differentially during virion morphogenesis in the raft tissue culture system compared to particle assembly in the 293T system cannot be discounted . Nevertheless , many observations strongly support the biological relevance of differentiation-independent ( e . g . , 293T cell-derived ) HPV particles for functional studies . Self-assembling VLP and PsV capsids containing L1 and L2 are structurally indistinguishable from wart-derived HPV virions [93] , [94] . Of specific importance , L1-only HPV VLPs mimic wart-derived virions functionally such that in vivo they elicit neutralizing antibodies that confer long-term protection from infection in animal models and in clinical trials [95] , [96] . Indeed , these L1-only VLPs are the basis for the successful HPV vaccines in use throughout the world today . Also of particular biological significance , a careful comparison of xenograft tissue-derived cotton tailed rabbit PV ( CRPV ) virions to 293T-produced CRPV virions established that the virion stocks were essentially indistinguishable as assayed by susceptibility to antibody-mediated neutralization , papilloma induction , and gene expression within lesions in rabbits [97] . HPV PsVs expressed from capsid genes of carcinogenic HPV types like HPV16 have a number of advantages over tissue-derived virions , especially given that virions for carcinogenic HPV types have never been purified in valuable levels from human lesions . High-titer , high-purity PsVs have utility in a wider variety of assays and in more rigorously controlled experiments than the more crude virions obtained from the organotypic tissue culture system [92] , [98] , [99] . Thus , sound evidence suggests that 293T-derived PsVs provide a functional and practical substitute for working with high-titer carcinogenic HPV virions in many situations [17] , [93] , [100] . Epithelial wounding , an important mediator of HPV infections in vivo [30] , leads to the influx and activation of many cell factors shown to interact with HPVs , including those we have identified in this work . GFs , cytokines and chemokines are key mediators of wound repair . EGF and KGF are released from cells , and heightened MMP activity causes an increase in HB-EGF shedding ( reviewed in [101] ) . EGF and cytokines are involved in the regulation of syndecan shedding [21] and KGF induces strong syndecan-1 expression beneath the basement membrane [102] . Further , syndecan-1 expression is strongly upregulated in migrating and proliferating keratinocytes . Syndecan-1 and -4 ectodomains are found in acute dermal wound fluids , where they regulate GF activity [103] , specifically the formation of HS-KGF complexes and actions of MMPs on shedding of EGFR ligands [104] . EGFR expression transiently increases after wounding [105] and KGFR is upregulated at the wound margin [106] . Alpha-6 beta-4 integrin , the classic core component of hemidesmosomes , performs adhesive functions by binding to laminin 332 in the basement membrane . Association of EGFR with alpha-6 beta-4 integrin and EGF-induced phosphorylation of beta-4 integrin is important for this disassembly of hemidesmosomes to promote cytokinesis and epithelial migration a wound-healing response ( reviewed in [90] ) . Taken together , our work illustrates additional means by which HPV has adapted to utilize the environment created during wounding , which not only allows the virus access to mitotically active basal cells , but also provides factors essential for the virus to infect cells with the boost of mitogenic signals . In a broader sense , it is of particular interest to reiterate that syndecans and other HSPG are bound by pathogens in addition to HPV , including some retroviruses , herpesviruses , flaviviruses , and bacteria like Chlamydia and Neisseria in their infection courses . Some of these pathogens , as discussed above , are also known to activate GFR pathways for infection . This brings up an exciting possibility that these other pathogens might also employ a soluble virus-HS-GF mode of infection under certain circumstances . Our study provides new insights into the transmission of a significant viral pathogen and reveals novel means whereby pathogens may repurpose normal cell functions during infection of their hosts . Likewise , this work uncovers new targets for prophylaxis of HPV , and potentially other pathogen infections .
The sources of different cell lines and their culture conditions , plasmids used , procedures to produce and purify HPV PsV , and the procedure for the exposure and infection of target cells are provided as Supporting Protocols S1 and S2 in Text S1 . 293T cells , HaCaT cells , CHO-K1 cells and derivative pgsd-677 were maintained as reported [46] , [83] , [107] , [108] . HPV PsVs encapsidating a luciferase reporter plasmid were generated via transfection in 293T cells and quantified for vge and L1/L2 capsid levels . SDS-PAGE and Coomassie Brilliant Blue staining were used to assess the purity of virus stocks [92] . Under our transfection conditions , capsids typically outnumber vge by 2- to 10-fold [88] . CsCl gradient-purified PsV stocks were sonicated , added to cells in various media and incubated at 4°C for 1 h to permit viral attachment . Inocula were aspirated , cells were extensively washed , and fresh culture media or Tyrode's buffer ( 10 mM HEPES pH 7 . 4 , 130 mM NaCl , 5 mM KCl , 1 . 4 mM CaCl2 , 1 mM MgCl2 , 5 . 6 mM glucose , and 0 . 05% BSA ) were added . Infections were allowed to proceed at 37°C , typically for 24 h before luciferase quantification . For the co-culture viral release assay , subconfluent donor cells grown on cover slips were incubated with PsVs at ∼2000 vge/cell for 1 h , 4°C ( Figure 4A ) . Cells were washed 3X to remove unbound PsVs , and coverslips transferred to 74-µm mesh plate inserts ( Corning ) . The PsV-exposed donor cell inserts were suspended above a subconfluent recipient cell monolayer with media covering both cultures ( Figure 4C ) . Donor and recipient cell infections were measured by luciferase assay . siRNA cell transfection was performed using Lipofectamine 2000 reagent ( Invitrogen ) , with EGFR siRNA ( Cell Signaling ) according to manufacturer's recommendations . A nonspecific siRNA was used as a negative control ( Dharmacon ) . Transfection was monitored using fluorescein-conjugated siRNA ( Cell Signaling ) . Scepter Cell Counting ( Millipore ) for viability and size and Trypan Blue exclusion staining were used to measure cell viability . Cells were incubated with 200 vge/cell of PsV for 1 h at 4°C , washed 3X with media and incubated at 37°C for various times . Experimental media were cleared by low speed centrifugation and the supernatant was concentrated by Amicon Ultra 30K filtration ( Millipore ) . Concentrated samples were fractionated on Sepharose 4B columns that had been preliminary calibrated with standard proteins as described [34] . The samples were applied to an equilibrated Sepharose 4B column and left for 3 min; eluate was collected as fraction 1 . PBS was applied and fraction 2 collected in 3 min and so on . Eluted fractions were analyzed by SDS-PAGE followed by immunoblotting for HPV16 L1 , proteoglycans and growth factors . Additional details of chromatography are given in Supporting Protocol S3 in Text S1 . HaCaT cells were incubated overnight following exposure to HPV PsV ( 100 vge/cell ) , culture supernatant was removed , cleared by centrifugation and concentrated by Amicon filtration . Concentrate was mixed with 6× non-reducing sample buffer and electrophoresed through a 8% acrylamide gelatin gel and analyzed as reported [35] . HaCaT cells were seeded onto glass cover slips and cultured overnight . Media were removed and the cells were starved for 2 h with Tyrode's buffer prior to PsV exposure at 4°C , 45 min . AF488-conjugated EGF was added and incubated an additional 15 min . Unbound materials were washed out and cells fixed . After extensive washes , cells were blocked and incubated with rabbit anti-HPV16 VLP antisera . Following PBS washes , slides were incubated with AF594-conjugated anti-rabbit IgG . Alternatively , for visualization of KGFR and HPV co-localization , BSA-blocked cells were incubated with anti-KGFR ( FGFR2IIIb ) mouse monoclonal and a rabbit anti-HPV VLP antisera . PBS washed slides were incubated with donkey anti-mouse-AF549 and AF488-conjugated anti-rabbit IgG secondary antibodies . For detection of ERK1/2 , fixed cells were permeabilized prior to adding anti phospho-44/42 MAPK rabbit monoclonal followed by Cy3-goat anti-rabbit IgG . All images were acquired with a Zeiss LSM 510 META confocal system using appropriate filters . Detailed immunofluorescence methods and antibody specifics are given in Supporting Protocol S4 in Text S1 . For co-IP of syndecan-1 from released material , HaCaT cells were seeded and incubated with virus as in Figure 4A with anti-HPV16 L1 mouse mAb attached to Dynabeads–Protein A in the lower chamber ( instead of recipient cells as in Figure 4C ) . After 2 or 20 h of incubation , beads were collected , washed , and solubilized in non-reducing sample buffer . Syndecan-1 was detected using rabbit anti-serum after SDS-PAGE by immunoblot ( details in Supporting Protocol S5 in Text S1 ) . GFRs were subject to co-IP with HPV16 PsVs bound to HaCaT cells at 500 vge/cell at 4°C , 1 h; mock-exposed cells were a negative control . Cells were solubilized with cold Triton lysis buffer ( 1% TX100 , 50 mM Tris-HCl , pH 7 . 5 , 150 mM NaCl , 1 mM EDTA , 1 mM PMSF , 10 ng/ml leupeptin , 10 ng/ml aprotinin ) . Insoluble materials were removed by centrifugation and supernatants were immunoprecipitated for 1 h at 4°C with rabbit anti-HPV16 VLP antibody attached to protein A-magnetic Dynabeads ( Invitrogen Dynal ) . Soluble proteins were resolved by 10% SDS-PAGE and were transferred onto PVDF membranes , which were probed with anti-syndecan-1 , anti-HB-EGF , anti-EGF , anti-EGFR , or p-FGFR and then HRP-conjugated secondary Ab . To deplete syndecan-1 and EGF from media , CM was incubated with anti-syndecan-1 mAb or anti-EGF mAb attached to Protein G Sepharose beads for 3 h at RT . The media was filtered to remove the bound material and used for infections . As a negative control we used CM incubated with Protein G Sepharose beads . Additional details of IPs are given in the Supporting Protocol S6 in Text S1 . Subconfluent HaCaT cells were serum-starved for 3–4 h in Tyrode's buffer containing 0 . 05% BSA . After adding ∼100 vge/cell HPV16 PsVs , 10 ng/ml EGF or 10 ng/ml KFG , cells were incubated at 37°C for 10 min before transferring to ice and solubilizing cells with RIPA buffer . In some experiments cells were incubated with various inhibitors in Tyrode's buffer for 45 min and after Tyrode's washes , were incubated with virus as above in the presence of inhibitors . Lysates were clarified , mixed with Laemmli buffer and boiled for 5 min prior to SDS-PAGE . Immunoblot was performed with various monoclonal and polyclonal antibodies: p-EGFR , p-KGFR , p-ERK , actin . For nuclear extractions , HaCaT cells were starved 4 h in Tyrode's solution containing 0 . 05% BSA , then exposed to HPV , EGF or KGF for various times . Cells were solubilized with NP40 lysis buffer and centrifuged . The pellet was incubated with nuclear extraction buffer . Following incubation on ice for 1 h , the extract was clarified and the supernatant subjected to SDS-PAGE and immunoblot for analysis of p-ERK content . Additional details and buffer constituents are given in Supporting Protocol S5 in Text S1 . Subconfluent HaCaT cells were pre-treated 45–60 min with 1 µM AG1478 ( Calbiochem ) , 100 nM PD168393 ( Calbiochem ) , 100 µM genistein ( Sigma ) , 100 µM daidzein ( Sigma ) , 1 µM PD173074 ( Calbiochem ) , 100–600 nM cetuximab ( ImClone ) , 1 µM to 100 µM MM ( Tocris Bioscience ) and 1 µM to 100 µM BM ( Tocris Bioscience ) . For dual inhibitor assays , cells were pre-treated 1 hr with 100 µM monastrol , pre-treated with monastrol plus 500 nM PD168393 for 1 h , or pre-treated with 500 nM PD168393 for 1 hr prior of adding 100 µM monastrol and incubated an additional 1 h . Cells were exposed to HPV16 or HPV31 PsV at 100 vge/cell for 1 h at 4°C , then shifted to 37°C in the presence of inhibitors for 24 h at which time they were analyzed for luciferase expression . These inhibitor concentrations are well documented not to cause cell toxicity; cell viability was ≥94% in each assay . | A subset of the >120 different types of human papillomaviruses ( HPVs ) are the most common cause of sexually transmitted infections . Certain HPVs are also associated with approximately 5% of all cancers worldwide . Like many pathogens , HPVs bind first to heparan sulfate proteoglycans ( HSPGs ) on cells before moving to more specific uptake receptors . However , relatively little is known about the mechanism ( s ) that triggers the translocation of HPV from HSPGs to the receptors that facilitate entry . As obligate parasites , viruses have evolved numerous means to hijack host cell functions to cause infection . We report two novel mechanisms of pathogen-host interactions . First , bound HPV particles are liberated from cells in an active complex with HSPGs and growth factors rather than dissociating from the sugars to engage secondary receptors . Second , HPV uses the specificity of the associated growth factors to bridge to their cognate receptors as opposed to direct binding to a cell internalization receptor . Signals transduced during these interactions are important for HPV infection . Our study provides new insights into the transmission of a significant viral pathogen and reveals novel means whereby microbes may repurpose normal cell functions during infection of their hosts . Likewise , this work uncovers new targets for HPV prophylaxis . | [
"Abstract",
"Introduction",
"Results",
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] | 2012 | Essential Roles for Soluble Virion-Associated Heparan Sulfonated Proteoglycans and Growth Factors in Human Papillomavirus Infections |
As nascent proteins are synthesized by the ribosome , they depart via an exit tunnel running through the center of the large subunit . The exit tunnel likely plays an important part in various aspects of translation . Although water plays a key role in many bio-molecular processes , the nature of water confined to the exit tunnel has remained unknown . Furthermore , solvent in biological cavities has traditionally been characterized as either a continuous dielectric fluid , or a discrete tightly bound molecule . Using atomistic molecular dynamics simulations , we predict that the thermodynamic and kinetic properties of water confined within the ribosome exit tunnel are quite different from this simple two-state model . We find that the tunnel creates a complex microenvironment for the solvent resulting in perturbed rotational dynamics and heterogenous dielectric behavior . This gives rise to a very rugged solvation landscape and significantly retarded solvent diffusion . We discuss how this non-bulk-like solvent is likely to affect important biophysical processes such as sequence dependent stalling , co-translational folding , and antibiotic binding . We conclude with a discussion of the general applicability of these results to other biological cavities .
The first microenvironment a protein experiences is that of the ribosome exit tunnel , the long cavity in the large ribosomal subunit through which the nascent peptide emerges during translation . Cryo-electron microscopy and x-ray crystallography have revealed this cavity to be about 100Å long with a diameter of 10–20 Å [1] , [2] . To a certain degree , the exit tunnel must be promiscuous in its interactions , so as to facilitate the translation of disparate protein sequences [2] , [3] . However , a number of protein sequences have been shown to stall translation , presumably via specific interactions with the exit tunnel . Most notable among these are the tnaC and SecM sequences [4] . Although the exact molecular mechanism for stalling has not yet been deduced , it is strongly believed that these sequences initiate stalling via interactions with the ribosome in a region where ribosomal proteins L22 and L4 protrude into the exit tunnel [4]–[6] . Lu et al have shown by site-specific chemical modification of particular nascent peptide sequences that the electrostatic potential in the tunnel is predominantly negative but also quite heterogeneous [7] . Nascent peptide sequences with stretches of positive amino acids have been shown to transiently stall translation in accordance with the known electrostatic characterization of the tunnel [8] . It has also been suggested that nascent peptides can fold to a limited extent in the tunnel [9]–[11] . For example , the SecM sequence is compacted while in the exit tunnel , and this compaction is necessary for stalling [12] . Additionally , recent cryo-electron microscopy has shown a nascent peptide folding into an alpha helix near the exit of the tunnel ( a region known as the vestibule ) [13] . Not all sequences may be able to fold in the tunnel however . The same group has also used cryo-EM to show that the nascent chain for the tnaC sequence adopts an expanded ( non-helical ) conformation in the exit tunnel [14] . In addition to these stalling sequences , there are a number of sequences that do not cause translational arrest , but have widely disparate rates of translation [8] , [15] , [16] . Macrolide antibiotics have been shown to bind to regions of the exit tunnel between the peptidyl transferase site and the constriction site [17] , [18] . Various macrolide antibiotics , which are known to bind in the ribosome exit tunnel , can have greatly different binding modes and disparate effects on translation [19] . Mutations to the rRNA or proteins in the area of the constriction site can also have an effect on antibiotic binding , with some mutations conferring resistance to antibiotic treatment [20] , [21] . The ribosome exit tunnel is a peculiar microenvironment from a nanoscopic point of view in that it is both heterogeneous ( containing both polar and non-polar residues ) and highly confined . Both of these characteristics are exacerbated when considering the presence of nascent peptide ( which due to the natural variation of protein sequences creates an environment that is even more heterogeneous in terms of hydrophobicity and even more spatially confined ) . We believe that an important step in understanding the complexity of molecular behavior in the ribosome exit tunnel requires an atomistic understanding of the thermodynamics and kinetics of the residues in the tunnel as well as the solvent confined to the exit tunnel . The behavior of water on short length scales has been studied both experimentally and via computer simulation [22]–[29] . The peculiar microenvironment created by highly confined water has been shown to have an effect on protein folding and stability [30] , [31] . Additionally , water is known to organize differently in the vicinity of hydrophilic and hydrophobic surfaces [32]–[35] further complicating the potential effect on the dynamics of confined peptides . For example , it has been recently shown that the different modes of organization confined water exhibits inside chaperonins can partially explain their ability to fold and unfold proteins [36] , [37] . Just as unstructured loops of proteins have large effects on their stability and activity , so may semi-structured confined water affect the behavior of nascent peptides and small molecules in the ribosome exit tunnel . Common structural methods usually treat biologically confined water as either a discrete tightly bound molecule , or a continuous , bulk-like dielectric fluid . Based on the known structural properties of the ribosome , we hypothesize that the majority of the water found in the ribosome exit tunnel falls between these endpoints . Although nano-confined water has been studied experimentally before , none of these methods are particularly well suited to deal with the large and complex ribosome and still capture the subtleties of the solvent confined within . For this reason , molecular simulation is an ideal way to probe the solvent confined in the exit tunnel . However , due to the complexity and computational demands of simulating macromolecular complexes of the scale of the ribosome , previous attempts to simulate the ribosome have often used simplified models for the solvent or only simulated a few trajectories at relatively short timescales [38]–[43] . Here we present the results of extensive atomistic molecular dynamics simulations , performed with the intent of studying solvent confined to the ribosome exit tunnel .
We modeled the ribosome exit tunnel in all-atom detail for the ribosome and solvent . Our simulations consisted of 91 , 787 atoms total , including solvent , ions , and a portion of the ribosome including 85 Å surrounding the ribosome tunnel . Since a high-resolution structure showing the conformation of a nascent peptide does not yet exist , we felt it best to consider the case of an empty tunnel . This allows us to more directly probe the general nature of the exit tunnel environment in terms of the nature of the water inside of the tunnel . Moreover , it is reasonable to assume that the nascent peptide would only further facilitate deviations from bulk behavior . In our model , 17 , 478 atoms were taken from the Haloarcula marismortui crystal structure [2] of the large subunit ( as shown in figure 1 and discussed in the Methods section ) . Using atomistic molecular dynamics and large-scale distributed computing , we performed an aggregate of 40µs ( 10µs for our main system and each of 3 controls ) of molecular dynamics simulation and then calculated a number of kinetic and thermodynamic properties of water inside the ribosome exit tunnel . We were able to observe bulk-like solvent properties beyond the mouth of the tunnel and significantly perturbed properties inside the tunnel . The results presented here are taken from a slice half way through the z-axis of the ribosome as shown in figure 1b . Although other slices show the same behavior , we chose this slice because it represents the plane between the portions of ribosomal proteins L4 and L22 that project into the tunnel ( an area known as the constriction site ) . A potential of mean force ( PMF ) , or free energy of finding a water molecule at a specific location inside the exit tunnel was calculated ( figure 2a ) . Such a PMF allows one to visualize spatial variations in solvation free energy relative to bulk , as they show the spatial dependence of the water density ( on a logarithmic scale ) . Examination of this PMF reveals that solvent confined in the tunnel is perturbed relative to the bulk , with the most significant perturbations occurring within 5 angstroms of the tunnel surface . Within these regions , fluctuations of 3–4kBT are readily seen , while fluctuations of 0 . 5–1kBT are seen farther from the surface . These peaks imply the existence of subpopulations of ordered water that are not present in the bulk . Some of the regions experiencing these large fluctuations , such as the constriction site between the ribosomal proteins L4 and L22 are believed to be of importance to translation . In these regions , a heterogeneous water PMF could directly affect the solvation of nascent chains as well as modulate specific ribosome-protein or ribosome-small molecule interactions . We also calculated the solvent rotational entropy as a function of position in the exit tunnel . Due to the importance of hydrogen bonding on solvent thermodynamics , rotational degrees of freedom are very important to water structure . The rotational entropy profile reveals that , like the PMF , the rotational degrees of freedom of solvent inside the exit tunnel are perturbed relative to bulk ( figure 2b ) . Near the walls of the tunnel , we find significant reduction in rotational entropy relative to bulk: up to −2 . 1kB . This is indicative of ordered water that has access to only approximately 10% of the rotational space that is available to bulk . It should be noted that rotational entropy is a very sensitive measure of orientational structure . For example regions that differ from bulk by 0 . 3 kB still suffer a 25% reduction in orientational freedom relative to bulk . At a distance of approximately 5Å from the tunnel surface , the rotational entropy of solvent returns to the bulk value . This corresponds to the region of the tunnel that has an elevated solvation free energy relative to bulk . The absence of entropy contours in this area can be explained by consideration of our measure of rotational entropy . Regions with a more favorable solvation than bulk will not have more rotational freedom than bulk ( the bulk value of rotational entropy should be a maximum for the system ) . When examining the relationship between rotational entropy and free energy we find that confinement to the ribosome exit tunnel facilitates a cooperative transition between highly ordered water with low internal energy and freely rotating water with high entropy ( figure S1 ) . This relationship allows us to use rotational entropy to define both bound and bulk-like states for water and examine where these two extrema occur in the ribosome exit tunnel . Considering water which samples 1 or 2 rotational states to be bound , and water than samples greater than 20 states to be bulk-like we can see ( figure S1 panel B ) that much of the water in the tunnel is between these two extremes of bound water and bulk-like continuum . This illustrates the inadequacy of the typical two-state water model to describe solvent behavior inside a nanoscopic cavity like the ribosome exit tunnel . Calculation of rotational entropy requires one to partition rotational space . To ensure that our observations are robust with respect to this partitioning , an additional metric for rotational dynamics is useful . Since water has a permanent molecular dipole moment , solvent molecules with decreased orientational freedom should also have reduced dielectric susceptibility . Because of highly varying local electric fields ( from the large amount of RNA and numerous counter ions in the system ) as well as geometric heterogeneity of the tunnel , the calculation of an actual spatially varying dielectric constant by typical methods [44] is extremely difficult . Thus , we calculate the solvent dipole fluctuation tensor as a function of position in the tunnel as a proxy of this value . Comparing the elements of this tensor to the values found outside the tunnel suggests the degree to which dielectric constant would be perturbed . Throughout the tunnel , the dipole moment fluctuation is significantly perturbed in a manner consistent with the PMF ( plotted in figure 2c ) . We find significantly reduced dipole moment fluctuations near the tunnel surface , supporting the idea that solvent molecules localized to that region are ordered . We also find various “hot spots” with very high dipole fluctuation . These spots seem to correspond to locations where there is low rotational entropy but favorable free energy . This is indicative of an ordered water molecule that is rapidly switching between spatially disparate states ( for example two orientations that may be 180 degrees apart ) . The off-diagonal elements of the tensor represent the degree to which polarization ( reduced orientational freedom ) in one dimension affects polarization in another direction , an effect that should only be non-zero in the case of significant solvent ordering . Indeed , we observe that the off diagonal elements of the tensor are equal to zero everywhere but near the edges of the tunnel ( figure 2d ) , another illustration of significant solvent ordering that is consistent with the rotational entropy profile ( figure 2b ) . Additionally , when comparing the individual components of the tensor , the dipole fluctuation is observed to be somewhat anisotropic ( figure S5 ) . Collectively , these results support our conclusion that highly perturbed rotational dynamics and dielectric behavior contribute to the complex solvation landscape inside the tunnel . These results imply that solvent confined to the ribosome does not behave as a continuous isotropic dielectric medium . In addition to non bulk-like thermodynamics , we also observe that solvent confined in the ribosome exit tunnel exhibits significantly retarded kinetic properties as well . Figure 3a shows the translational diffusion coefficient of water in the tunnel . Within the tunnel , diffusion is greatly reduced . This is reasonable considering translational diffusion involves traversing the rugged solvation landscape shown in figure 2a . Furthermore , the diffusion coefficient was found to be highly anisotropic inside the tunnel , particularly along the tunnel's y-axis ( figure S7 ) . This result makes sense , as the tunnel surface is quite heterogeneous both in terms of geometry and surface chemistry . In addition to translational diffusion , we also calculate the rotational diffusion coefficient as a function of position in the tunnel ( figure 3B ) . Rotational diffusion is marginally slowed throughout the tunnel , with severe restriction occurring only in specific locations . Indeed , there is a slight reduction in rotational diffusion around the edges of the tunnel with significant reduction only in regions with very low rotational entropy .
What might the effects of these properties be on biological processes ? We find that our characterization of water is consistent with previous biochemical experiments investigating the behavior of nascent peptides in the tunnel . Our PMF shows that the upper region of the tunnel can be relatively well characterized as hydroscopic ( shown as higher than bulk free energy of solvation throughout the middle of the tunnel ) an observation consistent with the work of Lu et al [7] . Additionally , numerous studies show the intersection between L22 and L4 to be a place of importance for sequence dependent stalling [2] , [4] , [12] , [14] as well as antibiotic binding [17] , [18] , [20] , [21] , [45] . Our data show that this region exhibits very complicated solvation behavior ( as L4 and L22 present both polar and non-polar moieties to the tunnel ) . This solvation behavior could explain the predominance of this region as an important site for stalling and in particular , account for the large energy barrier tryptophan ( conserved in SecM and tnaC stalling sequences ) must cross in order to proceed through the tunnel during translation [12] , [46] . Compaction of the nascent chain has been shown to exist in the region of the tunnel between the peptidyl-transferase center and the constriction site [10]–[12] . In the case of the SecM sequence this compaction is necessary for stalling . We believe that such compaction is quite compatible with our data . The surface of the tunnel in this region is largely hydrophilic and the SecM sequence is mostly hydrophobic in nature . The solvent confined between these two surfaces would have competing sets of thermodynamic demands on it , mediating a net repulsive force between the cavity and the peptide , thus favoring compaction . For hydrophilic sequences such as the tnaC stalling sequence , we would not predict for the chain to be collapsed or compacted . The low solvent entropy in this region of the exit tunnel implies that the difference in cost between solvent-solvent hydrogen bonds , and solvent-protein bonds is smaller than what would be observed in bulk . Thus a polar peptide such as tnaC would have less thermodynamic drive to form an alpha helix in this region of the tunnel . We would however expect for a polar sequence to become adsorbed onto the surface of the tunnel in this region . The solvent around the periphery of this section of the cavity is ordered ( as evidenced by low rotational entropy and low dielectric shown in our data ) and could only be easily displaced by polar residues of the nascent chain . This hypothesis is consistent with cryo-electron micrographs of tnaC in the exit tunnel , which show the tnaC peptide to be unfolded and making a number of tertiary interactions with the tunnel [14] . In addition to compaction of the nascent peptide in the region of the tunnel between the constriction site and the peptidyl transferase center , there have been recent observations of the nascent peptide folding into an alpha helix in the bottom portion of the tunnel close to the exit ( a region known as the vestibule ) . Deutsch and coworkers have shown via chemical cross-linking studies that this region of the tunnel seems to promote the folding of a nascent peptide into an alpha helix [47] , [48] . Additionally the hydrophilic ( EAAAK ) 5 sequence inserted near the n-terminus of the peptide DPAP-B has been observed via cryo-electron microscopy to be in an alpha helical conformation in the vestibule [13] . Our results offer a potential mechanistic explanation for these new observations . We show that the vestibule of the ribosome exit tunnel has a favorable chemical potential for water relative to bulk . Forming a helix in this region would be thermodynamically favorable since maintaining an expanded conformation would involve displacing a larger number of favorably solvated waters , and forming a larger number of protein-water hydrogen bonds in a region where solvent-solvent hydrogen bonding is likely to be highly favored . How might this non-bulk-like water affect other attempts to study the ribosome using simulation techniques ? The heterogeneous solvation landscape is likely to make prediction of binding free energies difficult , since desolvation would be a complicated function of position and orientation of the ligand in the exit tunnel ( rather than a constant based on bulk measurements such as a partition coefficient ) . Thus , mean field approaches such as Poisson-Boltzmann and Generalized Born [49] , which estimate the electrostatic solvation free energy based on a single isotropic dielectric constant , may have serious limitations due to the key role of correlations in solvent . The ruggedness of the solvation landscape and slow kinetics are likely to make convergence difficult in free energy calculations using methods such as free energy perturbation or thermodynamic integration . This ruggedness would not only affect attempts to predict the affinity of small molecules to the ribosome exit tunnel ( and thus confound attempts to discover new antibiotics ) but also may play a role in examining the interaction of nascent peptides with the tunnel ( such as the known stalling sequences ) . For this reason , it is expected to be difficult to obtain well-converged simulations of nascent peptides in tractable lengths of time . Systems in which there is extensive confinement ( such as the ribosome exit tunnel ) would greatly benefit from new advances in implicit solvation methodology , which could offer faster convergence and greater accuracy with respect to treatment of solvation . We have discussed these results in the context of the ribosome , but one can also consider other implications . We have observed ( please see text S1 ) that when water is confined to a “nonpolar ribosome” ( one where we have zeroed the partial atomic charges and removed counterions ) we also see highly perturbed thermodynamics , yet with different modes of organization in comparison to that seen in the original polar ribosome ( figure S2 ) . Thus , we have observed that restricted water entropy exists when water is confined to both polar and non-polar surfaces . Therefore , it is possible that our results can be generalized to a great number of important biological cavities . Among these are the chaperonin GroEL ( when a substrate is encapsulated ) , the proteasome , the cavity created by the ribosomal chaperone trigger factor , and the translocon pore . For the ribosome , it should be noted that the degree of confinement the solvent experiences would increase as a nascent peptide traverses the exit tunnel ( this would also hold true for trigger factor and the translocon pore ) . Considering the number of biological macromolecular complexes with some degree of confined water , we expect that the general principals described here may find wide applicability . The great cost of extensive simulations such as those performed in this study makes a strong case for the use of simplified models to study large macromolecular complexes . Unfortunately our results show that the details neglected in current simplified models are likely to have large effects on protein thermodynamics and kinetics in nanoscopic cavities . The need for simplified models that are both computationally efficient and capture the physics of solvent on short length-scales is apparent . Some candidate models could include Kovelenko & Hirata's 3D-RISM methods as well as Koehl and Delarue's PBL method [50] , [51] . Both attempt to treat the multi-body effects of solvent confined to nanoscopic cavities in an implicit manner . It still remains to be seen however if these promising models are computationally tractable for large-scale problems such as the study of the ribosome or chaperonins and similar macromolecular complexes .
In order to model solvent dynamics in the ribosome exit tunnel , a cutout from the large ribosomal subunit ( pdb code 1S72 ( 2 ) ) was constructed following Petrone et al [46] . The cutout was constructed by removing all atoms outside of a box ( 82 . 5Å by 85Å by 122Å ) centered on the exit tunnel . This simulation box was constructed such that there was a 140 cubic nanometer bulk solvent region outside the mouth of the tunnel . The three missing residues ( EVQ ) from L39 were modeled in and equilibrated . The broken chains that arose from constructing the cutout were capped with dummy atoms . All crystallographic ions were included and 338 additional sodium atoms were added to balance the charge . The system was solvated with TIP3P water [52] . The system was then allowed to equilibrate for 1ns and more water molecules were added to achieve bulk density in the region outside the exit tunnel ( the large amount of charge on the ribosome seemed to have an electrostrictive effect on the solvent ) . An additional 1ns of equilibration was then performed . Five thousand 2ns molecular dynamics simulations were performed with the GROMACS simulation package [53] on the Folding@Home distributed computing network [54] using the Amber99phi force field [55] . These simulations were performed in the NVT ensemble with temperature controlled via the Berendsen thermostat at 298K [56] and a time step of 2 femtoseconds . Electrostatics were treated with the reaction field method [57] using a cutoff of 1 nm . The above process was repeated four times . In the first set of simulations , the entire ribosome was held rigid ( leaving only ions and water to move about freely ) . For the second set of simulations , the residues within 1nm of the exit tunnel were allowed to move while the rest of the ribosome atoms were held rigid . For the third set of simulations , the ribosome was allowed to maintain flexibility in the tunnel as before , but the simulation temperature was reduced to 100K ( the crystallographic temperature ) . In the final set of simulations , all ribosome atoms were held rigid , but all ions were removed from the system and the charge on each ribosome atom was set to zero ( effectively turning the ribosome into a porous hydrophobic surface ) . The results for the first set of simulations are presented in the main text while the results of the three additional control sets of simulations are presented in the supporting material ( text S1 , figure S2 , figure S3 , figure S6 ) . All thermodynamic and kinetic properties were calculated as a function of position within the simulation box by dividing the box into a three-dimensional grid and calculating a histogram based on water positions . The data was broken into 50 sets for block averaging ( where the reported property is the average over all 50 blocks ) . The two most obvious thermodynamic quantities of interest to water dynamics are the potential of mean force for water within the ribosome exit tunnel as well as the rotational entropy of water at different locations inside the tunnel . The potential of mean force using position within the tunnel as a reaction coordinate was calculated as shown in equation 1 . ( 1 ) where N ( r ) is the three dimensional histogram of water oxygen positions across 100 trajectories . The rotational entropy was calculated by binning each water molecule into one of 28 rotational states . ( 2 ) After binning , the entropy was calculated from equation 2 where pi ( r ) is the probability of water at position r being in rotational state i . The rotational states were defined by partitioning space around the water oxygen into octants and assigning a state based on which octants contain the hydrogen atoms ( thus 8 octants choose 2 hydrogen atoms gives 28 states ) . Another important thermodynamic quantity is the local dielectric constant at different locations in the exit tunnel . Unfortunately , the traditional method for calculation of a local dielectric constant proposed by Kirkwood [44] would not apply in the non-isotropic environment of the ribosome exit tunnel . As a proxy of this value , the local dipole fluctuation tensor was calculated in a manner similar to that described in Lin et al . [58] . The elements of the tensor are given in equation 3 . ( 3 ) Where <μαβ ( r ) > is the average of the product of two components of the dipole moment of a water at position r , likewise <μα ( r ) > is the average of a given component of the dipole moment of water at position r . In order to simplify this , yet not lose the generality of including the off-diagonal elements of the tensor we report the following quantities defined in equations 4 and 5: ( 4 ) ( 5 ) We estimated the translational diffusion coefficient as a function of position in the tunnel by first assigning a water molecule to a grid position ( as in the previous analysis ) and measuring its mean square translational displacement 50 ps later . This was repeated for all trajectories and with the Einstein relation , yielded an average diffusion coefficient as a function of position in the exit tunnel . Additionally , we characterized rotational diffusion in a similar manner . For this method we measured the average cosine squared of the angle between the molecular dipole of water at time t and time t + 50ps . Grid resolution of 1Å resolution was used for all calculations . Error analysis was performed by the bootstrap method ( please see text S1 and figure S4 ) . | The physical properties of water are of critical importance to biomolecular processes such as protein folding and protein-ligand binding . The predominant view of water from a structural biological perspective , however , is that of a two-state model: water is either a tightly bound molecule ( as seen in x-ray crystallography ) or a continuous bulk fluid of constant dielectric . When water is confined on the nanometer length scale ( as it is in a number of critically important biological cavities ) the two-state characterization is likely to be inadequate . Using molecular dynamics simulations we report on the thermodynamic and kinetic properties of water confined to the ribosome exit tunnel . We show that this solvent is slow-diffusing and semi-structured , sampling a great variety of states between its characteristic ‘end-points’ of bulk-like and tightly bound . We conclude with a discussion of how the properties of this nano-confined solvent can affect the interpretation of recent experimental results regarding sequence dependant stalling and co-translational folding in the ribosome exit tunnel . | [
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] | 2010 | Non-Bulk-Like Solvent Behavior in the Ribosome Exit Tunnel |
Foot-and-mouth disease remains a major plague of livestock and outbreaks are often economically catastrophic . Current inactivated virus vaccines require expensive high containment facilities for their production and maintenance of a cold-chain for their activity . We have addressed both of these major drawbacks . Firstly we have developed methods to efficiently express recombinant empty capsids . Expression constructs aimed at lowering the levels and activity of the viral protease required for the cleavage of the capsid protein precursor were used; this enabled the synthesis of empty A-serotype capsids in eukaryotic cells at levels potentially attractive to industry using both vaccinia virus and baculovirus driven expression . Secondly we have enhanced capsid stability by incorporating a rationally designed mutation , and shown by X-ray crystallography that stabilised and wild-type empty capsids have essentially the same structure as intact virus . Cattle vaccinated with recombinant capsids showed sustained virus neutralisation titres and protection from challenge 34 weeks after immunization . This approach to vaccine antigen production has several potential advantages over current technologies by reducing production costs , eliminating the risk of infectivity and enhancing the temperature stability of the product . Similar strategies that will optimize host cell viability during expression of a foreign toxic gene and/or improve capsid stability could allow the production of safe vaccines for other pathogenic picornaviruses of humans and animals .
Foot-and-mouth disease ( FMD ) is a highly contagious viral disease of cloven-hoofed animals including cattle , sheep and pigs . Infection spreads rapidly through susceptible populations and can give rise to large scale epidemics , causing debilitation , pain and loss of productivity . Outbreaks of FMD such as that in the UK in 2001 , which resulted in the slaughter of over 6 million animals and cost in excess of £8 billion , highlight the need for vaccines that support a ‘vaccinate to live’ policy . Vaccination is currently reliant on the use of inactivated virus produced in large bioreactors in high containment facilities . This is unsatisfactory on several grounds: the set-up and running costs are very high , limiting global production capacity , storage and supply are constrained by the poor vaccine stability at ambient temperatures , and it can be difficult to distinguish vaccinated from infected animals . Thus , more options for FMD vaccine production are urgently required . FMDV is a non-enveloped single-stranded RNA virus belonging to the family Picornaviridae . The capsid consists of 60 copies each of four structural proteins ( VP1–VP4 ) . During assembly a 95 kDa polyprotein ( P1 ) is cleaved by the viral 3C protease to yield VP0 ( 36 kDa ) , VP1 ( 32 kDa ) and VP3 ( 27 kDa ) which self-assemble to form the capsid . Auto-catalytic cleavage of VP0 into VP2 ( 28 kDa ) and VP4 ( 8 kDa ) occurs during encapsidation of the viral genome to produce the mature virus [1] , [2] . During infection empty particles ( hereafter termed natural empty particles ) may also be produced , which resemble the mature virus in structure and antigenicity , but are inherently less stable [1] . The structural proteins are arranged in an icosahedral lattice of 12 pentameric building blocks which are the major structural intermediates for FMDV assembly and disassembly . The capsids are held together by electrostatic interactions , hydrogen bonds and weak hydrophobic interactions between the inter-pentameric subunits [3] , [4] and unlike enterovirus capsids which release RNA by receptor-mediated uncoating [5] , [6] , FMDV capsids appear to release their genome by dissociation into pentamers at pH<7 . 0 and elevated temperatures . This instability translates into vaccines with limited shelf-life , necessitating a cold chain in many parts of the world where they are distributed . Attempts to produce alternative vaccines have shown that intact virus particles stimulate the best immune response [7] . Picornavirus capsids can be synthesized using recombinant techniques by expressing ( minimally ) the P1 structural protein precursor and the 3C protease that cleaves it , since the capsid proteins spontaneously assemble to produce empty virus-like-particles ( VLPs ) [8] . The inherent problem is balancing the expression of P1 and 3C , the latter being toxic to cells , especially in the case of FMDV [2] , [9] , [10] . We demonstrate here the expression of FMDV VLPs at levels that are potentially viable commercially . We report the production of wild-type as well as stabilized VLPs , their characterization by X-ray crystallography and their ability to induce a sustained protective immunity in cattle .
Three-dimensional structures for several serotypes of FMDV [2] , [3] , [11] , [12] reveal the basis for the limited particle stability , highlighting specific atomic interactions along the interface between pentameric assemblies [4] , [13] . This allowed us to pick histidine 93 of VP2 , on the helix adjacent to the icosahedral 2-fold symmetry axis ( Figure 1a ) as a target for mutagenesis to a cysteine ( H2093C ) in order to form energetically favourable disulphide bonds across the inter-pentameric interface [14] . Initial production of empty capsids utilised infection of mammalian cells ( RK13 ) with recombinant vaccinia viruses that encode a P1–3C expression cassette , where P1 ( derived from FMDV A22 ) is either wild-type ( vA22-wt ) or carries the H2093C mutation in VP2 ( vA22-H2093C ) . This cassette was flanked by 5′ and 3′ UTRs to permit translation from the FMDV IRES [15] ( Figure S1 in Text S1 ) . Expression of capped transcripts was driven by a T7 polymerase promoter [14] . Provision of the T7 polymerase by co-infection with vaccinia virus recombinant vTF7 . 3 , effectively lowers the cytotoxicity due to 3C protease or its capsid product; use of an inducible promoter regulates the expression levels of the P1-2A-3C cassette compared to that of a constitutive promoter [9] , [16] . Infected cells were pelleted , lysed and the extracts analysed using 15–45% sucrose gradient sedimentation . Gradient fractions were collected from the bottom of the tubes and analysed by western blot using anti-FMDV A22 serum . FMDV capsid proteins were detected in the bottom half of the gradients ( fractions 4 in Figure 1b top panel ) , indicative of the ability of both the wild-type and mutant recombinant capsid proteins to assemble into empty particles [14] . To investigate the stability of the recombinant particles , aliquots ( from fractions 4 of the wild-type and H2093C gradients shown in Figure 1b top panel ) were subjected to either acidification or heating to 56°C for 2 h before repeat sedimentation on 15–45% sucrose gradients , with fractions analysed by western blot . Heat-treated A22-wt derived proteins remained near the top of the gradient in fraction 10 ( Figure 1b , middle left panel ) demonstrating dissociation . By contrast , proteins derived from heat-treated A22-H2093C were detected predominantly in fraction 4 , showing that the mutant particles had withstood heating ( Figure 1b , middle right panel ) . Acidification was to pH 5 . 2 with sodium acetate buffer . The samples were incubated for 15 min before sucrose gradient sedimentation . Analysis of gradient fractions by western blot showed that A22-wt capsids dissociated ( Figure 1b , bottom left panel ) , whereas A22-H2093C capsids remained intact ( Figure 1b , bottom right panel ) . To develop a practical method for novel vaccine production we explored the baculovirus expression system [17] . The same P1-3C cassettes were inserted into a baculovirus-compatible transfer vector , pOPINE [18] . In recombinant baculoviruses ( bA22-wt and bA22-H2093C ) , capsid expression is driven by the baculovirus promoter p10 . Capsid expression was optimised by reducing the activity and expression of 3C protease and thereby its toxic effects on the cell: this was achieved by site-directed mutagenesis in the vicinity of the 3C protease active site to reduce its activity and by insertion of a frameshift sequence upstream of the 3C gene to down-regulate its expression [19] . Following infection of suspension cultures of Sf9 cells [19] , capsids were purified by a procedure similar to that used for mammalian cells . FMDV capsids produced in insect cells sedimented at the same position on 15–45% sucrose gradients as those produced using vaccinia virus in mammalian cells ( Figure 2a top panel ) . A second lower band was observed on gradients loaded with the extract from insect cells ( Figure 2b top panel ) . SDS-PAGE confirmed that the upper band harboured FMDV capsid proteins whilst the lower band contained proteins from baculovirus nucleocapsids ( Figure 2b bottom panel ) . Yields ranged from 0 . 8 µg/ml for A22-H2093C to 1 . 2 µg/ml for A22-wt . VP0 was cleaved into VP2 and VP4 to a similar extent in recombinant A22 wt and H2093C . This type of cleavage had already been observed with A22 empty particles arising during an FMDV infection and progressed to completion on short term storage [2] . In situ room temperature X-ray crystallography [20] was used to determine the structure of both wild-type and mutant capsids produced using vaccinia virus at 2 . 2 Å and 2 . 9 Å resolution respectively . The crystals were essentially isomorphous to those obtained for both A22 virus and A22 natural empty particles [2] , [21] , and refinement , using strict 15-fold non-crystallographic symmetry and real space averaging gave reliable maps and models for both structures . Diffraction from crystals for baculovirus expressed particles was indistinguishable , demonstrating that the particles from both expression systems are iso-structural ( data not shown ) . The structures of recombinant A22 empty capsids were very similar to those previously reported for A22 virus and its natural empty particles [2] , [21] . However , one region on the surface of the particle showed a significant structural difference: residues 171–181 of the VP3 GH loop adopt essentially identical folds in the two natural particles , whereas in wt and H2093C recombinant particles their structure is more extended ( Figure S2 in Text S1 ) and almost identical to that seen in another serotype A virus , A10 [11] . We have previously shown that , for serotype O viruses , the VP3 GH loop conformation is modulated by changes in the adjacent VP1 GH loop [21] , so it is possible that one or more amino acid sequence changes occurred in the highly variable disordered VP1 GH loop during native A22 virus replication and account for the repacking of the VP3 loop ( no sequence changes were detected in the ordered portions of the native A22 virus capsid ) [2] . The electron density map for the A22-H2093C mutant particles showed that the disulphide bond across the 2-fold axis relating two pentamers was correctly formed whilst A22-wt showed the expected histidine side-chain density ( Figure 3a ) . A difference electron density map between the wt and H2093C recombinant particles ( Figure S3 in Text S1 ) revealed , apart from this mutated residue , no significant features on the particle surface . Changes are however apparent on the interior of the particles , with VP4 being similarly ordered in the recombinant wt particle and the A22 virus structure [2] whereas only two residues of VP4 were visualized in the electron density map for H2093C ( Figure 3b ) . It is possible that the greater rigidity of the H2093C particle inhibits movements required for VP4 to fully settle into the structure seen in mature virus following cleavage of VP0 . Structural superimposition gives rms deviations in Cαs of the native and mutant recombinant particles of 0 . 36 Å ( 661 equivalent residues ) and 0 . 4 Å ( 609 equivalent residues ) respectively compared to the A22 virus . To characterize the protective immunity induced by recombinant capsids produced with baculovirus , we immunised two groups of four out-bred cattle each with 12 µg of highly purified capsids formulated in oil adjuvant . One group received A22-wt and the other A22-H2093C capsids . All animals were re-immunised after 3 weeks . There was a rapid induction of neutralising antibodies after primary immunisation . The mean antibody titre of both groups was greater than 5 . 5 ( Log2 ) which is considered protective [22] . There was a significant increase in virus neutralising antibody titres ( VNT ) post-boost and antibodies were maintained at high titres , greater than 6 ( Log2 ) , up to 22 weeks post-immunisation , but had reduced to pre-boost levels after 34 weeks in both groups of animals ( Figure 4 ) . Throughout the experiment there were no significant ( P>0 . 05 ) differences in titres between animals vaccinated with wild-type empty capsids and those vaccinated with mutated empty capsids . At 34 weeks post immunisation the animals , plus two non-vaccinated control animals , were challenged by direct inoculation into the tongue with live A22 virus . Two of the four animals immunised with wild-type capsids and three of the four immunised with mutated capsids were fully protected using the criteria described in the European pharmacopeia [22] . The two control animals succumbed to full clinical signs ( e . g . vesicular lesion of all four feet ) . Large quantities of viral genome were detected in their sera ( greater than 6 . 5 log10 genomes/ml ) for three or four days ( Figure 5c ) . In contrast , lower quantities of viral genome were detected in the vaccinated animals ( Figure 5a and 5b ) . The total amount of virus produced , estimated by computing the area under the curve ( AUC ) for copy number versus time , showed that there were no significant differences in AUC between animals vaccinated with A22-wt empty capsids and those vaccinated with A22-H2093C empty capsids ( P = 0 . 23 ) , whilst the AUC was significantly higher for non-vaccinated compared with vaccinated animals ( P = 0 . 04 ) .
Here we have demonstrated the production of safe , effective FMDV empty capsids that do not require bio-containment during manufacture . Furthermore , enhanced stability of the empty capsids will reduce losses during production , storage and transport whilst maintaining antigenic structure and immunogenicity . In addition the complete absence of FMDV non-structural proteins from the vaccine formulation will allow the development of diagnostic tests to discriminate between infected and vaccinated animals ( DIVA ) . Disulphide bonds are used to stabilise many extracellular proteins and also certain virus capsids [23] . Such covalent cross-links are more robust than the non-covalent interactions that generally hold protein assemblies together . Here we have rationally engineered a disulfide bond by mutating a single histidine residue at position 93 of VP2 located at the icosahedral 2-fold axis between adjacent pentamers [4] . Baculovirus expressed wild-type and stabilised capsids produced equivalent titres of neutralising antibodies , following a standard immunisation regimen , over a 34 week period post immunisation . These results inform the debate on the effect of increased antigen stability on immunogenicity . Delamarre et al [24] showed that for two proteins with the same T cell and B cell epitopes but with different susceptibilities to lysosomal proteolysis , mediated by single point mutations , less digestible forms induced more efficient T cell priming and antibody responses . In contrast , recent studies with a model antigen in mice suggested that enhanced conformational stability resulted in reduced antigenicity [25] . Although as yet we do not know if forming a disulphide bridge will be possible for all serotypes , especially in the baculovirus expression system , our results demonstrate that capsid stability can be augmented without compromising immunogenicity and this might be a general tactic for improving vaccine efficacy . The rational structure-based approach initiated here should in principle allow the tuning of these parameters to match the particular circumstances of different viruses . For instance , enhancing the stability of capsids for highly prevalent FMDV serotype O , which are more labile than those of A22 [26] . Recent work on EV71 has demonstrated that maintaining the proper positions of the 2-fold helices ( which harbour the H2093C mutation in FMDV ) is essential for maintaining native antigenicity [6] , suggesting that the approach we have demonstrated here may be applicable across a wide range of human and animal picornaviruses , including polioviruses and coxsackieviruses .
An expression cassette based on the sequence of FMDV A22 Iraq was designed ( Figure S1 in Text S1 ) , synthesized de novo ( Geneart ) and cloned into the vaccinia virus transfer vector pBG200 [9] downstream of the T7 promoter . Substitution of a BstEII-SpeI fragment with a sequence encoding the H2093C mutation converted the pBG200-A22-wt plasmid to pBG200-A22-H2093C . The recombinant viruses were made by transfecting pBG200-A22-wt and pBG200-A22–H2093C into CV-1 cells infected with vaccinia virus ( VV ) strain WR . Recombinant VVs ( with an interrupted thymidine kinase gene ) were selected in HuTK-143 cells using 5-bromo-2-deoxyuridine . Three rounds of plaque purification in conjunction with screening by PCR using FMDV-specific primers were carried out to get stable recombinant VVs . These were amplified in RK13 cells and virus stocks titrated by plaque assay on BS-C-1 cells . All mammalian cells were grown in DMEM supplemented with 10% FCS and appropriate antibiotics at 37°C . A single 175 cm2 flask of RK13 cells was dually infected with either vA22-wt or vA22-H2093C at an MOI 10 and vTF7 . 3 at an MOI 5 . After 24 h cells were harvested by centrifugation at 2 , 000 g for 5 min at 4°C and the pellet resuspended in 1 ml 0 . 5% Ipegal ( Sigma ) in 40 mM sodium phosphate , 100 mM NaCl pH 7 . 6 . The sample was incubated on ice for 20 min , clarified , loaded onto a 15–45% sucrose gradient and spun for 20 h at 22 , 000 rpm ( SW41 rotor , Beckman ) at 12°C . Each gradient was fractionated into 12 fractions of 1 ml and aliquots were analysed by western blotting . A 200 µl aliquot of the empty capsid-containing fraction identified during the initial sedimentation experiment ( see above ) , was diluted 1/3 either ( i ) with phosphate buffer pH 7 . 6 and incubated in a water bath at 56°C for 2 h or ( ii ) with 50 mM sodium acetate buffer pH 4 . 6 , to give a final pH of 5 . 2 and incubated at room temperature for 15 min before neutralisation with NaOH . Treated samples were loaded onto 15–45% sucrose gradients , centrifuged and fractionated as above . Each fraction was precipitated with an equal volume of saturated ammonium sulphate overnight at 4°C . Precipitates were collected by centrifugation at 16 , 000 g for 15 min at 4°C and analysed by western blot . HEK293 cells grown in 2×2125 cm2 roller flasks were dually infected as described above . After 20 h , cells were pelleted at 3 , 500 g for 30 min at 4°C . Pellets were resuspended in phosphate buffer and lysed with 0 . 5% Igepal on ice for 20 min . Lysates were clarified at 10 , 000 g for 20 min at 4°C and the resulting pellets resuspended in a small volume of buffer for re-extraction with 1 volume of chloroform . The aqueous phases were pooled with the clarified extracts and pelleted over 30% sucrose cushions at 105 , 000 g for 5 h at 12°C . Pellets were resuspended in a small volume of buffer , treated with 200 mg/ml RNAse A in the presence of 0 . 1% Igepal for 30 min on ice , clarified and loaded onto a 15–45% sucrose gradient . Following centrifugation at 54 , 000 g for 22 h at 12°C , the gradient was fractionated and fractions analysed by SDS-PAGE . Sucrose was removed by desalting with a spin column ( Zeba , Pierce ) and samples concentrated by ultrafiltration ( Amicon ) . pTri-EX-derived plasmid pOPINE was used for In-Fusion cloning [18] of the FMDV coding sequence from pBG200-A22-wt resulting in pOPINE-A22-wt [19] . An overlapping PCR procedure which exchanged a P1 region for a fragment bearing the H2093C mutation resulted in plasmid pOPINE-A22-H2093C . Subsequent alterations within the P1-2A-3C expression cassette in order to down-regulate the 3C protease were as described [19] . Sf9 cells were grown in Insect-XPRESS ( Lonza ) supplemented with 2% FCS and antibiotics at 27 . 5°C . Transfer vector and AcMNPV bacmid KO1629 ( 0 . 5 µg of each ) were mixed in the presence of 3 µl Fugene ( Roche ) for 20 min at room temperature and used to transfect Sf9 cells at a density of 1 . 2×106/well in a 6-well plate . Since baculovirus DNA with gene knockout 1629 will not initiate an infection unless rescued by recombination with a baculovirus transfer vector , the AcMNPV harvested in the culture supernatant after 5 days was 100% recombinant virus [19] . Virus stocks were produced by infecting Sf9 cells at a confluence of 70% with 200 µl recombinant per 175 cm2 flask and harvested from culture supernatants after 5 days . For the expression of empty capsids , Sf9 cells at a density of 1–2 106/ml were infected with 1/10 volume of baculovirus stock . After 3 days virus extraction was as described for mammalian cells except that lysis was with 1% Triton X-100 in the presence of 5 µl/ml protease inhibitor cocktail ( Sigma ) . The crystal structures of FMDV serotypes A10 [11] , A22 [2] and O1bfs [3] were inspected and amino acid residues involved in inter-pentameric interactions were identified . An energetically favourable disulphide bond was predicted by manually measuring the pair-wise Cαi-Cαj and corresponding Cβi-Cβj distances using COOT [27] . Crystals were grown by the sitting-drop vapour diffusion method in Crystalquick X plates ( Greiner Bio-One ) using 100 nl virus plus 100 nl precipitant dispensed with a Cartesian liquid dispensing robot as described previously [28] . Micro-crystals of A22-wt empty capsids ( 3 mg/ml ) with average dimensions of 50×50×5 µm3 and A22-H2093C ( 2 . 3 mg/ml ) with average dimensions of 30×30×5 µm3 grew within 1 week at 294K with 4 M ammonium acetate , 100 mM bis-Tris Propane , pH 7 . 0 . Optimisation by varying the concentration of precipitant and pH around the initial condition produced sufficient crystals for structural solution . A 20×20 µm2 beam ( λ = 0 . 9778 Å; I24 micro-focus beamline , Diamond ) , was used for in situ diffraction image collection [20] at 294 K on a Pilatus 6 M detector . The structures of A22-wt and A22-H2093C were solved by molecular replacement . The orientation of the particles ( obtained from a self-rotation function ) was found to be the same as for the native A22 virus structure ( PDB id: 4GH4 ) . Hence the coordinates and non-crystallographic symmetry ( NCS ) operators from native virus were used for the refinement . Initial estimates of phases were obtained by rigid body refinement with CNS [29] . Iterative positional and B-factor refinement ( via CNS ) used strict NCS constraints ( Table S1 in Text S1 ) . Phases were further improved by 15-fold cyclic NCS averaging using the General Averaging Program ( GAP , D Stuart and J . Grimes , unpublished ) . There was good agreement between the observed data and those calculated from the averaged electron density map of R = 10 . 4% and CC = 97% for the wild-type and R = 12 . 3% and CC = 95% for the mutant . Model building used COOT [27] . Two groups of four 100 to 150 kg Holstein Friesian calves were vaccinated with either A22-wt or A22-H2093C capsids . Each animal received 12 µg of purified capsid formulated in oil adjuvant ( Seppic 206B ) as an intramuscular injection on week 0 and week 3 of the study . All eight animals plus two non-vaccinated control animals were needle challenged intradermolingually with 1×105 TCID50 of cattle adapted FMDV A22 on week 34 . Animals were examined clinically and blood sampled from the day of challenge until day 9 . Cattle were considered protected if lesions could not be detected at sites distal from the inoculation point . Animal experimentation was approved by the Pirbright Institute ( PI ) ethical review board under the authority of a Home Office project licence in accordance to the Home Office Guidance on the Operation of the Animals ( Scientific Procedures ) Act 1986 and associated guidelines . Sera from the 8 immunised cattle and control sera from 2 non-vaccinated animals were prepared from blood samples . Their neutralising activities were determined as reported previously; testing was in duplicate , in serial 2-fold dilutions and endpoints calculation was made as described [22] . Titres of FMDV-specific antibodies are expressed as the reciprocal value of the highest dilution giving ≥50% neutralisation of homologous virus growth . Total nucleic acid was extracted from 200 µl of serum and automated reverse transcription procedures were performed incorporating homologous FMDV RNA standards . Real-time PCR amplification was performed using A22 specific primers and standard curves constructed to provide a measure of the number of FMDV genome copies [30] . Virus neutralisation titres were analysed using linear mixed models including time since vaccination and vaccine type ( wt or H2093C empty capsids ) as factors and animal as a random effect . Model selection proceeded by stepwise deletion of non-significant ( P>0 . 05 ) terms , starting from a model including time since vaccination and vaccine and an interaction between them . The total amount of virus produced following challenge was estimated for each animal by computing the area under the curve ( AUC ) for copy number versus time using the trapezium rule . A Wilcoxon rank-sum test was used to check for significant ( P<0 . 05 ) differences in AUCs , first between animals vaccinated with A22-wt or A22-H2093C empty capsids and second between vaccinated ( with either capsid ) and non-vaccinated animals . | Picornaviruses are small RNA viruses , responsible for important human and animal diseases for example polio , some forms of the common cold and foot-and-mouth disease . Safe and effective picornavirus vaccines could in principle be produced from recombinant virus-like particles , which lack the viral genome and so cannot propagate . However the synthesis of stable forms of such particles at scale has proved very difficult . Two key problems have been that a protease required for the proper processing of the polyprotein precursor is toxic for host cells and the empty recombinant particles tend to be physically unstable in comparison to virus particles containing nucleic acid . This is particularly true in the case of Foot-and-Mouth Disease Virus ( FMDV ) . Here we report the production and evaluation of a novel vaccine against FMDV that addresses both of these shortcomings . Importantly , the strategies we have devised to produce improved FMDV vaccines can be directly applied to viruses pathogenic for humans . | [
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] | 2013 | Rational Engineering of Recombinant Picornavirus Capsids to Produce Safe, Protective Vaccine Antigen |
MicroRNAs ( miRNAs ) are stable , small non-coding RNAs that modulate many downstream target genes . Recently , circulating miRNAs have been detected in various body fluids and within exosomes , prompting their evaluation as candidate biomarkers of diseases , especially cancer . Kaposi's sarcoma ( KS ) is the most common AIDS-associated cancer and remains prevalent despite Highly Active Anti-Retroviral Therapy ( HAART ) . KS is caused by KS-associated herpesvirus ( KSHV ) , a gamma herpesvirus also associated with Primary Effusion Lymphoma ( PEL ) . We sought to determine the host and viral circulating miRNAs in plasma , pleural fluid or serum from patients with the KSHV-associated malignancies KS and PEL and from two mouse models of KS . Both KSHV-encoded miRNAs and host miRNAs , including members of the miR-17–92 cluster , were detectable within patient exosomes and circulating miRNA profiles from KSHV mouse models . Further characterization revealed a subset of miRNAs that seemed to be preferentially incorporated into exosomes . Gene ontology analysis of signature exosomal miRNA targets revealed several signaling pathways that are known to be important in KSHV pathogenesis . Functional analysis of endothelial cells exposed to patient-derived exosomes demonstrated enhanced cell migration and IL-6 secretion . This suggests that exosomes derived from KSHV-associated malignancies are functional and contain a distinct subset of miRNAs . These could represent candidate biomarkers of disease and may contribute to the paracrine phenotypes that are a characteristic of KS .
MicroRNAs ( miRNAs ) are small , non-coding RNAs that are capable of fine-tuning gene expression through translational repression and/or mRNA degradation . In the past , miRNAs have emerged as important regulators in nearly every cellular process , but perhaps the largest biological consequence of miRNA dysregulation is in cancer [1] , [2] , [3] , [4] , [5] , [6] . The relationship between intra-tumor miRNA signatures and cancer progression has been well established , leading to the discovery of specific miRNAs or miRNA clusters that modulate gene expression in cancer [7] , [8] , [9] . We and others have shown that miRNA signatures can classify tumors into distinct classes and are predictive of disease outcome [3] , [4] , [6] , [10] , [11] . In our prior study , we found that the host miRNA profile differed depending on the degree of transformation among cells , even though all samples were infected by the same virus and thus expressed similar levels of viral miRNAs [6] . This suggests that host miRNA profiles impart information about viral infection above that provided by detecting the presence of the infectious agent . MiRNA regulation is complex in malignancies associated with viral infection such as herpesvirus-associated cancers [2] , [6] , [12] , [13] . Viral infection can trigger changes in the miRNA profile through the expression of viral genes that modulate the host miRNA repertoire . Some viruses such as Kaposi's sarcoma-associated herpesvirus ( KSHV ) and Epstein-Barr Virus ( EBV ) in addition encode their own miRNAs , which fine-tune host gene expression to promote latent viral persistence , immune evasion , and tumor progression [8] , [9] , [14] , [15] , [16] , [17] . These viral miRNAs are often expressed within the tumor and can reveal important information regarding viral latency and disease progression [18] . Furthermore , recent studies have highlighted important functions of the viral miRNAs in regulation of the viral life cycle , immune evasion and angiogenesis through validated mRNA targets [7] , [14] , [19] , [20] , [21] , [22] , [23] . In KSHV-associated cancers , the KSHV miRNAs can account for as much as 20% of all mature miRNA species within a cell and are highly conserved among isolates ( Figure S1 and [12] , [14] , [17] ) . KSHV is the etiological agent of Kaposi's sarcoma ( KS ) , the most common AIDS-defining cancer worldwide [24] . KSHV is also associated with the B cell lymphoma Primary Effusion Lymphoma ( PEL ) and with the plasmablastic variant of Multicentric Castleman's Disease ( MCD ) . Despite the availability of Highly Active Anti-Retroviral Therapy ( HAART ) , KS continues to occur in the US and worldwide . Treatment of KS remains a challenge and stable , minimally invasive biomarkers for diagnosis are lacking [25] , [26] . Therefore , the discovery of plasma miRNA biomarkers for KSHV-associated malignancies could improve diagnostics through early detection and could influence treatment through non-invasive monitoring of tumor responses . MiRNA biomarkers can be sampled from blood , saliva , or other bodily fluids , offering a feasible diagnostic test even in resource-poor regions such as the “KS belt” in sub-Saharan Africa [24] , [27] . Viral microRNAs are the most attractive candidate biomarker because of their specificity for KSHV . However , a combination of viral microRNAs with cellular microRNA biomarkers is even more useful , as it may help differentiate among stages of KS progression or response to therapy and as it can identify cellular microRNAs that are common among KS and other cancers . We previously determined the cellular and viral miRNA profile in KS tumor biopsies as well as in PEL and found that the expression of viral miRNAs varies with disease state [3] , [4] , [6] . In addition to the viral miRNAs , key cellular miRNAs are involved in KSHV transformation and KS progression [8] , [9] , [28] , [29] . The detection of circulating miRNAs in plasma , serum and other bodily fluids suggests their utility as minimally invasive biomarkers for cancer diagnostics [11] , [30] , [31] , [32] , [33] , [34] . These circulating miRNAs are unusually stable ( i ) due to their packaging in microvesicles or exosomes , ( ii ) due to their RNA folding and size and/or ( iii ) due to their presence in Ago-containing ribonucleic acid:protein ( RNP ) complexes [32] , [34] , [35] , [36] . At this point it is unclear which of these mechanisms is the most efficient . Evidence suggests that all three mechanisms contribute to diagnostic utility by increasing miRNA stability . There are a variety of vesicles that are secreted from cells , each with slightly varying content and surface marker composition . Microvesicles can range in size from 30 nm–1000 nm and each follow different pathways of biogenesis ( reviewed in [37] , [38] , [39] ) . Recent studies have additionally shown that microvesicles from tumor cells may have altered morphology , size and surface markers , including the expression of tumor antigens compared to microvesicles that are released from non-tumor cells [40] , [41] , [42] , [43] . MiRNAs have been detected in microvesicles , exosomes and/or nanovesicles . This study refers to these vesicles collectively as exosomes based on common surface marker expression and morphological characteristics . Transfer of exosomes and their contents from tumor cells to surrounding , uninfected cells may be an important form of cellular communication and has been demonstrated in cell culture models , for instance in EBV-associated cancers [44] , [45] . Additionally , exosomes may provide a means of paracrine signaling from virally infected cells to adjacent , non-permissive cells [46] . This study attempts to bridge the gap between clinical samples and cell culture models . To do so we compared the detailed , circulating miRNome of KS in clinical human samples and in KS mouse models [47] , [48] , [49] . This confirms the presence of circulating KS and KSHV-specific miRNAs in vivo in the context of KSHV infection . Multiple KSHV miRNAs and members of the miR-17-92 cluster of cellular miRNAs were detected within patient exosomes . These circulating miRNA signatures may serve as a new mechanism of paracrine signaling for mediating KSHV pathogenesis and may represent a reservoir for novel biomarkers .
To date , most studies on viral exosomes have used tissue culture models of infection . To expand on these studies , we utilized a series of clinical samples and two novel robust mouse models of KSHV pathogenesis [47] , [48] , [49] . The sample groups and number of samples included in each group are outlined in Table S1 . Briefly , human plasma from healthy , KSHV-negative controls or from AIDS patients with either KS or a non-KS malignancy was used to isolate exosomes . The HIV viral load and CD4+ T cell counts were similar in both KS and non-KS malignancy groups ( data not shown ) . KS tumor biopsies and primary PEL pleural fluid were also included and served as positive controls for the presence of KSHV compared to control human plasma . We also used two mouse models previously characterized in our lab [47] , [48] , [49]: the 801 latency locus transgenic mouse model which expresses all viral miRNAs in B cells [50]; and a xenograft model using TIVE L1 tumor cells , which maintain KSHV [48] . These cells are xenografted into SCID mice , which results in robust and reproducible tumor formation [48] . H&E staining revealed similar phenotypes of KS and our TIVE xenograft mouse model while both of these differed from the staining observed in PEL ( Figure S2 ) . The KSHV-TIVE model [48] represents another instance of extended yet incomplete KSHV lytic transcription , as recently demonstrated in KSHV-infected lymphatic endothelial cell cultures under puromycin selection [51] and previously in KSHV-infected mouse endothelial cells [52] . Similarly , a KSHV cell line model of transformed rat mesenchymal precursors yields some lytic gene expression but with minimal amounts of virions produced [53] . The KSHV-TIVE endothelial cell model maintains KSHV in the absence of selection and like other long-term KSHV-infected endothelial cell cultures they remain tightly latent . Neither sodium butyrate nor exogenously provided RTA/Orf50 are able to induce infectious virus production ( R . Renne , personal communication ) or complete , genome-wide lytic transcription in TIVE L1 cells [48] . Subcutaneous implantation into mice can activate many viral genes , although these represent only approximately half of the genes turned on during lytic reactivation in PEL cells and are insufficient to produce infectious virions . A similar , abortive lytic expression profile has been observed in KSHV-infected human TIVE-L1 cells [48] as well as in KSHV-infected mouse and rat endothelial cells [52] , [53] . This incomplete transcription program is incompatible with virion production and in the case of KSHV-infected LEC has been termed a novel latency program [51] . For this reason , we refer to the TIVE xenograft mouse model as a latent KSHV model due to the lack of virions produced . Exosomes and circulating miRNAs were purified as shown in Figure 1A and detailed in methods . Following purification , total RNA was isolated from each sample group and used for Taqman-based qPCR profiling of the cellular miRNA repertoire ( 754 human miRNAs ) as described [3] , [4] , [54] . Agilent RNA analysis showed that exosomes expressed small RNAs but lacked both 18S and 28S ribosomal RNA ( Figure S3 ) . Figure 1 shows the distribution of miRNAs in different sample subsets ( Figure 1B–E ) . Each boxplot shows the expression levels for the different sample groups . The expression of individual microRNAs are denoted by solid circles . As demonstrated in Figure 1B , the majority of miRNAs present in control human plasma ( KSHV− ) in the supernatant fraction are susceptible to RNase , representing free , circulating miRNAs . These miRNAs are likely not encapsulated in Ago-RNP complexes nor microvesicles [35] . The exceptions were miRNAs miR-16 , miR-195 and miR-197 , which could be detected despite RNase treatment . This RNase resistance of these particular miRNAs is consistent with prior observations [35] . Levels of the C . elegans cel-mir-39 spike-in were abolished ∼16 , 000-fold after RNase treatment and were decreased when incubated with pleural fluid prior to RNA isolation ( Figure S4 ) . This verifies the activity of our RNase treatment and confirms that pleural fluid , like other body fluids , has some intrinsic RNase activity [33] , [34] , [35] . Therefore , the majority of RNAs that are stable in plasma and pleural fluid are likely RNase-resistant and protected within exosomes . In samples enriched for exosomes derived from either control human plasma or mouse serum , we were able to readily detect both human and mouse miRNAs ( Figure 1C ) . Figure 1D denotes the relative expression levels of miRNAs in cells , exosomes and the free , circulating fractions of control human plasma . As expected , exosomal and other circulating miRNAs are detectable but are present at lower levels compared with intracellular miRNAs . MiRNAs are readily detected in all sample types tested including tumor biopsies and exosomes from control plasma or serum and malignant effusions such as pleural fluid ( Figure 1E ) , though the miRNA yield was highest in tumor tissue . Although we used human plasma and serum from mice in the majority of experiments , we also performed miRNA profiling with control mouse plasma . Importantly , we did not observe significant differences in the levels of miRNAs found in plasma versus serum in this study . The comparison of this small subset of miRNAs across the different variables shown in Figure 1B–E did not afford us the statistical power to identify differences among individual miRNAs expressed in these samples . However , these data establish the framework for further analysis and confirms qPCR as a reliable platform for the profiling of miRNAs in a diverse group of clinical samples [54] , [55] . Furthermore , we validate the presence of exosomal miRNAs in cell-free patient plasma and mouse serum . Isolation of exosomes using the Exoquick method has previously been validated to yield similar electron microscopy ( EM ) structures and miRNA array populations as other techniques [56] . Nonetheless , we sought to confirm the presence of exosomes in our patient samples using two independent isolation techniques . Enriched exosomes from the Exoquick protocol revealed similar structures via electron microscopy compared to exosomes enriched by differential ultracentrifugation ( Figure 2A ) . However , while Exoquick samples did contain exosomes ( determined by size and morphological characteristics ) , they yielded images with high background by electron microscopy due to the crowding agent present in the ExoQuick solution . This background was not due to contaminating cellular debris , as high-speed centrifugation and elimination of cellular debris using a sucrose cushion failed to eliminate background in the EM images ( Figure S5 ) . By comparison , differential ultracentrifugation yielded exosomes of similar size and morphology with minimal background ( Figure 2A ) . Patient pleural fluid and BCBL1 cell supernatant yielded exosomes that appeared similar by EM . We therefore pursued the Exoquick method for further study , as these samples required much less sample input , a key benefit when working with clinical samples and mouse models . To further establish the purity of our exosomes , we performed Western blots for previously established exosomal markers including the tetraspanin CD9 , Hsp90 alpha/beta and flotillin [57] , [58] . We first analyzed the expression of flotillin , which is enriched in exosomes [58] , [59] , [60] . Flotillin was expressed in all human and mouse exosome samples ( Figure 2B , C ) but was not present in the supernatant fractions containing freely circulating miRNAs ( Figure 2D , S ) . Hsp90 alpha and beta , which are also highly enriched in exosomes , were detected in PEL cells and pleural fluid-derived exosomes but , as expected , were absent in the supernatant fraction ( Figure 2E–G ) . Finally , we assessed the expression of the tetraspanin CD9 , another exosomal marker . The KS exosomal subgroup ( KS-E ) expressed detectable levels of CD9 whereas the supernatant fraction ( S ) did not express the exosome marker ( Figure 2H ) . As a negative control for exosomes , we used the exosome-depleted supernatant fraction from BJAB cells ( - ) . The mouse exosome samples isolated from serum of control and transgenic ( Tg ) mice also showed robust expression of the CD9 exosome marker , indicating that these samples are enriched for exosomes ( Figure 2H ) . The increased expression observed in the mouse samples most likely reflects the ratio of input used to the total fluid volume present in human and mouse . A mouse has a total blood volume of 1 . 5 mls of which we use 250 ul ( ∼17% ) whereas human blood volume is approximately ∼5 L of which we used 250 ul ( 0 . 005% ) . We also tested the presence of exosomal markers in samples purified by ultracentrifugation and received similar results , validating the use of the Exoquick method for our study ( Figure S6 ) . Furthermore , the ExoQuick method yields more exosomes than other methods tested and uses approximately 100-fold less starting material . Increased expression of KSHV miRNAs correlates with disease state and tumor progression in endothelial cells [3] , [6] . EBV viral miRNAs have been detected in exosomes isolated from cultured lymphoma cell lines , NPC patients and xenografted mice [44] , [45] , but thus far it has not been shown that KSHV miRNAs are also loaded into exosomes . To address this question , we examined patient-derived exosomes for the presence of KSHV-encoded miRNAs . Figure 2I shows the qPCR products separated by size on a Caliper nanofluidics platform . Exosomes derived from serum of three independent KSHV-positive TIVE L1 xenograft tumor mice and PEL fluid contained KSHV miR-K2 ( Figure 2I ) . Total RNA from KSHV-positive latently-infected BCP-1 PEL cells were used as a positive control . KSHV miRs K12-4-5p , K12-4-3p , K12-5 , K12-6-5p , K12-10a and K12-11 were also detected in pleural fluid and xenograft tumor mice ( data not shown ) . KSHV miRNAs were undetectable in the KSHV-negative BJAB cell line ( Figure 2I ) . This shows that systemically circulating exosomes contain appreciable levels of mature KSHV miRNAs and therefore exosomes containing KSHV miRNAs can travel from the subcutaneous tumor graft into the bloodstream and are stable enough to circulate systemically . Since we harvested the blood at day 10–15 after tumor cell injection , this result is likely to reflect steady-state levels of exosomal miRNAs . Notably , the L1 TIVE xenograft model does not generate infectious virus [48] . Hence , exosome encapsulated KSHV miRNAs show promise as a highly sensitive marker for latent KSHV tumor cells . In order to study exosome-associated viral miRNAs in more detail , we used the BCBL1 PEL cell line to assess KSHV miRNA expression . We found that 14 out of 14 KSHV microRNAs tested were expressed at detectable levels in exosomes from latent BCBL1 cells ( Figure S7 ) . Methods for purifying virions and exosomes can lead to co-precipitating of both exosomes and virions , therefore making it difficult to physically separate them for analysis . To distinguish the source of these viral miRNAs as exosomal or virion-associated , we purified exosomes using three different techniques . In addition to using the ExoQuick method of purification , we also utilized differential ultracentrifugation and a new bead affinity purification technique that positively selects for CD63+ exosomes , an exosomal marker not present on KSHV virions . Expression of KSHV microRNAs was then assessed following enrichment of exosomes using either method ( Figure 3 , Figure S7 ) . In addition , we passed the samples through a 0 . 2 µm filter prior to exosome isolation but after the removal of cellular debris . Although KSHV virions are approximately 180 nm in size , they tend to aggregate , a phenomenon well-recognized in earlier studies studying infectivity of cell-free virus [61] , [62] , [63] . This aggregation of virions makes it difficult to clear even a 0 . 2 µm filter . This was experimentally confirmed by filtering concentrated KSHV stocks , which resulted in a decrease in titer of approximately 4 logs ( data not shown ) . Exosomes , however , which range in size from 30–100 nm , can easily pass through a 0 . 2 µm filter , as is evident from EM imaging of filtered patient-derived exosomes ( Figure 2A ) . Consistent with this , the expression levels of KSHV miRNAs were only slightly affected by filtering ( Figure 3A , Figure S7 ) . By contrast , filtering of exosome samples resulted in a dramatic decrease in viral load ( Figure 3B ) . We observed similar expression patterns of viral miRNAs in exosomes isolated and filtered following both ExoQuick and ultracentrifugation methods . This was confirmed by Caliper gel electrophoresis , which showed the presence of KSHV miRNA products in both the exosome and filtered exosome fractions ( Figure S7 ) . A lower shifted band corresponding to primer dimers was detected in the no template control reactions . Note also that the Caliper images represent non-quantitative accumulation of product after 55 cycles , whereas quantification was based on the exponential phase of the PCR reaction . Using the CD63+ exosome isolation method , we consistently observed expression of KSHV miRNAs regardless of filtration ( Figure 3A ) . The levels of viral miRNAs were not significantly different in exosome preparations from latent or lytically induced PEL cells ( Figure 3A ) . If these miRNAs were predominantly present within virions , we would expect a robust increase in viral miRNA levels concomitantly with increased virion production following reactivation as we observed for KSHV load ( Figure 3B ) . Furthermore , RNase treatment of samples slightly decreased viral miRNA levels in the CD63− supernatant but did not affect KSHV miRNA expression in CD63+ fractions , suggesting that these miRNAs are primarily protected within exosomes ( data not shown ) . Analysis of exosomes isolated by CD63 affinity capture confirmed the presence of CD9 , another well established exosomal marker ( Figure 3F ) . CD9 levels were unaffected by filtering samples and RNase treatment ( Figure 3F ) . Taken together , this demonstrates that the KSHV miRNAs are predominantly contained within exosomes released from latently-infected tumor cells . Having determined that viral miRNAs were present in exosomes , we next sought to analyze the distribution of KSHV DNA among our samples and biochemical fractions . There are two mechanisms that lead to KSHV viral DNA being detectable in body fluids: ( i ) virions [64] , ( ii ) tumor cell-released free viral DNA , as has been demonstrated for EBV [65] , [66] , [67] , [68] . To eliminate the contribution of cell-free viral DNA , we treated all samples with DNase prior to DNA isolation . We evaluated BCBL1-derived exosomes purified using different techniques for the presence of KSHV DNA ( Figure 3B , C ) . Exosome-enriched samples were passed through a 0 . 2 µm filter , which led to a drastic decrease in KSHV load using both purified virus stock and exosomes ( Figure 3B , data not shown ) . Although the viral load increased following reactivation , filtering of exosomes from lytic BCBL1 cells abolished viral load to approximately the limit of detection . DNase treatment of samples , which effectively eliminated freely circulating tumor-associated DNA , further decreased KSHV load in filtered fractions ( data not shown ) . We also compared the presence of viral DNA in exosome and supernatant fractions of samples enriched by CD63 bead affinity purification or differential centrifugation ( Figure 3C ) . Viral DNA was detected in the exosome-depleted supernatant fraction ( CD63− ) after bead affinity purification but was undetectable in the CD63+ exosome fraction . Conversely , viral DNA was enriched in the exosome pellet following differential ultracentrifugation , as both virions and exosomes sediment at similar densities during centrifugation . This establishes CD63-based affinity capture as an efficient way to separate exosomes and virions . Since we detected KSHV miRNAs , but not KSHV DNA in the CD63-affinity purified exosomes , this suggests that the primary source of the viral miRNAs we observe is exosomes rather than virions . We also evaluated viral load in exosomes purified using the ExoQuick method . The advantage of the ExoQuick method compared to CD63 capture is greater efficiency ( using only 250 µl as input ) , which is essential when profiling large numbers of clinical samples . We found KSHV DNA in both exosomal and free supernatant fractions of plasma and pleural fluid ( Figure 3D , E ) . The highest viral load was found in exosomes derived from PEL pleural fluid . No viral DNA was detected in our negative control samples or in exosomes purified from non-KS , HIV+ patients ( Figure 3D , 3E lanes CHP and AMT respectively ) . Both ExoQuick and ultracentrifugation methods yielded KSHV DNA in the KS patient plasma and PEL fluid exosome fraction ( Figure 3 ) . Thus , neither differential centrifugation not ExoQuick can with certainty be used to separate exosomes from virion particles . However , CD63+ exosomes contain little KSHV DNA , especially following filtering of exosome samples . This novel method confirms that the majority of the signal detected in the viral load assay was due to free DNA and KSHV virion DNA . To further address the possibility that virions may also be present in our exosome fraction and may contribute to our results , we looked for KSHV virions and viral proteins in our exosome-enriched samples . We did not detect any virions by EM following ExoQuick or ultracentrifugation isolation of exosomes ( Figure 2A ) . We analyzed more than 20 grids for the presence of virions in our exosome-enriched samples . Quantitative analysis of exosome-enriched pellets by differential centrifugation revealed 2 , 319 exosomes and no virions on three sample grids . The supernatant fraction was also imaged by EM for the presence of exosomes and only 13 exosomes were detected , validating ultracentrifugation as an efficient method for exosome isolation . We also compared the number of exosomes from latent and lytic BCBL1 cell supernatants by EM . Both latent and lytic samples had similar numbers of exosomes detected on representative grids , averaging 135 and 126 , respectively ( while the DNase resistant viral load differed by >10-fold ) . This suggests that the presence of exosomes in our samples may be static and independent of virus production . Finally , we probed exosome-enriched samples for KSHV structural proteins . KSHV K8 . 1 was readily detected in BCBL1 PEL cells following lytic reactivation ( Figure S8 ) . However , PF-derived exosomes , which contained the highest viral load of our samples , did not express K8 . 1 . These data confirm that our exosome-enriched samples do not contain appreciable levels of KSHV virions ( Figures S7 , S8 ) . Although KSHV protein and DNA are not found in exosomes , we find systemically circulating KSHV miRNAs in exosomes derived from patients , tissue culture models and mouse models of KS ( Figure 2I , Figures S7 , S8 ) . This establishes exosome-associated viral miRNAs as new biomarkers for KSHV-associated cancers . It also suggests that detecting viral miRNAs may offer greater sensitivity of diagnosing viral infection than viral load measurements . To obtain a more complete picture , we profiled the host miRNA repertoire in each of our sample groups using both exosomal and exosome-depleted supernatant preparations . The C . elegans cel-mir-39 spike-in was used as an internal normalizing control . Unsupervised clustering analysis revealed two distinct clusters , which are shown as projected onto the first three principal components ( Figure 4A ) . Unsupervised clustering groups samples and the different miRNAs based on similar expression levels . The result is typically shown as a heatmap . Principal component analysis is used to reduce the complexity of the data further without loss of statistical power . It combines the multiple measurements of each sample ( or each miRNA ) to such that the data can be represented in three dimensions ( the principal component axes ) . Individual analysis of the human and mouse profiling samples ( Figures 4B , C ) illustrates the more divergent clusters representing miRNAs elevated in tumor versus control samples in the mouse model . We expected to see more defined clusters in our mouse models since the xenografts represent biological replicates with limited variability compared with human clinical samples . The miRNA profile in the human samples alone clearly separated samples into KSHV-associated and control groups . When we further narrowed the miRNAs to known oncomiRs and tumor suppressor miRNAs , the classification improved ( Figure 4D ) . A list of these ∼150 oncomirs and tumor suppressor miRNAs is shown in Table S2 . As a negative control for our analysis we clustered an unrelated sample . We performed unsupervised clustering of miRNAs in HEK293 cells following infection with West Nile Virus ( WNV ) ( Chugh and Dittmer , unpublished data ) . This comparison yielded very different clusters of miRNAs compared with the KSHV exosome data as noted by further predicted target analysis ( Table 1 ) . This establishes a unique oncomir signature of KS- and PEL-associated exosomes . We further examined the expression of individual oncomirs and tumor suppressor miRNAs in the mouse exosome subset by heatmap analysis ( Figure 4E ) . Oncomirs were defined as host miRNAs readily studied for their role in tumorigenesis and related cancer signaling pathways while tumor suppressor miRNAs have been demonstrated to functionally inhibit these processes ( Table S2 ) . We identified this subset of oncogenic miRNAs because ( a ) we previously extensively validated these assays [3] , [4] , [55] and ( b ) they represent miRNAs with experimentally verified expression and function . The most distinct expression pattern was the apparent separation between TIVE xenograft and control mouse serum ( Figure 4E , Panel i ) . The majority of oncomiRs in this cluster were increased in exosomes from KS xenograft tumor models and were only minimally or not detectable in the control mice ( ctrl versus xeno ) . We next compared miRNA expression profiles of control and xenograft mice to our latency locus transgenic mouse model ( Figure 4E , Panel ii ) . In this novel model , only the KSHV latent genes and miRNAs are expressed in B cells [50] . However , none of the viral structural genes are present . We found that exosomes derived from the transgenic model differed from that of control mice and shared some oncogenic miRNA expression with the xenograft mice . As this transgenic mouse model phenotype represents B cell hyperplasia , these highly expressed miRNAs may be reflective of change in the miRNome regulated by the KSHV latency locus prior to tumor formation ( Figure 4E ) . We further compared the exosomal miRNA profile of transgenic mice to that of PEL-associated exosomes from primary pleural fluid ( Figure 4E , panels iii , iv ) . This yielded similarities in induced exosomal oncogenic miRNA expression between the 801 transgenic mouse model and PEL patient fluid ( Panel iii , Cluster 1 ) . Interestingly , we also identified a subset of microRNAs that was solely induced in the KSHV latency locus transgenic mouse model ( Figure 4E , Panel iv , Cluster 2 ) . Figure S9 further compares the exosomal miRNA profile in an independent set of transgenic and control mice and indicates elevated levels of oncogenic miRNA expression in the transgenic mouse model . Analysis of miRNA profiles in both Clusters 1 and 2 also revealed a subset of oncogenic miRNAs that were exclusively expressed in exosomes , suggesting that these miRNAs may be preferentially incorporated from the tumor site into exosomes for intercellular communication ( Figure 4E , Panels iii , iv , Figure S10 ) . Taken together , we find that the most elevated oncomiR levels in exosomes were observed in the TIVE xenograft tumor group , as these mice were bearing large , well-vascularized tumors , which facilitates expression and release of miRNAs . This demonstrates for the first time that exosomal miRNAs , including KSHV miRNAs , can be detected in mouse models of KS . Our human clinical samples of AIDS-KS recapitulated the trends in oncogenic miRNA expression observed in our mouse models ( Figure 4F , Figure S11 ) . Cluster 1 represents a subset of oncogenic miRNAs that are most highly expressed in exosomes derived from PEL pleural fluid ( Figure 4F ) . Several miRNAs in this cluster were also elevated in KS patient-derived exosomes . This pattern of miRNA expression may reflect a signature of KSHV-associated malignancies . A subset of miRNAs within this cluster could also represent miRNAs overexpressed in KS and other cancers since we observed oncogenic miRNA induction in other AIDS malignancies as well as KS-associated exosomes ( Figure 4F ) . Cluster 2 shows another subset of miRNAs with elevated expression in exosomes from KS or AIDS malignancy patients . This cluster also includes several miRNAs that seem to be preferentially expressed within exosomes compared to the supernatant fraction . We noticed little difference in the miRNA profile from control plasma exosomes versus RNase-treated control plasma exosomes , indicating that exosomes are indeed resistant to RNase treatment [35] ( Figure 4F , lanes CHP exo and RNase-CHP exo ) . We also compared the miRNA profile in pleural fluid-derived exosomes exposed to RNase to determine if they responded similarly to our exosomes from control human plasma . Exosomes from PEL patient pleural fluid exhibited higher levels of miRNA expression . RNase treatment only slightly changed the miRNA profile , similar to that observed in control exosomes ( Figure S12 ) . This demonstrates that different patient samples respond similarly to RNase treatment and further validates that the majority of our signal was derived from exosome-contained miRNAs . Since patient samples may display a high degree of genetic variability and therefore miRNA signatures could differ , we sought to address the issue of individual variance of patient miRNA profiles using three PEL patients . Pleural fluid-derived exosomes were independently isolated and the miRNA expression profile was compared to that of control human exosomes . Many of the “PEL signature” miRNAs were expressed in all three patients , suggesting that these could be used as novel biomarkers of PEL present in pleural fluid ( Figure S13 ) . Another subset of miRNAs was expressed in 2 out of 3 patients . Since PEL is a rare malignancy , we obtained only three patients , each of varying disease states . Different factors such as disease state , co-infection with EBV or HIV status could contribute to absence of these biomarkers in one of the patients . However , despite the inherent genetic variability among patients , we could identify multiple miRNAs that were expressed at high levels in all three PEL patients compared to controls ( Figure S13 ) . We further analyzed the expression of the oncomiR cluster in the exosome sample subsets . For this analysis , we defined a relative expression score based on the CT where a higher expression score corresponds to a lower CT . Specifically , we calculate the expression class by binning CTs such that a CT of 20–25 corresponds to an expression value of 3 . MicroRNAs expressed with CTs of 25–30 are assigned an expression value of 2 . 5 . Scores are assigned in 0 . 5 increments until CT of 45+ equals zero , or not detected . In addition to patient-derived exosomes , we determined the miRNA profile for KS biopsies and PBMCs derived from control human plasma . The full profiling data is shown in Figure S14 . Figure 5A demonstrates that , as expected , KS biopsies ( KS , left ) displayed the highest expression of oncomirs . By comparison , a large number of oncomirs were undetectable ( expression score = 0 ) in biofluids . KS-associated exosomes also contained oncomirs in moderate ( expression score = 1 ) and some at very high levels ( expression score = 2 ) . While oncomiRs are readily expressed in both control and malignant samples , we found that the number of highly expressed oncogenic miRNAs was lower in control exosomes ( Neg ) . Note that members of the miR-17–92 cluster are denoted by blue dots and are highly expressed in KS biopsies and KS-associated exosomes compared with controls ( expression score>1 . 5 ) . The levels of oncogenic miRNAs were abolished in exosome-depleted supernatant fractions treated with RNase ( Figure 5A ) . The exceptions were miRNAs including miR-16 , miR-195 and miR-197 , which were previously shown to be RNase-resistant ( Figure 1B , [35] ) . This demonstrates that most oncogenic miRNAs were present in exosomes . Oncomirs specifically expressed in tumor samples at the highest expression level included miR-106a , miR-17 , miR-454 , let-7e , miR-451 , miR-886-5p , miR-601 and miR-625 ( expression score of ≥2 , Figure 5A ) . Note , that a high expression score is the result of both the underlying high level of expression of the specific miRNA species and the sensitivity of the particular qPCR assay . One of the most well-studied oncogenic miRNA clusters is the miR-17-92 cluster . The 6 mature miRNA species in this cluster tend to be co-regulated [69] and we previously found this miR cluster upregulated in KS [3] , [4] . Members of the paralog cluster miR-106b/25 are also well-known for their role in tumorigenesis and share target genes with the miR-17-92 cluster [70] , [71] . We therefore investigated whether KS-associated exosomes contained members of these two miRNA clusters . In our clinical and mouse model samples , levels of the miR-17-92 and miR-106b/25 clusters were induced in exosomes derived from KSHV-associated mouse serum , primary human pleural fluid and KS biopsies compared with control exosomes ( Figure 5B , C ) . Since we did not observe a similar enrichment of all tumor-associated miRNAs within the exosomes , these miR-17-92 members are likely to be preferentially incorporated into exosomes . Members of these oncogenic clusters were slightly elevated in exosomes from KS patient plasma , although this was not statistically significant ( Figure 5B ) . However , exosomes derived from pleural fluid expressed much higher levels of the miR-17-92 and miR-106b-25 cluster members , with the exception of miR-25 and miR-92a ( Figure 5B ) . The increased expression of oncogenic miRNAs within PF-derived exosomes may be because of direct contact of the pleural fluid to PEL cells , suggesting that malignant effusions may be a very effective source for obtaining exosomes ( Figure 5B ) . Induction of the miR-17-92 cluster member miRNAs was most pronounced when we compared exosomes derived from the xenograft mouse model to control mouse exosomes ( Figure 5C , p≤ . 000059 ) . Therefore , we find that exosome-associated oncomirs are uniquely upregulated in samples from KS tumor-bearing animals and primary PEL patients . Interestingly , even the B cell hyperplasia latency locus transgenic mouse model showed increased levels of these miRNAs in systemically circulating exosomes ( Figure 5C , p≤0 . 05 ) . Several miRNAs seemed to be preferentially incorporated into exosomes ( Figure 4E , F ) . Therefore , we analyzed these in detail . As shown in Figure 5D , miRNAs miR-19a , miR-21 , miR-27a , miR-130 and miR-146a were enriched within exosomes and virtually undetectable as free , circulating miRNAs in the supernatant . Their relative expression levels were significantly elevated in mouse models of KS ( p≤4×10−5 , Figure 5D ) . To confirm these results , we performed Caliper gel electrophoresis analysis on the qPCR products , which confirmed that these miRNAs were overexpressed in exosomes from our transgenic and TIVE xenograft mouse models ( Figure S15 ) . Taken together , these data reveal that members of the miR-17-92 cluster are exclusively incorporated into exosomes and may exhibit diagnostic potential and contribute to tumor development and pathogenesis of malignancies such as KS . We profiled the circulating miRNAs in a second , independent pair of KS patients ( n = 2 ) and compared them to TIVE xenograft mice along with the appropriate controls ( Figure S16 ) . One of the KS patients profiled had an unusually high KSHV load , multiple internal lesions and cytokine dysregulation [72] . Unsupervised clustering analysis confirmed that the TIVE L1 xenograft mice had a distinct circulating miRNA profile from control mice , but also revealed that this mouse model shared similarities to the human miRNome detected in pleural fluid ( Figure S16 ) . Like the xenograft mice , the two KS case study patients expressed distinct circulating miRNA signatures when compared with control human plasma ( Figure S16 ) . The KS patient with more advanced disease ( DG1 , cytokine dysregulation and high KSHV load ) displayed a miRNA profile more similar to TIVE xenograft exosomes . These independent biological replicates and multiple clinical cases share a common , robust signature ( Figure S16 , Figure 4E , F ) . To gauge the importance of the KS exosome signatures , we analyzed the oncogenic miRNAs upregulated in tumor-derived exosomes using Gene Ontology pathway analysis and found that many of the miRNAs targeted pathways previously shown to be central to KSHV pathogenesis ( Table 1; asterisks ) . PI3K/Akt signaling is central to “Pathways in Cancer” , which had the highest correlation to upregulated miRNAs . It is known to be dysregulated following KSHV infection [73] , [74] , [75] . Many of the other pathways listed in Table 1 contribute to both the KEGG Pathways in Cancer and the Pancreatic Cancer pathway . For instance , MAPK is important in the control of replication and KSHV reactivation from latency while KSHV inhibits TGF-beta signaling through mechanisms including miRNA-targeted silencing [76] , [77] . KSHV LANA has been shown to bind GSK3-beta , leading to an upregulation of beta catenin in KS and PEL through the regulation of Wnt signaling [78] . TLR signaling has previously been shown to play a role in both primary infection of monocytes and reactivation from latency [79] , [80] . Finally , two of the pathway hits—focal adhesion and adherens junctions—are known to be important in viral entry , cytoskeletal remodeling and cell adhesion during KSHV infection and KS tumorigenesis including in adjacent KSHV-negative spindle cells within the KS lesion [81] . As control , we also analyzed the GO pathways associated with the miRNA signature of an unrelated virus ( WNV ) . This confirmed that the roles of the signaling pathways were unique to our exosome profiling of KSHV-associated malignancies ( Table 1 ) . We also performed GO pathway analysis using two additional , independent analysis databases: Panther and Ingenuity Pathway Analysis ( Table S4 and S5 ) . These revealed highly significant pathways targeted by oncomirs including angiogenesis , integrin signaling , transformation , migration and invasion ( Tables S4 , 5 ) . Previous studies of the oncogenic miR-17-92 cluster have also revealed roles in similar pathways such as NFkB signaling , angiogenesis , TLR , MAPK , STAT and TGF-beta signaling [69] , [82] , [83] , [84] , [85] , [86] , [87] , [88] , [89] , [90] , [91] . Since many of the GO analysis pathway hits have been previously functionally validated , it is likely that some of the exosomal miRNAs found overexpressed in this study contribute to KSHV signaling . One function that many of these pathways shared is the involvement in cell migration , which is important for tumorigenesis and noted in Table 1 . We therefore used cell migration as a bioassay to show that our exosome-enriched samples yielded intact , functional exosomes . Since cell migration was a shared functional outcome of several of the gene ontology pathway hits , we sought to test the effect of KS and PEL-derived exosomes on the migration of endothelial cells . hTERT-immortalized HUVECs [92] were treated with exosomes isolated using the ExoQuick kit . Exosomes derived from patient PEL pleural fluid were added to cells for 24 hours and the wound healing scratch assay was performed to test the migration capability of these cells . Figure 6A demonstrates that hTERT-HUVECs treated with patient-derived exosomes displayed enhanced cell migration by 8 hours post-initiation of the scratch assay . Cells treated with exosomes derived from control human plasma ( CHP ) showed delayed migration compared with cells receiving the exosomes derived from pleural fluid ( Figure 6A ) . This confirms that this effect was not due to ExoQuick itself , since control exosomes isolated using this protocol did not increase migration . Since we also detected KSHV DNA in the supernatant fraction of pleural fluid and the presence of virions can also affect migration , we analyzed the migration capability of cells treated with exosome-depleted supernatant ( PF sup ) . hTERT-HUVECs exposed to PF supernatant also displayed enhanced migration compared to control exosomes but cells treated with pleural fluid-derived exosomes still migrated more rapidly . As a positive control , we also treated hTERT-HUVECs with IL-6 , which resulted in increased migration similar to that observed with the pleural fluid supernatant fraction ( IL-6 versus PF sup ) . Of note , exosomes are known to carry proteins as well as miRNAs [44] . At this point , we cannot assign this exosome phenotype to either moiety . The data also suggests that while cytokines and virus present in the supernatant can affect cell migration , patient-derived exosomes further accelerate this process . Exosomes isolated from cell culture models and patients have been shown to express phosphatidylserine ( PS ) on their surface [39] , [93] . Since Annexin V can bind PS on the surface , annexin blocking of exosomes has been previously used as a means of inhibiting exosome fusion and transfer of exosomal contents [44] , [45] , [93] . Therefore , we also performed the scratch assay in the presence of annexin blocking ( Figure 6B ) . Exosomes and supernatants were incubated with Annexin prior to initiation of the scratch assay . Annexin blocking did not seem to affect the migration of hTERT-HUVECs treated with control ( CHP ) exosomes . However , the enhanced migration potential of cells treated with pleural fluid-derived exosomes was reversed with annexin blocking , demonstrating that this phenotype is due to exosomal transfer . Cells treated with exosome-depleted supernatants from pleural fluid were not affected by annexin blocking . Similarly , IL-6 enhanced cell migration regardless of annexin blocking . Therefore , any virus or cytokines present in this supernatant enhanced migration via a different mechanism independent of exosomes . Figure 6C provides a boxplot representation of the scratch assay data . This confirms that cells treated with pleural fluid-derived exosomes exhibit increased migration , which is reversed by treatment with annexin . This is also observed following treatment of cells with exosomes derived from the PEL cell line BCBL1 . We formally tested the individual contributions of each factor to the increased migration phenotype using a Dunnett confidence interval test which evaluates the significance of different treatments compared to a common control and adjusts for potential bias due to multiple comparisons being performed ( Figure 6D ) . As represented by the black circles ( with brackets representing the 95% confidence interval ( CI ) ) , treatment with IL-6 or exosomes from either pleural fluid or PEL cells independently led to significantly increased closure of the wound compared to exosomes isolated from KSHV-negative control human plasma ( CHP ) . By contrast exosome-free , mock treated cells behaved similarly to cells treated with exosomes from KSHV-negative CHP . All scratch assays were performed in triplicate for three independent biological replicates over a span of two weeks . In each biological replicate , we observed the same phenotype . Table 2 shows the linear , multivariate analysis of the data , which measures the difference between two experimental conditions after adjusting for all other factors . Exosomes derived from pleural fluid of a PEL patient ( p≤10−11 ) or from the BCBL1 PEL cell line ( p≤10−7 ) significantly enhanced migration of hTERT-HUVECs at 8 hours post-infection compared to CHP ( Table 2b–d ) . When comparing the supernatant and exosome fractions of pleural fluid and PEL cell supernatants , exosomes were more potent ( p≤0 . 031 ) , but we still observed a significant effect on HUVEC migration for the supernatant ( Table 2j ) . This is not entirely unexpected , since supernatants from PEL patients and PEL cells have large amounts of soluble IL-6 , IL-10 and VEGF [49] . Still exosomes independently confer an enhanced migration phenotype to hTERT-HUVECs . Annexin blocking of exosome fusion supports this ( p≤10−7 ) and resulted in reversal of the enhanced migration effect of PEL-derived exosomes ( Table 2k , columns b–d ) . This demonstrates that our purified exosomes have biological activity , and second that the KS and PEL patient-derived exosomes confer a phenotype of enhanced migration to endothelial cells , which is likely to contribute to KS-associated angiogenesis . We next analyzed migration of hTERT-HUVECs treated with exosomes using the xCelligence system , which allows for highly accurate , quantitative measurements of cell migration in real-time . The xCelligence Cell Invasion and Migration ( CIM ) Plate 16 consists of an upper and lower chamber separated by a microporous membrane coated with gold microelectrode sensors on the bottom side . As cells migrate toward the chemoattractant in the bottom chamber , the impedance signal increases and results in a corresponding increase in Cell Index ( proprietary readout , Roche application note ) . hTERT-HUVECs were treated with patient- , cell line- or mouse model-derived exosomes . Cells were then serum starved and plated into the upper chamber of the CIM Plate . Migration towards the chemoattractant FBS was continuously monitored every two minutes for a period of 24 hours . Figure 6E shows that hTERT-HUVECs treated with KSHV-associated exosomes exhibited increased migration compared with cells treated with exosomes from control human plasma ( red ) . This assay independently demonstrates that exosomes from patient PEL fluid , the BCBL1 PEL cell line , and a xenograft mouse model of KS confer an enhanced migration phenotype to hTERT-HUVEC cells . Since IL-6 plays a significant role in KSHV pathogenesis , we analyzed the levels of IL-6 present in the scratch assay supernatants by ELISA ( Figure 6F ) . hTERT-HUVECs treated with patient-derived exosomes secreted high levels of IL-6 . IL6 secretion in response to exosome treatment was decreased when the exosome fraction was incubated with annexin V ( p≤0 . 003 ) . These experiments suggest that efficient exosome transfer drives enhanced cell migration , possibly through the increased induction of cytokines such as IL-6 . Note , though , that these experiments did not distinguish between miRNA and protein components of the exosomes . In sum , the exosomal signature associated with KSHV-related malignancies could not only be a reservoir of clinically important diagnostic biomarkers but may also be a novel mechanism of paracrine signaling that mediates KSHV-associated pathogenesis and tumorigenesis .
Circulating miRNAs , especially those within exosomes , have emerged as novel biomarkers [31] , [32] , [33] , [94] , [95] . Their main advantage is stability and ease of detection as all miRNAs can be profiled with a common platform . We previously established and validated such a miRNA profiling platform [54] . Bodily fluids such as plasma can be obtained using minimally invasive techniques and lend themselves to repeat sampling , for instance to follow therapy . In the case of PEL , periodic ( in extreme cases every few days ) draining of pleural cavities is medically indicated . Although the exosomal miRNA profile of malignancies associated with EBV have been previously reported [44] , [45] , this is the first study to examine the circulating miRNA profile of KSHV-associated cancers . This is also one of a few studies to compare patient tumors to xenograft mouse models [96] . We extend previous findings on exosomal miRNAs , which were largely based on cell culture models . KSHV-encoded miRNAs were detectable in systemically circulating exosomes ( Figure 2I and Figure 3 ) , including in xenograft mouse models of KS . This suggests that viral miRNAs can have effects far from the site of the infected cell . Furthermore , viral microRNAs could potentially serve as highly specific biomarkers of KSHV-associated malignancies , particularly if the lesions are internal and comprised of mostly latently infected cells . We found similar levels of viral miRNAs in exosomes derived from latently infected PEL cells compared to PEL cells undergoing lytic reactivation ( Figure 3A ) . Most KS tumor cells and most PEL are latently infected and even if lytic gene expression is observed in a subset of cells , virions are seldom produced [97] , [98] . A significant complication of characterizing exosomal miRNAs in virally associated diseases is that miRNAs may be incorporated into virions . Previous studies have shown that viral RNAs can be detected within herpesvirus virions , including KSHV and EBV [99] , [100] . Recently , Lin et al . demonstrated the presence of viral , as well as cellular miRNAs in purified KSHV virions [64] . Exosomes are difficult to physically separate from virions due to their similar sedimentation velocities , buoyant densities , biogenesis and heterogeneous nature of exosomes [44] , [46] . Others have circumvented this issue using cell culture models that are incapable of virus production , such as HCV subgenomic replicon ( SGR ) cells [46] . Analogous to this model , we employed several latent models of KSHV infection , including the latently infected TIVE xenograft mice , the latency locus transgenic mice and the BCBL1 latent PEL cell line [48] , [50] . We believe that the majority of miRNAs we detect here are exosomal , rather than virion-associated . To support this interpretation , we offer three lines of evidence . First , we were able to detect all viral miRNAs in latent BCBL1 exosomes and filtering samples led to decreased viral load but did not significantly affect levels of KSHV miRNAs ( Figure 3 , Figure S7 ) . We detected similar amounts of KSHV miRNAs in exosomes isolated from latent PEL supernatant as in exosomes from supernatant of induced PEL ( Figure 3 ) . In the same samples , we observed a greater than 10-fold increase in viral DNA . This suggests that KSHV miRNAs are released into exosomes from latently infected PEL , analogous to exosomal EBV miRNAs which are released from latently infected cells [44] , [96] . Note , that we are able to detect KSHV miRNAs in exosomes from 250 µl of latently infected cell supernatant , whereas at least 500 mls were previously used to enrich for virion-associated miRNAs [64] . We could also detect KSHV miRNAs in the bloodstream of mice , which carry KSHV latently-infected TIVE-E1/L1 xenografts . These cells do not generate infectious virions [48] and ( R . Renne , personal communication ) . Second , we were able to isolate exosomes by CD63-mediated affinity purification ( Figure 3 ) . Herpesvirus virions and exosomes co-purify in almost all centrifugation schemas designed to enrich for exosomal fractions ( i . e . differential ultracentrifugation , sucrose gradients , ExoQuick solution ) . By contrast , anti-CD63 Dynabeads positively select exosomes which carry CD63 as one of their surface markers [57] , [101] while CD63 ( − ) virions are eliminated . This resulted in an enrichment of KSHV miRNAs and concomitant depletion of viral DNA ( Figure 3 ) , demonstrating that indeed viral miRNAs are present in exosomes . We were also unable to detect any contaminating virions in our samples enriched for exosomes by electron microscopy and structural viral proteins were absent in our exosome-enriched samples ( Figure 2A and Figure S8 ) . Thirdly , KSHV miRNAs could be detected in exosomes isolated from the serum of our xenograft mouse model . These xenograft mice harbor latently infected cells , which do not generate infectious virus . This suggests that viral miRNAs are constantly released and circulate systemically in exosomes in mice ( and patients ) who harbor KSHV latently infected cells . Taken together , these data suggest that KSHV latently infected cells can release viral miRNAs and further demonstrates that exosomes are the source of these circulating miRNAs . Human oncogenic miRNAs were easily detected in tumor-derived exosomes isolated from patient plasma and pleural fluid ( Figure 4 ) . Further analysis confirmed increased levels of the well-studied miR-17-92 cluster miRNAs . Our data also show potentially important similarities and differences in the miRNA profile from AIDS patients with KS compared to patients with other non-viral AIDS-associated malignancies ( Figure 4F ) . This subset of exosomal miRNAs could reflect differences between the varying progression of different malignancies in AIDS patients or similarities among AIDS-associated cancers and merits further study . Exosomal miRNAs are readily detected in pleural fluid samples , representing an alternate sample source with potentially higher correlation to disease state for patients with malignant effusions . Since pleural fluid is more proximal to the tumor site than plasma , which circulates throughout the body , we reason that the circulating miRNome from malignant effusions may be more reflective of the tumor itself . However , further studies comparing the miRNA signatures of pleural fluid-derived exosomes from PEL and other non-KSHV-associated malignancies such as lung cancer are necessary to reveal diagnostic biomarkers unique to PEL . We also demonstrated that human and viral miRNAs are present in circulating exosomes in xenografted mice ( Figures 2 , 4 ) . We used the TIVE L1 [48] xenograft model , which has been shown to be predictive of anti-KS therapies [98] , [102] . The KSHV miRNAs that we consistently detected in these mouse models could only stem from the human graft . Due to the high conservation of cellular miRNAs within the oncogenic clusters , the cross-species detection of miRNAs using the human assays makes it difficult to distinguish miRNAs of human versus mouse origin in these models ( Table S3 , ABI product information , miRBase ) . In some cases , the mature miRNAs share 100% sequence homology across the entire length , not just the seed region ( miRBase , [103] ) and in many cases the targets have co-evolved as well [69] . We observed greater levels of miRNAs in the mouse exosomes compared to human exosomes , which may be due to the fixed 250 µl sample size with respect to the overall amount of blood circulating within a human ( approximately 5 L ) or mouse ( approximately 0 . 0015 L ) . Specific host miRNA markers of tumorigenesis also emerged in our mouse models . We showed previously that host miRNAs are distinct for different stages of KS tumor progression [6] . Therefore , tumorigenic miRNAs combined with viral miRNAs would offer a very specific biomarker signature and may also identify biomarkers for other related cancers . Therefore , we analyzed expression of the oncogenic miR-17-92 and 106b/25 clusters and found that they were significantly enriched in exosomes from TIVE tumor-bearing mice compared with controls ( Figure 5 ) . Several of the oncogenic miRNAs expressed in exosomes were previously found at highly expressed levels in the TIVE cell line independently ( R . Renne , personal communication ) . The mir-17-92 cluster was previously shown to be upregulated in KS tumor biopsies [3] , [4] . This is the first demonstration that the miR-17-92 cluster miRNAs are incorporated into exosomes from KSHV-associated malignancies . These oncogenic miRNAs have also been detected in exosomes derived from leukemia cells and those derived from breast milk [56] , [104] , suggesting that their function is at least in part to mediate paracrine phenotypes . Viral and cellular miRNAs originating from the tumor enter the mouse circulatory system and are readily detected in serum . Since our mouse model exosome signatures recapitulate the clinical KS signatures , this supports the validity of xenograft mice as a reliable model system for KS . We also observed a subset of miRNAs that were highly induced in exosomes and were virtually undetectable in the free , circulating miRNA fraction ( Figure 5D ) . The miRNAs detected exclusively in the exosome fractions are either known to be oncogenic or shown to be upregulated by KSHV infection [105] , [106] . This suggests that certain miRNAs are preferentially incorporated into exosomes and that many proliferative and tumor-associated miRNAs fall into this class . Recently , Palma et al [107] found that selectively exported miRNAs from malignantly transformed cells may be incorporated into customized exosomal particles distinct from the microvesicles that originate from untransformed cells . It is conceivable that these have different systemic stability and thus become enriched in a blood sample . This may be the case with KSHV-induced miRNAs as well since we found miRNAs originating from our transgene model also to be enriched in this fraction . Exosomes serve as a means of intercellular communication with surrounding cells and the contents of exosomes can be shared between cells through the mechanism of exosomal transfer [44] , [45] . Exosomes can deliver functional miRNAs to recipient cells and consequently downregulate expression of target genes [44] , [45] , [108] . Leukemia cell-derived exosomes have recently been shown to affect endothelial cell function through microRNA transfer [56] . Moreover , tumor-derived microRNAs were recently reported to play a functional role through binding to Toll-like receptors , thereby inducing an inflammatory response and influencing tumor growth and metastasis [109] . This in vivo relevance was further demonstrated by inhibiting tumor-secreted miRNAs , which altered tumor formation in mice [109] . Dendritic cell-derived exosomes can be used to prime the immune response as cancer immunotherapy to suppress tumor burden [110] , [111] , [112] . Exosomes have also been recently tested in clinical trials to reduce tumor size [110] , [111] , [112] , [113] . Collectively , these studies further demonstrate the in vivo relevance of exosomes and their potential as mediators of disease phenotypes . In this study , we find stable , systemic KSHV miRNAs and oncomiRs . GO pathway analysis of predicted targets of the oncogenic miRNAs expressed in exosomes revealed a variety of pathways targeted by KSHV during pathogenesis ( Table 1 ) . Since several of these pathways shared a role in cell migration , we further tested the effects of patient-derived exosomes on migration of hTERT-HUVECs . Treatment of cells with exosomes from pleural fluid led to earlier , enhanced migration of endothelial cells , giving these patient-derived exosomes a functional biological role ( Figure 6 ) . Therefore , it is possible that miRNAs specifically expressed within exosomes play a role in disease progression and mediate paracrine effects , which are a hallmark of KSHV tumorigenesis .
De-identified human plasma samples were obtained from healthy controls , patients enrolled in the UNC AIDS Malignancy Trial IRB#09-1201 ( diagnosed with KS or other , non-KS malignancies ) and patients with Kaposi's sarcoma . Primary pleural effusion fluid from three patients was also obtained . For mouse controls , pooled plasma from C57/BL6 mice was obtained from Innovative Research ( Novi , Michigan ) . Blood was collected from C57/BL6 control mice and serum was isolated using a serum-gel tube ( Sarstedt ) . Blood sera were also purified from KSHV latency locus ( 801 ) transgenic mice [50] and Balb/c mice injected with TIVE , latently infected KSHV+ endothelial cells [48] . Purified serum was collected for each group . Samples from each group were pooled as shown in Table S1 to control for individual genetic variation among sample groups and to increase the material available for exosome isolation . Exosome isolations for each sample group were performed in duplicate . The mice were held in UNC animal facilities . Veterinary care was provided by the University veterinarians and support animal care staff . The animal facility is an American Association of Accreditation of Laboratory Animal Care ( AAALAC ) accredited facility . The mice were maintained according to AAALAC guidelines and approved by institutional animal care and use committee ( IACUC ) under protocol #10-247/“KSHV latency mice” . The UNC Chapel Hill animal welfare assurance number is: A-3410-01 . Human plasma , pleural fluid , mouse plasma or serum was centrifuged at 300×g for 10 minutes to pellet any cells . 250 µl of supernatant was transferred to a fresh tube and incubated with 63 µl Exoquick precipitation solution as per the manufacturers' instructions ( System Biosciences , Mountain View , California ) . After incubation for 16 hours at 4°C , contents of each tube were centrifuged for 30 minutes at 1 , 500×g to pellet exosomes . The supernatant containing free , circulating miRNAs was transferred to a fresh tube and the exosomal pellet was resuspended in 100 µl of nuclease-free , PCR-grade water ( Life Technologies , Carlsbad , California ) . Other studies also have validated the Exoquick protocol and have not detected any significant differences in exosome populations compared with ultracentrifugation methods [56] . Patient pleural fluid and tissue culture supernatants ( 35 mls ) were centrifuged for 30 minutes at 2 , 000×g to pellet cells . The supernatant was transferred to a fresh tube and centrifuged at 12 , 000×g for 30 minutes at 4°C . Filtering was performed after clearance of cellular debris and prior to ultracentrifugation where noted . Supernatants were transferred to ultracentrifuge tubes and spun in a SW32Ti swinging bucket rotor for 70 minutes at 110 , 000×g . The supernatant was discarded and the pellet was resuspended in 35 mls of sterile PBS and passed through a 0 . 2-micron filter . Exosomes were centrifuged at 110 , 000×g for an additional 70 minutes to wash . The supernatant was again discarded and the pellet was resuspended in 1 ml of sterile PBS . Samples were transferred to 1 . 5 ml ultracentrifuge tubes and concentrated by ultracentrifugation at 110 , 000×g for 70 minutes using a TLA-100 . 3 rotor . The resulting pellet was resuspended in a small volume and used for subsequent experiments . Samples ( 35 ml starting material ) were ultracentrifuged as previously described to obtain exosome-enriched samples ( ∼500 µl ) . These samples were further enriched for CD63+ exosomes using the CD63+ Dynabead exosome isolation kit according to manufacturer's instructions ( Invitrogen , Life Technologies #10606D ) . Briefly , 500 µl of sample was incubated with 100 µl CD63+ Dynabeads overnight at 4°C . Exosomes were positively selected using a Dynabeads magnet and samples were washed to eliminate non-specific binding . Bead-bound exosomes were resuspended in 300 µl PCR-grade water and approximately 100 µl was used as input for further RNA , DNA and protein analysis by Western blot and qPCR ( Figure 3 ) . Prior to DNA isolation using the Magnapure automated system ( Roche ) , beads were treated with Proteinase K ( 200 µg/ml ) for 2 hours at 55°C to dissociate beads and exosomes . To obtain filtered samples , cell supernatants or patient fluids were first cleared of cellular debris . The resulting supernatant was passed through either a ( 1 ) Nalgene 250 ml Rapid-flow filter unit , 0 . 2 µm CN membrane , 50 mm diameter ( Thermo Scientific , #126-0020 ) for ultracentrifugation and Dynabead methods or ( 2 ) Whatman Puradisc 25AS 0 . 2 µm polyethersulfone membrane filter ( #6780-2502 ) for the ExoQuick methods . The flow-through was then used as input for downstream exosome enrichment protocols ( ExoQuick , ultracentrifugation and CD63+ Dynabeads ) . Flow-through did not seem to affect exosome yield or loss of exosomal markers ( Figures 2 , Figure S6 and data not shown ) . Filtration of samples resulted in a decrease in KSHV load as determined by qPCR for LANA DNA ( Figure 3 ) . Select samples were treated with RNase prior to exosome isolation . RNase treatment was performed as described previously [34] . Briefly , samples were incubated with RNase ( Roche – product # 11119915001 , includes both RNase A and T ) at 37°C for 30 minutes to destroy any freely circulating RNAs ( Figure S4 ) . Exosomes were then isolated using the ExoQuick precipitation solution . Aliquots of purified exosome samples were absorbed directly onto glow-charged thin carbon foils on 400-mesh copper grids without fixation and stained with 2% ( w/v ) uranyl acetate in water . The grids were examined in an FEI Tecnai 12 ( Hillsboro , OR ) electron microscope at 80 kV . Images were captured on a Gatan Orius CCD Camera ( Gatan , Pleasanton , CA ) using Digital Micrograph software . Images for publication were arranged and contrast optimized using Adobe Photoshop CS4 . The supernatant and exosome fractions from each pooled group were used in full as input for RNA isolations . Total RNA was isolated using TRI reagent ( Molecular Research Center , Cincinnati , Ohio ) followed by a phenol/chloroform extraction and ethanol precipitation of RNA as previously described [3] , [4] , [6] . Prior to RNA isolation , 25 fmol of C . elegans cel-mir-39 RNA was added to each sample as a spike-in control [33] , [35] . Total RNA was resuspended in nuclease-free , PCR-grade water and the RNA concentration was determined using the NanoDrop spectrophotometer ( Thermo Scientific , Waltham , Massachusetts ) . Exosomes were isolated using the Exoquick kit as described above . Both exosomal fractions and supernatants were lysed in 100 µl NP40 lysis buffer ( 50 mM Tris , 150 mM NaCl , 1% NP-40 with 50 mM NaF , 1 mM sodium vanadate , 30 mM beta-glycerophosphate , 1 mM PMSF and protease inhibitor cocktail ( Sigma , St . Louis , Missouri ) . Lysates ( 10 µl ) were run on a 10% SDS-PAGE gel , transferred to a nitrocellulose membrane ( Hybond , GE Healthcare , Pittsburgh , Pennsylvania ) and blocked in 5% dry milk in Tris-buffered saline with 0 . 1% Tween 20 overnight at 4°C . CD9 was detected using the CD9 EXOAB antibody kit as per the manufacturer's instructions ( System Biosciences , Mountain View , California ) . Anti-flotillin-2 ( BD #610383 ) was used at 1∶5000 and anti-beta actin ( Sigma #A2228 ) , anti-Hsp90 alpha ( Assay Designs #SPS-771 ) and anti-Hsp90 beta ( Assay Designs #SPA-842 ) were used at 1∶2000 . Secondary HRP antibodies ( Vector Labs Cat# PI-1000 – rabbit , Cat#PI-2000 – mouse , Burlingame , California ) were used at 1∶10 , 000 and blots were developed using Pierce ECL Western blotting substrate ( Pierce , Rockford , Illinois ) . Approximately 1 µg of total RNA in 75 µl PCR-grade water was DNase-treated using the Turbo-DNase kit ( Life Technologies , Carlsbad , California ) . The RNA was run on an Agilent RNA Nano 6000 chip to assess RNA quality and the presence of small RNA populations . Next , samples ( 200 ng ) were used as input for cDNA synthesis using the Megaplex RT kit version 3 . 0 , Human Pools A and B ( Life Technologies , Carlsbad , California ) . Following cDNA synthesis , samples were further amplified using the Megaplex PreAmp kit version 3 . 0 , Human Pools A and B ( Life Technologies , Carlsbad , California ) . The PreAmp product was diluted 5-fold and the amplified cDNA samples were used as previously described [54] using a library of 754 Taqman cellular miRNA primers ( Life Technologies , Carlsbad , California ) and a robotic pipetting system for automated plate setup [54] ( Tecan , Männedorf , Switzerland ) . qPCR reactions were run on a Lightcycler 480 ( Roche , Indianapolis , Indiana ) . Automated plate setup and replicates correlated well , with little standard deviation between replicate CTs and no significant quadrant errors ( Figure S17 , average standard deviation among 4 replicates = 0 . 35 CT ) . Reactions were also performed to detect levels of the spike-in control cel-mir-39 and the KSHV miRNAs using individual Taqman RT and qPCR miRNA assays ( data not shown ) . PCR products of KSHV miR-K2 were run on an HTDNA 1K chip on the Caliper LabChip GX ( Caliper Life Sciences , Hopkinton , Massachusetts ) to confirm the results via gel electrophoresis . In-depth statistical analysis of technical replicates was performed in R and revealed little variation in CTs below 45 . CT variation among the same sample in each of 4 quadrants was also assessed and no significant deviation was observed ( Figure S17 ) . Cycle threshold ( CT ) values for each sample were averaged across two technical replicates ( one replicate from each exosome isolation ) and those with a CT greater than 45 were excluded and recorded as negative . The remaining data were assigned expression scores based on a specific range of CT values . The CT range of expression was 20–45 , with CT = 20 as the highest expression score ( expression score = 3 ) and CT = 45+ yielding the lowest score of 0 . Expression scores were assigned in increments of 0 . 5 , with one expression class including a range of 4 CTs . Therefore , any significant difference reported was confirmed as a difference greater than 4 CTs or approximately 16-fold . The expression scores were then subjected to unsupervised classical clustering with Pearson coefficient using Array Miner™ ( Optimal Design , Brussels , Belgium ) . PCA three-dimensional clustering figures and heatmaps of miRNA expression are shown . Exosomes were isolated using the Exoquick kit as described above ( System Biosciences , Mountain View , California ) . Exosome pellets and supernatants containing free , circulating miRNAs and proteins were resuspended in 200 µl PCR-grade water . Exosome-enriched samples were then treated with DNase for 30 minutes at 37°C according to manufacturer's instructions for the Turbo DNA-free kit ( Ambion , Life Technologies ) . DNase-treated samples were then adjusted to 500 µl volume and used as input for DNA extraction on the Magnapure ( Roche , Indianapolis , Indiana ) using the large volume kit and program settings for total nucleic acid from plasma samples . DNA was eluted in 100 µl total volume . Extracted DNA ( 5 µl ) from each sample was used to determine the presence of KSHV using primers: F primer 5′-GGAAGAGCCCATAATCTTGC-3′; R primer 5′- GCCTCATACGAACTCGAGGT-3′ . Ten-fold dilutions of a KSHV oligonucleotide target with the following sequence were used to generate a standard curve: 5′-GGAAGAGCCCATAATCTTGCACGACTCAGACCTGGAGTTCGTATGAGGC-3′ . PCR products were then loaded on an HTDNA 1K chip on the Caliper LabChip GX ( Caliper Life Sciences , Hopkinton , Massachusetts ) to confirm the presence of KSHV DNA . Oncomirs that were upregulated in the KSHV-associated sample groups were input into the microRNA target prediction database ( MetA MicroRNA target Interference ( MAMI ) , http://mami . med . harvard . edu/ ) . The settings used for target prediction were highest stringency and included only 3′UTR target sites . The Entrez IDs of the predicted targets were used as input for the GO pathway database DAVID . The KEGG pathway terms of highest correlation were determined along with statistical significance ( P value ) and the number of predicted targets in each pathway . Specific pathways involved in migration were determined by searching peer-reviewed literature that included mechanistic data for migration and each specific pathway . Pathway analysis was performed for microRNAs induced in KSHV-associated malignancies from our exosome study and for microRNAs induced by WNV infection in hTERT-HUVEC cells . hTERT-HUVEC cells [92] were seeded at 80% confluence in a 24-well plate and allowed to equilibrate overnight before treating with exosomes for a period of 24 hours . Annexin blocking was performed as previously described [44] . Briefly , exosomes or supernatant were incubated with Annexin V-FITC for 1 hour at room temperature prior to adding to cells . Cells were grown in EGM-2 media with all supplements ( Lonza , EGM-2 Bulletkit ) . Each well was scratched using a standard 200 µl pipette tip and the location of the scratch was marked to locate the initial scratch at subsequent time points . Cells were washed with media to eliminate floating cells and replaced with fresh media immediately after the wound initiation . Images were captured at 0 h , 8 h and 16 h after the initial scratch . Images are shown at 100× magnification and were obtained on a Leica DMIL microscope using a HI Plan 10×/0 . 25 PHI objective and QImaging camera ( Cooled color , RTV 10 bit ) paired with QCapture imaging software 3 . 0 . hTERT-HUVEC cells were treated with exosomes isolated using the ExoQuick method . After 24 hours of incubating with exosomes , cells were serum-starved for 6 hours and then lightly trypsinized for 3 minutes to detach cells . Trypsin was inactivated with media containing FBS and cells were centrifuged at 300 g for 5 minutes . Cells were washed with PBS and the remaining pellet was resuspended to a concentration of 300 , 000 cells/ml in serum-free EBM-2 media ( Lonza ) . 30 , 000 cells were plated per well of the upper chamber of an xCelligence CIM Plate 16 ( Acea Biosciences ) . Prior to CIM plate assembly , both sides of the membrane were coated with 20 µg/ml fibronectin . Media containing FBS was placed in the lower chamber as the chemoattractant . The upper and lower chambers of the CIM plate were assembled and reads were taken every 2 minutes for a period of 24 hours using the RTCA DP xCelligence instrument ( Acea Biosciences ) . Supernatants from the scratch assay ( hTERT-HUVECs treated with exosomes ) were analyzed for levels of IL-6 using ELISA according to manufacturer's protocol ( eBioscience , #88-7066-88 ) . Briefly , supernatants were collected at 16 hours post-scratch and were diluted 1∶10 for ELISA . A standard curve of IL-6 positive control was generated and levels of IL-6 ( pg/ml ) were calculated . The average of three technical replicates of two independent experiments was calculated . | Circulating microRNAs ( miRNAs ) , such as those found in exosomes , have emerged as diagnostic tools and hold promise as minimally invasive , stable biomarkers . Transfer of tumor-derived exosomal miRNAs to surrounding cells may be an important form of cellular communication . Kaposi's sarcoma-associated herpesvirus ( KSHV ) is the etiological agent of Kaposi's sarcoma ( KS ) , the most common AIDS-defining cancer worldwide . Here , we survey systemically circulating miRNAs and reveal potential biomarkers for KS and Primary Effusion Lymphoma ( PEL ) . This expands previous tissue culture studies by profiling clinical samples and by using two new mouse models of KSHV tumorigenesis . Profiling of circulating miRNAs revealed that oncogenic and viral miRNAs were present in exosomes from KS patient plasma , pleural effusions and mouse models of KS . Analysis of human oncogenic miRNAs , including the well-known miR-17-92 cluster , revealed that several miRNAs were preferentially incorporated into exosomes in our KS mouse model . Gene ontology analysis of upregulated miRNAs showed that the majority of pathways affected were known targets of KSHV signaling pathways . Transfer of these oncogenic exosomes to immortalized hTERT-HUVEC cells enhanced cell migration and IL-6 secretion . These circulating miRNAs and KS derived exosomes may therefore be part of the paracrine signaling mechanism that mediates KSHV pathogenesis . | [
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] | 2013 | Systemically Circulating Viral and Tumor-Derived MicroRNAs in KSHV-Associated Malignancies |
We investigated eight families with a novel subtype of congenital generalized lipodystrophy ( CGL4 ) of whom five members had died from sudden cardiac death during their teenage years . ECG studies revealed features of long-QT syndrome , bradycardia , as well as supraventricular and ventricular tachycardias . Further symptoms comprised myopathy with muscle rippling , skeletal as well as smooth-muscle hypertrophy , leading to impaired gastrointestinal motility and hypertrophic pyloric stenosis in some children . Additionally , we found impaired bone formation with osteopenia , osteoporosis , and atlanto-axial instability . Homozygosity mapping located the gene within 2 Mbp on chromosome 17 . Prioritization of 74 candidate genes with GeneDistiller for high expression in muscle and adipocytes suggested PTRF-CAVIN ( Polymerase I and transcript release factor/Cavin ) as the most probable candidate leading to the detection of homozygous mutations ( c . 160delG , c . 362dupT ) . PTRF-CAVIN is essential for caveolae biogenesis . These cholesterol-rich plasmalemmal vesicles are involved in signal-transduction and vesicular trafficking and reside primarily on adipocytes , myocytes , and osteoblasts . Absence of PTRF-CAVIN did not influence abundance of its binding partner caveolin-1 and caveolin-3 . In patient fibroblasts , however , caveolin-1 failed to localize toward the cell surface and electron microscopy revealed reduction of caveolae to less than 3% . Transfection of full-length PTRF-CAVIN reestablished the presence of caveolae . The loss of caveolae was confirmed by Atomic Force Microscopy ( AFM ) in combination with fluorescent imaging . PTRF-CAVIN deficiency thus presents the phenotypic spectrum caused by a quintessential lack of functional caveolae .
Congenital generalized lipodystrophies ( CGL1-3 , Berardinelli-Seip syndrome , MIM 608594 , 269700 , 612526 ) are autosomal recessive disorders characterized by almost complete absence of body fat associated with dyslipidemia and insulin resistance [1] , [2] . The discovery of different CGL-related gene defects confirmed its genetic heterogeneity and shed new light on adipocyte function . Causative mutations were found in an adipocyte differentiation factor ( Seipin , BSCL2 ) [3] , in an enzyme of the triglyceride and glycerophospholipid biosynthetic pathway ( AGPAT2 ) [4] , and in a lipid-binding protein essential for the formation of caveolae on adipocytes ( CAV1 ) [5] , [6] . Based on the clinical phenotype and genotyping results of patients from consanguineous Omani families , Rajab et al . delineated a novel , genetically distinct CGL-subtype ( CGL4 ) [7] , [8] . In contrast to the “classic” variants , symptoms were more widespread comprising myopathy , smooth and skeletal muscle hypertrophy , cardiac arrhythmias , osteopenia and distal metaphyseal deformation with joint stiffness . These peculiarities suggested an associated problem both with myocyte growth/function and bone formation . A recent report on two siblings with generalized lipodystrophy , muscle weakness and cervical instability also reminded us of this phenotype [9] . Here we report on the clinical and genetic evaluation of patients and their family members with CGL4 from Oman ( n = 10 ) and the UK ( n = 1 ) and describe the discovery of mutations in the gene PTRF-CAVIN ( Polymerase I and transcript release factor/Cavin ) which is an essential factor for the biogenesis of caveolae .
Data analysis of the Affymetrix GeneChip SNP data with HomozygosityMapper [14] delineated a single 2 Mbp region on chromosome 17 between SNPs rs9903086 and rs17531431 that was homozygous in all seven index patients ( red arrowheads in Figure 4A ) and could be verified with microsatellite markers . Prioritizing the 74 genes in this interval with GeneDistiller [15] for an expression in primarily affected tissues ( adipocytes , smooth and heart muscle ) , which was above three times the median intensity , produced PTRF-CAVIN as a single hit . This gene was an attractive candidate because it had been shown to interact with caveolin-1 [16] , whose mutations cause lipodystrophy [5] and with caveolin-3 , whose mutations cause rippling muscle disease [17] . Additionally , a Ptrf-Cavin knockout-mouse exhibited low body fat , dyslipidemia , glucose intolerance , and complete absence of caveolae [18] . We thus sequenced the two coding exons , intron-exon boundaries and the entire 5′ and 3′UTR of PTRF-CAVIN ( ENSG00000177469 ) . In both index patients we discovered homozygous mutations of PTRF-CAVIN ( Polymerase I and transcript release factor/Cavin ) that affected the open reading frame , led to a premature termination codon and subsequent complete absence of the protein from fibroblasts and muscle ( Figure 4 ) . Homozygosity for the c . 160delG mutation was verified in all patients with lipodystrophy from further six Omani families . Homozygous mutations showed complete penetrance in all families . The presence of the mutations was verified by RFLP-analysis via mutation dependent restriction sites ( Figure 4C and 4F ) Both mutations were absent in 476 chromosomes of healthy individuals from Oman ( n = 142 ) and from Central Europe ( n = 96 ) , thus excluding a common polymorphism . To verify the absence of PTRF-CAVIN at the protein level we performed a Western blot with protein-extracts from patient fibroblasts ( FI:201 ) and skeletal muscle ( FII:201 ) and probed it with anti-caveolin-1 and anti-PTRF antibodies . PTRF-CAVIN protein was completely absent from fibroblasts and skeletal muscle , whereas the caveolin-1 and caveolin-3 abundance was unchanged ( Figure 4D and 4G ) . Immunohistochemistry of patient muscle showed absence of PTRF-CAVIN from the smooth muscle layer of the intramuscular small vessels ( Figure 5 ) . As caveolae are particularly abundant on adipocyte membranes [19] , we searched for fat cells in the muscle of patient FII:201 and found complete absence of caveolin-1 immunoreactivity from the adipocyte cell membranes ( red arrowheads on Figure 5 ) . As caveolin-1 is essential for lipolysis , lipid droplet formation and lipoprotein metabolism [20] and patients with CAV1 mutations suffer from lipodystrophy [5] we thus show that a severe reduction of caveolae per se may lead to a similar adipose tissue phenotype . In contrast , subsarcolemmal caveolin-3 immunoreactivity was still present , albeit at reduced levels and in a patchy distribution ( Figure 5 ) . The patient muscle showed 25% regenerating fibers as verified by reemergence of the neonatal myosin heavy chain isoform ( neo-MHC ) . We used caveolin-1 immunoreactivity as a marker for caveolae on the fibroblast surfaces . Control fibroblasts showed a punctate staining pattern at the cell periphery that dissolved in the absence of PTRF-CAVIN ( Figure 6A and 6B , Figure 7E and 7F ) while the perinuclear caveolin-1 staining within the Golgi-apparatus remained unchanged . As caveolin-1 was present in patient fibroblasts in normal quantities ( Figure 4D ) lack of PTRF-CAVIN seems to disable recruitment of caveolin-1 into the caveolar microdomains of the outer cell membrane . However , transfection of a construct into patient fibroblasts , which contained the full-length , wild-type PTRF-CAVIN gene cloned downstream of the eukaryotic CMV-promoter , reconstituted the punctate staining pattern at the cell periphery that corresponds to the caveolae ( Figure 6E–6G ) . Morphometric analysis by transmission electron microscopy of the quantity of caveolae on the cell surface revealed a severe reduction by >97% in the patient's fibroblasts ( Figure 7A and 7B ) . Complementary to this technique we employed medium and high resolution Atomic Force Microscopy ( AFM ) to study the cell surface of native and mildly fixated cells from patient and control . This revealed a comparatively smooth cell surface in the patient with only occasional indentations of 50–100 nm corresponding to caveolae ( Figure 7C and 7D ) . In the patient , overlay between AFM and confocal images revealed the absence of the punctate caveolin-1 staining at the cell surface especially within the major indentations of the fibroblast cell membrane . Instead , the caveolin-3 immunoreactivity was diffusely distributed within the cytoplasm . As expected , PTRF-CAVIN immunoreactivity was entirely absent from the cell surface of patient fibroblasts ( Figure 7H ) .
PTRF-CAVIN is expressed in many tissues but highest mRNA-levels are found in adipocytes , muscle ( smooth , heart , and skeletal ) , osteoblasts , but not in neuronal tissue ( BioGPS , http://biogps . gnf . org/ ) . Hence its expression pattern is congruent with the multi-system disorder seen in our patients that spares the nervous system . In addition to lipodystrophy , most patients showed signs of smooth muscle hypertrophy in the gastrointestinal tract leading to dysmotility , dysphagia , ileus and infantile hypertrophic pyloric stenosis ( IHPS ) . Presently the genetic basis of IHPS is unknown , although several susceptibility loci have been discovered , with the Nitrate Oxide Synthase type 1 ( NOS1 , syn . nNOS ) locus amongst them and a NOS1-knockout mouse showing a gastric outlet obstruction [22] . As NOS1 co-localizes with caveolin-1 [23] and caveolin-3 [24] , we investigated the patient muscle with anti-NOS1 antibodies , and found an increased sub-sarcolemmal immunostaining ( Figure 5 ) . This finding is in accord with the data of Hayashi et al . who also found an overexpression of NOS1 in two muscle samples of their patients [13] as well as with results from myopathic mice with transgenic expression of mutant p . P104L caveolin-3 [25] . These findings make it unlikely that PTRF-CAVIN-deficiency acts via NOS1 depletion to cause IHPS . Various cardiac ion channels , especially the nodal pacemaker channel HCN4 ( hyperpolarization-activated cyclic nucleotide-gated potassium channel 4 ) , the voltage-gated Na+-channel ( Nav1 . 5 , SCN5A ) , which is important for excitability and propagation of cardiac depolarization waves , and the L-type Ca2+-channels ( CACNA1C ) necessary for excitation-contraction coupling [26] , are closely associated with caveolae and caveolin-3 ( CAV3 ) immunoreactivity . Mutations of SCN5A , CACNA1C and CAV3 are associated with ventricular tachycardia , long-QT syndromes ( LQT3 , 8 , 9 ) , and sudden cardiac death while mutations of HCN4 cause sick sinus syndrome type 2 [27] . Patients FI:201 and FII:201 showed features of both arrhythmias and additionally of LQT-syndrome , which we assume may be due to the simultaneous functional dissociation of all those ion channels that are functionally dependent on caveolae . Clearly , more functional and immune electron microscopic studies of cardiomyocytes are needed to verify the effect of PTRF-CAVIN on receptor clustering and function . The muscle weakness of our patients resembled the pattern seen in limb girdle muscular dystrophy type 1C ( LGMD1C ) and rippling muscle disease ( RMD ) , which are caused by dominant-negative mutations in CAV3 [17] . “Rippling” denotes rolling and sometimes painful muscle contractions which are not caused by sarcolemmal depolarization and thus silent on EMG [28] . One theory implicates the propagation of action potentials inside the muscle fiber through a malformed longitudinal tubular system [29] . Disruption of the T-tubular system in the absence of PTRF-CAVIN seems to be likely , because alteration of PTRF-CAVIN mRNA-levels considerably influenced tubular morphology [30] . Regarding the increased susceptibility of both patients to bacterial infections , it is noteworthy that caveolae have been found on stimulated B-lymphocytes ( plasma cells ) and that murine Cav1-/- B-lymphocytes showed reduced in vitro IgG3-secretion after LPS-stimulation [31] . These findings support the assumption that the structural integrity of caveolae is needed for a regular humoral immune reaction . Finally , caveolae are abundantly present on osteoblasts and are involved in the regulation of alkaline phosphate transcription and protein activity via the bone morphogenetic protein-2 ( BMP-2 ) signaling pathway [32] , while caveolin-1 has a role in bone matrix calcification [33] . Disruption of both functions through absence of caveolae might be the cause for abnormal bone growth ( osteopenia ) , reduced matrix calcification ( osteoporosis ) and reduced stability of the vertebrae and their ligaments ( atlanto-axial instability ) in our patients . In conclusion , the diverse clinical spectrum of PTRF-CAVIN deficiency displays the consequences of a quintessential lack of functional caveolae . We have shown that the spectrum mirrors the expression pattern of the gene and the distribution of caveolar functions according to current knowledge . The presence of generalized lipodystrophy in combination with muscle rippling/mounding , intestinal obstruction and the absence of acanthosis nigricans should let geneticists think of PTRF-CAVIN deficiency . Taken together , these features clearly delineate CGL4-patients from individuals with BSCL2 , AGPAT2 , and CAV1 mutations [2] . Clinicians should be alerted to perform detailed cardiac investigations to search for potentially serious arrhythmias and , if positive , consider the application of an implanted cardioverter defibrillator ( ICD ) device .
All patients and guardians provided written informed consent for genetic analysis according to the Declaration of Helsinki . The study was approved by the IRB of the Charité . DNA was extracted from EDTA-blood , saliva or mucosal swabs with the salt extraction method [34] . Hybridization and laser scanning of GeneChip Human Mapping 250K SNP-arrays ( Affymetrix ) were performed according to the specifications of the manufacturer . SNP haplotpye data were analyzed for homozygous regions with HomozygosityMapper [14] . The candidate locus on chromosome 17 was further verified with polymorphic microsatellite markers . Prioritization of candidate genes within the homozygous region was done with GeneDistiller [15] . HomozygosityMapper and GeneDistiller are freely available on the Internet at http://www . homozygositymapper . org and http://www . genedistiller . org . The coding region of PTRF-CAVIN with its 5′ and 3′UTR was PCR-amplified with genomic primers spanning the two exons and ca . 50 bp of the intron-exon boundary on each side ( Table S3 ) . Automatic sequencing was performed with the BigDye Terminator protocol ( Applied Biosystems , Darmstadt , Germany ) according to standard protocols and sequences were analyzed using MutationSurveyor v3 . 10 ( Softgenetics , State College , PA , USA ) . The mutations were further verified in the patients and excluded in normal controls by restriction fragment length polymorphism ( RFLP ) analysis: c . 160delG , the primer pair 5-CCC CAC GCT CTA TAT TGT CG-3 and 5-AGC TTG CTC ACC GTA TTG CT-3 amplifies a 320 bp fragment from genomic DNA . The restriction endonuclease MwoI cleaves the wildtype allele into 137+83+42+41+9+4+5 bp and the mutant allele into 67+70+83+42+41+9+4+5 bp fragments . c . 362dupT , the primer pair 5-GTC TCC CGC TCC AGC TC-3 and 5-TGT GGG CTC ACC TGG TAG AT-3 amplifies a 540 bp fragment from genomic DNA . The restriction endonuclease AclI does not cleave the wildtype allele whereas the mutant allele is cleaved into 416+124 bp . Protein was extracted from patient cultured fibroblasts ( patient FI:201 ) and muscle ( patient FII:201 ) after homogenization in RIPA buffer with a proteinase inhibitor cocktail ( Complete , Roche-Diagnostics , Basel , Switzerland ) , separated through denaturating SDS-PAGE with the Laemmli system and blotted on nitrocellulose membranes by the semidry method ( Biometra , Göttingen , Germany ) . The blots were probed with anti-caveolin-1 , anti-caveolin-3 and anti-PTRF as primary antibodies and corresponding peroxidase-labeled secondary antibodies . The myosin band on the Coomassie gel was used as loading control for muscle and the anti-β-tubulin band for fibroblasts . Bands were visualized by chemiluminescence . All antibodies used in this study are described in Table S2 . Patient fibroblasts were grown to semi-confluence in DMEM in the presence of 15% FCS and penicillin/streptomycin on uncoated cover slips , washed with PBS and fixed in 4% PFA . Control fibroblasts derived from diagnostic samples for numeric chromosomal aberrations that had turned out to be normal . After permeabilization with 0 , 1% ( v/v ) Triton X-100 , immunostaining was done with primary anti-caveolin-1 and anti-PTRF antibodies and appropriate secondary antibodies ( Table S2 ) according to standard procedures [35] . Fluorescent microscopic images were recorded with a Leica SPE laser confocal imaging system ( Leica Microsystems , Wetzlar , Germany ) . For muscle histology tissue was flash frozen and 6 µm sections were cut and mounted onto SuperFrost Plus slides . Immunolabeling was carried out using a standard protocol . Briefly , sections were equilibrated to room temperature and washed for 15 min in PBS pH 7 . 3 containing 0 . 1% Triton X-100 for membrane permeabilization . Sections were incubated overnight at 4°C in optimally diluted primary antibodies . The diluent contained 40% FCS and 0 . 1 M lysine . Following 2×10 min washes in PBS/Triton sections were incubated for 90 min at room temperature in 1∶100 HRP rabbit anti-mouse immunoglobulin ( Dako P260 ) . Sections were washed , visualized with DAB and counterstained with Carazzi's haematoxylin prior to dehydration and mounting . Control muscles were supplied from patients with orthopedic surgery . The full open reading frame of the PTRF-CAVIN gene was amplified from human fibroblast cDNA with a proof-reading polymerase ( PhusionTaq; New England Biolabs , Frankfurt a . M . , Germany ) with tailed primers ( forward 5-GGT GGT GGA TCC GGT CTC CCG CTC CAG CTC-3 , reverse 5-GGT GGT GTC GAC GTC GCT GTC GCT CTT GTC CA-3 ) which contained engineered BamHI and SalI restriction sites ( underlined ) . This PCR-fragment was purified by agarose electrophoresis and cloned into the BamHI+SalI multiple cloning sites of the pCMV-Tag4a vector ( Stratagene , Amsterdam , NL ) thus producing a C-terminally FLAG-tagged fusion protein of PTRF . The correct cloning was verified by automatic sequencing . For transfection of adherent fibroblasts 5 µg plasmid were mixed with Lipofectamine ( Invitrogen , Leek , NL ) and incubated for 24 h with cells growing in the exponential phase on cover slips . Cells were then double-stained with anti-caveolin-1 ( mouse-mAB ) and anti-FLAG ( rabbit-pAB ) and images recorded as described . The proper expression of the recombinant PTRF-FLAG protein via the construct was verified through double labeling of the transfected mutant cells with anti-PTRF ( mouse-mAB ) /anti-mouse-ALEXA555 and anti-FLAG ( rabbit-pAB ) /anti-rabbit-ALEXA488 antibodies ( Figure S2 ) The preparation of fibroblasts for transmission electron microscopy was done as published before [16] , [36] . Briefly , cells were grown on uncoated round glass cover slips in DMEM ( +15% FCS ) in 2 cm diameter plastic dishes to near confluence . After rinsing with PBS , cells were fixed in Na-cacodylate-buffered 2 . 5% glutaraldehyde ( pH 7 . 3 ) containing 1 mg/ml ruthenium red ( Sigma-Aldrich , Munich , Germany ) for 12 h at room temperature . Ruthenium red labels the cell surface only . Other staining protocols with OsO4 , uranylacetate or leadcitrate were omitted . The cell monolayer was embedded in Epon resin in situ and the cover slips were removed by flash freezing in liquid nitrogen . 70 nm sections were cut parallel to the culture substratum from the base of the cells with a microtome ( Reichert Ultracut , Vienna , Austria ) using a diamond knife and placed on Formvar-coated copper grids . Microscopic images were recorded with a Zeiss E905 transmission electron microscope . For quantification of the results , we counted the number of caveolae along the length of a total of 50 µm cell membrane in random samples . In control fibroblasts we found 923 and in the patient 27 caveolae per 50 µm cell membrane cumulating in a reduction of >97% in the patient . Atomic Force Microscopy ( AFM ) visualizes the surface membrane topography in living or only mildly fixated cells with a resolution in the nanometer range [37] . As opposed to scanning electron microscopy , dehydration of the samples is unnecessary , which allows simultaneous labeling with fluorescent antibodies , thus providing a powerful tool to see disease-specific changes on cell surfaces . Fibroblasts were cultured on glass bottom dishes ( WillCo Wells BV , Amsterdam , Netherlands ) . After cells had attached and flattened , they were fixed with 4% PFA for 20 min at room temperature , followed by a 3×5 min wash in PBS . Cells were labeled with anti-caveolin-1 and anti-PTRF antibodies and subsequently with appropriate secondary fluorescent antibodies . AFM measurements were carried out with NanoWizard II ( JPK Instruments , Berlin , Germany ) combined with a Leica Optical Microscope DMI6000B equipped with a DFC360FX CCD camera ( Leica Microsystems ) . The calibrated and deconvoluted fluorescent image from the same plane was then imported into the AFM software to secure the exact overlay between optical image and 3D surface structure . AFM measurements of the cell surface were performed in the tapping/intermittent contact mode with Si3N4-cantilevers of a nominal spring constant of 0 . 35 N/m ( type DNP , JPK Instruments ) . Selected squares of 10×10 µm ( medium resolution ) and of 1×1 µm ( high resolution ) were scanned at 0 . 5 Hz . AFM images were processed using the NanoWizard II Image Processing Software v3 . 2 ( JPK Instruments ) . A 3D-topography was generated and presented as a 2D-image . | Patients with generalized lipodystrophy have a marked lack of body fat . Several gene defects have been described that impede fat synthesis and maturation of fat cells . Here we report on mutations in a novel gene , called PTRF-CAVIN , causing congenital generalized lipodystrophy type 4 ( CGL4 ) that is additionally associated with muscle disease . Patients' muscles are large but weak and show an involuntary , rolling contraction pattern called “rippling . ” Further symptoms comprise life-threatening cardiac arrhythmias and a disorder of bone formation . We searched for shared segments in the genome of seven patients and found the responsible gene , called PTRF-CAVIN , on chromosome 17 . This gene is crucial for caveolae ( latin for “small caves” ) formation . These small indentations of the cell membrane are found on the surface of muscle , bone , fat , and immune cells and facilitate cell-to-cell communication and the absorption of substances from the extracellular space . Patients lack more than 97% of caveolae and artificial insertion of the correct gene into patient skin cells led to the reappearance of caveolae . As cardiac arrhythmia is a severe and potentially life-threatening condition , patients with CGL4 should be closely monitored by ECG and , if necessary , fitted with an implanted pacemaker and cardioverter defibrillator ( ICD ) device . | [
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] | 2010 | Fatal Cardiac Arrhythmia and Long-QT Syndrome in a New Form of Congenital Generalized Lipodystrophy with Muscle Rippling (CGL4) Due to PTRF-CAVIN Mutations |
The mitochondrial electron transport chain transforms energy satisfying cellular demand and generates reactive oxygen species ( ROS ) that act as metabolic signals or destructive factors . Therefore , knowledge of the possible modes and bifurcations of electron transport that affect ROS signaling provides insight into the interrelationship of mitochondrial respiration with cellular metabolism . Here , a bifurcation analysis of a sequence of the electron transport chain models of increasing complexity was used to analyze the contribution of individual components to the modes of respiratory chain behavior . Our algorithm constructed models as large systems of ordinary differential equations describing the time evolution of the distribution of redox states of the respiratory complexes . The most complete model of the respiratory chain and linked metabolic reactions predicted that condensed mitochondria produce more ROS at low succinate concentration and less ROS at high succinate levels than swelled mitochondria . This prediction was validated by measuring ROS production under various swelling conditions . A numerical bifurcation analysis revealed qualitatively different types of multistationary behavior and sustained oscillations in the parameter space near a region that was previously found to describe the behavior of isolated mitochondria . The oscillations in transmembrane potential and ROS generation , observed in living cells were reproduced in the model that includes interaction of respiratory complexes with the reactions of TCA cycle . Whereas multistationarity is an internal characteristic of the respiratory chain , the functional link of respiration with central metabolism creates oscillations , which can be understood as a means of auto-regulation of cell metabolism .
The electron transport chain links the central carbohydrate energy metabolism with ATP synthesis ( see Fig . 1 ) . It transforms the free energy released by the oxidation of NADH and succinate into a form of transmembrane electrochemical potential ( ΔΨ ) , which is used for ATP synthesis [1] . Reactive oxygen species ( ROS ) are byproducts of electron transport [2] . They play the roles of both metabolic signals and destructive agents [3]–[8] . These key roles of electron transport in cellular metabolism motivate the great interest in understanding the details of the dynamics of this process . Electron flow through the chain of carriers is controlled by levels of substrates ( NADH , succinate ) , levels of tricarboxylic acid ( TCA ) cycle intermediates , the rate of ATP consumption , oxygen availability , etc [9] . However , many interesting dynamical properties of the electron transport and linked ROS production are determined by the intrinsic properties of the electron transport chain , such as the structural and functional links between carriers ( topology of the system ) and values of parameters , e . g . reaction rate constants . Such intrinsic properties can determine physiologically important modes of respiratory chain operation and how transitions between these modes occur . This relationship between intrinsic properties and dynamics can be understood by analyzing a detailed model of electron transport . We have reported elsewhere that the Q-cycle mechanism of electron transport in respiratory complex III exhibits bistability [10] , i . e . two stable steady states may exist under the same microenvironmental conditions ( corresponding to two stable steady state solutions of a system of ordinary differential equations ( ODEs ) at the same parameter values ) . The importance of bistability resides , in particular , in the fact that it can be a main determinant of the destructive effects of hypoxia/reoxygenation [3] , [11] . Bistability also occurs in a model of the whole respiratory chain that agrees quantitatively with the measured forward and backwards fluxes through the respiratory chain [12] . Current experimental techniques make it possible to monitor the behavior of a single mitochondrion in living cells [13] . This has provided evidence that mitochondria can operate in numerous qualitatively distinct modes . They can persist in a steady state characterized by a high value of ΔΨ and a low rate of ROS production , can switch to another steady state characterized by a low value of ΔΨ and a high rate of ROS production , and can also enter into a mode of sustained oscillations [13]–[15] . The two qualitatively different steady states can be associated with the normal physiological state and a pathological one that can be approached after hypoxia/reoxygenation . The oscillatory behavior is probably very important for intracellular signaling , as it was found for Ca2+ signaling [16] . An application of a systematic method that reveals qualitatively different modes of model behavior and corresponding regions of relevant problem parameters , i . e . a bifurcation analysis of a mathematical model describing mitochondrial electron transport , would give insight into these physiologically important modes of mitochondrial functioning and the mechanism of switching between modes . The objective of the present study is to advance in this direction . This paper presents a bifurcation analysis of four increasingly complicated models . The first two of these describe only complex III ( Fig . 2 ) , in forms simplified compared with those previously presented [10] . The last two include elements of the respiratory chain , shown in Fig . 1 , that were previously modeled in [12] . For respiratory complex III , the models assume that the core of the complex contains four redox sites: cytochrome b with its two hemes , bH and bL , cytochrome c1 , and the Rieske protein containing iron-sulfur center ( bH-bL-c1-FeS ) . In addition , the core can bind a two-electron carrier ubiquinone either in the matrix ( Qi-Qi- ) or cytosolic ( -Qo-Qo ) side of the inner mitochondrial membrane . Symbols are repeated to represent the two electrons . This gives four different configurations of complex III: ( bH-bL-c1-FeS , bH-bL-c1-FeS-Qo-Qo , Qi-Qi-bH-bL-c1-FeS , Qi-Qi-bH-bL-c1-FeS-Qo-Qo ) . The models take into account that respiratory complexes constituting the respiratory chain consist of a number of fixed in space electron carriers that can be either reduced ( red ) or oxidized ( ox ) . A possible state of a complex is defined by a combination of redox states of individual carriers constituting it . The model variables correspond to these states . The repeated symbols each correspond to three possible states: either with two valence electrons and two corresponding protons ( ubiquinol ) , one valence electron ( semiquinone ) , or no valence electrons ( ubiquinol ) . Thus the configuration Qi-Qi-bH-bL-c1-FeS-Qo-Qo has 144 possible states , each corresponding to a variable in the model . These large numbers of variables make it difficult even to write down explicitly the corresponding systems of ordinary differential equations ( ODEs ) . Therefore an algorithm is designed and implemented to automatically compute the values of the right hand sides of the ODE system at each step of a numerical solution of a corresponding initial value problem ( IVP ) , based on the rules formulated in accordance with the reaction mechanism [10] . Two complimentary techniques are combined for numerical bifurcation analysis to systematically search for various types of model behavior and for intervals of parameter values corresponding to multiple steady state solutions or oscillatory solutions: 1 ) using IVP solvers to solve IVPs for our ODE systems , and 2 ) using the numerical bifurcation analysis software CL-MATCONTL [17] , [18] ( Text S1 ) to study the corresponding large equilibrium systems .
A numerical bifurcation analysis of the respiratory chain was first conducted for a model of complex III simplified to 145 equations ( referred to further as model 145 ) as described in Methods , with basic set of parameters listed in Table 1 . This model accounts for only one configuration of complex III , namely the core ( consisting of cytochrome b with its two hemes bH and bL , cytochrome c1 , and the Rieske iron-sulfur ( FeS ) center ) together with ubiquinone molecules bound at both inner ( i ) and outer ( o ) sites . This configuration is denoted as Qi-Qi-bH-bL-c1-FeS-Qo-Qo . Binding/dissociation of quinones in this model is accounted for by the replacement of reduced bound molecules at Qi by oxidized bound molecules and oxidized bound molecules at Qo by reduced bound molecules ( see Methods ) . Numerical continuation of steady state solutions , as a function of succinate concentration , ( proportional to VmSDH , eq . ( 1 ) ) , performed with CL_MATCONTL ( as described in Methods ) , revealed an interval of parameter values with multiple steady state solutions ( Fig . 3 , orange curves ) enclosed between two limit points ( LP ) indicating the points of fold bifurcations . This interval of the parameter values for which multiple steady state solutions exist , corresponds to two curve segments of stable steady states and one curve segment of unstable steady states in between . The method of numerical bifurcation analysis that we used allowed us to accurately and rigorously determine the bifurcation behavior of the whole system , without any simplifications . At the same time , the main process underlying the bifurcation behavior can , in some cases , be heuristically identified by reducing a system by taking into account different time scales . Such a reduction of Model 145 ( see Text S2 ) points to binding/dissociation of ubiquinone coupled with its reduction/oxidation as the main process responsible for the multistationarity of complex III ( Fig . S1 in Text S2 ) . A gradual increase of succinate from low concentrations towards the interval of multistationarity leads to the lowest branch of steady states for quinol ( QH2 ) as shown in Fig . 3A . Under such conditions complex III functions to give high electron flow and high ÄØ ( upper branches in Figs . 3B and 3C ) . It should be noted that , although the concentrations of QH2 that constitute the lower branch of steady states in Fig . 3A are low , they nevertheless are sufficient to maintain high levels of ΔΨ . The inset in Fig . 3C shows that ΔΨ drops to 0 as the concentration of succinate decreases to 0 . This drop is a consequence of QH2 deficiency . A decrease of succinate concentration from 15 to 0 corresponds to the almost linear decrease of QH2 levels from 0 . 02 to 0 nmol/mg prot . The above described “active” state , that provides highest electron flow , is characterized by low levels of semiquinone ( SQ ) at the quinol oxidase site ( Qo ) ( Fig . 3D ) . If succinate concentration surpasses the right limit point on Fig . 3A , the rate of ubiquinone reduction by succinate dehydrogenase outstrips the maximal capacity of its oxidation by complex III , therefore Q is almost completely converted into QH2 , as Fig . 3A shows . This is the biochemical mechanism of the bifurcation . The lack of an electron acceptor at Qi site results in a decrease of electron flow through the Q-cycle , a decrease of ΔΨ , and an increase in levels of SQ at Qo . If then succinate concentration decreases , the blocked electron transport cannot produce a sufficient amount of acceptors Q to activate electron flow . When the system is in such a blocked state , even decreased succinate dehydrogenase activity is sufficient to maintain low levels of Q and keep the system blocked . If initially the electron carriers are oxidized , the solution of an initial value problem for the ODE system approaches an “active” steady state , and if initially the carriers are reduced , the solution approaches a “blocked” steady state . Model 145 has 13 parameters . The number of parameters is much less than the number of equations because parameters characterize the types of electron transport reaction , which is a much smaller number than the number of combinations of redox states of carriers ( the number of equations ) . Different states participate in reactions of the same type with the same parameters , but they cannot be combined ( the system cannot be reduced without additional simplifications ) because the whole set of reactions for each state ( variable ) is different . Fig . 3 shows curve segments of multiple steady state solutions corresponding to an interval of values of one parameter . The shape and size of these curve segments depends on the values of other parameters . The width of this interval may be smaller , or the interval may even disappear . Fig . 3 shows an interval of relative succinate concentrations corresponding to multiple steady states , obtained using an algorithm ( described in Methods ) that scans all the parameters with the objective of finding as large interval as possible . Including in our model all of the four configurations of complex III and an explicit description of quinone binding/dissociation increases the number of equations to 257 ( see Methods ) . This more detailed model also has a region of multiple steady state solutions . Application of the same algorithm maximizing the region of relative succinate concentration characterized by multiple steady states resulted in the interval that is larger than the one in the case of model 145 , as is shown for ΔΨ in Fig . 4 . However , the qualitative behavior of the two models remains similar . Evidently , model 145 faithfully accounts for the main properties of complex III determined by the Q-cycle mechanism . Model 267 is obtained by adding to model 257 equations that account for reactions taking place in complex I ( described in Methods ) . This extended model contains almost all of the essential components of the respiratory chain model that we used for the analysis of experimental data [12] . Using it enables us to start the numerical bifurcation analysis for a “real” set of parameter values ( Table 2 ) that reproduces the measured dynamics of NADH reduction in the presence and absence of rotenone , and the maximal and state 4 respiration rates , when mitochondria are fueled by succinate or pyruvate/malate [12] . Numerical continuation of steady state solutions for ΔΨ as a function of succinate concentration uncovers the existence of an interval of parameter values with multiple solutions , in the form of an S-shaped curve , enclosed between two limit points ( Fig . 5 ) . There is also a Hopf bifurcation point in the vicinity of one limit point . The sustained oscillation , which can be simulated in the parameter interval between the left limit point and Hopf bifurcation , has very small amplitude ( inset in Fig . 5 ) . The mechanism of the fold bifurcations in this case is similar to that discussed for the simplest model . Similar to the case presented in Fig . 3 , the stable steady states with the lowest values of ÄØ have the highest levels of SQ radicals at the Qo site; this may be a physical basis for high ROS production rates . The measurements that were used to find the given set of parameters were performed in a suspension of isolated mitochondria . The rate of electron transport from cytochrome c1 to cytochrome c is the parameter most affected by the procedure of isolation , since it depends on the structure of intermembrane space , which is significantly changed after the isolation . In intact mitochondria the value of this parameter is expected to be higher than in isolated mitochondria . Increasing its value by less than an order of magnitude increases the interval of multiple steady state solutions to infinity ( Fig . 6 ) . Starting from an initially oxidized state , the system approaches a steady state , which , with numerical continuation with the substrate concentration as a parameter , results in the upper branch in Fig . 6 . This branch does not contain any bifurcation points . However , at a high substrate concentration , and starting from a reduced state , the system approaches another steady state located on a different branch marked blue in Fig . 6 . The lower segment of this branch is stable . A decrease of the substrate supply parameter ultimately leads to a limit point , and the upper segment of unstable steady states starts at this point . Note , this branch of unstable steady states is not connected to the upper branch of stable steady states . Similar to the cases analyzed above , the steady states corresponding to low ΔΨ values ( lowest blue branch ) are characterized by practically complete reduction of the free ubiquinone pool and maximal levels of free radicals SQ at the Qo site . Similarly to the cases analyzed above , the steady states corresponding to high ΔΨ ( yellow branch ) are accompanied by an oxidized free ubiquinone pool and low levels of SQ at the Qo site . The change of a single parameter that characterizes interaction between cytochromes c1 and c ( kc1c ) gives a qualitatively different behavior of model 267 , as seen in Fig . 6 from the comparison between the blue curve and the orange curve , which is redrawn from Fig . 5 . Such a difference in behavior can be induced by swelling/shrinking of mitochondria , as was mentioned above , but also hypoxia/reoxygenation can induce a similar change of parameters and , thus , similar effects . Hypoxia in our model can be simulated as a kc1c decrease . Assume that before hypoxia the system effectively functions at some point on the yellow curve ( Fig . 6 ) . Suppose the change of kc1c induced by hypoxia transforms the properties of the system so that the orange curve becomes the continuum of its steady states . If before hypoxia the functional steady state was to the right of the rightmost limit point in orange curve , then after hypoxia the system evolves until it reaches a steady state in the lower segment of orange curve ( coinciding with the lower segment of blue curve ) . If then the system is re-oxygenated , and the blue and yellow curves again become the continuum of steady states , it remains in the same state now located in the low segment of blue curve . Thus , hypoxia and re-oxygenation change the state of the system . Before hypoxia it generated high ΔΨ ( yellow curve in Fig . 6 ) and slowly produces ROS , whereas after re-oxygenation it stays in a state characterized by low ΔΨ ( blue curve with points in Fig . 6 ) and rapidly produces ROS . Further extension of the ODE model to 272 equations , as described in Methods , by including the reactions of the TCA cycle with the parameters listed in Table 3 , allowed us to study the interaction of the respiratory chain with central carbohydrate metabolism . In the extension , pyruvate is accounted for as a substrate for the TCA cycle , which provides succinate for complex II and NADH for complex I . Using parameter values verified by fitting the measured dynamics of NADH and respiration rates [12] , this model predicts the existence of wide range of pyruvate concentrations with two stable steady state segments ( Fig . 7 ) , as well as those described above with respect to succinate . Similarly to the cases considered above , the redox state of free ubiquinone pool determines the dynamics of the system . If the free ubiquinone pool ( Fig . 7A ) is not completely reduced , the electron flow through the respiratory chain ( Fig . 7B ) and ÄØ ( Fig . 7C ) is high , and the level of SQ at the Qo site ( Fig . 7D ) , determining the ROS production rate , is low . On the other hand , if ubiquinone is practically completely reduced , the electron flow through the respiratory chain and ÄØ is low , and the level of SQ at the Qo site is high . However , the bifurcations which determine a switch between the two steady state branches are different from those considered above . Specifically , when the system is in an oxidized state , the increase of the pyruvate concentration leads to a Hopf bifurcation . Oscillations in a neighborhood of this bifurcation point have insignificantly small amplitude ( see Fig . 8A ) . An increase of the control parameter makes the amplitude greater ( inset in Fig . 8B ) , but the trajectory comes to the zone of attraction of the lower segment of stable steady states ( Fig . 7C ) and approaches one of these states ( Fig . 8B ) . As the pyruvate concentration decreases , the system stays in this “reduced” stable curve segment until it reaches the limit point at ∼0 . 003 mM , where a curve segment of unstable steady states starts . An increase of the cytochrome c1 to cytochrome c electron transition rate ( kc1c ) to 782 s−1 and an increase in VmSDH ( equation ( 5 ) ) from 171 ( Table 3 ) to 1714 nmol/s/mg changes the bifurcation behavior as is shown in Fig . 9A . Although the bifurcation diagram here is also a basic S-shaped curve producing multi-stationarity , the entire segment of steady states between the two Hopf bifurcation points is unstable . Stable oscillations of high amplitude appeared between these Hopf bifurcation points ( Fig . 9B and 9C ) . ÄØ oscillates between 160 and 20 mV; such changes can be measured and , probably , this mechanism underlies the observed behavior [19] . As Figs . 9B and 9C show , ÄØ and the level of SQ at the Qo site ( defining the rate of ROS production ) oscillate in counter-phase . This also corresponds to the behavior monitored in intact mitochondria [14] . Variation of parameters can significantly change the durations of phases of low or high potential ( high or low ROS production rate , respectively ) . This could be a basis of the ROS signaling [20] . The mechanism of oscillations arises from an interaction of the ubiquinone reduction/oxidation with the TCA cycle . The switch from the “oxidized” curve segment of steady states to the “reduced” one is accompanied by a decrease of the electron flow , and , as a consequence , an increase of the NADH levels ( decrease of NAD+ ) . Since the conversion of pyruvate in the TCA cycle requires NAD+ , the production of succinate slows down . The high levels of NADH are maintained for some time due to reverse electron transport through complex I reducing NAD+ . A decrease of the substrate production in the TCA cycle and reverse electron transport result in an accumulation of a sufficient amount of the electron acceptor ubiquinone that activates electron transport , which results in switching back to the curve segment of the “oxidized” steady states , and then the cycle repeats again . The change from multi-stationarity shown in Fig . 7 to an oscillatory behavior shown in Fig . 9 is , in part , the result of a change of the rate constant kc1c , which accounts for interaction between cytochromes c1 and c . This rate constant depends on the volume of the intermembrane space , where the interaction takes place . The intermembrane volume can be controlled experimentally in a suspension of isolated mitochondria , and the correspondence of the model predictions and the measured ROS production rates under variations of the intermembrane space can be experimentally verified . The change of the ROS production rate ( characterized by the level of SQ at the Qo site ) , predicted for an “oxidized” state of model 272 with an increase of succinate concentration , is shown in Fig . 10A . The shape of the lower curve segment of steady state concentrations of SQ bound at the Qo site depends on parameter kc1c . Fig . 10A shows a superposition of curve segments of stable steady states obtained at two different values of kc1c . At a low value of this parameter ( ∼260 s−1 as shown in Table 1 ) , increasing the succinate concentration above 1 mM takes the system past the Hopf bifurcation point ( similar to that shown in Fig . 7D ) , and it switches to the upper curve segment of SQ stable steady state concentrations ( blue curve ) . Increasing kc1c shifts this Hopf bifurcation point to infinity , so that the upper branch of stable steady states , although it still exists , becomes inaccessible from the lower branch in the space of succinate concentrations ( orange curve ) . In Fig . 10A , the lower branch of steady states obtained at a higher value of kc1c crosses the lower branch obtained at a lower value of kc1c . At low succinate concentrations , the levels of SQ at the Qo site are higher when kc1c is high . At high succinate concentrations , this relationship is reversed . We have confirmed this experimentally , registering the rate of the ROS production as a measure of the SQ concentration at the Qo site . It is expected that kc1c decreases if the intermembrane space increases , thus diluting the concentration of cytochrome c that is included implicitly in this parameter . The light scattering technique allows measuring changes in the volume of the mitochondrial matrix and , implicitly , the intermembrane space . Light scattering is higher in KCl than in sucrose of the same osmolarity ( Fig . 10B ) . This indicates that the mitochondrial matrix is more compact in KCl than in a sucrose solution . The outer membrane is permeable for both solutes , hence the total mitochondrial volume , which it restricts , must be the same . Therefore the intermembrane space , estimated as the difference between total and matrix volumes , is greater in KCl media . Thus , mitochondria incubated in KCl are characterized by lower values of kc1c than those incubated in sucrose . The experimental results shown in Fig . 10C are consistent with the model . Indeed , in the media with sucrose , the rates of ROS production driven by low succinate concentrations ( 100–500 uM ) are higher than those in KCl supplemented media . In the range of succinate concentrations ≥500 uM , the situation was reversed: ROS production in KCl-based media exceeded that observed in sucrose-based media .
Bifurcation behavior , as revealed by the numerical bifurcation analysis of complex III models , is inherent in the Q-cycle mechanism of electron transport . The main process underlying fold bifurcations in the considered models of complex III was found to be reduction/oxidation and coupled binding/dissociation of ubiquinone in accordance with the Q-cycle mechanism ( Text S2 ) . We show here that a decrease of ÄØ accompanied by an increase of ROS production rate can take place as a consequence of perturbations in the respiratory chain operation . The most critical element in such bifurcation behavior is that the same metabolite is reduced at the Qi site and oxidized at the Qo site . The interaction of complex III with complex I increases the width of the maximal interval of multiple solutions , see Fig . 5 , or may even make it infinite , as shown in Fig . 6 . The width of this interval is sensitive to the parameter ( kc1c ) that characterizes the combined processes of electron transport from cytochrome c1 to c and further , ultimately reducing molecular oxygen . Thus , it can represent the availability of oxygen and , in this case , the comparison of Figs . 5 and 6 , given in Results , demonstrates how hypoxia and reoxygenation may perturb the system to a state of a very high ROS production . Further extending the model by including into it the reactions of the TCA cycle preserves the interval of parameters where multiple steady state solutions exist . In particular , there are two stable steady states at the parameter values defined in [12] by fitting measured experimental data , as shown in Fig . 7 . In experiments performed previously [10] , we confirmed that isolated mitochondria incubated with high succinate concentration can persist in one of two different steady states . Mitochondria can be switched from a high ROS production state to a low one by transient incubation with ADP , and then back to a high ROS production state by transient hypoxia . Another experimental confirmation of the predicted behavior of the electron transport chain is the consistency between the predicted curves of steady state levels of SQ at Qo attained at various concentrations of succinate for two different swelling conditions and the measured curves of ROS production rate ( Fig . 10 ) . Stable oscillations that can be obtained at the parameter values in a neighborhood of the Hopf bifurcation point have insignificantly small amplitude , and the region of their stability appears to be so small that it is practically undetectable . However , an increase of the values of the two parameters , which characterize succinate dehydrogenase activity and the rate of combined reactions upstream from cytochrome c1 , results in the appearance of an interval of succinate concentrations where high-amplitude oscillations exist and are stable ( Fig . 9 ) . Feedback interaction of the multistationary respiratory chain operation with the TCA cycle creates oscillations . NADH , as a common metabolite , provides such feedback ( see “Mechanism of oscillations” in Result section ) . The parameters shown in Tables 1–3 that were used for model 272 were determined from the best fit to the data from experiments performed in vitro in isolated mitochondria . One can expect that the volume of the intermembrane space increased after the procedure of isolation . Such a change of the intermembrane space dilutes cytochrome c , and thus decreases the rate of interaction of cytochromes c1 and c . Natural spatial variability of succinate dehydrogenase activity contributes to an uncertainty in its estimated value . Our results show that the change in these parameters , which can be expected in mitochondria of living cells , compared to the isolated ones , results in an oscillatory mode of operation . In fact , in mitochondria of living cells , flashes and oscillations of ROS production accompanied by the counter-phase changes of ΔΨ can be measured either as a response to laser excitation [13] , [14] , or as a spontaneous mitochondrial activity [15] , [19]–[25] . Usually , the measured in vivo decrease of ÄØ that accompanies the ROS flashes was ascribed to either a ROS-induced mitochondrial permeability transition ( MPT ) [13] , [14] or a ROS-activated inner membrane anion channel [26] . Our study opens a new direction in the investigation of the MPT that is of great importance for understanding intracellular signaling and regulation . In particular , it can help to solve the question: why is respiration inhibited during the MPT , when ÄØ is low and cytochrome c still remains in the intermembrane space ? Our hypothesis is that the MPT is secondary with respect to the change in the steady state of respiration; it happens when the electron transport chain switches to the “reduced” steady state , where respiration is inhibited by the mechanism considered above . There is more evidence supporting this hypothesis . Matrix pH is well known to be important for the MPT and models considering it as a main factor governing opening/closure of the MPT pore describe the observed events of the MPT [27] . However , the link between the change of matrix pH and the MPT was described phenomenologically; the concrete mechanism of the pH effect on the MPT remains elusive . The models presented here points to the mechanisms by which pH can affect electron transport: protons are explicitly involved in reduction/oxidation of ubiquinone , which is the main process defining the bifurcation . Alkalinization of the matrix must slow down SQ reduction at Qi site and , thus , block electron transport and facilitate the switch to the “reduced” state . If the change to a reduced steady state of the electron transport chain induces the MPT , this provides a concrete mechanism of pH-induced MPT , though it requires further investigation . Moreover , in some cases , ROS sparks and a decrease of ΔΨ may be a direct consequence of the functional organization of the electron transport , and may not require the involvement of other mechanisms . Thus , many experimentally observed effects , such as excessive ROS production after hypoxia/reoxygenation , or oscillations of ROS production and of ΔΨ , can be explained as a consequence of intrinsic properties of the respiratory chain and its interaction with the central metabolic pathways . These qualitatively different modes of behavior are manifestations of the same mechanism of electron transport , determined by its quantitative characteristics . Understanding the qualitatively different types of behavior requires a quantitative analysis of electron transport and the linked reactions of the central metabolism . The method presented here can be used for such an objective . However , the simplifications of reality used in our models should be taken into account . In particular , they represent complex III as a monomer , whereas it is known that the functional form is dimeric in living cells [28] . The functional link of monomers at the level of cyt bL was analyzed based on the edge-to-edge distance between cyt bL hemes of the two monomers [29] . It was found that the intermonomer interaction can affect the rate of electron transport , especially in the energized states and when the bH heme is reduced because of a lack of electron acceptors at the Qi site . Using our method to model the dimer would require solving ODE systems containing roughly the square of the number of equations that we analyze here . Such systems can be constructed , but solving them would create computational problems . Performing a preliminary analysis of a simplified model of the bc1 dimer containing cyt bL and bH , and Qo binding sites , we found that , despite intermonomer interactions , which quantitatively affect the kinetic behavior of complex III , qualitatively , the dimer demonstrates the same types of bifurcation behavior as the monomer in the situations analyzed in [29] . Another limitation of our models concerns the values for the parameters . The basic set of parameters shown in Tables 1–3 originally was taken from [30] and was then modified by fitting experimental data [12] . In principle , the rate constants can be determined based on the distances of electron tunneling [31] , [32] . However , the resolution of 2 . 1 Å in the determination of distances [33] and uncertainties in other parameters necessary for such determination ( as indicated e . g . in [29] ) can result in great variation in the values obtained for the rate constants . These uncertainties can be greater than an order of magnitude . The rate constants that we used are inside the range of possible variations admitted by the estimation based on the known distances between the electron carriers . It should be noted also that the TCA cycle is introduced in the model in a very schematic manner , however , keeping the stoichiometry of respiratory substrates , i . e . succinate and NADH production from pyruvate . Most of the reactions are lumped together and specific reaction mechanisms are not considered . Many of the enzymes performing consecutive reactions form multienzyme complexes [34] , [35] ( not considered here ) , where local concentrations of the metabolites can be different from their average concentrations in the matrix . Therefore we did not require average metabolite concentrations at equilibrium to be consistent with respective equilibrium constants . Simulation of a more realistic mechanism of the TCA cycle reactions might affect the bifurcation behavior of the whole system , since , as is indicated above , the appearance and location of Hopf bifurcations and related oscillations is a probable consequence of the interaction of the TCA cycle with reduction/oxidation of ubiquinone . This warrants further investigation . As is shown by our analysis , perturbations in metabolite concentrations or oxygen availability may induce critical changes in the modes of mitochondrial behavior that result in huge changes in ATP synthesis and ROS production . Changes in the energy supply or signaling or damaging events can have crucial consequences on cell operation in general . We have presented a general overview of the possible modes . At the same time , this approach opens a way to study effects of specific disease conditions on mitochondrial functioning , and to predict mitochondria related disease progressions . In particular , the primary disorder in the chronic obstructive pulmonary disease ( COPD ) is a decreased capacity of an organism to take up oxygen . The limits of oxygen uptake are measured clinically , and such limitations can be simulated by changing the value of the respective mitochondrial parameter , provided that the other model parameters are determined for the given tissue . In this way the role of the mitochondrial component in a disease progression can be elucidated . A similar approach can be developed , for instance , for diabetes , which results in an essential change in a substrate supply and composition . The effects of such a substrate change on the mitochondrial state and consequences for the whole cell functioning can be predicted . In this way the approach developed here opens a way to better understand progressions of many systemic diseases .
All procedures involving animals were approved by Children's Hospital of Pittsburgh and were in compliance with “Principles of Laboratory Animal Care” and the current laws of the United States . A detailed model of the respiratory chain is described elsewhere [12] . The general algorithm for constructing the ordinary differential equations ( ODEs ) for the model accounts for all the possible redox states of the respiratory complex III [10] interacting in accordance with the well accepted Q-cycle theory . It assumes that the core of the complex contains four redox sites: cytochrome b with its two hemes , bH and bL , cytochrome c1 , and the iron-sulfur containing Rieske protein ( bH-bL-c1-FeS ) . Each of these redox sites can carry one valence electron . The core can bind the two-electron carrier ubiquinone either on the matrix ( Qi ) or cytosolic ( Qo ) side of the inner mitochondrial membrane ( bH-bL-c1-FeS-Qo-Qo , Qi-Qi-bH-bL-c1-FeS , Qi-Qi-bH-bL-c1-FeS-Qo-Qo ) , giving four different configurations of the complex . The model describes binding/dissociation of ubiquinone/ubiquinol that results in interconversion of these four configurations . Bound electron carriers , as well as core redox centers occupy fixed binding sites and have fixed interactions within the respiratory complex . Therefore the probability that a complex is found with a given combination of reduced/oxidized states , including the states of the four redox sites and the states of the substrates , is considered as a variable of the model . The oxidized state of each redox site is coded as a binary “0” and the reduced state as a binary “1” . In this way various combinations of reduced and oxidized states of carriers can be represented as a four-digit binary numbers with values from 0 to 15 representing redox states of the core ( bH-bL-c1-FeS ) , six-digit binary numbers with values from 0 to 63 representing the redox states of each of two configurations containing one ubiquinone , ( Qi-Qi-bH-bL-c1-FeS and bH-bL-c1-FeS-Qo-Qo ) and eight-digit binary numbers with values ranging from 0 to 255 representing the redox states of the configurations containing two ubiquinones ( Qi-Qi-bH-bL-c1-FeS-Qo-Qo ) . The algorithm constructs an ODE system for all the configurations and their redox states . This system accounts for the transitions of electrons between carriers resulting in oxidation of the donor ( 1→0 ) and reduction of the acceptor ( 0→1 ) , and binding/dissociation of ubiquinone/ubiquinol . These reactions are simulated in accordance with the well accepted Q-cycle theory and are described in detail in [10] . All the models analyzed here consider the following electron transitions performed by complex III: With one exception ( described below ) all the models analyzed here consider the following reactions of binding/dissociation of ubiquinone/ubiquinol to/from complex III , ( eq ( 12–16 ) in [10] ) , : The rate constants of the reactions listed above , which were used as a base set of values for the analysis presented in the figures , are shown in Table 1 . The numerical continuation algorithm requires that the Jacobian matrix for the model differential equations is nonsingular and its rows are linearly independent . Earlier versions of our model [10] , [12] used two binary digits to model the state of ubiquinone , giving 4 combinations ( 00 , 01 , 10 , and 11 ) , although there are only 3 physically distinct states . In fact , 01 and 10 represent the same state ( semiquinone with one valence electron ) . The algorithm was originally designed so that only the state “01” can be produced , and the amount of configurations containing “10” as a state of semiquinone always was zero . The presence of equations describing such zero-concentration states did not change the result of numerical integration of the initial value problem . However , it did make the Jacobian matrix for the system singular as it contained linearly dependent rows . To perform a bifurcation analysis using CL_MATCONTL , such subsidiary equations had to be eliminated . We modified the algorithm for constructing the ODEs so that it does not include the equations for the states containing “10” semiquinone . The model simulates electron flow from succinate that reduces ubiquinone . Succinate concentration implicitly defines the maximal rate of ubiquinone reduction: ( 1 ) here VmSDH = k·S , where S is succinate concentration , k is a constant . Q is ubiquinone concentration . This reaction is accounted for as a term in the differential equation for ubiquinone , which also participates in other electron transport reactions of the respiratory chain . The structurally fixed reduction and oxidation of ubiquinone in complex III respectively on the matrix and cytosolic sides results in the translocation of protons and generation of a transmembrane electric potential . The conservation of the total amount of complex III and the total amount of ubiquinone is taken into account . The model constructed in this way contains 257 equations ( 255 equations for the redox states of complex III , and one each for ubiquinone ( oxidized ) , and transmembrane potential ) . This and all other models considered here account for a proton leak through the membrane that is exponentially dependent on ΔΨ: ( 2 ) where F = 96500 c/mol is the Faraday constant , R = 8 . 3 J/ ( mol×K ) is the gas constant , T = 298 K is temperature . Outside ( Ho = 0 . 0001 mM ) and matrix ( Hi = 0 . 00005 mM ) proton concentrations are considered to be fixed due to high buffer capacity , but ΔΨ is a variable whose dynamics are described by a differential equation that takes into account proton translocations coupled with electron transport [10] and a proton leak described by ( 2 ) . In accordance with the Q-cycle mechanism , ubiquinol bound in the Qo site is oxidized giving its electrons to the FeS center of the Rieske protein and cytochrome bH and releasing its protons into the intermembrane space . Then the ubiquinone formed is released . The next pair of electrons can be transported only after the next ubiquinol is bound to the same Qo site . For simplicity , release of ubiquinone and binding of new ubiquinol can be combined and described as an exchange of ubiquinone with ubiquinol at the Qo site . Similarly , the release of ubiquinol and binding of ubiquinone at Qi site can be combined . In this way , all the reactions of the Q-cycle can be described considering only one configuration of complex III that contains two bound ubiquinones ( Qi-Qi-bH-bL-c1-FeS-Qo-Qo ) . After removing “zero-states” containing “10” semiquinone ( as described above ) and taking into account the conservation of the total contents of complex III and ubiquinone , the above model is reduced to 145 equations . The model of 257 equations simulates the whole set of reactions of Q-cycle . The reduction of the number of equations from 400 , as explained above , does not change the biological model , but only simplifies its mathematical representation . Therefore we use this simplified set of equations as part of an extended mathematical description of electron transport in the respiratory chain . In addition to the reactions performed in complex III as described above , this extended model with 267 equations accounts for electron transport reactions performed by complex I , as described elsewhere [12]: The rate constants of the reactions listed above , which were used as a base set of values for the analysis presented in the figures , are shown in Table 2 . Substrate supply in model 267 is treated in the same way as in model 257 , with the exception that succinate oxidation depends on NAD+ . This accounts for NAD+-dependent reactions of succinate production in TCA cycle , lumped in the model with SDH and results in NADH production , which is oxidized by complex I: ( 3 ) NAD+ is a variable of the model and its dynamics are described by a following differential equation that is included in the ODE system describing the dynamics of various redox states of complexes I and III . It accounts for the rate of NAD+ production as a result of NADH oxidation by complex I , and the rate of NAD+ consumption in the TCA cycle reactions reducing it into NADH: ( 4 ) Mass conservation NAD++NADH = const is taken into account ( with const = 16 nmol/mg prot ) . In this model , consisting of 272 equations , the production of the substrates of respiration , succinate and NADH , is considered in more detail , although still in extremely simplified form . The expressions for some of the reaction rates lump several reactions together and account phenomenologically for the substrates rather than real reaction mechanisms . Succinate dehydrogenase now accounts for succinate ( suc ) , since it is a real variable of the given model: ( 5 ) The fumarate oxidation and malate dehydrogenase ( MDH ) reactions forming oxaloacetate ( oa ) assume that fumarate and malate are represented as a single pool ( mal ) : ( 6 ) The citrate synthase reaction assumes that pyruvate and acetyl CoA are combined in a single pool ( pyr ) : ( 7 ) Transport of pyruvate assumes a constant cytosolic concentration ( Cpyr ) and a variable mitochondrial concentrations ( pyr ) : ( 8 ) A number of TCA cycle reactions from citrate ( cit ) to succinate are combined . The whole set depends on citrate as input substrate and NAD+: ( 9 ) Succinate exchange to fumarate/malate assumes constant external concentrations ( Csuc , Cmal ) : ( 10 ) Succinate entry when it is added externally is modeled by: ( 11 ) Malic enzyme transforms malate into pyruvate: ( 12 ) The parameter values for the reactions listed above are shown in Table 3 . Dynamics of the new variables ( suc , mal , oa , pyr , cit ) are described by following ODEs incorporated in the model: ( 13 ) ( 14 ) ( 15 ) ( 16 ) ( 17 ) The dynamics of NAD+ are now described in a more complex way compared to Eq . ( 4 ) ( 18 ) The TCA cycle reactions from citrate to succinate reduce two molecules of NAD+ for each succinate produced , but the model also accounts for a molecule of NADH produced by transformation of pyruvate into acetyl CoA , which is not included explicitly . A numerical approach used to systematically search for various types of model behavior was implemented by combining two complimentary techniques: 1 ) using initial value problem ( IVP ) solvers to solve IVPs for our ODE systems , and 2 ) using the numerical bifurcation analysis software CL-MATCONTL to study the corresponding large equilibrium systems . The first method consists in numerical integration of two IVPs using the same parameter values but different initial states . One initial state is oxidized and one is reduced . Integration is continued until an approximate steady state is reached . A trajectory is considered to have reached an approximate steady state if the time derivatives of all variables were less than 1 . 0e-12 nmol· ( mg prot ) −1·s−1 . The numerical solution is obtained using the DASSL method [36] , as implemented in the NAG Fortran library ( http://www . nag . co . uk/numeric/fl/fldescription . asp ) . This Fortran code is incorporated within our C++ program . If the steady states reached from the two different initial states are different , this indicates that the given parameters correspond to multiple stable steady state solutions . The algorithm implemented in our software automatically scans parameter regions to find such points of multiple steady states . It detects multiple steady states even if they are located in disconnected branches ( as in Fig . 6 ) , but it can detect only stable equilibrium solutions . Parameter values corresponding to multiple steady state solutions are found using this method , and the interval of the chosen parameter is maximized ( optionally ) as described below . Within the interval thus obtained , we apply the second method to find steady state solution ( stable and unstable ) and exactly locate and characterize bifurcation points . The second method consists in numerical continuation and bifurcation analysis of equilibrium solutions to our ODE systems by CL-MATCONTL ( Text S1 ) [17] , [18] , a MATLAB package for bifurcation analysis of large equilibrium systems . These equilibrium systems are viewed as systems of nonlinear algebraic parameter dependent equations . To compute their solution branches one uses pseudo arch length ( numerical ) continuation , which is a technique to compute a sequence of consecutive points approximating the desired solution branch using Newton type methods . This allows an accurate computation of both stable and unstable equilibrium solutions on a branch . CL-MATCONTL requires an initial point on the solution branch to start continuation and can compute only a connected solution branch . It can miss a whole branch of steady states , if the complete solution consists of more than one unconnected branches ( as in Fig . 6 ) . At each point on the solution branch the Jacobian of the system is computed , and a bifurcation is detected and located when an eigenvalue of the Jacobian crosses the imaginary axis , See Text S1 for more details . Combining both the IVP and continuation methods allows one to find unconnected branches of steady states and various types of steady states and bifurcation points . | The mitochondrial respiratory chain shows a variety of modes of behavior . In living cells , flashes of ROS production and oscillations accompanied by a decrease of transmembrane potential can be registered . The mechanisms of such complex behavior are difficult to rationalize without a mathematical formalization of mitochondrial respiration . Our most complete model of mitochondrial respiration accounts for the details of electron transport , reproducing the observed types of behavior , which includes the existence of multiple steady states and periodic oscillations . This most detailed model contains hundreds of differential equations , and such complexity makes it difficult to grasp the main determinants of its behavior . Therefore the full model was reduced to a simplified description of complex III only , and numerical bifurcation analysis was used to study its behavior . Then the evolution of its behavior was traced in a sequence of models with increasing complexity leading back to the full model . This analysis revealed the mechanism of switching between the modes of behavior and the conditions for persistence in a given state , which defines ATP production , ROS signaling and destructive effects . This is important for understanding the biochemical basics of many systemic diseases . | [
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] | 2012 | Multistationary and Oscillatory Modes of Free Radicals Generation by the Mitochondrial Respiratory Chain Revealed by a Bifurcation Analysis |
Salmonella enterica subspecies enterica is traditionally subdivided into serovars by serological and nutritional characteristics . We used Multilocus Sequence Typing ( MLST ) to assign 4 , 257 isolates from 554 serovars to 1092 sequence types ( STs ) . The majority of the isolates and many STs were grouped into 138 genetically closely related clusters called eBurstGroups ( eBGs ) . Many eBGs correspond to a serovar , for example most Typhimurium are in eBG1 and most Enteritidis are in eBG4 , but many eBGs contained more than one serovar . Furthermore , most serovars were polyphyletic and are distributed across multiple unrelated eBGs . Thus , serovar designations confounded genetically unrelated isolates and failed to recognize natural evolutionary groupings . An inability of serotyping to correctly group isolates was most apparent for Paratyphi B and its variant Java . Most Paratyphi B were included within a sub-cluster of STs belonging to eBG5 , which also encompasses a separate sub-cluster of Java STs . However , diphasic Java variants were also found in two other eBGs and monophasic Java variants were in four other eBGs or STs , one of which is in subspecies salamae and a second of which includes isolates assigned to Enteritidis , Dublin and monophasic Paratyphi B . Similarly , Choleraesuis was found in eBG6 and is closely related to Paratyphi C , which is in eBG20 . However , Choleraesuis var . Decatur consists of isolates from seven other , unrelated eBGs or STs . The serological assignment of these Decatur isolates to Choleraesuis likely reflects lateral gene transfer of flagellar genes between unrelated bacteria plus purifying selection . By confounding multiple evolutionary groups , serotyping can be misleading about the disease potential of S . enterica . Unlike serotyping , MLST recognizes evolutionary groupings and we recommend that Salmonella classification by serotyping should be replaced by MLST or its equivalents .
For over 70 years , epidemiological investigations of Salmonella that infect humans and animals have depended on serotyping , the binning of isolates into serovars [1] , [2] . Salmonella serotyping depends on specific agglutination reactions with adsorbed antisera that are specific for epitopes ( ‘factors’ ) within either lipopolysaccharide ( O antigen; encoded by rfb genes ) or one of the two , alternate flagellar antigens ( phases 1 and 2 of H antigen , encoded by fliC and fljB ) . Various combinations of 46 O antigens and 85 H antigens have resulted in ∼1 , 500 serovars within S . enterica subspecies enterica and ∼1000 in the other subspecies of S . enterica plus S . bongori ( Fig . 1 ) [2] . The use of serotyping within Salmonella as a typing method is so widely accepted that governmental agencies have formulated guidelines intended to reduce human salmonellosis by targeting Typhimurium , Enteritidis and three other common serovars in domesticated animals ( European Union EC Regulation 2160/2003 of 12/12/2003 ) . Such regulations implicitly assume that serovars are associated with a particular disease potential [3] , [4] , an assumption that is also suggested by some of their names , e . g . Abortusequi , Abortusovis and Choleraesuis . These designations reflect a medical microbiological tradition of assigning distinctive taxonomic designations to microorganisms that are associated with particular diseases or hosts . However , this tradition is not necessarily warranted from an evolutionary perspective , as illustrated by the following examples . For some taxa , species designations have been used to designate genetically monomorphic clones of a broader species with a different pathogenic potential , e . g . the clone of Yersinia pseudotuberculosis that is called Y . pestis [5] , the host-specific ecotypes of the Mycobacterium tuberculosis complex that are designated M . bovis , M . microti , M . pinnipedii and M . caprae [6] , or the isolates of Escherichia coli that have been assigned to multiple species of the genus Shigella [7] . In other cases , taxonomic designations have grouped members of paraphyletic groups of microorganisms because they cause similar diseases , such as the anthrax toxin-producing variants of Bacillus cereus that are designated Bacillus anthracis [8] . That all isolates of an individual serovar of S . enterica share a common phylogenetic ancestry should therefore be considered to represent a working hypothesis that requires confirmation . Similarly , a supposed host and/or disease specificity needs to be confirmed by genetically informative methods with isolates from diverse geographical regions . These working hypotheses has been confirmed for serovar Typhi , which corresponds to a genetically monomorphic , recently evolved clone that causes typhoid fever in humans [9]–[11] . In contrast , multiple , discrete lineages have been identified within serovar Newport [12] . Close genetic relatedness and a monolithically uniform association with host/disease specificity remain to be demonstrated for most other serovars , especially because only few of them have yet been investigated in detail . Serovar designations are widely used for epidemiological purposes due to the belief that they are discriminatory , and because serovars represent a globally understandable form of communication . However , as noted by McQuiston et al . [13] , [14] , serotyping has multiple disadvantages , including low throughput , high expense , and a requirement for considerable expertise as well as numerous antibodies made by immunizing rabbits . As a result , various molecular methods have been proposed as potential alternatives to serotyping for subdividing Salmonella ( and other microbes ) [15] , [16] , ranging from PFGE ( Pulsed-Field Gel Electrophoresis ) [17] , [18] through to MLVA ( MultiLocus Variable number of tandem repeats Analysis ) [19] , [20] . These methods are possibly useful for recognizing a common source of microorganisms from a single outbreak [21] , but they are inappropriate for reliable assignments of isolates to one of the 2 , 500 S . enterica serovars . Still other attempts have been made to develop DNA-sequence based equivalents of serotyping [22]–[26] , including the detection of particular single nucleotide polymorphisms ( SNPs ) within flagellar antigens [13] , [14] . This approach shares with serotyping the assumption that serotyping reflects genetic relatedness or disease specificity , which needs not be generally true [12] . For example , genes encoding antigenic epitopes can be imported by horizontal genetic exchange and homologous recombination from unrelated lineages . As a result , genetically related serovars such as Heidelberg and Typhimurium possess very different fliC alleles whereas genetically distinct serovars can possess nearly identical alleles [27] . Thus , replacing serological determination by serotype-based molecular assays would maintain a system that does not necessarily reflect genetic relatedness . Furthermore , some serovar designations will need revision because they distinguish between minor antigenic variants of organisms that are genetically very similar , e . g . Dublin and Rostock [28] or Paratyphi A and Sendai [29] . We recommend another approach , namely using neutral markers to identify genetically related clusters of S . enterica . Serovar designations that reflect such groupings could be preserved , and possibly be detected by informative SNPs in those neutral markers , whereas other serovars need to be revised or possibly eliminated . Twenty years ago , a valiant attempt was made to identify natural groupings within S . enterica on the basis of MultiLocus Enzyme Electrophoresis ( MLEE ) [29]–[31] . MLEE data identified multiple monophyletic lineages that corresponded to individual serovars . Problematically , most serovars that were examined included exceptional isolates that were unrelated to the main lineage , and some serovars were composed of multiple , genetically unrelated lineages rather than one predominant lineage . MLEE was never generally accepted by microbiologists and these observations have not influenced the general use of serovar designations . Instead of MLEE , a sequence-based alternative , MultiLocus Sequence Typing ( MLST ) , has gained broad acceptance for many microbial species [32] . MLST is based on similar principles to MLEE , but has greater discrimination and is more objective because it is based on sequences of multiple housekeeping gene fragments rather than electrophoretic migration of proteins . Of equal importance , MLST schemes are community efforts because the data are publicly available online ( http://pubmlst . org/databases . shtml ) and data can be entered from decentralized sources . Isolates that possess identical alleles for all gene fragments are assigned to a common Sequence Type ( ST ) , and STs that share all but one or two alleles are grouped into ST-based clonal complexes [33] on the basis of eBurst [34] . An MLST scheme involving seven housekeeping gene fragments was developed for the analysis of serovar Typhi [9] , and subsequently tested with 110 isolates from 25 serovars of S . enterica subspecies enterica [35] , most of which were from Selander's SARB collection of reference strains for MLEE [30] . Subsequent analyses have used this scheme to survey serovars Newport [12] , [36] and Typhimurium [37]–[39] , as well as smaller numbers of isolates of various serovars from wild animals in Australia [40] and the mesenteric lymph nodes of cattle in Canada [41] . The same scheme has also been used to survey the genetic properties of antibiotic-resistant isolates among a global sample of various serovars [42] . These initial results suggested that MLST often correlates with serovar , with some exceptions . If this inference were correct , it would be advisable to replace serotyping by MLST for routine epidemiological purposes . We therefore embarked on a major , decentralized effort to test this hypothesis . We investigated isolates from diverse hosts , both diseased and healthy , as well as from the environment . We screened isolates from all continents and deliberately included representatives of rare serovars as well as unusual monophasic and diphasic variants from reference collections . All this data was submitted to a publically accessible MLST database ( http://mlst . ucc . ie/mlst/dbs/Senterica ) . In April , 2011 , that database included 4 , 257 isolates ( Table S1 ) from 554 serovars of S . enterica subspecies enterica that had been assigned to 1 , 092 STs . The database also contained 436 isolates from the other S . enterica subspecies as well as Salmonella bongori , whose properties will be described elsewhere , as will analyses of associations with host or geography . Here we describe the population structure of subspecies enterica on the basis of MLST , examine the extent of congruence between serotyping and MLST clusters , and conclude that serotyping of S . enterica should be replaced by MLST .
We initially recognized the existence of eBGs by visual examination of a minimal spanning tree ( MSTree ) of STs connected by the numbers of shared alleles . The MSTree of subspecies enterica shows multiple starburst-like clusters ( Fig . 2 ) , which in large part correspond to eBGs as defined here . Similar to eBurst groups in other species , most clusters radiate from a central node which contains numerous isolates , a phenomenon which is usually interpreted as representing monophyletic lineages of STs that have evolved from a single founder node [34] . We deferred interpretations on evolutionary history within eBGs , including the identification of founders , until genomic studies of historically representative isolates have been conducted , and therefore arbitrarily assigned an otherwise uninformative , unique number to each eBG . Historically , MLEE data of S . enterica were interpreted on the basis of phylogenetic trees [29]–[31] . Trees attempt to depict genealogies ( vertical descent from a common ancestor ) , and can be confounded by homologous recombination between unrelated lineages , a common occurrence in S . enterica [44] , [45] . Indeed , only one higher level population structure with strong statistical support has been identified within subspecies enterica; this structure has been referred to as Clade B [40] , [44] , [48] or Lineage 3 [45] . We confirmed the existence of Lineage 3 in our large dataset by a BAPS [49] cluster analysis of the allelic differences between STs using an upper bound of 2–7 clusters ( Fig . S2 ) . Similar results were obtained with concatenated sequences for all seven gene fragments regardless of upper bound , or when using Structure [50] . In order to assess the robustness of our eBG classification , we investigated the fine structure of subspecies enterica by three additional , independent clustering methods . Firstly , we analyzed concatenated sequences with ClonalFrame [51] , which determines tree topologies after stripping signals of lateral gene transfer and homologous recombination . ClonalFrame identified 163 lineages containing more than one ST ( Table 1 ) , each of which coalesced far from the root ( Fig . S3 ) . This result provides further support for the conclusion [44] , [45] that there is little deep phylogenetic signal within the MLST genes . Secondly , we analyzed the sequence data by a gene by gene bootstrap approach as described by Falush et al . [44] . A consensus UPGMA tree based on the concatenated sequences was then stripped of branches which did not find 50% support in 1000 gene by gene bootstrap trees . The bootstrap approach identified 167 clusters of STs . Finally , we used BAPS on allelic identities with an upper bound of 400 , which resulted in 216 clusters . For each of the three methods , many clusters each contained only one of the 138 eBGs and most or nearly all of the 138 eBGs contained isolates that were all assigned to a single cluster by each of the three alternative approaches ( Table 1 ) . The three methods were also largely congruent: for 108 eBGs , all the isolates were assigned to a single cluster by all three methods and for 24 others , the isolates were clustered together by two methods ( Fig . 3 ) . Finally , data permutation revealed that all of these correspondences between eBGs and the other methods were significantly non-random ( p<10−4 ) except for the number of eBGs per BAPS cluster where 9 . 5% of the permutations contained at least as many single eBGs per cluster as were found with the unpermutated data ( Table 1 ) . We conclude that the large majority of our assignments of STs to eBGs reflects the existence of natural genetic groupings that can also be identified by multiple other , independent clustering algorithms . We also note that the analysis of 300 Kb from 114 isolates of subspecies enterica identified only four clusters other than Lineage 3 , each containing isolates from one to three eBGs per cluster [45] . Thus , little phylogenetic information seems to exist above the cutoff imposed by our definition of an eBG , even when more extensive sequencing is applied . Some eBGs exhibit a unique one-to-one relationship with serovar , for example eBG13 ( Typhi ) , eBG11 ( Paratyphi A ) and eBG26 ( Heidelberg ) ( Table S1 ) . Of the 48 eBGs containing at least 15 isolates , 22 contain a single serovar , or its monophasic variants . In contrast , 26 other eBGs contain multiple serovars ( or isolates whose serovar is unknown ) , as indicated by white sectors in Fig . 2 . Similarly , of the 42 serovars from which we sampled at least 15 isolates , 17 were associated with a single eBG but the remaining 25 serovars were associated with multiple eBGs and/or STs . Particularly dramatic examples of serovars that encompass multiple , distinct eBGs are Newport [12] , Paratyphi B ( see below ) and Oranienburg ( Fig . 2 , Table S2 ) but multiple MLST clusters per serovar are common throughout the entire dataset , even in serovars from which only two isolates were tested ( Fig . S2 ) . Discrepancies between serotyping and assignments to eBGs by MLST might reflect mistakes in serotyping or MLST sequencing , or both . Due to the decentralized sources of data , such mistakes almost certainly exist within the database . However , the MLST database is actively curated . Each nucleotide within a new MLST allele must be supported by at least two independent sequence traces before that allele is accepted by the curator , which has led to the rejection of multiple submissions of new alleles . All STs containing novel combinations of known alleles are examined visually for internally consistent genetic relationships to other STs and serovars . In multiple cases , this curation has resulted in rejecting such STs and subsequent resequencing of the gene fragments revealed technical errors . However , the most common discrepancy which we have encountered has been inaccurate serotyping , which has plagued several percent of database entries from all the laboratories involved in this project , as well as in ring trials for testing laboratory accuracy [52] . In numerous cases where the serovar and the ST of new entries were discordant with other isolates , re-serotyping revealed that the original culture had been contaminated , or had been inaccurately serotyped . However , despite active curation and rechecking serotypes and STs , multiple discrepancies remain between genetic relationships of STs and serovar , which are described below in greater detail for four test cases of increasing complexity . eBG1 contained 482 isolates of serovar Typhimurium , which has the antigenic formula [1] , 4 , [5] , 12:i:1 , 2 ( Table S2 ) . [The colons divide the epitopes within the lipopolysaccharide ( LPS ) O antigen ( 4 , 12 ) from those in the phase 1 flagellar antigen ( i ) and the phase 2 flagellar antigen ( 1 , 2 ) . Numbers in square parentheses designate epitopes that are variably present within a serovar , in some cases due to lysogenic conversion by bacteriophages . ] eBG1 also contained so-called monophasic variants of Typhimurium , 88 isolates that do not express the phase 2 antigen and four isolates that do not express the phase 1 antigen , as well as rough and non-motile variants ( Fig . 4 , Table S2 ) . The presence of these serological variants within eBG1 indicates that they are genetically related to Typhimurium , and therefore these monophasic , rough and non-motile variants potentially represent mutations or recombination events affecting expression of LPS or the flagellar antigens encoded by fliC ( phase 1 ) and fljB ( phase 2 ) . Prior work has indicated that monophasic variants represent multiple , independent genetic events [53] , [54] , and our results support this interpretation . ST19 , the central ST in eBG1 , contains two distinct forms of monophasic variants , and both monophasic as well as diphasic variants are also found in ST34 . eBG1 also includes one isolate each of the serovars Hato and Farsta , whose antigenic formulas differ from Typhimurium at the phase 1 and 2 antigens , respectively ( Table S3 ) . Not all Typhimurium isolates are grouped in eBG1 ( Table S1 , S3 ) and exceptional isolates were found in eBG138 and ST513 . eBG138 shares only three identical alleles with eBG1 although it contains seven Typhimurium isolates plus nine monophasic Typhimurium isolates . Similarly , ST513 contains five Typhimurium isolates plus one Kunduchi isolate , whose phase 1 antigen differs from that of Typhimurium . ST513 also shares only three alleles with eBG1 . Thus , serotyping has conflated Typhimurium with isolates from genetically distant eBGs while failing to group related Typhimurium with its monophasic variants . Serotyping has also conflated genetically unrelated isolates of serovars Kunduchi , Farsta and Hato . Isolates of these serovars are found in six additional STs , each of which is unrelated to the others or to the STs containing Typhimurium ( Fig . 4 , Table S3 ) . Two hundred and forty two serovar Enteritidis isolates ( [1] , 9 , 12:g , m:- ) were present in eBG4 , as well as two non-motile variants ( Table S2 , Fig . 5 ) . eBG4 also includes several serovars that differ from Enteritidis by their phase 1 ( serovars Rosenberg , Moscow , Blegdam and Antarctica ) or O antigens ( Nitra ) ( Table S4 ) . In addition , eBG4 includes a discrete sub-lineage consisting of multiple isolates of the serovars Gallinarum and Gallinarum var . Pullorum ( henceforth referred to as Pullorum ) . In fact , Gallinarum and Pullorum are non-motile serological variants of Enteritidis that cause distinctive forms of lethal disease in poultry ( fowl typhoid and pullorum disease , respectively ) , but can otherwise be difficult to distinguish because they differ in nutritional capabilities ( biotypes ) rather than serologically [55] . According to MLST , four STs containing Gallinarum were closely related to ST11 , the most common ST in eBG4 . Two STs containing Pullorum isolates branched from the basal Gallinarum ST , ST470 ( Fig . 5 ) . Similar results have previously been obtained with MLEE [56] and a genomic comparison of one strain each of Enteritidis and Gallinarum also indicated a close relationship [57] . Two Enteritidis isolates were assigned to ST77 and ST6 , and a unique , diphasic Enteritidis isolate is in ST746 , which are all unrelated to eBG4 . Thus , like Typhimurium , most Enteritidis isolates are in one primary eBG but rare isolates are present in multiple unrelated eBGs and STs . Serovar Dublin ( [1] , 9 , 12 , [Vi]:g , p:- ) contains the flagellar p epitope rather than the m epitope in serovar Enteritidis . The majority ( 115 ) of Dublin isolates were grouped in eBG53 , which shares only three alleles with eBG4 , the main Enteritidis cluster , supporting this serological distinction . The remaining Dublin and Enteritidis isolates were found in eBG93 ( Enteritidis: 5 isolates , Dublin: 1 ) and ST74 of eBG32 ( Enteritidis: 1 , Dublin: 1 , Enteritidis/Dublin 1 ) . eBG93 is intermediate between eBG4 and eBG53 , sharing four alleles with each . ST74 shares none with either and other STs of eBG32 contained monophasic isolates of serovars Paratyphi B and Paratyphi B var . Java ( henceforth Java ) ( Fig . 5 ) , which only share the O12 antigen with Enteritidis or Dublin . It has previously been reported that strain RKS1550 ( also designated SARB14; MLEE ET Du2 ) has the phase 1 antigenic formula g , m , p , which is a combination of the phase 1 antigens found in Enteritidis ( g , m ) and Dublin ( g , p ) [28] . Its FliC sequence encodes Ala220 and Thr315 , which are typical of Enteritidis , as well as Ala318 , which is typical of Dublin [28] . SARB14 was one of the three strains assigned to ST74 . We confirmed by sequencing the presence of these three amino acids in its FliC sequence , and also found that the two other ST74 isolates possessed the same three substitutions . One of those two isolates had been serotyped as Dublin and the other as Enteritidis . However , we have now found that some such strains can be variably serotyped as Enteritidis , Dublin or both because different laboratories use different strains to generate and absorb serological typing sera . In agreement with observations from MLEE [28] , the primary Dublin eBG , eBG53 , also includes six isolates of serovar Rostock . It also includes one isolate each of serovars Naestved and Kiel . Serovars Rostock and Naestved contain additional epitopes in the phase 1 antigen while serovar Kiel contains a distinct epitope in the O antigen . Rostock , Naestved and Kiel have not yet been found outside eBG53 . The observation that eBG32 contained Paratyphi B and Java isolates as well as Enteritidis and Dublin stimulated a closer examination of Paratyphi B and Java . The genetic relationships between Paratyphi B and Java have long been a topic of discussion . Their serological formula ( [1] , 4 , [5] , 12:b:1 , 2 ) is identical and Java is treated as a variant of Paratyphi B that can ferment d-tartrate ( dTar+ ) whereas Paratyphi B sensus stricto is dTar- [58] . The dTar- phenotype has been attributed to a single nucleotide change in the start codon of the STM3356 gene , which is ATA in Paratyphi B rather than ATG [58] , [59] . Paratyphi B is thought to be associated with typhoid-like fever in humans whereas Java is associated with non-invasive gastroenteritis in animals and humans [60] , [61] . Our initial results did not allow a simple distinction between Paratyphi B and Java according to MLST , and these serovars seemed to be randomly distributed among various eBGs . After retesting all of the apparent exceptions plus numerous other isolates for their ability to ferment d-tartrate [58] as well as their phase 1 and phase 2 flagellar antigens , we found that the assignment to Paratyphi B or Java was inaccurate for 35/117 isolates , and that many Java isolates had been designated as Paratyphi B . Furthermore , many other isolates proved to be monophasic variants of Paratyphi B or Java ( Table 2 , S6 ) . We also sequenced the start codon in STM3356 from numerous isolates . The results of these analyses showed that all Paratyphi B isolates with the ATA codon were in eBG5 , within ST86 or five SLVs of ST86 ( Fig . 5 ) . Of these , ST86 and ST284 each contained one monophasic Paratyphi B isolate . However , three other monophasic Paratyphi B isolates were found in eBG32 , although these had the ATG codon that has been associated with Java . Thus , it seems likely that classical Paratyphi B with an ATA codon arose once within eBG5 whereas an inability to ferment d-tartrate is associated with other genetic causes among monophasic Paratyphi B in eBG32 . Java was much more diverse than Paratyphi B ( Fig . 5 ) . Some diphasic Java were in STs of eBG5 other than those associated with Paratyphi B and others were in eBG19 and eBG59 . Monophasic Java were found in eBG32 ( together with the unusual monophasic Paratyphi B and Enteritidis/Dublin isolates described above ) and in ST135 . Monophasic Java isolates were also present in eBG19 and dTar+ isolates with the same antigenic formula were in eBG214 , which is subspecies salamae . Taken together with a common inability to distinguish between Paratyphi B , Java and their monophasic variants , it is difficult to elucidate from the published literature just which eBGs are associated with typhoid-like fever and host specificity . Our results were generally consistent with prior assignments of Paratyphi B/Java to distinct groupings by MLEE [61] , molecular typing [62] and variable virulence determinants [60] , suggesting that such groupings may correspond to individual eBGs and STs ( Table 2 , S6 ) . However , even among the few isolates that were tested , we found multiple discrepancies between the different methods . Only 65/74 MLEE type Pb1 isolates were dTar- [61] versus 19/19 isolates within ST86 . Virulence groupings SPV1 , EPV1 and EPV3 [60] corresponded to ST86 , ST88 and ST28 , respectively , but EPV2 and EPV4 were each found in multiple eBGs . These comparisons also revealed additional sub-differentiation within individual eBGs and STs . Virulence groupings SPV1 and EPV2 , which differed in possession of SopE1 and frequency of SopD , correspond to distinct STs within eBG5 , which indicates that virulence antigens need not be uniform within an eBG . Similarly , Miko et al . [62] reported that two distinct molecular groupings ( Groups 2 and 3 ) of multidrug resistant ( MDR ) Java emerged in German poultry after 1994 . Both groups were in ST28 of eBG59 , showing that molecular fine typing can distinguish isolates within a single ST . Individual isolates within an ST can apparently also vary in regard to antibiotic resistance and its mechanisms because the Group 2 isolates possess a plasmid-borne class 1 integron whereas the Group 3 isolates contain a chromosomal Tn7-like class 2 integron [63] . Similarly , some MDR Java isolates from France carry the Salmonella genomic island 1 ( SGI1 ) , a ∼43-kb genomic island encoding multidrug resistance [64] . These isolates are in ST43 of eBG5 , together with EPV-2 and Group 1 , which do not contain SGI1 [63] . Thus , additional investigations are likely to reveal considerable diversity within eBGs and STs for virulence determinants and markers used for molecular typing . Typhimurium , Enteritidis , Dublin and Java are relatively common in Europe and the Americas , and have therefore been studied in considerable detail . In contrast , less information is available about subspecies enterica isolates with the antigenic formula 6 , 7:c:1 , 5 , which have now become rare in Europe and the Americas . However , 6 , 7:c:1 , 5 isolates continue to be an important cause of invasive human disease in Asia and possibly elsewhere ( Text S1 ) . 6 , 7:c:1 , 5 isolates with apparently different disease specificities have been assigned distinct serovar designations on the basis of their differential abilities to ferment dulcitol and tartrate [2] , [65] ( Table 3 ) , even though this distinction is based on biotyping rather than serotyping . Serovar Paratyphi C is associated with enteric fever in humans , Choleraesuis with septicemia in swine ( and humans ) and Typhisuis with chronic paratyphoid/caseous lymphadenitis in swine . Some Paratyphi C isolates express the Vi capsular antigen [66] , which is otherwise associated with serovars Typhi and Dublin . Further biotypic subdivisions on the basis of H2S production and the utilization of mucate can be used to subdivide Choleraesuis into its variants sensu stricto , Kunzendorf [67] and Decatur [65] ( Table 3 ) , but these subdivisions are usually reached only by highly specialized reference laboratories . Earlier MLEE data showed that most Paratyphi C , Choleraesuis and Typhisuis isolates were genetically related , but others were distinct , including all of variant Decatur . We examined 202 supposed 6 , 7:c:1 , 5 strains isolated from animals and humans from global sources as well as from reference collections ( Table S7 ) . Most of these isolates had been assigned to Paratyphi C , Choleraesuis sensu stricto or Choleraesuis var . Kunzendorf , and we were only able to obtain eight supposed Choleraesuis var . Decatur and seven Typhisuis isolates . The collection included isolates from the SARB collection that represents the diversity of 6 , 7:c:1 , 5 isolates on the basis of MLEE [30] . A comparison of the nutritional characteristics of all these isolates with the MLST results resulted in the slightly revised differentiation scheme that is shown in Table 3 . Our tests showed that 32 of the isolates had been serotyped incorrectly , or had not been assigned to the correct variant of Choleraesuis , including exceptional isolates according to MLEE . Two others were not even 6 , 7:c:1 , 5 . After correcting these faulty serovar assignments ( Table S7 ) , both MLEE and MLST assigned all Choleraesuis , Paratyphi C and Typhisuis isolates into a single complex of genetically related eBGs and STs that are subdivided by serovar ( Fig . 6 ) . All 48 Paratyphi C isolates were assigned to three STs within eBG20 . Early in the 20th century , microbiologists subdivided Paratyphi C into varieties Orientalis and Hirschfeld on the basis of their geographical site of isolation . The three isolates with such designations were in ST90 , which also contained standard Paratyphi C . When tested by a PCR assay for multiple genes within the SPI7 genomic island that encodes the Vi antigen , all Paratyphi C isolates that were tested were positive for the entire SPI7 island , or for a modified version designated ΔSPI7 because it contains an internal 5 kb deletion . All other 6 , 7:c:1 , 5 isolates tested were negative ( Table S7 ) . Typhisuis isolates were assigned to ST147 and ST636 , which differ by two alleles from each other and from the central ST of eBG20 . All 128 Choleraesuis isolates were grouped in eBG6 , which is a DLV of eBG20 ( Fig . 6 ) . Within eBG6 , two related STs are largely composed of Choleraesuis sensu stricto isolates , which do not produce H2S , whereas the other eight STs are largely composed of Choleraesuis var Kunzendorf , which do produce H2S . Paratyphi C isolates also produce H2S , suggesting that var Kunzendorf might have been ancestral and sensu stricto ( STs 68 and 139 ) corresponds to a lineage that has lost the ability to form H2S . The association between H2S production and ST is not absolute because one exceptional var Kunzendorf isolate was found in a sensu stricto ST and one sensu stricto isolate in a Kunzendorf ST . Other 6 , 7:c:1 , 5 isolates were unrelated to the complex consisting of eBG6 , eBG20 and Typhisuis . These isolates included strain SARB5 ( MLEE electrophoretic types Cs6 ) and SARB7 ( Cs13 ) . Published data [68] as well as our biotyping indicate that SARB5 ( Cs6 ) is a Choleraesuis var . Decatur , and MLST assigned it to eBG141 together with a second Decatur isolate ( Fig . 6 ) . Similarly , SARB7 ( Cs13 ) is Dulcitol-negative , H2S-positive , Mucate-positive , and Tartrate-positive , which we now also score as Choleraesuis var . Decatur ( Table 3 ) . SARB7 was assigned to eBG142 by MLST together with three other strains of the same biotype that were isolated in the same country ( Australia ) and year ( 1988 ) . Similarly , MLEE ET Ts3 ( SARB70 ) was supposed to be a Typhisuis isolate that was more closely related to Decatur than to other Typhisuis strains [30] . Once again , SARB70 is Choleraesuis var . Decatur , according to published data [30] plus our own biotyping results . SARB70 was assigned to ST70 by MLST along with SARB8 and two other Decatur isolates , one of which had originally been typed as Typhisuis var . Volsdagsen . Thus , Decatur consists of at least seven lineages ( eBG141 , eBG142 , eBG144 , ST70 , ST633 , ST637 and ST1581 ) , which are only distantly related to each other or to the main group of 6 , 7:c:1 , 5 isolates described above . These observations argue against the current concept that Decatur is a variant of Choleraesuis and also argue against assigning any common designation to them despite their similar biotypes . If Decatur are both diverse and genetically distinct from standard Choleraesuis , Paratyphi C and Typhisuis , why do they all share the same serotyping antigens ? To address this question , we sequenced almost all ( 1300/1500 bp ) of each of the phase 1 fliC and phase 2 fljB genes of representative isolates from various STs ( Table S8 ) . BLAST searches against GenBank with representative sequences from Paratyphi C or Choleraesuis isolates identified additional nearly identical sequences ( fliC: ≥97% identity , ≥97% coverage; fljB: ≥95% identity , 100% coverage ) , which were also included in the analyses . For fliC , strong BLAST hits were found not only among Choleraesuis and Paratyphi C isolates but also among other isolates that express the phase 1 c epitope in serovars Bury , Jericho , Goeteborg as well as in subspecies IIIb ( diarizonae ) ( Fig . 7 ) . Only a limited number of nucleotides were polymorphic in these sequences , and most of those polymorphisms were synonymous and did not introduce any amino acid changes . As a result , a total of only 12 amino acids were polymorphic in the FliC protein sequences , which subdivided the sequence variants into four slightly distinct groups ( Fig . S4 ) . Most of the polymorphic amino acids were associated with subspecies IIIb isolates , but five were polymorphic among Choleraesuis , Paratyphi C , Typhisuis and Decatur ( Fig . 7 ) . These polymorphisms were not uniquely associated with any serovar , nor did any single amino acid reliably distinguish Decatur from the main 6 , 7:c:1 , 5 groups . Choleraesuis s . s . was in FliC group C , Choleraesuis var Kunzendorf , Paratyphi C , Typhisuis , some Decaturs , Bury and Jericho were in FliC group A , and other Decaturs and Goeteborg were in FliC group B . The near identity of the FliC sequences from the genetically distinct isolates in various serovars likely reflect horizontal gene transfer by homologous recombination between these lineages . Greater diversity was observed for fljB , resulting in assignments to 11 amino acid groups , A through K ( Fig . 8 , supplementary Figures S5–S7 ) . This greater diversity arose in part because the BLAST searches had identified strongly homologous sequences expressing FljB epitopes 2 , 5 , 6 , or 7 in combination with epitope 1 . These have previously been referred to as the 1-Complex [14] . The 11 amino acid groups correlated in large part with the FljB serological epitopes , e . g . group A sequences were 1 , 2 while B sequences were 1 , 5 . However , multiple sequence clusters were found for each set of epitopes , e . g . 1 , 2 was associated with groups A , F and G and 1 , 5 was associated with groups B , C , D , J and K . The sequence differences between groups expressing the same serological epitopes were in part as large as the differences between distinct sets of epitopes . Genetically distinct eBGs and STs tended to belong to distinct FljB sequence groups: group B included the Australian Decatur isolates in eBG142; group C encompassed the related Choleraesuis , Paratyphi C and Typhisuis isolates; and group K encompassed the other Decatur isolates . Thus , it might be possible to develop molecular serotyping tools that could distinguish some of these distinct eBGs and STs . However , more efficient serology or molecular serology would not distinguish between eBGs 6 and 20 or eBGs 141 and 144 , because each of these paired groups contained identical FljB sequences . These results show that classical serotyping has been very efficient at recognizing identical or closely related sequences of FliC . It has been less efficient at distinguishing distinct sequences of FljB that differentiate Decatur and the Choleraesuis/Paratyphi C group , which has resulted in serological conflation of these genetically unrelated serovars . We were intrigued by the apparent rarity of non-synonymous polymorphisms , particularly in fliC . We therefore compared ω , the relative frequency of non-synonymous polymorphisms to synonymous polymorphisms in fliC and fljB , with ω in the individual MLST genes ( Table 4 ) . These results show that neither fliC nor fljB is particularly unusual , because dN , dS and ω are within the range found for MLST genes . A relative lack of non-synonymous polymorphisms within housekeeping genes is generally attributed to purifying selection due to the loss of deleterious mutations that led to amino acid changes . Given the similar values for ω in fliC and fljB , purifying selection should be considered as the null hypothesis for the relative absence of non-synonymous polymorphisms as well .
The definition of eBGs is based on longer branches between eBGs than within eBGs . Active curation of all new STs will help to prevent filling of such gaps through error , such as the artificial creation of mosaic STs due to mixed cultures or sequencing reactions . However , it might be expected that with time those gaps will be closed through the identification of rare , intermediate STs , such as result from homologous recombination . Indeed , the merging of clusters through recombination is predicted by simulations [43] and has been observed within many species , such as E . coli [7] , Y . pseudotuberculosis [74] or Campylobacter jejuni [75] . Within subspecies enterica , intermediate STs between eBGs of serovar Newport have also been attributed to homologous recombination [12] . In such cases , we recommend following the practise implemented for MLST of other pathogens such as Neisseria meningitidis , where eBurst group designations are maintained for groupings with distinct epidemiological patterns even when these groups become linked by rare , novel isolates . We anticipate a potential problem with eBGs within Lineage 3 ( Fig . S2 ) because only limited numbers of Lineage 3 isolates have been investigated and recombination among those isolates is particularly frequent [45] . Lineage 3 may in fact represent a connected network rather than multiple independent starbursts . In that case , eBGs within Lineage 3 might need to be merged into larger eBGs with time , as has occurred for particular lineages within N . meningitidis and E . coli , or the use of eBG designations might need to be discarded for Lineage 3 . However , we expect that most eBGs outside of Lineage 3 will continue to exist even after 10 , 000's of additional strains and genomic sequences have been obtained . Our optimism about the durability of most eBGs is based on the strong correlations between serotyping and eBG assignments for multiple eBGs , as well as our general failure to identify intermediate STs after extensive searches . For example , we were struck by the distinctiveness of eBG13 ( Typhi ) [10] , [11] and attempted to identify related STs by examining rare serovars with overlapping antigens . 100 , 000 s of isolates from subspecies enterica have been serotyped and 1500 serovars have been defined . Yet none of the rare isolates with overlapping serotypes were genetically related to eBG13 according to MLST ( data not shown ) . Similarly , we investigated 200 6 , 7:c:1 , 5 isolates from global sources , but failed to identify any ST that joined eBGs 6 ( Choleraesuis ) and 20 ( Paratyphi C ) . Our unpublished genomic analyses of serovars Paratyphi A and Agona confirm that each of these serovars represents a tight genetic grouping without close relatives . Thus , although we are somewhat uncertain about the durability of eBGs within Lineage 3 , we are confident that most eBGs represent natural groupings that will not be demolished by additional data . We also anticipate that some higher order relationships between eBGs may be detected by genomic analyses . For example , our distinction between eBG6 and eBG20 is based on a difference of two of the seven alleles between the most closely related pairs of STs within these eBGs . It maintains microbiological tradition and reflects distinctive disease and host properties . In contrast , eBG6 and eBG20 were clustered together in a higher order evolutionary grouping , Lineage 1 , according to analyses of multiple gene fragments spanning 300 kb [45] , and they also cluster together within the MSTree . Such higher order groupings may reveal details about longer term evolutionary history but do not invalidate the lower level clustering represented by eBGs . We conclude that eBGs represent natural groupings , but are uncertain about why they exist , how they arose and what can be predicted from an assignment to an eBG . Clearly , eBGs represent groups of closely related organisms related by descent from a common ancestor . However , the time scale of that descent is uncertain , within subspecies enterica as well as almost all other bacterial pathogens , because the mutational clock rate can vary by orders of magnitude between bacterial taxa [76] . It is tempting to equate eBGs with ecotypes , relatively uniform clusters of organisms sharing a common ecological niche which are continuously purified of diversity via competition and selective sweeps [77] . However , the utility of the ecotype concept is controversial for pathogens [78] , and even for environmental organisms [79]: Neutral processes such as bottlenecks and changes in population size can lead to reductions in diversity even in the apparent absence of selective sweeps [69] and uniformity does not necessarily reflect population wide replacement by a fitter variant because selection can be at the level of individual genes or gene clusters [79] . Thus , the evolutionary pressures leading to eBGs are currently best regarded as an interesting topic which warrants further investigations of evolutionary and population genetic history through genomic sequencing of defined collections . The predictive properties of eBGs are similar to those of serovars , some of which are thought to have undergone host-adaptation due to specific associations with host and disease type [80] . For example , serovars Typhi , Paratyphi A and Paratyphi C all cause typhoid or enteric fever ( exclusively ) in humans , and each belongs to a distinctive eBG . And even though they are genetically closely related , eBGs distinguish between Choleraesuis ( eBG20 ) , Paratyphi C ( eBG20 ) and Typhisuis , which differ in host adaptation: Choleraesuis can infect multiple mammalian species and causes a different form of invasive disease in humans than does Paratyphi C [81] . However , sufficient numbers of discrepancies exist between serovars and eBGs that the question of host-adaptation needs to be revisited for multiple eBGs . For example , Choleraesuis var . Decatur consists of multiple , genetically unrelated eBGs , each of which is also distinct from Choleraesuis . Paratyphi B var . Java and its monophasic variants are also distributed across multiple eBGs . Varying disease potential ( if any ) of these different eBGs will first become apparent after analyses of the correlations between disease state with eBG , which has not yet been performed . In some cases , serotyping may be more predictive of host-adaptation , e . g . Paratyphi B isolates form a sub-cluster within eBG5 , which otherwise contains Java isolates whose disease potential is uncertain . Similarly , serovars Gallinarum and Pullorum , which cause fowl typhoid and pullorum disease , are grouped within one sub-cluster of eBG4 . The other primary sub-cluster in eBG4 consists of serovar Enteritidis , which can cause a variety of other diseases in multiple hosts . Other observations also suggest occasional host-specificity at the ST level rather than the eBG level . ST183 in eBG4 ( Enteritidis ) contains phage type 11isolates from hedgehogs in Germany and humans in the UK . In eBG1 ( Typhimurium ) , phage type DT56var isolates from finches and humans in the UK were in ST568 [82] and phage type DT2 isolates from pigeons in Germany and France were in ST128 . The assignment of isolates to serovars on the basis of serotyping plus nutritional characteristics , the Kauffmann-White scheme , was initiated over 70 years ago , with the deliberate intention of providing a scheme with limited resolution that could be implemented in multiple laboratories [83] . Serovars were never intended to permit the complete differentiation of all antigenic diversity , nor was the serotyping scheme ever claimed to be complete or final [84] . Serovar designations continue to be updated regularly as new insights are acquired [2] , and some of the discrepancies between eBGs and serotyping have resulted in new serovar designations ( Table S1 , S2 ) that will be implemented in the next version of the scheme . The serovar concept is practised globally , providing a universal language of communication . 100 , 000's of isolates are serotyped annually and serovars are the basis for public health measures to reduce zoonotic diseases . However , in the interests of correctly identifying potential causes of disease with greater accuracy and higher speed , we recommend phasing out the routine use of serovars , and replacing it with a classification that is based on population genetic groupings such as eBGs and STs . This recommendation derives from the existence of multiple problems with assignments to serovars . Serotyping has multiple technical disadvantages , including low throughput , high expense , as well as a requirement for numerous antibodies made by immunizing rabbits plus considerable expertise [13] , [14] . Serotyping remains error-prone , even for the most common serovars , as demonstrated repeatedly here and in small scale ring trials [52] , and is not amenable to automation . However , our primary criticism of Salmonella serotyping reflects its information content . Some serovars are genetically relatively homogeneous , e . g . Typhimurium or Enteritidis , and most isolates from such serovars are closely related and belong to a common eBG . In contrast , numerous other serovars were distributed across multiple eBGs and/or STs ( Fig . 2 ) , and are therefore not necessarily uniform in virulence or epidemiology . Thus , serovars conflate eBGs with different biological properties , e . g . Decatur and Choleraesuis . For serovars such as Kentucky , Newport , and Java , it is not even possible to define a primary eBG because numerous isolates of those serovars were found in multiple distantly related groups ( Table S2 ) . At the same time , serovars differentiate between individual isolates that are closely related genetically but happen to possess distinct lipopolysaccharide or flagellar epitopes due to horizontal gene transfer or mutation , e . g . Dublin and Rostock , or Typhimurium and Farsta: 26 of the 48 eBGs containing at least 15 isolates included two or more serovars . Our results also show that serotyping is inconsistent . eBG1 contains monophasic variants that cannot be assigned a serovar designation because their epitopes are not unique whereas Java encompasses both diphasic and monophasic variants as well as multiple eBGs . And the assignment of an isolate to a serovar is often dependent not only on serology but also on nutritional properties , such as the differentiation between Choleraesuis , Paratyphi C and Typhisuis . We have primarily focused on well known serovars here because they represented the largest number of isolates that were tested by MLST . However , polyphyletic serovars are common , even those that are isolated only rarely in the USA or Europe ( Fig . S8 ) . Possibly the strongest arguments for continuing to assign isolates of S . enterica to serovars are tradition , the extensive infrastructure for serotyping in public health laboratories , and familiarity . Although it is difficult to discard a system that has been used so extensively for >70 years , and which is so embedded in microbiological thinking , the use of serotyping alone is often uninformative . Most of the S . enterica isolates in any European country belong to a very limited number of serovars , usually fewer than 10 ( Fig . S8 ) . In fact in recent years , most isolates belonged to Enteritidis , Typhimurium or Infantis , which results in relatively low discrimination . Furthermore , many current isolates of Typhimurium are monophasic and cannot be unambiguously recognized by serotyping [85] . Epidemiological investigations of outbreaks often depend on phage typing [86] , PFGE [17] , [18] or MLVA [19] , alone or in combination , usually after initial triage based on serotyping . These methods could continue to be used , and are likely to be even more effective if combined with an initial assignment to genetic groupings such as eBGs . MLST was first described in 1998 [87] and has now become the gold standard for long term epidemiology and population genetic analyses of pathogenic microbes . Of the 79 MLST databases that are publicly available ( http://pubmlst . org/databases . shtml ) , the S . enterica MLST database ( http://mlst . ucc . ie ) ranks fourth in number of isolates . This publicly accessible and actively curated web-based MLST database facilitates the global exchange of information . In particular , new alleles and new STs depend on user submissions rather than decisions by a central reference laboratory , and are immediately made publicly accessible . Similar global exchange of information at the strain level does not exist for serotyping . The database currently provides data for >500 of the 1 , 500 existing serovars in subspecies enterica , including all common serovars and many that are rare . These data have been accumulated through a decentralized global effort since 2002 and with time , we anticipate that representatives of all 1 , 500 serovars will be tested , thus providing a reasonably complete mapping between serovar and eBG/ST . The data presented here demonstrate that MLST is a valuable tool for the identification of genetic clusters and elucidating the diversity of known serovars . We also believe that it has the potential to completely replace serotyping , over which it possesses multiple advantages . Replacement of serotyping by MLST would involve changes in nomenclature . In cases where eBGs are relatively uniform in serovar and correspond to monophyletic groups , the serovar designations could be maintained together with the eBG designation for an interim period in order to provide continuity , e . g . eBG1 ( Typhimurium ) . For polyphyletic serovars , the serovar designation has little information content and should be eliminated as soon as possible , as is the case for other species for which MLST has become the common language . Even now , a surprisingly large numbers of entries are already being deposited at the MLST website without accompanying serovar information . In private discussions , some individuals have claimed that MLST is too technically demanding , expensive and slow . However , performing MLST does not require much more than a PCR machine plus training on working with DNA sequences . Our experience is that MLST does not require much technical competence , and laboratory scientists who are capable of handling serotyping can readily learn to handle MLST . MLST is cheaper than serotyping , sequencing of PCR products can be performed commercially and it can be automated . In our hands , with the help of robotic fluidics , one individual can easily complete the necessary manipulations from initial single colony isolation through to finished sequencing at the rate of 200 isolates per week and a cost per isolate of under €25 . A few days are needed to enter the sequence traces into a database and evaluate them with the help of dedicated scripts . In general , a small fraction of traces need to be repeated , which then doubles the time needed to provide definitive results for all 200 isolates . We anticipate that in the future , technical developments will allow even higher throughput of MLST assignments through multiplexed SNP-based typing and/or next-generation sequencing . Other individuals have claimed that MLST will soon be replaced by whole genome sequencing ( WGS ) , whose price is rapidly approaching that of MLST . Instead we argue that WGS and MLST are complementary , and should be pursued in parallel . WGS provides essential information for epidemiological tracking and will yield invaluable insights into the detailed population structure of bacterial pathogens [69] , [88] , including S . enterica . However , the evaluation of SNPs and genomic sequences from WGS takes much more time than the evaluation of paired traces from seven gene fragments . WGS currently suffers from differences between samples in quality and number of reads per nucleotide , which presents difficulties in extracting identical gene fragments from multiple genomes due to variable missing data . The S . enterica MLST database will probably contain data for >10 , 000 isolates in the near future , as do three other MLST databases today , whereas it would currently be difficult to extract information with comparable certainty from that many genomes . We propose that MLST should be used to provide a rapid overview of the population structure of S . enterica , which can then be used to identify selected isolates for investigation in greater detail by genome sequencing . Such efforts including the integration of genomic sequences and MLST data are already underway [89] . A third criticism of MLST for S . enterica is that it does not provide the fine resolution needed for outbreak analysis and short-term epidemiology . Indeed , MLST data does not generally have the same fine resolution as phage typing , PFGE , and MLVA . Multiple phage types were present within ST19 , the central ST in eBG1 ( Typhimurium ) , and within ST11 , the central ST of eBG4 ( Enteritidis , Gallinarum , Pullorum ) . However , MLST does provide somewhat greater resolution than serotyping because eBGs tends to contain multiple STs once a sufficient number of isolates has been tested . On occasion , MLST has also given hints of phylogeographic and host specificity . For example , invasive disease caused by Typhimurium in Africa is associated with ST313 and its descendent SLVs within eBG1 [39] . ST213 within eBG1 has only been isolated in Mexico [38] . Similarly , STs 66 and 634 of eBG6 ( Choleraesuis ) were first isolated in Canada ( 1978 ) and the USA ( 1981–1986 ) and subsequently from humans and swine in Taiwan ( 1998–2004 ) . A potential link between these isolates may have been breeding pigs , which have been imported into Taiwan from Canada and the USA since 1980 ( http://www . angrin . tlri . gov . tw/indexd/AGLP . htm ) . We conclude that MLST is a powerful candidate for the reference classification system for Salmonella , and can replace serotyping for that purpose . Similar to serotyping , additional methods will be needed to provide the fine resolution that is required for short term epidemiology . In other species where serotyping was previously the common language for strain tracking and epidemiology , such as E . coli or Klebsiella pneumoniae , it was rapidly replaced by MLST nomenclature after its introduction . We are confident that MLST designations will be also be adopted widely in the near future for S . enterica . By eliminating multiple misleading interpretations about strain relatedness associated with serotyping , this step would represent a major improvement for the epidemiology and control of Salmonella infections .
The analyses presented here are based on 4257 isolates whose data has been submitted to http://mlst . ucc . ie/mlst/dbs/Senterica by ourselves and others . Of these , 1770 are maintained in the strain collection of MA at University College Cork , and 1042 in the strain collection of FXW at the Institut Pasteur , for a total of 2643 in either or both of those collections . Biotyping and serotyping were performed in multiple laboratories but most of the tests were performed under the supervision of FXW or MC . Serotyping and biotyping were according to the modified Kauffmann-White scheme [2] , except as described below . Basic information on all isolates can be downloaded from the website . In addition , a detailed description of strain properties for Paratyphi B and Java isolates is presented in Table S6 . The distinction between Paratyphi B and Java was based on two tests , which gave concordant results after up to 7 days incubation: the lead acetate protocol 1 for d-tartrate fermentation described by Malorny et al . [58] and the ability to grow on d-tartrate as the sole carbon source as described by Weill et al . [64] . The start codon of STM3356 was sequenced as described by Malorny et al . [58] . Table S7 gives detailed information on results with 6 , 7:c:1 , 5 isolates . These were assigned to serovars on the basis of the biochemical properties which are summarized in Table 3 , and which are similar to the tests and recommendations by Le Minor et al . [65] . Mucate utilization , ducitol fermentation and H2S production were evaluated after 24 hrs incubation in standard media and tartrate fermentation was evaluated after 7 days , as described above . A separate manuscript is in preparation on differences between the contents of Selander's SARA and SARB collections . The conclusions drawn here were largely based on isolates stored by Kenneth E . Sanderson and corroborated by the collection of Fidelma Boyd . Serovar assignments were according to information uploaded to the website except that many atypical isolates and the Paratyphi B , Java and 6 , 7:c:1 , 5 isolates were retyped . MLST was performed on seven gene fragments as described [9] , [12] using the amplification and sequencing primers that are described on the MLST website . Sequences for each gene fragment were assembled from at least two independent PCR products , and trimmed to a constant length of 399–501 bp as indicated on the website . All allelic sequences and allelic combinations can be freely downloaded from the website . fliC and fljB were sequenced using the same oligonucleotide primers for PCR amplification and sequencing as previously described [90] , [91] . These primers each yield a ∼1500 bp product , which were trimmed to correspond to positions 73–1344 within the fliC gene and 109–1428 within the fljB gene , as shown in Figs . 6 and S5 . Sequences have been deposited in GenBank under the accession codes HQ871156–HQ871237 ( Table S8 ) . A custom oligonucleotide probe-based array was designed as previously described [92] in order to detect genes related to the absence and presence of SPI-7 . After labelling , probes were purified and applied to microarray slides [93] . Genomic DNA was sonicated to yield 200–500 bp fragments , purified and labelled with Cy3-dCTP using the BioPrime DNA Labelling System ( Invitrogen–BioSciences Ltd . , Dun Laoghaire , Ireland ) . Duplicate slides were hybridized with the dCTP labelled DNAs in 48% formamide at 55oC for 16–20 hrs in a humid chamber . The slides were washed at RT , washed again at 50oC , scanned ( GenepixR 4000B laser scanner , Axon Instruments , Redwood City , Calif . ) and processed ( GenePix Pro 3 . 0 ) . The full dataset was analyzed using R ( www . r-project . org ) , and Bioconductor ( www . bioconductor . org ) as described [94] . In brief , the bimodal distribution that was observed was treated as two overlapping Normal distributions . Means and 95% confidence intervals were determined for each distribution . Probes were scored “absent” if the log2 intensity was within or below the 95% CI for the “low” peak; “present” if the log2 intensity was within or above the 95% CI for the “high” peak and intermediate values were scored as “uncertain” . As a control , PCR tests similar to those described previously [95] were used to screen for presence or absence of larger regions of SPI-7 . Concatenated sequences from all seven gene fragments within 1092 STs were aligned using Mega 4 [96] and analyzed by ClonalFrame [51] , yielding the tree in Fig . S3 and a total of 903 clustered STs in 163 groups . Gene by gene bootstraps [44] were also performed on 1092 STs , except that for each of 1000 iterations , the seven gene fragments used for concatenation were chosen at random from the seven genes , with replacement . UPGMA trees were generated from all 1000 iterations using Paup [97] and a homemade script in Perl ( available on request ) was used to generate a 50% consensus tree based on the percentage support for each branch . 569 branches to individual STs that did not meet these criteria were excluded by this script . dN and dS were calculated on each gene fragment using Mega . UPGMA trees of the fliC and fljB nucleotide sequences and the FliC and FljB amino acid sequences were generated in Bionumerics 6 . 5 ( Applied Maths , Sint-Martens-Latem , Belgium ) , as shown in Figs . 7–8 and S4–S7 . Maximum likelihood topologies of synonymous and non-synonymous sites were calculated using PhyML [98] . A minimal spanning tree was generated from the allelic profiles of isolates using the predefined template in BioNumerics 6 . 5 designated as MST for categorical data , which preferentially joins single and double locus variants with the largest number of isolates per ST . For allelic comparisons , Baps 5 . 3 [49] was applied to the allelic profiles from each ST with an upper bound for group numbers ranging between 300 and 500 . The number of clusters ranged from 215 to 221 as the upper bound increased . The data presented here are based on an upper bound of 400 , which yielded 216 clusters . Baps was also used with allelic differences with an upper bound of 2–7 or with concatenated sequences ( Fig . S2 ) as described in Text S1 . | Microbiologists have used serological and nutritional characteristics to subdivide pathogenic bacteria for nearly 100 years . These subdivisions in Salmonella enterica are called serovars , some of which are thought to be associated with particular diseases and epidemiology . We used MultiLocus Sequence-based Typing ( MLST ) to identify clusters of S . enterica isolates that are related by evolutionary descent . Some clusters correspond to serovars on a one to one basis . But many clusters include multiple serovars , which is of public health significance , and most serovars span multiple , unrelated clusters . Despite its broad usage , serological typing of S . enterica has resulted in confusing systematics , with a few exceptions . We recommend that serotyping for strain discrimination of S . enterica be replaced by a DNA-based method , such as MLST . Serotyping and other non-sequence based typing methods are routinely used for detecting outbreaks and to support public health responses . Moving away from these methods will require a major shift in thinking by public health microbiology laboratories as well as national and international agencies . However , a transition to the routine use of MLST , supplemented where appropriate by even more discriminatory sequence-based typing methods based on entire genomes , will provide a clearer picture of long-term transmission routes of Salmonella , facilitate data transfer and support global control measures . | [
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] | 2012 | Multilocus Sequence Typing as a Replacement for Serotyping in Salmonella enterica |
Understanding how mutator strains emerge in bacterial populations is relevant both to evolutionary theory and to reduce the threat they pose in clinical settings . The rise of mutator alleles is understood as a result of their hitchhiking with linked beneficial mutations , although the factors that govern this process remain unclear . A prominent but underappreciated fact is that each mutator allele increases only a specific spectrum of mutational changes . This spectrum has been speculated to alter the distribution of fitness effects of beneficial mutations , potentially affecting hitchhiking . To study this possibility , we analyzed the fitness distribution of beneficial mutations generated from different mutator and wild-type Escherichia coli strains . Using antibiotic resistance as a model system , we show that mutational spectra can alter these distributions substantially , ultimately determining the competitive ability of each strain across environments . Computer simulation showed that the effect of mutational spectrum on hitchhiking dynamics follows a non-linear function , implying that even slight spectrum-dependent fitness differences are sufficient to alter mutator success frequency by several orders of magnitude . These results indicate an unanticipated central role for the mutational spectrum in the evolution of bacterial mutation rates . At a practical level , this study indicates that knowledge of the molecular details of resistance determinants is crucial for minimizing mutator evolution during antibiotic therapy .
Despite their increased load of deleterious mutations [1] , [2] , mutator strains of bacteria are isolated routinely in laboratory and clinical settings [3]–[8] . Theory [9]–[11] and experiments [5] , [12] , [13] explain these observations as a consequence of genetic hitchhiking , whereby mutator alleles reach high frequency by being co-selected with linked beneficial mutations . Mutator evolution is therefore dependent on the absence of horizontal gene transfer [14] and the availability of adaptive mutations with substantial fitness effects [11] – conditions frequently met during adaptation to a host or during antibiotic therapy , which have been invoked to explain the prevalence of mutators among pathogenic bacteria [15]–[17] . Whereas genetic hitchhiking provides a satisfactory mechanism to explain how mutator bacteria can be selected , the precise mechanistic details of this process are still a matter of research . Some authors emphasize that mutator fixation involves many consecutive hitchhiking events , prompted by frequent environmental shifts [9] , [18] , [19] or by the concurrence of multiple beneficial mutations [10] , [11] . Other authors , in contrast , consider mutator success as primarily the result of a single step in which one hitchhiking event takes mutator frequency from rareness to fixation [20] . Despite this theoretical effort , two major observations remain to be accounted for . First , although hitchhiking probability is predicted to increase with the extent of adaptive opportunity offered by the environment ( i . e . , the number and effects of available beneficial mutations ) [14] , [20] , , selection experiments with comparable adaptive opportunity report contrasting frequencies for mutator emergence [4] , [12] , [22] , [23] . Second , mutators of different strength are predicted to emerge according to the degree of adaptive opportunity [14] , [21]; nonetheless , both clinical and laboratory observations show a marked bias towards strong mutators , particularly those caused by defects in the mismatch repair system ( MMR ) [17] , [24] . To explain these deviations , it is argued that mutator alleles of other mutator strengths might be under-represented due to putative fitness disadvantages , and that MMR mutator mutants might be over-represented because they can also be selected for their high recombination rates [24] . An additional , previously unrecognized factor is that each mutator exhibits its own mutational spectrum , that is , mutation rate increase is limited to characteristic types of mutations [25] . This bias stems from the type of mutation avoidance mechanism that is altered in each mutator genotype; depending on which mechanism is affected , its failure will lead to a preferential increase in specific types of transitions , transversions or frameshifts [25] . In line with previous suggestions [12] , [26] , we hypothesized that if mutators and wild-type strains have differential access to specific beneficial mutations , they might produce mutants with different fitness levels , which could influence their evolutionary dynamics . Mutator alleles that on average generate stronger beneficial mutations will have a better chance to achieve fixation; conversely , those that more often produce weaker adaptive mutations will have limited spread . The fixation probability of a mutator allele would thus depend not only on its mutation rate , but also on its mutational spectrum . Here we test these predictions by characterizing beneficial mutations from wild-type and knockout strains of the mutT ( ΔmutT ) and mutY ( ΔmutY ) antimutator genes of Escherichia coli . mutT-defective strains show a strong mutator phenotype , leading specifically to A·T→C·G transversions [25] , whereas mutY defects lead to a moderate mutator phenotype that increases G·C→T·A transversions [25] . Both mutators were reported to arise spontaneously in evolution experiments with E . coli [4] , [12] . Since beneficial mutations are exceedingly rare and difficult to detect , our experimental design focused on mutations that confer antibiotic resistance , which are particularly suitable for this kind of study . We show that according to their mutational spectra , wild-type and mutator strains can generate distinct fitness distributions for antibiotic-resistant mutants . Notably , these dissimilarities can significantly alter the competitive abilities of mutators against the wild-type strain , as measured in direct competition experiments . Furthermore , using computer simulation of a simple population genetics model , we show that the hitchhiking dynamics is highly sensitive to average fitness deviations of the beneficial mutations with which mutator alleles hitchhike .
To test whether mutational spectrum differences translate into significant fitness differences , beneficial mutations were selected by plating cultures of wild-type , ΔmutT and ΔmutY strains in three antibiotics: rifampicin , streptomycin and tetracycline . Rifampicin- and streptomycin-resistant mutants showed marked colony size polymorphism ( Figure 1A ) . Resistance to these antibiotics arises readily through alterations in their targets , the RNA polymerase [27] and the 30S ribosomal subunit [28] , respectively . Since many alterations can affect the function of these essential machineries to different degrees , it is unsurprising that resistance mutants show growth differences . Consistent with the predictions of our hypothesis , wild-type and mutator strains displayed distinct colony size polymorphism in each antibiotic ( Figure 1A ) . To test whether access to beneficial mutations with varying selection coefficients was the only factor responsible for differences among strains , we used strains with an insertion bearing a tetracycline-resistance gene and its constitutive repressor [29] . As the probability of achieving resistance to high tetracycline concentrations through point mutation in E . coli is negligible ( mutant frequency <10−9 ) , resistance arises here only through loss-of-function mutations , which relieve the repression . All of these loss-of-function mutations can be considered equivalents , and no fitness differences are thus anticipated among tetracycline-resistant mutants . Indeed , these mutants showed little growth polymorphism ( Figure 1A ) . Small differences , apparently independent of the mutator background , probably reflect phenotypic lag or other sources of phenotypic variability . To quantify the fitness distribution of the mutants generated by each mutator , we randomly selected 42 independent resistant colonies from each combination of genotype and antibiotic , and measured growth rate as a proxy for Darwinian fitness . It is important to remark that we are dealing with the fitness distribution after selection , not the intrinsic fitness distribution; and so it should be understood in what follows . In rifampicin , the distribution of mutants generated by the ΔmutY strain is shifted toward higher growth rates compared with both those of wild-type and ΔmutT strains ( Figure 1B , centre ) , whereas in streptomycin the situation is the reverse ( Figure 1B , right ) ( n = 42 , P<0 . 0001 , Kolmogorov-Smirnov's one-sided two-sample test , in all cases ) . These results suggested that G·C→T·A substitutions in the rpoB gene ( the characteristic transversion increased in this mutator ) produced alleles encoding a high-fitness rifampicin-resistant RNA polymerase; coincidentally , the same transversion generated rpsL alleles encoding a low-fitness streptomycin-resistant ribosomal protein S12 . Similar reasoning could be applied to the ΔmutT strain results , which preferentially raises the A·T→C·G transversion . In the case of tetracycline , no significant differences were found between wild-type and any mutator strains ( Figure 1B , left ) ( n = 42 , P>0 . 18 , Kolmogorov-Smirnov's two-sided two-sample test ) . To confirm our interpretation , we randomly picked 10 colonies from each combination of antibiotic and strain , sequenced their rpoB and rpsL genes , and measured growth rates ( Figure 2 ) . Several G·C→T·A substitutions found in rpoB can explain the higher fitness of rifampicin-resistant ΔmutY mutants , supporting our hypothesis . The highest-fitness class ( Figure 1B , centre ) is likely to be composed of V146F mutants , the fastest-growing mutant detected ( Figure 2A ) ; other high-fitness mutations that resulted from this transversion were H526N and S531Y . The fitness distribution in rifampicin-resistant ΔmutT mutants can be explained , at least in part , by an A·T→C·G substitution that produces the low-fitness mutation Q513P . Among streptomycin-resistant mutants , idiosyncratic transversions in rpsL similarly help to explain the fitness differences . The low fitness of streptomycin-resistant ΔmutY mutants probably reflects predominance of the P90Q mutation ( 8/10 mutants tested ) , whereas the high fitness of ΔmutT mutants might be due to prevalence of the K42T mutation ( 9/10 ) . As a brief remark , 3/10 of the streptomycin-resistant mutants in the wild-type background carry the substitution P90L . This mutation is known to prevent growth in the absence of the antibiotic [28] , and offers a suggestive example of how spectra can determine the access to mutations with differences not only in fitness , but also in other related properties . The biochemical bases of the fitness cost of both rifampicin and streptomycin resistance have been discussed elsewhere . The costs of rpoB mutations are explained by the impairment of the transcription activity of the RNA polymerase [30] , [31] . Similarly , it has been long established that rpsL streptomycin resistance mutants exhibit hyperaccurate translation , resulting in a slower rate of protein synthesis and consequently , in a slower growth rate [32] . It is tempting to assume that if the fitness distribution of a given mutator is altered compared to that of wild-type , the average fitness of that mutator will change accordingly . These distributions nonetheless have distinct shapes and degrees of overlap ( Figure 1B ) ; it is thus of interest to determine the extent to which these differences translate into an overall fitness difference between each mutator and the wild-type genotypes . Direct competition experiments between mutators and wild-type strains in rifampicin and streptomycin showed significant differences in mean fitness ( Figure 3 ) ( n = 4 , P<0 . 012 , Mann-Whitney U-test , one-sided , all cases ) , confirming the predictions made by visual examination of Figure 1A . Our data provide evidence that in a specific environment , distinct mutators generate their own fitness distribution among newly-arising mutants , which can influence their competitive ability . Theory predicts the fixation probability of a mutator allele to be dependent on the selection coefficient of the driver allele with which the mutator hitchhikes [11] , [20] . According to our results , this coefficient can vary substantially depending on the mutational spectrum; the mutational spectrum should thus have some effect on the hitchhiking dynamics . To estimate how large this effect could be , we used simple computer simulations based on previous studies [10] , [11] , [20] . Briefly , we simulated the basic scenario of a non-mutator bacteria population growing in batch culture , where only one beneficial mutation is needed to achieve full adaptation . Mutators are generated at a constant rate , and produce the adaptive mutation with a 100-fold higher probability than the wild-type strain . Once the mutation is fixed in either background , the simulation ends . The mutational spectrum effect ( σ ) was introduced as a multiplicative factor to modify the selection coefficient ( s ) of the driver allele only on the mutator background ( see Material and Methods ) . The simulations showed that σ exerts a modest influence on the establishment of mutator genotypes bearing the adaptive mutation ( i . e . , on the probability that they escape random drift ) ( Figure 4 ) . This effect is not surprising , as the probability of a beneficial mutation surviving drift is approximately 2s [33] . In contrast , σ had a notable effect on the fate of established genotypes en route to fixation . In the absence of mutational spectrum effects ( σ = 1 ) , a mutator genotype bearing the adaptive mutation can only succeed if it reaches fixation before any adapted wild-type bacteria escapes drift; it will otherwise always be outcompeted due to its increased deleterious mutation load ( Figure 5 , upper row ) . When the average s of the driver allele is lower on the mutator background ( σ<1 ) , there are no qualitative changes . Success is further hindered because drift is more intense and time to fixation is longer , extending the period available for the establishment and subsequent selective sweep of an adapted wild-type strain . In contrast , when σ>1 a threshold appears above which the population dynamics switches . This threshold is determined by the value of σ that offsets the increased deleterious load of mutator genotypes . Above this value , the adapted mutator is fitter than its wild-type counterpart , and therefore needs only to escape drift to reach fixation ( Figure 5 , lower row ) ; as a consequence , fixation probability rises sharply ( Figure 4 ) . It is worth noting that , since the deleterious load is as small as the order of magnitude of the mutation rate [33] , only a slight spectrum-dependent fitness advantage is needed to substantially increase mutator success . Remarkably , the non-linear response of fixation probability to σ implies that previous models could have been underestimating the likelihood of mutator success by several orders of magnitude . This is clearly illustrated in Figure 4 where a change from σ = 0 . 56 to σ = 1 . 33 , which is equivalent to a change in relative fitness [37] from w = 0 . 96 to w = 1 . 03 , represents a ∼196-fold increase in fixation probability . Selection of mutators has been understood exclusively in terms of the increased number of beneficial mutations they generate [5] , [10] . Here we show that not only the number but also the type of these linked mutations are relevant . Our results indicate that mutator alleles can bias the average selection coefficient of the beneficial alleles with which they hitchhike . Besides , they suggest that the magnitude of this effect can easily be sufficient to drastically modify their probabilities to reach fixation . The only requirement for this bias is that the locus or loci under selection produce mutants with some variability for fitness . This is a fairly permissive condition , likely to be satisfied in several adaptive scenarios . Examples in which resistant mutants with varying degrees of fitness are commonly found include bacteriophage [34] and antibiotic resistance [35] , considered major drivers of mutator evolution [8] , [15] . It will be of interest to determine whether the observed relative abundance of each mutator is explained , at least in part , by the effect of its mutational spectrum on successive mutations undergone during adaptation . In the context of clinical infections , future work should address the extent to which antibiotic therapies show different propensities to select for mutator bacteria . Since mutators are recognized as a risk factor for treatment failure [15]–[17] , this knowledge could help to improve the design of safer therapeutic strategies . The fact that even slight mutational spectrum effects markedly alter hitchhiking , together with the apparent commonness of circumstances that potentially allow this to happen , lead us to conclude that the mutational spectrum is a major factor in the evolution of mutators in laboratory and clinical populations of bacteria [3]–[8] , as well as in certain cancers [36] .
Strains are E . coli MG1655 derivatives , obtained from Dr . I . Matic [29] . Bacteria were grown on Luria broth ( LB ) or LB agar plates ( 37°C ) . Antibiotics used were tetracycline ( 15 mg/L ) , rifampicin ( 100 mg/L ) and streptomycin ( 100 mg/L ) . Incubation time was 24 h , except for streptomycin plates ( 42 h ) . Overnight cultures of each strain were plated on each antibiotic at appropriate dilutions to ensure low colony density ( ∼50/plate ) . After incubation , independent colonies were picked at random ( by proximity to an arbitrary point ) and resuspended in saline solution . Population size ( N ) was estimated from viable counts by subsequent dilution and plating . Assuming that each colony originated from a single cell , generation number was calculated as log2N and the growth rate expressed as number of generations per hour . Note that this measurement integrates growth rate over all growth phases . Overnight cultures of each mutator and wild-type strain were mixed at a ratio based on their mutation rates , plated on antibiotic , and allowed to compete for 24 or 42 h . Initial competitor frequency was calculated by estimating the resistant-mutant frequency of each overnight culture . To distinguish them from the wild-type bacteria , mutators carried antibiotic resistance markers [29] , which entailed no significant fitness cost in these conditions ( n = 4 , P>0 . 28 , Mann-Whitney U-test , two-sided , both cases ) . After incubation , agar plates were washed in saline solution and the final mutator∶wild-type ratio obtained by plating on LB agar and selective media . Relative fitness was estimated using a standard formula [37] . | Natural and laboratory populations of bacteria can readily give rise to strains with high mutation rates . The evolution of these mutator bacteria—of particular concern in clinical situations—has been understood exclusively in terms of their increased capacity to generate beneficial mutations , such as those that confer antibiotic resistance . Current models , however , have largely overlooked that each mutator allele increases only characteristic types of mutations , a prominent fact whose evolutionary consequences remain unexplored . Using laboratory Escherichia coli populations , we show that this mutational bias determines the competitiveness of different mutators across environments . Computer simulation showed that this effect can markedly influence the evolutionary fate of mutator alleles . These results indicate that this unrecognized factor can be a major determinant in the evolution of mutator bacteria and suggest future experimental approaches for improving antibiotic therapy design . | [
"Abstract",
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] | 2013 | Mutational Spectrum Drives the Rise of Mutator Bacteria |
Set point viral load in HIV patients ranges over several orders of magnitude and is a key determinant of disease progression in HIV . A number of recent studies have reported high heritability of set point viral load implying that viral genetic factors contribute substantially to the overall variation in viral load . The high heritability is surprising given the diversity of host factors associated with controlling viral infection . Here we develop an analytical model that describes the temporal changes of the distribution of set point viral load as a function of heritability . This model shows that high heritability is the most parsimonious explanation for the observed variance of set point viral load . Our results thus not only reinforce the credibility of previous estimates of heritability but also shed new light onto mechanisms of viral pathogenesis .
The time course of viral load in HIV infected patients follows a characteristic pattern . During primary infection the viral load rapidly grows to very high levels . The peak viremia is attained within the first few weeks of infection . Thereafter the viral load declines rapidly over a period of several months and eventually settles down at a much lower level referred to as the viral set point . Set point viral load ( spVL ) is a central characteristic of the course of the disease . Firstly , the virus load measurements do fluctuate in patients , the time average of the viral load remains remarkably close to the spVL in most of patients over the time scale of several years [1 , 2] . Secondly , higher spVL is associated with faster disease progression [3] . The stability of spVL within patients is in strong contrast to the enormous variation in spVL observed between patients . While variation in spVL between patients ranges over 3–4 orders of magnitude [3–6] , the time trend over longitudinal viral load measurements typically changes by less then 0 . 1 log per year [1 , 2] . Given that spVL is a key predictor of disease progression , there is considerable interest in identifying the host and viral genetic factors underlying the variation in spVL . A well known example for the influence of naturally occurring variation in human genetic factors on viral load is the Δ32 deletion in the CCR5 gene [7] . Moreover polymorphisms in HLA-B and C alleles have been associated with variance in virus load and genome-wide association studies ( GWAS ) showed that about 20% of the variance in log spVL can be attributed to specific single nucleotide polymorphisms [8–11] . 20% is likely a lower bound for the overall contribution of host genetic factors , because GWAS generally suffer from the problem that they can only identify common genetic variants with strong effects and do not account for epistatic effects between host genes [12] . Natural variation in the virus can also affect spVL . For example the transmission of a nef-deficient virus through a contaminated blood sample resulted in a low viral load in the recipients [13] . Moreover , several studies have reported a correlation between predicted replicative capacity and viral load [14–16] . As this prediction is based only on the viral genotype a patient carries , this implies that naturally occurring variation in viruses does affect viral load . A number of recent studies attempted to estimate the contribution of the viral genotype to the variation in spVL by quantifying the statistical association of viral load between donors and recipients either directly in donor-recipient pairs or through phylogenetic analysis [17–22]; for reviews see Müller et al . [23] and Fraser et al . [24] . A meta-analysis of previously published donor-recipient studies correcting for various co-factors such as age and sex yielded a heritability of 33% with a 95% confidence interval of 20–46% [24] . The two studies that inferred heritability based on phylogenetic methods provided the most extreme estimates with 5 . 7% reported by Hodcroft et al . [22] and 59% reported by Alizon et al . [21] . While the phylogenetic approaches have an advantage over the donor-recipient based approaches in that they can use much larger patient populations , it is currently unclear to what extent the underlying assumptions of the phylogenetic approaches of no selection and high frequency of sampling affect the robustness of these results . The discrepant estimates call for a better quantitative understanding of the underlying factors determining heritability of log spVL in HIV . To this end we develop here a quantitative model that describes the change of the distribution of log spVL in a patient population in relation to heritability over a full transmission cycle . The model extends the approach of Shirreff et al . [25] and is similar in spirit to the integral projection models in ecology that are used to describe the temporal changes of distributions of a continuous phenotypic trait in populations [26–28] . In contrast to many applications in ecology , the application to distributions of log spVL has the advantage that all relevant processes and populations for which data are available , are numerically well approximated by a Gaussian function . This fact enables us to obtain complete analytical understanding of how spVL changes through time based on a model parametrized by available data .
We consider the change of the spVL distribution over one full reproduction cycle on the epidemiological level , i . e . from the current to the next generation of patients . We divide the patient population into “carriers” ( HIV infected individuals prior to selection for transmission ) , “donors” ( individuals that have been selected for transmission ) and “recipients” ( individuals that have just been infected by donors ) . Furthermore , we divide the reproduction cycle into three steps: ( i ) selection of donors from the carriers with replacement according to their transmission potential , ( ii ) transmission from donors to recipients , and ( iii ) intrahost evolution of the virus from the start of infection to the next transmission . Finally , we explicitly distinguish between factors contributing to set point viral load with regard to being transmissible ( i . e . viral genetic factors ) versus being non-transmissible ( i . e . host genetic factors , environmental factors , or any interaction between host , virus , and the environment ) . A schematic overview over the effects of these steps on the distribution of log spVL is shown in Fig . 1 . In the Supplementary Materials we show how the change of the spVL distribution can be computed for any distribution over a full transmission cycle . If all populations and processes are well approximated by Gaussian functions , then an approximation to the resulting log spVL distributions can be computed analytically ( see Methods and Supplementary Materials ) . Assuming that the population is in equilibrium we obtain for the mean , M̃C , variance , ṼC , and heritability , h2 , the following expressions: M ˜ C = μ o + μ i ( 1+ ν e + ν o V ˜ C − ν e ) , ( 1 ) V ˜ C = ν t + ν i 2 ( 1+ 1+4 ν e + ν o ν t + ν i ) + ν e , ( 2 ) h 2 =1− ν e V ˜ C . ( 3 ) Here the parameters μo and νo characterize the transmission potential [6] , i . e . the overall probability of a patient to transmit the infection as a function of log spVL ( Fig . 1 ( o ) – ( i ) ) . This transmission potential is given by the product of the rate of transmission per contact and the disease duration . As the former increases and the latter decrease with increasing spVL , the transmission potential has a maximum at intermediate levels of spVL [6] . The parameter νe gives the variance of the contribution of host/environmental effects on log spVL . The parameter νt describes the variance due to the bottleneck at transmission from donor to recipient , as a founder strain is selected randomly from the diverse population in the donor ( Fig . 1 ( i ) – ( ii ) ) . The parameters μi and νi describe the mean and variance of the contribution of intrahost evolution to log spVL ( Fig . 1 ( ii ) – ( iii ) ) . Our model assumes that the bottleneck at transmission is neutral with regard to selection on set point viral load . Note , that the assumption is without loss of generality . This is important because there is evidence for selection at transmission [29] , although it is unclear whether selection acts on spVL . Any selective effect at transmission , however , can be subsumed into the parameter μi . Hence , the effect of selection is effectively incorporated in our model . The parameter for the mean contribution by the host/environment , μe , does not appear in equations 1 or 2 . This is because the equations refer to the phenotypic value of spVL , i . e . the sum of the genetic contributions of the virus and the contributions from the host/environment . Any large environmental/host effect on the mean can always be compensated by correspondingly strong genetic effect of the virus on the mean but with opposite sign . The above results are applicable if , ( i ) if the population is approximately in equilibrium , and ( ii ) all populations and processes are numerically well approximated by Gaussian functions . Assumption ( i ) has been discussed in detail previously [6 , 25 , 30 , 31] . In essence , this assumption is supported by three observations . Firstly , the mean of the spVL distribution coincides with the optimum of the transmission potential ( see Fig . 2 and Fraser et al . [6] ) . Secondly , the rate of change of spVL has decreased over the last 25 years [31] . Thirdly , the rate of evolution is sufficiently rapid such that a spVL that is optimal for transmission could have evolved over the course of the epidemic [25] . These findings suggest that the distribution of set point viral load is indeed approximately in equilibrium , which in turn makes it is plausible to assume that the environmental and genetic factors determining set point viral load are also in equilibrium . Regarding assumption ( ii ) , we note that a Gaussian function describes a distribution or process by a main effect ( mean ) and some variational noise ( variance ) . Thus in absence of any better knowledge , a Gaussian distribution is a natural starting point to describe any process and simply represents a second order approximation to an unknown distribution . We can assess the validity of describing the distributions of spVL in carriers and the transmission potential graphically using available data . Inspection of Fig . 2A and Figure S1 in Supplementary S1 Text shows that the viral load amongst carriers is indeed numerically well approximated by a Gaussian with mean log spVL , MC ≈ 4 . 5 , and variance in log spVL , VC ≈ 0 . 5 . Also the fit of a Gaussian to the transmission potential ( see Fig . 2B ) is a very good approximation ( mean μo ≈ 4 . 6 and variance νo ≈ 1 . 0 ) , even though the transmission potential as estimated by Fraser et al . [6] is slightly right-skewed . There are no data to inform the shape of the processes of transmission and intrahost evolution . Using a description that has a mean effect with some variation around this mean is natural . Nonetheless , we test the effect of numerical deviations from a Gaussian with the following simulations . Firstly , we use the exact right-skewed transmission potential as given by Fraser et al . [6] . The analytical approximations for the distribution of the population in equilibrium remain excellent when the substantial deviations of the transmission potential from a Gaussian are incorporated ( see Figure S2 ) . Secondly , we study the robustness towards deviations from Gaussian functions in the processes describing intrahost evolution and the transmission bottleneck . Even when both processes are strongly skewed , the analytical approximations for mean and variance are excellent ( typically less than 2% deviation , see Figure S3 in Supplementary S1 Text ) . To assess what heritability values are compatible with the observed mean and variance of log SPVL in the carrier population we take a simple approach that is in essence Approximate Bayesian Computing with rejection sampling . To this end we define plausible prior distributions for the parameters of the model . Sampling randomly from the priors we determine the resulting means and variances of log spVL in carriers and reject sets of parameters that lead to means and variances outside a defined permissible range . The set of accepted parameters gives the posterior distribution . For the range of permissible mean log spVL we assume 4 < M̃C < 5 , which is compatible but somewhat larger than the observed range in the studies reported by Fraser et al . [6] and Geskus et al . [5] ( see Fig . 2A and Supplementary Materials , Section E ) . For the permissible range of variances of log spVL we assume that 0 . 3 < ṼC < 0 . 8 , which again is compatible but somewhat larger than the values reported by Fraser et al . [6] and Geskus et al . [5] ( see Supplementary Materials , Section E ) . We use uniform priors for all parameters . The parameters μo and νo , which describe mean and variance of the transmission potential , have thus far only been estimated only by a single peer reviewed study ( Fraser et al . [6] and Fig . 2B; see also [32] ) . To account for uncertainty in the estimates of these parameters we use 4 < μo < 5 and 0 . 5 < νo < 1 . 5 . Estimates for remaining parameters cannot be easily derived from the existing literature . To account for uncertainty in these parameters we assume 0 < νe < 1; −1 < μi < 1; 0 < νi < 0 . 3 and 0 < νt < 0 . 3 . Fig . 3 shows the posterior parameter distribution from the rejection sampling . Different colors in the scatter plots indicate different levels of mean heritability at given parameter combinations . The contour lines show the density of posterior distribution . The key result shown in the figure is that the majority of accepted parameter values result in high values of heritability ( purple to orange color at contour lines of highest posterior densities ) . While low values of heritability are also compatible with the observed mean and variance of log spVL , they occur rarely in the posterior sample and are at the edges of the prior distributions ( red to blue areas ) . The center of mass of the posterior sample is in areas with high heritability , higher in fact then what would seem compatible with current estimates of heritability and host genetic factors . There are two factors not included in this analysis: measurement error of spVL and prior knowledge of the host contribution to spVL . Increasing the measurement accuracy of spVL would increase heritability estimates based on both donor-recipient pairs and phylogenetic inference . Incorporating prior knowledge of the host genetic contribution would set an upper bound on the estimates of heritability in our analysis . Thus accounting for these two factors bring the center of mass of the heritability distribution closer to the measured values of heritability . The figure also highlights that generally wider priors would not change the posterior distribution because parameter values at the upper end of priors are never accepted . The change of the mean capacity of the virus to induce spVL through intrahost evolution , μi , is restricted to values smaller than 0 . 6 and decreases with increasing variance generated by the host/environment effects , νe . Increasing νe corresponds to decreasing heritability ( see eq . 3 ) and thus high levels of μi require high levels of heritability . The center of mass of the posterior sample suggests that the most parsimonious explanation of the observed mean and variance of log spVL implies both small intrahost evolution and high heritability . One criticism leveled against the transmission potential as quantified in [6] is that it does not appropriately reflect transmissions occuring during the acute or the AIDS phase . In the Supplementary Material , Section F . 3 , we show that our quantitative results are robust towards using a corrected transmission potential .
The above analysis shows that the most parsimonious explanation of the observed distribution of spVL in HIV carrier populations requires high heritability of spVL . Although low heritability values are also compatible with the observed distribution of spVL in HIV carrier populations , parameter combinations resulting in these low values have a small probability and occur at the edge of the realistic parameter range . The skepticism with which the estimated heritability values have been met in the field suggests that the general expectation is that heritability of spVL should be low . In contrast , our analysis shows that high heritability values are not only compatible with , but are also the more parsimonious explanation of the observed distribution in spVL in HIV carrier populations . Low heritability only occurs if the processes of intrahost evolution and the transmission bottleneck have a weak effect on spVL , i . e . if the parameters μi , νi and νt are small . An intuition can be obtained by noting that in equilibrium the variance generating and variance eliminating processes balance out . The transmission potential only exerts weak selection on log spVL and therefore only marginally reduces variance . The decrease of variance by selection for transmission has to be compensated by an increase in variance by intrahost evolution and the transmission bottleneck . For too low heritability , the genetic variance generated by intrahost evolution and transmission bottlenecks would overwhelm the reduction of variance due to selection by the transmission potential . While there are to our knowledge no data that allow to estimate the variance generated at transmission , νt , the posterior distributions of μi and νi are broadly compatible with the observed changes of virus load within patients [1 , 2 , 33] . Taken together our analysis suggests that the most parsimonious explanation of the distribution of log spVL is high h2 but low νt , μi and νi . Hence , heritability is high while the processes of intrahost evolution and transmission bottleneck have a small effect on the capacity of the virus to modulate log spVL . High heritability implies a substantial genetic control of the log spVL by the virus . The observation that at the same time the contribution of intrahost evolution to spVL is small raises an interesting question: How can a strongly heritable trait show little intrahost evolution ? Given the otherwise ample evidence for rapid intrahost evolution of HIV such as escape from drugs or the immune response , the absences of intrahost evolution of spVL is surprising . Generally a trait is expected to respond to selection , if ( i ) the trait is heritable , ( ii ) there is phenotypic variation of the trait in a population , and ( iii ) the trait is linked to fitness . That spVL is heritable has been reported previously [23 , 24] and our analysis reinforces the credibility of these findings . That there is phenotypic variation in the control of spVL by the virus is plausible given the large genetic variation of the virus population within an individual . What remains is whether it is conceivable that the capacity of a viral genotype to induce spVL is only weakly linked to fitness . One hypothesis that could reconcile high heritability with little intrahost evolution is that variation in viral load between patients is in part due to virus-induced activation of target cells . Difference in activation rate of target cells has previously been argued to account for a substantial part of the variation in viral load [4] . Furthermore , if target cell activation is at least partially under the control of the virus , then this control may indeed be weakly linked to intrahost fitness . If the target cell activation is systemic ( i . e . not locally confined to the inducing virus ) then increased target cell activation increases the pool of susceptible cells , but the benefit of increased target cell activation is not confined to the producer virus . As a result selection for virus induced activation rate is expected to be neutral or nearly neutral [34] . Indeed , an explicit model of the evolution of log spVL for a virus induced control of target cell activation can reconcile high heritability with absence of intrahost evolution [35] . Our modeling approach is based on describing how the distribution of a continuous phenotypic trait , here log spVL , changes in a population over a full cycle of reproduction . This approach is closely related to the method of integral projection models , which has been developed and widely applied in ecology and population biology [26–28 , 36 , 37] . The approach can in principle describe how arbitrary distributions change over time as a function of processes such as selection and reproduction . Here we are able to obtain a full analytical description of the temporal change of the spVL distribution , because all relevant distributions and processes can be well approximated by Gaussian functions . We also show that our analytical results remain robust even for substantial deviations numerical deviations from Gaussian functions ( see Supplementary Materials , Section F ) . Moreover , the model can be parametrized on the basis of available data . There are ample data for mean and variance of spVL and also most of the parameters can be confined to plausible ranges based on the literature . Our study clearly supports that high heritability is compatible with the observed distribution of log spVL in HIV carriers . High heritability of spVL does not preclude that also the host genotype has a considerable effect on virus load . However , it does lead to the expectation that over the course of infection the capacity to induce higher spVL should increase considerably unless this capacity is only weakly linked to intrahost fitness . This sheds new light onto the mechanisms controlling viral load . There should be identifiable genetic variation in the virus population that is associated with viral load , and moreover , the loci associated with control of viral load should be weakly linked to intrahost fitness . Genome-wide association studies mapping viral genetic polymorphisms to variance in log spVL seem a natural approach to test this prediction . A recent study by Bartha et al . [38] was unable to identify any statistical associations , but was powered only to detect individual non-synonymous mutations with an effect size of >4% on heritability . Larger studies will thus be required to identify whether and which viral polymorphisms are associated with set point viral load .
The spVL in a patient is generally determined by viral genetic factors , host genetic factors , the environment and interactions between these factors . Since only the virus is transmitted from donors to recipients , we subsume all non-transmissible effects such as the host genetic factors , environmental effects and all interactions between host , virus and the environment generically under “environmental effects” , e . The transmissible effects due to the viral genotype are the “genotypic effects” , g . The “phenotype” spVL is then given by g+e . We assume that the distribution of log spVL in the carrier population is given by a normal distribution N ( M C , V C ) , where MC and VC are the mean and variance , respectively . The transmission potential , defined as the overall probability of transmission of an HIV carrier integrated over the entire course of the disease , is assumed to be a function of log spVL which can be well approximated by a normal distribution N ( μ o , ν o ) ( see Fig . 2 ) . Here μo is the log spVL at which the transmission potential is maximal and νo characterizes how strongly the transmission potential selects for transmission at μo . We assume that g and e are independent and normally distributed in the carrier population with N ( m C , v C ) and N ( μ e , ν e ) , respectively . Here mC and vC are the variables that describe the mean and variance of the distribution of viral genotypes in the carrier population . Note that here the independence of g and e refers to the quantitative contribution of virus and host to spVL . Importantly , this independence does not imply an absence of virus genotype by host genotype interactions , such as an interaction between a particular viral epitope and a host HLA molecule . Genotype by genotype interactions are non-transmissible and thus subsumed in e . The parameters μe and νe describe mean and variance of the distribution of environmental effects , which comprise host effects , interactions and any non-transmissible effect . The distribution of phenotype log spVL in the carrier population is then given by a normal distribution with mean and variance , M C = m C + μ e , and V C = v C + ν e . ( 4 ) Selection for transmission acts on log spVL , i . e . on the sum of the genotypic and environmental effects , and is given by the transmission potential . Specifically , the probability of transmission for a given log spVL , ϕ , is given by ( see Fig . 2B ) , S ( ϕ ) = 1 2 π ν o e - ( ϕ - μ o ) 2 2 ν o . ( 5 ) Applying the above transmission potential to the carrier population , we find that the genotype and phenotype in the donor population are again normally distributed ( see Supplementary Materials , Equations B6 and B7 ) . The donor genotype has mean and variance , m D = m C ( ν e + ν o ) + ( μ o - μ e ) v C v C + ν e + ν o , and v D = v C ( ν e + ν o ) ν e + ν o + v C . ( 6 ) The donor phenotype has mean and variance ( see Supplementary Materials , Equations B8 and B9 ) , M D = M C ν o + μ o V C ν o + V C , and V D = V C ν o ν o + V C . ( 7 ) Note , that the mean and variance of the environmental effects ( i . e . the host effect ) is not given by the differences between the phenotypic and genotypic values , because environment and genotype in the donors are correlated . This is because selection for transmission acts on the sum of environmental and genotypic effects . In other words selection for transmission selects a subset of viral genotypes and host genotypes , and host and viral genotypes are correlated , because selection operates on their combined effect . When the virus is transmitted from the donor to the recipient population , the virus is “harvested” from a non-random distribution of environmental effects ( and thus also from a non-random set of hosts ) . The harvested virus is then redistributed over a random set of new hosts/environments in the recipient population . Thus all environmental effects in the donor population are erased at transmission and the environmental contribution in the recipients is redrawn from N ( μ e , ν e ) . To account for the fact that the virus population experiences a strong bottleneck from recipient to donor , we assume that the viral genotype is not transmitted exactly from donor to recipient but instead is assumed to be randomly drawn out of a distribution of genotypes in the donor patient . Assuming that this distribution is normal with mean mD and variance νt we obtain that both genotype and phenotype in the donor population are normally distributed . The recipient genotype has mean and variance , m R = m D , and v R = v D + ν t . ( 8 ) The recipient phenotype has mean and variance , M R = m R + μ e 0 , and V R = v R + ν e 0 , ( 9 ) where μ e 0 and ν e 0 are the mean and variance of the host/environmental effects prior to infection . The environmental effects are redrawn randomly , because they are not inherited from one transmission to the next . Note , that we assume here that the bottleneck at transmission is neutral . This assumption does not imply that there is no selection at the transmission stage , but rather that the bottleneck is neutral with regard to the spVL that the transmitted strains will eventually cause . Any selection at and after transmission on the viral genotypic contribution to log spVL is subsumed in the next step , intrahost evolution . After transmission the virus population in the recipient may change in a directed fashion according to intrahost evolution . Assuming that the overall change of the viral genotype due to intrahost evolution can be approximated by a normal distribution we find that the distribution of genotypes and phenotypes in the next generation of carriers , C′ is again normal . The distribution of the genotypes has a mean and variance m C ' = m R + μ i , and v C ' = v R + ν i . ( 10 ) The parameter μi thus describes any genetic change in the virus that affects log spVL across all patients in the same way . The parameter νi describes genetic changes that affect log spVL in a manner that is specific to the patient , i . e . it describes the effect of changes of log spVL due to genetic interactions between virus and host . As the environmental effects comprise the immune response by the host , the mean and variance in environmental effects may change in coevolution with the virus through μ e i and ν e i , respectively . Thus we obtain for mean and variance of the distribution of phenotypes , M C ' = m C ' + μ e 0 + μ e i , and V C ' = v C ' + ν e 0 + ν e i . ( 11 ) Note , that any selection for spVL at the transmission bottleneck can now be interpreted as a genotypic change that occurs during intrahost evolution . Thus the overall model is appropriate both for non-selective and selective bottlenecks . Heritability , h2 , is defined as fraction of genotypic variance relative to phenotypic variance in the carrier population [39] . Thus we have , h 2 = v C V C = 1 - ν e V C . ( 12 ) Heritability can be estimated in a parent-offspring regression [39] , where h2 is equal to the regression slope b . Donor-recipient pairs can be seen as parent-offspring pairs , where care must be taken since the donors are not randomly selected from the carrier population but are selected according to the transmission potential . Since , however , we are measuring the heritability of spVL and donors are selected based on spVL , the regression of recipients on selected donors is equal to heritability of spVL in carriers [24 , 39] . We now have a complete analytical description how mean and variance of log spVL change from the current to the next generation of carriers . The fact that the log spVL that maximizes the transmission potential and the mean of the distribution of log spVL in the carrier populations ( see Fig . 2 and Fraser et al . [6] ) are both around 4 . 5 , we can assume that the process is roughly at equilibrium . In equilibrium we have that the mean and variance of the distribution of phenotypes does not change , i . e . MC′ = MC and VC′ = VC . This will be fulfilled if the genetic and environmental contributions are also at equilibrium , implying in particular that μ e = μ e 0 + μ e i and ν e = ν e 0 + ν e i ( see Supplementary Materials section C . 1 ) . Using Equation 12 we can express the equilibrium mean and variance of log spVL as a function of νe , the variance of the contribution of the host/environment to log spVL ( see Supplementary Materials , Equations C9 and C10 ) , M ˜ C = μ o + μ i ( 1+ ν e + ν o V ˜ C − ν e ) , ( 13 ) V ˜ C = ν t + ν i 2 ( 1+ 1+4 ν e + ν o ν t + ν i ) + ν e . ( 14 ) or as a function the heritability h2 ( see Supplementary Materials , Equations C12 and C13 ) , M ˜ C = μ o + μ i ( 1+ ( 1− h 2 ) V ˜ C + ν o h 2 V ˜ C ) , ( 15 ) V ˜ C = ν t + ν i 2 ( h 2 ) 2 ( 1+ 1+ 4 ( h 2 ) 2 ν o ν t + ν i ) . ( 16 ) | Following an initial peak in viremia , the viral load in HIV infected patients settles down to a set point which remains more or less stable during chronic HIV infection . This set point viral load is one of the key factors determining the rate of disease progression . The extent to which it is determined by the virus versus host genetics is thus central to developing a better understanding of disease progression . Here we develop an analytical model that describes the changes of the distribution of set point viral load in the HIV carrier population over a full cycle of transmission . Applying this model to patient data we find that the most parsimonious explanation for the observed large variation of set point viral load across HIV patients is that set point viral load is highly heritable from donors to recipients . This implies that set point viral load is to a considerable extent under the genetic control of the virus . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | High Heritability Is Compatible with the Broad Distribution of Set Point Viral Load in HIV Carriers |
This study was conducted with the aim to understand some of the cultural belief systems in the management of wounds and patients practices that could contaminate wounds at the Obom sub-district of the Ga South Municipality of Ghana . This was an ethnographic study using in-depth interviews , Focus Group Discussions and participant observation techniques for data collection . Observations were done on Buruli ulcer patients to document how they integrate local and modern wound management practices in the day-to-day handling of their wounds . Content analysis was done after the data were subjected to thematic coding and representative narratives selected for presentation . It was usually believed that wounds were caused by charms or spirits and , therefore , required the attention of a native healer . In instances where some patients’ wounds were dressed in the hospital by clinicians whose condition/age/sex contradict the belief of the patient , the affected often redress the wounds later at home . Some of the materials often used for such wound dressing include urine and concoctions made of charcoal and gunpowder with the belief of driving out evil spirits from the wounds . Clinicians must therefore be aware of these cultural beliefs and take them into consideration when managing Buruli ulcer wounds to prevent redressing at home after clinical treatment . This may go a long way to reduce secondary infections that have been observed in Buruli ulcer wounds .
The Institutional Review Board of the Noguchi Memorial Institute for Medical Research , University of Ghana reviewed the study . The following ethical considerations were followed: Ethical principles of anonymity , confidentiality , and rights of withdrawal were ensured among participants ( buruli ulcer coordinators and patients , and all other respondents ) . Research participants were informed about the objectives , methods and what was expected of them by clarifying their roles in the study to them . It was made clear to participants that their participation in the study was voluntary and refusal to take part would not affect their access to services offered by the health facility . No form of inducement was used to entice participants to partake in the study . However , refreshment and transportation was provided after interviews . To help protect the identity of patients and prevent questioning by community members , both the questionnaire administration and individual interviews were held within an environment devoid of many people , which were largely chosen by respondents . To ensure participants’ right , an informed consent ( both oral and written ) was obtained from them before conducting the interview .
The study took place in the Obom sub-district of the Ga South Municipality . The Ga South Municipality was carved from the then Ga West District in November 2007 . The Municipality was established by Legislative instrument 1987 in 2007 with the capital at Mallam . The Ga South ( Weija ) Municipal Area lies within Latitude 5 degree 48’ North and within Longitudes 0o 8’ East and 0o 3’ West . It has total land coverage of approximately 517 . 2 sq . km . It shares boundaries with Accra Metropolitan Area to the South-East , Ga West to the East , Akwapim South to the North- East , West Akim to the North , Awutu Senya to the West , Gomoa to the South-West and the Gulf of Guinea to the South [3] . According to the extract from the 2010 National Population and Housing Census , the total population of the district is approximately 485 , 643 made up of 248 , 085 ( 51 . 1% ) females . The high population size is due to the Municipality’s closeness to the capital City Accra , making it home for many workers . According to the 2010 census , there are about 362 communities spread in the urban , peri-urban and rural areas of the Municipality . The coastal and the central portion of the Municipality have very dense population while the communities in the northern section are sparsely populated and scattered [3] . Obom , a sub-district in the Ga-South Municipality is located 15 kilometres to the north-east of Amasaman the district capital of Ga West Municipality . The Eastern part of the sub-district consists of low hills , interspersed with plains in the central parts . The river Densu , the largest water body in the district , runs through the sub-district . Other water bodies , which are tributaries of the Densu , are Adeiso , Honi and Ponpon rivers . There are also small ponds and seasonal streams . In addition , numerous surface water bodies have sprung up in the wake of extensive sand-winning activities to supply the building industry in the sub-district and the neighbouring Accra metropolis with sand . These water bodies are significant for economic activities such as fishing and farming as well as disease causation . Water—related diseases such as Buruli ulcer , schistosomiasis and malaria are endemic in the sub-district [4] . Apart from the two main health facilities ( one in Obom , the sub-district capital and the other in Amasaman , the district capital of Ga West Municipality ) that are accessible to residents in the sub-district , there are private clinics and maternity homes in the sub-district , some of which are at Mayra , KojoAshong , Domeabra , Oduman and Jei-Krodua . These facilities complement the efforts of the sub-district public health delivery , which could not reach majority of the people due to poor access and coverage . There are other decentralised health facilities ( CHPS compounds ) at Ashalagya , Balagono , Hobor and Kofikwei providing primary health care services to the populations that they serve . Owing to the poor condition of roads , the scarcity of means of transport and the fact that most communities are quite far from health facilities , access to health care is a major problem in the sub-district . The majority therefore utilise home treatment either homemade herbal treatment or over the counter medications usually bought from shops and itinerary vendors to manage ailments as a first line of action [4] . This was an ethnographic study using in-depth interviews , focus group discussions and participant observation techniques for data collection . Interviews and focus group discussions ( FGD ) were conducted with selected community elders , traditional healers , buruli ulcer patients and some patient caretakers in selected communities . In all , fifty five in-depth interviews and three focus group discussions were held in the study area . There were eight participants in each FGD session . Also , observations were done on buruli ulcer patients to document how they integrate local and modern wound management practices in the day to day handling of their wounds . Content analysis was done after the data were subjected to thematic coding and representative narrative selected for presentation . A recruitment strategy ( plan for identifying and enrolling people to participate in the research study ) was used . The strategy specified the criteria for screening potential participants , the number of people to be recruited , the location and the approach . In recruiting people for the FGDs the inclusion criteria were elderly male and female above age 55 years living in the communities that were sampled . Male and female above age 55 were considered elderly and appropriate to provide the necessary information on culture of wounds in this study based on consultations with the community chiefs and elders . With the aid of community volunteers and a disease control officer , both of whom had fair knowledge of the social setting of communities , a Ga , Ewe and Akan community elder , the three dominant ethnic groups living in the study area , were first approached by introducing the research team and the aims and objectives of the study to them , then asking them to kindly participate in the study . It was further explained that participation was voluntary and that one was at liberty to withdraw from the discussions anytime one deemed it necessary . However a promise to keep the discussions as confidential as possible was also made . When these elders consented to participate , we got the other members through a kind of snowballing in which the participant with whom the contact had been made and agreed used their social networks to refer the team to other elders who could potentially participate in the study . The same strategy was employed for selecting respondents for in-depth interviews with selected elderly community members . Convenient sampling was used to select buruli ulcer patients and their corresponding caretakers who came to the clinic for treatment or went to traditional healers for treatment . Traditional healers were identified using their association president . In-depth interviews were conducted with selected buruli ulcer patients on treatment at the Obom health center , caretakers of patients , selected elderly community members , and traditional healers to solicit information on buruli ulcer treatment , personal/community beliefs about wound care and perceptions of wound care at the biomedical health facilities . Fifty five in-depth interviews were conducted ( five buruli ulcer patients , five caretakers of patients ) ; 30 elderly community members ( 10 each of the dominant ethnic groups in the study area ) ( Ewe , Ga and Akan ) and five traditional healers . Also 10 key informant interviews were conducted with health care providers to appreciate the challenges facing the health system when it comes to Buruli ulcer control efforts . The key informant interviews were all done in English . With exception of the traditional healers who were all men , women were equally represented . Key-informants in this study were health care managers and providers comprising of the Director of the National Buruli ulcer control programme , the in-charge of the Obom health centre and nurses in the wound dressing room , the District Director of Health Service . Key-informant interviews allowed for the inclusion of providers’ perspective on wound care at the health facility . They also drew attention to the manner in which policies were applied and the challenges faced during day-to-day operations . Thus , key-informant interviews were critical in bringing biomedical context into the study . In other to generate interactive consensus building processes in the communities , three focus group discussions ( FGDs ) were held ( one each with the dominant ethnic groups in the study area ) ( Ga , Ewe and Akan ) respectively . Just like the in-depth interviews , FGDs were done to solicit information on buruli ulcer treatment , personal/community beliefs about wound care and perceptions on wound care at the biomedical health facilities . This method employed an interpretive paradigm which aimed at understanding the dynamics of the socio-cultural system as well as how communities interpret their world [5] . With this method , more time was spent in the communities in order to observe the daily activities of the people . The focus of the observation was on the daily management of wounds ( BU or suspected BU ) in selected households . The households selected were houses where buruli ulcer patients who started treatment at the health facility but defaulted or dropped out of treatment were residing . Additionally , community members who were identified as having wounds but decided to manage it at home were purposively visited . We also observed traditional healers’ ways of managing wounds of their clients . The observations were complemented by questions to clarify and understand individual actions taken in the process of wound care/management . Phenomenological analysis was performed on the qualitative data . Qualitative data from in-depth interviews and focus group discussions were categorised in a format that allows for manual coding by interview item for content analysis to be done . Data was analysed to clarify aspects of cultural beliefs of wound care , experiences of traditional treatment of wounds and treatment seeking behaviour of patients . Qualitative variables of interest were categorised and selected into common themes for presentation . This allowed the performance of phenomenological analysis on relevant coded segments to select representative narratives for presentation to complement the quantitative data .
From the medical perspective , a general wound is a type of injury in which the skin is torn , cut , or punctured ( an open wound ) , or where blunt force trauma causes a contusion ( a closed wound ) . However , we found that the biomedical care providers in our study also classified wounds into four main categories as follow: Further categorisations of wounds are acute and chronic wounds . Acute wounds are wounds that heal within a short period of time while chronic wounds are difficult to heal or last for a very long period of time . However , the following narratives by a biomedical care provider provided clear distinctions between other wounds and buruli ulcers wounds; He further explained that; Residents in BU endemic communities however , categorised wounds into two types—the normal wounds and the abnormal wounds . The normal wounds among the Ga it is called fla , the Ewe called it Abi and the Akan called it Akuro . These are wounds that were as a result of cuts , falls , boils , bites and accidents of all kinds . They heal very fast , usually within three months . The abnormal wounds , known locally as aboabone ( Ga ) , akuro bone ( Akan ) or abivordi ( Ewe ) are those that may start as normal wounds but due to other interventions , may take a very long time to heal or may not heal at all . Abnormal wounds ( could also be caused by supernatural forces notably witches , wizards , ancestral spirits , charms/sorcery/ Juju and the gods of the community . Thus , community members believed that wounds can be caused by natural factors , supernatural factors or both . They also believed that wounds are living beings that could sleep and wake up and this belief actually influences when a wound should be dressed and not to be exposed for all to see . They also believe that wounds must be dressed by elderly people but not young people . The people also believe that chronic wounds must be managed by traditional healers and not to be taken to the biomedical health facilities . According to some of the traditional healers visited , violations of some beliefs and prohibitions mentioned above could lead to serious consequences including the non-healing of wounds , barrenness , and even death . For some lucky victims , pacification rites in the form of libation are performed by linguist and fetish priest in the communities to avert the calamity or the punishment . The treatment of Buruli ulcer can be straight forward and less costly if the disease is detected early without complications . However , treatment becomes more costly if found in the advanced stage ( Fig 1 ) . The basic treatment consists of antibiotics taken daily , both orally and injection for 56 days , coupled with wound care/management . One of the following combinations may be used: a combination of rifampicin ( 10 mg/kg once daily ) and streptomycin ( 15mg/kg once daily ) ; or a combination of rifampicin ( 10 mg/kg once daily ) and clarithromycin ( 7 . 5 mg/kg twice daily ) ( Observations ) . Also , other interventions such as limited physiotherapy services are provided to minimise or prevent disabilities ( Fig 1 ) . Community members believed that normal wounds could be managed at home or at the biomedical health facilities while abnormal ones should be managed by the traditional healers/spiritualists/herbalists . To buttress this position , one community member said; Some community members believed that buruli ulcers wound by their nature , fall into the category of abnormal wounds and so need to be managed traditionally with spiritual backing for fast healing . The process of managing buruli ulcer wounds by traditional healers involves , first casting out the causal spirits before applying various herbs on the wound to aid healing . The processes of managing buruli ulcer by a traditional healer were observed in one study community . Traditional healers as follows: evil spirits are driven out of the wound by making the patient to drink concoctions made up of a mixture of palm wine and gun powder ( Fig 2 ) . For long lasting protection from the causal spirit , the patient is made to wear a red talisman around the waist ( Fig 3 ) . The final stage of the treatment process involves putting herbs such as cocoyam leaves ( kontomire ) among other herbal preparations on the wound to aid healing ( Fig 3 ) . Unlike the biomedical perspective , where moist wounds are considered to be in good conditions , community members perceived dry wounds as those that are showing signs of healing ( Fig 4 ) . This belief was confirmed by a buruli ulcer patient who dropped out of biomedical treatment and went to a traditional healer . In her narration , she said; This assertion was confirmed by the traditional healer who was managing the wound when he said: Respondents were of the opinion that , depending on the causes and classifications of wound , some could only be managed by traditional healers , spiritualists or herbalists while others could be managed effectively by biomedical health workers . This position was reflected in a statement by a 76 year old female respondent during a focus group discussion session when she said; The chronic wound ( Kisikro ) was also described by a 70 year old female during an in-depth interview session as follows: To buttress this belief , a 67 year old male participant had this to say: To show that the belief regarding wound causation is similar in Southern Ghana , we present what a 65 year old woman said during an in-depth interview session: Local beliefs about the causation of wounds affect the treatment practices of traditional healers in treating wounds in general and buruli ulcer wounds in particular . This has been expressed by a traditional healer during an in-depth interview session thus: Asked why the witches inflict wounds on victims , this was what the respondents said: Chronic wounds are perceived and treat them differently in line with the belief of the people . This belief was stated by a discussant in a focus group discussion session as captured in the following narratives: Despite the local perceptions that wounds are caused by spirits or witches , some community members including buruli ulcer patients are beginning to doubt these positions and this may be mainly due to the community outreach education programme being implemented in the study sub-district . This was reflected in a statement made by a 48 year old male Buruli ulcer patient during an in-depth interview when he said: Again , this was what a 72 year old female had to say about her perception of chronic wounds and how it has changed overtime: The socio-cultural beliefs and practices associated with wound care in the study district also had ramifications for patients and their caregivers . Almost all the respondents alluded to the fact that wound care was delicate , complex and mysterious and so its management is restricted to particular categories of people . Respondents maintained that there were people who are not fit to go near someone with a wound let alone to dress it . It came to light from responses that; women who are breastfeeding , women who are in their menstrual period , pregnant women , promiscuous young women and people with ‘evil eyes are not supposed to dress wound . It is also belief that more than one person ( multiple hands ) should not dress a wound . The reasons behind this believe is that when these categories of persons dress your wound , it will not heal fast or heal at all . According to a 68 year old woman shared her personal experience in the following narratives: Asked who was therefore qualified to take care of wounds , this was her response: On the other hand , most of the participants believed that people with wounds were not supposed to engage in the following acts if they want their wounds to heal fast: They must not have sex until the wound gets healed; They must not dress the wound in the afternoon; They must not dress the wound outside a room; They must not expose the wound for all to see as some people have evil eyes which could affect the wound . This position was confirmed in the following narratives: Asked why people had to hide their wounds , their responses were captured in the following narratives: In expressing the same sentiment about who is to dress wounds at the clinic , this was what a 64 year old female patient had to say: Asked whether she had ever been attended to by a pregnant nurse since she started coming for wound dressing , she replied: In stating her state of health and how she was being treated by the nurses at the clinic , this was what the respondent said: Findings revealed a number of cultural practices and beliefs which significantly affected patients' wound care and help seeking behaviour . These included cultural beliefs that prohibit certain category of people such as pregnant women , lactating mothers and women who menstruate from dressing wounds . Respondents believed that some wounds were caused by charms or spirits and , therefore , required the attention of traditional healers . In instances where patients’ wounds were dressed in the hospital by clinicians and the patients observed that the condition , age or sex of the clinician contradict their belief , the affected often redressed the wounds later at home for fear of the wound not healing . Some of the materials often used for such wound dressing include urine and concoctions made of charcoal and gun powder with the belief of driving out evil spirits from the wounds . These practices may cause secondary infection of wounds considering the conditions under which the mixtures ( concoctions ) are prepared . The kind of relationship that exists between patients and care providers has a great influence on their treatment seeking behaviour and adherence to treatment . The relationship has psychological effects on patients’ healing process . It is therefore vital to respect and take into considerations the beliefs and practices of patients as much as possible so as to avoid conflict with the biomedical treatment . We tried to understand how patients perceived biomedical health practitioners who handle their wounds during treatment at health facilities and the following narratives explained their positions: Asked how she saw the services being provided by the nurses , this was what she said: In suggesting how nurses are to treat patients with wounds at health facilities , a 66 year old male respondent said: While some of the patients interviewed maintained that they did not mind when pregnant nurses dressed their wounds for them at the clinic , others were not comfortable with that . These positions were represented in the following narratives: According to participants , there were some few individuals who neither practiced witchcraft nor juju /charms but they naturally have evil eyes from birth . Such people have ‘bad luck’ so when they see your wound it will not heal fast . According to them this was one of the reasons why many people will not come to the clinic for treatment . A 57 year old buruli ulcer patient made the following remark: Asked what has to be done to improve nurse-patient relationship to enhance effective wound care , this was what a respondent had to say:
Consistent with earlier findings , this study revealed that cultural practices and beliefs significantly affected patients' wound care [6 , 7 , and 9] . The management of wounds in the home or formal health facilities is dependent on community ideas of wound categorisations of ‘normal’ or ‘abnormal’ , which is similar to what was reported by Winch et al . , [10] in their malaria study in Tanzania , where some conditions were referred to as ‘‘out-of-the ordinary fevers” or ‘‘fevers which do not respond to hospital treatment” and so were regarded as best treated by the traditional practitioners . Many individuals integrate both African and Western viewpoints into their belief systems comfortably without any contradictions as reported by Rudick , [11] . It became evident from the findings that wound related cultural beliefs have influenced how buruli ulcer wounds were treated in the study area . Whether a wound is treated at home or by traditional healers prior to being seen at the hospital or a combination of modern and traditional treatments is dependent on the dominant causal belief appealed to by the patients and his/her significant others . It is worthwhile noting that despite healers’ often low level of education , professionals such as teachers , nurses and ministers of religion have been found to use their services [12] . It worth reporting that beliefs on causation of wounds , perceived seriousness of buruli ulcer infection , perceived effectiveness of medical treatment , fear of recurring infections , surgery and amputation constitute socio-cultural features of buruli ulcer that promote preference for traditional/herbal treatment , which then causes delayed in seeking medical treatment [13 , 14 , 15 , 16 , and 17] . In this regard Kargbo-labour [18] , recommended the need for African countries to consider local aetiology , perceptions and beliefs which are interwoven into the socio-cultural milieu of the African in disease control programmes . Findings from this study revealed that most respondents gave a maximum of three month for all normal wounds to heal and any wound that did not heal within three months was labeled as abnormal . These cultural beliefs could be impediments to buruli ulcer early case detection , treatment and adherence in the endemic communities of Ghana and other African countries [8] . This should be of concern to public health and a conscious effort should be made to understand the social , economic and cultural aspect of Buruli ulcer disease and its management in the local communities to aid its control . Equally important is the fact that most patients in this study started self-care or treatment at home when they noticed a nodule , boil , plaque or sustain any wound . This finding corroborated the earlier work by Grietens et al . , [7] where they reported that the commonest way of dressing wounds at home was the use of hot water to clean the wound and later applied ampicillin mixed in palm kernel oil . Herbs were used if after one month there was no improvement by way of healing . The self-care by buruli ulcer patients could be explained with the understanding of the clinical manifestations ( signs and symptoms ) of buruli ulcer where it starts as a painless itchy nodule , plague or wound [1 , 19–21] . Moreover , this follows medical logic of given ‘first aid’ at home . This could therefore be termed as a ‘normal folk’ medical practice that is ‘rationally’ attempted by most human beings with a physical malaise . It came to light that Traditional healers become the next point of care if the home treatment seemed not to be working for the person . This confirms what was reported from a study conducted in Ghana , which showed that in most rural communities like elsewhere in sub-Sahara Africa , traditional healers were more accessible to the general population than biomedical service providers [19] . It has also been stated that there was approximately one traditional healer for about 500 people while the ratio of doctor to population is 1:40 , 000 [22] . The belief that some wounds are caused by evil spirits and witches has influenced the mode of treatment in buruli ulcer endemic communities , which therefore give currency to some of the healing practices devised by traditional healers , including the attempt to ‘‘drive” out the evil spirits from the wounds of patients to aid recovery [8 , 10] . From respondents’ point of view , chronic wounds regarded , in some cases , as ‘‘bewitched wounds” cannot be treated by biomedical health practitioners . These wounds must of necessity be treated by traditional healers ( spiritualists or herbalist ) and this confirmed the belief reported byAgbenorku et al . , and Winch et al . , [20 and 10] that certain types of wounds and fevers are better treated by traditional methods and even made worse by Western medicine . It must be made clear that , in Buruli ulcer endemic communities these mainly include osteomyelitis and chronic leg ulcers . In fact , some patients do not seek any care for chronic ulcers because they are convinced that they will not be healed by help from biomedical practitioners because they were either bewitched or cursed to have the wound [24] . This was further explained by a study in Cameroon that although beliefs could influence health seeking behaviours for buruli ulcers , more compelling factors could also act on patients’ treatment paths , indicating that the choice of treatment was not decided upon solely with consideration to disease aetiology [7] . Similarly factors such as the effectiveness of treatment , place of treatment , difficulties of symptom recognition and acceptability of treatment were all paramount in deciding on treatment option to adopt [7] . Some of the beliefs associated with wound care are so strong that the people believed that any violation could lead to serious consequences such as the non-healing of wounds . These have serious effects on patients’ treatment seeking behaviour and adherence to biomedical treatments as they fit into the perception that some categories of wounds are not for biomedical treatments . For instance , buruli ulcer patients who believe that it is bad for a pregnant nurse to dress their wounds are more likely to drop out of treatment if a pregnant woman attends to them at any point in the management of their wounds . However , in the case of BU patients ( both men and women ) , it is not about the opposite sex but the biological condition such as pregnancy status and age of the female provider was critical in accepting to be treated by her . As a result of these perceptions some patients may resort to other actions like treating their wounds with all kinds of concoctions including urine , which might expose the wound to secondary infections [1] . There is therefore the need for intensification of education in endemic communities to provide adequate information that will help to address some of the strongly held beliefs on wound care for a changed behaviour . In this direction , Ackumey et al . , [25] suggested that providing education and knowledge at the individual level was not sufficient in itself to promote a change in behaviour . Health education programmes should be conducted to integrate the entire community especially traditional leaders , community volunteers , traditional healers and former patients to serve as change agents to help in accepting biomedical wound care [8] . This study has established the relationship between health care providers and their patients at the study area as an important element in treatment outcome [23] . It has demonstrated that respect for patients is an important step in addressing their health conditions and failure to do that would be a health service delivery in futility [11] . Some patients interviewed felt that they were not being treated well by health care providers and this must be addressed , if we want to win the confidence of patients to continue to accept and use the services provided at health facilities , it is often said that the best advertisement for a service provider are satisfied clients . Evidence from follow-ups on few dropped out patients revealed that the main reason why they defaulted or dropped out of treatment was disrespect or mistreatment by healthcare providers . Moreover , most clinicians do not listen to the patients well , especially with regards to their social , cultural and religious beliefs [11] . This might result in misunderstanding of some actions of the patients by health providers and also some actions of the health care providers by patients . This brings to the fore the recommendation that sociocultural factors affecting help-seeking practices for Buruli ulcer disease must feature strongly on the research agenda of the World Health Organisation ( WHO ) so as to generate data to guide public health strategies for the treatment and control of buruli ulcer in endemic countries [26] . | The study revealed a number of cultural practices and beliefs which influenced patients' wound care and health seeking behaviour . These included the beliefs that prohibit certain category of people such as pregnant women , lactating mothers and women who menstruate from dressing wounds . Respondents believed that some wounds were caused by charms or spirits and , therefore , required the attention of a traditional healer . In instances where patients’ wounds were dressed in the hospital by clinicians and the patients observed that the condition , age or sex of the clinician contradict their belief , they often redressed the wounds later at home for fear of the wound not healing . Some of the materials often used for such wound dressing include urine and concoctions made of charcoal and gunpowder with the belief of driving out evil spirits from the wounds . These practices may cause secondary infection of wounds considering the conditions under which the mixtures ( concoctions ) are prepared . It may require collaborative efforts of clinicians , public health promoters and the affected communities to find a common ground to manage Buruli ulcer wounds in a mutually acceptable way to aid healing . | [
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] | 2016 | Cultural Understanding of Wounds, Buruli Ulcers and Their Management at the Obom Sub-district of the Ga South Municipality of the Greater Accra Region of Ghana |
A disabling and disfiguring disease that “begins where the road ends” , yaws is targeted by WHO for eradication by the year 2020 . The global campaign is not yet financed . To evaluate yaws eradication within the context of the post-2015 development agenda , we perform a somewhat allegorical cost-effectiveness analysis of eradication , comparing it to a counterfactual in which we simply wait for more roads ( the end of poverty ) . We use evidence from four yaws eradication pilot sites and other mass treatment campaigns to set benchmarks for the cost of eradication in 12 known endemic countries . We construct a compartmental model of long-term health effects to 2050 . Conservatively , we attribute zero cost to the counterfactual and allow for gradual exit of the susceptible ( at risk ) population by road ( poverty reduction ) . We report mean , 5th and 95th centile estimates to reflect uncertainty about costs and effects . Our benchmark for the economic cost of yaws eradication is uncertain but not high –US$ 362 ( 75–1073 ) million in 12 countries . Eradication would cost US$ 26 ( 4 . 2–78 ) for each year of life lived without disability or disfigurement due to yaws , or US$ 324 ( 47–936 ) per disability-adjusted life year ( DALY ) . Excluding drugs , existing staff and assets , the financial cost benchmark is US$ 213 ( 74–522 ) million . The real cost of waiting for more roads ( poverty reduction ) would be 13 ( 7 . 3–20 ) million years of life affected by early-stage yaws and 2 . 3 ( 1 . 1–4 . 2 ) million years of life affected by late-stage yaws . Endemic countries need financing to begin implementing and adapting global strategy to local conditions . Donations of drugs and diagnostics could reduce cost to the public sector and catalyze financing . Resources may be harnessed from the extractive industries . Yaws eradication should be seen as complementary to universal health coverage and shared prosperity on the post-2015 development agenda .
Yaws is one of two neglected tropical diseases ( NTDs ) targeted by the World Health Organization ( WHO ) for eradication . A 2013 World Health Assembly resolution calls for its eradication by the year 2020 . A disabling and disfiguring disease that “begins where the road ends” it is found primarily among poor and isolated communities in warm , humid and tropical forest areas of Africa , South-East Asia and the Western Pacific . It is caused by a bacterium ( Treponema pallidum ssp pertenue ) related to syphilis but is not sexually-transmitted and mostly afflicts children . In its primary and secondary ( early ) stages it causes unsightly and often painful lesions of the skin ( especially face and feet ) , cartilage and bones . About 10% of untreated cases suffer tertiary ( late-stage ) yaws , with permanent disability and disfigurement of the face , lower limbs and hands . In 1950 , WHO estimated that 160 million people were infected with yaws [1] . Between 2008 and 2012 more than 300 000 new cases were reported to WHO [2] . Reporting yaws is not mandatory , however , and so the full burden of the disease is not currently known . In the field , diagnosis is primarily based on epidemiology and clinical symptoms . Laboratory-based serological tests are widely used to confirm clinical cases . The same tests are used to confirm syphilis but cannot distinguish between the two diseases . Recently , a rapid syphilis test has been demonstrated to be effective in confirming yaws and can be used in the field [3] . There is no vaccine for yaws . Prevention is based on the interruption of transmission through early diagnosis and treatment of individual cases and total ( mass ) or targeted treatment of affected populations . The epidemiology of the disease , the history of its control and the feasibility of eradication are described in detail elsewhere [1] [4] [5] [6] [7] [8] [9] [10] . WHO's strategy for yaws eradication is based on single-dose oral treatment with azithromycin at 30 mg/kg ( maximum 2 g ) [11] . Because of the simplicity and convenience , it is now preferred to the traditional treatment with injectable benzathine penicillin . Transmission has been shown to be interrupted with one or two rounds of treatment at high levels of population coverage [1] . The approach is known as total community treatment ( TCT ) – treatment of an entire endemic community irrespective of the number of active clinical cases . Total targeted treatment ( TTT ) – treatment of all active clinical cases and their contacts – is carried out to “mop-up” any cases that were missed in the TCT round . The definition of contacts may vary between settings , but normally includes household members and , in the case of school-age children , classmates . Confirmation of clinical cases during TCT ( for follow-up TTT ) may be carried out using a rapid dual point-of-care treponemal and non-treponemal serological test . “Proof of concept” pilot projects in Congo ( Bétou and Enyellé districts ) , Papua New Guinea ( Lihir island ) , Vanuatu ( Tafea Province ) and Ghana ( West Akyem district ) were successfully concluded in 2012 and 2013 . The tools exist for WHO and its partners to follow the global Guinea worm disease eradication campaign in eradicating another NTD . And yet , the effort is not financed , with no cash or in-kind donations yet received for a global yaws eradication campaign . As with other diseases of poverty , there is a tendency to hope that poverty reduction will resolve the problem . Unfortunately , the history of yaws suggests that a more concerted effort will be required . The dismantling of vertical yaws programs after 1964 led to a resurgence of yaws in the late 1970s , even as poverty rates declined [1] . To evaluate whether yaws eradication is a good investment within the broader post-2015 development agenda , we perform a cost and somewhat allegorical cost-effectiveness analysis of eradication , comparing it to a counterfactual in which we simply wait for more roads ( the end of poverty ) .
A review of the literature from 1950 to 2013 indicates that at least 85 countries have reported yaws [1] . Ecuador and India reported interruption of transmission of the disease in 2003 and 2006 respectively . 12 countries currently reporting cases to WHO require technical assistance and financing: Benin , Cameroon , Central African Republic , Congo , Cote d'Ivoire , Democratic Republic of the Congo ( DRC ) , Ghana , Indonesia , Papua New Guinea , Solomon Islands , Togo and Vanuatu . In 71 countries where no recent data are available , the absence of the disease needs to be verified . Expert opinion puts the population at risk for yaws at a minimum of 5 percent of the populations of ten of the twelve known endemic countries [15] . The exceptions are the small island states of Solomon Islands and Vanuatu , where 100 percent of the population is assumed at risk [16] . For an upper bound on population at risk we employ data from the G-Econ 4 . 0 ( May 2011 ) database [17] . G-Econ provides demographic and geophysical data for one-degree longitude by one-degree latitude cells – approximately 100 km by 100 km or the same size as most second administrative level boundaries . We summed the populations living in cells satisfying the following conditions favorable to the transmission of yaws: 1 ) average precipitation ( mm per year ) >500; 2 ) average annual temperature ( degrees Celsius ) >20; 3 ) tropical forest or woodland; and 4 ) population density per km2 <100 [18] . We adjusted the reported 2005 populations to 2015 using rural population projections [16] . In addition to demographic and geophysical data , G-Econ contains estimates of Gross Domestic Product ( GDP ) at the level of cells . We use Gross Cell Product ( GCP ) in the results section of this paper to assess the economic productivity of the lands on which populations at risk for yaws live and establish a threshold by which to assess cost-effectiveness . To kick-start the pilot project , WHO procured limited quantities of generic azithromycin ( Medopharm , India ) at US$ 0 . 17 per 500 mg tablet . We used population data disaggregated by age to estimate dosage: 0–4 years ( 500 mg ) , 5–9 years ( 1000 mg ) , 10–14 years ( 1500 mg ) and 15 years and older ( 2000 mg ) [16] . Based on experience from the pilot sites , TTT may be required . The number of index cases requiring mop-up is determined by the coverage , eligibility and cure rates in the model of health effects ( described below ) . For the purposes of the cost benchmarks , we assumed based on experience in India that mop-up reaches the active index case plus 10–20 close contacts that include the secondary cases . For the purposes of detailed planning and budgeting , local evidence will be needed . We assume that a 10% buffer stock is required . For the pilot , WHO also procured rapid dual non-treponemal and treponemal point-of-care serological tests ( Chembio Diagnostic System Inc . , New York , USA ) at a negotiated price of US$ 2 per test . The number of clinical cases that may be serologically tested during TCT and TTT is determined by the model of health effects ( below ) , with an allowance for clinical misdiagnosis of yaws-like lesions that increases the total number of tests by 10–30% . We have not estimated the cost of more expensive molecular tests ( polymerase chain reaction ) to monitor for drug resistance or to confirm eradication . We assume that surveillance ( below ) is clinical surveillance and we do not include costs for any additional tests . Experience from the pilot sites suggests that the cost of delivery will vary considerably across endemic countries . In some pilot sites , the cost per person was relatively low . On the island of Lihir ( Papua New Guinea ) , the financial cost of TCT of 16 941 people was about US$ 25 800 or US$ 1 . 52 per person . ( personal communication with Oriol Mitja ) This first round was completed in 40 days . Human resources included community and public health workers , a program coordinator , a nurse , a laboratory technician ( half-time ) and a driver . Six months later , the financial cost of TTT was US$ 11 400 or US$ 0 . 71 per person . Mop-up was completed in only 14 days because there was no serological testing and because fewer drugs were administered . In Ghana , the cost of TCT was even lower , but the program depended heavily on the contribution of volunteers . ( personal communication with Abdul Aziz Abdulai ) In other sites , the cost per person treated was relatively high . In Tafea province ( Vanuatu ) , the financial cost of TCT of 41 509 people distributed over five remote islands with very weak health and road infrastructure was about US$ 265 300 or US$ 8 . 27 per person . ( personal communication with Jacob Kool ) This amount included upfront investment in communication-for-behavioral-impact ( COMBI ) . COMBI is thought to have improved acceptance rates and general hygiene and thereby reduced the need for a mop-up round . Likewise in the remote Bétou and Enyellé districts of the Congo , upfront equipment costs pushed unit costs into the high single digits . The team offered a mobile clinic and measles vaccination ( requiring a costly cold-chain ) . ( personal communication with Matthew Coldiron ) . We did not assume that unit costs from the pilot sites were generalizable to other settings . In order to better understand the drivers of costs across settings , we reviewed the rather more expansive literature on the cost of mass drug administration ( MDA ) to control and eliminate other NTDs: lymphatic filariasis ( LF ) , schistosomiasis , soil-transmitted helminthiasis ( STH ) , onchocerciasis and trachoma . The 25 identified studies are referenced in the Supporting Information ( Table S1 ) . We extracted both financial ( F ) and economic ( E ) costs: planning , mapping and training activities ( F&E ) , drug shipment ( F&E ) , vehicles that were rented ( F&E ) or borrowed from other programs ( E ) , fuel and vehicle maintenance ( F&E ) , per diems ( F&E ) , project staff salaries ( F&E ) , Ministry of Health staff time ( E ) , office space ( E ) , utilities ( F&E ) and supplies ( F&E ) . We removed drugs from the cost of delivery . Capital cost annualization and overhead cost allocation were retained from the individual studies . We did not consider the cost of volunteer time because most studies did not report it . We extracted also the number of people treated and GDP per capita . We converted unit costs to constant 2012 US$ and ran multivariate regressions on the number of people treated to capture economies of scale , on GDP per capita ( constant 2012 US$ ) to capture differences in the quality and complexity of inputs , and on population density to capture differences in logistical difficulty . We opted for study/site fixed effects and a log-log specification . Regression results based on 103 observations from 57 study-countries are available in Supporting Information ( Table S2 ) . These results were used to generate country-specific benchmarks for the economic unit cost of TCT . With the mean and standard error of the log prediction , we simulated and re-transformed 1000 values and extracted the mean , 5th and 95th centile values for the best estimate and 90% credible interval ( CI ) . We used the same approach to generate benchmarks for the financial unit cost , using studies with financial cost estimates . In our review , financial costs were on average 66% ( interquartile range 28–86% ) of economic costs . Both economic and financial unit cost benchmarks are available in the Supporting Information ( Table S3 ) . All costs in this paper refer to economic costs , unless otherwise specified . We assumed , based on experience from Lihir that the cost of the TTT mop-up ( if required ) would be 30–50% of the cost of TCT . This assumption will be revisited as evidence comes in from other sites . We reviewed the literature from similar programs to estimate the cost of surveillance following TCT and TTT . The ( economic ) cost of prevalence surveys for trachoma was about US$ 1600–28 000 per district of 100 000–250 000 inhabitants in 2013 prices [19] . Only 6% of this cost was for supplies . In probabilistic sensitivity analysis , we assumed surveillance costs of US$ 2000–30 000 per 100 000 population at risk . We assumed clinical surveillance and did not include costs for any additional tests ( see cost of diagnostics , above ) . Surveillance is one of the aspects of yaws eradication that may require the most adaptation to local conditions . Yaws elimination in India and Guinea worm disease elimination in most countries used rumor investigation , including cash rewards of between US$ 10–1000 for the reporting of ( subsequently confirmed ) cases . In India , the cost of rewards was small relative to that of serological surveys . Our compartmental ( Markov ) model is depicted in Figure 1 . The population at risk moves to or through one of five possible states: primary , secondary , latent and tertiary yaws , or death ( the terminal state ) . Ours is not the first compartmental model of yaws transmission [20] . But it is the first to distinguish between the stages of infection , and the first used in a cost-effectiveness analysis . Start values for the model are based on the maximum number of new cases reported in any given year 2008–2012 [21] . We assumed conservatively that reported cases represented 30–90% of true incident cases . Based on experience with Buruli ulcer , another NTD affecting skin in poor and isolated populations , the number may be as low as 7% in the Democratic Republic of Congo and 18% in Cameroon [22] [23] . Transition probabilities from one state to another are determined by epidemiological parameters , with distributions for probabilistic sensitivity analysis ( Table 1 ) . We converted rates and durations into probabilities . We converted all probabilities in half-year ( 6-month ) cycle probabilities . For both the eradication scenario and counterfactual , we made an optimistic assumption that the susceptible ( at risk ) population and , by extension , the basic reproduction number will decrease 2–7% per year , as a result of more roads ( poverty reduction ) . These are the 50th and 75th centile values for the average annual rate of decline in the dollar-a-day poverty headcount of 98 developing countries over 1999–2013 [16] [24] . These correspond roughly to the values for India and China , respectively . This is an optimistic assumption resulting in a conservative estimate of cost-effectiveness . The eradication scenario has additional , programmatic assumptions related to coverage , eligibility ( for treatment ) and cure rates ( Table 1 ) . Covered , eligible and cured individuals in the primary and secondary states return to the susceptible population . Tertiary yaws is irreversible . The model assumes that TTT will treat all index cases and their contacts — this is a simplification but has no major effect on the model . The model has not been constructed to prove the feasibility of eradication , as this has already been done in the field . Future refinements of the model could , however , help identify the conditions under which it is more cost-effective to follow TCT with another round of TCT rather than TTT . We allowed the model to burn in over a period of 10 years , the maximum duration of progression to tertiary disease . The model was run to the year 2050 to capture some of the longer-term benefits of eradication . In reality the benefits of eradication could extend well beyond 2050 . We summed the number of ( discounted ) life-years spent in the primary and secondary ( early-stage ) and tertiary ( late-stage ) states , and compared the eradication scenario results to those of the counterfactual . There are no specific disability weights for early or late-stage yaws . We calculated disability-adjusted life-years ( DALYs ) using weights for comparable conditions [25] . We used 0 . 029 ( 0 . 016–0 . 048 ) for early stage yaws based on disfigurement level 1 with itch or pain , described as: “a slight , visible physical deformity that is sometimes sore or itchy . Others notice the deformity , which causes some worry and discomfort . ” This range contains an earlier point estimate of 0 . 048 for secondary syphilis [26] . We used 0 . 398 ( 95% CI 0 . 271–0 . 543 ) for late-stage yaws based on disfigurement level 3 , described as: “an obvious physical deformity that makes others uncomfortable , which causes the person to avoid social contact , feel worried , sleep poorly , and think about suicide . ” This range contains an earlier point estimate of 0 . 283 for tertiary syphilis . Our analysis does not take into account the potential knock-on benefits of total community treatment with azithromycin for trachoma , chancroid , chlamydia , syphilis , gastrointestinal and respiratory tract infections or malaria , nor of any of the other health services delivered during the campaigns . Reductions in child-mortality have been associated with mass administration of azithromycin for trachoma , but awaits confirmation by a randomized controlled trial [27] . In Vanuatu , the pilot sites saw a dramatic decrease in the number of diarrheal cases and all-cause hospitalizations . ( personal communication , Jacob Kool ) .
Expert opinion puts the minimum population at risk for yaws in the 12 known endemic countries in 2015 at 21 million . Using G-Econ data , we calculate that as many as 74 million people live under conditions favorable to yaws infection , as mapped by Figure 2 . The upper bound on the range of estimates produced for Indonesia comes close to that obtained in a recent , more detailed exercise by the national program . Extending this analysis to the 71 countries where cases are known to have occurred historically , we estimate that the population in need of verification of the absence of the disease is 210 million . In what follows , we calculate only the cost of surveillance for this population . Including buffer stock and mop-up , 75 ( 60–92 ) million grams of azithromycin are estimated to be required during 2015–2020 . At US$ 0 . 17 per 500 mg tablet , the cost would be US$ 28 ( 22–34 ) million . The number of serology tests required for confirmation of clinical cases in the 12 endemic countries is estimated at 0 . 4 ( 0 . 2–0 . 5 ) million , at a cost of US$ 0 . 7 ( 0 . 4–1 . 1 ) million . Best estimates from the regression models of the economic unit cost of delivery suggest a range , depending on the country , of US$ 0 . 20–10 . 41 per person . See Supporting Information for country-specific economic and financial unit cost benchmarks . The economic cost benchmarks imply that delivery would cost US$ 314 ( 31–1009 ) million . The financial cost would be lower . Both economic and financial cost benchmarks are reported in Table 2 . Two to three years of clinical surveillance in the 12 known endemic countries adds about US$ 18 ( 11–29 ) million . The total economic cost benchmark is therefore US$ 362 ( 75–1073 ) million . Excluding drugs , the economic cost benchmark is US$ 334 ( 48–1038 ) million . The benchmark cost of clinical surveillance in the 71 countries requiring verification of the absence of the disease is about US$ 33 ( 7 . 5–59 ) million . In the absence of an eradication campaign in the 12 known endemic countries , the number of years of life lived with early-stage yaws would be 13 ( 7 . 5–21 ) million in the period 2015–2050 . See Figure 3 . The credible intervals are large , reflecting our conservative choice of parameter distributions . The number of years of life lived with late-stage yaws would be 3 . 0 ( 1 . 5–5 . 4 ) million . See Figure 4 . This amounts to 16 ( 9 . 4–26 ) million years of life lived with yaws symptoms and 1 . 6 ( 0 . 8–2 . 9 ) million DALYs . Given that tertiary yaws is irreversible , eradication would avert most but not all of this burden , leaving 1 . 0 ( 0 . 6–1 . 7 ) million years of life lived with yaws symptoms and 0 . 3 ( 0 . 1–0 . 5 ) million DALYs . Due to the eradication campaign , 13 ( 7 . 3–20 ) million years of life would be lived without early-stage yaws and 2 . 3 ( 1 . 1–4 . 2 ) million years of life without late-stage yaws . 1 . 3 ( 0 . 6–2 . 4 ) million DALYs would be averted . The total economic cost per year of life lived without yaws symptoms is estimated at US$ 26 ( 4 . 2–78 ) for the 12 known endemic countries . There are no established thresholds for the acceptability of the cost per year of life lived without yaws symptoms , but Figure 5 shows that the probability of acceptability would exceed 50% at a threshold of US$ 11 . 35 and 90% at US$ 46 . The cost per DALY averted is US$ 324 ( 47–936 ) . The interval is large , but well below WHO thresholds for cost-effectiveness of three times GDP per capita [28] . We estimate GCP per capita of US$ 733 ( 2005 US$ ) for the 74 million people living under conditions favorable to yaws infection , and GDP per capita of US$ 799 for the 12 known endemic countries as a whole . Even under the most conservative assumptions , yaws eradication is cost-effective . Populations at risk for yaws are poor , but the areas in which they live are economically productive . G-Econ data suggest a GCP of US$ 37 400 per square kilometer ( 2005 US$ ) and US$ 51 billion in total , or 17% of GDP in the 12 known endemic countries . The best estimate of the cost of eradication represents less than 0 . 5% of GCP and less than 0 . 1% of GDP . It would appear to be affordable from the perspective of the economy as a whole . Recall also the difference between economic and financial costs . In practice , many costs will be covered by existing Ministry of Health staff and assets such as vehicles . Excluding drugs and Ministry of Health staff and assets , the financial cost of yaws eradication could be as little as US$ 213 ( 74–522 ) million in the 12 endemic countries .
This paper provides the first economic evaluation of yaws eradication . It is largely prospective and , as a consequence , conclusions are limited by our uncertainty about many parameters , for both costs and effects . Most but not all of this uncertainty was reflected in a probabilistic sensitivity analysis . The number and distribution of people at risk for yaws needs to be better mapped , to better model health effects , certainly , but also costs . Uncertainty around populations at risk is not fully reflected in the regression model estimates of the unit cost of delivery . In the case of DRC , for example , we may have overestimated economies of scale and underestimated logistical difficulty . The cost benchmarks presented in this study are certainly not a substitute for country plans and budgets . Recall that we have not included the cost of molecular tests to confirm eradication , though this may only need to start once clinical cases are no longer being found . There is also uncertainty about the 71 countries of unknown endemicity . While we have included the cost of clinical surveillance in these countries , we have not included the cost of serological and molecular tests , much less the cost of TCT , that might be incurred if clinical yaws cases are identified in any one of them . Given that some of these countries share borders with the 12 countries of known endemicity , cross-border issues will incur , at the very least , some coordination costs . That said , we have also not included the health benefits that would accrue in these 71 countries , so the influence that their inclusion would have on the cost-effectiveness result is ambiguous . The cost of the “end game” of any eradication effort is uncertain , with the emergence of complexities requiring some local adaptation of global strategies [29] . Much of the uncertainty will be resolved as endemic countries begin or , in the case of the pilot sites , continue to implement the program . Nonetheless , there are good reasons to believe that a global yaws eradication campaign could be established with a relatively modest investment in the period 2015–2020 – about US$ 100–500 million in the 12 endemic countries . The real cost of waiting for more roads ( the end of poverty ) would be millions of years of life lived with disability and disfigurement due to yaws . Yaws eradication appears to be very cost-effective under reasonable assumptions about its cost and effects , and even under optimistic scenarios of poverty reduction . The main question that remains is how to finance the next phase of implementation . The governments of endemic countries are encouraged to take ownership of national elimination efforts . But the global public good of yaws eradication will likely require global financing . The cost to the public sector would be significantly reduced by drug donations from pharmaceutical companies , similar to those being made for other preventable NTDs . Donations of diagnostic tests would also help . At least as important is the catalytic effect that these in-kind donations could have . Financial and in-kind resources could be better harnessed from the extractive industries ( e . g . mining , logging ) and others ( e . g . cocoa and coffee ) . These are industries with operations on or near the resource-rich lands where resource-poor populations still live with yaws . There is already some precedent for mining company support to yaws eradication implementation and research in Lihir [30] . If endemic countries and their financing partners deliver within the range of costs and effects considered in this study , yaws eradication will be cost-effective relative to WHO thresholds . Of course , the case for investment in yaws eradication does not rest on cost-effectiveness alone . Policy-makers may be confronted with choices between public health interventions of similar cost-effectiveness relative to WHO thresholds . In the context of universal health coverage , priority-setting should consider also equity , with priority given to the worse off . There is no doubt that efforts to eradicate yaws will benefit some of the world's least well off citizens . Yaws eradication should be seen as complementary to universal health coverage and shared prosperity on the post-2015 development agenda . | A disabling and disfiguring disease that “begins where the road ends” ( among poor and isolated communities ) , yaws is targeted by WHO for eradication by the year 2020 . The global campaign is not yet financed . We provide benchmarks for the cost and health effects of global yaws eradication , based on evidence from four yaws eradication pilot sites and other mass treatment campaigns . We suggest that a global yaws eradication campaign could be established with a relatively modest investment in the period 2015–2020 — as little as US$ 100 million in the 12 known endemic countries . Eradication would cost about US$ 26 for each additional year of life lived without disability or disfigurement due to yaws between the years 2015 and 2050 . The real cost of not doing anything but wait for more roads ( the end of poverty ) would be about 15 million years of life needlessly affected by disability and disfigurement . We expect that yaws eradication will be cost-effective . Importantly , from the perspective of universal health coverage , it will benefit some of the world's least well off citizens . Yaws eradication should therefore be seen as complementary to universal health coverage and shared prosperity on the post-2015 development agenda . | [
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] | 2014 | Where the Road Ends, Yaws Begins? The Cost-effectiveness of Eradication versus More Roads |
A wide range of organisms use sex pheromones to communicate with each other and to identify appropriate mating partners . While the evolution of chemical communication has been suggested to cause sexual isolation and speciation , the mechanisms that govern evolutionary transitions in sex pheromone production are poorly understood . Here , we decipher the molecular mechanisms underlying the rapid evolution in the expression of a gene involved in sex pheromone production in Drosophilid flies . Long-chain cuticular hydrocarbons ( e . g . , dienes ) are produced female-specifically , notably via the activity of the desaturase DESAT-F , and are potent pheromones for male courtship behavior in Drosophila melanogaster . We show that across the genus Drosophila , the expression of this enzyme is correlated with long-chain diene production and has undergone an extraordinary number of evolutionary transitions , including six independent gene inactivations , three losses of expression without gene loss , and two transitions in sex-specificity . Furthermore , we show that evolutionary transitions from monomorphism to dimorphism ( and its reversion ) in desatF expression involved the gain ( and the inactivation ) of a binding-site for the sex-determination transcription factor , DOUBLESEX . In addition , we documented a surprising example of the gain of particular cis-regulatory motifs of the desatF locus via a set of small deletions . Together , our results suggest that frequent changes in the expression of pheromone-producing enzymes underlie evolutionary transitions in chemical communication , and reflect changing regimes of sexual selection , which may have contributed to speciation among Drosophila .
Chemical communication is widespread in the animal world [1] . Pheromones can mediate aggregation , signal danger , attract mates , and elicit a variety of other behaviors . The species-specificity of pheromonal signals is of decisive importance in kin recognition and in sexual reproduction [2] . Evolutionary transitions in sexual communication have been suggested to govern the early stages of speciation [3] . Moreover , reproductively relevant traits such as sex pheromones are thought to evolve rapidly under sexual selection [4] . These observations raise the possibility that the genes that control how individuals communicate with each other are evolutionarily labile and may display a large degree of functional divergence across taxa . However , the molecular mechanisms that govern how pheromone signals evolve are not understood . In principle , pheromone signaling could evolve via changes in pheromone production , chemical structure , or reception . In order to decipher how pheromone signaling may evolve , the genes that contribute to pheromone signaling and the mutations that alter it must be identified . Drosophila male courtship behavior is triggered in part by female pheromones that act either by direct contact and/or by transmission over short distances . Sex pheromones in Drosophila are largely fatty-acid derived hydrocarbons that are present on the fly's cuticle [5] . Among Dipteran fly species , females exhibit considerable divergence in the number or position of double bonds in cuticular hydrocarbons [6] . In the Sophophora subgenus , females of some species , such as D . melanogaster [6]–[10] , D . sechellia [6] , [9] , [11] , and D . erecta [6] specifically produce long-chain dienes , which are hydrocarbons that contain two double bonds [6] . Alternatively , other species , such as D . serrata , D . pseudoobscura , and D . persimilis [12]–[14] produce these compounds in both sexes . Moreover , other species such as D . simulans , D . mauritiana , D . yakuba , D . teissieri , D . orena , and D . santomea [6] , [7] , [9] , [15] , [16] do not produce dienes . Cuticular hydrocarbons are suggested to have multiple roles ( e . g . , protection against dessication and cold resistance [17] , [18] ) , and evolutionary transitions in the production of these compounds have been linked to reproductive isolation [9] , [19] . Males from species that produce dienes dimorphically preferentially court heterospecific females that carry dienes over heterospecific females that do not harbor these compounds [9] , [20] . In D . melanogaster , the synthesis of dienes depends in part on the product of the desatF gene ( also known as Fad2 ) [21] , a desaturase that catalyzes the addition of a second double bond in cuticular hydrocarbons . This gene is transcribed female specifically in adults [21] . The loss of desatF activity causes both a decrease in the amount of dienes , and a decrease in the attractiveness of females to males during courtship [21] , suggesting a crucial role in mate recognition . Others have hypothesized that differences at the desatF locus may contribute to the difference in diene production between D . simulans and D . sechellia [22] and other Drosophila species [23] . Here , we investigate the evolution and regulation of the desatF gene across the subgenus Sophophora . First , we show that the desatF locus and its expression are extremely rapidly evolving across the subgenus Drosophila . Second , we demonstrate that the female-specific isoform of the protein encoded by the sex differentiation gene , doublesex ( dsx ) , directly activates desatF expression in species that express desatF female-specifically . Third , we reveal that one species evolved monomorphic expression of desatF by functional inactivation of an ancestral DSX-binding site in the desatF regulatory region . And finally , we uncover an apparent case of recent stabilizing selection on desatF expression and show that in D . melanogaster , new cis-regulatory inputs in the desatF enhancer have evolved by a series of small deletions . We suggest that rapid evolution in the expression desatF underlies changes in the synthesis of cuticular hydrocarbons , which are likely to alter chemical communication between and within Drosophila species .
It has been suggested that the biosynthesis of cuticular hydrocarbons takes place in specialized cells called oenocytes [21] , [24] , [25] , which are present underneath the dorsal and ventral abdominal cuticle . Histological , confocal , and electron microscopy studies have characterized adult oenocyte cells as being organized in metameric , transverse ribbon-like stripes , that do not cross the midline and are positioned just anterior to the intersegmental region of each segment in the dorsal abdomen [26] . Adult oenocytes cells are also present in each segment of the ventral abdomen [26] . While desatF is known to be transcribed female-specifically , spatially restricted expression in oenocytes has not been demonstrated since all other analyses relied on reverse transcription ( RT ) -PCR on whole flies [21] , [23] . We therefore developed an in situ hybridization protocol to visualize mRNA transcripts in adult abdomens . Consistent with previous studies , desatF expression is female-specific in D . melanogaster . Our in situ analysis revealed desatF expression in a pattern that is entirely consistent with previous histological descriptions [26] of adult oenocyte cells ( Figure 1A , purple stripes ) . Moreover , desatF expression in the adult abdomen is identical to the pattern revealed by a previously characterized GAL4 driver that is active in oenocyte tissue ( see “desatF Expression in Female Oenocytes Is Directly Activated by the Female-Specific DOUBLESEX Isoform , ” below; Figure 4A and 4B , [21] ) . desatF expression in oenocyte cells is consistent with its role in diene biosynthesis . Our in situ analysis was limited to oenocyte cells and we cannot rule out expression of desatF in other tissues . The profile of cuticular diene production in the Sophophora subgenus exhibits several states , depending on the species ( Figure 2 , middle column ) ( unpublished data; [6]–[16] ) . Across this group , diene production displays an apparent transition from sexual monomorphism to dimorphism in an ancestor of the D . melanogaster species subgroup ( Figure 2 , left column , green arrowhead ) and several transitions to a state of no production ( Figure 2 , left column , black arrow and not shown ) . Since desatF has been shown to be crucial for the production of dienes in D . melanogaster [21] , we asked whether evolutionary changes at this locus could provide an explanation for these differences . In order to do so , we tested whether desatF expression correlated with diene production . We cloned the desatF coding region and several kb of its upstream putative regulatory sequence from 24 species within the subgenus Sophophora . desatF expression was assessed by in situ hybridization in species where the gene lacked interruption in its reading frame . In 15 out of 24 species , desatF appeared to be intact ( Figure 2 , right column and summarized in Figure 7 ) . desatF expression correlated with diene production ( Figure 2 , compare the middle and right columns ) . In species where diene production is monomorphic ( e . g . , D . pseudoobscura , D . persimilis , D . serrata ) , desatF is expressed in oenocytes of both sexes . In species that do not produce dienes , desatF was either not expressed ( D . simulans , D . mauritiana , D . santomea , and D . teissieri ) or the gene was not intact ( D . yakuba and D . orena ) ( Figure 2 , right column and summarized in Figure 7 ) . Finally , in species where diene production is strongly female-biased , desatF was expressed only in female oenocytes ( D . melanogaster , D . sechellia , and D . erecta ) . Our survey revealed that the expression of desatF has evolved with extraordinary rapidity ( summarized in Figure 7 ) . Of the 24 species analyzed , spanning approximately 40 million years of evolution , we uncovered ten independent evolutionary transitions ( not including an additional sex-specific transition discussed below ) ( summarized in Figure 7 ) . On the basis of the phylogenetic tree ( adapted from [27] ) , desatF was disrupted six times by deletions and insertions of repetitive DNA ( summarized in Figure 7 , red bars ) . The expression of an intact desatF was lost independently at least three times ( summarized in Figure 7 , black bars ) . Based on our expression data , and in agreement with the inferences of others [23] , it appears that female-specific expression was gained once at the base of the D . melanogaster species subgroup ( Figure 2 , right column ) . These ten evolutionary transitions in the state of desatF expression among such recently diverged species mark the fastest evolving pattern of gene utilization that we know of . We were particularly interested in understanding the mechanism by which female-specific expression of desatF had evolved in the D . melanogaster species subgroup . Transitions of diene production from monomorphic to dimorphic states can be simply explained by a modification of desatF expression . In order to investigate the molecular basis of the regulatory changes underlying transitions in desatF expression , we first identified the location of a cis-regulatory element ( CRE ) within the desatF locus that governs gene expression in oenocytes . A screen of the 15-kb upstream intergenic region was conducted to find CREs driving enhanced green fluorescent protein ( eGFP expression ) in 4-d-old D . melanogaster oenocytes . This reporter gene assay revealed a 638-bp CRE immediately upstream of desatF in D . melanogaster , which drove eGFP expression in a manner identical to endogenous gene expression ( Figures 3B and 3C , and S1 , region 4 ) . We next tested whether differences in desatF expression evolved by changes in its identified CRE , or by changes in trans-factors that regulate its expression . We distinguished among these possibilities by analyzing the activity of orthologous CREs of desatF from seven species in reporter gene assays in adult D . melanogaster . If cis-regulatory sequence evolution accounts for differences in desatF expression , then reporter activity driven by the CREs should recapitulate the expression pattern of desatF in the species from which the CRE was derived . All transgenes tested ( except D . sechellia; Figures 3D and 3E; discussed below ) recapitulated endogenous temporal- , spatial- , and sex-specific-expression of desatF of the species from which it was derived ( Figures 3F–3M ) . Thus , the changes underlying the transition between monomorphic and dimorphic expression of desatF largely occurred in the cis-regulatory regions of desatF . In order to elucidate how and when dimorphic desatF expression evolved , we next sought to dissect the molecular mechanisms regulating its expression in dimorphic species . In order to elucidate factors regulating dimorphic expression of desatF , we first delineated the minimal D . melanogaster and D . erecta CREs capable of recapitulating sex-specific expression . In D . melanogaster , we identified a 271-bp element ( mel-oe2; oe is an abbreviation for oenocyte element ) , upstream of desatF , which drives reporter activity in a pattern identical to the larger element ( Figure S1 ) . Further subdivisions led us to isolate a smaller CRE ( mel-oe1 ) , which also confers full reporter activity ( Figures 4J and 4K ) . In D . erecta , a 710-bp CRE upstream of desatF ( ere-oe ) was found to recapitulate endogenous expression in transgenic reporter assays in D . melanogaster adults ( Figures 4N , 4O , and S2 ) . To identify putative transcription factor binding sites within the desatF enhancer , we compared the sequence of mel-oe1 with ere-oe . This comparison revealed a single orthologous putative binding site in both elements for the sex-specific transcription factors encoded by the doublesex gene ( dsx ) [28] . Female- and male-specific isoforms of the DSX transcription factor specify sexual development of soma [29] . The Yolk protein coding genes ( yp1 and yp2 ) and the bric-a-brac locus ( bab ) are the only known direct targets of DSX regulation [30]–[32] . In both cases , DSX proteins regulate target genes sex-specifically by modulating the function of tissue-specific activators; DSX-F enhances target gene expression in females , and DSX-M represses expression in males . The presence of a putative DSX-binding site in the desatF CREs from D . melanogaster and D . erecta raised the possibility that desatF is a direct target of DSX regulation . This prediction was confirmed by a variety of genetic , biochemical , and transgenic reporter experiments . If DSX directly regulates desatF , then flies lacking DSX function should be altered in desatF expression . Indeed , in situ hybridization analyses indicated that females lacking DSX activity completely lacked the expression of desatF in adult oenocyte cells ( Figure S3 ) . Furthermore , depletion of dsx transcripts in oenocytes using a UAS-dsx-RNAi transgene driven by an oenocyte-specific Gal4 transgene ( OK72-Gal4; Figure 4A and 4B ) caused the loss of desatF expression in adult oenocyte cells ( compare controls in Figures 4C and 4E with Figure 4G ) . We next tested whether the DSX protein bound to the putative DSX binding sites in the desatF CREs from D . melanogaster and D . erecta . Electrophoretic mobility shift assays ( EMSAs ) demonstrated that the DSX-DNA-binding domain ( DSX-DBD ) specifically and efficiently bound the wild-type site ( Figure 4I , lanes 1–5 ) , and this binding was abolished when the DSX-binding site was mutated ( Figure 4I , lanes 6–10 ) . Furthermore , mutations in the DSX binding site of an otherwise wild-type mel-oe1 ( Figures 4J and 4K ) and ere-oe ( Figures 4N and 4O ) caused a complete loss of reporter activity in vivo ( Figure 4L and 4M and 4P and 4Q , respectively ) . Taken together , these data demonstrate that DSX-F directly activates desatF expression in adult oenocyte tissue . Note that while DSX-F is directly required for female-specific expression of desatF , it is not sufficient , and additional cis-regulatory inputs are also necessary for gene expression ( see “cis-Regulatory Sites in desatF Were Gained by a Series of Small Deletions during D . melanogaster Evolution” below ) . Our experiments did not indicate a repressive function for DSX-M in regulating desatF expression . The loss of DSX function in males did not lead to an upregulation of desatF in oenocyte cells ( compare controls in Figure 4D and 4F with 4H ) , and mutations in the DSX binding site of mel-oe1 and ere-oe did not lead to a gain of reporter expression in males ( Figure 4M and 4Q ) . Thus , while DSX-F is required directly to activate desatF expression in females ( Figure 4 ) , unlike other known targets of DSX proteins [30]–[32] , DSX-M apparently does not regulate desatF in males . Our studies thus demonstrate an additional mode of DSX target gene regulation . Our phylogenetic analysis of desatF expression in species within the Sophophora subgenus led us to infer that sexually dimorphic expression arose in the ancestor of the D . melanogaster species subgroup ( Figure 2 , left column , green arrowhead ) . Given that DSX-F directly activates desatF expression in female oenocytes of D . melanogaster and D . erecta , we posited that the origin of female-specific expression arose with the DSX-binding site . In order to test this hypothesis , we investigated the ancestry of this site . If the DSX-binding site at desatF evolved concomitantly with the origin of dimorphic expression , then all outgroup species to the D . melanogaster species subgroup analyzed in our study , which display either monomorphic expression , or no expression of desatF , should lack an orthologous DSX-binding site in the oenocyte CRE of desatF . While we did not find an orthologous DSX-binding site in the outgroup species D . pseudoobscura , D . persimilis , or D serrata , which display monomorphic expression of desatF , we were surprised to find several outgroup species that contained an orthologous sequence similar to the DSX-binding site consensus [28] within the upstream regulatory region of desatF ( Figure 5A ) . In D . prostipennis , D . paralutea , and D . eugracilis , all of which lack desatF expression in adult oenocytes , and in D . takahashii , which expresses desatF in both sexes , there is an orthologous sequence that matches the D . melanogaster DSX-binding site for at least ten out of 13 base pairs ( Figure 5A , right panel ) . In D . paralutea , the orthologous site matches the D . melanogaster site perfectly . These data indicate that the origin of the DSX-binding site most likely predated the origin of the D . melanogaster species subgroup ( Figure 5A , red ) . If true , then the origin of the DSX-binding site would be deeper in the phylogeny ( Figure 5A , green arrowhead ) than the origin of dimorphic expression originally inferred from our phylogenetic expression analyses ( Figure 5A , black arrowhead ) , and those from others [23] . One interpretation of the observations above is that dimorphic expression of desatF actually arose with the acquisition of the DSX-binding site in the desatF CRE and was subsequently lost in the D . takahashii subgroup and in D . eugracilis . If this were true , then the state of desatF expression in D . takahashii , D . prostipennis , D . paralutea , and D . eugracilis evolved from an ancestor that expressed desatF female-specifically by direct DSX regulation . Moreover , monomorphic expression of desatF in D . takahashii would then be predicted to have evolved at least in part by loss of direct DSX regulation . In order to test this possibility , we examined the D . takahashii desatF CRE and putative DSX-binding site in greater detail . We observed that the putative DSX-binding site present in the desatF CRE of D . takahashii diverges from the consensus sequence in two positions , one of which is located in the core of the site , where an A is found instead of the consensus C ( Figure 5A ) . We note that this difference is likely to be derived in D . takahashii as all other species with the orthologous site contain a consensus C at this position ( Figure 5A ) . Given that this change is located in the core of the sequence , it suggested to us that DSX proteins might not bind the D . takahashii site . EMSAs revealed that , in fact , the DSX-DBD failed to bind the orthologous site in D . takahashii desatF ( Figure 5C , lanes 1–5 ) . Furthermore , introducing the identical C-to-A mutation in the core of the D . melanogaster DSX-binding site greatly diminished binding of the DSX-DBD relative to wild type ( Figure 5B , compare lanes 1–5 with lanes 6–10 ) , and when introduced in an otherwise wild-type mel-oe1 ( Figures 5D and 5E ) , this C-to-A mutation caused a complete loss of reporter activity ( Figure 5F ) . These results show that the putative D . takahashii DSX-binding site is nonfunctional . Our data are consistent with an evolutionary scenario in which monomorphic expression of desatF in D . takahashii evolved from a dimorphic ancestor by a loss-of-function mutation in the ancestral DSX-binding site . We tested this scenario by assessing whether the restoration of a functional DSX-site by an A-to-C transition in the D . takahashii oenocyte CRE would result in dimorphic reporter expression . A 296-bp CRE upstream of desatF from D . takahashii is fully sufficient to recapitulate monomorphic expression of desatF in transgenic reporter assays in D . melanogaster ( Figure 5H and 5I ) . EMSAs confirmed that an A-to-C mutation in the core of the D . takahashii DSX-like binding site , which converts it to the consensus sequence , is sufficient to restore binding by the DSX-DBD ( Figure 5C , compare lanes 1–5 with lanes 6–10 ) . Remarkably , this change was also sufficient to increase reporter expression in females and to decrease reporter expression in males relative to wild-type constructs ( Figure 5J and 5K ) . This modified CRE is thus functionally dimorphic in contrast to the wild-type functionally monomorphic construct ( compare Figure 5H and 5I with 5J and 5K ) . These results are consistent with the monomorphic expression of desatF in D . takahashii having evolved , at least in part , by the inactivation of the DSX-binding site that was present in an ancestor that expressed desatF female-specifically . Furthermore , this brings us to a total of two transitions in the sex-specificity of desatF expression ( summarized in Figure 7 , pink bars ) : a gain of dimorphism and a subsequent transition to monomorphism . In the course of our studies of desatF regulation , we were surprised to discover that while D . melanogaster , D . sechellia , and D . erecta express desatF similarly in female oenocytes ( Figure 2 , right column ) , their respective oenocyte CREs were significantly different in structure . We found that sequences orthologous to the mel-oe2 CRE from D . erecta ( i . e . , ere-oe2; Figure S2 ) and D . sechellia ( i . e . , sec-oe2; Figure 3 , compare 3B with 3D ) failed to drive expression in transgenic reporter assays in D . melanogaster . These results suggested that female-specific desatF expression in D . erecta and D . sechellia rely at least in part on different cis-regulatory sites than those characterized in D . melanogaster . Indeed , for D . erecta , additional sequences outside the ere-oe2 region are required for reporter activity . By extending the 5′-end of ere-oe2 by 190 bp ( ere-oe3; Figure S2 ) , we obtained full reporter activity in D . melanogaster female oenocytes . Importantly , this 190-bp region , by itself , is not sufficient for reporter function . Furthermore , the orthologous region from D . melanogaster is clearly not required for CRE function , as mel-oe2 is a fully functional CRE despite lacking the orthologous 190-bp region ( Figure S1 ) . Thus , the functional D . melanogaster and D . erecta CREs share common necessary features ( e . g . , the DSX-binding site ) , but also exhibit critical differences ( e . g . , the 190-bp region required for D . erecta CRE activity ) . For D . sechellia , an exhaustive search of all intergenic sequences upstream and downstream of desatF failed to identify a region that drove reporter expression in the D . melanogaster genetic background ( unpublished data ) . This may indicate that the D . sechellia CRE for oenocyte expression is located outside of the regions searched and/or that there are trans-acting regulatory differences between the species , which are key for desatF expression in D . sechellia females . We note there is a putative DSX-binding site in the D . sechellia desatF CRE that matches the consensus site described for D . melanogaster [28] . Thus the absence of reporter expression from the D . sechellia desatF CRE in the D . melanogaster molecular background is not likely to be due to a failure of DSX to bind the site . Together , our data indicate that D . melanogaster , D . sechellia , and D . erecta express desatF female-specifically in part by distinct cis-regulatory mechanisms . One explanation for these results is that stabilizing selection has maintained phenotypic constancy for desatF expression while mutational turnover of functionally important sites has taken place . This phenomenon has been previously reported [33]–[38] , but described for species that have diverged over relatively long periods of evolutionary time ( 40 millions y or more ) . It is surprising that drastic alterations in the cis-regulatory mechanisms at desatF occurred in a short period of time ( D . melanogaster and D . sechellia diverged only 2–3 million y ago ) . More detailed investigation of the D . melanogaster CRE uncovered cis-regulatory sites specific to this species and important for desatF female-specific expression . While investigating the cis-regulatory differences at desatF between the three dimorphic species , we uncovered an unusual feature specific to D . melanogaster . We found eight copies of the hexamer AATTTG in its upstream regulatory region ( Figure 6A ) , i . e . , within mel-oe1 and -oe2 , which was overrepresented with high statistical significance ( occurrence p = 0 . 0000051; from the Oligo-analysis program [39] ) . The motif does not match any binding site consensus to our knowledge . In order to test whether these motifs were functionally relevant , we introduced point mutations in six of these motifs in an otherwise wild-type mel-oe2 CRE . This led to a complete loss of reporter activity in female oenocytes ( unpublished data ) , indicating that these motifs are indeed necessary for CRE function . Most of these hexamer motifs were absent in the orthologous region from D . erecta , D . sechellia ( Figure 6A ) , and all other species examined in our study ( unpublished data ) , suggesting new cis-regulatory sites evolved recently in the D . melanogaster lineage . The evolution of these hexamer motifs could have occurred through a variety of mechanisms [40] . Rearrangement events such as transposition and duplication , and binding site formation by point mutation , are the two main modes by which new cis-regulatory content has been suggested to evolve [41]–[43] . In order to understand the mutational path that produced these hexamer motifs at desatF in D . melanogaster , we compared the mel-oe2 sequence to its ortholog in closely related species , D . simulans and D . erecta . Of particular interest is the cluster of three motifs in the forward direction in mel-oe2 ( Figure 6A ) . A sequence alignment of mel-oe2 and its ortholog from D . erecta and D . simulans revealed that , except for the hexamers , this region is largely conserved , excluding a transposition event ( Figure 6D; see below ) . Closer scrutiny of the alignment in the hexamer region revealed in D . simulans and D . erecta the presence of common insertions/deletions disrupting each of the three hexamer motifs in the cluster ( Figure 6D ) . On the basis of the phylogenetic relationships among these three species , we infer that the three hexamer motifs were gained during D . melanogaster evolution by a series of nonidentical small deletions . We tested if these three particular motifs were required for CRE function by introducing point mutations in the mel-oe1 CRE . We found that they caused a complete loss of reporter activity ( Figure 6B and 6C ) . To test whether the AT content of these motifs , instead of their sequence , could explain their functional relevance , we mutated the three sites in mel-oe1 without altering their AT percentage . This construct also failed to produce reporter activity ( Figure S4 ) . We suggest that these hexamer sequences are binding sites for a transcription factor and that they evolved via a series of small deletions .
A long-standing question in evolutionary biology is how sexually dimorphic traits evolve [49] . For example , monomorphic patterns can evolve from dimorphic patterns and vice versa , however , the molecular mechanisms that govern these transitions have seldom been addressed . In Lepidoptera and Diptera , duplication or structural changes of genes encoding desaturases have been suggested to [23] or shown to contribute to evolutionary alterations in pheromone signals [50]–[52] , however , none of these phenomena alone could account for evolutionary transitions in sex-specificity of pheromone production . Here , we have provided evidence that cis-regulatory sequence evolution led to transitions from monomorphic to dimorphic expression of desatF , and its reversion , and concomitant changes in diene production . By pinpointing one of these transitions at the level of individual base-pairs , we propose that monomorphic expression of desatF in D . takahashii evolved from a dimorphic ancestor through a derived mutation in a single critical residue inactivating the orthologous DSX-binding site ( Figure 4 ) . A simple model for the origin of monomorphic gene expression is that a mutation in the DSX-binding site abrogated repression by DSX-M , in turn , up-regulating desatF expression in males . Furthermore , the loss of regulation by DSX-F would lead to a decrease in desatF expression in females . These alterations , together , would produce monomorphic expression of desatF in D . takahashii . However , we note that this model is at odds with our finding that DSX-M appears to not regulate desatF in D . melanogaster . This suggests that the ancestor of D . melanogaster and D . takahashii regulated desatF dimorphically by either a D . melanogaster-like mechanism , or by a mode that involved repression by DSX-M . While we currently cannot polarize these possibilities , both models implicate the inactivation of the ancestral DSX-binding site as a necessary step in the transition to monomorphic expression . In order to understand the mechanisms that drive speciation , the genetic changes that lead to reproductive isolation must be elucidated . It has been suggested that , “speciation genes are those that contribute to reproductive isolation , often in the form of hybrid inviability , sterility or behavioral aberration” [53] . While progress has been made in identifying genes that contribute to postzygotic isolation ( such as Xmrk2 [54] , [55] , OdsH [53] , Nup96 [56] , see review [57] ) , little is known of genes that contribute to prezygotic isolation . We suggest that desatF could be one such gene . There is evidence that diene production contributes to reproductive isolation . For example , it has been documented that dienes inhibit D . simulans male courtship behavior [58] . Moreover , D . simulans/D . melanogaster hybrid females lacking desatF expression elicit greater levels of courtship activity from D . simulans males , relative to hybrids expressing desatF female-specifically [59] . Taken together , these data indicate that expression of desatF and the production of dienes in D . melanogaster females contribute to the reproductive isolation between these sibling species . desatF expression has evolved numerous times during Drosophila evolution . If , as others have suggested [12] , transitions in dienes contribute to sexual behavior in species other than D . melanogaster , then the contribution of desatF to speciation may be widespread .
Wild-type stocks were obtained from the University of California , San Diego ( UCSD ) stock center ( see Table S1 ) . Gal4-UAS analyses were performed using the following lines: OK72-Gal4 was obtained from the Bloomington Stock Center; dsx1 pp , UAS-dsxRNAi , and UAS-lacZ were provided by M . McKeown ( Brown University ) . Images of in situ hybridizations and X-Gal stained adult abdomens were taken using an Olympus SZX16 Stereo Microscope equipped with an Olympus DP71 microscope digital camera . Adult transgenic eGFP-reporter line samples were imaged using an Olympus Fluoview FV 1000 confocal microscope and software . Wings and head were removed from 4-d-old adults , which were then mounted in Halocarbon 700 oil for confocal analysis . Sequences for D . melanogaster , D . simulans , D . sechellia , D . yakuba , D . erecta , D . ananassae , and D . pseudoobscura were obtained from their respective genome databases . All other sequences were obtained by cloning and sequencing of orthologous sequences using genomic DNA prepared from species stocks obtained from the UCSD Drosophila stock center ( see Table S1 ) . Sequences were PCR amplified using different sets of degenerate primers and then fused to give rise to the sequence of the whole locus . Details are available upon request to the authors . Novel sequences have been deposited in GenBank ( http://www . ncbi . nlm . nih . gov/Genbank , submission numbers are listed in Table S1 ) . Orthologous sequences were aligned using ClustalW2 [60] with subsequent manual alignment in problematic regions . We used the GenePalette program to analyze our sequences ( www . genepalette . org ) . We used Oligo-analysis to look for overrepresented motifs in our sequence [39] . This program calculates the probability that the analyzed sequence contains an oligonucleotide sequence at a frequency greater than that expected at random . In situ hybridization was performed as previously described [61] with minor modifications . The complete adult abdominal in situ protocol is available at http://www . molbio . wisc . edu/carroll/ . Primers used to amplify probes are listed in Table S2 . EMSAs were performed as previously described [27] , [32] . PAGE-purified oligos used in EMSAs are listed in Table S3 . All transgenic lines were produced by using the Phage φC31 Integrase system . Embryos from flies containing the X-chromosome attP docking site VK00046 [62] were injected as previously described [32] . Primers used to clone the constructs are listed in Table S4 . | Mate selection is a complex process involving communication between potential partners on many levels , such as visual , aural , and olfactory cues . Many animals use chemical signals in the form of pheromones to communicate and correctly recognize individuals of the appropriate species and sex during reproduction . Evolutionary changes in the production of these chemicals have been suggested to contribute to speciation . Yet , the molecular mechanisms governing these transitions have seldom been addressed . Here , we show that expression of the gene desatF , which encodes an enzyme involved in the production of the Drosophila pheromones known as dienes , is highly variable and rapidly evolving across Drosophila species . Changes in desatF gene expression correlate with changes in sex- and species-specific production of dienes . Further , these changes in diene production can be explained by simple modifications in the regulatory regions of the desatF gene , providing a molecular level understanding of the evolution of pheromone production in Drosophila . | [
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] | 2009 | Rapid Evolution of Sex Pheromone-Producing Enzyme Expression in Drosophila |
The interplay of different virus species in a host cell after infection can affect the adaptation of each virus . Endogenous viral elements , such as endogenous pararetroviruses ( PRVs ) , have arisen from vertical inheritance of viral sequences integrated into host germline genomes . As viral genomic fossils , these sequences can thus serve as valuable paleogenomic data to study the long-term evolutionary dynamics of virus–virus interactions , but they have rarely been applied for this purpose . All extant PRVs have been considered autonomous species in their parasitic life cycle in host cells . Here , we provide evidence for multiple non-autonomous PRV species with structural defects in viral activity that have frequently infected ancient grass hosts and adapted through interplay between viruses . Our paleogenomic analyses using endogenous PRVs in grass genomes revealed that these non-autonomous PRV species have participated in interplay with autonomous PRVs in a possible commensal partnership , or , alternatively , with one another in a possible mutualistic partnership . These partnerships , which have been established by the sharing of noncoding regulatory sequences ( NRSs ) in intergenic regions between two partner viruses , have been further maintained and altered by the sequence homogenization of NRSs between partners . Strikingly , we found that frequent region-specific recombination , rather than mutation selection , is the main causative mechanism of NRS homogenization . Our results , obtained from ancient DNA records of viruses , suggest that adaptation of PRVs has occurred by concerted evolution of NRSs between different virus species in the same host . Our findings further imply that evaluation of within-host NRS interactions within and between populations of viral pathogens may be important .
Similar to virus–host interactions , virus–virus interactions , especially those occurring during mixed plant virus infections in nature , have complex outcomes ranging from antagonism to synergism [1 , 2] . Such interactions between different virus species affect their adaptation [1 , 2] . Numerous virus-derived sequences , referred to as endogenous viral elements ( EVEs ) , have recently been discovered in various eukaryotic genomes [3–6] . In addition to EVEs derived from retroviruses , EVEs originating from viruses without active reverse-transcription or integration abilities have been identified [4 , 7–10] . Because these elements are vertically inherited viral sequences integrated into the germline genome of a host , they are viral genomic fossils and hence serve as invaluable historical records [3 , 11 , 12] . Although EVEs may provide an unprecedented opportunity to advance our understanding of evolutionary-scale virus–virus interactions , these records have rarely been exploited to explore such interactions . Pararetroviruses ( PRVs ) , including Caulimoviridae and Hepadnaviridae families , are reverse-transcribing double-stranded DNA viruses that lack an integrase and a process for integration [5 , 13] . PRVs also possess EVEs called endogenous PRVs that originated from the incidental integration of PRV DNA into host genomes through non-homologous end-joining [14 , 15] . Endogenous PRVs have been identified in an increasing number of plant genomes and have also been recently discovered in bird and reptile genomes [4 , 5 , 11 , 16–18] . PRVs are thought to be distantly related to long terminal repeat ( LTR ) retrotransposons [19] . Interestingly , many LTR retrotransposons are non-autonomous with respect to their parasitic life cycle in host cells , i . e . , they have lost most or all of their coding capability but can amplify themselves by using the protein machinery of autonomous LTR retrotransposons that are functionally and structurally intact [20–22] . A hallmark of the parasitism of non-autonomous LTR retrotransposons on their autonomous partners is the substantial sequence similarity of their LTRs—the location of noncoding regulatory sequences ( NRSs ) [22–24] . Plant PRVs have open circular genomes and encode a movement protein ( MP ) , a capsid protein ( CP ) harboring a zinc finger motif , a protease ( PR ) , and a reverse transcriptase with RNase H activity ( RT/RH ) [25] . In addition to the domains encoding these essential proteins , diverse non-standard domains or open reading frames ( ORFs ) have frequently been found in plant PRV genomes , the protein products of which generally play roles in vector transmission or immune suppression [26 , 27] . The intergenic region ( IGR ) of plant PRVs , a highly diverse noncoding region containing multiple NRSs , is crucial for viral transcription , translation , and replication [25 , 27] . All known PRVs encode all essential proteins and are thus autonomous PRV species during their parasitic life cycle in host cells . Limited cases of non-autonomous virus species have been previously documented . One well-known example is adeno-associated virus ( Dependoparvovirus , a single-stranded DNA virus ) , which has been applied as a gene therapy vector [28] . No non-autonomous PRV species have been reported from nature to date . In this study , we uncovered paleogenomic evidence for non-autonomous PRVs and revealed their interplay with different PRV species through an analysis of endogenous PRVs in grass family ( Poaceae ) genomes ( S1 Table ) . We discovered two examples of virus–virus interactions: a possible commensal partnership between a non-autonomous PRV and an autonomous PRV species , and a possible mutualistic partnership between two functionally complementary non-autonomous PRV species . Unexpectedly , we found that the two partners in each interplaying system have frequently exchanged ( >18 estimated major recombination events ) their NRSs with each other via region-specific recombination to maintain partnership and coevolution . The NRS homogenization between partner viruses led by such recombination events suggests that concerted evolution has occurred in these proposed partnerships . Our results provide paleoviral insights into the genesis and adaptation of complex virus systems .
We previously identified the first known endogenous PRV family in the genome of rice ( Oryza sativa ) [29] . This family , derived from a sister species of rice tungro bacilliform virus ( RTBV ) —an autonomous PRV that infects O . sativa—has been designated as endogenous RTBV-like ( eRTBVL ) [14 , 29 , 30] . In the present study , we observed domain reshuffling in at least 13 eRTBVL segments in the O . sativa genome , 7 of which formed a long cluster on chromosome 8 with segments of eRTBVL-X ( the youngest group of eRTBVL [30] ) ( S1 Fig ) . These reshuffled sequences exhibited a consensus pattern among the 13 segments ( S2 Fig ) , which suggests that the domain reshuffling must have occurred in the corresponding viral genome prior to integration . We named this reshuffled eRTBVL as endogenous RTBV-like 2 ( eRTBVL2 ) and reconstructed its ancestral virus circular genome ( Fig 1A ) . Instead of an RT/RH domain and a third ORF , this eRTBVL2 possessed a functionally unknown domain , henceforth referred to as the SFKTE domain ( for the conserved five-residue SFKTE present in all homologous sequences ) ( Fig 1A and 1B ) . A BLAST search for the SFKTE domain sequence in the O . sativa genome identified 15 loci ( e-value < 4 . 00 × 10−44 ) that have recently been annotated as endogenous PRVs similar to petunia vein clearing virus ( PVCV ) sequences; these PRVs are hereafter referred to as endogenous PVCV-like ( ePVCVL ) ( Fig 2A; [18] ) . By aligning the regions around the identified sequences , we constructed the ancestral virus circular genome for these ePVCVL segments ( Fig 1A; details in S3 Fig ) . The results of a detailed sequence comparison using consensus sequences of viral genomes imply a possible recombination event between the viruses of eRTBVL and ePVCVL that may have generated a recombinant virus responsible for eRTBVL2 ( Fig 1B ) . Recombination analyses with multiple methods statistically validated this recombination event ( P = 7 . 18 × 10−309; S2 Fig ) . Examination of presumed recombination breakpoints revealed no obvious sequence similarity between the parent sequences; instead , we detected a small microhomologous region at the left breakpoint ( S2 Fig ) , which suggests an illegitimate recombination event . Three predicted essential domains ( MP , CP , and PR ) were confirmed by conserved motif alignment , but the RT/RH domain indispensable for replication was not detected in eRTBVL2 or ePVCVL ( S2 Table and S4 Fig ) . Despite the absence of the RT/RH domain , the presence of multiple genomic fossils of these viruses ( 13 eRTBVL2 and 24 ePVCVL segments in the O . sativa genome; S2 Fig and S3 Table ) suggests the success of their proliferation . We therefore propose that the viruses of eRTBVL2 and ePVCVL are non-autonomous PRV species . To achieve replication , non-autonomous PRVs of eRTBVL2 and ePVCVL should require an autonomous partner virus or other related elements . Considering the high sequence similarity of IGRs carrying NRSs ( Fig 1B; predicted NRSs in S5 Fig ) , we hypothesized that the virus of eRTBVL2 may depend on the protein machinery of the virus of eRTBVL ( an autonomous PRV ) for proliferation , similar to the case of parasitic interactions between non-autonomous and autonomous LTR retrotransposon pairs [20–22] . We thus tested the spatio-temporal likelihood of this proposed interplay . In a phylogenetic tree of IGR sequences of eRTBVL and eRTBVL2 ( Fig 1C ) , most eRTBVL2 sequences were placed within or close to the eRTBVL-X clade , with three other eRTBVL2 sequences each falling into one of three older eRTBVL clades ( -A1 , -A2 and -B ) [30] . Phylogenetic trees of other homologous regions ( ORF1 , MP , CP , and PR domains ) between eRTBVL and eRTBVL2 had topologies similar to the IGR-based tree ( see S6 Fig for these four ORF/domains ) . The results of these phylogenetic analyses suggest that recombination may have occurred between the viruses of eRTBVL and eRTBVL2 at IGRs and other homologous regions , implying their spatio-temporal coexistence . Detailed recombination analyses confirmed the contribution of the virus of eRTBVL to the recombination of the viruses of the three eRTBVL2 sequences phylogenetically close to eRTBVL-A1 , -A2 , and -B clades , and also supported recombination events between the viruses of eRTBVL-X and other eRTBVL2 sequences ( P = 1 . 37 × 10−9 to 1 . 44 × 10−181; S7A Fig ) . We next analyzed the temporal relationship of eRTBVL2 segments based on a phylogeny of the SFKTE domain ( S8 Fig ) . We rooted the phylogenetic tree of SFKTE amino acid sequences of eRTBVL2 and ePVCVL ( S8 Fig ) using the oldest ePVCVL segment , where the relative antiquity of the latter was determined by a bidirectional genome-wide orthology analysis of ePVCVL loci in Oryza species ( see Materials and Methods and S4 and S5 Tables; PCR and Sanger sequencing validation in S9 Fig ) . In the generated SFKTE domain tree ( S8 Fig ) , the eRTBVL2 segments related to the eRTBVL-X group ( Fig 1C ) were the latest branching sequences , whereas the three eRTBVL2 segments related to eRTBVL-A1 , -A2 , and -B groups ( Fig 1C ) branched earlier ( S8 Fig ) . Because the eRTBVL-X group is the youngest eRTBVL group and eRTBVL-A1 , -A2 , and -B groups are older [30] , the SFKTE phylogeny indicates that the evolution of the virus of eRTBVL2 is temporally consistent with that of eRTBVL . Taken together , these results strongly support the coexistence and coevolution of the viruses of eRTBVL2 and eRTBVL and provide evidence for a possible partnership between the two viruses during mixed infection . The virus of eRTBVL2 did not seem to be a parasite on the virus of eRTBVL , because we observed no higher magnitude of proliferation in the former relative to the latter ( Fig 1C and S6 Fig ) . Taking into account the observation that the replication dependence of the virus of eRTBVL2 on the virus of eRTBVL had no recognizable deleterious effect on the latter , we suggest a possible commensal partnership between the viruses of eRTBVL2 and eRTBVL . Although our search for the autonomous partner of the virus of ePVCVL revealed no such candidate in the genomes of O . sativa or other Oryza species , we noticed another endogenous PVCV-like family ( hereafter ePVCVL2 ) showing defective structures ( Fig 2B and 2C; [18] ) . We successfully reconstructed the ancestral virus circular genome of ePVCVL2; this ancestral genome possessed MP , PR , and RT/RH domains but the CP domain was absent ( Fig 3A; details in S2 Table , S3 and S4 Figs ) . The composition of this genome suggests that the virus of ePVCVL2 is structurally and functionally complementary to the virus of ePVCVL . Given the existence of the naturally defective genome as well as multiple fossils of the virus of ePVCVL2 ( 11 segments in the O . sativa genome; S3 Table ) , we suggest that this virus is another non-autonomous PRV species . Detailed comparison of ePVCVL and ePVCVL2 consensus sequences revealed a high degree of local similarity between their IGRs as well as their MP domains ( 97 . 2% nucleotide identity: 99 . 3% for IGR and 95 . 3% for MP ) ( Fig 3B ) . Given that IGR sequence identities between eRTBVL groups ( intraspecies level ) ranged from 72 . 6% to 92 . 8% , this interspecies similarity of IGRs is exceptionally high . Both ePVCVL and ePVCVL2 encode a PR domain , but the nucleotide sequence of this region was very dissimilar between these two types of endogenous PRVs ( Fig 3B ) . This dissimilarity of PR domains , extraordinarily high IGR sequence similarity ( identical NRSs between IGRs; predicted NRSs in S5 Fig ) , and observed functional complementarity between the viruses of ePVCVL and ePVCVL2 all suggest a possible mutualistic partnership in which the two viruses mutually compensate to facilitate proliferation . To confirm the proposed partnership , we performed a bidirectional genome-wide orthology analysis of ePVCVL2 loci in Oryza genomes ( the same analysis of ePVCVL loci mentioned above ) . This analysis revealed that ePVCVL and ePVCVL2 segments are species-specific , except for four shared ePVCVL loci and two shared ePVCVL2 loci , and coexist in each analyzed Oryza genome ( Fig 2C; details in S9 Fig , S4 and S5 Tables ) , thereby supporting the coexistence of the viruses of ePVCVL and ePVCVL2 during host divergence . No major ePVCVL cluster related to a major ePVCVL2 cluster was present in the phylogenetic tree of ePVCVL and ePVCVL2 IGR sequences in the O . sativa genome ( Fig 3C ) . On the contrary , three ePVCVL IGR sequences clustered with three ePVCVL2 IGR sequences in a strongly supported clade ( Fig 3C ) . To confirm this finding , we examined single nucleotide polymorphisms ( SNPs ) among the six IGR sequences , which revealed six SNP sites shared by the IGRs of ePVCVL and ePVCVL2 ( Fig 3D ) . We further carried out recombination analyses on these ePVCVL and ePVCVL2 sequences , which resulted in the identification of significant recombination events between the IGRs of the viruses of ePVCVL and ePVCVL2 ( P = 1 . 28 × 10−8 to 2 . 90 × 10−23; S7B Fig ) . When we extended our phylogenetic analysis of IGR sequences to segments in other Oryza genomes , we also found that the IGR sequences of ePVCVL and ePVCVL2 clustered together ( S10 Fig ) . The recombination of IGR sequences between the viruses of ePVCVL and ePVCVL2 , implied by the phylogenetic analysis , was likewise confirmed by recombination analyses of ePVCVL and ePVCVL2 sequences in these Oryza genomes ( P = 4 . 06 × 10−4 to 6 . 87 × 10−23; S7C Fig ) . Taken together , these data thus provide strong evidence that two non-autonomous PRVs in a possible mutualistic partnership have recombined their IGR sequences to continue their coevolution during mixed infection . By searching for homologous sequences of eRTBVL2 , ePVCVL , and ePVCVL2 and reexamining reported endogenous PRVs in non-Oryza grass genomes [18] , we found both ePVCVL and ePVCVL2 homologous sequences coexisting in the genomes of sorghum ( Sorghum bicolor ) and switchgrass ( Panicum virgatum ) ( Fig 2 and S3 Table ) . These sequences formed a phylogenetic sister group ( non-Oryza group ) to either ePVCVL or ePVCVL2 segments of analyzed Oryza genomes ( Oryza group ) ( Fig 2A and 2B ) . We constructed two ancestral virus circular genomes for these sequences ( Fig 4A; details in S3 Fig ) . One was structurally equivalent to Oryza group ePVCVLs , that is , the RT/RH domain was absent . The other genome resembled Oryza group ePVCVL2s , but lacked both MP and CP domains ( this genome contained a region slightly resembling the CP domain but without an essential zinc finger motif ) ( Fig 4A and 4B; details in S2 Table , S3 and S4 Figs ) . IGR sequences of ePVCVL and ePVCVL2 non-Oryza groups shared extremely high nucleotide identities ( 97 . 1%; Fig 4B ) , whereas IGR sequence similarities between ePVCVL Oryza and non-Oryza groups and between ePVCVL2 Oryza and non-Oryza groups were low ( 43 . 6% and 44 . 6% nucleotide identities , respectively; S11 Fig ) . In a phylogenetic tree based on sequences from the S . bicolor genome , IGR sequences of non-Oryza ePVCVL and ePVCVL2 groups were mixed together ( Fig 4C ) . ( The number of IGR sequences in the P . virgatum genome was too limited for phylogenetic analysis ) . We also performed recombination analyses on these sequences in the S . bicolor genome , which resulted in the detection of significant recombination events occurring between the IGRs of the viruses of ePVCVL and ePVCVL2 non-Oryza sequences ( P = 6 . 30 × 10−10 to 1 . 23 × 10−22; S7D Fig ) . A close examination of the S . bicolor sequences revealed that virus-derived small insertion/deletion ( indel ) variations in IGRs were shared between partial non-Oryza ePVCVL and ePVCVL2 segments ( Fig 4D ) . The presence of these indels is direct evidence that IGRs have frequently been recombined between the virus genomes of ePVCVL and ePVCVL2 . Taking all of these results into consideration , we conclude that non-autonomous PRVs have adapted to a long-term partnership via IGR homogenization mediated by frequent recombination , leading to concerted evolution of NRSs .
The discovery and analysis of various EVEs in eukaryotic genomes has contributed to our understanding of viral origin and evolution as well as long-term interactions between viruses and hosts [3 , 31–33] . Endogenous PRVs in plant genomes have been frequently reported [5 , 18 , 34] , and extreme cases of endogenous PRV reactivation under certain conditions , such as in endogenous banana streak virus , have been well documented [35–38] . Using grass endogenous PRVs as ancient DNA records of viruses , we performed paleogenomic analyses of PRVs to explore their long-term virus–virus interactions . In contrast to all previously known PRVs , which are autonomous , three non-autonomous PRV species were identified in this study , namely , the viruses of eRTBVL2 , ePVCVL , and ePVCVL2 . Our examination of ePVCVL and ePVCVL2 sequences , which were first described by Geering et al . [18] , revealed the adaptation strategies of their corresponding non-autonomous viruses . We have proposed two adaptation strategies used by non-autonomous PRVs: a possible commensal partnership with autonomous PRVs and a possible mutualistic partnership with other non-autonomous PRVs ( summarized in Fig 5A ) . These proposed partnerships have been enabled by the existence of shared common NRSs in their IGRs . We have also demonstrated the evolutionary dynamics of these partnerships: frequent recombination of IGRs ( >18 estimated major events; see below ) between two partners leading to NRS homogenization between different PRV species during host divergence . This concerted evolution of NRSs is responsible for the maintenance of such partnerships and has driven the coevolution of interacting viruses . The consensus NRSs of two partner viruses would be expected to recruit the same virus-encoded proteins and host factors to complete their life cycles in hosts . In the possible commensal partnership suggested by this study ( Fig 5A ) , the non-autonomous virus of eRTBVL2 should benefit from sharing the RT/RH protein of the autonomous virus of eRTBVL . With respect to the SFKTE domain of the virus of eRTBVL2 , neither the RT-like motif nor its degenerate residues could be distinguished in this domain by amino acid alignment with all known types of RT-like domains ( S4 Fig and S1 Dataset ) or by using HHpred , a sensitive detection method based on profile hidden Markov models ( S2 Table; see Materials and Methods ) [39] . Although the possibility cannot be completely excluded and future biochemical verification is needed , the likelihood of RT activity in SFKTE proteins is very low . In fact , plant PRV genomes usually possess various additional non-standard domains or ORFs that often play a role in vector transmission or immune suppression [26 , 27] . SFKTE proteins may have functions similar to those of well-known additional PRV proteins , such as interaction with insect vector proteins or host antiviral factors [26 , 27] . Although not necessary for its replication , the virus of eRTBVL may also benefit , to some extent , from such a function of SFKTE proteins encoded by the virus of eRTBVL2 during mixed infection . Consequently , an alternative relationship may exist between the two viruses: a mutualistic partnership . In the possible mutualistic partnership suggested for the viruses of ePVCVL and ePVCVL2 ( Fig 5A ) , the two non-autonomous viruses benefit from each other via functional complementary . The RT/RH protein from the virus of ePVCVL2 reverse transcribes its own pregenomic RNA as well as that of the virus of ePVCVL , while the CP protein from the virus of ePVCVL assembles its own viral particles as well as those of the virus of ePVCVL2 . Products from additional domains/ORFs of these two viruses ( the SFKTE domain of the virus of ePVCVL and ORF2 of the virus of ePVCVL2 ) may also contribute to the putative mutualistic partnership . In the case of the non-Oryza group , the MP protein from the virus of ePVCVL is responsible not only for its own cell-to-cell movement , but also for that of the virus of ePVCVL2; at the same time , the region in the virus of ePVCVL2 slightly similar to the CP domain but lacking a zinc finger motif may encode defective CP proteins ( i . e . , those lacking viral DNA binding activity because of missing zinc finger motifs ) to bind host antiviral proteins to disable viral-CP-binding activities . This system of two interplaying viruses is reminiscent of extant complex viruses possessing multiple polynucleotide sequences , which suggests that functional complementarity and co-regulation may have contributed to the origin of multipartite viruses . The interspecies recombination event that generated the virus of eRTBVL2 ( Fig 1B and S2 Fig ) occurred between the viruses of eRTBVL ( Tungrovirus-related species [29] ) and ePVCVL ( Petuvirus-related species; Fig 2A ) , which belong to different genera and possess distinct genomic structures with very weak sequence similarities . The presence of reshuffled domain combinations in the viral genome of eRTBVL2 relative to the virus of eRTBVL ( Fig 1A and 1B ) supports the theory of modular evolution that has been considered to be applicable to all known virus types [40 , 41] . Putative interspecies recombination events have frequently been reported in viruses [42–46] . We propose that interspecies recombination is one of the mechanisms driving viral modular evolution . We particularly note that the frequent exchange of IGRs revealed in this study implies that modular evolution applies not only to coding domains , but also possibly to NRSs . Other studies have observed that recombination between endogenous and exogenous retroviruses has occasionally occurred and produced recombinant viruses [47–52] . This recombination may occur when exogenous and endogenous retroviral RNAs are coexpressed in host cells [47] . Recombination between endogenous and exogenous PRVs has not been reported to date [12] . Although in our study we also found no evidence to support the origin of any non-autonomous PRVs from such recombination , consideration of the evolutionary influence of this type of occasional albeit hypothetical recombination event is still of interest . Concerted evolution has been widely observed to accompany the sequence homogenization process of some duplicated genes or elements in prokaryotic and eukaryotic genomes; one notable example is the sequence homogenization of ribosomal DNA repeats within a species [53 , 54] . Concerted evolution has also been reported in nanoviruses , which are single-stranded DNA viruses [55 , 56] . In our study , concerted evolution was observed during the homogenization of IGRs between a pair of partner viruses . IGRs are noncoding and highly divergent across PRV genomes; for example , IGRs of RTBV and PVCV respectively share less than 44 . 4% and 35 . 1% nucleotide identities with those of other PRVs ( NCBI genome database ) . Nevertheless , the overall set of IGRs ( and neighboring regions ) between the two partner viruses in this study displayed an extraordinarily high sequence similarity ( Figs 1B , 3B and 4B ) . This finding suggests that recombination , rather than mutation selection , is the main contributor to IGR homogenization between partner viruses . The results of our detailed phylogenetic and recombination analyses support the idea that persistent recombinations have driven this IGR concerted evolution ( Figs 1C , 3C and 4C; S7 and S10 Figs ) . When we generated consensus sequences for eRTBVL2 , ePVCVL , and ePVCVL2 , we found a consensus pattern for each recombination breakpoint ( Figs 1B , 3B and 4B , S2 and S3 Figs ) . This discovery suggests that these recombinations took place between homologous localized regions of two partner viruses; in other words , the recombinations were region-specific [23] . We propose the following model to explain the process of concerted evolution of IGR sequences ( Fig 5B ) . Once illegitimate recombination produced identical ( or highly similar ) IGR sequences between the viruses of eRTBVL and eRTBVL2 , mutations accumulated in these IGRs over time; however , region-specific recombination within homologous IGRs ( and neighboring regions ) of the two viruses exchanged these mutations between virus populations during mixed infection , with subsequent recombination within a viral population able to further spread the exchanged mutations . The constant repetition of this mutation–recombination cycle caused the two viruses in the putative partnership to maintain highly similar IGRs . As one of the two partner viruses diverged into a new lineage during evolution , the other coevolved via region-specific recombination between their homologous regions; this resulted in different viruses of eRTBVL2 possessing different IGRs that were highly similar to those of each of the viral lineages of eRTBVL groups ( Fig 1C ) . Likewise , the constant repetition of this mutation–recombination cycle during the evolution of the viruses of ePVCVL and ePVCVL2 caused each partner of the virus pair infecting the same grass species to always maintain highly similar IGRs , even as the viruses of ePVCVL/ePVCVL2 diverged into distinct lineages infecting different host species in different habitats ( Figs 3C and 4C , and S10 Fig ) . Consequently , divergent evolution occurred in each of the four studied virus species , whereas concerted evolution took place between the IGRs of each pair of partner viruses ( Fig 5B ) . Although precise quantification of the recombination frequency in these viral partnerships appears to be difficult , we tried to estimate the number of major recombination events between IGRs of partner viruses based on phylogeny . Phylogenetic clustering of eRTBVL2 IGRs with those of each of four eRTBVL groups ( Fig 1C ) suggested the occurrence of more than four major recombination events . Similarly , a total of 10 major recombination events were suggested by phylogenetic analyses of ePVCVL and ePVCVL2 IGRs ( Figs 3C and 4C , and S10 Fig ) . In regards to the remaining grass genomes , which were not phylogenetically analyzed because of the high truncation and limited number of sequences , the independent endogenization and IGR concerted evolution of ePVCVL and ePVCVL2 in each genome imply that more than one major recombination event has taken place in each genome ( a total of four ) ( Fig 2C and S3 Table ) . We consequently detected more than 18 independent major recombination events , which supports the idea that partner viruses have frequently recombined IGRs with each other to maintain partnership and coevolution . Although recombination has probably been much more frequent than we have estimated , these major events have had significant impacts on viral phylogeny during long-term evolution . Similar to the recombination of retroviruses , PRVs such as cauliflower mosaic virus ( CaMV ) have been thought to recombine mostly through intermolecular template switching during reverse transcription in the host cytoplasm [23 , 57 , 58] . In our study , however , locational patterns of viral strand discontinuities ( primer binding sites and polypurine tracts ) did not correspond well to patterns of sequence similarity between viral genomes ( Figs 1 , 3 and 4 , S2 and S3 Figs ) . When present in the host nucleus , PRV DNA is organized into minichromosomes [27] , and indirect evidence exists that CaMV recombinations sometimes take place between viral minichromosomes [59 , 60] . Consequently , the region-specific recombinations identified in this study may have occurred mainly through homologous recombination between local homologous regions of viral minichromosomes with the help of host recombination machinery . One homologous recombination mechanism , gene conversion , has been suggested to be responsible for the concerted evolution of ribosomal DNA and other genes [53 , 61 , 62] . Our study has provided paleogenomic evidence for non-autonomous PRVs as well as their adaptation . Considering the abundance of diverse EVEs harbored in eukaryotic genomes and the rapid accumulation of genomic data [3] , many EVEs derived from previously unknown unusual virus types may still await discovery and analysis . At the same time , plentiful remnants of ancient virus–virus interactions may have been recorded in host genomes; our study has revealed one such paleovirological case of interplay between viral NRSs . One important future research focus should be evaluation of the prevalence and dynamics of NRS interactions between viral pathogens in mixed infections in plants and humans or within a viral population , as these may have significant impacts on viral evolution and pathology .
Whole-genome sequences of 20 grass species were downloaded mainly from the Gramene database [63] ( detailed data sources in S1 Table ) . To identify endogenous PRVs , we first performed a BLASTn search ( with default settings ) using the BLAST+ 2 . 2 . 27 utility and previously reported sequences [64] . The hit sites ( e-values < 1 × 10−10 and lengths >100 bp ) along with their 5 , 000-bp upstream and downstream sequences were retrieved and assembled into consensus sequences ( the nucleotide with the highest frequency at each position in the alignment was selected ) using the Vector NTI Advance 11 . 5 toolkit ( Invitrogen ) . A second round of BLASTn searching and a BLASTp search were then performed using these consensus sequences and their translated amino acid sequences , respectively . Only hit sequences longer than 100 bp were retained . Each translated protein sequence was subjected to the HHpred server [39] , with all standard HHM databases ( as of 3 May 2014 ) chosen for homologous domain detection ( using default parameters ) . To check unidentified domains/ORFs , their amino acid sequences were resubmitted to the HHpred server and also subjected to BLASTp and tBLASTn searches against NCBI databases . Identified domains were confirmed by conserved motif alignment . Coordinates of eRTBVL2 , ePVCVL , and ePVCVL2 sequences and their genes/regions in grass genomes are available in S2 Dataset ( BED format ) . Dot plots were generated using the EMBOSS package ( word size = 10; threshold = 45 ) [65] . Nucleotide sequences of each dataset were aligned in ClustalW [66] followed by manual editing . After being translated from the aligned nucleotide sequences , amino acid sequences of each dataset were realigned using MUSCLE [67] followed by manual editing . Highly truncated sequences ( generally shorter than 80% of the entire region ) and ambiguous regions were removed from the final alignments . Best-fitting substitution models were determined for each aligned dataset according to the Akaike information criterion calculated using jModelTest version 2 . 1 . 4 [68] or ProtTest version 3 . 2 [69] . For eRTBVL2 datasets comprising IGR ( nucleotide positions 6063–6704 of the consensus genome ) , MP ( 486–1853 ) , CP ( 1854–2845 ) , PR ( 2831–4090 ) , and ORFx ( 48–485 ) sequences , the best-fitting models were HKY+G , TrN+G , GTR+G , TrN+I+G , and TrN+G , respectively , with JTT+I+F chosen for the SFKTE sequences corresponding to amino acid positions 1220–1741 of the ORF2 protein sequence . Models VT+F+G and LG+I+F+G were respectively selected for the ePVCVL CP dataset ( amino acid positions 709–996/722–1010 of the protein sequence of Oryza/non-Oryza groups ) and the ePVCVL2 RT/RH dataset ( amino acid positions 1017–1414/945–1342 of the ORF1 protein sequence of Oryza/non-Oryza groups ) . Models HKY+G , GTR+G , and HKY+G were respectively chosen for the IGR datasets of ePVCVL and ePVCVL2 of O . sativa , genus Oryza , and S . bicolor genomes ( nucleotide positions 5878–6415/5786–6323 , 5878–6611/5786–6519 , and 6008–6659/5691–6317 of the consensus genomes of ePVCVL/ePVCVL2 , respectively ) . Maximum-likelihood ( ML ) phylogenetic analyses were performed in PhyML version 3 . 0 [70] or MEGA version 6 . 06 ( only for Fig 1C and S6 Fig for display purposes ) [71] . Branch support in all trees was calculated using 1 , 000 bootstrap replicates . The tree for the SFKTE domain of eRTBVL2 and ePVCVL segments was rooted using the oldest ePVCVL segment as determined by orthology analysis of ePVCVL loci in Oryza species ( see below ) . ePVCVL was assumed to be older than eRTBVL2 , as eRTBVL2 only exists in a subspecies of O . sativa , whereas ePVCVL is present in all O . sativa subspecies ( see S8 Fig ) . All sequence alignments for phylogenetic analyses are available in S3 Dataset . Sequences suggested as having a high probability of recombination according to the phylogenetic analyses and sequence alignments were subjected to recombination analyses using RDP version 4 . 72 [72] . We used six different methods ( RDP [73] , GENECONV [74] , BootScan [75] , MaxChi [76] , Chimaera [77] , and SiScan [78] ) in this program to identify potential recombination events and perform statistical tests . Sequence alignments for the recombination analyses were generally extracted from the alignment datasets of phylogenetic analyses . In the case where no suitable phylogenetic dataset was available , sequence alignments used for recombination analyses were made in MUSCLE [67] followed by manual editing . Default parameters were used for each method , except that the reference sequence parameter of the RDP method , in accordance with the RDP manual , was adjusted to “internal references only” when many closely related sequences existed in the alignment [72] . For each method , P < 0 . 005 was used as a threshold value for possible recombination events . Only the recombination events independently detected by more than three methods with statistical significance were considered reliable , and the best P value for each event was chosen . These recombination events were checked and displayed in BootScan plots ( window size = 300 nt; step size = 10 nt ) using the RDP program . All alignments used for recombination analyses are available in S4 Dataset . If an ePVCVL/ePVCVL2 segment in an Oryza genome was located next to or near another ePVCVL or ePVCVL2 segment ( i . e . , less than 5 kb away on the same chromosome or scaffold ) , the two ( or more ) segments were generally considered to be one locus for the analysis . The left and right 5-kb flanking sequences of each locus of ePVCVL and ePVCVL2 in the O . sativa genome were first mapped onto five other Oryza genomes ( O . glaberrima , O . glumaepatula , O . longistaminata , O . meridionalis , and O . punctata ) using BLASTn . The mapping results were rechecked using genome collinearity data ( genome-wide alignments between Oryza genomes ) obtained from the Gramene database [63] . Both 5-kb flanking sequences of each locus of ePVCVL and ePVCVL2 in the five above-mentioned Oryza genomes were next mapped onto the O . sativa genome and rechecked in the same manner . Some flanking sequences in O . glumaepatula and O . meridionalis genomes contained many uncharacterized ( ‘N’ ) bases; the examined length of these flanking sequences was therefore extended to 15 kb . Genomic PCR and Sanger sequencing were used to confirm orthologous loci of ePVCVL and ePVCVL2 . Loci shared among Oryza species were examined; in addition , representative O . sativa-specific ePVCVL and ePVCVL2 loci were selected and analyzed . Wild and cultivated rice plants ( accession numbers in S9 Fig ) were grown in a greenhouse at Hokkaido University , Sapporo , Japan . Total DNA was extracted from leaf samples using cetyltrimethylammonium bromide extraction buffer . DNA concentrations were all diluted to the same order of magnitude . PCR amplifications were performed using Ex Taq or LA Taq polymerase ( Takara ) on a PTC-200 thermal cycling system ( GMI ) . PCR products were resolved on a 1–2% agarose gel , stained with ethidium bromide , and viewed using an AE-6933FXES Printgraph system ( ATTO ) . Sanger sequencing was performed on an ABI 3730 DNA Analyzer ( Applied Biosystems ) using a BigDye Terminator v3 . 1 cycle sequencing kit ( Applied Biosystems ) according to the manufacturer’s protocol . Information on the primers used in this study is provided in S6 Table . All relevant data are within the paper and its Supporting Information files except for the assembled sequences of non-autonomous PRVs , which are available from DDBJ database under accession numbers BR001403–BR001407 . | This paper addresses the adaptive strategies of ancient defective viruses recorded in grass genomes . We mined numerous virus segments from various grass genomes and assembled several defective pararetrovirus ( non-autonomous PRV ) species . We attempted to understand how these non-autonomous PRVs can complete parasitic life cycles in host plants . We determined that these non-autonomous PRV species have participated in interplay with autonomous PRVs or different non-autonomous PRV species . This interplay between different virus genomes has involved the exchange of noncoding regulatory sequences , which consequently evolved to be extraordinarily highly similar in different viruses within the same host . In non-autonomous PRVs , adaptive strategies to compensate for a lack of functionality have consequently involved concerted evolution of noncoding sequences establishing the partnerships . | [
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] | 2017 | Genomic fossils reveal adaptation of non-autonomous pararetroviruses driven by concerted evolution of noncoding regulatory sequences |
The physiopathology of dengue hemorrhagic fever ( DHF ) , a severe form of Dengue Fever , is poorly understood . We are unable to identify patients likely to progress to DHF for closer monitoring and early intervention during epidemics , so most cases are sent home . This study explored whether patients with selected co-morbidities are at higher risk of developing DHF . A matched case-control study was conducted in a dengue sero-positive population in two Brazilian cities . For each case of DHF , 7 sero-positive controls were selected . Cases and controls were interviewed and information collected on demographic and socio-economic status , reported co-morbidities ( diabetes , hypertension , allergy ) and use of medication . Conditional logistic regression was used to calculate the strength of the association between the co-morbidities and occurrence of DHF . 170 cases of DHF and 1 , 175 controls were included . Significant associations were found between DHF and white ethnicity ( OR = 4 . 70; 2 . 17–10 . 20 ) , high income ( OR = 6 . 84; 4 . 09–11 . 43 ) , high education ( OR = 4 . 67; 2 . 35–9 . 27 ) , reported diabetes ( OR = 2 . 75; 1 . 12–6 . 73 ) and reported allergy treated with steroids ( OR = 2 . 94; 1 . 01–8 . 54 ) . Black individuals who reported being treated for hypertension had 13 times higher risk of DHF then black individuals reporting no hypertension . This is the first study to find an association between DHF and diabetes , allergy and hypertension . Given the high case fatality rate of DHF ( 1–5% ) , we believe that the evidence produced in this study , when confirmed in other studies , suggests that screening criteria might be used to identify adult patients at a greater risk of developing DHF with a recommendation that they remain under observation and monitoring in hospital .
Dengue is the most important viral vector-transmitted disease worldwide in terms of the total number of cases , disease morbidity and mortality [1] . This arboviral disease affects large areas of countries in tropical and subtropical regions of the world . As the physiopathology of the severe presentations , dengue hemorrhagic fever ( DHF ) /Dengue Shock Syndrome ( DSS ) , remains poorly understood , there are no effective means to predict or prevent progression to this severe clinical expression of the infection [2] , [3] . According to the World Health Organization ( WHO ) [4] , around 500 , 000 cases of DHF/DSS , occur annually , with 22 , 000 deaths . Until the 1980's years , Southeast Asia was the region most affected by dengue; then it spread to Central and South America and now it is present from the 35th parallel north to the 35th parallel south [2] . In Southeast Asia the incidence of DHF/DSS is high and the disease typically affects mainly children [5] . Until 2007 , the clinical pattern of dengue was very different in Brazil ( currently the country that reports most cases of dengue fever to the WHO ) : the majority of cases had occurred among adults and the percentage of the severe forms of the disease was low; but since the 2008 epidemic in Rio de Janeiro , the risk of morbidity and mortality due DHF/DSS began to rise in children [6] , [7] . It is not clear why some cases of dengue progress to DHF; understanding this process is essential for preventing it . There is evidence that sequential infections by different dengue viral serotypes plays a important role [8] . However , even during widespread epidemics of dengue fever , in populations with high levels of antibodies against dengue virus ( indicating a previous infection ) , the proportion of cases that progress to DHF is small , ranging from <0 . 5% to 4% of all cases [9] , [10] indicating that other factors are involved in disease severity . Several hypotheses have been raised but so far none supported by solid evidence . It has been suggested that some preexisting chronic diseases such as diabetes , hypertension and bronchial asthma increase the risk of progression to severe forms of dengue [11] , [12] based on case series with no control group . The objective of the case-control study reported here was to evaluate the contribution of comorbidities to the development of DHF .
This is a matched case-control study carried out in two coastal cities in the northeast of Brazil , Salvador and Fortaleza , with populations of roughly two and a half million each . The minimal sample size ( 159 cases and 636 controls ) was determined to be able to detect an increase of 2 . 3 fold in the risk of DHF in cases of dengue who also had diabetes ( prevalent in 7% of the controls , the least common of comorbidities studied ) , with 95% precision , 80% power and a ratio of at least 4 controls for each case . We eventually studied 170 cases and 1 , 175 controls . The study population consisted of subjects with a history of dengue , confirmed serologically . Only cases and controls who tested positive ( IgG ) for the anti-dengue antibodies were included in the study , since we wanted to investigate reasons for progression to DHF , not for infection with dengue virus . Cases were selected among those notified with dengue hemorrhagic fever during the period 2002 and 2003 in Salvador and 2003–2005 in Fortaleza . In Salvador the research was conducted in 2004 , while in Fortaleza , it was conducted in 2005 . Selection of cases: Cases of DHF registered in the national surveillance system ( SINAN ) in residents of these two cities between 2003 and 2005 were identified as potential cases for the study . Their surveillance records were examined by two physicians and included in the study if they met the criteria for DHF used by Brazilian Health Service [13] which is very similar to the WHO [14]: ( i ) fever and positive serology for anti dengue virus IgM and/or viral isolation and characterization by cell culture or RT-PCR ( ii ) at least two signs or symptoms of dengue fever ( headache or retroorbital pain , myalgia , arthralgia , prostration , exanthema ) and ( iii ) all of the following signs: a ) hemorrhagic manifestations ( at least one type ) ; b ) hemoconcentration with an increase of at least 10% in basal hematocrit level ( in 80% of the selected cases the increase was 20% or greater , in 13% between 12 and 18% and in 7% between 10 and 12% ) and/or hematocrit values >38% in the case of a child , >40% in the case of an adult female and >45% in the case of an adult male; c ) thrombocytopenia ( ≤100 , 000/mm3 ) . We did not consider ascites , pleural effusion , as these were very rarely recorded in the SINAN at the time . In Salvador , of the 91 cases of DHF notified to SINAN , 26 did not meet the criteria and were not included in the study and 65 met the criteria and were invited to participate in the study; 55 agreed to participate . No deaths from DHF were recorded during the study period . In Fortaleza , 194 cases of DHF were notified to SINAN , 55 did not meet the criteria and 139 were invited to participate in the study . Of these 5 had died from others causes since the notification , and 19 refused to participate . The remaining 115 were included in the study . Cases which were excluded had severe dengue but did not meet the criteria for DHF or their records did not have sufficient information . For each case , a pool of 7–8 potential controls were selected from individuals living in the neighborhood of each case , according to a rule ( from the first case on either side of the case's residence , until a suitable control was found , limited to the block ) matched by sex and age ( within five years ) , and who reported dengue fever in the same year as the case . Blood was drawn from the pool of potential controls by venous puncture for exclusion of those without seropositivity for dengue . The serum was separated by centrifugation and stored at −20°C . The Department of Arbovirology and Hemorrhagic Fevers of the Evandro Chagas Institute performed ELISA for anti-dengue IgG antibodies , with titers were above 1∶40 [15] being considered positive . 64 potential controls ( 5 . 2% ) did not have anti-dengue IgG antibodies titers above 1∶40 and were excluded from the study . 1 , 175 controls remained in the study . Cases and controls were interviewed ( during 2005–2006 ) at home by teams of trained interviewers using a previously tested , standardized questionnaire , collecting the following types of information: demographic and biological ( name , address , age , sex ) , socio economic ( years of schooling , family income expressed as a multiple of the legal minimum salary ( US$120 a month at the time of the interview ) and self-reported skin color . Clinical information collected included signs and symptoms of dengue and reported morbidity with respect to diabetes , hypertension , allergy , asthma , kidney failure , liver failure and sickle-cell anemia at the time of the reported dengue , and use of medication for these illnesses at the moment of the interview . When the individual reported having had one of these conditions , he/she was asked whether the diagnosis had been made by a doctor and the interviewer asked to see the prescription and/or packaging of any medication . To avoid bias , interviewers were blind to the study objectives and all field work was supervised . Analysis: To reduce the data , cases and controls were grouped according to their biological and social characteristics and the frequency of self-reported chronic diseases . Reported kidney failure , liver failure and sickle-cell anemia were not considered in this analysis because they were either not reported or reported by very few cases or controls . The crude association ( OR and 95%CI ) between the predictors of interest , presence of co-morbidities ( hypertension , diabetes , allergy and asthma ) and outcome of interest , presence of DHF was investigated using cross tabulations based on matched pair analysis , the McNemar's test [16] ( since this was a matched study ) . Adjusted measures for the association were estimated using multivariate logistic regression adjusting for potential confounding variables . As this was a matched case control study using individual matching , conditional logistic regression was used to estimate the association between the predictors of interest ( independent variables: hypertension , diabetes , allergy and asthma ) and the occurrence of DHF ( dependent variable: presence or absence of DHF ) within each matched set of case and controls . Since the study was matched for age , sex , neighborhood and city , the conditional logistic regression adjusted for these variables as potential confounders . To establish whether skin color , schooling and income were also potential confounding variables , we explored the association between these variables and DHF . The subsequent regression models exploring the association between comorbidities and DHF included those variables as potential confounders . A separate model was built for each of the co-morbidities of interest and also for each co-morbidity stratified by number and type of drug used . We did not include a model with all co- morbidities because we did not have sufficient power . Since black skin color has been reported to be a protective factor against DHF [17] , [18] , and is also known to be associated with hypertension [12] , a specific analysis was carried out on cases and controls who described themselves as being black . The STATA® software program , version 9 , was used to perform data processing and analysis . Ethical approval was granted by the Research Ethics Committee , Instituto de Saúde Coletiva , Federal University of Bahia , Salvador , Brazil . Cases and controls who agreed to participate in the study gave written informed consent .
Of the 170 cases of DHF included in the study , 55 . 9% were women and around 80% occurred in individuals over 15 years of age . With respect to skin color , 43 . 5% of cases considered themselves white , 50 . 0% mixed race and 6 . 5% black . Over 51% of cases had 11 years or more of schooling and 47 . 1% had an income of at least four minimum salaries . Of the 1 , 175 controls , 54 . 6% were female , 80 . 9% were over 15 years of age; 27 . 1% considered themselves white , 64 . 5% mixed race and 8 . 4% black; 31 . 5% had 11 years or more of schooling; and 17 . 5% had an income of at least 4 minimum salaries . Of these demographic variables , statistically significant differences ( p<0 . 05 ) were found between the two groups only with respect to skin color , years of schooling and family income . Statistically significant differences ( p<0 . 05 ) were also found between the cases and controls with respect to self-reported diabetes and allergy ( Table 1 ) . Skin color , schooling , and income were all independently associated with DHF . The likelihood ( crude , matched OR ) of a white individual having been affected by DHF was 4 . 60 times higher than that of a black participant . Family income ≥4 minimum salaries ( OR = 7 . 02 ) and ≥11 years of schooling ( OR = 4 . 66 ) were also found to be risk factors for this clinical form of dengue . There was little variation in the values of these measurements of association following adjustment for chronic diseases , differences remaining statistically significant ( Table 2 ) . When each one of the self-reported chronic diseases was adjusted for the ethnic and social variables , only diabetes remained associated with DHF ( adjusted OR = 2 . 75; 95% CI: 1 . 12–6 . 73 ) ( Table 3 ) . When the self-reported diseases were classified according to use of the respective medications , with the category “absence of disease” as reference , the use of medication ( steroids and no steroids ) by allergic individuals was found to be positively associated with DHF; however , following adjustment for the ethnic and social variables , the association was maintained only for allergy treated with steroids ( OR = 2 . 94; 95%CI: 1 . 01–8 . 54 ) . Of the individuals who reported hypertension , an increasing gradient was found in the crude and adjusted OR when the use of medication was considered ( no treatment , treatment with only one antihypertensive drug or treatment with more than one hypertensive drug ) ; however , these differences were not statistically significant ( Table 4 ) . In the subgroup analysis of the cases and controls who described themselves as black , the individuals who had hypertension and used more than one antihypertensive medication were found to have a 13-fold greater likelihood of having DHF ( 95%CI: 1 . 42–118 . 8 ) compared to black individuals who did not have hypertension . Black individuals with hypertension who did not use medication or who used only one type of antihypertensive medication were around four times more likely to have had DHF when compared to black individuals without hypertension; however , this difference was not statistically significant ( data not shown on tables ) .
The results of this study showed that individuals who reported having allergies ( and for which they used steroids ) , or those who reported diabetes , were two and a half times as likely to have DHF . DHF is known to be more common in repeat dengue infections . Current knowledge about the physiopathology of DHF suggests amplification of the immune response due to the presence of heterotypic antibodies against a serotype of the dengue virus at the time of new infection [8] , [11] as an explanation for the higher frequency of DHF in repeat dengue infections . The immune system in allergic individuals may be persistently activated with signs of inflammation in tissues and capillaries [19] , [20] , and if we consider the use of steroids for allergy as a marker of the severity of the allergy , one can conclude that severe allergy is even more likely to lead to inflammation and liberation of pro inflammatory cytokines in tissues , particularly in the endothelium . An alternative explanation for this finding might be that steroid use itself may increase risk of DHF since they can produce capillary fragility . The inflammation hypothesis is consistent also with the increased risk with diabetes . Type II diabetes , a metabolic disorder of adults that reduces the use of glucose by the organism , changes the anatomical and physiological integrity of the endothelium due to a permanent inflammatory condition caused by activation of T-lymphocytes . This process leads to the release of pro-inflammatory cytokines such as gamma interferon ( IFNy ) and TNFα [21] , [22] . These cytokines are known to have a fundamental role in one of the main phenomena responsible for the clinical manifestations of DHF , the third space fluid shift [23] , which is a consequence of endothelial dysfunction and results in hemoconcentration , hypotension and shock . It would appears that triggering endothelial dysfunction may be the common biological mechanism by which allergy and diabetes increase the risk of progression to DHF , by increasing the intrinsic permeability of the endothelial surface of hosts who have been previously infected by another serotype , permitting the occurrence of fluid shift . Although no statistically significant association was found between hypertension and DHF , it is interesting that when individuals without hypertension were taken as a reference group , a clear trend was found for an increased likelihood of DHF among those who reported having hypertension but did not use any antihypertensive medication followed by hypertensive individuals who used more than one antihypertensive drug . On the other hand , the thirteen-fold higher association between individuals who consider themselves black and use an antihypertensive drug and DHF , when compared with non-hypertensive black individuals , strengthens the hypothesis that preexisting diseases in which physiopathology detrimentally affects endothelial function may help trigger the phenomenon of fluid shift resulting from the increased vascular permeability that characterizes DHF . The increase in DHF in subjects with white skin color is well described [11] , [17] and recent in vivo study associated these differences with genetic markers of African ancestry [18] . In the cities where the study was conducted the proportion of individuals of African descent ( black and mixed ) is over 68% , the majority belonging to a lower socioeconomic stratum than the white population , with lower family income and poorer education [24] . The greater likelihood of DHF in white individuals with higher education levels and higher family income is consistent with both a mixed ancestry subjects being more likely to call themselves white if they are rich and well educated or the presence of some other undetected lifestyle factors associated with an increase the risk of DHF . The growth in the frequency of allergic and atopic diseases that has been observed in recent decades has emphasized the need for research studies capable of explaining the mechanisms involved in this phenomenon . Changes in lifestyle , initially in countries of Eastern Europe , have occurred in parallel with a growth in the rate of asthma and other allergic diseases [25] , [26] , [27] . There is evidence that individuals living in cleaner environments , who are consequently less exposed to infections , are at a greater risk of developing allergic diseases ( “the hygiene hypothesis” ) . Although this hypothesis has been controversial , there evidence that some infections early in life may protect individuals against allergic and atopic diseases years later [28] , [29] . It is therefore feasible to speculate that white individuals with higher education levels and family income , who in this study were found to have a 7-fold greater risk of having been affected by DHF , would also constitute the segment of the population with the highest risk of suffering from allergies or of having higher levels of allergic sensitization , which may act as triggers of the mechanisms of amplification of the immune response . This hypothesis should be the object of new studies designed to clarify the issue . This study was retrospective , based on reported co-morbidities , and a self-reported date of history of dengue fever in controls . The diagnosis of DHF was based on the information from review of cases investigated by the surveillance system . These potential limitations create methodological concerns . However , to be included in this analyses , available hospital data was reviewed by experts and found to satisfy the clinical and laboratory criteria described in Methods . To increase the validity of reported co-morbidities and medication , the investigators requested to see medical prescriptions and drug packaging to ensure that the information obtained indirectly was as reliable . The data collection procedures were standardized and identical for the two comparison groups . A potential bias in this study is that dengue infection rates varied in Salvador and Fortaleza by skin color . However , there is good evidence that skin colour is not associated with dengue infection [30] . As to likelihood that a diagnosis and notification of DHF varies by skin color , this is also very unlikely because Brazil has universal health care free at the point of use . As for notifications of cases to health authorities by provider , is known that private health providers less often notify cases to National Health Service than public sector provider . This would tend to introduce a bias toward cases reported among lower incoming and black patients . Subgroup analysis in blacks had not been planned . We hypothesized that hypertension was a risk and we knew of course that hypertension is more common in blacks . We did the subgroup analysis because hypertension risk was not found to be a risk factor in the role patient population . We suggest a cautious interpretation of this finding but urge that this be investigated in future studies . We did not present results separately for the two cities . This is appropriate as matching for cities would have controlled for any confounding; it would not have been appropriate if the associations between co-morbidities and DHF were different in the two cities . We did examine the magnitude of the associations in the two cities and judged them to be similar . The exclusion of individuals who were seronegative under the sample constraints described assured that all cases and controls , had dengue infection in the past . DENV1 and DENV2 have been circulating intensely in the two cities since the 1990s and the populations of these cities have high levels of antibodies against these serotypes [31] , [32] . Most cases of DHF included in this study were caused by DENV3 , which was introduced into these cities only in 2002 [10] . Because this is a retrospective study , we do not know the date of or number of dengue infections in control . It is possible that many individuals were infected prior to 2005 or 2006 . It is important to emphasize that controls reported having had a dengue like illness during the same period of the matched case and reported never having had DHF . This is the first case-control study to investigate the association between hospitalization with a diagnosis of DHF and evidence of diabetes and allergy , and hypertension and the results reported here are the initial evidence for this very important association . In dengue epidemics most dengue fever cases are seen in out-patients settings and then sent home , as there are too many for all to be hospitalized for observation , in spite of the potential for progression to DHF . If it were possible to identify those with higher risk of progression to DHF , and to keep them for observation , for to early detection of signs , symptoms and alterations in laboratory tests suggestive of DHF , this would enable timely and effective clinical management . Early intervention is such cases will reduce mortality substantially , since the case fatality rate of DHF in SE Asian patients is high ( 1–5% ) . We believe the evidence produced in this study when confirmed suggests that screening criteria might be used to identify adult patients at a greater risk of developing DHF with a recommendation that they remain under observation and monitoring in hospital . We also recommend further clinical studies to define new protocols on the evolution of dengue infections in patients with diabetes , allergies and hypertension ( particularly with respect to drugs used ) and appropriate medical management . Finally , cross immunologic pathophysiologic studies based on the associations between diabetes , allergy and high socioeconomic status and DHF , are urgently needed to investigate the intricate mechanism controlling severe forms of dengue . | Dengue is an arboviral disease that affects large areas of countries in tropical and subtropical regions of the world . Around 500 , 000 cases and 22 , 000 deaths of dengue hemorrhagic fever ( DHF ) /Dengue Shock Syndrome ( DSS ) , the most severe presentations of this disease , occur annually . It is unclear why some cases of dengue fever ( 0 . 5% to 4% ) progress to DHF/DSS . There is weak evidence that some diseases could have a role in this process , such as diabetes , hypertension , and allergies . In epidemics most dengue fever cases are sent home as there are too many to be kept in observation , but if it were possible to identify those with a higher risk of progression to DHF , they could be kept for observation , for early detection of signs , symptoms and alterations in laboratory tests suggestive of DHF , to enable timely and effective clinical management and early intervention . We study this issue and we believe that the evidence produced in this study , when confirmed in other studies , suggests that screening criteria might be used to identify adult patients at a greater risk of developing DHF with a recommendation that they remain under observation and monitoring in a hospital . | [
"Abstract",
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"Methods",
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] | [
"infectious",
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] | 2010 | Allergies and Diabetes as Risk Factors for Dengue Hemorrhagic Fever: Results of a Case Control Study |
Embryonic development requires a correct balancing of maternal and paternal genetic information . This balance is mediated by genomic imprinting , an epigenetic mechanism that leads to parent-of-origin-dependent gene expression . The parental conflict ( or kinship ) theory proposes that imprinting can evolve due to a conflict between maternal and paternal alleles over resource allocation during seed development . One assumption of this theory is that paternal alleles can regulate seed growth; however , paternal effects on seed size are often very low or non-existent . We demonstrate that there is a pool of cryptic genetic variation in the paternal control of Arabidopsis thaliana seed development . Such cryptic variation can be exposed in seeds that maternally inherit a medea mutation , suggesting that MEA acts as a maternal buffer of paternal effects . Genetic mapping using recombinant inbred lines , and a novel method for the mapping of parent-of-origin effects using whole-genome sequencing of segregant bulks , indicate that there are at least six loci with small , paternal effects on seed development . Together , our analyses reveal the existence of a pool of hidden genetic variation on the paternal control of seed development that is likely shaped by parental conflict .
Post-fertilisation development is a complex process that involves dynamic interactions between maternally and paternally derived genomes . A correct balancing of parental genomes is essential for embryonic development , and disruptions of this balance ( e . g . by crossing individuals with different ploidies ) often lead to embryo inviability [1–6] . Genomic imprinting , an epigenetic mechanism that leads to differential expression of alleles in a parent-of-origin-dependent manner , is responsible for many parental asymmetries during embryo and seed development in mammals and flowering plants [7 , 8] . Transcriptome profiling of developing seeds has revealed the existence of hundreds of candidate imprinted genes in the embryo and/or endosperm , a biparental nourishing tissue that derives from a second fertilisation event ( reviewed in [9–11] ) . However , the functional role of genomic imprinting is still a matter of considerable theoretical debate [12] . The parental conflict ( or kinship ) theory of genomic imprinting proposes that imprinting can evolve as the manifestation of a conflict of interests between maternal and paternal alleles over resource allocation during embryogenesis or seed development [13–15] . This conflict arises due to the asymmetric genetic relatedness between maternal and paternal alleles in polyandrous ( multiple paternity ) species , where maternal alleles are more likely to be shared between siblings than paternal alleles . The parental conflict theory is supported not only by mutant phenotypes in mice [16–18] but also by the discovery of MEDEA ( MEA ) , a major regulator of imprinting and the maternal control of seed development in Arabidopsis thaliana ( L . ) Heynh ( referred to as Arabidopsis hereafter ) . Only the maternal MEA allele is expressed ( before fertilization in the embryo sac that contains the female gametes and later in the embryo and endosperm derived from these gametes ) [19 , 20] , and seeds that maternally inherit a loss-of-function mea allele undergo excessive cell proliferation and eventually abort [21] . MEA encodes a SET-domain histone methyltransferase that catalyses the trimethylation of H3K27—a repressive epigenetic mark associated with gene silencing—as part of a seed-specific version of the Polycomb Repressive Complex 2 ( FIS-PRC2 ) [22] . This suggests that an important function of MEA is to maternally restrain seed growth by negatively regulating the expression of genes that would otherwise promote embryo and endosperm growth . While the parental conflict theory predicted the existence of maternal regulators of seed development such as MEDEA ( MEA ) , the paternal genotype has no or very small effects on seed growth [23–30] . Here we show that there is a large hidden pool of natural variation in the paternal control of seed development that can be exposed using a maternal mutant mea background . Using a combination of classic quantitative trait analysis and a novel method for whole-genome sequencing of bulk segregants ( Bulk-Seq ) , we determined that at least six loci contribute to the paternal rescue of mea seeds . Together , our results indicate that there is a large pool of natural variation in loci exerting paternal effects on seed development in Arabidopsis . These paternal effects are buffered by maternal MEA activity , suggesting that they were likely shaped by parental conflict .
When mea ovules are pollinated with wild-type pollen from the Landsberg erecta accession ( hereafter referred to as Ler ) , seeds undergo excessive cell proliferation and abort before completing embryogenesis [21] . However , mea ovules pollinated with pollen from other Arabidopsis accessions ( such as Cvi-0 or C24 ) can give rise to viable plump mea seeds ( Fig 1A and S1 Fig ) . To dissect the relative paternal and maternal contributions to mea seed rescue , we introgressed mea-2 ( originally in the Ler background ) into Cvi-0 and C24 . After six generations of backcrossing , we crossed three independent Cvi-0mea/MEA and C24mea/MEA lines with pollen from Ler , C24 , and Cvi-0: all the pollinations made with Ler pollen resulted in high rates of seed abortion , whereas the pollinations made with Cvi-0 or C24 resulted in mostly viable plump seeds , independently of the genotype of the maternal plant used ( Fig 1B ) . The magnitude of the Cvi-0 and C24 paternal rescue was modulated by the maternal genotype ( e . g . in a Cvi-0mea/MEA maternal background the paternal effect of Cvi-0 was stronger and the effect of C24 was very weak ) . This suggests that the rescue of mea seeds is primarily a paternal-specific effect that can be partially modulated by the maternal genotype , indicating the existence of strong reciprocal interactions between the two parental genomes . Since MEA is an imprinted gene ( only its maternal allele is expressed ) a potential explanation for mea seed rescue could be an activation of the paternal wild-type MEA allele . However , this hypothesis cannot easily be tested using allele-specific expression assays , because maternal mea mutations already induce low levels of paternal MEA expression [20 , 31–33] . To determine genetically if the paternal MEA allele is required for mea seed rescue , we examined the F2 progeny of crosses between mea-2 and different Arabidopsis accessions . If a paternal MEA allele was required for the rescue , we would not expect to recover viable homozygous mea/mea seeds . However , we recovered 9–20% of viable mea/mea plants in the F2 progeny of crosses with the accessions C24 , Hs-0 , and Lomm1-1 ( S1 Table ) . This result clearly indicates that a paternal MEA allele is not required for mea seed rescue in these crosses . In the crosses with Cvi-0 we only recovered 3% of homozygous mea/mea seeds; when these different F2 homozygous Cvi-0mea/mea individuals were self-fertilized , however , we observed a range of 1–60% plump F3 seeds in their progeny ( S2 Fig ) . This finding suggests that in mea crosses with Cvi-0 , the paternal MEA allele ( or a closely linked locus ) can enhance but is not required for the rescue of mea seeds . MEA encodes a subunit of the FIS-PRC2 complex , which also contains the zinc finger protein FERTILIZATION-INDEPENDENT SEED2 ( FIS2 ) and the WD40 domain protein FERTILIZATION-INDEPENDENT ENDOSPERM ( FIE ) [22] . To test whether Cvi-0 and C24 can also rescue seed abortion caused by mutations in these genes , we crossed heterozygous fis2/FIS2 and fie/FIE plants with pollen from Ler , Cvi-0 , and C24 ( Fig 1C ) . While Cvi-0 could rescue fis2 seeds , there was no significant seed rescue using C24 pollen . fie seeds could not be rescued by pollen of either Cvi-0 or C24 . These results indicate that the mea seed paternal rescue does not simply occur at the FIS-PRC2 level; rather it supports the hypotheses that the different FIS-PRC2 subunits play distinct roles [34] and that MEA participates in multiple protein complexes during seed development [32] . To determine the extent of species-wide variation on MEA-dependent parental interactions , we pollinated 164 Arabidopsis accessions with mea-2 to generate F1s that were allowed to self-fertilize to examine seed viability rates in the F2 generation . Each of the F2 populations segregates ( 1 ) MEA and mea , and ( 2 ) chromosomes from Ler and the respective parental accessions: therefore , we expected to obtain 50% viable seeds from accessions that do not modify the penetrance of mea ( such as Ler ) , and up to 75% viable seeds from accessions with a strong paternal rescue effect ( assuming no epistatic effects ) . Accordingly , we observed 52% plump seeds in the control Ler crosses , while in the Cvi-0 and C24 crosses there were 65% and 68% plump seeds , respectively ( Fig 2A–2C ) . Roughly half of the accessions tested showed between 55% and 70% plump seeds , suggesting that alleles that modify the penetrance of mea seed abortion are widespread among natural Arabidopsis accessions . Half of the strongest mea rescuers originate from latitudes more southern than 45° ( Fig 2A ) , but we found no significant linear correlation between the geographical origin of the accessions and their effect on the penetrance of mea . We compared the mea rescue effect of these accessions with over 100 phenotypes reported for a large set of A . thaliana accessions [35] . The only statistically significant traits correlated with the rescue of mea were in planta magnesium and calcium concentrations ( Pearson correlation , 5% false discovery rate ) ( S3 Fig ) . We also found evidence for an association between flowering time and mea rescue , as half of the strongest mea rescuers included very early flowering accessions under field or short day conditions ( Cvi-0 , C24 , Se-0 , Ts-1 and Co ) ( S3 Fig ) . We did not find a correlation between mea rescue and the size of self-fertilized or outcrossed Arabidopsis seeds [30 , 36] . We performed a genome-wide association study ( GWAS ) to identify regions in the genome whose species-level variation is linked to mea rescue . However , we were unable to detect clear statistically significant associations ( Fig 2D ) , likely due to the weak power of GWAS to detect polygenic traits with low effect sizes [37] . Nevertheless , some of the most highly associated SNPs were in the vicinity of the regions identified with the Bulk-Seq analysis ( see below ) . We crossed homozygous mea/mea plants ( generated using an inducible MEA-glucocorticoid receptor system ) with pollen from 80 Cvi-0/Ler recombinant inbred lines ( RILs ) for which a detailed genetic map is available [38] . The percentage of plump seeds that originated from these crosses followed a continuous distribution ( Fig 3A ) , indicating that the rescue of mea seeds is a polygenic trait . The broad-sense heritability H2 ( the percentage of total phenotypic variance that can be explained by genetic factors ) is 85% , indicating that mea seed rescue is under strong genetic control . We used maximum likelihood standard interval mapping to identify regions that are significantly associated with mea seed rescue . As expected from the continuous phenotype distribution , we identified multiple QTL peaks on several chromosomes ( Fig 3B ) . Using a multiple-QTL approach [39] , we narrowed down these regions to six QTLs , located on chromosome 1 ( 64 . 3cM and 101cM ) , chromosome 2 ( 65cM ) , chromosome 3 ( 77cM ) and chromosome 5 ( 21 and 63 . 5cM ) ( Fig 3C ) . The six QTLs contribute independently to seed rescue ( i . e . there was no evidence for epistatic interaction between QTLs ) and together explain 73 . 1% of the phenotypic variance . Each QTL has a relatively small effect and explains a small proportion ( 5–11% ) of the overall phenotypic variation ( Fig 3D and Table 1 ) . Nevertheless , the effect of multiple QTLs increases exponentially: every additional Cvi-0 QTL increases the rescued seed rate by roughly 50% ( e . g . pollen donors with two , three or four Cvi-0 QTLs generate on average 18% , 28% or 42% plump seeds , respectively ) ( Fig 3E ) . We then crossed mea/mea homozygous plants with pollen from a population of Cvi-0/Ler near-isogenic lines ( NILs ) [40] . Unlike RILs , which have mosaic genomes with a similar proportion of Ler and Cvi-0 genetic backgrounds , these NILs contain only one or a few small introgressions of Cvi-0 in an otherwise homogenous Ler genetic background . We used 33 NILs that together cover 93–98% of the genome with isogenic Cvi-0 fragments . While Cvi-0 pollen gave rise to 85% mea plump seeds , almost all the NILs showed no significant differences from Ler ( 3% viable seeds ) ( Fig 4A ) . The three NILs that clearly showed an effect ( 13–23% viable seeds ) actually contain multiple Cvi-0 fragments that overlap two or three of the identified QTLs ( Fig 4B ) . Together , the RIL and NIL analyses suggest the existence of at least six loci in Cvi-0 that contribute to the rescue of mea seeds . We also scored seed abortion in the F2 progeny of a cross between mea-2 and C24 ( genotyped at 14 markers throughout the genome ) . Despite the low statistical power caused by the segregation of MEA in this population , we found evidence for one QTL at the bottom of chromosome 1 that could explain 15% of the observed phenotypic variation ( S4 Table ) . Thus , even this analysis with limited power identified one of the loci on chromosome 1 that was mapped using RILs and NILs . To independently validate the results of the QTL analyses , we developed a novel method for mapping parent-of-origin effects using whole-genome sequencing . The strategy is to create an F2 population that contains one set of chromosomes from one parent but inherits two segregating sets from the other parent . These two sets should have opposing effects in pre- or post-fertilisation fitness or viability , so that they will not be equally transmitted . DNA is then extracted from pools of viable F2 seedlings , and whole-genome sequencing is used to identify genomic regions that exhibit biased transmission of the two segregating paternal ( or maternal ) genotypes . In this case , we took advantage of the differential survival of mea seeds depending on the inheritance of Cvi-0 against Ler paternal alleles . First , we generated F1 hybrid plants by reciprocally crossing Ler and Cvi-0 plants ( Fig 5A ) . The Ler/Cvi-0 hybrids were then used to pollinate ( 1 ) Ler plants and ( 2 ) homozygous mea/mea plants ( Ler background ) . The resulting F2 progenies will therefore exclusively inherit Ler chromosomes from the mother ( with and without mea ) but different combinations of Ler and Cvi-0 alleles from the father ( due to recombination and segregation of chromosomes during male gametogenesis in the F1 plant ) . Only mea F2 seeds that inherit Cvi-0 alleles that rescue mea are able to generate viable seedlings: therefore , genomic regions that are linked to these Cvi-0 alleles will be predominantly transmitted to viable F2 plants . We can identify these by sequencing pools of viable plants and quantifying the relative proportion of Cvi-0 and Ler SNPs throughout the genome . To account for biases in the paternal transmission of Cvi-0 and Ler SNPs that occur independently of mea , we determined the transmission of Cvi-0 SNPs in a control cross using wild-type Ler instead of mea plants . In this wild-type ( WT ) control ( 'WT pool' ) , we expect the percentage of Cvi-0 reads throughout the genome to be close to 25%; in the 'mea pool' , regions that are associated with mea rescue will be enriched in Cvi-0 reads ( up to 50% ) . We pooled genomic DNA from a total of 2400 viable mea seedlings and 1400 WT seedlings in three biological replicates . We then used a dataset of known Ler and Cvi-0 polymorphisms [41 , 42] to estimate the proportion of Cvi-0 reads throughout the genome ( Fig 5B and S4 Fig ) . In the WT pool , there is clear evidence for segregation distortion in several genomic regions , including a low proportion of Cvi-0 reads at the top of chromosome 3 and the middle of chromosome 1 , and a high proportion at the bottom of chromosome 1 and the top of chromosomes 2 and 4 . Most of these regions were previously shown to exhibit segregation distortion in crosses between Ler and Cvi-0 [38] . To identify the regions that are associated with mea seed rescue , we calculated the difference in the proportion of Cvi-0 reads between the mea and WT pools ( Fig 5C ) . There was an overall increase in Cvi-0 reads throughout the genome in the mea pool , likely reflecting the highly polygenic nature of mea seed rescue; but the enrichment in Cvi-0 reads was particularly pronounced in the middle and bottom of chromosome 1 , the bottom of chromosomes 2 and 3 , and in the top and middle of chromosome 5: in these regions there was an increase of 15–30% in the proportion of Cvi-0 alleles relative to the WT pool ( Fig 5C , Table 2 ) . These peaks were reproducible between the three biological replicates ( S4 Fig ) . Each of the peaks identified by Bulk-Seq is located in the vicinity of the QTLs identified by the RIL-QTL analysis ( Table 1 ) ; some of the peaks ( particularly b , d , and g ) are also close to SNPs that were identified by the GWAS analysis as associated ( although non-significantly ) with mea rescue ( Fig 2D ) . Taken together , the Bulk-Seq analysis provides strong support to the existence and predicted location of the multiple Cvi-0 alleles that underlie the rescue of mea seeds .
Our results demonstrate that there is a pool of hidden variation in the paternal regulation of seed development in Arabidopsis . This paternal variation is released upon maternal loss of mea , suggesting that the maternal genome actively buffers the manifestation of paternal effects during seed development . While in the past the effects of the paternal genotype on seed growth were found to be very small or non-existent [23–30] , our results clearly indicate that paternal effects exist but are buffered by the maternal genome . This observation is consistent with predictions of the paternal conflict theory , which proposes that the maternal genome counteracts the effect of paternally inherited alleles that would otherwise place extra demands on seed growth . We hypothesize that , in a maternal Ler background , the ( potential ) paternal growth demands of Cvi-0 and C24 are lower than the ones of most other accessions ( including Ler itself ) . Upon maternal loss of the buffering mechanism mediated by MEA , the paternal growth demands of Ler ( and most accessions ) lead to excessive seed growth , resulting in mea seed collapse; however , the paternal growth demands of Cvi-0 and C24 are not as strong and allow mea seeds to complete development . Interestingly , paternal effects on mea seed development are , in turn , dependent on the maternal genetic background: we showed that C24 paternal alleles can strongly rescue mea seeds in a maternal Ler or C24 background , but this rescue is much weaker in a Cvi-0 maternal background ( Fig 1B ) . This indicates that there are multiple reciprocal interactions between maternal and paternal alleles in the regulation seed growth . In many ways , this paternal variation is a classic example of cryptic genetic variation ( CGV ) . Natural genotypes often harbour extensive CGV that is only released upon severe environmental or genetic perturbations [43–45] . Typical examples of CGV include variation in the number of Drosophila bristles in a scute mutant background [46] , inflorescence architectures in maize crossed to its wild ancestor teosinte [47] , body size in oceanic stickleback upon exposure to low salinity environments [48] , or genetic background-dependent phenotypic variation upon disruption of the heat shock protein Hsp90 in Drosophila and Arabidopsis [49 , 50] . CGV usually has no or little effects on phenotypical variation , but it can modify phenotypes under atypical environmental conditions or following the introduction of novel alleles . By acting as a standing pool of genetic information , CGV has been hypothesized to play an important role in adaptation and the evolution of novel characters [51 , 52] . One explanation for the origin of CGV is that as new mutations appear , their potential phenotypic effect is suppressed by existing buffering mechanisms [44] . During Arabidopsis seed development , MEA could act as a buffering mechanism that prevents the expression of mutations that would otherwise disrupt the balance of paternal genomes . Another possibility that could explain the hidden paternal variation is the predominantly self-fertilizing behaviour of Arabidopsis . Although imprinting can be maintained in species with a low outcrossing rate [53 , 54] , high kin genetic relatedness is predicted to decrease the intensity of parental conflict [55] . The transition of Arabidopsis from an outcrossing to a self-fertilizing species around one million years ago [56] , could have resulted in an erosion of the functional importance of imprinting mechanisms and the strength of the parental effects . This could make Arabidopsis seeds more resistant to unbalanced crosses and mask the manifestation of parental effects . Supporting this hypothesis , Arabidopsis seeds are unusually tolerant of unbalanced interploidy crosses [1 , 6] . Our genetic analyses demonstrate that multiple loci contribute independently to the paternal rescue of mea seeds , but the effect of each individual locus is small . We predict that the underlying genes encode factors that fine-tune embryo and endosperm growth during the early stages of embryogenesis , particularly endosperm cellularization and the transition from radial ( globular stage ) to bilateral ( heart stage ) embryo symmetry . We showed that the rescue is directional ( e . g . Cvi-0mea x Ler seeds abort , but Lermea x Cvi-0 seeds are rescued ) . This suggests that the rescue is parent-of-origin-specific , and should therefore be meditated by imprinted genes . However , at this point we cannot distinguish whether the underlying alleles are paternally expressed or paternally repressed in Cvi-0 . The MADS-box gene PHE1 , a paternally expressed gene that is a direct target of MEA , is located close to the QTL peak at 101cM in chromosome 1 ( peaks b and c in the Bulk-Seq analysis ) . phe1 mutants develop slightly lighter seeds and can partially rescue mea seeds [57–59] , suggesting that variation in PHE1 may underlie this QTL . mea seeds can be paternally rescued by the ddm1 mutant or an anti-sense met1 line [19 , 60] . Such lines have lower levels of CG methylation , which has been hypothesised to act antagonistically to H3K27 trimethylation to regulate the expression of paternally-derived alleles [61] . Interestingly , Cvi-0 has lower levels of CG methylation in embryos and endosperm than Ler or Col-0 [11] . However , we found no correlation between mea seed rescue and global levels of CG methylation in vegetative tissues of over 50 A . thaliana accessions [62] . The overgrowth phenotype of mea seeds strongly resembles the phenotype of interploidy crosses where the ploidy of the male is higher than that of the female [6 , 63] , suggesting that MEA is an important contributor to maternal genome dosage . Accordingly , paternal excess crosses can be rescued by increasing the expression of MEA [63] , while mea seeds are viable in maternal excess crosses [64] . Interestingly , hypomethylated pollen can also rescue seeds resulting from unbalanced crosses where the ploidy of the male is higher than the female [65] , confirming the existence of overlaps between mechanisms that regulate parental dosage , Polycomb activity , and DNA methylation in developing Arabidopsis seeds . Overall , we demonstrate here that there is a large pool of hidden intra-specific variation in the paternal control of seed development . Recent transcriptome studies have shown that 5–15% of A . thaliana and maize imprinted genes have allele-specific imprinting [11 , 66 , 67] , while MEA has been found to be under positive selection in the genus Arabidopsis [68–70] . This suggests that the balancing of parental information during seed development is a very dynamic evolutionary process , and provides strong support to the parental conflict theory for the evolution of imprinting . Importantly , this standing pool of cryptic genetic variation in wild and domesticated species could have important uses in plant breeding programs [71] that aim to regulate seed size or overcome inter-specific hybridizations [72–75] .
The mea-1 and mea-2 [21] , fis2-1 [76] and fie ( SALK_042962 ) [77 , 78] mutants , as well as the RIL and NIL Ler/Cvi populations [38 , 40] were previously described . The Landsberg erecta ( Ler-1 ) , Cape Verde Islands ( Cvi-0 ) , and C24 accessions used in this study are derived from lines N22618 , N22614 , and N22620 , respectively , and were a gift of Ortrun Mittelsten Scheid ( GMI Vienna ) . All plants were grown on standard soil ( ED73 , Einheitserde , Germany ) in a greenhouse chamber with 16h light at 20°C and 8h dark at 18°C with an average of 60% humidity . For seed viability assays , individually crossed siliques were harvested 1–2 days before dehiscence; seeds were then examined under a stereomicroscope and categorised as plump or aborted based on shape , size , and colour . For embryo quantification , seeds at different stages of development were fixed overnight at -20°C with 90% acetone , cleared with chloral hydrate/water/glycerol ( 8:2:1 w/v/v ) , and analysed under a Leica DMR microscope . For the generation of Cvi-0mea/MEA and C24mea/MEA lines , mea-2/MEA plants ( Ler background ) were used to pollinate Cvi-0 and C24 . The F1 progeny was selected in Murashige and Skoog ( MS ) medium supplemented with 50μg/ml kanamycin ( mea-2 is marked by a kanamycin resistance gene ) and backcrossed to Cvi-0 and C24 , respectively . Individual backcrossed ( BC1 ) kanamycin-resistant plants were used to pollinate Cvi-0 or C24 and generate independent BC2 lines . Backcrossing was performed for another four generations; BC6 plants were genotyped with a sequence length polymorphism marker linked to the MEA locus ( 3 . 286 Mbp distance ) using primers 5'-AATTGAAGCTTTTCTGC-3' and 5'-AGAAAATGAAAAACTTATGG-3' to select plants with homozygous Cvi-0 introgressions close to mea . Seed abortion was scored in the progeny of a single BC6 individual from each of three independently generated Cvi-0mea/MEA and C24mea/MEA lines . Cvi-0 , Hs-0 , C24 , and Lom-1 were crossed with pollen from mea-2 , the F1 populations were allowed to self-fertilize , and DNA was extracted from viable seedlings . mea-2 plants were genotyped using primers 5'-CCAATGCACAAATCGACAATG-3' and 5'-CACCAAGAGTGCCATCTCCA-3' ( WT genomic DNA ) , and 5'-CGATTACCGTATTTATCCCGTTCG-3' ( Ds insertion tightly linked to the mea-2 allele [21] ) . To obtain pMEA::MEA-GR , an 8 . 6 kb genomic fragment ( 4 . 4kb upstream region + 4 . 2 kb MEA ORF ) was amplified from the previously generated plasmid pCambia 1381Z [20] using primers proMEA-BPfor ( AAAAAGCAGGCTCACTAAGATATGTTGGGTC ) and MEA-BP-GRrev ( AGAAAGCTGGGTCTGCTCGACCTGCCCGA ) , and recombined into pDONR207 using Gateway cloning ( Invitrogen ) . The resulting entry vector was subsequently recombined into pDEST-GR ( pARC146 without 35S promoter ) [79] , and the fusion between MEA and the GR domain sequence was confirmed by sequencing . The construct was introduced into the Agrobacterium strain GV3101::pMP90 , and transformed into Ler plants using floral dip [80] . Lines expressing the construct were crossed with mea-1 , and the offspring was continuously watered with 10 μM dexamethasone ( DEX ) ( Sigma cat . D1756 ) to identify reduced seed abortion and thus rescue by the construct . Several lines were identified that complemented the mea-1 seed abortion phenotype to a large extent only in the presence of 10 μM DEX . The best complementing line ( line 18 ) was used to raise the homozygous mea-1/mea-1 plants used in this study . A total of 47 , 619 seeds derived from crosses between homozygous mea-1/mea-1 plants and 80 Ler/Cvi RILs were scored . The genotype map for these lines included 144 markers [38 , 81] and was kindly provided by Joost Keurentjes ( Wageningen University and Research Centre ) . Broad-sense heritability was estimated with an analysis of variance of a linear mixed-effects model , using the lmer function of the 'lme4' R package [82] . Means and confidence intervals for each RIL were estimated using a binomial regression , and normalised using a cubic root transformation . QTL analyses were performed using the 'R/qtl' R package [39] . Genotype data across the genome was estimated using multiple imputation at a 1cM density , and interval mapping was calculated using standard maximum likelihood estimation; the LOD ( logarithm of odds ) threshold at 1% was calculated using a permutation test with 5000 replicates . Multiple-QTL models were selected using a combination of automated stepwise model selection and iterative individual QTL location refinement as implemented in 'R/qtl'; the penalized LOD scores used to guide model selection were derived using a permutation test with 1000 replicates of a two-dimensional , two-QTL genome scan . The best fit-model we identified had six loci and no epistatic interactions between the QTLs . Finer localizations of each of the QTLs of the best-fit model along the chromosomes were estimated using a Bayesian approach , as implemented in the bayesint function of 'R/qtl' . The effect of individual QTLs was estimated as the proportion of phenotypic variance they explain . The approximate physical location was estimated using the physical location of genetic markers [38 , 81] . For the mea-2 x C24 QTL analysis , an F2 population of 247 individuals was generated and genotyped using 14 sequence length polymorphism markers . An ANOVA regression was calculated for each marker: the values on S4 Table are the p-values for a regression made using a subset of 35 homozygous mea/mea plants . Ler and Cvi-0 were reciprocally crossed to generate Ler/Cvi-0 hybrids . These were then used to pollinate homozygous mea/mea or Ler plants . Three independent replicates were generated , each using different parental individuals and at different days . In total , around 10 , 000 and 4 , 000 F2 seeds were generated from mea/mea and Ler mothers , respectively . Seeds were surface sterilised for 10 minutes using 1% sodium hypochlorite , washed extensively with water , and sown in MS medium . After 10–14 days of growth at 22°C , leaves from viable seedlings were collected ( 825 , 800 , and 775 seedlings for each of the WT pool replicates; 600 , 400 , and 400 individuals for each of the mea pool replicates ) . DNA was extracted in groups of 50 leaves using the DNeasy Plant Mini Kit ( Qiagen cat . 69104 ) , and quantified using the Qubit dsDNA HS Assay kit ( Life Technologies cat . Q32854 ) . DNA from the different extractions was pooled in equi-amounts , precipitated using sodium acetate and isopropanol , and resuspended in 25 μl TE buffer for each replicate at a final concentration of 100–150 ng/μl . The TruSeq DNA Sample Prep Kit v2 ( Illumina ) was used in the succeeding steps . DNA samples ( 1 μg ) were sonicated and the fragmented DNA samples end-repaired and polyadenylated . TruSeq adapters containing the index for multiplexing were ligated to the fragmented DNA samples . The ligated samples were run on a 2% agarose gel and the desired fragment length was excised ( 50bp +/- the target fragment length ) . DNA from the gel was purified with MinElute Gel Extraction Kit ( Qiagen ) . Fragments containing TruSeq adapters on both ends were selectively enriched with PCR . The quality and quantity of the enriched libraries were validated using Qubit ( 1 . 0 ) Fluorometer and the Caliper GX LabChip GX ( Caliper Life Sciences ) . The product is a smear with an average fragment size of approximately 260 bp . The libraries were normalized to 10nM in Tris-Cl 10 mM , pH8 . 5 with 0 . 1% Tween 20 . The TruSeq SR Cluster Kit ( Illumina ) was used for cluster generation using 5 pM of pooled normalized libraries on the cBOT . Sequencing of single reads was performed on one lane of the Illumina HiSeq 2000 using the TruSeq SBS Kit v3-HS ( Illumina Inc , USA ) . Reads were quality-checked using FastQC ( http://www . bioinformatics . babraham . ac . uk/projects/fastqc ) and trimmed with the FASTX-Toolkit ( http://hannonlab . cshl . edu/fastx_toolkit/ ) . Processed reads were aligned to the TAIR10 version of the Arabidopsis genome ( Col-0 accession ) using Bowtie2 [83] and the SAMtools package [84] . SNPs were called using the mpileup command from SAMtools . Known SNPs from Ler-1 and Cvi-0 [41 , 42] were retrieved from http://1001genomes . org/data/MPI/MPISchneeberger2011/releases/current//Ler-1/Marker/Ler-1 . SNPs . TAIR9 . txt ( 461 , 070 Ler-1/Col-0 SNPs ) and from http://signal . salk . edu/atg1001/data/Salk/quality_variant_filtered_Cvi_0 . txt ( 657027 Cvi-0/Col-0 SNPs ) . After removing common Ler/Cvi-0 SNPs , we obtained a list with 765 , 643 SNPs . This list was combined with the SNP calls from the bulk sequencing dataset; for each of the datasets ( three replicates of each pool ) , we then removed positions that were ( 1 ) ambiguous in the reference sequence; ( 2 ) did not match between the Bulk-Seq samples and the published SNPs; and ( 3 ) had very high coverage ( above the 99% percentile of a Poisson distribution with λ = median coverage of the sample ) . These quality-filtering steps resulted in an average of 390 , 000 SNPs for each of the six datasets . The three replicates were combined by summing up the number of reads at each SNP position . SNP positions that had no Ler or no Cvi-0 reads in the combined dataset were discarded , as were the top and low 1% quantiles of coverage . This resulted in a final combined dataset of 352 , 491 SNPs with an average read coverage of 16 Ler and 6 Cvi-0 reads , respectively ( S3 Table ) . The same quality filtering steps were performed in each of the three replicates . To calculate the relative proportion of Cvi-0 and Ler reads across the genome , we first summed the read counts of groups of 50 neighbouring SNPs across the genome using a rolling window . We then calculated the proportion of Cvi-0 and Ler reads , and the relative enrichment of Cvi-0 reads in the mea pool relative to the WT pool: %Cvi enrichment=%Cvimeapool−%CviWT pool%CviWT pool Finally , we smoothed Cvi enrichment across neighbouring positions by computing the median of Cvi-0 enrichment with a rolling window of 100 SNPs . We used mea-2 to pollinate 167 Arabidopsis accessions obtained from the Nottingham Arabidopsis Stock Centre ( NASC ) or as a kind gift from Ortrun Mittelsten Scheid ( Gregor Mendel Institute ) and Takashi Tsuchimatsu ( University of Zurich ) . Accessions are detailed in S1 Table . The F1 progeny was selected in MS medium supplemented with 50μg/ml kanamycin and allowed to self-fertilize . We collected individual fruits 1–2 days before dehiscence and visually scored seed phenotypes; some fruits had a high proportion of autonomous seeds ( mea autonomous endosperm development depends on the genetic background [85] ) ; to avoid a bias in the aborted/plump seed ratio calculations , we did not use fruits that had more than 8% autonomous seeds ( 225 out of 2046 fruits; there was no correlation between mea rescue and autonomous seed development ) . We discarded three outliers ( accessions with a low number of scored seeds or relatively high percentage of autonomous seed development ) and obtained a final dataset consisting of 93 , 884 seeds from 164 accessions . Genotypic information ( 250k snp data v3 . 06 ) was downloaded from https://cynin . gmi . oeaw . ac . at/home/ . For correlation analysis with the dataset of 107 phenotypes [35] , we calculated Pearson correlations and corrected p-values for multiple testing using the Benjamini-Hochberg false discovery rate procedure . Genome-wide association mapping was performed with compressed mixed linear models [86] implemented in the 'GAPIT' R package [87] and in the web-based GWAPP portal [88] using the proportion of plump seeds as a phenotype ( cubic root normalised ) . Plots were generated using the 'ggplot2' R package [89] . | In plants and mammals , embryo development occurs under the protection and nourishment of maternal tissues . In polygamous species , this can lead to competition between siblings for privileged access to maternal nutrients . According to the parental conflict theory—a variation of the kinship theory—the asymmetric genetic relatedness between offspring from multiple fathers may lead to the evolution of parent-of-origin-dependent developmental regulation; paternally inherited alleles would benefit from maximizing embryo growth ( at the expense of siblings ) , whereas maternally inherited alleles would benefit from restraining growth ( to equalize sibling resource allocation ) . The kinship theory assumes that the paternal genome can actually influence embryo development; however , plant seed development is under strong maternal control . Here , we show that there is a hidden pool of variation in paternal effect loci that can be released upon loss of MEDEA , a major maternal regulator of seed development . Our results demonstrate that the maternal genome actively buffers the effects of paternal genomes on seed development , thereby providing strong functional support to the parental conflict theory . | [
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] | 2016 | Quantitative Genetics Identifies Cryptic Genetic Variation Involved in the Paternal Regulation of Seed Development |
Polycomb proteins play an essential role in maintaining the repression of developmental genes in self-renewing embryonic stem cells . The exact mechanism allowing the derepression of polycomb target genes during cell differentiation remains unclear . Our project aimed to identify Cbx8 binding sites in differentiating mouse embryonic stem cells . Therefore , we used a genome-wide chromatin immunoprecipitation of endogenous Cbx8 coupled to direct massive parallel sequencing ( ChIP-Seq ) . Our analysis identified 171 high confidence peaks . By crossing our data with previously published microarray analysis , we show that several differentiation genes transiently recruit Cbx8 during their early activation . Depletion of Cbx8 partially impairs the transcriptional activation of these genes . Both interaction analysis , as well as chromatin immunoprecipitation experiments support the idea that activating Cbx8 acts in the context of an intact PRC1 complex . Prolonged gene activation results in eviction of PRC1 despite persisting H3K27me3 and H2A ubiquitination . The composition of PRC1 is highly modular and changes when embryonic stem cells commit to differentiation . We further demonstrate that the exchange of Cbx7 for Cbx8 is required for the effective activation of differentiation genes . Taken together , our results establish a function for a Cbx8-containing complex in facilitating the transition from a Polycomb-repressed chromatin state to an active state . As this affects several key regulatory differentiation genes this mechanism is likely to contribute to the robust execution of differentiation programs .
First identified in Drosophila , the polycomb group of proteins share conserved domains and play an important role in coordinated gene repression during vertebrate and invertebrate development [1] . The prevailing view is that PRC2 and PRC1 act in a sequential manner . The association of the three proteins Eed , Suz12 and Ezh2 or Ezh1 leads to the formation of the core PRC2 complex . Ezh1 and Ezh2 are histone methyl transferases that mediate the addition of up to three methyl groups to lysine 27 of histone H3 ( H3K27me1–3 ) . The trimethylated mark is recognized by PRC1 complexes that further mediate ubiquitination of H2A and gene repression [2]–[4] . More recently , it has been shown that PRC1 can also be recruited to chromatin in the absence of a functional PRC2 complex [5]–[8] . In contrast to PRC2 , the composition of PRC1 is highly modular and much more variable . The ubiquitin ligase that provides the catalytic activity to the PRC1 complex can be either Ring1a or Ring1b . The complex additionally includes one of six Pcgf proteins; one of three orthologs of polyhomeiotic and five mutually exclusive Cbx proteins can occupy the position of the Drosophila Polycomb protein . Cbx proteins differ in some of their domains suggesting that they could convey different functional and regulatory properties to PRC1 [9] . In addition a variant complex in which RYBP replaces Cbx proteins has been shown to mediate repression independent of the methylation status of H3K27 [7] . Mouse embryonic stem ( ES ) cells are characterized by their ability to self-renew and their potential to differentiate into any of the three germ layers . PRC maintain the pluripotency of the cells by maintaining the developmental regulators repressed [10]–[12] . On differentiation ES cells acquire cell-type specific gene expression patterns that strongly depend on the genome-wide redistribution of the Polycomb proteins [12] . Activation of tissue specific genes correlates with the displacement of Polycomb proteins and a decrease of the H3K27me3 mark during retinoic acid induced neuronal differentiation [13] . However , it has been recently shown that Polycomb proteins can also be recruited to activated genes to attenuate the retinoic acid associated transcriptional activation of specific genes [14] . The important function of Polycomb complexes in the epigenetic changes induced by retinoic acid in mouse embryonic stem cells has been recently reviewed by Gudas [15] . The composition of the PRC1 complex changes during the differentiation of ES cells . Cbx7 is the primarily expressed Polycomb ortholog in ES cells but it is quickly downregulated during differentiation while Cbx2 , Cbx4 and Cbx8 are induced [16] , [17] . These studies showed that the integrity of Cbx7 was required for stable ES cell maintenance , while Cbx2 and Cbx4 were required for balanced lineage specification . It is worth noting that similar results have been obtained for hematopoietic stem cells [18] . However , some important questions about Polycomb proteins remain unanswered . Despite their overt relevance for ES cell differentiation , it is poorly understood how Polycomb repressed states are established and resolved . How PRCs are initially recruited to target genes is a matter of continuous debate ( discussed in [1] ) . Similarly it is unclear how the transition from a PRC repressed state to an active state is achieved . How changes in PRC composition relate to these transitions has not been investigated . Here , we analyzed the genome wide recruitment of Cbx8 in ES cells induced to differentiate . We provide compelling evidence suggesting that Cbx8 is part of a transitory PRC1 complex facilitating the activation of Cbx7-PRC1-repressed genes during the commitment to differentiation .
We used retinoic acid ( RA ) to induce mouse E14 ES cells to start differentiating towards the neuronal lineage . We confirmed previous results [16] , [17] showing that Cbx8 was virtually absent in self-renewing ES cells but potently induced on protein and RNA levels after three days of RA-induced differentiation ( Fig . 1A and S1A Figure ) . To assess the genome wide distribution of Cbx8 in differentiating ES cells , we enriched Cbx8-bound chromatin by chromatin immunoprecipitation ( ChIP ) using an antibody generated against the unique part of the protein ( S1B-G Figure ) and analyzed the co-precipitated DNA by direct massive parallel sequencing ( ChIP-seq ) . Taking advantage of the fact that Cbx8 is virtually absent in untreated , self-renewing ES cells ( Fig . 1A ) , we decided to use both IgG as well as Cbx8 ChIPs from untreated cells as negative controls . We were able to uniquely map 9–16 million reads per sample ( S2A Figure ) . For our further analysis we used a set of high confidence binding sites that were identified by the overlap of peaks that were called by MACS comparing Cbx8 ChIP from RA-treated ES cells to IgG and those called comparing Cbx8 ChIP from RA-treated ES cells to the antibody-specific background ChIPed from untreated cells ( S2B Figure ) . By this method we were able to identify a subset of 171 peaks corresponding to Cbx8 binding sites of high confidence ( S1 Table ) . Peaks were annotated to the nearest gene if the center of the peak was inside a window flanking the transcribed region by 3 kb . Plotting the average read coverage on genes with annotated Cbx8 indicated that Cbx8 tends to accumulate on gene bodies but also to spread into upstream and downstream regions ( Fig . 1C ) . Using this annotation we found that the large majority ( 141/171 ) of identified peaks are associated to annotated genes ( Fig . 1D ) . We performed a microarray analysis comparing self renewing untreated ES cells and differentiating cells after 3 days of RA treatment and crossed the data with our ChIP-seq to identify possible transcriptional changes on Cbx8 target genes . Taking into consideration the established function of Polycomb proteins in gene repression , we expected target genes to either not change because they are maintained in a repressed state or to be downregulated . We could extract data for 121 of the 141 gene-associated peaks . We found that about one third of these Cbx8 binding sites ( 53 peaks ) annotated to genes that displayed a more than 1 . 5-fold change in gene expression . To our surprise the large majority of these genes ( 44/53 ) was not down- but upregulated ( Fig . 1E ) . Most of these upregulated genes were repressed in untreated cells as indicated by very low average probe intensities ( Fig . 1F ) . Though a previous report has already shown that Cbx8 can be found on a handful of activated genes in differentiating cells [19] , our data suggested that this could actually be true for a substantial fraction of Cbx8 target genes . In order to confirm this , we selected a panel of target and control genes and simultaneously analyzed Cbx8 recruitment and the corresponding mRNA levels . We were able to confirm differentiation-induced recruitment of Cbx8 on all target genes tested while control genes were negative ( Fig . 2A ) . Target genes included many important key differentiation genes such as Sox9 , Gata6 and Nkx6-1 whose expression was potently induced ( Fig . 2B ) . In order to exclude the possibility that Cbx8 binding and active transcription might occur on different and exclusive alleles within the cell population , we analyzed the co-occurrence of Cbx8 and H3K36me3 , which is a mark of active transcription [20] , by coupled ChIP in differentiating ES cells treated for three days with RA . First , we have analyzed five Cbx8 target genes and four non-target genes that included Oct4 , Nanog , Gapdh and Rpo . Despite the fact that Nanog is downregulated after three days of RA treatment ( Fig . 2B ) it still retained some H3K36me3 that was in a similar range as on the activated gene Sox9 or the constitutively active gene Gapdh ( Fig . 2C , left panel ) suggesting that removal of the active mark H3K36me3 follows a slower dynamic than the actual gene repression . Taking advantage of the fact that with Gapdh , Nanog , and Sox9 we had identified target and non-target genes of Cbx8 with comparable H3K36me3 levels , we have used anti-Cbx8 antibody-enriched chromatin as input material for a secondary ChIP with IgG and anti-H3K36me3 antibody . As shown in the right panel of Fig . 2C , we found a clear enrichment of H3K36me3 over IgG on Sox9 and on the other Cbx8 target genes but not on non-target genes such as Gapdh or Nanog . As chromatin ranging in size between 300–500 bps has been used for these experiments H3K36me3 and binding of Cbx8 could be occurring on different H3 tails on the same or even neighboring nucleosomes . The observation that Cbx8 can be simultaneously detected on the same locus provides further support to the idea that Cbx8 is recruited to genes that become actively transcribed . To study the functional importance of Cbx8 for the activation of differentiation genes , we used lentiviral vectors expressing two different short hairpin RNAs ( shRNA ) directed specifically against the Cbx8 transcript . After selection stably transduced cells were used for the study . Both shRNAs efficiently repressed the expression of Cbx8 on both the mRNA and protein levels in ES cells treated with RA ( Fig . 3A , B ) . The activation of upregulated Cbx8 target genes was significantly decreased in cells depleted for Cbx8 ( Fig . 3C ) . Among the Cbx8-sensitive genes were several pivotal regulators of differentiation processes such as Sox9 and Nkx6-1 , that have been shown to be important transcription factors required for normal brain development [21] , [22] . The reduction of Cbx8 occupancy was similarly efficient on up- as well as downregulated genes ( Fig . 3D ) , however , the reduction in Cbx8 levels didn't affect the repression of its target genes Prdm14 and Otx2 ( Fig . 3C ) . Importantly , the non-target genes Oct4 and Nanog , which encode the regulators of pluripotency , were similarly repressed in Cbx8 deficient and control cells ( Fig . 3C , D ) . Plotting enriched gene ontologies according to their similarity in a semantic space illustrates a clear overrepresentation of both transcriptional and differentiation regulators ( Fig . 4A ) , which are the classical categories of Polycomb target genes in self-renewing ES cells [12] . Gene ontologies related to neuronal development were preferentially enriched in the subgroup of activated Cbx8 genes but not those target genes that did not show any change in gene expression ( S3A Figure ) . Downregulated genes were not sufficient in number to yield a result in gene ontology analysis . We compared our genome wide Cbx8 binding profile in differentiating ES cells with published ChIP-seq data obtained from self-renewing ES cells [17] . The binding of Cbx8 in RA-treated differentiating ES cells mirrors the binding of PRC1 proteins Ring1b and Cbx7 within H3K27me3 domains in untreated self-renewing ES cells ( Fig . 4B ) . As shown in Fig . 4C , this holds true for the vast majority of Cbx8 binding sites in RA-treated ES cells as 133/171 overlapped with sites bound by Cbx7 in self-renewing ES cells . Similar overlaps were observed with Ring1b and H3K27me3 ( S3B Figure ) . The unexpected link between Cbx8 recruitment and gene activation prompted us to analyze whether Cbx8 acted alone , as part of a PRC1 or part of a different complex . First we compared the dynamics in occupancy of Cbx8 and Ring1b , which is the least variant component of PRC1 . As already suggested by the ChIP-seq data ( Fig . 4B ) , Ring1b was strongly enriched on target genes in self-renewing ES cells . While the recruitment of Cbx8 initially increased and peaked after three days of retinoic acid induction , the binding of Ring1b decreased progressively reaching background levels at day five of differentiation ( Fig . 5A ) . At day five of retinoic acid induced differentiation , the Cbx8 occupancy dropped to very low levels similar to Ring1b . In contrast , H2A ubiquitination , mark set by Ring1b [23] , persisted over the entire time course ( Fig . 5A ) . In order to understand whether Cbx8 and Ring1b are acting together , we decided to identify the proteins that bind Cbx8 during ES cell differentiation . Therefore , we generated ES cells stably expressing epitope-tagged Cbx8 ( Fig . 5B ) . Exogenous Cbx8 expressed in untreated ES cells bound to the same target genes as endogenous Cbx8 in RA-treated ES cells suggesting that the epitope does not affect its function ( S4A Figure ) . We harvested cells at day three of differentiation which corresponds to the time point with maximal recruitment of endogenous Cbx8 to target genes ( Fig . 5A ) . Cbx8 and interacting proteins were enriched by affinity purification , analyzed by mass spectrometry and significantly enriched proteins were ranked according to their abundance ( S2 Table ) . After the bait protein Cbx8 , the top five ranked proteins were the PRC1 subunits Ring1a/b , Phc2 and the Pcgf proteins Mel18 and Bmi1 . Three additional PRC1 subunits were found in lower abundance ( Fig . 5B ) . Next , we tested whether Ring1b and H2A ubiquitination co-occur with Cbx8 on genes . Therefore , we have used anti-Cbx8 antibody-enriched chromatin as input material for a secondary ChIP with IgG , anti-Ring1b and anti-ubiquitinated H2A antibody . As shown in Fig . 5C we could detect co-enrichment on the target genes Sox9 , Nkx6-1 , Lhx2 and Gata2 but not on the non-target genes Oct4 , Rpo or Gapdh . Taken together , these results suggested that Cbx8 acts as part of a PRC1 complex . To further support this , we have analyzed the occupancy of Cbx8 target genes by Ring1b , Cbx7 and H2A ubiquitination in RA-treated cells after Cbx8 knockdown . The resulting reduction of Cbx8 occupancy ( Fig . 3D ) correlated with a small but consistent reduction of Ring1b on several Cbx8 target genes without affecting non-target genes ( Fig . 6 ) . Notably , global Ring1b protein levels were not affected ( S4B Figure ) . However , we observed an increased incorporation of Cbx7 that could partially compensate for Cbx8 loss ( Fig . 6 ) . This was not the consequence of an upregulation of Cbx7 expression as knockdown of Cbx8 did not affect the mRNA levels of Cbx7 or other Cbx proteins ( S4C Figure ) . Moreover we found that Cbx8 loss did not affect the levels of H2A ubiquitination on its target genes ( Fig . 6 ) . In order to address the question whether Cbx8-containing PRC1 on activated genes is repressive or activating , we stably interfered with the expression of Ring1b , the least variant component of PRC1 . When analyzing Ring1b knockdown cell after 3 days of RA treatment we did not observe any compensation by Ring1a but a slight increase in Cbx8 mRNA ( S4D Figure ) . Under these conditions the Cbx8 target gene Gata2 was further upregulated while other target genes such as Nkx6-1 and Sox17 were less activated ( S4D Figure ) . These results are difficult to interpret as knockdown of Ring1b interferes with all PRC1 complexes present prior and after RA treatment . We then focused our attention on the mechanism that recruits Cbx8 to its target genes . An obvious possibility is that it binds directly to H3K27me3 . Although activated Cbx8 target genes progressively reduced their H3K27me3 levels , in contrast to Ring1b , this reduction occurred only very slowly and even after five days of differentiation genes still retained half-maximal or even higher levels of H3K27me3 ( Fig . 7A ) . Depletion of Cbx8 did not affect the amount of H3K27me3 detectable 3 days after RA treatment ( Fig . 7B ) . We then analyzed by ChIP-ReChIP if Cbx8 and H3K27me3 co-occur on target genes in ES cells treated for 3 days with RA . Indeed , we found that Cbx8 and H3K27me3 co-existed on Cbx8 target genes ( Fig . 7C ) . This suggests that also in the context of gene activation the interaction of Cbx8 with H3K27me3 is the most likely mechanism of recruitment to chromatin . In addition to H3K27me3 , at day 3 of RA treatment we also detected some acetylation of the same residue on Cbx8 target genes ( S5B Figure ) . This H3K27ac was of low level when compared to an enhancer that was previously described to be marked by H3K27ac in ES cells ( S5A Figure ) [24] . Curiously , knockdown of Cbx8 lead to a modest but significant decrease of H3K27ac ( S5B Figure ) . The presence of these two mutually exclusive H3K27 marks provided us with a valuable tool to interrogate the preferential binding of Cbx8 . First we titrated both antibodies to reach similar enrichment in ChIP assays ( S5C Figure ) . Then we used the enriched material to perform a sequential ChIP for Cbx8 . As shown in S5C Figure , Cbx8 preferentially bound K27me3-marked chromatin . Cbx7 and Cbx8 are expressed in an almost mutually exclusive manner in self-renewing and differentiating ES cells , respectively [16] , [17] . To further gain additional insight into the functional relevance of the switch from Cbx7 to Cbx8 on target genes , we generated mouse embryonic stem cells that stably express exogenous Cbx8 and analyzed them in self-renewing conditions while cells expressing exogenous Cbx7 were analyzed in differentiating cells after 3 days of retinoic acid induction ( Fig . 8A ) . When expressed in self-renewing cells , exogenous Cbx8 was able to efficiently outcompete Cbx7 for its target genes ( Fig . 8B ) . The enforced recruitment of exogenous Cbx8 achieved under these conditions was two-to-three-fold higher than that observed in differentiating cells for the endogenous protein ( Fig . 8B ) , although , this did not affect the low expression level of these genes ( Fig . 8C ) . In the converse experiment during differentiation , Cbx7 overexpression significantly reduced the activation of Cbx8 target genes ( Fig . 8C ) . Although enrichment of overexpressed Cbx7 on target genes did not reach the levels of the endogenous protein in self-renewing cells , it resulted in an efficient displacement of Cbx8 ( Fig . 8D ) .
Taken together our results support a model in which PRC1 containing Cbx8 replace PRC1 containing Cbx7 on developmental genes , which facilitates the transition from a repressed chromatin state to gene activation during early ES cell differentiation ( Fig . 8E ) . Prolonged gene activation results in eviction of PRC1 complexes despite some persisting H3K27me3 and H2A ubiquitination . This mechanism affects several key regulatory genes and thus probably contributes to a robust execution of differentiation programs . Our data supports the idea that Cbx8 acts in the context of an intact PRC1 complex . Initially , we considered also two other possibilities: Cbx8 could act as monomer competing with repressive PRC1 complexes for H3K27me3 binding sites , or Cbx8 acts in complex with non-polycomb proteins that are activating . Indeed , in leukemia cells Cbx8 has already been described as a component of activating complexes containing Tip60 and MLL-AF9 [25] . However , performing mass spectrometric analysis of affinity purified Cbx8-complexes from differentiating ES cells we could not detect any of these proteins , but rather found other PRC1-components acting as main binding proteins ( Fig . 5B ) . Moreover , we have shown that Ring1b was enriched on Cbx8 immunoprecipitated chromatin compared to IgG ( Fig . 5C ) and that the knockdown of Cbx8 resulted in a small but detectable reduction of Ring1b occupancy on genes ( Fig . 6 ) . Since we would have expected the opposite if Cbx8 acted as a monomer on target genes , this observation taken together with our co-immunoprecipitation data strongly argued for Cbx8 to be functioning in the context of a PRC1 complex . One fundamental question is how a Cbx8-containing PRC1 is able to contribute to gene activation . A plausible explanation could be that Cbx8-containing PRC1 is simply less repressive than the PRC1 containing Cbx7 that it replaces . This prompted us to test whether Cbx8 has an influence on PRC1 activity . When monitoring the ubiquitination of H2A , we found that its levels on Cbx8 target genes neither decreased during the first five days of differentiation nor changed on depletion of Cbx8 ( Figs . 5A , 6 ) . It has been shown that Ring1b mediates chromatin compaction and gene repression independently of its catalytic activity [26] . In that regard , it would be interesting to test whether changes in PRC1 composition affects its capacity to compact chromatin . If Cbx7-containing PRC1 complexes induced a higher degree of chromatin compaction , this could possibly be relaxed on replacement by Cbx8 . Knockdown of Ring1b itself let to the further activation of a Cbx8 target gene but to a reduction in the activation of several other Cbx8 target ( S4D Figure ) . It is intriguing to speculate that the outcome could depend on the ratio of Cbx7 and Cbx8-containing PRC1 complexes present at the time point of analysis and the gene-specific kinetics of activation . However , the interpretation of such data is complicated by the large number of binding sites of Ring1b as its other target genes could exert indirect effects and the fact that Ring1b occurs in both PRC1 and non-PRC1 complexes [27] . Finally , of course we cannot exclude the possibility that Cbx8-containing PRC1 complexes are able to recruit activating proteins in a very transient way , which would not be captured by our purification and mass spectrometric analysis . Both the replacement of Cbx7-containing PRC1 for Cbx8 loaded complexes and the complete eviction of all PRC1 after prolonged activation occurs in the context of persisting H3K27me3 and H2A ubiquitination . This reiterates that these marks have a slow turnover and are not necessary a reflection of gene activity or repression , in particular during dynamic cell fate transitions . On Cbx8 target genes we could detect low levels of H3K27ac that were further reduced mildly but significantly in cells depleted for Cbx8 ( S5B Figure ) . Whether Cbx8-dependent H3K27ac is a cause or consequence of enhanced transcription is unclear . H3K27ac could positively affect chromatin accessibility for transcription-supporting factors . However it is more likely that the observed low level of H3K27ac is a collateral consequence of an increased concentration of histone acetylases travelling with Polymerase II . The modularity of PRC1 is illustrated by the fact that there are 180 theoretical combinations for assembling the different PRC subunits . The real number of different PRC1 complexes existing under different physiological conditions is likely to be much lower due to mutually exclusive expression patterns and preferential binding between subunits . The systematic proteomic and epigenomic analysis of all six PCGF proteins shed some initial light on the different compositions and genomic distributions of PRC1 complexes [28] . On the one hand proteomic studies allowed the identification of new PRC1 components such as RYBP and YAF2 , while on the other hand they increased doubts about the genuineness of subunits that had previously been considered to be canonical; such as Cbx6 . A major challenge for the field is to understand how the modularity of PRC1 is regulated and how it contributes to cellular functions . Self-renewing ES cells primarily express two PRC1 complexes containing either Cbx7 or RYBP [7] . Whereas Cbx7-PRC1 mediates early repression of differentiation genes by binding to H3K27me3 , RYBP-PRC1 binds independently of the methylation status of H3K27 and is associated with lower levels of Ring1b and H2A ubiquitination and occupies genes that are less repressed [29] . Cbx7 and Cbx8 are expressed in an almost mutually exclusive manner in self-renewing and differentiating ES cells , respectively . Although in vitro the chromodomain of Cbx7 has higher affinity to H3K27me3 than the one of Cbx8 [30] , in cells we found that both proteins were able to efficiently replace each other on genes . Enforced expression of Cbx7 in differentiating cells was able to compete with Cbx8 for its target genes and to reduce gene activation . In the converse experiment similarly efficient replacement of Cbx7 by exogenous Cbx8 in self-renewing ES cells was not sufficient to induce derepression ( Fig . 8A–D ) . These results place Cbx7 over Cbx8 in the functional hierarchy of Polycomb proteins . Future studies will have to assess in greater detail how different PRC1 complexes regulate cell fate decisions . Our preliminary data shows that ES cells that maintain 30–50% reduced Cbx8 expression are qualitatively able to differentiate into Tuj1-positive neurons with neurite outgrowth following 11 days of a long-term differentiation protocol ( adapted from [31] ) , but seem to do so in a less efficient way ( S6 Figure ) . The number of observations linking Polycomb proteins and active gene transcription are increasing . Here we report that the transient recruitment of PRC1 containing Cbx8 facilitates the transition from a Polycomb-repressed to a fully active state of key regulatory genes during early differentiation . Others have suggested that binding of Cbx8 to active genes could mark these for later repression [19] . Our data does not support this as we find Cbx8 recruitment to be only transient and all PRC1 to be entirely evicted after prolonged gene activation ( Fig . 5 ) . It is worth to point out that Cbx8 target genes that got repressed after treatment with retinoic acid were not affected by the knockdown of Cbx8 . This can be explained by a possible compensation by other canonical repressing PRC1 complexes or a recent finding showing that Polycomb protein recruitment is rather a consequence than a cause of initial gene silencing [32] . Studying differentiating myocytes , others have reported that Ezh1 associates with actively transcribed genes and further argued for a positive function in which Ezh1 could be required for the recruitment of Polymerase II [33] . In contrast , Pombo and colleagues suggested that their observed Polycomb-binding to transcribed metabolic genes was the consequence of a continuous switching between an active and a Polycomb-repressed state restraining transcriptional elongation [34] . In Drosophila cells , PRC1 was found to indirectly associate with some active as well as inactive genes by binding to structural cohesin proteins [35] . The authors argued again for a more active role on active genes by suggesting that PRC1 could be required for allowing the phosphorylation that makes Polymerase II elongation competent . A large body of additional work is needed to sort out the relation between Polycomb-bound and transcribed chromatin states in greater detail . In this endeavor it will be important to carefully distinguish between passive contributions from reductions in repressive potential and genuine contributions to transcription initiation and elongation .
We produced a specific polyclonal antibody against mouse Cbx8 by immunizing rabbits with a His-tagged fragment of Cbx8 protein encompassing amino acids 201–360 . Serum was precleared with sepharose and passed over a column containing a fusion protein of glutathione-S-transferase ( GST ) and amino acids 201–360 of Cbx8 covalently cross-linked to Glutathione sepharose . Anti-Cbx8 antibody was eluated with low pH , dialyzed and stored in PBS with 20% glycerol . The antibody performed in a similar way to antibodies previously described and kindly provided by Kristian Helin [13]; ( S1 Figure ) . In addition we made use of the following antibodies: anti-IgG ( Abcam ) , anti-H3 C-terminal ( Abcam ) and anti-H3K27me3 ( Millipore ) , anti-Flag M2 ( Sigma-Aldrich ) , anti-Ring1b provided by Luciano Di Croce [36] , anti-H3K27Ac and anti-Cbx7 ( Abcam ) , anti-H3K36me3 ( Abcam ) and rabbit monoclonal anti-ubiquityl-H2A ( Lys119 , D27C4 , Cell Signaling Technology ) . The amounts and concentrations used for ChIP and western blotting is given in S3 Table . Expression plasmids and pLKO-1 constructs for shRNA-mediated knockdown were generated with standard PCR and cloning techniques . For stable expression in ES cells , cDNAs were cloned in frame with a multiple-epitope tag into a vector containing CAG promoter and an IRES-puromycin resistance gene cassette [37] . A modified and gateway cloning-adapted ( Life Technologies ) version was kindly provided by Diego Pasini . E14Tg2A . 4 mouse ES cells were cultured as previously described [38] . Cells were transduced with lentiviral shRNA cassettes essentially as described before [39] . Transduced cells were selected with 2 µg/ml puromycin . For shRNA sequences see S3 Table . For the generation of stably expressing ES cell clones , the above described CAG promoter driven vectors were transfected into mouse ES cells E14 using Lipofectamine 2000 ( Life Technologies ) . Stable transfectants were selected with 2 µg/ml puromycin and analyzed by RT-PCR and western blot for transgene expression . For neuronal differentiation , 1 µM all trans retinoic acid ( RA ) was added directly to cells in medium without leukemia inhibitory factor . Unless indicated otherwise in Figure legends , cells were collected after 3 days of RA treatment . Lysis and western blot analyses were performed as previously described [40] . Following the supplier's instructions , RNA was purified from 2×106 cells using the RNeasy minikit ( Qiagen ) , with a DNase I digestion step to avoid any potential DNA contamination . Total RNA ( 1 µg ) was reverse transcribed using a cDNA synthesis kit ( Roche Diagnostics ) and oligo ( dT ) primers . Relative cDNA levels were quantified by quantitative PCR ( qRT-PCR ) . Values were normalized to the expression of two housekeeping genes ( Rpo and Gapdh ) . For gene expression analysis , four biological replicates of RA treated ( 3 days ) and untreated ES cells were used for each condition and samples were prepared and hybridized to SurePrint G3 Mouse GE 8×60K Microarrays ( Agilent technologies ) following the supplier's instructions . Analyses were essentially performed as described [41] selecting differentially expressed probes with a FDR of 0 . 05 and fold change of >1 . 5 . Chromatin fragmented to a size ranging from 300–500 bps and immunoprecipitation ( ChIP ) experiments were performed essentially as previously described [42] . ChIP-reChIP experiment was performed as described elsewhere [34] . The sequences of all oligonucleotides used here are provided in the S3 Table . Unless indicated otherwise , ChIP results are given as the percentage of the amount of ChIP-enriched DNA relative to the amount of DNA isolated from one tenth of input material measured by quantitative PCR . For ChIP-sequencing ( ChIP-seq ) , 10 ng of DNA was enriched by ChIP and fluorimetrically quantified with PicoGreen . Library generation and direct massive parallel sequencing on an Illumina genome analyzer were performed according to the supplier's instructions . Reads obtained were cleaned based on quality , trimmed using the ShortRead package in R [43] and aligned with the mouse genome ( NCBIM37/mm9 ) using Bowtie version 0 . 12 . 7 [44] , two mismatches were allowed for the alignment within the seed , only reads mapping to a single position in the genome were used . To detect genomic regions with significant enrichment we used MACS software version 1 . 4 . 1 [45] . For peak calling of Cbx8 in RA-treated ES cells we used a p-value cut-off of 1×10−4 and a FDR of 5% . Both IgG and Cbx8 from self-renewing cells ( that do not express Cbx8 ) were independently used as control libraries . Only peaks called in both cases ( minimal overlap of 50 bps ) were accepted as high confidence target peaks and further analyzed . A subset was validated by direct ChIP . Peaks were annotated using ChIPpeakAnno package [46] . Genes were considered to be target genes if the center of a peak was found in the transcribed region ±3 kb using the transcript set of Mouse Ensembl Gene ( based on assembly NCBIM37/mm9 ) . In cases where a peak annotated to two genes , the nearest gene was selected and identified by the minimal distance between peak center and transcribed region . Un ambiguous peak that was found inside an intron of three differently annotated transcripts ( chr10:3310058 , 3311329 ) was excluded from the analysis . To calculate the normalized enrichment profile we have used ngs . plot ver 2 . 0 [47] . The gene ontology analysis was performed using the function “getEnrichedGO” from ChippeakAnno package , we used the Ensembl gene ID and accepted GO categories with an adjusted P-value of 0 . 001 or less . P-values were calculated using the multiple adjusted Benjamini-Hochberg method . A value of ten was set as minimum count in the genome for a GO term to be included . For visualizing GO categories we used REVIGO [48] , using the following parameters: “Medium” for the allowed similarity and “SimRel” for semantic similarity measure . Gene ontologies were grouped and genes were scored if they were associated to one or more ontology terms per group . For proteomic analysis , 2×108 differentiated ES cells expressing Flag-epitope tagged Cbx8 and parental control cells were collected with PBS . Nuclei were isolated using sucrose buffer and nuclear extract was separated from chromatinic fraction by a high salt extraction protocol followed by ultracentrifugation ( 1 h 50000 rpm ) . The soluble fraction containing nuclear extract was diluted to isotonic concentration followed by a preclear step using sepharose beads . Binding to Anti-Flag M2 Beads ( Sigma-Aldrich ) was performed in a rotating wheel during 2 h . Then , Anti-Flag M2 Beads were passed into a column for further enrichment and washing steps . Beads were collected into tubes for elution with 50 mM NaHCO3 and 0 , 5% SDS . Eluated proteins were precipitated with cold acetone and frozen dry pellets were sent to a Proteomics facility for mass spectrometry analysis . Samples were digested with trypsin and 1 µg of each sample was injected in an Orbitrap Velos to LC-MS/MS analysis . Data was searched using an internal version of the search algorithm Mascot against a SwissProt_Mouse database ( July 2013 ) . Protein identification and peptides identified for each protein were identified using Proteome Discoverer v1 . 4 , which gives an approximate estimation of protein amount with the average peak area of the 3 top peptides for a given protein . The number of peptides identified for each protein is a parameter of quality , the more peptides the better . Peptides were filtered based on the 1%FDR . Only proteins enriched more than 10 fold and with a coverage of at least 5% were considered for analysis . Unless indicated otherwise , qRT-PCR and ChIP data is represented as the mean of three independent experiments and errors denote the standard deviation and stars indicate p-values below 0 . 05 as determined by the two-tailed Student's T-tests . Individual ChIP experiments are normalized to the average enrichment observed in the experiment . Means and errors of three experiments are scaled to represent the average of all experiments . ChIP-seq and Microarray data have been deposited in the GEO database under accession number GSE54053 . | Cell fate transitions have long been known to be accompanied by alterations in chromatin structure . But only during the last few years has it become clear that chromatin modifications form the molecular basis of an epigenetic memory that defines cell identity . The Polycomb Group Proteins ( PcGs ) form two major protein complexes known as polycomb repressive complexes 1 and 2 ( PRC1 and PRC2 ) . Their function is essential for the maintenance of transcriptional repression during embryogenesis through the methylation of the lysine 27 on histone H3 and the subsequent ubiquitination of histone H2A . The chromobox homolog 8 , Cbx8 , which is part of the PRC1 complex , is therefore generally defined as a repressor of gene transcription . The genome wide profiling of Cbx8 during the early steps of mouse embryonic stem ( mES ) cells differentiation provided us with surprising results involving Cbx8 in gene activation . Our results point out that Cbx8 is part of a PRC1 complex involved in the transition from a Polycomb repressed state to an active state . | [
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] | 2014 | A Cbx8-Containing Polycomb Complex Facilitates the Transition to Gene Activation during ES Cell Differentiation |
Sprouting angiogenesis , where new blood vessels grow from pre-existing ones , is a complex process where biochemical and mechanical signals regulate endothelial cell proliferation and movement . Therefore , a mathematical description of sprouting angiogenesis has to take into consideration biological signals as well as relevant physical processes , in particular the mechanical interplay between adjacent endothelial cells and the extracellular microenvironment . In this work , we introduce the first phase-field continuous model of sprouting angiogenesis capable of predicting sprout morphology as a function of the elastic properties of the tissues and the traction forces exerted by the cells . The model is very compact , only consisting of three coupled partial differential equations , and has the clear advantage of a reduced number of parameters . This model allows us to describe sprout growth as a function of the cell-cell adhesion forces and the traction force exerted by the sprout tip cell . In the absence of proliferation , we observe that the sprout either achieves a maximum length or , when the traction and adhesion are very large , it breaks . Endothelial cell proliferation alters significantly sprout morphology , and we explore how different types of endothelial cell proliferation regulation are able to determine the shape of the growing sprout . The largest region in parameter space with well formed long and straight sprouts is obtained always when the proliferation is triggered by endothelial cell strain and its rate grows with angiogenic factor concentration . We conclude that in this scenario the tip cell has the role of creating a tension in the cells that follow its lead . On those first stalk cells , this tension produces strain and/or empty spaces , inevitably triggering cell proliferation . The new cells occupy the space behind the tip , the tension decreases , and the process restarts . Our results highlight the ability of mathematical models to suggest relevant hypotheses with respect to the role of forces in sprouting , hence underlining the necessary collaboration between modelling and molecular biology techniques to improve the current state-of-the-art .
Sprouting angiogenesis—a process by which new blood vessels grow from existing ones—is an ubiquitous phenomenon in health and disease of higher organisms [1] . It plays a crucial role in organogenesis [2] , wound healing [3] , inflammation [4 , 5] , as well as on the onset and progression of over 50 different diseases such as cancer , rheumatoid arthritis and diabetes [6 , 7] . Currently , many cancer therapies are designed to inhibit the surrounding vasculature depriving the tumor of oxygen and nutrients , but at the same time to facilitate the delivery of chemotherapeutic drugs [8–13] . However , in order to achieve a vasculature that best suits the aim of hindering the tumor development , a detailed knowledge of the regulatory mechanisms of angiogenesis has to be reached . A better understanding of the processes involved in angiogenesis may have a critical impact concerning the strategies to tackle tumor progression as well as in the treatment of several other pathologies where angiogenesis plays an important role . Angiogenesis is a complex process where a myriad of biological signals , such as the activation of signalling pathways by the binding of growth factors to receptors at the cell membrane , are converted into mechanical forces that originate cell movement . Subsequently , the concerted movement of several endothelial cells lead up to the formation of tubular structures . Sprouting angiogenesis starts when endothelial cells of existing capillaries acquire the tip cell phenotype by the action of a protein cocktail produced typically by tissue cells in a hypoxic micro-environment [14] . Tip cells lead the growth of new capillary sprouts towards increasing concentrations of relevant growth factors , such as VEGF ( vascular endothelial growth factor ) . Endothelial cells behind the tip cell acquire the stalk cell phenotype , being their proliferation rate regulated by VEGF . The stalk cells that follow the tip cell build the body of the growing sprout [14] , and after their spatial rearrangement will form a lumen where blood can flow . When two of these sprouts merge , the blood is able to circulate . The shear forces exerted by the blood flow on the capillary wall trigger signalling pathways in the endothelial cells that lead to further remodelling at the newly formed vasculature , contributing to the shaping of an hierarchical vasculature , with thicker arteries branching into thinner vessels [15–18] . It is rather challenging to study experimentally the articulation between biological and mechanical events in angiogenesis . Recently , some progresses have been reached , for example , the expression of remodelling extracellular proteases and VEGF receptors have been found to be regulated by mechanical stress [18 , 19] , and there is now a detailed knowledge concerning the mobility of endothelial cells in matrices of different rigidities and subjected to different levels of chemotactic agents [20–22] . Another approach to study the interplay between mechanical and biological processes in angiogenesis is through mathematical modelling . In fact , mathematical modelling is able to combine in a simulation various mechanisms of angiogenesis , thus challenging the current paradigms , and providing new testable hypothesis [23–26] . For research in tumor growth , the use of mathematical modelling is already evident in the community , with complex models able to predict tumor spread [27] and development [28 , 29] . The phenomenon of sprouting angiogenesis has been extensively modelled in the last two decades using a large variety of strategies . Both the production of angiogenic proteins and the mechanisms that lead to the formation of complex vascular networks have been simulated in detail . The production of VEGF is intricately dependent on the levels of transcription factors that detect O2 levels , such as the Hif-1α , and these coupled signalling pathways have been analysed extensively in the modelling literature [30 , 31] . The bioavailability of VEGF in the tissue depends on its interaction with the matrix , and this interplay has also been simulated computationally [25 , 32–36] . These models enabled a much better understanding of the pathways that regulate the production and signalling of VEGF in angiogenesis . During these last two decades , research groups have simulated the growth of the new vasculature either via macroscopic continuous models , or using discrete approaches . Continuous models of angiogenesis describe the evolution of average endothelial cell density in a point of the tissue [37–42] . This density field invades the hypoxic tissue while being regulated by the VEGF gradient and/or by the gradient of matrix stiffness ( durotaxis ) . These models allowed the quantitative study of several other biological regulatory mechanisms of angiogenesis such as angiopoietin-1 and angiopoietin-2 levels [42] , and were able to monitor the evolution of the pericyte levels in the vessels , thus describing the degree of vascular maturation [41] . However , these models are not able by design to predict the morphology of the growing vascular network . Discrete approaches , on the other hand , aim at modelling the ramified vasculature that results from the angiogenic process [43–46] . An extensive amount of work has been done in these models , where the growing vessels are modelled by reinforced random walks directed towards the gradient of VEGF or other factors in the matrix . Researchers were able to predict vessel flow with these models and to use flow as a further remodelling agent of the vasculature [46] . Discrete models of angiogenesis have been coupled to tumor growth models [47 , 48] and used to describe neo-vascularisation in the retina [49] . However they do not model the shape of the growing vessels , and therefore , the interplay with the matrix rigidity is hard to implement . More detailed cell-based approaches , on the other hand , model how individual cells along the vessel interact with each other while they move towards higher VEGF levels [50–53] . These models have much more detail and are able to unravel the mechanisms of cell-cell interactions coupled with signalling pathways in angiogenesis . In spite of cell-based models being able to predict the morphology of the small vasculatures , the large number of parameters and rules often hamper the full exploration of the model’s parameter space . In the last five years , researchers developed hybrid models that combine a continuous description of the vessel sprout with a cell-based approach for tip cell creation and movement [25 , 54 , 55] . Using techniques such as phase-field modelling , which is able to describe the dynamics of complex boundaries such as capillary walls , these models allowed the study of the shape of vessel sprouts with a smaller number of parameters and rules . However , until now they do not include the ability of matrix rigidity to control tip cell movement . For reviews on the literature of angiogenesis modelling see [17 , 56–59] . This effort on angiogenesis simulation has thus provided important insights into this process by unravelling the dynamics of the Notch membrane protein transcription that regulates tip cell selection and the growth and interaction between endothelial cells’ filopodia [52 , 60] . Mathematical modeling has helped to identify the role of mechanical traction versus chemotaxis on endothelial cell patterning on a matrix in vitro [51 , 61] . It has also addressed sprout regression [62] and described the complex movement of cells at the tip of a growing sprout [63 , 64] . In this latter case , it has been predicted and observed experimentally the exchange of phenotype between the cells at the front of the sprout , leading to the overtaking of the first cell by the second cell , which becomes the new tip cell . This complex dynamics guarantees the presence of a cell at the front of every growing sprout with the tip cell phenotype that is capable of exerting a pulling force on the matrix , and is characterised by the presence of filopodia capable of sensing the gradient of the angiogenic factors , such as VEGF . Even after 30 years of research in sprouting angiogenesis there are still many questions regarding the mechanics of vessel sprout elongation and growth that remain elusive . Importantly , the role of the tip cell as the main driver of sprout elongation is still not clear . On one hand the tip cell uses the filopodia to sense the VEGF gradients , attaches itself to the matrix and then exerts a contractile force pulling the cells behind it . On the other hand , the stalk cells proliferate , possibly pushing the tip cell forward . There is still no clear information on the relevance of each of these two opposite mechanisms in the process of vessel sprout growth and elongation . As mentioned , mathematical modelling has already addressed several issues related to tip cell dynamics , and may constitute a suitable approach to elucidate the role of each mechanism in this process . In this work , we introduce the first phase-field continuous model of sprouting angiogenesis in the literature capable of combining sprout morphology prediction with the elastic properties of the matrix and the forces exerted by the cells . The model we present has the clear advantage of a reduced number of parameters or rules , while being able to run within several partial differential equation solvers . We use this model to shed light on the regulation by local stresses of endothelial cell proliferation in angiogenesis . We finalize by proposing a mechanism that describes the distribution of forces in a sprout that lead to proper vessel formation . In the next section we introduce the mathematical model that describes vessel growth coupled with the mechanical characteristics of the tissue . In the Results and Discussion section we start by describing the growth of a single sprout , characterising the conditions for the rupture of this sprout . We then study how vessel proliferation has to be regulated to prevent sprout breakage , and finally we end by drawing the conclusions of this work .
Phase-field models , originally developed by the physics community in the context of non-equilibrium systems , have achieved great success over the past decades in describing a whole range of materials science phenomena related to nucleation and domain growth [65] . The phase-field permits an elegant and multifaceted numerical description of complex nonlinear problems with moving boundaries . It is a tailorable method , which can be easily adapted to describe quantitatively a vast range of mechanical and dynamical properties of interfaces as a function of bulk properties . Phase-field models are focused on the movement of the boundaries between the domains , and not on an exhaustive description of the transport properties within each domain . For this reason , they require a reduced number of parameters , making them suitable to model morphology and growth of biological systems . So far this type of models has been used in the study of cell shape and movement [66–68] , solid tumor growth [28 , 29 , 69] and angiogenesis [25 , 55] . In this work we introduce for the first time a phase-field model of angiogenesis capable of describing vessel sprouting as a function of the mechanical characteristics of the microenvironment . The evolution of the vessel morphology depends also on the concentration of diffusible angiogenic factors . In the present model we consider for simplicity only one diffusible angiogenic factor , which we will deem VEGF , though we can extend this model to include several angiogenic factors [70] and VEGF isoforms that are also able to be captured by the matrix [25 , 71] . The gradient of VEGF determines the direction of the tip cell movement . The tip cell is described in the present model by the traction force it is able to exert on the matrix . We consider that the stalk cells do not exert a traction force in the direction of VEGF gradient , due to contact-inhibited chemotaxis [51] . We also include an adhesion force able to be exerted by all cells . The adhesion pulls the cells in the direction of increasing cell density [51] , and it is balanced by the corresponding increase in free energy associated to high cell densities . In detail , an order parameter ϕ ( r ) distinguishes between capillaries and the extra cellular matrix ( ECM ) , taking the values ϕ = 1 and ϕ = −1 respectively . Values of ϕ larger than ϕ = 1 correspond to areas with high proliferation of endothelial cells which will lead to the widening of the capillary . The position of the capillary wall is given at the level set ϕ ( r ) = 0 . In the transition between the two phases the order parameter varies continuously within a small distance ε . We assume that the dynamics of stalk endothelial cells is the result of the balance between the term describing the dynamics of the vessel and the cell proliferation term , which is regulated by the concentration of angiogenic factor and by the mechanical stresses [25] . In this work we include in the capillary wall dynamics term the effect of the elastic properties of the cells and the ECM . We model these tissues as homogeneous and isotropic , though differing in their rigidity moduli . In this case , the equation for the evolution of ϕ ( r ) is given by ( see the first 2 sections of S1 Text and [72] ) : ∂ t ϕ = M{ ρ ϕ ∇ 2 ( - ϕ + ϕ 3 - ϵ 2 ∇ 2 ϕ ) - μ 1 ∇ 2 ( ∑ i j ∂ i j w ∂ i j w - 1 d ( ∇ 2 w ) 2 ) + α L 0[ ∇ 2 χ t + 2 μ 1 ∑ i j ∂ i j ( ϕ ∂ i j w - δ i j d ϕ ∇ 2 w ) ] } + α p [ V ( r ) , ϕ ( r ) , w ( r ) ] , ( 1 ) where μ1 is the difference between the rigid moduli of the sprout and the ECM , α represents the adhesion force between the endothelial cells , and L0 is an elastic constant related to the average compressibility and rigidity moduli of the endothelial cells and the ECM . In this equation , M is the mobility constant that sets the timescale of the problem ( see the first section of S1 Text ) , and ρϕ is a free energy density that determines the balance between the role of the surface tension and of the elasticity in determining sprout morphology and growth . The field w depends on the zeroth order displacement field , u0 ( r ) , with respect to an unstressed reference configuration: u0 ( r ) = ∇w ( r ) . The active force exerted by the tip cell , f t ( r ) , is included through the field χt ( r ) , such that ∇ χ t = - f t . In the tissue , the displacement field u0 ( r ) , and therefore the field w ( r ) , is a function of the forces exerted by the cells . In Eq ( 1 ) the evolution of the endothelial cells in the tissue depends on the tension along the capillary wall , on the difference in stiffness between the two tissues , on the tissue strain , on the adhesion with neighbouring endothelial cells and on the proliferation ( see Fig 1 ) . The derivation of the different terms of the equation is carried out in the first two sections of S1 Text . We expect that the proliferation of a cell may depend on the average strain along the cell body , and on the concentration of the angiogenic factors at its surface . Therefore , we set the proliferation term αp as a functional of ϕ ( r ) , the local strains trough the field w ( r ) and the concentration of the angiogenic factor V ( r ) . In practice , the term αp[V ( r ) , ϕ ( r ) , w ( r ) ] at a specific point r corresponds to the average of the proliferation rates in a circular neighbourhood of approximately the size of an endothelial cell around that point . Below we will explore how the proliferation rate depends on the strain and on the VEGF concentration . Eq ( 1 ) is obtained after considering that the mechanical relaxation of the system occurs in a much shorter timescale than the one associated with cell movement . In this quasi-equilibrium approach , the divergence of the stress tensor is related to the external force density , f ext , according to a force balance relation [73] which here simply becomes L 0 ∇ ( ∇ 2 w ) = - f ext ( see the second section of S1 Text ) . These external force densities derive from the adhesion forces exerted between the cells ( that pull cells together and are controlled by the parameter α ) , and from the traction forces exerted by the tip cells that drive sprout extension , f t ( r ) . Therefore , w ( r ) can be obtained from the following Laplace equation: L 0 ∇ 2 w = - α ϕ + χ t . ( 2 ) We integrate Eq ( 1 ) numerically using a discretised finite differences scheme . We start by fixing the force field created by the tip cell at the front of the growing sprout . At every time step we obtain w by solving Eq ( 2 ) , which is then used to integrate Eq ( 1 ) numerically . After a time tcell ≈ 5 min we find again the location of the tip of the sprout and change the force field to be centred at that site . The Young’s module of the ECM that is considered in this work is 3 . 0 KPa , and its Poisson ratio is 0 . 13 [74] . We refer the reader to the fourth section of S1 Text for the numerical value of the other parameters used in the model . In that section the reader can also find the justification for the values chosen for each parameter . The VEGF dynamics is also modelled . We consider that it follows a diffusion process with consumption at the vessel [25]: ∂ t V = D V ∇ 2 V - α V V ϕ Θ ( ϕ ) , ( 3 ) where Θ ( ϕ ) is the Heaviside function , αV is the VEGF consumption rate and DV is the VEGF diffusion constant in the tissue . The relatively high value of the VEGF diffusion constant in the tissue ( see fourth section of S1 Text and [75] ) guarantees that the diffusion occurs in a very fast timescale in comparison to cell movement [25] .
We start by using this model to study the elongation of a single sprout . Each sprout in vivo is lead by a tip cell that exerts a contractile force in the surrounding matrix [20 , 61] . The direction of this force is aligned with the direction of the tip endothelial cell polarisation , which can be altered by the angiogenic factors present in the tissue [14] . The traction exerted by the endothelial cells has been measured experimentally in detail [20] , and is approximately radial and directed towards the center of the cell body . The contraction of the tip cell is able to produce the observed movement of the sprout in the direction of the angiogenic factor’s gradient . In our simulation we start with a vessel directed vertically and located on the left side of the simulated region . We consider a gradient of VEGF that is perpendicular to this vessel . The levels of VEGF are defined relative to the VEGF concentration at the hypoxic cells , on the right boundary of the simulation unit , where we set V = 1 as a boundary condition . In this continuous model , the effect of the tip cell is solely modelled by the force it exerts on the tissue . According to what it is observed experimentally , we use a contractile force aligned preferentially with the VEGF gradient ( see Fig 2A and the third section of S1 Text ) . Here , we keep the force profile as simple as possible: centred at the vessel boundary and different from zero within a circular region with 5 μm radius ( approximately the radius of an endothelial cell ) . We initially position the center of the traction force field at a site on the right boundary of the vessel ( Fig 2B ) . Hence , this force mimics the distribution of forces exerted by a tip cell [61] . The movement of endothelial cells to regions where the matrix has larger strains is used in the literature to model tip cell movement on matrices [61] . Similarly , in our model the soft tissue ( the vessel ) will always move to the regions where the more rigid tissue is being compressed in order to minimise the free energy ( see the first section of S1 Text ) . Therefore , the endothelial cells move to occupy the neighbouring regions where the ECM is strained , since the vessel’s rigidity modulus is lower than the rigidity modulus of the ECM . They will also follow the local gradient of matrix rigidity ( durotaxis ) [59] . As the vessel is being pulled by the traction force exerted by the tip cell , the back part of the vessel also becomes curved , as it is also pulled forward ( Fig 2B ) . After some time ( tcell ) , we find the new location of the interface between the vessel and the ECM , and reposition the force field in a way it is centred at this new location . We observe that in most situations , due to the surface tension term , the growing velocity of the sprouts decreases sharply , achieving values below than 2 μm/hr within less than 8 hours . Therefore the vessel effectively stops growing . In Fig 2C we represent ( in a color scale ) the observed elongation of the sprout after 14 . 5 hours ( effectively the maximum sprout length ) for different values of the maximum traction force exerted by the tip cell and the value of the adhesion α . To create this figure we simulate 640 different sprouting events with the values of traction and adhesion assigned randomly in the intervals [0 , 6 . 0] KPa and [0:1 . 0] KPa respectively . For each run we measured the total length of the sprout if it was intact , or recorded if the tip cell broke away from the original vessel . We observe that for a given value of the adhesion , the larger the traction force , the larger is the resulting sprout . However , for very large tractions , the sprout breaks and its tip separates from the vessel and continues the migration . In the simulation we observe that for a given value of the traction force exerted by the tip cell , an increased value of the adhesion between the cells also leads to a larger vessel . The adhesion allows the cells of the vessel that are behind the tip cell to follow the leading sprout . Also , if the forces that link the tip cell to the cells behind are very low , when the tip contracts it cannot pull the rest of the sprout , and therefore the vessel will not grow . In that situation , the contraction of the tip cannot lead neither to sprout extension nor to vessel rupture . This is exactly what is observed: we obtain smaller vessels for low adhesion and low traction forces . In particular , for a smaller adhesion coefficient , the traction force required to grow a sprout up to a specific length is higher . On the other hand , if the adhesion is very large , when the tip cell contracts , the vessel behind follows readily the tip cell and eventually breaks up ( Fig 2C ) . Importantly , vessel sprouts that grow indefinitely without breaking ( i . e . that are able to maintain their growing velocity ) were never observed in our simulations without accounting for endothelial cell proliferation . Experimentally , isolated endothelial cell migration is observed in ex vivo assays ( Fig 3A and 3B ) . In the conditions of the aortic ring experiment preformed , these cells migrate approximately a distance of 230 μm , independently of the initial concentration of VEGF in the medium ( Fig 3B ) . We can use this fact to fit the time scale of the model , and the value of the parameter M that we will use in our simulations ( see section 4 in S1 Text ) . Nevertheless , we do not observe isolated endothelial cells in Matrigel plugs inserted in live mice ( Fig 3C ) . That may occur because the values of the in vivo cell adhesion , traction force and the tissue Young’s modulus fall within in a region where the sprouts do not break up , because the cell proliferation is able to prevent tip cell migration ( see below ) or because the original isolated endothelial cells have died , since they need to be in contact with each other for survival in vivo . Below we will explore the role that endothelial cell proliferation has in allowing the growth of long sprouts without breaking up . Endothelial cell proliferation is necessary to overcome the short ramification phenotype that we observed in the previous section . It has been reported that the endothelial cells with the stalk phenotype increase their proliferation rate when in the presence of VEGF [14] . On the other hand , it is also known that mechanical stresses can alter significantly the behaviour of endothelial cells , with shear stress being an important trigger factor in vessel remodelling and in endothelial cell proliferation [16–18 , 76–78] . Therefore , it is relevant to understand the role of the concentration of angiogenic growth factors ( e . g . VEGF ) and mechanical stresses in regulating endothelial cell proliferation . With the present model we can test different possible regulatory mechanisms of VEGF and mechanical stresses , and observe the resulting morphology of the vessel sprouts . Since the presence of individual cells is often seen experimentally ( Fig 3A ) , in the following simulations we start by setting fixed values for the maximum traction and adhesion that lead to the sprout breaking in the absence of proliferation . It is also observed experimentally that the maximum traction exerted by the tip cell is normally on the order of the magnitude of the matrix’s Young’s modulus ( see for example [20 , 21] ) , and so we chose a maximum traction force of 3 . 0 KPa ( approximately equal to the Young’s modulus of the matrix ) , and α = 0 . 47 KPa ( sufficient to have individual cell migration at maximum traction force of 3 . 0 KPa , according to Fig 2C ) . In the remainder of this section we explore several possible mechanisms for regulating endothelial cell proliferation and their consequences regarding vessel formation . We expect that the proliferation is able to prevent isolated tip cell migration . However , endothelial cell proliferation should be located in specific sites for the resulting vessel to grow straight . We consider four different ways for the VEGF concentration and the strain to regulate the proliferation rate , and compare the shape of the resulting sprouts . Proliferation regulated by local strain . We start by considering that the proliferation only occurs in the regions of the vessel where the cells have positive strain , i . e . where the cells are being stretched . In here , we consider that the proliferation rate increases linearly with the strain until it achieves the maximum proliferation ( MP ) , at a particular strain value we deem the limit strain , LS . To measure the strain we use the divergence of the zeroth order displacement vector , i . e . ∇ ⋅ u0 = ∇2 w = −αϕ+χt . The areas where the cells are stretched have ∇2 w larger than its equilibrium value in the capillary ( corresponding to ϕ = 1 ) , i . e . −αϕ+χt > −α . Hence , the strain that is referred to in Figs 4A and 5A corresponds to α ( 1 − ϕ ) + χt . In the sprout , the region of positive strain is small and localised behind the tip cell , with the first stalk cells being the ones that are typically stretched the most . In Fig 4A we identify the type of observed sprouts for the different values of MP and LS . Different types of sprouts are identified with different colours . It is clear from the graphic that the resulting morphology does not significantly depend on the value of the limit strain . The site in the vessel with large positive values of strain is small , and the strain varies dramatically in that small region of the vessel . Therefore , considering different values of the limit strain ( i . e , the strain at which the maximum proliferation is reached ) does not alter significantly the spatial region where the maximum proliferation is attained , leading to a similar sprout morphology . However , the value of MP influences dramatically the resulting morphology: for low values of MP the tip cell is still able to break apart from the growing sprout ( Fig 4B1 ) , while for values of MP larger than ≈ 0 . 35 hr−1 , the vessel does not break up , but it adopts a triangular shape ( Fig 4B2 ) . When the proliferation is controlled solely by the strain , it occurs very far back in the vessel , and therefore the vessel widens significantly at the back . Proliferation regulated by local strain , but triggered by VEGF . Similarly to the previous type of regulation , in this case we consider that the proliferation is still proportional to the strain ( until it reaches the maximum proliferation at the strain value of LS ) , but we only allow proliferation if the concentration of VEGF is larger than a certain cut-off . Here we set this cutoff to 0 . 05 ( relative to the concentration of VEGF at the right end of the simulating box ) . In this way , a small concentration of VEGF serves as a trigger for proliferation . Similarly to the previous case , we observe that the type of vessel still does not depend significantly on LS . We also still observe that for low values of MP there is individual cell migration , while for large maximum proliferations the vessel is malformed ( Fig 5A ) . However , since VEGF serves as a trigger for proliferation , the proliferation occurs preferentially in the sites with higher VEGF concentration , i . e . closer to the front of the vessel . In this way , instead of the malformed vessels adopting a triangular shape , they are not straight , presenting a thicker section at the middle of the vessel than at their root ( Fig 5B3 and 5B4 ) . There is however a very short band of values for the parameters where the proliferation is enough to prevent the vessel from breaking up and small enough to not present the bulging at the front . These vessels are considered well formed ( Fig 5B2 ) . Proliferation regulated by VEGF concentration . Another type of vessel proliferation regulation is admitting that it increases linearly with the VEGF concentration until it reaches the maximum proliferation , MP , at a limit VEGF concentration , LV . The higher concentration of VEGF in the sprout occurs at its tip , and therefore in this scenario the new material resultant from the proliferation appears primarily at the front of the vessel , extending it and resulting in sprouts that are straighter than in previous scenarios ( Fig 6A ) . Since the VEGF concentration varies smoothly along the ECM , the value of the limit VEGF concentration has an important role in determining the final morphology . If this value is small , the maximum proliferation is achieved at very low VEGF concentrations and , therefore , large regions of the sprout reach MP . On the other hand , if the LV is large , the proliferation increases slowly with VEGF until it reaches MP only at large values of VEGF concentration . In this case , the regions of the sprout with high proliferation rate are solely the ones with high VEGF levels . Accordingly , the sprout morphologies associated with low proliferation are found for low values of MP and high values of LV . In this region of the parameter space the sprout still breaks ( Fig 6B1 ) , leading to individual endothelial cell migration . As the proliferation rate increases , the sprouts stop breaking , and we observe vessels that are straight in the majority of the regimes tried ( Fig 6B2 and 6B3 ) . Only at very large proliferations ( high Mp and low values of LV ) does the sprout becomes deformed and triangular ( Fig 6B4 ) . However , when we consider that the endothelial proliferation is proportional to the VEGF concentration , all the cells proliferate , and not only the cells at the sprout . At the parental vessel , the concentration of VEGF is low but different from zero , thus this vessel is also able to become thicker , as it is clearly observed in our simulations for the larger proliferations ( see Fig 6A , red and orange points , and Fig 6B3 ) . This thickening of the original vessel is clearly not normally observed during sprouting angiogenesis , where the developing is normally centred at the growing sprout [14 , 79] . Moreover , while in sprouting angiogenesis in vivo there is no proliferation of the tip cell [14] , in the present hypothesis the larger proliferation occurs at the sprout tip . Therefore this hypothesis allocates a proliferation rate that is too high at the tip of the sprout and at the parental vessel . Proliferation regulated by VEGF concentration but triggered by strain . Finally we consider that there is only proliferation in the areas where the cells are being stretched . In this scenario , the proliferation is linearly proportional to the VEGF concentration until the maximum proliferation MP is reached ( at VEGF concentration LV ) , but only if the strain is positive and larger than a cut-off , which we set as Sm = 0 . 05 . This small strain , which serves as a trigger for proliferation , is equal to the smallest value of LS tested in Figs 4A and 5A . For low values of the proliferation ( low MP and high LV ) the tip cell still separates from the sprout . However , as the proliferation increases , the region in parameter space with well formed vessels is extremely large ( Fig 7A ) . As in the cases where the proliferation was regulated by the strain , here the parental vessel never becomes thicker ( Fig 7B2 , 7B3 and 7B4 ) . Even for the largest proliferation rates , where the vessels become triangular for very high MP and low LV , the parental vessel is thin and the proliferation is confined to the growing sprout ( Fig 7B4 ) . Most of the region of the parameters tested either produces vessels that are straight or vessels that break off ( at low proliferation rates ) . Malformed triangular sprouts only occur for very large proliferations . In all these types of regulation , we observe that endothelial cell proliferation is able to prevent the sprouts from breaking when the traction force and adhesion are high . In the same way , endothelial cell proliferation is able to cause the sprout to grow indefinitely at lower adhesion and/or traction , when initially the sprout achieved a maximum length and did not break up ( Figs 2C and 3C ) . In Fig 8 we plot the equivalent graphics of Figs 6A and 7A but for adhesion α = 0 . 31 KPa and maximum traction equal to 3 . 0 KPa . For these parameters , in the absence of proliferation the sprout would not break and only reach 40 μm ( Fig 2 ) after 14 . 5 hours . For proliferation regulated by VEGF concentration , we observe that for low proliferation the sprouts still grow very slowly . We mark in black in Fig 8A the situations where the vessel is not able to extend 60 μm ( approximately six cell lengths ) during our simulation time . We never observe breaking as in Figs 6A and 7A . As the proliferation rate of the endothelial cell increases , we observe first well formed long vessels ( in green ) , then thick parental vessels ( in orange ) , and finally malformed triangular vessels ( in red ) , similarly to what is observed in Fig 6 . For proliferation regulated by VEGF concentration but triggered by strain the thickening of the parental vessel is not present ( Fig 8B ) . We clearly observe in Figs 4 , 5 , 6 , 7 and 8 that the largest region in parameter space with well formed sprouts is obtained always when the proliferation is triggered by endothelial cell strain and its rate grows with VEGF concentration . The endothelial cell strain ∇2 w = −αϕ + χt is composed of two terms: the first term is regulated by the adhesion α and becomes positive when the order parameter is below ϕ = 1 , and the second term depends on χt , being related to the pulling force exerted upon the stalk cells when the tip cell contracts . Therefore , if the proliferation is triggered by strain , there are two ways to prompt cell replication: either the contraction of the tip cell stretches the tissue , or there is a lower density of cells ( ϕ < 1 ) at some point . The latter scenario is clearly observed in endothelial cell cultures where contact inhibition is present , since in these cultures cell proliferation only occurs if there is space between the cells . We conclude that in our sprouts the tip cell has the role of creating a tension in the cells that follow its lead . On those first stalk cells , this tension produces strain and/or empty spaces , inevitably triggering cell proliferation . The new cells occupy the space behind the tip , the tension decreases , and the process restarts . In this work we introduced a new phase-field model for angiogenesis that is able to describe sprout development as a function of both chemical factors ( VEGF levels ) and the mechanical environment of the surrounding tissue . The presented model is very compact , with the dynamics of the vessel being dependent on only 6 parameters ( α , L0 , μ1 , ϵ , ρϕ and M ) plus the location and profile of the tip cells’ traction fields and the definition of the endothelial cell proliferation , both of which can be obtained experimentally . The 6 parameters can be directly related to experimental measurements . The values of L0 and μ1 depend directly from the Young’s modulus and shear ratio of the vessel and ECM ( see fourth section of S1 Text ) and the coefficient α can be obtained from the measurement of the adhesion forces per unit area between two endothelial cells ( either by atomic force microscopy [80] or by traction force microscopy [81] ) . The mobility coefficient of the simulation can be chosen by matching the velocity of the endothelial cells in the simulation to the velocity observed experimentally ( as it is done in the present article ) , while the value of the ρϕ defines the maximum length of a single sprout if there is no proliferation . The results of the model are independent of the value chosen for ϵ , which is the width of the capillary wall in the simulation ( in phase-field models the interface width is the smallest length scale in the simulation [65] ) . A different ϵ can be compensated by altering the value of ρϕ in order to match the observed vessel length . This model consists of the integration of a single partial differential equation where one of the parameters , w , related to the strain of the microenvironment , is obtained at every iteration as a function of the applied forces . The absence of several rule based mechanisms in the model will permit in a short time its implementation within several PDE solving engines , allowing it to run in parallel computing environments . This will allow running larger systems , and even consider its extension to 3D , which will enable the study of lumen formation and aneurisms . Also , this framework will allow the study of the action of flow in vessel remodelling , since we can couple local forces with cell movement to obtain the final vessel morphology . Moreover , the use of phase-field modelling permits the inclusion of more stable minima by alteration of the Ginzburg-Landau potential ( in the first section of S1 Text ) . The new minima can describe the presence of other tissues , or of more complex ECM formations such as a basement membrane , for example . The formation of a basement membrane occurs around mature vessels , and ends up by surrounding them . Phase-field has been used to model surfactant phases that occupy the boundary between two other phases [82] , and also surfactant phases that are chemically formed during the evolution of the simulation [83] , similarly to what occurs with the basement membrane . Therefore the present modelling strategy could in principle be extended to model a basement membrane as a third component with specific mechanical properties . Such a model could be used to study how the basement membrane creation/degradation dynamics impacts vessel extension and regression . The ECM is a very complex system per se , which can be described as a mixture of two phases: the interstitial fluid and the ECM fibres [84] . At the scale of the problem discussed in this article , the ECM can be viewed morphologically as a single phase . However , since the diffusible growth factors diffuse on the interstitial fluid , we could expect that the characterisation of the diffusion of VEGF should take into consideration a more complex description of the ECM . This is certainly a very relevant direction of future research . Also , the capture of the heavier VEGF isoforms by the matrix fibres [25 , 71] could be included in the model ( similarly to what was done in [25] ) in a way to improve the characterisation of the VEGF concentration in live tissues . Descriptions of ECM as a complex two-phase system have been carried out in tumor growth models using mixture theory [85] , and can also be implemented into a phase-field description . The present model can also be used to study the role of tissue mechanics during vessel sprouting , extension and anastomosis in large vessel networks . We are currently working on this topic where we will compare quantitatively the morphology of the resulting networks with sprout formations observed in vitro . When several vessel sprouts are present , the Notch-Dll4 signalling pathway that regulates the activation of endothelial cells has to be considered [52 , 60] . This can be done by forbidding the activation of a new tip cell ( i . e . the inclusion of a traction force field as described in this article ) at any point that is closer than two cell diameters from any center of an already existing traction force field . The implementation of this exact mechanism was already considered in [25] . In the present work we used this method to couple the stress exerted by the tip cells to the final morphology observed in order to highlight the importance of the regulation of cell proliferation by stress in sprouting angiogenesis . We demonstrated that well formed sprouts are possible for the largest region of parameter space if proliferation is controlled by VEGF concentration specifically in sites where cells are under strain . The sites of high positive strain identify the localisations where new endothelial tissue is required for the sprout to grow effectively . The triggering of endothelial cell proliferation by the strain is clearly supported in the literature [18 , 76–78] , and suggests a mechanism for sprout extension , where the role of the tip cell is to create the strain that sets off cell proliferation close to the front of the growing vessel . | Sprouting angiogenesis—a process by which new blood vessels grow from existing ones—is an ubiquitous phenomenon in health and disease of higher organisms , playing a crucial role in organogenesis , wound healing , inflammation , as well as on the onset and progression of over 50 different diseases such as cancer , rheumatoid arthritis and diabetes . Mathematical models have the ability to suggest relevant hypotheses with respect to the mechanisms of cell movement and rearrangement within growing vessel sprouts . The inclusion of both biochemical and mechanical processes in a mathematical model of sprouting angiogenesis permits to describe sprout extension as a function of the forces exerted by the cells in the tissue . It also allows to question the regulation of biochemical processes by mechanical forces and vice-versa . In this work we present a compact model of sprouting angiogenesis that includes the mechanical characteristics of the vessel and the tissue . We use this model to suggest the mechanism for the regulation of proliferation within sprout formation . We conclude that the tip cell has the role of creating a tension in the cells that follow its lead . On those first cells of the stalk , this tension produces strain and/or empty spaces , inevitably triggering cell proliferation . The new cells occupy the space behind the tip , the tension decreases , and the process restarts . The modelling strategy used , deemed phase-field , permits to describe the evolution of the shape of different domains in complex systems . It is focused on the movement of the interfaces between the domains , and not on an exhaustive description of the transport properties within each domain . For this reason , it requires a reduced number of parameters , and has been used extensively in modelling other biological phenomena such as tumor growth . The coupling of mechanical and biochemical processes in a compact mathematical model of angiogenesis will enable the study of lumen formation and aneurisms in the near future . Also , this framework will allow the study of the action of flow in vessel remodelling , since local forces can readily be coupled with cell movement to obtain the final vessel morphology . | [
"Abstract",
"Introduction",
"Models",
"Results",
"and",
"Discussion"
] | [] | 2015 | The Force at the Tip - Modelling Tension and Proliferation in Sprouting Angiogenesis |
Multicellular tubes consist of polarized cells wrapped around a central lumen and are essential structures underlying many developmental and physiological functions . In Drosophila compound eyes , each ommatidium forms a luminal matrix , the inter-rhabdomeral space , to shape and separate the key phototransduction organelles , the rhabdomeres , for proper visual perception . In an enhancer screen to define mechanisms of retina lumen formation , we identified Actin5C as a key molecule . Our results demonstrate that the disruption of lumen formation upon the reduction of Actin5C is not linked to any discernible defect in microvillus formation , the rhabdomere terminal web ( RTW ) , or the overall morphogenesis and basal extension of the rhabdomere . Second , the failure of proper lumen formation is not the result of previously identified processes of retinal lumen formation: Prominin localization , expansion of the apical membrane , or secretion of the luminal matrix . Rather , the phenotype observed with Actin5C is phenocopied upon the decrease of the individual components of non-muscle myosin II ( MyoII ) and its upstream activators . In photoreceptor cells MyoII localizes to the base of the rhabdomeres , overlapping with the actin filaments of the RTW . Consistent with the well-established roll of actomyosin-mediated cellular contraction , reduction of MyoII results in reduced distance between apical membranes as measured by a decrease in lumen diameter . Together , our results indicate the actomyosin machinery coordinates with the localization of apical membrane components and the secretion of an extracellular matrix to overcome apical membrane adhesion to initiate and expand the retinal lumen .
Multicellular tubes are fundamental structures required for the transport of gases , liquids , or cells and are necessary for the generation and function of tissues and organs such as lung , kidney , blood vessels , neural tubes , and mammary gland . The main feature of a tubular network is a luminal space lined by apical membranes of polarized epithelial or endothelial cells . To construct a functional tube , there needs to be mechanisms to first generate a luminal space and then regulate the expansion and determination of the final diametrical size of the lumen . Cells utilize multiple pathways to organize themselves to form an initial tubular network ( reviewed in [1]–[3] ) and likewise diametric luminal growth appears to be under precise genetic control [4] . To date lumen growth has been characterized as a process of directed and regulated apical secretion of components into a central space and a reorganization of the apical membrane . Secretion likely provides a mechanical expansion force that drives the diametrical growth of the tube lumen [5]–[7] . Secreted components can include solid extracellular matrices of proteoglycans and collagens . Increase in lumen size can also be achieved by increasing the osmotic lumen pressure via ion pumps and channels [8]–[10] . Furthermore , the fusion of secretory vesicles with apical plasma membranes often changes the cells apical domain antigens , which in return drive the expansion of apical membrane permitting an increase in the diameter of the lumen [4] , [11] , [12] . The Drosophila compound eye provides an ideal model system to study lumen formation . The Drosophila eye consists of approximately 800 individual units known as ommatidia . In each ommatidium , a tubular structure is generated by the concerted efforts of the eight photoreceptors . Over a period ∼60 hours ( h ) , the reorganization of the photoreceptor apical membranes drives a dramatic morphogenesis from a single epithelial sheet to a single tube containing eight cells surrounding a central lumen matrix , termed the inter-rhabdomeral space ( IRS ) [13] . Furthermore , each photoreceptor projects its corresponding light sensing organelle , the rhabdomere , within the luminal space . Consequentially , the IRS is required to shape the rhabdomeres and optically position each rhabdomere to achieve proper visual sensitivity [14] , [15] . Genetic dissection of retinal lumen formation has provided key insights into fundamental questions about lumen formation , such as the mechanism through which adherent juxtaposed membranes separate . To date Drosophila retinal lumen formation is known to be dependent on secretion of an extracellular matrix [16] , [17] and a concurrent steric hindrance of Chaoptin ( Chp ) based adhesion [17] . The major constituent of the IRS matrix is the proteoglycan protein Eyes shut ( EYS ) , which is also known as Spacemaker . Loss of EYS results in a complete failure of the IRS to form [16] , [17] . Nonetheless , secretion of EYS is not sufficient for the generation of a continuous lumen . In addition to being secreted , EYS must be localized around the developing apical rhabdomeres through an interaction with the five-pass transmembrane protein Prominin ( Prom ) [17] , [18] . In the absence of Prom , the lumen space is present but not continuous and the residual fusion between rhabdomeres is the result of the adhesion between the rhabdomeric apical membranes mediated by the GPI-anchored membrane protein Chp [17] , [19] , [20] . Although these previous results demonstrate the interplay between secretion and adhesion , here we reveal an additional mechanism contributing to retinal lumen formation and expansion . Our results implicate that actin and non-muscle myosin II ( MyoII ) generate a contractile force at the apical domain of photoreceptors to separate the initial juxtaposed membranes . Rhabdomere adhesion is enhanced upon reduction of components of the actomyosin complex or its upstream activators in our sensitized genetic background . Additionally , knockdown of MyoII in a wild-type background led to a narrower lumen space . Temporal profiling of the key molecules for retinal lumen formation indicates the actomyosin complex is the first to localize to the apical surface followed by Prom and EYS . Thus the actomyosin machinery would be providing an initial apical based tension on the Chp based juxtaposed membranes , assisting the initiation and expansion of subsequently deposited extracellular matrix represented by EYS . All together , our genetic analysis has revealed an unappreciated facet of lumen formation and outlined the temporal steps and coordination required to achieve membrane separation .
The separation of rhabdomeres and formation of the retinal lumen , the IRS , within each ommatidium depends on the fine balance between an adhesion force , provided by Chp [19]–[22] , and an expansion force provided by EYS [16] , [17] and Prom [17] . When one copy of both eys and prom are removed there is a partial failure to form a continuous open IRS , exhibited by the presence of juxtaposed rhabdomeres ( compare Figure 1 A , B ) . However , the phenotype is not fully penetrant ( Figure 1B , E ) and manipulating the levels Prom , EYS , or Chp can modulate the appearance of a continuous retinal lumen [17] . Utilizing this sensitized genetic background , we performed a genetic screen to identify other genes required for the formation of the IRS . Defined genomic deletions were introduced into the eys , prom trans-heterozygote ( EP-TH ) background and scored for the ability to enhance the EP-TH adhesion phenotype and thus Drosophila eyes were screened for the loss of the deep pseudopupil [23] . From our screen , we identified deletion Df ( 1 ) ED6829 ( www . flybase . org ) as a potential candidate . Transmission electron microscopy ( TEM ) analysis confirmed that rhabdomere fusion was significantly enhanced upon the inclusion of the deletion in the double heterozygous background . The addition of Df ( 1 ) ED6829 in the EP-TH resulted in an 41% increase in rhabdomere fusion ( Figure 1 C , E ) . Analysis of overlapping deletions and testing of individual mutants for genes within the deleted interval mapped the responsible locus to Actin5C ( Act5C ) , as alleles of Act5C as well as mild RNAi knockdown of Act5C in the EP-TH background completely phenocopied the phenotype observed with the deficiency ( Figure 1D , E and Figure S1 ) . In Drosophila there are six actin genes: Act5C and Act42A are ubiquitously expressed , while Act57B , Act79B , Act87E , and Act88F are muscle-specific [24]–[26] . To test the specificity of our enhancement , we re-examined deletions that individually remove each actin gene . TEM analyses of the corresponding deficiencies for the other five actin genes did not show any enhancement of fused rhabdomeres in the EP-TH background ( Figure S2 ) . These data suggested that Act5C was a specific and key component in generating the retinal lumen . Photoreceptors are dependent upon actin for overall structure and integrity . In particular , the microvilli of the rhabdomeres contain an actin cytoskeleton core [27] and the rhabdomeres are supported by the actin-based structure the rhabdomere terminal web ( RTW ) [28] . The RTW is also responsible for directing delivery of molecules to the rhabdomere [28] , [29] . Thus , the enhancement of rhabdomere fusion observed in the triple heterozygote may be an indirect result of disruption of photoreceptor actin based structures . To test this possibility , we examined the ability of the rhabdomere to extend the entire length of the photoreceptor cell body to the cone cell plate [30] . We found the removal of one genetic copy of Act5C in a wild type or EP-TH background does not affect the ability of the rhabdomeres to extend the entire length ( Figure S3 ) . Nor did we observe any change in rhabdomere diameter , an indication that microvilli formation and extension is normal ( Figure 1 ) . With respect to the formation of the RTW , heterozygous mutation of Act5C does not affect the localization of Moesin ( Moe ) ( Figure S4 ) and RNAi knockdown of moe in the EP-TH background led to rhabdomere degeneration as observed in a moe mutant [28] . Thus our results do not indicate that the increase in juxtaposed rhabdomeres was the result of a general defect in the actin-based structures of photoreceptors . What is the specific primary defect upon the genetic reduction Act5C ? Besides structural support , the actin cytoskeleton provides tracks in the cell to allow intracellular transportation of membrane and non-membrane-bound cargos [31] , [32] . Furthermore , in mammalian photoreceptors the mammalian Prominin ortholog , Prominin1 , directly interacts with actin; actin co-immunoprecipitates with Prominin1 and the binding is attenuated by the Prominin1 R373C mutation [33] . Thus it is conceivable that the increase in rhabdomere fusion upon the genetic reduction of Actin results from either mislocalized or missing apical components or defects in secretion of EYS . We addressed these possibilities by first surveying whether there was any difference in the spatial localization pattern of Prom and EYS in the Act5C/+; EP-TH background . We did not observe any detectable decrease or mislocalized EYS ( Figure 2 A , D ) or Prom ( Figure 2 B , E ) . In addition to EYS and Prom , the transmembrane protein Crumbs ( Crb ) has also been implicated in rhabdomere separation and Drosophila salivary gland lumen formation [11] , [21] , [34] , [35] . In photoreceptors , Crb localizes to the stalk membrane , the portion of the apical membrane void of microvilli , and is critical for regulating the length of the stalk membrane . In crb mutant flies neighboring adjacent rhabdomeres remain juxtaposed [21] , [34]–[37] implying the shortening of the stalk membrane permitted adjacent rhabdomeres to remain together . In the Act5C/+; EP-TH background we did not observe any visible alteration in Crb localization ( Figure 2 C , F ) . Taken together these results implied that the increase in juxtaposed rhabdomeres , upon the decrease in Act5C genetic dosage , was not due to an obvious mislocalization of known retinal lumen formation proteins . To identify the mode of action responsible for the loss of a continuous luminal space in Act5C/+; EP-TH , we next examined the role of the actomyosin network . Actin is well known to interact with non-muscle myosin II ( MyoII ) to generate a contraction force to induce cellular morphological changes ( for reviews see [38]–[40] ) . Previous studies in Drosophila photoreceptors have demonstrated a role of the actomyosin complex in regulating photoreceptor cell body position , adherens junction formation and apical contraction within the morphogenetic furrow [41]–[43] . Therefore , a plausible explanation was that the reduction of Act5C dosage decreased a contractile force in photoreceptors resulting in inability of the apical membranes to pull away from each other during the initial phase of lumen formation . Non-muscle myosin II molecules are hexamers comprised of three pairs of subunits [44]: two heavy chains , encoded by zipper ( zip ) , two regulatory light chains , spaghetti squash ( sqh ) , and two essential light chains [45]–[47] . Unlike the loss of one copy of Act5C , a deletion that removes zip as well as specific zip alleles did not enhance the EP-TH phenotype . Nonetheless , only removing one genetic dosage of zip may not have been sufficient to lower protein levels . In an attempt to further reduce Zip protein levels we employed RNAi against zip . The ability of zip RNAi to decrease Zip protein levels was confirmed by immunofluorescence detection in 48 h after puparium formation ( APF ) pupal eyes with utilization of zip RNAi flip-out clones ( Figure S5 A , B ) . Upon RNAi knockdown , we observed a significant increase in fused rhabdomeres in the EP-TH background that mimicked the loss of one copy of Act5C ( Figure 3 A , B , F ) . To demonstrate phenotypic specificity , co-expression of UAS-GFP-zip along with UAS-zip-RNAi rescued the rhabdomere adhesion phenotype back to the EP-TH background levels ( Figure S5 E , F ) . To further eliminate the possibility of RNAi off-target effects , we assayed the ability of a dominant-negative ( DN ) form of Zip ( UAS-GFP-zip-Neck-Rod ) [48] , [49] to enhance rhabdomere fusion . We obtained a significant enhancement with the expression of the dominant-negative form of Zip in the EP-TH background ( Figure 3 D , F ) . Similar to the MyoII heavy chain Zip , reduction of the myosin regulatory light chain , Sqh , also displayed an essential role in retinal lumen formation . RNAi knockdown of sqh , confirmed by immunofluorescence in flip-out clones ( Figure S5 C , D ) , substantially enhanced the presence of juxtaposed rhabdomeres in the EP-TH background ( Figure 3 C , F ) . In contrast , the co-expression of a constitutively active form of Sqh ( UAS-sqhE20E21 ) [50] with the sqh RNAi construct reduced the rhabdomere enhancement back to EP-TH levels ( Figure S5 G , H ) . More importantly , the expression of the constitutively active Sqh alone in the EP-TH background rescued the rhabdomere adhesion . The frequency of observed juxtaposed rhabdomeres was significantly reduced from 15% per ommatidium to 3% ( Figure 3 E , F ) . The effect of the reduction of the actomyosin machinery components , Act5C , Zip , and Sqh , on the formation of a continuous IRS was not limited to the sensitized EP-TH background . In an eys or prom single-heterozygous mutant background rhabdomere fusion is never observed ( Figure S6 A , C , F , H ) , but in these single-heterozygous backgrounds the reduction of Act5C , Zip , or Sqh resulted in the appearance of fused rhabdomeres ( Figure S6 ) . Notably , in the wild-type background in which neither EYS nor Prom level is altered , the RNAi knockdown of zip , sqh as well as mild Act5C knockdown is sufficient in generating juxtaposed rhabdomeres ( Figure 4 , Figure S7 ) ; strong knockdown of Act5C with the GMR-GAL4 leads to loss of rhabdomere structures ( Figure S1 H ) . These results demonstrated that the actomyosin complex is not only involved in , but is also required and essential for retinal lumen formation . If the actomyosin machinery was a key element in the formation of the retinal lumen we should find common phenotypes among the potential upstream regulators of MyoII . To date there are more than a dozen of kinases reported to phosphorylate and activate MyoII regulatory light chain in invertebrate and vertebrate model organisms ( reviewed in [40] ) . Fortuitously , our assay permitted a screening of potential regulators in Drosophila photoreceptor cells ( Table S1 ) . From our limited screen we found three candidates that when knocked-down were capable of enhancing the rhabdomere fusion in the EP-TH background: Rho-kinase ( Rok ) [51] , and the upstream activator of Rok , the small GTPase Rho1 [52] , and the upstream transcriptional regulator Snail [53] ( Figure 5 A , B , D , E ) . However , Rok not only activates Sqh through direct phosphorylation , it also indirectly activates Sqh by inhibiting its inhibitor , the Myosin binding subunit ( Mbs ) of the myosin light chain phosphatase complex [54] . To test the possibility that the regulation of retinal lumen formation involves Mbs-mediated Sqh dephosphorylation , we reasoned that the overexpression of a constitutively active form of Mbs ( MbsN300 ) , that lacks the Rok regulatory target site [43] would result in an increase in rhabdomere fusion in the EP-TH background . With the expression of MbsN300 , we observed an increase in rhabdomere fusion ( Figure 5 C , E ) , suggesting Mbs is a negative regulator for actomyosin contraction in Drosophila retina . Jointly , these results indicated that in Drosophila photoreceptor cells , Sqh was positively regulated by Rok and Rho1 , and negatively regulated by Mbs . To investigate how the actomyosin machinery might contribute to retinal lumen formation , we first determined the sub-cellular localization of MyoII . Utilizing a GFP tagged Sqh under the transcriptional control of its native promoter [55] , we observed that Sqh-GFP was localized to the apical cytosol of photoreceptor cells just basal to the developing rhabdomeres ( Figure 6 ) , which is consistent with Zip antibody staining pattern in Drosophila photoreceptors reported previously [56] . This localization pattern of Sqh was not dependent upon an extracellular matrix , since Sqh-GFP localized normally in the eys null ( Figure S8 A , B ) and the prom null background ( Figure S8 C , D ) . The apical localization pattern of MyoII predicts that the potential contraction may act on the apical membranes . To detect potential defects in retraction of the apical membrane , we investigated whether the distance between apical rhabdomere membranes was altered upon the reduction of MyoII in a wild-type background . We generated sqh RNAi ( Figure 7 A ) and zip dominant-negative ( GFP-Zip-Neck-Rod ) ( Figure 7 C ) flip-out clones and measured the distance between the apical rhabdomeres . In particular we measured the width of the IRS between the apical membranes of rhabdomeres R2 and R4 , two diametrically opposing rhabdomeres . The distance between apical rhabdomere membranes , which is equal to the width of the retinal lumen , was visualized by EYS staining . Our assay demonstrated that the lumen width decreased by 28% in sqh RNAi flip-out clones and by 33% in GFP-zip-Neck-Rod flip-out clones compared with their neighboring wild-type clones ( Figure 7 A–D ) . These results are consistent with a hypothesis that the actomyosin machinery generates a contraction force at the apical photoreceptor cell membranes to pull the apical membranes inward to initiate and expand the retinal lumen ( Figure 7 E–G ) . We also tested other aspects of cell morphology , and we found that this apically localized actomyosin machinery is not sufficient to induce a whole cell size change in photoreceptors nor does reduction of MyoII affect the overall extension of the rhabdomeres ( Figure S9 ) . However , we also noted changes in rhabdomere width , oblong rhabdomeres , with the knockdown of the actomyosin machinery but this phenotype did not correlate with the enhanced rhabdomere adhesion observed ( Figure S10 ) . Temporally , we know that the photoreceptor cell apical membranes are juxtaposed to each other as early as 24 h APF [30] and as expected the adhesive membrane protein Chaoptin was detected on the apical surface linking the membranes together ( Figure 8 D ) . Concurrently , we also observed not only an accumulation of F-actin at the apical surface but also the phosphorylated form of Sqh ( Figure 8 A ) . In contrast , at 24 h APF , neither Prom nor EYS was detected on the apical surface ( Figure 8 A , D ) . Subsequently , as the apical membranes initiate their transformation we observed the accumulation of Prom at 45 h APF on the apical surface ( Figure 8 E , F ) only then followed by the appearance of EYS at 48 h APF ( Figure 8 B , C ) . Consistent with the early localization of MyoII to the apical surface , we can detect MyoII-induced rhabdomere fusion as early as 48 h APF ( Figure 9 ) ; without MyoII knockdown , the eys heterozygous mutant alone does not lead to rhabdomere fusion ( Figures S6 C ) . TEM analysis at 72 h clearly demonstrated the interlocking of microvilli between rhabdomeres of different photoreceptors ( Figure 9 H ) . These data indicated that the critical developmental role of the actomyosin machinery was occurring prior to 48 h APF .
There are several morphological strategies to generate luminal spaces ( reviewed in [2] , [3] ) . Chord hollowing is the process in which cells create a de novo lumen between their apical domains , as exemplified by Madin-Darby canine kidney ( MDCK ) cysts [57] . The process of chord hollowing best describes the mechanism observed in each Drosophila ommatidium . In Drosophila , the encasing of the photoreceptors by the overlying cones cells results in the apical membranes of the eight photoreceptors cells to rotate 90 degrees inward and in the end are now juxtaposed to each other [30] . Subsequently , the depositing of an extracellular matrix between the apical domains generates the retinal lumen , the IRS [16] , [17] . As described lumen formation involves a few common design principles [1] , [2] and with respect to chord hollowing the critical step being how space is initiated between adherent cells . Initially , our genetic analysis of mutations for Prom , a glycosylated surface protein , and EYS , a secreted extracellular protein [17] , strengthened the notion that there are molecules that provide anti-adhesive properties and the concurrent depositing of an extracellular matrix or osmotic pressure results in the permanent separation of the membranes . In Drosophila photoreceptors , Prom has the potential to act as an anti-adhesive molecule as described for Mucin1 and members of the CD34 family of proteins ( reviewed in [2] ) . Prom is a five-transmembrane protein with two large extracellular loops containing a minimum of four sites that are N-glycosylated [18] . Prom is present on the apical surface before secretion of the extracellular matrix and in combination with EYS provides a barrier to prevent interactions between the adhesive molecule Chp [17] , [20] , [22] . Nonetheless , the exact mechanism of its anti-adhesive properties remains ambiguous . We cannot separate the role of N-glycosylation from proper trafficking of Prom to the membrane [18] . Furthermore we have not been able to confirm whether Prom's interaction with EYS is essential for membrane separation or whether the anti–adhesive properties of Prom is sufficient to prevent the interaction of Chaoptin between rhabdomeres as long as a force is supplied to keep the membranes apart . With respect to the force , the secretion of EYS fulfills this role . Not only is the separation of the membranes and formation of the retinal lumen dependent on the presence of EYS [16] , [17] but alteration of the amount of EYS is sufficient to modulate the diameter of the lumen [17] . Nevertheless , even with these two well-defined parameters , anti-adhesion and secretion , numerous questions remain about membrane separation . For instance , do the photoreceptor cells themselves generate an active force for separating the rhabdomere membranes ? Thus our sensitized EP-TH genetic background provided an opportunity to not only address potential questions related to the regulation of Prom and EYS but also reveal additional mechanisms for retinal lumen formation . Here our results demonstrated that the specific reduction in the genetic dosage of Act5C increased the likelihood that the apical membranes remaining juxtaposed . The reduction of Act5C genetic dosage did not affect rhabdomere morphogenesis , the trafficking of components to the apical membrane , or secretion of EYS but rather revealed the critical role of actomyosin machinery in separating the apical membranes; the reduction of the myosin heavy chain , Zip , and the myosin regulatory light chain , Sqh , phenocopied the results obtained with Act5C . In light of the fact that the loss of actomyosin machinery results in rhabdomere fusion in the presence of wild-type levels of Prom and EYS demonstrates that the role of the actomyosin machinery is not an accessory process but a required mechanism for retinal lumen formation . Thus all together our data advocate a temporal framework for initiation of the retinal luminal space ( Figure 7 E–G ) . Based on our results , the accumulation of the actomyosin complex on the apical surface occurs prior to the secretion of an extracellular matrix . The combination of MyoII and the actin meshwork generates a contractile force that pulls the apical membrane towards the center of the photoreceptor cell . Furthermore , the polarized localization of MyoII , rather than a circumferential cable pattern or a dispersed pattern , suggested that in photoreceptor cells the actomyosin contraction may not be a “purse string” or a “ratchet-like” mechanism which contracts the cell from the periphery to the center , as reported in vertebrate neurulation and in Drosophila mesoderm invagination ( see review [38] ) . We propose that together with the subsequent accumulation of Prom , the actomyosin contraction provides the necessary separation and weakening of inter-apical membrane interactions . Lastly , the secretion of EYS and its interaction with Prom provides an additional separation force and permanent barrier to prevent the adhesive properties of Chp from interacting between rhabdomeric membranes , thus fully establishing and stabilizing the retinal lumen space . Whether the role of actomyosin contraction is limited to Drosophila retinal lumen formation or chord hollowing remains to be tested . Nonetheless , actomyosin contraction has been implicated in other luminal systems and is likely another core process required for lumen formation . For example , during murine vascular lumen formation , MyoII fails to localize to the apical cell membrane upon pharmacological inhibition of ROCK or its upstream activator vascular endothelial growth factor A , and the vascular tubes formed to a lesser extent [58] . Furthermore , the actomyosin contraction observed in Drosophila photoreceptors may also extend to vertebrate ciliary photoreceptors . Previous work has demonstrated the functional conservation of EYS and Prom among rhabdomeric and ciliary photoreceptors [18] . Interestingly , both Prominin1 and MyoII localize to the basal region of the outer segment where the nascent discs are formed in mammalian photoreceptors [59]–[61] . Therefore , one possibility is that actomyosin contraction is involved in the intra-cellular disk lumen formation . Specifically , actomyosin contraction might be required to pull the plasma ciliary membrane inwards to form and morphologically flatten the nascent discs [62] , [63] . Overall , utilization of Drosophila retinal lumen as a model tissue , in particular our EP-TH sensitized genetic background , to perform unbiased screens will further define the mechanisms for activation of the actomyosin machinery and elucidate mechanisms for lumen formation and regulation .
All crosses and staging were performed at 23°C unless otherwise noted . Drosophila stocks used in this study include: prom1 , eys1 [17] , UAS-GFP-zip , UAS-GFP-zip-Neck-Rod ( Dr . D . Kiehart ) , UAS-MbsN300 ( Dr . J . Treisman ) , sqh-GFP ( Dr . A . Martin ) , GMR>w+ STOP>Gal4 ( Dr . C . Desplan ) , and UAS-sqhE20E21 ( Dr . M . Birnbaum ) . The following fly stocks were obtained from the Bloomington Drosophila Stock Center: w1118 , GMR-Gal4 , UAS-mCD8-GFP , Act5CG0245 , Act5CG0009 , Act5CG0010 , Act5CG0025 , Act5CG0177 , zip1 , sqhAX3 , Df ( 1 ) ED6829 , Df ( 1 ) ED418 , Df ( 2R ) ED1484 , Df ( 2R ) ED3791 , Df ( 3L ) BSC223 , Df ( 3R ) ED5613 , Df ( 3R ) ED5705 , UAS-zip RNAi ( TRiP #HMS01618 ) , UAS-sqh RNAi ( TRiP #HMS00437 ) , UAS-Rho1 RNAi ( TRiP #JF02809 ) , UAS-rok RNAi ( TRiP #JF03225 ) , UAS-crb RNAi ( TRiP #JF02777 ) , UAS-Act5C RNAi ( TRiP #HMS02487 ) , UAS-atg1 RNAi ( TRiP #JF02273 , and #GL00047 ) , UAS-cta RNAi ( TRiP #JF01607 , and #HMS02362 ) , UAS-fog RNAi ( TRiP #GL00529 ) , UAS-mist RNAi ( TRiP #HMS02327 ) , UAS-RhoGEF2 RNAi ( TRiP #HMS01118 ) , UAS-sna RNAi ( TRiP #HMS01252 ) , UAS-SNF1A RNAi ( TRiP #JF01951 , #HMS00362 , and #GL00004 ) , UAS-sqa RNAi ( TRiP #JF02277 ) , UAS-Strn-Mlck RNAi ( TRiP #JF02278 , #JF02170 , and #HMS01665 ) , UAS-T48 RNAi ( TRiP #HMS02248 ) , and UAS-twi RNAi ( TRiP #JF02003 , and #HMS01317 ) . Pph13-Gal4 was generated by inserting the immediate upstream 1 . 6 kb of genomic DNA extending from the first coding Methionine of the Pph13 locus into pCHS-GAL4 . prom-Gal4 was generated by inserting the immediate upstream 3 . 6 kb of genomic DNA extending from the first coding Methionine of the prominin locus into pCHS-GAL4 . To generate flip-out clones , UAS-zip RNAi , UAS-GFP-zip-Neck-Rod , or UAS-sqh RNAi males were crossed with hs-Flp; GMR>w+ STOP>Gal4; UAS-mCD8-GFP females , and the 24 h–48 h larvae were subject to 1 h heatshock at 37°C and then returned to 23°C to generate clones . GMR promoter starts to drive Gal4 expression in photoreceptors of flip-out clones starting from the third instar larval stage . Clones were marked by the presence of mCD8-GFP . To generate MARCM clones , sqhAX3 , Frt19A; GMR-Gal4 females were crossed with hs-Flp , tub-Gal80 , Frt19A; UAS-mCD8-GFP males , and the third instar larvae were subject to 1 h heatshock at 37°C and then returned to 23°C . Mutant clones were marked by the presence of mCD8-GFP . Drosophila eye samples were prepared for transmission electron microscopy ( TEM ) as previously described [17] . All crosses were maintained at 23°C and adult heads were fixed within 8 h after eclosion . Standard fixation and staining protocols were used for immunofluorescence staining . Briefly , pupal retinas were staged at 23°C , dissected in PBS , and fixed in PBS containing 4% formaldehyde for 10 min ( 24 h , 45 h , and 48 h APF pupae , as well as 72 h APF pupae for anti-Prom staining ) or 40 min ( 72 h and 96 h APF pupae ) . The primary antibodies used were: mouse anti-EYS ( mAb 21A6 , 1∶50 , Developmental Studies Hybridoma Bank ) [17]; rabbit anti-Prom ( 1∶100 ) [17]; rat anti-Crb ( 1∶400 , Dr . E . Knust ) [64]; rabbit anti-Zip ( #656 , 1∶400 , Dr . D . Kiehart ) [65]; guinea pig anti-Sqh ( GP#21 , 1∶400 , Dr . D . Kiehart ) [66]; mouse anti–phospho–Sqh ( Ser19 , analogous to D . melanogaster Sqh Ser21 ) ( pMRLC , 1∶100 , Cell Signaling Technology ) ; mouse anti-Chp ( mAb 24B10 , 1∶100 , Developmental Studies Hybridoma Bank ) [19]; mouse anti-Na+ K+ ATPase alpha subunit ( a5 , 1∶100 , Developmental Studies Hybridoma Bank ) [29]; rabbit anti-phospho-Moesin ( pMoe ) ( mAb 41A3 , 1∶100 , Cell Signaling ) . Rhodamine ( 1∶200 ) or Alexa Fluor 647 ( 1∶50 ) conjugated phalloidin ( Life Technologies ) was used for the detection of F-actin . The FITC or RX conjugated secondary antibodies ( 1∶200 ) were obtained from Jackson ImmunoResearch Laboratories . Confocal images were taken on a Leica TCS SP5 and TEM was performed with a JOEL 1010 , and all pictures were processed in Adobe Photoshop . | Biological tubes are integral units of tissues and organs such as lung , kidney , and the cardiovascular system . The fundamental design of tubes involves a central lumen wrapped by a sheet of cells . To function properly , the tubes require a precise genetic control over their creation , the diametric growth and maintenance of the lumen during development . In the fruit fly , Drosophila melanogaster , the photoreceptor cells of the eye form a tubular structure . The formation of the retinal lumen is critical for separating and positioning the light sensing organelles of each photoreceptor cell to achieve visual sensitivity . In an effort to investigate the mechanisms of Drosophila retinal lumen formation , we identified a contractile machinery that was present at the apical portion of photoreceptor cells . Our data is consistent with the idea that a contractile force contributes to the initial separation of the juxtaposed apical membranes and subsequent enlargement of the luminal space . Our work suggests that building a biological tube requires not only an extrinsic pushing force provided by the growing central lumen , but also a cell intrinsic pulling force powered by contraction of cells lining the lumen . Our findings expand and demonstrate the coordination of several molecular mechanisms to generate a tube . | [
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] | 2014 | The Actomyosin Machinery Is Required for Drosophila Retinal Lumen Formation |
Apicomplexans employ a peripheral membrane system called the inner membrane complex ( IMC ) for critical processes such as host cell invasion and daughter cell formation . We have identified a family of proteins that define novel sub-compartments of the Toxoplasma gondii IMC . These IMC Sub-compartment Proteins , ISP1 , 2 and 3 , are conserved throughout the Apicomplexa , but do not appear to be present outside the phylum . ISP1 localizes to the apical cap portion of the IMC , while ISP2 localizes to a central IMC region and ISP3 localizes to a central plus basal region of the complex . Targeting of all three ISPs is dependent upon N-terminal residues predicted for coordinated myristoylation and palmitoylation . Surprisingly , we show that disruption of ISP1 results in a dramatic relocalization of ISP2 and ISP3 to the apical cap . Although the N-terminal region of ISP1 is necessary and sufficient for apical cap targeting , exclusion of other family members requires the remaining C-terminal region of the protein . This gate-keeping function of ISP1 reveals an unprecedented mechanism of interactive and hierarchical targeting of proteins to establish these unique sub-compartments in the Toxoplasma IMC . Finally , we show that loss of ISP2 results in severe defects in daughter cell formation during endodyogeny , indicating a role for the ISP proteins in coordinating this unique process of Toxoplasma replication .
The phylum Apicomplexa contains numerous obligate intracellular pathogens that are the cause of serious disease in humans and animals , greatly influencing global health and causing significant economic loss worldwide . The phylum includes Plasmodium falciparum , the causative agent of malaria which claims 1–2 million human lives annually , and Toxoplasma gondii , a pathogen that infects more than thirty percent of the world's population and causes severe neurological disorders and death in immunocompromised individuals [1] . Most of the drugs used to treat apicomplexans target metabolic pathways or the chloroplast-derived apicoplast [2] , [3] , [4] , but these parasites also possess elaborate and unique structures that are required for replication and invasion and thus represent attractive new targets for therapeutic intervention . Apicomplexans are grouped with dinoflagellates and ciliates in the alveolata infrakingdom [5] . The unifying morphological characteristic of this group is the presence of alveoli: membrane sacs located beneath the plasma membrane . Molecular phylogenetic data supports this grouping , as does the identification of a conserved family of articulin-like membrane skeleton proteins , the alveolins , which associate with alveoli in all three phyla [6] , [7] . While the presence of alveoli is conserved , each of these groups has adapted this peripheral membrane structure for different cellular functions to fit their distinct niches . In dinoflagellates , the alveoli sometimes contain cellulose-based plates that function as protective armor [8] . In contrast , ciliate alveoli are calcium storage devices thought to play roles in regulation of cilia , exocytosis from cortical organelles known as extrusomes , and control of cytoskeletal elements [9] , [10] , [11] . In apicomplexans , the alveoli in conjunction with an underlying filamentous network are termed the inner membrane complex ( IMC ) [12] , [13] . Flattened alveoli underlie the entirety of the plasma membrane except for a small gap at the apex and base of the cell [14] . These cisternae are organized into a patchwork of rectangular plates capped by a single cone-shaped plate at the apex of the cell . Freeze-fracture studies of the IMC plates expose a lattice of intramembranous particles ( IMPs ) , an arrangement that suggests an association with proteins of the underlying filamentous network and subtending cortical microtubules [15] , [16] , [17] . Together , these features of the IMC are the foundation for a unique form of gliding motility used for host cell invasion and also serve as the scaffold for daughter cell formation during division [18] , [19] . Toxoplasma tachyzoites replicate by endodyogeny , a process of internal cell budding that produces two daughters within an intact mother parasite . Following centriole duplication , daughter cell formation begins with the concurrent assembly of an apical and basal complex [20] . Although these two structures consist of cytoskeletal components that will eventually cap opposite ends of the mature parasite , they are initiated in close spatial and temporal proximity . IMC construction then proceeds by the extension of the basal complex away from the daughter apical complex , generating a bud into which replicated organelles are packaged . Parasite division is completed by a number of maturation steps terminating with the adoption of the maternal plasma membrane [21] . The apical , cone-shaped cisterna is unique in form and presumably the earliest membrane component deposited into the nascent IMC [19] . A number of cytoskeletal IMC markers localize to a region at the parasite apex thought to correspond to this apical-most IMC plate . A GFP fusion of the dynein light chain , TgDLC , can be detected in an apical cap region but predominantly localizes to the conoid and is also found in the basal complex , spindle poles and centrioles . TgCentrin2 , the most divergent of the three Toxoplasma centrin homologues , labels the preconoidal rings and a peripheral ring of ∼6 annuli located at the lower boundary of the TgDLC cap . It has been suggested that these annuli lie at the juncture between the apical cap plate and the flanking set of IMC plates [20] . Additionally , PhIL1 , a cytoskeletal IMC protein of unknown function , is detected throughout the IMC but strongly enriched in the apical cap and basal complex [22] . Only a few proteins are known to directly associate with the IMC membranes . These include a number of proteins associated with gliding motility [23] , [24] , [25] , as well as the heat shock protein Hsp20 [26] and one isoform of the purine salvage enzyme hypoxanthine-xanthine-guanine phosphoribosyltransferase [27] . Thus , despite the central role of this conserved membrane system in apicomplexan biology , little is known of its composition , organization , and construction . We present here a family of proteins unique to the Apicomplexa that localize to three distinct sub-compartments of the Toxoplasma IMC . ISP1 localizes to a region corresponding to the apical cap , ISP2 occupies a central IMC region , and ISP3 resides in both the central IMC region and a basal IMC compartment . ISP1 and 3 are early markers for bud formation and label previously unobserved daughter IMC structures in the absence of parasite cortical microtubules , indicating that microtubules are not required for initial assembly of IMC membranes . We show that the ISPs are initially targeted to the IMC by conserved residues predicted for coordinated myristoylation and palmitoylation in the extreme N-terminus of each of these proteins . Interestingly , deletion of ISP1 results in the relocalization of ISP2 and 3 to the apical cap , demonstrating an interactive , hierarchical targeting among this family of proteins to these distinct sub-compartments of the IMC . Finally , disruption of ISP2 results in a severe loss of parasite fitness and dramatic defects in daughter cell formation . Although the ISP2 knockout parasites ultimately compensate for these defects , this data shows an important role for these proteins in the coordination of daughter cell assembly .
We previously generated a panel of monoclonal antibodies against a mixed fraction of T . gondii organelles [28] . One of the antibodies , 7E8 , stains a cone-shaped structure at the periphery of the apical end of the parasite ( Figure 1A ) . This staining pattern extends from a gap at the extreme apex ( Figure 1A , arrow ) ∼1 . 5 µm along the length of the parasite , a localization suggestive of the apical IMC plate observed by electron microscopy [14] . Colocalization with TgCentrin2 shows that 7E8 staining is delimited at its apex and base by this apical cap marker , indicating that 7E8 does indeed detect a protein associated with the anterior-most IMC plate ( Figure 1D ) . During early endodyogeny , 7E8 staining is visible in daughter parasites as a pair of small rings within each mother parasite ( Figure 1B , arrows ) . As daughter formation proceeds , this structure enlarges and extends to form the apical cap seen in mature tachyzoites ( Figure 1C ) . The association with forming daughter scaffolds together with the extreme apical gap further suggests that 7E8 labels the apical sub-compartment of the IMC . We also frequently observe 7E8 staining a single dot near the basal border of the cone ( Figure 1C , arrow ) which is distinct from TgCentrin2 annuli ( Figure 1D , inset ) . Western blot analysis of Toxoplasma lysates with mAb 7E8 revealed a single band at ∼18 kDa ( Figure 1E ) . We used the 7E8 antibody to isolate its target protein by immunoaffinity chromatography . The isolated protein was separated by SDS-PAGE ( Figure 1F ) , digested with trypsin , and seven peptides were identified by mass spectrometry corresponding to the hypothetical T . gondii protein TGGT1_009340 ( Figure 1G ) . EST and cDNA sequencing confirmed that the gene model is correct . Due to its unique localization , we named this protein IMC Sub-compartment Protein 1 ( ISP1 ) . Examination of the 176 amino acid sequence of ISP1 reveals that it contains a high number of charged residues ( ∼30% ) . While there are a relatively large number of ESTs encoding ISP1 , the protein lacks conserved domains that could suggest its function . The protein contains a glycine at position two , which is predicted to be myristoylated [29] as well as a pair of cysteines at positions seven and eight strongly predicted to be palmitoylated [30] . Since ISP1 lacks a predicted signal peptide or transmembrane domain , these residues suggested a mechanism for IMC membrane association . BLAST analysis of the ISP1 sequence revealed orthologues across the apicomplexan phylum , including Neospora , Theileria , Cryptosporidia , Babesia , and Plasmodium ( Figure S1 ) . Orthologues were also found in Eimeria by BLAST against EST libraries ( data not shown ) . ISP1 also showed significant homology in its C-terminal region to CP15/60 , a poorly characterized putative surface glycoprotein in Cryptosporidia [31] , [32] . No ISP1 orthologues were identified outside of the phylum indicating that this protein is restricted to the Apicomplexa . BLAST analysis of the T . gondii genome using the ISP1 sequence identified two additional hypothetical proteins with considerable sequence similarity to ISP1 , which we named ISP2 ( TGGT1_058450 ) and ISP3 ( TGGT1_094350 ) ( Figure 2A ) . The greatest degree of sequence similarity between these three proteins exists within the C-terminal two-thirds of their sequences . The N-terminal regions of the proteins are more divergent , but each contain a conserved glycine at position two as well as a pair of conserved cysteines predicted to be myristoylated and palmitoylated , respectively ( Figure 2A , boxed residues ) . ISP2 additionally contains a third cysteine at position five predicted to be palmitoylated . Similar to ISP1 , these proteins are highly charged and have a relatively large number of corresponding ESTs . OrthoMCL analysis of the ISPs indicates two ortholog groups within Apicomplexa . ISP1 and ISP2 segregate with one group while ISP3 segregates with another ( Figure S1 ) . The Toxoplasma genome may encode a fourth ISP family member ( TGGT1_063420 ) , although it does not segregate with any OrthoMCL group . This predicted protein lacks the conserved glycine and cysteine residues present in the N-termini of other ISP proteins . Only a single EST is present for TGGT1_063420 , indicating that it is poorly expressed relative to the other ISPs , and thus it was not investigated further . To localize ISP2 and ISP3 in T . gondii , we expressed each gene under the control of its endogenous promoter with a C-terminal HA epitope tag . Intriguingly , ISP2 localizes to a previously unrecognized central sub-compartment of the IMC , which begins at the base of the ISP1 apical cap and extends approximately two-thirds the length of the cell . The apical boundary of this compartment is delineated by the TgCentrin2 annuli ( Figure 2B ) . The posterior boundary has a jagged edge suggesting it corresponds to discrete IMC plates ( Figure 2D , arrows ) . While the ISP2 signal terminates near the end of the subpellicular microtubules , the termini for these two structures are not identical ( Figure S3 , WT ) . Antisera raised against recombinant ISP2 confirmed this central IMC sub-compartment localization , ensuring that exclusion of ISP2 from the apical cap and basal IMC is not an artifact of epitope tagging ( Figure S2A ) . Similar to ISP2 , ISP3 stains the central section of the IMC . However , ISP3 staining extends to the posterior end of the complex , identifying a third sub-compartment of the IMC ( Figure 2C ) . A small gap in ISP3 staining is observed in the posterior region similar to that seen for other IMC proteins [23] . Antisera raised against recombinant ISP3 gave a poor signal by IFA , but was sufficient to confirm localization to both the IMC central and basal sub-compartments ( Figure S2B ) . As with ISP1 , ISP2 and ISP3 are visible in forming daughter parasites . Whereas the maternal signals of ISP1 and ISP2 appear to remain stable throughout endodyogeny , the maternal ISP3 signal rapidly attenuates with the onset of endodyogeny while it concentrates in daughters ( Figure 2E ) . Attenuation of ISP3 in mothers and enrichment in daughters was also observed with our polyclonal antibody , indicating this is not the result of a C-terminal processing event that removes the HA epitope tag ( Figure S2C ) . Thus , ISP3 provides an excellent marker for bud initiation , growth , and maturation during endodyogeny ( Figure 2E and Video S1 ) . The observations that the ISPs are visible at the periphery of forming daughters prior to adoption of the maternal plasma membrane and that gaps are present at the extreme apex and base suggests an association with the IMC . To confirm IMC association , we treated extracellular parasites with Clostridium septicum alpha-toxin . This vacuolating toxin causes a dramatic separation of the plasma membrane and the underlying IMC , enabling differential localization of these closely apposed membrane systems [33] . In toxin-treated parasites , the ISP proteins segregate with the IMC and not with the plasma membrane , confirming that the ISPs are indeed IMC proteins ( Figure 3A–B ) . To ascertain if the ISPs are embedded in the IMC protein meshwork that includes the articulin-like protein IMC1 , we performed detergent extractions of extracellular parasites in 0 . 5% NP-40 . In these conditions , each ISP was solubilized similar to the control protein ROP1 , while IMC1 remained in the insoluble pellet fraction ( Figure 3C ) . This extraction profile demonstrates that the ISPs are not embedded in the detergent resistant protein meshwork that underlies the IMC membranes . We disrupted microtubules in intracellular parasites to assess whether the underlying microtubules influence ISP localization . Apicomplexan microtubules are selectively susceptible to disruption by dinitroanilines , such as oryzalin [34] . After 40 hours of 2 . 5 µM oryzalin treatment , all tubulin is unpolymerized and dispersed . Without spindle microtubules ( mitosis ) and subpellicular microtubules ( budding ) , productive daughter formation repeatedly fails resulting in an undivided , amorphous mother cell with a polyploid DNA content [35] ( Figure 4A ) . Intriguingly , we observe ISP1 labeling numerous small rings that are centrally located within oryzalin-treated parasites ( Figure 4A , inset ) of approximately the same dimensions as ISP1 early daughter buds in untreated , replicating parasites ( compare with Figure 1B , arrows ) . Since polymerization of subpellicular microtubules is essential to drive bud extension , these rings likely represent failed attempts to build new daughter buds [36] . A larger peripheral patch of ISP1 with a central hole is also observed , likely representing the original parent apical cap ( Figure 4A , arrows ) . While ISP2 was not observable in these early bud rings ( Figure 4B ) , we did detect ISP3 in these structures within oryzalin-treated parasites ( Figure 4C , inset arrows ) , suggesting that both the apical cap and remaining IMC sub-domains are formed independently of microtubules at a very early stage of bud development . While membrane skeleton proteins are likely candidates for providing the foundation for these structures , we were unable to detect the articulin-like protein IMC1 in these early bud rings , even at lower oryzalin concentrations ( 0 . 5 µM ) that only disrupt cortical microtubules ( Figure 4D and Video S2 ) . The greatest sequence similarity within the ISP family is present in the C-terminal two-thirds of the proteins while the N-terminal region is more divergent ( Figure 2A ) , thus we reasoned that the unique targeting of each ISP family member might be controlled by its N-terminal region . To test if the N-terminal region of ISP1 is necessary for targeting , we eliminated the first 63 residues to create a truncated protein fused to YFP . ISP164–176-YFP does not target to the IMC but is instead distributed throughout the cytoplasm and nucleus , showing that this N-terminal region is necessary for apical cap targeting ( Figure 5A ) . To determine if the ISP1 N-terminal region is sufficient for targeting , we fused the first 65 residues of ISP1 ( containing the putative acylation sequence and divergent N-terminal region ) to YFP and expressed this construct in Toxoplasma . The ISP11–65-YFP fusion traffics to the apical cap in an identical fashion to endogenous ISP1 ( Figure 5B ) . To further narrow the N-terminal region required for apical cap targeting , we generated an additional fusion of the first 29 residues of ISP1 ( containing the putative acylation sequence ) to YFP . This fusion also traffics in a manner identical to full length ISP1 ( Figure 5C ) , demonstrating that this N-terminal domain is both necessary and sufficient for apical cap targeting . To assess targeting of ISP2 and ISP3 , we also created fusions of their N-terminal regions ( residues 1–41 and 1–36 respectively ) to YFP . The ISP31–36-YFP fusion targets to the central and basal sub-compartments of the IMC but is restricted from the apical cap ( Figure 5D ) , showing that this region is sufficient for proper sub-compartment targeting . In contrast , ISP21–41-YFP localized to the entire IMC , overlapping with endogenous ISP1 in the apical cap and extending into the basal IMC sub-compartment ( data not shown ) . To ensure this change in targeting for ISP21–41 was not an artifact of the YFP fusion , we replaced YFP with an HA tag ( shown to have no effect on the targeting of full length ISP2 , Figure 5E ) . The ISP21–41-HA protein also localized throughout the IMC ( Figure 5F ) , demonstrating that the N-terminal domain of ISP2 is sufficient for targeting to the IMC , but not for correct sub-compartment localization . Protein myristoylation occurs co-translationally through the action of an N-myristoyl transferase [37] . This modification is sufficient to promote transient association with membranes for otherwise cytosolic proteins . This weak membrane affinity can then be stabilized by addition of one or more palmitoylations through the action of a palmitoyl acyltransferase ( PAT ) , effectively locking a protein into a target membrane system in a mechanism known as “kinetic trapping” . The ISPs each contain a second position glycine followed by cysteines within the first 10 residues that are predicted to be myristoylated and palmitoylated , respectively ( Figure 2 , boxed residues ) . We mutated the glycine and cysteine residues in HA epitope tagged ISP constructs to examine their effect on targeting . As predicted by the kinetic trapping model , mutation of the second position glycine to an alanine abolished IMC targeting in each family member ( Figure 6 and Figures S3 and S4 , G2A ) , resulting in proteins distributed throughout the cytoplasm . Mutation of the cysteine residues to serine was performed individually and together . While only minor defects in targeting were observed with individual cysteine mutations , mutation of both cysteines abolished ISP1 and ISP3 targeting ( Figure 6 and Figure S4 ) . In the case of ISP2 , targeting was only abolished when all three cysteines were coordinately mutated ( Figure S3 ) . While coordinated cysteine mutants of the ISPs are distributed in the cytoplasm similar to G2A mutants , we also often observed perinuclear staining that is especially concentrated just apical of the nucleus ( arrows , Figure 6 and Figure S3 and S4 ) . Presumably , myristoylation of these proteins still occurs , but without palmitoylation , these mutants are left to transiently sample the different membrane systems within the cell and therefore may appear concentrated as they associate with the ER and Golgi membranes present in this region . These results demonstrate that these residues are essential to ISP sorting and indicate that coordinated acylation of the ISPs is responsible for IMC membrane targeting . To assess the function of ISP1 , we disrupted the ISP1 gene by homologous recombination ( Figure 7A ) . We identified clones which lacked ISP1 expression by IFA and Western blot ( Figure 7B–C ) , indicating successful disruption of the ISP1 locus and demonstrating that ISP1 is not necessary for in vitro propagation of T . gondii . Disruption of ISP1 did not result in any gross defect in parasite growth . However , we were surprised to find that both ISP2 and ISP3 were relocalized in the Δisp1 strain . In the parental strain , ISP2 staining terminates sharply at the ring of TgCentrin2 annuli bordering the base of the apical cap ( Figure 7D , arrowheads ) . However , in Δisp1 parasites , ISP2 staining extends past this border , relocalizing to the apical cap sub-compartment of the IMC ( Figure 7D ) . Apical cap relocalization is also observed for ISP3 in the Δisp1 strain ( Figure 7E ) . To ensure the ISP2 and ISP3 relocalization to the apical cap is truly a result of the absence of ISP1 , we reintroduced the ISP1 gene with a C-terminal YFP fusion into the Δisp1 strain . This fusion protein targets correctly to the apical cap and , importantly , reestablishes the wild-type localization of ISP2 ( Figure 8A , insets ) and ISP3 ( data not shown ) , excluding them from the apical cap . Thus , ISP1 exhibits a gate-keeping effect on ISP2 and 3 , preventing access to the apical cap and establishing a hierarchy of protein targeting among these IMC sub-compartments . To determine if ISP1 performs a broader scaffolding function within the apical cap , we evaluated the localization of TgDLC1 using a GFP fusion; however , we observed no change in the localization of this protein in the absence of ISP1 ( data not shown ) . Given the ability of ISP1 to exclude other family members from the apical cap , we exploited our ISP11–65-YFP construct to determine whether or not the N-terminal region that is sufficient for apical cap targeting also plays a role in exclusion from this compartment . Expression of this construct in Δisp1 parasites does not result in exclusion of ISP2 ( Figure 8B ) or ISP3 ( data not shown ) from the apical cap , demonstrating that distal sequences present in the more conserved regions of ISP1 ( residues 66–176 ) are necessary for exclusion . To further assess whether the C-terminal region from another ISP family member could substitute for the ISP1 C-terminal domain and function in exclusion , we constructed a hybrid protein containing the N-terminal 65 amino acids of ISP1 and the C-terminal region of ISP2 ( residues 43–160 ) fused to YFP . Similar to the ISP11–65-YFP construct , the ISP1N/2C-YFP chimera targets to the apical cap but does not exclude ISP2 ( Figure 8C ) or ISP3 ( data not shown ) . These results demonstrate that the exclusion activity of the C-terminal region of ISP1 is specific to this family member and cannot be replaced by the complementary region from ISP2 . We created an additional chimera consisting of the N-terminal region of ISP2 ( residues 1–41 ) fused to the C-terminal region of ISP1 ( residues 67–176 ) . While the N-terminal region of ISP2 alone targets YFP or HA throughout the IMC ( Figure 5F ) , inclusion of the C-terminal region of ISP1 restricts the localization to the apical cap and central regions of the IMC ( Figure 8D , see discussion ) . In parasites expressing this chimera , ISP2 and 3 are mostly relocalized into the base portion of the IMC ( Figure 8E–F , brackets ) . The fact that the ISP1 C-terminal region is able to exhibit exclusion activity against the other ISPs when artificially targeted to other domains of the IMC strengthens the conclusion that the ISP1 C-terminal region constitutes an ISP exclusion domain . To further investigate the function of the ISP proteins , we disrupted the genes encoding ISP2 and ISP3 by homologous recombination . To accomplish this , we employed a recently developed Δku80 parasite strain that is highly efficient at homologous recombination [38] . We first removed HPT from the Ku80 locus by homologous recombination and negative selection using 6-thioxanthine , creating Δku80Δhpt strain parasites . We then used this strain to disrupt ISP2 or ISP3 and confirmed these deletions by IFA ( not shown ) and Western blot ( Figure 9A and Figure S5 ) . In contrast to our findings for Δisp1 parasites , localization of other ISP family members was unchanged in both Δisp2 and Δisp3 strains ( data not shown ) . While no gross phenotype was seen in Δisp3 parasites , the Δisp2 strain parasites were obviously defective in growth as the knockout was rapidly lost from transfected populations and its isolation required cloning early following transfection . To assess this loss in fitness , we performed competition growth assays between parent and Δisp2 parasites by mixing these strains in culture and monitoring the culture composition at each passage . The parental strain rapidly out competed the Δisp2 parasites , confirming a severe fitness loss in these parasites ( Figure 9B ) . Further analysis by IFA revealed that Δisp2 parasites display a number of defects in parasite division . Most frequently , we observed the construction of >2 daughters per mother cell in each round of endodyogeny with some parasites assembling as many as 8 daughters ( Figure 9C ) . To quantify this defect , we stained for ISP1 , an early marker for bud formation during endodyogeny , and counted vacuoles containing parasites undergoing endodyogeny and assembling >2 buds . As expected , we saw a dramatic increase in the number of parasites producing more than two daughters in the Δisp2 strain ( Figure 9D ) . Neither Δisp1 or Δisp3 parasites showed any aberration in daughter cell assembly compared to wild-type parasites ( data not shown ) . Assembly of >2 daughters in Δisp2 parasites sometimes occurred around a single polyploid nucleus with karyokinesis accompanying budding ( bottom left parasite , Figure 9C ) while other parasites assembled the spindle apparatus and underwent karyokinesis without budding , resulting in a mother parasite with two nuclei ( Figure 9E ) . We also observe parasites containing two discrete nuclei in the process of budding >2 daughters ( outlined parasites , Figure 9F ) . Less frequently , we observed a catastrophic failure of Δisp2 parasites to appropriately segregate nuclei , resulting in anucleate zoids and nuclei extruded in the vacuole ( Figure 9G ) . These vacuoles also show major defects in apicoplast segregation with a few cells receiving both a nucleus and an apicoplast while some received only an apicoplast and others received neither . Finally , some vacuoles with nuclear segregation defects contained many immature buds within the vacuole ( Figure 9H ) . These buds appear to be outside of any intact parasite and it is unclear if they were initiated within a mother cell and then somehow liberated into the vacuolar space or if they were the result of a budding event that was initiated within the vacuolar space itself . In these vacuoles , several elongated apicoplasts are strung throughout the vacuolar space , associated with the extracellular buds and nuclei . Surprisingly , the Δisp2 parasites recovered from both the fitness and replication defects after approximately two months of culture ( data not shown ) , preventing complementation by genetic rescue . To ensure these phenotypes are specific to the disruption of ISP2 and not the consequence of any off target effects , we generated a second independent Δisp2 line . This line displayed the same loss of fitness and cell division defects , indicating these phenotypes are specifically linked to disruption of the ISP2 locus ( data not shown ) .
Alveoli are the unifying morphological feature among ciliates , dinoflagellates and apicomplexans where these unique membrane stacks have been adapted to suit these divergent organisms in vastly different niches . In apicomplexans , the membrane stacks ( the IMC ) have been exploited to provide unique and critical roles in parasite replication , motility and invasion . Freeze-fracture studies reveal a highly sophisticated arrangement of IMC plates with dissimilar organization of IMPs in the apical versus lower plates indicating compositional differences between these regions [14] . Identification of the ISPs clearly demonstrates that the protein constitution of the membrane cisternae is not uniform . The ISP compartments have sharp boundaries ( Figure 2B–D ) , suggesting that they correspond to discrete cisterna or groups thereof ( Figure 10A ) . ISP1 localizes to the apical cap compartment that is delimited by TgCentrin2 and thus represents the first membrane associated protein of this apical-most IMC plate . Previously , the cytoskeleton-associated proteins PhIL1 and TgDLC1 were shown to localize in part to the apical cap region [20] , [22] . The C-terminal half of PhIL1 is sufficient for apical cap localization and also for retaining cytoskeletal association . This portion of the protein lacks predicted transmembrane domains or acylation signals , indicating that it links directly to a sub-domain of the cytoskeleton independent of the membrane stacks . Electron micrographs of detergent-extracted parasites show substantial differences in the cytoskeletal filaments in this region ( e . g . thicker filaments and a parallel instead of interwoven arrangement ) , indicating that distinct sub-domains exist in both the IMC membranes and underlying network [13] . Localization of ISP2 and 3 revealed two additional sub-compartments of the IMC that have not been previously observed: a central compartment labeled by ISP2 and a basal compartment labeled by ISP3 . The abutment of ISP2 and ISP3 staining against the posterior end of the apical cap likely corresponds to the junction between the apical cap and the rectangular plates constituting the remainder of the IMC . The presence of TgCentrin2 annuli at this border is striking as centrins are calcium-binding contractile proteins known to play a role in the duplication of microtubule organizing centers [39] . While the ISP3 sub-compartment clearly terminates at the posterior end of the IMC , it is unclear what accounts for the basal boundary of the ISP2 sub-compartment which lies approximately two-thirds down the length of the parasite . One possibility is an association with the cortical microtubules that also terminate in this region [40] . However , the microtubules and ISP2 signal do not consistently terminate at the same point . Alternatively , the signal termination may correspond to another junction of IMC plates and the exclusion of ISP2 from the basal region of the IMC may reflect another point of hierarchical targeting , as we discovered for ISP1 in the apical cap . While ISP1 and 2 are both retained in mother parasites during endodyogeny , ISP3 maternal staining dissipates as daughter parasites form . The strong ISP3 signal in early buds along with the rapid attenuation of ISP3 signal in the mother during endodyogeny provides an unhampered view of the membranes of the daughter buds ( Figure 2E and Video S1 ) . Expression of IMC proteins is tightly regulated during the cell cycle including the ISPs , which show an expression profile similar to that of IMC1 ( Michael White , personal communication ) . Thus , the bright ISP3 staining in daughters and concomitant loss of signal in mother cells could be due to synthesis in daughters and degradation in mothers . Alternatively , since palmitoylation is a reversible lipid modification , recycling by de-palmitoylation at the parent IMC and re-palmitoylation at daughter IMCs could account for the ISP3 dynamics observed . ISP1 and 3 are localized to numerous ring structures in oryzalin-treated parasites , indicating that initiation of bud IMC assembly repetitively occurs under these conditions and is not dependent on microtubules . Microtubule polymerization is essential for cell division and cortical microtubule extension is thought to drive bud growth , explaining why buds in parasites lacking microtubules never elongate [36] . ISP1 and 3 are localized to distinct compartments in forming daughter cells , demonstrating that IMC sub-compartmentalization is established early during endodyogeny ( Video S1 ) . The ISP1 and 3 signals are not always perfectly overlapping in oryzalin-treated cells , suggesting that IMC membrane specialization may be established even in these early bud rings , although the rings are too small to clearly visualize distinct sub-domains . The absence of ISP2 from these rings may indicate later recruitment to daughter buds or simply be a consequence of drastic perturbation of the cell under these conditions . Some nucleating scaffold element must provide a foundation for these early IMC membrane bud rings . The earliest signs of daughter bud formation observed by electron microscopy are a dome-shaped vesicle and associated microtubules [41] . The basal complex protein TgMORN1 is the earliest protein marker of bud generation , forming a pair of rings around the centrioles after their duplication at approximately the same time daughter conoids are assembled [42] . In oryzalin-treated parasites observed during the first few hours following drug addition , initial TgMORN1 ring formation still occurs and can be followed until cells attempt to bud , at which point the inability to polymerize new microtubules results in drastic loss of parasite morphology . After 24 hours of oryzalin treatment , TgMORN1 localizes in patches sparsely associated with peripheral sheets of IMC membrane skeleton marker IMC1 but does not label anything resembling the bud rings observed for ISP1 and 3 [20] , [43] . In our study , IMC1 did not localize to ISP1-labeled bud rings in oryzalin-treated parasites , demonstrating that it is not required for bud initiation . Furthermore , TgMORN1 has been disrupted and shown to be non-essential for parasite growth [44] . Future studies with ISP1 and 3 will enable the discovery of the critical nucleating factors that mediate bud initiation . Protein acylation is a widely employed eukaryotic mechanism to mediate membrane association of proteins that lack a transmembrane domain . Our mutation of conserved N-terminal residues that are predicted to be myristoylated and palmitoylated indicates that these modifications are responsible for IMC membrane targeting . These mutagenesis studies also agree with our deletion analysis demonstrating that the N-terminal regions of ISP1 and 3 are sufficient for correct targeting . Together , these data suggest a kinetic trapping model for ISP localization in which ISP proteins are first co-translationally myristoylated in the cytosol enabling sampling of membranes , then recognized and palmitoylated by a unique PAT ( or PAT activity ) that is present in each sub-compartment , thus locking the protein into the appropriate membrane sub-compartment ( Figure 10B ) . For ISP1 and 3 , this multiple PAT model agrees with our deletion analysis showing that N-terminal regions of the proteins are sufficient for sub-compartment localization . Recognition of each ISP protein as a substrate would be determined by the context of the sequences immediately surrounding the residues required for myristoylation and palmitoylation . Indeed , additional deletion analysis showed that the first ten residues of ISP1 mostly retain apical cap targeting ( data not shown ) . In contrast , while the N-terminal region of ISP2 is sufficient for general IMC membrane association , deletion of the C-terminal region or its substitution in the ISP2N/1C chimera alters sub-compartment specificity . These structural changes to ISP2 may remove important information for establishing stringent PAT specificity , permitting incorporation into other IMC sub-compartments . A similar effect was recently discovered for the palmitoylated protein Vac8 in Saccharomyces cerevisiae . While palmitoylation of wild-type Vac8 was only catalyzed by one of the five S . cerevisiae PATs tested , truncation of the Vac8 C-terminus resulted in its palmitoylation by all five PATs [45] . Alternatively , it is possible that palmitoylation of the ISP family is facilitated by a single PAT that is localized throughout all three IMC compartments and regulated by additional cofactors . Modulation of PAT activity against certain substrates by additional protein cofactors has been shown in both yeast and mammalian systems [46] , [47] . The presence of a Asp-His-His-Cys-cysteine-rich domain ( DHHC-CRD ) is the hallmark of PAT activity and has allowed for the identification of several PATs in other systems , including 7 in S . cerevisiae and 23 in mammalian genomes [48] . Within the Toxoplasma genome , 18 DHHC-CRD containing proteins are predicted to be encoded , a relatively higher number among protists ( e . g . the Giardia lamblia and Trypanosoma brucei genomes are predicted to contain 9 and 12 PATs , respectively [49] , [50] ) , indicating a more extensive PAT network may be present to accommodate protein sorting within the numerous unique membrane systems in apicomplexans . Future work localizing and characterizing the putative Toxoplasma PATs will distinguish between the possible models for ISP sorting suggested by our data . Relocalization of the other ISP family members into the apical cap may explain the lack of any gross phenotype in Δisp1 parasites . Whereas targeting to the apical cap is mediated by the N-terminal region of ISP1 , relocalization of other family members into this sub-compartment is dependent on the C-terminal portion of this protein . Both the ISP1N/2C and ISP2N/1C chimeras support the conclusion that this gate-keeping is specific to ISP1 and directed against ISP2/3 . Interestingly , while distal sequences of ISP2 are also required for its exclusion ( as shown by ISP21–41-HA ) , this is not the case for a comparable truncation of ISP3 . Perhaps the simplest explanation for the mechanism of ISP2/3 exclusion from the apical cap is provided by our multiple PAT model ( Figure 10B ) . This model would suggest that in wild-type parasites , the presence of ISP1 , either directly or indirectly via other proteins , modulates PAT activity in the apical cap , thus preventing recognition of ISP2 and 3 . In the absence of the ISP1 C-terminal domain , ISP2 and 3 are able to be recognized as substrates of the apical cap PAT and also localize to this compartment . This model would also suggest that the exclusion insensitivity of truncated ISP2 ( Figure 5F ) , as compared to truncated ISP3 ( Figure 5D ) , may simply result from a change in the ability of PATs to specifically recognize and act upon this altered molecule ( discussed in the previous section ) . Alternatively , deletion of ISP1 may result in relocalization of a central sub-compartment PAT into the apical cap , thus enabling ISP2 and 3 to localize to this membrane region . Finally , it is also possible that ISP1 exclusion is the result of a receptor in the apical cap , which the C-terminal domain of ISP1 binds with a higher affinity than ISP2 or 3 . The absence of the ISP1 C-terminal domain would then allow binding of the similar regions of ISP2 and 3 to the receptor in the apical cap . However , the variable exclusion observed in C-terminal truncations of ISP2 and 3 argues against this scenario . We have attempted to identify ISP1 binding partners by immunoprecipitation under gentle conditions but have had no success , indicating that if partners do exist , they are not strongly interacting . Regardless of the precise mechanism , the targeting of the ISP family demonstrates that organization of the Toxoplasma IMC is an interactive , complex process . To our knowledge , this hierarchical targeting is a completely unprecedented mechanism for sorting of palmitoylated proteins in any membrane system . It will be interesting to see if similar mechanisms of membrane organization are present in other members of the eukarya . Disruption of ISP2 results in defects in daughter cell formation , indicating that ISP2 is important for proper coordination of daughter parasite assembly . Our observation that ∼5% of wild-type parental strain vacuoles assemble >2 daughters is in agreement with previous studies [51] . Toxoplasma populations have been reported to undergo flux in the percentage of parasites displaying this trait due to certain stresses [52] , however the dramatic ( ∼60% ) effects on daughter parasite assembly in the Δisp2 strain vastly exceed these previous reports . Furthermore , the severe fitness loss in these parasites indicates this failure to properly coordinate cell division has serious consequences for parasite biology . This could be due to abortive replication events , as we do observe ultrastructural and organelle partitioning defects that are likely terminal ( e . g . parasites lacking a nucleus or apicoplast and immature daughter buds within the vacuole , Figure 9G–H ) . However , many of the Δisp2 progeny produced in parasites assembling >2 siblings appear viable as they seem to properly assembly the IMC and cortical cytoskeleton and also receive nuclear DNA , an apicoplast and a mitochondrion ( data not shown ) . In these cases , poor control over the number of daughter cells being assembled may also render a fitness cost on parasites during the normally efficient proliferative tachyzoite life stage . The increase in the number of daughter parasites per mother cell results in several outcomes . In some parasites , DNA replication and karyokinesis occur prior to bud formation ( Figure 9E–F ) , while in others , multiple rounds of DNA replication appear to occur without karyokinesis , resulting in large nuclei that are segregated in a single step among multiple daughters ( Figure 9D ) . In either case , mother parasites that produce greater than 2 daughters are no longer performing endodyogeny , but instead replicating by one form or another of endopolygeny [19] , [51] , [53] . The presence of replication abnormalities in Δisp2 parasites reminiscent of division in other Toxoplasma life stages and other apicomplexan species suggests this protein plays a role in coordinating progress along the proper cell division pathway in tachyzoites and that this coordination is needed to maintain parasite fitness . It is unclear how Δisp2 parasites ultimately recover from these defects and return to normal growth and replication . In both of the independent ISP2 knockouts performed months apart , the defects in growth and daughter formation were stable for at least two months . Recovery may be due to compensation via the other ISP proteins or may instead involve other players . It will be interesting to determine whether double knockouts of the ISP proteins , or even a triple knockout , will yield a more severe and stable phenotype . These functional implications for ISP2 underscore the idea that apicomplexan-specific processes are likely tied to the many hypothetical genes encoded within these parasites , some of which will provide novel therapeutic targets . The conservation of this family throughout the phylum suggests that the unique ISP targeting mechanism is conserved and raises the possibility that these proteins are more broadly involved in coordinating the various pathways of cell division that are critically important to the pathogenesis of apicomplexan parasites .
Antibodies were raised in mice under the guidelines of the Animal Welfare Act and the PHS Policy on Humane Care and Use of Laboratory Animals . Specific details of our protocol were approved by the UCLA Animal Research Committee . T . gondii RHΔhpt ( parental ) strain and modified strains were maintained in confluent monolayers of human foreskin fibroblast ( HFF ) host cells as previously described [54] . Monoclonal antibodies ( mAb ) were generated against a mixed fraction of organelles from T . gondii [28] . For immunization , ∼100 µg of purified organelles [55] were injected in RIBI adjuvant into a BALB/c mouse . Following four injections , the spleen was isolated , hybridoma lines were prepared , and supernatants from individual clones screened for antibody reactivity . The following primary antibodies were used in IFA or Western blot: rabbit polyclonal anti-tubulin [35] , rabbit polyclonal anti-SAG1 [56] , anti-IMC1 mAb 45 . 15 [33] , anti-ROP1 mAb TG49 [57] , and anti-ATrx1 mAb 11G8 [28] . Hemagglutinin ( HA ) epitope was detected with mAb HA . 11 ( Covance ) or rabbit polyclonal anti-HA ( Invitrogen ) . Fixation and immunofluorescence staining of T . gondii were carried out as previously described [55] . All cells imaged in this study were formaldehyde-fixed except parasites in Figure S2B , which were fixed with methanol . Image stacks were collected at z-increments of 0 . 2 µm with an AxioCam MRm CCD camera and AxioVision software on an Axio Imager . Z1 microscope ( Zeiss ) using a 100x oil immersion objective . Deconvolved images were generated using manufacturer specified point-spread functions and displayed as maximum intensity projections . The protein recognized by monoclonal antibody 7E8 was isolated from 5×109 T . gondii RH tachyzoites lysed in radioimmunoprecipitation assay ( RIPA ) buffer ( 50 mM Tris [pH 7 . 5] , 150 mM NaCl , 0 . 1% sodium dodecyl sulfate [SDS] , 0 . 5% NP-40 , 0 . 5% sodium deoxycholate ) . Insoluble material was removed from the lysate by centrifugation at 10 , 000× g for 30 min after which the remaining soluble lysate fraction was incubated with mAb 7E8 cross-linked to protein G-Sepharose beads ( Amersham ) using dimethylpimelimidate as previously described [58] . After washing in RIPA buffer , the bound protein was eluted using high pH ( 100 mM triethylamine , pH 11 . 5 ) and the eluate was separated by SDS-polyacrylamide gel electrophoresis ( PAGE ) . Coomassie staining identified a single 18-kDa band , which was excised and trypsin digested before analysis by mass spectrometry at the Vincent Coates Foundation Mass Spectrometry Laboratory , Stanford University Mass Spectrometry ( http://mass-spec . stanford . edu ) . YFP-αTubulin and mRFP-TgCentrin2 were expressed in parasites using previously described plasmids [20] , [59] . HA epitope-tagged lines and YFP fusions pISP1/2/3-HA/YFP were generated by cloning the genomic loci of ISP1 ( primers P1/P2 ) , ISP2 ( primers P3/P4 ) or ISP3 ( primers P5/P6 ) into the expression plasmids pNotI-HA-HPT or pNotI-YFP-HPT using the restriction sites HindIII/NotI . These vectors contain a C-terminal HA tag or YFP fusion and selectable marker HPT driven by the DHFR promoter [60] . The ISP11–65 truncation was generated by cloning YFP ( primers P7/P8 ) at the restriction sites EcoRV/PacI in pISP1-YFP . The ISP21–41 truncation was generated by cloning YFP ( primers P9/P8 ) at the restriction sites RsrII/NotI in pISP2-YFP . The ISP31–36 truncation was generated by cloning the ISP3 promoter and residues 1–36 ( primers P10/P11 ) at the restriction sites PmeI/AvrII in the previously described vector ptubYFP-YFP/sagCAT [61] . The ISP164–176 truncation was generated by cloning the ISP1 promoter and start codon ( primers P1/P12 ) at the restriction sites HindIII/EcoRV in pISP1-YFP . The ISP1N/2C chimera was generated by cloning ISP243–160 ( primers P13/P4 ) at the restriction sites EcoRV/NotI in pISP1-YFP . The ISP2N/1C chimera was generated by cloning ISP167–176-YFP ( primers P14/P8 ) at the restriction sites RsrII/PacI in pISP2-HA . For expression , 1 . 6×107 parasites were transfected with 30 µg of plasmid and then analyzed by IFA as specified in figure legends . Separation of the parasite IMC and plasma membrane was achieved by treatment with C . septicum alpha-toxin as previously described [33] . Briefly , freshly lysed , extracellular parasites were washed and incubated 4 hrs in serum free media with or without 20 nM activated alpha-toxin . Following treatment , cells were fixed in 3 . 5% formaldehyde , allowed to settle on glass slides and analyzed by IFA . Tachyzoites were allowed to infect HFF monolayers on coverslips in media containing 0 . 5 or 2 . 5 µM oryzalin ( Sigma ) . Parasites were allowed to grow 30–40 hrs post-infection and then fixed and examined by IFA . The coding sequences for ISP2 ( primers P15/P16 ) and ISP3 ( primers P17/P18 ) were PCR amplified from T . gondii cDNA and cloned into pET101/D-TOPO ( Invitrogen ) . Constructs were transformed into E . coli BL21DE3 cells , grown to A600 of 0 . 6–0 . 8 and induced with 1 mM isopropyl 1-thio-β-D-galactopyranoside ( Sigma ) for 5 hrs at 37°C . Recombinant ISP2 and ISP3 were purified over Qiagen Ni-NTA agarose under denaturing conditions and eluted with a low-pH buffer as per the manufacturer's instructions . Eluted proteins were dialyzed against PBS and ∼75 µg was injected per immunization into BALB/c mice ( Charles River ) on a 21 day immunization schedule . Polyclonal antiserum was collected from mice after the second boost and screened by IFA and Western blot analysis . For detergent extraction experiments , 3×107 freshly lysed parasites were washed in PBS , pelleted and lysed in 1 mL TBS ( 50 mM Tris-HCl [pH 7 . 4] , 150 mM NaCl ) containing 0 . 5% NP-40 and complete protease inhibitors ( Roche ) for 15 min at 4°C and then centrifuged for 15 min at 14 , 000× g . Equivalent amounts of total , supernatant and pellet fractions were separated on a 15% gel , transferred to nitrocellulose and blotted using anti-IMC1 , anti-ROP1 , mAb 7E8 , polyclonal anti-ISP2 , and polyclonal anti-ISP3 . Mutations were generated by Quick Change Mutagenesis ( Strategene ) using HA-tagged , wild-type ISP1 , 2 or 3 with mutagenesis primers as follows ( forward primer given , reverse compliment was also used ) : ISP1: G2A ( P19 ) , C7S ( P20 ) , C8S ( P21 ) , C7 , 8S ( P22 ) . ISP2: G2A ( P23 ) , C5S ( P24 ) , C8S ( P25 ) , C9S ( P26 ) , C8 , 9S ( P27 ) , C5 , 8 , 9S ( P28 ) . ISP3: G2A ( P29 ) , C6S ( P30 ) , C7S ( P31 ) , C6 , 7S ( P32 ) . PCR amplified products were treated with DpnI to digest wild-type template and transformed into E . coli . Recovered clones were sequenced to confirm mutations . The deletion of the ISP1 gene was accomplished by double homologous recombination using a construct derived from the pMini-GFP . ht knockout vector [62] which contains the selectable marker hypoxanthine-xanthine-guanine phosphoribosyltransferase ( HPT ) and also contains the green fluorescent protein ( GFP ) as a downstream marker to distinguish homologous and heterologous recombinants . The 5′ flank ( 3 , 147 bp ) and 3′ flank ( 3 , 042 bp ) of ISP1 were amplified from strain RH genomic DNA using primer pairs P33/P34 and P35/P36 , respectively . These genomic flanks were then cloned into pMini-GFP . ht upstream and downstream of HPT , resulting in the vector pISP1-KO-HPT . After linearization with NheI , 30 µg of pISP1-KO-HPT was transfected into RHΔhpt parasites and selection for HPT was applied 12 hours post-transfection using 50 µg/ml mycophenolic acid and 50 µg/ml xanthine . Surviving parasites were cloned by limiting dilution eight days post-transfection and screened for GFP by fluorescence microscopy . GFP-negative clones were assessed for absence of mAb 7E8 staining by IFA . Western blot analysis was carried out on whole-cell lysates of Δisp1 clones and parental strains using mAb 7E8 and anti-ROP1 antibody as previously described [55] . The HPT gene was removed from RHΔisp1 + HPT by a second round of double homologous recombination . The pISP1-KO-HPT vector was digested by EcoRV/NheI to remove the HPT gene and then blunted using Klenow enzyme and re-circularized by ligation . The resulting vector was linearized by EcoRI and transformed into RHΔisp1 + HPT , followed by selection for the absence of HPT on 200 µg/ml 6-thioxanthine ( Sigma ) . After 3 weeks of selection , parasites were cloned and screened for the absence of GFP expression . Clones that were GFP-negative were then assessed for the inability to grow in mycophenolic acid and xanthine , indicating loss of HPT . One such clone was chosen and deletion of the ISP1 locus was confirmed by PCR . This clone was designated Δisp1 . The HPT selectable marker was removed from the Ku80 locus of the previously described Δku80 strain [38] . Briefly , 10 µg of a PCR fusion construct containing a 5′ Ku80 flank ( primers P37/P38 ) fused to a 3′ Ku80 flank ( primers P39/P40 ) was transfected into RHΔku80-HPT parasites . Selection against HPT with 6-thioxanthine and confirmation of marker loss were carried out as described above . For disruption of ISP2 , a knockout vector was generated by cloning ∼3 kb 5′ ( primers P41/P42 ) and 3′ ( primers P43/P44 ) genomic flanks into a modified version of pMiniGFP . ht in which HPT was replaced by the selectable marker DHFR-TSc3 , yielding the vector pISP2KO-DHFR-TSc3 . After linearization by NotI , 30 µg of this vector was transfected into Δku80Δhpt parasites and selection was applied 12 hours post-transfection using 1 µM pyrimethamine . Parasites were cloned and confirmed to lack ISP2 as described above . For disruption of ISP3 , the vector pISP3-KO-HPT was generated by cloning ∼3 kb 5′ ( primers P45/P46 ) and 3′ ( primers P47/P48 ) genomic flanks into pMiniGFP . ht . After linearization by KpnI and transfection into the Δku80Δhpt strain , parasites were selected for HPT , cloned and confirmed to lack ISP3 as described above . Freshly lysed parental and Δisp2 parasites were counted and mixed in desired ratios before infection of 3 . 3×106 parasites into a T25 flask of confluent HFFs . Parasites were allowed to disrupt the monolayer before passing into a fresh T25 . At initial infection and at each passage , samples of the mixed culture were infected into coverslips and allowed to grow 32 hours before fixation and staining with polyclonal anti-ISP2 and rabbit polyclonal anti-tubulin as a co-marker to monitor mixed culture composition . At least 500 vacuoles were counted from each of 4 coverslips per passage . Values represent mean 3 standard deviations for a representative experiment . Parental line and Δisp2 parasites were infected onto coverslips and allowed to grow 18–24 hours before fixation and staining with mAb 7E8 as a marker for daughter buds and rabbit polyclonal anti-tubulin as a co-marker . Fifty vacuoles containing parasites undergoing bud formation were counted from each of 3 coverslips per sample . Vacuoles containing one or more parasites assembling >2 daughters were scored as aberrant . Values represent the mean ± SD from a representative experiment . | Apicomplexans are the cause of important diseases in humans and animals including malaria ( Plasmodium falciparum ) , which claims over a million human lives each year , and toxoplasmosis ( Toxoplasma gondii ) , which causes birth defects and neurological disorders . These parasites possess a unique cortical system of membrane sacs arranged on a cytoskeletal meshwork , together referred to as the inner membrane complex ( IMC ) . The IMC is the anchor point for the gliding motility machinery necessary for host invasion and also a scaffold around which new parasites are constructed during replication . Here we have uncovered new insights into the organization and function of this structure by identifying and characterizing ISP1-3 , a family of proteins that define novel sub-compartments within the Toxoplasma IMC . Residues predicted for myristoylation and palmitoylation are critical in the membrane targeting of these proteins , suggesting that multiple palmitoyl acyltransferase activities reside within the IMC and dictate its organization . Surprisingly , ISP1 is required for proper sub-compartment sorting of ISP2 and 3 , revealing a novel hierarchical targeting mechanism for the organization of this membrane system . Disruption of ISP2 results in defects during endodyogeny and a dramatic loss in parasite fitness , revealing that the ISP proteins play an important role in coordinating parasite replication . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
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] | [
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] | 2010 | A Novel Family of Toxoplasma IMC Proteins Displays a Hierarchical Organization and Functions in Coordinating Parasite Division |
Integration of genome-wide association studies ( GWAS ) and expression quantitative trait loci ( eQTL ) studies is needed to improve our understanding of the biological mechanisms underlying GWAS hits , and our ability to identify therapeutic targets . Gene-level association methods such as PrediXcan can prioritize candidate targets . However , limited eQTL sample sizes and absence of relevant developmental and disease context restrict our ability to detect associations . Here we propose an efficient statistical method ( MultiXcan ) that leverages the substantial sharing of eQTLs across tissues and contexts to improve our ability to identify potential target genes . MultiXcan integrates evidence across multiple panels using multivariate regression , which naturally takes into account the correlation structure . We apply our method to simulated and real traits from the UK Biobank and show that , in realistic settings , we can detect a larger set of significantly associated genes than using each panel separately . To improve applicability , we developed a summary result-based extension called S-MultiXcan , which we show yields highly concordant results with the individual level version when LD is well matched . Our multivariate model-based approach allowed us to use the individual level results as a gold standard to calibrate and develop a robust implementation of the summary-based extension . Results from our analysis as well as software and necessary resources to apply our method are publicly available .
Recent technological advances allow interrogation of the genome to a high level of coverage and precision , enabling experimental studies that query the effect of genotype on both complex and molecular traits . Among these , GWAS have successfully associated genetic loci to human complex traits . GWAS meta-analyses with ever increasing sample sizes allow the detection of associated variants with smaller effect sizes [1–3] . However , understanding the mechanism underlying these associations remains a challenging problem . Another approach is the study of expression quantitative trait loci ( eQTLs ) , measuring association between genotype and gene expression . These studies provide a wealth of biological information but tend to have smaller sample sizes . A similar observation applies to QTL studies of other traits such methylation , metabolites , or protein levels . The importance of gene expression regulation in complex traits [4–7] has motivated the development of methods to integrate eQTL studies and GWAS . To examine these mechanisms we developed PrediXcan [8] , which tests the mediating role of gene expression variation in complex traits . Briefly , PrediXcan tests the hypothesis that genetic variants affect phenotypes through the regulation of gene expression traits . To do that , it correlates genetically predicted gene expression and the phenotype with the idea that causal genes are likely to show a significant association . Linear prediction models of expression using genetic variation in the vicinity of the gene are trained in reference transcriptome datasets such as Genotype-Tissue Expression project ( GTEx ) [9] . Due to sharing of eQTLs across multiple tissues , we have shown the benefits of an agnostic scanning across all available tissues [10] . Despite the increased multiple testing burden ( for Bonferroni correction , the total number of gene-tissue pairs must be used when determining the threshold ) , we gain considerably in number of significant genes . However , given the substantial correlation between different tissues [9] , Bonferroni correction can be too stringent increasing the false negative rate . In order to aggregate evidence more efficiently , we present here a method termed MultiXcan , which tests the joint effects of gene expression variation from different tissues . Furthermore , we develop and implement a method that only needs summary statistics from a GWAS: Summary-MultiXcan ( S-MultiXcan ) . We make our implementation publicly available to the research community in https://github . com/hakyimlab/MetaXcan . We apply this method to simulated and real data ( 222 traits from the UK Biobank study [11] and 109 public GWAS ) to show the performance and proper calibration of p-values . We make all of the results publicly available at https://doi . org/10 . 5281/zenodo . 1402225 .
To integrate information across tissues , MultiXcan regresses the phenotype of interest on the predicted expression of a gene in multiple tissues as follows: y = μ + t 1 g 1 + t 2 g 2 + ⋯ + t p g p + e ( 1 ) where y is the n-dimensional phenotype vector , μ is an intercept term , ti is standardized predicted expression of the gene in tissue i , gi is its effect size , and e an error term with variance σ e 2; p is the number of available tissue models . We use an F-test to assess the joint significance of the regression . Expression predictions across tissues can be highly correlated . We predicted expression for individuals from the UK Biobank cohort using models trained on 44 GTEx tissues ( as presented in [10] ) , and found a median pair-wise correlation of rp50 = 0 . 56 ( IQR = 0 . 69 ) between different tissue models in a given gene , across genes ( see Methods for details ) . To avoid numerical issues caused by collinearity , we use principal components of the predicted expression data matrix as explanatory variables , and discard the axes of smallest variation ( PCA regularization ) . Additional covariates can be added to the regression seamlessly . Fig 1-a displays an overview of the method; see further details in the Methods section . S1 Fig shows an example of the correlation between tissues of predicted expression of the gene SLC5A6 . We applied MultiXcan to 222 traits from the UK Biobank cohort . The traits were chosen based on several criteria , such as availability of well-established literature , binary traits having enough cases , or potential interest for a phenome-wide study ( allergy , behavioral , metabolic and anthropometric phenotypes ) . We used Elastic Net prediction models trained on 44 tissues from GTEx , originally presented in [10] . We compared three approaches for assessing the significance of a gene jointly across all tissues: 1 ) running PrediXcan using the most relevant tissue; 2 ) running PrediXcan using all tissues , one tissue at a time; 3 ) running MultiXcan . Fig 1-b illustrates the results from each approach . We summarize a comparison between approaches 2 ) and 3 ) in Table 1 . PrediXcan overcomes MultiXcan only in 21 traits , all of them with less than 50 significant associations across both methods . MultiXcan detects more associations in 103 traits . Fig 2-a and 2-b show a comparison of detections for both MultiXcan and PrediXcan . See S1 Dataset for a summary of detections per trait , and S2 and S3 Datasets for the full list of significant MultiXcan and PrediXcan results respectively . As an illustrative example , we examined more closely the results for self-reported high cholesterol phenotype ( http://biobank . ctsu . ox . ac . uk/crystal/field . cgi ? id=20002 ) . We used 50 , 497 cases and 100 , 994 controls . After Bonferroni correction , MultiXcan was able to detect a larger number of significantly associated genes ( 251 detections ) than PrediXcan using all tissues ( 196 detections ) or only a single tissue ( whole blood , 33 detections ) . 172 genes were detected by both PrediXcan and MultiXcan . Fig 2-c shows the QQ-plot for associations in these three approaches . There are 79 genes associated to high cholesterol via MultiXcan and not PrediXcan . Among them , we find genes related to lipid metabolism ( APOM [12] , PAFAH1B2 [13] ) , glucose transport ( SLC5A6 [14] ) , and vascular processes ( NOTCH4 [15] ) . The well known gene SORT1 is detected by both MultiXcan and PrediXcan . To evaluate MultiXcan’s performance in different known scenarios , we simulated traits as a function of different numbers of causal tissues for each gene: a single tissue , multiple tissues , all available tissues . We executed PrediXcan , MultiXcan without PCA regularization , and MultiXcan with PCA regularization . We show proper calibration under the null hypothesis of no association in S3 Fig , and robustness of the regularization approach in S6 Fig . See further details in S1 Supplementary Note . As expected , when there is a known single causal tissue , PrediXcan with the known tissue yields more significant associations . However , when there are multiple causal tissues , MultiXcan yields more significant associations than the best single tissue PrediXcan results . In traits simulated from a single causal tissue , PrediXcan outperforms MultiXcan in 99 . 9% of the cases ( AOV p-value < 10−16 ) . MultiXcan performs best in scenarios with multiple causal tissues ( 84 . 4% of the times when a few tissues are causal , and 99 . 5% when all tissues are causal; AOV p-value < 10−16 in both cases ) . One caveat is that the simulation does not cover cases when the prediction in the single tissue has low quality . In such an scenario , borrowing information from other tissues will still be beneficial . To expand the applicability of our method to massive sample sizes and to studies where individual level data are not available , we extend our method to use summary results rather than individual-level data . We call this extension Summary-MultiXcan ( S-MultiXcan ) . We infer the joint estimates of effect sizes of predicted expression on phenotype ( Eq 1 ) using the marginal estimates . We also compute the covariance matrix of the effect sizes and leverage the asymptotic multivariate normality of the estimates , to compute a statistic that is approximately χ p 2 ( p number of tissues ) . The final expression is equivalent to the omnibus test mentioned in [16] , which can be interpreted as a specific case of general weighted association analysis [17] . Fig 3-a illustrates our approach and the details can be found in the Methods section . As with the individual level approach , the correlation between tissues leads to numerical problems ( due to near singular covariance matrices that need to be inverted ) . We address this by using a pseudo inverse approach which , in a nutshell , uses singular value decomposition ( SVD ) of the covariance matrix to keep only the components of large variation . This is analogous to the PCA regularization used for the individual level approach . Thus we test for significance using χ k 2 with k the number of surviving components . See details in the Methods Section . A robust implementation for calculating predicted expression correlation is critical to avoid unnecessary false positive results . In principle , it is possible to simply calculate the correlation between tissues using predicted expression in a reference set . However , we found that this approach can lead to large differences between the individual level data results ( our gold standard ) and the summary level ones when SNPs from the reference set are missing in the GWAS results . An example of this is shown in S8 Fig with the Type 1 Diabetes study from the Wellcome Trust Case-Control Consortium ( WTCCC ) ; association data is included in S9 Dataset . To avoid this problem , we calculate the covariance matrix between tissues using only the predictor SNPs that are common in both the GWAS summary and the reference LD set . Fig 4 displays a few examples of the general agreement between the individual-level MultiXcan and S-MultiXcan . The summary-based version’s results tend to be slightly more conservative than MultiXcan , as illustrated in S2 Fig . As a general comparison to the individual-level method , we list a summary of S-MultiXcan’s application to the 222 UK Biobank traits on Table 2; we observe an adequate similarity between S-MultiXcan’s and MultiXcan’s summaries . The small loss in power arises from the imperfect match of LD between the UK cohort and the reference panel . To reduce false positives due to LD misspecification when dealing with GWAS summary statistics , we discard any significant association result for a gene if the best single tissue result has p-value greater than 10−4 ( “suspicious associations” ) . In other words , we keep significant associations if at least one single gene-tissue pair association is borderline significant or better ( 10−5 is the Bonferroni threshold for a typical tissue model ) . This is rather conservative since it is possible that evidence with modest significance from weakly correlated tissues can lead to very significant combined association when their effects get aggregated . For example among Bonferroni significant genes in the individual level analysis , a median of 8 . 3% across traits ( IQR = 5 . 7% ) have the most significant marginal ( PrediXcan ) p-value greater than 10−4 . We list the number of such genes for each of the 222 UK Biobank traits in S8 Dataset . We applied S-MultiXcan to 109 traits on publicly available GWAS , chosen with a similar criteria as UK Biobank’s traits . Like the individual level method , we observed S-MultiXcan to detect more associations than S-PrediXcan in most cases ( average detection increase 10 ) , as shown in Fig 3-b , after discarding suspicious associations . We also show the QQ-plots for a sample trait ( Schizophrenia ) on Fig 3-c and the total number of associations across all public GWAS traits in 3-d . We display a summarized comparison between S-MultiXcan and S-PrediXcan in S1 Table , after discarding suspicious associations . The list of analyzed traits can be found in S4 and S5 Datasets contains a summary of significant associations for each trait and for each method . S6 Dataset lists the significant S-MultiXcan results for each trait . These results have been uploaded to https://doi . org/10 . 5281/zenodo . 1402225 .
We make our software publicly available on a GitHub repository: https://github . com/hakyimlab/MetaXcan . Prediction model weights and covariances for different tissues can be downloaded from http://predictdb . org/ . A short working example can be found on the GitHub page; more extensive documentation can be found on the project’s https://github . com/hakyimlab/MetaXcan/wiki . The results of S-MultiXcan applied to the 44 human tissues and a broad set of phenotypes can be queried on http://gene2pheno . org . The data used in this paper is publicly available in https://doi . org/10 . 5281/zenodo . 1402225 .
This study uses de-identified genotype and phenotype data from public repositories including dbGaP , EGA , and UK Biobank . Our study has been determined to be non-human subject research by the University of Chicago’s IRB protocol number IRB16-0921 . MultiXcan consists of fitting a linear regression of the phenotype on predicted expression from multiple tissue models jointly: y = ∑ j = 1 p t j g j + e = T g + e ( 2 ) where y is a centered vector of phenotypes for n individuals , tj is an n-vector of standardized predicted gene expression for model j , gj is the effect size for the predicted gene expression j , e is an error term with variance σ e 2 , and p is the number of tissues . Thus , T is a data matrix where each column j contains the values from tj , and g is the p-vector of effect sizes gj . The high degree of eQTL sharing between different tissues induces a high correlation between predicted expression levels . In order to avoid collinearity issues and numerical instability , we decompose the predicted expression matrix into principal components and keep only the eigenvectors of non negligible variance . To select the number of components , we used a condition number threshold of λ max λ i < 30 , where λi is an eigenvalue of the matrix Tt T . As a side effect , we observe moderate increases in significance levels because less informative components of tissue expression are discarded from the model . A range of values between 10 and 100 yielded similar results in the simulations described in S1 Supplementary Note as displayed in S6 Fig . Lastly , we use an F-test to quantify the significance of the joint fit . We use Bonferroni correction to determine the significance threshold . For MultiXcan , we use the total number of genes with a prediction model in at least one tissue , which yields a threshold approximately at 0 . 05/17500 ∼ 2 . 9 × 10−6 . For PrediXcan across all tissues , we use the total number of gene-tissue pairs , which yields a threshold approximately at 0 . 05/200 , 000 ∼ 2 . 5 × 10−7 . Since the tested hypotheses are not independent , Bonferroni correction is overly conservative , as can be seen when counting the number of associations via FDR in S7 Fig . We have demonstrated that S-PrediXcan can accurately infer PrediXcan results from GWAS Summary Statistics and LD information from a reference panel [10] , with the added benefits of reduced computational and regulatory burden . Here we extend MultiXcan in a similar fashion . Summary-MultiXcan ( S-MultiXcan ) infers the individual-level MultiXcan results , using univariate S-PrediXcan results and LD information from a reference panel . It consists of the following steps: Prediction Models were obtained from http://predictdb . org/ resource . These models were trained using Elastic Net as implemented in R’s package glmnet [38] , with a mixing parameter α = 0 . 5 , on 44 tissue studies from GTEx’ release version 6p . The underlying GTEx study data was obtained from dbGaP with accesion number phs000424 . v6 . p1 . Please see [10] for details . We implemented MultiXcan and S-MultiXcan using python scientific packages , working up from existing software in the MetaXcan package . S-PrediXcan , PrediXcan , MultiXcan and S-MultiXcan analysis were computed using the Center for Research Informatics’ high performance cluster at the University of Chicago . PrediXcan , S-PrediXcan , MultiXcan and S-MultiXcan results have been uploaded to the http://gene2pheno . org resources . The databases are open to the research community for arbitrary programmatic query . | We develop a new method , MultiXcan , to test the mediating role of gene expression variation on complex traits , integrating information available across multiple tissue studies . We show this approach has higher power than traditional single-tissue methods . We extend this method to use only summary-statistics from public GWAS . We apply these methods to 222 complex traits available in the UK Biobank cohort , and 109 complex traits from public GWAS and discuss the findings . | [
"Abstract",
"Introduction",
"Results",
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] | [
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] | 2019 | Integrating predicted transcriptome from multiple tissues improves association detection |
Dengue is associated with explosive urban epidemics and has become a major public health problem in many tropical developing countries , including Brazil . The laboratory diagnosis of dengue can be carried out using several approaches , however sensitive and specific assays useful to diagnose in the early stage of fever are desirable . The flavivirus non-structural protein NS1 , a highly conserved and secreted glycoprotein , is a candidate protein for rapid diagnosis of dengue in endemic countries . We aimed to evaluate the potential use of 3 commercial kits in a panel of 450 serum samples for early diagnosis of dengue in Brazil . The PanBio Early ELISA ( PanBio Diagnostics ) showed a sensitivity of 72 . 3% ( 159/220 ) and a specificity of 100% , while the sensitivity of the Platelia™ NS1 assay ( Biorad Laboratories ) was 83 . 6% ( 184/220 ) . However , the highest sensitivity ( 89 . 6%; 197/220 ) was obtained by using the NS1 Ag Strip ( Biorad Laboratories ) . A lower sensitivity was observed in DENV-3 cases by all 3 kits . Serum positive by virus isolation were more often positive than cases positive by RT-PCR by all three assays and a higher detection rate was observed during the first four days after the onset of the symptoms . The presence or absence of IgM showed no influence in the confirmation by the pan-E Early ELISA ( P = 0 , 6159 ) . However , a higher confirmation by both Platelia™ NS1 ( Biorad ) and Dengue NS1 Ag Strip ( Biorad ) in the absence of IgM was statistically significant ( P<0 , 0001 and P = 0 , 0008 , respectively ) . Only the Platelia™ NS1 test showed a higher sensitivity in confirming primary infections than secondary ones . The results indicate that commercial kits of dengue NS1 antigen are useful for the laboratory diagnosis of acute primary and secondary dengue . It can be used in combination with the MAC-ELISA for case detection and as screening test to complement viral isolation .
Dengue is associated with explosive urban epidemics and has become a major public health problem [1] . Annually , the World Health Organization estimates that 50–100 million people are infected with dengue virus ( DENV ) worldwide with estimated 250 , 000–500 , 000 cases of dengue haemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) with about 25 , 000 deaths occurring . One or more of four serotypes of DENV ( DENV1–4 ) , a mosquito-borne , positive-strand RNA virus in the genus Flavivirus , family Flaviviridae cause the disease in more than 100 endemic countries in tropical areas [2] . The geographical spread of all four DENV serotypes throughout the subtropical regions of the world has led to larger and more severe outbreaks and the accurate and efficient diagnosis of the disease is important for clinical care , surveillance , pathogenesis studies and vaccine research . Furthermore , an efficient diagnosis is an important tool to support Epidemiological Surveillance Programs considering the difficulties in confirming dengue cases based only on the clinical symptoms , especially during inter-epidemic periods . Dengue is an enveloped virus with a single-stranded , positive sense RNA genome of about 11 kb containing a single open reading frame enconding a single polyprotein co- and pos-translationally cleaved into 3 structural ( C , prM and E ) and 7 nonstructural proteins ( NS1 , NS2A , NS2B , NS , NS4A , NS4B and NS5 ) [3] . Dengue is a major public health problem in many tropical and subtropical countries in the world . The accurate and efficient diagnosis of dengue is important for clinical care , surveillance , pathogenesis studies , and vaccine research . The most used techniques use for dengue serodiagnosis are based on the anti-DENV IgM and IgG detection by using MAC-ELISA and IgG-ELISA [4] . However , one of the limitations consists in the variations on the detection rate during the acute phase of the disease . Usually , it takes from 3 to 5 days after the onset of the symptoms to detect anti-DENV IgM and from 1 to 14 days to anti-DENV IgG to become detectable , depending on whether the patient has primary or secondary infections [5] . During the acute phase , however , the NS1 exists as secreted as well as a membrane-associated protein and both forms are demonstrated to be immunogenic [6] , [7] , [8] , [9] , [10] . High NS1 level was demonstrated to circulate in the acute phase of dengue by antigen capture ELISAs , found in the sera of patients with primary and secondary DENV infections , up to the ninth day after the onset of the symptoms [10] , [11] . The availability of commercial kits for the detection of anti-DENV NS1 in acute serum provides an alternative to the existing methods such as PCR , serology and virus isolation . Previous studies have shown the sensitivity and specificity of NS1 capture commercial kits for the laboratorial diagnosis of dengue infections [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] . Recently , the Brazilian Ministry of Health has establish this new approach in sentinel clinics throughout the country after the 2008 dengue epidemic , however without a full evaluation of the commercial tests available . In the study , we aimed to evaluate the sensitivity and specificity of 3 commercially-available dengue NS1 antigen kits to demonstrate its potential use for the early laboratory confirmation of acute dengue infection in Brazil . This constitutes the first report of a comparison of NS1 antigen capture assays performed in the country .
The samples belong to a previously-gathered collection from an ongoing Project in the Laboratory approved by the Ethics Committee on Human Research ( CEP: 274/05 ) . Laboratory-positive DENV infection was defined in patients experiencing a febrile illness consistent with dengue according to WHO criteria [20] in which infection was confirmed by DENV isolation [21] , detection of DENV RNA by RT-PCR [22] , detection of anti-DENV IgM antibodies by MAC-ELISA[23] , and/or a >4-fold rise in anti-DENV IgG-ELISA titer in paired acute and convalescent sera [24] . Individuals negative for DENV infection by using all the methods described above and health individuals were classified as non-dengue cases . The serum samples ( days 1st to 9th after the onset of the symptoms ) analyzed in this study by the pan-E Early ELISA ( PanBio Diagnostics , Brisbane , Australia- first generation ) , Platelia™ ( Biorad Laboratories , Marnes-La-Coquette , France ) and NS1 Ag Strip ( Biorad Laboratories , Marnes-La-Coquette , France ) belong to a previously-gathered serum collection of the Laboratory of Flavivirus at Oswaldo Cruz Institute , FIOCRUZ , Brazil , from epidemics occurred from 1986 to 2008 . A panel of 450 sera ( 220 dengue positive sera and 230 non-dengue sera ) was divided into eleven Groups as follows: Groups A to C , sera from patients infected with DENV-1 ( n = 50 ) , DENV-2 ( n = 50 ) , and DENV-3 ( n = 58 ) , respectively; Group D , sera from patients with dengue infection serologically confirmed by MAC-ELISA with negative virus isolation and RT-PCR ( n = 62 ) ; Group E , sera from healthy individuals ( n = 30 ) ; Group F , sera from individuals negative for dengue ( n = 86 ) ; Group G , sera from yellow fever positive individuals ( n = 20 ) ; Group H , sera from individuals vaccinated for yellow fever and negative for anti-DENV antibodies ( n = 44 ) ; Group I , sera from measles patients ( n = 16 ) and Group J , sera from rubella patients ( n = 34 ) . Virus isolation was performed by inoculation into C6/36 Aedes albopictus cell line [21] and isolates were identified by indirect fluorescent antibody test ( IFAT ) using serotype-specific monoclonal antibodies [25] . RT—PCR for detecting and typing DENV was performed as described previously [22] . Briefly , consensus primers were used to anneal to any of the four DENV types and amplify a 511-bp product in a reverse transcriptase-polymerase reaction . A cDNA copy of a portion of the viral genome was produced in a reverse transcriptase reaction . After a second round of amplification ( nested PCR ) with type-specific primers , DNA products of unique sizes for each DENV serotype were generated and analyzed by gel electrophoresis . The in-house MAC-ELISA was carried out for dengue cases confirmation as described previously [23] . The IgG—ELISA previously described by Miagostovich [24] was performed for the characterization of dengue immune response as primary or secondary infections in dengue cases previously confirmed by virus isolation , RT—PCR and/or MAC-ELISA . Briefly , 96-well plates were coated with hyper immune ascitic fluid ( a mixture of anti-DENV-1 to 4 ) , followed by the addition of a mixture of the four DENV antigens . Serum diluted 1∶40 was added to the first well and four-fold dilutions were carried out up to the eighth well . After incubation , anti-human IgG conjugated to horseradish peroxidase was added Acute phase serum samples ( <6 days after onset of symptoms ) with IgG-ELISA titers of 1∶160 or greater are considered to be secondary infections . Likewise , samples with titers >1∶10 , 240 on days 6–9 , or >1∶40 , 960 on days 10–15 after onset are secondary responses . The sensitivities , specificities , efficiency , negative and positive predicted values were calculated as follows: Sensitivity: a/a+c X 100% Specificity: d/d+b X 100% Efficiency: a+d/a+b+c+d X 100% Negative Predicted Value: d/d+c X100% Positive Predicted Value: a/a+b X 100%; where: a = number of true positive , b = number of false positive , c = number of false negative and d = number of true negative . The derived data was tabulated in appropriate worksheets using the Microsoft Excel and evaluated by chi-square test using the Epi Info 6 ( Center for Disease Control and Prevention , Atlanta ) for any statistical significant association .
A panel of 450 ( n = 220 dengue cases and n = 230 non-dengue cases ) was used to evaluate three NS1 antigen capture tests commercially available . The overall sensitivities were 72 . 3% ( 159/220 ) for the pan-E Early ELISA ( PanBio ) test , 83 . 6% ( 184/220 ) for the Platelia™ NS1 ( BioRad ) kit , and 89 . 6% ( 197/220 ) for the NS1 Ag Strip kit ( BioRad ) , Table 1 . The differences observed in the sensitivities between the three kits analyzed were statistically significant ( P = 0 . 0009 ) . The pan-E Early ELISA ( PanBio ) showed a higher sensitivity in confirming DENV-2 infections ( Group B; 82 . 0% ) than confirming DENV-1 and DENV-3 infections . The Platelia™ NS1 kit ( BioRad ) was more sensitive in the detection of DENV-1 cases ( Group A; 98 . 0% ) than in the detection of DENV-2 and DENV-3 infections . The Dengue NS1 Ag Strip kit ( BioRad ) showed the same sensitivity in confirming DENV-1 and DENV-2 infections ( 98 . 0% ) . DENV-3 infections were the detected less often by all the three kits tested ( 65 . 5% , 86 . 2% and 88 . 0% for the pan-E Early ELISA ( PanBio ) , the Platelia™ NS1 kit ( BioRad ) and the Dengue NS1 Ag Strip kit ( BioRad ) , respectively ( Table 1 ) . Specificities were 100% , 98 . 7% and 99 . 1% for the PanBio kit , for the Platelia™ NS1 kit ( Biorad ) and for the NS1 Ag Strip ( BioRad ) , respectively , based on the analysis of sera of healthy individuals ( Group E ) and individuals negative for dengue ( Group F ) , Table 1 . No cross-reactivity was observed with sera from yellow fever infected patients ( Group G ) ; however both Biorad kits showed cross-reactivity with one yellow fever vaccinee ( Group H ) . None of the measles sera ( Group I ) were recognized by the NS1 tests . One rubella positive case ( Group J ) showed cross-reactivity with both Platelia™ NS1 kit ( Biorad ) and for the NS1 Ag Strip ( BioRad ) kit . The overall evaluations according to the different Groups analyzed are shown in Table 1 . A higher sensitivity ( 71 . 5% , 94 . 8% and 98 . 7% ) was observed in cases positive by virus isolation only than in cases previously positive by RT-PCR ( 62 . 3% , 82 . 3% and 82 . 3% ) for the pan-E Early ELISA , Platelia™ NS1 and Dengue NS1 Ag Strip , respectively ( Table 2 ) . The detection rate by the pan-E Early ELISA , Platelia™ NS1 and Dengue NS1 Ag Strip in the presence of IgM was 69 . 4% , 64 . 5% and 77 . 4% , respectively ( Table 2 ) . In this study , the presence or absence of IgM did not influence detection by the pan-E Early ELISA ( P = 0 , 6159 ) . However , a higher detection rate by both Platelia™ NS1 ( Biorad ) and Dengue NS1 Ag Strip ( Biorad ) in the absence of IgM was statistically significant ( P<0 , 0001 and P = 0 , 0008 , respectively ) . The sensitivities of all NS1 tests were evaluated according to the number of days of illness . A higher detection rate by the three tests analyzed was during the first four days after the onset of the symptoms ( Day 3 , in Figure 1 , considering day 0 as the first day of fever ) . The sensitivity of Platelia™ NS1 ( Biorad ) decreased to 75% of detection after that and maintained the same rate until day 6 of illness . However , after the 4th day , the NS1 Ag Strip ( Biorad ) showed 89 . 0% of sensitivity up to the 7th of symptoms . From day five to the 7th , the pan-E Early ELISA ( Panbio ) confirms about 60 . 0% of the cases ( Figure 1 ) . Although dengue NS1 antigen detections up to the 9th day are observed , here we plotted cases only up to the 7th day due to the low number of samples representing 8th and 9th days in our population . We also aimed to compare the cases confirmation by the dengue NS1 antigen capture to the confirmation by other methodologies used in this study according to the number of the days of illness . In this comparison , we considered a NS1 positive case , as a case positive in any of the three tests used . Figure 2 shows NS1 confirmation around 90% of the cases up to the 7th day of illness , as previously shown . RT-PCR and virus isolation detections rate were around 80% in the first three days of illness , decreasing after that . However , on the other hand IgM detection rates increase only after the 4th day of illness . The serologic response could be characterized by IgG-ELISA in 54 samples , where a second specimen was available . There were 40 primary and 14 secondary infections . No differences were observed by the pan-E Early ELISA ( Panbio ) ( P = 0 . 96 ) and by the NS1 Ag Strip ( Biorad ) ( P = 0 . 76 ) in confirming primary and secondary infections ( Table 3 ) . However , the Platelia™ NS1 test showed a higher sensitivity in confirming primary infections than secondary ones ( P = 0 . 01 ) . In our study , the pan-E Early ELISA test ( Panbio ) was less efficient in detecting acute dengue infections ( 86 . 1% ) when compared to the Platelia™ NS1 test ( 91 . 3% ) and the NS1 Ag Strip ( 95 . 0% ) . Positive predictive values were 98 . 3% , 99 . 5% and 100% for the Platelia™ NS1 ( Biorad ) , NS1 Ag Strip ( Biorad ) and pan-E Early ELISA tests ( Panbio ) , respectively . However , the pan-E Early ELISA ( Panbio ) showed the lowest negative predictive value ( 78 . 3% ) , followed by the Platelia™ NS1 test ( Biorad ) with 86 . 3% and the NS1 Ag Strip ( Biorad ) with 91 . 1% ( Table 4 ) .
The techniques of dengue serologic diagnosis which have been widely used are based on the detection of IgM antibodies by MAC-ELISA and IgG by IgG-ELISA . However , one of the limitations of these techniques is the inability to detect antibodies to DENV in the acute phase of disease [5] , [26] . It takes 3 to 5 days for IgM antibodies and anti-DENV 10–14 days for IgG anti-DENV to become detectable . Moreover , primary and secondary infections have different profiles of production of these antibodies [27] . According to previous studies the presence of NS1 in human sera can be confirmed between days 0 to 9 [28] , [29] , [30] and with a peak at days 6 to 10 [31] . Currently , commercial kits such as the Dengue EARLY ELISA ( Panbio Diagnostics , Brisbane , Australia ) , Platelia™ Dengue NS1 Ag-ELISA and Dengue NS1 Ag STRIP ( BioRad Laboratories Marnes La Coquette , France ) are available for early diagnosis of dengue based on NS1 antigen capture and several studies have been conducted in many laboratories [12] , [15] , [18] , [19] , [29] , [31] , [32] , [33] , [34] , [35] , [36] . In this study , we had the opportunity to evaluate and compare three NS1 antigen capture kits available with a panel of samples ( n = 450 ) from cases occurred since the introduction of dengue in Rio de Janeiro , Brazil in 1986 to 2008 . The NS1 Ag Strip test ( Biorad ) was the most sensitive in confirming dengue cases , followed by Platelia™ NS1 ( BioRad ) . The least sensitive was the pan-E Early ELISA ( PanBio ) with 72 . 3% of sensitivity . However , in this study PanBio kit was the most specific ( 100% ) while both kits from BioRad showed 98 . 7% and 99 . 1% of specificity , respectively . A recent evaluation in Malaysia showed that the NS1 Ag Strip had 90 . 4% of sensitivity and 99 . 5% of specificity [17] . Studies performed in Vietnam [18] and French Guyana [29] showed sensitivities of 82% and 88% , respectively for the Platelia™ NS1 test . However , sensitivities varying from 63 . 2% to 93 . 3% have also been reported for this kit [12] , [15] . Even though different DENV genotypes may circulate in the Americas and Asia , NS1 kits evaluations in countries from those area show the ability of those tests to detect DENV in infected patients . Our observations are consistent with previous studies in which the pan-E Early ELISA had lower sensitivities [13] , [14] , [16] , [37] . However , to increase diagnostic performance , Panbio has recently released an improved second generation for their NS1 capture kit with changes in key reagents and procedure [38] . All NS1 tests were more sensitive in confirming cases positive by virus isolation than in cases positive by RT-PCR . Dussart [29] confirmed 94 . 1% of cases positive by virus isolation and 85% of the cases RT-PCR positive using the Platelia™ NS1 test . Recently , McBride [16] showed that the NS1 antigen capture was positive in 87% of the cases positive by RT-PCR . In our study , the Dengue NS1 Ag Strip confirmed 98 . 7% of the cases positive by virus isolation and 82 . 3% of RT-PCR positive cases , results similarly observed by Zainah [17] . In the presence of IgM antibodies , the Dengue NS1 Ag Strip confirmed more cases ( 77 . 4% ) than the pan-E Early ELISA ( 69 . 4% ) and the Platelia™ NS1 ( 64 . 5% ) . The presence or absence of IgM did not influence in the cases confirmation by the pan-E Early ELISA ( P = 0 , 6159 ) . However , the higher confirmation by both Platelia™ NS1 and the Dengue NS1 Ag Strip in the absence of IgM were statistically significant . Sekaran [32] showed that the NS1 detection rates decrease as IgM levels rise , in agreement with our results . The pan-E Early ELISA ( PanBio ) showed a higher sensitivity in confirming DENV-2 infections and the Platelia™ NS1 kit ( BioRad ) in DENV-1 infections . However , the Dengue NS1 Ag Strip kit ( BioRad ) showed the same sensitivity in confirming DENV-1 and DENV-2 infections . DENV-3 infections were the least confirmed by all three kits . The apparent inability in confirming infection by this serotype has been shown previously [33] . Furthermore , differences in the inter-serotype sensitivities have been reported for all three kits . McBride [16] recently showed lower sensitivities by the pan-E Early ELISA ( PanBio ) in DENV-2 and DENV-4 infections . The latter was also found in previous studies performed by Bessoff [37] and Dussart [14] and most recently in a study performed in Venezuela [36] . Due to the absence of DENV-4 circulating in Brazil , we were not able to access the assays sensitivities in cases infected by this serotype . Both Biorad kits ( Platelia™ NS1 and Dengue NS1 Ag Strip ) showed a lower sensitivity in DENV-2 infections from Vietnam [18] and Venezuela [36] . A higher detection rate by the three tests was found during the first four days after the onset of the symptoms . Although dengue NS1 antigen detections up to the 9th day are described , here we analyzed cases only up to the 7th day due to the low number of samples representing 8th and 9th days in our population . The lack of later samples in this study did not allow us to determine when NS1 detection would decrease . However , previous studies found NS1 antigen in 82% to 83% of patients with dengue from day 1 to 9th after the onset of fever [11] , [30] . The Platelia™ NS1 test showed a higher sensitivity in confirming primary infections than secondary ones , as previously observed [12] , [15] , [17] , [18] , [32] , [34] . False negative results by NS1 antigen capture in secondary infections may also be due to the immune-complexes formation by the anti- DENV IgG sequestration [39] . Efforts to dissociate immune complexes by acid treatment can enhance the assays sensitivities , as previously shown [15] . However , in our study no attempts were made to dissociate those complexes . To further analyze the sensitivity of those tests in confirming secondary cases , a larger number of cases should be tested . Among the kits evaluated , the Dengue NS1 Ag Strip ( BioRad ) was the most efficient in confirming dengue infections by capturing NS1 antigen from infected patients . Moreover , it was more convenient to be used , as the results can be obtained in 15 minutes , easy to perform and its performance does not involve the use of special laboratory equipment . Previous studies have demonstrated a diagnostic strategy combining NS1 Ag detection in acute-phase sera and DENV IgM detection in early-convalescent-phase sera , providing a sensitivity of about 90% for dengue diagnosis [29] , [34] . In conclusion , this evaluation has shown that NS1 antigen capture assays are indeed an alternative tool for the early diagnosis of dengue infections , may be used as a screening test prior virus isolation and used in combination with IgM capture can increase the rate of cases confirmation , especially in endemic areas where secondary infections are expected to occur due to the co-circulation of the different DENV serotypes , such as seen in Brazil . This evaluation was performed for research purposes only and authors have no financial interest . The pan-E Early ELISA from Panbio and the Dengue NS1 Ag Strip from BioRad were kindly provided for evaluation . | Dengue is the one of the most prevalent arthropod-borne viral diseases in tropical regions of the world . Manifestations may vary from asymptomatic to potentially fatal complications . Laboratorial diagnosis is essential to diagnose dengue and differentiate it from other diseases . Dengue virus non-structural protein 1 ( NS1 ) may be used as a marker of acute dengue virus infection . Our results , based in the comparison of three NS1 antigen capture assays available , have shown that this approach is reliable for the early diagnosis of dengue infections , especially in the first four days after the onset of the symptoms . A lower sensitivity was observed in DENV-3 cases . Serum positive by virus isolation were more often detected than those positive by RT-PCR by all three assays . Only the Platelia™ NS1 test showed a higher sensitivity in confirming primary infections than secondary ones . In conclusion , NS1 antigen capture commercial kits are useful for diagnosis of acute primary and secondary dengue infections and , in endemic countries where secondary infections are expected to occur , may be used in combination with MAC-ELISA to increase the overall sensitivity of both tests . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"virology/diagnosis"
] | 2010 | Comparison of Three Commercially Available Dengue NS1 Antigen Capture Assays for Acute Diagnosis of Dengue in Brazil |
In budding yeast , meiotic commitment is the irreversible continuation of the developmental path of meiosis . After reaching meiotic commitment , cells finish meiosis and gametogenesis , even in the absence of the meiosis-inducing signal . In contrast , if the meiosis-inducing signal is removed and the mitosis-inducing signal is provided prior to reaching meiotic commitment , cells exit meiosis and return to mitosis . Previous work has shown that cells commit to meiosis after prophase I but before entering the meiotic divisions . Since the Ndt80 transcription factor induces expression of middle meiosis genes necessary for the meiotic divisions , we examined the role of the NDT80 transcriptional network in meiotic commitment . Using a microfluidic approach to analyze single cells , we found that cells commit to meiosis in prometaphase I , after the induction of the Ndt80-dependent genes . Our results showed that high-level expression of NDT80 is important for the timing and irreversibility of meiotic commitment . A modest reduction in NDT80 levels delayed meiotic commitment based on meiotic stages , although the timing of each meiotic stage was similar to that of wildtype cells . A further reduction of NDT80 resulted in the surprising finding of inappropriately uncommitted cells: withdrawal of the meiosis-inducing signal and addition of the mitosis-inducing signal to cells at stages beyond metaphase I caused return to mitosis , leading to multi-nucleate cells . Since Ndt80 enhances its own transcription through positive feedback , we tested whether positive feedback ensured the irreversibility of meiotic commitment . Ablating positive feedback in NDT80 expression resulted in a complete loss of meiotic commitment . These findings suggest that irreversibility of meiotic commitment is a consequence of the NDT80 transcriptional positive feedback loop , which provides the high-level of Ndt80 required for the developmental switch of meiotic commitment . These results also illustrate the importance of irreversible meiotic commitment for maintaining genome integrity by preventing formation of multi-nucleate cells .
During gametogenesis , cells integrate external signals with internal cell-cycle control mechanisms to initiate and sustain meiosis , and eventually to differentiate into gametes . Although the external signals that initiate the switch into meiosis in various organisms are quite diverse , many of the features of meiosis are universal in the production of haploid meiotic products from a diploid progenitor cell [1] , [2] . After cells enter into meiosis , maintenance of meiosis is important to ensure proper gametogenesis . In humans , an inability to properly maintain meiosis can result in developmental abnormalities or possibly oncogenesis in the germ line [1] . In many organisms , cells that have initiated meiosis pass through an irreversible transition near the end of prophase I . These cells irreversibly commit to undergoing the meiotic divisions . Cells in prophase I have undergone pre-meiotic DNA replication and have initiated meiosis-specific events such as double strand break ( DSB ) formation , pairing of homologous chromosomes , synaptonemal complex ( SC ) assembly , and the initiation of recombination , but they have not yet entered into the meiotic divisions [2] . Indeed , human , mouse , and frog oocytes arrest at the end of prophase I and enter into the meiotic divisions only if stimulated by hormones [3] , [4] . The hormones induce resumption of meiosis and the oocyte becomes committed to finishing meiosis I . In the D . melanogaster ovarian cyst , 16 cells enter into meiosis; however , at prophase I , only 1 cell , chosen as the oocyte , continues meiosis [5]–[8] . The other 15 cells will exit meiosis and enter an endocycle . In S . cerevisiae , cells commit to meiosis as they exit prophase I and enter the meiotic divisions [9] . In a process termed return-to-growth ( RTG ) , budding yeast cells in prophase I exit meiosis and return to mitosis if the meiosis-inducing signal is withdrawn and the mitosis-inducing signal is provided [10]–[14] . Once they have passed the commitment point , cells are committed to meiosis even without the continued presence of the meiosis-inducing signal . An understanding of the regulatory mechanisms that drive cells through meiotic commitment points will provide insight into mechanisms that constrain cells to a developmental path . The ability of budding yeast cells to make the developmental switch from meiosis back to mitosis confers upon them the advantage to alter their developmental program in response to fluctuating environmental conditions [9] . Nutrient limitation induces the process of sporulation , in which cells enter meiosis and then package the meiotic products into spores . The spores can survive adverse conditions and then germinate when nutrients become available . If nutrient-rich conditions return prior to cells reaching the meiotic commitment point , cells exit meiosis and return to mitosis . In budding yeast , the temporal coordination of cell-cycle events in meiosis is tightly controlled and intertwined with transcriptional cascades [15] , [16] . Cells initiate meiosis when starved of nitrogen and glucose but provided acetate as an energy source . The starvation signal stimulates the expression of the Ime1 transcription factor , which induces a class of early genes required for the entry into meiosis , pre-meiotic DNA replication , and prophase I . At pachytene of prophase I , the paired chromosomes have formed extensive synaptonemal complex and have initiated crossing-over . To exit pachytene of prophase I , the Ndt80 transcription factor is induced and turns on ∼150 middle meiosis genes by binding to a midsporulation element ( MSE ) in the promoter of the genes [17]–[20] . Once the Ndt80-dependent genes are expressed , the SC disassembles , a meiotic spindle forms , and cells segregate homologous centromeres in meiosis I and sister centromeres in meiosis II [16] , [21] . As cells are undergoing meiosis II , a wave of late genes is expressed , many of which encode proteins required for the packaging of the four meiotic products into spores [15] . Here , we investigated the irreversibility of meiotic commitment . We hypothesized that the NDT80 transcriptional regulatory network was essential for generating irreversibility due to its requirement in meiotic commitment , its sensitivity to nutritional changes , and its activation through positive feedback to give a high-level burst of NDT80 expression [16] , [17] , [21] , [22] . The Ime1 transcription factor induces NDT80 transcription by binding an upstream regulatory sequence within the promoter [17] . NDT80 is expressed later than most Ime1-dependent genes because the NDT80 promoter is regulated by a repressor complex comprised of Sum1 , Rfm1 , and a histone deacetylase Hst1 [23] , [24] . The loss of repression occurs after the phosphorylation of Sum1 by multiple kinases at the end of pachytene [25]–[27] . A high-level of NDT80 expression is induced by an autoregulatory positive feedback loop in which Ndt80 binds to MSEs in its own promoter and enhances its own transcription [16] , [17] . Since positive feedback loops are often found in irreversible cell-cycle transitions [28] , the NDT80 transcriptional network becomes a strong candidate for generating the irreversibility of meiotic commitment . Using single-cell analysis , we analyzed the role of the NDT80 transcriptional network in regulating the irreversibility of meiotic commitment . We found that cells commit to meiosis in prometaphase I , after the induction of the Ndt80-dependent genes . And , that high-level induction of NDT80 was needed for the irreversibility of meiotic commitment . By making strains that allowed us to manipulate both the timing and level of NDT80 expression , we showed that decreasing the levels of NDT80 could uncouple the entrance into meiosis and meiotic commitment . We found cells that were inappropriately uncommitted to meiosis; these cells underwent meiosis I and then returned to mitosis instead of finishing meiosis II , becoming multi-nucleate polyploid cells . Further reducing the levels of NDT80 by making NDT80 promoter mutations to disrupt positive feedback resulted in a complete loss of meiotic commitment . With complete medium addition , all of the cells returned to mitosis from stages beyond metaphase I . Our work suggests that a threshold level of Ndt80 is needed for the irreversibility of meiotic commitment and that positive feedback in the NDT80 transcriptional regulatory network ensures that threshold level .
Past studies performed on populations of cells and one study on individual cells showed that meiotic commitment occurs after pachytene , but before the first meiotic division [9] , [29] . The meiotic commitment point was thought to occur at the end of prophase I [10] , [16] , and predicted to be associated with the initiation of the separation of spindle pole bodies ( SPBs ) , the yeast equivalent of the centrosome [9] , [30] . We wanted to define the stage of meiotic commitment more precisely and needed to establish markers to differentiate prophase I exit , prometaphase I , metaphase I , and anaphase I . As cells exit prophase I , the transcription factor Ndt80 induces the transcription of the middle meiosis genes , including the M-phase cyclins , which are needed for spindle assembly and the meiotic divisions [15] , [21] , [31] , [32] . As the bipolar spindle assembles , the cells are transitioning from prometaphase I to metaphase I . In anaphase I , the spindle elongates . Therefore , we decided to use the changes in spindle length to determine the meiotic stages . With time-lapse microscopy , we captured images every 10 minutes as cells underwent meiosis . We followed the expression of three proteins tagged with fluorescent markers: Spc42-mCherry , Zip1-GFP , and GFP-Tub1 ( Figure 1A ) . Spc42 , a component of the spindle pole body ( SPB ) , the yeast equivalent of the centrosome , was fused to mCherry , allowing us to monitor the separation of the SPBs , and therefore the length of the spindle [33] . Zip1 , a component of the SC , marks pachytene [34] , [35] . The disassembly of the SC and concomitant loss of Zip1-GFP localization represents the end of prophase I . GFP-Tub1 , which encodes α-tubulin fused to GFP , allowed us to monitor different meiotic stages based on spindle morphology [36] , [37] . Although Zip1 and Tub1 are both fused to GFP , the locations of the proteins are morphologically distinct [37] , [38] ( Figure 1A ) . To ensure that we could indeed differentiate between Zip1-GFP and GFP-Tub1 , we measured the time from SC disassembly to SPB separation in cells with Zip1-GFP and Spc42-mCherry and compared this time to cells with all three marked proteins . In both strains , we see that the SPBs separate approximately 3 minutes after SC loss ( n = 100 cells per genotype , SI Figure S1A , B ) , suggesting that we can differentiate between Zip1-GFP and GFP-Tub1 . To more precisely define prometaphase I and metaphase I , we measured the distance between SPBs to determine spindle lengths as cells progressed from prophase I to anaphase I . The analysis of the spindle length showed that after the SC disassembles , the spindle undergoes a period of elongation for 31±1 mins ( average time ± S . E . , n = 75 cells ) ( Table 1 ) . Once the spindle reaches a length of 3 . 5±0 . 05 µm , the spindle maintains that length for 28±1 mins . The spindle will increase its length to 4 . 4±0 . 07 µm and then elongates further and chromosomes segregate in anaphase I . In Figure 1B , we plotted the spindle length at timepoints between the end of prophase I and the beginning of anaphase I for 5 of the 75 cells that we analyzed . We defined prometaphase I as the time after SC disassembly in which the SPBs are separating and the spindle is elongating from 0 . 1–3 . 4 µm . We defined the start of metaphase I as the time in which the spindle reaches the stable length of 3 . 5 µm . To determine whether the spindle elongation from to 3 . 5 µm to 4 . 4 µm marked the transition into anaphase I , we used a strain with Spc42-mCherry and GFP-tagged securin ( Pds1 in budding yeast ) . Since Pds1 is degraded at the metaphase I to anaphase I transition , we monitored the spindle length at the timepoint just prior to Pds1-GFP degradation to define the spindle length at the end of metaphase I ( Figure 1C ) . We found that the spindle was on average 3 . 5±0 . 05 µm at the last timepoint the cells were in metaphase I ( n = 70 cells , average spindle length ± S . E . ) . In the first timepoint after Pds1 degradation , the cells enter anaphase I and the spindle lengths ranged from 3 . 8 to 9 . 1 µm . Therefore , we defined metaphase I as the time in which the spindle has reached the length of 3 . 5 µm and anaphase I at the time in which the spindle elongates beyond 3 . 5 µm ( Figure 1D ) . We next used these defined meiotic stages to pinpoint meiotic commitment . To monitor meiotic commitment , we used a microfluidics assay coupled to time-lapse microscopy to monitor individual cells [38] . W303 cells were placed in microfluidic chambers and introduced to sporulation medium to induce meiosis . After 12 hours , cells were at a variety of meiotic stages , and synthetic complete medium was flowed into the chambers . Cells were scored based on their spindle length when exposed to complete medium and their cell-cycle outcomes: returned to mitosis , finished meiosis , or arrested . We found that as the spindle length increased , the percent of cells committed to meiosis also increased ( n = 300 , Figure 2A , Sup . Table S2 ) . If complete medium was added to cells with a spindle length between 1 . 0–1 . 99 µm , the cells returned to mitosis . If complete medium was added to cells with a spindle length from 3 . 0–5 . 5 µm , the cells finished meiosis . In cells with spindle lengths between 2 . 0–2 . 99 µm , some cells returned to mitosis and some cells finished meiosis . Since spindle assembly requires the activity of the M-phase cyclins whose transcription is dependent on Ndt80 , our findings suggest that cells commit to meiosis after the expression of the Ndt80-dependent genes . We next assigned the cells a meiotic stage based on their spindle length ( Figure 2B ) , and found that 100% of cells in prophase I returned to mitosis upon complete medium addition , as previously reported ( n = 100 , Figure 2B , C , Supp . Table S3 ) [9] . Of cells in prometaphase I upon complete medium addition , 64% returned to mitosis ( Video S1 , Figure 2B , D ) , 34% finished meiosis ( Video S2 , Figure 2B , E ) , and 2% arrested , in meiosis I ( n = 100 ) . Finally , 99% of cells finished meiosis upon complete medium addition in metaphase I ( n = 100 , Figure 2B , F ) . These data show that the meiotic commitment point lies in prometaphase I ( Figure 2G ) . Prior experiments did not have the sensitivity to resolve these variations in cell-cycle outcomes upon nutrient addition in prometaphase I [9] . Prometaphase I occurs after the synaptonemal complex is disassembled and after spindle formation initiates . This suggests that prior to commitment , Ndt80 has started transcribing the Ndt80-dependent genes such as the M phase cyclins , which are needed for spindle formation . To verify that the Ndt80-dependent genes were indeed transcribed and translated prior to commitment , we monitored the timing of the accumulation of the encoded protein of the Ndt80-dependent gene CDC5 , which encodes polo kinase . Cdc5 is necessary and sufficient for SC disassembly at the end of prophase I [39] . To determine whether Cdc5 was present prior to commitment in prometaphase I , we tagged Cdc5 with Superfolder-GFP ( Cdc5-sfGFP ) , a fast folding variant of GFP [40] , [41] in a strain with Spc42-mCherry . As expected , cells in prophase I that have not yet expressed NDT80 do not have Cdc5-sfGFP present ( Figure 3A ) . In contrast , 100% of cells in prometaphase I , with a spindle length between 0 . 1–3 . 4 µm , have Cdc5-GFP present ( n = 44 cells ) ( Figure 3B-C ) . These results demonstrate that the Ndt80-dependent gene CDC5 is expressed and the protein is present in the cell prior to meiotic commitment . Although cells commit to meiosis after the initiation of the expression of the Ndt80-dependent genes , we considered that Ndt80 could have a role in the irreversibility of meiotic commitment due to its tightly regulated transcriptional activation . We asked if modifying NDT80 expression could lead to an alteration in the establishment of meiotic commitment . We placed NDT80 under the control of the GAL1 , 10 promoter ( PGAL1 , 10-NDT80 ) and the Gal4 activator was fused to the estradiol receptor so that the addition of estradiol activated NDT80 expression via Gal4 ( GAL4-ER ) [32] , [42] . The PGAL1 , 10-NDT80/PGAL1 , 10-NDT80 cells undergo meiosis and form spores with the addition of estradiol , as previously described [32] . Because the NDT80 promoter is absent in the PGAL1 , 10-NDT80 cells , NDT80 transcription is no longer subject to transcriptional positive feedback , but instead can be regulated by an inducible promoter to limit the duration of transcription . PGAL1 , 10-NDT80/PGAL1 , 10-NDT80 cells in microfluidics chambers were exposed to sporulation medium to induce meiosis until they arrested at pachytene ( due to the lack of Ndt80 ) . Estradiol was added to induce NDT80 , and , at 10-minute timepoints after the induction of NDT80 , we introduced complete medium into separate chambers and recorded cell-cycle outcomes of each chamber ( Figure 4A ) . The addition of complete medium , which contains glucose but not estradiol , represses the transcription of the GAL1 , 10 promoter . In Figure 4B , we have plotted the percent of cells for each cell-cycle outcome ( y-axis ) when complete medium was added at a timepoint after the cells disassembled the SC and exited prophase I ( x-axis ) ( n = 100 cells counted for each timepoint ) ( Supp . Table S4 ) . When complete medium was added ten minutes after SC loss , 99% of the cells returned to mitosis . However , if complete medium was added 30 minutes after SC loss , only 5% of cells returned to mitosis; 60% finished meiosis; 3% arrested; and , the remaining 32% of cells took another path , described below . By 50 minutes after SC loss , enough Ndt80 had accumulated such that 100% of the cells were committed to meiosis and finished meiosis after the addition of complete medium . Importantly , we found an additional cell-cycle outcome in the inducible NDT80 strain: those cells that completed meiosis I , assembled two spindles , then returned to mitosis as demonstrated by bud formation ( Video S3 , Figure 4B , C ) . After budding , both nuclei divided , resulting in multi-nucleate cells . We defined these cells as inappropriately uncommitted –they underwent meiosis I but were not committed to finishing meiosis II and instead returned to mitosis . This is a novel behavior representing the absence of commitment , and different from the cells that are not yet committed to meiosis I and returned to mitosis from prophase I . When complete medium was added 20 minutes and 30 minutes after SC loss , 36% and 32% of cells were inappropriately uncommitted , respectively . Also , at the 20- and 30-minute timepoints , the percentage of cells that returned to mitosis decreased and those that finished meiosis increased when compared to the 10-minute timepoint . These data suggest that , with an inducible NDT80 , either the duration of NDT80 transcription or the level of Ndt80 protein after SC breakdown is important for meiotic commitment . With NDT80 expressed for a short time period ( 0–10 mins ) after SC disassembly , the cells returned to mitosis . With NDT80 expressed for a longer time period ( 50 minutes or more ) , the cells committed to meiosis . We propose that at timepoints in between ( 20–40 minutes ) , Ndt80 may be at intermediate levels: i ) some cells had Ndt80 below a threshold level and returned to mitosis; ii ) some cells had Ndt80 above a threshold level and committed to meiosis , and; iii ) some cells had enough Ndt80 to finish meiosis I , but not enough Ndt80 to commit to meiosis II and returned to mitosis after meiosis I . We considered that insufficient levels of Ndt80 protein in the PGAL1 , 10-NDT80 strain could cause the inappropriately uncommitted cell phenotype . It has previously been shown that expression from the GAL1 , 10 promoter in meiosis does not lead to a large overproduction of the expressed protein [32] . Furthermore , the addition of complete medium with glucose should repress NDT80 transcription from the GAL1 , 10 promoter . Therefore , we asked whether Ndt80 protein levels in the PGAL1 , 10-NDT80 cells decrease with the addition of complete medium . Cells were induced to enter meiosis . When cells arrested at pachytene , estradiol was added to induce the expression of Ndt80 . After 110 minutes of Ndt80 induction , complete medium was added and aliquots were taken every 30 minutes for 2 . 5 hours . The 110-minute timepoint was chosen due to the large fraction of inappropriately uncommitted cells at this timepoint ( after 110-minutes of estradiol addition , most of the cells had disassembled their SC 30 minutes earlier , and therefore this timepoint corresponds to the 30-minute timepoint shown in Figure 4B ) . Analysis of Ndt80 levels by western blot showed that the Ndt80 protein levels decreased by 86% within 30-minutes of complete medium addition and remained at that very low level ( Figure 5A , upper panel ) . In a control experiment , cells that continued in meiosis with estradiol in the sporulation medium maintained the high level of Ndt80 protein ( Figure 5A , lower panel ) . We next determined if there was a difference between Ndt80 protein levels in the PGAL1 , 10-NDT80 strain with the addition of complete medium compared to a strain with NDT80 under control of its endogenous promoter . Since the cells with PNDT80-NDT80 are not as synchronous as the PGAL1 , 10-NDT80 cells , we added a cdc20 meiotic null to both strains so that the cells would arrest in metaphase I due to a loss of Cdc20 and remain in meiosis . Cells were induced to enter meiosis . When the PGAL1 , 10-NDT80 cells arrested at pachytene , estradiol was added to induce the expression of NDT80 . After 110 minutes of NDT80 induction , complete medium was added and aliquots were taken at 0 , 15 , and 30 minutes to isolate protein . For the PNDT80-NDT80 cells , complete medium was added after 12 hours since the cells were in prometaphase I- metaphase I at this timepoint . The protein was isolated at 0 , 15 , and 30-minute timepoints after complete medium addition . Analysis of Ndt80 by western blot showed that in the PGAL1 , 10-NDT80 cells , the Ndt80 protein decreased by 92% within 15-minutes of complete medium addition ( Figure 5B ) . In contrast , in the PNDT80-NDT80 cells , there was only a 27% decrease of Ndt80 protein 15 minutes and a 55% decrease 30 minutes after the introduction of complete medium . These results suggest that insufficient levels of Ndt80 in PGAL1 , 10-NDT80 cells after complete medium addition is likely to result in the inappropriately uncommitted cells . Since the levels of Ndt80 decreased with the addition of complete medium in the inducible NDT80 strain , we predicted that lower levels of Ndt80 could result in an altered commitment point . To test our prediction , we further decreased Ndt80 levels by making a PGAL1 , 10-NDT80/ndt80Δ strain . In this strain , one copy of NDT80 is under the GAL1 , 10 promoter and the other copy of NDT80 is deleted . These cells will progress through meiosis and form spores after the induction of NDT80 with estradiol . Using our microfluidics assay , we monitored cell cycle outcomes after complete medium addition . In Figure 5C , we compare the cell cycle outcomes of PGAL1 , 10-NDT80/PGAL1 , 10-NDT80 and PGAL1 , 10-NDT80/ndt80Δ . Our results are presented as a graph of the percent of cells with each cell cycle outcome ( y-axis ) at the different meiotic stages in which complete medium was added ( x-axis ) ( Figure 5C , Supp . Table S5 ) . Our data show an important difference in the percent of committed cells between the different strain backgrounds . In the cells with two copies of PGAL1 , 10-NDT80 , there are inappropriately uncommitted cells upon nutrient addition in prometaphase I , metaphase I , and anaphase I ( n>200 cells counted at each stage ) . However , most cells were committed to meiosis upon nutrient addition in metaphase I and anaphase I . In contrast , in cells with only one copy of PGAL1 , 10-NDT80 , most cells did not finish meiosis upon nutrient addition . Of the PGAL1 , 10-NDT80/ndt80Δ cells in prometaphase I upon nutrient addition , 98% returned to mitosis ( n = 178 cells ) . Of the cells in metaphase I upon nutrient addition 51% returned to mitosis , 8% finished meiosis , 14% were inappropriately uncommitted , and the remainder arrested in either meiosis I or meiosis II ( n = 157 cells ) . Of the cells in anaphase I upon nutrient addition , 84% were inappropriately uncommitted , 8% arrested in meiosis II , and only 8% finished meiosis ( n = 100 cells ) . These results show that by decreasing the levels of Ndt80 , fewer cells commit to finishing meiosis . A comparison of the two strains , PGAL1 , 10-NDT80/PGAL1 , 10-NDT80 and PGAL1 , 10-NDT80/ndt80Δ , suggests that decreasing the levels of NDT80 resulted in fewer cells committed to meiosis . Therefore , we predict that there is a threshold level of Ndt80 needed to drive cells through meiotic commitment . In a normal meiosis , the high-level burst of NDT80 expression at the end of pachytene is driven by an auto-regulatory feedback loop in which Ndt80 enhances its own transcription by binding to MSE sequences found in its own promoter [17] , [43] . We hypothesize that positive feedback in NDT80 induction may ensure that cells reach that switch threshold level of NDT80 required for meiotic commitment . To test this hypothesis , we eliminated positive feedback by deleting the two 9 bp MSEs in the NDT80 promoter ( PNDT80-MSE1Δ , 2Δ-NDT80 ) . In this strain , NDT80 can be activated by the Ime1 transcription factor , which binds to an upstream regulatory sequence , but cannot be activated through an Ndt80-dependent autoregulatory loop . We verified by western blot that the PNDT80-MSE1Δ , 2Δ-NDT80 cells had a substantially lower level of Ndt80 protein ( Figure 5D ) . We next tested whether the meiotic commitment point was altered in the PNDT80-MSE1Δ , 2Δ-NDT80 cells . We performed our microfluidics assay and found that the cells were not committed to meiosis . In Figure 5E , we plot the percent of cells with each cell cycle outcome ( y-axis ) at the different meiotic stages in which complete medium was added ( x-axis ) ( n = 100 cells counted for each cell-cycle stage ) ( Supp . Table S6 ) . Of the cells in prometaphase I at the time of complete medium addition , 99% returned to mitosis . PNDT80-MSE1Δ , 2Δ-NDT80 cells in metaphase I at the time of nutrient addition gave several cell-cycle outcomes: i ) 16% were inappropriately uncommitted and underwent meiosis I , then budded and underwent mitosis , dividing both nuclei; ii ) 5% arrested in meiosis I; and iii ) 79% budded and returned to mitosis from metaphase I ( Video S4 , Figure 5E , 6A ) ( n = 100 cells counted ) . The PNDT80-MSE1Δ , 2Δ-NDT80 cells in anaphase I at the time of complete medium addition were also not committed to meiosis; they finished meiosis I with complete medium addition , but 100% of the cells budded and underwent mitosis instead of meiosis II ( Figure 6B , n = 100 cells counted ) . Cells beyond anaphase I were also inappropriately uncommitted to meiosis; 98% of cells in prophase II at the time of complete medium addition budded and underwent mitosis ( n = 100 cells counted ) . These results show that meiotic commitment requires positive feedback in NDT80 expression and support our hypothesis that a threshold level of Ndt80 is needed to drive meiotic commitment . We next asked whether the PNDT80-MSE1Δ , 2Δ-NDT80 cells could go through meiosis . We used time-lapse microscopy to monitor the cells undergoing meiosis and found that 78% of the cells that entered meiosis underwent meiosis I but then arrested at metaphase II ( Figure 6C , n = 100 ) . Only 5% of the cells finished both meiotic divisions , 10% arrested at pachytene , and 7% arrested in metaphase I . This suggests that the levels of Ndt80 in the PNDT80-MSE1Δ , 2Δ-NDT80 strain are sufficient to allow most cells to enter into meiosis I and II , but not sufficient to finish meiosis II . Since the PNDT80-MSE1Δ , 2Δ-NDT80 cells do not commit to meiosis , a higher-level induction of NDT80 is required for meiotic commitment . To determine if modestly lower levels of NDT80 result in an alteration of the meiotic commitment point , we decreased Ndt80 levels by making an NDT80/ndt80Δ heterozygote . In this strain , one copy of NDT80 was under the control of its native promoter and the other copy was deleted . Using our microfluidics assay , we found that of the NDT80/ndt80Δ cells that were in prometaphase I at complete medium addition , 92% returned to mitosis and 7% finished meiosis ( n = 100 cells counted , Figure 7 , Supp . Table S3 ) . Of the cells in metaphase I at complete medium addition , 2% returned to mitosis , 84% finished meiosis , 11% arrested in metaphase II , and 3% were inappropriately uncommitted ( n = 100 cells counted ) ( Figure 7 ) . The results show that in NDT80/ndt80Δ cells , fewer cells commit to meiosis in prometaphase I when compared to NDT80/NDT80 , suggesting that the commitment point shifted to the end of prometaphase I/beginning of metaphase I . We conclude that reducing the NDT80 levels alters the timing of the meiotic commitment point . Once NDT80/ndt80Δ cells exit prophase I , the timing of meiosis was not delayed in comparison to wildtype cells . In wildtype cells , the duration of prometaphase I and metaphase I was 31±1 mins and 28±1 mins , respectively ( n = 75 cells counted , average time in minutes ± S . E . ) . In NDT80/ndt80Δ cells , the duration of prometaphase I and metaphase I was 28±1 mins and 21±1 mins , respectively ( n = 80 cells , average time in minutes ± S . E . ) . The timing of the other stages of meiosis were also not delayed ( Table 1 ) . These results suggest that although two copies of NDT80 are needed for the proper timing of meiotic commitment , one copy of NDT80 is sufficient for the normal duration of each of the meiotic stages . The importance of the irreversibility of meiotic commitment can be demonstrated through phenotypic analysis of the inappropriately uncommitted cell; the failure to commit to meiosis beyond prometaphase I results in the formation of multi-nucleate cells . The PNDT80-GAL1 , 10NDT80/PNDT80-GAL1 , 10NDT80 inappropriately uncommitted cells underwent a first meiotic division , creating two nuclei in the mother cell . After the first meiotic division , the uncommitted cells budded and underwent mitosis instead of finishing meiosis II ( Figure 4C ) . After the mitotic division , depending on how the nuclei divided , the mother cell had either 2 or 3 nuclei and the daughter had either 2 nuclei or 1 nucleus . There were three nuclear segregation phenotypes ( Figure 8A-C ) : i ) 36% segregated both nuclei across the bud neck , resulting in two nuclei each in the mother and daughter cells ( Figure 8A ) ; ii ) 32% segregated one nucleus in the mother cell and one nucleus across the bud neck , resulting in three nuclei in the mother and one in the daughter cell ( Figure 8B ) , and; iii ) 32% segregated one nucleus in the mother cell and one nucleus in the daughter cell , resulting in two nuclei each in the mother and daughter cells ( Figure 8C ) ( n = 100 ) . The multi-nucleate mother and daughter cells continued to divide ( Video S3 ) and could increase genome copy number in the subsequent divisions due to the nuclear segregation phenotype described for Figure 8B . Our results show that the loss of meiotic commitment can have the drastic consequence of the loss of genome integrity . To determine the ploidy of the inappropriately uncommitted cells , we asked if DNA re-replication occurred after meiosis I but prior to the mitotic division . We monitored the nuclear localization of a component of the replicative helicase , Mcm7 , tagged with GFP ( Mcm7-GFP ) . The replicative helicase enters the nucleus and loads onto origins prior to DNA replication [44] . We have previously found that Mcm7-GFP does not enter into the nucleus when cells return to mitosis from prophase I , and these cells also do no re-replicate their DNA [38] , [45] . To determine if the inappropriately uncommitted cells license their origins , we monitored the localization of Mcm7-GFP from the strain with PNDT80-GAL1 , 10NDT80/ndt80Δ . Using time-lapse microscopy , we found that in the inappropriately uncommitted cells , Mcm7-GFP entered the nuclei after meiosis I but prior to the mitotic division in 92% of the cells ( n = 27 cells ) . Figure 8D shows Mcm7-GFP localized in the two nuclei of an inappropriately uncommitted cell . These results suggest that in the inappropriately uncommitted cells , the levels of CDK decrease after meiosis I such that Mcm7-GFP enters the nuclei and licenses origins . The data support the conclusion that the cell exits meiosis after the first division and then begins the mitotic cell cycle . To confirm that DNA replication did indeed occur , we monitored a marked chromosome after segregation . We labeled one copy of chromosome III with a lactose operator array ( LacO ) near the centromere in a strain expressing GFP fused to the lactose repressor ( LacI-GFP ) [46] . We scored the inappropriately uncommitted cells that were in mitotic anaphase from the PGAL1 , 10-NDT80/PGAL1 , 10-NDT80 strain . We observed that 74% of the inappropriately uncommitted cells had 3-4 GFP-labeled chromosomes ( n = 100 , Figure 8A-C ) , indicating that the cells replicated their DNA prior to the mitotic division . Twenty-six percent had 2 GFP-labeled chromosomes , suggesting that they did not replicate their chromosomes . However , this assay likely under-represents the percent of cells that replicated their chromosomes; due to close attachment of centromeres to the SPB , the GFP labels can often be difficult to resolve [45] , [47] . These results indicate that at least 74% of the cells exit meiosis I and begin a mitotic cell cycle in which the cells bud , replicate their DNA ( in two separate nuclei ) , and then segregate their chromosomes . In these cells , there is a 2N DNA content per nucleus after the mitotic division . Therefore , the mother cells with 2-3 nuclei have a 4N-6N DNA content . These results demonstrate that establishing meiotic commitment through the NDT80 positive feedback loop is important in maintaining ploidy .
Previous work has shown that upon rich medium addition , cells in pachytene will return to mitosis whereas cells that have entered the meiotic divisions will remain in meiosis [10]–[14] . This led to the prediction that cells commit to meiosis at the end of prophase I as the SPBs begin to separate and meiosis I spindle formation initiates . By monitoring the timing of commitment with respect to the separation of SPBs in individual cells , we were able to show that commitment occurs in prometaphase I , after SPBs have initiated separation , but prior to metaphase I spindle formation . As mentioned by Simchen ( 2009 ) , the separation of SPBs leads to the establishment of the meiosis I spindle , which may be an important component of commitment . In addition , other possible important components of commitment that occur in prometaphase I could include the expression of the Ndt80-dependent genes , the attachment of homologous chromosomes to spindle microtubules , and the activation of Cdk bound to the M phase cyclins [15] , [48] , [49] . After these meiotic events occur , the cell will be unable to maintain genome stability in the absence of meiotic commitment . Indeed , we show that inappropriately uncommitted cells that return to mitosis after meiosis I become multi-nucleate with an increase in genome copy number ( Figure 8A-C ) . Our results showed that cells become committed to meiosis after the Ndt80-dependent genes are expressed and the encoded proteins are active , including the M phase cyclins Clb1 and Clb4 , which initiate meiosis I spindle formation with CDK , and the polo kinase Cdc5 , which is needed for SC disassembly [15] , [31] , [32] , [39] ( Figure 3 ) . We propose that once cells have entered metaphase I , the levels of proteins , whose expression are dependent on Ndt80 , are high enough to drive the cells through the meiotic divisions , even in the absence of the meiosis-inducing signal . We find that altering NDT80 expression results in a disruption of commitment . Expression of NDT80 under the GAL1 , 10 promoter resulted in some cells that were inappropriately uncommitted and would return to mitosis after undergoing meiosis I ( Figure 4B , C ) . These cells entered into meiotic divisions but did not commit to meiosis . Western blot analysis shows that the protein levels on Ndt80 drop sharply within 15 minutes of complete medium addition in the PGAL1 , 10-NDT80/PGAL1 , 10-NDT80 cells ( Figure 5B ) . We show that further decreasing the levels of Ndt80 , by deleting one of the copies of PGAL1 , 10-NDT80 , results in a substantial decrease in the percent of cells that commit to finishing meiosis ( Figure 5C ) . We suggest a model in which there are different threshold levels of Ndt80 required for the entrance into the meiotic divisions and for meiotic commitment: a lower level of Ndt80 promotes meiosis I and meiosis II , but a much higher level is needed to maintain meiosis in the absence of the meiosis-inducing signal and the introduction of the mitosis-inducing signal . Previous work has shown that the RNA levels of NDT80 and the Ndt80-dependent genes decrease within 30 minutes of nutrient addition , even in committed cells [22] . Therefore , high-level induction of NDT80 may be needed to obtain a threshold level of protein to sustain meiosis beyond commitment . This suggests that the level of induction of NDT80 transcription is in excess of what is needed for meiosis , but is required to ensure that the cells can maintain the meiotic pathway once the they have entered into metaphase I . Besides the genes needed for the meiotic divisions , Ndt80 also transcribes genes whose encoded proteins are required for spore formation [15] , [48] . Past work has shown that cells can inappropriately return to mitosis at postmeiotic stages of sporulation [16] . For example , in the absence of SPO14 , a phospholipase that regulates the formation of the prospore membrane , cells arrest and are able to return to mitosis if rich medium is added [13] , [50] , [51] . Blocking prospore membrane closure with a mutant of SSP1 or by temperature upshift also results in the return to mitosis post meiosis [12] , [52] . These results suggest that ensuring proper spore formation , possibly through the high-level expression of NDT80 , is also important to prevent the inappropriate return to mitosis during sporulation . The amplification of a signal through positive feedback can help to make transitions between states irreversible and switch-like . Therefore , positive feedback is an important component of many networks involved in cell-cycle regulation , including those in meiosis [28] . Our work showed that positive feedback of NDT80 expression has an additional role besides ensuring the switch-like entrance into meiosis: positive feedback ensures the irreversibility of meiotic commitment . NDT80 expression is highly regulated through a meiosis-specific transcription factor , a repressor complex that binds to the NDT80 promoter , and an autoregulatory positive feedback loop [15] , [16] , [23] , [24] . This regulation ensures that NDT80 is expressed only in cells that are exiting prophase I , and that once it is expressed , a high-level induction ensues . We asked how this regulation affects meiotic commitment . We deleted the transcriptional positive feedback loop by mutating the Ndt80 binding sites within the NDT80 promoter . In these cells , Ime1 can still induce the expression of NDT80 but Ndt80 cannot feed back to induce its own expression . In the absence of positive feedback , the cells were inappropriately uncommitted and could return to mitosis from any stage of meiosis . These results show that the high-level induction of NDT80 through positive feedback is essential for meiotic commitment . Positive feedback is a common feature in networks that drive irreversible cell-cycle transitions and is also used in the progesterone-dependent commitment to meiotic resumption in the X . laevis oocyte . The amphibian oocyte arrests at prophase of meiosis I and the addition of the steroid hormone progesterone releases that arrest by inducing translation of the protein kinase Mos , which activates the mitogen-activated protein kinase cascade ( MAPK ) and cyclin-dependent kinase ( CDK ) bound to cyclin B [4] , [53] . The cells commit to meiotic maturation 2–3 hours after the addition of progesterone . The cells will remain in the mature state even if only exposed to a transient threshold level of progesterone [54]–[57] . Positive feedback in Mos translation from the activities of Mos , MAPK , and CDK ensures the irreversibility of the commitment to meiotic maturation [54] . When positive feedback is blocked , the response to progesterone becomes transient and reversible . A further study has shown that the threshold response to progesterone can be modulated in response to environmental conditions through linked double-negative feedback loops [58] . In the future , it will be important to determine if nested feedback loops tune the response to the environmental factors that influence meiotic commitment in budding yeast . Comparisons of the network architectures that ensure the irreversibility of the transition through meiotic commitment in different organisms will provide insight into the general properties that govern meiotic commitment points .
Strains are derivatives of W303 and are listed in Supporting Information Table S1 . Deletion and tagged strains were made using standard methods [59]–[61] . Chromosomes were tagged with the LacO array and LacI-GFP as described [46] . The inducible NDT80 strain was made as described [32] . ZIP1-GFP with GFP located at the end of the second coiled-coil domain at position 700 , replaced ZIP1 at the endogenous locus as described [35] . To visualize tubulin , constructs containing PTUB1GFP-TUB1 or PHIS3mCherry-TUB1 were integrated into the URA3 locus . The NDT80 MSE1Δ MSE2Δ promoter mutations were made by first cloning NDT80 with 500 bps of the promoter region to a yeast integrating plasmid ( pRS403 ) . The MSE1 and MSE2 9 bp elements were deleted sequentially using a site-directed mutagenesis kit ( Finnzymes ) . Deletions were confirmed by sequencing . The plasmid was integrated into ndt80Δ strains at the HIS3 locus . We used the following media: YPD ( 1% bacto-yeast extract , 2% bacto-peptone , 2% glucose ) , YPA ( 1% yeast extract , 2% bacto-peptone , 1% potassium acetate ) , sporulation medium ( 1% potassium acetate ) , and synthetic complete medium ( referred to as complete medium , 0 . 67% bacto-yeast nitrogen base without amino acids , 0 . 2% dropout mix with all amino acids , 2% glucose ) . In the microfluidics assays , cells were sporulated in liquid culture by growing in YPD at 30°C for 24 hours , diluted into YPA at 30°C for 12–15 h , washed with water and resuspended in sporulation medium at 25°C . Cells were introduced into microfluidics chambers ( CellAsic Y04D yeast perfusion plates ) . Unless otherwise specified , after 12 hours in sporulation medium , synthetic complete medium was flowed into the chambers and cells were monitored by time-lapse microscopy . In the PGAL1 , 10-NDT80 experiments , cells were allowed to reach a prophase arrest in sporulation medium for 11–12 . 5 hours and then beta-estradiol ( 1 µM , Sigma ) was flowed into the chambers . Synthetic complete medium was then flowed into the chambers at 10-minute timepoints after the addition of beta-estradiol . The RTG assay was performed using the CellAsic Onix microfluidics perfusion platform . The data presented were confirmed in at least three independent experiments . Cells were imaged using a Nikon Ti-E inverted microscope equipped with a 60× objective ( PlanApo n . a . = 1 . 4 oil ) , a Lambda 10-3 optical filter changer and smartshutter ( Sutter instrument ) , GFP and mCherry filters ( Chroma Technology ) , and a CoolSNAPHQ2 CCD camera ( Photometrics ) at 25°C . Z-stacks of 4–7 sections were acquired in 10 minute intervals for 12–15 h using a 12 . 5% or 25% ND filter and exposure times of 50–700 ms . Z-stacks were combined into a single maximum intensity projection with NIS-Elements software ( Nikon ) . To measure spindle lengths , 7 Z-stacks were taken at 0 . 8 µm intervals . The spindle length was measured using the X , Y , and Z with NIS-Elements software . For immunofluorescence following the return to mitosis , meiosis-induced cells were cultured in sporulation medium for 10–12 h at 25°C , transferred to synthetic complete medium and incubated for 6 . 5–7 h at 25°C . Cells were fixed with 4% paraformaldehyde overnight at 4°C , washed twice in phosphate-buffered saline ( PBS ) , resuspended in 1 M sorbitol , and the cell wall was partially digested in 1–1 . 5 mg/ml Zymolyase ( Zymo Research ) buffered with 1 M sorbitol for 5–10 minute at room temperature . After washing twice with PBS , cells were immersed in PBS containing 0 . 2% Triton-X for 10-minutes , then blocked with 5% bovine serum albumin ( Sigma ) for 1 h at 30°C . Cells were incubated with the primary antibodies , chicken anti-GFP ( Molecular Probes , 1∶100 ) , and rabbit anti-Tub1 ( 1∶250 ) , for 1 h at 30°C . Cells were washed twice in PBS , once in PBS containing 0 . 1% Tween 20 , then once in PBS . The secondary antibodies , Alexa 488 anti-chicken ( Molecular Probes , 1∶100 ) and Alexa 594 anti-rabbit ( Molecular Probes , 1∶100 ) were used . Cells were washed twice in PBS , then stained with 1 µg/ml of DAPI . For western blots , protein was isolated from 6 mls of cells after complete medium addition . Strains were sporulated in liquid culture by growing in YPD at 30°C to saturation , diluted into YPA for 12–15 h at 30°C , washed with water and resuspended in sporulation medium at 25°C . For the PGAL1 , 10NDT80 strains , beta-estradiol ( 1 µM , Sigma ) was added 14 hours after the resuspension in potassium acetate . After 110 minutes , cells were washed and synthetic complete medium was added to the cells . For the cells in which NDT80 was continually expressed , the cells were washed and resuspended in sporulation medium with beta-estradiol . Protein was isolated using the TCA method from cells every 15–30 minutes after the addition of synthetic complete medium for a total of 150 minutes . The following antibodies for western blot analysis were used: anti-Ndt80 ( gift of M . Lichten , 1∶10000 ) , anti-Nop1 ( Toronto Research Chemicals , 1∶10000 ) . The blots shown ( Figure 5A , B , D ) were cut in two , with the top half probed with anti-Ndt80 and the bottom half probed with anti-Nop1 . The secondary antibody used was a donkey anti-rabbit IgG ECL antibody conjugated to HRP for Ndt80 and goat anti-mouse IgG ECL antibody conjugated to HRP . Protein was detected using Amersham ECL western blotting reagents ( GE healthcare ) . | There are two main types of cell division cycles , mitosis and meiosis . During mitosis , DNA is replicated and then chromosomes segregate , producing two daughter cells with the same ploidy as the progenitor cell . During meiosis , DNA is replicated and then chromosomes undergo two rounds of segregation , producing four gametes with half the ploidy of the progenitor cell . As the cell enters into the meiotic divisions , it irreversibly commits to finishing meiosis and cannot return to mitosis . The molecular mechanisms that define meiotic commitment are not well understood . In this study , we asked whether the regulatory network involved in the transcription of NDT80 has a role in meiotic commitment . Ndt80 is a transcription factor that induces the genes needed for the meiotic divisions . We found that a high-level of Ndt80 activity is required for meiotic commitment; in wildtype cells , this is achieved through a transcriptional positive feedback loop– a regulatory mechanism in which the Ndt80 protein increases the transcription of its own gene . In the absence of positive feedback , cells escape meiosis inappropriately , resulting in an aberrant cell cycle that causes an increase in genome copy number . This study shows the important role of positive feedback in meiotic commitment and in the maintenance of genome integrity . | [
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] | 2014 | Positive Feedback of NDT80 Expression Ensures Irreversible Meiotic Commitment in Budding Yeast |
During interphase chromosomes decondense , but fluorescent in situ hybridization experiments reveal the existence of distinct territories occupied by individual chromosomes inside the nuclei of most eukaryotic cells . We use computer simulations to show that the existence and stability of territories is a kinetic effect that can be explained without invoking an underlying nuclear scaffold or protein-mediated interactions between DNA sequences . In particular , we show that the experimentally observed territory shapes and spatial distances between marked chromosome sites for human , Drosophila , and budding yeast chromosomes can be reproduced by a parameter-free minimal model of decondensing chromosomes . Our results suggest that the observed interphase structure and dynamics are due to generic polymer effects: confined Brownian motion conserving the local topological state of long chain molecules and segregation of mutually unentangled chains due to topological constraints .
Nowadays , the large-length scale structure of decondensed chromosomes can be experimentally studied using Fluorescence in situ Hybridization ( FISH ) : nucleic acids are chemically modified to incorporate fluorescent probes and specific sequences on single chromosomes can be detected [11] . In particular , it is possible to mark different portions of the genome ( chromosome painting ) and to determine locations of and spatial distances between targeted sites [11] . Chromosome painting indicates that chromosome territories in human nuclei have an ellipsoidal shape with radii of the order of 1 µm [4] . In contrast and as already discovered by Rabl , the interphase nuclei of organisms like newt or Drosophila are organized in elongated territories oriented between two poles of the nucleus [2] , [3] . Furthemore , there are also organisms such as budding yeast whose chromosomes appear to mix freely or , at least , considerably less organized [4] , [5] . The localization of territories inside the nucleus exhibits regular patterns: gene-rich chromosomes in human lymphocytes preferably locate in the nuclear interior while gene-poor chromosomes are typically found closer to the periphery [12] , [13]; in contrast , in human fibroblasts positioning of territories was shown to correlate with chromosome size and not with its gene content [14] . In general , interactions between specific chromosome regions and structural elements within the nuclear envelope , such as nuclear pores or nuclear lamina , are believed to shape chromatin organization [15] . Data on the ( relative ) position and motion of target sites provide further insight into the organization of interphase chromosomes . In Figure 1A we show average spatial distances between targeted sites as a function of their genomic separation . The figure contains FISH data for yeast chromosomes 6 and 14 ( Chr6 and Chr14 , brown ○ ) [16] , human chromosome 4 ( Chr4 , blue ○ and ◊ ) [17] and Drosophila chromosome 2L ( Chr2L , orange and green ○ ) [18] . In the latter case , orange symbols refer to embryos in DS5 phase and green symbols to the DS1 phase which appears later in the cell cycle [19] . Two-dimensional spatial distances between sites on Chr4 measured in fibroblasts cells fixed on microscope slides [17] were here rescaled by 3/2 to obtain the corresponding 3 d distances . Observations for the various organisms agree on short length scales and coincide with the known properties of the ( 30 nm ) chromatin fiber [16] . Given further the rather structureless appearance of interphase nuclei in the light microscope , a useful starting point for a theoretical description is a confined , equilibrated semi-dilute solution of “worm-like” chromatin fibers [12] . Within the worm-like chain ( WLC ) model , the fiber statistics can be calculated analytically [7] . It is characterized by a crossover from rigid rod to random coil behavior at a characteristic length scale , the Kuhn length lK≈300 nm of the 30 nm chromatin fiber [16] , [20] . Consider two points located at N1 and N2 Mbp from one chosen end of the fiber . They are separated by L = |N2−N1|×10 µm Mbp−1 along the contour of the chromatin fiber [16] . The fiber is essentially stiff with a mean square spatial distance R2 ( L≫lK ) = L2 on small scales and bent by thermal fluctuations on large scales with R2 ( L ) = lKL . The full crossover is described by [21] ( 1 ) ( black continuous line , Figure 1 ) . In particular , Equation 1 holds in the bulk of semi-dilute solutions where chains strongly overlap . Given the typical contour length of Lc = 1 mm of the chromatin fiber of a human chromosome with ≈100 Mbp , the expected chain extension of largely exceeds the nuclear radius of 5 µm . In an equilibrated solution , the fibers should fill the nucleus homogeneously with mean-square internal distances saturating at a limiting value ( black dashed line , Figure 1A ) . ( The exact probability distribution function of the square internal distances R2 ( |N2−N1| ) of a polymer without self-interactions obeys diffusion equation [7] with null boundary conditions ( in our case the boundary is the sphere which models the nucleus ) . ) In contrast , the smaller yeast ( S . cerevisiae ) chromosome ( ≈1 Mbp , Lc≈0 . 01 mm , ) should be only weakly affected by a confinement to its nucleus of ≈1 µm radius [22] while Drosophila chromosomes ( ≈20 Mbp , Lc≈0 . 2 mm , ) in embryonic cells ( for which FISH data are avalaible [18] ) are confined inside nuclei whose radius grows from ≈2 µm to ≈5 µm in ≈30 minutes . Not surprisingly , the large-scale statistics of human and Drosophila chromosomes does not agree at all with the predictions of a WLC model assuming confinement at the scale of the nucleus ( Figure 1A ) . Rather , the data reflect the different territory shapes observed for the two species . Note , however , that confinement on large scales alone cannot explain the unexpectedly small distances on intermediate scales |N2−N1|>4 Mbp for Chr4 ( blue ◊ ) . There is less data available for the dynamics of interphase chromosomes . In mammalian cells chromatin domains of ∼1 µm diameter display little or no motion in a period of several hours [23] . Cabal et al . [24] followed the motion of a marked active gene ( GAL ) in in-vivo yeast nuclei . They observed a mean-square displacement ( msd ) g1 ( t = 100 s ) ≈0 . 1 µm2 for their largest observation interval , i . e . , much less than the typical territory size in organisms with larger chromosomes . In particular , the authors reported anomalous diffusion with g1 ( t ) ∼t0 . 4 . To rationalize this result , it is again useful to consider “worm-like” chromatin fibers in equilibrated semi-dilute solutions at typical nuclear densities . Neglecting entanglement effects , g1 ( t ) displays crossovers between different regimes: ( 1 ) g1 ( t ) ∼t0 . 75 up to length scales of ≈1 Kuhn length [25]; ( 2 ) g1 ( t ) ∼t0 . 5 ( Rouse behavior ) up to length scales of the chain radius of gyration [7]; and ( 3 ) g1 ( t ) ∼t at larger times , when the monomer motion is dominated by the center-of-mass diffusion ( cyan line , Figure 2 ) . In semidilute solutions , linear chains with a contour length exceeding a characteristic value , L≫Le , become mutually entangled , leading to confinement to a tube-like region following the coarse-grained chain contour and a drastically altered , “reptation” dynamics [7] . Estimating Le is not a trivial task . How strongly linear polymers entangle with each other depends on their stiffness and on the contour length density of the polymer melt or solution [26] . The latter is most suitably expressed in terms of the density of Kuhn segments , ρK . In loosely entangled systems with the mean-free chain length between collisions is larger than the Kuhn length , leading to random coil behavior between entanglement points . In contrast , for filaments are tightly entangled and exhibit only small bending fluctuations between entanglement points . For a solution of chromatin fibers at a typical nuclear density of ≈0 . 012 bp/nm3 and a Kuhn length of 300 nm ( Table 1 ) , i . e . the system is loosely entangled , but close to the crossover between the limiting cases . The entanglement contour length , Le , can be estimated via [26] , yielding Le≈1 . 2 µm or four times the Kuhn length . To a first approximation , chains can thus be considered to be flexible on the tube scale , i . e . , we expect around a msd of a crossover from Rouse behavior to a g1 ( t ) ∼t0 . 25 regime characteristic of reptation [7] . Interestingly , this estimate coincides with the observations of Cabal et al . [24] , who reported an intermediate effective power law g1 ( t ) ∼t0 . 4 for msd 0 . 05 µm2≤g1 ( t ) ≤0 . 17 µm2 . Using their data , we can obtain estimates for the entanglement time , τe≃32 s , as well as for the disentanglement times , τd≈τe ( Lc/Le ) 3 [7] , of the order of τd≃2×104 s , 2×108 s ( ≈5 years ) and 2×1010 s ( ≈500 years ) for yeast , Drosophila and human chromosomes , respectively . Since this exceeds the life time of the entire organism ( not to mention the much shorter cell cycle of most animal cells [1] ) , Drosophila and human chromosomes do not have the time to equilibrate during interphase . ( This conclusion does not change , if we take into account entanglement relaxation via topo-II discussed in [9] . At best , this mechanism could completely remove the barrier for chain crossing , thus converting the system to a solution of phantom chains whose relaxation time is given by the Rouse time τR≈τe ( Lc/Le ) 2 [7] . Yeast , Drosophila and human chromosomes would relax in , respectively , 2×103 s , 106 s ( ≈10 days ) , and 2×107 s ( ≈250 days ) . ) While the discussion up to this point has shed some light on various aspects of the structure and dynamics of interphase chromosomes , we have so far evaded the central question , the origin of the observed chromosome territories . A priori , segregation or ( micro ) phase separation due to small chemical differences between chains is a common phenomenon in polymeric systems [21] . Organisms could , in principle , render different chromosomes immiscible by a labeling technique akin to chromosome painting . In practice , it is difficult to conceive a corresponding , self-organizing molecular mechanism . Here we propose that the formation of chromosome territories could be related to a different , less well-known effect , the segregation of unentangled ring polymers in concentrated solutions due to topological barriers [10] , [27] . Well-separated metaphase chromosomes are clearly unentangled at the onset of interphase . Initially , decondensing chains can only rearrange locally and spread out uniformly without changing the global topological state . Up to the extremely long relaxation times for large chromosomes , interphase nuclei should therefore show a behavior similar to concentrated solutions of unentangled ring polymers . In particular , the chromosomes should remain segregated ! It is instructive to compare this explanation to previously published models describing interphase chromosomes as equilibrium structures . The unexpectedly small distances on intermediate scales |N2−N1|>4 Mbp for Chr4 ( blue ◊ ) were rationalized in terms of giant loops of fibers departing from an underlying ( protein ) backbone [17] or alternatively , in terms of random loops on all length scales resulting from specific chromatin-chromatin interactions [28] . Simulations of a multi-loop subcompartment polymer model reproduced the experimental observations on human Chr4 , by imposing ( and hence not explaining ) confinement to a spherical territory [20] , [29] . We do not exclude the possibility of such contacts . However , we claim that territories should also form , if the involved proteins are disabled . For the inverse test—to keep the linking proteins , but to equilibrate a nucleus with disabled local topology conservation—it would be instructive to investigate the structure of nuclei in long-living cells arrested in interphase and to devise ways to maximize the efficiency of topo-II . ( This conclusion does not change , if we take into account entanglement relaxation via topo-II discussed in [9] . At best , this mechanism could completely remove the barrier for chain crossing , thus converting the system to a solution of phantom chains whose relaxation time is given by the Rouse time τR≈τe ( Lc/Le ) 2 [7] . Yeast , Drosophila and human chromosomes would relax in , respectively , 2×103 s , 106 s ( ≈10 days ) , and 2×107 s ( ≈250 days ) . ) We note that a few cross-links or attachment points to a residual skeleton would be sufficient to suppress chromosome equilibration via reptation [30] . Long-lived contacts could thus stabilize the observed structures without being at their origin . How much of the experimental observations can be explained by this topology-conserving , parameter-free , minimal model of decondensing chromosomes ? Unfortunately , it is difficult to derive quantitative predictions from an analytical theory due to the non-trivial initial conformation , the simultaneous presence of various crossovers ( stiff/flexible , loosely/tightly entangled ) , and the lack of a theory describing the conformational statistics and dynamics of the unentangled ring polymer melts . We have therefore resorted to Molecular Dynamics ( MD ) computer simulations as a tool which allows us to study the model without further approximations . With a spatial discretization of 30 nm ( corresponding to the bead diameter ) , the employed generic bead-spring polymer model [31] accounts for the linear connectivity , self-avoidance and bending stiffness of the chromatin fiber ( Materials and Methods ) . In particular , there is an energy barrier of 70KBT to prevent chain crossing [32] . We emphasize that our description does not invoke any protein-like machinery as the nuclear matrix [33] . Furthermore , we neglect local changes of the chromatin fiber as they occur , e . g . , as a result of chromatin remodeling during transcription [34] , because these processes do not alter the local topological state of the fiber and are therefore irrelevant for the phenomenon we discuss . This argument does not hold for the action of topo-II whose role is precisely to ( dis ) entangle DNA allowing strands to cross [9] , [20] . Non-directed topology changes with a particular rate could be included by suitable modifications of the energy barrier for chain crossing [35] . Similarly , it is straightforward to include ( protein-mediated ) interactions between specific DNA sites or effects such as confinement by or anchoring to the nuclear envelope [11] , [33] , [36] . However , here we concentrate on the generic case of decondensing long , internally and mutually unentangled polymers at total concentrations far above the overlap concentration . As initial states of our simulations we chose linear or ring-shaped helical structures remnant of metaphase chromosomes ( Materials and Methods ) . Given the anisotropic shape of our “metaphase” chromosomes , we were interested to see how the decondensation is affected by the presence of other chains . The l . h . s . panel in Figure 3 shows the initial chromosome conformations in our simulations on a common scale , indicated by a typical human nuclear radius of 5 µm . For Drosophila ( marked “B” , only one chromosome is shown for clarity ) we assumed that 8 Chr2L model chromosomes are initially aligned along the common axis of a rectangular simulation box ( nematic orientation ) . In the case of yeast ( marked “C” ) and of the human ( marked “A” ) , we followed the decondensation of 6 respectively 4 chromosomes of equal size which were oriented randomly in the simulation box [14] . For comparison we have also studied ring shaped chromosomes ( see inset of Fig . 1F ) of different length under conditions corresponding to those of the human cell nucleus . 27 small rings ( Lc = 2×2 . 7 Mbp ) were randomly oriented inside the simulation box , while for larger rings ( Lc = 2×48 . 6 Mbp and Lc = 2×97 . 2 Mbp ) we limited ourselves to simulations of single chains in contact with their periodic copies in adjacent simulation cells . The setup as a ring allows us to eliminate chain end effects which otherwise play an important role . All simulations were performed in a constant isotropic pressure ensemble using rectangular simulation boxes with three independently fluctuating linear dimensions . The imposed pressure leads to the final density corresponding to the experimental value of ≈0 . 012 bp/nm3 for human nuclei or 10% of volume fraction of chromatin ( Table 1 ) . This appears a reasonable choice because the experimental density in yeast nuclei is only two times lower ( ≈0 . 006 bp/nm3 , Table 1 ) , while the rapid growing size of Drosophila embryos nuclei [19] does not allow for a univocal choice . We emphasize that the employed periodic boundary conditions do not introduce confinement to the finite volume of the simulation box . Using properly unfolded coordinates , chains can extend over arbitrarily large distances ( see Figure 4 for the example of a MD simulation using a similar model but with a strongly reduced barrier for chain crossing ) . To give an idea of the computational effort , we consider the example of Chr4 , where we simulated four model chromosomes of half of the actual length of Chr4 . Each chromosome is modeled as a chain of 32 , 400 beads with a total contour length of 10−3 m or 97 . 2 Mbp . Using ≈7×104 single-processor CPU-hours on a CRAY XD1 parallel computer , we followed the dynamics over 12×106 MD time steps . The comparison to the measured single-site mobility for yeast [24] in Figure 2 suggests the value of τ ≈2×10−2 s used throughout the paper . According to this estimate , we followed the chain dynamics over 240 , 000 s ( ≈3 days ) of real time . However , it is clear that more experimental data are needed to reliably fix the absolute time scale of our simulations .
Since there are no attractive interactions in our model of the chromatin fiber , the bent and kinked initial state is unstable and unfolds rapidly . The initial rapid expansion stops when chromosomes come into contact with others , including their periodic replicas in adjacent simulation cells . Our simulation time is sufficient to mix and equilibrate the short ( 1 Mbp ) yeast chromosomes ( Figure 3 ) . Fast equilibration of yeast chromosomes explains why apparently there is no territorial organization in yeast nuclei [5] . ( Most chromosomes in yeast have a size smaller than 1 Mbp , corresponding to a disentanglement time comparable to the time duration of the relative interphase ( ∼1 hour [37] ) . ) In this case memory of the initial condition is rapidly lost: a simulation where the chains are initially prepared as rods oriented along the same direction produces similar results ( data not shown ) . The much longer Drosophila and human chromosomes remain confined to distinct territories ( Figure 3 ) . For the nematically ordered initial state we assumed for Drosophila , we observed that decondensation leads to the formation of Rabl-like elongated territories . In contrast , in isotropically arranged copies of the human Chr4 , the preferred axial expansion is suppressed and the resulting territory shapes resemble elongated ellipsoids . Our ring chromosomes essentially reproduce the latter behavior . More quantitatively , the shape of the human Chr4 territory is described by the average of the 3 eigenvalues Λ1 , Λ2 , and Λ3 of the corresponding gyration tensor ( [38] and Materials and Methods ) and the ratios of the two largest eigenvalues Λ1 and Λ2 over Λ3 are two quantities which could experimentally be tested . We have found that averaging over the configurations of all the possible sections of half the total ring size gives Λ1∶Λ2∶Λ3 = 6 . 4 ( ±1 . 4 ) ∶1 . 9 ( ±0 . 4 ) ∶1 . 0 ( 2×97 . 2 Mbp ) , Λ1∶Λ2∶Λ3 = 5 . 5 ( ±1 . 2 ) ∶2 . 1 ( ±0 . 5 ) ∶1 . 0 ( 2×48 . 6 Mbp ) and Λ1∶Λ2∶Λ3 = 6 . 9 ( ±0 . 7 ) ∶2 . 2 ( ±0 . 2 ) ∶1 . 0 ( 2×2 . 7 Mbp ) , while averaging over all the 4-Chr4 configurations gives Λ1∶Λ2∶Λ3 = 11 . 0 ( ±1 . 2 ) ∶1 . 5 ( ±0 . 3 ) ∶1 . 0 . The ∼2 times larger value found in the latter case is probably an artifact of the setup ( see also below ) . In Figure 1 ( panels B to E ) we compare the simulation results for mean-square spatial distances between marked sites on the chromosomes to the experimental findings shown in Figure 1A and discussed in the introduction . Gray lines represent spatial distances between sites in the initial , compact “metaphase” configuration . To give an impression of the time dependence of the results , we have averaged the R2 ( N2−N1| ) curves over three exponentially spaced time windows: 240 s<t<2 , 400 s , 2 , 400 s<t<24 , 000 s , 24 , 000 s<t<240 , 000 s ( dark red , magenta and cyan lines respectively ) . In panels B–D we show results averaged over the entire length of the simulated Drosophila chromosome Chr2L , yeast Chr6 and Chr14 and human Chr4 . While the former two are in excellent agreement with the experimental data , this is not the case for our first set of results for the human Chr4 . Here simulation and experimental data agree quantitatively only on short length scales . It turns out , that there are different explanations for the deviations on intermediate and on large length scales . Figure 1F shows the corresponding comparison to our data for ring chromosomes . In this case , the experimentally observed conformational statistics of human Chr4 on large scales is perfectly reproduced . In fact , when we reanalyzed data for the linear chromosome assuming the existence of a “centromere-hinge , ” we found nearly perfect agreement with the ring data ( not shown ) . This suggests to interpret the ( nearly linear ) large scale behavior of our simulation results in Figure 1D as an artifact of the straight initial configuration . Interestingly , the simulation data follow the experimentally observed effective power law R2∼L2ν with ν≈0 . 32 [29] already on intermediate scales ( L>1 Mbp ) . ( We note that the relation between the square of the gyration radius and the mean square internal distances of a polymer R2 ( |N2−N1| ) , [7] , is compatible with chromosome territories being compact objects with . However , the reverse conclusion [20] , [29] is incorrect: globular polymer conformations also follow , but do not have a fractal structure where the same exponent characterises the entire chain conformation ( see , for example the dashed line in Figure 1 ) . Simple confinement alone cannot explain the chain structure . ) This behavior seems to be robust , since all our simulation data for linear chains and rings of different size beautifully collapse onto each other . Similar , quasi-fractal structures were reported in [10] . Taken together this suggests that our ring samples are relatively well-equilibrated and that ( in agreement with our working hypothesis ) long , unentangled linear chains initially relax to a very similar structure . However , we still require an explanation for the deviations between this apparently quite robust prediction and the experimental data in Figure 1D . Reptation theory [30] would suggest that the further equilibration of linear chromosomes proceeds by a very slow escape of the terminal parts of the chain from their initial environment . Qualitatively , this effect is directly observable in Figure 3 where we have marked the terminal parts of our model chromosomes in red . Interestingly , the experimental data for the spatial distances between sites with genomic distances in the Mbp range on human Chr4 were determined in the ≈4 . 5 Mbp 4p16 . 3 region which is located at the end of p-arm [17] . A good way to quantify the consequences is to measure R2 ( |N2−N1| , N1 = 0 ) , i . e . , mean-square spatial distances between the chain ends and points along the fiber ( Figure 1E and Figure 5 ) . These distances show a stronger time dependence than results averaged over the entire chain . In particular , they follow the WLC prediction up to much larger contour length distances before crossing over to the bulk averages . The point of departure from the WLC prediction can be used to estimate up to which distance from the end the chains are equilibrated after a certain time . ( The temporal behavior of the ratio between the escaped portion of the chain and the whole contour length Lc at short times t is compatible with the power-law ∼t1/2 predicted by reptation theory ( [30] , data not shown ) . ) The comparison to the experimental data in Figure 1E suggests that the 4p16 . 3 region of the human Chr4 was nearly equilibrated in the experimental situation . We emphasize that we expect spatial distances between marked sites in the interior of long chromosomes to fall onto the corresponding simulation data in Figure 1D and 1F . This is at least qualitatively supported by a remark in [39] where van den Engh et al . report the more centrally located 6p21 region on human Chr6 to be more compact than the 4p16 . 3 region near the end of Chr4 . As a final point , we turn to the dynamics of chromosomes during interphase . Figure 2 shows the msd of the 6 inner beads ( g1 ( t ) ) after the complete ( yeast ) and initial ( human , Drosophila ) relaxation in comparison to the respective gyration radii . By adjusting the time scale of the simulations , the simulation data can be mapped on the experimental results from [24] . The good agreement indicates that our model provides a simple , quantitative explanation for the reported anomalous diffusion . In particular , the model reproduces the dynamic ( entanglement ) length scale with no adjustable parameters . Moreover , the dynamic range of the simulation data ( 0 . 1 s<t<3 days ) significantly exceeds the observation window in [24] , allowing us to extrapolate to longer times . For comparison , in Figure 2 we have included data for equilibrated yeast chromosome solutions from simulation of a model without excluded volume interactions and topological barriers ( cyan line ) . All simulations exhibit identical short time behavior in agreement with theoretical expectations [25] . A Rouse regime for is only observable in simulations without topological barriers . The yeast chromosomes in equilibrated entangled solutions exhibit instead g1 ( t ) ∼t0 . 25 reptation dynamics . Interestingly , our data for human and Drosophila chromosomes show the same behavior in spite of the very different microscopic topological state and the ( on these scales ) weakly perturbed chain statistics . ( The small deviations from the yeast data are artifacts of the constant-pressure simulations used for human and Drosophila chromosomes . ) In our simulations the asymptotic free diffusion regime—where the center of mass has moved farther than the chain size [7]—is reached only for yeast chromosomes . ( Note that the corresponding simulation data cannot be compared directly to experiments , since we have neglected the nuclear confinement in the present study . ) While human and Drosophila chromosomes remain confined to their territories and do not equilibrate , individual sites are extremely dynamic . Cabal et al . [24] reported that invidual loci on yeast chromosomes explore regions of linear size ∼0 . 4 µm . The simulations indicate that msd's of ∼1 µm2 are reached on the time scale of ∼5 hours .
We have studied the decondensation , structure and dynamics of interphase chromosomes using Molecular Dynamics simulations of a bead-spring model of the 30 nm chromatin fiber . Our results suggest that for sufficiently long chromosomes territories form as a consequence of a generic polymer effect , the preservation of the local topological state in solutions of long chain molecules undergoing Brownian motion . In fact , we argue that such interphase nuclei never equilibrate and behave like concentrated solutions of unentangled ring polymers , which segregate due to topological constraints [10] . Such cases are also know from material science where they result in unusual material properties [40] . The slow kinetics leads to memory effects . For example , different chromosome arrangements in the nucleus at the end of metaphase provide a rationale for the different territory shapes observed in humans and flies . Similarly , the negligible relative motion of territories provides a natural explanation for the tendency of chromosomes to “reappear” at the end of interphase at similar relative positions as those occupied at the end of the preceeding anaphase [41] . Our simulations confirm this tendency: the centers of mass of the large human chromosomes remain confined to small regions of linear size ≈0 . 1 µm and retain their relative positions ( Figure 6 ) . In contrast , individual sites are extremely dynamic inside the territories and explore much larger regions up to a linear size of ≈1 µm ( Figure 2 ) . We emphasize that conservation of the local topology during decondensation discussed in the present work considerably simplifies the reverse process of chromosome condensation at the end of interphase , a process which takes only about 1 hour in most animal cells [1] and which is difficult to conceive for fully equilibrated nuclei [9] . Obviously , there is more to the structure and dynamics of eukaryotic nuclei than can be captured by the present model in its basic form . However , our results suggest that effects such as active transport [22] , chromosome anchoring to the nuclear envelope [36] , replication [34] and homologous pairing [19] should be investigated in the framework of the polymer description presented here . As we have shown , computer simulations along the present lines can now reach the relevant time and length scales .
To model chromatin fiber we used the generic bead-spring polymer model of Kremer and Grest [31] . Chains are composed of interacting beads of diameter σ . There are three types of interactions: ULJ , UFENE , and Ustiff . ULJ is a shifted , purely repulsive Lennard-Jones potentialbetween any two monomers . The potentialgives the additional interaction between nearest neighbours along the chain . Finally , the stiffness of the fiber is modeled bywhere θ is the angle formed by the oriented unit vectors of two consecutive bonds . The bead diameter σ equals 30 nm , thus each bead corresponds to 3 , 000 bp [2] . The other parameters are given by R0 = 1 . 5σ , k = 30 . 0ε/σ2 , and temperature KBT = 1 . 0ε [31] . Since the Kuhn's length of the 30-nm fiber is lK = 300 nm = 10σ [16] , [20] , the stiffness constant βkθ is taken = 5 [42] . Experimental evidence suggests that metaphase chromosomes are folded into loops 30–100 kbp long ( rosettes ) , arranged radially along the axis of the chromatid ( see [9] and references therein ) . Metaphase chromosome are ≈700 nm thick and the length of each chromosome is related to its size [6] . On average , a typical human chromosome has 108 bp , i . e . , a contour length Lc = 106 nm and a length hchr≈5 , 000 nm [43] . As a starting configuration , we have placed chain beads along the generalized helix described by the equation:where rchr = 12σ , p = σ , and hchr = 170σ . With this choice of parameters , the length of each turn is approximately = 200σ . Given an average loop length of 50 kbp ≈17σ , we have ≈12 loops/turn . That fixes the remaining parameters k = 6 and x = 0 . 38 . The contour lengths of the simulated human Chr4 and Drosophila Chr2L are , respectively , Lc = 97 . 2 Mbp and Lc = 21 . 6 Mbp , which corresponds to chains composed of 32 , 400 and 7 , 200 beads . The ring setup is described by the following equation:where the period , , and rt = 42σ . The simulations have been performed in a constant isotropic pressure ensemble . Since the value of the pressure which must be imposed to the system is not known a priori , we have designed the following procedure: the decondensation of a ring chain of contour length Lc = 5 . 4 Mbp ( 1 , 800 beads ) has been simulated in a constant volume ensemble and the average diagonal components of the pressure tensor Pαβ ( α , β = x , y , z ) [44] have been calculated . We have found Pxx = Pyy = Pzz = 0 . 01 and this value has been used throughout the paper . However , simulations of yeast chromosomes dynamics have been performed in a constant volume ensemble , because in the constant pressure ensemble the small system size leads to large unphysical fluctuations of the simulation box . In this constant volume ensemble , simulated yeast chromosomes have been initially arranged in an equilibrated configuration . We have chosen the integration time tint = 0 . 012τ , where τ = σ ( m/ε ) 1/2 is the Lennard-Jones time and m is the mass of each bead [31] . Each simulation runs up to time 109tint = 12×106τ . Since we have sampled each 106tint , each running produces 103 configurations . Notice that the time behavior of the msd of the 6 inner beads ( g1 ( t ) ) ( Figure 2 ) has been calculated after shifting to the frame where the center of mass of the whole system is at rest . For human and Drosophila chromosomes , g1 ( t ) and have been calculated neglecting the first 6×105τ≈12 , 000 s of the simulated trajectory . The gyration tensor T of an object composed of N beads is the 3×3 symmetric matrix whose elements are , where rl is the vector pointing at the lth bead , is the center of mass of the beads and i , j = 1 , 2 , 3 are the three indices for cartesian components . The trace of T , where is the square of the gyration radius of the object [7] . It also equals Λ1+Λ2+Λ3 , where Λi is the ith eigenvalue of T . For objects with spherical symmetry , Λ1 = Λ2 = Λ3 . Then , differences between the eigenvalues measure the anisotropy of the object [38] . | Eukaryotic genomes are organized in sets of chromosomes . Each chromosome consists of a single continuous DNA double-helix and associated proteins that organize locally in the form of a chromatin fiber . During cell division ( mitosis ) chromosomes adopt a compact form that is suitable for transport . During periods of normal cell activity ( interphase ) , they decondense inside the cell nucleus . Being long-chain molecules ( in the case of human chromosomes the contour length of the chromatin fiber is on the order of 1 mm ) , the random thermal motion of interphase chromatin fibers is hindered by entanglements , similar to those restricting the manipulation of a knotted ball of wool . We have studied the consequences of this effect using computer simulations . Most importantly , we find that entanglement effects cause sufficiently long chromosomes to remain segregated during interphase and to form “territories . ” Our model ( 1 ) reproduces currently avaliable experimental results for the existence and shape of territories as well as for the internal chromosome structure and dynamics in interphase nuclei and ( 2 ) explains why entanglement effects do not interfere with the reverse process of chromosome condensation at the end of interphase . | [
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] | 2008 | Structure and Dynamics of Interphase Chromosomes |
Aspergillus fumigatus forms ubiquitous airborne conidia that humans inhale on a daily basis . Although respiratory fungal infection activates the adaptor proteins CARD9 and MyD88 via C-type lectin , Toll-like , and interleukin-1 family receptor signals , defining the temporal and spatial pattern of MyD88- and CARD9-coupled signals in immune activation and fungal clearance has been difficult to achieve . Herein , we demonstrate that MyD88 and CARD9 act in two discrete phases and in two cellular compartments to direct chemokine- and neutrophil-dependent host defense . The first phase depends on MyD88 signaling because genetic deletion of MyD88 leads to delayed induction of the neutrophil chemokines CXCL1 and CXCL5 , delayed neutrophil lung trafficking , and fatal pulmonary damage at the onset of respiratory fungal infection . MyD88 expression in lung epithelial cells restores rapid chemokine induction and neutrophil recruitment via interleukin-1 receptor signaling . Exogenous CXCL1 administration reverses murine mortality in MyD88-deficient mice . The second phase depends predominately on CARD9 signaling because genetic deletion of CARD9 in radiosensitive hematopoietic cells interrupts CXCL1 and CXCL2 production and lung neutrophil recruitment beyond the initial MyD88-dependent phase . Using a CXCL2 reporter mouse , we show that lung-infiltrating neutrophils represent the major cellular source of CXCL2 during CARD9-dependent recruitment . Although neutrophil-intrinsic MyD88 and CARD9 function are dispensable for neutrophil conidial uptake and killing in the lung , global deletion of both adaptor proteins triggers rapidly progressive invasive disease when mice are challenged with an inoculum that is sub-lethal for single adapter protein knockout mice . Our findings demonstrate that distinct signal transduction pathways in the respiratory epithelium and hematopoietic compartment partially overlap to ensure optimal chemokine induction , neutrophil recruitment , and fungal clearance within the respiratory tract .
Aspergillus fumigatus is a leading cause of infectious morbidity and mortality in patients with hematologic malignancies , allogeneic stem cell transplant recipients and in patients receiving immune suppressive therapies [1 , 2] . Airborne conidia ( asexual spores ) represent the infectious propagules that humans inhale daily . Due to their small size ( ∼2–3 μm diameter ) , conidia can bypass mucociliary clearance and reach terminal airways [3 , 4] . In immune competent individuals , lifelong asymptomatic clearance of inhaled conidia prevents the formation of tissue-invasive hyphae . Neutrophils represent essential effector phagocytes that are rapidly recruited to infected airways and act against A . fumigatus conidia and hyphae [5–7] . Neutrophil recruitment to the portal of infection depends on rapid chemokine induction , with a prominent role for ELR+ CXC chemokines and their receptors in this process . In mice , antibody-mediated blockade [8] or genetic deficiency of the chemokine receptor CXCR2 [7] results in delayed neutrophil airway recruitment and the development of invasive aspergillosis ( IA ) . Consistent with this notion , overexpression of a CXCR2 ligand ( i . e . CXCL1/KC ) in the murine lung improves outcomes in neutropenic mice infected with A . fumigatus [9] . Although a number of signaling pathways have been linked to the induction of CXCR2 ligands ( i . e . the CXCL chemokines CXCL1/KC , CXCL2/MIP-2 , and CXCL5/LIX ) and to protective innate immune responses , the events that couple fungal recognition to neutrophil recruitment remain incompletely defined . Conidial swelling , the first step in fungal germination to filamentous hyphae , leads to stage-specific exposure of ligands that activate C-type lectin receptor ( CLR ) [10–12] , NOD-like receptor ( NLR ) [13] , Toll-like-receptor ( TLR ) , and interleukin-1-receptor ( IL-1R ) signaling in vitro and in vivo [14 , 15] . The CLRs Dectin-1 ( i . e . CLEC7A ) [16 , 17] and Dectin-2/-3 ( i . e . CLEC4N/CLEC4D ) [18 , 19] recognize fungal β-glucan and α-mannan respectively , and transduce signals via spleen tyrosine kinase ( Syk ) , protein kinase C-δ , and caspase recruitment domain-containing protein 9 ( CARD9 ) [20–22] . The latter forms a trimeric complex with Bcl10 ( i . e . B cell lymphoma/leukemia 10 ) and MALT1 ( mucosa associated lymphoid tissue lymphoma translocation protein 1 ) and activates NF-κB-dependent transcription , for example of the tnf and il1β genes [20 , 23] . Both Dectin-1 and CARD9 have been linked to the induction of CXCR2 ligands during respiratory A . fumigatus infection [14 , 23] . In vitro , A . fumigatus can induce Syk-dependent assembly of cytoplasmic multiprotein complexes termed inflammasomes that incorporate NLRP3 ( Nod-like receptor family , pyrin domain-containing 3 ) and the scaffold protein ASC ( Apoptosis-associated speck-like protein containing a CARD ) and result in caspase-dependent proteolytic cleavage of pro-IL-1β into active IL-1β [13 , 24 , 25] . In vivo , respiratory A . fumigatus and systemic Candida albicans infection induce IL-1α and IL-1β production [14 , 23 , 26] , linking fungal recognition to IL-1 receptor type I ( IL-1R ) signaling . IL-1R and most TLR superfamily members activate the signal transducer MyD88 ( Myeloid differentiation primary response gene 88 ) . Similar to CARD9 , MyD88 activation following A . fumigatus challenge has been linked to the induction of NF-κB-dependent production of CXCR2 ligands in vitro and in vivo [10 , 23 , 27–29] . Although TLR2- and TLR4-deficient macrophages and dendritic cells show defects in the production of CXCR2 ligands in vitro [10 , 11 , 30] , the relative contribution of TLR-MyD88 versus IL1R-MyD88 signaling in neutrophil recruitment and in protective innate immunity in the lung following respiratory A . fumigatus challenge remains to be elucidated . During respiratory fungal challenge , the mechanism by which the host integrates signals from CLR/CARD9 , IL-1R1/MyD88 , and TLR/MyD88 pathways to achieve rapid chemokine-dependent influx of effector phagocytes and conidial clearance remains undefined . One possible scenario is that CARD9- and MyD88-dependent signals act within the same cellular compartment and in the same time frame for optimal chemokine induction , as has been demonstrated for Dectin-1/CARD9 and TLR2/MyD88-dependent macrophage TNF and IL-12 release in response to the model fungal particle zymosan [21 , 31] . In a second scenario , MyD88- and CARD9-coupled signals may act in distinct cellular compartments in the lung within the same time phase post-infection ( p . i . ) to induce chemokines and coordinate innate immune responses . The latter scenario is exemplified by Pseudomonas aeruginosa- or Legionella pneumophila-induced IL-1α/IL-1β production by hematopoietic cells; this process , in turn , rapidly activates IL-1R1 signaling in pulmonary epithelial cells to promote airway neutrophil influx [32–34] . However , since gram-negative bacteria associated with pneumonia are not known to induce CLR/CARD9 signaling in vivo , conclusions derived from the above studies may not extrapolate to respiratory fungal infection models . A common prediction of the first and second scenario is that a defect in either signaling pathway would diminish chemokine induction and neutrophil recruitment in the same time period p . i . These scenarios could be distinguished by identifying common or distinct cellular compartments in which the signaling pathways operate . Alternatively , CLR/CARD9 and TLR/MyD88 or IL1R1/MyD88 signaling pathways may act primarily in temporally distinct phases , either in the same or in distinct cellular compartments , creating two additional scenarios . In these scenarios , each protein would mediate a distinct phase of chemokine induction and neutrophil recruitment that is largely independent of the phase induced by the other signaling adapter . For example , MyD88- and type I interferon signaling induce sequential phases of CCL2/MCP-1 , a chemokine that regulates inflammatory monocyte trafficking during systemic listeriosis [35] . In this study , we demonstrate that IL1R1/MyD88 signaling plays a crucial role in innate host defense by mediating rapid CXCL chemokine induction and airway neutrophil recruitment during respiratory fungal infection . This initial phase of chemokine induction and effector cell recruitment occurs independently of CARD9 signaling and depends on IL-1R and MyD88 expression within lung epithelial cells . The mortality defect observed in MyD88 ( −/− ) mice can be significantly ameliorated by administration of a single dose of recombinant CXCL1 at the onset of infection , consistent with the notion that MyD88-coupled signals act to orchestrate neutrophil recruitment . In contrast , CARD9 signaling within hematopoietic cells mediates CXCL chemokine induction and neutrophil recruitment in the second phase , with neutrophils representing a major cellular source of CXCL2 . Since cell-intrinsic MyD88 and CARD9 expression in neutrophils are dispensable for the induction of neutrophil conidiacidal activity , our data indicate that MyD88- and CARD9-coupled signals act primarily to orchestrate sequential phases of neutrophil recruitment during respiratory A . fumigatus infection . Thus , our data illustrate an essential role for lung epithelial cells in innate antifungal immunity and delineate compartment-specific and additive roles for MyD88 and CARD9 signaling that collectively regulate chemokine induction and neutrophil trafficking during pulmonary infection with A . fumigatus .
To define the role of MyD88 during respiratory fungal challenge , MyD88 ( −/− ) and C57BL/6 control mice were challenged with 7 × 107 A . fumigatus Af293 conidia and monitored for survival . The median survival time for MyD88 ( −/− ) mice was 3 days , while all C57BL/6 mice survived the 17 day observation period ( Fig . 1A ) . Mortality in MyD88 ( −/− ) mice correlated with an increased fungal burden ( Fig . 1B ) and greater lung damage , as measured by bronchoalveolar lavage fluid ( BALF ) albumin ( Fig . 1C ) and lactate dehydrogenase ( LDH ) release ( Fig . 1D ) , compared to control mice at 48 h p . i . Histopathologic analysis demonstrated differences in the pattern of inflammation between the two groups at 48 h p . i . ( S1A-S1B Fig . in S1 Text ) . Within WT lung sections moderate to severe , multifocal to coalescing inflammation was widespread ( S1A and S1C Fig . in S1 Text ) . Although inflammatory foci in MyD88 ( −/− ) lung sections were not as widespread as in control mice , the foci were admixed with cellular debris indicative of severe tissue necrosis ( S1B and S1D Fig . in S1 Text ) , consistent with the development of necrotizing fungal bronchopneumonia . Although MyD88 ( −/− ) mice exhibited a higher fungal burden in the lung than WT mice , overt hyphal tissue invasion was not apparent in either group . In sum , these results indicate that early MyD88 function is essential for effective fungal clearance and prevents tissue damage that compromises murine survival . To dissect the mechanism by which MyD88 mediates early host protection against A . fumigatus , we compared the recruitment and functional properties of neutrophils in MyD88 ( −/− ) and C57BL/6 mice . Neutrophil numbers in the lung and BALF were consistently decreased in MyD88 ( −/− ) mice compared to C57BL/6 controls at 10 h p . i . ( Fig . 2A & 2B ) with similar neutrophil numbers observed in both compartments at 36 h p . i . , consistent with an initial MyD88-dependent phase of cell recruitment following respiratory A . fumigatus infection . In contrast , the number of lung monocytes , lung and alveolar macrophages , and lung CD11b+ DCs was similar in MyD88 ( −/− ) and control mice at 10 h p . i . ( S2A-S2D Fig . in S1 Text; see S2E-S2G Fig . in S1 Text for leukocyte gating strategy ) . Fluorescent A . fumigatus reporter ( FLARE ) conidia ( S2 Text ) [23] contain two fluorophores ( dsRed and Alexa Fluor 633 ) that enable lung leukocytes to be distinguished on the basis of conidial uptake and killing because dsRed fluorescence , but not Alexa Fluor 633 fluorescence , is extinguished when conidia are killed . Using FLARE conidia , we observed that neutrophil fungal cell uptake in MyD88 ( −/− ) mice was defective at 10 h p . i . ( S3A-S3E Fig . in S1 Text ) . In MyD88 ( −/− ) mice , a lower frequency of lung neutrophils internalized fungal cells at 10 h p . i . compared to lung neutrophils in WT mice ( S3B and S3D Fig . in S1 Text ) . Furthermore , the frequency of live fungal cells within neutrophils , corrected for fungal cell uptake , was higher in neutrophils in MyD88 ( −/− ) mice compared to neutrophils in control mice at 10 h p . i . ( S3C and S3E Fig . in S1 Text ) . Differences in neutrophil fungal cell uptake and killing in MyD88 ( −/− ) and control mice were no longer apparent at 36 h p . i . ( S3B-S3E Fig . in S1 Text ) , suggesting that MyD88-dependent differences in neutrophil recruitment underlie the apparent reduction in phagocytic and conidiacidal activity observed at 10 h p . i . To determine whether cell-intrinsic MyD88 function in neutrophils impacts their survival , trafficking , and effector functions , we generated mixed bone marrow ( BM ) chimeric mice ( CD45 . 2+ MyD88 ( −/− ) and CD45 . 1+ WT MyD88 ( +/+ ) BM cells injected into lethally irradiated CD45 . 1+CD45 . 2+ MyD88 ( +/+ ) recipients ) and compared the behavior of MyD88 ( −/− ) and MyD88 ( +/+ ) neutrophils during respiratory fungal infection . The relative frequency of CD45 . 1+ MyD88 ( +/+ ) and CD45 . 2+ MyD88 ( −/− ) neutrophils was unchanged as neutrophils exited the BM , entered the circulation , and trafficked to the lung and BALF 10 h p . i . ( Fig . 2C-2D ) . These data indicate that neutrophil-intrinsic MyD88 is dispensable for neutrophil trafficking and survival during respiratory fungal infection . Using the same experimental set-up , we next compared conidial uptake and killing by MyD88 ( −/− ) and MyD88 ( +/+ ) neutrophils within the same lung , eliminating differences in cell trafficking and in inflammation observed in MyD88 ( −/− ) and in C57BL/6 mice . Conidial uptake by and conidial viability in lung neutrophils was statistically indistinguishable in MyD88 ( −/− ) and MyD88 ( +/+ ) counterparts isolated from BM chimeric mice ( S4A-S4B Fig . in S1 Text ) . Furthermore , in vitro conidial uptake by bone marrow neutrophils ( BMNF ) isolated from WT and MyD88 ( −/− ) mice was similar ( S4C Fig . in S1 Text ) . These data indicate that the induction of neutrophil phagocytic and conidiacidal activities is not coupled to neutrophil-intrinsic MyD88 function during respiratory fungal infection . To investigate a link between the MyD88-dependent neutrophil recruitment defect and chemokine induction in the lung , we focused on the transcription and translation of the neutrophil-recruiting chemokines CXCL1/KC , CXCL2/MIP-2 , and CXCL5/LIX since these chemoattractants act as agonists for the chemokine receptor CXCR2 . Administration of anti-CXCR2 antibodies exacerbates mortality [8] and genetic deficiency in CXCR2 leads to delayed neutrophil recruitment following A . fumigatus challenge [7] . Because CXCL1 mRNA stability is regulated in part by inflammatory stimuli [36] , we carried out in situ mRNA hybridization on lung sections of 3 × 107 Af293 or PBS administered MyD88 ( −/− ) and WT mice , with gene-specific riboprobes for cxcl1 and cxcl2 . Representative high power micrographs from lung sections demonstrated a severely attenuated signal for cxcl1 but not for cxcl2 in infected MyD88 ( −/− ) mice compared to infected WT mice ( Fig . 3A ) . No riboprobe signal was observed in uninfected controls ( S5 Fig . in S1 Text ) . Although we could not unambiguously identify the cell types that produce CXCL1 and CXCL2 mRNA in infected mice , these data indicate that MyD88-dependent signals are required for CXCL1 but not for CXCL2 mRNA induction or stability in the lung following A . fumigatus challenge . We next examined cytokine induction in A . fumigatus-infected WT and MyD88 ( −/− ) at the protein level by ELISA . The lung and BALF tissues in MyD88 ( −/− ) mice displayed a marked decrease in CXCL1 and CXCL5 levels , but not in CXCL2 levels at 10 h p . i . ( Fig . 3B-3C , S6A-S6F Fig . in S1 Text ) consistent with a prior study [29] . BALF and lung TNF were increased in MyD88 ( −/− ) mice . However , a number of other cytokines assayed ( IL-12p70 , IL-6 , IL-17A , IL-1β ) were not significantly reduced or elevated in BALF and lung tissues of MyD88 ( −/− ) mice at 10 h p . i . ( Fig . 3B-3C ) . Thus , consistent with the MyD88-dependent defect in neutrophil recruitment , we observed a MyD88-dependent reduction of the chemokines CXCL1 and CXCL5 in the lung . Since MyD88 is critical for signal transduction from IL-1R family members and many TLRs , we focused on neutrophil recruitment and chemokine induction in IL-1R ( −/− ) , IL-18R ( −/− ) , TLR2 ( −/− ) , and TLR4 ( −/− ) mice . Defective BALF and lung neutrophil recruitment was observed in IL-1R ( −/− ) mice , but not in IL-18R ( −/− ) , TLR2 ( −/− ) , and TLR4 ( −/− ) mice at 10 h p . i . ( Fig . 4A-4D ) . In parallel , lung and BALF CXCL1 and CXCL5 , but not CXCL2 levels were diminished in IL-1R ( −/− ) mice at 10 h p . i . ( Fig . 4E-4J ) , matching the results observed in MyD88 ( −/− ) mice ( Figs . 2A-2B , 3B-3C , S6A-S6F in S1 Text ) . BALF CXCL1 , CXCL2 , and CXCL5 levels in TLR2 ( −/− ) and TLR4 ( −/− ) mice were not substantially different from those in WT mice at this time point ( S7 Fig . in S1 Text ) . At 36 h p . i . , IL-1R ( −/− ) mice displayed similar BALF neutrophil recruitment and CXCL1 and CXCL2 levels as in WT mice ( S8A-S8C Fig . in S1 Text ) . BALF CXCL5 were decreased in IL-1R ( −/− ) mice ( S8D Fig . in S1 Text ) , similar to observations in MyD88 ( −/− ) mice at this time point ( S6F Fig . in S1 Text ) . These data indicate that IL-1R signaling mediates the initial MyD88-dependent chemokine induction and neutrophil recruitment during respiratory A . fumigatus infection . To investigate the cellular compartment in which MyD88 signaling drives early neutrophil recruitment in our model , we generated bone marrow chimeric mice that lack MyD88 either in radioresistant or in radiosensitive cells . MyD88 expression in radioresistant cells ( i . e . in WT → MyD88 ( −/− ) mice ) was required for rapid neutrophil recruitment ( Fig . 5A-5B ) , while MyD88 expression in radiosensitive hematopoietic cells ( i . e . in MyD88 ( −/− ) → WT mice ) was dispensable for this process . Consistent with this finding , IL-1R expression in radioresistant cells , but not in radiosensitive cells , was critical for airway neutrophil recruitment in A . fumigatus-infected BM chimeric mice ( Fig . 5C-5D ) . To examine the contribution of lung epithelial cells to chemokine induction and neutrophil recruitment , we analyzed MyD88 ( −/− ) mice that express MyD88 under control of the club cell 10 kDa protein ( CC10 ) promoter , restricting MyD88 expression to lung epithelial cells [33 , 37] . CC10 promoter-driven MyD88 expression rescued neutrophil recruitment ( Fig . 5E ) and CXCL1 and CXCL5 induction ( Fig . 5F-5H ) in A . fumigatus-challenged mice compared to non-transgenic , globally MyD88-deficient littermate controls . These data indicate that early IL-1R/MyD88-dependent signals in lung epithelial cells recruit neutrophils to the infection site . To determine whether ELR+ CXC chemokine induction and neutrophil recruitment represents the major mechanism by which MyD88 mediates rapid host defense against A . fumigatus conidia , we administered a single dose of recombinant CXCL1 ( rCXCL1 ) , CXCL2 ( rCXCL2 ) , CXCL5 ( rCXCL5 ) , or PBS diluent via the i . t . route to MyD88 ( −/− ) mice 4 h p . i . and monitored murine survival and neutrophil recruitment . The median survival of rCXCL1-treated mice was 8 days compared to 3 days for PBS-treated controls ( Fig . 5I ) . The prolonged survival in rCXCL1-treated MyD88 ( −/− ) mice correlated with partial restoration of early neutrophil recruitment ( S9 Fig . in S1 Text ) . In contrast , rCXCL2 or rCXCL5 did not prolong murine survival ( S10 Fig . in S1 Text ) . Thus , murine mortality caused by a global deficiency in MyD88 signaling is partially reversed by administration of a single chemokine at a single time point , illustrating a central role of airway epithelial MyD88-dependent chemokine induction and neutrophil recruitment at the earliest stage of respiratory A . fumigatus infection . In a previous study , we found that CARD9 was dispensable for neutrophil recruitment and CXCL chemokine induction in the first 12 h p . i . , However , our group and others found that Dectin-1 ( −/− ) and CARD9 ( −/− ) mice display defective chemokine production and neutrophil recruitment at 24 and 36 h p . i . , respectively [14 , 23] , time points that lie outside the MyD88-dependent period . We therefore investigated whether MyD88 and CARD9 act in the same or in different cellular compartments . By analyzing neutrophil recruitment in BM chimeric mice at 36 h p . i . we found that CARD9 deficiency in radiosensitive hematopoietic cells ( i . e . CARD9 ( −/− ) → WT ) leads to defective neutrophil airway and lung recruitment , similar to BM chimeric mice that are globally defective in CARD9 ( i . e . CARD9 ( −/− ) → CARD9 ( −/− ) ) ( Fig . 6A ) . Consistent with this finding , BALF CXCL1 , CXCL2 , and CXCL5 chemokine levels were reduced in CARD9 ( −/− ) → WT mice , but not in WT → CARD9 ( −/− ) mice ( Fig . 6B ) , indicating that CARD9 acts in hematopoietic cells to mediate the second phase of ELR+ CXC chemokine induction and lung neutrophil recruitment . Thus , MyD88 and CARD9 act in different cellular compartments to regulate these processes . To determine whether fungal stimulation directly induces ELR+ CXC chemokine induction in hematopoietic cells , we cultured WT and CARD9 ( −/− ) BM macrophages ( BMMs ) with A . fumigatus germlings . BMMs produced CXCL1 and CXCL2 in a CARD9-dependent manner ( Fig . 7A ) , though CXCL5 was not detected in these co-culture experiments . To visualize the cellular source of CXCL2 in the murine lung , we generated a CXCL2 reporter mouse in which GFP expression was placed under control of the murine CXCL2 promoter ( Fig . 7B ) . GFP transgene expression in BMMs prepared from CXCL2 reporter mice correlated with CXCL2 release triggered by TLR2 agonist stimulation ( Fig . 7C ) . To define the cellular sources of CXCL2 and to examine CARD9-dependent regulation of CXCL2 , we crossed to the transgene to the CARD9 ( −/− ) background and compared its expression in CARD9 ( +/+ ) and CARD9 ( −/− ) mice infected with A . fumigatus . GFP expression was observed primarily in neutrophils , and to a lower frequency , in inflammatory monocytes and monocyte-derived CD11b+ DCs ( Fig . 7D ) . In contrast , GFP-expressing CD3+ , CD19+ , or NK1 . 1+ cells were not observed in lungs of A . fumigatus-infected mice at 36 h p . i . Lung-infiltrating neutrophils thus represent the most abundant cellular source of CXCL2 and its expression was dependent on CARD9 . To define the temporal process by which MyD88- and CARD9-coupled signals mediate lung neutrophil recruitment , we compared these processes in MyD88 ( −/− ) , CARD9 ( −/− ) , MyD88 ( −/− ) CARD9 ( −/− ) , and in C57BL/6 control mice . Consistent with a two-phase model of neutrophil recruitment , we observed MyD88-dependent , CARD9-independent neutrophil recruitment in the first phase and MyD88-independent , CARD9-dependent neutrophil recruitment in the second phase of respiratory fungal infection ( Fig . 8A-8B ) . Beyond its role in neutrophil recruitment , global CARD9 deficiency is associated with a defect in killing A . fumigatus [23] and C . albicans [38] . It is possible that CARD9 expression in neutrophils is essential for conidiacidal activity or , alternatively , that neutrophil-extrinsic CARD9 signaling regulates neutrophil conidiacidal activity . To distinguish these possibilities , we analyzed neutrophil conidial uptake and killing in mixed BM chimeric mice ( CD45 . 2+ CARD9 ( −/− ) or CD45 . 2+ MyD88 ( −/− ) CARD9 ( −/− ) BM cells mixed in a 1:1 ratio with CD45 . 1+ MyD88 ( +/+ ) CARD9 ( +/+ ) BM cells and injected into lethally irradiated CD45 . 1+CD45 . 2+ MyD88 ( +/+ ) CARD9 ( +/+ ) recipients ) using FLARE conidia . In this experimental setting , CARD9 ( −/− ) and MyD88 ( −/− ) CARD9 ( −/− ) neutrophils were not defective in conidial uptake or killing compared to WT ( i . e . MyD88 ( +/+ ) CARD9 ( +/+ ) ) counterparts isolated from the same lung ( S11A - S11D Fig . in S1 Text ) . Thus , CARD9 signaling regulates neutrophil conidiacidal activity in a cell-extrinsic manner , since neutrophil-intrinsic CARD9 and MyD88 expression both appear dispensable for this process . To examine the cumulative effect of MyD88 and CARD9 function in host defense , we challenged double knockout , single knockout , and control mice with a conidial dose ( 3 × 107 ) that is sub-lethal for single adapter protein knockout strains . MyD88 ( −/− ) CARD9 ( −/− ) mice rapidly and universally succumbed to this infectious inoculum , while the mice in all other groups survived the infection ( Fig . 8C ) . Histopathological examination of hematoxylin and eosin ( H&E ) stained lung sections from MyD88 ( −/− ) CARD9 ( −/− ) mice demonstrated severe disruption of pulmonary architecture and multifocal to coalescing areas of necrosis centered on and around vessels and bronchioles ( Fig . 8Di-8Dii ) . Necrotic areas were characterized by abundant cellular and nuclear debris admixed with many fungal hyphae ( Fig . 8Dii , arrow ) . GAS stained lung sections of MyD88 ( −/− ) CARD9 ( −/− ) mice contained many germinating conidia and fungal hyphae expanding and surrounding vessels and bronchioles , indicative of severe tissue destruction associated with IA ( Fig . 8Diii ) . Mice in other groups however , did not develop tissue-invasive hyphae during the course of study ( S12 Fig . in S1 Text , arrows show regions of hyphal proliferation ) . Collectively , these data indicate a sequential and additive role for MyD88 and CARD9 signaling in host defense against A . fumigatus , with critical contributions from lung epithelial and hematopoietic cells in orchestrating chemokine and neutrophil recruitment to prevent tissue-invasive disease .
Rapid neutrophil influx is essential to prevent the germination and tissue invasion of airborne conidia that are inhaled daily during human life . A . fumigatus activates multiple innate immune signaling pathways that act in distinct cell types to induce an abundance of neutrophil chemotactic molecules . Deciphering the timing and importance of each component to the orchestration of effective innate immune defense has been challenging . In this study , we demonstrate that IL-1R/MyD88-mediated signals in lung epithelial cells and CARD9-mediated signals in hematopoietic cells provide sequential contributions to ELR+ CXC chemokine induction and to airway neutrophil recruitment . In the absence of either signaling pathway within the relevant cellular compartment , lung ELR+ CXC chemokine expression levels are diminished and neutrophil recruitment is either delayed at onset of fungal challenge ( i . e . in MyD88 deficiency ) or lags in the ensuing phase ( i . e . in CARD9 deficiency ) . Among the ELR+ CXC chemokines , loss of MyD88 signaling was associated with defects in CXCL1 and to a lesser extent in CXCL5 , while loss of CARD9 signaling was associated with defects in CXCL1 and CXCL2 . Although this study and our previous work [23] demonstrate that loss in either signal adapter protein increases murine susceptibility to A . fumigatus challenge , host defense against inhaled conidia was profoundly compromised when both MyD88- and CARD9-mediated signals were absent . Neutrophil-intrinsic MyD88 and CARD9 function is dispensable for the induction of neutrophil phagocytic and conidiacidal activities , supporting the view that the primary role of both adaptors is to orchestrate neutrophil recruitment . This idea is reinforced by the observation that a single application of exogenous rCXCL1 prolongs survival in infected MyD88 ( −/− ) mice . Given the essential requirement for CXCR2 ligands in host defense , our study did not focus on other neutrophil chemoattractants that can emerge in the vicinity of microorganisms , e . g . complement component C5a , or at endothelial sites , e . g . leukotriene B4 [39 , 40] . Other chemoattractants implicated in neutrophil chemotaxis include CCL3 , CCL6 , and CCL9 ( via CCR1 ) , platelet-activating factor ( via 2 receptors ) , and CXCL12 ( via CXCR4 ) [40] . Thus , it is possible that rCXCL1 administration enhances neutrophil recruitment by replenishing endogenous CXCL1 or by compensating for MyD88-dependent deficits in other chemoattractants that may act in series or parallel [39] . However , under the experimental conditions tested , rCXCL2 and rCXCL5 did not ameliorate MyD88-dependent mortality . One possible explanation is that endogenous CXCL2 and CXCL5 were relatively preserved in infected MyD88 ( −/− ) mice at 10 h p . i . , unlike CXCL1 , blunting the impact of exogenous administration . Jeyaseelan and colleagues reported that CXCL1 regulates expression of CXCL2 and CXCL5 during murine Klebsiella pneumoniae pneumonia [41] . During murine pulmonary A . fumigatus infection , the early defect in CXCL1 , and to a lesser extent CXCL5 , in MyD88 ( −/− ) mice was not associated with reduced CXCL2 levels , consistent with the notion that CXCL2 production in this model is not strictly dependent on CXCL1 . Our results indicate that lung epithelial cells play a central role in coordinating innate immune responses to inhaled conidia . We provide evidence for this statement in the form of experiments that demonstrate a critical role for MyD88 signaling in radioresistant cells , but not in radiosensitive cells , for CXCL1 and CXCL5 induction and neutrophil recruitment . In addition , expression of MyD88 under control of the well-characterized CC10 promoter [33] is sufficient to rescue CXCL1 and CXCL5 induction and neutrophil recruitment . A previous study on inflammatory responses to conidia in immortalized lung cell lines ( BEAS-2B ) implicated phosphatidylinositol-3-kinase , p38 MAP kinase , and ERK1/2 signaling pathways in the induction of interleukin-8 ( IL-8 ) [42] , a human equivalent of murine CXCL1 . In that study , transfection of a dominant negative MyD88 mutant gene did not interfere with fungus-induced IL-8 release , though the findings were not extended to primary cells or to the lung tissue context . It is unclear whether co-culture of BEAS-2B cells and A . fumigatus induces IL-1R ligands in vitro . This step however , appears to be critical for epithelial cell activation in the lung and ensuing neutrophil airway trafficking following respiratory A . fumigatus challenge . This view is further supported by neutrophil recruitment studies in the lung following challenge with common bacterial agents of pneumonia including Legionella pneumophila [32 , 34] , Pseudomonas aeruginosa [33] , and Streptococcus pneumonia [43] . Additionally , in a cutaneous Staphylococcus aureus infection model , IL1R-MyD88 signaling mediates neutrophil recruitment in a TLR-independent manner [44 , 45] . How does A . fumigatus trigger IL-1R signaling in lung epithelial cells ? Within 2 hours following intratracheal ( i . t . ) A . fumigatus challenge , alveolar macrophages transcribe Il1α and Il1β genes [46] , and bioactive IL-1α and IL-1β is detectable in the lungs at 6 hours p . i . ( Caffrey A . et al . , manuscript submitted ) coincident with the onset of a neutrophilic cellular influx [7] . In a parallel study , Obar and colleagues unveil a dominant role for IL-1α in early lung CXCL1 chemokine induction and neutrophil recruitment ( Caffrey A . et al . , manuscript submitted ) . Lung-resident myeloid cells represent a potential source of IL-1α and IL-1β at the earliest time point post-infection . A . fumigatus infection activates the transcription factor hypoxia-inducible factor-1α ( HIF1α ) in myeloid cells and this process is linked to defective IL-1α and CXCL1 induction , impaired neutrophil recruitment , and murine susceptibility to IA [47] . Epithelial cells may contribute to early IL-1α release as well , particularly in the context of tissue damage or injury , since pro-IL-1α constitutively expressed in lung epithelial cells , and upon necroptotic cell death , is processed via calpain-dependent cleavage and released [48 , 49] . IL-1α can rapidly induce chemotactic mediators and neutrophil recruitment to sites of inflammation , as demonstrated in sterile models of ischemic and hypoxic injury [48 , 49] . This model of IL-1α function is expanding to include microbial agents of inflammation , e . g . L . pneumophila within the respiratory tract [34] . Since respiratory fungal infection induces hypoxic tissue environments in the lung [50] , receptor-interacting protein kinase 3 ( RIP3 ) -dependent induction of necroptotic cell death [51] by inhaled conidia may provide a stimulus for IL-1α release . Although the impact of A . fumigatus conidia on necroptotic cell death—which favors IL-1α release—has not been investigated in detail , conidial melanin appears to inhibit apoptotic cell death—which favors IL-1β release—in macrophages and in epithelial cells in vitro [52–55] . Thus , the presence of conidial melanin and modulation of host cell death pathways may shape early IL-1α/β responses during respiratory fungal infection . MyD88 acts as a transducer not only for IL-1R-mediated signals , but also for other IL-1R family members [48] . Previous studies indicate that A . fumigatus induces the synthesis of IL-1 receptor antagonist ( IL-1RA ) and the transcription of IL-36γ and IL-36 receptor antagonist in human peripheral blood mononuclear cells [56 , 57] . Although our studies and those of Obar and colleagues demonstrate a critical role for IL-1R/MyD88 signaling in host defense against A . fumigatus , our findings do not exclude a role for other IL-1R family members , e . g . IL-36R , in linking IL-1R-dependent signals to ELR+ CXC chemokine induction . Our data did not reveal a requirement for CARD9 signaling in IL-1β and CXCL1 , CXCL2 , and CXCL5 induction and in airway neutrophil recruitment within the first 10 h p . i . In contrast , we and other investigators reported Dectin-1- and CARD9-dependent defects in IL-1α/β and CXCL1 and CXCL2 production at later time points , starting at 24 h post-infection [14 , 23] . In response to fungal stimuli , Dectin-1/Syk/CARD9 signaling is associated with activation of canonical NLRP3/ASC/caspase-1 inflammasomes [13 , 24] and of non-canonical ASC/caspase-8-containing inflammasomes [25]; both mediate the conversion of pro-IL-1β to biologically active IL-1β . Obar and colleagues observe that genetic deletion of ASC , critical for assembly of caspase-1- and caspase-8-containing inflammasomes does not diminish lung neutrophil recruitment nor increase susceptibility to IA , in contrast to IL-1R deficiency ( Caffrey A . et al . , manuscript submitted ) . Thus , CARD9-dependent production of IL-1β does not appear to be the primary trigger for IL-1R signaling , CXCL1 induction , and neutrophil recruitment in the initial phase of infection , though CLR/CARD9-dependent signals play a significant role in amplifying and sustaining neutrophil recruitment at later time points . In the respiratory fungal infection model , MyD88-dependent CXCL1 and CXCL5 release correlates with early phase lung and airway neutrophil recruitment . Remarkably , administration of recombinant CXCL1 ameliorates murine mortality in MyD88 ( −/− ) mice , suggesting that MyD88-dependent control over neutrophil chemotaxis represents the major physiologically relevant function of early IL-1R/MyD88 signaling . In addition , MyD88 signaling negatively regulates TNF induction in A . fumigatus-infected mice , as demonstrated by our study and others [29] . Thus , TNF-induced lung tissue damage and necrosis , associated with defective ELR+ CXC chemokine induction , defective neutrophil recruitment , and defective fungal clearance , likely contributes to observed infectious outcomes . In our study , CARD9 regulates CXCL1 and CXCL2 release in the second phase of neutrophil recruitment , with neutrophils acting as the primary hematopoietic source of CXCL2 . Several explanations are possible for the differential temporal regulation and roles of CXCR2 ligands during pulmonary aspergillosis . First , lung and airway CXCL1 levels increase more rapidly than CXCL2 levels at the onset respiratory fungal infection [23 , 29] and thus CXCR2-dependent trafficking steps may reflect the relative abundance of cognate ligands . Second , since different resident and recruited cell types ( e . g . neutrophils for CXCL2 ) may represent the major sources of these chemoattractants , the arrival and position of cellular CXCL1 or CXCL2 producers during neutrophil lung influx may determine the relative timing and role of CXCL1 and CXCL2 during this process . In addition , the relative access of individual ELR+ chemokines to glycosaminoglycans during fungal challenge is likely to play a key role in forming gradients and directing neutrophil trafficking into the lung [58 , 59] . To address these issues , the development of a complete set of transgenic reporters and conditional ablation strategies for CXCR2 ligands will facilitate more incisive experiments to determine the relative contribution , anatomic localization , cellular sources , and regulation of individual CXCR2 ligands during respiratory fungal infection . In humans , Mendelian defects in MyD88 and CARD9 signaling do not lead to the spontaneous development of aspergillosis , unlike individuals with Mendelian defects in NADPH oxidase activity ( i . e . chronic granulomatous disease ) . Children that lack MyD88 signaling develop pyogenic infections , primarily due to Streptococcus pneumonia [60] , and individuals with CARD9 deficiency develop mucocutaneous and disseminated candidiasis and dermatophytosis [38 , 61 , 62] . These observations support the model that IL-1R/MyD88 and CLR/CARD9 signaling provide redundancy in orchestrating sterilizing immunity against inhaled conidia . Since humans typically inhale several hundred to several thousand conidia daily [63] , the presence of a single functional adapter pathway is sufficient to mediate fungal clearance in an otherwise immune competent host . Consistent with this idea , we observe that genetic deletion of both adapter proteins significantly increases murine susceptibility to invasive disease compared to deletion of a single adapter protein . In the context of allogeneic hematopoietic cell transplantation ( HCT ) , genetic variations in the IL-1 gene cluster are associated with susceptibility to IA , though these data must be interpreted cautiously since an independent validation of this cohort has not been reported [64] . Allelic differences in components of CLR/CARD9 and in MyD88 signaling pathways have been linked to susceptibility to IA in allogeneic HCT patients in multiple cohorts [12 , 65–68] . Thus , genetic variants in CARD9- or in MyD88-dependent signaling pathways may predispose to IA only in the setting of innate immune damage ( e . g . in patients with neutropenia or in patients that receive corticosteroids ) . In sum , our study reveals coordinated regulation of chemokine induction and neutrophil recruitment by IL-1R/MyD88 and CLR/CARD9 signaling pathways that operate in a biphasic manner and in epithelial and hematopoietic compartments to orchestrate sterilizing immunity against A . fumigatus . Further insight into the signals and temporal sequence of events required for innate immune orchestration is likely to inform strategies to identify and intervene in patients at high risk for aspergillosis .
All animal experiments were conducted with sex- and age-matched mice and performed with approval from the FHCRC ( protocol number 1813 ) and MSKCC ( protocol number 13-07-008 ) Institutional Animal Care and Use Committee . Animal studies were compliant with all applicable provisions established by the Animal Welfare Act and the Public Health Services Policy on the Humane Care and Use of Laboratory Animals . Chemicals and cell culture reagents were purchased from Sigma-Aldrich and Gibco respectively . A . fumigatus strain Af293 was used for all experiments . MyD88 ( −/− ) ( Stock No . 009088 , Jackson Laboratories ) and CARD9 ( −/− ) [69] were crossed to generate MyD88 ( −/− ) CARD9 ( −/− ) mice . IL-1R ( −/− ) ( Stock No . 003245 ) , IL-18R ( −/− ) ( Stock Number 004131 ) , C57BL/6 ( CD45 . 2+ ) , C57BL/6 . SJL ( CD45 . 1+ ) , mice were from Jackson Laboratories . TLR2 ( −/− ) and TLR4 ( −/− ) mice were obtained by the Pamer laboratory from the Medzhitov laboratory in 2000 and backcrossed at least 10 generations on the C57BL/6 background . All mouse strains were bred and housed in the FHCRC Comparative Medicine Shared Resources or in the MSKCC Research Animal Resource Center under specific pathogen-free conditions . MyD88 ( −/− ) CC10-MyD88 [33] and non-transgenic MyD88 ( −/− ) littermate controls were bred and maintained at the Yale University Animal Resources Center and shipped to MSKCC for experimental use . CXCL2 reporter mice were generated by bacterial artificial chromosome ( BAC ) -mediated transgenesis using the recombineering strategy developed by Heintz and colleagues [70] . The endogenous CXCL2 locus on BAC clone RP23-87C13 ( BACPAC Resources Center , Children’s Hospital Oakland research Institute ) was modified 3’ to the terminal Asn residue in the fourth exon to encode the following insertion in the 5’ to 3’ direction: an HA peptide ( -YPYDVPDY- ) , a -GSG- linker , a 2A peptide ( -GSGAPVKQTLNFDLLKLAGDVESNPGP- ) and amino acids 1–238 of enhanced GFP followed by a stop codon . The BAC modification procedures were performed as described in [71 , 72] and the modified BAC was analyzed by DNA sequencing and Southern blotting to verify GFP integration at the CXCL2 locus . The purified modified RP23-87C13 BAC was injected into fertilized B6/129 oocytes at the University of Washington Transgenic Resources Program . Four potential founder animals were identified among offspring screened by PCR . One candidate , designated 3090 , transmitted the CXCL2-GFP transgene to one-half of the progeny and was the founder of the CXCL2 reporter colony . CXCL2 reporter mice were backcrossed 10 generations to C57BL/6 mice . To generate CXCL2 reporter mice on the CARD9 ( −/− ) background , N8 CXCL2 reporter mice were crossed to CARD9 ( −/− ) mice and littermate non-transgenic CARD9 ( −/− ) controls were used in experiments . BM chimeric mice were generated by reconstituting lethally irradiated ( 9 . 5 Gy ) recipients ( C57BL/6 . SJL , CD45 . 1+CD45 . 2+ C57BL/6 , MyD88 ( −/− ) , or CARD9 ( −/− ) mice ) mice with 2–5 × 106 MyD88 ( −/− ) , CARD9 ( −/− ) , MyD88 ( −/− ) CARD9 ( −/− ) or C57BL/6 BM cells . The mice were treated with enrofloxacin or amoxicillin-clavulanate in the drinking water for 14 days to prevent bacterial infections and rested for 6–8 weeks prior to use in experiments . BAL and lung suspensions were prepared for flow cytometry as described in [72] . Briefly , lung digest and , if applicable , BAL cells were enumerated and stained with the following Abs: anti-Ly6C ( clone AL-21 ) , anti-Ly6G ( clone 1A8 ) , anti-CD11b ( clone M1/70 ) , anti-CD11c ( clone HL3 ) , anti-CD45 . 1 ( clone A20 ) , anti-CD45 . 2 ( clone 104 ) , anti-Ly6B . 2 ( clone 7/4 ) , anti-MHC class II ( clone M5/114 . 15 . 2 ) . Neutrophils were identified as CD45+CD11b+Ly6CloLy6G+Ly6B . 2+ , monocytes as CD45+CD11b+Ly6ChiLy6G−Ly6B . 2+ , lung macrophages as autofluorescent CD45+CD11c+MHC class II variable population , CD11b DCs as CD45+CD11c+MHC class II variable CD11b+ population and alveolar macrophages as SSChiCD45+CD11b+CD11c+ BALF cells . Flow cytometry data was collected on a BD LSR II flow cytometer and analyzed on FlowJo , version 9 . 7 . 6 ( Treestar ) . Perfused murine lungs were homogenized in 2 mL of PBS , 0 . 025% Tween-20 for colony-forming units ( CFUs ) and for ELISA . For histology , perfused lungs were fixed in 10% neutral-buffered formalin , embedded in paraffin , sectioned at 4μm and stained with hematoxylin & eosin ( H&E ) or Gomori’s ammoniacal silver ( GAS ) . Slides were reviewed by a board certified pathologist ( SEK ) . Images were captured from whole slide images , acquired with the Aperio ScanScope ( Aperio Technologies ) using ×20 and ×40 objectives at the FHCRC’s Experimental Histopathology Shared Resource . Images for S12 Fig . were captured using a Zeiss Mirax Midi slide scanner with 20×/0 . 8NA objective and analyzed using Pannoramic Viewer ( v1 . 15 . 3 ) at the MSKCC’s Molecular Cytology Core Facility ( MCCF ) . To assess fungal burden , infected mice were euthanized and lungs were immediately frozen in liquid nitrogen . Samples were freeze-dried , homogenized with glass beads on a Mini Beadbeater ( BioSpec Products , Inc . , Bartlesville , OK ) , and DNA extracted with E . N . Z . A fungal DNA kit ( Omega Bio-Tek , Norcross , GA ) or phenol chloroform extraction . RT-PCR was performed as described previously [73] . LDH ( CytoTox96 non-radioactive cytotoxicity assay kit; Promega , Cat . No . G1780 ) and albumin assays ( Albumin ( BCG ) reagent set; Eagle Diagnostics , Cat . No . 2050-1 ) were performed on BALF collected from infected mice using the manufacturers’ protocols as follows . 100 μl of BALF was added to equal volumes of the recommended reagents and incubated for either 30 min ( LDH ) or 5 min ( Albumin ) and absorbance measured at 490 nm ( LDH assay ) or 630 nm ( Albumin assay ) . For in situ hybridization experiments , gene-specific riboprobes were synthesized by in vitro transcription using a Maxiscript SP6/T7 kit ( Ambion ) and unincorporated nucleotides were removed using RNA Mini Quick Spin columns ( Roche ) . Paraffin embedded lung sections were pretreated as described [74] , following deparaffinization in xylenes and rinsing in ethanol . In situ hybridization with 35S-UTP-labeled riboprobes ( antisense or control sense ) was performed as described previously [75] with 0 . 1M dithiothreitol included in the hybridization mix . Hybridizations were performed at 50°C overnight . After stringent washing to remove unbound riboprobes , tissue sections were coated with NTB emulsion ( Kodak ) and exposed at 10°C for 14 days . Parallel hybridizations using the control sense riboprobes did not give rise to specific autoradiographic signals . For supplementation experiments , infected mice were administered 50 ng of recombinant rCXCL1 ( Cat . No . 573702 , BioLegend ) , rCXCL2 ( Cat . No . 582502 ) or rCXCL5 ( Cat . No . 573302 ) in 50 μl PBS or PBS alone via the i . t . route ( at 4 h p . i . ) and monitored for survival or assayed for neutrophil recruitment . 50 ng of recombinant rCXCL1 was analyzed using the Limulus amoebocyte lysate assay kit ( Lonza , Cat . No . 50647U ) and endotoxin content was below the level of detection ( 0 . 1 EU/ml ) . BMM stimulation with A . fumigatus germlings was performed as described in [23] . Briefly , BMMs derived from WT and CARD9 ( −/− ) mice were stimulated with UV-inactivated germlings ( MOI = 1 ) for 18 h . Chemokine levels in culture supernatants were measured using ELISA kits from R&D Systems . All results are expressed as mean ( ±SEM ) derived from 3 independent experiments , unless stated otherwise . A Mann-Whitney U test was used for unpaired two groups or Wilcoxon signed-rank test for paired two groups comparison . Non-parametric Kruskal-Wallis one-way analysis of variance followed by Dunn’s post test was used for three groups comparison . Survival data was analyzed by long rank test unless stated otherwise . All statistical analyses were performed with GraphPad Prism software , v6 . 0c . A p value < 0 . 05 was considered significant and indicated with an asterisk . | Our understanding of how epithelial and hematopoietic cells in the lung coordinate immunity against inhaled fungal conidia ( spores ) remains limited . The mold Aspergillus fumigatus is a major cause of infectious mortality in immune compromised patients . Host defense against A . fumigatus involves the activation of two host signal transducers , MyD88 and CARD9 , leading to neutrophil recruitment to the infection site . In this study , we define how MyD88- and CARD9-coupled signals operate in epithelial and hematopoietic compartments to regulate neutrophil-mediated defense against A . fumigatus . Our studies support a two-stage model in which MyD88 activation in epithelial cells , via the interleukin-1 receptor , supports the rapid induction of neutrophil-recruiting chemokines . This process is essential for the first phase of neutrophil recruitment . Mortality observed in MyD88-deficient mice can be significantly reversed by administration of a chemokine termed CXCL1 to infected airways . The second phase of neutrophil recruitment is initiated by CARD9 signaling in hematopoietic cells . Loss of both phases of chemokine induction and neutrophil recruitment dramatically increases murine susceptibility to tissue-invasive disease . In sum , our study defines a temporal sequence of events , initiated by interleukin-1 receptor/MyD88 signaling in the pulmonary epithelium and propagated by CARD9 signaling in hematopoietic cells , that induces protective immunity against inhaled fungal conidia . | [
"Abstract",
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"Methods"
] | [] | 2015 | Compartment-Specific and Sequential Role of MyD88 and CARD9 in Chemokine Induction and Innate Defense during Respiratory Fungal Infection |
Tuberculous latency and reactivation play a significant role in the pathogenesis of tuberculosis , yet the mechanisms that regulate these processes remain unclear . The Mycobacterium tuberculosis universal stress protein ( USP ) homolog , rv2623 , is among the most highly induced genes when the tubercle bacillus is subjected to hypoxia and nitrosative stress , conditions thought to promote latency . Induction of rv2623 also occurs when M . tuberculosis encounters conditions associated with growth arrest , such as the intracellular milieu of macrophages and in the lungs of mice with chronic tuberculosis . Therefore , we tested the hypothesis that Rv2623 regulates tuberculosis latency . We observed that an Rv2623-deficient mutant fails to establish chronic tuberculous infection in guinea pigs and mice , exhibiting a hypervirulence phenotype associated with increased bacterial burden and mortality . Consistent with this in vivo growth-regulatory role , constitutive overexpression of rv2623 attenuates mycobacterial growth in vitro . Biochemical analysis of purified Rv2623 suggested that this mycobacterial USP binds ATP , and the 2 . 9-Å-resolution crystal structure revealed that Rv2623 engages ATP in a novel nucleotide-binding pocket . Structure-guided mutagenesis yielded Rv2623 mutants with reduced ATP-binding capacity . Analysis of mycobacteria overexpressing these mutants revealed that the in vitro growth-inhibitory property of Rv2623 correlates with its ability to bind ATP . Together , the results indicate that i ) M . tuberculosis Rv2623 regulates mycobacterial growth in vitro and in vivo , and ii ) Rv2623 is required for the entry of the tubercle bacillus into the chronic phase of infection in the host; in addition , iii ) Rv2623 binds ATP; and iv ) the growth-regulatory attribute of this USP is dependent on its ATP-binding activity . We propose that Rv2623 may function as an ATP-dependent signaling intermediate in a pathway that promotes persistent infection .
Mycobacterium tuberculosis , one of the most successful human pathogens , infects one-third of the world's population , causing nearly two million deaths per year [1] . Epidemiological data estimate that , in the immunocompetent host , only ∼10% of M . tuberculosis infection progress to active pulmonary disease . The remaining 90% of the infected individuals are asymptomatic , and are generally believed to harbor latent bacilli that can reactivate to cause tuberculous diseases , sometimes decades after the initial infection . Recrudescence of latent bacilli contributes significantly to the incidence of adult tuberculosis [2] , yet the physiological state of latent bacilli and the signals that promote dormancy in the host remain incompletely defined . Understanding the dynamic interaction between host and pathogen during the establishment of persistent M . tuberculosis infection will guide the design of novel treatment for the latently infected population . An intracellular pathogen , M . tuberculosis must possess a finely tuned signaling network to sense and transduce complex environmental signals , ensuring survival of the bacilli within host cells . Nitric oxide ( NO ) produced by infected macrophages and relative hypoxia are signals likely to be encountered within tuberculous lesions that are believed , based on in vitro studies , to promote latency by prompting the M . tuberculosis dormancy response . Exposure to these stimuli results in the induction of ∼50 M . tuberculosis genes , designated the dormancy regulon , via the two-component regulatory system DosR-DosS ( see Table S1 for accession numbers ) [3] , [4] , [5] . Among this set of genes is rv2623 , one of eight M . tuberculosis genes annotated as containing the universal stress protein ( USP ) domain [6] , [7] . Members of this ancient and conserved family of proteins are found in all forms of life and can be induced by a variety of environmental stresses [8] , [9] . However , the roles of USP proteins in microbial pathogenesis are incompletely understood . Interestingly , rv2623 is one of the most strongly induced transcripts of the dormancy regulon [3] , [4] , [5] . Increased expression of rv2623 was also observed following phagocytosis by macrophages [10] and in the lungs of chronically infected mice [11] , supporting a functional role during persistent M . tuberculosis infection . The present study reveals that: i ) deletion of rv2623 confers hypervirulence on the tubercle bacillus in animal models , suggesting that expression of Rv2623 may be conducive to the establishment of persistence in vivo; ii ) overexpression of Rv2623 results in growth retardation of recipient strains in vitro , further supporting a growth-regulatory role; iii ) Rv2623 binds ATP; and finally , through mutagenesis study guided by crystallographic analysis of Rv2623 ( the first such study for a tandem-domain USP ) , we show that iv ) the growth-regulating attribute of this M . tuberculosis USP is linked to its ATP-binding capacity .
An rv2623-deletion mutant of the virulent M . tuberculosis Erdman strain was generated by specialized transduction [12] . The rv2623-specific allelic exchange construct was delivered via recombinant mycobacteriophage phAE159 and transformants were analyzed by Southern blot , confirming replacement of rv2623 with the hyg gene , which confers hygromycin resistance ( Figure 1A ) . Aliquots of a single knockout clone , designated as Δrv2623 , were stored at −70°C . Deletion of rv2623 is not likely to affect transcription of neighboring genes , given the sequence-confirmed precise excision of the rv2623 coding region and the gene organization at the rv2623 locus ( the downstream rv2624c is transcribed in the direction opposite to that of rv2623 ) ( Figure 1B ) . Deletion of specific USPs in E . coli results in growth defects in vitro [8] , [13] , [14] . For example , an E . coli strain deficient for UspA exhibits reduced survival in stationary phase culture [14] . However , the in vitro growth kinetics of Δrv2623 M . tuberculosis in OADC-supplemented Middlebrook 7H9 or minimal Sauton's medium is comparable to that of wildtype Erdman up to 14 days post-inoculation ( Figure 2A ) . We reasoned that a potential growth-regulating attribute of Rv2623 might be masked by functional redundancy among the M . tuberculosis USP homologs . Indeed , partial functional overlap has been demonstrated among the E . coli USPs [9] , [15] . We therefore examined the effect of overexpression of this USP in the rapidly growing M . smegmatis strain mc2155 [16] . As seen in Figure 2B , constitutive overexpression of M . tuberculosis rv2623 using the multi-copy plasmid pMV261 resulted in growth deficiency of the recipient strain both on solid medium ( Middlebrook 7H10 agar ) and in the liquid medium-based BD BACTEC 9000MB system . These results strongly suggest that M . tuberculosis Rv2623 regulates mycobacterial growth in vitro . Although USP family proteins are expressed by many bacterial pathogens [7] , [8] , to date , there has only been one in vivo study , which showed that a Salmonella USP promotes virulence in mice [17] . The observation that Rv2623 modulates mycobacterial growth in vitro prompted us to examine the effect of this USP on the in vivo kinetics of M . tuberculosis infection . Low dose aerosol infection of outbred Hartley guinea pigs with ∼30 CFU revealed a clear growth advantage of the Δrv2623 mutant strain relative to wildtype . As early as 20 days post-infection , the number of M . tuberculosis bacilli present in the lungs of Δrv2623-infected guinea pigs was ∼10-fold higher ( p<0 . 05 ) than those infected with wildtype Erdman , and continued to rise , attaining a 15-fold ( p<0 . 001 ) difference by 60 days post-infection ( Figure 3A ) . Guinea pigs are able to control the growth of Erdman bacilli following the onset of adaptive immunity at ∼3 weeks post-infection , as evident by the relatively stable pulmonary bacterial burden beyond the 3 week time point , yet levels of Δrv2623 bacilli continued to increase at a reduced but steady rate resulting in a rapidly progressing infection . Moreover , Δrv2623-infected guinea pigs were moribund at 60 days post-infection , while those challenged with wildtype Erdman remained relatively healthy , providing further evidence that the mutant strain is hypervirulent in this model . Finally , complementation with a single integrated copy of rv2623 expressed from a constitutive mycobacterial promoter ( Δrv2623 attB::Phsp60Rv2623 ) abrogated the growth advantage of the deletion mutant ( Figure 3A ) . Also consistent with the fulminate disease progression displayed by Δrv2623-infected guinea pigs are the more severe pathological changes observed as early as 20 days post-infection in the lungs of these animals , as assessed by histopathological studies , including the semi-quantitative Total Lung Score analysis ( Figure 3B and Protocol S1 ) . Overall , the progression of pulmonic lesions was accelerated in Δrv2623-infected animals compared to those infected with wildtype Erdman , accompanied by more extensive necrosis and widespread fibrosis . This increase in lung pathology was also largely reversed in animals infected with the complemented Δrv2623 attB::Phsp60Rv2623 strain ( Figure 3B and C ) . Results of the complementation experiments were further validated using a complemented strain Δrv2623 attB::Prv2623Rv2623 , whose expression of the wildtype universal stress protein is driven by the native rv2623 promoter [18] ( Figure 3D and E ) . In contrast to the result of the guinea pig study , we observed no difference in the kinetics of infection between C57BL/6 mice infected with wildtype M . tuberculosis , Δrv2623 , or the attB::Phsp60 Rv2623 complemented strain in a low dose aerogenic model [19] , as assessed by lung bacterial burden ( Figure 4A ) . However , the mouse is a relatively resistant host to M . tuberculosis , particularly in strains such as C57BL/6 [20] , [21] . In fact , evidence exists that M . tuberculosis triggers an immune response in mice that is in excess of that required for controlling the infection [22] , [23] . Thus , the hypervirulence phenotype of Δrv2623 observed in the susceptible guinea pig model could have been masked in the C57BL/6 mice . Consequently , we examined the virulence of Δrv2623 in the relatively susceptible C3H/HeJ mouse strain [24] . Indeed , the Δrv2623 mutant was markedly more virulent relative to wildtype Erdman M . tuberculosis following aerogenic infection , as assessed by the mean survival time of C3H/HeJ mice infected with these strains ( 62 and 25 . 5 days post infection for Erdman- and Δrv2623-infected mice , respectively , p = 0 . 0014; Figure 4B ) . In agreement with the survival data , quantification of tissue bacterial burden revealed a growth advantage for the Rv2623-deficient mutant relative to wildtype M . tuberculosis Erdman ( Figure 4C ) . Manifestation of this hypervirulence phenotype is apparent as early as 3 weeks post-infection , with the lung bacterial burden of mice infected with Δrv2623 M . tuberculosis ∼100 fold higher than that in the wildtype-infected animals . As in the guinea pig studies , results of complementation experiments involving the reintroduction of a single copy of wildtype rv2623 into Δrv2623 M . tuberculosis reverses the hypervirulence ( Figure 4C ) exhibited in the C3H/HeJ model , thus indicating that the observed growth phenotype of the tubercle bacillus deficient for the universal stress protein is rv2623-specific . Finally , survival of Δrv2623-infected mice was also significantly reduced in another susceptible mouse strain , C3HeB/FeJ ( Figure S1 ) . Together , the animal studies provide strong evidence that Rv2623 regulates the growth of M . tuberculosis in vivo: in the absence of Rv2623 , the tubercle bacillus fails to establish a chronic persistent infection , exhibiting a hypervirulent phenotype . Although the functions of universal stress proteins have yet to be completely defined , there is evidence that many USPs play differential roles in protecting microbes against various environmental stresses [9] . Therefore , the hypervirulence of Δrv2623 in guinea pigs and susceptible mice is intriguing; if Rv2623 provides M . tuberculosis protection against stress , it might be expected that the Rv2623-deficient mutant would be attenuated in vivo . The growth kinetics and survival of the Δrv2623 strain was examined under various stress conditions , including those likely to be present during M . tuberculosis infection . These included oxidative stress ( superoxide anion , O2− ) , DNA damage ( UV irradiation , mitomycin C ) , heat shock ( 53°C ) , and acidic culture ( pH 4 . 0 ) . The use of streptonigrin , an antibiotic whose toxicity correlates with levels of free iron , was based on the observation that the intracellular environment of macrophages can induce a iron-scavenging response in mycobacteria [25] , perhaps as a means of maintaining adequate levels of this important growth factor , and that an E . coli USP was shown to regulate iron uptake [9] . The results showed that the mutant strain was no more susceptible to growth inhibition than was wild type Erdman under all of the stress conditions tested ( Figure S2 ) . These results support the notion that it is unlikely that M . tuberculosis Rv2623 is essential for resistance to stresses encountered in the host , which is consistent with the observed in vivo hypervirulence phenotype of Δrv2623 . We began a biochemical characterization of Rv2623 in order to gain insight into the relationship between the molecular structure/function of this USP and it's growth-regulatory properties . M . tuberculosis Rv2623 was expressed in E . coli and purified to homogeneity for biochemical studies . SDS-PAGE analysis of affinity-purified His6-Rv2623 revealed a single band that approximates the predicted molecular mass of ∼31 . 6 kDa , which was identified by immunoblotting as Rv2623 ( Figure S3 ) . Gel filtration analysis of native His6-Rv2623 revealed that the purified protein exists primarily as a single species with an apparent molecular mass of 61±1 kDa; suggesting that Rv2623 is a dimer under native conditions ( Figure S3 ) , an observation that was later confirmed using nano electrospray ionization ( nano ESI ) mass spectrometry ( data not shown ) . The nucleotide-binding capacity of a subset of USPs was discovered following the observation that MJ0577 , a single-domain USP from Methanococcus jannaschii , co-purifies and co-crystallizes with ATP [26] . On the basis of structures of ATP-binding and non-ATP-binding USPs , a G-2X-G-9X-G ( S/T ) motif was suggested to be essential for the binding of ATP [27] . The presence of this motif in each of the two tandem USP domains of Rv2623 [7] raised the possibility that this protein possesses ATP binding activity . An HPLC-based examination of supernatants from boiled samples of His6-Rv2623 demonstrated that His6-Rv2623 co-purifies with both ATP and ADP ( Figure 5 ) . Analysis of E . coli-expressed Rv2623 using nano ESI mass spectrometry also demonstrated that an ATP-saturated form of dimeric Rv2623 ( composed of 2 bound ATP molecules per monomer ) constitutes at least half of the purified sample ( data not shown ) . Measurement of the binding stoichiometry , which comprised HPLC-based quantification of adenine nucleotides from the boiled supernatant and spectral analysis of heat denatured Rv2623 following reconstitution in 6 M guanidine-HCl , yields 1 . 4±0 . 2 nucleotide equivalents/monomer with an overall content of 86±4% ATP ( 14±4% ADP ) . Thus , Rv2623 binds endogenous adenine nucleotides in E . coli , and the association is sufficiently tight that nearly 75% of the nucleotide binding sites are occupied upon purification . Indeed , nucleotide did not completely dissociate from the protein following an extensive , two-week dialysis with multiple changes against nucleotide-free buffer ( approximately 0 . 3 nucleotide equivalents per monomer remain ) . It is conceivable that the presence of ADP is the consequence of an Rv2623-associated ATP activity and this putative ATPase function is currently under investigation . To examine the biochemical mechanisms responsible for Rv2623 function , we determined the crystal structure of wild-type Rv2623 at a resolution of 2 . 9 Å . The structure reveals a compact , 2-fold symmetric dimer . Each monomer is composed of tandem USP domains [residues 6–154 ( domain 1 ) , 155–294 ( domain2 ) ] that share 26% sequence identity and significant structural homology ( residues 6–154 and 155–294 comprise domains 1 and 2 , respectively; interdomain rms = 2 . 04 Å for 140 equivalent Cα's ) . Individual domains , which consist of a twisted , five-stranded , parallel β sheet flanked by four α helices , unite through an antiparallel , cross-strand ( β5–β10 ) interaction that produces a central dyad axis between β5/β10 and a continuous , ten-stranded , mixed β sheet in the complete monomer . Each domain possesses a pair of conserved βαβ motifs ( domain 1: β1-L1-α1- β2 , β4-L2-α4-β5; domain 2: β6-L3-α5-β7 , β9-L4-α8-β10 ) that encompass four loops ( designated L1–L4 ) responsible for ATP recognition ( Figure 6A and C ) . A “U-shaped” ATP molecule that lies within a cleft near the monomer surface is stabilized by 1 ) a cluster of hydrophobic residues ( I14 , V41 , H42 , V116/132/261/277/281 , L136 , A175 ) that forge the adenine/ribose-binding scaffold , 2 ) a pair of conserved L1/L3 aspartates ( D15-L1/D167-L3 ) , and 3 ) small phosphoryl/ribosyl-binding residues within the G-2X-G-9X-G ( S/T ) motifs that comprise L2/L4 ( G120/265/267/268 and S131/276 ) ( Figures 6A , C and 7A ) . Dimerization of Rv2623 occurs along a 2-fold axis orthogonal to the intramonomer dyad and juxtaposes ATP binding pockets from opposing monomers ( Figure 6B ) . Phylogenomic analysis places Rv2623 in a Uniprot/TrEMBL family ( Q5YVE7 ) of 370 tandem-domain USPs , and a 113-member subfamily ( N631 ) that consists almost exclusively of actinobacterial representatives ( Text S1 ) . Structure-based sequence alignments of both Rv2623 domains with the N631 consensus suggest that domain 2 , which exhibits significantly higher conservation than domain 1 across global and ATP-binding subfamily consensus sequences , represents the ancestral domain among ATP-binding USPs with tandem-type architectures . Interestingly , the domain fold and interdomain organization observed for Rv2623 is broadly conserved: these features are shared among single domain USP structures , both monomeric and dimeric , that are presently represented within the PDB . As this manuscript was under preparation , a second , lower resolution ( 3 . 2 Å ) crystal form of Rv2623 ( PDB ID 2JAX ) was released for public access . This structure is nearly identical to the present model as demonstrated by superposition over the ATP ligands and the monomeric and dimeric forms ( rmsds are 0 . 57 and 0 . 81 for 258 and 517 matched CA's , respectively ) . The differences localize primarily to flexible loop regions ( residues 44–58 , 150–159 ) that , while disordered in 2JAX , are partially stabilized in the present structure by local crystal contacts . To gain insight into the ATP-binding mode ( s ) exhibited by Rv2623 , the structural features of the ATP-binding pocket of domains 1/2 were compared to the monomer fold of the representative ATP-binding USP , MJ0577 ( PDBID 1MJH ) [26] . Overlay of these structures reveals very considerable similarity for the residues that form the binding pockets and the associated ATP molecules , for which the triphosphoryl moieties assume virtually indistinguishable conformations . Relatively subtle structural and phylogenetic differences that exist between the ATP-binding pockets might nevertheless confer divergent binding and/or regulatory properties to the tandem domains . To explore the relationship between the putative ATP-dependent biochemical function of Rv2623 and the growth-regulating attribute of Rv2623 , we engineered mutations within the L1 ( D15E ) and β4 ( G117A ) conserved residues that were predicted , on the basis of the crystal structure , to disrupt ATP recognition ( Figure 7A ) . In silico replacement of the β4 G117 side chain hydrogen with a methyl group suggested that any residue larger than glycine at this position is likely to perturb both of the conserved loop regions in contact with the nucleotide . Similarly , extension of the D15 side chain to glutamate was also predicted to interfere with the ATP-binding conformation ( Figure 7A ) . HPLC analysis of nucleotides extracted from Rv2623D15E and Rv2623G117A revealed that the mutant proteins are indeed deficient in ATP-binding , exhibiting ∼34% ( p<0 . 001 ) and ∼29% ( p = 0 . 0018 ) of the amount of ATP bound by wild-type Rv2623 , respectively ( Figure 7B ) . Likewise , following an overnight incubation with [α-33P] ATP at 4°C , the amount of protein-bound radioactivity , which represented a very small fraction of the total ATP binding sites , was significantly less for the mutant proteins than wild-type Rv2623 ( data not shown ) . Importantly , thermal denaturation profiles of wild-type Rv2623 , Rv2623D15E and Rv2623G117A demonstrated virtually identical Tm values , implying that the native Rv2623 fold was not destabilized by these mutations ( Figure 7C ) . It is therefore likely that the D15E and G117A mutations produced local structural changes in the ATP binding loops that contributed directly to the reduced levels of bound ATP in comparing to wild-type Rv2623 . We next sought to probe the relationship between the nucleotide-binding capacity and growth regulation by this mycobacterial USP . Both the D15E and G117A mutant proteins were overexpressed in M . smegmatis mc2155 at levels equivalent to that of wild-type Rv2623 ( Figure S4 ) . Results of these studies demonstrated that while overexpression of wildtype Rv2623 retards the growth of the recipient strain relative to cells transformed with vector alone , growth of the strains overexpressing ATP-binding-deficient mutant Rv2623 are only minimally affected by overexpression as assessed by spotting serial dilutions of the cultures of the appropriate strains onto solid Middlebrook 7H10 agar ( data not shown ) as well as by monitoring the time to detection using the BD BACTEC 9000MB system ( Figure 8A ) . The distinct effects exhibited by the wild type and the G117A and D15E mutants defective in ATP binding suggests a direct correlation between growth attenuation and ATP binding ( Figure 7B ) . To examine whether the effects of overexpression of Rv2623 on M . smegmatis are operative in virulent M . tuberculosis , the growth kinetics of the Erdman strain overexpressing wildtype Rv2623 , as well as the Rv2623G117A and the Rv2623D15E mutant proteins , were evaluated in vitro using the BACTEC 9000MB system ( Figure 8B ) . As in the M . smegmatis studies , the results show that overexpression of Rv2623 in M . tuberculosis results in marked retardation of growth . Furthermore , this growth attenuation is not observed in M . tuberculosis strains overexpressing the G117A or the D15E mutant Rv2623 ( Figure 8B ) . Taken together , these data strongly suggest that the ability of Rv2623 to regulate growth of M . smegmatis and M . tuberculosis is dependent on an ATP-dependent process .
Despite the significance of M . tuberculosis latency in pathogenesis , the mechanisms by which the tubercle bacillus establishes and maintains the latent state remain incompletely defined . Identification of M . tuberculosis genes that are induced by hypoxia and nitric oxide ( NO ) in vitro provides a framework for understanding the physiology of dormant bacilli [3] , [4] , [5] . These genes , referred to as the dormancy regulon , are transcriptionally regulated by the mycobacterial two-component system DosR-DosS under hypoxic conditions [4] . Indeed , it has been shown that both the cognate sensor histidine kinase DosS ( a member of the dormancy regulon ) as well as an “orphan” kinase , DosT , functioning as redox and hypoxia sensors , respectively; can regulate DosR activity , and that O2 , NO , and CO can modulate the activity of these two kinases via interaction with a haem prosthetic group [28] , [29] , [30] , [31] , [32] . The biological significance of the dormancy regulon has been underscored by in vitro studies of dosR mutants of BCG and M . tuberculosis , which demonstrated the requirement of this transcription factor for survival under hypoxic conditions [3] , [33] . Further , upregulation of the expression of certain dormancy regulon genes have been implicated in tuberculosis transmission as well as the virulence of the epidemiologically important W-Beijing lineage of M . tuberculosis [34] , [35] . There are eight genes in the M . tuberculosis genome annotated to encode USP family proteins [7] . We studied the M . tuberculosis USP rv2623 because it is one of the most highly induced genes in the dormancy regulon when bacilli are subjected to hypoxia and nitrosative stress [3] , [4] , [5] , [36] , [37] . More important , rv2623 was also shown to be up-regulated when the tubercle bacillus is internalized by human and mouse macrophages [10] , [38] as well as in the lungs of mice with persistent M . tuberculosis infection [11] . These latter observations suggest that the induction of rv2623 may have biological relevance . The precise mechanisms by which Rv2623 expression is regulated remain to be defined . Recent transcriptional analysis of Rv2623 , while confirming the essentiality of the two 18 bp palindromic DosR-binding motifs that are present in the promoter region of this gene [38] for induction of Rv2623 under low oxygen conditions , also demonstrated the presence of additional regulatory elements within the rv2623 5′-untranslated region [18] . These results suggest that the regulation of Rv2623 is likely complex . The M . tuberculosis dormancy response features a dramatic decrease in metabolic activity , resulting in a rapid decrease in bacterial replication [39] . Therefore , it is possible that deficiency in certain members of the dormancy regulon could result in inability of the tubercle bacillus to enter a latent state in the infected host , leading to unrestrained growth and thus , hypervirulence . Indeed , specific members of the M . tuberculosis dormancy regulon whose insufficiency results in a hypervirulence phenotype have been reported [40] , [41] . In certain experimental tuberculosis animal models , DosR deficiency has been associated with a hypervirulence phenotype [41] . However , DosR deficiency has also been reported to have no effect on M . tuberculosis virulence or to lead to an attenuated phenotype [42] , [43] . The discrepancies regarding M . tuberculosis virulence in these DosR studies are unclear , but could be due to differences in experimental systems employed . Insufficiency of the chaperone-like α-crystallin encoded by M . tuberculosis hspX ( acr ) has also been shown to be associated with hypervirulence in a BALB/c mouse model of tuberculosis [40] . In the present study , an rv2623 knockout mutant of virulent M . tuberculosis Erdman fails to establish a chronic persistent infection , displaying a hypervirulent phenotype in susceptible hosts , as assessed by lung bacterial burden , histopathology , and mortality . Results of the complementation studies indicate that the phenotype is Rv2623-specific . This growth-regulating phenomenon is echoed by the observation that ectopic overexpression of Rv2623 results in attenuation of mycobacterial growth . Together , these data strongly suggest that the M . tuberculosis USP Rv2623 has the ability to regulate growth in vitro and in vivo , and is required for the establishment of a persistent infection . Intriguingly , ectopic overexpression of HspX by the same means employed by our study also resulted in an attenuated growth phenotype compared with LacZ-overexpressing controls [44] , suggesting that these two tightly co-regulated “stress” proteins might have similar growth-regulatory roles during dormancy . Bioinformatic and experimental evidence suggest that nucleotide-binding capacity represents a discriminating biochemical feature that facilitates USP protein classification . Putative functional differences between USPs are implied by their assignment to two subclasses: one whose members do not bind ATP and another whose constituents bind and hydrolyze adenine nucleotide substrates [8] , [26] , [27] , [45] , [46] . A structural comparison between the prototypic members of the two subclasses , the non-ATP-binding UspA homolog ( H . influenzae , PDB ID 1JMV ) and the ATP-binding USP , MJ0577 ( M . jannaschii , PDB ID 1MJH ) revealed that while both proteins exhibit a similar fold , conserved glycine residues within the ATP-binding loop of the latter are substituted with bulky amino acids that preclude ATP recognition in the former [26] , [27] . The unique nucleotide-binding pocket of this protein family is structurally distinct from those commonly encountered in ATP-binding proteins [26] , [47] , [48] . Specific roles for USP family proteins are just beginning to be characterized , and early functional classifications have been informed by ATP-binding capacity [9] . While the non-ATP-binding UspA homologs appear to play diverse roles in promoting survival under a variety of environmental insults [15] , [49] , [50] , [51] , the function ( s ) of ATP-binding type USPs remain unclear [9] . Based on in silico analyses , Florczyk et al . classified Rv2623 as belonging to a novel class of ATPases [52] , although formal evidence for ATP binding by this protein has not been reported . This study has provided substantial biochemical and structural evidence that M . tuberculosis Rv2623 is a bona fide nucleotide-binding USP: i ) E . coli-expressed His6-Rv2623 co-purifies with tightly bound ATP and ADP; ii ) analysis of the 2 . 9 Å -resolution Rv2623 crystal structure , the first molecular model of a tandem-type USP , reveals four ATP-bound nucleotide-binding pockets; and iii ) point mutations ( D15E , G117A ) within the conserved L1 ( D15E ) and β4 ( G117A ) regions of the structure , which were predicted to disrupt nucleotide-binding , yielded mutant proteins with attenuated ATP-binding capacity . Furthermore , given that the attenuated growth phenotype caused by overexpression of Rv2623 could be abrogated by mutations that interfere with the binding of this protein to its nucleotide substrate , it is likely that the mycobacterial growth-regulatory faculty of Rv2623 is mediated by an ATP-dependent function . In summary , the results of the present study have revealed that the M . tuberculosis USP Rv2623 has the ability to regulate mycobacterial growth , as evident by the in vivo hypervirulence phenotype of Δrv2623 , which fails to establish a persistent infection in susceptible hosts , as well as the growth attenuation observed in mycobacteria overexpressing this USP . Thus , M . tuberculosis Rv2623 may serve the function of promoting mycobacterial transition into latency . The latent state allows persistence in infected individuals of tubercle bacilli that can reactivate to cause active disease and to disseminate when the immune status of the host is compromised . As a result , Rv2623 may contribute significantly to the propagation of the tubercle bacillus in the human host and the difficulties in eradicating tuberculosis . Mechanistically , results of the mutagenesis studies have shown that Rv2623 regulates growth through ATP-dependent function . Clearly , much remains to be learned regarding how the ATP-dependent function of Rv2623 contributes to growth regulation . It has been proposed that a nucleotide-binding USP from M . jannaschii , MJ0577 , whose ability to hydrolyze ATP is dependent on interaction with factor ( s ) present in the cell extract of this hyperthermophile [26] , functions as a molecular switch much like the Ras protein family , whose GTP hydrolysis ability is modulated by interaction with a number of regulatory proteins [53] , [54] , [55] . The fact that E . coli-expressed Rv2623 co-purifies with ADP as well as ATP suggests the possibility that this mycobacterial USP , like MJ0577 , is capable of ATP hydrolysis . It is therefore conceivable that M . tuberculosis Rv2623 , as a component of the yet-to-be defined dormancy signaling pathway ( s ) , functions as a molecular switch by virtue of its ATP-binding and putative ATP-hydrolyzing properties , to mediate the establishment of tuberculous latency . Experiments designed to investigate the potential ATP-hydrolyzing activity of Rv2623 are currently underway . Recent identification of the DosR-dependent dormancy regulon [3] , [4] , [5]; the DosR-independent enduring hypoxic response , which involves over 200 mycobacterial genes , including those known to regulate bacteriostasis [42]; and the demonstration that M . tuberculosis redox and hypoxia sensors can interact with multiple ligands that differentially modulate the activity of these important kinases [28] , [29] , [30] , [31] , [32] , predict a complex regulatory network for tuberculous latency . Elucidation of how ATP-binding and , potentially , the hydrolysis of ATP by Rv2623 regulate M . tuberculosis dormancy-signaling pathways will likely illuminate the mechanisms by which the tubercle bacillus establishes persistence .
Liquid cultures of M . tuberculosis and M . smegmatis strains were grown in Middlebrook 7H9 medium ( Becton Dickinson , Sparks , MD ) supplemented with 0 . 2% glycerol ( Sigma , St . Louis , MO ) , 0 . 05% Tween 80 ( Sigma , St . Louis , MO ) , and 10% oleic acid-albumin-dextrose-catalase ( OADC ) enrichment media ( Becton Dickinson , Sparks , MD ) . For the determination of the number of colony forming units ( CFU ) and examination of growth on solid media , Middlebrook 7H10 agar medium ( Becton Dickinson ) supplemented with 0 . 5% glycerol and 10% OADC was used . The Δrv2623 mutant strain was maintained in media supplemented with 50 µg/ml hygromycin B ( Roche ) and cultures of complemented , Rv2623-overexpressing strains contained kanamycin ( 40 µg/ml ) . Growth was also examined in minimal Sauton's medium ( 4 g asparagine , 2 g sodium citrate , 0 . 5 g K2HPO4·3H2O , 0 . 5 g MgSO4·7H2O , 0 . 05 g ferric ammonium citrate , 60 g glycerol in 1 L of H2O supplemented with 0 . 05% Tween80 and antibiotics as required ) . For some experiments , growth was monitored by the BD BACTEC 9000 system ( Becton Dicinson ) . Stationary phase M . tuberculosis or M . smegmatis cultures were inoculated in triplicates ( 105 or 104 CFU ) into vials of liquid medium containing a sensor compound that fluoresces upon depletion of oxygen as a result of bacterial growth . The time to detection reflects the rate of bacterial growth . Replacement of genomic rv2623 was performed by allelic exchange using a specialized transducing phage delivery system as previously described [12] . Transformants were analyzed by PCR and Southern blot to confirm replacement of rv2623 with a hygromycin cassette , yielding Δrv2623 . A complemented strain was generated as described previously [56] by transformation of Δrv2623 with a plasmid vector that integrates at the attB site and bears the rv2623 coding sequence under transcriptional control of the constitutive hps60 promoter or the endogenous rv2623 promoter [18] , yielding Δrv2623 attB::Phsp60 Rv2623 and Δrv2623 attB::Prv2623 Rv2623; respectively . The M . tuberculosis hsp60 promoter fusion was also used to overexpress Rv2623 via subcloning of this region into pMV261 , a non-integrating variant of pMV361 to yield pMV261::rv2623 , which is self-replicating at 3–5 copies/cell [57] ( Text S1 ) . Log phase cultures ( OD600 = 0 . 8–1 . 0 ) of Erdman and Δrv2623 were diluted 1∶10 into Sauton's media containing various stress-inducing chemical agents ( phenazine methosulfate , streptonigrin and mitomycin C ) at the indicated concentrations or for acid stress into 7H9+10% OADC+0 . 05% Tween 80 ( pH = 4 . 0 ) and grown at 37°C for several days . Growth was monitored by OD600 . Survival of these strains following heat shock was compared after a shift of log phase cultures from 37°C to 53°C and determining the number of CFU/ml at various time points thereafter . For irradiation with UV light , cells were plated onto solid 7H10 agar supplemented with 0 . 05% Tween80 and exposed to increasing amounts of UV energy ( UV Stratalinker 1800 , Stratagene ) . Surviving cells were enumerated and the data are expressed as percent survival as compared to unexposed controls . In addition , cells were treated with the indicated concentrations of mitomycin C for a period of 1 hour followed by determination of surviving CFU/ml . See Figure S2 for details . Outbred Hartley guinea pigs ( ∼500 g body weight ) ( Charles River Laboratories , North Wilmington , MA ) were given a low dose of M . tuberculosis using a Madison chamber aerosol generation device calibrated to deliver ∼30 CFU [58] . Guinea pigs were sacrificed ( n = 5 ) at 20 , 40 , and 60 days post infection for histological analysis and determination of organ bacterial burden . Histological analysis of infected tissues was performed by scoring individual tissue sections based on criteria described in Protocol S1 . For the murine tuberculosis model , six-to-eight-week-old mice ( Jackson Laboratories , Bar Harbor , Maine ) were infected with M . tuberculosis via aerosol ( In-Tox Products , Albuquerque , NM ) as previously described [19] with ∼100 CFU ( C57BL/6 ) or 750–1000 CFU ( C3H/HeJ , C3HeB/FeJ ) . For CFU determination , mice were sacrificed ( n = 3 ) at the times indicated and portions of the lung , liver and spleen were homogenized in PBS+0 . 05% Tween 80 , diluted , and plated onto solid 7H10 media . The coding sequence of rv2623 was PCR-amplified from M . tuberculosis Erdman genomic DNA and subcloned into the expression vector pQE80L ( Qiagen , Inc . ) , which encodes an N-terminal His6-tag , producing the plasmid pQE-rv2623 ( Text S1 ) . Expression was carried out following isopropyl beta-D-thiogalactoside ( IPTG ) induction of BL21 E . coli transformed with pQE-rv2623 . His6-Rv2623 was then affinity-purified to homogeneity from BL21 cell lysates using Ni-NTA agarose ( Qiagen , Inc . ) according to the manufacturer's instructions . For crystallization , purified Rv2623 was concentrated to 12 mg/ml using a 10 kDa Molecular Weight Cut Off ( MWCO ) centrifugal filter ( Amicon ) , and frozen at −80°C . A Superdex 200 10/300 GL column ( GE Healthcare Life Sciences ) was equilibrated with Rv2623 dialysis buffer ( Text S1 ) and calibrated using the following molecular mass standards: aldolase ( 158 kDa ) , bovine serum albumin ( 67 kDa ) , ovalbumin ( 43 kDa ) , chymotrypsinogen A ( 25 kDa ) as described in the Amersham Pharmacia technical notes ( GE Healthcare Life Sciences ) . The flow rate was set to 0 . 15 ml/min and elution of the protein was monitored at 280 nm . Nucleotides were extracted from purified Rv2623 by boiling . Samples were then loaded onto an analytical , anion-exchange HPLC column ( AX300 , Eprogen Inc . or Mono Q HR 5/5 , GE Healthcare ) . Samples were eluted isocratically using NaH2PO4 , pH 5 . 5 ( AX300 ) or using a ammonium phosphate pH 7 . 0 ( 0 . 02–1 . 0 M ) step gradient ( Mono Q ) ( Text S1 ) . Nucleotides were identified on the basis of retention time relative to nucleotide standards , and quantified by peak area . Following Ni affinity chromatography , His6-Rv2623 samples for stoichiometry measurements ( 500 µl , 8–120 µM protein ) were attained by subjecting the Ni-purified fractions to rapid desalting over a HiPrep Desalting 26/10 column ( GE Healthcare ) equilibrated with 50 mM NaCl , 2 mM MgCl2 , 10% glycerol , 20 mM HEPES pH 8 . 0 and/or an additional purification using a MonoQ 10/100 GL column ( GE Healthcare ) equilibrated with the desalting buffer and eluted with a linear salt gradient ranging from 50 mM to 250 mM NaCl . Amicon centrifugal filtration concentrators ( MWCO = 30 kDa ) were employed for final concentration steps prior to analysis . Bound nucleotide was then released by boiling and quantified by HPLC ( under the Mono Q HR 5/5 conditions ) according to the methodology described above and Text S1 . The heat-precipitated protein was subsequently reconstituted in 6 M guanidine HCl and its concentration was determined by spectrophotometric measurement of the protein peak at 280 nM and a molar extinction coefficient for Rv2623 at 280 nm ( 54 , 640/Mcm ) . This extinction coefficient was determined using a 6 M GuHCl-reconstituted ( nucleotide-free ) sample of Rv2623 that had been subject to quantitative amino acid analysis at the Yale Keck Facility . The nucleotide binding stoichiometry was calculated as the molar ratio of the released nucleotide to protein . Single amino acid substitutions were incorporated into the rv2623 coding region contained in appropriate expression vectors by mismatched PCR priming ( Text S1 ) . Individual PCR reactions were performed using either pMV261::rv2623 or pQE-rv2623 plasmid templates for mycobacterial overexpression and protein purification , respectively . Then the pMV261:: rv2623 mutants expression vector DNA was used to transformed into M . smegamatis mc2155 and the DNA of pQE-rv2623 mutants was transformed into E . coli BL21 ( DE3 ) . Thermal denaturation curves were determined for purified wild type and mutant Rv2623 using an IQ5 Real Time PCR Detection System ( Bio-Rad ) following incubation with SYPRO Orange protein gel stain ( Invitrogen ) ( Text S1 ) . ATP and MgCl2 were added to final concentrations of 0 . 8 mM and 1 . 8 mM , respectively , in the protein sample prior to crystallization by sitting-drop vapor-diffusion at 4°C ( Text S1 ) . Diffraction data were collected at the National Synchrotron Light Source beamline X29A on an ADSC Q315 detector through the Macromolecular Crystallography Research Resource ( PXRR ) mail-in crystallography program . Data processing and scaling was performed with the HKL2000 suite . The structure of M . tuberculosis Rv2623 , which contains two USP domains in tandem , and whose first domain shares 25% sequence identity with the USP M . tuberculosis Rv1636 , was solved using the molecular replacement method and a CHAINSAW-generated search model consisting of the Rv1636 dimer ( PDB ID 1TQ8 chains A , B ) , using a 2 . 9 Å , C2221 dataset ( a = 173 , b = 241 . 5 , c = 241 . 7 ) . A starting polyalanine model ( R/Rfree = . 56/ . 57 ) of four dimers was subject to four refinement cycles , each consisting of multi-domain rigid-body refinement in Molrep , a single cycle of restrained MLF refinement in Refmac5 ( to obtain input FOMs for DM ) , 20 cycles of phase extension in DM ( as above ) , and manual rebuilding of the polyalanine backbone in Coot . As R-factors converged ( R/Rfree = . 40/ . 42 ) , ∼80% of the side chains were positioned and the Rv2623 dimers were further rebuilt and refined ( R/Rfree = . 31/ . 33 ) in CNS using high NCS restraint weights ( 400 kcal/mol ) with rigid-body , energy minimization , grouped isotropic B factor , and simulated annealing refinement protocols . ATP and Mg2+ were built within composite omit density ( calculated in CNS ) during the final rebuilding/refinement cycles conducted with relaxed NCS restraints in Arp-waters and Refmac5 , yielding final R/Rfree = 24 . 5/26 . 5 . SigmaA-weighted difference maps calculated with the refined model reveal weak , fragmented density for a pseudotranslated copy of the Rv2623 dimer whose corresponding NCS translational vector ( uvw = . 500 , . 012 , . 494 ) appears in the native patterson at 7 . 7% of the origin peak height . Data collection and refinement statistics are summarized in Protocol S1 . The coordinates of M . tuberculosis Rv2623 have been submitted to the protein databank ( PBDID 3CIS ) . | Mycobacterium tuberculosis poses serious threats to public health worldwide . The ability of this pathogen to establish in the host a clinically silent , persistent latent infection that can subsequently reactivate to cause diseases constitutes a major challenge in controlling tuberculosis . Our study showed that an M . tuberculosis mutant that is deficient in a universal stress protein ( USP ) designated Rv2623 fails to establish a chronic persistent infection in animal hosts . The mutant strain exhibits a hypervirulent phenotype as assessed by increased bacillary growth , pathology , and mortality in infected animals relative to the parental strain . Consistent with this in vivo growth-regulating attribute , we demonstrated that Rv2623 , when expressed in mycobacteria at levels higher than that of the wild-type strain , retards bacterial growth in vitro . Using biochemical and biophysical analyses , including the Rv2623 crystal structure , we showed that this USP binds to ATP within a novel ATP-binding pocket . Through targeted mutagenesis studies , we further determined that the ability of Rv2623 to regulate bacillary growth is dependent on its ATP-binding capacity . Our data strongly suggest Rv2623 as a critical component that regulates the entry of M . tuberculosis into a chronic persistent growth phase , and therefore provide valuable insight into tuberculous dormancy and uncover new opportunities for the development of novel anti-tuberculous therapies . | [
"Abstract",
"Introduction",
"Results",
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"Methods"
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] | 2009 | Mycobacterium tuberculosis Universal Stress Protein Rv2623 Regulates Bacillary Growth by ATP-Binding: Requirement for Establishing Chronic Persistent Infection |
Despite internal complexity , tumor growth kinetics follow relatively simple laws that can be expressed as mathematical models . To explore this further , quantitative analysis of the most classical of these were performed . The models were assessed against data from two in vivo experimental systems: an ectopic syngeneic tumor ( Lewis lung carcinoma ) and an orthotopically xenografted human breast carcinoma . The goals were threefold: 1 ) to determine a statistical model for description of the measurement error , 2 ) to establish the descriptive power of each model , using several goodness-of-fit metrics and a study of parametric identifiability , and 3 ) to assess the models' ability to forecast future tumor growth . The models included in the study comprised the exponential , exponential-linear , power law , Gompertz , logistic , generalized logistic , von Bertalanffy and a model with dynamic carrying capacity . For the breast data , the dynamics were best captured by the Gompertz and exponential-linear models . The latter also exhibited the highest predictive power , with excellent prediction scores ( ≥80% ) extending out as far as 12 days in the future . For the lung data , the Gompertz and power law models provided the most parsimonious and parametrically identifiable description . However , not one of the models was able to achieve a substantial prediction rate ( ≥70% ) beyond the next day data point . In this context , adjunction of a priori information on the parameter distribution led to considerable improvement . For instance , forecast success rates went from 14 . 9% to 62 . 7% when using the power law model to predict the full future tumor growth curves , using just three data points . These results not only have important implications for biological theories of tumor growth and the use of mathematical modeling in preclinical anti-cancer drug investigations , but also may assist in defining how mathematical models could serve as potential prognostic tools in the clinic .
Neoplastic growth involves a large number of complex biological processes , including regulation of proliferation and control of the cell cycle , stromal recruitment , angiogenesis and escape from immune surveillance . In combination , these cooperate to produce a macroscopic expansion of the tumor volume , raising the prospect of a possible general law for the global dynamics of neoplasia . Quantitative and qualitative aspects of the temporal development of tumor growth can be studied in a variety of experimental settings , including in vitro proliferation assays , three-dimensional in vitro spheroids , in vivo syngeneic or xenograft implants ( injected ectopically or orthotopically ) , transgenic mouse models or longitudinal studies of clinical images . Each scale has its own advantages and drawbacks , with increasing relevance tending to coincide with decreasing measurement precision . The data used in the current study are from two different in vivo systems . The first is a syngeneic Lewis lung carcinoma ( LLC ) mouse model , exploiting a well-established tumor model adopted by the National Cancer Institute in 1972 [1] . The second is an orthotopic human breast cancer xenografted in severe combined immunodeficient ( SCID ) mice [2] . Tumor growth kinetics has been an object of biological study for more than 60 years ( see e . g . [3] as one of the premiere studies ) and has been experimentally investigated extensively ( see [4] for a thorough review and [5]–[8] for more recent work ) . One of the most common findings for animal [9] and human [10]–[12] tumors alike is that their relative growth rates decrease with time [13]; or equivalently , that their doubling times increase . These observations suggest that principles of tumor growth might result from general growth laws , often amenable to expression as ordinary differential equations [14] . The utility of these models can be twofold: 1 ) testing growth hypotheses or theories by assessing their descriptive power against experimental data and 2 ) estimating the prior or future course of tumor progression [9] , [15] either as a personalized prognostic tool in a clinical context [16]–[20] , or in order to determine the efficacy of a therapy in preclinical drug development [21] , [22] . Cancer modeling offers a wide range of mathematical formalisms that can be classified according to their scale , approach ( bottom-up versus top-down ) or integration of spatial structure . At the cellular scale , agent-based models [23] , [24] are well-suited for studies of interacting cells and implications on population-scale development , but computational capabilities often limit such studies to small maximal volumes ( on the order of the mm3 ) . The tissue scale is better described by continuous partial differential equations like reaction-diffusion models [19] , [25] or continuum-mechanics based models [26] , [27] , when spatial characteristics of the tumor are of interest . When focusing on scalar data of longitudinal tumor volume ( which is the case here ) , models based on ordinary differential equations are more adapted . A plethora of such models exist , starting from proliferation of a constant fraction of the tumor volume , an assumption that leads to exponential growth . This model is challenged by the aforementioned observations of non-constant tumor doubling time . Consequently , investigators considered more elaborate models; the most widely accepted of which is the Gompertz model . It has been used in numerous studies involving animal [9] , [28]–[31] or human [12] , [15] , [30] , [32] data . Other models include logistic [30] , [33] or generalized logistic [11] , [31] formalisms . Inspired by quantitative theories of metabolism and its impact on biological growth , von Bertalanffy [34] derived a growth model based on balance equations of metabolic processes . These considerations were recently developed into a general law of biological growth [35] and brought to the field of tumor growth [36] , [37] . When the loss term is neglected , the von Bertalanffy model reduces to a power law ( see [5] , [38] for applications to tumor growth ) . An alternative , purely phenomenological approach led others [39] to simply consider tumor growth as divided into two phases: an initial exponential phase then followed by a linear regimen . Recently , influences of the microenvironment have been incorporated into the modeling , an example being the inclusion of tumor neo-angiogenesis by way of a dynamic carrying capacity [40] , [41] . Although several studies have been conducted using specific mathematical models for describing tumor growth kinetics , comprehensive work comparing broad ranges of mathematical models for their descriptive power against in vivo experimental data is lacking ( with the notable exception of [30] and a few studies for in vitro tumor spheroids [33] , [42]–[44] ) . Moreover , predictive power is very rarely considered ( see [42] for an exception , examining growth of tumor spheroids ) , despite its clear relevance to clinical utility . The aim of the present study is to provide a rational , quantitative and extensive study of the descriptive and predictive power of a broad class of mathematical models , based on an adapted quantification of the measurement error ( uncertainty ) in our data . As observed by others [45] , specific data sets should be used rather than average curves , and this is the approach we adopted here . In the following sections , we first describe the experimental procedures that generated the data and define the mathematical models . Then we introduce our methodology to fit the models to the data and assess their descriptive and predictive powers . We conclude by presenting the results of our analysis , consisting of: 1 ) analysis of the measurement error and derivation of an appropriate error model , subsequently used in the parameters estimation procedure , 2 ) comparison of the descriptive power of the mathematical models against our two datasets , and 3 ) determination of the predictive abilities of the most descriptive models , with or without adjunction of a priori information in the estimation procedure .
Animal tumor model studies were performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . Protocols used were approved by the Institutional Animal Care and Use Committee ( IACUC ) at Tufts University School of Medicine for studies using murine Lewis lung carcinoma ( LLC ) cells ( Protocol: #P11-324 ) and at Roswell Park Cancer Institute ( RPCI ) for studies using human LM2-4LUC+ breast carcinoma cells ( Protocol: 1227M ) . Institutions are AAALAC accredited and every effort was made to minimize animal distress . For all the models , the descriptive variable is the total tumor volume , denoted by V , as a function of time t . It is assumed to be proportional to the total number of cells in the tumor . To reduce the number of degrees of freedom , all the models ( except the exponential V0 ) had a fixed initial volume condition . Although the number of cells that actually remain in the established tumor is probably lower than the number of injected cells ( ∼60–80% ) , we considered 1 mm3 ( cells [49] , i . e . the number of injected cells ) as a reasonable approximation for V ( t = 0 ) . For a given animal j and model M , the general setting considered for prediction was to estimate the model's parameter set using only the first n data points and to use these to predict at a depth d , i . e . to predict the value at time , provided that a measurement exists at this day ( in which case it will be denoted by ) . The resulting best-fit parameter set will be denoted .
The following method was used for analysis of the error made when measuring tumor volume with calipers . One volume per time point per cage was measured twice within a few minutes interval . This gave a total of 133 measurements over a wide range of volumes ( 20 . 7–1429 mm3 ) . These were subsequently analyzed by considering the following statistical representationwhere Y is a random variable whose realizations are the measured volumes , is the true volume , ε is a reduced centered Gaussian random variable , and σE is the error standard deviation . The two measures , termed y1 and y2 , were , as expected , strongly correlated ( Figure 1A , ) . Statistical analysis rejected variance independent of volume , i . e . constant E ( , χ2 test ) and a proportional error model ( E = Y ) was found only weakly significant ( , χ2 test , see Figure 1B ) . We therefore introduced a dedicated error model , defined by ( 22 ) Two main rationales guided this formulation . First , we argued that error should be larger when volume is larger , a fact that is corroborated by larger error bars for larger volumes on growth data reported in the literature ( see Figure 4 in [2] for an example among many others ) . This was also supported by several publications using a proportional error model when fitting growth data ( such as [42] , [60] ) . Since here such a description of the error was only weakly significant , we added a power to account for lower-than-proportional uncertainty in large measurements . Second , based on our own practical experience of measuring tumor volumes with calipers , for very small tumors , the measurement error should stop being a decreasing function of the volume because of detectability limits . This motivated the introduction of the threshold Vm . After exploration of several values of Vm and α , we found to be able to accurately describe dispersion of the error in our data ( , χ2 test , see Figure 1C ) . This yielded an empirical value of We did not dispose of double measurements for the breast tumor data and the error analysis was performed using the lung tumor data set only . However , the same error model was applied to the breast tumor data , as both relied upon the same measurement technique . This result allowed quantification of the measurement error inherent to our data and was an important step in the assessment of each model's descriptive power . We tested all the models for their descriptive power and quantified their respective goodness of fit , according to various criteria . Two distinct estimation procedures were employed . The first fitted each animal's growth curve individually ( minimization of weighted least squares , with weights defined from the error model of the previous section , see Material and Methods ) . The second method used a population approach and fitted all the growth curves together . Results are reported in Figure 2 and Tables 1 and 2 . Parameter values resulting from the fits are reported in Tables 3 and 4 . Figure 2A depicts the representative fit of a given animal's growth curve for each data set using the individual approach . From visual examination , the exponential 1 ( 1 ) , logistic ( 2 ) and exponential-linear ( 1 ) models did not well explain lung tumor growth and the exponential 1 ( 1 ) and logistic ( 2 ) models did not satisfactorily fit the breast tumor growth data . The other models seemed able to describe tumor growth in a reasonably accurate fashion . These results were further confirmed by global quantifications over the total population , such as by residuals analysis ( Figure 2C ) and global metrics reported in Tables 1 and 2 . When considering goodness-of-fit only , i . e . looking at the minimal least squared errors possibly reached by a model to fit the data ( metric in Tables 1 and 2 ) , the generalized logistic model ( 3 ) exhibited the best results for both data sets ( first column in Tables 1 and 2 ) . This indicated a high structural flexibility that allowed this model to adapt to each growth curve and provided accurate fits . On the other hand , the exponential 1 ( 1 ) and logistic ( 2 ) models clearly exhibited poor fits to the data , a result confirmed by almost all the metrics ( with the exception of the AICc ) . The two models that were shown unable to describe our data in the previous section , namely the exponential 1 ( 1 ) and logistic ( 3 ) models , were excluded from further analysis . The remaining ones were assessed for their predictive power . The challenge considered was to predict future growth based on parameter estimation performed on a subset of the data containing only n data points ( with n<Ij for a given j ) . We refer to the Materials and Methods section for the definitions of prediction metrics and success scores . When relatively fewer data points were used , for example with only three , individual predictions based on individual fits were shown to be globally limited for the lung tumor data , especially over a large time frame ( Figure 4 . A , Table 5 ) . However , this situation is likely to be the clinically relevant since few clinical examinations are performed before the beginning of therapy . On the other hand , large databases might be available from previous examinations of other patients and this information could be useful to predict future tumor growth in a particular patient . In a preclinical setting of drug investigation , tumor growth curves of animals from a control group could be available and usable when inferring information on the individual time course of one particular treated animal . An interesting statistical method that could potentiate this a priori information consists in learning the population distribution of the model parameters from a given database and to combine it with the individual parameter estimation from the available restricted data points on a given animal . We investigated this method in order to determine if it could improve the predictive performances of the models . Each dataset was randomly divided into two groups . One was used to learn the parameter distribution ( based on the full time curves ) , while the other was dedicated to predictions ( limited number of data points ) . For a given animal of this last group , no information from his growth curve was used to estimate the a priori distributions . The full procedure was replicated 100 times to ensure statistical significance , resulting in respectively 2000 and 3400 fits performed for each model . We refer to the Materials and Methods for more technical details . Results are reported in Figure 5 . Predictions obtained using this technique were significantly improved for the lung tumors , going from an average success score of 14 . 9%±8 . 35% to 62 . 7%±11 . 9% ( means ± standard deviations ) for prediction of the total future curve with the power law model ( 6 ) ( see Figure 5 . A ) . Prediction success rates were improved even at large future depths . For instance , predictions 7 days in the future reached an average success rate of 50 . 6% , power law model ( 6 ) , see Figure 5 . C , while their success rate was very low with direct individual prediction ( 6 . 07% ) . Prediction successes reached 90% ( power law model ( 6 ) ) at the closer horizon of the next day data point ( ) , while success rate was only 57 . 1% using an individual approach ( Figure 5 . B ) . Other small horizon depths also reached excellent prediction scores ( Figure 5 . C ) . The largest improvement of success rates for the power law model was observed for that went from an average score of 6 . 86% ( with standard deviation 7 . 47 ) to an average score of 75 . 2% ( with standard deviation 12 . 9 ) , representing more than an 11-fold increase . We report in Figure S5 the details of predictions with and without a priori information for all the animals within a given forecast group from the lung tumor data set ( power law model ( 6 ) ) . It can be appreciated how additional information on the parameter distribution in the estimation procedure significantly improved global prediction of the tumor growth curves . The impact of the addition of the a priori information was however less important when using more data points for the estimation ( results not shown ) . For the breast data , due to its already high prediction score without adjunction of a priori information , the exponential-linear model did not benefit from the method . For the next day data point of the breast tumor growth curves , predictability was already almost maximal without adjunction of a priori information and thus no important impact was observed . For both data sets , not all the models equally benefited from the addition of a priori information ( Figure 5 ) . Models having the lowest parameter inter-animal variability , such as the power law ( 6 ) , Gompertz ( 4 ) , exponential-linear ( 1 ) , and exponential V0 ( 1 ) models ( Table 3 ) , which also had better practical identifiability ( Tables 2 and S3 ) , exhibited great benefit . In contrast , the models with three parameters showed only modest benefit or even decrease of their success rates ( see and for the von Bertalanffy model ( 6 ) on the breast tumor data in Figure 5 . B ) , with the exception of the generalized logistic model ( 3 ) on the breast tumor data . In these cases , adjunction of a priori information translated into poor enhancement of predictive power because the mean population parameters did not properly capture the average behavior within the population and were therefore not very informative . On the other hand , models such as the power law model ( 6 ) on the lung tumor data set , whose coefficient γ characterized particularly well the growth pattern ( Table 3 ) , had a more informative a priori distribution that translated into the highest improvement of predictive power . For the generalized logistic model ( 3 ) on the breast data , the mean parameters were able to inform the linear regimen of the growth phase and thus protected the model from too early saturation . These results demonstrated that addition of a priori information in the fit procedure considerably improved the forecast performances of the models , in particular when using a small number of data points and low-parameterized models for data with low predictability , such as the power law model for the lung tumor data set .
In our analysis , constant variance of the error was clearly rejected and although a proportional error ( used by others [33] ) was not strictly rejected by statistical analysis ( p = 0 . 08 ) , a more adequate error model to our data was developed . However , using a proportional or even constant error model did not significantly affect conclusions as to the descriptive power of the models , identifying the same models ( Tables 1 , 2 ) as most adequate for description of tumor growth ( results not shown ) . Nevertheless , the use of an appropriate error model could have important implications in the quantitative assessment of a model's descriptive performance and rejection of inaccurate tumor growth theories . For instance , using the same human tumor growth data , Bajzer et al . [60] found the assumption of proportional error variance to favor the Gompertz model for descriptive ability , whereas Vaidya and Alexandro [30] had observed the logistic model to be favored , under a constant-variance assumption . The error model used might additionally have important implications on predictions . Although detailed analysis of the impact of the error model on prediction power is beyond the scope of the present study , we performed a prospective study of predictive properties when using a constant error model on the lung tumor data and found changes in the ranking of the models ( results not shown ) . As expected , our results confirmed previous observations [9]–[11] , [13] , [29] , [32] that tumor growth is not continuously exponential ( constant doubling time ) in the range of the tumor volumes studied , ruling out the prospect of a constant proliferating fraction . A less expected finding was that the logistic model ( linear decay in volume of the relative growth rate ) was also unable to describe our data , although similar results have been observed in other experimental systems [31] , [33] . On the other hand , the Gompertz and power law models could give an accurate and identifiable description of the growth slowdown , for both data sets . More elaborate models such as the generalized logistic , von Bertalanffy , and dynamic CC models could describe them as well . However , their parameters were found not to be identifiable from only tumor growth curves , in the ranges of the observed volumes . Additional data could improve identifiability , such as related to later growth and saturation details . It should be noted in this case that the dynamic CC model was not designed with the intent to quantify tumor growth , but rather to describe the effects of anti-angiogenic agents on global tumor dynamics . Because the model carries angiogenic parameters that are not directly measureable , or even inferable , from the experimental systems we used , it stands to reason that they would not be easily identifiable from the data . Kinetics under the influence of antiangiogenic therapy might thus provide useful additional information that could render this model identifiable . For the breast tumor experimental system , the slowdown was characterized by linear dynamics and was most accurately fitted by the exponential-linear model . Observed was exponential growth from the number of injected cells ( during the unobserved phase ) that switched smoothly to a linear phase ( exponential-linear model ) . It should be noted that in the breast tumor data set , no data were available during the initiation phase ( below 200 mm3 ) and only the linear part of a putative exponential-linear growth was observed . Explorations of the kinetics of growth during the initial phase ( at volumes below the mm3 ) are needed for further clarification . Despite structural similarities , important differences were noted in the parameter estimates between the two experimental models , in agreement with other studies emphasizing differences between ectopic and orthotopic growth [61] , [62] . Our results and methodology may help to identify the impact on kinetics of the site of implantation , although explicit comparisons could not be made here due to the differences in the cell lines used . The Gompertz model ( exponential decay in time of the relative growth rate ) was able to fit both data sets accurately , consistently with the literature [13] , [15] , [29] , [31] , [33] . One of the main criticisms of the Gompertz model is that the relative tumor growth rate becomes arbitrarily large ( or equivalently , the tumor doubling time gets arbitrarily small ) for small tumor volumes . Without invoking a threshold this becomes biologically unrealistic . This consideration led investigators [13] , [63] to introduce the Gomp-exp model that consists in an initial exponential phase followed by Gompertzian growth when the associated doubling time becomes realistic . This approach could also be applied to any decreasing relative growth rate model . We did not consider it in our analysis due to the already large initial volume and the lack of data on the initiation phase where the issue is most relevant . The power law model was also able to describe the experimental data and appeared as a simple , robust , descriptive and predictive mathematical model for murine tumor growth kinetics . It suggests a general law of macroscopic in vivo tumor growth ( in the range of the volumes observed ) : only a subset of the tumor cells proliferate and this subset is characterized by a constant , possibly fractional , Hausdorff dimension . In our results , this dimension ( equal to 3γ ) was found to be significantly different from two or three ( p<0 . 05 by Student's t-test ) in 14/20 mice for the lung tumor data set and 13/34 mice for the breast tumor data , effectively suggesting a fractional dimension . A possible explanation of this feature could come from the fractal nature of the tumor vasculature [64] , [65] , an argument supported by others who have investigated the link between tumor dynamics and vascular architecture [37] . More precisely , the branching nature of the vascularization generates a fractal organization [37] , [64] , [65] that could in turn produce a contact surface of fractional Hausdorff dimension . Considering further that the fraction of proliferative cells is proportional to this contact surface ( for instance because proliferative cells are limited to an area at fixed distance from a blood vessel or capillary , due to diffusion limitations ) , this could make the connection between fractality of the vasculature and proliferative tissue . These considerations could therefore provide a mechanistic explanation for the growth rate decay that naturally happens when the dimension of the proliferative tissue is lower than three . Our results were obtained using two particular experimental systems: an ectopic mouse syngeneic lung tumor and an orthotopic human xenograft breast tumor model . Although consistent with other studies that found the power law model adequate for growth of a murine mammary cell line [38] or for description of human mammography density distribution data [5] , these remain to be confirmed by human data . This model should also be taken with caution when dealing with very small volumes ( at the scale of several cells for instance ) for which the relative growth rate becomes very large . Indeed , the interpretation of a fractional dimension then fails , since the tumor tissue can no longer be considered a continuous medium . In this instance , it may be more appropriate to consider exponential growth in this phase [37] . Our results showed that a highly descriptive model ( associated to large flexibility ) such as the generalized logistic model , might not be useful for predictions , while well-adapted rigidity – as provided by the exponential-linear model on the breast tumor data – could lead to very good predictive power . Interestingly , our study revealed that models having low identifiability ( von Bertalanffy and dynamic CC ) could nevertheless exhibit good predictive power . Indeed , over a limited time span , different parameter sets for a given model could generate the same growth curves , which would be equally predictive . For the Gompertz model , predictive power might be improved by using possible correlations between the two parameters of this model , as reported by others [15] , [63] , [66]–[68] and suggested by our own parameter estimates ( R = 0 . 99 for both data sets , results not shown ) . If a backward prediction is desired ( for instance for the identification of the inception time of the tumor ) , the use of exponential growth might be more adapted for the initial , latency phase , e . g . by employment of the Gomp-exp model [13] , [63] . Translating our results to the clinical setting raises the possibility of forecasting solid tumor growth using simple macroscopic models . Use of a priori information could then be a powerful method and one might think of the population distribution of parameters being learned from existing databases of previous patient examinations . However , the very strong improvement of prediction success rates that we obtained partly comes from the important homogeneity of our growth data ( in particular the LLC data ) that generated a narrow and very informative distribution of some parameters ( for instance parameter γ of the power law model ) , which in turn powerfully assisted the fitting procedure . In more practical situations such as with patient data , more heterogeneity of the growth data should be expected that could alter the benefit of the method . For instance , in some situations , growth could stop for arbitrarily long periods of time . These dormancy phases challenge the universal applicability of a generic growth law such as the Gompertz or power law [69] . Description of such dormancy phenomena could be integrated using stochastic models that would elaborate on the deterministic models reviewed here , as was done by others [70] to describe breast cancer growth data using the Gompertz model . Moreover , further information than just tumor volume could be extracted from ( functional ) imaging devices , feeding more complex mathematical models that could help design more accurate in silico prediction tools [18] , [71] . Our analysis also has implications for the use of mathematical models as valuable tools for helping preclinical anti-cancer research . Such models might be used , for instance , to specifically ascertain drug efficacy in a given animal , by estimating how importantly the treated tumor deviates from its natural course , based on a priori information learned from a control group . Another application can be for rational design of dose and scheduling of anti-cancerous drugs [22] , [72] , [73] . Although integration of therapy remains to be added ( and validated ) to models such as the power law , more classical models ( exponential-linear [39] or dynamic CC [41] ) have begun to predict cytotoxic or anti-angiogenic effects of drugs on tumor growth . Our methods have allowed precise quantification of their respective descriptive and predictive powers , which , in combination with the models' intrinsic biological foundations , could be of value when deciding among such models which best captures the observed growth behaviors in relevant preclinical settings . | Tumor growth curves display relatively simple time curves that can be quantified using mathematical models . Herein we exploited two experimental animal systems to assess the descriptive and predictive power of nine classical tumor growth models . Several goodness-of-fit metrics and a dedicated error model were employed to rank the models for their relative descriptive power . We found that the model with the highest descriptive power was not necessarily the most predictive one . The breast growth curves had a linear profile that allowed good predictability . Conversely , not one of the models was able to accurately predict the lung growth curves when using only a few data points . To overcome this issue , we considered a method that uses the parameter population distribution , informed from a priori knowledge , to estimate the individual parameter vector of an independent growth curve . This method was found to considerably improve the prediction success rates . These findings may benefit preclinical cancer research by identifying models most descriptive of fundamental growth characteristics . Clinical perspective is also offered on what can be expected from mathematical modeling in terms of future growth prediction . | [
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] | 2014 | Classical Mathematical Models for Description and Prediction of Experimental Tumor Growth |
Salmonella Typhimurium sequence type ( ST ) 313 causes invasive nontyphoidal Salmonella ( iNTS ) disease in sub-Saharan Africa , targeting susceptible HIV+ , malarial , or malnourished individuals . An in-depth genomic comparison between the ST313 isolate D23580 and the well-characterized ST19 isolate 4/74 that causes gastroenteritis across the globe revealed extensive synteny . To understand how the 856 nucleotide variations generated phenotypic differences , we devised a large-scale experimental approach that involved the global gene expression analysis of strains D23580 and 4/74 grown in 16 infection-relevant growth conditions . Comparison of transcriptional patterns identified virulence and metabolic genes that were differentially expressed between D23580 versus 4/74 , many of which were validated by proteomics . We also uncovered the S . Typhimurium D23580 and 4/74 genes that showed expression differences during infection of murine macrophages . Our comparative transcriptomic data are presented in a new enhanced version of the Salmonella expression compendium , SalComD23580: http://bioinf . gen . tcd . ie/cgi-bin/salcom_v2 . pl . We discovered that the ablation of melibiose utilization was caused by three independent SNP mutations in D23580 that are shared across ST313 lineage 2 , suggesting that the ability to catabolize this carbon source has been negatively selected during ST313 evolution . The data revealed a novel , to our knowledge , plasmid maintenance system involving a plasmid-encoded CysS cysteinyl-tRNA synthetase , highlighting the power of large-scale comparative multicondition analyses to pinpoint key phenotypic differences between bacterial pathovariants .
S . enterica serovar Typhimurium ( S . Typhimurium ) infects a wide range of animal hosts and generally causes self-limiting gastroenteritis in humans . Variants of this serovar , belonging to sequence type ( ST ) 313 , are associated with invasive nontyphoidal Salmonella ( iNTS ) disease in susceptible HIV+ , malaria-infected , or malnourished individuals in sub-Saharan Africa [1] . iNTS causes around 681 , 000 deaths per year worldwide , killing 388 , 000 people in Africa alone [2] . The multidrug resistance of ST313 isolates complicates patient treatment and accounts for the high case fatality rate ( 20 . 6% ) of iNTS disease [3] . Two ST313 lineages have been associated with iNTS , and the clonal replacement of lineage 1 by lineage 2 is hypothesized to have been driven by the gain of chloramphenicol ( Cm ) resistance by lineage 2 [4] . Genetically distinct ST313 isolates that do not belong to lineages 1 and 2 have been described in the United Kingdom [5] and in Brazil [6] . The globally distributed S . Typhimurium ST19 causes gastroenteritis in humans and invasive disease in mice . Following oral ingestion , these bacteria colonize the gut and stimulate inflammation by a Salmonella pathogenicity island ( SPI ) -1-mediated process . Subsequently , ST19 can survive and proliferate in a “Salmonella-containing vacuole” ( SCV ) within epithelial cells or macrophages that involves the SPI-2 type three secretion system responsible for systemic disease in mammalian hosts [7] . Host restriction of other Salmonella pathovariants has been associated with genome degradation caused by pseudogene formation [8–11] . This process involves the loss or inactivation of virulence genes required for colonization of the mammalian gut while the ability to thrive inside macrophages is maintained . Phenotypic differences between ST313 and ST19 have been summarized previously [12] , and new studies have since been published . S1 Table lists 20 phenotypic features that differentiate ST313 from ST19 isolates at the level of metabolism , motility , and stress resistance [13–24] . In terms of infection biology , reports of the relative ability of ST313 and ST19 isolates to invade epithelial cells and macrophages have yielded conflicting results ( S1 Table ) [6 , 13 , 15 , 17 , 25–27] . It is clear that ST313 infection of macrophages stimulates lower levels of cytotoxicity and inflammasome response than ST19 infections [13 , 25] . Following treatment with human serum , more complement was required for antibody-mediated bactericidal killing of ST19 than for ST313 isolates [14] . Animal infection experiments have demonstrated that ST313 isolates can infect nonhuman hosts , including mice , cows , chickens , and macaques [15–18 , 28 , 29] . Taken together , these findings confirm that ST313 is a distinct pathovariant of S . Typhimurium [30] . However , the molecular mechanisms responsible for the phenotypic signature of the ST313 pathovariant remain to be understood and require a bespoke experimental approach . D23580 is the ST313 lineage 2 reference strain , a typical representative Malawian strain isolated from an HIV-negative child in 2004 [19] . We previously defined the transcriptional start sites ( TSSs ) of this strain and identified a SNP in the promoter of the pgtE gene specific to ST313 lineage 2 that modulated virulence [20] . To investigate whether the ability of ST313 and ST19 of S . Typhimurium to cause different types of human disease was a genetic characteristic of the two types of bacteria , we identified all genomic differences between D23580 and 4/74 . We then generated a comprehensive dataset for studying the mechanisms of infection-relevant differences between ST313 and ST19 listed in S1 Table . We hypothesized that transcriptional differences between the two strains would account for specific phenotypic differences , and we present a multicondition transcriptomic comparison of the ST313 strain , D23580 , with the ST19 strain , 4/74 ( S1 Fig ) .
S . Typhimurium D23580 was the first ST313 isolate to be genome sequenced [19] . At that time , the presence of one D23580-specific plasmid , pBT1 , was reported . To facilitate a robust transcriptomic analysis of D23580 , we resequenced the strain using a combination of long-read PacBio and short-read Illumina technologies . Following a hybrid assembly approach ( Materials and Methods ) , three contigs were identified: the 4 , 879 , 402 base pair ( bp ) chromosome , the 117 , 046 bp pSLT-BT plasmid , and the 84 , 543 bp pBT1 plasmid ( accession: PRJEB28511 ) . Comparison with the published D23580 genome ( accession: FN424405 ) [19] identified just three nucleotide differences in the chromosome . Specifically , an extra nucleotide at the 304 , 327 position ( 1 bp downstream of Asp-transfer RNA [tRNA] ) , at the 857 , 583 position ( 1 bp upstream of Lys-tRNA ) , and one nucleotide change at position 75 , 492 ( T-to-C; intergenic region ) were identified . The sequence of the pSLT-BT plasmid had a single-nucleotide deletion difference at position 473 in an intergenic region . The sequence of the pBT1 plasmid has not been reported previously , and a primer-walking approach was used to sequence the two remaining small plasmids carried by D23580 ( Materials and Methods ) , pBT2 and pBT3 ( 2 , 556 bp and 1 , 975 bp , respectively ) ( accession: PRJEB28511 ) . To maximize the functional insights to be gained from a transcriptomic analysis , a well-annotated genome is required . The published annotation for D23580 dates back to 2009 [19] and lacked certain essential bacterial genes such as the genes encoding the two outer membrane proteins LppA and LppB [31] . Accordingly , we searched for important nonannotated bacterial genes and used D23580 transcriptomic data ( described below ) to cross-reference the locations of transcripts with the location of coding genes ( S1 Text ) . This analysis allowed us to update the published annotation of D23580 by adding 86 new coding genes and 287 small RNAs ( sRNAs ) and correcting the start or end locations of 13 coding genes ( S2 Table ) . The resequenced and reannotated S . Typhimurium D23580 genome is subsequently referred to as D23580_liv ( accession: PRJEB28511 ) . Previously , the D23580 genome had been compared with the attenuated laboratory S . Typhimurium LT2 strain [19 , 32 , 33] . To assess the similarities and differences between the ST313 strain D23580 and a virulent ST19 isolate , a detailed comparative genomic analysis was performed against the ST19 strain 4/74 ( S1 Text ) . 4/74 is a prototrophic S . Typhimurium ST19 strain that is highly virulent in four animal models [34] and is the parent of the widely used SL1344 auxotrophic strain [35] . D23580 and 4/74 share 92% and 95% of coding genes and sRNAs , respectively ( S2 Table ) . Genetic differences included 788 SNPs , three multinucleotide polymorphisms ( MNPs ) [36] , and 65 indels , as well as 77 D23580-specific pseudogenes that have been listed elsewhere [19] . Analysis of the SNPs , using the 4/74 annotation as a reference , showed that 379 were nonsynonymous , 255 were synonymous , six were located in sRNAs , nine generated stop codons in coding genes , and seven lost stop codons in D23580 ( S3 Table ) . The final 132 SNPs were in intergenic regions . Fig 1 compares the chromosome and pSLT plasmid organization of strains 4/74 and D23580 and shows the distribution of the indels and three SNP classes that differentiate the two strains . We recently reported the presence of 3 , 597 TSSs in D23580 and discussed the key differences in comparison with the 3 , 838 TSSs identified in 4/74 [20 , 37] . Seventeen of the SNPs and indels were located ≤40 nucleotides upstream of one of the D23580 TSSs [20] , raising the possibility of a direct influence upon the level of transcription . Regarding prophage complement , SopEϕ [38] was absent from D23580 and present in strain 4/74 [19 , 24] . As we established earlier , D23580 carries two ST313-specific prophages , BTP1 and BTP5 [5 , 19 , 24] . In terms of plasmids , the genome of 4/74 includes pSLT4/74 , pCol1B94/74 , and pRSF10104/74 [35] . In contrast , D23580 carries a distinct plasmid complement , namely , pSLT-BT , pBT1 , pBT2 , and pBT3 [19] . The pSLT-BT plasmid of D23580 carries a Tn21-based insertion element that encodes resistance to five antibiotics [19] . The D23580 and 4/74 strains carry 4 , 396 orthologous coding genes ( S1 Text ) . Ten of the orthologs were encoded by the D23580-specific prophages BTP1 and BTP5 or by the 4/74-specific pRSF10104/74 plasmid and so were excluded from further analysis . A total of 279 orthologous sRNAs were found in both strains ( S2 Table ) . The sRNA-associated differences included three 4/74-specific sRNAs ( STnc3640 , STnc1400 , and STnc3800 ) , and the duplication of IsrB-1 in D23580 . Eight new sRNAs were found in the BTP1 prophage region of D23580 , and the existence of four was confirmed by northern blot ( S2 Fig ) . We identified 93 D23580-specific chromosomal genes that were encoded within prophage regions and absent from 4/74 ( S2 Table ) : specifically , 59 BTP1 genes , 27 BTP5 genes , one Gifsy-2 gene , and six Gifsy-1 genes . We found 89 chromosomal genes that were 4/74-specific and were absent from D23580 ( S2 Table ) . Most were associated with the SopEϕ prophage region ( 68 genes ) or located in the Def2 remnant phage ( 13 genes ) or in three separate non-phage–associated regions in D23580: allB ( associated with allantoin utilization ) , the SPI-5 genes orfX and SL1344_1032 , and an approximately 4-kb deletion that included genes SL1344_1478 to SL1344_1482 . A total of 4 , 675 orthologous coding genes and noncoding sRNAs were shared by strains D23580 and 4/74 . The sRNA IsrB-1 was removed from the list of orthologs because it was duplicated in D23580 . To search for a distinct transcriptional signature of D23580 , the expression levels of the 4 , 674 orthologs was compared between D23580 and 4/74 using a transcriptomic approach . To discover the similarities and differences in the transcriptome of strains D23580 and 4/74 , we first used our established experimental strategy: the transcriptome of D23580 was determined using RNA isolated from 16 infection-relevant in vitro growth conditions [37] and during intra-macrophage infection [39 , 40] . To allow direct comparison of the D23580 transcriptomic data with previously published 4/74 data , experiments were performed exactly as Kröger and colleagues [37] and Srikumar and colleagues ( Materials and Methods ) [40] . The RNA-sequencing ( RNA-seq ) -derived reads were mapped to the D23580_liv chromosome and the pSLT-BT , pBT1 , pBT2 , and pBT3 plasmid sequences ( Materials and Methods ) . Numbers of mapped sequence reads and other RNA-seq-derived statistical information are detailed in S4 Table . The level of expression of individual genes and sRNAs was calculated as transcripts per million ( TPM ) [41 , 42] for the chromosome and the pSLT-BT and pBT1 plasmids ( S5 Table ) . To achieve a complete transcriptomic comparison , we first reanalyzed our published 4/74 transcriptomic data [37 , 40] to add all transcripts expressed by the three plasmids pSLT4/74 , pCol1B94/74 , and pRSF10104/74 ( Materials and Methods , S4 Table ) . Initial analysis focused on the expression characteristics of the strains D23580 and 4/74 in 17 distinct environmental conditions . The number of genes and sRNAs expressed in at least one condition for strain D23580 was 4 , 365 ( 85% ) out of 5 , 110 . 745 genes and sRNAs ( 15% ) were not expressed in any of the 17 conditions . For strain 4/74 , the number of genes and sRNAs that were expressed in at least one condition was 4 , 306 ( 86% ) out of 5 , 026 , consistent with our earlier findings [37] ( S3 Fig ) . 3 , 958 of the 4 , 674 orthologous coding genes and sRNAs shared by strains D23580 and 4/74 were expressed in at least one growth condition in both strains . A small minority ( 117 ) of orthologous genes were expressed in at least one condition in strain 4/74 but not in any of the conditions in D23580 , with most showing low levels of expression ( close to the threshold TPM = 10 ) ( S5 Table ) . In contrast , we identified 82 orthologous coding genes and sRNAs that were expressed in at least one of the 17 growth conditions for D23580 but not expressed in 4/74 ( S5 Table ) . To compare the expression profiles of D23580 and 4/74 , we made 17 individual pairwise comparisons between the 17 growth conditions with the two strains ( Materials and Methods , S3 Fig ) . The data confirmed that S . Typhimurium reacts to particular infection-relevant stresses with a series of defined transcriptional programs that we detailed previously [37] . By comparing the transcriptomic response of two pathovariants of S . Typhimurium , the conservation of the transcriptional response is apparent ( S3 Fig ) . A complementary analytical approach was used to identify the transcriptional differences that relate to the distinct phenotypes of the ST313 and ST19 pathovariants ( S1 Table ) . Overall , 1 , 031 of the orthologous coding genes and sRNAs were differentially expressed ( ≥3 fold-change ) between strains D23580 and 4/74 in at least one growth condition ( Fig 2A , S5 Table ) . Transcriptional differences are highlighted in S3 Fig . The terms “D23580-up-regulated” and “D23580-down-regulated” refer to genes that show a higher or lower level of expression in D23580 compared to 4/74 . Three coding genes were D23580-up-regulated , and six genes were D23580-down-regulated in almost all growth conditions ( Fig 2B and 2C ) . The up-regulated genes included pgtE , a gene that is highly expressed in D23580 , responsible for resistance to human serum killing and linked to virulence [20] . The other two up-regulated genes were nlp , encoding a ner-like regulatory protein , and the STM2475 ( SL1344_2438 ) gene , which encodes a hypothetical protein . Two sRNAs , STnc3750 and STnc2050 , were D23580-up-regulated . STnc3750 overlaps , and is transcribed in the same direction , with the last 32 nucleotides of the 3′ end of pgtE and is up-regulated in strain 4/74 in the intra-macrophage environment [40] . The function of these two Hfq-associated sRNAs remains unknown . Three of the genes that were D23580-down-regulated in most conditions ( pSLT043-5 ) were located downstream of the Tn21-like element in the pSLT-BT plasmid ( S4 Fig ) . Because the Tn21-like multidrug resistance island was inserted between the mig-5 promoter region and the pSLT043-5 genes , we hypothesize that the differential expression reflects transcriptional termination mediated by the Tn21 cassette . Two other D23580-down-regulated genes were located in the Gifsy-1 prophage region , dinI-gfoA . The presence of a SNP in the PdinI-gfoA promoter of D23580 is responsible for the lack of viability of the Gifsy-1 phage in D23580 [24] . The final gene that was D23580-down-regulated in most growth conditions was the cysS chromosomal gene , which encodes a cysteinyl-tRNA synthetase . Aminoacyl-tRNA synthetases are generally essential genes , required for cell growth and survival [43] . The unexpected low level of cysS expression in D23580 in several growth conditions ( TPM values ranging from 5 to 18 excluding the late stationary phase and shock conditions ) was investigated further ( see below ) . Intriguing patterns of differential expression were observed between strains D23580 and 4/74 in particular growth conditions for certain functional groups of Salmonella genes . For example , the flagellar regulon and associated genes showed a characteristic pattern of expression in the phosphate carbon nitrogen ( PCN ) -related minimal media and inside macrophages ( S5 Fig ) . To allow us to make statistically significant findings , a larger-scale experiment was designed . To generate a robust transcriptional signature of D23580 , we focused on the five environmental conditions with particular relevance to Salmonella virulence , namely , early stationary phase ( ESP ) , anaerobic growth , SPI-2–noninducing ( NonSPI2 ) and SPI-2–inducing ( InSPI2 ) conditions , and intra-macrophage . The ESP and anaerobic growth conditions stimulate expression of the SPI-1 virulence system , and SPI-2 expression is induced by the InSPI2 and macrophage conditions [37 , 40] . RNA was isolated from three biological replicates of both D23580 and 4/74 grown in the four in vitro environmental conditions . For the two strains , three biological replicates were generated in parallel in a new set of experiments for this study . Additionally , RNA was extracted from additional biological replicates of intra-macrophage S . Typhimurium following infection of murine RAW264 . 7 macrophages for both D23580 ( two additional replicates ) and 4/74 ( one additional replicate ) . Following RNA-seq , the sequence reads were mapped to the D23580 and 4/74 genomes using our bespoke software pipeline ( Materials and Methods ) . The RNA-seq mapping statistics are detailed in S4 Table . To ensure that biologically meaningful gene expression differences were reported , we used very conservative cutoffs to define differential expression ( Materials and Methods ) . Following RNA-seq analysis of the three biological replicates of D23580 and 4/74 in five growth conditions , differential expression analysis of orthologous genes and sRNAs was performed with a rigorous statistical approach ( Materials and Methods , S6 Table ) . We identified 677 genes and sRNAs that showed ≥2 fold-change ( false discovery rate [FDR] ≤0 . 001 ) in at least one growth condition ( Fig 3A ) . Between 6% ( anaerobic growth ) and 2% ( InSPI2 condition ) of orthologous genes and sRNAs were differentially expressed between the two strains ( Fig 3B ) . The ability to swim in semisolid agar is a key phenotypic difference between D23580 and 4/74 [13 , 16] . We confirmed that D23580 was less motile than 4/74 ( S5 Fig ) but did not observe significant differences in motility gene expression in complex media between the strains at the transcriptional level ( S5 Fig ) . One nucleotide deletion and 11 SNP differences were found in the flagellar regulon between the two strains: one mutation in the promoter region of mcpA; three synonymous mutations in flgK , cheA , and fliP; four nonsynonymous mutations in flhA , flhB , fliB , and mcpC; and three mutations in the 5′ untranslated regions ( UTRs ) of motA , flhD , and mcpA . FlhA and FlhB are transmembrane proteins that are essential for flagellar protein export [44] . The SNP in flhB was conserved in all ST313 strains tested but also in the ST19 strains LT2 and 14028 . The D23580 flhA SNP was specific to ST313 lineage 2 . To investigate the function of the 4/74 flhA SNP , the mutation was introduced to the chromosome of D23580 by single-nucleotide engineering ( D23580 flhA4/74 ) . Motility of the D23580 flhA4/74 mutant was significantly increased compared to the D23580 wild-type ( WT ) strain ( S5 Fig ) . We originally hypothesized that the flhA SNP was related to the reported decreased inflammasome activation in macrophages , which is thought to contribute to the stealth phenotype of S . Typhimurium ST313 that involves evasion of the host immune system during infection [25] . However , no significant differences in cell death due to inflammasome activation were found between WT D23580 and the D23580 flhA4/74 mutant ( S5 Fig ) . The transcriptomic data did offer an explanation for the reduced motility of D23580 on minimal media . In the NonSPI2 condition , all flagellar genes were D23580-down-regulated , with the exception of the master regulators flhDC ( S5 Fig ) . In contrast , in the InSPI2 and intra-macrophage condition , only the flagellar class 2 genes ( such as flgA ) were significantly down-regulated . RflP ( YdiV ) is a post-transcriptional negative regulator of the flagellar master transcriptional activator complex FlhD4C2 [45–47] . We speculate that the down-regulation of the flagellar regulon in NonSPI2 could be due to a significant up-regulation ( 3 . 5 fold-change ) of rflP in this low-nutrient environmental condition . This differential expression was not seen in the InSPI2 growth condition , which only differs from NonSPI2 by a lower pH ( 5 . 8 versus 7 . 4 ) and a reduced level of phosphate [37] . We identified six genes and sRNAs that were D23580-up-regulated in all five growth conditions , specifically pgtE , nlp , ydiM , STM2475 ( SL1344_2438 ) , the ST64B prophage-encoded SL1344_1966 , and the sRNA STnc3750 ( Fig 3C ) . Just four genes were D23580-down-regulated in all conditions , namely , pSLT043-5 ( SLP1_0062–4 ) and cysS ( Fig 3D ) . These findings confirmed that biologically significant information can be extracted from the initial 17-condition experiment ( Fig 2B and 2C ) because similar genes were up-/down-regulated across the multiple conditions of the replicated experiment . The transcriptomic data were interrogated to identify virulence-associated genes that were differentially expressed between D23580 and 4/74 . Coding genes and sRNAs located within SPI-1 , SPI-2 , SPI-5 , SPI-12 , and SPI-16 showed differential expression between D23580 and 4/74 in at least one growth condition ( S6 Fig ) . The SPI-5-encoded sopB gene ( encoding a SPI-1 effector protein ) and its associated chaperone gene ( pipC ) were significantly D23580-up-regulated in the InSPI2 and intra-macrophage conditions . In contrast , the SPI-12-associated genes STM2233-7 ( SL1344_2209–13 ) were D23580-down-regulated in the same two growth conditions . Most SPI-2 genes were significantly D23580-up-regulated in the ESP condition , raising the possibility that the noninduced level of expression of SPI2 is higher in D23580 than 4/74 . The most highly differentially expressed genes in ESP ( ≥4 fold-change , FDR ≤0 . 001 ) ( Fig 4 ) included the D23580-up-regulated genes required for itaconate degradation ( ripC ) , myo-inositol utilization ( reiD ) , and proline uptake ( putA ) . D23580-down-regulated genes in the same growth condition included those involved in uptake of uracil and cytosine ( uraA and codB ) , melibiose utilization ( melAB ) , carbamoyl-phosphate metabolism and pyrimidine biosynthesis ( carAB and pyrEIB ) , nitrate reductase ( napDF ) , and sulfate metabolism ( cysPU and sbp ) . We also identified genes that were differentially expressed between D23580 and 4/74 during infection of RAW264 . 7 macrophages ( Fig 4 ) . The 16 genes that were most highly D23580-up-regulated ( ≥4 fold-change , FDR ≤0 . 001 ) included a β-glucosidase , STM3775 ( SL1344_3740 ) ; genes involved in cysteine metabolism , cdsH; oxidation of L-lactate , STM1621 ( SL1344_1551 ) ; and the transcriptional regulator rcsA . Genes that were D23580-down-regulated during infection of macrophages were involved in the uptake of sialic acid ( nanM ) and maltose or maltodextrin ( malEFK ) or the secretion and import of siderophores ( iroC and iroD ) . Malaria is one of the risk factors frequently associated to iNTS disease in sub-Saharan Africa , especially in young children . The recent work of Lokken and colleagues [48] reported an increase in intracellular iron availability in liver mononuclear cells caused by the infection of the malaria parasite . In addition , systemic growth of a S . Typhimurium mutant that was unable to acquire exogenous iron was significantly increased in a malaria coinfection mouse model [48] . These observations raise the possibility that the D23580-down-regulation of iron acquisition genes reflects adaptation to the host-associated niche . Six of the 677 genes and sRNAs that were differentially expressed between D23580 and 4/74 were among the 17 genes that had a SNP/indel in the promoter region [20]: pgtE , yohM , mcpA , yrbL , nanM , and STM2475 . The promoter region of STM2475 was further studied ( S7 Fig ) because STM2475 was D23580-up-regulated in all five growth conditions tested . Three TSSs controlled expression of this gene in 4/74: one primary , one secondary , and one internal [37] . The same number of TSSs was identified in D23580 [20] . Further investigation of the region revealed the absence of the 4/74 internal TSS in D23580 . The 4/74 secondary TSS became the primary TSS in D23580 , likely because of the presence of an “A” insertion in the −10 element of the STM2475 promoter region . The 4/74 primary TSS corresponded to the secondary TSS in D23580 . D23580 carried a third STM2475 TSS within the upstream gene ypfG . We speculate that the “A” insertion in the −10 element in D23580 could explain the differential expression between strains . The key features of the transcriptional signature of D23580 included the differential expression of the flagellar and associated genes , genes involved in aerobic and anaerobic metabolism , and iron-uptake genes . Specifically , the aerobic respiratory pathway cyoABCDE was D23580-up-regulated in the anaerobic growth condition , and anaerobic-associated-pathway pdu , cbi , and tdc operons were D23580-down-regulated . Importantly , genes associated with the acquisition of iron through production and uptake of siderophores were D23580-down-regulated in the intra-macrophage environment . In summary , the transcriptional signature of D23580 suggests that the biology of ST313 lineage 2 differs from ST19 under anaerobic conditions in vitro and during infection of murine macrophages . The challenge of data reproducibility in experimental science is widely acknowledged [49 , 50] . To assess the robustness of our experiments , the RNA-seq-derived expression profiles that we generated from five replicated conditions were compared with five relevant individual conditions . There was a high level of correlation between the individual versus replicated datasets ( correlation coefficients between 0 . 88 and 0 . 97 ) ( S8 Fig ) . However , different levels of expression were seen between the individual and the replicated ESP growth conditions of D23580 for a small minority of genes . The main variations in terms of functional gene groups involved cysteine metabolism , carbamoyl-phosphate and pyrimidine biosynthesis , and nitrate metabolism . Variation in expression of the ripCBA-lgl operon was also observed during anaerobic growth . We speculate that these alterations in gene expression reflect experimental variations such as the use of different batches of media . RNA-seq-based transcriptomic analysis does not reflect the translational and post-translational levels of regulation [51] . To identify proteins that differentiated strains D23580 and 4/74 , we used a proteomic strategy that involved a liquid chromatography-tandem mass spectrometry ( LC-MS/MS ) platform and analyzed proteins from D23580 and 4/74 bacteria grown in the ESP condition ( S7 Table ) . A label-free quantification approach identified 66 differentially expressed orthologous proteins ( ≥2 unique peptides , ≥2 fold-change , p-value <0 . 05 ) ( Fig 5 ) , including 54 D23580-up-regulated proteins and 12 D23580-down-regulated proteins . The most highly D23580-up-regulated protein was PgtE , corroborating our previous study [20] . Up-regulated proteins included those required for carbamoyl-phosphate and pyrimidine biosynthesis ( CarAB and PyrIB ) , some SPI-1 proteins and associated effectors ( PrgH , SipAB , InvG , SlrP , SopB , SopE2 , SopA , and SopD ) , RipAC ( itaconate degradation ) , and Lgl ( methylglyoxal detoxification ) . To identify genes that were differentially expressed at both the transcriptional and translational levels , the quantitative proteomic data were integrated with the transcriptomic data . Eight D23580-up-regulated proteins ( YciF , SopA , PgtE , STM2475 , RipC , RibB , Nlp , and STM3775 ) were significantly up-regulated in the transcriptomic data ( ≥2 fold-change , FDR ≤0 . 001 ) . The promoter regions of two of the related genes , pgtE and STM2475 , carried a D23580-specific SNP ( S3 Table ) . Four differentially expressed proteins ( pSLT043 , CysS , YgaD , and MelA ) were D23580-down-regulated at the transcriptomic level ( ≥2 fold-change , FDR ≤0 . 001 ) ( Fig 6 ) . Overall , 12 genes were differentially expressed at both the transcriptional and protein levels . The melibiose utilization system consists of three genes: melR , which encodes an AraC-family transcriptional regulator; melA , encoding the alpha-galactosidase enzyme; and melB . MelB is responsible for the active transport of melibiose across the bacterial cell membrane . We found that the melAB genes were D23580-down-regulated at the transcriptomic level ( Fig 7A ) . The differential expression of melA was confirmed at the proteomic level ( Fig 5 ) . In strain D23580 , the melibiose utilization genes contain three nonsynonymous SNPs ( 4/74 → D23580 ) . Two are present in melB ( Pro → Ser at the 398 AA and Ile → Val at the 466 AA ) and one in melR ( Phe → Leu ) ( Fig 7B ) . The three SNPs were analyzed in the context of a phylogeny of 258 genomes of S . Typhimurium ST313 that included isolates from Malawi , as well as more distantly related ST313 genomes from the UK [5] ( S8 Table ) . All three SNPs were found to be monophyletic , allowing us to infer the temporal order in which they arose and representing an accumulation of SNPs in melibiose utilization genes over evolutionary time . The first SNP , melB I466V , was present in all 258 ST313 strains tested and therefore arose first . The second SNP , in melR , was present in all ST313 lineage 2 and UK-ST313 genomes , suggesting that it appeared prior to the divergence of these phylogenetic groups [5] . The final SNP , melB P398S , was present in all ST313 lineage 2 and a subset of UK-ST313 genomes , consistent with this being last of the three mutations to arise ( Fig 7C ) . ST313 strains can therefore be classified into groups of strains containing one , two , or three SNPs in melibiose utilization genes . It has been reported that D23580 did not ferment melibiose , whereas a ST313 lineage 1 isolate ( A130 ) , S . Typhimurium SL1344 , and S . Typhi Ty2 were able to utilize melibiose as a sole carbon source [18] . MelB catalyzes the symport of melibiose with Na+ , Li+ , or H+ [53] . We confirmed that ST19 strains and strains belonging to the ST313 lineage 1 were positive for alpha-galactosidase activity . In contrast , isolates representing the ST313 lineage 2 and a subset of UK-ST313 strains were unable to utilize melibiose . To determine the biological role of the SNPs in the melB and melR genes , we employed a genetic approach . Single-nucleotide engineering was used to generate isogenic strains that reflect all three melibiose gene SNP states for determination of the role of the SNP differences between ST313 lineage 2 and ST19 in the alpha-galactosidase ( MelA ) -mediated phenotypic defect ( Fig 7D ) . Melibiose utilization in D23580 was rescued by nucleotide exchange of the three SNP mutations ( D23580 melB+ melR+ ) ( Fig 7E , S1 Data ) . D23580 recovered its ability to grow with melibiose as the sole carbon source after exchanging only the melR SNP with 4/74 ( D23580 melR+ ) . In contrast , D23580 did not grow in the same medium when the exchange only involved the two melB SNPs ( D23580 melB+ ) . 4/74 lost its ability to utilize melibiose as sole carbon source when we introduced the D23580 melR SNP ( 4/74 melR− and 4/74 melB− melR− ) . However , an exchange of the two nucleotides in melB did not eliminate the ability of 4/74 to grow in minimal medium with melibiose ( 4/74 melB- ) . These data correlated with the alpha-galactosidase activity of the mutants , although a slight difference was observed between strains D23580 melR+ ( light green ) and D23580 melB+ melR+ ( green ) and between strains 4/74 ( green ) and 4/74 melB− ( light green ) ( Fig 7F ) , suggesting an altered efficiency of melibiose utilization between the two strains . To completely restore alpha-galactosidase activity in D23580 , the reversion of the nonsynonymous SNPs in both the melR and melB genes was required . Our data suggest that the melR SNP is critical for the loss of function of the melibiose utilization system . In a chicken infection model , the melA transcript of S . Typhimurium strain F98 is more highly expressed in the caecum than during in vitro growth [54] . In a chronic infection model , accumulation of melibiose was observed in the murine gut after infection with S . Typhimurium strain 14028s [55] . More recently , it has been reported that some gut bacteria are able to extracellularly hydrolyze raffinose into melibiose and fructose , causing the accumulation of melibiose [56] . We speculate that the ability to metabolize melibiose could provide a fitness advantage to S . Typhimurium ST19 during gut colonization and that the loss of the melibiose catabolic pathway in S . Typhimurium ST313 lineage 2 could reflect niche adaptation . The inactivation of melibiose catabolism by SNPs that are conserved throughout the ST313 lineage 2 is consistent with a functional role in ST313 virulence , and we are currently examining this possibility . The dramatic down-regulation of the chromosomal cysS gene at both the transcriptomic ( Fig 8A ) and proteomic levels ( Fig 5 ) was studied experimentally . The coding and noncoding regulatory regions of the chromosomal cysS were identical at the DNA level in strains D23580 and 4/74 . The chromosomal cysS gene encodes a cysteinyl-tRNA synthetase , which is essential for cell growth in S . Typhimurium and other bacteria [43 , 57 , 58] . To investigate cysS gene function , we consulted a transposon-insertion sequencing ( TIS ) dataset for S . Typhimurium D23580 ( S1 Data ) . Genes that show the absence or low numbers of transposon-insertion sites are considered to be “required” for bacterial growth in a particular condition [58 , 59] . The data suggested that a functional chromosomal cysS was not required for growth in rich medium ( Fig 8B ) . We searched for S . Typhimurium D23580 genes that encoded a cysteinyl-tRNA synthetase and identified the pBT1-encoded gene , pBT1-0241 ( cysSpBT1 ) , which the TIS data suggested to be “required” for growth in rich medium ( Fig 8B ) . To investigate cysteinyl-tRNA synthetase function in D23580 , individual knock-out mutants were constructed in the chromosomal cysS gene ( cysSchr ) and the cysSpBT1 gene . These genes were 89% identical at the amino acid level and 79% at the nucleotide level . The cysSpBT1 mutant was whole-genome sequenced to confirm the absence of secondary unintended mutations . The pBT1 plasmid was also cured from D23580 . We determined the relative fitness of the two cysS mutants and the pBT1-cured strain . The WT D23580 and D23580 ΔcysSchr and D23580 ΔpBT1 mutants grew at similar rates in Lennox broth ( LB ) , while the D23580 ΔcysSpBT1 mutant showed an extended lag phase ( Fig 9A , S1 Data ) . The D23580 ΔcysSpBT1 mutant showed a more dramatic growth defect in minimal medium with glucose as the sole carbon source ( Fig 9B , S1 Data ) . To determine whether the presence of the pBT1 plasmid was linked to the decrease in cysSchr expression , RNA from two biological replicates was isolated from the pBT1-cured strain in the ESP growth condition . Differential expression analysis between this mutant and the WT D23580 strain showed a significant increase in expression of cysSchr , with TPM levels close to those seen in 4/74 ( Fig 9C , S5 Table , S1 Data ) . These results suggested the pBT1 plasmid is responsible for the down-regulation of cysSchr expression in D23580 . The conservation of pBT1 was studied among 233 ST313 strains and compared to the presence of the pSLT-BT plasmid , which was found in all lineage 2 isolates ( Fig 9D , S8 Table ) . Approximately 37% of ST313 lineage 2 isolates carried the pBT1 plasmid . The pBT1 plasmid has rarely been seen previously but did show significant sequence similarity to five plasmids found in Salmonella strains isolated from reptiles and elsewhere ( 98% to 99% nucleotide identity over 92% to 97% of the plasmid sequence; accessions JQ418537 , JQ418539 , CP022141 , CP022036 , and CP022136 , S1 Text ) . Examples of essential bacterial genes located on plasmids are rare , and this phenomenon has been previously explored [62] . We conclude that the essentiality of the cysSpBT1 gene provides a novel strategy , to our knowledge , for plasmid maintenance in a bacterial population . To allow scientists to gain new biological insights from analysis of this rich transcriptomic dataset , we have made it available as an online resource for the visualization of similarities and differences in gene expression between ST313 ( D23580 ) and ST19 ( 4/74 ) , using an intuitive heat map-based approach ( http://bioinf . gen . tcd . ie/cgi-bin/salcom_v2 . pl ) . To examine the transcriptional data in a genomic context , we generated two strain-specific online browsers that can be accessed from the previous link , one for D23580 and one for 4/74 . The value of this type of online resource for the intuitive interrogation of transcriptomic data has been outlined recently [63] .
To investigate the functional genomics of S . Typhimurium ST313 , we first resequenced and reannotated the genome of the D23580 isolate . Our comparative genomic analysis of two S . Typhimurium ST313 and ST19 isolates confirmed the findings of Kingsley and colleagues [19] , identifying 856 SNPs and indels , many instances of genome degradation , and the presence of specific prophages and plasmids . To discover the genetic differences that impact upon the biology of S . Typhimurium ST313 , we used a functional transcriptomic approach to show that the two S . Typhimurium pathovariants shared many responses to environmental stress . By investigating global gene expression in multiple infection-relevant growth conditions , we discovered that 677 genes and sRNAs were differentially expressed between strains D23580 and 4/74 . A parallel proteomic approach confirmed that many of the gene expression differences led to alterations at the protein level . The differential expression of 199 genes and sRNAs within macrophages allowed us to predict functions of African S . Typhimurium ST313 that are modified during infection . The comparative gene expression data were used to predict key phenotypic differences between the pathovariants , which are summarized in S1 Table . The power of our experimental approach is highlighted by our discovery of the molecular basis of the melibiose utilization defect of D23580 and a novel , to our knowledge , bacterial plasmid maintenance system that relied upon a plasmid-encoded essential gene . In the future , similar functional transcriptomic approaches could shed light on the factors responsible for the phenotypic differences that distinguish the pathovariants of many bacterial pathogens .
The clinical isolate S . enterica serovar Typhimurium D23580 was obtained from the Malawi-Liverpool-Wellcome Trust Clinical Research Programme , Blantyre , Malawi [19] . This strain , isolated from the blood of an HIV− child from Malawi , is used as a representative of the Salmonella ST313 after approval by the Malawian College of Medicine ( COMREC ethics no . P . 08/14/1614 ) . S . Typhimurium 4/74 was originally isolated from the bowel of a calf with salmonellosis [64] and is used as a representative strain of Salmonella ST19 . Other Salmonella strains referenced in this study are listed in S9 Table . All strains were routinely grown in LB containing 10 g/L tryptone , 5 g/L yeast extract , and 5 g/L NaCl . Liquid bacterial cultures were incubated at 37°C , 220 rpm for 16 h . Agar plates were prepared with 1 . 5% Bacto Agar ( BD Difco , Franklin Lakes , NJ , USA ) . To test the ability to grow with melibiose as the sole carbon source , strains were grown in M9 minimal medium with 0 . 4% of melibiose . M9 minimal medium consisted of 1× M9 Minimal Salts ( Sigma Aldrich , St . Louis , MO , USA ) , 2 mM MgSO4 , and 0 . 1 mM CaCl2 . Glucose was added at a final concentration of 0 . 4% to M9 minimal medium to study growth behavior of the cysS mutants . Media were supplemented with antibiotics when required: kanamycin ( Km ) 50 μg/mL , gentamicin ( Gm ) 20 μg/mL , tetracycline ( Tc ) 20 μg/mL , nalidixic acid ( Nal ) 50 μg/mL , and Cm 20 μg/mL . Details for growing bacteria in the 16 in vitro infection-relevant conditions and inside murine RAW264 . 7 macrophages ( ATCC TIB-71 ) have been published previously [37 , 40] and are summarized in S10 Table . For PacBio sequencing , S . Typhimurium D23580 was grown for 16 h in Lennox medium at 37°C , 220 rpm . DNA was extracted using the Bioline mini kit for DNA purification ( Bioline , London , UK ) . Genomic quality was assessed by electrophoresis in a 0 . 5% agarose gel at 30–35 V for 17–18 h . A 10 kb library was prepared for DNA sequencing using three SMRT cells on a PacBio RSII ( P5/C3 chemistry ) at the Centre for Genomic Research , University of Liverpool , UK . Illumina sequencing of S . Typhimurium D23580 was performed by MicrobesNG , University of Birmingham , UK . All the SNP and indel differences found between the chromosome and pSLT-BT sequences of the D23580 strain used in this study ( accession: PRJEB28511 ) and the published D23580 ( accession: FN424405 and FN432031 ) were confirmed by PCR with external primers and subsequent Sanger sequencing . Draft sequences of the pBT2 and pBT3 plasmids were provided by Robert A . Kingsley [19] and were used to design oligonucleotides for primer-walking sequencing ( all primer sequences are listed in S11 Table; Eurofins Genomics , Luxembourg , Luxembourg ) . Plasmid DNA from S . Typhimurium D23580 was isolated using the ISOLATE II Plasmid Mini Kit ( Bioline ) . For pBT2 , the following oligonucleotides were used: Fw-pBT2-1 and Rv-pBT2-1 , Fw-pBT2-2 and Rv-pBT2-2; and for pBT3 , the following oligonucleotides were used: Fw-pBT3-3 and Rv-pBT3-3 , Fw-pBT3-1 and Rv-pBT3-4 , Fw-pBT3-4 and Rv-pBT3-2 . The resulting genome sequence was designated D23580_liv ( accession: PRJEB28511 ) . HGAP3 [65] was used for PacBio read assembly of the D23580 chromosome and for the large plasmids pSLT-BT and pBT1 . A hybrid assembly approach , Unicycler v0 . 4 . 5 [66] , was used to combine the long reads from PacBio and the short reads from the Illumina platform in order to assemble small plasmids ( not covered by PacBio due to size selection in library preparation ) and to improve the large plasmid assemblies . Total RNA from S . Typhimurium D23580 grown in 16 in vitro infection-relevant conditions ( EEP , MEP , LEP , ESP , LSP , 25°C , NaCl shock , bile shock , low Fe2+ shock , anaerobic shock , anaerobic growth , oxygen shock , NonSPI2 , InSPI2 , peroxide shock , and nitric oxide shock ) and murine RAW264 . 7 macrophages was isolated using TRIzol and treated with DNase I , as described previously [37 , 40] . For a more robust comparative transcriptomic analysis , a second round of RNA-seq experiments involved RNA isolation from three new D23580 biological replicates grown in ESP , anaerobic growth , NonSPI2 , and InSPI2 , and two more intra-macrophage samples . In addition , RNA purifications of three S . Typhimurium 4/74 biological replicates grown in ESP , anaerobic growth , NonSPI2 , and InSPI2 , and one more biological replicate from the intra-macrophage environment were performed for this study . For RNA-seq , cDNA libraries were prepared and sequenced by Vertis Biotechnologie AG ( Freising , Germany ) . Briefly , RNA samples were fragmented with ultrasound ( 4 pulses of 30 sec at 4°C ) , treated with Antarctic phosphatase , and rephosphorylated with polynucleotide kinase ( PNK ) . RNA fragments were poly ( A ) -tailed , and an RNA adapter was ligated to the 5′-phosphate of the RNA . First-strand cDNA synthesis was carried out using an oligo ( dT ) -adapter primer and M-MLV reverse transcriptase . cDNA was subsequently amplified by PCR to 20–30 ng/μL and purified using the Agencourt AMPure XP kit ( Beckman Coulter Genomics , Chaska , MN , USA ) . cDNA samples were pooled in equimolar amounts , size selected to 150–500 bp , and sequenced on an Illumina HiSeq 2000 system ( single-end 100 bp reads ) . Minor changes were applied to different RNA-seq runs . For the third macrophage biological replicate of D23580 , cDNA was PCR amplified to 10–20 ng/μL and size selected to 200–500 bp , and samples were sequenced on an Illumina HiSeq 2500 platform ( 1 × 100 bp ) . For RNA samples of D23580 and 4/74 grown in the four in vitro growth conditions with three biological replicates , the third macrophage replicate of 4/74 , and the D23580 pBT1-cured strain , cDNA was PCR amplified to 10–20 ng/μL and size selected to 200–500 bp , and cDNA libraries were single-read sequenced on an Illumina NextSeq 500 system using 75-bp read length . The quality of each RNA-seq library was assessed using FastQC v0 . 11 . 5 ( http://www . bioinformatics . babraham . ac . uk/projects/fastqc/ ) and then processed with Trimmomatic v0 . 36 [67] to remove Illumina TruSeq adapter sequences , leading and trailing bases with a Phred quality score below 20 , and trim reads with an average base quality score of 20 over a 4-bp sliding window . All reads less than 40 nucleotides in length after trimming were discarded from further analysis . The remaining reads of each library were aligned to the corresponding genomes using Bowtie2 v2 . 2 . 9 [68] , and alignments were filtered with Samtools v1 . 3 . 1 [69] using a MAPQ cutoff of 15 . For S . Typhimurium D23580 , reads were aligned to the sequences of the chromosome and the pSLT-BT , pBT1 , pBT2 , and pBT3 plasmids ( accession: PRJEB28511 ) . For S . Typhimurium 4/74 , reads were aligned to the sequences of the published 4/74 chromosome and the plasmids pSLTSL1344 , pCol1B9SL1344 , and pRSF1010SL1344 ( accession: CP002487 , HE654724 , HE654725 , and HE654726 , respectively ) . The RNA-seq mapping statistics are detailed in S4 Table . Reads were assigned to genomic features using featureCounts v1 . 5 . 1 [70] . The complete RNA-seq pipeline used for this study is described in https://github . com/will-rowe/rnaseq . Two strain-specific browsers were generated for the visualization of the transcriptional data in a genomic context online ( http://bioinf . gen . tcd . ie/cgi-bin/salcom_v2 . pl ) . The different tracks in each JBrowse [52] were normalized using a published approach [71] . Expression levels of strain D23580 were calculated as TPM values [41 , 42] that were generated for coding genes and noncoding sRNAs in the chromosome and pSLT-BT and pBT1 plasmids using the reannotated D23580_liv genome ( S2 Table ) . For strain 4/74 , TPM values were recalculated from our published RNA-seq data [37 , 40] for coding genes and noncoding sRNAs in the chromosome and the three plasmids pSLT4/74 , pCol1B94/74 , and pRSF10104/74 [35] . Based on those values and following previously described Materials and Methods , the expression cutoff was set as TPM > 10 for genes and sRNAs [37] . For comparative analysis between the two S . Typhimurium strains D23580 and 4/74 , TPM values were obtained for the 4 , 675 orthologous genes and noncoding sRNAs . These values were used to calculate fold-changes between strains . TPM values ≤10 ( representing nonexpressed genes or sRNAs ) were set to 10 before calculation of fold-changes . Because of the availability of only one biological replicate per growth condition , a conservative cutoff of ≥3 fold-change was used as a differential expression threshold between strains . Raw read counts from the 4 , 674 orthologous coding genes and noncoding sRNAs for the three replicates of the five conditions ( ESP , anaerobic growth , NonSPI2 , InSPI2 , and macrophage ) for 4/74 and D23580 were uploaded into Degust ( S2 Data ) ( http://degust . erc . monash . edu/ ) . To identify statistically significant gene expression differences between the two bacterial strains , data were analyzed using the Voom/Limma approach [72 , 73] with an FDR of ≤0 . 001 and Log2 fold-change of ≥1 . Pairwise comparisons were generated between the two strains for each specific condition . To remove genes with low counts across all samples , thresholds of ≥10 read counts and ≥1 count per million ( CPM ) in at least the three biological replicates of one sample were used [72 , 74] . For differential expression analysis of the D23580 ΔpBT1 strain grown in ESP , two RNA-seq biological replicates were compared with the three biological replicates of D23580 WT . An LC-MS/MS ( Q Exactive Orbitrap; Thermo Fisher Scientific , Waltham , MA , USA ) 4-h reversed-phase C18 gradient was used to generate proteomic data from six biological replicates of each strain , 4/74 and D23580 , grown in the ESP condition in LB . The pellet from bacterial cultures was resuspended in 50 mM phosphate buffer ( pH 8 ) , sonicated ( 10 sec on , 50 sec off , for 10 cycles at 30% amplitude ) , and supernatants were analyzed after centrifugation at 16 , 000 × g for 20 min . Subsequent experimental procedures were performed at the Centre for Proteome Research at the University of Liverpool , UK . In brief , 100 μg of protein were digested ( RapiGest , in-solution trypsin digestion ) , and 1 μg of digested protein was run on an LC-MS/MS platform . A database was generated merging the amino acid sequences of the annotated genes in 4/74 [35] and our reannotated D23580 to allow homologous proteins as well as strain-specific proteins to be identified . The merged database was clustered using the program Cd-hit and an identity threshold of 95% [75] . Clusters with a single protein , representing strain-specific proteins , were included in the database with their accession ID . Clusters with more than one protein represented orthologs , and only peptides common to all proteins of the cluster were included in the database . Common peptides allowed label-free comparison of proteins that had a low level of sequence variation . Raw data obtained from the LC-MS/MS platform ( data available from the ProteomeXchange Consortium via the PRIDE database [76] ) were loaded into the Progenesis QI software ( Nonlinear Dynamics , Newcastle upon Tyne , UK ) for label-free quantification analysis . Differential expression analysis between the two strains , 4/74 and D23580 , is shown in S7 Table . From those results ( 2 , 013 proteins ) , multihit proteins ( peptides assigned to more than one protein in the same strain ) were removed , leaving a total of 2 , 004 proteins . Cutoffs of ≥2 unique peptides per identified protein ( 1 , 632 proteins ) , ≥2 fold-change expression , and p-value <0 . 05 between strains ( 121 proteins ) were used . Among the 121 proteins , 25 were 4/74-specific , 30 were D23580-specific , and 66 were encoded by orthologous genes between strains . To assess alpha-galactosidase activity , strains were grown on Pinnacle Salmonella ABC ( chromogenic Salmonella medium , Lab M ) . Bacteria that are able to produce alpha-galactosidase in the absence of beta-galactosidase appear as green colonies on this medium because of the hydrolysis of X-alpha-Gal . This enzymatic activity was correlated to the ability to grow in M9 minimal medium with melibiose as the sole carbon source . Two strategies were used for single-nucleotide replacement as previously described [20] . For D23580 flhA4/74 , a single-strand DNA oligonucleotide recombination approach was used [77] . Briefly , the flhA-474SNP oligonucleotide containing the SNP in 4/74 ( “C” ) was used to replace the SNP in D23580 ( “T” ) . The methodology followed the same strategy used for λ Red recombination explained below . After electroporation of the ssDNA oligonucleotide into D23580 carrying the pSIM5-tet plasmid , screening for D23580 recombinants was performed using a PCR with a stringent annealing temperature and primers Fw-flhA and Rv-flhA-474SNP . The reverse primer contained the 4/74 SNP in flhA . The SNP mutation in D23580 flhA4/74 was confirmed by Illumina whole-genome sequencing ( MicrobesNG , University of Birmingham ) . Variant-calling bioinformatic analysis confirmed the presence of the intended mutation and the absence of any secondary mutations . The second strategy for constructing scarless SNP mutants followed a previously described approach based on the pEMG suicide plasmid [24 , 78] . Oligonucleotides melR-EcoRI-F and melR-BamHI-R were used to PCR amplify , in 4/74 and D23580 , an melR region containing the SNP described between strains . Additionally , primers melB-EcoRI-F and melB-BamHI-R were used for amplification , in 4/74 and D23580 , of a melB region containing the two SNPs described in this gene . PCR products were cloned into the pEMG suicide plasmid and transformed into Escherichia coli S17-1 λpir . The resulting recombinant plasmids were conjugated into 4/74 or D23580 , depending on the strain that was used for the PCR amplification . For S . Typhimurium 4/74 , transconjugants were selected on M9 minimal medium with 0 . 2% glucose and Km . For S . Typhimurium D23580 , transconjugants were selected on LB Cm Km plates . As described previously [24] , transconjugants were transformed with the pSW-2 plasmid to promote the loss of the integrated pEMG by a second homologous recombination . The single-nucleotide substitutions were confirmed by PCR amplification with external primers and sequencing . Mutants D23580 melR+ and D23580 melR+melB+ were confirmed by Illumina whole-genome sequencing ( MicrobesNG , University of Birmingham ) . Variant-calling bioinformatic analysis confirmed the intended mutations and the absence of any secondary mutations in D23580 melR+melB+ . The D23580 melR+ mutant had a secondary spontaneous synonymous mutation at the chromosomal location 436 , 081 in STMMW_04211 ( GCC → GCA ) . The D23580 mutants in cysSchr ( STMMW_06051 ) and cysSpBT1 ( pBT1-0241 ) were constructed using the λ Red recombination strategy [79] . The Km resistance cassette ( aph ) of pKD4 was amplified by PCR using the primer pairs NW_206/NW_207 and NW_210/NW_211 , respectively . The resulting PCR fragments were electroporated into D23580 carrying the recombineering plasmid pSIM5-tet following the previously described methodology [20 , 80] . The ΔcysSchr::aph mutation was transduced into WT D23580 using the high-frequency-transducing bacteriophage P22 HT 105/1 int-201 [81] as previously described [24] . The D23580 ΔcysSpBT1::aph mutant was whole-genome sequenced using the Illumina technology ( MicrobesNG , University of Birmingham ) . Variant-calling bioinformatic analysis confirmed the intended mutation and the absence of secondary nonintended mutations with the exception of a six-nucleotide insertion in a noncoding region at the chromosomal position 2 , 755 , 248 ( A → AGCAAGG ) . The Km resistance cassettes of the two recombinant strains , ΔcysSchr::aph and D23580 ΔcysSpBT1::aph , were flipped out using the FLP recombinase expression plasmid pCP20-TcR [22] . The pBT1 plasmid was cured from D23580 using published methodology [82] . First , the pBT1-0211 gene of pBT1 , encoding a putative RelE/StbE replicon stabilization toxin , was replaced by an I-SceI-aph module by λ Red recombination . The I-SceI-aph module was amplified from pKD4-I-SceI [24] using primers NW_163 and NW_164 , and the resulting PCR fragment was electroporated into D23580 carrying pSIM5-tet . The resulting ΔpBT1-0211::I-SceI-aph mutants were selected on LB Km plates , and the mutation was transduced into WT D23580 as described above . D23580 ΔpBT1-0211::I-SceI-aph was subsequently transformed with the I-SceI meganuclease-producing plasmid pSW-2 [78] , and transformants were selected on LB Gm agar plates supplemented with 1 mM m-toluate , which induces high expression of the I-SceI nuclease from pSW-2 . The absence of pBT1 was confirmed by whole-genome sequencing of the D23580 ΔpBT1 strain ( MicrobesNG , University of Birmingham ) . Overnight bacterial cultures were washed twice with PBS and resuspended in the specific growth medium at an optical density ( OD ) 600 nm of 0 . 01 . Growth curves of strains grown in LB and M9 minimal medium supplemented with melibiose or glucose were based on OD at 600 nm measurements every hour of samples growing in a 96-well plate . Microplates were incubated at 37°C on an orbital shaker set at 500 rpm in a FLUOstar Omega ( BMG Labtech ) plate reader . Only the values of the OD600 nm at 8 h were plotted for strains grown in M9 melibiose medium . The conservation of the two SNPs in melB and one SNP in melR that distinguished the S . Typhimurium strains D23580 and 4/74 was analyzed in the genomes of 258 S . Typhimurium ST313 isolates from Malawi and the United Kingdom . The A5 assembly pipeline [83] and ABACAS [84] were used when a reference-quality genome was not available . The PanSeq package allowed the identification of core genome SNPs [85] , and the concatenated SNP alignment served to obtain a maximum-likelihood phylogenetic tree using PhylML [86] . BLASTn was used to identify the genotype of the melibiose SNPs shown in Fig 7C in all genomes ( S8 Table ) . For phylogenetic analysis of ST313 isolates , all available FASTQ data were downloaded from the ENA using FASTQ dump v2 . 8 . 2 ( accessions in S8 Table , ENA access date: 01 . 02 . 2017 ) . Data quality was assessed using FastQC v0 . 11 . 5 ( http://www . bioinformatics . babraham . ac . uk/projects/fastqc/ ) and then processed with Trimmomatic v0 . 36 [67] to any adapter sequences , leading and trailing bases with a Phred quality score below 20 , and trim reads with an average base quality score of 20 over a 4-bp sliding window . All reads less than 40 nucleotides in length after trimming were discarded from further analysis . A multiple-sequence alignment was generated by mapping isolate FASTQ data to the ST313 D23580 reference genome ( pSLT-BT and pBT1 plasmids ) ( accession: PRJEB28511 ) using Bowtie2 v2 . 2 . 9 [68] . Alignments were filtered ( MAPQ cutoff 15 ) and then deduplicated , sorted , and variant called with Samtools v1 . 3 . 1 [69] . For each alignment , recombination was masked using Gubbins v2 . 2 . 0 [87] and the variable sites were used to construct a maximum-likelihood tree using RAxML [88] . Phylogenetic trees were visualized using Figtree ( http://tree . bio . ed . ac . uk/software/figtree/ ) and Dendroscope [89] . Coverage information was extracted from the alignment files using bedtools v2 . 26 . 0 [90] and visualized using R . Results are shown in Fig 9D ( S8 Table ) . One-way ANOVA and Tukey’s multiple comparison test were performed using GraphPad Prism 6 . 0 ( GraphPad Software Inc . , La Jolla , CA , USA ) . | Invasive nontyphoidal Salmonella ( iNTS ) is associated with a major and largely unreported tropical disease that is responsible for hundreds of thousands of deaths per year in Africa . The main causative agent is a pathovariant of Salmonella Typhimurium called ST313 , which is closely related to the well-characterized ST19 sequence type of Salmonella that causes gastroenteritis globally . ST313 and ST19 vary by just 856 core genome single-nucleotide polymorphisms ( SNPs ) . To understand how genetic changes generate phenotypic and mechanistic differences between African and global Salmonella , we used functional transcriptomic and proteomic approaches . By investigating the transcriptome of African and global S . Typhimurium in 17 growth conditions , we discovered that 677 genes and small RNAs were differentially expressed between strains D23580 ( ST313 ) and 4/74 ( ST19 ) . A parallel proteomic approach linked gene expression differences to alterations at the protein level . We also identified differentially expressed genes during the actual infection of murine macrophages . Our data revealed the genetic basis of the loss of a carbon source utilization in African Salmonella and the discovery of a new mechanism for maintaining a plasmid in a bacterial population involving a plasmid-encoded essential bacterial gene . | [
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] | 2019 | Adding function to the genome of African Salmonella Typhimurium ST313 strain D23580 |
In mammals , both testis and ovary arise from a sexually undifferentiated precursor , the genital ridge , which first appears during mid-gestation as a thickening of the coelomic epithelium on the ventromedial surface of the mesonephros . At least four genes ( Lhx9 , Sf1 , Wt1 , and Emx2 ) have been demonstrated to be required for subsequent growth and maintenance of the genital ridge . However , no gene has been shown to be required for the initial thickening of the coelomic epithelium during genital ridge formation . We report that the transcription factor GATA4 is expressed in the coelomic epithelium of the genital ridge , progressing in an anterior-to-posterior ( A-P ) direction , immediately preceding an A-P wave of epithelial thickening . Mouse embryos conditionally deficient in Gata4 show no signs of gonadal initiation , as their coelomic epithelium remains a morphologically undifferentiated monolayer . The failure of genital ridge formation in Gata4-deficient embryos is corroborated by the absence of the early gonadal markers LHX9 and SF1 . Our data indicate that GATA4 is required to initiate formation of the genital ridge in both XX and XY fetuses , prior to its previously reported role in testicular differentiation of the XY gonad .
In male and female mammals alike , the embryonic gonad initially forms as a sexually bipotential structure called the genital ridge . The genital ridge subsequently differentiates to become a testis or an ovary . Formation of the genital ridge begins with increasing proliferation of coelomic epithelial cells to establish a dense and pseudostratified layer on the ventromedial surface of the mesonephros [1]–[4] . At about the same time , the underlying basement membrane becomes fragmented , allowing the epithelial cells to migrate inward and form a thickened , multilayered structure [5]–[7] . These morphological changes , which first appear anteriorly and gradually extend in a posterior direction , create the genital ridge [1] , [3] , [4] , [8] . In mouse embryos , formation of the genital ridge starts at about embryonic day 10 . 5 ( E10 . 5 ) and continues until E11 . 5-E12 . 0 , when sexual differentiation of the gonad becomes evident [9] , [10] . Full development of the genital ridge requires a set of genes that includes Steroidogenic factor 1 ( Sf1 ) [11] , Lim homeobox protein 9 ( Lhx9 ) [12] , Wilms tumor 1 ( Wt1 ) [13] , and Empty spiracles homeobox 2 ( Emx2 ) [7] , [14] . In mouse embryos homozygous for a null mutation in any one of these genes , the coelomic epithelial layer shows initial thickening but regresses before the genital ridge is fully formed . Deficiency of either Sf1 or Wt1 results in the death of somatic cells within the developing genital ridge , whereas loss of Lhx9 disrupts proliferation of these cells [11] , [12] , [15] , [16] . Emx2 deletion impairs the migration of epithelial cells through the basement membrane to form a multilayered structure [7] . Each of these genes is thus required for growth and maintenance of the genital ridge , but not for its initial formation . How the coelomic epithelium begins to differentiate into the genital ridge remains unknown . Previously reported mouse mutants that lack gonads during fetal development show signs of epithelial thickening or various levels of gonadal development before its regression . Examples include those mentioned above and Osr1-null embryos [17] ( Y . C . Hu and D . C . Page , unpublished data ) . Mouse mutants lacking the epithelial thickening that heralds the genital ridge have not yet been reported . GATA4 is an evolutionarily conserved transcription factor that is essential for early development of multiple organs , including heart , foregut , liver , and ventral pancreas [18]–[20] . Interestingly , Gata4 is also expressed in the genital ridge , and this expression pattern is conserved across many organisms , including mammals [21]–[23] , chicken [24] , fish [25] , and turtles [26] . Gata4's expression in the genital ridge has been linked to its role in testis differentiation . Specifically , GATA4 , together with WT1 , synergistically activates transcription of Sry [27] , which triggers testicular differentiation of the genital ridge . In XY mouse embryos homozygous for a Gata4 knock-in allele ( Gata4ki ) that abrogates GATA4 binding to the cofactor FOG2 ( or FOG1 ) , genital ridges form , but further differentiation into testes is blocked , and the transcriptional program downstream of Sry is greatly attenuated [28] . Mouse embryos heterozygous for Gata4ki on specific genetic backgrounds also show sex reversal of genetic males to phenotypic females [29] . In another study where Gata4 was removed conditionally from XY genital ridges after E10 . 5 , subsequent testis differentiation was disrupted and ovarian somatic markers were upregulated [30] . These studies clearly indicate a requirement for Gata4 in testis determination and differentiation . In this study , we investigated whether Gata4 plays a role in formation of the genital ridge , prior to the point of testis determination . Gata4-null embryos die before the genital ridges form [19] , [20] , so we utilized the tamoxifen-inducible Cre/loxP system to conditionally delete Gata4 in mouse embryos after E8 . 75 . Our approach allows mutant embryos to survive to ∼E11 . 5 , thus providing us an opportunity to investigate Gata4's role in early gonadal development . Here we report that embryos conditionally deficient for Gata4 show neither coelomic epithelial thickening nor expression of the early gonadal differentiation factors LHX9 and SF1 . Therefore , our study indicates that Gata4 is required for formation of the genital ridge , prior to its previously reported role in testis determination .
The earliest stage of gonadogenesis is characterized by epithelial thickening , which begins in mouse embryos at ∼E10 . 5 ( 8 tail somite [ts] stage ) [5] , [7] . According to morphological studies , formation of the genital ridge progresses in an A-P direction along the axis of the mesonephros [1] . If GATA4 is involved in the signaling pathway that drives genital ridge formation , it should be expressed in the coelomic epithelium before initial thickening and ideally in a similar A-P direction . In order to investigate the timing and location of GATA4 expression , we performed whole-mount immunofluorescence on C57BL/6 embryos at E10 . 0 , E10 . 2 and E10 . 4 . The nascent gonads first appear as a paired coelomic epithelial layer lying along the surface of the mesonephroi , lateral to the dorsal mesentery , and extending between the front and hind limbs . After dissection to expose the entire length of the genital ridges , we were able to image them longitudinally by confocal laser scanning microscopy ( Figure 1A ) . We found that GATA4 protein was present in the anterior half of the genital ridge as early as ∼E10 . 0 ( 26–27 total somites ) and extended to the posterior half by ∼E10 . 2 ( 2 ts ) ( Figure 1B ) . Thus , expression of GATA4 protein progressed in an A-P direction . This expression did not extend into the metanephric region ( Figure S1 ) , suggesting that GATA4 is restricted to the region that will later form the genital ridge . Sf1 has been suggested as one of the earliest markers of gonadal cells [31] . Therefore , we compared the expression of GATA4 and SF1 proteins in the same tissues . In the sagittal images , SF1 was clearly detected at the anterior half of the genital ridge at ∼E10 . 2 ( 2 ts ) and later extended into the posterior half ( Figure 1B ) . Similar to GATA4 , SF1 was also expressed in an A-P direction . However , expression of GATA4 preceded expression of SF1 . To confirm and extend these observations , we transversely sectioned the stained embryos with a vibratome and took sections from anterior , middle , and posterior parts of the genital ridges for confocal imaging ( Figure 1A ) . Consistent with the results from the sagittal images , at ∼E10 . 0 GATA4 was expressed homogeneously in the coelomic epithelial layer of the genital ridge from the anterior to the middle portions , while SF1 was expressed only sporadically in the same areas ( Figure 1C , white arrowheads ) . At ∼E10 . 4 ( 6 ts ) , GATA4 expression had already spread to the posterior end of the genital ridge , while SF1 expression had spread only to the middle region ( Figure 1D ) . Therefore , the A-P expression of GATA4 was earlier than that of SF1 , suggesting the possibility that GATA4 might function upstream of SF1 . In addition , we consistently observed that the coelomic epithelium at the anterior of the ridge was developmentally more advanced than that at the posterior . For instance , at ∼E10 . 4 , the coelomic epithelium already showed more than one layer of cells in the anterior region ( Figure 1D , yellow arrowhead ) , while it remained a monolayer in the middle-to-posterior areas . Taken together , these findings demonstrate that GATA4 is expressed in the genital ridge epithelium in an A-P direction and just before thickening occurs , thus fitting the profile of a candidate gene that regulates the formation of the genital ridge . Having observed that expression of GATA4 correlates with genital ridge formation , we wanted to know whether GATA4 is required for the earliest event , the initial thickening of the coelomic epithelium . Gata4-null embryos die between E7 . 5 and E9 . 5 [19] , [20] , so we used the tamoxifen-inducible Cre/loxP system to inactivate Gata4 gene activity in a temporally specific manner . We first intercrossed CAG-CreER; Gata4+/Δ male to Gata4flox/flox female mice to produce embryos carrying CAG-CreER; Gata4flox/Δ , where the CAG promoter drives ubiquitous expression of CreER [32] . We then injected the dams with tamoxifen at E8 . 75 days to generate Gata4 conditional knockout embryos , hereafter referred to as Gata4 cKO ( CAG-CreER ) . Gata4flox/+ embryos from the same litters served as controls . We first examined whether GATA4 expression was ablated by our conditional deletion strategy , and whether the epithelial thickening was affected in mutant embryos . Following tamoxifen treatment , Gata4 cKO ( CAG-CreER ) embryos died between E11 . 0 and E11 . 5 , likely due to the requirement for Gata4 in early development of multiple organs , including the heart , gut , and liver . We therefore collected embryos at ∼E10 . 7 ( 10 ts ) . In control embryos , GATA4 was clearly detected in the thickened coelomic epithelium of the developing gonad ( Figure 2A , arrows ) as well as in cells of the genital mesenchyme , mesentery , and gut endoderm . In Gata4 cKO ( CAG-CreER ) embryos , GATA4 was almost undetectable in the corresponding coelomic epithelium and in the rest of the embryo section , indicating that the tamoxifen-induced Gata4 deletion was nearly complete ( Figure 2B ) . We found that the coelomic epithelium of mutant embryos remained as a single layer of cells ( Figure 2B ) , suggesting that initial thickening was disrupted by the loss of Gata4 . Given that Gata4 has a widespread function in organogenesis , we wanted to confirm that the lack of epithelial thickening is a direct consequence of Gata4 removal in the genital ridges instead of a secondary effect caused by ubiquitous deletion of Gata4 via CAG-CreER . We used two tissue-specific CreER systems , Osr1-eGFP-CreERt2 [33] and Wt1-CreERt2 [34] , to replace the CAG-CreER in the experiment described above . In these two systems , eGFP-CreERt2 and CreERt2 cassettes were targeted into the endogenous Osr1 and Wt1 loci , respectively , to create knock-in alleles . Osr1 is expressed in the intermediate mesoderm and its derivatives , including genital ridges , with peak expression between E8 . 5 and E9 . 5 [35] . The gene Wt1 is thought to act downstream of Osr1 and is detected in the urogenital ridges starting at ∼E9 . 5 [17] , [36] . Wt1 and Osr1 are also expressed in the embryonic heart and some other tissues during the time frame of the study . The temporal and spatial activity of the CreERt2 cassettes in Osr1eGFP-CreERt2/+ and Wt1CreERt2/+ embryos correlated well with that of the respective endogenous genes , as evaluated by the CAG-LSL-tdTomato reporter using Gt ( ROSA ) 26SorCAG-tdTomato mice ( data not shown ) . We then introduced these knock-in alleles into Gata4+/Δ males , which were bred with Gata4flox/flox females to produce Gata4 conditional mutant embryos . Gata4 deletion efficiency in the genital ridge was assayed by immunohistochemical staining for GATA4 protein . Following the previously described tamoxifen injection scheme , we assessed GATA4 expression and gonadal phenotype of the mutant embryos carrying Wt1CreERt2/+; Gata4flox/Δ or Osr1eGFP-CreERt2/+; Gata4flox/Δ , hereafter referred to as Gata4 cKO ( Wt1CreER ) and Gata4 cKO ( Osr1CreER ) , respectively . Compared to the controls at E10 . 7 ( 10 ts ) , the number of GATA4-expressing cells was greatly reduced in the mutant coelomic epithelium , while GATA4 expression in the mesentery and gut endoderm remained unaffected ( Figure 2C , 2D ) . Thickening of the coelomic epithelium was much less prominent in both mutants ( Figure 2C , 2D ) . To improve the efficiency of tamoxifen-induced Gata4 deletion , we introduced both knock-in alleles into the same animals , hereafter referred to as Gata4 cKO ( Wt1CreER;Osr1CreER ) . As expected , we obtained a greater degree of reduction in the number of GATA4-expressing cells in the coelomic epithelium of Gata4 cKO ( Wt1CreER;Osr1CreER ) embryos , and the coelomic epithelium remained as a single cell layer of cells ( Figure 2E ) . However , it should be noted that the efficiency of Wt1CreER- and/or Osr1CreER-mediated Gata4 deletion varied among mutants . Only mutants that displayed a high degree of Gata4 deletion were chosen for detailed study , and these embryos died between E11 . 0 and E11 . 5 . Mutant embryos that survived beyond E12 . 0 did not show sufficient deletion of Gata4 to block gonadal initiation . To assess the degree of epithelial thickening in control and mutant embryos , we determined the number of cell layers by staining for the basement membrane with an antibody against laminin . While control genital ridges had grown to a multilayered epithelial structure , the mutant coelomic epithelium remained a single cell layer without clear signs of thickening ( Figure 2F ) . Thus , we conclude that Gata4 is essential for the initial thickening of the coelomic epithelium that gives rise to the genital ridge . Note that the absence of this thickening was seen in both XX and XY Gata4 cKO mutants , which is consistent with the fact that genital ridge formation precedes gonadal sex differentiation . Epithelial thickening is first observed around E10 . 3–E10 . 4 ( 5–6 ts ) at the anterior genital ridge . It has been reported that an increase in proliferation of the coelomic epithelium immediately precedes the thickening event [1] , [3] , [4] , [37] . Therefore , we tested whether the lack of epithelial thickening in Gata4 cKO embryos is caused by lack of epithelial cell proliferation . We collected BrdU-treated control and Gata4 cKO ( Wt1CreER;Osr1CreER ) embryos at two different time points: ∼E10 . 3 ( 3–5 ts ) and ∼E10 . 7 ( 9–11 ts ) , representing just before and after the occurrence of epithelial thickening . After immunostaining sections with antibodies against BrdU and laminin ( Figure 3A ) , we analyzed the fraction of gonadal somatic cells that had incorporated BrdU . We found that the control epithelium exhibited a significantly higher proliferation rate , compared to the Gata4 cKO ( Wt1CreER;Osr1CreER ) epithelium , just before thickening occurred ( Figure 3B ) . After thickening had taken place , the control epithelium proliferated at a similar rate as the mutant epithelium ( Figure 3B ) . These data suggest that Gata4 is required to increase the proliferation of the coelomic epithelium , which seemingly contributes to the subsequent thickening event . For thickening to occur , the basement membrane underneath the coelomic epithelium has to lose its continuity , permitting epithelial cells to migrate inward to form additional layers . To determine the integrity of the basement membrane underneath control and Gata4 cKO ( Wt1CreER;Osr1CreER ) coelomic epithelia , we labeled embryo sections with laminin antibody . We observed that the basement membrane in the controls had become fragmented by ∼E10 . 3 ( 5 ts ) ( Figure 3A , yellow arrows ) . In contrast , the basement membrane in the mutants remained continuous , thus hindering the thickening process . Therefore , Gata4 cKO coelomic epithelium does not show any features of gonadal differentiation . To determine whether increased cell death also contributed to the lack of epithelial thickening in Gata4 cKO embryos , we measured cell apoptosis by TUNEL staining . We found that mutant coelomic epithelial layers showed slightly higher TUNEL labeling indices compared to controls , but the difference did not reach statistical significance ( data not shown ) . To substantiate the finding that Gata4 deficiency disrupts formation of the genital ridge , we determined whether expression of early gonadal regulators was affected in Gata4 cKO mutants . Each of the genes Lhx9 , Sf1 , Wt1 , and Emx2 is known to control the growth and maintenance of the genital ridge [11]–[14] . Lhx9 and Sf1 are expressed specifically in the coelomic epithelium of the developing genital ridge , whereas Wt1 and Emx2 are expressed more broadly . Having found that GATA4 is primarily expressed in the coelomic epithelial layer of the genital ridge during the initiation period ( Figure 1 ) , we first tested whether LHX9 and SF1 are regulated by Gata4 , using whole-mount immunofluorescence . We examined embryos with near complete deletion of Gata4 at ∼E10 . 7–E10 . 8 for the experiment . In control embryos , both GATA4 and LHX9 were co-expressed and co-localized in the epithelial cells along the length of the genital ridges ( Figure 4A , 4B ) . By contrast , the Gata4 cKO ( Wt1CreER ) coelomic epithelium showed minimal expression of GATA4 and LHX9 ( Figure 4A , 4B ) . There was some residual expression at the epithelium of the dorsal mesentery . Likewise , in control genital ridges , SF1 was restricted to the thickened epithelial layer , and all SF1-positive cells were also GATA4-positive ( Figure 4C ) . SF1 did not extend to the dorsal mesentery , where many cells expressed GATA4 alone . By contrast , SF1 was absent in the coelomic epithelium of both Gata4 cKO ( Wt1CreER ) and Gata4 cKO ( CAG-CreER ) embryos ( Figure 4C and Figure S2 ) . These results suggest that Gata4 is required for expression of LHX9 and SF1 , and that these two factors act downstream of GATA4 . Notably , Gata4 cKO mutants that failed to express LHX9 or SF1 also did not exhibit thickening of the coelomic epithelium ( Figure 4B , 4C , yellow brackets ) . To further confirm the loss of Lhx9 and Sf1 expression in the absence of Gata4 , we measured the mRNA expression levels of these genes in XY and XX urogenital ridges that contained gonad/mesonephros complexes and the dorsal part of the mesentery . In this experiment , we used Gata4 cKO embryos carrying CAG-CreER , instead of Wt1CreER or Osr1CreER , to avoid any potential influence of Osr1 or Wt1 haploinsufficiency on gonadal gene expression . As expected , at E10 . 5–E10 . 8 ( 8–11 ts ) , mRNA for Lhx9 and Sf1 was significantly reduced in the Gata4 cKO samples of both sexes ( Figure 4D ) . These findings indicate that Gata4 cKO coelomic epithelial cells fail to express the early gonadal differentiation regulators Lhx9 and Sf1 , supporting the hypothesis that Gata4 is required for initiation of genital ridge formation . In contrast to the dependence of LHX9 and SF1 expression on Gata4 , both WT1 and EMX2 were expressed in the coelomic epithelium of Gata4 cKO ( Wt1CreER;Osr1CreER ) and Gata4 cKO ( CAG-CreER ) embryos ( Figure 5 and Figure S3 ) . In addition , the overall expression patterns of WT1 and EMX2 in the mesonephric region were indistinguishable between control and Gata4 cKO embryos . Therefore , WT1 and EMX2 are not downstream of GATA4 . Although both Wt1 and Emx2 play an essential role in genital ridge development , they are not required to initiate the process 13 , 14 . Likewise , Gata4 , which is required for initiation , is present in both Wt1- and Emx2-deficient genital ridges [7] , [38] . Thus , regulation of Wt1 and Emx2 is independent of Gata4 in gonadogenesis , and vice versa . The presence of EMX2 staining in the coelomic epithelium of Gata4 cKO embryos allowed us to quantitate the reduction in epithelial cell number in Gata4 cKO embryos , compared to controls . We counted the number of EMX2-positive coelomic epithelial cells per unit length . By E10 . 6–10 . 7 , the EMX2-positive epithelial layer in Gata4 cKO ( Wt1CreER;Osr1CreER ) embryos contained about half the number of cells seen in controls ( Figure 5C ) , reflecting the absence of epithelial thickening in Gata4 cKO embryos . Primordial germ cells ( PGCs ) migrate from the base of the allantois to the developing genital ridge , arriving there between E10 . 0 and E11 . 5 [39]–[41] . A defect in genital ridge growth does not block PGC migration in Lhx9−/− , Sf1−/− , Wt1−/− , and Emx2−/− mutants [11]–[14] . One possible explanation is that the initial genital ridge formation seen in these mutants is sufficient to attract PGCs . Having shown that the genital ridge does not begin to form in the absence of Gata4 , we tested whether migration of PGCs was affected in Gata4 cKO embryos . We performed whole-mount immunofluorescence on embryos at E10 . 3 ( 3 ts ) , when PGCs are arriving at the genital ridges . In control embryos ( Gata4flox/+;Oct4-EGFP ) , a longitudinal view showed that GATA4-expressing cells were present along the entire length of the genital ridge , and PGCs marked by an Oct4-EGFP transgene [42] had arrived in the coelomic epithelium of the genital ridge at this time point ( Figure 6 ) . In Gata4 cKO ( Wt1CreER ) ;Oct4-EGFP embryos , GATA4 was absent yet PGCs still migrated to the corresponding coelomic epithelium despite the absence of genital ridge formation ( Figure 6 ) . We next looked at transverse sections of mesonephric regions from additional lines , Gata4 cKO ( CAG-CreER ) and Gata4 cKO ( Wt1CreER;Osr1CreER ) . We found that PGCs , labeled with germ cell marker SSEA1 , had migrated to the coelomic epithelium on the ventromedial side of the mesonephros despite the absence there of the epithelial thickening that would normally mark the beginning of genital ridge formation ( Figure S4 ) . These results suggest that neither Gata4 nor gonadal development is required for migration of PGCs during embryogenesis .
The genital ridge is the sexually undifferentiated , or bipotential , precursor of testis or ovary . We explored the question of how formation of the genital ridge is initiated . More specifically , we investigated the genetic regulation of the thickening of the coelomic epithelium , which constitutes the first step in gonadogenesis . Our data show that GATA4 expression in the coelomic epithelium precedes thickening and progresses in an A-P direction , correlating well with the A-P progression of genital ridge formation [1] . When we conditionally deleted Gata4 from the E8 . 75 embryos , we found that formation of the genital ridge was Gata4-dependent . Our results were consistent using a number of different tamoxifen-inducible Cre drivers . Collectively , in the absence of Gata4 , we did not observe the cellular changes critical for thickening of the coelomic epithelium , such as increasing proliferation and basement membrane fragmentation . Therefore , the coelomic epithelium shows no features of gonadal differentiation in the absence of Gata4 . Failure of genital ridge formation is further confirmed by the lack of LHX9 and SF1 expression in Gata4 cKO embryos . Thus , Gata4 is the first genetic regulator shown to be required for the initiation of gonadogenesis ( Figure 7 ) . Gata4 has been previously studied as an important regulator of the testis determination pathway [27]–[30] . Correct dosage of fully functional Gata4 is critical for properly initiating the testis differentiation program in the genital ridge . Manuylov et al . showed that when Gata4 deletion was induced by tamoxifen injection at E10 . 5 via Wt1CreER , testis differentiation was blocked and sex reversal was observed [30] . When Gata4 deletion was induced later , at E11 . 5 , testis formation occurred although the testicular cords were somewhat disorganized [30] . In the present study , we induced Gata4 deletion by tamoxifen injection at an earlier time point , E8 . 75 , and noted the complete absence of genital ridge formation in both XX and XY embryos . These results suggest that Gata4 plays at least two crucial roles in early gonadal development: initiation of genital ridge formation , followed by testis differentiation of the genital ridge . Therefore , the specific timing of Gata4 loss leads to divergent phenotypes in mouse embryonic gonads . Moreover , previous studies of the Gata4ki allele have indicated the importance of GATA4-FOG interaction in mouse testis differentiation [28] , [29] . Because the genital ridge is still formed in Gata4ki animals , GATA4 likely exerts its specific function in gonadal initiation independent of its interaction with FOG cofactors . Our Gata4 cKO embryos died before ∼E11 . 5 , due to broad Gata4 deletion induced by the CreER or CreERt2 drivers used in the present study . This early death prevented us from studying sexual differentiation and later development of embryos that lack a gonad . We did not observe any signs of coelomic epithelial thickening , or expression there of LHX9 or SF1 , in Gata4 cKO embryos . However , we cannot rule out the possibility that the onset of gonadogenesis may simply be delayed in these mutants . Therefore , generation of a genital ridge-specific Cre mouse line will be necessary for future studies . We have shown that Lhx9 and Sf1 are not expressed in the coelomic epithelium of Gata4 cKO embryos , indicating that expression of these genes depends on Gata4 . Given that the promoters of both genes contain consensus GATA4 binding sites [43] , [44] , it is plausible that GATA4 may directly activate Lhx9 and Sf1 transcription in the coelomic epithelium . However , GATA4 is known to function as both a transcriptional activator and a transcriptional repressor . For instance , in the epicardial mesothelium , GATA4 , together with its cofactor FOG2 , suppresses Lhx9 expression at E11 . 5 by binding directly to conserved binding sites in the promoter [43] . Furthermore , an in vitro biochemical study suggests that GATA4 can either activate or inhibit the activity of the Sf1 promoter through the consensus binding site depending on the cellular environment [44] . In the cellular context of the coelomic epithelium , it is possible that GATA4 directly stimulates the Lhx9 and Sf1 promoters by cooperating with a specific set of cofactors . Another possibility is that GATA4 indirectly activates transcription of Lhx9 and Sf1 through repression of their repressors . The regulation of Lhx9 expression is rather specific to Gata4 , as Lhx9 expression is affected in Gata4 cKO but not in Sf1−/− , Wt1−/− , and Emx2−/− mutants [7] , [12] , [45] . In contrast , Sf1 expression is inactivated in the coelomic epithelia of all known mutants defective in genital ridge development , including Lhx9−/− , Wt1−/− , Emx2−/− , and Gata4 cKO embryos [7] , [12] , [45] , implying that Sf1 may be a common downstream target of multiple signaling cascades that control the formation and growth of the genital ridge . Indeed , the Sf1 promoter contains functional binding sites for multiple factors , including GATA4 , WT1 , and LHX9 [44] , [45] . We also found that expression of SF1 is restricted to the genital ridge epithelium , while expression of GATA4 and LHX9 extends to the dorsal end of the mesentery area ( Figure 1D and Figure 4B , 4C ) [7] . Therefore , these data imply that SF1 marks the identity of true gonadal somatic precursor cells . GATA4 is a transcription factor whose functions appear to be conserved among vertebrates . For instance , Gata4−/− ES cell-derived mouse embryos and Gata4-deficient zebrafish embryos have strikingly similar phenotypes , including defects in heart tube looping , ventricle expansion , liver bud expansion , and derivation of the pancreas from the foregut [20] , [46]–[48] . Xenopus embryos also require Gata4 for heart and liver development [49] . Considering the conserved expression of Gata4 in the genital ridge across vertebrates [21]–[26] , we propose that the gonad is initiated through an evolutionarily conserved mechanism in which differentiation of the coelomic epithelium into the genital ridge is dependent on a common regulator , Gata4 . In the current study , we established that Lhx9 and Sf1 act downstream of Gata4 during the initiation of gonadogenesis in mouse embryos ( Figure 4 ) . Conserved expression of Lhx9 and Sf1 with Gata4 in the genital ridges of different species would support a conserved role of Gata4 in gonadal initiation . Indeed , Gata4 , Lhx9 , and Sf1 are all expressed in the genital ridges , in both sexes , of chicken embryos [24] . Sf1 was also detected in the genital ridges of dogs and turtles [50] , [51] . PGCs migrate from the base of the allantois through the hindgut and mesentery to their final destination , the gonads , where they ultimately give rise to gametes . Our data show that PGCs were able to migrate to the coelomic epithelium on the ventromedial side of the mesonephros in Gata4 cKO embryos ( Figure 6 and Figure S4 ) , suggesting that neither Gata4 nor genital ridge formation is required for PGC migration . In agreement with this finding , it has been shown that PGCs actively migrate out of the hindgut toward the presumptive gonadal region at ∼E9 . 5 before the genital ridges are formed [41] . In addition , somatic factors that guide PGC migration , such as the chemokine SDF1/CXCL2 and the transcription factor FOXC1 , are expressed not only in genital ridges , but also in the mesentery and mesonephros , starting as early as E9 . 0 [52]–[54] . We conclude that these somatic factors , expressed independently of Gata4 , are sufficient to direct PGCs to their final destination , or to its proximity , even in the absence of the genital ridges . Gata4 cKO mutants show no signs of gonadal initiation or differentiation , unlike the previously reported mutants ( Lhx9−/− , Sf1−/− , Wt1−/− , and Emx2−/− ) where the genital ridge is formed but then degenerates . Gata4 exhibits a functional role in gonadogenesis earlier than Lhx9 , Sf1 , Wt1 , and Emx2 . Notably , GATA4 is a transcription factor that lies in the middle of signaling cascades . Identification of upstream regulators and additional downstream targets of Gata4 will provide insights into the regulation of gonadal initiation . Thus , our findings open to study the earliest steps in the formation of testes and ovaries .
All experiments involving mice were approved by the Committee on Animal Care at the Massachusetts Institute of Technology . Gata4flox/+ [47] , CAG-CreER [32] , Osr1eGFP-CreERt2/+ [33] , Wt1CreERt2/+ [34] , and Gt ( ROSA ) 26SorCAG-tdTomato mice were obtained from Jackson Laboratory ( Stock Numbers 008194 , 004682 , 009061 , 010912 , and 007908 , respectively ) and then intercrossed for the experiment . Mice carrying an Oct4-EGFP transgene were also obtained from Jackson Laboratory ( Stock Number 004654 ) and then backcrossed to the C57BL/6 strain ( Taconic Farms ) for at least 11 generations . Gata4-conditional-mutant embryos were generated by mating males carrying Gata4+/Δ and the indicated CreER to Gata4flox/flox females . Tamoxifen ( Sigma ) was dissolved in corn oil ( Sigma ) at a concentration of 20 mg/ml . Dams were injected intraperitoneally at 8 . 75 days postcoitum with a single shot of tamoxifen ( 4–5 mg/40 g body weight ) to induce excision of the floxed Gata4 allele . The injection scheme was optimized for maximum embryo survival and Gata4 excision efficiency . Embryos were collected between E10 . 0 and E11 . 5; tail somites were counted to determine precise age . Genotypes were assayed by PCR according to protocols from the Jackson Laboratory website . Mouse embryos were either left whole or dissected to remove heads , limbs , body walls , and internal organs . Embryos were fixed at 4°C overnight in 4% paraformaldehyde and then blocked with 3% BSA/5% donkey serum/0 . 1% Triton X-100/PBS for another night . After washing with 0 . 1% Triton X-100/PBS , embryos were incubated at 4°C overnight with antibodies against GATA4 ( sc-25310 or sc-1237 , Santa Cruz Biotechnology ) , SF1 ( PP-N1665-00 , R&D Systems ) and/or LHX9 ( sc-19348 , Santa Cruz Biotechnology ) . After washing for at least 8 hours , embryos were then incubated at 4°C overnight with donkey secondary antibodies conjugated with FITC , Rhodamine Red X , or DyLight 649 ( Jackson ImmunoResearch ) . All antibodies were diluted 1∶100 in 1% BSA/0 . 1% Triton X-100/PBS solution . After washing for at least 8 hours , embryos were preserved in SlowFade Gold Antifade reagent ( Life Technologies ) . Images were taken using an LSM710 confocal microscope ( Zeiss ) . For transverse views , embryos were embedded in 7 . 5% low-melting point agarose , cut into 300 µm-thick transverse sections with a vibratome , and imaged via confocal microscopy . Immunohistochemical staining of embryonic sections was carried out as described previously [55] . Briefly , whole embryos were fixed at 4°C overnight in 4% paraformaldehyde , paraffin embedded , and sectioned . Sections representing the anterior portion of the genital ridges were used for all experiments . Slides were then dewaxed , rehydrated , and antigen-retrieved by microwaving in citrate buffer . After blocking , slides were incubated with primary antibodies . For colorimetric staining , slides were incubated with rabbit or mouse ImmPress reagent ( Vector Labs ) , developed using DAB substrate kit ( Vector Labs ) , and counterstained with hematoxylin . For fluorescent staining , slides were incubated with donkey secondary antibodies conjugated with FITC , Rhodamine Red X or DyLight 649 ( Jackson ImmunoResearch ) and mounted with ProLong Gold Antifade reagent with DAPI ( Life Technologies ) . For BrdU incorporation experiments , pregnant mice were injected intraperitoneally with BrdU ( 100 mg/kg body weight ) six hours before sacrifice . Embryos were removed and processed for immunofluorescent staining , following the procedure described above , except that slides were denatured with 3 . 5N HCl for 30 seconds before blocking . Germ cells , characterized by their large , round , clear nuclei with clumps of chromatin scattered around the nuclear periphery , were excluded from counting . TUNEL staining was carried out with In Situ Cell Death Detection Kit ( Roche Applied Science ) according to the manufacturer's instructions . Primary antibodies against GATA4 ( sc-25310 , Santa Cruz Biotechnology ) , SF1 ( PP-N1665-00 , R&D Systems ) , laminin ( L9393 , Sigma ) , BrdU ( OBT0030 , Accurate Chemical and Scientific ) , WT1 ( RB-9267 , Thermo Scientific ) , and EMX2 ( a kind gift of Ken-ichirou Morohashi , Kyushu University , Fukuoka , Japan ) [7] , [56] were used in the study . Urogenital ridges were collected , submerged in TRIzol ( Life Technologies ) , and then stored at −80°C until genotyping was completed . Total RNA was prepared according to the manufacturer's instructions and DNase-treated using DNA Free Turbo ( Ambion ) . Three hundred ng of total RNA was reverse transcribed , and qPCR was performed with SYBR Green dye , as previously described [55] . qPCR primer pairs employed were as follows: 5′- CGGAAGCCCAAGAACCTGAATAAATC-3′ and 5′- GCTGCTGTGCCCATAGTGAGATGAC-3′ for Gata4 , 5′- GAGTTCGTCTGTCTCAAGTTCCTCATCC-3′ and 5′- ACCTCCACCAGGCACAATAGCAAC-3′ for Sf1 , 5′- ACCAGCAGCCTTATCCACCTTCACAG-3′ and 5′- TGTAATGCCCCAAGATTTGTTCTCCC-3′ for Lhx9 , and 5′- GAGAGCCAGCCTACCATCC-3′ and 5′- GGGTCCTCGTGTTTGAAGGAA-3′ for Wt1 . Results were analyzed using the delta-delta Ct method with β-actin as a normalization control . | During mammalian fetal development , the precursor of the testis or ovary first appears as a simple thickening , in a specific region , of the epithelial cell layer that lines the body cavity . The resulting structure is called the genital ridge , which then differentiates into either testis or ovary , depending on whether the sex chromosome constitution is XY or XX . A handful of genes , including Lhx9 , Sf1 , Wt1 , and Emx2 , are required to sustain the growth of the genital ridge . However , mice with mutations in any of these genes still undergo the initial step of epithelial thickening , suggesting that an additional step ( or factor ) is required to initiate genital ridge formation . We found that the evolutionarily conserved transcription factor GATA4 is expressed in the epithelium of the genital ridge before initial thickening . We produced a mouse with a Gata4 mutation in this tissue and demonstrated that the initial thickening does not take place; the mutant embryos fail to initiate gonad development . In support of this observation , the Gata4 mutant does not express the early gonadal markers LHX9 and SF1 . These findings indicate that a genetically discrete , Gata4-dependent initiation step precedes the previously known processes that result in formation of testes and ovaries . | [
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] | 2013 | Gata4 Is Required for Formation of the Genital Ridge in Mice |
Nutrient stresses trigger a variety of developmental switches in the budding yeast Saccharomyces cerevisiae . One of the least understood of such responses is the development of complex colony morphology , characterized by intricate , organized , and strain-specific patterns of colony growth and architecture . The genetic bases of this phenotype and the key environmental signals involved in its induction have heretofore remained poorly understood . By surveying multiple strain backgrounds and a large number of growth conditions , we show that limitation for fermentable carbon sources coupled with a rich nitrogen source is the primary trigger for the colony morphology response in budding yeast . Using knockout mutants and transposon-mediated mutagenesis , we demonstrate that two key signaling networks regulating this response are the filamentous growth MAP kinase cascade and the Ras-cAMP-PKA pathway . We further show synergistic epistasis between Rim15 , a kinase involved in integration of nutrient signals , and other genes in these pathways . Ploidy , mating-type , and genotype-by-environment interactions also appear to play a role in the controlling colony morphology . Our study highlights the high degree of network reuse in this model eukaryote; yeast use the same core signaling pathways in multiple contexts to integrate information about environmental and physiological states and generate diverse developmental outputs .
Baker's yeast , Saccharomyces cerevisiae , is most often described as a simple , unicellular organism . Despite this perception , S . cerevisiae displays a surprising array of behaviors , many of them involving complex interactions between cells . Under nutrient rich conditions , S . cerevisiae grows via “yeast form , ” mitotic growth , rapidly dividing and forming smooth , round colonies on solid media . Limitation of one or more key nutrients can trigger a variety of developmental responses . For example , nitrogen starvation of diploid cells induces pseudohyphal growth , which is characterized by elongated cells , agar invasion and unipolar budding , where mother and daughter cells remain attached [1]–[3] . Haploid invasive growth , a similar behavior , is observed in haploid cells grown under dextrose limitation [4] , or in the presence of various alcohols [5]–[7] . Nitrogen starvation combined with a non-fermentable carbon source induces sporulation and meiosis [8]–[11] . A number of yeast developmental responses result in multicellular structures . For example , biofilm mat formation is induced by growth on solid media with low agar and dextrose concentrations [12] . The combination of plating on hard agar followed by UV irradiation has been shown to trigger the growth of multicellular , macroscopic stalks [13] . Cell-cell adhesion is a necessary component of these responses and is induced by several different stresses including carbon and nitrogen starvation and changes in ethanol concentration and pH [14] . Recent work suggests a quorum sensing mechanism in S . cerevisiae based on the autostimulatory aromatic alcohols phenylethanol and tryptophol . This quorum sensing mechanism has been shown to enhance filamentous growth , and presumably contributes to other developmental responses as well [15] . In addition to the developmental responses described above , S . cerevisiae can form colonies consisting of complex , organized , macroscopic structures ( Figure 1 ) . We refer to the induction of this phenotype as the “colony morphology response . ” The determinants and function of the colony morphology response are poorly understood in yeast . Complex colonies produce an extensive extracellular matrix that is absent from simple colonies [16] , and it has been proposed that complex colonies help protect yeast cells against a hostile environment [17] . It has been observed that starvation results in the reorganization of yeast colonies at the cellular level [18] , and there is evidence that budding patterns and distributions of cell shape are different in complex colonies than simple colonies [19] . Microarray expression analysis comparing a strain with a complex colony phenotype and a strain with smooth colonies , derived from the first by passaging on rich media , found numerous differences in their transcriptional profiles [16] . However , it is impossible to tell which of these changes are cause , which are effect , and which are unrelated to the colony morphology response . The colony morphology response is a promising system for the study of simple multicellular developmental processes because it involves cell-cell communication , cellular differentiation and specialization , and cell-adhesion . While the mechanisms involved in the development of complex yeast colonies are unlikely to be evolutionarily related to the developmental pathways regulating multicellularity in metazoans , S . cerevisiae offers the opportunity to explore the principles underlying multicellular differentiation in an extremely tractable model system . As a “facultative” multicellular behavior of a unicellular organism , complex colony formation raises interesting questions of cooperative behavior and the repeated evolution of multicellularity across the tree of life [20] . Similar colony morphologies are observed in many undomesticated bacteria [21] . This gross similarity at the macroscopic scale begs the question of whether such structures represent convergent , adaptive solutions that microbial lineages have evolved to deal with similar environmental challenges . In this report , we define key environmental and genetic determinants of complex colony morphology in S . cerevisiae . By studying the phenotypes of a genetically diverse panel of S . cerevisiae isolates under a large number of growth conditions we have determined that fermentable carbon source limitation plus an abundant nitrogen source are the key nutritional signals for inducing complex colony morphology . We show that the complex colony response requires the filamentous growth MAP kinase ( FG MAPK ) cascade and Ras-cAMP-PKA signaling and that mutations at the RIM15 locus exhibit synergistic epistasis with components of these pathways . We also demonstrate that ploidy and mating type quantitatively contribute to the intensity of colony morphology and that genotype-by-environment effects are common for this trait .
We studied eight strains of S . cerevisiae ( BY4743 , BY4739 , MLY40α , MLY61a/α , YJM224 , YJM311 , OS17 , NKY292 ) under a variety of growth conditions ( Table S1 ) in order to determine the most important environmental triggers for complex colony morphology ( CCM ) . This strain panel was chosen to include common laboratory strain backgrounds - S288c ( BY4743 [diploid] and BY4749 [haploid] ) , SK1 ( OS17 [diploid] and NKY292 [haploid] ) , and Σ1278b ( MLY61a/α [diploid] and MLY40α [haploid] ) - as well as a distillery strain ( YJM224 [diploid] ) and a clinical isolate ( YJM311 [diploid] ) . Σ1278b and SK1 are standard backgrounds for studying yeast development ( sporulation in SK1 , filamentous growth in Σ1278b ) and their inclusion here facilitates comparisons between developmental processes . We varied the conditions of growth along five major axes: carbon source type and concentration , non-carbon nutrient concentration , media water content , media hardness ( agar content ) , and temperature . Growth was monitored daily for six days , and each plate was scored for colony morphology ( Figure 2 ) . This survey showed that induction of colony morphology is primarily carbon source dependent , with the strongest effects induced by reduced dextrose ( 1% dextrose w/v ) and non-fermentable carbon sources ( isopropanol , ethanol , acetate ) . Increasing dextrose concentration ( 4% Dextrose YEPD ) inhibits the colony morphology response , providing further evidence that carbon source limitation is a primary trigger for CCM . In contrast , media water content and hardness had little if any effect on CCM induction . The only obvious effect of temperature was slow growth at lower temperature , which prolonged the time course of colony development . We further investigated the impact of carbon availability on CCM induction by growing the same strains on YEPD plates containing a range of dextrose concentrations , from 2% ( standard YEPD ) to 0 . 0625% ( Figure 3 ) . We observed two major trends in this experiment . First , the lowest concentrations of dextrose caused the fastest induction of CCM . On lower dextrose concentrations CCM is observable as early as day two for some strains ( Figure 3 ) . Second , there is strain-to-strain variation in dextrose sensitivity . By day six most CCM competent strains exhibit the phenotype on 1% dextrose ( MLY40α , OS17 , NKY292 , and YJM311 ) and even weakly on 2% dextrose ( NKY292 ) , while others ( YJM224 ) required a dextrose concentration of 0 . 5% or less to induce the colony morphology response . At the lower end of the dextrose concentrations tested , colonies were smaller at each time point , presumably because they exhausted all available carbon , or the low levels of carbon induced growth regulation . At the lowest dextrose concentrations some strains failed to demonstrate the strain specific colony morphotypes observed at intermediate concentrations , likely because of growth limitations . Other nutrients also play a role in the complex colony response . Reducing yeast extract and peptone to half of the normal YEPD levels inhibits complex morphology , and doubling these nutrients induces it ( Figure 2 ) . We suspected that nitrogen might be the key nutrient causing this effect . To test this hypothesis we assayed colony morphology on synthetic media ( SC ) with and without the addition of glutamate , a preferred nitrogen source [22] , [23] . None of the strains tested exhibited complex morphologies on 0 . 5% Dextrose SC ( SCLD ) , but when the synthetic media is supplemented with 50mM glutamate ( SCLD+Glu ) , some strains developed complex morphologies like those observed on YEPLD , while others developed intermediate morphologies ( Figure 4 and Figure S1 ) . The most glucose sensitive of the strains in our survey ( YJM224 ) displayed only simple morphology on the glutamate supplemented SCLD media . Higher levels of glutamate ( 200mM ) resulted in little if any additional changes in colony morphology ( data not shown ) . Because there are significant pleiotropic interactions between developmental pathways in yeast [24] we hypothesized that the signaling and regulatory pathways controlling the colony morphology response would show some degree of overlap with those regulating other developmental responses , such as pseudohyphal growth , haploid invasive growth , and sporulation . To test this , we assayed colony morphology phenotypes in a panel of knockout mutants of genes known to be involved in developmental processes . This panel consisted of over 150 strains representing more than 50 different gene knockouts in MATa , MATα , and MATa/MATα strains of two lineages of the Σ1278b background . Wild-type diploid Σ1278b shows simple colony morphology in our assays while haploid Σ1278b shows strong complex morphology ( see section on ploidy below ) . We identified thirteen haploid loss-of-CCM mutants and four diploid gain-of-CCM mutants ( Table 1 ) . We found that some gene-knockouts behaved differently in the different lineages of the Σ1278b background . For example , the tpk3Δ/ tpk3Δ diploid mutants exhibit a gain of CCM in the “Heitman” Σ1278b background [25] , but not in the Sigma2000 background [26] . This variation is likely due to small genetic differences between these strains ( see below ) resulting from distinct histories of strain construction [27] . In some cases we observed differences in the phenotypes of gene-knockouts between MATa and MATα strains ( Table 1 , Figure S2 and Figure S3 ) . In addition to the four diploid mutants listed in Table 1 , we observed that a hog1Δ/hog1Δ mutant had a gain-of-CCM when grown on YEPD , YEPLD , YEPHD , and YEPEthanol ( Figure S4 ) . This pattern of induction suggests that crosstalk between various signal transduction pathways , which has been observed to cause inappropriate responses to environmental signals [28]–[30] , can also induce complex colony morphology as well . In order to gain a more comprehensive understanding of the genes and pathways affecting colony morphology phenotypes , we carried out a transposon mutagenesis screen using the mTn7-mutagenized genome library created by Kumar et al [31] . This screen identified seven additional genes exhibiting loss-of-CCM mutant phenotypes: YTA7 , RSC1 , RGT1 , RRT12 , TRM9 , ELP4 , and PET122 . Most of these genes have been previously described as affecting developmental pathways . Both ELP4 and TRM9 are members of the tRNA modification elongator complex . Other members of the elongator complex are required for filamentous growth and elp2Δ mutants show reduced biofilm mat formation [32] . Fischer et al showed that deletion of RSC1 impairs FLO11 expression and hence leads to a loss of invasive and pseudohyphal growth [33] . YTA7 is involved in chromatin silencing and maintains a barrier between heterochromatin and euchromatin upstream of the silent HMR locus [34] . In other screens , YTA7 mutants have been found to have a loss of “fluffy” colony morphology [35] and decreased filamentous growth [36] . RGT1 encodes a glucose responsive transcription factor and mutations in this gene are known to cause sporulation defects , though this may result from decreased cell size in these mutants [37] . RRT12 ( OSW3 ) encodes a protein involved in the formation of a protective dityrosine coat required for spore wall assembly [38] . As described above , we observed phenotypic differences among knockout mutants in different lineages of the Σ1278b background , and in some cases we noted differences between MATa and MATα strains , particularly in the “Heitman” Σ1278b background . Because this variation was consistent between experimental replicates , we reasoned that the phenotypic variation we observed was due to mutations that accumulated in each lineage during laboratory domestication . We used SNP calls from high-throughput sequencing data ( Magwene , in prep . ) to identify heterozygous sites in the diploid strain MLY61a/α , created from a cross of MLY40α and MLY41a . We then predicted which of these sites were heterozygous for premature stop codons ( relative to the predicted peptide sequences of the reference strain S288c ) . Among the heterozygous sites we identified was a nonsense mutation in RIM15 , a G >T transversion at position 1216 that converts a Gly codon to an opal stop codon ( rim15:1216G>T ) . Rim15p is a protein kinase shown to play a key role in mediating developmental responses to nutrient conditions [39] , [40] . The wild-type RIM15 encodes a 1770aa long protein . The rim15:1216G>T allele encodes a truncated protein with a predicted length of 406aa , which includes two putative functional domains ( PAS and zinc-finger ) [39] , but not the kinase domain ( Figure 5A ) . We confirmed the presence of two distinct alleles in the Heitman lineage by sequencing a 312bp portion of RIM15 covering the polymorphic site , from MLY61a/α , MLY40α , MLY41a , and G85 ( Sigma2000 ) . This confirmed that MLY61a/α was heterozygous , MLY40α , bore the predicted rim15:1216G>T allele , and MLY41a encodes the full length ( wild-type ) RIM15 . G85 is homozygous for the wild-type allele . The MATα strain , MLY40α , reproducibly develops a subtly weaker form of the complex colony phenotype than does the MATa strain , MLY41a ( Figure 5B , top ) . We predicted that this was due to a partial or complete loss of Rim15p function . To test this we compared the colony morphology of XPY90a and XPY90α ( rim15Δ::HygB derivatives of MLY41a and MLY40α respectively ) [41] with that of MLY41a and MLY40α . As predicted , the rim15Δ mutants ( Figure 5B , bottom ) exhibited a colony morphology phenotype very similar to that of MLY40α and decreased relative to MLY41a ( compare top and bottom rows of Figure 5B ) . We also noted differences between MATa and MATα strains for several of the deletion mutants we tested ( Figure 5C and 5D ) . We predicted that these differences reflected epistatic interactions between RIM15 and the gene knocked out , such that a gene deleted in MLY41a was the expected single knockout , whereas the same deletion in MLY40α was effectively a double-mutant with rim15:1216G>T . To test this we crossed XPY5a ( MATa , tpk2Δ ) with XPY90α ( MATα , rim15Δ ) and MLY179α ( MATα , mga1Δ ) with XPY90a ( MATa , rim15Δ ) and analyzed how colony morphology segregated in tetrads relative to mating type and the gene deletions . The results of these crosses indicate the following: 1 ) both mutations at the RIM15 locus ( rim15Δ and rim15:1216G>T ) interact epistatically with mutations at the TPK2 and MGA1 loci such that the degree of colony morphology loss is greater than the sum of the single mutants ( rim15Δ , tpk2Δ < rim15Δ or tpk2Δ and rim15Δ , mga1Δ < rim15Δ or mga1Δ ) ; 2 ) the rim15:1216G>T allele may maintain some functionality because the degree of CCM reduction observed in mutants with this background are typically milder than those for comparable mutants in the rim15Δ background and; 3 ) there is still an effect of mating type on the degree of colony morphology independent of the RIM15 locus . These findings are illustrated in Figure 5B–5D . Results of the crosses are thus consistent with a model of synergistic epistatic interaction between RIM15 and other genes involved in colony morphology . In addition to nutritional determinants , we observed a role for ploidy in the colony morphology response . Several strains that have simple or mild colony morphologies as diploids ( MLY61a/α and OS17 ) exhibit strong colony phenotypes as haploids ( MLY40α and NKY292 ) ( contrast Figure 6C and 6E with Figure 6D and 6F ) . To further explore the colony morphology differences between isogenic haploids and diploids , we constructed haploid derivatives of a clinical isolate ( YJM311 ) that exhibits a strong CCM phenotype as a diploid . We observed variation in colony morphology among the haploid derivatives of this strain , presumably due to allelic heterozygosity in the parental strain , but many displayed a morphology similar to that found in other haploid strains ( compare Figure 6H with Figure 6D and 6F ) . In order to confirm the role of ploidy in the colony morphology response we tested a set of isogenic haploid , diploid , triploid , and tetraploid strains [42] for colony morphology phenotypes in the Σ1278b . . We found an inverse correlation between ploidy and colony morphology; strains with ploidy of 2N and greater showed mild or no signs of complex colony morphology ( Figure S5 ) . Here as well mating type has a weak but noticeable affect on colony morphology independent of ploidy . The diploids heterozygous at the MAT locus ( the normal state for diploids; Figure S5E ) have simple morphology , while those homozygous for MAT have colonies that are somewhat elaborated ( Figure S5B ) . During our survey of growth conditions , we observed that colony morphology exhibits genotype-by-environment ( G×E ) effects . To provide a framework for study of G×E interactions we defined six morphotype classes: spokes ( with weak concentric rings in this case ) ( Figure 1A ) , concentric rings ( Figure 1B ) , lacy ( Figure 1C ) , coralline ( similar to lacy , but the cable-like structures are more angular , and tend to have more height ) ( Figure 1D ) , mountainous ( possibly a variation on spokes ) ( Figure 1E ) , and irregular ( which includes a wide range of forms that have no obvious regularity ) ( Figure 1F ) . For example , YJM311 grown on YEPLD media has a “lacy” morphotype ( Figure 1C ) . The same strain grown on YEPEthanol , YEPIsopropanol , or YEPAcetate ( Figure S6A , S6B , S6C ) has a morphology that closely resembles a tangle of string ( a variation of the lacy morphotype ) . On galactose , sucrose , and 1% agar YEPD the same strain exhibits the spoke morphotype , although each media induces a distinct version of the spoke morphotype ( Figure S6D , S6E , S6F ) . Having identified the key signals for the colony morphology response , we expanded our survey to include all 35 S . cerevisiae strains from the Saccharomyces Genome Resequencing Project ( SGRP; [43] ) . Our goal was to determine the prevalence and diversity of complex colony morphologies and to identify strains of interest for future work . Of these thirty-five strains , by day six of growth , thirteen exhibited non-smooth or stronger colonies ( anything beyond a smooth , shiny colony ) on at least one media type . For most of these , this was simply a bumpy or textured colony surface , but six of these thirteen had at least “signs of CCM” ( score of two or greater ) ( Figure S7 ) .
In common with other developmental switches in yeast , the complex colony morphology response is induced by nutritional signals . Fermentable carbon source limitation coupled with an abundant nitrogen source appears to be the key trigger . Taking our results together with information on other developmental responses sheds light on how S . cerevisiae responds to variable nutritional environments ( Figure 7A ) . Haploid invasive growth , like complex colony morphology , is induced by dextrose limitation [4] . What seems to distinguish the two is the availability of other nutrients , particularly nitrogen . CCM competent strains grown on low dextrose synthetic media do not generally exhibit complex morphology . However , supplementing this synthetic media with glutamate is sufficient to induce the colony morphology response in most competent strains . In contrast , nitrogen availability seems to have little effect on haploid invasive growth [4] . Our findings also suggest a link between complex colony morphology and S . cerevisiae biofilm formation [12] . Like complex colony morphology , reduced dextrose is a trigger for biofilm development , and biofilms exhibit gross structural features resembling some of the colony structures we have observed [12] , [44] . Cellular level organizational changes observed in starving colonies [18] might help explain how starvation signals result in macroscopic changes in both colony and biofilm structure . The emerging picture of yeast development suggests that S . cerevisiae uses the core elements of two key signaling pathways , a MAP kinase cascade and a Ras-cAMP-PKA pathway , in multiple contexts [1] , [3] , [45] . The colony morphology phenotypes we observed in knockout strains implicate both of these pathways as playing key roles in regulating colony architecture ( Figure 7B ) . The filamentous growth/mating MAPK cascade ( consisting , in part , of the kinases Ste20p , Ste11p , and Ste7p ) regulates mating , filamentous and invasive growth , and cell wall integrity , in response to pheromone , nutrient limitation and osmolar stress respectively [46] . The mating and filamentous growth pathways both involve the transcription factor Ste12p , which induces expression of mating genes by binding pheromone response elements ( PREs ) , and dimerizes with Tec1p to bind filamentous response elements ( FREs ) in the promoters of filamentation genes . Dig1p and Dig2p inhibit activation by Ste12p at PREs and by the Ste12p/Tec1p heterodimer at FREs [47] . Because multiple signals flow through the same core kinases of the MAPK cascade , several mechanisms are employed to prevent incorrect output . Knocking out genes in the cascade can disrupt this “insulation , ” resulting in crosstalk between the pathways [28]–[30] . Such crosstalk is observed in hog1Δ mutants , which can be induced to mate by osmolar stress [29] . We observe similar crosstalk in the regulation of colony morphology . The diploid hog1Δ/ hog1Δ mutant exhibits colony morphology on low dextrose , high dextrose or alcohol containing media ( Figure S4 ) . The crosstalk observed in MAPK cascade mutants complicates interpretation , but the loss of CCM in ste20Δ , ste11Δ , ste7Δ , ste12Δ , and tec1Δ mutants demonstrates that the MAPK cascade plays a key role in the regulation of colony morphology . We observed no gain of CCM in a diploid ste12Δ/ste12Δ , dig1Δ/dig1Δ , dig2Δ/dig2Δ triple mutant strain , but we did find a gain of CCM in the diploid tec1Δ/tec1Δ , dig1Δ/dig1Δ , dig2Δ/dig2Δ triple mutant . Our interpretation of this result is that when Dig1p/Dig2p repression of Ste12p is relieved , Ste12p is capable of activating a set of Tec1p independent targets , as has been show previously [48] , and that this subset of targets affects colony morphology . Our identification of ELP4 and TRM9 in the mutagenesis screen further argues for an important role of the MAPK cascade in regulating complex colony morphology . Abdullah and Cullen recently demonstrated a role for the elongator complex and other tRNA modification proteins in the MAPK dependent regulation of filamentous growth [32] . Elongator affects this pathway via starvation dependent induction of the signaling mucin gene MSB2 , which interacts with Cdc42 to activate MAPK signaling [49] . Our independent identification of elp4Δ and trm9Δ mutants in this study adds to the evidence for a role for elongator and other tRNA modification complexes in regulating yeast development via the MAPK pathway . In addition to the MAP kinase cascade , the colony morphology response also requires a functional Ras-cAMP-PKA pathway . Mutants that inhibit this pathway exhibit an attenuation of colony morphology , while those that up-regulate cAMP levels and/or PKA activation show an increased expression of complex morphology in diploid backgrounds . A ras2Δ haploid mutant shows a loss of CCM consistent with similar decreases in biofilm formation and pseudohyphal growth observed for ras2Δ mutants [44] , [50] . We also confirmed the observation of Halme et al . [51] that deletion mutants of IRA2 exhibit an increased colony morphology phenotype . Ira2p promotes Ras inactivation by stimulating GTPase activity , and treatment of cells with glucose destabilizes Ira2p , allowing active Ras proteins to induce cAMP production by adenylate cyclase [52] . There are three catalytic subunits of yeast PKA , Tpk1p , Tpk2p , and Tpk3p . Previous studies [41] , [53] , [54] have demonstrated distinct developmental and physiological roles for each of these subunits . For example , Tpk2p promotes filamentous growth and expression of Flo11p while Tpk1 and Tpk3p inhibit filamentous growth [41] , [54] . Similar to these previous studies , we observed distinct effects of the PKA subunits on the colony morphology response . We found a loss of CCM in haploid tpk2Δ mutants as well as in tpk1Δ , tpk2Δ double mutants . The tpk2Δ , tpk3Δ double mutant showed a mild decrease in CCM . In diploids the tpk3Δ/tpk3Δ single mutant showed a background dependent increase in colony morphology . The tpk1Δ/ tpk1Δ , tpk3Δ/tpk3Δ double mutant also showed an increase in colony morphology . We did not observe a definite colony morphology phenotype in haploid or diploid TPK1 mutant strains or diploid TPK2 mutants . The opposite phenotypes of TPK2 and TPK3 mutants can be explained by a model put forth by Pan and Heitman [41] that suggests Tpk3p ( and possibly Tpk1p ) act in a negative feedback loop that attenuates cAMP levels . A candidate target for this feedback interaction via Tpk3p is the low-affinity phosphodiesterase Pde1p [55] . Our interpretation of this model and the mutants described above is that an active cAMP-PKA pathway is required for the development of complex colonies . Mutations that lead to a decrease in cAMP and/or PKA activation ( ras2Δ and tpk2Δ ) also decrease complex colony morphology and those that increase cAMP levels ( ira2Δ and tpk3Δ ) promote the development of complex colonies . Given that a good nitrogen source seems to be a requirement for complex colony morphology , it is perhaps surprising that we observed a loss of CCM in a gln3Δ mutant . Gln3p is a transcriptional activator that activates “nitrogen starvation genes , ” genes repressed by preferred nitrogen sources such as glutamate and ammonium . Under good nitrogen conditions , Gln3p is sequestered in the cytoplasm by Ure2p . Nitrogen deprivation leads to dissociation of Gln3p from Ure2p , Gln3 then localizes to the nucleus [23] . However , ours is not the first study to observe unintuitive results with respect to the effects of nitrogen catabolite repression pathway mutants on yeast development . Lorenz and Heitman [56] found that both a gln3Δ/gln3Δ mutant and a ure2Δ/ure2Δ mutant are defective in pseudohyphal growth . These results suggest that a balance between Ure2p and Gln3p may be necessary for appropriate response to nitrogen levels . We find that ploidy has a major effect on colony morphology phenotypes . In some strains , this is manifested as a decrease in colony morphology in diploids relative to haploids; in others , there is simply a change in the stereotyped colony morphotype with ploidy . For example , colonies of Σ1278b haploids strains develop complex morphologies within six days , whereas diploid strains take much longer [19] . It has been proposed that this ploidy difference in colony morphology is linked to the ploidy specific expression of FLO11 [19] , [42] , [57] . The role of ploidy in the colony morphology response is another link between colony morphology and biofilm formation , which is also stronger in haploids [12] . There is presumably also a connection to the ploidy specificity of filamentous growth [58] . Pseudohyphal growth is a behavior of diploids starved for nitrogen , whereas the similar haploid invasive growth is induced by fermentable carbon limitation [4] . The crosses we carried out using rim15 mutants demonstrate that some of the mating type differences we observed in the Heitman Σ1278b lineage were the result of polymorphism for a loss-of-function allele in the RIM15 locus ( rim15:1216G>T ) . This allele , present in the MATα background , was associated with a weaker CCM phenotype . However , after breaking this linkage , we still find residual CCM variation that segregates with mating type . MATα strains consistently exhibit a weaker version of the CCM phenotype than do matched MATa strains in the Heitman background , regardless of the allelic state of RIM15 . We observe a similar direction of difference between MATa and MATα in the Fink Σ1278b lineages . The flocculin Flo11p is known to be involved in several developmental processes , including filamentous growth [59] and biofilm formation [12] . There is a great deal of previous evidence of a role for FLO11 in colony morphology . For example , FLO11 was shown to be required for the “wrinkled” colony morphology observed in Ira- mutants [51] , and insertion of a wild “flor” allele of FLO11 into a laboratory-domesticated strain induces the formation of “compact fluffy colonies” [35] . Finally , FLO11 is expressed at higher levels in a strain with complex morphology than a strain with simple morphology , but at very low levels in both [16] . Our finding that haploid flo11Δ strains fail to form complex colonies is consistent with these observations . The key stimuli we identify here , glucose and nitrogen , are both known to influence the expression of FLO11 [59] , [60] . However , high levels of FLO11 expression are clearly not the sole determinant of colony morphology , since FLO11 is upregulated in diploid cells grown on SLAD ( low nitrogen , high glucose ) [59] . Growth on SLAD triggers pseudohyphal growth , but not the complex colony response . Rim15p is a protein kinase that is thought to play a central role in the integration of nutrient signals [39] . RIM15 was first identified in a screen for mutants defective in the expression of genes expressed early in meiosis [61] . Subsequent studies [40] , [62] have demonstrated that Rim15p helps to regulate entry into G0 ( stationary phase ) in response to nutrient depletion , particularly glucose , by regulating the expression of a large number of stress responsive genes . Current models [39] , [63] , [64] posit that Rim15p integrates signals from at least three major nutrient signaling pathways , the Ras-cAMP-PKA , Sch9 , and TOR pathways . Rim15-dependent effects on transcription are mediated by the transcription factors Msn2 , Msn4 , and Gis1 [65] . We identified and analyzed a loss-of-function mutation in RIM15 ( rim15:1216G>T ) that contributes to variation in colony morphology phenotypes among lineages of the laboratory strain Σ1278b . Our results support a model of genetic interactions in which RIM15 mutations have a modest effect on colony morphology by themselves , but can exhibit significant epistatic interactions in combination with mutations at other loci . The SNP we observed is also a strong candidate as a contributor to subtle colony morphology differences between the Heitman Σ1278b lineage and the Sigma2000 lineage . This mutation may also contribute to differences in related developmental responses , such as pseudohyphal growth , that have been noted by other investigators [27] . Since glucose limitation causes hyperphosphorylation and nuclear accumulation of Rim15p [62] , and glucose limitation is also a strong inducer of complex colony morphology , we hypothesize that the CCM defects we observe in RIM15 mutants are due to a failure to trigger the upregulation of stress responsive genes via Gis1 and Msn2/4 . However , the mutant phenotypes also point to the existence of one or more RIM15 independent pathways , since RIM15 mutants do not show a complete loss of colony morphology , even when combined with knockouts at other loci . One possibility is that FLO11 expression is necessary but not sufficient to induce robust colony morphology , and that Rim15p signaling might be needed to amplify or intensify the strength of the CCM response in a FLO11 independent manner . What role , if any , does the complex colony response play in yeast ecology ? It has been proposed that complex morphologies help to protect against a hostile environment [17] , and the observation that some strains switch to simple morphologies after a small number of passages on rich media ( i . e . auspicious conditions ) may support this hypothesis [16] . It has been observed that starvation results in reorganization of yeast colonies at the cellular level [18] , and there is evidence that budding patterns and distributions of cell shape are different in complex colonies than simple colonies [19] . Extensive extracellular matrix is produced by complex colonies , and is absent from simple colonies [16] . The role that we demonstrate here for RIM15 in mediating colony morphology helps to more clearly link colony morphology to stressors such as oxidative stress [65] and calorie restriction [64] , where Rim15p plays an important role in mounting transcriptional responses . Colony morphology is a phenotype that is ripe for further research . The work presented herein provides a foundation , in terms of signals and pathways , for future studies of the developmental circuitry underlying the complex colony response . While we have found important genetic intersections between colony morphology and other developmental pathways , there is clearly not a complete overlap . We found no clear change in colony morphology in many of the knockout strains we tested that are known to have altered filamentous growth . Conversely , we have identified a number of genes , such as RRT12 and RIM15 , that are known to affect sporulation , but have never been shown to have filamentous growth phenotypes . The key cellular factors that contribute to the morphogenesis of complex colonies are largely undefined . Factors such as strength of adhesion , bud location , cell shape , spatially and temporally variable rates of cell division and cell death , secretion of extracellular matrix , and other such variables must contribute in some way to establishing and maintaining colony architecture during colony growth . Future studies that exploit genetic variation among strains along with mutants and cellular reporters will help to unravel this fascinating morphogenetic process . Complex colony morphology , together with mating , filamentous growth , biofilm formation and sporulation , represent outputs of a complex decision-making machinery that integrates information on internal cell state , nutrients , potential mating partners , and various environmental stresses . A major challenge moving forward will be to better understand how simple eukaryotes such as yeast are able to correctly discriminate between different combinations of signals and how they are able to generate a diversity of responses given that the same core signaling pathways are used in different contexts .
YEPD and Hartwell's Complete ( HC ) media , were made as described in Burke et al . ( 2000 ) . YPD+G418 and YPD+G418+HygB contained 200mg/L geneticin . YPD+HygB and YPD+G418+HygB contained 300mg/L hygromycin B . YEPGalactose , YEPSucrose , YEPAcetate , YEPEthanol , YEPIsopropanol are the same as YEPD , except with 2% of the named carbon source ( e . g . galactose in YEPGalactose ) substituted for 2% dextrose . Modified YEPD media were made in the same manner as YEPD with changes as noted: 1% agar YEPD; 4% agar YEPD; 0 . 5% yeast extract 1% peptone YEPD; 2% yeast extract 4% peptone YEPD; 4% dextrose YEPD; 1% dextrose YEPD; 0 . 5% dextrose YEPD ( YEPLD ) ; 0 . 25% dextrose YEPD; 0 . 125% dextrose YEPD; 0 . 0625% dextrose YEPD . For “wetted” media , 400 µl of water was added to each plate and allowed to absorb; “dried” media was treated by incubation at 40C for two days . Modified synthetic complete ( SC ) media were made according to Kaiser et al . [66] , with the following changes: 0 . 5% Dextrose SC ( SCLD ) ; 0 . 5% Dextrose SC , 50mM L-Glutamic acid monosodium salt monohydrate ( SCLD+Glu ) ; 0 . 5% Dextrose SC -uracil , 50mM L-Glutamic acid monosodium salt monohydrate ( SCLD-Ura+Glu ) . All strains used in this work are listed in Table S2 . Strains used are of diverse origin , including laboratory strains as well as clinical , distillery isolates . To generate haploid derivatives of the homothallic diploid YJM311 , the HO endonuclease was knocked out by transformation with the HO-poly-KanMX4-HO plasmid [67] . Knockouts were confirmed by PCR of the HO locus , then sporulated and tetrads were dissected . Haploid gene knockout strains PMY566 , PMY568 , PMY570 , PMY572 , PMY575 , PMY577 , PMY579 , PMY581 , PMY583 , PMY585 , and PMY589 were derived from diploids [26] by sporulation and tetrad dissection . The environmental conditions tested are detailed in Table S1 . Cells were plated with a targeted density of 20 or 60 cfu/plate . Several of the strains used in this study form flocs and/or aggregates of incompletely budded cell clusters . In order to accurately determine titers to plate a consistent number of cells , cultures were washed , then incubated for 15 minutes at room temperature in deflocculation buffer ( 90 mM mannose , 20 mM citrate , ( pH 7 . 0 ) , 5 mM EDTA ) [68] , briefly sonicated , then counted by hemocytometer . In addition to , or instead of this spreading procedure , some assays of colony morphology were conducted by pinning a small amount of yeast cells from a colony or water suspension directly to the assay plate . For the initial survey of growth conditions , most strain-by-condition combinations were tested at two plating densities: 20 cfu/plate ( results shown here ) and 60 cfu/plate ( data not shown ) . Results were similar for both plating densities . The strain-by-condition combinations not replicated are ones that showed no CCM: neither of the S288C derivatives ( BY4743 and BY4739 ) , were replicated; the wetted , dried , and room temperature conditions were also not replicated . Once carbon limitation was determined to induce the colony morphology response , we found that YEPD with 0 . 5% Dextrose ( Yeast Extract , Peptone , Low Dextrose - YEPLD ) , to be nearly optimal , strongly inducing the response while allowing sufficient colony growth to permit development of characteristic morphology ( Figure 3 ) . This medium was therefore used as a standard for subsequent experiments . For the treatment screen , all plates were scored for colony morphology every day from day one to day six . Haploid derivatives of YJM311 were scored on day six . Mutant strains were scored on day 6 and compared to parental wild-type colonies . We developed a qualitative method of scoring colony morphology using a scale from zero to five , based on the complexity and definition ( depth ) of morphology structures . While this framework is subjective , all scoring was performed by a single individual to ensure consistency . Scores were determined based on the survey of all the colonies on a plate , rather than a single colony ( although for almost all plates , the colonies on a plate all had very similar morphology ) . The numerical scores have the following meanings: ( 0 ) No colonies or microcolonies; ( 1 ) Simple colony morphology; ( 2 ) Hints of colony morphology; ( 3 ) Weak or early colony morphology; ( 4 ) Strong colony morphology; ( 5 ) Very strong colony morphology ( Figure S8 ) . In summary , colonies that have no signs of CCM , but have a non-smooth surface texture receive a score greater than one but less than two . Colonies that have some signs of CCM receive a score of two or greater but less than three . Colonies that have definite morphology receive a score of three or greater . Genome-wide transposon-mediated mutagenesis was carried out following the methods of Kumar and Snyder [69] , with modifications as noted , using an mTn7 mutagenized S . cerevisiae genome library generated by Kumar et al . [31] . Briefly , individual pools of mutagenized library were digested with Not I to linearize , then transformed [70] into PMY574 . The transformation reactions were plated onto SCLD-Ura+Glu , to simultaneously select for transformants and induce colony morphology . Colonies displaying a loss of complex morphology relative to PMY574 were picked and pinned to YEPLD to confirm the colony morphology phenotype . DNA was extracted from loss-of-morphology mutants using the DNeasy Blood & Tissue Kit ( Qiagen ) , following the supplementary protocol for yeast DNA . Transposon insertion locations were identified by two-step PCR ( ST-PCR ) [71] . Primers mTn [69] and THG . SEQ [72] were used as ST-PCR primer 1 and primer 3 respectively to amplify from the “left” end of mTn7 , and primers mTn7_5895R ( GCACTGTTTTTATGTGTGCGATA ) and mTn7_6007R ( GCCGTTTACCCATACGATGT ) were used as ST-PCR primer 1 and primer 3 respectively to amplify from the “right” end of mTn7 . Primers 2 and 4 were as described [71] . Primers THG . SEQ and mTn7_6007R were used for sequencing ST-PCR products from the left and right ends , respectively . Finally , sequencing reads were BLASTed against the S . cerevisiae genome in order to locate their position within the genome . Genes identified by mutagenesis were confirmed for colony morphology phenotypes by construction of knockout mutants in the PMY574 and PMY575 backgrounds . Primers used for gene deletion and deletion confirmation were based on primer sequences generated by the Saccharomyces Genome Deletion Project [73] , however the UP_45 and DOWN_45 ORF specific oligonucleotides were joined with primers specific for the pRS400 plasmid series , and were used to amplify the URA3 fragment from pRS406 [74] . Transformants were selected on SC –uracil , then assayed for colony morphology phenotype by growth on YEPLD . XPY5a was crossed with XPY90α to generate diploids heterozygous for deletions at the RIM15 and TPK2 loci . MLY179α was crossed with XPY90a to generate diploids heterozygous for deletions at the RIM15 and MGA1 loci . Diploids were selected by growth on YPD+G418+HygB , then sporulated and tetrads were dissected . Segregation of the RIM15 , TPK2 , and MGA1 alleles was determined by assaying growth of segregants on YPD+HygB , YPD+G418 , and YPD+G418 respectively . Mating type of segregants was determined by crossing with AAY1017 and AAY1018 , then assaying for growth on SD . Colony morphology phenotypes of segregants were assayed by growth on YEPLD . | Baker's yeast forms smooth round colonies when grown in favorable conditions . When starved for one or more nutrients , yeast can alter its growth pattern to produce complex structures consisting of numerous interacting cells . One mode of growth , the colony morphology response , produces visually striking , lacy colony architectures . We describe both conditions that induce this morphology and also genes and pathways that are required for the response . We demonstrate that low levels of carbon combined with abundant nitrogen trigger complex colony formation . Using a candidate gene approach coupled with genome-wide mutagenesis , we identified genes involved in the production of complex colony morphology . Many of these genes are components of either a MAP kinase cascade or the Ras-cAMP-PKA pathway , two well-studied signaling pathways that are conserved across eukaryotic organisms . Yeast use these pathways to mediate cellular responses to changes in their environment . We observe shared characteristics between complex colonies and biofilms , which are organized communities of microorganisms with relevance to human health and human infrastructure , making colony morphology a candidate model for understanding how microorganisms interact to form complex structures . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
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] | 2010 | Environmental and Genetic Determinants of Colony Morphology in Yeast |
Yeast successfully adapts to an environmental stress by altering physiology and fine-tuning metabolism . This fine-tuning is achieved through regulation of both gene expression and protein activity , and it is shaped by various physiological requirements . Such requirements impose a sustained evolutionary pressure that ultimately selects a specific gene expression profile , generating a suitable adaptive response to each environmental change . Although some of the requirements are stress specific , it is likely that others are common to various situations . We hypothesize that an evolutionary pressure for minimizing biosynthetic costs might have left signatures in the physicochemical properties of proteins whose gene expression is fine-tuned during adaptive responses . To test this hypothesis we analyze existing yeast transcriptomic data for such responses and investigate how several properties of proteins correlate to changes in gene expression . Our results reveal signatures that are consistent with a selective pressure for economy in protein synthesis during adaptive response of yeast to various types of stress . These signatures differentiate two groups of adaptive responses with respect to how cells manage expenditure in protein biosynthesis . In one group , significant trends towards downregulation of large proteins and upregulation of small ones are observed . In the other group we find no such trends . These results are consistent with resource limitation being important in the evolution of the first group of stress responses .
Unicellular organisms are sensitive to environmental challenges . Their internal milieu acts as a buffer against such changes by mounting an adaptive response involving modifications at different cellular levels . Appropriate adaptive responses require intracellular signaling , changes in the conformation and activity of proteins , changes in transcription and translation of genes , etc . [1] . Many of the cellular modifications that characterize any adaptive response are due to the need for acquiring new protein functionalities while shutting down other protein functionalities that are not required in the new conditions . These changes ultimately fine tune the mechanisms and processes that allow the cell to function appropriately and survive under changing environments . Such fine tuning is shaped by various functional requirements and physiological constraints . The functional requirements are a result of the specific demands that are imposed on cell survival by the environment . On the other hand , the physiological constraints are defined by the limits within which the cell is physically capable of changing the activity of its component parts to meet the functional requirements . From a global point of view , adaptive responses can be seen as a multi-optimization problem because cells evolved appropriate responses to cope with different types of stress , while optimizing different parts of its metabolism for each of those responses [2] , [3] . For example , cells simultaneously have to increase the concentration of specific metabolites and proteins , while decreasing the concentration of other components to prevent an increase in the concentration of unneeded metabolites . Such an increase could strain cell solubility capacity or increase spurious reactivity to dangerous levels . These and other functional constraints are likely to provide sustained evolutionary pressures that ultimately select a specific gene expression profile that leads to suitable adaptive responses . With these arguments in mind , it is thus important to identify the functional requirements and quantitative physiological constraints that may significantly shape adaptive responses . Among others , minimization of energetic expenditure plays an important role in cells growing exponentially in a rich medium . Several signatures that are consistent with minimization of metabolic cost have already been identified in the properties of the set of proteins that is expressed when cells are growing in rich media ( basal conditions ) . For example , genes coding for proteins that are highly abundant under basal conditions have a pattern of synonymous codon usage that is well adapted to the relative abundance of synonymous tRNAs in the yeast S . cerevisiae and in Escherichia coli [4] , [5] . Another signature that is found in genes that are highly expressed under basal conditions is a sequence bias that minimizes transcriptional and translational costs [6] . This minimization of metabolic cost is further observed in the relative amino acid composition of abundant proteins under the same conditions . These proteins are enriched with metabolically cheaper amino acids [7] . A final example of a general signature is the codon bias of long genes . This bias is such that the probability of missense errors is reduced during translation [6] , [8] , [9] . These biases suggest that reducing overall costs in metabolism , whenever possible , may significantly increase cellular fitness . This view is consistent with the observation that small changes in gene expression affecting the levels of protein synthesis influence the fitness of specific E . coli strains [10] . This body of results strongly supports the notion that metabolic cost acts as a selective pressure in shaping the properties of cells growing in a rich medium , in absence of environmental stresses . Thus , one might ask if minimization of metabolic cost is also an important factor in the evolution of adaptive responses to stress conditions . It is predictable that this evolutionary pressure might leave stronger signatures in adaptive responses that require the use of higher ATP amounts by the cell , such as adaptation to heat , weak organic acids , or NaCl . In these three cases , it has been reported that ATP concentrations decrease due to a high energy demand [11] . Given that protein synthesis is one of the costliest biosynthetic efforts for the cell [12] , the minimization of metabolic cost might have biased the properties of proteins whose expression change during adaptation . Therefore , here we ask the following questions . Is there a signature that is consistent with a selective pressure for minimizing metabolic cost in proteins synthesis during adaptive responses to stress ? Can one find general signatures in the physicochemical properties proteins and in the expression patterns of genes that are involved in the adaptive response to different environmental challenges ? If so , what physiological constraints are consistent with those signatures ? We address these questions by investigating how is the value of several properties of proteins ( size and molecular weight of proteins , codon adaptation index , aromaticity , average cost per amino acid , etc . ) related to changes in gene expression levels during various environmental changes . We find that genes whose expression is upregulated during different types of adaptive responses tend to code for proteins that are small , while genes whose expression is downregulated during the same responses tend to code for proteins that are large . This is a signature that is consistent with a selective pressure for minimizing metabolic cost in proteins synthesis . It is more significant in adaptive responses where changes in gene expression levels affect a large fraction of the genome . To our knowledge , this is the first general and global signature that has been identified for the properties of proteins involved in adaptive responses to stress .
The microarray data we analyze provide information regarding relative up and downregulation ( UpCF and DownCF , respectively ) of gene expression with respect to a pre-stress control condition . To facilitate comparison between upregulated and downregulated genes , we use the inverse of the ratio for downregulated genes . Thus , all values for the ratios of changes in gene expression discussed below are greater than 1 . Changes in gene expression during stress responses are dynamic and , for the most part , transient . Because of this , we take the maximum value of up or downregulation as an approximated measure of the maximal change in gene expression during the transient stress response . Changes in gene expression are underestimated for genes that undergo very strong up or downregulation , due to intrinsic limitations of the microarray technology [26] . To minimize any errors that may come from this limitation we use the 98th quantile of all the ratio values for a given gene as a proxy of its maximum UpCF or DownCF .
Some of the protein properties we consider are strongly correlated ( see Table S1 ) . For example , different measures of codon preference towards the major tRNA isoacceptors , such as CAI , CB , and FOP , are highly correlated ( r = 0 . 83–0 . 97 ) . Length and molecular weight of proteins are , in practice , equivalent . Protein and mRNA abundance show a correlation of r = 0 . 56 . Protein abundance is also positively correlated to CAI , to CB and to FOP ( r = 0 . 53–0 . 54 ) , and to mRNA abundance under basal growth conditions ( r = 0 . 60–0 . 64 ) . Similarly , average amino acid cost ( ACPA ) is highly correlated to aromaticity ( r = 0 . 84 ) , because the most expensive amino acids are aromatic . Thus , if a protein has a high percentage of aromatic amino acids ( which is proportional to aromaticity ) it will have a larger average cost per amino acid than proteins with lower percentage of aromatic amino acids . The only type of data that is available for both , the entire genome and a comprehensive set of yeast adaptive responses , is gene expression data from microarray experiments [13] , [27] . Thus , in order to search for trends between the adaptive responses and the protein properties , we analyze how the value of those properties is related to the changes in gene expression during the response . We analyze microarray data for fourteen stress responses and two control conditions ( change in carbon source — C Source — and return to basal conditions after osmotic shock — ↓Sorbitol ) . Short proteins with a high relative composition of metabolically cheaper amino acids are highly abundant under basal conditions , which is consistent with the hypothesis that lowering protein cost is a driving force in shaping the protein complement of yeast in those conditions [7] . It is also well known that the process leading up to protein synthesis is one of the costliest components of cellular metabolism [12] and that during response to many environmental stress signals yeast shuts down gene expression and decreases the number of ribosomes [13] , [14] . It has been proposed that gene expression profiles have signatures that are specific to the conditions under which they have evolved [2] , [28] . If metabolic cost in general , and cost of protein synthesis in particular , is a significant factor in shaping adaptive profiles , then one might expect that the stronger the resource limitation is , the larger its signature will be . It would then be reasonable to expect that adaptive responses where a resource limitation exists may have similar qualitative bulk expenditure in protein synthesis . To find support for this hypothesis we must estimate that cost for the different stress responses . Changes in protein levels can be roughly estimated over the whole genome by the changes in the levels of gene expression [29] . Thus , an index that approximately estimates the changing costs of protein synthesis during a given adaptive response i can be defined as: ( 1 ) In this equation Ak is the basal abundance of protein k and Lk is the primary sequence length of that protein UpCFik and DownCFik represent the change-fold of up- or downregulation of the gene that codes for protein k . It is likely that specific functional requirements during any given stress response will lead to the synthesis of new proteins whose functionality is required for survival under the new conditions . By calculating a cost index for each of the twenty five Gene Ontology ( GO ) categories of cellular components defined in the SGD Slim Mapper Tool , we can analyze if the requirement for new functions is restricted to specific categories of the GO classification or not . Such a discriminating cost index can be defined as: ( 2 ) The index refers to stress condition and GO category . For each protein within the GO category , the up- ( UpCFijk ) or down-change fold ( DownCFijk ) is multiplied by its length . If in the GO category the expression of genes coding for small proteins is preferably upregulated and the expression of genes coding for large proteins is preferably downregulated , the index will be negative . This index provides a rough bulk estimate of how much a cell invests in synthesizing new proteins ( the upregulation term ) subtracting how much the cell saves by decreasing the synthesis of other proteins ( the downregulation term ) . The index under basal conditions is 0 because the difference between up- and down-expression is null in that case . A cluster analysis of the twenty five dimensional vectors built for each adaptive response with the index calculated for each GO category is shown in Figure 3 . Four clusters can be distinguished from this analysis . Responses to ↓Sorbitol , C source , Menadione and Acid cluster together with basal conditions ( Basal Cluster ) and apart from the other responses . Interestingly , this Basal Cluster includes stress responses in which the previous analytical methods find a low correlation between protein cost and changes in gene expression ( Tables 1–2 and Figures 1–2 and S1 ) . Because we could not find an accurate bootstrap statistical test to calculate significance for the clusters in Figure 3 we further tested similitude between the conditions using a discriminant analysis of the data used to build the clusters . Two dimensions explain 99 . 9% of the variance in the data and separate all four groups found in the cluster analysis ( Figure S4 ) . The normalized values of each component of the vector for each type of adaptive response plotted in Figure 4 show the similarity between the different responses . For reference purposes , the basal condition is represented by a dashed circle in each of the panels of that figure . Lines below that circle indicate negative values for while any lines above the basal condition circle indicate positive values for . Conditions included in the Basal Cluster show low absolute values for this index in all GO categories . This is different for the other groups that , overall , have larger negative values for in categories “Cytoplasm” , “Nucleus” , and “Ribosome” . The four clusters are also consistent with the gradation observed in the moving-quantile plots for length and abundance ( Figure 2 and S1 ) . Altogether , the results presented in this section , suggest two broad types of adaptive responses . In one type , corresponding to responses in the Basal Cluster , the changes in gene expression are small . In this group of responses , we find no correlation between protein properties and gene expression . In the other type of stress , responses have evolved in a way that is consistent with a significant pressure to minimize the metabolic cost of the response . The previous results suggest that the stress conditions considered can be classified in two broad types with respect to metabolic economy . On one hand , we have the Basal Cluster in Figures 3 and 4 . This cluster includes the adaptive responses to Menadione , Acid and the two controls , C Source and ↓Sorbitol . The results do not support a significant pressure by metabolic economy in shaping these responses . On the other hand , all other responses can be clustered into three subgroups . Nevertheless , they all appear to be shaped to some degree by metabolic economy . Therefore , we lump together change-folds for gene expression of all these later stress responses . By doing this we create a data set that has a stronger signal than that found in responses to the individual stresses when we relate properties and gene expression changes . The stronger signal in this combined data set also allows us to analyze patterns within each GO category for function , biological processes and cellular location of the proteins .
What type of general selective pressure might explain the correlations we find between changes in gene expression and protein abundance or length during stress response ? One answer to this question is that minimizing the cost of protein synthesis is a significant pressure that shapes changes in gene expression during adaptive responses . Why would minimizing metabolic costs improve fitness of S . cerevisiae ? As the cell optimizes the expenditure of resources for metabolic maintenance , it will have more resources available for survival and reproduction , thus out competing organisms . This seem logical , but it also raises another question , which is why would one expect this pressure to be felt at the level of proteins ? Calculations based on the typical cellular composition of yeast and bacteria predict that protein synthesis uses more metabolic resources and ATP molecules than the formation of other macromolecules and it is a limiting step for yield [12] , [34] , [35] , [36] , [37] . As proteins of a shorter size use less amino acids , evolving fully functional short proteins leads to faster protein synthesis with less usage of cellular resources . It must be stressed that this argument cannot be seen as defending that cell will , over time , simply loose all large proteins and use smaller proteins to perform all necessary molecular functions . Evolution is constrained by life history . Specifically , the evolutionary unit of proteins is the functional domain [38] . Such functional domains have on average appeared only once in evolution and examples of domain convergent evolution are rare . Protein function often depends on how a small number of amino acids are located within the 3D structure of these domains . Therefore , a shorter protein may not have a 3D structure that will allow for the maintenance of an appropriate biological activity . This will constrain the amount of resources that can be saved by evolving shorter proteins . Under stress , availability of resources may be significantly limited , and the cell must adapt quickly in order to survive . For challenging stress conditions , resource limitation may impose severe limitations to the adaptive response . Exposure to these kinds of stresses causes the cell to deviate considerable resources from its steady state metabolism towards the adaptive response and imposes important constraints to cell economy [11] , [39] . For example during exposure to high NaCl concentrations , additional energy expenditure for growth increased between 14% and 31% [40] , and the activity of the plasma membrane H+-ATPase ( highest consumer of ATP ) is repressed during heat shock or in the presence of a weak acid [11] , [41] . Another situation that has been put forward as supporting a cellular energetic shortage during stress response is the hypersensitivity to oxidative stress of mutants that lack mitochondrial function and of yeast treated with mitochondrial inhibitors [42] . These authors suggest that the oxidative sensitivity is due to a defect in an energy-requiring process that is needed for detoxification of ROS or for the repair of oxidative molecular damage . Further support for the importance of protein cost as a selective pressure in the evolution of adaptive changes in gene expression is found in different studies . For example , pathways appear to have evolved to maximize flux for a minimum amount of protein , because the enzyme concentration may be limited by both the protein synthesizing capacity and the solvent capacity of a cell [43] . In fact , theoretical studies suggest that adaptive responses of yeast to environmental changes trigger a gene expression profile that is optimal under the constraint of minimal total enzyme production [2] , [44] , [45] . There are three aspects that the cell can tune to decrease cost of protein synthesis . First , it can decrease the amount of protein that it synthesizes per time units . If we take changes in gene expression as a proxy of changes in protein synthesis , we find that , in many cases the overall protein synthesis during stress response is decreased ( the yij index defined above is negative ) . Second , the cell may decrease cost of protein synthesis by expressing at higher levels proteins that are small . This would decrease the biosynthetic cost per protein chain and is consistent with our results . Finally , the cell may decrease the cost of protein synthesis by increasing the half life of proteins . We find no evidence for this strategy . In summary , if decreasing the cost of protein synthesis significantly contributes to shaping the gene expression profile of an adaptive response , we should find trends in the composition of the changing protein complement that are consistent with the following predictions: The results of our analysis are broadly consistent with these predictions ( see Figure 3 for a summary ) and support the hypothesis that response to the various stresses has evolved under a selective pressure for minimizing the cost of protein synthesis . GO analysis show that the results are not biased by a specific type of proteins and that the hypotheses are consistent with the results over a wide variety of GO categories . We also see that proteins involved in molecular complexes have changes in gene expression that are similar to proteins that are very large . A more detailed analysis of this later result would require an accurate knowledge about the stoichiometry of the complexes . Further analysis that would directly establish whether there are limitations on resources and energy usage during a given adaptive response would require data about ATP usage and production under each relevant condition . Such data would allow us to better understand which constraints are important in shaping the evolution of those responses . | Although different environmental stresses trigger specific sets of protective changes in the gene expression of yeast , the adaptive responses to these stresses also share some common features . We hypothesize that minimization of metabolic costs may contribute to shaping such adaptive responses . If this is so , then such pressure should be more noticeable in the costliest biosynthetic processes . One of these is protein synthesis . Thus , we analyze the set of genes and proteins whose expression changes during the responses and look for evidence to support or falsify our hypothesis . We find that protein properties that are indicative of protein cost correlate to changes in gene expression in a way that is consistent with that hypothesis for a large number of adaptive responses . However , if changes in gene expression are small during the adaptive response , we find no evidence of protein cost as a factor in shaping the adaptive response . | [
"Abstract",
"Introduction",
"Materials",
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"biology/bioinformatics"
] | 2010 | Minimization of Biosynthetic Costs in Adaptive Gene Expression Responses of Yeast to Environmental Changes |
Chemotherapy resistance is a major challenge to the effective treatment of cancer . Thus , a systematic pipeline for the efficient identification of effective combination treatments could bring huge biomedical benefit . In order to facilitate rational design of combination therapies , we developed a comprehensive computational model that incorporates the available biological knowledge and relevant experimental data on the life-and-death response of individual cancer cells to cisplatin or cisplatin combined with the TNF-related apoptosis-inducing ligand ( TRAIL ) . The model’s predictions , that a combination treatment of cisplatin and TRAIL would enhance cancer cell death and exhibit a “two-wave killing” temporal pattern , was validated by measuring the dynamics of p53 accumulation , cell fate , and cell death in single cells . The validated model was then subjected to a systematic analysis with an ensemble of diverse machine learning methods . Though each method is characterized by a different algorithm , they collectively identified several molecular players that can sensitize tumor cells to cisplatin-induced apoptosis ( sensitizers ) . The identified sensitizers are consistent with previous experimental observations . Overall , we have illustrated that machine learning analysis of an experimentally validated mechanistic model can convert our available knowledge into the identity of biologically meaningful sensitizers . This knowledge can then be leveraged to design treatment strategies that could improve the efficacy of chemotherapy .
Though chemotherapy is one of the most successful tools in the fight against cancer [1] , treatment often fails due to a diverse set of resistance mechanisms occurring in cells [2–5] . To minimize the probability of resistance , patients are typically treated with multiple chemotherapy drugs with separate molecular mechanisms . The theoretical justifications are that resistance to one drug will not confer resistance to others and the occurrence of cells harboring multiple resistance mechanisms are rare . Although better than any single drug , combination chemotherapy presents its own set of challenges , since the drugs interact in complex ways and in some cases might even antagonize one another . Moreover , individual drugs elicit responses on a wide range of timescales , therefore dosing schedules can have a profound effect on the response . In extreme cases , the interaction between drugs can be inverted; that is , one dose schedule can be synergistic while another is antagonistic [6] Given the large number of chemotherapy drugs available , and the almost limitless possibilities for dose schedules , identifying the optimal treatment protocols with experiments alone would be too time and resource consuming to be feasible . Computational models , which capture the key molecular events of the chemotherapy response , could drastically facilitate the identification of optimal treatment protocols as they can explore treatment space much more rapidly than experimental methods . To this end , we employed a computational modeling strategy to understand the response of HCT116 colon cancer cells at the single cell level to two drugs , the cross linking agent cisplatin and the TNF-related apoptosis-inducing ligand ( TRAIL ) . These drugs induce cell death through two very different mechanisms . Cisplatin induces DNA damage that stabilizes the transcription factor p53 and ultimately leads to activation of the intrinsic apoptosis pathway . In contrast , TRAIL is a cytokine that induces the extrinsic apoptosis pathway by binding to DR4/5 receptors and activating caspase 8 through a p53 independent mechanism [7–9] . Importantly , quantitative single-cell data exists for the response to each treatments making them ideal modeling candidates as the models can be tested to ensure they match the data [5 , 10] . To accurately model the response of these two drugs , we combined our previous models on p53 signaling [11] and the intrinsic apoptosis pathway [12] together with recent studies on how intercellular variability contribute to cell fate in response to pro-apoptosis signals . In particular , a recent study by Márquez-Jurado et al . revealed that cell-to-cell heterogeneity in mitochondrial mass results in different levels of pro- and anti- apoptotic regulating proteins ( e . g . BCL family member MCL , BH3 family member Bid , and the caspase family proteins ) and fine-tunes the apoptotic responses of individual tumor cells [13] . Based on these findings , we incorporated variability in apoptosis regulators ( BCL family Bax and BH3 proteins ) as well as variability in caspases . We first validated this expanded model by testing predictions on the cellular response to a cisplatin and TRAIL combination treatment . Our model predicts that the combination with TRAIL considerably increases the rate of cell death induced by cisplatin treatment , which we experimentally verified using population measurements . In addition , the model suggests the combination cisplatin/TRAIL treatment leads to a bimodal distribution in the time of death for individual tumor cells . We experimentally confirmed this prediction using live single-cell time lapse microscopy . Moreover , the precisely determined timing of the 2nd wave of death shown by microscopy allows us to refine the decay rate of TRAIL in the model . Such consistency between model prediction and experimental validation suggests that the expanded model serves as a reasonable framework to connect observed cellular fates to available knowledge on the molecular control network . We further analyzed how cell-to-cell differences in other regulators in the molecular control network ( e . g . p53 , Bcl-2 and caspase family proteins ) impact the cellular response ( survival or death ) to cisplatin treatment . Due to the large number of components , our validated model is too complicated to yield an analytical solution . Therefore , to gain insight into this complex process , we subjected a heterogeneous population of models that represent individual tumor cells to systematic analyses using an ensemble of machine learning methods . These methods included Partial Least Squares regression ( PLS ) , Random Forest ( RF ) , Logistic Regression ( LR ) and Support Vector Machine ( SVM ) . The results of these different methods were cross-compared to reduce the chance of overfitting as well as potential bias induced by any single method . Collectively , these analyses revealed that the fate of cisplatin treated cells are most sensitive to the levels of the classical apoptosis inhibitor BCL family proteins , the pro-apoptotic protein Bax , the positive feedback controlling p53 activation , and the degradation rate of the Bcl-2 homology domain 3 ( BH3 ) proteins , among other regulators . These results are consistent with available knowledge of the biological system . Indeed , the role of Bcl-2 in fine-tuning fates of cisplatin treated cells has been experimentally demonstrated in previous studies [10] . These biologically meaningful results , achieved from machine learning analysis of the solid , experimentally validated models , provided a ‘proof of principle’ of our novel pipeline to integrate knowledge and data to cope with uncertainties and design novel treatments . Overall , we have successfully integrated high resolution experimental data and currently available knowledge into a validated and comprehensive model that connects the non-genetic variations within cells to their fates in response to combination chemotherapy treatment . The model then represents our knowledge in a suitable framework for machine learning methods to make quantitative discoveries on chemotherapy sensitizers . Furthermore , the model allows us to extract causal insights on how the sensitizers work . Our innovative pipeline , in which mechanistic modeling chaperones machine learning , is empowered by the strength of these two different approaches , and could provide a way to efficiently identify effective drug combinations for a diverse set of cancers .
The current model for fractional killing incorporates three sources of variability: p53 regulation , BCL family proteins , and caspase proteins ( Fig 1 ) . Heterogeneity in the apoptosis regulator BCL proteins as well as caspases was incorporated due to recent work by Márquez-Jurado et al . [13] , who showed that variation in the pro- and anti- apoptotic proteins Bcl-2 , Bax , Smac , XIAP , Caspase-8 and Caspase-9 fine-tuned the apoptotic responses among individual cells [13] . The influence diagram ( Fig 1 ) of the expanded model was translated into a set of ordinary differential equations ( ODEs , details in Methods ) . Holding these equations constant , we varied equation parameters to simulate a population of models each representing a single cell , thus mimicking a population of tumor cells with cell-to-cell variability . The temporal simulations of representative cells are shown in Fig 2 . In response to an identical concentration of cisplatin , some cells died ( red curves , Fig 2 ) , while others survived ( blue curves , Fig 2 ) . Cells with different fates activate p53 at different rates , with dead ones activating p53 faster ( Fig 2A ) , matching previously published experimental results [10] . In apoptotic cells , rapidly accumulating p53 activates Baxm proteins , which results in the release of CytoC from mitochondria ( Fig 2B ) . Meanwhile , Smac is also released from mitochondria and the apoptosis inhibitor XIAP is sequestered into the inactivated Smac:XIAP dimer form ( Fig 2C ) . Together , CytoC and Smac result in the activation of caspase-9 ( Fig 2D ) , which cleaves inactive pro-caspase-3 into the activated form of caspase-3 ( Fig 2E and 2F ) . Once activated , caspase-3 can directly induce cell death [5 , 12] . Cell death is triggered and simulation stops when caspase 3 activity rises above a threshold level of 0 . 3 . We next validated this expanded model by experimentally examining its predictions . Cancer cells also undergo fractional killing in response to treatment with TRAIL ligand [5] , and our model predicts that the incorporation of TRAIL would enhance the killing efficiency of cisplatin ( Fig 3A and 3B ) . Kaplan-Meier plotting suggests that the combined therapy results in more rapid death as well as a higher cellular mortality rate . Cisplatin alone induces the death of 40 . 6% of treated cells , while the combination of cisplatin with TRAIL increases the rate of cellular death to around 73 . 7% ( Fig 3A ) . All other settings in these two populations are identical . Furthermore , density plots of the distribution of death times suggest that the cisplatin and TRAIL combination induces “two waves of death”: the initial peak is around 18 hours after treatment and corresponds to the peak time observed with TRAIL alone; and the 2nd peak time is around 65 hours , similar to the single peak induced by cisplatin alone ( Fig 3B ) . From the temporal simulations following combination therapy , we could see that as the p53 dynamics changes little in response to the combination therapy , the combination results in two waves of activations of Caspase 9 and Caspase 3 ( Fig 3C–3E ) . This predicted ‘two waves of death’ indicates that the level of TRAIL ligand does not remain constant but decays over time , as assumed in the current model . Indeed , if an alternative assumption is made and the level of TRAIL is held constant , the combined therapy results in only one large wave of cellular death ( S1A and S1B Fig ) . Although the timing of cell death is sensitive to the TRAIL decay rate , the prediction of enhanced cellular death is robust and insensitive to it; a higher fraction of cells will die in response to the combination treatment , whether TRAIL decays or remains constant ( Fig 3A and S1A Fig ) . Hence , the model generates two predictions , one robust prediction of higher death fraction and one less robust prediction of ‘two waves of death’ ( Fig 3 ) . We proceeded to test these model predictions of death time and death rate . We first generated dose-response curves for HCT116 colon cancer cells treated with cisplatin alone or a combination of cisplatin with a low dose of TRAIL ( Fig 4A ) . Consistent with the model’s robust prediction , the combination treatment led to a decrease in cell viability when compared to cisplatin alone ( Fig 4A , cisplatin EC50 7 . 1 μM V . S . cisplatin + TRAIL EC50 1 . 7 μM ) . The population measurements generated by the dose-response curves cannot detect the dynamical features of the cells , such as time to death . Testing whether there are ‘two waves of death’ requires observing death events in single cells with high temporal resolution . Therefore , we used time-lapse microscopy to track the kinetics of cisplatin and TRAIL induced cell death over time . We used a previously engineered colon cancer cell line where one allele of p53 was tagged with the Venus fluorescent protein at the endogenous locus and harbored mCerulean fused to a nuclear localization signal to track nuclear p53 levels ( HCT116 p53-VKI , [10 , 14] ) . In these single cells , p53-Venus levels and cell fate ( death or survival ) were measured following the treatments with cisplatin alone and cisplatin + TRAIL by time-lapse fluorescence microscopy every 15 minutes over a 74 hour period ( Fig 4B and 4C , S1–S3 Movies ) . Kaplan-Meier plotting revealed that the combination TRAIL plus cisplatin treatment killed more cells and at a faster rate when compared to cisplatin treatment alone ( Fig 4D ) . Furthermore , the experimental data revealed two waves of death in the cisplatin + TRAIL combination treatment ( Fig 4E ) . Time-lapse images were also acquired for the TRAIL mono treatment , however due to the large number of divisions we were unable to track single cells when they receive only TRAIL . We did measure the times of death of single cells to get the death distributions in Fig 4E . The first peak of death aligned closely with the peak observed with TRAIL treatment alone , while the second peak of death aligned to the cisplatin only therapy . Hence , these data confirm the model assumption that TRAIL actively decays rather than remaining at its initial concentration . The experimental confirmation of the model’s predictions show that the model assumptions are reasonable and indicates that our expanded model can serve as a faithful ‘in silico’ representation of the biological system . Since the expanded model incorporates the relevant data into a suitable , knowledge constrained framework , we hypothesized that rigorous analysis of this model could yield biologically meaningful results . To test this hypothesis , the expanded model was simulated with a total of 6000 different sets of parameters whose values were randomly selected ( details in Methods ) . In this way , these simulations served as an “in silico” proxy of 6000 cisplatin treated tumor cells and are referred to as in silico cells ( ISCs ) hereafter . Among the population , an ISC is labelled as “dead” if caspase-3 is activated past a specific threshold and as “alive” if caspase-3 activity is kept below the threshold . In this way , cell fate is determined by the peak value of caspase-3 , which is a continuous response variable . As the level of caspase-3 is a systems level property and controlled by many parameters , Partial Least Squares regression ( PLS ) was used to reduce the parameter dimension and to identify the parameters that contribute most to the value of this continuous variable [15] . PLS identified two principal factors that together explain about 60% of the variance that characterizes the change of peak caspase-3 level ( Fig 5A ) . Based on their relative contributions to these principal factors , PLS then generated a rank of the contributions by individual parameters ( Fig 5B , top panel ) . With this rank , PLS revealed that caspase-3 activity is most sensitive to variabilities characterizing the level of Bcl-2 , the level of Bax , the positive feedback controlling the transcription factor p53 , and the stability of BH3 . To avoid bias induced by any single analysis method , we subjected the discrete life-death responses of ISCs to two additional machine learning methods: random forest and logistic regression . For our analysis , the random forest established 2000 decision trees to calculate the relative contribution of each variable in distinguishing living cells from dead ones , while logistic regression identified the dominant factors using backward elimination [15] . Consistent with the results from PLS , these two alternative methods also identified the variability of four components ( Bcl-2 , Bax , p53 self-activation and BH3 stability ) as the dominant factors contributing to cell death in response to cisplatin ( Fig 5B , middle and bottom panels ) . The sensitizers identified by these machine learning methods are consistent with previous studies . Our previous work has shown that cell death in response to cisplatin can be greatly enhanced by inhibiting BCL proteins [10] . Meanwhile , other studies using either gain-of-function [16–18] or loss-of-function [19–21] of Bax showed its importance in regulating cell death under diverse conditions . This partial confirmation indicates that indeed , machine learning analysis of knowledge based , data constrained mechanistic models can yield biologically meaningful sensitizers that are informed by both experimental data and biological knowledge . Furthermore , the identified controlling components have opposite effects on cellular death . As the elevation of p53 positive feedback strength and Bax level promote cellular death; the increase of the other two components ( BCL level and BH3 stability ) promote cellular survival . On the basis of these biological considerations , we then carried out support vector machine ( SVM ) analysis with a linear combination of the two life promoting components ( Fig 5C , y-axis ) and the two death promoting components ( Fig 5C , x-axis ) . The combination coefficients were identified using independent logistic regressions ( details in Method ) . As a result , SVM plotting identified a two dimensional plane with two clearly distinguished regions: the top left region is mainly occupied by living cells , while the bottom right region contains mostly dead cells ( Fig 5C ) . Starting from a high dimensional space of over 30 parameters , this SVM identified a 2-dimentional plane that correctly identified the fates of a majority of cells ( 84 . 5% ) . In this way , the SVM identified boundary derived from simulated data provides an elegant way to simplify and to understand the complex control of cellular fates .
Mechanistic modeling has been widely used to aid in the design of effective cancer therapies or to identify biomarkers for personalized treatment [22–35] . Recently , machine learning methods have also gained popularity in multiple areas including biomedicine [36–38] . Given their distinct power and limitations , it is reasonable to expect that the integration of these two different methods could result in a powerful tool for the biomedical community [39] . Indeed , Gong and Sobie have elegantly integrated mechanistic modeling and machine learning to predict drug responses across different types of cultured cells [40] . In this work , we illustrate a novel way to use mechanistic modeling to chaperone and improve machine learning to extract biologically meaningful results . By incorporating existing knowledge and providing mechanistic explanations , mechanistic modeling allows us to cope with two major limitations of machine learning performed in isolation: being unable to incorporate available knowledge and lack of causality . Hence , this novel , integrated approach utilizes the power of machine learning and reduces its limitations . Recently , machine learning analysis has gained rapid popularity and there is concern on how the biomedical community should apply this new tool in conjunction with traditional mechanistic modeling [39 , 41 , 42] . In this exploration , we revealed one plausible way to integrate these two , in which the machine learning algorithms were used to shed light on the control of the mechanistic model when all its parameters are changed simultaneously . In doing so , we achieved a robust and consistent ranking of the model components , as well as a clearer representation of the system on the reduced two dimensional SVM plane . By efficiently utilizing these methodologies , we believe that the traditional field of mechanistic modeling can benefit from the rapid development of machine learning algorithms . Meanwhile , our work also indicates that solid and well-validated models are essential for this integrated approach to be successful . We believe that mechanistic models can only become solid if they are continuously validated and improved against experimental findings , and we have insisted in continuously testing and modifying our model with novel experimental results . A brief time line of the model’s history and its interaction with relevant experimental studies is shown in Fig 6 . Initiated by dynamical studies on p53 oscillations [43 , 44] , our original model for the pulsatile dynamics of p53 in 2007 [11] . Meanwhile in 2009 , we also developed a computational framework for the intrinsic cell death pathway [12] . At the time , the model of programmed cell death made a novel assumption . It assumed that the inactivation of the mitochondrial form of Baxm is catalyzed by the apoptosis inhibitor BCL family proteins . In other words , BCL family proteins serve not only as stoichiometric inhibitors , but also as enzymes to accelerate the inactivation of Baxm from the mitochondria and shuttle them back to the cytoplasm . This essential model assumption was then validated independently by Edlich et al . [45] . By observing fluorescence loss in photobleaching , Edlich et al . demonstrated that the translocation of Bax from the mitochondria to the cytoplasm is sped up when a member of the BCL family , Bcl-xl , is overexpressed; on the contrary , this translocation rate decreased when Bcl-xl is inhibited , as reported in Fig 6D from [45] . This independent confirmation of the model assumption suggested that the model is reasonable and can be used for further expansion and predictions . In 2016 , the high temporal resolution data reported by Paek , et al . suggested an intriguing relationship between the dynamical activation of p53 and cellular fates [10] . Following this report , we developed a comprehensive model that combined our models of p53 and apoptosis while incorporating the newly found incoherent pathways activated by the chemotherapy drug cisplatin . The model successfully recaptured and explained the observed dynamic relation [46] . In the current study , we further expanded the available model with recent data from the literature and from the Paek lab . When additional data is available in the future , the model will be further improved to better represent our growing knowledge of the biological system . Since our mechanistic model has been continuously updated and validated with experimental data from multiple sources , it currently serves as a general apoptosis framework . Because each component of the combined model was derived from models tested on different cell types , it yields results that broadly apply to many different cell types . We hope that in the near future , the apoptosis model can be constrained with data from a single cell line . Then , integrating the cell line specific model with machine learning analysis will promise to yield novel , specific results in that particular cell line . Meanwhile , the methodology we apply in the manuscript integrates the powers of both mechanistic modeling and machine learning , and achieves more than either approach in isolation . This approach can be generally applied to study a broad range of cancers as well as other complex diseases .
The wiring diagram of the model was translated into a set of ordinary differential equations ( S1 Table ) and simulated following our established protocol [46] . The structure and basal parameter values of the model are mostly inherited from previous publications [11 , 12 , 46] . The rate constants are characterized by the inverse of the time scale of model ( hour -1 ) . The levels of the control molecules , as well as the regulatory strengths , are dimensionless . For the population level simulations , the parameters were randomly sampled from uniform distributions ranging between 70%–120% of their basal levels , which were used to properly recapture the corresponding experimental observations from our previous publication [10 , 46] . The models are simulated with stiff methodology in the software Xppaut ( http://www . math . pitt . edu/~bard/xpp/xpp . html ) . The simulated living cells and death cells in response to different drug treatments , these simulated cells are then subject to analysis with machine learning methods . An ensemble of machine learning methods was used to analyze the model simulated data . Partial Least Square ( PLS ) Analysis was used for dimension reduction with multivariate linear regression [15] . Similar to Principal Component Analysis ( PCA ) , PLS also identifies a smaller set of factors that are linear combinations of the original parameters . However , in contrast to an unsupervised PCA , PLS is supervised and the factors identified here do not always include all the original parameters . Rather , only part of the original parameters are included in the PLS-identified factors on the basis of the comparison between the observation and statistical prediction . In the factor space with reduced dimensions , the composed linear regression model bears the smallest distance ( least squares ) between the measured values and the fitted ones . The PLS was carried out with the standard procedure within the statistical software SAS ( SAS Institute Inc . , Cary , NC , USA ) . By assembling multiple Decision Trees , Random Forest was used to improve the classification results and generate a rank of how each parameter contributes to the prediction accuracy [15] . Computation was done in R using the Random forests algorithm within the standard package [47] . Default parameters were used along with 10-fold cross-validation . Logistic regression , which models the logistic transformation of probability of the binary response cell fates [15] , was used to rank the contribution of the control parameters as well as to identify the linear combination of the life promoting and death promoting parameters . The logistic regression , together with backward elimination and 10-fold cross validation , was computed in SAS programming environment using the standard method and default parameter setting . For ranking the control parameters , all parameters are incorporated at the beginning; for identifying the linear combinations , the selected parameters were used . The Support Vector Machine ( SVM ) is a popular classification method . With a subset of the models from the population , SVM computes the boundaries that separate different populations of data with maximal margin . The chosen data are referred to as “support vectors” since they are used to compute the boundaries . The SVM was carried out within R . The “life factor” used in SVM is a linear combination of the BCL-2 level and BH3 degradation rate ( −17 . 6132 + 14 . 5786 * nbcl2total + 6 . 9014 * kdbh3 ) , while the “death factor” is a linear combination of the strength of p53 positive feedback and the level of Bax ( −6 . 0579 + 1 . 5398 * Rp53p53 + 3 . 3541 * baxtotal ) . These coefficients are identified with logistic regression that best fits the probability of cellular survival with Bcl2 level and Bh3 degradation rate; and then fits the probability of cellular death with the p53 positive feedback strength and Bax level . The one parameter bifurcation and the two parameter bifurcation analysis were carried out with death factor and life factor as control parameters , and the position of the threshold between life and death regions was tracked with the freely available Oscill8 software ( http://oscill8 . sourceforge . net ) . Approximately 3 , 000 HCT116 cells were plated to each well of a 96 well plate in McCoys 5A + 10% FBS . After 24 hours , cisplatin ( Sigma , 1134357 ) and/or TRAIL ( Sigma , T9701 ) was added to the media . 72 hours after drug treatment , cells were washed 2x by PBS and fixed in PBS containing 3 . 5% paraformaldehyde for 20 minutes at room temperature . Cells were then permeabilized using PBS containing . 1% Triton X-100 for 1 hour at room temperature and then stained using Cell Tag 700 stain ( LiCor 926–41090 ) which stains the nucleus and cytoplasm of cells . Total cell viability was measured using the 700nm channel of a LiCor Odyssey scanner . Cell viability was normalized to untreated cells . EC50 was estimated by fitting the data using MATLAB to the hill function: EHill=1+E∞−11+ ( EC50C ) H where C is the concentration of cisplatin , E∞ is the maximum effect , EC50 is the 50% effective concentration and H is the hill exponent . To measure p53 dynamics and cellular death in single human colon cancer derived , HCT116 cells , we used the HCT116 p53-VKI cell line which has one allele of p53 tagged at the C-terminus with the Venus fluorescent protein and mCerulean-NLS as described previously [10 , 14] . We have previously shown that the p53-Venus reporter matches endogenous p53 dynamics [10] . Cellular morphology was used to determine whether cells have enacted apoptosis ( See Fig 4 ) . For microscopy , ~10 , 000 cells were plated on poly-d-lysine coated glass bottom dishes ( MatTek , P35GC-1 . 5-14-C ) and allowed to attach to plates for 72 hours in McCoy’s media 5A Media with 10% FBS . Media was washed out with PBS and replaced with DMEM FluoroBrite ( ThermoFisher ) with 5% FBS and 1% Glutamax ( ThermoFisher ) . Cells were imaged on a Nikon Eclipse Ti-E microscope enclosed in an OKO labs incubation chamber to maintain humidity , a temperature of 37°C and 5% CO2 . Images were captured every 15 minutes for 74 hours . Drugs were added 1 . 5 hours into the experiment , and mineral oil was added to prevent evaporation of media . We used previously written custom MATLAB scripts to extract p53 dynamics and cell death from single-cells . | Combination chemotherapy is frequently used in the fight against cancer as treatment with multiple chemotherapy drugs of different molecular mechanisms reduces the chance of resistance . The complex mechanisms involved makes it essential to develop a comprehensive computational model that comprehends experimental data and biological knowledge to facilitate design of combination therapies . As computational models grow and capture more and more molecular events governing the chemotherapy response , it becomes harder to explore the treatment space efficiently and systematically . To facilitate the extraction of unbiased solutions from complicated models , we have conducted systematic analysis using a series of machine learning methods including Partial Least Squares regression ( PLS ) , Random forest ( RF ) , Logistic Regression ( LR ) and Support Vector Machine ( SVM ) . The results of these different methods were cross-validated to reduce the chance of overfitting or bias by any single method . Overall , we propose a novel computational pipeline , where machine learning analysis of experimentally validated models is used to generate unbiased predictions of novel chemotherapy targets . | [
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] | 2019 | Designing combination therapies with modeling chaperoned machine learning |
Mutations in the splicing factor SF3B1 are found in several cancer types and have been associated with various splicing defects . Using transcriptome sequencing data from chronic lymphocytic leukemia , breast cancer and uveal melanoma tumor samples , we show that hundreds of cryptic 3’ splice sites ( 3’SSs ) are used in cancers with SF3B1 mutations . We define the necessary sequence context for the observed cryptic 3’ SSs and propose that cryptic 3’SS selection is a result of SF3B1 mutations causing a shift in the sterically protected region downstream of the branch point . While most cryptic 3’SSs are present at low frequency ( <10% ) relative to nearby canonical 3’SSs , we identified ten genes that preferred out-of-frame cryptic 3’SSs . We show that cancers with mutations in the SF3B1 HEAT 5-9 repeats use cryptic 3’SSs downstream of the branch point and provide both a mechanistic model consistent with published experimental data and affected targets that will guide further research into the oncogenic effects of SF3B1 mutation .
One of the biggest surprises to emerge from the growing catalog of somatic mutations in various cancer types is the recurrent mutation of genes encoding the RNA spliceosome [1] . Recurrent mutations in the highly conserved HEAT 5–9 repeats of splicing factor 3B subunit 1 ( SF3B1 ) have been reported in myelodysplastic syndrome , chronic lymphocytic leukemia ( CLL ) , breast cancer ( BRCA ) , uveal melanoma ( UM ) , and pancreatic cancer [2–7] . SF3B1 mutation is associated with poor prognosis in CLL but improved prognosis in myelodysplasia and UM [2 , 7–9] . Prior studies have shown that mutated SF3B1 CLL samples have differential exon inclusion and use some cryptic 3’ splice sites ( 3’SSs ) relative to wild-type SF3B1 CLL samples [5 , 6 , 8 , 10 , 11] . However , it is unknown whether SF3B1 mutation is associated with the same 3’SS selection defects in different cancers . The mechanism underlying the cryptic 3’SS selection and the functional consequences thereof remain unresolved as well . SF3B1 is a core part of the U2-small nuclear ribonucleoprotein ( U2-snRNP ) complex and stabilizes the binding of the U2-snRNP to the branch point ( BP ) , a degenerate sequence motif usually located 21–34 bp upstream of the 3’SS [12 , 13] . SF3B1 also interacts with other spliceosomal proteins such as U2AF2 , which binds the polypyrimidine tract ( PPT ) downstream of the BP [2 , 14 , 15] . The binding of the U2-snRNP and other spliceosome proteins around the BP prevents 3’SS selection in a ~12–18 bp region directly downstream of the BP due to steric hindrance [16 , 17] . Inherited cis-acting splicing mutations beyond this ~12–18 bp region downstream of the BP that result in the use of cryptic 3’SSs have been shown to occur in Mendelian disease genes [18] . Additionally , a competitive region exists ~12 bp downstream from the first 3’SS after the protected region where AG dinucleotides can compete to be used as 3’SSs based on sequence characteristics such as the PPT length , distance from the BP , nucleotide preceding the AG dinucleotide , and other features [17] . The role of SF3B1 and the U2-snRNP in recognizing and binding the BP and the localization of mutations to HEAT 5–9 repeats suggest that SF3B1 mutations are dominant drivers that may alter 3’SS selection [6] . To test this , we examined splice site usage in transcriptome data from SF3B1 mutant and SF3B1 wild-type CLL , UM and BRCA cases . We identified 619 cryptic 3’SSs used more frequently in SF3B1 mutants and clustered 10–30 bp upstream of canonical 3’SSs . The majority of these cryptic 3’SSs were observed in all three tumor types despite the divergent clinical implications of SF3B1 mutation . Our analysis of tumors with SF3B1 mutations shows that cryptic 3’SS selection occurs only in samples with missense mutations at ~10 amino acid hotspots in the fifth to ninth HEAT repeats . We analyzed the organization of splicing motifs around the cryptic 3’SSs and found that only introns with an AG dinucleotide at the boundary of the sterically protected region downstream of the BP but >10 bp upstream of the canonical 3’SS are susceptible to cryptic 3’SS selection in SF3B1 mutants . We assessed the functional impact of SF3B1 mutation and found that the cryptic 3’SSs are typically used at low frequency in the SF3B1 mutants ( <10% relative to the canonical splice site ) and are sometimes present in the SF3B1 wild-types but at an even lower frequency ( <0 . 5% relative to the canonical splice site ) . However , we identified 10 candidate genes , some previously implicated in tumorigenesis , for which there is a high amount of out-of-frame cryptic splice site usage that may affect the function of these genes .
We used RNA-sequencing data from SF3B1 mutated and SF3B1 wild-type chronic lymphocytic leukemia ( CLL; seven mutant , nine wild-type ) , breast cancer ( BRCA; 14 mutant , 18 wild-type ) , and uveal melanoma ( UM; four mutant , four wild-type ) samples ( S1 Fig . , S1 File ) to test 219 , 476 splice junctions present in the Gencode v14 gene annotation [19] along with 87 , 941 novel splice junctions ( not annotated in Gencode ) for differential usage by comparing junction-spanning reads using a generalized linear model as implemented in DEXSeq [20] . A splice junction is considered differentially used between mutant and wild-type samples if the expression level of that junction differs significantly after accounting for overall expression differences of the corresponding gene locus . All tested junctions were covered by at least 20 reads summed over all cancer samples in a given analysis , shared a 5’ splice site and/or 3’SS with a Gencode splice junction , and had a known splice site motif . We identified 1 , 749 junctions that were significantly differentially used between the SF3B1 mutant and SF3B1 wild-type samples across the three tumor types including 1 , 330 novel junctions , of which 1 , 117 are novel 3’SSs ( BH-adjusted p < 0 . 1 , S2 File ) . These 1 , 749 significant junctions were highly enriched for novel splice junctions compared to annotated junctions ( Fisher exact , p < 10-200 ) and the novel junctions were enriched for novel 3’SSs ( Fisher exact , p < 10-200 ) showing that SF3B1 mutations result in the usage of a large number of novel 3’SSs . These 1 , 749 significant junctions include 61 of 79 splice sites recently reported as specific to CLL cases with SF3B1 mutations [11] supporting the specificity of our approach while demonstrating an increased sensitivity that has allowed us to identify many more cryptic 3’SSs than previously reported . We plotted the distance between each significant novel 3’SS and its associated canonical 3’SS ( defined as the nearest Gencode 3’SS that shared the same 5’ splice site—see Methods ) . Of the 1 , 117 significant novel 3’SSs , 619 were proximal cryptic 3’SSs clustered 10–30 bp upstream of their associated canonical 3’SSs while the remaining 498 cryptic 3’SSs were widely distributed ( herein referred to as distal cryptic 3’SSs ) ( Fig . 1A , S3 File ) . All of the 619 proximal cryptic 3’SSs were used more often in the SF3B1 mutant samples compared to the wild-type samples and 58% were out-of-frame relative to the nearby canonical 3’SSs , suggesting that these are not canonical 3’SSs missing from Gencode . 417 of the 498 distal cryptic 3’SSs were also used more highly in the SF3B1 mutants ( S4 File ) . The distribution of the 1 , 117 significant novel 3’SSs is different from that of novel 3’SSs whose usage did not differ significantly between the SF3B1 mutants and wild-types ( Fig . 1B , C ) , further demonstrating that the usage of proximal cryptic 3’SSs is a property of SF3B1 mutants . Examining each tumor type individually , we observed the same enrichment of cryptic 3’SSs 10–30 bp upstream of canonical splice sites ( S2 Fig . ) . Given these observations , SF3B1’s role in binding the BP , and the organization of the BP and splicing motifs in the last 30 bp of the intron [12] , we focused our initial analyses on the 619 proximal cryptic 3’SSs . We clustered all samples based on the read coverage of the 619 proximal cryptic 3’SSs and found that four SF3B1-mutated BRCA samples did not cluster with the other mutants ( Fig . 1D ) . The SF3B1 mutation for one of these BRCA samples was a nonsense mutation not located in the HEAT 5–9 repeats while another sample had a subclonal ( 8 . 4% ) HEAT 5–9 mutation with attenuated cryptic 3’SS selection ( S3 Fig . ) . The other two samples had mutations in the HEAT 5–9 repeats but outside of the apparent ~10 amino acid mutational hotspots ( Fig . 1E ) . We observed cryptic 3’SS selection in a TCGA lung adenocarcinoma sample with a hotspot mutation but not in lung cancer samples with SF3B1 mutations outside of the five hotspots ( S4 Fig . ) . These results show that cryptic 3’SS selection only occurs in tumors carrying mutations in one of the five ~10 amino acid hotspots in the HEAT 5–9 repeats and is not limited to cancers in which SF3B1 is recurrently mutated . The majority of the 619 proximal cryptic 3’SSs were used in SF3B1-mutated samples in all three cancer types suggesting that the mechanism of cryptic 3’SS selection in SF3B1-mutated tumors is the same between different cancers ( Fig . 1D ) . Some cryptic 3’SSs were not used in one or two of the cancer types due to lower expression of the corresponding genes in those cancers . Differences in cryptic 3’SS usage due to varying gene expression may contribute to the divergent prognostic implications of SF3B1 mutation in various cancers [2 , 7] . To characterize the roles of the genes affected by cryptic 3’SS usage , we performed a gene set enrichment analysis for the 912 genes that contained the 619 proximal and 417 distal cryptic 3’SSs used significantly more often in the SF3B1 mutant samples ( S5 File ) . The gene set with the second smallest p-value consists of genes up-regulated in chronic myelogenous leukemia and the seventh gene set contains genes up-regulated in aggressive uveal melanoma samples ( GSEA [21] , q < 10-35 ) . These results may reflect the fact that we are more likely to identify cryptic 3’SSs in genes that are highly expressed which may bias such a gene set enrichment analysis . Nonetheless , several gene sets with potential importance for cancer development are enriched such as genes positively correlated with BRCA1 , ATM , and CHEK2 expression across normal tissues ( GSEA , q < 10-28 ) . We characterized the sequence features of the 619 proximal cryptic 3’SSs and their associated canonical 3’SSs to gain further insights into the mechanism of cryptic 3’SS selection ( Fig . 2A ) . We chose 23 , 066 control 3’SSs ( see Methods ) and plotted the nucleotide frequency [22] for the last 50 bp of the introns for all control , associated canonical , and cryptic 3’SSs as well as the enrichment of adenines relative to the control introns . The control introns have a typical nucleotide composition with a 4–24 bp PPT preceding the 3’SS ( Fig . 2B ) [13] . The associated canonical 3’SS introns are enriched for adenines ~15–20 bp upstream of the 3’SS since the proximal cryptic 3’SSs are located in this region ( Fig . 2C ) . However , the introns for proximal ( Fig . 2D ) and distal ( Fig . 2E ) cryptic 3’SSs have a strong enrichment of adenines concentrated ~15 bp upstream of the splice sites . These results suggest that the increased usage of the 619 proximal and 417 distal cryptic 3’SSs in the SF3B1 mutants may result from the same mechanism . The human BP motif is highly degenerate except for a largely invariant adenine [13] leading us to suspect that the adenine signal upstream of the cryptic 3’SSs is caused by the associated canonical 3’SSs’ BP adenines . We used SVM_BP [23] to predict BPs for the associated canonical 3’SSs and calculated the distance from the highest scoring predicted BPs to the cryptic splice sites . We found that AG dinucleotides that serve as cryptic 3’SSs are enriched ~13–17 bp downstream from the predicted BP ( Fig . 3A ) relative to random AG dinucleotides present in control 3’SS introns ( Fig . 3B , p < 10-7 , Mann Whitney U ) . For cryptic 3’SSs not located 13–17 bp downstream from the highest scoring BP in Fig . 3A , we calculated the distance from the second highest scoring BP to the cryptic 3’SSs and found that overall , the majority of the cryptic 3’SSs were located 13–17 bp from either the highest or second highest scoring BP ( Fig . 3C ) . 3’SSs are typically not located within ~12–18 bp downstream of the BP because the proteins bound to the BP sterically hinder AG dinucleotides in this region and prevent them from being used as 3’SSs [16] . Our results suggest that AG dinucleotides serving as cryptic 3’SSs in SF3B1 mutants are located at the end of this sterically protected region downstream of the BP ( Fig . 3D ) . Additionally , during the splicing reaction , the spliceosome searches ~12 bp downstream from the first 3’SS after the BP for any other 3’SSs and chooses the strongest 3’SS based on sequence features [16] . The lack of cryptic 3’SSs in the last 10 bp of the intron ( Fig . 1A ) indicates that cryptic 3’SSs used in SF3B1 mutants are located far enough upstream of the associated canonical 3’SSs to avoid competition for splicing . We observed that the distance between associated canonical 3’SSs and their predicted BPs is significantly greater than the distance between control 3’SSs and their BPs such that the cryptic 3’SSs at the edge of the protected region do not compete with the canonical 3’SS for splicing ( p < 10-23 , Mann Whitney U , Fig . 3E , F ) . We also predicted BP’s for the 619 proximal and 417 distal cryptic 3’SSs ( as opposed to above where we predicted BP’s for the canonical 3’SSs associated with the 619 proximal 3’SSs ) and found that the majority of these cryptic 3’SSs were 13–17 bp downstream of their predicted BP’s ( S5 Fig . ) providing further evidence that most cryptic 3’SSs ( both proximal and distal ) associated with SF3B1 mutations are located at the edge of the sterically protected region . Our results suggest that the mechanism of cryptic 3’SS selection in SF3B1 mutants is not altered BP recognition because a more varied distribution of distances from the cryptic 3’SS to the canonical 3’SS BP would be expected if BP recognition was altered . Studying the role of cryptic 3’SS in inherited Mendelian disease genes , Královicová et al . 2005 used splicing reporters with cryptic 3’SSs located in the PPT and found that moving the cryptic 3’SS into the ~12–18 bp sterically protected region reduced or eliminated cryptic 3’SS selection . On the other hand , moving an AG dinucleotide out of the sterically protected region allowed for its selection as a cryptic 3’SS [18] . These published experimental results and the rigid distance between the BP and the cryptic 3’SSs observed in our study are consistent with a model of altered 3’SS selection in SF3B1 mutants due to a change in the size of the sterically hindered region downstream of the BP . To test whether the sequences requirements defined here are sufficient for cryptic 3’SS usage , we identified 11 , 302 introns whose canonical 3’SSs passed our coverage cutoff of 20 reads summed over all samples and had potential cryptic 3’SSs ( intronic AG dinucleotides that were 10–30 bp upstream of an annotated 3’SS and 13–17 bp downstream of the highest-scoring predicted BP ) . For 900 of these introns , the potential cryptic 3’SSs also passed the coverage cutoff , of which 310 were used significantly more often in the SF3B1 mutants . This analysis demonstrates that not every potential cryptic 3’SS is differentially used in the mutants , so the sequence requirements described here appear to be necessary for cryptic 3’SS usage but not sufficient . Although the cryptic splice sites described here are used significantly more often in the SF3B1 mutants , the biological effects are likely dependent on the proportion of transcripts that use the cryptic 3’SSs relative to the canonical 3’SSs . We therefore calculated the percent spliced in ( PSI ) for the proximal cryptic 3’SSs relative to their associated canonical 3’SSs in the CLL samples since they have a higher sequencing depth than the other tumor samples ( S1 Fig . ) that allows for more accurate quantification of splicing and because the distribution of well-characterized low- and high-risk CLL prognostic factors was similar between the SF3B1 mutated and wild-type samples ( Fig . 4A ) . To calculate PSI for the 325 proximal cryptic 3’SSs used significantly more often in the SF3B1 mutants from the CLL-only analysis ( S6–S7 Files ) , we divided the number of reads that span the cryptic 3’SS by the number of reads that span both the cryptic 3’SS and its associated canonical 3’SS . We observed that some cryptic 3’SSs are used exclusively in SF3B1 mutants while others are also used in SF3B1 wild-type samples but at a lower frequency relative to the mutants ( Fig . 4A ) . 67% of the cryptic 3’SSs were included in <10% of transcripts compared to their associated canonical 3’SS . These results suggest that the cryptic splice sites are either included rarely even in the SF3B1 mutants or that transcripts with cryptic splice sites are subject to a higher rate of nonsense-mediated decay ( NMD ) . To investigate the potential role of NMD , we identified differentially expressed genes between the SF3B1 mutant and wild-type samples in a joint analysis of all three cancers and performed a gene set enrichment analysis . We found that genes in the “Reactome NMD enhanced by the exon junction complex” set were enriched ( GSEA [21] , q < 10-28 ) among the 272 differentially expressed genes ( DESeq2 , BH-adjusted p < 0 . 1 , S8–S9 Files ) suggesting that NMD may be different between the SF3B1 mutants and wild-types . 33 of the 582 genes that contained the 619 proximal cryptic 3’SSs were differentially expressed with the expression of 29/33 of these genes lower in the SF3B1 mutants . Genes containing a proximal cryptic 3’SSs were more likely to be differentially expressed ( Fisher exact , p < 10-8 ) and more likely to have lower expression in SF3B1 mutants ( Fisher exact , p = 0 . 0009 ) . These results suggest that cryptic 3’SS selection may affect gene expression for a subset of genes . However , the observation that in-frame cryptic 3’SSs likely not subject to NMD and out-of-frame cryptic 3’SSs potentially subject to NMD are included at similar rates relative to their associated canonical 3’SSs ( Fig . 4A ) suggests that most genes’ expression are not affected by cryptic 3’SS selection and most cryptic 3’SSs are observed at a low frequency because they are spliced in infrequently compared to their associated canonical 3’SSs . To identify cryptic 3’SSs with relatively high PSI values in the SF3B1 mutant versus wild-type samples , we searched for cryptic 3’SSs that were 1 ) used more than 50% of the time in the CLL SF3B1 mutants; 2 ) used less than 20% of the time in wild-type samples; and 3 ) had an average coverage of at least 30 junction-spanning reads in the mutant samples . Despite the generally low PSI values for the 325 cryptic 3’SSs from the CLL-only analysis , we identified four genes previously implicated in cancer ( TTI1 [24–26] , MAP3K7 [27–29] , FXYD5 [30] , PFDN5 [31] ) and six others ( YIF1A , ORAI2 , ZNF91 , ZNF548 , RP11–1280I22 . 1 , RP11–532F12 . 5 ) with out-of-frame cryptic 3’SSs that were consistently preferred to the associated canonical 3’SS in the CLL SF3B1 mutant samples ( Fig . 4B ) . Ferreira et al . identified the junctions in ORAI2 , ZNF91 , and TTI1 in CLL SF3B1 mutants as well [11] . Nine of the ten junctions were significant in our BRCA-only analysis and showed high differences in relative inclusion ( S6 Fig . , S10–S11 Files ) . These genes are not differentially expressed between the CLL SF3B1 mutant and wild-type samples ( S12 File ) but the frequent inclusion of out-of-frame cryptic 3’SSs may affect their biological function .
Here we have shown that a consequence of SF3B1 mutations in different cancer types is genome-wide selection of hundreds of cryptic 3’SSs . We have shown the cryptic 3’SSs have specific sequence requirements; AG dinucleotides used as cryptic 3’SSs in SF3B1 mutants are located at the end of the sterically protected region ~13–17 bp downstream of the BP but are >10 bp upstream of nearby canonical 3’SSs allowing them to avoid competition for splicing . These sequence requirements limit the introns susceptible to cryptic 3’SS selection to those where the BP is located farther from the 3’SS than the typical ~24 bp . While these requirements appear necessary for cryptic 3’SS usage , they are not sufficient , as we did not detect cryptic 3’SS usage in all introns with AG dinucleotides that satisfy these requirements . Characteristics such as RNA conformation , RNA binding protein sites , BP prediction inaccuracies , cryptic or downstream canonical 3’SS strength , gene/transcript expression , sequencing depth , or other factors may also play a role in determining whether cryptic 3’SSs are used and detected by RNA sequencing . Examining differential splice junction usage allowed us to identify many more cryptic 3’SSs than previous studies while still identifying 61 of 79 cryptic 3’SSs recently reported for CLL SF3B1 mutants using a method based on relative inclusion [5 , 6 , 8 , 10 , 11] . When examining the three cancer types in our study individually , the number of cryptic 3’SSs identified was highly dependent on the sequencing depth of the samples ( S1–S2 Figs . , S2 File ) . Additionally , examining cryptic 3’SSs expressed higher in the SF3B1 mutants but not significantly ( Fig . 1B ) shows a modest enrichment of novel 3’SSs 10–30 bp upstream of canonical 3’SSs . These observations suggest that deeper sequencing will continue to reveal proximal cryptic 3’SSs in SF3B1 mutants that are used very infrequently or are present in lowly expressed genes . Selection of cryptic 3’SSs in the region downstream of the BP has been reported for some inherited diseases including those resulting from disrupted tumor suppressor genes such as ATM , NF1 , and TP53 [18] . Using a curated a list of aberrant splice sites associated with different diseases from the literature , Královicová et al . 2005 found that in cases where cryptic 3’SS selection was not caused by mutation of the 3’YAG consensus sequence , cryptic 3’SSs were often located ~19 bp upstream of associated canonical 3’SSs and ~11–15 bp downstream of the BP [18] . Most of the diseases considered in Královicová et al . 2005 are Mendelian diseases where a cryptic 3’SS disrupts or abolishes the function of a single disease gene . In these cases , a mutation in the PPT between the sterically protected and competitive regions has introduced a cryptic 3’SS ( Fig . 3D ) . For cancers with SF3B1 mutations , we suspect that the size of the sterically protected region is slightly altered allowing for existing AG dinucleotides to be used as cryptic 3’SSs in hundreds of genes . It is also possible SF3B1 mutations could cause destabilization of the U2 snRNP complex or alter interactions with U2AF2 , affecting the ability to recognize the canonical 3’SS and leading to cryptic 3’SS selection . However , the rigid distance ( ~13–17 bp ) from the predicted BPs to the cryptic 3’SSs for most of the cryptic 3’SSs is most consistent with a change in the size of the sterically protected region downstream of the branch point . We found that cryptic 3’SS selection is limited to tumors with mutations in the five ~10 amino acid hotspots in the SF3B1 HEAT 5–9 repeats and that these mutations are associated with cryptic 3’SS selection across different cancer types and even in cancers in which SF3B1 is not recurrently mutated . 58% of these cryptic 3’SSs are out-of-frame relative to nearby canonical 3’SSs , but the biological impact of these cryptic 3’SSs is likely a function of how frequently they are used relative to the nearby canonical 3’SSs . We found that while the cryptic 3’SSs are used more often in the SF3B1 mutated samples compared to wild-type samples , they are used relatively infrequently ( <10% ) compared to nearby canonical 3’SSs . While the differentially expressed genes between the SF3B1 mutated and wild-type samples are enriched for genes in the NMD pathway , even in-frame cryptic 3’SSs are used at a low frequency indicating that the associated canonical 3’SS is mostly preferred to the cryptic 3’SS even in SF3B1 mutants . Nonetheless , we identified ten genes , including four with known roles in cancer , which had a high frequency of cryptic splice site usage relative to the nearby canonical splice site . Further studies are required to determine whether low-frequency cryptic 3’SS selection in hundreds of genes , high-frequency cryptic 3’SS selection in a small group of genes , and/or other splicing alterations drive the oncogenic effect of SF3B1 mutation .
Ethics statement . For the chronic lymphocytic leukemia ( CLL ) samples , the UCSD IRB approved the study and all subjects gave informed consent ( Project #080918 ) . Refer to the informed consent for The Cancer Genome Atlas and Harbour et al . for consent information for other cancer samples [7] . CLL . Seven SF3B1-mutated CLL cases and nine SF3B1 wild-type CLL cases were identified from the CLL Consortium database . The mutations were originally characterized by PCR and verified in the RNA-sequencing data [9] . Sample dates were chosen on average 95 days prior to treatment and at least 287 days after prior treatment to select samples with high tumor cell count . Samples were chosen to have relatively similar numbers of IGHV mutated/unmutated and ZAP-70 positive/negative samples ( Fig . 4 ) . BRCA , LUAD , and LUSC . SF3B1 mutant samples were identified using the Broad GDAC TCGA analysis ( http://gdac . broadinstitute . org/runs/analyses__2013_02_22/ ) in TCGA tumor types with no publication restrictions . Samples with SF3B1 mutations outside of Gencode version 14 exons were excluded . We excluded any cancer types with less than four SF3B1 mutants or for which paired-end RNA-sequencing data was not available leaving breast cancer ( BRCA ) , lung adenocarcinoma ( LUAD ) , and lung squamous cell carcinoma ( LUSC ) . We chose 1 . 25 as many SF3B1 wild-type controls as mutated samples for each cancer type randomly from samples without mutations in SF3B1 or other splicing factors . RNA sequencing data was downloaded from CGHub [32] . UM . Uveal melanoma samples were downloaded from the Short Read Archive ( SRA062359 ) [7] . As reported in Furney et al . , four uveal melanoma samples had SF3B1 mutations in codon 625 and four had wild-type copies of SF3B1 [33] . RNA was extracted from peripheral blood mononucleocytes from seven SF3B1-mutated CLL cases and nine SF3B1 wild-type cases per the manufacturer’s specifications using Qiagen RNeasy mini-spin columns , and RIN scores determined using an Agilent Bioanalyzer . RNA was polyA selected and processed using SMART cDNA synthesis ( Clontech ) to prepare sequencing libraries . Samples were sequenced on Illumina HiSeq2000 instruments generating an average of 239 million paired 75 bp reads per sample ( S1 Fig . ) . Sequencing adapters and poly-A/T tails were trimmed for CLL samples only using cutadapt version 1 . 1 ( -m 20—n 10—b AAGCAGTGGTATCAACGCAGAGTACTTTTTTTTTTT—b AAGCAGTGGTATCAACGCAGAGTACGCGGG—b AAGCAGTGGTATCAACGCAGAGT—b TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT—b AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA ) [34] . Read pairs where or one of both reads were of length less than 20 were removed . RNA-seq reads were aligned to the human genome ( hg19 ) using STAR 2 . 3 . 0e ( —alignSJDBoverhangMin 1—seedSearchStartLmax 12—alignSplicedMateMapLminOverLmate 0 . 08—outFilterScoreMinOverLread 0 . 08—outFilterMatchNminOverLread 0 . 08—outFilterMultimapNmax 100—outFilterIntronMotifs RemoveNoncanonicalUnannotated—outSJfilterOverhangMin 6 6 6 6 ) and a splice junction database consisting of junctions from Gencode , UCSC knownGene , AceView , lincRNAs , and H-Inv [19 , 35–39] . Duplicate read pairs were removed prior to alignment by comparing the sequences of all read pairs and keeping only one read pair per set of read pairs with identical sequences . Splice junction read coverages were obtained from the SJ . out . tab output file from STAR . Novel splice junctions were defined as those junctions identified by STAR not present in Gencode version 14 that ( i ) were covered by at least 20 reads summed over all cancer samples in a given analysis , ( ii ) shared a 5’ splice site and/or 3’SS with a Gencode junction , and ( iii ) had one of the following motifs: GU/AG , CU/AC , GC/AG , CU/GC , AU/AC , GU/AU . Novel junctions were calculated separately for each analysis . Known and novel junctions that had a coverage of at least 20 reads over all samples , used a known intron motif , and contained a known Gencode 5’ splice site or 3’SS were aggregated by gene and tested for differential usage using DEXSeq’s testForDEUTRT function ( v1 . 8 . 0 , R v3 . 0 . 3 ) [20] . Splice junctions used in more than one Gencode gene were removed . When multiple cancer types were analyzed , we provided cancer type as a covariate to DEXSeq . Raw p-values were adjusted for multiple hypothesis testing using the Benjamini Hochberg procedure . To examine the impact of the coverage cutoff of 20 reads summed over all samples on our results , we increased the cutoff to 50 , 75 , and 100 reads summed over all samples and found that 42% , 32% , and 24% of the significant novel 3’SSs remained at each of these cutoffs . The enrichment for proximal cryptic 3’SS remained at all cutoffs , so we used the 20 read cutoff to maximize sensitivity . Associated canonical 3’SSs were identified for novel/cryptic 3’SSs as follows . First , all Gencode splice sites that shared a 5’ splice site with the novel 3’SS were identified . Then , the closest Gencode 3’SS from these splice sites that was downstream of the cryptic 3’SS was chosen as the associated canonical 3’SS for that cryptic 3’SS . If there was no Gencode 3’SS downstream of the cryptic 3’SS , the closest Gencode 3’SS upstream of the cryptic 3’SS was chosen as the associated canonical 3’SS . We performed a gene set enrichment analysis using GSEA [21] for the genes that contained cryptic 3’SSs by combining the genes that contained the 619 proximal ( S3 File ) and the 417 distal cryptic 3’SSs ( S4 File ) . We identified 23 , 066 control 3’SSs by choosing splice sites that are annotated in Gencode , whose average coverage over BRCA , CLL , and UM samples is greater than 100 , and whose 5' splice site does not have any novel 3'SSs . We characterized intronic AG dinucleotides for these control junctions by analyzing the intronic sequence downstream of the predicted branch points minus the last 10 bp of the intron since alternative 3’SSs can be located in the last 10 bp of the intron . All heatmap rows and columns were clustered using scipy . cluster . hierarchy . linkage with either the “complete” or “single” distance metric . Mutant allele frequency was determined by calculating per-base coverages using unique properly paired reads with samtools mpileup for the SF3B1 locus and counting the number of reads supporting either the reference or alternate alleles . Reads that were not contained within Gencode v14 exons in the STAR genomic alignment were discarded . The remaining reads were re-aligned to the Gencode v14 transcriptome using Bowtie2 ( v2 . 1 . 0 , -t-k 400-X 400—no-mixed—no-discordant ) and transcript expression was estimated using eXpress ( v1 . 3 . 0 , —max-indel-size 20 ) [40 , 41] . Gene expression was estimated by summing together the effective counts or FPKM values for all transcripts contained in a gene . For the green heatmap in Fig . 1D , the average expression ( FPKM ) of each gene containing a cryptic 3’SS was determined for each cancer type . The average expression values were then normalized for each gene by dividing by the largest average expression of the three cancers for that gene . Therefore each column in the green heatmap in Fig . 1D has one value of 1 . 0 while the other two values are between 0 . 0 and 1 . 0 and represent the expression of the gene in that cancer relative to the maximum . HEAT repeat locations were defined according to the definition of HEAT repeats in Wang et al . 1998 [15] . COSMIC v66 complete export was downloaded and the number of mutations at each location in the SF3B1 heat domains 5–9 was plotted for locations with at least two observed mutations in COSMIC [42] . Nucleotide frequency plots were constructed using WebLogo ( unit_name = ’probability’ ) [22] . Adenine enrichment was calculated by counting the number of adenines and non-adenines at each intron position for a given splice site class and comparing to the number of adenines and non-adenines in control 3’SSs using a Fisher exact test . SVM_BP was used to predict branch points [23] . The SVM_BP code was altered to allow for branch points eight bp from the 3’SS by setting mindist3ss = 8 in svm_getfeat . py ( see https://github . com/cdeboever3/svm-bpfinder ) . SVM_BP was run with options “Hsap 50 . ” When multiple branch points were predicted for one 3’SS , we chose the branch point with the highest sequence score ( bp_scr ) . In some instances , there was more than one cryptic 3’SS associated with a canonical 3’SS , so we randomly chose only one of these cryptic splice sites for further analysis . For Fig . 3C , we plotted the distance from highest scoring BP predicted for canonical 3’SSs to their associated cryptic 3’SSs as in Fig . 3A . However , the distances for cryptic 3’SSs located less than 13 bp or more than 17 bp from the BP in Fig . 3A were replaced with the distance from the second highest scoring BP . S5C–S5D Fig . were created similarly . Gene expression was estimated as described above . We summed the effective counts from eXpress for all transcripts from each gene to obtain effective read counts for each gene . We provided these read counts to DESeq2 ( v1 . 2 . 10 , R v3 . 0 . 3 ) and tested for differential gene expression using nbinomWaldTest using cancer type as a covariate for the analysis with different cancers [43] . We only tested genes where the sum of effective read counts over all samples was greater than 100 . p-values were adjusted using the Benjamini-Hochberg procedure . Gene set enrichment analysis was performed using GSEA [21] . Percent spliced in ( PSI ) values for cryptic 3’SSs relative to canonical 3’SSs were calculated by dividing the number of reads that span the cryptic 3’SS ( c ) by the number of reads that span the cryptic 3’SS plus the number of reads that span the canonical 3’SS ( a ) , cc+a , for each sample . The ten 3’SSs with high PSI values in CLL were identified by identifying cryptic 3’SSs whose median PSI was greater than 50% in the CLL SF3B1 mutants but less than 20% in the wild-type samples and whose average coverage was at least 30 junction-spanning reads in the CLL mutant samples . These junctions were also chosen to be out-of-frame although the cryptic 3’SS in ORAI2 is located in the 5’ untranslated region . We have made the code and intermediate data files needed to replicate this study available on Github ( https://github . com/cdeboever3/deboever-sf3b1-2015 ) and Figshare ( http://dx . doi . org/10 . 6084/m9 . figshare . 1120663 ) . Instructions are provided in the Github repository for reproducing our figures , tables , and statistical analyses . Sequencing data is available through dbGaP ( phs000767 ) . | A key goal of cancer genomics studies is to identify genes that are recurrently mutated at a rate above background and likely contribute to cancer development . Many such recurrently mutated genes have been identified over the last few years , but we often do not know the underlying mechanisms by which they contribute to cancer growth . Unexpectedly , several genes in the spliceosome , the collection of RNAs and proteins that remove introns from transcribed RNAs , are recurrently mutated in different cancers . Here , we have examined mutations in the splicing factor SF3B1 , a key component of the spliceosome , and identified a global splicing defect present in different cancers with SF3B1 mutations by comparing the expression of splice junctions using generalized linear models . While prior studies have reported a limited number of aberrant splicing events in SF3B1-mutated cancers , we have established that SF3B1 mutations are associated with usage of hundreds of atypical splice sites at the 3’ end of the intron . We have identified nucleotide sequence requirements for these cryptic splice sites that are consistent with a proposed mechanistic model . These findings greatly expand our understanding of the effect of SF3B1 mutations on splicing and provide new targets for determining the oncogenic effect of SF3B1 mutations . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | Transcriptome Sequencing Reveals Potential Mechanism of Cryptic 3’ Splice Site Selection in SF3B1-mutated Cancers |
In mouse female preimplantation embryos , the paternal X chromosome ( Xp ) is silenced by imprinted X chromosome inactivation ( iXCI ) . This requires production of the noncoding Xist RNA in cis , from the Xp . The Xist locus on the maternally inherited X chromosome ( Xm ) is refractory to activation due to the presence of an imprint . Paternal inheritance of an Xist deletion ( XpΔXist ) is embryonic lethal to female embryos , due to iXCI abolishment . Here , we circumvented the histone-to-protamine and protamine-to-histone transitions of the paternal genome , by fertilization of oocytes via injection of round spermatids ( ROSI ) . This did not affect initiation of XCI in wild type female embryos . Surprisingly , ROSI using ΔXist round spermatids allowed survival of female embryos . This was accompanied by activation of the intact maternal Xist gene , initiated with delayed kinetics , around the morula stage , resulting in Xm silencing . Maternal Xist gene activation was not observed in ROSI-derived males . In addition , no Xist expression was detected in male and female morulas that developed from oocytes fertilized with mature ΔXist sperm . Finally , the expression of the X-encoded XCI-activator RNF12 was enhanced in both male ( wild type ) and female ( wild type as well as XpΔXist ) ROSI derived embryos , compared to in vivo fertilized embryos . Thus , high RNF12 levels may contribute to the specific activation of maternal Xist in XpΔXist female ROSI embryos , but upregulation of additional Xp derived factors and/or the specific epigenetic constitution of the round spermatid-derived Xp are expected to be more critical . These results illustrate the profound impact of a dysregulated paternal epigenome on embryo development , and we propose that mouse ROSI can be used as a model to study the effects of intergenerational inheritance of epigenetic marks .
In mammals , as in all diploid organisms with a sexual reproduction cycle , the diploid zygote is formed upon fertilization by combination of the haploid maternal and paternal genomes . Sperm and egg each contribute a complete set of chromosomes , and in addition the gametes carry sex-specific epigenetic information that is important for correct execution of the early developmental gene expression program . A striking epigenetic difference between the paternal and maternal epigenomes is caused by the fact that the paternal chromatin undergoes two rounds of complete remodelling in the reproductive cycle . First , during the final post-meiotic phase of spermatogenesis , in elongating and condensing spermatids , the vast majority of histones is replaced by protamines , generating the compact sperm nucleus . Second , immediately following fertilization , the protamines are replaced by maternal histones . The maternally provided histones H3 and H4 on the paternal pronucleus are devoid of lysine di- and tri-methylation marks , which leads to clear global differences in heterochromatin organization between the paternal and maternal genomes that are maintained up to the 8-cell stage [1 , 2] . In addition to a haploid set of autosomes , a spermatozoon contributes either an X or a Y chromosome to the zygote . The sex chromosomes are more drastically remodelled than the autosomes during spermatogenesis , because the heterologous X and Y chromosomes undergo meiotic sex chromosome inactivation ( MSCI ) in spermatocytes ( reviewed by [3] ) , which is associated with chromosome wide nucleosome exchange [4] . After meiosis , silencing of X- and Y-linked genes is largely maintained during spermatid differentiation through post-meiotic sex chromatin repression ( PSCR ) [5] . Noteworthy , a number of X- and Y-linked genes , single and multi-copy , escape PSCR and become specifically reactivated [5–7] until the global transcriptional silencing that accompanies the histone-to-protamine transition sets in , in condensing spermatids . Subsequently , after fertilization , the X chromosome of paternal origin ( Xp ) will always be inactivated in female pre-implantation embryos and this is maintained in the extra-embryonic tissues of post-implantation embryos . This imprinted X chromosome inactivation ( iXCI ) depends on expression and spreading in cis of the Xist noncoding RNA on the Xp [8] . X-encoded RNF12 is a known and important XCI trans activator , acting through a dose-dependent mechanism in the activation of Xist transcription [9–11] . Maternal expression of RNF12 has been shown to be essential for iXCI , whereas deletion of the paternal copy is compatible with normal female embryo development and establishment of iXC [9] . Thus , the inactive state established on the Xp by MSCI and PSCR in spermatogenesis is not directly transmitted to female pre-implantation embryos but has to be re-established . However , whether the epigenetic events associated with the presence of unsynapsed chromatin are involved in establishing a paternal imprint at the Xic is not fully clear . In one study , expression of the Xist transgene was observed in preimplantation embryos only when the transgene was inherited from the father , independently of hemi- or homozygosity , indicating that imprinting was normally established on the single copy Xist transgene[12] . However , in a more recent study , correct imprinted expression was observed only when transgenic inserted Xist was transmitted from a hemizygous father[13] . Here the transgene was present in multiple copies . Irrespective of the mechanistic background of the paternal Xic imprint , it is clear that paternal X-linked genes are transcriptionally active at the 2-cell stage and are then gradually inactivated de novo via Xist RNA-dependent silencing [12 , 14] . During iXCI Xist RNA spreading on the Xp triggers the recruitment of chromatin-modifying protein complexes , which in turn will establish repressive epigenetic marks on the Xp , rendering it transcriptionally inactive . At the blastocyst stage , while iXCI is stably maintained in extra-embryonic tissues , the Xp becomes reactivated in the epiblast followed by random XCI without a parent-of-origin bias [15] . Paternal inheritance of a Xist deletion ( ΔXist ) completely abolishes iXCI of the Xp [16] , arrests development around E6 . 5 , and results in reabsorption of mutant female embryos by E12 . 5 [17] . The strong bias towards Xp inactivation in iXCI is favoured by the presence of an imprinting mark on the X chromosome of maternal origin ( Xm ) that prevents it from expressing Xist [18 , 19] . In addition , Xp may also be imprinted to become preferentially inactivated , as might be inferred from the recently reported effects from the pairing status of an Xist transgene during male meiotic prophase [13] , although the nature of such an imprint remains elusive . It has been proposed that iXCI occurs as a two-step process [20] . First , pre-inactivated intergenic repeat regions on the Xp may carry transgenerational epigenetic information from the paternal germline to the zygote , predisposing the Xp for iXCI independently of Xist [20] . This might rely on the inheritance of sperm-derived nucleosomes and their associated modifications . Second , subsequent establishment of genic silencing strictly depends on Xist expression from the paternal allele [20] . Alternatively , it has been suggested that the preferential inactivation of Xp may simply rely on early and robust activation of the paternal Xist gene [21] . This may be facilitated , upon fertilization , by the protamine-to-histone transition , during which the protamine-based chromatin of the sperm acquires newly deposited histones lacking most heterochromatic marks . The transcriptionally permissive chromatin signature deposited on the haploid genome in the paternal pronucleus would then allow Xist expression from the paternal allele . Conclusive evidence for the contribution of the protamine-to-histone transition in the initiation of iXCI is lacking . To test if the chromatin rearrangement in spermatids impacts on iXCI , we made use of mouse round spermatids to fertilize oocytes . When a round spermatid is injected into a mouse oocyte ( ROSI ) , the paternal genome has a histone-based chromatin constitution , contrary to the protamine-packaged chromatin of spermatozoa . Hence , ROSI evades the protamine-to-histone replacement in the male pronucleus , but rather provides for a paternal genome with a spermatogenic histone-based chromatin composition . Here , by using ROSI as an experimental tool , and a method to visualize individual chromosomes in fixed early embryos , we could establish that the chromatin constitution of the X chromosome in round spermatids is maintained in ROSI-derived zygotes . Next , we observed that absence of genome wide paternal chromatin remodelling did not affect the timing of Xist expression in ROSI-derived female zygotes , on a wild type background . We then asked if the transcriptionally repressed state and the heterochromatin marks present on the X chromosome of round spermatids , because of MSCI and PSCR , might be sufficient to establish iXCI independently of Xist-mediated silencing . This was tested using round spermatids from male mice lacking a functional Xist gene ( XpΔXist ) for ROSI . Here , we would expect rescue of the early embryonic lethality of XpΔXist female embryos through maintenance of MSCI- and PSCR-mediated inactivation of Xp , which is Xist-independent . Our results showed that injection of XpΔXist round spermatids indeed prevents the female lethality , that is always observed upon fertilization with mature XpΔXist spermatozoa . Surprisingly , this rescue occurred through inactivation of the Xm and not by Xist-independent Xp silencing . In addition , we observed high levels of the XCI activator RNF12 , in all ROSI derived embryos , independent of sex and genotype . These findings are discussed in the context of our current views on iXCI , and possible implications for transfer of dysregulated paternal epigenetic information to the embryo are described in relation to the clinical application of assisted reproductive technology .
To analyze the histone modification patterns of round spermatid-derived paternal chromatin in early embryos , and in particular the epigenetic profile of the Xp , we arrested ROSI-derived mouse embryos at the pro-metaphase stage of the first or second cleavage divisions [22] . This allows the visualization of epigenetic marks on individual chromosomes . As a control for staining specificity , zygotes obtained by intracytoplasmic sperm injection ( ICSI ) , using epididymal spermatozoa , were subjected to the same experimental procedure . Following the histone-to-protamine transition in spermatids , mouse epididymal spermatozoa contain approximately 1% of residual histones [23 , 24] . After sperm decondensation by heparin treatment and immunostaining for histone H3 . 1/2 and centromeres ( with anti-centromere antibody , ACA ) , limited histone retention associated with pericentromeric regions was visible ( Fig 1A , left panel ) , as previously shown [24] . After fertilization , when the protamine-to-histone transition has taken place , we did not detect any H3K9me3 at paternal prometaphase chromosomes of ICSI-derived zygotes ( Fig 1A , right panel ) , while prometaphase chromosomes of maternal origin were strongly enriched for H3K9me3 at pericentromeric regions and displayed moderate H3K9me3 levels along the chromosome arms . These results are in accordance with results from previous studies on in vivo fertilized embryos , that showed epigenetic asymmetry between maternally and paternally inherited chromatin up to the third cleavage division [1 , 2 , 25 , 26] . In round spermatids , the X and Y chromosomes , as well as the constitutive pericentric heterochromatin clustered in the chromocenter , are enriched for H3K9me3 ( Fig 1B , left panel ) , in accordance with previously published data [4] . In ROSI-derived zygotes at the first cleavage division , we detected persistence of H3K9me3 at the DAPI-dense heterochromatic chromosome ends of paternal origin and on the entire Xp ( Fig 1B , right panel ) . This epigenetic profile mirrors the H3K9me3 pattern observed in round spermatids . ROSI-derived 2-cell stage female embryos that were arrested at the pro-metaphase of the second cleavage division displayed maintenance of a high enrichment for H3K9me3 in particular on one of the two X chromosomes ( Fig 1C ) , as confirmed by Xist DNA FISH ( note that some residual H3K9me3 signal generates background staining after the FISH procedure Fig 1C , right panel and enlargements ) . One blastomere ( the lower one , in Fig 1C ) showed DNA FISH staining also on the Xm , confirming that the embryo was indeed female . The present results are in agreement with previous observations , that a substantial fraction of modified histones present in round spermatid chromatin is maintained in early pre-implantation embryos generated by ROSI [27] . Here , we show that this concerns in particular the X chromosome , where the H3K9me3 chromatin signature covers the entire chromosome . We then aimed to investigate if absent or limited remodeling of paternal chromatin in ROSI-derived female zygotes , and the heterochromatic epigenetic signature of the X chromosome specifically , might affect the timing of Xist expression and interfere with iXCI establishment . In ROSI-derived female embryos , Xist expression started normally at the 4-cell stage ( Fig 1D ) , when clear Xist RNA FISH clouds are already visible , and these are further enhanced at the 8-cell stage ( Fig 1E ) , similarly to what has been described for in vivo fertilized control embryos [28] . We cannot discriminate between the two parental X chromosomes in this experiment , but since we never observed Xist clouds , nor Xist mRNA expression above background in male wild type ROSI-derived morulas ( see below ) , and since the timing of Xist expression is normal , we infer that it is the Xp that is inactivated . Thus , the data suggest that the protamine-to-histone transition does not play a major role in the activation of the paternal Xist gene . In addition , the enrichment of H3K9me3 along the X chromosome does not appear to interfere with Xist transcription . It therefore seems more likely that another type of imprint , or a different mechanism , controls preferential Xist expression from Xp . Female embryos inheriting an Xist deletion on the Xp can no longer be recovered by E12 , because lack of imprinted inactivation of the Xp leads to embryonic lethality [16] . We tested if transmission of an Xp carrying the Xist deletion through ROSI , instead of fertilization with mature sperm , might rescue the embryonic lethal phenotype . This experiment was based on the hypothesis that inheritance of an Xp that carries the transcriptionally silenced PSCR heterochromatic signature , might provide sufficient dosage compensation of X-linked genes in the absence of Xist , and thereby would allow female embryo survival . We performed ROSI with XpΔXist round spermatids from the C57BL/6 ΔXist mouse line generated by Csankovszki et al . [29] . We also performed ICSI with XpΔXist mature sperm from the C57BL/6 mouse line . At E15 , we obtained 17 pups after ROSI and 11 after ICSI ( Table 1 ) . This was approximately 10% of the number of 2-cell stage embryos that were transferred to pseudopregnant females , and 25% of the counted implantations , for both techniques . The sex of the embryos was determined by visual inspection of the isolated gonads and confirmed by genotyping PCR for all females and most males ( S1A and S1B Fig ) . The ICSI experiments yielded only males , as expected . In contrast , 5 out of the 17 E15 embryos generated by ROSI were female ( p<0 . 05 , chi square test ) . All male and female surviving embryos appeared normal in size and appearance and there was no overt difference between ICSI versus ROSI embryos , or between males and females . Interestingly , female embryos could not be generated when using XpΔXist spermatids from M . musculus castaneus ( CAST/EiJ ) males ( Table 1 , male sex assessed by the presence of testes ) . Male fertility parameters of CAST/EiJ mice differ significantly from those of C57BL/6 mice [30] . Although CAST/EiJ males are normally fertile , this result indicates that there may be critical differences in gene expression between the two subspecies . At present we cannot point to any specific causal difference that would explain our failure to rescue on this background . For all our subsequent experiments we continued with C57BL/6 spermatids . We genotyped all ROSI-derived female embryos , and found both a wild type Xist allele and a deleted Xist allele in embryonic and extraembryonic tissues of four of the female embryos , while one embryo was an XO female which had lost the mutated paternal X chromosome ( Fig 2A ) . Next , we analysed the X chromosome to autosome ratio ( X:A ) for all embryos using quantitative PCR on genomic DNA , and observed the expected 1:1 ratio for the four XX embryos , and their placentas and isolated gonads ( Fig 2B ) . We then verified if ROSI-derived XpΔXist female embryos might develop to term . To this end , we collected pups in the morning after birth ( P0 ) . We obtained 7 live born pups ( 10% survival of 2-cell embryos that were transferred , Table 1 ) , of which 5 were males and 2 were females . Sex was confirmed by PCR for UbeX and UbeY ( S1C Fig ) . The two XpΔXist females were comparable in size and body weight to the male wild type siblings ( Fig 2C ) . Genotyping for the mutated and wild type Xist allele confirmed heterozygosity of these females ( Fig 2C ) . Our recovery of embryos following ICSI or ROSI is relatively low , compared to previously published data [31] . Part of the low recovery yield from the ICSI can be explained by the lethality of females due to the paternal Xist deletion . For the ROSI experiments , the rescue that we observe appears to be only partial . This is based on a comparison between the male to female sex ratio of 2 . 83 observed when XpΔXist ROSI embryos and pups are taken together ( n = 23 ) , and published sex ratios observed in 12 different published mouse ROSI experiments , involving comparable numbers of mice per experiment , whereby a mean male:female sex ratio of 0 . 93 ±0 . 48 was observed , with a maximum observed sex ratio of 2 . 33 ( S1 Table ) . The unexpected survival of XpΔXist female embryos might be explained by maintenance of the PSCR state of the round spermatid-derived Xp , as we initially hypothesized . However , it cannot be excluded that survival of the embryos might be explained by inactivation of the wild type Xm , replacing iXCI of the mutant Xp . In order to distinguish between these different possibilities , we analyzed Xist expression levels by qPCR in three E15 control male and female placentas and in the ROSI-derived XpΔXist female placentas for which RNA samples were available . As expected , male placentas showed very low Xist expression , which most probably reflects expression from a very small number of maternal cells through decidua contamination ( Fig 3A ) . Conversely , Xist expression was very high in wild type female placentas , in accordance with maintenance of stable iXCI of the Xp in this tissue , as is required for proper extraembyonic tissue development . Surprisingly , Xist RNA levels of ROSI-derived XpΔXist female placentas were comparable to those of wild type female placentas ( Fig 3A ) . Since the paternal Xist allele was deleted , this expression is expected to be explained by robust transcription occurring from the wild type Xm . To investigate this further , we first checked Xist expression by RNA FISH on E15 placenta sections obtained from ROSI-derived XpΔXist female embryos ( Fig 3B ) . By using the Reichert’s membrane as reference for the embryonic side of the placenta , we verified that Xist RNA clouds were formed in the whole population of labyrinth cells of embryonic origin . Next , we performed a combined DNA/RNA FISH experiment , using two different probes; one recognizing both the wild type and mutant X chromosome , and the other recognizing only the wild type X chromosome . The results show that most cells display an Xist RNA cloud signal with both probes on the maternal wild type X , and that Xist RNA clouds are never observed on the paternal ΔXist X chromosome ( Fig 3C ) . Together , these results indicate that a switch from Xp inactivation to Xm inactivation has occurred in the XpΔXist female embryos that were obtained by ROSI . This is consistent with previous data showing that Xist mRNA is retained by the Xi of its origin [32] . Next , Xist mRNA expression was quantified through qRT-PCR on RNA isolated from morulas from normal fertilization with wild type and XpΔXist males , and from morulas generated by ROSI with XpΔXist round spermatids . We determined the sex of the embryos from the presence or absence of the Y-specific transcript Eif2s3y . As expected , Xist was present at very high levels in wild type female morulas , but absent from males . Also , none of the in vivo fertilized XpΔXist male and female morulas showed any Xist expression above background ( Fig 4A ) . Interestingly , Xist levels were variable in ROSI-derived XpΔXist female morulas , and only one out of 11 analysed female embryos did not display any Xist expression . None of the wild type male morulas arising from these ROSI experiments with XpΔXist round spermatids showed Xist expression above background ( Fig 4A ) . We further analysed the onset of Xm inactivation , and its variability . In wild type ROSI-derived female embryos , Xist clouds were prominent from the 4-cell stage onwards ( Figs 1D , 1E , 4B and 4C ) . This pattern was not significantly different from what was observed upon in vivo fertilization of wild type embryos . In contrast , none of the XpΔXist , 8-cell ROSI embryos that we analysed displayed Xist clouds in any of the cells ( Fig 4B and 4C ) , but we did observe clear Xist RNA FISH clouds at the morula stage . The number of cells with an Xist cloud appeared somewhat more variable compared to what was observed in wild type female ROSI derived or in vivo fertilized morulas , and the average fraction of positive cells was lower , when compared to in vivo fertilized morulas , and on the border of significance for wild type ROSI derived females ( Fig 4C ) . Thus , Xm Xist activation in the XpΔXist female preimplantation embryos is delayed compared to what is observed following ROSI or in vivo fertilization using wild type spermatids or sperm , respectively . The observed variation in the number of cells that have formed an Xist cloud at the morula stage , including two embryos with no Xist clouds ( Fig 4C ) , is consistent with the variation in Xist level that we detected in the qRT-PCR experiments ( Fig 4A ) . The lack of clouds in some embryos is also in accordance with the notion that we did not rescue all XpΔXist females by performing ROSI . Previous reports have shown that also in diploid parthenogenetic embryos one of the two maternal X chromosomes starts to express Xist around the morula stage [33 , 34] . It was then suggested that this could occur because a repressive imprint on the Xm Xist allele , preventing its expression , is not retained throughout pre-implantation development . However , in a similar situation , when in vivo fertilized blastocysts disomic for Xm were analysed , Xm derived Xist clouds were hardly ever observed [35 , 36] . Thus , in this latter situation , the presence of a paternal genome most likely somehow helps to maintain the maternal imprint up to the blastocyst stage . Consistent with these findings , we also did not observe Xist expression above background levels in any of the XpΔXist female morulas obtained by natural mating ( Fig 4A ) . To further substantiate that the observed Xist clouds in the XpΔXist female morulas result in robust XCI , we investigated the immunolocalisation of H3K27me3 in female blastocysts derived from in vivo wild type fertilization in comparison to ROSI-derived XpΔXist female blastocysts . H3K27me3 is one of the earliest known histone modification that accompanies XCI , and is detectable as a domain covering the inactive Xp in wild type trophoblast cells [37] and Fig 5A . We also observed such H3K27me3 domains in ROSI-derived XpΔXist female embryos , ( Fig 5B ) . These data are consistent with the occurrence of robust maternal XCI in trophoblast cells in ROSI-derived , XpΔXist female embryos . Still , not all cells may be able to activate Xist expression from Xm , and only if the fraction of cells that manage to do so is high enough , the embryo may be rescued . Recently , it was shown that expression of the X-linked XCI activator RNF12 is reduced in in vitro fertilized mouse embryos , and that this causes impaired iXCI , leading to skewed sex ratios of the offspring [38] . In our ROSI model , we anticipated an opposite situation with high Rnf12 expression , since Rnf12 is one of the X-linked genes that becomes specifically reactivated in spermatids [5] . If this status is maintained upon ROSI , then it may lead to higher RNF12 levels compared to what is observed following fertilization with mature sperm . We analysed RNF12 protein levels at the eight-cell stage , in wild type and XpΔXist ROSI derived , and in vivo fertilized female and male embryos . This time point was chosen because it is just prior to the initiation of Xist cloud formation in the XpΔXist female ROSI embryos . Interestingly , the overall RNF12 level was increased approximately three-fold in all ROSI-derived embryos compared to in vivo fertilized embryos ( Fig 6A and 6B ) . However , no difference between male and female embryos was noted . In addition , RNF12 levels of all in vivo derived embryos , fertilized either by wild type or ΔXist sperm , were similar ( Fig 6A and 6B ) . This latter observation indicates that failure to inactivate the paternal X does not lead to a measurable significant increase in RNF12 levels using this type of semi-quantitative immunocytochemical analysis . RNF12 expression from the paternally inherited postmeiotically reactivated X chromosome by itself cannot easily explain our findings , since ROSI-derived males also display high RNF12 levels . Somehow , the injection of a round spermatid nucleus must either transfer a substantial amount of very stable RNF12 protein or mRNA , or other , autosomal spermatid-expressed genes ensure continuous Rnf12 expression from the maternal X in males , and perhaps from both X chromosomes in females . It might be suggested that the observed enhanced RNF12 expression could contribute to the ability of XpΔXist embryos to overcome the maternal imprint on the Xic , and allow maternal XCI . However , other X-linked factors are most likely more critically involved in lowering the threshold for activation of the Xm Xist gene in the XpΔXist female embryos , because Xm inactivation was never observed in male ROSI embryos . Alternatively , or in addition , the chromatin structure of the paternal X chromosome , being heavily marked by silencing histone modifications , may titrate away factors that are important for maintenance of the inactive status of the maternal Xist gene . In future experiments , comparative global gene expression analyses of ROSI derived and ICSI derived embryos might be used to identify novel XCI factors involved in both imprinted and random X chromosome inactivation . From this perspective , it will also be interesting to compare the gene expression profiles of purified round spermatids from C57BL/6 mice and CAST/EiJ mice , since we failed to rescue the lethality of paternal Xist deletion using ROSI on the latter genetic background . Microarray analyses of gene expression using total testis mRNAs of M . musculus musculus and M . musculus castaneus identified a relatively small number of differentially expressed spermatogenesis genes [39] . In this dataset , expression of Rnf12 was not significantly different between the M . musculus subspecies [39] . Thus , we speculate that differences in regulation of expression of genes other than Rnf12 may be critical for inducing maternal Xist expression in ROSI derived XpΔXist embryos on the C57BL/6 background only . In the model in Fig 7 , we schematically depict the differences between regulation of iXCI following in vivo fertilization , ROSI , or induction of parthenogenesis . When iXCI is initiated in wild type embryos carrying an Xp and an Xm ( as opposed to two Xms in the parthenogenic situation ) , the Xp most likely is more responsive to XCI trans activator ( s ) such as RNF12 than the Xm . This differential response is related to an imprint of the Xist promoter on the Xm , which prevents Xist expression , and which is absent from the promoter on Xp [19] . In addition , Xp may carry an ( MSCI-dependent ) imprint to facilitate Xist expression . At this stage , RNF12 expression is relatively high , due to the maternally provided store , and paternal Xist activation occurs independent of the X:A ratio . Transcription of the XCI activator ( s ) would reach the threshold for Xist expression from the Xp in all blastomeres by the 4-cell stage , but virtually never reach the threshold for activation of Xist expression from Xm . When the paternal copy of Xist is deleted , iXCI can not occur , and the maternal Xist gene remains repressed due to a paternal inhibitory effect that is missing in parthenogenetic embryos . ROSI somehow leads to elevated levels of RNF12 in morulas , but this by itself will not be enough to activate Xm in ROSI-derived males , consistent with earlier findings using Rnf12 overexpression [38] . Somehow , either the presence of two X chromosomes , or the specific epigenetic constitution of the Xp , contributes to efficient stimulation of Xist expression from Xm . Subsequent Xm silencing most likely allows rescue of XpΔXist females . In addition , prior to the establishment of Xist mediated Xm inactivation , the silencing epigenetic marks that are carried by the round spermatid-derived Xp may also contribute to a more optimal gene-expression balance . This may exert an additional positive effect on the fitness of the ROSI-derived XpΔXist female embryos . Taken together , the present experiments have demonstrated that ROSI allows activation of Xist transcription from the Xm in preimplantation mouse embryos in the absence of a paternal Xist gene . We propose that correct regulation of expression of X-linked trans activators of XCI from both the paternal and maternal X chromosome is of critical importance in iXCI in mouse . In humans , X chromosome inactivation is initiated later than in mouse , and most likely is not imprinted ( reviewed in [40] ) . Still , our results do provide evidence that disturbances of the paternal epigenome impact on embryonic gene regulation , and this is relevant for considerations on human embryo quality . In humans , there is an increase in the histone:protamine ratio when sperm from male factor subfertility patients is compared with sperm from fertile men [41 , 42] . Also , when sperm is extracted from the testis and used for ICSI , it cannot be excluded that spermatids with an incomplete histone-to-protamine transition are selected for injection into oocytes , so that ICSI resembles ROSI . Furthermore , the birth of 14 ROSI-derived babies was recently described [43] , making careful assessment of possible associated epigenetic risks more topical than ever . Future clinical and basic animal research should go hand in hand to evaluate if there is a relation between embryo paternal epigenome quality and oocyte injection using spermatids or spermatozoa in which the histone-to-protamine transition has not been completed or is disturbed .
For all experiments we aimed to reduce pain and stress as much as possible by housing animals in groups whenever possible , and using appropriate anesthetic agents during operation , followed by treatments to reduce pain . Animals more than one week old were killed using cervical dislocation . Embryos collected after day 13 of embryonic development , and pups younger than 1 week old were killed by decapitation and immediate collection of heads in liquid nitrogen . All animal experiments were approved by and were performed in strict accordance with the recommendations by the local animal experiments committee DEC-consult ( approval numbers EMC2448 and EMC3200 ) . B6D2F1 mice ( C57BL/6 × DBA/2 ) were used as oocyte donors . We used B10CBA females that were mated with vasectomized males as pseudopregnant surrogates for transfer of ICSI- and ROSI-derived two-cell stage embryos . C57Bl6 mice carrying an Xist deletion ( ΔXist ) were those originally generated by Csankovszki and colleagues [29] , the allele was also crossed into a CAST/EiJ background for several generations , but round spermatids isolated form ΔXist males with this background did not result in retrieval of viable female embryos . Control wild type C56BL/6 males were also used as spermatid and spermatozoa donors . To obtain embryos from in vivo fertilized oocytes , superovulated B6D2F1 females ( see below for the superovulation protocol ) were mated with wild type or ΔXist males and zygotes were retrieved from the oviduct and cultured for different applications as described in the expanded view . ROSI was carried out as described previously [44] with minor modifications: ICSI was carried out as described previously [45] . Injected oocytes were cultured for 24–30 h in G-1 PLUS medium until the two-cell stage . Thereafter , 10–15 two-cell embryos were transferred to each oviduct of surrogate females on day 1 of pseudopregnancy . Alternatively , embryos were cultured up to the 4-cell , 8-cell , or morula stage in G-1 PLUS medium and further processed for different applications as described below . Zygotes or two-cell embryos were incubated with colcemid ( 1 . 5 μg/ml ) to arrest cells at prometaphase until pronuclei had disappeared . To obtain chromosome spreads , after zona pellucida removal with Acidic Tyrode’s Solution ( Sigma ) , arrested zygotes were incubated in hyposolution ( 25% v/v FCS , 0 . 5% w/v sodium citrate ) for 5 min and subsequently transferred to a drop of fixative ( 1% v/v paraformaldehyde , 0 . 2% v/v Triton X-100 , 0 . 1 mM dithiothreitol , pH 9 . 2 ) on a glass slide . After horizontal drying for 1 h , the slides were washed with 0 . 08% Photo-Flo ( Kodak ) and air dried . All slides were stored at −20°C until further use . Decondensation of wild type mouse caput sperm was performed as described previously [24] . Nuclei of wild type mouse spermatogenic cells were spread as previously described [46] . For immunofluorescence stainings , the zona pellucida of the 8-cell embryos was removed with incubation in Acidic Tyrode’s Solution ( Sigma ) at room temperature for 1–2 min . Afterwards , embryos were washed in M2 medium , fixed in 4% PFA for 15’ at room temperature and then washed again in M2 medium . Subsequently , embryos and slides containing zygote or embryo chromosome spreads , decondensed sperm , or spread spermatid nuclei , were rinsed in PBS-phosphate-buffered saline PBS-T ( PBS , 0 . 01% v/v Tween-20 ) and blocked with blocking solution ( PBS-T , 2% w/v bovine serum albumin ( BSA fraction V ) , 5% v/v normal goat serum ) for 30 minutes and incubated with primary antibodies at 4°C overnight . The following antibodies were used in this study: rabbit polyclonal against H3K9me3 ( 1:200 , Abcam Ab8898-100 ) , mouse monoclonal anti H3 . 1/2 ( 1:1000 , gift from dr . P . de Boer , for validation see [4] ) , mouse monoclonal against RNF12 ( 1:50 Abnova ) , and human centromere autoantigen ( ACA , 1:1000 , Fitzgerald Industries , 90C-CS1058 ) . After washing with PBS-T , slides were incubated with the appropriate secondary antibodies for 1 hour , washed with PBS-T and mounted with ProLong Gold mounting solution for DNA counterstaining . Images were obtained using a LSM700 confocal laser scanning microscope ( Zeiss ) and processed with Fiji and Adobe Photoshop CS3 software . Imaging of RNF12 stained embryos was performed using the same exposure time for each embryo . Quantification of total RNF12 levels per embryo was performed using Image J ( Fiji ) software . Subsequently , statistical significance was determined by Student’s t test ( *P≤0 . 05 , significant ) . Pre-implantation embryos were treated with Acidic Tyrode’s Solution ( Sigma ) to remove the zona pellucida . The method for Xist RNA-FISH has been described [32 , 47] , and we used a 5 . 5 Kb BglII cDNA fragment , covering exons 3-7 ( in part ) as a probe . For DNA FISH on chromosome spreads of prometaphase arrested embryos and on 8 cell embryos after RNA FISH or immunostaining ( for sex determination ) , slides were denatured in 70% v/v formamide/2x SSC/10mM phosphate buffer for 5’ at 78°C followed by dehydration in ice cold ethanol series ( 70% , 85% and 100% ) 3 minutes each . Slides were left to dry for a few minutes at room temperature and then , the same Xist probe used for RNA FISH was applied on the slide . Detection was performed as for RNA FISH . Placentas were removed at E15 . The tissues were snap frozen and stored at -80°C until use . For RNA FISH , 14 μm-thick frozen sections were made from frozen tissues on a cryostat and mounted on glass slides . Sections were briefly air-dried , extracted with 0 . 5% Triton X-100 in phosphate-buffered saline ( PBS ) on ice , fixed in 4% formaldehyde , 5% acetic acid for 18 min at room temperature , washed 3 times in PBS for 5 min each , dehydrated in 70–100% ethanol series and air-dried . Then the probes were applied . For DNA FISH the same procedure as described above for RNA/DNA FISH on preimplantation embryos’ was followed . Both for RNA and DNA FISH , an additional Xist probe was used that is specific for the wild type X chromosome . This probe covers a 8 . 4 Kb fragment lying within the deleted area of the ΔXist . It was generated by combination of PCR products obtained with the following primer sets: Fwprom CCCTCTGGAAGAGCAGTCAG and Rvprom GCCATAAGGCTTGGTGGTAG ( ~1 , 7Kb ) , Fw1 GCCAACCAATGAGACCACTT and Rv1 TGGCATGATGGAATTGAGAA ( ~2 . 5Kb ) , Fw2 CTACCCACCCCAGTACATGC and Rv2 TTGGCTCAGTGCTTATGGTG ( ~2 . 1Kb ) , Fw3 CAGTTGCCTTCTCCTTGCTC and Rv3 AGCTGTTAGTGCCGTCCAGT ( ~2 . 1Kb ) . The PCR conditions were: initial denaturation 94°C for 5 min , followed by 35 cycles of 94°C 30 sec , 55°C 30 sec , 72°C 3 min , and final extension at 72°C for 5 min . PCR products were loaded on a 1% agarose gel , bands were extracted and DNA was isolated using NucleoSpin Extract II ( Macherey Nagel ) according to the manufacturer’s protocol . Subsequently , the probe was made using the Biotin Nick translation mix ( Roche diagnostics ) , according to the manufacturer’s instructions . The primer pairs used to assess the genotype of the mice for the presence or absence of the Xist deletion , and to detect Ube1x and Ube1y have been previously described ( Xist deletion: [48] , Ube1x/y: [49] ) . For Sry we used forward primer 5’GTGGTCCCGTGGTGAGAG3’ , and reversed primer 5’TTTTGTTGAGGCAACTGCAG3’ , generating a 250bp fragment . PCR conditions were as follows: Initial hold for 2 minutes at 98°C , followed by 35 cycles of 98°C for 10 seconds , 63°C for 15 seconds , and 72°C for 30 seconds , and finally 72°C for 5 minutes . For quantitative RT–PCR ( RT–qPCR ) of single embryos , the Taqman Cells-to-Ct Kit ( Applied Biosystems ) was used according to the manufacturer's protocol . All samples were analyzed in triplicate in a 10 μl final reaction volume using the BioRad CFX 384 Real-time System . The reaction mixture contained SYBR Green PCR Master Mix ( Applied Biosystems ) , primers ( for Actin , Xist , or Eif2s3y ) and 2 . 5 μl of cDNA . The following primers were used: Xist for GGATCCTGCTTGAACTACTGC and Xist rev CAGGCAATCCTTCTTCTTGAG [50] , Actin for AACCCTAAGGCCAACCGTGAAAAG and rev CATGGCTGGGGTGTTGAAGGTCTC , Eif2s3y for CCAGGGACCAAAGGAAACTT and rev TAGCCTGGCTTTCTTTCACC [51] . For copy number qPCR on genomic DNA , primers were designed for the X chromosome on the Tsix promoter region ( for CCGAGATATCCACGCATCTT and rev AGCTGGCTATCACGCTCTTC ) and for chromosome 12 on the Rex1 allele ( for GGTGCAAGAAGAAGCTGAGG and rev GTTTCGAGCTCTCCGTGAAG ) . After an initial hold at 94°C for 2 minutes , reaction mixtures underwent 40 cycles of 30s at 94°C , 30s at 60°C , and 30s at 72°C . Results were expressed as Cycle threshold ( Ct ) values . Gene expression levels were normalized over Actin gene expression , according to the 2- ΔCT method [52] . In order to be able to use a relative quantification approach to compare expression levels we ensured that the primer pairs have similar amplification efficiencies ( E = 100 ± 10% ) . | In sexual reproduction , maternal and paternal haploid sets of DNA are combined in one new diploid individual . However , not only DNA , but also epigenetic information , defined by DNA and histone modifications , is transferred to the zygote . Specific inactivation of the paternally inherited X chromosome ( Xp ) in the preimplantation female mouse embryo is required for embryo survival . This imprinted X chromosome inactivation ( iXCI ) is initiated by transcription of the Xist gene from Xp . In contrast , the maternal Xist gene is imprinted during oogenesis to remain silent . We have investigated the consequences of elimination of the histone-to-protamine and protamine-to-histone transitions on iXCI , by fertilization through injection of immature round spermatids into oocytes ( ROSI ) . Interestingly , when the round spermatids used for ROSI carried an X chromosome with an Xist deletion ( ΔXist ) , we found that the Xist gene on the maternal X chromosome was activated , which rescued the female lethality of embryos that is invariably observed upon fertilization with mature ΔXist spermatozoa . This striking result is best explained by deregulation of embryonic gene expression , in particular from Xp , when the paternal genome originates from round spermatids rather than spermatozoa . From this , we suggest that the use of round spermatids has unforeseen consequences for embryonic gene expression and its use in human assisted reproduction must be carefully considered . | [
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] | 2016 | Round Spermatid Injection Rescues Female Lethality of a Paternally Inherited Xist Deletion in Mouse |
Dengue fever induces a robust immune response , including massive T cell activation . The level of T cell activation may , however , be associated with more severe disease . In this study , we explored the level of CD8+ T lymphocyte activation in the first six days after onset of symptoms during a DENV2 outbreak in early 2010 on the coast of São Paulo State , Brazil . Using flow cytometry we detected a progressive increase in the percentage of CD8+ T cells in 74 dengue fever cases . Peripheral blood mononuclear cells from 30 cases were thawed and evaluated using expanded phenotyping . The expansion of the CD8+ T cells was coupled with increased Ki67 expression . Cell activation was observed later in the course of disease , as determined by the expression of the activation markers CD38 and HLA-DR . This increased CD8+ T lymphocyte activation was observed in all memory subsets , but was more pronounced in the effector memory subset , as defined by higher CD38 expression . Our results show that most CD8+ T cell subsets are expanded during DENV2 infection and that the effector memory subset is the predominantly affected sub population .
Dengue is the most prevalent arthropod-born viral disease in tropical and subtropical areas of the globe , affecting approximately 400 million people annually [1] . The World Health Organization estimates that nearly 40% of the world’s population lives in areas at risk for dengue transmission . Dengue cases in Central and Latin America have increased almost five-fold in the last 30 years . During 2008 , up to one million cases were reported in Americas , and higher numbers of deaths were documented in the South [2] . In the latest decades , Brazil has been hard hit by the disease , accounting for more than 60% of the total reported cases in the Americas [2] . The continuing occurrence of the disease in resource limited countries and the lack of novel therapeutic approaches or a highly effective vaccine make dengue fever a neglected disease . Surveillance for dengue is absent in most countries , and no existing model for predicting an outbreak in endemic regions is widely available . Therefore , it is important to increase our knowledge of disease pathogenesis , with the goal of developing new strategies to fight the epidemic . The mechanisms by which the dengue virus ( DENV ) causes severe illness remain to be elucidated . Both biological properties of the viral isolates and immunogenic host factors seem to contribute to the level of pathogenicity [3 , 4 , 5 , 6] . Whereas immunity induced by natural infection is believed to provide serotype-specific lifelong protection , previous infection by a distinct serotype is considered to increase the risk for the development of dengue hemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) [5 , 7] . The immunological processes during dengue infection are not yet completely defined . However , incidence of mild dengue manifestations and occasional progression to the more severe disease likely reflect a complex interplay between host and viral factors including cytokine production by inflammatory cells . Previous studies reported increased levels of circulating cytokines and soluble receptors in DHF patients when compared to those with dengue fever ( DF ) , suggesting that immune activation may be related to disease severity [8] . T cell activation mechanisms are based on the binding of specific T cell receptors ( TCRs ) to MHC molecules [9] . CD8+ T cells are one of the most important cell types to recognize and eliminate infected cells . Some authors have suggested that high numbers of CD8+ T cells might be protective by reducing viral load [10] . Memory T lymphocytes remain present in the absence of antigenic stimulation and have the capacity to expand rapidly upon secondary challenge . In the last decade , several surface markers have been used to distinguish among effector memory ( TEM ) , central memory ( TCM ) , and terminally differentiated memory cells ( TEMRA ) [11] . In this work , we explored the state of CD8+ T cell activation in different compartments during the acute phase of dengue fever .
All procedures adopted in this study were performed according to the terms agreed by the Institutional Review Board from the Hospital das Clínicas , University of São Paulo ( CAPPesq—Research Projects Ethics Committee ) . This study was approved by CAPPesq under protocol 0652/09 . Written informed consents were obtained from all study volunteers . Whole-blood samples were collected , using sterile EDTA-treated Vacutainer tubes ( BD Brazil ) , from patients with DENV2 dengue at the Ana Costa Hospital , Santos , State of São Paulo , during the 2010 first semester outbreak [12] . Patients with suspected dengue fever , dengue with warning signs or severe dengue were invited to participate in the study . A rapid rest was performed to confirm the diagnosis of acute dengue disease , followed by the detection of dengue viral load determination ( see next sections ) . Primary dengue infection was considered when dengue IgG-specific antibodies were not detected in the presence of reactive dengue IgM-specific antibody and/or NS1 antigenemia . Secondary dengue infection was considered in the presence of dengue IgG-specific antibodies at acute phase up to 6 days of symptoms . A commercial Dengue Duo Test Bioeasy ( Standard Diagnostic Inc . 575–34 , Korea ) , a rapid test kit , was used for dengue diagnosis , by detection of both dengue virus NS1 antigen and IgM- and IgG-specific antibodies in human blood . Samples were considered positive for acute dengue fever when NS1 or IgM bands were reactive in the testing kit . We also considered an acute case whenever DENV2 RNA was detected . The IgG avidity test was used to determine if patients presented with a primary or secondary DENV infection [13] . Samples with low avidity IgG antibodies were classified as primary DENV infection , whereas samples with high avidity IgG antibodies were classified as secondary . Samples in which IgG antibodies were not detected could not be classified , although the majority were probably from primary DENV infection . Viral load was determined by an “in-house” real-time polymerase chain reaction ( RT-PCR ) method . RNA was extracted from 140μL of plasma using the Qiagen Viral RNA kit ( Qiagen , USA ) . All RT-PCR reactions were performed in duplicate . RT-PCR was conducted using the SuperScript III Platinum SYBR Green One-Step qRT-PCR kit with ROX ( Invitrogen , USA ) and 0 . 4 μM of primers covering all four DENV serotypes [14] . Cycling conditions were: 10 minutes reverse transcription at 60°C , 1 min Taq polymerase activation at 95°C , followed by 45 cycles consisting of 95°C without holding time , 60°C for 3 seconds , and 72°C for 10 seconds . The reaction was run on an ABI 7300 RT-PCR equipment ( Applied Biosystems , Brazil ) . As an internal control Bovine Diarrhea Virus ( BDV ) was added to the samples before RNA extraction and also run in a parallel RT-PCR assay . Supernatant from DENV-3 cell cultures was included as an external control . DENV-3 supernatant was previously quantified by a commercial dengue RT-PCR kit ( RealArt; artus/QIAGEN , Germany ) [15] and used to generate a standard curve . The detection limit of this assay was 100 copies/ml . Peripheral blood absolute CD4+ and CD8+ T cell counts were assessed using the BD Multitest CD3-FITC/CD8-PE/CD45-PerCP/CD4-APC monoclonal antibody ( mAb ) cocktail from BD Biosciences ( San Diego , CA ) , according to the manufacturer’s instruction , using a FACSCanto flow cytometer ( BD Biosciences ) . Cell surface staining was routinely performed on 100 μL fresh whole blood . Peripheral blood mononuclear cells ( PBMCs ) were isolated from fresh EDTA-treated blood by Ficoll-Hypaque gradient centrifugation and frozen in liquid nitrogen as previously described [16] . PBMC were isolated from volunteers and stored in liquid nitrogen until used in the assays . To characterize the activation profile of CD8+ T lymphocytes , we used the markers HLA DR and CD38 . HLA DR is a transmembrane glycoprotein encoded by genes within the Human Leucocyte Antigen ( HLA ) complex . CD38 is a nonlineage-restricted type II transmembrane glycoprotein that has emerged as a multifunctional protein . Cells expressing both markers are likely to be activated . The following monoclonal antibodies were used in the FACS assays: anti-CD8-peridin chlorophyll protein ( PerCP ) ( clone SK1 ) , CD45RA-fluorescein isothiocyanate ( FITC ) ( clone L48 ) , CD38-phycoerythrin ( PE ) ( clone HB7 ) , from BD Biosciences ( San Jose , CA , USA ) ; CCR7-phycoerythrin—cyanine ( PE-Cy7 ) ( clone 3D12 ) , HLADR-Alexa 700 ( clone L243 ) , CD27-APCH7 ( clone M-T271 ) , CD4-Pacific Blue ( clone RAPA-T4 ) , from BD Pharmingen ( San Jose , CA , USA ) ; CD3-ECD ( clone UCHT1 ) , from Beckman Coulter ( Marseille , France ) and Fixable Aqua dead cell stain kit , from Molecular Probes ( Oregon , USA ) . After thawing , cells were centrifuged at 1 , 500 rpm for 5 minutes and transferred into 96 V bottom well plates ( Nunc , Denmark ) in 100 L of staining buffer ( PBS supplemented with 0 . 1% sodium azide [Sigma] and 1% FBS , pH 7 . 4–7 . 6 ) with the surface monoclonal antibodies panel . Cells were incubated at 4C in the dark for 30 minutes , washed twice , and resuspended in 100 L of fixation buffer ( 1% paraformaldehyde Polysciences , Warrington , PA in PBS , ( pH 7 . 4–7 . 6 ) . Fluorescence minus one ( FMO ) was used for gating strategy [17] . The strategy is shown in S1 Fig . CD8+ T cell proliferation was assessed using a Ki67 staining protocol . Ki67 is a cell-cycle-associated antigen expressed exclusively in proliferating cells . After staining with surface markers CD3-PERCP ( clone SK7 ) , CD8-allophycocyanin cyanine-7 ( APC-Cy7 ) ( clone SK7 ) , from BD Biosciences ( San Jose , CA , USA ) ; CD4-Alexa 700 ( clone RAPA-T4 ) , from BD Pharmingen ( San Jose , CA , USA ) and Fixable Aqua dead cell stain kit , as described above , cells were fixed with 4% fixation buffer for 10 minutes . Cells were washed with staining buffer once and re-suspended in 100 L of permeabilization buffer from BD Biosciences ( San Jose , CA , USA ) and incubated for 15 minutes . Cells were washed with staining buffer twice . Ki-67-FITC ( clone B-56 ) was added and cells incubated at 4C in darkness for 30 minutes . Finally , the cells were washed twice , and re-suspended in 100 L of 1% fixation buffer . Samples were acquired on a FACSFortessa , using FACSDiva software ( BD Biosciences ) , and then analyzed with FlowJo software version 9 . 4 ( Tree Star , San Carlo , CA ) . Fluorescence voltages were determined using matched unstained cells . Compensation was carried out with CompBeads ( BD Biosciences ) single-stained with . Samples were acquired until at least 200 , 000 events were collected in a live lymphocyte gate . The analysis strategy is shown in S2 Fig . Because most continuous variables presented an overdispersed distribution , results were summarized as medians and 25% to 75% interquartile ranges ( IQR ) and compared across dengue patient groups and non exposed controls , using nonparametric Kruskal-Wallis or Mann-Whitney tests ( continuous variables ) . When the Kruskal-Wallis test indicated a statistically significant difference ( P<0 . 05 ) among more than two groups , a Dunn’s multiple comparison post-tests was carried out to determine between which groups the differences were sustained . Potential correlations were explored using Spearman rank correlation tests . The software Prism , version 5 . 0 , was used for analyses ( GraphPad Software , San Diego , CA ) .
Peripheral venous blood was obtained from 74 patients with acute dengue fever and 17 matched donors who were asymptomatic and negative for DENV IgM , NS1 , and RNA . The characteristics of the dengue fever patients and healthy controls are depicted in Table 1 . No differences were seen in gender and age distribution comparing both groups . As expected , dengue fever patients had lower number of platelets ( median 152 , 000 cells/μl , interquartile range 25%–75% [IQR] , 110 , 000–207 , 000 ) when compared to controls ( median 226 , 000 cells/μl , IQR , 166 , 000–310 , 000 ) , p<0 . 0001 . Platelets decreased during the first days of disease , with a median of 174 , 000 cells/μl ( IQR , 147 , 000–232 , 000 ) on days 1 and 2 , 153 , 000 cells/μl ( IQR , 115 , 000–206 , 000 ) on days 3 and 4 , and 94 , 000 cells/μl ( IQR , 28 , 000–154 , 000 ) on days 5 and 6 after the onset of symptoms . Overall leukocyte counts were also lower ( median 4 , 400 cells/μl , IQR , 3 , 275–6 , 400 ) compared to controls ( median 8 , 100 cells/μl , IQR , 6 , 140–9 , 335 ) , p<0 . 0001 . Numbers were lower on days 1 and 2 ( median 5 , 100 cells/μl , IQR , 3 , 750–6 , 450 ) and 3 and 4 ( median 3 , 600 cells/μl , IQR , 3 , 100–5 , 100 ) , p<0 . 001 , but recovered on days 5 and 6 to levels of the control group . A subset of 30 dengue fever patients ( Table 2 ) was selected for expanded immunophenotyping experiments . To be representative of the disease natural history after onset of symptoms , 10 of these patients were at days 1 and 2 , 10 patients at days 3 and 4 , and 10 patients at days 5 and 6 , as detailed in Table 2 , along with 17 healthy controls . We first evaluated the percentages and the absolute numbers of CD8+ T lymphocytes in acute dengue fever patients . As shown in Fig . 1A , the percentage of CD8+ T cells of overall circulating lymphocytes remained constant up to the fourth day after onset of symptoms . However , this percentage increased on days 5 and 6 , with an increased median of 38% , IQR , 29–53 ( p<0 . 05 ) , and this was higher than observed in healthy controls . Absolute numbers of CD8+ T cells were lower from the first to the fourth days after onset of symptoms ( median 253 cells/μl , IQR , 151–358 for days 1 and 2; median 201 cells/μl , IQR , 158–345 for days 3 and 4 ) when comparing dengue patients with healthy controls ( median 465 cells/μl , IQR , 329–605 ) . On the fifth and sixth days , we observed a higher number of cells ( median 534 cells/μl , IQR , 285–1644 ) , with wider distribution values ( Fig . 1B ) . Dengue viral load was evaluated in the course of dengue fever . As expected , higher viral loads were observed in the first and second days after onset of symptoms , as shown in Fig . 2A , decreasing thereafter . Remarkably , dengue viral load negatively correlated with the number of circulating CD8+ T lymphocytes . As demonstrated in Fig . 2 , we observed that higher viral load was seen only when CD8+ T lymphocytes remained below 450 cells/μl ( arbitrary dotted line parallel to the y axis ) , whereas higher CD8+ T lymphocyte counts were associated with Dengue viral load bellow 1 , 050 copies/ml ( arbitrary dotted line parallel to the x axis ) . These results imply that these CD8+ T cells may be playing a role in the control of DENV replication in the acute phase of disease . Of note , statistically significant correlations ( p<0 . 05 ) were seen on days 1 and 2 ( r = -0 . 6 ) ( Fig . 2B ) and on days 5 and 6 ( r = -0 . 5 ) ( Fig . 2D ) . In contrast , no correlation was observed on days 3 and 4 ( Fig . 2C ) . Samples from 30 patients with dengue fever were selected for the remaining experiments . The aim was to be representative of the disease natural history , up to six days after onset of symptoms . Results are shown for days 1 and 2 ( 10 patients ) , 3 and 4 ( 10 patients ) , and 5 and 6 ( 10 patients ) , as detailed in Table 2 . These were compared to the 17 healthy controls . We observed that more CD8+ T lymphocytes expressed Ki67 in dengue fever cases when compared to controls , either expressed in absolute numbers or percentage of stained cells ( median 14 cells/μl , IQR , 7–40 vs . 4 cells/μl , IQR , 3–12 , p = 0 . 002; median 4% , IQR , 2–14 vs . median 1% , IQR , 1–2 , p<0 . 0001 ) . However , this increase in expression was largely seen on days 5 and 6 ( median 113 cells/μl , IQR , 21–418 and median 20% , IQR , 10–31 ) , suggesting that these cells proliferate later in the course of the disease ( Fig . 3 ) . We addressed the levels of CD8+ T lymphocytes activation using surface staining for CD38 and HLA-DR . Coinciding with CD8+ T lymphocyte expansion and proliferation , higher cell activation could be detected later in the course of disease , on days 5 and 6 , compared to controls either in percentages ( median 34% , IQR , 20–59 vs . median 3 , IQR , 3–5 , p<0 . 0001 ) or in absolute numbers ( median 114 cells/μl , IQR , 45–1110 vs . median 14 cells/μl , IQR , 12–21 , p<0 . 0001 ) , as depicted in Fig . 4 . The cellular activation profile was different among the subpopulations of CD8+ T lymphocytes . Using comprehensive staining panels , we did not observe statistically significant differences in activation of naïve cells ( Fig . 5A and 5B ) . On the other hand , higher activation was observed on days 5 and 6 in the central memory ( TCM ) , effector memory ( TEM ) , and terminally effector memory ( TEMRA ) cell percentages ( Fig . 5C , 5E , and 5G , respectively ) . Nevertheless , this effect was only seen in the TEM subpopulation when absolute numbers were evaluated ( Fig . 5F ) , suggesting that TEM cells are largely responsible for this phenomenon . We also observed that the TEM CD8+ T cell subset was negatively correlated with DENV viral load , suggesting that the activation of such particular phenotype may have a central role in controlling virus replication; however , given the post-hoc nature of this analysis this result needs to be interpreted with caution , requiring confirmatory experiments .
Changes in lymphocyte subsets in dengue fever have long been recognized , including an increase in CD8+ T lymphocyte numbers [8 , 18 , 19] . Using samples collected in a DENV2 outbreak in the coast of the State of São Paulo , Brazil [12] , we were able to demonstrate that the percentage CD8+ T cell count increased later in the course of disease , after onset of symptoms . This was associated with higher Ki67 expression , suggesting a proliferative rebound that follows the peak viremia . This phenomenon may be related to increased cell activation , as has been suggested by others [20] . In this paper we explored in more detail the activation status of different CD8+ T cell subpopulations in adult patients with dengue fever . During high viral burden , several circulating cells are activated including monocytes [21] , NK cells , CD4+ and CD8+ T cells [8 , 22 , 23] . This activation seems to be a natural immune response to the pathogen and reflects its efforts to control viral replication . Our results show that the expanding number of CD8+ T cells was associated with lower viremia , especially later in the disease course . Dung et al . , also demonstrated that activated CD8+ T cell expansion ( evaluated by the expression of HLA-DR and CD38 molecules ) was associated with viral control [24] . However , it is possible that high levels of cellular activation may be harmful to the host and may be related to disease severity . A number of studies have found increased markers of immune cell activation in patients with dengue hemorrhagic fever ( DHF ) compared with patients with classic dengue fever ( DF ) [25] . Indeed , children who developed DHF had higher percentages of CD8+ T cells and NK cells expressing CD69 , an early activation marker than those with DF during the febrile period of illness [8 , 26] . Also , children admitted with acute dengue fever had increased levels of NK cells and T lymphocyte activation and the severity of disease was associated with higher activation status [23] . In the last few years considerable progress has been made in identifying different T cell memory subsets to dissect the heterogeneity of human immune responses [27] . In this paper , we evaluated in different CD8+ T subpopulations in adults with dengue fever using a comprehensive panel of antibodies . Our current study demonstrates differences in activation status among the various CD8+ T cell subpopulations in dengue fever patients . The percentages and numbers of effector memory ( TEM ) cells , characterized by the CCR7-CD27+CD45RA+/- phenotype [28] , were the most activated in the later phase of the disease , as demonstrated by the expression of HLADR and CD38 molecules . Other subpopulations also exhibited increased activation , including central memory ( TCM ) and terminally differentiated memory cells ( TEMRA ) . However this finding was restricted to the percentage and not the absolute numbers of these two TCM and TEMRA subsets suggesting that TEM cells are the most activated subset in the later stages of acute dengue fever . TEM cells have immediate effector function , by secreting IL-2 , IFNγ , and other cytokines in response to infectious pathogens [29 , 30 , 31] . One limitation of our study is the lack of any data regarding antigen-specific responses since the observed expansion of TEM cells was described in CD8+ T cells using only surface staining . Further studies using DENV-derived proteins or peptides for stimulation of PBMC from acute dengue cases are warranted . Antigen-specific CD8+ T cell responses have been recently described in general population from Sri Lanka hyperendemic area , with higher magnitude and more polyfunctional responses for HLA alleles associated with decreased susceptibility to severe disease [32] . DENV-reactive CD8+ T cells are important in the control of viral replication [33] and may have different responses to different epitopes [34] . DENV serotype-cross-reactivity of CD8+ T cells has also been demonstrated after primary infection [35] . The observed expansion of TEM cells , which may contain such cells , should be explored in future studies to verify their antigen-specific characteristics [24] . The analysis of human memory T and B cells has the capacity to identify the antigens that are targeted by effector T cells , thus providing a rational for vaccine design . In fact , many dengue vaccine candidates have been using replicating virus , including chimeric dengue virus [36] , which can induce a significant immune reaction against the vaccine [37] [38] [39] . Based on our findings that different CD8+ T cell subpopulation are activated to different levels , it may be important to investigate the status of CD8+ T cell differentiation when analyzing antigen-specific responses . Considering the key role of CD8+ T cell activation and antigen-specific responses in the pathogenesis of dengue fever , further investigation should be conducted to explore the mechanisms of activation pathways in disease pathogenesis . | Dengue is a disease affecting approximately 400 million people annually , especially in tropical and subtropical areas of the globe . The immune response against the dengue virus is still under investigation and it is important to understand why the disease can be fatal in a small proportion of cases . In this work , we explored how an important cell type of the immune system , namely the CD8+ T cell , reacts during dengue infection . Using a method known as flow cytometry , we demonstrated that these cells expand and become highly activated , during the days following the onset of dengue fever symptoms . This expansion is associated with a decreased dengue virus load in the patients’ blood , suggesting that CD8+ T cells play an important role in viral control . Interestingly , we found that a subset of CD8+ T cells , called effector memory , is greatly expanded during dengue infection . Our results are important because they might contribute to the understanding of disease mechanisms during dengue infection and may help in the development of a novel vaccine against dengue . | [
"Abstract",
"Introduction",
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] | [] | 2015 | CD8+ T Lymphocyte Expansion, Proliferation and Activation in Dengue Fever |
Large , naturally evolved biomolecular networks typically fulfil multiple functions . When modelling or redesigning such systems , functional subsystems are often analysed independently first , before subsequent integration into larger-scale computational models . In the design and analysis process , it is therefore important to quantitatively analyse and predict the dynamics of the interactions between integrated subsystems; in particular , how the incremental effect of integrating a subsystem into a network depends on the existing dynamics of that network . In this paper we present a framework for simulating the contribution of any given functional subsystem when integrated together with one or more other subsystems . This is achieved through a cascaded layering of a network into functional subsystems , where each layer is defined by an appropriate subset of the reactions . We exploit symmetries in our formulation to exhaustively quantify each subsystem’s incremental effects with minimal computational effort . When combining subsystems , their isolated behaviour may be amplified , attenuated , or be subject to more complicated effects . We propose the concept of mutual dynamics to quantify such nonlinear phenomena , thereby defining the incompatibility and cooperativity between all pairs of subsystems when integrated into any larger network . We exemplify our theoretical framework by analysing diverse behaviours in three dynamic models of signalling and metabolic pathways: the effect of crosstalk mechanisms on the dynamics of parallel signal transduction pathways; reciprocal side-effects between several integral feedback mechanisms and the subsystems they stabilise; and consequences of nonlinear interactions between elementary flux modes in glycolysis for metabolic engineering strategies . Our analysis shows that it is not sufficient to just specify subsystems and analyse their pairwise interactions; the environment in which the interaction takes place must also be explicitly defined . Our framework provides a natural representation of nonlinear interaction phenomena , and will therefore be an important tool for modelling large-scale evolved or synthetic biomolecular networks .
Complex biochemical reaction networks serve a broad variety of different tasks within the cell . Systems Biology researchers apply a range of systems analysis techniques to these networks to identify and model functional subsystems and their interaction structure . In the context of biomolecular networks , the subsystems that can be identified often have biological interpretations: for example , the heat shock response and the chemotaxis pathways represent two functional subsystems within a model describing the complete biomolecular reaction network of Escherichia coli; the synthesis pathways of individual products represent distinct functional subsystems within a metabolic network; and so on . Other functional subsystems may also have system-theoretic interpretations: for example , interacting , distributed feedback control mechanisms; or subsystems which can sense , compute , or actuate on the cell and its environment . This tangle of different objectives within the same network leads to trade-off situations: evolutionary or synthetic changes to one functional subsystem can lead to declining performance or unexpected side effects with respect to another . A fundamental challenge of Systems Biology is to not only establish the behaviour of each functional subsystem in isolation , but also to understand how they dynamically influence one another . This problem is particularly acute when applying the modelling and analysis tools of Systems Biology to adapt and redesign modular biomolecular networks in Synthetic Biology [1–3] . The dynamics of many functional subsystems , whether evolved biochemical networks or synthetic devices , often do not proceed as modelled when integrated into a cell due to their interactions with one another and the environment of their cellular host . Possible sources of nonlinear interactions between pairs of functional subsystems and the cellular environment include retroactivity in genetic [4–6] and signalling [7] networks , crosstalk between parallel signalling pathways [8 , 9] , and the coupling of multiple transcription or translation rates through competition for shared resources [10–12] . In each of these settings , the change in input–output behaviour of a given subsystem upon integration with its context is examined . There are two complementary goals of this paper . First , we investigate the behaviour of each subsystem between the two extremes of ‘isolated’ or ‘integrated’ , when it is integrated with any subset of the other subsystems . The second goal , which is achieved as a consequence of the first , is to then systematically quantify each of the pairwise interactions between the network’s subsystems . The approach we will take in this work is to define a functionality as a group of reactions which corresponds to an identified functional subsystem of a biomolecular network . The reactions that determine each functionality can be selected either through biological insight , or by applying existing computational approaches such as elementary flux mode ( EFM ) analysis [13–15] ( see also Results ) . We characterise the behaviour , or effect , of a functionality as the solution of an ordinary differential equation ( ODE ) model determined by the particular group of reactions . This approach exploits the recently-introduced decomposition technique known as layering [16 , 17] . As depicted in Fig 1 , such an approach is distinct from established modular approaches to network decomposition , which are characterised by identifying sets of species with a high connectivity inside the module , and significantly lower connectivity to species in other modules [18–26] . While often many species and reactions in a given network are implicated in multiple network functions , these modular approaches generally do not allow for such a high degree of overlap between modules . For example , if a network of two pathways responds to two external signals with a single output species , a modular decomposition of this network requires the common output species to be assigned to a module representing exactly one of the pathways , or potentially to an additional separate module . Either way , the input–output behaviour of both pathways cannot be easily defined . However , in the layered framework , the common output is associated with both layers , and hence the output of each layer can be defined in terms of its biological function . Thus , in some cases , layers are preferable to modules for defining the functional subsystems of the network , since the layered framework explicitly allows for overlap in species and reaction subsets [16 , 17] , as will be illustrated further in ‘Mapping Functionalities to Layers’ below . Most importantly , we make it explicit that the behaviour of any functionality also depends on the other functionalities with which it is integrated , to which we refer as the context of the functionality . This contextual dependence is formalised by developing a notational framework that will unambiguously define a functionality’s behaviour in a particular context . The resulting concept of conditional dynamics will be key to our understanding of each functionality as being defined only in the context of others , allowing us to systematically investigate the interdependence of an entire network’s functionalities . The subsequent aim of this framework is to characterise all of the interactions between each pair of functionalities , each of which is also context-dependent . Our approach is a formalisation and extension of previous investigations into additive ( i . e . independent ) , synergistic , or antagonistic subsystem interactions . Examples of these phenomena include the cytokine secretion by macrophages in response to stimulation with different sets of ligands [27] , the response of bacteria to different combinations of drugs [28] , or calcium signalling responses to different stimuli [29] . Importantly , we demonstrate how the strength and the type of interactions between functionalities depends on mediated indirect interactions with the other functionalities comprising their context . The relationship of our approach to the concepts in [27–29] is further discussed in the section “Calculating With Layers” . In addition to the previous literature on context-dependent dynamics , our theoretical framework is also related to steady-state methods . For instance , the third of our examples will exploit EFMs , a technique designed to analyse the steady-state flux distribution in metabolic networks [13–15] . Furthermore , modular and hierarchical control/response analysis is concerned with the different behaviour of subsystems both in isolation and integrated in larger systems , with particular reference to the steady-state responses of biochemical networks to parameter perturbations [6 , 30–34] . The key distinction between our method and these is that we analyse the dynamics of kinetic models [35 , 36] represented by sets of ODEs , rather than steady states . Moreover , our method is not based on linearisation , allowing us to adequately capture nonlinear interactions between functionalities . Quantifying the dynamic interactions of each pair of functionalities in all possible contexts requires multiple ODE simulations; for its practical applicability , it is important to minimise the computational effort involved . This paper is structured as follows: in the Methods section we show how to use a layered decomposition to identify the incremental effect of a functionality , making its context-dependence explicit . We continue by defining the interdependence , or mutual dynamics , between any two functionalities . We summarise this interdependence by the incompatibility and the cooperativity between functionalities . In the final part of the Methods section we describe how to analyse all functionalities and their dependencies with minimal computation . We demonstrate our method on three familiar biomolecular networks in the Results section . The first example is of two signalling pathways with two crosstalk mechanisms , in which we use our approach to quantify the nonlinear interactions between crosstalk mechanisms . In the second example we analyse an unstable pathway stabilised by two integral feedback loops , finding the interactions between each controller and the pathway , and also between the controllers . Finally , we consider the glycolytic pathway in Saccharomyces cerevisiae , with functionalities defined by an EFM analysis . We apply our approach to compare how different knock-out strategies in metabolic engineering influence the yield of ethanol , industrially relevant in biofuel production .
Consider a biochemical reaction network with NX species Xi of time-varying concentrations xi ( t ) , taking part in NR reactions R1 , … , RNR , each of which proceeds at the concentration-dependent rate vj ( x1 , … , xNX ) for j = 1 , … , NR . Forming vectors v = ( v1 , … , vNR ) T and x = ( x1 , … , xNX ) T gives an ODE model of the system x ˙ ( t ) = S v ( x ( t ) ) , x ( 0 ) = x 0 , ( 1 ) where the stoichiometric matrix S maps reaction rates to the rate of change of concentrations . The layered decomposition strategy [16 , 17] defines NL new stoichiometric matrices S 1 , … , S N L such that S = S 1 + ⋯ + S N L , and defines NL associated state variables xl taking values x l ( t ) ∈ ℝ N X ( see Fig 1B ) . Each layer’s state xl has dynamics x ˙ l ( t ) = S l v ( x 0 + x l ( t ) + ∑ k ≠ l x k ( t ) ) , ( 2 ) from initial conditions xl ( 0 ) = 0 , for l = 1 , … , NL . The original state’s dynamics are recovered by summing the layers’ states x ( t ) = x 0 + ∑ l = 1 N L x l ( t ) . Denote by r = rank ( S ) the dimension of the original system , which defines the dimension of the manifold in ℝ N X in which x ( t ) evolves . It follows that rl = rank ( Sl ) defines the dimension of the state space of each layer . Hence , even though the state space of each layer is also embedded in ℝ N X , each layer is a lower-dimensional system than the original system if rl < r . In our previous work , we have applied this decomposition strategy by choosing the matrices Sl to reflect timescale separation [17] , and to reflect the propagation of steady-state responses to parametric perturbations [16] . A feature of both of these approaches was that , in the form ( 2 ) , each layer’s dynamics depend on all other layers’ states ( as in Fig 1B , for example ) . Consequently , all layers had to be numerically integrated together , and the effect of one specific layer on all others could not be easily determined . Also , the approach was constrained to define layers by strict partitions of the reaction set , somewhat limiting its flexibility to capture the widest possible range of functional subsystems . In this article , we significantly extend the layering framework in two ways . First , we introduce the concept of functionalities , which are possibly overlapping sets of reactions working together for a common purpose . To enforce a cascade structure between the functionalities , we adapt the layered dynamics corresponding to each functionality depending on its position in the cascade . The following section will use this cascade structure to define the incremental dynamic effect of each functionality . Let a functionality Fi of a network be defined for i = 1 , … , NL to be a subset of N R i reactions Fi ⊆ {R1 , … , RNR} necessary to fulfil a given task of the network , where superscript integers index functionalities and their properties . It is assumed for the remainder of this section that these subsets are given , and that all reactions take part in at least one functionality . The question of how to choose each subset Fi ⊆ {R1 , … , RNR} remains out of the scope of this work . Nevertheless , there are numerous non-modular decomposition strategies taken in recent related research that we can use to justify this definition of a functionality . For example , Oishi and Klavins [37] identify control blocks as specific groups of reactions , connected by shared species . Kurata et al . [38] identified reaction groups forming ‘flux modules’ in the Escherichia coli heat shock response system . Similarly , the decomposition of signalling networks into component pathways exhibiting crosstalk [8] also identifies functionalities as groups of reactions . Finally , we can also consider elementary flux modes ( EFMs ) of metabolic networks [13] as being sets of reactions with the specific ‘task’ of converting one or more substrates into given products . Several of these examples are explored further in the Results section of this paper . In this section , we will assume that the functionalities are ordered by their index F 1 , … , F N L . We first identify the dynamics of the isolated functionality F1 as the dynamics of a biomolecular network consisting of only the reactions associated with F1 . We then identify the conditional dynamics of the next functionality in the cascade as the effect of extending the pre-existing network with the reactions in the new functionality . First consider , without loss of generality , the network defined by only the subset of reactions making up functionality F1 ⊆ {R1 , … , RNR} , taken in isolation from the other reactions . For given initial conditions x0 , we now identify the isolated dynamics of this functionality as the solution to the layer x ˙ 1 ( t ) = S 1 v ( x 0 + x 1 ( t ) ) , x 1 ( 0 ) = 0 . ( 3 ) Here , the stoichiometric matrix S1 is defined S j k 1 = { S j k R k ∈ F 1 , 0 otherwise , by copying the columns of the original stoichiometric matrix S in ( 1 ) corresponding to the reactions in F1 and setting the other columns to zero . We will denote this trajectory x1 = L ( F1 ) , where the notation L represents a map from the functionality F1 to the solution x1 of the dynamics ( 3 ) from initial conditions x1 ( 0 ) = 0 . Note that L ( F1 ) depends on the specific initial condition x0 of the network ( 3 ) , which is in general distinct from the initial condition x1 ( 0 ) = 0 of the layer’s state . To make this dependence explicit , it is sometimes helpful ( see Examples 2 and 3 ) to define a ‘zero layer’ F0 with constant trajectory L ( F0 ) = x0 . We can then make clear that L ( F1 ) is dependent on the initial conditions by writing it as L ( F1∣F0 ) . The layered framework also implies that the absolute concentrations in this network are modelled by the translated trajectory x0 + L ( F1∣F0 ) . We next consider extending the functionality F1 by combining it with the reactions in F2 . The extended network can be simulated through a similar process to the original network above , as follows . Define S1 , 2 as S j k 1 , 2 = { S j k R k ∈ F 1 ∪ F 2 , 0 otherwise , considering only the reactions in at least one of F1 or F2 . Using S1 , 2 we can then simulate the layer corresponding to the extended network x ˙ 1 , 2 ( t ) = S 1 , 2 v ( x 0 + x 1 , 2 ( t ) ) , x 1 , 2 ( 0 ) = 0 , ( 4 ) the solution of which can be written L ( F1 , F2∣F0 ) = x1 , 2 . This denotes the trajectory of the combined functionalities F1 and F2 . The fact that each of ( 3 ) and ( 4 ) are layers with states in ℝ N X implies that we can calculate the difference between each of the trajectories . This difference is clearly interpreted as the incremental effect of extending a network made up of the initial conditions F0 and the isolated functionality F1 , by also including F2 . We thus define L ( F 2 | F 1 , F 0 ) = L ( F 1 , F 2 | F 0 ) - L ( F 1 | F 0 ) ( 5 ) as the conditional dynamics of F2 , given the specified context of F1 and the initial condition layer F0 . However , rather than simulating the layer ( 4 ) representing the combined functionalities , we may further exploit the layered framework described above to directly simulate L ( F2∣F1 , F0 ) . Suppose we already have x1 = L ( F1∣F0 ) , found as the solution to the dynamics ( 3 ) . We now define the layer x ˙ 2 ( t ) = S 1 , 2 v ( x 0 + x 1 ( t ) + x 2 ( t ) ) - S 1 v ( x 0 + x 1 ( t ) ) , x 2 ( 0 ) = 0 , ( 6 ) with S1 and S1 , 2 given above . Note that this layer is downstream of ( 3 ) , since it depends on the state x1 . It is clear from summing the vector fields in ( 3 ) and ( 6 ) that the sum ( x1 + x2 ) of the layers’ states follows exactly the same dynamics as the combined network’s state x1 , 2 in ( 4 ) . Thus , since x2 = x1 , 2 − x1 it follows that the dynamics ( 6 ) directly simulate L ( F2∣F1 , F0 ) , with the input L ( F1∣F0 ) simulated by ( 3 ) . We can rewrite the dynamics ( 6 ) corresponding to the simulation of L ( F2∣F1 , F0 ) as x ˙ 2 ( t ) = S 2 v ( x 0 + x 1 ( t ) + x 2 ( t ) ) + S 1 v a l t ( x 0 + x 1 ( t ) , x 2 ( t ) ) ( 7a ) where S2 = S1 , 2 − S1 corresponds to the reactions in F2\F1 , and v a l t ( x 0 + x 1 , x 2 ) = v ( x 0 + x 1 + x 2 ) - v ( x 0 + x 1 ) ( 7b ) are the rates of the ‘altered reactions’: the rates of reactions in F1 which are modified by the presence of F2 ( shown as broken green arrows in Fig 1C ) . This description allows us to see the degree to which F2 is ‘downstream’ of F1 . For example , if valt = 0 , then we can say that the reactions in F1 are independent of those in F2 and that F2 is strictly downstream of F1 . Note that , especially for larger networks , many of the altered reaction rates in valt are zero and can be omitted ( see Example 3 ) , simplifying simulation . Given that the trajectory of x1 is already determined from simulating ( 3 ) , we can simulate either ( 6 ) or ( 7a ) , using x1 ( t ) as a time-dependent input to obtain the conditional dynamics L ( F2∣F1 ) . The latter approach is taken in our examples ( see Results section ) . The definitions above easily extend to larger combinations of functionalities . In full generality , we can consider the network defined by the combination of n1 functionalities F 1 , … , F n 1 , and its extension through the additional n2 functionalities F n 1 + 1 , … , F n 1 + n 2 . By writing F ‾ 1 = F 1 ∪ ⋯ ∪ F n 1 and F ‾ 2 = F n 1 + 1 ∪ ⋯ ∪ F n 1 + n 2 , the definitions above can apply in the simulation of L ( F 1 , … , F n 1 | F 0 ) = L ( F ¯ 1 | F 0 ) , L ( F n 1 + 1 , … , F n 1 + n 2 | F 0 , F 1 , … , F n ) = L ( F ¯ 2 | F 0 , F ¯ 1 ) . Here we have defined a notation for the trajectory L ( F 1 , … , F n 1 ∣ F 0 ) of the biochemical network made up of the reactions which comprise an arbitrary combination of functionalities F 1 , … , F n 1 . We have also defined the change in trajectory L ( F n 1 + 1 , … , F n 1 + n 2 ∣ F 0 , F 1 , … , F n 1 ) incurred by extending that network with the additional reactions in F n 1 + 1 , … , F n 1 + n 2 . Finally , we have shown how to identify the dynamical systems that can simulate these trajectories . Consider the two layers L ( F2∣F1 ) and L ( F2 ) that both describe the effect of the functionality F2 . This effect is different depending on the presence or absence of F1 . The difference between these two trajectories defines how the presence of F1 changes the behaviour of F2; that is , the dependence of F2 on F1 . We will now demonstrate how our layered analysis allows us to define the interdependence between two functionalities , thereby capturing the nonlinear effects arising from modelling a biomolecular network as being constructed from a combination of functional subsystems . In order to quantify the interactions between functionalities , we can exploit the layered formulation above . For simplicity , from this point on we suppress the F0 notation , with the acknowledgement that all of the trajectories depend on the system’s initial conditions L ( F0 ) = x0 . The definition of conditional dynamics in ( 5 ) implies that L ( F1 , F2 ) = L ( F2∣F1 ) + L ( F1 ) . This represents a layered cascade , where the dynamics of an integrated network are the linear combination of the conditional dynamics of its functionalities . There are two natural questions associated with this approach . First , how is the contribution of functionality F2 , considered in isolation , different from the conditional dynamics of F2 when integrated with F1 ? Secondly , how is the behaviour of the integrated F1 , F2 network different from the linear combination of the isolated functionalities ? That is , how different is L ( F1 , F2 ) from L ( F1 ) + L ( F2 ) ? The answers to these two questions are the same . We denote the error incurred by approximating the integrated system as the linear combination of the isolated dynamics by the quantity M ( F1; F2 ) , defined as M ( F 1 ; F 2 ) = L ( F 1 ) + L ( F 2 ) - L ( F 1 , F 2 ) , which we call mutual dynamics . This can be interpreted as the nonlinearity that arises from integrating the two functionalities together . Note that , since M is defined symmetrically , we can use ( 5 ) to rewrite M as M ( F 1 ; F 2 ) = L ( F 2 ) - L ( F 2 | F 1 ) = L ( F 1 ) - L ( F 1 | F 2 ) . ( 8 ) Therefore M measures how the function of F2 ( or F1 ) is changed when considered in the context of F1 ( or F2 ) . Thus M ( F1; F2 ) is a symmetric measure of the interdependence between the two functionalities . We use ( 8 ) to calculate the the mutual dynamics between two functionalities , which requires us to first obtain either both trajectories L ( F1 ) and L ( F1∣F2 ) , or alternatively both trajectories L ( F2 ) and L ( F2∣F1 ) . These trajectories can be simulated , as described in the previous section , or calculated by the methods described in ‘Reducing Computational Burden’ below . We have been careful to make explicit through our Bayesian-style notation that the dynamics of all functionalities are context-dependent . It is also the case that the interdependence between any two functionalities is context-dependent . Therefore , we need to extend the definition of mutual dynamics to consider how the interdependence between F1 and F2 is dependent on the wider context of the network , which we denote by another functionality , F3 . Similarly to the definition above , we can define the conditional mutual dynamics between F1 and F2 , given F3 , with the formula M ( F 1 ; F 2 | F 3 ) = L ( F 1 | F 3 ) + L ( F 2 | F 3 ) - L ( F 1 , F 2 | F 3 ) , to quantify the difference between the dynamics of the integrated and isolated functionalities F1 and F2 , in the context of F3 . As before , this can also be expressed in terms of layered dynamics as M ( F 1 ; F 2 | F 3 ) = L ( F 2 | F 3 ) - L ( F 2 | F 1 , F 3 ) = L ( F 1 | F 3 ) - L ( F 1 | F 2 , F 3 ) , to quantify how the effect of F2 on its context changes with the presence of F1 , and vice versa . The geometric intuition underlying the conditional mutual dynamics can be seen in Fig 2 . The key interpretation of M is that it captures the nonlinearities that arise from combining F1 and F2 into a single network , conditioned on F3 if necessary . The conditional mutual dynamics M ( F1; F2∣F3 ) is a time-varying , vector trajectory . We can base on M the following time-varying scalar , which we call the incompatibility and denote I ( F1; F2∣F3 ) with formula I ( F 1 ; F 2 | F 3 ) = ∥ M ( F 1 ; F 2 | F 3 ) ∥ ∥ L ( F 1 | F 3 ) + L ( F 2 | F 3 ) ∥ . ( 9 ) Here , ‖ . ‖ represents the Euclidean norm . In this paper we use the unweighted Euclidean norm , but in certain cases it might be appropriate to introduce a weight , for example if the concentrations of the species in a network are at different orders of magnitude . One might also decide to set the weight of certain intermediate species of limited interest to zero ( see below ) . This incompatibility measures the relative size of the error made by approximating the integration of two functionalities as the sum of their individual behaviour . To gain some intuition about this number , we can consider a number of special cases . If I ( F1; F2∣F3 ) = 0 , then this indicates that the trajectory of the integrated functionalities is simply the sum of the isolated functionalities’ trajectories: L ( F1 , F2∣F3 ) = L ( F1∣F3 ) + L ( F2∣F3 ) . If the incompatibility is nonzero but small then L ( F1 , F2∣F3 ) ≈ L ( F1∣F3 ) + L ( F2∣F3 ) is a reasonable approximation , since the incurred error is relatively small . However , if I is of significant size , then the dynamics of the integrated functionalities can be expected to significantly differ from their individual behaviours . Besides I , which measures the relative size of the mutual dynamics M , the direction of M is also important , since this determines if the integration of two functionalities together enhances or attenuates their individual dynamics , or causes other effects . We define the cooperativity C as the cosine of the angle between −M and the sum of the isolated layers: C ( F 1 ; F 2 | F 3 ) = - M ( F 1 ; F 2 | F 3 ) · ( L ( F 1 | F 3 ) + L ( F 2 | F 3 ) ) ∥ M ( F 1 ; F 2 | F 3 ) ∥ ∥ L ( F 1 | F 3 ) + L ( F 2 | F 3 ) ∥ , ( 10 ) with ⋅ denoting the scalar product . See Fig 2 for a geometric representation of C . Note that when using a weighted norm , the scalar product should be weighted accordingly . Again , we consider a number of special cases . Suppose that C ( F1; F2∣F3 ) equals or is close to minus one , so that M is approximately parallel to and pointing in the same direction as L ( F1∣F3 ) + L ( F2∣F3 ) . In this case the integrated dynamics can be approximated as an attenuation of the isolated behaviour L ( F 1 , F 2 | F 3 ) = L ( F 1 | F 3 ) + L ( F 2 | F 3 ) - M ( F 1 ; F 2 | F 3 ) ≈ ( 1 - I ( F 1 ; F 2 | F 3 ) ) ( L ( F 1 | F 3 ) + L ( F 2 | F 3 ) ) . Conversely , if the cooperativity equals or is close to one , the two functionalities enhance each other , in the sense that we can approximate the integrated behaviour as an amplification of the isolated behaviours L ( F 1 , F 2 | F 3 ) ≈ ( 1 + I ( F 1 ; F 2 | F 3 ) ) ( L ( F 1 | F 3 ) + L ( F 2 | F 3 ) ) . However , once the cooperativity C equals or is close to zero , the mutual dynamics M are orthogonal to L ( F1∣F3 ) + L ( F2∣F3 ) . This means that when the functionalities are integrated , both isolated functionalities are maintained , but there are also additional interactions ( with an effect of strength I ) in directions orthogonal to the summed isolated dynamics . For example , suppose that the functionalities F1 and F2 correspond to sets of reactions responsible for mediating the cellular responses to two different input signals . By setting the influence of all but the common output ( measured ) species to zero , an incompatibility I ( F1; F2 ) close to zero corresponds to an additive interaction of the input signals . If I ( F1; F2 ) is larger , it may correspond to either a synergetic ( for C ( F1; F2 ) = 1 ) or antagonistic ( for C ( F1; F2 ) = −1 ) interaction ( see e . g . [28] ) . Note that for a scalar output , orthogonal dynamics are not possible . For systems composed of many different functionalities , one might also take the time averages ⟨I⟩ and ⟨C⟩ of the incompatibility , respectively the cooperativity , over the simulation time ΔT to obtain single measures quantifying the interactions between layers: ⟨ I ⟩ ( F 1 ; F 2 | F 3 ) = 1 Δ T ∫ 0 Δ T I ( F 1 ; F 2 | F 3 ) d t ⟨ C ⟩ ( F 1 ; F 2 | F 3 ) = 1 Δ T ∫ 0 Δ T C ( F 1 ; F 2 | F 3 ) d t Although they are useful for obtaining a first impression of how functionalities interact , time averages should be carefully applied . They can hide transient interactions between functionalities , including potential sign changes of the state-dependent cooperativities ( see Example 1 ) . We have now identified how to measure the interdependence between two functionalities , which we have defined as the change in the dynamics of one functionality when the other is present . We have made explicit how this interdependence is itself dependent on the context of the rest of the network . In the remainder of this section we will describe how to minimise the computational burden incurred when calculating all possible interactions between functionalities . The notation L ( F1 ) and L ( F2∣F1 ) describing the map from a functionality ( or set of functionalities ) to the resulting trajectory simplifies the calculations we may wish to carry out to understand how the functionalities combine . Using the key definition of ‘conditional dynamics’ given by ( 5 ) , we can prove a number of rules for combining layers which appear analogous to those known from Information Theory [39] . For example , for two random variables X and Y , the well-known quantities of joint entropy H ( X , Y ) = H ( X ) + H ( Y∣X ) and mutual information I ( X; Y ) = H ( X ) − H ( X∣Y ) are each definitions of the same form as those given above of conditional dynamics ( 5 ) and mutual dynamics ( 8 ) respectively . However , it is important to note that this similarity is only superficial , and any intuition gained by seeking analogies between our work and information theoretic concepts should be applied carefully . This caveat applies in particular to the two results below . Two lemmas allowing the quick combination of layer dynamics can be easily proved directly from the definitions of L ( F1 ) and L ( F2∣F1 ) , their extensions to larger combinations of functionalities , and Eq ( 5 ) . The first is an analogue of Bayes’ Rule , given by L ( F 1 | F 2 , F 3 ) = L ( F 2 | F 1 , F 3 ) + L ( F 1 | F 3 ) - L ( F 2 | F 3 ) . ( 11 ) We will demonstrate how this rule can be used for quickly deducing the incremental effects of layers when combined in a different order . This is fundamental , since a natural ordering of the layers is generally not given . A second rule , which is analogous to Bayes’ Factor , is given by L ( F 3 | F 1 ) - L ( F 2 | F 1 ) ︸ Posterior Dynamics = L ( F 1 | F 3 ) - L ( F 1 | F 2 ) ︸ Bayes Factor B 32 | 1 + L ( F 3 ) - L ( F 2 ) ︸ Prior dynamics . ( 12 ) This rule applies when we have a choice between integrating two functionalities to the F1-only network . The difference in their effects is decomposed into the difference L ( F3 ) − L ( F2 ) between their isolated behaviours , summed with the difference L ( F1∣F3 ) − L ( F1∣F2 ) in the incremental effect of F1 on each . We can use these rules to compute all possible functionality combinations with a minimal amount of simulation . In order to answer particular biological questions , we may be interested in the incremental effect of a given functionality on a specific ‘base’ network , such as those described in Examples 1 and 2 in the Results section . In other cases we may be interested in all possible interactions between the functionalities , such as the situation in Example 3 . In a biochemical network whose reactions are decomposed into NL functionalities , the latter case suggests that we must simulate all NL layers for each of the NL ! different orderings of the functionalities , resulting in ( NL + 1 ) ! − NL ! layers to be numerically solved . This burden can be significantly reduced using the calculation rules deduced above . The cascaded layers representing all possible orderings of functionalities can be arranged in an acyclic directed graph , shown in Fig 3 . Each node represents the trajectory arising from the incremental addition of a new functionality , given those already present . The graph is organised into levels , corresponding to the position of the new layer in the sequence . The root of the layering graph ( referred to as Level 0 ) represents the given initial conditions x0 . Each of the subsequent levels l = 1 , … , NL consists of ( NL−l+1 ) ( NLl−1 ) nodes . Each node represents the dynamics of a functionality Fi conditioned on a subset of size l − 1 of the remaining functionalities Fj , j ≠ i . A directed edge from a node in Level l to a node in Level l + 1 exists if the node in Level l + 1 is conditioned on all functionalities taking part in the node in Level l . Each directed path from Level 0 to Level NL ( i . e . from the root to a leaf ) represents one of the NL ! possible orderings of functionalities . By adding up the layer dynamics corresponding to the nodes in each path , the trajectory of the complete network is obtained . Each node in Level 1 represents the dynamics of an isolated functionality . The dynamics represented by the nodes at the leaves of the layering graph are also of specific interest . Multiplying the layer dynamics in a given node in Level NL ( corresponding to a particular functionality Fi ) by −1 gives the change in the dynamics that results from removing Fi from the system while keeping all other functionalities intact ( see Example 3 ) . To obtain the dynamics of all layers for all orderings of functionalities , we can use rules ( 11 ) and ( 12 ) to exploit certain symmetries in the layering graph and reduce the number of numerical integrations of layer ODE systems . If all of the dynamics in Level l − 1 are already known , it is only necessary to numerically solve ( N L l ) layer dynamics in Level l . The rest of the level can then be deduced using Bayes’ rule ( 11 ) . For example , suppose we have the trajectories of all nodes in Layer 1 , and have simulated L ( F1∣F0 , F2 ) in Level 2 . Using ( 11 ) we can deduce L ( F2∣F0 , F1 ) without having to simulate again . In fact , when considering a network with NL functionalities , only ∑ l = 1 N L ( N L l ) = 2 N L - 1 layers have to be numerically integrated ( see Example 1 ) , compared to ( NL + 1 ) ! − NL ! integrations necessary when analyzing all orderings separately . Although the number of integrations still exponentially grows with NL , it nevertheless becomes possible to analyse systems with up to ten or more distinct functionalities in reasonable time . For NL = 10 , integrating every layer would require more than 3 . 5 ⋅ 104 times as much computational time as is required by exploiting ( 11 ) . Furthermore , the symmetry of the layering graph allows a certain degree of freedom in choosing which layers to simulate , and which to deduce from ( 11 ) . In particular , one should always choose to simulate the simplest layers , with respect to the number of states or reactions in the respective ODE system . Fig 3 shows the situation where NL = 3: we need to simulate 23 − 1 = 7 layers . We have decided to , wherever possible , simulate F3 , ahead of F2 , ahead of F1 . After simulating the isolated behaviour L ( F1∣F0 ) of the functionality F1 , we can calculate its behaviour in any other context without having to simulate it again . For example , L ( F1∣F0 , F2 ) can be calculated , using ( 11 ) , as L ( F2∣F0 , F1 ) + L ( F1∣F0 ) − L ( F2∣F0 ) . Similarly , substituting the resulting trajectory into ( 11 ) once more means that we can calculate L ( F1∣F0 , F2 , F3 ) as the linear combination L ( F3∣F0 , F1 , F2 ) + L ( F1∣F0 , F2 ) − L ( F3∣F0 , F2 ) . As can be seen by the indicated nodes in Fig 3 , only one layer corresponding to F1 had to be numerically simulated , whereas we simulated layers corresponding to F2 twice , and layers corresponding to F3 four times . Thus we can reduce computation time even further by avoiding the repeated simulation of high-dimensional layers .
The high osmolarity glycerol and the pheromone response mitogen-activated protein ( MAP ) kinase pathways in S . cerevisiae share the common species Ste11 [40] . Such a common species can constitute a mutually excitatory crosstalk mechanism by cross-activating one pathway upon activation of the other . However , McClean et al . [8] observed that only one pathway responds , and deduced that a second , mutually inhibitory crosstalk mechanism exists . They constructed a model , available as Model 115 in the BioModels Database [41] , that includes both crosstalk mechanisms . As an initial example of our approach we implemented a layered decomposition of this model , shown in Fig 4 . The mathematical description of the complete model and of all layers can be found in the Supporting Information . We applied our framework to systematically investigate the function of each of the crosstalk mechanisms . We first considered the effects of each crosstalk mechanism on the signalling pathways . This gives some indication of the function of each crosstalk mechanism , but our framework takes this analysis further . The effect of excitatory crosstalk on the network is altered by the presence of inhibitory crosstalk , and vice versa . The conditional mutual dynamics between the two crosstalk mechanisms , integrated with the crosstalk-free network , can be used to quantify their interdependence . In the model , we set the strength of the mutually excitatory crosstalk to ka = 0 . 1 and the strength of the mutually inhibitory crosstalk to kd = 1 , corresponding to a monostable network ( see Fig . 1 in [8] ) . We assume zero initial conditions . The three reactions R1 , R2 , and R3 represent the first signal transduction pathway , which affects species concentrations X1–X3 , so that we define the functionality F1 = {R1 , R2 , R3} . Similarly , we choose F2 = {R4 , R5 , R6} to represent the second signal transduction pathway , affecting species Y1–Y3 . The mutually inhibiting crosstalk between the two pathways comprises F3 = {R7 , R8} . Finally , the mutually excitatory crosstalk is given by F4 = {R9 , R10} . We first calculated all of the possible incremental effects of each functionality Fi integrated with each possible subset {Fj ∣ j ≠ i} of the others . Since we have NL = 4 functionalities we only need to simulate 2 N L − 1 = 15 layers to be able to calculate all NL ⋅ NL ! = 96 layer dynamics for all orderings of the functionalities . Fig 5 shows the trajectories corresponding to several of these nodes . The plots were generated for time-varying inputs S1 and S2 given by [ S 1 ( t ) , S 2 ( t ) ] = { [ 0 , 0 ] 0 ≤ t < 20 , [ 5 , 0 ] 20 ≤ t < 40 , [ 5 , 5 ] 40 ≤ t < 80 , [ 0 , 5 ] 80 ≤ t < 100 , [ 5 , 5 ] 100 ≤ t . 10 . 1371/journal . pcbi . 1004235 . g005 Fig 5 Layer dynamics of the crosstalk example . Dynamics of the layers for switching between different input combinations: ( -/- ) S1 = S2 = 0; ( +/- ) S1 = 5 , S2 = 0; ( +/+ ) S1 = S2 = 5; and ( -/+ ) S1 = 0 , S2 = 5 . The top left figure corresponds to the dynamics of the complete network , the central and right top figure to the dynamics of the two isolated signalling pathways without any crosstalk . If the mutually inhibitory crosstalk L ( F3∣F1 , F2 ) is added first , the network becomes bistable , then monostable again by then including the mutually excitatory crosstalk L ( F4∣F1 , F2 , F3 ) . Adding first the excitatory crosstalk L ( F4∣F1 , F2 ) leads to strong cross-activation of the pathways , which is significantly weakened by the mutual inhibitory crosstalk L ( F3∣F1 , F2 , F4 ) . The switching times between the input combinations were chosen such that , at the end of each period , the species of the network have converged to their corresponding steady-state concentrations . The top-left plot in Fig 5 shows the response L ( F1 , F2 , F3 , F4 ) of the entire network to this input pattern . Each of the other plots depicts the incremental effects of integrating a certain functionality with a group of others . Fig 6 depicts the trajectories of the mutual dynamics between several pairs of functionalities , also calculated from the 15 simulations of layered ODE systems . Below these trajectories we plot their corresponding incompatibilities I and cooperativities C . We consider the basic network comprised only of F1 and F2 , where neither crosstalk mechanism is active . From L ( F1 ) and L ( F2 ) in Fig 5 we see that after t = 20 ( respectively , t = 40 ) the concentrations of species X1–X3 ( respectively , Y1–Y3 ) quickly saturate . It is easily shown that the mutual dynamics between the two isolated pathways M ( F1; F2 ) = 0 . This means that there is no interdependency between the pathways , and their integrated dynamics equal the sum of their isolated dynamics . Of course , this conclusion is intuitively clear because we are considering a network with neither crosstalk mechanism active . We are now in a position to investigate what each crosstalk mechanism does to this basic network , by identifying the effects of each of F3 and F4 in turn . We first identify the effect of the mutual inhibitory crosstalk F3 on the basic network . This is depicted in Fig 5 by L ( F3∣F1 , F2 ) . The input signals S1 = S2 = 5 are both ‘on’ during two time intervals t ∈ [40 , 80] and t ≥ 100 . In each of these time intervals , we can observe in L ( F3∣F1 , F2 ) two different steady states of X1–X3 and Y1–Y3 . Thus one effect of mutual inhibition is that the resulting network is bistable . We can also observe that the X1–X3 values of L ( F3∣F1 , F2 ) , which show the effect of F3 on the Xi concentrations , become sufficiently negative during t ∈ [80 , 100] that they cancel out the positive values of X1–X3 in L ( F1 , F2 ) . Hence , we can conclude that , by integrating mutual inhibition with the basic network , the removal of S1 at t = 80 now causes the corresponding pathway to deactivate . We can also analyse the interaction between mutual inhibition and the basic network in terms of the mutual dynamics M ( F3; F1 , F2 ) , as depicted in Fig 6 . For t ≥ 40 , the cooperativity C ( F3; F1 , F2 ) is always negative , and the incompatibility I ( F3; F1 , F2 ) is high . Thus , the effect of integrating F3 with ( F1 , F2 ) is to strongly attenuate the levels of all species from their saturated state . We next consider the effect on the basic network of mutually excitatory crosstalk F4 , depicted by L ( F4∣F1 , F2 ) in Fig 5 . We will focus in particular on the time interval t ∈ [20 , 40] , where S1 is first activated . For this time interval , the concentrations of Y2 and Y3 increase , while the concentrations of X1–X3 are transiently reduced . Thus the effect of including mutual excitation is that a non-zero input S1 is sufficient to activate the second pathway , as well as the first . As in the previous case , we can also analyse the interaction between mutual excitation and the basic network in terms of the mutual dynamics M ( F4; F1 , F2 ) , as depicted in Fig 6 . In this time interval , the cooperativity C ( F4; F1 , F2 ) is briefly negative before returning to zero , while the incompatibility I ( F4; F1 , F2 ) increases monotonically and converges to around 0 . 81 . Negative cooperativity implies that mutual excitation attenuates the isolated dynamics , although only marginally since I is small . This attenuation is also only transient; C approaches zero and I approaches approximately 0 . 81 as t→40 . Thus , by the end of this time interval , integrating mutual excitation creates a significant additional effect in a direction orthogonal to the isolated dynamics . This is intuitively clear , since the incremental effect of mutual excitation is the additive excitation of Y2 and Y3 , orthogonal to the saturation of X1–X3 during t ∈ [20 , 40] . We have identified the effect of each crosstalk mechanism on the basic network . However , we can see from the plots of L ( F3∣F1 , F2 , F4 ) and L ( F4∣F1 , F2 , F3 ) that the function of each crosstalk mechanism ( when defined as its incremental effect on a network ) is very different when the other crosstalk mechanism is present . We can see from the first of these trajectories that one effect of integrating inhibition into the cross-activated network is to effectively insulate the Y1–Y3 pathway , since the excitation shown by L ( F4∣F1 , F2 ) during t ∈ [20 , 40] is cancelled out by the values of L ( F3∣F1 , F2 , F4 ) . Furthermore , on comparing L ( F3∣F1 , F2 , F4 ) with L ( F3∣F1 , F2 ) we can see that mutual inhibition has remarkably different incremental effects depending on whether or not mutual excitation is present . This can be quantified by observing the trajectory of M ( F 3 ; F 4 | F 1 , F 2 ) = L ( F 3 | F 1 , F 2 ) - L ( F 3 | F 1 , F 2 , F 4 ) and the associated C and I values in the right-hand plots of Fig 6 . For t ∈ [20 , 40] , the value of I ( F3; F4∣F1 , F2 ) is large , and C ( F3; F4∣F1 , F2 ) is close to −1 . This confirms the assertion above that , in this time interval , the interdependence between crosstalk mechanisms causes them to approximately cancel each other out , so that the behaviour of the entire system is much like that of the isolated pathways . We can observe that , in this time interval , the effect of mutual inhibition on the basic network L ( F3∣F1 , F2 ) is zero . However , non-zero mutual dynamics M ( F3; F4∣F1 , F2 ) in this time interval means that the presence of mutual excitation causes the inhibition crosstalk to have a non-zero function . To summarise , we have demonstrated how to use the concepts of conditional dynamics L ( Fi∣F1 , F2 ) and mutual dynamics M ( Fi; F1 , F2 ) to analyse the effect of each crosstalk mechanism on a pair of isolated pathways . We extended this analysis by using M ( F3; F4∣F1 , F2 ) to quantify how , by integrating the crosstalk mechanisms together , they influence one another’s isolated functions . This example illustrates the application of our layered framework to a toy network , comprised of a biomolecular cascade containing two integral feedback motifs ( Fig 7 ) . This toy network was adapted from a motif identified in [42] as sufficient to exhibit perfect adaptation to changing external input concentrations , and underlying the ability of the chemotaxis pathway in E . coli to show near-perfect adaptation to extracellular chemoattractant concentrations [43–45] . The network consists of two intermediates , Y1 and Y2 . The species Y1 is produced with a time-varying rate k ( t ) depending on the concentrations of one or more external species . Intermediate Y1 is irreversibly converted to Y2 at rate V1 ( y1 ) and Y2 is consumed with rate V2 ( y2 ) , each following Michaelis-Menten kinetics ( see Fig 7 ) . For certain production rates of Y1 , V1 is close to saturation so that the concentration of Y1 eventually becomes unstable for high rates k ( t ) . To stabilise the network and achieve perfect adaptation of the concentrations of Y1 and Y2 to slowly changing production rates k ( t ) , the network needs to be controlled . Such controllers can be implemented through additional Michaelis-Menten reactions Va ( y1 ) and Vb ( y2 ) forming species A and B from Y1 and Y2 , respectively . A and B are consumed with Michaelis-Menten rates V−a ( a ) and V−b ( b ) respectively , both of which are assumed to be close to saturation . The two control loops are closed by non-competitive inhibition of the production of Y1 and of the conversion of Y1 to Y2 modelled by adapting the production rates with the factors ϕa ( a ) and ϕb ( b ) respectively , as shown in Fig 7 . The dynamics of the system can be modelled by the following ODEs: [ y ˙ 1 y ˙ 2 a ˙ b ˙ ] ︸ = : x ˙ = [ 1 - 1 0 - 1 0 0 0 0 0 0 1 - 1 0 0 0 - 1 0 0 0 0 0 0 1 - 1 0 0 0 0 0 0 0 0 0 0 1 - 1 ] ︸ = : S [ k ( t ) ϕ a ( a ) V 1 ( y 1 ) ϕ b ( b ) V 2 ( y 2 ) V a ( y 1 ) V a ( y 1 ) V - a ( a ) V b ( y 2 ) V b ( y 2 ) V - b ( b ) ] ︸ v ( x ) , with V i ( x ) = k i x m i + x , ϕ i ( x ) = 1 p i + x . Reactions Va and Vb have two distinct effects on the pathway: first , they decrease the concentration of Yi independently of any interaction of the controller; and second , they increase the concentration of A and B and are thus the means by which the controller observes the state of the network . To distinguish these two effects mathematically , in the dynamics above we have distributed each of Va and Vb into two reactions with the same rates , one only affecting the controller , and the other only the pathway . To analyse the system , we define three functionalities , corresponding to the uncontrolled pathway F1 ( columns 1–4 and 7 in S ) and the two controllers F2 ( columns 5 and 6 ) and F3 ( columns 8 and 9 ) . The initial condition x0 was set to represent the steady state concentrations of the species for k = 3 . Note that , in order to make explicit the dependency of the layered dynamics on this initial condition , we created a ‘zero layer’ F0 with constant trajectory L ( F0 ) = x0 as described in the Methods section . The ODEs describing the layers’ dynamics for each ordering of the functionalities , the parameter values , as well as the precise initial conditions for this example can be found in the Supporting Information . We then determined the dynamics of the layers when applying a step change of the Y1 production rate from k = 3 to k = 10 at t = 50 . Fig 8 displays the dynamics of the pathway , the conditional dynamics of the controllers given the pathway for both orderings of the controllers , and the complete dynamics of the system . Similarly to the previous example , we will consider a basic network consisting of only the uncontrolled pathway , F1 . We are then able to identify the effect of each controller as the conditional dynamics of F2 and F3 given the basic network . However , the effect on the basic network of both controllers will be different to the sum of each controller’s individual effect . We will thus compute the mutual dynamics between the two controllers in order to analyse how the controllers interact with one another when both are integrated in the context of the basic network . We first consider the isolated dynamics L ( F1∣F0 ) of the uncontrolled pathway which makes up the basic network , depicted in Fig 8 . After the step increase at t = 50 , the two reactions decreasing Y1 quickly saturate . The isolated system is therefore unstable , and the Y1 component of L ( F1∣F0 ) increases to infinity at a positive constant slope . We will now use this basic network to define the function of each of the controllers . Integrating the controller F2 with the pathway stabilises the network . This is indicated by the trajectory of L ( F2∣F0 , F1 ) in Fig 8 . In particular , the Y1 component of this trajectory decreases to minus infinity with approximately −1 times the rate of increase of Y1 in the isolated pathway . Another way to observe this stabilising effect is to consider the mutual dynamics M ( F1; F2 ) between the pathway and the controller , shown in Fig 9 . The inconsistency I ( F1; F2∣F0 ) between pathway and controller approaches one , while the cooperativity C ( F1; F2∣F0 ) minus one . We interpret these values as the two functionalities cancelling one another out; in other words , F2 stabilising F1 . We next consider the effect of the second controller F3 , depicted in Fig 8 by L ( F3∣F0 , F1 ) . The Y1 component of L ( F3∣F0 , F1 ) also diverges to positive infinity , albeit at a much slower rate than that of the basic network . Intuitively , the second controller cannot stabilise the basic network , since it has no means to observe the unstable Y1 component . Instead , its interventions further destabise the pathway . We can quantify this through the mutual dynamics between the controller and pathway . Indeed , since C ( F1; F3∣F0 ) ≈ 1 , the second controller amplifies the unstable dynamics . However , since I ( F1; F3∣F0 ) ≪ 1 , this effect is only minimal . We now consider the interaction between the two controllers when combined with the uncontrolled pathway . Interestingly , the effect of the second controller when combined with the first is only transient , as shown by L ( F3∣F0 , F1 , F2 ) in Fig 8 . However , the mutual dynamics M ( F2; F3∣F0 , F1 ) between the two controllers have a Y1 component increasing slowly to infinity , and the cooperativity between the controllers C ( F2; F3∣F0 , F1 ) approaches 1 . This positive cooperativity indicates that the first controller increases its interventions to stabilise the network when the second controller is present . That the two controllers given the pathway act cooperatively might initially appear surprising , since the first controller stabilises the network while the second destabilises the network . This indicates the utility of our notation in allowing a rigorous quantification of how functionalities interact , given their environment . Consider the following three cases for how the two controllers F2 and F3 interact . First consider the interaction M ( F2; F3∣F0 , F1 ) between the two controllers when integrated with the pathway . Since the presence of F3 increases the control action by F2 necessary to stabilise the pathway , the cooperativity given the pathway C ( F2; F3∣F0 , F1 ) approaches 1 , as described above . Next , consider the interaction M ( F2; F3∣F0 ) between the two controllers without the pathway . They cannot influence each other since the respective subnetworks are not connected . Consequently they do not interact and hence M ( F2; F3∣F0 ) = C ( F2; F3∣F0 ) = I ( F2; F3∣F0 ) = 0 . Finally , consider the interaction M ( F2; ( F1 , F3 ) ∣F0 ) between the first controller F2 and the unstable network comprised of the pathway F1 extended with F3 . The first controller stabilises the extended pathway , which is represented by the cooperativity C ( F2; ( F1 , F3 ) ∣F0 ) approaching minus one , as F2 acts to attenuate the instability of F1 , F3 . Thus , we have demonstrated that the rigorous definition of interdependence using our cascaded layering framework allows us to identify which components of a network amplify or attenuate each other , by defining the components which interact and the context in which they do so . Glycolysis is a central ten-step process in most organisms responsible for the production of energy in form of ATP and NADH by catabolism of glucose and other sugars ( see [46] , p . 88 ff . ) . Besides being one of the best studied metabolic pathways , it is also an important starting point for biotechnological processes [47] . For non-growing S . cerevisiae cells , a kinetic model of the glycolytic pathway was created by Hynne et al . [48] , including fermentation , glycerol production , lactonitrile and glycogen formation , and cellular import and export processes for glucose and other metabolites . We will use this model ( available at the BioModels Database [41] , model 61 ) to exemplify how to apply our layering approach in the context of metabolic engineering . In this section we use Elementary Flux Modes ( EFMs ) [13] , minimal functional pathways which can carry non-zero fluxes at steady state and which fulfil positivity constraints for irreversible reactions . From the unique set of EFMs of a network , all steady-state flux distributions can be obtained by non-negative linear combinations of EFMs . Due to their simplicity , EFMs can often be associated with certain elementary ‘tasks’ of a network , like the production of one or more final products from various available extracellular substrates . Thus EFMs are natural candidates to represent functionalities . The model in [48] includes the dynamics of the cofactors NAD+ , NADH , AMP , ADP , and ATP , with the consumption of ATP by the rest of the cell modelled by first order mass action kinetics . The cofactor concentrations can be interpreted as control inputs to the junctions of the glycolytic pathway dynamically channelling the metabolite flux into the different branches depending on cellular requirements , e . g . during hyperosmotic conditions [49] . The feedback loop is closed by an integral controller ‘observing’ the consumption and production of the cofactors by the glycolytic pathway and other cellular processes . We established a mixed layer structure for the glycolysis model ( Fig 10 ) . This mixed layer structure consists of the EFMs ( not taking into account mass balance of the cofactors ) as functionalities in a cascaded layer structure . The dynamics of cofactors , together with ATP consumption ( ATP→ADP , Reaction 23 in [48] ) and the AK reaction ( ATP + AMP ↔ 2ADP , Reaction 24 ) form a control layer that communicates with all layers in the cascade without being part of the cascade . Using the software efmtool [50] , we identified eight EFMs for the modified network where the cofactor dynamics were removed by setting the appropriate rows in the stoichiometric matrix to zero . Three of the identified EFMs were non-negative linear combinations of other EFMs , a consequence of treating each reversible reaction as two separate irreversible reactions . Based on the concept of simplicity ( condition C3 in [13] ) , we removed the linearly-dependent EFMs with the lowest number of zero entries . The remaining five EFMs can be interpreted as representing elementary ‘tasks’ of the network: Glycogen buildup: production and storage of glucose-6-phospate G6P Production and excretion of glycerol Glyc Fermentation: production and excretion of ethanol EtOH Production and excretion of acetaldehyde ACA Lactonitrile lacto formation . Since the cofactors participate in many of the reactions , our approach to not take them into account when calculating the EFMs is conceptually similar to classifying metabolites taking part in more than a threshold number of reactions as ‘external’ , as proposed in [51] . Both approaches result in the same significantly simpler and biologically interpretable set of EFMs . However , unlike the subnetworks identified in [48] , the EFMs are not necessarily redox neutral . We will briefly discuss the consequences of this at the end of this section . In Fig 10 we represent the common reactions of the five EFMs by a graph . Surprisingly , the graph is a binary tree , indicating that , while there are junctions in the reaction pathway of the metabolites , there are no joins . Furthermore , there exists one main branch , consisting of Reactions 1 − 11 and Reaction 18 , and each EFM can be represented by a unique junction from this main branch: EFM F1 is the junction after Reaction 3 , F2 after Reactions 6 and 7 , F3 after Reaction 11 , and F4 and F5 after Reaction 18 . This property provides a natural order for the EFMs , with F1 at the top of the cascade and F5 at the bottom . The ODEs describing the layers’ dynamics for this ordering of the functionalities , the dynamics of the control layer , the parameter values , and the initial conditions can be found in the Supporting Information . The layers and their interconnection can be represented as a species-reaction graph ( SR graph , see [18] ) depicting the reactions , the non-zero altered reactions , the species with non-zero dynamics in each layer , and the inter-layer communication ( Fig 10 ) . Recall from ( 7b ) that the vector of altered reactions for layer i is defined by v a l t ( x 1 + ⋯ + x i - 1 , x i ) ≔ v ( x 1 + ⋯ + x i ) - v ( x 1 + ⋯ + x i - 1 ) . In this example we identified reactions j where vj ( x1 + … + xi ) = vj ( x1 + … + xi−1 ) , meaning the rate of the reaction is not altered by the interconnection of functionality Fi . A sufficient condition [18] to conclude that altered reaction j does not change the dynamics of Layer i and thus can be omitted is if the jth element of I ( δ δ x v ) exp ( I ( ∑ k = 1 i - 1 S k ) I ( δ δ x v ) ) · I ( S i ) · 1 ( 13 ) is zero . Here , exp ( . ) represents the matrix exponent , Sk the stoichiometric matrix of layer k , I the element-wise indicator function being one if the respective element is non-zero and zero otherwise , and 1 the NR × 1 vector with all elements being one . Similarly , the integration of a species can be omitted in a layer if it is not affected by any of that layer’s reactions . Fig 10 shows that each layer is comparatively small and includes few altered reactions . Furthermore , since reactions R13 and R14 are linear ( in the metabolites ) , information about the metabolite concentrations involved in these reactions is not required to be transmitted between the layers ( for example , EtOH3 and EtOHX3 are not transmitted to Layer 4 and 5 ) . This demonstrates that , for many biological networks , layers and the interfaces between them are comparatively simple . We equilibrated the model for a mixed flow glucose concentration of 10 mM and initialised the dynamics of the initial condition layer L ( F0 ) = x0 to these steady-state values . This corresponds to low glucose conditions in the range of the Km values of high-affinity glucose transporters [52] , and slightly below the mixed flow glucose concentration for which the parameters of the model were identified ( 18 . 5 mM ) to prevent glycogenic oscillations ( see [48] ) . All other parameters and medium conditions were kept unchanged as compared to the version of the glycolysis model [48] available at the BioModels Database . We then calculated the layer dynamics for each ordering of the EFMs into all possible cascade structures by simulating the layering graph for 1000 min as described in Methods . This requires the simulation of 25 − 1 = 31 layers to populate the 5 ⋅ 5 ! = 600 nodes of the graph . Based on the layering graph we then analysed all pairwise interactions between Fi and Fj , given all possible combinations of other functionalities . In Fig 11 , we display the steady-state incompatibilities and cooperativities between the EFMs after convergence at the end of the simulation . Note that although the incompatibilities and cooperativities converge , this is not necessarily true for the corresponding layer and mutual dynamics , which diverge for some EFMs ( compare Fig 9 ) . Several pairs have an incompatibility close to one and a cooperativity close to minus one . This combination is typical if one of the functionalities ( given its environment ) is unstable but is stabilised by the other . For example , in the isolated layer L ( F3∣F0 ) , pyruvate increases to infinity since the PDC reaction ( Reaction 11 in [48] ) is saturated and becomes rate limiting . Integrating either F1 or F2 with F3 reduces the production rate of pyruvate and thus stabilises the network , since both L ( F1 , F3∣F0 ) and L ( F2 , F3∣F0 ) are stable . On the other hand , in the isolated layer L ( F5∣F0 ) both pyruvate and acetaldehyde ( ACA ) concentrations are unstable , the latter due to limited cyanide ( CNX ) availability . Integrating F1 or F2 with F5 only stabilises the pyruvate concentration , but not acetaldehyde . The prediction of infinite growth of species concentrations is unlikely to be observed experimentally . Instead , the instability might indicate that the conversion of pyruvate to acetaldehyde catalysed by the pyruvate decarboxylase ( pdc ) becomes a rate limiting step when synthetically increasing the flow through the respective EFM . Indeed , it was experimentally validated [53] that under certain conditions over-expression of pdc after reducing glycerol synthesis can lead to increased growth rates and ethanol yield . The incompatibilities between two EFMs , given the three other EFMs , are of specific interest ( rightmost ten interactions in Fig 11 ) . They indicate that F1 and F3 are significantly more incompatible than F2 and F3 . Recall that F3 corresponds to the fermentation capability of the network . To increase biofuel production one would intuitively expect that the highest yields could be obtained by knocking out or down the EFMs which are most incompatible to F3 , thereby removing EFMs which act to attenuate its function . Recall that the effect of knocking-out a certain functionality Fi on the overall dynamics of the network is equal to −L ( Fi∣Fj , j ≠ i ) . Thus , the effect of knocking out GPD ( Reaction 15 ) and , thus , glycerol production F2 is −L ( F2∣F0 , F1 , F3 , F4 , F5 ) . The effect of a double knock-out of glycerol production together with glycogen build-up F1 corresponds to −L ( F1 , F2∣F0 , F3 , F4 , F5 ) . The theoretical effect on fermentation efficiency of all possible combinations of knock-outs ( Fig 12 ) can thus be directly derived , given the layering graph . Indeed , this analysis confirms that fermentation F3 is significantly more incompatible with glycogen build-up F1 than with glycerol production F2 at the given experimental setup . The effect of knocking out more than one functionality is non-linear , and a double knock-out of the two functionalities yielding the highest gains when knocked out separately is not necessarily optimal , as shown in Fig 12 . Thus , the layering graph provides an effective tool to analyse such potential interactions . It is out of the scope of this article to take side effects of the proposed genetic modifications into account , such as possible viability issues when knocking out some of the pathways . Our intention was to provide a proof of principle of how to apply our layering framework in the context of metabolic engineering . Notably , our analysis is based on a model of a non-industrial strain of S . cerevisia cells grown under glucose starvation , and which was created mainly to explain glycogenic oscillations at low glucose conditions [48] rather than being optimised for metabolic engineering purposes . Furthermore , we essentially avoided the challenge of balancing the redox state ( NAD+/NADH ratio ) by our mixed layering approach ( Fig 10 ) in which the control layer was always receiving the cofactor consumption and production rates of all cascaded layers while simulating the layering graph . Nevertheless , our analysis indicates that ethanol yield can be increased by approximately 6% by knocking out glycerol formation ( F2 ) . This prediction is in good agreement with a reported [54] experimental increase of about 7% after inhibition of glycerol formation from dihydroxyacetone phosphate ( DHAP ) by knock-down of glycerol-3-phosphate dehydrogenase ( GPD ) . In [54] the redox state was balanced by synthetically engineering an alternative route in the endogeneous Embden–Meyerhof–Parnas pathway based on NADP+ and NADPH instead of NAD+ and NADH .
We have described a cascaded layering approach which can be used to systematically identify the interactions of functionalities ( i . e . functional subsystems ) in a decomposed network . Functionalities are defined as sets of reactions which together accomplish a given purpose . We have identified the dynamics of an isolated functionality as the dynamics of the subnetwork made up of only those reactions . However , the dynamics of a functionality are different when it is in the context of others . We have formulated the conditional dynamics of a functionality as the incremental change to the dynamics caused by integrating it with its context . We have also demonstrated that the computational burden associated with this layered framework can be minimised by exploiting symmetries within the definitions of interdependence . We have used the conditional dynamics to define the mutual dynamics , which describe the context-dependent interaction between any pair of functionalities . We can thus identify if the interaction between them is strong or weak ( incompatibility ) , and to what extent they amplify or attenuate each other ( cooperativity ) . This interdependence is also context-dependent , so that two functionalities may interact in vastly different ways depending on the wider system in which they are integrated . Our framework allows the unambiguous quantification of nonlinear interactions between functionalities . Finally , we illustrated our layering framework with three examples . The first considered signalling pathways interconnected by two distinct crosstalk mechanisms , a mutually inhibiting and a mutually excitatory crosstalk . We not only quantified how each mechanism separately influences the dynamics , but also how the crosstalk mechanisms interact with each other . Our second example concerned a pathway stabilised by two integral feedback mechanisms . For certain input signals , the pathway alone becomes unstable . It gets stabilised by the first integral feedback , but the second feedback further destabilises the system , as displayed by the negative and positive cooperativity respectively . Thus each feedback has an opposite effect on the pathway to the other when each is considered in isolation . However , when they are integrated together they interact cooperatively , due to the first controller increasing its strength to stabilise the network when the second controller is present . Third , we showed how to apply our layering framework in the context of metabolic engineering to analyse the dynamic interactions between elementary flux modes in glycolysis . Interestingly , the effect of abolishing an EFM by knocking out its enzymes can be easily described as the negative of the conditional dynamics of that EFM , given the rest of the network . The incompatibility between two EFMs , on the other hand , gives information about the expected increase in the flux through one EFM if the other one is knocked out . These interpretations allowed us to efficiently calculate the expected increase of ethanol yield in biofuel production when knocking out arbitrary combinations of EFMs . Interestingly , this yield is not additive , and a double knock-out of the two EFMs giving the highest increases in ethanol yield alone is not necessarily optimal . Instead , our method to calculate the integrated effect of all combinations of knock-outs allowed us to assess the best metabolic engineering strategy with a minimum amount of simulations required . As discussed in the Introduction , the layering framework [16 , 17] differs from modularization approaches [18–26] . Each of these frameworks allows different network architectures to be considered , reflecting a distinction between vertical and horizontal network decomposition [55] . Layered networks represent the overlay of multiple ( possibly competing ) functional subnetworks , while the modular framework interprets biological networks in terms of engineered interconnections of input–output systems . Although it is a modular phenomenon , previous work on quantifying retroactivity [4 , 5] can be related to our layered formulation . A goal of both techniques is to measure the difference in a given subsystem’s isolated and integrated behaviours . In particular , an upper bound on the Euclidean norm of the difference in a module’s isolated and integrated trajectories has been derived in terms of the system parameters in [5] . In the Supporting Information to this paper , we show in detail how this norm relates to our formulation of the incremental dynamics L ( F2∣F1 ) , and the mutual dynamics M ( F1 , F2 ) . Although our layering framework and the concept of retroactivity were developed to quantify the interactions between different kinds of subsystems , this comparison suggests that our additional measures of incompatibility and cooperativity as defining an interaction direction may also be applicable to further understand retroactivity in multi-dimensional modular systems . The computational method employed to quantify the interaction strength between a given functionality pair is clearly dependent on the kinetic constants and other parameter values in the model , including the initial conditions . A repetition of this computation across a range of such values may give a more informative picture of how uncertain biological functionalities interact , but will be difficult to visualise and expensive to evaluate . Possible directions for future work may include the derivation of analytical estimates in terms of the system’s parameter values for the mutual dynamics , incompatibility , or cooperativity , of the type produced for layered steady state perturbations in [16] or retroactive perturbations in [5] . It would also be interesting to consider if any estimates of our measures can be computationally derived to hold for all parameters , similarly to the structural results on the direction of steady state responses to parameter perturbations given in [34] . Alternatively , semi-definite programming may be used to calculate worst-case estimates of the difference between the trajectories L ( F1 ) and L ( F1∣F2 ) without resorting to simulation , similarly to previous work on model reduction error estimation [56] . An important assumption made in this paper was that the network functionalities Fi are given . The examples in this paper provide two different strategies for how the functionalities necessary for our layering framework can be defined . In the first two examples this was done by intuition and prior knowledge , while in the third example we applied an already available method to group functionally related reactions in metabolic pathways , namely EFMs . Clearly , this is not a comprehensive list of how functionalities can be defined , and ( depending on the precise application ) other strategies might be more promising . One such example may arise in the context of Synthetic Biology , from the specification of synthetic biomolecular devices such as toggle switches , oscillators , and so on , the dynamics of which are significantly affected when combined into large-scale systems [2] . Such an application of our framework to Synthetic Biology would require these biomolecular devices–typically designed and modelled as modules–to be mathematically expressed as layers , respectively functionalities . If this is possible without requiring modifications in the biomolecular implementation remains a question for future research . Our layering approach provides a general and concise framework for the quantification of the nonlinear interactions between functionalities of signalling and metabolic pathways . It offers a great flexibility since it only requires that each reaction must be part of a functionality , but allows reactions to be part of more or even all functionalities . Besides the mathematical definition of the model , the only input data needed is the problem-specific definition of the sets of reactions which make up a functionality . For many signalling pathways these definitions are typically given by biological insight , and for metabolic networks implementations of efficient algorithms to determine the sets of EFMs are available ( e . g . [50] ) . To allow a quick assessment of our approach , we have implemented a MATLAB ( The MathWorks , Natick , MA ) toolbox , available under the GNU General Public License from sysos . eng . ox . ac . uk/control/sysos/index . php/User:Prescott/Code . This toolbox provides algorithms to automatically construct the models of the layers from SBML files , to derive the layering graph with minimal numerical simulation , and to easily assess the mutual dynamics , the incompatibility and the cooperativity between functionalities . We hope that our implementation can serve as a starting point to integrate our layering framework into standard software solutions for the analysis of biomolecular networks . | To better understand the dynamic behaviour of cells and their interaction with the environment , mathematical models describing the interplay between proteins , metabolites or signalling molecules are used extensively in Systems Biology . Typically , such models focus on single functional subsystems and neglect the rest of the biochemical reaction network . However , the behaviour of multiple functional subsystems when integrated together can differ significantly from each subsystem’s isolated behaviour . In this article we describe a methodology for assessing the nonlinear effects of combining multiple functional subsystems of a biological system . This is key for answering questions related to Systems and Synthetic Biology as well as Metabolic Engineering . For example , if we can identify the isolated behaviours of two subsystems , we can determine if they persist when the subsystems interact . Similarly , we can show how modifications to single functional subsystems ( such as increasing particular metabolic yields ) have different effects in the context of the integrated system . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Quantification of Interactions between Dynamic Cellular Network Functionalities by Cascaded Layering |
Cellular adaptation relies on the development of proper regulatory schemes for accurate control of gene expression levels in response to environmental cues . Over- or under-expression can lead to diminished cell fitness due to increased costs or insufficient benefits . Positive autoregulation is a common regulatory scheme that controls protein expression levels and gives rise to essential features in diverse signaling systems , yet its roles in cell fitness are less understood . It remains largely unknown how much protein expression is ‘appropriate’ for optimal cell fitness under specific extracellular conditions and how the dynamic environment shapes the regulatory scheme to reach appropriate expression levels . Here , we investigate the correlation of cell fitness and output response with protein expression levels of the E . coli PhoB/PhoR two-component system ( TCS ) . In response to phosphate ( Pi ) -depletion , the PhoB/PhoR system activates genes involved in phosphorus assimilation as well as genes encoding themselves , similarly to many other positively autoregulated TCSs . We developed a bacteria competition assay in continuous cultures and discovered that different Pi conditions have conflicting requirements of protein expression levels for optimal cell fitness . Pi-replete conditions favored cells with low levels of PhoB/PhoR while Pi-deplete conditions selected for cells with high levels of PhoB/PhoR . These two levels matched PhoB/PhoR concentrations achieved via positive autoregulation in wild-type cells under Pi-replete and -deplete conditions , respectively . The fitness optimum correlates with the wild-type expression level , above which the phosphorylation output saturates , thus further increase in expression presumably provides no additional benefits . Laboratory evolution experiments further indicate that cells with non-ideal protein levels can evolve toward the optimal levels with diverse mutational strategies . Our results suggest that the natural protein expression levels and feedback regulatory schemes of TCSs are evolved to match the phosphorylation output of the system , which is determined by intrinsic activities of TCS proteins .
Cells constantly face challenges from a wide variety of environmental perturbations that require evolution of appropriate mechanisms for adaptive responses . Cellular adaptation is often through modulation of gene expression that benefits cells under specific conditions . However , expressing proteins using cellular resources carries a fitness cost . Hence , evolutionary adaptation relies on development of proper signaling and gene regulatory schemes to produce appropriate amounts of proteins under particular environmental conditions , balancing cost and benefit to maximize fitness . Bacteria use the two-component system ( TCS ) as one of the major signal transduction schemes to respond to environmental cues . A sensor histidine kinase ( HK ) , whose autokinase , phosphotransferase and/or phosphatase activities can be tuned by input signals , adjusts the phosphorylation level of its cognate response regulator ( RR ) , ultimately determining output responses , mostly via transcriptional regulation [1]–[3] . Naturally , not only the physico-chemical properties but also the quantities of TCS proteins can influence the output , and thus could be subject to evolutionary optimization . Adaptation to various environments requires appropriate expression levels of TCS-regulated genes as well as genes encoding TCS proteins themselves to provide fitness advantages . How different environments shape the fitness profile and select particular TCS quantities remains largely unknown . In many cases , the quantities of HK and RR are autoregulated . In E . coli , nearly half of the 30 RR transcription factors auto-activate expression of operons encoding themselves [4] . Genetic mechanisms and regulatory features of this feedback control have been explored [4]–[8] but the potential fitness benefit of TCS autoregulation in environmental adaptation are less examined experimentally . Positive feedback can lead to an ultra-sensitive switch-like response , an increase of regulatory capacity , a delay of response time , and promotion of a bistable system that can yield all-or-none output [4] , [5] , [9] . The ability to switch between two discrete “ON” and “OFF” states or to defer responses after multiple cell division cycles could be beneficial to some differentiation and developmental processes [10]–[12] . Elimination of the positive feedback has been shown to affect regulatory and temporal precision of these developmental processes [12] , [13] . However , a binary or greatly delayed response is not likely preferred by all signaling systems; rather , a continuous well-defined output in relation to input signals allows cells to make accurate and prompt adjustments . It has been suggested that most TCSs tend to be monostable and the TCS autoregulatory architecture is distinct from conventional positive feedback loops due to the negative phosphatase activities of bifunctional HKs [6] , [7] , [14] . Constitutively expressed TCSs often complement the loss of autoregulated systems without causing apparent differences in steady-state outputs under laboratory conditions [7] , [15]–[19] . This raises the question of what evolutionary advantages autoregulation brings over constitutive expression in these systems and demands a comprehensive mapping of cell fitness at different TCS expression levels to understand the evolution of TCS autoregulation . Fitness benefits of expressing TCS regulators rely upon the concerted transcription of ensemble of TCS-regulated output genes . The fitness landscape may well be correlated with TCS concentration-dependent output profiles . Intuitively , selection should produce TCSs that in environments of stimulation will have optimal concentrations of TCS proteins to elicit sufficient output to offset the cost of TCS expression . Autoregulation reflects the need for different optimal TCS levels in different environments . To examine the dependence of fitness on TCS expression levels and output responses , the autoregulated E . coli PhoB/PhoR system was used as a model system because the relation between its output phosphorylation profiles and protein levels has been well defined [19] . The PhoB/PhoR system activates genes responsible for assimilation of phosphorus in response to limitation of environmental phosphate ( Pi ) concentrations [20] , [21] . Phosphorylated RR , PhoB , also binds to the pho box at its own promoter and stimulates the expression of PhoB and PhoR ( Figure 1A ) . A ∼20 fold increase of PhoB concentration has been observed upon Pi-depletion . Replacing the autoregulatory phoB promoter with IPTG-inducible promoters allowed the characterization of phosphorylation profiles at different PhoB/PhoR levels [19] . Here we report comparison of output responses of individual cells for the autoregulated and constitutively expressed PhoB/PhoR system to examine whether bistability exists for the autoregulated WT system and how autoregulation contributes to cell fitness . The correlation of output to PhoB/PhoR levels prompted a thorough examination of cell fitness at different constitutive PhoB/PhoR levels in the absence and presence of stimuli . A competition assay in continuous cultures was developed for revealing the dependence of cell fitness on phosphorylation output and PhoB/PhoR expression levels . Under Pi-replete conditions high constitutive expression of PhoB/PhoR caused a decrease in fitness . In contrast , under Pi-deplete conditions , cell fitness peaked at an expression level close to the wild-type ( WT ) PhoB/PhoR concentration , at which the phosphorylation output starts to plateau . Further increase of expression apparently no longer offers sufficient advantages to overcome the cost of protein expression . To investigate whether bacteria can tune their expression levels to optimality through evolution , laboratory evolution experiments were performed for cells with unfavorable amounts of proteins under specific Pi conditions . These conditions led to evolution of diverse mutants that all shifted protein expression toward optimal levels to have greater fitness . Our results demonstrate that expression levels of TCS proteins are evolutionarily optimized and the autoregulatory scheme of the PhoB/PhoR system allows cells to adapt to both Pi- replete and -deplete conditions by expressing different optimal levels of TCS proteins to balance the cost and benefit .
To examine output responses of single cells , the gene encoding yellow fluorescent protein ( yfp ) was fused to the promoter of the PhoB-regulated gene phoA and placed into the chromosome . The resulting strain still carries the original copy of phoA encoding an alkaline phosphatase ( AP ) and showed identical AP response curves to Pi concentrations as the WT strain ( Figure S1A ) . YFP fluorescence followed a similar increasing trend as AP activities when the starting Pi concentration in the medium decreased ( Figure 1B ) . Once the phoBR operon was placed behind a non-autoregulatory lac promoter in the LAC strain , mean fluorescence output clearly depended on expression levels of PhoB and PhoR ( Figure 1B ) . In the absence of IPTG , PhoB concentration of the LAC strain is at a low level similar to the uninduced WT level observed under Pi-replete conditions ( Table S1 ) and fluorescence output is correspondingly limited . The presence of 150 µM IPTG resulted in a level of PhoB comparable to the autoregulated WT level under Pi-deplete conditions and the output response curves were also comparable . It appears that positive autoregulation allows auto-amplification of PhoB and PhoR levels , leading to amplification of output responses . Analyses of single-cell fluorescence indicated that all strains , constitutive or autoregulated , displayed a graded dependence of output on Pi concentrations ( Figure 1C and Figure S1 ) . Bistability , or bimodal distribution of responses , was not observed under experimental conditions . At the timescale of the experiments , no significant delay of response time was observed for the autoregulated PhoB/PhoR system because the level of phosphorylated PhoB ( PhoB∼P ) was shown to increase with comparable response time in either the autoregulated WT strain or the constitutive LAC strain [19] . The observed concentration-dependent output differences may have a direct consequence on cell fitness , which is often assessed by comparing cell growth rates under various conditions . Despite different constitutive levels of PhoB achieved in the LAC , TRC or KON strains whose phoBR promoter was replaced by IPTG-inducible ( LAC and TRC ) or constitutive ( KON ) promoters ( Figure 2A and Table S1 ) , cells displayed similar growth curves in MOPs medium , indistinguishable from the autoregulated WT strain ( Figure 2B ) . As reported previously [22] , [23] , Pi-depletion causes a halt of the logarithmic growth and cells enter into a Pi-limited stationary phase . There is minimal difference in optical densities within the Pi-limited growth phase for these strains . Constitutive expression of the PhoB/PhoR TCS proteins also did not result in significant growth defects for the logarithmic growth phase and all shared similar doubling times ( Figure 2C ) . Therefore , it appears that either the autoregulated WT strain does not confer fitness advantages over constitutive strains or the fitness costs of constitutively high expression are too small to be manifested under experimental conditions . Batch cultures in MOPs medium only allowed approximately 2 h of logarithmic growth before reaching the stationary phase , which may not be adequate to reveal any growth differences . To examine potential fitness costs and benefits , bacteria competition was assayed in continuous cultures to allow prolonged constant growth . Because bacteria growth in continuous cultures is significantly different from that in batch cultures and bacteria consumed different amount of Pi in batch and continuous cultures to reach different O . D . , AP activities were used to define the activating Pi-deplete and inactive Pi-replete conditions in continuous cultures ( Figure S2 ) . All strains were competed against a yfp-carrying WT strain , WT-yfp , which allows determination of the population distribution by cellular fluorescence . Starting from a 50∶50 mixture , the population of the non-fluorescent WT strain remained constant under both Pi-deplete and -replete conditions ( Figure 3A ) , suggesting an equal fitness for WT and WT-yfp under both experimental conditions . In contrast , the constitutive LAC strain that expresses PhoB at a low level displayed a fitness dependence on Pi concentrations . Under the Pi-replete condition , LAC is equally fit as WT-yfp , while a gradual decrease in population was observed under the Pi-deplete condition , presumably due to its limited output response . After 24 h , the population of the LAC strain decreased to less than 1% of the total bacteria population . Bacteria populations after 24 h of competition against WT-yfp were thus used to evaluate cell fitness at a range of PhoB expression levels ( Figure 3B , 3C and Table S1 ) . Strains with no or low PhoB expression , such as the phoB deletion or the WT strain , had high fitness under Pi-replete conditions . Increasing PhoB/PhoR concentrations reduced cell fitness ( Figure 3B ) . Because high levels of PhoB/PhoR can promote a modest increase of expression of PhoB-activated genes in the absence of stimuli [19] , fitness reduction can be attributed to cost of protein production as well as activities of proteins encoded by these PhoB-activated genes , which are tailored for Pi-depleted environments and detrimental under Pi-replete conditions [20] , [24] , [25] . However , the elevated basal activity of PhoB at high expression levels is dependent on the conserved D53 residue in PhoB [19] yet the D53A mutant showed similar fitness to the constitutive KON strain without the mutation , arguing against the protein activities as a main cost of fitness . When the system is stimulated under Pi-deplete conditions , cell fitness followed a different pattern of dependence on TCS expression levels ( Figure 3C ) . Cells with no or low PhoB expression were unable to compete with WT-yfp that expressed PhoB at a high level through autoregulation . For the same LAC strain , increasing IPTG concentrations raised PhoB levels as well as the output responses . Correspondingly , the fitness of bacteria increased until the PhoB concentration became comparable to the WT level . Further increase of PhoB levels eventually led to diminishing fitness of cells . As discovered previously [19] , the output of the system , indicated by the concentration of phosphorylated PhoB ( PhoB∼P ) shown on the right axis of Figure 3C , increased along with PhoB/PhoR levels and started to saturate around the WT concentration of PhoB ( solid line , Figure 3C ) . Thereafter , increase of PhoB concentration does not enhance the beneficial output further but still carries great costs of protein production under Pi-depleted conditions , reducing the overall cell fitness . It appears that the WT level of PhoB has been optimized to provide close to maximal fitness under Pi-deplete conditions . The optimal PhoB level of WT is likely a result of adaptive evolution to balance costs and benefits . For a given E . coli strain with non-optimal expression of PhoB and reduced fitness , such as the LAC strain in the absence of IPTG ( LAC0 ) , do bacteria actually evolve toward the optimal expression level ? We tested this by following laboratory evolution of bacteria in Pi-limited continuous cultures . At the start of the continuous culture , the LAC strain in the absence of IPTG displayed an extremely low level of output in AP activity as well as minimal PhoB levels ( Figure 4 ) . Only after 48 h of growth , approximately ∼18 generations , AP activity rose to a level comparable to that of the WT strain and the PhoB expression level reached the optimal level observed for the WT strain . This adaptation is clearly dependent on stimuli because PhoB concentration and AP activity remained constantly low when Pi was replete . On the other hand , when 150 µM IPTG is present in continuous cultures ( LAC150 ) , LAC displayed an optimal PhoB level , high output and high fitness that are all comparable to WT , thus no further increase of PhoB level or AP activity were observed . To investigate mechanisms behind the observed adaptation in the LAC0 continuous culture , individual colonies were isolated to compare their PhoB expression levels and output responses to the original LAC strain in batch cultures ( Figure 5 ) . Not surprisingly , as bacteria in LAC150 cultures can manage adequate output responses with sufficient levels of PhoB , colonies isolated from LAC150 cultures showed similar outputs in AP activity to the original LAC strain , implying the absence of mutations in the PhoB/PhoR pathway . In contrast , all six colonies isolated from LAC0 cultures showed elevated AP activities even in the absence of IPTG while the low PhoB concentration of the original LAC strain under the same condition only gave limited output ( Figure 5A ) . Increased AP activities are not the result of stimuli-independent constitutive activation of phoA expression , and all colonies maintained low AP activities under Pi-replete conditions . Among six LAC0 colonies , colony 4 and 6 showed slightly lower AP activities while other colonies had high AP activities , similar to WT , even in the absence of IPTG . Correspondingly , colonies 4 and 6 showed sub-optimal levels of PhoB while other LAC0 colonies expressed phoB at the optimal WT level in the absence of IPTG ( Figure 5B ) . The lac promoters before phoB in colonies 1 , 2 , 3 and 5 appeared to be no longer regulated by IPTG and the regulation in colonies 4 and 6 was weakened with a high basal expression ( Figure 5C ) . The adaptation observed in LAC0 cultures occurred via altering promoter regulation that shifted the PhoB concentration toward the optimal level . The lac promoter is repressed by LacI through binding to lacO regions of the promoter . Therefore the lac promoter , lacI and phoB were sequenced to map the adaptive mutations . Consistent with the fact that PhoB expression and AP activity profiles of LAC150 colonies were similar to those of the original LAC strain , no mutations were found in these regions for colonies of the LAC150 culture . In contrast , colonies 4 and 6 isolated from the evolved LAC0 culture carry a G to A mutation in the lacO1 region at the promoter of phoBR while lacI appeared to be missing in other LAC0 colonies ( Figure 5D ) . No mutations were identified in the coding sequences of phoB for any colonies . Re-introduction of the same G to A mutation into the lac promoter ( LAC* IV ) resulted in identical AP activity profiles and PhoB expression levels observed for LAC0 colonies 4 and 6 while deletion of lacI ( lacI− ) in the LAC strain gave the same phenotype as other LAC0 colonies ( Figure 5A and 5C ) . Further , introduction of an additional plasmid-encoded lacIq into LAC0 colonies re-established repression of the lac promoter and suppressed expression of phoB ( Figure 5E ) . Combined , the above analyses indicate that LAC0 cells evolved to express phoB at or close to the optimal level by mutating different promoter regulatory elements that allowed them to generate sufficient output responses to survive under Pi-deplete conditions . In LAC0 cultures , PhoB concentration is extremely limited at the initial stage and cells faced a great challenge of Pi-depletion that drove the evolution . An intermediate concentration ( 40 µM ) of IPTG in continuous cultures induced the PhoB concentration to a moderate and sub-optimal level in LAC40 cultures , yet a Pi-deplete environment still prompted evolution of LAC cells , although at a slower pace ( Figure 6 ) . Increase of PhoB levels was observed after 48 h of continuous growth in LAC40 cultures while only 24 h were required for the LAC0 culture to show the first sign of adaptation ( Figure 6A ) . Correspondingly , for individual colonies isolated from 24 h growth samples , all LAC40 colonies displayed a low PhoB level in the absence of IPTG similar to that of the original LAC strain while one adaptive mutant with high PhoB expression started to emerge in LAC0 cultures ( Figure 6C and S3 ) . At 48 h , the majority of LAC0 colonies had already adapted with high levels of PhoB comparable to the optimal WT level . At the same time , adaptation was observed in less than half of the LAC40 colonies . Further growth until 86 h increased the population of adapted cells in LAC40 cultures . Interestingly , most of the adapted LAC40 colonies shared a similar phenotype with intermediate uninduced PhoB levels , different from the high optimal PhoB level seen in adapted LAC0 cells . Sequencing results revealed that all colonies with high PhoB levels lost functional LacI through deletion , frame-shifting insertion or early termination of lacI . In contrast , most of the LAC40 colonies with intermediate PhoB levels had an unaltered lacI but carried diverse mutations in the LacI repressing site lacO1 ( Figure 6D ) . Such mutations relaxed LacI repression and 40 µM IPTG was sufficient to induce the PhoB level to the optimal WT level in these mutants ( Figure 6E ) . Despite different initial conditions and diverse genotypes in adapted cells , evolution in LAC0 and LAC40 cultures both led to the optimal PhoB concentration that confers maximal fitness to cells in Pi-deplete environments . As discussed above , a high concentration of PhoB does not provide benefits under Pi-replete conditions and the cost of protein production reduced cell fitness . Therefore , a laboratory evolution experiment similar to LAC0 and LAC40 continuous cultures was performed to examine whether a strain with a high PhoB level would adapt to Pi-replete environments by reducing PhoB expression , thus increasing its fitness . An IPTG concentration of 15 µM induced a high level of PhoB in the TRC strain and continuous growth in the Pi-replete culture resulted in a gradual decrease of PhoB levels ( Figure 7A ) . Analyses of individual colonies revealed that adapted cells with reduced or abolished PhoB expression already emerged after 48 h of growth ( Figure 7B ) . After 86 h , approximately 30 generations , the majority of colonies isolated from the culture no longer expressed significant amounts of PhoB . Again , different mutational strategies were discovered among these adapted cells that yielded similar phenotypes with reduced PhoB expression ( Figure 7C ) .
Production of unneeded proteins often carries a fitness cost [32] . One of the most extensively studied examples is the E . coli lac operon that encodes genes for utilization of lactose . In the absence of lactose , gratuitous induction of the lac operon reduces the growth rate and this reduction reflects the deleterious activity of the LacY permease as well as the cost of producing unneeded proteins with cellular resources [27] , [28] , [30] . Similarly , the fitness cost of high TCS expression can arise from protein production cost and potential detrimental protein activities of RR-regulated genes . It has been shown that constitutive activation of PhoB-regulated genes , particularly the pst operon encoding a Pi-transport system , hampers growth under Pi-replete conditions [24] , [25] . However , high levels of PhoB/PhoR do not cause significant phosphorylation of PhoB in the absence of stimuli and only a minor basal activation of PhoB-regulated genes were observed at high PhoB levels [19] . Mutation of the conserved D53 residue almost completely abolished the basal activation [19] yet high expression of the mutant gave similar fitness reduction as high expression of intact PhoB/PhoR proteins . Thus detrimental activities of PhoB-regulated genes do not appear to be a major contributor to fitness reduction under the tested Pi-replete conditions although it cannot be excluded that some fitness cost may still originate from residual phosphorylation-independent basal activation of PhoB-regulated genes . Under Pi-replete conditions , WT E . coli cells produce approximately 0 . 13 pmol of PhoB per 0 . 3 OD*ml of cells ( Table S1 ) , corresponding to a concentration of ∼0 . 5 µM or 200–300 molecules per cell . Apparently , maintaining such a low level of this 26-kDa protein and an even lower level of PhoR does not have much fitness cost . Increasing expression 50–100 fold in the constitutive TRC strain results in a PhoB concentration of only 25–50 µM . It has been reported that full induction of the lac operon yields ∼50 µM LacZ molecules ( 116 kDa ) [33] and causes a 4 . 5% reduction in batch culture growth rates [27] . Therefore , it is not surprising that a fitness cost of PhoB overexpression was not revealed above the observed 5% data variance of growth rates in batch cultures . In contrast , a gradual fitness reduction caused by PhoB production was apparent in continuous cultures . Because production of useless proteins is an inefficient use of limited resources in nitrogen-limited chemostat cultures , fitness differences likely were magnified . Cost of protein production is clearly dependent on growth conditions and cells tend to reduce the cost with various mechanisms [32] , [34] , including complete abolishment of expression as observed in our adaptation experiments . Pi-depletion leads to an ∼20 fold increase of PhoB level in WT cells . The cost of producing ∼6000 PhoB molecules per cell is compensated by the beneficial function of PhoB/PhoR proteins to provide a close-to-peak fitness . Fitness benefits arise from PhoB∼P-dependent expression of regulated genes , such as the pst operon encoding the Pi transporter system and the outermembrane porin gene phoE . Although the exact fitness contribution of individual PhoB-regulated genes is difficult to track , the overall fitness landscape correlates well with the output PhoB phosphorylation profile . Peak fitness occurs at a PhoB level close to where PhoB∼P starts to saturate , a point determined by the specific balance of PhoR kinase and phosphatase activities [19] . The constitutive strain in laboratory evolution experiments and the autoregulated WT strain all evolved to express PhoB close to this optimal level for maximal fitness . Above this level , high PhoB levels presumably increase cost without providing further benefits because of the saturation of phosphorylation . RR phosphorylation saturation is not unique to the PhoB/PhoR system but rather a result of the intrinsic HK-RR phosphorylation cycle determined by TCS protein activities [7] , [19] , [35] , [36] . A similar output saturation profile has also been revealed for the autoregulated E . coli PhoQ/PhoP system and the induced WT PhoP level is again close to the beginning of saturation [7] . It remains to be investigated whether the expression levels of PhoQ/PhoP and other TCSs are similarly optimized to the phosphorylation output profile . The laboratory evolution experiments indicated that bacteria with non-ideal TCS expression could increase their fitness through mutations that produced optimal levels of TCS proteins for the specific environment . The majority of mutations were at the promoter of phoBR or the regulatory gene of the promoter , suggesting the expression of TCS as a convenient and efficient evolutionary target for environmental adaptation . As the lac promoter of phoBR in the LAC strain is under negative regulation by LacI , any loss-of-function mutants of lacI can result in higher expression of phoBR and this may contribute to the rapid evolution pace observed in continuous cultures . For bacteria with different initial sub-optimal fitness , such as the LAC0 and LAC40 cultures , adaptation occurred at different rates yet produced similar optimal PhoB expression levels with distinct genotypes . In the absence of IPTG , null lacI results in an unrepressed optimal PhoB level that gives adapted LAC cells higher fitness than lacO mutants whose PhoB levels are below the optimal level , thus lacI null mutants predominate in LAC0 cultures . In continuous cultures with 40 µM IPTG , both lacI deletion and lacO mutations were able to give optimal expression of TCS proteins . All isolated lacO mutants carry G:C-to-T:A substitutions and constitute a majority of evolved cells in LAC40 cultures . The dominance of lacO mutants may reflect a higher mutation frequency for single nucleotide substitution than for frame-shifting insertion or gene deletion observed in lacI null mutants . Indeed , nutrient-limited continuous cultures have been known to cause mutations in a mismatch repair gene mutY , which greatly increases the frequency of G:C-to-T:A transversions [37] . Despite diverse types of mutations observed in the evolved population , adapted bacteria cells all converged to similar phenotypes in TCS expression to match the demand of the environment . Responsive gene regulation is generally a favored mechanism for adaptation to variable environments with conflicting demands of protein expression . In natural habitats where E . coli cells face Pi-rich conditions , such as intestinal lumen , and Pi-limited conditions , such as aquatic environments , Pi-responsive autoregulation of the PhoB/PhoR regulators appears to be an evolutionary consequence that achieves optimal PhoB/PhoR levels under both environments . Under constant environments in continuous cultures , the autoregulated WT strain does not confer apparent fitness advantages over the constitutive strains as long as phoB expression matches the WT concentration . However , fixed constitutive expression can only achieve optimal fitness in one particular environment whereas autoregulation gives cells maximal fitness in variable habitats . Positive autoregulation allows elevated output responses and higher fitness benefits under stimulated conditions . It is this output-amplifying feature of positive autoregulation that enables cells to reduce protein production cost in the absence of stimuli without sacrificing the capacity of output responses . Other features commonly associated with positive autoregulation , such as bistability and response delay , were not observed for the WT PhoB/PhoR system . For strains with non-native genetic background , all-or-none responses have been documented when PhoR is absent and PhoB is cross-phosphorylated by overexpressed non-cognate HKs [38] . However , such cross-phosphorylation is suppressed by the phosphatase activity of the cognate HK and may not be physiologically relevant [19] , [39] . Phosphatase-defective or monofunctional HKs are known to promote bistability [7] , [40] , thus bimodal responses in these non-native cross-talk systems may arise from the lack of negative phosphatase activity in non-cognate HKs [41] . Positive autoregulation has been shown advantageous for the Salmonella PhoQ/PhoP system to promote virulence in mice [42] . The fitness gain over the constitutive strain was suggested to be associated with a transient surge in RR phosphorylation observed for the autoregulated strain even though the activation surge was later attributed to intrinsic negative feedback of biochemical activities in bifunctional HKs [8] , [43] . It is possible that part of the fitness advantages for the autoregulated PhoQ/PhoP system may come from lowered costs in unstimulated environments , similarly to fitness profiles observed for the PhoB/PhoR system . Balancing costs and benefits by positive autoregulation may be a recurring scheme in TCSs to select proper TCS protein quantities and biochemical activities .
The strains and plasmids used in this study are listed in Table S2 . λ red recombination [44] was used to make chromosomal gene disruption or alteration in strain BW25113 or derivatives of BW25113 similarly to the constitutive strains , RU1616 ( LAC ) , RU1617 ( KON ) and RU1618 ( TRC ) , in which the WT autoregulated phoB promoter was replaced with constitutive ( KON ) or IPTG-inducible ( LAC and TRC ) promoters [19] . Strains with the chromosomal reporter phoA-yfp or YFP marker were created using the reported recombination strategies [45] . Briefly , the plasmid pRG261 containing PphoA-yfp reporter or pRG278 containing Ptet-yfp was integrated into the chromosome of indicated strains at the HK022 or lamda phage attachment sites to generate RU1465 ( WT , phoA-yfp ) , RU1653 ( LAC , phoA-yfp ) and RU1622 ( WT-yfp ) , respectively . Details of strain and plasmid construction were described in Text S1 . Bacteria batch cultures were grown in MOPs minimal media [46] with 0 . 4% glucose , containing either 2 mM ( Pi-replete ) or 50 µM ( Pi-deplete ) KH2PO4 . Continuous cultures were grown at 37°C in a home-built chemostat modified from the design described in [47] . Fresh feed medium was supplied to a 50-ml glass vessel through silicone tubing with a peristaltic pump at a flow rate of 6 ml/h while bacteria culture flowed out through an outflow tube at the same rate . The total culture volume is 24 ml , set by the depth of the outflow tube in the chemostat vessel . Thus the dilution rate was 0 . 25 h−1 , corresponding to a generation time of ∼2 . 8 h . The feed medium was identical to the MOPs medium used in batch cultures except that the concentration of NH4Cl was limited at 250 µM instead of 5 mM in batch cultures . Optical densities ( 600 nm ) of the nitrogen-limited chemostat were ∼0 . 09 , corresponding to ∼2 . 2×109 cells in the chemostat culture . Pi concentrations of 300 µM and 12 µM were included in feed media for Pi-replete and -deplete conditions , respectively . As described previously [19] , to assay bacterial responses to phosphate concentrations , cells from fresh Pi-replete MOPs cultures were inoculated in MOPs medium containing 2 mM ( Pi-replete ) , 50 µM ( Pi-deplete ) or indicated concentrations of KH2PO4 with a starting OD600 at 0 . 04 followed by 3 h growth . For YFP reporter assays , fluorescence of individual cells was measured with a Beckman Coulter FC500 flow cytometer . Mean fluorescence of the whole population was calculated from the fitted lognormal distribution . For AP activities and protein level determination , bacteria pellets equivalent to 0 . 3 OD600*ml from batch or continuous cultures were collected and assayed as described before [19] . AP activities were determined by monitoring the rate of absorbance change at 420 nm using a microplate reader ( Varioskan , ThermoFisher ) following addition of 7 mM p-nitrophenylphosphate . Values of absorbance changing rates were multiplied by 100000 to represent absolute AP activities while they are compared to AP activity of the WT strain for relative AP activities . PhoB levels from sample lysates were determined by western blot . Blots probed with anti-PhoB primary antisera and Cy5-conjugated secondary antibodies were visualized by fluorescence imaging with a FluorChem Q ( Alpha Innotech ) . Selected blots were simultaneously probed with the anti-Sigma70 and Cy3-conjugated secondary antibodies to confirm equal loading of samples . Cell fitness was evaluated with growth rates in batch cultures and competition assays in continuous cultures . Indicated strains were induced with indicated IPTG concentrations in MOPs media for 2–3 h to achieve different PhoB/PhoR levels . Cells from these fresh MOPs cultures were inoculated in 96-well plates and grown at 37°C with a starting OD600 of ∼0 . 03 under respective IPTG conditions . The exponential growth rates before Pi-depletion were determined by fitting the data with a single exponential function . Competition assays were performed in continuous cultures with the indicated non-fluorescent strains and WT-yfp . Cells from fresh MOPs cultures were mixed at a 1∶1 ratio and inoculated into the chemostat with a starting OD600 of ∼0 . 04 . Indicated IPTG concentrations were included in the feed medium to achieve different PhoB levels . After 24 h of growth , cells collected from the outlet stream were analyzed by microscope imaging or flow cytometry to determine the population of individual strains . Bacteria cells were immobilized on 1% agarose pads made with MOPs medium as described [48] . Microscope images were obtained using an Olympus IX70 microscope ( Olympus ) with a 100× NA PlanApo 1 . 3 objective , 100 W mercury lamp and the HiQ fluorescein filter set ( Chroma ) . Phase-contrast images were taken to determine the total cell numbers and fluorescent images were used to count the population of fluorescent WT-yfp cells . Cell numbers were counted using ImageJ software ( NIH ) and bacteria population was determined from at least 1000 cells from a total of 6–10 images . In selected samples , cell populations measured by microscope imaging were confirmed by flow cytometry . Indicated bacteria strains were grown in continuous cultures for ∼4 days . Pi concentrations of 300 µM ( Pi-replete ) and 12 µM ( Pi-deplete ) were included in feed media together with indicated concentrations of IPTG to yield non-optimal PhoB expression levels under different Pi conditions . Cells were collected from the outlet stream at indicated time intervals . Collected cultures were pelleted and 0 . 3 OD600*ml of cells were stored at −80°C for later examination of AP activities and PhoB levels . Diluted cultures from the chemostat were streaked on LB plates for colony isolation . Single colonies were randomly chosen to grow in MOPS batch cultures and characterized for their phenotypes in PhoB expression and AP activities . To investigate the genotype of individual clones , colony PCR was performed to amplify the chromosomal DNA regions corresponding to the lacI gene , phoB and its promoter . Sequences of these regions were determined and compared to the WT sequence . For some colonies , primers specific to lacI yielded no PCR products and further use of additional primers corresponding to different coding , upstream and downstream regions of lacI still did not give PCR products , suggesting the loss of lacI . | Different proteins are expressed at different levels that may have evolved for optimal fitness under specific environmental conditions . Additionally , cells regulate protein expression levels in response to environmental changes . For signaling proteins whose benefits are not immediately proportional to their expression levels , it is less understood whether expression levels are optimized and how protein expression levels correlate with the output responses they regulate . We developed a continuous culture competition assay to examine cell fitness at different expression levels of the E . coli PhoB/PhoR system . The PhoB/PhoR system , which induces expression of genes for phosphate assimilation under phosphate ( Pi ) -limited conditions , represents an archetype of the widely distributed prokaryotic two-component signal transduction scheme . Wild-type cells express different levels of PhoB/PhoR under different Pi conditions; these levels provide near maximal fitness under respective conditions . Under Pi-deplete conditions where PhoB-regulated gene expression is important for survival , the optimal expression level appears to correlate with the phosphorylation output of the PhoB/PhoR system . Challenging cells with expression of unfavorable levels of proteins led to diverse mutations that all shifted protein expression towards optimal levels . Our results indicate that the autoregulatory scheme and expression levels of the PhoB/PhoR system are evolved to provide optimal fitness . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Evolutionary Tuning of Protein Expression Levels of a Positively Autoregulated Two-Component System |
Mitochondrial dynamics is an essential physiological process controlling mitochondrial content mixing and mobility to ensure proper function and localization of mitochondria at intracellular sites of high-energy demand . Intriguingly , for yet unknown reasons , severe impairment of mitochondrial fusion drastically affects mtDNA copy number . To decipher the link between mitochondrial dynamics and mtDNA maintenance , we studied mouse embryonic fibroblasts ( MEFs ) and mouse cardiomyocytes with disruption of mitochondrial fusion . Super-resolution microscopy revealed that loss of outer mitochondrial membrane ( OMM ) fusion , but not inner mitochondrial membrane ( IMM ) fusion , leads to nucleoid clustering . Remarkably , fluorescence in situ hybridization ( FISH ) , bromouridine labeling in MEFs and assessment of mitochondrial transcription in tissue homogenates revealed that abolished OMM fusion does not affect transcription . Furthermore , the profound mtDNA depletion in mouse hearts lacking OMM fusion is not caused by defective integrity or increased mutagenesis of mtDNA , but instead we show that mitochondrial fusion is necessary to maintain the stoichiometry of the protein components of the mtDNA replisome . OMM fusion is necessary for proliferating MEFs to recover from mtDNA depletion and for the marked increase of mtDNA copy number during postnatal heart development . Our findings thus link OMM fusion to replication and distribution of mtDNA .
Mammalian mitochondria are dynamic organelles present as long interconnected tubules or individual units that may undergo intracellular transport [1 , 2] . A family of dynamin-related GTPases regulate mitochondrial morphology through fission and fusion of the mitochondrial membranes [3–6] . The dynamin-related protein 1 ( DRP1 ) mediates division of mitochondria , mitofusin 1 and 2 ( MFN1 and MFN2 ) control outer mitochondrial membrane fusion , whereas optic atrophy 1 ( OPA1 ) control inner mitochondrial membrane fusion . The above mentioned proteins are all essential for embryonic development [2 , 7 , 8] , and mutated forms are known to cause human disease with phenotypes such as encephalopathy [9] , peripheral neuropathy [10 , 11] , and optic atrophy [12 , 13] . Mitochondrial dynamics is important for distribution and maintenance of mtDNA . Single mtDNA molecules are packaged by the DNA-binding mitochondrial transcription factor A ( TFAM ) into mitochondrial nucleoids that are evenly distributed throughout the mitochondrial network [14–16] . Previous research has reported that absence of mitochondrial fission results in an elongated mitochondrial network with bulbs harboring aggregates of mitochondrial nucleoids [17 , 18] . Additionally , inter-organellar contacts between the endoplasmic reticulum ( ER ) and the OMM have been proposed to facilitate the initial steps of mitochondrial division [19] , and to be critical for mtDNA segregation after replication [20] . Thus , proteins acting on the OMM to promote mitochondrial division , possibly in coordination with ER contact sites , help distribute mitochondrial nucleoids . However , little is known about the role of mitochondrial fusion proteins in the distribution of nucleoids , in particular concerning mitofusins , which are also implicated in OMM-ER tethering . Maintenance of mtDNA is absolutely dependent on mitochondrial fusion in budding yeast , whereas loss of fusion in mammals leads to depletion but not total loss of mtDNA [21–23] . The role for mitochondrial fusion in mtDNA maintenance is nicely illustrated by the double Mfn1 and Mfn2 heart conditional knockout animals ( dMfn KO ) , where a strong reduction in mtDNA copy number impairs oxidative phosphorylation ( OXPHOS ) and cardiac function [23–25] . In skeletal muscle , the mtDNA depletion caused by absence of OMM fusion has been attributed to instability and increased mutagenesis of mtDNA [23] . Moreover , loss of IMM fusion through inducible OPA1 ablation in adult hearts leads to severe mtDNA depletion [26] . However , loss of mitochondrial fission induced by conditional knockout of DRP1 or reduced mitochondrial fusion due to conditional knockout of either MFN1 or MFN2 did not affect mtDNA copy number in cardiac tissue [23 , 27–29] . Fusion of the OMM and IMM , which serves to facilitate membrane and/or matrix content mixing , is thus critical for mtDNA copy number maintenance . In this study , we generated dMfn KO conditional heart knockout mice and also characterized fusion-deficient mouse embryonic fibroblasts to decipher how nucleoid distribution and mtDNA maintenance are linked to mitochondrial fusion . Using sequencing of DNA , we found no increase in mtDNA rearrangements or point mutations in dMfn heart KO animals . Therefore , the low levels of mtDNA observed in the absence of OMM fusion are unlikely to be explained by increased mtDNA mutagenesis leading to instability . Additionally , we reveal that loss of OMM fusion leads to clustering of mitochondrial nucleoids , but this does not impair mitochondrial transcription . Notably , loss of OMM or IMM fusion alters the replisome protein composition , which impairs mtDNA replication activity . Altogether , we found that mitochondrial fusion is necessary to sustain high rates of mtDNA replication , but it is dispensable for maintaining integrity and transcription of mtDNA .
To study the link between mitochondrial fusion and mtDNA maintenance , we generated heart dMfn KO mice , which were born at the expected Mendelian ratios ( S1A Fig ) and showed loss of transcripts encoding MFN1 and MFN2 in heart tissue ( S1B Fig ) . Most heart dMfn KO animals survived until the postnatal age of 5 weeks and displayed a significant increase in the heart weight to body weight ratio ( Fig 1A ) . Transmission electron microscopy analysis of heart tissue revealed that dMfn KO mice exhibited a significant increase in ratio of mitochondrial/cytoplasmic area associated with aberrant mitochondrial ultrastructure and disruption of the myofibril organization ( Fig 1B and 1C ) . In line with previous fusion-deficient models , qPCR and Southern blot analysis revealed a marked reduction of the mtDNA copy number in heart tissue from dMfn KO animals ( Fig 1D and 1E ) and in fusion-deficient dMfn KO and Opa1 KO mouse embryonic fibroblasts ( Fig 1F ) . High-resolution respirometry showed a significant decrease of respiration under phosphorylating and uncoupled conditions in dMfn KO heart mitochondria ( Fig 1G ) and in OMM and IMM fusion-deficient MEFs ( S1C Fig ) , consistent with the observed mtDNA depletion ( Fig 1D–1F ) . This respiratory defect correlated with a reduction in OXPHOS proteins in dMfn KO heart mitochondria ( Fig 1H ) , which rely on mtDNA expression for their stability [30] , and caused growth impairment of fusion-deficient MEFs ( S1D Fig ) . It has been shown that the absence of MFN1 and MFN2 in skeletal muscle severely reduces mtDNA copy number and leads to mtDNA integrity defects [23] . This observation has led to the prevailing theory stating that mitochondrial fusion safeguards mtDNA integrity and prevents mtDNA mutations [23 , 31] . To examine the integrity of mtDNA in heart tissue in the absence of OMM fusion , we performed paired-end Illumina sequencing to detect unique breakpoints consistent with rearrangements of mtDNA . Similar frequencies of mtDNA rearrangements were found in both negative control samples and dMfn KO hearts ( Fig 2A and 2B ) . The positive control sample , which is derived from muscle tissue of a mouse that harbors a mutation in the DNA helicase Twinkle ( A360T ) , which is known to cause mtDNA rearrangements in patients [32] , contained abundant breakpoints consistent with mtDNA rearrangements ( Fig 2C ) . Although this model has been described as a deletor mouse [32] , the pattern of rearrangements we observed ( Fig 2C ) is consistent with abundant mtDNA duplications . Moreover , we found no difference in levels of mtDNA point mutations in heart tissue between control and dMfn KO animals by using post-PCR cloning and sequencing ( Fig 2D ) . These results demonstrate that loss of OMM fusion in the heart reduces mtDNA copy number without influencing mtDNA integrity or increasing point mutations . To investigate if the distribution of nucleoids throughout the mitochondrial network depends on mitochondrial fusion , we performed immunofluorescence staining against DNA and mitochondria in various fusion-deficient ( Mfn1 KO , Mfn2 KO , dMfn KO , and Opa1 KO ) MEFs . Control MEFs immunostained with anti-DNA and anti-TOM20 antibodies , and imaged by confocal microscopy , showed abundant evenly distributed mitochondrial nucleoids throughout the mitochondrial network ( Fig 3A ) . In contrast , dMfn KO MEFs displayed reduced abundance of nucleoids that often were present as enlarged foci in a subset of the enlarged-fragmented mitochondria ( Fig 3A , white arrowheads; S2A and S2B Fig ) . The Gaussian distribution of confocal-determined nucleoid diameters revealed a high proportion of enlarged nucleoids in dMfn KO MEFs ( Fig 3B ) , with ~40% of nucleoids having diameters ≥300 nm ( Fig 3C ) . The abnormal size of nucleoids prompted us to further examine nucleoid morphology and diameter with stimulated emission depletion ( STED ) super-resolution microscopy [15] . In dMfn KO MEFs , the enlarged nucleoids observed by confocal microscopy were resolved into multiple nucleoids in very close proximity by STED microscopy ( Fig 3D , white arrowheads ) . This finding was obtained by using either PicoGreen or anti-DNA antibody staining to determine nucleoid morphology ( Fig 3D and S2C Fig ) . Control MEFs and fusion knockout MEFs of different genotypes all exhibited nucleoid diameters of about 100 nm when analyzed by STED microscopy ( Fig 3E and 3F , and S2D and S2E Fig ) , in agreement with published reports [33 , 34] . These observations show that loss of OMM fusion in MEFs does not alter nucleoid size but instead causes nucleoids to cluster . We also visualized nucleoids by confocal microscopy using antibodies against TFAM , which is the core protein packaging mtDNA into nucleoids , together with antibodies against DNA . As expected , we observed that TFAM is present in every nucleoid in both control and dMfn KO MEFs ( Fig 3G ) . The morphology of nucleoids was similar when visualized with TFAM or DNA antibodies . Notably , the stoichiometry between TFAM and mtDNA was maintained among the nucleoid population , as clustered nucleoids exhibit more TFAM reactivity than single nucleoids . This is in line with our finding that the enlarged nucleoids are composed of multiple nucleoids , and thus exhibit greater TFAM reactivity . This observation suggests that insufficient compaction by TFAM cannot account for the enlarged nucleoids observed by confocal microscopy . To rule out the possibility that the observed nucleoid clustering phenotype could be restricted to cultured MEFs , we investigated nucleoid appearance in vivo . The mitochondrial network and nucleoids were immunostained in heart tissue sections from control and conditional dMfn KO animals . In line with the results from MEFs , confocal imaging of dMfn KO heart sections confirmed the presence of apparently large nucleoids that were resolved into multiple nucleoids by STED microscopy ( Fig 4A ) . Furthermore , the different topological conformations of mtDNA in heart tissue were examined and we found no difference in the amount of mtDNA catenanes , which are physically interlocked molecules ( Fig 4B ) . Therefore , changes in mtDNA topology could not explain the clustering of nucleoids in dMfn KOs . Altogether , our analyses reveal that OMM fusion does not influence nucleoid size , but it is required for proper nucleoid distribution in cultured cells and in vivo . To determine how mitochondrial fusion impacts mtDNA copy number , we first evaluated mitochondrial transcription as the mitochondrial RNA polymerase ( POLRMT ) provides the RNA primers required for initiation of mtDNA replication [35] . There was a general reduction of steady-state levels of mitochondrial transcripts in hearts of conditional dMfn KO animals , whereas the positive ( Mterf4 KO ) and negative ( Polrmt KO ) control samples showed the expected increase and decrease in transcripts , respectively ( S3A Fig ) . Also , levels of the promoter-proximal 7S RNA , previously shown to correlate with transcription initiation [35] , were reduced in heart dMfn KO animals ( S3B Fig ) . The reduction of mitochondrial transcript levels including 7S RNA was proportional to the reduction of mtDNA levels ( Fig 1D ) . Furthermore , the levels of the key mitochondrial transcription factors TFAM and mitochondrial transcription factor B2 ( TFB2M ) were normal , whereas POLRMT levels were increased ( Fig 5A ) . These findings suggest that the reduced abundance of mtDNA templates for transcription explains the reduced levels of mitochondrial transcripts . To investigate this possibility further , we performed in organello transcription assays on isolated heart mitochondria and observed an impaired mitochondrial transcription in dMfn KO heart mitochondria ( Fig 5B and 5C ) . However , there was no difference between control and dMfn KO heart mitochondria when levels of de novo transcripts were normalized to the levels of mtDNA templates ( Fig 5D ) . We proceeded to assess transcription in individual nucleoids by applying FISH to detect the mtDNA-encoded CoxI mRNA in MEFs ( Fig 5E ) . The levels of CoxI mRNA as determined by FISH were reduced in dMfn KO MEFs ( Fig 5F ) , consistent with the northern blot results ( S3A Fig ) . Importantly , the CoxI mRNA was detected in close proximity to almost all nucleoids in both control and dMfn KO MEFs ( Fig 5E and 5G ) showing that nucleoid clustering does not alter transcription activity . As the FISH analyses of CoxI mRNA does not directly reflect ongoing mtDNA transcription , we also performed bromouridine ( BrU ) labeling to assess de novo transcription in MEFs . In agreement with the results from FISH analysis of the CoxI mRNA , we found that mitochondrial BrU incorporation was decreased by more than 50% in dMfn and Opa1 KO MEFs ( Fig 5H and 5I ) . Notably , the vast majority of nucleoids in control as well as in OMM or IMM fusion-deficient MEFs incorporated BrU into newly synthesized transcripts ( Fig 5H and 5J ) . These results show that mitochondrial transcription is proportional to the number of nucleoids , i . e . the abundance of mtDNA templates , and that clustering of nucleoids does not impair mtDNA transcription . The finding of preserved transcription in dMfn KO animals ( Fig 5 ) makes it unlikely that RNA primer formation limits mtDNA replication . Normally , the vast majority of all initiated mtDNA replication events are prematurely terminated after about 650 nucleotides to generate a short single-stranded species denoted 7S DNA [36] . Southern blot ( S3C Fig ) and qPCR analyses ( S3D Fig ) consistently showed that the levels of 7S DNA normalized to mtDNA levels were unchanged in dMfn KO hearts . We also included control samples from Mgme1 whole body KO and Polrmt heart KO animals that showed the expected increase and decrease of 7S DNA levels , respectively [35 , 37] . Our results thus suggest that initiation of mtDNA replication is unaffected in OMM fusion-deficient hearts and the mtDNA depletion must therefore be caused by events downstream of initiation . The basal mitochondrial replisome is composed of the mitochondrial DNA polymerase γ ( POLγ ) , which consists of the catatytic ( POLγA ) and the accessory ( POLγB ) subunits . In addition , the DNA helicase TWINKLE and the single-stranded DNA binding protein 1 ( SSPB1 ) are components of the basal replisome . Western-blot analyses of POLγA , TWINKLE , and SSBP1 levels revealed a striking imbalance between the steady-state levels of replisome components in dMfn KO hearts and MEFs ( Fig 6A ) . Low levels of SSBP1 were consistently observed in both dMfn KO hearts ( Fig 6A and 6D ) and MEFs ( Fig 6C ) . TWINKLE levels were normal in mutant MEFs but upregulated in dMfn KO hearts . In contrast , the PolγA levels were drastically reduced in IMM and OMM fusion incompetent MEFs and near normal in dMfn KO hearts ( Fig 6A–6D ) . Interestingly , the severity of replisome imbalance observed between fusion incompetent mitochondria in heart tissue and MEFs ( Fig 6A–6D ) nicely correlate with the extent of mtDNA depletion observed between these two models ( Fig 1D–1F ) . To further define the nucleoid protein composition , we subjected Triton-X-lysed MEF mitochondria to density gradient centrifugation to enrich for nucleoid-associated proteins under native conditions . We found a lower ratio of POLγA per mtDNA in the nucleoid-enriched fraction in both dMfn and Opa1 KO MEFs ( Fig 6E–1G ) . Notably , all the fusion-deficient mitochondria analyzed by density gradients consistently showed an altered stoichiometry of mitochondrial replisome factors associated with the mtDNA template ( Fig 6F and 6G ) . In support of this finding , image analysis showed that SSBP1 co-localized to a greater extent with nucleoids in IMM and OMM incompetent MEFs compared to nucleoids in control MEFs ( Fig 6H–6J ) . Mitochondrial DNA replication not only requires a functional replisome but also relies on stable supply of deoxyribonucleotides ( dNTPs ) . To rule out changes in the cellular dNTP pools as a causative reason for mtDNA depletion in fusion-deficient MEFs , we determined the abundance of dNTPs by UPLC-MS and found no difference between control , dMfn KO , and Opa1 KO MEFs ( S4A Fig ) . The postnatal development of cardiomyocytes involves substantial mtDNA replication leading to an almost 13-fold increase of mtDNA copy number during the first four weeks of postnatal life ( Fig 7A ) . Interestingly , the dMfn KO hearts cannot sustain this increase and develop severe mtDNA depletion at age 4 weeks ( Fig 7A ) . The low steady-state levels of mtDNA show that loss of OMM fusion in heart tissue prevents the mtDNA increase normally occurring during postnatal heart development . To investigate whether the replisome protein imbalance directly impacts mtDNA replication in cells lacking mitochondrial fusion , we treated MEFs with ethidium bromide ( EtBr ) and studied mtDNA depletion and repletion dynamics . We observed that the rate of mtDNA depletion was faster in control MEFs than in dMfn or Opa1 KO MEFs ( Fig 7B and 7C ) , suggesting that the rate of mtDNA loss induced by EtBr may depend on mtDNA replication rates . Notably , after six days of EtBr treatment , clustered nucleoids were no longer detected in dMfn KO MEFs ( Fig 7D ) . However , during the repopulation phase , clustered nucleoids reemerged extensively in dMfn KO MEFs ( Fig 7D ) . Moreover , during the mtDNA repopulation phase , MEFs with loss of IMM or OMM fusion showed a severe decrease in the mtDNA synthesis rate ( Fig 7B and 7E ) . To further study the mtDNA replication process , we performed in organello assays with dMfn KO heart mitochondria and followed the incorporation of a radioactively labeled nucleotide into newly synthesized mtDNA . A marked increase in incomplete mtDNA replication products , smaller than full-length mtDNA , was visible as smears surrounding the 7S DNA in dMfn KO heart mitochondria ( Fig 7F and 7G ) . We have previously observed that the formation of similar incomplete mtDNA replication products are linked to mtDNA replication stalling and mtDNA depletion in Mgme1 KO animals [37] . It is thus plausible that dMfn KOs exhibit processivity defects during mtDNA replication in the absence of mitochondrial fusion . Altogether , our analyses reveal that mitochondrial fusion is necessary to maintain high replicative activity of mtDNA .
The mechanism whereby loss of OMM fusion leads to mtDNA depletion is unclear and a popular theory has been that fusion stabilizes mtDNA by preventing mutagenesis and instability . However , this hypothesis , based on the observation that skeletal muscle dMfn KO mice accumulate point mutations and deletions of mtDNA [23] , is unsatisfactory because the absolute levels of point mutations and deletions were extremely low ( 2-3x10-6 mutations/bp and 1x10-6 deletions/mtDNA ) . If one assumes ~103 mtDNA copies per nucleus in skeletal muscle cells , then only 1:103 of the nuclear domains in skeletal muscle will contain a deletion or a point mutation of mtDNA . Contrary to this previous model , we show here that heart dMfn KO mice have no increase of point mutations or deletions of mtDNA , which excludes this mechanism as a cause for mtDNA depletion . Importantly , our results are based on loss-of-function mutations and it should be pointed out that missense mutations in MFN2 or OPA1 in humans have been reported to cause mtDNA deletions , whereas , in support of our findings , frameshift or premature stop mutations that lead to loss of the fusion protein , do not seem to cause mtDNA deletions in humans [38 , 39] . Characterization of heart dMfn KO animals revealed that loss of OMM fusion causes a severe imbalance of replisome factors with mtDNA depletion as a direct consequence . However , in contrast to MEFs , TWINKLE protein levels in dMfn KO hearts are strongly upregulated , consistent with the findings in other heart conditional KO mice with mtDNA replication deficiencies . The increase of TWINKLE in KO hearts may represent a compensatory response to counteract mtDNA depletion [35] . The lack of a similar response in KO MEFs could explain why the replisome factor imbalance and mtDNA depletion are more severe in fibroblasts than in heart tissue . Altogether , our results show that both OMM and IMM fusion ensure rapid rates of mtDNA replication . This may be achieved by equilibrating the stoichiometry of replisome factors in order to allow the formation of functional replisomes throughout the mitochondrial network . This is well in line with previous in vitro studies that have shown that a balanced replisome composition is essential for efficient mtDNA replication [40–43] . Consistent with these data , mtDNA depletion phenotypes have also been reported in vivo when replisome proteins have been overexpressed or knocked-out in animals [44–49] . The results we present here thus suggest that mitochondrial content mixing induced by mitochondrial dynamics is necessary to maintain the delicate protein composition balance of the mitochondrial replisome . Our study also shows that OMM fusion is an important player that determines segregation of mitochondrial nucleoids . The lack of nucleoid distribution was not strictly related to a general loss of fusion , because Opa1 KO mitochondria did not display nucleoid clustering . Instead , this phenomenon appears to be specific to cells with a combined loss of both MFN1 and MFN2 . It has previously been reported that distribution of mitochondrial nucleoids depends on fission induced by DRP1 and that mtDNA replication preferentially may occur at ER-OMM contact sites [17 , 20 , 50] . It is thus possible that the OMM serves as an important platform for mtDNA distribution and that mitofusins are involved in this process . In line with this hypothesis , clustered nucleoids were no longer visible in dMfn KO MEFs during EtBr treatment but reappeared during the mtDNA repopulation phase . This suggests that nucleoid clusters are not caused by altered mtDNA stability , but rather result from impaired mtDNA distribution when OMM fusion is absent . Future studies are warranted to further understand the molecular link between nucleoid distribution and OMM dynamics . In summary , we report here an unexpected link between mitochondrial fusion and mammalian mtDNA replication , whereby mitochondrial fusion coordinates high rates of mtDNA replication and promotes mitochondrial nucleoid distribution .
Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact , Nils-Göran Larsson ( nils-goran . larsson@ki . se ) All animal procedures were conducted in accordance with European , national and institutional guidelines and protocols were approved by the Landesamt für Natur , Umwelt und Verbraucherschutz , Nordrhein-Westfalen , Germany . Animal work also followed the guidelines of the Federation of European Laboratory Animal Science Associations ( FELASA ) . C57BL/6N mice with loxP-flanked Mfn1 and Mfn2 genes were previously described in Lee et al . , 2012 [51] . To generate Mfn1 and 2 heart conditional double knockout mice , male mice homozygous for mitofusin 1 , heterozygous for mitofusin 2 , and heterozygous for expression of cre-recombinase in heart and skeletal muscle ( Mfn1loxP/loxP , Mfn2+/loxP; Ckmm-cre-/+ ) , were crossed with female mice homozygous for loxP-flanked mitofusin 1 and 2 genes ( Mfn1loxP/loxP , Mfn2loxP/loxP ) . Littermates lacking the transgenic Cre-recombinase allele were used as controls . In some crosses , an allele allowing for the ubiquitous expression of a mitochondria-targeted YFP from the ROSA26 locus was also introduced . Immortalized MEFs with homozygous knockout for Mfn1 , Mfn2 or both Mfn1 and Mfn2 , were originally generated in the lab of Dr . David Chan [2 , 52] . Mitofusin and Opa1 KO MEFs were obtained from Dr . Thomas Langer . MEFs were maintained in DMEM GlutaMax containing 25 mM glucose ( Thermo Fisher Scientific , 31966–021 ) supplemented with 10% fetal bovine serum ( Thermo Fisher Scientific , 10270–106 ) , 1% penicillin/streptomycin ( Thermo Fisher Scientific , 15070–063 ) , 1% non-essential amino acids ( Thermo Fisher Scientific , 11140–050 ) , and 50 μg/ml uridine . Cells were passaged every 3 to 4 days . Electron micrographs of heart mitochondria from control and dMfn heart double knockout mice were obtained as previously described [53] . Small pieces from the left myocardium were fixed in a mix of 2% glutaraldehyde and 1% paraformaldehyde in 0 . 1 M phosphate buffer pH 7 . 4 , at room temperature for 30 min , followed by 24 hours at 4°C . Specimens were rinsed in 0 . 1 M phosphate buffer and post-fixed in 2% OsO4 for 2 hours , dehydrated and embedded in LX-112 resin . Ultra-thin sections ( approximately 50–60 nm ) were cut and sections were examined in a transmission electron microscope ( Tecnai 12 , FEI Company , Netherlands ) at 80 kV . Digital images at a final magnification of 8 , 200x were randomly taken on myofibrils from sections of the myocardium . The volume density ( Vv ) of mitochondria was calculated on printed digital images by point counting using a 2 cm square lattice [54] . To determine the number of images needed for an appropriate sampling , we used a cumulative mean plot [54] . In total , 15 randomly taken images were used from each animal . Mice were sacrificed by cervical dislocation and hearts were quickly washed in ice-cold PBS . Hearts were then minced and gently homogenized using a Potter S homogenizer ( Sartorius ) in 5 ml of mitochondria isolation buffer ( MIB , 310 mM sucrose , 20 mM Tris-HCl , and 1 mM EGTA ) . Differential centrifugation of homogenates was performed to isolate intact mitochondria . First , homogenates were centrifuged for 10 min at 1 , 000xg at 4°C . Then , supernatants were collected and centrifuged again for 10 min at 4 , 500xg at 4°C . Crude mitochondria were resuspended in MIB and the protein concentration was determined by using the DC Protein assay ( Bio-Rad ) . The flux of mitochondrial oxygen consumption in isolated heart mitochondria was measured as previously described [55] . The assay was performed on 100–125 μg of crude heart mitochondria diluted in 2 ml of mitochondria respiration buffer ( 120 mM sucrose , 50 mM KCl , 20 mM Tris-HCl , 4 mM KH2PO4 , 2 mM MgCl2 , and 1 mM EGTA , pH 7 . 2 ) in an Oxygraph-2K ( Oroboros Instruments ) at 37°C . The mitochondria oxygen consumption rate was evaluated using either 10 mM pyruvate , 5 mM glutamate , and 5 mM malate . The oxygen consumption flux was assessed in the phosphorylating state with 1 mM ADP or in the non-phosphorylating state by addition of 2 . 5 μg/ml oligomycin . Lastly , mitochondrial respiration was uncoupled by successive addition of CCCP up to 3 μM to reach maximal oxygen consumption . The respiratory control ratio values were >10 with pyruvate/glutamate/malate and >5 with succinate/rotenone based on control heart mitochondria . Heart tissue and whole cells were lyzed in RIPA buffer ( 150 mM sodium chloride , 1 . 0% NP-40 or Triton X-100 , 0 . 5% sodium deoxycholate , 0 . 1% SDS ( sodium dodecyl sulfate ) , 50 mM Tris , pH 8 . 0 ) . Isolated heart mitochondria were resuspended in NuPAGE LDS Sample Buffer ( novex , NP0007 ) with 0 , 1M dithiothreitol , and proteins were separated by SDS-PAGE using 4–12% precast gels ( Invitrogen ) , followed by blotting onto polyvinylidene difluoride ( PVDF ) membranes or in particular cases onto nitrocellulose membranes ( GE Healthcare ) . Membranes were blocked in 3% milk/TBS . The following primary antibodies were used: mouse anti-OXPHOS cocktail ( 1:1000 , ab110413 ) , mouse ATP5A ( 1:1000 , ab14748 ) , rabbit anti-POLγA ( 1:250 , ab12899 ) , rabbit anti-TFAM ( 1:1000 , ab131607 ) , mouse anti-VDAC ( 1:1000 , ab14734 ) , all from Abcam , and rabbit anti-SSBP1 ( 1:500 , HPA002866 ) from Sigma . Rabbit polyclonal antisera were generated by Agrisera and recognize the mouse TWINKLE , TFB2M , LRPPRC , and POLRMT proteins [48 , 56 , 57] . All TWINKLE , TFB2M , and POLRMT rabbit polyclonal antisera were affinity purified using the corresponding recombinant protein . The following secondary antibodies were applied at a 1:10 , 000 dilution: donkey anti-rabbit IgG ( NA9340V ) and sheep anti-mouse ( NxA931 ) both from GE Healthcare . Detection of HRP-conjugated secondary antibodies was achieved by enhanced chemiluminescence ( Immun-Star HRP Luminol/Enhancer from Bio-Rad ) . Mitochondrial isolation from cells and glycerol density gradients were performed as described [58] . In brief , crude mitochondria were isolated from cells by differential centrifugation , treated with DNase and RNase and purified in sucrose gradients . A 15–40% glycerol gradient containing a 20% glycerol / 30% iodixanol pad at the bottom was cast using a gradient master ( model 107 , Biocomp ) with the following settings: 2 min and 31 sec run time , 81 . 65 angle , and speed set to 14 . Mitochondria ( 250–600 μg ) were lyzed in 2% Triton X-100 for 15 min on ice and afterwards the total lysate was carefully layered on top of the gradient . Samples were centrifuged at 151 , 000 x g at 4°C for 4 hours . The gradient was manually fractionated in 750 μl increments from top to bottom . Fractions were then divided in half , whereby one portion was used to precipitate proteins overnight with 12% trichloroacetic acid and 0 . 02% sodium deoxycholate , followed by centrifugation at 20 , 000 x g for 20 min at 4°C and acetone washes , while the other half of fraction 1 was used to isolated mtDNA by proteinase K treatment followed by phenol/chloroform extraction . Thereafter , protein samples were processed for western blot analysis and mtDNA samples for Southern blot analysis . Cells were grown on 100 mm dishes in DMEM GlutaMax containing 25 mM glucose and supplemented with 10% FBS , 1% penicillin and streptomycin , 1% non-essential amino acids , and 50 μg/ml uridine . Positive control cells were treated with hydroxyurea ( Sigma , H8627 ) at 2 mM for 30 hours to mildly deplete purines . Cells were quickly washed with ice-cold PBS , detached by trypsinization with 0 . 05% trypsin and the total number of cells was determined using a Vi-cell XR cell analyzer ( Beckman Coulter ) . Approximately 2 million cells were centrifuged at 800xg for 5 min , washed with ice-cold PBS , and resuspended in 1 ml of ice-cold 60% methanol . Samples were then vortexed rigorously , frozen for 30 min at -20°C , and sonicated for 15 min on ice . After sonication , samples were centrifuged at 1000xg for 5 min at 4°C and supernatants collected . Extraction solution was evaporated using an Eppendorf Concentrator plus at room temperature . Dried pellets were dissolved in 100 μl Milli-Q water , vortexed and sonicated for 2 min . After sonication samples were vortexed again and filtrated through a 0 . 2 μm modified nylon centrifugal filter ( VWR ) with an Eppendorf centrifuge 5424R set to 8°C and 12000 rpm . The external standard calibration curve was prepared in concentrations from 5 to 600 ng/ml of the dNTPs . All solutions were daily fresh prepared from stock solutions of 100 μg/ml and dissolved in Milli-Q water . dNTP analysis was conducted using a Dionex ICS-5000 ( Thermo Fisher ) Anion exchange chromatography using a Dionex Ionpac AS11-HC column ( 2 mm x 250 mm , 4 μm particle size , Thermo Fisher ) at 30°C . A guard column , Dionex Ionpac AG11-HC b ( 2 mm x 50 mm , 4 μm particle size , Thermo Fisher ) , was placed before the separation column . The eluent ( KOH ) was generated in-situ by a KOH cartridge and deionized water . At a flow rate of 0 . 380 mL/min a gradient was used for the separation: 0–3 min 10 mM KOH , 3–12 min 10–50 mM , 12–19 min 50–100 mM , 19–21 min 100 mM 21–25 min 10 mM . A Dionex suppressor AERS 500 , 2 mm was used for the exchange of the KOH and operated with 95 mA at 15°C . The suppressor pump flow was set at 0 . 6 mL/min . 10 μL of sample in a full loop mode ( overfill factor 3 ) was injected . Autosampler was set to 6°C . The Dionex ICS-5000 was connected to a XevoTM TQ ( Waters ) and operated in negative ESI MRM ( multi reaction monitoring ) mode . The source temperature was set to 150°C , desolvation temperature was 350°C and desolvation gas was set to 50 L/h , cone gas to 50 L/h . The following MRM transitions were used for quantification: dGTP , transition 505 . 98 → 408 . 00 , cone 30 , collision 20; dATP , transition 490 . 02 → 158 . 89 , cone 30 , collision 26; dTTP , transition 480 . 83 → 158 . 88 , cone 28 , collision 46; dCTP , transition 465 . 98 → 158 . 81 , cone 28 , collision 30 . The software MassLynx and TargetLynx ( Waters ) were used for data management and data evaluation & quantification . The calibration curve presented a correlation coefficient: r2 > 0 . 990 for all the compounds ( response type: area; curve type linear ) . Quality control standards were tested during the sample analysis . The deviation along the run was between 0 . 5% and 40% respectively . Blanks after the standards , quality control and samples did not present significant peaks . RNA was isolated from heart tissue either by using the ToTALLY RNA isolation kit ( Ambion ) or by using TRIzol Reagent ( Invitrogen ) , and subjected to DNase treatment ( TURBO DNA-free , Ambion ) . 1–2 μg of total RNA was denatured in RNA Sample Loading buffer ( Sigma ) , electrophoresed in 1 or 1 . 8% formaldehyde-MOPS agarose gels prior transfer onto Hybond-N+ membranes ( GE Healthcare ) . After UV crosslinking the membranes were successively hybridized with various probes at 65°C in RapidHyb buffer ( Amersham ) and then washed in 2x SSC/0 . 1% SDS and 0 . 2x SSC/0 . 1% SDS before exposure . Mitochondrial probes used for visualization of mRNA and rRNA levels were restriction fragments labeled with α-32P-dCTP and a random priming kit ( Agilent ) . Different tRNAs and 7S rRNA were detected using specific oligonucleotides labeled with γ-32P-dATP . Radioactive signals were detected by autoradiography . Total RNA from frozen heart tissue was isolated using RNeasy Mini Kit ( Qiagen ) following the manufacturer’s instructions , DNase treated using TURBO DNA-free Kit ( Thermo Fisher Scientific ) , and RNA subjected to reverse transcription PCR ( RT-PCR ) for cDNA synthesis using High Capacity cDNA reverse transcription kit ( Applied Biosystems ) . Real-time quantitative reverse transcription PCR ( qRT-PCR ) was performed using the following Taqman probes from Thermo Fisher Scientific: Mfn1 ( Mm01289372_m1 ) , Mfn2 ( Mm00500120_m1 ) , and β-2-microglobulin ( B2M , Mm00437762_m1 ) . The quantity of transcripts was normalized to B2M as a reference gene transcript . Total DNA was extracted from heart and the somatic mtDNA mutation load was determined by post-PCR , cloning , and sequencing as described previously [59] . We used primers that amplify a region of the mtDNA spanning the 3’ end of mtND2 through approximately a third of mtCO1 ( nucleotide pair 4950–5923 of the mouse reference mtDNA sequence GenBank NC_005089 ) . The resulting clones were filtered to remove sequences derived from the known nuclear mitochondrial sequences ( NUMTs ) . DNA from isolated heart mitochondria was used to generate libraries for sequencing to detect mtDNA deletions . Total genomic DNA from the Deletor mouse was provided by Dr . Anu Suomalainen-Wartiovaara and used as a positive control for the detection of mtDNA rearrangements [32] . A standard Illumina TrueSeq paired-end library was prepared with ~500 base pair fragment inserts . Paired-end 100 base pair sequencing was conducted using an Illumina HiSeq 2500 . The reads were mapped to the genomic sequence without the mitochondria using bowtie ( VN: 2 . 1 . 0 ) to remove nuclear-genomic sequences [60] . The unmapped reads were then mapped to the mitochondrial sequence ( GenBank JF286601 . 1 ) using bwa ( n = 0 . 04 , VN: 0 . 6 . 2-r126 ) [61] with unmapped reads undergoing an additional mapping round after trimming fastx_trimmer , VN: 0 . 0 . 13 . 2 ) to ensure higher mapping results . Using samtools [62] ( VN: 1 . 0: samtools -f1 –F14 ) reads , where the two paired sequences were observed to be greater than 600 base pair apart were identified as those containing deletion breakpoints . These reads were extracted for additional analysis . The data presented in this publication have been deposited in NCBI's Gene Expression Omnibus [63] and are accessible through GEO Series accession number GSE124420 ( https://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE124420 ) . Total DNA was extracted from 20 mg of mouse heart tissue . Briefly , the minced tissue was lyzed in 600 μl of lysis buffer ( 100 mM Tris-HCl pH 7 . 5 , 100 mM EDTA , 100 mM NaCl , 0 . 5% SDS , 0 . 5 mg/ml Proteinase K ) at 55°C for 3 hours followed by 2 hours incubation on ice with premixed LiCl and K-acetate ( final concentration 270 mM K-acetate , 820 mM LiCl ) to precipitate contaminants . To remove the precipitate , the sample was centrifuged for 10 min at 10 , 000 rpm at room temperature and thereafter the DNA was precipitated with isopropanol overnight . The pelleted DNA was washed with 75% ethanol and resuspended to 10 mM Tris-HCl , 1 mM EDTA pH 8 . 0 followed by quantification with Qubit . To analyze the major topological isomers of the mtDNA , 200 ng of total DNA was resolved in a 0 . 4% agarose gel ( 15 x 15 cm ) without ethidium bromide at 35 V for 20 hrs , followed by transfer onto Hybond-N+ membrane ( GE Healthcare ) . The mtDNA was detected using [α-32P]-dCTP labeled probe ( pAM1 ) . To identify different topological isomers of the mtDNA , 200 ng of control total DNA was incubated at 37°C for 30 min with only buffer , SacI ( New England Biolabs; 20 U ) , Nt . BbvCI ( New England Biolabs; 10 U ) , Topo I ( New England Biolabs; 5 U ) , Topo II ( USB Affymetrix; 20 U ) , DNA gyrase ( New England Biolabs; 5 U ) . The method was modified from [64] . Isolated heart mitochondria were purified by differential centrifugation in 320 mM sucrose , 10 mM Tris-HCl and 2 mM EDTA . From the same mitochondrial preparation: i ) 300 μg of mitochondria were labeled with 50 μCi of [α-32P]-UTP ( Perkin-Elmer ) and processed as previously described to assess de novo mitochondrial transcription [57]; ii ) as a loading control , 30 μg of mitochondria were subjected to SDS-PAGE and immunoblotted using mouse anti-VDAC antibody ( Calbiochem ) ; and iii ) 300 μg of mitochondria were used to measure the ratio between 7S DNA and mitochondrial DNA . In the latter case , the mitochondrial pellet was resuspended in 50 mM Tris-HCl pH 7 . 5 , 75 mM NaCl , 6 . 25 mM EDTA , 1% SDS and 1 , 2 mg/ml of Proteinase K and incubated for 1 hr at 37°C . After boiling for 5 min at 95°C , samples were electrophoresed in 0 . 8% agarose gels and transferred onto nylon membranes by Southern blotting . Both , mitochondrial DNA and 7S DNA were detected using [α-32P]-dCTP-labeled DNA probes specific for 7S DNA . Isolated heart mitochondria were purified by differential centrifugation and 15 μg of mitochondria were used to assess loading . For de novo mtDNA replicaton , 300 μg of mitochondria were washed with 500 μl ice-cold incubation buffer ( 25 mM sucrose , 75 mM sorbitol , 10 mM K2HPO4 , 100 mM KCl , 0 . 05 mM EDTA , 5 mM MgCl2 , 10 mM Tris-HCl pH 7 . 4 , 10 mM glutamate , 2 . 5 mM malate , 1 mg/ml BSA , and fresh 1 mM ADP , final pH 7 . 2 ) , centrifuged at 9 , 000 rpm for 4 min at 4°C , resuspended in incubation buffer containing 50 μM of dCTP , dGTP , dTTP and 20 μCi of radioactive α-32P-dATP , and incubated for 2 hours at 37°C . Thereafter , mitochondria were centrifuged at 9 , 000 rpm for 4 min at 4°C and washed 3x with 500 μl ice-cold washing buffer ( 10% glycerol , 0 . 15 mM MgCl2 , 10 mM Tris-HCl pH 6 . 8 ) . Mitochondria were treated with 1 , 2 mg/ml proteinase K and DNA extracted with phenol/chloroform . The mtDNA was resuspended in 30 μl TE buffer and one half boiled at 95°C for 5 min . Next , DNA was separated by electrophoresis on a 0 . 8% agarose gel for 15 hours at 20 V and transferred onto nylon membranes ( Hybond-N+ , GE Healthcare ) , followed by membrane cross-linking . Both Phosphor Imager ( Fuji Film FLA-7000 ) and autoradiography films ( GE Healthcare ) were used to detect the radioactive signal . After the radioactive signal had decayed , steady-state mitochondrial DNA and 7S DNA levels were assessed by using an α-32P-dATP-labeled DNA probe for 7S DNA . The growth rate of log phase cells was assessed by equilibrating cells to galactose media ( DMEM supplemented with 15 mM galactose , 1mM sodium pyruvate ( Thermo Fisher Scientific , 11360–039 ) , 10% FBS , 1% Penicillin/Streptomycin , 1% non-essential amino acids , and 50 μg/ml uridine ) for 3 days , followed by plating 27 , 000 cells in a six-well plate containing 3 ml of galactose media . Cells were collected by trypsinization with 1 ml of 0 . 05% Trypsin-EDTA ( Thermo Fisher Scientific , 25300–054 ) every 24 hours and viable cells counted using a Vi-Cell XR analyzer ( Beckman Coulter ) . MEFs in log phase were grown in DMEM GlutaMax containing 25 mM glucose and supplemented with 1% dialyzed fetal bovine serum , 1% penicillin/streptomycin , 1% non-essential amino acids , and 50 μg/ml uridine for 5 to 6 days . MEFs were passaged once during this timeframe . Cells were then collected by trypsinization and counted . The flux of mitochondrial oxygen consumption was determined using 1 million viable cells as described for isolated heart mitochondria , see Mitochondrial respiration , expect for the following modifications . Cells were permeabilized with 0 . 02 mg/ml digitonin and the oxygen consumption rate in state 3 was assessed using 10 mM succinate and 5 mM glycerol-3-phosphate . MEFs were cultivated in 18 ml of DMEM GlutaMax containing 25 mM glucose and supplemented with 10% FBS , 1% penicillin and streptomycin , 1% non-essential amino acids , and fresh 100 ng/ml ethidium bromide . After 6 days of cultivation in the presence of ethidium bromide , cells were switched to medium containing no ethidium bromide for 6 days . Cells were passaged every 2–3 days during both conditions . Total DNA was extracted using the DNeasy Tissue and Blood Kit ( QIAGEN ) and mtDNA levels determined , see mtDNA quantification by qPCR . For experiments requiring dual visualization of mRNA and proteins , FISH was performed prior to immunocytochemistry , by using the QuantiGene ViewRNA ISH Cell Assay ( Affymetrix ) following the manufacturer’s instructions . To visualize mitochondrial RNA , cells were hybridized 3 hours at 40°C using the Cox1mRNA probe ( 1:500 , cat# VB4-17017 , Affymetrix ) . To confirm probe specificity towards RNA , treatments with RNAse T1 and DNAse1 ( both at 250U/mL , 37°C ) were performed prior to hybridization . Cells grown on coverslips for 24 hours in DMEM medium , without uridine supplementation were incubated with 5 mM BrU for 1 hour at 37°C and 5% CO2 . A short chase using DMEM medium containing 50 μg/ml uridine was performed followed by a PBS wash prior to fixation . BrU was prepared fresh for each experiment . Standard immunocytochemistry procedures were carried out to visualize the mitochondrial network , mtDNA , and newly synthesized BrU-labeled RNA . Imaging of heart sections was performed using a Leica TCS SP8 gated STED ( gSTED ) microscope , equipped with a white light laser and a 93x objective lens ( HC PL APO CS2 93x GLYC , NA 1 . 30 ) . For confocal images of mitochondria and DNA , Z-stacks in accordance with the Nyquist sampling criteria were taken by exciting the fluorophores at 488 nm and 594 nm , respectively , and Hybrid detectors collected fluorescent signals . Stimulated emission depletion of DNA channel was performed with a 775 nm depletion laser . 2D confocal and gSTED images were acquired sequentially with the optical zoom set to obtain a voxel size of 17 x 17 nm . Excitation was provided at 594 nm and Hybrid detectors collected signal . Gating between 0 . 3–6 ns was applied . Images were deconvolved with the Huygens software . Performance of the microscope and optimal depletion laser power were tested as previously described [65] . Data are presented as mean ± SEM unless otherwise indicated in figure legends . Sample number ( n ) indicates the number of independent biological samples ( individual mice , number of cells , or wells of cells ) in each experiment . Sample numbers and experimental repeats are indicated in the figures . Data were analyzed in Graphpad Prism using the unpaired Student’s t-test , one-way ANOVA using Turkey’s multiple comparison test , two-way ANOVA using Bonferroni multiple comparison test between group comparison , as appropriate . A p-value ≤ 0 . 05 was considered statistically significant . | Mammalian mitochondria contain multiple copies of the mitochondrial genome ( mtDNA ) , which encodes genes that are essential for the oxidative phosphorylation system . An important feature of mtDNA is that it is evenly distributed throughout the mitochondrial network . Dynamin-related GTPase proteins help control the size and shape of mitochondria by fusion and fission events and are intimately linked to maintenance and distribution of mtDNA . Certain human mutations in mitofusin 2 ( MFN2 ) and optic atrophy protein 1 ( OPA1 ) cause disease phenotypes , such as peripheral neuropathy and optic atrophy , which are often also associated with mtDNA depletion . However , the mechanism whereby MFNs and OPA1 are involved in maintenance of mtDNA is unclear . In this study , we demonstrate that rapid mtDNA synthesis in proliferating tissue-culture cells or cardiomyocytes during post-natal heart development requires mitochondrial fusion . However , the absence of mitochondrial fusion in mouse heart is not associated with mtDNA integrity defects but instead affects the replication of mtDNA . These findings provide direct evidence for the importance of mitochondrial fusion in maintaining mtDNA replication . | [
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] | 2019 | Mitochondrial fusion is required for regulation of mitochondrial DNA replication |
There is an urgent need to field test dengue vaccines to determine their role in the control of the disease . Our aims were to study dengue epidemiology and prepare the site for a dengue vaccine efficacy trial . We performed a prospective cohort study of children in primary schools in central Thailand from 2006 through 2009 . We assessed the epidemiology of dengue by active fever surveillance for acute febrile illness as detected by school absenteeism and telephone contact of parents , and dengue diagnostic testing . Dengue accounted for 394 ( 6 . 74% ) of the 5 , 842 febrile cases identified in 2882 , 3104 , 2717 and 2312 student person-years over the four years , respectively . Dengue incidence was 1 . 77% in 2006 , 3 . 58% in 2007 , 5 . 74% in 2008 and 3 . 29% in 2009 . Mean dengue incidence over the 4 years was 3 . 6% . Dengue virus ( DENV ) types were determined in 333 ( 84 . 5% ) of positive specimens; DENV serotype 1 ( DENV-1 ) was the most common ( 43% ) , followed by DENV-2 ( 29% ) , DENV-3 ( 20% ) and DENV-4 ( 8% ) . Disease severity ranged from dengue hemorrhagic fever ( DHF ) in 42 ( 10 . 5% ) cases , dengue fever ( DF ) in 142 ( 35 . 5% ) cases and undifferentiated fever ( UF ) in 210 ( 52 . 5% ) cases . All four DENV serotypes were involved in all disease severity . A majority of cases had secondary DENV infection , 95% in DHF , 88 . 7% in DF and 81 . 9% in UF . Two DHF ( 0 . 5% ) cases had primary DENV-3 infection . The results illustrate the high incidence of dengue with all four DENV serotypes in primary school children , with approximately 50% of disease manifesting as mild clinical symptoms of UF , not meeting the 1997 WHO criteria for dengue . Severe disease ( DHF ) occurred in one tenth of cases . Data of this type are required for clinical trials to evaluate the efficacy of dengue vaccines in large scale clinical trials .
Dengue virus ( DENV ) infection with any one of the four virus serotypes ( DENV-1 to -4 ) , and 4 ) can produce a spectrum of outcomes , ranging from asymptomatic infection to mild undifferentiated fever ( UF ) , classic dengue fever ( DF ) and the most severe form of illness , dengue hemorrhagic fever ( DHF ) [1] . Dengue is an important cause of morbidity and mortality in tropical and subtropical regions of the world [2] . In Thailand , dengue was first recognized in Bangkok in 1958 , and in 1987 the largest epidemic ever recorded occurred with 174 , 285 cases [3]–[5] . Data from 1974 to 1993 showed that dengue was common in children aged less than 15 years of age and the incidence rates among children hospitalized with dengue have been consistently highest in the 5–9 year age group [6] . Disease has been caused by all four DENV serotypes and has become an intractable public health problem in the country [6] , [7] . There is no specific antiviral therapeutic licensed for treatment of dengue and prevention relies on mosquito control . As several promising live-attenuated vaccines candidates are in the later stages of clinical development , there is an urgent need to field test dengue vaccines , which may ultimately control the accelerating spread of dengue worldwide [8] , [9] . Population-based , laboratory confirmed background data on the epidemiology of dengue in high risk age-specific populations along with field site operational suitability are critical for clinical dengue vaccine trials [8] , [10] . Our aims were to collect accurate dengue incidence data for four transmission years in primary school children in a dengue hyper-endemic area , and to establish infrastructure for potential large scale trials of candidate tetravalent dengue vaccine . In 2005 , a pilot epidemiologic study of symptomatic dengue infection in 481 school-children aged 3–10 years was conducted , which led to this study conducted during 2006–2009 .
The study protocol was approved by the Ethical Review Committee for Research in Human Subjects , Ministry of Public Health , Thailand , and the Institutional Review Board , International Vaccine Institute , Seoul Korea . The study was carried out in the sub-district Namuang ( downtown ) of Muang district of Ratchaburi province , which is located approximately 100 km west of Bangkok , and lies between the Maeklong River on the east and the Thai-Myanmar border on the west . The sub-district has a population of 38 , 835 ( census 2006 ) and a total area of 8 . 7 km2 . The principal medical care facility for the province is Ratchaburi Provincial Hospital ( RH ) , a 855-bed tertiary care facility with 90 pediatric beds and 12 pediatricians on staff . In 2005 the hospital served approximately 1 , 520 outpatients per day . There were 207 , 197 and 214 clinically diagnosed dengue patients , admitted to the pediatric dengue ward in 2003 , 2004 , and 2005 , respectively . This was a prospective cohort study of children attending 7 primary schools . Schools were selected based on their desire to participate in the study , and location within 6 km from RH and the Provincial Health Office ( figure 1 ) . Following school-based informational meetings with parents , informed parental consent and signed assent for children >7 years of age were obtained from potential participants . Enrollment criteria were healthy children , no history of chronic illness , ages 3–11 years ( grades 1–5 ) at the time of enrollment , attendance at one of the study schools , and living in a village of Muang district . Exclusion criteria included intent to move outside of the study area within the study period . Children were eligible to remain in the study until graduation from sixth grade . During each January of the study , new children aged 4–5 years were offered the opportunity to enroll to replace children who graduated from the sixth grade . During the entire four-year study period , active surveillance for school absence and/or children who had a documented fever was conducted by contacting teacher-coordinators daily during school-term and telephoning parents or conducting home visits twice a week during school vacations . School absenteeism was identified each morning by teacher-coordinators at participating schools by comparing names of study participants with reported absences . Participant absenteeism was recorded on a web-based child tracking application at the study field office . Absenteeism was reviewed by the research staff and parents of absent students were contacted by the research staff each afternoon to determine if the child was absent due to a febrile episode . All parents and teacher-coordinators were provided digital thermometers and were instructed in their use . Parents of a child with a temperature ≥37 . 5°C were asked to take them to the RH outpatient department ( OPD ) where there was a special fast track unit with research pediatricians to examine study participants . Children with a fever of ≥38°C or who were considered severely ill were admitted to the inpatient dengue ward ( IPD ) . All illness data were reported at RH on a web- based reporting application . At the OPD or IPD , an acute-phase venous blood sample ( S1 ) was obtained from each febrile study participant and a convalescent-phase venous blood sample ( S2 ) was obtained 7 to 14 days later . S1 and S2 samples were drawn into serum separator tubes , allowed to clot at room temperature for 1–2 h , then stored at 4°C . Serum was separated into aliquots within 24 hours , and stored at –70°C until laboratory testing . Serum samples were transported in dry ice from RH to Bangkok monthly for dengue and Japanese encephalitis ( JE ) laboratory testing . Over the course of the study , diagnostic testing was performed in two laboratories at Mahidol University- Center for Vaccine Development ( 2006 ) , and at the Faculty of Tropical Medicine , Mahidol ( 2007–2009 ) using the same diagnostic algorithm . S1 and S2 were tested for dengue virus specific IgM/IgG by capture enzyme-linked immunosorbent assay ( EIA ) , as described previously [11] . An IgM anti-DENV level ≥40 units was considered indicative of an acute DENV infection . To exclude Japanese encephalitis virus infection and antibody cross-reactivity , specimens were tested concurrently for JE-specific IgM by EIA [11] . S1 samples of the cases with IgM anti-DENV level ≥40 units were further tested for DENV serotype . In 2006 , this was performed by mosquito inoculation in Toxorhynchites splendens [12] with detection and serotyping by immunofluorrescence . In 2007–9 , a modified nested serotype-specific reverse-transcriptase polymerase chain reaction ( RT-PCR ) [13] was used to serotype DENV . While study participants were tracked by name , school , and home address throughout the study , they were given a unique identification number upon enrollment that was used in the electronic data base for transmittal of epidemiologic , clinical and laboratory data , and for data analyses . Individual identifier information was kept in a secured location separate from data forms . Data were entered within 24 hours after the staff identified a participant as being absent , febrile or at the hospital . Data quality was assured by the study field office and hospital staff managers on a daily basis during weekdays . Data from all sources were automatically transferred through a web-based application to a database located at the Data Management Unit ( DMU ) of the Faculty of Tropical Medicine , Mahidol University , in Bangkok which uses the Mahidol University Information Technology Department Data Procedure SOP . The DMU monitored inconsistency of data entry daily . On a monthly basis , the entire dataset for the study children was exported into SAS format and archived with a CD backup . Statistical analyses were performed by using SPSS software for Windows ( version 17 . 0; SPSS Inc . , Chicago , Illinois ) . All incidences of the confirmed dengue were calculated as per 100 person-years ( percent ) . Incidence rates in all the children and children aged ≤4 , 5–9 and 10–14 years were determined by using the age-specific study population at the time of surveillance as denominator . Chi-square tests were used for determining the differences among the proportions of clinical spectra , dengue serotypes and annual incidences .
In February 2006 , 3 , 015 students aged 3–13 years were enrolled in the study for the start of surveillance . In the subsequent 3 years , loss of students was a result of the graduation of sixth graders and families' relocation . Losses were 51 ( 1 . 7% ) in 2006 , 254 ( 8% ) in 2007 , and 384 ( 14% ) in 2008 . The higher dropout rate in 2008–2009 was due to 150 subjects' terminations . They were enrolled into Sanofi Pasteur's dengue vaccine efficacy trial which began in February 2009 [ClinicalTrials . gov Identifier: NCT00842530 , http://www . clinicaltrials . gov] . Following replacement of dropouts , there were 3 , 220 ( 3– . 14 years old ) subjects in 2007 which declined to 2 , 833 ( 4–14 years ) in 2008 . Since there was no subject replacement in 2009 , only 2 , 316 ( 5–15 years ) subjects remained at the end of the study . No differences in gender distribution were noted from year to year or between schools ( data not shown ) . Mean number of subjects enrolled in the four years was 2 , 846 , with male to female ratio of 1 . 04∶1 . Median age of participants shifted from 9 years in 2006 and 2007 to 10 years in 2008 and 11 years in 2009 . Over the 4-year study period there were 36 , 934 student-absence episodes- 8 , 429 , 9 , 438 , 10 , 007 and 9 , 060 , for each respective year of the study . During this same period there were 5 , 842 febrile illness episodes −1 , 892 , 1 , 401 , 1 , 527 and 1 , 022 for each respective year of the study . The mean student-absence episodes and mean febrile episodes per child per each study year are shown in table 1 . Per year , the mean number of absences per student was 3 . 39 and each child had a mean febrile episodes of 0 . 53 ( table 1 ) . Of the study participants with a febrile illness over the 4-year study period , 73% were brought to RH by their parents . The majority of febrile children ( 53% ) visited RH on days 1–2 after onset of fever , while 30% , 14% and 3% visited RH on days 3–4 , days 5–6 and days 7–9 after onset of fever , respectively . In 2006 , of 1892 children who had febrile illness , 734 ( 39% ) came to RH of which 154 ( 8% ) were admitted to the IPD and 580 ( 31% ) required only OPD care . Twenty nine percent visited a private hospital and other clinics ( IPD 1% and OPD 28% ) , and the remaining 32% of them bought medicine independently from pharmacy without seeking medical care ( self treatment ) . The rate of hospital visits increased significantly from 39% in 2006 to 94% ( 1316/1401 ) in 2007 , 88% ( 1347/1527 ) in 2008 and 87% ( 887/1022 ) in 2009 . Numbers of febrile children who were admitted to RH IPD were 257 ( 18% ) in 2007 , 161 ( 10% ) in 2008 and 90 ( 8% ) in 2009 . During the study period 58 . 2% ( 3401of 5842 ) of febrile children had acute samples collected ( S1: 625 from IPD and 2776 from OPD ) and 57 . 7% ( 3368 febrile children ) had paired sera collected ( S2: 625 from IPD and 2743 from OPD ) and tested for DENV and JEV infection . There were 394 serological confirmed dengue cases ( 215 boys and 179 girls ) . The case number was lowest ( 51 cases ) in 2006 , then rose to 111 cases in 2007 , reached a peak at 156 cases in 2008 and declined to 76 cases in 2009 . Dengue occurred all year round but the highest number was from June to August in 2006 , 2008 and 2009 . In 2007 the disease was highest from June to December ( figure 2 ) . Dengue accounted for 2 . 73% , 7 . 78% , 10 . 18% and 7 . 50% of the febrile illness episodes in each of the study years , respectively and averaged 6 . 74% of the children with a febrile illness . Over the 4 years of the study , there were 2882 . 32 , 3103 . 66 , 2717 . 27 and 2312 . 45 person-years of follow-up for the respective years ( table 1 ) . There was statistically significant difference in the incidence of dengue year by year ( p<0 . 001 ) . The incidence in 2006 was the lowest ( 1 . 77% ) , increased to 3 . 58% in 2007 , peaked in 2008 ( 5 . 74% ) and then declined to 3 . 29% in 2009 ( table 1 ) . Mean dengue incidence over the 4 years was 3 . 6% . Dengue incidence varied by years and by schools . Over the 4 years , school 4 had the highest mean incidence ( 4% ) and school 7 had the lowest mean incidence ( 2 . 31% ) ( figure 3 ) . Among the 3 , 401 and 3368 samples of S1 and S2 tested , there were 9 serologically-determined acute JEV infection cases ( 6 and 3 cases in 2007 and 2008 , respectively ) . Among the serologically confirmed 394 cases , 42 ( 10 . 7% ) were DHF , 142 ( 36% ) were DF and 210 ( 53 . 3% ) were UF . Overall the proportion of males and females was 54 . 6% and 45 . 4% , respectively , and this male predominance persisted in all disease categories although it was not statistically significant ( p = 0 . 89 chi square test ) . The age of cases ranged from 4–14 years ( mean , 9 . 4; median , 10; mode 11 ) with slightly more cases in children aged 10–14 years old than in the 5–9 years old group: 200 ( 50 . 8% ) vs . 192 ( 48 . 7% ) , respectively . The older age group also exhibited higher numbers of both DHF and DF ( 25 and 72 cases ) than those of the younger age group ( 17 and 69 cases ) , respectively . However , there were no significant differences in disease severity , DHF vs DF between the two age groups ( p>0 . 05 , chi-square test ) . Only two children aged 3 and 4 years had dengue ( one DF and one UF ) , so comparative analysis of the less than 4 year-age group with the older age groups was not performed . Mean ages for children with differing DHF grades was: I-11 years ( n = 29 ) , II- 10 years ( n = 6 ) and III – 6 years ( n = 7 ) . Mean ages of DF and UF were 9 . 3 and 9 . 4 years , respectively . There were no deaths . Further details of clinical manifestations have been described [14] . Diagnosis of serotype was attempted on acute serum samples of 394 children who had serological confirmed dengue virus infection . These tests yielded DENV from 333 ( 84 . 5% ) children , and the detection rate varied with only 67% in 2006 using mosquito isolation compared to 83% in 2007 , 86% in 2008 , and 96% in 2009 using RT-PCR . Viral detection also varied by day of sample collection , the highest rate ( 95% ) was from the samples collected on days 1 and 2 after onset of fever , followed by days 3 and 4 ( 89% ) and days 5 and 6 ( 69% ) . All four DENV serotypes circulated in every study year ( table 2 ) . DENV-1 was the most common serotype detected with 144 isolates ( 43% ) and predominated in every study year , ranging from 34 to 50 percent . DENV-2 ( 98 isolates , 29% ) was the next most common , followed by DENV-3 ( 66 isolates , 20% ) and DENV-4 ( 25 isolates , 8% ) . DENV-2 and DENV-3 had the lowest proportions of isolates in 2006 ( 3% and 9% , respectively ) . There was a 30 fold increase of DENV-2 to 33% of isolates in 2007 and a further increase to 37% of isolates in 2008 and declining to 25% of isolates in 2009 . There was an 8 fold increase of DENV-3 to 26% of isolates in 2007 , declining to 10% of isolates in 2008 and again rising to 34% of isolates in 2009 . In 2006 DENV-4 was at its highest number: 13 isolates ( 38% ) , declining to 7 , 3 , and 2 isolates in 2007–9 , respectively ( table 2 ) . DENV serotype specific incidence differed statistically by year ( p<0 . 002 ) and varied between schools and between years ( number of serotype-specific cases divided by number of children with acute dengue virus infection in the corresponding school ) . Over the study years , all four DENV serotypes were found in all 7 schools ( figure 4 ) . The highest incidence of DENV-1 , DENV-2 , DENV-3 and DENV-4 was found in schools 5 , 4 , 1 and 2 , respectively . All 4 DENV serotypes were associated with all illness categories ( DHF , DF and UF ) ( table 3 ) . DENV-1 , DENV-2 and DENV-3 were associated with DHF grades 1 , 2 , and 3 . DENV-4 was only observed among children with DHF grade 1 . The proportions of DEN1 , DEN2 , DEN3 and DEN4 among children with DHF were 9 . 7% ( 14/144 ) , 10 . 2% ( 10/98 ) , 15 . 2% ( 10/66 ) and 8% ( 2/25 ) , respectively . The proportions of DENV-1 , DENV-2 , DENV-3 and DENV-4 among children with DF were 39 . 6% ( 57/144 ) , 31 . 6% ( 31/98 ) , 39 . 4% ( 26/66 ) , 44% ( 11/25 ) , and in UF were 50 . 7% ( 73/144 ) , 58 . 2% ( 57/98 ) , 45 . 4% ( 30/66 ) and 48% ( 12/25 ) , respectively . It was found that DENV serotypes had no significant correlation with disease severity ( p>0 . 05 between DHF vs . non-DHF and p>0 . 05 between UF vs . non-UF ) or for rate of hospitalization ( p>0 . 05 ) . Of all 394 dengue cases , secondary DENV infection was found in 86 . 3% , the remainder ( 13 . 7% ) having a primary DENV infection ( table 3 ) . The rates of secondary DENV infection found in DHF , DF and UF cases were 95% , 88 . 7% and 81 . 91% , respectively . Primary DENV infection in 2 DHF Grade 1 cases was due to DEN3 . There was no statistically significant difference in rate of secondary DENV infection in DHF ( 40/42 ) vs . those in DF and UF ( 298/352 ) ( p>0 . 05 ) . Altogether 193 cases ( 47% ) were hospitalized . The hospitalization rate by disease severity category was: DHF - 42 ( 100% ) , DF - 119 ( 84% ) , and UF - 32 ( 15% ) . The proportion of hospitalized children with UF ( 32/172 ) was significantly lower than that of children with DF ( 119/142 ) ( p<0 . 0001 ) , which was significantly lower than those of DHF ( 42/42 ) ( p<0 . 001 ) ( table 3 ) .
Our study is a population based epidemiological study driven by a clear objective of site preparation for a future dengue vaccine field trial . The Namuang sub-district of Muang district , Ratchaburi province , was chosen for the study site because it had high reported rates of dengue virus transmission . The incidence of dengue disease was determined from a prospective , long- term active fever surveillance of study participants in a well-defined cohort of school children throughout a 48-month period . Daily tracking of school absenteeism during school days and telephone contact with parents twice a week during school holidays made a broader capture of dengue possible . Most febrile illness cases detected in the cohort surveillance presented at Ratchaburi hospital were examined by pediatricians and had samples collected for dengue diagnostic testing . An important aspect of this study was that our surveillance system captured children with febrile illness who sought medical care in both the public and private sectors . Prior to the beginning of the study , we met with a private hospital director and pediatricians who worked on the private hospital and clinics in Muang district to inform them of the study project . They were asked to inform us whenever participants attended their hospital/clinics . As a result , our staff visited all participants admitted to the private hospital and obtained acute serum samples from febrile patients . Convalescent samples were subsequently collected at RH . In addition , by end of the first year , we had strengthened parent-staff communications and education by use of telephone explanation and written documents that explained provision of study care with no charge fast track service and telephone consultation service at RH . Our efforts to educate parents were evidenced by increased in parent initiated hospital visits for febrile illness to 87% in the years 2007–2009 . Our rates of dengue incidences over the four-year period , 1 . 77 , 3 . 58 , 5 . 74 and 3 . 29 per 100 person-years in 2006–2009 reflect a parallel pattern of dengue incidence reported from the national surveillance data base in 2006–9 for Muang district , Ratchaburi Province: 159 , 200 , 361 and 155 per 100 , 000 , respectively . Our prospectively determined dengue incidence in Na-muang sub-district is 11 to 21 ( average 16 . 5 ) fold higher than those derived from the national surveillance database in Muang district . One difference between our incidence rates and the national rates is that we prospectively determined DHF , DF and UF cases among febrile illnesses of primary school children , whereas , the national data presents mainly DHF reported from hospitalized patients in all age groups . Although there were some methodological differences in how active surveillance was conducted in our study and previously reported studies in childhood cohorts from Kampang Phet , Thailand [15] , Kampong Cham , Cambodia [16] and Managua , Nicaragua [17] , the incidences of laboratory confirmed dengue found by active surveillance for acute febrile illness were much higher than that of the corresponding national reporting data . Using a calculated expansion factor to estimate differences between national reporting and laboratory based surveillance for dengue incidence , Wichmann et al . 2011 found the average under-recognition of dengue across three cohort studies ( Kampang Phet , Ratchaburi and Kampong Cham ) of dengue incidence to be more than 8-fold [18] . The Nicaraguan study found under-reporting of dengue cases in relation to national surveillance systems to be 21 . 3 fold [17] . The national under-reporting of dengue incidence of cases hinders accurate knowledge of disease burden . All four DENV serotypes were involved in all levels of disease severity observed over the study period . Although there was strong seasonal variation , laboratory confirmed dengue virus infections were observed in every month of the four-year observation period in all 7 schools indicating that there is continuous DENV transmission in Ratchaburi . DENV-1 was the most commonly virus isolated ( 43% of total ) , followed by DENV-2 ( 29% ) and DENV-3 ( 20% ) . New introduction in 2007 of DENV-2 and DENV-3 after a period of relatively low frequency in occurrence was followed by a severe outbreak in 2008 wherein DEN2 frequency was 30-fold increased and DENV-3 8-fold increased . This observation is consistent with a previous report that in Thailand DENV-2 was a dominant isolate during moderately severe dengue outbreak years and DENV-3 was associated with subsequent severe outbreaks after a time of relatively low frequency [6] . Dengue virus isolation methods differed between 2006 and the subsequent years in that PCR was used from 2007–2009 . A lower DENV isolation rate was observed when culture in C6/36 cells and mosquito inoculation ( 67% ) were used compared to detection of virus genome using RT-PCR ( >83% ) in this study is likely due to lower sensitivity of mosquito methods compared to PCR molecular methods [19] , [20] . The overall viral identification rate using combined mosquito inoculation and PCR tests of 84 . 5% reported in this study is higher than the 65% in the previous report which also used both methods [7] . Secondary-type DENV infection described in this study was based on the ratio of anti-DENV IgM: IgG<1∶8 [11] . It has been observed from serology test results that there is cross reaction between JEV IgG and DENV IgG in S1 as well as in S2 with resulting inability to distinguish JEV IgG from DENV IgG . This observation had also been reported previously [21] . As the Thai National Immunization Program has included JE vaccination since 2000 , JEV IgG from vaccination might have had some influence on the observed DENV IgG titers and may have confounded the interpretation of secondary infection data reported here . However , the majority ( 84 . 5% ) of the patients with secondary DENV infection was accompanied with positive dengue virus detection and had low JEV IgG titer in their acute ( S1 ) specimens suggesting a minimal impact on the validity of anti-DENV IgM/IgG ratio used to estimate secondary DENV infection . There appears to have been a shift in modal age of dengue incidence over the past four decades in Thailand . A retrospective hospital based study of laboratory confirmed dengue in 15 , 376 patients from Bangkok , Thailand reported a modal age of 5 years during 1973–79 which increased to 8 years during 1990–99 [6] . We found a modal age of 11 years in the present study . This finding might reflect a somewhat older group presenting with less severe disease ( UF ) in outpatient settings , but it is consistent with the increasing modal age previously reported [6] . Hospitalization rates for acute symptomatic dengue infection could be considered a measurement of dengue disease severity . The hospitalization rate for DHF , DF , and UF were 100- 84- and 15- percent , respectively . Over the four years of our study , the provision of fast track service without payment , led to progressively better compliance of febrile cases from the cohort reporting directly to hospital OPD for evaluation , minimizing the possibility that cases may have been missed , especially in years 2007–9 . However it is possible that the study missed some mild febrile cases that did not attend RH in 2006 . Because of the sensitivity of our surveillance system only 184 ( 46% ) of laboratory confirmed dengue cases ( DHF 42 cases and DF 142 cases ) met the 1997 WHO case definition [1] . Clinical manifestations of 210 ( 52 . 5% ) of laboratory confirmed UF cases did not meet the WHO case definition for dengue [1] . UF was the most common clinical manifestation of children infected with dengue virus , whether primary or secondary DENV infections , and can be difficult to distinguish from other childhood febrile illnesses [14] . Our findings that a high proportion of dengue cases manifested as UF has an important impact on present vector control practices . In Ratchaburi province , mosquito control campaigns against dengue occur broadly over the province , four times per year . In addition , implementation of mosquito control measures including source reduction , application of larvicides , and spraying residual insecticide , are implemented in the area of 100 meters in diameter around all the reported houses of patients with dengue infection at all times of year . Due to the current national surveillance system only DHF/DF cases observed in clinical facilities are reported . Mosquito control measures are not implemented around houses of UF patients . This may contribute to inability to adequately control dengue and result in the long persistence and wide spread of dengue in the province as well as in Thailand in general . We performed an extensive dengue epidemiology study and report accurate background data on dengue incidence in a cohort of individuals at high risk of dengue , children ages 3–15 years living in Namuang subdistrict of Muang district of Ratchaburi province , Thailand . We found sufficiently high incidence of four DENV serotypes for four consecutive years to make vaccine efficacy studies possible . Our study methods and findings fulfilled the epidemiological criteria recommended by WHO for dengue vaccine trial site selection [8] . Following the reports of safety and immunogenicity of phase I studies of live-attenuated tetravalent dengue vaccine in dengue–naïve and dengue-endemic populations [22] , [23] , the site was selected for the first dengue vaccine efficacy and safety trial , phase 2b . This trial uses a live-attenuated yellow fever vaccine virus based chimeric tetravalent dengue vaccine in a 3-dose vaccine administration of doses spaced at six-month intervals . This trial was launched in early 2009 and dose administration in approximately 4000 participants was completed in March 2011 [ClinicalTrials . gov Identifier: NCT00842530 ) . Safety of the vaccine within the trial enrollment and vaccine administration period has been established . An analysis for vaccine efficacy is planned in 2012 . If the vaccine is sufficiently immunogenic and efficacious after long term follow up , it is anticipated that the vaccine might be available for use in 2015 . | There is an urgent need to field test dengue vaccine . Efficacy trials need to be conducted in study sites with sufficiently high dengue incidence to make a robust estimate of vaccine efficacy and where all dengue virustypes circulate frequently . In this paper , we report on dengue disease surveillance on approximately 3000 primary-school children in seven schools in Muang district of Ratchaburi province , central Thailand , from 2006 through 2009 . We report on the characteristics of children in this cohort who fell ill with laboratory confirmed dengue disease . The study showed that approximately four percent of the children had laboratory confirmed dengue per year . All four dengue virus types were found to be the causes of illness in children in all seven schools . This study has shown Muang district of Ratchaburi province to be suitable for dengue vaccine testing and the site has been selected for the world’s first dengue vaccine safety and efficacy study , being conducted from 2009–2014 in children aged 4–11 years . | [
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] | 2012 | Dengue Infection in Children in Ratchaburi, Thailand: A Cohort Study. I. Epidemiology of Symptomatic Acute Dengue Infection in Children, 2006–2009 |
Current WHO recommendations for lymphatic filariasis ( LF ) surveillance advise programs to implement activities to monitor for new foci of transmission after stopping mass drug administration ( MDA ) . A current need in the global effort to eliminate LF is to standardize diagnostic tools and surveillance activities beyond the recommended transmission assessment survey ( TAS ) . TAS was first conducted in American Samoa in 2011 ( TAS 1 ) and a repeat TAS was carried out in 2015 ( TAS 2 ) . Circulating filarial antigen ( CFA ) and serologic results from both surveys were analyzed to determine whether interruption of LF transmission has been achieved in American Samoa . A total of 1 , 134 and 864 children ( 5–10 years old ) were enrolled in TAS 1 and TAS 2 , respectively . Two CFA-positive children were identified in TAS 1 , and one CFA-positive child was identified in TAS 2 . Results of both surveys were below the threshold for which MDA was warranted . Additionally , 1 , 112 and 836 dried blood spots from TAS 1 and TAS 2 , respectively were tested for antibodies to Wb123 , Bm14 and Bm33 by luciferase immunoprecipitation system ( LIPS ) assay and multiplex bead assay . In 2011 , overall prevalence of responses to Wb123 , Bm14 , and Bm33 was 1 . 0% , 6 . 8% and 12 . 0% , respectively . In 2015 , overall prevalence of positive Bm14 and Bm33 responses declined significantly to 3 . 0% ( p<0 . 001 ) and 7 . 8% ( p = 0 . 013 ) , respectively . Although passing TAS 1 and TAS 2 and an overall decline in the prevalence of antibodies to Bm14 and Bm33 between these surveys suggests decreased exposure and infection among young children , there were persistent responses in some schools . Clustering and persistence of positive antibody responses in schools may be an indication of ongoing transmission . There is a need to better understand the limitations of current antibody tests , but our results suggest that serologic tools can have a role in guiding programmatic decision making .
Lymphatic filariasis ( LF ) , endemic in 72 countries , is a debilitating mosquito-transmitted parasitic disease caused by filarial worms ( Wuchereria bancrofti and Brugia spp . ) [1] . In 1997 , at the 50th World Health Assembly ( WHA ) , a resolution was passed to eliminate LF as a public health problem by 2020 [2] . Shortly thereafter , in 1999 , the Pacific Program for the Elimination of Lymphatic Filariasis ( PacELF ) was established to eliminate the disease in the Pacific Region through a strategy of annual rounds of mass drug administration ( MDA ) [3] . The following year , the Global Program to Eliminate Lymphatic Filariasis ( GPELF ) was established to assist all LF-endemic countries in achieving this elimination goal through the same MDA strategy . At the start of GPELF it was estimated that approximately 1 . 4 billion people were at risk for infection . By the end of 2016 , MDA had been implemented in 66 of 72 LF-endemic countries , with a cumulative total of 6 . 7 billion treatments delivered since the start of GPELF [4] . After multiple rounds of MDA , LF elimination programs must be able to determine when it is appropriate to stop treatment . The World Health Organization ( WHO ) -recommended transmission assessment survey ( TAS ) was designed as a decision-making tool to determine when transmission of LF is presumed to have reached a level low enough that it cannot be sustained even in the absence of MDA [5] . In areas where W . bancrofti is the principal LF pathogen , infection is assessed in the TAS by measuring circulating filarial antigen ( CFA ) . Since its integration into national programs in 2011 , TAS has successfully been implemented across LF endemic countries , and based on the results , MDA has been discontinued in multiple locations . The global number of people requiring MDA has been reduced from 1 . 4 billion in 2000 to 856 . 4 million in 2016 [4] . Effective monitoring and evaluation ( M&E ) is not only necessary during the MDA period but important throughout the lifespan of the LF program , including after MDA has stopped . Current WHO recommendations for post-MDA surveillance include periodic surveys: repeating TAS twice at 2- to 3-year intervals after stopping MDA . Beyond the TAS , post-MDA surveillance guidance has not been standardized . Current WHO recommendations for surveillance advise programs to implement activities to monitor for new foci of transmission through the assessment of microfilaremia , antigenemia , or antibodies [5] . After effective MDA , microfilaremia and antigenemia begin to decline in populations and become increasingly difficult to detect [6] . Detection of antifilarial antibodies appears to provide the earliest indicator of filarial exposure [7] , and the absence of detectable antibody responses may provide evidence that transmission has been interrupted . Surveys conducted in 1999 indicated that 17% of residents in 18 villages in American Samoa were infected with W . bancrofti [3] . This established American Samoa as one of the areas with the highest filarial infection levels in the Pacific Region and the only U . S . territory endemic for LF . The American Samoa Department of Health ( DOH ) started MDA in 2000 . Annual MDA coverage was low ( <50% ) prior to 2003 . After reassessment and modification of the communication and distribution strategies , the program treated an estimated 70% and 65% of the population in 2003 and 2004 , respectively [8] . Results from surveys in four sentinel sites showed an overall decline in CFA levels from 13% in 2003 to 0 . 95% in 2006 [9] . An island-wide survey was conducted in 2007 , and CFA prevalence was 2 . 3% , with the majority of the antigenemia detected in adults [10] . Because LF was presumed to be at very low levels , minimal programmatic activities were conducted from 2008–2010 . In accordance with WHO recommendations , TAS 1 was conducted in American Samoa in 2011 and was repeated in 2015 ( TAS 2 ) . The DOH opted to include antifilarial antibody testing in both surveys to complement antigen testing . In this paper we report CFA and serologic results from the two TAS that were conducted to determine whether or not interruption of LF transmission has been achieved in American Samoa .
The surveys were approved by the DOH Institutional Review Board ( IRB ) and the U . S . Centers for Disease Control and Prevention ( CDC ) as program evaluation , non-research . In preparation for the TAS , survey details were described in a written document distributed to school officials and parents or guardians of potential participants . In accordance with DOH and Department of Education policies , parents or guardians provided written permission for participation of children . Additionally , children ≥7 years of age were asked to provide oral assent for their participation on the day of the survey . All data were collected electronically , and identifiable information was kept confidential and maintained by using a secure database with access restricted to essential survey personnel . American Samoa , a U . S . territory , is located in the South Pacific comprising of seven small islands and atolls . More than 90% of the total population live on the main island of Tutuila with the remainder of the residents dispersed on the adjacent island of Aunu’u and the outer Manu’a islands of Ta’u , Ofu and Olosega . Tutuila and Aunu’u comprised the evaluation unit for TAS . TAS 1 was carried out in February 2011 and TAS 2 was conducted in April 2015 . Surveys were implemented according to WHO guidelines for conducting TAS in areas where Aedes spp . are the main LF vectors [5] . Because of high school enrollment rates ( >95% ) , school-based surveys were conducted at both time points , and grades 1 and 2 were used as a proxy for the recommended age ( 6–7 years ) . Systematic sampling was recommended for both surveys , but due to low rates of consent , all children with signed consent forms were enrolled . The target sample sizes in 2011 and 2015 were 1 , 042 and 1 , 014 , respectively . The critical cutoff , the maximum number of observed positive results that is consistent with a threshold of < 1% , for both surveys was six antigen-positive children . For both surveys approximately 160 μL of blood was collected via a single finger stick into an EDTA-coated blood collection tube ( Ram Scientific , Yonkers , NY ) . One hundred microliters of blood was used for the detection of CFA by immunochromatographic card test ( ICT ) ( Alere; Scarborough , ME ) . The cards were read at 10 min and marked as either positive or negative according to the manufacturer’s instructions . The remaining 60 μL of blood ( 10 μL per extension x 6 extensions ) was spotted onto filter paper ( Cellabs , Sydney , Australia ) , dried and stored at -20°C until shipped to National Institutes of Health ( NIH ) for antifilarial antibody testing by luciferase immunoprecipitation system ( LIPS ) assay [11] or CDC for testing by multiplex bead assay ( MBA ) [12–14] ( described below ) . In TAS 1 , IgG responses to Wb123 were determined by previously described LIPS assay [11] . One modification was made to accommodate the use of dried blood spots ( DBS ) instead of serum . DBS were eluted in 200 μl of PBS , and 40 μl of the eluted material was used for the assay . Cutoff values were calculated from receiver operator characteristic ( ROC ) curves using sera from W . bancrofti-infected patients and presumed negative sera from North Americans with no history of foreign travel . Antifilarial antibody responses to Bm14 [15] and Bm33 [16] for samples collected during TAS 1 and responses to Wb123 , Bm14 and Bm33 for samples collected during TAS 2 were determined by previously described MBA [7 , 12–14] . Briefly , DBS were eluted to yield a sample dilution of 1:400 in PBS buffer ( pH 7 . 2 ) containing 0 . 3% Tween-20 , 0 . 02% sodium azide , 0 . 5% casein , 0 . 5% polyvinyl alcohol ( PVA ) , 0 . 8% polyvinylpyrrolidone ( PVP ) , and 3 μg/ml Escherichia coli extract . E . coli extract was added to the buffer to absorb antibodies to any residual E . coli proteins that may not have been eliminated in the antigen purification process . Samples having a coefficient of variation of >15% between duplicate wells for ≥2 positive LF antibody responses were repeated . The average of the median fluorescent intensity ( MFI ) values from the duplicate wells minus the background ( bg ) fluorescence from the buffer-only blank was reported as MFI-bg . Cutoff values were calculated from ROC curves using sera from W . bancrofti-infected patients and presumed negative sera from US citizens with no history of foreign travel . Parents or guardians of individuals who were ICT positive were notified of test results , and the children were offered a standard single dose of diethylcarbamazine ( DEC ) ( 6 mg/kg ) and albendazole ( 400 mg ) . Analyses were performed in R version 3 . 3 . 0 [17] with the survey package [18] using a 5% level of significance . Because a high percentage of the American Samoa population of 1st and 2nd graders participated , samples from TAS 1 and TAS 2 were treated as clustered samples with a finite population correction . Differences in frequencies were evaluated with a Rao-Scott Χ2 statistic [19] . Confidence intervals for proportions utilize the incomplete beta function [20] . Changes in MFI were evaluated with the complex sampling version of Mood’s test for differences in medians [21] .
A total of 1 , 134 children from 25 of 26 public and private elementary schools were enrolled in TAS 1; 50 . 6% were male , and the mean age was 6 . 8 years ( range 5–10 years ) . Because written informed parental consent was required for participation , systematic sampling of children could not be applied as intended . All children with signed consent forms from parents/guardians were enrolled in the survey . One small private school ( St . Theresa ) was not sampled because of school officials refusal to participate . Demographic information was not available for 57 students from Tafuna Elementary . For 197 ( 17 . 4% ) children enrolled , no blood sample was collected or the quantity of blood collected was insufficient for testing by ICT . Of the samples tested , 2/937 ( 0 . 2% , 95% upper confidence limit ( CL ) 0 . 8% ) were antigen positive . Both positive children were from the same school , Lupelele Elementary . Demographic information and number of samples tested by ICT are summarized by school in Table 1 . By 2015 , two elementary schools that had existed in 2011 were closed and six new schools had opened . A total of 864 children from all 30 public and private elementary schools were enrolled in TAS 2; 48 . 4% were male , and the mean age was 7 . 0 years . As with TAS 1 , written parental consent was required for participation and all children with signed consent forms were enrolled in the survey . For 96 ( 11 . 1% ) children enrolled , no blood sample was collected or the quantity of blood collected was insufficient for ICT testing . Of the samples tested , 1/768 ( 0 . 1% , 95% CL 0 . 3% ) was positive . The antigen-positive child was from Lupelele Elementary , the same school where the two antigen-positive children were identified in TAS 1 four years earlier . Demographic information and number of samples tested by ICT for TAS 2 are given in Table 1 , stratified by school . In 2011 , a total of 1 , 112 DBS were prepared for antibody testing . Overall prevalence of responses to Wb123 , Bm14 , and Bm33 was 1 . 0% , 6 . 8% and 12 . 0% , respectively . There was at least one Wb123 antibody-positive child in 6/25 ( 24 . 0% ) schools . Distribution of responses to Bm14 and Bm33 responses was more widespread than responses to Wb123 with at least one antibody-positive child identified in 88 . 0% and 80 . 0% of schools , respectively . In 2015 , a total of 836 DBS were collected for antibody testing . Overall prevalence of Bm14 and Bm33 responses declined significantly to 3 . 0% ( p<0 . 001 ) and 7 . 8% ( p = 0 . 013 ) , respectively . The prevalence of Wb123 responses was 3 . 6% in TAS 2 , but results were not directly compared to those from TAS 1 because of the different testing platform used . The distribution of Wb123 responses was more widespread in TAS 2 than TAS 1 with at least one antibody-positive child identified in 12/30 ( 40 . 0% ) schools . Distribution of Bm14 responses was more focal in 2015 than in 2011 with at least one antibody-positive child identified in 12/30 ( 40 . 0% ) schools; only one of these 12 schools did not have any Bm14-positive children in TAS 1 . Distribution of positive Bm33 responses was the most widespread ( 56 . 7% of schools ) of the three markers assessed , but was still more focally distributed in 2015 compared to 2011 . Antibody responses are summarized by school in Fig 1 and Table 2 . Change in MFI-bg for Bm14 and Bm33 was compared for the 22 schools included in both surveys . There were significant declines in the median quantitative MBA responses for Bm14 and Bm33 in 21/22 ( 95 . 5% ) schools . Median MFI-bg values are summarized in Table 3 , stratified by school . All three ICT-positive children identified in the surveys had positive antibody responses to Wb123 , Bm14 and Bm33 . However , concordance of individual antibody responses for antigen-negative children was relatively poor at both time points . Only 21/161 ( 13 . 0% ) antibody-positive children in TAS 1 and 20/73 ( 27 . 4% ) positive children in TAS 2 had positive responses to at least two markers . Similar discordance was also observed for samples tested where the antigen status was unknown . Test concordance for antigen negative children is summarized in Table 4 . Test concordance for children whose antigen status was unknown is summarized in Table 5 .
The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention . | Lymphatic filariasis ( LF ) , endemic in 72 countries , is a debilitating mosquito-transmitted parasitic disease caused by filarial worms . The Global Program to Eliminate Lymphatic Filariasis ( GPELF ) aims to interrupt transmission through mass drug administration ( MDA ) and to reduce suffering caused by the disease . At the start of GPELF in 2000 it was estimated that approximately 1 . 4 billion people were at risk for infection . By the end of 2016 , primarily through successful MDA programs , the global number of people requiring interventions was reduced to 856 . 4 million . Current recommendations by the World Health Organization for LF surveillance advise programs to implement activities to monitor for new foci of transmission after stopping MDA . A current need in the global effort to eliminate LF is to standardize diagnostic tools and surveillance activities beyond the recommended transmission assessment survey ( TAS ) . Two TAS were conducted in American Samoa; first in 2011 ( TAS 1 ) and repeated in 2015 ( TAS 2 ) . In our evaluation , circulating filarial antigen and serologic results from both surveys were analyzed to determine whether interruption of LF transmission has been achieved in American Samoa . Despite passing TAS 1 and TAS 2 , clustering and persistence of positive antibody responses in schools may be an indication of ongoing transmission . Results from our evaluation suggest that serologic tools can have a role in guiding programmatic decision-making . | [
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] | 2018 | Comparison of antigen and antibody responses in repeat lymphatic filariasis transmission assessment surveys in American Samoa |
Central nervous system ( CNS ) melioidosis is rare . However , delayed diagnosis and treatment could lead to fatality . To identify knowledge of CNS melioidosis , we systematically review case reports and case series . We searched through PubMed , Web of Science and Thai-Journal Citation Index databases as well as Google Scholar with the last date on July 10 , 2018 . The diagnosis of CNS melioidosis had to be confirmed with culture , serology or polymerase chain reaction . We excluded the animal cases and the studies that the clinical data were not available . We identified 1170 relevant studies , while 70 studies with a total of 120 patients were analyzed . Ninety-three percent of patients were reported from the endemic area of melioidosis . Median age was 40 years ( IQR 18–53 ) , and 70% were men . A total of 60% had one or more risk factors for melioidosis . The median duration from clinical onset to diagnosis was ten days ( IQR 5–25 ) . Fever ( 82% ) , headache ( 54% ) , unilateral weakness ( 57% ) and cranial nerve deficits ( 52% ) are among the prominent presentation . Most patient ( 67% ) had at least one extraneurological organ involvement . The CSF profile mostly showed mononuclear pleocytosis ( 64% ) , high protein ( 93% ) and normal glucose ( 66% ) . The rim-enhancing pattern ( 78% ) is the most frequent neuroimaging finding in encephalomyelitis and brain abscess patients . Both brainstem ( 34% ) and frontal lobe ( 34% ) are the most affected locations . Mortality rate was 20% . This study is the most extensive systematic review of case reports and case series of CNS melioidosis in all age groups . However , the results should be cautiously interpreted due to the missing data issue . The propensity of brainstem involvement which correlates with prominent cranial nerve deficits is the characteristic of CNS melioidosis especially encephalomyelitis type . The presenting features of fever and neurological deficits ( especially cranial nerve palsies ) along with the mononuclear CSF pleocytosis in a patient who lives in the endemic area and also has the risk factor for melioidosis should raise the CNS melioidosis as the differential diagnosis .
Melioidosis is an infectious disease caused by the gram-negative bacterium , Burkholderia pseudomallei . The regions considered to be endemic include Southeast Asia , northern Australia , South Asia ( including India ) , and China . The disease can involve many organs , but the central nervous system ( CNS ) melioidosis is rare . Only 1 . 5 to 5 percent of the melioidosis cases have been reported to have neurological involvement [1 , 2] . Although there is a systematic review conducted in pediatric patients [3] , most CNS melioidosis cases occur in adults . Therefore , the knowledge of the CNS melioidosis including epidemiology , clinical manifestations , laboratory findings , treatment , and outcome are still limited and solely depend on data of case reports . The present study aimed to perform a systematic review of individual participant data of case reports and case series on the CNS melioidosis in all age group .
We conducted a systematic review following the Preferred Reporting Items for a Systematic Review and Meta-analysis of Individual Participant Data ( PRISMA-IPD ) guideline [4] . However , we did not register our study protocol on the international database of systematic reviews . Inclusion criteria for the study and case recruitment were as follows: 1 ) the study must be a case report or case series 2 ) the patient was diagnosed with the CNS melioidosis which includes encephalomyelitis , brain abscess , isolated meningitis and isolated extra-axial collection ( defined as subdural empyema or epidural abscess with the absence of encephalomyelitis and brain abscess ) 3 ) the diagnosis of melioidosis was confirmed with either of culture , serology or polymerase chain reaction ( PCR ) 4 ) the data of clinical manifestations were available . Exclusion criteria included 1 ) not case report or case series 2 ) not the CNS melioidosis case ( melioidosis related peripheral nervous system disease , e . g . , Guillain-Barré syndrome was also excluded . ) 3 ) animal study 4 ) no abstract or full text available 5 ) no clinical data 6 ) the report language was neither English nor Thai . We searched through bibliographic databases ( PubMed , Web of Science , and Thai-Journal Citation Index ) and Google Scholar as well as grey literature sources ( Bielefeld Academic Search Engine , and Thailand Library Integrated System ) . The search strategies were as follows: "melioidosis"[mh] AND ( "central nervous system infections"[mh] OR meningitis OR "central nervous system" OR neurological OR brain OR cranial OR "spinal cord" ) for PubMed , TS = melioidosis AND TS = ( meningitis OR "central nervous system" OR neurological OR brain OR cranial OR "spinal cord" ) for Web of Science , "melioidosis" for Thai-Journal Citation Index , and “melioidosis” AND “neurological” AND "case report" for Google Scholar . The filters included human study , and English or Thai language . The last date of searching was on July 10 , 2018 . The selection process started with firstly , the literature from all sources were gathered and then the studies that duplicated among the databases were excluded . Secondly , abstracts and full texts were screened against the criteria , and individual participant data were sought . The author of the original study was contacted if necessary . The aggregate data was acceptable in case the individual data was eventually not available . The collected data on the individual basis included epidemiology , the duration between the onset of illness and the diagnosis , clinical manifestations , cerebrospinal fluid ( CSF ) profile , imaging findings , diagnostic techniques , types of disease , systemic involvement , treatment , and outcome . The data item that was not available in the study would be recorded as "not reported" . Only the reported data would be analyzed . We used the IBM SPSS Statistics for Windows , Version 25 . 0 for the analysis . Nominal data were reported as frequency and percentage . Scale data were analyzed as mean with SD or median with IQR . Pooled statistics were calculated for analyzing the individual data and aggregate data together . For example , the mean age for all patients was calculated by ∑nix¯i/∑ni where ni was the size of patient group i ( individual or aggregate data ) , and x¯i was the mean age of patient group i . However , pooled statistics cannot be computed for a median , in case the data is not normally distributed . The authors received approval from the Human Ethics Committee of Srinakharinwirot University to conduct this study ( SWUEC/E-369/2560 ) . No written informed consent was required for this systematic review .
We identified 1170 articles from the bibliographic databases search ( PubMed , Web of Science and Thai-Journal Citation Index ) including Google Scholar , as well as 82 articles from the grey literature sources . After removal of duplicates , the remaining 1091 records were then assessed for the eligibility through abstracts and full texts . We excluded 1021 studies for particular reasons , and the rest of the 70 studies were sought for the individual participant data . We found 69 studies with 159 subjects for which individual participant data were provided . However , nine individual patients were excluded because the participants duplicate others . Moreover , we excluded the additional 42 cases because the diagnosis was not CNS melioidosis . Finally , we recruited 69 reports including 108 subjects for which individual participant data were provided . For another study that individual participant data were not provided , the aggregate data of 12 participants were available . In total , we recruited 70 studies with 120 patients for the analysis ( Fig 1 ) . The first report of a CNS melioidosis patient was presented in 1977 . The number of patients per publication ranged from 1 to 12 cases . The characteristics of the included studies and individual patients are summarized in the S1 Table . Most cases ( 93% ) were reported from the endemic area of melioidosis in which Australia , Thailand , India , and Malaysia accounted for a majority of patients ( Fig 2 ) . Ninety-three percent of Australian patients were reported from the northern part , while 65% of Thai cases were from the northeastern region . Eight cases ( 7% ) were reported from the non-endemic countries including the USA , Belgium , Japan , Norway , and UAE . These patients traveled , lived or served as a soldier in the endemic region at some points of time before the disease development . Fifteen cases worked as rice farmers . Three patients reported the histories of preceding cranial injuries . The median age of the CNS melioidosis patients was 40 years ( IQR 18–53 ) . The 10-day newborn was the youngest reported case , while the oldest was 75 years of age . Most patients ( 77% ) were adults ( ≥19 years ) , and 70% of patients were men . Risk factors for melioidosis were shown in Table 1 . Most patients ( 60% ) had one or more risk factors . Diabetes mellitus was the most common ( 43% ) , but in Australia , excessive alcohol use is the most prominent risk factor ( 39% ) . Five cases from Australia were reportedly heavy drinkers of kava , a drink prepared from the powder of the root plant Piper methysticum . Contrary to adult patients , most pediatric cases ( 67% ) did not have any risk factors for melioidosis . However , diabetes mellitus remained the only risk factor among children . The median duration from clinical onset to diagnosis was ten days ( IQR 5–25 ) , and it ranged from 1 to 150 days . Majority of CNS melioidosis patients ( 91% ) was classified as acute melioidosis ( less than two months of onset ) . Encephalomyelitis ( 37% ) and brain abscess ( 35% ) were the two most common disease types . Regarding the age group , encephalomyelitis was most observed in the adult ( 39% ) , while brain abscess was the most common type in the children ( 44% ) . The cases of encephalomyelitis were reported mainly from Australia ( 63% ) . Other types of CNS melioidosis included isolated meningitis and isolated extra-axial collection , which accounted for 16 and 12 percent of cases , respectively . Clinical manifestations of all CNS melioidosis and its types are described in Table 2 . Fever was the top presentation for all CNS melioidosis , while other features included headache , altered consciousness , neck stiffness , seizures , unilateral weakness , paraplegia , quadriplegia , and cranial nerve palsies . The facial nerve was the most common cranial nerve affected . In encephalomyelitis patients , the fever and cranial nerve deficits were the prominent manifestations . In brain abscess cases , the most common presentations were the fever and unilateral weakness . Fifteen cases were reported to present with craniofacial swelling . Sixty-seven percent of patients had one or more extraneurological involvement . More than half of those had a pulmonary infection , while one-fourth had localized skin infection ( Table 3 ) . Fifty-eight cases underwent lumbar puncture . The CSF profile showed pleocytosis in 91% of the cases , and 64% of patients had CSF mononuclear cell predominance . The CSF WBC count had a median of 190 cells/μL ( IQR 54–413 ) , while no CSF RBC was detected in 78% of patients . CSF protein was high ( >0 . 45 g/L ) in 93% of cases , and the median protein value was 1 . 16 g/L ( IQR 0 . 82–1 . 64 ) . CSF glucose was low ( <2 . 2 mmol/L ) in 34% of patients , and the mean glucose level was 2 . 8 mmol/L ( SD 1 . 5 ) . Both CT and MRI of the brain were equally done ( 60% vs . 58% of cases ) . However , 33% of patients who did the brain CT initially had a normal imaging result , while the brain MRI findings were abnormal in all cases . The imaging findings included hyperintensity on T2W in all MRI cases and also brain edema in 97% of CT and MRI results . There were 49 patients of encephalomyelitis and brain abscess reported having the abnormal contrast enhancement in neuroimaging which the rim-enhancing pattern was the most common characteristic ( 78% ) . Other features included nodular ( 14% ) , irregular ( 14% ) , leptomeningeal ( 14% ) and linear enhancement ( 2% ) . Brainstem ( 34% ) , frontal lobe ( 34% ) and parietal lobe ( 33% ) were among the most common locations affected in the encephalomyelitis and brain abscess patients ( Table 1 ) . Lesions involving contiguous parts of the CNS , which included supra- and infratentorial brain as well as spinal cord , occurs in 29 percent of encephalomyelitis cases . Skull and scalp lesions were present in 19% and 16% of cases , respectively . Twenty-two percent of patients having brain parenchymal diseases also had concurrent involvement of adjacent parts encompassing extra-axial spaces , skull or extracranial structures . The diagnosis of melioidosis was confirmed with the positive culture in 110 CNS melioidosis patients ( 92% ) . Blood was the most common specimen ( 41% ) that grew the organism . Positive cultures of the CNS specimens , which includes brain tissue or pus , CSF and extra-axial collection , were present in 32 , 21 and 10 cases , respectively ( Table 4 ) . Nine patients ( 8% ) depended on only the serology for diagnosis , which the antibody titer ranged from 1:16 to 1:1 , 280 . There was one case that was diagnosed by only polymerase chain reaction test of blood buffy coat . Ceftazidime ( 54% of cases ) , meropenem ( 36% ) and trimethoprim/sulfamethoxazole ( TMP/SMX , 41% ) were the primary drugs used for intensive-phase therapy . Others included chloramphenicol ( 13% ) , imipenem ( 7% ) and doxycycline ( 5% ) ( Table 1 ) . Eight or more weeks of recommended treatment duration was reported in 30 percent of non-death cases . For eradication phase , TMP/SMX was the most common prescription ( 73% ) among others that encompassed doxycycline ( 27% ) and amoxicillin/clavulanic acid ( 12% ) . The treatment duration of at least six months was given in 24 percent of non-death patients . Adjunctive abscess drainage was performed in 58 percent of cases . After treatment , 37 percent of the CNS melioidosis patients recovered completely or nearly completely . Thirty-one percent had moderate neurological improvement , while 13 percent did not recover and suffered neurological disability . Overall mortality was 20 percent of patients .
The CNS melioidosis mostly occurs in adults , and diabetes mellitus is the most common risk factor . Encephalomyelitis and brain abscess are the major types of the disease which fever , unilateral weakness , and cranial nerve deficits are the most prominent presenting features . The CSF profile usually shows mononuclear pleocytosis with high protein and normal glucose level . Contrast-enhanced MRI is the most sensitive imaging study for detecting brain lesions . The rim-enhancing pattern is the most frequent finding , and the brainstem is the most affected location . One-fifth of the patients is fatal . The interpretation from this study may be biased as there were several missing data from the case reports and case series . | Melioidosis is an infectious disease caused by the bacterium , Burkholderia pseudomallei . It is the disease of the tropical region especially Southeast Asia and northern Australia where the microbes are present in soil and water . Melioidosis can cause a variety of symptoms depending on the site of infection . Brain and spinal cord infections are rare but fatal , and the knowledge of their characteristics is still limited . In this study , the authors found that most patients with central nervous system melioidosis have risk factors especially diabetes mellitus . Fever is the most common presentation , and brainstem is the primary site of attack causing symptoms and signs of cranial nerve abnormalities . Cerebrospinal fluid findings usually show mononuclear cells accompanying with high protein and normal sugar level . The diagnosis is made by isolation of B . pseudomallei mostly from brain tissue , brain pus or blood . This knowledge would help physicians to recognize the disease which leads to early diagnosis and treatment along with the better outcome . | [
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] | 2019 | Central nervous system melioidosis: A systematic review of individual participant data of case reports and case series |
The cellular immune response against parasitoid wasps in Drosophila involves the activation , mobilization , proliferation and differentiation of different blood cell types . Here , we have assessed the role of Edin ( elevated during infection ) in the immune response against the parasitoid wasp Leptopilina boulardi in Drosophila melanogaster larvae . The expression of edin was induced within hours after a wasp infection in larval fat bodies . Using tissue-specific RNAi , we show that Edin is an important determinant of the encapsulation response . Although edin expression in the fat body was required for the larvae to mount a normal encapsulation response , it was dispensable in hemocytes . Edin expression in the fat body was not required for lamellocyte differentiation , but it was needed for the increase in plasmatocyte numbers and for the release of sessile hemocytes into the hemolymph . We conclude that edin expression in the fat body affects the outcome of a wasp infection by regulating the increase of plasmatocyte numbers and the mobilization of sessile hemocytes in Drosophila larvae .
Parasitoid wasps are natural enemies of insects such as the fruit fly Drosophila melanogaster . In the course of a successful wasp infection , a female wasp lays an egg in a fruit fly larva and the wasp larva hatches . Thereafter , the wasp larva develops inside the Drosophila larva using the host tissue as a source of nutrition to ultimately emerge as an adult wasp , unless the wasp larva is eliminated by the host’s immune response [1] . The initial oviposition of a wasp egg triggers changes in gene expression in the fruit fly and activates both humoral and cellular defense mechanisms [2–4] . The role of the humoral defense , i . e . the production of antimicrobial peptides by the fat body , via the Imd and Toll pathways in response to a microbial challenge , is well characterized in response to microbial challenge ( reviewed in [5 , 6] ) . However , in the context of wasp parasitism , cellular immunity is more striking than the humoral response . The cellular immune responses are mediated by three types of blood cells , or hemocytes: plasmatocytes , lamellocytes and crystal cells ( reviewed for example in [7 , 8] ) . The round and small plasmatocytes are the most abundant type tallying up to 95% of all of the larval hemocytes . Plasmatocytes are responsible for phagocytosing invading microorganisms and apoptotic particles and are also required for a normal resistance against bacteria [9–12] . Crystal cells comprise around 5% of all hemocytes and they contain phenoloxidase-containing crystals that are released in the melanization response [13] . Lamellocytes , on the other hand , are solely found in larvae and are rarely present in individuals that are not immune-challenged . The main task of lamellocytes is to participate in encapsulating objects that are too large to be phagocytosed , such as the eggs of parasitoids wasps . However , the encapsulation of wasp eggs requires the concerted action of all three types of hemocytes [7] . Upon a wasp infection , the presence of a wasp egg is first recognized . Plasmatocytes are the first cells that adhere to the wasp egg and they spread around the surface of the egg forming the first layer of the capsule [14] . A wasp infection also leads to the differentiation of a large number of lamellocytes [15–17] , which migrate towards the wasp egg and attach onto the plasmatocyte-covered egg . During a successful immune response lamellocytes , together with plasmatocytes , form a multilayered capsule that surrounds the wasp egg . The capsule is melanized , phenol oxidases and reactive oxygen species are released within the capsule [18] , and the wasp is ultimately killed . Although many pathways , such as the Toll and JAK/STAT pathway , have been shown to have a role in the encapsulation response [3] , the phenomenon is still insufficiently understood . In this current study , we investigate the role of Edin ( elevated during infection ) in a wasp infection . Edin is a small peptide that is secreted into the hemolymph upon infection [19 , 20] , and it is required for the immune response against Listeria monocytogenes [21] . Earlier , we have shown that the expression of edin is induced after a bacterial infection , and it has a minor role in the resistance against Enterococcus faecalis [20] . In this study , we investigated whether edin expression is induced by a wasp infection using the Leptopilina boulardi strain G486 . We also examined the role of Edin in the encapsulation response and in the activation and formation of hemocytes upon a wasp infection . We report that edin expression is required in the fat body upon a wasp infection in order to mount an effective encapsulation response , and that knocking down edin in the fat body causes defects in hemocyte mobilization in Drosophila larvae .
We have previously shown that edin is induced both in vitro and in vivo upon a microbial infection , but were unable to find any essential role for Edin in this context [20] . To test whether a wasp infection induces the expression of edin , we infected Canton S larvae with the parasitoid wasp Leptopilina boulardi strain G486 , and determined the expression levels of edin in whole larvae three hours after infection using qRT-PCR . As is seen in Fig 1A , the wasp infection led to a 7-fold induction in the expression levels of edin compared to uninfected larvae . Because the fat body is the main immune-responsive organ in the fruit fly , we next looked at edin mRNA levels in the fat bodies of wasp-infected larvae 24 hours post-infection . As is shown in Fig 1B , the expression of edin was more highly induced in the fat bodies of the wasp-infected larvae than in whole larvae ( 80-fold induction ) . Our results indicate that edin is upregulated after a wasp infection in larvae and that the fat body is a main source for its expression . Fruit fly larvae can mount an effective immune response against invading parasitoids by encapsulating the wasp egg . To address the functional significance of edin expression for the encapsulation process upon an L . boulardi infection , we used the UAS-GAL4 system to knock down edin expression . The normal response against the wasp egg is the formation of a visible melanized capsule around the parasitoid egg , and in our hands , 45–66% of control larvae had a melanized capsule . First , we crossed edin14289 RNAi flies ( #14289 , hereafter referred to as edin14289 ) with flies carrying the C564-GAL4 driver , which is expressed in many organs , including the fat body , salivary glands and lymph glands [22] , and looked for the presence of melanized capsules 27–29 hours after the wasp parasitization ( Fig 2A ) . Parasitized w1118 controls showed an encapsulation rate of 47% . Similarly , w1118 crossed with C564-GAL4 or edin14289 showed encapsulation rates of 52% and 53% , respectively , while only 15% of edin14289 crossed with C564-GAL4 showed melanized capsules . To ensure that the observed phenotype was caused by reduced edin expression , we analyzed the encapsulation response of another edin RNAi line ( #109528 , hereafter referred to as edin109528 ) . Similarly to the edin14289 line , edin109528 crossed with the driver line showed a clearly decreased encapsulation efficiency of 7% ( Fig 2A ) , when compared to edin109528 crossed with w1118 . We next used a fat body-specific driver to examine specifically whether the lowered encapsulation response was due to the role of edin in the fat body . We crossed both the edin14289 and edin109528 RNAi lines with the Fb-GAL4 driver line and examined the encapsulation response of the offspring . Fb-GAL4 crossed with w1118 showed encapsulation levels of 45% ( Fig 2A ) , whereas edin RNAi flies crossed with Fb-GAL4 showed an encapsulation activity of only 8% ( edin14289 ) and 7% ( edin109528 ) . In addition , similar results were also obtained with another fat body-specific driver , Lsp2-GAL4 ( edin109528 , S1 Fig ) . We also analyzed the encapsulation activity of edin RNAi larvae crossed with the pan-hemocyte driver HmlΔ;He-GAL4 and were not able to see any effect with either of the RNAi lines ( 60% and 70% encapsulation , Fig 2A ) . Together , these data suggest that Edin is required for a normal encapsulation response after parasitization , and that its expression is required in the larval fat body but not in the hemocytes . Scoring for the ability of the fly larva to melanize the wasp egg does not indicate whether the fruit fly larva is actually able to overcome the parasitization . Therefore , we replicated the experimental setting in Fig 2A , but scored for the presence of living or dead wasp larvae 48–50 hours post infection . The parasite was scored as killed by the fruit fly larva if a melanized wasp egg was found in the hemocoel in the absence of a living wasp larva . As is seen in Fig 2B , the percentage of dead wasps in control larvae varied between 20–34% . When edin14289 RNAi was induced with either the C564-GAL4 or Fb-Gal4 driver , the percentage of dead wasps was significantly reduced ( 8% in both cases ) . A significant decrease was also observed with the combination of the edin109528 RNAi line and the C564-GAL4 driver ( 9% killing rate ) . These results , together with the encapsulation phenotype , indicate that edin is required for the resistance against wasp parasitism in Drosophila larvae . Lamellocytes have a central role in the resistance against L . boulardi parasitism . They are not found in the hemocoel of healthy , unchallenged Drosophila larvae , but they are formed in response to a wasp infection [15–17] . To investigate whether the expression of edin in the fat body is required for lamellocyte formation , we bled hemocytes of wasp-challenged larvae 48–50 hours after infection . Plasmatocytes and lamellocytes were visualized using the eaterGFP ( green ) and msnCherry ( red ) reporters , respectively . As is shown in Fig 3A and 3B , all of the hemocytes in the unchallenged larvae express the eaterGFP reporter and are msnCherry-negative , indicating that only plasmatocytes are present . Lamellocytes are msnCherry-positive , large , and flat cells . They are present only in the infected larvae ( Fig 3A’ and 3B’ ) and are found both in RNAi treated and control larvae , indicating that edin expression in the fat body is not required for lamellocyte formation upon a wasp infection ( Fig 3B’ ) . It is noteworthy that the infected larvae contain cells that express both eaterGFP and msnCherry reporters , showing that some of the cells are undergoing plasmatocyte to lamellocyte transition and are not yet fully differentiated lamellocytes ( Fig 3A’ and S2 Fig ) . In order to obtain additional information about the role of Edin after wasp infection , we used flow cytometry and the msnCherry , eaterGFP reporter to analyze hemocytes of larvae , where edin was knocked down in the fat body . Fig 3C–3D’ show representative scatter plots of hemocytes of uninfected and infected larvae with edin RNAi in the fat body as well as age-matched uninfected and infected control larvae at the 27–29 hour time point . Lamellocytes were induced in spite of edin depletion in the fat body . When comparing hemocyte numbers of uninfected and infected control larvae and edin RNAi larvae , we found that although lamellocyte numbers of infected animals did not differ ( p = 0 . 061 , Fig 3E ) , the plasmatocyte numbers generally increased approximately two to three fold after infection in controls but remained constant in edin knock-down larvae ( Fig 3E ) . Taken together , Edin was dispensable for lamellocyte formation but seemed to be necessary to increase plasmatocyte numbers after a wasp infection . In order to properly encapsulate wasp eggs , blood cells must adhere and spread on the egg surface until the egg is finally encapsulated . The Rac GTPase Rac2 regulates the actin cytoskeleton that mediates the spreading of plasmatocytes on the wasp egg [23] . To ensure that the defect in encapsulation is not caused by a defective plasmatocyte function , we tested whether plasmatocytes adhere and spread normally on glass slides and on wasp eggs . In our experimental setting , lamellocytes appear 20 hours after parasitization . To get only plasmatocytes , we bled larvae 14 hours after wasp infection and stained the microtubules and the actin cytoskeleton ( Fig 4A and 4B” ) . We measured the tubulin to actin ratio from approximately 120 hemocytes of larvae with edin RNAi in fat body and control larvae , and found no significant difference in the spreading behavior ( control: tubulin/actin = 0 . 46 , standard deviation = 0 . 18; edin RNAi: tubulin/actin = 0 . 42 , standard deviation = 0 . 21; p = n . s . , S1 Table ) . Another way of looking at spreading behavior is assaying the distribution of the NimC1 protein that is specific for plasmatocytes . The NimC1 protein forms a cytoplasmic ring in control cells , whereas it accumulates in the center of the cell in Rac2 mutants [23] . NimC1 antibody staining of plasmatocytes on the wasp egg 14 hours after parasitization of edin RNAi larvae was indistinguishable from controls ( Fig 4C and 4D ) indicating normal adhesion and spreading of plasmatocytes in vivo . The defining early events of capsule formation are the recognition of the wasp egg by plasmatocytes [14] and a significant increase of hemocytes in circulation . [24] . To study whether edin expression is required to increase plasmatocyte numbers in the early stages of an infection , we counted plasmatocytes 14 hours after wasp infection using flow cytometry . As is shown in Fig 4E , edin RNAi in the fat body resulted in more than three times fewer cells compared to controls ( p<0 . 001 ) . Taken together , Edin is dispensable for lamellocyte formation but it is necessary to increase plasmatocyte numbers in circulation in the early stages of a wasp infection . Sessile plasmatocytes reside attached to the skin of Drosophila larvae and form a hematopoietic compartment that releases blood cells in response to a wasp infection [25 , 26] . In order to see if the decreased numbers of plasmatocytes were due to a defect in releasing the sessile plasmatocytes into circulation , we imaged the Fb-GAL4-driven edin RNAi larvae and the respective control crosses 27–29 hours after the wasp parasitization , and again used the msnCherry , eaterGFP reporter line to allow the visualization of plasmatocytes ( green ) and lamellocytes ( red ) . In the uninfected controls ( Fig 5A–5D , top row ) , the banded pattern of plasmatocytes and the lymph gland could been seen . The bands represented plasmatocytes that resided in the sessile compartment in the absence of an immune stimulus . When the larvae were infected by wasps , the green banded pattern disappeared ( Fig 5E–5G ) and lamellocytes appeared in the hemolymph ( Fig 5E’–5G’ ) . This was due to the activation of the hemocytes in the sessile compartment in response to the wasp infection , which causes the cells to leave the compartment and enter the circulation , where many differentiate into lamellocytes [25 , 26] . Consistent with our flow cytometry data ( Fig 3 ) , when edin was knocked down in the fat body , lamellocytes still appeared in the circulation showing that Edin did not affect the formation of lamellocytes ( Fig 5H’ ) . However , in the edin knockdown larvae the banded pattern of plasmatocytes was not disrupted as in the controls ( Fig 5H and 5H’ ) . Of note , overexpression of edin in the fat body did not disrupt the banded pattern indicating that the overexpression of edin alone was not sufficient for releasing the sessile hemocytes into the circulation ( S3 Fig ) . In conclusion , our data suggest that edin expression in the fat body affects plasmatocyte activation and release from the sessile compartment . This suggests that the silencing of edin results in a compromised response to L . boulardi parasitism in the early stages of the infection , and that the altered resistance is due to insufficient plasmatocyte numbers in circulation .
Encapsulation is a complex response against a wasp attack in fruit fly larvae and it requires the concerted action of activated hemocytes . In the course of the encapsulation response , plasmatocytes and the encapsulation-specific lamellocytes form a multilayered capsule around the wasp egg and sequester the invading parasite from the hemocoel of the larva . In addition to inducing the encapsulation response , a wasp infection causes changes in the expression profile of the fruit fly genes [3 , 4] . Our results show that edin was rapidly induced in response to an infection by the endoparasitoid wasp Leptopilina boulardi and that edin expression in the fat body , but not in hemocytes , was required to mount a normal encapsulation response against the wasp . Encapsulation was not blocked entirely , however , as approximately 10% of the larvae encapsulated the wasp egg , when edin was knocked down in the fat body . Nevertheless , lamellocyte numbers were unaffected and plasmatocyte spreading behavior was normal . Instead , in larvae where edin was knocked down in the fat body , fewer plasmatocytes were present in circulation , while more hemocytes were retained within the sessile compartment . These data indicate that the presence of lamellocytes alone is not enough for the fruit fly larva to kill the wasp egg . Sufficient numbers of plasmatocytes are also needed . We discovered that knocking down edin in the fat body did not affect lamellocyte differentiation but compromised the increase of plasmatocyte numbers after a wasp infection . The impaired encapsulation response observed in our study could be therefore due to the misregulation of hemocyte proliferation and/or activation . Because plasmatocyte function was not impaired , as the cells were able to attach and spread normally onto glass slides and wasp eggs , the lowered plasmatocyte number could be the cause of the defects observed in the encapsulation response . Other studies have shown that high hemocyte numbers are associated with an increased resistance against parasitoid wasps in D . melanogaster as well as in other Drosophila species [27–30] , although the molecular mechanisms behind this phenomenon are not understood . In our study , the lowered numbers of plasmatocytes are observed already early on during the wasp infection ( 14 h post infection ) , suggesting that the function of Edin is critical at the onset of an immune response . This might be the case also in the context of an antimicrobial response , where edin knock down seems to have a modest effect on the levels of some antimicrobial peptides during the early phases of a bacterial infection [20] . Studies have shown that , when hemocytes are activated after an immune stimulus , the banded pattern formed by plasmatocytes is disrupted and the cells are released into the circulation [25 , 26 , 31] , where they can differentiate into lamellocytes [16 , 17 , 26] . The mobilization of sessile cells occurs prior to the release of hemocytes from the lymph gland [17 , 26] , and this disruption of the banded pattern is caused by changes in the adhesive properties of the cells . Several genes have been reported to be involved in the attachment of the sessile hemocytes to the sessile compartment [25 , 32] . For example , the conserved Rho family of GTPases , namely Rac1 and Rho , regulate the release of sessile cells through the regulation of the adhesive properties of the cells [33 , 34] . It has also been suggested that sessile hemocytes adhere to laminin under the larval integument in a syndecan-dependent manner [35] . Additionally , the EGF-repeat containing receptor Eater , which was originally identified for its role in the phagocytosis of bacteria [36] , was recently reported to be required in plasmatocytes for the adhesion of hemocytes to the sessile compartment [37] . In our current study , we show that sessile plasmatocytes of edin RNAi larvae did not leave the sessile bands , and the numbers of circulating plasmatocytes did not change after a wasp infection , yet normal amounts of lamellocytes were formed . Despite comparatively normal amounts of lamellocytes , the encapsulation response was impaired when the sessile plasmatocytes could not be mobilized . Hence , besides forming the first layer of the capsule and giving rise to lamellocytes , plasmatocytes have other functions in the encapsulation response that are dependent on edin expression in the fat body . Our results imply that the effect of Edin is non-cell autonomous and that it seems to act as a molecule that signals from fat body to hemocytes either directly or indirectly . Although the humoral and cellular aspects of Drosophila immunity are often depicted as separate , several studies have provided evidence of the interaction between hemocytes and the fat body . For example , the antimicrobial peptide response to an E . coli infection in domino mutants which lack hemocytes , is normal , but these mutants fail to induce Diptericin during a gut infection by Erwinia carotovora suggesting that hemocytes mediate a signal from the gut to the fat body [38 , 39] . In line with these data , Brennan et al . have shown that Psidin acts in the hemocytes to activate the production of Defensin in the fat body [40] . Another example of crosstalk between hemocytes and the fat body is the requirement of Upd3 expression in hemocytes to activate the JAK-STAT pathway in the fat body of adult flies [41] . Furthermore , in larvae , the production of the cytokine Spätzle by hemocytes is needed for the activation of Toll-mediated AMP production in the fat body [42] . Hemocytes are also mediators of the transport of the nitric oxide from its site of production in the gut epithelia to the fat body , where AMP production via the Imd pathway is activated [43 , 44] . However , contradicting data also exist for adult flies showing that the ablation of hemocytes by apoptosis does not affect AMP induction in the fat body [45 , 46] . A more recent study has shown that the interaction between the fat body and hemocytes is crucial in controlling tumor cell death [47] . Recently , we also showed that Toll signaling in the fat body controlled hemocyte differentiation and activation , but that it did not play a major role in the immune response against L . boulardi as the wasps were able to suppress Toll signaling in the fat body [48] . These examples point to the existence of active tissue-to-tissue signaling that orchestrates appropriate immune responses against different immune challenges . According to our results , Edin functions as a cytokine-like molecule , but the receptor for Edin and its localization remain to be studied . Edin might signal directly from the fat body to the hemocytes , but it may also signal to other tissues or cells that then affect the function of the hemocytes in the sessile compartment ( Fig 6 ) . Although Edin is not structurally conserved outside brachyrecan flies [20] , its cytokine-like function might be conserved , as in the case of the Spätzle-like function of the vertebrate nerve growth factor β [49] , for example . Based on our results Edin appears to be a key regulator in the cross-talk between fat body and hemocytes in the context of a wasp infection . As in the encapsulation response , the granuloma formation in vertebrates also requires the recruitment of many different cell types . For example , the adult zebrafish responds to a Mycobacterium marinum infection by enclosing the infectious foci in granulomas [50 , 51] , but also the intracellular bacterium Listeria monocytogenes is sequestered inside granulomas to constrain the infection [52] . Whether information obtained from genetically tractable model organisms such as Drosophila melanogaster , will lead to a better understanding of the pathophysiology of granuloma formation remains to be studied .
UAS-edin RNAi ( CG32185 ) flies #109528 and #14289 ( hereafter called edin109528 and edin14289 ) were obtained from the Vienna Drosophila Resource Center . The driver lines used in this study were the fat body-specific driver Fb-GAL4 , the hemocyte-specific driver HmlΔ;He-GAL4 [48] and C564-GAL4 , which was obtained from Prof . Bruno Lemaitre ( Global Health Institute , EPFL , Switzerland ) . The C564-GAL4 driver is expressed in many tissues such as the fat body , lymph gland , salivary glands , gut and brain but not in hemocytes [22] . The hemocyte reporter lines eaterGFP ( for plasmatocytes ) [53] and MSNF9mo-mCherry ( for lamellocytes , hereafter called msnCherry ) [54] were obtained from Robert Schulz’s laboratory . The lines were crossed to create the msnCherry , eaterGFP reporter line . The mCherry , eaterGFP reporter was further crossed with Fb-GAL4 and edin RNAi109528 to obtain the mCherry , eaterGFP;Fb-GAL4 and mCherry , eaterGFP;edin109528 lines . Canton S flies were used for RNA extractions . Ten GAL4-driver virgin females were crossed with five RNAi male flies and allowed to lay eggs at +25°C . w1118 flies and GAL4-driver virgin females crossed with w1118 males and w1118 virgin females crossed with RNAi males were used as controls . The flies were transferred daily into fresh vials and the vials containing eggs were transferred to +29°C . On the third day after egg-laying , the larvae were infected with 20 female and 10 male wasps of the Leptopilina boulardi strain G486 . The larvae were infected for 2 hours at room temperature after which the wasps were removed and the larvae were transferred back to +29°C . The encapsulation properties were assayed 27–29 hours after the infection and the killing ability of the larval immune system 48–50 hours after the wasp infection . The egg was scored as encapsulated when traces of melanin were found on it . To analyze the killing ability of the Drosophila larva , three types of phenotypes were scored . The wasp was scored as killed if a melanized wasp egg or melanized wasp larva without other living wasp larvae was found in the hemolymph , whereas the wasp was scored as living when a living wasp that had escaped a melanized capsule was present or when a living wasp larva without any melanized particles was found in the hemocoel . Eight to ten Canton S larvae per sample were snap frozen on dry ice at 0 hours or 3 hours after the wasp infection . The fat bodies were dissected in 1x PBS 24 hours after the wasp infection and kept on ice . Both larvae and fat bodies were homogenized in TRIsure reagent ( Bioline , London , UK ) and total RNAs were extracted according to the manufacturer’s instructions . Quantitative RT-PCR was carried out using the iScript One-Step RT-PCR kit with SYBR Green ( Bio-Rad , Hercules , CA , USA ) and the Bio-Rad CFX96 ( Bio-Rad ) instrument according to the manufacturer’s instructions . Results were analyzed with the Bio-Rad CFX Manager software version 1 . 6 . Actin5C was used as a housekeeping gene . The following primers were used: Forward 5’-CTCGTGTCCTGCTGTCTG-3’ and reverse 5’-GCCTTCGTAGTTGTTCCG-3' for edin and forward 5’-CGAAGAAGTTGCTGCTCTGG-3’ and reverse 5’-AGAACGATACCGGTGGTACG-3’ for Actin5C . Drosophila larvae were imaged using 3rd instar larvae 27–29 hours after the wasp infection . The larvae were washed three times in H2O and embedded on microscope slides in a drop of ice-cold glycerol . The larvae were immobilized at -20°C before imaging . The Zeiss ApoTome . 2 was used for live imaging of larvae . For hemocyte imaging , the larvae were washed three times in H2O , and the hemocytes were bled into 1 x PBS 48–50 hours after the wasp infection . Uninfected controls of the same age were also used . The hemocytes were let to adhere to the glass surface of a microscope slide for 30 minutes , after which they were fixed with 3 . 7% paraformaldehyde for 5 minutes . The samples were washed with PBS and mounted with the Prolong Gold Anti-Fade reagent with DAPI ( Molecular Probes ) . Hemocyte imaging was carried out with the Zeiss AxioImager . M2 microscope with Zeiss AxioCam and the Zen Blue 2011 software and with the Zeiss LSM780 in the case of the antibody-stained hemocytes . The hemocyte images were processed with ImageJ 1 . 49p ( Rasband WS , ImageJ , U . S . National Institutes of Health , Bethesda , Maryland , USA , imagej . nih . gov/ij , 1997–2012 ) . Hemocytes from infected and control larvae were bled into 1 x PBS with 8% BSA to obtain the hemocytes . Flow cytometry was used to detect eaterGFP-positive and msnCherry-positive cells in these samples . The Accuri C6 flow cytometer ( BD , Franklin Lakes , NJ , USA ) was used to run the samples , and the data was analyzed using the BD Accuri C6 software . The gating strategy is explained in S2 Fig . For F-actin and α-tubulin stainings , hemocytes were bled from 15 larvae per cross into 20 μl of 1 x PBS with 8% BSA in pools of three larvae per well and allowed to spread on a glass slide for 45 minutes . Cells were fixed with 3 . 7% paraformaldehyde/PBS solution for 10 minutes , washed three times with PBS and permeabilized for 5 minutes with 0 . 1% Triton X-100 before antibody staining . Cells were incubated for 2 hours with an unconjugated mouse α-tubulin monoclonal antibody ( Life Technologies , 1μg/ml concentration ) followed by one hour incubation with the Alexa Fluor 405 goat anti-mouse secondary antibody ( Life Technologies , a 1:500 dilution in 1% BSA in PBS ) . F-actin was visualized by incubating the cells for 30 minutes with the Alexa Fluor 680 nm Phalloidin stain ( Invitrogen ) diluted to 1:50 in 1x PBS with 1% BSA . After this , the cells were washed 3 times with PBS and mounted using the ProLong Gold antifade mountant ( Life Technologies ) . We measured the area of Phalloidin and α-tubulin staining with ImageJ 1 . 49p and calculated the ratio of α-tubulin to Phalloidin areas . Wasp eggs with hemocytes attached onto them were collected from fly larvae 12–14 hours after infection in a drop of 8% BSA in 1 x PBS , fixed with 3 . 7% paraformaldehyde/PBS solution for 10 minutes , washed three times with PBS , and stained for 4 hours with an undiluted mixture of monoclonal P1a and P1b ( NimC1 ) plasmatocyte-specific antibodies [55] . Thereafter , the samples were washed 3 times with PBS and incubated with the Alexa Fluor 405 goat anti-mouse secondary antibody ( Life Technologies , 1:500 dilution ) . The eggs were mounted with 50% glycerol prior to imaging . Three eggs per cross were imaged . Edin expression data was analyzed using an independent samples two-tailed T-test , with unequal variances assumed . The analysis was carried out using Microsoft Office Professional Plus Excel 2013 . The threshold for statistical significance was established as p<0 . 05 . We applied a Generalized Linear Model ( glm ) in R 3 . 1 . 2 ( 2014-10-31 ) — “Pumpkin Helmet” ( R Development Core , 2003 ) to analyze the encapsulation and parasite killing data ( R Core Team 2014 , R: A language and environment for statistical computing . R Foundation for Statistical Computing , Vienna , Austria , http://www . R-project . org/ ) . The categorical explanatory variable was “Cross” and the binary response variable was numbers of “successful encapsulation” or “killed parasites” and numbers of “failed encapsulation” or “failed parasite killing” . Differences between specific crosses were analyzed by Chi-square tests . We analyzed the cell spreading data and cell numbers 14–16 hours post infection with Welch’s T-test implemented in R 3 . 1 . 2 ( 2014-10-31 ) ( R Development Core , 2003 ) . The data were log-transformed prior to the analyses to obtain normal distribution . Full factorial analysis of variance ( ANOVA ) was applied to data on plasmatocyte and lamellocyte numbers 27–29 hours after infection with cross and infection status ( infected or not infected ) as explanatory variables . The data did not meet the requirement for normal distribution and was log transformed prior to the analyses . In the analysis of plasmatocyte numbers , a significant interaction term was found between cross and infection status and therefore plasmatocyte numbers were further analyzed conducting ANOVAs separately for each cross with infection status as explanatory variable . This data was analyzed using IBM SPSS Statistics version 22 . | The events leading to a successful encapsulation of parasitoid wasp eggs in the larvae of the fruit fly Drosophila melanogaster are insufficiently understood . The formation of a capsule seals off the wasp egg , and this process is often functionally compared to the formation of granulomas in vertebrates . Like granuloma formation in humans , the encapsulation process in fruit flies requires the activation , mobilization , proliferation and differentiation of different blood cell types . Here , we have studied the role of Edin ( elevated during infection ) in the immune defense against the parasitoid wasp Leptopilina boulardi in Drosophila larvae . We demonstrate that edin expression in the fat body ( an immune-responsive organ in Drosophila functionally resembling the mammalian liver ) is required for a normal defense against wasp eggs . Edin is required for the release of blood cells from larval tissues and for the subsequent increase in circulating blood cell numbers . Our results provide new knowledge of how the encapsulation process is regulated in Drosophila , and how blood cells are activated upon wasp parasitism . Understanding of the encapsulation process in invertebrates may eventually lead to a better knowledge of the pathophysiology of granuloma formation in human diseases , such as tuberculosis . | [
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] | [] | 2015 | Edin Expression in the Fat Body Is Required in the Defense Against Parasitic Wasps in Drosophila melanogaster |
Epstein-Barr Nuclear Antigen 1 ( EBNA1 ) is essential for Epstein-Barr virus to immortalize naïve B-cells . Upon binding a cluster of 20 cognate binding-sites termed the family of repeats , EBNA1 transactivates promoters for EBV genes that are required for immortalization . A small domain , termed UR1 , that is 25 amino-acids in length , has been identified previously as essential for EBNA1 to activate transcription . In this study , we have elucidated how UR1 contributes to EBNA1's ability to transactivate . We show that zinc is necessary for EBNA1 to activate transcription , and that UR1 coordinates zinc through a pair of essential cysteines contained within it . UR1 dimerizes upon coordinating zinc , indicating that EBNA1 contains a second dimerization interface in its amino-terminus . There is a strong correlation between UR1-mediated dimerization and EBNA1's ability to transactivate cooperatively . Point mutants of EBNA1 that disrupt zinc coordination also prevent self-association , and do not activate transcription cooperatively . Further , we demonstrate that UR1 acts as a molecular sensor that regulates the ability of EBNA1 to activate transcription in response to changes in redox and oxygen partial pressure ( pO2 ) . Mild oxidative stress mimicking such environmental changes decreases EBNA1-dependent transcription in a lymphoblastoid cell-line . Coincident with a reduction in EBNA1-dependent transcription , reductions are observed in EBNA2 and LMP1 protein levels . Although these changes do not affect LCL survival , treated cells accumulate in G0/G1 . These findings are discussed in the context of EBV latency in body compartments that differ strikingly in their pO2 and redox potential .
Epstein-Barr nuclear antigen 1 ( EBNA1 ) has two functions that are necessary for Epstein-Barr virus ( EBV ) to immortalize naïve human B-cells . EBNA1 is essential for the replication and partitioning of EBV genomes in latently-infected cells [1] , and activates the transcription of EBV genes that are essential for immortalization [2] . In earlier studies , the ability of EBNA1 to activate transcription was closely correlated to its ability to support EBV genome replication and partitioning during latency . Both activities require EBNA1 to bind a series of cognate binding sites termed the family of repeats ( FR ) , and alterations in repeat number caused proportional variations in both functions [3] . There have been recent advances in understanding how EBNA1 activates the transcription from EBV promoters . It is known that occupancy of specific sequences by the chromatin boundary factor , CTCF , regulates EBNA1's ability to activate specific viral promoters during latency [4] . Additionally , studies have also distinguished the ability of EBNA1 to activate transcription from its ability to support the replication and partitioning of EBV genomes . The amino-terminal half of EBNA1 contains two positively-charged regions with alternating glycines and arginines that can bind AT-rich DNA in a manner similar to AT-hook proteins [5] . Indeed , the amino-terminal 450 amino-acids of EBNA1 can be replaced with a cellular AT-hook protein , HMGA1a , and the resulting chimera HMGA1a-DBD , supports replication and partitioning of oriP-plasmids and EBV genomes in transformed cell-lines [2] , [6] , [7] . Considering the role of HMGA1a in the formation of transcription enhanceosomes [8] , [9] , HMGA1a-DBD is surprisingly deficient in the ability to activate transcription from EBV promoters [2] , [6] . These studies indicate that either AT-hook function is insufficient for transcription activation , or that it is irrelevant to the ability of EBNA1 to activate transcription . Complementing the conclusions drawn using chimeric proteins such as HMGA1a-DBD , a 25 amino acid-long domain has been identified in the amino-terminus of EBNA that is required for EBNA1 to transactivate [10] . This domain , termed unique region 1 ( UR1 ) , is juxtaposed to the first of EBNA1's two AT-hooks . Deletion of UR1 does not abrogate EBNA1's ability to support the replication and partitioning of EBV-derived plasmids , but reduces its transactivation activity to the levels observed with HMGA1a-DBD [10] . Emphasizing the importance of UR1 in transactivation , addition of multiple copies of UR1 to HMGA1a-DBD restores its ability to activate transcription and facilitate the immortalization of naïve B-cells [2] . The mechanism by which UR1 contributes to EBNA1's ability to activate transcription is unknown . Here , we report that while UR1 is necessary for EBNA1 to transactivate , it is not sufficient for transactivation . We demonstrate that UR1 coordinates zinc via two essential cysteines residues . Chelation of cellular zinc selectively and significantly reduces the ability of EBNA1 to activate transcription . Consistent with these results , mutation of the essential cysteines , prevents the coordination of zinc in vitro , and also causes a transactivation defect in vivo . By co-immunoprecipitation and bimolecular fluorescence complementation , we show that UR1 can interact homotypically , and that this ability correlates strongly with EBNA1's ability to activate transcription . Further , when UR1 dimerizes by coordinating zinc , it facilitates interactions between dimers of EBNA1 , and the latter are required for EBNA1 to transactivate cooperatively . We demonstrate that the essential , conserved cysteines within UR1 are regulated by redox , and that the ability of EBNA1 to activate transcription is subject to the oxygen levels prevalent in the environment . At oxygen levels that mimic that found in lymph nodes , EBNA1 activates transcription from the EBV BamHI-C promoter in a sustained manner; whereas at higher oxygen levels , transactivation is dampened rapidly . Exposure of a lymphoblastoid cell-line ( LCL ) to mild oxidative stress decreases transcription from the BamHI-C promoter , a promoter active in latency III . Reductions are observed in the levels of EBNA2 and LMP1 , genes whose promoters are transactivated by EBNA1 [11] , [12] , [13] . Although these changes do not affect the survival of treated cells , they accumulate in G0/G1 . The results reported in this study provide the first molecular insights into the mechanism by which EBNA1 activates transcription , and describe environmental changes that modulate transactivation . We discuss these results in the context of gene-expression observed in proliferating EBV-immortalized cells in hypoxic environments such as the lymph node , and non-proliferating EBV-infected cells observed in the relatively oxygen-rich periphery .
EBNA1 augments transcription of reporter plasmids bearing the family of repeats ( FR ) by both retaining FR-containing DNAs within cells to increase the number of available transcriptional templates [10] , [14] , and by directly transactivating FR-dependent promoters [2] , [6] , [10] . While plasmid retention requires the AT-hook domains of EBNA1 [5] , indicated as ATH1 and ATH2 in Figure 1A , an EBNA1 derivative with a deletion of a . a . 65–89 failed to activate transcription from a chromosomally integrated FR-dependent transcription reporter . The latter still contains ATH1 and ATH2 , indicating that AT-hooks are irrelevant or insufficient for transcription activation . This conclusion is supported by results obtained using a chimera between the cellular AT-hook protein , HMGA1a , and the DBD of EBNA1 . This chimera , HMGA1-DBD , retains oriP-plasmids as efficiently as EBNA1 , but fails to activate transcription from the same chromosomally integrated transcription reporter [6] . HMGA1a-DBD activates transcription from transiently transfected FR-containing reporter plasmids with approximately 10% the efficiency of EBNA1 ( ibid ) . Therefore , to distinguish the ability of EBNA1 derivatives to increase reporter transcription by plasmid retention versus transactivation , we have compared results obtained with wild type and mutant EBNA1 proteins to those obtained with HMGA1a-DBD ( Figure 1B ) . In transient transfections of C33a cells , using FR-TKp-luciferase as the reporter plasmid , EBNA1 transactivates the TK-promoter approximately 85-fold over the EBNA1 DNA binding domain ( DBD ) alone . In contrast , a mutant of EBNA1 with a . a . 71–88 deleted , EBNA1Δ ( 71–88 ) , activated transcription approximately two-times as well as the DBD alone . Using the same reporter , HMGA1a-DBD was observed to activate transcription approximately six-fold over the DBD . Having confirmed that the UR1 region of EBNA1 was necessary for transactivation to be observed even with episomal reporter plasmids , we tested if UR1 was sufficient for transactivation , using an UR1-DBD fusion protein . UR1-DBD failed to activate transcription from the FR-TKp-luciferase reporter , and was statistically indistinguishable from the effects of the DBD alone . Similar results were obtained upon transfection of EBV-negative BJAB Burkitt's lymphoma cells with oriP-BamHI-Cp-luciferase used as the reporter ( data not shown ) . Immunoblot analyses indicated that the failure of EBNA1Δ ( 71–88 ) and UR1-DBD to transactivate could not be attributed to lower expression levels or degradation ( Figure S1 ) . In this report , we have examined the mechanism by which UR1 facilitates transactivation by EBNA1 . Sequence comparison of the EBNA1 proteins from EBV and three related gammaherpesviruses indicated that a portion of UR1 , corresponding to a . a . 75–85 of EBV's EBNA1 protein , was conserved in all four proteins ( Figure 1C ) . A PSI-BLAST search conducted using these four conserved sequences matched portions of several zinc-binding proteins , particularly one-half of a conserved C4 zinc finger [15] present in the catalytic subunit of DNA polymerase δ from several species ( Figure 1C ) . The matched sequences contain a pair of cysteine residues separated by two amino acids , which consist of an aliphatic amino acid and an invariant glycine . The cysteines are flanked by basic amino-acids , with an amino-acid whose side-chain contains a hydroxyl or sulfhydryl group immediately adjacent to the first cysteine . In light of the UR1's homology to half a zinc-binding domains , we tested whether the conserved cysteines residues in UR1 were necessary for EBNA1's ability to transactivate FR-dependent promoters . For this , both cysteines were altered to serines . A schematic of the mutant protein , EBNA1 ( CC→SS ) is shown in Figure 1D , and by immunoblot analysis , EBNA1 ( CC→SS ) was observed to be expressed as well as wild-type EBNA1 ( Figure S2 ) . Transactivation assays indicated that this mutant is severely impaired in its ability to activate transcription from the FR-TKp-luciferase reporter in C33a cells ( Figure 1B ) and oriP-BamHI-Cp-luciferase reporter BJAB cells ( data not shown ) . In zinc-finger proteins , four cysteines , or a combination of four cysteines and histidines are used to coordinate zinc [15] . However , there are no other conserved cysteine or histidine residues present in the EBNA1 proteins from the four gammaherpesviruses , making it unlikely that these proteins coordinate zinc as monomers . Sequences that support multimerization were mapped previously to a fragment of EBNA1 containing UR1 [16] . Therefore , we postulated that the UR1 regions of adjacent EBNA1 molecules might coordinate zinc by contributing two cysteines apiece , and thereby dimerize . This was addressed experimentally by testing whether UR1 could coordinate zinc , and if zinc was required for EBNA1 to transactivate . To determine if UR1 coordinates zinc , peptides corresponding to a . a 58–89 of EBNA1 and EBNA1 ( CC→SS ) , indicated as WT and M in Figure 1D , were tested for their ability to bind radioactive zinc by a zinc-blot procedure previously used to identify and characterize several proteins that bind zinc [17] , [18] , [19] . As shown in Figure 2A , the wild-type peptide , but not the mutant , was observed to bind Zn65 . Parallel blots were stained with amido-black to confirm that both peptides bound PVDF with similar efficiencies . To test whether zinc was necessary for EBNA1 to transactivate , transfected cells were exposed to TPEN , a chelator known to have high specificity for zinc . Previously , TPEN has been used to probe the ability of p53 and other proteins to bind zinc within cells [20] , [21] , [22] . As shown in Figure 2B , TPEN diminished transactivation by EBNA1 in dose-dependent manner , such that exposure to 5 µM TPEN for 15 hours reduced transactivation by EBNA1 to approximately 4% of the untreated control . In contrast , a higher level of TPEN , approximately 40 µM , was required to affect the function of p53 [21] . EBNA1's ability to transactivate was also reduced in the presence of 1 . 5 µM and 3 µM TPEN ( data not shown ) . Because exposure to TPEN for shorter or similar lengths of time has been shown to induce apoptosis in some cell-lines [20] , a cell-cycle analysis was performed on TPEN-treated transfected C33a cells , and is summarized in Figure 2B . C33a cells were more resistant to TPEN-induced apoptosis than HeLa cells , BJAB cells and lymphoblastoid cell lines ( data not shown ) . At 45 µM TPEN , a substantial sub-G1 peak was observed . In contrast , at lower concentrations of TPEN the cell-cycle profiles of TPEN-treated cells were similar to those observed for untreated cells . Immunoblot analysis indicated that TPEN did not decrease transactivation by inhibiting the synthesis , or inducing the degradation of EBNA1 ( Figure 2B ) . The effect of TPEN was also examined on a co-transfected CMV-EGFP reporter plasmid , and found to not significantly change the fraction of live , GFP-positive cells , or the mean fluorescence index of these cells . To investigate the possibility that transcription from the TK promoter require other zinc-dependent transcription factors affected by TPEN , we examined whether TPEN altered transactivation by a chimeric protein in which the DBD was fused to the acidic activation domain of VP16 [23] ( DBD-VP16 ) . DBD-VP16 , which lacks the N-terminal 450 a . a of EBNA1 , activates transcription from the FR-TKp-luciferase reporter ( Figure 2C ) , and this activation is not reduced by treatment with 5 µM TPEN . Thus , exposure to TPEN specifically reduces the ability of EBNA1 to transactivate the FR-dependent transcription reporter plasmids . Next , we tested whether inhibition by TPEN was reversible by the subsequent addition of zinc or other metal ions ( Figure 2D ) . Transfected cells were treated with 5 µM TPEN for 15 hours , following which zinc or other metal ions were added for an additional 15 hours prior to analysis . It was observed that addition of Zn2+ or Cd2+ effectively restored transactivation , in contrast to Mg2+ , Ca2+ , Mn2+ and Fe2+ ( Figure 2D ) . Addition of Zn2+ or Cd2+ did not affect basal transcription from the FR-TKp-luciferase reporter , or transactivation of this reporter by DBD-VP16 ( data not shown ) . The ability of zinc to restore EBNA1-dependent transactivation in TPEN-treated cells was concentration dependent , and did not affect expression from a co-transfected CMV-EGFP reporter ( Figure S3 ) . We initially postulated that UR1 might facilitate a zinc-dependent interaction between EBNA1 and a cellular transcription co-activator . To identify such proteins , a yeast two-hybrid screen was performed using the first 94 amino acids of wild-type EBNA1 as the bait protein against a cDNA library from HeLa cells , a cell-line in which EBNA1 has been observed to activate transcription . No protein identified in this screen associated specifically with the bait protein , but not an UR1-deleted derivative ( data not shown ) . In an effort to test our hypothesis that UR1 associates with cellular transcription factors , we tested whether co-expression of EBNA1's amino-terminal 450 a . a . interferes with EBNA1's ability to activate transcription from an FR-dependent reporter plasmid , by squelching its interaction with putative cellular co-activators or general transcription factors [24] , [25] . As shown in Figure 3 , co-transfecting increasing amounts of an expression plasmid encoding a fusion between the first 450 a . a . of EBNA1 and the DNA binding domain of the BPV-1 E2 protein , 3xF-EBNA1 ( 1-450 ) -E2DBD , did not affect the ability of EBNA1 to activate transcription from the FR-TKp-luciferase reporter , over the pcDNA3 control . In contrast , co-transfecting a DBD expression plasmid decreased the expression observed from FR-TKp-luciferase , by expressing a protein that competes efficiently with EBNA1's ability to bind FR [26] . While 3xF-EBNA1 ( 1-450 ) -E2DBD does not interfere with the function of wild-type EBNA1 , it retains the ability to activate transcription from a TKp-luciferase reporter plasmid containing twenty E2-binding sites ( Figure S4 ) , and this transactivation is sensitive to treatment with 5 µM TPEN ( Figure S5 ) . Thus , the inability of 3xF-EBNA1 ( 1-450 ) -E2DBD to squelch transactivation of FR-TKp-luciferase by EBNA1 is not because it is incapable of activating transcription via a mechanism similar to that used by EBNA1 . Some proteins transactivate by directly altering DNA template structure . For example , the prototypic cellular AT-hook protein , HMGA1a , bends DNA to mediate transcription enhanceosome formation [8] , [27] . HMGA1a , p53 , Ikaros , and other proteins are also known to homotypically interact , loop intervening DNA , and thereby juxtapose a distal enhancer to a promoter [28] , [29] , [30] . EBNA1 has AT-hooks , and is known to loop oriP-DNA [31] , [32] . Looping activity correlates to domains in the first 450 a . a . of EBNA1 , particularly UR1 [16] , [33] , [34] . Therefore , we tested whether UR1 functions as a zinc-dependent self-association domain . Co-immunoprecipitation was used to test if EBNA1 ( 1-450 ) -E2DBD associates with wild-type EBNA1 , and EBNA1 ( CC→SS ) . For this , EBNA1 ( 1-450 ) -E2DBD , which is amino-terminally tagged with a 3xFLAG epitope , was co-expressed in 293 cells along with EBNA1 or EBNA1 ( CC→SS ) . Lysates were incubated with a monoclonal anti-Flag antibody linked to sepharose beads , and examined by immunoblot . As shown in Figure 4A , EBNA1 was co-precipitated with EBNA1 ( 1-450 ) -E2DBD using the anti-FLAG antibody . In contrast , EBNA1 ( CC→SS ) was never observed to associate with EBNA1 ( 1-450 ) -E2DBD . Immunoblots using anti-EBNA1 antibody indicated that EBNA1 and EBNA1 ( CC→SS ) were expressed equivalently in lysates . Having determined that the amino-terminal 450 a . a . of EBNA1 could self-associate if the sequence of UR1 was not altered , we tested whether UR1 was sufficient to mediate homotypic interactions using bimolecular fluorescence complementation [35] . For this , UR1 was fused to amino acids 2-154 of EYFP , or to amino acids 155-238 of EYFP . Expression plasmids were co-transfected into 293 cells , and examined by fluorescence microscopy . While no fluorescence was observed using the parental plasmids expressing both halves of EYFP ( Figure 4B ) , or with each of the UR1 plasmids transfected singly ( data not shown ) , the UR1 fusions complemented each other ( Figure 4B ) . The ability of zinc chelators to disrupt complementation was tested using 1 , 10-phenanthroline , which has been used widely to dissect the role of zinc association with various proteins [36] , [37] . TPEN could not be used for this experiment because even low levels of TPEN cause 293 cells to dissociate from the coverslip . Transfected cells were treated with 80 µM 1 , 10-phenanthroline for 24 hours , and then examined by microscopy . Treatment of transfected cells with 80 µM 1 , 10-phenanthroline for 24 hours caused a large decrease in the number and intensity of EYFP-positive cells ( Figure 4B ) . These results indicate that UR1 can self-associate in a manner dependent on metal ions . While the previous results indicate that UR1 can mediate dimerization as evaluated by bimolecular fluorescence complementation , they do not distinguish between direct association and indirect association mediated by a cellular protein . If zinc coordination results in direct association , it is relevant to consider that in addition to forming dimers , pairs of cysteines can form trimers and tetramers , as observed in the GAL4 DBD [38] , or in the RING finger [39] . To distinguish between such multimeric forms , we examined the migration properties of the wild-type UR1 peptide in the presence and absence of zinc by size exclusion chromatography . After establishing conditions to observe a highly reproducible elution profile for molecular weight standards , the peptides were examined by chromatography in the presence and absence of 1 mM zinc sulfate . The results of this analysis are shown in Figure 5A . The apparent molecular weight ( MW ) calculated from the peak retention time for the peptides in the absence of zinc were close to their theoretical MWs . WT peptide eluted as a single peak in the absence of zinc with an apparent molecular weight of 3 . 6 kD ( Figure 5A ) . When the wild-type peptide was analyzed in the presence of zinc , two peaks were observed: 1 ) a small peak , representing approximately 5% of the total , migrated at an apparent molecular weight of 1 . 6 kD; 2 ) a large peak , consistent with size of a dimer that migrated at a apparent molecular weight of 6 . 8 kD . The presence or absence of zinc had no effect on the retention time of the mutant peptide ( data not shown ) . These data indicate that at least in the context of a peptide , UR1 dimerizes in the presence of zinc . The ability of UR1 to dimerize indicates that either EBNA1 contains a second intra-molecular dimerization domain at its N-terminus , or that dimers of EBNA1 may form larger inter-molecular complexes through UR1 . To distinguish between these two possibilities , attempts were made using cross-linkers or Blue native electrophoresis to resolve complexes formed by wild-type EBNA1 within transfected cells . In such gels , we could not distinguish between multimers of EBNA1 dimers , and EBNA1 associated with cellular proteins that interact with EBNA1 , such as p32/TAP/gC1qR [40] , Importin-α/Rch1 [41] , p40/EBP2 [42] , and Brd4 [43] ( data not shown ) . Therefore , we resorted to an alternative approach in an effort to distinguish between intra-molecular and inter-molecular dimerization mediated by UR1 . This approach was chosen on the basis of two alternative hypotheses for the mechanism by which UR1 dimerization may contribute to transactivation . In the first , it was hypothesized that UR1 was necessary to form intra-molecular dimers that are essential for transactivation . Alternatively , it was hypothesized that inter-molecular dimerization by UR1 was necessary to form a structured array of EBNA1 bound to FR , and that such arrays were necessary for transactivation . These two hypotheses can be distinguished by examining how transactivation by wild-type EBNA1 or EBNA1 ( CC→SS ) varies with the number of EBNA1-binding sites . If UR1 is necessary to form intra-molecular dimers required for transactivation , then it is predicted that EBNA1 ( CC→SS ) will transactivate poorly relative to wild-type EBNA1 , independent of the number of binding sites . In contrast , if UR1 is necessary to form inter-molecular dimers required for transactivation , then it is predicted that for a single binding site or a small number of binding sites , EBNA1 ( CC→SS ) will transactivate equivalently to EBNA1 , with a defect in transactivation being exposed as the number of binding sites is increased . Therefore , the ability of wild-type EBNA1 and EBNA1 ( CC→SS ) to transactivate a series of reporter plasmids containing one , three , five , seven , ten and 20 binding-sites was examined under conditions previously determined to be optimal to observe cooperative transactivation ( Figure 5B ) . The white bars in Figure 5B indicate the level of transactivation predicted with increases in the number of binding sites , assuming a simple additive relationship between the two . In contrast , as shown in Figure 5B , and observed previously [3] , [44] , transactivation by EBNA1 ( black bars ) is strongly cooperative , with a synergism that is proportional to the number of binding sites . Under optimal conditions , EBNA1 bound to 20 binding-sites transactivates approximately 150-fold over EBNA1 bound to a single binding-site , and it is important to note that the latter is reproducibly two to three-fold over what is observed in the absence of EBNA1 . Transactivation by EBNA1 ( CC→SS ) ( gray bars ) could not be distinguished statistically from wild-type EBNA1 for reporter plasmids containing one , three or five binding sites . We note that for such small numbers of binding sites , wild-type EBNA1 does not display significant synergism . In contrast to wild-type EBNA1 , at higher numbers of binding sites , EBNA1 ( CC→SS ) does not transactivate cooperatively . We interpret these results to indicate that the major contribution of UR1 toward EBNA1's ability to transactivate is by facilitating cooperativity; the latter being more consistent with inter-molecular interactions than intra-molecular dimerization . When the correlation between transactivation and number of binding sites is fit to a sigmoidal dose-response curve by non-linear regression , strong positive cooperativity is observed for wild-type EBNA1 , with a Hill coefficient ( HC ) of 3 . 9±0 . 48 ( Figure 5C ) , and a goodness of fit ( R2 ) of 0 . 9996 . No cooperativity is observed when transactivation by EBNA1 ( CC→SS ) is fit to the same sigmoidal dose-response curve ( HC = 31 . 77±40698 ) , and the data fit this model poorly ( R2 = 0 . 9459 ) ( Figure 5D ) . In conjunction with other results presented here ( Figure 4 and Figure 5A ) , it is concluded that UR1 facilitates cooperative transactivation by mediating inter-molecular interactions between adjacent EBNA1 dimers bound to FR . It remains to be determined whether such cooperative interactions facilitate architectural changes at the promoter , or association with a coactivator . We note that if a multimerization-dependent coactivator mediates transactivation , it is equally inefficient in associating with wild-type EBNA1 and EBNA1 ( CC→SS ) when these proteins are bound to one , three or five binding-sites . While our data favor a role for UR1 in inter-molecular associations over intra-molecular dimerization , we reiterate that either interaction remains dependent on the ability of two cysteines residues within UR1 to coordinate zinc . The ability of cysteines to associate with zinc is regulated by their redox status . Reduced cysteines can coordinate zinc , whereas oxidized cysteines cannot . For zinc-finger proteins , and other transcription factors , it has been observed that the redox state of cysteine modulates activity . Transactivation by AP-1 , NFκB , and zinc-binding proteins such as p53 and Egr-1 is modulated by intracellular redox , and is increased two to three-fold by over-expression of redox factor 1 ( Ref-1/APE1 ) [45] , [46] , [47] , [48] , [49] . The oxidation status of zinc-coordinating cysteines regulates the activity of several zinc-finger transcription factors , such as Sp1 , Egr-1 and steroid hormone receptors [45] , [50] , [51] , [52] , because oxidation of sulfhydryl groups ( -SH ) results in the progressive generation of sulfenic acid ( -SOH ) , sulfinic acid ( -SO2H ) and sulfonic acid ( -SO3H ) , none of which can coordinate zinc . Therefore cysteine oxidation alters the structure , and consequentially the function of zinc-binding proteins [53] . Menadione , a redox-reactive p-quinone has been used to study the effect of redox on proteins containing reactive cysteines , including estrogen receptor and the SMN complex [54] , [55] . Because zinc coordination is required for EBNA1 to activate transcription , we tested whether exposure to menadione affected transactivation . C33a cells co-transfected with an EBNA1-expression plasmid and the FR-TKp-luciferase reporter plasmid were split six hours post-transfection and exposed to increasing levels of menadione for 18 hours prior to analysis . Menadione reduced transactivation in a dose-dependent manner ( Figure 6A ) , without affecting expression of a co-transfected CMV-EGFP reporter plasmid ( data not shown ) . Although transactivation was reduced to as little as 20% , no profound change in the cell-cycle profile of transfected cells was observed upon menadione treatment at concentrations up to 2 µM ( Figure 6A ) . The highest concentration of menadione used here is considerably lower than concentrations of menadione used to induce apoptosis via oxidative stress [56] . A similar 50% decrease in transactivation was observed upon treatment of transfected cells with 150 µM of the mild oxidant paraquat ( Figure S6 ) . Menadione did not affect transactivation by DBD-VP16 from the same reporter ( Figure S7 ) , indicating that it is unlikely to have inactivated components of the basal transcription apparatus used at the HSV-1 Tk promoter . Bimolecular fluorescence complementation was used to examine whether oxidative stress impaired the ability of UR1 to support dimerization , which is essential for transactivation . Cells transfected with the UR1-EYFP derivatives described above were split six hours post-transfection , and then exposed to increasing concentrations of menadione for 18 hours prior to analysis by fluorescence microscopy . At concentrations of menadione above 0 . 6 µM , a statistically significant reduction in the fraction of fluorescent cells was observed ( Figure 6B ) , with a concomitant decrease in fluorescence intensity ( Figure 6B and data not shown ) . In the absence of menadione , approximately 20 fluorescent cells were observed for every 100 adherent cells . In the presence of 1 . 4 µM menadione , where a 50% reduction in transactivation was observed ( Figure 6A ) , approximately five fluorescent cells were observed for every 100 adherent cells . Together , these results indicate that the reduction in transactivation observed upon oxidative stress correlates with an impaired ability of UR1 to support dimerization . During oxidative stress , thioredoxin directly reduces cysteines on target transactivators , or increases the amount of reduced Ref-1/APE1 available to reduce cysteines on target proteins [57] , [58] . One characteristic of cysteines that are reduced by Ref-1/APE1 is their close juxtaposition to lysine and/or arginine residues . The conserved cysteines in UR1 lie in such a connotation ( Figure 1C ) . It was therefore tested whether over-expression of Ref-1/APE1 would affect EBNA1's ability to transactivate . For this , C33a cells were co-transfected with an EBNA1-expression plasmid , the FR-TKp-luciferase reporter plasmid , and increasing amounts of an expression plasmid for Ref-1/APE1 . This analysis , shown in Figure 6C , indicated that over-expression of Ref-1/APE1 increased the ability of EBNA1 to activate transcription by as much as 3-fold , a magnitude of increase similar to its effect on other redox-sensitive transcription factors [45] , [46] , [47] , [48] , [49] . Ref-1/APE1 did not increase transactivation by DBD-VP16 from the same reporter , and did not affect expression of either DBD-VP16 or EBNA1 ( data not shown ) . Because Ref-1/APE1 augmented EBNA1's ability to transactivate , we tested whether over-expression of Ref-1/APE1 would ameliorate the effect of oxidative stress induced by menadione . For this , C33a cells were transfected with an EBNA1-expression plasmid , the FR-TKp-luciferase reporter plasmid and either an APE1-expression plasmid , or empty vector control . Cells were split six hours post-transfection , and one-half treated with 1 . 4 µM menadione . As is shown in Figure 6D , cells that over-express Ref-1/APE1 are resistant to menadione treatment . Similarly , over-expression of Ref-1/APE1 reversed the effect of 150 µM of paraquat on EBNA1 ( Figure S8 ) . Thus , as reported for several other viral and cellular transactivators , Ref-1/APE1 also facilitates EBNA1's ability to transactivate by modulating redox . B-cells infected latently by EBV reside in the periphery and internal body compartments such as lymph nodes and tonsilar tissue . These compartments differ considerably in the availability of O2 , so that the oxygen partial pressure ( pO2 ) is approximately 80 torr ( 10 . 5% O2 ) in the periphery while the pO2 in lymph ranges between 20–40 torr ( 2 . 5%–5% O2 ) [59] , [60] , or lower [61] . The latently infected cells in these compartments are dramatically different , with distinct patterns of viral gene expression . Cells in the lymph node express all the EBV genes necessary to drive proliferation of latently-infected cells ( latency III ) . In contrast , latently-infected cells in the periphery are largely quiescent , and do not express any viral proteins , with the exception of EBNA1 when cells divide ( latency 0/I ) [62] , [63] . While it is clear that there are epigenetic changes on the EBV genome associated with these two distinct patterns of latency [4] , [64] , [65] , the contributions of viral proteins toward this “imprinting” is unknown . Transactivators , such as NFκB , Egr-1 , p53 and AP-1 , whose activities are increased by over-expression of Ref-1/APE1 , are also more active under hypoxic conditions [47] , [66] , [67] , [68] . We therefore tested the effect of hypoxia on EBNA1's ability to transactivate . For this , C33a cells were co-transfected with an EBNA1-oriP expression plasmid , and the oriP-BamHI-Cp-luciferase reporter plasmid . Transfected cells were split six hours post-transfection , and incubated in parallel under normoxic ( 21% O2 ) and hypoxic ( 4% O2 ) conditions for six days , and it is emphasized that the latter mimic the pO2 found in lymph nodes [59] , [60] , [61] . The expression of luciferase was measured daily during this time-course , and the amount of replicated oriP-BamHI-Cp-luciferase reporter plasmid was measured at the end of six days . At the earliest measurement point , 48 hours post-transfection , the levels of luciferase were approximately 25% higher in cells maintained under hypoxia than cells maintained in normoxic conditions ( Figure 7A ) . For both conditions , the level of luciferase expression decreased over time; however , the rate of decrease was more rapid under normoxic conditions than hypoxic conditions . As a consequence of the difference in rates , by six days post-transfection , the average amount of luciferase expressed in hypoxic cells was 3 . 5-fold the level expressed in normoxic cells ( Figure 7A ) . Southern blots performed to quantify the level of replicated oriP-BamHI-Cp-luciferase reporter plasmids six days post transfection , indicated equivalent numbers of plasmids per cell under both conditions ( Figure 7B ) . The latter result indicates that differences in luciferase expression between hypoxic and normoxic conditions cannot be attributed to differences in EBNA1's association with its cognate binding sites in oriP under these two conditions . To confirm this , an expression plasmid for DBD-VP16 was co-transfected with FR-TKp-luciferase into C33a cells that were subsequently maintained at hypoxic or normoxic conditions for 72 hours . No difference in luciferase expression was detected ( Figure 7C ) , reiterating the conclusion that the difference in luciferase activity under normoxic and hypoxic reflects a difference in EBNA1's ability to activate transcription under these two conditions , and not its ability to bind FR or support oriP replication and maintenance . These experiments provide evidence that UR1 can act as a molecular sensor that modulates EBNA1's ability to activate transcription in response to environmental conditions . To determine the relevance of these observations for EBV gene-expression within immortalized cells , we examined the effects of oxidative stress on transcription from the BamHI-C promoter in a LCL . These experiments were conducted in an LCL , NOLA-1 , immortalized using the B95-8 strain of EBV . In preliminary experiments , it was observed that while treatment of NOLA-1 with 1 . 4 µM menadione was toxic within 18 hours , treatment with 150 µM paraquat was not toxic for at least 72 hours . Therefore , NOLA-1 cells were treated with 150 µM paraquat . Aliquots of cells were removed at 48 and 72 hours to assay transcription from the BamHI-Cp promoter by real-time PCR , the results of which are shown in Figure 8A . Within 48 hours of treatment with 150 µM paraquat , the level of BamHI-Cp transcripts was observed to decrease to approximately 60% of the level observed in control cells . This decrease was significant , with a p-value of less than 0 . 01 . After 72 hours of treatment , a further decrease in the level of BamHI-Cp transcripts was observed , such that it was approximately 40% the level observed in control cells ( p<0 . 01 ) . Treatment with paraquat did not decrease the viability of cells as estimated by mitochondrial respiration for up to 72 hours ( data not shown ) . In addition , a cell-cycle analysis indicated that paraquat treatment did not increase the fraction of sub-G1 cells , although paraquat-treated cells clearly accumulated in G0/G1 after 72 hours of exposure ( Figure 8A ) . Staining with Annexin V and propidium iodide indicated a small increase in the numbers of doubly-positive necrotic cells , and annexin V-positive apoptotic cells as a consequence of paraquat treatment . However , we emphasize that the majority of cells remained doubly-negative ( Figure 8B ) . Therefore , the decreases observed in BamHI-Cp transcription do not result from cellular necrosis or apoptosis . The decrease in BamHI-Cp transcription was confirmed by examining protein levels for EBNA2 and LMP1 by immunoblot , along with the levels of EBNA1 . This analysis is shown in Figure 8B . No significant decrease in EBNA1 protein levels were observed after 72 hours of exposure to paraquat , indicating that the decrease in BamHI-Cp transcription does not result from a reduction in EBNA1 . At the same time-point , an approximately 50% decrease in the levels of EBNA2 and LMP1 were observed . Both these proteins have substantially shorter half-lives than EBNA1 [69] , [70] , [71] , and at least for EBNA2 the decreased protein level parallels the decrease in BamHI-Cp transcription . The decrease in levels of LMP1 could either result from a diminution in transactivation by EBNA1 , or by a reduction in the levels of EBNA2 , as both proteins are known to transactivate the LMP1 promoter [11] , [12] , [13] , [72] , [73] . Finally , indirect immunofluorescence was used to determine whether oxidative stress altered the sub-cellular localization of EBNA1 , as has been observed for other transactivators . This analysis is shown in Figure 8C , and indicates that although small changes in the nuclear localization of EBNA1 are observed even by 48 hours of exposure to paraquat , the majority of EBNA1 remains nuclear . During microscopy , mitotic figures were observed for approximately 5% of control cells , with punctate EBNA1 staining ( two such cells are shown in Figure 8C ) . Such mitotic cells were rarely observed after even 48 hours of paraquat treatment , consistent with the observation that treated cells accumulate in G0/G1 . In summary , limited oxidative stress does not alter the levels or nuclear localization of EBNA1 , but decreases BamHI-Cp transcription , and reduces EBNA2 and LMP1 levels . Concurrent with these changes , treated cells accumulate in G0/G1 .
At the onset of these studies , it was known that deletion of UR1 abolished EBNA1's ability to transactivate [10] , and that UR1-deleted EBV failed to immortalize naïve B-cells [2] . We have dissected the mechanism by which UR1 functions for EBNA1 to transactivate , and observed that transactivation is subject to regulation by environmental conditions . We have determined that the UR1 domain of EBNA1 coordinates zinc through two conserved , essential cysteines , and upon doing so self-associates . Point mutation of these cysteines eliminates the ability of EBNA1 to self-associate , coordinate zinc , or activate transcription . In addition , chelation of intracellular zinc severely impacts the ability of EBNA1 to activate transcription , and blocks the ability of UR1 to support self-association . Together these data provide the first evidence that a domain of EBNA1 necessary for transactivation coordinates zinc , and upon doing so , allows EBNA1 to self-associate . Because short motifs containing two cysteines residues have been known to mediate heterotypic protein interactions , as exemplified by the interaction between the lck kinase and CD4 or CD8 [74] , we explored the possibility that through zinc coordination UR1 permits EBNA1 to interact with another protein such as a cellular transcription co-activator . Indeed , it has been shown recently that the UR1 region of EBNA1 interacts with the cellular Brd4 protein , and interpreted that this interaction contributes to transactivation [43] . Our failure to find proteins such as Brd4 could either reflect their rarity , or that co-activators only associate with an array of EBNA1 bound to FR , a condition not met by our two-hybrid screen . However , we note , that co-activators were also not found in previous one-hybrid screens in which FR-bound EBNA1 was used as bait [42] , [75] . In addition , while deletion of UR1 severely reduces transactivation , an EBNA1 derivative with multiple copies of UR1 did not activate transcription to higher levels than wild-type [76] , as might be expected if UR1 functions by recruiting a transcription coactivator . Therefore , we propose that UR1 contributes structurally to EBNA1's ability to transactivate , when it is bound to FR . Such a structural contribution likely explains our observation that while the amino-terminal 450 a . a . of EBNA1 contains domains sufficient for transactivation , it does not squelch EBNA1's ability to transactivate . The inefficiency with which FLAG-tagged EBNA1 ( 1-450 ) -E2DBD pulls down EBNA1 reinforces our belief that for intact EBNA1 , self-association through UR1 is facilitated upon binding FR . An implication of the results obtained when comparing transactivation by EBNA1 and EBNA1 ( CC→SS ) is that an intact UR1 domain is required for cooperative transactivation . Thus , cooperative association with an array of cognate binding sites by the DBD of EBNA1 does not solely underlie EBNA1's ability to activate transcription cooperatively . The mechanisms for transactivation that we have considered are grounded in the prior observation that EBNA1 contains a self-interacting domain that maps to UR1 [16] , and that several zinc-coordinating domains are known to self-associate through homotypic interactions . Examples of the latter include zinc-binding domains within the Ikaros family of proteins [29] , the E7 protein of HPV18 [19] , and the Tat protein of HIV-1 [77] . Studies on many transactivators indicate that homotypic interactions are often associated with structural changes in the template that facilitate transactivation . These include architectural proteins such as HMGA1a [28] , cellular transcription factors such as p53 , Sp1 , and Ikaros [30] , [32] , [78] , and the E2 transactivator of HPV [79] , [80] . Using these observations as precedent , and the knowledge that for transactivation EBNA1 requires both UR1 and AT-hook domains , we propose two models by which UR1 contributes to transactivation . In the first model , it is speculated that UR1 mediates loop formation between EBNA1 bound to FR and as yet unidentified promoter proximal sites , and that such loops facilitate the association of EBNA1's AT-hooks with AT-rich sequences present at EBV promoters such as BamHI-Cp . In the second model , which we believe is more consistent with our data , we propose that UR1 facilitates interactions between EBNA1 dimers bound to adjacent sites in FR to create a structured array of EBNA1 at FR . Further , that the structure presented by such an array facilitates the interaction of EBNA1's AT-hooks with promoter proximal AT-rich sequences , and the possible recruitment of co-activators . The mechanism by which UR1 dimerizes suggests that transactivation can be regulated by the availability of zinc , and by modulating the ability of the two conserved cysteines in UR1 to participate in zinc coordination . Zinc levels are known to differ in various body compartments , and the function of immune cells is dependent on zinc [81] , [82] . Therefore , it is possible that the site a latently-infected cell resides in affects the function of EBNA1 within that cell . Cellular zinc homeostasis is known to be regulated finely [83] , and has recently been shown to be relevant to the pathogenesis of oncogenic papillomaviral infections , through the action of the viral E6 and E7 proteins on cellular zinc-binding proteins such as metallothioneins [84] , and modulation of cellular zinc transporters by the viral E5 protein [85] . Cellular zinc availability directly affects the activity of the eukaryotic transactivators such as MTF-1 in human cells [86] , [87] , ZafA in Aspergillus [88] , and Zap1 in Saccharomyces [89] . The oxidation status of cysteines affects the activities of many proteins that participate in gene expression including transcription factors and the SMN splicing complex [53] , [55] . Such an effect is pronounced for zinc-binding proteins whose structure and function are altered by cysteine oxidation . Indeed , it is estimated that estrogen receptor isolated from approximately one-third of untreated ER-positive breast tumors is unable to bind its cognate binding site [52] , a defect that can be reversed by treating cell extracts with reducing agents such as dithiothreitol [90] . For some zinc-finger proteins such as Egr-1 , over-expression of the cellular redox mediator , Ref-1/APE1 , and hypoxic conditions are shown to increase activity both by increasing the synthesis of Egr-1 , and facilitating its nuclear translocation [91] . For the glucocorticoid receptor , redox modulates nuclear translocation in addition to DNA binding [92] . It is intriguing that the ability of EBNA1 to transactivate is modulated by intracellular redox , and that the activity of EBNA1 differs under normoxic and hypoxic conditions . B-cells latently infected by EBV exist as proliferating blasts in lymph nodes expressing all the latency-associated genes necessary for cell proliferation ( latency III ) , and as a subset of quiescent memory B-cells in the periphery where the only viral protein expressed is EBNA1 , and that too only when cells divide ( latency 0/I ) [62] , [63] . This striking difference in phenotype correlates with epigenetic changes associated with viral promoters active during latency III and shutdown during latency I . There are prominent physiological differences between these two niches that latently-infected cells reside in . Sites like the spleen , thymus and lymph nodes are hypoxic with an effective oxygen level of between 2% and 5% . In contrast , the periphery is relatively oxygen-rich , with an effective oxygen level of approximately 10% . Studies conducted over the last 40 years have indicated that oxygen levels affect the behavior of B-cells; Mishell and Dutton [93] first showed that the proliferation and antibody production properties of murine splenic B-cells was augmented considerably under hypoxic conditions . There have been studies since then indicating that cytokine production properties of T-cells and B-cell differ considerably when the cells are grown under hypoxic conditions that mimic oxygen levels in vivo , or under normoxic conditions that typically prevail when cells are grown in culture [59] , [94] . Exposure of a proliferating EBV-immortalized LCL to mild oxidative stress decreases transcription from the viral BamHI-C promoter , consistent with observation made with transcription reporter plasmids in transformed cell-lines . This decrease in transcription manifests itself as decreased levels of the viral EBNA2 and LMP1 proteins , which are essential for the continued proliferation of EBV-immortalized cells [95] , [96] . Treatment was not observed to affect LCL survival , although treated cells did accumulate in G0/G1 . As such , there are parallels between these results , and those obtained with recombinant EBVs expressing conditional derivatives of EBNA2 and LMP1 ( ibid ) . Conditional inactivation of EBNA2 after immortalization results in a rapid depletion of LMP1 . Such cells exit the cell-cycle , and undergo necrosis [95] . It is likely that our treated cells do not undergo necrosis because EBNA2 and LMP1 are only partially depleted . Conditional inactivation of LMP1 causes newly-infected cells to become cell-cycle arrested , but survive quiescently [96] . It is known that the expression level of LMP1 , as well as its sub-cellular localization can dramatically affect cellular physiology [97] , [98] , [99] , [100] . Thus although we observe only a 50% decrease in the level of LMP1 , this decrease may be sufficient to down-modulate mitogenic signals required for cell proliferation . While it has been long known that hypoxic conditions alter the propagation and pathogenicity of bacterial , viral and eukaryotic pathogens , the molecular details elucidating these effects have emerged more recently . Redox controls the activity of the OxyR family of prokaryotic transactivators , such that changing culture conditions from anaerobic to aerobic induces the expression of OxyR-induced genes [101] . The Tat protein of HIV-1 induces apoptosis of naïve T-cells under normoxic conditions; yet , at physiological oxygen levels ( hypoxic conditions ) , Tat stimulates T-cell proliferation , and primes them for infection by HIV-1 [102] . The ability of the papillomaviral E2 protein [103] , and EBV's lytic transactivator BZLF1/Zta [104] to support replication is regulated by redox . Our results raise the intriguing possibility that in the severely hypoxic conditions prevalent in lymph nodes EBNA1 transactivates the expression of viral genes required for cell proliferation , but when such cells escape to the periphery , higher oxygen levels gradually diminish the ability of EBNA1 to transactivate while leaving intact its ability to maintain viral genomes . This model is consistent with the observation that EBNA1 with UR1 deleted continues to support EBV genome replication and partitioning , and is not inconsistent with the ability of EBV to efficiently immortalize B-cells grown in vitro under normoxic conditions . It is known that exposure of primary cells under hypoxic conditions to normoxia induces the expression of redox genes , particularly thioredoxin , and consistent with this , established EBV-positive lymphoma lines grown in vitro express considerably higher levels of thioredoxin than primary B-cells [105] . In summary , our studies provide mechanistic insights that underpin the ability of EBNA1 to self-associate and to activate transcription . Transactivation by EBNA1 relies on its ability to interact homotypically by coordinating zinc through two conserved cysteines . EBNA1's ability to transactivate is subject to regulation by the availability of zinc , and oxygen levels in the environment , implying a mechanism to modulate EBNA1's ability to transactivate without affecting its ability to stably maintain EBV episomes . The regulatory model proposed here for EBNA1 is likely applicable to proteins of other viral , prokaryotic , and eukaryotic pathogens that traffic between environmentally distinct niches within the body .
Plasmids expressing wild-type EBNA1 , the EBNA1 DBD and HMGA1a-DBD have been described previously [6] , [26] , [106] . Similar plasmids were constructed to express EBNA1Δ ( 71-88 ) , EBNA1 ( CC→SS ) , and UR1-DBD . The chimeric protein 3xF-EBNA1 ( 1-450 ) -E2DBD was constructed by fusing a . a . 1–450 of EBNA1 fused to a . a . 239–410 of BPV-1 E2 , with a 3xFLAG epitope tag at the amino-terminus of EBNA1 . The chimeric protein DBD-VP16 was constructed by fusing the EBNA1 DBD to a . a . 413–490 of HSV-1 VP16 . For bimolecular fluorescence complementation , a . a . 61–90 of EBNA1 was fused independently to a . a . 155–238 , and 2–154 of EYFP . FR-TKp-luciferase [3] reporter plasmid and oriP-BamHI-Cp-luciferase reporter plasmid [26] were described previously . The 2xMME-TKp-luciferase reporter , was constructed by replacing FR in FR-TKp-luciferase with two copies of the BPV-1 MME [107] . The Ref-1/APE1 expression plasmid was a gift from Dr . Tadahide Izumi . Derivatives of the FR-TKp-luciferase reporter plasmids with one , three , five , seven , ten , and 20 binding sites have been described previously [3] . Experiments were performed in C33a ( HPV-negative ) cervical cancer cells , BJAB ( EBV-negative ) Burkitt's lymphoma cells , and 293 human embryonic kidney epithelial cells , propagated and transfected as described previously [3] , [5] , [6] . NOLA-1 is a lymphoblastoid cell-line established from the peripheral B-cells of an HIV-1 negative donor by infection with B95-8 EBV , using established protocols [108] . Cells used for these experiments have been passaged fewer than fifteen times after an immortalized clone was obtained . Proteins were immunoblotted as described earlier [5] , [6] . Rabbit polyclonal antibody K67 . 3 was used to detect EBNA1 , and the M2 mouse monoclonal anti-FLAG antibody ( Sigma , St . Louis , MO ) was used to detect FLAG-tagged proteins . Goat polyclonal antibody 2824 was used to detect EBNA2 , and rabbit polyclonal antibodies 2825/2826 were used to detect LMP1 . Polyclonal antibodies against EBNA1 , EBNA2 and LMP1 were obtained from Pacific Immunology using three antigenic peptides from each protein . Mouse monoclonal antibody ( 8226 ) against β-actin was purchased from Abcam ( Cambridge , MA ) . Reporter assays were performed as described earlier [3] using 500 ng of the reporter plasmid and 10 µg of the effector plasmid . Flow cytometry was used to normalize raw luciferase values to correct for the percent of live-transfected cells ( PI-negative , GFP-positive ) in each transfection . In experiments examining cooperative transactivation , 100 ng of each reporter plasmid was co-transfected with 1 µg of the effector plasmid and 500 ng of a GFP expression plasmid . Raw luciferase values were corrected for the percent of live-transfected cells . Cooperativity plots were generated by non-linear regression analysis fit to a sigmoidal dose-response curve using Prism ( GraphPad Software , La Jolla , CA ) . N , N , N′ , N′-Tetrakis- ( 2-pyridylmethyl ) -Ethylenediamine ( TPEN ) ( Sigma , St Louis , MO ) was used to chelate zinc . After transfection , cells were incubated with TPEN for 15 hours , following which they were analyzed to determine the percent of live-transfected cells , the cell-cycle profile , and luciferase activity . In supplementation experiments , the indicated metal salt was added to media already containing TPEN . Metals were added as solutions of the followings salts , zinc acetate , cadmium acetate , ferrous acetate , calcium chloride , manganese chloride , and magnesium sulfate . All salts were purchased at the highest purity available from Fisher Scientific , Pittsburgh , PA . Radioactive zinc blotting was done as described previously [17] , [18] , [19] . Briefly , synthetic peptides were obtained at >99% purity and resuspended in 50 mM Tris pH 7 . 4 at 10 mg/ml ( Peptide 2 . 0 , Chantilly , VA ) . The WT peptides corresponded to a . a 58–89 of wild type EBNA1 ( WT ) or the cysteine to serine mutant ( M ) described above . WT and M peptides were blotted on a PVDF membrane . Membranes were incubated with approximately 50 µCi of 65ZnCl2 ( specific activity . 0442 mCi/µg , Oak Ridge National Laboratory , Oak Ridge , TN ) for 30 minutes at room temperature , washed and analyzed by phosphor-imaging . Membranes prepared in parallel , were stained with Amido-Black . 293 cells were co-transfected with plasmids expressing EBNA1 ( 1-450 ) -E2DBD , and EBNA1 or EBNA1 ( CC→SS ) . Co-immunoprecipitation was performed as described [5] using the M2 mouse monoclonal antibody conjugated to sepharose beads . Immunoprecipitates were detected using M2 anti-FLAG antibody ( Sigma , St . Louis , MO ) , or K67 . 3 anti-EBNA1 antibody . 293 cells were co-transfected with plasmids expressing EYFP ( 2-154 ) and EYFP ( 155-38 ) , or the corresponding UR1 fusions . Fixed cells were examined by fluorescence microscopy 48 hours post-transfection . In some experiments , the zinc chelator 1 , 10-phenanthroline ( Fisher Scientific , Pittsburgh , PA ) was added 24 hours prior to microscopy . To analyze the effect of redox , menadione ( MP Biomedicals , Solon , OH ) in various concentrations was added to the culture medium six hours post-transfection . Size exclusion chromatography was performed using a Biosep-SEC-S2000 ( 600×30 mm ) ( Phenomenex , Torrance , CA ) column on a Bio-Rad BioLogic Duo Flow system , to examine the ability of WT and M peptides to form dimers or higher order multimers . The column was equilibrated with a buffer consisting of 50 mM NaCl and 50 mM Tris ( pH 7 . 5 ) , and run in the same buffer at 0 . 8 ml/min ( approximately 730 psi ) for 50 minutes after sample injection . Molecular weights were calculated after the column was calibrated with Bio-Rad gel filtration standards ranging in size from 1 . 35 to 670 kD . Elution from the column was monitored by absorption at 280 , 225 , 215 and 205 nm using a QuadTech inline detector . For calculation , the log of the molecular weights of standards was plotted against retention time . The method was found to be as accurate as using the ratio of the elution volume to the void volume , and was validated by calculating the molecular weight of purified ubiquitin and cytochrome C based on their retention times . When the effect of zinc on multimerization was tested , it was added to the peptide at a final concentration of 1 mM . C33a cells were transfected as described [3] with plasmids as depicted in Figure 6 . Six hours post-transfection , cells were split and treated with menadione or paraquat for 18 hours following which they were analyzed for their cell-cycle profile , and luciferase activity . C33a cells were transfected as described [3] . Six hours post-transfection , cells were split , and grown under normoxic conditions , or hypoxic conditions . For hypoxic conditions , cells were grown in a sealed modular incubation chamber ( Billups-Rothenberg , Inc , Del Mar , CA ) placed at 37oC . Prior to sealing , the chamber was flushed with a mixture of 4%O2 , 5%CO2 and 91%N2 gas ( AirGas , Theodore , AL ) for five minutes . Culture media was replaced with fresh media every 24 hours that was pre-equilibrated to normoxic or hypoxic conditions , at which time the chamber was re-flushed and re-sealed . No significant differences in doubling rate were observed between normoxic and hypoxic conditions . Southern blots were performed as described [3] using probes specific for oriP-BamHI-Cp-luciferase . Apoptosis and necrosis was evaluated by flow cytometry using the Annexin V-FITC apoptosis detection kit ( EMD Biosciences , Gibbstown , NJ ) as per the manufacturers protocol , and examined using a FACSCalibur flow cytometer . Cell-cycle analysis was performed after PI-staining of fixed cells using a FACSCalibur . Total cellular RNA was extracted using TRI Reagent ( Ambion , Austin , TX ) , following which cDNA was prepared using TaqMan reverse transcriptase reagents ( Applied Biosystems , Foster City , CA ) . Real time PCR was performed using the ABsolute blue QPCR Rox mix ( Thermo Fisher , Germantown , PA ) on an ABI 7300 real time PCR system ( Applied Biosystems , Foster City , CA ) . The primer and probe sequences used for the detection of BamHI-Cp transcripts and the endogenous control GAPDH mRNA have been described [109] . Relative quantitation was performed by the dCt method , and is expressed relative to the level of normalized BamHI-Cp transcripts detected in control cells . Control or treated NOLA-1 cells were examined by immunofluorescence using microscopy conditions described previously [76] . Rabbit polyclonal antibodies 2638 or K67 . 3 was used to detect EBNA1 with a TexasRed conjugated anti-rabbit secondary antibody ( Jackson ImmunoResearch , West Grove , PA ) . All the statistical analysis where indicated was performed using MSTAT version 5 ( N , Drinkwater , McArdle Laboratory for Cancer Research , University of Wisconsin Medical School ) . The Wilcoxon rank sum test was used for pair-wise comparisons . | Epstein-Barr virus ( EBV ) infects human B-cells and immortalizes them . Immortalization results in diseases that range from infectious mononucleosis to malignancies such as lymphomas . During immortalization , EBV expresses a small number of viral genes that modulate cellular proliferation and differentiation . One of the genes expressed by EBV , Epstein-Barr nuclear antigen 1 ( EBNA1 ) , activates the expression of the other viral genes required for immortalization . In this report , we have explored the mechanism by which EBNA1 activates gene expression . We have determined that EBNA1 uses the micronutrient zinc to self-associate , and that self-association is necessary for it to activate gene expression . Further , we have determined that environmental conditions such as oxygen tension and oxidative stress modulate EBNA1's capacity to self-associate , and therefore to activate gene expression . The gene expression profile and proliferative phenotype of EBV-infected cells is known to vary in differing environmental niches in the human body , such as lymph nodes and in peripheral circulation . We interpret our results to postulate that these differences arise as a consequence of varying oxygen tension in these microenvironments on EBNA1's capacity to activate viral gene expression . Our findings can be exploited to devise novel therapeutics against EBV-associated diseases that target EBNA1 through oxidative stress . | [
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] | 2009 | Zinc Coordination Is Required for and Regulates Transcription Activation by Epstein-Barr Nuclear Antigen 1 |
The major transition to eusociality required the evolution of a switch to canalize development into either a reproductive or a helper , the nature of which is currently unknown . Following predictions from the ‘theory of facilitated variation’ , we identify sex differentiation pathways as promising candidates because of their pre-adaptation to regulating development of complex phenotypes . We show that conserved core genes , including the juvenile hormone-sensitive master sex differentiation gene doublesex ( dsx ) and a krüppel homolog 2 ( kr-h2 ) with putative regulatory function , exhibit both sex and morph-specific expression across life stages in the ant Cardiocondyla obscurior . We hypothesize that genes in the sex differentiation cascade evolved perception of alternative input signals for caste differentiation ( i . e . environmental or genetic cues ) , and that their inherent switch-like and epistatic behavior facilitated signal transfer to downstream targets , thus allowing them to control differential development into morphological castes .
The mechanisms underlying the evolution of phenotypic novelty are hotly debated [1–4] . A fundamental question is how small genetic or epigenetic changes can produce a set of simultaneous , complementary phenotypic changes required to generate new adaptive trait combinations . A key prediction of the ‘theory of facilitated variation’ [5] is that regulation acts on evolutionarily conserved switch mechanisms , which then modulate expression of target loci controlling development . This process may facilitate large and complex evolutionary steps because it brings together new combinations of inputs ( internal or external stimuli ) and outputs ( phenotypes ) but does not rely on evolution of genes involved in the processes per se . Importantly , the reliance of this mode of evolution on conserved genetic and developmental processes increases the likelihood that the outputs will be functionally integrated and thus non-lethal , similar to the ‘two-legged goat effect’ , a striking example of phenotypic accommodation in which developmental robustness allows the animal to ‘adapt’ to a previously unselected bipedal lifestyle [6] . Evolution by facilitated variation may be especially important to the origin of developmental polyphenisms in which organisms develop into two or more discrete forms , since polyphenisms typically result from plastic activity of regulatory genes . Additionally , it is likely that regulatory mechanisms controlling one set of polyphenism are pre-adapted to evolve control over newly evolving polyphenisms , for two reasons . Firstly , such mechanisms’ pre-existing sensitivities to variable cues make it more likely that they will evolve the ability to perceive alternative gradients of novel cues , relative to constitutively expressed genes . Secondly , their downstream target genes already show inter-individual variability in expression , and the organism will thus already have evolved alternative responses to this variability . Gerhart and Kirschner [5] made predictions about the properties of the “core components” which they hypothesize to be the principal drivers of evolutionary novelty , namely that these components should display both robustness and adaptability , as well as exploratory behavior , state-dependent expression and regulatory compartmentation . The sex differentiation pathways exhibit all these properties , making them prime candidates for facilitating the evolution of new forms of polyphenism . Some components of the sex differentiation pathway ( such as the doublesex-mab3 ( DM ) gene family; [7–9] ) are evolutionarily ancient and conserved across diverse metazoa , and thus could potentially be involved in generating novel polyphenism in multiple distantly related taxa . In insects , the sequence of sex determination has been called hourglass-shaped [10] , with highly variable input signals and downstream targets , but a small set of conserved core regulatory genes including transformer ( tra ) and doublesex ( dsx ) . doublesex is alternatively spliced depending on the presence of an active TRA protein , and its sex-specific isoforms act as transcription factors causing sex-specific gene expression and development through their differential effects on multiple downstream targets [11 , 12] . The two social insect ‘castes’—queens and workers—differ radically from one another in their developmental environment ( e . g . nutritional environment ) resulting in differences in size , fecundity , behavior and physiology . Ultimately , the evolution of caste polyphenism thus required concerted evolution of environmental input signals and corresponding developmental responses [13] . Eusociality has evolved at least twice within the Hymenoptera [14] , but we presently lack a well-evidenced theory of the genetic mechanisms that allowed caste-specific gene expression to originate . There is increasing evidence that the evolution of polyphenism in ants , bees and wasps was achieved primarily through evolution of regulatory genes , rather than gene content or composition [15–17] , but the core components involved are largely unknown . Here , we propose that conserved parts of the sex differentiation cascade , including the transcription factor doublesex , evolved sensitivity to new environmental input signals ( e . g . nutritional signals ) , thereby triggering caste-specific gene expression that sends larvae on divergent developmental trajectories . To test this hypothesis , we identified dsx and its female- and male-specific isoforms , and measured their expression across life stages in the four discrete morphs ( queens , workers , winged males and wingless males ) of the ant Cardiocondyla obscurior . We find that dsx sex-specific isoforms are expressed both sex-specifically and morph-specifically in larvae , pupae and adults . Moreover , ninety other conserved genes with sex-biased expression showed morph-specific expression patterns during larval development , suggesting that co-option of the genes regulating sex differentiation via sex-specific alternative splicing was involved in the origin of morphologically distinct castes .
Queens and workers produced from inter-population crosses were heterozygous for diagnostic microsatellite markers , whereas emerging winged and wingless males as well as one sex mosaic individual expressing both male and female characters exclusively carried the maternal alleles ( S1 Table ) . Although single locus complementary sex determination is unlikely because the species regularly engages in inbreeding [18] , C . obscurior appears to use standard haplodiploid sexual reproduction . The C . obscurior genome [19] has four paralogs containing the DM domain of doublesex ( dsx ) ( pfam00751; Cobs_01393 , Cobs_07724 , Cobs_09254 and Cobs_18158 ) , representing the ancestral state in holometabolous insects [20] . Sex-specific splice forms are only known from one paralog per species ( e . g . , in Apis [21] and Nasonia [20] ) , and the function of the others is unclear . In C . obscurior , only Cobs_01393 was differentially expressed in male and female larval RNAseq data ( S2 Table ) . Moreover , Cobs_01393 had the highest sequence homology to functional dsx in other insects ( S1 Fig ) . Finally , we found that Cobs_01393 was located within ~79 kb of prospero; microsynteny of prospero and dsx is conserved across the Hymenoptera [20] . We thus conclude that Cobs_01393 is the functional paralog of dsx . We identified the full-length sequence and sex-specific isoforms of the functional paralog of dsx using 3’ rapid amplification of cDNA ends ( RACE ) ( S2 Fig ) . The first four exons are identical in both isoforms . The DM domain ( pfam00751 ) is located in exon 2 and the dsx dimerization domain ( pfam08828 ) in exon 4 . The female-typical isoform dsxF contains one exon specific to dsxF , whereas the male-typical isoform dsxM excludes that exon but includes two others that are absent in dsxF ( Fig 1A and S3 Table ) . This splicing pattern , with a shortened female transcript , has been inferred for the fire ant Solenopsis invicta [22] , and matches dsx sex-specific isoforms in Drosophila melanogaster and Apis mellifera , but not Nasonia vitripennis [20] . While the sex-signaling function of dsx is conserved across highly divergent lineages , recent evidence shows that dsx sequence evolves rapidly [23–25] , causing substantial inter-specific variation in dsx splicing patterns . A higher level of divergence in dsx compared to other DM domain-containing proteins in our phylogenetic analysis confirms this result ( S1 Fig ) . We designed primers that spanned the exon boundary of the DM domain-containing exon ( to measure the overall expression of both isoforms ) , as well as primers specific to both isoforms for use in RT-qPCR . We found significantly higher expression of the DM domain in adult males ( pooling winged males and wingless “ergatoid” males; WM and EM ) compared to females ( pooling queens and workers; WO and QU ) ( nEM = 8 , nWM = 8 , nWO = 10 , nQU = 10; Welch two sample t-test: t25 . 7 = -8 . 7 , p<0 . 001 , Fig 1B ) . Expression of the DM-domain was similar in queens and workers ( t-test with Benjamini-Hochberg ( BH ) correction: p = 0 . 672 ) , but higher in winged males compared to wingless males ( p = 0 . 009 ) . We then compared expression of dsxF and dsxM across all four morphs in pupae ( nEM = 10 , nWM = 10 , nWO = 9 , nQU = 10 ) and adults ( nEM = 7 , nWM = 7 , nWO = 7 , nQU = 7; Fig 1C and 1D ) . We found morph-specific signatures of expression in both life stages for dsxF ( ANOVA: pupae: F ( 3 , 35 ) = 42 . 33 , p<0 . 001; adults: F ( 3 , 24 ) = 3 . 75 , p = 0 . 024 ) as well as for dsxM ( Kruskal Wallis rank sum test with df = 3: pupae: X2 = 30 . 2 , p<0 . 001; adults: X2 = 22 . 6 , p<0 . 001 ) . Worker pupae showed significantly higher dsxF expression than queen pupae ( pairwise t-test with BH correction: p = 0 . 013 ) and worker pupae and adults showed significantly lower dsxM expression than queen pupae and adults , respectively ( Wilcoxon Tests with BH correction: pupae: p = 0 . 012; adults: p = 0 . 0014 ) . Neither dsxF nor dsxM expression differed significantly between the two male morphs ( dsxF: pairwise t-test with BH correction: pupae: p = 0 . 480 , adults: p = 0 . 277; dsxM: pairwise Wilcoxon tests with BH correction: pupae: p = 0 . 481 , adults: p = 0 . 805 ) . However , overall expression of both isoforms was higher in winged compared to wingless males ( Fig 1B ) . Our finding that dsx is differentially expressed and alternatively spliced across morphs in pupae and adults suggests that dsx might play a role in controlling polyphenic development . To confirm that expression of dsx isoforms corresponds with phenotypic tissue differentiation , we used qPCR to analyze dsxM and dsxF expression in male and female-typical tissues dissected from aberrant “sex mosaic” individuals that express both male and female characters . C . obscurior sex mosaics are typically laterally separated into female and male halves , indicating that intersexuality is caused by single , early developmental aberrations such as anomalous fertilization events , loss of sex locus expression or inheritance of maternal effects [26–28] . The expression of dsxF and dsxM was male-typical in male tissue and female-typical in female tissue for all individuals except one , which had similar levels of dsxM in both tissue types ( S3 Fig ) . As in previous studies [29 , 30] , we only observed individuals possessing queen and winged male traits , or worker and wingless male traits; other trait combinations were absent ( S4 Table ) , implying that common mechanisms control morph differentiation in males and females . We analyzed published RNAseq data [31] from individual early 3rd instar larvae ( QU , EM , WM , WO; n = 7 each ) on an exon-level with DEXSeq [32] . We found morph-biased expression in each of the seven dsx exons , and confirmed sex-specific expression of the DM domain , dsxF , and dsxM in the early 3rd larval stage ( S4 Fig and S5 Table ) . Overall , dsx expression was higher in males than in females , and higher in wingless morphs compared to winged morphs ( EM > WM , WO > QU ) . We hypothesized that other genes with sex-specific alternative splicing have been similarly co-opted for morph differentiation . Using a conservative false discovery rate of 0 . 005 , DEXSeq analysis identified 179 exons of 91 genes with sex-biased expression ( S6 Table ) . Dsx exon 5 ( = dsxF ) is ranked 5th among the top 10 differentially expressed exons and exons 6 and 7 ( = dsxM ) are the two most significant differentially expressed exons across all samples . To test for co-option of this set of exons into morph differentiation , we performed a hierarchical clustering analysis based on log-transformed exon counts . Queens and workers , as well as winged and wingless males , were clearly separated by the set of sex-biased exons , with the exception of two male samples that clustered with the wrong male morph ( bootstrap node support: QU/WO = 75 , WM/EM = 68 ) ( Figs 2 , S5 and S6 for bootstrap support for all nodes ) . Because terminal switch points for morph differentiation in male and female larvae may differ [31] , misclassification of two male samples ( WM34 & EM29 ) in hierarchical clustering may reflect higher plasticity in males compared to females at this particular developmental stage . Accordingly , in C . obscurior 3rd instar larvae , more genes are differentially expressed between queens and workers than between winged and wingless males [31] . To identify the sex-biased exons that most strongly affect separation between sexes and morphs , we performed a principal component analysis ( PCA ) of the 179 normalized exon counts . PC 1 separated sexes ( 29 . 9% explained variation ) , PC 2 ( 15 . 3% ) and PC 4 ( 6 . 8% ) separated female and male morphs , respectively ( Fig 3; linear discriminant analysis using Wilk’s test on PCs 1 , 2 and 4; factor sex: F ( 1 , 28 ) = 95 . 81 , p < 0 . 001; factor morph: F ( 3 , 28 ) = 27 . 70 , p < 0 . 001 ) , while PC 3 ( 7 . 7% ) did not separate between sexes or morphs ( linear discriminant analysis using Wilk’s test on PC 3; factor sex: F ( 1 , 28 ) = 0 . 06 , p = 0 . 80; factor morph: F ( 3 , 28 ) = 1 . 81 , p = 0 . 17 ) . From the 179 exons , we identified those with the strongest influence on sex ( PC 1 ) , female morph ( PC 2 ) , and male morph ( PC 4 ) by extracting the exon loadings that fell in either the 10% or 90% quantiles for each PC ( S6 Table ) . Using these lists , we identified dsx ( replicating the RT-qPCR results ) and seven other genes that showed both sex-specific and morph-specific alternative splicing , of which kr-h2 has a putative transcription factor function ( Table 1 ) . All eight genes are conserved across the Insecta , and a Gene Ontology ( GO ) term enrichment analysis with topGO [33] suggests that they serve basic metabolic and other core functions ( S7 Table ) .
Our study suggests provides evidence that the sex differentiation pathway has been co-opted to control morph-specific development , as we predicted from the theory of facilitated variation . The major candidate gene dsx was alternatively spliced in males and females , and differentially expressed between queens and workers and between winged and wingless males . We independently replicated these results using qRT-PCR and RNAseq data from different individuals and life stages . Strikingly , we found that exons showing sex-biased expression were also differentially expressed between morphs , suggesting that dsx and other sex-biased genes mediate polyphenism within each of the sexes . The RNAseq analysis conservatively identified eight genes that have sex-specific and morph-specific alternative splicing; all of these genes were evolutionarily conserved and had GO terms associated with basic cellular functions . While dsx encodes sex-specific transcription factors and co-ordinates expression of a large number of downstream genes [34] , except for a putative role of kr-h2 ( see below ) the other genes exhibit no transcription factor function . We confirmed that the sex-specific isoforms of dsx correlated with tissue type by analyzing male and female-typical tissue dissected from aberrant sex mosaic individuals . Finally , we reaffirmed that sex mosaics are always either hybrids of a queen and a winged male , or a worker and a wingless male , implying common morph differentiation control mechanisms in both sexes , especially regarding winglessness . Interestingly , dsx has been shown to be a central hub gene involved in generating evolutionary novelty and polyphenism in other taxa . In a butterfly , genetic variation in dsx is associated with a heritable female-limited wing color/shape polymorphism , suggesting that dsx has been co-opted to control a novel , female-limited trait as well as maintaining its function in sex differentiation [24] . In the genus Drosophila , new localizations of dsx are thought to have facilitated the evolution of a novel male-limited trait ( the sex combs ) , highlighting how the preexisting sex determination system was co-opted to produce a new polyphenism [35] . In the dung beetle Onthophagus taurus , RNAi experiments suggested that variation in dsx splicing mediates the difference in the presence of horns between males and females , and also controls a nutritionally dependent , male-limited polyphenism between large-horned and small-horned males [36] . A subsequent study of another horned beetle showed that different dsx isoforms control the sensitivity of the mandibles to juvenile hormone ( JH ) , such that male mandibles are stimulated to grow by JH while those of females are not [37] . Thus it appears that dsx first evolved to mediate male-limited expression of horns by elevating the sensitivity of male horn tissue to JH [37] and perhaps also the IGF signaling pathway [38] , and was then secondarily co-opted to control a nutrition-sensitive , male-limited polyphenism . The beetle dsx data are thus highly congruent with the theory of facilitated variation: the male polyphenism evolved using pre-existing genetic switches and developmental mechanisms to link a novel combination of stimuli and outputs ( here , larval nutrition and horn phenotype ) . Pre- and posttranscriptional genetic tools are not yet well established in ants but there is circumstantial evidence for similar links between dsx and JH in C . obscurior . A previous experiment showed that JH is involved in the development of larvae of both sexes into winged morphs [39] , and the present study found differences in dsx splicing and expression between winged and wingless morphs . Thus , we speculate that the isoforms of dsx may mediate the responsiveness of developing tissues to JH , as hypothesized for beetles [37] . Significant differences in feminizer expression between queens and workers in the stingless bee Melipona [40] , an upstream signal of dsx in bees [41] , likewise suggests co-option of sex differentiation genes into caste differentiation in bees . There are no homologues of csd and feminizer in ants , because csd evolved in the Apis lineage by duplication of feminizer [42] . In ants , the closest homologue to feminizer is transformer . In C . obscurior , we could not detect morph-specific expression of the two transformer paralogues ( tra1: Cobs_03145 and tra2: Cobs_18309 ) , although they were expressed in a sex-specific manner . In addition to dsx , we found a second sex-biased transcript with putative regulatory function . This ortholog to kr-h2 was alternatively spliced in queen and worker larvae , rendering the Kruppel homolog family a promising candidate for modulating plastic responses to the environment . kr-h2 has structural similarity to the JH-inducible transcription factor kr-h1 [43] , which is involved in the initiation of metamorphosis in other insects [44 , 45] . kr-h2-induced differences in developmental timing may explain why metamorphosis is delayed in queens compared to workers [39] , and further points to a link between sex-specific transcription , function in transcriptional regulation , sensitivity to JH , and evolutionary co-option into within-sex polyphenism . We believe that the hypothesis advanced here , i . e . co-option of sex differentiation pathways into social insect caste polyphenism , is complementary to a previous theory regarding the proximate mechanisms underlying the origin of eusociality , termed the reproductive groundplan hypothesis ( RGPH ) . Based on the ovarian ground plan hypothesis [46] , the RGPH posits that eusociality arose via changes in the regulation of pre-existing gene sets relating to reproductive physiology and behavior , for example when genes involved in nest provisioning and brood care began to be expressed in unmated , non-reproductive individuals [47] . Research on the RGPH has stressed the importance of genes with nutrition-sensitive expression in delimiting the queen and worker “genetic toolkits” , in light of evidence that caste fate is nutrition-sensitive [48] , that diet preference , reproduction and behavior are pleiotropically linked [49] , and that some nutrition-related genes such as IRS and TOR influence caste fate [50] . Juvenile hormone , which is involved in regulatory feedback loops with some nutrition-related gene networks , has also been linked to caste differences [48 , 50]—including in our study species C . obscurior [31 , 39]—as well as to within-caste polymorphisms ( e . g . [51] ) . Our hypothesis and the RGPH both argue that regulatory evolution caused conserved genes to acquire caste-specific expression . Our hypothesis is distinct in that it explicitly proposes that this regulatory evolution takes place in sex differentiation genes , but leaves the targets of these genes unspecified . By contrast , the RPGH makes predictions about which gene networks produce caste-biased phenotypes ( e . g . ovary development , [52] ) , but makes no prediction regarding the identity of the regulatory sequences controlling these networks . Thus , the hypotheses do not overlap , and both may be correct . Analyses of potential regulatory links between the pathways presented here and those implicated with the RGPH will reveal to what extent they are connected . Co-option of conserved genes involved primarily with sex differentiation in novel contexts allows functionally integrated gene networks to produce discrete phenotypes . Together with the horned beetle data reviewed above , our study suggests that core components of the sex differentiation pathway such as dsx can produce evolutionary novelty by acting as a switch for nutrition and JH-sensitive growth and development . Although many mechanisms of gene regulation have been implicated in controlling caste-specific development in social insects ( e . g . methylation [53] , transcription factors [31] , small RNAs acting post-transcription [17] , RNA editing [54] or structural chromatin modification [55] ) , all of these depend on some higher-level genetic switch to trigger differential activity . We propose that highly conserved hub genes such as dsx , which can translate variable input signals into large transcription differences using intermediate-level regulators , were the most basic mechanism responsible for the repeated evolutionary transition to eusociality and caste polyphenism .
Crosses between five queens of a C . obscurior population from Japan ( JP ) and five wingless males of a C . obscurior population from Brazil ( BR ) were set up by placing sexual pupae together with some brood and ~20 workers in plaster-filled Petri dishes . Nests were checked twice a week , provided with water , honey and pieces of dead insects and kept at constant conditions ( 12h 28°C light , 12h 23°C dark ) . We sampled emerging F1 hybrid QU , WO , EM and WM pupae and extracted DNA from the 10 parental and 71 F1 individuals ( 23 EM , 3 WM , 22 QU , 22 WO , 1 GY = gynandromorph , for sample sizes per family see S1 Table ) . Each individual was analyzed at three variable microsatellite loci ( Cobs_1 . 1 , Cobs_8 . 3 , Cobs_8 . 4; for primer sequences see S8 Table ) . PCRs were performed using the BIO-X-ACT Short Mix ( Bioline ) and microsatellite analyses were carried out on an ABI PRISM ( Applied Biosystems ) . To find the functional dsx ortholog of C . obscurior , we identified DM domain-containing proteins of Drosophila melanogaster , Nasonia vitripennis , Apis mellifera , Pogonomyrmex barbatus , Acromyrmex echinatior and C . obscurior by BLASTp and tBLASTn analyses ( S9 Table ) and aligned them with MUSCLE [56] . We extracted the DM domain region from the manually corrected alignment ( S7 Fig ) and built a phylogenetic tree in MEGA [57] , applying a WAG+G+I phylogenetic model and bootstrap resampling with 1 , 000 replicates ( S1 Fig ) . We reanalyzed previously published RNAseq data of larvae [31] . After removing adapter sequences with cutadapt and performing quality filtration with Trimmomatic , the reads were mapped against the reference genome with tophat2 ( v2 . 0 . 8 ) and bowtie2 ( v2 . 1 . 0 ) in sensitive mode . We generated count tables with HTseq based on the Cobs1 . 4 official gene set and used DESeq2 [58] to assess sex-specific expression of the four dsx paralogs following size factor normalization . We applied RACE ( Rapid Amplification of cDNA Ends ) for identification of dsx isoforms . Total RNA was extracted from three females ( QU adult , QU pupa , WO pupa ) and three wingless males ( one pupa , two adults ) using the peqGOLD MicroSpin Total RNA Kit ( peqlab ) . Transcription to cDNA was performed with the AffinityScript Multiple Temperature cDNA Synthesis Kit ( Agilent Technologies ) , using the 3’ RACE Adapter GCGAGCACAGAATTAATACGACTCACTATAGGTTTTTTTTTTTTVN . 3’ RACE was performed in a nested PCR using two gene-specific 3’ primers ( dsx4_for4 , Co_dsx_p3_for , for primer sequences see S8 Table ) and the 5’ primer provided in the First Choice RLM-RACE Kit ( Ambion ) . PCRs were performed using the BIO-X-ACT Short Mix ( Bioline ) with the following protocol: 94°C ( 3 min ) , followed by 35 cycles 94°C ( 30 sec ) , 60°C ( 30 sec ) , 72°C ( 2 min ) and a final elongation of 72°C ( 7 min ) . The products were purified with the NucleoSpin Gel and PCR Clean-up ( Macherey-Nagel ) and Sanger sequenced at LGC Berlin . Total RNA was extracted from adults ( 8 EM , 8 WM , 10 QU , 10 WO ) using the RNeasy Plus Mini Kit ( Qiagen ) and transcribed to cDNA using the AffinityScript Multiple Temperature cDNA Synthesis Kit ( Agilent Technologies ) . Expression of the DM domain was quantified by qPCR using the primer pair dsx4_for4/dsx4_rev1 and normalized with two housekeeping genes ( RPS2_new , RPL32; see S8 Table for primer sequences ) . We further used qPCR to measure isoform-specific dsx expression ( dsxM and dsxF ) in pupae and adults of all four morphs , and in tissue from four sex mosaic pupae . We dissected the head and thoraces of the sex mosaics ( for morphological descriptions see S4 Table ) laterally into male and female halves and stored male and female tissue parts separately in RNAlater-ICE ( Ambion ) , resulting in one female and one male sample per individual . We extracted total RNA from 9–10 pupae and seven adults of each of the four morphs , and from the sex mosaic tissue using the peqGOLD MicroSpin Total RNA Kit ( peqlab ) including a DNA digestion step with the peqGOLD DNase I Digest Kit ( peqlab ) . After cDNA synthesis with iScript cDNA Synthesis Kit ( Bio-Rad ) we quantified gene expression of dsxF and dsxM using isoform specific , intron-spanning primers ( dsxF: 4for/F5rev , dsxM: 4for/M5rev; see Fig 1A for position of primers ) and two housekeeping genes ( RPS2_new , Y45F10D_JO1 ) . All qPCR reactions were performed in triplicates ( repeatability was uniformly high , so we took the mean of the three replicates prior to analysis ) . Data analysis was carried out according to [59] , using the geometric mean of the two housekeeping genes for normalization . We analyzed published RNAseq data [31] from 3rd star instar larval QU , WO , WM and EM ( n = 7 each ) and assessed differential exon-specific expression with DEXSeq [32] . Raw reads were trimmed and passed through quality filtration as described in [31] and mapped to the reference genome Cobs1 . 4 [19] using STAR [60] . We corrected the dsx and tra gene model using the RACE results for dsx , and split the tra gene model into two paralogs ( tra1 and tra2 ) , as observed in other ants [42 , 61 , 62] . For all other genes we used gene models of the Cobs1 . 4 official gene set . We followed the default workflow of DEXSeq and tested for differential exon usage between males and females based on a false discovery rate of 0 . 005 . In the resulting 179 sex-specific exons , we tested for morph-specific exon profiles using hierarchical clustering ( implemented by the R function hclust using the ward . D2 method [63] ) of pairwise Manhattan distances between log-transformed normalized exon counts . We assessed the support for each node in the cluster analysis using bootstrap resampling with 10 , 000 replicates using the pvclust package in R 3 . 1 . 2 . We conducted a PCA with normalized exons counts . We visually identified principal components that best separated between sexes ( PC 1 ) , female morphs ( PC 2 ) and male morphs ( PC 4 ) and confirmed that these components suffice to separate among sexes and morphs with linear discriminant analysis and subsequent Wilk’s tests in R 3 . 1 . 2 . Based on loadings of exons on each component , we identified exons that fell in either 10% or 90% quantiles ( S6 Table ) as those with the strongest influence on PC 1 , PC 2 and PC 4 . From this list , we extracted only those genes that contained multiple exons with strong influence on both sex ( PC 1 ) and morph ( PC 2 and/or PC 4 ) . This yielded a list of eight candidate genes showing alternative splicing between sexes as well as morphs ( Table 1 ) . | Division of labor into reproductive queens and helper workers in the societies of ants , bees and wasps is achieved by phenotypic plasticity , which allows individuals to embark on discrete developmental trajectories in response to variable signals . These signals can be genetic , epigenetic or environmental , thereby resembling the extreme variation in signals for sex determination across multicellular animals . We show that common developmental pathways downstream of these input signals , including the conserved sex differentiation gene doublesex , regulate sex and caste-specific phenotypic differentiation in the ant species Cardiocondyla obscurior . Many different mechanisms of gene regulation have been implicated in controlling caste-specific development in social insects but these all depend on a higher-level genetic switch . We propose that highly conserved hub genes such as dsx , which can translate variable input signals into large transcription differences using intermediate-level regulators , are tightly linked with the repeated evolutionary transition to eusociality and caste polyphenism . | [
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] | 2016 | Evolution of Social Insect Polyphenism Facilitated by the Sex Differentiation Cascade |
Current limitations in quantitatively predicting biological behavior hinder our efforts to engineer biological systems to produce biofuels and other desired chemicals . Here , we present a new method for calculating metabolic fluxes , key targets in metabolic engineering , that incorporates data from 13C labeling experiments and genome-scale models . The data from 13C labeling experiments provide strong flux constraints that eliminate the need to assume an evolutionary optimization principle such as the growth rate optimization assumption used in Flux Balance Analysis ( FBA ) . This effective constraining is achieved by making the simple but biologically relevant assumption that flux flows from core to peripheral metabolism and does not flow back . The new method is significantly more robust than FBA with respect to errors in genome-scale model reconstruction . Furthermore , it can provide a comprehensive picture of metabolite balancing and predictions for unmeasured extracellular fluxes as constrained by 13C labeling data . A comparison shows that the results of this new method are similar to those found through 13C Metabolic Flux Analysis ( 13C MFA ) for central carbon metabolism but , additionally , it provides flux estimates for peripheral metabolism . The extra validation gained by matching 48 relative labeling measurements is used to identify where and why several existing COnstraint Based Reconstruction and Analysis ( COBRA ) flux prediction algorithms fail . We demonstrate how to use this knowledge to refine these methods and improve their predictive capabilities . This method provides a reliable base upon which to improve the design of biological systems .
Systems biology aims to understand and predict how a cell’s behavior emerges from the interaction of its molecular parts [1–3] . Determination of metabolic fluxes ( i . e . , the number of metabolites traversing each biochemical reaction per unit time [4 , 5] ) is crucial to this effort because they map how carbon and electrons flow through metabolism to enable cell function [2 , 5] . Metabolic fluxes typically cannot be measured directly , but must be inferred from experimental data through computational algorithms [4] . Among the most popular methods for studying metabolic fluxes are Metabolic Flux Analysis ( MFA , [6 , 7] ) , Flux Balance Analysis ( FBA , [8] ) and 13C Metabolic Flux Analysis ( 13C MFA , [4 , 5 , 9] ) . MFA calculates fluxes by using a stoichiometric model for the major intracellular reactions and assuming no metabolite accumulation [6] . The inputs are extracellular fluxes obtained through measurements of external concentrations of metabolites such as glucose or lactate as a function of time . If more flux measurements are available than degrees of freedom , the system is said to be overdetermined and a unique solution can be obtained . In the opposite case , the system is underdetermined and several flux profiles are compatible with the experimental data . MFA has been used to study fluxes in ( e . g . ) chinese hamster ovary [10] , S . cerevisiae [11] and hybridoma cells [12] . Present-day FBA enhances MFA by expanding the network to include all reactions in metabolism , or at least as many as can be inferred from the genome through a metabolic reconstruction that yields a genome-scale stoichiometric model [13] . Since the degrees of freedom for such a model are usually over a hundred and measured fluxes are usually an order of magnitude less , the system is grossly underdetermined . Hence , fluxes are determined through linear programming ( LP ) by assuming that metabolism is tuned , due to evolutionary pressure , to maximize growth rate ( typically; but see Schuetz et al . [14] for other suggested alternatives ) . The use of this objective function to interpret stoichiometric models is a key feature of FBA , even when stoichiometric models were not genome-scale . FBA forms the basis of a family of flux analysis methods named COnstraint-Based Reconstruction and Analysis ( COBRA ) methods [15] , some of which can be used to produce flux predictions which are often used in bioengineering . These predictions can be full ( when fluxes are determined without data from the actual experiment [16] ) or partial ( when some data from the experiment , like glucose consumption , is used for the prediction [17] ) , qualitative ( e . g . , prediction of whether an organism will grow or not under given conditions [18] ) or quantitative ( e . g . , the value of the flux is predicted [19] ) . Full predictions are particularly useful for bioengineering purposes since they enable quick testing of the consequences of engineering approaches [20] . These methods have been used to facilitate the large-scale industrial production of 1 , 4-butanediol , a commodity chemical used to manufacture over 2 . 5 million tons annually of high-value polymers [21] . Recently , this rationally developed strain was used for a 5 million pound commercial production [22] , and BASF has licensed this strain for future production of renewable 1 , 4-butanediol [23] . FBA has applications beyond bioengineering , including the prediction of ratios of microbial species in a simple microbial community [24] and providing valuable insights into tumor cell metabolism [25 , 26] . 13C MFA improves on MFA by using data obtained from 13C substrate labeling experiments together with a limited reaction stoichiometry and measured extracellular fluxes to measure intracellular fluxes . In these experiments , the organism under study is grown on 13C labeled substrate and the labeling pattern ( i . e . , the fraction of molecules with 0 , 1 , 2 , … 13C atoms incorporated or Mass Distribution Vector , MDV [27] ) is measured for a set of metabolites . Since the labeling pattern is highly dependent on the flux profile , it is possible to back-calculate the fluxes that best explain the measured labeling pattern if we know the fate of each carbon atom ( carbon transitions , see [9] ) for all reactions in the model . This involves a nonlinear fitting problem where the fluxes are the parameters . The usual approach is to consider only a small subset of all metabolism assumed to be comprehensive enough to explain the labeling of the measured metabolites , typically central carbon metabolism [28 , 29] . Hence the model exhibits relatively few degrees of freedom that can be fully constrained by the labeling data . 13C MFA has found applications in metabolic engineering [30] , biotechnology [31] and biomedicine [32] . Each of these methods involves its own advantages and disadvantages . Unlike 13C MFA , FBA uses the comprehensive description of metabolism contained in a genome-scale model and explicitly takes into account the system-wide balances of metabolites that can be crucial for host engineering [33] . Because of the exhaustive description of metabolism incorporated in genome-scale models , they often point towards completely unexpected regions of metabolism involved in the studied processes . For example , these models have been used to show that biosynthesis and degradation of heme compensates for the lack of a functional TCA cycles in cancer cells [26] . Furthermore , FBA can be used in combination with COBRA methods to make full predictions . 13C MFA , on the other hand , is a descriptive method for determining the metabolic fluxes compatible with the accrued experimental data but does not postulate general principles that can be used to make predictions for experiments that have not been performed . However , 13C MFA does not rely on maximum growth assumptions , the general applicability of which has been questioned [14 , 34 , 35] and shown to be inaccurate for engineered strains that are not under long-term evolutionary pressure [17] . Moreover , the comparison of measured and fit labeling patterns provides a degree of validation and falsifiability that FBA does not possess: an inadequate fit to the experimental data indicates that the underlying model assumptions are wrong . In contrast , FBA produces a solution for almost any input . Several attempts to combine the complementary virtues of 13C MFA and FBA have been reported . For example , they have often been combined to test new FBA-based methods , since 13C MFA is considered to be the most authoritative determination of fluxes . Both Segrè et al [16] and Yizhak et al [19] used 13C MFA to validate MOMA and IOMA , respectively . More recently , Schuetz et al [14] , used 13C MFA-derived fluxes to compare predictions from an MFA model containing ∼ 100 reactions and using different objectives , and also to demonstrate the applicability of pareto optimality to predict fluxes [35] . Choi et al [36] combined both methods by using flux ratios obtained from 13C MFA to constrain FBA for genome-scale models through the use of artificial metabolites . Although genome-scale models were not used , Suthers et al [27] presented 13C MFA for a large scale model containing 350 reactions . The OPENflux open source software by Quek et al [37] , allows for certain reactions in 13C MFA to be used only for stoichiometric modeling purposes . More recently , Chen et al [38] for the first time modeled the same E . coli strain under the same conditions using FBA and 13C MFA , using some of the information of the latter to constrain the former . In a similar vein , Kuepfer et al [39] constrained a S . cerevisiae genome-scale model with fluxes obtained from 13C MFA and determined the flux distribution by using the minimization of the overall intracellular flux as the objective function . However , to date there has been no attempt to use the data from 13C labeling experiments to constrain fluxes for a genome-scale model without assuming that metabolism is evolutionarily tuned to optimize an objective function ( such as the growth rate optimization typically used in FBA ) . The traditional underlying assumption in the field of 13C MFA is that the degrees of freedom of the model must carefully match the amount of information obtained from the labeling patterns . Indeed , if the mathematical formulation of 13C MFA were that of a linear programming problem ( as for FBA ) it would be pointless to try to constrain over a hundred degrees of freedom with the approximately 50 measurements of labeling used in this study , for example . However , 13C MFA is a nonlinear fitting problem ( with fluxes being the parameters ) and these problems behave very differently for the underdetermined case , a special case of what is known as “sloppy” models in statistical mechanics [40–43] . These underdetermined nonlinear fits exhibit some degrees of freedom which are highly constrained ( the parameters cannot be changed without noticeable measurable effects ) and other degrees of freedom barely constrained at all ( parameters can be changed freely , see section 3 . 3 in Brown et al [40] for a concrete example ) . Even if all degrees of freedom are not fully determined , the model can still be used to test hypotheses effectively [44] . These characteristics have been shown to be of general nature and apply to a variety of nonlinear problems appearing in systems biology [44] , insect flight and variational quantum wave functions [45] , interatomic potentials [46] and a model of the next-generation international linear collider [42] . In this paper , we present a systematic and rigorous framework to take full advantage of the nonlinear nature of the flux fitting problem and find all fluxes compatible with the 13C labeling data for a genome-scale model . In order to constrain fluxes effectively , we make the biologically relevant assumption that metabolic flux flows from core metabolism ( defined below ) to peripheral metabolism and does not flow back . The simultaneous use of 13C labeling data and genome-scale models produces a new computational approach combining the advantages of both FBA and 13C MFA: two-scale 13C Metabolic Flux Analysis ( 2S-13C MFA , see Fig 1 ) . 2S-13C MFA determines fluxes for a full genome-scale model , taking into account the system-wide balances of metabolites . However , 2S-13C MFA does not rely on maximum growth assumptions: instead it uses the data obtained from 13C labeling experiments to constrain feasible fluxes . The use of this data is shown to constrain glycolytic and pentose phosphate pathway fluxes 8–50 fold more effectively than using only measured extracellular fluxes . We use this new method to compare different predictive methods and show where and why they fail . Based on that information , we develop a new predictive method that is able to produce a full quantitative prediction of 48 labeling measurements , going beyond the usual qualitative ( e . g . grow/no grow ) predictions .
In 13C MFA , most reactions in the cell’s enormous metabolic network have a very limited contribution to the 13C labeling of the observed metabolites ( regardless of whether these are free metabolites [47] or proteogenic amino acids [4 , 9] ) . Thus , since the complexity of the problem of flux measurement from labeling information scales nonlinearly with the number of reactions , the metabolic network in 13C MFA consists of a minimal set of reactions ( core set ) that most influence labeling patterns ( typically central carbon metabolism but may include other extra reactions , such as those describing protein turnover [48] ) , stemming from the literature or the researcher’s experience . This approach is able to convincingly explain labeling patterns for amino acids and intracellular metabolites for model organisms ( e . g . E . coli [49 , 50] and S . cerevisiae [51 , 52] ) under well-studied conditions ( e . g . glucose feed ) . The good fits to experimental data support , to a good approximation , the underlying assumptions that carbon precursors flow from a central core metabolism to peripheral metabolism and do not flow back . However , it would be desirable to generate the minimal core network systematically through a computational method , so the technique can be generally applied to non-standard cases: e . g . non-model organisms , bioengineered strains , alternative non-standard carbon sources , human cells or microbial communities . Most crucially , such a method would explicitly test the assumption that reactions not included in the minimal network do not significantly influence labeling . As an added benefit , it would take advantage of the significant community efforts that have been put into developing and improving genome-scale metabolic models [53] . Another important benefit of integrating 13C MFA with genome-scale metabolic models is to check for consistency of the inferred core fluxes with peripheral metabolism . The central metabolic network needs to produce not only carbon precursors for peripheral metabolism , but also ATP and reducing equivalents . Genome scale networks offer the possibility to track every reaction consuming and producing energy or reducing equivalents , and therefore can be used effectively to study the interplay between peripheral and core metabolism . On one hand , since core metabolism involves the reactions with largest fluxes in metabolism , once these are set by the 13C labeling data , peripheral metabolism is expected to be highly constrained . On the other hand , changes in the usage of core intermediates in peripheral metabolism will also have significant effects on core fluxes since they need to provide these carbon intermediates , on top of ATP and reducing equivalents . While this has been already predicted in terms of small changes in biomass composition having significant effects on central carbon fluxes [54] , the effect is an order of magnitude more important in bioengineered strains [55] . In such cells , peripheral metabolism can be vastly altered , and it is crucial to have a method that can take the diverse changes into account in a systematic manner . 2S-13C MFA addresses both of these issues and provides a complete rigorous framework for estimating fluxes using 13C labeling data in the context of a genome scale metabolic network . The algorithm is comprised of a set of optimization problems to be applied sequentially , as shown in the outline in Fig 2 . Our approach divides the metabolites and reactions in a genome-scale model into two groups to be modeled at different scales of resolution ( Fig 3A ) , in the spirit of the multi-scale approach in engineering and physics [56] . For “core” metabolites and reactions both stoichiometry and carbon labeling are tracked . For the remaining “non-core” metabolites and reactions , only stoichiometry is tracked and their contribution to the core set labeling is ignored . Crucially , the algorithm recursively adjusts the division into core and non-core reactions according to the error in the labeling fitting . This procedure is illustrated with a small illustrative network ( see Fig 3B and 3C ) throughout the following subsections . Notice that , because of this core readjustment and the inclusion of other reactions needed to fully explain labeling [48] , the core set needs not be the same as is usually referred to as central carbon metabolism . 2S-13C MFA is supported by two independent data sets: 13C labeling experimental data and genome-scale stoichiometry . We will now show that the method is robust to errors in the 13C labeling experimental data and more robust than FBA to stoichiometric errors . Fluxes calculated through 2S-13C MFA are robust with respect to the experimental accuracy of the 13C labeling data . We show this by generating new sets of 13C data where the new labeling is randomly chosen within the experimental error . The calculated profiles do not change for this new labeling significantly from the initial profiles , as can be observed in Fig 7 . Cofactor ambiguities in the reconstruction of genome-scale models introduce errors in determining fluxes which are much smaller than for FBA . Reconstruction errors are still possible in genome-scale models , in spite of the care used to develop them and the continous improvement in the reconstruction for each new release [13] . We studied the resulting flux profiles after a reconstruction error was simulated ( by changing NADPH to NADH dependence for a high flux reaction , G6PDH2r ) and found that the change in fluxes was much less severe than for FBA ( Fig 8 ) . We found similar results for other reactions ( S16 Fig ) . We attribute this difference to FBA relying heavily on stoichiometric information whereas 2S-13C MFA can additionally count on 13C labeling data to constrain fluxes . Furthermore , changes in the biomass composition reaction do not affect calculated fluxes significantly ( S17 Fig ) . 2S-13C MFA can produce cofactor balance information which cannot be produced using either FBA ( does not provide confidence intervals bounded by 13C data ) or 13C MFA ( does not consider all possible cofactor-producing reactions in the cell metabolism ) . Discerning cofactor production and consumption can be crucial to understand cell behavior . Not only does cofactor balancing have an essential bearing on tumor cell death [65] , but it can also have a significant impact on final production for bioengineered cells [33 , 55 , 66 , 67] . Core metabolism and biosynthetic pathways are linked through cofactor balancing , so an expansion of 13C MFA to include genome-scale stoichiometry such as that provided by 2S-13C MFA is of great interest for bioengineering . While some previous 13C MFA efforts [27 , 28 , 37] did take cofactor balances into account , this has not been done for all metabolites in a genome-scale network . This capability is illustrated ( Fig 6 ) with the main electron carriers in metabolism , NADPH and NADH , responsible for redox balances and , ultimately , for respiration . A majority of NADPH production ( 47–55% , obtained by normalizing data from Fig 6 ) in wild type E . coli is produced by the pentose phosphate pathway ( reactions G6PDH2r and GND: 25–27% ) and the TCA cycle ( reaction ICDHyr: 22–29% ) . Notice that , unlike FBA , 2S-13C MFA produces confidence intervals bounded by 13C data: for example , if the flux through reaction GND were to be more than 3 . 2 mMol/gdw/hr ( 30% of total NADPH creation according to upper left part of Fig 6 ) , the computationally derived labeling for ( e . g . ) m = 0 , 4 for malate ( mal-L ) , for m = 1 for phosphoenolpyruvate ( pep ) and for m = 1 , 2 for ribose 5-phosphate ( r5p ) would differ from the measured value by more than the CEMS measuring error ( see Eq 23 and S1–S3 Figs ) . The transhydrogenase reaction THD2 , transferring electrons from NADH to NADP , also contributes significantly ( ∼ 44% ) but , unfortunately , the flux through this reaction is only very loosely constrained by the current data set . The main NADPH sink involves glutamate production ( reaction GLUDy: 29–65% ) , with fatty acid biosynthesis ( C181SN ) in a distant second place ( 7% ) and a collection of other reactions at equally low levels ( < 12% ) . NADPH consumption and production are radically altered when the gene encoding the initial enzyme in glycolysis ( PGI ) is knocked out ( Fig 6 , right upper panel ) . This genetic manipulation forces the rerouting of all glycolytic flux into the PPP and a lower growth rate ( 0 . 17–0 . 23 vs 0 . 83–0 . 9 h-1 ) . 2S-13C MFA allows us to study the impact of this flux rerouting on the rest of the cell metabolism . For example , the overall NADPH demand falls three fold ( vs a ∼ four fold decrease in growth rate ) . Furthermore , the demand is not equally met by the pentose phosphate pathway and the TCA cycle anymore: in the Δpgi , the PPP enzymes G6PDH2r and GND produce most of the NADPH ( 81% ) with a much less important role played by the TCA cycle ( 13–19% ) . Interestingly , the absolute flux through the PPP for the pgi KO and the wild type is very similar . NADH is generated mostly by the glyceraldehyde-3-phosphate ( 36–37% , GAPD ) , malate ( 10–27% , MDH ) , pyruvate ( 15–25% , PDH ) and 2-Oxoglutarate dehydrogenases ( 7–11% , AKGDH ) , which provide 79–98% of all NADH . Consumption is dominated by the ubiquinone-8 NADH dehydrogenase ( NADH6 , 43–83% ) . After pgi is knocked out , NADH demand decreased ∼ 4 fold , with relative production by MDH undergoing the biggest drop ( from 10–27% to 5–7% ) . These results are consistent with previous 13C MFA calculations for NADPH production [68] , which validates this approach . Furthermore , they also provide information on secondary metabolism and NADH balances in a systematic manner . A similar analysis can be done for acetyl-CoA , a key metabolite and common bottleneck in metabolic engineering [69 , 70] or any other relevant metabolite such as ATP , AMP , Coenzyme A or FADH , which would provide detailed information on the organism’s underlying physiology . The 2S-13C MFA results pinpoint which ( non measured ) metabolites are expected to be detected in the extracellular medium based on the values of exchange fluxes as constrained by the 13C labeling experiments ( S13 Fig ) . Knowledge and quantification of the full range of excreted metabolites ( also known as exometabolome or metabolic footprint [71] ) is desirable for a full understanding of the biochemical impact of the cell on its environment . For metabolic engineering purposes , this knowledge provides important clues as to how to close the carbon balance and whether toxic compounds are being produced in the fermentation . For human metabolism , better prediction of extracellular fluxes can yield improved metabolic predictions when integrated with physiologically-based pharmokinetic models [72] . The exchange fluxes predictions typically have large confidence intervals , but these intervals are much smaller than for FBA ( see S13 Fig ) . Metabolites expected to be detected in the medium are those whose exchange fluxes have net positive maximum and minimum values for a period of time sufficiently long so as to reach detection limits . For the E . coli strains considered here , urea , glycolate ( glyc , S14 Fig ) , fumarate ( fum , S15 Fig ) and acetaldehyde ( acald ) are the non-typical metabolites expected to be present in the medium ( acetate is already measured ) . This prediction of atypical metabolites is of particular interest in light of the recent discovery of extended overflow metabolism [73] . A full prioritized list can be obtained from the exchange flux information and used to direct mass spectrometry , NMR or vibrational spectroscopy efforts to find the missing metabolites until carbon balance is met . For future improvement of intracellular metabolic flux predictions , constraints introduced by extracellular metabolite measurements are very effective and usually easy to measure , but it is necessary to know which metabolites to look for and 2S-13C MFA provides precisely that type of insights . 2S-13C MFA produces nearly the same results as 13C MFA for central carbon metabolism ( see e . g . S4 Fig and S18 Fig ) . This similarity is not surprising since 2S-13C MFA is designed to mimic 13C MFA for this part of metabolism ( see “Limiting flux to core reactions” section ) . The only difference for the current data set can be found in the TCA cycle flux , as described below . These differences arise because genome-scale models account for fluxes to biomass in a more detailed and realistic manner and because they do not rule out unexpected metabolic routes compatible with the available data . Flux through the TCA cycle is lower in the 13C MFA solution ( S4 Fig vs S18 Fig ) because of an inaccurate account of fluxes to biomass in the 13C MFA model . Specifically , the large fluxes draining acetyl-CoA into biomass and to the exterior of the cell as acetate ( each a third of the pyruvate dehydrogenase flux , PDH ) imposed in the original publication [47] have been overestimated in this particular case . While the typical biomass function used in 13C MFA involves a specific stoichiometry of central carbon intermediates converted directly to biomass , this is only an approximation since some of the metabolites required for biomass growth are not represented in the minimal network model and need to be substituted by their requirements in terms of intermediates present in the minimal network ( acetyl-CoA , in this case ) . These effects require significant effort to be accurately incorporated into a small-scale model , but they are elegantly handled by the genome-scale model . In this case a large flux of acetyl-CoA to biomass is assumed in the 13C MFA solution , while the 2S-13C MFA solution determines the requirements on core metabolism by setting the biomass flux to the measured growth rate , and automatically determining the fluxes needed to provide the metabolites present in the biomass equation through the comprehensive stoichiometry network reflected in Eq 2 . Hence , genome-scale models provide an automatic and detailed accounting of biomass requirements . The difference in TCA cycle flux extends to the glyoxylate shunt in some cases in which the isocitrate lyase ( ICL ) is active for the 2S-13C MFA ( S5 and S6 Figs ) solution , but not in the 13C MFA solution due to a limited 13C MFA model . In the 2S-13C MFA solution , the objective function ( Eq 1 ) becomes slightly lower by diverting flux into the ICL and then shuttling it out of the core set from glyoxylate ( glx ) into glycolate . This route is not included in the 13C MFA model and activating the ICL for this model would produce a large amount of flux through the malate synthase ( MLS ) . This MLS flux would significantly deteriorate the fit to malate labeling , so the glyoxylate shunt remains inactive for 13C MFA case . The shuttling of glyoxylate into glycolate is unexpected and , in fact , could be the result of a numerical artifact since the only labeling data available in the full TCA cycle is that of malate ( mal-L ) and small errors in the labeling measurements ( or their confidence intervals ) for this metabolite can lead to erroneous solutions . The glyoxylate shunt is known to be inactive under the given conditions and one could use this information and constrain its flux to zero . However , we aim to produce a general method; of use under conditions where this information may not be available ( e . g . exotic feeds or bioengineered cells ) . Hence , we decided not to constrain the glyoxylate flux in order to show how the genome-scale model constrained by measured data can produce testable and falsifiable consequences to detect this type of errors . In this case , the shuttling of glyoxylate through glycolate results in glycolate being exported out to the medium ( S14 and S15 Figs ) and detectable through ( e . g . ) MS methods . If no glycolate is found , that extracellular flux can be set to zero and the glyoxylate shunt flux will decrease to zero , so as not to deteriorate the fit of the malate labeling pattern . Alternatively , the availability of labeling patterns for additional metabolites ( fumarate , fum and glyoxylate , glx ) would confirm or deny the ICL activity . In this way , 2S-13C MFA can fruitfully use available data to suggest and test unexpected metabolic activity; such as the surprising heme degradation in cancer cells with a non-functional TCA cycle [26] . Our goal in developing 2S-13C MFA is to make a clear distinction between highly reliable constraints such as those induced by 13C labeling data , measured extracellular fluxes , carbon transition information and reaction stoichiometry , and reasonable hypotheses ( such as growth rate optimization ) that may not be universally applicable . 2S-13C MFA has been developed to provide a self-consistent and unbiased determination of the range of metabolic fluxes compatible with available experimental data . Unlike other COBRA methods , the fluxes obtained by 2S-13C MFA are backed up by the extra validation provided by the correct fit of 48 relative labeling measurements ( see S1–S3 Figs ) . Hence , we use it here as a reference to compare various COBRA predictive methods based on different hypotheses . This procedure permits us to determine which method predicts fluxes most accurately . We compared flux profile predictions for pyk and pgi gene knock outs calculated at three different time points ( 5 , 6 and 7 hrs for wild type and pyk KO , and 16 , 21 and 23 hrs for pgi KO ) using six different methods: FBA maximizing either growth and ATP production [74] , Minimization of Metabolic Adjustment ( MOMA [16] ) , Regulatory On/Off Minimization ( ROOM [75] ) and two new methods developed in this paper ( Fig 9 and S19 and S20 Figs ) . These new methods are 13C MOMA and 13C ROOM , which leverage 2S-13C MFA flux profiles obtained for the wild type strain to improve flux predictions ( see S3 Text ) , similar in spirit to the approach by Kuepfer et al [39] . Growth maximizing FBA provides predictions that are not quantitatively accurate , but seem approximately right for most cases ( S19 and S20 Figs ) . This fact probably explains its success in metabolic engineering applications . Since FBA is typically used to make partial flux predictions by using some measured flux information for the predicted experiment , we tested this variation as well . Once fluxes for growth rate and excreted metabolites were constrained to the measured values , pgi predictions became extremely accurate ( S20 Fig ) . However , when a full prediction ( i . e . , not using any data from the predicted experiment ) was sought , FBA noticeably failed for this KO due to its inability to predict the drop in growth rate . ATP maximizing FBA fails most noticeably for the pyk KO , probably because the PYK reaction is involved in ATP production and its elimination significantly changes the ATP balance when ATP is to be maximized . MOMA , ROOM , 13C MOMA and 13C ROOM flux predictions fail most blatantly for the pgi knockout strain because growth rates change radically and the method tries to maintain the previous flux levels ( S19 Fig ) . Hence , we can expect these methods to offer good results only when changes expected in glucose intake and growth rates are relatively small . An improvement of these methods could be obtained if only the relative flux profiles are used for the prediction in the algorithm and the growth rate is separately obtained . Since 13C MOMA and 13C ROOM use 2S-13C MFA flux profiles from the wild type strain , we expected that they would more accurately predict fluxes than would MOMA and ROOM . We find that is the case for fluxes in glycolysis and PPP , but the TCA cycle flux values are less accurate ( see S19 Fig ) . This phenomena is most likely due to our initial flux profile not being very accurate for the TCA cycle ( large confidence intervals , see S4–S6 Figs ) since many fewer labeling measurements are available for TCA metabolites ( only malate vs eight metabolites available for glycolysis and PPP ) . Labeling data for more metabolites next to branch points ( e . g . fumarate , glyoxylate , isocitrate , alphaketoglutarate ) would help reduce the flux confidence intervals for this area of metabolism . These comparisons illustrate how 2S-13C MFA can be used to test COBRA methods , see where they fail and why , and use this information to improve them . The combination of 13C labeling data and genome-scale models with COBRA prediction methods allows us to make some predictions we cannot do using either 13C MFA or FBA . The ability to make accurate predictions of metabolism is of fundamental importance to make metabolic engineering a more predictive discipline . The 13C MOMA predictions of glycolysis and PPP fluxes for the pyk KO at the 5-hr time point surpass those of all other methods ( Fig 9 and S19 Fig ) . In fact , the flux predictions are precise enough that we can even predict with reasonable accuracy the metabolite labeling to be expected from that strain at that time point ( Fig 10 ) . This is a prediction of directly measured data instead of a derived measurement such as flux . No information from the pyk strain at 5 hrs was used . We used the 2S-13C MFA flux calculations from the wild type strain at 5 hrs to obtain fluxes compatible with those measurements and then , through 13C MOMA , obtain the predicted fluxes if pyk were to be knocked out . The corresponding labeling patterns were then derived . Notice that this is very different from the partial predictions of labeling that 13C MFA routinely produces , where the labeling for the present experiment is predicted . Hence , this constitutes a full quantitative prediction of metabolite labeling from a 13C labeling experiment , which has not been reported before , to our knowledge . The prediction of metabolite labeling is particularly relevant as validation because they refer to a directly measured quantity ( i . e . the MDV obtained from the CE-TOFMS ) . Fluxes are derived quantities relying on a variety of implicit assumptions ( i . e . the two-scale approximation , metabolic pseudo-steady state , no accumulation of intermediate metabolites , genome-scale model completeness and accuracy , cell homogeneity… ) . The real test that these assumptions are not severely violated and that the method provides reliable flux profiles is to use them to predict directly measured quantities for other experiments ( in this case , labeling patterns ) . Moreover , this example shows that coupling 13C labeling data with COBRA methods opens the possibility to go beyond qualitative predictions ( e . g . grow/no grow ) . In this manuscript , we have shown how to maximize the information obtained from 13C data to constrain genome-scale models , and that once core metabolism is set by 13C labeling data information , the rest of metabolism is generally highly constricted . As is a usual behavior in “sloppy” nonlinear fitting problems [40] , some fluxes are very effectively constrained and some others only loosely . Confidence intervals obtained through 2S-13C MFA immediately identify these two types of fluxes and show that the use of 13C labeling experimental data produce much narrower confidence intervals than those produced by FBA . The method is generally applicable to any genome-scale model or feed , and can be used to expand the use of 13C-based flux analysis beyond the customary cases to tackle non-standard feeds , exotic organisms and systems described by large stochiometric matrices such as the human metabolic network [76] , those derived from adding macromolecular synthesis [77] or microbial communities [24] . 2S-13C MFA produces similar results as 13C MFA for the region where the latter is valid: central carbon metabolism . 2S-13C MFA , however , extrapolates the constraints induced by the 13C labeling data to a genome-scale model , providing fluxes not only central carbon metabolism but also for peripheral metabolism . This was illustrated firstly by a detailed description of NADPH and NADH production and consumption and , secondly , by predicting unmeasured metabolites expected in the extracellular medium . 2S-13C MFA does not use an evolutionary optimization principle ( such as growth rate optimization ) but , rather characterizes all flux profiles compatible with the experimental data . The extra validation gained by matching the measured labeling values is used to test the validity of the maximization hypothesis by comparing these results with predictions obtained through FBA and other COBRA methods . The comparison of flux profiles predicted with COBRA methods and those obtained through 2S-13C MFA provided not only a ranking of accuracy for predictions but also insight as to how to improve predictive methods . An improved version of MOMA using 2S-13C MFA profiles as a starting point is able to predict the outcome of 48 direct measurements of metabolite labeling for a pyk KO experiment using only data from a different experiment involving the wild type . This capability shows that using 13C labeling experimental data enables accurate predictions beyond qualitative cases ( e . g . grow/no grow ) . This method represents another step in the effort to make bioengineering a more predictable endeavour . The new method hinges on a simple assumption: flux flows from the core set to peripheral metabolism and does not flow back . This assumption is supported by the good fits obtained in general by 13C MFA methods thus far . There might be situations where this two-scale assumption is not applicable and these will be pinpointed by unacceptable fits to the labeling data . The core set of reactions , however , is flexible and can be enlarged as needed to provide acceptable fits to the labeling data . Hence , phenomena such as protein turnover [48 , 78] or cell scavenging in stationary phase can be included by adding the appropiate reactions to the core . The availability of carbon transitions for genome-scale models [79] facilitates a systematic core enlargement . In spite of its simplicity , FBA-based modelling has already exhibited significant success: only three elements are used in this modelling scheme ( genome-scale stochiometry , measured extracellular fluxes and an optimization principle ) but they have been successfully used to rationally engineer strains used for large-scale industrial production [21–23] . However , certainly not every single flux in a flux profile obtained through FBA can be trusted . 2S-13C MFA unites the informative constraints of 13C labeling experiments with genome-scale stoichiometry to improve the determination of internal metabolic fluxes and set confidence intervals based on experimental data . 2S-13C MFA completes and improves 13C MFA by enforcing a global balance of metabolites instead of balancing only a few chosen metabolites . We believe it will be a tool of extreme utility in bioengineering , at a time when a variety of different frameworks for flux prediction for genome-scale models are becoming available [80 , 81] . Furthermore , we think that its widespread use to determine metabolic fluxes will affect our understanding of fundamental biological problems [82] beyond bioengineering .
The two-scale approximation assumes that non-core reactions do not contribute directly to the labeling of core metabolites , since carbon precursors flow from core metabolism into peripheral metabolism and do not flow back . This is represented in terms of a genome-scale model by limiting to zero the flux of reactions flowing into core metabolism ( see Algorithm 1 ) . The first step in 2S-13C MFA ( Fig 2 ) hence consists in taking each reaction that has a product in core metabolism and setting the upper bound to zero . However , it may be the case that this extra constraint makes it impossible to meet the measured growth rate ( we check this by solving the corresponding FBA problem ) . In that case , setting the upper bound to a fraction of the glucose uptake rate is tested ( first 0 . 05 and then 0 . 2 for this case ) . Since the labeling of core metabolism can be impacted by reversible reactions with reactants included in the core set as well , we cover this case by limiting the lower bound of the reaction to zero or the lowest value that permits growth . The impact of the reactions that could not be set to zero will be checked later through External Labeling Variability Analysis ( ELVA , Figs 2 and 4 ) . The input for the first step ( Fig 2 ) is the genome-scale model with the carbon transitions for the core set integrated in it . The output consists of the genome-scale model with lower and upper bounds modified by this “Limiting flux to core” procedure . A detailed description of this first step in the diagram shown in Fig 2 can be found in the pseudo code in Algorithm 1 . Algorithm 1 . “Limiting flux to core” pseudo code for each reaction j flowing into core: limits = [0 , 0 . 05 , 0 . 2]*glucose_uptake limit = limits . next ( ) goOn = True while goOn: if reaction j has forward flux: ub[j] = min ( ub[j] , limit ) else if reaction j has backward flux: lb[j] = sign ( lb[j] ) *min ( abs ( lb[j] ) , limit ) solve FBA problem goOn = ( FBA problem has no solution ) and ( limit is not the last value in limits ) limit = limits . next ( ) Where limits . next ( ) obtains next value in list limits ( the first one if uninitiated ) , and glucose_uptake is the value of the glucose uptake rate . Solve FBA problem refers to finding the solution to the problem given by equations 1–3 in S1 Text . has forward flux refers to the reaction having a possible positive flux ( i . e . positive upper bound , ub ) flowing into the core set and has backward flux refers to the reaction having a possible negative flux ( i . e . negative lower bound , lb ) flowing into the core set for reversible reactions . sign ( lb[j] ) is the sign of the lower bound lb[j] . ub[j] and lb[j] denote upper and lower bounds for reaction j , respectively . The second step in 2S-13C MFA ( Fig 2 ) involves fitting the measured metabolite labeling by solving the optimization problem in Eqs 1–7 , where the upper ( ubj ) and lower bounds ( lbj ) have been limited by the previous step ( “Limiting flux to core” ) . 2S-13C MFA is a hybrid of FBA and 13C MFA ( see Fig 1 and S1 Text ) where the stoichiometry constraint is applied to the full genome-scale network , as in the case of FBA , and the labeling constraints [58] are applied only to the core set of reactions and metabolites , as is the case for 13C MFA . Unlike previous efforts [36 , 39] , these constraints are enforced simultaneously , instead of sequentially using the results of 13C MFA to constrain the FBA problem . This simultaneous approach is more rigorous than using slack coefficients ( δ in Kuepfer et al [39] ) , does not need to invoke an optimization principle to calculate fluxes and allows for the global metabolite balance to affect core metabolism fluxes . Furthermore , it is also self-consistent whereas in the sequential approach one might find that fluxes that flow into core metabolism are active , even though they were not taken into account to do the initial 13C MFA fit . In the notation of Suthers et al [58] ( GAMS files available in S1 Code ) : Minimize OF= ( ∑e∈Emeasm∈Me ( femexp−femΔem ) 2/| Me | ) /| Emeas | ( 1 ) Subject to: ∑ j S i j v j = 0 ∀ i ∈ I N , j ∈ J ( 2 ) l b j ≤ v j ≤ u b j ∀ j ∈ J ( 3 ) ∑ m ∈ M e f e m = 1 ∀ e ∈ E c o ( 4 ) Σe′∈Eco ( ( Σl|EMMe′−>el>0EMMe′−>elVl ) fe′m ) + ( Σl|Sil*<0Sil*Vl ) fem=0∀m∈Me , e∈Ei , i∈IcoN ( 5 ) f e m = ∑ w ∈ W e m ∏ n = 1 | E e | f e n m n ∀ m ∈ M e , e ∈ E c o c ( 6 ) v j = ∑ l ∈ J c o B m a p j l V l ∀ j ∈ J ( 7 ) where: S e t s _ I ≡ { i } : Set of all metabolites . I c o ⊂ I : Set of core metabolites . I c o N ⊂ I c o : Set of non-exchange core metabolites . J : Set of fluxes . J c o ⊂ J : Set of core fluxes . J B : Set of fluxes with backward and forward fluxes differentiated , e . g . PGI_f , PGI_b , PGL…etc . J c o B ⊂ J B : Set of core fluxes for J B . E = { e } : Elementary Metabolite Units ( EMUs ) . E c ⊂ E : Combined EMUs . E i ⊂ E : EMUs from metabolite i ∈ I . E c o c ⊂ E c : Core combined EMUs . E e ⊂ E : EMUs that produce combined EMU e . E c o ⊂ E : EMUs corresponding to core metabolites . E m e a s ⊂ E : EMUs corresponding to measured EMUs . W e m : Set of every possible mass isotopomer multiplet of E e that produce the mass isotopomer m of e . M e : m values for MDV of emu e : 0 , 1 , ⋯ , # of carbons ine . P a r a m e t e r s _ E M M e ′ - > e l = 1 k ife ′ produces e through reactionl ∈ J c o B , 0 otherwise . See Suthersetal[58] . S i j : Stoichiometry matrix . S i l * : Stoichiometry matrix with backward and forward fluxes differentiated . u b j , l b j : Upper and lower bounds for reaction j . f e m e x p ∈ [ 0 , 1 ] : Experimentally measured MDV for emu e from metabolite m . Δ e m : Measurement error for f e m e x p . m a p j l = 1 * g l u c u p t ifl corresponds to forward flux of j . = - 1 * g l u c u p t ifl corresponds to backward flux of j . V a r i a b l e s _ v j : Flux value of reaction j ∈ J , in mmol/gdw/h . V l : Flux value of reaction l ∈ J c o B , normalized to glucose input rate . f e m ∈ [ 0 , 1 ] : Mass isotopomer fraction ( MDV ) for emu e from metabolite m . Notice that S i j* is not the same as Sij , since J and JB are slightly different sets of fluxes . In fact: S i l * = S i j if l is the forward version of j . S i l * = - S i j if l is the backward version of j . ( 8 ) Notice that Eqs 2 and 3 are the traditional FBA contraints and that Eqs 4–6 are the 13C labeling constraints , but they have been limited to core metabolites and reactions . The mapping ( Eq 7 ) converts the fluxes from the 13C MFA description ( higher resolution ) where fluxes are normalized to the glucose uptake rate ( glucupt ) into the FBA description ( lower resolution ) . We describe the 13C MFA description as of higher resolution because the 13C labeling data can pick up differences in forward and backward fluxes ( JB set ) , whereas the purely stochiometric approach of FBA can only constrain net fluxes ( J set ) . For all 2S-13C MFA calculations all input flux was supposed to be routed through GLCpts ( glucose transport via PEP:pyr pts [57] ) . Optimization problems were run N = 30 times and the one with lowest objective function picked . The value of N was chosen so as to avoid relative minima of OF . A plot of how the OF saturated as N increased can be found in S21 Fig in the supplementary material . In addition to the OF , the more typical sum-of-squares residuals ( SSR ) for the fits have been included in the legends of S1–S3 Figs . However , standard good-of-fitness metrics , such as those proposed by Antoniewicz et al [85] , are not applicable to 2S-13C MFA . By using genome-scale models , the number of estimated free fluxes ( p ) is higher than the number of independent measurements ( n = 48 − 9 = 39 in this case ) and the χ2 ( n − p ) distribution of a negative number of degrees of freedom is then not properly defined . This apparent paradox arises because of the implicit assumptions in the χ2 statistics approach to these types of fits . The null model assumes that each of the terms in the SSR are independent random variables , hence the degrees of freedom are the number of terms in the SSR [86] . Nonetheless , we know that , for 13C MFA , the terms in the SSR are typically not independent: the labeling pattern ( MDV ) of related compounds ( i . e . amino acids arising from the same precursors ) are very similar . Hence , Antoniewicz et al [85] , decided to use as degrees of freedom the number of individual measurements minus the number of estimated free fluxes . This proposal seems reasonable for standard 13C MFA , but breaks down for genome-scale models with a much larger number of degrees of freedom . One could choose a different null model for the χ2 statistics ( and previous approaches have shown that the standard χ2 goodness-of-fit approach is probably too conservative , see Fig 3A in [27] ) but the crux of the matter is that we believe that using p-values < 0 . 05 as an absolute arbitrary threshold for significant vs insignificant results is too simplistic , as do other biological researchers [87] or R . A . Fisher himself [88] . Hence , what we report here ( in S1–S3 Figs ) is what we believe is a better ( and more intuitive ) way to estimate how good a fit is: the average objective function normalized to the measurement error ( Eq 1 ) . This is the answer to the question: how different are my fits from the experimentally measured values , measured in units of the experimental error ? This is in accordance in spirit with the suggestion of presenting measures of significance without arbitrary thresholds [87] . Notice that none of the objective functions ( OF ) in S1–S3 Figs is smaller than one , indicating that the difference between experiment and theory cannot be explained through experimental error . This may be because the experimental error for the labeling pattern was underestimated or because the model fails to explain the full labeling pattern . These results are in line with the general trend that fits to intracellular metabolites tend to be worse [89] than fits to proteogenic amino acids [49] . The discarding of the 0 . 05 p-value criterium does not imply that the considerable effort employed in obtaining excellent fits to data [49 , 51] goes unrewarded . Under the 2S-13C MFA method , a bad fit to the experimental data results in a larger value for δe m in Eq 23 and wider confidence intervals . Hence , worse fits beget less flux resolution . Finally , we think that the best validation of the of a flux fit is using the flux distribution to predict the results of another experiment , as we did in Figs 9 and 10 . Once a core set is chosen , ELVA establishes the maximum impact of non-core metabolism in the labeling of the measured metabolites . In order to do so , only core metabolism is considered and the impact of non-core metabolism is represented through “inflow” metabolites and reactions ( see Fig 3C for an example ) . Inflow reactions agglomerate all non-core reactions flowing into ( or out of ) a particular core metabolite ( see S22 Fig in supplementary material ) and are assigned trivial carbon transitions ( e . g . abc --> abc for a three carbon metabolite ) . Inflow metabolites are dummy metabolites with the same number of carbons as the involved core metabolite . ELVA constrains fluxes to the solution obtained in the “Fit data” step ( Eq 10 ) and maximizes and minimizes the computational MDV for each m in each of the labeled metabolites . Since fluxes are fixed for all reactions and labeling is fixed for all metabolites except inflow metabolites , the optimization problem in Eqs 9–15 quantifies the maximum and minimum effect that this unknown labeling ( since it comes from non-core metabolism ) could have on the labeling pattern for the measured metabolites ( fem∀m , e ∈ Emeas ) : Min/max f e m ∀ m ∈ M e , e ∈ E m e a s ( 9 ) Subject to: V j = V ¯ j j ∈ J c o e x t B ( 10 ) ∑ j S i j * V j = 0 ∀ i ∈ I c o e x t N , j ∈ J c o e x t B ( 11 ) l b j ≤ V j ≤ u b j ∀ j ∈ J c o e x t B ( 12 ) ∑ m ∈ M e f e m = 1 ∀ e ∈ E ( 13 ) ∑e′∈E ( ( ∑j|EMMe′−>ej>0EMMe′−>ejVj ) fe′m ) + ( ∑j|Sij*<0Sij*Vj ) fem=0∀m∈Me , e∈Ei , i∈IN ( 14 ) f e m = ∑ w ∈ W e m ∏ n = 1 | E e | f e n m n ∀ m ∈ M e , e ∈ E c o e x t c ( 15 ) where symbols are as explained before , with the addition of: S e t s _ I c o e x t B : Set of extended metabolites . J c o e x t B : Set of extended fluxes with backward and forward fluxes differentiated . P a r a m e t e r s _ V ¯ l : solutions to the problem given by equations 1 - 7 J c o e x tB and Icoext are the set of reactions and metabolites obtained after expanding the core set to meet stoichiometry requirements ( Eq 11 ) as is explained in supplementary S22 Fig . The input for this step is the flux profile obtained from the data fit and the output is an ELVA plot ( Fig 4 ) used to decide whether the solution is self-consistent or not . Once a self-consistent core set has been determined through the recursive procedure in Fig 2 , flux ranges compatible with the experimental data are obtained through the following optimization problem: Min/max V j ∀ j ∈ J ( 16 ) Subject to: ∑ j S i j v j = 0 ∀ i ∈ I N , j ∈ J ( 17 ) l b j ≤ v j ≤ u b j ∀ j ∈ J ( 18 ) ∑ m ∈ M e f e m = 1 ∀ e ∈ E c o ( 19 ) Σe′∈Eco ( ( Σl|EMMe′−>el>0EMMe′−>elVl ) fe′m ) + ( Σl|Sil*<0Sil*Vl ) fem=0∀m∈Me , e∈Ei , i∈IcoN ( 20 ) f e m = ∑ w ∈ W e m ∏ n = 1 | E e | f e n m n ∀ m ∈ M e , e ∈ E c ( 21 ) v j = ∑ l ∈ J c o B m a p j l V l ∀ j ∈ J ( 22 ) ( f e m - f e m e x p ) 2 ≤ δ e m 2 ∀ e ∈ E m e a s , m ∈ M e ( 23 ) where symbols are as explained before , with the addition of: P a r a m e t e r s _ δ e m : Maximum error allowed for f e m . δ e m = Δ e mifΔ e m > ϵ e m and δ e m = 1 . 1 * ϵ e m if Δ e m < = ϵ e m where ϵ e m = f e m f i t - f e m e x p from the solution to equations1-7 . The fluxes vj and Vj are initialized to the values obtained from solving Eqs 1–7 . The results of the minimization and maximization give the flux upper and lower bound compatible with the experimental data from the 13C labeling experiments . This procedure is similar to the FluxRange procedure in Suthers et al [27] , with the exception that we use set constraints for each measured data point ( Eq 23 ) instead of only for the objective function . This approach guarantees that fluxes produce labeling patterns within the experimental error in a much more efficient way than a monte carlo approach . For example , consider these reactions which conform a futile cycle: NDPK 1 : g d p c + a t p c < = > g t p c + a d p c ADK 1 : a m p c + a t p c < = > 2 . 0 a d p c ADK2 : g t p c + a m p c < = > g d p c + a d p c and for which the 13C labeling data cannot constrain the flux values in any fashion . This is shown correctly in the confidence intervals obtained with the method expressed through Eqs 16–23: NDPK1 : [ - 497 . 4 - 500 ] ADK1 : [ - 497 . 4 - 500 ] ADK2 : [ - 498 . 0 - 499 . 3 ] whereas if we perform a Monte Carlo , where labeling data is randomly chosen within the experimental error ( 100 instances ) , we do not obtain the proper range: NDPK 1 : [ - 490 . 8 - - 489 . 7 ] ADK 1 : [ 500 . 0 - 500 . 0 ] ADK2 : [ - 498 . 0 - - 498 . 0 ] The input for this step is the flux profile obtained in the “Fit data” step and the outputs are the flux profile with corresponding confidence intervals , as shown in S4–S12 Figs . | While metabolic fluxes constitute the most direct window into a cell’s metabolism , their accurate measurement is non trivial . The gold standard for flux measurement involves providing a labeled feed where some of the carbon atoms have been substituted by isotopes with higher atomic mass ( 13C instead of 12C ) . The ensuing labeling found in intracellular metabolites is then used to computationally infer the metabolic fluxes that produced the observed pattern . However , this procedure is typically performed with small metabolic models encompassing only central carbon metabolism . The genomic revolution has afforded us easily available genomes and , with them , comprehensive genome-scale models of cellular metabolism . It would be desirable to use the 13C labeling experimental data to constrain genome-scale models: these data constrain fluxes very effectively and provide in the labeling data fit an obvious proof that the underlying model correctly explains measured quantities . Here , we introduce a rigorous , self-consistent method that uses the full amount of information contained in 13C labeling data to constrain fluxes for a genome-scale model where underlying assumptions are explicitly stated . | [
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] | [] | 2015 | A Method to Constrain Genome-Scale Models with 13C Labeling Data |
While RNA interference ( RNAi ) has been deployed to facilitate gene function studies in diverse helminths , parasitic nematodes appear variably susceptible . To test if this is due to inter-species differences in RNAi effector complements , we performed a primary sequence similarity survey for orthologs of 77 Caenorhabditis elegans RNAi pathway proteins in 13 nematode species for which genomic or transcriptomic datasets were available , with all outputs subjected to domain-structure verification . Our dataset spanned transcriptomes of Ancylostoma caninum and Oesophagostomum dentatum , and genomes of Trichinella spiralis , Ascaris suum , Brugia malayi , Haemonchus contortus , Meloidogyne hapla , Meloidogyne incognita and Pristionchus pacificus , as well as the Caenorhabditis species C . brenneri , C . briggsae , C . japonica and C . remanei , and revealed that: ( i ) Most of the C . elegans proteins responsible for uptake and spread of exogenously applied double stranded ( ds ) RNA are absent from parasitic species , including RNAi-competent plant-nematodes; ( ii ) The Argonautes ( AGOs ) responsible for gene expression regulation in C . elegans are broadly conserved , unlike those recruited during the induction of RNAi by exogenous dsRNA; ( iii ) Secondary Argonautes ( SAGOs ) are poorly conserved , and the nuclear AGO NRDE-3 was not identified in any parasite; ( iv ) All five Caenorhabditis spp . possess an expanded RNAi effector repertoire relative to the parasitic nematodes , consistent with the propensity for gene loss in nematode parasites; ( v ) In spite of the quantitative differences in RNAi effector complements across nematode species , all displayed qualitatively similar coverage of functional protein groups . In summary , we could not identify RNAi effector deficiencies that associate with reduced susceptibility in parasitic nematodes . Indeed , similarities in the RNAi effector complements of RNAi refractory and competent nematode parasites support the broad applicability of this research genetic tool in nematodes .
RNA interference ( RNAi ) is a reverse genetics technique which permits the ablation of mRNA by introduction of complementary double-stranded RNA ( dsRNA ) , through cellular mechanisms common to most eukaryotes ( for review , see [1] ) and provides a functional genomics platform in a range of organisms , including those intractable to traditional genetic manipulations . One such group of organisms are the parasitic nematodes for which there have been recent expansions in transcriptomic and genomic datasets [2]–[5] . Several groups have attempted to apply the RNAi protocols pioneered in Caenorhabditis elegans to parasitic nematodes . Significant progress has been made in plant-parasitic nematodes ( PPNs ) in which RNAi is an established experimental technique [6]–[8] , and may have utility for parasite control in plants genetically engineered to express PPN-transcript-specific dsRNA [9] , [10] . In contrast , RNAi experiments in animal- and human-parasitic nematodes have had variable levels of success ( for reviews , see [11]–[13] ) . Of note are experiments reporting inefficient or inconsistent transcript knockdown , highlighted by successful silencing of only 3 of 8 Ostertagia ostertagi genes [14] and 2 of 11 Haemonchus contortus genes [15] . In H . contortus , one feature of successful RNAi appears to be the location of target gene expression , since genes predicted to be expressed in environmentally-exposed tissues are more readily silenced [16] . RNAi difficulties have also been seen in Heligmosomoides polygyrus [17] and the non-parasitic species Pristionchus pacificus and Oscheius sp1 CEW1 [18]–[20] . Notably , inter-species differences are apparent even within the genus Caenorhabditis , where C . briggsae ( unlike C . elegans ) is unable to take up dsRNA from the environment , due to a SID-2 which displays aberrant RNAi functionality [21] . Hypotheses to explain RNAi difficulties in parasitic nematodes have been reported , and include: ( i ) the lack of appropriate in vitro culture systems for parasitic nematodes [15]; ( ii ) inappropriate methods of dsRNA delivery , i . e . delivered externally , where microinjection directly into the worm is more effective in C . elegans [13]; ( iii ) differences in RNAi effector protein functionality [13] , [15]; and ( iv ) differences in the complement of RNAi effectors between nematodes [12] , [13] , [15] , [17] . The latter hypothesis has been confirmed for the apicomplexan Plasmodium spp . ( the causative agents of malaria ) , which are refractory to RNAi due to deficiencies in key pathway components [22]–[24] . Here , we test this hypothesis in nematodes by investigating the complement of RNAi pathway proteins in selected nematode datasets . Using 77 C . elegans RNAi pathway proteins as query sequences , we performed BLAST trawls of nematode-derived genomic and transcriptomic resources . Our searches focused on high-quality sequence datasets , including the draft genomes of Trichinella spiralis ( Clade I/clade 2; here and throughout , we utilize clade delineations of both Blaxter et al . ( denoted clades I–V [25] ) and Holterman et al . ( denoted clades 1–12 [26] ) , Ascaris suum ( Clade III/clade 8 ) , Brugia malayi ( Clade III/clade 8 ) , Meloidogyne incognita ( Clade IV/clade 12 ) , Meloidogyne hapla ( Clade IV/clade 12 ) , Caenorhabditis brenneri , Caenorhabditis briggsae , Caenorhabditis japonica , Caenorhabditis remanei ( Clade V/clade 9 ) , Haemonchus contortus ( Clade V/clade 9 ) , and Pristionchus pacificus ( Clade V/clade 9 ) as well as the transcriptomes of Oesophagostomum dentatum ( Clade V/clade 9 ) and Ancylostoma caninum ( Clade V/clade 9 ) . We find that the RNAi effector complements of these species , whilst quantitatively different are qualitatively similar with regard to the presence of functional groupings , yielding no major inter-species differences except that all were notably less diverse than in Caenorhabditis spp . These data suggest that variable susceptibilities to RNAi amongst parasitic nematodes cannot be adequately explained by differences in RNAi effector complement between such species .
Seventy-seven C . elegans proteins known to be involved in core aspects of RNAi were identified from literature ( Figure 1 ) . These proteins were separated into five core functional groups; namely , small RNA biosynthesis , dsRNA uptake and spreading , AGOs and RISC , RNAi inhibitors , and nuclear effectors . Protein sequences were retrieved from WormBase ( www . wormbase . org; release WS206 ) and used as search strings in a series of primary translated nucleotide ( tBLASTn ) and protein BLASTs ( BLASTp ) [27] against genome and transcriptome databases described below . All primary BLAST hits returning with a bitscore ≥40 and an expect value ≤0 . 01 were manually translated to amino acid sequence in six reading frames ( www . expasy . ch/tools/dna . html ) , and analysed for identity and domain structure by BLASTp ( through NCBI's Conserved Domain Database service ) and InterProScan ( www . ebi . ac . uk/Tools/InterProScan ) . The appropriate reading frame in each case ( usually that with the largest uninterrupted open reading frame [ORF] , however this was determined empirically on a case by case basis ) was then subjected to reciprocal tBLASTn and BLASTp against the C . elegans non-redundant nucleotide and protein databases on the NCBI BLAST server ( http://www . ncbi . nlm . nih . gov/BLAST ) , using default settings . The identity of the top-scoring reciprocal BLAST hit was accepted as identity of the relevant primary hit , as long as that identity was also supported by domain structure analysis ( see Datasets S1 , S2 , S3 , S4 , S5 ) . In the case of H . contortus , primary tBLASTn searches were performed and the separate high scoring return sequences were concatenated into a single sequence ( to facilitate reciprocation ) and used as reciprocal tBLASTn and BLASTp searches against C . elegans , as before . The M . incognita ( http://www . inra . fr/meloidogyne_incognita/genomic_resources ) and B . malayi ( http://blast . jcvi . org/er-blast/index . cgi ? project=bma1 ) genomes were searched using BLASTp to predicted protein sets , in addition to tBLASTn against available contig assembly , unplaced reads and associated ESTs [3] , [5] . The M . hapla genome was searched using BLASTp against public release 4 ( HapPep4: www . hapla . org ) of the hand annotated and experimentally-validated M . hapla protein set [28] , in addition to tBLASTn against the 10× contig assembly [4] . The H . contortus genome was searched using tBLASTn against the supercontig 26/08/09 database ( http://www . sanger . ac . uk/cgi-bin/blast/submitblast/h_contortus ) . A . suum , A . caninum , T . spiralis and O . dentatum primary BLASTp and tBLASTn searches were performed using the datasets generated at Washington University , St Louis ( available at www . nematode . net , [29] ) , as above; reciprocal BLAST searches against C . elegans datasets were then performed as before . Using the core eukaryotic genes as a reference [30] , we estimated that 93% of the A . caninum [31]; and 87% of the O . dentatum transcriptome is identified , making these two dataset comparable to the full proteomes predicted from the genomes of the other species included in this study . C . brenneri , C . briggsae , C . japonica and C . remanei datasets were accessed through WormBase . Searches were also performed against publically-available nematode expressed sequence tags ( ESTs ) available through GenBank ( www . ncbi . nlm . nih . gov ) , using methods as described above .
Perhaps the most striking observation is that each of the parasite species considered here possessed only a fraction of our original search set of 77 C . elegans RNAi proteins ( Table 6 ) , with all displaying a greatly contracted suite of RNAi effector proteins; of the original 77 C . elegans search strings , H . contortus returned 46 , A . suum 44 , A . caninum 40 , O . dentatum 38 , P . pacificus 36 , B . malayi 35 , M . hapla 28 , M . incognita 27 , and T . spiralis 22 . This reduction in diversity ( which could suggest either that: ( i ) orthologs of the C . elegans proteins are absent from the species in question; ( ii ) they have diverged to a degree that is unrecognisable on a primary sequence level , or ( iii ) our datasets possess significant areas of inadequate coverage such that additional RNAi effector genes await discovery in these species ) was observed across all of the functional groupings in our dataset , but was most pronounced within the proteins responsible for uptake/spread of dsRNA . In contrast , the other Caenorhabditid species possessed an RNAi effector complement much closer to that of C . elegans; C . briggsae 65 , C . remanei 65 , C . brenneri 63 , and C . japonica 60 ( Table 6 ) . However , both parasitic and free-living species returned only a subset of putative AGO orthologs relative to C . elegans . AGO analysis presented a significant challenge within our sequence similarity searches , due in part to significant areas of sequence similarity between functionally disparate C . elegans proteins . In many cases our BLAST analysis presented a clustering of multiple distinct AGOs around an individual C . elegans ortholog . Additionally , in some examples we could identify putative AGO orthologs which reciprocated to non-cleavage competent C . elegans proteins , but which encoded catalytic residues consistent with cleavage-competency themselves [32] . Clearly , using gross sequence similarity as an identification tool for AGOs underestimates functional diversity ( data not shown ) , and as a result , we considered that an in depth analysis of AGO family diversity was beyond the scope of this study . This did not represent an issue for the analysis of other RNAi pathway protein families . Small RNA-based genetic regulatory pathways are ubiquitous in eukaryotes , and represent a set of proteins with conserved function and structure in evolutionarily distant organisms . As such , our analysis of proteins that perform nuclear biosynthesis , nuclear export and cytoplasmic processing of small RNAs such as miRNAs ( Figure 1; for recent review , see [1] ) should provide a positive control measure for both our approach , and sequence data quality . These core proteins were well conserved within our dataset ( Table 1; Dataset S1 ) - transcripts encoding many of the proteins required for siRNA and miRNA processing , including RNase III enzymes ( drosha , DRSH-1; pasha , PASH-1; dicer , DCR-1 ) , RNA helicases ( dicer-related helicases DRH-1 and -3 ) , and exportins ( XPO-1 and -3 ) are highly conserved across the genomic and transcriptomic datasets considered here , although orthologs of the dsRNA-binding protein and dicer-complex cofactor , RDE-4 , were notably absent from all of the parasites except B . malayi and A . caninum . Our dataset recognizes five C . elegans genes putatively responsible for dsRNA uptake and spread , identified from mutant screens for defects in systemic RNAi ( the RNAi spreading defective mutants rsd-2 , -3 and -6 , and the systemic RNAi defective mutants sid-1 and -2 ) . Much interest has centered on SIDs as core determinants of dsRNA uptake/spreading mechanisms . These transmembrane proteins were first described in C . elegans as mediators of systemic and environmental RNAi due to their role in transmembrane transport of dsRNA [21] , [33] . Putative SID orthologs have since been described in disparate organisms including mammalian cells [34] , trematode flatworms [35] , crustaceans [36] and insects [37] , [38] ( although Drosophila melanogaster does not possess known SID orthologs , heterologous expression of C . elegans SID-1 sensitizes Drosophila cells to RNAi by soaking [39] ) . Similarly , expression of SID-1 in C . elegans neurons reverses the neuronal intractability of this species [40] . The role of SID-2 in environmental RNAi has been demonstrated by functional expression of C . elegans SID-2 in C . briggsae , a transformation which confers susceptibility to environmental RNAi in this species [21] . Given the importance of SID-1 and -2 to functional RNAi in C . elegans , it is surprising that these proteins are so poorly conserved in other nematodes , where putative SID-1 orthologs were identified in H . contortus and O . dentatum only ( Table 2 ) and sid-2 was not identified outside the Caenorhabditis genus . Similarly poor conservation was observed with RSD-2 ( not identified ) and RSD-6 ( seen only in P . pacificus ) . RSD-3 is the sole perfectly conserved spreading protein in our dataset , occurring in all 13 species ( see Table 2; Dataset S2 ) . Evidence from C . elegans implicates RSD-3 in intercellular spread since rsd-3 null mutants are able to take up dsRNA from the gut lumen , but are unable to distribute this dsRNA into the germline [41] . Despite lacking identifiable orthologs of SID-1 , and -2 , as well as RSD-2 and -6 , plant-parasitic Meloidogyne and Globodera spp . display systemic RNAi following soaking in dsRNA/siRNA [7] , [8] , [42] , [43] , suggesting that alternative uptake proteins ( e . g . fed mutants; see [44] ) , or mechanisms are involved , perhaps similar to the receptor-mediated endocytotic dsRNA uptake process seen in insect gut cells [45] . Intriguingly , our own unpublished data demonstrate a phenomenon of well conserved miRNA target transcript up-regulation in response to dsRNA/siRNA soaking of M . incognita , G . pallida and A . suum , possibly in response to a ubiquitous saturation of RNAi pathway effectors shared between exogenous ( dsRNA/siRNA ) and endogenous ( miRNA ) small RNA pathways , which could indicate that uptake is not limiting for these nematodes ( [46]; unpublished observations ) . Additionally , we cannot discount the possibility that poorly-characterised morphological differences , such as cuticle permeability , better enable dsRNA uptake or propagation in PPNs relative to other parasite species . In C . elegans , plants [47] , and Neurospora [48] , the RNAi effect is greatly amplified by the action of RNA-dependent RNA polymerases ( RdRPs ) , which produce a population of secondary siRNAs from the target mRNA template [41] , [49]–[52] . Further examples of RdRP-catalyzed amplification mechanisms have recently been reported in Paramecium tetraurelia , where multiple RdRPs appear to exist [53] , and in Drosophila , where a non-canonical RdRP has been identified [54] . The most well-conserved RdRP in our dataset is EGO-1 ( Enhancer of Glp-One [glp-1] ) , which appears in seven species ( Table 2 ) . RRF-3 ( RNA-dependent RNA polymerase family member 3 ) , which coordinates complex and ill-understood interactions between RNAi inhibition and amplification of the secondary siRNA response is reasonably well conserved , with RRF-1 less so . EGO-1 is an RdRP with core functions in transcription of “WAGO” ( worm-specific AGO [55] ) -interacting 22G-RNAs responsible for silencing events involved in genome surveillance [56] , [57] and with additional roles in germline development [58] , heterochromatin assembly [59] , [60] , holocentric chromosome segregation [61] , and P-granule function [62] . In light of these core roles , the inter-species conservation of EGO-1 is unsurprising . RRF-3 , which is also reasonably well-conserved , was traditionally referred to as an inhibitory RdRP [63] , although through recent work has been implicated in the production of secondary 26G-RNAs which seed a two-step process of secondary amplification against endogenous targets ( endo-siRNAs ) [57] , [64] , [65] . It is also believed that nonsense-mediated decay ( NMD ) proteins SMG-2 ( Suppressor with Morphological effects on Genitalia 2 ) , -5 and -6 may play a role in the induction and maintenance of secondary amplification [66] , a hypothesis supported by analysis of smg null mutants which are defective for RNAi initiation [67] . SMG-2 and -6 are perfectly conserved across the genomes and transcriptomes considered here , while SMG-5 is not well conserved ( see Table 2; Dataset S2 ) . Conservation of EGO-1 suggests that all of the nematode species examined here are capable of some degree of secondary RNAi amplification , consistent with previous observations of the potency of RNAi in PPNs , where soaking in as little as 0 . 1 µg/ml dsRNA was capable of eliciting significant and consistent knockdown of transcripts in Globodera pallida and M . incognita second stage juveniles ( J2s ) [8] . C . elegans possesses at least 27 distinct AGOs ( including pseudogenes C06A1 . 4 and C14B1 . 7 ) [32] , which constitute the central effectors of the RNA-induced silencing complex ( RISC ) , conferring both function and specificity to RISC . All of the nematodes in our dataset possessed multiple distinct AGOs ( Table 3 ) . A subset of well-conserved AGOs ( defined according to closest C . elegans BLAST match ) included the miRNA-interacting AGO , ALG-1 ( Argonaute [Plant]-Like Gene ) , as well as several endo-siRNA-interacting AGOs including the 26G-RNA-interacting ALG-4 [68] , and the 22G-RNA-interacting WAGOs , R06C7 . 1 and F58G1 . 1 [55] . Some members of the PIWI-clade of AGOs , such as PRG-1 ( Piwi-Related Gene 1 ) , PRG-2 , ERGO-1 ( Endogenous Rnai deficient arGOnaute 1 ) and the AGO/PIWI-clade secondary AGOs SAGO-1 and SAGO-2 , are not well conserved . Surprisingly , RDE-1 , which is believed to be the main AGO involved in silencing events triggered by exogenous dsRNA in C . elegans , was only identified in the animal parasitic nematodes A . suum , H . contortus and A . caninum . Thus the AGOs known in C . elegans to be responsible for endogenous regulation of gene expression are well conserved , while the AGOs responsible for executing RNAi triggered by exogenous dsRNA are not . However , as previously stated , our identification strategy does not account for the possibility that other uncharacterized AGOs exist in each nematode species , performing roles comparable to those AGOs which we could not identify . A further four C . elegans AGOs ( M03D4 . 7; T23D8 . 7; ZK218 . 8 , NRDE-3 ) did not appear to be present within our parasite dataset . The AGO NRDE-3 , is responsible for nuclear translocation of RNAi triggers in C . elegans , and is involved in processes which lead to heritability of gene silencing events . As NRDE-3 is completely absent from the parasite datasets considered here , this may indicate that silencing events cannot be passed between generations of parasitic nematodes . Our data suggest that most nematodes have smaller AGO complements than C . elegans , although the impact this has on functional diversity is unknown . The contracted complement of AGOs identified in the parasite species relative to C . elegans is consistent with their propensity for gene loss [69] . This could indicate redundancy in the function of individual AGOs within C . elegans , or conversely a reduced functionality within the parasites considered here . Interestingly , ERGO-1 is involved in the function of endogenous siRNA populations within C . elegans [57] , [65] but is poorly conserved perhaps indicating a differential small RNA population dynamic between species . Again , the poor conservation of such proteins in RNAi-competent plant-parasitic species would seem to suggest that such deficiencies need not undermine RNAi functionality . In addition to the catalytic AGO protein , RISCs also comprise several protein co-factors , including multiple dsRNA-binding proteins and exonucleases which are thought to pass from elements of the biosynthetic machinery ( Figure 1 ) , although these co-factors are in fact quite poorly characterized , even in C . elegans . Our analysis reveals that TSN-1 ( Tudor Staphylococcal Nuclease 1 ) , which is a common component of RISC in C . elegans , Drosophila and mammalian cells [70] , is well conserved across the species considered here ( Table 3; Dataset S3 ) . The ALG interacting protein AIN-1 , responsible for targeting miRNA-bound ALGs to P-bodies [71] , [72] , is also reasonably well-conserved , being present in seven species . VIG-1 , the C . elegans ortholog of Drosophila VASA intronic gene which regulates transition between larval and adult cellular fates though interaction with the let-7 miRNA [73] , was identified in five of our eight species . Proteins with RNAi-inhibiting function were first characterized in C . elegans , leading to the identification of RNAi-hypersensitive null mutant strains of RRF-3 [63] and ERI-1 [74] . Only two RNAi inhibitor orthologs , the DEDDh-like 3′-5′ siRNA exonuclease ERI-1 and the miRNA 5′-3′ exonuclease XRN-2 ( XRN RiboNuclease related 2 ) , are fully conserved across our genomic and transcriptomic datasets ( Table 4; Dataset S4 ) . Sporadically-conserved inhibitors included the adenosine deaminases ADR-1 and -2 [75] , and LIN-15b , while orthologs of ERI-3 , -5 and -6/7 [76] were not identified outside Caenorhabditis spp . The RNAi pathway affects a number of poorly understood nuclear silencing mechanisms . We found that an uncharacterized nuclear effector , EKL-1 ( Enhancer of KSR-1 Lethality 1 [KSR-1 is a Ras-ERK signaling scaffold protein] [77] ) was the most highly conserved between species ( Table 5; Dataset S5 ) . Other chromatin-associated proteins , helicases and methylation factors are conserved to varying degrees , however MES-3 ( Maternal Effect Sterile 3 ) , RDE-2 ( RNAi Defective 2 ) , EKL-5 and MUT-16 were only found in Caenorhabditis spp . In spite of the contrasting experimental evidence from published studies , our data indicate that diverse nematode species possess the machinery required to facilitate an RNAi response . Our inability to culture many animal parasitic nematodes under in vitro conditions may represent one of the main reasons why RNAi is difficult to perform in these species . Certainly , where RNAi has been most successful in nematodes it has been in species/life-stages amenable to laboratory culture , e . g . free living species such as C . elegans or free-living stages of parasites such as PPN J2 larvae , and more recently in vivo in mosquito-stage Brugia [78] , although some readily-cultured species seem refractory to RNAi [12] . Additionally , given that small non-coding RNAs are heavily involved in various cellular stress responses [79] , it may be that adverse culture conditions lead to their increased expression , resulting in saturation of available RISC proteins , which would interfere with the organism's ability to direct an RNAi response to an exogenous trigger . If such saturation events varied between cells and/or tissues , then this could account for differing knockdown susceptibilities between some genes . Further , we have little information on differences in RNAi effector protein expression level or localization between species and/or life-stages , which might account for the observed variability . Other possible explanations for RNAi disparities include factors for which we have limited information , such as uncharacterized morphological differences between species ( e . g . permeability of the cuticle to nucleic acids ) , or allelic diversity in discrete worm populations which may affect RNAi susceptibility in a similar fashion to drug susceptibility/resistance . In summary , our data do not support inter-species disparities in RNAi effector protein complements as an explanation for differences in RNAi competencies . Whilst the Caenorhabditid spp . encode significantly more RNAi pathway effectors than the other nematodes considered here , qualitative similarities in functional groupings across species with variable RNAi susceptibilities validate our conclusion . | Many organisms regulate gene expression through an RNA interference ( RNAi ) pathway , first characterized in the nematode Caenorhabditis elegans . This pathway can be triggered experimentally using double-stranded ( ds ) RNA to selected gene targets , thereby allowing researchers to ‘silence’ individual genes and so investigate their function . It is hoped that this technology will facilitate gene silencing in important parasitic nematodes that impose a considerable health and economic burden on mankind . Unfortunately , differences in RNAi susceptibility have been observed between species . Here we investigated the possibility that differences in the complement of effector proteins involved in the RNAi pathway are responsible for these differences in susceptibility . Our data revealed that most facets of the RNAi pathway are well represented across parasitic nematodes , although there were fewer pathway proteins in other nematodes compared to C . elegans . In contrast , the proteins responsible for uptake and spread of dsRNA are not well represented in parasitic nematodes . However , the importance of these differences is undermined by our observation that the protein complements in all the parasites were qualitatively similar , regardless of RNAi-susceptibility . Clearly , differences in the RNAi pathway of parasitic nematodes do not explain the variations in susceptibility to experimental RNAi . | [
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] | 2011 | RNAi Effector Diversity in Nematodes |
Strongyloides stercoralis is the only soil-transmitted helminth with the ability to replicate within its host , leading to long-lasting and potentially fatal infections . It is ubiquitous and its worldwide prevalence has recently been estimated to be at least half that of hookworm . Information on the epidemiology of S . stercoralis remains scarce and modalities for its large-scale control are yet to be determined . A community-based two-year cohort study was conducted among the general population in a rural province in North Cambodia . At each survey , participants infected with S . stercoralis were treated with a single oral dose of ivermectin ( 200μg/kg BW ) . Diagnosis was performed using a combination of the Baermann method and Koga agar plate culture on two stool samples . The cohort included participants from eight villages who were either positive or negative for S . stercoralis at baseline . Mixed logistic regression models were employed to assess risk factors for S . stercoralis infection at baseline and re-infection at follow-up . A total of 3 , 096 participants were examined at baseline , revealing a S . stercoralis prevalence of 33 . 1% . Of these participants , 1 , 269 were followed-up over two years . Re-infection and infection rates among positive and negative participants at baseline were 14 . 4% and 9 . 6% at the first and 11 . 0% and 11 . 5% at the second follow-up , respectively . At follow-up , all age groups were at similar risk of acquiring an infection , while infection risk significantly decreased with increasing village sanitation coverage . Chemotherapy-based control of S . stercoralis is feasible and highly beneficial , particularly in combination with improved sanitation . The impact of community-based ivermectin treatment on S . stercoralis was high , with over 85% of villagers remaining negative one year after treatment . The integration of S . stercoralis into existing STH control programs should be considered without further delay .
Infection with Strongyloides stercoralis , a soil-transmitted helminth ( STH ) that occurs worldwide , is probably the most neglected of all neglected tropical diseases ( NTDs ) [1–5] . S . stercoralis is endemic in humid and warm regions where sanitation and hygiene conditions are poor [2 , 5 , 6] . Its global prevalence is largely underestimated due to the use of inadequate diagnostic techniques . A rough ( and probably still conservative ) estimate of 220–370 million infection cases has recently been put forward , corresponding to half of the number of hookworm cases [4 , 7] . Many epidemiological aspects of S . stercoralis infection are unknown or poorly documented , but data from recent studies suggest prevalence rates between 10% and 40% , and possibly up to 60% , in the tropics and subtropics [6] . S . stercoralis larvae living in soil contaminated by human faeces penetrate the intact human skin and eventually reach the small intestine . Walking barefoot or prolonged contact with contaminated soil when farming are known risk factors for infection in areas with poor sanitation facilities [8] . Although about half of all infections remain asymptomatic , chronic symptomatic strongyloidiasis commonly involves diarrhoea , abdominal pain , urticaria and the so-called “larva currens” [8–10] . A particularity of S . stercoralis is its ability to replicate within its host , known as “autoinfection” , which may lead to potentially long-lasting and perpetual infections and to potentially fatal dissemination of the parasite [11–14] . Control programs against soil-transmitted helminths target infections with Ascaris lumbricoides , Trichuris trichiura and hookworms . The mainstay of the WHO’s “preventive chemotherapy” strategy is regular chemotherapy with albendazole or mebendazole , either through targeted treatment of specific at-risk groups or by mass-drug administration to entire populations [15 , 16] . Health education and sanitation improvement are also recommended because they contribute to reducing transmission and are necessary for sustainable control [17] . Mebendazole and albendazole have a suboptimal effect on S . stercoralis and require a long-term treatment schedule , which precludes their use for large-scale control of this parasite [18 , 19] . Ivermectin is the drug of choice against S . stercoralis and a single oral dose has been shown to be highly efficacious [19–21] . To date , there is no control strategy against S . stercoralis . Current evidence calls for action , but key aspects of S . stercoralis infections need to be documented to assess the effectiveness of preventive chemotherapy and to adequately target control measures [1–4 , 22 , 23] . A major question is whether S . stercoralis control could be integrated into existing STH control programs . The purpose of this large two-year cohort study was to assess the impact of ivermectin for chemotherapy-based control , to determine which age groups should be targeted and to identify risk factors for S . stercoralis incident infections among the general population in an endemic setting .
Ethical approval was obtained from the National Ethics Committee for Health Research , Ministry of Health , Cambodia ( NECHR , reference number 192 , dated December 19 , 2011 ) and from the Ethics Committee of Basel and Baselland , Switzerland ( EKBB , reference number 18/12 , dated February 23 , 2012 ) . All participants received an explanation of the study goals and procedures prior to enrollment . Written informed consent was obtained from all participating adults , while consent from participants aged 2–18 years was obtained from the parents or legal guardians . The study was conducted among the general population of Preah Vihear , a rural province in Northern Cambodia with an estimated population of 234 , 370 in 2013 [24] . STH , including S . stercoralis , are highly endemic [22] . Eight villages that had not been previously exposed to S . stercoralis treatment were selected in the Rovieng district . In each village , all households were included and all household members over two years old were eligible . This study was a two-year prospective intervention ( cohort ) study , consisting of a baseline survey conducted between February and June 2012 and two follow-up surveys of enrolled individuals , conducted after 12 months ( January-March 2013 ) and 24 months ( February-March 2014 ) . The study cohort included participants who submitted at least one stool sample and were either positive or negative at baseline . All S . stercoralis positive participants at baseline ( 2012 ) were enrolled . A subset of negative participants at baseline was asked to participate in the cohort and was selected as follows: in each of the eight villages , 18 to 26 households were randomly selected and visited again in 2013 . Household members who were present and S . stercoralis negative at baseline ( 2012 ) were asked to participate in the two follow-up surveys . A study diagram is presented in Fig 1 . The intervention consisted of face-to-face health education on worm infections and hygiene and of administering a single oral dose of ivermectin ( 200μg/kg BW ) to all S . stercoralis cases at baseline and follow-up . A sample of 290 S . stercoralis-infected participants at baseline was followed-up 21 days after treatment to estimate the cure rate . Other parasitic infections were treated according to the national guidelines [25] . Data on demographic features ( age , sex , main occupation , level of education ) , knowledge about worms ( sources of infection , health problems caused by worms ) and hygiene practices ( hand washing , shoe wearing , main defecation place ) were obtained from each participant with a pre-tested questionnaire . Heads of households were interviewed about the size of the household , water and sanitation conditions , house construction material and ownership of household assets . At each survey , two stool samples were collected on consecutive days from each participant . S . stercoralis was diagnosed using the Koga agar plate ( KAP ) culture [26] and the Baermann technique [27] , performed on each sample . S . stercoralis larvae were identified through examination under a microscope and based on morphology . Combining those two methods on two samples has a 92 . 8% sensitivity rate [28] . A detailed description of this laboratory procedure is given elsewhere [22] . Technicians were specifically trained to identify S . stercoralis larvae . Throughout the study period , technicians were rigorously supervised by a qualified microscopist from the Swiss Tropical and Public Health Institute ( Swiss TPH ) , Basel , Switzerland . Any unclear diagnosis was immediately discussed with both the qualified microscopist and the study supervisor . All ( questionnaire and laboratory ) data were double-entered and validated in EpiData version 3 . 1 ( EpiData Association; Odense , Denmark ) . Data management and statistical analyses were performed in STATA version 13 . 0 ( StataCorp LP; College Station , United States of America ) . Two risk definitions of S . stercoralis infection were considered: the risk of S . stercoralis infection at baseline ( prevalence ) and the risk of S . stercoralis infection at follow-up ( incidence ) . The latter included all cases occurring at any follow-up survey , either among participants found positive at baseline , treated and considered negative after treatment ( “re-infection” ) or participants diagnosed negative at baseline ( ”new-infection” ) . A participant was considered S . stercoralis positive if at least one larva was found in any of the four samples or S . stercoralis negative if no larva was detected in the four samples . Participants with only negative results but with fewer than four analyzed samples were not included in the analysis . The risk of infection at follow-up was defined as a binary outcome , which took the value of one if a participant was infected at any of the follow-up surveys , regardless of their infection status at baseline , and zero otherwise . All reported results in this paper use the definitions described above , unless specified otherwise . Age was centered at the mean of each sample ( i . e . baseline or follow-up ) and both squared and cubic terms were calculated . A socioeconomic index was built based on house construction material and asset ownership variables , using Principal Component Analysis ( PCA ) [29 , 30] . Households were classified into wealth tertiles , with the first tertile corresponding to the least poor and the third tertile to the poorest . To ensure the comparability of household socio-economic status ( SES ) across time , the same asset weights were used to calculate the index of each year . Therefore , differences in SES between 2012 and 2014 relate to changes in the ownership of assets . For some of the subjects , age and education variables were inconsistent across the surveys . To resolve inconsistencies , the following procedure was used: if two of the three values were consistent then the third one was corrected so as to achieve consistency across all three values . Pearson’s chi-squared ( χ2 ) test was used to compare proportions . The cure rate achieved by ivermectin was assumed to be the proportion of S . stercoralis patients who had four negative diagnosis results ( Baermann method and Koga agar plate tests on two consecutive days ) 21 days after treatment . Mixed-effects logistic regression models were used to investigate the association of each risk definition with explanatory demographic , socioeconomic and behavioral variables , i . e . S . stercoralis infection risk at baseline ( “baseline model” ) and infection risk at follow-up ( “follow-up model” ) . Village-level correlation was taken into account with a village-level random effect in both models . For the follow-up model , within-individual correlation was accounted for using an individual-level random effect . The model building process was similar for the two models . Age and sex were not submitted for variable selection . The same set of demographic , socioeconomic , water and sanitation , and behavioral variables was subjected to selection for each outcome , with the exception of village-level prevalence at baseline and an indicator for baseline infection status that were used only in the follow-up model . Those variables are presented in S1 Table . For the follow-up model , age , occupation and level of education as of the previous year were used as explanatory variables . For behavior and knowledge , current year values were used because health education was always delivered after administering questionnaires and current values were also deemed more representative of the knowledge and behavior of the past months than the values of the previous survey . Variable selection was first performed using mixed-effects bivariate logistic regressions , including the appropriate random effects as described above . Variables exhibiting an association at a significance level of 15% in the likelihood ratio test ( LRT ) were included in the multivariate logistic regression models . In case two explanatory variables were strongly correlated , only one of them was included in the model and this variable was selected based on the Akaike Information Criterion ( AIC ) of the resulting model . Additionally , sex was checked to determine if it could be an effect modifier of any other variable in the baseline and follow-up models . For the follow-up model , the interaction between defecation place and occupation was checked , as well as whether infection status at baseline could be an effect modifier of any other variable in the model , in case infection status at baseline involved different risk factors at follow-up . Finally , the significance of the village-level random effect was assessed and the random effect was removed if the AIC indicated a better fit in absence of the random effect . We also conducted a mixed Poisson regression analysis providing incidence rate ratios instead of odds ratios [31] . To illustrate the impact of village-level sanitation coverage , the STATA command “margins” was used to predict the risk of infection at follow-up as a function of village-level sanitation coverage , while adjusting for all other covariates of the underlying risk factor model .
At baseline , 3 , 837 participants were present and 3 , 697 had complete questionnaire data and at least one available diagnostic result for S . stercoralis , i . e . with either Baermann and/or KAP results available for one day ( Fig 1 ) . All 1 , 026 S . stercoralis positive participants were enrolled and 477 S . stercoralis negative participants were eligible for enrollment . The compliance rate was 86 . 7% among the 1 , 503 eligible cohort participants . There were no differences in the proportions of males and females or in reported defecation place between compliant and non-compliant participants at baseline . The proportion of preschool-aged children and of adults who declared staying at home , having a small business or working in the tertiary sector were higher in the non-compliant group , while participants who attained primary school were better represented in the compliant group . Therefore , these variables were adjusted for in the risk factor analysis . According to the outcome definition adopted in this work , samples from 3 , 096 participants at baseline were analyzed , while the study cohort included 1 , 269 participants , of whom 873 were positive and 396 were negative at baseline . For the follow-up , 935 cohort participants were present at both surveys , 274 were present only at the first follow-up and 60 were present only at the second follow-up . Hence , 1 , 269 participants were included in the analysis of infection risk at follow-up , i . e . all participants who were present at one or both follow-up surveys . Fig 1 displays compliance levels and the number of participants excluded from the analysis at each survey . The characteristics of the baseline and cohort participants are given in S1 Table . Overall , the cohort appeared to be representative of the baseline sample , with similar distributions of covariates . The only notable difference between the baseline and cohort samples was the frequency of females , which was higher at baseline ( n = 1 , 702 , 55 . 0% ) while more males were represented in the cohort ( n = 653 , 51 . 4% ) . This difference is due to the higher risk of infection among males and was statistically significant ( χ2 = 14 . 9 , p<0 . 001 ) . Cohort participants were older than non-participants ( median = 23 vs . 25 years ) at baseline . The most frequent occupation was rice farmer ( baseline: 50 . 5%; cohort: 54 . 4% ) . About half ( baseline: 55 . 9%; cohort: 55 . 1% ) of participants did not have access to sanitation and 57 . 9% of participants declared regularly defecating in the open at baseline . Almost all existing sanitation facilities were latrines types recognized as efficient in preventing from exposure to excreta , with 93% of them being ventilated improved pit latrines ( 96% ) or pour-flush toilets . No village had full sanitation coverage and the village-level proportion of households owning a latrine ranged from 4 . 2% to 77 . 6% . The prevalence of S . stercoralis infection at baseline was 33 . 1% , ( 95% Confidence Interval ( CI ) : 31 . 5–34 . 8 ) . Prevalence was similar across the eight study villages ( χ2 = 10 . 62 , p = 0 . 16 ) and ranged from 24 . 2% ( 95%CI: 19 . 7–29 . 2 ) to 33 . 8% ( 95%CI: 27 . 6–40 . 4 ) . Among the cohort participants who were infected at baseline , 120/833 and 75/681 participants were found to have been re-infected , yielding re-infection rates of 14 . 4% ( 95%CI: 12 . 1–17 . 0 ) and 11 . 0% ( 95%CI: 8 . 6–13 . 6 ) at the first ( 2013 ) and second ( 2014 ) follow-up , respectively . Among the cohort participants who were infection-free at baseline , 36/376 and 36/314 were found to have been infected at the first and second follow-up , respectively , resulting in new infection rates of 9 . 6% ( 95%CI: 6 . 8–13 . 0 ) and 11 . 5% ( 95%CI: 8 . 1–15 . 5 ) in 2013 and 2014 , respectively . Re-infection rates significantly varied across villages ( χ2 = 32 . 2 , p<0 . 001 ) and ranged from 8 . 3% ( 95%CI: 3 . 1–17 . 3 ) to 20 . 1% ( 15 . 5–25 . 3 ) . Compared to the positive cohort , the rate of infection was significantly lower among the negative cohort at the first follow-up , but there was no difference at the second follow-up . Fig 2 displays the rates of S . stercoralis infection among participants testing positive or negative at baseline , at each follow-up . Of the 290 participants who were enrolled for follow-up 21 days after treatment , 261 ( 90 . 0% ) were present at follow-up and of those , 206 ( 78 . 9% ) had complete diagnostic testing ( i . e . four results regardless of the infection status ) . 7/206 patients had not been cured , so the cure rate achieved by ivermectin was 96 . 6% ( 95%CI: 93 . 1–98 . 6 ) . The results of the bivariate mixed-effects logistic regression models at baseline ( prevalent cases ) and follow-up ( incident cases ) are presented in S2 Table . At baseline , the village-level random effect was not significant in any bivariate model , so risk factors for infection at baseline were explored using a simple logistic regression model . At follow-up , the village-level random effect lost significance upon introduction of the proportion of households owning a latrine in each village , which suggests that this variable accounted for most of the between-village differences in infection risk . The results of the multivariate logistic regression models built for baseline and follow-up are presented in Table 1 . No interaction was found either in the baseline or follow-up model . At baseline , females had lower odds of being infected and infection risk increased with age . The poorest were at a higher risk for infection , as were participants with a higher education level . At follow-up , the risk of acquiring a new infection decreased with increasing sanitation coverage at village level . Neither age nor sex was significantly associated with the risk of acquiring an S . stercoralis infection at follow-up . Rice farmers had higher odds of acquiring an infection , as did participants who reported regularly defecating in rice fields or water . Another behavioral risk factor was not wearing shoes , both at home and when going to the toilet . The odds ratios obtained with this multivariate model were similar to risk ratios produced by the mixed Poisson multivariate model . Incidence rate ratios obtained from this model are presented in S3 Table . Fig 3 shows the predicted risk of S . stercoralis infection at follow-up as a function of the village-level proportion of households owning a latrine and reported individual defecation place . The risk of S . stercoralis infection at follow-up decreases with increasing sanitation coverage as expressed by the village-level proportion of households owning improved latrines . While the risk of individuals regularly defecating in rice fields or water is the highest , the risk of acquiring a S . stercoralis infection at follow-up for individuals usually defecating in toilets vs . in the forest or behind the house appear similar and reflect the absence of a significant protective effect of defecation in latrines , at individual level .
This two-year community-based cohort study documents , for the first time to our knowledge , incidence rates and risk factors for incidence of S . stercoralis among the general population and provides essential information to guide control efforts: ivermectin chemotherapy against S . stercoralis infections is highly beneficial , and its impact is enhanced by community-level improved sanitation coverage . About one in three of the 3 , 000 participants present at baseline were infected with S . stercoralis . A cohort of 1 , 269 participants was followed-up over two years and S . stercoralis re-infection rates were 14% and 11% at the first and second follow-up , respectively . The rates of newly acquired infections among participants who were negative at baseline were 10% and 11% in 2013 and 2014 , respectively . The re-infection rates estimated here confirm results by Khieu and colleagues who found a re-infection rate of 31% in a 2-year cohort study of 300 schoolchildren in semi-rural Cambodia [32] . Since only eight individuals were positive at both follow-ups , a rate of similar magnitude might have been observed after two years if cases found at the first follow-up had not been treated . Most importantly , the S . stercoralis infection risk at follow-up was low , with almost 90% of cohort participants testing negative one year after treatment or after having been diagnosed negative at baseline , indicating that populations strongly benefited from ivermectin treatment . Indeed , an incidence rate of around 13% is particularly low , even compared to that of hookworm , which has the lowest re-infection rates of the three other STH [33] . A striking finding of the present work was the corresponding decrease of S . stercoralis infection risk at follow-up with increasing sanitation coverage measured at community level . While the rationale behind improving sanitation for STH control is to prevent re-infection by decreasing transmission , evidence supporting this fact remains rare and mostly arises from cross-sectional studies using latrine availability or use at individual level . We did not find any protective effect of defecating in latrines on S . stercoralis infection risk at baseline or follow-up . The association between S . stercoralis infections and sanitation has rarely been studied so far . Varying results were obtained mostly from cross-sectional studies , which either found no association or a decreased risk associated with defecation in or access to improved latrines [22 , 34–36] . In our study , we found a strong impact of sanitation coverage in combination with treatment which explained the differences in infection rates at follow-up across villages . Two key aspects underlie this finding . First , coverage was strongly correlated with use in this setting , with almost all participants living in a house equipped with latrines declaring regularly defecating in them . Second , more than 90% of existing sanitation facilities were improved latrines , which effectively prevent environmental contamination [37 , 38] . This result is in line with another study that also found a protective effect of 75% sanitation coverage and above on STH infection [39] . Unlike infection risk at follow-up , baseline prevalence was similar across villages and was not associated with sanitation coverage . This suggests that , in absence of treatment , the protective effect of sanitation coverage levels-off with time , with parasites steadily accumulating in the environment . In that case , the association between infection risk and sanitation coverage and use may not be observable in the absence of a longitudinal approach . Additionally , participants who declared not wearing shoes , both at home and when going to defecate , had a three-fold higher risk of acquiring an infection by the time of follow-up . This result adequately reflects that infection occurs due to exposure of bare feet to larvae and is in line with meta-analysis findings showing that the risk of infection with S . stercoralis was almost halved by footwear use ( OR: 0 . 56 , 95%CI: 0 . 38–0 . 83 ) [40] . It is widely accepted by the STH control community that water , sanitation and hygiene ( WASH ) improvement is important for controlling and preventing those infections [41–43] . However , sanitation measures have not concretely been included in STH intervention packages because implementing them is particularly challenging due to their high cost , complexity , need for cross-sectorial collaboration , and lack of perceived need by communities [41 , 42] . Regarding the latter , promoting behavior change and/or triggering demand for sanitation through initiatives like community-led total sanitation ( CLTS ) and targeted social marketing or social networking appears to be useful but actual evidence of their impact is scarce [42 , 44–46] . There is still a need to assess and quantify which sanitation improvement measures are effective in reducing S . stercoralis ( as well as other STH ) transmission , including combinations with chemotherapy , and according to which ecological and socio-cultural settings . Infection at follow-up was not associated with age , so no age risk group could be identified as a target for control . Given the implications of this result , various additional models were run throughout the model building process to test the relationship between infection risk at follow-up and age . None provided evidence in favor of a significant role of age , indicating that the result presented here is robust . Yet , S . stercoralis prevalence at baseline did increase with age , a result consistent with other findings from all continents [5 , 9 , 22 , 35 , 47] . Combined , the relationships between age and prevalence or incidence indicate that S . stercoralis infections are permanently acquired through life and that higher prevalence rates observed among adults result from infections that are maintained and accumulated over time [22] . Finally , regarding other risk factors , farmers were found to have higher odds of acquiring an infection , which was reflected by the positive association between infection risk at follow-up and both occupation and defecating in rice fields . This result is consistent with farmers’ frequent and intense contact with soil and is in line with the fact that S . stercoralis infection is known to be an occupational disease of farmers and miners , even in temperate climates [6] . Our study has some limitations . First , we restricted the inclusion of S . stercoralis negative participants to those who had four negative diagnostic results to ensure a high specificity while keeping the maximum number of positive observations in the sample . This approach resulted in a slight overestimation of prevalence at baseline , i . e . 33 . 1% vs . 29 . 3% with a complete case analysis , but it did not significantly affect the incidence rates . Moreover , because S . stercoralis can replicate within its host , an undetected infection can reappear through auto-infection [11] . This may have led to overestimation of the ivermectin cure rate and infection risk at follow-up . Given the high cure rate achieved by ivermectin and the rapid clearance of parasites after a single oral dose , the number of re-emerging infections and uncured patients should be low . Nonetheless , the genotyping of parasites before and after treatment is needed to assess the proportion of re-emerging vs . new infections , which is an important aspect of S . stercoralis infection [32 , 48] . The cure rate achieved by ivermectin has likely been overestimated due to the use of coprological methods , but this issue might have been mitigated by the highly sensitive diagnostic approach used in this study [3 , 49] . An alternative way to confirm cure would be to use serological tests which would involve measuring antibody titers 6–12 months after treatment but which would be inappropriate in a high risk setting where reinfection occurs [50 , 51] . Second , one village had a very low proportion of households owning a latrine ( 4% ) , which could have biased the association with follow-up infection risk . However , the association remained significant after excluding this village from the analysis ( the results from this model are available in S4 Table ) . Additionally , a more flexible model ( i . e . including a quadratic term for sanitation coverage ) was run to assess the potential non-linearity of this effect . Although this term was not significant , the resulting dose-response curve suggested that the effect of sanitation coverage of S . stercoralis might level off at around 60% . The existence of such a threshold would be consistent with other findings [39 , 52] . This aspect might be of importance when setting goals for sanitation improvement measures . While we are confident in our results about sanitation coverage , additional studies including a larger number of villages could help to assess whether there is indeed a threshold . The possibility that sanitation acted in a fashion similar to herd immunity as suggested by our results , would have far-reaching implications in terms of equity , since even partial sanitation coverage would benefit entire communities , including those who cannot afford latrines . Finally , the generalization of findings presented here will necessitate additional studies and modeling across different settings to account for variation in geographic and living conditions , including access to improved sanitation across regions , when assessing the impact of both treatment and sanitation on S . stercoralis infections [53] . The high efficacy of ivermectin and the low incidence rates estimated in the present work suggest that mass ivermectin chemotherapy as a control measure against S . stercoralis would have a strong impact . Our results actually confirm recent findings from a retrospective study conducted in Ecuador which found that mass drug administration of ivermectin targeting onchocerciasis had a significant impact on S . stercoralis prevalence [54] . Whether treatment should be delivered annually or more or less frequently cannot be addressed by the present study design , but a cost-effectiveness analysis could help to assess this component of control . In the absence of age-specific morbidity data for chronic infections and because the risk of developing hyper-infection is similar at any age , the similarity of incident infection risk across ages and the highest prevalence rates among adults in populations naïve to treatment suggest a community-wide approach . Reaching entire communities may raise feasibility and treatment availability issues , although the experience of lymphatic filariasis and onchocerciasis show that community-wide mass treatment is feasible [55] . For Cambodia , this should not be a major issue given its effective and well-established delivery systems of treatment against other STH , working through health centres with community health workers and , lately , even in factories [56–58] . The affordability of treatment is of greater concern . Ivermectin is not subsidized or donated in Cambodia , where a tablet produced by a certified good manufacturing practice company is available for Cambodia at 10 USD and two to four tablets are needed to treat one individual . Unfortunately , and whatever the target population , such a cost currently precludes the implementation of large-scale S . stercoralis control . Although the difficulties of improving sanitation are widely acknowledged , cost-effectiveness studies for sanitation measures might be of interest in the framework of S . stercoralis control in Cambodia , given the current high cost of ivermectin and the potential impact of sanitation measures on other STH and diarrhoeal diseases . Additional research on sanitation impact should include assessments of latrine promotion and behavior change measures . The findings presented here indicate that S . stercoralis should be integrated into existing STH control programs and include adults . In addition , improved sanitation enhances the effect of successful treatment by reducing S . stercoralis transmission , so sanitation improvement measures using new participatory approaches such as CLTS or social marketing should be considered [59 , 60] . However controlling S . stercoralis will not be achievable unless funds are made available or generic ivermectin is produced at an affordable price so that low- and middle-income countries , which are the most affected , can start tackling the S . stercoralis problem . | Strongyloides stercoralis is an intestinal parasite that can replicate within its host , leading to long-lasting and potentially life-threatening infections . Occurring worldwide , it thrives where the climate is warm and sanitation is poor . S . stercoralis is not well detected by standard field techniques and its global prevalence is largely underestimated . Strongyloidiasis is arguably the most neglected of neglected tropical diseases . No control strategy currently exists for it . Ongoing control programs against other similar parasites regularly deliver treatment that has little effect on S . stercoralis . This parasite is a major public health issue in Cambodia , where prevalence rates of up to 40% were recently found . We diagnosed , treated and followed-up ( over two years ) more than 1 , 200 villagers in rural Cambodia to assess the impact of ivermectin on S . stercoralis at community level and assess whether S . stercoralis control could be integrated into existing programs . Our results indicate that ivermectin treatment was highly beneficial , with more than 85% of participants testing negative one year after treatment . All ages were at similar risk of acquiring an infection . The effect of community-based treatment was enhanced by increasing village sanitation coverage . We conclude that S . stercoralis control is feasible and strategies should be designed without further delay . | [
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] | 2016 | Ivermectin Treatment and Sanitation Effectively Reduce Strongyloides stercoralis Infection Risk in Rural Communities in Cambodia |
Zika virus ( ZIKV ) is an emerging mosquito-borne flavivirus linked to devastating neurologic diseases . Immune responses to flaviviruses may be pathogenic or protective . Our understanding of human immune responses to ZIKV in vivo remains limited . Therefore , we performed a longitudinal molecular and phenotypic characterization of innate and adaptive immune responses during an acute ZIKV infection . We found that innate immune transcriptional and genomic responses were both cell type- and time-dependent . While interferon stimulated gene induction was common to all innate immune cells , the upregulation of important inflammatory cytokine genes was primarily limited to monocyte subsets . Additionally , genomic analysis revealed substantial chromatin remodeling at sites containing cell-type specific transcription factor binding motifs that may explain the observed changes in gene expression . In this dengue virus-experienced individual , adaptive immune responses were rapidly mobilized with T cell transcriptional activity and ZIKV neutralizing antibody responses peaking 6 days after the onset of symptoms . Collectively this study characterizes the development and resolution of an in vivo human immune response to acute ZIKV infection in an individual with pre-existing flavivirus immunity .
Zika virus ( ZIKV ) is an emerging arthropod-borne flavivirus . It is primarily transmitted by Aedes sp . mosquitos but can also be transmitted person to person vertically from mother to child , sexually and in blood during transfusions [1] . Clinical manifestations occur in approximately 20% of infections and can include an acute onset low grade fever , pruritic erythematous macular papular rash , arthralgias and conjunctivitis [2] . Clinically these symptoms can be confused with dengue virus ( DENV ) or chikungunya virus ( CHIKV ) infections that are transmitted by the same mosquito vectors and can co-circulate with ZIKV [3] . During pregnancy , ZIKV can cause congenital Zika syndrome and other severe birth defects in fetuses [2] . In adults , ZIKV is associated with life-threatening Guillain-Barré Syndrome ( GBS ) [4 , 5] . The details of how ZIKV bypasses immune restriction to cause disease are still under investigation . The relationship between flaviviruses and the immune system is complex [6] . On one hand , the immune system can exacerbate viral pathogenesis . For example , ZIKV , like DENV and West Nile virus ( WNV ) , infect innate immune white blood cells early in infection [7–11] . Studies in ZIKV infected children identified monocytes , in particular CD14+CD16+ intermediate monocytes , and myeloid dendritic cells as the main targets of ZIKV infection in peripheral blood mononuclear cells ( PBMCs ) [9] . These infected cells may act like a “Trojan horse” to increase spread of the virus to different tissue compartments . Antibody ( Ab ) responses to flaviviruses are often cross-reactive and have the potential to mediate antibody-dependent enhancement ( ADE ) . While there is no evidence that ADE alters ZIKV pathogenesis in humans , in a mouse model of ZIKV infection , administration of DENV or WNV convalescent plasma increased ZIKV morbidity and mortality through ADE [12] . On the other hand , the development of protective adaptive immune responses is thought to be critical to clear ZIKV infection [6] . Therefore , increasing our understanding of human immune responses to ZIKV infection can lead to better understanding of ZIKV clinical manifestations and pathogenesis and inform the development of vaccines . Only a small number of studies have examined human responses to ZIKV infection in vivo . Analysis of serum inflammatory markers during acute ZIKV infection identified some potential biomarkers associated with neurologic complications [13] and viremia plus moderate symptoms [14] . Monoclonal Abs isolated from four donors infected with ZIKV demonstrated that neutralizing Abs primarily recognized the envelope protein domain III of ZIKV and that Abs recognizing different ZIKV epitopes could alternatively protect against ZIKV challenge or enhance subsequent DENV infection in mice [15] . Another study tracking the development of Ab responses to ZIKV in three DENV-experienced and one DENV-naïve individual found that acute-phase Abs developing during ZIKV infection in DENV-experienced individuals were highly cross-reactive but poorly neutralizing [16] . In a single flavivirus naïve individual , anti-ZIKV B-cell plasma neutralization activity and T-cell responses peaked later between day 15 and day 21 [17] . A large study examining T cell responses to ZIKV in DENV-naïve and DENV-immune patients revealed that DENV exposure prior to ZIKV infection influences the timing , magnitude , and quality of the T cell response [18] . In another study that examined both innate and adaptive immune responses in 5 individuals infected with ZIKV , Lai et . al . observed that flavivirus-experienced individuals developed rapid cross-reactive antibody responses against both DENV and ZIKV as well as activated CD8+ T cell responses , albeit few ZIKV-specific CD8+ T cells were identified [19] . These studies provide insight into human ZIKV infection , but our understanding remains limited due to the small number of reported cases . Additionally , published reports have utilized conventional approaches to study the in vivo immune responses to ZIKV . Combining these approaches with genome-wide next-generation sequencing ( NGS ) analyses could bring new insight into human ZIKV responses and inform direction and design of future studies of immune responses during infection in larger cohorts . As a step towards improving our understanding of human immune responses to acute ZIKV infection through new approaches , we present a detailed immunologic characterization of the innate and adaptive temporal and cell type-specific responses to an acute ZIKV infection in a DENV-experienced patient .
This research study was approved by the UCSD IRB with Human Research Protections Program # 161060 . Written informed consent was obtained from the adult human subject described in this report . After obtaining written informed consent , blood was collected on five occasions d3 , d6 , d17 , d48 , and d240 post-onset of symptoms ( POS ) . Urine was collected on d3 and d6 only . Serum was isolated by collecting blood into a plain tube containing no anticoagulant , allowed to clot at room temperature for 20 minutes followed by centrifugation at 1500xg for 10 minutes in a refrigerated centrifuge . Serum was frozen in single use aliquots at -80°C . Peripheral blood mononuclear cells ( PBMCs ) were isolated from heparinized blood using Histopaque-1077 per manufacturer's instructions and subjected to flow activated cell sorting ( FACS ) or cryopreserved in 5 million cell aliquots in 90% FBS + 10% DMSO ( Hybri-max Sigma ) using a Nalgene Mr . Frosty at -80°C for 24 hours before transfer to liquid nitrogen . Cryopreserved cells were thawed rapidly to 37°C and slowly diluted with pre-warmed growth media , followed by gentle pelleting and resuspension in cold FACS staining buffer . Five microliters of d3 POS serum or blood was inoculated into a T25 flask of C6/36 mosquito ( Aedes albopictus ) cells . Supernatants ( 5 mL ) were harvested seven days after culture and titrated via BHK-21 cell-based focus forming assay ( FFA ) and anti-Flavivirus envelope ( E ) protein antibody clone 4G2 . The urine culture supernatant had a titer of 2 . 0 x 104 focus forming units ( FFU ) /mL . Infectious virus in the serum culture supernatant was undetectable . Viral RNA from 0 . 2ml of C6/36 supernatant that was inoculated with d3 POS urine was extracted using the Roche High Pure Viral RNA Kit ( Roche ) and reversed transcribed using a primer specific method for ZikaBr ( Forward primer AGTGGAGACGATTGYTGTNGT , Reverse primer AACATGTCTTCTGTGGTCATCCA ) ( SuperScript III First-Strand Synthesis System for RT-PCR , Invitrogen ) . cDNA was amplified using Taq polymerase ( Roche ) , cleaned using QIAquick PCR Purification Kit ( Qiagen ) and sequenced using BDT v3 . 1 on the ABI 3130xl Genetic Analyzer . Forward and reverse sequences were used to make a contig and manually edited using Bioedit [ref http://www . mbio . ncsu . edu/BioEdit/bioedit . html] . The Basic Alignment Search Tool ( BLAST ) [ref:] was then used with the resultant sequence [ref: https://blast . ncbi . nlm . nih . gov/Blast . cgi ? PROGRAM=blastn&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome] which most closely aligned with other ZIKV NS5 sequences . For phylogenetic analyses , RNA from ZIKV SD001 infected primary human macrophages were aligned to the human hg19 genome using STAR [PMID: 23104886] . Any unmapped reads were used as input for strand-specific de novo transcriptome assembly with Trinity [PMID: 21572440] . The longest assembled transcripts were approximately 9 kb , and corresponded to near full-length viral genomes . The resulting alignment from ZIKV SD001 and 435 publicly available ZIKV sequences from NCBI viral genomes resource [20] were used to perform an approximate maximum likelihood phylogenetic tree with PhyML [21] . The tree was rooted with ZIKV ( GenBank accession number KY241712 ) isolated in Asia . For innate immune cell sorting ten million PBMCs were stained with antibodies against CD3 PE-Cy7 , CD19 PE-Cy7 CD20 PE-Cy7 , HLADR BV421 , CD11c AF700 , CD123 PE , CD14 AF488 , CD16 APC , CD56 APC-Cy7 , and Zombie Aqua Fixable viability dye and separated as shown . For T cell sorting , five million cryopreserved PBMCs were stained with CD16 BV510 , CD56 BV510 , CD4 APC-eFluor780 , CD3 AF700 , CD8 BV785 , CD45RA BV570 , CCR7 PE-Cy7 , CXCR5 BV421 , CXCR3 BV605 , TCR V_24-J_18 BV711 , CD226 BB515 , CCR6 PerCP-Cy5 . 5 , CCR4 PE , CD25 PE-Dazzle 594 , and CD127 AF647 and sorted into CD3+ T cell CD4+ and CD8+ populations . T cells were further analyzed for effector or memory phenotypes , CD4 T helper ( Th ) subsets based on the expression of chemokine receptors ( Th1: CCR6-CCR4-CXCR3+; Th2: CCR6-CCR4+CXCR3-; Th1/17: CCR6+CCR4-CXCR3+; and Th17: CCR6+CCR4+CXCR3- ) as well as the cytotoxicity marker CD226 . Stained PBMCs were sorted in the La Jolla Institute ( LJI ) Flow Cytometry Core Facility on a FACSAria Fusion sorter . Sequencing libraries were prepared using a low input RNA-seq prepared according to the Smart-seq2 method [22] with some modifications . 5000–15 , 000 PBMCs ( pre-sort ) or FACS isolated cell populations were lysed in TRIzol and RNA extracted using Direct-zol RNA Microprep ( Zymo ) with on-column DNAseI treatment . 10 μL purified RNA was mixed with 5 . 5 μL of SMARTScribe 5X First-Strand Buffer ( Clontech ) , 1 μL polyT-RT primer ( 2 . 5 μM , 5’-AAGCAGTGGTATCAACGCAGAGTAC ( T30 ) VN , 0 . 5 μL SUPERase-IN ( Ambion ) , 4 μL dNTP mix ( 10 mM , Invitrogen ) , 0 . 5 μL DTT ( 20 mM , Clontech ) and 2 μL Betaine solution ( 5 M , Sigma ) , incubated 50°C 3 min . 3 . 9 μL of first strand mix , containing 0 . 2 μL 1% Tween-20 , 0 . 32 μL MgCl2 ( 500 mM ) , 0 . 88 μL Betaine solution ( 5 M , Sigma ) , 0 . 5 μL ( 5 M , Sigma ) SUPERase-IN ( Ambion ) and 2 μL SMARTScribe Reverse Transcriptase ( 100 U/μL Clontech ) was added and incubated one cycle 25°C 3 min . , 42°C 60 min . 1 . 62 μL template switch ( TS ) reaction mix containing 0 . 8 μL biotin-TS oligo ( 10 μM , Biotin-5’-AAGCAGTGGTATCAACGCAGAGTACATrGrG+G-3’ ) , 0 . 5 μL SMARTScribe Reverse Transcriptase ( 100 U/μL Clontech ) and 0 . 32 μL SMARTScribe 5X First-Strand Buffer ( Clontech ) was added , then incubated at 50°C 2 min . , 42°C 80 min . , 70°C 10 min . 14 . 8 μL second strand synthesis , pre-amplification mix containing 1 μL pre-amp oligo ( 10 μM , 5’AAGCAGTGGTATCAACGCAGAGT-3’ ) , 8 . 8 μL KAPA HiFi Fidelity Buffer ( 5X , KAPA Biosystems ) , 3 . 5 μL dNTP mix ( 10 mM , Invitrogen ) and 1 . 5 μL KAPA HiFi HotStart DNA Polymerase ( 1U/μL , KAPA Biosystems ) , was added , then amplified by PCR: 95°C 3 min . , 5 cycles 98°C 20 sec , 67°C 15 sec and 72°C 6 min , final extension 72°C 5 min . The synthesized dsDNA was purified using Sera-Mag Speedbeads ( Thermo Fisher Scientific ) with final 8 . 4% PEG8000 , 1 . 1M NaCl , then eluted with 13 μL UltraPure water ( Invitrogen ) . The product was quantified by Qubit dsDNA High Sensitivity Assay Kit ( Invitrogen ) and libraries were prepared using the Nextera DNA Sample Preparation kit ( Illumina ) . Tagmentation mix containing 11 μL 2X Tagment DNA Buffer and 1 μL Tagment DNA Enzyme was added to 10 μL purified DNA , then incubated at 55°C 15 min . 6 μL Nextera Resuspension Buffer ( Illumina ) was added and incubated at room temperature for 5 min . Tagmented DNA was purified using Sera-Mag Speedbeads ( Thermo Fisher Scientific ) with final 7 . 8% PEG8000 , 0 . 98M NaCl , then eluted with 25 µL UltraPure water ( Invitrogen ) . Final enrichment amplification was performed with Nextera primers , adding 1 μL Index 1 primers ( 100 μM , N7xx ) , 1 μL Index 2 primers ( 100 μM , N5xx ) and 27 μL NEBNext High-Fidelity 2X PCR Master Mix ( New England BioLabs ) , then amplified by PCR: 72°C 5 min . , 98°C 30 sec . , 6–12 cycles 98°C 10 seconds , 63°C 30 sec . , and 72°C 1 min . Libraries were size selected , quantified using the Qubit dsDNA HS Assay Kit ( Thermo Fisher Scientific ) , pooled and sequenced on a Hi-Seq 2000 sequencer using single-end 50bp reads at a depth of 25 to 30 million single end reads per sample . 50 , 000 FACS isolated classical monocytes or NK cells were lysed in 50 μl lysis buffer ( 10 mM Tris-HCl ph 7 . 5 , 10 mM NaCl , 3 mM MgCl2 , 0 . 1% IGEPAL , CA-630 , in water ) on ice and nuclei were pelleted by centrifugation at 500 RCF for 10 min . Nuclei were then resuspended in 50 μl transposase reaction mix ( 1x Tagment DNA buffer ( Illumina 15027866 ) , 2 . 5 μl Tagment DNA enzyme I ( Illumina 15027865 ) , in water ) and incubated at 37°C for 30 min on a PCR cycler . DNA was then purified with Zymo ChIP DNA concentrator columns ( Zymo Research D5205 ) and eluted with 10 μl of elution buffer . DNA was then amplified with PCR mix ( 1 . 25 μM Nextera primer 1 , 1 . 25 μM Nextera index primer 2-bar code , 0 . 6x SYBR Green I ( Life Technologies , S7563 ) , 1x NEBNext High-Fidelity 2x PCR MasterMix , ( NEBM0541 ) ) for 8–12 cycles , size selected for fragments ( 160–290 bp ) by gel extraction ( 10% TBE gels , Life Technologies EC62752BOX ) and single-end sequenced for 51 cycles on a HiSeq 4000 or NextSeq 500 . RNA-seq reads were aligned to the GRCh38/hg38 assembly of the human genome using STAR ( version 2 . 5 . 2a ) using default parameters [23] . Gene expression values were calculated as fragments per kilobase per million mapped reads ( FPKM ) across GENCODE transcript exons ( release 24 ) [24] using HOMER [25] . To remove possible contamination from genomic DNA in the RNA-seq samples , FPKM measurements were calculated for long introns ( >10 kb ) and the median intron FPKM per experiment was subtracted from each exon FPKM values to remove background signal . Gene expression FPKM values across all samples set to a minimum of zero and then quantile normalized . Only GENCODE transcripts with length greater than 300 bp were considered . Log2 fold change ratios were calculated using a pseudo count by adding a FPKM of 4 to both numerator ( i . e . day 3 , 6 , 17 ) and denominator ( i . e . day 48/convalescent ) to reduce the impact of low expression noise and contamination on the lists of regulated genes . Functional enrichment was performed using HOMER using pathway definitions from Gene Ontology and HALLMARK pathways from MSigDB [26] . Promoter known motif enrichment was calculated using HOMER using sequence from -300 bp to +50 bp relative to annotated transcription start sites . Hierarchical clustering of correlated gene expression profiles , motif enrichment , and GO/pathway function enrichment values were performed using Cluster 3 . 0 [27] and visualized using Java TreeView [28] . For ATAC-seq , fastq files were trimmed and aligned to hg38 using bowtie2 . Reads mapping to Mitochondrial DNA were removed and PCR duplicates were removed . Peaks were called using a standardized peak size using HOMER ( 300 bp ) . To compare classical monocytes and NK cells the appropriate peak files were merged and differential peaks identified using getDifferentialPeaks command ( HOMER ) with threshold of fold change >3 and P-value < 0 . 001 . Motif analysis was performed on differential peak files using findMotifsGenome . pl ( HOMER ) . All human RNA-seq and ATAC-seq data described in this manuscript are available at the National Center for Biotechnology Information ( NCBI ) Gene Expression Omnibus ( GEO ) accession number GSE123541 . Affymetrix gene expression microarray CEL files were downloaded from NCBI GEO for longitudinal DENV infection in humans ( GSE43777 ) and ZIKV infection in Rhesus macaques ( GSE93861 ) and processed into gene expression values using R/Bioconductor using GCRMA with default options . For the human DENV infection data , only samples performed on whole genome HG-U133plus2 microarrays were used for the comparison . Samples for the human DENV study were identified based on their annotated number of days since initial fever ( G1 , G2 , etc . ) and averaged to generate per day expression values . Rhesus macaque ZIKV infection gene expression values were averaged based on the day post infection , and human orthologues were assigned using one-to-one orthologues defined by ENSEMBL BIOMART ( https://www . ensembl . org/biomart ) . For each study , log2 activation ratios were calculated using the average expression for each day compared to the average of the convalescent samples ( human ) or pre-infection samples ( Rhesus ) . Microarray and RNA-seq activation ratios were compared by linking the datasets using gene symbols , using data from the highest expressed isoform in the cases where multiple isoforms exist per gene . Flow cytometry-based neutralization assay was used to evaluate SD001 serum neutralization of ZIKV ( strains FSS13025 and SD001 [29] ) and DENV ( DENV1 strain West pacific 74 and DENV4 strain TVP-360 ) in vitro . 2×104 FFU DENV or ZIKV were incubated with or without serial 3-fold dilutions ( starting at 1:10 ) of heat-inactivated SD001 serum in 96-well round bottom plates for 1-hour at 37°C . U937 cells stably expressing DC-SIGN ( 1x105 ) were seeded in each well and incubated for 2 h at 37°C with occasional rocking . After incubation , the plates were centrifuged for 5 minutes at 1500 rpm , supernatants aspirated and fresh medium added followed by incubation for 16 h at 37°C . U937 cells were then fixed , permeabilized , stained with anti-CD209 PE and 4G2 FITC ( to detect ZIKV ) or 2H2 FITC ( to detect DENV ) and analyzed using an LSRII . Percent inhibition was calculated by determining the relative infection in virus incubated with serial diluted patient serum ( tests ) versus no serum ( control ) . Best fit curves and neutralizing titer 50 ( NT50 ) were determined using Prism 7 . 0 ( GraphPad ) . Serum from 8 months prior to infection ( pre-infection ) as well as d3 , d6 , d17 , and d48 POS were prepared in duplicate using the Bio-Plex Pro Human Cytokine 27-plex Assay ( Bio-rad #M500KCAF0Y ) per manufacturers protocol and read using a Luminex machine . Cytokine concentrations were calculated from standard curves generated using references included in the kit . The following cytokines were measured FGF basic , Eotaxin , G-CSF , GM-CSF , IFN-γ , IL-1β , IL-1ra , IL-2 , IL-4 , IL-5 , IL-6 , IL-7 , IL-8 , IL-9 , IL-10 , IL-12 ( p70 ) , IL-13 , IL-15 , IL-17 , IP-10 , MCP-1 ( MCAF ) , MIP-1α , MIP-1β , PDGF-BB , RANTES , TNF-α , and VEGF .
A middle-aged , previously healthy , dengue virus ( DENV ) -experienced woman developed fatigue , an erythematous pruritic macular rash , and arthralgias six days after traveling to Caracas , Venezuela in March 2016 ( Fig 1A and 1B ) . She presented on day 3 ( d3 ) post-onset of symptoms ( POS ) . A comprehensive metabolic panel and complete blood count were within normal limits except for slight elevations in ALT ( 50 U/L , normal range 0–41 U/L ) and AST ( 44 U/L , normal range 0–40 U/L ) . Serologic testing was consistent with acute flaviviral infection but did not differentiate between DENV and ZIKV infection ( Table 1 ) [30] . A research-use nucleic acid amplification test ( NAAT ) ( Hologic ) was positive for ZIKV infection in d3 POS blood and urine samples ( Table 1 ) . Blood and urine on d3 and d6 POS were negative for DENV , as determined via qRT-PCR . Urine , from d3 and d6 POS , inoculated onto C6/36 cells produced infectious virus as measured by focus forming assay ( FFA ) . Sequence analysis of C6/36 amplified virus was confirmed to be ZIKV using a validated population-based sequencing protocol for ZikaBr targeting ZIKV NS5 . Phylogenetic analysis of the near complete viral genome ( >9kb ) showed the ZIKV San Diego isolate ( ZIKV SD001 [29] ) was most closely related to other Latin American ZIKV isolates downloaded from Genbank ( Fig 1C ) [31] . To characterize the systemic immune response to ZIKV infection , we first measured circulating serum cytokine levels . Serum was collected on d3 , d6 , d17 and d48 POS . These samples were compared to baseline pre-infection serum collected from this individual 8 months prior to infection . We found that only a small number of cytokines , including IP-10 , MCP-1 and IL-1RA , showed dramatic increases during early infection ( Fig 2A ) . Each of these cytokines peaked on d3 POS before returning toward baseline . The levels of many inflammatory cytokines , including IFNγ and TNFα , did not change or minimally changed throughout infection ( S1 Fig ) . To evaluate the cellular response to infection , we first performed RNA sequencing ( RNA-seq ) on PBMCs . To identify induced and repressed genes during infection we compared transcriptomes at d3 , d6 and d17 POS with d48 ( convalescent ) ( Fig 2B ) . Hierarchical clustering of normalized PBMC transcriptional profiles showed dynamic induction patterns with strong d3 up-regulation of many interferon-stimulated genes ( ISGs ) , which steadily declined at d6 and d17 . Published human PBMC studies during acute DENV infections demonstrated sequential waves of gene expression with early induction of ISGs and inflammatory chemokines followed by a switch to induction of genes involved in cell proliferation [34] . During ZIKV infection in this individual , there was similar strong induction of type I ISGs , exemplified by MX1 , OAS3 , RSAD2 , and IFI27 genes ( Cluster 2 ) , but minimal coincident induction of chemokines involved in leukocyte chemotaxis ( Cluster 1 ) ( Fig 2B ) . This includes CXCL10 and CCL2 , that encode the chemokines IP-10 and MCP-1 , that were elevated at the protein level d3 POS . Genes associated with cell differentiation and proliferation , such as BUB1 , DLGAP5 , PBK and CEP55 ( Cluster 3 ) were upregulated during DENV infection but not ZIKV , while EGR1 , HBEGF and MAFB ( Cluster 4 ) , were up-regulated at d17 POS during ZIKV infection ( Fig 2B ) . To better understand if low-level cytokine gene induction in PBMCs was characteristic of ZIKV infection , we analyzed a published study where temporal gene expression profiles were measured in rhesus macaques following ZIKV infection [33] . PBMCs from our patient and rhesus macaques showed similar early transcriptional upregulation of ISGs but minimal chemokine gene induction with the possible exception of CXCL10 in monkeys ( Fig 2C ) . Analyzing PBMC transcription and serum cytokines provides important information about global immune responses but lacks cell population-level resolution . To better understand how individual cell populations responds to ZIKV infection , we isolated three monocyte subsets; classical , intermediate , and non-classical; natural killer ( NK ) cells; two dendritic cell ( DC ) subsets; myeloid DCs ( mDCs ) and plasmacytoid DCs ( pDCs ) ; as well as CD4+ and CD8+ T cells at d3 , d6 , d17 and d48 POS using Fluorescence-activated cell sorting ( FACS ) ( S2 Fig ) . RNA-seq transcriptional analysis of individual cell types and PBMCs together identified 1 , 147 genes induced at least 2-fold at d3 , d6 , or d17 when compared to d48 ( Fig 3A ) . A similar analysis of PBMCs alone identified only 452 induced genes ( Fig 3A ) . Innate immune cells ( monocytes and DCs ) induced the highest number of genes on d3 POS ( Fig 3B ) . Genes up-regulated in innate immune subsets were most enriched for functional annotations associated with interferon ( IFN ) and immune responses at d3 and d6 POS ( Fig 3C ) . Additionally , the promoters of genes induced at d3 and d6 POS in innate immune cells were most significantly enriched for ISRE , IRF-composite and STAT1 motifs ( Fig 3D ) . Together , this data is consistent with early activation of type I IFN responses in innate immune populations through activation of interferon regulatory factors ( IRFs ) and interferon-stimulated gene factor 3 ( ISGF3 ) transcription factors [35 , 36] . In contrast to innate immune cells , the peak of T cell gene up-regulation was delayed ( Fig 3B ) . Genes induced in CD8+ T cells were functionally enriched for terms associated with cell cycle progression such as E2F and MYC targets and G2M checkpoint ( Fig 3C ) . Additionally , the promoters of these induced genes were enriched for E2F , NFY and POU binding motifs where transcription factors involved in controlling cell cycle and cell differentiation can bind ( Fig 3D ) . Like innate immune populations , NK cells responded rapidly to infection by inducing IFN pathways ( Fig 3C ) . However , NK cells also activated cell cycle progression pathways like CD8+ T cells but did so earlier , d3 compared to d6 POS , during infection ( Fig 3C and 3D ) . Although PBMC analysis identified fewer induced genes , PBMC functional and promoter motif enrichment analyses captured many core components observed in both individual innate immune and T cell analyses ( Fig 3C and 3D ) . An unbiased analysis of gene expression profiles using hierarchical clustering of the top induced genes in all individual cell types and PBMCs revealed both temporal and cell type specific patterns of gene expression ( Fig 3E ) . Gene expression at early time points , d3 and d6 POS , generally cluster together and apart from d17 responses ( Fig 3E ) . The exception is T cells , where only d6 POS gene expression cluster in the early group . The genes driving this difference are largely induced in a time dependent manner , with anti-viral genes ( Cluster B ) being up-regulated early and other immune pathway genes ( Cluster D ) later in infection ( Fig 3E ) . Additionally , at d3 and d6 POS , transcriptional responses cluster by cell type suggesting early transcriptional responses are in part cell type specific ( Fig 3E ) . Population-specific induction of genes is evident from genes that are up-regulated exclusively in pDCs ( Cluster A ) or NK and T cells ( Cluster C , Fig 3E ) . In contrast to all other cell types , classical and intermediate monocytes cluster together based on day POS suggesting that gene induction in these two cell types is more dependent on time POS than cell type . Many genes , including AIM2 , an ISG involved in inflammasome activation in macrophages , is induced in both time and cell-type specific manners ( Fig 3F and 3G ) . Additionally , CXCL10 , CCL2 , and IL1RN that encode the cytokines , IP-10 , MCP-1 and IL1RA , were upregulated at least 2-fold in certain monocyte populations at d3 POS even though they were not significantly induced in PBMCs as a whole ( S3 Fig ) . Chromatin accessibility is a major component of genome regulation . Open regions of chromatin are putatively associated with genomic regulatory regions , including both promoters and enhancers . The Assay for Transposase Accessible Chromatin using sequencing ( ATAC-seq ) can be used to identify transcription factors ( TFs ) involved in regulating important functions , such as differentiation and gene regulation through the analysis of open chromatin . We did not obtain high quality d3 ATAC-seq data . However , high quality ATAC-seq data were produced using samples from d6 and d17 POS . Comparing ATAC-seq peaks in classical monocytes with NK cells on d6 POS we identified 13 , 792 and 13 , 200 peaks unique to classical monocytes and NK cells respectively . De novo motif analysis of these peaks identified PU . 1 , CEBP and AP-1 in monocytes and ETS1 , RUNX and T-box in NK cells as the most enriched TF binding motifs ( Fig 4A ) . Each of these TFs have been identified as important lineage-determining transcription factors ( LDTFs ) in monocytes and NK cells , respectively . To investigate TFs that may be important during the cellular response to infection we examined dynamic changes in chromatin accessibility over time . We identified 1 , 493 and 1 , 261 ATAC-seq peaks that were significantly upregulated at d6 POS compared to d17 POS in classical monocytes or NK cells respectively . In addition to cell type specific LDTFs in monocytes and NK cells , motif analysis of upregulated ATAC-seq peaks at d6 POS demonstrated increased enrichment of PU . 1:IRF8 and bZIP TF binding motifs in classical monocytes ( Fig 4B ) . In contrast , ISRE/IRF motifs were equally represented in regulated ATAC-seq peaks in classical monocytes and NK cells ( Fig 4B ) . To help illustrate how these data characterize individual gene loci , we considered the open chromatin landscape at genes with both common and cell-type specific patterns of regulation . Both classical monocytes and NK cells upregulated the ISGs IFIT2 and IFIT3 early in infection and ATAC-seq peaks were identified at sites containing ISRE motifs ( Fig 4C ) . Although the ISRE associated peaks were common at these loci , the other ATAC-seq peaks were monocyte or NK specific and were associated with TF binding motifs enriched in the corresponding cell-type . This suggests that although both cell types induce IFIT2 and IFIT3 they may utilize cell type specific TFs to help regulate gene expression . Another ISG , APOBEC3A , was induced in monocytes but not in NK cells ( Fig 4D ) . At this gene locus , the ATAC-seq peaks were all monocyte specific and were associated with monocyte-enriched TF binding motifs ( Fig 4D ) . The gene MKI67 encodes the protein Ki-67 and is a marker of proliferation . This gene was induced in NK cells early in infection but was never induced in classical monocytes ( Fig 4E ) . The ATAC-seq peaks associated with this gene are NK-specific and associated with NK enriched TF binding motifs except for one common peak associated with an E2F motif ( Fig 4E ) . These examples help illustrate how open chromatin patterns associated with cell-type specific transcription factors may play a role in defining common and cell-type specific patterns of gene expression ( Fig 4D and 4E ) . We next evaluated the temporal development of adaptive immune responses . Prior to the acute ZIKV infection , this individual had low but detectable neutralizing Abs to both DENV and ZIKV strains ( Fig 5A ) . Neutralizing Ab titers to ZIKV and DENV rapidly increased after infection , peaking on d6 POS ( Fig 5A–5D ) . The highest neutralizing titer 50 ( NT50 ) developed against the patient’s own virus followed by the related ZIKV FSS13025 ( Cambodia , 2010 ) ( Fig 5A and 5B ) [37] . The NT50 also increased against both DENV1 and DENV4 but to a lesser degree than either ZIKV strain . These results are consistent with the idea that ZIKV infection can induce cross-reactive neutralizing Ab responses to DENV especially in individuals with prior flavivirus experience with faster kinetics relative to naïve people [17 , 19] . Lastly , we assessed the T cell response by flow cytometry . Published studies have shown that the majority of DENV-specific and ZIKV-specific T cells display an effector or memory phenotype based on expression of CD45RA and CCR7 [18 , 38 , 39] . Moreover , in secondary DENV infections , the T cell response is associated with an expansion of T effector memory RA ( TEMRA ) and T effector memory ( TEM ) cells that can be more vigorous than in primary DENV infection [39] . Accordingly , our data on bulk populations of unstimulated T cells showed higher proportions of CD4+ TEMRA cells and lower proportion of naïve CD8+ T cells ( TN ) at d6 POS as compared to 3 healthy DENV-naïve and 2 DENV-immune control individuals ( Fig 6A and 6B ) . We also examined CD4 T helper ( Th ) subsets based on the expression of chemokine receptors ( Th1: CCR6-CCR4-CXCR3+; Th2: CCR6-CCR4+CXCR3-; Th1/17: CCR6+CCR4-CXCR3+; and Th17: CCR6+CCR4+CXCR3- ) . No specific Th profile was observed in this individual ( S2 Table ) , consistent with the published observation that the majority of DENV-specific CD4+ T cells are not associated with common Th subsets [39] . Studies of DENV-infected individuals have suggested that expanded CD4+ TEMRA cells can exhibit a virus-specific cytotoxic phenotype that has been associated with protection against severe DENV disease [39 , 40] . Cytotoxic CD4 T cells are CD45RA+CCR7- ( TEMRA ) with increased expression of CD8α , cytotoxic effector molecules such as granzyme B and perforin , and CD226 , a co-stimulatory molecule that enhances CD8 effector and cytotoxic functions . A CD4+ T cell population with low level CD8 expression ( CD4+CD8dim ) was detected in our individual with acute ZIKV patient ( Fig 6C ) . The frequency of this population was between 3 . 9 and 9 . 1% of all CD3+ T cells on d3 , d6 , d17 and d48 POS but decreased to 1 . 1% by day d240 ( Fig 6C and 6D ) . The CD4+CD8dim population was less than 0 . 6% in three ZIKV-naïve controls ( Fig 6C ) . In two DENV-immune individuals this population was 0 . 6% and 2 . 7% of all T cells ( Fig 6C and 6D ) . At d3 POS 52 . 4% of CD4+CD8dim cells were also CD45RA+CD226+ and negative for three chemokine receptors ( CRs ) CCR6 , CCR4 and CXCR3 ( Fig 6E and 6F ) . By d240 POS the frequency of CD4+CD8dim cells that were CD45RA+CD226+CR- fell to 0 . 2% . In the DENV-immune control with a significant CD4+CD8dim population , 14 . 5% of this population was CD45RA+CD226+CR- ( Fig 6E and 6F ) . Based on these markers , the CD4+CD8dimCD45RA+CD226+CRs- subset is likely to be cytotoxic CD4+ T cells .
Herein , we combine global PBMC and cell type-specific transcriptional and epigenetic analyses to characterize the development and resolution of an in vivo human immune response to an acute viral infection . This is a single patient study and therefore broad conclusions cannot be drawn . However , given the limited number and scope of published ZIKV in vivo response data , we feel that our study presents a unique and detailed perspective of both the innate and adaptive immune responses to ZIKV , and provides important considerations for designing future studies . Approximately ten years prior to the ZIKV infection reported here , this individual was infected with DENV . She has no known subsequent exposure to DENV or ZIKV and has not lived in an endemic region where exposure is likely during this interval . Studies have demonstrated that prior DENV exposure influences the timing , magnitude , and quality of adaptive immune responses to ZIKV infection [18 , 19] . The influence of prior DENV-exposure on innate immune responses are not understood . ZIKV is transmited by the same vector as DENV and circulates in geographical regions where DENV is endemic or hyper-endemic . Morever , ZIKV vaccine candidates have been designed for testing and deployment in DENV-endemic countries . Thus , understanding ZIKV immune responses in individuals with DENV-immunity is highly relevant . Our analyses of PBMC and cell-specific responses demonstrate that , during acute ZIKV infection , a robust type I IFN transcriptional response was induced at early time points . Based on promoter motif analysis , this IFN response is likely driven by activation of JAK/STAT and IRF transcription factor signaling . Induction of ISG genes broadly are common to all innate immune cells tested , including monocytes ( classical , intermediate and non-classical ) , mDCs , pDCs and NK cells . However , induction of some individual ISGs , such as AIM2 , are induced in cell-type specific manners . Transcription analysis of bulk PBMCs is sufficient to capture a significant proportion of the response at both a pathway and gene-specific level but individual cell analysis identifies specific gene regulation and the cell type responsible for those responses during ZIKV infection that is not appreciated in the PBMC analysis . ATAC-seq enables assessment of enhancer elements distal from promoters that play important roles in modulating the immune response . This assay identified common changes in ISRE/IRF motifs in NK and classical monocytes , but also indicated substantial chromatin remodeling at sites containing cell-type specific TF binding motifs that help to explain the observed changes in gene expression . The ATAC-seq analysis was limited to d6 and d17 samples . ATAC-seq analysis at earlier time points or inclusion of other cell types could provide higher resolution of time- and cell-type dependent changes in chromatin accessibility . Analysis of ZIKV infected cohorts in Brazil and Singapore demonstrated elevations in many serum cytokines , including IFNγ , MCP-1 , IL1RA , IL-18 , IL-10 , IP-10 and TNFα [13 , 14] . We found similar elevations in IP-10 , MCP-1 and IL1RA at d3 POS but other cytokines tested showed smaller variation that is difficult to interpret . At the transcriptional level , PBMCs showed minimal induction of most chemokine genes . This included the genes CXCL10 , CCL2 and IL1RN , that encode for IP-10 , MCP-1 and IL1RA . This low-level chemokine gene induction was similar to what was observed during in vivo ZIKV infection of macaques [33] . In contrast to PBMCs , CXCL10 , CCL2 and IL1RN were induced at least 2-fold in specific monocyte populations . Thus , specific populations , such as monocytes , or non-PBMCs may be the source of elevated IP-10 , MCP-1 and IL1RA . In our individual , neutralizing Ab titers increased rapidly post infection , peaking at d6 POS . We observed robust increases in neutralizing anti-ZIKV Ab responses with more modest increases in cross-reactive DENV-neutralization titers . This pace of neutralizing response is consistent with previous findings that humoral responses develop rapidly in DENV-immune individuals [19] . Analysis of serum from 8 months prior to infection revealed this individual had pre-existing low level neutralizing Ab titers ( 1:83 ) against ZIKV SD001 that did not prevent symptomatic ZIKV infection . Previous studies have demonstrated that individuals with remote exposure to DENV infrequently have cross-neutralizing Abs to ZIKV [41 , 42] . In two studies , 0 of 19 [41] and 3 of 17 ( 18% ) convalescent-phase [42] sera from recovered individuals with single DENV infections , had detectable cross-neutralizing Abs against ZIKV . Among persons exposed to repeat DENV infections , 3 ( 23% ) of 13 [41] and 6 of 16 ( 38% ) [42] convalescent-phase sera had ZIKV neutralizing Abs . Most of these individuals who develop cross-neutralizing Abs against ZIKV had relatively low Ab titers ( <1:100 ) . During DENV infections , higher levels of cross-reactive pre-infection neutralizing Ab titers in humans correlate with reduced probability of symptomatic secondary DENV infection [43] . In our individual , prior DENV exposure induced low-level ZIKV cross-neutralizing Abs that did not protect against subsequent ZIKV infection . During our T cell phenotyping , we found a significant CD4+CD8dim T cell subset on d3 through d48 POS that largely resolved by d240 POS . Previous studies have shown that CD4+CD8dim T cell populations can be highly enriched for cells recognizing DENV , HCMV and HIV antigens [39 , 44 , 45] . In our patient , more than 50% of the CD4+CD8dim T cells during acute ZIKV infection were CD45RA+CD226+CR- . This expression pattern is suggestive of cytotoxic T cells , a population not yet reported during ZIKV infection . Increased frequencies of these cells have been observed after primary and secondary DENV infections , particularly in individuals expressing HLA alleles that are associated with protection against DENV [39] . Our study provides a rationale and framework for investigating the importance of the CD4+CD8dim T cell response in ZIKV immunity . Collectively , these results detail the global and cell type-specific innate immune responses during an acute ZIKV infection and highlight the rapid development of neutralizing Ab and effector memory T cell responses in a DENV experienced host . These data supports accumulating evidence that prior exposure to DENV accelerates and alters adaptive immune responses likely via the presence of cross-reactive epitopes [16–19 , 46] . Measuring time point- and cell type-specific transcriptional signatures of innate vs . adaptive immune cell populations in the blood of individuals with and without a history of flavivirus infection and vaccination can elucidate how prior flavivirus exposure might alter the magnitude , specificity , breadth , phenotype , and functionality of both humoral and cellular immune response to ZIKV . Our findings indicate that information is lost using conventional approaches and that genomic assays have the potential to provide substantial additional mechanistic insight . Combining detailed longitudinal systems biology analysis with classic immunologic techniques in future clinical studies has great potential to improve our understanding of human immune responses to pathogens at a broad level by identifying communication pathways that connect innate and adaptive immunity and regulate the balance between protection and pathogenesis . More urgently towards solving the global ZIKV and DENV problem , this approach may be invaluable in investigating the human immune response in the context of natural infection and vaccination , thereby leading to the generation of ZIKV and DENV vaccines with maximal safety and efficacy . | Zika virus ( ZIKV ) is an emerging flaviviral infection that causes significant clinical disease . It is estimated that approximately one half of the world’s population is at risk for ZIKV infection . There are only a limited number of studies describing the human immune response to ZIKV infection . Carlin et al . combined conventional and genomic approaches to longitudinally analyze the innate and adaptive immune responses to acute ZIKV infection and its resolution in a person who was infected while traveling in Venezuela during the 2016 ZIKV epidemic year . Genome-wide sequencing in individual cell types revealed that although many populations respond to interferon stimulation , only specific cell populations within peripheral blood mononuclear cells upregulate important inflammatory cytokine gene expression . Additionally , analysis of open chromatin using ATAC-seq suggests that chromatin remodeling at sites containing cell-type specific transcription factor binding motifs may help us understand changes in gene expression . Consistent with previous reports , this individual with prior exposure to dengue virus ( DENV ) , rapidly developed neutralizing anti-ZIKV responses that were cross-reactive with multiple DENV serotypes . Collectively this study combines traditional and genomic approaches to characterize the cell-type specific development of an in vivo human immune response to acute ZIKV infection . | [
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] | 2018 | A longitudinal systems immunologic investigation of acute Zika virus infection in an individual infected while traveling to Caracas, Venezuela |
Knotted proteins , because of their ability to fold reversibly in the same topologically entangled conformation , are the object of an increasing number of experimental and theoretical studies . The aim of the present investigation is to assess , on the basis of presently available structural data , the extent to which knotted proteins are isolated instances in sequence or structure space , and to use comparative schemes to understand whether specific protein segments can be associated to the occurrence of a knot in the native state . A significant sequence homology is found among a sizeable group of knotted and unknotted proteins . In this family , knotted members occupy a primary sub-branch of the phylogenetic tree and differ from unknotted ones only by additional loop segments . These “knot-promoting” loops , whose virtual bridging eliminates the knot , are found in various types of knotted proteins . Valuable insight into how knots form , or are encoded , in proteins could be obtained by targeting these regions in future computational studies or excision experiments .
Since the early 90's , when the first crystal structures of knotted proteins became available , the number of known knotted protein chains has increased to comprise several hundred PDB [1] instances spanning a few different folds and functional families [2] , . Even before the discovery of knotted proteins , the possible existence of non-trivial topological entanglements , or lack thereof , in proteins was a matter of debate [4] , [5] . From a general polymer physics point of view , sufficiently long heteropolymers in canonical equilibrium would be expected to be highly knotted [6]–[10] . The quantitative theoretical estimates of the fraction of knotted molecules hold well for biopolymers such as DNA in a variety of physical situations [11]–[15] . Yet , these estimates cannot be extended to naturally-occurring proteins where the incidence of knots is far lower than what expected for randomly-collapsed flexible polymers [16] . The discrepancy , may reflect the action of several evolutionary mechanisms that have arguably accompanied the selection of viable protein folds . In support of this view it should be stressed that proteins differ from globular flexible polymers not only in terms of the low incidence of knots but especially because , in the absence of any specific cellular machinery , the same knot type is formed reversibly and reproducibly in the same protein location [17] , [18] . This experimental fact poses several conceptual challenges particularly regarding the relationship between the interplay of local folding events and the highly non-local degree of coordination that is intuitively required to tie a given knot in a certain protein position . These considerations have stimulated an increasing number of experimental and theoretical studies aimed at understanding the kinetic and thermodynamic processes leading to knot formation in proteins or the implications for the molecular mechanical stability [2] , [3] , [17]–[27] . Specifically , numerical studies employing steered molecular dynamics towards the native state have shown that knotted structures are less accessible targets compared to generic unknotted ones [3] , [26] . Furthermore , it was suggested that knots are formed from a single ( and local ) loop threading event [3] , [26] . At the same time experiments [17] , [18] indicate that the knot formation process is not hindered by the presence , at the protein's termini , of large , structured , additional chains . This suggests the existence of a global coordination of the protein chain dynamics while still unfolded [2] , [18] , [20] , [21] , [23] . In line with this view , computational studies [22] indicate non-native interactions between highly-hydrophobic segments as possible driving forces enhancing the dynamical accessibility of a knotted native state . The present work aims at complementing the insight offered by these studies through a systematic quantitative comparative investigation of knotted and unknotted proteins . Our first aim is to assess , on the basis of available PDB entries , the level of sequence and structure discontinuity between knotted and unknotted proteins . The question is tackled by means of a systematic search of significant sequence- and structure-based correspondences between knotted and unknotted protein pairs . The second aim is to obtain clues about the possible mechanisms leading to the formation of knotted native states by searching for salient systematic differences between knotted/unknotted protein pairs . Indeed , the PDB-wide sequence and structural comparison indicates that various types of protein knots are associated to the presence of loop segments that are absent from sequence-homologous or structurally-similar unknotted proteins . The removal ( virtual bridging ) of these segments , which include a region of a knotted transcarbamylase previously identified by Virnau et al . [19] , manifestly results in unknotted configurations , thus suggesting that the protein segments corresponding to these “knot-promoting” regions have a direct impact on the protein knotted state . Based on these observation it can be expected that valuable insight into the way that knots form , or are encoded , in proteins could be obtained by targeting these regions in future in vitro experiments or with numerical computations .
The 1 . 2 protein chains contained in PDB entries as of December 2009 were processed to establish their knotted or unknotted state . Out of this initially-large number of chains only 247 ( from 229 distinct PDB entries ) were identified as being knotted . The list , provided in Table S1 , has broad overlaps with previously-published tables of knotted proteins [19] , [28] based on knot detection criteria different from the one adopted here , see Materials and Methods section . The set of all knotted proteins found in the PDB is highly redundant; for example , as many as 194 of the 229 knotted proteins , are carbonic anhydrases . The primary sequence comparison of the entries revealed that less than 50 chains are non-identical in sequence . The dataset was hence processed to achieve a uniform , minimally-redundant , coverage in sequence space . The culling procedure returned 11 representative knotted chains , which are listed in Table 1 along with their salient structural and functional characteristics . The simplest knot type , , also known as trefoil knot , is by far the most abundant knot type in the initial redundant set , and is also the most abundant in the representative list of Table 1 . Indeed , 7 of the 11 entries are trefoils . Among the trefoil representatives in Table 1 we have identified the shortest known knot , consisting of only 10 amino acids . The knot is found in the cryo-em resolved PDB entry 1s1hI ( ribosomal 80S-eEF2-sordarin complex ) [29] . Several clues point to its possible artifactual nature: the knotted region ( from a . a . 98 to 105 ) is listed in the structure file as having highly non-standard stereochemical parameters . Furthermore , the associated temperature-factor values are in excess of 100 , and are hence indicative of poor compliance with the electron-density map . For these reasons the knot in entry 1s1hI is probably artifactual and will be excluded from further considerations . The more complex knot types , , , are represented by two and one entries respectively in Table 1 and , in any case , by very few chains in the redundant set . The survey of the December 2009 PDB release did not return knots more complex than the type , which was recently reported in ref . [3] . It is interesting to observe a parallel between the chronological succession of the first PDB release of the various types of protein knots and the complexity of the knots . In fact , the first structures containing , , and knots were resolved or released , respectively , in 1988 ( PDB entries 4cac and 5cac [30] ) , 1996 ( PDB entry 1yve [31] ) , 2004 ( PDB entry 1xd3 [32] ) and 2007 ( PDB entry 3bjx [33] ) . Although the steady increase of the PDB cannot be viewed as resulting from the repeated addition of structures sampled uniformly in “protein structure space” , it is natural to assume that the chronological succession of the knots “discovery” is inversely correlated to the abundance of the various knot types . This qualitative consideration is supported by the fact that , in compact flexible polymers , the abundance of the simplest knot types decreases with knot complexity [12] , [34] . One notable point of these polymeric reference systems is that , for entropic reasons , the knot type is appreciably less abundant than , which has the same nominal complexity [12] , [13] . The absence of the knot in presently-available proteins ( a fact previously also related to the unknotting number [2] ) , may thus reflect the still limited pool of known knotted proteins and might hence populate in the future . Finally , we discuss the extent to which knots of different handedness occur among knotted proteins . Apart from the knot which is achiral , knots , and can exist in left- and right-handed versions . Previous observations made on a redundant set of proteins folded in trefoil knots concluded that , except for a single protein entry , all other ones were right-handed trefoils . For the most numerous family of knotted proteins , namely carbonic anhydrases , the bias towards right-handed knots was related to the intrinsic chirality of the motif adopted by such enzymes [2] . The investigation of the handedness in this latest dataset , where sequence redundancy has been removed , provides a novel context for examining the problem . As reported in Table 1 , the balance between right- and left-handed knots is 5 to 3 , respectively . The near equality of the populations is thus compatible with the null hypothesis that left- and right-handed protein knots occur in equal proportion ( after removal of the biases of representation due to sequence redundancy of otherwise detectable evolutionary relationships ) . Simulations of the protein folding of knotted proteins , based on simplified steered dynamics targeted towards the known native state , have reported a much lower degree of efficiency in reaching the native state from an extended conformation compared to unknotted proteins [3] , [22] , [26] . This difference could be associated to the expectedly higher level of protein motion coordination required to fold correctly in a knotted conformation versus an unknotted one . One would therefore conclude that the topological property of being knotted takes the difficulty of the folding process to a level that is considerably more challenging than for unknotted proteins . This consideration is here taken as the motivation for a systematic survey of whether , and to what extent , knotted proteins are discontinuously related by sequence and structure to unknotted ones . In this section we tackle one facet of the problem . Specifically , we shall examine how primary-sequence similarities reverberate in relatedness of the knotted/unknotted topological state . To this purpose , for each of the 11 representatives in Table 1 we performed a PDB-wide BLAST [35] search for related sequences . The search was restricted to sequences of proteins of known structure ( i . e . contained in the PDB ) because without the structural data it would not be possible to compare the knottedness of pairs with related primary sequences . The BLAST queries were run with a stringent E-value threshold ( 0 . 1 ) for returned matches , so that false positives are not expected to occur appreciably among the returned entries . Only for three protein chains , namely 5cacA , 2fg6C and 2ha8A , the number of significant matches was larger or equal to 10 . Incidentally we mention that , consistently with the probable artifactual origin of the knot in entry 1s1hI , all the 10 significant BLAST matches of 1s1hI were unknotted protein chains . All the returned matches for the 5cacA human carbonic anhydrase and the 2ha8A methyltransferase domain of the human TAR RNA binding protein ( TARBP1-MTd ) , consisted esclusively of a dozen knotted proteins , all with the same knot type . These matches are therefore not informative for the purpose of understanding if and how differences in sequence reverberate into differences of knotted state . On the contrary , the BLAST matches of the trefoil-knotted N-succinyl-ornithine transcarbamylase ( SOTCase ) , associated to the PDB entry 2fg6C [36] , proved particularly interesting as only 7 of the tens of matching entries are knotted ( all in a trefoil knot ) . To advance the understanding of the precise type of sequence relatedness of the SOTCase and its knotted and unknotted homologs , the matching BLAST sequences were used as input for a CLUSTALW multiple sequence alignment [37] . The results were used , in turn , to establish a phylogenetic relationship between the related proteins using a neighbour-joining bootstrapping algorithm [38] . The method associates to each branch of the phylogenetic tree a percent confidence estimated from the occurrence of the branch in 1000 repeated phylogenetic reconstructions using only a subset of the aligned amino acids . The phylogenetic tree for the SOTCase is represented in Fig . 1a . The tree shows that the knotted entries appear in two terminal branches sharing a common root . Each branch gathers entries that are highly similar in sequence; in fact their sequence identity ( computed by dividing the number of aligned identical amino acids by the average length of the two compared proteins ) is not smaller than 90% . The sequence identity across the two branches has the much smaller , but still significant , average value of 40% . The homology relation among all members of the phylogenetic tree is further confirmed by the fact that those , for which CATH [39] code is known , belong to the same CATH family . On the other hand , the robustness of the separation of the knotted sequence subgroup from the unknotted one is strongly suggested by the bootstrap algorithm , with a confidence level larger than 99% . Amongst the knotted and unknotted entries , the average level of sequence identity is about 20% , with a standard deviation of 7% . Indeed , it is interesting to observe that few knotted/unknotted pairs can have a level of mutual sequence identity even larger than knotted pairs . For example the knotted chain 2g68A has a sequence identity of 33% and 38% respectively , against 1js1X ( knotted ) and 1pvvA ( unknotted ) . As , to the best of our knowledge , no previous study had pointed out meaningful relationships of knotted and unknotted proteins , the present results offer a novel insight into the possible mechanisms that have led to the appearance of knotted proteins . In particular , the phylogenetic tree structure suggests the existence of a simple evolutionary lineage between the sets of knotted and unknotted proteins shown in Fig . 1a . In fact , both groups of trefoil knotted proteins , which have a limited mutual sequence identity , appear to have commonly diverged from the main tree of unknotted entries . The implications are twofold . On the one hand , the robust conservation of the knotted fold in the two sequence-diverged knotted groups suggest the functionally-oriented characteristics of the knotted topology . Indeed , it had already been pointed out for one member of this family , see ref . [19] , that the active site is located close to the knotted region , a fact that led to speculate that knottedness would confer a necessary mechanical rigidity to the protein as a whole or to the active site [24] , [25] . On the other hand , the existence of a single knotted branch indicates that the knot appearance , and its subsequent conservation , are rare evolutionary events . Further clues about the biological rationale behind the evolutionary pathways that have led to the emergence/conservation of the knotted structures in Fig . 1a ought to be addressed using more powerful tools than the present sequence-based analysis , in particular , a more general reconstruction of the phylogenetic relatedness should be accomplished within a genome-wide perspective for the organisms involved . Valuable insight into the fundamental similarities and differences in the entries appearing in the tree of Fig . 1a can be obtained by inspecting their structural alignment . To this purpose we used the MISTRAL [40] multiple structural alignment web server which was recently developed by some of us . The use of this multiple structural alignment method , which is non-sequential , appears to be particularly appropriate , since correspondences are sought between proteins with different knotted state , and hence with expected differences in fold organization . The proteins appearing in the phylogenetic tree can be all simultaneously structurally-aligned . Their aligned core consists of as many as 192 amino acids , which is a substantial fraction of the full proteins ( which have an average length of about 310 a . a . ) . Over the core region , the average RMSD of any pair of matching amino acids is less than 2 Å . The good structural superposability of the protein set ( which we recall includes protein pairs with average mutual sequence identity of about 20% ) is exemplified in Fig . 1b where the alignment of 6 proteins taken from the various primary branches of the phylogenetic tree is shown . The detailed pairwise structural comparison indicates that members of the two knotted branches admit a good structural superposition over the full protein length ( and , in particular , over the knotted region ) . To highlight the salient differences between the knotted and unknotted entries in the tree we analysed all the pairwise structural superpositions of the knotted SOTCase with the unknotted homologs . This investigation generalises the structural comparative inspection of two specific instances of knotted and unknotted carbamylases carried out in ref . [19] . The results are best illustrated considering the closest matching pair , namely the SOTCase and PDB entry 1ortA . In spite of their limited mutual sequence identity , which is about 25% , these proteins admit a very good structural superposition , see Fig . 2a , b . Indeed , as many as 246 of their amino acids ( which are 321 and 335 in total for chains SOTCase and chain 1ortA , respectively ) can be superposed with an RMSD as small as 2 . 5Å . The alignment respects the overall sequence directionality of the chains . The few non-matching regions are typically insertions in exposed stretches of the sequence , corresponding to small loops protruding out of the surface of the molecule , which have no particular bearing on the protein topology . The case is different for two regions of the SOTCase: the proline-rich segment comprising amino acids 174–182 , and the segment 235–255; both regions are located in proximity of the active site ( residues 176–178 , 252 ) . As shown in Fig . 2a , these loops , which do not contain highly hydrophobic segments ( see Figure S1 ) , have a particular mutual concatenation which directly impacts on the protein knotted state . In fact , the virtual excision ( bridging ) of these two segments , which both have a small end-to-end separation , results in the elimination of the knot from SOTCase . We remark that Virnau et al . [19] had recently observed that the knottedness of the transcarbamylase of X . Campestris was probably due to the excess length of the region comprising a . a . 176 with respect to the human analog . This observation is reinforced by the present general sequence- and structure-based systematic comparison which additionally points out the systematic absence of a second loop segment 235–255 in the unknotted homologs of the SOTCase . The results provide a quantitative basis for suggesting that some light on the process of protein knot formation can be shed by targeting these regions in suitable mutagenesis experiments . It would be particularly interesting to analyse whether both of the identified “knot-promoting” loops need to be excised to produce an unknotted native state , or if only one would suffice . The results discussed in the previous section indicate that knotted proteins appear to be sparsely distributed in sequence space . In fact , only for one of the representatives in Table 1 , it was possible to establish significant sequence-based relationships with unknotted proteins . Here we investigate whether , irrespective of the level of primary sequence relatedness , there exist meaningful structural similarities between knotted and unknotted proteins . The search was performed , by carrying out MISTRAL structural alignments of each of the knotted representatives in Table 1 , against an extensive set of about 2 . 4 10 unknotted protein chains . The latter set was obtained by culling the full set of all available PDB chains as of December 2009 using standard criteria based on mutual sequence identity , see Materials and Methods section . The top-ranking alignments are reported in Table S2 . Hereafter we focus on a limited number of cases which , regardless of their ranking in alignment quality , can be aptly used to highlight interesting relationships between knotted and unknotted pairs . In particular , they might possibly be used to shed light on important kinetic or thermodynamic mechanisms that guide or otherwise favor the formation of knots in naturally occurring proteins . In particular , we start by discussing the limited number of cases where the alignment suggests the presence of knot-promoting loop segments , analogously to the case of the SOTCase and chain 1ortA . These segments are identified using two main criteria: ( i ) the segments ends must be sufficiently close that they could be virtually bridged by very few amino acids; ( ii ) the bridging/excision operation should lead to an unknotted conformation . The automated search for such segments returned positive matches for three representatives . One of them was the same SOTCase chain , which we discussed in previous sections . The other chains were the aforementioned TARBP1-MTd and the photosensory core module of Pseudomonas aeruginosa bacteriophytochrome ( PaBphP , PDBid 3c2wH ) . TARBP1-MTd aligns well with two unknotted protein representatives that have very different overall structural organization . Despite the differences , discussed hereafter , the alignments consistently indicate that loop 101–123 is a knot-promoting loop for chain A of TARBP1-MTd . The alignment against the unknotted protein chain 1b93A [41] comprises 87 amino acids ( at 3 . 5 Å RMSD ) and covers the entire knotted region with the exception of the above mentioned segment . The fact that the ends of the segments are less than 5Å apart , readily suggests that the excision of the fragment ought to result in an unknotted protein with structure analogous to the 1b93A chain . The inspection of the hydrophobicity profile based on the Kyte and Doolittle scale [42] ( see Figure S2 ) indicates that one of the regions with high hydrophobicity falls within the knot-promoting loop . In analogy with what suggested in ref . [22] for YibK , it is therefore possible that the kinetic accessibility of the knotted state is enhanced by contacts that this region forms with other parts of the protein . The topologically-important role of the segment is further highlighted by the alignment with the 1hdoA chain . At variance with the case of 1b93A , the good alignment does not involve regions that have the same succession , along the primary sequence , in the two proteins . This is readily ascertained by the inspection of the structural diagram of Fig . 3a , b where it is possible to appreciate the different “rewiring” of several corresponding secondary structure elements . In this case too , the alignment comprises the knotted region with the exception of the previously mentioned segment . This reinforces the previous suggestion that the removal of the segment ought to result in an unknotted folded configuration . The “figure-of-eight” knot in protein PaBphP [43] spans a very large portion of the photosensory core module of PaBphP ( a . a . 24 to 282 ) . This protein is composed of three domains: named PAS ( Per-ARNT-Sim ) , GAF ( cGMP phosphodiesterase/adenyl cyclase/FhlA ) and PHY ( phytochrome ) domains . The GAF domain is known to be present in several sequence-unrelated proteins and , in fact , it represent the core region of the good alignment of PaBphP photosensory core module with the non-homologous chain 2b18A [44] . The alignment singles out the segment of amino acids 203 to 256 as a knot-promoting loop . Indeed , while the knot length is very large , the knot appears to result from the “threading” of the N-terminal domain through the above mentioned loop . As for SOTCase , the hydrophobicity profile ( see Figure S3 ) does not provide a definite indication that the loop region takes part to contacts aiding the kinetic accessibility of the knotted native state . The removal of the loop , as readily seen from Fig . 4 , leads to an unknotted structure , and therefore suggests that , like the other cases , it could be profitably targeted in mutagenesis experiments to ascertain its role in the process of knot formation . The above analysis was based on the identification of knot-promoting regions suggested by significant alignments of the knotted representatives in Table 1 against unknotted representatives . Only for the three representatives discussed above it was possible to identify such correspondences on the basis of available structural data . Yet , it is interesting to point out that for two other representatives , namely chains 2etlA ( ubiquitin carboxy-terminal hydrolase , UCH ) and 2p02A ( alpha subunit of human S-adenosylmethionine synthetase , hereafter -SAM-S ) , good structural matches involving the knotted region were found against unknotted structures . At variance with previous cases , however , these matches do not suggest the possibility to unknot the protein by a simple excision operation . Yet , they are interesting for the purpose of understanding how continuous is the structure space between knotted and unknotted PDB entries . The two examples are shown in Fig . 5 . Panel ( b ) presents a superposition of the knotted UCH [45] , which is the only knot representative , against the unknotted entry 1aecA [46] . The alignment , though not spanning the entirety of the protein structures , highlights a good correspondence of secondary and tertiary structure elements . Analogous considerations , hold for the alignment of -SAM-S [47] and 2b×4A [48] ( Fig . 6 ) , whose mutual sequence identity is less than 10% . The alignment highlights the threefold symmetry of the knotted protein , which however , builds on a non-trivial domain organization which results in a trefoil knot . In this study we presented a database-wide comparative analysis of pairs of knotted and unknotted proteins . The study was aimed at understanding if , and to what extent , the rare instances of known knotted proteins are discontinuously related in sequence or structure space to unknotted proteins . The analysis proceeded by first identifying minimally-redundant sets for the knotted protein chains found among the presently-available PDB entries . Specifically , the latter were found to be fully represented by 11 entries . These non-homologous and structurally-different representatives cover all the 4 different types of knots which have been found to date in proteins . Most of the represented knots are chiral . Excluding from considerations a trefoil-knotted protein whose origin is probably artifactual , it is found that left- and right-handed chiral knots are almost equally represented . This fact , which had not been pointed out before , is well compatible with the null hypothesis that left- and right-handed protein knots occur in equal proportions in non-redundant datasets . In order to understand what type of primary sequence relatedness exists between knotted and unknotted proteins , a PDB-wide BLAST [35] search was performed for each of the knotted representatives to identify the sequence homologs . For nearly all of the representatives , the analysis did not return significant sequence-based matches with unknotted proteins . One notable exception was constituted by a specific SOTCase , namely 2fg6C , whose phylogenetic tree comprises both knotted and unknotted entries . The knotted homologs fully occupied two commonly-rooted sub-branches of the tree , suggesting the existence of a single evolutionary event at the basis of the divergence of the knotted group from the main unknotted tree . The structural alignment of members of the knotted SOTCase phylogenetic tree highlighted that the knotted domains differed from the unknotted counterparts , for the presence of two additional short segments with a small end-to-end separation . The bridging of these knot-promoting loop segments , one of which was identified in ref . [19] using a different approach , that is their removal from the primary sequence , ought to result in an unknotted native state equivalent to the one of the unknotted homologs . The insight offered by the sequence comparative investigation was finally complemented by one based on pairwise structural alignments . At variance with the sequence case , the structural one revealed several significant knotted/unknotted correspondences . In an appreciable number of instances , these correspondences involved a substantial fraction of the region where the knot is accommodated . Also in these cases , knotted proteins appeared to differ from the unknotted partner by the presence of knot-promoting segments analogous to those identified in the alignments involving the SOTCase . The results therefore point to the key role that these specific , local , protein segments play for the global knotted topology of the folded protein . These regions might represent ideal candidates for mutagenesis or excision experiments to monitor the impact of these regions on the process of knot formation .
The PDB database as of December 2009 contained 6 . 2 entries , which were parsed into single chains . From the resulting dataset we retained only those chains with length matching the nominal one ( provided in the SEQRES PDB field ) to within 25 amino acids . Very short ( less than 50 a . a . ) and very long ( more than 1000 a . a . ) chains , as well as those with missing coordinates were not considered . This sieving procedure returned 1 . 2 chains . Detecting and characterizing the presence of knots in proteins requires a suitable generalization of the mathematical notion of knottedness [49]–[51] . The latter is rigorously defined only for circular , closed , chains [52] , [53] . In such contexts , at variance with the case of linear open-ended polymers such as proteins , knots cannot be untied by any manipulation preserving the connectivity and self-avoidance of the circular chain . The mathematical concept of knottedness can be extended to protein chains whenever a simple , non-ambiguous way exists to bridge the two termini , such as by prolonging them into an arc that does not intersect the protein hull . Such virtual circularization procedures are actually possible for most protein chains because the N and C termini are usually exposed at the protein surface . The closure algorithm applied here first performs the identification of those chains with both termini exposed on the surface: this condition is satisfied if one can pass a plane through each terminus , such that all other residues occupy only one of the two subspaces created by the plane . In these cases the chain can be unambiguously closed by adding a segment connecting the termini “at infinity” without intersecting the protein chain . As many as 6 . 4 10 chains could be circularised with this procedure . For proteins constituted by identical monomeric chains , only one representative chain was considered , reducing the number of considered entries to 4 . 5 10 . The dataset of the 4 . 5 10 circularised protein chains was further processed to establish the knot topology of each entry; the knot type was determined using the scheme of refs . [12] , [13] which is based on the KNOTFIND algorithm . Only 247 protein chains were found to have nontrivial topology . These two sets are affected by a large sequence redundancy , which was removed at the stringent 10% sequence identity level using the web tool developed by Cedric Notredame and available at http://www . expasy . ch/tools/redundancy . The culling procedure returned the 11 representatives shown in Table 1 . No significant structural relatedness was found among any pair of these representatives . The large set of unknotted proteins was processed with the UniqueProt [54] standalone program to efficiently remove the overall sequence similarity . Its iterative application with default parameters returned 2 . 4 unknotted representatives . The publicly-accessible MISTRAL multiple structural alignment tool [40] was used for the systematic structural comparison of knotted and unknotted proteins . The alignment tool was used for two reasons . First , it has been shown to yield a reliable estimate of the statistical significance of a given alignment and , secondly , it can detect structurally-corresponding regions that do not have the same succession or directionality along the primary sequence of the input proteins . The necessity to account for such generalised relationships in proteins has emerged recently [55] . It appears particularly relevant in this context given the expected difficulty in establishing overall correspondences of knotted and unknotted proteins from a standard ( sequential ) sequence-based perspective . All pairwise structural alignments between the representatives of the unknotted and knotted proteins were computed . Among those with a -value smaller than we singled out those which involved at least 40% of the protein region that encompasses the knot . The latter is defined by taking the chain portion that is strictly occupied by the knot according to the criterion of ref . [50] and extending it by 20 amino acids on both sides of the primary sequence ( unless a terminus is closer ) . The selected alignments are provided in Table S2 . | Out of the tens of thousands of known protein structures , only a few hundred are knotted . The latter epitomize , better than unknotted proteins , the degree of coordinated motion of the backbone required to fold reversibly in a specific native conformation , which indeed must contain a precise knot in a specific protein region . In the present work we search for salient features associated to protein “knottedness” through a systematic sequence and structure comparison of knotted and unknotted protein chains . A significant sequence relatedness is found within a sizeable group of knotted and unknotted proteins . Their tree of sequence relatedness suggests that the knotted entries all diverged from a specific evolutionary event . The systematic structural comparison further indicates that the knottedness of several different types of proteins is likely ascribable to the presence of short “knot-promoting” loops . These segments , whose bridging eliminates the knot , are natural candidates for future experimental/computational studies aimed at clarifying whether the global knotted state of a protein is influenced by specific regions of the primary sequence . | [
"Abstract",
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] | [
"biophysics",
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"biology"
] | 2010 | Knotted vs. Unknotted Proteins: Evidence of Knot-Promoting Loops |
It is well established that Epstein-Barr virus nuclear antigen 3C ( EBNA3C ) can act as a potent repressor of gene expression , but little is known about the sequence of events occurring during the repression process . To explore further the role of EBNA3C in gene repression–particularly in relation to histone modifications and cell factors involved–the three host genes previously reported as most robustly repressed by EBNA3C were investigated . COBLL1 , a gene of unknown function , is regulated by EBNA3C alone and the two co-regulated disintegrin/metalloproteases , ADAM28 and ADAMDEC1 have been described previously as targets of both EBNA3A and EBNA3C . For the first time , EBNA3C was here shown to be the main regulator of all three genes early after infection of primary B cells . Using various EBV-recombinants , repression over orders of magnitude was seen only when EBNA3C was expressed . Unexpectedly , full repression was not achieved until 30 days after infection . This was accurately reproduced in established LCLs carrying EBV-recombinants conditional for EBNA3C function , demonstrating the utility of the conditional system to replicate events early after infection . Using this system , detailed chromatin immunoprecipitation analysis revealed that the initial repression was associated with loss of activation-associated histone modifications ( H3K9ac , H3K27ac and H3K4me3 ) and was independent of recruitment of polycomb proteins and deposition of the repressive H3K27me3 modification , which were only observed later in repression . Most remarkable , and in contrast to current models of RBPJ in repression , was the observation that this DNA-binding factor accumulated at the EBNA3C-binding sites only when EBNA3C was functional . Transient reporter assays indicated that repression of these genes was dependent on the interaction between EBNA3C and RBPJ . This was confirmed with a novel EBV-recombinant encoding a mutant of EBNA3C unable to bind RBPJ , by showing this virus was incapable of repressing COBLL1 or ADAM28/ADAMDEC1 in newly infected primary B cells .
Epstein-Barr virus ( EBV ) is a large DNA virus that belongs to the gamma subfamily of herpes viruses and infects persistently >90% of the human population . Infection with EBV is aetiologically associated with several types of human cancer , including Burkitt lymphoma , Hodgkin lymphoma , peripheral natural killer/T-cell lymphoma , nasopharyngeal and gastric carcinoma [1] . Infection of B cells with EBV results in activation and transformation of resting cells into proliferating B blasts , in which the viral genome resides as an extra-chromosomal episome within the nucleus . In vivo , early after infection , all EBV latency-associated genes are expressed , producing six EBV nuclear antigens [EBNA1 , 2 , 3A , 3B , 3C and leader protein ( LP ) ] , three latent membrane proteins ( LMP1 , 2A and 2B ) , two small non-coding RNAs ( EBER1 and 2 ) and micro-RNA transcripts from the BamHI A region ( BARTs ) [1 , 2] . In vitro , infection of primary resting B cells with EBV creates continuously proliferating lymphoblastoid cell lines ( LCL ) that constitutively express all latency-associated EBV genes [1] . The genes encoding EBNA3A , 3B and 3C are arranged in tandem in the EBV genome and share the same gene structure with a short 5’ exon and a long 3’ exon . The proteins originate , through alternative splicing , from the B cell specific EBNA2/LP/3A/3B/3C transcription unit resulting in very long mRNAs initiated primarily from the Cp promoter . There are only a few copies of EBNA3 mRNAs in LCLs , probably due to tight transcriptional regulation–for example it has been reported that less than 3 mRNA copies of EBNA3C per cell can be detected [3]–and associated with slow turnover of the proteins [4] . The EBNA3s form a family of transcriptional co-regulators that can cooperate to regulate host gene expression [5–7] . EBNA3 proteins do not bind DNA directly , but are assumed to be tethered to target genes by associating with DNA sequence-binding factors , an example being RBPJ ( also known as RBP-jk , CBF1 , CSL , Suppressor of Hairless and Lag1 ) [8–12] . RBPJ is a component of the Notch signalling pathway that was first discovered in Drosophila , but is highly conserved across species and has an important role in developmental processes in embryonic and adult tissue , e . g . cell lineage decisions ( reviewed in [13 , 14] ) . In vertebrates–in the absence of active Notch signalling–RBPJ represses Notch target genes through interaction with TFIIA and TFIID to prevent transcription [15] and also recruitment of repressor complexes containing histone deacetylase 1 and 2 ( HDAC1 and 2 ) , silencing mediator of retinoid and thyroid hormone receptors ( SMRT/NcoR ) , SMRT/HDAC1-associated repressor protein ( SHARP/MINT/SPEN ) , CBF1-interacting co-repressor ( CIR ) , C-terminal binding protein ( CtBP ) , CtBP-interacting protein ( CtIP ) and KyoT2 [16–20] . Ligand binding to the Notch receptors induces a series of proteolytic cleavages of the receptor resulting in the release of Notch intracellular domain ( NICD ) from the cell membrane [21–23] . NICD translocates into the nucleus where it binds to RBPJ via its RBPJ-associated molecule ( RAM ) domain WΦP ( Φ = hydrophobic residue ) motif ( WFP ) [24] and via its ankyrin repeats [25–27] . Binding of NICD to RBPJ disrupts the association with repressor complexes [16] and additional binding to the strong co-activator Mastermind [28–30] leads to formation of a stable activating complex and full activation of the repressed Notch signalling target genes . EBNA2 also binds to RBPJ via a RAM domain WWP motif [31–34] . EBNA2 , one of the first viral genes expressed after infection of B cells and a transcriptional transactivator of the other latent viral genes as well as cellular genes , operationally resembles NICD [35] . All the EBNA3s share a highly conserved N-terminal homology domain ( HD ) that contains RBPJ binding sites [9 , 11] . EBNA2 and EBNA3s , however , form mutually exclusive complexes through competitive binding to the same binding site on RBPJ [8 , 36] . EBNA3/RBPJ complexes were shown to disrupt DNA binding of RBPJ in vitro , in electrophoretic mobility shift assays [8 , 9 , 37] and to repress EBNA2-mediated activation in transient reporter assays [8 , 11 , 37 , 38] . This repression is dependent on the ability of EBNA3 to bind to RBPJ . Mutation of four core residues within the HD of EBNA3C from 209TFGC to 209AAAA ( HDmut ) that affect binding to RBPJ produced a protein that did not disrupt RBPJ/DNA binding and that failed to repress EBNA2-mediated transcriptional activation in transient reporter assays [11 , 39] . The HDmut EBNA3C also failed to sustain LCL proliferation when transfected into LCL with conditional EBNA3C after inactivation of EBNA3C [40 , 41] . In addition to the earlier identified core 209TFGC motif [11] , Calderwood and colleagues more recently identified a RAM-like motif ( 227WTP ) in EBNA3C but not EBNA3A and EBNA3B [42] . The W227S mutant EBNA3C successfully repressed EBNA2-mediated transcriptional activation in transient reporter assays and sustained LCL proliferation in back-complementation assays , however , both 209AAAA and W227S mutations were required for an effective loss of RBPJ binding as determined by co-immunoprecipitation [42] . Originally , a model was proposed in which EBNA2 acts as a viral analogue of NCID , producing transcriptional activation when bound to RBPJ; this is counteracted by competitive binding of EBNA3s to RBPJ and destabilisation of RBPJ binding to DNA [1 , 43 , 44] . An alternative model was then proposed in which EBNA3s directly recruit repressors to RBPJ that remains statically bound to its responsive elements [45] . When targeted directly to DNA by fusion with the DNA-binding domain of Gal4 , all EBNA3 proteins exhibit strong repressor activity in reporter assays [37 , 46 , 47] . Moreover , EBNA3C can interact with cellular factors that are involved in transcriptional repression , these include HDAC1 , HDAC2 , CtBP , Sin3A and NcoR [48–50]–this would be consistent with the repressor recruitment model . However , it is fair to say that currently the role of RBPJ in gene regulation by EBNA3C remains largely unknown . From previous microarray analyses in EBNA3A knockout ( KO ) LCL [5] , BL31 infected with EBNA3A or EBNA3C KO viruses [6] , BJAB cells stably expressing EBNA3C [51] and EBNA3C-conditional LCL [52] it is known that EBNA3A and EBNA3C repress ADAM28 and ADAMDEC1 , two members of a disintegrin and metalloprotease ( ADAM ) family that are encoded in adjacent genomic loci . McClellan and colleagues identified an intergenic EBNA3 binding site that loops to the transcription start site ( TSS ) of both genes only in the presence of EBNA3C and repression involved reduced levels of activation-associated H3K9/14ac mark and increased levels of the repressive H3K27me3 mark within ADAM28 and at the TSS of ADAMDEC1 [51 , 53] . However , these observations were obtained from stable transfectants of EBNA3C in the EBV-negative B cell lymphoma line BJAB in the absence of the other latent viral gene products and nothing is known about the temporal sequence of events at these regulatory sites and TSS , or the factors that are involved in EBNA3C-mediated gene repression early after infection of primary B cells with EBV . In addition to confirming repression of both ADAMs , a microarray study of EBNA3C-conditional LCL identified COBLL1 as the gene most robustly repressed by EBNA3C ( S1 Table , [52] ) . The function of the COBLL1 gene product is unknown and it has not previously been characterised as an EBNA3C repressed gene . Based on the previous studies and the microarrays , we selected ADAM28 , ADAMDEC1 and COBLL1 in order to explore in more detail the temporal sequence of events and factors that are involved in EBNA3C-mediated gene regulation . Here , we show that all three genes are highly repressed in B cells following infection of primary CD19+ cells with EBV , only when EBNA3C is expressed and functional . Using LCLs conditional for EBNA3C function we could show for a first time that this system can be used efficiently to replicate EBNA3C-mediated changes in gene expression very similar to those seen early after infection of primary B cells with EBV . Using the conditional system we were able to explore further the temporal changes in epigenetic marks at regulatory elements and TSSs leading to repression of transcription and showed that it involved two-steps , rapid initial loss of activation-associated histone marks that led to repression of mRNA expression , followed by recruitment of polycomb proteins and increases of repressive histone H3K27me3 mark . Furthermore , we show that RBPJ is only recruited when EBNA3C is functional and that repression is absolutely dependent on the ability of EBNA3C to bind to RBPJ . This is the first time that EBNA3C-mediated transcriptional repression has been described in such detail and it provides novel insights into temporal sequence of events occurring early after infection and the dynamic role of RBPJ in EBV-mediated gene repression .
Interrogation of Affymetrix Exon 1 . 0 ST microarray analysis from the EBNA3C conditional system ( 3CHT ) indicated that COBLL1 , ADAM28 and ADAMDEC1 required expression of functional EBNA3C for very significant levels of repression in LCL ( S1 Table; http://www . epstein-barrvirus . org . uk ) . In order to establish that ADAM28 , ADAMDEC1 and COBLL1 are regulated by EBNA3C during viral infection of B cells–and to determine whether EBNA3A and/or EBNA3B are involved in the regulation–primary CD19+ cells were infected with previously characterised wild type ( B95 . 8-BAC ) EBV or recombinant EBNA3 KO or revertant ( Rev ) viruses ( Fig 1A–1C ) [54] . Infections with wild type , Rev and EBNA3B KO viruses resulted in a reduction of both ADAM28 ( 2–3 log fold ) and ADAMDEC1 ( 1–2 log fold ) levels of mRNA , over a period of 30 days after infection . In the absence of EBNA3C ( 3CKO ) and to a lesser extent EBNA3A ( 3AKO ) there was a failure to repress ADAM28 ( Fig 1A ) and ADAMDEC1 ( Fig 1B ) –this is consistent with reports derived from stable cell lines [51] . All infections with EBNA3C competent viruses led to a remarkable 3–4 log fold reduction of COBLL1 mRNA over the same period of time , but this was not seen after infection with 3CKO virus–here the levels detected in primary B cells were maintained ( Fig 1C ) . These differences in gene expression between the various virus infections were not observed for the two control genes ALAS1 ( S1A Fig ) and GNB2L1 ( S1B Fig ) , neither of which are known to be targets of EBNA3 proteins or EBV . After establishing that all three genes were robustly repressed after infection of primary B cells with EBV , which confirmed previous findings from stable cell lines that ADAM28 and ADAMDEC1 were repressed by EBNA3C and EBNA3A and identifying that COBLL1 was repressed by EBNA3C alone , we wanted to determine whether it was possible to recapitulate this repression in LCLs carrying EBV-recombinants conditional for EBNA3C ( 3CHT ) . In this cell line , EBNA3C activity is conditional on the presence of 4-hydroxytamoxifen ( HT ) and proliferation of the cells does not decrease in its absence due to the homozygous deletion of p16INK4A , a primary target of EBNA3C [52] . This cell line could therefore be established having never expressed functional EBNA3C ( 3CHT A13 ) and the expression of ADAM28 , ADAMDEC1 and COBLL1 in this cell line is similar to uninfected primary B cells . Activation of EBNA3C by the addition of HT to these cells ( +HT ) resulted in a 2-log fold repression of ADAM28 ( Fig 2A ) , ADAMDEC1 ( Fig 2B ) and a 4-log fold repression of COBLL1 ( Fig 2C ) , which was not seen when EBNA3C was kept inactive ( -HT ) over this 60-day period . The repression of all three genes was fully reversible . Inactivation of EBNA3C , by washing out HT on day 30 , led to an increase in expression of mRNAs corresponding to ADAM28 , ADAMDEC1 and COBLL1 up to levels similar to those at the start of the time-course . The observed repression of ADAM28 , ADAMDEC1 and COBLL1 in the 3CHT system was a direct consequence of the HT-induced activation of EBNA3C , because adding HT to a non-conditional EBNA3C KO LCL , grown out from the primary B cell infection ( see below ) , did not change the expression levels of any of the three genes ( S1C–S1E Fig ) . The repression of COBLL1 by EBNA3C could also be observed at the protein level . Over the 3CHT A13 60-day time-course , COBLL1 protein levels quickly disappeared . They were barely detectable three days after activation of EBNA3C ( by the addition of HT ) and were completely undetectable thereafter . However , COBLL1 protein did not reappear until about 20 days after inactivation of EBNA3C ( Fig 2D ) . In addition , a rare LCL that grew from an infection of primary B cells with EBNA3C KO virus showed that only EBNA3C-deficient LCLs expressed COBLL1 ( S2A Fig ) . Consistent with previous published data [52] , cells from the EBNA3C KO virus infection underwent the expected crisis around 3–4 weeks post-infection but in a single experiment a sub-population survived and grew into a stable LCL . Immunoblot analysis revealed–in addition to high levels of COBLL1 –low levels of retinoblastoma protein ( Rb ) , phosphorylated Rb ( P-Rb ) and loss of p16INK4a in this unusual LCL ( S2A Fig ) –we and others have seen this type of clonal selection , more frequently , with cells infected with EBNA3A KO virus [5 , 55] . It should be noted that protein expression data for ADAM28 and ADAMDEC1 was not included because , in our hands , the commercial antibodies that we tested did not produce convincing or reproducible results . The results obtained from the 3CHT A13 time-course were highly reproducible , not only in the same cell line , but also in 3CHT C19 , another 3CHT cell line created by an independent 3CHT recombinant virus clone on the p16-null background , but grown out in the presence of HT and then washed ( S1F–S1H Fig ) . The dynamic range of COBLL1 repression was remarkable , following a similar highly exponential repression profile in both 3CHT A13 and C19 conditional cell lines and also in newly infected primary B cells ( S3A Fig ) . Repression of ADAM28 and ADAMDEC1 was rather more variable between cell lines , but showed a similar exponential repression profile ( S3B and S3C Fig ) . Taken together these results showed for the first time , that the EBNA3C conditional cell lines could be used to recapitulate efficiently EBNA3C-mediated gene repression observed in B cells early after infection with EBV . Having shown that the EBNA3C conditional system could be efficiently used to replicate gene expression changes seen early after EBV infection of primary B cells , we wanted to determine the sequence of events that led to such robust repression of COBLL1 and the ADAM28-ADAMDEC1 locus by employing chromatin immunoprecipitations ( ChIP ) on samples harvested throughout the 3CHT A13 time-course . COBLL1 is located on chromosome 2 , has multiple transcript variants and a CpG island around the promoter region . ChIP coupled to high throughput DNA sequencing ( ChIP-seq ) using LCLs expressing tandem-affinity purification ( TAP ) tagged EBNA3s ( [56] and K . Paschos et al . , manuscript in preparation ) identified a single intragenic EBNA3A and EBNA3C binding site , hereafter called the COBLL1 peak ( Fig 3A ) . This binding site was confirmed in the LCLs used here by ChIP-qPCR ( S4 Fig ) . ChIP analysis on samples from the 3CHT A13 time-course and probed across the COBLL1 locus , showed that after activation of conditional EBNA3C , there was a sustained decrease in the activation-associated histone marks H3K9ac , H3K27ac and H3K4me3 and an increase in the repressive histone mark H3K27me3 primarily at the TSS of COBLL1 ( Fig 3B–3E ) . These changes were not observed at GAPDH or Myoglobin , two controls for expressed and repressed genes , respectively . Next , since H3K27me3 is catalysed by polycomb repressive complex 2 ( PRC2 ) and polycomb complexes were previously found to be involved in the repression of BCL2L11 ( BIM ) [57] and CDKN2A ( p16INK4A ) [55] we explored whether they also play a role in the repression of COBLL1 . ChIP for PRC2-family member SUZ12 showed increased enrichment at the TSS of COBLL1 as it is repressed ( Fig 3F ) . In contrast and rather unexpected , ChIP for the polycomb repressive complex 1 ( PRC1 ) family member BMI1 revealed recruitment of BMI1 to the COBLL1 peak , but not the TSS , with highest BMI1 levels nine days after EBNA3C activation ( Fig 3G ) . Finally , since the EBNA3 proteins cannot bind directly to DNA and RBPJ is the most well characterised DNA binding factor to which they have all been reported to bind , ChIP for RBPJ was performed . This revealed that RBPJ accumulated on the COBLL1 peak , but only when EBNA3C was functional–with the highest levels appearing six days after activation of EBNA3C by HT ( Fig 3H ) . ADAM28 and ADAMDEC1 are encoded on chromosome 8 and also have multiple transcript variants , but no distinct CpG island . Previous studies by McClellan and colleagues [51] identified an intergenic EBNA3A and EBNA3C binding site ( hereafter called the ADAM peak ) , which was also detected in our ChIP-seq performed on EBNA3A-TAP and EBNA3C-TAP LCLs ( Fig 4A ) and confirmed by ChIP-qPCR ( S4 Fig ) . ChIP for activation associated histone marks on samples of the 3CHT A13 time-course and across the ADAM locus again showed a loss of H3K9ac , H3K27ac and H3K4me3 largely at the TSS of ADAMDEC1 , but also ADAM28 , when EBNA3C was made functional by the addition of HT ( Fig 4B–4D ) . There was an increase in the repressive H3K27me3 mark across the ADAM28-ADAMDEC1 locus , but at considerably lower levels than seen at the COBLL1 TSS ( Fig 4E ) . This is consistent with previous data from McClellan and colleagues that showed less H3K9/14ac and increased H3K27me3 in a stable EBNA3C expressing BJAB cell line [51] . Unlike at the COBLL1 locus , no increase in SUZ12 enrichment could be detected across the ADAM28-ADAMDEC1 locus ( Fig 4F ) . However , similar to COBLL1 , ChIP for BMI1 revealed recruitment to the ADAM peak ( but again not to either TSS ) with highest levels nine days after EBNA3C activation ( Fig 4G ) . Although repression of this locus had not been previously described as being RBPJ-dependent , as with COBLL1 , RBPJ enrichment also increased at the ADAM peak only when functional EBNA3C was induced , with highest levels appearing six days after activation ( Fig 4H ) . In order to confirm these temporal changes of histone marks and factor recruitment at both the COBLL1 and ADAM28-ADAMDEC1 loci , ChIP samples taken during a biological replicate time-course–using the 3CHT C19 LCL–were analysed in a similar way to the 3CHT A13 samples and showed very similar results ( S5 and S6 Figs ) . Unfortunately , we were unable to reproducibly perform ChIP for EBNA3C using a commercial polyclonal antibody against EBNA3C that also precipitates EBNA3A and EBNA3B [53] . In order to reliably ChIP for EBNA3C during an extended time-course experiment , the conditional EBNA3C would also need to be TAP-tagged , but these viruses are not currently available . Immunoblot analysis showed that there were no consistent changes to the levels of EBNA3A , EBNA3B , BMI1 , SUZ12 and RBPJ proteins during either the 3CHT A13 or 3CHT C19 time-courses ( S2B and S2C Fig ) . The similarity of the two time-course experiments allowed a more detailed analysis of the temporal development of histone marks and factor recruitment to regulatory elements . For this , ChIP enrichment levels relative to input from both 3CHT A13 and 3CHT C19 time-courses were normalised . Activation-associated histone marks were expressed relative to the first time point–HT ( day 3 ) and repressive histone marks relative to the last time point +HT ( day 30 ) . This revealed that at the TSS of all three genes loss of the activation-associated histone marks H3K9ac , H3K27ac and H3K4me3 preceded any increase in the repressive histone mark H3K27me3 ( Fig 5A–5C top ) . Furthermore , comparison between the changes in histone marks and corresponding mRNA expression levels ( Fig 5A–5C bottom ) of each gene over time revealed that the initial repression of mRNA expression was caused by loss of all three activation-associated histone modifications ( H3K9ac , H3K27ac and H3K4me3 ) and was independent of the appearance of the repressive H3K27me3 modification , which was only deposited after most mRNA was depleted . At the TSS of COBLL1 , maximal SUZ12 enrichment levels were reached by day nine , which precedes the substantial increase in H3K27me3 at this site about 15 days after activation of EBNA3C ( Fig 6A ) . Analysis of the BMI1 recruitment to both ADAM peak and COBLL1 peak showed that maximal BMI1 levels were reached nine days after activation of EBNA3C ( Fig 6B ) . However , these high BMI1 levels were not maintained and relative enrichment levels of BMI1 subsequently dropped , but they remained significantly higher than in cells where EBNA3C was kept inactive . Interestingly , comparing the recruitment profile of SUZ12 to the TSS of COBLL1 with that of BMI1 to the COBLL1 peak , it appeared that both increased simultaneously at the distinct sites , but in contrast to BMI1 , SUZ12 levels were maintained at a high level for at least 60 days ( Fig 3F ) . Analysis of the accumulation of RBPJ at both the ADAM and COBLL1 peaks revealed again what appeared to be a transient recruitment of RBPJ with maximal enrichment 3–6 days after the addition of HT ( Fig 6C ) . This preceded the recruitment of BMI1 to the two EBNA3-binding sites . Taken together , these analyses showed consistently that activation-associated histone marks were removed first , consistent with this being the main cause for repression of mRNA expression , before repressive marks were established . Furthermore , the dynamic recruitment of both RBPJ and BMI1 was dependent on functional EBNA3C , with RBPJ recruitment probably preceding BMI1 and EBNA3C involved in recruiting both . We were interested to see whether it was possible to recapitulate the repression of COBLL1 and ADAM28 in transient reporter assays . Therefore , initially , the promoter region of COBLL1 was cloned upstream of a luciferase cassette either in the presence or absence of the COBLL1 peak inserted downstream of luciferase ( Fig 7A ) . The ideal B cell line for transient reporter assays is the readily transfectable EBV-negative Burkitt’s lymphoma cell line DG75 [58] . However , luciferase activity of the COBLL1 construct was very low in this cell line , which made it impossible to study repression , perhaps because DG75 cells do not express endogenous COBLL1 . Therefore , a panel of transfectable B cell lines was screened for luciferase activity of the COBLL1 construct and it was found to be robust in the EBV-positive , but EBNA3C-null cell line Raji [59] . This expresses endogenous COBLL1 . The presence of COBLL1 peak in the plasmid led to an increase in luciferase activity ( ~10 fold ) relative to the construct that only has the promoter region of COBLL1 –indicating that , in the absence of EBNA3C , the COBLL1 peak acts as an enhancer ( Fig 7B ) . Co-transfection of expression plasmids for the EBNA3s along with the luciferase constructs that contain the COBLL1 peak showed that EBNA3C repressed luciferase activity , but neither EBNA3A nor EBNA3B had this effect ( Fig 7C ) . This was consistent with the results from the primary B cell infections presented above , confirming EBNA3C as sole repressor of COBLL1 . Co-transfection of the COBLL1 reporter with an expression plasmid encoding a mutant EBNA3C that is unable to bind to RBPJ based on the double mutant described in Calderwood et al . ( see Introduction and Materials and Methods ) , failed to repress luciferase activity ( Fig 7D ) . In order to recapitulate the repression of ADAM28 a similar approach was used in the EBV-negative Burkitt’s lymphoma cell line DG75 [58] , here luciferase activity of the ADAM28 construct was robust ( Fig 8A ) . Again the presence of the ADAM peak included downstream of the luciferase gene led to an increase in luciferase activity , but this was not as substantial as for COBLL1 ( <3 fold ) ( Fig 8B ) . For this construct , co-transfection of plasmids expressing either EBNA3A or EBNA3C , but not those expressing EBNA3B , resulted in a reduction in luciferase activity ( Fig 8C ) –again consistent with results from the primary B cell infection experiments . As for the COBLL1 reporters , the repression of the ADAM reporter was dependent on the presence of RBPJ , since neither EBNA3A nor EBNA3C induced a reduction in luciferase activity in RBPJ-null DG75 cells ( SM224 . 9 [60] , Figs 8D and S2D ) . Moreover , co-transfection of the ADAM reporter with the plasmid expressing the EBNA3C RBPJ-binding mutant also failed to repress luciferase activity ( Fig 8E ) . These results showed that transient reporter assays can be used to recapitulate the repression of COBLL1 by EBNA3C and the repression of ADAM28 by both EBNA3A and EBNA3C and that the ability of EBNA3s to bind and recruit RBPJ was likely to be important for the repression of both loci in the context of latent EBV infection . In order to determine whether binding of EBNA3C to RBPJ is necessary for the repression of the endogenous COBLL1 and ADAM28-ADAMDEC1 locus in the context of infection , a new EBV recombinant encoding the RBPJ binding mutant of EBNA3C ( RBPJ BM EBNA3C ) was constructed . The RBPJ BM EBNA3C was based on the double mutant described by Calderwood and colleagues [42] and comprised the newly identified W227S mutation and the previously identified mutation of residues 209TFGC→AAAA ( [11] , see Introduction and Fig 9A ) . NotI and SalI restriction sites were introduced in order to allow restriction digest confirmation of successful mutagenesis ( S7A Fig ) and rescued BACs from HEK293 virus producing clones ( S7B Fig ) that maintained general BAC integrity . DNA sequencing of rescued episomes confirmed the mutations that had been engineered . Infection of primary CD19+ cells with this RBPJ BM EBNA3C-recombinant virus resulted in outgrowth and establishment of an LCL . This was unexpected because previous back-complementation experiments using similar RBPJ BM EBNA3C transfected into LCL with conditional EBNA3C , failed to rescue LCL proliferation after inactivation of the conditional EBNA3C [40 , 42] . Cell proliferation , measured by the incorporation of thymidine analogue EdU 36 days after primary B cell infection , showed that 22 . 9% of RBPJ BM EBNA3C cells were synthesising DNA , which is double the 11 . 7% of cells infected with EBNA3C KO virus , but considerably less than 55% of cells infected with wild type or revertant viruses ( Fig 9B ) . Immunoblot analysis of the established RBPJ BM EBNA3C LCL 56 days after primary B cell infection showed similar EBNA3A , EBNA3B , EBNA3C , EBNALP and RBPJ levels compared with wild type or EBNA3C revertant LCLs ( Fig 9C ) . EBNA2 expression , and probably as a consequence LMP1 expression , appeared to be significantly increased in the RBPJ BM EBNA3C LCL , even in comparison to EBNA3C knockout LCL . This suggests that the ability of EBNA3C to interact with RBPJ is important for the regulation of viral genes in the context of infection . The inability of RBPJ BM EBNA3C to bind to RBPJ was confirmed by immunoprecipitation of RBPJ from these LCLs . This very efficiently pulled down wild type EBNA3C , but only trace amounts of RBPJ BM EBNA3C ( Fig 9D ) . RBPJ was immunoprecipitated efficiently from both LCLs . Finally , in order to determine whether binding of EBNA3C to RBPJ was necessary for the repression of the endogenous COBLL1 and ADAM28-ADAMDEC1 locus , RNA samples taken every 5 days from the time of infection of primary B cells with the recombinant RBPJ BM EBNA3C virus were analysed . Consistent with the results of the luciferase reporter assays and the ChIP studies , infection with the RBPJ BM EBNA3C virus was unable to repress ADAM28 , ADAMDEC1 or COBLL1 ( Fig 10A–10C ) . Expression levels of all three genes were similar compared to changes seen after infection with EBNA3C KO virus , whereas infection with EBNA3C revertant or wild type viruses resulted in robust repression of all three genes as seen in the previous primary B cell infections ( Fig 1 ) . As before , these differences in gene expression between the various viruses were not seen for the control gene ALAS1 ( S1I Fig ) . In conclusion , these results showed that the ability of EBNA3C to interact with RBPJ is not only essential for repression in transient reporter assays , but also for the repression of the endogenous COBLL1 and ADAM28-ADAMDEC1 locus in the context of viral infection .
Although it is well established that EBNA3C is essential for transformation of normal primary B cells and for repression of host tumour suppressor genes ( e . g . BCL2L11 and CDKN2A ) , the precise molecular mechanisms by which EBNA3C regulates gene expression remain largely unknown . Here , by using two genomic loci very robustly repressed by EBNA3C , we have explored some of the molecular interactions involved in EBNA3C-mediated gene repression . This has produced new insights into the temporal sequence of events during the repression and challenges existing models based on DNA-binding transcription factors remaining relatively static on chromatin . Most surprising was the dynamic recruitment of and/or stabilisation of what we take to be RBPJ/EBNA3C complexes on both COBLL1 and ADAM peaks . This is in contrast to the paradigm that RBPJ is stably bound to its DNA recognition sequences and that , in the absence of Notch signalling , recruits repressors that are replaced by activators upon signalling [13 , 61] . Furthermore , it is in contrast to previous reports that suggest EBNA3C disrupts RBPJ binding to DNA in order to prevent transactivation by EBNA2 [8 , 9 , 37] . It is tempting to speculate that interaction of EBNA3C with RBPJ increases the binding to–or stabilises RBPJ/EBNA3C complexes on–regulatory elements that control expression of these EBNA3C target genes . Recent studies in Drosophila [62] and mammalian cells [63] have revealed that activation of Notch signalling can induce de novo binding or increased binding of RBPJ mainly at regulatory elements . A similar observation was made for some EBNA2 target genes , where EBNA2 expression appeared to increase the occupancy of RBPJ at these genes during activation [64] . However , increased binding of RBPJ has not , to our knowledge , been reported during gene repression . It might be that EBNA3C , through interaction with one or more other transcription factors ( S8 and S9 Figs ) , increases the on-rate in the dynamic equilibrium of RBPJ binding to its recognition sites . Using publically available transcription factor binding prediction software ( PROMO [65 , 66] and Patch 1 . 0 [BIOBASE] ) , it was not possible to identify any strictly canonical RBPJ binding sites within 1kb around either the ADAM peak or the COBLL1 peak , but if a more relaxed interpretation was used , several possible sites were found at both EBNA3C peaks . It is not possible to determine which , if any , of these are responsible for targeting RBPJ and it is more than likely that other transcription factors are also involved . Alternatively a combination of EBNA3C and some other factor could redirect RBPJ to cryptic sites . Previous studies found that EBNA3 binding sites coincided with various transcription factors , e . g . BATF , BCL11A , IRF4 , PAX5 and RUNX3 [53 , 67 , 68] , all of which seem to co-occupy the EBNA3 binding sites at COBLL1 and ADAM28/ADAMDEC1 ( S8B and S9B Figs ) . Any of these could act as a co-factor in directing RBPJ/EBNA3C complexes to these particular loci . This raises the question of precisely what role RBPJ plays in repression here , or more generally in the context of infections with other gamma-herpesviruses such as KSHV [43 , 44 , 69] . Our best guess is that the interaction between RBPJ and EBNA3C is needed for the assembly of a multi-protein platform of co-repressors ( see Introduction and below ) that is unable to efficiently assemble in the absence of either RBPJ or EBNA3C . It is possible that the assembly of these multi-protein complexes can mask the epitope detected by the anti-RBPJ antibody in ChIP experiments , which might be an explanation for what appeared to be lower RBPJ occupancy at ADAM peak and COBLL1 peak at later time-points after activation of EBNA3C . Recently RBPJ has been found to be retained on mitotic chromatin–book-marking the transcriptional state of genes through cell division–and also to interact with CTCF , which might be involved in formation of higher-order chromosome structures [70] . We cannot exclude either of these functions being important here . The functional importance of RBPJ for the repression of ADAM28 and COBLL1 was indicated in transient reporter assays using RBPJ BM EBNA3C and RBPJ knockout cells . In these assays EBNA3C binding appeared to convert enhancer-like elements into repressor elements . The magnitude of repression in these transient reporter assays was far less than the repression seen in the context of viral infection and the host chromosomes . Consequently we constructed the RBPJ BM EBNA3C recombinant virus–based on the most recent assessment of RBPJ binding sites in EBNA3C . The primary B cell infection with this virus unequivocally demonstrated that the RBPJ BM EBNA3C virus is completely unable to repress ADAM28 , ADAMDEC1 and COBLL1 , providing compelling evidence that the EBNA3C:RBPJ interaction is essential in the context of viral infection . The only caveat here is that we cannot formally exclude the possibility that these two mutations in EBNA3C alter other yet to be described interactions required for gene repression . However , the failure of wild type EBNA3C to repress ADAM28 luciferase constructs in the RBPJ-null DG75 cells also showed that presence of RBPJ is essential for repression . The RBPJ BM EBNA3C virus was able to establish a stable LCL ( in culture >2 months ) that , although it proliferated slowly , was in contrast to the previous back-complementation studies that suggested the interaction was essential to rescue LCLs when EBNA3C was inactivated [40–42] . Further studies are required to characterise the importance of the EBNA3C:RBPJ interaction and to determine which other factors are required for the dynamic binding of these complexes to specific regulatory elements in response to EBNA3C . The repression of ADAM28/ADAMDEC1 and COBLL1 was highly reproducible and very similar between primary B cell infection and EBNA3C conditional LCL after activation of EBNA3C , which further validated the full functionality of the 3CHT fusion proteins . The kinetics followed a highly exponential repression profile over the first two weeks after infection or activation of EBNA3C ( S3 Fig ) , but the fully repressed state was only achieved after 30 days or even later . Full de-repression , through inactivation of EBNA3C , required a similar time period . Currently , we do not understand why these changes in gene expression take place over such a long period of time . At least for COBLL1 the initial repression was relatively rapid , with a 10-fold drop in gene expression by day three and a complete disappearance of COBLL1 protein in immunoblot by day six . However , reactivation of COBLL1 expression and reappearance of COBLL1 protein from the fully repressed state took at least 20 days after inactivation of EBNA3C . One possible explanation is that it takes that long for repressive histone marks to be fully established during repression or fully removed during reactivation . The comparison between histone marks and mRNA expression suggested that the initial repression of both loci is independent of polycomb protein recruitment , but requires removal of activation-associated histone acetylation and the H3K4me3 mark . There are 18 human proteins with deacetylase activity that are grouped into four families according to their homology ( HDAC family 1–4 ) [71] . EBNA3C has been shown to bind to and recruit the class one HDAC family members 1 and 2 , which makes both of them likely candidates involved in the initial repression [48 , 49] . There are more than 30 proteins in the Jumonji C family of demethylases that are able to remove mono- , di- or trimethylation on lysine residues and at least four of them , KDM5A/B/C/D , have been shown to be able to catalyse the removal of H3K4me3 [72–77] . Multiprotein complexes composed of both HDAC1 and HDAC2 together with KDM5A/Sin3 or KDM5C/NcoR/REST have been identified [76 , 78 , 79] . Besides HDAC1 and HDAC2 , EBNA3C can bind to both Sin3 , NcoR and CtBP [49 , 50] , so it seems likely that EBNA3C recruits one of these multifunctional complexes to remove histone acetylation and H3K4me3 in the initial phase of repression . Furthermore RBPJ can independently recruit similar complexes of repressors ( see Introduction ) , adding further support to the idea that by physically interacting EBNA3C and RBPJ synergise in the early phase of repression . Following the initial repression , PRC1 and PRC2 were recruited probably to maintain or further extend the repressive state by depositing H3K27me3 . In more recent studies the classical sequential recruitment model in which PRC2-induced modifications recruit PRC1 has been challenged . It has been shown that PRC1 can be recruited independently from PRC2 and the H3K27me3 modification [80–83] . Furthermore , PRC1 can actually recruit PRC2 through the deposition of H2AK119Ub [84–86] . In our study , however , the ChIP analysis revealed that–in contrast to the classical and more recent models of polycomb repressive complex recruitment–it appeared that PRC1 and PRC2 complexes were recruited not only in the same time frame , but also to different genomic loci . BMI1 , as part of PRC1 , was found at the EBNA3C binding site located in the regulatory elements of ADAM28/ADAMDEC1 and COBLL1 , whereas the PRC2 subunit SUZ12 was found at the TSS of COBLL1 . No direct SUZ12 recruitment could be detected at various sites across the ADAM28/ADAMDEC1 locus although H3K27me3 levels increased at these sites , albeit to much lower levels than at the TSS of COBLL1 . Recruitment of SUZ12 to a discrete site might not have been detected because of the choice of primers , this is however unlikely , because a previous study in human embryonic stem cells identified that 95% of SUZ12 binding sites localised within 1kb of TSS and 40% were within 1kb of CpG islands [87] . Further studies verified this and showed that CpG islands could recruit PRC2 and led to the establishment of H3K27me3 [88–90] . The TSS of both ADAM28 and ADAMDEC1 were included in the ChIP analysis . Furthermore , there is a CpG island around the TSS of COBLL1 , but not at the ADAM28/ADAMDEC1 locus . This might explain the direct recruitment of SUZ12 and the much higher H3K27me3 levels at the TSS of COBLL1 relative to the ADAM28/ADAMDEC1 locus . It was very surprising that SUZ12 and BMI1 were recruited to two distinct sites at COBLL1 . Polycomb complexes have been shown to mediate the formation of higher-order chromosome structures [91–93] ( reviewed in [94] ) . So perhaps chromatin looping between the COBLL1 peak and the TSS of COBLL1 would bring BMI1 ( PRC1 ) and SUZ12 ( PRC2 ) together . We attempted chromosome conformation capture to analyse this locus but we could not reproducibly show looping between COBLL1 peak and the TSS . We do not know the reason for this , however , the same technique has been successfully used to show repressive loop formation between ADAM peak and the TSS of ADAM28 and ADAMDEC1 when EBNA3C was expressed [53] and we have used it to show looping between a distal enhancer and TSS during EBNA3A/3C-mediated activation of a micro-RNA cluster [56] . It is currently unclear whether BMI1 and SUZ12 are recruited by direct interaction with EBNA3C or if this represents a default mechanism of gene repression at these two genomic loci once activation-associated histone marks are removed . A similar two-step model has been proposed recently for the EBNA3A-mediated repression of CXCL9 and CXCL10 [45] . At the CXCL9/CXCL10 locus , EBNA3A binding to the regulatory sites displaced EBNA2 resulting in an initial state of repression ( or de-activation ) , which was subsequently maintained or further extended by the recruitment of polycomb proteins . This is very similar to what we have observed , however , no occupancy of EBNA2 has been reported on the ADAM or COBLL1 peaks ( see below ) . Furthermore , it is currently unclear why EBNA3A plays only a subsidiary role in the regulation of ADAM28 and ADAMDEC1 compared to EBNA3C , or why EBNA3A is found on the COBLL1 peak ( S4 Fig ) even though in the primary B cell infection the presence of EBNA3A is clearly not required in the repression of COBLL1 . This is unlikely to be an artefact of our TAP-tagged LCL cell lines , because two recently published ChIP-seq experiments using anti-HA antibody in EBNA3A-HA [68] or EBNA3C-HA [67] LCL obtained similar results and detected both EBNA3A and EBNA3C at ADAM and COBLL1 peaks ( S8A and S9A Figs ) . Furthermore , confirming our RBPJ ChIP-qPCR results , ChIP-seq for RBPJ in the IB4 LCL [95] revealed steady-state occupancy of RBPJ on both sites ( S8A and S9A Figs ) . Similarly , ChIP-seq in latency III expressing BL cell line MutuIII using the sheep polyclonal anti-EBNA3C antibody , which also precipitates EBNA3A and EBNA3B , identified the same peaks at both loci , but EBNA2 ChIP-seq revealed no binding at these loci ( [53] , S8B and S9B Figs ) . It is worth noting that RP11-624C23 . 1 –number four in the list of EBNA3C repressed genes found in the microarray transcriptome analysis ( S1 Table ) –is a long non-coding RNA with four isoforms of different lengths that run across the ADAM28/ADAMDEC1 locus on the negative strand ( S9B Fig ) . Together with ADAM28 and ADAMDEC1 , RP11-624C23 . 1 is repressed by EBNA3C ( http://www . epstein-barrvirus . org . uk ) , again consistent with the whole locus being co-ordinately regulated . In summary , a detailed analysis of the mechanisms involved in EBNA3C-mediated gene repression of two genomic loci has provided novel insights into the temporal sequence of events during the repression of transcription and the dynamics of factor recruitment . First , it seems that histone marks associated with activation are removed in an initial step of the repression before repressive marks are deposited . Second , the sequential model of polycomb recruitment was not observed , but rather both PRC1 and PRC2 appeared to be recruited at the same time , but to different sites . Third , the paradigm that RBPJ is stably bound on DNA will need to be reassessed to accommodate a more dynamic recruitment and/or stabilisation model of RBPJ/EBNA3C complexes in repression , as has been proposed for RBPJ-mediated activation [62–64] . Although it is clearly essential , the role of RBPJ in EBNA3C-mediated repression examined here has still to be defined . Finally , the EBNA3C mutant incapable of binding RBPJ was able to sustain cell proliferation and to establish a stable LCL , suggesting a robust interaction between EBNA3C and RBPJ is not an absolute requirement for B cell proliferation .
Recombinant EBV KOs , Revs and wild type have been described previously [54] . Virus production and B cell isolation were performed as previously described [52 , 55] . Primary B cells were isolated from anonymous buffy coat residues ( UK Blood Transfusion Service ) . B cell purity was assessed to be >90% CD20+ using anti-CD20-APC ( eBioscience ) staining and flow cytometric analysis . Three million primary B cells in 1 . 5 ml were infected with 0 . 5 ml of supernatant containing a total of 1–3x105 Raji green units [55] . RNA from three million uninfected primary B cells was taken on the day of infection and infected cells were incubated in RPMI 1640 ( Life Technologies ) supplemented with 15% foetal bovine serum , penicillin , and streptomycin at 37°C and 5% CO2 . For a period of 30 days , every three days—or five days for the second primary B cell infection with RBPJ binding mutant virus ( see below ) - 0 . 5 ml of cells were harvested for RNA extraction and replaced by fresh medium . After this , cells were , where possible , grown into LCL and analysed by immunoblotting . All cells were cultured in RPMI 1640 medium ( Life Technologies ) supplemented with 10% foetal bovine serum , penicillin , and streptomycin either in absence or presence of 400nM 4-hydroxytamoxifen ( HT , Sigma ) at 37°C and 10% CO2 . For the time-course experiments EBNA3C conditional LCLs ( 3CHT ) on the p16-null background were used . These have been described previously [52 , 55] . 3CHT A13 ( established in the absence of HT ) were used in a time-course experiment over 60 days with samples taken for RNA , protein and ChIP every three days over the first 30 days and every ten days until day 60 . Cells were counted and diluted to 3x105 cells/ml at every time-point until day 30 and three times a week subsequently , but seeded at 3x105 cells/ml the day prior to harvesting . After harvesting cells on day 30 , half of the +HT culture was centrifuged and the medium replaced by fresh medium without HT and cultured without HT until day 60 ( washed ) . 3CHT C19 ( established in the presence of HT , washed and subsequently grown without HT in the medium for more than three months before the experiment was started ) were used in a time-course experiment over 30 days . Cells were counted and split to 3x105 cells/ml the day before harvesting samples for RNA , protein and ChIP . RT-qPCR was performed essentially as described previously [57] . RNA from 4 . 5x106 cells was extracted using the RNeasy mini kit ( Qiagen ) and 10ng of cDNA was used for each qPCR reaction . GAPDH or GNB2L1 were used as housekeeping genes as indicated and gene expression was expressed relative to primary B cells or LCL -HT on d0 . The sequences of the primers used in this study are listed in S2 Table . Immunoblotting was performed essentially as described previously [54 , 55 , 57] . A total amount of 30 μg of RIPA protein extract was separated on 12 , 10 or 7 . 5% SDS-PAGE , as appropriate , using a mini-PROTEAN II cell ( BioRad ) and transferred onto Protran nitrocellulose membrane . Antibodies used in this study are listed in S3 Table . ChIPs for histone modifications and SUZ12 were performed as described previously [57] . For anti-Flag , BMI1 and RBPJ ChIPs , 4 . 5x106 cells were incubated for 20 min in 1 ml swelling buffer ( 25 mM HEPES pH 7 . 8 , 1 . 5 mM MgCl2 , 10 mM KCl , 0 . 1% NP-40 , 1 mM DTT , 1 mM PMSF , 1 μg/ml aprotinin and 1 μg/ml pepstatin A ) . Nuclei were resuspended in 1 ml sonication buffer ( 50 mM HEPES pH 7 . 8 , 140 mM NaCl , 1 mM EDTA , 1% Triton-X-100 , 0 . 1% sodium deoxycholate , 0 . 1% SDS , 1 mM PMSF , 1 μg/ml aprotinin and 1 μg/ml pepstatin A ) and sonicated for one hour using a Covaris M220 ( 75 W peak power , 26 duty cycle , 200 cycles/burst and 6°C set temperature ) . Thereafter , the ChIP assay kit from Millipore ( 17–295 ) was used , according to the manufacturer’s protocol . DNA was cleaned using QIAquick PCR purification Kit ( Qiagen ) and was assayed by qPCR on QuantStudio 7 Flex ( Life technologies ) . Input DNA was 5% of DNA used in immunoprecipitations and diluted to 2 . 5% prior to PCR quantification . Enrichment relative to input was calculated using four 5-fold-dilution series and error bars calculated as standard deviations from triplicate PCR reactions for both input and IP . Antibodies used are listed in S3 Table and sequences of the primers used in these assays are listed in S4 Table . Genomic DNA extracted from GM12878 LCL cells using Blood & Cell Culture DNA Midi kit ( Qiagen ) was used to PCR amplify the promoter region 1 kb upstream of ADAM28 and COBLL1 ( short transcripts ) , the ADAM peak ( 1 kb around the EBNA3 binding peak at ADAM28-ADAMDEC1 ) or the COBLL1 peak ( 1 . 5 kb around the EBNA3 binding peak at COBLL1 ) ( Primers are listed in S5 Table ) . The promoter regions were cloned upstream of the luciferase gene in pGL3-basic vector using the MluI restriction site . ADAM peak and COBLL1 peak were cloned downstream of the luciferase gene using the SalI restriction site . All vectors were screened for the correct orientation and were sequence verified . DG75 ( for ADAM28 constructs ) or Raji cells ( for COBLL1 constructs ) were electroporated with 1 μg of pGL3-luciferase vectors , 1 μg pSV-beta-galactosidase and varying amounts of pCDNA3-EBNA3 expression plasmids . Total amounts of DNA were balanced using an empty pCDNA3 expression plasmid . Electroporations were performed as described previously for DG75 [96] . For Raji a voltage of 240V was used and all electroporations were harvested after 48h . Luciferase and beta-galactosidase assays were performed as described previously [96] and measured on FLUOstar Omega ( BMG Labtech ) . Beta-galactosidase activity was used to normalise luciferase activities for transfection efficiencies . For the creation of the RBPJ binding mutant ( BM ) EBNA3C recombinant virus , the N terminus of EBNA3C was cloned from the B95 . 8 EBV-BAC [97] by XbaI digestion at position 98 , 398 and BglII at site 99 , 749 ( relative to GenBank entry V01555 . 2 ) into a modified pBlueScrit II SK+ . The two in previous studies identified RBPJ binding site of EBN3C were mutated to generate the RBPJ binding mutant EBNA3C . In-Fusion PCR mutagenesis ( Clonetech ) was employed to first substitute EBNA3C residues 209TFGC for 209AAAA while introducing a NotI recognition site ( forward primer: 5’-GCGGCCGCAGCTCAAAATGCGGCACGAACT-3’ , reverse primer: 5’-AGCTGCGGCCGCGGCAGTTAACATGATGCTGT-3’ ) . PCR products were purified ( Diffinity RapidTip2 ) and circularised using In-Fusion cloning ( Clonetech ) . Plasmid DNA was screened by NotI restriction digest before introducing the W227S mutation together with a SalI recognition site ( forward primer: 5’-GCCACCGTGTCGACACCACCCCATGCTGGACCAA-3’ , reverse primer: 5’-CGACACGGTGGCAGAGAAGGTGT-3’ ) . The RBPJ binding mutant fragment of EBNA3C was subcloned into the shuttle plasmid pKovKanΔCm [98] and verified by DNA sequencing . The recombinant EBV was created by RecA based homologous recombination between the B95 . 8 EBV-BAC and the shuttle plasmid as previously described [98] . At each stage of recombineering BAC DNA was isolated and validated by restriction digest and pulsed-field gel electrophoresis . RBPJ binding mutant EBNA3C virus producing 293 cell clones were established as previously described [54] . Episome rescue of EBV BACs from 293 producing cell lines was performed as previously described for low molecular weight DNA [99] . IPs were performed essentially as described previously [96] . Briefly , RBPJ was immunoprecipitated from protein extracts of 107 wild type EBNA3C or RBPJ BM EBNA3C LCLs for two hours at 4°C using RBPJ rat monoclonal antibody 1F1 . Then , 30 μl of protein G-Sepharose beads were added and incubated under rotation for 1h at 4°C , washed four times in IP buffer and immunoprecipitated proteins were resolved by SDS-PAGE and probed for EBNA3C ( A10 ) or RBPJ ( ab25949 ) . A cell proliferation assay–based on measuring the incorporation of EdU at day 36 after primary B cell infection–was performed as described previously for established cell lines [52] . | The Epstein-Barr nuclear protein EBNA3C is a well-characterised repressor of host gene expression in B cells growth-transformed by EBV . It is also well established that EBNA3C can interact with the cellular factor RBPJ , a DNA-binding factor in the Notch signalling pathway conserved from worms to humans . However , prior to this study , little was known about the role of the interaction between these two proteins during the repression of host genes . We therefore chose three genes–the expression of which is very robustly repressed by EBNA3C –to explore the molecular interactions involved . Hitherto these genes had not been shown to require RBPJ for EBNA3C-mediated repression . We have described the sequence of events during repression and challenge a widely held assumption that if a protein interacts with RBPJ it would be recruited to DNA because of the intrinsic capacity of RBPJ to bind specific sequences . We show that interaction with RBPJ is essential for the repression of all three genes during the infection of B cells by EBV , but that RBPJ itself is only recruited to the genes when EBNA3C is functional . These data suggest an unexpectedly complex interaction of multiple proteins when EBNA3C prevents the expression of cellular genes . | [
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] | [] | 2016 | EBNA3C Directs Recruitment of RBPJ (CBF1) to Chromatin during the Process of Gene Repression in EBV Infected B Cells |
Visual short-term memory tasks depend upon both the inferior temporal cortex ( ITC ) and the prefrontal cortex ( PFC ) . Activity in some neurons persists after the first ( sample ) stimulus is shown . This delay-period activity has been proposed as an important mechanism for working memory . In ITC neurons , intervening ( nonmatching ) stimuli wipe out the delay-period activity; hence , the role of ITC in memory must depend upon a different mechanism . Here , we look for a possible mechanism by contrasting memory effects in two architectonically different parts of ITC: area TE and the perirhinal cortex . We found that a large proportion ( 80% ) of stimulus-selective neurons in area TE of macaque ITCs exhibit a memory effect during the stimulus interval . During a sequential delayed matching-to-sample task ( DMS ) , the noise in the neuronal response to the test image was correlated with the noise in the neuronal response to the sample image . Neurons in perirhinal cortex did not show this correlation . These results led us to hypothesize that area TE contributes to short-term memory by acting as a matched filter . When the sample image appears , each TE neuron captures a static copy of its inputs by rapidly adjusting its synaptic weights to match the strength of their individual inputs . Input signals from subsequent images are multiplied by those synaptic weights , thereby computing a measure of the correlation between the past and present inputs . The total activity in area TE is sufficient to quantify the similarity between the two images . This matched filter theory provides an explanation of what is remembered , where the trace is stored , and how comparison is done across time , all without requiring delay period activity . Simulations of a matched filter model match the experimental results , suggesting that area TE neurons store a synaptic memory trace during short-term visual memory .
Visual short-term , or working , memory is often tested with a sequential delayed match-to-sample ( DMS ) task . First an image to be remembered ( the sample ) is presented . Then a sequence of images ( the tests ) , separated by short delays , is presented . The subject is supposed to respond when the remembered image reappears ( the match trial ) . The comparison between images presented at different times requires the brain to compare its current neuronal response with the one that occurred earlier . How this memory task is performed is not well understood , but where it is performed is well known . Analysis of behavior following selective ablations has shown that two large brain regions are important for performing this task: inferior temporal cortex ( ITC ) and prefrontal cortex ( PFC ) [1]–[4] . Selective ablations within ITC , particularly perirhinal cortex , interfere with visual memory [5]–[9] , but ablations of area TE have different effects than ablations of perirhinal cortex [10] , [11] . For example , after area TE ablation , memory at both short and long delays is impaired , whereas after ablations of perirhinal cortex only memory at long delays is impaired [11] . Neurons in both area TE and perirhinal cortex are selective for visual patterns [12]–[15] . In match-to-sample or stimulus-stimulus association tasks , the selective neuronal activity representing the sample or pair-associate image persists during the interstimulus interval for a minority of neurons in both area TE and in perirhinal cortex [16]–[19] . This delay period activity has been thought to play a critical role in maintaining short-term memory . However , the delay-period activity in perirhinal neurons is less consistently selective for the sample stimuli after distractors are presented [15] , [20] . Delay period activity during the DMS task is also found in lateral PFC , but in less than half of the neurons [20] . This activity persists and keeps its selectivity for the sample despite distractors [20] . The delay-period activity in prefrontal cortex has also been linked to motor-response selection [20]–[29] . Stimulus-selective delay-period activity has been hypothesized to be the memory trace , and consequently short-term memory has been extensively modeled with attractor networks or feedback networks that maintain their activity after the stimulus goes away [30]–[37] . In contrast , Eskandar et al . [38] developed a multiplicative neural network model that successfully predicted the responses ( of ITC neurons in area TE ) to both matching and nonmatching test images . In their experimental data few neurons showed stimulus-selective delay-period activity [12] . Thus , their model did not depend on a reverberating circuit , and in fact did not propose any mechanism for storing the memory trace . Instead , it proposed a generic model that used correlation of a stored sample response that was somehow “played back” and compared with each test response . Here we report data from a DMS task showing that single neurons in area TE , but not perirhinal cortex , of inferior temporal cortex , have significant trial-by-trial correlations in the fluctuation of their activity ( noise ) across sample and match periods . These correlations suggest that some proportion of the neuronal response elicited by the sample stimulus is stored locally , and acts on subsequent stimulus elicited activity . We present a computational model , based on single-trial learning in a matched filter , showing that the observed correlations could arise from storage of a working memory trace using rapid , short-term synaptic plasticity , and show how the outputs of these neurons could be utilized to detect the match . In this model , the brain does not detect the noise correlations themselves , but simply looks at the total level of activity in the TE neurons to perform the DMS task . Nonetheless , the noise correlations are important because many models of brain function could reproduce the DMS behavior of the monkeys , hence correct performance by itself is not a good criterion for selecting a model . For example , consider recording music on either an analog magnetic tape or on a digital memory stick . If you play back either recording , they will both reproduce the music . And , if you repeat the recordings hundreds of times , the average sound reproduction across these trials will be the same from both . However , if you carefully analyze the sound from each trial , certain systematic anomalies will arise . The analog tape will not move at constant speed , giving rise to shifts in frequencies ( wow and flutter ) , and the digital recording will show only a finite set of levels ( quantization ) . These imperfections have nothing to do with the task of reproducing the sound , but are a unique signature of the recording mechanism and can be used to differentiate between them . Similarly , we argue that the noise correlations are a clue to the mechanism used in short-term visual memory . The matched filter is performing the DMS task by noticing when the overall response is high , but it is also leaving the signature of its mechanism on the responses in the correlated noise . Thus , we can use the correlated noise to infer something about the mechanism , even though it is an epiphenomenon unnecessary for the DMS task .
The experimental results described above lead us to hypothesize that the noise correlation is related to short-term memory , i . e . , that the correlated noise is a side-effect of the mechanisms of short-term memory . Our new hypothesis of short-term memory storage and recognition processes is similar to an engineering tool called a matched filter , which is commonly used ( e . g . , in radar , radiology , etc . ) to compare an unknown signal with a known signal [40] . Signal encoding and learning mechanisms are required in any episodic memory model , but we do not speculate about them here . We also deal only with the information processing required , and not with details ( architecture , connections , and dynamics ) of how a neuronal circuit in cortex could perform the processing . We concentrate instead on the model's memory architecture . When the image is the sample to be remembered , a learning command ( Learn in Figure 4 ) causes each input synapse of a TE neuron to set its weight proportional to its current input ( the learning mechanism is not specified here , but any short-term process that made the synaptic excitability high after strong inputs and low after weak inputs would be sufficient . ) During the sample presentation of the i-th pattern , the image is sparsely encoded across all N axons that project to TE neurons . A non-exclusive subset of these N axons then project to a given TE neuron ( Figure 4 ) . We model the connections from the encoder population to the TE neuron with algebraic synapses ( i . e . , inputs are graded , and can be positive or negative ) . The nature of the encoder is not specified here . It is simply assumed that after the presentation of the i-th pattern the spike count on the m-th axon branch is cmi . Our results deal with two aspects of the data , the average response of the neurons in the DMS task , and the noise on individual responses . To analyze these two aspects , we present the model in two forms , a deterministic model that predicts the average responses of the neurons , and a stochastic model that predicts the noise correlations . In the deterministic model no noise is added to the encoder's activity , so the input to the m-th synapse on the k-th neuron after the i-th pattern is simply: ( 1 ) where M is the total number of synapses on the k-th neuron . In the stochastic model , which is used to predict the correlated noise seen in our experiments , several types of noise are added to the encoder's output . First , there is a common noise term ( δ ) that is added to each encoder output , representing a level of arousal . Each encoder also has two independent noise contributions drawn , for each synapse , from a distribution common across synapses: a multiplicative ( α ) and an additive ( β ) noise . The input to the m-th synapse on the k-th neuron is then: ( 2 ) where αkm , βkm , and δ are samples of independent noise sources ( αhas mean 1 and β and δ have mean zero ) , and M is the number of synapses on the downstream neuron . All noise in the stochastic model is referred to the output of the encoder neurons . The samples of noise are drawn each time they are needed ( e . g . , three times for sample-nonmatch-match trials ) . The cmi thus represent the average , or expected value of each encoder output for a given stimulus , and the xkmi represent the particular ( noiseless or noisy ) sample . When no learning is present in the model ( e . g . , for perirhinal neurons ) , the synaptic weights at the m-th synapse of the k-th neuron are all set to unity gain: ( 3 ) When the i-th pattern is shown and learning is triggered , the weight ( strength ) of the m-th synapse of the k-th neuron receiving that input is set to: ( 4 ) These synaptic weights are the memory trace . For each subsequent image the output of the TE neuron will be the product of the encoder output elicited by the test stimulus and the synaptic weights that hold the memory trace . Thus , the output of each TE or perirhinal neuron is a correlation: the sum of the product of the input activity with the stored weights . The response of the k-th neuron to the pattern pair ( sample i , test j ) is simply the sum over all M synapses: ( 5 ) In the matched filter theory , the test image is considered as a match if the activity summed across neurons is above a threshold . Some subset of neurons will respond strongly ( on average ) to a given sample pattern . When a nonmatch stimulus is later presented , not all of these neurons will fire strongly , so the product of weights and inputs ( Equation 5 ) for at least some of those neurons will be low ( even though the weight is high ) , and the sum of all those responses will not exceed the threshold for recognizing a match . When a match stimulus is presented , each neuron in the subset will again fire strongly , and be multiplied by the high weight , giving a sum of responses across neurons that exceed the threshold for recognizing a match . Thus , to recognize matches , the matched filter relies on the fact that a neuron that responded strongly to a given stimulus once will , on average , respond strongly to that stimulus again . The matched filter simply relies on standard stimulus selectivity–different stimuli evoke different average responses . The matched filter mechanism for detecting matches does not make use of , but does give rise to , the “noise” correlations observed in TE neurons in our data . As just explained , the matched filter relies on differences in the average response to different stimuli to detect a match . But the weight stored after presentation of the sample image depends not on the average response to the sample image , but on the individual response to that presentation . The response of a neuron to an individual presentation of the sample image can be higher or lower than the average across presentations for that image . If , on a given trial , the response of a TE neuron to the sample image is higher than average , the synaptic weight will be set higher than the average during that trial . When the match is presented , the encoder signal can be lower , higher , or the same as the average . However , the multiplicative interaction between the input and the weight will bias the average of many such interactions to be higher than average if the stored weight is higher than average . Similarly , lower sample responses lead to lower synaptic weights and a lower than average response to the match . Put another way , the deviations of the responses from their means for the sample and test patterns will be correlated within a trial . Note that whereas individual neurons can be above or below average for any given trial , these fluctuations will average out , and the total activity across the population will only be above threshold for the repeat of the sample image . We test this model in two ways . First , a single-synapse ( scalar ) version of the deterministic model was applied to the average data collected in the experiment , to test the model's ability to perform the DMS task , on average , like the neurons . Tests with the average responses can not evaluate the correlation of the response noise across conditions , so a second test was needed . We used a stochastic vector model ( shown in Figure 4 ) with a simple visual encoder and two types of noise ( additive and multiplicative ) on the input to the downstream neuron's synapses . We also added a noise source shared by all synapses , to test the hypothesis that changes in arousal could explain the noise correlations in our TE data . The model was simulated and the noise parameters were adjusted to best fit the correlation between the noise in the responses to the sample and the noise in the responses to the test stimuli found in the experimental data . It is important to emphasize that this tuning only sets the relative size of the correlations; the fact that there is a correlation depends upon the matched-filter model's structure . Indeed , the same tuning did not create correlations in the stochastic model of the perirhinal neurons , because they have no memory trace . The inputs to the model are unknown and must be estimated . It is possible to train a neural network to find the xki that solves Equation 5 ( not shown ) . However , this approach yields a model with a very large number of free parameters , and thus provides only weak support for our hypothesis . We can make a stronger test of our hypothesis by noting that in the DMS task there is a special case , the response of a neuron to the matching image , which has the response: ( 6 ) To compare our model to the data , we can only consider scalar variables , because the single-unit recordings give only the spike count in the response to a stimulus . The individual encoder outputs are not known . Thus , a scalar approximation of the encoder output , eki , the unknown input to the k-th neuron for the i-th pattern , can be estimated as the square root of the average of the responses across N repetitions of the match stimulus: ( 7 ) where rkn is the spike count on the k-th neuron on the n-th experimental trial . This corresponds to a model where each neuron has only a single synapse . This is obviously a major simplification of the model , but it is necessary because we can not observe the output of the encoder by recording from single neurons . Furthermore , Equation 7 is only a rough approximation , because it takes the square root of the sum , instead of the sum of the square roots , of the individual rkn . The advantage of this approach , which offsets the coarseness of the approximation , is that we have a parameter-free , deterministic model that performs the DMS task , with all of the assumptions explicit in the structure of the matched filter . The predicted response , r* , of a neuron for the nonmatching j-th pattern following the sample i-th pattern with a memory effect would then be: ( 8 ) Similarly , the predicted response of a neuron to the matching i-th stimulus would be: ( 9 ) For completeness , the predicted response of the matched filter model to the sample j-th image is calculated based on the assumption that the memory trace is still set to the previous sample , say the p-th image ( i . e . , this assumes the previously stored signal persists until a new learning command occurs ) . The old memory trace probably decays away over time , but this assumption lets us calculate a conservative estimate of the sample response: ( 10 ) The encoder output is estimated from the response of the neuron to the match stimulus ( Equation 7 ) . Thus , the deterministic model can only be used to predict the responses to sample and nonmatch stimuli ( Figure 5 ) . For the population of TE neurons , the correlations between sample response and prediction ( R = 0 . 74 ) and nonmatch response and prediction ( R = 0 . 73 ) are significant ( Figure 5A; p<0 . 001; R2 = 0 . 54 for TE sample predictions , and 0 . 53 for TE nonmatch predictions ) . For the population of perirhinal neurons , the correlations are lower , but still significant ( Figure 5B; p<0 . 001 , R2 = . 31 and 0 . 39 , sample and nonmatch , respectively ) . This is consistent with our expectations , because both types of neurons showed stimulus selectivity ( see Figure 2A ) . Thus , the scalar matched filter model , with no free parameters and with the simplistic approximation of Equation 7 , successfully predicts the responses of the neurons during the DMS task , accounting for a bit more than 50% of the variance in the TE data . In Figure 6 , instead of predicting the responses of the neurons to the sample or nonmatch stimulus on each trial , we predict the deviation of that response from its mean . This roughly matches what we found in the data ( see Figure 3 ) . The variance explained in the prediction of the response deviation was much less than the variance explained in the prediction of the response itself for perirhinal neurons ( R2 = 0 . 05 and 0 . 06 for sample and nonmatch deviations in TE , and R2<0 . 001 for deviations in perirhinal neurons ) . Figure 7A shows the results of computing the match-nonmatch performance for the set of 64 population responses for the 35 TE neurons . Each row ( sample ) and column ( test ) begins with the corresponding stimulus . The average population response is printed for each nonmatch and match decision . The diagonal values show the match responses ( in spikes per 400 ms epoch ) . A number colored in blue is a correct match decision ( or hit , based on a threshold of 6 . 15 ) , and an orange number is a miss . The off-diagonal elements show the nonmatch responses . A green number is a correct rejection , and a red number is a false alarm . Overall , the matched filter based on these 35 neurons scored 50% correct ( ROC d' = 1 . 02; random would have been 1/64 or 1 . 56% correct ) on the DMS task . The same comparison is made in Figure 7B for the perirhinal neurons , which scored 55% correct ( d' = 0 . 72 ) . The similarity in scores is not surprising , as TE neurons project to perirhinal cortex . However , the d' value ( which is the separation of the means of the probability density functions of occurrence , with and without signal , divided by the standard deviation of the distributions ) is much smaller in perirhinal neurons . This suggests that signals that were separate in TE have become confounded in perirhinal cortex . The second test of our model is whether it can give rise to noise correlations , for which the noise on the visual encoder must be modeled parametrically . In the stochastic vector model ( Equation 2 ) , there are three parameters ( to specify the variances of the three noise processes α , β and δ ) . Note that these three parameters are fit independently in the model for both TE and perirhinal neurons ( see Methods ) , but their presence alone is not sufficient to generate the noise correlations in our data . It is the presence of learning that introduces the noise correlations , which is clear because the perirhinal neurons do not learn , and do not show this correlation . Above , predicted responses were computed from the average responses of the experimental data . To simulate the DMS task with a matched filter model with noise on a trial-by-trial basis , we need to generate an encoder output . For simplicity , we chose the discrete Fourier transform ( DFT ) to represent the encoder . Each 8×8 stimulus was placed on a 16×16 gray background . The stimuli ( Figure 8 , top row ) were first converted to their 16×16 DFTs ( Figure 8 , middle row ) . Each DFT image thus represents activity in 256 encoder cells ( represented as a vector of length 256 ) . The output of the model is just the dot product of the sample and test responses ( Figure 8 , bottom row . NB: the luminance levels in the figure are a poor indicator of their importance , because of the log transformation used in plotting ) . The average output power ( calculated using root-sum-of-squares of population activity , with the brightest pixel across all stimulus pairs normalized to 1 . 0 ) across the entire population is given on the left ( 0 . 452 for the match , and 0 . 149 for the nonmatch case ) . The output results for all 64 combinations of stimulus and test patterns are shown in Figure 9 . As in Figure 7 , the matches are on the diagonal , and the nonmatches are on the off-diagonals . The normalized output power is printed above each response image for a population of 256 encoder neurons ( shown as a 16×16 icon ) . Green numbers are correct hits , blue are correct rejections , orange are misses ( none in this example ) , and red are false alarms . With the threshold set to 0 . 225 , the model makes only two mistakes ( both false alarms ) . This gives the model a success rate of 97% ( d' = 3 . 34; the correction for p ( hit ) = 1 was made using p ( hit ) = 1−0 . 5/ ( Nhit+Nmiss ) , [41] ) . The average success rate of our two monkeys was 98% . ( These two rates are so close because the noise in the model was tuned to match these monkeys , so it is a fit , not a prediction; see Methods ) . A quantitative comparison of the performance of the model with that of the monkeys is shown in Figure 10 . The average correlations between the sample and match response deviations are shown for actual and simulated TE neuronal responses ( Figure 10A and 10B , respectively ) . The response correlation was larger between the sample and match phase than between the sample and nonmatch phase ( for simulated response: paired t-test , p≤0 . 00001; for actual response: p< = 0 . 001 ) . As a control , the same model was used to simulate a population of neurons in perirhinal cortex by fixing the synaptic weights ( Figure 4 , Wkm ) to 1 . 0 . Without synaptic plasticity , noise correlations in the perirhinal simulations were the same for all phase pairs ( Figure 10D ) , as was found in the experimental data ( Figure 10C ) . Thus , our simple matched filter model shows that the unexpected correlation between noises at different times for sample vs . match responses is an emergent property of a multiplicative matched filter model that stores its memory trace locally with one-trial learning of synaptic weights . Note that one parameter in the noise model , δ , is shared by all the TE neurons . It represents a kind of alertness level . If δ varied slowly over time , it could introduce a correlation between sample and test responses . However , as the nonmatch response is always equal to or closer in time to the sample response than is the match response , the effect of the δ noise must make the noise correlations on sample-nonmatch responses the same or larger than the noise correlations on the sample-match response . This is the opposite of our data ( sample-match noise correlations were larger , Figure 3 ) . Hence , in this model the δ noise process contributes to the height of the correlations in Figure 10B and 10D , but not to the height differences in either panel . The magnitude of the correlation is also adjusted by tuning the α- and β-noise processes . However , without the multiplicative effects of the matched-filter model there would be no difference in heights of the bars for area TE ( Figure 10B ) . They would be like the bars for perirhinal cortex ( Figure 10D ) . The usual interpretation of the average delay period activity in a DMS task is that it reflects activity in a reverberatory circuit ( attractor network ) that is holding the memory . It is interesting to ask what happens to a matched filter between stimulus presentations . If all inputs are set to zero , then there is no output from the matched filter . However , if there is noise on the inputs to the matched filter during the interstimulus interval , the matched filter would produce an output ( Figure 11 , top row ) . The output looks like the template , but with a much reduced signal-to-noise ratio ( SNR ) . If this response to noise were averaged over several trials , the SNR would improve ( as the , where N is the number of trials in the average; see Figure 11 , bottom row ) . This example shows that another interpretation of the delay-period activity is possible: it may be the response of a matched filter to noise , which reflects the current setting of the synaptic weights .
The noise correlations arise because of the multiplication stage in the matched filter . The brain cannot detect these noise correlations; it only detects the total activity in the population after the test image is presented . The experimenter can observe the noise correlations in the data , and infer from them something about the mechanism that is acting . That these correlations are irrelevant to performing the task is obvious because of the large number of neurons involved . To make a match or nonmatch judgment , the brain must take some kind of average over a population of neurons . Furthermore , this population must contain about the same number of neurons responding above and below their average responses . Thus , over the population the correlated noise would average out . Another inference follows from our observations: that the synaptic weights holding the memory trace must be on the TE neurons from which we are recording . The responses of TE neurons can not simply be reflecting an input from a different area , say prefrontal cortex , which was recalling the previous input . If another area held the memory , then they would have to be holding the previous output of area TE . The exact same cells that projected to each neuron in the memory area would have to receive a return projection from that neuron . In other words , the mapping from TE to the memory area would have to be 1:1 . No cortical brain area we know of contains a 1:1 mapping . Instead , neurons seem to have a large degree of fan-out and fan-in , so each neuron connects with many others , and many others connect to it . In such a many:many mapping the exact value of the response of the TE neurons to the sample stimulus would be averaged out by the time it returned to TE during recall . But that would destroy the very noise correlations we observed . Thus , the synapses that hold the memory trace must be on the TE neurons themselves . In our model ( Figure 4 , and Materials and Methods ) , the “signals” are the neurons that provide inputs to TE , and the weights are encoded by the strength of their synapses onto TE neurons . Each neuron remembers only its own input , and thus learning happens locally , by the modification of synaptic weights . The model is biologically plausible–the multiplication , addition , and threshold operations are easily available to neurons [46]–and no signals need to be transmitted to , or recalled from , any other part of the brain for comparison . Note that this model has only an implicit recall; there is no actual reconstruction of the original signal to compare with the current signal . However , one surprising feature of the matched filter is that when excited by a random signal ( e . g . , white noise inputs ) , the average of its response will be an approximation of the original signal ( cf . Figure 11 ) . Our model is similar to most others formulated to describe memory in that it uses synaptic plasticity to create a stored memory . Here we specifically propose using rapid synaptic plasticity gated by a learning command . Although this rapid type of synaptic plasticity has not been observed , its existence has been hypothesized by others when considering how working memory might arise [47] , [48] . Our hypothesis only requires that synapses of TE neurons are altered according to their current input: if a particular input is high the weight is set high and if a particular input is low the weight is set low . The synapse therefore has a memory of the input , which consists of both signal ( mean response ) and noise ( deviation from the mean ) . Every subsequent signal is multiplied by this adapted synaptic weight . This multiplication correlates the response with the remembered signal–the mean plus the deviation . Our hypothesis of memory storage does not require delay activity , which has been seen mainly in perirhinal cortex [15] and , even there , only a relatively small proportion of neurons show this property [16] , [18] , [19] , [49] . In our data , the noise correlation is significant for most TE neurons , suggesting that most of these neurons participate in this simple working memory . A consequence of the matched filter model ( Equation 5 ) is that the average response over the population shows match enhancement and nonmatch suppression . This is the basis for the filter's discrimination . For an individual member of that population , however , nonmatch responses can be larger than match responses , depending upon the selectivity of the neuron . This can be seen in the simulation by comparing individual pixels ( i . e . , simulated neurons ) down a column in Figure 9 . A systematic match enhancement or suppression has been seen in neurons in perirhinal cortex ( see Figure 12 ) [14] , [15] , [49] , a cortical region that seems heavily involved in decisions about remembered stimuli [7] , [8] , [10] , [13] , [50] . Our data do not show such systematic changes in perirhinal cortex , so our model does not deal with this behavior . Our model also does not try to account for other effects of novelty , recency or familiarity , in which the responses to previously seen stimuli are sometimes smaller on subsequent presentations , because this is an effect observed mainly in neurons medial to the anterior middle temporal sulcus , in perirhinal cortex [51] . Some cells in perirhinal cortex showed both match suppression ( a short term memory effect that decreased responses to the matching stimulus ) and a familiarity effect ( a long term response decrement ) over long times when the stimuli were repeatedly presented [52] . The neurons we recorded that showed the noise-correlation effect were all in area TE , lateral to the anterior middle temporal sulcus , where previous reports did not find match-suppression [12] , [49] . The neurons we recorded that did not show the noise-correlation effect were medial to this sulcus , in perirhinal cortex . It is important to emphasize that in this model the brain does not perform the DMS task by detecting the noise correlations . The DMS task is performed by comparing the level of activity in a population of TE neurons with a threshold . The threshold determines the sensitivity of the detector , and thus is probably under behavioral control . The noise correlations are observable only by the experimenter after the task , and not by the brain during the task . The noise correlations are thus a clue to the mechanism used to solve the DMS task , in this case , a multiplicative model . Our findings and model here extend our previous work [12] , [38] . In those studies we found that “responses to the nonmatch stimuli carried significant amounts of information about the pattern of the previous sample stimuli . ” This is consistent with our findings here , because the response of an inferior temporal ( IT ) neuron would be the product of the visual codes for the sample and test stimuli . We hypothesized then that “the role of IT neurons in visual memory tasks is to compare the internal representations of current visual images with the internal representations of recalled images . ” This is exactly what we are proposing here , but now we have a specific hypothesis for the memory mechanism that eliminates the need for recalling the response to the sample image . This new theory is applicable in any area of the brain that depends upon synaptic changes , rather than persistent activity , to hold a memory trace . Other types of working memory should be studied with this in mind . This work may even be relevant in areas that hold a memory as delay-period activity in an attractor network , such as prefrontal cortex , because synaptic plasticity is required to create the attractor representing the object that is being remembered [31] , [37] . There are many forms of memory , and DMS just tests one particular type of explicit , or declarative , memory . For example , Standing [53] studied free-recall or recognition tasks . He showed that thousands of pictures or words could be recognized as familiar after being seen only once . As Standing pointed out , this is different from the limited “memory-span” ( about seven items ) required to deal with ordered lists . Although the DMS task is more like a memory-span task than a familiarity task , our matched filter model may be applicable to recognition tasks as well . Some part of the brain would have to be organized as many little matched filters , and it would have to set the weights in a different matched filter for every picture on which the subject concentrated . Obviously , this area would need a huge capacity , but it would be much more efficient to build a large capacity memory out of synaptic weights than out of reverberating circuits . Then , during testing , the matched filter would implicitly test the incoming picture against all stored pictures simultaneously . If any little filter responded with a total power above some threshold , the familiar object would be recognized . Another advantage of this approach to recognition memory is that the recall is implicit , and thus in a sense , free . There is no computation other than the weighted sum of inputs performed by the biophysical properties of the soma . This could be vitally important to the animal , which otherwise would have to actively search through thousands of memories to find a match . No matter how large the number of matched filters , the time to test an incoming pattern is fixed regardless of memory size . In search schemes , the time would grow with memory size . Delay period activity in prefrontal cortex during memory tasks , which is proposed as playing a critical role in storing sensory signals , has been an important discovery for unraveling neuronal mechanisms of working memory since the 1970s [21] , [54] , [55] . However , delay period activity in prefrontal cortex is related to storing the sensory signal , and that signal can be modulated depending on whether the stored signal would be used to execute or suppress an action , that is , for response selection , indicating that the delay activity is also , or even mainly , related to executive function [26] , [56] . A recent study shows that prefrontal cortex holds the decision during a memory task , while the middle temporal visual area computes comparisons between sample and test visual motion [56] . Our proposal supports the suggestion that stimulus-selective working memory signals are held in higher sensory areas ( area TE in our case , MT for Zaksas and Pasternak [56] ) . Our study shows evidence for working memory storage with a silent storage mechanism using rapidly adapting synaptic weights , and a matched filter provides a specific proposal of how to utilize the outputs of TE neurons to detect a match . Our new model explains the noise correlations across time in our data . The matched filter theory also formulates and answers many important questions about the mechanism of visual working memory: what is remembered ( the entire output of the encoder population ) ; how and where it is remembered ( as synaptic weights of TE neurons ) ; how it is recalled ( recall is not needed in a matched filter mechanism ) ; and how the memory trace is compared with a new response ( correlation by multiplication of input activity and synaptic weights ) . The output of TE could thus be used to make the match/nonmatch decision , simply by applying a threshold to the total population activity . Our hypothesis arose from the need to explain the noise correlation seen in our experimental data from area TE of inferior temporal cortex . If the correlations in our data were due to an artifact arising from some stimulus-dependence that remained after the mean was subtracted , we might have seen a similar pattern of correlations in perirhinal cortex . Also , breaking the serial relationships within single trials by shuffling the match responses within each stimulus pattern group should not have destroyed the correlations ( cf . Results ) . If the correlations arose from a slowly varying signal having nothing to do with memory or the stimulus ( e . g . , due to arousal ) the correlations should be higher between sample and nonmatch than between sample and match in sample-nonmatch-match trials . Thus , our explanation for the correlations between noise in sample and test responses in TE neurons is that they are a consequence of the multiplicative mechanism in a local , synaptic , short-term memory . This model does not specify how a neuronal circuit might perform matched-filtering . Cortical architecture , connections and dynamics are all ignored . Nonetheless , this model does more than merely provide a representation of the data . Our data show only that cells are selective for stimuli , respond differently depending upon the condition ( sample , nonmatch , match ) , and that there is correlated noise on their responses across sample-test presentations . The filter model demonstrates ( thus , it is an existence proof ) that the responses themselves ( not the noise ) could come from a multiplicative interaction between a synaptic memory trace and a current input . Note that this part of the model is parameter-free: there is no tuning or fitting involved . Thus , it is a very strong argument in favor of the matched filter theory of short-term memory , even without a biophysically detailed model . Furthermore , the matched filter hypothesis explains why the noise correlation exists , even though it makes no contribution to solving the memory task . It is important to remember that the noise-correlations are not built into the model by fitting it to the data ( e . g , perirhinal neurons are fit the same way but have no such correlations ) . Thus , this “black-box” filter model , although simplistic with respect to cortical circuitry , provides a mechanistic explanation of how the noise correlations could arise , and thus is a plausible model for short-term memory .
Computer simulations of the matched filter model of TE were carried out in MATLAB ( The MathWorks , Natick , MA ) . The scalar matched filter model used for the data analysis has no parameters . To introduce a stochastic vector model of physiological neuronal data with noise , three kinds of noise were added to the model: slowly varying input noise common to all neurons ( δ ) , encoder noise ( αkm ) and population noise ( βkm ) ( Figure 4 ) . Because the variance of a neuronal response is often proportional to its amplitude , we used multiplicative encoder noise . The other noise sources were additive . ( An important point to make here is that we chose the matched filter model because it is the simplest nonlinear model that can explain our results , not because it is the only model that can do so . ) The three noise parameters were fit to the data using the simplex algorithm in the modeFrontier optimization program ( ESTECO , Italy ) . The input to the model is an encoding of the image by a population of visual system neurons . Because the exact nature of the visual input signals are not known for either area TE or perirhinal cortex , we chose an arbitrary encoding , where each of 256 neurons represented one component of a two-dimensional , 16×16 pixel , discrete Fourier transform ( DFT ) . These 256 neurons create a visual encoding of 8×8 Walsh images on a 16×16 gray background ( e . g . , Figure 8 ) . The 256 encoder neurons projected to 256 inputs of a matched filter . On each trial , for each neuron , for each pattern , a new sample of white noise was chosen , uniformly distributed in the range 1+ ( −Kα , Kα ) . The value of Kα sets the range of the simulated data . Population noise was drawn from a white noise process with a normal distribution ( mean zero and standard deviation Kβ ) . When Kβ was zero , the matched filter discriminated the match stimulus perfectly . As Kβ increased discrimination decreased . For each neuron , there is a draw of alpha and beta from the same distribution for each stimulus . A shared input noise , δ , the same for all neurons and all phases ( sample , nonmatch , and match ) in a single trial , was used to test the hypothesis that the noise correlations between sample and match came from exogenous sources . The δ noise was common to all the neurons , as might happen if the animal's attention or level of arousal changed from trial-to-trial . The result was to change the values of noise correlation of the sample response with the nonmatch and match response together ( not shown ) , whereas the data showed a higher correlation between noise on sample and match than on sample and nonmatch . DMS trials were repeated 40 times to match the size of the experimental data sets for each neuron . This process was repeated 30 times to match the number of neurons . Images are shown with a logarithmic gray scale . Model responses are plotted as the normalized sum of the squared magnitude of the discrete Fourier transform . Frequency-domain figures contain data with a wide range of values . To better visualize the low values ( where the noise is most apparent ) , we plotted the logarithmically transformed values of each cell , z , using log ( 1+z ) . Although the matched filter model is parameter-free , the stochastic model needed three parameters to simulate the type and amount of noise in the neuronal responses . Noise parameters of the model were adjusted to fit the mean values of the data in Figure 10 , and to perform the DMS task successfully ( high power for match , low power for nonmatch stimuli ) . The simplex does not converge to a fixed point for this model , because each run contains a new noise sample . Instead of a fixed point the parameters approach a limit cycle . The values used to obtain the data summarized in Figure 10 were: Kα = 0 . 482 , Kβ = 0 . 094 , and δ = 0 . 01 ( TE ) or 0 . 097 ( perirhinal ) . | To know whether one is looking at an object seen a few seconds ago or not depends on visual short-term memory . To study short-term memory , we recorded single neuronal activity from two brain areas of monkeys , the TE and the perirhinal cortex of the temporal lobe , known to be important in visual pattern recognition and memory . The monkeys performed a short-term visual memory task , a sequential match-to-sample . The monkeys had to signal when a sample stimulus reappeared in a short sequence of stimuli . In area TE only , small fluctuations occurring for a sample-elicited response were correlated with the responses when a match stimulus reappeared , as if a snapshot of the sample-induced response was stored and recalled . In our modeling , we propose that each TE neuron stores and compares the signals during short-term memory by storing the response to the sample in local and rapidly adapting synapses . Subsequent stimulus-elicited responses are then automatically multiplied by the locally stored signal . Here , we show that the match can be detected when the sum of the outputs of the population of TE neurons crosses a threshold . Correlated fluctuations will be a signature this type of local memory storage wherever it occurs in the brain . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"neuroscience",
"neuroscience/cognitive",
"neuroscience",
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] | 2008 | Short-Term Memory Trace in Rapidly Adapting Synapses of Inferior Temporal Cortex |
Recent studies have demonstrated how the competition for the finite pool of available gene expression factors has important effect on fundamental gene expression aspects . In this study , based on a whole-cell model simulation of translation in S . cerevisiae , we evaluate for the first time the expected effect of mRNA levels fluctuations on translation due to the finite pool of ribosomes . We show that fluctuations of a single gene or a group of genes mRNA levels induce periodic behavior in all S . cerevisiae translation factors and aspects: the ribosomal densities and the translation rates of all S . cerevisiae mRNAs oscillate . We numerically measure the oscillation amplitudes demonstrating that fluctuations of endogenous and heterologous genes can cause a significant fluctuation of up to 50% in the steady-state translation rates of the rest of the genes . Furthermore , we demonstrate by synonymous mutations that oscillating the levels of mRNAs that experience high ribosomal occupancy ( e . g . ribosomal “traffic jam” ) induces the largest impact on the translation of the S . cerevisiae genome . The results reported here should provide novel insights and principles related to the design of synthetic gene expression circuits and related to the evolutionary constraints shaping gene expression of endogenous genes .
During the gene expression process various macromolecules ( e . g . ribosomes , RNA polymerase ( RNAP ) , transcription factors , elongation factors , spliceosome , transfer RNA ( tRNA ) molecules , etc . ) process the genetic material ( DNA , mRNA , pre-mRNA ) in order to generate proteins [1] . The number of gene expression macromolecules and factors in the cell is finite; for example , there are about 200 , 000 ribosomes and 30 , 000 RNAP-II molecules in the S . cerevisiae cell [2 , 3] . Thus , this limited resource budget induces competition between the different molecules/regions encoding the genetic material , resulting in non-trivial correlations and couplings between the different gene expression stages , and between the processed genetic material molecules . Some previous studies have suggested that such competition should be considered when designing synthetic gene expression circuits [4–9] , and that they significantly affect the evolution of genomes [10] . For example , [9] considered a stochastic model to analyze the competition of two types of mRNAs ( two genes ) for the limited ribosomal resource , where the total number of mRNAs and ribosomes fluctuate randomly . It was shown that the strength of the couplings ( or cross-talk ) between the translation of the two protein types strongly depends on whether the ribosomes are underloaded ( i . e . , there are more ribosomes than mRNAs ) or overloaded ( i . e . , there are more mRNAs than ribosomes ) . Specifically , it was also suggested that the competition for the limited main resources in transcription ( RNAP [11] ) and translation ( ribosomes [12] ) is a primary factor in the cellular economy of the cell . The competition for the available resources , which leads to an indirect coupling between expressions of different genes , might be one of the reasons why levels of genes , mRNAs , and proteins in the cell do not necessarily correlate [4 , 10 , 12–16] . The expression levels of large sets of genes and relevant gene expression factors are fluctuating or oscillating in different physiological conditions ( e . g . cell cycle [17–21] ) . In addition , there are many cases of oscillating genes that are significant ( up to hundreds of genes oscillating with a ratio of up to about three folds between highest to lowest mRNA levels ) in all domains of life [22–34] . Furtheremore , various synthetic circuits and cell free systems include oscillators [35–42] . The couplings , due to competition , may link the oscillations related to one gene expression stage ( e . g . transcription ) to oscillations in a different gene expression stage ( e . g . translation ) . In this study , we suggest for the first time that finite intracellular resources induce non-trivial and significant coupling between different gene expression stages ( transcription and translation ) in endogenous and heterologous genes . For example , increased mRNA levels in one gene affects the translation levels of all other genes . To this end , we perform a whole-cell simulation of translation [43 , 44] , which captures fundamental properties of translation , with parameters estimated from experimental data that enables us to comprehensively quantify these effects for the first time . This type of information is currently not available experimentally , and we believe that our results are expected to reflect well the reality . We specifically demonstrate by Monte Carlo simulations that by periodically changing the mRNA levels of a single gene or a set of genes , i . e . by periodically modifying the transcription process , the translation of all S . cerevisiae genes are affected in a periodic manner , with the same periodicity as the mRNA levels periodicity . Importantly , we numerically estimate , for the first time , the exact impact of the mRNA levels periodicity on the translation process dynamics , as well as on the dynamics of the free ribosomal pool and the way it is affected by parameters such as the codon composition of the oscillating gene , its initiation rate and mRNA levels .
At the first step , we aim at evaluating the impact of fluctuating mRNA levels of a S . cerevisiae endogenous gene set on the translation of the entire S . cerevisiae transcriptome . Fig 2 panels ( a ) and ( b ) depict the results as a function of the number of oscillating endogenous genes . In the figure we plot both the effect of the average/typical oscillating gene set , and the effect of the oscillating set with maximal mRNA levels ( see the Materials and methods section for more details ) . As can be seen , oscillating the mRNA levels of a typical large set of 1 , 000 S . cerevisiae genes with normalized amplitude A = 1/2 is expected to typically induce an amplitude of about 9% on the rest of the genes translation rates; the maximal effect of a set of 1 , 000 S . cerevisiae genes is very high and close to 50% . During the life cycle of a cell large sets of genes may fluctuate/oscillate together ( e . g . due to a common regulatory mechanism ) at the transcription level and the results reported here demonstrate that these oscillations should have non-negligible effect on the rest of the genes at the translation levels . Note that the results for za and ρ ‾ a when oscillating a typical gene set are very similar ( the solid-line for za cannot be distinguished from the solid-line for ρ ‾ a ) . One phenomena that involves large scale gene expression oscillation is the cell cycle process . Ref . [17] identified 800 protein-encoding transcripts in S . cerevisiae that are cell cycle regulated , i . e . genes whose transcript levels vary periodically during the cell cycle process . These genes are involved in different cell cycle related functions such as cell cycle control , DNA replication , DNA repair , budding , glycosylation , nuclear division and mitosis . We evaluate the effect of oscillating these genes on the translation of the rest of the genes as a function of the normalized amplitude A ∈ [0 . 1 , 0 . 9] . These are depicted in Fig 2 panels ( c ) and ( d ) . It may be noticed that the amplitudes increase linearly with A , and that the amplitude of the free ribosomal pool and the translation amplitudes are very similar . Note that the variance of the steady-state mean density amplitude hardly change as a function of A , whereas the variance of the steady-state translation rate amplitude increases from zero to about 0 . 12 for A = 0 . 9 . This suggests that the steady-state mean density amplitudes of all S . cerevisiae genes vary much less than the corresponding steady-state translation rate amplitudes . Since ρ a i measures the average of the steady-state density amplitudes of gene i , it is indeed expected that its variance over all genes will be less than the variance of the steady-state translation rate amplitudes over all genes . Next , we aim at understanding the effect of oscillating the mRNA levels of a heterologous gene on the free ribosomal pool , and on the translation rate and ribosomal density of the endogenous genes . Note that there are many synthetic systems where the mRNA levels of a single heterologous gene occupy dozens of percentages of the total number of mRNAs in the cell ( see , e . g . , [49 , 50] ) . This analysis should specifically provide some intuition related to the effect of synthetic gene expression oscillation circuits on the translation of the rest of the genes . ( Note that there are many examples of synthetic genes with oscillatory mRNA levels [36 , 51–57] ) . It should also teach us about the effect of fluctuations in the expression levels of highly expressed heterologous genes on the expression levels of the rest of the genes . To this end , we add to our whole-cell model a heterologous GFP gene with periodically varying mRNA levels . Fig 3 depicts the average steady-state translation rate amplitude ( R ‾ a ) and mean density amplitude ( ρ ‾ a ) for different ( typical ) values of GFP nominal mRNA levels L ˜ h and initiation rates α for A = 1/2 , T = 16 , and z ‾ = 30 % . It may be seen that both R ‾ a and ρ ‾ a increase with both α and L ˜ h . This is expected since increasing α or L ˜ h increases the dynamic assignment of ribosomes to the GFP mRNAs , which in turn increases the impact of ribosomes assignment to the S . cerevisiae genes via the shared pool . For example , for L ˜ h = 30 % , R ‾ a [ρ ‾ a] ranges from about 2 . 5% [2 . 5%] to about 13 . 5% [14%] . Another observation is that the impacts on R ‾ a and ρ ‾ a are very similar . This suggests that by measuring the periodic amplitude of the translation rates at steady-state one can reasonably conclude the average amplitude of the mean ribosomal densities at steady-state . Fig 3 also depicts R ‾ a and ρ ‾ a as a function of A ∈ ( 0 , 1/2] for α = 0 . 8 , L ˜ h = 20 % , T = 16 , and z ‾ = 30 % . It may be noticed that the translation rate and ribosome density increase linearly with A ∈ ( 0 , 1/2] . Similar observations were made for several other values of L ˜ h and α . We conclude that highly expressed heterologous genes can have an effect of up to about 20% on the amplitude of the translation rate and ribosome density of the rest of the endogenous genes . This should be considered when designing the properties of a synthetic circuit . Note that by the analysis done in the previous section , oscillations of large number of endogenous genes should also affect the heterologous genes . In this section different synonymous substitutions are introduce to the heterologous GFP gene to study their effect , separately , on the translation of the endogenous genes . The goal here is to evaluate the effect of the coding region ( and thus the induced ribosomal density and translation rate ) on translation oscillation . In brief , we consider various variants of the GFP coding region; all of them code the same GFP protein but with different codons ( a detailed description of each synonymously mutated GFP can be found in the Materials and methods section ) . The mutated GFP genes considered are: Table 2 lists the steady-state translation rate R and mean densities ρ of each mutated GFP modeled to include initiation rate equals to 0 . 8 ( which is the median initiation rate of the S . cerevisiae genome [47] ) . The table also lists two metrics ( η and η ˜ ) for ranking the codon decoding times of the coding region ( named decoding time measure ( DTM ) ) . The DTM provides a score of how fast the ORF can be decoded; a value of zero means that it is composed of the fastest synonymous codons , and a larger value of DTM indicates that slower codons are used in the ORF . Specifically , in η all codons contribute equally to the DTM , whereas in η ˜ the codon impact on the DTM increases as we move closer to the 3’-UTR end of the gene ( see the Materials and methods section for more details ) . The following may be concluded from Table 2: Fig 4 depicts the translation normalized statistics as a function of the nominal mRNA levels L ˜ h , for α = 0 . 8 and α = 3 . 2 , for each of the GFP mutated genes , when translated ( separately ) with the S . cerevisiae gene pool , for A = 1/2 , T = 16 , and z ‾ = 30 % . Each data point in the figure represents the corresponding statistics per 600 mutated GFP mRNAs ( 1% of the total S . cerevisiae mRNA levels ) , i . e . we divide the statistics values by the corresponding L ˜ h and multiply by 600 . This represents the impact on the translation process per a unit of 600 GFP mRNAs ( a normalized measure can then be used to compare results between different values of L ˜ h ) . We first observe that the normalized statistics increase with α for each L ˜ h value . This is obviously expected since large values of α imply high periodic variations of assigned ribosomes to the GFP mRNAs , and thus also to the S . cerevisiae genes mRNAs ( due to the shared pool ) , and so we expect the amplitudes of the free pool , translation rates and mean densities to increase . We also note that the statistics variations over the different mutations increase with α . For example , for L ˜ h = 25 % , the normalized za varies between 0 . 55% and 0 . 67% ( in case of α = 3 . 2 ) , and between 0 . 35% and about 0 . 4% ( in the case of α = 0 . 8 ) . This is expected since , for example , a low value of α means that the initiation is the rate limiting factor , and in this case the GFP ORF mutations ( affecting the elongation rates ) less affect the parameters . In addition , it may be seen that the normalized statistics maintain a particular ranking for different L ˜ h values: they achieve their maximal values when oscillating the GFP_HIGH_RD mutation , are reduced when oscillating the GFP original gene , and achieve their minimal values when oscillating the GFP_LOW_RD mutation . For example , for L ˜ h = 10 % and α = 3 . 2 , the normalized R ‾ a is about 0 . 57% when oscillating GFP_HIGH_RD , is about 0 . 55% when oscillating GFP , and is about 0 . 45% when oscillating GFP_LOW_RD . This correlates with the mean steady-state ribosomal densities of these mutations , as well as with their non-homogeneous DTMs ( η ˜ ) . This suggests that mRNAs with “traffic jams” at steady-state ( i . e . mRNAs that occupy large number of ribosomes at steady-state ) have a substantial impact on the translation of the other genes via the shared ribosomal pool . The normalized za , R ‾ a and ρ ‾ a seem to slightly decrease with L ˜ h , implying that the non-normalized parameters increase sub-linearly with L ˜ h . Oscillating the mRNA levels of the GFP gene increases and decreases periodically the assigned number of ribosomes to the GFP mRNAs , which in turn decreases and increases periodically the amount of free ribosomes , respectively . This affects the actual initiation rate to the mRNAs . However , due to the finite , shared pool of ribosomes , the oscillation effect caused by an increase in mRNA levels admits a linear region which is eventually saturated ( similar to most physical systems ) . Finally , it may be observed that the corresponding variances increase with both L ˜ h and α , indicating , as expected , that for large oscillating mRNA levels , and/or initiation rates , the variations of the amplitudes over all genes increase . Note that the variance values are few order of magnitudes less than the corresponding average values; for example , for L ˜ h = 20 % and α = 3 . 2 , R ^ a [ρ ^ a] is about 0 . 7% [0 . 03%] of the corresponding average values . In general , both the steady-state translation rate and the mean density of the mutated or the original GFP gene affect the parameters . For example , the effect of the gene GFP_SPD_TR on the statistics is less severe than the effect of the gene GFP_MDN_RD , even-though GFP_SPD_TR consumes more ( by 25% ) ribosomes at steady-state ( see Table 2 ) . However , the steady-state translation rate of GFP_SPD_TR is larger ( by about 26% ) than the steady-state translation rate of GFP_MDN_RD , thus ribosomes in the GFP_SPD_TR mutation case are released faster to the pool and thus are available more for translating other genes . In addition , we can observe a ‘diminishing marginal utility’ effect: the results depicted in Fig 4 suggest that oscillating a larger number of mRNAs of the mutated GFP gene decreases the amplitude of the free pool and of the genes translation rate and mean density per GFP mRNA level . This effect is partially due to the limited and shared ribosome pool . Fig 5 depicts the translation normalized statistics when oscillating the mutated GFP_HIGH_RD gene for several values of the average steady-state free ribosomal pool z ‾ . It may be seen that za decreases with z ‾ , whereas R ‾ a and ρ ‾ a are slightly affected by z ‾ . For example , for L ˜ h = 30 % and α = 0 . 8 , the normalized za decreases from about 1 . 4% for z ‾ = 10 % to about 0 . 4% for z ‾ = 30 % , whereas both the normalized R ‾ a and ρ ‾ a hardly vary and are equal to about 0 . 32% and 0 . 34% , respectively . One possible explanation for this is as follows: As z ‾ decreases ( i . e . as less ribosomes are free thus more are assigned to the mRNAs ) the effective initiation rates to the mRNA increases . This increases the oscillation amplitude induced by the GFP_HIGH_RD mRNAs , and thus the relative effect on z ‾ increases ( recall that za denotes the free pool oscillation amplitude relative to z ‾ ) . On the other hand , an increase in the effective initiation rates increases both the steady-state translation rates and mean densities of all the S . cerevisiae mRNAs , and so the effect on R ‾ a and ρ ‾ a is small . However , as suggested by Fig 5 , the corresponding variances increase slightly as z ‾ decreases , implying that the amplitude variations over all genes do not change much as z ‾ decreases from 30% to 10% . Note that , again , the variance values are few order of magnitudes less than the corresponding average values ( for example , for L ˜ h = 20 % , R ^ a [ρ ^ a] is about 1 . 0% [0 . 05%] of the corresponding average values ) . The results depicted in Fig 5 suggest that the fluctuations of the translation rates and mean ribosomal densities are hardly affected by the affinity of ribosomes to the mRNA molecules ( this affinity may be controlled by initiation efficiency , for example ) . However , the fluctuations of the free ribosomal pool increase as more ribosomes are translating the mRNA molecules . As another example , Table 3 depicts the ( non-normalized ) statistics when oscillating the mutated GFP_HIGH_RD gene for A = 0 . 35 , T = 16 , α = 0 . 8 , L ˜ h = 20 % , and for several values of the average free ribosomal pool at steady-state z ‾ . The same conclusions can be derived here as well . In summary , the current subsection teaches us that when designing highly expressed heterologous genes that are expected to fluctuate/oscillate we should carefully choose their codons composition: to induce low effect on the other genes we should minimize the ribosome density , and on the other hand high ribosomal density results in large effect on the other genes . As was demonstrated here the exact profiles that maximize/minimize ribosome densities are not simply the ones with optimal/slowest codons along the coding region , respectively; thus , it is important to develop models and algorithms for engineering and manipulating ribosome density of endogenous and heterologous genes . The results reported here with the heterologous gene may be further validated experimentally in the future using in-vitro and/or in-vivo systems with oscillating GFP proteins [51] . However , we believe that with the current experimental approach it should be challenging to directly study the coupling we reported here due to the following reasons . First , oscillating endogamous systems probably includes various effects and feedbacks that may “cancel” or blur the phenomena presented here . Second , in order to be able to measure , with the current techniques , the effect reported here large portion of the mRNA molecules in the cell should oscillate . Finally , this study analyzes oscillations during the translation stage . Thus , to study them one should directly measure translation rate; the conventional experimental approaches ( e . g . RNA-seq , ribo-seq or approaches based on quantitative mass spectrometry ) measure variables that are expected to be related/correlated with the translation rate but are not the actual translation rate . Our results should be specifically considered when designing large intra-cellular circuits with many components/genes . In such cases , among others , the oscillation in transcription levels of some parts of the circuit should affect the other part of the circuit . We provide here some initial guidelines related to this topic . First , if we are not interested in cross-talk between the different oscillating genes we should engineer their transcript to minimize the induced oscillations ( e . g . designing codon profiles that minimize ribosome density and if possible decrease their initiation rate ) . Second , in some cases we may want to design genes that induce oscillations on the rest/other genes; in these cases , we will design them accordingly ( e . g . high initiation rate and ribosome density ) . Third , our ( or similar ) models can be used to estimate potential “noise” due to oscillation cross-talk . These estimations can be considered when designing the circuit and assuring its performance . The goal of this study is to understand and carefully quantify the impact of oscillations over wide range of conditions and parameters ( e . g . , large range of A , L ˜ h , α , z ‾ , and different GFP mutations ) . This is important , as the severity of the oscillations impact ( in terms of its phenotypic or biological-significant effect ) is , in general , gene and condition specific . It might depend on the function of the genes ( e . g . structural genes , transcription factors , signaling proteins , etc . ) , the exact condition ( e . g . initiation rate , mRNA levels ) , the organisms type , etc . The computational model used in this study is deterministic , enabling rigorous analysis of its properties using tools from systems and control theory . In addition , it was shown to admit high correlation with the stochastic TASEP model of translation ( e . g . see [45] ) , and furthermore using it to simulate large-scale translation with competition is simple . The processes in the cell are stochastic in nature , and future study may employ stochastic whole cell models to study the effect of oscillations in a “noisy” environment . In S6 Fig we provide initial results that suggest that noise in the model parameters should not affect our conclusions . It is important to emphasize that the results reported here are relevant also in cases where the time scales of translation and cell cycle differ . Note that it has been suggested that translation of cell cycle related genes is regulated by periodically varying tRNA levels [18] . This implies , among others , that the time scales are quite similar . Specifically , the translation time in general can be longer than the cell cycle period . For concreteness , consider the case of S . cerevisiae . The cell cycle period in S . cerevisiae is less than 87 minutes [58] . Cell cycle period can be much shorter in eukaryotes; for example , it was reported that the duration of cell cycle in early embryo of the fruit fly D . melanogaster is only eight minutes [59] . The translation rate in S . cerevisiae was estimated to be higher than 0 . 956 codons per second ( the slowest codon is CUU ) [60] with average rate over all codons of 10 codons per second ( in mouse the average codon translation rate was estimated to be about five codons per second [61] ) . In practice , this rate can be much slower due to strong folding of the mRNA molecule and interaction of the translated amino-acid peptide with the exit channel of the ribosome [62 , 63] . In S . cerevisiae the ORF length is between 75 and 14 , 733 nucleotides . The longest gene corresponds to an upper bound on the translation time of a gene , which is about 82 minutes ( assuming a lower bound on translation rate of one codon per second , which may be lower in practice ) , an estimated translated time of this protein based on mean codon translation time is 8 . 2 minutes . In mammals the mean codon decoding time is five codons per second , and the longest human protein ( Titin–TTN ) , which consists of 33 , 000 amino acids , corresponds to estimated translation time of 110 minutes . This suggests that periodically varying mRNA levels in cell cycle related genes may be similar to the time scale of mRNA translation . We will conclude with the main lessons from the analysis performed here based on our whole-cell computational model: 1 ) Competitions for limited resources in the cell lead to indirect couplings between the gene expression stages , and these couplings must be considered when analyzing the cellular economy of the cell; 2 ) A whole-cell computational model of translation that takes into account fundamental properties of translation , with parameters estimated based on experimental measurements , can comprehensively quantify the effect of oscillations on the ribosomal densities and translation rates of all genes; 3 ) Careful considerations must take place when designing highly express heterologous genes that are expected to fluctuate , as their codon compositions and translation initiation rates may have high effect on all genes translation rate . We demonstrate specific cases with high and low effect on fluctuations; 4 ) Quantitative estimation ( based on parameters estimated from experimental data ) of the magnitude of these oscillations in endogenous and heterologous genes is provided here; and 5 ) The conclusions reported here in general should also be relevant to other aspects of gene expression and/or intracellular phenomenon . For example , when considering oscillations in tRNA levels , the number of DNA copies of a virus , intracellular transport factors , etc .
We first sort the S . cerevisiae genes according to mRNA levels and evaluate the steady-state mean density and translation rate amplitudes as a function of the number of oscillating genes chosen sequentially from the sorted list of genes , starting from the gene with the largest mRNA levels ( dashed-lines in Fig 2 , panels ( a ) and ( b ) ) . For example , when using p genes with oscillating mRNA levels , the p genes with the largest mRNA levels are used . This provides a bound on the maximal oscillating amplitudes when any arbitrary p S . cerevisiae genes are oscillating . The “typical” selected genes were chosen randomly from the S . cerevisiae gene pool . Here the oscillation amplitudes and variances for each number of “typical” oscillating genes set is averaged over 30 repetitions . The results of oscillating these genes are depicted , using solid-lines , in Fig 2 , panels ( a ) and ( b ) . The assumption in this study is that large set of genes can be regulated ( oscillate ) independently of the ribosomal pool . This is motivated by: 1 ) The regulations at the translation and transcription stages are not tightly coupled [64]; 2 ) There may be delays between the two stages [65]; 3 ) In the case of heterologous genes ( and the corresponding promoters and gene expression circuits ) that are engineered by design there is no reason to assume that the ribosome pool is also regulated . Ref . [17] identified 800 protein-encoding transcripts in S . cerevisiae that are cell cycle regulated . We evaluate the parameters when oscillating 770 of these genes , as we lack mRNA measurements for 30 of the reported 800 cell cycle related genes . The 770 cell cycle genes used are listed in S1 Table . Table 4 lists the 30 genes we lack mRNA measurements for . The ribosome flow model network with pool ( RFMNP ) [43] is a deterministic computational model for large-scale simultaneous mRNA translation and competition for ribosomes . It is based on combining several ribosome flow models with input and outputs ( RFMIOs ) [45 , 66] , interconnected via a pool of free ribosomes . Each gene is modeled by a single RFMIO , and all the RFMIOs are sharing the same pool of ribosomes . The dynamics of the system is expressed by a set of ordinary differential equations that describes the time evolution of the ribosomal densities in the different RFMIOs and the free pool . In this paper we utilize the RFMNP to simulate a whole-cell S . cerevisiae simultaneous translation and competition for ribosomes . Each of the S . cerevisiae gene ( and the GFP gene ) is modeled by a single RFMIO . We next describe in details the RFMIO and the RFMNP . The ribosome flow model ( RFM ) [45] is a deterministic mathematical model for mRNA translation that can be derived by a mean-field approximation of an important model from statistical physics called the totally asymmetric simple exclusion process ( TASEP ) ( see , e . g . , [67] and [68] ) . In the RFM , mRNA molecules are coarse-grained into n consecutive sites of codons . The state variable x i ( t ) : ℝ + → [ 0 , 1 ] , i = 1 , … , n , describes the normalized ribosomal occupancy level at site i at time t , where xi ( t ) = 1 [xi ( t ) = 0] indicates that site i is completely full [empty] at time t . The model includes n + 1 positive parameters that regulate the transition rate between the sites: the initiation rate into the chain λ0 , the elongation ( or transition ) rate from site i to site ( i + 1 ) λi , i = 1 , … , n − 1 , and the exit rate λn ( see Fig 6 ) . The dynamics of the RFM with n sites is given by n nonlinear first-order ordinary differential equations: x ˙ 1 = λ 0 ( 1 - x 1 ) - λ 1 x 1 ( 1 - x 2 ) , x ˙ 2 = λ 1 x 1 ( 1 - x 2 ) - λ 2 x 2 ( 1 - x 3 ) , x ˙ 3 = λ 2 x 2 ( 1 - x 3 ) - λ 3 x 3 ( 1 - x 4 ) , ⋮ x ˙ n - 1 = λ n - 2 x n - 2 ( 1 - x n - 1 ) - λ n - 1 x n - 1 ( 1 - x n ) , x ˙ n = λ n - 1 x n - 1 ( 1 - x n ) - λ n x n . ( 2 ) If we let x0 ( t ) ≔ 1 and xn+1 ( t ) ≔ 0 , then ( 2 ) can be written more succinctly as x ˙ i = h i - 1 ( x ) - h i ( x ) , i = 1 , & , n , ( 3 ) where hi ( x ) ≔ λixi ( 1 − xi+1 ) . This can be explained as follows . The flow of ribosomes from site i to site i + 1 at time t is λixi ( 1 − xi+1 ) . This flow increases with the density at site i , and decreases as site i + 1 becomes fuller . This corresponds to a “soft” version of a simple exclusion principle . Since the ribosomes have volume , the input rate to site i decreases as the number of ribosomes in that site increases . Note that the maximal possible flow from site i to site i + 1 is the transition rate λi . Thus , Eq ( 3 ) simply states that the change in the density at site i at time t is the input rate to site i ( from site i − 1 ) minus the output rate ( to site i + 1 ) at time t . The ribosome exit rate from site n at time t is equal to the protein translation rate at time t , and is denoted by R ( t ) ≔ λn xn ( t ) . Denote by x ( t , a ) the solution of ( 3 ) at time t ≥ 0 for the initial condition x ( 0 ) = a . Since the state-variables correspond to normalized occupancy levels , we always assume that a belongs to the closed n-dimensional unit cube C n ≔ { x ∈ R n : x i ∈ [ 0 , 1 ] , i = 1 , & , n } . Let int ( Cn ) denote the interior of Cn . Ref . [69] showed that the RFM is a tridiagonal cooperative dynamical system [70] , and that this implies that ( 2 ) admits a unique steady-state point e = e ( λ0 , …λn ) ∈ int ( Cn ) , that is globally asymptotically stable , that is , limt→∞ x ( t , a ) = e , for all a ∈ Cn ( see also [71] ) . In particular , the translation rate converges to the steady-state value R ≔ λnen . We denote by ρ ≔ ∑ i = 1 n e i n the steady-state mean ribosomal density along the mRNA . The RFM can be extended into a single-input single-output ( SISO ) control system , by defining the translation rate as the output , and by introducing a time-varying input control u : ℝ + → ℝ + representing the flow of ribosomes from the “outside environment” into the mRNA ( which is related to the rate ribosomes diffuse to the 5’end ( in eukaryotes ) or the RBS ( in prokaryotes ) of the mRNA ) . This is referred to as the RFM with input and output ( RFMIO ) [66] . Thus , the equation for the change in the density at site 1 in the RFMIO becomes x˙1=λ0u ( 1−x1 ) −λ1x1 ( 1−x2 ) , and all other equations for x ˙ i , i = 2 , … , n , are the same as in the RFM . The RFMIO can then be written in a compact-form as x˙=f ( x , u ) , y=λnxn , ( 4 ) where y denotes the output . In this study , each S . cerevisiae gene is modeled by a RFMIO , where each RFMIO site contains 10 consecutive codons ( the ribosome footprint is assumed to be about 10 codons wide ) . In [43] , a network of m RFMIOs interconnected via a pool of free ribosomes ( called the RFM network with pool ( RFMNP ) ) was introduced for analyzing large-scale translation while competing for the available , limited ribosomal resource . Competition for the available ribosomal resource leads to indirect coupling between the different mRNAs . For example , if more ribosomes bind to a certain mRNA molecule then the pool of free ribosomes in the cell is depleted , and this may lead to lower initiation rates in the other mRNAs . Let z ( t ) :ℝ+→ℝ+ denote the free ribosomal pool occupancy at time t . For an RFMNP with m RFMIOs , let nj , i = j , … , m , denote the jth RFMIO dimension , yj ≔ Rj ( t ) its output rate at time t , and λ0j , … , λnjj its transition rates . The input to the jth RFMIO is uj = Gj ( z ) where the function Gj ( ⋅ ) :ℝ+→ℝ+ satisfies: ( 1 ) Gj ( 0 ) = 0; ( 2 ) Gj ( z ) is strictly increasing on ℝ+; and ( 3 ) for all z > 0 sufficiently small Gj ( z ) is linearly proportional to z . Typical examples are Gj ( z ) = z , and Gj ( z ) = aj tanh ( z/bj ) with aj , bj > 0 ( see S1 Fig and [43] for more details ) . Thus , the RFMNP is given by x˙1=f ( x1 , u1 ) , y1=λn11xn11 , ⋮x˙m=f ( xm , um ) , ym=λnmmxnmm , ( 5 ) and z˙=∑j=1myj-∑j=1mλ0j ( 1-x1j ) Gj ( z ) . ( 6 ) Eq ( 6 ) implies that the change in the free pool , as a function of time , is the sum of all output rates of the RFMIOs ( input flow to the free pool ) minus the total flow of ribosomes that bind to the mRNA molecules ( output flow from the free pool ) . The RFMNP is then a dynamical system with ( 1+∑j=1mnj ) state-variables . Since the RFMNP is a closed system , the total number of ribosomes H ( t ) ≔z ( t ) +∑j=1m∑i=1njxij ( t ) is conserved , that is H ( t ) ≡ H ( 0 ) for all t ≥ 0 . It was proven in [43] that for any given number of total ribosomal pool H ( 0 ) , the RFMNP admits a unique steady-state point that depends on the rates and H ( 0 ) but not on the initial conditions . Furthermore , if one or more of the RFMIOs rates are time-periodic functions , with a common minimal period T > 0 , then the RFMNP entrains to the periodic excitations in the λijs , i . e . every state-variable converges to a periodic solution with period T . This also means that each of the translation rates and mean densities converge to periodic solutions with period T . Thus , we do not need to evaluate different values of T , and the value T = 16 is used throughout this paper ( e . g . using T = 20 instead yields the same behavior but with periodicity T = 20 ) . In this paper we simulate the RFMNP while periodically changing the mRNA levels of the heterologous GFP gene or several endogenous genes . Assume , for example , that the GFP mRNA levels are changing ( periodically ) between a minimal value of β1 > 0 and a maximal value of β2 > β1 . It is straightforward to verify that this is equivalent to an RFMNP with β2 copies of the gene GFP , while periodically changing the initiation rates of these copies . Thus , [43] provides a rigorous proof to the periodicity we observed at steady-state in the state-variables . Finally , the parameters of the model used here are based on [72]; see more details below . We use ribo-seq data to infer the codon decoding rates [73] , and normalize these rates so that the median codon elongation rate of all S . cerevisiae mRNAs becomes 6 . 4 codons per second [48] . This holds for all endogenous genes and the GFP . The ribo-seq data and the decoding rates are used also for inferring the initiation rates . The ribo-seq data and mRNA levels were taken from [74]; the number of S . cerevisiae ribosomes used in the simulation is 200 , 000 [75] , with 60 , 000 mRNAs [46] , scaled according to the mRNA levels from [74] . Thus , the correlations between the predicted ribosome densities from our model and measured ribosome densities in the analyzed conditions are very high ( correlation coefficient r > 0 . 7 for sites size of 10 codons ) and is similar to the correlations between two experimental replications in the field [76] . Note that the large-scale measurements of mRNA levels and ribosome profiling suggest that almost all genes have certain mRNA levels and ribosome densities; this suggests that most of the genes are transcribed/translated at the same time but at ( possibly extremely ) different rates/levels ( the differences among genes can be very significant: up to four orders of magnitudes ) . Let q ≔ 10 denote the number of codons per RFMIO site . Given an S . cerevisiae gene ORF consisting of K codons ( excluding the stop codon ) , we model it using RFMIO with n sites as follows . The mRNA is divided into ( n + 1 ) pieces: the first piece contains ( q − 1 ) codons ( that are also related to later stages of initiation [14] ) , pieces 2 to n contain each q non-overlapping codons , and the last piece contains between q/2 and 3q/2 codons . For example , for q = 10 and K = 146 , the first piece contains 9 codons , pieces 2 to 14 contain each 10 codons , and piece 15 contains 7 codons , thus n = 14 . The first piece corresponds to λ0 , and pieces 2 to n + 1 correspond to λ1 to λn , respectively , as described next . The initiation rate ( that corresponds to the first piece ) is estimated based on the ribosome density per mRNA level , as this value is expected to be approximately proportional to the initiation rate when initiation is the rate limiting factor [45 , 77] . We apply a normalization that sets the median initiation rate of all S . cerevisiae mRNAs to 0 . 8 [47] . The RFMIO rates , per S . cerevisiae gene , are then set as follows: Let τi denote the decoding time of codon i in the ORF , and let ψ ( i ) denote the minimum among the decoding times of codon i and its synonymous mutations . Define the decoding-time measure ( DTM ) of a gene by η≔∑i=1K ( τi-ψ ( i ) ) wiK , ( 7 ) where K denotes the number of codons in the ORF ( excluding the stop codon ) , and wi > 0 , i = 1 , … , K , is the weight given to the non-negative cost ( τi − ψ ( i ) ) . The DTM then provides a score of how fast the ORF can be decoded; a value of zero means that the ORF is composed from the fastest synonymous codons , and a larger value of η indicates that slower codons are used in the ORF . One might expect that in general η should be inversely proportional to the steady-state translation rate . However , since η doesn’t provide information about the distribution of the decoding time costs along the ORF , this might not always hold . For example , a slow codon in the middle of the ORF can impact the steady-state translation rate more than a slow codon in the boundaries . Another possible interpretation of η is in describing the “speed-budget” relative to the optimum ( η = 0 corresponds to the fastest possible decoding times ) . Thus , two genes with similar DTMs correspond to the same speed-budget . In the case where w1 = … = wK = 1 , the DTM is referred to as the homogeneous DTM . A monotone-increasing weights describes the hypothesis that slower codons toward the 3’ UTR increase ribosomal “traffic jams” on the mRNA , resulting in larger number of ribosomes on the mRNA at steady-state ( see S2 Fig ) . The GFP protein sequence is from gi:1543069 . Recall that the GFP gene ORF consists of 239 codons ( excluding the stop codon ) . The mutated GFP genes are generated by performing the following synonymous substitutions relative to the GFP gene ( see also S3 Fig ) : | Each cell contains a limited number of macromolecules and factors that participate in the gene expression process . These expression resources are shared between the different molecules that encode the genetic code , resulting in non-trivial couplings and competitions between the different gene expression stages . Such competitions should be considered when analyzing the cellular economy of the cell , the genome evolution , and the design of synthetic expression circuits . Here we study the effect of couplings and competitions for ribosomes by performing a whole-cell simulation of translation of S . cerevisiae , with parameters estimated from experimental data . We demonstrate that by periodically changing the mRNA levels of a single gene ( endogenous or heterologous ) or a set of genes , the translation of all S . cerevisiae genes are affected in a periodic manner . We numerically estimate the exact impact of the mRNA levels periodicity on the translation process dynamics , as well as on the dynamics of the free ribosomal pool and the way it is affected by parameters such as the codon composition of the oscillating gene , its initiation rate and mRNA levels . Furthermore , we show that the codon compositions of synthetically highly expressed heterologous genes that are expected to oscillate must be carefully considered . For example , synonymous mutations resulting in “traffic jams” of ribosomes along the fluctuated mRNAs may cause significant fluctuations of up to 50% in the steady-state translation rates of all genes . | [
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] | 2018 | Computational analysis of the oscillatory behavior at the translation level induced by mRNA levels oscillations due to finite intracellular resources |
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